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sam-m-caldwell/christmas_problem
|
https://github.com/sam-m-caldwell/christmas_problem
|
43bd7621264f778355483d50d4d889c87d7b78db
|
d7323dcd9c3ddd534296a440fd4ac640fb8db738
|
a94b7fefc55a0b5591c7a48d71bf44e5fd5b39d8
|
refs/heads/master
| 2021-08-29T18:46:03.201106 | 2017-12-14T17:07:07 | 2017-12-14T17:07:07 | 114,274,132 | 0 | 0 | null | null | null | null | null |
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"text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec 14 11:54:49 2017\n\n@author: Sam Caldwell\n\"\"\"\n\n#==============================================================================\n# Libraries\n#==============================================================================\n\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\nimport os\nimport pulp as pp\nos.chdir('C:\\\\Users\\\\Sam Caldwell\\\\Desktop\\\\kaggle')\n\ntest = False\n#==============================================================================\n# Read data\n#==============================================================================\n\nif test:\n int_gifts_max = 2\n \n ar_child_wishlist = pd.read_csv(\n \"Data/child_wishlist_test.csv\",\n #\"Data/child_wishlist.csv\",\n header=None\n ).drop(0, 1).values\n \n ar_gift_wishlist = pd.read_csv(\n \"Data/child_wishlist_test.csv\",\n #\"Data/gift_goodkids.csv\",\n header=None\n ).drop(0, 1).values\n \n ls_twins = [(0,1)]\nelse:\n int_gifts_max = 1000\n \n ar_child_wishlist = pd.read_csv(\n #\"Data/child_wishlist_test.csv\",\n \"Data/child_wishlist.csv\",\n header=None\n ).drop(0, 1).values\n \n ar_gift_wishlist = pd.read_csv(\n #\"Data/child_wishlist_test.csv\",\n \"Data/gift_goodkids.csv\",\n header=None\n ).drop(0, 1).values\n \n ls_twins = [(2*k,2*k+1) for k in range(int(0.004*len(ar_child_wishlist)/2))]\n\n#==============================================================================\n# Assign parameters\n#==============================================================================\nint_gifts = len(ar_gift_wishlist)\nint_kids = len(ar_child_wishlist)\nint_kid_pref = ar_child_wishlist.shape[1]\nint_gift_pref = ar_gift_wishlist.shape[1]\nint_rel_happiness = (int_kids/int_gifts)**2\n\nint_ratio = 2\nint_rel_happiness = (int_kids/int_gifts)**2\n\nls_kids = range(int_kids)\n#ls_twins = range(int(0.004*int_kids))\n\nls_gifts = range(int_gifts)\nint_max_kid_happy = int_kids * int_ratio\nint_max_gift_happy = int_gifts * int_ratio\n\nint_gift_pref = ar_gift_wishlist.shape[0]\nint_child_pref = ar_gift_wishlist.shape[1]\n\n#==============================================================================\n# Enumerate benefits\n#==============================================================================\n\n#dict_obj_child = {\n# (k, g) : \n# 2*(10 - sum(ar_child_wishlist[k] == g))/int_max_kid_happy\n# for g in ls_gifts\n# for k in ls_kids}\n# \n#dict_obj_gifts = {\n# (k, g) : \n# -1 if g not in ar_gift_wishlist[k] \n# else 2*(10 - sum(ar_gift_wishlist[k] == g))\n# for g in ls_gifts\n# for k in ls_kids}\n \ndef obj_child(k, g) :\n child_happiness = (int_gift_pref - np.where(ar_child_wishlist[k]==g)[0])\n if not child_happiness:\n child_happiness = -1\n return int(child_happiness)\n\ndef obj_gifts(k, g) :\n gift_happiness = (int_child_pref - np.where(ar_gift_wishlist[g]==k)[0]) * int_rel_happiness\n if not gift_happiness:\n gift_happiness = -1 * int_rel_happiness\n return int(gift_happiness)\n\nls_set = [(k, g) for k in ls_kids for g in ar_child_wishlist[k]]\n\ndict_obj = {\n (k,g) :\n int(int_gift_pref - np.where(ar_child_wishlist[k]==g)[0]) + \\\n int(int_child_pref - np.where(ar_gift_wishlist[g]==k)[0]) * int_rel_happiness\n for (k, g) in ls_set\n}\n \n#==============================================================================\n# Define problem\n#==============================================================================\n \nprob = pp.LpProblem(\"Santa optimisation\", pp.LpMaximize)\n\n# Variable\ncat = pp.LpVariable.dicts(\n 'sel', ls_set, \n lowBound = 0, upBound = 1, cat = 'Integer')\n\n# Objective\n#prob += pp.lpSum([\n# (obj_child(k, g)+obj_gifts(k, g))*cat[k,g] \n# #for g in ls_gifts for k in ls_kids])\n# for k in ls_kids for g in ar_child_wishlist[k]])\nprob += pp.lpSum([dict_obj[k,g]*cat[k,g] for (k, g) in ls_set])\n\n# Constraint 1 - one gift per child\nfor k in ls_kids: \n prob += pp.lpSum([cat[k, g] for g in ls_gifts if (k,g) in ls_set]) == 1\n \n# Constraint 2 - Only 1000 of each gift\nfor g in ls_gifts: \n prob += pp.lpSum([cat[k, g] for k in ls_kids if (k,g) in ls_set]) <= int_gifts_max\n \n# Constraint 3 - twins have same gifts\nfor k in ls_twins:\n for g in ls_gifts:\n if (k,g) in ls_set:\n prob += cat[k[0], g] - cat[k[1], g] == 0\n\nprob.solve()\nprint pp.LpStatus[prob.status]\nyo = {(k, g): cat[k,g].varValue for (k, g) in ls_set}\n#var_obj = sum([\n# (obj_child(k, g) + obj_gifts(k, g))*cat[k,g].varValue\n# for g in ls_gifts for k in ls_kids])\nvar_obj = sum([\n (dict_obj(k,g))*cat[k,g].varValue\n for (k, g) in ls_set])"
}
] | 1 |
jrizkalla/environment
|
https://github.com/jrizkalla/environment
|
ad83e3620ec2216ad87d540339162ab6882fbcb9
|
e0b82b5a0f5998c4d8d257b392a0188b387269af
|
4a7f33f0cad178ee722d9211abb711036aaf1ccb
|
refs/heads/master
| 2021-07-13T13:45:04.741418 | 2020-07-31T19:44:15 | 2020-07-31T19:44:15 | 52,281,730 | 0 | 0 | null | null | null | null | null |
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"text": "#!/usr/bin/env python3\n\nfrom urllib import request as http\nfrom urllib.parse import quote\nimport re\nimport typing as T\nimport sys\nimport argparse\nfrom os import path\nfrom itertools import chain\n\ntry:\n eval('f\"\"')\nexcept SyntaxError:\n print(\"Python 3.6 required\", file=sys.stderr)\n\n\nLIST_SEPERATOR = re.compile(\"[\\s,]\")\nTMP_CACHE = \"/tmp/mkgitignore_langs.txt\"\n\ndef get_langs(url: str = \"https://www.gitignore.io/api/list\") -> T.Set[str]:\n try:\n with open(TMP_CACHE, \"r\") as tmp_file:\n return set(l.strip() for l in tmp_file.readlines())\n except:\n # fallback on the url\n resp = http.urlopen(url)\n if resp.getcode() != 200: \n raise http.URLError(f\"Error: {resp.getcode()} {resp.reason}\")\n res = set(LIST_SEPERATOR.split(resp.read().decode().strip()))\n try:\n with open(TMP_CACHE, \"w\") as tmp_file:\n for r in res:\n print(r, file=tmp_file)\n except: pass\n return res\n\ndef get_gitignore(langs: T.Iterable[str], base_url=\"https://www.gitignore.io/api/\"):\n langs = list(langs)\n \n supported_langs = get_langs()\n \n for l in langs:\n if l not in supported_langs:\n raise Exception(f\"language '{l}' is not supported by gitignore.io\")\n # make sure that they're all supported\n \n query = quote(\",\".join(l.lower().strip() for l in langs))\n if query == \"\":\n return \"\"\n url = base_url + (\"\" if base_url[-1] == \"\" else \"/\") + query\n try:\n resp = http.urlopen(url)\n except:\n print(f\"Failed to get url: {url}\")\n raise\n \n if resp.getcode() != 200: \n raise http.URLError(f\"Error: {resp.getcode()} {resp.reason}\")\n return resp.read().decode()\n\narg_parser = argparse.ArgumentParser(\n description=\"Download .gitignore from gitignore.io\")\narg_parser.add_argument(\"langs\", nargs=\"*\", \n help=\"Languages included in .gitignore\")\narg_parser.add_argument(\"--list\", action=\"store_true\", default=False, \n help=\"List supported languages and exit\")\narg_parser.add_argument(\"-o\", \"--output\",\n metavar=\"FILE\", type=argparse.FileType(\"w\"), \n default=\".gitignore\", help=\"Output file\")\narg_parser.add_argument(\"--bare\", action=\"store_true\", default=False,\n help=\"Don't add extra languages (like Vim and Mac OS)\")\n\ndef main(langs, list, output, bare):\n if list:\n for lang in get_langs():\n print(lang)\n if not bare:\n langs = set(langs + \"vim macos\".split(\" \"))\n print(get_gitignore(langs), file=output)\n\nif __name__ == \"__main__\":\n try:\n ret = main(**vars(arg_parser.parse_args()))\n except Exception as e:\n ret = 1\n print(f\"Error: {e}\")\n sys.exit(ret if ret is not None else 0)\n"
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"text": "import json;\nfrom collections import UserDict\nfrom collections.abc import Mapping\n\nfrom typing import Any\n\nclass JSObject(UserDict):\n '''\n Implements a Javascript Object (for the lack of a better term).\n JSObjects are essentially dictionaries that allow getting, setting, and deleting keys using the \n dot syntax.\n '''\n \n @classmethod\n def load(Class, *args, **kwargs):\n '''\n Load a JSObject from a JSON file.\n *args and **kwargs are passed directly to json.load.\n '''\n kwargs['object_hook'] = lambda d: Class(d)\n return json.load(*args, **kwargs)\n \n @classmethod\n def loads(Class, *args, **kwargs):\n '''\n Load a JSObject from a JSON string.\n *args and **kwargs are passed directly to json.loads.\n '''\n kwargs['object_hook'] = lambda d: Class(d)\n return json.loads(*args, **kwargs)\n \n def __init__(self, data={}):\n '''\n Create a new JSObject from a dictionary.\n '''\n \n data = dict(data)\n for k in data:\n value = data[k]\n if isinstance(value, Mapping) and type(value) != self.__class__:\n value = JSObject(value)\n \n data[k] = value\n \n self.__dict__['data'] = {}\n super().__init__(data)\n \n def dump(self, f, **kargs) -> None:\n json.dump(self, f, **kargs);\n def dumps(self, f, **kargs) -> str:\n return json.dumps(self, f, **kargs);\n \n def __getattr__(self, attr: str) -> Any:\n try:\n return self[attr];\n except KeyError as e:\n raise AttributeError from e;\n \n def __setattr__(self, attr: str, value: Any):\n if attr in self.__dict__:\n self.__dict__[attr] = value\n else:\n self[attr] = value\n \n def __delattr__(self, attr: str):\n if attr in self.__dict__:\n del self.__dict__[attr]\n else:\n del self.data[attr]\n \n def __repr__(self) -> str:\n return '{self.__class__.name}({repr})'.format(self=self, repr=super().__repr__())\n \n def __str__(self) -> str:\n return '{self.__class__.name}({str})'.format(self=self, str=super().__str__())\n"
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"text": "# Open .xcodeproj file in any parent directory using the verison of xcode pointed to by xcode-select\n\nif [ $# -ne 0 ]; then\n echo 'Usage: openproj' >&2\n exit 1\nfi\n\n# find the correct version of xcode\nxcode_loc=\"$(cd `xcode-select -p` && cd ../.. && pwd)\"\n\ncwd=\"$(pwd)\"\nwhile [ \"$(pwd)\" != \"/\" ]; do\n xcode_proj=\"$((ls -d *.xcodeproj) 2>/dev/null)\"\n if [ $? -eq 0 ]; then break; fi\n cd ..\ndone\nxcode_dir=\"$(pwd)\"\ncd \"$cwd\"\n\nif [ -z \"$xcode_proj\" ]; then\n echo \"Error: no .xcodeproj file found\" >&2\n exit 1\nfi\necho open -a $xcode_loc \"$xcode_dir/$xcode_proj\"\nopen -a \"$xcode_loc\" \"$xcode_dir/$xcode_proj\"\n"
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"text": "\n# Created by .config\nexport PATH=\"$PATH:/Users/jrizkalla/environment/bin:/Users/jrizkalla/environment/C/bin\"\nexport PYTHONPATH=\"$PYTHONPATH:/Users/jrizkalla/environment/python\"\nexport ENV=\"/Users/jrizkalla/environment\"\n\nexport OSA_LIBRARY_PATH=\"/Users/jrizkalla/environment/jxa/lib\"\n"
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"text": "#!/usr/bin/env python3\n\nimport sys\nvers = sys.version_info\nif vers.major < 3 or vers.minor < 6:\n print(\"Please run with Python 3.6 or newer. Exiting\")\n sys.exit(5)\ndel vers\n\nEXEC_NAME = \"~/server.py\"\n\nfrom typing import *\nimport os\nimport argparse\nfrom pathlib import Path\nimport json\nimport tempfile\nimport contextlib\nimport functools\nfrom subprocess import call, check_call, check_output, DEVNULL\nimport uuid\nimport time\nfrom itertools import chain\nimport getpass\nfrom copy import copy, deepcopy\n\nclass parser:\n main = argparse.ArgumentParser(description=\"Manage multiple ssh servers without going crazy\",\n epilog=\"Server JSON format: { servers: [ {id: str, hostname; str, username: str} ])\")\n main.add_argument(\"--master\", nargs=\"?\", help=\"Force run as the master. Must provide .server_config.json file\", const=\"~/.server_config.json\")\n main.add_argument(\"--minion\", nargs=2, help=\"Force run as a minion. Must provide the server hostname and username. Must be able to ssh into the server without a password\")\n main.add_argument(\"--redirect-form\", help=\"Specify commands in Json format\")\n main.add_argument(\"--dry-run\", action=\"store_true\", default=False, help=\"Don't make any permenant changes. Just print the commands\")\n main.add_argument(\"-s\", \"--silent\", action=\"store_true\", default=False, help=\"Don't print anything that's not an error\")\n sub = main.add_subparsers()\n \n info = sub.add_parser(\"info\", help=\"Display information about the current configuration.\")\n info.set_defaults(action=\"info\")\n \n serv = sub.add_parser(\"server\", help=\"Server commands\")\n serv.set_defaults(action=\"serv\")\n \n ls = sub.add_parser(\"ls\", help=\"List all the servers\")\n ls.set_defaults(action=\"ls\")\n ls.add_argument(\"--json\", action=\"store_true\", default=False, help=\"Ouput the list of servers in json format\")\n \n ssh = sub.add_parser(\"ssh\", help=\"ssh into one of the servers\")\n ssh.set_defaults(action=\"ssh\")\n ssh.add_argument(\"server_name\", nargs=\"?\", default=None, help=\"The server name. Defaults to the master server\")\n ssh.add_argument(\"--print-url\", action=\"store_true\", default=False, help=\"Just print the ssh URL\")\n \n vnc = sub.add_parser(\"vnc\", help=\"vnc one of the servers\")\n vnc.set_defaults(action=\"vnc\")\n vnc.add_argument(\"server_name\", nargs=\"?\", default=None, help=\"The server name. Defaults to the master server\")\n vnc.add_argument(\"--print-url\", action=\"store_true\", default=False, help=\"Just print the vnc URL\")\n \n rsync = sub.add_parser(\"rsync\", help=\"Move a file from one server to another\")\n rsync.set_defaults(action=\"rsync\")\n rsync.add_argument(\"from_server\", help=\"From server name and location\")\n rsync.add_argument(\"to_server\", help=\"To server name and location. If location is not specified, send the file to the same location on the server.\")\n \n send = sub.add_parser(\"send\", help=\"Send a file to a server\")\n send.set_defaults(action=\"send\")\n send.add_argument(\"server\", help=\"The server name and location. If location is not specified, send the file to the same location on the server.\")\n \n get = sub.add_parser(\"get\", help=\"Get a file from a server\")\n get.set_defaults(action=\"get\")\n get.add_argument(\"server\", help=\"The server \")\n \n\n\n# util functions\ndef error(text: str, ret: int = 1):\n print(text, file=sys.stderr)\n sys.exit(ret)\n \[email protected]\ndef tmpfile(fl: Path):\n try:\n yield\n finally:\n if fl.exists():\n fl.unlink()\ndef _iterdir_noerr(self):\n i = iter(self.iterdir())\n while True:\n try:\n yield next(i)\n except FileNotFoundError: pass\n except StopIteration: break\nPath.iterdir_noerr = _iterdir_noerr\n \n\n\nclass Config:\n \n def __init__(self):\n self.dry_run = False\n \n @property\n def config_json(self):\n try:\n return self._config_json\n except AttributeError:\n with self.master_config.open(\"r\") as jf:\n self._config_json = json.load(jf)\n return self._config_json\n \n def print_cmd(self, cmd):\n if self.verbosity <= 0: return\n \n if len(cmd) >= 2 and cmd[0] == cmd[1]:\n cmd = cmd[1:]\n \n cmd_str = \" \".join(cmd)\n print(f\"$> {cmd_str}\")\n \n\n def __run_subpro(self, fn, cmd_args, *args, **kwargs):\n if self.dry_run and not (\"override\" in kwargs and kwargs[\"override\"]) :\n cmd_args = [\"echo\"] + [f\"'{a}'\" for a in cmd_args]\n if \"stdout\" in kwargs: del kwargs[\"stdout\"]\n if \"override\" in kwargs: del kwargs[\"override\"]\n return fn(cmd_args, *args, **kwargs)\n \n def call(self, *args, **kwargs):\n self.print_cmd(args[0])\n if self.dry_run and not (\"override\" in kwargs and kwargs[\"override\"]): return\n if \"override\" in kwargs: del kwargs[\"override\"]\n return call(*args, **kwargs)\n \n def check_call(self, *args, **kwargs):\n self.print_cmd(args[0])\n if self.dry_run and not (\"override\" in kwargs and kwargs[\"override\"]): return\n if \"override\" in kwargs: del kwargs[\"override\"]\n return check_call(*args, **kwargs)\n \n def execv(self, cmd, args, override=False):\n self.print_cmd([cmd] + args)\n if self.dry_run and not override: return\n \n os.execv(cmd, args)\n\ndef parse_server_name(server: str) -> Tuple[Optional[str], Optional[Path]]:\n if server.find(\":\") == -1:\n return (server, None)\n \n srv, fl, *err = server.split(\":\")\n if len(err) > 0:\n return (None, None)\n else:\n return (srv, Path(fl))\n \n\n\n\ndef send_to_master(config: Config, args: argparse.Namespace, return_output=False):\n # create a \"unique\" file on the server using the client mac address, process number, and timestamp\n req_file = Path(tempfile.gettempdir()) / f\"req_{uuid.getnode()}_{os.getpid()}_{time.time()}.json\"\n \n filtered_args = { k: v for k, v in vars(args).items() if k not in \"master,minion,redirect_form\".split(\",\") }\n \n with tmpfile(req_file):\n with open(req_file, \"w\") as tmp_file:\n json.dump(filtered_args, tmp_file)\n # scp the file to the server\n config.check_call([\"scp\", req_file, f\"{config.minion_username}@{config.minion_hostname}:/tmp/{req_file.parts[-1]}\"], stdout=DEVNULL, override=True)\n # and finally, run the command\n args = ([\"ssh\", f\"{config.minion_username}@{config.minion_hostname}\", EXEC_NAME, f\"--redirect-form /tmp/{req_file.parts[-1]}\"], )\n if return_output:\n return config.check_output(*args, override=True).decode()\n else:\n config.check_call(*args, override=True)\n\ndef server_fn(fn: Callable[[argparse.Namespace], int]):\n @functools.wraps(fn)\n def _server_fn(config: Config, args: argparse.Namespace):\n if config.is_master:\n fn(config, args)\n else:\n send_to_master(config, args)\n \n return _server_fn\n\n\n@server_fn\ndef ls_fn(config, args):\n if args.json:\n print(json.dumps(config.config_json[\"servers\"]))\n else:\n for srv in config.config_json[\"servers\"]:\n print(f\"{srv['id']:>30} -- {srv['username']}@{srv['hostname']}\")\n\ndef mk_sshvnc_fn(protocol: str):\n def _fn(config, args):\n if args.server_name is None:\n if config.is_master:\n # url of myself?\n uname = os.uname()\n username = getpass.getuser()\n hostname = os.uname().nodename\n else:\n username = config.minion_username\n hostname = config.minion_hostname\n else:\n if config.is_master:\n # lookup the hostname\n try:\n serv = next(serv for serv in config.config_json[\"servers\"] if serv[\"id\"] == args.server_name)\n except StopIteration:\n exit(f\"Unknown server name '{args.server_name}'\")\n username = serv[\"username\"]\n hostname = serv[\"hostname\"]\n else:\n # ask the master\n args2 = deepcopy(args)\n args2.print_url = True\n res = send_to_master(config, args2, return_output=True)\n # parse it\n _, res = res.strip().split(\"://\")\n username, hostname = res.split(\"@\")\n \n if args.print_url:\n print(f\"{protocol}://{username}@{hostname}\")\n else:\n # actually ssh\n call([\"echo\", f\"{protocol}\", f\"{username}@{hostname}\"])\n if protocol == \"ssh\":\n config.execv(\"/usr/bin/ssh\", [\"/usr/bin/ssh\", f\"{username}@{hostname}\"])\n else:\n config.execv(\"/usr/bin/open\", [\"/usr/bin/open\", f\"vnc://{username}@{hostname}\"])\n return _fn\n\n\n@server_fn\ndef rsync_fn(config, args):\n print(args)\n srv, srv_fl = parse_server_name(args.from_server)\n if srv_fl is None:\n error(\"Unable to parse server name and file\" if srv is None else \"Please provide a file name to rsync\")\n clt, clt_fl = parse_server_name(args.to_server)\n if clt_fl is None:\n clt_fl = srv_fl.parent\n \n # lookup the server and client\n try:\n srv = next(serv for serv in config.config_json[\"servers\"] if serv[\"id\"] == srv)\n clt = next(serv for serv in config.config_json[\"servers\"] if serv[\"id\"] == clt)\n except StopIteration:\n error(\"Unable to find server or client name\")\n srv = f\"{srv['username']}@{srv['hostname']}\"\n clt = f\"{clt['username']}@{clt['hostname']}\"\n \n \n with tempfile.TemporaryDirectory() as tmp_dir:\n # get the file\n print(tmp_dir)\n config.check_call([\"/usr/bin/rsync\", \"-avz\", f\"{srv}:{srv_fl}\", tmp_dir], stdout=DEVNULL)\n \n # send the file\n tmp_fl = Path(tmp_dir) / srv_fl.name\n config.check_call([\"/usr/bin/rsync\", \"-avz\", f\"{tmp_fl}\", f\"{clt}:{clt_fl}\"])\n \n\ndef not_implemented(config, args):\n error(\"Not implemented yet\")\n\nhandlers = {\n \"ls\": ls_fn,\n \"ssh\": mk_sshvnc_fn(\"ssh\"),\n \"vnc\": mk_sshvnc_fn(\"vnc\"),\n \"rsync\": rsync_fn,\n \"send\": not_implemented,\n \"get\": not_implemented,\n }\n\ndef main(args):\n \n if args.redirect_form is not None:\n # override all the args with this form.\n rf = Path(args.redirect_form)\n with rf.open(\"r\") as fm:\n args_dict = json.load(fm)\n # delete the form\n rf.unlink()\n \n args = argparse.Namespace()\n args.__dict__.update(args_dict)\n # fix the missing args\n if \"master\" not in args:\n args.master = None\n if \"minion\" not in args:\n args.minion = None\n \n # figure out if we're the client of the server\n if args.master is not None and args.minion is not None:\n exit(\"Can't be master and minion at the same time\")\n \n master_config = Path(args.master if args.master is not None \n else os.environ.get(\"SERVER_MASTER_CONFIG\" ,\"~/.server_config.json\")).expanduser().resolve()\n if args.master is not None and not master_config.is_file():\n exit(\"The master config file must be readable json file. Run with --help for the required format\")\n elif not master_config.is_file():\n master_config = None\n \n try:\n minion_hostname, minion_username = (args.minion if args.minion is not None \n else (os.environ[\"SERVER_MASTER_HOSTNAME\"], os.environ[\"SERVER_MASTER_USERNAME\"]))\n except KeyError:\n minion_hostname, minion_username = (None, None)\n \n if args.master is None and args.minion is None:\n is_master = master_config is not None\n else:\n is_master = args.master is not None\n \n # validation that we have the correct information\n if is_master and master_config is None:\n exit(\"Missing master configuration. Please provide a ~/.server-config.json file (see help for format)\")\n elif not is_master and minion_hostname is None:\n exit(\"Missing master hostname and username. Please provide them with SERVER_MASTER_HOSTNAME and SERVER_MASTER_USERNAME environment variables\")\n \n \n if \"action\" not in args:\n parser.main.print_help()\n exit()\n if args.action == \"info\":\n print(f\"Running as {'master' if is_master else 'minion'}\")\n if is_master:\n print(f\"Will fetch list of minions from {master_config}\")\n else:\n print(f\"Will redirect most work to master at {minion_username}@{minion_hostname}\")\n sys.exit(0)\n \n config = Config()\n config.is_master = is_master\n config.master_config = master_config\n config.minion_hostname = minion_hostname\n config.minion_username = minion_username\n config.dry_run = args.dry_run\n config.verbosity = 0 if args.silent else 1\n handlers[args.action](config, args)\n\nif __name__ == \"__main__\":\n main(parser.main.parse_args())\n"
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"text": "#!/bin/bash\n\nfunction print_help {\n >&2 echo -e 'Pretty: make output of commands pretty using Vim'\n >&2 echo -e 'Commands:'\n >&2 echo -e '\\tls (default args: -lhF)'\n}\n\nif [ $# -lt 1 ]; then\n >&2 echo 'Error: missing command.'\n print_help\n exit 1\nfi\n\nif [ $1 == 'ls' ]; then\n $1 -lhF ${@:2} | view - --cmd 'let g:fast_startup=1' +'set ft=prls'\nfi\n"
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"text": "#!/usr/bin/env python3\n\nimport urllib.parse as urllib\nimport urllib3\n\nRANDOM_WORD_WEBSITE='http://watchout4snakes.com/wo4snakes/Random/RandomWord';\n\ndef getRandomWord():\n ''' Returns a random word using an online service'''\n \n #req = urllib2.Request(RANDOM_WORD_WEBSITE, urllib.urlencode({\"LastWord\":\"\"}));\n#? req.add_header(\"User-Agent\", \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.75.14 (KHTML, like Gecko) Version/7.0.3 Safari/7046A194A\");\n http = urllib3.PoolManager()\n req = http.request(\"GET\", RANDOM_WORD_WEBSITE +\"?\"+ urllib.urlencode({\"LastWord\": \"\"}),\n headers={\"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.75.14 (KHTML, like Gecko) Version/7.0.3 Safari/7046A194A\"\n });\n#? assert req.get_method() == \"POST\";\n#? websiteFile = urllib2.urlopen(req);\n \n word = req.data.decode(\"utf-8\")\n#? word = websiteFile.read();\n#? websiteFile.close();\n \n return word;\n\n\ndef getPassword(numWords):\n '''Returns a string of numWords random words'''\n \n passwd = \"\";\n for i in range(0, numWords):\n passwd += getRandomWord();\n \n return passwd;\n\n\nif __name__ == \"__main__\": # running as a script\n import sys;\n num = 1;\n silent = False;\n \n try:\n num = int(sys.argv[1]);\n if len(sys.argv) >= 3:\n if sys.argv[2] == '-s':\n silent = True;\n except:\n sys.stderr.write(\"Usage: %s <number of words> [-s]\\n\" % sys.argv[0]);\n sys.exit(1);\n \n try:\n if silent:\n print(getPassword(num));\n else:\n pswd = \"\"\n sys.stdout.write(\"Sentence: \");\n for i in range(0, num):\n word = getRandomWord();\n pswd += word;\n sys.stdout.write(\"%s \" % word);\n sys.stdout.flush();\n sys.stdout.write(\"\\nPassword: %s\\n\" % pswd);\n except:\n sys.stderr.write(\"Something wrong! Check your internet connection\\n\");\n raise\n"
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"text": "#!/usr/bin/env bash\n\n\n# Simple wrapper around git difftool that doesn't spam the output with\n# a million vimdiff commands\n\ngit status > /dev/null \nif [ $? -ne 0 ]; then exit $?; fi\n\n# go to the root or git diff --name-only won't work properly\ncd \"$(git rev-parse --show-toplevel)\"\n\nFZF_PREVIEW=\"git diff $@ --color=always -- {-1}\"\n\nfls_str=\"$(git difftool --name-only $@)\"\nif [ $? -ne 0 ]; then exit $?; fi\n\nIFS=$'\\n' read -ra fls -d '\\0' <<< \"$fls_str\"\nunset IFS\n\nif [ \"${#fls[@]}\" -eq 0 ]; then\n echo \"No diff\"\n exit 0\nfi\n \n\nfls_orig=(\"${fls[@]}\") \n\n\n# make everything yellow\nfor i in \"${!fls[@]}\"; do\n fls[$i]=$\"\\e[33m\"\n fls[$i]+=\"${fls_orig[$i]}\"\n fls[$i]+=$\"\\e[0m\"\ndone\n\nfunction search_arr {\n needle=\"$1\"\n shift\n haystack=(\"$@\")\n \n for i in \"${!haystack[@]}\"; do\n if [ \"$needle\" == \"${haystack[$i]}\" ]; then\n echo $i\n return 0\n fi\n done\n return 1\n}\n\n\nwhile [ 1 ]; do\n fl=\"$(printf '%b\\n' \"${fls[@]}\" | fzf --no-sort --no-multi --ansi --preview \"$FZF_PREVIEW\")\"\n if [ -z \"$fl\" ]; then\n # empty file. Exit\n exit 0\n fi\n \n git difftool \"$@\" -- \"$fl\"\n \n # mark the file as viewed by changing it's color\n idx=$(search_arr $fl \"${fls_orig[@]}\")\n if [ $? -ne 0 ]; then \n echo \"Fatal error\"\n exit 1\n fi\n \n # change the color of that file\n \n fls[$idx]=$\"\\e[32m\"\n fls[$idx]+=\"${fls_orig[$idx]}\"\n fls[$idx]+=$\"\\e[0m\"\n \ndone\n\n"
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"text": "#!/usr/bin/env python\n\nimport tree;\nimport ctree;\n\nset = tree.ParseSettings();\nset.ignoreHiddenFiles = True;\n\nt = tree.FileTree(settings=set);\nctree.shape_tree(t);\nctree.print_latex(t);\n"
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"text": "#!/bin/bash\n\nif [ $# -le 0 ]; then \n\techo \"error! What process id?\"\n\texit 1\nfi\nps -A | egrep -i $1 | head -n 1 | awk '{print $1}'\n\n"
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"text": "#!/usr/bin/env python3\nfrom pathlib import Path\nimport json\nfrom subprocess import check_output, CalledProcessError\nimport sys\nimport argparse\n\narg_parser = argparse.ArgumentParser(description=\"Get radar title from id by quering Radar\")\narg_parser.add_argument(\"num\", help=\"The radar id\")\narg_parser.add_argument(\"--cache-only\", action=\"store_true\", default=False,\n help=\"Only check the local cache. Makes quering very quick\")\n\nGET_TITLE_SCRIPT = \"\"\"\ntell application \"Radar\"\n GetProblemData columnNames \"problemtitle\" problemID \"{}\"\nend tell\n\"\"\"\n\nDICT_FILE = Path.home() / \".radar_names\"\n\ndef get_title(num, cache_only):\n try:\n with open(DICT_FILE, \"r\") as df:\n problems = json.load(df)\n except FileNotFoundError:\n problems = {}\n \n try:\n return problems[num]\n except KeyError:\n if cache_only: raise\n # fetch it from Radar\n \n script = GET_TITLE_SCRIPT.format(num)\n title = check_output(f\"osascript -e '{script}'\", shell=True).decode().strip()\n if title.startswith(\"0\\n\"):\n raise CalledProcessError(0, \"osascript\")\n problems[num] = title\n # flush dictionary\n try:\n with open(DICT_FILE, \"w\") as df:\n json.dump(problems, df)\n except: pass\n \n return title\n\n\nif __name__ == \"__main__\":\n try:\n print(get_title(**vars(arg_parser.parse_args())))\n except:\n sys.exit(1)\n"
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"text": "#!/usr/bin/env python\n\nimport os;\nimport copy;\n\nclass ParseSettings:\n def __init__(self):\n self.ignoreHiddenFiles = False;\n self.blacklist = ['.DS_Store']; # ignore these files\n self.detectType = True;\n self.maxLevel = 99999999; # basically infinity\n \n def filter_func(self, file):\n ''' Checks if a file is hidden and checks if it is in the blacklist'''\n \n if len(file) == 0:\n raise ValueError('File must be at least one char long');\n \n if self.ignoreHiddenFiles and file[0] == '.':\n return False;\n \n try:\n index = self.blacklist.index(file);\n except ValueError:\n pass\n else:\n return False;\n \n return True;\n \n \n\nclass File(object):\n ''' The baseclass for a node in FileTree\n \n Attributes:\n -- name: the name of the file\n -- parent: the parent of this file. A file with\n None as its parent is the root\n -- type: the type of the file. An empty string represents an unknown type\n '''\n \n def __init__(self, name, parent = None):\n self.name = name;\n self.parent = parent;\n self.type = ''\n \n def __str__(self):\n return self.name + ' [' + self.type + ']';\n \nclass Dir(File):\n ''' A directory that can have multiple children\n \n Attributes:\n -- children: an array of children (of type File)\n Note: it is recommended to avoid using children directly\n '''\n \n def __init__(self, name, parent = None):\n File.__init__(self, name, parent);\n self.children = [];\n self.type = 'directory';\n self.subtype = 'plaindir';\n \n def append_child(self, file):\n ''' Appends file (of type File) to this Dir.'''\n \n if not isinstance(file, File):\n raise TypeError('Only Files can be appended to the children of Dir');\n \n self.children.append(file);\n file.parent = self;\n \n def delete_child(self, file):\n ''' Deletes file from this Dir.\n File can be:\n - A File object\n - A string\n '''\n \n if isinstance(file, File):\n self.children.remove(file);\n file.parent = None;\n elif type(file) is str:\n # Look for the file 'manually'\n for i in range(len(self.children)):\n if self.children[i].name == file:\n self.children[i].parent = None;\n del self.children[i];\n break;\n else:\n raise ValueError('Unable to find child')\n else:\n raise ValueError('paramter not file or string');\n \n def delete_all_children(self):\n ''' Deletes all children'''\n for ch in self.children:\n ch.parent = None;\n self.children = [];\n\n\nclass FileTree:\n ''' An actual file tree (from actual files).\n \n The root is a File (or a subclasses of File)\n '''\n \n def __init__(self, directory='', settings = ParseSettings()):\n ''' Creates a FileTree from the directory in directory (if it exists)\n \n If directory is empty, it uses the current working directory\n '''\n self.settings = settings;\n \n if len(directory) == 0:\n directory = os.getcwd();\n \n directory = os.path.abspath(directory);\n if not os.path.exists(directory):\n raise ValueError('directory passed to FileTree.__init__ is not a real directory');\n \n # is the root a directory:\n self.root = None;\n if os.path.isdir(directory):\n self.root = Dir(directory);\n for dir in os.listdir(directory):\n self._discover_tree(dir, self.root, 1);\n \n \n def _discover_tree(self, dir, parent, level):\n if level > self.settings.maxLevel:\n return;\n \n if not self.settings.filter_func(dir):\n return;\n \n fullPath = self.path_from_root(parent) + '/' + dir;\n if os.path.isdir(fullPath):\n file = Dir(dir);\n parent.append_child(file);\n \n try:\n for f in os.listdir(fullPath):\n self._discover_tree(f, file, level+1);\n except OSError:\n # Permission denied. That's ok\n pass\n else:\n name, ext = os.path.splitext(dir);\n file = File(name + ext);\n if self.settings.detectType:\n file.type = ext[1:]; # remove the . in the extention\n parent.append_child(file);\n \n \n def path_from_root(self, file):\n path = [file.name];\n # Append them in reverse for more efficiency\n file = file.parent;\n while (file != None):\n path.append(file.name);\n file = file.parent;\n \n pathstr = path[-1];\n i = len(path) - 2;\n while (i >= 0):\n pathstr += '/' + path[i];\n i -= 1;\n return pathstr;\n\n def _create_str(self, file, level):\n res = (level * 4) * ' ' + str(file) + '\\n';\n if file.type == 'directory':\n for child in file.children:\n res += self._create_str(child, level+1);\n \n return res;\n \n def __str__(self):\n return self._create_str(self.root, 0);\n \n \n def _filter(self, node, accept_file):\n if accept_file(node):\n if node.type == 'directory':\n children = copy.copy(node.children);\n for child in children:\n self._filter(child, accept_file);\n else:\n # Completely remove the node (and all of its children) from the tree\n if node.parent:\n node.parent.delete_child(node);\n else:\n root = None;\n \n def _prune_branch(self, node):\n ''' Prunes node (but does not remove node itself).\n Returns true if there was a file in node and false otherwise'''\n \n # Go to the end. And while walking up, remove unwanted\n # branches\n if node.type == 'directory':\n files = False;\n i = 0;\n while(i < len(node.children)):\n if not self._prune_branch(node.children[i]):\n node.delete_child(node.children[i]);\n i-=1;\n else:\n files = True;\n i+=1;\n return files; \n else:\n return True;\n \n def prune(self):\n ''' Removes all branches that don't have files at the end.\n '''\n # Keep the root anyway\n self._prune_branch(self.root);\n \n \n def filter(self, accept_file, cut_empty_branches=False):\n ''' Filters the tree based on accept_file (func).\n \n Arguments:\n -- accept_file is a function that takes in a File as a paramter\n and returns a boolean\n -- cut_empty_branches: if it is true all brances that don't end in files\n are cut off. Otherwise they are kept as they are\n '''\n self._filter(self.root, accept_file);\n if cut_empty_branches:\n self.prune();\n \n \n def _expand(self, file, expand_file):\n newFile, newFiles = expand_file(file);\n if len(newFiles) > 0 and (not isinstance(newFile, Dir) or newFile.type != 'directory'):\n raise ValueError('The file returned by expand_file must be a directory if there are new files');\n if file.type == 'directory' and (newFile.type != 'directory' or not isinstance(newFile, Dir)):\n raise ValueError('expand_file cannot change a directory to a regular file');\n \n # newFile could be the same as file...\n if (newFile == file):\n if len(newFiles) > 0:\n for f in newFiles:\n file.append_child(f);\n if file.type == 'directory':\n for c in file.children:\n self._expand(c, expand_file);\n return;\n \n \n parent = file.parent;\n newFile.parent = parent;\n if file.type == 'directory':\n # Remove all files from file and add them to newFile\n children = file.children;\n file.delete_all_children();\n for ch in children:\n newFile.append_child(ch);\n \n # add the new file\n for ch in newFiles:\n newFile.append_child(ch);\n \n # Replace file with newFile (in parent)\n i = parent.children.index(file);\n parent.children[i] = newFile;\n file.parent = None;\n file.name = 'ERROR';\n return;\n \n # Recurse on all children\n if newFile.type == 'directory':\n for ch in newFile.children:\n self._expand(ch, expand_file);\n \n \n def expand(self, expand_file):\n ''' Expands the tree by processing files\n \n Arguments:\n -- expand_file is a function that takes a File as input and returns\n a new Dir (or subchild) and a list of files that are added to it (return\n only the new file. Not the files that were already children of file)\n Note: all directories must have type == 'directory'. Directory subtypes are\n placed in Dir.dirtype\n '''\n \n # Root cannot be expanded:\n if self.root.type == 'directory':\n for f in self.root.children:\n self._expand(f, expand_file);\n \n \n def _apply(self, node, level, apply_func):\n apply_func(level, node);\n \n if node.type == 'directory':\n for ch in node.children:\n self._apply(ch, level+1, apply_func);\n \n def apply(self, apply_func):\n ''' Applies a function on all nodes\n \n Arguments:\n -- apply_func (level, file) -> None\n A function that takes the level and the file and edits file\n level starts at 0 (for the root)\n '''\n \n self._apply(self.root, 0, apply_func)\n \n \n \n \n \n# Shell script part:\n\n\nif __name__ == '__main__': # Run as a script (not imported)\n # Parse the command line arguments and run the tree\n import sys;\n def print_help(retcode=0, extend=False):\n print 'Usage: ' + sys.argv[0] + ' -[cpflht] -help';\n if extend:\n sys.stderr.write('''\n Arguments:\n -help print this help message\n -c don't use colors\n -p print the full path of the files, not just their names\n -f <format> change the prefix string to format\n -l <num> set the max number of levels to descend\n -h look at hidden files\n -t print the type of file\n -d <dir> start at <dir> not the cwd\n''');\n sys.exit(retcode);\n \n class PrintSettings:\n pass\n \n set = ParseSettings();\n set.detectType = False;\n set.ignoreHiddenFiles = True;\n pset = PrintSettings();\n pset.color = True;\n pset.fullPath = False;\n pset.prefix = 4 * ' ';\n \n startDir = '';\n \n # Parse the arguments\n i = 1;\n while (i < len(sys.argv)):\n arg = sys.argv[i].lower();\n if (arg == '-help' or arg == '--help' or arg == 'help'):\n print_help(extend=True);\n elif (arg == '-c'):\n pset.color = False;\n elif (arg == '-p'):\n pset.fullPath = True;\n elif (arg == '-h'):\n set.ignoreHiddenFiles = False;\n elif (arg == '-t'):\n set.detectType = True;\n elif (arg == '-f' or arg == '-l' or arg == '-d'):\n if ((i+1) == len(sys.argv)):\n print_help(2);\n param = sys.argv[i+1];\n i += 1;\n \n if (arg == '-f'):\n pset.prefix = param;\n elif (arg == '-l'):\n try:\n param = int(param);\n except ValueError:\n sys.stderr.write('Error: unable to parse -l <num>');\n print_help(3);\n set.maxLevel = param;\n elif (arg == '-d'):\n startDir = param;\n else:\n print_help(1);\n \n i += 1;\n \n \n # Create the tree and print it\n try:\n tree = FileTree(startDir, settings=set);\n except ValueError:\n sys.stderr.write('Error: not a directory');\n print_help(4);\n else:\n def _print_node(file, level):\n res = '';\n if pset.fullPath:\n res += tree.path_from_root(file);\n else:\n res += file.name;\n if set.detectType:\n res += ' [' + file.type + ']';\n \n if file.type == 'directory':\n if pset.color:\n # make directories blue on a white background\n res = level * pset.prefix + '\\033[34;m' + res + '\\033[0;m'\n else:\n res = level * pset.prefix + res;\n print res;\n for child in file.children:\n _print_node(child, level + 1);\n else:\n res = level * pset.prefix + res;\n print res;\n \n _print_node(tree.root, 0);\n \n \n \nelse:\n pass # Do nothing\n"
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"path": "/python/build_script_finder.py",
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"text": "#!/usr/bin/env python3\n\nimport sys\nfrom pathlib import Path\nfrom subprocess import check_output, run, PIPE\nimport json\n\ndef main(argv):\n \n if len(argv) > 2 and argv[1] == \"add\":\n script = argv[2]\n try:\n with (Path.home() / \".build.json\").open(\"r\") as json_file:\n scripts = json.load(json_file)\n except FileNotFoundError:\n scripts = {}\n \n full_path = Path(script).expanduser().absolute().resolve()\n curr_branch = check_output(\"git branch | grep --color=auto \\* | cut -d ' ' -f2\", shell=True).decode().strip()\n key = str(Path.cwd().absolute()) + \":\" + curr_branch\n key = key.lower().strip()\n if key in scripts:\n resp = input(\"Warning. Script for {} exists. Override? (y/n) \".format(key))\n if not resp.lower() in (\"yes\", \"y\"):\n return 1\n \n scripts[key] = {\n \"path\": str(full_path),\n \"args\": argv[3:]\n }\n \n with (Path.home() / \".build.json\").open(\"w\") as json_file:\n json.dump(scripts, json_file, indent=2)\n \n elif len(argv) >= 2 and argv[1] == \"show\":\n \n curr_dir = Path.cwd().absolute()\n curr_branch = check_output(\"git branch | grep --color=auto \\* | cut -d ' ' -f2\", shell=True).decode().strip().lower()\n \n try:\n with (Path.home() / \".build.json\").open(\"r\") as json_file:\n scripts = json.load(json_file)\n \n \n # Walk up the directory tree\n dir_ = curr_dir\n while dir_ != Path(\"/\"):\n script_id = str(dir_).lower() + \":\" + curr_branch\n if script_id in scripts: break\n dir_ = dir_.parent\n \n script = scripts[str(dir_).lower() + \":\" + curr_branch]\n if len(argv) > 2 and argv[2] in (\"-a\", \"--all\"):\n print(\"Script: {}\\nArgs: {}\".format(script[\"path\"], script[\"args\"]))\n else:\n print(script[\"path\"])\n except:\n print(\"No script for current {} branch {}\".format(curr_dir.name, curr_branch))\n \n else:\n curr_dir = Path.cwd().absolute().resolve()\n curr_branch = check_output(\"git branch | grep --color=auto \\* | cut -d ' ' -f2\", shell=True).decode().strip().lower()\n \n try:\n with (Path.home() / \".build.json\").open(\"r\") as json_file:\n scripts = json.load(json_file)\n except FileNotFoundError:\n scripts = {}\n \n # Walk up the directory tree\n dir_ = curr_dir\n while dir_ != Path(\"/\"):\n script_id = str(dir_).lower() + \":\" + curr_branch\n if script_id in scripts: break\n dir_ = dir_.parent\n \n \n try:\n script = scripts[script_id]\n except KeyError:\n print(\"Error. Script for {} and branch {} doesn't exist.\\n Use `{} add` to add one\".format(curr_dir.name, curr_branch, argv[0]))\n return 1\n \n print(f\"Running {[script['path']] + script['args'] + argv[1:]}\")\n run([script[\"path\"]] + script[\"args\"] + argv[1:])\n\nif __name__ == \"__main__\":\n sys.exit(main(sys.argv))\n"
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"text": "''' Provides a few functions for outputing LaTeX. '''\n\n\n\nLATEX_CHARS = ['\\\\', '&', '%', '$', '#', '_', '{', '}', '~', '^'];\nLATEX_CHARS_REP = [r'\\textbackslash{}', r'\\&', r'\\%', r'\\$', r'\\#', r'\\_', r'\\{', r'\\}', r'\\textasciitilde{}', r'\\textasciicircum{}'];\n\ndef sanitize(s):\n ''' Sanitizes a string for LaTeX (non math mode)\n \n Arguments:\n -- s any string\n Returns a copy of s sanitized for LaTeX (outside math mode)\n '''\n \n lst = [];\n for c in s:\n for i in range(len(LATEX_CHARS)):\n if c == LATEX_CHARS[i]:\n lst.append(LATEX_CHARS_REP[i]);\n break;\n else:\n # Not a special char at all\n lst.append(c);\n \n return ''.join(lst);\n"
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"text": "sesh='Home'\n\ntmux new -s $sesh -d\n\ntmux rename-window -t $sesh 'home'\n\ntmux split-window -h \"tail -f ~/.log_file\"\ntmux select-pane -t 1\ntmux resize-pane -R 40\n\ntmux select-pane -t 0\n\ntmux attach -t $sesh\n"
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"text": "'''\n CTree\n Defines a few more types for C and C header files\n and filters out unneeded files.\n \n Also reads functions in a C files using ctags\n'''\n\nimport tree;\nimport subprocess;\nimport sys;\nimport latex;\n\nclass CFunc(tree.File):\n def __init__(self, name, parent = None, code = '', line = -1):\n tree.File.__init__(self, name, parent);\n self.type = 'C function';\n self.code = code;\n self.line = line;\n \n def __str__(self):\n return str(self.line) + ' ' + self.name + ': ' + self.code;\n\nclass CFile(tree.Dir):\n def __init__(self, name, parent = None):\n tree.Dir.__init__(self, name, parent);\n self.subtype = 'c';\n \n def read_functions(self, full_path):\n ''' Reads the functions from the file at full_path\n \n Uses ctags to read full_path. Returns a list of all\n functions\n \n Arguments:\n -- full_path: the full path of this C file\n '''\n \n output = subprocess.check_output('ctags -x \"' + full_path + '\"', shell=True);\n \n # Output looks like this:\n # <name> <line_num> <file> <code>\n result = [];\n for line in iter(output.splitlines()):\n linelst = line.split();\n cfunc = CFunc(linelst[0], code = ' '.join(linelst[3:]), line = linelst[1]);\n result.append(cfunc);\n return result;\n \n def __str__(self):\n return self.name + ' [' + self.subtype + ']';\n \ndef _filter_func(file):\n return file.type == 'directory' or file.type == 'c' or file.type == 'h';\n \ndef _make_expand_func(t):\n def _expand_func(file):\n if file.type == 'c':\n cfile = CFile(name=file.name);\n return (cfile, cfile.read_functions(t.path_from_root(file)));\n else:\n return (file, []);\n return _expand_func;\n \n \ndef shape_tree(t):\n ''' Filters and expands t and then returns it.\n \n t is a FileTree\n \n Filters out all non-c (.c, .h, and directories) files\n then expands all .c files using ctags\n '''\n \n t.filter(_filter_func, True);\n t.expand(_make_expand_func(t));\n return t;\n\n\n# And as a bonus :)...\n\ndef print_node_dirtree(file, level, header, out):\n ''' Prints a node to out\n Used with print_latex to print a tree.\n This function formats the output for the dirtree package\n '''\n \n if level == -1:\n # Print the header\n out.write('\\definecolor{c-func-color}{rgb}{.3, .3, .3}\\n\\dirtree{%\\n');\n return;\n elif level == -2:\n out.write('}\\n')\n return;\n \n if not header:\n return;\n \n level += 1;\n \n if file.type == 'directory':\n out.write('.' + str(level) + ' {' + latex.sanitize(file.name) + '}.\\n');\n if isinstance(file, CFile):\n pass # C file\n else:\n pass # Normal directory\n else:\n if isinstance(file, CFunc):\n # C function\n out.write('.' + str(level) + ' {\\\\color{c-func-color}' + latex.sanitize(file.code) + '}.\\n');\n else:\n # Normal (probably header) file\n out.write('.' + str(level) + ' {' + latex.sanitize(file.name) + '}.\\n');\n \n \n\ndef _print_latex(node, level, out, node_printer):\n node_printer(node, level, True, out);\n if node.type == 'directory':\n for c in node.children:\n _print_latex(c, level+1, out, node_printer);\n node_printer(node, level, False, out);\n \ndef print_latex(t, out=sys.stdout, node_printer=print_node_dirtree):\n ''' Prints the C Tree in LaTeX.\n \n Arguments:\n -- t the tree to print\n -- out the file to print to (stdout by default)\n -- node_printer(file, level, out) -> None: a function used to print each node.\n By default, this is set to print_node_dirtree.\n If level is -1, node_printer must print the header.\n If level is -2, node_printer must print the footer.\n '''\n \n node_printer(None, -1, None, out)\n _print_latex(t.root, 0, out, node_printer);\n node_printer(None, -2, None, out)\n"
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"text": "source $HOME/.bash_profile\n\nfpath=($ENV/zsh/plugins/complete_funcs $fpath)\n\n# Load colors\nautoload -U colors && colors\n\n# Customize the prompt\nsetopt prompt_subst\nsetopt extendedglob\nsetopt hist_ignorespace\n\nalias kk=' echo \"$(fc -ln -1)\" && eval \"$(fc -ln -1)\"'\n\nBASE_PROMPT1=\"%{$fg[yellow]%}%!. [%{$fg[cyan]%}%D{%I:%M:%S %p}%{$fg[yellow]%}]\" # 1. [time]\nBASE_PROMPT2=\"%{$fg[yellow]%}$> %{$reset_color%}\" #$> \n# Set a timeout to 1 second (to update the clock)\n# \n#TMOUT=1\n#function TRAPALRM {\n# if [ \"$WIDGET\" != \"expand-or-complete\" ]; then\n# zle reset-prompt\n# fi\n#}\n\nBASE_RPROMPT=\"%M %{$fg[white]%}%{$fg[cyan]%}%1d%{$reset_color%}\"\nRPROMPT=\"$BASE_RPROMPT\"\n\n#PS2='%_ > '\n\n\n# Enable auto completion\nzmodload zsh/complist\nautoload -U compinit && compinit\nzstyle ':completion:*' menu select\nzstyle ':completion:*' list-colors \"${(@s.:.)LS_COLORS}\"\nsetopt completealiases\nsetopt menu_complete\n\n# Type the name of the directory without cd\nsetopt autocd\n# don't enable correction (I don't like it)\n# setopt correctall\n\n# Set vi mode\nbindkey -v\n#export KEYTIMEOUT=20 # Kill the lag (don't need this with jk instead of <esc>)\nbindkey -M viins 'jk' vi-cmd-mode\nbindkey '^?' backward-delete-char\nfunction ctrl-z-widget {\n clear\n fg\n}\nzle -N ctrl-z-widget\nbindkey '^z' ctrl-z-widget\n\n\n# Add an indicator of the current mode (normal or insert)\nfunction zle-keymap-select {\n mode=\"n\"\n if [ \"$KEYMAP\" = \"main\" ]; then\n mode=\"i\"\n fi\n PROMPT=\"$BASE_PROMPT1 %(?.%{$fg[green]%}.%{$fg[red]%})$mode$BASE_PROMPT2\"\n zle reset-prompt\n}\nzle -N zle-keymap-select\n\n# Git branch in status\nexport _rprompt_use_radar_title=false\n\nfunction zle-line-init {\n zle && zle zle-keymap-select\n \n if [ $_rprompt_use_radar_title = \"true\" ]; then\n curr_radar=$(curr-radar --id-only 2>/dev/null)\n if [ -n \"$curr_radar\" ]; then\n branch=$(radartitle $curr_radar)\n else\n branch=\"\"\n fi\n else\n branch=\"$(curr-radar 2>/dev/null)\"\n fi\n if [ -z \"$branch\" ]; then\n branch=$(basename \"$(git symbolic-ref HEAD 2>/dev/null)\")\n fi\n # Set the color based on wether the current repo is clean or not\n git diff --cached --exit-code >/dev/null 2>&1\n if [ $? -eq 0 ]; then\n # are there untracked files?\n if [ -z \"$(git status --porcelain)\" ]; then\n color='green'\n else\n color='red'\n fi\n else\n color='red'\n fi\n \n if [ -n \"$branch\" ]; then\n branch=\"[%{$fg_bold[$color]%}$branch%{$reset_color%}]\"\n fi\n \n # also get the Python virtual environment name (if it exists)\n if [ -n \"$VIRTUAL_ENV\" ]; then\n virtual_env=\"(%{$fg[yellow]%}$(basename $VIRTUAL_ENV)%{$reset_color%})\"\n else\n virtual_env=\"\"\n fi\n \n base_prompt=\"$BASE_RPROMPT\"\n \n # Color the directory green if the parent of the current dir is called 'green'\n # (don't ask why)\n #if [ \"$(basename $(dirname $(pwd)))\" = \"green\" ]; then\n if dir-contains \"$(pwd)\" \"green\"; then\n base_prompt=\"%{$fg[green]%}%1d%{$reset_color%}\"\n fi\n \n RPROMPT=\"$base_prompt$virtual_env $branch\"\n zle && zle reset-prompt\n}\nzle -N zle-line-init\n\nfunction curr-radar-title {\n if [ \"$_rprompt_use_radar_title\" = \"true\" ]; then\n export _rprompt_use_radar_title=false\n else\n export _rprompt_use_radar_title=true\n fi\n zle zle-line-init\n}\nzle -N curr-radar-title\nbindkey -a 's' curr-radar-title\n\n\n\n# Save the history between sessions\nHISTSIZE=1000\nHISTFILE=\"$HOME/.zsh_history_file\"\nSAVEHIST=1000\n\n# Share the history\nsetopt SHARE_HISTORY\n\n# Enable fish like syntax highlighting\nsource \"$ENV/zsh/plugins/zsh-syntax-highlighting/zsh-syntax-highlighting.zsh\"\n# Enable fish like autosuggestions\nsource \"$ENV/zsh/plugins/zsh-autosuggestions/zsh-autosuggestions.zsh\"\n\nbindkey '^f' autosuggest-accept\nbindkey '^g' forward-word\n\n# make ctrl-a clear the display since l is being used for something else\nbindkey -s '^a' '^l'\n\n\n# Add RVM to PATH for scripting. Make sure this is the last PATH variable change.\nexport PATH=\"$PATH:$HOME/.rvm/bin\"\n\n\n[ -f ~/.fzf.zsh ] && source ~/.fzf.zsh\nexport PATH=\"/usr/local/opt/ruby/bin:$PATH\"\n\ntest -e \"${HOME}/.iterm2_shell_integration.zsh\" && source \"${HOME}/.iterm2_shell_integration.zsh\"\n"
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"text": "#!/usr/bin/env sh\n\nfunction default_css {\n cat <<END\n * {\n font-family: \"Helvetica\", sans-serif;\n }\n code,\n code * {\n font-family: \"Courier New\";\n }\nEND\n}\n\nwhich pandoc > /dev/null 2>&1\nif [ $? -ne 0 ]; then\n echo \"Error: please install pandoc to continue (brew install pandoc)\" >&2\n exit 1\nfi\nwhich textutil > /dev/null 2>&1\nif [ $? -ne 0 ]; then\n echo \"Error: missing textutil command. Are you on MacOS?\" >&2\n exit 1\nfi\n\nmd_tmp_file=$(mktemp -t compose.md)\nrtf_tmp_file=$(mktemp -t compose.rtf)\nhtml_tmp_file=$(mktemp -t compose.html)\n\nfunction cleanup {\n rm -r \"$md_tmp_file\"\n rm -r \"$rtf_tmp_file\"\n rm -r \"$html_tmp_file\"\n}\ntrap cleanup EXIT\n\nVIM=vim\nwhich mvim >/dev/null >&2\nif [ $? -eq 0 ]; then VIM=\"mvim --nofork\"; fi\n\n$VIM \"$md_tmp_file\" '+set ft=markdown'\n\n# Create a style file\nstyle=\"\"\nif [ -e \"$HOME/.compose.css\" ]; then\n style=\"$(cat \"$HOME/compose.css\")\"\nelse\n style=\"$(default_css)\"\nfi\n\n# Convert the file to rtf\necho \"<style>$style</style>\" > $html_tmp_file\npandoc \"$md_tmp_file\" --standalone -t html --highlight-style tango >> $html_tmp_file\ncat $html_tmp_file | textutil -stdin -format html -convert rtf -output \"$rtf_tmp_file\"\n\ncat \"$rtf_tmp_file\" | pbcopy\n\n# For debugging. TODO: remove\ncp \"$rtf_tmp_file\" /tmp/compose.rtf\ncp \"$md_tmp_file\" /tmp/compose.md\ncp \"$html_tmp_file\" /tmp/compose.html\n"
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"text": "\nalias X11Compile='g++ -L/usr/X11/lib -I/usr/X11/include -lX11'\nalias bopen='open -g'\nalias c='clear'\nalias delete='\\rm -i -r'\nalias egrep='egrep --color=auto'\nalias grep='grep --color=auto'\n# Does ls accept -G or --color=auto for color?\nls -G > /dev/null 2>&1\nif [ $? -eq 0 ]; then\n alias la='ls -G -A -F'\n alias ls='ls -G -F'\nelse\n alias la='ls --color=auto -A -F'\n alias ls='ls --color=auto -F'\nfi\nalias process='ps -A | egrep -i '\nalias rm='trash'\n#alias lock=\"/System/Library/CoreServices/Menu\\ Extras/User.menu/Contents/Resources/CGSession -suspend\"\nalias linux=\"ssh [email protected]\"\nalias linux2=\"ssh [email protected]\"\nalias linux4=\"ssh [email protected]\"\nalias linux6=\"ssh [email protected]\"\nalias glinux=\"ssh -X [email protected]\"\nalias xcode=\"open -a Xcode \"\nalias less=\"less -r\"\nalias stop=\"kill SIGTSTP\"\n\nalias pmux=\"./scripts/tmux.sh\"\n\nalias branch='BRANCH_FROM=$(pwd); cd'\nalias goback='echo \"cd $BRANCH_FROM\"; cd \"$BRANCH_FROM\"'\n\nalias autolatex='latexmk -pdf -pvc -interaction=nonstopmode -synctex=1'\nalias skim='open -a Skim'\nalias glog=\"git log --decorate --graph --abbrev-commit\"\n\n# Vim\n# Is MacVim installed?\nls \"/Applications/MacVim.app/Contents/MacOS/Vim\" > /dev/null 2>&1\nif [ $? -eq 0 ]; then\n alias vim=\"/Applications/MacVim.app/Contents/MacOS/Vim\"\n alias mvim=\"mvim -p\" # multiple tabs in vim\n alias cvim=\"mvim -p -c 'call CFamily_OpenAll()'\"\nfi\n# TODO: fix\nalias vim=\"mvim -v\"\n\n# Out of the box vim and mvim\nalias vimootb='vim -u NONE'\nalias mvimootb='mvim -u NONE'\n\nalias fork='open -a $TERM_PROGRAM .' # open this directory in another terminal window/tab\n# customize the prompt\nexport PS1=\"\\e[33m[\\t] $>\\e[0m \"\n\n# add a new directory to the list of directories that have the executable files\nexport PATH=\"$PATH:/Library/TeX/Distributions/Programs/texbin\"\n\n# for os 161\n#export PATH=\"$PATH:$HOME/sys161/bin:$HOME/sys161/tools/bin\"\n\n# Specifically for OS X:\nif [ \"$(uname)\" = \"Darwin\" ]; then\n alias save=\"open /System/Library/Frameworks/ScreenSaver.framework/Versions/Current/Resources/ScreenSaverEngine.app\"\nfi\n\n# icloud locations\nif [ \"$USER\" = 'johnrizkalla' -a \"$(uname)\" = 'Darwin' ]; then\n export ICLOUD='/Users/johnrizkalla/Library/Mobile Documents/com~apple~CloudDocs'\n export DOCUMENTS=\"$ICLOUD/Documents\"\n export CURRENT_TERM=\"$DOCUMENTS/2015 - 2016 Academic Year/Winter 2015 (4A term)\"\n\n # Shortcuts for common folders\n alias icloud='cd \"$ICLOUD\"'\n alias documents='cd \"$ICLOUD\"; cd Documents'\n alias term='cd \"$ICLOUD\"; cd Documents; cd \"$CURRENT_TERM\"'\n alias home=\"cd ~\"\n alias downloads=\"cd ~/Downloads\"\n alias desktop=\"cd ~/Desktop\"\n #and a summary:\n alias folders='echo -e icloud; echo -e \"\\tdocuments\"; echo -e \"\\t\\tterm\"; echo home; echo -e \"\\tdownloads\"; echo -e \"\\tdesktop\"'\nfi\n\n##\n# Your previous /Users/johnrizkalla/.bash_profile file was backed up as /Users/johnrizkalla/.bash_profile.macports-saved_2015-05-05_at_17:43:35\n##\n\n# Setting PATH for Python 3.4\n# The orginal version is saved in .bash_profile.pysave\nPATH=\"/Library/Frameworks/Python.framework/Versions/3.4/bin:/usr/local/bin:${PATH}\"\nexport PATH\n\n# Set the log file (for scripts running in the background)\nexport LOG_FILE=\"$HOME/.log_file\"\n# flog can be used to print to a log file\n\n# Some functions\ntype qlmanage >/dev/null 2>&1 # Quick look (OS X)\nif [ $? -eq 0 ]; then\n function preview {\n qlmanage -p $1 >/dev/null 2>&1 &\n }\nfi\n\nsource \"$HOME/.env_bash_profile\"\n\nif [ -x \"$HOME/.local_bash_profile\" ]; then\n source \"$HOME/.local_bash_profile\"\nfi\n\nfunction armdump {\n arm-none-eabi-objdump -D $@ | vim - -c 'source ~/.vim/scripts/ObjDumpToARM.vim'\n}\n\n# Encrypt and zip a directory\nfunction enczip {\n zip -r \"$1.zip\" \"$1\"\n err=\"$?\"\n if [ \"$err\" -ne 0 ]; then\n return $err\n fi\n openssl des3 -in \"$1.zip\" -out \"$1.zipenc\"\n err=\"$?\"\n if [ \"$err\" -ne 0 ]; then\n \\rm -rf \"$1.zip\"\n return $err\n fi\n \\rm -rf \"$1\" \"$1.zip\"\n}\n\nfunction deczip {\n ext=\"${1##*.}\"\n name=\"${1%.*}\"\n \n openssl des3 -d -in \"$1\" -out \"$name.zip\"\n err=\"$?\"\n if [ \"$err\" -ne 0 ]; then\n \\rm -f \"$name.zip\"\n return $err\n fi\n unzip \"$name.zip\"\n err=\"$?\"\n if [ \"$err\" -ne 0 ]; then\n \\rm -rf \"$name.zip\"\n return $err\n fi\n \\rm -f \"$name.zip\" \"$name.$ext\"\n}\n\nfunction automd {\n filename=\"$1\"\n if [ ! -f \"$filename.md\" ]; then\n # Does the file end with md?\n filename=\"$(basename \"$filename\" \".md\")\"\n if [ ! -f \"$filename.md\" ]; then\n echo \"Cannot open file $filename.md\"\n return 1\n fi\n fi\n \n #echo \"$filename.md\" | entr sh -c pandoc --standalone \"$filename.md\" -o \"$filename.tex\" &&\n # sed -i.bak '1s/$/\\\\usepackage{tikz}\\\\usetikzlibrary{calc,positioning}/' \"$filename.tex\" &&\n # latexmk -pdf \"$filename.tex\" &&\n # \\rm *.^(md|pdf)\n trap \"return;\" SIGINT SIGTERM\n while [ 1 ]; do\n fswatch -L -0 -1 \"$filename.md\" > /dev/null\n echo \"Recompiling $filename.md\"\n pandoc --verbose \"$filename.md\" -o \"$filename.pdf\"\n done\n}\n\nfunction help {\n man $* 2>/dev/null\n if [ $? -ne 0 ]; then\n \"$1\" --help 2>&1 | less\n fi\n}\n\nfunction winmgr {\n cmd=$(echo $1 | tr \"[:upper:]\" \"[:lower\"])\n if [ \"$cmd\" = \"on\" ]; then\n launchctl load ~/Library/LaunchAgents/com.koekeishiya.khd.plist\n launchctl load ~/Library/LaunchAgents/com.koekeishiya.chunkwm.plist\n else\n launchctl unload ~/Library/LaunchAgents/com.koekeishiya.khd.plist\n launchctl unload ~/Library/LaunchAgents/com.koekeishiya.chunkwm.plist\n fi\n}\n\n\nfunction activate {\n script=\"env/bin/activate\"\n if [ -n \"$1\" ]; then script=\"$1\"; fi\n source \"$script\"\n}\n\nif [ -x /usr/local/bin/egrep ]; then\n GREP=/usr/local/bin/egrep\nelse\n GREP=egrep\nfi\n\nfunction psearch {\n case_sensitive=true\n for arg in \"$@\"; do\n if [ \"${arg:0:1}\" != \"-\" ]; then\n term=\"$arg\"\n elif [ \"$arg\" = \"-i\" ]; then\n case_sensitive=false\n fi\n done\n if [ -z \"$term\" ]; then\n echo \"Error: search for what?\" >&2\n return 1\n fi\n \n if [ $case_sensitive = \"true\" ]; then\n case_sensitive_flag=\"\"\n else\n case_sensitive_flag=\"\\c\"\n fi\n egrep -rnI --exclude-dir=build $@ . | vim -R - \"+syntax match String \\\"\\v$case_sensitive_flag$term\\\"\" \"+syntax match Keyword \\\"\\v\\<$case_sensitive_flag$term\\>\\\"\"}\n\n\n# Remove gaps in window numbers in the current tmux session\nfunction tmux-gap-remove {\n while true; do\n # find the gap\n window_nums=($(tmux list-windows | awk '{print $1}' | cut -c -1))\n echo '\"' \"${window_nums[*]}\" '\"'\n for i in $(seq 1 $(( ${#window_nums[*]} - 1 ))); do\n difference=$(( ${window_nums[$i+1]} - ${window_nums[$i]} ))\n if [ $difference -gt 1 ]; then\n break;\n fi\n done\n \n if [ $difference = 1 ]; then break; fi\n for j in $(seq $(( $i + 1 )) $(( ${#window_nums[*]} ))); do\n echo tmux move-window -s $j -t $(( $j - $difference + 1))\n tmux move-window -s $j -t $(( $j - $difference + 1))\n done\n done\n}\n\nfunction vlog {\n log $@ | vim -\n}\n\nfunction curr-branch {\n git branch | grep \\* | cut -d ' ' -f2\n}\n\nfunction default-branch {\n git remote show origin | grep 'HEAD branch' | sed 's/.*: //'\n}\n\nfunction dir-contains {\n haystack=\"$(cd $1 && pwd)\"\n needle=$2\n \n while [ \"$haystack\" != \"/\" ] && [ \"$haystack\" != \"\" ]; do\n curr=\"$(basename \"$haystack\")\"\n if [ \"$curr\" = \"$needle\" ]; then return true; fi\n haystack=\"$(dirname \"$haystack\")\"\n done\n \n false\n}\n\n[[ -s \"$HOME/.rvm/scripts/rvm\" ]] && source \"$HOME/.rvm/scripts/rvm\" # Load RVM into a shell session *as a function*\n\n\nFZF_BASE_OPTS='--border --color fg:-1,bg:-1,hl:230,fg+:3,bg+:233,hl+:229 --color info:150,prompt:110,spinner:150,pointer:167,marker:174'\nexport FZF_DEFAULT_OPTS=\"$_FZF_BASE_OPTS --preview '[[ \\$(file --mime {}) =~ binary ]] && xxd {} || (highlight --out-format=xterm256 {} || cat {}) 2>/dev/null'\"\nexport FZF_TMUX=1\nexport FZF_TMUX_HEIGHT=95%\n\nexport PATH=\"/usr/local/opt/ruby/bin:/usr/local/lib/ruby/gems/2.6.0/bin:$PATH\"\n\n\nif [ -r ~/.bashrc ]; then source ~/.bashrc; fi\n"
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"text": "# Environment\nMy Unix (Mac OS and Linux) environment setup. Includes bash scripts, python scripts, and vim configuration\n\n## Usage\nTo install the environment simply run `./config.sh`.\nTo undo the changes run `./cleanup.sh`.\n\n## Contents\nThis environment is my personal setup and was not written with the intend of distribution.\nHowever, there are some nice scripts that may be useful to other people (plus I ran out of private repositories in GitHub)\n\n### Bash Scripts\n* `bash_profile.sh` -- my bash profile\n* `pid.sh` -- print the process id of the first process that contains the argument\n* `trash.sh` -- move the argument to ~/.Trash\n* `waitforprocess.sh` -- use `caffeinate` and `pid.sh` to make the computer wait for a process before sleeping\n* `csconfig.sh` -- configuration of the `cs` command which does a `cd` and a `ls`\n* `settitle.sh` -- set the title of the terminal (using xterm escape codes)\n* `tree.sh` -- print the current directory in a tree format\n* `log.sh` -- log to a file (`$HOME/.log_file`)\n\n### Python Scripts\n* `tree.py` -- provides an interface for accessing the filesystem as a tree structure. Also provides a command line interface similar to that of `tree.sh` (Bash scripts)\n* `latex.py` -- provides a function to sanitize strings for LaTeX\n* `ctree.py` -- uses `tree.py`, `latex.py`, and `ctags` to generate a tree in LaTeX of C files and the functions in them.\n* `passwordgen.py` -- generates passwords by getting random workds [online](http://watchout4snakes.com/wo4snakes/Random/RandomWord)\n* `music.py` -- provides an interface for controlling iTunes from the command line\n\n### bin/\nThe following symbolic links are automatically placed in bin/ by `config.sh`:\n\n| tool | Linked too |\n| ----:|:---------- |\n| `csconfig` | [`bash/csconfig.sh`](bash/csconfig.sh) |\n| `flog` | [`bash/log.sh`](bash/log.sh) |\n| `passwordgen` | [`python/passwordgen.py`](python/passwordgen.py) |\n| `pid` | [`bash/pid.sh`](bash/pid.sh) |\n| `play` | [`python/music.py`](python/music.py) |\n| `trash` | [`bash/trash.sh`](bash/trash.sh) |\n| `tree` | [`python/tree.py`](python/tree.py) |\n| `waitforprocess` | [`bash/waitforprocess.sh`](bash/waitforprocess.sh) |\n"
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"text": "#!/bin/bash\n\n# Debug\nif [ -n \"$DEBUG_TRASH\" ]; then\n function dbprint {\n echo $@\n }\nelse\n function dbprint {\n echo -n\n }\nfi\n\nif [ -z \"$TRASH_DIR\" -o ! -d \"$TRASH_DIR\" ]; then\n TRASH_DIR=\"$HOME/.Trash\"\nfi\ndbprint \"using TRASH_DIR=$TRASH_DIR\"\n\n\n# Pull out the options (files that start with -)\nfiles=()\nargs=()\n\nfor something in $@; do\n if [ ${something:0:1} = '-' ]; then\n args+=(\"$something\")\n else\n files+=(\"$something\")\n fi\ndone\n\ndbprint \"Args: ${args[@]}\"\ndbprint \"Files: ${files[@]}\"\n\nfor file in \"${files[@]}\"; do\n # Look for an unused filename to move to\n baseFilename=\"$(basename \"$file\")\"\n dbprint \"base filename: $baseFilename\"\n if [ -e \"$TRASH_DIR/$baseFilename\" ]; then\n counter=1\n while [ -e \"$TRASH_DIR/copy$counter-$baseFilename\" ]; do\n counter=$((counter+1))\n done\n filename=\"$TRASH_DIR/copy$counter-$baseFilename\"\n else\n filename=\"$TRASH_DIR/$baseFilename\"\n fi\n \n dbprint \"mv $args $file $filename\"\n mv $args \"$file\" \"$filename\"\ndone\n"
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"text": "#!/usr/bin/env bash\n# Script to install a basic Vimrc on a new system (over ssh)\n\nif [ $# -ne 1 ]; then\n echo \"Usage: $0 username@hostname[:port]\" >&2\n exit 1\nfi\n\nhostname=\"$1\"\nvimrc=\"$(mktemp)\"\n\nfunction _cleanup {\n rm -f \"$vimrc\"\n}\n\ntrap _cleanup EXIT\n\ncat > \"$vimrc\" <<EOF\n\nset nocompatible\nsyntax on\nset number\nset relativenumber\n\ninoremap jk <esc>\ninoremap jK <esc>\ninoremap Jk <esc>\ninoremap JK <esc>\n\nset autoindent\nset tabstop=4\nset shiftwidth=4\nset expandtab\n\nset smartcase\nset mouse=a\n\nEOF\n\nscp \"$vimrc\" \"$hostname\":~/.vimrc\n"
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"text": "#!/bin/bash\n\n# make sure there are at least 1 argument passed\nif [ $# -eq 0 ]; then\n\techo \"Please provide at least one process name\"\n\texit 1\nfi\n\n# caffienate all the processes passed\nfor process in $@ ; do\n\t# get the PID\n\tpid=$(ps -A | egrep -i $process | head -n 1 | awk '{print $1}')\n\techo \"Caffeinating \\\"$(ps -A | egrep -i $process | head -n 1)\\\"\"\n\tcaffeinate -d -w $pid & disown\ndone\n"
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"path": "/python/tests/testjsonschema.py",
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"text": "#!/usr/bin/env python3\nimport unittest;\nimport json;\nimport os.path as path;\nimport os;\nimport copy;\nfrom jsonschema import Schema;\n\nclass SchemaTestCase(unittest.TestCase):\n def setUp(self):\n # Create a fairly complex JSON object (and output it in a file)\n self.json_data = {\n 'key1' : 123,\n 'key2' : 324.343,\n 'key3' : 'string',\n 'key4' : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n 'key5' : ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine'],\n 'key6' : {\n 'key6_1' : 123,\n 'key6_2' : 324.343,\n 'key6_3' : 'string',\n 'key6_4' : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n 'key6_5' : ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine'],\n },\n 'key7' : [{\n 'key7_1' : 0,\n }, {\n 'key7_2' : 0,\n }],\n 'invalid-py-key1' : 0,\n '342' : 0,\n 'invalid key 2' : 0,\n };\n # Look for a working filename\n for i in range(1000):\n self.filename = 'testjsonschema-testfile-%d.json' % i;\n if not path.isfile(self.filename):\n break;\n else:\n self.filename = '';\n raise Exception('Unable to find empty filename!');\n # Actually write the information to the file\n with open(self.filename, 'w') as f:\n json.dump(self.json_data, f, indent = 4);\n \n def tearDown(self):\n # Delete the file\n os.remove(self.filename);\n \n def check_schema(self, s):\n self.assertEqual(s, s, 'schema not equal to itself');\n self.assertEqual(s, copy.deepcopy(s), \"schema not equal to it's copy\");\n \n # Assert that all the keys are in there\n default_keys = {'key1':0, 'key2':0, 'key3':0, 'key4':0, 'key5':0, 'key6':0, 'key7':0, 'invalid-py-key1':0, '342':0, 'invalid key 2':0};\n keys = {};\n for k,v in s.items():\n keys[k] = 0;\n self.assertEqual(keys, default_keys, 'Missing or extra keys');\n \n for k in default_keys:\n self.assertTrue(k in s, str(k) + \"should be in s but it isn't\");\n \n self.assertTrue(type(s.key6) is Schema, 's.key6 is not a Schema');\n self.assertTrue(type(s['key6']) is Schema, 's[\"key6\"] is not a Schema');\n self.assertTrue(type(s['key7'][0]) is Schema, 's[\"key7\"][0] is not a Schema');\n \n self.assertEqual(s.key1, s['key1']);\n self.assertEqual(s.key2, s['key2']);\n self.assertEqual(s.key3, s['key3']);\n self.assertEqual(s.key4, s['key4']);\n self.assertEqual(s.key5, s['key5']);\n self.assertEqual(s.key6, s['key6']);\n self.assertEqual(s.key7, s['key7']);\n \n \n def test_create_from_dict(self):\n s = Schema(self.json_data);\n self.check_schema(s);\n def test_create_from_file(self):\n s = Schema(self.filename);\n self.check_schema(s);\n def test_writing_out(self):\n with open(self.filename, 'w+') as f:\n f.seek(0, 0);\n json.dump(self.json_data, f);\n self.assertEqual(self.json_data, Schema(self.filename));\n \nsuite = unittest.TestLoader().loadTestsFromTestCase(SchemaTestCase);\nif __name__ == '__main__':\n unittest.TextTestRunner(verbosity=2).run(suite);\n"
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"text": "# Make open work\n# Default command (fallback order):\n# reattach-to-username with zsh\n# reattach-to-username with bash\n# zsh\n# bash\n# sh\n#set-option -g default-command 'reattach-to-user-namespace -l /usr/local/bin/zsh'\nset-option -g default-command \"$HOME/environment/bin/tmux-default-command\"\nset-option -g focus-events on\nset-option -g history-limit 100000\n\n# Vim copy mode\nset-window-option -g mode-keys vi\nbind-key -T copy-mode-vi v send-keys -X begin-selection\nbind-key -T copy-mode-vi y send-keys -X copy-selection\n\n\n# Some useful mappings:\n#bind-key h select-pane -L\n#bind-key l select-pane -R\n#bind-key k select-pane -U\n#bind-key j select-pane -D\nbind -n C-h run \"(tmux display-message -p '#{pane_current_command}' | grep -iq vim && tmux send-keys C-h) || tmux select-pane -L\"\nbind -n C-j run \"(tmux display-message -p '#{pane_current_command}' | grep -iq vim && tmux send-keys C-j) || tmux select-pane -D\"\nbind -n C-k run \"(tmux display-message -p '#{pane_current_command}' | grep -iq vim && tmux send-keys C-k) || tmux select-pane -U\"\nbind -n C-l run \"(tmux display-message -p '#{pane_current_command}' | grep -iq vim && tmux send-keys C-l) || tmux select-pane -R\"\nbind-key C-0 select-pane -t 0\nbind-key C-1 select-pane -t 1\nbind-key C-2 select-pane -t 2\nbind-key C-3 select-pane -t 3\nbind-key C-4 select-pane -t 4\nbind-key C-5 select-pane -t 5\nbind-key C-6 select-pane -t 6\n\nbind-key C-a send-keys -R \\; clear-history\n\nbind-key C-b last-window\n\nbind-key C-a send-keys -R \\; clear-history\n\n# Bind pane splitting\nbind-key - split-window v\nbind-key | split-window h\n\nbind-key C-q confirm-before kill-session\n\n# Useful shortcuts\nbind-key a set-window-option synchronize-panes\n\n# Enable mouse control\n#set -g mouse-select-window on\n#set -g mouse-select-pane on\n#set -g mouse-resize-pane on\nset -g mouse on\n\n# Set TERM to screen-256color instead of just screen\nset -g default-terminal \"screen-256color\"\n\n\n# Some color:\nset -g status-fg white\nset -g status-bg '#005f5f'\nset -g status-right \"#{prefix_highlight} CPU: #{cpu_percentage} %a %h-%d %H:%M\"\nset-window-option -g window-status-current-bg '#5fffff'\nset-window-option -g window-status-current-fg black\n\nset -g pane-border-fg '#005f5f' # same color as Tmux's status line\nset -g pane-active-border-fg '#008787' # same color as Vim's status line\n\n# Set the clock to 12 hour not 24 hour\nset-window-option -g clock-mode-style 12\n\n# WORST CONFIG EVER\n#set-option -g destroy-unattached\n\n# Plugins\n\n# set -g @plugin \"github_username/plugin_name\"\nset -g @plugin \"tmux-plugins/tmux-yank\"\nset -g @plugin \"tmux-plugins/tmux-cpu\"\nset -g @plugin \"tmux-plugins/tmux-prefix-highlight\"\nset -g @plugin \"tmux-plugins/tmux-battery\"\nset -g @plugin 'tmux-plugins/tmux-copycat'\nset -g @plugin \"tmux-plugins/tmux-resurrect\"\nset -g @resurrect-capture-pane-contents 'on'\nset -g @resurrect-strategy-vim 'session'\n#set -g @resurrect-strategy-nvim 'session'\nset -g @plugin \"tmux-plugins/tmux-continuum\"\nset -g @plugin \"tmux-plugins/tmux-copycat\"\n\n\n\n# Initialize TMUX plugin manager\nrun \"~/.tmux/plugins/tpm/tpm\"\n"
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"text": "#!/bin/sh\n\nreat=$(which reattach-to-user-namespace 2> /dev/null)\nif [ $? -eq 0 ]; then\n zsh=\"$(which zsh 2> /dev/null)\"\n if [ $? -eq 0 ]; then\n $reat -l \"$zsh\"\n else\n $reat -l \"/bin/bash\"\n fi\nelse\n zsh=\"$(which zsh 2> /dev/null)\"\n if [ $? -eq 0 ]; then\n zsh -l\n else\n bash=\"$(which bash 2>/dev/null)\"\n if [ $? -eq 0 ]; then\n $bash -l\n else\n sh -l\n fi\n fi\nfi\n"
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"path": "/C/lock.c",
"repo_name": "jrizkalla/environment",
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"text": "// Uses a private API. compile using:\n// clang -F /System/Library/PrivateFrameworks -framework login\n \n \nextern void SACLockScreenImmediate();\n\nint main(){\n SACLockScreenImmediate();\n return 0;\n}\n"
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"path": "/python/run_cmd.py",
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"text": "#!/usr/bin/env python3\n\"\"\"\nSave commands with a description and run them later\n\"\"\"\n\nimport os\nimport stat\nimport sys\nimport json\nimport subprocess\nimport tempfile\nfrom pathlib import Path\nimport argparse\nfrom typing import *\nfrom datetime import datetime\nfrom dataclasses import dataclass\n\nclass UserError(Exception):\n pass\n\n@dataclass\nclass Command:\n __keys = \"name desc command script date script_source_file\".split(\" \")\n name: str\n desc: str\n command: str\n script: Optional[str]\n date: datetime\n script_source_file: Optional[str]\n \n def to_dict(self) -> Dict[str, Any]:\n d = { k : self.__dict__[k] \n for k in type(self).__keys if self.__dict__[k] is not None }\n d[\"date\"] = d[\"date\"].isoformat()\n return d\n \n @classmethod\n def from_dict(cls, d: Dict[str, Any]) -> \"Command\":\n attrs = {}\n for k in cls.__keys:\n attrs[k] = d.get(k, None)\n attrs[\"date\"] = datetime.fromisoformat(attrs[\"date\"])\n return Command(**attrs)\n \nclass Database:\n def __init__(self, loc):\n self.loc = loc\n try:\n with open(loc, \"r\") as cf:\n self.cmds = json.load(cf)\n except FileNotFoundError:\n # empty db\n self.cmds = {}\n\n def add(self, cmd: Command, override: Union[bool, Callable[[], bool]]=False):\n lname = cmd.name.lower().strip()\n \n if lname in self.cmds:\n if not isinstance(override, bool):\n _override = override()\n if not _override:\n raise KeyError(f\"'{name}' already defined\")\n self.cmds[lname] = cmd.to_dict()\n self.save()\n \n def get(self, name: str) -> Command:\n cmd = self.cmds[name.lower().strip()]\n return Command.from_dict(cmd)\n \n def save(self):\n # convert cmds\n with open(self.loc, \"w\") as cf:\n json.dump(self.cmds, cf, indent=4)\n\n def __iter__(self):\n for _, cmd in self.cmds.items():\n yield Command.from_dict(cmd)\n\n\n\ndef _no_cmd(db: Database, args) -> int:\n parser.print_help()\n return 0\n\ndef _list(db: Database, args) -> int:\n if args.names:\n fmt = \"{cmd.name}\"\n elif args.date:\n fmt = \"({cmd.date.year}/{cmd.date.month}/{cmd.date.day}) {cmd.name:>20} -- {cmd.desc}\"\n else:\n fmt = \"{cmd.name:>15} -- {cmd.desc}\"\n for cmd in db:\n print(fmt.format(cmd=cmd))\n\ndef _save(db: Database, args) -> int:\n if args.script:\n command = \"\"\n try:\n text = Path(args.command[0]).read_text()\n except IndexError:\n raise UserError(f\"No script file specified\")\n else:\n command = \" \".join(args.command)\n text = None\n cmd = Command(\n name=args.name,\n desc=args.desc,\n command=command,\n script=text,\n date=datetime.now(),\n script_source_file=args.source_file\n )\n def _override():\n answer = input(f\"Command '{cmd.name}' already exists. Override it? (y/n) \")\n return answer.lower().strip() == \"y\"\n try:\n db.add(cmd, override=_override)\n except KeyError as e:\n print(\"Refusing to override. Exiting.\")\n \ndef _run(db: Database, args) -> int:\n # lookup the command\n try:\n cmd = db.get(args.name)\n except KeyError:\n print(f\"Unable to find command '{args.name}'\")\n return 1\n \n if not args.silent:\n print(f\"\\033[1m{cmd.command}\\033[0m\")\n if cmd.script is not None:\n # run it as a script by creating a temp file.\n with tempfile.NamedTemporaryFile(mode=\"w\") as tf:\n if cmd.script[0:2] != \"#!\":\n tf.write(\"#!/usr/bin/env bash\\n\")\n if cmd.script_source_file is not None:\n tf.write(\"#\" + \"-\" * 78 + \"\\n\")\n tf.write(f'source \"{cmd.script_source_file}\"\\n')\n tf.write(\"#\" + \"-\" * 78 + \"\\n\\n\")\n \n injected_commands = \"\\n\".join(args.inject)\n tf.write(f\"\\n{injected_commands}\\n\\n\")\n tf.write(cmd.script)\n tf.write(\"\\n\")\n tf.flush()\n os.chmod(tf.name, stat.S_IXUSR | stat.S_IRUSR)\n \n res = subprocess.run(tf.name)\n \n \n else:\n res = subprocess.run(cmd.command, shell=True)\n return res.returncode\n\ndef _view(db: Database, args) -> int:\n try:\n cmd = db.get(args.name)\n except KeyError:\n print(f\"Unable to find command '{args.name}'\")\n return 1\n \n out = \"\"\n if not any((args.all, args.date, args.desc, args.cmd)):\n args.all = True\n \n if args.all or args.date:\n out += f\"{cmd.date.strftime('%Y/%m/%d %I:%M %p')}\\n\"\n if args.all or args.desc:\n out += f\"{cmd.desc}\\n\"\n if args.all or args.cmd:\n out += f\"{cmd.command}\\n\"\n \n end = \"\\n\" if out == \"\" else \"\"\n print(out, end=end)\n \n \n\nparser = argparse.ArgumentParser(description=\"Save commands and run them later\")\nsubparsers = parser.add_subparsers()\nparser.set_defaults(__func=_no_cmd)\n \nlist_parser = subparsers.add_parser(\"list\", help=\"View list of commands\")\nlist_parser.set_defaults(__func=_list)\nlist_parser.add_argument(\"--names\", action=\"store_true\", default=False, help=\"List saved command names only\")\nlist_parser.add_argument(\"-d\", \"--date\", action=\"store_true\", default=False, help=\"Print the dates.\")\n\nrun_parser = subparsers.add_parser(\"run\", help=\"Run a command\")\nrun_parser.set_defaults(__func=_run)\nrun_parser.add_argument(\"name\", help=\"Command to run.\")\nrun_parser.add_argument(\"-s\", \"--silent\", action=\"store_true\", default=False, help=\"Don't print anything other than the command output.\")\nrun_parser.add_argument(\"-i\", \"--inject\", action=\"append\", default=[], help=\"If the command is a script, inject a shell line before the script runs.\")\n\nsave_parser = subparsers.add_parser(\"save\", help=\"Save a command\")\nsave_parser.set_defaults(__func=_save)\nsave_parser.add_argument(\"-d\", \"--desc\", default=\"\", help=\"Provide a description.\")\nsave_parser.add_argument(\"--script\", default=False, action=\"store_true\", help=\"Read the contents of the file and run it as a script.\")\nsave_parser.add_argument(\"--source-file\", default=None, help=\"Source this file when running the script.\")\nsave_parser.add_argument(\"name\", help=\"The name of the command\")\nsave_parser.add_argument(\"command\", nargs=\"*\", help=\"The command and it's arguments or, if --script is specified, a path to a shell script.\")\n\n\nview_parser = subparsers.add_parser(\"view\", help=\"View details about a command\")\nview_parser.set_defaults(__func=_view)\nview_parser.add_argument(\"name\", help=\"Command to view.\")\nview_parser.add_argument(\"-a\", \"--all\", action=\"store_true\", default=False, help=\"View all attributes of a command.\")\nview_parser.add_argument(\"-e\", \"--desc\", action=\"store_true\", default=False, help=\"View description.\")\nview_parser.add_argument(\"-d\", \"--date\", action=\"store_true\", default=False, help=\"View date.\")\nview_parser.add_argument(\"-c\", \"--cmd\", action=\"store_true\", default=False, help=\"View command.\")\n\n\ndef main(args):\n db_loc = Path(os.environ.get(\"SAVED_COMMANDS\", Path.home() / \".saved_commands\")).expanduser()\n db = Database(db_loc)\n \n try:\n return args.__func(db, args)\n except UserError as e:\n print(f\"Error: {e.args[0]}\", file=sys.stderr)\n return 1\n \nif __name__ == \"__main__\":\n sys.exit(main(parser.parse_args()))\n"
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"text": "#!/usr/bin/env bash\n\ngit clone https://github.com/zsh-users/zsh-syntax-highlighting \"$ENV/zsh/plugins/zsh-syntax-highlighting\"\ngit clone https://github.com/zsh-users/zsh-autosuggestions \"$ENV/zsh/plugins/zsh-autosuggestions\"\nln -s \"$ENV/zsh/my-plugins/complete_funcs\" \"$ENV/zsh/plugins/complete_funcs\"\n"
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"text": "#!/bin/bash\n\nenv=\"$HOME/environment\"\nexport ENV=\"$env\"\nsource $env/bash/pretty_script.sh\n\n# Is brew installed?\ntype brew >/dev/null 2>&1\nret=$?\nif [ $ret -ne 0 ]; then\n - /usr/bin/ruby -e \\\"\\$\\(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install\\)\\\"\nfi\n\ntype brew >/dev/null 2>&1\nif [ $? -eq 0 ]; then\n - brew install fzf\n - brew install highlight\n - brew install macvim\n - brew install python3\n - brew install tmux\nelse\n echo \"Not installing fzf, macvim, python3, and tmux because brew is missing\"\nfi\n\ncheckpoint lns \"Settings up environment in $env\"\n\n# Set up the links (ignoring errors)\n- ln -f -s \\\"$env/bash/bash_profile.sh\\\" \\\"$HOME/.bash_profile\\\"\n- ln -f -s \\\"$env/zsh/zshrc\\\" \\\"$HOME/.zshrc\\\"\n- ln -f -s \\\"$env/vim/vimrc\\\" \\\"$HOME/.vimrc\\\"\n- ln -f -s \\\"$env/vim\\\" \\\"$HOME/.vim\\\"\n- ln -f -s \\\"$env/tmux\\\" \\\"$HOME/.tmux\\\"\n- ln -f -s \\\"$env/wm/khdrc\\\" \\\"$HOME/.khdrc\\\"\n- ln -f -s \\\"$env/wm/chunkwmrc\\\" \\\"$HOME/.chunkwmrc\\\"\n\n# tmux links\n- ln -f -s \\\"$env/tmux/tmux.conf.sh\\\" \\\"$HOME/.tmux.conf\\\"\n\n# Change .bash_profile\necho -e \"\\n# Created by .config\\nexport PATH=\\\"\\$PATH:$env/bin:$env/C/bin\\\"\\nexport PYTHONPATH=\\\"\\$PYTHONPATH:$env/python\\\"\" > \"$env/bash/env_bash_profile.sh\"\necho -e \"export ENV=\\\"$env\\\"\\n\" >> \"$env/bash/env_bash_profile.sh\"\nif [ $(uname) = 'Linux' ]; then\n echo -e 'export TRASH_DIR=\"$HOME/.local/share/Trash\"' >> \"$env/bash/env_bash_profile.sh\"\nfi\necho -e \"export OSA_LIBRARY_PATH=\\\"$env/jxa/lib\\\"\" >> \"$env/bash/env_bash_profile.sh\"\n\n- ln -s \"$env/bash/env_bash_profile.sh\" \"$HOME/.env_bash_profile\"\n\necho \"Settings up links in $env/bin\"\n\n- ln -f -s \\\"$env/bash/pid.sh\\\" \\\"$env/bin/pid\\\"\n- ln -f -s \\\"$env/bash/settitle.sh\\\" \\\"$env/bin/settitle\\\"\n- ln -f -s \\\"$env/bash/trash.sh\\\" \\\"$env/bin/trash\\\"\n- ln -f -s \\\"$env/bash/waitforprocess.sh\\\" \\\"$env/bin/waitforprocess\\\"\n- ln -f -s \\\"$env/bash/csconfig.sh\\\" \\\"$env/bin/csconfig\\\"\n- ln -f -s \\\"$env/bash/tmux-default-command.sh\\\" \\\"$env/bin/tmux-default-command\\\"\n- ln -f -s \\\"$env/bash/pretty.sh\\\" \\\"$env/bin/pretty\\\"\n- ln -f -s \\\"$env/bash/tmuxnew.sh\\\" \\\"$env/bin/tmuxnew\\\"\n- ln -f -s \\\"$env/bash/compose.sh\\\" \\\"$env/bin/compose\\\"\n- ln -f -s \\\"$env/bash/openproj.sh\\\" \\\"$env/bin/openproj\\\"\n- ln -f -s \\\"$env/bash/init_vim_on_ssh.sh\\\" \\\"$env/bin/init_vim_on_ssh\\\"\n- ln -f -s \\\"$env/bash/difftool.sh\\\" \\\"$env/bin/difftool\\\"\n\n- ln -f -s \\\"$env/python/tree.py\\\" \\\"$env/bin/tree\\\"\n- ln -f -s \\\"$env/python/passwordgen.py\\\" \\\"$env/bin/passwordgen\\\"\n- ln -f -s \\\"$env/python/music.py\\\" \\\"$env/bin/play\\\"\n- ln -f -s \\\"$env/python/mkgitignore.py\\\" \\\"$env/bin/mkgitignore\\\"\n- ln -f -s \\\"$env/python/radartitle.py\\\" \\\"$env/bin/radartitle\\\"\n- ln -f -s \\\"$env/python/run_cmd.py\\\" \\\"$env/bin/cmd\\\"\n- ln -f -s \\\"$env/python/build_script_finder.py\\\" \\\"$env/bin/bld\\\"\n- ln -f -s \\\"$env/python/server.py\\\" \\\"$env/bin/srv\\\"\n\n- ln -f -s \\\"$env/tmux/scripts/tmuxhome.sh\\\" \\\"$env/bin/tmuxhome\\\"\n\n# Compile C files\necho \"Compiling C programs...\"\ncd \"$env/C/\"\n+ make\n\n# Download Vim plugins\n+ vim +PlugInstall +qall\n\n# Install tmux plugin manager\n- git clone https://github.com/tmux-plugins/tpm \"$env/tmux/plugins/tpm\"\n# install zsh plugins\n+ \\\"$env/zsh/install-plugins.sh\\\"\n\n# install tmux plugins\n+ \\\"$env/tmux/plugins/tpm/bin/install_plugins\\\"\n"
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"text": "#level Library to pretty print commands\n# Usage:\n# source pretty_script file\n# + error checked command\n# - error ignored command\n# Note: shell special chars must be escaped\n# e.g. + echo test '&&' echo test2\n# - echo something \\| sed 's/h//g'\n\n\n# Functions:\n# + run a command. Fail the script if it fails\n# - run a command. Keep going if it fails\n# checkpoint \"id\" \"description\" register a checkpoint. Caller can skip to checkpoints\n# noop Does nothing and doesn't get printed\n\n# Variables: Change the variables after sourcing pretty_script to change settings\n# int VERBOSITY: verbosity level (defaults to 0, changed by command line arguments)\n# bool COLOR : Color on or off (defaults to on, changed by command line arguments)\n# bool DRY_RUN : Dry run or actually execute commands (changed by command line arguments)\n# string INDENT: indent inserted at command stdout and stderr (default \" \")\n# string CURR_CHECKPOINT: the current checkpoint\n# string PPROMPT: Prompt printed before + commands\n# string MPROMPT: Prompt printed before - commands\n\nsource ~/.bashrc\n\n\n# read verbosity level from command line\nVERBOSITY=0\nCOLOR=1\nINDENT=\" \"\nDRY_RUN=0\n\n# Checkpoint stuff\nCURR_CHECKPOINT=\"__start__\"\nPREV_CHECKPOINT=\"__start__\"\n_START_AT=\"__start__\"\n_STOP_AFTER=\"__stop__\"\n_STARTED=0\n_STOPPED=0\n_CHECKPOINT_FMT=\"normal\"\nASK_FOR_CONFIRMATION=0\n\n_AT_ERROR=\"\"\n\nfunction _parse_arg {\n # TODO: generalize command line arguments\n case $1 in\n h|help)\n echo \"Usage: command [-v|--verbose] [-c|--color] [--dry-run] [--start-at=<start>] [--stop-after=<stop>] [-l|--list-checkpoints] [--ask]\"\n exit 0\n ;;\n v|verbose)\n VERBOSITY=$(($VERBOSITY + 1))\n ;;\n c|color)\n COLOR=$(( ! $COLOR ))\n ;;\n dry-run)\n DRY_RUN=1\n ;;\n start-at=*)\n _START_AT=\"${1#start-at=}\"\n ;;\n stop-after=*)\n _STOP_AFTER=\"${1#stop-after=}\"\n ;;\n l|list-checkpoints)\n _START_AT=\"__end__\" # skip everything\n _CHECKPOINT_FMT=\"list\"\n _VERBOSITY=3 # make sure checkpoints are printed\n ;;\n ask)\n ASK_FOR_CONFIRMATION=1\n ;;\n esac\n}\n\nfor arg in $@; do\n if [ \"${arg:0:2}\" = \"--\" ]; then\n _parse_arg \"${arg#--}\"\n elif [ \"${arg:0:1}\" = \"-\" ]; then\n while read -n1 char; do\n _parse_arg $char\n done < <(echo \"${arg}\")\n fi\ndone\n\nCLR_EMPH=\"\"\nCLR_RESET=\"\"\nCLR_GREEN=\"\"\nCLR_ERR=\"\"\nif [ $COLOR -eq 1 ]; then\n CLR_EMPH=\"$(tput bold)\"\n CLR_GREEN=\"$(tput setaf 2)\"\n CLR_ERR=\"$(tput setaf 1)\"\n CLR_RESET=\"$(tput sgr0)\"\nfi\n\nPPROMPT=\"$CLR_GREEN> $CLR_RESET\" # prompt for +\nMPROMPT=\"$CLR_GREEN- $CLR_RESET\" # prompt for -\n\n\nfunction _print_cmd {\n if [ \"$1\" = \"noop\" ]; then return 0; fi\n \n cmd=$1; shift\n args=$@\n \n ask=\"\\n\"\n if [ $ASK_FOR_CONFIRMATION -eq 1 ]; then\n ask=\". Run? (y/n) \"\n fi\n \n if [ $VERBOSITY -ge 1 ]; then\n printf \"%s%s%s\" \"$PROMPT\" $CLR_EMPH \"$cmd\"\n if [ $VERBOSITY -ge 2 ]; then\n printf \" %s%s$ask\" \"$args\" $CLR_RESET\n else\n printf \"%s$ask\" $CLR_RESET\n fi\n fi\n}\n\n# Verbosity levels:\n# 0 - print errors only\n# 1 - print cmd and errors\n# 2 - print cmd, params, and errors\n# 3 - print cmd, params, stdout, and errors\n\nfunction run {\n \n if [ $_STARTED -eq 0 ]; then # not started\n if [ \"$CURR_CHECKPOINT\" != \"$_START_AT\" ]; then\n return 0\n fi\n fi\n _STARTED=1\n \n if [ $_STOPPED -eq 1 ] || [ \"$PREV_CHECKPOINT\" == \"$_STOP_AFTER\" ]; then \n _STOPPED=1\n return 0\n fi\n \n _print_cmd $@\n \n if [ $DRY_RUN -eq 1 ]; then return 0; fi\n \n if [ $ASK_FOR_CONFIRMATION -eq 1 ] && [ \"$1\" != \"noop\" ]; then\n read answer\n answer=\"$(echo $answer | tr '[A-Z]' '[a-z]' | tr -d '[:space:]')\"\n if [ \"$answer\" != \"\" ] && [ \"$answer\" != \"y\" ] && [ \"$answer\" != \"yes\" ]; then\n echo \"Cancelled\"\n return 0\n fi\n fi\n \n if [ $VERBOSITY -ge 3 ]; then\n # \"$@\" | sed \"s/^/$INDENT/g\" WONT WORK: bash executes all pipes in subshells\n # Optiona 2: Create a temp file, output everything to it, then cat it. \n # the problem with that is that nothing is printed until the command finishes\n \n eval \"$@\" > >(sed \"s/^/$INDENT/g\") 2>&1\n \n ret=$?\n \n if [ $ret -ne 0 ]; then\n printf \"${CLR_ERR}Error (%s): process returned $ret$CLR_RESET\\n\" \"$CURR_CHECKPOINT\"\n fi\n \n return $ret\n else\n stdout_file=\"$(mktemp -t stout_$(basename $0))\"\n stderr_file=\"$(mktemp -t stout_$(basename $0))\"\n \n eval $@ >$stdout_file 2>$stderr_file\n \n ret=$?\n if [ $ret -ne 0 ]; then\n printf \"${CLR_ERR}Error (%s): process returned $ret$CLR_RESET\\n\" \"$CURR_CHECKPOINT\"\n printf \"stdout:\\n\"\n cat $stdout_file | sed \"s/^/$INDENT/g\"\n printf \"stderr:\\n\"\n cat $stderr_file | sed \"s/^/$INDENT/g\"\n fi\n \n rm -f \"$stdout_file\"\n rm -f \"$stderr_file\"\n \n return $ret\n fi\n}\n\nfunction + {\n PROMPT=$PPROMPT\n run $@\n ret=$?\n if [ $ret -ne 0 ]; then\n eval \"$_AT_ERROR\"\n exit $ret;\n fi\n}\n\nfunction - {\n PROMPT=$MPROMPT\n run $@\n}\n\n\n# Usage: checkpoint id \"description\"\nfunction checkpoint {\n id=$1\n desc=$2\n PREV_CHECKPOINT=\"$CURR_CHECKPOINT\"\n CURR_CHECKPOINT=$1\n if [ $VERBOSITY -ge 3 ] || [ \"$_CHECKPOINT_FMT\" = \"list\" ]; then\n if [ \"$_CHECKPOINT_FMT\" = \"list\" ]; then\n printf \"%30s -- %s\\n\" \"$id\" \"$desc\"\n else\n checkpoint=\"Checkpoint $id $desc\"\n border=$(head -c ${#checkpoint} < /dev/zero | tr '\\0' '-')\n printf \"\\n Checkpoint ${CLR_EMPH}%s${CLR_RESET} %s\\n\" \"$id\" \"$desc\"\n printf \" %s \\n\" \"$border\"\n fi\n fi\n}\n\n# execute command on failure\nfunction at-error {\n echo \"Arg[0]: $0\"\n _AT_ERROR=\"$@\"\n}\n\nfunction noop {\n true\n}\n\nif [ $VERBOSITY -ge 4 ]; then\n echo \"Maximum verbosity enabled\"\n if [ $COLOR -eq 1 ]; then echo \"Color enabled\";\n else echo \"Color disabled\"; fi\nfi\n\n+ noop # kick start the script (for checkpoints)\n"
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"text": "#!/usr/bin/env python\n\ndef mult_table(height, width):\n '''Produces a multiplication table.\n \n The result is a list of rows with width items.'''\n \n res = [];\n for row in range(height):\n res.append([]);\n for col in range(width):\n res[row].append((row+1) * (col+1));\n return res;\n\ndef format_table(table):\n '''Creates a string with a nicely formated multiplication table'''\n \n # Get the max number and count the number of digits in it\n max_digits = len(str(table[-1][-1])) + 1;\n \n res = ''.ljust(max_digits) + ' | ';\n # Print the header\n for i in range(len(table[0])):\n res += str(i+1).ljust(max_digits) + ' ';\n res += '\\n';\n \n res += max_digits * '-' + '-+-' + (len(table[0]) * (max_digits +1)) * '-' + '\\n';\n \n # Print the table\n for row in range(len(table)):\n res += str(row+1).rjust(max_digits) + ' | ';\n for num in table[row]:\n res += str(num).ljust(max_digits) + ' ';\n res += '\\n';\n \n return res;\n"
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"text": "env=\"$HOME/environment\"\n\nif [ $# -eq 1 ]; then\n env=\"$1\"\nfi\n\nif [ -L \"$HOME/.bash_profile\" ]; then\n rm -f \"$HOME/.bash_profile\"\nfi\nif [ -L \"$HOME/.vimrc\" ]; then\n rm -f \"$HOME/.vimrc\"\nfi\nif [ -L \"$HOME/.vim\" ]; then\n rm -f \"$HOME/.vim\"\nfi\nif [ -L \"$HOME/.env_bash_profile\" ]; then\n rm -f \"$HOME/.env_bash_profile\"\nfi\nif [ -L \"$HOME/.zshrc\" ]; then\n rm -f \"$HOME/.zshrc\"\nfi\nif [ -L \"$HOME/.tmux.conf\" ]; then\n rm -f \"$HOME/.tmux.conf\"\nfi\nif [ -L \"$HOME/.tmux\" ]; then\n rm -f \"$HOME/.tmux\"\nfi\n\n\necho \"Removing all links in $env/bin\"\nfor file in $(ls \"$env/bin\"); do\n if [ -L \"$env/bin/$file\" ]; then\n rm -f \"$env/bin/$file\"\n fi\ndone\n\nrm -f \"$HOME/.radar_names\"\n\necho \"Removing $env/bash/env_bash_profile.sh\"\nrm -f \"$env/bash/env_bash_profile.sh\"\n\necho \"Removing compiled C files at $env/C/bin/\"\nrm -f \"$env/C/bin/\"*\n\necho \"Removing downloaded vim plugins\"\nrm -rf vim/plugins/*\n\necho \"Removing Zsh plugins\"\nrm -rf zsh/plugins/*\n"
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"text": "#!/usr/bin/env python\n\n'''\nProvides a simple interface for iTunes.\nUses Javascript and the `osascript` tool. Requires OS X 10.10 or higher\n\nProvides two classes:\n Parser -- parses strings\n Play -- plays a parsed string (Parser)\n \nThe module also provides a few convenience functions:\n play() -- plays the music\n printTrack() -- prints the current track in JSON\n pause() -- pauses the music\n stop() -- stops the music\n playPause() -- plays the music if it's paused or pauses it if it's playing\n backtrack() -- go back or go to the beginning of the track\n nextTrack() -- go forward to the next track\n rewind()\n fastForward()\n resume() -- stop rewind and fastForward\n increaseVolume() -- increases the volume by 1 percent\n decreaseVolume() -- decreases the volume by 1 percent\n printVolume() -- prints the volume\n printState() -- prints the state\n'''\n\nimport sys;\nimport copy;\nimport subprocess;\nimport fileinput;\n\nclass Parser:\n '''\n Parses a simple language for interacting with iTunes.\n \n The output is an array of ``Operation``s\n '''\n \n class Operation:\n '''\n An Operation consists of number of times, name, and params.\n \n ```self.numTimes``` is the number of times this operation should be exeucted\n ```self.name``` is it's name\n ```self.params``` is an array of strings representing the parameters of the operation\n \n An operation, in text format, looks like this: <num times><name>(<param1>, <param2>, ...)\n '''\n def __init__(self):\n self.numTimes = 1;\n self.name = \"\";\n self.params = [];\n def __str__(self):\n st = str(self.numTimes);\n st += self.name;\n st += \"(\"\n for param in self.params:\n st += param + \", \";\n if len(self.params) > 0:\n st = st[:-2];\n st += \")\";\n return st;\n def __repr__(self):\n return self.__str__();\n \n class UnableToParse(Exception):\n '''\n An exception thrown when the Parser is unable to parse a line\n '''\n def __init__(self, source, reason):\n self.source = source;\n self.reason = reason;\n def why(self):\n return (\"Unable to parse \\\"%s\\\": %s\" % (self.source, self.reason));\n \n def __init__(self, rawInput):\n '''\n Parses rawInput. Throws UnableToParse if it can't.\n \n Input should look like this:\n ```operation : operation : ...```\n Where ``operation`` consists of:\n ```<num times><name>(<param1>, <param2>, ...)```\n '''\n \n self.operations = [];\n self.numTimes = \"\";\n self.name = \"\";\n self.param = \"\";\n self.params = [];\n \n def nextStateFn (state, c):\n if state == 0:\n if c == ':':\n return 0;\n elif ord(c) >= ord('0') and ord(c) <= ord('9'):\n self.numTimes += c;\n return 1;\n else:\n self.name += c;\n return 2;\n elif state == 1:\n if ord(c) >= ord('0') and ord(c) <= ord('9'):\n self.numTimes += c;\n return 1;\n else:\n self.name += c;\n return 2;\n elif state == 2:\n if ord(c) >= ord('0') and ord(c) <= ord('9'):\n self.op = Parser.Operation();\n self.op.numTimes = int(self.numTimes) if len(self.numTimes) > 0 else 1;\n self.op.name = self.name;\n self.name = \"\";\n self.numTimes = \"\";\n self.operations.append(copy.copy(self.op));\n self.numTimes += c;\n return 1;\n elif c == ':':\n self.op = Parser.Operation();\n self.op.numTimes = int(self.numTimes) if len(self.numTimes) > 0 else 1;\n self.op.name = self.name;\n self.name = \"\";\n self.numTimes = \"\";\n self.operations.append(copy.copy(self.op));\n return 0;\n elif c == '(':\n return 3;\n else:\n self.name += c;\n return state;\n elif state == 3:\n if c == ',':\n if (len(self.param) > 0):\n self.params.append(self.param);\n self.param = \"\";\n return 4;\n elif c == ')':\n if (len(self.param) > 0):\n self.params.append(self.param);\n self.param = \"\";\n return 5;\n else:\n self.param += c;\n return state;\n elif state == 4:\n if c != ',':\n self.param += c;\n return 3;\n else:\n return -1;\n elif state == 5:\n self.op = Parser.Operation();\n self.op.numTimes = int(self.numTimes) if len(self.numTimes) > 0 else 1;\n self.op.name = self.name;\n self.op.params = self.params;\n self.numTimes = \"\";\n self.name = \"\";\n self.params = [];\n self.param = \"\";\n self.operations.append(copy.copy(self.op));\n if ord(c) >= ord('0') and ord(c) <= ord('9'):\n return 1;\n else:\n return 2;\n \n nextState = 0;\n for c in rawInput:\n nextState = nextStateFn(nextState, c);\n if nextState == -1:\n raise UnableToParse(c, \"unable to parse\");\n if nextState == 0 or nextState == 2 or nextState == 5:\n self.op = Parser.Operation();\n self.op.numTimes = int(self.numTimes) if len(self.numTimes) > 0 else 1;\n self.op.name = self.name;\n self.op.params = self.params;\n self.operations.append(copy.copy(self.op));\n \n \n \nclass Play:\n '''\n Plays a set of operation produced by a parser.\n '''\n def __init__(self, parser):\n self.p = parser;\n self.script = r'''\napp = Application(\"iTunes\");\ncurrApp = Application.currentApplication();\ncurrApp.includeStandardAdditions = true;\n\nfunction getJSON(item){\n json = \"{\\n\";\n for (prop in item){\n value = item[prop];\n json += \"\\t\\\"\" + prop + \"\\\":\";\n if (typeof value == \"string\"){\n json += \"\\\"\" + value.replace(/\\\"/g, '\\\\\\\"') + \"\\\",\\n\";\n } else if (typeof value == \"object\"){\n json += \"\\\"\" + value + \"\\\",\\n\";\n //json += stringify(value) + \",\\n\";\n } else {\n json += value + \",\\n\";\n }\n }\n json = json.substring(0, json.length - 2);\n json += \"\\n}\\n\"\n return json;\n}\n\nfunction inArr(item, arr){\n for (var i = 0; i < arr.length; i++){\n if (item == arr[i]){\n return true;\n }\n }\n return false;\n}\n\nfunction getItems(item, props){\n var obj = {};\n for (prop in item){\n if (inArr(prop, props)){\n obj[prop] = item[prop];\n }\n }\n return obj;\n}\n'''\n \n def __call__(self):\n '''\n Runs an array of operations found in Parser.\n \n Operations supported are:\n `compile` -- print the script instead of executing it\n `|>` or `play` -- play the music\n `|>?` or `play?` -- print the current song in JSON. If params are provided, only print the attributes listed in params\n `||` or `pause` -- pause the music\n `s' or `stop` -- stop the music\n `|` or `playpause` -- play the music if it's paused and pause it if it's played\n `<` or `backtrack` -- go back or go to the beginning of the track\n `<p` or `previoustrack` -- go back to the previous track\n `>` or `nexttrack` -- go forward to the next track\n `<<` or `rewind` -- rewind for one second\n `>>` or `fastforward` -- fast forward for one second\n `<<!` or `rewind!` -- rewind forever\n `>>!' or `fastforward!` -- fast forward forever\n `resume` -- resume from a fast forward or rewind\n `+` or `increasevolume` -- increase the volume. If a parameter is given, it sets the volume to it\n `-` or `decreasevolume` -- decrease the volume. If a parameter is given, it sets the volume to it\n `+?` or `-?` -- print the volume\n `state?` -- print the state of the player. 'playing', 'paused', 'stopped', 'fast forwarding', or 'rewinding'.\n '''\n script = self.script;\n compile = False\n for op in self.p.operations:\n if op.name == \"compile\":\n compile = True;\n elif op.name == \"|>\" or op.name == \"play\":\n for i in range(0, op.numTimes):\n script += \"app.play();\\n\";\n elif op.name == \"|>?\" or op.name == \"play?\":\n # Filter out by params\n for i in range(0, op.numTimes):\n if len(op.params) > 0:\n script += \"props = [\";\n for param in op.params:\n script += '\"' + param.strip() + '\", ';\n script = script[:-2];\n script += \"];\\n\";\n script += \"console.log(getJSON(getItems(app.currentTrack().properties(), props)));\\n\"\n else:\n script += \"console.log(getJSON(app.currentTrack().properties()));\\n\";\n \n elif op.name == \"||\" or op.name == \"pause\":\n for i in range(0, op.numTimes):\n script += \"app.pause();\\n\"\n elif op.name == \"s\" or op.name == \"stop\":\n for i in range(0, op.numTimes):\n script += \"app.stop();\\n\";\n elif op.name == \"|\" or op.name == \"playpause\":\n for i in range(0, op.numTimes):\n script += \"app.playpause();\\n\";\n elif op.name == \"<\" or op.name == \"backtrack\":\n for i in range(0, op.numTimes):\n script += \"app.backTrack();\\n\";\n elif op.name == \"<p\" or op.name == \"previoustrack\":\n for i in range(0, op.numTimes):\n script += \"app.previousTrack();\\n\";\n elif op.name == \">\" or op.name == \"nexttrack\":\n for i in range(0, op.numTimes):\n script += \"app.nextTrack();\\n\";\n elif op.name == \"<<\" or op.name == \"rewind\":\n script += \"app.rewind();\\n\";\n script += \"currApp.doShellScript('sleep \" + str(op.numTimes) + \"');\\napp.resume();\\n\";\n elif op.name == \">>\" or op.name == \"fastforward\":\n script += \"app.fastForward();\\n\";\n script += \"currApp.doShellScript('sleep \" + str(op.numTimes) + \"');\\napp.resume();\\n\";\n elif op.name == \"<<!\" or op.name == \"rewind!\":\n script += \"app.rewind();\\n\";\n elif op.name == \">>!\" or op.name == \"fastforward!\":\n script += \"app.fastForward();\\n\"\n elif op.name == \"resume\":\n script += \"app.resume();\\n\";\n elif op.name == \"+\" or op.name == \"increasevolume\" or op.name == \"setvolume\":\n if len(op.params) == 1:\n try:\n vol = int(op.params[0]);\n script += \"currApp.setVolume(\" + str((float(vol) / 100.0)*7) + \");\\n\";\n except:\n pass\n else:\n script += \"vol = (currApp.getVolumeSettings().outputVolume/100)*7;\\n\";\n script += \"currApp.setVolume(vol + \" + str(float(op.numTimes) * 0.1) + \");\\n\";\n elif op.name == \"-\" or op.name == \"decreasevolume\":\n if len(op.params) == 1:\n try:\n vol = int(op.params[0]);\n script += \"currApp.setVolume(\" + str((float(vol) / 100.0)*7) + \");\\n\";\n except:\n pass\n else:\n script += \"vol = (currApp.getVolumeSettings().outputVolume/100)*7;\\n\";\n script += \"currApp.setVolume(vol - \" + str(float(op.numTimes) * 0.1) + \");\\n\";\n elif op.name == \"+?\" or op.name == \"-?\":\n for i in range(0, op.numTimes):\n script += \"console.log(currApp.getVolumeSettings().outputVolume);\\n\";\n elif op.name == 'state?':\n for i in range(0, op.numTimes):\n script += \"console.log(app.playerState());\\n\";\n else:\n return;\n \n \n #script = script.replace(\" \", \"\");\n #script = script.replace(\"\\n\", \";\");\n if compile:\n print(script);\n else:\n p = subprocess.Popen(['osascript', '-l', 'JavaScript'], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE);\n stdout, stderr = p.communicate(script);\n if len(stdout.strip()) > 0 or len(stderr.strip()):\n print(stderr.strip());\n\ndef run(str):\n p = Play(Parser(str))();\n \n# Some convenient functions\ndef play():\n run(\"play\");\ndef printTrack():\n run(\"play?\");\ndef pause():\n run(\"pause\");\ndef stop():\n run(\"stop\");\ndef playPause():\n run(\"playpause\");\ndef previousTrack():\n run(\"previoustrack\");\ndef backtrack():\n run(\"backtrack\");\ndef nextTrack():\n run(\"nexttrack\");\ndef rewind():\n run(\"rewind!\");\ndef fastForward():\n run(\"fastforward!\");\ndef resume():\n run(\"resume\");\ndef increaseVolume():\n run(\"+\");\ndef decreaseVolume():\n run(\"-\");\ndef printVolume():\n run(\"+?\");\ndef printState():\n run(\"state?\");\n \nif __name__ == \"__main__\":\n if len(sys.argv) == 1:\n run(\"|\");\n elif len(sys.argv) >= 2 and sys.argv[1] == \"-i\":\n # Run interactive mode\n while True:\n try:\n line = raw_input();\n if len(line.strip()) == 0:\n run(\"|\");\n else:\n run(line);\n except EOFError:\n sys.exit(0);\n except KeyboardInterrupt:\n print\n pass;\n # Cat all the args in a string (in a smart way)\n commands = \"\";\n openParen = False;\n for arg in sys.argv[1:]:\n commands += \" \" if openParen else \":\";\n commands += arg;\n numOpen = arg.count('(');\n numClosed = arg.count(')');\n if numClosed > numOpen:\n openParen = False;\n elif numOpen < numClosed:\n openParen = True;\n # remove whitespace (also in an inteligent way)\n numOpen = 0;\n i = 0;\n length = len(commands);\n while i < length:\n if commands[i] == '(':\n numOpen+=1;\n elif commands[i] == ')':\n numOpen-=1;\n elif commands[i] == ' ' and numOpen == 0:\n # remove it\n commands1 = commands[:i];\n commands2 = commands[i+1:];\n commands = commands1 + commands2;\n i-=1;\n length-=1;\n i+=1;\n run(commands);\n"
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"text": "#!/bin/bash\n\n\n# This script prints all of the current directory (recursively) as a tree\n\ncolors=\"true\"\nfunction printDir() {\n\tif [ $colors == \"true\" ]; then\n\t\techo -e -n \"\\x1b[0;;36m\"\n\t\techo $@\n\t\techo -e -n \"\\x1b[0;0m\"\n\telse\n\t\techo $@\n\tfi\n}\n\nfunction printBlockedDir () {\n\tif [ $colors == \"true\" ]; then\n\t\techo -e -n \"\\x1b[0;;34m\"\n\t\techo $@\n\t\techo -e -n \"\\x1b[0;0m\"\n\telse\n\t\techo $@\n\tfi\n}\n\nfunction printError () {\n\tif [ $colors == \"true\" ]; then\n\t\techo -e -n \"\\x1b[0;;31m\"\n\t\techo $@\n\t\techo -e -n \"\\x1b[0;0m\"\n\telse\n\t\techo $@\n\tfi\n}\n\n\nfunction printHelpMessage() {\n\techo \"use tree [args]\"\n\techo \"args:\"\n\techo -e \"\\t-h \\tprints this help message\";\n\techo -e \"\\t-c \\tdon't use colors\";\n\techo -e \"\\t-p \\tprints the full path of the files not just their names\"\n\techo -e \"\\t-f format \\tchanges the string used to indicate the levels to format\"\n\techo -e \"\\t-l num \\tDescends maximum num levels\"\n}\n\nfullPath=\"false\"\nspace=\" \"\nmaxLevels=999999999 # infinity?\n\n#read the global variables\nargs=( \"$@\" )\ni=0\nwhile [ $i -lt $# ]; do\n\tif [ ${args[$i]} == \"-h\" ]; then\n\t\tprintHelpMessage\n\t\texit 0\n\telif [ ${args[$i]} == \"-p\" ]; then\n\t\tfullPath=\"true\"\n\telif [ ${args[$i]} == \"-c\" ]; then\n\t\tcolors=\"false\"\n\telif [ ${args[$i]} == \"-f\" ]; then\n\t\t# read the next arg\n\t\tif [ $(($i + 1)) -eq $# ]; then\n\t\t\tprintError \"Missing argument for -f. Use -h for help\"\n\t\t\texit 1\n\t\tfi\n\t\ti=$(($i + 1))\n\t\tspace=${args[$i]}\n\telif [ ${args[$1]} == \"-l\" ]; then\n\t\t#read the next arg\n\t\tif [ $(($i + 1)) -eq $# ]; then\n\t\t\tprintError \"Missing argument for -l. Use -h for help\"\n\t\t\texit 1\n\t\tfi\n\t\ti=$(($i + 1))\n\t\tmaxLevels=${args[$i]}\n\telse\n\t\tprintError \"Unrecognized argument. Use -h for help\"\n\t\texit 1\n\tfi\n\ti=$(($i + 1))\ndone\n\n\nfunction printFiles () {\n\tif [ $fullPath == \"true\" ]; then\n\t\tls -d -1 $PWD/** 2>/dev/null\n\telse\n\t\tls\n\tfi\n}\n\nindentLevel=\"0\";\n\n\nfunction printIndent() {\n\ti=0;\n\twhile [ $i -lt $indentLevel ]; do\n\t\techo -n -e \"$space\"\n\t\ti=$(($i + 1))\n\tdone\n}\n\n\nlevel=-1\n# prints the current directory\nfunction printDirectory() {\n\tlevel=$(($level + 1))\n\tif [ $level -ge $maxLevels ]; then\n\t\tlevel=$(($level - 1))\n\t\treturn\n\tfi\n\n\tfor dir in $(printFiles) ; do \n\t\tif [ -d $dir ]; then\n\t\t\t\n\t\t\tprintIndent\n\t\t\tcd $dir 2>/dev/null\n\t\t\tif [ $? -eq 0 ]; then # we can go in the directory, continue\n\t\t\t\tcd ..\n\n\t\t\t\tprintDir $dir\n\t\t\t\t# recurse on $dir\n\t\t\t\tindentLevel=$(($indentLevel + 1))\n\t\t\t\tcd $dir\n\t\t\t\tprintDirectory;\n\t\t\t\tcd ..\n\t\t\t\tindentLevel=$(($indentLevel - 1))\n\n\t\t\telse \n\t\t\t\t#the directory is blocked for some reason\n\t\t\t\tprintBlockedDir $dir\n\t\t\tfi\n\t\t\t\n\t\telse \n\t\t\tprintIndent;\n\t\t\techo $dir\n\t\tfi\n\tdone\n\tlevel=$(($level - 1))\n}\n\nprintDirectory\n"
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"text": "#!/usr/bin/env python\nimport tree;\nimport os;\nfrom shutil import rmtree;\n\ndef _print_html(file, html, level=0, path=\"\"):\n indent = \" \" * level;\n \n html += indent + '<div class=\"%s\" type=\"%s\" level=\"%d\">' % (file.type, ('directory' if isinstance(file, tree.Dir) else 'file'), level);\n html += '<div class=\"name\"><a href=\"' + path + \"/\" + file.name + '\">';\n html += file.name + '</a></div>\\n';\n \n if isinstance(file, tree.Dir):\n html += indent + '<div class=\"dir-contents\">\\n';\n for child in file.children:\n html = _print_html(child, html, level+1, path + '/' + file.name);\n html += indent + '</dir>\\n';\n \n html += indent + '</div>\\n';\n return html;\n \n \ndef create_html(file):\n html = '''<html>\n<head>\n <title>Directory</title>\n</head>\n<body>\n'''\n\n file.root.name = file.root.name[file.root.name.rfind(\"/\")+1:];\n html += '<a href=\".\">%s</a>\\n' % file.root.name;\n for child in file.root.children:\n html = _print_html(child, html, 0, path=\".\");\n html += '</body>\\n</html>';\n return html;\n\n# Create the .html_fs_tree\ndirectory_path = \".html_fs_tree\";\ntry:\n directory = os.mkdir(directory_path);\nexcept OSError:\n sys.stderr.write(directory_path + \" already exsits. Do you want to override it (y/n)? \");\n line = sys.stdin.readline();\n if line.lower() == \"yes\" || line.lower() == \"y\":\n # Delete .html_fs_tree and everything in it\n rmtree(directory_path);\n else:\n sys.exit(1);\n \n\n"
}
] | 40 |
noah-j/mouserack
|
https://github.com/noah-j/mouserack
|
01e8fc79caabc9728eedd810a4ec9beacbf15a73
|
5975548744cc69fba4542f9cc868844a7bd7a130
|
a12374f58e2874fc59255f195affcc9a30f96e80
|
refs/heads/master
| 2020-04-14T23:49:05.025973 | 2015-10-29T22:55:31 | 2015-10-29T22:55:31 | 30,135,790 | 0 | 0 | null | null | null | null | null |
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"text": "#I could have a button that asks if the cage is a BU/stock/plug etc.\n#upon initialization\n\n#TODO: display cage details upon mouseover of a particular cage!\n#TODO: allow users to specify a slot as empty\n\nfrom Tkinter import *\n\nclass Cage(Tk):\n\n\n\n def __init__(self,cageName,rack_frame):\n self.cageName = cageName\n\n\n\n\n '''\n #I am going to move the view_edit function to the rack file.\n def view_edit(self,rack_frame):\n This command displays the cage contents on the sidebar\n the cage label is added as a label at the top of the field. Below\n this there is a yes/no button asking if the cage is a BU or not.\n Depending on the users answer different entry fields will be displayed.\n BU: male and female genotype, from, ET #, DOB (automatically generate\n breed date from this info using date/time package) ; set-up date,\n last litter (older litters will also be displayed but as labels, not\n as entry fields), NOTE: last litter will automatically generate a\n weaning TODO that the user can override, misc TODO field with date\n input\n\n For other cages the user will input the number of individuals and a\n series of fields will be generated as listed below\n Other cage: M/F, individual ID fields, genotype info for each ID input,\n TODO fields for each mouse\n\n\n\n #in order for buttons to have multiple functions when clicked\n #just write the command function to do everything you want it to.\n #duh.\n\n def bu_init():\n\n fields = ['Genotype','Ear tag #','from','DOB']\n row_counter = 2\n column_counter = numCols+1\n for field in fields:\n field_label = Label(rack_frame,text=field)\n field_entry = Entry(rack_frame)\n field_label.grid(row=2 , column=numCols+1)\n field_entry.grid(row=3 , column=numCols+1)\n column_counter+=1\n\n\n\n def other_init():\n pass\n\n cage_name_display = Label(rack_frame,text=self.cageName)\n cage_name_display.grid(row=1,column=numCols+1)\n\n\n BUbutton = Button(rack_frame, text='Breeding unit', command=bu_init)\n otherbutton = Button(rack_frame,text='Other cage', command=other_init)\n '''\n\n\n\n\n\n\n\n\n\n'''Version #1\nclass Cage(Tk):\n\n\n\n def __init__(self):\n\n cageFrame = Toplevel()\n cageFrame.grid()\n cageFrame.title('Enter cage ID')\n\n self.cageIdLabel = Label(cageFrame, text='Please enter cage ID')\n self.cageIdLabel.grid(row=0,column=1)\n self.cageId = Entry(cageFrame)\n self.cageId.grid(row=0, column=2)\n\n\n\n def view(self):\n pass\n\n\n\n'''"
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"text": "# mouse_rack\n\nI am working on this simple program in Tkinter to learn more about python 2.7. The mouse rack program is intended to help \nlab workers who are responsible for mouse husbandry keep track of what's in their mouse room and keep up on deadlines\nrelated to their cages.\n\n**NOTE 7/21/15:** I have abandonded this project. The time investment isn't worth the resultant program. It could be useful if it was integrated with a barcode scanner and cage specific barcodes but that's way beyond my means.\n"
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"src_encoding": "UTF-8",
"text": "\nimport string\nfrom Tkinter import *\nimport cage\nfrom collections import *\n'''\nThis is v1 of the mouseRack application in Pyhton. It will contain an initialization class\nthat takes user input for rack name, and number of rows and columns. The program will then\ninitialize a GUI rack with the appropriate number of 'slots' for cages. Each slot will\nbe fillable with a cage Class. Cage classes will have a number of different info slots\nand will have programable deadlines. They will change color (become 'hotter') as the due\ndate for the task approaches.\n\nPersistance management:\n'''\nclass Rack(Tk):\n\n\n def __init__(self, rack_frame,numRows, numCols):\n\n alpha = list(string.ascii_uppercase)\n\n self.numRows = int(numRows)\n self.numCols = int(numCols)\n\n\n def edit_cage(cage_name,frame):\n cage_name = cage.Cage(self,frame)\n Rack.view_edit(cage_name,frame)\n\n def add_cage():\n #I don't think I should do this as a nested function.\n #break it out ASAP\n def initializeCage():\n cageRow=int(rowInput.get())\n cageCol=colInput.get()\n cageName=idInput.get()\n\n #alphaDict converts a letter designation back to the appropriate\n #column number so tkinter grid can appropriately place the cage\n alphaDict = {}\n number = 0\n for letter in alpha:\n alphaDict[letter] = number\n number+=1\n\n\n\n activeFrame = label_frame_list[alphaDict[cageCol]][cageRow-1]\n activeEmptyLabel = label_list[alphaDict[cageCol]][cageRow-1]\n activeEmptyLabel.grid_forget()\n\n cageLabel= Button(activeFrame,text=cageName,command=lambda: edit_cage(cageName,rack_frame))\n\n cageLabel.grid()\n\n idInputLabel.grid_forget()\n idInput.grid_forget()\n rowInputLabel.grid_forget()\n rowInput.grid_forget()\n colInputLabel.grid_forget()\n colInput.grid_forget()\n addButton.grid_forget()\n\n\n\n idInputLabel=Label(rack_frame,text=\"Cage ID\")\n idInput= Entry(rack_frame)\n idInputLabel.grid(row=1,column=self.numCols+1)\n idInput.grid(row=1,column=self.numCols+2)\n\n rowInputLabel=Label(rack_frame,text=\"Row\")\n rowInput=Entry(rack_frame)\n rowInputLabel.grid(row=2,column=self.numCols+1)\n rowInput.grid(row=2,column=self.numCols+2)\n\n colInputLabel=Label(rack_frame,text=\"Column\")\n colInput=Entry(rack_frame)\n colInputLabel.grid(row=3,column=self.numCols+1)\n colInput.grid(row=3,column=self.numCols+2)\n\n addButton = Button(rack_frame,text=\"Add\",command=initializeCage)\n addButton.grid(row=4,column=self.numCols+2)\n\n\n #I am trying to put my labelframe and label objects into a list so I can access them easily\n #from elsewhere in the program\n #learn more about hte defaultdict class\n #TODO: LEARN more about defaultdict and understand how my two lists are being built here\n label_frame_list = defaultdict(list)\n label_list = defaultdict(list)\n for col in range(self.numCols):\n for r in range(self.numRows):\n label_frame_list[col].append(LabelFrame(rack_frame,text='{0}-{1}'.format(alpha[col],r+1)))\n l = label_frame_list[col][r]\n l.grid(row=r,column=col)\n\n\n label_list[col].append(Label(label_frame_list[col][r],text='empty'))\n label = label_list[col][r]\n label.grid()\n toolBar = Menu(rack_frame)\n toolBar.add_command(label=\"Add cage\",command=add_cage)\n toolBar.add_command(label=\"Census\")\n\n rack_frame.config(menu=toolBar)\n\n def view_edit(rack_frame):\n '''This command displays the cage contents on the sidebar\n the cage label is added as a label at the top of the field. Below\n this there is a yes/no button asking if the cage is a BU or not.\n Depending on the users answer different entry fields will be displayed.\n BU: male and female genotype, from, ET #, DOB (automatically generate\n breed date from this info using date/time package) ; set-up date,\n last litter (older litters will also be displayed but as labels, not\n as entry fields), NOTE: last litter will automatically generate a\n weaning TODO that the user can override, misc TODO field with date\n input\n\n For other cages the user will input the number of individuals and a\n series of fields will be generated as listed below\n Other cage: M/F, individual ID fields, genotype info for each ID input,\n TODO fields for each mouse\n '''\n\n\n #in order for buttons to have multiple functions when clicked\n #just write the command function to do everything you want it to.\n #duh.\n\n def bu_init():\n\n fields = ['Genotype','Ear tag #','from','DOB']\n row_counter = 2\n column_counter = numCols+1\n for field in fields:\n field_label = Label(rack_frame,text=field)\n field_entry = Entry(rack_frame)\n field_label.grid(row=2 , column=numCols+1)\n field_entry.grid(row=3 , column=numCols+1)\n column_counter+=1\n\n\n\n def other_init():\n pass\n\n cage_name_display = Label(rack_frame,text=self.cageName)\n cage_name_display.grid(row=1,column=numCols+1)\n\n\n BUbutton = Button(rack_frame, text='Breeding unit', command=bu_init)\n otherbutton = Button(rack_frame,text='Other cage', command=other_init)\n BUbutton.grid(row=2, column=numCols+1)\n otherbutton.grid(row=2, column=numCols+2)\n\n #toolBar = Menu(rack_frame)\n #toolBar.add_command(label=\"Add cage\",command=add_cage)\n #toolBar.add_command(label=\"Census\")\n\n #rack_frame.config(menu=toolBar)\n\n\n\n\n\n\n\n\n'''VERSION #2\nclass Rack(Tk):\n\n\n def __init__(self, rack_frame,numRows, numCols):\n\n alpha = list(string.ascii_uppercase)\n\n self.numRows = int(numRows)\n self.numCols = int(numCols)\n\n\n def add_cage():\n #I don't think I should do this as a nested function.\n #break it out ASAP\n def initializeCage():\n cageRow=int(rowInput.get())\n cageCol=colInput.get()\n cageName=idInput.get()\n #alphaDict converts a letter designation back to the appropriate\n #column number so tkinter grid can appropriately place the cage\n alphaDict = {}\n number = 0\n for letter in alpha:\n alphaDict[letter] = number\n number+=1\n #How do I access labelframes from the original rack grid?\n cageLabel= Label(rack_frame,text=cageName)\n\n cageLabel.grid(row=cageRow-1,column=alphaDict[cageCol])\n\n\n\n idInputLabel=Label(rack_frame,text=\"Cage ID\")\n idInput= Entry(rack_frame)\n idInputLabel.grid(row=1,column=self.numCols+1)\n idInput.grid(row=1,column=self.numCols+2)\n\n rowInputLabel=Label(rack_frame,text=\"Row\")\n rowInput=Entry(rack_frame)\n rowInputLabel.grid(row=2,column=self.numCols+1)\n rowInput.grid(row=2,column=self.numCols+2)\n\n colInputLabel=Label(rack_frame,text=\"Column\")\n colInput=Entry(rack_frame)\n colInputLabel.grid(row=3,column=self.numCols+1)\n colInput.grid(row=3,column=self.numCols+2)\n\n addButton = Button(rack_frame,text=\"Add\",command=initializeCage)\n addButton.grid(row=4,column=self.numCols+2)\n\n\n\n\n for col in range(self.numCols):\n for r in range(self.numRows):\n\n L= LabelFrame(rack_frame,text='{0}-{1}'.format(alpha[col],r+1))\n L.grid(row=r,column=col)\n\n b= Label(L, text=\"Empty\")\n b.grid()\n\n\n toolBar = Menu(rack_frame)\n toolBar.add_command(label=\"Add cage\",command=add_cage)\n toolBar.add_command(label=\"Census\")\n\n rack_frame.config(menu=toolBar)\n'''\n\n\n\n\n\n\n\n\n\n\n'''VERSION #1\nclass Rack(Tk):\n\n\n def __init__(self, rack_frame,numRows, numCols):\n alpha = list(string.ascii_uppercase)\n\n #rackFrame = Toplevel()\n #rackFrame.grid()\n\n self.numRows = int(numRows)\n self.numCols = int(numCols)\n\n #Going to use the labelframe widget type to populate the racks\n #slots = LabelFrame(rack_frame,padx=5, pady=5)\n #slots.grid()\n def add_cage():\n\n Cage()\n\n\n\n #right now I'm using this to generate the rightBar item below. This isn't a good solution\n\n for col in range(self.numCols):\n\n for r in range(self.numRows):\n L = LabelFrame(rack_frame,text='{0}-{1}'.format(alpha[col],r+1))\n L.grid(row=r,column=col)\n\n b= Button(L, text=\"Add cage\",command=add_cage)\n b.grid()\n\n #TODO: implement this right action bar area that will host the cage entry and details section\n #rightBar = Frame(rack_frame,row=0, column=colCounter+1, rowspan=rowCounter+1)\n #rightBar.grid()\n\n\n #NOTE: instead of using text for the labels for my labelframes\n #containing the cages I should use labelwidget and make the widget\n #a drop down menu that allows the user to delete/change cages!\n\n\n\n\n\n\n\n\n#root = Tk()\n\n#rack_window = Toplevel()\n#root.mainloop()\n'''"
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"text": "\n\nimport string\nfrom Tkinter import *\nfrom rack import *\n\nclass Startup(object):\n\n def __init__(self, master):\n frame = Frame(master)\n\n frame.grid()\n\n\n self.rowLabel = Label(master, text=\"# of rows\")\n self.rowLabel.grid(row=0, column=1)\n self.rowPicker= Entry(master)\n self.rowPicker.grid(row=0,column=2)\n\n self.colLabel = Label(master, text=\"# of columns\")\n self.colLabel.grid(row=1, column=1)\n self.colPicker = Entry(master)\n self.colPicker.grid(row=1, column=2)\n\n self.nameLabel= Label(master, text=\"Name of rack\")\n self.nameLabel.grid(row=2, column=1)\n self.rackName = Entry(master)\n self.rackName.grid(row=2, column=2)\n\n\n\n def launch():\n\n name = self.rackName.get()\n rows = self.rowPicker.get()\n cols = self.colPicker.get()\n rackFrame = Toplevel()\n rackFrame.title('{}'.format(self.rackName.get()))\n rackFrame.grid()\n\n try:\n #I don't actually want to kill all the original widgets here\n #I want the new Rack instance to launch in its own seperate window\n\n #self.rowLabel.destroy()\n #self.colLabel.destroy()\n #self.nameLabel.destroy()\n #self.rowPicker.destroy()\n #self.colPicker.destroy()\n #self.rackName.destroy()\n #self.launchButton.destroy()\n ###I got it. The issue was not specifying the frame in the rack method\n ### __init__ at the Label button\n name = Rack(rackFrame,rows, cols)\n\n except ValueError:\n #This should be made into a pop up window warning about\n #using the correct input types\n exceptionPrompt=Label(rackFrame,text='please enter integers for row and column numbers ')\n\n nGet = self.rackName.get()\n rGet = self.rowPicker.get()\n cGet = self.colPicker.get()\n self.launchButton = Button(master, text=\"Create rack\", command=launch)\n self.launchButton.grid(row=3)\n\n\n\n\n\n\n\nroot= Tk()\nstarter = Startup(root)\nroot.mainloop()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#version 1\n'''\nclass Startup:\n\n def __init__(self, master):\n frame = Frame(master)\n frame.pack()\n l1 = Label(root,text='MouseRack v1')\n l1.pack()\n\n self.rowPicker= Listbox(master, selectmode=SINGLE)\n self.rowPicker.pack()\n\n self.colPicker = Listbox(master, selectmode=SINGLE)\n self.colPicker.pack()\n\n for item in range(10):\n self.rowPicker.insert(END, item)\n self.colPicker.insert(END, item)\n\n\n def selection(self,master):\n\n numRows = self.rowPicker.curselection()\n numCols = self.colPicker.curselection()\n\n\n rackName = raw_input('Please enter rack ID')\n\n\n self.choiceButton = Button(master, text='Initialize', command=rackname.Rack(numRows,numCols))\n\n\nroot= Tk()\nstarter = Startup(root)\nroot.mainloop()\n'''"
}
] | 4 |
Johannse1/Textbook
|
https://github.com/Johannse1/Textbook
|
a0e0cdf7d87cdd9b075f9b2e8dfdbaf6bd31766a
|
22b6b795e445042435577481e566bb331b8abd74
|
0619cc5340512dd943ceed920c5ff0177fb5cd73
|
refs/heads/master
| 2020-09-14T00:34:40.219673 | 2019-11-23T04:58:36 | 2019-11-23T04:58:36 | 222,954,838 | 0 | 0 | null | null | null | null | null |
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"text": "from person import Person\n\nclass Book():\n def __init__(self,title,first, last, age,edition,ISBN_number,publisher,year_published,quantity,price):\n self.title = title\n self.author = Person(first, last, age)\n self.edition = edition\n self.number = ISBN_number\n self.publisher = publisher\n self.year = year_published\n self.quantity = quantity\n self.price = price\n\n def textbook(self):\n print(f\"{self.title}, {self.author}, {self.edition}, {self.number}, {self.price}, {self.year}, {self.quantity}, {self. price}\")\n\n def add(self,qty):\n self.quantity = self.quantity + qty\n\n def deduct(self,qty):\n if self.quantity >= qty:\n self.quantity = self.quantity - qty\n return 0\n else:\n return 1\n\n"
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"text": "from textbook import Book\nprint(\"Please enter the requiered information for the 1st book.\")\n\n\ndef book_description():\n title = str(input(\"Title: \"))\n first = str(input(\"First name:\"))\n last = str(input(\"Last name:\"))\n age = str(input(\"Age: \"))\n edition = str(input(\"Edition: \"))\n ISBN_number = str(input(\"ISBN number: \"))\n publisher = str(input(\"Publisher: \"))\n year_published = str(input(\"Year published: \"))\n quantity = str(input(\"Number of books: \"))\n price = str(input(\"Price: \"))\n book = Book(title,first, last, age,edition,ISBN_number,publisher,year_published,quantity,price)\n return book\n\n\nbook1 = book_description()\nbook2 = book_description()\nmenu_choice = 0\nwhile menu_choice != 4:\n print(\"What would you like to do, please select from the menu below.\")\n print(\"1. add to inventory.\")\n print(\"2. deduct from inventory.\")\n print(\"3. Modify the price of a book.\")\n print(\"4. Quit the programm\")\n\n menu_choice = int(input())\n\n if menu_choice == 1:\n print(f\"Which book, '{book1.title}' or '{book2.title}'\")\n choice = int(input())\n if choice == book1.title:\n qty = int(input(\"How much would you like to add?\"))\n book1.add_quantity(qty)\n print(\"The quantity in inventory is now \" + str(book1.quantity + \"\\n\\n\"))\n elif choice == book2.title:\n qty = int(input(\"How much would you like to add?\"))\n book2.add_quantity(qty)\n print(\"The quantity in inventory is now \" + str(book2.quantity + \"\\n\\n\"))\n else:\n print(\"Error, please enter the correct title.\")\n elif menu_choice == 2:\n print(f\"Which book, '{book1.title}' or '{book2.title}'\")\n choice = int(input())\n if choice == book1.title:\n qty = int(input(\"How much would you like to remove?\"))\n result = book1.deduct_quantity(qty)\n if result == 0:\n print(\"The quantity in inventory is now 1\" + str(book1.quantity) + \"\\n\\n\")\n else:\n print(\"You do not have enough books in inventory to remove that quantity\")\n print(\"Curretn inventory in stock is\" + str(book1.quantity) + \"\\n\\n\")\n\n elif choice == book2.title:\n qty = int(input(\"How much would you like to remove?\"))\n result = book2.deduct_quantity(qty)\n if result == 0:\n print(\"The quantity in inventory is now 1\" + str(book2.quantity) + \"\\n\\n\")\n else:\n print(\"You do not have enough books in inventory to remove that quantity\")\n print(\"Current inventory in stock is\" + str(book2.quantity) + \"\\n\\n\")\n else:\n print(\"Error, please enter the correct title.\")\n\n elif menu_choice == 3:\n\n print(f\"Which book, '{book1.title}' or '{book2.title}'\")\n choice = int(input())\n if choice == book1.title:\n price = float(input(\"What will the new price be: \"))\n book1.price = price\n print(f\"The price of {book1.title} is now {book1.price}\")\n elif choice == book2.title:\n price = float(input(\"What will the new price be: \"))\n book2.price = price\n print(f\"The price of {book2.title} is now {book2.price}\")\n else:\n print(\"Error, please enter the correct title.\")\n\n elif menu_choice == 4:\n print(\"Thank you for using the system!\")\n else:\n print(\"Error, please enter a number on the menu.\")\n"
}
] | 2 |
Eric-Arellano/bathroom-classification
|
https://github.com/Eric-Arellano/bathroom-classification
|
d8118937d0bab6ceeed8d9e145b9c078278b7dbb
|
a9f861ed323fecad1a8637be4a6f3f70b8d0dcea
|
9bf9e697fbd01a1d590486f1ce629813a139c6d8
|
refs/heads/master
| 2020-03-15T22:34:55.664375 | 2018-12-18T21:37:46 | 2018-12-18T21:37:46 | 132,376,533 | 0 | 0 |
MIT
| 2018-05-06T21:14:06 | 2018-12-18T21:37:49 | 2019-10-21T16:04:46 |
Python
|
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"text": "from typing import List, Tuple\nfrom glob import glob\n\nimport numpy as np\nimport cv2\n\nFilePath = str\nClassification = Tuple[str, str]\n\n\ndef main() -> None:\n images = load_images()\n classifications = classify_images(images)\n save_as_csv(classifications)\n\n\ndef load_images() -> List[np.ndarray]:\n images = [cv2.imread(file) for file in glob('../data/*.jpg')]\n print(f'Number of images loaded: {len(images)}')\n return images\n\n\ndef classify_images(image_path: List[np.ndarray]) -> List[Classification]:\n cv2.namedWindow()\n cv2.imshow('image', )\n\n\ndef save_as_csv(classifications: List[Classification]) -> None:\n raise NotImplementedError\n\n\nif __name__ == '__main__':\n main()\n"
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"text": "# Bathroom Classification\nUses ML classification to determine if bathroom is gender-inclusive and accessible to people with disabilities.\n\n## Prereqs\n1. Chrome Driver, `brew cask install chromeodriver` (only if scraping data)\n\n## To install\n1. `python3 -m venv .`\n1. `source bin/activate`\n1. `pip install -r requirements.txt`\n\n## To run\n1. Activate venv: `source bin/activate`\n1. Scrape images if not already downloaded: `python src/scrape_images.py`\n1. Label images if not already labeled: `python src/labeler.py`\n"
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"text": "import os\nfrom pathlib import Path\nfrom typing import List\n\nimport cv2\nimport requests\nfrom selenium import webdriver\n\nQuery = str\nUrl = str\nFilePath = str\n\n\ndef main() -> None:\n urls = get_urls_multiple_queries(['bathroom sign',\n 'male bathroom sign',\n 'men bathroom sign',\n 'female bathroom sign',\n 'women bathroom sign',\n 'unisex bathroom sign',\n 'family bathroom sign',\n 'ada bathroom sign',\n 'accessible bathroom sign',\n 'gender neutral bathroom sign',\n 'gender inclusive bathroom sign',\n 'all gender bathroom sign',\n ])\n image_data = download(urls)\n image_data = remove_empty_images(image_data)\n image_data = deduplicate(image_data)\n file_paths = save_images(image_data)\n remove_corrupt_images(file_paths)\n\n\ndef get_urls_multiple_queries(queries: List[Query]) -> List[Url]:\n \"\"\"\n Return concatenated URLs of all search results on Google Images for every query.\n \"\"\"\n distinct_urls = [get_urls(query) for query in queries]\n flattened = [y for x in distinct_urls for y in x]\n print(f'Total image URLs: {len(flattened)}')\n return flattened\n\n\ndef get_urls(query: Query) -> List[Url]:\n \"\"\"\n Return URLs of all search results on Google Images for query.\n \"\"\"\n print(f'Getting image URLs for \"{query}\".')\n # build driver\n options = webdriver.ChromeOptions()\n options.add_argument('--headless')\n driver = webdriver.Chrome(chrome_options=options)\n # get page\n encoded_query = query.replace(' ', '+')\n driver.get(f'https://www.google.com/search?tbm=isch&q={encoded_query}')\n # scrape image URLs\n result = driver.execute_script(r'''\n const nodes = document.querySelectorAll('.rg_di .rg_meta')\n const urls = Array.from(nodes).map(x => JSON.parse(x.textContent).ou).join('\\n');\n return urls;\n ''')\n driver.close()\n return result.split('\\n')\n\n\ndef download(urls: List[Url]) -> List[bytes]:\n \"\"\"\n Get image file.\n \"\"\"\n\n def get_image(url: Url) -> bytes:\n try:\n print(f'Attempting to download {url}.')\n r = requests.get(url, timeout=45)\n return r.content\n except:\n print(f'Failed...skipping.')\n\n return [get_image(url) for url in urls]\n\n\ndef remove_empty_images(image_data: List[bytes]) -> List[bytes]:\n \"\"\"\n Remove any bytes that are None.\n \"\"\"\n print(f'Size before removing empty images: {len(image_data)}')\n cleaned = [image for image in image_data if image is not None]\n print(f'Size after removing empty images: {len(cleaned)}')\n return cleaned\n\n\ndef deduplicate(image_data: List[bytes]) -> List[bytes]:\n \"\"\"\n Remove any identical images.\n \"\"\"\n print(f'Size before de-duplication: {len(image_data)}')\n uniques = []\n for image in image_data:\n if not any(image == unique_image for unique_image in uniques):\n uniques.append(image)\n print(f'Size after de-duplication: {len(uniques)}')\n return uniques\n\n\ndef save_images(image_data: List[bytes]) -> List[FilePath]:\n \"\"\"\n Save as .jpg files with names indexed from 0 to n.\n \"\"\"\n current_file_path = Path(os.path.realpath(__file__))\n data_folder = str(current_file_path.parents[1].joinpath('data'))\n file_paths = []\n for index, image in enumerate(image_data):\n file_name = f'{data_folder}/{str(index).zfill(4)}.jpg'\n file_paths.append(file_name)\n with open(file_name, 'wb') as file:\n file.write(image)\n return file_paths\n\n\ndef remove_corrupt_images(file_paths: List[FilePath]) -> None:\n print(f'Size before removing corrupt images: {len(file_paths)}')\n corrupt = [file_path for file_path in file_paths\n if cv2.imread(file_path) is None]\n print(f'Size after removing corrupt images: {len(file_paths) - len(corrupt)}')\n for file in corrupt:\n os.remove(file)\n\n\nif __name__ == '__main__':\n main()\n"
}
] | 4 |
divchaturvedi/Movie-Recommender-System
|
https://github.com/divchaturvedi/Movie-Recommender-System
|
89480919e4c9dedb1aca1d2c2fd9d379faf43d6f
|
78ee2667f41ecd79fe9655c2423cee925056b886
|
6612d1f5e738c04ba83c4c9fc1df0bbca3acee4d
|
refs/heads/master
| 2023-01-03T04:20:12.203713 | 2020-11-01T07:35:50 | 2020-11-01T07:35:50 | 298,371,935 | 1 | 2 | null | null | null | null | null |
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"path": "/data_mining project/ml-100k/isdiff.sh",
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"text": "#!/bin/sh\ncmp u60_1.base u60_2.base"
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"text": "# Movie Recommender System\nRecommender System in python using autoencoders as part of Data Mining project.\n\nContributors:\n\nAasav Badera - 18075001,\nDeepesh Tank - 18075017,\nDishant Chourasia - 18075018,\nDivyansh Chaturvedi - 18074005\n"
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"text": "#include<bits/stdc++.h> \n#include <ext/pb_ds/assoc_container.hpp>\n#include <ext/pb_ds/tree_policy.hpp> \n// #pragma GCC optimize(\"-O3\")\n// #pragma GCC optimize(\"Ofast\")\n// #pragma GCC target(\"avx,avx2,fma\")\nusing namespace std;\nusing namespace __gnu_pbds;\n#ifdef ON_LINUX \n#include <sys/resource.h>\n#define meminc rlimit rlim;if (getrlimit(RLIMIT_STACK, &rlim)) return 1;rlim.rlim_cur = 26843556;if (setrlimit(RLIMIT_STACK, &rlim)) return 2;\n#else\n#define meminc \n#endif\n#ifdef LOCAL\n#define dbg(args...) { string _s = #args; replace(_s.begin(), _s.end(), ',', ' '); stringstream _ss(_s); istream_iterator<string> _it(_ss); err(_it, args); }\n#else\n#define dbg(args...)\n#endif\nstruct custom_hash {\n static uint64_t splitmix64(uint64_t x) {\n // http://xorshift.di.unimi.it/splitmix64.c\n x += 0x9e3779b97f4a7c15;\n x = (x ^ (x >> 30)) * 0xbf58476d1ce4e5b9;\n x = (x ^ (x >> 27)) * 0x94d049bb133111eb;\n return x ^ (x >> 31);\n }\n\n size_t operator()(uint64_t x) const {\n static const uint64_t FIXED_RANDOM = chrono::steady_clock::now().time_since_epoch().count();\n return splitmix64(x + FIXED_RANDOM);\n }\n};\nvoid err(istream_iterator<string> it) {}\ntemplate<typename T, typename... Args>\nvoid err(istream_iterator<string> it, T a, Args... args) {\n cerr << *it << \" = \" << a << endl;\n err(++it, args...);\n}\ntemplate<typename T,typename U> std::ostream& operator<<(std::ostream& out, std::pair<T,U> a) {\n out<< a.first << \" \" << a.second;\n return out;\n}\n \ntemplate<typename T,typename U> std::istream& operator>>(std::istream& in, std::pair<T,U> &a) {\n in >> a.first >> a.second;\n return in;\n}\n#define int long long int\n#define ll int\n#define pb push_back\n#define pii pair<int, int>\n#define ff first\n#define se second\n#define vii vector<pii>\n#define vi vector<int >\n#define ordered_set tree<int, null_type,less<int>, rb_tree_tag,tree_order_statistics_node_update>\n// -------------------Standard Traversal Moves---------------------\n// vi fx = {1 ,-1 ,0, 0}, fy = {0, 0, -1, 1};\n// vi fx = {2, -2, 2, -2, 1, -1, 1, -1}, fy = {1, 1, -1, -1, 2, 2, -2, -2};\n// vi fx = {1, 1, 1, -1, -1 , -1, 0, 0}, fy = {1, -1, 0, 1, -1, 0, 1, -1};\n// ----------------------------------------------------------------\n\n#define rep(i, a, b) for(int i=a;i<b;i++)\n#define all(a) (a).begin(),(a).end()\n#define sz(x) (int )x.size()\n#define yes cout << \"YES\" << endl\n#define no cout << \"NO\" << endl\n#define endl '\\n' \n// const int hell = (int)998244353;\nconst int hell = (int)1e9 + 7;\nconst int inf = 3223372036854775807;\nconst double PI = 3.14159265;\nconst int N = (int) 1e6 + 5;\nint n, m, k, a[N];\n int counter = 1;\nint rand(int a,int b)\n{\n return a + (b - a + 1) * rand();\n}\nint32_t main()\n{\n // meminc;\n ios::sync_with_stdio(false);\n cin.tie(nullptr);\n cerr.precision(10);\n cout.precision(25);\n cout << fixed;\n #ifdef LOCAL\n // for getting input from input.txt\n freopen(\"basefile\", \"r\", stdin);\n // for writing output to output.txt\n #endif\n int tests = 1;\n // cin >> tests;\n rep(test, 1, tests+1)\n {\n string filename[2] = {\"u90_1.test\", \"u90_1.base\"};\n ofstream outputfile[2];\n rep(i, 0, 2)\n {\n outputfile[i].open(filename[i].c_str());\n }\n n = 100000;\n m = n / 10;\n m *= 9;\n k = n - m;\n srand(20387520947);\n rep(i, 0, n)\n {\n int f1, f2, f3, f4;\n counter++;\n cin >> f1 >> f2 >> f3 >> f4;\n // string g;\n // cin >> g;\n // g += \"::\";\n // f1 = f2 = f3 = f4 = -1;\n // int le = -1;\n // rep(j, 0, sz(g) - 1)\n // {\n // if(g[j] == ':' and g[j + 1] == ':')\n // {\n // if(f1 == -1)\n // {\n // f1 = stoi(g.substr(le + 1, j - le));\n // continue;\n // }\n // else if(f2 == -1)\n // {\n // f2 = stoi(g.substr(le + 1, j - le));\n // continue;\n // }\n // else if(f3 == -1)\n // {\n // f3 = stoi(g.substr(le + 1, j - le));\n // continue;\n // }\n // else\n // {\n // f4 = stoi(g.substr(le + 1, j - le));\n // }\n // }\n // else\n // {\n // le = j;\n // }\n // }\n string to_write;\n to_write += to_string(f1);\n to_write += ',';\n to_write += to_string(f2);\n to_write += ',';\n to_write += to_string(f3);\n to_write += ',';\n to_write += to_string(f4);\n to_write += ',';\n if(!m)\n {\n outputfile[0] << to_write << endl;\n continue;\n }\n if(!k)\n {\n outputfile[1] << to_write << endl;\n continue;\n }\n int key = rand(0, 1e9);\n key &= 1;\n if(!key)\n {\n m--;\n outputfile[1] << to_write << endl;\n continue;\n }\n else{\n k--;\n outputfile[0] << to_write << endl;\n }\n }\n }\n #ifdef LOCAL\n // cerr << \"Time elapsed: \" << 1.0 * clock() / CLOCKS_PER_SEC << \" s.\\n\";\n #endif\n return 0;\n}"
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"text": "\"\"\"##Importing the libraries\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.optim as optim\nimport torch.utils.data\nfrom torch.autograd import Variable\n\n\n\"\"\"## training set and the test set\"\"\"\n\ntraining_set_1 = pd.read_csv('ml-100k/u1.base', delimiter = '\\t')\ntraining_set_1 = np.array(training_set_1, dtype = 'int')\ntest_set_1 = pd.read_csv('ml-100k/u1.test', delimiter = '\\t')\ntest_set_1 = np.array(test_set_1, dtype = 'int')\n\ntraining_set_2 = pd.read_csv('ml-100k/u2.base', delimiter = '\\t')\ntraining_set_2 = np.array(training_set_1, dtype = 'int')\ntest_set_2 = pd.read_csv('ml-100k/u2.test', delimiter = '\\t')\ntest_set_2 = np.array(test_set_1, dtype = 'int')\n\ntraining_set_3 = pd.read_csv('ml-100k/u3.base', delimiter = '\\t')\ntraining_set_3 = np.array(training_set_1, dtype = 'int')\ntest_set_3 = pd.read_csv('ml-100k/u3.test', delimiter = '\\t')\ntest_set_3 = np.array(test_set_1, dtype = 'int')\n\ntraining_set_4 = pd.read_csv('ml-100k/u4.base', delimiter = '\\t')\ntraining_set_4 = np.array(training_set_1, dtype = 'int')\ntest_set_4 = pd.read_csv('ml-100k/u4.test', delimiter = '\\t')\ntest_set_4 = np.array(test_set_1, dtype = 'int')\n\ntraining_set_5 = pd.read_csv('ml-100k/u5.base', delimiter = '\\t')\ntraining_set_5 = np.array(training_set_1, dtype = 'int')\ntest_set_5 = pd.read_csv('ml-100k/u5.test', delimiter = '\\t')\ntest_set_5 = np.array(test_set_1, dtype = 'int')\n\n\"\"\"## Getting the number of users and movies\"\"\"\n\nuser_count_training_set = int(max(training_set_1[:, 0]))\nuser_count_test_set = int(max(test_set_1[:, 0]))\nuser_count = int(max(user_count_test_set, user_count_training_set))\n\nmovies_count_training_set = int(max(training_set_1[:, 1]))\nmovies_count_test_set = int(max(test_set_1[:, 1]))\nmovies_count = int(max(movies_count_test_set, movies_count_training_set))\n\n\"\"\"## Converting the data into an array with users in rows and movies in columns\"\"\"\n\ndef modify(data):\n listoflist = []\n for ui in range(1, user_count+1):\n mi =data[:, 1][data[:, 0]==ui]\n ri =data[:, 2][data[:, 0]==ui]\n listofratings =np.zeros(movies_count)\n listofratings[mi-1] =ri\n listoflist.append(list(listofratings))\n return listoflist\n\n\ntraining_set_1 = modify(training_set_1)\ntest_set_1 = modify(test_set_1)\n\ntraining_set_2 = modify(training_set_2)\ntest_set_2 = modify(test_set_2)\n\ntraining_set_3 = modify(training_set_3)\ntest_set_3 = modify(test_set_3)\n\ntraining_set_4 = modify(training_set_4)\ntest_set_4 = modify(test_set_4)\n\ntraining_set_5 = modify(training_set_5)\ntest_set_5 = modify(test_set_5)\n\"\"\"## Converting the data into Torch tensors\"\"\"\n\ntraining_set_1=torch.FloatTensor(training_set_1)\ntest_set_1=torch.FloatTensor(test_set_1)\n\ntraining_set_2=torch.FloatTensor(training_set_2)\ntest_set_2=torch.FloatTensor(test_set_2)\n\ntraining_set_3=torch.FloatTensor(training_set_3)\ntest_set_3=torch.FloatTensor(test_set_3)\n\ntraining_set_4=torch.FloatTensor(training_set_4)\ntest_set_4=torch.FloatTensor(test_set_4)\n\ntraining_set_5=torch.FloatTensor(training_set_5)\ntest_set_5=torch.FloatTensor(test_set_5)\n\n\"\"\"## Creating the architecture of the Neural Network\"\"\"\n\nclass MODELL(nn.Module):\n def __init__(self, ):\n super(MODELL, self).__init__()\n self.layer1 =nn.Linear(movies_count, 20)\n self.layer2 =nn.Linear(20, 10)\n self.layer3 =nn.Linear(10, 20)\n self.layer4 =nn.Linear(20, movies_count)\n self.activation = nn.Sigmoid()\n def forward(self, x):\n x =self.activation(self.layer1(x))\n x =self.activation(self.layer2(x))\n x =self.activation(self.layer3(x))\n x =self.layer4(x)\n return x\n\narchi = MODELL()\ncriterion = nn.MSELoss()\noptimizer = optim.RMSprop(archi.parameters(), lr = 0.01, weight_decay = 0.5)\n\n\"\"\"## Training the autoencoder : part 1\"\"\"\n\ne_counts = 100\nfor epoch in range(e_counts):\n train_loss1 , s1 =0 , 0. \n for ui in range(user_count):\n input_info = Variable(training_set_1[ui]).unsqueeze(0)\n target_info = input_info.clone()\n if torch.sum(target_info.data > 0) > 0:\n output_info =archi(input_info)\n target_info.require_grad = False\n output_info[target_info ==0] =0\n l =criterion(output_info, target_info)\n adj_cons = movies_count/float(torch.sum(target_info.data > 0) + 1e-10)\n l.backward()\n train_loss1 +=np.sqrt(l.data*adj_cons)\n s1 +=1.\n optimizer.step()\n print('train_1 loss at epoch number '+ str(epoch+1)+ ' is ' +str(train_loss1/s1))\nprint()\n\n\"\"\"## Testing the autoencoder : part 1\"\"\"\n\ntest_loss1 ,s1 = 0 , 0.\n \nfor ui in range(user_count):\n input_info= Variable(training_set_1[ui]).unsqueeze(0)\n target_info =Variable(test_set_1[ui]).unsqueeze(0)\n if torch.sum(target_info.data>0)> 0:\n output_info =archi(input_info)\n target_info.require_grad= False\n output_info[target_info== 0] = 0\n l= criterion(output_info, target_info)\n adj_cons =movies_count/float(torch.sum(target_info.data > 0) + 1e-10)\n test_loss1 += np.sqrt(l.data*adj_cons)\n s1+= 1.\nprint('test_1 loss: '+str(test_loss1/s1))\nprint()\n\n\narchi = MODELL()\ncriterion = nn.MSELoss()\noptimizer = optim.RMSprop(archi.parameters(), lr = 0.01, weight_decay = 0.5)\n\n\"\"\"## Training the autoencoder : part 2\"\"\"\n\ne_counts = 100\nfor epoch in range(e_counts):\n train_loss2 , s2 =0 , 0. \n for ui in range(user_count):\n input_info = Variable(training_set_2[ui]).unsqueeze(0)\n target_info = input_info.clone()\n if torch.sum(target_info.data > 0) > 0:\n output_info =archi(input_info)\n target_info.require_grad = False\n output_info[target_info ==0] =0\n l =criterion(output_info, target_info)\n adj_cons = movies_count/float(torch.sum(target_info.data > 0) + 1e-10)\n l.backward()\n train_loss2 +=np.sqrt(l.data*adj_cons)\n s2 +=1.\n optimizer.step()\n print('train_2 loss at epoch number '+ str(epoch+1)+ ' is ' +str(train_loss2/s2))\nprint()\n\n\"\"\"## Testing the autoencoder : part 2\"\"\"\n\ntest_loss2 ,s2 = 0 , 0.\n \nfor ui in range(user_count):\n input_info= Variable(training_set_2[ui]).unsqueeze(0)\n target_info =Variable(test_set_2[ui]).unsqueeze(0)\n if torch.sum(target_info.data>0)> 0:\n output_info =archi(input_info)\n target_info.require_grad= False\n output_info[target_info== 0] = 0\n l= criterion(output_info, target_info)\n adj_cons =movies_count/float(torch.sum(target_info.data > 0) + 1e-10)\n test_loss2 += np.sqrt(l.data*adj_cons)\n s2+= 1.\nprint('test_2 loss: '+str(test_loss2/s2))\nprint()\n\n\narchi = MODELL()\ncriterion = nn.MSELoss()\noptimizer = optim.RMSprop(archi.parameters(), lr = 0.01, weight_decay = 0.5)\n\n\"\"\"## Training the autoencoder : part 3\"\"\"\n\ne_counts = 100\nfor epoch in range(e_counts):\n train_loss3 , s3 =0 , 0. \n for ui in range(user_count):\n input_info = Variable(training_set_3[ui]).unsqueeze(0)\n target_info = input_info.clone()\n if torch.sum(target_info.data > 0) > 0:\n output_info =archi(input_info)\n target_info.require_grad = False\n output_info[target_info ==0] =0\n l =criterion(output_info, target_info)\n adj_cons = movies_count/float(torch.sum(target_info.data > 0) + 1e-10)\n l.backward()\n train_loss3 +=np.sqrt(l.data*adj_cons)\n s3 +=1.\n optimizer.step()\n print('train_3 loss at epoch number '+ str(epoch+1)+ ' is ' +str(train_loss3/s3))\nprint()\n\n\"\"\"## Testing the autoencoder : part 3\"\"\"\n\ntest_loss3 ,s3 = 0 , 0.\n \nfor ui in range(user_count):\n input_info= Variable(training_set_3[ui]).unsqueeze(0)\n target_info =Variable(test_set_3[ui]).unsqueeze(0)\n if torch.sum(target_info.data>0)> 0:\n output_info =archi(input_info)\n target_info.require_grad= False\n output_info[target_info== 0] = 0\n l= criterion(output_info, target_info)\n adj_cons =movies_count/float(torch.sum(target_info.data > 0) + 1e-10)\n test_loss3 += np.sqrt(l.data*adj_cons)\n s3+= 1.\nprint('test_3 loss: '+str(test_loss3/s3))\nprint()\n\narchi = MODELL()\ncriterion = nn.MSELoss()\noptimizer = optim.RMSprop(archi.parameters(), lr = 0.01, weight_decay = 0.5)\n\n\"\"\"## Training the autoencoder : part 4\"\"\"\n\ne_counts = 100\nfor epoch in range(e_counts):\n train_loss4 , s4 =0 , 0. \n for ui in range(user_count):\n input_info = Variable(training_set_4[ui]).unsqueeze(0)\n target_info = input_info.clone()\n if torch.sum(target_info.data > 0) > 0:\n output_info =archi(input_info)\n target_info.require_grad = False\n output_info[target_info ==0] =0\n l =criterion(output_info, target_info)\n adj_cons = movies_count/float(torch.sum(target_info.data > 0) + 1e-10)\n l.backward()\n train_loss4 +=np.sqrt(l.data*adj_cons)\n s4 +=1.\n optimizer.step()\n print('train_4 loss at epoch number '+ str(epoch+1)+ ' is ' +str(train_loss4/s4))\nprint()\n\n\"\"\"## Testing the autoencoder : part 4\"\"\"\n\ntest_loss4 ,s4 = 0 , 0.\n \nfor ui in range(user_count):\n input_info= Variable(training_set_4[ui]).unsqueeze(0)\n target_info =Variable(test_set_4[ui]).unsqueeze(0)\n if torch.sum(target_info.data>0)> 0:\n output_info =archi(input_info)\n target_info.require_grad= False\n output_info[target_info== 0] = 0\n l= criterion(output_info, target_info)\n adj_cons =movies_count/float(torch.sum(target_info.data > 0) + 1e-10)\n test_loss4 += np.sqrt(l.data*adj_cons)\n s4+= 1.\nprint('test_4 loss: '+str(test_loss4/s4))\nprint()\n\narchi = MODELL()\ncriterion = nn.MSELoss()\noptimizer = optim.RMSprop(archi.parameters(), lr = 0.01, weight_decay = 0.5)\n\n\"\"\"## Training the autoencoder : part 5\"\"\"\n\ne_counts = 100\nfor epoch in range(e_counts):\n train_loss5 , s5 =0 , 0. \n for ui in range(user_count):\n input_info = Variable(training_set_5[ui]).unsqueeze(0)\n target_info = input_info.clone()\n if torch.sum(target_info.data > 0) > 0:\n output_info =archi(input_info)\n target_info.require_grad = False\n output_info[target_info ==0] =0\n l =criterion(output_info, target_info)\n adj_cons = movies_count/float(torch.sum(target_info.data > 0) + 1e-10)\n l.backward()\n train_loss5 +=np.sqrt(l.data*adj_cons)\n s5 +=1.\n optimizer.step()\n print('train_5 loss at epoch number '+ str(epoch+1)+ ' is ' +str(train_loss5/s5))\nprint()\n\n\"\"\"## Testing the autoencoder : part 5\"\"\"\n\ntest_loss5 ,s5 = 0 , 0.\n \nfor ui in range(user_count):\n input_info = Variable(training_set_5[ui]).unsqueeze(0)\n target_info = Variable(test_set_5[ui]).unsqueeze(0)\n if torch.sum(target_info.data>0)> 0:\n output_info =archi(input_info)\n target_info.require_grad= False\n output_info[target_info== 0] = 0\n l= criterion(output_info, target_info)\n adj_cons =movies_count/float(torch.sum(target_info.data > 0) + 1e-10)\n test_loss5 += np.sqrt(l.data*adj_cons)\n s5+= 1.\nprint('test_5 loss: '+str(test_loss5/s5))\nprint()\n\ntotal_error = ((test_loss1)/s1) + ((test_loss2)/s2) + ((test_loss3)/s3) + ((test_loss4)/s4) + ((test_loss5)/s5)\ntotal_error /= 5.0\nprint(\"Total average Loss after 5-fold cross Validation : \" + str(total_error))\n\n"
}
] | 4 |
abdallahshaaban/Machine-Learning-Nano-Degree
|
https://github.com/abdallahshaaban/Machine-Learning-Nano-Degree
|
355e6e8e71f1d92b2627fa9777d4109689155614
|
bc81580e283a925246e910fb214fa8c769137c68
|
a9ceb64dbbf4e062fac9ddb1c239edcf91cdab11
|
refs/heads/master
| 2020-03-15T15:55:03.606313 | 2018-10-09T16:28:10 | 2018-10-09T16:28:10 | 132,223,819 | 0 | 0 | null | null | null | null | null |
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"path": "/Capstone/README.md",
"repo_name": "abdallahshaaban/Machine-Learning-Nano-Degree",
"src_encoding": "UTF-8",
"text": "\nSpeech Recognition\nAlgorithm that understands simple speech commands\nSpeech Commands dataset : http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz\nThe Speech Commands Datasets includes 65,000 one-second long utterances of 30 short words, by thousands of different people."
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"path": "/Dectect Spam/Naive Bayes Classifier.py",
"repo_name": "abdallahshaaban/Machine-Learning-Nano-Degree",
"src_encoding": "UTF-8",
"text": "import pandas as pd\nfrom collections import Counter\nfrom sklearn.feature_extraction.text import CountVectorizer\n\ndf = pd.read_table('E:/materials/ML\\MLND/Dectect Spam/smsspamcollection/SMSSpamCollection',sep='\\t',header=None,names=['label','sms_message'])\ndf.head()\ndf.shape\n\ndf['label']=df.label.map({'ham':0,'spam':1})\ndf.head()\n\ndocuments = ['Hello, how are you!',\n 'Win money, win from home.',\n 'Call me now.',\n 'Hello, Call hello you tomorrow?']\n\nlower_case_documents = []\nfor i in documents:\n lower_case_documents.append(i.lower())\nprint(lower_case_documents)\n\nsans_punctuation_documents = []\nimport string\n\nfor i in lower_case_documents:\n sans_punctuation_documents.append(i.translate(str.maketrans('', '', string.punctuation))) \nprint(sans_punctuation_documents)\n\npreprocessed_documents = []\nfor i in sans_punctuation_documents:\n preprocessed_documents.append(i.split(' '))\nprint(preprocessed_documents)\n\nfrequency_list = []\n\nfor i in preprocessed_documents:\n frequency_counts = Counter(i)\n frequency_list.append(frequency_counts) \nprint(frequency_list)\n\n\ncount_vector = CountVectorizer()\ncount_vector.fit(documents)\ncount_vector.get_feature_names()\n\ndoc_array = count_vector.transform(documents).toarray()\ndoc_array"
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"path": "/Capstone/requirements.txt",
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"text": "numpy\nos\nmatplotlib\nlibrosa\nkeras\nscipy\nwave\nsklearn\nitertools\n"
}
] | 3 |
modiprabal/dvc
|
https://github.com/modiprabal/dvc
|
8c8ed92a79042a62414403f982d8e62b0536fda9
|
d81b56978fd2206c6e7e7056ac18ab350cb9a6c8
|
bfbcc789e29238b8559c0d5dedf7275b94cb831d
|
refs/heads/master
| 2022-07-11T20:46:27.875261 | 2020-05-12T21:17:05 | 2020-05-12T21:17:05 | null | 0 | 0 | null | null | null | null | null |
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"content_id": "a98ef154a993eb8c58a089b0130ef7ba4099ca54",
"detected_licenses": [
"Apache-2.0"
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"language": "Python",
"length_bytes": 2946,
"license_type": "permissive",
"max_line_length": 79,
"num_lines": 116,
"path": "/dvc/stage/exceptions.py",
"repo_name": "modiprabal/dvc",
"src_encoding": "UTF-8",
"text": "from dvc.exceptions import DvcException\n\n\nclass StageCmdFailedError(DvcException):\n def __init__(self, stage, status=None):\n msg = \"failed to run: {}\".format(stage.cmd)\n if status is not None:\n msg += \", exited with {}\".format(status)\n super().__init__(msg)\n\n\nclass StageFileFormatError(DvcException):\n def __init__(self, fname, e):\n msg = \"DVC-file '{}' format error: {}\".format(fname, str(e))\n super().__init__(msg)\n\n\nclass StageFileDoesNotExistError(DvcException):\n def __init__(self, fname):\n from dvc.dvcfile import DVC_FILE_SUFFIX, is_dvc_file\n\n msg = \"'{}' does not exist.\".format(fname)\n\n sname = fname + DVC_FILE_SUFFIX\n if is_dvc_file(sname):\n msg += \" Do you mean '{}'?\".format(sname)\n\n super().__init__(msg)\n\n\nclass StageFileAlreadyExistsError(DvcException):\n def __init__(self, relpath):\n msg = \"not overwriting '{}'\".format(relpath)\n super().__init__(msg)\n\n\nclass StageFileIsNotDvcFileError(DvcException):\n def __init__(self, fname):\n from dvc.dvcfile import DVC_FILE_SUFFIX, is_dvc_file\n\n msg = \"'{}' is not a DVC-file\".format(fname)\n\n sname = fname + DVC_FILE_SUFFIX\n if is_dvc_file(sname):\n msg += \" Do you mean '{}'?\".format(sname)\n\n super().__init__(msg)\n\n\nclass StageFileBadNameError(DvcException):\n pass\n\n\nclass StagePathOutsideError(DvcException):\n pass\n\n\nclass StagePathNotFoundError(DvcException):\n pass\n\n\nclass StagePathNotDirectoryError(DvcException):\n pass\n\n\nclass StageCommitError(DvcException):\n pass\n\n\nclass StageUpdateError(DvcException):\n def __init__(self, path):\n super().__init__(\n \"update is not supported for '{}' that is not an \"\n \"import.\".format(path)\n )\n\n\nclass MissingDataSource(DvcException):\n def __init__(self, missing_files):\n assert len(missing_files) > 0\n\n source = \"source\"\n if len(missing_files) > 1:\n source += \"s\"\n\n msg = \"missing data '{}': {}\".format(source, \", \".join(missing_files))\n super().__init__(msg)\n\n\nclass StageNotFound(KeyError, DvcException):\n def __init__(self, file, name):\n super().__init__(\n \"Stage '{}' not found inside '{}' file\".format(name, file.relpath)\n )\n\n\nclass StageNameUnspecified(DvcException):\n def __init__(self, file):\n super().__init__(\n \"Stage name not provided.\"\n \"Please specify the name as: `{0}:stage_name`\".format(file.relpath)\n )\n\n\nclass DuplicateStageName(DvcException):\n def __init__(self, name, file):\n super().__init__(\n \"Stage '{name}' already exists in '{relpath}'.\".format(\n name=name, relpath=file.relpath\n )\n )\n\n\nclass InvalidStageName(DvcException):\n def __init__(self,):\n super().__init__(\"Stage name cannot contain punctuation characters.\")\n"
}
] | 1 |
aredev/side-channel-1
|
https://github.com/aredev/side-channel-1
|
95cdb9ee736ca1b805f8a700df25c7891de6da19
|
d8007671e909fd05cf63185d957e449c9a36f089
|
59738228f6fdcd7698e0e4e63587aa929ad26812
|
refs/heads/master
| 2021-01-19T12:15:32.106057 | 2017-03-23T18:58:11 | 2017-03-23T18:58:11 | 84,455,847 | 0 | 0 | null | null | null | null | null |
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"max_line_length": 130,
"num_lines": 167,
"path": "/analysis.py",
"repo_name": "aredev/side-channel-1",
"src_encoding": "UTF-8",
"text": "import scipy.io\nimport numpy\nfrom scipy.stats.stats import pearsonr\nimport matplotlib.pyplot as plt\n\n__author__ = \"Tom Sandmann (s4330048) & Abdullah Rasool (s4350693)\"\n\nsbox = dict([\n (hex(0), hex(12)),\n (hex(1), hex(5)),\n (hex(2), hex(6)),\n (hex(3), hex(11)),\n (hex(4), hex(9)),\n (hex(5), hex(0)),\n (hex(6), hex(10)),\n (hex(7), hex(13)),\n (hex(8), hex(3)),\n (hex(9), hex(14)),\n (hex(10), hex(15)),\n (hex(11), hex(8)),\n (hex(12), hex(4)),\n (hex(13), hex(7)),\n (hex(14), hex(1)),\n (hex(15), hex(2)),\n])\n\nrows = 14900 # invalues\ncolumns = 16 # keys\n\n\n# Read the input from the in.mat file\ndef read_inputs_file():\n file = scipy.io.loadmat('in.mat')\n return file['in']\n\n\n# Read the input from the traces.mat file\ndef read_traces_file():\n file = scipy.io.loadmat('traces.mat')\n return file['traces']\n\n\n# Generates all 2^4 possibilities for k, from 1 to 16\ndef create_all_keys():\n keys = []\n for i in range(0, 16):\n keys.append(i)\n return keys\n\n\n# Use the values of in, the key and the sbox to craft y\ndef create_value_prediction_matrix(in_values, keys):\n matrix = numpy.zeros((rows, columns))\n row = 0\n for i in in_values:\n for k in keys:\n i_xor_k = hex(i[0] ^ k)\n y = sbox[i_xor_k]\n matrix[row][k] = int(y, 16) # Access matrix by column row\n row += 1\n\n return matrix\n\n\n# Converts the value prediction matrix into the power predication matrix, using the hamming weigth\n# of the values\ndef create_power_prediction_matrix(value_prediction_matrix):\n matrix = numpy.zeros((rows, columns))\n for row in range(rows):\n for column in range(columns):\n value_in_bin = bin(int(value_prediction_matrix[row][column]))\n matrix[row][column] = value_in_bin.count(\"1\")\n\n return matrix\n\n\n# Compute the pearson correlation coefficient for every column in the power prediction matrix\n# with every column of the traces matrix\ndef create_column_wise_correlation(traces, power_predication_matrix):\n candidates = []\n\n for candidate in range(columns):\n time_samples = []\n coefficients = []\n for time_sample in range(6990):\n # pearsonnr() returns tuple, first element is correlation value, second is p-value.\n corcoef = abs(pearsonr(power_predication_matrix[:, candidate], traces[:, time_sample])[0])\n time_samples.append(time_sample)\n coefficients.append(corcoef)\n candidates.append((time_samples, coefficients, candidate, max(coefficients)))\n\n sorted_candidates = sorted(candidates, key=lambda tup: tup[3], reverse=True)\n print(\"Sorted candidates: \")\n print(\"Candidate:\\t\\tCorrelation Value:\")\n for c in sorted_candidates:\n print(str(c[2]), str(c[3]), sep=\"\\t\\t\\t\")\n\n return sorted_candidates\n\n\n# Create a plot of the correlations\ndef create_candidate_plot(candidates):\n # candidates[0]: First candidate\n # candidates[0][0]: time data of first candidate\n # candidates[0][1]: coefficient data of first candidate\n # candidates[0][2]: 'name' of first candidate\n\n # Get the candidate with the highest correlation coefficient\n highest_correlated_candidate = candidates[0][2]\n print('Candidate with highest correlation : ' + str(candidates[0][2]) + ' with correlation value ' +\n str(candidates[0][3]))\n\n for p in candidates:\n if p[2] != highest_correlated_candidate:\n plt.plot(p[0], p[1], 'r', label=p[2])\n else:\n plt.plot(p[0], p[1], 'g', label=p[2])\n\n plt.ylabel('Correlation')\n plt.xlabel('Time')\n plt.legend()\n plt.show()\n\n return highest_correlated_candidate\n\n\n# Calculate the correlation for a different amount (ie. rows) of traces with each of the columns of the\n# power prediction matrix. We need to plot the ranking of the highest correlated candidate from the previous\n# step\ndef create_stepped_power_traces_graph(traces, power_prediction_matrix, highest_correlated_candidate):\n nr_of_traces = [500, 1000, 2000, 4000, 8000, 12000]\n ranking = []\n\n for attack_size in nr_of_traces:\n attack_ranking = []\n for candidate in range(columns):\n coefficients = []\n for time_sample in range(6990):\n corcoeff = abs(pearsonr(traces[0:attack_size, time_sample], power_prediction_matrix[0:attack_size, candidate])[0])\n coefficients.append(corcoeff)\n\n attack_ranking.append((candidate, max(coefficients)))\n\n sorted_attack_ranking = sorted(attack_ranking, key=lambda tup: tup[1], reverse=True)\n ranking.append((attack_size, sorted_attack_ranking))\n\n rankings_of_highest_correlated_candidate = []\n for attack in ranking:\n ranking_of_best_correlation = attack[1]\n rank = [y[0] for y in ranking_of_best_correlation].index(highest_correlated_candidate)\n rankings_of_highest_correlated_candidate.append(rank+1)\n\n plt.plot(nr_of_traces, rankings_of_highest_correlated_candidate)\n plt.xlabel('Nr of traces')\n plt.ylabel('Ranking of candidate')\n plt.show()\n\n\nin_values = read_inputs_file()\nkeys = create_all_keys()\nvpm = create_value_prediction_matrix(in_values, keys)\nppm = create_power_prediction_matrix(vpm)\ntraces = read_traces_file()\n# print(len(traces[0:500, 0])) # To get the first n from a column do this\ncandidates = create_column_wise_correlation(traces, ppm)\nhcc = create_candidate_plot(candidates)\ncreate_stepped_power_traces_graph(traces, ppm, hcc)\n"
},
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"repo_name": "aredev/side-channel-1",
"src_encoding": "UTF-8",
"text": "\nCopyright (C) 2017 Team RASMAN\n"
}
] | 2 |
sumansta/daleydai
|
https://github.com/sumansta/daleydai
|
ce5c4edb099181d910da5206010727e72f177678
|
e5eb9e0cd56043f36d61dee442c67a60fc18911a
|
af5a7a0fd4a4b5db7cee2fe5d7420b018af0d246
|
refs/heads/main
| 2023-08-18T18:40:51.222249 | 2021-05-04T03:25:38 | 2021-05-04T03:25:38 | 348,916,994 | 0 | 0 | null | 2021-03-18T02:33:34 | 2021-05-04T03:09:34 | 2021-05-04T03:21:21 |
Python
|
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"num_lines": 21,
"path": "/daleydai/commands/broker.py",
"repo_name": "sumansta/daleydai",
"src_encoding": "UTF-8",
"text": "import click\n\nfrom daleydai.scraper import fetch_broker_data\n\n\[email protected]()\[email protected](\"id\")\ndef buyer(id: int):\n \"\"\"\n Fetch bought shares by broker id\n \"\"\"\n fetch_broker_data(id, isBuy=True)\n\n\[email protected]()\[email protected](\"id\")\ndef seller(id: int):\n \"\"\"\n Fetch sold shares by broker id\n \"\"\"\n fetch_broker_data(id, isBuy=False)\n"
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"repo_name": "sumansta/daleydai",
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"text": "from pathlib import Path\n\n\"\"\"\nURLs\n\"\"\"\nSTOCK_LIST_URL = \"https://newweb.nepalstock.com/api/nots/company/list\"\nTODAY_PRICE_URL = (\n \"https://newweb.nepalstock.com/api/nots/nepse-data/today-price?&size=500\"\n)\n\nheaders = {\n \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36\"\n}\n\nCONFIG_DIR = f\"{Path.home()}/.daleydai\"\nCONFIG_FILE = f\"{CONFIG_DIR}/meroshares.json\"\nSTOCKS_LIST_FILE = f\"{CONFIG_DIR}/stocks.json\"\n"
},
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"max_line_length": 68,
"num_lines": 19,
"path": "/daleydai/app.py",
"repo_name": "sumansta/daleydai",
"src_encoding": "UTF-8",
"text": "import click\n\nfrom daleydai.commands import init, buyer, seller, show, add, remove\n\n\[email protected]()\ndef cli():\n \"\"\"\n karod bata road....\n \"\"\"\n pass\n\n\ncli.add_command(init)\ncli.add_command(buyer)\ncli.add_command(seller)\ncli.add_command(show)\ncli.add_command(add)\ncli.add_command(remove)\n"
},
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"path": "/daleydai/commands/__init__.py",
"repo_name": "sumansta/daleydai",
"src_encoding": "UTF-8",
"text": "\"\"\"\nExports for CLI\n\"\"\"\nfrom daleydai.commands.broker import buyer, seller\nfrom daleydai.commands.meroshares import init, add, show, remove\n"
},
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"max_line_length": 88,
"num_lines": 27,
"path": "/setup.py",
"repo_name": "sumansta/daleydai",
"src_encoding": "UTF-8",
"text": "import setuptools\n\nwith open(\"README.md\", \"r\") as f:\n long_description = f.read()\n\nrequirements = [\"requests\", \"pandas\", \"click\", \"rich\"]\n\nsetuptools.setup(\n name=\"daleydai\",\n version=\"0.0.3\",\n author=\"Suman Shrestha\",\n url=\"https://github.com/sumansta/daleydai\",\n description=\"karod bata road...\",\n license=\"MIT\",\n packages=setuptools.find_packages(exclude=[\"dist\", \"build\", \"*.egg-info\", \"tests\"]),\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n install_requires=requirements,\n entry_points={\"console_scripts\": [\"daleydai = daleydai.app:cli\"]},\n classifiers=[\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Operating System :: OS Independent\",\n \"License :: OSI Approved :: MIT License\",\n ],\n)\n"
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"max_line_length": 153,
"num_lines": 80,
"path": "/daleydai/scraper.py",
"repo_name": "sumansta/daleydai",
"src_encoding": "UTF-8",
"text": "import json\nimport requests\nimport pandas as pd\n\nfrom daleydai.constants import headers, TODAY_PRICE_URL, CONFIG_FILE, STOCKS_LIST_FILE\n\n\npd.set_option(\"display.max.columns\", None)\npd.set_option(\"display.max.rows\", None)\npd.set_option(\"display.precision\", 10)\n\n\ndef request_api(url):\n response = requests.get(url, headers=headers)\n if response.status_code == 200:\n return response.json()\n else:\n print(response)\n raise Exception(\"Bad Request !!\")\n\n\ndef fetch_stocks_and_dump_to_file():\n jsonResponse = request_api(TODAY_PRICE_URL)\n with open(STOCKS_LIST_FILE, \"w+\") as file:\n file.truncate()\n json.dump(jsonResponse, file)\n pass\n\n\ndef fetch_broker_data(BROKER_ID, isBuy):\n\n buyOrSell = \"buyerBroker\" if isBuy else \"sellerBroker\"\n MAIN_URL = f\"https://newweb.nepalstock.com/api/nots/nepse-data/floorsheet?&size=500&{buyOrSell}={BROKER_ID}&sort=contractId,desc\"\n\n dataFrames = pd.DataFrame()\n jsonResponse = request_api(MAIN_URL)\n summaryData = pd.json_normalize(jsonResponse[\"floorsheets\"])\n totalPages = summaryData[\"totalPages\"].values[0]\n nextPage = 0\n while nextPage <= totalPages:\n print(f\"Fetching page {nextPage + 1}\")\n PAGED_URL = f\"https://newweb.nepalstock.com/api/nots/nepse-data/floorsheet?page={nextPage}&size=500&{buyOrSell}={BROKER_ID}&sort=contractId,desc\"\n pagedResponse = request_api(PAGED_URL)\n pagedDf = pd.json_normalize(pagedResponse[\"floorsheets\"][\"content\"])\n dataFrames = pd.concat([pagedDf, dataFrames], sort=False)\n nextPage += 1\n\n unsortedDf = dataFrames.groupby(\"stockSymbol\").agg(\n totalKittas=pd.NamedAgg(column=\"contractQuantity\", aggfunc=\"sum\"),\n totalAmount=pd.NamedAgg(column=\"contractAmount\", aggfunc=\"sum\"),\n totalTransactions=pd.NamedAgg(column=\"stockSymbol\", aggfunc=\"count\"),\n maxPrice=pd.NamedAgg(column=\"contractRate\", aggfunc=\"max\"),\n minPrice=pd.NamedAgg(column=\"contractRate\", aggfunc=\"min\"),\n )\n\n sortedDf = unsortedDf.sort_values(\"totalKittas\", ascending=False)\n print(sortedDf)\n\n\ndef read_stocks_from_file():\n # read meroshares file\n meroshareData = json.load(open(CONFIG_FILE))\n\n # read stocks price file\n data = json.load(open(STOCKS_LIST_FILE))\n df = pd.DataFrame(data[\"content\"])\n\n filteredDf = df[df.symbol.isin(meroshareData)]\n print(\n filteredDf[\n [\n \"symbol\",\n \"openPrice\",\n \"highPrice\",\n \"lowPrice\",\n \"closePrice\",\n \"businessDate\",\n ]\n ]\n )\n"
},
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"language": "Python",
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"license_type": "no_license",
"max_line_length": 72,
"num_lines": 58,
"path": "/daleydai/utils.py",
"repo_name": "sumansta/daleydai",
"src_encoding": "UTF-8",
"text": "import os\nimport sys\nimport json\nimport pandas as pd\n\nfrom daleydai import console\n\nfrom daleydai.constants import CONFIG_DIR, CONFIG_FILE, STOCKS_LIST_FILE\n\n\ndef create_required_files():\n console.print(\"[blue]Creating config files\")\n if not os.path.exists(CONFIG_DIR):\n os.mkdir(CONFIG_DIR)\n\n if not os.path.exists(CONFIG_FILE):\n with open(CONFIG_FILE, \"w+\"):\n pass\n\n if not os.path.exists(STOCKS_LIST_FILE):\n with open(STOCKS_LIST_FILE, \"w+\"):\n pass\n\n\ndef add_mero_shares(stock: str):\n with open(CONFIG_FILE, \"r+\") as file:\n data = file.readline()\n\n if not data:\n listData = [stock]\n json.dump(listData, file)\n sys.exit()\n\n shareList = json.loads(data)\n\n if stock not in shareList:\n shareList.append(stock)\n\n file.seek(0)\n json.dump(shareList, file)\n\n\ndef remove_mero_shares(stock: str):\n with open(CONFIG_FILE, \"r+\") as file:\n data = file.readline()\n\n if not data:\n sys.exit()\n\n data_dict = json.loads(data)\n if stock not in data_dict:\n sys.exit()\n\n data_dict.remove(stock)\n\n file.seek(0)\n json.dump(data_dict, file)\n file.truncate()\n"
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"max_line_length": 93,
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"path": "/README.md",
"repo_name": "sumansta/daleydai",
"src_encoding": "UTF-8",
"text": "# daleydai\n\n## karod bata road...\n\n## Installing package\n```bash\n$ pip install daleydai\n```\n\n## Commands\n\n### Fetch latest stock price and dump it to local file. Run it once a day to get latest data.\n\n```bash\n$ daleydai init\n```\n\n## Portfolio Commands\n\n### Show my shares\n\n```bash\n$ daleydai show\n```\n\n### Add my shares\n\n```bash\n$ daleydai add <stock symbol>\n```\n\n### Remove from my shares\n\n```bash\n$ daleydai remove <stock_symbol>\n```\n\n## Broker Commands\n\nShow bought or sold stocks by particular broker\n\n```bash\n$ daleydai buyer <broker_id>\n```\n\n```bash\n$ daleydai seller <broker_id>\n```\n"
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"is_generated": false,
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"license_type": "no_license",
"max_line_length": 93,
"num_lines": 57,
"path": "/daleydai/commands/meroshares.py",
"repo_name": "sumansta/daleydai",
"src_encoding": "UTF-8",
"text": "import click\nimport json\nimport sys\nimport os\n\n\nfrom daleydai import console\n\nfrom daleydai.constants import CONFIG_FILE\nfrom daleydai.utils import create_required_files, add_mero_shares, remove_mero_shares\nfrom daleydai.scraper import fetch_stocks_and_dump_to_file, read_stocks_from_file\n\n\[email protected]()\ndef init():\n \"\"\"\n Initialize App\n \"\"\"\n console.print(\"[blue]Initializing App\")\n create_required_files()\n fetch_stocks_and_dump_to_file()\n console.print(\"[green]App initialization complete\")\n\n\[email protected]()\ndef show():\n \"\"\"\n Show my shares\n \"\"\"\n with open(CONFIG_FILE, \"r+\") as file:\n data = file.readline()\n\n if not data:\n console.print(\n \"[yellow]Your shares are empty. Use [green]add <stock> [yellow]to add shares\"\n )\n sys.exit()\n\n read_stocks_from_file()\n\n\[email protected]()\[email protected](\"stock\")\ndef add(stock: str):\n \"\"\"\n Add My Shares\n \"\"\"\n add_mero_shares(stock)\n\n\[email protected]()\[email protected](\"stock\")\ndef remove(stock: str):\n \"\"\"\n Remove My Shares\n \"\"\"\n remove_mero_shares(stock)\n"
}
] | 9 |
zzhmark/insitu-GMM-on-umap
|
https://github.com/zzhmark/insitu-GMM-on-umap
|
870beab3ef2f842f25dc461ab28d0b19905d9296
|
01f003475a9c82f98aee5aab6af874732d3049bb
|
f2c28aad65f8a18964c3dd19aa7a7722f3c6b538
|
refs/heads/main
| 2023-01-01T04:34:36.527909 | 2020-10-13T04:57:46 | 2020-10-13T04:57:46 | 303,254,870 | 0 | 0 | null | null | null | null | null |
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"max_line_length": 80,
"num_lines": 53,
"path": "/GMM/score.py",
"repo_name": "zzhmark/insitu-GMM-on-umap",
"src_encoding": "UTF-8",
"text": "import numpy as np\n\nfrom sklearn.metrics import normalized_mutual_info_score\nimport pandas as pd\n\n\ndef blob_score(pts1: np.ndarray, pts2: np.ndarray,\n mean1: pd.Series, mean2: pd.Series):\n \"\"\"\n :param pts1: blob1 points coordinate list\n :param pts2: blob2 points coordinate list\n :param mean1: the mean intensity of blob1\n :param mean2: the mean intensity of blob2\n :return: a score\n Calculate 2 blobs' similarity score, determined by\n the relative difference of their intensity and their\n relative overlap area.\n \"\"\"\n grad_term = 1 - np.abs(mean1 - mean2) / 256\n set1, set2 = set(tuple(i) for i in pts1), set(tuple(i) for i in pts2)\n overlap_term = len(set1.intersection(set2)) / len(set2.union(set2))\n return grad_term * overlap_term\n\n\ndef local_gmm_score(label1: np.ndarray, label2: np.ndarray,\n means1: pd.Series, means2: pd.Series) -> float:\n \"\"\"\n :param label1: labeled image, 2D numpy array\n :param label2: labeled image, 2D numpy array\n :param means1: means for points in different levels\n :param means2: means for points in different levels\n :return: the local score\n Calculate 2 images' similarity score, by working out similarity\n scores between any 2 blobs, and sum up the best matches.\n \"\"\"\n n1, n2 = len(means1), len(means2)\n # List points for different levels.\n pts_list1 = [label1 == i + 1 for i in range(n1)]\n pts_list2 = [label2 == i + 1 for i in range(n2)]\n score_blobs = np.zeros((n1, n2))\n for i, pts1, mean1 in zip(range(n1), pts_list1, means1):\n for j, pts2, mean2 in zip(range(n2), pts_list2, means2):\n score_blobs[i, j] = blob_score(pts1, pts2, mean1, mean2)\n return np.max(score_blobs, axis=0).sum() + np.max(score_blobs, axis=1).sum()\n\n\ndef global_gmm_score(label1: np.ndarray, label2: np.ndarray):\n \"\"\"\n :param label1: labeled image, 2D numpy array\n :param label2: labeled image, 2D numpy array\n :return: score\n \"\"\"\n return normalized_mutual_info_score(label1, label2)\n"
},
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"num_lines": 2,
"path": "/README.md",
"repo_name": "zzhmark/insitu-GMM-on-umap",
"src_encoding": "UTF-8",
"text": "# insitu-GMM-on-umap\nApply insitu Gaussian mixture model segmentation on umap results.\n"
},
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"alphanum_fraction": 0.6272104978561401,
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"language": "Python",
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"num_lines": 90,
"path": "/GMM/segment.py",
"repo_name": "zzhmark/insitu-GMM-on-umap",
"src_encoding": "UTF-8",
"text": "import numpy as np\nfrom typing import Tuple, List\n\nfrom sklearn.mixture import GaussianMixture, BayesianGaussianMixture\nimport pandas as pd\n\n\ndef gmm(data: np.ndarray, n: int, method: str = 'default') -> \\\n Tuple[List[int], List[int]]:\n \"\"\"\n :param data: list of input\n :param n: number of components\n :param method: 'default' or 'bayesian'\n :return: (labels, means)\n \"\"\"\n # To avoid error, the number of components should be\n # no more than the length of input data.\n noc = min(len(data), n)\n if method.lower() == 'bayesian':\n model = BayesianGaussianMixture(n_components=noc, random_state=123)\n model.fit(data)\n else:\n model = GaussianMixture(n_components=noc, random_state=123)\n model.fit(data)\n return model.predict(data), model.means_\n\n\ndef global_gmm(expr: np.ndarray, n: int):\n \"\"\"\n :param expr: a list of values representing expression\n :param n: number of components\n :return: (labels, means table)\n Solve GMM for the heat map, iteratively find\n the minimalist mean of GMM models and separate the\n corresponding points.\n \"\"\"\n # Down sample the images and masks to reduce calculation.\n label_out = np.zeros(expr.shape, dtype=np.uint8)\n min_expr = np.min(expr)\n expr_copy = expr - min_expr\n nol = 0 # The count of labels.\n model_out = pd.DataFrame(columns=[0, 1])\n global_mean = np.mean(expr_copy)\n while True:\n labels, means = gmm(expr_copy, n)\n max_pts, max_mean = labels == np.argmax(means), max(means)\n # When the minimum mean reach the global mean, break the loop.\n if max_mean < global_mean:\n break\n # Otherwise, label the points in the output mask,\n # and dump them in the next run.\n nol += 1\n label_out[max_pts] = nol\n expr_copy[max_pts] = 0\n model_out = model_out.append([[nol, max_mean + min_expr]])\n model_out.columns = ['label', 'mean']\n model_out = model_out.set_index('label')\n return label_out, model_out\n\n\ndef local_gmm(coord: np.ndarray, label: np.ndarray,\n global_model: pd.DataFrame, n: int) -> \\\n Tuple[np.ndarray, np.ndarray, pd.DataFrame]:\n \"\"\"\n :param coord: a list of coordinates\n :param global_model: parameters of global gmm, pandas data frame\n :param n: maximum number of components for the bayesian algorithm\n :return: (labels, means table)\n Solve GMM for points' local distribution within\n each grayscale level generated by the global model.\n \"\"\"\n label_out = np.zeros(label.shape)\n model_out = pd.DataFrame(columns=['label', 'mean'])\n # Iterate over different expression levels in the global model.\n for i, mean in zip(global_model.index, global_model['mean']):\n pts = coord[label == i] # Retrieve points with a specific label.\n labels = gmm(pts, n, 'bayesian')[0]\n # Adjust labels from 0..n-1 to 1..n.\n # Because labels can be discontinuous.\n levels = np.unique(labels)\n labels = [np.where(levels == i)[0][0] + 1 for i in labels]\n # Label the areas on the output mask.\n start = np.max(label_out)\n for p, label in zip(pts, labels):\n label_out[tuple(p)] = start + label\n model = pd.DataFrame({'label': [*range(start, start + max(labels))],\n 'mean': [mean] * max(labels)})\n model_out = model_out.append(model)\n model_out = model_out.set_index('label')\n return label_out, model_out\n"
}
] | 3 |
etlert/hello-world
|
https://github.com/etlert/hello-world
|
fdc16c1e88ff00ffa7bf622a8f7a449d1645ac8f
|
01785288f6c2e82e626ae8fd2b48a1abd1e90155
|
901809f1cd59c8adb0f76695f6ba8fbe7f2c265d
|
refs/heads/master
| 2021-06-15T13:58:55.400131 | 2021-02-28T13:04:20 | 2021-02-28T13:04:20 | 163,947,059 | 0 | 0 | null | 2019-01-03T08:50:38 | 2019-01-03T09:29:20 | 2019-01-03T09:41:06 | null |
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"max_line_length": 21,
"num_lines": 3,
"path": "/1_elso.py",
"repo_name": "etlert/hello-world",
"src_encoding": "UTF-8",
"text": "import sys\nprint(\"Hello World!\")\nsys.exit()\n"
},
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"is_generated": false,
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"language": "Markdown",
"length_bytes": 58,
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"max_line_length": 15,
"num_lines": 6,
"path": "/README.md",
"repo_name": "etlert/hello-world",
"src_encoding": "UTF-8",
"text": "# hello-world\nkezdeti lépések\n\nHi humans+\nIt is me.\nbye\n"
}
] | 2 |
kerneltux0/playing-with-django
|
https://github.com/kerneltux0/playing-with-django
|
9756cf860914f4bc7c90b3a6cf9f8693f7598f74
|
6265be9525b4011f281f4b6fa7edd49a384cdd6c
|
9547efc9571566e23d978b6449f645f153f39556
|
refs/heads/master
| 2022-10-13T22:26:26.554744 | 2020-06-09T16:55:20 | 2020-06-09T16:55:20 | 267,619,405 | 0 | 0 | null | null | null | null | null |
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"text": "from django.shortcuts import render\nfrom django.http import HttpResponse\n\n# Create your views here.\ndef home_view(request, *args, **kwargs):\n my_context = {\n 'my_text': 'Take a guess',\n 'this_num': 3.14,\n 'my_list': [\n 'whatever',\n 123,\n 123.456\n ]\n }\n return render(request, \"home.html\", my_context)\n \ndef contact_view(request, *args, **kwargs):\n return render(request, \"contact.html\", {})"
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"text": "from django.shortcuts import render, get_object_or_404, redirect\nfrom .models import Testing\nfrom .forms import TestForm\n\n# Create your views here.\n\ndef testing_create_view(request):\n form = TestForm(request.POST or None)\n if form.is_valid():\n form.save()\n form = TestForm()\n\n context = {\n 'form': form\n }\n\n\n return render(request, \"testing_create.html\", context)\n\ndef testing_list_view(request):\n querySet = Testing.objects.all()\n\n context = {\n 'object_list': querySet\n }\n\n return render(request, 'testing_list.html', context)\n\ndef testing_delete_view(request, id):\n obj = get_object_or_404(Testing, id=id)\n if request.method == 'POST':\n obj.delete()\n return redirect('../../')\n\n context = {\n 'object': obj\n }\n\n return render(request, 'testing_delete.html', context)\n\ndef dynamic_lookup_view(request, id):\n obj = get_object_or_404(Testing, id=id)\n context = {\n 'object': obj\n }\n\n return render(request, 'testing_detail.html', context)\n\ndef testing_detail_view(request):\n obj = Testing.objects.get(id=1)\n\n context = {\n 'object': obj\n }\n\n return render(request, \"testing_detail.html\", context)"
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"text": "from django.db import models\nfrom django.urls import reverse\n\n# Create your models here.\nclass Testing(models.Model):\n title = models.CharField(max_length=120)\n info = models.TextField()\n coolStuff = models.BooleanField(default=False)\n\n def get_absolute_url(self):\n return reverse(\"testing-detail\", kwargs={\"id\": self.id})"
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"text": "from django import forms\nfrom .models import Testing\n\nclass TestForm(forms.ModelForm):\n class Meta:\n model = Testing\n fields = [\n 'title',\n 'info',\n 'coolStuff'\n ]\n \n def clean_title(self, *args, **kwargs):\n title = self.cleaned_data.get('title')\n if not \"something\" in title:\n raise forms.ValidationError(\"Wrong question!\")\n return title"
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"text": "# Generated by Django 3.0.6 on 2020-06-02 15:16\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('testing', '0001_initial'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='testing',\n name='coolStuff',\n field=models.BooleanField(default=False),\n ),\n ]\n"
}
] | 7 |
aavileli/K8sPurger
|
https://github.com/aavileli/K8sPurger
|
1e5184461a3c74a0f979ddf63c5410f937345ee9
|
dc203b265d0215c63d4d8c57c3b37698d5e53948
|
ba1facce447fc872c68a4ccefa4c69a1677c3a45
|
refs/heads/main
| 2023-04-29T14:33:46.955311 | 2021-05-17T00:05:31 | 2021-05-17T00:05:31 | 368,009,932 | 0 | 0 |
Apache-2.0
| 2021-05-17T00:01:52 | 2021-05-16T13:10:35 | 2021-05-12T06:16:14 | null |
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"src_encoding": "UTF-8",
"text": "<!--\nLicensed to the Apache Software Foundation (ASF) under one\nor more contributor license agreements. See the NOTICE file\ndistributed with this work for additional information\nregarding copyright ownership. The ASF licenses this file\nto you under the Apache License, Version 2.0 (the\n\"License\"); you may not use this file except in compliance\nwith the License. You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n-->\n\n# K8SPurger\n\n## A Simple script to hunt and delete unused Kubernetes resources such as Secret, ConfigMap, and Persistent Volume Claim\n\nNAQ (Nobody asked Question).\n\n1) What this script do?\n\nA:- This will find all unused resources and show them in a nice format and optionally remove them.\n\n2) Why you need this?\n\nA:- This is not required. I was learning Python and according to my Moto \"Fail Fast.. Learn Faster\" I started this project as we have 15+ Kubernetes clusters and we have a lot of such unused resources because of kustamize configmap/secret and a lot of POC which come and go which pile up such resources.\n\n3) Is this cause any effect on my cluster?\n\nA:- By default script will run in Dry-Run mode which should only get the details of the resource. There is an optional --delete=true flag if you want to delete the unused resource but it is recommended that you should run as Dry-run first to avoid any impact to the cluster. \n\nNote:- You should not trust strangers' words on the internet so browse the script as it is under apache 2 License and try on dummy cluster.\n\n4) How this work? Can I just use the kubectl command to do the same?\n\nA:- The kubectl does not directly give these details you have to invest a lot of time. If you know a short way, Please let me know via raising the issue (sharing is caring). This script will get all pods in all namespaces and scan them for these resources and make a list and then get the resource in Kubernetes and just give you the difference.\n\n5) So if I understood correctly it will scan the pod only. what if I have deployement/StatefullSet which has zero replica set?\n\nA:- Yes, in that case, the resource will be shown as unused. That's why this script runs in dry-run mode by default so you can see and take action yourself. If you have zero replicas mean you are not using that resource.\n\n6) Why PVC why not PV?\n\nA:- Normally we use PVC to manage PV and when we delete claims, PV will be deleted or retained as per storage-class configuration. To avoid any potential data loss I choose to work with PVC only.\n\n7) What if I hit a bug or required any feature?\n\nA:- You can raise an issue. I will try to fix the bug. The feature has to look into how much time is required.\n\n8) I did check the script your python code is awful?\n\nA:- Yes. As I said I am learning any guidance is welcome. You can raise PR so I can understand how PRO writes the code and improve my code.\n\n\n\n## Installation and Configuration\n\nThis script use [Python client for Kuberntes](https://github.com/kubernetes-client/python). We need to install that first\n\n```\npip install kubernetes\npython K8sPurger.py\n```\n\n### By default, it will run in dry-run mode and just show you the unused resources such as.\n\n```\nyogesh$ python K8sPurger.py\n\nThis script is created to find unused resource in Kubernetes and delete them\n\nExtra Secrets are 3 which are as below\n---------------------------\n| Secrets |Namespace |\n---------------------------\n| appsnewsecret |apps |\n| testsecret |apps |\n| testsecret |default |\n---------------------------\n \n\nExtra ConfigMap are 4 which are as below\n-------------------------\n| ConfigMap |Namespace |\n-------------------------\n| test |apps |\n| testing |apps |\n| test |default |\n| thisisnewcm |default |\n-------------------------\n \n\nExtra PV Claim are 1 which are as below\n---------------------------\n| PV Claim |Namespace |\n---------------------------\n| task-pv-claim |default |\n---------------------------\n```\n### If you are sure you can delete the resources by appending flag --delete=true\n\n```\nyogesh$ python K8sPurger.py --delete=true\n\nThis script is created to find an unused resource in Kubernetes and delete them\n\n\nExtra Secrets are 3 which are as below\n---------------------------\n| Secrets |Namespace |\n---------------------------\n| appsnewsecret |apps |\n| testsecret |apps |\n| testsecret |default |\n---------------------------\n \n\nExtra ConfigMap are 4 which are as below\n-------------------------\n| ConfigMap |Namespace |\n-------------------------\n| test |apps |\n| testing |apps |\n| test |default |\n| thisisnewcm |default |\n-------------------------\n \n\nExtra PV Claim are 1 which are as below\n---------------------------\n| PV Claim |Namespace |\n---------------------------\n| task-pv-claim |default |\n---------------------------\n \nYou have selected to delete unused items which are as above you want to continue?\nType yes or y to continue or any key to exit.\ny\n\nDeleting secretappsnewsecret....\nDeleting secrettestsecret....\nDeleting secrettestsecret....\nDeleted All Unused Secret.\n\nDeleting ConfigMap test....\nDeleting ConfigMap testing....\nDeleting ConfigMap test....\nDeleting ConfigMap thisisnewcm....\nDeleted All Unused ConfigMap.\n\nDeleting PVC task-pv-claim....\nDeleted All Unused PVC.\n```\n### You can say no even after passing --delete=true flag and script won't delete the resources.\n\n```\nyogesh$ python K8sPurger.py --delete=true\n\nThis script is created to find unused resource in Kubernetes and delete them\n\nHurray You don't have a unused Secrets\n \nHurray You don't have a unused ConfigMap\n \nHurray You don't have a unused PV Claim\n \nYou have selected to delete unused items which are as above you want to continue?\nType yes or y to continue or any key to exit.\nn\nYou choose not to auto delete. Great choice! You can clean them up manually.\n```\n\n### NOTE:- You can browse code and if like idea provides star for encouragement or provide feedback to me one below social networks.\n\nTwitter https://twitter.com/yogeshkunjir LinkedIn https://www.linkedin.com/in/yogeshkunjir/"
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"text": "#!/usr/bin/python\n\nfrom kubernetes import config, client\nimport argparse\n\nUsedSecret, UsedConfigMap, UsedPVC = [], [], []\nSecrets, ConfigMap, PVC = [], [], []\n\n\ndef main():\n print(\"\\nThis script is created to find unused \"),\n print(\"resource in Kubernetes and delete them\\n\")\n parser = argparse.ArgumentParser(description='Parcer to get delete value')\n parser.add_argument('-d', '--delete', help='Input file name', required=False)\n args = parser.parse_args()\n try:\n config.load_kube_config()\n v1 = client.CoreV1Api()\n except Exception as e:\n print(\"Not able to read Kubernetes cluster check Kubeconfig\")\n raise RuntimeError(e)\n GetUsedResources(v1)\n DefinedSecret(v1)\n DefinedConfigMap(v1)\n DefinedPersistentVolumeClaim(v1)\n ExtraSecret = Diffrance(Secrets, UsedSecret)\n PrintList(ExtraSecret, \"Secrets\")\n ExtraConfigMap = Diffrance(ConfigMap, UsedConfigMap)\n PrintList(ExtraConfigMap, \"ConfigMap\")\n ExtraPVC = Diffrance(PVC, UsedPVC)\n PrintList(ExtraPVC, \"PV Claim\")\n DeleteEnabled(v1, args, ExtraSecret, ExtraConfigMap, ExtraPVC)\n\n\ndef DeleteEnabled(v1, args, ExtraSecret, ExtraConfigMap, ExtraPVC):\n arg = {'true', 'True', 'TRUE'}\n yes = {'yes', 'y'}\n if args.delete in arg:\n print(\"You have selected to delete unused items which are as above you want to continue?\")\n print(\"Type yes or y to continue or any key to exit.\")\n choice = raw_input().lower()\n if choice in yes:\n DeleteSecret(v1, ExtraSecret)\n DeleteCM(v1, ExtraConfigMap)\n DeletePVC(v1, ExtraPVC)\n else:\n print(\"You choose not to auto delete. Great choice! You can clean them up manually.\")\n\n\ndef Diffrance(listA, listB):\n listC = []\n for i in listA:\n if i not in listB:\n listC.append(i)\n return listC\n\n\ndef PrintList(Toprint, name):\n if len(Toprint) == 0:\n print(\"hurray You don't have a unused \" + name)\n else:\n print(\"\\nExtra \" + name + \" are \" + str(len(Toprint)) + \" which are as below\")\n size1 = max(len(word[0]) for word in Toprint)\n size2 = max(len(word[1]) for word in Toprint)\n borderchar = '|'\n linechar = '-'\n # print(name + \" Namespaces\")\n print(linechar * (size1 + size2 + 7))\n print('{bc} {:<{}} {bc}'.format(name, size1, bc=borderchar) + '{:<{}} {bc}'.format(\"Namespace\", size2, bc=borderchar))\n print(linechar * (size1 + size2 + 7))\n for word in Toprint:\n print('{bc} {:<{}} {bc}'.format(word[0], size1, bc=borderchar) + '{:<{}} {bc}'.format(word[1], size2, bc=borderchar))\n print(linechar * (size1 + size2 + 7))\n print(\" \")\n\n\ndef GetUsedResources(v1):\n try:\n ApiResponce = v1.list_pod_for_all_namespaces(watch=False)\n except Exception as e:\n print(\"Not able to reach Kubernetes cluster check Kubeconfig\")\n raise RuntimeError(e)\n for i in ApiResponce.items:\n if \"kube-system\" in i.metadata.namespace or \"kube-public\" in i.metadata.namespace:\n pass\n else:\n container = i.spec.containers\n for item in container:\n if item.env is not None:\n for env in item.env:\n if env.value_from is not None:\n if env.value_from.secret_key_ref is not None:\n UsedSecret.append(\n [env.value_from.secret_key_ref.name, i.metadata.namespace])\n elif env.value_from.config_map_key_ref is not None:\n UsedConfigMap.append(\n [env.value_from.config_map_key_ref.name, i.metadata.namespace])\n if i.spec.volumes is not None: \n for volume in i.spec.volumes:\n if volume.secret is not None:\n UsedSecret.append([volume.secret.secret_name, i.metadata.namespace])\n elif volume.config_map is not None:\n UsedConfigMap.append([volume.config_map.name, i.metadata.namespace])\n elif volume.persistent_volume_claim is not None:\n UsedPVC.append([volume.persistent_volume_claim.claim_name, i.metadata.namespace])\n\n\ndef DefinedSecret(v1):\n try:\n ApiResponce = v1.list_secret_for_all_namespaces(watch=False)\n except Exception as e:\n print(\"Not able to reach Kubernetes cluster check Kubeconfig\")\n raise RuntimeError(e)\n for i in ApiResponce.items:\n if \"kube-system\" in i.metadata.namespace or \"kube-public\" in i.metadata.namespace:\n pass\n elif i.type in \"kubernetes.io/tls\" or i.type in \"kubernetes.io/service-account-token\":\n pass\n else:\n Secrets.append([i.metadata.name, i.metadata.namespace])\n\n\ndef DefinedConfigMap(v1):\n try:\n ApiResponce = v1.list_config_map_for_all_namespaces(watch=False)\n except Exception as e:\n print(\"Not able to reach Kubernetes cluster check Kubeconfig\")\n raise RuntimeError(e)\n for i in ApiResponce.items:\n if \"kube-system\" in i.metadata.namespace or \"kube-public\" in i.metadata.namespace:\n pass\n else:\n ConfigMap.append([i.metadata.name, i.metadata.namespace])\n\n\ndef DefinedPersistentVolumeClaim(v1):\n try:\n ApiResponce = v1.list_persistent_volume_claim_for_all_namespaces(watch=False)\n except Exception as e:\n print(\"Not able to reach Kubernetes cluster check Kubeconfig\")\n raise RuntimeError(e)\n for i in ApiResponce.items:\n PVC.append([i.metadata.name, i.metadata.namespace])\n\n\ndef DeleteSecret(v1, ExtraSecret):\n if len(ExtraSecret) == 0:\n print(\"No Unused Secret to delete. Skipping deletion.\")\n else:\n for item in ExtraSecret:\n print(\"Deleting secret\" + item[0] + \"....\")\n try:\n _ = v1.delete_namespaced_secret(item[0], item[1])\n except Exception as e:\n print(\"Not able to reach Kubernetes cluster check Kubeconfig\")\n raise RuntimeError(e)\n print(\"Deleted All Unused Secret.\\n\")\n\n\ndef DeleteCM(v1, ExtraConfigMap):\n if len(ExtraConfigMap) == 0:\n print(\"No Unused ConfigMap to delete. Skipping deletion.\")\n else:\n for item in ExtraConfigMap:\n print(\"Deleting ConfigMap \" + item[0] + \"....\")\n try:\n _ = v1.delete_namespaced_config_map(item[0], item[1])\n except Exception as e:\n print(\"Not able to reach Kubernetes cluster check Kubeconfig\")\n raise RuntimeError(e)\n print(\"Deleted All Unused ConfigMap.\\n\")\n\n\ndef DeletePVC(v1, ExtraPVC):\n if len(ExtraPVC) == 0:\n print(\"No Unused Persistent volume claim to delete. Skipping deletion.\")\n else:\n for item in ExtraPVC:\n print(\"Deleting PVC \" + item[0] + \"....\")\n try:\n _ = v1.delete_namespaced_persistent_volume_claim(item[0], item[1])\n except Exception as e:\n print(\"Not able to reach Kubernetes cluster check Kubeconfig\")\n raise RuntimeError(e)\n print(\"Deleted All Unused PVC.\\n\")\n\n\nif __name__ == '__main__':\n main()\n"
}
] | 2 |
TomiMustapha/Shleep
|
https://github.com/TomiMustapha/Shleep
|
29c0446579ff12eb708649d1fb26e1243e76d920
|
4664d9342b1cd2fdb39e524253e57dc0cebf8dc6
|
7d979d9ace06452d5f190fb4a6954ff8126bac1e
|
refs/heads/master
| 2021-05-15T03:03:07.895077 | 2017-09-17T19:58:48 | 2017-09-17T19:58:48 | 103,787,056 | 0 | 0 | null | null | null | null | null |
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"text": "## MAIN APP\n##\n##\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom week import Week\nfrom dataset import DataSet\n\ndef inputData(dataset):\n i = np.size(dataset.weeks)-1\n done = False\n while(not done):\n try:\n x = int(input(\"Insert hours of sleep (Press Enter to Quit) : \\n\" ) ) \n except ValueError:\n print(\"Quit and show plot? (y/n) : \\n \")\n finish = str(input(\" \"))\n if (finish == 'y'):\n done = True\n break\n continue\n \n try:\n \n dataset.weeks[i].insert_node(x)\n \n except ValueError:\n dataset.insert(x)\n i+=1\n \ndef getDayAverage(dataset,day):\n average = np.array([])\n for i in range(0, np.size(dataset.weeks)):\n if(np.size(dataset.weeks[i].nodes) > day):\n average= np.append(average,dataset.weeks[i].nodes[day])\n else:\n break\n return np.average(average) \n \n\n# initialize the DataSet\n\ndata = DataSet()\n\nfile = np.load('data.npy')\n\nfor week in file:\n data.insert_week(week)\n \n \nprint(\"Previous data: \\n\")\ndata.print()\nprint(\"\\n\")\n\n# The x axis will always be 1-7\n# this indicates the days of the week\n\ninputData(data)\n\nnp.save('data.npy', data.weeks)\n\ni = 1\nfor week in data.weeks:\n y = week.nodes\n x = np.arange(1, y.size+1, 1)\n #print(y)\n plt.scatter(x,y)\n lines = plt.plot(x,y)\n plt.setp(lines, label=\"Week \" + str(i))\n i+=1\n\n \n \n \n \n#Create an array that keeps track of the average sleep per week \naverageSleep = np.array([]) \nfor week in data.weeks:\n x = np.average(week.nodes)\n averageSleep = np.append(averageSleep,x)\n#Calculate the average sleep over all the weeks \naverageSleepTotal = np.average(averageSleep) \n#Calculate the average sleep for each day \naveragePerDay = np.array([])\nfor i in range (0,7):\n x = getDayAverage(data,i)\n averagePerDay = np.append(averagePerDay, x)\nprint(averagePerDay) \n#print(averageSleep)\n#print(averageSleepTotal)\n# Ideal sleep schedule to compare to\n\nideal_y = np.array([8, 8, 8, 8, 8, 8, 8])\nideal_x = np.arange(ideal_y.size)+1\nideal_err = 1\nideal = plt.plot(ideal_x, ideal_y)\nxDay = np.arange(1,8) \nplt.setp(ideal, label=\"Ideal Schedule\")\nperDay = plt.plot(xDay,averagePerDay)\nplt.setp(perDay, label=\"Average Sleep Per Day\")\n\n\nplt.title(\"Sleep Distribution\")\nplt.xlabel(\"Day of the Week\")\nplt.ylabel(\"Hours of Sleep\")\nplt.legend(loc='best')\nplt.figtext(0.5, .95, 'Your Average Sleep is '+str(round(averageSleepTotal,2))+' hours per day', horizontalalignment='right')\n\n \n \nplt.show() \n\n\n\n\n\n\n"
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"text": "## Data Set class\n## \n## Each data set is an array of weeks\n## So every entry is an array of nodes\n\nimport numpy as np\nfrom week import Week\n\nclass DataSet():\n \n def __init__(self):\n self.weeks = np.array([])\n \n def insert_week(self, week):\n self.weeks = np.append(self.weeks, week)\n \n def insert(self, x):\n newWeek = Week()\n newWeek.insert_node(x)\n self.insert_week(newWeek)\n \n \n def print(self):\n i = 1\n for week in self.weeks:\n print(\"Week: \" + str(i))\n print(week.nodes)\n i+=1\n \n \n\n"
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"path": "/test.py",
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"text": "\r\nfrom week import Week\r\nfrom dataset import DataSet\r\n\r\nnode1 = Node(5)\r\nnode2 = Node(20)\r\nnode3 = Node(20)\r\nnode4 = Node(20)\r\nnode5 = Node(20)\r\nnode6 = Node(20)\r\nnode7 = Node(20)\r\n\r\n\r\nweek = Week()\r\n\r\nweek.insert_node(node1)\r\nweek.insert_node(node2)\r\nweek.insert_node(node3)\r\nweek.insert_node(node4)\r\nweek.insert_node(node5)\r\nweek.insert_node(node6)\r\nweek.insert_node(node7)\r\n\r\ndataset = DataSet()\r\n\r\ndataset.insert_week(week)\r\n\r\nweek2 = Week()\r\n\r\nweek2.insert_node(node2)\r\nweek2.insert_node(node1)\r\n\r\ndataset.insert_week(week2)\r\n\r\ndataset.print()\r\n\r\n"
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"path": "/week.py",
"repo_name": "TomiMustapha/Shleep",
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"text": "## Week class\r\n##\r\n## Each week is a list of fixed size 7 of positive ints\r\n## Represents a week in which we log hours of sleep\r\n## We can then combine weeks into a matrix to be represented graphically\r\n\r\nimport numpy as np\r\n\r\n\r\nclass Week():\r\n\r\n def __init__(self):\r\n\r\n self.nodes = np.array([])\r\n\r\n def insert_node(self, node):\r\n if (not type(node) == int):\r\n raise TypeError(\"Not an int.\")\r\n elif (self.nodes.size >= 7):\r\n raise ValueError(\"Week has exceeded 7\")\r\n else:\r\n self.nodes = np.append(self.nodes, node)\r\n \r\n\r\n \r\n\r\n \r\n\r\n \r\n"
}
] | 4 |
canoejun/dataBaseHomeWork
|
https://github.com/canoejun/dataBaseHomeWork
|
c56ebd68b02b9e36529f6978a3ada63337948638
|
8010b0643c17ebd3d25080cfe374c5525027fe16
|
523c9a8ae4b05822d68263c64ded183d0c951da5
|
refs/heads/master
| 2020-05-15T21:58:04.932358 | 2019-04-24T08:20:31 | 2019-04-24T08:20:31 | 182,514,347 | 0 | 0 | null | null | null | null | null |
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"path": "/springmvc_project/src/cn/springmvc/News.java",
"repo_name": "canoejun/dataBaseHomeWork",
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"text": "package cn.springmvc;\r\n\r\npublic class News {\r\n\tprivate String title;\r\n\tprivate String media;\r\n\tprivate String time;\r\n\tprivate String url;\r\n\tprivate String pictureUrl;\r\n\r\n\tpublic void setTitle(String title) {\r\n\t\tthis.title = title;\r\n\t}\r\n\tpublic String getTitle() {\r\n\t\treturn title;\r\n\t}\r\n\tpublic void setMedia(String media) {\r\n\t\tthis.media = media;\r\n\t}\r\n\tpublic String getMedia() {\r\n\t\treturn media;\r\n\t}\r\n\tpublic void setTime(String time) {\r\n\t\tthis.time = time;\r\n\t}\r\n\tpublic String getTime() {\r\n\t\treturn time;\r\n\t}\r\n\tpublic void setUrl(String url) {\r\n\t\tthis.url = url;\r\n\t}\r\n\tpublic String getUrl() {\r\n\t\treturn url;\r\n\t}\r\n\tpublic void setPictureUrl(String pictureUrl) {\r\n\t\tthis.pictureUrl = pictureUrl;\r\n\t}\r\n\tpublic String getPictureUrl() {\r\n\t\treturn pictureUrl;\r\n\t}\r\n}\r\n"
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"text": "import urllib.request\r\nimport urllib.parse\r\nimport re\r\n\r\n#获取网页的源码\r\ndef get_content():\r\n #网址\r\n url = 'https://xiaoyuan.zhaopin.com/full/0/0_0_0_0_0_0_0_1_0'\r\n #打开网址\r\n a = urllib.request.urlopen(url)\r\n #读取源代码并转为unicode\r\n html = a.read().decode('utf-8')\r\n return html\r\n\r\n#正则匹配要爬取的内容\r\ndef get(html):\r\n #正则匹配式\r\n reg = re.compile(r'class=\"searchResultJobName.*?\">.*?<a target=\"_blank\" joburl href=\"//(.*?)\" class=\"fl __ga__fullResultcampuspostname_clicksfullresultcampuspostnames_001\">(.*?)</a>.*?<p class=\"searchResultCompanyname\"><span>(.*?)</span>.*?<span>城市：<em class=\"searchResultJobCityval\">(.*?)</em></span>.*?<span>发布时间：<em></em>(.*?)</span>.*?<span>招聘人数：<em>(.*?)</em></span>.*?职责描述：<span>(.*?)</span>',re.S)\r\n #进行匹配\r\n items = re.findall(reg,html)\r\n print(items)\r\n #计算匹配到的数目（一整条记录算一个）\r\n return items\r\n\r\nitems= get(get_content())\r\n\r\ndef work_data():\r\n return items\r\n"
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"path": "/app/Podfile",
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"text": "target '三创赛' do\n # Uncomment the next line if you're using Swift or would like to use dynamic frameworks\n # use_frameworks!\n\n # Pods for 三创赛\n pod 'AFNetworking','~> 3.2.1'\n pod 'Masonry'\n pod 'MJExtension'\n pod 'SVProgressHUD'\n\nend"
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"text": "import pymysql\r\nimport re\r\n# import urllib.request\r\n\r\n\r\nimport news_mod\r\nimport work_mod\r\n\r\n# 链接数据库\r\nconn = pymysql.connect(host='127.0.0.1',user='root',passwd='jun123',db='info_manage',port=3306,charset='utf8')\r\n\r\nprint(\"打印数据库链接对象{}\".format(conn))\r\n# 获取游标\r\ncur = conn.cursor()\r\n#-------------------------------------part1-新闻模块--------------------------------------------------------------------\r\ncur.execute(\"drop table if EXISTS news\")\r\nsql=\"\"\"create table news (\r\n title VARCHAR(500) NOT NULL PRIMARY KEY,\r\n time VARCHAR(500),\r\n media VARCHAR(500),\r\n url VARCHAR(700),\r\n pic VARCHAR(700)\r\n )\"\"\"\r\ncur.execute(sql)\r\n#cur.execute(sql) 这个只是执行了你 sql 中的语句，如果对表进行了修改 只执行这一句并没有作用，\r\n#需要在后面加上conn.commit()提交增删改数据到数据库\r\n\r\n#添加数据\r\ntry:\r\n for i in news_mod.news_data():\r\n # print(i[\"title\"], \"-\", i[\"url\"], i[\"media_name\"])\r\n title = i[\"title\"]\r\n print(title)\r\n url = i[\"url\"]\r\n print(url)\r\n time = re.findall(r'[0-9].*-.*-[0-9][0-9]',url)\r\n\r\n pic_url = news_mod.getpic(url)\r\n print(pic_url)\r\n time2 = time[0]\r\n print(time[0])\r\n\r\n\r\n print(type(time2))\r\n\r\n media_name = i[\"media_name\"]\r\n print(media_name)\r\n\r\n value = \"'\" + title + \"','\" + time2 + \"','\" + media_name + \"','\" + url + \"','\" + pic_url + \"')\"\r\n print('valus='+value)\r\n sql = \"insert into news(title,time,media,url,pic)values(\" + value\r\n print(sql)\r\n # 执行sql语句\r\n cur.execute(sql)\r\n # 提交到数据库执行\r\n conn.commit()\r\nexcept:\r\n #发生错误时回滚\r\n conn.rollback()\r\n\r\n#--------------------------------part2-工作模块------------------------------------------------------------------------\r\n\r\ncur.execute(\"drop table if EXISTS works\")\r\nsql=\"\"\"create table works (\r\n url VARCHAR(500) NOT NULL PRIMARY KEY,\r\n work VARCHAR(500),\r\n company VARCHAR(500),\r\n city VARCHAR(500),\r\n time VARCHAR(500),\r\n recruits_number VARCHAR(500),\r\n request VARCHAR(500)\r\n )\"\"\"\r\ncur.execute(sql)\r\n\r\ntry:\r\n for item in work_mod.work_data():\r\n url = item[0]\r\n work = item[1]\r\n company = item[2]\r\n city = item[3]\r\n time = item[4]\r\n recruits_number = item[5]\r\n request = item[6]\r\n\r\n value = \"'\" + url + \"','\" + work + \"','\" + company + \"','\" + city + \"','\" + time + \"','\" + recruits_number + \"','\" + request + \"')\"\r\n sql = \"insert into works(url,work,company,city,time,recruits_number,request)values(\" + value\r\n # 执行sql语句\r\n cur.execute(sql)\r\n # # #提交到数据库执行\r\n conn.commit()\r\nexcept:\r\n #发生错误时回滚\r\n conn.rollback()\r\n"
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"path": "/springmvc_project/src/cn/springmvc/GetInfoServlet.java",
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"text": "package cn.springmvc;\r\n\r\nimport java.util.Enumeration;\r\nimport java.util.HashMap;\r\nimport java.util.List;\r\nimport java.util.Map;\r\n\r\nimport javax.servlet.http.HttpServletRequest;\r\nimport javax.servlet.http.HttpServletResponse;\r\nimport org.springframework.stereotype.Controller;\r\nimport org.springframework.web.bind.annotation.RequestMapping;\r\nimport org.springframework.web.bind.annotation.RequestMethod;\r\n\r\n\r\n@Controller\r\npublic class GetInfoServlet {\r\n//\t获取工作信息\r\n\t@RequestMapping(\"/getAllJobs\")\r\n\tpublic void getAllJobs(HttpServletRequest request,HttpServletResponse response) {\r\n\t\tSystem.out.println(\"getAllJobs\");\r\n\t\tList<Works> list = new GetInfoServiceImpl().getAllJobs();\r\n\t\tListObject listObject = new ListObject();\r\n\t\tlistObject.setItems(list);\r\n\t\tlistObject.setCode(StatusCode.CODE_SUCCESS);\r\n\t\tlistObject.setMsg(\"访问成功\");\r\n\t\tResponseUtils.renderJson(response, JackJsonUtils.toJson(listObject));\r\n\t}\r\n//\t获取新闻信息\r\n\t@RequestMapping(\"/getAllNews\")\r\n\tpublic void getAllNews(HttpServletRequest request,HttpServletResponse response) {\r\n\t\tSystem.out.println(\"getAllNews\");\r\n\t\tList<News> list = new GetInfoServiceImpl().getAllNews();\r\n\t\tListObject listObject = new ListObject();\r\n\t\tlistObject.setItems(list);\r\n\t\tlistObject.setCode(StatusCode.CODE_SUCCESS);\r\n\t\tlistObject.setMsg(\"访问成功\");\r\n\t\tResponseUtils.renderJson(response, JackJsonUtils.toJson(listObject));\r\n\t}\r\n//\t登录\r\n\t@RequestMapping(value=\"login\", method= RequestMethod.POST)\r\n\tpublic void login(HttpServletRequest request,HttpServletResponse response) {\r\n//\t Map<String, Object> paramMap = new HashMap<String, Object>();\r\n//\t\tSystem.out.println(request);\r\n//\t Enumeration<String> enume = request.getParameterNames();\r\n//\t while (enume.hasMoreElements()) {\r\n//\t \tString key = (String) enume.nextElement();\r\n//\t String[] values = request.getParameterValues(key);\r\n//\t System.out.println(key+\" \"+values);\r\n//\t }\r\n\t\t\r\n\t\tString phoneNumber = request.getParameter(\"phoneNumber\");\r\n\t\tString password = request.getParameter(\"password\");\r\n\t\tSystem.out.println(\"login\");\r\n\t\tboolean isPasswordTrue = new GetInfoServiceImpl().login(phoneNumber, password);\r\n\t\tListObject listObject = new ListObject();\r\n\t\tif (isPasswordTrue) {\r\n\t\t\tlistObject.setItems(null);\r\n\t\t\tlistObject.setCode(StatusCode.CODE_SUCCESS);\r\n\t\t\tlistObject.setMsg(\"访问成功\");\r\n\t\t\tResponseUtils.renderJson(response, JackJsonUtils.toJson(listObject));\r\n\t\t\treturn ;\r\n\t\t}\r\n\t\tlistObject.setItems(null);\r\n\t\tlistObject.setCode(StatusCode.CODE_ERROR);\r\n\t\tlistObject.setMsg(\"访问失败\");\r\n\t\tResponseUtils.renderJson(response, JackJsonUtils.toJson(listObject));\r\n\t}\r\n\t\r\n\t@RequestMapping(value=\"addUser\", method= RequestMethod.POST)\r\n\tpublic void addUser(HttpServletRequest request,HttpServletResponse response) {\r\n\t\tString phoneNumber = request.getParameter(\"phoneNumber\");\r\n\t\tString password = request.getParameter(\"password\");\r\n\t\tboolean isAddUserSuccess = new GetInfoServiceImpl().addUser(phoneNumber, password);\r\n\t\tListObject listObject = new ListObject();\r\n\t\tif (isAddUserSuccess) {\r\n\t\t\tlistObject.setItems(null);\r\n\t\t\tlistObject.setCode(StatusCode.CODE_SUCCESS);\r\n\t\t\tlistObject.setMsg(\"访问成功\");\r\n\t\t\tResponseUtils.renderJson(response, JackJsonUtils.toJson(listObject));\r\n\t\t\treturn ;\r\n\t\t}\r\n\t\tlistObject.setItems(null);\r\n\t\tlistObject.setCode(StatusCode.CODE_ERROR);\r\n\t\tlistObject.setMsg(\"访问失败\");\r\n\t\tResponseUtils.renderJson(response, JackJsonUtils.toJson(listObject));\r\n\t}\r\n\t@RequestMapping(value=\"changePassword\", method= RequestMethod.POST)\r\n\tpublic void changePassword(HttpServletRequest request,HttpServletResponse response) {\r\n\t\tString phoneNumber = request.getParameter(\"phoneNumber\");\r\n\t\tString password = request.getParameter(\"password\");\r\n\t\tboolean isChangePasswordSuccess = new GetInfoServiceImpl().changePassword(phoneNumber, password);\r\n\t\tListObject listObject = new ListObject();\r\n\t\tif (isChangePasswordSuccess) {\r\n\t\t\tlistObject.setItems(null);\r\n\t\t\tlistObject.setCode(StatusCode.CODE_SUCCESS);\r\n\t\t\tlistObject.setMsg(\"访问成功\");\r\n\t\t\tResponseUtils.renderJson(response, JackJsonUtils.toJson(listObject));\r\n\t\t\treturn ;\r\n\t\t}\r\n\t\tlistObject.setItems(null);\r\n\t\tlistObject.setCode(StatusCode.CODE_ERROR);\r\n\t\tlistObject.setMsg(\"访问失败\");\r\n\t\tResponseUtils.renderJson(response, JackJsonUtils.toJson(listObject));\r\n\t}\r\n\t\r\n\t@RequestMapping(value=\"deleteUser\", method= RequestMethod.POST)\r\n\tpublic void deleteUser(HttpServletRequest request,HttpServletResponse response) {\r\n\t\tString phoneNumber = request.getParameter(\"phoneNumber\");\r\n\t\tString password = request.getParameter(\"password\");\r\n\t\tboolean isDeleteUserSuccess = new GetInfoServiceImpl().deleteUser(phoneNumber, password);\r\n\t\tListObject listObject = new ListObject();\r\n\t\tif (isDeleteUserSuccess) {\r\n\t\t\tlistObject.setItems(null);\r\n\t\t\tlistObject.setCode(StatusCode.CODE_SUCCESS);\r\n\t\t\tlistObject.setMsg(\"访问成功\");\r\n\t\t\tResponseUtils.renderJson(response, JackJsonUtils.toJson(listObject));\r\n\t\t\treturn ;\r\n\t\t}\r\n\t\tlistObject.setItems(null);\r\n\t\tlistObject.setCode(StatusCode.CODE_ERROR);\r\n\t\tlistObject.setMsg(\"访问失败\");\r\n\t\tResponseUtils.renderJson(response, JackJsonUtils.toJson(listObject));\r\n\t}\r\n\t\r\n} \r\n"
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"text": "package cn.springmvc;\r\n\r\nimport java.util.List;\r\n\r\npublic interface GetInfoService {\r\n\tpublic List<Works> getAllJobs();\r\n\tpublic List<News> getAllNews();\r\n\tpublic boolean login(String _phoneNumber,String password);\r\n\tpublic boolean addUser(String _phoneNumber,String password);\r\n\tpublic boolean changePassword(String _phoneNumber,String password);\r\n\tpublic boolean deleteUser(String _phoneNumber,String password);\r\n}\r\n"
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"text": "package cn.springmvc;\r\n\r\nimport java.sql.ResultSet;\r\nimport java.sql.SQLException;\r\nimport java.util.ArrayList;\r\nimport java.util.List;\r\n\r\n\r\npublic class GetInfo {\r\n\tpublic static List<Works> getAllJobs(){\r\n\t\tList<Works> list = new ArrayList<Works>();//list对象\r\n\t\tString sql = null;\r\n\t\tDBHelper db1 = null;\r\n\t\tsql = \"select *from works\";// SQL\r\n\t\tdb1 = new DBHelper(sql);//创建DBHelper对象\r\n\t\tResultSet ret = null;//创建结果集对象，执行sql后返回的数据集合\r\n//\t\tSystem.out.println(\"fbadfbd\");\r\n\t\ttry {\r\n\t\t\tret = db1.pst.executeQuery();//这个方法就类似于执行了SELECT语句一样！\r\n\t\t\twhile (ret.next()) {\r\n//\t\t\t\t\t链接地址\r\n\t\t\t\tString url = ret.getString(1);;\r\n\t\t\t\t//\t 职位\r\n\t\t\t\tString work = ret.getString(2);;\r\n\t\t\t\t//\t 公司\r\n\t\t\t\tString company = ret.getString(3);;\r\n\t\t\t\t//\t 城市\r\n\t\t\t\tString city = ret.getString(4);;\r\n\t\t\t\t//\t 发布时间\r\n\t\t\t\tString time = ret.getString(5);;\r\n\t\t\t\t//\t 需求人数\r\n\t\t\t\tString recruits_number = ret.getString(6);;\r\n\t\t\t\t//\t 岗位要求\r\n\t\t\t\tString request = ret.getString(7);;\r\n\r\n\t\t\t\tWorks works = new Works();\r\n\t\t\t\tworks.setUrl(url);\r\n\t\t\t\tworks.setWork(work);\r\n\t\t\t\tworks.setCompany(company);\r\n\t\t\t\tworks.setCity(city);\r\n\t\t\t\tworks.setTime(time);\r\n\t\t\t\tworks.setRecruits_number(recruits_number);\r\n\t\t\t\tworks.setRequest(request);\r\n//\t\t\t\tSystem.out.println(works.getCompany());\r\n\t\t\t\tlist.add(works);\r\n\t\t\t} //循环从结果集中获取数据并设置到list列表对象中\r\n\t\t\tret.close();//关闭对象\r\n\t\t\tdb1.close();//关系数据库连接\r\n\t\t} catch (SQLException e) {\r\n\t\t\t// TODO Auto-generated catch block\r\n\t\t\te.printStackTrace();\r\n\t\t} //\r\n\t\treturn list;//返回结果\r\n\t}\r\n\t\r\n\tpublic static List<News> getAllNews() {\r\n\t\tList<News> list = new ArrayList<News>();//list对象\r\n\t\tString sql = null;\r\n\t\tDBHelper db1 = null;\r\n\t\tsql = \"select *from news\";// SQL\r\n\t\tdb1 = new DBHelper(sql);//创建DBHelper对象\r\n\t\tResultSet ret = null;//创建结果集对象，执行sql后返回的数据集合\r\n\t\ttry {\r\n\t\t\tret = db1.pst.executeQuery();//这个方法就类似于执行了SELECT语句一样！\r\n\t\t\twhile (ret.next()) {\r\n\t\t\t\tString title = ret.getString(1);\r\n\t\t\t\tString time = ret.getString(2);\r\n\t\t\t\tString media = ret.getString(3);\r\n\t\t\t\tString url = ret.getString(4);\r\n\t\t\t\tString pictureUrl = ret.getString(5);\r\n\t\t\t\tNews news = new News();\r\n\t\t\t\tnews.setTitle(title);\r\n\t\t\t\tnews.setMedia(media);\r\n\t\t\t\tnews.setTime(time);\r\n\t\t\t\tnews.setUrl(url);\r\n\t\t\t\tnews.setPictureUrl(pictureUrl);\r\n\t\t\t\tlist.add(news);\r\n\t\t\t} //循环从结果集中获取数据并设置到list列表对象中\r\n\t\t\tret.close();//关闭对象\r\n\t\t\tdb1.close();//关系数据库连接\r\n\t\t} catch (SQLException e) {\r\n\t\t\t// TODO Auto-generated catch block\r\n\t\t\te.printStackTrace();\r\n\t\t} //\r\n\t\treturn list;//返回结果\r\n\t}\r\n\t\r\n\t\r\n\tpublic static boolean login(String _phoneNumber,String password) {\r\n\t\tString sql = null;\r\n\t\tDBHelper db1 = null;\r\n\t\tsql = \"select * from users where phoneNumber =\" + _phoneNumber;// sql\r\n\t\tdb1 = new DBHelper(sql);//创建DBHelper对象\r\n\t\tResultSet ret = null;//创建结果集对象\r\n\t\tUsers users = new Users();\r\n\t\ttry {\r\n\t\t\tret = db1.pst.executeQuery();//正常来说，这个结果集只有一个对象\r\n\t\t\twhile (ret.next()) {\r\n\t\t\t\tString phoneNumber = ret.getString(1);//第一列是手机号\r\n\t\t\t\tString realPassword = ret.getString(2);//第二列是密码\r\n\t\t\t\tusers.setPhoneNumber(phoneNumber);\r\n\t\t\t\tusers.setPassword(realPassword);\r\n\t\t\t} //循环从结果集中获取数据并设置到对象中（正常来说，这个循环只执行一次）\r\n\t\t\tret.close();//关闭对象\r\n\t\t\tdb1.close();//关系数据库连接\r\n\t\t} catch (SQLException e) {\r\n\t\t\t// TODO Auto-generated catch block\r\n\t\t\te.printStackTrace();\r\n\t\t} // \r\n\t\tif (password.equals(users.getPassword())) {\r\n\t\t\treturn true;\r\n\t\t}\r\n\t\treturn false;\r\n\t}\r\n\tpublic static boolean addUser(String _phoneNumber,String password) {\r\n\t\tString sql = null;\r\n\t\tDBHelper db1 = null;\r\n\t\tint result = 0;\r\n\t\tsql = \"insert into users values('\"+_phoneNumber+\"','\"+password+\"')\";\r\n\t\tSystem.out.println(sql);\r\n\t\tdb1 = new DBHelper(sql);\r\n\t\ttry {\r\n\t\t\tresult = db1.pst.executeUpdate();\r\n\t\t\tdb1.close();//关系数据库连接\r\n\t\t} catch (SQLException e) {\r\n\t\t\t// TODO Auto-generated catch block\r\n\t\t\te.printStackTrace();\r\n\t\t} // \r\n\t\tif (result != 0) {\r\n\t\t\treturn true; \r\n\t\t}\r\n\t\treturn false;\r\n\t}\r\n\t\r\n\tpublic static boolean changePassword(String _phoneNumber,String password) {\r\n\t\tString sql = null;\r\n\t\tDBHelper db1 = null;\r\n\t\tsql = \"update users set password = '\"+password+\"' where phoneNumber =\" + _phoneNumber;// sql\r\n\t\tSystem.out.println(sql);\r\n\t\tdb1 = new DBHelper(sql);//创建DBHelper对象\r\n\t\tint result = 0;//创建结果集对象\r\n\t\ttry {\r\n\t\t\tresult = db1.pst.executeUpdate();//正常来说，这个结果集只有一个对象\r\n\t\t\tdb1.close();//关系数据库连接\r\n\t\t} catch (SQLException e) {\r\n\t\t\t// TODO Auto-generated catch block\r\n\t\t\te.printStackTrace();\r\n\t\t} // \r\n\t\tif (result == 1) {\r\n\t\t\treturn true; \r\n\t\t}\r\n\t\treturn false;\r\n\t}\r\n\t\r\n\tpublic static boolean deleteUser(String _phoneNumber,String password) {\r\n\t\tString sql = null;\r\n\t\tDBHelper db1 = null;\r\n\t\tsql = \"delete from users where phoneNumber =\" + _phoneNumber;// sql\r\n\t\tSystem.out.println(sql);\r\n\t\tdb1 = new DBHelper(sql);//创建DBHelper对象\r\n\t\tint result = 0;//创建结果集对象\r\n\t\ttry {\r\n\t\t\tresult = db1.pst.executeUpdate();//正常来说，这个结果集只有一个对象\r\n\t\t\tdb1.close();//关系数据库连接\r\n\t\t} catch (SQLException e) {\r\n\t\t\t// TODO Auto-generated catch block\r\n\t\t\te.printStackTrace();\r\n\t\t} // \r\n\t\tif (result == 1) {\r\n\t\t\treturn true; \r\n\t\t}\r\n\t\treturn false;\r\n\t}\r\n}\r\n"
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"path": "/app/三创赛/Classes/Other/Constants.h",
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"src_encoding": "UTF-8",
"text": "//\n// Constants.h\n// 三创赛\n//\n// Created by 张俊 on 2019/3/27.\n// Copyright © 2019年 zhangjun. All rights reserved.\n//\n\n#ifndef Constants_h\n#define Constants_h\n\n#define SCREEN_WIDTH [UIScreen mainScreen].bounds.size.width\n#define SCREEN_HEIGHT [UIScreen mainScreen].bounds.size.height\n#define WEEK_SCROLLERVIEW_HEIGHT 40\n\n#define NAVA_MAXY (CGRectGetMaxY(self.navigationController.navigationBar.frame))\n\n\n#endif /* Constants_h */\n"
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"path": "/爬虫/news_mod.py",
"repo_name": "canoejun/dataBaseHomeWork",
"src_encoding": "UTF-8",
"text": "# 虽然网页源码里有feed-card-item类，但是实际上找不到这个类，是因为他\r\n# 是通过JavaScript动态生成的，不在静态HTML页面，所以通过访问下面的那个URL来捕获\r\n# 也就是说，该网页并不是一次加载出全部资源，而是动态加载资源\r\n\r\nimport requests\r\nimport json\r\nimport urllib.request\r\nimport re\r\n\r\n\r\nurl = \"https://feed.sina.com.cn/api/roll/get?pageid=121&lid=1356&num=20&versionNumber=1.2.4&page=1&encode=utf-8&callback=feedCardJsonpCallback&_=1540471652371\"\r\nres = requests.get(url).text\r\nres = res.rstrip(\"tch(e){};\")\r\nres = res.rstrip(\";}ca\")\r\nres = res.rstrip(\")\")\r\nres = res.lstrip(\"try{feedCardJsonpCallback\")\r\nres = res.lstrip(\"(\")\r\njso = json.loads(res)\r\n\r\ndef getpic(url):\r\n # 打开网址\r\n a = urllib.request.urlopen(url)\r\n # 读取源代码并转为unicode\r\n html = a.read().decode('utf-8')\r\n reg = re.compile(r'<div class=\"img_wrapper\"><img.*?src=\"(.*?)\" alt=\"',re.S)\r\n # 进行匹配\r\n items = re.findall(reg, html)\r\n if (not items):\r\n return \"\"\r\n else:\r\n picurl = items[0]\r\n return picurl\r\n\r\n\r\ndef news_data():\r\n return jso[\"result\"][\"data\"]\r\n\r\n\r\n"
}
] | 9 |
arushk1/solaris-telemetry
|
https://github.com/arushk1/solaris-telemetry
|
d1f0027e4fe48065ac58681fb23b2658b6428b1e
|
ed7beb868d8bfb0a58d36c31da95d4b65b3c0168
|
d42739f82f8f5c51c6d8346b12b4c139807ecc25
|
refs/heads/master
| 2021-03-12T20:15:27.511942 | 2014-12-24T22:23:14 | 2014-12-24T22:23:14 | 21,133,130 | 0 | 0 | null | null | null | null | null |
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"text": "#!/usr/bin/env python\n\nimport sys\nfrom Tkinter import *\nimport time, os\nimport RPi.GPIO as GPIO\nfrom random import randint\nfrom gps import *\nimport threading\n\nmgui = Tk()\nmgui.geometry(\"600x300\")\nmgui.title(\"Helios Suite\")\n\ngpsd = None #seting the global variable\nDEBUG = 1\nGPIO.setmode(GPIO.BCM)\nSPICLK = 18\nSPIMOSI = 24\nSPIMISO = 23\nSPICS = 25\n \n\nclass GUI:\n\n def __init__(self,mgui): \n self.mgui = mgui\n print \"First Func\"\n self.initpins()\n self.createlabels()\n self.createentry()\n self.updategui()\n time.sleep(0.5)\n \n #Define Sensor and output pins\n #Call display after defining\n #Put in loop of delay 1 sec to update values\n\n def initpins(self):\n\n print \"Initialize pins\"\n\n GPIO.setup(SPIMOSI, GPIO.OUT)\n GPIO.setup(SPIMISO, GPIO.IN)\n GPIO.setup(SPICLK, GPIO.OUT)\n GPIO.setup(SPICS, GPIO.OUT)\n\n def createlabels(self):\n\n print \"Creates GUI\"\n \n #Frame Information\n global frame1\n global frame2\n global frame3\n global frame4\n global frame5\n \n frame1 = Frame(mgui,bd=4,padx=2,pady=2,relief=GROOVE,width=200,height=125)\n frame2 = Frame(mgui,bd=4,padx=2,pady=2,relief=GROOVE,width=200,height=175)\n frame3 = Frame(mgui,bd=4,padx=2,pady=2,relief=GROOVE,width=200,height=125)\n frame4 = Frame(mgui,bd=4,padx=2,pady=2,relief=GROOVE,width=200,height=175)\n frame5 = Frame(mgui,bd=4,padx=2,pady=2,relief=GROOVE,width=200,height=125)\n frame6 = Frame(mgui,bd=4,padx=2,pady=2,relief=GROOVE,width=200,height=175)\n frame1.grid_propagate(0)\n frame1.grid(row=0,column=0,sticky=W)\n frame2.grid_propagate(0)\n frame2.grid(row=1,column=0,sticky=W)\n frame3.grid_propagate(0)\n frame3.grid(row=0,column=1)\n frame4.grid_propagate(0)\n frame4.grid(row=1,column=1)\n frame5.grid_propagate(0)\n frame5.grid(row=0,column=2,sticky=E)\n frame6.grid_propagate(0)\n frame6.grid(row=1,column=2,sticky=E)\n\n\n #Frame 1 Labels\n pantemp = Label(frame1,text=\"Panel Temperature :\").grid(row=0,column=0,sticky=W)\n mottemp = Label(frame1,text=\"Motor Temperature :\").grid(row=1,column=0,sticky=W)\n mpptemp = Label(frame1,text=\"MPPT Temperature :\").grid(row=2,column=0,sticky=W)\n motrrpm = Label(frame1,text=\"Cabin Temperature :\").grid(row=3,column=0,sticky=W)\n\n \n\n\n #Frame2 Labels\n\n panc = Label(frame2,text=\"Aux Current :\").grid(row=0,column=0,sticky=W)\n motc = Label(frame2,text=\"Motor Current :\").grid(row=1,column=0,sticky=W)\n batc = Label(frame2,text=\"Battery Current :\").grid(row=2,column=0,sticky=W)\n panp = Label(frame2,text=\"MPPT 1 :\").grid(row=3,column=0,sticky=W)\n motp = Label(frame2,text=\"MPPT 2 :\").grid(row=4,column=0,sticky=W)\n batp = Label(frame2,text=\"MPPT 3 :\").grid(row=5,column=0,sticky=W)\n\n \n #Frame 3 - Speedometer, to be done later\n\n speed = Label(frame3, text=\"Speed :\").grid(row=0,column=0,sticky=W)\n lat = Label(frame3, text=\"Latitude :\").grid(row=0,column=0,sticky=W)\n longitude = Label(frame3, text=\"Longitude :\").grid(row=0,column=0,sticky=W)\n\n #Frame 4 - Strategy\n\n chglft = Label(frame5,text=\"Charge Left :\").grid(row=0,column=0,sticky=W)\n dstleft = Label(frame5,text=\"Distance Left :\").grid(row=1,column=0,sticky=W)\n dsttrv = Label(frame5,text=\"Dist Travelled :\").grid(row=2,column=0,sticky=W)\n\n\n \n\n def createentry(self):\n\n \n #Initialise values\n #Using Random Values to test\n #Replace Random variables with BBB inputs\n pttxtvar = str(randint(1,20))\n mttxtvar = str(randint(1,20))\n mptxtvar = str(randint(1,20))\n mrtxtvar = str(randint(1,20))\n pttxtvar = StringVar(value=pttxtvar)\n mttxtvar = StringVar(value=mttxtvar)\n mptxtvar = StringVar(value=mptxtvar)\n mrtxtvar = StringVar(value=mrtxtvar)\n #Frame1 Values\n pttxt = Entry(frame1,bg=\"White\",textvariable=pttxtvar).grid(row=0,column=1,sticky=W)\n mttxt = Entry(frame1,bg=\"White\",textvariable=mttxtvar).grid(row=1,column=1,sticky=W)\n mptxt = Entry(frame1,bg=\"White\",textvariable=mptxtvar).grid(row=2,column=1,sticky=W)\n mrtxt = Entry(frame1,bg=\"White\",textvariable=mrtxtvar).grid(row=3,column=1,sticky=W)\n\n\n #Initialise Values\n\n pcsns = \"\"\n mcsns = \"\"\n bcsns = \"\"\n ppsns = \"\"\n mpcsns = \"\"\n bpsns = \"\"\n apsns = \"\"\n pctxtvar = StringVar(value=pcsns)\n mctxtvar = StringVar(value=mcsns)\n bctxtvar = StringVar(value=bcsns)\n pptxtvar = StringVar(value=ppsns)\n mptxtvar = StringVar(value=mpcsns)\n bptxtvar = StringVar(value=bpsns)\n aptxtvar = StringVar(value=apsns)\n\n #Frame2 Values\n\n pctxt = Entry(frame2,bg=\"White\",textvariable=pctxtvar).grid(row=0,column=1)\n mctxt = Entry(frame2,bg=\"White\",textvariable=mctxtvar).grid(row=1,column=1)\n bctxt = Entry(frame2,bg=\"White\",textvariable=bctxtvar).grid(row=2,column=1)\n pptxt = Entry(frame2,bg=\"White\",textvariable=pptxtvar).grid(row=3,column=1)\n mptxt = Entry(frame2,bg=\"White\",textvariable=mptxtvar).grid(row=4,column=1)\n bptxt = Entry(frame2,bg=\"White\",textvariable=bptxtvar).grid(row=5,column=1)\n aptxt = Entry(frame2,bg=\"White\",textvariable=aptxtvar).grid(row=6,column=1)\n\n #Initialise Values\n\n clsns = \"\"\n dlsns = \"\"\n dtsns = \"\"\n clvar = StringVar(value=clsns)\n dlvar = StringVar(value=dlsns)\n dtvar = StringVar(value=dtsns)\n\n #Initialise Frame3 Values\n\n speedtxt = \"\"\n lattxt = \"\"\n longtxt = \"\"\n speedtxtvar = StringVar(value=speedtxt)\n lattxtvar = StringVar(value=lattxt)\n longtxtvar = StringVar(value=longtxt)\n \n #Frame3 Values\n \n speedtext = Entry(frame3,bg=\"White\",textvariable=speedtxtvar).grid(row=0,column=1)\n lattext = Entry(frame3,bg=\"White\",textvariable=lattxtvar).grid(row=1,column=1)\n longtext = Entry(frame3,bg=\"White\",textvariable=longtxtvar).grid(row=2,column=1)\n \n #Frame 5 Values\n\n cltxt = Entry(frame5,bg=\"White\",textvariable=clvar).grid(row=0,column=1,sticky=W)\n dltxt = Entry(frame5,bg=\"White\",textvariable=dlvar).grid(row=1,column=1,sticky=W)\n dttxt = Entry(frame5,bg=\"White\",textvariable=dtvar).grid(row=2,column=1,sticky=W)\n \n\n\n def updategui(self):\n\n global mgui\n global gpsc\n\n #Frame 1\n\n print \"Update GUI\"\n pttxtvar = (readadc(0, SPICLK, SPIMOSI, SPIMISO, SPICS)/1023.0)*33\n mttxtvar = (readadc(1, SPICLK, SPIMOSI, SPIMISO, SPICS)/1023.0)*33\n mptxtvar = (readadc(2, SPICLK, SPIMOSI, SPIMISO, SPICS)/1023.0)*33\n mrtxtvar = (readadc(3, SPICLK, SPIMOSI, SPIMISO, SPICS)/1023.0)*33\n pttxtvar = StringVar(value=pttxtvar)\n mttxtvar = StringVar(value=mttxtvar)\n mptxtvar = StringVar(value=mptxtvar)\n mrtxtvar = StringVar(value=mrtxtvar)\n pttxt = Entry(frame1,bg=\"White\",textvariable=pttxtvar).grid(row=0,column=1,sticky=W)\n mttxt = Entry(frame1,bg=\"White\",textvariable=mttxtvar).grid(row=1,column=1,sticky=W)\n mptxt = Entry(frame1,bg=\"White\",textvariable=mptxtvar).grid(row=2,column=1,sticky=W)\n mrtxt = Entry(frame1,bg=\"White\",textvariable=mrtxtvar).grid(row=3,column=1,sticky=W)\n\n #Frame 3\n\n speedtxt = gpsc.fix.speed\n lattxt = gpsc.fix.latitude\n longtxt = gpsc.fix.longitude\n speedtxtvar = StringVar(value=speedtxt)\n lattxtvar = StringVar(value=lattxt)\n longtxtvar = StringVar(value=longtxt)\n speedtext = Entry(frame3,bg=\"White\",textvariable=speedtxtvar).grid(row=0,column=1)\n lattext = Entry(frame3,bg=\"White\",textvariable=lattxtvar).grid(row=1,column=1)\n longtext = Entry(frame3,bg=\"White\",textvariable=longtxtvar).grid(row=2,column=1)\n\n\n\n\n\n\n \n self.mgui.after(500, self.updategui)\n\n\nclass GpsController(threading.Thread):\n def __init__(self):\n threading.Thread.__init__(self)\n self.gpsd = gps(mode=WATCH_ENABLE) #starting the stream of info\n self.running = False\n \n def run(self):\n self.running = True\n while self.running:\n # grab EACH set of gpsd info to clear the buffer\n self.gpsd.next()\n\n def stopController(self):\n self.running = False\n \n @property\n def fix(self):\n return self.gpsd.fix\n\n @property\n def utc(self):\n return self.gpsd.utc\n\n @property\n def satellites(self):\n return self.gpsd.satellites\n\ndef readadc(adcnum, clockpin, mosipin, misopin, cspin):\n if ((adcnum > 7) or (adcnum < 0)):\n return -1\n GPIO.output(cspin, True)\n\n GPIO.output(clockpin, False) # start clock low\n GPIO.output(cspin, False) # bring CS low\n\n commandout = adcnum\n commandout |= 0x18 # start bit + single-ended bit\n commandout <<= 3 # we only need to send 5 bits here\n for i in range(5):\n if (commandout & 0x80):\n GPIO.output(mosipin, True)\n else:\n GPIO.output(mosipin, False)\n commandout <<= 1\n GPIO.output(clockpin, True)\n GPIO.output(clockpin, False)\n\n adcout = 0\n # read in one empty bit, one null bit and 10 ADC bits\n for i in range(12):\n GPIO.output(clockpin, True)\n GPIO.output(clockpin, False)\n adcout <<= 1\n if (GPIO.input(misopin)):\n adcout |= 0x1\n\n GPIO.output(cspin, True)\n\n adcout /= 2 # first bit is 'null' so drop it\n return adcout\n\n\n \n\n \n#create controller\ngpsc = GpsController()\n\n#start controller\ngpsc.start()\napp = GUI(mgui)\nmgui.mainloop()\n"
}
] | 1 |
grevian/AppEngine-Example
|
https://github.com/grevian/AppEngine-Example
|
e0787b731012d5b77c29eb75631bcee9738f4d0b
|
fc7addff04b62a91cc8f0bd7bfaf309c1fa6e99f
|
66773b2af85f5dabc2ac0a038a8a5518237cb63e
|
refs/heads/master
| 2021-03-12T23:44:45.588349 | 2013-12-11T01:08:46 | 2013-12-11T01:08:46 | 14,697,366 | 1 | 1 | null | 2013-11-25T20:25:52 | 2013-12-11T06:23:32 | 2013-12-11T06:17:21 |
CSS
|
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"text": "import jinja2\nimport logging\nimport os\nimport webapp2\n\nfrom google.appengine.api import memcache\nfrom google.appengine.api import users\n\nfrom models.content import Article, Comment\nfrom auth import user_vars\n\n# This just says to load templates from the same directory this file exists in\njinja_environment = jinja2.Environment(\n loader=jinja2.FileSystemLoader(os.path.dirname('resources/templates/')))\n\nclass IndexHandler(webapp2.RequestHandler):\n\n def get(self): \n \"\"\"Generate the main index page\"\"\"\n template_values = {}\n\n # Load any user specific values to pass into the template\n template_values.update(user_vars())\n\n # Check memcache for the list of front page articles\n articles_list = memcache.get(\"articles_list\")\n\n # If it wasn't in memcache, generate the list and place it into memcache\n if not articles_list:\n logging.info(\"Front page not found in memcache, requerying\")\n article_list = []\n articles = Article.query().order(-Article.rating, -Article.submitted).fetch(20)\n\n for article in articles:\n article_properties = { 'title': article.title,\n 'rating': article.rating,\n 'submitted': article.submitted,\n 'id': article.key.id(),\n }\n\n # This is actually an anti-pattern, I should show the appstats waterfall here and have a PR to fix it\n # Though it does show exactly why you'd want to memcache a heavier object like this\n if article.submitter:\n submitter = article.submitter.get()\n # We test this in case a user was deleted\n if submitter:\n article_properties['submitter'] = submitter.nickname\n\n article_list.append(article_properties)\n memcache.add(\"articles_list\", articles_list, time=60)\n\n # Add the article list to the template\n template_values['articles'] = article_list\n\n # If users aren't logged in, I could cache and return the entire rendered front page\n template = jinja_environment.get_template('index.html')\n self.response.out.write(template.render(template_values))\n\n"
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"text": "import jinja2\nimport logging\nimport os\nimport webapp2\n\nfrom google.appengine.api import memcache\nfrom google.appengine.api import users\n\nfrom models.content import Article, Comment\nfrom models.auth import JedditUser\n\n# This just says to load templates from the same directory this file exists in\njinja_environment = jinja2.Environment(\n loader=jinja2.FileSystemLoader(os.path.dirname('resources/templates/')))\n\nclass AddArticleHandler(webapp2.RequestHandler):\n\n def post(self):\n article = Article(title=self.request.POST['article-title'], content=self.request.POST['article-content'])\n\n # Attach our user if the submitter is logged in, but we do allow anonymous posts\n user = users.get_current_user()\n if user:\n article.submitter = JedditUser.key_from_user(user)\n article_key = article.put()\n\n # Invalidate the article list in memcache, It will get rebuilt next time the front page is loaded\n memcache.delete(\"articles_list\")\n\n # Redirect on POST is a common web technique, designed to keep people from accidentally\n # resubmitting the same form repeatedly\n return self.redirect('/article/%d' % article_key.id(), body=\"Thanks for your submission!\")\n\nclass AddCommentHandler(webapp2.RequestHandler):\n def post(self, article_id):\n article_id = int(article_id)\n google_user = users.get_current_user()\n user = JedditUser.get_or_create_by_user(google_user)\n article = Article.get_by_id(article_id)\n comment_content = self.request.POST['comment']\n if not comment_content or comment_content.strip() == '':\n return self.redirect('/article/%d' % article_id, body=\"Empty comment submitted\")\n comment = Comment(user=user.key, article=article.key, content=comment_content)\n comment.put()\n return self.redirect('/article/%d' % article_id, body=\"Thank you for your comment\")\n\nclass ViewArticleHandler(webapp2.RequestHandler):\n\n def get(self, article_id):\n \"\"\"Generate a page for a specific article\"\"\"\n template_values = {}\n\n user = users.get_current_user()\n if user:\n template_values['user'] = user\n template_values['google_logout_url'] = users.create_logout_url('/')\n else:\n template_values['google_login_url'] = users.create_login_url('/login?final=/article/%d' % int(article_id))\n\n article = Article.get_by_id(int(article_id))\n article_values = {\n 'title': article.title,\n 'content': article.content,\n 'submitted': article.submitted,\n 'rating': article.rating,\n 'id': article_id,\n }\n\n if article.submitter:\n submitter = article.submitter.get()\n if submitter:\n article_values['submitter'] = submitter.nickname\n\n\n # Merge the two sets of variables together\n template_values.update(article_values)\n\n comment_list = []\n # Add in any comments that might exist\n for comment in Comment.query(Comment.article == article.key).order(Comment.posted):\n comment_values = {}\n comment_values['id'] = comment.key.id()\n # Another fine example of an anti-pattern\n comment_values['user'] = comment.user.get()\n comment_values['posted'] = comment.posted.strftime(\"%A, %d. %B %Y %I:%M%p\")\n comment_values['content'] = comment.content\n comment_list.append(comment_values)\n\n template_values['comments'] = comment_list\n template = jinja_environment.get_template('article.html')\n self.response.out.write(template.render(template_values))\n\n"
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"text": "from google.appengine.ext import ndb\n\nclass JedditUser(ndb.Model):\n user = ndb.UserProperty(required=True, indexed=False)\n joined = ndb.DateProperty(auto_now_add=True)\n about = ndb.TextProperty()\n\n # In case users don't want to display their Google \"name\", they can override it here\n local_nickname = ndb.StringProperty(indexed=False)\n\n @classmethod\n def key_from_user(cls, user):\n # Construct the key using the user_id that is assured to be constant as our identifier\n # This also serves as a good example of how to tie entities together without requiring a query\n return ndb.Key('JedditUser', user.user_id())\n\n @classmethod\n def create(cls, user):\n key = cls.key_from_user(user)\n return cls(key=key, user=user)\n\n @classmethod\n def get_or_create_by_user(cls, user):\n key = cls.key_from_user(user)\n existing_user = key.get()\n if not existing_user:\n existing_user = cls.create(user)\n existing_user.put()\n return existing_user \n\n @property\n def nickname(self):\n if self.local_nickname:\n return self.local_nickname\n else:\n return self.user.nickname()\n\n"
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"text": "import webapp2\n\nfrom google.appengine.ext import ndb\nfrom google.appengine.api import users\n\nfrom models.content import Article\nfrom models.vote import Vote\nfrom models.vote import UPVOTE, DOWNVOTE\nfrom models.auth import JedditUser\n\nclass AddVoteHandler(webapp2.RequestHandler):\n\n def post(self, article_id, vote_type): \n article = Article.get_by_id(int(article_id))\n\n user = users.get_current_user()\n if user:\n user_key = JedditUser.key_from_user(user)\n \n # TODO Votes are now being created properly, Add update requests to a pull queue\n if vote_type == 'down':\n vote = Vote.create(article_key=article.key, user_key=user_key, value=DOWNVOTE)\n article.rating = article.rating - 1.0\n else:\n vote = Vote.create(article_key=article.key, user_key=user_key, value=UPVOTE)\n article.rating = article.rating + 1.0\n\n ndb.put_multi([article, vote])\n\n return self.redirect('/', body=\"Thanks for your vote!\")\n\n def get(self, article_id, vote_type):\n return self.post(article_id, vote_type)\n\n"
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"text": "import webapp2\n\nfrom index import IndexHandler\nfrom auth import LoginHandler\nfrom article import AddArticleHandler, ViewArticleHandler, AddCommentHandler\nfrom vote import AddVoteHandler\n\n# Here we can set up more advanced routing rules\nAPP = webapp2.WSGIApplication([\n (r'/', IndexHandler),\n (r'/login', LoginHandler),\n (r'/submit', AddArticleHandler),\n (r'/article/(\\d+)', ViewArticleHandler),\n (r'/article/(\\d+)/comment', AddCommentHandler),\n (r'/vote/(\\d+)/(\\w+)', AddVoteHandler),\n], debug=True)\n\n"
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"text": "import logging\nimport webapp2\n\nfrom google.appengine.api import users\n\nfrom models.auth import JedditUser\n\nclass LoginHandler(webapp2.RequestHandler):\n\n def post(self):\n # Here you could attempt to create an account through other APIs that could have been passed in\n # Or log them into your local API using a username/password in self.request.POST perhaps\n # but for this app, we just give up and tell them we couldn't figure it out.\n self.response.write('You could not be logged in')\n\n def get(self):\n # Get the logged in user from the Google Users API\n # https://developers.google.com/appengine/docs/python/users/\n user = users.get_current_user()\n if user:\n # Use a user entity in our datastore so we can get their nickname etc. for other users to see\n # Also to tie content created by them on Jeddit back to their user account\n existing_user = JedditUser.get_or_create_by_user(user)\n logging.info(\"%s has logged in\" % existing_user.user.email())\n \n # The docs indicate that an existing user could change their email address or nickname, which could require\n # the user entity to be updated, so handle that case here\n if existing_user.user != user:\n logging.info(\"User %s has updated their google account\")\n existing_user.user = user\n new_user.put()\n\n if self.request.get('final'):\n return self.redirect(self.request.get('final'), body=\"Thanks for logging in\")\n \n return self.redirect('/', body=\"Thanks for logging in\")\n else:\n # You could display a login form here if you had alternative methods of logging in\n logging.warning(\"A user could not be logged in\")\n self.response.write('You could not be logged in')\n\ndef user_vars():\n template_values = {}\n\n user = users.get_current_user()\n\n if user:\n template_values['user'] = JedditUser.get_or_create_by_user(user)\n else:\n template_values['google_login_url'] = users.create_login_url('/login')\n\n return template_values\n"
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"text": "from google.appengine.ext import ndb\n\nfrom auth import JedditUser\n\n# Our basic user submitted content\nclass Article(ndb.Model):\n title = ndb.StringProperty(required=True)\n content = ndb.TextProperty(required=True)\n submitted = ndb.DateTimeProperty(auto_now_add=True)\n submitter = ndb.KeyProperty(kind=JedditUser)\n rating = ndb.FloatProperty(default=0.5)\n\nclass Comment(ndb.Model):\n article = ndb.KeyProperty(kind=Article)\n user = ndb.KeyProperty(kind=JedditUser)\n posted = ndb.DateTimeProperty(auto_now_add=True)\n content = ndb.TextProperty(required=True)\n\n"
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"repo_name": "grevian/AppEngine-Example",
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"text": "Jeddit: An AppEngine Example\n============================\n\nThis is a simple reddit clone made to demonstrate some core concepts of Google App Engine\n\nTo deploy this app, check out the source code and edit app.yaml, change the \"application\" to your application name\nwhich you can create in the [Google Cloud Dashboard](https://cloud.google.com/console#/project) then download\nthe [App Engine SDK](https://developers.google.com/appengine/downloads#Google_App_Engine_SDK_for_Python) and run\n\n appcfg.py update path/to/jeddit\n\nYou should then be able to access the Application at \"your-project-name.appspot.com\"\n\n"
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"path": "/models/vote.py",
"repo_name": "grevian/AppEngine-Example",
"src_encoding": "UTF-8",
"text": "from google.appengine.ext import ndb\n\nfrom auth import JedditUser\nfrom content import Article\n\nUPVOTE = 1\nDOWNVOTE = -1\n\n# Two models used to calculate the content rating\nclass Vote(ndb.Model):\n article = ndb.KeyProperty(kind=Article)\n user = ndb.KeyProperty(kind=JedditUser)\n voted = ndb.DateTimeProperty(auto_now_add=True)\n value = ndb.IntegerProperty(choices=(UPVOTE,DOWNVOTE), required=True)\n\n @classmethod\n def create(cls, article_key, user_key, value):\n # Compose a key that ensures a user can only vote on the same article once\n # any other votes will just overwrite the same entity no matter the type\n key = ndb.Key('Vote', '%s:%s' % (article_key.id(), user_key.id()))\n return Vote(key=key, user=user_key, article=article_key, value=value)\n\n"
}
] | 9 |
MjRauff/waxterapp
|
https://github.com/MjRauff/waxterapp
|
81554b33764d894f663a060e183ca6f7de5bc757
|
bf0dc912351ab4a9cdd04bc0a63c2f9c3124c024
|
819a76d0b9fee15133a7ce026ad810872eed3d90
|
refs/heads/master
| 2022-12-15T05:22:32.320493 | 2020-09-07T00:19:56 | 2020-09-07T00:19:56 | 279,781,594 | 0 | 0 | null | null | null | null | null |
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"path": "/minavaxter/plants/urls.py",
"repo_name": "MjRauff/waxterapp",
"src_encoding": "UTF-8",
"text": "from django.urls import path\nfrom . import views\n\napp_name = \"plants\"\n\nurlpatterns = [\n path(\"\", views.PlantsListView.as_view(), name=\"plants_list\"),\n path('<slug:slug>/<int:pk>', views.PlantsDetailView.as_view(), name='plants_detail'),\n path('<slug:slug>/<int:pk>/delete', views.PlantsDeleteView.as_view(), name='plants_delete'),\n path('<slug:slug>/<int:pk>/#watered', views.watered, name='watered'),\n path('<slug:slug>/<int:pk>/#fertilizer', views.fertilizer, name='fertilizer'),\n path(\"family\", views.PlantsTypeListView.as_view(), name=\"plants_list_type\"),\n path(\"family/<slug:slug>/delete\", views.PlantsTypeDelete.as_view(), name=\"delete_type\"),\n path(\"family/create\", views.PlantsTypeCreate.as_view(), name=\"create_type\"),\n path(\"<slug:slug>/<int:pk>/upload-image\", views.PlantPicCreate.as_view(), name=\"upload_pic\"),\n path(\"<slug:slug>/<int:pk>/<int:pk2>/delete-image\", views.PlantPicDelete.as_view(), name=\"delete_pic\"),\n]\n"
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"repo_name": "MjRauff/waxterapp",
"src_encoding": "UTF-8",
"text": "# Generated by Django 3.0.3 on 2020-09-06 17:24\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('plants', '0002_auto_20200729_1331'),\n ]\n\n operations = [\n migrations.AlterModelOptions(\n name='image_profile',\n options={'ordering': ('date_create', 'plant')},\n ),\n migrations.AlterField(\n model_name='image_profile',\n name='image',\n field=models.ImageField(blank=True, null=True, upload_to='plants/'),\n ),\n ]\n"
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"path": "/minavaxter/minavaxter/views.py",
"repo_name": "MjRauff/waxterapp",
"src_encoding": "UTF-8",
"text": "from django.views import generic\nfrom django.shortcuts import render, redirect\n\nclass IndexView(generic.TemplateView):\n def get(self, request):\n return redirect(\"plants:plants_list\")\n"
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"path": "/minavaxter/plants/models.py",
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"src_encoding": "UTF-8",
"text": "from django.db import models\nfrom django.urls import reverse\nfrom django.utils.text import slugify\nfrom django.forms import ModelForm\n# Create your models here.\nICON_CHOICES = [\n (\"MISSING_DATA\", \"Missing Data\"),\n (\"LOW\", \"Low\"),\n (\"MEDIUM\", \"Medium\"),\n (\"HIGH\", \"High\"),\n]\n\nclass Plantfamily(models.Model):\n name = models.CharField(max_length=100, blank=True)\n slug = models.SlugField(allow_unicode=True, unique=True, blank=True)\n species = models.CharField(max_length=100, blank=True)\n light = models.CharField(max_length=100, choices=ICON_CHOICES, default=\"MISSING_DATA\")\n light_details = models.TextField(max_length=2000, blank=True)\n water = models.CharField(max_length=100, choices=ICON_CHOICES, default=\"MISSING_DATA\")\n water_details = models.TextField(max_length=2000, blank=True)\n info = models.TextField(max_length=2000, blank=True)\n link = models.URLField(max_length=1000, blank=True)\n date_create = models.DateTimeField(auto_now_add=True)\n date_update = models.DateTimeField(auto_now=True)\n def light_color(self):\n if self.light == \"LOW\":\n return \"text-danger\"\n elif self.light == \"MEDIUM\":\n return \"text-warning\"\n elif self.light == \"HIGH\":\n return \"text-success\"\n else:\n print(\"Error Color Light\")\n def water_color(self):\n if self.water == \"LOW\":\n return \"text-danger\"\n elif self.water == \"MEDIUM\":\n return \"text-warning\"\n elif self.water == \"HIGH\":\n return \"text-success\"\n else:\n print(\"Error Color Water\")\n def __str__(self):\n return self.name\n def profile_pic(self):\n image = Image_profile.objects.filter(plant=self)\n return image.order_by(\"-date_create\").first()\n def save(self, *args, **kwargs):\n self.slug = slugify(self.name)\n super().save(*args, **kwargs)\n class Meta:\n ordering = (\"name\", \"date_create\",)\n\nclass Plantprofile(models.Model):\n plant = models.ForeignKey(Plantfamily, on_delete=models.CASCADE, blank=True, null=True)\n watered = models.DateField(null=True, blank=True, default=\"2000-01-01\")\n fertilizer = models.DateField(null=True, blank=True, default=\"2000-01-01\")\n kia = models.BooleanField(default=False)\n date_create = models.DateTimeField(auto_now_add=True)\n date_update = models.DateTimeField(auto_now=True)\n def __str__(self):\n return \"{} ( {} )\".format(self.plant.name, self.pk)\n def profile_pic(self):\n image = Image_profile.objects.filter(plant=self)\n return image.order_by(\"-date_create\").first()\n def length(self):\n object = Groth_rate.objects.filter(plant=self).first()\n try:\n return object.length\n except:\n return \"???\"\n class Meta:\n ordering = (\"plant\", \"date_create\",)\n\nclass Image_profile(models.Model):\n plant = models.ForeignKey(Plantprofile, on_delete=models.CASCADE, blank=True, null=True)\n image = models.ImageField(upload_to=\"plants/\", blank=True, null=True)\n date_create = models.DateTimeField(auto_now_add=True)\n def __str__(self):\n return \"{} {}\".format(self.plant, self.date_create.strftime('%B %d %Y'))\n class Meta:\n ordering = (\"date_create\",\"plant\",)\n\nclass Groth_rate(models.Model):\n plant = models.ForeignKey(Plantprofile, on_delete=models.CASCADE, blank=True, null=True)\n length = models.IntegerField(default=0)\n date_create = models.DateTimeField(auto_now_add=True)\n def __str__(self):\n return \"{} {}\".format(self.plant, self.date_create.strftime('%B %d %Y'))\n class Meta:\n ordering = (\"-date_create\",\"plant\",)\n"
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"path": "/minavaxter/plants/migrations/0002_auto_20200729_1331.py",
"repo_name": "MjRauff/waxterapp",
"src_encoding": "UTF-8",
"text": "# Generated by Django 3.0.3 on 2020-07-29 11:31\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('plants', '0001_initial'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='plantfamily',\n name='link',\n field=models.URLField(blank=True, max_length=1000),\n ),\n ]\n"
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"language": "HTML",
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"path": "/minavaxter/plants/templates/plants/all_plants_type.html",
"repo_name": "MjRauff/waxterapp",
"src_encoding": "UTF-8",
"text": "{% extends \"base.html\" %}\n\n{% block contant %}\n<div class=\"\">\n <div class=\"jumbotron\">\n <ul class=\"list-group list-group-flush\">\n <a class=\"w-100 btn btn-dark text-white mt-2 p-3\" href=\"{% url 'plants:create_type' %}\"><span class=\"fas fa-tree mr-2\"></span>Add plant type</a>\n {% for object in object_list %}\n <form class=\"p-3 mt-2 w-100\" method=\"post\">\n {% csrf_token %}\n <a class=\"delete_btn\" href=\"{% url 'plants:delete_type' slug=object.slug %}\">delete</a>\n <input class=\"w-100 btn btn-success text-white p-3\" name=\"grab_type\" type=\"submit\" value=\"{{object.name}}\">\n </form>\n {% endfor %}\n </ul>\n </div>\n</div>\n{% endblock %}\n"
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"num_lines": 15,
"path": "/minavaxter/plants/forms.py",
"repo_name": "MjRauff/waxterapp",
"src_encoding": "UTF-8",
"text": "from django.forms import ModelForm\nfrom . import models\n\nclass PlantsTypeForm(ModelForm):\n class Meta:\n model = models.Plantfamily\n fields = [\"name\", \"species\", \"light\", \"light_details\", \"water\", \"water_details\", \"info\", \"link\"]\n\nclass PlantsPicForm(ModelForm):\n class Meta:\n model = models.Image_profile\n fields = [\"image\",]\n def __init__(self, *args, **kwargs):\n super(PlantsPicForm, self).__init__(*args, **kwargs)\n self.fields['image'].required = True\n"
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"num_lines": 117,
"path": "/minavaxter/plants/views.py",
"repo_name": "MjRauff/waxterapp",
"src_encoding": "UTF-8",
"text": "from django.views import generic\nfrom . import models\nfrom . import forms\nfrom datetime import date as dt\nfrom django.shortcuts import render, redirect\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.http import HttpResponseRedirect\nfrom django.urls import reverse_lazy\n\nclass PlantsListView(LoginRequiredMixin, generic.ListView):\n model = models.Plantprofile\n template_name = \"plants/all_plants.html\"\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n return context\n\n\nclass PlantsDetailView(LoginRequiredMixin, generic.DetailView):\n model = models.Plantprofile\n template_name = \"plants/plant_profile.html\"\n\n def post(self, request, slug, pk):\n if request.method == 'POST':\n length = request.POST.get(\"length\", \"\")\n length = int(length)\n plant = models.Plantprofile.objects.get(pk=pk)\n groth_rate = models.Groth_rate\n groth_rate(plant=plant, length=length).save()\n return redirect(\"plants:plants_detail\", slug=slug, pk= pk)\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n plant = models.Plantprofile.objects.get(pk=self.kwargs[\"pk\"])\n dt_water_days = dt.today() - plant.watered\n dt_fertilizer_days = dt.today() - plant.fertilizer\n groth_rate = models.Groth_rate.objects.filter(plant=plant).order_by(\"date_create\")\n context[\"groth_rate\"] = groth_rate\n context[\"groth_rate_last\"] = groth_rate.order_by(\"date_create\", \"length\").first()\n context[\"gallery\"] = models.Image_profile.objects.filter(plant=plant)\n context[\"dt_water\"] = dt_water_days.days\n context[\"dt_fertilizer\"] = dt_fertilizer_days.days\n return context\n\nclass PlantsDeleteView(LoginRequiredMixin, generic.DeleteView):\n model = models.Plantprofile\n template_name = \"plants/plant_profile_delete.html\"\n success_url = reverse_lazy(\"plants:plants_list\")\n\n\n\nclass PlantsTypeListView(LoginRequiredMixin, generic.ListView):\n model = models.Plantfamily\n template_name = \"plants/all_plants_type.html\"\n\n def post(self, request):\n if request.method == 'POST':\n grab_type = request.POST.get(\"grab_type\", \"\")\n plant_type = models.Plantfamily.objects.get(name=grab_type)\n new_plant = models.Plantprofile\n new_plant(plant=plant_type).save()\n return redirect(\"home\")\n\nclass PlantsTypeDelete(LoginRequiredMixin, generic.DeleteView):\n model = models.Plantfamily\n template_name = \"plants\\delete_plant_type.html\"\n success_url = reverse_lazy(\"plants:plants_list_type\")\n\nclass PlantsTypeCreate(LoginRequiredMixin, generic.CreateView):\n form_class = forms.PlantsTypeForm\n success_url = reverse_lazy(\"plants:plants_list_type\")\n template_name = \"plants\\create_plant_type.html\"\n\nclass PlantPicCreate(LoginRequiredMixin, generic.CreateView):\n form_class = forms.PlantsPicForm\n template_name = \"plants\\plant_upload_pic.html\"\n\n def get_success_url(self):\n slug = self.kwargs[\"slug\"]\n pk = self.kwargs[\"pk\"]\n return reverse_lazy('plants:plants_detail', kwargs={'pk': pk, 'slug': slug})\n\n def form_valid(self, form):\n plant = models.Plantprofile.objects.get(pk=self.kwargs[\"pk\"])\n form.instance.plant = plant\n form.save()\n return super(PlantPicCreate, self).form_valid(form)\n\nclass PlantPicDelete(LoginRequiredMixin, generic.DeleteView):\n model = models.Image_profile\n template_name = \"plants\\delete_plant_type.html\"\n def get_success_url(self):\n slug = self.kwargs[\"slug\"]\n pk = self.kwargs[\"pk2\"]\n return reverse_lazy('plants:plants_detail', kwargs={'pk': pk, 'slug': slug})\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n object = models.Image_profile.objects.get(pk=self.kwargs[\"pk\"])\n context[\"test\"] = object.image.url\n return context\n\n@login_required\ndef watered(request, slug, pk):\n plant = models.Plantprofile.objects.get(pk=pk)\n dt_days = dt.today()\n plant.watered = dt_days\n plant.save()\n return redirect(\"plants:plants_detail\", slug=slug, pk=pk)\n\n@login_required\ndef fertilizer(request, slug, pk):\n plant = models.Plantprofile.objects.get(pk=pk)\n dt_days = dt.today()\n plant.fertilizer = dt_days\n plant.save()\n return redirect(\"plants:plants_detail\", slug=slug, pk=pk)\n"
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"num_lines": 7,
"path": "/minavaxter/plants/admin.py",
"repo_name": "MjRauff/waxterapp",
"src_encoding": "UTF-8",
"text": "from django.contrib import admin\nfrom . import models\n# Register your models here.\nadmin.site.register(models.Plantprofile)\nadmin.site.register(models.Image_profile)\nadmin.site.register(models.Groth_rate)\nadmin.site.register(models.Plantfamily)\n"
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"repo_name": "MjRauff/waxterapp",
"src_encoding": "UTF-8",
"text": "# Generated by Django 3.0.3 on 2020-07-15 19:47\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Plantfamily',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(blank=True, max_length=100)),\n ('slug', models.SlugField(allow_unicode=True, blank=True, unique=True)),\n ('species', models.CharField(blank=True, max_length=100)),\n ('light', models.CharField(choices=[('MISSING_DATA', 'Missing Data'), ('LOW', 'Low'), ('MEDIUM', 'Medium'), ('HIGH', 'High')], default='MISSING_DATA', max_length=100)),\n ('light_details', models.TextField(blank=True, max_length=2000)),\n ('water', models.CharField(choices=[('MISSING_DATA', 'Missing Data'), ('LOW', 'Low'), ('MEDIUM', 'Medium'), ('HIGH', 'High')], default='MISSING_DATA', max_length=100)),\n ('water_details', models.TextField(blank=True, max_length=2000)),\n ('info', models.TextField(blank=True, max_length=2000)),\n ('link', models.URLField(max_length=1000)),\n ('date_create', models.DateTimeField(auto_now_add=True)),\n ('date_update', models.DateTimeField(auto_now=True)),\n ],\n options={\n 'ordering': ('name', 'date_create'),\n },\n ),\n migrations.CreateModel(\n name='Plantprofile',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('watered', models.DateField(blank=True, default='2000-01-01', null=True)),\n ('fertilizer', models.DateField(blank=True, default='2000-01-01', null=True)),\n ('kia', models.BooleanField(default=False)),\n ('date_create', models.DateTimeField(auto_now_add=True)),\n ('date_update', models.DateTimeField(auto_now=True)),\n ('plant', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='plants.Plantfamily')),\n ],\n options={\n 'ordering': ('plant', 'date_create'),\n },\n ),\n migrations.CreateModel(\n name='Image_profile',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('image', models.ImageField(blank=True, upload_to='plants/')),\n ('date_create', models.DateTimeField(auto_now_add=True)),\n ('plant', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='plants.Plantprofile')),\n ],\n options={\n 'ordering': ('-date_create', 'plant'),\n },\n ),\n migrations.CreateModel(\n name='Groth_rate',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('length', models.IntegerField(default=0)),\n ('date_create', models.DateTimeField(auto_now_add=True)),\n ('plant', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='plants.Plantprofile')),\n ],\n options={\n 'ordering': ('-date_create', 'plant'),\n },\n ),\n ]\n"
}
] | 10 |
kralbeg/github-testing1
|
https://github.com/kralbeg/github-testing1
|
60bfaced3814c134056c5f7e315f685dde3c7047
|
68adf4e2f4e923d4345495f6564ae421375746f9
|
7000fcaf564b6e6360942d226178a7ddf68b40ad
|
refs/heads/master
| 2020-04-05T19:07:09.077041 | 2018-11-11T21:01:19 | 2018-11-11T21:01:19 | 157,119,198 | 0 | 0 | null | null | null | null | null |
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"language": "Markdown",
"length_bytes": 167,
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"num_lines": 13,
"path": "/gitExample/README.md",
"repo_name": "kralbeg/github-testing1",
"src_encoding": "UTF-8",
"text": "# git-test\n\n## Goal of Project\n\n**Its a bold written word.**\n\n*Its a italic written word.*\n\n`git status`\n\n[Google Link](www.google.com)\n\n\n"
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"num_lines": 3,
"path": "/gitExample/minus.py",
"repo_name": "kralbeg/github-testing1",
"src_encoding": "UTF-8",
"text": "def minus (x,y)\n\tprint(x-y)\nminus(25,10)\n"
}
] | 2 |
mhalstrom/pointCloudAwesomeness
|
https://github.com/mhalstrom/pointCloudAwesomeness
|
934bbfd57f34ff26602d2e8684e0afdfa59492f7
|
794c9d92c3ff11a143964e1f4434b06ce1d16bc7
|
aa899f22c89bdd899fe4f3fea005057e63e6d2b6
|
refs/heads/master
| 2020-05-20T09:20:43.545127 | 2019-05-08T00:38:35 | 2019-05-08T00:38:35 | 185,498,000 | 0 | 0 | null | null | null | null | null |
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"path": "/icp.py",
"repo_name": "mhalstrom/pointCloudAwesomeness",
"src_encoding": "UTF-8",
"text": "import csv\nimport numpy as np\nimport time\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\ndef read_off(fileName):\n retPts = []\n with open(fileName, 'r') as f:\n offReader = csv.reader(f, delimiter=' ')\n # get first two lines\n for i, row in enumerate(offReader):\n if i > 1:\n # have an additional 1 in order to use homogeuns xforms\n retPts.append((float(row[0]), float(row[1]), float(row[2]), 1))\n\n return retPts\n\n\ndef read_asc(fileName):\n retPts = []\n with open(fileName, 'r') as f:\n\n for i, line in enumerate(f):\n row = [val.strip() for val in line.strip().split()]\n\n retPts.append((float(row[0].strip()), float(\n row[1].strip()), float(row[2].strip()), 1))\n\n return retPts\n\n# functions for finding point pairs\n\n\ndef find_best_point_pairs_mark_01(pts1, pts2):\n pts1_mean = np.mean(pts1[:, :3], axis=0)\n pts2_mean = np.mean(pts2[:, :3], axis=0)\n\n distances_pts1, indexesPts1 = np.unique(np.linalg.norm(\n pts1-pts2_mean, axis=1), return_index=True)\n distances_pts2, indexesPts2 = np.unique(np.linalg.norm(\n pts2-pts1_mean, axis=1), return_index=True)\n\n distancesIndexes = np.stack((indexesPts1, indexesPts2), axis=1)\n reordered = np.argsort(distancesIndexes, axis=0)\n return distancesIndexes[reordered[:, 0], 1]\n\n# calculate the homogoenus transform\n\n\ndef get_current_transform(p_pts, q_pts):\n p_mean = np.mean(p_pts[:, :3], axis=0)\n q_mean = np.mean(q_pts[:, :3], axis=0)\n\n p_prime = p_pts[:, :3] - p_mean\n q_prime = q_pts[:, :3] - q_mean\n\n W = np.dot(q_prime.T, p_prime)\n\n U, S, V_T = np.linalg.svd(W)\n\n rotation = np.dot(V_T.T, U.T)\n translation = p_mean - np.dot(rotation, q_mean.T)\n ht = np.identity(4)\n ht[:3, :3] = rotation\n ht[:3, 3] = translation\n\n return ht\n\n\n# pre processing\ndef trim_outliers(pts):\n meanPts = np.mean(pts, axis=0)\n distances = np.sum((pts-meanPts)**2, axis=1)\n\n # trim 90th percential\n return np.logical_xor(distances < np.percentile(distances, 90), distances > np.percentile(distances, 10))\n\n\np_pts = np.unique(\n np.array(read_asc('/media/Data/icpData/ICP-dental/W10.asc')), axis=0)\nq_pts = np.unique(\n np.array(read_asc('/media/Data/icpData/ICP-dental/W9.asc')), axis=0)\nfinal_p_pts = p_pts.copy()\nfinal_q_pts = q_pts.copy()\n# first make sure we have the same number of pts to begin with\np_pts = p_pts[trim_outliers(p_pts[:, :3]), :]\nq_pts = q_pts[trim_outliers(q_pts[:, :3]), :]\n\nmaxPts = min(p_pts.shape[0], q_pts.shape[0])\nprint(max(p_pts.shape[0], q_pts.shape[0]), maxPts)\np_pts = p_pts[:maxPts, :].copy()\nq_pts = q_pts[:maxPts, :].copy()\n\n\nstartTime = time.time()\n'''\n# target\nP_file = './P_new.off'\n\n# start\nQ_file = './Q_new.off'\n\np_pts = np.array(read_off(P_file))\nq_pts = np.array(read_off(Q_file))\n'''\n\nnewQ_pts = np.ones_like(q_pts)\noldQ_pts = np.copy(q_pts)\nindicies = find_best_point_pairs_mark_01(p_pts[:, :3], oldQ_pts[:, :3])\n\nfor i in range(11):\n indicies = find_best_point_pairs_mark_01(p_pts[:, :3], oldQ_pts[:, :3])\n ht = get_current_transform(p_pts.copy(), oldQ_pts[indicies, :].copy())\n\n oldQ_pts = ht.dot(oldQ_pts.T).T\n\n print(np.sum((p_pts[:, :3]-oldQ_pts[indicies, :3])**2))\n\nf_ht = get_current_transform(oldQ_pts.copy(), q_pts.copy())\nfinalOut = f_ht.dot(final_q_pts.T).T\nnp.savetxt('test.off', finalOut, delimiter=' ',\n fmt='%f', header='COFF\\n19300 0 0', comments='')\n# np.savetxt('README.md', f_ht, fmt='%f')\nprint(f_ht)\nprint(time.time()-startTime)\n\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\n\nax.scatter(finalOut[:, 0], finalOut[:, 1],\n finalOut[:, 2], s=2, c='r', marker='.')\nax.scatter(final_p_pts[:, 0], final_p_pts[:, 1],\n final_p_pts[:, 2], s=2, c='b', marker='.')\nplt.show()\n\n\n''' \n1. how to find the \"closest\" points?????\n1a. kdtree\n1b. extract features find -> find regions with similar features then call those closest.....\n1c. Do PCA then make very thing line up?\n1d. clustering, not sure what to do here yet\n\n\n2. Define metrics for defining what is a \"GOOD\" result\n2a. apply metrics to all of the above methods and compare\n2b. Do we want to talk about speed?\n\n3. Make nice plots and put in presentations and be done.\n4. We could do a demo\n'''\n"
}
] | 1 |
byAbaddon/Advanced-Course-PYTHON-May-2020
|
https://github.com/byAbaddon/Advanced-Course-PYTHON-May-2020
|
56af34f512832422c322c37cfa433f8f9d86f5b9
|
8d223a4b88bf6d39ffbe33eaffdfae7ea1fa578d
|
ddcb5136ba9163cbb1fbea886454ece7ac6b1ecf
|
refs/heads/main
| 2023-06-12T04:39:45.828451 | 2021-06-29T21:41:43 | 2021-06-29T21:41:43 | 375,830,932 | 0 | 0 | null | null | null | null | null |
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"src_encoding": "UTF-8",
"text": "collection = []\ndef combination(names, loop):\n \n if len(collection) == loop:\n print(', '.join(collection))\n return\n\n for i in range(len(names)):\n collection.append(names[i])\n combination(names[i + 1:], loop)\n collection.pop()\n\n\ncombination(input().split(', '), int(input()))"
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"repo_name": "byAbaddon/Advanced-Course-PYTHON-May-2020",
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"text": "row, col = [int(x) for x in input().split(', ')]\nmatrix = []\nindex = 0\n\nfor _ in range(row):\n matrix.append([int(x) for x in input().split()])\n\n\nwhile True:\n matrix_col_sum = 0\n for i in range(len(matrix)):\n matrix_col_sum += matrix[i][index]\n \n print(matrix_col_sum)\n index += 1 \n\n if index == col:\n break"
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"text": "import os\nos.system('clear')\n\ndef rounded(num):\n return list(map( int, filter(lambda x: not int(x) & 1 , num.split())))\n\ndef even_num(num):\n return list(map( int, filter(lambda x: not int(x) & 1 , num.split())))\n\nprint(even_num(input())) "
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"path": "/Exam - 27 June 2020/01. Bombs/01. Bombs.py",
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"text": "one_bomb_list = [int(x) for x in input().split(', ')]\ntwo_bomb_list = [int(x) for x in input().split(', ')]\n\nbomb_dict = {40 : 'Datura Bombs', 60 :'Cherry Bombs', 120 : 'Smoke Decoy Bombs'}\ncount_bomb_dict = {'Cherry Bombs' : 0,'Datura Bombs' : 0 , 'Smoke Decoy Bombs' : 0}\nsuccessfully_filled_bomb = False\n \nwhile len(one_bomb_list) > 0 and len(two_bomb_list) > 0:\n bomb_sum = one_bomb_list[0] + two_bomb_list[-1]\n if bomb_sum in bomb_dict:\n count_bomb_dict[bomb_dict[bomb_sum]] += 1\n del one_bomb_list[0]\n del two_bomb_list[-1]\n if count_bomb_dict['Datura Bombs'] >= 3 and count_bomb_dict['Cherry Bombs'] >= 3 and count_bomb_dict['Smoke Decoy Bombs'] >= 3:\n successfully_filled_bomb = True\n break \n else:\n two_bomb_list[-1] -= 5 \n\n \n \nif successfully_filled_bomb:\n print('Bene! You have successfully filled the bomb pouch!')\nelse:\n print(\"You don't have enough materials to fill the bomb pouch.\")\nif one_bomb_list: \n print('Bomb Effects:', str(one_bomb_list)[1:-1])\nelse:\n print('Bomb Effects: empty')\nif two_bomb_list:\n print('Bomb Casings:', str(two_bomb_list)[1:-1])\nelse:\n print('Bomb Casings: empty')\n\nfor key, val in count_bomb_dict.items():\n print(f'{key}: {val}')\n\n\n\n"
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"text": "n = int(input())\nparking_list = set()\n\nfor _ in range(n):\n status, number = input().split(', ')\n if status == 'IN':\n parking_list.add(number)\n else:\n parking_list.remove(number)\n\nif not parking_list:\n print('Parking Lot is Empty')\nelse: \n for car in parking_list:\n print(car)\n\n"
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"text": "#------------------------------------------------TODO -------------WTF ???-------- 80pts \nsize_matrix = int(input())\nmatrix = [row.split() for row in [' '.join(input())for _ in range(size_matrix)]]\napple = 0\nrow, col = 0, 0\n\nfor row_index in range(len(matrix)):\n for col_index in range(len(matrix[row_index])): \n if matrix[row_index][col_index] == 'S':\n matrix[row_index][col_index] = '.'\n row, col = row_index, col_index\n \ndef teleport(row, col):\n first_B = (row, col) \n for row_index in range(len(matrix)):\n for col_index in range(len(matrix[row_index])): \n if matrix[row_index][col_index] == 'B' and (row_index, col_index) != first_B:\n second_B = (row_index, col_index)\n return second_B\n\nwhile True:\n try:\n direction = input()\n\n if direction == 'right':\n col += 1 \n elif direction == 'left':\n col -= 1 \n elif direction == 'up': \n row -= 1 \n elif direction == 'down':\n row += 1 \n\n\n if matrix[row][col] == 'B':\n matrix[row][col] = '.'\n row, col = teleport(row, col)\n elif matrix[row][col] == '*':\n apple += 1\n if apple == 10:\n matrix[row][col] = 'S'\n break \n \n matrix[row][col] = '.'\n\n except: \n break\n\n \nif apple == 10: \n print(f'You won! You fed the snake.\\nFood eaten: 10')\nelse:\n print(f'Game over!\\nFood eaten: {apple}')\n \n[print(''.join(mtx)) for mtx in matrix]"
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"text": "quantity = int(input())\nclients_list = [int(x) for x in input().split()]\n\nprint(max(clients_list))\n\nwhile clients_list:\n if quantity >= clients_list[0]:\n quantity -= clients_list.pop(0)\n else:\n print('Orders left:', \" \".join(list(map( str, clients_list))))\n break\nelse: \n print('Orders complete')\n\n \n "
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"text": "def palindrome(word, index):\n reversed_word = ''.join(list(reversed(word)))\n if word == reversed_word:\n return f'{word} is a palindrome'\n else:\n return f'{word} is not a palindrome'\n\n"
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"text": "def list_manipulator(arr, command, position, *args):\n if not args:\n if command == 'remove':\n if position == 'beginning':\n return arr[1:]\n else:\n return arr[0:-1] \n elif command == 'add':\n if position == 'beginning':\n arr = list(args) + arr\n return arr\n else: # add end\n arr = arr + list(args)\n return arr\n else: #command == remove\n if position == 'beginning':\n return arr[args[0]:]\n else: # remove end\n return arr[0:-args[0]]\n \n\n"
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"text": "matrix = [ [int(x) for x in input().split()] for x in range(int(input()))]\n\nwhile True:\n try:\n command, row, col, val = [ int(x) if x.isdigit() else x for x in input().split()]\n\n if command == 'Add':\n try:\n matrix[row][col] += val \n except:\n print('Invalid coordinates')\n else:\n try:\n matrix[row][col] -= val \n except:\n print('Invalid coordinates') \n\n except:\n [print(*x) for x in matrix]\n break\n\n\n"
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"text": "tuple_list = list(zip([x for x in input().split(', ')], [x for x in input().split(', ')]))\n[print(' -> '.join(x)) for x in tuple_list]\n\n\n"
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"text": "vowels_list = ['a', 'o', 'u', 'e', 'i']\n[print(x, end = '') for x in input() if x not in vowels_list]"
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"text": "numbers_list = input().split(', ')\nprint(f'Positive: {\", \".join([x for x in numbers_list if int(x) >= 0])}')\nprint(f'Negative: {\", \".join([x for x in numbers_list if int(x) < 0])}')\nprint(f'Even: {\", \".join([x for x in numbers_list if not int(x) & 1 ])}')\nprint(f'Odd: {\", \".join([x for x in numbers_list if int(x) & 1])}')\n\n\n\n"
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"text": "clothes = [int(x) for x in input().split()]\nrack = int(input())\nnum_racks, current = 0, 0\n\nwhile clothes:\n out = clothes.pop(0)\n current += out\n if rack == current:\n num_racks += 1\n current = 0\n continue\n elif rack < current:\n num_racks += 1\n clothes.insert(0, out)\n current = 0\n continue\n \n if len(clothes) == 0:\n num_racks += 1 \n\n\nprint(num_racks)\n\n"
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"text": "import math\n\ncalc_list = [x for x in input().split()]\n\ndef calculator(arr, operator):\n for i in range(len(arr) + len(arr) // 2):\n if i & 1:\n arr.insert(i, operator)\n \n result = eval(' '.join(arr))\n if type(result) == float:\n result = math.floor(result) \n return str(result)\n \n\nwhile len(calc_list) != 1:\n current_list = []\n for n in calc_list:\n if n in ['-', '+', '*', '/'] :\n operator_index = calc_list.index(n)\n operator = calc_list.pop(operator_index)\n current_list = calc_list[0 : operator_index ]\n calc_list = calc_list[len(current_list) : ]\n calc_list.insert(0, calculator(current_list, operator))\n break\n\n\nprint(*calc_list) \n\n\n "
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"text": "def get_repeating_DNA(test):\n result = []\n for i in range(len(test)):\n if test[i: i + 10] in test[i+1:]:\n if len(test[i: i+10]) == 10 and test[i: i+10] not in result:\n result.append(test[i: i+10])\n\n return result\n"
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"text": "\n# Advanced-Course-PYTHON-May-2020\nAdvanced-Course-PYTHON-May-2020\n"
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"text": "def absolute(num_list):\n return[ abs(float(x)) for x in num_list.split()]\n \n\nprint(absolute(input()))\n "
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"text": "data = input()\nprint('NO' if len(data) % 2 != 0 or data[3] == ']' else 'YES')\n \n\n \n"
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"text": "loop = int(input())\nodd_num_set = set()\neven_num_set = set()\n\nfor index in range(1, loop + 1):\n ascii_sum = sum([ord(x) for x in input()]) // index\n if ascii_sum & 1:\n odd_num_set.add(ascii_sum)\n else:\n even_num_set.add(ascii_sum)\n \nodd_sum = sum(odd_num_set)\neven_sum = sum(even_num_set)\n\nif even_sum == odd_sum:\n result = odd_num_set.union(even_num_set)\n print(', '.join(str(x) for x in result))\nelif odd_sum > even_sum:\n result = odd_num_set.difference(even_num_set)\n print(', '.join(str(x) for x in result)) \nelse:\n result = odd_num_set.symmetric_difference(even_num_set)\n print(', '.join(str(x) for x in result)) "
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"text": "number_of_guests = int(input())\nguests_list = [input() for _ in range(number_of_guests)]\nvip_list = []\n\nwhile True: \n guest = input()\n if guest == 'END':\n print(len(guests_list))\n break\n else:\n if guest in guests_list:\n guests_list.remove(guest)\n\n\nfor guest in guests_list:\n if guest[0].isdigit():\n vip_list.append(guest)\n guests_list.remove(guest)\n\nsorted_vip_list = sorted(vip_list)\nfor vip in sorted_vip_list:\n print(vip)\n\nsorted_guests_list = sorted(guests_list)\nfor ordinary in sorted_guests_list:\n print(ordinary) \n\n\n\n"
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"text": "names_list = []\n\nwhile True:\n token = input()\n if token == 'End':\n print(f'{len(names_list)} people remaining.')\n break\n elif token == 'Paid':\n for _ in range(len(names_list)):\n print(names_list.pop(0))\n else:\n names_list.append(token) "
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"text": "phone_dict = {}\n\nwhile True:\n token = input().split('-')\n if len(token) == 2:\n name, phone = token[0], token[1]\n if name not in phone_dict:\n phone_dict[name] = ''\n phone_dict[name] = phone\n else:\n loop = int(token[0])\n for _ in range(loop):\n name = input()\n if name not in phone_dict:\n print(f'Contact {name} does not exist.')\n else:\n print(name, '->', phone_dict[name])\n break \n"
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"text": "def age_assignment(*args, **kwargs):\n info_dict = {}\n for name in args:\n info_dict[name] = kwargs[name[0:1]] \n\n return info_dict \n"
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"text": "matrix_size = int(input())\nleft, right = 0, 0\n\nfor i in range(matrix_size):\n row = [int(x) for x in input().split()]\n left += row[i]\n right += row[len(row) - 1 - i]\n\nprint(abs(left -right))"
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"text": "loop = int(input())\nstudent_dict = {}\nstring = ''\n\nfor _ in range(loop):\n token = input().split()\n key, val = token[0], float(token[1])\n if key not in student_dict:\n student_dict[key] = []\n student_dict[key].append(val)\n\n\nfor key, val in student_dict.items():\n for v in val:\n string += f'{v:.2f} '\n print(f'{key} -> {string}(avg: {sum(val) / len(val):.2f})')\n string = ''"
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"text": "def rounded(num):\n return [round(float(x)) for x in num.split()]\n\nprint(rounded(input())) "
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"text": "def even_odd(*args):\n command = args[-1]\n if command == 'even':\n return list(filter(lambda x: not int(x) & 1, args[0:-1] ))\n else:\n return list(filter(lambda x: int(x) & 1, args[0:-1]))\n\n"
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"text": "rows, cols = [int(x) for x in input().split()]\nmatrix = []\n\nfor _ in range(rows):\n matrix.append(input().split())\n\nwhile True:\n token = input().split()\n \n if len(token) == 1:\n break\n elif 'swap' not in token:\n print('Invalid input!')\n elif len(token) == 5:\n swap, row1, col1, row2, col2 = [int(x) if x.isdigit() else x for x in token]\n if row1 <= rows and row2 <= rows and col1 <= cols and col2 <= cols:\n first_change = matrix[row1][col1]\n second_change = matrix[row2][col2]\n matrix[row1][col1] = second_change\n matrix[row2][col2] = first_change\n\n for row in matrix:\n print(*row)\n\n else:\n print('Invalid input!')\n else:\n print('Invalid input!')"
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"text": "size_matrix = int(input())\nmatrix = []\ndiagonal_sum = 0\n\nfor _ in range(size_matrix):\n matrix.append([int(x) for x in input().split()])\n\nindex = 0\nfor row in matrix:\n diagonal_sum += row[index] \n index += 1\n\nprint(diagonal_sum)"
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"text": "def find_strongest_eggs(*test):\n eggs_list, length = test[0], test[1]\n collection_list = [[] for _ in range(length)]\n strong_eggs_list = []\n\n index = 0\n for egg in eggs_list:\n collection_list[index].append(egg)\n index += 1\n if index == length:\n index = 0\n\n for row in collection_list:\n for el in range(len(row)):\n left = row[(len(row) // 2) - 1] \n middle = row[len(row) // 2]\n right = row[(len(row) // 2) + 1] \n \n if left < middle > right and left < right:\n strong_eggs_list.append(middle)\n \n return strong_eggs_list\n\n"
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"text": "bunker_dict = {items : {} for items in input().split(', ')}\nfor _ in range(int(input())):\n item, product, params = input().split(' - ')\n quan, qual = [ int(x[x.index(':') + 1:]) for x in params.split(';')]\n bunker_dict[item][product] = (quan, qual)\n \n\nprint('Count of items:', sum([sum(x[0] for x in tuple(v.values())) for k, v in bunker_dict.items()]))\nprint(f'Average quality: {sum([sum(x[1] for x in tuple(v.values())) for k, v in bunker_dict.items()]) / len(bunker_dict):.2f}')\n[print(f'{items} -> { \", \".join([x for x in bunker_dict[items].keys()])}') for items in bunker_dict]\n\n\n\n#--------------------------------------------------------------------(2)-------------------------------------------------\n\nbunker_dict = {items : {} for items in input().split(', ')}\nquantity , quality = 0, 0\n\nfor _ in range(int(input())):\n item, product, params = input().split(' - ')\n quan, qual = [ int(x[x.index(':') + 1:]) for x in params.split(';')]\n bunker_dict[item][product] = ()\n quantity += quan \n quality += qual \n \n\nprint(f'Count of items: {quantity}\\nAverage quality: {quality / len(bunker_dict):.2f}')\n[print(f'{k} -> { \", \".join([x for x in v.keys()])}') for k, v in bunker_dict.items()]"
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"text": "import os\nos.system('clear')\n\nsize_matrix = int(input())\nmatrix = [char for char in [input().split() for _ in range(size_matrix)]]\ndirections_dict = {'up': (-1, 0), 'right': (0, 1), 'down': (1, 0), 'left': (0, -1)}\ntargets = 0\ndestroyed_targets = 0\nmission_done = False\n\nfor row_index in range(len(matrix)):\n for col_index in range(len(matrix[row_index])): \n if matrix[row_index][col_index] == 't':\n targets += 1 \n destroyed_targets += 1 # count target\n\nfor _ in range(int(input())):\n for row_index in range(len(matrix)):\n for col_index in range(len(matrix[row_index])):\n if matrix[row_index][col_index] == 'p':\n plain_row, plain_col = row_index, col_index #find plane position\n\n command, direction, step = [int(x) if x.isdigit() else x for x in input().split()]\n \n row_ch, col_ch = directions_dict[direction]\n row = plain_row + (row_ch * step)\n col = plain_col + (col_ch * step)\n\n if col >= size_matrix or row >= size_matrix or row < 0 or col < 0:\n continue\n \n if command == 'shoot':\n if matrix[row][col] == 't':\n destroyed_targets -= 1\n matrix[row][col] = 'x'\n\n if destroyed_targets == 0:\n mission_done = True\n break\n \n elif command == 'move':\n if matrix[row][col] != 'x' and matrix[row][col] != 't':\n matrix[plain_row][plain_col ]= '.' \n matrix[row][col] = 'p'\n\n \nif mission_done:\n print(f'Mission accomplished! All {targets} targets destroyed.')\nelse:\n print(f'Mission failed! {destroyed_targets} targets left.')\n\n[print(*mtx) for mtx in matrix]\n\n\n\n"
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"text": "import os\nos.system('clear')\n\npeople = input().split()\nn = int(input())\n\nwhile len(people) != 1:\n for i in range(1, n):\n people.append(people.pop(0))\n else:\n print('Removed', people.pop(0))\n\nprint('Last is', people.pop())"
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"text": "import sys\n\ncups = [int(x) for x in input().split()]\nbottles = list(reversed([int(x) for x in input().split()]))\n\nwater = 0\n\nwhile True:\n for i in range(10):\n try:\n if cups[i] <= bottles[i]:\n if cups[i] < bottles[i]:\n water += abs(cups[i] - bottles[i])\n cups.pop(i)\n bottles.pop(i)\n break\n else: \n cups.pop(i)\n bottles.pop(i)\n break\n\n elif cups[i] > bottles[i]:\n cups[i] -= bottles[i]\n bottles.pop(i)\n break\n except:\n if cups:\n print('Cups:', *cups)\n else: \n print('Bottles:', *bottles)\n\n print(f'Wasted litters of water: {water}')\n sys.exit() "
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"text": "num_list = input().split()\npositive_sum = sum([int(x) for x in num_list if int(x) > 0 ])\nnegative_sum = sum([int(x) for x in num_list if int(x) < 0 ])\n\nprint(f'{negative_sum}\\n{positive_sum}')\n\nif abs(negative_sum) > positive_sum:\n print('The negatives are stronger than the positives')\nelse:\n print('The positives are stronger than the negatives')\n\n"
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"text": "matrix = [input().split(', ') for x in range(int(input()))]\n\nleft_sum, right_sum = 0, 0\nfor i in range(len(matrix)):\n left_sum += int(matrix[i][i])\n right_sum += int(matrix[i][len(matrix) - 1 - i])\n\nprint('First diagonal:', ', '.join([matrix[i][i] for i in range(len(matrix))]) + '.','Sum:', left_sum)\nprint('Second diagonal:', ', '.join([matrix[i][len(matrix) - 1 - i] for i in range(len(matrix))]) + '.','Sum:', right_sum )\n\n\n\n"
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"text": "def get_damage(row,col,matrix): #copy / paste - code\n counter=0\n if row-2>=0 and col-1>=0:\n if matrix[row-2][col-1]=='K':\n counter+=1\n if row-2>=0 and col+1<len(matrix):\n if matrix[row-2][col+1]=='K':\n counter+=1\n if row-1>=0 and col-2>=0:\n if matrix[row-1][col-2]=='K':\n counter+=1\n if row-1>=0 and col+2<len(matrix):\n if matrix[row-1][col+2]=='K':\n counter+=1\n if row+1<len(matrix)and col-2>=0:\n if matrix[row+1][col-2]=='K':\n counter+=1\n if row +1<len(matrix)and col+2<len(matrix):\n if matrix[row+1][col+2]=='K':\n counter+=1\n if row+2<len(matrix) and col-1>=0:\n if matrix[row+2][col-1]=='K':\n counter+=1\n if row+2<len(matrix) and col+1<len(matrix):\n if matrix[row+2][col+1]=='K':\n counter+=1\n return counter\n\n\n\nrow_count=int(input())\nmatrix=[]\npositon=[]\ndeleted_knights=0\n\n\nfor i in range(row_count):\n matrix.append([i for i in input()])\n\nwhile True:\n max_damage = 0\n for row in range(row_count):\n for col in range(row_count):\n current=matrix[row][col]\n if current=='K':\n damage=get_damage(row,col,matrix)\n if damage>max_damage:\n max_damage=damage\n position=[row,col]\n if max_damage==0:\n break\n\n first_pos=position[0]\n second_pos=position[1]\n matrix[first_pos][second_pos]='0'\n position=[]\n deleted_knights+=1\n\n\nprint(deleted_knights)"
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"text": "num_list = [float(x) for x in input().split()]\ncount_dict = {}\n\nfor n in num_list:\n if n not in count_dict:\n count_dict[n] = 0\n count_dict[n] += 1\n\nfor k, v in count_dict.items():\n print(f'{k} - {v} times')"
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"text": "#---------------------------------WTF------------TODO \nimport datetime, sys\n\nrobot_dict = { x.split('-')[0]: int(x.split('-')[1]) for x in input().split(';')}\nh, m, s = list(map(int,input().split(':')))\n\ndef time(add_sec):\n time = datetime.datetime(2020, 7, 16, h, m, s)\n new_time = time + datetime.timedelta(0, add_sec)\n return new_time.time()\n\n\nsec = 0\n\nfor key in robot_dict:\n item = input()\n sec += 1\n print(f'{key} - {item} [{time(sec)}]') \n\n\nsort_robot_dict = dict(sorted(robot_dict.items(), key = lambda x: x[1]))\n \nitem_list = []\nwhile True:\n item = input()\n if item == 'End':\n break\n item_list.append(item)\n\n\ntime = datetime.datetime(2020, 7, 16, h, m, s) \n\nwhile True:\n for key, val in sort_robot_dict.items():\n try:\n item = item_list.pop()\n \n new_time = time + datetime.timedelta(0, sec + val)\n time = datetime.datetime(2020, 7, 16,new_time.hour, new_time.minute, new_time.second)\n sec = 0\n print(f'{key} - {item} [{new_time.time()}]')\n \n except:\n sys.exit()\n\n \n \n \n\n\n"
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"text": "expression = input()\nstack = []\n\nfor i in range(len(expression)):\n if expression[i] == '(':\n stack.append(i) \n\n if expression[i] == ')':\n start_index = stack.pop()\n end_index = i \n print(expression[start_index: end_index + 1])\n "
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"text": "from functools import reduce \n\ndef multiply (*args):\n return reduce(lambda a, x: a * x , args)\n\n"
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"text": "loop = int(input())\nstack = []\n\nfor _ in range(loop):\n token = list(map(int, input().split()))\n if token[0] == 1:\n stack.append(token[1])\n elif token[0] == 2 and len(stack) > 0:\n stack.pop()\n elif token[0] == 3 and len(stack) > 0:\n print(max(stack))\n elif token[0] == 4 and len(stack) > 0 :\n print(min(stack))\n\nprint(', '.join(map(str ,list(reversed(stack))))) "
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"text": "row, col = [ int(x) for x in input().split(', ')]\nmatrix = []\nmatrix_sum = 0\n\nfor i in range(row):\n matrix.append([int(x) for x in input().split(', ')])\n matrix_sum += sum(matrix[i])\n\nprint(matrix_sum)\nprint(matrix)"
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"text": "water = int(input())\npeople = []\n\nwhile True:\n token = input()\n if token == 'Start':\n break\n people.append(token)\n\nwhile True:\n token = input().split()\n if token[0] == 'End':\n print(f'{water} liters left')\n break\n elif token[0] == 'refill':\n water += int(token[1])\n else:\n person_name = people.pop(0)\n if water >= int(token[0]):\n water -= int(token[0]) \n print(f'{person_name} got water')\n else:\n print(f'{person_name} must wait')\n\n "
}
] | 70 |
vicky1412/Burplauncher
|
https://github.com/vicky1412/Burplauncher
|
73bec86e7c131d39d2a15702204990a7b1831d87
|
ec976f11b62869fd34a4a7d1826047b49f8322f3
|
30a359e433a7460b46d548b90694b2a9fd7eb938
|
refs/heads/main
| 2023-06-19T13:05:32.842711 | 2021-07-13T16:23:16 | 2021-07-13T16:23:16 | 385,661,887 | 0 | 0 | null | null | null | null | null |
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"text": "# Burplauncher\n\nIt is a app launcher application.\n\nI am have burpsuite application which is used for web pentestisng.\nI have the cracked version only\n\nSo, every time i need to patch the application withs the key in the ,jar java file to open the main\napplication.\nIt takes time and i am lasy\nso coded to automatically do the patch for me and hide the meshy cmd black screen \n\nand added the shortcut to my desktop so whenever i want i just click the burplauncher application to open \nthe burp application.\n\nNote: the burp folde which contains burplauncher.jar must be at the same directory\n\nlanguage : python\n\nmodule used : pynput(keyboard controller), subprocess\n\n\n"
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"text": "import subprocess\r\nimport threading\r\nfrom pynput.keyboard import Key,Controller\r\nimport time\r\n\r\nimport os\r\n\r\n\r\ndef th():\r\n keyboard = Controller()\r\n time.sleep(1)\r\n keyboard.press(Key.tab)\r\n keyboard.release(Key.tab)\r\n time.sleep(1)\r\n keyboard.press(Key.enter)\r\n keyboard.release(Key.enter)\r\n\r\ndef kill():\r\n time.sleep(2)\r\n subprocess.call(\"taskkill/IM javaw.exe\",shell=True)\r\n\r\nt = threading.Thread(target=th)\r\ns = threading.Thread(target=kill)\r\n\r\n\r\nt.start()\r\ns.start()\r\nos.chdir(\"G:\\\\Application\\\\burp\")\r\nsubprocess.call(\"burploader.jar\",shell=True)"
}
] | 2 |
dixonliang/SentimentAnalysisSoccer
|
https://github.com/dixonliang/SentimentAnalysisSoccer
|
d59eb59c56159961dc4b9bf448d2dcc72fd0adbb
|
a7030b91a1d4d6c601928ff3a3c38983d7938e93
|
fe0a8e7f19b41dcb2a5ebb33fa03e9b66018680a
|
refs/heads/main
| 2023-01-24T12:53:44.753163 | 2020-12-06T20:53:33 | 2020-12-06T20:53:33 | 319,126,328 | 1 | 2 | null | null | null | null | null |
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"text": "# Sentiment Analysis for Soccer Games and Documentation\n\n### Introduction\n\nThe main idea of this repo is to provide the code, documentaion, and demo for a basic sentiment analysis for soccer games using Python, Tweepy, TextBlob, and BM25Okapi. **The easiest way to use this code is to use the Jupyter Notebook demo in this repo.** A video tutorial on Youtube is also provided. The source code is available as well. This was originallyfor my course project for CS 410 Text Information Systems for my Masters in Computer Science at University of Illinois Urbana-Champaign. Please feel free to reach out to me if you would like to collaborate :) .\n\n#### Files \n\nYouTube Demo Link: https://www.youtube.com/watch?v=UuY7dO8bq0M&ab_channel=DixonLiang\n\nmaincode.py - The main source code\n\n12_5_20_ChelseaLeeds.ipynb - The demo code / output that was used for the YouTube video tutorial \n\ndemo.ipynb - Empty demo code in Jupyter Notebook for free use\n\nChelseaLeeds_Presentation.pptx - The powerpoint presentation used in the video tutorial\n\nteam1_sentiment.png - Example sentiment bar chart for Team 1 (new file will be saved down if main code is run)\nteam1_BM25positive.png - Example positive BM25 average ranking for Team 1 (new file will be saved down if main code is run)\nteam1_BM25negative.png - Example negative BM25 average ranking for Team 1 (new file will be saved down if main code is run)\n\nteam2_sentiment.png - Example sentiment bar chart for Team 2 (new file will be saved down if main code is run)\nteam2_BM25positive.png - Example positive BM25 average ranking for Team 2 (new file will be saved down if main code is run)\nteam2_BM25negative.png - Example negative BM25 average ranking for Team 2 (new file will be saved down if main code is run)\n\n### Background\n\nWe will be using Tweepy to source tweets from the Twitter API and TextBlob to provide a framework for natural language processing to provide sentiment analysis. In addition, we will use PyPi's implementation of BM25Okapi to provide context of the sentiment analysis.\n\nIdeally, the result of this code will show the relative sentiment of a player's performance during a recent game. By using wisdom of the crowds, we hope to gain an idea of how the player performed. Using BM25Okapi, we will also be able to use relevant terms to see what might have caused sentiment to go way or another (ex. player scored a goal or provided an assist, etc.) Using PyPlot, we will also be able to visualize the results.\n\nTechnically, this code can be used for any soccer game, but given the popularity and language barrier, EPL games are likely to provide the most meaningful results. Adjustments could be made for La Liga or Serie A using Spanish or Italian NLP. Please feel free to reach out as I welcome any collaboration as the code can be improved and applied to different sports or different applications all together :) .\n\nA run through of the source code is provided below. \n\n### Code Documentation\n\n#### Introduction \n\nPackages Needed: To begin, we need several packages installed and imported. These are: Tweepy, TextBlob, Numpy, Rank_BM25, and Matplotlib.pyplot. Documentation and links are found here: http://docs.tweepy.org/en/latest/api.html https://textblob.readthedocs.io/en/dev/api_reference.html https://numpy.org/doc/ https://pypi.org/project/rank-bm25/ https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.html\n\nMost importantly, we will need access to the Twitter API, which can be gained by having a Twitter profile. You will be provided four keys of strings of letters and numbers which you will need to enter in the box below: consumer key, consumer secret, access token, access token secret. These will be used in the below code area. \n\n```shell\nconsumer_key = \"\"\nconsumer_secret = \"\"\naccess_token = \"\"\naccess_token_secret = \"\"\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\n\napi = tweepy.API(auth,wait_on_rate_limit=True)\n``` \n\n#### Game Parameters\n\nWe will need to set the parameters for the game we are interested in; this includes the two teams names and the starting 11 for each team. \n\n```shell\nteam1 = \"\"\nteam2 = \"\"\n\n#team1\nteam1_Player1 = \"\"\nteam1_Player2 = \"\"\nteam1_Player3 = \"\"\nteam1_Player4 = \"\"\nteam1_Player5 = \"\"\nteam1_Player6 = \"\"\nteam1_Player7 = \"\"\nteam1_Player8 = \"\"\nteam1_Player9 = \"\"\nteam1_Player10 = \"\"\nteam1_Player11 = \"\"\n\n#team2\nteam2_Player1 = \"\"\nteam2_Player2 = \"\"\nteam2_Player3 = \"\"\nteam2_Player4 = \"\"\nteam2_Player5 = \"\"\nteam2_Player6 = \"\"\nteam2_Player7 = \"\"\nteam2_Player8 = \"\"\nteam2_Player9 = \"\"\nteam2_Player10 = \"\"\nteam2_Player11 = \"\"\n```\nAfter setting the game parameters, there are a few algorithm paramters we will need to set. To begin, the number of tweets that we want to retrieve is set as a parameter for the algorithm. This may also affect how quickly the algorithm runs because of limitations in the package and the free version of the Twitter API. The threshold for objectivity/subjectivity is also set. 0 is defined as purely objective and 1 is defined as subjective. Ideally for the most results, we want a low threshold, 0.10 has been suggested, but any threshold can be set. The date periods for when we want to retrieve tweets from is also se; for best results, it is suggested to only use the day of the game and the day after the game. The free version of the Twitter API limits searches to within the 7 days. \n\nSentiment Analysis: \n\n```shell\n### define the number of tweets we want to sort for and subjective threshold\n\nnumber_of_tweets = 100 # how many tweets we want to search for\nthreshold = 0.10 # threshold for subjectivity [0,1]\n\n### setting date range, ideally run day after the game\n\ndate_since = \"2020-11-21\"\ndate_until = \"2020-11-22\"\n```\n\nFor the BM25Okapi algorithm, there are just two sets of parameters we must set. The first is the set positive terms we want to use for context. Some suggestions are in the default query already. Similarily, for the second set of parameters, it is a set of negative terms. \n\nBM25Okapi: \n\n```shell\npositive_terms = \"assist good excellent great\" # search queries, positive terms\nnegative_terms = \"poor bad miss own awful\" # negative terms\n```\n\nThe BM25Okapi portion of the code will combine all of the tweets for every player together which will they treat each tweet as a document as part of a corpus. Then using the positive array, it will then go through each document ranking it based on how many of the positive terms each tweet matches. The higher ranked the tweet is, the more relevant it is to that query. Given we are using positive terms, the idea is that the tweet is more reflective of positive results in relation to those terms during the game for the respective player. The same will be done with the negative query. Once the rankings are done, each players average ranking for each query is provided, similarily to the sentiment array above. These two arrays will then be used for charting. \n\nBM25 incorporates search ranking concepts such as IDF (inverse document frequency), which is a filter for commonly used terms as well as TF (term frequency), which gives higher ranking for more matching of terms. A brief summary of how exactly the formula ranks can be found here: https://nlp.stanford.edu/IR-book/html/htmledition/okapi-bm25-a-non-binary-model-1.html\n\n\n#### Running the Code\n\nAfter setting the above parameters, the entire \"maincode.py\" can be run which will then output the relevant visualizations for this task. The code will retrieve the set number of tweets for each player and then use TextBlob's sentiment analysis tool to rate the sentiment of each tweet. If the tweet crosses the set threshold, the senitment for that tweet will be used for an average of all of the sentiment for that respective player. This array of sentiments of players will then be used for our graphs below. \n\nThe functions that are used for the generation of these visualizations are listed below. \n\n#### Visual Output Functions\n\nplot_bar_team1_sentiment(): \n\nUsing pyplot, this function will chart Team 1's senitment by player in the form of a horizontal bar chart. The function will take the sentiment array as mentioned above and plot the respective average for each player. If the sentiment is more towards the right, the player's sentiment for that game will be more positive. If the senitment is more towards the left, the player's sentiment for that game will be more negative. \n\nplot_bar_team2_sentiment():\n\nSame as the above but with the players for Team 2. \n\n<img src=\"./team1_sentiment.png\" alt=\"alt text\" width=400 height=300>\n\nplot_bar_team1_BM25positive():\n\nUsing pyplot again, this function will chart Team 1's BM25Okapi rankings in the form of a horizontal bar chart. \n\nplot_bar_team2_BM25positive():\n\nSame as above but for Team 2. \n\n<img src=\"./team1_BM25positive.png\" alt=\"alt text\" width=400 height=300>\n\nplot_bar_team1_BM25negative():\n\nSame as above but for the negative query and Team 1. \n\nplot_bar_team2_BM25negative():\n\nSame as above but for the negative query and Team 2. \n\n<img src=\"./team1_BM25negative.png\" alt=\"alt text\" width=400 height=300>\n\n#### Text Output Functions\n\ndisplay_tweets(team, player_number): \n\nThis function will take in two arguments, the team name and the player number (which can be referenced above on the parameters). The function will then display the ten highest and ten lowest sentiment tweets for that player. \n\n```shell\n['RT @SiPhillipsSport: Chelsea keep the ball for about 5 minutes, thennnnn Rudiger.', 'RT @goal: Thiago Silva ❌\\nHavertz ❌\\nPulisic ❌\\n\\nRudiger ✅\\nChilwell ✅\\nWerner ✅\\n\\nChelsea reveal their team to play Newcastle 🔵\\n\\n#NEWCHE https:/…', '@ChelseaFC Chelsea had a clean with Rudiger and Zouma playing together. 😉 We are winning this league', 'RT @SiPhillipsSport: Chelsea keep the ball for about 5 minutes, thennnnn Rudiger.', 'NEWCASTLE 0-2 CHELSEA: GODFREY 🗣️ \"The only player wey dun improve Chelsea na Mendy, ZOUMA AND RUDIGER STILL NO GET… https://t.co/TYZOd3mZ9X', 'RT @kingmali_: @ChelseaFC MOTM kante\\nLovely clean sheet Mendy\\nWell done Tammy\\nRudiger is not fit to be a Chelsea player PERIOD!\\nEmerson is…', 'RT @SiPhillipsSport: Chelsea keep the ball for about 5 minutes, thennnnn Rudiger.', 'RT @AbsoluteChelsea: Frank Lampard says Antonio Rudiger was brilliant on his first Premier League start of the season for #Chelsea against…', \"Are you more confident about Chelsea's defensive options and depth than at the start of the season?\\n\\nhttps://t.co/enuSsURsmJ\"]\n```\n\nrank_top(corpus,terms):\n\nThis function is in relation to the BM25Okapi rankings. It takes in two arguments, a corpus (in this case, will be a series of tweets) and then a search query (in this case, positive or negative term array). This function will display the top ten ranked tweets in the corpus given the query. An example would be if we wanted to see the top ranked tweets for a specific player. \n\n```shell\n['@Chelsea_Era @EBL2017 Werner was playing bumdesliga, I don’t doubt he’s got a good scoring record in that league. H… https://t.co/veBdvRxnxQ',\n \"https://t.co/cTxtOa9fGf\\nMendy & Chilwell both had their 'worst' game in a Chelsea shirt today, and were still excel… https://t.co/gmjF62mTX3\",\n '@AlexGoldberg_ Kovacic done ok today but gives the ball away too much in dangerous areas, against a better team Che… https://t.co/d0y3hpDjgX',\n '@afcjxmes Kovacic was Chelsea’s worst midfielder today, gave the ball away in dangerous areas too many times, Kante… https://t.co/rPLqctpkjc',\n \"Timo Werner is 'undroppable'.\\n\\nN'Golo Kante is back doing what he does best.\\n\\nFrank Lampard is about to settle on a… https://t.co/oXxsMrxKh7\",\n \"Timo Werner is 'undroppable'.\\n\\nN'Golo Kante is back doing what he does best.\\n\\nFrank Lampard is about to settle on a… https://t.co/oXxsMrxKh7\",\n 'RT @Football__Tweet: Edouard Mendy has kept 7 clean sheets in his first 9 Chelsea games.\\n\\nTalk about an upgrade on the most expensive goalk…',\n '@tessderry1 Ths international break suckssss...timo chilwell mount grealish theirlegs lookd tired.....\\n\\nNext match… https://t.co/L57jzK0DyO',\n 'Frank Lampard expressed his delight as Chelsea kept another clean sheet in their 0-2 win against Newcastle at St Ja… https://t.co/d3HMLpYWoX',\n 'Saturdays added assist:\\nMount (Chelsea v Newcastle) pass leading to own goal. https://t.co/xtIdUJXHLQ']\n```\n\n\n#### Helper Functions \n\nsentiment_element(element): \n\nThis is a simple function that will be used for Python's sort implementation for an array. In this case, we are interested in sorting be the second element (for each entry in the serntiment array is the sentiment score) which is what this function does. \n\nrank_scores(corpus,terms):\n\nThis function is in relation to the BM25Okapi rankings. It takes in two arguments, a corpus (in this case, will be a series of tweets) and then a search query (in this case, positive or negative term array). It is the function that will actually use PyPi's implementation of BM25Okapi to give each tweet a rank in relation the entire corpus. Before passing into the implementation, both the corpus and term query will be tokenized. \n"
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"text": "\n### Sentiment Anaylsis for Soccer Games\n#Author: Dixon Liang\n#Below is the source code, documentation can be found in my GitHub\n\n# import necessary packages\n\nimport tweepy # import tweepy aka Twitter API\nfrom textblob import TextBlob # import textblob aka NLP package\nimport numpy as np # import numpy\nfrom rank_bm25 import BM25Okapi # import BM25\nimport matplotlib.pyplot as plt # import plotting\nplt.ion()\n\n# need Twitter access keys\n\nconsumer_key = \"\"\nconsumer_secret = \"\"\naccess_token = \"\"\naccess_token_secret = \"\"\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\n\napi = tweepy.API(auth,wait_on_rate_limit=True)\n\n### initialization of team names and starting 11 from the game\n\n# enter the team names in the quotes as a string, same applies for the players\n\nteam1 = \"\"\nteam2 = \"\"\n\n#team1\nteam1_Player1 = \"\"\nteam1_Player2 = \"\"\nteam1_Player3 = \"\"\nteam1_Player4 = \"\"\nteam1_Player5 = \"\"\nteam1_Player6 = \"\"\nteam1_Player7 = \"\"\nteam1_Player8 = \"\"\nteam1_Player9 = \"\"\nteam1_Player10 = \"\"\nteam1_Player11 = \"\"\n\n#team2\nteam2_Player1 = \"\"\nteam2_Player2 = \"\"\nteam2_Player3 = \"\"\nteam2_Player4 = \"\"\nteam2_Player5 = \"\"\nteam2_Player6 = \"\"\nteam2_Player7 = \"\"\nteam2_Player8 = \"\"\nteam2_Player9 = \"\"\nteam2_Player10 = \"\"\nteam2_Player11 = \"\"\n\ntotal_players = 11\n\nteam1_player_array = [team1_Player1, team1_Player2, team1_Player3, team1_Player4, team1_Player5, team1_Player6, team1_Player7,\nteam1_Player8, team1_Player9, team1_Player10, team1_Player11] # player array for Team 1\n\nteam2_player_array = [team2_Player1, team2_Player2, team2_Player3, team2_Player4, team2_Player5, team2_Player6, team2_Player7,\nteam2_Player8, team2_Player9, team2_Player10, team2_Player11] # player array for Team 2\n\nteam1_player_sentiment = [] # place holder array for players senitment scores\nteam1_player_tweets = [] # place holder for the tweets for each player\nteam1_player_combined = [] # place holder for the tweets for each player and sentiment\n\nteam2_player_sentiment = [] # place holder array for players senitment scores\nteam2_player_tweets = [] # place holder for the tweets for each player that match threshold\nteam2_player_combined = [] # place holder for the tweets for each player and sentiment\n\n### define the number of tweets we want to sort for and subjective threshold\n\nnumber_of_tweets = 100 # how many tweets we want to search for\nthreshold = 0.10 # threshold for subjectivity [0,1]\n\n### setting date range, ideally run day after the game\n\ndate_since = \"2020-11-21\"\ndate_until = \"2020-11-22\"\n\n### code to sort by sentiment rating\n\ndef sentiment_element(element): # define sorting function\n return element[1]\n\n### PART1: Basic Sentiment Analysis without any adjustments\n\n### Loop for Team 1 to find sentiment\n\n\nfor i in team1_player_array: # loop through each player\n search_words = [i, team1] # search array for each player\n tweets = tweepy.Cursor(api.search,search_words,lang=\"en\",since=date_since,until=date_until).items(number_of_tweets) # find tweets for each player\n tweet_array = []\n sentiment_array = []\n combined_array = []\n\n for tweet in tweets:\n tweet_array.append(tweet.text)\n sentiment_array.append(TextBlob(tweet.text).sentiment) # append the sentiment into array\n\n for j in range(0,len(tweet_array)): # create combined array to sort\n combined_array.append([tweet_array[j],sentiment_array[j][0]])\n\n combined_array.sort(key=sentiment_element) # sort tweet array by sentiment (remember that lowest sentiment is first)\n\n team1_player_tweets.append(tweet_array) # create array of just the tweets\n team1_player_combined.append(combined_array) # create array of all of the respective player tweets, which are now sorted by sentiment\n\n sentiment_count = 0 # want to only count sentiments that are subjective\n sentiment_total = 0 # keep track for average\n for sentiment in sentiment_array:\n if (sentiment[1] >= threshold): # set threshold for objectivity, 0 = objective, 1 = subjective\n sentiment_count = sentiment_count + 1\n sentiment_total = sentiment_total + sentiment[0]\n\n if (sentiment_total == 0):\n team1_player_sentiment.append([i,0,sentiment_count]) # handle 0 count\n else:\n team1_player_sentiment.append([i,sentiment_total/sentiment_count,sentiment_count])\n\n\n### Loop for Team 2 to find sentiment\n\nfor i in team2_player_array: # loop through each player\n search_words = [i, team2] # search array for each player\n tweets = tweepy.Cursor(api.search,search_words,lang=\"en\",since=date_since,until=date_until).items(number_of_tweets) # find tweets for each player\n tweet_array = []\n sentiment_array = []\n combined_array = []\n\n for tweet in tweets:\n tweet_array.append(tweet.text)\n sentiment_array.append(TextBlob(tweet.text).sentiment) # append the sentiment into array\n\n for j in range(0,len(tweet_array)): # create combined array to sort\n combined_array.append([tweet_array[j],sentiment_array[j][0]])\n\n combined_array.sort(key=sentiment_element) # sort tweet array by sentiment (remember that lowest sentiment is first)\n\n team2_player_tweets.append(tweet_array) # create array of just the tweets\n team2_player_combined.append(combined_array) # create array of all of the respective player tweets, which are now sorted by sentiment\n\n sentiment_count = 0 # want to only count sentiments that are subjective\n sentiment_total = 0 # keep track for average\n for sentiment in sentiment_array:\n if (sentiment[1] >= threshold): # set threshold for objectivity\n sentiment_count = sentiment_count + 1\n sentiment_total = sentiment_total + sentiment[0]\n\n\n if (sentiment_total == 0):\n team2_player_sentiment.append([i,0,sentiment_count]) # handle 0 count\n else:\n team2_player_sentiment.append([i,sentiment_total/sentiment_count,sentiment_count])\n\n\n### display the top 10 min and max sentiment tweets for a player based on team\n\ndef display_tweets(team, player_number):\n if (team == team1):\n print(team1_player_combined[player_number-1][0:9]) # negative sentiment\n print(team1_player_combined[player_number-1][number_of_tweets-11:number_of_tweets-1]) # positive sentiment\n else:\n print(team2_player_combined[player_number-1][0:9]) # negative sentiment\n print(team2_player_combined[player_number-1][number_of_tweets-11:number_of_tweets-1]) # positive sentiment\n\n\n### sort each senitment array and organize for plotting\n\nteam1_player_sentiment.sort(key=sentiment_element)\nteam2_player_sentiment.sort(key=sentiment_element)\n\n# create index for team 1\nteam1_Index = []\nteam1_Sentiment = []\nfor i in team1_player_sentiment:\n team1_Index.append(i[0])\n team1_Sentiment.append(round(i[1],3))\n\n# create index for team 2\nteam2_Index = []\nteam2_Sentiment = []\nfor i in team2_player_sentiment:\n team2_Index.append(i[0])\n team2_Sentiment.append(round(i[1],3))\n\n\n### create bar graphs for Part 1 displaying data and then save down\n\ndef plot_bar_team1_sentiment():\n fig, ax = plt.subplots()\n ax.barh(team1_Index, team1_Sentiment, color = \"lightblue\")\n plt.title(team1 + ' Sentiment')\n plt.xlabel('Sentiment Score [-1,1]')\n for i, v in enumerate(team1_Sentiment):\n ax.text(v, i, \" \" + str(v), color='black', va = 'center', fontweight='bold')\n plt.savefig('team1_sentiment.png')\n\nplot_bar_team1_sentiment()\n\ndef plot_bar_team2_sentiment():\n fig, ax = plt.subplots()\n ax.barh(team2_Index, team2_Sentiment, color = \"orange\")\n plt.title(team2 + ' Sentiment')\n plt.xlabel('Sentiment Score [-1,1]')\n for i, v in enumerate(team2_Sentiment):\n ax.text(v, i, \" \" + str(v), color='black', va = 'center', fontweight='bold')\n plt.savefig('team2_sentiment.png')\n\nplot_bar_team2_sentiment()\n\n\n### PART2 Using BM25Okapi with user generated terms for context into sentiment\n\n\n### implementiation for specific terms relating to the game, ranking for BM25Okapi\n\npositive_terms = \"assist good excellent great\" # search queries, positive terms\nnegative_terms = \"poor bad miss own awful\" # negative terms\n\n\n### implementation of BM25Okapi to rank relevant of tweets in relation the game\n\ndef rank_scores(corpus, terms): # give each tweet a score based on query\n bm25 = BM25Okapi(corpus)\n tweet_scores = bm25.get_scores(terms)\n return tweet_scores\n\ndef rank_top(corpus, terms): # show the top 10 based on query\n bm25 = BM25Okapi(corpus)\n top_10_tweets = bm25.get_top_n(terms, corpus, n=10)\n return top_10_tweets\n\n\n### sentiment in relation to using BM25 as context\n\nteam1_total_tweets = []\nteam2_total_tweets = []\nteam1_positive_results = []\nteam2_positive_results = []\nteam1_negative_results = []\nteam2_negative_results = []\n\nfor i in range(0,len(team1_player_tweets)):\n team1_total_tweets = team1_total_tweets + team1_player_tweets[i] # combine all player tweets into one corpus\n team2_total_tweets = team2_total_tweets + team2_player_tweets[i]\ntotal_tweets = team1_total_tweets + team2_total_tweets # combine both player tweets into one corpus\ntokenized_tweets = [doc.split(\" \") for doc in total_tweets] # tokenize the tweets for function\n\ntokenized_query_positive = positive_terms.split(\" \")\ntokenized_query_negative = negative_terms.split(\" \")\n\n# positive array\npositive_array = rank_scores(tokenized_tweets,tokenized_query_positive)\nteam1_positive_array = positive_array[0:number_of_tweets*total_players] # break into positive array for the two teams for sum\nteam2_positive_array = positive_array[number_of_tweets*total_players:len(positive_array)]\n\n# negative array\nnegative_array = rank_scores(tokenized_tweets,tokenized_query_negative)\nteam1_negative_array = negative_array[0:number_of_tweets*total_players] # break into positive array for the two teams for sum\nteam2_negative_array = negative_array[number_of_tweets*total_players:len(negative_array)]\n\n# postive tweets\nteam1_positive_results = np.sum(np.reshape(team1_positive_array,(total_players,number_of_tweets)),axis=1) / number_of_tweets\nteam2_positive_results = np.sum(np.reshape(team2_positive_array,(total_players,number_of_tweets)),axis=1) / number_of_tweets\n\n# negative tweets\nteam1_negative_results = np.sum(np.reshape(team1_negative_array,(total_players,number_of_tweets)),axis=1) / -number_of_tweets\nteam2_negative_results = np.sum(np.reshape(team2_negative_array,(total_players,number_of_tweets)),axis=1) / -number_of_tweets\n\n### create bar graphs for Part 2 displaying data and then save down\n\n\n# reshape sum arrays for graphing\n\nteam1_positive_results = np.round(np.reshape(team1_positive_results,(1,total_players)).tolist(),3)\nteam2_positive_results = np.round(np.reshape(team2_positive_results,(1,total_players)).tolist(),3)\nteam1_negative_results = np.round(np.reshape(team1_negative_results,(1,total_players)).tolist(),3)\nteam2_negative_results = np.round(np.reshape(team2_negative_results,(1,total_players)).tolist(),3)\n\n\n# team 1 BM25 charts\n\ndef plot_bar_team1_BM25positive():\n fig, ax = plt.subplots()\n ax.barh(team1_Index, team1_positive_results[0], color = \"lightblue\")\n plt.title(team1 + ' BM25 Positive Context' + '\\n' + \"Terms: \" + positive_terms)\n plt.xlabel('Avg. BM25 Ranking')\n for i, v in enumerate(team1_positive_results[0]):\n ax.text(v, i, \" \" + str(v), color='black', va = 'center', fontweight='bold')\n plt.savefig('team1_BM25positive.png')\n\nplot_bar_team1_BM25positive()\n\ndef plot_bar_team1_BM25negative():\n fig, ax = plt.subplots()\n ax.barh(team1_Index, team1_negative_results[0], color = \"lightblue\")\n plt.title(team1 + ' BM25 Negative Context' + '\\n' + \"Terms: \" + negative_terms)\n plt.xlabel('Avg. BM25 Ranking')\n for i, v in enumerate(team1_negative_results[0]):\n ax.text(v, i, \" \" + str(v), color='red', va = 'center', fontweight='bold')\n plt.savefig('team1_BM25negative.png')\n\nplot_bar_team1_BM25negative()\n\n\n# team 2 BM25 charts\n\ndef plot_bar_team2_BM25positive():\n fig, ax = plt.subplots()\n ax.barh(team2_Index, team2_positive_results[0], color = \"orange\")\n plt.title(team2 + ' BM25 Positive Context' + '\\n' + \"Terms: \" + positive_terms)\n plt.xlabel('Avg. BM25 Ranking')\n for i, v in enumerate(team2_positive_results[0]):\n ax.text(v, i, \" \" + str(v), color='black', va = 'center', fontweight='bold')\n plt.savefig('team2_BM25positive.png')\n\nplot_bar_team2_BM25positive()\n\ndef plot_bar_team2_BM25negative():\n fig, ax = plt.subplots()\n ax.barh(team2_Index, team2_negative_results[0], color = \"orange\")\n plt.title(team2 + ' BM25 Negative Context' + '\\n' + \"Terms: \" + negative_terms)\n plt.xlabel('Avg. BM25 Ranking')\n for i, v in enumerate(team2_negative_results[0]):\n ax.text(v, i, \" \" + str(v), color='red', va = 'center', fontweight='bold')\n plt.savefig('team2_BM25negative.png')\n\nplot_bar_team2_BM25negative()\n\n\n### end of source code\n\n\n\n\n\n\n\n\n\n\n\n"
}
] | 2 |
CanalTP/fab_kirin
|
https://github.com/CanalTP/fab_kirin
|
f1467a52134fd32b7075df503671eb8cbc52cd21
|
b8f04f48005ec6f3a5dd01c4dee44ac8c2dfb8d4
|
f0b8f2567d3783005c7b745130b211696d0e5d74
|
refs/heads/master
| 2021-11-28T15:57:49.252524 | 2021-11-24T16:19:32 | 2021-11-24T16:19:32 | 76,439,730 | 0 | 3 | null | 2016-12-14T08:26:49 | 2021-11-17T09:51:03 | 2021-11-24T16:19:32 |
Shell
|
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"text": "from fabric.api import *\nimport base\n\n\ndef local_dockerized_deps():\n env.name = 'local_dockerized_deps'\n env.path = '~/fab_kirin_workspace' # directory must be available on host\n env.is_local = True\n\n env.roledefs = {\n 'kirin': ['localhost'],\n 'kirin-beat': ['localhost']\n }\n\n env.kirin_host = '172.17.0.1' # as seen from `ip a` -> docker0 -> inet value (host IP from container)\n env.kirin_host_port = '54746'\n\n env.docker_image_kirin = 'kirin'\n env.previous_docker_tag = 'local' # tag of the image to deploy\n env.current_docker_tag = 'local' # same, as no platform-chaining is done\n\n env.use_load_balancer = False\n\n env.navitia_url = 'http://172.17.0.1:5000/' # Navitia on host, must be reachable from container\n\n env.postgres_database = '172.17.0.1'\n env.postgres_port = 35432\n env.user_kirin_postgres = 'kirin'\n env.pwd_kirin_postgres = 'kirin'\n\n env.redis_host = '172.17.0.1'\n env.redis_port = 36379\n env.redis_db = 1\n\n env.rabbitmq_url = '172.17.0.1' # Navitia RabbitMQ on host, must be reachable from container\n env.user_rabbitmq = 'navitia'\n env.pwd_rabbitmq = 'navitia'\n env.rabbitmq_vhost = '/'\n\n # Note: The user `guest` is not authorized to connect by remote. Create 'kirin' user with access to 'kirin' vhost\n env.celery_broker_url = 'pyamqp://kirin:<pass>@172.17.0.1:5672/kirin?heartbeat=60'\n\n env.use_logger = True\n env.use_syslog = False\n"
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"text": "# coding=utf-8\n\n\nfrom fabfile import *\n"
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"text": "# fab_kirin local demo\n\nDeploy kirin locally\n\nThe goal is to showcase local deployment of Kirin using mostly the same mechanism\nthan for any other platform.\\\nPlease adapt to your needs (location of dependencies, navitia, passwords, params...).\nNote that `local_dockerized_deps` overloads `base` values.\n\n\n## Use\n\nFirst build a Kirin image from sources, tagged `kirin:local` as described in\nhttps://github.com/CanalTP/kirin#docker.\n\nProvide a Navitia instance, running on `localhost:5000` and reachable from within a container\n(maybe change flask's host to `0.0.0.0` to allow that).\n\nMake sure that user `navitia` has read/write access to RabbitMQ's vhost used by\nKraken (`/` in current conf)\n\nCreate a workspace for fab_kirin:\n\n```bash\nmkdir -p ~/fab_kirin_workspace # matching 'env.path' in local_dockerized_deps\nmkdir -p ~/fab_kirin_workspace/postgres-data # matching 'kirin_db' volume mounted in docker-compose_deps.yml\n```\n\nInstall python dependencies (using a virtualenv is recommended) with:\n\n```bash\ncd /path/to/fab_kirin\npip install -r requirements.txt -U\n```\n\nAs good security practice redis by default does not allow remote client to connect to the server.\nOne possibility is to configure Redis to accept remote connections:\n\nOpen redis configuration file /etc/redis/redis.conf\nThe following line should be uncommented\n```\nprotected-mode no\n```\n\nThe IP binding must be open for an access from remote client\n```\n#The following line should be commented\n#bind 127.0.0.1 ::1\n```\n\nCreate and run containers for dependencies:\n\n```bash\ndocker-compose -f demo/deps/docker-compose_deps.yml up -d\n```\n\nFor a first-time deployment:\n\n```bash\nPYTHONPATH=demo/conf fab use:local_dockerized_deps deploy:first_time=True\n# During process, you should be asked a ssh passphrase and then a session (sudo) password to localhost.\n```\n\nYou should now be able to request successfully (everything should be OK):\n\n```bash\ncurl localhost:54746/status\n```\n\nEnjoy, you can now create a contributor, and use it.\n"
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"text": "# coding=utf-8\nimport json\nfrom fabric.api import local, run, env, abort, settings, task, execute, roles\nfrom importlib import import_module\nimport abc\nimport requests\nimport time\nfrom retrying import Retrying\nfrom fabric.contrib.files import upload_template as _upload_template\nimport os\n\n\nclass DeploymentManager(object):\n @abc.abstractmethod\n def enable_node(self, node):\n pass\n\n @abc.abstractmethod\n def disable_node(self, node):\n pass\n\n\nclass NoSafeDeploymentManager(DeploymentManager):\n def enable_node(self, node):\n \"\"\" Null impl \"\"\"\n\n def disable_node(self, node):\n \"\"\" Null impl \"\"\"\n\n\ndef convert2bool(v):\n if isinstance(v, bool):\n return v\n return str(v).lower() in (\"yes\", \"y\", \"true\", \"t\", \"1\")\n\n\ndef manage_local():\n if not hasattr(env, \"is_local\"):\n env.is_local = False\n\n if env.is_local:\n env.run_func = local\n else:\n env.run_func = run\n\n\ndef check_node(query, headers=None):\n \"\"\"\n poll on state of execution until it gets a 'succeeded' status\n \"\"\"\n response = None\n try:\n if headers:\n response = requests.get(query, headers=headers, verify=False)\n else:\n response = requests.get(query, verify=False)\n print('waiting for enable node ...')\n except Exception as e:\n print(\"Error : {}\".format(e))\n\n # Return full response\n return response\n\n\nclass SafeDeploymentManager(DeploymentManager):\n # avoid the message output : InsecureRequestWarning: Unverified HTTPS request is being made.\n # Adding certificate verification is strongly advised.\n # See: https://urllib3.readthedocs.io/en/latest/security.html\n requests.packages.urllib3.disable_warnings()\n\n def __init__(self):\n super(SafeDeploymentManager, self).__init__()\n self.http_header = {'Content-Type': 'application/json',\n 'Accept': 'application/json',\n 'X-Rundeck-Auth-Token': env.rundeck_token}\n\n def enable_node(self, node):\n node = hostname2node(node)\n print(\"The {} node will be enabled\".format(node))\n\n args = {'argString': '-nodename {} -state enable'.format(node)}\n\n switch_power_on = requests.post(\"{}/api/18/job/{}/run\"\n .format(env.rundeck_url, env.rundeck_job, node),\n headers=self.http_header, data=json.dumps(args), verify=False)\n response = switch_power_on.json()\n\n request = '{}/api/18/execution/{}/state?{}'.format(env.rundeck_url, response['id'], env.rundeck_job)\n\n try:\n Retrying(stop_max_delay=60000, wait_fixed=500,\n retry_on_result=lambda resp: resp is None or\n resp.json().get('executionState') != 'SUCCEEDED')\\\n .call(check_node, request, self.http_header)\n except Exception as e:\n abort(\"The {} node cannot be enabled:\\n{}\".format(node, e))\n\n print(\"The {} node is enabled\".format(node))\n\n def disable_node(self, node):\n node = hostname2node(node)\n print(\"The {} node will be disabled\".format(node))\n\n args = {'argString': '-nodename {} -state disable'.format(node)}\n\n switch_power_off = requests.post(\"{}/api/18/job/{}/run\"\n .format(env.rundeck_url, env.rundeck_job, node),\n headers=self.http_header, data=json.dumps(args), verify=False)\n response = switch_power_off.json()\n\n request = '{}/api/18/execution/{}/state?{}'.format(env.rundeck_url, response['id'], env.rundeck_job)\n\n try:\n Retrying(stop_max_delay=60000, wait_fixed=500,\n retry_on_result=lambda resp: resp is None or\n resp.json().get('executionState') != 'SUCCEEDED')\\\n .call(check_node, request, self.http_header)\n except Exception as e:\n abort(\"The {} node cannot be disabled:\\n{}\".format(node, e))\n\n print(\"The {} node is disabled\".format(node))\n\n\ndef deploy_kirin_container_safe(server, node_manager, first_time=False):\n \"\"\" Restart kirin on a specific server,\n in a safe way if load balancers are available\n \"\"\"\n with settings(host_string=server):\n node_manager.disable_node(server)\n dc_filepath = '{path}/docker-compose_kirin.yml'.format(path=env.path)\n migrate(dc_filepath)\n restart(dc_filepath, first_time=first_time)\n test_deployment()\n node_manager.enable_node(server)\n\n\ndef deploy_kirin_beat_container_safe(server, first_time=False):\n \"\"\" Restart kirin on a specific server\n \"\"\"\n with settings(host_string=server):\n dc_filepath = '{path}/docker-compose_kirin-beat.yml'.format(path=env.path)\n restart(dc_filepath, first_time=first_time)\n\n\ndef pull_kirin_image():\n \"\"\"\n Retrieve new kirin image\n \"\"\"\n if not env.is_local:\n env.run_func('docker pull {image}:{new_tag}'.format(image=env.docker_image_kirin, new_tag=env.current_docker_tag))\n\n\ndef update_kirin_docker_tag():\n \"\"\"\n To tag the image, we pull the previous tag, tag it as our own and push it\n \"\"\"\n if not env.is_local:\n local('docker pull {image}:{prev_tag}'\n .format(image=env.docker_image_kirin, prev_tag=env.previous_docker_tag))\n local('docker tag {image}:{prev_tag} {image}:{new_tag}'\n .format(image=env.docker_image_kirin,\n prev_tag=env.previous_docker_tag,\n new_tag=env.current_docker_tag))\n local('docker push {image}:{new_tag}'\n .format(image=env.docker_image_kirin, new_tag=env.current_docker_tag))\n\n\n@task\ndef deploy(first_time=False):\n \"\"\"\n Deploy Kirin services\n \"\"\"\n first_time = convert2bool(first_time)\n manage_local()\n\n # Unless platform is empty, display status before\n if not first_time:\n print_status()\n update_kirin_docker_tag()\n execute(deploy_kirin, first_time=first_time)\n execute(deploy_kirin_beat, first_time=first_time)\n print_status()\n\n\ndef print_status():\n\n def check_and_print_response(query, header=None):\n response = check_node(query, header)\n if response is None or response.status_code != 200:\n return False\n else:\n print(\"\")\n print(\"curl {}\".format(query))\n print(response.json())\n print(\"\")\n return True\n\n api_root_global = env.kirin_host\n if env.is_local: # for a local deployment, port 80 is not mandatory\n api_root_global = '{}:{}'.format(env.kirin_host, env.kirin_host_port)\n request = 'http://{}/status'.format(api_root_global)\n try:\n Retrying(stop_max_delay=30000, wait_fixed=100,\n retry_on_result=lambda res: not res)\\\n .call(check_and_print_response, request)\n except Exception as e:\n abort(e)\n\n\n@task()\n@roles('kirin-beat')\ndef deploy_kirin_beat(first_time=False):\n \"\"\"\n Deploy Kirin beat\n :return:\n \"\"\"\n first_time = convert2bool(first_time)\n manage_local()\n\n if len(env.roledefs['kirin-beat']) != 1:\n abort('Error : Only one beat can exist, you provided kirin-beat role on {}'\n .format(env.roledefs['kirin-beat']))\n\n env.docker_nodename = hostname2node(env.host_string)\n upload_template('kirin.env', '{}'.format(env.path), context={'env': env})\n upload_template('docker-compose_kirin-beat.yml', '{}'.format(env.path), context={'env': env})\n\n # Deploy NewRelic\n if env.new_relic_key:\n upload_template('newrelic.ini', '{}'.format(env.path), context={'env': env})\n\n pull_kirin_image()\n\n deploy_kirin_beat_container_safe(env.host_string, first_time=first_time)\n\n # need to wait between both node execution because using same token\n time.sleep(5)\n\n\n@task()\n@roles('kirin')\ndef deploy_kirin(first_time=False):\n \"\"\"\n Deploy Kirin\n :return:\n \"\"\"\n first_time = convert2bool(first_time)\n manage_local()\n\n if env.use_load_balancer:\n node_manager = SafeDeploymentManager()\n else:\n node_manager = NoSafeDeploymentManager()\n\n env.docker_nodename = hostname2node(env.host_string)\n upload_template('kirin.env', '{}'.format(env.path), context={'env': env})\n upload_template('docker-compose_kirin.yml', '{}'.format(env.path), context={'env': env})\n\n # Deploy NewRelic\n if env.new_relic_key:\n upload_template('newrelic.ini', '{}'.format(env.path), context={'env': env})\n\n pull_kirin_image()\n\n deploy_kirin_container_safe(env.host_string, node_manager, first_time=first_time)\n\n # need to wait between both node execution because using same token\n time.sleep(5)\n\n\ndef remove_targeted_image(id_image):\n \"\"\" Remove an image \"\"\"\n with settings(warn_only=True):\n env.run_func('docker rmi {}'.format(id_image))\n\n\ndef remove_targeted_images():\n \"\"\" Remove several images \"\"\"\n images_to_remove = env.run_func(\"docker images | grep kirin | awk '{print $3}' && \"\n \"docker images -f dangling=true -q\")\n for image in images_to_remove.split('\\n'):\n remove_targeted_image(image.strip('\\r'))\n\n\ndef start_container(compose_file):\n \"\"\" Start targeted containers in daemon mode and restart them if crash \"\"\"\n env.run_func('docker-compose -f {} up --force-recreate -d'.format(compose_file))\n\n\ndef stop_container(compose_file):\n \"\"\" Stop targeted containers \"\"\"\n env.run_func('docker-compose -f {} stop'.format(compose_file))\n\n\ndef remove_container(compose_file):\n \"\"\" Remove targeted containers without asking confirmation and\n remove volumes associated with containers\n \"\"\"\n env.run_func('docker-compose -f {} rm -v -f'.format(compose_file))\n\n\ndef migrate(compose_file, revision='head'):\n env.run_func('docker-compose -f {} run --rm --no-deps kirin flask db upgrade {}'\n .format(compose_file, revision))\n\n\ndef restart(compose_file, first_time=False):\n \"\"\" Restart containers properly \"\"\"\n # Unless the platform is empty, previous container needs to be managed\n if not first_time:\n stop_container(compose_file)\n remove_container(compose_file)\n remove_targeted_images()\n start_container(compose_file)\n\n\ndef test_deployment():\n \"\"\" Verify api kirin is OK \"\"\"\n\n headers = {'Host': env.kirin_host}\n api_root_node = env.host_string\n if env.is_local: # for a local deployment, port 80 is not mandatory\n api_root_node = '{}:{}'.format(env.host_string, env.kirin_host_port)\n request = 'http://{}/status'.format(api_root_node)\n\n try:\n Retrying(stop_max_delay=30000, wait_fixed=100,\n retry_on_result=lambda resp: resp is None or resp.status_code != 200)\\\n .call(check_node, request, headers)\n except Exception as e:\n abort(e)\n print(\"{} is OK\".format(request))\n\n\n@task\ndef use(module_path, *args):\n pos = module_path.rfind(\".\")\n if pos == -1:\n path, f_name = module_path, module_path\n else:\n path, f_name = module_path[:pos], module_path[pos+1:]\n module = import_module(path)\n getattr(module, f_name)(*args)\n\n\ndef hostname2node(host):\n \"\"\" Return the node name equivalent to hostname\"\"\"\n # clean the host which can contain usernames from fabric\n # for example : [email protected] -> hostname\n suffix_to_remove = getattr(env, 'hostname_suffix_to_remove', '')\n host_only = host.replace(env.user + '@', '').replace(suffix_to_remove, '')\n\n return host_only\n\n\ndef upload_template(filename, destination, context=None, **kwargs):\n kwargs['use_jinja'] = True\n kwargs['template_dir'] = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'templates')\n kwargs['context'] = context\n kwargs['use_sudo'] = False\n kwargs['backup'] = False\n _upload_template(filename, destination, **kwargs)\n"
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"text": "FROM python:3.9.1-slim\n\n# add docker cli\nARG DOCKER_VERSION=\"5:19.03.13~3-0~debian-buster\"\nRUN BUILD_DEPENDENCIES=\"apt-transport-https curl gnupg-agent software-properties-common\" \\\n\t&& apt update \\\n\t&& apt install --yes ca-certificates ${BUILD_DEPENDENCIES} \\\n\t&& curl -fsSL https://download.docker.com/linux/debian/gpg | apt-key add \\\n\t&& add-apt-repository \"deb [arch=amd64] https://download.docker.com/linux/debian $(lsb_release -cs) stable\" \\\n\t&& apt update \\\n\t&& apt -y install docker-ce-cli=${DOCKER_VERSION} \\\n\t&& apt -y purge ${BUILD_DEPENDENCIES} \\\n\t&& apt autoremove --yes \\\n\t&& rm -rf /var/lib/apt/lists/*\n\n# install dependencies for kirin fabric\nCOPY requirements.txt /\nRUN pip install -r /requirements.txt -U\n\n# setup kirin fabric\nRUN mkdir /fabfile\nCOPY fabfile /fabfile\nWORKDIR /fabfile\nENV PYTHONPYCACHEPREFIX=/tmp\n"
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"text": "from fabric.api import *\n\nenv.docker_image_kirin = 'navitia/kirin'\n\nenv.kirin_host = 'localhost' # global host\nenv.kirin_host_port = '9090'\nenv.kirin_docker_port = '9090'\n\nenv.path = '~/fab_kirin_workspace' # directory must be available on host\nenv.is_local = False\n\nenv.new_relic_key = None\n\nenv.postgres_database = 'localhost' # Postgres, must be reachable from container\nenv.postgres_port = 5432\nenv.user_kirin_postgres = 'kirin'\nenv.pwd_kirin_postgres = 'kirin'\nenv.kirin_postgres_database = 'kirin'\n\nenv.rundeck_token = None\n\nenv.rabbitmq_url = 'localhost' # Navitia RabbitMQ, must be reachable from container\nenv.rabbitmq_port = 5672\nenv.user_rabbitmq = 'navitia'\nenv.pwd_rabbitmq = 'navitia'\nenv.rabbitmq_vhost = 'navitia' # vhost to communicate with Navitia (Kraken)\nenv.heartbeat_rabbitmq = 180\n\n# Kirin's Celery RabbitMQ, must be reachable from container\n# Note: The user `guest` is not authorized to connect by remote. Create 'kirin' user with access to 'kirin' vhost\nenv.celery_broker_url = 'pyamqp://kirin:<pass>@localhost:5672/kirin?heartbeat=60'\nenv.celery_concurrency = 3\n\nenv.use_logger = False\nenv.use_syslog = True\nenv.use_json = True\nenv.use_complete_gtfsrt = True\n\nenv.redis_host = 'localhost' # Redis, must be reachable from container\nenv.redis_port = 6379\nenv.redis_password = '' # No password is needed by default\n\nenv.cots_par_iv_circuit_breaker_max_fail = 4\nenv.cots_par_iv_circuit_breaker_timeout_s = 60\nenv.cots_par_iv_timeout_token = 30*60\nenv.cots_par_iv_cache_timeout = 60*60\nenv.cots_par_iv_request_timeout = 2\nenv.gtfs_rt_timeout = 5\n"
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"text": "# fab_kirin\nKirin's deployment mechanisms\n\n## Invocation\n\n### from source code with a working setup\n\nFor a regular deployment (on a platform with Kirin already running):\n```bash\nPYTHONPATH=/path/to/kirin_deployment_conf/ fab use:<platform_file_name> deploy\n```\n\nFor a first-time deployment on an empty platform:\n```bash\nPYTHONPATH=/path/to/kirin_deployment_conf/ fab use:<platform_file_name> deploy:first_time=True\n```\n\n### using Docker image\n\nSame principle as above for `fabric` usage, but this is an example to focus on specific options to use for Docker:\n\n``` bash\ndocker run --volume \"/var/run/docker.sock:/var/run/docker.sock\" --volume \"/path/to/folder/containing/kirin_conf/<platform_file_name>.py:/kirin_conf\" --env \"PYTHONPATH=/kirin_conf\" navitia/fab_kirin fab use:<platform_file_name> deploy\n```\n\nThe binding of the Docker socket is required since some Docker commands are run by `fabric`.\n\nThe `kirin_conf` volume is used to share `<platform_file_name>.py`. The `PYTHONPATH` environment variable must be set to the same value than the target of the shared volume to make the `<platform_file_name>.py` reachable by `fabric`.\n\n## Usage\n\n### Demo\n\nA demo for local deployment is available, please see [instructions](demo/README.md).\n\n### deployment files\n\nFile should look like:\n\n```python\nfrom fabric.api import *\nimport common\n\n\ndef prod():\n env.name = 'prod'\n\n env.roledefs = {\n 'kirin': ['<user>@<kirin_platform1>', '<user>@<kirin_platform2>'],\n 'kirin-beat': ['<user>@<kirin-beat_platform>'] # only one beat can exist\n }\n\n env.kirin_host = '<kirin_host_name>'\n\n env.previous_docker_tag = '<prev_tag>'\n env.current_docker_tag = '<prod_tag>'\n\n env.use_load_balancer = True # or False\n\n env.postgres_database = '<SQL_db_platform>'\n env.navitia_url = 'https://api.navitia.io'\n env.rabbitmq_url = '<rabbitmq_platform>' # rabbitmq where disruptions are published for navitia\n\n env.celery_broker_url = 'pyamqp://<user>:<mdp>@<platform>:<port>/<vhost>?heartbeat=60' # beware to open access to vhost for user in rabbitmq (for beat-worker communication)\n\n env.use_logger = True\n\n env.cots_par_iv_api_key = '<cots-api-key>'\n env.cots_par_iv_motif_resource_server = '<ParIV-motif-url>'\n env.cots_par_iv_token_server = '<ParIV-token-url>'\n env.cots_par_iv_client_id = '<ParIV-username'\n env.cots_par_iv_client_secret = '<ParIV-password>'\n\n env.cots_par_iv_circuit_breaker_max_fail = 4\n env.cots_par_iv_circuit_breaker_timeout_s = 60\n env.cots_par_iv_timeout_token = 30*60\n env.cots_par_iv_cache_timeout = 60*60\n env.cots_par_iv_request_timeout = 2\n```\n"
}
] | 8 |
Dave-Guangyao-Li/music
|
https://github.com/Dave-Guangyao-Li/music
|
98db3adb1346d334c3005af5a2538ea9e39e071b
|
03c263b11fff4e512d6f97aba1933949b73b19a7
|
5aa178be867748acbbb387b9bb99c32dd35c837d
|
refs/heads/master
| 2022-09-18T19:42:19.857812 | 2020-05-23T14:56:56 | 2020-05-23T14:56:56 | null | 0 | 0 | null | null | null | null | null |
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"text": "from django.db import models\nfrom django.contrib.auth.models import AbstractUser\nfrom index.models import Song\nclass MyUser(AbstractUser):\n qq = models.CharField('QQ号码', max_length=20)\n weChat = models.CharField('微信账号', max_length=20)\n mobile = models.CharField('手机号码', max_length=11, unique=True)\n liked_song = models.ManyToManyField(Song, null=True, blank=True, verbose_name='已收藏歌曲') #多对多模型，存储用户与收藏歌曲\n # 设置返回值\n def __str__(self):\n return self.username\n\n\n\n# 定义一个邮件数据模型, 添加需要的字段\nfrom datetime import datetime\n# 邮箱验证类\nclass EmailVeriRecord(models.Model, BaseException):\n # 验证码\n code = models.CharField(max_length=20, verbose_name='验证码')\n # 用户邮箱\n email = models.EmailField(max_length=50, verbose_name='用户邮箱')\n # datetime.now 在创建对象的时候, 再执行函数获取时间\n # 发送时间\n send_time = models.DateTimeField(default=datetime.now, verbose_name='发送时间', null=True, blank=True)\n # 过期时间\n exprie_time = models.DateTimeField(null=True)\n # 邮件类型\n # choices 枚举选项, 必须从指定的项中选择一个\n email_type = models.CharField(choices=(('register', '注册邮件'), ('forget', '找回密码')), max_length=10)\n class Meta:\n verbose_name = '邮件验证码'\n verbose_name_plural = verbose_name"
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"text": "# Generated by Django 2.0 on 2020-05-02 03:26\n\nimport datetime\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('user', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='EmailVeriRecord',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('code', models.CharField(max_length=20, verbose_name='验证码')),\n ('email', models.EmailField(max_length=50, verbose_name='用户邮箱')),\n ('send_time', models.DateTimeField(blank=True, default=datetime.datetime.now, null=True, verbose_name='发送时间')),\n ('exprie_time', models.DateTimeField(null=True)),\n ('email_type', models.CharField(choices=[('register', '注册邮件'), ('forget', '找回密码')], max_length=10)),\n ],\n options={\n 'verbose_name': '邮件验证码',\n 'verbose_name_plural': '邮件验证码',\n },\n ),\n ]\n"
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"text": "from django.shortcuts import render\nfrom index.models import *\ndef rankingView(request):\n # 搜索歌曲\n search_song = Dynamic.objects.select_related('song').order_by('-dynamic_search').all()[:4]\n # 歌曲分类列表\n All_list_type = Song.objects.values('song_type').distinct()\n # 歌曲标签列表\n All_list_label = Song.objects.values('label_id').distinct()\n # 歌曲语种列表\n All_list_language = Song.objects.values('song_languages').distinct()\n # 歌曲列表信息\n song_type = request.GET.get('type', '')\n song_label = request.GET.get('label', '')\n song_language = request.GET.get('language','')\n if song_type:\n song_info = Dynamic.objects.select_related('song').filter(song__song_type=song_type).order_by('-dynamic_plays').all()[:10]\n elif song_label:\n song_info = Song.objects.select_related('label').filter(label__label_id=int(song_label)).order_by('-dynamic__dynamic_plays').all()[:10]\n elif song_language:\n song_info = Dynamic.objects.select_related('song').filter(song__song_languages=song_language).order_by('-dynamic_plays').all()[:10]\n else:\n song_info = Dynamic.objects.select_related('song').order_by('-dynamic_plays').all()[:10]\n return render(request, 'ranking.html', locals())\n\n\n\n# 通用视图\nfrom django.views.generic import ListView\nclass RankingList(ListView):\n # context_object_name设置Html模版的某一个变量名称\n context_object_name = 'song_info'\n # 设定模版文件\n template_name = 'ranking.html'\n # 查询变量song_info的数据\n def get_queryset(self):\n # 获取请求参数\n song_type = self.request.GET.get('type', '')\n song_language = self.request.GET.get('language', '')\n if song_type:\n song_info = Dynamic.objects.select_related('song').filter(song__song_type=song_type).order_by('-dynamic_plays').all()[:10]\n elif song_language:\n song_info = Dynamic.objects.select_related('song').filter(song__song_languages=song_language).order_by('-dynamic_plays').all()[:10]\n else:\n song_info = Dynamic.objects.select_related('song').order_by('-dynamic_plays').all()[:10]\n return song_info\n\n # 添加其他变量\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n # 搜索歌曲\n context['search_song'] = Dynamic.objects.select_related('song').order_by('-dynamic_search').all()[:4]\n # 所有歌曲分类\n context['All_list_type'] = Song.objects.values('song_type').distinct()\n # 所有歌曲语种\n context['All_list_language'] = Song.objects.values('song_languages').distinct()\n return context"
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"text": "# from django.contrib.auth.forms import UserCreationForm\n# from .models import MyUser\n# from django import forms\n# from captcha.fields import CaptchaField\n#\n# # 定义MyUser的数据表单，用于用户注册\n# class MyUserCreationForm(UserCreationForm):\n# # 重写初始化函数，设置自定义字段password1和password2的样式和属性\n# def __init__(self, *args, **kwargs):\n# super(MyUserCreationForm, self).__init__(*args, **kwargs)\n# self.fields['password1'].widget = forms.PasswordInput(attrs={'class': 'txt tabInput', 'placeholder':'密码,4-16位数字/字母/特殊符号(空格除外)'})\n# self.fields['password2'].widget = forms.PasswordInput(attrs={'class': 'txt tabInput', 'placeholder':'重复密码'})\n# captcha = CaptchaField()\n# class Meta(UserCreationForm.Meta):\n# model = MyUser\n# # 在注册界面添加模型字段：手机号码和密码\n# fields = UserCreationForm.Meta.fields + ('mobile',)\n# # 设置模型字段的样式和属性\n# widgets = {\n# 'mobile': forms.widgets.TextInput(attrs={'class': 'txt tabInput', 'placeholder':'手机号'}),\n# 'username': forms.widgets.TextInput(attrs={'class': 'txt tabInput', 'placeholder':'用户名'}),\n# }\n\n\nfrom django import forms\n# 引入验证码的CaptchaField\nfrom captcha.fields import CaptchaField\n\n# 表单\nclass UserForm(forms.Form):\n username = forms.CharField(label=\"用户名\", max_length=128, widget=forms.TextInput(attrs={'class': 'form-control'}))\n # widget=forms.PasswordInput用于指定该字段在form表单里表现为<input type='password' />，也就是密码输入框。\n password = forms.CharField(label=\"密码\", max_length=256, widget=forms.PasswordInput(attrs={'class': 'form-control'}))\n captcha = CaptchaField(label='验证码')\n # # 用户名\n # username = forms.CharField(required=True, min_length=4, error_messages={'invalid': '用户名长度不能少于四个字符'})\n # # 邮箱\n # email = forms.EmailField(required=True, error_messages={'invalid': '请填写正确的邮箱地址'})\n # # 密码\n # password = forms.CharField(required=True, min_length=6, error_messages={'invalid': '密码不能少于6位'})\n # rePassword = forms.CharField(required=True, min_length=6, error_messages={'invalid': '密码不能少于6位'})\n # # 验证码\n # captcha = CaptchaField(required=True, error_messages={'invalid': '验证码错误'})\n\nclass RegisterForm(forms.Form):\n username = forms.CharField(label=\"用户名\", max_length=128, widget=forms.TextInput(attrs={'class': 'form-control'}))\n password1 = forms.CharField(label=\"密码\", max_length=256, widget=forms.PasswordInput(attrs={'class': 'form-control'}))\n password2 = forms.CharField(label=\"确认密码\", max_length=256,\n widget=forms.PasswordInput(attrs={'class': 'form-control'}))\n email = forms.EmailField(label=\"邮箱地址\", widget=forms.EmailInput(attrs={'class': 'form-control'}))\n qq = forms.CharField(label=\"QQ号\", max_length=20, widget=forms.TextInput(attrs={'class': 'form-control'}))\n weChat = forms.CharField(label=\"微信号\", max_length=20, widget=forms.TextInput(attrs={'class': 'form-control'}))\n mobile = forms.CharField(label=\"电话号\", max_length=11, widget=forms.TextInput(attrs={'class': 'form-control'}))\n captcha = CaptchaField(label='验证码')"
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"text": "# music\n A music website implemented with Python and Django\n"
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"text": "import itertools\nimport math\n\nfrom django.shortcuts import render, redirect\nfrom django.http import StreamingHttpResponse, HttpResponseRedirect\nfrom index.models import *\nfrom user.models import MyUser\nfrom django.contrib.auth import login, logout\nfrom django.contrib.auth.hashers import check_password\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.contrib import messages\n\n\n\n# 歌曲播放页面\ndef playView(request, song_id):\n # 热搜歌曲\n search_song = Dynamic.objects.select_related('song').order_by('-dynamic_search').all()[:6]\n # 歌曲信息\n song_info = Song.objects.get(song_id=int(song_id))\n # 播放列表\n play_list = request.session.get('play_list', [])\n song_exist = False\n if play_list:\n for i in play_list:\n if int(song_id) == i['song_id']:\n song_exist = True\n if song_exist == False:\n play_list.append({'song_id': int(song_id), 'song_singer': song_info.song_singer, 'song_name': song_info.song_name, 'song_time': song_info.song_time})\n request.session['play_list'] = play_list #存入session\n # 歌词\n if song_info.song_lyrics != '暂无歌词':\n f = open('static/songLyric/' + song_info.song_lyrics, 'r', encoding='utf-8')\n song_lyrics = f.read()\n f.close()\n\n # 添加播放次数\n # 扩展功能：可使用session实现每天只添加一次播放次数\n dynamic_info = Dynamic.objects.filter(song_id=int(song_id)).first()\n # 判断歌曲动态信息是否存在，存在就在原来基础上加1\n if dynamic_info:\n dynamic_info.dynamic_plays += 1\n dynamic_info.save()\n # 动态信息不存在则创建新的动态信息\n else:\n dynamic_info = Dynamic(dynamic_plays=1, dynamic_search=0, dynamic_down=0, dynamic_like=0, song_id=song_id)\n dynamic_info.save()\n\n return render(request, 'play.html', locals())\n\n\n\n@login_required(login_url='/user/login.html')\ndef recommendView(request, song_id):\n # 热搜歌曲\n search_song = Dynamic.objects.select_related('song').order_by('-dynamic_search').all()[:6]\n # 歌曲信息\n song_info = Song.objects.get(song_id=int(song_id))\n # 播放列表\n play_list = request.session.get('play_list', [])\n song_exist = False\n if play_list:\n for i in play_list:\n if int(song_id) == i['song_id']:\n song_exist = True\n if song_exist == False:\n play_list.append(\n {'song_id': int(song_id), 'song_singer': song_info.song_singer, 'song_name': song_info.song_name,\n 'song_time': song_info.song_time})\n request.session['play_list'] = play_list # 存入session\n # 歌词\n if song_info.song_lyrics != '暂无歌词':\n f = open('static/songLyric/' + song_info.song_lyrics, 'r', encoding='utf-8')\n song_lyrics = f.read()\n f.close()\n # 基于内容统计的推荐\n # 推荐相关歌曲 推荐同一类型，同一个歌手，同一语种的歌曲，以及混合综合推荐，查询结果合并\n song_type = Song.objects.values('song_type').get(song_id=int(song_id))['song_type']\n song_singer = Song.objects.values('song_singer').get(song_id=int(song_id))['song_singer']\n song_languages = Song.objects.values('song_languages').get(song_id=int(song_id))['song_languages']\n # 结果根据播放量降序排序\n song_relevant1 = Dynamic.objects.select_related('song').filter(song__song_type=song_type).order_by('-dynamic_plays').all()\n song_relevant2 = Dynamic.objects.select_related('song').filter(song__song_singer=song_singer).order_by('-dynamic_plays').all()\n song_relevant3 = Dynamic.objects.select_related('song').filter(song__song_languages=song_languages ).order_by('-dynamic_plays').all()\n\n # 同曲风推荐的结果存储进song_relevant_type中\n song_relevant_type = []\n # 存入列表 歌曲推荐只需显示名字，歌手名，专辑封面图即可\n for result in song_relevant1:\n if result.song.song_id != int(song_info.song_id): # 不存入当前正在播放的歌曲信息\n song_relevant_type.append({'song_id': int(result.song.song_id), 'song_singer': result.song.song_singer,\n 'song_name': result.song.song_name, 'song_img': result.song.song_img})\n request.session['song_relevant_type'] = song_relevant_type # 存入session\n\n # 同歌手推荐的结果存储进song_relevant_singer中\n song_relevant_singer = []\n # 存入列表 歌曲推荐只需显示名字，歌手名，专辑封面图即可,不存入当前正在播放的歌曲信息\n for result in song_relevant2:\n if result.song.song_id != int(song_info.song_id): # 不存入当前正在播放的歌曲信息\n song_relevant_singer.append({'song_id': int(result.song.song_id), 'song_singer': result.song.song_singer,\n 'song_name': result.song.song_name, 'song_img': result.song.song_img})\n request.session['song_relevant_singer'] = song_relevant_singer # 存入session\n\n # 同语种推荐的结果存储进song_relevant_languages中\n song_relevant_languages = []\n # 存入列表 歌曲推荐只需显示名字，歌手名，专辑封面图即可,不存入当前正在播放的歌曲信息\n for result in song_relevant3:\n if result.song.song_id != int(song_info.song_id): # 不存入当前正在播放的歌曲信息\n song_relevant_languages.append({'song_id': int(result.song.song_id), 'song_singer': result.song.song_singer,\n 'song_name': result.song.song_name, 'song_img': result.song.song_img})\n request.session['song_relevant_languages'] = song_relevant_languages # 存入session\n # 根据song_id查找歌曲信息\n song_info = Song.objects.get(song_id=int(song_id))\n\n\n\n\n # 基于用户的系统过滤推荐cfUser实现\n '''目前一个缺陷就是：如果是新注册的用户，没有收藏歌曲，或某一个用户收藏的歌曲与其他任何用户都没有交集\n 都会导致在计算W时出现除以零的异常，导致页面出错。目前通过设置布尔型变量解决，交集为0直接此用户相似度直接赋值为0，防止异常\n '''\n\n # train用来存储用户id:对应收藏歌曲id列表，字典存储\n train = {}\n # 获取所有用户的id\n all_user_list = []\n all_user = MyUser.objects.all().values() # 获取所有用户信息\n for i in all_user: # 存储所有用户的Id\n all_user_list.append(int(i[\"id\"]))\n # 分别查询出用户喜欢的歌曲\n for uid in all_user_list:\n temp_current_user = MyUser.objects.get(id=int(uid))\n # 查出关联的所有喜欢的歌曲信息\n liked_song_info_result = temp_current_user.liked_song.all()\n liked_song_info = [] # 收藏歌曲列表\n for result in liked_song_info_result:\n liked_song_info.append(int(result.song_id))\n train[uid] = liked_song_info\n # 得到train数据集包含用户和对应喜爱歌曲信息：{1: [2, 3, 4, 5, 6], 2: [4, 5, 6, 7, 8], 7: [1, 2, 3, 4, 5], 8: [5, 6, 7, 8, 9], 9: [6, 7, 8, 9, 10], 10: [7, 8, 9, 10, 11]}\n\n # 1. 计算相似度\n W = dict() # 存储相似度\n current_user = MyUser.objects.get(username=request.user.username)\n current_user_id = current_user.id # 获取当前用户id\n # exit_flag = False # 当W为0直接退出循环，防止发生除以零的异常\n for u in train.keys(): # 遍历所有用户列表，跳过自身\n # for v in train.keys():\n if u == current_user_id:\n continue\n W[u] = len(set(train[u]) & set(train[current_user_id])) # 计算交集\n if W[u] == 0: #W为0直接赋值为0，进入下一次循环，防止发生除以零的异常\n # exit_flag = True\n continue\n else:\n W[u] /= math.sqrt(len(train[u]) * len(train[current_user_id]) * 1.0)\n\n # 2.推荐和用户最相似的3个用户,将推荐用户感兴趣的推荐给目标用户，去除相同项\n rank = dict() # 存储歌曲相关度的排序列表,如r[2]存储与歌曲id为2的歌曲的兴趣度\n interacted_items = train[current_user_id] # 当前用户的收藏列表\n top_k_result = sorted(W.items(), key=lambda item: item[1], reverse=True)[0:3] # 倒序从大到小输出相似度前三的用户及其相似度\n for r in top_k_result:\n # print(r[1])\n for i in train[r[0]]: # 计算当前用户对歌曲i的可能兴趣度\n if i in interacted_items: # 排除当前用户已经收藏过的歌曲\n continue\n rank[i] = 0\n rank[i] += r[1] * 1.0\n rank = sorted(rank.items(), key=lambda item: item[1], reverse=True)[0:6] # 按兴趣度大小从大到小倒序排序，取前六个显示,注意排序后的字典中键值对变成了元组的形式存储在列表中\n # 基于用户的协同过滤推荐的结果存储进song_relevant_overall中\n song_cfrelevant_overall = []\n # 将rank中的歌曲信息存入列表 歌曲推荐只需显示名字，歌手名，专辑封面图即可\n no_recommend_relevant = False\n for song_relevant_info in rank:\n cf_song_info = Song.objects.get(song_id=song_relevant_info[0])\n song_cfrelevant_overall.append(\n {'song_id': int(cf_song_info.song_id), 'song_singer': cf_song_info.song_singer,\n 'song_name': cf_song_info.song_name, 'song_img': cf_song_info.song_img, 'relevant_measure': song_relevant_info[1]})\n # 如果这个相关歌曲推荐表所有歌曲兴趣推测度都为0，则no_recommend_relevant置为True,用于前端页面显示\n test_list = []\n overall = 0.0\n for test in song_cfrelevant_overall: # 测试此用户是否有与其音乐兴趣相似的推荐，如果没有需要在前端页面另外提示\n test_list.append(test['relevant_measure'])\n overall = sum(test_list) # 如果每一个歌曲的兴趣度都是0，总和也即为0\n if overall == 0.0:\n no_recommend_relevant = True\n request.session['song_relevant_overall'] = song_cfrelevant_overall # 存入session\n messages.success(request, \"已为您显示个性化推荐！\")\n return render(request, 'play.html', locals())\n\n\n\n\n\n# 歌曲下载\n# 设置用户登录限制\n@login_required(login_url='/user/login.html')\ndef downloadView(request, song_id):\n # 根据song_id查找歌曲信息\n song_info = Song.objects.get(song_id=int(song_id))\n # 添加下载次数\n dynamic_info = Dynamic.objects.filter(song_id=int(song_id)).first()\n # 判断歌曲动态信息是否存在，存在就在原来基础上加1\n if dynamic_info:\n dynamic_info.dynamic_down += 1\n dynamic_info.save()\n # 动态信息不存在则创建新的动态信息\n else:\n dynamic_info = Dynamic(dynamic_plays=0, dynamic_search=0, dynamic_like=0, dynamic_down=1, song_id=song_id)\n dynamic_info.save()\n # 读取文件内容\n file = 'static/songFile/' + song_info.song_file\n def file_iterator(file, chunk_size=512):\n with open(file, 'rb') as f:\n while True:\n c = f.read(chunk_size)\n if c:\n yield c\n else:\n break\n # 将文件内容写入StreamingHttpResponse对象，并以字节流方式返回给用户，实现文件下载\n filename = str(song_id) + '.mp3'\n response = StreamingHttpResponse(file_iterator(file))\n response['Content-Type'] = 'application/octet-stream'\n response['Content-Disposition'] = 'attachment; filename=\"%s\"' % (filename)\n return response\n\n\n\n# 歌曲收藏\n# 设置用户登录限制\n@login_required(login_url='/user/login.html')\ndef likeView(request, song_id):\n # 根据song_id查找歌曲信息\n song_info = Song.objects.get(song_id=int(song_id))\n # 为当前用户添加收藏曲目信息\n current_user = MyUser.objects.get(username=request.user.username)\n # 在多对多模型表中添加对应的收藏数据\n current_user.liked_song.add(song_info)\n # 弹出消息提示收藏成功\n messages.success(request, \"歌曲收藏成功！\")\n # 添加收藏次数\n dynamic_info = Dynamic.objects.filter(song_id=int(song_id)).first()\n # 判断歌曲动态信息是否存在，存在就在原来基础上加1\n if dynamic_info:\n dynamic_info.dynamic_like += 1\n dynamic_info.save()\n # 动态信息不存在则创建新的动态信息\n else:\n dynamic_info = Dynamic(dynamic_plays=0, dynamic_search=0, dynamic_like=1, dynamic_down=0, song_id=song_id)\n dynamic_info.save()\n return HttpResponseRedirect(\"/play/\" + str(song_info.song_id) + \".html\")\n"
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"text": "from django.urls import path\nfrom . import views\nurlpatterns = [\n # 用户的登录\n path('login.html', views.loginView, name='login'),\n # 用户的注册\n path('register.html', views.registerView, name='register'),\n # 用户中心\n path('home/<int:page>.html', views.homeView, name='home'),\n # 退出用户登录\n path('logout.html', views.logoutView, name='logout'),\n # path('custom.html', views.customView, name='custom'),\n # 验证码验证API接口\n path('ajax_val', views.ajax_val, name='ajax_val'),\n # 取消收藏歌曲\n path('unlike/<int:song_id>.html', views.unlikeView, name='unlike'),\n # 用户查看自己的注册信息\n path('userinfo.html', views.userinfoView, name='userinfo'),\n # 用户编辑自己的个人信息\n path('edit.html', views.editView, name='edit'),\n # # 用户重置密码\n # path('resetpsw.html', views.resetpswView, name='resetpsw')\n]\n"
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"path": "/play/urls.py",
"repo_name": "Dave-Guangyao-Li/music",
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"text": "from django.urls import path\nfrom . import views\nurlpatterns = [\n # 歌曲播放页面\n path('<int:song_id>.html', views.playView, name='play'),\n # 歌曲下载\n path('download/<int:song_id>.html', views.downloadView, name='download'),\n # 歌曲收藏\n path('like/<int:song_id>.html', views.likeView, name='like'),\n # 歌曲个性化推荐（协同过滤）\n path('recommend/<int:song_id>.html', views.recommendView, name='recommend'),\n]"
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"text": "import hashlib\n\nfrom django.contrib.auth.models import User\nfrom django.contrib import messages\n\n\nfrom django.shortcuts import render, redirect\nfrom index.models import *\nfrom user.models import *\nfrom django.db.models import Q\nfrom django.contrib.auth import login, logout, authenticate\nfrom django.contrib.auth.hashers import check_password\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n# from .form import CaptchaTestForm\n\n\n\n\nfrom django.shortcuts import render, HttpResponse\nfrom user.form import RegisterForm\nfrom user.form import UserForm\nfrom user.models import MyUser\n# make_password引入密码加密的函数\nfrom django.contrib.auth.hashers import make_password\n# Create your views here.\nfrom utils.email_util import send_email\n\n# 用户注册与登录\ndef loginView(request):\n # 搜索歌曲\n search_song = Dynamic.objects.select_related('song').order_by('-dynamic_search').all()[:4]\n # if request.session.get('is_login', None):\n # return redirect(\"/user/home/1.html\")\n if request.method == \"POST\":\n login_form = UserForm(request.POST)\n message = \"请检查填写的内容！\"\n if login_form.is_valid():\n # 校验通过后获取数据\n username = login_form.cleaned_data['username']\n password = login_form.cleaned_data['password']\n try:\n user = MyUser.objects.get(username=username)\n if check_password(password, user.password): # 利用check_password函数进行密码校验，因为数据库中密码已加密，不能直接相比\n # 将信息存入session\n login(request, user) # 登录\n request.session['is_login'] = True\n request.session['user_id'] = user.id\n request.session['user_name'] = user.username\n # 弹出消息提示成功\n messages.success(request, \"登录成功，跳转到用户中心...\")\n return redirect('/user/home/1.html', messages)\n else:\n message = \"密码不正确！\"\n except:\n message = \"用户不存在！\"\n return render(request, 'login.html', locals())\n # 非POST请求返回给用户一个空表\n login_form = UserForm()\n return render(request, 'login.html', locals())\n\n # # 表单提交\n # if request.method == 'POST':\n # form = RegisterFrom()\n # # user = MyUserCreationForm()\n # # # form = CaptchaTestForm(request.POST)\n # # # 判断表单提交是用户登录还是用户注册\n # # 用户登录\n # if request.POST.get('loginUser', ''):\n # loginUser = request.POST.get('loginUser', '')\n # password = request.POST.get('password', '')\n # if MyUser.objects.filter(Q(email=loginUser) | Q(username=loginUser)):\n # user = MyUser.objects.filter(Q(email=loginUser) | Q(username=loginUser)).first()\n # if check_password(password, user.password):\n # login(request, user)\n # return redirect('/user/home/1.html')\n # else:\n # tips = '密码错误'\n # else:\n # tips = '用户不存在'\n # # # 用户注册\n # # else:\n # # user = MyUserCreationForm(request.POST)\n # # if user.is_valid():\n # # user.save()\n # # tips = '注册成功'\n # # else:\n # # if user.errors.get('username', ''):\n # # tips = user.errors.get('username', '注册失败')\n # # else:\n # # tips = user.errors.get('mobile', '注册失败')\n # # else:\n # # user = MyUserCreationForm()\n # return render(request, 'login.html', locals())\n\ndef registerView(request):\n # 搜索歌曲\n search_song = Dynamic.objects.select_related('song').order_by('-dynamic_search').all()[:4]\n # if request.session.get('is_login', None):\n # # 登录状态不允许注册。你可以修改这条原则！\n # return redirect(\"/index/\")\n if request.method == \"POST\":\n register_form = RegisterForm(request.POST)\n message = \"请检查填写的内容！\"\n if register_form.is_valid(): # 获取数据\n username = register_form.cleaned_data['username']\n password1 = register_form.cleaned_data['password1']\n password2 = register_form.cleaned_data['password2']\n email = register_form.cleaned_data['email']\n qq = register_form.cleaned_data['qq']\n weChat = register_form.cleaned_data['weChat']\n mobile = register_form.cleaned_data['mobile']\n if password1 != password2: # 判断两次密码是否相同\n message = \"两次输入的密码不同！\"\n return render(request, 'register.html', locals())\n else:\n same_name_user = MyUser.objects.filter(username=username)\n if same_name_user: # 用户名唯一\n message = '用户已经存在，请重新选择用户名！'\n return render(request, 'register.html', locals())\n same_email_user = MyUser.objects.filter(email=email)\n if same_email_user: # 邮箱地址唯一\n message = '该邮箱地址已被注册，请使用别的邮箱！'\n return render(request, 'register.html', locals())\n\n # 当一切都OK的情况下，创建新用户\n new_user = MyUser.objects.create()\n new_user.username = username\n new_user.password = make_password(password1) # 使用加密密码\n new_user.email = email\n new_user.qq = qq\n new_user.weChat = weChat\n new_user.mobile = mobile\n # # 账户状态 未激活\n # new_user.is_active = 0\n new_user.save()\n\n # # 发送注册邮件\n # if send_email(email, send_type='app'):\n # # 注册邮件发送成功\n # return HttpResponse('恭喜您注册成功, 激活邮件已发送至您的邮箱, 请登录后进行激活操作')\n # else:\n # return HttpResponse('恭喜您注册成功, 激活邮件发送')\n # # return redirect('login.html') # 自动跳转到登录页面\n # else:\n # # 返回form表单\n # # 返回注册页面, 信息回填, 显示错误信息\n # return render(request, 'register.html', locals())\n\n # 弹出消息提示注册成功\n messages.success(request, \"恭喜您注册成功，跳转到登录页面...\")\n # 重定向到登陆界面\n return redirect('login.html', messages)\n # return render(request, 'login.html', locals())\n register_form = RegisterForm()\n return render(request, 'register.html', locals())\n # if request.method == 'GET':\n # # 构建form对象, 为了显示验证码\n # form = RegisterFrom()\n # return render(request, 'register.html', {'form': form})\n # if request.method == 'POST':\n # # 验证form提交的数据\n # form = RegisterFrom(request.POST)\n # # 判断是否合法\n # if form.is_valid():\n # # 判断密码是否一致\n # username = form.cleaned_data['username']\n # email = form.cleaned_data['email']\n # pwd = form.cleaned_data['password']\n # rePwd = form.cleaned_data['rePassword']\n # # 两次密码不一致\n # if pwd != rePwd:\n # # 返回注册页面和错误信息\n # # error = '两次密码不一致!'\n # return render(request, 'register.html', {'form': form, 'error': '两次密码不一致!'})\n # # 判断用户是否存在\n # # 根据email查找用户, 如果用户存在, 返回错误信息\n # if MyUser.objects.filter(email=email):\n # # 用户已存在\n # # errMsg = \"该用户已存在!\"\n # return render(request, 'register.html', {'form': form, 'errMsg': '该用户已存在!'})\n # # 创建用户\n # user = MyUser(username=username, email=email, password=make_password(pwd))\n # # 对用户传递过来的密码进行加密, 将加密之后的数据进行保存\n # # 账户状态 未激活\n # user.is_active = 0\n # # # 保存为邮箱地址, 可以使用邮箱登录后台\n # # user.username = email\n # # 保存用户\n # user.save()\n # # 发送注册邮件\n # if send_email(email, send_type='app'):\n # # 注册邮件发送成功\n # return HttpResponse('恭喜您注册成功, 激活邮件已发送至您的邮箱, 请登录后进行激活操作')\n # else:\n # return HttpResponse('恭喜您注册成功, 激活邮件发送')\n # else:\n # # 返回form表单\n # # 返回注册页面, 信息回填, 显示错误信息\n # return render(request, 'register.html', {'form': form})\n # # return render(request, 'register.html', locals())\n\n\n\n\n\n# 用户中心\n# 设置用户登录限制\n@login_required(login_url='/user/login.html')\ndef homeView(request, page):\n # 热搜歌曲\n search_song = Dynamic.objects.select_related('song').order_by('-dynamic_search').all()[:4]\n # 分页功能1\n song_info = request.session.get('play_list', [])\n paginator = Paginator(song_info, 4)\n try:\n contacts = paginator.page(page)\n except PageNotAnInteger:\n contacts = paginator.page(1)\n except EmptyPage:\n contacts = paginator.page(paginator.num_pages)\n\n # 展示用户已收藏歌曲\n # 先查出当前用户所有喜欢的歌曲\n current_user = MyUser.objects.get(username=request.user.username)\n # 查出关联的所有喜欢的歌曲信息\n liked_song_info_result = current_user.liked_song.all()\n liked_song_info = [] # 收藏歌曲列表\n for result in liked_song_info_result:\n liked_song_info.append({'song_id': int(result.song_id), 'song_singer': result.song_singer, 'song_name': result.song_name, 'song_time': result.song_time})\n request.session['liked_song_info'] = liked_song_info # 存入session\n # 分页功能展示已收藏歌曲\n liked_song_paginator = Paginator(liked_song_info, 4)\n try:\n liked_song_contacts = liked_song_paginator.page(page)\n except PageNotAnInteger:\n liked_song_contacts = liked_song_paginator.page(1)\n except EmptyPage:\n liked_song_contacts = liked_song_paginator.page(liked_song_paginator.num_pages)\n\n return render(request, 'home.html', locals())\n\n# 取消当前用户的某歌曲收藏\n# 设置用户登录限制\n@login_required(login_url='/user/login.html')\ndef unlikeView(request, song_id):\n # 查询当前用户\n current_user = MyUser.objects.get(username=request.user.username)\n current_liked_song_id = current_user.liked_song.get(song_id=int(song_id))\n # 在多对多模型表中删除当前歌曲对应的收藏记录\n current_user.liked_song.remove(current_liked_song_id)\n return HttpResponseRedirect('/user/home/1.html')\n\n\n# 退出登录\ndef logoutView(request):\n logout(request)\n return redirect('/')\n\n# ajax接口，实现动态验证验证码\nfrom django.http import JsonResponse, HttpResponseRedirect\nfrom captcha.models import CaptchaStore\ndef ajax_val(request):\n if request.is_ajax():\n # 用户输入的验证码结果\n response = request.GET['response']\n # 隐藏域的value值\n hashkey = request.GET['hashkey']\n cs = CaptchaStore.objects.filter(response=response, hashkey=hashkey)\n # 若存在cs，则验证成功，否则验证失败\n if cs:\n json_data = {'status':1}\n else:\n json_data = {'status':0}\n return JsonResponse(json_data)\n else:\n json_data = {'status':0}\n return JsonResponse(json_data)\n\n# # 实现密码加密\n# def hash_code(s, salt='mysite_login'):\n# h = hashlib.sha256()\n# s += salt\n# h.update(s.encode()) # update方法只接收bytes类型\n# return h.hexdigest()\n\n# 实现用户信息显示\ndef userinfoView(request):\n # 搜索歌曲\n search_song = Dynamic.objects.select_related('song').order_by('-dynamic_search').all()[:4]\n current_user = MyUser.objects.get(username=request.user.username)\n current_user_id = current_user.id # 获取当前用户id\n # 分别获取用户的相关信息,传到前端\n username = MyUser.objects.values('username').get(id=int(current_user_id))['username']\n email = MyUser.objects.values('email').get(id=int(current_user_id))['email']\n qq = MyUser.objects.values('qq').get(id=int(current_user_id))['qq']\n weChat = MyUser.objects.values('weChat').get(id=int(current_user_id))['weChat']\n mobile = MyUser.objects.values('mobile').get(id=int(current_user_id))['mobile']\n\n return render(request, 'userinfo.html', locals())\n\n# 实现用户注册信息修改\ndef editView(request):\n '''\n 先把已有的用户信息读出来，然后判断用户请求是POST还是GET。如果是GET，则显示表单\n 并将用户已有信息也显示在其中，如果是POST，则接收用户提交的表单信息，然后更新各个数据模型实例属性的值\n '''\n # 搜索歌曲\n search_song = Dynamic.objects.select_related('song').order_by('-dynamic_search').all()[:4]\n if request.method == \"POST\":\n data = request.POST\n edit_form = RegisterForm(request.POST)\n message = \"请检查填写的内容！\"\n # if edit_form.is_valid(): # 获取数据\n # # userChange = edit_form.cleaned_data\n username = data.get('username')\n email = data.get('email')\n qq = data.get('qq')\n weChat = data.get('weChat')\n mobile = data.get('mobile')\n same_name_user = MyUser.objects.filter(username=username) #确保数据库中没有其它相同用户名存在\n if same_name_user: # 用户名唯一\n message = '用户已经存在，请重新选择用户名！'\n return render(request, 'edit.html', locals())\n same_email_user = MyUser.objects.filter(email=email)\n if same_email_user: # 邮箱地址唯一\n message = '该邮箱地址已被注册，请使用别的邮箱！'\n return render(request, 'edit.html', locals())\n # 当一切都OK的情况下，更新\n MyUser.objects.filter(id=int(request.user.id)).update(username=username, email=email, qq=qq, weChat=weChat, mobile=mobile)\n messages.success(request, \"信息修改成功!\")\n return redirect('userinfo.html', {\"messages\": messages, \"search_song\":search_song})\n # 如果是GET请求则显示当前用户信息到页面上\n else:\n current_user = MyUser.objects.get(id=int(request.user.id))\n edit_form = RegisterForm(initial={\"username\": current_user.username, \"email\": current_user.email,\\\n \"qq\": current_user.qq, \"weChat\": current_user.weChat,\\\n \"mobile\": current_user.mobile})\n return render(request, 'edit.html', {\"edit_form\": edit_form, \"search_song\": search_song})\n\n# # 实现用户密码重置\n# def resetpswView(request):\n# pass\n\n"
},
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"text": "from django.core.mail import send_mail\nfrom user.models import EmailVeriRecord\nimport random\nimport datetime\nfrom music import settings\nfrom datetime import timedelta\n\n\n# 随机产生验证码的函数\ndef random_codechr(length=16):\n # 随机大小写组合的验证码\n chars = 'quFDGDbtwehykjahuhufHFCUHNCWEHAFDONCJUHU'\n codechr = ''\n for x in range(length):\n # 随机取出一个字符\n codechr += random.choice(chars)\n return codechr\n\n\n#\ndef send_email(to_email, send_type='app'):\n \"\"\"\n :param to_email: 收件人的邮箱\n :param send_type: 邮件类型\n :return: 邮件发送结果\n \"\"\"\n email = EmailVeriRecord()\n # 获取验证码\n email.code = random_codechr()\n # 收件人\n email.email = to_email\n # 过期时间\n email.exprie_time = datetime.datetime.now() + datetime.timedelta(days=7)\n # 邮件类型\n email.send_type = send_type\n # 发送邮件\n try:\n res = send_mail('网站用户激活邮件', '', settings.EMAIL_HOST_USER, [to_email],\n html_message='欢迎注册Lgy6533的音乐网站, 点击链接激活你的账户:<a href=\"127.0.0.1:8000/active/{}\">127.0.0.1:8000/active/{}</a>'.format(\n email.code, email.code))\n if res == 1:\n # 保存邮件记录\n email.save()\n return True\n else:\n return False\n except EmailVeriRecord as e:\n print(e)\n return False\n\n# ————————————————\n# 版权声明：本文为CSDN博主「窒息的鱼」的原创文章，遵循CC\n# 4.0\n# BY - SA版权协议，转载请附上原文出处链接及本声明。\n# 原文链接：https: // blog.csdn.net / qq_41664526 / article / details / 80069158"
}
] | 10 |
acoadmarmon/lanl-earthquake-predict
|
https://github.com/acoadmarmon/lanl-earthquake-predict
|
3fd7f272f3c2253391b20a03b5012f7a88ba8dd1
|
130932494370eed558700a2a703814023ca38613
|
acb63eead42ecef751b9be9cce8506ba21bfa342
|
refs/heads/master
| 2021-06-27T00:08:18.584775 | 2020-12-01T04:27:38 | 2020-12-01T04:27:38 | 189,081,451 | 0 | 0 | null | null | null | null | null |
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"text": "# lanl-earthquake-predict\nMy solution to the lanl-earthquake kaggle competition. I transferred this code from my private gitlab repo where I initially worked on it.\n\n## Technology:\nI used Tensorflow and Keras to build my model and Google Cloud's ML Engine to train. Much of the GCP training boilerplate code comes from https://github.com/GoogleCloudPlatform/training-data-analyst/\n\n## Methodology:\nI tried many different approaches to train a model to predict when an earthquake will occur given sesmic signals, including:\n- Manual Feature Extraction -> Random Forest\n- 1D CNN\n- 1D CNN feature extractor -> LSTM prediction\n- Convolutional LSTM (CNN applied to each step in the LSTM to create a low dimensional feature representation that is passed forward to future LSTM steps)\n- Chunked Manual Feature Extraction (100, 1000, 10000 signal example chunks) -> LSTM\n"
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"text": "import argparse\nimport json\nimport os\n\nfrom . import keras_model\n\nimport tensorflow as tf\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--bucket',\n help = 'GCS path to data',\n required = True\n )\n parser.add_argument(\n '--output_dir',\n help = 'GCS location to write checkpoints and export models',\n required = True\n )\n parser.add_argument(\n '--batch_size',\n help = 'Number of examples to compute gradient over.',\n type = int,\n default = 128\n )\n parser.add_argument(\n '--job-dir',\n help = 'this model ignores this field, but it is required by gcloud',\n default = 'junk'\n )\n\n parser.add_argument(\n '--train_examples',\n help = 'Number of examples (in thousands) to run the training job over. If this is more than actual # of examples available, it cycles through them. So specifying 1000 here when you have only 100k examples makes this 10 epochs.',\n type = int,\n default = 5000\n )\n parser.add_argument(\n '--pattern',\n help = 'Specify a pattern that has to be in input files. For example * will process all',\n default = '*'\n )\n parser.add_argument(\n '--eval_steps',\n help = 'Positive number of steps for which to evaluate model. Default to None, which means to evaluate until input_fn raises an end-of-input exception',\n type = int,\n default = None\n )\n parser.add_argument(\n '--env',\n help = 'Positive number of steps for which to evaluate model. Default to None, which means to evaluate until input_fn raises an end-of-input exception',\n default = 'gcp'\n )\n parser.add_argument(\n '--mode',\n help = 'TRAIN or PREDICT',\n default = 'TRAIN'\n )\n\n ## parse all arguments\n args = parser.parse_args()\n arguments = args.__dict__\n\n # unused args provided by service\n arguments.pop('job_dir', None)\n arguments.pop('job-dir', None)\n\n ## assign the arguments to the model variables\n output_dir = arguments.pop('output_dir')\n keras_model.JOB_DIR = output_dir\n keras_model.ENV = arguments.pop('env')\n keras_model.BUCKET = arguments.pop('bucket')\n keras_model.BATCH_SIZE = arguments.pop('batch_size')\n keras_model.TRAIN_EXAMPLES = arguments.pop('train_examples')\n keras_model.EVAL_STEPS = arguments.pop('eval_steps')\n #print (\"Will train for {} steps using batch_size={}\".format(model.TRAIN_STEPS, model.BATCH_SIZE))\n keras_model.PATTERN = arguments.pop('pattern')\n\n # Append trial_id to path if we are doing hptuning\n # # This code can be removed if you are not using hyperparameter tuning\n # output_dir = os.path.join(\n # output_dir,\n # json.loads(\n # os.environ.get('TF_CONFIG', '{}')\n # ).get('task', {}).get('trial', '')\n # )\nif arguments.pop('mode') == 'TRAIN':\n keras_model.train_and_evaluate(output_dir)\nelse:\n keras_model.predict(output_dir)\n"
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"text": "import tensorflow as tf\nimport numpy as np\nimport os\nfrom numpy import genfromtxt\nimport pickle\nfrom tensorflow.python.lib.io import file_io\nimport subprocess\nimport sys\nimport pandas as pd\n\n\n\nENV = 'gcp'\nBUCKET = None # set from task.py\nPATTERN = '*' # gets all files\n\n#Hyperparameters\nTRAIN_STEPS = 10000\nEVAL_STEPS = None\nBATCH_SIZE = 256\n\nCSV_COLUMNS = ['signal_observation']\nINPUT_COLUMNS = [\n tf.feature_column.numeric_column('signal_observation')\n]\nDEFAULTS = [[0.0]]\ndef read_dataset(prefix, mode, batch_size = 512):\n def load_and_preprocess_signal(path, label):\n #print(path)\n return np.reshape(genfromtxt(file_io.FileIO(path.decode(), 'r'),dtype='float32', delimiter=','), (150, 1000)), label\n\n print('Reading {} data.'.format(prefix))\n\n labels_ds = None\n file_list = None\n if mode != tf.estimator.ModeKeys.PREDICT:\n label_file_path = 'gs://lanl-earthquake-gpu-large/{}_labels.pkl'.format(prefix)\n with file_io.FileIO(label_file_path, 'rb') as f:\n labels = pickle.load(f)\n\n file_list = ['{}/'.format(prefix) + i for i in labels.keys()]\n labels_ds = tf.data.Dataset.from_tensor_slices(tf.cast(list(labels.values()), tf.float32))\n else:\n file_list = ['{}/'.format(prefix) + i for i in os.listdir('{}/'.format(prefix))]\n with file_io.FileIO('./data/15000_processed_data/file_names.pkl', 'wb') as f:\n pickle.dump(file_list, f)\n labels_ds = tf.data.Dataset.from_tensor_slices(tf.cast([0.0 for i in range(len(file_list))], tf.float32))\n\n path_ds = tf.data.Dataset.from_tensor_slices(file_list)\n dataset = tf.data.Dataset.zip((path_ds, labels_ds))\n dataset = dataset.map(lambda filename, label: tuple(tf.py_func(load_and_preprocess_signal, [filename, label], [tf.float32, label.dtype])))\n\n if mode == tf.estimator.ModeKeys.TRAIN:\n num_epochs = None # indefinitely\n dataset = dataset.shuffle(buffer_size = 10 * batch_size)\n else:\n num_epochs = 1 # end-of-input after this\n\n dataset = dataset.repeat(num_epochs).batch(batch_size)\n\n return dataset\n\ndef cnn_model_fn(features, labels, mode):\n \"\"\"Model function for CNN.\"\"\"\n features = tf.reshape(features, [-1, 150000, 1])\n labels = tf.reshape(labels, [-1, 1])\n\n # Convolutional Layer #1\n conv1 = tf.layers.conv1d(\n inputs=features,\n filters=4,\n kernel_size=500,\n padding=\"same\",\n activation=tf.nn.leaky_relu)\n # Pooling Layer #1\n pool1 = tf.layers.max_pooling1d(inputs=conv1, pool_size=2, strides=2)\n\n # Convolutional Layer #2 and Pooling Layer #2\n conv2 = tf.layers.conv1d(\n inputs=pool1,\n filters=8,\n kernel_size=250,\n padding=\"same\",\n activation=tf.nn.leaky_relu)\n pool2 = tf.layers.max_pooling1d(inputs=conv2, pool_size=2, strides=2)\n\n conv3 = tf.layers.conv1d(\n inputs=pool2,\n filters=16,\n kernel_size=125,\n padding=\"same\",\n activation=tf.nn.leaky_relu)\n pool3 = tf.layers.max_pooling1d(inputs=conv3, pool_size=2, strides=2)\n\n stacked_lstm = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.LSTMBlockCell(16), tf.contrib.rnn.LSTMBlockCell(8), tf.contrib.rnn.LSTMBlockCell(4)])\n\n init_state = stacked_lstm.zero_state(tf.shape(features)[0], tf.float32)\n outputs, state = tf.nn.dynamic_rnn(stacked_lstm, pool3,\n initial_state=init_state,\n dtype=tf.float32)\n\n\n #ACCOUNT FOR BATCH_SIZE HERE\n dense_1 = tf.layers.dense(inputs=outputs[:, -1, :], units=10)\n predictions = tf.layers.dense(inputs=dense_1, units=1)\n\n\n if mode == tf.estimator.ModeKeys.PREDICT:\n return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)\n\n # Calculate Loss (for both TRAIN and EVAL modes)\n loss = tf.losses.mean_squared_error(labels, predictions)\n\n accuracy = tf.metrics.mean_absolute_error(labels=labels, predictions=predictions)\n tf.summary.scalar('MAE', accuracy[1])\n # Configure the Training Op (for TRAIN mode)\n if mode == tf.estimator.ModeKeys.TRAIN:\n optimizer = tf.train.AdamOptimizer(learning_rate=0.001)\n train_op = optimizer.minimize(\n loss=loss,\n global_step=tf.train.get_global_step())\n return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)\n\n return tf.estimator.EstimatorSpec(\n mode=mode, loss=loss)\n\n\ndef train_and_evaluate(output_dir):\n eval_filename = 'eval.tar.gz'\n train_filename = 'train.tar.gz'\n\n subprocess.check_call(['gsutil', 'cp', os.path.join('gs://lanl-earthquake-gpu-large', eval_filename), eval_filename], stderr=sys.stdout)\n subprocess.check_call(['gsutil', 'cp', os.path.join('gs://lanl-earthquake-gpu-large', train_filename), train_filename], stderr=sys.stdout)\n\n subprocess.call(['tar', '-xf', eval_filename])\n\n subprocess.call(['tar', '-xf', train_filename])\n\n EVAL_INTERVAL = 3600\n\n run_config = tf.estimator.RunConfig(save_summary_steps=10, save_checkpoints_secs = EVAL_INTERVAL,\n keep_checkpoint_max = 3)\n signal_regressor = tf.estimator.Estimator(\n model_fn=cnn_model_fn, model_dir=output_dir, config = run_config)\n\n hook = tf.contrib.estimator.stop_if_no_decrease_hook(signal_regressor, 'loss', 300)\n\n train_spec = tf.estimator.TrainSpec(\n input_fn = lambda: read_dataset(\"train\", mode=tf.estimator.ModeKeys.TRAIN, batch_size=BATCH_SIZE),\n max_steps = TRAIN_STEPS, hooks=[hook])\n\n eval_spec = tf.estimator.EvalSpec(\n input_fn = lambda: read_dataset(\"eval\", mode=tf.estimator.ModeKeys.EVAL, batch_size=BATCH_SIZE))\n\n tf.estimator.train_and_evaluate(signal_regressor, train_spec, eval_spec)\n\ndef predict(output_dir):\n signal_regressor = tf.estimator.Estimator(\n model_fn=cnn_model_fn, model_dir=output_dir)\n\n predictions = list()\n for i in signal_regressor.predict(lambda: read_dataset(\"./data/15000_processed_data/test\", mode=tf.estimator.ModeKeys.PREDICT, batch_size=BATCH_SIZE)):\n predictions.append(i)\n print(i)\n\n\n\n with file_io.FileIO('predictions.pkl', 'wb') as f:\n pickle.dump(predictions, f)\n\n with file_io.FileIO('./data/15000_processed_data/file_names.pkl', 'rb') as f:\n file_list = pickle.load(f)\n\n df = pd.DataFrame(list(zip([i.split('/')[4][:-4] for i in file_list], [i[0] if i[0] < 0.0 else 0.0 for i in predictions])), columns=['seg_id', 'time_to_failure'])\n df['time_to_failure'] = df['time_to_failure'].apply(lambda x: 0.0 if x < 0.0 else x)\n df.to_csv('./data/15000_processed_data/submission_standard.csv', index=False)\n"
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"text": "import tensorflow as tf\nimport numpy as np\nimport os\nfrom numpy import genfromtxt\nimport pickle\nfrom tensorflow.python.lib.io import file_io\nimport subprocess\nimport sys\nimport pandas as pd\n\nENV = 'gcp'\nBUCKET = None # set from task.py\nPATTERN = '*' # gets all files\n\nTRAIN_EXAMPLES = 6712\nJOB_DIR='gs://lanl-earthquake-gpu-large'\n\ndef read_dataset(prefix, num_epochs, mode, batch_size = 512):\n def load_and_preprocess_signal(path, label):\n #print(path)\n features = np.reshape(genfromtxt(file_io.FileIO(path.decode(), 'r'),dtype='float32', delimiter=','), (1500, 100, 1))\n #print(features)\n return features, label\n\n def set_shape(features, label):\n features.set_shape([1500, 100, 1])\n label.set_shape([1])\n return features, label\n print('Reading {} data.'.format(prefix))\n\n labels_ds = None\n file_list = None\n if mode != tf.estimator.ModeKeys.PREDICT:\n label_file_path = 'gs://lanl-earthquake-gpu-large/{}_labels.pkl'.format(prefix)\n with file_io.FileIO(label_file_path, 'rb') as f:\n labels = pickle.load(f)\n\n file_list = ['{}/'.format(prefix) + i for i in labels.keys()]\n labels_ds = tf.data.Dataset.from_tensor_slices(tf.cast(list(labels.values()), tf.float32))\n else:\n file_list = ['{}/'.format(prefix) + i for i in os.listdir('{}/'.format(prefix))]\n with file_io.FileIO('./data/15000_processed_data/file_names.pkl', 'wb') as f:\n pickle.dump(file_list, f)\n labels_ds = tf.data.Dataset.from_tensor_slices(tf.cast([0.0 for i in range(len(file_list))], tf.float32))\n\n path_ds = tf.data.Dataset.from_tensor_slices(file_list)\n dataset = tf.data.Dataset.zip((path_ds, labels_ds))\n dataset = dataset.map(lambda filename, label: tuple(tf.py_func(load_and_preprocess_signal, [filename, label], [tf.float32, label.dtype])))\n dataset = dataset.map(lambda features, label: set_shape(features, label))\n #if mode == tf.estimator.ModeKeys.TRAIN:\n # dataset = dataset.shuffle(buffer_size = 10 * batch_size)\\\n\n dataset = dataset.repeat(num_epochs).batch(batch_size)\n return dataset\n\ndef conv_lstm_model_function():\n\n # 30092513\n # inputs = tf.keras.layers.Input(shape=(1000,1))\n #\n # # a layer instance is callable on a tensor, and returns a tensor\n # x = tf.keras.layers.Conv1D(filters=32,\n # kernel_size=16,\n # padding=\"same\")(inputs)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.MaxPooling1D(pool_size=5, strides=5)(x)\n # x = tf.keras.layers.Conv1D(filters=64,\n # kernel_size=16,\n # padding=\"same\")(x)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.MaxPooling1D(pool_size=5, strides=5)(x)\n # x = tf.keras.layers.Conv1D(filters=128,\n # kernel_size=16,\n # padding=\"same\")(x)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.MaxPooling1D(pool_size=5, strides=5)(x)\n # vector_rep = tf.keras.layers.Flatten()(x)\n # model = tf.keras.models.Model(inputs=inputs, outputs=vector_rep)\n #\n # inputs = tf.keras.layers.Input(shape=(150,1000,1))\n # x = tf.keras.layers.TimeDistributed(model)(inputs)\n # x = tf.keras.layers.LSTM(2048)(x)\n # x = tf.keras.layers.Dense(2048)(x)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.Dense(256)(x)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.Dense(128)(x)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # predictions = tf.keras.layers.Dense(1)(x)\n\n # keras_large_conv_lstm_model_on_gpu\n # inputs = tf.keras.layers.Input(shape=(1000,1))\n #\n # # a layer instance is callable on a tensor, and returns a tensor\n # x = tf.keras.layers.Conv1D(filters=4,\n # kernel_size=16,\n # padding=\"same\")(inputs)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.MaxPooling1D(pool_size=5, strides=5)(x)\n # x = tf.keras.layers.Conv1D(filters=8,\n # kernel_size=16,\n # padding=\"same\")(x)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.MaxPooling1D(pool_size=5, strides=5)(x)\n # x = tf.keras.layers.Conv1D(filters=16,\n # kernel_size=16,\n # padding=\"same\")(x)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.MaxPooling1D(pool_size=5, strides=5)(x)\n # vector_rep = tf.keras.layers.Flatten()(x)\n # model = tf.keras.models.Model(inputs=inputs, outputs=vector_rep)\n #\n # inputs = tf.keras.layers.Input(shape=(150,1000,1))\n # x = tf.keras.layers.TimeDistributed(model)(inputs)\n # x = tf.keras.layers.LSTM(128)(x)\n # x = tf.keras.layers.Dense(128)(x)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.Dense(64)(x)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # predictions = tf.keras.layers.Dense(1)(x)\n\n\n # keras_1000000_conv_lstm_model\n with tf.device('/cpu:0'):\n inputs = tf.keras.layers.Input(shape=(100,1))\n\n # a layer instance is callable on a tensor, and returns a tensor\n x = tf.keras.layers.Conv1D(filters=16,\n kernel_size=10,\n padding=\"same\")(inputs)\n x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n x = tf.keras.layers.MaxPooling1D(pool_size=5, strides=5)(x)\n #x = tf.keras.layers.Permute((2, 1))(x)\n x = tf.keras.layers.CuDNNLSTM(32)(x)\n model = tf.keras.models.Model(inputs=inputs, outputs=x)\n\n inputs = tf.keras.layers.Input(shape=(1500,100,1))\n x = tf.keras.layers.TimeDistributed(model)(inputs)\n x = tf.keras.layers.CuDNNLSTM(64, return_sequences=True)(x)\n x = tf.keras.layers.CuDNNLSTM(32)(x)\n x = tf.keras.layers.Dense(128)(x)\n x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n x = tf.keras.layers.Dropout(0.2)(x)\n predictions = tf.keras.layers.Dense(1)(x)\n model = tf.keras.models.Model(inputs=inputs, outputs=predictions)\n\n print(model.summary())\n parallel_model = tf.keras.utils.multi_gpu_model(model, gpus=4)\n parallel_model.compile(optimizer='adam',\n loss='mean_squared_error',\n metrics=['mae', 'acc'])\n return parallel_model\n\n # keras 35000 conv lstm model\n # with tf.device('/cpu:0'):\n # inputs = tf.keras.layers.Input(shape=(1000,1))\n #\n # # a layer instance is callable on a tensor, and returns a tensor\n # x = tf.keras.layers.Conv1D(filters=2,\n # kernel_size=16,\n # padding=\"same\")(inputs)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.MaxPooling1D(pool_size=10, strides=10)(x)\n # x = tf.keras.layers.Conv1D(filters=4,\n # kernel_size=16,\n # padding=\"same\")(x)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.MaxPooling1D(pool_size=10, strides=10)(x)\n # x = tf.keras.layers.Conv1D(filters=6,\n # kernel_size=16,\n # padding=\"same\")(x)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.MaxPooling1D(pool_size=10, strides=10)(x)\n # vector_rep = tf.keras.layers.Flatten()(x)\n # model = tf.keras.models.Model(inputs=inputs, outputs=vector_rep)\n #\n # inputs = tf.keras.layers.Input(shape=(150,1000,1))\n # x = tf.keras.layers.TimeDistributed(model)(inputs)\n # x = tf.keras.layers.CuDNNLSTM(64, return_sequences=True)(x)\n # x = tf.keras.layers.CuDNNLSTM(32, return_sequences=True)(x)\n # x = tf.keras.layers.CuDNNLSTM(16)(x)\n # x = tf.keras.layers.Dense(16, activation='relu')(x)\n # predictions = tf.keras.layers.Dense(1)(x)\n # model = tf.keras.models.Model(inputs=inputs, outputs=predictions)\n # #print(model.count_params())\n # #model = tf.keras.utils.multi_gpu_model(model, gpus=4)\n # model.compile(optimizer='adam',\n # loss='mean_squared_error',\n # metrics=['mae', 'acc'])\n # return model\n\n # keras_8000000_conv_lstm_model\n # with tf.device('/cpu:0'):\n # inputs = tf.keras.layers.Input(shape=(1000,1))\n #\n # # a layer instance is callable on a tensor, and returns a tensor\n # x = tf.keras.layers.Conv1D(filters=2,\n # kernel_size=16,\n # padding=\"same\")(inputs)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.MaxPooling1D(pool_size=2, strides=2)(x)\n # x = tf.keras.layers.Conv1D(filters=4,\n # kernel_size=16,\n # padding=\"same\")(x)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.MaxPooling1D(pool_size=2, strides=2)(x)\n # x = tf.keras.layers.Conv1D(filters=6,\n # kernel_size=16,\n # padding=\"same\")(x)\n # x = tf.keras.layers.LeakyReLU(alpha=0.3)(x)\n # x = tf.keras.layers.MaxPooling1D(pool_size=2, strides=2)(x)\n # vector_rep = tf.keras.layers.Flatten()(x)\n # model = tf.keras.models.Model(inputs=inputs, outputs=vector_rep)\n #\n # inputs = tf.keras.layers.Input(shape=(150,1000,1))\n # x = tf.keras.layers.TimeDistributed(model)(inputs)\n # x = tf.keras.layers.CuDNNLSTM(1024, return_sequences=True)(x)\n # x = tf.keras.layers.CuDNNLSTM(512, return_sequences=True)(x)\n # x = tf.keras.layers.CuDNNLSTM(256)(x)\n # x = tf.keras.layers.Dense(256, activation='relu')(x)\n # x = tf.keras.layers.Dropout(0.2)(x)\n # x = tf.keras.layers.Dense(128, activation='relu')(x)\n # x = tf.keras.layers.Dropout(0.2)(x)\n # predictions = tf.keras.layers.Dense(1)(x)\n # model = tf.keras.models.Model(inputs=inputs, outputs=predictions)\n #\n # parallel_model = tf.keras.utils.multi_gpu_model(model, gpus=4)\n # parallel_model.compile(optimizer='adam',\n # loss='mean_squared_error',\n # metrics=['mae', 'acc'])\n # return parallel_model\n\n\n\ndef train_and_evaluate(output_dir):\n\n # EVAL_INTERVAL = 3600\n #\n # run_config = tf.estimator.RunConfig(save_summary_steps=10, save_checkpoints_secs = EVAL_INTERVAL,\n # keep_checkpoint_max = 3)\n # signal_regressor = tf.keras.estimator.model_to_estimator(keras_model=conv_lstm_model_function(), model_dir=output_dir, config = run_config)\n\n eval_filename = 'eval.tar.gz'\n train_filename = 'train.tar.gz'\n\n subprocess.check_call(['gsutil', 'cp', os.path.join('gs://lanl-earthquake-gpu-large', eval_filename), eval_filename], stderr=sys.stdout)\n subprocess.check_call(['gsutil', 'cp', os.path.join('gs://lanl-earthquake-gpu-large', train_filename), train_filename], stderr=sys.stdout)\n\n subprocess.call(['tar', '-xf', eval_filename])\n\n subprocess.call(['tar', '-xf', train_filename])\n\n #tf.estimator.Estimator(\n # model_fn=cnn_model_fn, model_dir=output_dir, config = run_config)\n\n training_dataset = read_dataset(\"train\", num_epochs=30, mode=tf.estimator.ModeKeys.TRAIN, batch_size=BATCH_SIZE)\n\n # Pass a numpy array by using DataFrame.values\n validation_dataset = read_dataset(\"eval\", num_epochs=1, mode=tf.estimator.ModeKeys.EVAL, batch_size=67)\n\n keras_model = conv_lstm_model_function()\n\n tensorboard_cb = tf.keras.callbacks.TensorBoard(\n os.path.join(JOB_DIR, 'keras_tensorboard'),\n histogram_freq=1)\n early_stop = tf.keras.callbacks.EarlyStopping(\n monitor='val_loss',\n min_delta=0,\n patience=3)\n history = keras_model.fit(training_dataset,\n epochs=30,\n steps_per_epoch=int(TRAIN_EXAMPLES/BATCH_SIZE),\n validation_data=validation_dataset,\n validation_steps=25,\n callbacks=[tensorboard_cb, early_stop],\n verbose=1)\n export_path = tf.contrib.saved_model.save_keras_model(keras_model, JOB_DIR + '/keras_export')\n print(\"Model exported to: \", export_path)\n\n\n\ndef predict(output_dir):\n signal_regressor = tf.keras.estimator.model_to_estimator(keras_model=conv_lstm_model_function(), model_dir=output_dir)\n\n\n predictions = list()\n for i in signal_regressor.predict(lambda: read_dataset(\"./data/15000_processed_data/test\",num_epochs=1,mode=tf.estimator.ModeKeys.PREDICT, batch_size=BATCH_SIZE)):\n predictions.append(i)\n print(i)\n\n\n\n with file_io.FileIO('predictions.pkl', 'wb') as f:\n pickle.dump(predictions, f)\n\n with file_io.FileIO('./data/15000_processed_data/file_names.pkl', 'rb') as f:\n file_list = pickle.load(f)\n\n df = pd.DataFrame(list(zip([i.split('/')[4][:-4] for i in file_list], [i[0] if i[0] < 0.0 else 0.0 for i in predictions])), columns=['seg_id', 'time_to_failure'])\n df['time_to_failure'] = df['time_to_failure'].apply(lambda x: 0.0 if x < 0.0 else x)\n df.to_csv('./data/15000_processed_data/submission_standard.csv', index=False)\n"
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"src_encoding": "UTF-8",
"text": "import pandas as pd\nfrom sklearn.preprocessing import StandardScaler\nimport pickle\nimport random\nimport numpy as np\nimport pywt\nimport os\n\ntrain_df = pd.read_csv('./data/train.csv')\nprint('done')\n\n# def get_signal_features(df_segment, window_size):\n# df = pd.DataFrame(index=range(150000/window_size ), dtype=np.float64)\n# fft = np.fft.fft(df_segment.acoustic_data)\n# realFFT = np.real(zc)\n# imagFFT = np.imag(zc)\n# df['Rmean'] = realFFT.mean()\n# X.loc[seg_id, 'Rstd'] = realFFT.std()\n# X.loc[seg_id, 'Rmax'] = realFFT.max()\n# X.loc[seg_id, 'Rmin'] = realFFT.min()\n# X.loc[seg_id, 'Imean'] = imagFFT.mean()\n# X.loc[seg_id, 'Istd'] = imagFFT.std()\n# X.loc[seg_id, 'Imax'] = imagFFT.max()\n# X.loc[seg_id, 'Imin'] = imagFFT.min()\n# scaler = StandardScaler(copy=False)\n# train_df['acoustic_data'] = scaler.fit_transform(train_df['acoustic_data'].values.reshape(-1, 1))\n\n#eval_index = list(np.random.permutation([i for i in range(4194)]))[:838]\n\nimport matplotlib.pyplot as plt\ntrain_labels = dict()\neval_labels = dict()\nprev_is_eval = False\nfor i in range(4194*2 - 838*2):\n train_labels['signal_' + str(i) + '.csv'] = train_df['time_to_failure'].iloc[i*75000 + 150000]\n wave = train_df['acoustic_data'].iloc[i*75000: i*75000 + 150000]\n\n fft = np.fft.fft(df_segment.acoustic_data)\n\n realFFT = np.real(fft)\n imagFFT = np.imag(fft)\n for i in range(1000):\n x = wave.values[150*i: 150*i + 150]\n r = realFFT[150*i: 150*i + 150]\n i = imagFFT[150*i: 150*i + 150]\n df['mean'] = x.mean()\n df['max'] = x.max()\n df['min'] = x.min()\n df['std'] = x.std()\n df['Rmean'] = r.mean()\n df['Rstd'] = r.std()\n df['Rmax'] = r.max()\n df['Rmin'] = r.min()\n df['Imean'] = i.mean()\n df['Istd'] = i.std()\n df['Imax'] = i.max()\n df['Imin'] = i.min()\n\n #train_df['acoustic_data'].iloc[i*75000: i*75000 + 150000].to_csv('./data/15000_processed_data/train/signal_' + str(i) + '.csv', index=False, header=False)\n\nfor i in range(4194*2 - 838*2, 4194*2 - 1, 1):\n eval_labels['signal_' + str(i) + '.csv'] = train_df['time_to_failure'].iloc[i*75000 + 150000]\n train_df['acoustic_data'].iloc[i*75000: i*75000 + 150000].to_csv('./data/15000_processed_data/eval/signal_' + str(i) + '.csv', index=False, header=False)\n\nwith open('./data/15000_processed_data/eval_labels.pkl', 'wb') as f:\n pickle.dump(eval_labels, f, protocol=2)\n# with open('./data/15000_processed_data/train_labels.pkl', 'wb') as f:\n# pickle.dump(train_labels, f, protocol=2)\n"
}
] | 5 |
matteolee72/sgcarmart_carchecker
|
https://github.com/matteolee72/sgcarmart_carchecker
|
fa894b9334b46c9ce703dfa21d820855e40e8f13
|
7193a4de73dc2594898049efd114671a60c613d0
|
d6454079841676f8d5956a431fb03dfab4e4ec96
|
refs/heads/master
| 2023-03-31T06:35:59.967970 | 2021-03-31T04:07:48 | 2021-03-31T04:07:48 | 348,904,995 | 0 | 0 | null | null | null | null | null |
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"text": "from bs4 import BeautifulSoup\nimport requests\nimport pandas as pd\nimport re\n\ndata = pd.read_csv('Sgcarmart_Webscraping_Data.csv')\n\nheaders = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36\"\n}\n\ncar_names=data.Name.tolist()\n\nurls=data.Link.tolist()\n\nlimits = data.Limit.tolist()\n\nfor i in range(len(car_names)):\n car_count=0\n car_name=car_names[i]\n url=urls[i]\n limit=limits[i]\n #pulling html from website\n source = requests.get(url).text\n #parsing the website for depre\n soup = BeautifulSoup(source, features=\"html.parser\")\n article=soup.find('div',{\"id\":\"contentblank\"})\n depre=article.findAll(style=\"width:101px;\")\n name=article.findAll(style=\"width:186px;padding-left:4px;\")\n \n #looping through each depre to check for matches\n z = 0\n for d in depre:\n n = name[z].a.text\n p = d.text.strip()\n x = re.sub(r\"[^0-9]\",\"\",p)\n x = int(x)\n z+=1\n \n if (x <= limit):\n if (n.endswith(\"(OPC)\")!=True):\n car_count+=1\n\n if car_count > 0:\n print(car_name + \": \" + str(car_count))\n \ninput(\"Press enter to exit\")\n"
}
] | 1 |
BenPorebski/tepitope
|
https://github.com/BenPorebski/tepitope
|
46f094cbbb7f55149a57c59d2a3adf9487ca036b
|
a1e8c92a8cf27672b4552c5f771712010fa0740a
|
e58138fb81aa251bb5c9121e075e9b597e6fa7a3
|
refs/heads/master
| 2021-01-17T07:10:19.119604 | 2020-05-15T09:41:20 | 2020-05-15T09:41:20 | 43,042,439 | 3 | 1 | null | 2015-09-24T03:24:46 | 2016-02-14T06:39:05 | 2016-04-01T23:01:35 |
Python
|
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"src_encoding": "UTF-8",
"text": "# tepitope\nMHC class II epitope predictor based on the tepitope positional scoring matrices.\n"
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"text": "\"\"\"A local implementation of tepitope - an MHC class II binding predictor.\"\"\"\n\n\nclass HLAmatrix:\n \"\"\"class for loading and scoring a matrix\"\"\"\n\n def load_matrix(self, matrix_file):\n \"\"\"Loads and parses the matrix into memory\"\"\"\n with open(matrix_file) as matrix_handle:\n percentage_threshold_tmp = ''\n scoring_tmp = ''\n for line in matrix_handle:\n if len(line.split(',')[0]) <= 2:\n line = line.replace('-', '-0')\n # print line\n self.matrix_data[line.split(',')[0][0]] = map(float, (line.strip().split(',')[1:]))\n\n if line.split(',')[0] == 'Percent Threshold':\n percentage_threshold_tmp = line.rstrip().split(',')[1:]\n if line.split(',')[0] == 'Numerical Score':\n scoring_tmp = line.rstrip().split(',')[1:]\n\n for i, threshold in enumerate(percentage_threshold_tmp):\n thresh_clean = threshold.split('%')[0]\n if int(thresh_clean) == self.threshold:\n self.threshold_score = float(scoring_tmp[i])\n\n def score(self, nonamer):\n \"\"\"Scores a nonamer based on the loaded matrix\"\"\"\n score = 0\n for i, residue in enumerate(nonamer):\n try:\n tmp_score = self.matrix_data[residue][i]\n score = score + tmp_score\n except:\n continue\n\n return (nonamer, score)\n\n\n def score_sequence(self, sequence):\n \"\"\"Scores an entire sequence based on the loaded matrix and threshold\"\"\"\n returned_score = []\n for i in range(len(sequence)-9):\n scored = self.score(sequence[i:i+9])\n if scored[1] >= self.threshold_score:\n returned_score.append( (scored[0],scored[1],i,i+8) )\n\n return sorted(returned_score, key=lambda score: score[1], reverse=True)\n\n\n def __init__(self, csv_file, threshold):\n self.matrix_data = {}\n self.threshold = threshold\n self.threshold_score = 0\n self.matrix_file = csv_file ## Set the matrix file variable\n self.allele = os.path.splitext(os.path.basename(csv_file))[0] ## Set the allele name\n self.load_matrix(csv_file) ## Load the matrix\n\n\nif __name__ == '__main__':\n\n import os, glob, sys, argparse, re\n\n parser = argparse.ArgumentParser()\n parser.add_argument('-t', \"--threshold\", dest='threshold', type=int, default=5)\n parser.add_argument('-c', '--csv', dest='csv_filename', default=None, help='filename for CSV output')\n parser.add_argument('--no-header', dest='csv_header', action='store_false',\n help='omit the header row from the CSV file')\n parser.add_argument('-f', '--file', '--sequence-file', dest='filename', default=None,\n help='file which contains the sequence')\n parser.add_argument('-i', '--starting-index', dest='base_index', default=1, type=int,\n help='the index of the first peptide in the sequence, used to offset the begin and end indexes '\n 'of the results')\n parser.add_argument(\"sequence\", default=None, nargs='?', help='the peptide sequence to analyze, if not using -f')\n args = parser.parse_args()\n\n # get sequence\n if args.sequence is not None and args.filename is not None:\n print >> sys.stderr, 'You must specify only one of -f, --file, --sequence-file, or the sequence as a command-line argument'\n exit(1)\n if args.sequence is None and args.filename is None:\n print >> sys.stderr, 'You must specify -f, --file, --sequence-file, or put the sequence as a command-line argument'\n exit(1)\n if args.sequence:\n sequence = args.sequence\n else:\n with open(args.filename) as f:\n sequence = re.sub(r'\\s', '', f.read())\n\n # generate results\n results = set()\n for matrix_file in glob.glob('matrices/*.csv'):\n matrix = HLAmatrix(matrix_file, args.threshold)\n for epitope, score, begin, end in matrix.score_sequence(sequence):\n results.add( (score, begin + args.base_index, end + args.base_index, epitope, matrix.allele) )\n\n results = sorted( results, key=lambda row:row[0], reverse=True )\n\n # output\n if args.csv_filename is not None:\n # CSV output\n import csv\n with open(args.csv_filename, 'wb') as csv_file:\n out = csv.writer(csv_file,lineterminator=os.linesep)\n if args.csv_header:\n out.writerow(('score', 'begin', 'end', 'epitope', 'allele'))\n for result in results:\n out.writerow(result)\n else:\n # stdout\n for result in results:\n print '%.2f\\t%d\\t%d\\t%s\\t%s'%result\n\n\n ##### Example 2 #####\n\n # sequence = \"PSPPGNLRVTDVTSTSVTLSWEPPPGPITGYRVEYREAGGEWKEVTVPGSETSYTVTGLKPGTEYEFRVRAVNGAGEGPPSSVSVTT\"\n # # print 'Sequence: %s' % (sequence)\n # drb1_0101 = HLAmatrix('matrices/HLA-DRB1_0101.csv', 5)\n # print drb1_0101.score_sequence(sequence)\n # drb1_0801 = HLAmatrix('matrices/HLA-DRB1_0801.csv', 5)\n # print drb1_0801.score_sequence(sequence)\n"
}
] | 2 |
Yi-61/Image_Completion_CS230
|
https://github.com/Yi-61/Image_Completion_CS230
|
a5edd10ca4c2226859f681c50d1868e748adfd55
|
238fb3268fc4e10685e5cf79c26d09dffcd380ae
|
30ba78bf43ec20f57f562f283eafcc217e5872e0
|
refs/heads/master
| 2021-04-27T22:05:28.956983 | 2018-03-23T00:26:52 | 2018-03-23T00:26:52 | 122,412,626 | 0 | 0 | null | null | null | null | null |
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"text": "from keras.models import Sequential\nfrom keras.layers import Dense, Input, Reshape, LeakyReLU, Conv2D, Conv2DTranspose\nfrom keras.layers.core import Activation\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.layers.convolutional import UpSampling2D\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.layers.core import Flatten\nfrom keras.optimizers import Adam, SGD\nfrom keras.datasets import mnist\nfrom keras import backend as K\nimport numpy as np\nfrom PIL import Image\nimport argparse\nimport math\nimport load_pickle\nimport matplotlib.pyplot as plt\n\ndef generator_model(n_x = 80, n_color = 3, n_c = 8):\n model = Sequential()\n model.add(Dense(input_dim=100, units=512))\n model.add(Activation('tanh', name = 'tanh01'))\n\n model.add(Dense(512*5*5))\n model.add(BatchNormalization())\n model.add(Activation('tanh', name = 'tanh02'))\n model.add(Reshape((5, 5, 512), input_shape=(512*5*5,))) #output: (5,5,128)\n\n model.add(UpSampling2D(size=(2, 2))) #output: (10,10,128)\n model.add(Conv2D(256, (3, 3), padding='same')) #output: (10,10,64)\n model.add(Activation('tanh', name = 'tanh03'))\n\n model.add(UpSampling2D(size=(2, 2))) #output: (20,20,64)\n model.add(Conv2D(128, (3, 3), padding='same')) #output: (20,20,32)\n model.add(Activation('tanh', name = 'tanh04'))\n\n model.add(UpSampling2D(size=(2, 2))) #output: (40,40,32)\n model.add(Conv2D(64, (3, 3), padding='same')) #output: (40,40,16)\n model.add(Activation('tanh', name = 'tanh05'))\n\n model.add(UpSampling2D(size=(2, 2))) #output: (80,80,16)\n model.add(Conv2D(n_color, (3, 3), padding='same')) #output: (80,80,1)\n model.add(Activation('tanh', name = 'tanh06'))\n\n return model\n\ndef discriminator_model(n_x = 80, n_c = 16, n_color = 3):\n model = Sequential()\n model.add(Conv2D(32, (3, 3), padding='same', input_shape=(n_x, n_x, n_color) ) )\n model.add(Activation('tanh', name = 'tanh01'))\n\n model.add(MaxPooling2D(pool_size=(2, 2)))\n model.add(Conv2D(64, (3, 3)))\n model.add(Activation('tanh', name = 'tanh02'))\n\n model.add(MaxPooling2D(pool_size=(2, 2)))\n model.add(Conv2D(128, (3, 3)))\n model.add(Activation('tanh', name = 'tanh03'))\n\n model.add(MaxPooling2D(pool_size=(2, 2)))\n model.add(Conv2D(256, (3, 3)))\n model.add(Activation('tanh', name = 'tanh04'))\n\n # model.add(MaxPooling2D(pool_size=(2, 2)))\n model.add(Flatten())\n # model.add(Dense(512))\n model.add(Dense(1024))\n model.add(Activation('tanh', name = 'tanh05'))\n model.add(Dense(1))\n model.add(Activation('sigmoid'))\n\n return model\n\nd = discriminator_model()\ng = generator_model()\nd.load_weights('discriminator_4')\ng.load_weights('generator_4')\n\ninpu _image\nprint(g.summary())\nprint(g.get_layer('tanh01').output.get_shape())\n\n# inp = d.input # input placeholder\n# outputs = [layer.output for layer in d.layers] # all layer outputs\n# functor = K.function([inp]+ [K.learning_phase()], outputs ) # evaluation function\n#\n# # Testing\n# test = np.random.random(input_shape)[np.newaxis,...]\n# layer_outs = functor([test, 1.])\n# print layer_outs\n"
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"text": "#Save losses into a txt file\r\n#Only keeping one BatchNorm for the generator is enough.\r\n#More BatchNorm layers and relu activations are bad\r\n#add: np.random.shuffle(X_train) for each epoch\r\n#Try: more dense layers in generator, and reduce dense size - no used\r\n#Try: less unitsin the 1st dense layer in generator (1024 -> 512) - works\r\n#Try: one less conv layer in discriminator; smaller dense layer at the end - no use\r\n#Try: more filters in generator - works. g_loss becomes smallers (1-3)\r\n#Uses 1-100000 dataset\r\n\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Input, Reshape, LeakyReLU, Conv2D, Conv2DTranspose\r\nfrom keras.layers.core import Activation\r\nfrom keras.layers.normalization import BatchNormalization\r\nfrom keras.layers.convolutional import UpSampling2D\r\nfrom keras.layers.convolutional import MaxPooling2D\r\nfrom keras.layers.core import Flatten\r\nfrom keras.optimizers import Adam, SGD\r\nfrom keras.datasets import mnist\r\nimport numpy as np\r\nfrom PIL import Image\r\nimport argparse\r\nimport math\r\nimport load_pickle\r\nimport matplotlib.pyplot as plt\r\n\r\ndef generator_model(n_x = 80, n_color = 3, n_c = 8):\r\n model = Sequential()\r\n model.add(Dense(input_dim=100, units=512))\r\n model.add(Activation('tanh'))\r\n\r\n model.add(Dense(512*5*5))\r\n model.add(BatchNormalization())\r\n model.add(Activation('tanh'))\r\n model.add(Reshape((5, 5, 512), input_shape=(512*5*5,))) #output: (5,5,128)\r\n\r\n model.add(UpSampling2D(size=(2, 2))) #output: (10,10,128)\r\n model.add(Conv2D(256, (3, 3), padding='same')) #output: (10,10,64)\r\n model.add(Activation('tanh'))\r\n\r\n model.add(UpSampling2D(size=(2, 2))) #output: (20,20,64)\r\n model.add(Conv2D(128, (3, 3), padding='same')) #output: (20,20,32)\r\n model.add(Activation('tanh'))\r\n\r\n model.add(UpSampling2D(size=(2, 2))) #output: (40,40,32)\r\n model.add(Conv2D(64, (3, 3), padding='same')) #output: (40,40,16)\r\n model.add(Activation('tanh'))\r\n\r\n model.add(UpSampling2D(size=(2, 2))) #output: (80,80,16)\r\n model.add(Conv2D(n_color, (3, 3), padding='same')) #output: (80,80,1)\r\n model.add(Activation('tanh'))\r\n\r\n return model\r\n\r\ndef discriminator_model(n_x = 80, n_c = 16, n_color = 3):\r\n model = Sequential()\r\n model.add(Conv2D(32, (3, 3), padding='same', input_shape=(n_x, n_x, n_color) ) )\r\n model.add(Activation('tanh'))\r\n\r\n model.add(MaxPooling2D(pool_size=(2, 2)))\r\n model.add(Conv2D(64, (3, 3)))\r\n model.add(Activation('tanh'))\r\n\r\n model.add(MaxPooling2D(pool_size=(2, 2)))\r\n model.add(Conv2D(128, (3, 3)))\r\n model.add(Activation('tanh'))\r\n\r\n model.add(MaxPooling2D(pool_size=(2, 2)))\r\n model.add(Conv2D(256, (3, 3)))\r\n model.add(Activation('tanh'))\r\n\r\n # model.add(MaxPooling2D(pool_size=(2, 2)))\r\n model.add(Flatten())\r\n # model.add(Dense(512))\r\n model.add(Dense(1024))\r\n model.add(Activation('tanh'))\r\n model.add(Dense(1))\r\n model.add(Activation('sigmoid'))\r\n\r\n return model\r\n\r\ndef generator_containing_discriminator(g, d):\r\n model = Sequential()\r\n model.add(g)\r\n d.trainable = False\r\n model.add(d)\r\n return model\r\n\r\ndef plot_loss(g_mat, d_mat, epoch):\r\n epochs = np.arange(epoch+1)\r\n plt.plot(epochs, g_mat, 'r')\r\n plt.plot(epochs, d_mat, 'b')\r\n plt.legend(['generator_loss', 'discriminator_loss'], loc = 'best')\r\n plt.savefig('loss_' + str(epoch) + '.png')\r\n plt.close()\r\n\r\ndef save_loss(EPOCH, INDEXES, D_loss_save, G_loss_save): #save loss into a txt file\r\n epochs = [EPOCH] * len(INDEXES)\r\n loss_save = np.stack((epochs, INDEXES, D_loss_save, G_loss_save))\r\n f_handle = open('losses.txt','ab')\r\n np.savetxt(fname = f_handle, X = loss_save.T, fmt = '%10.5f')\r\n f_handle.close()\r\n\r\ndef train(X_train, BATCH_SIZE):\r\n noise_dim = 100\r\n k_d = 2\r\n k_g = 1\r\n\r\n d = discriminator_model()\r\n g = generator_model()\r\n d_loss_mat = []\r\n g_loss_mat = []\r\n d.load_weights('discriminator')\r\n g.load_weights('generator')\r\n d_on_g = generator_containing_discriminator(g, d)\r\n d_optim = Adam(lr=0.0002, beta_1=0.5, beta_2=0.999, decay=0.0, amsgrad=False)\r\n g_optim = Adam(lr=0.0002, beta_1=0.5, beta_2=0.999, decay=0.0, amsgrad=False)\r\n # d_optim = SGD(lr=0.0002, momentum=0.9, nesterov=True)\r\n # g_optim = SGD(lr=0.0002, momentum=0.9, nesterov=True)\r\n g.compile(loss='binary_crossentropy', optimizer=\"Adam\")\r\n d_on_g.compile(loss='binary_crossentropy', optimizer=g_optim)\r\n d.trainable = True\r\n d.compile(loss='binary_crossentropy', optimizer=d_optim)\r\n\r\n # g_loss = 10\r\n # d_loss = 10 #initialize\r\n\r\n for epoch in range(100):\r\n d_loss_save = []\r\n g_loss_save = []\r\n indexes = []\r\n d_train_batch = np.zeros((1,80,80,3))\r\n np.random.shuffle(X_train)\r\n print(\"========================Epoch is\", epoch, \"===========================\")\r\n print(\"Number of batches\", int(X_train.shape[0]/BATCH_SIZE))\r\n for index in range(int(X_train.shape[0]/BATCH_SIZE)):\r\n noise = np.random.uniform(-1, 1, size=(BATCH_SIZE, noise_dim))\r\n image_batch = X_train[index*BATCH_SIZE:(index+1)*BATCH_SIZE]\r\n generated_images = g.predict(noise, verbose=0)\r\n # print(X_train[0,...].shape)\r\n # print(image_batch.shape)\r\n # print(generated_images.shape)\r\n X = np.concatenate((image_batch, generated_images))\r\n y = [1] * BATCH_SIZE + [0] * BATCH_SIZE\r\n\r\n # d_loss = d.train_on_batch(X, y)\r\n\r\n if index % k_d == 0: # Train d once every k iterations\r\n # d_train_batch = np.concatenate( (d_train_batch, image_batch), axis = 0 )\r\n # d_train_batch = np.delete(d_train_batch, 0, 0)\r\n # noise = np.random.uniform(-1, 1, size=(BATCH_SIZE * d_train_batch.shape[0], noise_dim))\r\n # # image_batch = X_train[index*BATCH_SIZE:(index+k_d)*BATCH_SIZE]\r\n # generated_images = g.predict(noise, verbose=0)\r\n # # X = np.concatenate((image_batch, generated_images))\r\n # y = [1] * d_train_batch.shape[0] + [0] * generated_images.shape[0]\r\n # X = np.concatenate((d_train_batch, generated_images), axis = 0)\r\n d_loss = d.train_on_batch(X, y)\r\n # # d_loss = d.train_on_batch(X, y)\r\n # d_train_batch = np.zeros((1,80,80,3))\r\n # else:\r\n # d_train_batch = np.concatenate( (d_train_batch, image_batch), axis = 0 )\r\n\r\n # if index % k == 0: # Train g once every k iterations\r\n if index % k_g == 0:\r\n noise = np.random.uniform(-1, 1, (BATCH_SIZE * k_g, noise_dim))\r\n d.trainable = False\r\n g_loss = d_on_g.train_on_batch(noise, [1] * BATCH_SIZE * k_g)\r\n d.trainable = True\r\n\r\n\r\n n_save = 100\r\n if index % n_save == 0:\r\n print(\"------------------------------------------------\")\r\n print(\"batch %d d_loss : %f\" % (index, d_loss))\r\n print(\"batch %d g_loss : %f\" % (index, g_loss))\r\n d_loss_save.append(d_loss)\r\n g_loss_save.append(g_loss)\r\n indexes.append(index)\r\n\r\n if index % 100 == 0: #output a picture\r\n noise = np.random.uniform(-1, 1, size=(1, noise_dim))\r\n sample_images = g.predict(noise, verbose=0)\r\n image = sample_images[0]\r\n image = image * 127.5 + 127.5\r\n image = np.squeeze(image)\r\n Image.fromarray(image.astype(np.uint8)).save(\"Samples/\" + str(epoch) + \"_\" + str(index) + \".png\")\r\n\r\n save_loss(epoch, indexes, d_loss_save, g_loss_save)\r\n\r\n d_loss_mat.append(d_loss)\r\n g_loss_mat.append(g_loss)\r\n if (epoch+1) % 2 == 0: #save weights & plot loss\r\n g.save_weights('generator_' + str(epoch), True)\r\n d.save_weights('discriminator_'+ str(epoch), True)\r\n plot_loss(g_loss_mat, d_loss_mat, epoch)\r\n\r\n\r\n\r\nX_train_1 = load_pickle.load('000001_050000.pickle')\r\nX_train_2 = load_pickle.load('050000_100000.pickle')\r\nX_train = np.concatenate( (X_train_1, X_train_2), axis = 0 )\r\nprint(X_train.shape)\r\nX_train = (X_train - 127.5)/127.5\r\n# X_train = X_train / 255\r\n# X_train_red = X_train[:,:,:,0]\r\n# X_train_green = X_train[:,:,:,1]\r\n# X_train_blue = X_train[:,:,:,2]\r\n# X_train_gray = 0.2989 * X_train_red + 0.5870 * X_train_green + 0.1140 * X_train_blue\r\n# X_train_gray = X_train_gray.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2], 1)\r\n# X_train_gray = (X_train_gray - 0.5 ) / 0.5\r\nprint(X_train[0,0,0,0])\r\n\r\ntrain(X_train, BATCH_SIZE = 20)\r\n"
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"repo_name": "Yi-61/Image_Completion_CS230",
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"text": "# Image_Completion_CS230\nThis is a class project for CS230 2017-2018 Winter.\n\nThe Image_Generation folder contains scripts for data preprocessing and DCGAN implementation.\n\nThe Sample_Data folder contains a small training dataset.\n"
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"text": "from PIL import Image\nimport numpy as np\nimport os.path\nimport pickle\nimport time\n\n# Read landmarks\n# Delete the first two rows of 'list_landmarks_align_celeba.txt' and rename it to 'landmarks_align.txt'\ndef read_landmarks(landmark_folder_path):\n landmarks_path = os.path.join(landmark_folder_path, 'landmarks_align.txt')\n landmarks_file = open(landmarks_path, 'r')\n landmarks = np.loadtxt(landmarks_file, usecols = (1,2,3,4,5,6,7,8,9,10))\n return landmarks\n\n# Return an image\ndef read_one_image(image_folder_path, image_name):\n image_path = os.path.join(image_folder_path, image_name)\n image = Image.open(image_path)\n return image\n\n# Crop one image and resize it\n# Return ndarray\ndef resize_one_image(index, image, landmarks, save_flag = False, save_folder_path = './celeba_data/image_align_processed'):\n image_data = np.asarray(image, dtype = \"int32\")\n width, height = image_data.shape[0], image_data.shape[1]\n left_eye_x, left_eye_y, right_eye_x, right_eye_y, nose_x, nose_y, left_mouth_x, left_mouth_y, right_mouth_x, right_mouth_y = landmarks[index-1]\n length_crop = 80\n edge_to_eye = 20\n edge_to_mouth = 60\n length_resize = 80\n if left_eye_x - edge_to_eye < 0:\n left = 0\n right = left + length_crop\n elif right_eye_x + edge_to_eye > width - 1:\n right = width - 1\n left = right - length_crop\n else:\n left = left_eye_x - edge_to_eye\n right = left + length_crop\n\n mouth_mean_y = np.mean([left_mouth_y, right_mouth_y])\n if mouth_mean_y - edge_to_mouth < 0:\n upper = 0\n lower = upper + length_crop\n elif mouth_mean_y + (length_crop - edge_to_mouth) > height - 1:\n lower = height - 1\n upper = lower - length_crop\n else:\n upper = mouth_mean_y - edge_to_mouth\n lower = upper + length_crop\n\n image_cropped = image.crop((left, upper, right, lower))\n image_resized = image_cropped.resize((length_resize, length_resize))\n\n if save_flag:\n resized_image_name = 'resized_' + str(length_resize) + '_' + str(index).zfill(6) + '.jpg'\n image_resized.save(os.path.join(save_folder_path, resized_image_name))\n\n return image_resized\n\n# Write array of image data to pickle files\ndef write_to_pickle(landmark_folder_path = './celeba_data', image_folder_path = './celeba_data/image_align',\n save_folder_path = './celeba_data/pickle', start_index = 1, end_index = 202599, verbose_step = 1000, save_step = 10000):\n if start_index < 1:\n start_index = 1\n if end_index > 202599:\n end_index = 202599\n\n image_data_list = []\n landmarks = read_landmarks(landmark_folder_path)\n print('Landmarks read')\n\n last_saved_index = start_index - 1\n tic = time.time()\n for index in range(start_index, end_index + 1):\n image_name = str(index).zfill(6) + '.jpg'\n image = read_one_image(image_folder_path, image_name)\n image_resized = resize_one_image(index, image, landmarks)\n image_resized_data = np.asarray(image_resized, dtype='uint8')\n image_data_list.append(image_resized_data)\n\n if index % verbose_step == 0:\n print('Completed image index: ' + str(index))\n\n if index % save_step == 0 or index == end_index:\n image_data_array = np.array(image_data_list)\n\n pickle_name = str(last_saved_index + 1).zfill(6) + '_' + str(min(end_index, index)).zfill(6) +'.pickle'\n pickle_path = os.path.join(save_folder_path, pickle_name)\n pickle.dump(image_data_array, open(pickle_path,'wb'))\n\n toc = time.time()\n print('Saved to file: ' + pickle_name)\n print('Time for this batch is: ' + str(toc - tic) + 's')\n\n image_data_list = []\n last_saved_index = index\n tic = time.time()\n\n# Run this line\nwrite_to_pickle()\n"
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"repo_name": "Yi-61/Image_Completion_CS230",
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"text": "from PIL import Image\nimport numpy as np\nimport pickle\nimport os.path\n\ndef load(pickle_name, folder_path = './celeba_data/'):\n pickle_path = os.path.join(folder_path, pickle_name)\n data_read = np.load(pickle_path)\n return data_read\n\n'''\n# For test purposes\nfor i in range(100):\n img_test = Image.fromarray(data_read[i, :, :, :].astype(\"uint8\"), 'RGB')\n img_test.save(str(i) + '_processed.jpg')\n'''\n"
}
] | 5 |
lfihk-thibault/ConverterRepo
|
https://github.com/lfihk-thibault/ConverterRepo
|
80f5a9e546d87b483eb857df996b6cda5c333c06
|
554590e064ad82620f429f2806a9891a93229b21
|
d726ff0bfed4a47a04aa027180c527bb3364d82d
|
refs/heads/master
| 2021-01-10T22:38:10.265433 | 2016-10-01T11:05:31 | 2016-10-01T11:05:31 | 69,572,160 | 0 | 0 | null | null | null | null | null |
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"max_line_length": 59,
"num_lines": 36,
"path": "/BinToDec.py",
"repo_name": "lfihk-thibault/ConverterRepo",
"src_encoding": "UTF-8",
"text": "#Binary to Decimal\r\n#By ImOverlord\r\n\r\n\r\ndef main():\r\n\tbin = raw_input(\"Enter Binary: \")\r\n\tbinLen = len(bin)\r\n\r\n\t#Check if input is Binary\r\n\tfor i in range(binLen):\r\n\t \r\n\t if not bin[i] in [\"1\",\"0\"]:\r\n\t print \"Error, Input entered not in binary\"\r\n\t main()\r\n\r\n\r\n\t#VARs\r\n\tx = binLen\r\n\ty = 0\r\n\tz = \"\"\r\n\r\n\tprint ' '.join([\"\\nConverting\",bin,\"to decimal\\n\"])\r\n\r\n\t#Convertion Code\r\n\tfor i in range(binLen):\r\n\t\t\r\n\t\ty = y + (int(bin[i])*pow(2,binLen))/2\r\n\t\t\r\n\t\tbinLen = binLen - 1\r\n\r\n\r\n\ta = str(y)\r\n\tprint ' '.join([bin,\"is\",a,\"in decimal\"])\r\n\texit() #EXT\r\n\r\nmain()"
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"path": "/DecToBin.py",
"repo_name": "lfihk-thibault/ConverterRepo",
"src_encoding": "UTF-8",
"text": "\r\ndef DecToBin():\r\n\tdec = raw_input(\"\\nEnter Decimal: \")\r\n\tfor i in range(len(dec)):\r\n\t\tif not dec[i] in [\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"]:\r\n\t\t\tprint \"Error, Input is not a number\"\r\n\t\t\tDecToBin()\r\n\r\n\ta = dec\r\n\tb = 0\r\n\tc = 0\r\n\tbin = \"\"\r\n\tbin1 = \"\"\r\n\tprint ' '.join(['\\nConverting', dec, 'to binary\\n'])\r\n\twhile a != 0:\r\n\t\tb = int(a) / 2\r\n\t\tb = int(b)\r\n\t\tc = int(a) - (b * 2)\r\n\t\ta = b\r\n\t\tbin = ''.join([bin,str(c)])\r\n\tfor i in range(len(bin)):\r\n\t\ts = (len(bin) - 1) - i\r\n\t\tbin1 = ''.join([bin1,bin[s]])\r\n\t\r\n\tprint ' '.join([dec, 'is ',bin1,'in binary'])\r\n\texit()\r\nDecToBin()\r\n"
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"path": "/README.md",
"repo_name": "lfihk-thibault/ConverterRepo",
"src_encoding": "UTF-8",
"text": "# ConverterRepo\nSimple programs that convert information\n\n- Binary to Decimal\n- Decimal to Binary\n\nUpcoming:\n\n- Hexadecimal to Binary\n- Binary to Hexadecimal\n"
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"repo_name": "lfihk-thibault/ConverterRepo",
"src_encoding": "UTF-8",
"text": "#Binary/Decimal Converter\r\n#By ImOverlord\r\n\r\n\r\n\r\n\r\ndef DecToBin():\r\n\tdec = raw_input(\"\\nEnter Decimal: \")\r\n\tfor i in range(len(dec)):\r\n\t\tif not dec[i] in [\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"]:\r\n\t\t\tprint \"Error, Input is not a number\"\r\n\t\t\tDecToBin()\r\n\r\n\ta = dec\r\n\tb = 0\r\n\tc = 0\r\n\tbin = \"\"\r\n\tbin1 = \"\"\r\n\tprint ' '.join(['\\nConverting', dec, 'to binary\\n'])\r\n\twhile a != 0:\r\n\t\tb = int(a) / 2\r\n\t\tb = int(b)\r\n\t\tc = int(a) - (b * 2)\r\n\t\ta = b\r\n\t\tbin = ''.join([bin,str(c)])\r\n\tfor i in range(len(bin)):\r\n\t\ts = (len(bin) - 1) - i\r\n\t\tbin1 = ''.join([bin1,bin[s]])\r\n\t\r\n\tprint ' '.join([dec, 'is ',bin1,'in binary'])\r\n\texit()\r\n\r\n\r\ndef BinToDec():\r\n\tbin = raw_input(\"\\nEnter Binary: \")\r\n\tbinLen = len(bin)\r\n\r\n\t#Check if input is Binary\r\n\tfor i in range(binLen):\r\n\t \r\n\t if not bin[i] in [\"1\",\"0\"]:\r\n\t print \"Error, Input entered not in binary\"\r\n\t BinToDec()\r\n\r\n\t#VARs\r\n\tx = binLen\r\n\ty = 0\r\n\tz = \"\"\r\n\r\n\tprint ' '.join([\"\\nConverting\",bin,\"to decimal\\n\"])\r\n\r\n\t#Convertion Code\r\n\tfor i in range(binLen):\r\n\t\t\r\n\t\ty = y + (int(bin[i])*pow(2,binLen))/2\r\n\t\t\r\n\t\tbinLen = binLen - 1\r\n\r\n\r\n\ta = str(y)\r\n\tprint ' '.join([bin,\"is\",a,\"in decimal\"])\r\n\texit() #EXT\r\n\r\ndef Main():\r\n\tprint \"What Convertion do you want to do? \\n \\n 1) Binary To Decimal \\n 2) Decimal To Binary\"\r\n\tinput1 = raw_input(\"\\n> \")\r\n\r\n\tif not input1 in [\"1\",\"2\"]:\r\n\t\tprint \"Input not recognized\\n \\n\"\r\n\t\tMain()\r\n\telif input1 == \"1\":\r\n\t\tBinToDec()\r\n\telse:\r\n\t\tDecToBin()\r\n\r\n\r\nMain()"
}
] | 4 |
AishreeRamesh/simplewebserver
|
https://github.com/AishreeRamesh/simplewebserver
|
62d9154a78479aa2a53068b394aaa74e80bb11eb
|
267b475d3436cd6e86f179e2033c796b5c9b1ffc
|
323296cff2a1dc050440640365e3b60522b5544b
|
refs/heads/main
| 2023-03-02T00:17:18.943788 | 2021-02-10T19:18:02 | 2021-02-10T19:18:02 | 337,796,938 | 0 | 0 | null | 2021-02-10T17:17:59 | 2021-02-08T01:38:33 | 2021-02-08T01:38:30 | null |
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"text": "# Developing a Simple Webserver\n## AIM:\nTo develop a simple webserver to serve html pages.\n\n## DESIGN STEPS:\n### Step 1: \nHTML content creation\n### Step 2:\nDesign of webserver workflow\n### Step 3:\nImplementation using Python code\n### Step 4:\nServing the HTML pages.\n### Step 5:\nTesting the webserver\n\n## PROGRAM:\n\n```\nfrom http.server import HTTPServer, BaseHTTPRequestHandler\ncontent = \"\"\"\n<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n<style>\nh1 {text-align: center;}\n.hello {text-align: center;}\nh1 {color:blue;}\n.hello {color:green;}\nh1 {font-family:'Arial Narrow';}\n.hello {font-family:verdana;}\n</style>\n<title>MY WEBSERVER</title>\n</head>\n<body>\n<h1><u>Multiplication table of 11</u></h1>\n<p class = \"hello\">4 x 0 = 0</p>\n<p class = \"hello\">4 x 1 = 4</p>\n<p class = \"hello\">4 x 2 = 8</p>\n<p class = \"hello\">4 x 3 = 12</p>\n<p class = \"hello\">4 x 4 = 16</p>\n<p class = \"hello\">4 x 5 = 20</p>\n<p class = \"hello\">4 x 6 = 24</p>\n<p class = \"hello\">4 x 7 = 28</p>\n<p class = \"hello\">4 x 8 = 32</p>\n<p class = \"hello\">4 x 9 = 36</p>\n<p class = \"hello\">4 x 10 = 40</p>\n<p class = \"hello\">4 x 11 = 44</p>\n<p class = \"hello\">4 x 12 = 48</p>\n</body>\n</html>\n\"\"\"\n\nclass myhandler(BaseHTTPRequestHandler):\n def do_GET(self):\n print(\"request received\")\n\n self.send_response(200)\n self.send_header('content-type', 'text/html; charset=utf-8')\n self.end_headers()\n\n self.wfile.write(content.encode())\n\nserver_address = ('',80)\n\nhttpd = HTTPServer(server_address,myhandler)\n\nprint(\"my webserver is running...\")\nhttpd.serve_forever()\n```\n\n## OUTPUT:\n\n\n\n## Validation Report:\n\n\n\n\n## RESULT:\n\nThus, a webserver is designed to display multiplication table and is hosted in the URL http://aishree.student.saveetha.in/"
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"text": "from http.server import HTTPServer, BaseHTTPRequestHandler\ncontent = \"\"\"\n<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n<style>\nh1 {text-align: center;}\n.hello {text-align: center;}\nh1 {color:blue;}\n.hello {color:green;}\nh1 {font-family:'Arial Narrow';}\n.hello {font-family:verdana;}\n</style>\n<title>MY WEBSERVER</title>\n</head>\n<body>\n<h1><u>Multiplication table of 11</u></h1>\n<p class = \"hello\">4 x 0 = 0</p>\n<p class = \"hello\">4 x 1 = 4</p>\n<p class = \"hello\">4 x 2 = 8</p>\n<p class = \"hello\">4 x 3 = 12</p>\n<p class = \"hello\">4 x 4 = 16</p>\n<p class = \"hello\">4 x 5 = 20</p>\n<p class = \"hello\">4 x 6 = 24</p>\n<p class = \"hello\">4 x 7 = 28</p>\n<p class = \"hello\">4 x 8 = 32</p>\n<p class = \"hello\">4 x 9 = 36</p>\n<p class = \"hello\">4 x 10 = 40</p>\n<p class = \"hello\">4 x 11 = 44</p>\n<p class = \"hello\">4 x 12 = 48</p>\n</body>\n</html>\n\"\"\"\n\nclass myhandler(BaseHTTPRequestHandler):\n def do_GET(self):\n print(\"request received\")\n\n self.send_response(200)\n self.send_header('content-type', 'text/html; charset=utf-8')\n self.end_headers()\n\n self.wfile.write(content.encode())\n\nserver_address = ('',80)\n\nhttpd = HTTPServer(server_address,myhandler)\n\nprint(\"my webserver is running...\")\nhttpd.serve_forever()"
}
] | 2 |
pratikshap3/my_caluclator_demo
|
https://github.com/pratikshap3/my_caluclator_demo
|
55562e197d02e3cb1e7f6e60360aac8cfd214031
|
3071f1e9fc138862c049b4a5312bfa3caa18a15e
|
df6178298e8a7011e9ca7dcc74de4a25a8ca475c
|
refs/heads/master
| 2023-01-25T01:24:16.342348 | 2020-12-11T11:52:55 | 2020-12-11T11:52:55 | 299,605,967 | 0 | 1 | null | null | null | null | null |
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"text": "import cal_code\nimport unittest\n\nclass TestCalculator(unittest.TestCase):\n def test_addition(self):\n result = cal_code.add(3, 5)\n self.assertEqual(result, 8)\n \n def test_subtraction(self):\n result = cal_code.subtract(5, 2)\n self.assertEqual(result, 3)\n \n \nif __name__ == '__main__':\n unittest.main()\n"
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"src_encoding": "UTF-8",
"text": "# my_caluclator_demo\nFor ecode\n"
}
] | 2 |
oalejel/scioly-room-sign-generator
|
https://github.com/oalejel/scioly-room-sign-generator
|
452f61fe86cde060afd9aa244554f1bc0ae67e73
|
9a353e1bceeae603d7f9ecba3cb4d24ed3f8d1d0
|
0043d6cd93ecf7358e8f8ca2831248b021b0543a
|
refs/heads/master
| 2020-04-21T15:06:21.961696 | 2019-02-09T01:51:27 | 2019-02-09T01:51:27 | 169,657,912 | 0 | 0 | null | null | null | null | null |
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"src_encoding": "UTF-8",
"text": "#!/usr/bin/env python\n\"Make Science Olympiad room signs.\"\n\n\"\"\"\nFOR EVENT SIGNS:\nlist all event names in ALPHABETICAL ORDER in indexed-event-names.json if some titles are too long, place a\nnewline character (forward slash n) to separate the indexed-images folder should\ncontain images named after their events. This program matches images with event \nnames by alphabetically ordering the image names. Make names lower-case letters.\n\nExample: { \"event-names\": [ ... ] }\n\nNOTE: check to make sure images match titles after running!\n\n\nFOR HOMEROOM SIGNS:\nIt's ok if you have duplicate team names; the program will throw those out.\nMake sure school-names has an array of strings with key \"school-names\"\nExample: { \"school-names\": [ ... ] }\n\nNOTE: you can also include room names like \"GRADING ROOM\" in the array.\n\"\"\"\n\nimport os\nfrom sys import argv\n\nfrom reportlab.pdfgen import canvas\nfrom reportlab.pdfbase.pdfmetrics import registerFont\nfrom reportlab.pdfbase.ttfonts import TTFont\nfrom reportlab.pdfbase.pdfmetrics import stringWidth\nfrom reportlab.rl_config import defaultPageSize\nimport json\n\n# filled later in program\nevent_names = list()\nschool_names = list()\n\n# define some dimensions\npoint = 1\ninch = 72\nPAGE_WIDTH = 8.5 * inch\nPAGE_HEIGHT = 11 * inch\nMARGIN = 0.5 * inch\n\n# inserts newlines to make text fit width of a page \n# goals: 1) minimize number of newlines \n# 2) maximize width used in text without splitting words\ndef fit_text_to_width(canv, max_width, text):\n words = text.split(' ')\n word_widths = [canv.stringWidth(w) for w in words]\n space_width = canv.stringWidth(' ')\n\n index = 0\n while index < len(word_widths):\n width = word_widths[index]\n # furthest index of words to include.\n # inclusion_index - index is number of spaces to consider\n inclusion_index = index\n \n # if we can fit more words in this line\n if width < max_width:\n while inclusion_index < len(word_widths):\n # see what the width of adding the next word (with spaces) is\n words_sum = sum(word_widths[index:inclusion_index + 2])\n space_sum = space_width * (inclusion_index - index)\n test_sum = words_sum + space_sum\n if test_sum <= max_width:\n inclusion_index += 1\n else:\n break\n # once we have optimize the placement of a newline, modify \n # the words array to place a newline symbol at the end of the last word\n if inclusion_index < len(word_widths) - 1:\n words[inclusion_index] += '\\n'\n \n index = inclusion_index\n index += 1\n \n # insert spaces in relevant places\n for i in range(0, len(words) - 1):\n if words[i][-1] != '\\n':\n words[i] += ' '\n \n # then join our padded words\n return \"\".join(words)\n\ndef make_event_pdf(output_filename, event_name, img_name):\n c = canvas.Canvas(output_filename, pagesize=(PAGE_WIDTH, PAGE_HEIGHT))\n c.setStrokeColorRGB(0,0,0)\n c.setFillColorRGB(0,0,0)\n registerFont(TTFont('UniversCondensed','univcond.ttf'))\n \n # draw event image in bottom center \n c.drawImage(img_name, \n x=MARGIN, \n y=50, \n width=PAGE_WIDTH - (2 * MARGIN),\n height=300,\n preserveAspectRatio=True)\n \n # add a header that says Science Olympiad with our logo on right \n header_text_height = 25\n c.setFont(\"UniversCondensed\", header_text_height)\n c.drawString(MARGIN, PAGE_HEIGHT - (MARGIN + header_text_height), \"Science Olympiad\")\n \n # draw icon in top right\n img_aspect_ratio = 1.10376 # height / 1.10376\n img_width = 60.0\n c.drawImage(\"icon.png\", \n x=PAGE_WIDTH-(MARGIN + img_width), \n y=PAGE_HEIGHT-(img_width * img_aspect_ratio + MARGIN), \n width=img_width, \n height=img_width*img_aspect_ratio)\n \n # we want vertical center of grouped title, that may be multiline\n # to be at height (PAGE_HEIGHT - title_height * 3.5)\n # must compute an offset from this centering if we have multiple lines to write for the title\n # get estimated width and height of title \n title_width = c.stringWidth(event_name)\n title_height = 100 * point\n # set our title font to 100 pts\n c.setFont(\"UniversCondensed\", title_height)\n \n width_limit = PAGE_WIDTH - (2*MARGIN)\n adjusted_text = fit_text_to_width(c, width_limit, event_name)\n \n vertical_offset = PAGE_HEIGHT - title_height * 3.4\n lines = adjusted_text.split('\\n')\n vertical_offset += (float(len(lines)) // 2) * title_height\n \n for subtline in lines:\n c.drawCentredString(PAGE_WIDTH * 0.5, vertical_offset, subtline)\n vertical_offset -= title_height\n print(lines)\n\n c.showPage()\n c.save()\n \ndef generate_event_signs():\n try:\n with open('indexed-event-names.json') as f:\n event_names = json.load(f)[\"event-names\"]\n except:\n print(\"File not found: indexed-event-names.json\")\n print(\"This script needs this file to generate PDFs.\")\n \n # make a folder for event pdfs \n if not os.path.exists(\"event-pdfs-output\"):\n os.mkdir(\"event-pdfs-output\")\n \n # sort event names in case people don't read instructions ;)\n event_names.sort()\n \n # if no images folder, the complain\n image_names = list()\n try:\n image_names = os.listdir(\"images\")\n image_names.sort() # sort alphabetically\n # then remove any hidden files\n image_names = [i for i in image_names if i[0] != '.']\n except:\n print(\"Missing images folder!\")\n\n for index, event in enumerate(event_names):\n filename = \"{}-room.pdf\".format(event.replace('\\n', ''))\n img_name = \"images/{}\".format(image_names[index])\n make_event_pdf(\"event-pdfs-output/{}\".format(filename), event, img_name)\n print(\"Wrote\", filename)\n \ndef make_homeroom_pdf(output_filename, school_name):\n global PAGE_WIDTH, PAGE_HEIGHT \n # swap to draw in landscape \n# PAGE_WIDTH, PAGE_HEIGHT = PAGE_HEIGHT, PAGE_WIDTH\n \n c = canvas.Canvas(output_filename, pagesize=(PAGE_WIDTH, PAGE_HEIGHT))\n c.setStrokeColorRGB(0,0,0)\n c.setFillColorRGB(0,0,0)\n registerFont(TTFont('UniversCondensed','univcond.ttf'))\n \n # add a header that says Science Olympiad with our logo on right \n header_text_height = 25\n c.setFont(\"UniversCondensed\", header_text_height)\n c.drawString(MARGIN, PAGE_HEIGHT - (MARGIN + header_text_height), \"UMSO\")\n \n # draw icon in top right\n img_aspect_ratio = 1.10376 # height / 1.10376\n img_width = 60.0\n c.drawImage(\"icon.png\", \n x=PAGE_WIDTH-(MARGIN + img_width), \n y=PAGE_HEIGHT-(img_width * img_aspect_ratio + MARGIN),\n width=img_width,\n height=img_width*img_aspect_ratio)\n \n # we want vertical center of grouped title, that may be multiline\n # to be at height (PAGE_HEIGHT - title_height * 3.5)\n # must compute an offset from this centering if we have multiple lines to write for the title\n # get estimated width and height of title \n title_width = c.stringWidth(school_name)\n title_height = 100 * point\n # set our title font to 100 pts\n c.setFont(\"UniversCondensed\", title_height)\n \n width_limit = PAGE_WIDTH - (2*MARGIN)\n adjusted_text = fit_text_to_width(c, width_limit, school_name)\n \n vertical_offset = PAGE_HEIGHT - title_height * 4\n lines = adjusted_text.split('\\n')\n vertical_offset += (len(lines) // 2) * title_height\n \n for subtline in lines:\n c.drawCentredString(PAGE_WIDTH * 0.5, vertical_offset, subtline)\n vertical_offset -= title_height\n \n # add soinc logo at bottom \n # draw icon in top right\n c.drawImage(\"soinc-logo.png\", \n x=MARGIN, \n y=25,\n width=width_limit,\n height=200,\n preserveAspectRatio=True);\n \n c.showPage()\n c.save()\n \n # undo our flip for landscape\n# PAGE_WIDTH, PAGE_HEIGHT = PAGE_HEIGHT, PAGE_WIDTH\n \ndef generate_homeroom_signs():\n try:\n with open('school-names.json') as f:\n school_names = json.load(f)[\"school-names\"]\n except:\n print(\"File not found: school-names.json\")\n print(\"This script needs this file to generate homeroom PDFs.\")\n \n # make a folder for homeroom pdfs \n if not os.path.exists(\"homeroom-pdfs-output\"):\n os.mkdir(\"homeroom-pdfs-output\")\n \n for name in school_names:\n filename = \"homeroom-pdfs-output/{}.pdf\".format(name)\n filename.replace('\\n', '')\n filename.replace(' ', '')\n make_homeroom_pdf(filename, name)\n \nif __name__ == \"__main__\":\n # unconditionally regenerate all\n # consider reading arguments to decide which of the two to print\n generate_homeroom_signs()\n generate_event_signs()\n \n"
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"text": "# Science Olympiad Room Sign Generator\n\nUsage\n* Place all images named after each Science Olympiad event in the images folder\n* Update indexed-event-names.json by listing all event names in JSON format\n* Run pdf-generator.py\n"
}
] | 2 |
ktodorov/historical-ocr
|
https://github.com/ktodorov/historical-ocr
|
f40eac59353a3811a93e9e5b5dcb51e72ff5d893
|
d4d7bf0addf5ff98b7182c00ff716e79c97e050e
|
dfe87fb2217fefe80eb02a8f4ad5fa3d0cc91f96
|
refs/heads/main
| 2023-04-08T06:30:26.646143 | 2021-12-22T16:19:37 | 2021-12-22T16:19:37 | 304,329,922 | 0 | 0 | null | null | null | null | null |
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"text": "import os\nfrom typing import Dict, List\nimport numpy as np\nimport torch\nimport pickle\nfrom overrides import overrides\n\nfrom datasets.document_dataset_base import DocumentDatasetBase\nfrom services.arguments.ocr_quality_arguments_service import OCRQualityArgumentsService\nfrom services.log_service import LogService\n\nfrom services.process.word2vec_process_service import Word2VecProcessService\n\nclass Word2VecDataset(DocumentDatasetBase):\n def __init__(\n self,\n arguments_service: OCRQualityArgumentsService,\n process_service: Word2VecProcessService,\n log_service: LogService,\n **kwargs):\n super().__init__()\n\n self._arguments_service = arguments_service\n self._log_service = log_service\n self._process_service = process_service\n\n self._text_corpus = process_service.get_text_corpus(ocr_output_type=self._arguments_service.ocr_output_type)\n self._log_service.log_debug(f'Loaded {self._text_corpus.length} entries in word2vec dataset')\n\n def __len__(self):\n return self._text_corpus.length\n\n def __getitem__(self, id):\n return id\n\n def use_collate_function(self) -> bool:\n return True\n\n def collate_function(self, ids):\n skip_gram_entries = self._text_corpus.get_entries(ids)\n batch_size = len(ids)\n\n target_tokens = [x.target_token for x in skip_gram_entries]\n context_tokens_lists = [x.context_tokens for x in skip_gram_entries]\n\n lengths = [len(sequence) for sequence in context_tokens_lists]\n max_length = max(lengths)\n\n padded_contexts = np.zeros((batch_size, max_length), dtype=np.int64)\n\n for i, l in enumerate(lengths):\n padded_contexts[i][0:l] = context_tokens_lists[i][0:l]\n\n return (\n torch.from_numpy(padded_contexts).long().to(self._arguments_service.device),\n torch.LongTensor(target_tokens).to(self._arguments_service.device))\n\n def get_indices_per_document(self) -> Dict[int, List[int]]:\n return self._text_corpus.get_indices_per_document()"
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"text": "from torch.utils.data import Dataset\n\nfrom overrides import overrides\n\nclass DatasetBase(Dataset):\n def __init__(self, **kwargs):\n super().__init__()\n\n def __len__(self) -> int:\n return len(super())\n\n def __getitem__(self, idx):\n return super().__getitem__(idx)\n\n def use_collate_function(self) -> bool:\n return False\n\n def collate_function(self, sequences):\n pass\n"
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"path": "/optimizers/adamw_optimizer.py",
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"text": "from torch import optim\nfrom torch.optim.optimizer import Optimizer\nfrom overrides import overrides\n\nfrom models.model_base import ModelBase\n\nfrom optimizers.optimizer_base import OptimizerBase\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\n\n\nclass AdamWOptimizer(OptimizerBase):\n def __init__(\n self,\n arguments_service: ArgumentsServiceBase,\n model: ModelBase):\n super().__init__(arguments_service, model)\n self._weight_decay = arguments_service.weight_decay\n\n def _init_optimizer(self) -> Optimizer:\n optimizer = optim.AdamW(\n self._model.optimizer_parameters(),\n lr=self._learning_rate,\n weight_decay=self._weight_decay)\n\n return optimizer\n\n def step(self):\n self._optimizer.step()\n\n def zero_grad(self):\n self._optimizer.zero_grad()\n"
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"text": "from services.experiments.process.neighbourhood_overlap_process_service import NeighbourhoodOverlapProcessService\nfrom services.plot_service import PlotService\nfrom services.file_service import FileService\nfrom services.arguments.ocr_evaluation_arguments_service import OCREvaluationArgumentsService\nfrom typing import Dict, List\nfrom entities.plot.figure_options import FigureOptions\nfrom entities.plot.plot_options import PlotOptions\nfrom enums.experiment_type import ExperimentType\n\n\nclass SetSizedBasedPlotService:\n def __init__(\n self,\n arguments_service: OCREvaluationArgumentsService,\n file_service: FileService,\n plot_service: PlotService,\n neighbourhood_overlap_process_service: NeighbourhoodOverlapProcessService):\n self._file_service = file_service\n self._arguments_service = arguments_service\n self._plot_service = plot_service\n self._neighbourhood_overlap_process_service = neighbourhood_overlap_process_service\n\n def plot_set_size_bases(self):\n experiments_folder = self._file_service.get_experiments_path()\n experiment_type_folder = self._file_service.combine_path(\n experiments_folder,\n ExperimentType.OverlapSetSizeComparison.value,\n create_if_missing=True)\n\n ax = self._plot_service.create_plot(\n PlotOptions(\n figure_options=FigureOptions(\n seaborn_style='whitegrid')))\n percentages = {}\n\n for set_size in range(100, 1050, 50):\n overlaps_by_config_and_seed = self._neighbourhood_overlap_process_service.get_calculated_overlaps(\n set_size)\n\n for (configuration, is_random_initialized), overlaps_by_seed in overlaps_by_config_and_seed.items():\n if (configuration, is_random_initialized) not in percentages.keys():\n percentages[(configuration, is_random_initialized)] = {}\n\n if all(x is None for x in list(overlaps_by_seed.values())):\n continue\n\n combined_overlaps = self._neighbourhood_overlap_process_service.combine_seed_overlaps(\n overlaps_by_seed,\n set_size,\n max_bins=set_size)\n\n percentage_value = self._get_overlap_percentage(\n combined_overlaps, set_size)\n percentages[(configuration, is_random_initialized)\n ][set_size] = percentage_value\n\n keys_to_delete = [\n key for key, values in percentages.items() if all(x == 0 for x in values)]\n for key in keys_to_delete:\n del percentages[key]\n\n set_sizes = [x for x in range(100, 1050, 50)]\n y_values = [[0 for _ in range(len(set_sizes))]\n for _ in range(len(percentages.keys()))]\n for i, ((configuration, is_random_initialized), percentages_by_set_size) in enumerate(percentages.items()):\n for k, set_size in enumerate(set_sizes):\n if set_size in percentages_by_set_size.keys():\n y_values[i][k] = percentages_by_set_size[set_size]\n\n labels = [\n f'{configuration.value}' +\n ('[random]' if is_random_initialized else '')\n for (configuration, is_random_initialized) in percentages.keys()]\n\n self._plot_service.plot_lines(\n x_values=set_sizes,\n y_values=y_values,\n labels=labels,\n plot_options=PlotOptions(\n ax=ax,\n figure_options=FigureOptions(\n title=f'{ExperimentType.OverlapSetSizeComparison.value}-{self._arguments_service.language.value}',\n save_path=experiment_type_folder,\n filename=f'overlap-set-size-comparison-{self._arguments_service.language.value}')))\n\n def _get_overlap_percentage(self, combined_overlaps: Dict[int, List[int]], set_size: int):\n result = 0\n count = 0\n\n for overlap_amount, overlap_values in combined_overlaps.items():\n current_percentage = overlap_amount / set_size\n for overlap_value in overlap_values:\n if overlap_value is None:\n continue\n\n result = result + (overlap_value * current_percentage)\n count = count + overlap_value\n\n final_percentage = result / count\n return final_percentage\n"
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"text": "import random\nfrom typing import List\nfrom overrides import overrides\n\nfrom datasets.dataset_base import DatasetBase\n\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\n\n\nclass JointDataset(DatasetBase):\n def __init__(\n self,\n sub_datasets: List[DatasetBase],\n **kwargs):\n super(JointDataset, self).__init__()\n\n self._datasets = sub_datasets\n\n def __len__(self):\n return max([len(dataset) for dataset in self._datasets])\n\n def __getitem__(self, idx):\n ids = [dataset[self.correct_id(idx, len(dataset))]\n for dataset in self._datasets]\n\n max_len = 10\n ids = [idx[:max_len] if len(idx) > max_len else idx for idx in ids]\n return ids\n\n def correct_id(self, idx: int, data_list_length: int) -> int:\n result = idx\n if idx > data_list_length:\n result = random.randint(0, data_list_length)\n\n return result\n\n def use_collate_function(self) -> bool:\n return True\n\n def collate_function(self, sequences):\n if not sequences:\n return []\n\n result_list = [None for _ in range(len(sequences[0]))]\n entries = [[] for _ in range(len(sequences[0]))]\n\n for i, sequence in enumerate(sequences):\n for k, entry in enumerate(sequence):\n entries[k].append(entry)\n\n for k in range(len(sequences[0])):\n result_list[k] = self._datasets[i].collate_function(entries[k])\n\n return result_list\n"
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"text": "from enums.argument_enum import ArgumentEnum\n\nclass Configuration(ArgumentEnum):\n BERT = 'bert'\n ALBERT = 'albert'\n XLNet = 'xlnet'\n RoBERTa = 'roberta'\n BART = 'bart'\n CBOW = 'cbow'\n SkipGram = 'skip-gram'\n PPMI = 'ppmi'\n GloVe = 'glove'\n\n @staticmethod\n def get_friendly_name(configuration) -> str:\n if configuration == Configuration.BERT:\n return 'BERT'\n elif configuration == Configuration.ALBERT:\n return 'ALBERT'\n elif configuration == Configuration.SkipGram:\n return 'Skip-gram'\n elif configuration == Configuration.CBOW:\n return 'CBOW'\n elif configuration == Configuration.PPMI:\n return 'PPMI'\n elif configuration == Configuration.GloVe:\n return 'GloVe'\n\n return None"
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"text": "from models.transformers.bert import BERT\nfrom tests.entities.embedding_configuration import EmbeddingConfiguration\nfrom typing import List\nfrom models.model_base import ModelBase\nfrom services.vocabulary_service import VocabularyService\nimport numpy as np\nimport pickle\nfrom models.simple.skip_gram import SkipGram\nfrom models.simple.cbow import CBOW\nfrom tests.fakes.log_service_fake import LogServiceFake\nfrom enums.language import Language\nfrom enums.configuration import Configuration\nfrom enums.challenge import Challenge\nfrom enums.ocr_output_type import OCROutputType\nfrom entities.cache.cache_options import CacheOptions\nimport os\nfrom tests.fakes.non_context_service_fake import NonContextServiceFake\nfrom dependency_injection.ioc_container import IocContainer\nimport dependency_injector.providers as providers\nimport torch\nimport unittest\nfrom sklearn.metrics.pairwise import cosine_distances, cosine_similarity\nfrom scipy import spatial\nimport csv\nimport pandas as pd\n\nimport tests.constants.embedding_models as embedding_models\n\ndef initialize_container(\n configuration: Configuration,\n ocr_output_type: OCROutputType,\n language: Language,\n seed: int = 13,\n override_args: dict = None) -> IocContainer:\n custom_args = {\n 'learning_rate': 1e-3,\n 'data_folder': os.path.join('tests', 'data'),\n 'challenge': Challenge.OCREvaluation,\n 'configuration': configuration,\n 'language': language,\n 'output_folder': os.path.join('tests', 'results'),\n 'ocr_output_type': ocr_output_type,\n 'seed': seed\n }\n\n if override_args is not None:\n for key, value in override_args.items():\n custom_args[key] = value\n\n container = IocContainer()\n container.arguments_service.override(\n providers.Factory(\n NonContextServiceFake,\n custom_args))\n\n container.log_service.override(providers.Factory(LogServiceFake))\n\n return container\n\ndef calculate_context_words(\n configuration: Configuration,\n vocabulary_service: VocabularyService,\n model: ModelBase,\n target_word: str,\n neighbourhood_set_size: int = 50) -> List[str]:\n target_id = vocabulary_service.string_to_id(target_word)\n\n target_embeddings = model.get_embeddings([target_word])\n\n all_embeddings = None\n if configuration == Configuration.SkipGram:\n all_embeddings = list(model._embedding_layer._embeddings_target.parameters())[0].detach().cpu().tolist()\n elif configuration == Configuration.CBOW:\n all_embeddings = list(model._embeddings.parameters())[0].detach().cpu().tolist()\n\n similarities = []\n for j in range(len(all_embeddings)):\n print(f'Processing {target_word} - {j}/{len(all_embeddings)} \\r', end='')\n if j == target_id:\n assert all_embeddings[j] == target_embeddings\n\n # similarity = cosine_similarity(target_embeddings, all_embeddings[j])\n similarity = 1 - spatial.distance.cosine(target_embeddings, all_embeddings[j])\n similarities.append(similarity)\n\n indices = np.argsort(similarities)[::-1]\n sorted_similarities = [similarities[x] for x in indices]\n\n assert sorted_similarities[-2] < sorted_similarities[1]\n sorted_words = vocabulary_service.ids_to_strings(indices)\n\n return sorted_words[:neighbourhood_set_size]\n\ndef initialize_model(\n arguments_service,\n vocabulary_service,\n data_service,\n log_service,\n tokenize_service,\n ocr_output_type: OCROutputType,\n language: Language,\n configuration: Configuration,\n initialize_randomly: bool,\n learning_rate: float):\n\n model = create_model(\n configuration,\n arguments_service,\n vocabulary_service,\n data_service,\n log_service,\n tokenize_service,\n ocr_output_type=ocr_output_type)\n\n model.load(\n path=os.path.join('results', 'ocr-evaluation', configuration.value, language.value),\n name_prefix='BEST',\n name_suffix=None,\n load_model_dict=True,\n use_checkpoint_name=True,\n checkpoint_name=None,\n overwrite_args={\n 'initialize_randomly': initialize_randomly,\n 'configuration': configuration.value,\n 'learning_rate': learning_rate,\n 'minimal_occurrence_limit': 5 if configuration != Configuration.BERT else None,\n # 'checkpoint_name': 'local-test-pre',\n })\n\n return model\n\ndef create_model(\n configuration: Configuration,\n arguments_service,\n vocabulary_service,\n data_service,\n log_service,\n tokenize_service,\n ocr_output_type: OCROutputType,\n pretrained_matrix = None):\n if configuration == Configuration.SkipGram:\n result = SkipGram(\n arguments_service=arguments_service,\n vocabulary_service=vocabulary_service,\n data_service=data_service,\n log_service=log_service,\n pretrained_matrix=pretrained_matrix,\n ocr_output_type=ocr_output_type)\n elif configuration == Configuration.CBOW:\n result = CBOW(\n arguments_service=arguments_service,\n vocabulary_service=vocabulary_service,\n data_service=data_service,\n log_service=log_service,\n pretrained_matrix=pretrained_matrix,\n ocr_output_type=ocr_output_type)\n elif configuration == Configuration.CBOW:\n result = BERT(\n arguments_service=arguments_service,\n data_service=data_service,\n log_service=log_service,\n tokenize_service=tokenize_service,\n overwrite_initialization=False)\n\n return result\n\ntarget_words = {\n Language.English: ['man', 'new', 'time', 'day', 'good', 'old', 'little', 'one', 'two', 'three'],\n Language.Dutch: ['man', 'jaar', 'tijd', 'mensen', 'dag', 'huis', 'dier', 'afbeelding', 'werk', 'naam', 'groot', 'kleine', 'twee', 'drie', 'vier', 'vijf']\n}\n\ndef log_neighbourhoods(\n vocabulary_service: VocabularyService,\n model: ModelBase,\n embedding_configuration: EmbeddingConfiguration,\n output_folder: str):\n for target_word in target_words[embedding_configuration.language]:\n context_words = calculate_context_words(embedding_configuration.configuration, vocabulary_service, model, target_word)\n save_context_words('skip-gram-base', target_word, context_words, output_folder, embedding_configuration)\n\n\ndef save_context_words(\n model_name: str,\n target_word: str,\n context_words: List[str],\n output_folder: str,\n embedding_configuration: EmbeddingConfiguration):\n csv_fieldnames = ['language', 'configuration', 'randomly_initialized', 'ocr_type', 'learning_rate', 'target_word', 'context_words']\n file_path = os.path.join(output_folder, 'context-words.csv')\n\n init_header = not os.path.exists(file_path)\n\n with open(file_path, 'a', encoding='utf-8') as csv_file:\n csv_writer = csv.DictWriter(csv_file, fieldnames=csv_fieldnames)\n if init_header:\n csv_writer.writeheader()\n\n csv_writer.writerow({\n 'language': embedding_configuration.language,\n 'configuration': embedding_configuration.configuration,\n 'randomly_initialized': embedding_configuration.initialize_randomly,\n 'learning_rate': str(embedding_configuration.lr),\n # 'configuration': embedding_configuration.lr,\n 'ocr_type': embedding_configuration.ocr_output_type,\n 'target_word': target_word,\n 'context_words': ', '.join(context_words)\n })\n\ndef log_embedding_layers(model):\n print(f'Base Context mean: {model._embedding_layer._embeddings_context.weight.mean()}')\n print(f'Base Input mean: {model._embedding_layer._embeddings_target.weight.mean()}')\n\n\nclass TestBaselineSkipGram(unittest.TestCase):\n def test_baseline_convergence(self):\n output_folder = os.path.join('tests', 'results')\n file_path = os.path.join(output_folder, 'context-words.csv')\n if os.path.exists(file_path):\n os.remove(file_path)\n\n for language, configurations in embedding_models.configurations.items():\n for configuration, lrs in configurations.items():\n for lr, initialize_randomly_to_output_types in lrs.items():\n for initialize_randomly, output_types in initialize_randomly_to_output_types.items():\n for ocr_output_type in output_types:\n # if configuration != Configuration.SkipGram:\n # continue\n\n container_base = initialize_container(configuration, ocr_output_type, language)\n\n vocabulary_service = container_base.vocabulary_service()\n arguments_service = container_base.arguments_service()\n skip_gram_base = initialize_model(\n arguments_service,\n vocabulary_service,\n container_base.data_service(),\n container_base.log_service(),\n container_base.tokenize_service(),\n ocr_output_type=ocr_output_type,\n language=language,\n configuration=configuration,\n initialize_randomly=initialize_randomly,\n learning_rate=lr)\n\n # log_embedding_layers(skip_gram_base)\n\n embedding_configuration = EmbeddingConfiguration(language, configuration, lr, initialize_randomly, ocr_output_type)\n log_neighbourhoods(vocabulary_service, skip_gram_base, embedding_configuration, output_folder=output_folder)\n\nif __name__ == '__main__':\n unittest.main()\n\n"
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"text": "import os\n\nfrom typing import Tuple, List\n\nfrom overrides import overrides\n\nimport sentencepiece as spm\n\nfrom transformers import PreTrainedTokenizerFast, XLNetTokenizerFast\nfrom transformers.tokenization_utils_fast import PreTrainedTokenizerFast\n\nfrom enums.configuration import Configuration\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\n\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\n\nclass TransformerTokenizeService(BaseTokenizeService):\n def __init__(\n self,\n arguments_service: PretrainedArgumentsService):\n super().__init__()\n\n pretrained_weights = arguments_service.pretrained_weights\n self._tokenizer: PreTrainedTokenizerFast = self._tokenizer_type.from_pretrained(pretrained_weights)\n\n def encode_tokens(self, tokens: List[str]) -> List[int]:\n result = self._tokenizer.convert_tokens_to_ids(tokens)\n return result\n\n def decode_tokens(self, character_ids: List[int]) -> List[str]:\n result = self._tokenizer.decode(character_ids)\n return result\n\n def decode_string(self, character_ids: List[int]) -> List[str]:\n result = self._tokenizer.decode(character_ids)\n return result\n\n def id_to_token(self, character_id: int) -> str:\n result = self._tokenizer.decode([character_id])\n return result\n\n def encode_sequence(self, sequence: str) -> Tuple[List[int], List[str], List[Tuple[int,int]], List[int]]:\n encoded_representations = self._tokenizer.encode_plus(sequence)\n if len(encoded_representations.encodings) > 1:\n raise Exception('More than one encoding found during `encode_plus` operation')\n\n encoded_representation = encoded_representations.encodings[0]\n return (\n encoded_representation.ids,\n encoded_representation.tokens,\n encoded_representation.offsets,\n encoded_representation.special_tokens_mask)\n\n def encode_sequences(self, sequences: List[str]) -> List[Tuple[List[int], List[str], List[Tuple[int,int]], List[int]]]:\n encoded_representations = self._tokenizer.encode_plus(sequences)\n return [(x.ids, x.tokens, x.offsets, x.special_tokens_mask) for x in encoded_representations]\n\n @property\n def vocabulary_size(self) -> int:\n return self._tokenizer.vocab_size\n\n @property\n def mask_token(self) -> str:\n return '<mask>'\n\n @property\n def _tokenizer_type(self) -> type:\n return PreTrainedTokenizerFast"
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"text": "from tests.fakes.dataset_service_fake import DatasetServiceFake\nfrom tests.fakes.train_service_fake import TrainServiceFake\nfrom tests.fakes.log_service_fake import LogServiceFake\nfrom tests.fakes.non_context_service_fake import NonContextServiceFake\nfrom tests.fakes.model_fake import ModelFake\n\nimport os\nfrom dependency_injection.ioc_container import IocContainer\nimport dependency_injector.providers as providers\nimport unittest\n\nfrom enums.language import Language\nfrom enums.configuration import Configuration\nfrom enums.challenge import Challenge\nfrom enums.ocr_output_type import OCROutputType\n\nfrom losses.loss_base import LossBase\nfrom optimizers.optimizer_base import OptimizerBase\n\n\ndef initialize_container(\n ocr_output_type: OCROutputType = None, \n override_args: dict = None) -> IocContainer:\n custom_args = {\n 'challenge': Challenge.OCREvaluation,\n 'configuration': Configuration.SkipGram,\n 'language': Language.English,\n 'output_folder': os.path.join('tests', 'results'),\n 'experiments_folder': os.path.join('tests', 'experiments'),\n 'cache_folder': os.path.join('tests', '.cache'),\n 'ocr_output_type': ocr_output_type,\n 'checkpoint_name': 'local-test',\n 'minimal_occurrence_limit': 5,\n 'initialize_randomly': False,\n 'patience': 10000,\n 'consider_equal_results_as_worse': True\n }\n\n if override_args is not None:\n for key, value in override_args.items():\n custom_args[key] = value\n\n container = IocContainer()\n\n container.arguments_service.override(\n providers.Factory(\n NonContextServiceFake,\n custom_args))\n\n container.log_service.override(providers.Factory(LogServiceFake))\n container.train_service.override(\n providers.Singleton(\n TrainServiceFake,\n arguments_service=container.arguments_service,\n dataloader_service=container.dataloader_service,\n loss_function=container.loss_function,\n optimizer=container.optimizer,\n log_service=container.log_service,\n file_service=container.file_service,\n model=container.model))\n\n container.model.override(\n providers.Factory(\n ModelFake,\n arguments_service=container.arguments_service,\n log_service=container.log_service,\n data_service=container.data_service))\n\n container.optimizer.override(\n providers.Factory(\n OptimizerBase,\n arguments_service=container.arguments_service,\n model=container.model))\n\n container.loss_function.override(\n providers.Factory(\n LossBase))\n\n container.dataset_service.override(\n providers.Factory(\n DatasetServiceFake,\n arguments_service=container.arguments_service,\n cache_service=container.cache_service,\n process_service=container.process_service,\n log_service=container.log_service))\n\n return container\n\ndef distinct(seq):\n seen = set()\n seen_add = seen.add\n return [x for x in seq if not (x in seen or seen_add(x))]\n\nclass TestTraining(unittest.TestCase):\n\n def test_batch_order_different_corpora_same_seed(self):\n pass\n # # Raw model\n container_1 = initialize_container(ocr_output_type=OCROutputType.Raw)\n container_1.main()\n\n train_service_1 = container_1.train_service()\n ids_1 = train_service_1.data_loader_train.dataset._ids\n unique_ids_1 = distinct(ids_1)\n\n # Ground truth model\n container_2 = initialize_container(\n ocr_output_type=OCROutputType.GroundTruth)\n container_2.main()\n\n train_service_2 = container_2.train_service()\n ids_2 = train_service_2.data_loader_train.dataset._ids\n unique_ids_2 = distinct(ids_2)\n\n self.assertListEqual(unique_ids_1, unique_ids_2)\n\n def test_batch_order_different_corpora_different_seed(self):\n pass\n # # Raw model\n container_1 = initialize_container(ocr_output_type=OCROutputType.Raw)\n container_1.main()\n\n train_service_1 = container_1.train_service()\n ids_1 = train_service_1.data_loader_train.dataset._ids\n ids_1_string = ''.join([str(x) for x in distinct(ids_1)])\n\n # Ground truth model\n container_2 = initialize_container(\n ocr_output_type=OCROutputType.GroundTruth,\n override_args={\n 'seed': 7\n })\n container_2.main()\n\n train_service_2 = container_2.train_service()\n ids_2 = train_service_2.data_loader_train.dataset._ids\n ids_2_string = ''.join([str(x) for x in distinct(ids_2)])\n\n self.assertNotEqual(ids_1_string, ids_2_string)\n\nif __name__ == '__main__':\n unittest.main()\n"
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"text": "from datasets.document_dataset_base import DocumentDatasetBase\nfrom services.log_service import LogService\nfrom samplers.document_sampler import DocumentSampler\nimport numpy as np\n\nfrom typing import Tuple\n\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom transformers import BertTokenizer\n\nfrom enums.run_type import RunType\n\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\nfrom services.dataset_service import DatasetService\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\n\n\nclass DataLoaderService:\n\n def __init__(\n self,\n arguments_service: ArgumentsServiceBase,\n dataset_service: DatasetService,\n log_service: LogService):\n\n self._dataset_service = dataset_service\n self._arguments_service = arguments_service\n self._log_service = log_service\n\n def get_train_dataloaders(self) -> Tuple[DataLoader, DataLoader]:\n \"\"\"Loads and returns train and validation(if available) dataloaders\n\n :return: the dataloaders\n :rtype: Tuple[DataLoader, DataLoader]\n \"\"\"\n data_loader_train = self._initialize_dataloader(\n run_type=RunType.Train,\n batch_size=self._arguments_service.batch_size,\n shuffle=self._arguments_service.shuffle)\n\n data_loader_validation = None\n if not self._arguments_service.skip_validation:\n data_loader_validation = self._initialize_dataloader(\n run_type=RunType.Validation,\n batch_size=self._arguments_service.batch_size,\n shuffle=False)\n\n return (data_loader_train, data_loader_validation)\n\n def get_test_dataloader(self) -> DataLoader:\n \"\"\"Loads and returns the test dataloader\n\n :return: the test dataloader\n :rtype: DataLoader\n \"\"\"\n data_loader_test = self._initialize_dataloader(\n run_type=RunType.Test,\n batch_size=self._arguments_service.batch_size,\n shuffle=False)\n\n return data_loader_test\n\n def _initialize_dataloader(\n self,\n run_type: RunType,\n batch_size: int,\n shuffle: bool) -> DataLoader:\n\n self._log_service.log_debug(\n f'Initializing dataset for run type \\'{run_type.value}\\'')\n dataset = self._dataset_service.initialize_dataset(run_type)\n data_loader: DataLoader = DataLoader(\n dataset,\n batch_size=batch_size,\n shuffle=shuffle)\n\n if dataset.use_collate_function():\n data_loader.collate_fn = dataset.collate_function\n\n self._log_service.log_debug(\n f'Created dataloader for run type \\'{run_type.value}\\' [shuffle: {shuffle} | batch size: {batch_size} | collate function: {dataset.use_collate_function()}]')\n return data_loader\n"
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"text": "from services.metrics_service import MetricsService\nfrom services.vocabulary_service import VocabularyService\nfrom tests.fakes.log_service_fake import LogServiceFake\nfrom services.cache_service import CacheService\nfrom services.string_process_service import StringProcessService\nfrom services.data_service import DataService\nfrom services.download.ocr_download_service import OCRDownloadService\nfrom services.process.word2vec_process_service import Word2VecProcessService\nfrom services.tokenize.cbow_tokenize_service import CBOWTokenizeService\nfrom models.simple.cbow import CBOW\nfrom services.file_service import FileService\nfrom enums.challenge import Challenge\nfrom enums.language import Language\nfrom enums.configuration import Configuration\nimport os\nfrom tests.fakes.argument_service_fake import ArgumentServiceFake\nimport dependency_injector.containers as containers\nimport dependency_injector.providers as providers\n\n\nclass TestContainer(containers.DeclarativeContainer):\n\n arguments_service = providers.Factory(\n ArgumentServiceFake,\n custom_values={\n 'data_folder': os.path.join('tests', 'data'),\n 'challenge': Challenge.OCREvaluation,\n 'configuration': Configuration.CBOW,\n 'language': Language.English,\n 'output_folder': os.path.join('tests', 'results')\n })\n\n file_service = providers.Factory(\n FileService,\n arguments_service=arguments_service)\n\n log_service = providers.Factory(LogServiceFake)\n\n data_service = providers.Factory(DataService)\n\n string_process_service = providers.Factory(StringProcessService)\n\n cache_service = providers.Singleton(\n CacheService,\n arguments_service=arguments_service,\n file_service=file_service,\n data_service=data_service)\n\n ocr_download_service = providers.Factory(\n OCRDownloadService,\n data_service=data_service,\n string_process_service=string_process_service,\n cache_service=cache_service)\n\n vocabulary_service = providers.Singleton(\n VocabularyService,\n data_service=data_service,\n file_service=file_service,\n cache_service=cache_service\n )\n\n metrics_service = providers.Factory(\n MetricsService\n )\n\n tokenize_service = providers.Singleton(\n CBOWTokenizeService,\n vocabulary_service=vocabulary_service)\n\n process_service = providers.Singleton(\n Word2VecProcessService,\n arguments_service=arguments_service,\n ocr_download_service=ocr_download_service,\n cache_service=cache_service,\n log_service=log_service,\n vocabulary_service=vocabulary_service,\n file_service=file_service,\n tokenize_service=tokenize_service)\n\n model = providers.Singleton(\n CBOW,\n arguments_service=arguments_service,\n process_service=process_service,\n data_service=data_service,\n vocabulary_service=vocabulary_service)\n"
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"text": "import re\nfrom services.vocabulary_service import VocabularyService\nimport string\nfrom typing import List, Tuple\nfrom nltk.tokenize import RegexpTokenizer\n\n\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\n\nclass CBOWTokenizeService(BaseTokenizeService):\n def __init__(\n self,\n vocabulary_service: VocabularyService):\n super().__init__()\n\n self._vocabulary_service = vocabulary_service\n self._tokenizer = RegexpTokenizer(r'\\w+')\n\n def encode_tokens(self, tokens: List[str]) -> List[int]:\n pass\n\n def decode_tokens(self, character_ids: List[int]) -> List[str]:\n result = self._vocabulary_service.ids_to_strings(character_ids)\n return result\n\n def decode_string(self, character_ids: List[int]) -> List[str]:\n pass\n\n def id_to_token(self, character_id: int) -> str:\n pass\n\n def encode_sequence(self, sequence: str) -> Tuple[List[int], List[str], List[Tuple[int,int]], List[int]]:\n pass\n\n def encode_sequences(self, sequences: List[str]) -> List[Tuple[List[int], List[str], List[Tuple[int,int]], List[int]]]:\n result = [([self._vocabulary_service.string_to_id(x)], None, None, None) for x in sequences]\n return result\n\n def tokenize_sequences(self, sequences: List[str]) -> List[List[str]]:\n result = [self._tokenizer.tokenize(self._clean_text(sequence)) for sequence in sequences]\n return result\n\n def _clean_text(self, text):\n # remove numbers\n text_nonum = re.sub(r'\\d+', '', text)\n # remove punctuations and convert characters to lower case\n text_nopunct = \"\".join([char.lower() for char in text_nonum if char not in string.punctuation])\n # substitute multiple whitespace with single whitespace\n # Also, removes leading and trailing whitespaces\n text_no_doublespace = re.sub('\\s+', ' ', text_nopunct).strip()\n return text_no_doublespace"
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"text": "from datetime import datetime, timedelta\nfrom services.log_service import LogService\nimport torch\nimport numpy as np\n\nfrom entities.metric import Metric\nfrom entities.data_output_log import DataOutputLog\n\n\nclass LogServiceFake(LogService):\n def __init__(self):\n pass\n\n def log_progress(\n self,\n current_step: int,\n all_steps: int,\n epoch_num: int = None,\n evaluation: bool = False):\n\n pass\n\n def initialize_evaluation(self):\n pass\n\n def log_evaluation(\n self,\n train_metric: Metric,\n validation_metric: Metric,\n epoch: int,\n iteration: int,\n iterations: int,\n new_best: bool,\n metric_log_key: str = None):\n \"\"\"\n logs progress to user through tensorboard and terminal\n \"\"\"\n\n pass\n\n def log_info(self, message: str):\n print(message)\n\n def log_debug(self, message: str):\n print(message)\n\n def log_error(self, message: str):\n print(message)\n\n def log_exception(self, message: str, exception: Exception):\n log_message = f'Exception occurred. Message: {message}\\nOriginal exception: {exception}'\n print(log_message)\n\n def log_warning(self, message: str):\n print(message)\n\n def log_summary(self, key: str, value: object):\n pass\n\n def log_batch_results(self, data_output_log: DataOutputLog):\n pass\n\n def log_incremental_metric(self, metric_key: str, metric_value: object):\n pass\n\n def log_arguments(self):\n pass\n\n def log_heatmap(\n self,\n heatmap_title: str,\n matrix_values: np.array,\n x_labels: list,\n y_labels: list,\n show_text_inside: bool = False):\n pass\n\n def start_logging_model(self, model: torch.nn.Module, criterion: torch.nn.Module = None):\n pass\n\n def get_time_passed(self) -> timedelta:\n result = timedelta(minutes=60)\n return result\n\n def _get_current_step(self) -> int:\n return 0\n"
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"path": "/tests/test_bert.py",
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"text": "from tests.fakes.log_service_fake import LogServiceFake\nfrom enums.language import Language\nfrom enums.configuration import Configuration\nfrom enums.challenge import Challenge\nfrom enums.ocr_output_type import OCROutputType\nfrom enums.pretrained_model import PretrainedModel\nimport os\nfrom tests.fakes.non_context_service_fake import NonContextServiceFake\nfrom dependency_injection.ioc_container import IocContainer\nimport dependency_injector.providers as providers\nimport torch\nimport unittest\n\n\ndef initialize_container(ocr_output_type: OCROutputType = None, override_args: dict = None) -> IocContainer:\n custom_args = {\n 'data_folder': 'data',\n 'challenge': Challenge.OCREvaluation,\n 'configuration': Configuration.BERT,\n 'language': Language.English,\n 'output_folder': os.path.join('tests', 'results'),\n 'ocr_output_type': ocr_output_type,\n 'include_pretrained_model': True,\n 'pretrained_weights': 'bert-base-cased',\n 'pretrained_model_size': 768,\n 'pretrained_max_length': 512,\n 'pretrained_model': PretrainedModel.BERT,\n }\n\n if override_args is not None:\n for key, value in override_args.items():\n custom_args[key] = value\n\n container = IocContainer()\n container.arguments_service.override(\n providers.Factory(\n NonContextServiceFake,\n custom_args))\n\n container.log_service.override(providers.Factory(LogServiceFake))\n\n return container\n\n\nclass TestBERT(unittest.TestCase):\n\n def test_embedding_matrix_english_initialization(self):\n tokens = ['test', 'token', 'bert', 'vocabulary', 'units', 'python']\n\n main_container = initialize_container()\n metrics_service = main_container.metrics_service()\n\n # Raw model\n container_1 = initialize_container(ocr_output_type=OCROutputType.Raw)\n\n tokenize_service_1 = container_1.tokenize_service()\n encoded_sequences_1 = [\n tokenize_service_1.encode_sequence(token) for token in tokens]\n ids_1 = [torch.Tensor(ids) for ids, _, _, _ in encoded_sequences_1]\n ids_tensor_1 = torch.nn.utils.rnn.pad_sequence(\n ids_1, batch_first=True, padding_value=0).long()\n\n model_1 = container_1.model()\n word_evaluations_1 = model_1.get_embeddings(tokens, ids_tensor_1)\n\n # Ground truth model\n container_2 = initialize_container(\n ocr_output_type=OCROutputType.GroundTruth)\n\n tokenize_service_2 = container_2.tokenize_service()\n encoded_sequences_2 = [\n tokenize_service_2.encode_sequence(token) for token in tokens]\n ids_2 = [torch.Tensor(ids) for ids, _, _, _ in encoded_sequences_2]\n ids_tensor_2 = torch.nn.utils.rnn.pad_sequence(\n ids_2, batch_first=True, padding_value=0).long()\n\n model_2 = container_2.model()\n word_evaluations_2 = model_2.get_embeddings(tokens, ids_tensor_2)\n\n # Assert\n for word_evaluation_1, word_evaluation_2 in zip(word_evaluations_1, word_evaluations_2):\n self.assertEqual(word_evaluation_1.get_embeddings(\n 0), word_evaluation_2.get_embeddings(0))\n self.assertEqual(metrics_service.calculate_cosine_distance(\n word_evaluation_1.get_embeddings(0), word_evaluation_2.get_embeddings(0)), 0.0)\n\n def test_embedding_matrix_dutch_initialization(self):\n override_args = {\n 'language': Language.Dutch,\n 'pretrained_weights': 'wietsedv/bert-base-dutch-cased'\n }\n\n tokens = ['test', 'token', 'bert', 'vocabulary', 'units', 'python']\n main_container = initialize_container(\n override_args=override_args)\n\n metrics_service = main_container.metrics_service()\n\n # Raw model\n container_1 = initialize_container(\n ocr_output_type=OCROutputType.Raw,\n override_args=override_args)\n\n tokenize_service_1 = container_1.tokenize_service()\n encoded_sequences_1 = [\n tokenize_service_1.encode_sequence(token) for token in tokens]\n ids_1 = [torch.Tensor(ids) for ids, _, _, _ in encoded_sequences_1]\n ids_tensor_1 = torch.nn.utils.rnn.pad_sequence(\n ids_1, batch_first=True, padding_value=0).long()\n\n model_1 = container_1.model()\n word_evaluations_1 = model_1.get_embeddings(tokens, ids_tensor_1)\n\n # Ground truth model\n container_2 = initialize_container(\n ocr_output_type=OCROutputType.GroundTruth,\n override_args=override_args)\n\n tokenize_service_2 = container_2.tokenize_service()\n encoded_sequences_2 = [\n tokenize_service_2.encode_sequence(token) for token in tokens]\n ids_2 = [torch.Tensor(ids) for ids, _, _, _ in encoded_sequences_2]\n ids_tensor_2 = torch.nn.utils.rnn.pad_sequence(\n ids_2, batch_first=True, padding_value=0).long()\n\n model_2 = container_2.model()\n word_evaluations_2 = model_2.get_embeddings(tokens, ids_tensor_2)\n\n # Assert\n for word_evaluation_1, word_evaluation_2 in zip(word_evaluations_1, word_evaluations_2):\n self.assertEqual(word_evaluation_1.get_embeddings(\n 0), word_evaluation_2.get_embeddings(0))\n self.assertEqual(metrics_service.calculate_cosine_distance(\n word_evaluation_1.get_embeddings(0), word_evaluation_2.get_embeddings(0)), 0.0)\n\n def test_embedding_matrix_same_different_seed(self):\n tokens = ['test', 'token', 'bert', 'vocabulary', 'units', 'python']\n main_container = initialize_container()\n metrics_service = main_container.metrics_service()\n\n # Raw model\n container_1 = initialize_container(\n ocr_output_type=OCROutputType.Raw,\n override_args={\n 'seed': 13\n })\n\n tokenize_service_1 = container_1.tokenize_service()\n encoded_sequences_1 = [\n tokenize_service_1.encode_sequence(token) for token in tokens]\n ids_1 = [torch.Tensor(ids) for ids, _, _, _ in encoded_sequences_1]\n ids_tensor_1 = torch.nn.utils.rnn.pad_sequence(\n ids_1, batch_first=True, padding_value=0).long()\n\n model_1 = container_1.model()\n word_evaluations_1 = model_1.get_embeddings(tokens, ids_tensor_1)\n\n # Ground truth model\n container_2 = initialize_container(\n ocr_output_type=OCROutputType.Raw,\n override_args={\n 'seed': 42\n })\n\n tokenize_service_2 = container_2.tokenize_service()\n encoded_sequences_2 = [\n tokenize_service_2.encode_sequence(token) for token in tokens]\n ids_2 = [torch.Tensor(ids) for ids, _, _, _ in encoded_sequences_2]\n ids_tensor_2 = torch.nn.utils.rnn.pad_sequence(\n ids_2, batch_first=True, padding_value=0).long()\n\n model_2 = container_2.model()\n word_evaluations_2 = model_2.get_embeddings(tokens, ids_tensor_2)\n\n # Assert\n for word_evaluation_1, word_evaluation_2 in zip(word_evaluations_1, word_evaluations_2):\n self.assertEqual(\n word_evaluation_1.get_embeddings(0),\n word_evaluation_2.get_embeddings(0))\n\n self.assertEqual(\n metrics_service.calculate_cosine_distance(\n word_evaluation_1.get_embeddings(0),\n word_evaluation_2.get_embeddings(0)),\n 0.0)\n\n\nif __name__ == '__main__':\n unittest.main()\n"
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"text": "from entities.word_evaluation import WordEvaluation\nfrom typing import List\nfrom scipy.spatial import procrustes\nimport numpy as np\n\nfrom services.log_service import LogService\n\nclass WordAlignmentService:\n def __init__(\n self,\n log_service: LogService):\n self._log_service = log_service\n\n def align_word_embeddings(self, evaluations: List[WordEvaluation]) -> List[WordEvaluation]:\n if len(evaluations) == 0:\n raise Exception('Evaluations list is empty')\n\n embeddings_size = evaluations[0].get_embeddings_size()\n model1_embeddings = np.zeros((len(evaluations), embeddings_size))\n model2_embeddings = np.zeros((len(evaluations), embeddings_size))\n\n for i, word_evaluation in enumerate(evaluations):\n model1_embeddings[i] = word_evaluation.get_embeddings(0)\n model2_embeddings[i] = word_evaluation.get_embeddings(1)\n\n standardized_model1_embeddings, standardized_model2_embeddings, disparity = procrustes(\n model1_embeddings, model2_embeddings)\n self._log_service.log_debug(f'Disparity found: {disparity}')\n\n new_evaluations = []\n for i, word_evaluation in enumerate(evaluations):\n new_evaluations.append(WordEvaluation(\n word_evaluation.word,\n [standardized_model1_embeddings[i],\n standardized_model2_embeddings[i]]))\n\n return new_evaluations"
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"text": "from services.log_service import LogService\nfrom enums.ocr_output_type import OCROutputType\nfrom entities.word_evaluation import WordEvaluation\nfrom typing import List\nfrom enums.language import Language\nimport os\nfrom overrides import overrides\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.nn import init\n\nfrom models.model_base import ModelBase\n\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\nfrom services.process.word2vec_process_service import Word2VecProcessService\nfrom services.data_service import DataService\nfrom services.vocabulary_service import VocabularyService\n\n\nclass CBOW(ModelBase):\n def __init__(\n self,\n arguments_service: ArgumentsServiceBase,\n vocabulary_service: VocabularyService,\n data_service: DataService,\n log_service: LogService,\n process_service: Word2VecProcessService = None,\n ocr_output_type: OCROutputType = None,\n pretrained_matrix = None):\n super().__init__(data_service, arguments_service, log_service)\n\n self._arguments_service = arguments_service\n self._vocabulary_service = vocabulary_service\n self._log_service = log_service\n\n if ocr_output_type is not None:\n dataset_string = self._arguments_service.get_dataset_string()\n vocab_key = f'vocab-{dataset_string}-{ocr_output_type.value}'\n self._vocabulary_service.load_cached_vocabulary(vocab_key)\n\n randomly_initialized = False\n freeze_embeddings = True\n if pretrained_matrix is None and process_service is not None:\n pretrained_matrix, randomly_initialized = process_service.get_pretrained_matrix()\n freeze_embeddings = False\n\n if pretrained_matrix is not None:\n self._log_service.log_debug('Embedding matrix provided. Initializing embeddings from it')\n embedding_size = pretrained_matrix.shape[-1]\n self._embeddings = nn.Embedding.from_pretrained(\n embeddings=pretrained_matrix,\n freeze=freeze_embeddings,\n padding_idx=self._vocabulary_service.pad_token)\n\n if randomly_initialized and not freeze_embeddings:\n initrange = 1.0 / embedding_size\n init.uniform_(self._embeddings.weight.data, -initrange, initrange)\n else:\n self._log_service.log_debug('Embedding matrix is not provided. Initializing embeddings randomly')\n embedding_size = self._get_embedding_size(arguments_service.language)\n self._embeddings = nn.Embedding(\n num_embeddings=self._vocabulary_service.vocabulary_size(),\n embedding_dim=embedding_size,\n padding_idx=self._vocabulary_service.pad_token)\n\n self._linear = nn.Linear(\n embedding_size, self._vocabulary_service.vocabulary_size())\n\n def forward(self, input_batch, **kwargs):\n context_tokens, targets = input_batch\n embedded_representation = self._embeddings.forward(context_tokens)\n\n hidden = self._linear.forward(embedded_representation)\n squeezed_hidden = torch.mean(hidden, dim=1)\n output = F.log_softmax(squeezed_hidden, dim=1)\n\n return (output, targets)\n\n def _get_embedding_size(self, language: Language):\n if language == Language.English:\n return 300\n elif language == Language.Dutch:\n return 320\n elif language == Language.French:\n return 300\n elif language == Language.German:\n return 300\n\n raise NotImplementedError()\n\n def get_embeddings(self, tokens: List[str], skip_unknown: bool = False) -> List[WordEvaluation]:\n vocab_ids = torch.Tensor([self._vocabulary_service.string_to_id(token) for token in tokens]).long().to(self._arguments_service.device)\n\n embeddings = self._embeddings.forward(vocab_ids)\n embeddings_list = embeddings.squeeze().tolist()\n\n if skip_unknown:\n unk_vocab_id = self._vocabulary_service.unk_token\n embeddings_list = [x if vocab_ids[i] != unk_vocab_id else None for i, x in enumerate(embeddings_list)]\n\n return embeddings_list\n"
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"text": "import re\n\nfrom typing import List\n\n\nclass StringProcessService:\n def __init__(self):\n self._charmap = {\n 0x201c: u'\"',\n 0x201d: u'\"',\n 0x2018: u\"'\",\n 0x2019: u\"'\",\n 'ff': u'ff',\n 'fi': u'fi',\n 'fl': u'fl',\n 'ffi': u'ffi',\n 'ffl': u'ffl',\n '″': u'\"',\n '′': u\"'\",\n '„': u'\"',\n '«': u'\"',\n '»': u'\"'\n }\n\n self._number_regex = '^(((([0-9]*)(\\.|,)([0-9]+))+)|([0-9]+))'\n\n def convert_string_unicode_symbols(self, text: str) -> str:\n result = text.translate(self._charmap)\n return result\n\n def convert_strings_unicode_symbols(self, texts: List[str]) -> List[str]:\n result = [self.convert_string_unicode_symbols(x) for x in texts]\n return result\n\n def replace_string_numbers(self, text: str) -> str:\n result = re.sub(self._number_regex, '0', text)\n return result\n\n def replace_strings_numbers(self, texts: List[str]) -> List[str]:\n result = [self.replace_string_numbers(x) for x in texts]\n return result\n\n def remove_string_characters(self, text: str, characters: List[str]) -> str:\n result = text\n for character in characters:\n result = result.replace(character, '')\n\n return result\n\n def remove_strings_characters(self, texts: List[str], characters: List[str]) -> List[str]:\n result = [self.remove_string_characters(x, characters) for x in texts]\n return result\n"
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"text": "from overrides import overrides\n\nfrom datasets.skip_gram_dataset import SkipGramDataset\nfrom services.arguments.ocr_quality_arguments_service import OCRQualityArgumentsService\nfrom services.log_service import LogService\nfrom services.cache_service import CacheService\nfrom services.process.skip_gram_process_service import SkipGramProcessService\n\n\nclass SkipGramDatasetFake(SkipGramDataset):\n def __init__(\n self,\n arguments_service: OCRQualityArgumentsService,\n process_service: SkipGramProcessService,\n log_service: LogService,\n cache_service: CacheService):\n super().__init__(arguments_service, process_service, log_service)\n\n self._ids = []\n\n def __getitem__(self, idx):\n document_ids = [x.document_index for x in self._text_corpus.get_entries(idx)]\n self._ids.extend(document_ids)\n\n return super().__getitem__(idx)"
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"path": "/services/experiments/process/neighbourhood_overlap_process_service.py",
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"text": "from collections import defaultdict\nfrom entities.plot.legend_options import LegendOptions\nfrom entities.plot.label_options import LabelOptions\nfrom enums.overlap_type import OverlapType\nfrom enums.plots.line_style import LineStyle\n\nimport numpy as np\nfrom services.cache_service import CacheService\nfrom services.arguments.ocr_evaluation_arguments_service import OCREvaluationArgumentsService\nfrom entities.cache.cache_options import CacheOptions\nfrom typing import Dict, List, Tuple\nfrom entities.plot.plot_options import PlotOptions\nfrom enums.value_summary import ValueSummary\nfrom enums.configuration import Configuration\nfrom matplotlib.axes import Axes\n\n\nclass NeighbourhoodOverlapProcessService:\n def __init__(\n self,\n arguments_service: OCREvaluationArgumentsService,\n cache_service: CacheService):\n self._arguments_service = arguments_service\n self._cache_service = cache_service\n\n def get_all_overlaps(self, neighbourhood_set_size: int) -> Dict[Tuple[Configuration, bool], Dict[int, dict]]:\n configurations = [\n Configuration.CBOW,\n Configuration.PPMI,\n Configuration.SkipGram,\n Configuration.BERT,\n ]\n\n seeds = [7, 13, 42]\n\n result = {}\n\n for configuration in configurations:\n for is_random_initialized in [False, True]:\n result[(configuration, is_random_initialized)] = {}\n\n for seed in seeds:\n config_overlaps = self._cache_service.get_item_from_cache(\n CacheOptions(\n 'neighbourhood-overlaps',\n key_suffixes=[\n '-rnd' if is_random_initialized else '',\n f'-{neighbourhood_set_size}'\n ],\n configuration=configuration,\n seed=seed))\n\n result[(configuration, is_random_initialized)][seed] = config_overlaps\n\n return result\n\n def get_overlaps(\n self,\n overlap_types: List[OverlapType],\n include_randomly_initialized: bool = False) -> Dict[Configuration, Dict[str, Dict[OverlapType, Dict[int, dict]]]]:\n lrs = ['0.01', '0.001', '0.0001', '0.00001']\n configs = [Configuration.BERT, Configuration.ALBERT, Configuration.SkipGram, Configuration.CBOW, Configuration.PPMI, Configuration.GloVe]\n seeds = [1, 7, 13, 42]\n randomly_initializations = [False]\n if include_randomly_initialized:\n randomly_initializations.append(True)\n\n result = {}\n\n for config in configs:\n result[config] = {}\n for overlap_type in OverlapType:\n if overlap_type not in overlap_types:\n continue\n\n result[config][overlap_type] = {}\n for randomly_initialized in randomly_initializations:\n result[config][overlap_type][randomly_initialized] = {}\n for lr in lrs:\n result[config][overlap_type][randomly_initialized][lr] = {}\n\n for seed in seeds:\n overlaps = self._cache_service.get_item_from_cache(\n CacheOptions(\n 'neighbourhood-overlaps',\n key_suffixes=[\n '-lr' if (config != Configuration.PPMI and config != Configuration.GloVe) else '',\n lr if (config != Configuration.PPMI and config != Configuration.GloVe) else '',\n '-',\n str(overlap_type.value),\n '-rnd' if randomly_initialized else ''\n ],\n configuration=config,\n seed=seed,\n seed_specific=True))\n\n result[config][overlap_type][randomly_initialized][lr][seed] = overlaps\n\n # If we are processing PPMI, we only have one LR so we break\n if config == Configuration.PPMI or config == Configuration.GloVe:\n break\n\n return result\n\n def combine_seed_overlaps(\n self,\n overlaps_by_seed: Dict[int, Dict[str, int]],\n neighbourhood_set_size: int,\n max_bins: int = 100) -> Dict[int, List[int]]:\n if all(x is None for x in overlaps_by_seed.values()):\n return None\n\n reduce_factor = 1\n if neighbourhood_set_size > max_bins:\n reduce_factor = neighbourhood_set_size / max_bins\n\n combined_overlaps = defaultdict(\n lambda: [None for _ in range(len(overlaps_by_seed.keys()))])\n\n for i, current_overlaps in enumerate(overlaps_by_seed.values()):\n if current_overlaps is None:\n continue\n\n for overlap_amount in current_overlaps.values():\n overlap_amount = int(overlap_amount / reduce_factor)\n\n if combined_overlaps[overlap_amount][i] is None:\n combined_overlaps[overlap_amount][i] = 0\n\n combined_overlaps[overlap_amount][i] = combined_overlaps[overlap_amount][i] + 1\n\n return combined_overlaps\n\n def extract_value_summaries(self, combined_overlaps: Dict[int, List[int]]) -> Dict[ValueSummary, List[int]]:\n value_summaries = {\n ValueSummary.Maximum: {},\n ValueSummary.Average: {},\n ValueSummary.Minimum: {},\n }\n\n for i in range(0, self._arguments_service.neighbourhood_set_size, 1):\n if i not in combined_overlaps.keys():\n continue\n\n valid_overlaps = [x for x in combined_overlaps[i] if x is not None]\n\n value_summaries[ValueSummary.Minimum][i] = min(valid_overlaps)\n value_summaries[ValueSummary.Maximum][i] = max(valid_overlaps)\n value_summaries[ValueSummary.Average][i] = int(np.mean(valid_overlaps))\n\n return value_summaries\n\n def get_distribution_plot_options(\n self,\n ax: Axes,\n configuration: Configuration,\n overlap_type: OverlapType,\n learning_rate_str: str,\n value_summary: ValueSummary) -> PlotOptions:\n alpha_values = {\n ValueSummary.Maximum: .3,\n ValueSummary.Average: 1,\n ValueSummary.Minimum: 1,\n }\n\n fill = {\n ValueSummary.Maximum: True,\n ValueSummary.Average: False,\n ValueSummary.Minimum: True,\n }\n\n linewidths = {\n ValueSummary.Maximum: 0,\n ValueSummary.Average: 1,\n ValueSummary.Minimum: 0,\n }\n\n colors = {\n OverlapType.BASEvsGT: {\n ValueSummary.Maximum: 'goldenrod',\n ValueSummary.Average: 'goldenrod',\n ValueSummary.Minimum: 'white',\n },\n OverlapType.BASEvsOG: {\n ValueSummary.Maximum: 'cadetblue',\n ValueSummary.Average: 'cadetblue',\n ValueSummary.Minimum: 'white',\n },\n OverlapType.BASEvsOCR: {\n ValueSummary.Maximum: 'darkred',\n ValueSummary.Average: 'darkred',\n ValueSummary.Minimum: 'white',\n }\n }\n\n lr_types = {\n f'{Configuration.BERT.value}-0.0001': 'aggressive',\n f'{Configuration.BERT.value}-0.00001': 'slow',\n f'{Configuration.CBOW.value}-0.001': 'aggressive',\n f'{Configuration.CBOW.value}-0.0001': 'slow',\n f'{Configuration.SkipGram.value}-0.001': 'aggressive',\n f'{Configuration.SkipGram.value}-0.0001': 'slow',\n f'{Configuration.PPMI.value}': 'aggressive'\n }\n\n line_styles_per_lr_type = {\n 'aggressive': LineStyle.Solid,\n 'slow': LineStyle.Dashed\n }\n\n line_style_key = f'{configuration.value}'\n lr_label = 'default'\n lr_type = 'aggressive'\n if configuration != Configuration.PPMI:\n line_style_key = f'{line_style_key}-{learning_rate_str}'\n lr_type = lr_types[line_style_key]\n lr_label = lr_type\n\n\n result = PlotOptions(\n color=colors[overlap_type][value_summary],\n linestyle=line_styles_per_lr_type[lr_type],\n fill=fill[value_summary],\n label=lr_label,\n alpha=alpha_values[value_summary],\n line_width=linewidths[value_summary],\n ax=ax,\n xlim=(0,100),\n legend_options=LegendOptions(show_legend=False))\n\n return result"
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"text": "import os\nfrom typing import Counter\nimport nltk\n\nlanguage = 'eng'\n\nicdar_path = os.path.join('data', 'newseye')\nicdar_2017_path = os.path.join(icdar_path, '2017', 'full')\nicdar_2019_path = os.path.join(icdar_path, '2019', 'full')\n\npaths = []\nif language == 'eng' or language == 'fr':\n paths.extend([\n os.path.join(icdar_2017_path, f'{language}_monograph'),\n os.path.join(icdar_2017_path, f'{language}_periodical')\n ])\n\npaths.extend([\n os.path.join(icdar_2019_path, language[:2])\n])\n\nif language == 'nl':\n paths = [\n os.path.join(icdar_2019_path, language[:2], 'NL1')\n ]\n\n\nwords_set = Counter()\n\nfor path in paths:\n txt_files = os.listdir(path)\n for txt_file in txt_files:\n txt_file_path = os.path.join(path, txt_file)\n with open(txt_file_path, 'r', encoding='utf8') as file:\n words = file.read().lower().split()\n for word in words:\n words_set[word] += 1\n\nsorted_word_pairs = sorted(words_set.items(), key=lambda x: x[1], reverse=True)\n\nprint('Top 20 words:')\nprint(sorted_word_pairs[:20])\nprint('-----------------')\n\nif language == 'eng':\n labels = ['NOUN', 'ADJ' ]\n # sorted_words = [x[0] for x in sorted_word_pairs]\n pos_tags = [(nltk.pos_tag([word], tagset='universal')[0], occurences) for word, occurences in sorted_word_pairs[:250]]\n remaining_pos_tags = [(x[0], occurences) for x, occurences in pos_tags if x[1] in labels]\n print('Top 20 filtered POS words:')\n print(remaining_pos_tags[:20])\n print('-----------------')\n\nwords = {\n 'nl': ['man', 'jaar', 'tijd', 'mensen', 'dag', 'kinderen', 'hand', 'huis', 'dier', 'afbeelding', 'werk', 'naam', 'groot', 'kleine'],\n 'eng': ['man', 'new', 'time', 'men', 'day', 'good', 'old', 'house', 'people', 'work', 'name', 'world', 'little']\n}\n\nprint('Selected words:')\nprint([x for x in sorted_word_pairs if x[0] in words[language]])\n\n\n# print(remaining_pos_tags[50:100])"
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"text": "from typing import List\n\n# from MulticoreTSNE import MulticoreTSNE as TSNE\nimport numpy as np\n\n\nclass FitTransformationService:\n def __init__(self):\n pass\n\n def fit_and_transform_vectors(\n self,\n number_of_components: int,\n vectors: list):\n \"\"\"Fits a list of vectors using TSNE into `number_of_components`\n\n :param number_of_components: The number of components the vectors will be fitted into\n :type number_of_components: int\n :param vectors: The vectors to be fitted in\n :type vectors: list\n :return: Returns the TSNE result\n :rtype: [type]\n \"\"\"\n\n tsne = TSNE(\n n_components=number_of_components,\n random_state=0,\n n_jobs=4)\n\n if not isinstance(vectors, np.ndarray):\n vectors = np.array(vectors)\n\n tsne_result = tsne.fit_transform(vectors)\n return tsne_result"
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"text": "from services.log_service import LogService\nfrom entities.tokens_occurrence_stats import TokensOccurrenceStats\nfrom overrides import overrides\n\nfrom datasets.dataset_base import DatasetBase\nfrom services.arguments.ocr_quality_arguments_service import OCRQualityArgumentsService\n\nfrom services.process.ppmi_process_service import PPMIProcessService\n\nclass PPMIDataset(DatasetBase):\n def __init__(\n self,\n arguments_service: OCRQualityArgumentsService,\n process_service: PPMIProcessService,\n log_service: LogService,\n **kwargs):\n super().__init__()\n\n self._arguments_service = arguments_service\n self._log_service = log_service\n\n self._occurrence_stats: TokensOccurrenceStats = process_service.get_occurrence_stats(ocr_output_type=self._arguments_service.ocr_output_type)\n self._log_service.log_debug(f'Loaded occurrence matrix with shape {self._occurrence_stats.mutual_occurrences.shape}')\n\n def __len__(self):\n return 1 # TODO Check\n\n def __getitem__(self, idx):\n return self._occurrence_stats\n\n def use_collate_function(self) -> bool:\n return True\n\n def collate_function(self, sequences):\n stats = sequences[0]\n return stats\n"
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"text": "from services.log_service import LogService\nimport numpy as np\nimport torch\nimport random\n\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\nfrom services.data_service import DataService\nfrom services.train_service import TrainService\nfrom services.test_service import TestService\nfrom services.experiments.experiment_service_base import ExperimentServiceBase\n\ndef initialize_seed(seed: int, device: str):\n torch.manual_seed(seed)\n np.random.seed(seed)\n random.seed(seed)\n\n if device == 'cuda':\n torch.backends.cudnn.benchmark = False\n torch.cuda.manual_seed_all(seed)\n\n\ndef main(\n arguments_service: ArgumentsServiceBase,\n train_service: TrainService,\n test_service: TestService,\n experiment_service: ExperimentServiceBase,\n log_service: LogService):\n\n log_service.log_arguments()\n initialize_seed(arguments_service.seed, arguments_service.device)\n\n try:\n if arguments_service.evaluate:\n log_service.log_debug('Starting TEST run')\n test_service.test()\n elif not arguments_service.run_experiments:\n log_service.log_debug('Starting TRAIN run')\n train_service.train()\n else:\n log_service.log_debug('Starting EXPERIMENT run')\n experiment_service.execute_experiments(arguments_service.experiment_types)\n except Exception as exception:\n log_service.log_exception('Stopping program execution', exception)\n raise exception"
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"text": "from enums.argument_enum import ArgumentEnum\n\n\nclass Challenge(ArgumentEnum):\n OCREvaluation = 'ocr-evaluation'\n"
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"text": "from typing import List, Dict\n\nimport torch\n\nfrom entities.batch_representation import BatchRepresentation\n\nfrom enums.evaluation_type import EvaluationType\n\n\nclass BaseEvaluationService:\n\n def evaluate_batch(\n self,\n output: torch.Tensor,\n batch_input: BatchRepresentation,\n evaluation_types: List[EvaluationType],\n batch_index: int) -> Dict[EvaluationType, List]:\n \"\"\"Evaluates the generated output based on the chosen evaluation types\n\n :param output: the generated output from the model\n :type output: torch.Tensor\n :param evaluation_types: list of different types of evaluations that should be performed\n :type evaluation_types: List[EvaluationType]\n :return: a dictionary with evaluation scores for every type\n :rtype: Dict[EvaluationType, List]\n \"\"\"\n return {}\n\n def save_results(self, evaluation: Dict[EvaluationType, List], targets: List[str]):\n print(evaluation)\n"
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"text": "from losses.skip_gram_loss import SkipGramLoss\nfrom optimizers.sparse_adam_optimizer import SparseAdamOptimizer\nfrom services.fit_transformation_service import FitTransformationService\nfrom services.tagging_service import TaggingService\nfrom services.process.ppmi_process_service import PPMIProcessService\nfrom losses.simple_loss import SimpleLoss\nfrom services.experiments.process.word_neighbourhood_service import WordNeighbourhoodService\nfrom services.experiments.process.metrics_process_service import MetricsProcessService\nfrom models.evaluation_model import EvaluationModel\n\nimport dependency_injector.containers as containers\nimport dependency_injector.providers as providers\n\nfrom dependency_injection.selector_utils import *\n\nimport main\n\n\nfrom losses.loss_base import LossBase\nfrom losses.transformer_loss_base import TransformerLossBase\nfrom losses.cross_entropy_loss import CrossEntropyLoss\n\nfrom models.model_base import ModelBase\nfrom models.transformers.bert import BERT\nfrom models.transformers.albert import ALBERT\nfrom models.transformers.xlnet import XLNet\nfrom models.transformers.bart import BART\nfrom models.simple.cbow import CBOW\nfrom models.simple.skip_gram import SkipGram\nfrom models.simple.ppmi import PPMI\n\nfrom optimizers.optimizer_base import OptimizerBase\nfrom optimizers.sgd_optimizer import SGDOptimizer\nfrom optimizers.adam_optimizer import AdamOptimizer\nfrom optimizers.adamw_transformer_optimizer import AdamWTransformerOptimizer\n\nfrom services.arguments.ocr_quality_arguments_service import OCRQualityArgumentsService\nfrom services.arguments.ocr_quality_non_context_arguments_service import OCRQualityNonContextArgumentsService\nfrom services.arguments.ocr_evaluation_arguments_service import OCREvaluationArgumentsService\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\n\nfrom services.download.ocr_download_service import OCRDownloadService\n\nfrom services.process.process_service_base import ProcessServiceBase\nfrom services.process.transformer_process_service import TransformerProcessService\nfrom services.process.word2vec_process_service import Word2VecProcessService\nfrom services.process.evaluation_process_service import EvaluationProcessService\n\nfrom services.data_service import DataService\nfrom services.dataloader_service import DataLoaderService\nfrom services.dataset_service import DatasetService\nfrom services.file_service import FileService\nfrom services.log_service import LogService\nfrom services.mask_service import MaskService\nfrom services.metrics_service import MetricsService\nfrom services.test_service import TestService\n\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\nfrom services.tokenize.bert_tokenize_service import BERTTokenizeService\nfrom services.tokenize.albert_tokenize_service import ALBERTTokenizeService\nfrom services.tokenize.xlnet_tokenize_service import XLNetTokenizeService\nfrom services.tokenize.bart_tokenize_service import BARTTokenizeService\nfrom services.tokenize.camembert_tokenize_service import CamembertTokenizeService\nfrom services.tokenize.cbow_tokenize_service import CBOWTokenizeService\n\nfrom services.train_service import TrainService\nfrom services.vocabulary_service import VocabularyService\nfrom services.plot_service import PlotService\nfrom services.experiments.experiment_service_base import ExperimentServiceBase\nfrom services.experiments.ocr_quality_experiment_service import OCRQualityExperimentService\nfrom services.cache_service import CacheService\nfrom services.string_process_service import StringProcessService\n\nfrom services.experiments.process.neighbourhood_overlap_process_service import NeighbourhoodOverlapProcessService\nfrom services.experiments.process.neighbourhood_similarity_process_service import NeighbourhoodSimilarityProcessService\n\nfrom services.plots.baseline_neighbour_overlap_plot_service import BaselineNeighbourOverlapPlotService\nfrom services.plots.ocr_neighbour_overlap_plot_service import OCRNeighbourOverlapPlotService\nfrom services.plots.individual_metrics_plot_service import IndividualMetricsPlotService\nfrom services.plots.set_sized_based_plot_service import SetSizedBasedPlotService\n\nfrom services.embeddings.word_alignment_service import WordAlignmentService\nfrom services.embeddings.word_embeddings_service import WordEmbeddingsService\n\nimport logging\n\n\nclass IocContainer(containers.DeclarativeContainer):\n \"\"\"Application IoC container.\"\"\"\n\n logger = providers.Singleton(\n logging.Logger,\n name='historical-ocr logger')\n\n # Services\n\n arguments_service_base = providers.Singleton(\n ArgumentsServiceBase,\n raise_errors_on_invalid_args=False)\n\n argument_service_selector = providers.Callable(\n get_arguments_service,\n arguments_service=arguments_service_base)\n\n arguments_service: providers.Provider[ArgumentsServiceBase] = providers.Selector(\n argument_service_selector,\n base=providers.Singleton(ArgumentsServiceBase),\n evaluation=providers.Singleton(OCREvaluationArgumentsService),\n ocr_quality=providers.Singleton(OCRQualityArgumentsService),\n ocr_quality_non_context=providers.Singleton(OCRQualityNonContextArgumentsService))\n\n log_service = providers.Singleton(\n LogService,\n arguments_service=arguments_service,\n logger=logger)\n\n data_service = providers.Factory(\n DataService,\n log_service=log_service)\n\n file_service = providers.Factory(\n FileService,\n arguments_service=arguments_service\n )\n\n cache_service = providers.Singleton(\n CacheService,\n arguments_service=arguments_service,\n file_service=file_service,\n data_service=data_service,\n log_service=log_service)\n\n plot_service = providers.Factory(\n PlotService,\n data_service=data_service\n )\n\n vocabulary_service: providers.Provider[VocabularyService] = providers.Singleton(\n VocabularyService,\n data_service=data_service,\n file_service=file_service,\n cache_service=cache_service,\n log_service=log_service\n )\n\n string_process_service = providers.Factory(StringProcessService)\n\n ocr_download_service = providers.Factory(\n OCRDownloadService,\n arguments_service=arguments_service,\n data_service=data_service,\n string_process_service=string_process_service,\n cache_service=cache_service,\n log_service=log_service)\n\n tokenize_service_selector = providers.Callable(\n get_tokenize_service,\n arguments_service=arguments_service)\n\n tokenize_service: providers.Provider[BaseTokenizeService] = providers.Selector(\n tokenize_service_selector,\n bert=providers.Singleton(\n BERTTokenizeService,\n arguments_service=arguments_service,\n file_service=file_service,\n ocr_download_service=ocr_download_service),\n albert=providers.Singleton(\n ALBERTTokenizeService,\n arguments_service=arguments_service,\n file_service=file_service,\n ocr_download_service=ocr_download_service),\n xlnet=providers.Singleton(\n XLNetTokenizeService,\n arguments_service=arguments_service),\n bart=providers.Singleton(\n BARTTokenizeService,\n arguments_service=arguments_service),\n camembert=providers.Singleton(\n CamembertTokenizeService,\n arguments_service=arguments_service),\n cbow=providers.Singleton(\n CBOWTokenizeService,\n vocabulary_service=vocabulary_service),\n skip_gram=providers.Singleton(\n CBOWTokenizeService,\n vocabulary_service=vocabulary_service),\n ppmi=providers.Singleton(\n CBOWTokenizeService,\n vocabulary_service=vocabulary_service))\n\n mask_service = providers.Factory(\n MaskService,\n tokenize_service=tokenize_service,\n arguments_service=arguments_service\n )\n\n metrics_service = providers.Factory(MetricsService)\n\n tagging_service = providers.Factory(TaggingService)\n\n process_service_selector = providers.Callable(\n get_process_service,\n arguments_service=arguments_service)\n\n process_service: providers.Provider[ProcessServiceBase] = providers.Selector(\n process_service_selector,\n evaluation=providers.Singleton(\n EvaluationProcessService,\n arguments_service=arguments_service,\n cache_service=cache_service,\n log_service=log_service,\n vocabulary_service=vocabulary_service,\n tokenize_service=tokenize_service,\n tagging_service=tagging_service),\n word2vec=providers.Singleton(\n Word2VecProcessService,\n arguments_service=arguments_service,\n ocr_download_service=ocr_download_service,\n cache_service=cache_service,\n log_service=log_service,\n vocabulary_service=vocabulary_service,\n file_service=file_service,\n tokenize_service=tokenize_service),\n transformer=providers.Singleton(\n TransformerProcessService,\n arguments_service=arguments_service,\n ocr_download_service=ocr_download_service,\n tokenize_service=tokenize_service,\n cache_service=cache_service,\n log_service=log_service),\n ppmi=providers.Singleton(\n PPMIProcessService,\n ocr_download_service=ocr_download_service,\n arguments_service=arguments_service,\n cache_service=cache_service,\n vocabulary_service=vocabulary_service,\n tokenize_service=tokenize_service,\n log_service=log_service))\n\n dataset_service = providers.Factory(\n DatasetService,\n arguments_service=arguments_service,\n mask_service=mask_service,\n process_service=process_service,\n log_service=log_service)\n\n dataloader_service = providers.Factory(\n DataLoaderService,\n arguments_service=arguments_service,\n dataset_service=dataset_service,\n log_service=log_service)\n\n model_selector = providers.Callable(\n get_model_type,\n arguments_service=arguments_service)\n\n model: providers.Provider[ModelBase] = providers.Selector(\n model_selector,\n eval=providers.Singleton(\n EvaluationModel,\n arguments_service=arguments_service,\n data_service=data_service,\n vocabulary_service=vocabulary_service,\n process_service=process_service,\n log_service=log_service,\n file_service=file_service,\n cache_service=cache_service,\n tokenize_service=tokenize_service),\n bert=providers.Singleton(\n BERT,\n arguments_service=arguments_service,\n data_service=data_service,\n log_service=log_service,\n tokenize_service=tokenize_service),\n albert=providers.Singleton(\n ALBERT,\n arguments_service=arguments_service,\n data_service=data_service,\n log_service=log_service,\n tokenize_service=tokenize_service),\n xlnet=providers.Singleton(\n XLNet,\n arguments_service=arguments_service,\n data_service=data_service,\n log_service=log_service),\n bart=providers.Singleton(\n BART,\n arguments_service=arguments_service,\n data_service=data_service,\n log_service=log_service),\n cbow=providers.Singleton(\n CBOW,\n arguments_service=arguments_service,\n process_service=process_service,\n data_service=data_service,\n vocabulary_service=vocabulary_service,\n log_service=log_service),\n skip_gram=providers.Singleton(\n SkipGram,\n arguments_service=arguments_service,\n process_service=process_service,\n data_service=data_service,\n vocabulary_service=vocabulary_service,\n log_service=log_service),\n ppmi=providers.Singleton(\n PPMI,\n arguments_service=arguments_service,\n data_service=data_service,\n vocabulary_service=vocabulary_service,\n log_service=log_service))\n\n loss_selector = providers.Callable(\n get_loss_function,\n arguments_service=arguments_service)\n\n loss_function: providers.Provider[LossBase] = providers.Selector(\n loss_selector,\n base=providers.Singleton(LossBase),\n cross_entropy=providers.Singleton(CrossEntropyLoss),\n simple=providers.Singleton(SimpleLoss),\n skip_gram=providers.Singleton(SkipGramLoss),\n transformer=providers.Singleton(TransformerLossBase))\n\n optimizer_selector = providers.Callable(\n get_optimizer,\n arguments_service=arguments_service)\n\n optimizer: providers.Provider[OptimizerBase] = providers.Selector(\n optimizer_selector,\n base=providers.Singleton(\n OptimizerBase,\n arguments_service=arguments_service,\n model=model),\n sgd=providers.Singleton(\n SGDOptimizer,\n arguments_service=arguments_service,\n model=model),\n adam=providers.Singleton(\n AdamOptimizer,\n arguments_service=arguments_service,\n model=model),\n sparse_adam=providers.Singleton(\n SparseAdamOptimizer,\n arguments_service=arguments_service,\n model=model),\n transformer=providers.Singleton(\n AdamWTransformerOptimizer,\n arguments_service=arguments_service,\n model=model))\n\n evaluation_service = None\n\n fit_transformation_service = providers.Factory(\n FitTransformationService)\n\n neighbourhood_similarity_process_service = providers.Factory(\n NeighbourhoodSimilarityProcessService,\n arguments_service=arguments_service,\n file_service=file_service,\n log_service=log_service,\n tagging_service=tagging_service)\n\n word_neighbourhood_service = providers.Factory(\n WordNeighbourhoodService,\n arguments_service=arguments_service,\n metrics_service=metrics_service,\n plot_service=plot_service,\n file_service=file_service,\n log_service=log_service,\n fit_transformation_service=fit_transformation_service,\n cache_service=cache_service,\n neighbourhood_similarity_process_service=neighbourhood_similarity_process_service,\n process_service=process_service)\n\n neighbourhood_overlap_process_service = providers.Factory(\n NeighbourhoodOverlapProcessService,\n arguments_service=arguments_service,\n cache_service=cache_service)\n\n metrics_process_service = providers.Factory(\n MetricsProcessService,\n metrics_service=metrics_service)\n\n baseline_neighbour_overlap_plot_service = providers.Factory(\n BaselineNeighbourOverlapPlotService,\n arguments_service=arguments_service,\n file_service=file_service,\n plot_service=plot_service,\n log_service=log_service,\n neighbourhood_overlap_process_service=neighbourhood_overlap_process_service)\n\n ocr_neighbour_overlap_plot_service = providers.Factory(\n OCRNeighbourOverlapPlotService,\n arguments_service=arguments_service,\n file_service=file_service,\n plot_service=plot_service,\n log_service=log_service,\n neighbourhood_overlap_process_service=neighbourhood_overlap_process_service)\n\n individual_metrics_plot_service = providers.Factory(\n IndividualMetricsPlotService,\n arguments_service=arguments_service,\n file_service=file_service,\n plot_service=plot_service,\n log_service=log_service)\n\n set_sized_based_plot_service = providers.Factory(\n SetSizedBasedPlotService,\n arguments_service=arguments_service,\n file_service=file_service,\n plot_service=plot_service,\n neighbourhood_overlap_process_service=neighbourhood_overlap_process_service)\n\n word_alignment_service = providers.Factory(\n WordAlignmentService,\n log_service=log_service)\n\n word_embeddings_service = providers.Factory(\n WordEmbeddingsService,\n arguments_service=arguments_service,\n log_service=log_service,\n vocabulary_service=vocabulary_service,\n word_alignment_service=word_alignment_service)\n\n experiment_service_selector = providers.Callable(\n get_experiment_service,\n arguments_service=arguments_service)\n\n experiment_service = providers.Selector(\n experiment_service_selector,\n ocr_quality=providers.Factory(\n OCRQualityExperimentService,\n arguments_service=arguments_service,\n dataloader_service=dataloader_service,\n file_service=file_service,\n metrics_service=metrics_service,\n cache_service=cache_service,\n word_neighbourhood_service=word_neighbourhood_service,\n log_service=log_service,\n metrics_process_service=metrics_process_service,\n baseline_neighbour_overlap_plot_service=baseline_neighbour_overlap_plot_service,\n ocr_neighbour_overlap_plot_service=ocr_neighbour_overlap_plot_service,\n individual_metrics_plot_service=individual_metrics_plot_service,\n set_sized_based_plot_service=set_sized_based_plot_service,\n word_embeddings_service=word_embeddings_service,\n model=model),\n none=providers.Object(None))\n\n test_service = providers.Factory(\n TestService,\n arguments_service=arguments_service,\n dataloader_service=dataloader_service,\n evaluation_service=evaluation_service,\n file_service=file_service,\n model=model\n )\n\n train_service_selector = providers.Callable(\n include_train_service,\n arguments_service=arguments_service)\n\n train_service: providers.Provider[TrainService] = providers.Selector(\n train_service_selector,\n include=providers.Factory(\n TrainService,\n arguments_service=arguments_service,\n dataloader_service=dataloader_service,\n loss_function=loss_function,\n optimizer=optimizer,\n log_service=log_service,\n model=model,\n file_service=file_service),\n exclude=providers.Object(None))\n\n # Misc\n\n main = providers.Callable(\n main.main,\n arguments_service=arguments_service,\n train_service=train_service,\n test_service=test_service,\n experiment_service=experiment_service,\n log_service=log_service)\n"
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"text": "from services.log_service import LogService\nfrom entities.plot.legend_title_options import LegendTitleOptions\nfrom entities.plot.legend_options import LegendOptions\nfrom enums.configuration import Configuration\nfrom entities.plot.figure_options import FigureOptions\nfrom enums.value_summary import ValueSummary\nfrom services.plot_service import PlotService\nfrom services.experiments.process.neighbourhood_overlap_process_service import NeighbourhoodOverlapProcessService\nfrom enums.overlap_type import OverlapType\nfrom enums.experiment_type import ExperimentType\nfrom services.arguments.ocr_evaluation_arguments_service import OCREvaluationArgumentsService\nfrom services.file_service import FileService\n\nclass BaselineNeighbourOverlapPlotService:\n def __init__(\n self,\n arguments_service: OCREvaluationArgumentsService,\n file_service: FileService,\n plot_service: PlotService,\n log_service: LogService,\n neighbourhood_overlap_process_service: NeighbourhoodOverlapProcessService):\n self._file_service = file_service\n self._arguments_service = arguments_service\n self._plot_service = plot_service\n self._log_service = log_service\n self._neighbourhood_overlap_process_service = neighbourhood_overlap_process_service\n\n def plot_baseline_overlaps(self):\n self._log_service.log_info('Generating baseline overlap plots')\n\n output_folder = self._get_output_folder()\n overlaps_by_config = self._neighbourhood_overlap_process_service.get_overlaps(\n self._arguments_service.neighbourhood_set_size,\n overlap_types=[OverlapType.BASEvsGT, OverlapType.BASEvsOCR, OverlapType.BASEvsOG])\n fig, config_axs = self._plot_service.create_plots(\n len(overlaps_by_config.keys()),\n share_x_coords=True)\n\n for i, (configuration, overlaps_by_type) in enumerate(overlaps_by_config.items()):\n sub_titles = {}\n types_plotted = []\n plot_count = 0\n for overlap_type, overlaps_by_lr in overlaps_by_type.items():\n for learning_rate, overlaps_by_seed in overlaps_by_lr.items():\n if all(x is None for x in list(overlaps_by_seed.values())):\n continue\n\n if overlap_type not in types_plotted:\n sub_titles[plot_count] = OverlapType.get_friendly_name(\n overlap_type)\n types_plotted.append(overlap_type)\n plot_count += 1\n\n plot_count += 1\n combined_overlaps = self._neighbourhood_overlap_process_service.combine_seed_overlaps(\n overlaps_by_seed,\n self._arguments_service.neighbourhood_set_size)\n\n value_summaries = self._neighbourhood_overlap_process_service.extract_value_summaries(\n combined_overlaps)\n\n for value_summary, overlap_line in value_summaries.items():\n # Skip max and min value summaries\n if value_summary != ValueSummary.Average:\n continue\n\n config_axs[i] = self._plot_service.plot_distribution(\n counts=overlap_line,\n plot_options=self._neighbourhood_overlap_process_service.get_distribution_plot_options(\n config_axs[i],\n configuration,\n overlap_type,\n learning_rate,\n value_summary))\n\n self._plot_service.set_plot_properties(\n ax=config_axs[i],\n figure_options=FigureOptions(\n hide_y_labels=True,\n figure=fig,\n super_title=f'Neighbourhood overlaps [Baseline vs. GT] ({self._arguments_service.language.value.capitalize()})',\n title=Configuration.get_friendly_name(configuration)),\n legend_options=LegendOptions(\n show_legend=len(sub_titles) > 0,\n legend_title_options=LegendTitleOptions(\n sub_titles=sub_titles)))\n\n self._plot_service.save_plot(\n save_path=output_folder,\n filename=f'neighbourhood-overlaps-{self._arguments_service.neighbourhood_set_size}',\n figure=fig)\n\n def _get_output_folder(self):\n experiments_folder = self._file_service.get_experiments_path()\n result = self._file_service.combine_path(\n experiments_folder,\n f'{ExperimentType.NeighbourhoodOverlap.value}-base-vs-gt',\n self._arguments_service.language.value,\n create_if_missing=True)\n\n return result"
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"text": "[< go back to main page](../../README.md)\n\n# Base arguments\n\n## Description\n\nThese arguments are generic and applicable to every configuration and challenge.\n\n## Argument list\n\n| Parameter | Type | Default value | Description |\n| ------------- | ------------- | -------------- |-------------|\n| `epochs` | `int` | 500 | Maximum number of epochs |\n| `eval-freq` | `int` | 50 | Evaluate every x batches |\n| `batch-size` | `int` | 8 | Size of batches |\n| `max-training-minutes` | `int` | 1440 (6 days) | Maximum minutes of training before save-and-kill |\n| `device` | `str` | cuda | Device to be used. Pick from `cpu`/`cuda`. If default none is used automatic check will be done |\n| `seed` | `int` | 42 | Random seed |\n| `evaluate` | `bool` | False | Run in evaluation mode |\n| `patience` | `int` | 30 | How long will the model wait for improvement before stopping training |\n| `language` | [`Language`](../../enums/language.py) | English | Which language to train on |\n| `shuffle` | `bool` | True | Shuffle training dataset |\n| `learning-rate` | `float` | 1e-5 | Learning rate for training models |\n| `weight-decay` | `float` | 1e-8 | Weight decay for optimizer |\n| `momentum` | `float` | 0 | Momentum for optimizer |\n| `checkpoint-name` | `str` | None | name that can be used to distinguish checkpoints |\n| `resume-training` | `bool` | False | Resume training using saved checkpoints |\n| `resume-checkpoint-name` | `str` | None | Checkpoint name that will be used to resume training from. If None is given, then current checkpoint name will be used |\n| `skip-best-metrics-on-resume` | `bool` | False | Whether to skip loading saved metrics and continuing from last best checkpoint |\n| `data-folder` | `str` | data | Folder where data will be taken from |\n| `output-folder` | `str` | results | Folder where results and checkpoints will be saved |\n| `checkpoint-folder` | `str` | None | Folder where checkpoints will be saved/loaded. If it is not provided, the output folder will be used |\n| `evaluation-type` | [`EvaluationType`](../../enums/evaluation_type.py) | None | What type of evaluations should be performed |\n| `output-eval-format` | [`OutputFormat`](../../enums/output_format.py) | None | What the format of the output after evaluation will be |\n| `challenge` | [`Challenge`](../../enums/challenge.py) | None | Challenge that the model is being trained for. Data and output results will be put into such specific folder |\n| `configuration` | [`Configuration`](../../enums/configuration.py) | KBert | Which configuration of model to load and use |\n| `metric-types` | [`MetricType`](../../enums/metric_type.py) | JaccardSimilarity | What metrics should be calculated |\n| `joint-model` | `bool` | False | If a joint model should be used instead of a single one |\n| `joint-model-amount` | `int` | 2 | How many models should be trained jointly |\n| `enable-external-logging` | `bool` | False | Should logging to external service be enabled |\n| `train-dataset-limit-size` | `int` | None | Limit the train dataset |\n| `validation-dataset-limit-size` | `int` | None | Limit the validation dataset |\n| `skip-validation` | `bool` | False | Whether validation should be skipped, meaning no validation dataset is loaded and no evaluation is done while training |\n| `run-experiments` | `bool` | False | Whether to run experiments instead of training or evaluation |\n| `experiment-types` | [`ExperimentType`](../../enums/experiment_type.py) | None | What types of experiments should be run |\n| `reset-training-on-early-stop` | `bool` | False | Whether resetting of training should be done if early stopping is activated and the first epoch has not yet been finished |\n| `resets-limit` | `int` | 1 | How many times should the training be reset during first epoch if early stopping is activated |\n| `training-reset-epoch-limit` | `int` | 1 | Until which epoch the training reset should be performed |\n| `save-checkpoint-on-crash` | `bool` | False | If this is set to true, then in the event of an exception or crash of the program, the model's checkpoint will be saved to the file system |\n| `save-checkpoint-on-finish` | `bool` | False | If this is set to true, then when the model has converged, its checkpoint will be saved to the file system. Keep in mind that this will not be the best model checkpoint as the stopping will occur after some amount of iterations without any improvement |\n\nAdditionally, the following arguments infer from [`PretrainedArgumentsService`](../../services/arguments/pretrained_arguments_service.py) and are used currently in all implemented challenge argument services\n\n| Parameter | Type | Default value | Description |\n| ------------- | ------------- | -------------- |-------------|\n| `pretrained-weights` | `str` | bert-base-cased | Weights to use for initializing HuggingFace transformer models |\n| `include-pretrained-model` | `bool` | False | Should a pretrained model be used to provide more information |\n| `pretrained-model-size` | `int` | 768 | The hidden size dimension of the pretrained model |\n| `pretrained-max-length` | `int` | None | The maximum length the pretrained model (if any) |\n| `learn-new-embeddings` | `bool` | False | Whether new embeddings should be learned next to the pretrained representation |\n| `fasttext-model` | `str` | None | fasttext model to use for loading additional information |\n| `include-fasttext-model` | `bool` | False | Should a fasttext model be used to provide more information |\n| `fasttext-model-size` | `int` | 300 | The hidden size dimension of the fasttext model |\n| `pretrained-model` | [`PretrainedModel`](../../enums/pretrained_model.py) | BERT | Pretrained model that will be used to tokenize strings and generate embeddings |\n| `fine-tune-pretrained` | `bool` | False | If true, the loaded pre-trained model will be fine-tuned instead of being frozen |\n| `fine-tune-after-convergence` | `bool` | False | If true, the loaded pre-trained model will be fine-tuned but only once the full model has converged |\n| `fine-tune-learning-rate` | `float` | None | Different learning rate to use for pre-trained model. If None is given, then the global learning rate will be used |"
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"text": "#!/bin/bash\n#SBATCH --job-name=tr-ocr\n#SBATCH --ntasks=1\n#SBATCH --cpus-per-task=3\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=72:00:00\n#SBATCH -p gpu_shared\n#SBATCH --gpus=1\n\nmodule purge\nmodule load 2020\n# module load Python\nmodule load Python/3.8.2-GCCcore-9.3.0\n\necho 'PATH IS'\nprintenv PATH\n\necho 'PATH_modshare IS'\nprintenv PATH_modshare\n\n# source activate ocr-uva-2019\n\nCONF=\"$CONFMODEL\"\nINCLUDEPRETRARG=\"\"\nPRETRMODELARG=\"\"\nif [ ! -z \"$USEPRETR\" ]\nthen\n INCLUDEPRETRARG=\"--include-pretrained-model\"\n PRETRMODELARG=\"--pretrained-model $CONFMODEL\"\nfi\n\nPRETRWEIGHTSARG=\"--pretrained-weights bert-base-cased\"\nif [ ! -z \"$PRETRWEIGHTS\" ]\nthen\n PRETRWEIGHTSARG=\"--pretrained-weights $PRETRWEIGHTS\"\nfi\n\nLEARNINGRATE=\"$LR\"\nif [ -z \"$LR\" ]\nthen\n LEARNINGRATE=\"1e-3\"\nfi\n\nPATIENCEARG=\"$PATIENCE\"\nif [ -z \"$PATIENCE\" ]\nthen\n PATIENCEARG=\"100\"\nfi\n\nEVALFREQARG=\"$EVALFREQ\"\nif [ -z \"$EVALFREQ\" ]\nthen\n EVALFREQARG=\"100\"\nfi\n\nBATCHSIZEARG=\"$BATCHSIZE\"\nif [ -z \"$BATCHSIZE\" ]\nthen\n BATCHSIZEARG=\"2\"\nfi\n\nLEARNINGRATE=\"$LR\"\nif [ -z \"$LR\" ]\nthen\n LEARNINGRATE=\"1e-3\"\nfi\n\nOUTPUTTYPEARG=\"$OUTPUTTYPE\"\nif [ -z \"$OUTPUTTYPE\" ]\nthen\n OUTPUTTYPEARG=\"raw\"\nfi\n\nLANGUAGEARG=\"english\"\nif [ ! -z \"$LANGUAGE\" ]\nthen\n LANGUAGEARG=\"$LANGUAGE\"\nfi\n\nSEEDARG=\"13\"\nif [ ! -z \"$SEED\" ]\nthen\n SEEDARG=\"$SEED\"\nfi\n\nPADDINGIDXARG=\"0\"\nif [ ! -z \"$PADDINGIDX\" ]\nthen\n PADDINGIDXARG=\"$PADDINGIDX\"\nfi\n\nRANDOMINITARG=\"\"\nif [ ! -z \"$RANDOMINIT\" ]\nthen\n RANDOMINITARG=\"--initialize-randomly\"\nfi\n\nDATASETSARG=\"\"\nif [ ! -z \"$DATASETS\" ]\nthen\n DATASETSARG=\"--datasets $DATASETS\"\nfi\n\nRESUMETRAININGARG=\"\"\nif [ ! -z \"$RESUMETRAINING\" ]\nthen\n RESUMETRAININGARG=\"--resume-training\"\nfi\n\necho 'EXECUTING... srun python -u run.py --configuration ' $CONF ' --challenge ocr-evaluation --epochs 500000 --device cuda --eval-freq ' $EVALFREQARG ' --seed ' $SEEDARG ' --learning-rate ' $LEARNINGRATE ' --skip-validation --metric-types levenshtein-distance jaccard-similarity --language ' $LANGUAGEARG ' --batch-size ' $BATCHSIZEARG ' --ocr-output-type ' $OUTPUTTYPEARG ' --patience ' $PATIENCEARG ' ' $INCLUDEPRETRARG ' ' $PRETRMODELARG ' --pretrained-model-size 768 --pretrained-max-length 512 ' $PRETRWEIGHTSARG ' --enable-external-logging --padding-idx ' $PADDINGIDXARG ' ' $RANDOMINITARG $DATASETSARG $RESUMETRAININGARG\n\nsrun python -u run.py --configuration $CONF --challenge ocr-evaluation --epochs 500000 --device cuda --eval-freq $EVALFREQARG --seed $SEEDARG --learning-rate $LEARNINGRATE --skip-validation --metric-types levenshtein-distance jaccard-similarity --language $LANGUAGEARG --batch-size $BATCHSIZEARG --ocr-output-type $OUTPUTTYPEARG --patience $PATIENCEARG $INCLUDEPRETRARG $PRETRMODELARG --pretrained-model-size 768 --pretrained-max-length 512 $PRETRWEIGHTSARG --enable-external-logging --padding-idx $PADDINGIDXARG $RANDOMINITARG $DATASETSARG $RESUMETRAININGARG"
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"text": "from enums.configuration import Configuration\nfrom enums.challenge import Challenge\n\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\n\n\ndef get_arguments_service(arguments_service: ArgumentsServiceBase):\n result = 'base'\n challenge = arguments_service.challenge\n run_experiments = arguments_service.run_experiments\n configuration = arguments_service.configuration\n\n if challenge == Challenge.OCREvaluation:\n if run_experiments:\n result = 'evaluation'\n elif configuration in [Configuration.CBOW, Configuration.SkipGram, Configuration.PPMI]:\n result = 'ocr_quality_non_context'\n else:\n result = 'ocr_quality'\n\n return result\n\n\ndef get_optimizer(arguments_service: ArgumentsServiceBase):\n if arguments_service.evaluate or arguments_service.run_experiments:\n return 'base'\n\n result = 'base'\n challenge = arguments_service.challenge\n configuration = arguments_service.configuration\n if challenge == Challenge.OCREvaluation:\n if configuration == Configuration.CBOW:\n result = 'adam'\n elif configuration == Configuration.SkipGram:\n result = 'sparse_adam'\n elif configuration == Configuration.PPMI:\n result = 'base'\n else:\n result = 'transformer'\n\n return result\n\n\ndef get_loss_function(arguments_service: ArgumentsServiceBase):\n loss_function = None\n challenge = arguments_service.challenge\n configuration = arguments_service.configuration\n\n if challenge == Challenge.OCREvaluation:\n if configuration == Configuration.CBOW:\n return 'cross_entropy'\n elif configuration == Configuration.SkipGram:\n return 'skip_gram'\n elif configuration == Configuration.PPMI:\n return 'base'\n else:\n return 'transformer'\n\n return loss_function\n\n\n# def register_evaluation_service(\n# arguments_service: ArgumentsServiceBase,\n# file_service: FileService,\n# plot_service: PlotService,\n# metrics_service: MetricsService,\n# process_service: ProcessServiceBase,\n# vocabulary_service: VocabularyService,\n# data_service: DataService,\n# joint_model: bool,\n# configuration: Configuration):\n# evaluation_service = None\n\n# return evaluation_service\n\n\ndef get_model_type(arguments_service: ArgumentsServiceBase):\n\n run_experiments = arguments_service.run_experiments\n configuration = arguments_service.configuration\n\n model = None\n\n if run_experiments:\n model = 'eval'\n else:\n model = str(configuration.value).replace('-', '_')\n\n return model\n\n\ndef get_process_service(arguments_service: ArgumentsServiceBase):\n result = None\n\n challenge = arguments_service.challenge\n run_experiments = arguments_service.run_experiments\n configuration = arguments_service.configuration\n\n if challenge == Challenge.OCREvaluation:\n if run_experiments:\n result = 'evaluation'\n elif configuration == Configuration.CBOW or configuration == Configuration.SkipGram:\n result = 'word2vec'\n elif configuration == Configuration.PPMI:\n result = 'ppmi'\n else:\n result = 'transformer'\n\n return result\n\n\ndef get_tokenize_service(arguments_service: ArgumentsServiceBase) -> str:\n pretrained_model_type = None\n if isinstance(arguments_service, PretrainedArgumentsService):\n pretrained_model_type = arguments_service.pretrained_model\n\n if pretrained_model_type is None:\n configuration = arguments_service.configuration\n return str(configuration.value).replace('-', '_')\n\n return pretrained_model_type.value\n\n\ndef get_experiment_service(arguments_service: ArgumentsServiceBase):\n\n run_experiments = arguments_service.run_experiments\n\n if not run_experiments:\n return 'none'\n\n return 'ocr_quality'\n\ndef include_train_service(arguments_service: ArgumentsServiceBase):\n if arguments_service.run_experiments or arguments_service.evaluate:\n return 'exclude'\n\n return 'include'\n\ndef get_dataset_type(arguments_service: ArgumentsServiceBase):\n joint_model: bool = arguments_service.joint_model\n configuration: Configuration = arguments_service.configuration\n challenge: Challenge = arguments_service.challenge\n result = 'base'\n\n if not joint_model:\n if challenge == Challenge.OCREvaluation:\n if configuration == Configuration.CBOW:\n result = 'word2vec'\n elif configuration == Configuration.SkipGram:\n result = 'skip_gram'\n elif configuration == Configuration.PPMI:\n result = 'ppmi'\n else:\n result = 'transformer'\n elif joint_model:\n result = 'evaluation'\n\n return result\n"
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"text": "from tests.fakes.train_service_fake import TrainServiceFake\nfrom tests.fakes.log_service_fake import LogServiceFake\nfrom enums.language import Language\nfrom enums.configuration import Configuration\nfrom enums.challenge import Challenge\nfrom enums.ocr_output_type import OCROutputType\nfrom enums.experiment_type import ExperimentType\nimport os\nfrom tests.fakes.non_context_service_fake import NonContextServiceFake\nfrom tests.fakes.evaluation_service_fake import EvaluationServiceFake\nfrom dependency_injection.ioc_container import IocContainer\nimport dependency_injector.providers as providers\nimport torch\nimport unittest\nfrom shutil import copyfile\n\n\ndef initialize_container(\n ocr_output_type: OCROutputType = None, \n override_args: dict = None,\n evaluation: bool = False) -> IocContainer:\n custom_args = {\n 'challenge': Challenge.OCREvaluation,\n 'configuration': Configuration.SkipGram,\n 'language': Language.English,\n 'output_folder': os.path.join('tests', 'results'),\n 'experiments_folder': os.path.join('tests', 'experiments'),\n 'cache_folder': os.path.join('tests', '.cache'),\n 'ocr_output_type': ocr_output_type,\n 'checkpoint_name': 'local-test',\n 'minimal_occurrence_limit': 5,\n 'initialize_randomly': False,\n 'patience': 1,\n 'consider_equal_results_as_worse': True\n }\n\n if override_args is not None:\n for key, value in override_args.items():\n custom_args[key] = value\n\n container = IocContainer()\n\n if evaluation:\n container.arguments_service.override(\n providers.Factory(\n EvaluationServiceFake,\n custom_args))\n else:\n container.arguments_service.override(\n providers.Factory(\n NonContextServiceFake,\n custom_args))\n\n container.log_service.override(providers.Factory(LogServiceFake))\n container.train_service.override(\n providers.Factory(\n TrainServiceFake,\n arguments_service=container.arguments_service,\n dataloader_service=container.dataloader_service,\n loss_function=container.loss_function,\n optimizer=container.optimizer,\n log_service=container.log_service,\n file_service=container.file_service,\n model=container.model))\n\n return container\n\n\nclass TestNeighbourhoodPlots(unittest.TestCase):\n\n def test_neighbourhood_plot_of_new_models(self):\n language = 'english'\n preferred_tokens_path = os.path.join('experiments', f'preferred-tokens-{language}.txt')\n tests_preferred_tokens_path = os.path.join('tests', preferred_tokens_path)\n if os.path.exists(preferred_tokens_path) and not os.path.exists(tests_preferred_tokens_path):\n copyfile(preferred_tokens_path, tests_preferred_tokens_path)\n\n raw_checkpoint_filepath = os.path.join('tests', 'results', 'ocr-evaluation', 'skip-gram', 'english', 'BEST_en-skip-gram-raw-lim-5-local-test.pickle')\n if os.path.exists(raw_checkpoint_filepath):\n os.remove(raw_checkpoint_filepath)\n\n grt_checkpoint_filepath = os.path.join('tests', 'results', 'ocr-evaluation', 'skip-gram', 'english', 'BEST_en-skip-gram-grt-lim-5-local-test.pickle')\n if os.path.exists(grt_checkpoint_filepath):\n os.remove(grt_checkpoint_filepath)\n\n # Raw model\n container_1 = initialize_container(ocr_output_type=OCROutputType.Raw)\n container_1.main()\n\n assert os.path.exists(raw_checkpoint_filepath)\n\n # Ground truth model\n container_2 = initialize_container(\n ocr_output_type=OCROutputType.GroundTruth)\n container_2.main()\n\n assert os.path.exists(grt_checkpoint_filepath)\n\n experiments_container = initialize_container(\n override_args={\n 'separate_neighbourhood_vocabularies': False,\n 'run_experiments': True,\n 'experiment_types': [ExperimentType.CosineSimilarity, ExperimentType.CosineDistance],\n 'batch_size': 128,\n 'joint_model': True\n },\n evaluation=True)\n\n experiments_container.main()\n\n assert len(os.listdir(os.path.join('tests', 'experiments', 'neighbourhoods', 'en-skip-gram'))) > 0\n\n\n def test_neighbourhood_plot_of_new_random_models(self):\n language = 'english'\n preferred_tokens_path = os.path.join('experiments', f'preferred-tokens-{language}.txt')\n tests_preferred_tokens_path = os.path.join('tests', preferred_tokens_path)\n if os.path.exists(preferred_tokens_path) and not os.path.exists(tests_preferred_tokens_path):\n copyfile(preferred_tokens_path, tests_preferred_tokens_path)\n\n raw_checkpoint_filepath = os.path.join('tests', 'results', 'ocr-evaluation', 'skip-gram', 'english', 'BEST_en-skip-gram-rnd-raw-lim-5-local-test.pickle')\n if os.path.exists(raw_checkpoint_filepath):\n os.remove(raw_checkpoint_filepath)\n\n grt_checkpoint_filepath = os.path.join('tests', 'results', 'ocr-evaluation', 'skip-gram', 'english', 'BEST_en-skip-gram-rnd-grt-lim-5-local-test.pickle')\n if os.path.exists(grt_checkpoint_filepath):\n os.remove(grt_checkpoint_filepath)\n\n # Raw model\n container_1 = initialize_container(\n ocr_output_type=OCROutputType.Raw,\n override_args={\n 'initialize_randomly': True\n })\n container_1.main()\n\n assert os.path.exists(raw_checkpoint_filepath)\n\n # Ground truth model\n container_2 = initialize_container(\n ocr_output_type=OCROutputType.GroundTruth,\n override_args={\n 'initialize_randomly': True\n })\n container_2.main()\n\n assert os.path.exists(grt_checkpoint_filepath)\n\n experiments_container = initialize_container(\n override_args={\n 'separate_neighbourhood_vocabularies': True,\n 'run_experiments': True,\n 'experiment_types': [ExperimentType.CosineSimilarity, ExperimentType.CosineDistance],\n 'batch_size': 128,\n 'joint_model': True,\n 'initialize_randomly': True\n },\n evaluation=True)\n\n experiments_container.main()\n\n assert len(os.listdir(os.path.join('tests', 'experiments', 'neighbourhoods', 'en-skip-gram-rnd'))) > 0\n\n\nif __name__ == '__main__':\n unittest.main()\n"
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"text": "from enums.configuration import Configuration\nfrom enums.word_evaluation_type import WordEvaluationType\nfrom enums.overlap_type import OverlapType\nfrom services.experiments.process.neighbourhood_similarity_process_service import NeighbourhoodSimilarityProcessService\nfrom enums.font_weight import FontWeight\nfrom entities.plot.label_options import LabelOptions\nfrom entities.plot.figure_options import FigureOptions\nfrom entities.plot.plot_options import PlotOptions\nfrom entities.cache.cache_options import CacheOptions\nfrom services.cache_service import CacheService\nfrom scipy import sparse\nfrom scipy.sparse import vstack\nfrom tqdm import tqdm\n\nfrom entities.word_neighbourhood_stats import WordNeighbourhoodStats\nfrom services.log_service import LogService\nfrom entities.plot.legend_options import LegendOptions\nfrom typing import Dict, List, Tuple\nfrom matplotlib.pyplot import plot\nimport math\nimport numpy as np\n\nfrom entities.word_evaluation import WordEvaluation\n\nfrom services.arguments.ocr_evaluation_arguments_service import OCREvaluationArgumentsService\nfrom services.file_service import FileService\nfrom services.metrics_service import MetricsService\nfrom services.plot_service import PlotService\nfrom services.fit_transformation_service import FitTransformationService\nfrom services.process.evaluation_process_service import EvaluationProcessService\n\n\nclass WordNeighbourhoodService:\n def __init__(\n self,\n arguments_service: OCREvaluationArgumentsService,\n metrics_service: MetricsService,\n plot_service: PlotService,\n file_service: FileService,\n log_service: LogService,\n fit_transformation_service: FitTransformationService,\n cache_service: CacheService,\n neighbourhood_similarity_process_service: NeighbourhoodSimilarityProcessService,\n process_service: EvaluationProcessService):\n\n self._arguments_service = arguments_service\n self._metrics_service = metrics_service\n self._plot_service = plot_service\n self._file_service = file_service\n self._log_service = log_service\n self._fit_transformation_service = fit_transformation_service\n self._cache_service = cache_service\n self._neighbourhood_similarity_process_service = neighbourhood_similarity_process_service\n\n # load previously cached word similarity calculations\n common_tokens = process_service.get_common_words()\n self._word_similarity_indices, self._cache_needs = self._load_cached_calculations(\n common_tokens)\n\n def plot_word_neighbourhoods(\n self,\n target_word_evaluation: WordEvaluation,\n word_neighbourhood_stats: WordNeighbourhoodStats):\n\n self._log_service.log_debug(\n f'Plotting neighbourhoods for word \\'{target_word_evaluation.word}\\'')\n\n all_words = word_neighbourhood_stats.get_all_words()\n\n all_word_embeddings = []\n for i in range(word_neighbourhood_stats.neighbourhoods_amount):\n all_word_embeddings.append(\n target_word_evaluation.get_embeddings(i))\n\n all_word_embeddings.extend(\n word_neighbourhood_stats.get_all_embeddings())\n\n assert all(not np.isnan(x).any()\n for x in all_word_embeddings), \"Invalid values found in word embeddings\"\n\n fitted_result = self._fit_transformation_service.fit_and_transform_vectors(\n number_of_components=word_neighbourhood_stats.neighbourhoods_amount,\n vectors=all_word_embeddings)\n\n self._plot_fitted_result(\n fitted_result[:word_neighbourhood_stats.neighbourhoods_amount],\n fitted_result[word_neighbourhood_stats.neighbourhoods_amount:],\n target_word_evaluation,\n all_words,\n word_neighbourhood_stats)\n\n def get_word_neighbourhoods(\n self,\n word_evaluation: WordEvaluation,\n vocabulary_evaluations: List[WordEvaluation],\n neighbourhood_set_sizes: List[int],\n overlap_type: OverlapType,\n include_embeddings: bool = False) -> Dict[int, WordNeighbourhoodStats]:\n self._log_service.log_debug(\n f'Extracting neighbourhoods for word \\'{word_evaluation.word}\\'')\n result = {\n neighbourhood_set_size: WordNeighbourhoodStats(\n word_evaluation.word, neighbourhoods=[])\n for neighbourhood_set_size in neighbourhood_set_sizes\n }\n\n model_indices = []\n if overlap_type == OverlapType.BASEvsGT:\n model_indices = [2, 1]\n elif overlap_type == OverlapType.BASEvsOCR:\n model_indices = [2, 0]\n elif overlap_type == OverlapType.BASEvsOG:\n model_indices = [2, 3]\n elif overlap_type == OverlapType.GTvsOCR:\n model_indices = [1, 0]\n\n for i in model_indices:\n word_neighbourhoods_per_set_size = self._get_word_neighbourhood(\n word_evaluation,\n vocabulary_evaluations,\n embeddings_idx=i,\n neighbourhood_set_sizes=neighbourhood_set_sizes,\n output_full_evaluations=include_embeddings)\n\n for neighbourhood_set_size, word_neighbourhood in word_neighbourhoods_per_set_size.items():\n result[neighbourhood_set_size].add_neighbourhood(word_neighbourhood)\n\n return result\n\n def _plot_fitted_result(\n self,\n target_word_fitted_vectors: np.ndarray,\n fitted_vectors: np.ndarray,\n target_word_evaluation: WordEvaluation,\n all_words: List[str],\n word_neighbourhoods: WordNeighbourhoodStats):\n ax = self._plot_service.create_plot()\n\n labels_colors = ['crimson', 'royalblue', 'darkgreen']\n word_neighbourhood_length = word_neighbourhoods.neighbourhood_size\n\n plot_options = PlotOptions(\n ax=ax,\n legend_options=LegendOptions(show_legend=False),\n figure_options=FigureOptions(\n show_plot=False))\n\n for i in range(word_neighbourhoods.neighbourhoods_amount):\n target_word_fitted_vector = target_word_fitted_vectors[i]\n current_fitted_vectors = fitted_vectors[(\n i * word_neighbourhood_length):(i+1)*word_neighbourhood_length]\n\n x_coords = target_word_fitted_vector[0] + \\\n current_fitted_vectors[:, 0]\n y_coords = target_word_fitted_vector[1] + \\\n current_fitted_vectors[:, 1]\n\n self._plot_service.plot_scatter(\n x_coords,\n y_coords,\n plot_options=plot_options)\n\n current_words = [target_word_evaluation.word] + all_words[(\n i*word_neighbourhood_length):((i+1)*word_neighbourhood_length)]\n current_word_colors = [labels_colors[i]] + [labels_colors[i] if all_words.count(\n x) == 1 else labels_colors[-1] for x in current_words[1:]]\n\n labels_options = [\n LabelOptions(\n x=x_coords[k],\n y=y_coords[k],\n text=current_words[k],\n text_color=current_word_colors[k])\n for k in range(word_neighbourhood_length)]\n\n labels_options[0]._font_weight = FontWeight.Bold\n labels_options[0]._font_size = 15\n\n self._plot_service.plot_labels(labels_options, plot_options)\n\n self._plot_service.set_plot_properties(\n ax=ax,\n figure_options=FigureOptions(\n title=f'Neighbourhoods `{target_word_evaluation.word}`',\n hide_axis=True),\n legend_options=LegendOptions(\n show_legend=True,\n legend_colors=labels_colors,\n legend_labels=['raw', 'ground truth', 'overlapping']))\n\n experiments_folder = self._file_service.get_experiments_path()\n neighbourhoods_folder = self._file_service.combine_path(\n experiments_folder,\n 'neighbourhoods',\n self._arguments_service.get_configuration_name(),\n create_if_missing=True)\n\n self._plot_service.save_plot(\n save_path=neighbourhoods_folder,\n filename=f'{target_word_evaluation}-neighborhood-change')\n\n def _token_already_calculated(self, token: str, embeddings_idx: int) -> bool:\n if (token not in self._word_similarity_indices.keys() or\n embeddings_idx not in self._word_similarity_indices[token].keys() or\n self._word_similarity_indices[token][embeddings_idx] is None):\n return False\n\n return True\n\n def _get_word_neighbourhood(\n self,\n word_evaluation: WordEvaluation,\n model_evaluations: List[WordEvaluation],\n embeddings_idx: int,\n neighbourhood_set_sizes: List[int],\n output_full_evaluations: bool = False) -> Dict[int, List[WordEvaluation]]:\n # We check if we have already calculated this word neighbourhood for the selected embeddings id\n if (not output_full_evaluations and self._token_already_calculated(word_evaluation.word, embeddings_idx)):\n indices = self._word_similarity_indices[word_evaluation.word][embeddings_idx]\n else:\n # If no calculation is available, we calculate and cache\n target_embeddings = np.array(\n [word_evaluation.get_embeddings(embeddings_idx)])\n model_embeddings = np.array([model_evaluation.get_embeddings(\n embeddings_idx) for model_evaluation in model_evaluations])\n\n distances = self._metrics_service.calculate_cosine_similarities(\n target_embeddings, model_embeddings)\n\n indices = np.argsort(distances.squeeze())[::-1]\n\n if not output_full_evaluations:\n self._cache_needs[WordEvaluationType(embeddings_idx)] = True\n if word_evaluation.word not in self._word_similarity_indices.keys():\n self._word_similarity_indices[word_evaluation.word] = {}\n\n # We mark the indices to be cached because we add a new entry\n self._word_similarity_indices[word_evaluation.word][embeddings_idx] = indices\n\n result = {}\n for neighbourhood_set_size in neighbourhood_set_sizes:\n if neighbourhood_set_size > len(indices):\n self._log_service.log_error(\n f'Neighbourhood set size ({neighbourhood_set_size}) is larger than the collection ({len(indices)}). Using the entire collection instead')\n raise Exception('Invalid set size')\n\n max_indices = indices[:neighbourhood_set_size]\n\n if output_full_evaluations:\n result_evaluations = [x for i, x in enumerate(\n model_evaluations) if i in max_indices]\n result[neighbourhood_set_size] = result_evaluations\n continue\n\n result[neighbourhood_set_size] = max_indices\n\n return result\n\n def generate_neighbourhood_plots(\n self,\n word_evaluations,\n cosine_distances: Dict[str, float]):\n target_tokens = self._neighbourhood_similarity_process_service.get_target_tokens(\n cosine_distances)\n neighbourhood_set_size = 50\n\n for target_token in tqdm(target_tokens, desc=\"Processing target tokens\", total=len(target_tokens)):\n i = next(i for i, word_evaluation in enumerate(\n word_evaluations) if word_evaluation.word == target_token)\n\n if i is None:\n continue\n\n word_evaluation = word_evaluations[i]\n remaining_words = [word_evaluation for idx, word_evaluation in enumerate(\n word_evaluations) if word_evaluation.contains_all_embeddings(OverlapType.GTvsOCR) and idx != i]\n word_neighbourhood_stats = self.get_word_neighbourhoods(\n word_evaluation,\n remaining_words,\n neighbourhood_set_sizes=[neighbourhood_set_size],\n overlap_type=OverlapType.GTvsOCR,\n include_embeddings=True)\n\n self.plot_word_neighbourhoods(\n word_evaluation,\n word_neighbourhood_stats[neighbourhood_set_size])\n\n def generate_neighbourhood_similarities(\n self,\n word_evaluations: List[WordEvaluation],\n overlap_type: OverlapType) -> Dict[str, int]:\n self._log_service.log_debug(\n f'Generating neighbourhood similarity results for overlap type \\'{overlap_type.value}\\'')\n\n # get all indices of words that support the current overlap type\n common_words_indices = [\n i\n for i, word_evaluation in enumerate(word_evaluations)\n if word_evaluation.contains_all_embeddings(overlap_type)]\n\n percentages = list(range(1, 101, 1)) # 1..20\n words_amounts = [\n int(len(common_words_indices) * (float(percentage)/ 100)) - (1 if percentage == 100 else 0)\n for percentage in percentages]\n\n result = {\n percentage: {}\n for percentage in percentages\n }\n\n self._log_service.log_summary(\n f'Total \\'{overlap_type.value}\\' neighbourhood overlaps', len(common_words_indices))\n for i in tqdm(iterable=common_words_indices, desc=f'Calculating neighbourhood overlaps [\\'{overlap_type.value}\\']', total=len(common_words_indices)):\n # get the target word evaluation\n word_evaluation = word_evaluations[i]\n\n # get the remaining valid word evaluations\n remaining_words = [word_evaluations[idx]\n for idx in common_words_indices if idx != i]\n\n # calculate the word neighbourhood stats for this word\n word_neighbourhood_stats_per_set_size = self.get_word_neighbourhoods(\n word_evaluation,\n remaining_words,\n neighbourhood_set_sizes=words_amounts,\n overlap_type=overlap_type)\n\n # we only need the overlaps amount\n for words_amount, percentage in zip(words_amounts, percentages):\n result[percentage][word_evaluation.word] = word_neighbourhood_stats_per_set_size[words_amount].overlaps_amount\n\n # occasionally cache the calculations performed so far in case the process is interrupted\n if i % 500 == 0:\n self._log_service.log_summary(\n f'Processed \\'{overlap_type.value}\\' neighbourhood overlaps', i)\n self._save_calculations()\n\n self._save_calculations()\n\n return result\n\n def _load_cached_calculations(self, common_tokens: List[str]) -> Dict[str, Dict[int, list]]:\n result = {token: {} for token in common_tokens}\n cache_needs = {}\n\n for i, word_evaluation_type in enumerate(WordEvaluationType):\n cache_needs[word_evaluation_type] = False\n word_similarities_cache_options = self._create_cache_options(\n word_evaluation_type)\n\n current_word_similarity_indices: Dict[str, Dict[int, list]] = self._cache_service.get_item_from_cache(\n word_similarities_cache_options)\n\n if current_word_similarity_indices is None:\n cache_needs[word_evaluation_type] = True\n continue\n\n for token, value in current_word_similarity_indices.items():\n if token not in result.keys():\n result[token] = {}\n\n result[token][i] = value\n\n return result, cache_needs\n\n def _save_calculations(self):\n for i, word_evaluation_type in enumerate(WordEvaluationType):\n if not self._cache_needs[word_evaluation_type]:\n continue\n\n cache_options = self._create_cache_options(word_evaluation_type)\n current_value = {token: embeddings[i] if i in embeddings.keys(\n ) else None for token, embeddings in self._word_similarity_indices.items()}\n\n self._cache_service.cache_item(\n current_value,\n cache_options)\n\n self._cache_needs[word_evaluation_type] = False\n\n def _create_cache_options(self, word_evaluation_type: WordEvaluationType):\n random_suffix = ''\n if word_evaluation_type == WordEvaluationType.Baseline or self._arguments_service.initialize_randomly:\n random_suffix = '-rnd'\n\n configuration_value = None\n if word_evaluation_type == WordEvaluationType.Baseline:\n configuration_value = Configuration.SkipGram\n\n word_eval_type_suffix = ''\n if word_evaluation_type != WordEvaluationType.Baseline:\n word_eval_type_suffix = f'-{str(word_evaluation_type.value)}'\n\n if word_evaluation_type != WordEvaluationType.CurrentOriginal:\n word_eval_type_suffix = f'{word_eval_type_suffix}-lr{self._arguments_service.learning_rate}'\n\n result = CacheOptions(\n 'word-similarities',\n seed_specific=True,\n key_suffixes=[\n word_eval_type_suffix,\n '-sep' if self._arguments_service.separate_neighbourhood_vocabularies else '',\n random_suffix,\n '-min',\n str(self._arguments_service.minimal_occurrence_limit)\n ],\n configuration=configuration_value)\n\n return result\n"
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"text": "from datasets.document_dataset_base import DocumentDatasetBase\nfrom torch.utils.data.sampler import Sampler\n\nimport numpy as np\nfrom random import shuffle\n\n\nclass DocumentSampler(Sampler):\n\n def __init__(\n self,\n dataset: DocumentDatasetBase,\n shuffle: bool = True,\n batch_size=64):\n\n self._total_length = len(dataset)\n self._shuffle = shuffle\n self._ids_per_doc = dataset.get_indices_per_document()\n self.batch_size = batch_size\n self.iter_list = self._create_iter_list()\n\n def _create_iter_list(self):\n data_buckets = {\n k: np.asarray(v) for k, v in self._ids_per_doc.items()\n }\n\n bucket_keys = list(data_buckets.keys())\n\n if self._shuffle:\n shuffle(bucket_keys)\n\n iter_list = []\n for k in bucket_keys:\n np.random.shuffle(data_buckets[k])\n if len(data_buckets[k]) < self.batch_size:\n iter_list.append(data_buckets[k])\n continue\n\n iter_list += (np.array_split(\n data_buckets[k],\n int(data_buckets[k].shape[0]/self.batch_size)))\n\n # shuffle all the batches so they are not ordered by bucket\n # size\n\n self._total_length = len(iter_list)\n return iter_list\n\n def __iter__(self):\n for i in self.iter_list:\n yield i.tolist() # as it was stored in an array\n\n def __len__(self):\n return self._total_length"
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"text": "from typing import Dict\nfrom overrides import overrides\nimport argparse\n\nfrom services.arguments.ocr_quality_arguments_service import OCRQualityArgumentsService\n\nfrom enums.ocr_output_type import OCROutputType\n\n\nclass OCRQualityNonContextArgumentsService(OCRQualityArgumentsService):\n def __init__(self):\n super().__init__()\n\n def get_configuration_name(self, overwrite_args: Dict[str, object] = None) -> str:\n result = super().get_configuration_name(overwrite_args)\n\n rnd_value = self._get_value_or_default(overwrite_args, 'initialize_randomly', self.initialize_randomly)\n if rnd_value:\n result += f'-rnd'\n\n min_occurrence_value = self._get_value_or_default(overwrite_args, 'minimal_occurrence_limit', self.minimal_occurrence_limit)\n if min_occurrence_value is not None:\n result += f'-min{min_occurrence_value}'\n\n ocr_output_value = self._get_value_or_default(overwrite_args, 'ocr_output_type', self.ocr_output_type)\n output_type_suffix = ''\n if ocr_output_value == OCROutputType.GroundTruth:\n output_type_suffix = f'-grt'\n else:\n output_type_suffix = f'-{ocr_output_value.value}'\n\n result = result.replace(output_type_suffix, '')\n result += output_type_suffix\n\n return result\n\n def _add_specific_arguments(self, parser: argparse.ArgumentParser):\n super()._add_specific_arguments(parser)\n\n parser.add_argument('--minimal-occurrence-limit', type=int, default=5,\n help='Minimal occurrence limit for words or tokens to be included in the vocabulary. This setting is not taken into account for configurations using pre-trained vocabularies')\n\n parser.add_argument('--initialize-randomly', action='store_true',\n help='If this is set to True, then the initial embeddings will be initialized randomly.')\n\n parser.add_argument('--window-size', type=int, default=5,\n help='Window size to be used for models which rely on one such as CBOW and Skip-gram')\n\n @property\n def minimal_occurrence_limit(self) -> int:\n return self._get_argument('minimal_occurrence_limit')\n\n @property\n def initialize_randomly(self) -> bool:\n return self._get_argument('initialize_randomly')\n\n @property\n def window_size(self) -> int:\n return self._get_argument('window_size')"
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"text": "from matplotlib.figure import Figure\nimport pandas as pd\nfrom utils.math_utils import get_square, is_square\nfrom entities.plot.histogram_options import HistogramOptions\nfrom entities.plot.label_options import LabelOptions\nfrom entities.plot.figure_options import FigureOptions\nfrom enums.plot_legend_position import PlotLegendPosition\nfrom entities.plot.legend_options import LegendOptions\nfrom entities.plot.plot_options import PlotOptions\nimport sys\nimport seaborn as sns\nimport numpy as np\nfrom sklearn.metrics import confusion_matrix\nfrom services.data_service import DataService\nfrom typing import Dict, List\nimport matplotlib.pyplot as plt\nimport matplotlib\nfrom matplotlib.pyplot import cm, plot\nfrom matplotlib.lines import Line2D\nfrom matplotlib.artist import Artist\nfrom matplotlib.axes import Axes\nfrom collections import Counter\n\n\nplt.rcParams[\"figure.figsize\"] = (15, 10)\nplt.rcParams['font.family'] = 'sans-serif'\nplt.rcParams['font.sans-serif'] = 'cm'\n\n\nclass PlotService:\n def __init__(\n self,\n data_service: DataService):\n sns.set()\n sns.set(font_scale=2) # crazy big\n # sns.set_style(\"ticks\")\n\n self._data_service = data_service\n\n def create_plot(self, plot_options: PlotOptions = None) -> Axes:\n if plot_options is not None and plot_options.ax is not None:\n return plot_options.ax\n\n if plot_options is not None and plot_options.figure_options is not None:\n sns.set_style(plot_options.figure_options.seaborn_style)\n else:\n sns.set_style('ticks')\n\n fig = plt.figure()\n\n fig.canvas.start_event_loop(sys.float_info.min) # workaround for Exception in Tkinter callback\n\n ax = fig.add_subplot(1, 1, 1)\n return ax\n\n def create_plots(self, plots_count: int, share_x_coords: bool = False, share_y_coords: bool = False) -> List[Axes]:\n sns.set_style('ticks')\n\n rows_count = plots_count\n columns_count = 1\n if is_square(plots_count):\n square_count = get_square(plots_count)\n rows_count = square_count\n columns_count = square_count\n\n fig, all_axs = plt.subplots(rows_count, columns_count, sharex=share_x_coords, sharey=share_y_coords)\n axs = [x for col_axs in all_axs for x in col_axs]\n\n return fig, axs\n\n def plot_histogram(\n self,\n values: list,\n plot_options: PlotOptions,\n number_of_bins: int = None):\n ax = self.create_plot(plot_options)\n\n if number_of_bins is None:\n number_of_bins = len(set(values))\n\n if plot_options.xlim is None:\n start_x, end_x = min(values), max(values)\n else:\n start_x, end_x = plot_options.xlim\n\n distance_bin = (end_x - start_x) / number_of_bins\n\n bins = np.arange(start_x, end_x, distance_bin)\n\n ax.hist(values, bins=bins, edgecolor='none')\n\n self._add_properties(ax, plot_options)\n return ax\n\n def autolabel_heights(self, ax, rects, rotation: int = 0):\n \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n y_offset = 3 if rotation == 0 else 10\n for rect in rects:\n height = rect.get_height()\n if height == 0:\n continue\n\n ax.annotate(\n '{}'.format(height),\n xy=(rect.get_x() + rect.get_width() / 2, height),\n xytext=(0, y_offset), # 3 points vertical offset\n textcoords=\"offset points\",\n ha='center',\n va='bottom',\n rotation=rotation)\n\n def plot_counters_histogram(\n self,\n counter_labels: List[str],\n counters: List[Counter],\n plot_options: PlotOptions,\n histogram_options: HistogramOptions,\n counter_colors: List[str] = None):\n\n ax = self.create_plot(plot_options)\n\n unique_labels = list(\n sorted(set([label for x in counters for label in x.keys()])))\n\n values = []\n for counter in counters:\n values.append([(counter[label] if label in counter.keys() else 0)\n for label in unique_labels])\n\n total_width = 1 - histogram_options.bars_padding # the width of the bars\n dim = len(counters)\n dimw = total_width / dim\n\n x = np.arange(len(unique_labels)) # the label locations\n\n if counter_colors is None:\n counter_colors = cm.rainbow(np.linspace(0, 1, dim))\n\n rects = []\n for i, counter_values in enumerate(values):\n rects.append(\n ax.bar(x + (i * dimw), counter_values, dimw, label=counter_labels[i], color=counter_colors[i]))\n\n xticks = x + (total_width - dimw) / 2\n xtick_labels = unique_labels\n if plot_options.xticks_count is not None:\n\n indices = np.round(np.linspace(\n 0, len(xticks) - 1, plot_options.xticks_count)).astype(int)\n leftover_ticks = [xticks[idx] for idx in indices]\n xtick_labels = [unique_labels[idx] for idx in indices]\n\n xticks = leftover_ticks\n\n ax.set_xticks(xticks)\n labels = ax.set_xticklabels(\n xtick_labels, rotation=plot_options.x_labels_rotation_angle)\n\n if plot_options.space_x_labels_vertically:\n for i, label in enumerate(labels):\n label.set_y(label.get_position()[1] - (i % 2) * 0.075)\n\n if histogram_options.plot_values_above_bars:\n for rect in rects:\n self.autolabel_heights(\n ax, rect, rotation=histogram_options.values_above_bars_rotation)\n\n x1, x2, y1, y2 = ax.axis()\n ax.axis((x1, x2, y1, y2 + 5))\n\n self._add_properties(ax, plot_options)\n\n return ax\n\n def plot_distribution(\n self,\n counts,\n plot_options: PlotOptions):\n ax = self.create_plot(plot_options)\n\n counts_list = counts\n if isinstance(counts, dict):\n for k, v in counts.items():\n for _ in range(v):\n counts_list.append(k)\n\n ax = sns.kdeplot(\n data=counts_list,\n color=plot_options.color,\n fill=plot_options.fill,\n linestyle=plot_options.linestyle.value,\n label=plot_options.label,\n legend=plot_options.legend_options.show_legend,\n linewidth=plot_options.line_width,\n alpha=plot_options.alpha,\n ax=ax)\n\n self._add_properties(ax, plot_options)\n\n return ax\n\n def plot_line_variance(\n self,\n pd_dataframe: pd.DataFrame,\n x: str,\n y: str,\n plot_options: PlotOptions):\n\n ax = self.create_plot(plot_options)\n\n ax = sns.lineplot(\n data=pd_dataframe,\n x=x,\n y=y,\n label=plot_options.label,\n ax=ax,\n markersize=14,\n color=plot_options.color,\n linestyle=plot_options.linestyle.value,\n ci=95)\n\n self._add_properties(ax, plot_options)\n\n return ax\n\n def plot_scatter(\n self,\n x_values: list,\n y_values: list,\n plot_options: PlotOptions):\n ax = self.create_plot(plot_options)\n ax.scatter(x_values, y_values, color=plot_options.color)\n self._add_properties(ax, plot_options)\n return ax\n\n def plot_overlapping_bars(\n self,\n numbers_per_type: List[List[int]],\n bar_titles: List[str],\n plot_options: PlotOptions,\n colors: List[str] = None):\n\n ax = self.create_plot(plot_options)\n\n unique_numbers = set([item for v in numbers_per_type for item in v])\n counters_per_type = {\n bar_titles[i]: Counter(v)\n for i, v in enumerate(numbers_per_type)\n }\n\n normalized_counters_per_type = {\n type_name: Counter({\n n: (float(v)/sum(unnormalized_counter.values())) * 100\n for n, v in unnormalized_counter.items()\n })\n for type_name, unnormalized_counter in counters_per_type.items()\n }\n\n argmaxes = {}\n for i, number in enumerate(unique_numbers):\n occs = np.array([x[number]\n for _, x in normalized_counters_per_type.items()])\n arg_sort = np.argsort(np.argsort(occs, kind='heapsort'))\n sorted_occs = sorted(occs)\n\n a = np.zeros(len(occs))\n for i, index in enumerate(arg_sort):\n if index == 0:\n a[i] = 0\n else:\n a[i] = sorted_occs[index-1]\n\n argmaxes[number] = a\n\n for i, counter_values in enumerate(normalized_counters_per_type.values()):\n x = list(sorted(counter_values.keys()))\n\n y = np.array([counter_values[key] for key in x])\n p = np.array([argmaxes[a][i] for a in x])\n norm_y = y - p\n\n ax.bar(x, norm_y, width=x[1]-x[0], color=colors[i], bottom=p)\n\n self._add_properties(ax, plot_options)\n\n return ax\n\n def plot_labels(\n self,\n labels_options: List[LabelOptions],\n plot_options: PlotOptions):\n ax = self.create_plot(plot_options)\n\n for label_options in labels_options:\n label_color = label_options.text_color if label_options.text_color is not None else plot_options.color\n\n ax.annotate(\n label_options.text,\n xy=(label_options.x, label_options.y),\n xytext=(0, 0),\n textcoords='offset points',\n color=label_color,\n weight=label_options.font_weight.value,\n fontsize=label_options.font_size)\n\n self._add_properties(ax, plot_options)\n return ax\n\n def plot_arrow(\n self,\n x: float,\n y: float,\n dx: float,\n dy: float,\n plot_options: PlotOptions):\n ax = self.create_plot(plot_options)\n\n ax.annotate(\"\", xy=(x+dx, y+dy), xytext=(x, y),\n arrowprops=dict(arrowstyle=\"-|>\", color=plot_options.color),\n bbox=dict(pad=7, facecolor=\"none\", edgecolor=\"none\"))\n\n self._add_properties(ax, plot_options)\n return ax\n\n def plot_confusion_matrix(\n self,\n true_values: list,\n predicted_values: list,\n plot_options: PlotOptions,\n labels: List[str] = None,\n normalize: bool = False):\n ax = self.create_plot(plot_options)\n\n cm = confusion_matrix(true_values, predicted_values, labels)\n\n vmin = cm.min()\n vmax = cm.max()\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n vmin = 0\n vmax = 1\n\n sns_heatmap = sns.heatmap(\n cm,\n ax=ax,\n vmin=vmin,\n vmax=vmax,\n cmap='RdYlGn_r',\n square=True)\n\n ax.set_xlabel('Predicted values') # , labelpad=20)\n ax.set_ylabel('True values')\n\n if labels is not None:\n ax.set_ylim(0, len(labels) + 0.5)\n ax.set_ylim(0, len(labels) + 0.5)\n\n sns_heatmap.set_yticklabels(labels, rotation=0)\n sns_heatmap.set_xticklabels(\n labels, rotation=45, horizontalalignment='right')\n\n self._add_properties(ax, plot_options)\n return ax\n\n def plot_heatmap(\n self,\n values: np.array,\n plot_options: PlotOptions,\n labels: List[str] = None,\n vmin: float = None,\n vmax: float = None,\n show_colorbar: bool = True):\n ax = self.create_plot(plot_options)\n\n if vmin is None:\n vmin = np.min(values)\n\n if vmax is None:\n vmax = np.max(values)\n\n sns_heatmap = sns.heatmap(\n values,\n ax=ax,\n vmin=vmin,\n vmax=vmax,\n cmap='Greens',\n square=True,\n cbar=show_colorbar)\n if labels is not None:\n ax.set_ylim(0, len(labels) + 0.5)\n ax.set_ylim(0, len(labels) + 0.5)\n\n sns_heatmap.set_yticklabels(labels, rotation=0)\n sns_heatmap.set_xticklabels(\n labels, rotation=45, horizontalalignment='right')\n\n self._add_properties(ax, plot_options)\n return ax\n\n def plot_lines(\n self,\n x_values: List[float],\n y_values: List[List[float]],\n plot_options: PlotOptions,\n labels: List[str] = None):\n ax = self.create_plot(plot_options)\n\n if labels is not None:\n for value_list, label in zip(y_values, labels):\n ax.plot(x_values, value_list, label=label)\n else:\n label = plot_options.label\n ax.plot(x_values, y_values, label=label)\n\n self._add_properties(ax, plot_options)\n return ax\n\n def show_plot(self):\n plt.show()\n\n def set_plot_properties(\n self,\n ax: Axes,\n figure_options: FigureOptions,\n legend_options: LegendOptions = None):\n\n if figure_options.tight_layout:\n plt.tight_layout()\n\n if figure_options.hide_axis:\n ax.axis('off')\n\n if figure_options.hide_x_labels:\n ax.axes.xaxis.set_visible(False)\n\n if figure_options.hide_y_labels:\n ax.axes.yaxis.set_visible(False)\n\n if legend_options is not None:\n self.show_legend(ax, legend_options)\n\n if figure_options.super_title is not None and figure_options.figure is not None:\n figure_options.figure.suptitle(figure_options.super_title)\n\n if figure_options.title is not None:\n ax.set_title(figure_options.title, pad=figure_options.title_padding,\n fontdict={'fontweight': 'bold'})\n\n\n def plot_confidence_lines(\n self,\n x_values: List[float],\n y_values: List[List[float]],\n plot_options: PlotOptions,\n labels: List[str] = None):\n ax = self.create_plot(plot_options)\n\n # if labels is not None:\n # for value_list, label in zip(y_values, labels):\n # ax.plot(x_values, value_list, label=label)\n # else:\n # label = plot_options.label\n # ax.plot(x_values, y_values, label=label)\n sns.lineplot(x_values, y_values)\n\n self._add_properties(ax, plot_options)\n return ax\n\n\n def show_legend(\n self,\n ax: Axes,\n legend_options: LegendOptions):\n\n if legend_options is None or not legend_options.show_legend:\n return\n\n bbox_to_anchor = None\n legend_location = None\n lg_obj = None\n\n if legend_options.legend_position == PlotLegendPosition.Outside:\n bbox_to_anchor = (1.04, 1)\n legend_location = \"upper left\"\n elif (legend_options.legend_position != PlotLegendPosition.Outside and\n legend_options.legend_position != PlotLegendPosition.Automatic):\n legend_location = legend_options.legend_position.value\n\n if legend_options.legend_colors is not None and len(legend_options.legend_colors) > 0:\n legend_lines = self._create_legend_lines(\n legend_options.legend_colors)\n if legend_options.legend_labels is not None and len(legend_options.legend_labels) > 0:\n lg_obj = ax.legend(legend_lines, legend_options.legend_labels,\n bbox_to_anchor=bbox_to_anchor, loc=legend_location)\n else:\n lg_obj = ax.legend(legend_lines, bbox_to_anchor=bbox_to_anchor,\n loc=legend_location)\n elif legend_options.legend_title_options is not None:\n sub_title_pairs = legend_options.legend_title_options.get_sub_titles()\n if sub_title_pairs is not None:\n handles, labels = ax.get_legend_handles_labels()\n for position_id, text in sub_title_pairs:\n handles.insert(position_id, text)\n labels.insert(position_id, '')\n\n lg_obj = ax.legend(handles, labels, handler_map={str: legend_options.legend_title_options},handlelength=10, markerscale=100)\n else:\n lg_obj = ax.legend()\n\n if lg_obj is not None and legend_options.marker_scale is not None:\n leg_lines = lg_obj.get_lines()\n plt.setp(leg_lines, linewidth=legend_options.marker_scale)\n\n def save_plot(self, save_path: str, filename: str, figure: Figure = None):\n self._data_service.save_figure(save_path, filename, fig=figure)\n\n def _create_legend_lines(\n self,\n legend_colors: List[str]) -> List[Artist]:\n lines = [Line2D([0], [0], color=color, lw=4)\n for color in legend_colors]\n return lines\n\n def _add_properties(\n self,\n ax: Axes,\n plot_options: PlotOptions):\n\n self.show_legend(ax, plot_options.legend_options)\n self._set_labels(ax, plot_options)\n self._set_plot_limits(ax, plot_options)\n self.set_plot_properties(ax, plot_options.figure_options)\n\n if plot_options.figure_options is None:\n return\n\n if plot_options.figure_options.save_path is not None and plot_options.figure_options.filename is not None:\n self.save_plot(plot_options.figure_options.save_path,\n plot_options.figure_options.filename)\n elif plot_options.figure_options.show_plot:\n self.show_plot()\n\n if plot_options.clear_figure:\n plt.clf()\n\n def _set_labels(self, ax: Axes, plot_options: PlotOptions):\n if plot_options.ylabel_options.text is not None:\n ax.set_ylabel(\n plot_options.ylabel_options.text,\n fontweight=plot_options.ylabel_options.font_weight.value)\n\n if plot_options.xlabel_options.text is not None:\n ax.set_xlabel(\n plot_options.xlabel_options.text,\n fontweight=plot_options.xlabel_options.font_weight.value)\n\n def _set_plot_limits(self, ax: Axes, plot_options: PlotOptions):\n if plot_options.ylim is not None:\n ax.set_ylim(plot_options.ylim[0], plot_options.ylim[1])\n\n if plot_options.xlim is not None:\n ax.set_xlim(plot_options.xlim[0], plot_options.xlim[1])\n"
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"text": "from enums.overlap_type import OverlapType\nfrom typing import List\n\nclass WordEvaluation:\n def __init__(self, word: str, embeddings_list: List[List[List[float]]] = None):\n self._word = word\n self._embeddings = embeddings_list\n\n def add_embeddings(self, embeddings: List[int], idx: int):\n if self._embeddings is None:\n self._embeddings = []\n\n # make sure the list is big enough\n while len(self._embeddings) <= idx:\n self._embeddings.append(None)\n\n if embeddings is not None:\n self._embeddings[idx] = embeddings\n\n def get_embeddings(self, idx: int) -> list:\n if idx > len(self._embeddings):\n raise Exception('Invalid embeddings index')\n\n return self._embeddings[idx]\n\n def get_embeddings_size(self) -> int:\n filled_embeddings = list(filter(lambda x: x is not None, self._embeddings))\n if len(filled_embeddings) == 0:\n return None\n\n return len(filled_embeddings[0])\n\n @property\n def word(self) -> str:\n return self._word\n\n def contains_embeddings(self, embeddings_idx: int) -> bool:\n return self._embeddings[embeddings_idx] is not None\n\n def contains_all_embeddings(self, overlap_type: OverlapType = None) -> bool:\n if overlap_type is None:\n result = len(self._embeddings) >= 3 and all([x is not None for x in self._embeddings])\n return result\n\n # result = len(self._embeddings) >= 3 and self._embeddings[2] is not None\n result = True\n if overlap_type != OverlapType.GTvsOCR:\n result = len(self._embeddings) >= 3 and self._embeddings[2] is not None\n\n if overlap_type == OverlapType.BASEvsGT:\n return (result and self._embeddings[1] is not None)\n elif overlap_type == OverlapType.BASEvsOCR:\n return (result and self._embeddings[2] is not None)\n elif overlap_type == OverlapType.BASEvsOG:\n return (result and len(self._embeddings) >= 4 and self._embeddings[3] is not None)\n elif overlap_type == OverlapType.GTvsOCR:\n return (result and self._embeddings[1] is not None and self._embeddings[0] is not None)\n\n raise NotImplementedError(f'Overlap type {overlap_type.value} is not implemented')\n\n def __str__(self):\n return self._word\n"
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"text": "import numpy as np\nfrom overrides import overrides\nimport torch\n\nfrom datasets.dataset_base import DatasetBase\nfrom services.arguments.ocr_quality_arguments_service import OCRQualityArgumentsService\nfrom services.process.evaluation_process_service import EvaluationProcessService\nfrom services.log_service import LogService\n\nclass EvaluationDataset(DatasetBase):\n def __init__(\n self,\n arguments_service: OCRQualityArgumentsService,\n process_service: EvaluationProcessService,\n log_service: LogService):\n self._arguments_service = arguments_service\n self._process_service = process_service\n self._log_service = log_service\n\n self._target_tokens = self._process_service.get_target_tokens()\n self._log_service.log_debug(f'Loaded {len(self._target_tokens)} target tokens in evaluation dataset')\n\n def __len__(self):\n return len(self._target_tokens)\n\n def __getitem__(self, idx):\n target_token = self._target_tokens[idx]\n return target_token"
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"text": "import torch.nn as nn\nfrom overrides import overrides\n\nfrom losses.loss_base import LossBase\n\nclass JointLoss(LossBase):\n def __init__(self):\n super(JointLoss, self).__init__()\n\n def backward(self, models_outputs):\n for model_output in models_outputs:\n model_output.backward()\n\n result = [model_output.item() for model_output in models_outputs]\n return result\n\n def calculate_loss(self, models_outputs):\n result = [model_output.item() for model_output in models_outputs]\n return result\n"
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"text": "from enums.language import Language\nfrom entities.cache.cache_options import CacheOptions\nimport enum\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\nfrom services.cache_service import CacheService\nfrom services.file_service import FileService\nfrom services.log_service import LogService\nfrom services.process.process_service_base import ProcessServiceBase\nfrom models.simple.ppmi import PPMI\nfrom entities.word_evaluation import WordEvaluation\nfrom models.simple.skip_gram import SkipGram\nfrom torch.utils import data\nfrom enums.configuration import Configuration\nimport os\nimport numpy as np\nfrom overrides.overrides import overrides\nimport torch\nfrom typing import List\nimport torch\nfrom copy import deepcopy\n\nfrom enums.ocr_output_type import OCROutputType\n\nfrom entities.models.model_checkpoint import ModelCheckpoint\nfrom entities.batch_representation import BatchRepresentation\n\nfrom models.model_base import ModelBase\nfrom models.transformers.bert import BERT\nfrom models.transformers.albert import ALBERT\nfrom models.simple.cbow import CBOW\n\nfrom services.arguments.ocr_evaluation_arguments_service import OCREvaluationArgumentsService\nfrom services.data_service import DataService\nfrom services.vocabulary_service import VocabularyService\n\n\nclass EvaluationModel(ModelBase):\n def __init__(\n self,\n arguments_service: OCREvaluationArgumentsService,\n data_service: DataService,\n vocabulary_service: VocabularyService,\n process_service: ProcessServiceBase,\n log_service: LogService,\n file_service: FileService,\n cache_service: CacheService,\n tokenize_service: BaseTokenizeService):\n super().__init__(data_service, arguments_service, log_service)\n\n self._arguments_service = arguments_service\n self._data_service = data_service\n self._vocabulary_service = vocabulary_service\n self._process_service = process_service\n self._log_service = log_service\n self._tokenize_service = tokenize_service\n\n self._ocr_output_types = [OCROutputType.Raw, OCROutputType.GroundTruth]\n\n self._inner_models: List[ModelBase] = torch.nn.ModuleList([\n self._create_model(self._arguments_service.configuration, ocr_output_type) for ocr_output_type in self._ocr_output_types])\n\n # self._inner_models.append(\n # # BASE\n # SkipGram(\n # arguments_service=self._arguments_service,\n # vocabulary_service=VocabularyService(\n # self._data_service,\n # file_service,\n # cache_service,\n # log_service,\n # overwrite_configuration=Configuration.SkipGram),\n # data_service=self._data_service,\n # log_service=self._log_service,\n # ocr_output_type=OCROutputType.GroundTruth)\n # )\n\n # if self._arguments_service.configuration in [Configuration.SkipGram, Configuration.CBOW, Configuration.BERT]:\n # pretrained_matrix = None\n # if self._arguments_service.configuration in [Configuration.SkipGram, Configuration.CBOW]:\n # pretrained_matrix = cache_service.get_item_from_cache(\n # CacheOptions(\n # f'word-matrix-{self._arguments_service.get_dataset_string()}-{OCROutputType.GroundTruth.value}',\n # seed_specific=True))\n\n # pretrained_matrix = pretrained_matrix.to(self._arguments_service.device)\n\n # self._inner_models.append(\n # self._create_model(\n # self._arguments_service.configuration,\n # OCROutputType.GroundTruth,\n # pretrained_matrix=pretrained_matrix,\n # overwrite_initialization=True))\n\n def forward(self, tokens: torch.Tensor):\n self._log_service.log_warning('Joint model currently does not have a forward pass implemented properly. Please use `get_embeddings` instead')\n raise NotImplementedError()\n\n def get_embeddings(self, tokens: List[str], skip_unknown: bool = False) -> torch.Tensor:\n word_evaluation_sets = []\n for model in self._inner_models:\n embeddings_list = model.get_embeddings(tokens, skip_unknown=skip_unknown)\n word_evaluation_sets.append(embeddings_list)\n\n result = self._combine_word_evaluations(tokens, word_evaluation_sets)\n return result\n\n def _create_model(\n self,\n configuration: Configuration,\n ocr_output_type: OCROutputType,\n pretrained_matrix = None,\n overwrite_initialization: bool = False):\n result = None\n if configuration == Configuration.BERT:\n result = BERT(\n arguments_service=self._arguments_service,\n data_service=self._data_service,\n log_service=self._log_service,\n tokenize_service=self._tokenize_service,\n overwrite_initialization=overwrite_initialization)\n elif configuration == Configuration.ALBERT:\n result = ALBERT(\n arguments_service=self._arguments_service,\n data_service=self._data_service,\n log_service=self._log_service,\n tokenize_service=self._tokenize_service)\n elif configuration == Configuration.CBOW:\n result = CBOW(\n arguments_service=self._arguments_service,\n vocabulary_service=deepcopy(self._vocabulary_service),\n data_service=self._data_service,\n log_service=self._log_service,\n pretrained_matrix=pretrained_matrix,\n ocr_output_type=ocr_output_type)\n elif configuration == Configuration.SkipGram:\n result = SkipGram(\n arguments_service=self._arguments_service,\n vocabulary_service=deepcopy(self._vocabulary_service),\n data_service=self._data_service,\n log_service=self._log_service,\n pretrained_matrix=pretrained_matrix,\n ocr_output_type=ocr_output_type)\n elif configuration == Configuration.PPMI:\n result = PPMI(\n arguments_service=self._arguments_service,\n vocabulary_service=deepcopy(self._vocabulary_service),\n data_service=self._data_service,\n log_service=self._log_service,\n process_service=self._process_service,\n ocr_output_type=ocr_output_type)\n\n if result is None:\n self._log_service.log_error('Joint model inner type is not implemented')\n raise NotImplementedError()\n\n return result\n\n def load(\n self,\n path: str,\n name_prefix: str = None,\n name_suffix: str = None,\n load_model_dict: bool = True,\n use_checkpoint_name: bool = True,\n checkpoint_name: str = None) -> ModelCheckpoint:\n self._log_service.log_debug('Loading joint models..')\n\n for (ocr_output_type, model) in zip(self._ocr_output_types, self._inner_models[:2]):\n ocr_output_type_str = 'grt' if ocr_output_type == OCROutputType.GroundTruth else ocr_output_type.value\n model.load(\n path=path,\n name_prefix=name_prefix,\n name_suffix=f'-{ocr_output_type_str}',\n load_model_dict=load_model_dict,\n use_checkpoint_name=use_checkpoint_name,\n checkpoint_name=checkpoint_name)\n\n if len(self._inner_models) > 2:\n skip_gram_model = self._inner_models[2]\n skip_gram_overwrite_args = {\n 'initialize_randomly': True,\n 'configuration': Configuration.SkipGram.value,\n 'learning_rate': 1e-3,# if self._arguments_service.language == Language.English else 1e-2,\n 'minimal_occurrence_limit': 5\n }\n\n skip_gram_model.load(\n path=path.replace(self._arguments_service.configuration.value, Configuration.SkipGram.value),\n name_prefix=name_prefix,\n name_suffix=f'-{ocr_output_type_str}',\n load_model_dict=load_model_dict,\n use_checkpoint_name=use_checkpoint_name,\n checkpoint_name=checkpoint_name,\n overwrite_args=skip_gram_overwrite_args)\n\n self._log_service.log_debug('Loading joint models succeeded')\n return None\n\n\n def _combine_word_evaluations(self, tokens: List[str], embeddings_list: List[List[List[float]]]) -> List[WordEvaluation]:\n # unique_tokens = set([word_evaluation.word for word_evaluations in word_evaluations_sets for word_evaluation in word_evaluations])\n\n result = []\n\n for i, token in enumerate(tokens):\n we = WordEvaluation(token, embeddings_list=[\n x[i] for x in embeddings_list\n ])\n\n result.append(we)\n\n # we_dict = {}\n # for unique_token in unique_tokens:\n # new_word_evaluation = WordEvaluation(unique_token)\n\n # for i, word_evaluations in enumerate(word_evaluations_sets):\n # for word_evaluation in word_evaluations:\n # if word_evaluation.word != unique_token:\n # continue\n\n # new_word_evaluation.add_embeddings(word_evaluation.get_embeddings(0), idx=i)\n\n # we_dict[unique_token] = new_word_evaluation\n\n # result = list(we_dict.values())\n return result"
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"text": "from entities.cache.cache_options import CacheOptions\nfrom logging import error\nimport os\nfrom services.file_service import FileService\n\nfrom overrides import overrides\nfrom torch._C import dtype\nimport gensim\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\nfrom typing import Dict, List, Tuple\n\nfrom enums.ocr_output_type import OCROutputType\nfrom enums.language import Language\n\nfrom entities.word2vec.word2vec_corpus import Word2VecCorpus\n\nfrom services.process.icdar_process_service import ICDARProcessService\n\nfrom services.download.ocr_download_service import OCRDownloadService\nfrom services.arguments.ocr_quality_non_context_arguments_service import OCRQualityNonContextArgumentsService\nfrom services.cache_service import CacheService\nfrom services.log_service import LogService\nfrom services.vocabulary_service import VocabularyService\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\n\n\nclass Word2VecProcessService(ICDARProcessService):\n def __init__(\n self,\n ocr_download_service: OCRDownloadService,\n arguments_service: OCRQualityNonContextArgumentsService,\n cache_service: CacheService,\n log_service: LogService,\n vocabulary_service: VocabularyService,\n file_service: FileService,\n tokenize_service: BaseTokenizeService):\n super().__init__(\n ocr_download_service=ocr_download_service,\n arguments_service=arguments_service,\n cache_service=cache_service,\n vocabulary_service=vocabulary_service,\n tokenize_service=tokenize_service,\n log_service=log_service)\n\n self._arguments_service = arguments_service\n self._cache_service = cache_service\n self._vocabulary_service = vocabulary_service\n self._file_service = file_service\n\n def get_text_corpus(self, ocr_output_type: OCROutputType) -> Word2VecCorpus:\n limit_size = self._arguments_service.train_dataset_limit_size\n text_corpus = self._load_text_corpus(ocr_output_type, limit_size)\n return text_corpus\n\n def get_embedding_size(self) -> int:\n if self._arguments_service.language == Language.English:\n return 300\n elif self._arguments_service.language == Language.Dutch:\n return 320\n elif self._arguments_service.language == Language.French:\n return 300\n elif self._arguments_service.language == Language.German:\n return 300\n\n error_message = 'Unsupported embeddings language'\n self._log_service.log_error(error_message)\n raise Exception(error_message)\n\n def get_pretrained_matrix(self) -> Tuple[torch.Tensor, bool]:\n if not self._vocabulary_service.vocabulary_is_initialized():\n raise Exception('Vocabulary not initialized')\n\n ocr_output_type = self._arguments_service.ocr_output_type\n\n random_suffix = ''\n if self._arguments_service.initialize_randomly:\n random_suffix = '-rnd-init'\n\n token_matrix = self._cache_service.get_item_from_cache(\n CacheOptions(\n f'word-matrix-{self._get_dataset_string()}-{ocr_output_type.value}{random_suffix}',\n seed_specific=True),\n callback_function=self._generate_token_matrix)\n\n token_matrix = token_matrix.to(self._arguments_service.device)\n return token_matrix, self._arguments_service.initialize_randomly\n\n def _generate_token_matrix(self):\n data_path = self._file_service.combine_path(\n self._file_service.get_challenge_path(),\n 'word2vec',\n self._arguments_service.language.value)\n\n word2vec_model_name, word2vec_binary = self._get_word2vec_model_info()\n word2vec_model_path = os.path.join(data_path, word2vec_model_name)\n self._log_service.log_debug(f'Loading word2vec model from \\'{word2vec_model_path}\\'')\n word2vec_weights: gensim.models.KeyedVectors = gensim.models.KeyedVectors.load_word2vec_format(\n word2vec_model_path, binary=word2vec_binary)\n\n initialized_tokens: Dict[str, list] = None\n initialized_tokens_cache_key = f'initialized-tokens-{self._get_dataset_string()}'\n if not self._arguments_service.initialize_randomly:\n initialized_tokens = self._cache_service.get_item_from_cache(\n CacheOptions(\n initialized_tokens_cache_key,\n seed_specific=True),\n callback_function=lambda: {})\n\n vocab_size = self._vocabulary_service.vocabulary_size()\n pretrained_weight_matrix = np.random.uniform(\n low=(-0.5 / word2vec_weights.vector_size),\n high=(0.5 / word2vec_weights.vector_size),\n size=(vocab_size, word2vec_weights.vector_size))\n\n if not self._arguments_service.initialize_randomly:\n self._log_service.log_debug(f'Populating pretrained matrix...')\n vocabulary_items = tqdm(self._vocabulary_service.get_vocabulary_tokens(\n ), desc=\"Generating pre-trained matrix\", total=self._vocabulary_service.vocabulary_size())\n for (index, token) in vocabulary_items:\n if token in word2vec_weights.key_to_index.keys():\n pretrained_weight_matrix[index] = word2vec_weights[token]\n elif initialized_tokens is not None and token in initialized_tokens.keys():\n pretrained_weight_matrix[index] = initialized_tokens[token]\n else:\n initialized_tokens[token] = pretrained_weight_matrix[index]\n\n self._log_service.log_debug(f'Populating pretrained matrix finished successfully')\n self._cache_service.cache_item(\n initialized_tokens,\n CacheOptions(\n initialized_tokens_cache_key,\n seed_specific=True))\n\n result = torch.from_numpy(pretrained_weight_matrix).float()\n return result\n\n def _get_word2vec_model_info(self) -> Tuple[str, bool]:\n if self._arguments_service.language == Language.English:\n return 'GoogleNews-vectors-negative300.bin', True\n elif self._arguments_service.language == Language.Dutch:\n return 'combined-320.txt', False\n elif self._arguments_service.language == Language.French:\n return 'frwiki_20180420_300d.txt', False\n elif self._arguments_service.language == Language.German:\n return 'dewiki_20180420_300d.txt', False\n\n error_message = 'Unsupported word2vec language'\n self._log_service.log_error(error_message)\n raise Exception(error_message)\n\n def _load_text_corpus(\n self,\n ocr_output_type: OCROutputType,\n reduction: int) -> Word2VecCorpus:\n corpus = self._cache_service.get_item_from_cache(\n CacheOptions(\n f'word2vec-data',\n key_suffixes=[\n '-',\n self._get_dataset_string(),\n '-',\n str(ocr_output_type.value),\n '-ws-',\n str(self._arguments_service.window_size)\n ]),\n callback_function=self._generate_ocr_corpora)\n\n total_amount = corpus.length\n if reduction is not None:\n corpus.cut_data(reduction)\n\n self._log_service.log_info(f'Loaded {corpus.length:,} entries out of {total_amount:,} total for {ocr_output_type.value}')\n self._log_service.log_summary(key=f'\\'{ocr_output_type.value}\\' entries amount', value=corpus.length)\n\n return corpus\n\n def _generate_corpora_entries(self, data_ids):\n return Word2VecCorpus(data_ids, window_size=self._arguments_service.window_size)"
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"path": "/models/transformers/xlnet.py",
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"text": "from services.log_service import LogService\nfrom overrides import overrides\n\nfrom models.transformers.transformer_base import TransformerBase\nfrom transformers import XLNetLMHeadModel\n\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\nfrom services.data_service import DataService\n\nclass XLNet(TransformerBase):\n def __init__(\n self,\n arguments_service: PretrainedArgumentsService,\n data_service: DataService,\n log_service: LogService,\n output_hidden_states: bool = False):\n super().__init__(arguments_service, data_service, log_service, output_hidden_states)\n\n def forward(self, input_batch, **kwargs):\n input, labels, attention_masks = input_batch\n outputs = self.transformer_model.forward(input, labels=labels, attention_mask=attention_masks)\n return outputs.loss\n\n @property\n def _model_type(self) -> type:\n return XLNetLMHeadModel\n\n @property\n def transformer_model(self) -> XLNetLMHeadModel:\n return self._transformer_model"
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"path": "/services/embeddings/word_embeddings_service.py",
"repo_name": "ktodorov/historical-ocr",
"src_encoding": "UTF-8",
"text": "from services.embeddings.word_alignment_service import WordAlignmentService\nfrom services.vocabulary_service import VocabularyService\nfrom services.log_service import LogService\nfrom models.evaluation_model import EvaluationModel\nfrom services.arguments.ocr_evaluation_arguments_service import OCREvaluationArgumentsService\nfrom typing import List\nfrom tqdm import tqdm\n\nfrom entities.word_evaluation import WordEvaluation\nfrom enums.ocr_output_type import OCROutputType\nfrom torch.utils.data import DataLoader\n\n\nclass WordEmbeddingsService:\n def __init__(\n self,\n arguments_service: OCREvaluationArgumentsService,\n log_service: LogService,\n vocabulary_service: VocabularyService,\n word_alignment_service: WordAlignmentService):\n\n self._arguments_service = arguments_service\n self._vocabulary_service = vocabulary_service\n self._log_service = log_service\n self._word_alignment_service = word_alignment_service\n\n def generate_embeddings(self, model: EvaluationModel, dataloader: DataLoader) -> List[WordEvaluation]:\n result: List[WordEvaluation] = []\n\n self._log_service.log_debug('Processing common vocabulary tokens')\n dataloader_length = len(dataloader)\n for i, tokens in tqdm(iterable=enumerate(dataloader), desc=f'Processing common vocabulary tokens', total=dataloader_length):\n outputs = model.get_embeddings(tokens)\n result.extend(outputs)\n\n if self._arguments_service.separate_neighbourhood_vocabularies:\n processed_tokens = [we.word for we in result]\n for ocr_output_type in [OCROutputType.Raw, OCROutputType.GroundTruth]:\n self._log_service.log_debug(\n f'Processing unique vocabulary tokens for {ocr_output_type.value} type')\n vocab_key = f'vocab-{self._arguments_service.get_dataset_string()}-{ocr_output_type.value}'\n self._vocabulary_service.load_cached_vocabulary(vocab_key)\n unprocessed_tokens = []\n for _, token in self._vocabulary_service.get_vocabulary_tokens(exclude_special_tokens=True):\n if token in processed_tokens:\n continue\n\n unprocessed_tokens.append(token)\n\n batch_size = self._arguments_service.batch_size\n with tqdm(desc=f'Unique {ocr_output_type.value} vocabulary tokens', total=len(unprocessed_tokens)) as progress_bar:\n for i in range(0, len(unprocessed_tokens), batch_size):\n tokens = unprocessed_tokens[i:i+batch_size]\n word_evaluations = model.get_embeddings(\n tokens, skip_unknown=True)\n result.extend(word_evaluations)\n processed_tokens.extend(tokens)\n progress_bar.update(len(tokens))\n\n # if self._arguments_service.initialize_randomly:\n # TODO\n # result = self._word_alignment_service.align_word_embeddings(result)\n\n return result\n"
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"path": "/services/plots/individual_metrics_plot_service.py",
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"text": "from collections import Counter\nfrom services.log_service import LogService\nfrom services.file_service import FileService\nfrom services.plot_service import PlotService\nfrom entities.plot.figure_options import FigureOptions\nfrom entities.plot.legend_options import LegendOptions\nfrom entities.plot.plot_options import PlotOptions\nfrom enums.overlap_type import OverlapType\nfrom enums.experiment_type import ExperimentType\nfrom typing import Dict\nfrom services.arguments.ocr_evaluation_arguments_service import OCREvaluationArgumentsService\n\n\nclass IndividualMetricsPlotService:\n def __init__(\n self,\n arguments_service: OCREvaluationArgumentsService,\n file_service: FileService,\n plot_service: PlotService,\n log_service: LogService):\n self._file_service = file_service\n self._arguments_service = arguments_service\n self._plot_service = plot_service\n self._log_service = log_service\n\n def plot_individual_metrics(self, result: Dict[ExperimentType, Dict[str, float]]):\n for experiment_type, word_value_pairs_by_overlap in result.items():\n if experiment_type == ExperimentType.NeighbourhoodOverlap:\n for overlap_type, word_value_pairs in word_value_pairs_by_overlap.items():\n self._save_individual_experiment(\n experiment_type, overlap_type, word_value_pairs)\n else:\n self._save_individual_experiment(\n experiment_type, None, word_value_pairs_by_overlap)\n\n def _save_individual_experiment(\n self,\n experiment_type: ExperimentType,\n overlap_type: OverlapType,\n word_value_pairs):\n experiments_folder = self._file_service.get_experiments_path()\n experiment_type_folder = self._file_service.combine_path(\n experiments_folder,\n experiment_type.value,\n create_if_missing=True)\n\n if overlap_type is not None:\n experiment_type_folder = self._file_service.combine_path(\n experiment_type_folder,\n overlap_type.value,\n create_if_missing=True)\n\n self._log_service.log_debug(\n f'Saving \\'{experiment_type.value}\\' experiment results at \\'{experiment_type_folder}\\'')\n\n values = [round(x, 1) for x in word_value_pairs.values()]\n if values is None or len(values) == 0:\n return\n\n counter = Counter(values)\n filename = f'{self._arguments_service.get_configuration_name()}-{self._arguments_service.neighbourhood_set_size}'\n self._plot_service.plot_distribution(\n counts=counter,\n plot_options=PlotOptions(\n legend_options=LegendOptions(show_legend=False),\n figure_options=FigureOptions(\n title=experiment_type.value,\n save_path=experiment_type_folder,\n filename=filename),\n color='royalblue',\n fill=True,\n xlim=(0, self._arguments_service.neighbourhood_set_size)))\n"
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"text": "\nfrom enums.language import Language\nfrom enums.configuration import Configuration\nfrom enums.challenge import Challenge\nfrom enums.ocr_output_type import OCROutputType\n\nconfigurations = {\n Language.English: {\n Configuration.SkipGram: {\n 1e-3: {\n True: [\n OCROutputType.GroundTruth\n ],\n False: [\n OCROutputType.GroundTruth,\n OCROutputType.Raw,\n ]\n },\n 1e-4: {\n False: [\n OCROutputType.GroundTruth,\n OCROutputType.Raw,\n ]\n }\n },\n Configuration.CBOW: {\n 1e-3: {\n True: [\n OCROutputType.GroundTruth\n ],\n False: [\n OCROutputType.GroundTruth,\n OCROutputType.Raw,\n ]\n },\n 1e-4: {\n False: [\n OCROutputType.GroundTruth,\n OCROutputType.Raw,\n ]\n }\n },\n # Configuration.BERT: {\n # 1e-5: {\n # False: [\n # OCROutputType.GroundTruth,\n # OCROutputType.Raw,\n # ]\n # },\n # # 1e-4: {\n # # False: [\n # # OCROutputType.GroundTruth,\n # # OCROutputType.Raw,\n # # ]\n # # }\n # },\n }\n}\n"
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"text": "import os\nfrom services.file_service import FileService\nfrom services.arguments.ocr_evaluation_arguments_service import OCREvaluationArgumentsService\nfrom services.tagging_service import TaggingService\nfrom services.log_service import LogService\nfrom enums.part_of_speech import PartOfSpeech\nfrom typing import Dict, List, Tuple\n\n\nclass NeighbourhoodSimilarityProcessService:\n def __init__(\n self,\n arguments_service: OCREvaluationArgumentsService,\n file_service: FileService,\n log_service: LogService,\n tagging_service: TaggingService):\n self._arguments_service = arguments_service\n self._file_service = file_service\n self._log_service = log_service\n self._tagging_service = tagging_service\n\n def get_target_tokens(\n self,\n cosine_distances: Dict[str, float],\n pos_tags: List[PartOfSpeech] = [PartOfSpeech.Noun, PartOfSpeech.Verb, PartOfSpeech.Adjective]) -> List[str]:\n metric_results = [(word, distance)\n for word, distance in cosine_distances.items()\n if self._tagging_service.word_is_specific_tag(word, pos_tags)]\n\n metric_results.sort(key=lambda x: x[1], reverse=True)\n\n most_changed_100 = [result[0] for result in metric_results[-100:]]\n most_changed_100_string = ', '.join(most_changed_100)\n self._log_service.log_debug(\n f'Most changed 100 words: [{most_changed_100_string}]')\n\n most_changed = self._map_target_tokens(\n metric_results,\n targets_count=10)\n\n log_message = f'Target words to be used: [' + \\\n ', '.join(most_changed) + ']'\n self._log_service.log_info(log_message)\n\n return most_changed\n\n def _map_target_tokens(\n self,\n ordered_tuples: List[Tuple[str, float]],\n targets_count: int) -> List[str]:\n result_tuples = []\n preferred_tokens = self._get_preferred_target_tokens()\n\n for tuple in ordered_tuples:\n if preferred_tokens is None or tuple[0] in preferred_tokens:\n result_tuples.append(tuple[0])\n\n if len(result_tuples) == targets_count:\n return result_tuples\n\n return result_tuples\n\n def _get_preferred_target_tokens(self) -> List[str]:\n preferred_tokens_path = os.path.join(\n self._file_service.get_experiments_path(),\n f'preferred-tokens-{self._arguments_service.language.value}.txt')\n\n if not os.path.exists(preferred_tokens_path):\n return None\n\n preferred_tokens = []\n with open(preferred_tokens_path, 'r', encoding='utf-8') as tokens_file:\n file_lines = tokens_file.readlines()\n if file_lines is None or len(file_lines) == 0:\n return None\n\n preferred_tokens = [x.strip().lower() for x in file_lines]\n\n return preferred_tokens\n"
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"text": "from overrides import overrides\nfrom transformers import BartTokenizerFast\n\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\n\nfrom services.tokenize.transformer_tokenize_service import TransformerTokenizeService\n\nclass BARTTokenizeService(TransformerTokenizeService):\n def __init__(\n self,\n arguments_service: PretrainedArgumentsService):\n super().__init__(arguments_service)\n\n @property\n def _tokenizer_type(self) -> type:\n return BartTokenizerFast"
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"text": "from overrides import overrides\n\nimport torch\nimport torch.nn.functional as F\n\nfrom losses.loss_base import LossBase\n\nclass SkipGramLoss(LossBase):\n def __init__(self):\n super().__init__()\n\n def backward(self, embeddings):\n loss = self._calculate_loss(embeddings)\n loss.backward()\n return loss.item()\n\n def calculate_loss(self, embeddings) -> torch.Tensor:\n loss = self._calculate_loss(embeddings)\n return loss.item()\n\n def _calculate_loss(self, embeddings):\n (emb_target, emb_context, emb_negative) = embeddings\n emb_target = emb_target.unsqueeze(2)\n\n pos_loss = torch.bmm(emb_context, emb_target)\n pos_loss = -F.logsigmoid(pos_loss)\n\n neg_loss = torch.bmm(emb_negative, emb_target).squeeze()\n neg_loss = -torch.sum(F.logsigmoid(-neg_loss), dim=1)\n total_loss = torch.mean(pos_loss + neg_loss)\n return total_loss"
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"text": "from typing import overload\nimport unittest\nimport os\nimport numpy as np\n\nfrom dependency_injection.ioc_container import IocContainer\nimport dependency_injector.providers as providers\nfrom tests.fakes.non_context_service_fake import NonContextServiceFake\nfrom tests.fakes.log_service_fake import LogServiceFake\nfrom run import initialize_seed\n\ndef initialize_container(override_args = None) -> IocContainer:\n custom_args = {\n }\n \n if override_args is not None:\n for key, value in override_args.items():\n custom_args[key] = value\n\n container = IocContainer()\n container.arguments_service.override(\n providers.Factory(\n NonContextServiceFake,\n custom_values=custom_args))\n\n container.log_service.override(providers.Factory(LogServiceFake))\n return container\n\n\nclass TestInitializationService(unittest.TestCase):\n def test_stochasticity_when_same_seed_different_container(self):\n # first initialization\n container_1 = initialize_container(override_args={\n 'seed': 13,\n 'device': 'cpu'\n })\n\n arguments_service_1 = container_1.arguments_service()\n initialize_seed(arguments_service_1.seed, arguments_service_1.device)\n\n rand_vector_1 = np.random.randint(512, size=512)\n rand_list_1 = list(rand_vector_1)\n\n # second initialization\n container_2 = initialize_container(override_args={\n 'seed': 13,\n 'device': 'cpu'\n })\n\n arguments_service_2 = container_2.arguments_service()\n initialize_seed(arguments_service_2.seed, arguments_service_2.device)\n\n rand_vector_2 = np.random.randint(512, size=512)\n rand_list_2 = list(rand_vector_2)\n\n\n self.assertEqual(rand_list_1, rand_list_2)\n\n def test_stochasticity_when_different_seed(self):\n # first initialization\n container_1 = initialize_container(override_args={\n 'seed': 13,\n 'device': 'cpu'\n })\n\n arguments_service_1 = container_1.arguments_service()\n initialize_seed(arguments_service_1.seed, arguments_service_1.device)\n\n rand_vector_1 = np.random.randint(512, size=512)\n rand_list_1 = list(rand_vector_1)\n\n # second initialization\n container_2 = initialize_container(override_args={\n 'seed': 42,\n 'device': 'cpu'\n })\n\n arguments_service_2 = container_2.arguments_service()\n initialize_seed(arguments_service_2.seed, arguments_service_2.device)\n\n rand_vector_2 = np.random.randint(512, size=512)\n rand_list_2 = list(rand_vector_2)\n\n # assert\n self.assertNotEqual(rand_list_1, rand_list_2)\n\n\n def test_stochasticity_when_same_seed_same_container(self):\n # first initialization\n container_1 = initialize_container(override_args={\n 'seed': 13,\n 'device': 'cpu'\n })\n\n arguments_service_1 = container_1.arguments_service()\n initialize_seed(arguments_service_1.seed, arguments_service_1.device)\n\n rand_vector_1 = np.random.randint(512, size=512)\n rand_list_1 = list(rand_vector_1)\n\n rand_vector_2 = np.random.randint(512, size=512)\n rand_list_2 = list(rand_vector_2)\n\n # assert\n self.assertNotEqual(rand_list_1, rand_list_2)\n\n\nif __name__ == '__main__':\n unittest.main()\n"
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"text": "from typing import Dict, List\n\nfrom overrides.overrides import overrides\nfrom datasets.dataset_base import DatasetBase\n\n\nclass DocumentDatasetBase(DatasetBase):\n def __init__(self):\n super().__init__()\n\n def get_indices_per_document(self) -> Dict[int, List[int]]:\n return {}\n\n def use_collate_function(self) -> bool:\n return True\n\n def collate_function(self, sequences):\n return sequences[0]\n"
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"text": "from tests.fakes.log_service_fake import LogServiceFake\nfrom enums.language import Language\nfrom enums.configuration import Configuration\nfrom enums.challenge import Challenge\nimport os\nfrom tests.fakes.non_context_service_fake import NonContextServiceFake\nfrom dependency_injection.ioc_container import IocContainer\nimport dependency_injector.providers as providers\nimport unittest\n\ndef initialize_container() -> IocContainer:\n container = IocContainer()\n container.arguments_service.override(\n providers.Factory(NonContextServiceFake,\n custom_values={\n 'data_folder': os.path.join('tests', 'data'),\n 'challenge': Challenge.OCREvaluation,\n 'configuration': Configuration.CBOW,\n 'language': Language.English,\n 'output_folder': os.path.join('tests', 'results')\n }))\n\n container.log_service.override(providers.Factory(LogServiceFake))\n return container\n\n\nclass TestFileService(unittest.TestCase):\n def test_combine_path_missing(self):\n container = initialize_container()\n file_service = container.file_service()\n\n path_to_test = os.path.join('tests', 'results', 'temp')\n\n if os.path.exists(path_to_test):\n os.rmdir(path_to_test)\n\n self.assertRaises(Exception, lambda: file_service.combine_path('tests', 'results', 'temp', create_if_missing=False))\n self.assertFalse(os.path.exists(path_to_test))\n\n def test_combine_path_create(self):\n container = initialize_container()\n file_service = container.file_service()\n\n path_to_test = os.path.join('tests', 'results', 'temp')\n\n if os.path.exists(path_to_test):\n os.rmdir(path_to_test)\n\n file_service.combine_path('tests', 'results', 'temp', create_if_missing=True)\n\n self.assertTrue(os.path.exists(path_to_test))\n\n os.rmdir(path_to_test)\n\nif __name__ == '__main__':\n unittest.main()"
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"text": "from typing import Dict\nfrom overrides import overrides\nimport argparse\n\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\n\nfrom enums.ocr_output_type import OCROutputType\n\n\nclass OCRQualityArgumentsService(PretrainedArgumentsService):\n def __init__(self):\n super().__init__()\n\n def get_configuration_name(self, overwrite_args: Dict[str, object] = None) -> str:\n result = super().get_configuration_name(overwrite_args)\n\n ocr_output_value = self._get_value_or_default(overwrite_args, 'ocr_output_type', self.ocr_output_type)\n output_type_suffix = ''\n if ocr_output_value == OCROutputType.GroundTruth:\n output_type_suffix = f'-grt'\n else:\n output_type_suffix = f'-{ocr_output_value.value}'\n\n result = result.replace(output_type_suffix, '')\n result += output_type_suffix\n\n return result\n\n def _add_specific_arguments(self, parser: argparse.ArgumentParser):\n super()._add_specific_arguments(parser)\n\n parser.add_argument('--ocr-output-type', type=OCROutputType, choices=list(OCROutputType), required=True,\n help='OCR output type to be used')\n\n @property\n def ocr_output_type(self) -> OCROutputType:\n return self._get_argument('ocr_output_type')\n"
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"text": "#!/bin/bash\n#SBATCH --job-name=ft-ocr\n#SBATCH --ntasks=1\n#SBATCH --cpus-per-task=3\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=48:00:00\n#SBATCH --mem=60000M\n#SBATCH -p gpu_shared\n#SBATCH --gres=gpu:1\n\nmodule purge\nmodule load 2020\nmodule load Python\n\n# module load Miniconda3\n# source activate historical-ocr\n\nCONF=\"$CONFMODEL\"\nINCLUDEPRETRARG=\"\"\nPRETRMODELARG=\"\"\nMINIMALOCCURRENCELIMITARG=\"--minimal-occurrence-limit 5\"\nSEPARATEVOCABSARG=\"--separate-neighbourhood-vocabularies\"\nif [ ! -z \"$USEPRETR\" ]\nthen\n INCLUDEPRETRARG=\"--include-pretrained-model\"\n PRETRMODELARG=\"--pretrained-model $CONFMODEL\"\n MINIMALOCCURRENCELIMITARG=\"\"\n SEPARATEVOCABSARG=\"\"\nfi\n\nPRETRWEIGHTSARG=\"--pretrained-weights bert-base-cased\"\nif [ ! -z \"$PRETRWEIGHTS\" ]\nthen\n PRETRWEIGHTSARG=\"--pretrained-weights $PRETRWEIGHTS\"\nfi\n\nBATCHSIZEARG=\"$BATCHSIZE\"\nif [ -z \"$BATCHSIZE\" ]\nthen\n BATCHSIZEARG=\"128\"\nfi\n\nLANGUAGEARG=\"english\"\nif [ ! -z \"$LANGUAGE\" ]\nthen\n LANGUAGEARG=\"$LANGUAGE\"\nfi\n\nSEEDARG=\"13\"\nif [ ! -z \"$SEED\" ]\nthen\n SEEDARG=\"$SEED\"\nfi\n\nPADDINGIDXARG=\"0\"\nif [ ! -z \"$PADDINGIDX\" ]\nthen\n PADDINGIDXARG=\"$PADDINGIDX\"\nfi\n\nRANDOMINITARG=\"\"\nif [ ! -z \"$RANDOMINIT\" ]\nthen\n RANDOMINITARG=\"--initialize-randomly\"\nfi\n\nDATASETSARG=\"\"\nif [ ! -z \"$DATASETS\" ]\nthen\n DATASETSARG=\"--datasets $DATASETS\"\nfi\n\nLEARNINGRATE=\"$LR\"\nif [ -z \"$LR\" ]\nthen\n LEARNINGRATE=\"1e-3\"\nfi\n\nsrun python3 -u run.py --run-experiments --configuration $CONF --challenge ocr-evaluation --device cuda --seed $SEEDARG --learning-rate $LEARNINGRATE --language $LANGUAGEARG --batch-size $BATCHSIZEARG $INCLUDEPRETRARG $PRETRMODELARG --pretrained-model-size 768 --pretrained-max-length 512 $PRETRWEIGHTSARG --padding-idx $PADDINGIDXARG $SEPARATEVOCABSARG $MINIMALOCCURRENCELIMITARG --joint-model --neighbourhood-set-size 1000 --experiment-types neighbourhood-overlap cosine-similarity cosine-distance $DATASETSARG --enable-external-logging $RANDOMINITARG"
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"text": "from overrides import overrides\n\nimport torch\nimport torch.nn as nn\n\nfrom losses.loss_base import LossBase\n\nclass CrossEntropyLoss(LossBase):\n def __init__(self):\n super().__init__()\n\n self._criterion = nn.CrossEntropyLoss()\n\n def backward(self, model_output):\n prediction, target = model_output\n loss = self._criterion.forward(prediction, target)\n loss.backward()\n\n return loss.item()\n\n def calculate_loss(self, model_output) -> torch.Tensor:\n prediction, target = model_output\n loss = self._criterion.forward(prediction, target)\n return loss.item()\n\n\n @property\n def criterion(self) -> nn.Module:\n return self._criterion"
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"repo_name": "ktodorov/historical-ocr",
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"text": "from typing import List, Dict\n\nimport torch\n\nfrom services.evaluation.base_evaluation_service import BaseEvaluationService\n\nfrom entities.batch_representation import BatchRepresentation\nfrom enums.evaluation_type import EvaluationType\n\n\nclass OCRQualityEvaluationService(BaseEvaluationService):\n def __init__(self):\n super().__init__()\n\n def evaluate_batch(\n self,\n output: torch.Tensor,\n batch_input: BatchRepresentation,\n evaluation_types: List[EvaluationType],\n batch_index: int) -> Dict[EvaluationType, List]:\n return {}\n\n def save_results(self, evaluation: Dict[EvaluationType, List], targets: List[str]):\n print(evaluation)\n"
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"path": "/datasets/transformer_lm_dataset.py",
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"text": "from typing import Dict, List\nimport numpy as np\nimport torch\nfrom overrides import overrides\n\nfrom datasets.document_dataset_base import DocumentDatasetBase\nfrom services.arguments.ocr_quality_arguments_service import OCRQualityArgumentsService\nfrom services.mask_service import MaskService\nfrom services.log_service import LogService\n\nfrom services.process.transformer_process_service import TransformerProcessService\n\nclass TransformerLMDataset(DocumentDatasetBase):\n def __init__(\n self,\n arguments_service: OCRQualityArgumentsService,\n process_service: TransformerProcessService,\n mask_service: MaskService,\n log_service: LogService,\n **kwargs):\n super().__init__()\n\n self._mask_service = mask_service\n self._arguments_service = arguments_service\n self._log_service = log_service\n\n self._entries = process_service.get_entries(self._arguments_service.ocr_output_type)\n self._log_service.log_debug(f'Loaded {len(self._entries)} entries in transformer dataset')\n\n def __len__(self):\n return len(self._entries)\n\n def __getitem__(self, id):\n return id\n\n def use_collate_function(self) -> bool:\n return True\n\n def collate_function(self, ids):\n entries = [self._entries[idx] for idx in ids]\n batch_size = len(ids)\n\n tokens_ids = [entry.token_ids for entry in entries]\n masks = [entry.mask_ids for entry in entries]\n\n lengths = [len(sequence) for sequence in tokens_ids]\n max_length = max(lengths)\n\n padded_sequences = np.zeros((batch_size, max_length), dtype=np.int64)\n if self._arguments_service.padding_idx != 0:\n padded_sequences.fill(self._arguments_service.padding_idx)\n\n padded_masks = np.ones((batch_size, max_length), dtype=np.int64)\n\n for i, l in enumerate(lengths):\n padded_sequences[i][0:l] = tokens_ids[i][0:l]\n padded_masks[i][0:l] = masks[i][0:l]\n\n return self._sort_batch(\n torch.from_numpy(padded_sequences).to(\n self._arguments_service.device),\n torch.from_numpy(padded_masks).bool().to(\n self._arguments_service.device),\n torch.tensor(lengths).to(self._arguments_service.device))\n\n def _sort_batch(self, sequences, masks, lengths):\n seq_lengths, perm_idx = lengths.sort(0, descending=True)\n seq_tensor = sequences[perm_idx]\n mask_tensor = masks[perm_idx]\n return self._mask_service.mask_tokens(seq_tensor, mask_tensor, seq_lengths)\n\n def get_indices_per_document(self) -> Dict[int, List[int]]:\n total_documents = len(set([x.document_index for x in self._entries]))\n result = {\n i: []\n for i in range(total_documents)\n }\n\n for i, entry in enumerate(self._entries):\n result[entry.document_index].append(i)\n\n return result"
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"text": "import torch\nimport torch.nn as nn\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\nfrom overrides import overrides\nfrom models.model_base import ModelBase\n\n\nclass CharacterRNN(ModelBase):\n\n def __init__(\n self,\n vocabulary_size: int,\n character_embedding_size: int,\n hidden_size: int,\n number_of_layers: int,\n bidirectional_rnn: bool = True,\n dropout: float = 0.0):\n super().__init__()\n\n self._dropout = nn.Dropout(dropout)\n self._embedding_layer = nn.Embedding(vocabulary_size, character_embedding_size)\n self._character_rnn = nn.LSTM(\n character_embedding_size,\n hidden_size,\n num_layers=number_of_layers,\n batch_first=True,\n bidirectional=bidirectional_rnn)\n\n def forward(self, char_seq_tensor: torch.Tensor, char_seq_len: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Get the last hidden states of the LSTM\n input:\n char_seq_tensor: (batch_size, sent_len, word_length)\n char_seq_len: (batch_size, sent_len)\n output:\n Variable(batch_size, sent_len, char_hidden_dim )\n \"\"\"\n batch_size = char_seq_tensor.size(0)\n sent_len = char_seq_tensor.size(1)\n char_seq_tensor = char_seq_tensor.view(batch_size * sent_len, -1)\n char_seq_len = char_seq_len.view(batch_size * sent_len)\n\n char_seq_len = torch.where(char_seq_len == 0, torch.ones(char_seq_len.shape, device=char_seq_len.device, dtype=char_seq_len.dtype), char_seq_len)\n\n sorted_seq_len, permIdx = char_seq_len.sort(0, descending=True)\n _, recover_idx = permIdx.sort(0, descending=False)\n sorted_seq_tensor = char_seq_tensor[permIdx]\n\n character_embeddings = self._embedding_layer.forward(sorted_seq_tensor)\n character_embeddings = self._dropout.forward(character_embeddings)\n pack_input = pack_padded_sequence(\n character_embeddings,\n sorted_seq_len,\n batch_first=True)\n\n rnn_output, hidden = self._character_rnn.forward(pack_input, None)\n ## char_hidden = (h_t, c_t)\n # char_hidden[0] = h_t = (2, batch_size, lstm_dimension)\n # char_rnn_out, _ = pad_packed_sequence(char_rnn_out)\n # transpose because the first dimension is num_direction x num-layer\n # before view, the size is ( batch_size * sent_len, 2, lstm_dimension) 2 means 2 direciton..\n hidden = hidden[0].transpose(1, 0).contiguous().view(batch_size * sent_len, 1, -1)\n\n result = hidden[recover_idx].view(batch_size, sent_len, -1)\n return result\n\n"
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"text": "import os\nfrom services.log_service import LogService \nimport torch\nfrom overrides import overrides\n\nfrom models.model_base import ModelBase\nfrom transformers import PreTrainedModel, PretrainedConfig\n\nfrom entities.models.model_checkpoint import ModelCheckpoint\nfrom entities.metric import Metric\n\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\nfrom services.data_service import DataService\n\nclass TransformerBase(ModelBase):\n def __init__(\n self,\n arguments_service: PretrainedArgumentsService,\n data_service: DataService,\n log_service: LogService,\n output_hidden_states: bool = False,\n overwrite_initialization: bool = False):\n super(TransformerBase, self).__init__(data_service, arguments_service, log_service)\n\n self._output_hidden_states = output_hidden_states\n\n if overwrite_initialization:\n self._log_service.log_debug('Skipping the initialization of the transformer model due to configuration settings')\n self._transformer_model = None\n else:\n self._log_service.log_debug(f'Initializing transformer model of type {str(self._model_type)} using \\'{arguments_service.pretrained_weights}\\' weights')\n\n config = self._config_type.from_pretrained(arguments_service.pretrained_weights, return_dict=True)\n config.output_hidden_states = output_hidden_states\n\n self._transformer_model: PreTrainedModel = self._model_type.from_pretrained(\n arguments_service.pretrained_weights,\n config=config)\n\n self._arguments_service = arguments_service\n\n @property\n def transformer_model(self) -> PreTrainedModel:\n return self._transformer_model\n\n def forward(self, input_batch, **kwargs):\n pass\n\n def named_parameters(self):\n return self._transformer_model.named_parameters()\n\n def parameters(self):\n return self._transformer_model.parameters()\n\n def compare_metric(self, best_metric: Metric, new_metrics: Metric) -> bool:\n if best_metric.is_new or best_metric.get_current_loss() > new_metrics.get_current_loss():\n return True\n\n return False\n\n def save(\n self,\n path: str,\n epoch: int,\n iteration: int,\n best_metrics: object,\n resets_left: int,\n name_prefix: str = None) -> bool:\n\n self._log_service.log_debug(f'Saving transformer model')\n\n model_name = self._get_model_name(name_prefix)\n\n saved = super().save(path, epoch, iteration, best_metrics,\n resets_left, name_prefix, save_model_dict=False)\n\n if not saved:\n self._log_service.log_debug(f'Saving transformer model failed')\n return saved\n\n pretrained_weights_path = self._get_pretrained_path(\n path, model_name, create_if_missing=True)\n\n self._log_service.log_debug(f'Saving transformer model weights at \\'{pretrained_weights_path}\\'')\n self._transformer_model.save_pretrained(pretrained_weights_path)\n\n return saved\n\n def load(\n self,\n path: str,\n name_prefix: str = None,\n name_suffix: str = None,\n load_model_dict: bool = True,\n use_checkpoint_name: bool = True,\n checkpoint_name: str = None) -> ModelCheckpoint:\n\n model_name = self._get_model_name(name_prefix, name_suffix)\n\n model_checkpoint = super().load(\n path,\n model_name,\n load_model_dict=False,\n use_checkpoint_name=False)\n if model_checkpoint is None:\n return None\n\n if load_model_dict:\n self._load_transformer_model(path, model_name)\n\n return model_checkpoint\n\n @property\n def _model_type(self) -> type:\n return PreTrainedModel\n\n @property\n def _config_type(self) -> type:\n return PretrainedConfig\n\n def _load_transformer_model(self, path: str, name_prefix: str):\n pretrained_weights_path = self._get_pretrained_path(path, name_prefix)\n self._log_service.log_debug(f'Attempting to load transformer model weights from \\'{pretrained_weights_path}\\'')\n\n config = PretrainedConfig.from_pretrained(pretrained_weights_path)\n config.output_hidden_states = True\n\n self._transformer_model = self._model_type.from_pretrained(\n pretrained_weights_path, config=config).to(self._arguments_service.device)\n\n def _get_pretrained_path(self, path: str, name_prefix: str, create_if_missing: bool = False):\n file_name = f'{name_prefix}_weights'\n pretrained_weights_path = os.path.join(path, file_name)\n\n if create_if_missing and not os.path.exists(pretrained_weights_path):\n os.mkdir(pretrained_weights_path)\n\n return pretrained_weights_path"
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"text": "import os\n\nfrom typing import Tuple, List\n\nfrom overrides import overrides\n\nfrom tokenizers import BertWordPieceTokenizer\n\nfrom tokenizers.implementations import ByteLevelBPETokenizer\nfrom tokenizers.processors import BertProcessing\n\n\nimport sentencepiece as spm\n\nfrom enums.configuration import Configuration\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\n\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\nfrom services.file_service import FileService\nfrom services.download.ocr_download_service import OCRDownloadService\n\nclass BERTTokenizeService(BaseTokenizeService):\n def __init__(\n self,\n arguments_service: PretrainedArgumentsService,\n file_service: FileService,\n ocr_download_service: OCRDownloadService):\n super().__init__()\n\n self._arguments_service = arguments_service\n self._file_service = file_service\n self._ocr_download_service = ocr_download_service\n\n pretrained_weights = self._arguments_service.pretrained_weights\n\n pretrained_weights = arguments_service.pretrained_weights\n self._tokenizer = self._load_tokenizer(pretrained_weights)\n if self._tokenizer is None:\n self._tokenizer = self._train_tokenizer(pretrained_weights)\n\n self._tokenizer._tokenizer.post_processor = BertProcessing(\n (\"[SEP]\", self._tokenizer.token_to_id(\"[SEP]\")),\n (\"[CLS]\", self._tokenizer.token_to_id(\"[CLS]\")),\n )\n\n # self._arguments_service = arguments_service\n # vocabulary_path = os.path.join(arguments_service.data_folder, 'vocabularies', f'{pretrained_weights}-vocab.txt')\n # if not os.path.exists(vocabulary_path):\n # raise Exception(f'Vocabulary not found in {vocabulary_path}')\n\n # self._tokenizer: BertWordPieceTokenizer = BertWordPieceTokenizer(\n # vocabulary_path, lowercase=False)\n\n def encode_tokens(self, tokens: List[str]) -> List[int]:\n result = [self._tokenizer.token_to_id(x) for x in tokens]\n return result\n\n def decode_tokens(self, character_ids: List[int]) -> List[str]:\n result = [self._tokenizer.id_to_token(\n character_id) for character_id in character_ids]\n return result\n\n def decode_string(self, character_ids: List[int]) -> List[str]:\n result = self._tokenizer.decode(character_ids)\n return result\n\n def id_to_token(self, character_id: int) -> str:\n result = self._tokenizer.id_to_token(character_id)\n return result\n\n def encode_sequence(self, sequence: str, add_special_tokens: bool = True) -> Tuple[List[int], List[str], List[Tuple[int,int]], List[int]]:\n encoded_representation = self._tokenizer.encode(sequence, add_special_tokens=add_special_tokens)\n return (\n encoded_representation.ids,\n encoded_representation.tokens,\n encoded_representation.offsets,\n encoded_representation.special_tokens_mask)\n\n def encode_sequences(self, sequences: List[str]) -> List[Tuple[List[int], List[str], List[Tuple[int,int]], List[int]]]:\n encoded_representations = self._tokenizer.encode_batch(sequences)\n return [(x.ids, x.tokens, x.offsets, x.special_tokens_mask) for x in encoded_representations]\n\n @property\n def vocabulary_size(self) -> int:\n return self._tokenizer.get_vocab_size()\n\n def _load_tokenizer(self, pretrained_weights: str) -> ByteLevelBPETokenizer:\n result = None\n vocabulary_path = os.path.join(\n self._arguments_service.data_folder, 'vocabularies', self._arguments_service.language.value, f'{pretrained_weights}-vocab.json')\n merges_path = os.path.join(\n self._arguments_service.data_folder, 'vocabularies', self._arguments_service.language.value, f'{pretrained_weights}-merges.txt')\n\n if os.path.exists(vocabulary_path) and os.path.exists(merges_path):\n result = ByteLevelBPETokenizer(vocabulary_path, merges_path)\n\n return result\n\n def _train_tokenizer(self, pretrained_weights: str) -> ByteLevelBPETokenizer:\n file_paths = self._ocr_download_service.get_downloaded_file_paths(self._arguments_service.language)\n tokenizer = ByteLevelBPETokenizer()\n\n tokenizer.train(\n files=file_paths,\n min_frequency=2,\n special_tokens=[\n \"[PAD]\",\n \"[CLS]\",\n \"[SEP]\",\n \"[UNK]\",\n \"[MASK]\",\n ])\n\n save_path = self._file_service.combine_path(self._arguments_service.data_folder, 'vocabularies', self._arguments_service.language.value, create_if_missing=True)\n tokenizer.save_model(save_path, pretrained_weights)\n\n return tokenizer"
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"text": "from overrides import overrides\nimport argparse\n\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\n\nfrom enums.metric_type import MetricType\nfrom enums.configuration import Configuration\nfrom enums.pretrained_model import PretrainedModel\n\n\nclass PretrainedArgumentsService(ArgumentsServiceBase):\n def __init__(self, raise_errors_on_invalid_args=True):\n super().__init__(raise_errors_on_invalid_args)\n\n def _add_specific_arguments(self, parser: argparse.ArgumentParser):\n super()._add_specific_arguments(parser)\n\n parser.add_argument('--pretrained-weights', type=str, default='bert-base-cased',\n help='weights to use for initializing transformer models')\n parser.add_argument('--include-pretrained-model', action='store_true',\n help='Should a pretrained model be used to provide more information')\n parser.add_argument('--pretrained-model-size', type=int, default=768,\n help='The hidden size dimension of the pretrained model. Default is 768 for BERT')\n parser.add_argument('--pretrained-max-length', type=int, default=None,\n help='The maximum length the pretrained model(if any). Default is None')\n parser.add_argument('--learn-new-embeddings', action='store_true',\n help='Whether new embeddings should be learned next to the pretrained representation')\n\n parser.add_argument('--fasttext-model', type=str, default=None,\n help='fasttext model to use for loading additional information')\n parser.add_argument('--include-fasttext-model', action='store_true',\n help='Should a fasttext model be used to provide more information')\n parser.add_argument('--fasttext-model-size', type=int, default=300,\n help='The hidden size dimension of the fasttext model. Default is 300')\n parser.add_argument(\"--pretrained-model\", type=PretrainedModel, choices=list(PretrainedModel), default=None,\n help=\"Pretrained model that will be used to tokenize strings and generate embeddings\")\n parser.add_argument('--fine-tune-pretrained', action='store_true',\n help='If true, the loaded pre-trained model will be fine-tuned instead of being frozen. Default is `false`')\n parser.add_argument('--fine-tune-after-convergence', action='store_true',\n help='If true, the loaded pre-trained model will be fine-tuned but only once the full model has converged. Default is `false`')\n parser.add_argument(\"--fine-tune-learning-rate\", type=float, default=None,\n help=\"Different learning rate to use for pre-trained model. If None is given, then the global learning rate will be used. Default is `None`.\")\n\n @property\n def pretrained_weights(self) -> str:\n return self._get_argument('pretrained_weights')\n\n @property\n def include_pretrained_model(self) -> bool:\n return self._get_argument('include_pretrained_model')\n\n @property\n def pretrained_model_size(self) -> int:\n return self._get_argument('pretrained_model_size')\n\n @property\n def pretrained_max_length(self) -> int:\n return self._get_argument('pretrained_max_length')\n\n @property\n def learn_new_embeddings(self) -> bool:\n return self._get_argument('learn_new_embeddings')\n\n @property\n def fasttext_model(self) -> str:\n return self._get_argument('fasttext_model')\n\n @property\n def include_fasttext_model(self) -> bool:\n return self._get_argument('include_fasttext_model')\n\n @property\n def fasttext_model_size(self) -> int:\n return self._get_argument('fasttext_model_size')\n\n @property\n def pretrained_model(self) -> PretrainedModel:\n return self._get_argument('pretrained_model')\n\n @property\n def fine_tune_pretrained(self) -> bool:\n return self._get_argument('fine_tune_pretrained')\n\n @property\n def fine_tune_after_convergence(self) -> bool:\n return self._get_argument('fine_tune_after_convergence')\n\n @property\n def fine_tune_learning_rate(self) -> float:\n return self._get_argument('fine_tune_learning_rate')"
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"text": "from entities.cache.cache_options import CacheOptions\nimport os\nfrom services.file_service import FileService\n\nfrom torch._C import dtype\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\nfrom typing import List, Tuple\n\nfrom enums.ocr_output_type import OCROutputType\nfrom enums.language import Language\n\nfrom services.process.process_service_base import ProcessServiceBase\n\nfrom services.download.ocr_download_service import OCRDownloadService\nfrom services.arguments.ocr_quality_non_context_arguments_service import OCRQualityNonContextArgumentsService\nfrom services.cache_service import CacheService\nfrom services.log_service import LogService\nfrom services.vocabulary_service import VocabularyService\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\n\n\nclass ICDARProcessService(ProcessServiceBase):\n def __init__(\n self,\n ocr_download_service: OCRDownloadService,\n arguments_service: OCRQualityNonContextArgumentsService,\n cache_service: CacheService,\n vocabulary_service: VocabularyService,\n tokenize_service: BaseTokenizeService,\n log_service: LogService):\n\n self._arguments_service = arguments_service\n self._cache_service = cache_service\n self._ocr_download_service = ocr_download_service\n self._vocabulary_service = vocabulary_service\n self._tokenize_service = tokenize_service\n self._log_service = log_service\n\n self._min_occurrence_limit = self._arguments_service.minimal_occurrence_limit\n self._vocab_key = f'vocab-{self._get_dataset_string()}-{arguments_service.ocr_output_type.value}'\n\n if not self._vocabulary_service.load_cached_vocabulary(self._vocab_key):\n self._log_service.log_debug(\n 'Vocabulary was not loaded. Attempting to initialize...')\n self._initialize_vocabulary()\n else:\n self._log_service.log_debug('Vocabulary loaded successfully')\n\n def _initialize_vocabulary(self):\n self._ocr_download_service.download_data(\n self._arguments_service.language)\n\n ocr_data, gs_data = self._read_data()\n tokenized_data = self._tokenize_service.tokenize_sequences(\n gs_data if self._arguments_service.ocr_output_type == OCROutputType.GroundTruth else ocr_data\n )\n self._log_service.log_debug(\n f'Tokenized {len(tokenized_data)} strings successfully')\n\n self._vocabulary_service.initialize_vocabulary_from_corpus(\n tokenized_data,\n min_occurrence_limit=self._min_occurrence_limit,\n vocab_key=self._vocab_key)\n\n def _generate_ocr_corpora(self):\n ocr_data, gs_data = self._read_data()\n tokenized_ocr_data = self._tokenize_service.tokenize_sequences(\n ocr_data)\n tokenized_gs_data = self._tokenize_service.tokenize_sequences(gs_data)\n\n self._save_common_tokens(tokenized_ocr_data, tokenized_gs_data)\n\n ocr_output_type = self._arguments_service.ocr_output_type\n data_ids = [self._vocabulary_service.string_to_ids(\n x) for x in (tokenized_ocr_data if ocr_output_type == OCROutputType.Raw else tokenized_gs_data)]\n\n result = self._generate_corpora_entries(data_ids)\n return result\n\n def _generate_corpora_entries(self, data_ids):\n return None\n\n def _save_common_tokens(self, tokenized_ocr_data: List[List[str]], tokenized_gs_data: List[List[str]]):\n \"\"\"Saves the intersection of the tokens from both output types, as well as the ids of these tokens for the current output type\n\n :param tokenized_ocr_data: The tokenized data for OCR output type\n :type tokenized_ocr_data: List[List[str]]\n :param tokenized_gs_data: The tokenized data for GT output type\n :type tokenized_gs_data: List[List[str]]\n \"\"\"\n self._log_service.log_debug('Saving common tokens')\n token_pairs_cache_key = f'common-t-pairs-{self._get_dataset_string()}-{self._arguments_service.ocr_output_type.value}-lim-{self._arguments_service.minimal_occurrence_limit}'\n if self._cache_service.item_exists(CacheOptions(token_pairs_cache_key)):\n return\n\n common_tokens = self._cache_service.get_item_from_cache(\n CacheOptions(\n f'common-tokens-{self._get_dataset_string()}',\n configuration_specific=False),\n callback_function=lambda: self._combine_common_words(tokenized_ocr_data, tokenized_gs_data))\n\n token_id_pairs = []\n for common_token in common_tokens:\n token_ids = [self._vocabulary_service.string_to_id(common_token)]\n if token_ids[0] == self._vocabulary_service.unk_token:\n token_ids = None\n\n token_id_pairs.append((common_token, token_ids))\n\n self._cache_service.cache_item(\n token_id_pairs,\n CacheOptions(token_pairs_cache_key))\n\n self._log_service.log_debug(\n f'Saved {len(token_id_pairs)} common token pairs successfully')\n\n def _combine_common_words(self, tokenized_ocr_data: List[List[str]], tokenized_gs_data: List[List[str]]):\n ocr_unique_tokens = set(\n [item for sublist in tokenized_ocr_data for item in sublist])\n gs_unique_tokens = set(\n [item for sublist in tokenized_gs_data for item in sublist])\n\n common_tokens = list(ocr_unique_tokens & gs_unique_tokens)\n return common_tokens\n\n def _load_file_data(self):\n number_of_files = len(self._arguments_service.datasets)\n\n ocr_file_data = []\n gs_file_data = []\n\n for i, dataset in enumerate(self._arguments_service.datasets):\n print(f'{i}/{number_of_files} \\r', end='')\n result = self._ocr_download_service.get_downloaded_dataset(dataset)\n if result is None:\n self._log_service.log_debug(\n f'Did not find \\'{dataset}\\' dataset to load')\n continue\n else:\n self._log_service.log_debug(f'Loading \\'{dataset}\\' data')\n\n ocr_file_data.extend(result[0])\n gs_file_data.extend(result[1])\n\n return ocr_file_data, gs_file_data\n\n def _read_data(self):\n ocr_file_data, gs_file_data = self._cache_service.get_item_from_cache(\n CacheOptions(\n f'ocr-gs-file-data-{self._get_dataset_string()}',\n configuration_specific=False),\n callback_function=self._load_file_data)\n\n return ocr_file_data, gs_file_data\n\n def _get_dataset_string(self):\n return '-'.join(sorted(self._arguments_service.datasets))"
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"text": "\nfrom enums.language import Language\nfrom enums.metric_type import MetricType\n\ndefault_values = {\n 'epochs': 500,\n 'eval_freq': 50,\n 'batch_size': 8,\n 'max_training_minutes': 24 * 60,\n 'device' : 'cpu',\n 'seed': 42,\n 'evaluate': False,\n 'patience': 30,\n 'consider_equal_results_as_worse': False,\n 'language': Language.English,\n 'shuffle': True,\n 'learning_rate': 1e-5,\n 'weight_decay': 1e-8,\n 'momentum': 0,\n 'checkpoint_name': None,\n 'resume_training': False,\n 'resume_checkpoint_name': None,\n 'skip_best_metrics_on_resume': False,\n 'data_folder': 'data',\n 'experiments_folder': 'experiments',\n 'cache_folder': '.cache',\n 'output_folder': 'results',\n 'checkpoint_folder': None,\n 'metric_types': MetricType.JaccardSimilarity,\n 'joint_model': False,\n 'joint_model_amount': 2,\n 'enable_external_logging': False,\n 'train_dataset_limit_size': None,\n 'validation_dataset_limit_size': None,\n 'skip_validation': True,\n 'run_experiments': False,\n 'experiment_types': None,\n 'reset_training_on_early_stop': False,\n 'resets_limit': 1,\n 'training_reset_epoch_limit': 1,\n 'save_checkpoint_on_crash': False,\n 'save_checkpoint_on_finish': False,\n 'log_folder': '.logs',\n 'enable_verbose_logging': False,\n\n 'pretrained_weights': 'bert-base-cased',\n 'include_pretrained_model': False,\n 'pretrained_model_size': 768,\n 'pretrained_max_length': None,\n 'learn_new_embeddings': False,\n\n 'fasttext_model': None,\n 'include_fasttext_model': False,\n 'fasttext_model_size': 300,\n 'pretrained_model': None,\n 'fine_tune_pretrained': False,\n 'fine_tune_after_convergence': False,\n 'fine_tune_learning_rate': None,\n 'minimal_occurrence_limit': 5,\n 'initialize_randomly': False,\n 'datasets': ['icdar-2017', 'icdar-2019'],\n}"
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"text": "import numpy as np\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\nfrom services.log_service import LogService\nfrom typing import List\nfrom entities.word_evaluation import WordEvaluation\nfrom overrides import overrides\nimport torch\n\nfrom models.transformers.transformer_base import TransformerBase\nfrom transformers import BertForMaskedLM, BertConfig\n\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\nfrom services.data_service import DataService\n\n\nclass BERT(TransformerBase):\n def __init__(\n self,\n arguments_service: PretrainedArgumentsService,\n data_service: DataService,\n log_service: LogService,\n tokenize_service: BaseTokenizeService,\n output_hidden_states: bool = False,\n overwrite_initialization: bool = True):\n super().__init__(arguments_service, data_service, log_service, output_hidden_states, overwrite_initialization=True)\n\n self._tokenize_service = tokenize_service\n\n self._transformer_model = BertForMaskedLM(config=BertConfig())\n\n def forward(self, input_batch, **kwargs):\n input, labels, attention_masks = input_batch\n outputs = self.transformer_model.forward(\n input, labels=labels, attention_mask=attention_masks)\n\n return outputs.loss\n\n @property\n def _model_type(self) -> type:\n return BertForMaskedLM\n\n @property\n def transformer_model(self) -> BertForMaskedLM:\n return self._transformer_model\n\n def get_embeddings(self, tokens: List[str], skip_unknown: bool = False) -> List[WordEvaluation]:\n # encode the tokens\n encoded_sequences = self._tokenize_service.encode_sequences(tokens)\n vocab_ids_lists = [vocab_ids_list for vocab_ids_list, _, _, _ in encoded_sequences]\n\n lengths = [len(sequence) for sequence in vocab_ids_lists]\n max_length = max(lengths)\n\n batch_size = len(vocab_ids_lists)\n padded_vocab_ids = np.zeros((batch_size, max_length), dtype=np.int64)\n if self._arguments_service.padding_idx != 0:\n padded_vocab_ids.fill(self._arguments_service.padding_idx)\n\n for i, l in enumerate(lengths):\n padded_vocab_ids[i][0:l] = vocab_ids_lists[i][0:l]\n\n vocab_ids = torch.Tensor(padded_vocab_ids).long().to(self._arguments_service.device)\n\n # process through the pipeline\n mask = (vocab_ids != self._arguments_service.padding_idx)\n outputs = self._transformer_model.forward(\n vocab_ids, mask, output_hidden_states=True)\n\n # BatchSize X MaxLength X EmbeddingSize\n padded_embeddings = outputs[1][0]\n mask = mask.unsqueeze(-1).repeat(1, 1, 768).float()\n embeddings_means = (torch.sum(padded_embeddings * mask, dim=1) /\n mask.sum(dim=1)).cpu().tolist()\n\n return embeddings_means\n"
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"text": "# Evaluation of OCR quality importance over historical texts\n\n[](LICENSE)\n"
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"text": "from collections import defaultdict\n\nfrom matplotlib import pyplot as plt\nfrom entities.plot.grouping import Grouping\nfrom entities.plot.handlers.grouping_handler import GroupingHandler\nfrom typing import Dict, List, Tuple\nfrom entities.plot.plot_options import PlotOptions\nfrom enums.plots.line_style import LineStyle\nfrom services.log_service import LogService\nfrom entities.plot.legend_title_options import LegendTitleOptions\nfrom entities.plot.legend_options import LegendOptions\nfrom enums.configuration import Configuration\nfrom entities.plot.figure_options import FigureOptions\nfrom enums.value_summary import ValueSummary\nfrom services.plot_service import PlotService\nfrom services.experiments.process.neighbourhood_overlap_process_service import NeighbourhoodOverlapProcessService\nfrom enums.overlap_type import OverlapType\nfrom enums.experiment_type import ExperimentType\nfrom services.arguments.ocr_evaluation_arguments_service import OCREvaluationArgumentsService\nfrom services.file_service import FileService\nfrom matplotlib.axes import Axes\nimport numpy as np\nimport pandas as pd\nfrom copy import deepcopy\n\n\nclass OCRNeighbourOverlapPlotService:\n def __init__(\n self,\n arguments_service: OCREvaluationArgumentsService,\n file_service: FileService,\n plot_service: PlotService,\n log_service: LogService,\n neighbourhood_overlap_process_service: NeighbourhoodOverlapProcessService):\n self._file_service = file_service\n self._arguments_service = arguments_service\n self._plot_service = plot_service\n self._log_service = log_service\n self._neighbourhood_overlap_process_service = neighbourhood_overlap_process_service\n\n def plot_ocr_ground_truth_overlaps(self, total_words_count: int):\n self._log_service.log_info(\n 'Generating OCR vs Ground Truth overlap plots')\n\n output_folder = self._get_output_folder()\n overlaps_by_config = self._neighbourhood_overlap_process_service.get_overlaps(\n overlap_types=[OverlapType.GTvsOCR], include_randomly_initialized=True)\n ax = self._plot_service.create_plot()\n\n ax2 = plt.axes([.37, .17, .25, .25])\n\n groupings = []\n for configuration, overlaps_by_type in overlaps_by_config.items():\n for _, overlaps_by_random_initialization in overlaps_by_type.items():\n for randomly_initialized, overlaps_by_lr in overlaps_by_random_initialization.items():\n for learning_rate, overlaps_by_seed in overlaps_by_lr.items():\n if all(x is None for x in list(overlaps_by_seed.values())):\n continue\n\n pd_dataframe = self._combine_seed_overlaps(\n overlaps_by_seed,\n total_words_count=total_words_count)\n\n plot_options, grouping = self._get_distribution_plot_options(\n ax,\n configuration,\n randomly_initialized,\n learning_rate,\n ValueSummary.Average)\n\n groupings.append(grouping)\n\n ax = self._plot_service.plot_line_variance(\n pd_dataframe,\n x='neighbours',\n y='overlap',\n plot_options=plot_options)\n\n plot_options_mini = PlotOptions(\n ax2,\n legend_options = LegendOptions(show_legend=False),\n xlim = (0.01, 0.2),\n ylim=(0.1, 0.85),\n color=plot_options.color,\n linestyle=plot_options.linestyle)\n\n ax2 = self._plot_service.plot_line_variance(\n pd_dataframe,\n x='neighbours',\n y='overlap',\n plot_options=plot_options_mini)\n\n ax2.set(xlabel=None)\n ax2.set(ylabel=None)\n\n self._plot_service.set_plot_properties(\n ax=ax,\n figure_options=FigureOptions(\n title=f'Neighbourhood overlap ({self._arguments_service.language.value.capitalize()})'))\n\n self._plot_service.set_plot_properties(\n ax=ax2,\n figure_options=FigureOptions(\n title=f'Zoom-in'))\n\n filtered_groupings = list({x.group_name : x for x in groupings}.values())\n ax.legend(\n filtered_groupings,\n ['' for _ in filtered_groupings],\n loc='lower right',\n handler_map={Grouping: GroupingHandler()},\n handlelength=13.3)\n\n self._plot_service.save_plot(\n save_path=output_folder,\n filename=f'neighbourhood-overlaps')\n\n def _get_output_folder(self):\n experiments_folder = self._file_service.get_experiments_path()\n result = self._file_service.combine_path(\n experiments_folder,\n f'{ExperimentType.NeighbourhoodOverlap.value}-gt-vs-ocr',\n self._arguments_service.language.value,\n create_if_missing=True)\n\n return result\n\n def _extract_value_summaries(self, combined_overlaps: Dict[int, List[int]]) -> Dict[ValueSummary, List[int]]:\n value_summaries = {\n ValueSummary.Maximum: {},\n ValueSummary.Average: {},\n ValueSummary.Minimum: {},\n }\n\n avg_overlaps, min_overlaps, max_overlaps = combined_overlaps\n for percentage in avg_overlaps.keys():\n valid_avg_overlaps = [x for x in avg_overlaps[percentage] if x is not None]\n valid_min_overlaps = [x for x in min_overlaps[percentage] if x is not None]\n valid_max_overlaps = [x for x in max_overlaps[percentage] if x is not None]\n\n value_summaries[ValueSummary.Minimum][percentage] = (min(valid_avg_overlaps), min(valid_min_overlaps), min(valid_max_overlaps))\n value_summaries[ValueSummary.Maximum][percentage] = (max(valid_avg_overlaps), max(valid_min_overlaps), max(valid_max_overlaps))\n value_summaries[ValueSummary.Average][percentage] = (np.mean(valid_avg_overlaps), np.mean(valid_min_overlaps), np.mean(valid_max_overlaps))\n\n return value_summaries\n\n def _combine_seed_overlaps(\n self,\n overlaps_by_seed: Dict[int, Dict[str, int]],\n total_words_count: int) -> Dict[int, List[int]]:\n if all(x is None for x in overlaps_by_seed.values()):\n return None\n\n overlaps_by_percentage = {}\n\n for overlaps_by_set_percentage in overlaps_by_seed.values():\n if overlaps_by_set_percentage is None:\n continue\n\n for percentage, current_overlaps in overlaps_by_set_percentage.items():\n total_words_for_current_percentage = int(total_words_count * (percentage / 100.0))\n\n overlap_values = list(current_overlaps.values())\n\n overlap_values = [x[0] if isinstance(x, list) else x for x in overlap_values]\n overlap_values = [(x / total_words_for_current_percentage) for x in overlap_values]\n\n if percentage not in overlaps_by_percentage.items():\n overlaps_by_percentage[percentage] = []\n\n overlaps_by_percentage[percentage].extend(overlap_values)\n\n flatten_overlaps = [((percentage / 100.0), overlap) for percentage, overlaps in overlaps_by_percentage.items() for overlap in overlaps]\n result = pd.DataFrame(flatten_overlaps)\n result.columns = ['neighbours', 'overlap']\n return result\n\n def _get_distribution_plot_options(\n self,\n ax: Axes,\n configuration: Configuration,\n randomly_initialized: bool,\n learning_rate_str: str,\n value_summary: ValueSummary) -> Tuple[PlotOptions, Grouping]:\n alpha_values = {\n ValueSummary.Maximum: .3,\n ValueSummary.Average: 1,\n ValueSummary.Minimum: 1,\n }\n\n fill = {\n ValueSummary.Maximum: True,\n ValueSummary.Average: False,\n ValueSummary.Minimum: True,\n }\n\n linewidths = {\n ValueSummary.Maximum: 0,\n ValueSummary.Average: 1,\n ValueSummary.Minimum: 0,\n }\n\n colors = {\n Configuration.BERT: {\n ValueSummary.Maximum: 'goldenrod',\n ValueSummary.Average: 'goldenrod',\n ValueSummary.Minimum: 'white',\n },\n Configuration.ALBERT: {\n ValueSummary.Maximum: 'forestgreen',\n ValueSummary.Average: 'forestgreen',\n ValueSummary.Minimum: 'white',\n },\n Configuration.CBOW: {\n ValueSummary.Maximum: 'cadetblue',\n ValueSummary.Average: 'cadetblue',\n ValueSummary.Minimum: 'white',\n },\n Configuration.SkipGram: {\n ValueSummary.Maximum: 'darkred',\n ValueSummary.Average: 'darkred',\n ValueSummary.Minimum: 'white',\n },\n Configuration.PPMI: {\n ValueSummary.Maximum: 'black',\n ValueSummary.Average: 'black',\n ValueSummary.Minimum: 'white',\n },\n Configuration.GloVe: {\n ValueSummary.Maximum: 'darkmagenta',\n ValueSummary.Average: 'darkmagenta',\n ValueSummary.Minimum: 'white',\n }\n }\n\n lr_types = {\n f'{Configuration.BERT.value}-0.0001': 'fast',\n f'{Configuration.BERT.value}-0.00001': 'slow',\n f'{Configuration.ALBERT.value}-0.0001': 'fast',\n f'{Configuration.ALBERT.value}-0.00001': 'slow',\n f'{Configuration.CBOW.value}-0.001': 'fast',\n f'{Configuration.CBOW.value}-0.025': 'fast',\n f'{Configuration.CBOW.value}-0.0001': 'slow',\n f'{Configuration.SkipGram.value}-0.001': 'fast',\n f'{Configuration.SkipGram.value}-0.025': 'fast',\n f'{Configuration.SkipGram.value}-0.0001': 'slow',\n f'{Configuration.PPMI.value}': 'fast',\n f'{Configuration.GloVe.value}': 'fast'\n }\n\n line_styles_per_lr_type = {\n 'fast': LineStyle.Solid,\n 'slow': LineStyle.Dashed\n }\n\n line_style_key = f'{configuration.value}'\n lr_label = 'default'\n lr_type = 'fast'\n if configuration != Configuration.PPMI and configuration != Configuration.GloVe:\n line_style_key = f'{line_style_key}-{learning_rate_str}'\n lr_type = lr_types[line_style_key]\n lr_label = lr_type\n\n result = PlotOptions(\n color=colors[configuration][value_summary],\n linestyle=line_styles_per_lr_type[lr_type],\n fill=fill[value_summary],\n label=f'{Configuration.get_friendly_name(configuration)} [{lr_label}]',\n alpha=alpha_values[value_summary],\n line_width=linewidths[value_summary],\n ax=ax,\n ylim=(0, 1),\n xlim=(0, 1),\n legend_options=LegendOptions(show_legend=True, marker_scale=6))\n\n return result, Grouping(Configuration.get_friendly_name(configuration), colors[configuration][value_summary])\n"
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"text": "from torch import optim\nfrom torch.optim.optimizer import Optimizer\nfrom overrides import overrides\n\nfrom models.model_base import ModelBase\n\nfrom optimizers.optimizer_base import OptimizerBase\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\n\nfrom transformers import AdamW\n\n\nclass JointAdamWTransformerOptimizer(OptimizerBase):\n def __init__(\n self,\n arguments_service: ArgumentsServiceBase,\n model: ModelBase):\n super().__init__(arguments_service, model)\n self._weight_decay = arguments_service.weight_decay\n\n def _init_optimizer(self) -> Optimizer:\n model1_parameters, model2_parameters = self._model.optimizer_parameters()\n optimizer1 = AdamW(model1_parameters, lr=self._learning_rate, weight_decay=self._weight_decay)\n optimizer2 = AdamW(model2_parameters, lr=self._learning_rate, weight_decay=self._weight_decay)\n return (optimizer1, optimizer2)\n\n def step(self):\n self._optimizer[0].step()\n self._optimizer[1].step()\n\n def zero_grad(self):\n self._optimizer[0].zero_grad()\n self._optimizer[1].zero_grad()"
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"text": "from tests.fakes.skip_gram_dataset_fake import SkipGramDatasetFake\nfrom datasets.dataset_base import DatasetBase\nfrom enums.run_type import RunType\n\nfrom services.cache_service import CacheService\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\nfrom services.log_service import LogService\nfrom services.process.process_service_base import ProcessServiceBase\n\n\nclass DatasetServiceFake:\n def __init__(\n self,\n arguments_service: ArgumentsServiceBase,\n cache_service: CacheService,\n process_service: ProcessServiceBase,\n log_service: LogService):\n\n self._arguments_service = arguments_service\n self._cache_service = cache_service\n self._process_service = process_service\n self._log_service = log_service\n\n def initialize_dataset(self, run_type: RunType) -> DatasetBase:\n return SkipGramDatasetFake(\n arguments_service=self._arguments_service,\n process_service=self._process_service,\n log_service=self._log_service,\n cache_service=self._cache_service)"
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"text": "from enums.configuration import Configuration\nfrom entities.cache.cache_options import CacheOptions\nfrom enums.part_of_speech import PartOfSpeech\nfrom services.tagging_service import TaggingService\nfrom enums.ocr_output_type import OCROutputType\nfrom typing import Any, Dict, List, Tuple\n\nfrom entities.token_representation import TokenRepresentation\n\nfrom services.arguments.ocr_evaluation_arguments_service import OCREvaluationArgumentsService\nfrom services.process.process_service_base import ProcessServiceBase\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\n\nfrom services.vocabulary_service import VocabularyService\nfrom services.cache_service import CacheService\nfrom services.log_service import LogService\n\n\nclass EvaluationProcessService(ProcessServiceBase):\n def __init__(\n self,\n arguments_service: OCREvaluationArgumentsService,\n cache_service: CacheService,\n log_service: LogService,\n vocabulary_service: VocabularyService,\n tokenize_service: BaseTokenizeService,\n tagging_service: TaggingService):\n super().__init__()\n\n self._arguments_service = arguments_service\n self._cache_service = cache_service\n self._log_service = log_service\n self._vocabulary_service = vocabulary_service\n self._tokenize_service = tokenize_service\n self._tagging_service = tagging_service\n\n def get_target_tokens(self, pos_tags: List[PartOfSpeech] = None) -> List[TokenRepresentation]:\n common_tokens = self.get_common_words()\n\n self._log_service.log_info(\n f'Loaded {len(common_tokens)} common words')\n\n if pos_tags is not None:\n common_tokens = [\n token\n for token in common_tokens\n if self._tagging_service.get_part_of_speech_tag(token) in pos_tags\n ]\n\n self._log_service.log_info(\n f'Filtered common words. Left with {len(common_tokens)} common words')\n\n return common_tokens\n\n def get_common_words(self) -> Dict[str, List[List[int]]]:\n configurations_to_skip = [\n Configuration.BERT,\n Configuration.XLNet,\n Configuration.BART,\n Configuration.RoBERTa,\n Configuration.GloVe,\n Configuration.ALBERT]\n\n result = None\n\n for config in Configuration:\n # some configurations do not work with vocabularies or support all words\n if config in configurations_to_skip:\n continue\n\n # get the config vocabularies\n config_raw_vocabulary = self._cache_service.get_item_from_cache(\n CacheOptions(\n 'vocab',\n key_suffixes=[\n '-',\n self._arguments_service.get_dataset_string(),\n '-',\n OCROutputType.Raw.value\n ],\n configuration=config))\n\n config_gt_vocabulary = self._cache_service.get_item_from_cache(\n CacheOptions(\n 'vocab',\n key_suffixes=[\n '-',\n self._arguments_service.get_dataset_string(),\n '-',\n OCROutputType.GroundTruth.value\n ],\n configuration=config))\n\n if config_raw_vocabulary is None or config_gt_vocabulary is None:\n self._log_service.log_warning(\n f'Configuration {config.value} does not have both vocabularies initialized')\n continue\n\n # extract the tokens from the vocabularies\n raw_tokens = list(config_raw_vocabulary[0].keys())[4:]\n gt_tokens = list(config_gt_vocabulary[0].keys())[4:]\n\n # intersect\n intersected_tokens = list(set(raw_tokens) & set(gt_tokens))\n\n self._log_service.log_debug(\n f'Configuration {config.value} tokens intersection - [raw: {len(raw_tokens)}; GT: {len(gt_tokens)}; intersected: {len(intersected_tokens)}')\n\n # update the current result\n if result is None:\n result = intersected_tokens\n else:\n result = list(set(result) & set(intersected_tokens))\n\n return result\n\n def _intersect_words(\n self,\n current_result: Dict[str, List[List[int]]]) -> Dict[str, List[List[int]]]:\n common_tokens_config_cache_options = CacheOptions(\n f'common-tokens-{self._get_dataset_string()}-all-config',\n configuration_specific=False)\n\n common_tokens_all_configs = self._cache_service.get_item_from_cache(\n common_tokens_config_cache_options,\n callback_function=lambda: {})\n\n common_tokens_all_configs[self._arguments_service.configuration] = list(\n current_result.keys())\n\n self._cache_service.cache_item(\n common_tokens_all_configs,\n common_tokens_config_cache_options)\n\n all_words_per_config = list(common_tokens_all_configs.values())\n words_intersection = set(all_words_per_config[0]).intersection(\n *all_words_per_config)\n\n result = {k: v for k, v in current_result.items()\n if k in words_intersection}\n return result\n\n def _get_dataset_string(self):\n return '-'.join(sorted(self._arguments_service.datasets))\n"
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"text": "from entities.cache.cache_options import CacheOptions\nfrom typing import List, Tuple\nimport random\n\nfrom enums.ocr_output_type import OCROutputType\n\nfrom entities.transformers.transformer_entry import TransformerEntry\n\nfrom services.arguments.ocr_quality_arguments_service import OCRQualityArgumentsService\nfrom services.process.process_service_base import ProcessServiceBase\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\nfrom services.download.ocr_download_service import OCRDownloadService\n\nfrom services.vocabulary_service import VocabularyService\nfrom services.cache_service import CacheService\nfrom services.log_service import LogService\n\n\nclass TransformerProcessService(ProcessServiceBase):\n def __init__(\n self,\n arguments_service: OCRQualityArgumentsService,\n ocr_download_service: OCRDownloadService,\n tokenize_service: BaseTokenizeService,\n cache_service: CacheService,\n log_service: LogService):\n super().__init__()\n\n self._arguments_service = arguments_service\n self._tokenize_service = tokenize_service\n self._ocr_download_service = ocr_download_service\n self._cache_service = cache_service\n self._log_service = log_service\n\n self._preprocess_max_string_length = 128\n\n def get_entries(self, ocr_output_type: OCROutputType):\n entries = None\n limit_size = self._arguments_service.train_dataset_limit_size\n\n entries = self._load_transformer_entries(\n ocr_output_type,\n limit_size)\n\n return entries\n\n def _generate_entries(self):\n self._ocr_download_service.download_data(\n self._arguments_service.language, max_string_length=self._preprocess_max_string_length)\n\n ocr_file_data, gs_file_data = self._read_data()\n\n encoded_ocr_sequences = self._tokenize_service.encode_sequences(\n ocr_file_data)\n encoded_gs_sequences = self._tokenize_service.encode_sequences(\n gs_file_data)\n\n ocr_entries = [TransformerEntry(i, ids, special_tokens_mask)\n for i, (ids, _, _, special_tokens_mask) in enumerate(encoded_ocr_sequences)]\n gs_entries = [TransformerEntry(i, ids, special_tokens_mask)\n for i, (ids, _, _, special_tokens_mask) in enumerate(encoded_gs_sequences)]\n\n return ocr_entries, gs_entries\n\n def _load_transformer_entries(\n self,\n ocr_output_type: OCROutputType,\n reduction: int) -> List[TransformerEntry]:\n ocr_entries, gs_entries = self._cache_service.get_item_from_cache(\n CacheOptions(f'entries-{self._get_datasets_string()}'),\n callback_function=self._generate_entries)\n\n entries = ocr_entries if ocr_output_type == OCROutputType.Raw else gs_entries\n\n total_amount = len(entries)\n if reduction is not None:\n entries = entries[:reduction]\n\n self._log_service.log_info(\n f'Loaded {len(entries)} entries out of {total_amount} total')\n self._log_service.log_summary(\n key=f'entries amount', value=len(entries))\n\n return entries\n\n def _load_file_data(self):\n number_of_files = len(self._arguments_service.datasets)\n\n ocr_file_data = []\n gs_file_data = []\n\n for i, dataset in enumerate(self._arguments_service.datasets):\n print(f'{i}/{number_of_files} \\r', end='')\n result = self._ocr_download_service.get_downloaded_dataset(\n dataset, self._preprocess_max_string_length)\n if result is None:\n self._log_service.log_warning(\n f'Did not find \\'{dataset}\\' dataset to load')\n continue\n else:\n self._log_service.log_debug(f'Loading \\'{dataset}\\' data')\n\n ocr_file_data.extend(result[0])\n gs_file_data.extend(result[1])\n\n return ocr_file_data, gs_file_data\n\n def _get_datasets_string(self):\n datasets_string = '-'.join(sorted(self._arguments_service.datasets))\n return datasets_string\n\n def _read_data(self):\n\n (ocr_file_data, gs_file_data) = self._cache_service.get_item_from_cache(\n CacheOptions(\n f'ocr-gs-file-data-{self._get_datasets_string()}-{self._preprocess_max_string_length}',\n configuration_specific=False),\n callback_function=self._load_file_data)\n\n return ocr_file_data, gs_file_data\n"
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"text": "from overrides import overrides\n\nimport torch\nimport torch.nn as nn\n\nfrom losses.loss_base import LossBase\n\nclass SimpleLoss(LossBase):\n def __init__(self):\n super().__init__()\n\n def backward(self, loss):\n loss.backward()\n return loss.item()\n\n def calculate_loss(self, loss) -> torch.Tensor:\n return loss.item()"
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"text": "from enums.challenge import Challenge\nfrom enums.configuration import Configuration\nfrom enums.run_type import RunType\n\nfrom datasets.dataset_base import DatasetBase\nfrom datasets.joint_dataset import JointDataset\nfrom datasets.transformer_lm_dataset import TransformerLMDataset\nfrom datasets.word2vec_dataset import Word2VecDataset\nfrom datasets.evaluation_dataset import EvaluationDataset\nfrom datasets.ppmi_dataset import PPMIDataset\n\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\nfrom services.file_service import FileService\nfrom services.mask_service import MaskService\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\nfrom services.log_service import LogService\nfrom services.vocabulary_service import VocabularyService\nfrom services.metrics_service import MetricsService\nfrom services.data_service import DataService\nfrom services.process.process_service_base import ProcessServiceBase\n\n\nclass DatasetService:\n def __init__(\n self,\n arguments_service: ArgumentsServiceBase,\n mask_service: MaskService,\n process_service: ProcessServiceBase,\n log_service: LogService):\n\n self._arguments_service = arguments_service\n self._mask_service = mask_service\n self._process_service = process_service\n self._log_service = log_service\n\n def initialize_dataset(self, run_type: RunType) -> DatasetBase:\n \"\"\"Loads and returns the dataset based on run type ``(Train, Validation, Test)`` and the language\n\n :param run_type: used to distinguish which dataset should be returned\n :type run_type: RunType\n :rtype: DatasetBase\n \"\"\"\n joint_model: bool = self._arguments_service.joint_model\n configuration: Configuration = self._arguments_service.configuration\n challenge: Challenge = self._arguments_service.challenge\n result = None\n\n if run_type == RunType.Test:\n pass\n\n if not joint_model:\n if challenge == Challenge.OCREvaluation:\n if configuration == Configuration.CBOW or configuration == Configuration.SkipGram:\n self._log_service.log_debug('Initializing Word2Vec dataset')\n result = Word2VecDataset(\n arguments_service=self._arguments_service,\n process_service=self._process_service,\n log_service=self._log_service)\n elif configuration == Configuration.PPMI:\n self._log_service.log_debug('Initializing PPMI dataset')\n result = PPMIDataset(\n arguments_service=self._arguments_service,\n process_service=self._process_service,\n log_service=self._log_service)\n else:\n self._log_service.log_debug('Initializing Transformer dataset')\n result = TransformerLMDataset(\n arguments_service=self._arguments_service,\n process_service=self._process_service,\n mask_service=self._mask_service,\n log_service=self._log_service)\n elif joint_model:\n self._log_service.log_debug('Initializing Evaluation dataset')\n result = EvaluationDataset(\n arguments_service=self._arguments_service,\n process_service=self._process_service,\n log_service=self._log_service)\n\n return result\n"
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"text": "from services.arguments.ocr_evaluation_arguments_service import OCREvaluationArgumentsService\nfrom overrides import overrides\nfrom tests.utils.argument_utils import default_values\n\nclass EvaluationServiceFake(OCREvaluationArgumentsService):\n def __init__(self, custom_values = {}):\n super().__init__()\n\n self._custom_values = custom_values\n\n def _parse_arguments(self):\n return\n\n def _get_argument(self, key: str) -> object:\n if key not in self._custom_values.keys():\n return default_values[key]\n\n return self._custom_values[key]\n"
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"text": "import os\nfrom typing import List, Tuple\n\nimport numpy as np\nimport torch\n# import sklearn.manifold.t_sne\n\nfrom enums.experiment_type import ExperimentType\n\nfrom entities.batch_representation import BatchRepresentation\n\nfrom models.evaluation_model import EvaluationModel\n\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\nfrom services.dataloader_service import DataLoaderService\nfrom services.file_service import FileService\n\nclass ExperimentServiceBase:\n def __init__(\n self,\n arguments_service: PretrainedArgumentsService,\n dataloader_service: DataLoaderService,\n file_service: FileService,\n model: EvaluationModel):\n\n self._model = model.to(arguments_service.device)\n self._dataloader_service = dataloader_service\n self._file_service = file_service\n\n self._dataloader = self._dataloader_service.get_test_dataloader()\n\n checkpoints_path = self._file_service.get_checkpoints_path()\n self._model.load(checkpoints_path, 'BEST')\n self._model.eval()\n\n def execute_experiments(self, experiment_types: List[ExperimentType]):\n pass\n\n"
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"text": "from entities.cache.cache_options import CacheOptions\nimport os\nfrom services.file_service import FileService\n\nfrom torch._C import dtype\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\nfrom typing import List, Tuple\nfrom entities.tokens_occurrence_stats import TokensOccurrenceStats\n\nfrom overrides import overrides\n\nfrom enums.ocr_output_type import OCROutputType\nfrom services.download.ocr_download_service import OCRDownloadService\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\nfrom services.cache_service import CacheService\nfrom services.log_service import LogService\nfrom services.vocabulary_service import VocabularyService\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\nfrom services.process.icdar_process_service import ICDARProcessService\n\nclass PPMIProcessService(ICDARProcessService):\n def __init__(\n self,\n ocr_download_service: OCRDownloadService,\n arguments_service: ArgumentsServiceBase,\n cache_service: CacheService,\n vocabulary_service: VocabularyService,\n tokenize_service: BaseTokenizeService,\n log_service: LogService):\n super().__init__(\n ocr_download_service=ocr_download_service,\n arguments_service=arguments_service,\n cache_service=cache_service,\n vocabulary_service=vocabulary_service,\n tokenize_service=tokenize_service,\n log_service=log_service)\n\n def get_occurrence_stats(self, ocr_output_type: OCROutputType) -> TokensOccurrenceStats:\n occurrence_stats: TokensOccurrenceStats = self._cache_service.get_item_from_cache(\n CacheOptions(f'tokens-occurrences-stats-{ocr_output_type.value}'),\n callback_function=self._generate_ocr_corpora)\n\n return occurrence_stats\n\n def _generate_corpora_entries(self, data_ids):\n token_stats = TokensOccurrenceStats(data_ids, self._vocabulary_service.vocabulary_size())\n return token_stats"
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"path": "/models/transformers/bart.py",
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"text": "from services.log_service import LogService\nfrom overrides import overrides\n\nfrom models.transformers.transformer_base import TransformerBase\nfrom transformers import BartModel, BartConfig\n\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\nfrom services.data_service import DataService\n\nclass BART(TransformerBase):\n def __init__(\n self,\n arguments_service: PretrainedArgumentsService,\n data_service: DataService,\n log_service: LogService,\n output_hidden_states: bool = False):\n super().__init__(arguments_service, data_service, log_service, output_hidden_states)\n\n def forward(self, input_batch, **kwargs):\n input, labels, attention_masks = input_batch\n outputs = self.transformer_model.forward(input, labels=labels, attention_mask=attention_masks)\n return outputs.loss\n\n @property\n def _model_type(self) -> type:\n return BartModel\n\n @property\n def _config_type(self) -> type:\n return BartConfig\n\n @property\n def transformer_model(self) -> BartModel:\n return self._transformer_model"
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"text": "from logging import error\nimport os\nimport sys\nimport time\nimport math\nfrom datetime import datetime\nfrom typing import Dict, List, Tuple\nimport numpy as np\nimport json\n\nimport torch\nfrom torch.optim.optimizer import Optimizer\nfrom torch.utils.data import DataLoader\n\nfrom losses.loss_base import LossBase\nfrom models.model_base import ModelBase\nfrom optimizers.optimizer_base import OptimizerBase\n\nfrom entities.models.model_checkpoint import ModelCheckpoint\nfrom entities.metric import Metric\nfrom entities.data_output_log import DataOutputLog\n\nfrom enums.metric_type import MetricType\n\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\nfrom services.dataloader_service import DataLoaderService\nfrom services.file_service import FileService\nfrom services.log_service import LogService\n\nfrom utils.dict_utils import stringify_dictionary\n\nfrom transformers import BertTokenizer\n\n\nclass TrainService:\n def __init__(\n self,\n arguments_service: PretrainedArgumentsService,\n dataloader_service: DataLoaderService,\n loss_function: LossBase,\n optimizer: OptimizerBase,\n log_service: LogService,\n file_service: FileService,\n model: ModelBase):\n\n self._arguments_service = arguments_service\n self._model_path = file_service.get_checkpoints_path()\n self._optimizer_base = optimizer\n\n self._log_service = log_service\n self._dataloader_service = dataloader_service\n\n self._loss_function = loss_function\n self._model = model.to(arguments_service.device)\n self.data_loader_train: DataLoader = None\n self.data_loader_validation: DataLoader = None\n\n self._initial_patience = self._arguments_service.patience\n # if we are going to fine-tune after initial convergence\n # then we set a low patience first and use the real one in\n # the second training iteration set\n if self._arguments_service.fine_tune_after_convergence:\n self._initial_patience = 5\n\n def train(self) -> bool:\n \"\"\"\n main training function\n \"\"\"\n epoch = 0\n\n try:\n self._log_service.initialize_evaluation()\n\n best_metrics = Metric(amount_limit=None)\n patience = self._initial_patience\n\n metric = Metric(amount_limit=self._arguments_service.eval_freq)\n\n start_epoch = 0\n start_iteration = 0\n resets_left = self._arguments_service.resets_limit\n reset_epoch_limit = self._arguments_service.training_reset_epoch_limit\n\n if self._arguments_service.resume_training:\n self._log_service.log_debug('Resuming training...')\n model_checkpoint = self._load_model()\n if model_checkpoint and not self._arguments_service.skip_best_metrics_on_resume:\n best_metrics = model_checkpoint.best_metrics\n start_epoch = model_checkpoint.epoch\n start_iteration = model_checkpoint.iteration\n resets_left = model_checkpoint.resets_left\n metric.initialize(best_metrics)\n else:\n model_exists, model_name = self._model_already_exists()\n if model_exists and not self._arguments_service.overwrite_previous_model:\n raise Exception(f'Model \\'{model_name}\\' already exists. You must provide `--overwrite-previous-model` if you want to overwrite the previous model')\n\n self.data_loader_train, self.data_loader_validation = self._dataloader_service.get_train_dataloaders()\n self._optimizer = self._optimizer_base.get_optimizer()\n self._log_service.start_logging_model(\n self._model, self._loss_function.criterion)\n\n # run\n epoch = start_epoch\n model_has_converged = False\n while epoch < self._arguments_service.epochs:\n self._log_service.log_summary('Epoch', epoch)\n\n best_metrics, patience = self._perform_epoch_iteration(\n epoch, best_metrics, patience, metric, resets_left, start_iteration)\n\n start_iteration = 0 # reset the starting iteration\n\n # flush prints\n sys.stdout.flush()\n\n if patience == 0:\n # we only prompt the model for changes on convergence once\n should_start_again = not model_has_converged and self._model.on_convergence()\n if should_start_again:\n self._log_service.log_debug(\n 'Patience depleted but the model has not converged. Starting training from last best model again...')\n model_has_converged = True\n model_checkpoint = self._load_model()\n if model_checkpoint is not None:\n best_metrics = model_checkpoint.best_metrics\n start_epoch = model_checkpoint.epoch\n start_iteration = model_checkpoint.iteration\n resets_left = model_checkpoint.resets_left\n metric.initialize(best_metrics)\n\n self._initial_patience = self._arguments_service.patience\n patience = self._initial_patience\n epoch += 1\n elif (self._arguments_service.reset_training_on_early_stop and resets_left > 0 and reset_epoch_limit > epoch):\n patience = self._initial_patience\n resets_left -= 1\n self._log_service.log_debug(\n f'Patience depleted but reset training on early stop is enabled. Continuing training.\\n - Resets left: {resets_left}\\n - Reset epoch limit: {reset_epoch_limit}\\n - Current epoch: {epoch}')\n self._log_service.log_summary(\n key='Resets left', value=resets_left)\n else:\n self._log_service.log_info(\n 'Stopping training due to depleted patience')\n break\n else:\n epoch += 1\n\n if epoch >= self._arguments_service.epochs:\n self._log_service.log_info(\n f'Stopping training due to depleted epochs ({self._arguments_service.epochs})')\n\n except KeyboardInterrupt as e:\n self._log_service.log_exception(\n 'Training stopped as the program was killed by user', e)\n if self._arguments_service.save_checkpoint_on_crash:\n self._model.save(\n self._model_path,\n epoch,\n 0,\n best_metrics,\n resets_left,\n name_prefix=f'KILLED_at_epoch_{epoch}')\n\n return False\n except Exception as e:\n self._log_service.log_exception(\n 'Exception occurred during training', e)\n if self._arguments_service.save_checkpoint_on_crash:\n self._model.save(\n self._model_path,\n epoch,\n 0,\n best_metrics,\n resets_left,\n name_prefix=f'CRASH_at_epoch_{epoch}')\n raise e\n\n # flush prints\n sys.stdout.flush()\n\n if self._arguments_service.save_checkpoint_on_finish:\n self._log_service.log_debug('Training finished')\n self._model.save(\n self._model_path,\n epoch,\n 0,\n best_metrics,\n resets_left,\n name_prefix=f'FINISHED_at_epoch_{epoch}')\n\n return True\n\n def _perform_epoch_iteration(\n self,\n epoch_num: int,\n best_metrics: Metric,\n patience: int,\n metric: Metric,\n resets_left: int,\n start_iteration: int = 0) -> Tuple[Metric, int]:\n \"\"\"\n one epoch implementation\n \"\"\"\n data_loader_length = len(self.data_loader_train)\n\n for i, batch in enumerate(self.data_loader_train):\n if i < start_iteration:\n continue\n\n self._log_service.log_progress(i, data_loader_length, epoch_num)\n\n loss_batch, accuracies_batch, _ = self._perform_batch_iteration(\n batch)\n assert not math.isnan(\n loss_batch), f'loss is NaN during training at iteration {i}'\n\n metric.add_loss(loss_batch)\n metric.add_accuracies(accuracies_batch)\n\n # calculate amount of batches and walltime passed\n batches_passed = i + (epoch_num * data_loader_length)\n\n # run on validation set and print progress to terminal\n # if we have eval_frequency or if we have finished the epoch\n if self._should_evaluate(batches_passed):\n self._log_service.log_debug(\n f'Starting evaluation at step {i} of epoch {epoch_num}. Total batches passed: {batches_passed}')\n if not self._arguments_service.skip_validation:\n validation_metric = self._evaluate()\n else:\n self._log_service.log_debug(\n f'Skip evaluation selected. Using default metric')\n validation_metric = Metric(metric=metric)\n\n assert not math.isnan(metric.get_current_loss(\n )), f'combined loss is NaN during training at iteration {i}; losses are - {metric._losses}'\n\n new_best = self._model.compare_metric(\n best_metrics, validation_metric)\n if new_best:\n best_metrics, patience = self._save_current_best_result(\n validation_metric, epoch_num, i, resets_left)\n else:\n patience -= 1\n\n self._log_service.log_evaluation(\n metric,\n validation_metric,\n epoch_num,\n i,\n data_loader_length,\n new_best,\n metric_log_key=self._model.metric_log_key)\n\n self._log_service.log_summary(\n key='Patience left', value=patience)\n\n self._model.finalize_batch_evaluation(is_new_best=new_best)\n\n # check if runtime is expired\n self._validate_time_passed()\n\n if patience == 0:\n break\n\n return best_metrics, patience\n\n def _perform_batch_iteration(\n self,\n batch: torch.Tensor,\n train_mode: bool = True,\n output_characters: bool = False) -> Tuple[float, Dict[MetricType, float], List[str]]:\n \"\"\"\n runs forward pass on batch and backward pass if in train_mode\n \"\"\"\n\n if train_mode:\n self._model.train()\n if self._optimizer is not None:\n self._optimizer.zero_grad()\n else:\n self._model.eval()\n\n outputs = self._model.forward(batch)\n\n if train_mode:\n loss = self._loss_function.backward(outputs)\n self._model.clip_gradients()\n if self._optimizer is not None:\n self._optimizer.step()\n\n self._model.zero_grad()\n else:\n loss = self._loss_function.calculate_loss(outputs)\n\n metrics, current_output_log = self._model.calculate_accuracies(\n batch, outputs, output_characters=output_characters)\n\n return loss, metrics, current_output_log\n\n def _load_model(self) -> ModelCheckpoint:\n model_checkpoint = self._model.load(self._model_path, 'BEST')\n if not model_checkpoint:\n model_checkpoint = self._model.load(self._model_path)\n\n return model_checkpoint\n\n def _model_already_exists(self) -> bool:\n model_name = self._model._get_model_name('BEST')\n model_exists = os.path.exists(os.path.join(self._model_path, f'{model_name}.pickle'))\n return (model_exists, model_name)\n\n def _evaluate(self) -> Metric:\n metric = Metric(amount_limit=None)\n data_loader_length = len(self.data_loader_validation)\n full_output_log = DataOutputLog()\n\n for i, batch in enumerate(self.data_loader_validation):\n if not batch:\n continue\n\n self._log_service.log_progress(\n i, data_loader_length, evaluation=True)\n\n loss_batch, metrics_batch, current_output_log = self._perform_batch_iteration(\n batch, train_mode=False, output_characters=(len(full_output_log) < 100))\n\n if math.isnan(loss_batch):\n error_message = f'Found invalid loss during evaluation at iteration {i}'\n self._log_service.log_error(error_message)\n raise Exception(error_message)\n\n if current_output_log is not None:\n full_output_log.extend(current_output_log)\n\n metric.add_accuracies(metrics_batch)\n metric.add_loss(loss_batch)\n\n final_metric = self._model.calculate_evaluation_metrics()\n metric.add_accuracies(final_metric)\n self._log_service.log_batch_results(full_output_log)\n\n assert not math.isnan(metric.get_current_loss(\n )), f'combined loss is NaN during evaluation at iteration {i}; losses are - {metric._losses}'\n\n return metric\n\n def _should_evaluate(\n self,\n batches_passed: int):\n # If we don't use validation set, then we must not evaluate before we pass at least `eval_freq` batches\n if self._arguments_service.skip_validation and batches_passed < self._arguments_service.eval_freq:\n return False\n\n result = (batches_passed % self._arguments_service.eval_freq) == 0\n return result\n\n def _validate_time_passed(self):\n time_passed = self._log_service.get_time_passed()\n if ((time_passed.total_seconds() > (self._arguments_service.max_training_minutes * 60)) and\n self._arguments_service.max_training_minutes > 0):\n interrupt_message = f\"Process killed because {self._arguments_service.max_training_minutes} minutes passed\"\n self._log_service.log_error(interrupt_message)\n raise KeyboardInterrupt(interrupt_message)\n\n def _save_current_best_result(\n self,\n validation_metric: Metric,\n epoch_num: int,\n i: int,\n resets_left: int):\n best_metrics = validation_metric\n self._model.save(self._model_path, epoch_num, i,\n best_metrics, resets_left, name_prefix='BEST')\n\n best_accuracies = best_metrics.get_current_accuracies()\n\n for key, value in best_accuracies.items():\n self._log_service.log_summary(\n key=f'Best - {str(key)}', value=value)\n\n self._log_service.log_summary(\n key='Best loss', value=best_metrics.get_current_loss())\n patience = self._initial_patience\n\n best_metrics_str = stringify_dictionary(\n dict(list(best_accuracies.items()) + list({'loss': str(best_metrics.get_current_loss())}.items())))\n\n self._log_service.log_debug(\n f'Saved current best metrics:\\n{best_metrics_str}')\n\n return best_metrics, patience\n"
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"text": "class EmbeddingConfiguration:\n def __init__(\n self,\n language,\n configuration,\n lr,\n initialize_randomly,\n ocr_output_type):\n\n self.language = language\n self.configuration = configuration\n self.lr = lr\n self.initialize_randomly = initialize_randomly\n self.ocr_output_type = ocr_output_type\n"
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"text": "from enums.overlap_type import OverlapType\nfrom entities.word_evaluation import WordEvaluation\nfrom typing import Dict, List\nfrom services.metrics_service import MetricsService\n\n\nclass MetricsProcessService:\n def __init__(\n self,\n metrics_service: MetricsService):\n self._metrics_service = metrics_service\n\n def calculate_cosine_similarities(self, word_evaluations: List[WordEvaluation]) -> Dict[str, float]:\n result = {}\n for word_evaluation in word_evaluations:\n if not word_evaluation.contains_all_embeddings():\n continue\n\n result[word_evaluation.word] = self._metrics_service.calculate_cosine_similarity(\n list1=word_evaluation.get_embeddings(idx=0),\n list2=word_evaluation.get_embeddings(idx=1))\n\n return result\n\n def calculate_cosine_distances(self, word_evaluations: List[WordEvaluation]) -> Dict[str, float]:\n result = {}\n for word_evaluation in word_evaluations:\n if not word_evaluation.contains_all_embeddings(OverlapType.GTvsOCR):\n continue\n\n cosine_distance = self._metrics_service.calculate_cosine_distance(\n list1=word_evaluation.get_embeddings(idx=0),\n list2=word_evaluation.get_embeddings(idx=1))\n\n result[word_evaluation.word] = cosine_distance\n\n return result\n\n def calculate_euclidean_distances(self, word_evaluations: List[WordEvaluation]) -> Dict[str, float]:\n result = {}\n\n for word_evaluation in word_evaluations:\n if not word_evaluation.contains_all_embeddings():\n continue\n\n result[word_evaluation.word] = self._metrics_service.calculate_euclidean_distance(\n list1=word_evaluation.get_embeddings(idx=0),\n list2=word_evaluation.get_embeddings(idx=1))\n\n return result"
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"text": "from tests.fakes.log_service_fake import LogServiceFake\nfrom enums.language import Language\nfrom enums.configuration import Configuration\nfrom enums.challenge import Challenge\nfrom enums.ocr_output_type import OCROutputType\nimport os\nfrom tests.fakes.non_context_service_fake import NonContextServiceFake\nfrom dependency_injection.ioc_container import IocContainer\nimport dependency_injector.providers as providers\nimport torch\nimport unittest\n\n\ndef initialize_container(\n ocr_output_type: OCROutputType = None,\n override_args: dict = None) -> IocContainer:\n custom_args = {\n 'data_folder': os.path.join('tests', 'data'),\n 'challenge': Challenge.OCREvaluation,\n 'configuration': Configuration.CBOW,\n 'language': Language.English,\n 'output_folder': os.path.join('tests', 'results'),\n 'ocr_output_type': ocr_output_type\n }\n\n if override_args is not None:\n for key, value in override_args.items():\n custom_args[key] = value\n\n container = IocContainer()\n container.arguments_service.override(\n providers.Factory(\n NonContextServiceFake,\n custom_args))\n\n container.log_service.override(providers.Factory(LogServiceFake))\n\n return container\n\n\nclass TestCBOW(unittest.TestCase):\n\n def test_embedding_matrix_english_initialization(self):\n main_container = initialize_container()\n metrics_service = main_container.metrics_service()\n\n # Raw model\n container_1 = initialize_container(ocr_output_type=OCROutputType.Raw)\n vocabulary_service_1 = container_1.vocabulary_service()\n\n tokens_1 = vocabulary_service_1.get_vocabulary_tokens()\n\n ids_1 = [id for id, _ in tokens_1]\n ids_tensor_1 = torch.Tensor(ids_1).long()\n\n model_1 = container_1.model()\n word_evaluations_1 = model_1.get_embeddings(tokens_1, ids_tensor_1)\n\n # Ground truth model\n container_2 = initialize_container(\n ocr_output_type=OCROutputType.GroundTruth)\n vocabulary_service_2 = container_2.vocabulary_service()\n\n tokens_2 = vocabulary_service_2.get_vocabulary_tokens()\n\n ids_2 = [id for id, _ in tokens_2]\n ids_tensor_2 = torch.Tensor(ids_2).long()\n\n model_2 = container_2.model()\n word_evaluations_2 = model_2.get_embeddings(tokens_2, ids_tensor_2)\n\n # Assert\n for word_evaluation_1, word_evaluation_2 in zip(word_evaluations_1, word_evaluations_2):\n self.assertEqual(\n word_evaluation_1.get_embeddings(0),\n word_evaluation_2.get_embeddings(0))\n\n self.assertEqual(\n metrics_service.calculate_cosine_distance(\n word_evaluation_1.get_embeddings(0),\n word_evaluation_2.get_embeddings(0)),\n 0.0)\n\n def test_embedding_matrix_dutch_initialization(self):\n main_container = initialize_container(\n override_args={'language': Language.Dutch})\n\n metrics_service = main_container.metrics_service()\n\n # Raw model\n container_1 = initialize_container(\n ocr_output_type=OCROutputType.Raw,\n override_args={'language': Language.Dutch})\n\n vocabulary_service_1 = container_1.vocabulary_service()\n\n tokens_1 = vocabulary_service_1.get_vocabulary_tokens()\n\n ids_1 = [id for id, _ in tokens_1]\n ids_tensor_1 = torch.Tensor(ids_1).long()\n\n model_1 = container_1.model()\n word_evaluations_1 = model_1.get_embeddings(tokens_1, ids_tensor_1)\n\n # Ground truth model\n container_2 = initialize_container(\n ocr_output_type=OCROutputType.GroundTruth,\n override_args={'language': Language.Dutch})\n\n vocabulary_service_2 = container_2.vocabulary_service()\n\n tokens_2 = vocabulary_service_2.get_vocabulary_tokens()\n\n ids_2 = [id for id, _ in tokens_2]\n ids_tensor_2 = torch.Tensor(ids_2).long()\n\n model_2 = container_2.model()\n word_evaluations_2 = model_2.get_embeddings(tokens_2, ids_tensor_2)\n\n # Assert\n for word_evaluation_1, word_evaluation_2 in zip(word_evaluations_1, word_evaluations_2):\n self.assertEqual(\n word_evaluation_1.get_embeddings(0),\n word_evaluation_2.get_embeddings(0))\n\n self.assertEqual(\n metrics_service.calculate_cosine_distance(\n word_evaluation_1.get_embeddings(0),\n word_evaluation_2.get_embeddings(0)),\n 0.0)\n\n def test_embedding_matrix_same_different_seeds(self):\n main_container = initialize_container()\n metrics_service = main_container.metrics_service()\n\n # Raw model\n container_1 = initialize_container(\n ocr_output_type=OCROutputType.Raw,\n override_args={\n 'seed': 13\n })\n\n vocabulary_service_1 = container_1.vocabulary_service()\n\n tokens_1 = vocabulary_service_1.get_vocabulary_tokens()\n\n ids_1 = [id for id, _ in tokens_1]\n ids_tensor_1 = torch.Tensor(ids_1).long()\n\n model_1 = container_1.model()\n word_evaluations_1 = model_1.get_embeddings(tokens_1, ids_tensor_1)\n\n # Ground truth model\n container_2 = initialize_container(\n ocr_output_type=OCROutputType.Raw,\n override_args={\n 'seed': 42\n })\n\n vocabulary_service_2 = container_2.vocabulary_service()\n\n tokens_2 = vocabulary_service_2.get_vocabulary_tokens()\n\n ids_2 = [id for id, _ in tokens_2]\n ids_tensor_2 = torch.Tensor(ids_2).long()\n\n model_2 = container_2.model()\n word_evaluations_2 = model_2.get_embeddings(tokens_2, ids_tensor_2)\n\n # Assert\n for word_evaluation_1, word_evaluation_2 in zip(word_evaluations_1, word_evaluations_2):\n self.assertEqual(\n word_evaluation_1.get_embeddings(0),\n word_evaluation_2.get_embeddings(0))\n\n self.assertEqual(\n metrics_service.calculate_cosine_distance(\n word_evaluation_1.get_embeddings(0),\n word_evaluation_2.get_embeddings(0)),\n 0.0)\n\n\nif __name__ == '__main__':\n unittest.main()\n"
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"path": "/models/simple/ppmi.py",
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"text": "from collections import Counter\nfrom services.log_service import LogService\nfrom enums.ocr_output_type import OCROutputType\nfrom services.process.evaluation_process_service import EvaluationProcessService\nfrom services.process.process_service_base import ProcessServiceBase\nfrom entities.word_evaluation import WordEvaluation\nfrom typing import List\nfrom entities.tokens_occurrence_stats import TokensOccurrenceStats\nimport numpy as np\nimport math\nimport torch\nfrom scipy import sparse\nfrom itertools import product\nimport tqdm\n\nimport multiprocessing\n\nfrom models.model_base import ModelBase\n\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\nfrom services.data_service import DataService\nfrom services.vocabulary_service import VocabularyService\n\n\n# Positive Pointwise Mutual Information\nclass PPMI(ModelBase):\n def __init__(\n self,\n arguments_service: ArgumentsServiceBase,\n vocabulary_service: VocabularyService,\n data_service: DataService,\n log_service: LogService,\n process_service: ProcessServiceBase = None,\n ocr_output_type: OCROutputType = None):\n super().__init__(data_service, arguments_service, log_service)\n\n self._arguments_service = arguments_service\n self._vocabulary_service = vocabulary_service\n self._log_service = log_service\n\n if ocr_output_type is not None:\n dataset_string = self._arguments_service.get_dataset_string()\n vocab_key = f'vocab-{dataset_string}-{ocr_output_type.value}'\n self._vocabulary_service.load_cached_vocabulary(vocab_key)\n\n self._process_service = process_service\n\n # needed for column intersection during evaluation\n self._common_word_ids: List[int] = self._get_common_word_ids()\n\n self._initialized = False\n self._ppmi_matrix = sparse.dok_matrix(\n (self._vocabulary_service.vocabulary_size(),\n self._vocabulary_service.vocabulary_size()),\n dtype=np.float32)\n\n def forward(self, stats: TokensOccurrenceStats):\n if self._initialized:\n return\n\n result = self._calculate_pmi(\n stats.mutual_occurrences.todense(),\n positive=True)\n\n self._ppmi_matrix = sparse.dok_matrix(result)\n\n self._initialized = True\n\n def _calculate_pmi(\n self,\n matrix,\n positive=True):\n col_totals = matrix.sum(axis=0)\n total = col_totals.sum()\n row_totals = matrix.sum(axis=1)\n expected = np.outer(row_totals, col_totals) / total\n matrix = matrix / expected\n # Silence distracting warnings about log(0):\n with np.errstate(divide='ignore'):\n matrix = np.log(matrix)\n\n matrix[np.isinf(matrix) | np.isnan(matrix)] = 0.0 # log(0) = 0\n if positive:\n matrix[matrix < 0] = 0.0\n\n return matrix\n\n def get_embeddings(self, tokens: List[str], skip_unknown: bool = False) -> List[WordEvaluation]:\n vocab_ids = np.array([np.array([self._vocabulary_service.string_to_id(token)]) for token in tokens])\n\n embeddings = self._ppmi_matrix[vocab_ids, self._common_word_ids].toarray()\n\n assert all(not np.isnan(x).any() for x in embeddings)\n assert len(embeddings) == len(vocab_ids)\n assert len(embeddings[0]) == len(self._common_word_ids)\n\n if skip_unknown:\n unk_vocab_id = self._vocabulary_service.unk_token\n embeddings = [\n x if vocab_ids[i][0] != unk_vocab_id else None for i, x in enumerate(embeddings)]\n\n return embeddings\n\n def save(\n self,\n path: str,\n epoch: int,\n iteration: int,\n best_metrics: object,\n resets_left: int,\n name_prefix: str = None,\n save_model_dict: bool = True) -> bool:\n checkpoint_name = self._get_model_name(name_prefix)\n saved = self._data_service.save_python_obj(\n self._ppmi_matrix,\n path,\n checkpoint_name)\n\n return saved\n\n def load(\n self,\n path: str,\n name_prefix: str = None,\n name_suffix: str = None,\n load_model_dict: bool = True,\n use_checkpoint_name: bool = True,\n checkpoint_name: str = None):\n\n if checkpoint_name is None:\n if not use_checkpoint_name:\n checkpoint_name = name_prefix\n else:\n checkpoint_name = self._arguments_service.resume_checkpoint_name\n if checkpoint_name is None:\n checkpoint_name = self._get_model_name(\n name_prefix, name_suffix)\n\n if not self._data_service.python_obj_exists(path, checkpoint_name):\n raise Exception(\n f'PPMI model checkpoint \"{checkpoint_name}\" not found at \"{path}\"')\n\n self._ppmi_matrix = self._data_service.load_python_obj(\n path, checkpoint_name)\n self._initialized = True\n return None\n\n def _get_common_word_ids(self) -> List[int]:\n if ((not self._arguments_service.evaluate and not self._arguments_service.run_experiments) or\n self._process_service is None or\n not isinstance(self._process_service, EvaluationProcessService)):\n self._log_service.log_debug(f'Skipping loading common token ids')\n return None\n\n common_words = self._process_service.get_common_words()\n common_word_ids = [self._vocabulary_service.string_to_id(\n common_word) for common_word in common_words]\n\n self._log_service.log_debug(f'Loaded {len(common_word_ids)} common token ids')\n return common_word_ids\n"
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"text": "from models.embedding.skip_gram_embedding_layer import SkipGramEmbeddingLayer\nfrom services.log_service import LogService\nfrom entities.word_evaluation import WordEvaluation\nfrom typing import List\nfrom enums.ocr_output_type import OCROutputType\nfrom enums.language import Language\nimport os\nfrom overrides import overrides\n\nimport torch\nfrom torch.nn.functional import embedding\n\nfrom models.model_base import ModelBase\n\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\nfrom services.process.word2vec_process_service import Word2VecProcessService\nfrom services.data_service import DataService\nfrom services.vocabulary_service import VocabularyService\n\n\nclass SkipGram(ModelBase):\n def __init__(\n self,\n arguments_service: ArgumentsServiceBase,\n vocabulary_service: VocabularyService,\n data_service: DataService,\n log_service: LogService,\n process_service: Word2VecProcessService = None,\n ocr_output_type: OCROutputType = None,\n pretrained_matrix=None):\n super().__init__(data_service, arguments_service, log_service)\n\n self._arguments_service = arguments_service\n self._ocr_output_type = ocr_output_type\n self._vocabulary_service = vocabulary_service\n self._log_service = log_service\n\n randomly_initialized = False\n freeze_embeddings = True\n if pretrained_matrix is None and process_service is not None:\n pretrained_matrix, randomly_initialized = process_service.get_pretrained_matrix()\n freeze_embeddings = False\n\n if ocr_output_type is not None:\n dataset_string = self._arguments_service.get_dataset_string()\n vocab_key = f'vocab-{dataset_string}-{ocr_output_type.value}'\n self._vocabulary_service.load_cached_vocabulary(vocab_key)\n\n self._vocabulary_size = self._vocabulary_service.vocabulary_size()\n self._embedding_layer = SkipGramEmbeddingLayer(\n log_service,\n self._arguments_service.language,\n self._vocabulary_size,\n pretrained_matrix,\n randomly_initialized,\n freeze_embeddings,\n pad_token=self._vocabulary_service.pad_token)\n\n self._negative_samples = 10\n\n def forward(self, input_batch, **kwargs):\n context_words, target_words = input_batch\n batch_size = target_words.size()[0]\n\n emb_context = self._embedding_layer.forward_context(context_words)\n emb_target = self._embedding_layer.forward_target(target_words)\n\n neg_samples = self._get_negative_samples(batch_size)\n emb_negative = self._embedding_layer.forward_negative(neg_samples)\n\n return (emb_target, emb_context, emb_negative)\n\n def _get_negative_samples(self, batch_size: int):\n noise_dist = torch.ones(self._vocabulary_size)\n num_neg_samples_for_this_batch = batch_size * self._negative_samples\n negative_examples = torch.multinomial(\n noise_dist, num_neg_samples_for_this_batch, replacement=True)\n negative_examples = negative_examples.view(\n batch_size, self._negative_samples).to(self._arguments_service.device)\n return negative_examples\n\n def get_embeddings(self, tokens: List[str], skip_unknown: bool = False) -> List[WordEvaluation]:\n vocab_ids = torch.Tensor([self._vocabulary_service.string_to_id(\n token) for token in tokens]).long().to(self._arguments_service.device)\n\n embeddings = self._embedding_layer.forward_target(vocab_ids)\n embeddings_list = embeddings.squeeze().tolist()\n\n if skip_unknown:\n unk_vocab_id = self._vocabulary_service.unk_token\n embeddings_list = [\n x if vocab_ids[i] != unk_vocab_id else None for i, x in enumerate(embeddings_list)]\n\n return embeddings_list\n"
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"path": "/services/experiments/ocr_quality_experiment_service.py",
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"text": "from services.plots.ocr_neighbour_overlap_plot_service import OCRNeighbourOverlapPlotService\nfrom services.embeddings.word_embeddings_service import WordEmbeddingsService\nfrom enums.configuration import Configuration\nfrom enums.overlap_type import OverlapType\n\nfrom services.experiments.process.metrics_process_service import MetricsProcessService\n\n\nfrom entities.cache.cache_options import CacheOptions\nfrom entities.word_evaluation import WordEvaluation\nfrom services.cache_service import CacheService\nfrom overrides.overrides import overrides\nfrom typing import Callable, List, Dict\nfrom overrides import overrides\n\nfrom enums.experiment_type import ExperimentType\n\nfrom models.model_base import ModelBase\n\nfrom services.arguments.ocr_evaluation_arguments_service import OCREvaluationArgumentsService\nfrom services.dataloader_service import DataLoaderService\nfrom services.experiments.experiment_service_base import ExperimentServiceBase\nfrom services.file_service import FileService\nfrom services.metrics_service import MetricsService\nfrom services.experiments.process.word_neighbourhood_service import WordNeighbourhoodService\nfrom services.log_service import LogService\n\nfrom services.plots.baseline_neighbour_overlap_plot_service import BaselineNeighbourOverlapPlotService\nfrom services.plots.individual_metrics_plot_service import IndividualMetricsPlotService\nfrom services.plots.set_sized_based_plot_service import SetSizedBasedPlotService\n\n\nclass OCRQualityExperimentService(ExperimentServiceBase):\n def __init__(\n self,\n arguments_service: OCREvaluationArgumentsService,\n dataloader_service: DataLoaderService,\n file_service: FileService,\n metrics_service: MetricsService,\n cache_service: CacheService,\n word_neighbourhood_service: WordNeighbourhoodService,\n log_service: LogService,\n metrics_process_service: MetricsProcessService,\n baseline_neighbour_overlap_plot_service: BaselineNeighbourOverlapPlotService,\n ocr_neighbour_overlap_plot_service: OCRNeighbourOverlapPlotService,\n individual_metrics_plot_service: IndividualMetricsPlotService,\n set_sized_based_plot_service: SetSizedBasedPlotService,\n word_embeddings_service: WordEmbeddingsService,\n model: ModelBase):\n super().__init__(arguments_service, dataloader_service, file_service, model)\n\n self._arguments_service = arguments_service\n self._metrics_service = metrics_service\n self._cache_service = cache_service\n self._word_neighbourhood_service = word_neighbourhood_service\n self._log_service = log_service\n self._metrics_process_service = metrics_process_service\n self._word_embeddings_service = word_embeddings_service\n\n # plot services\n self._baseline_neighbour_overlap_plot_service = baseline_neighbour_overlap_plot_service\n self._ocr_neighbour_overlap_plot_service = ocr_neighbour_overlap_plot_service\n self._individual_metrics_plot_service = individual_metrics_plot_service\n self._set_sized_based_plot_service = set_sized_based_plot_service\n\n self._random_suffix = '-rnd' if self._arguments_service.initialize_randomly else ''\n self._separate_suffix = '-sep' if self._arguments_service.separate_neighbourhood_vocabularies else ''\n self._lr_suffix = f'-lr{self._arguments_service.get_learning_rate_str()}' if self._arguments_service.configuration != Configuration.PPMI else ''\n\n def execute_experiments(self, experiment_types: List[ExperimentType]):\n experiment_types_str = ', '.join([x.value for x in experiment_types])\n self._log_service.log_debug(\n f'Executing experiments: {experiment_types_str}')\n\n result = {experiment_type: {} for experiment_type in experiment_types}\n\n # Load word evaluations\n word_evaluations = self._load_word_evaluations()\n\n # Cosine distances\n self._load_experiment_result(ExperimentType.CosineDistance, experiment_types,\n result, lambda: self._load_cosine_distances(word_evaluations))\n\n # Neighbourhood overlaps\n self._load_experiment_result(ExperimentType.NeighbourhoodOverlap, experiment_types,\n result, lambda: self._load_neighbourhood_overlaps(word_evaluations))\n\n # Neighbourhood plots\n # if ExperimentType.CosineDistance in experiment_types and ExperimentType.NeighbourhoodOverlap in experiment_types:\n # self._word_neighbourhood_service.generate_neighbourhood_plots(\n # word_evaluations,\n # result[ExperimentType.CosineDistance])\n\n # Save final results and generate plots\n self._save_experiment_results(result, word_evaluations)\n\n self._log_service.log_info(\n 'Experiments calculation completed successfully')\n\n def _load_word_evaluations(self):\n word_evaluations: List[WordEvaluation] = self._cache_service.get_item_from_cache(\n CacheOptions(\n f'word-evaluations',\n key_suffixes=[\n self._random_suffix,\n self._separate_suffix,\n self._lr_suffix\n ],\n seed_specific=True),\n callback_function=lambda: self._word_embeddings_service.generate_embeddings(self._model, self._dataloader))\n\n self._log_service.log_info('Loaded word evaluations')\n\n return word_evaluations\n\n def _load_experiment_result(\n self,\n experiment_type: ExperimentType,\n experiment_types: List[ExperimentType],\n result_dict: Dict[ExperimentType, dict],\n callback_func: Callable):\n if experiment_type not in experiment_types:\n return\n\n result_dict[experiment_type] = callback_func()\n\n def _load_cosine_distances(self, word_evaluations: List[WordEvaluation]):\n result = self._cache_service.get_item_from_cache(\n CacheOptions(\n f'cosine-distances',\n key_suffixes=[\n self._random_suffix,\n self._lr_suffix\n ],\n seed_specific=True),\n callback_function=lambda: self._metrics_process_service.calculate_cosine_distances(word_evaluations))\n\n self._log_service.log_info('Loaded cosine distances')\n return result\n\n def _load_neighbourhood_overlaps(self, word_evaluations: List[WordEvaluation]):\n result = {}\n for overlap_type in OverlapType:\n if ((self._arguments_service.initialize_randomly and overlap_type != OverlapType.GTvsOCR) or\n (self._arguments_service.configuration == Configuration.PPMI and overlap_type == OverlapType.BASEvsOG) or\n (overlap_type == OverlapType.BASEvsOG)):\n continue\n\n result[overlap_type] = self._cache_service.get_item_from_cache(\n CacheOptions(\n 'neighbourhood-overlaps',\n key_suffixes=[\n self._lr_suffix,\n '-',\n overlap_type.value,\n self._random_suffix\n ],\n seed_specific=True),\n callback_function=lambda: self._word_neighbourhood_service.generate_neighbourhood_similarities(\n word_evaluations,\n overlap_type=overlap_type))\n\n self._log_service.log_info('Loaded neighbourhood overlaps')\n return result\n\n def _load_euclidean_distances(self, word_evaluations: List[WordEvaluation]):\n result = self._cache_service.get_item_from_cache(\n CacheOptions(\n 'euclidean-distances',\n key_suffixes=[\n self._random_suffix,\n self._lr_suffix\n ],\n seed_specific=True),\n callback_function=lambda: self._metrics_process_service.calculate_euclidean_distances(word_evaluations))\n\n self._log_service.log_info('Loaded euclidean distances')\n return result\n\n def _save_experiment_results(self, result: Dict[ExperimentType, Dict[str, float]], word_evaluations: List[WordEvaluation]):\n self._log_service.log_info('Saving experiment results')\n\n # Plot individual metrics\n # self._individual_metrics_plot_service.plot_individual_metrics(result)\n\n # Baseline vs. others plots\n # self._baseline_neighbour_overlap_plot_service.plot_baseline_overlaps()\n\n # GT vs OCR plots\n total_words_count = len(\n [1 for x in word_evaluations if x.contains_all_embeddings(OverlapType.GTvsOCR)])\n self._ocr_neighbour_overlap_plot_service.plot_ocr_ground_truth_overlaps(\n total_words_count)\n\n # Set size based plots\n # self._set_sized_based_plot_service.plot_set_size_bases()\n"
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"text": "from genericpath import isdir\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\nfrom entities.cache.cache_options import CacheOptions\nimport os\nfrom services.log_service import LogService\nimport urllib.request\nimport random\nfrom shutil import copyfile\nfrom multiprocessing import Pool, TimeoutError\nimport functools\nimport sys\nimport pickle\n\nfrom typing import Callable, List\n\nfrom enums.language import Language\n\nfrom services.data_service import DataService\nfrom services.string_process_service import StringProcessService\nfrom services.cache_service import CacheService\n\n\nclass OCRDownloadService:\n def __init__(\n self,\n arguments_service: ArgumentsServiceBase,\n data_service: DataService,\n string_process_service: StringProcessService,\n cache_service: CacheService,\n log_service: LogService):\n self._data_service = data_service\n self._string_process_service = string_process_service\n self._cache_service = cache_service\n self._log_service = log_service\n self._arguments_service = arguments_service\n\n self._languages_2017 = [\n Language.English,\n Language.French\n ]\n\n self._datasets = arguments_service.datasets\n\n def get_downloaded_file_paths(self, language: Language) -> List[str]:\n folder_paths = []\n prefixes = self._get_folder_language_prefixes(language)\n\n if language in self._languages_2017:\n folder_paths_2017 = [os.path.join('data', 'newseye', '2017', 'full', prefix) for prefix in prefixes]\n folder_paths.extend([x for x in folder_paths_2017 if os.path.exists(x)])\n\n folder_paths_2019 = [os.path.join('data', 'newseye', '2019', 'full', prefix) for prefix in prefixes]\n folder_paths.extend([x for x in folder_paths_2019 if os.path.exists(x)])\n\n result = []\n for folder_path in folder_paths:\n if not os.path.isdir(folder_path):\n result.append(folder_path)\n continue\n\n inner_level_names = os.listdir(folder_path)\n folder_paths.extend([os.path.join(folder_path, x) for x in inner_level_names])\n\n return result\n\n def download_data(self, language: Language, max_string_length: int = None):\n key_length_suffix = ''\n if max_string_length is not None:\n key_length_suffix = f'-{max_string_length}'\n\n if language in self._languages_2017:\n self._download_dataset(\n 'icdar-2017',\n f'icdar-2017{key_length_suffix}',\n extraction_function=lambda: self.process_newseye_files(\n language,\n os.path.join('data', 'newseye', '2017'),\n max_string_length=max_string_length))\n\n self._download_dataset(\n 'icdar-2019',\n f'icdar-2019{key_length_suffix}',\n extraction_function=lambda: self.process_newseye_files(\n language,\n os.path.join('data', 'newseye', '2019'),\n max_string_length=max_string_length))\n\n if language == Language.English:\n self._download_dataset(\n 'trove',\n 'trove',\n extraction_function=self._process_trove_data)\n\n def get_downloaded_dataset(\n self,\n dataset:str,\n max_string_length: int = None):\n cache_key = dataset\n if dataset != 'trove' and max_string_length is not None:\n cache_key = f'{dataset}-{max_string_length}'\n\n return self._cache_service.get_item_from_cache(\n CacheOptions(\n cache_key,\n configuration_specific=False))\n\n def _download_dataset(\n self,\n dataset: str,\n dataset_cache_key: str,\n extraction_function: Callable):\n # we skip this dataset if it is not one of the used ones for the current run\n if dataset not in self._datasets:\n return\n\n cache_options = CacheOptions(\n dataset_cache_key,\n configuration_specific=False)\n\n # if the dataset was already processed before we do not need to do anything else\n if self._cache_service.item_exists(cache_options):\n return\n\n self._log_service.log_debug(f'Processing \"{dataset}\" dataset...')\n\n # get the data from the extraction callable function and cache it\n dataset_data = extraction_function()\n self._cache_service.cache_item(dataset_data, cache_options)\n\n def _cut_string(\n self,\n text: str,\n chunk_length: int):\n invalid_characters = ['#', '@']\n\n if chunk_length is None:\n return self._string_process_service.remove_string_characters(text, characters=invalid_characters)\n\n string_chunks = [\n self._string_process_service.convert_string_unicode_symbols(\n self._string_process_service.remove_string_characters(\n text=text[i:i+chunk_length],\n characters=invalid_characters))\n for i in range(0, len(text), chunk_length)]\n\n return string_chunks\n\n def process_newseye_files(\n self,\n language: Language,\n data_path: str,\n start_position: int = 14,\n max_string_length: int = 500,\n subfolder_to_use: str = 'full'):\n ocr_sequences = []\n gs_sequences = []\n\n language_prefixes = self._get_folder_language_prefixes(language)\n\n for subdir_name in os.listdir(data_path):\n if subdir_name != subfolder_to_use:\n continue\n\n subdir_path = os.path.join(data_path, subdir_name)\n if not os.path.isdir(subdir_path):\n continue\n\n for language_name in os.listdir(subdir_path):\n if not any([language_name.startswith(language_prefix) for language_prefix in language_prefixes]):\n continue\n\n language_path = os.path.join(subdir_path, language_name)\n subfolder_names = os.listdir(language_path)\n subfolder_paths = [os.path.join(\n language_path, subfolder_name) for subfolder_name in subfolder_names]\n subfolder_paths = [\n x for x in subfolder_paths if os.path.isdir(x)]\n subfolder_paths.append(language_path)\n\n for subfolder_path in subfolder_paths:\n filepaths = [os.path.join(subfolder_path, x)\n for x in os.listdir(subfolder_path)]\n filepaths = [x for x in filepaths if os.path.isfile(x)]\n for filepath in filepaths:\n with open(filepath, 'r', encoding='utf-8') as data_file:\n data_file_text = data_file.read().split('\\n')\n ocr_strings = self._cut_string(\n data_file_text[1][start_position:], max_string_length)\n gs_strings = self._cut_string(\n data_file_text[2][start_position:], max_string_length)\n\n if type(ocr_strings) is list:\n ocr_sequences.extend(ocr_strings)\n else:\n ocr_sequences.append(ocr_strings)\n\n if type(gs_strings) is list:\n gs_sequences.extend(gs_strings)\n else:\n gs_sequences.append(gs_strings)\n\n result = tuple(zip(*[\n (ocr_sequence, gs_sequence)\n for (ocr_sequence, gs_sequence)\n in zip(ocr_sequences, gs_sequences)\n if ocr_sequence != '' and gs_sequence != ''\n ]))\n\n return result\n\n def _process_trove_data(self):\n cache_item_keys = self._cache_service.get_item_from_cache(\n CacheOptions(\n 'trove-item-keys',\n configuration_specific=False),\n callback_function=self._download_trove_files)\n\n title_prefix = '*$*OVERPROOF*$*'\n separator = '||@@||'\n\n ocr_sequences = []\n gs_sequences = []\n\n for cache_item_key in cache_item_keys:\n # Get the downloaded file from the cache, process it and add it to the total collection of items\n file_content: str = self._cache_service.get_item_from_cache(\n CacheOptions(\n cache_item_key,\n configuration_specific=False)\n ).decode('utf-8')\n\n file_content_lines = file_content.splitlines()\n for file_line in file_content_lines:\n if file_line.startswith(title_prefix) or file_line == separator:\n continue\n\n text_strings = file_line.split(separator)\n text_strings = self._string_process_service.convert_strings_unicode_symbols(\n text_strings)\n text_strings = self._string_process_service.remove_strings_characters(\n text_strings, characters=['#', '@', '\\n'])\n\n ocr_sequences.append(text_strings[0])\n gs_sequences.append(text_strings[1])\n\n result = tuple(zip(*[\n (ocr_sequence, gs_sequence)\n for (ocr_sequence, gs_sequence)\n in zip(ocr_sequences, gs_sequences)\n if ocr_sequence != '' and gs_sequence != ''\n ]))\n\n return result\n\n def _download_trove_files(self):\n cache_item_keys = []\n\n # Download and cache all files from dataset #1\n dataset1_file_urls = [\n f'http://overproof.projectcomputing.com/datasets/dataset1/rawTextAndHumanCorrectionPairs/smh{i}.txt' for i in range(1842, 1955)]\n\n for i, file_url in enumerate(dataset1_file_urls):\n cache_key = f'trove-d1-{i}'\n cached_successfully = self._cache_service.download_and_cache(\n file_url,\n CacheOptions(\n cache_key,\n configuration_specific=False),\n overwrite=False)\n\n if cached_successfully:\n cache_item_keys.append(cache_key)\n\n # Download and cache dataset #2\n dataset2_file_url = 'http://overproof.projectcomputing.com/datasets/dataset2/rawTextAndHumanCorrectionAndOverproofCorrectionTriples/allArticles.txt'\n dataset2_key = 'trove-d2'\n cached_successfully = self._cache_service.download_and_cache(\n dataset2_file_url,\n CacheOptions(\n dataset2_key,\n configuration_specific=False),\n overwrite=False)\n\n if cached_successfully:\n cache_item_keys.append(dataset2_key)\n\n # Download and cache dataset #3\n dataset3_file_url = 'http://overproof.projectcomputing.com/datasets/dataset3/rawTextAndHumanCorrectionAndOverproofCorrectionTriples/allArticles.txt'\n dataset3_key = 'trove-d3'\n cached_successfully = self._cache_service.download_and_cache(\n dataset3_file_url,\n CacheOptions(\n dataset3_key,\n configuration_specific=False),\n overwrite=False)\n\n if cached_successfully:\n cache_item_keys.append(dataset3_key)\n\n return cache_item_keys\n\n def _get_folder_language_prefixes(self, language: Language) -> List[str]:\n if language == Language.English:\n return ['eng', 'EN']\n elif language == Language.French:\n return ['fr', 'FR']\n elif language == Language.German:\n return ['DE']\n elif language == Language.Dutch:\n return ['NL']\n else:\n raise NotImplementedError()\n"
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"path": "/services/tokenize/xlnet_tokenize_service.py",
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"text": "from overrides import overrides\nfrom transformers import XLNetTokenizerFast\n\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\n\nfrom services.tokenize.transformer_tokenize_service import TransformerTokenizeService\n\nclass XLNetTokenizeService(TransformerTokenizeService):\n def __init__(\n self,\n arguments_service: PretrainedArgumentsService):\n super().__init__(arguments_service)\n\n @property\n def _tokenizer_type(self) -> type:\n return XLNetTokenizerFast"
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"text": "import os\n\nfrom typing import Tuple, List\n\nfrom overrides import overrides\n\nfrom transformers import CamembertTokenizer, CamembertConfig, XLNetTokenizer\n\nimport sentencepiece as spm\n\nfrom enums.configuration import Configuration\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\nfrom services.file_service import FileService\n\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\n\nclass CamembertTokenizeService(BaseTokenizeService):\n def __init__(\n self,\n arguments_service: PretrainedArgumentsService):\n super().__init__()\n\n pretrained_weights = arguments_service.pretrained_weights\n configuration = arguments_service.configuration\n\n self._arguments_service = arguments_service\n\n self._tokenizer: CamembertTokenizer = CamembertTokenizer.from_pretrained(pretrained_weights)\n self._sign_tokens = [',', '.', ';']\n self._subword_prefix_symbol = '▁'\n\n def encode_tokens(self, tokens: List[str]) -> List[int]:\n result = self._tokenizer.convert_tokens_to_ids(tokens)\n return result\n\n def decode_tokens(self, character_ids: List[int]) -> List[str]:\n result = self._tokenizer.convert_ids_to_tokens(character_ids)\n return result\n\n def decode_string(self, character_ids: List[int]) -> List[str]:\n result = self._tokenizer.decode(character_ids)\n return result\n\n def id_to_token(self, character_id: int) -> str:\n result = self._tokenizer.convert_ids_to_tokens([character_id])\n return result[0]\n\n def encode_sequence(self, sequence: str) -> Tuple[List[int], List[str], List[Tuple[int,int]], List[int]]:\n tokens: List[str] = self._tokenizer.tokenize(sequence)\n\n offsets_result = []\n tokens_result = []\n tokens_to_encode = []\n counter = 0\n for token in tokens:\n token_str = token\n if token == self._subword_prefix_symbol:\n if counter > 0:\n counter += 1\n\n continue\n elif token.startswith(self._subword_prefix_symbol):\n if counter > 0:\n counter += 1\n\n token_str = token[1:]\n\n offsets_result.append((counter, counter + len(token_str)))\n counter += len(token_str)\n\n if not token.startswith(self._subword_prefix_symbol) and token not in self._sign_tokens:\n token_str = f'##{token_str}'\n\n tokens_result.append(token_str)\n tokens_to_encode.append(token)\n\n\n token_ids = self._tokenizer.convert_tokens_to_ids(tokens_to_encode)\n return (\n token_ids,\n tokens_result,\n offsets_result,\n None)\n\n def encode_sequences(self, sequences: List[str]) -> List[Tuple[List[int], List[str], List[Tuple[int,int]], List[int]]]:\n return [self.encode_sequence(sequence) for sequence in sequences]\n\n @property\n def vocabulary_size(self) -> int:\n return self._tokenizer.vocab_size"
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"text": "import os\nfrom transformers import PreTrainedModel, BertModel, CamembertModel\nimport fasttext\n\nimport torch\nfrom torch import nn\n\nfrom overrides import overrides\n\nfrom services.file_service import FileService\n\nfrom entities.options.pretrained_representations_options import PretrainedRepresentationsOptions\n\nfrom enums.pretrained_model import PretrainedModel\n\nfrom models.model_base import ModelBase\n\nclass PretrainedRepresentationsLayer(ModelBase):\n def __init__(\n self,\n file_service: FileService,\n device: str,\n pretrained_representations_options: PretrainedRepresentationsOptions):\n super().__init__()\n\n self._device = device\n self.do_not_save: bool = (not pretrained_representations_options.fine_tune_pretrained and\n not pretrained_representations_options.fine_tune_after_convergence)\n\n self._include_pretrained = pretrained_representations_options.include_pretrained_model\n self._pretrained_model_size = pretrained_representations_options.pretrained_model_size\n self._pretrained_weights = pretrained_representations_options.pretrained_weights\n self._pretrained_max_length = pretrained_representations_options.pretrained_max_length\n self._pretrained_model: PreTrainedModel = None\n\n self._fine_tune_pretrained = pretrained_representations_options.fine_tune_pretrained\n self._fine_tune_after_convergence = pretrained_representations_options.fine_tune_after_convergence\n\n self._include_fasttext_model = pretrained_representations_options.include_fasttext_model\n\n if self._include_pretrained and self._pretrained_model_size and self._pretrained_weights:\n if pretrained_representations_options.pretrained_model == PretrainedModel.BERT:\n self._pretrained_model = BertModel.from_pretrained(\n pretrained_representations_options.pretrained_weights)\n elif pretrained_representations_options.pretrained_model == PretrainedModel.CamemBERT:\n self._pretrained_model = CamembertModel.from_pretrained(\n pretrained_representations_options.pretrained_weights)\n\n if pretrained_representations_options.fine_tune_pretrained:\n self._pretrained_model.train()\n else:\n self._pretrained_model.eval()\n\n if self._include_fasttext_model:\n assert pretrained_representations_options.fasttext_model is not None, 'fast text model is not supplied when include-fasttext-model is set to true'\n\n data_path = file_service.get_initial_data_path()\n fasttext_path = os.path.join(\n data_path, 'fasttext', pretrained_representations_options.fasttext_model)\n assert os.path.exists(\n fasttext_path), f'fast text model not found in {fasttext_path}'\n\n self._fasttext_dimension = pretrained_representations_options.fasttext_model_size\n self._fasttext_model = fasttext.load_model(fasttext_path)\n\n def get_pretrained_representation(self, input):\n if self._pretrained_model is None:\n return []\n\n if input.shape[1] > self._pretrained_max_length:\n overlap_size = 5\n window_size = self._pretrained_max_length - (overlap_size * 2)\n offset_pairs = self.get_split_indices(input.shape[1], window_size, overlap_size)\n result_tensor = torch.zeros(input.shape[0], input.shape[1], self._pretrained_model_size, device=input.device)\n\n for (start_offset, end_offset) in offset_pairs:\n current_input = input[:, start_offset:end_offset]\n current_output = self._pretrained_model.forward(current_input)\n current_representations = current_output[0]\n\n if start_offset > 0:\n result_tensor[:, start_offset+overlap_size:end_offset] = current_representations[:, overlap_size:]\n # we get the mean of the overlapping representations\n result_tensor[:, start_offset:start_offset+overlap_size] = torch.mean(\n torch.stack([\n result_tensor[:, start_offset:start_offset+overlap_size],\n current_representations[:, :overlap_size]]))\n else:\n result_tensor[:, :end_offset] = current_representations\n\n else:\n output = self._pretrained_model.forward(input)\n result_tensor = output[0]\n\n return result_tensor\n\n def get_split_indices(self, full_length: int, window_size: int, overlap_size=5):\n\n offset_pairs = []\n for position in range(0, full_length, window_size-overlap_size):\n start_offset = position\n end_offset = position + window_size\n\n if end_offset > full_length:\n end_offset = full_length\n\n offset_pairs.append((start_offset, end_offset))\n\n if end_offset >= full_length:\n break\n\n return offset_pairs\n\n def get_fasttext_representation(\n self,\n sequences_strings):\n batch_size = len(sequences_strings)\n sequence_length = max([len(s) for s in sequences_strings])\n\n fasttext_tensor = torch.zeros(\n (batch_size, sequence_length, self._fasttext_dimension)).to(self._device)\n for b in range(batch_size):\n current_string = sequences_strings[b]\n for i, token in enumerate(current_string):\n if token.startswith('##'):\n token = token[2:]\n\n fasttext_representation = self._fasttext_model.get_word_vector(\n token)\n fasttext_tensor[b, i, :] = torch.Tensor(\n fasttext_representation).to(self._device)\n\n return fasttext_tensor\n\n @property\n def keep_frozen(self) -> bool:\n return not self._fine_tune_pretrained\n\n\n def on_convergence(self) -> bool:\n if self._fine_tune_after_convergence and not self._fine_tune_pretrained:\n print('Starting to fine-tune pre-trained...')\n self._fine_tune_pretrained = True\n return True\n\n return False"
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"path": "/models/embedding/embedding_layer.py",
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"text": "import torch\nfrom torch import nn\n\nfrom typing import List\n\nfrom overrides import overrides\nimport fasttext\n\nfrom entities.batch_representation import BatchRepresentation\nfrom entities.options.embedding_layer_options import EmbeddingLayerOptions\n\nfrom enums.embedding_type import EmbeddingType\n\nfrom models.embedding.character_rnn import CharacterRNN\nfrom models.pretrained.pretrained_representations_layer import PretrainedRepresentationsLayer\nfrom models.model_base import ModelBase\n\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\nfrom services.tokenize.base_tokenize_service import BaseTokenizeService\nfrom services.file_service import FileService\n\n\nclass EmbeddingLayer(ModelBase):\n def __init__(\n self,\n file_service: FileService,\n embedding_layer_options: EmbeddingLayerOptions):\n super().__init__()\n\n self._output_embedding_type = embedding_layer_options.output_embedding_type\n self._merge_subword_embeddings = embedding_layer_options.merge_subword_embeddings\n\n self._include_pretrained = embedding_layer_options.pretrained_representations_options.include_pretrained_model\n self._include_fasttext_model = embedding_layer_options.pretrained_representations_options.include_fasttext_model\n\n self._output_size = 0\n if self._include_pretrained or self._include_fasttext_model:\n self._pretrained_layer = PretrainedRepresentationsLayer(\n file_service=file_service,\n device=embedding_layer_options.device,\n pretrained_representations_options=embedding_layer_options.pretrained_representations_options)\n\n if self._include_pretrained:\n self._output_size += embedding_layer_options.pretrained_representations_options.pretrained_model_size\n\n if self._include_fasttext_model:\n self._output_size += embedding_layer_options.pretrained_representations_options.fasttext_model_size\n\n self._learn_subword_embeddings = embedding_layer_options.learn_subword_embeddings\n\n if self._learn_subword_embeddings:\n self._subword_embedding = nn.Embedding(\n embedding_layer_options.vocabulary_size,\n embedding_layer_options.subword_embeddings_size)\n self._subword_embedding_dropout = nn.Dropout(\n embedding_layer_options.dropout)\n self._output_size += embedding_layer_options.subword_embeddings_size\n\n self._learn_character_embeddings = embedding_layer_options.learn_character_embeddings\n if self._learn_character_embeddings:\n if embedding_layer_options.output_embedding_type == EmbeddingType.Character:\n self._character_embedding = nn.Embedding(\n embedding_layer_options.vocabulary_size,\n embedding_layer_options.character_embeddings_size)\n self._character_embedding_dropout = nn.Dropout(\n embedding_layer_options.dropout)\n self._output_size += embedding_layer_options.character_embeddings_size\n else:\n self._character_embedding = CharacterRNN(\n vocabulary_size=embedding_layer_options.vocabulary_size,\n character_embedding_size=embedding_layer_options.character_embeddings_size,\n hidden_size=embedding_layer_options.character_rnn_hidden_size,\n number_of_layers=1,\n bidirectional_rnn=True,\n dropout=0)\n\n self._output_size += (\n embedding_layer_options.character_rnn_hidden_size * 2)\n\n self._learn_word_embeddings = embedding_layer_options.learn_word_embeddings\n if self._learn_word_embeddings:\n if embedding_layer_options.pretrained_word_weights is None:\n self._word_embedding = nn.Embedding(\n embedding_layer_options.vocabulary_size,\n embedding_layer_options.word_embeddings_size)\n else:\n self._word_embedding: nn.Embedding = nn.Embedding.from_pretrained(\n embedding_layer_options.pretrained_word_weights,\n freeze=False)\n\n self._word_embedding_dropout = nn.Dropout(\n embedding_layer_options.dropout)\n self._output_size += self._word_embedding.embedding_dim\n\n self._learn_manual_features = embedding_layer_options.learn_manual_features\n if self._learn_manual_features:\n self._manual_features_layer = nn.Embedding(\n num_embeddings=(\n embedding_layer_options.manual_features_count * 2) + 1,\n embedding_dim=1)\n\n self._output_size += embedding_layer_options.manual_features_count\n\n self._device = embedding_layer_options.device\n\n def forward(\n self,\n batch_representation: BatchRepresentation, \n skip_pretrained_representation: bool = False):\n subword_embeddings = None\n word_embeddings = None\n character_embeddings = None\n\n include_pretrained = self._include_pretrained and batch_representation.subword_sequences is not None\n\n if self._learn_word_embeddings:\n word_embeddings = self._word_embedding.forward(\n batch_representation.word_sequences)\n word_embeddings = self._word_embedding_dropout.forward(\n word_embeddings)\n\n if self._learn_subword_embeddings:\n subword_embeddings = self._subword_embedding.forward(\n batch_representation.subword_sequences)\n subword_embeddings = self._subword_embedding_dropout.forward(\n subword_embeddings)\n\n if self._learn_character_embeddings:\n if self._output_embedding_type == EmbeddingType.Character:\n character_embeddings = self._character_embedding.forward(\n batch_representation.character_sequences)\n\n if self._character_embedding_dropout is not None:\n character_embeddings = self._character_embedding_dropout.forward(\n character_embeddings)\n else:\n character_embeddings = self._character_embedding.forward(\n batch_representation.character_sequences,\n batch_representation.subword_characters_count)\n\n if include_pretrained and not skip_pretrained_representation:\n pretrained_embeddings = self._pretrained_layer.get_pretrained_representation(\n batch_representation.subword_sequences)\n\n if self._include_fasttext_model and not skip_pretrained_representation:\n fasttext_embeddings = self._pretrained_layer.get_fasttext_representation(\n batch_representation.tokens)\n\n if self._learn_manual_features:\n manual_feature_embeddings = self._manual_features_layer.forward(\n batch_representation.manual_features)\n\n manual_feature_embeddings = manual_feature_embeddings.squeeze(-1)\n\n result_embeddings = None\n if self._output_embedding_type == EmbeddingType.Character:\n result_embeddings = character_embeddings\n\n if result_embeddings is None and not include_pretrained and not self._include_fasttext_model:\n raise Exception('Invalid configuration')\n\n if self._learn_subword_embeddings:\n # TODO Concat sub-word embeddings to character embeddings\n pass\n\n if include_pretrained and not skip_pretrained_representation:\n result_embeddings = self._add_subword_to_character_embeddings(\n result_embeddings,\n pretrained_embeddings,\n batch_representation.offset_lists)\n\n if self._include_fasttext_model and not skip_pretrained_representation:\n result_embeddings = self._add_subword_to_character_embeddings(\n result_embeddings,\n fasttext_embeddings,\n batch_representation.offset_lists)\n\n elif self._output_embedding_type == EmbeddingType.SubWord:\n result_embeddings = None\n if subword_embeddings is not None:\n result_embeddings = subword_embeddings\n\n if result_embeddings is None and not include_pretrained and not self._include_fasttext_model:\n raise Exception('Invalid configuration')\n\n if self._learn_character_embeddings:\n result_embeddings = self._add_character_to_subword_embeddings(\n batch_representation.batch_size,\n result_embeddings,\n character_embeddings,\n batch_representation.subword_characters_count)\n\n if include_pretrained and not skip_pretrained_representation:\n if result_embeddings is None:\n result_embeddings = pretrained_embeddings\n else:\n result_embeddings = torch.cat(\n (result_embeddings, pretrained_embeddings), dim=2)\n\n if self._include_fasttext_model:\n if result_embeddings is None:\n result_embeddings = fasttext_embeddings\n else:\n result_embeddings = torch.cat(\n (result_embeddings, fasttext_embeddings), dim=2)\n\n if self._learn_manual_features:\n if result_embeddings is None:\n result_embeddings = manual_feature_embeddings\n else:\n result_embeddings = torch.cat(\n (result_embeddings, manual_feature_embeddings), dim=2)\n\n if self._merge_subword_embeddings and batch_representation.position_changes is not None:\n result_embeddings, batch_representation._subword_lengths = self._restore_position_changes(\n position_changes=batch_representation.position_changes,\n embeddings=result_embeddings,\n lengths=batch_representation.subword_lengths)\n\n elif self._output_embedding_type == EmbeddingType.Word:\n result_embeddings = word_embeddings\n\n return result_embeddings\n\n def _add_subword_to_character_embeddings(\n self,\n character_embeddings,\n subword_embeddings,\n offset_lists):\n batch_size = character_embeddings.shape[0]\n pretrained_embedding_size = subword_embeddings.shape[2]\n\n new_character_embeddings = torch.zeros(\n (batch_size, character_embeddings.shape[1], character_embeddings.shape[2] + subword_embeddings.shape[2])).to(self._device)\n\n new_character_embeddings[:, :,\n :character_embeddings.shape[2]] = character_embeddings\n\n for i in range(batch_size):\n inserted_count = 0\n last_item = 0\n for p_i, offset in enumerate(offset_lists[i]):\n current_offset = 0\n if offset[0] == offset[1]:\n current_offset = 1\n\n for k in range(offset[0] + inserted_count, offset[1] + inserted_count + current_offset):\n if offset[0] < last_item:\n continue\n\n last_item = offset[1]\n\n new_character_embeddings[i, k, -pretrained_embedding_size:\n ] = subword_embeddings[i, p_i]\n\n if offset[0] == offset[1]:\n inserted_count += 1\n\n return new_character_embeddings\n\n def _restore_position_changes(\n self,\n position_changes,\n embeddings,\n lengths):\n batch_size, sequence_length, embeddings_size = embeddings.shape\n\n new_max_sequence_length = max(\n [len(x.keys()) for x in position_changes])\n\n new_embeddings = torch.zeros(\n (batch_size, new_max_sequence_length, embeddings_size), dtype=embeddings.dtype).to(self._device)\n new_lengths = torch.zeros(\n (batch_size), dtype=lengths.dtype).to(self._device)\n\n for i, current_position_changes in enumerate(position_changes):\n new_lengths[i] = len(current_position_changes.keys())\n\n for old_position, new_positions in current_position_changes.items():\n if len(new_positions) == 1:\n new_embeddings[i, old_position,\n :] = embeddings[i, new_positions[0], :]\n else:\n new_embeddings[i, old_position, :] = torch.mean(\n embeddings[i, new_positions], dim=0)\n\n return new_embeddings, new_lengths\n\n def _add_character_to_subword_embeddings(\n self,\n batch_size: int,\n subword_embeddings: torch.Tensor,\n character_embeddings: torch.Tensor,\n subword_characters_count: List[List[int]]):\n\n if subword_embeddings is None:\n return character_embeddings\n\n subword_dimension = subword_embeddings.shape[2]\n concat_dimension = subword_dimension + character_embeddings.shape[2]\n\n result_embeddings = torch.cat(\n [subword_embeddings, character_embeddings], dim=-1)\n\n return result_embeddings\n\n @property\n def output_size(self) -> int:\n return self._output_size\n"
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"path": "/losses/transformer_loss_base.py",
"repo_name": "ktodorov/historical-ocr",
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"text": "from overrides import overrides\n\nfrom losses.loss_base import LossBase\n\nclass TransformerLossBase(LossBase):\n def __init__(self):\n super().__init__()\n\n def backward(self, model_output):\n # print(model_output)\n # model_output.mean().backward()\n model_output.backward()\n\n return model_output.item()\n\n def calculate_loss(self, model_output):\n loss = model_output\n return loss.item()\n"
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"text": "import argparse\n\nfrom typing import List, Dict\n\nfrom entities.arguments.custom_argument_parser import CustomArgumentParser\n\nfrom enums.evaluation_type import EvaluationType\nfrom enums.language import Language\n# from enums.output_format import OutputFormat\nfrom enums.challenge import Challenge\nfrom enums.configuration import Configuration\nfrom enums.metric_type import MetricType\nfrom enums.experiment_type import ExperimentType\n\n\nclass ArgumentsServiceBase:\n def __init__(self, raise_errors_on_invalid_args: bool = True):\n self._raise_errors_on_invalid_args = raise_errors_on_invalid_args\n self._arguments: argparse.Namespace = {}\n\n self._parse_arguments()\n\n def get_arguments_dict(self) -> Dict[str, object]:\n return self._arguments\n\n def get_configuration_name(self, overwrite_args: Dict[str, object] = None) -> str:\n language_value = self._get_value_or_default(overwrite_args, 'language', str(self.language)[:2])\n config_value = self._get_value_or_default(overwrite_args, 'configuration', str(self.configuration))\n checkpoint_value = self._get_value_or_default(overwrite_args, 'checkpoint_name', self.checkpoint_name)\n seed_value = self._get_value_or_default(overwrite_args, 'seed', self.seed)\n lr_value = self._get_value_or_default(overwrite_args, 'learning_rate', self.get_learning_rate_str())\n\n result = f'{language_value}-{config_value}'\n if checkpoint_value is not None:\n result += f'-{str(checkpoint_value)}'\n\n result += f'-s{seed_value}'\n\n if config_value != Configuration.PPMI.value:\n result += f'-lr{lr_value}'\n\n return result\n\n def _parse_arguments(self):\n parser = CustomArgumentParser(\n raise_errors_on_invalid_args=self._raise_errors_on_invalid_args)\n\n self._add_base_arguments(parser)\n self._add_specific_arguments(parser)\n self._arguments: Dict[str, object] = vars(parser.parse_args())\n self._validate_arguments(parser)\n\n def _add_specific_arguments(self, parser: argparse.ArgumentParser):\n pass\n\n def _add_base_arguments(self, parser: argparse.ArgumentParser):\n parser.add_argument('--epochs', default=500,\n type=int, help='max number of epochs')\n parser.add_argument('--eval-freq', default=50,\n type=int, help='evaluate every x batches')\n parser.add_argument('--batch-size', default=8,\n type=int, help='size of batches')\n parser.add_argument('--max-training-minutes', default=72 * 60, type=int,\n help='max mins of training before save-and-kill')\n parser.add_argument(\"--device\", type=str, default='cuda',\n help=\"Device to be used. Pick from cpu/cuda. If default none is used automatic check will be done\")\n parser.add_argument(\"--seed\", type=int, default=42,\n metavar=\"S\", help=\"random seed (default: 42)\")\n parser.add_argument(\"--evaluate\", action='store_true',\n help=\"run in evaluation mode\")\n parser.add_argument(\"--patience\", type=int, default=30,\n help=\"how long will the model wait for improvement before stopping training\")\n parser.add_argument(\"--consider-equal-results-as-worse\", action='store_true',\n help='If this is set to true, then equal results after evaluation are not considered better')\n parser.add_argument(\"--language\", type=Language, choices=list(Language), default=Language.English,\n help=\"which language to train on\")\n parser.add_argument(\"--shuffle\", action='store_false',\n help=\"shuffle datasets while training\")\n parser.add_argument(\"--learning-rate\", type=float, default=0.00001,\n help=\"learning rate for training models\")\n parser.add_argument(\"--weight-decay\", type=float, default=1e-8,\n help=\"weight decay for optimizer. Default is `1e-8`\")\n parser.add_argument(\"--momentum\", type=float, default=0,\n help=\"momentum for optimizer. Default is `0`\")\n parser.add_argument(\"--checkpoint-name\", type=str, default=None,\n help=\"name that can be used to distinguish checkpoints\")\n parser.add_argument(\"--resume-training\", action='store_true',\n help=\"resume training using saved checkpoints\")\n parser.add_argument(\"--resume-checkpoint-name\", type=str, default=None,\n help=\"Checkpoint name that will be used to resume training from. If None is given, then current checkpoint name will be used. Default is `None`\")\n parser.add_argument(\"--overwrite-previous-model\", action='store_true',\n help=\"If training is not resumed and previous model exists, this setting must be provided in order for the existing model to be overwritten\")\n parser.add_argument(\"--skip-best-metrics-on-resume\", action='store_true',\n help=\"Whether to skip loading saved metrics and continuing from last best checkpoint. Default is `False`\")\n parser.add_argument(\"--data-folder\", type=str, default='data',\n help='folder where data will be taken from')\n parser.add_argument(\"--cache-folder\", type=str, default='.cache',\n help='folder where cache will be taken from')\n parser.add_argument(\"--experiments-folder\", type=str, default='experiments',\n help='folder where experiments results will be saved to')\n parser.add_argument(\"--output-folder\", type=str, default='results',\n help='folder where results and checkpoints will be saved')\n parser.add_argument('--checkpoint-folder', type=str, default=None,\n help='folder where checkpoints will be saved/loaded. If it is not provided, the output folder will be used')\n parser.add_argument('--evaluation-type', type=EvaluationType, choices=list(EvaluationType), nargs='*',\n help='what type of evaluations should be performed')\n # parser.add_argument('--output-eval-format', type=OutputFormat, choices=list(OutputFormat),\n # help='what the format of the output after evaluation will be')\n parser.add_argument(\"--challenge\", type=Challenge, choices=list(Challenge), required=True,\n help='Optional challenge that the model is being trained for. If given, data and output results will be put into a specific folder')\n parser.add_argument('--configuration', type=Configuration, choices=list(Configuration), required=True,\n help='Which configuration of model to load and use. Default is kbert')\n parser.add_argument('--metric-types', type=MetricType, choices=list(MetricType), default=MetricType.JaccardSimilarity, nargs='*',\n help='What metrics should be calculated. Default is only Jaccard similarity')\n parser.add_argument('--joint-model', action='store_true',\n help='If a joint model should be used instead of a single one')\n parser.add_argument('--joint-model-amount', type=int, default=2,\n help='How many models should be trained jointly')\n parser.add_argument('--enable-external-logging', action='store_true',\n help='Should logging to external service be enabled')\n parser.add_argument('--train-dataset-limit-size', type=int, default=None,\n help='Limit the train dataset. By default no limit is done.')\n parser.add_argument('--validation-dataset-limit-size', type=int, default=None,\n help='Limit the validation dataset. By default no limit is done.')\n parser.add_argument('--skip-validation', action='store_true',\n help='Whether validation should be skipped, meaning no validation dataset is loaded and no evaluation is done while training. By default is false')\n parser.add_argument('--run-experiments', action='store_true',\n help='Whether to run experiments instead of training or evaluation')\n parser.add_argument('--experiment-types', type=ExperimentType, choices=list(ExperimentType), default=None, nargs='*',\n help='What types of experiments should be run')\n parser.add_argument('--reset-training-on-early-stop', action='store_true',\n help='Whether resetting of training should be done if early stopping is activated and the first epoch has not yet been finished')\n parser.add_argument('--resets-limit', type=int, default=1,\n help='How many times should the training be reset during first epoch if early stopping is activated. Default is 1')\n parser.add_argument('--training-reset-epoch-limit', type=int, default=1,\n help='Until which epoch the training reset should be performed. Default is 1')\n\n\n parser.add_argument('--save-checkpoint-on-crash', action='store_true',\n help='If this is set to true, then in the event of an exception or crash of the program, the model\\'s checkpoint will be saved to the file system. Default is `False`')\n parser.add_argument('--save-checkpoint-on-finish', action='store_true',\n help='If this is set to true, then when the model has converged, its checkpoint will be saved to the file system. Keep in mind that this will not be the best model checkpoint as the stopping will occur after some amount of iterations without any improvement. Default is `False`')\n\n parser.add_argument(\"--log-folder\", type=str, default='.logs',\n help='The folder where log files will be saved. Default is .logs folder in the root project directory')\n parser.add_argument('--enable-verbose-logging', action='store_true',\n help='Optionally enable verbose logging which will output details about most operations being performed during runs')\n\n parser.add_argument('--padding-idx', type=int, default=0,\n help='Idx of the PAD token if used')\n\n parser.add_argument('--datasets', nargs='+', default=['icdar-2019', 'icdar-2017'],\n help='What datasets should be used')\n\n def _validate_arguments(self, parser: argparse.ArgumentParser):\n pass\n\n def _get_argument(self, key: str) -> object:\n \"\"\"Returns an argument value from the list of registered arguments\n\n :param key: key of the argument\n :type key: str\n :raises LookupError: if no argument is found, lookup error will be raised\n :return: the argument value\n :rtype: object\n \"\"\"\n if key not in self._arguments.keys():\n raise LookupError(f'{key} not found in arguments')\n\n return self._arguments[key]\n\n @property\n def epochs(self) -> int:\n return self._get_argument('epochs')\n\n @property\n def eval_freq(self) -> int:\n return self._get_argument('eval_freq')\n\n @property\n def batch_size(self) -> int:\n return self._get_argument('batch_size')\n\n @property\n def max_training_minutes(self) -> int:\n return self._get_argument('max_training_minutes')\n\n @property\n def device(self) -> str:\n return self._get_argument('device')\n\n @property\n def seed(self) -> int:\n return self._get_argument('seed')\n\n @property\n def evaluate(self) -> bool:\n return self._get_argument('evaluate')\n\n @property\n def patience(self) -> int:\n return self._get_argument('patience')\n\n @property\n def consider_equal_results_as_worse(self) -> bool:\n return self._get_argument('consider_equal_results_as_worse')\n\n @property\n def language(self) -> Language:\n return self._get_argument('language')\n\n @property\n def shuffle(self) -> bool:\n return self._get_argument('shuffle')\n\n @property\n def learning_rate(self) -> float:\n return self._get_argument('learning_rate')\n\n def get_learning_rate_str(self) -> str:\n learning_rate_str = '{:f}'.format(self.learning_rate)\n while learning_rate_str.endswith('0'):\n learning_rate_str = learning_rate_str[:-1]\n\n return learning_rate_str\n\n @property\n def momentum(self) -> float:\n return self._get_argument('momentum')\n\n @property\n def weight_decay(self) -> float:\n return self._get_argument('weight_decay')\n\n @property\n def checkpoint_name(self) -> str:\n return self._get_argument('checkpoint_name')\n\n @property\n def resume_training(self) -> bool:\n return self._get_argument('resume_training')\n\n @property\n def resume_checkpoint_name(self) -> str:\n return self._get_argument('resume_checkpoint_name')\n\n @property\n def overwrite_previous_model(self) -> bool:\n return self._get_argument('overwrite_previous_model')\n\n @property\n def skip_best_metrics_on_resume(self) -> bool:\n return self._get_argument('skip_best_metrics_on_resume')\n\n @property\n def data_folder(self) -> str:\n return self._get_argument('data_folder')\n\n @property\n def experiments_folder(self) -> str:\n return self._get_argument('experiments_folder')\n\n @property\n def cache_folder(self) -> str:\n return self._get_argument('cache_folder')\n\n @property\n def output_folder(self) -> str:\n return self._get_argument('output_folder')\n\n @property\n def checkpoint_folder(self) -> str:\n return self._get_argument('checkpoint_folder')\n\n @property\n def evaluation_type(self) -> List[EvaluationType]:\n return self._get_argument('evaluation_type')\n\n @property\n def challenge(self) -> Challenge:\n return self._get_argument('challenge')\n\n @property\n def configuration(self) -> Configuration:\n return self._get_argument('configuration')\n\n @property\n def metric_types(self) -> List[MetricType]:\n return self._get_argument('metric_types')\n\n @property\n def train_dataset_limit_size(self) -> int:\n return self._get_argument('train_dataset_limit_size')\n\n @property\n def validation_dataset_limit_size(self) -> int:\n return self._get_argument('validation_dataset_limit_size')\n\n @property\n def joint_model(self) -> bool:\n return self._get_argument('joint_model')\n\n @property\n def joint_model_amount(self) -> int:\n return self._get_argument('joint_model_amount')\n\n @property\n def enable_external_logging(self) -> bool:\n return self._get_argument('enable_external_logging')\n\n @property\n def skip_validation(self) -> bool:\n return self._get_argument('skip_validation')\n\n @property\n def run_experiments(self) -> bool:\n return self._get_argument('run_experiments')\n\n @property\n def experiment_types(self) -> List[ExperimentType]:\n return self._get_argument('experiment_types')\n\n @property\n def reset_training_on_early_stop(self) -> bool:\n return self._get_argument('reset_training_on_early_stop')\n\n @property\n def resets_limit(self) -> int:\n return self._get_argument('resets_limit')\n\n @property\n def training_reset_epoch_limit(self) -> int:\n return self._get_argument('training_reset_epoch_limit')\n\n @property\n def save_checkpoint_on_crash(self) -> bool:\n return self._get_argument('save_checkpoint_on_crash')\n\n @property\n def save_checkpoint_on_finish(self) -> bool:\n return self._get_argument('save_checkpoint_on_finish')\n\n @property\n def log_folder(self) -> str:\n return self._get_argument('log_folder')\n\n @property\n def verbose_logging(self) -> bool:\n return self._get_argument('enable_verbose_logging')\n\n @property\n def padding_idx(self) -> int:\n return self._get_argument('padding_idx')\n\n @property\n def datasets(self) -> List[str]:\n return self._get_argument('datasets')\n\n def get_dataset_string(self) -> str:\n return '-'.join(sorted(self.datasets))\n\n def _get_value_or_default(self, value_dict: Dict[str, object], value_key: str, default_value: object):\n if value_dict is None or value_key not in value_dict.keys():\n return default_value\n\n return value_dict[value_key]"
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"text": "import numpy as np\nfrom copy import deepcopy\nimport math\n\nfrom tests.fakes.log_service_fake import LogServiceFake\nfrom enums.language import Language\nfrom enums.configuration import Configuration\nfrom enums.challenge import Challenge\nimport os\nfrom tests.fakes.non_context_service_fake import NonContextServiceFake\nfrom dependency_injection.ioc_container import IocContainer\nimport dependency_injector.providers as providers\nimport unittest\n\n\ndef initialize_container() -> IocContainer:\n container = IocContainer()\n container.arguments_service.override(\n providers.Factory(NonContextServiceFake,\n custom_values={\n 'data_folder': os.path.join('tests', 'data'),\n 'output_folder': os.path.join('tests', 'results')\n }))\n\n container.log_service.override(providers.Factory(LogServiceFake))\n return container\n\n\nclass TestMetricsService(unittest.TestCase):\n def test_cosine_distance_zero_for_equal_vectors(self):\n container = initialize_container()\n metrics_service = container.metrics_service()\n\n rand_vector = np.random.randint(512, size=512)\n rand_list = list(rand_vector)\n rand_list_clone = deepcopy(rand_list)\n\n self.assertEqual(metrics_service.calculate_cosine_distance(rand_list, rand_list_clone), 0.0)\n\n\nif __name__ == '__main__':\n unittest.main()\n"
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"text": "from entities.metric import Metric\nfrom enums.metric_type import MetricType\nfrom typing import Dict, List, Tuple\nfrom overrides import overrides\nimport torch\n\nfrom services.train_service import TrainService\n\n\nclass TrainServiceFake(TrainService):\n def __init__(\n self,\n arguments_service,\n dataloader_service,\n loss_function,\n optimizer,\n log_service,\n file_service,\n model):\n super().__init__(\n arguments_service,\n dataloader_service,\n loss_function,\n optimizer,\n log_service,\n file_service,\n model)\n\n\n def _perform_batch_iteration(\n self,\n batch: torch.Tensor,\n train_mode: bool = True,\n output_characters: bool = False) -> Tuple[float, Dict[MetricType, float], List[str]]:\n return (0, {}, None)\n"
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"text": "from typing import Dict\nfrom overrides import overrides\nimport argparse\n\nfrom services.arguments.pretrained_arguments_service import PretrainedArgumentsService\n\nfrom enums.ocr_output_type import OCROutputType\n\n\nclass OCREvaluationArgumentsService(PretrainedArgumentsService):\n def __init__(self):\n super().__init__()\n\n def get_configuration_name(self, overwrite_args: Dict[str, object] = None) -> str:\n result = super().get_configuration_name(overwrite_args)\n\n rnd_value = self._get_value_or_default(overwrite_args, 'initialize_randomly', self.initialize_randomly)\n if rnd_value:\n result += f'-rnd'\n\n min_occurrence_value = self._get_value_or_default(overwrite_args, 'minimal_occurrence_limit', self.minimal_occurrence_limit)\n if min_occurrence_value is not None:\n result += f'-min{min_occurrence_value}'\n\n return result\n\n def _add_specific_arguments(self, parser: argparse.ArgumentParser):\n super()._add_specific_arguments(parser)\n\n parser.add_argument('--minimal-occurrence-limit', type=int, default=None,\n help='Minimal occurrence limit for words or tokens to be included in the vocabulary. This setting is not taken into account for configurations using pre-trained vocabularies')\n\n parser.add_argument('--separate-neighbourhood-vocabularies', action='store_true',\n help='If this is set to True, then the neighbourhood similarity graph will use separate vocabularies of the models')\n\n parser.add_argument('--initialize-randomly', action='store_true',\n help='If this is set to True, then the initial embeddings will be initialized randomly.')\n\n parser.add_argument('--neighbourhood-set-size', type=int, default=1000,\n help='The neighbourhood_set_size set size. Larger values tend to produce more stable results. Default value is 1000.')\n\n @property\n def minimal_occurrence_limit(self) -> int:\n return self._get_argument('minimal_occurrence_limit')\n\n @property\n def separate_neighbourhood_vocabularies(self) -> bool:\n return self._get_argument('separate_neighbourhood_vocabularies')\n\n @property\n def initialize_randomly(self) -> bool:\n return self._get_argument('initialize_randomly')\n\n @property\n def neighbourhood_set_size(self) -> int:\n return self._get_argument('neighbourhood_set_size')"
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"text": "from overrides import overrides\n\nfrom enums.language import Language\n\nfrom models.model_base import ModelBase\n\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\nfrom services.data_service import DataService\nfrom services.log_service import LogService\n\n\nclass ModelFake(ModelBase):\n def __init__(\n self,\n arguments_service: ArgumentsServiceBase,\n data_service: DataService,\n log_service: LogService):\n super().__init__(data_service, arguments_service, log_service)\n\n def forward(self, input_batch, **kwargs):\n return None\n\n def _get_embedding_size(self, language: Language):\n if language == Language.English:\n return 300\n elif language == Language.Dutch:\n return 320\n\n raise NotImplementedError()"
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"text": "from services.log_service import LogService\nfrom entities.word_evaluation import WordEvaluation\nimport os\n\nimport torch\nimport torch.nn as nn\n\nfrom datetime import datetime\n\nfrom typing import Dict, List, Tuple\n\nfrom overrides import overrides\n\nfrom entities.models.model_checkpoint import ModelCheckpoint\nfrom entities.metric import Metric\nfrom entities.batch_representation import BatchRepresentation\nfrom enums.metric_type import MetricType\n\nfrom services.data_service import DataService\nfrom services.arguments.arguments_service_base import ArgumentsServiceBase\n\nclass ModelBase(nn.Module):\n def __init__(\n self,\n data_service: DataService,\n arguments_service: ArgumentsServiceBase,\n log_service: LogService):\n super(ModelBase, self).__init__()\n\n self._data_service = data_service\n self._arguments_service = arguments_service\n self._log_service = log_service\n self.do_not_save: bool = False\n\n self.metric_log_key: str = None\n\n def forward(self, batch_representation: BatchRepresentation):\n return None\n\n def calculate_accuracies(\n self,\n batch: BatchRepresentation,\n outputs,\n output_characters=False) -> Tuple[Dict[MetricType, float], List[str]]:\n return ({}, None)\n\n def compare_metric(self, best_metric: Metric, new_metric: Metric) -> bool:\n if best_metric.is_new:\n return True\n\n current_best = 0\n new_result = 0\n\n if self.metric_log_key is not None:\n current_best = best_metric.get_accuracy_metric(self.metric_log_key)\n new_result = new_metric.get_accuracy_metric(self.metric_log_key)\n\n if current_best == new_result:\n best_loss = best_metric.get_current_loss()\n current_loss = new_metric.get_current_loss()\n result = (best_loss > current_loss) or (not self._arguments_service.consider_equal_results_as_worse and best_loss == current_loss)\n else:\n result = current_best < new_result\n\n return result\n\n def clip_gradients(self):\n torch.nn.utils.clip_grad_norm_(self.parameters(), max_norm=1.0)\n\n def save(\n self,\n path: str,\n epoch: int,\n iteration: int,\n best_metrics: object,\n resets_left: int,\n name_prefix: str = None,\n save_model_dict: bool = True) -> bool:\n self._log_service.log_debug(f'Saving model [epoch: {epoch} | iteration: {iteration} | resets left: {resets_left}]')\n\n assert self._data_service is not None\n assert self._arguments_service is not None\n\n model_checkpoint = ModelCheckpoint(\n model_dict=self.state_dict() if save_model_dict else {},\n epoch=epoch,\n iteration=iteration,\n best_metrics=best_metrics,\n resets_left=resets_left)\n\n checkpoint_name = self._get_model_name(name_prefix)\n saved = self._data_service.save_python_obj(\n model_checkpoint, path, checkpoint_name)\n\n return saved\n\n def load(\n self,\n path: str,\n name_prefix: str = None,\n name_suffix: str = None,\n load_model_dict: bool = True,\n use_checkpoint_name: bool = True,\n checkpoint_name: str = None,\n overwrite_args: Dict[str, object] = None) -> ModelCheckpoint:\n self._log_service.log_debug(f'Loading model [path: {path} | name_prefix: {name_prefix} | name_suffix: {name_suffix}]')\n assert self._data_service is not None\n assert self._arguments_service is not None\n\n if checkpoint_name is None:\n if not use_checkpoint_name:\n checkpoint_name = name_prefix\n else:\n checkpoint_name = self._arguments_service.resume_checkpoint_name\n if checkpoint_name is None:\n checkpoint_name = self._get_model_name(name_prefix, name_suffix, overwrite_args)\n\n if not self._data_service.python_obj_exists(path, checkpoint_name):\n error_message = f'Model checkpoint \"{checkpoint_name}\" not found at \"{path}\"'\n self._log_service.log_error(error_message)\n raise FileNotFoundError(error_message)\n\n model_checkpoint: ModelCheckpoint = self._data_service.load_python_obj(\n path, checkpoint_name)\n\n if model_checkpoint is None:\n error_message = 'Model checkpoint is empty'\n self._log_service.log_error(error_message)\n raise Exception(error_message)\n\n if load_model_dict:\n model_dict = model_checkpoint.model_dict\n for module_name, module in self.named_modules():\n if isinstance(module, ModelBase):\n if module.do_not_save:\n for parameter_name, parameter_value in module.named_parameters():\n model_dict[f'{module_name}.{parameter_name}'] = parameter_value\n\n module.before_load()\n\n self.load_state_dict(model_dict)\n\n for module_name, module in self.named_modules():\n if isinstance(module, ModelBase):\n module.after_load()\n\n self._log_service.log_debug(f'Loaded model dictionary successfully')\n\n return model_checkpoint\n\n def _get_model_name(self, name_prefix: str = None, name_suffix: str = None, overwrite_args: Dict[str, object] = None) -> str:\n result = self._arguments_service.get_configuration_name(overwrite_args)\n if name_prefix is not None:\n result = f'{name_prefix}_{result}'\n\n if name_suffix is not None:\n result = f'{result}{name_suffix}'\n\n return result\n\n def on_convergence(self) -> bool:\n self._log_service.log_debug(f'Model converged')\n\n result = self._on_convergence(self)\n for _, module in self.named_modules():\n result = result or self._on_convergence(module)\n\n return result\n\n def _on_convergence(self, main_module) -> bool:\n result = False\n for module_name, module in main_module.named_modules():\n if module_name == '':\n continue\n\n if isinstance(module, ModelBase):\n result = result or module.on_convergence()\n\n return result\n\n def state_dict(self, destination=None, prefix='', keep_vars=False):\n if self.do_not_save:\n return None\n\n result = super().state_dict(\n destination=destination,\n prefix=prefix,\n keep_vars=keep_vars)\n\n return result\n\n @property\n def keep_frozen(self) -> bool:\n return False\n\n def optimizer_parameters(self):\n return self.parameters()\n\n\n def calculate_evaluation_metrics(self) -> Dict[str, float]:\n return {}\n\n def finalize_batch_evaluation(self, is_new_best: bool):\n pass\n\n def before_load(self):\n pass\n\n def after_load(self):\n pass\n\n def get_embeddings(self, tokens: List[str], vocab_ids: List[torch.Tensor], skip_unknown: bool = False) -> List[WordEvaluation]:\n raise NotImplementedError()"
}
] | 94 |
liuxianghong/study
|
https://github.com/liuxianghong/study
|
087efead785bd2dfe817ff7dfa324f501f1f0b90
|
9d107e04ce23576416dbc01d03ed30f0ab9ede01
|
5461cfea0a8037918a50ad85751d71e03d136a79
|
refs/heads/master
| 2021-01-11T21:41:30.436733 | 2017-01-16T10:31:30 | 2017-01-16T10:31:30 | 78,835,700 | 0 | 0 | null | null | null | null | null |
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"text": "from pydelicious import get_popular,get_userposts,get_urlposts\nimport time\nimport random\n\ndef initiallizeUserDict(tag, count = 5):\n\tuser_dict = {}\n\tredic = get_popular(tag = tag)[0:count]\n\tprint redic\n\tfor p1 in redic:\n\t\tprint p1['href']\n\t\tfor p2 in get_urlposts('https://shop.icio.us/sales/the-limited-edition-black-hawk-drone-hd-camera?utm_source=del.icio.us&utm_medium=referral&utm_campaign=the-limited-edition-black-hawk-drone-hd-camera'):\n\t\t\tuser = p2['user']\n\t\t\tuser_dict[user] = {}\n\treturn user_dict\n\n\ndef fillItems(user_dict):\n\tall_items = {}\n\n\tfor user in user_dict:\n\t\tposts = {}\n\t\tfor i in xrange(3):\n\t\t\ttry:\n\t\t\t\tposts = get_userposts(user)\n\t\t\t\tbreak\n\t\t\texcept Exception as e:\n\t\t\t\tprint \"Failed user \" + user + \" , retrying \"\n\t\t\t\ttime.sleep(3)\n\t\tfor post in posts:\n\t\t\turl = post['href']\n\t\t\tprint url\n\t\t\tuser_dict[user][url] = 1.0\n\t\t\tall_items[url] = 1\n\n\tfor ratings in user_dict.values():\n\t\tfor item in all_items:\n\t\t\tif item not in ratings:\n\t\t\t\tratings[item] = 0.0\n\ndef randomUser(user_dict):\n\tuser = user_dict.keys()[random.randint(0, len(user_dict) - 1)]\n\treturn user\n\t\n"
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"text": "critics={'Lisa':{'Lady':1.8, 'Snakes1':3.0, 'Snakes2':3.0, 'Snakes3':3.0}\n,'Lisb':{'Lady':5.5, 'Snakes1':2.0, 'Snakes2':4.0, 'Snakes3':3.5, 'Snakes4':1.8}\n,'Lisc':{'Lady':4.5, 'Snakes1':6.0, 'Snakes2':4.4, 'Snakes3':2.5, 'Snakes4':2.8}\n,'Lisd':{'Lady':2.5, 'Snakes1':2.1, 'Snakes2':4.5, 'Snakes3':1.5, 'Snakes4':3.4}\n,'Lise':{'Lady':1.8, 'Snakes1':3.0, 'Snakes2':3.0, 'Snakes3':3.0, 'Snakes4':5.0}}\n\nfrom math import sqrt\n\ndef sim_distance(prefs,person1,person2):\n\tsi = {}\n\tfor item in prefs[person1]:\n\t\tif item in prefs[person2]:\n\t\t\tsi[item] = 1\n\tif len(si) == 0:\n\t\treturn 0\n\tsum_of_squares = sum(pow(prefs[person1][item] - prefs[person2][item],2) for item in prefs[person1] if item in prefs[person2])\n\treturn 1/(1+sqrt(sum_of_squares))\n\ndef sim_pearson(prefs,person1,person2):\n\tsi = {}\n\tfor item in prefs[person1]:\n\t\tif item in prefs[person2]:\n\t\t\tsi[item] = 1\n\tn = len(si)\n\tif n == 0:\n\t\treturn 1\n\tsum1 = sum([prefs[person1][item] for item in si])\n\tsum2 = sum([prefs[person2][item] for item in si])\n\n\tsum1sq = sum([pow(prefs[person1][item],2) for item in si])\n\tsum2sq = sum([pow(prefs[person2][item],2) for item in si])\n\n\tpsunm = sum([prefs[person1][item] * prefs[person2][item] for item in si])\n\n\tnum = psunm - (sum1*sum2/n)\n\n\tsq = (sum1sq - pow(sum1,2)/n) * (sum2sq - pow(sum2,2)/n)\n\tden = sqrt(sq)\n\tif den == 0:\n\t\treturn 0\n\n\tr = num/den\n\treturn r\n\ndef topMatches(prefs,person,n=5,similarity=sim_pearson):\n\tscores = [(similarity(prefs,person,other), other) \n\t\t\t\tfor other in prefs if other!= person]\n\tscores.sort()\n\tscores.reverse()\n\treturn scores[0:n]\n\ndef getRecommend(prefs,person,similarity=sim_pearson):\n\ttotals = {}\n\tsimSums = {}\n\tfor other in prefs:\n\t\tif other == person:\n\t\t\tcontinue\n\t\tsim = similarity(prefs,person,other)\n\t\tif sim <= 0:\n\t\t\tcontinue\n\t\tfor item in prefs[other]:\n\t\t\tif item not in prefs[person] or prefs[person][item] == 0:\n\t\t\t\ttotals.setdefault(item,0)\n\t\t\t\ttotals[item] += prefs[other][item] * sim\n\t\t\t\tsimSums.setdefault(item,0)\n\t\t\t\tsimSums[item] += sim\n\trankings = [(total / simSums[item],item) for item,total in totals.items()]\n\trankings.sort()\n\trankings.reverse()\n\treturn rankings\n\ndef transformPrefs(prefs):\n\tresult = {}\n\tfor person in prefs:\n\t\tfor item in prefs[person]:\n\t\t\tresult.setdefault(item,{})\n\t\t\tresult[item][person] = prefs[person][item]\n\treturn result\n\t\t\t\ndef calculateSimilarItems(prefs,n=10):\n\tresult = {}\n\titemPrefs = transformPrefs(prefs)\n\tc = 0\n\tfor item in itemPrefs:\n\t\tc += 1\n\t\tif c % 100 == 0:\n\t\t\tprint \"%d / %d\" % (c,c.len(itemPrefs))\n\t\tscores = topMatches(itemPrefs, item, n = n, similarity = sim_distance)\n\t\tresult[item] = scores\n\treturn result\n\n\n"
}
] | 2 |
Rohan-Chaudhury/Shape-Detection-Using-OpenCV
|
https://github.com/Rohan-Chaudhury/Shape-Detection-Using-OpenCV
|
cfa47a9f9a5a56b096d67a7e5b3372d87e6fe202
|
8737295ff5441d23a30bf444e7889179be35b914
|
53fa717fb661bbf6e8b89e41f545a075b04c0d76
|
refs/heads/master
| 2021-01-25T14:49:37.992106 | 2018-03-03T21:43:28 | 2018-03-03T21:43:28 | null | 0 | 0 | null | null | null | null | null |
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"text": "# import the necessary packages\r\nimport cv2\r\n\r\nimport argparse\r\nimport imutils\r\nimport cv2\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n \r\nclass ShapeDetector:\r\n\tdef __init__(self):\r\n\t\tpass\r\n \r\n\tdef detect(self, c):\r\n\t\t# initialize the shape name and approximate the contour\r\n\t\tshape = \"unidentified\"\r\n\t\tperi = cv2.arcLength(c, True)\r\n\t\tapprox = cv2.approxPolyDP(c, 0.04 * peri, True)\r\n\t\t# if the shape is a triangle, it will have 3 vertices\r\n\t\tif len(approx) == 3:\r\n\t\t\tshape = \"triangle\"\r\n \r\n\t\t# if the shape has 4 vertices, it is either a square or\r\n\t\t# a rectangle\r\n\t\telif len(approx) == 4:\r\n\t\t\t# compute the bounding box of the contour and use the\r\n\t\t\t# bounding box to compute the aspect ratio\r\n\t\t\t(x, y, w, h) = cv2.boundingRect(approx)\r\n\t\t\tar = w / float(h)\r\n \r\n\t\t\t# a square will have an aspect ratio that is approximately\r\n\t\t\t# equal to one, otherwise, the shape is a rectangle\r\n\t\t\tshape = \"square\" if ar >= 0.95 and ar <= 1.05 else \"rectangle\"\r\n \r\n\t\t# if the shape is a pentagon, it will have 5 vertices\r\n\t\t#elif len(approx) == 5:\r\n\t\t#\tshape = \"pentagon\"\r\n \r\n\t\t# otherwise, we assume the shape is a circle\r\n\t\telif len(approx) >4:\r\n\t\t\tshape = \"circle\"\r\n \r\n\t\t# return the name of the shape\r\n\t\treturn shape\r\n\r\n# construct the argument parse and parse the arguments\r\n#ap = argparse.ArgumentParser()\r\n#ap.add_argument(\"-i\", \"--image\", required=True,help=\"a,jpg\")\r\n#args = vars(ap.parse_args())\r\n# load the image and resize it to a smaller factor so that\r\n# the shapes can be approximated better\r\ncap= cv2.VideoCapture(0)\r\ncodec=cv2.VideoWriter_fourcc(*'XVID')\r\nout=cv2.VideoWriter('outpu.avi',codec,80.0,(640,480))\r\n\r\nwhile True:\r\n ret,image=cap.read()\r\n resized = imutils.resize(image, width=650)\r\n ratio = image.shape[1] / float(resized.shape[1])\r\n hsv=cv2.cvtColor(image,cv2.COLOR_BGR2HSV)\r\n s=15\r\n w=np.array([30-s,100,100])\r\n s=np.array([30+s,255,255])\r\n\r\n thresh=cv2.inRange(hsv,w,s) \r\n\r\n \r\n kernel=np.ones((11,11),np.uint8)\r\n kernel2=np.ones((10,10),np.uint8)\r\n #thresh = cv2.GaussianBlur(thresh, (5, 5), 0)\r\n thresh=cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel)\r\n thresh = cv2.GaussianBlur(thresh, (11, 11), 4)\r\n #thresh=cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,kernel2)\r\n cv2.imshow('a',thresh)\r\n # find contours in the thresholded image and initialize the\r\n # shape detector\r\n cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\r\n cnts = cnts[0] if imutils.is_cv2() else cnts[1]\r\n sd = ShapeDetector()\r\n\r\n # loop over the contours\r\n for c in cnts:\r\n # compute the center of the contour, then detect the name of the\r\n # shape using only the contour\r\n M = cv2.moments(c)\r\n if M[\"m00\"]>0:\r\n cX = int((M[\"m10\"] / M[\"m00\"]) * ratio)\r\n cY = int((M[\"m01\"] / M[\"m00\"]) * ratio)\r\n shape = sd.detect(c)\r\n \r\n # multiply the contour (x, y)-coordinates by the resize ratio,\r\n # then draw the contours and the name of the shape on the image\r\n c = c.astype(\"float\")\r\n c *= ratio\r\n c = c.astype(\"int\")\r\n cv2.drawContours(image, [c], -1, (0, 255, 0), 2)\r\n cv2.putText(image, shape, (cX, cY), cv2.FONT_HERSHEY_SIMPLEX,0.5, (255, 255, 255), 2)\r\n #out.write(image)\r\n # show the output image\r\n cv2.imshow(\"Image\", image)\r\n if cv2.waitKey(1) & 0xFF== ord('q'):\r\n break\r\n\r\ncap.release()\r\ncv2.destroyAllWindows()\r\n\r\n"
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"text": "import cv2\r\nimport numpy as np\r\n\r\ncap= cv2.VideoCapture(0)\r\n\r\nwhile True:\r\n _,image=cap.read()\r\n hsv=cv2.cvtColor(image,cv2.COLOR_BGR2HSV)\r\n s=25\r\n w=np.array([60-s,100,50])\r\n s=np.array([60+s,255,255])\r\n\r\n mask=cv2.inRange(hsv,w,s)\r\n\r\n cv2.imshow('Image',image)\r\n cv2.imshow('res',mask)\r\n if cv2.waitKey(1) & 0xFF== ord('q'):\r\n break\r\ncap.release\r\ncv2.destroyAllWindows()\r\n"
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"text": "import cv2\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nimg = cv2.imread('a.jpg')#number for grayscale -0 instead of imread_grayscale you can just write 0\r\n#IMREAD_COLOR -1\r\n#IMREAD_UNCHANGED - (-1)\r\n\r\ncv2.imshow('abd',img)\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()\r\n\r\nplt.imshow(img, cmap='gray', interpolation='bicubic')\r\nplt.plot([50,100],[80,100],'c',linewidth=5)\r\nplt.show()\r\n\r\ncv2.imwrite('c.png',img)\r\n"
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"repo_name": "Rohan-Chaudhury/Shape-Detection-Using-OpenCV",
"src_encoding": "UTF-8",
"text": "# Shape-Detection-Using-OpenCV\nShape Detection in various images using the OpenCV library in python\n"
}
] | 4 |
njblur/dogcat
|
https://github.com/njblur/dogcat
|
9b2c4432c5c1cffbbc08fe14f10b7aa3256e2687
|
8ce347a6a70530e5d7881c80ab1bdf5aec4bb096
|
ca042e3d343a6b38d8a5c835e4557d778562fecb
|
refs/heads/master
| 2021-06-30T19:15:51.211629 | 2017-09-17T14:13:26 | 2017-09-17T14:13:26 | 103,832,212 | 1 | 0 | null | null | null | null | null |
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"text": "## where are dog and cat ?\n\n\n* Inspired by YOLO, CNN could not only make classification, it could be used to detect the object position.\n* Here is the case that we know what the object is in a picture, we just want to know where it is. \n* In this network, we could the detect the positions of a cat and also a dog in the picture, you could add any more objects you want, like eyes,nose, mouth, ears in a face to make it a face landmarker.\n\n"
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"repo_name": "njblur/dogcat",
"src_encoding": "UTF-8",
"text": "import os\nimport sys\nimport numpy as np\nimport tensorflow as tf\nimport cv2\nimport matplotlib.pyplot as plt\nimport IPython\n\ndef combine_image(fg,fg2,bg,x,y,x2,y2,scale=1.0):\n shape = fg.shape\n width = shape[1]\n height = shape[0]\n scaled_width = int(width*scale+0.5)\n scaled_height = int(height*scale+0.5)\n bg_shape = bg.shape\n bg_width = bg_shape[1]\n bg_height = bg_shape[0]\n angle = 0.0\n #angle = -(minute-15)*360/60\n # matrix = cv2.getRotationMatrix2D((center_x,center_y),angle,scale)\n # fg_s = cv2.warpAffine(fg,matrix,(scaled_width,scaled_height))\n fg_s = cv2.resize(fg,(scaled_width,scaled_height))\n combined = np.copy(bg)\n to_put = combined[y:y+scaled_height,x:x + scaled_width]\n to_put[fg_s[:,:,0]>0] = fg_s[fg_s[:,:,0]>0]\n\n shape = fg2.shape\n width = shape[1]\n height = shape[0]\n scaled_width = int(width*scale+0.5)\n scaled_height = int(height*scale+0.5)\n fg_s = cv2.resize(fg2,(scaled_width,scaled_height))\n\n to_put = combined[y2:y2+scaled_height,x2:x2 + scaled_width]\n to_put[fg_s[:,:,0]>0] = fg_s[fg_s[:,:,0]>0]\n\n return combined\ndef generate_data(fg,fg2,bg,data_size):\n fg_shape = fg.shape\n bg_shape = bg.shape\n x_margin = bg_shape[1] - fg_shape[1]\n y_margin = bg_shape[0] - fg_shape[0]\n\n fg2_shape = fg2.shape\n x2_margin = bg_shape[1] - fg2_shape[1]\n y2_margin = bg_shape[0] - fg2_shape[0]\n\n # scales = np.random.random(size=[data_size])*0.4+0.8\n Xs = np.random.randint(0,x_margin,size=[data_size])\n Ys = np.random.randint(0,y_margin,size=[data_size])\n X2s = np.random.randint(0,x2_margin,size=[data_size])\n Y2s = np.random.randint(0,y2_margin,size=[data_size]) \n train_data = [combine_image(fg,fg2,bg,x,y,x2,y2) for x,y,x2,y2 in zip(Xs, Ys, X2s,Y2s)]\n target_data = [[x*1.0/bg_shape[1],y*1.0/bg_shape[0],x2*1.0/bg_shape[1],y2*1.0/bg_shape[0]] for x,y,x2,y2 in zip(Xs, Ys,X2s,Y2s)]\n return np.array(train_data),np.array(target_data)\ndef main(train):\n batch_size = 50\n cat=cv2.imread('cat.png')\n dog=cv2.imread('dog.png')\n background=cv2.imread('background.png')\n cat = cat[:,:,[2,1,0]]\n cat[cat[:,:,0]>245]=[0,0,0]\n dog = dog[:,:,[2,1,0]]\n dog[dog[:,:,0]>245]=[0,0,0]\n background = background[:,:,[2,1,0]]\n input = tf.placeholder(shape=[batch_size,224,224,3],dtype=tf.float32)\n target = tf.placeholder(dtype=tf.float32,shape=[batch_size,4])\n filter1_weights = tf.Variable(tf.truncated_normal(shape=[5,5,3,32],stddev=0.01))\n filter1_bias = tf.Variable(tf.zeros(shape=[32]))\n filter2_weights = tf.Variable(tf.truncated_normal(shape=[3,3,32,64],stddev=0.01))\n filter2_bias = tf.Variable(tf.zeros(shape=[64]))\n\n conv1 = tf.nn.conv2d(input,filter1_weights,strides=[1,2,2,1],padding=\"SAME\")\n conv1 = tf.nn.bias_add(conv1,filter1_bias)\n conv1 = tf.nn.relu(conv1)\n conv1 = tf.nn.max_pool(conv1,ksize=[1,2,2,1],strides=[1,2,2,1],padding=\"SAME\")\n\n conv1 = tf.nn.conv2d(conv1,filter2_weights,strides=[1,2,2,1],padding=\"SAME\")\n conv1 = tf.nn.bias_add(conv1,filter2_bias)\n conv1 = tf.nn.relu(conv1)\n conv1 = tf.nn.max_pool(conv1,ksize=[1,2,2,1],strides=[1,2,2,1],padding=\"SAME\")\n\n shape = conv1.get_shape().as_list()\n\n batch = shape[0]\n size = shape[1]*shape[2]*shape[3]\n\n flat = tf.reshape(conv1,[batch,size])\n\n fc1_weights = tf.Variable(tf.truncated_normal(shape=[size,128],stddev=0.01))\n fc1_bias = tf.Variable(tf.zeros(dtype=tf.float32,shape=[128]))\n\n fc1 = tf.matmul(flat,fc1_weights) + fc1_bias\n\n fc2_weights = tf.Variable(tf.truncated_normal(shape=[128,4],stddev=0.01))\n fc2_bias = tf.Variable(tf.zeros(dtype=tf.float32,shape=[4]))\n\n fc2 = tf.matmul(fc1,fc2_weights) + fc2_bias\n\n loss = tf.nn.l2_loss((fc2-target))/batch_size\n\n trainer = tf.train.GradientDescentOptimizer(0.001)\n\n step = trainer.minimize(loss)\n\n epoch = 200\n \n saver = tf.train.Saver()\n with tf.Session() as sess:\n init = tf.global_variables_initializer()\n sess.run(init)\n if(train):\n train_data_g, target_data_g = generate_data(cat,dog,background,batch_size*100)\n for i in range(epoch):\n for j in range(100):\n train_data = train_data_g[j*batch_size:j*batch_size+batch_size]\n target_data = target_data_g[j*batch_size:j*batch_size+batch_size]\n [l,s] = sess.run([loss,step],feed_dict={input:train_data,target:target_data})\n print \"loss is \" + str(l)\n saver.save(sess,'cat.model')\n else:\n saver.restore(sess,'cat.model')\n v,l = generate_data(cat,dog,background,batch_size)\n [o] = sess.run([fc2],feed_dict={input:v})\n offset = o*224\n plotimage(v[0],offset[0],cat.shape,dog.shape)\n\ndef plotimage(image,pos,shape1,shape2):\n width = shape1[1]\n height = shape1[0]\n width2 = shape2[1]\n height2 = shape2[0]\n ax=plt.subplot(111)\n ax.axis('off')\n box=plt.Rectangle((pos[0],pos[1]),width,height,fill=False,color='blue')\n box2=plt.Rectangle((pos[2],pos[3]),width2,height2,fill=False,color='green')\n ax.add_patch(box)\n ax.add_patch(box2)\n plt.imshow(image)\n plt.show()\n\nif __name__ == '__main__':\n main(False)\n\n\n"
}
] | 2 |
yq911122/digit-clf
|
https://github.com/yq911122/digit-clf
|
a3e3a7404ad24331f04a27f4ed394f64afa24760
|
723720a576667d01adbb5f15ffdf1f4085ddb209
|
4526d18e68aa96db6411d1e7a4d4b02423e6c060
|
refs/heads/master
| 2016-08-08T19:03:29.733634 | 2016-01-14T06:12:26 | 2016-01-14T06:12:26 | 49,623,398 | 0 | 0 | null | null | null | null | null |
[
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"avg_line_length": 26.839284896850586,
"blob_id": "e64f39271b1689170582b98658122514d253f9e0",
"content_id": "16df34e1c9b8f54738d752470609c3671944aa5f",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,
"language": "Python",
"length_bytes": 1558,
"license_type": "no_license",
"max_line_length": 77,
"num_lines": 56,
"path": "/NumReg.py",
"repo_name": "yq911122/digit-clf",
"src_encoding": "UTF-8",
"text": "from pandas.io.parsers import read_csv\nfrom pandas import DataFrame\nimport numpy as np\n\nfrom sklearn.grid_search import GridSearchCV\n\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import RandomForestClassifier\n\nTESTDATAURL = \"test.csv\"\nTRAINDATAURL = \"train.csv\"\n\ndef load_data(test = False):\n\tif test:\n\t\tdf = read_csv(TESTDATAURL)\n\t\treturn df\n\tdf = read_csv(TRAINDATAURL)\n\treturn df\n\n\nclass digi_classifier(object):\n\t\"\"\"digit classifier, implement fit(), predict(), err_rate()\"\"\"\n\n\tdef __init__(self):\n\t\tsuper(digi_classifier, self).__init__()\n\t\tself.classifier = RandomForestClassifier(n_estimators=100)\t\n\t\t# self.classifier = SVC()\n\n\tdef fit(self, X, Y):\n\t\t# cross validation to tune params for SVM\n\t\t# params = {\"n_estimators\": [10, 50, 100], \"min_samples_leaf\": [5, 15, 30]}\n\t\t# clf = GridSearchCV(self.classifer, params)\n\t\t# clf.fit(X, Y)\n\t\t# print clf.grid_scores_\n\t\t# self.classifer = clf.best_estimator_\n\t\tself.classifier.fit(X,Y)\n\n\tdef predict(self, X):\n\t\tpd = DataFrame(self.classifier.predict(X))\n\t\tpd.index += 1\n\t\treturn pd\n\n\tdef error_rate(self, PredValues, ActualValues):\n\t\ttotal = len(ActualValues)\n\t\tdiff = [1 for i in range(total) if PredValues[i] != ActualValues[i]]\n\t\treturn sum(diff)/float(total)\n\nif __name__ == \"__main__\":\n\ttrainData = load_data()\n\ttestData = load_data(test = True)\n\tclf = digi_classifier()\n\n\tcols = [col for col in trainData.columns if col not in ['label']]\n\tclf.fit(trainData[cols], trainData['label'])\n\tpredValues = clf.predict(testData)\n\tpredValues.to_csv(\"result.csv\", header=['Label'], index_label='ImageId')"
}
] | 1 |
artragis/Python-ZMarkdown
|
https://github.com/artragis/Python-ZMarkdown
|
bf92cbced65db5bb131dbacde911cca78920c2d8
|
d4b5750ab3edc64c5d414ea0fb55ba943c2d6d03
|
60f5262b7fa563603931c00b302e79c8cec51144
|
refs/heads/master-zds
| 2021-01-21T07:44:43.910934 | 2016-01-04T17:58:26 | 2016-01-04T18:15:13 | 48,980,134 | 0 | 0 | null | 2016-01-04T06:57:15 | 2016-01-04T06:57:16 | 2015-10-31T16:00:58 |
HTML
|
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"path": "/markdown/extensions/typographie.py",
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"src_encoding": "UTF-8",
"text": "# -*- coding: utf-8 -*-\n\n# Gestion fine de la typographie française, du moins, ce qui peut être\n# automatisé, par Dominus Carnufex. Lointainement inspiré de l’extension\n# SmartyPants. Licence CeCIll-B.\n\nimport markdown\nfrom ..inlinepatterns import HtmlPattern\n\n\nclass RemplacerPattern(HtmlPattern):\n def __init__(self, motif, remplacement, markdown):\n \"\"\" Replacer le motif par un simple texte. \"\"\"\n HtmlPattern.__init__(self, motif)\n self.remplacement = remplacement\n self.markdown = markdown\n\n def handleMatch(self, m):\n return self.markdown.htmlStash.store(self.remplacement, safe=True)\n\n\nclass TypographieExtension(markdown.extensions.Extension):\n def __init__(self, *args, **kwargs):\n self.config = {\n 'apostrophes': [True, 'Apostrophes typographiques'],\n 'cadratins': [True, 'Tirets cadratins'],\n 'demi-cadratins': [True, 'Tirets demi-cadratins'],\n 'espaces': [True, 'Espaces insécables'],\n 'guillemets': [True, 'Guillemets français'],\n 'pour-mille': [True, 'Symbole pour mille'],\n 'suspension': [True, 'Points de suspension'],\n }\n super(TypographieExtension, self).__init__(*args, **kwargs)\n\n def remplacerApostrophes(self, md):\n apostrophesPattern = RemplacerPattern(\"'\", \"’\", md)\n self.remplacements.add('apostrophes', apostrophesPattern, '_begin')\n\n def remplacerCadratins(self, md):\n cadratinsPattern = RemplacerPattern(r'(?<!-)---(?!-)', \"—\", md)\n self.remplacements.add('cadratins', cadratinsPattern, '_begin')\n\n def remplacerDemiCadratins(self, md):\n demiCadratinsPattern = RemplacerPattern(\n r'(?<!-)--(?!-)', \"–\", md\n )\n self.remplacements.add(\n 'demi-cadratins', demiCadratinsPattern, '_begin'\n )\n\n def remplacerEspaces(self, md):\n espacePointVirgulePattern = RemplacerPattern(\" ; \", \" ; \", md)\n espaceDeuxPointsPattern = RemplacerPattern(\" : \", \" : \", md)\n espaceInterrogationPattern = RemplacerPattern(\" \\?\", \" ?\", md)\n espaceExclamationPattern = RemplacerPattern(\" !\", \" !\", md)\n espacePourCentPattern = RemplacerPattern(\" %\", \" %\", md)\n espacePourMillePattern = RemplacerPattern(\n \" ‰\".decode(\"utf8\"), \" ‰\", md\n )\n espaceGuillemetOuvrantPattern = RemplacerPattern(\n \"« \".decode(\"utf8\"), \"« \", md\n )\n espaceGuillemetFermantPattern = RemplacerPattern(\n \" »\".decode(\"utf8\"), \" »\", md\n )\n\n self.remplacements.add(\n 'espace-point-virgule', espacePointVirgulePattern, '_end'\n )\n self.remplacements.add(\n 'espace-deux-points',\n espaceDeuxPointsPattern,\n '<espace-point-virgule'\n )\n self.remplacements.add(\n 'espace-interrogation',\n espaceInterrogationPattern,\n '<espace-deux-points'\n )\n self.remplacements.add(\n 'espace-exclamation',\n espaceExclamationPattern,\n '<espace-interrogation'\n )\n self.remplacements.add(\n 'espace-pour-cent',\n espacePourCentPattern,\n '<espace-exclamation'\n )\n self.remplacements.add(\n 'espace-pour-mille',\n espacePourMillePattern,\n '<espace-pour-cent'\n )\n self.remplacements.add(\n 'espace-guillemet-ouvrant',\n espaceGuillemetOuvrantPattern,\n '<espace-pour-mille'\n )\n self.remplacements.add(\n 'espace-guillemet-fermant',\n espaceGuillemetFermantPattern,\n '<espace-guillemet-ouvrant'\n )\n\n def remplacerGuillemets(self, md):\n guillemetsOuvrantsPattern = RemplacerPattern(r'\\<\\<', \"«\", md)\n guillemetsFermantsPattern = RemplacerPattern(r'\\>\\>', \"»\", md)\n self.remplacements.add(\n 'guillemets-ouvrants', guillemetsOuvrantsPattern, '_begin'\n )\n self.remplacements.add(\n 'guillemets-fermants',\n guillemetsFermantsPattern,\n '>guillemets-ouvrants'\n )\n\n def remplacerGuillemetsEspaces(self, md):\n guillemetsOuvrantsPattern = RemplacerPattern(\n r'\\<\\< ', \"« \", md\n )\n guillemetsFermantsPattern = RemplacerPattern(\n r' \\>\\>', \" »\", md\n )\n self.remplacements.add(\n 'guillemets-ouvrants', guillemetsOuvrantsPattern, '_begin'\n )\n self.remplacements.add(\n 'guillemets-fermants',\n guillemetsFermantsPattern,\n '>guillemets-ouvrants'\n )\n\n def remplacerPourMille(self, md):\n pourMillePattern = RemplacerPattern(\"%o\", \"‰\", md)\n self.remplacements.add('pour-mille', pourMillePattern, '_begin')\n\n def remplacerPourMilleEspaces(self, md):\n pourMillePattern = RemplacerPattern(\" %o\", \" ‰\", md)\n self.remplacements.add('pour-mille', pourMillePattern, '_begin')\n\n def remplacerSuspension(self, md):\n suspensionPattern = RemplacerPattern(\n r'(?<!\\.)\\.{3}(?!\\.)', \"…\", md\n )\n self.remplacements.add('suspension', suspensionPattern, '_begin')\n\n def extendMarkdown(self, md, md_globals):\n configs = self.getConfigs()\n self.remplacements = markdown.odict.OrderedDict()\n if configs['apostrophes']:\n self.remplacerApostrophes(md)\n if configs['cadratins']:\n self.remplacerCadratins(md)\n if configs['demi-cadratins']:\n self.remplacerDemiCadratins(md)\n if configs['espaces']:\n self.remplacerEspaces(md)\n if configs['guillemets'] and not configs['espaces']:\n self.remplacerGuillemets(md)\n if configs['guillemets'] and configs['espaces']:\n self.remplacerGuillemetsEspaces(md)\n if configs['pour-mille'] and not configs['espaces']:\n self.remplacerPourMille(md)\n if configs['pour-mille'] and configs['espaces']:\n self.remplacerPourMilleEspaces(md)\n if configs['suspension']:\n self.remplacerSuspension(md)\n traitement = markdown.treeprocessors.InlineProcessor(md)\n traitement.inlinePatterns = self.remplacements\n md.treeprocessors.add('typographie', traitement, '_end')\n md.ESCAPED_CHARS.extend([\"'\"])\n\n\ndef makeExtension(*args, **kwargs):\n return TypographieExtension(*args, **kwargs)"
},
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"detected_licenses": [],
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"path": "/test.py",
"repo_name": "artragis/Python-ZMarkdown",
"src_encoding": "UTF-8",
"text": "# coding: utf-8\n\nimport codecs\nimport markdown\nfrom markdown.extensions.zds import ZdsExtension\nimport time\n from __future__ import print_function\n \n# Markdowns customs extensions :\ndef get_markdown_instance(Inline=False):\n zdsext = ZdsExtension({\"inline\": Inline, \"emoticons\": {\"TOTOTO\":\"truc\"}, \"js_support\": True})\n # Generate parser\n md = markdown.Markdown(extensions=(zdsext,),\n safe_mode = 'escape',\n # Protect use of html by escape it\n enable_attributes = False,\n # Disable the conversion of attributes.\n # This could potentially allow an\n # untrusted user to inject JavaScript\n # into documents.\n tab_length = 4,\n # Length of tabs in the source.\n # This is the default value\n output_format = 'html5', # html5 output\n # This is the default value\n smart_emphasis = True,\n # Enable smart emphasis for underscore syntax\n lazy_ol = True,\n # Enable smart ordered list start support\n )\n return md\n\ninput_file = codecs.open(\"prob.md\", mode=\"r\", encoding=\"utf-8\")\ntext = input_file.read()\nprint(get_markdown_instance(Inline=False).convert(text).encode('utf-8'))\n\n"
}
] | 2 |
ynigam51/python-practice
|
https://github.com/ynigam51/python-practice
|
a2c16bf43cfee481430d6d8d1f1c03042e0d9240
|
fc52096ff5319e4ea494e13c1ac7bf52c07aedcd
|
98992923763058095424817a12726c546ac3a4d4
|
refs/heads/main
| 2023-03-25T18:09:27.722605 | 2021-03-25T12:12:35 | 2021-03-25T12:12:35 | 350,045,743 | 0 | 0 | null | null | null | null | null |
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"blob_id": "dd7147602f4c3d6eaf3003e50d085f7d70acc710",
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"max_line_length": 22,
"num_lines": 9,
"path": "/Membership OP.py",
"repo_name": "ynigam51/python-practice",
"src_encoding": "UTF-8",
"text": "a=[2,3,4,'YASH']\r\nprint(2 in a)\r\nprint(3 in a)\r\nprint(4 in a)\r\nprint('YASH' in a)\r\nprint(2 not in a)\r\nprint(3 not in a)\r\nprint(6 not in a)\r\nprint('YASH' not in a)\r\n"
},
{
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"alphanum_fraction": 0.682692289352417,
"avg_line_length": 22.076923370361328,
"blob_id": "1656b44bdbaafe24d319c59d1efcf5b1ce28062f",
"content_id": "02739a3dc6c7842564f6cb515451f8799594aff3",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,
"language": "Python",
"length_bytes": 312,
"license_type": "no_license",
"max_line_length": 94,
"num_lines": 13,
"path": "/membership OP 2.py",
"repo_name": "ynigam51/python-practice",
"src_encoding": "UTF-8",
"text": "Python 3.9.2 (tags/v3.9.2:1a79785, Feb 19 2021, 13:44:55) [MSC v.1928 64 bit (AMD64)] on win32\r\nType \"help\", \"copyright\", \"credits\" or \"license()\" for more information.\r\n>>> \r\n= RESTART: C:/Users/admin/Desktop/Online python class/my practise/Logical OP.py\r\nTrue\r\nTrue\r\nTrue\r\nTrue\r\nFalse\r\nFalse\r\nTrue\r\nFalse\r\n>>> "
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"text": "n1=int(input(\"Enter a number:\"))\r\nn2=int(input(\"Enter a number:\"))\r\nprint(n1==n2)\r\nprint(n1<=n2)\r\nprint(n1>=n2)\r\nprint(n1!=n2)\r\nprint(n1<n2)\r\nprint(n1>n2)\r\n"
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"text": "a=int(input(\"Enter a number:\"))\r\nb=int(input(\"Enter a number:\"))\r\nprint(a and b)\r\nprint(a or b)\r\nprint(not a)\r\nprint(not b)\r\n\r\n"
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"text": "Python 3.9.2 (tags/v3.9.2:1a79785, Feb 19 2021, 13:44:55) [MSC v.1928 64 bit (AMD64)] on win32\r\nType \"help\", \"copyright\", \"credits\" or \"license()\" for more information.\r\n>>> a,b=4,6\r\n>>> a=b\r\n>>> print(a)\r\n6\r\n>>> a+=b\r\n>>> print(b)\r\n6\r\n>>> print(a)\r\n12\r\n>>> a%=b\r\n>>> print(a)\r\n0\r\n>>> a//=b\r\n>>> print(a)\r\n0\r\n>>> a-=b\r\n>>> print(a)\r\n-6\r\n>>> a**=b\r\n>>> print(a)\r\n46656\r\n>>> a*=b\r\n>>> print(a)\r\n279936\r\n>>> "
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"repo_name": "ynigam51/python-practice",
"src_encoding": "UTF-8",
"text": "n1=int(input(\"Enter a number:\"))\r\nn2=int(input(\"Enter a number:\"))\r\nprint(n1 & n2)\r\nprint(n1 | n2)\r\nprint(~n1)\r\nprint(~n2)\r\nprint(n1^n2)\r\n"
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"text": "Python 3.9.2 (tags/v3.9.2:1a79785, Feb 19 2021, 13:44:55) [MSC v.1928 64 bit (AMD64)] on win32\r\nType \"help\", \"copyright\", \"credits\" or \"license()\" for more information.\r\n>>> a,b=2,4\r\n>>> print(a+b)\r\n6\r\n>>> print(a-b)\r\n-2\r\n>>> print(a%b)\r\n2\r\n>>> print(a//b)\r\n0\r\n>>> print(a+=b)\r\nSyntaxError: invalid syntax\r\n>>> print(a/b)\r\n0.5\r\n>>> print(a**b)\r\n16\r\n>>> print(a*b)\r\n8\r\n>>> "
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"path": "/membership.y.py",
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"text": "Python 3.9.2 (tags/v3.9.2:1a79785, Feb 19 2021, 13:44:55) [MSC v.1928 64 bit (AMD64)] on win32\r\nType \"help\", \"copyright\", \"credits\" or \"license()\" for more information.\r\n>>> a=[2,4,5,6]\r\n>>> 2 in a\r\nTrue\r\n>>> 4 in a\r\nTrue\r\n>>> 5 in a\r\n \r\nSyntaxError: unexpected indent\r\n>>> 5 in a\r\nTrue\r\n>>> 6 in a\r\nTrue\r\n>>> 7 in a\r\nFalse\r\n>>> 2 not in a\r\nFalse\r\n>>> 4 not in a\r\nFalse\r\n>>> 5 not in a\r\nFalse\r\n>>> 6 not in a\r\nFalse\r\n>>> 7 not in a\r\nTrue\r\n>>> 9 not in a\r\nTrue\r\n>>> "
}
] | 11 |
zihanNU/TrafficEstimation_Prediction_KalmanFilter_Python
|
https://github.com/zihanNU/TrafficEstimation_Prediction_KalmanFilter_Python
|
201b4dc154ad879f908a88fe22a775a0b32a8b9c
|
2285f06ad28a0e3acc8142980a2a85a19990cda9
|
584163e8802068eddf8c65f375c4065b034a2887
|
refs/heads/master
| 2021-01-19T20:49:10.514132 | 2017-04-17T23:15:14 | 2017-04-17T23:15:14 | 88,559,043 | 2 | 0 | null | null | null | null | null |
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"text": "#datapath = r'\\\\fukuda_lab_nhd\\BackUP\\Tonny\\Research\\EvaluationbyTaxiProbeData\\DATA\\03ProbeMaster\\DRM1800'\ndatapath = r'C:\\Users\\Zihan\\Documents\\STUDY\\GPSdata'\n\n\ndef calcu_meas():\n import os\n dirs=os.listdir(datapath)\n curdir=0\n count=0\n num=0\n for diri in dirs:\n num=num+1\n record=open(str(num),'w')\n print(\"Current dir:\",dir)\n curdir=curdir+1\n print count\n print(\"Processed dirs: %d/%d\"%(curdir,len(dirs)))\n csvs=os.listdir(datapath+'\\\\'+diri)\n length=len(csvs)\n print length\n for csv in csvs:\n print (\"csv\",csv)\n GPSdata=open(datapath+'\\\\'+diri+'\\\\'+csv,'r')\n lines=GPSdata.readlines()\n countline=0\n for line in lines:\n if countline ==0:\n countline=countline+1\n continue\n linearray=line.split(',')\n carnum=linearray[1]\n \tspeed=linearray[27]\n \t\tlat=float(linearray[6])\n \t\tlon=float(linearray[7])\n \tdata=linearray[3]\n \ttime=linearray[4]\n \tcellnumber=-1\n # provide the longitude and latitude range for each cell\n if lon>=139.7418 and lon<=139.76320 and lat>=35.6495 and lat<=35.6525:\n if lon >=139.7418 and lon<=139.7431 and lat<=35.6524 and lat>=35.6521:\n cellnumber=1\n if lon >=139.743 and lon<=139.744 and lat<=35.6522 and lat>=35.6520:\n cellnumber=2\n if lon >=139.744 and lon<=139.745 and lat<=35.652 and lat>=35.6517:\n cellnumber=3\n if lon >=139.745 and lon<=139.747 and lat<=35.6517 and lat>=35.6516:\n cellnumber=4\n if lon >=139.747 and lon<=139.748 and lat<=35.6516 and lat>=35.6515:\n cellnumber=5\n if lon >=139.748 and lon<=139.749 and lat<=35.6515 and lat>=35.6513:\n cellnumber=6\n if lon >=139.749 and lon<=139.751 and lat<=35.6507 and lat>=35.6502:\n cellnumber=7\n if lon >=139.750 and lon<=139.751 and lat<=35.6502 and lat>=35.6499:\n cellnumber=8\n if lon >=139.751 and lon<=139.753 and lat<=35.6499 and lat>=35.6498:\n cellnumber=9\n if lon >=139.753 and lon<=139.754 and lat<=35.6501 and lat>=35.6498:\n cellnumber=10\n if lon >=139.754 and lon<=139.755 and lat<=35.6503 and lat>=35.6501:\n cellnumber=11 \n if lon >=139.755 and lon<=139.756 and lat<=35.6503 and lat>=35.6501:\n cellnumber=12\n if lon >=139.756 and lon<=139.757 and lat<=35.6501 and lat>=35.6499:\n cellnumber=13\n if lon >=139.757 and lon<=139.758 and lat<=35.6499 and lat>=35.6498:\n cellnumber=14\n if lon >=139.758 and lon<=139.759 and lat<=35.6498 and lat>=35.6497:\n cellnumber=15\n if lon >=139.759 and lon<=139.760 and lat<=35.6497 and lat>=35.6496:\n cellnumber=16\n if lon >=139.760 and lon<=139.761 and lat<=35.64972 and lat>=35.64958:\n cellnumber=17\n if lon >=139.761 and lon<=139.762 and lat<=35.64972 and lat>=35.64958:\n cellnumber=18\n if lon >=139.762 and lon<=139.763 and lat<=35.6503 and lat>=35.6496:\n cellnumber=19 \n record.write(carnum+','+data+','+time+','+str(lat)+','+str(lon)+','+str(cellnumber)+','+speed+'\\n') \n\n count=count+1\n GPSdata.close\n record.close\n return count\n\nif __name__=='__main__':\n logfile=open('log.txt','w')\n logfile.close()\n \n count=calcu_meas()\n logfile.close()\n record.close()\n"
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"repo_name": "zihanNU/TrafficEstimation_Prediction_KalmanFilter_Python",
"src_encoding": "UTF-8",
"text": "from matplotlib import pylab\nfrom numpy import *\n\nwith open('velocity_a_test32.csv','r') as file1:\n lines=file1.readlines()\n z=[]\n m=[]\n for i in range(0,180):\n linearray=lines[i].split(',')\n for j in range(126):\n for k in range(1):\n p=float(linearray[j])\n z.append(p)\n for k in range(1):\n p=float(linearray[126][1:7].rstrip('\\n'))\n z.append(p)\n z=array(z).reshape(180,127)\n m=z.T\n print z.shape\n## print z\n## pylab.xticks(arange(20),'0','5','10','15','20')\n## pylab.xticks(arange(180),'7.30','8.00','8.30','9.00','9.30','10.00','11.30')\n pylab.subplot(111)\n pylab.ylabel('Cell Number')\n pylab.xlabel('Time')\n## pylab.xmajorLocator=fig.MultipleLocator(10)\n## pylab.ylim(0,100)\n pylab.title('2004.Nov.3(Wen.), 7:30-10:30')\n pylab.pcolor(m,vmin= 10,vmax= 110)\n b=pylab.colorbar(shrink=0.76)\n pylab.imshow(pylab.clip(m,10,110))\n## b.shrink(0.8)\n pylab.savefig('yokohane_a_11032t.png',dpi=150)\n print 'over'\n"
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"text": "import os\nfrom numpy import *\ndatapath = r'C:\\Users\\hong\\Desktop\\master thesis\\coding and test for traffic count\\yokohane\\04-11-03'\n\ndef tcdata(manageid,numberid):\n# read the overall original data\n tccsv=os.listdir(datapath)\n dataitem=['','','','','']\n file_redata=open(numberid+'_'+manageid+'.csv','w')\n for csv in tccsv:\n file_data=open(datapath+'\\\\'+csv,'r')\n lines=file_data.readlines()\n linearray=lines[10].split(',')\n for j in range(1,len(linearray)):\n s=linearray[j].decode('s-jis')\n managementid=s[0:2]+'-'+s[3:5]+'-'+s[6:9] # to format the sensor id\n if managementid==(manageid):\n for k in range(462,642):\n lineitem=lines[k].split(',') \n dataitem=[lineitem[0],lineitem[j],lineitem[j+2],lineitem[j+4],lineitem[j+6]] # to select time, traffic volume, large vehicle volume, speed and occupancy\n datajoin=','.join(item for item in dataitem)\n file_redata.write(datajoin+'\\n')\n file_redata.close()\n return\n else:\n j=j+8 # for next sensor data\n file_data.close()\n file_redata.close()\n return \n\nif __name__ == \"__main__\":\n manageid=[]\n numberid=[]\n H=[]\n for i in range(108*145): # sensor number and cell number, all together for a H matrix\n H.append(0)\n H=array(H).reshape(108,145)\n file_H=open('H3.csv','w')\n file_sensor=open('1.csv','r')\n lines=file_sensor.readlines()\n for i in range (0,len(lines)):\n linearray=lines[i].split(',')\n print int(linearray[9]),i\n H[i][int(linearray[9])-1]=1 # when a cell has a sensor, the related H matrix item should be changed into 1 otherwise remain 0\n for i in range(108):\n for j in range(144):\n file_H.write(str(H[i][j])+',')\n file_H.write(str(H[i][144])+'\\n')\n manageid.append(linearray[1]) # sensor id\n numberid.append(linearray[10]) # cellnumber\n file_sensor.close()\n file_H.close()\n## for i in range(0,len(manageid)):\n## print manageid[i],numberid[i]\n## tcdata(manageid[i],numberid[i])\n print 'over'\n \n"
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"text": "from matplotlib import pylab\nfrom matplotlib.backends.backend_pdf import PdfPages\nimport os\nimport numpy as np\n\nfile1=open('ttotal.csv','r')\nt1=[]\nt2=[]\nt3=[]\nt4=[]\nt5=[]\nt6=[]\nlines=file1.readlines()\nfor i in range(1,181):\n linearray1=lines[0].split(',')\n linearray2=lines[1].split(',')\n linearray3=lines[2].split(',')\n linearray4=lines[3].split(',')\n linearray5=lines[4].split(',')\n linearray6=lines[5].split(',')\n t1.append(linearray1[i])\n t2.append(linearray2[i])\n t3.append(linearray3[i])\n t4.append(linearray4[i])\n t5.append(linearray5[i])\n t6.append(linearray6[i])\nfig=pylab.figure(figsize=(16,9))\nax1=fig.add_subplot(131)\nl1=ax1.plot(t1,'b-',markersize=30)\nl4=ax1.plot(t4,'k.-',markersize=5)\nax2=fig.add_subplot(132)\nl2=ax2.plot(t2,'r-',markersize=30)\nl5=ax2.plot(t5,'k.-',markersize=5)\nax3=fig.add_subplot(133)\nl3=ax3.plot(t3,'g-',markersize=30)\nl6=ax3.plot(t6,'k.-',markersize=5)\ntext=['7:30','7:50','8:10','8:30','8:50','9:10','9:30','9:50','10:10','10:30']\nfig.legend((l4,l5,l6),('Wangan Nov.01','Wangan Nov.02','Wangan Nov.03'),loc='lower right')\nax1.set_title('Travel Time, Nov 01',fontsize=15)\nax1.set_xlabel('Departure Time',fontsize=15)\nax1.set_ylabel('Travel Time1 (minute)',fontsize=15)\nax1.set_ylim([20,60])\nax2.set_title('Travel Time Difference, Nov 02',fontsize=15)\nax2.set_xlabel('Departure Time',fontsize=15)\nax2.set_ylabel('Travel Time (minute)',fontsize=15)\nax2.set_ylim([20,60])\nax1.set_xticks([0,60,120,180])\nax1.set_xticklabels(['7:30','8:30','9:30','10:30'])\nax2.set_xticks([0,60,120,180])\nax2.set_xticklabels(['7:30','8:30','9:30','10:30'])\nax3.set_xticks([0,60,120,180])\nax3.set_xticklabels(['7:30','8:30','9:30','10:30'])\nax3.set_title('Travel Time Difference, Nov 03',fontsize=15)\nax3.set_xlabel('Departure Time',fontsize=15)\nax3.set_ylabel('Travel Time (minute)',fontsize=15)\nax3.set_ylim([20,60])\npylab.xticks([0,60,120,180],['7:30','8:30','9:30','10:30'])\n##fig.legend((l1,l2,l3),('More minute on Yokohane Line, Nov.01','More minute on Yokohane Line, Nov.02','More minute on Yokohane Line, Nov.03'),loc='lower center')\n##pylab.show()\n##ax3.set_xticks(np.arange(180),text)\n##fig.legend((l1,l2,l3,l4,l5,l6),('Yokohane Nov.01','Yokohane Nov.02','Yokohane Nov.03','Wangan Nov.01','Wangan Nov.02','Wangan Nov.03'),loc='upper leftpylab.xticks([0,60,120,180],['7:30','8:30','9:30','10:30'])\n##pylab.show()\npylab.savefig('t22.png',dpi=150)\nfile1.close()\nprint 'over'\n"
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"text": "import os\nfrom scipy.optimize import fmin_bfgs\nfrom scipy.linalg import det\nfrom numpy import *\n#datapath = r'C:\\Users\\hong\\Desktop\\master thesis\\200411'\ndatapath = r'/Users/tonny/Desktop/untitled folder/para_opti/200411'\n\ndef get_measurement(count):\n csv9=['0411_27.csv', '0411_28.csv','0411_29.csv', '0411_30.csv', '0411_31.csv', '0411_33.csv','0411_34.csv', '0411_35.csv', '0411_37.csv']\n csv7=['0411_27.csv', '0411_29.csv', '0411_30.csv', '0411_31.csv', '0411_34.csv', '0411_35.csv', '0411_37.csv']\n csv5=['0411_27.csv', '0411_29.csv', '0411_31.csv', '0411_34.csv', '0411_37.csv']\n y=[]\n for csv in csv7:\n # reverse\n file_y=open(datapath+'/'+csv,'r')\n lines=file_y.readlines()\n for i in range ((count-1)*T+3392,count*T+3392):\n linearray=lines[i].split(',')\n y.append(float(linearray[5]))\n file_y.close()\n## x=[]\n## for i in range(20):\n## x.append(0)\n## x[1]=y[8]\n## x[3]=y[7]\n## x[5]=y[6]\n## x[7]=y[5]\n## x[10]=y[4]\n## x[12]=y[3]\n## x[15]=y[2]\n## x[18]=y[1]\n## x[19]=y[0]\n## line=','.join(str(speed)for speed in x)\n## file_ysave.write(line+'\\n') ## to record all measurement in one \n ramp=[]\n csvramp=['0411_32.csv','0411_33.csv', '0411_36.csv']\n for csv in csvramp:\n # reverse\n file_r=open(datapath+'/'+csv,'r')\n lines=file_r.readlines()\n for i in range ((count-1)*T+3392,count*T+3392):\n linearray=lines[i].split(',')\n ramp.append(float(linearray[4]))\n ramp=array(ramp).reshape(3,1)\n file_r.close()\n rampin=ramp[2]\n belta=ramp[0]/ramp[1] \n return array(y).reshape(7,1)\n \n\ndef initial():\n v_mean=[]\n v_variance=[]\n file_ini=open('meanvariance.csv','r')\n lines=file_ini.readlines()\n for i in range(len(lines)):\n linearray=lines[i].split(',')\n v_mean.append(float(linearray[0]))\n v_variance.append(float(linearray[1]))\n## print v_mean\n file_ini.close()\n return array(v_mean).reshape(20,1),array(v_variance).reshape(20,1)\n\ndef get_velocity(rho,para):\n vmax =para[0]\n rhomax=para[1]\n rhoc=para[2]\n wf=rhoc*vmax/rhomax\n vc = vmax*(1-rhoc/rhomax)\n QM= vc*rhoc\n v=[]\n for i in range(cellnumber):\n rhoi=rho[i][0]\n# print rhoi,rhomax\n if rhoi<=rhoc:\n v.append(vmax*(1-rhoi/rhomax))\n else:\n v.append(-wf*(1-rhomax/rhoi))\n return array(v).reshape(20,1)\n\ndef get_rho(v,para):\n vmax =para[0]\n rhomax=para[1]\n rhoc=para[2]\n wf=rhoc*vmax/rhomax\n vc = vmax*(1-rhoc/rhomax)\n QM= vc*rhoc\n rho=[]\n for i in range(cellnumber):\n vi=v[i][0]\n #print v[i],vc\n if vi>=vc:\n rho.append(rhomax*(1-vi/vmax))\n else:\n rho.append(rhomax*(1/(1+vi/wf)))\n## print rho\n return array(rho).reshape(20,1)\n\ndef calculate(rho1,rho2,rho3,nettype,para):\n vmax =para[0]\n rhomax=para[1]\n rhoc=para[2]\n wf=rhoc*vmax/rhomax\n vc = vmax*(1-rhoc/rhomax)\n QM= vc*rhoc\n Si_minus=min(vmax*rho1,QM)\n Ri=min(QM,wf*(rhomax-rho2))\n Si=min(vmax*rho2,QM)\n Ri_plus=min(QM,wf*(rhomax-rho3))\n if nettype==0:\n flowin=min(Si_minus,Ri)\n flowout=min(Si,Ri_plus)\n if nettype==1:\n if Si_minus+rampin<=Ri:\n flowin=Si_minus+rampin\n else:\n flowin=Ri\n flowout=min(Si,Ri_plus)\n if nettype==2:\n flowin=min(Si_minus,Ri)\n flouout=min(Si,Ri_plus/(1-belta))\n return flowin-flowout\n \ndef get_flow(v,rho,para):\n vmax =para[0]\n rhomax=para[1]\n rhoc=para[2]\n wf=rhoc*vmax/rhomax\n vc = vmax*(1-rhoc/rhomax)\n QM= vc*rhoc\n v_minus=abs(random.normal(v_ini[0],v_var[0]))\n v_plus=abs(random.normal(v_ini[cellnumber-1],v_var[cellnumber-1]))\n if v_minus>=vc:\n rhoi_minus=rhomax*(1-v_minus/vmax)\n else:\n rhoi_minus=rhomax*(1/(1+v_minus/wf))\n if v_plus>=vc:\n rhoi_plus=rhomax*(1-v_plus/vmax)\n else:\n rhoi_plus=rhomax*(1/(1+v_plus/wf))\n nettype=0\n deltaflow=[]\n for i in range(cellnumber):\n if cellnumber==9:\n nettype=2 #offramp\n if cellnumber==13:\n nettype=1 #onramp\n if i!=cellnumber-1:\n rhoi_plus=rho[i+1]\n if i!=0:\n rhoi_minus=rho[i-1]\n rhoi=rho[i]\n dflow=calculate(rhoi_minus,rhoi,rhoi_plus,nettype,para)\n deltaflow.append(dflow)\n return array(deltaflow).reshape(20,1)\n\ndef get_H():\n h=[]\n file_h=open('H_7sensors.csv','r')\n lines=file_h.readlines()\n for i in range (len(lines)):\n linearray=lines[i].split(',')\n for j in range(cellnumber):\n h.append(float(linearray[j]))\n h=array(h)\n h=h.reshape(7,20)\n## print h.shape\n file_h.close()\n return h\n\ndef enkf(v_analysis,y,para):\n vmax =para[0]\n rhomax=para[1]\n rhoc=para[2]\n wf=rhoc*vmax/rhomax\n vc = vmax*(1-rhoc/rhomax)\n QM= vc*rhoc\n v_forecast=[]\n rho=[] # cellnumber\n v_forecast=array(v_analysis)\n v_analysis=array(v_analysis)\n for j in range(k):\n## print v_analysis[j]\n rho=get_rho(v_analysis[j],para)\n deltaflow=get_flow(v_analysis[j],rho,para)\n n=random.normal(0,Q)\n v_forecast[j]=get_velocity(rho-T/60/0.1*deltaflow,para)+n\n sumv_f=v_forecast[0]\n for i in range(1,k):\n sumv_f=sumv_f+v_forecast[i]\n v_mean=sumv_f/k\n errorT=matrix(v_forecast[0]-v_mean)\n error=errorT.T\n sumerror=abs(error*errorT)\n for i in range(1,k):\n error=matrix(v_forecast[i]-v_mean) #horinzon\n errorT=error.T #vertical\n sumerror=sumerror+abs(error*errorT)\n P_ens=sumerror/(k-1)\n## print 'P',P_ens\n H=get_H()\n HT=H.T\n m=matrix(H)*matrix(HT)+Rn\n G_ens=P_ens*HT*(m.I)\n## print 'G',G_ens\n likeli_ens=0\n for i in range(k):\n x=random.normal(0,R)\n v_analysis[i]=v_forecast[i]+G_ens*(y-matrix(H)*matrix(v_forecast[i])+x)\n delta=y-matrix(H)*matrix(v_forecast[i])\n Rm=matrix(Rn)\n likeli_ens=exp(-0.5*delta.T*Rm.I*delta)+likeli_ens\n sumv_a=v_analysis[0]\n for i in range(1,k):\n sumv_a=sumv_a+v_analysis[i]\n v_anamean=sumv_a/k\n v_anamean=v_anamean.reshape(1,20)\n v_mean=sumv_f/k\n result=v_mean.reshape(1,20)\n line1=','.join(str(speed)for speed in v_anamean[0])\n## line2=','.join(str(speed)for speed in result[0])\n## file_vforecast.write(line2+'\\n')\n## file_vanalysis.write(line1+'\\n')\n return -log(likeli_ens)\n\ndef get_likelihood(para):\n hood=0\n print para\n for i in range(120/T):\n ymeasure=get_measurement(i)\n hood=enkf(v_analysis,ymeasure,para)+hood\n print \"again\"\n print \"hood=:\",hood\n return hood\n\nif __name__ == \"__main__\": \n global T,rampin,rampout,belta,k,cellnumber,Q,R,Rn,v_ini,v_var\n likelihood=[]\n T=1\n rampin=0.0\n belta=0.0\n k=100\n Rn=[]\n ymeasure=[]\n v_ini=[]\n v_var=[]\n## v_estimate=[]\n v_analysis=[]\n cellnumber=20\n R=[6.4,6.4,6.4,6.4,6.4,6.4,6.4]\n Q=[3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2]\n## R=[4,4,4,4,4]\n## Q=[2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2]\n Q=array(Q).reshape(20,1)\n R=array(R).reshape(7,1)\n## file_vforecast=open('velocity_72sensors_forecast_1.csv','w')\n## file_vanalysis=open('velocity_7sensors_analysis_1.csv','w')\n## file_ysave=open('y20041103_9.30_10.00','w')\n for i in range(49):\n Rn.append(0)\n Rn=array(Rn).reshape(7,7)\n for i in range(7):\n for j in range(7):\n if i==j:\n Rn[i][j]=6.4*6.4\n print Rn.shape\n v_ini,v_var=initial()\n for i in range(k):\n p=abs(random.normal(v_ini,sqrt(v_var)))\n if p.any>=0:\n v_analysis.append(p)\n file_hood=open('hood','w') \n for vm in range(80,120):\n for rhom in range(160,200):\n for rhoc in range(30,50):\n para=[vm,rhom,rhoc]\n hood=get_likelihood(para)[0][0]\n likelihood.append(likelihood)\n file_hood.write(str(likelihood)+'\\n')\n# para0=[98,178,40]\n# para_o=fmin_bfgs(get_likelihood,para0)\n## print 'Estimater parameters: ', paraopti\n## print 'real parameters:',para\n a=array(likelihood)\n print min(a)\n print a.index(min(a))\n print 'over'\n file_hood.close()\n## file_vforecast.close\n## file_vanalysis.close\n \n \n## print v_hat\n \n"
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"text": "def func(sss):\n from matplotlib import pylab\n from numpy import *\n from matplotlib.colors import LinearSegmentedColormap\n\n cdict = {'red': ((0.0, 0.0, 0.0),\n (0.25,0.0, 0.0),\n (0.5, 0.8, 1.0),\n (0.75,1.0, 1.0),\n (1.0, 0.4, 1.0)),\n\n 'green': ((0.0, 0.0, 0.0),\n (0.25,0.0, 0.0),\n (0.5, 0.9, 0.9),\n (0.75,0.0, 0.0),\n (1.0, 0.0, 0.0)),\n\n 'blue': ((0.0, 0.0, 0.4),\n (0.25,1.0, 1.0),\n (0.5, 1.0, 0.8),\n (0.75,0.0, 0.0),\n (1.0, 0.0, 0.0))\n }\n blue_red = LinearSegmentedColormap('BlueRed', cdict)\n pylab.register_cmap(cmap=blue_red)\n\n pylab.rcParams['image.cmap'] = 'BlueRed'\n\n with open('velocity_9sensors_analysis_1.csv','r') as file1:\n lines=file1.readlines()\n z1=[]\n m1=[]\n norm=[]\n for i in range(0,120):\n linearray=lines[i].split(',')\n for j in range(19):\n for k in range(5):\n p=float(linearray[j])\n z1.append(p)\n for k in range(5):\n p=float(linearray[19].rstrip('\\n'))\n z1.append(p)\n z1=array(z1).reshape(120,100)\n m1=z1.T\n with open('velocity_'+sss+'sensors_analysis_1.csv','r') as file2:\n lines=file2.readlines()\n z2=[]\n m2=[]\n for i in range(0,120):\n linearray=lines[i].split(',')\n for j in range(19):\n for k in range(5):\n p=float(linearray[j])\n z2.append(p)\n for k in range(5):\n p=float(linearray[19].rstrip('\\n'))\n z2.append(p)\n z2=array(z2).reshape(120,100)\n m2=z2.T\n ## print z\n ## pylab.xticks(arange(20),'0','5','10','15','20')\n ## pylab.yticks(arange(120),'8.30','9.00','9.30','10.00','10.00')\n fig=pylab.figure()\n ax=fig.add_subplot(111)\n ax.set_ylabel('Cell Number')\n ax.set_xlabel('Time')\n ## pylab.xmajorLocator=fig.MultipleLocator(10)\n ## pylab.ylim(0,100)\n ax.set_title('2004.Nov.3(Wed.), 8:30-10:30')\n diff=(m2-m1)\n max=diff.max()\n min=diff.min()\n for i in diff:\n for j in i:\n if (j>0):\n print j\n j=j/max\n print j\n else:\n j=-j/min\n norm.append(j)\n norm=array(norm).reshape(100,120)\n imag=pylab.imshow(norm)\n cb=pylab.colorbar(imag,shrink=0.87,ticks=[-3,-2,-1,0,1,2,3,4])\n cb.set_ticklabels([str(round(min,2)),'0',str(round(max,2))],update_ticks=True)\n #pylab.show()\n pylab.savefig('9-'+sss+'.png',dpi=150)\n print 'over'\nif __name__=='__main__':\n print 's'\n func('7')\n func('72')\n func('73')\n func('5')\n"
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"text": "import os\nfrom numpy import *\ndatapath = r'C:\\Users\\hong\\Desktop\\master thesis\\coding and test for traffic count\\yokohane\\20041102_yokohane'\n\ndef get_measurement(count):\n tccsv=os.listdir(datapath)\n y=[]\n for i in range (127):\n y.append(0.0)\n for i in range(0,len(tccsv)):\n # reverse\n file_y=open(datapath+'\\\\'+tccsv[i],'r')\n lines=file_y.readlines()\n linearray=lines[count].split(',')\n if len(linearray)>3 and linearray[3]!='':\n y[int(tccsv[i][0:3])-1] = float(linearray[3])\n else:\n y[int(tccsv[i][0:3])-1]= 0\n file_y.close()\n line=','.join(str(speed)for speed in y)\n file_ysave.write(line+'\\n') ## to record all measurement in one\n\nif __name__ == \"__main__\":\n file_ysave=open('y_measure_1102.csv','w')\n for i in range(1,180+1):\n print i\n ymeasure=get_measurement(i)\n file_ysave.close()\n"
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"text": "from matplotlib import pylab\nfrom numpy import *\n\nwith open('velocity_9sensors_analysis_1.csv','r') as file1:\n lines=file1.readlines()\n z1=[]\n m1=[]\n for i in range(0,120):\n linearray=lines[i].split(',')\n for j in range(19):\n for k in range(5):\n p=float(linearray[j])\n z1.append(p)\n for k in range(5):\n p=float(linearray[19].rstrip('\\n'))\n z1.append(p)\n z1=array(z1).reshape(120,100)\n m1=z1.T\nwith open('velocity_73sensors_analysis_1.csv','r') as file2:\n lines=file2.readlines()\n z2=[]\n m2=[]\n for i in range(0,120):\n linearray=lines[i].split(',')\n for j in range(19):\n for k in range(5):\n p=float(linearray[j])\n z2.append(p)\n for k in range(5):\n p=float(linearray[19].rstrip('\\n'))\n z2.append(p)\n z2=array(z2).reshape(120,100)\n m2=z2.T\n## print z\n## pylab.xticks(arange(20),'0','5','10','15','20')\n## pylab.yticks(arange(120),'8.30','9.00','9.30','10.00','10.00')\n pylab.ylabel('Cell Number')\n pylab.xlabel('Time')\n## pylab.xmajorLocator=fig.MultipleLocator(10)\n## pylab.ylim(0,100)\n pylab.title('2004.Nov.3(Wen.), 8:30-10:30')\n pylab.imshow(m1-m2)\n pylab.colorbar()\n pylab.savefig('9-73.png',dpi=150)\n print 'over'\n"
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"text": "import os\nfrom numpy import *\nimport math\nfrom scipy.optimize import minimize\nfrom scipy.linalg import det\ndatapath1 = r'C:\\Users\\hong\\Desktop\\master thesis\\coding and test for traffic count\\yokohane\\20041101_yokohane'\ndatapath2 = r'C:\\Users\\hong\\Desktop\\master thesis\\coding and test for traffic count\\yokohane\\yokonane_rampin1101'\ndatapath3 = r'C:\\Users\\hong\\Desktop\\master thesis\\coding and test for traffic count\\yokohane\\yokohane_rampout1101'\ndatapath4 = r'C:\\Users\\hong\\Desktop\\master thesis\\coding and test for traffic count\\yokohane\\yokohane_base1101'\n\ndef get_measurement(count):\n tccsv=os.listdir(datapath1)\n y=[]\n for i in range(102):\n y.append(-1.0) # if no measurement, tag -1.0; no measurement will make no correction\n for i in range(0,len(tccsv)):\n # reverse\n file_y=open(datapath1+'\\\\'+tccsv[i],'r')\n lines=file_y.readlines()\n## os.system('pause')\n## print lines[180],i\n linearray=lines[count].split(',')\n if linearray[3]!='' and float(linearray[3])!=0.0:\n y[i]=float(linearray[3])\n## y[i]=y[i-1]\n## else:\n## y[i]=75\n file_y.close()\n## x=[]\n## for i in range(127):\n## x.append(0)\n## x[1]=y[8]\n## x[3]=y[7]\n## x[5]=y[6]\n## x[7]=y[5]\n## x[10]=y[4]\n## x[12]=y[3]\n## x[15]=y[2]\n## x[18]=y[1]\n## x[19]=y[0]\n## line=','.join(str(speed)for speed in x)\n## file_ysave.write(line+'\\n') ## to record all measurement in one \n rampincsv=os.listdir(datapath2)\n ramp_in=[]\n ramp_out=[]\n base=[]\n lenrmpin=len(rampincsv)\n for csv in rampincsv:\n # reverse\n file_r=open(datapath2+'\\\\'+csv,'r')\n lines=file_r.readlines()\n## print '1',csv\n## print lines[180],i,'ramoin'\n linearray=lines[count].split(',')\n if linearray[1]!='':\n ramp_in.append(float(linearray[1]))\n else:\n ramp_in.append(0.0)\n ramp_in.append(int(csv[0:3])-1)\n rampin=array(ramp_in).reshape(lenrmpin,2).T\n file_r.close()\n rampoutcsv=os.listdir(datapath3)\n lenrmpout=len(rampoutcsv)\n for csv in rampoutcsv:\n file_r=open(datapath3+'\\\\'+csv,'r')\n lines=file_r.readlines()\n## print '2',csv\n## print lines[180],i,'rampout'\n linearray=lines[count].split(',')\n if linearray[1]!='':\n ramp_out.append(float(linearray[1]))\n else:\n ramp_out.append(-1.0)\n ramp_out.append(int(csv[0:3])-1)\n file_r.close\n rampout=array(ramp_out).reshape(lenrmpout,2).T\n## os.system('pause')\n baseadd=os.listdir(datapath4)\n for i in range(lenrmpout):\n file_r=open(datapath4+'\\\\'+baseadd[i],'r')\n lines=file_r.readlines()\n## print '3',baseadd[i]\n## print lines[180],i,'base'\n linearray=lines[count].split(',')\n if linearray[1]!='':\n base.append(float(linearray[1]))\n else:\n base.append(-1.0)\n file_r.close\n basearray=array(base).reshape(1,lenrmpout)\n belta=[rampout[0]/basearray[0],rampout[1]]\n beta=array(belta).reshape(2,lenrmpout)\n for j in range(lenrmpout):\n if beta[0][j]>=1:\n beta[0][j]=0.99\n if basearray[0][j]==-1.0:\n beta[0][j]=-1.0\n if rampout[0][j]==-1.0:\n beta[0][j]=-1.0\n## print 'beta', beta\n return array(y).reshape(102,1),rampin,beta\n \n\ndef initial():\n v_mean=[]\n v_variance=[]\n file_ini=open('meanvariance.csv','r')\n lines=file_ini.readlines()\n for i in range(len(lines)):\n linearray=lines[i].split(',')\n v_mean.append(float(linearray[0]))\n v_variance.append(float(linearray[1]))\n file_ini.close()\n return array(v_mean).reshape(127,1),array(v_variance).reshape(127,1)\n\ndef get_velocity(rho,para):\n v=[]\n vmax =para[0]\n rhomax=para[1]\n rhoc=para[2]\n wf=rhoc*vmax/rhomax\n vc = vmax*(1-rhoc/rhomax)\n QM= vc*rhoc\n for i in range(cellnumber):\n v1=vmax*(1-rho[i][0]/rhomax)\n v2=-wf*(1-rhomax/rho[i][0])\n if rho[i][0]<=rhoc:\n v.append(v1)\n if rho[i][0]>rhoc:\n v.append(v2)\n v=array(v).reshape(127,1)\n for i in range(cellnumber):\n if v[i][0]<0 or v[i][0]>vmax:\n## print 'v',v[i][0],rho[i][0],rho[i-1][0],rho[i+1][0]\n if v[i][0]<0:\n v[i][0]=0.0\n else:\n v[i][0]=vmax\n## os.system('pause')\n## print rho[i][0],i\n return v\n\ndef get_rho(v,para):\n rho=[]\n vmax =para[0]\n rhomax=para[1]\n rhoc=para[2]\n wf=rhoc*vmax/rhomax\n vc = vmax*(1-rhoc/rhomax)\n QM= vc*rhoc\n for i in range(cellnumber):\n rho1=rhomax*(1-v[i][0]/vmax)\n rho2=rhomax*(1/(1+v[i][0]/wf))\n if v[i][0]>=vc:\n m=rho1\n else:\n if v[i][0]<vc:\n m=rho2\n rho.append(m)\n rho=array(rho).reshape(127,1)\n for i in range(cellnumber):\n if rho[i][0]<0 or rho[i][0]>rhomax:\n## print 'rho',rho[i][0],v[i][0],t,i\n if rho[i][0]<0:\n rho[i][0]=1\n else:\n rho[i][0]=rhomax\n## os.system('pause')\n## else:\n## print 'mis',v[i][0],t,i\n## os.system('pause')\n\n return rho\n\ndef calculate(rho1,rho2,rho3,nettype,cell_no,rampin,beta,para):\n vmax =para[0]\n rhomax=para[1]\n rhoc=para[2]\n wf=rhoc*vmax/rhomax\n vc = vmax*(1-rhoc/rhomax)\n QM= vc*rhoc\n rampin=array(rampin).reshape(2,17)\n beta=array(beta).reshape(2,13)\n Si_minus=min(vmax*rho1,QM)\n Ri=min(QM,wf*(rhomax-rho2))\n Si=min(vmax*rho2,QM)\n Ri_plus=min(QM,wf*(rhomax-rho3))\n vmax =para[0]\n rhomax=para[1]\n rhoc=para[2]\n wf=rhoc*vmax/rhomax\n vc = vmax*(1-rhoc/rhomax)\n QM= vc*rhoc\n## if Si_minus<0 or Si<0 or Ri<0 or Ri_plus<0:\n## print 'flow ability',Si_minus, Si, Ri, Ri_plus\n beta_cell=-2.0\n rampin_cell=-2.0\n if nettype==0:\n flowin=min(Si_minus,Ri)\n flowout=min(Si,Ri_plus)\n if nettype==1:\n for i in range (17):\n if rampin[1][i]==cell_no:\n rampin_cell=rampin[0][i]\n if Si_minus+rampin_cell<=Ri:\n flowin=Si_minus+rampin_cell\n else:\n flowin=Ri\n flowout=min(Si,Ri_plus)\n if nettype==2:\n for i in range (13):\n betacount=0\n if beta[0][i]==-1.0: # change beta where no sensor data for beta\n betacount=betacount+1\n for i in range (13):\n if beta[0][i]==-1.0:\n beta[0][i]=(sum(beta[0])+betacount)/(13-betacount)\n if beta[1][i]==cell_no:\n beta_cell=beta[0][i]\n flowin=min(Si_minus,Ri)\n flowout=min(Si,Ri_plus/(1-beta_cell))\n deltaflow=flowin-flowout\n## if abs(deltaflow)>1000:\n## print 'd',deltaflow,nettype,flowin, flowout,cell_no\n## print Si_minus, Si, Ri, Ri_plus\n## os.system('pause')\n return deltaflow\n\n\ndef get_flow(v,rho,rampin,beta,para,v_ini):\n vmax =para[0]\n rhomax=para[1]\n rhoc=para[2]\n wf=rhoc*vmax/rhomax\n vc = vmax*(1-rhoc/rhomax)\n QM= vc*rhoc\n v_minus=(random.normal(v_ini[0][0],Q))\n v_plus=(random.normal(v_ini[cellnumber-1][0],Q))\n deltaflow=[]\n vmax =para[0]\n rhomax=para[1]\n rhoc=para[2]\n wf=rhoc*vmax/rhomax\n vc = vmax*(1-rhoc/rhomax)\n QM= vc*rhoc\n for i in range(cellnumber):\n nettype=0\n outno=[9,12,14,19,27,34,47,69,75,86,89,99,118]\n inno=[2,10,16,22,30,37,44,56,71,76,80,84,93,94,96,106,121]\n if v_minus>=vc:\n rhoi_minus=rhomax*(1-v_minus/vmax)\n else:\n rhoi_minus=rhomax*(1/(1+v_minus/wf))\n if v_plus>=vc:\n rhoi_plus=rhomax*(1-v_plus/vmax)\n else:\n rhoi_plus=rhomax*(1/(1+v_plus/wf))\n if i+1 in outno:\n nettype=2 #offramp\n if i+1 in inno:\n nettype=1 #onramp\n if i!=cellnumber-1:\n rhoi_plus=rho[i+1][0]\n if i!=0:\n rhoi_minus=rho[i-1][0]\n rhoi=rho[i][0]\n dflow=calculate(rhoi_minus,rhoi,rhoi_plus,nettype,i,rampin,beta,para)\n deltaflow.append(dflow)\n return array(deltaflow).reshape(127,1)\n\ndef get_H():\n h=[]\n file_h=open('H2.csv','r')\n lines=file_h.readlines()\n for i in range (len(lines)):\n linearray=lines[i].split(',')\n for j in range(cellnumber):\n h.append(float(linearray[j]))\n h=array(h)\n h=h.reshape(102,127)\n file_h.close()\n return h\n\ndef enkf(v_ana,y,rampin,beta,para0,v_ini):\n para0=array(para0).reshape(3,1)\n vmax = para0[0]\n rhomax= para0[1]\n rhoc= para0[2]\n para=[vmax,rhomax,rhoc]\n wf=rhoc*vmax/rhomax\n vc = vmax*(1-rhoc/rhomax)\n QM= vc*rhoc\n v_forecast=[]\n v_analysis=[]\n y_revise=y.reshape(102,1)\n rho=[] # cellnumberv\n v_forecast=array(v_ana).reshape(k,cellnumber,1)\n v_analysis=array(v_ana).reshape(k,cellnumber,1)\n for j in range(k):\n rho=get_rho(v_analysis[j],para)\n## print v_analysis[j].T\n deltaflow=get_flow(v_analysis[j],rho,rampin,beta,para,v_ini)\n n=random.normal(0,Q) \n deltarho=T/60.0/0.25*deltaflow\n newrho= rho+T/60.0/0.25*deltaflow\n for n in range(cellnumber-1):\n if newrho[n][0]<0 or newrho[n][0]>rhomax:\n## print 'newrho',newrho[n][0], rho[n][0],deltarho[n][0],deltaflow[n][0]\n## print 'newrho',n\n newrho[n][0]=(newrho[n+1][0]+newrho[n-1][0])*0.5\n if newrho[cellnumber-1][0]<0 or newrho[cellnumber-1][0]>rhomax:\n newrho[cellnumber-1][0]=newrho[n-1][0]+random.normal(0,2)\n## print newrho[n][0]\n## os.system('pause')\n## print 'new',newrho.T,newrho.shape\n v_forecast[j]=get_velocity(newrho,para)\n sumv_f=v_forecast[0]\n for i in range(1,k):\n sumv_f=sumv_f+v_forecast[i]\n v_mean=sumv_f/k\n error=matrix(v_forecast[0]-v_mean)\n errorT=error.T\n sumerror=(error*errorT)\n for i in range(1,k):\n error=matrix(v_forecast[i]-v_mean) #horinzon]\n errorT=error.T #vertical\n sumerror=sumerror+(error*errorT)\n P_ens=sumerror/(k-1)\n## print 'P',P_ens\n H=get_H()\n HT=H.T\n m=matrix(H)*P_ens*matrix(HT)+Rn\n G_ens=(P_ens*HT*(m.I)).reshape(127,102)\n for i in range(k):\n x=random.normal(0,R)\n## print 'x',x\n HX=(matrix(H)*matrix(v_forecast[i])).reshape(102,1)\n for j in range(102):\n y_revise[j][0]=y[j][0]\n if y[j][0]==-1:\n y_revise[j][0]=HX[j][0]\n## print 'no y',y_revise[j][0],j\n## os.system('pause')\n v_analysis[i]=v_forecast[i]+G_ens*(y_revise-HX+random.normal(0,R))\n for n in range(cellnumber):\n## if v_analysis[i][n][0]>vmax or v_analysis[i][n][0]<0:\n## print 'cellnumber=',n,'v1=',v_forecast[i][n][0],'v2=',v_analysis[i][n][0],matrix(G_ens*(y_revise-HX))[n][0]\n## print y_revise, HX\n## os.system('pause')\n if n ==cellnumber-1:\n v_analysis[i][n][0]=v_analysis[i][n-1][0]*0.5\n else:\n v_analysis[i][n][0]=v_analysis[i][n-1][0]*0.5+v_analysis[i][n+1][0]*0.5\n## print 'newv',v_analysis[i][n][0],n,t\n sumv_a=v_analysis[0]\n likeli_ens=0\n for i in range(k):\n delta=y_revise-HX\n if abs(delta[0])>100:\n continue\n print delta.T*Rn.I*delta,i\n likeli_ens=exp(-0.5*delta.T*Rn.I*delta)+likeli_ens\n for i in range(1,k):\n sumv_a=sumv_a+v_analysis[i]\n v_anamean=sumv_a/k\n result1=v_anamean.reshape(1,127)\n result2=v_mean.reshape(1,127)\n line1=','.join(str(speed)for speed in result1[0])\n line2=','.join(str(speed)for speed in result2[0])\n## file_vforecast.write(line2+'\\n')\n## file_vanalysis.write(line1+'\\n')\n ## print 'v shape', v_analysis.shape\n print -log(likeli_ens)\n return -log(likeli_ens)\n\n\ndef get_likelihood(para):\n print para\n hood=0\n ymeasure,rampin,beta=get_measurement(0)\n H=get_H()\n for t in range(0,180/T+1):\n v_a=[]\n## print t\n ymeasure,rampin,beta=get_measurement(t)\n v_ini=array((matrix(H)).I*ymeasure).reshape(cellnumber,1) # y is start from before estimation and to make the initial velocity\n for i in range(0,cellnumber-1):\n if i in [72,124]:\n v_ini[i][0]=(v_ini[i-2][0]+v_ini[i-1][0])/2.0\n if v_ini[i][0]==0.0 or v_ini[i][0]<0: # no sensor, make the average\n v_ini[i][0]=(v_ini[i-1][0]/2.0+v_ini[i+1][0]/2.0)\n if v_ini[cellnumber-1][0]<0:\n v_ini[cellnumber-1][0]=v_ini[cellnumber-1-1][0]\n for j in range(0,k):\n p=(random.normal(v_ini,Q))\n v_a.append(p)\n v_a=array(v_a).reshape(k,cellnumber,1)\n ymeasure,rampin,beta=get_measurement(t+1)\n hood=enkf(v_a,ymeasure,rampin,beta,para,v_ini)+hood\n print \"hood=:\",hood\n return hood\n\nif __name__ == \"__main__\": \n global t,T,vmax,rhomax,rhoc,wf,vc,QM,rampin,beta,k,cellnumber,Q,R,Rn,v_ini,v_a\n rampin=[]\n beta=[]\n T=1\n k=10\n Rn=[]\n ymeasure=[]\n v_ini=[]\n a=[]\n cellnumber=127\n Q=3.2\n R=6.4\n file_vforecast=open('velocity_f_para.csv','w')\n file_vanalysis=open('velocity_a_para.csv','w')\n for i in range(102*102):\n Rn.append(0)\n Rn=array(Rn).reshape(102,102)\n for i in range(102):\n for j in range(102):\n if i==j:\n Rn[i][j]=R*R\n Rn=matrix(Rn)\n para0=[120.0,180.0,40.0]\n para_esti=minimize(get_likelihood,para0,method='Powell')\n print para_esti\n print 'over'\n## file_ysave.close()\n file_vforecast.close\n file_vanalysis.close\n print 'over'\n \n## print v_hat\n \n"
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"text": "1. the name for the measurement is: cellnumber_sensor id\n2. sensors on the ramps include ramp_in; ramp_out and base. ramp_out and base should be considered together to calculate the ramp_out ratio"
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"text": "import os\nfrom numpy import *\nimport math\ndatapath1 = r'C:\\Users\\hong\\Desktop\\master thesis\\coding and test for traffic count\\yokohane\\20041101_wangan'\ndatapath2 = r'C:\\Users\\hong\\Desktop\\master thesis\\coding and test for traffic count\\yokohane\\wangan_rampin1101'\ndatapath3 = r'C:\\Users\\hong\\Desktop\\master thesis\\coding and test for traffic count\\yokohane\\wangan_rampout1101'\ndatapath4 = r'C:\\Users\\hong\\Desktop\\master thesis\\coding and test for traffic count\\yokohane\\wangan_base1101'\n\ndef get_measurement(count): # get measurement according to time step, measurement at the main road, and ramps\n tccsv=os.listdir(datapath1)\n y=[]\n for i in range(108):\n y.append(-1.0) # if no measurement, tag -1.0; no measurement will make no correction\n for i in range(0,len(tccsv)):\n # reverse\n file_y=open(datapath1+'\\\\'+tccsv[i],'r')\n lines=file_y.readlines()\n linearray=lines[count].split(',')\n if linearray[3]!='' and float(linearray[3])!=0.0:\n y[i]=float(linearray[3])\n file_y.close()\n rampincsv=os.listdir(datapath2)\n ramp_in=[]\n ramp_out=[]\n base=[]\n lenrmpin=len(rampincsv)\n for csv in rampincsv:\n # reverse\n file_r=open(datapath2+'\\\\'+csv,'r')\n lines=file_r.readlines()\n\n linearray=lines[count].split(',')\n if linearray[1]!='':\n ramp_in.append(float(linearray[1]))\n else:\n ramp_in.append(0.0)\n ramp_in.append(int(csv[0:3])-1)\n rampin=array(ramp_in).reshape(lenrmpin,2).T\n file_r.close()\n rampoutcsv=os.listdir(datapath3)\n lenrmpout=len(rampoutcsv)\n for csv in rampoutcsv:\n file_r=open(datapath3+'\\\\'+csv,'r')\n lines=file_r.readlines()\n## print '2',csv\n## print lines[180],i,'rampout'\n linearray=lines[count].split(',')\n if linearray[1]!='':\n ramp_out.append(float(linearray[1]))\n else:\n ramp_out.append(-1.0)\n ramp_out.append(int(csv[0:3])-1)\n file_r.close\n rampout=array(ramp_out).reshape(lenrmpout,2).T\n## os.system('pause')\n baseadd=os.listdir(datapath4)\n for i in range(lenrmpout):\n file_r=open(datapath4+'\\\\'+baseadd[i],'r')\n lines=file_r.readlines()\n## print '3',baseadd[i]\n## print lines[180],i,'base'\n linearray=lines[count].split(',')\n if linearray[1]!='':\n base.append(float(linearray[1]))\n else:\n base.append(-1.0)\n file_r.close\n basearray=array(base).reshape(1,lenrmpout)\n belta=[rampout[0]/basearray[0],rampout[1]]\n beta=array(belta).reshape(2,lenrmpout)\n for j in range(lenrmpout):\n if beta[0][j]>=1:\n beta[0][j]=0.99\n if basearray[0][j]==-1.0:\n beta[0][j]=-1.0\n if rampout[0][j]==-1.0:\n beta[0][j]=-1.0\n## print 'beta', beta\n return array(y).reshape(108,1),rampin,beta\n \n\ndef initial(): # initial with the velocity_mean and velocity_variance\n v_mean=[]\n v_variance=[]\n file_ini=open('meanvariance.csv','r')\n lines=file_ini.readlines()\n for i in range(len(lines)):\n linearray=lines[i].split(',')\n v_mean.append(float(linearray[0]))\n v_variance.append(float(linearray[1]))\n file_ini.close()\n return array(v_mean).reshape(145,1),array(v_variance).reshape(145,1)\n\ndef get_velocity(rho): # calculate the estimated velocity according to the estimated density\n v=[]\n for i in range(cellnumber):\n v1=vmax*(1-rho[i][0]/rhomax)\n v2=-wf*(1-rhomax/rho[i][0])\n if rho[i][0]<=rhoc:\n v.append(v1)\n if rho[i][0]>rhoc:\n v.append(v2)\n v=array(v).reshape(145,1)\n for i in range(cellnumber):\n if v[i][0]<0 or v[i][0]>vmax:\n## print 'v',v[i][0],rho[i][0],rho[i-1][0],rho[i+1][0]\n if v[i][0]<0:\n v[i][0]=0.0\n else:\n v[i][0]=vmax\n## os.system('pause')\n## print rho[i][0],i\n return v\n\ndef get_rho(v): # calculate the estimated density\n rho=[]\n for i in range(cellnumber):\n rho1=rhomax*(1-v[i][0]/vmax)\n rho2=rhomax*(1/(1+v[i][0]/wf))\n if v[i][0]>=vc:\n m=rho1\n else:\n if v[i][0]<vc:\n m=rho2\n rho.append(m)\n rho=array(rho).reshape(145,1)\n for i in range(cellnumber):\n if rho[i][0]<0 or rho[i][0]>rhomax:\n## print 'rho',rho[i][0],v[i][0],t,i\n if rho[i][0]<0:\n rho[i][0]=1\n else:\n rho[i][0]=rhomax\n## os.system('pause')\n## else:\n## print 'mis',v[i][0],t,i\n## os.system('pause')\n\n return rho\n\ndef calculate(rho1,rho2,rho3,nettype,cell_no,rampin,beta): # calculate the delta flow for each cell\n rampin=array(rampin).reshape(2,9)\n beta=array(beta).reshape(2,9)\n Si_minus=min(vmax*rho1,QM)\n Ri=min(QM,wf*(rhomax-rho2))\n Si=min(vmax*rho2,QM)\n Ri_plus=min(QM,wf*(rhomax-rho3))\n## if Si_minus<0 or Si<0 or Ri<0 or Ri_plus<0:\n## print 'flow ability',Si_minus, Si, Ri, Ri_plus\n beta_cell=-2.0\n rampin_cell=-2.0\n if nettype==0:\n flowin=min(Si_minus,Ri)\n flowout=min(Si,Ri_plus)\n if nettype==1:\n for i in range (9):\n if rampin[1][i]==cell_no:\n rampin_cell=rampin[0][i]\n if Si_minus+rampin_cell<=Ri:\n flowin=Si_minus+rampin_cell\n else:\n flowin=Ri\n flowout=min(Si,Ri_plus)\n if nettype==2:\n for i in range (9):\n betacount=0\n if beta[0][i]==-1.0: # change beta where no sensor data for beta\n betacount=betacount+1\n for i in range (9):\n if beta[0][i]==-1.0:\n beta[0][i]=(sum(beta[0])+betacount)/(9-betacount)\n if beta[1][i]==cell_no:\n beta_cell=beta[0][i]\n flowin=min(Si_minus,Ri)\n flowout=min(Si,Ri_plus/(1-beta_cell))\n deltaflow=flowin-flowout\n## if abs(deltaflow)>1000:\n## print 'd',deltaflow,nettype,flowin, flowout,cell_no\n## print Si_minus, Si, Ri, Ri_plus\n## os.system('pause')\n return deltaflow\n\n\ndef get_flow(v,rho,rampin,beta): # calculate cell type, original, ramp_in or ramp_out and then calculate the deltaflow with 'calculate'\n v_minus=(random.normal(v_ini[0][0],Q))\n v_plus=(random.normal(v_ini[cellnumber-1][0],Q))\n deltaflow=[]\n for i in range(cellnumber):\n nettype=0\n outno=[2,3,48,66,78,85,107,124,142]\n inno=[7,58,69,89,104,111,117,130,143]\n if v_minus>=vc:\n rhoi_minus=rhomax*(1-v_minus/vmax)\n else:\n rhoi_minus=rhomax*(1/(1+v_minus/wf))\n if v_plus>=vc:\n rhoi_plus=rhomax*(1-v_plus/vmax)\n else:\n rhoi_plus=rhomax*(1/(1+v_plus/wf))\n if i+1 in outno:\n nettype=2 #offramp\n if i+1 in inno:\n nettype=1 #onramp\n if i!=cellnumber-1:\n rhoi_plus=rho[i+1][0]\n if i!=0:\n rhoi_minus=rho[i-1][0]\n rhoi=rho[i][0]\n dflow=calculate(rhoi_minus,rhoi,rhoi_plus,nettype,i,rampin,beta)\n deltaflow.append(dflow)\n return array(deltaflow).reshape(145,1)\n\ndef get_H(): # get the H matrix\n h=[]\n file_h=open('H3.csv','r')\n lines=file_h.readlines()\n for i in range (len(lines)):\n linearray=lines[i].split(',')\n for j in range(cellnumber):\n h.append(float(linearray[j]))\n h=array(h)\n h=h.reshape(108,145)\n file_h.close()\n return h\n\ndef enkf(v_ana,y,rampin,beta):\n v_forecast=[]\n v_analysis=[]\n y_revise=y.reshape(108,1)\n rho=[] # cellnumberv\n v_forecast=array(v_ana).reshape(k,cellnumber,1)\n v_analysis=array(v_ana).reshape(k,cellnumber,1)\n for j in range(k): # calculate the estimated density for next time step according to the current density and delta flow\n rho=get_rho(v_analysis[j])\n deltaflow=get_flow(v_analysis[j],rho,rampin,beta)\n n=random.normal(0,Q) \n deltarho=T/60.0/0.25*deltaflow\n newrho= rho+T/60.0/0.25*deltaflow\n for n in range(cellnumber-1):\n if newrho[n][0]<0 or newrho[n][0]>rhomax:\n newrho[n][0]=(newrho[n+1][0]+newrho[n-1][0])*0.5\n if newrho[cellnumber-1][0]<0 or newrho[cellnumber-1][0]>rhomax:\n newrho[cellnumber-1][0]=newrho[n-1][0]+random.normal(0,2)\n v_forecast[j]=get_velocity(newrho)\n sumv_f=v_forecast[0]\n for i in range(1,k):\n sumv_f=sumv_f+v_forecast[i]\n v_mean=sumv_f/k\n error=matrix(v_forecast[0]-v_mean)\n errorT=error.T\n sumerror=(error*errorT)\n for i in range(1,k):\n error=matrix(v_forecast[i]-v_mean) #horinzon]\n errorT=error.T #vertical\n sumerror=sumerror+(error*errorT)\n P_ens=sumerror/(k-1)\n## print 'P',P_ens\n H=get_H()\n HT=H.T\n m=matrix(H)*P_ens*matrix(HT)+Rn\n G_ens=(P_ens*HT*(m.I)).reshape(145,108)\n for i in range(k):\n x=random.normal(0,R)\n## print 'x',x\n HX=(matrix(H)*matrix(v_forecast[i])).reshape(108,1)\n for j in range(108):\n y_revise[j][0]=y[j][0]\n if y[j][0]==-1:\n y_revise[j][0]=HX[j][0]\n if y[j][0]>vmax:\n y_revise[j][0]=vmax\n print 'no y',y_revise[j][0],j\n## os.system('pause')\n v_analysis[i]=v_forecast[i]+G_ens*(y_revise-HX+random.normal(0,R)) # correct the estimation with the forecast and the kalman gain \n for n in range(cellnumber):\n if n ==cellnumber-1:\n v_analysis[i][n][0]=v_analysis[i][n-1][0]*0.5\n else:\n v_analysis[i][n][0]=v_analysis[i][n-1][0]*0.5+v_analysis[i][n+1][0]*0.5\n sumv_a=v_analysis[0]\n for i in range(1,k):\n sumv_a=sumv_a+v_analysis[i]\n v_anamean=sumv_a/k\n result1=v_anamean.reshape(1,145)\n result2=v_mean.reshape(1,145)\n line1=','.join(str(speed)for speed in result1[0])\n line2=','.join(str(speed)for speed in result2[0])\n file_vforecast.write(line2+'\\n')\n file_vanalysis.write(line1+'\\n')\n print 'v shape', v_analysis.shape\n return 1\n\nif __name__ == \"__main__\": \n global t,T,vmax,rhomax,rhoc,wf,vc,QM,rampin,beta,k,cellnumber,Q,R,Rn,v_ini,v_a\n rampin=[]\n beta=[]\n vmax =140.0\n rhomax=190.0\n rhoc=40.0\n wf=rhoc*vmax/rhomax\n vc = vmax*(1-rhoc/rhomax)\n QM= vc*rhoc\n T=1\n k=1000\n Rn=[]\n ymeasure=[]\n v_ini=[]\n a=[]\n cellnumber=145\n Q=2\n R=4\n file_vforecast=open('velocity_f_w.csv','w')\n file_vanalysis=open('velocity_a_w1101t.csv','w')\n for i in range(108*108):\n Rn.append(0)\n Rn=array(Rn).reshape(108,108)\n for i in range(108):\n for j in range(108):\n if i==j:\n Rn[i][j]=R*R\n Rn=matrix(Rn)\n ymeasure,rampin,beta=get_measurement(0)\n H=get_H()\n for t in range(0,180/T):\n v_a=[]\n print t\n ymeasure,rampin,beta=get_measurement(t)\n for yi in range(108-5):\n if ymeasure[yi]<0:\n ymeasure[yi]=ymeasure[yi-4]/2.0+ymeasure[yi+4]/2.0\n v_ini=array((matrix(H)).I*ymeasure).reshape(cellnumber,1) # y is start from before estimation and to make the initial velocity\n for i in range(0,cellnumber-1):\n if v_ini[i][0]==0.0 or v_ini[i][0]<0: # no sensor, make the average\n v_ini[i][0]=(v_ini[i-1][0]/2.0+v_ini[i+1][0]/2.0)\n if v_ini[cellnumber-1][0]<0:\n v_ini[cellnumber-1][0]=v_ini[cellnumber-1-1][0]\n for j in range(0,k):\n p=(random.normal(v_ini,Q))\n v_a.append(p)\n v_a=array(v_a).reshape(k,cellnumber,1)\n ymeasure,rampin,beta=get_measurement(t+1)\n a=enkf(v_a,ymeasure,rampin,beta)\n \n## file_ysave.close()\n file_vforecast.close\n file_vanalysis.close\n print 'over'\n \n## print v_hat\n \n"
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"text": "import os\nfrom numpy import *\ndatapath = r'C:\\Users\\hong\\Desktop\\master thesis\\coding and test for traffic count\\yokohane\\04-11-03'\n\ndef tcdata(manageid,numberid):\n tccsv=os.listdir(datapath)\n dataitem=['','','','','']\n file_redata=open(numberid+'_'+manageid+'.csv','w')\n for csv in tccsv:\n file_data=open(datapath+'\\\\'+csv,'r')\n lines=file_data.readlines()\n linearray=lines[10].split(',')\n for j in range(1,len(linearray)):\n s=linearray[j].decode('s-jis')\n managementid=s[0:2]+'-'+s[3:5]+'-'+s[6:9] # format the sensor id and compare with the sensor list\n if managementid==(manageid):\n for k in range(461,643):\n lineitem=lines[k].split(',') \n dataitem=[lineitem[0],lineitem[j],lineitem[j+2],lineitem[j+4],lineitem[j+6]]\n datajoin=','.join(item for item in dataitem)\n file_redata.write(datajoin+'\\n')\n file_redata.close()\n return\n else:\n j=j+8\n file_data.close()\n return \n\nif __name__ == \"__main__\":\n manageid=[]\n numberid=[]\n file_sensor=open('1.csv','r')\n lines=file_sensor.readlines()\n for i in range (0,len(lines)):\n linearray=lines[i].split(',')\n manageid.append(linearray[1]) # sensor id\n numberid.append(linearray[9]) # cell number\n file_sensor.close()\n for i in range(0,len(lines)):\n tcdata(manageid[i],numberid[i])\n print 'v'\n \n"
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"text": "import os\nfrom numpy import *\ndatapath = r'C:\\Users\\hong\\Desktop\\master thesis\\200411'\n\ndef get_measurement(count): # get measurement\n csv9=['0411_27.csv', '0411_28.csv','0411_29.csv', '0411_30.csv', '0411_31.csv', '0411_33.csv', '0411_34.csv','0411_35.csv', '0411_37.csv']\n csv7=['0411_27.csv', '0411_29.csv', '0411_30.csv', '0411_31.csv', '0411_34.csv', '0411_35.csv', '0411_37.csv']\n csv5=['0411_27.csv', '0411_29.csv', '0411_31.csv', '0411_34.csv', '0411_37.csv']\n y=[]\n for csv in csv9:\n # reverse\n file_y=open(datapath+'\\\\'+csv,'r')\n lines=file_y.readlines()\n for i in range ((count-1)*T+3392,count*T+3392):\n linearray=lines[i].split(',')\n y.append(float(linearray[5]))\n file_y.close()\n return array(y).reshape(9,1)\n\ndef initial(): # intialize the EnKF with the v_mean and v_variance\n v_mean=[]\n v_variance=[]\n file_ini=open('meanvariance.csv','r')\n lines=file_ini.readlines()\n for i in range(len(lines)):\n linearray=lines[i].split(',')\n v_mean.append(float(linearray[0]))\n v_variance.append(float(linearray[1]))\n## print v_mean\n file_ini.close()\n return array(v_mean).reshape(20,1),array(v_variance).reshape(20,1)\n\ndef get_velocity(rho): # get the forecast velocity\n v=[]\n for i in range(cellnumber):\n if rho[i]<=rhoc:\n v.append(vmax*(1-rho[i]/rhomax))\n else:\n v.append(-wf*(1-rhomax/rho[i]))\n return array(v).reshape(20,1)\n\ndef get_rho(v): # get estimate density\n rho=[]\n for i in range(cellnumber):\n #print v[i],vc\n if v[i]>=vc:\n rho.append(rhomax*(1-v[i]/vmax))\n else:\n rho.append(rhomax*(1/(1+v[i]/wf)))\n## print rho\n return array(rho).reshape(20,1)\n\ndef calculate(v1,v2): # calculate the flow-in and flow-out with Daniel Work's function\n if v1>=v2:\n if vc>=v2:\n flow=v2*rhomax*(1/(1+v2/wf))\n else:\n if vc>=v1:\n v_temp=vc\n else:\n v_temp=v1\n flow=v_temp*rhomax*(1-v_temp/vmax)\n else:\n if v1>=vc:\n rho1=rhomax*(1-v1/vmax)\n else:\n rho1=rhomax*(1/(1+v1/wf))\n if v2>=vc:\n rho2=rhomax*(1-v2/vmax)\n else:\n rho2=rhomax*(1/(1+v2/wf))\n flow=min(rho1*v1,rho2*v2)\n return flow\n \n\ndef get_flow(v): # calculate delta flow with two steps of flow-in and flow-out\n v_minus=abs(random.normal(v_ini[0],v_var[0]))\n v_plus=abs(random.normal(v_ini[cellnumber-1],v_var[cellnumber-1]))\n deltaflow=[]\n for i in range(cellnumber):\n if i==cellnumber-1:\n vj=v_plus #vj to present v[i+1]\n else:\n vj=v[i+1]\n if i==0:\n vk=v_minus #vk to present v[i-1]\n else:\n vk=v[i-1]\n v1=v[i]\n v2=vj\n v3=vk\n flow1=calculate(v1,v2) #get flow-in\n flow2=calculate(v3,v1) #get flow-out\n deltaflow.append(flow1-flow2) \n return array(deltaflow).reshape(20,1)\n\ndef get_H():\n h=[]\n file_h=open('H_9sensors.csv','r')\n lines=file_h.readlines()\n for i in range (len(lines)):\n linearray=lines[i].split(',')\n for j in range(cellnumber):\n h.append(float(linearray[j]))\n h=array(h)\n h=h.reshape(9,20)\n## print h.shape\n file_h.close()\n return h\n\ndef enkf(v_analysis,y):\n v_forecast=[]\n rho=[] \n v_forecast=array(v_analysis)\n v_analysis=array(v_analysis)\n for j in range(k):\n## print v_analysis[j]\n rho=get_rho(v_analysis[j])\n deltaflow=get_flow(v_analysis[j])\n n=random.normal(0,Q)\n v_forecast[j]=get_velocity(rho-T/60/0.1*deltaflow)+n\n sumv_f=v_forecast[0]\n for i in range(1,k):\n sumv_f=sumv_f+v_forecast[i]\n v_mean=sumv_f/k\n errorT=matrix(v_forecast[0]-v_mean)\n error=errorT.T\n sumerror=abs(error*errorT)\n for i in range(1,k):\n error=matrix(v_forecast[i]-v_mean) #horinzon\n errorT=error.T #vertical\n sumerror=sumerror+abs(error*errorT)\n P_ens=sumerror/(k-1) # get the covariance matrix\n## print 'P',P_ens\n H=get_H()\n HT=H.T\n m=matrix(H)*matrix(HT)+Rn\n G_ens=P_ens*HT*(m.I) # get the kalman gain\n## print 'G',G_ens\n for i in range(k):\n x=random.normal(0,R)\n v_analysis[i]=v_forecast[i]+G_ens*(y-matrix(H)*matrix(v_forecast[i])+x) # correct the forecast velocity with the forecast and kalman gain\n sumv_a=v_analysis[0]\n for i in range(1,k):\n sumv_a=sumv_a+v_analysis[i]\n v_anamean=sumv_a/k\n v_anamean=v_anamean.reshape(1,20)\n v_mean=sumv_f/k\n result=v_mean.reshape(1,20)\n line1=','.join(str(speed)for speed in v_anamean[0])\n line2=','.join(str(speed)for speed in result[0])\n file_vforecast.write(line2+'\\n')\n file_vanalysis.write(line1+'\\n')\n return line1\n\nif __name__ == \"__main__\": \n global T,vmax,rhomax,rhoc,wf,vc,k,cellnumber,Q,R,Rn,v_ini,v_var,v_estimate\n vmax =100.24606798\n rhomax=184.46672118\n rhoc=40.29179605\n wf=rhoc*vmax/rhomax\n vc=vmax*(1-rhoc/rhomax)\n k=1000\n T=1\n Rn=[]\n ymeasure=[]\n v_ini=[]\n v_var=[]\n v_hat=[]\n v_estimate=[]\n v_analysis=[]\n cellnumber=20\n## R=[12.8,12.8,12.8,12.8,12.8,12.8,12.8,12.8,12.8]\n## Q=[6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4]\n R=[6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4,6.4]\n Q=[3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2,3.2]\n Q=array(Q).reshape(20,1)\n R=array(R).reshape(9,1)\n file_vforecast=open('velocity_9sensors_forecast_1test_opt.csv','w')\n file_vanalysis=open('velocity_9sensors_analysis_1test_opt.csv','w')\n## file_ysave=open('y20041103_9.30_10.00','w')\n for i in range(81):\n Rn.append(0)\n Rn=array(Rn).reshape(9,9)\n for i in range(9):\n for j in range(9):\n if i==j:\n Rn[i][j]=6.4*6.4\n print Rn.shape\n v_ini,v_var=initial()\n for i in range(k):\n p=abs(random.normal(v_ini,sqrt(v_var)))\n if p.any>=0:\n v_analysis.append(p)\n for i in range(120/T):\n ymeasure=get_measurement(i)\n v_hat.append(enkf(v_analysis,ymeasure))\n## print v_hat\n print 'over'\n## file_ysave.close()\n file_vforecast.close\n file_vanalysis.close\n \n \n## print v_hat\n \n"
}
] | 13 |
ksisant3/tensorFlow
|
https://github.com/ksisant3/tensorFlow
|
1ee8db6993b84f8a92658ab2f249b751c0224fc8
|
b2e909f8349bd156462d53b2e01d2150597ced54
|
cade4773b90f49a76630f289eedba4645d2ffbc5
|
refs/heads/master
| 2020-07-31T02:59:46.237962 | 2019-09-27T18:45:40 | 2019-09-27T18:45:40 | 210,461,275 | 0 | 0 | null | null | null | null | null |
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"text": "from __future__ import absolute_import, division, print_function, unicode_literals\n\n# TensorFlow and tf.keras\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras.layers import *\nfrom tensorflow.keras.callbacks import TensorBoard\n\n# Helper libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cv2\nfrom tqdm import tqdm\nimport pickle\nimport random\nimport os\n\nNAME = \"phenocam-snow-cnn\"\n\ntensorBoard = TensorBoard(log_dir=f'logs/{NAME}')\ntrain_data = \"pictures/trainImages\"\ntest_data = \"pictures/testImages\"\n\n\n# Assign choice to array snow [1,0] or no snow [0,1]\ndef one_shot_label(img):\n label = img.split('.')[0]\n if label == 'snow':\n one_shot = np.array([1,0])\n else:\n one_shot = np.array([0,1])\n return one_shot\n\ndef train_data_with_label():\n train_images = []\n for index in tqdm(os.listdir(train_data)):\n path = os.path.join(train_data, index)\n img = cv2.imread(path, 0)\n img = cv2.resize(img, (128, 128))\n train_images.append([np.array(img), one_shot_label(index)])\n random.shuffle(train_images)\n return train_images\n\ndef test_data_with_label():\n test_images = []\n for index in tqdm(os.listdir(test_data)):\n path = os.path.join(test_data, index)\n img = cv2.imread(path, 0)\n img = cv2.resize(img, (128, 128))\n test_images.append([np.array(img), one_shot_label(index)])\n return test_images\n\ntraining_images = train_data_with_label()\ntesting_images = test_data_with_label()\n\ntr_img_data = np.array([i[0] for i in training_images]).reshape(-1,128,128,1)\ntr_lbl_data = np.array([i[1] for i in training_images])\n\ntst_img_data = np.array([i[0] for i in testing_images]).reshape(-1,128,128,1)\ntst_lbl_data = np.array([i[1] for i in testing_images])\n\nmodel = keras.Sequential()\n\n# 1st Convolution Layer\nmodel.add(InputLayer(input_shape=[128,128,1]))\nmodel.add(Conv2D(filters=32,kernel_size=5,strides=1,padding='same'))\nmodel.add(Activation(\"relu\"))\nmodel.add(MaxPooling2D(pool_size=5,padding='same'))\n\n# 2nd Convolution Layer. More filters to reach more details.\n# i.e Snow on ground not on trees, difference in glare and snow.\nmodel.add(Conv2D(filters=50,kernel_size=5,strides=1,padding='same'))\nmodel.add(Activation(\"relu\"))\nmodel.add(MaxPooling2D(pool_size=5,padding='same'))\n\n# 3rd Convolution Layer. More filters to reach more details.\n# i.e Snow on ground not on trees, difference in glare and snow.\nmodel.add(Conv2D(filters=80,kernel_size=5,strides=1,padding='same'))\nmodel.add(Activation(\"relu\"))\nmodel.add(MaxPooling2D(pool_size=5,padding='same'))\n\n# Dropout Layer to avoid overfitting, Flatten before\n# being fed into fully connected layers.\nmodel.add(Dropout(0.25))\nmodel.add(Flatten())\nmodel.add(Dense(512))\nmodel.add(Dropout(rate=0.5))\nmodel.add(Activation(\"relu\"))\n\n# The number of neurons in the output layer will\n# be equal to number of classes in the problem\nmodel.add(Dense(2, activation= 'softmax'))\n\nmodel.compile(optimizer=\"adam\",\n loss=\"categorical_crossentropy\",\n metrics=[\"accuracy\"])\n\nmodel.fit(x=tr_img_data, y=tr_lbl_data,epochs=25)\n\n# Plot predicted data of test images.\nfig=plt.figure(figsize=(40,40))\n\nfor count, data in enumerate(testing_images[0:]):\n y = fig.add_subplot(6,5,count+1)\n img = data[0]\n data = img.reshape(1,128,128,-1)\n model_out = model.predict([data])\n\n # If the argmax of the prediction is 1 or [0,1]\n # then it has no snow\n if np.argmax(model_out) == 1:\n str_label = \"No Snow\"\n else:\n str_label = \"Snow\"\n\n y.imshow(img)\n plt.title(str_label)\n y.axes.get_xaxis().set_visible(False)\n y.axes.get_yaxis().set_visible(False)\nplt.show()\n"
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"text": "from __future__ import absolute_import, division, print_function, unicode_literals\n\n# TensorFlow and tf.keras\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D\nfrom tensorflow.keras.callbacks import TensorBoard\n\n# Helper libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cv2\nimport pickle\nimport random\nimport os\n\nNAME = \"phenocam-snow-cnn\"\n\ntensorBoard = TensorBoard(log_dir=f'logs/{NAME}')\nDATADIR = \"pictures/\"\nCATEGORIES = [\"noSnow\",\"Snow\"]\n\nIMG_SIZE = 100\n\ntraining_data = []\n\ndef create_training_data():\n for category in CATEGORIES:\n path = os.path.join(DATADIR, category) # path to snow or no snow\n class_num = CATEGORIES.index(category)\n for img in os.listdir(path):\n try:\n img_array = cv2.imread(os.path.join(path,img))\n new_array = cv2.resize(img_array,(IMG_SIZE, IMG_SIZE))\n training_data.append([new_array, class_num])\n except Exception as e:\n pass\n\ncreate_training_data()\n\nrandom.shuffle(training_data)\n\ntraining_features = []\ntraining_labels = []\n\nfor features, label in training_data:\n training_features.append(features)\n training_labels.append(label)\n\ntraining_features = np.array(training_features).reshape(-1,IMG_SIZE,IMG_SIZE,3)\n\npickle_out = open(\"training_features.pickle\", \"wb\")\npickle.dump(training_features, pickle_out)\npickle_out.close()\n\npickle_out = open(\"training_labels.pickle\", \"wb\")\npickle.dump(training_labels, pickle_out)\npickle_out.close()\n\ntraining_features = pickle.load(open(\"training_features.pickle\", \"rb\"))\ntraining_labels = pickle.load(open(\"training_labels.pickle\", \"rb\"))\n\n# Scale values to range from 0 to 1\ntraining_features = training_features / 255.0\n\nmodel = keras.Sequential()\nmodel.add(Conv2D(64, (3,3), input_shape = training_features.shape[1:]))\nmodel.add(Activation(\"relu\"))\nmodel.add(MaxPooling2D(pool_size=(2,2)))\n\nmodel.add(Conv2D(64, (3,3)))\nmodel.add(Activation(\"relu\"))\nmodel.add(MaxPooling2D(pool_size=(2,2)))\n\nmodel.add(Flatten())\n\nmodel.add(Dense(64))\nmodel.add(Activation(\"relu\"))\n\nmodel.add(Dense(1))\nmodel.add(Activation(\"sigmoid\"))\n\nmodel.compile(optimizer=\"adam\",\n loss=\"binary_crossentropy\",\n metrics=[\"accuracy\"])\n\nmodel.fit(training_features, training_labels, epochs=10)\n\n# A prediction is an array of 10 numbers. These describe the \"confidence\"\n# of the model that the image corresponds to each of the\n# 10 different articles of clothing.\npredictions = model.predict(test_images)\nprint(predictions[0])\n\nprint(np.argmax(predictions[0]))\n\n# Plot the first X test images, their predicted label, and the true label\n# Color correct predictions in blue, incorrect predictions in red\nnum_rows = 5\nnum_cols = 3\nnum_images = num_rows*num_cols\nplt.figure(figsize=(2*2*num_cols, 2*num_rows))\nfor i in range(num_images):\n plt.subplot(num_rows, 2*num_cols, 2*i+1)\n plot_image(i, predictions, test_labels, test_images)\n plt.subplot(num_rows, 2*num_cols, 2*i+2)\n plot_value_array(i, predictions, test_labels)\nplt.show()\n"
}
] | 2 |
brunosinister/flask-docker-vue-bootstrap
|
https://github.com/brunosinister/flask-docker-vue-bootstrap
|
8c03434f0df205bb86fdb4ff8affe0881bbbeb9c
|
2fb110cca518a21475566454fa74ddb2c64d3db9
|
31a7e7dbd2db82d9415c8b2ce1ca50b321e40b9b
|
refs/heads/master
| 2020-03-29T17:26:39.424429 | 2018-09-20T17:46:56 | 2018-09-20T17:46:56 | 150,163,081 | 0 | 0 | null | 2018-09-24T20:17:48 | 2018-09-20T17:47:24 | 2018-09-21T18:43:38 | null |
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"path": "/api/app/__init__.py",
"repo_name": "brunosinister/flask-docker-vue-bootstrap",
"src_encoding": "UTF-8",
"text": "from flask_restplus import Api, Resource\nfrom .books import api as books\n\napi = Api(\n title='Book API',\n version='1.0',\n description='A simple book demo API',\n)\n\[email protected]('/ping')\nclass HealthCheck(Resource):\n def get(self):\n return {'message': 'pong!'}\n\napi.add_namespace(books)"
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"path": "/api/run.py",
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"text": "from flask import Flask\nfrom werkzeug.contrib.fixers import ProxyFix\n\nfrom app import api\n\napp = Flask(__name__)\napp.url_map.strict_slashes = False\napp.wsgi_app = ProxyFix(app.wsgi_app)\n\napi.init_app(app)\n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0', debug=True)"
}
] | 2 |
Crian69/Last-Man-Stading-Discord-Py
|
https://github.com/Crian69/Last-Man-Stading-Discord-Py
|
9fc18e23d1c22e3e3de4c8ad5ecd0c68a6380a8d
|
43a91f4b6550bdb81f179e3956c7b1db19b49ca7
|
2fbf3c9a71cfcbff3e2c31f634ad545233a2e16b
|
refs/heads/master
| 2023-05-06T00:11:24.271418 | 2021-05-08T05:51:35 | 2021-05-08T05:51:35 | 365,429,404 | 0 | 0 | null | null | null | null | null |
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"path": "/main.py",
"repo_name": "Crian69/Last-Man-Stading-Discord-Py",
"src_encoding": "UTF-8",
"text": "import discord\nimport os\nfrom discord.ext import commands\nfrom discord.ext.commands import BucketType\nfrom discord.ext.commands import MaxConcurrencyReached\n\nintent: discord.Intents = discord.Intents.all()\nclient: commands.Bot = commands.Bot(command_prefix='!', help_command=None, intent=intent)\nTOKEN = os.getenv('TOKEN')\n\n\[email protected]\nasync def on_ready():\n print('Bot Is Online')\n\n\[email protected]\nasync def on_command_error(ctx, exc):\n if isinstance(exc, MaxConcurrencyReached):\n await ctx.reply('Contest Already Running PLease End To Start Again')\n else:\n raise exc\n\n\[email protected]()\[email protected]_concurrency(1, BucketType.channel)\nasync def lms(ctx: discord.ext.commands.Context, channel: discord.TextChannel, timeout=10, length=3):\n def check(m: discord.Message):\n return m.channel == channel and len(m.content) >= length\n\n embed = discord.Embed(title='Last Man Standing Contest')\n embed.add_field(name='RULES',\n value=f\"Minimum Amount Of Letter In Message >= `{length}` \\nTime For No Next Message To Win `{timeout}`secs\",\n inline=False)\n embed.add_field(value='CONTEST WILL BEGIN AFTER NEXT MESSAGE IS SENT IN THIS CHANNEL', name=\"\", inline=False)\n await channel.send(embed=embed)\n\n start_message: discord.Message = await client.wait_for('message', check=check)\n message: discord.Message = None\n\n while True:\n try:\n message = await client.wait_for('message', timeout=timeout, check=check)\n except Exception as E:\n if message is None:\n await start_message.reply(\n f'CONGRATULATIONS {start_message.author.mention} You have won the LMS contest')\n return\n else:\n await message.reply(f\"CONGRATULATIONS {message.author.mention} You have won the LMS contest\")\n return\n else:\n continue\n\n\nclient.run(TOKEN)\n"
}
] | 1 |
SumonRayy/python-face-detector
|
https://github.com/SumonRayy/python-face-detector
|
b3906fd7c01051936c79cb4932465dbae87d800d
|
596a93a78f388a731630f8faea5f5045553612fc
|
ed342ea69ab57e9b0bd44a9a763df5874e1399f6
|
refs/heads/main
| 2023-04-08T19:25:00.252522 | 2021-04-27T07:22:34 | 2021-04-27T07:22:34 | 361,655,307 | 0 | 0 | null | null | null | null | null |
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"path": "/image_face_detector.py",
"repo_name": "SumonRayy/python-face-detector",
"src_encoding": "UTF-8",
"text": "# Importing the OpenCV Library :\nimport cv2\n\n# Importing the Trained Dataset :\ntrained_face_data = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n\n# Reading the Image :\nimg = cv2.imread('assets/hc.jpg')\n\n# Changing the Color Format to Grayscale :\ngrayScaled_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n# Taking The Face Coordinates :\nfc = trained_face_data.detectMultiScale(grayScaled_img)\n\n# Draw The Rectangle :\nfor (x, y, w, h) in fc:\n cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)\n\ncv2.imshow(\"Image Viewer\", img)\n\ncv2.waitKey()\n\nprint(\"Code Completed\")\n"
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"repo_name": "SumonRayy/python-face-detector",
"src_encoding": "UTF-8",
"text": "# Python Face Detector AI 😃🐍\n\n<i><p>It's an AI which detects faces from a picture or in realtime from the webcam. This Project uses Python and OpenCV dataset.</p></i>\n\n-------------\n\n## How to Use? \n\n1. Clone the Repo.\n2. Create and Activate a virtual environment for this project by using ```virtualenv venv``` and ```source venv/bin/activate``` or ```venv\\Scripts\\activate```\n3. Install the Required modules by using ```pip install -r requirements.txt```\n4. Upload any image inside the repo to detect one or multiple faces from it. (Optional if you're using Realtime Face detector).\n5. Just Run/Execute any of the following Python Program by using ```python <module_name>``` \n\nThere Are two main modules in this repo : \n\n- ```image_face_detector.py``` : This module can be used to detect the face from an image.\n\n- ```camera_face_detector.py``` : This module can be used to detect the face from a webcam RealTime.\n\n--------------\n## contributing\n\nYou can contribute to improve this project by:\n\n- edit the code\n- creating a pull request\n- submitting new ideas / features suggestions\n- reporting a bug\n--------------\n\n### Thank You For Checking This Out 🥰🤗 Please give it a Start ⭐ if You've Liked it . . .\n## And Follow Me On GitHub 🙏🏻\n\n"
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"text": "# Importing the OpenCV Library :\nimport cv2\n\n# Importing the Trained Dataset :\ntrained_face_data = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n\nwebcam = cv2.VideoCapture(0)\n\nwhile True:\n sfr, frame = webcam.read()\n # Changing the Color Format to Grayscale :\n grayScaled_img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n # Taking The Face Coordinates :\n fc = trained_face_data.detectMultiScale(grayScaled_img)\n\n # Draw The Rectangle :\n for (x, y, w, h) in fc:\n cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)\n\n cv2.imshow(\"Camera Viewer\", frame)\n key = cv2.waitKey(1)\n\n # Stop Key :\n if key == 81 or key == 113:\n break\n\nwebcam.release()\n"
}
] | 3 |
dreamer2018/YWL
|
https://github.com/dreamer2018/YWL
|
1e2dcbcfe05021a543116bdbecde853d09885967
|
c1e8e63c9e23696362c6ac8eaa02293493b9666c
|
f17e2ac10f2556fd79eb89bfcba9a068de40bf9b
|
refs/heads/master
| 2021-05-24T04:20:58.975449 | 2016-08-31T08:15:56 | 2016-08-31T08:15:56 | 65,357,220 | 0 | 0 | null | null | null | null | null |
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"num_lines": 16,
"path": "/ywl_site/admin.py",
"repo_name": "dreamer2018/YWL",
"src_encoding": "UTF-8",
"text": "from django.contrib import admin\nfrom ywl_site.models import *\n\n\n\n# Register your models here.\n\nadmin.site.register(news_type)\nadmin.site.register(activity_type)\nadmin.site.register(donate_type)\nadmin.site.register(jobs_type)\nadmin.site.register(news)\nadmin.site.register(activity)\nadmin.site.register(join)\nadmin.site.register(donate)\nadmin.site.register(picture)\n"
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"text": "#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\n\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render_to_response\nfrom django.template import RequestContext\nfrom login.form import ContactForm\nfrom login.models import user, education\n\n\n# Create your views here.\n# 登录页面\ndef login(request):\n return render_to_response('login.html', {\n 'url': '../static/',\n }, context_instance=RequestContext(request))\n\n\n# 登录信息验证界面\ndef login_info(request):\n html = \"\"\n if request.POST:\n phone = request.POST['phone']\n passwd = request.POST['password']\n try:\n u = user.objects.get(phone=phone)\n except user.DoesNotExist:\n html += '<html><body><h2>帐号不存在，请<a href = \"/login/ \">重试</a>或去<a href = \"/register/\">注册</a></h2></body>'\n else:\n if u.passwd == passwd:\n request.session['id'] = u.id\n html += '<html><head><meta http-equiv=\"refresh\" content=\"3;url=/\"><head><body><h2>登录成功，' \\\n '正在跳转.......</h2></body>'\n else:\n html += '<h2>帐号或密码错误，请<a href = \"/login/ \">重试</a></h2>'\n return HttpResponse(html)\n\n\n# 注销\ndef logout(request):\n del request.session['id']\n return HttpResponseRedirect('/login/')\n\n\n# 注册页面\ndef register(request):\n return render_to_response('register.html', {\n 'url': '../static/',\n 'education': education.objects.all(),\n }, context_instance=RequestContext(request))\n\n\n# 获取注册信息\ndef register_info(request):\n html = ''\n if request.POST:\n # 获取数据\n nick_name = request.POST['nick_name']\n real_name = request.POST['real_name']\n phone = request.POST['phone']\n email = request.POST['email']\n password = request.POST['password']\n password_confirm = request.POST['password_confirm']\n birthday = request.POST['birthday']\n education = request.POST['education']\n address = request.POST['address']\n introduction = request.POST['introduction']\n\n # 解析数据正确性\n if len(nick_name) > 20 or len(nick_name) < 2:\n html += '<p>昵称必须大与2小与20</p><br/>'\n if len(real_name) > 20 or len(real_name) < 2:\n html += '<p>姓名必须大与2小与20</p><br/>'\n if len(phone) != 11:\n html += '<p>手机号码输入错误</p><br/>'\n if password != password_confirm:\n html += '<p>两次输入的密码不一致</p></br/>'\n if len(password) < 6 or len(password) > 20:\n html += '<p>密码长度不合法</p><br/>'\n if len(birthday) != 10:\n html += '<p>日期不合法</p><br/>'\n if len(address) > 40:\n html += '<p>地址过长</p><br/>'\n try:\n user.objects.get(phone=phone)\n except user.DoesNotExist:\n pass\n else:\n html += '<p>手机号码已被注册，请换个手机号码重试！<p><br/>'\n if not len(html):\n u = user()\n u.nick_name = nick_name\n u.real_name = real_name\n u.phone = phone\n u.email = email\n u.education = education\n u.passwd = password\n u.birthday = birthday\n u.address = address\n u.about = introduction\n u.save()\n else:\n html = '<html><head><meta http-equiv=\"refresh\" content=\"3;url=/register/\"><head><body><h2>Null</h2></body>'\n if not len(html):\n html = '<html><head><meta http-equiv=\"refresh\" content=\"3;url=/login/\"><head><body><h2>注成功，正在跳转.......' \\\n '</h2></body>'\n return HttpResponse(html)\n\n # if request.method == 'POST':\n # form = ContactForm(request.POST)\n # if form.is_valid():\n # cd = form.cleaned_data\n # u = user()\n # u.nick_name = cd['nick_name']\n # u.real_name = cd['real_name']\n # u.phone = cd['phone']\n # u.email = cd['email']\n # u.education = cd['education']\n # u.passwd = cd['password']\n # u.birthday = cd['birthday']\n # u.address = cd['address']\n # u.about = cd['introduction']\n # u.save()\n # return '<html><head><meta http-equiv=\"refresh\" content=\"3;url=/register/\"><head><body><h2>Null</h2></body>'\n # else:\n # form = ContactForm()\n # return render_to_response('register.html', {'form': form})\n\n\ndef develop(request, offset):\n return render_to_response('develop.html')\n"
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"text": "from django.contrib import admin\nfrom login.models import *\n# Register your models here.\nadmin.site.register(user)\nadmin.site.register(education)\n"
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"path": "/static/js/rolling.js",
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"text": "// JavaScript Document\r\nvar speedx=50; //数字越大速度越慢\r\nvar tabx=document.getElementById(\"demox\"); \r\nvar tab1x=document.getElementById(\"demo1x\"); \r\nvar tab2x=document.getElementById(\"demo2x\"); \r\ntab2x.innerHTML=tab1x.innerHTML; \r\nfunction Marqueex(){ \r\nif(tab2x.offsetWidth-tabx.scrollLeft<=0) \r\ntabx.scrollLeft-=tab1x.offsetWidth \r\nelse{\r\ntabx.scrollLeft++; \r\n} \r\n} \r\nvar MyMarx=setInterval(Marqueex,speedx); \r\ntabx.onmouseover=function() {clearInterval(MyMarx)}; \r\ntabx.onmouseout=function() {MyMarx=setInterval(Marqueex,speedx)}; \r\n \r\n \r\n"
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"text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\"\"\"\n Created by zhoupan on 8/30/16.\n\"\"\"\nfrom django import forms\n\n\nclass ContactForm(forms.Form):\n nick_name = forms.CharField(min_length=2, max_length=20)\n real_name = forms.CharField(min_length=2, max_length=20)\n phone = forms.CharField(min_length=11, max_length=11)\n email = forms.EmailField()\n passwd = forms.CharField(min_length=2, max_length=20)\n passwd_confirm = forms.CharField(min_length=2, max_length=20)\n birthday = forms.CharField(max_length=10, required=False)\n address = forms.CharField(max_length=40, required=False)\n education = forms.IntegerField()\n about = forms.CharField(widget=forms.Textarea)\n"
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"text": "#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n# Create your views here.\nfrom django.shortcuts import render_to_response\n\nfrom login.models import *\nfrom ywl_site.models import *\n\n\ndef index(request):\n render = {\n 'current_name': '首页',\n 'url': '../static/',\n 'donate': donate.objects.all(),\n 'news': news.objects.all(),\n 'activity': activity.objects.all(),\n 'join': join.objects.all(),\n 'about': picture.objects.all()[0:4],\n 'picture': picture.objects.all(),\n }\n if request.session.has_key('id'):\n render['user'] = user.objects.get(id=request.session['id'])\n return render_to_response('index.html', render)\n\n\ndef newss(request):\n render = {\n 'current_name': '新闻动态',\n 'url': '../static/',\n 'news': news.objects.all(),\n 'new': news.objects.all()[0:5],\n 'hot': news.objects.order_by('read')[0:5]\n }\n if request.session.has_key('id'):\n render['user'] = user.objects.get(id=request.session['id'])\n return render_to_response('news.html', render)\n\n\ndef news_view(request, offset):\n render = {\n 'url': '../../static/',\n 'current_name': '新闻动态',\n 'id': offset,\n 'news': news.objects.get(id=offset),\n 'new': news.objects.all()[0:5],\n 'hot': news.objects.order_by('-read')[0:5]\n }\n n = news.objects.get(id=offset)\n n.read += 1\n n.save()\n\n if request.session.has_key('id'):\n render['user'] = user.objects.get(id=request.session['id'])\n return render_to_response('news_view.html', render)\n\n\ndef activitys(request):\n render = {\n 'current_name': '专题活动',\n 'url': '../static/',\n 'activity': activity.objects.all(),\n 'new': activity.objects.all()[0:5],\n 'hot': activity.objects.all()[0:5]\n }\n if request.session.has_key('id'):\n render['user'] = user.objects.get(id=request.session['id'])\n return render_to_response('activity.html', render)\n\n\ndef activity_view(request, offset):\n render = {\n 'url': '../../static/',\n 'current_name': '专题活动',\n 'id': offset,\n 'activity': activity.objects.get(id=offset),\n 'new': activity.objects.all()[0:5],\n 'hot': activity.objects.all()[0:5]\n }\n if request.session.has_key('id'):\n render['user'] = user.objects.get(id=request.session['id'])\n return render_to_response('activity_view.html', render)\n\n\ndef joins(request):\n render = {\n 'current_name': '公益招募',\n 'url': '../static/',\n 'join': join.objects.all(),\n 'new': join.objects.all()[0:5],\n 'hot': join.objects.all()[0:5],\n }\n if request.session.has_key('id'):\n render['user'] = user.objects.get(id=request.session['id'])\n return render_to_response('join.html', render)\n\n\ndef join_view(request, offset):\n render = {\n 'url': '../../static/',\n 'current_name': '公益招募',\n 'id': offset,\n 'join': join.objects.get(id=offset),\n 'new': join.objects.all()[0:5],\n 'hot': join.objects.all()[0:5],\n }\n if request.session.has_key('id'):\n render['user'] = user.objects.get(id=request.session['id'])\n return render_to_response('join_view.html', render)\n\n\ndef donates(request):\n render = {\n 'current_name': '乐捐',\n 'url': '../static/',\n 'donate': donate.objects.all(),\n 'new': donate.objects.all()[0:5],\n 'hot': donate.objects.all()[0:5],\n }\n if request.session.has_key('id'):\n render['user'] = user.objects.get(id=request.session['id'])\n return render_to_response('donate.html', render)\n\n\ndef donate_view(request, offset):\n render = {\n 'url': '../../static/',\n 'current_name': '乐捐',\n 'id': offset,\n 'donate': donate.objects.get(id=offset),\n 'new': donate.objects.all()[0:5],\n 'hot': donate.objects.all()[0:5],\n }\n if request.session.has_key('id'):\n render['user'] = user.objects.get(id=request.session['id'])\n return render_to_response('donate_view.html', render)\n\n\ndef about(request):\n render = {\n 'current_name': '关于我们',\n 'url': '../static/',\n }\n if request.session.has_key('id'):\n render['user'] = user.objects.get(id=request.session['id'])\n return render_to_response('about.html', render)\n\n\ndef contact(request):\n render = {\n 'current_name': '联系我们',\n 'url': '../static/',\n }\n if request.session.has_key('id'):\n render['user'] = user.objects.get(id=request.session['id'])\n return render_to_response('contact.html', render)\n\n\ndef test(request, offset):\n render = {\n 'activity': activity.objects.get(id=offset),\n }\n return render_to_response('test.html', render)\n\n\ndef pictures(request, offset):\n render = {\n 'url': '../../static/',\n 'current_name': '新闻动态',\n 'picture': picture.objects.get(id=offset)\n }\n if request.session.has_key('id'):\n render['user'] = user.objects.get(id=request.session['id'])\n return render_to_response('picture.html', render)\n\n\ndef test_plus(request, offset):\n render = {\n 'url': '../../static/',\n 'current_name': '新闻动态',\n 'id': offset,\n 'news': news.objects.get(id=offset)\n }\n if request.session.has_key('id'):\n render['user'] = user.objects.get(id=request.session['id'])\n return render_to_response('news_view.html', render)\n"
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"text": "#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\nfrom django.db import models\n\n\n# Create your models here.\n# 新闻类型\nclass news_type(models.Model):\n name = models.CharField(max_length=20) # 新闻类型名\n\n def __unicode__(self):\n return u'%s' % self.name\n\n\n# 活动类型\nclass activity_type(models.Model):\n name = models.CharField(max_length=20) # 活动类型名\n\n def __unicode__(self):\n return u'%s' % self.name\n\n\n# 捐助类型\nclass donate_type(models.Model):\n name = models.CharField(max_length=20) # 捐助类型名\n\n def __unicode__(self):\n return u'%s' % self.name\n\n\n# 公益招募职位类型\nclass jobs_type(models.Model):\n name = models.CharField(max_length=20) # 公益招募职位名\n\n def __unicode__(self):\n return u'%s' % self.name\n\n\n# 新闻动态\nclass news(models.Model):\n type = models.ForeignKey(news_type) # 新闻类型\n title = models.CharField(max_length=40) # 新闻标题\n author = models.CharField(max_length=20) # 作者，编辑\n time = models.DateTimeField(auto_now_add=True) # 发布时间\n url = models.URLField() # 来源\n read = models.IntegerField() # 阅读量\n text = models.TextField() # 主要内容\n\n def __unicode__(self):\n return u'%s' % self.title\n\n\n# 专题活动\nclass activity(models.Model):\n type = models.ForeignKey(activity_type) # 活动类型，线上，线下\n title = models.CharField(max_length=40) # 活动标题\n sponsor = models.CharField(max_length=40) # 活动发起方\n start = models.DateTimeField() # 活动开始时间\n end = models.DateTimeField() # 活动结束时间\n address = models.CharField(max_length=100) # 活动地址\n text = models.TextField() # 活动主要内容\n\n def __unicode__(self):\n return u'%s' % self.title\n\n\n# 公益招募\nclass join(models.Model):\n type = models.ForeignKey(jobs_type) # 职位名\n address = models.CharField(max_length=20) # 招募地点\n name = models.CharField(max_length=40) # 机构名称\n job = models.CharField(max_length=20) # 招募职位\n number = models.IntegerField() # 招聘人数\n time = models.DateTimeField() # 发布时间\n\n def __unicode__(self):\n return u'%s' % self.name\n\n\n# 公益捐赠\nclass donate(models.Model):\n title = models.CharField(max_length=40) # 捐助标题\n type = models.ForeignKey(donate_type) # 捐助类型 \n time = models.DateTimeField() # 捐助日期\n text = models.TextField() # 捐助内容\n\n def __unicode__(self):\n return u'%s' % self.title\n\n\n# 图片\nclass picture(models.Model):\n title = models.CharField(max_length=40)\n url = models.URLField()\n text = models.TextField()\n\n def __unicode__(self):\n return u'%s' % self.title\n"
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"text": "#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\n\"\"\"\n Create By zhoupan At 2016.08.17\n\"\"\"\n\nfrom django.db import models\n\n\n# Create your models here.\nclass education(models.Model):\n name = models.CharField(max_length=10)\n\n def __unicode__(self):\n return u'%s' % self.name\n\n\nclass user(models.Model):\n nick_name = models.CharField(max_length=20) # 昵称\n real_name = models.CharField(max_length=20) # 真名\n phone = models.CharField(max_length=11, unique=True) # 手机号码\n email = models.EmailField() # 常用邮箱\n passwd = models.CharField(max_length=20) # 密码\n birthday = models.CharField(max_length=10, blank=True) # 生日\n address = models.CharField(max_length=40, blank=True) # 常住地址\n education = models.IntegerField() # 学历\n about = models.TextField(blank=True) # 个人说明\n def __unicode__(self):\n return u' %s ' % self.nick_name\n"
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"path": "/ywl_site/migrations/0001_initial.py",
"repo_name": "dreamer2018/YWL",
"src_encoding": "UTF-8",
"text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='activity',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(max_length=40)),\n ('sponsor', models.CharField(max_length=40)),\n ('start', models.DateTimeField()),\n ('end', models.DateTimeField()),\n ('address', models.CharField(max_length=100)),\n ('text', models.TextField()),\n ],\n ),\n migrations.CreateModel(\n name='activity_type',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=20)),\n ],\n ),\n migrations.CreateModel(\n name='donate',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(max_length=40)),\n ('time', models.DateTimeField()),\n ('text', models.TextField()),\n ],\n ),\n migrations.CreateModel(\n name='donate_type',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=20)),\n ],\n ),\n migrations.CreateModel(\n name='jobs_type',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=20)),\n ],\n ),\n migrations.CreateModel(\n name='join',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('address', models.CharField(max_length=20)),\n ('name', models.CharField(max_length=40)),\n ('job', models.CharField(max_length=20)),\n ('number', models.IntegerField()),\n ('time', models.DateTimeField()),\n ('type', models.ForeignKey(to='ywl_site.jobs_type')),\n ],\n ),\n migrations.CreateModel(\n name='news',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(max_length=40)),\n ('author', models.CharField(max_length=20)),\n ('time', models.DateTimeField(auto_now_add=True)),\n ('url', models.URLField()),\n ('read', models.IntegerField()),\n ('text', models.TextField()),\n ],\n ),\n migrations.CreateModel(\n name='news_type',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=20)),\n ],\n ),\n migrations.CreateModel(\n name='picture',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(max_length=40)),\n ('url', models.URLField()),\n ('text', models.TextField()),\n ],\n ),\n migrations.AddField(\n model_name='news',\n name='type',\n field=models.ForeignKey(to='ywl_site.news_type'),\n ),\n migrations.AddField(\n model_name='donate',\n name='type',\n field=models.ForeignKey(to='ywl_site.donate_type'),\n ),\n migrations.AddField(\n model_name='activity',\n name='type',\n field=models.ForeignKey(to='ywl_site.activity_type'),\n ),\n ]\n"
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"text": "# YWL\n一个小小的网站\n"
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"text": "{% extends 'base.html' %}\n{% block main %}\n <div class=\"clear\"></div>\n <div class=\"wt1002 fwmain\">\n <div class=\"fwmain_nright r mTop10\">\n <div class=\"label\">\n <div class=\"label_head\">\n <div class=\"label_title\">新闻内容</div>\n </div>\n <div class=\"about_con\">\n <div class=\"news_detail_title\">{{ news.title }}</div>\n <div class=\"news_detail_info\">\n <div class=\"news_detail_time\">{{ news.time | date:\"Y-m-d\" }}</div>\n <div class=\"news_detail_from\">来源:<a href=\"{{ news.url }}\">{{ news.url }}</a></div>\n <div class=\"news_detail_tool\">点击数:{{ news.read }} 作者:{{ news.author }}</div>\n </div>\n <div class=\"news_detail_cont\"> {{ news.text | safe }}</div>\n </div>\n </div>\n </div>\n <div class=\"fwmain_nleft l mTop10\">\n <div class=\"label_head\">\n <div class=\"label_title\">最新发布</div>\n </div>\n <div class=\"label_contents\">\n {% for item in new %}\n <h1><a href=\"/news/{{ item.id }}/\">{{ item.title }}</a></h1>\n {% endfor %}\n </div>\n <div class=\"label_head mTop10\">\n <div class=\"label_title\">最多关注</div>\n </div>\n <div class=\"label_contents\">\n {% for item in hot %}\n <h1><a href=\"/news/{{ item.id }}/\">{{ item.title}}</a></h1>\n {% endfor %}\n </div>\n </div>\n <div class=\"clear\"></div>\n </div>\n <div class=\"clear\"></div>\n{% endblock %}"
}
] | 11 |
missmaggiemo/pheasant-call
|
https://github.com/missmaggiemo/pheasant-call
|
c15223229f0b4e1ee43b0e7545d4d0594c600f0e
|
eae6287f62971599f0e3c9f642777fd0d57fe439
|
3a82b3d981fd05d15bb4ea9d8dbe4af151556bdb
|
refs/heads/master
| 2020-03-23T11:55:55.525730 | 2018-07-19T16:48:41 | 2018-07-19T16:48:41 | 141,527,256 | 0 | 0 | null | null | null | null | null |
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"text": "# requirements.txt for pip3\n\nFlask==1.0.2\nFlask-SQLAlchemy==2.3.2\npytest==3.6.3\n"
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"text": "from flask import Flask, request\nfrom flask_sqlalchemy import SQLAlchemy\nimport json\n\nproviders_app = Flask(__name__)\nproviders_app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///providers.sqlite3'\n# There was a warning in the logs about SQLALCHEMY_TRACK_MODIFICATIONS\n# adding overhead, and since I'm not rapidly iterating on models, I'll disable it\n# for cleaner logs, see http://flask-sqlalchemy.pocoo.org/2.3/config/\nproviders_app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb = SQLAlchemy(providers_app)\n\n\n# Basic index making sure everything works\n@providers_app.route('/')\ndef hello():\n return 'Hello, Bain Challenge!'\n\n\n# Providers model to give structure for entering data, querying\nclass Providers(db.Model):\n # Model attrs, DB columns\n id = db.Column('id', db.Integer, primary_key = True)\n drg_definition = db.Column(db.String(100))\n provider_id = db.Column(db.Integer)\n provider_name = db.Column(db.String(100))\n provider_street_address = db.Column(db.String(100))\n provider_city = db.Column(db.String(100))\n provider_state = db.Column(db.String(100))\n provider_zip_code = db.Column(db.Integer)\n hospital_referral_region_description = db.Column(db.String(100))\n total_discharges = db.Column(db.Integer)\n average_covered_charges = db.Column(db.Float)\n average_total_payments = db.Column(db.Float)\n average_medicare_payments = db.Column(db.Float)\n\n # Fields for JSON serialization\n SERIAL_FIELDS = {\n 'provider_name': 'Provider Name',\n 'provider_street_address': 'Provider Street Address',\n 'provider_city': 'Provider City',\n 'provider_state': 'Provider State',\n 'provider_zip_code': 'Provider Zip Code',\n 'hospital_referral_region_description': 'Hospital Referral Region Description',\n 'total_discharges': 'Total Discharges',\n 'average_covered_charges': 'Average Covered Charges',\n 'average_total_payments': 'Average Total Payments',\n 'average_medicare_payments': 'Average Medicare Payments',\n }\n\n def __init__(self, drg_definition, provider_id, provider_name,\n provider_street_address, provider_city, provider_state,\n provider_zip_code, hospital_referral_region_description,\n total_discharges, average_covered_charges,\n average_total_payments, average_medicare_payments):\n self.drg_definition = drg_definition\n self.provider_id = provider_id\n self.provider_name = provider_name\n self.provider_street_address = provider_street_address\n self.provider_city = provider_city\n self.provider_state = provider_state\n self.provider_zip_code = provider_zip_code\n self.hospital_referral_region_description = hospital_referral_region_description\n self.total_discharges = total_discharges\n self.average_covered_charges = average_covered_charges\n self.average_total_payments = average_total_payments\n self.average_medicare_payments = average_medicare_payments\n\n def serialize(self):\n \"\"\" Serialize for JSON \"\"\"\n serial_dict = {}\n for key in self.__class__.SERIAL_FIELDS.keys():\n if key == 'provider_zip_code':\n serial_dict[self.__class__.SERIAL_FIELDS[key]] = str(getattr(self, key))\n elif key[0:7] == 'average':\n serial_dict[self.__class__.SERIAL_FIELDS[key]] = '${:,.2f}'.format(getattr(self, key))\n else:\n serial_dict[self.__class__.SERIAL_FIELDS[key]] = getattr(self, key)\n return serial_dict\n\n\n# Route to run the script to load the data\n@providers_app.route('/load_data')\ndef load_csv_data():\n \"\"\" Load the master data file into the DB\n\n Example\n localhost:5000/load_data\n\n Also takes query parameter 'filename' a local file name\n for loading data from other sources, used in testing.\n \"\"\"\n file = './Inpatient_Prospective_Payment_System__IPPS__Provider_Summary_for_the_Top_100_Diagnosis-Related_Groups__DRG__-_FY2011.csv'\n # Unless we have specified a local file\n if request.args.get('filename'):\n file = request.args.get('filename')\n column_names = []\n line_num = 1\n with open(file, 'r') as f:\n for line in f:\n raw_fields = line.split(',')\n # Capture and transform col names\n if 'DRG Definition' == raw_fields[0]:\n column_names = [fi.strip().lower().replace(' ', '_') for fi in raw_fields]\n continue\n # Getting the field values\n fields = []\n i = 0\n while i < len(raw_fields):\n if raw_fields[i][0] == '\"':\n # This means that there was a ',' somewhere in the middle\n # of this field value and we need to group the rest of\n # raw fields that were split up\n concat_str = raw_fields[i]\n while i < len(raw_fields) - 1:\n # We're incrementing i here because we don't want to\n # re-read the concatenated fields\n i += 1\n concat_str = concat_str + ',' + raw_fields[i]\n if raw_fields[i][-1] == '\"':\n break\n # Strip of quotes for DB storage\n concat_str = concat_str.replace('\"', '')\n fields.append(concat_str)\n else:\n fields.append(raw_fields[i])\n i += 1\n # Writing to DB\n try:\n model_values = {column_names[i]: fields[i] for i in range(len(fields))}\n # Data transformations\n model_values['provider_id'] = int(model_values['provider_id'])\n model_values['provider_zip_code'] = int(model_values['provider_zip_code'])\n model_values['total_discharges'] = int(model_values['total_discharges'])\n model_values['average_covered_charges'] = float(model_values['average_covered_charges'].replace('$', ''))\n model_values['average_total_payments'] = float(model_values['average_total_payments'].replace('$', ''))\n model_values['average_medicare_payments'] = float(model_values['average_medicare_payments'].strip().replace('$', ''))\n # Make model instance and write\n new_provider = Providers(**model_values)\n db.session.add(new_provider)\n except Exception as e:\n # If there is an issue, we want to log and continue\n print('Line {} Exception {} on fields: '.format(str(line_num), e))\n print(fields)\n # Flush to file on every 100th entry\n if line_num % 100 == 0:\n db.session.commit()\n # Keeping track of line number for logging\n line_num += 1\n # Commit anything left over\n db.session.commit()\n # Helpful return text\n ret_text = \"Finished {} lines\".format(str(line_num))\n return ret_text\n\n\n@providers_app.route('/providers')\ndef get_data():\n \"\"\" JSON API route\n\n Serves the following query parameters\n max_discharges: The maximum number of Total Discharges\n min_discharges: The minimum number of Total Discharges\n max_average_covered_charges: The maximum Average Covered Charges\n min_average_covered_charges: The minimum Average Covered Charges\n max_average_medicare_payments: The maximum Average Medicare Payment\n min_average_medicare_payments: The minimum Average Medicare Payment\n state: The exact state that the provider is from\n limit: A limit for the number of results to return, defaults to 0\n\n Example query\n localhost:5000/providers?max_discharges=11max_average_covered_charges=50000&state=GA\n \"\"\"\n ret_items = None\n # Get and parse query params\n p_query = Providers.query\n if not request.args:\n # If no query params, return first 10 results because there are _a lot_\n p_query = p_query.limit(10)\n else:\n # Query DB for matching providers with query params\n # These filter params are additive\n try:\n if request.args.get('max_discharges'):\n p_query = p_query.filter(Providers.total_discharges <= int(request.args.get('max_discharges')))\n if request.args.get('min_discharges'):\n p_query = p_query.filter(Providers.total_discharges >= int(request.args.get('min_discharges')))\n if request.args.get('max_average_covered_charges'):\n p_query = p_query.filter(Providers.average_covered_charges <= int(request.args.get('max_average_covered_charges')))\n if request.args.get('min_average_covered_charges'):\n p_query = p_query.filter(Providers.average_covered_charges >= int(request.args.get('min_average_covered_charges')))\n if request.args.get('max_average_medicare_payments'):\n p_query = p_query.filter(Providers.average_medicare_payments <= int(request.args.get('max_average_medicare_payments')))\n if request.args.get('min_average_medicare_payments'):\n p_query = p_query.filter(Providers.average_medicare_payments >= int(request.args.get('min_average_medicare_payments')))\n if request.args.get('state'):\n p_query = p_query.filter(Providers.provider_state == request.args.get('state'))\n # I added 'limit' for my own testing purposes\n if request.args.get('limit'):\n p_query = p_query.limit(int(request.args.get('limit')))\n except Exception as e:\n # If query params are malformed, I'd like to return a useful error\n data = {\n 'text': 'Malformed query parameters',\n 'error': str(e)\n }\n response = providers_app.response_class(\n response=json.dumps(data),\n status=400,\n mimetype='application/json'\n )\n return response\n\n # Fetch the models from the DB\n models = p_query.all()\n # Return JSON for model values\n serialized = [m.serialize() for m in models]\n response = providers_app.response_class(\n response=json.dumps(serialized),\n status=200,\n mimetype='application/json'\n )\n return response\n\n\n# Break out the DB init method so that we can initialize another DB for testing\ndef init_db():\n \"\"\" Methods to initialize the DB \"\"\"\n db.create_all()\n\n\nif __name__ == '__main__':\n init_db()\n providers_app.run(host='0.0.0.0')\n"
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"text": "# This is a Makefile, see\n# https://www.gnu.org/software/make/manual/make.html#Introduction\n# if you aren't already familiar\n\nVENV_NAME = venv\nAPP_NAME = providers\nVENV_PIP = $(VENV_NAME)/bin/pip\nVENV_PYTHON = $(VENV_NAME)/bin/python\nSYS_PYTHON3 = $(shell which python3)\nDATA = Inpatient_Prospective_Payment_System__IPPS__Provider_Summary_for_the_Top_100_Diagnosis-Related_Groups__DRG__-_FY2011.csv\n\nrun: venv\n\techo \"App will run on localhost:5000\"\n\t$(VENV_PYTHON) $(APP_NAME).py\n\nvenv:\n\t$(SYS_PYTHON3) -m pip install virtualenv\n\tvirtualenv $(VENV_NAME)\n\t$(VENV_PIP) install -r requirements.txt\n\ntest: venv\n\thead -10 $(DATA) > test_data.csv\n\t$(VENV_NAME)/bin/pytest\n\nload_data:\n\techo \"Must have app running\"\n\tcurl \"localhost:5000/load_data\"\n\nclean:\n\t(rm test_data.csv; \\\n\trm *.sqlite3; \\\n\trm *.log; \\\n\trm -rf $(VENV_NAME))\n"
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"text": "# Bain Coding Challenge 2018-07-18\nApplicant: Maggie Moreno\n\n## Overview\n\n* http://54.146.129.15:5000/providers\n\n* Python 3 Flask app, see http://flask.pocoo.org/ if you're not familiar\n\n* Data in SQLite\n\n - run `make load_data` to load the database the first time the app is run locally\n\n* Uses python virtualenv, see https://virtualenv.pypa.io/en/stable/ if you're not familiar\n\n* One endpoint, /providers, that responds to a GET, parses query params, and spits back data\n\n - Empty query params spits back the first 10 results\n\n* Original Prompt https://gist.github.com/rbrowngt/4690f19441adf54872b4cee43ee86cef\n\n\n## How to use\n\n### Get data from the remote app\n```\ncurl http://54.146.129.15:5000/providers\n```\n\n### Run the app locally, in the background\n```\nmake run\n```\n\n### Load the data into the SQLite DB, if it isn't already there\n```\nmake load_data\n```\n\n### Run the app locally, in the background\n```\nmake test\n```\n\n## Technical Desicions Explanation\n\n### Why Flask?\n\nFlask is a light-weight Python web server middleware framework. I've done most of my development in Python lately, so in the interest of time, I'm sticking with that. Since the assignment asks for a read-only API for what is effectively just one table of data, I don't need a CRUD-oriented framework like Django.\n\n### Why SQLite?\n\nSQLite doesn't requite another process running anywhere else, and I've used with with Python projects before as a just-get-going starter DB. It's easy enough to swap in another SQL-like database in its place when it comes time to scale.\n\n### Why did I deploy on AWS instead of something like Heroku?\n\nI'm not that familiar with deploying anything other than a Rails app on Heroku, and I haven't done that since 2014. At my former company, we deployed everything on AWS, and I already had an instance standing. To be honest though, I think this was a poor decision on my part. I ran into a number of issues deploying my little app simply because I wasn't familiar enough with the AWS-Linux way of doing things and expected it to be more like the Ubuntu way of doing things. I learned a lot, for sure, but I think this project would have moved a little more quickly for me had I just committed to learning about using Postgres with Flask. There are plenty of docs for deploying on Heroku with Flask and Postgres-- a little more research would have gone a long way.\n\n\n## TODO\n\n- [x] How to use\n- [x] Flask app\n- [x] Load data into SQLite\n- [x] API endpoint to return data (JSON)\n- [x] Parse query params\n- [x] README\n- [x] Launch on my AWS instance `http://54.146.129.15:5000/providers`\n- [x] Tests\n"
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"text": "# Tests for the app\nimport os\nimport pytest\nimport json\nfrom providers import providers_app, init_db\n\nTESTDB = 'test.sqlite3'\n\n\ndef dollar_to_float(dollar):\n\treturn float(dollar.replace('$', '').replace(',', ''))\n\n\[email protected]\ndef client():\n providers_app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///{}'.format(TESTDB)\n providers_app.config['TESTING'] = True\n client = providers_app.test_client()\n init_db()\n yield client\n os.remove(TESTDB)\n\n\ndef test_index(client):\n \"\"\"Start with a blank database.\"\"\"\n\n rv = client.get('/')\n assert b'Hello, Bain Challenge!' in rv.data\n\n\ndef test_load_sample_data(client):\n rv = client.get('/load_data?filename=test_data.csv')\n assert b'Finished 10 lines' in rv.data\n\n\ndef test_sample_query_limit(client):\n client.get('/load_data?filename=test_data.csv')\n rv = client.get('/providers?limit=2')\n data = json.loads(rv.data)\n assert len(data) == 2\n\n\ndef test_sample_query_state(client):\n client.get('/load_data?filename=test_data.csv')\n rv = client.get('/providers?state=AL')\n data = json.loads(rv.data)\n assert len(data) == 9\n assert data[0]['Provider State'] == 'AL'\n\n\ndef test_sample_query_max_discharges(client):\n client.get('/load_data?filename=test_data.csv')\n rv = client.get('/providers?max_discharges=100')\n data = json.loads(rv.data)\n assert len(data) == 8\n assert data[0]['Total Discharges'] < 100\n\n\ndef test_sample_query_min_discharges(client):\n client.get('/load_data?filename=test_data.csv')\n rv = client.get('/providers?min_discharges=90')\n data = json.loads(rv.data)\n assert len(data) == 2\n assert data[0]['Total Discharges'] > 90\n\n\ndef test_sample_query_max_average_covered_charges(client):\n client.get('/load_data?filename=test_data.csv')\n rv = client.get('/providers?max_average_covered_charges=32000')\n data = json.loads(rv.data)\n assert len(data) == 6\n assert dollar_to_float(data[0]['Average Covered Charges']) < 32000\n\n\ndef test_sample_query_min_average_covered_charges(client):\n client.get('/load_data?filename=test_data.csv')\n rv = client.get('/providers?min_average_covered_charges=32000')\n data = json.loads(rv.data)\n assert len(data) == 3\n assert dollar_to_float(data[0]['Average Covered Charges']) > 32000\n\n\ndef test_sample_query_max_average_medicare_payments(client):\n client.get('/load_data?filename=test_data.csv')\n rv = client.get('/providers?max_average_medicare_payments=5000')\n data = json.loads(rv.data)\n assert len(data) == 6\n assert dollar_to_float(data[0]['Average Medicare Payments']) < 5000\n\n\ndef test_sample_query_min_average_medicare_payments(client):\n client.get('/load_data?filename=test_data.csv')\n rv = client.get('/providers?min_average_medicare_payments=5000')\n data = json.loads(rv.data)\n assert len(data) == 3\n assert dollar_to_float(data[0]['Average Medicare Payments']) > 5000\n"
}
] | 5 |
developerhupeng/jiemianzidonghua
|
https://github.com/developerhupeng/jiemianzidonghua
|
78ea0f8f08fdd957f777c2bfbd7c3396eb732623
|
3224c7bff3983dc01f6883624d3600e36bdbcfdf
|
4689d83b58bf7f9e020435b92a291e6291276b06
|
refs/heads/master
| 2020-09-09T09:18:13.841993 | 2019-11-14T09:56:40 | 2019-11-14T09:56:40 | 221,409,898 | 0 | 0 | null | null | null | null | null |
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"text": "from time import sleep\n\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC #定义了变量EC表示expected_conditions\n\n\nimport autoit\nfrom selenium.webdriver import ActionChains\nfrom selenium.webdriver.common.keys import Keys\n#纯文本\n#取出公共变量driver（打开浏览器）\ndef test_input(driver):\n #打来浏览器获取IP地址http://ui.yansl.com/#/input\n driver.get(\"http://ui.yansl.com/#/input\")\n #停留2秒钟\n sleep(2)\n #取input为变量名，定位浏览器文本元素的位置\n input = driver.find_element_by_xpath(\"//input[@name='t1']\")\n #清空输入框\n input.clear()\n #输入数据\n input.send_keys(\"小鲁，你好骚啊\")\n #停留2秒\n sleep(2)\n#单选框\n#取出公共变量driver（打开浏览器）\ndef test_rabio(driver):\n #打开浏览器获取地址\n driver.get(\"http://ui.yansl.com/#/radio\")\n #停留2秒\n sleep(2)\n #取radio为变量名，打开浏览器并定位点击元素的位置\n radio = driver.find_element_by_xpath(\"//input[@name='sex'][2]\")\n #进行点击\n radio.click()\n #停留2秒\n sleep(2)\n#下拉框\n#取出公共变量driver（打开浏览器）\ndef test_select(driver):\n #打开浏览器并获取它的ip\n driver.get(\"http://ui.yansl.com/#/select\")\n #进入网页后停留2秒\n sleep(2)\n #定位元素位置\n radio = driver.find_element_by_xpath(\"//*[@id='form']/form/div[3]/div/div/div[2]/input\")\n #进行点击\n radio.click()\n #停留2秒\n sleep(2)\n #接着定位元素\n option = driver.find_element_by_xpath(\"(//span[text()='双皮奶'])[last()]\")\n #取变量，把鼠标点击浏览器的功能给变量\n actions = ActionChains(driver)\n #在赋值变量中模仿鼠标运行点击变量option（双皮奶）接着继续运行一下\n actions.move_to_element(option).perform()\n #停留2秒\n sleep(2)\n #进行点击\n option.click()\n #停留2秒\n sleep(2)\n#模块\ndef test_slider(driver):\n driver.get(\"http://ui.yansl.com/#/slider\")\n sleep(2)\n #点击\n slider = driver.find_element_by_xpath(\"(//div[@class='el-tooltip el-slider__button'])[last()]\")\n slider.click()\n sleep(2)\n actions = ActionChains(driver)\n #模仿鼠标滑动，拖动\n actions.drag_and_drop_by_offset(slider,0,-200).perform()\n sleep(2)\n\n#时间\ndef test_time(driver):\n driver.get(\"http://ui.yansl.com/#/dateTime\")\n sleep(2)\n\n t1 = driver.find_element_by_xpath(\"//*[@id='form']/form/div[1]/div[2]/div/div/input\")\n #清空\n t1.clear()\n #输入\n t1.send_keys(\"14:17;00\")\n sleep(2)\n#文件\ndef test_upload(driver):\n driver.get(\"http://ui.yansl.com/#/upload\")\n sleep(2)\n\n upload = driver.find_element_by_xpath(\"//*[@id='form']/form/div[1]/div/input\")\n #清空\n upload.clear()\n #输入\n upload.send_keys(\"c:\\\\Users\\guoya\\\\Desktop\\\\178847431362956833.png\")\n sleep(2)\n\n#点击上传\ndef test_upload2(driver):\n driver.get(\"http://ui.yansl.com/#/upload\")\n sleep(2)\n upload = driver.find_element_by_xpath(\"//*[@id='form']/form/div[2]/div/div/div[1]/button/span\")\n upload.click()\n sleep(2)\n autoit.win_wait(\"打开\", 10)\n sleep(1)\n # autoit.control_send(\"打开\", \"Edit1\", os.path.abspath(file_path))\n autoit.control_set_text(\"打开\", \"Edit1\", \"c:\\\\Users\\guoya\\\\Desktop\\\\178847431362956833.png\")\n sleep(2)\n autoit.control_click(\"打开\", \"Button1\")\n#窗口切换\ndef test_windows(driver):\n driver.get(\"http://192.168.1.128:8082/xuepl/demo.html\")\n sleep(2)\n\n dang_dang = driver.find_element_by_link_text(\"当当\")\n actions = ActionChains(driver)\n actions.key_down(Keys.CONTROL).click(dang_dang).key_up(Keys.CONTROL).perform()\n sleep(2)\n jd = driver.find_element_by_link_text(\"京东\")\n actions = ActionChains(driver)\n #抬起鼠标，点击京东，运行京东\n actions.key_down(Keys.CONTROL).click(jd).key_up(Keys.CONTROL).perform()\n sleep(2)\n dn = driver.find_element_by_partial_link_text(\"度娘\")\n actions = ActionChains(driver)\n actions.key_down(Keys.CONTROL).click(dn).key_up(Keys.CONTROL).perform()\n sleep(2)\n\n # 获取所有窗口的句柄\n handles = driver.window_handles\n for h in handles:\n # 根据窗口句柄，切换窗口\n driver.switch_to.window(h)\n sleep(2)\n #如果运行到包含京东的页面，\n if driver.title.__contains__(\"京东\"):\n #结束\n break\n#框架切换\ndef test_iframe(driver):\n driver.get(\"http://192.168.1.128:8082/xuepl1/frame/main.html\")\n sleep(2)\n\n frame = driver.find_element_by_xpath('/html/frameset/frameset/frame[1]')\n driver.switch_to.frame(frame)\n sleep(2)\n driver.find_element_by_link_text('京东').click()\n sleep(2)\n #退出当前iframe\n driver.switch_to.parent_frame()\n #回到初始页面\n #driver.switch_to.default_content()\n sleep(2)\n iframe = driver.find_element_by_xpath(\"//*[@id='menu']/ul/li[2]/a\")\n driver.switch_to.frame(iframe)\n sleep(2)\n inpu = driver.find_element_by_xpath('//input[@id]')\n inpu.click()\n inpu.send_keys(\"手机\")\n sleep(5)\n#页面等待\ndef test_wait(driver):\n driver.get(\"http://ui.yansl.com/#/loading\")\n bt =driver.find_element_by_xpath(\"//span[contains(text(),'指令方式')]\")\n bt.click()\n #显示等待\n WebDriverWait(driver, 5, 0.5).until(\n EC.presence_of_element_located((By.XPATH, '//tbody/tr[1]/td[2]/div'))\n )\n bg = driver.find_element_by_xpath(\"//tbody/tr[1]/td[2]/div\")\n print(bg.text)\n sleep(2)\n#文本断言\ndef test_text(driver):\n driver.get(\"http://ui.yansl.com/#/message\")\n buttons = driver.find_elements_by_xpath(\"//label[contains(text(),'自动关闭提示')]/..//span[text()='消息']\")\n buttons[0].click()\n message = driver.find_element_by_xpath(\"//div[@role='alert']/p\")\n #消息\n text = message.text\n # 断言 这是一条消息在文本中\n assert \"这是一条消息\" in text\n sleep(2)\n#对它的界面源代码做断言\ndef test_page_source(driver):\n driver.get(\"http://ui.yansl.com/\")\n driver.find_element_by_xpath(\"//*[@id='app']/section/section/aside/ul/li[3]/div\").click()\n driver.find_element_by_xpath(\"//*[@id='app']/section/section/aside/ul/li[3]/ul/li/ul/li[3]\").click()\n #获取页面源代码\n source = driver.page_source\n print(source)\n # 断言 这是一条消息在文本中\n assert \"手工关闭提示\" in source\n sleep(2)\n"
},
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"avg_line_length": 15.095237731933594,
"blob_id": "ba2b131e7c3270beac3e2c50ae6cbb81c18315c3",
"content_id": "96ede725eb0c6f895bf9ba8928f84fcde2745bd9",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,
"language": "Python",
"length_bytes": 363,
"license_type": "no_license",
"max_line_length": 38,
"num_lines": 21,
"path": "/666/test_brower.py",
"repo_name": "developerhupeng/jiemianzidonghua",
"src_encoding": "UTF-8",
"text": "from asyncio import sleep\n\nfrom selenium import webdriver\n#休眠\n#sleep(1)\n\ndef test_brower(driver):\n driver.get(\"http://www.baidu.com\")\n sleep(1)\n driver.get(\"http://www.jd.com\")\n #后退\n driver.back()\n sleep(2)\n #前进\n driver.forward()\n sleep(2)\n #刷新\n driver.refresh()\n sleep(2)\n #关闭浏览器\n driver.quit()"
}
] | 2 |
meshuai/crash_wine
|
https://github.com/meshuai/crash_wine
|
3b562dca752cdbca078d114db7c288f43be3837b
|
6f9c91c36dbebecb6b2db0a5341fda145d931337
|
03a019ef7946303bd008174e6f3535baca88d9ee
|
refs/heads/master
| 2021-01-01T15:40:46.419397 | 2015-04-19T11:17:17 | 2015-04-19T11:17:17 | 34,195,395 | 0 | 0 | null | null | null | null | null |
[
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"blob_id": "3e1765e0328712ed5dbd307bcd27376e96770556",
"content_id": "a7f4ba2e516affec16bfc9f356e7e02c7349a473",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,
"language": "JavaScript",
"length_bytes": 442,
"license_type": "no_license",
"max_line_length": 54,
"num_lines": 19,
"path": "/static/js/crash_wine/core.js",
"repo_name": "meshuai/crash_wine",
"src_encoding": "UTF-8",
"text": "\n$(function() {\n\n $('#start_game #start').on('click tap',function(){\n $('#start_game').addClass('hidden');\n add_red_wine();\n $('#game_board').removeClass('hidden');\n\n game_over();\n });\n\n $('#restart').on('click tap', function(){\n $('#game_over').addClass('hidden');\n $('#score').data('score', 0).text(0);\n $('#game_board').removeClass('hidden');\n\n game_over();\n });\n \n});"
},
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"avg_line_length": 29,
"blob_id": "35c2ce3fe6bc7796e33af34c112776efafdfb16b",
"content_id": "04616bb9ca3ed32b0c815060cb11c753ad64a3f8",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,
"language": "Python",
"length_bytes": 270,
"license_type": "no_license",
"max_line_length": 70,
"num_lines": 9,
"path": "/crash_wine/views.py",
"repo_name": "meshuai/crash_wine",
"src_encoding": "UTF-8",
"text": "from django.shortcuts import render\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.views.decorators.clickjacking import xframe_options_exempt\n\n@xframe_options_exempt\n@csrf_exempt\ndef index(request):\n\n return render(request, 'crash_wine/index.html')\n"
},
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"blob_id": "4cfcfdf624f9bc7227ece82767598202fb2f6094",
"content_id": "5a251738a5d86a88e856a61c4b9b901c0b6bfbdc",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,
"language": "JavaScript",
"length_bytes": 2054,
"license_type": "no_license",
"max_line_length": 68,
"num_lines": 83,
"path": "/static/js/crash_wine/game.js",
"repo_name": "meshuai/crash_wine",
"src_encoding": "UTF-8",
"text": "/* game main functions */\n\nvar number = 1;\nvar red_wine_width = 64;\nvar red_wine_height = 85;\n\nfunction random_position(holder, member){\n var width = holder.width();\n var height = holder.height();\n\n var _width = member.width();\n var _height = member.height();\n\n var left = Math.floor(Math.random() * (width - _width));\n var top = Math.floor(Math.random() * (height - _height));\n \n return [top, left];\n}\n\n\nfunction click_wine(score_holder){\n var score = parseInt(score_holder.data('score'));\n score += 1;\n score_holder.data('score', score);\n score_holder.text(score);\n}\n\nfunction create_red_wine(){\n var red_wine = $(\"<div>\", {class: \"red_wine\"});\n red_wine.css({\n width: red_wine_width + 'px', height: red_wine_height + 'px'\n });\n\n red_wine.on('click tap', function(e) {\n clearInterval(red_wine.interval_time);\n var target = e.currentTarget;\n $(target).off('click tap');\n click_wine($('#score'));\n // remove red_wine;\n red_wine.animate({\n opacity: 0.02\n }, 800, function(){\n $(target).remove();\n //add_red_wine();\n });\n add_red_wine();\n });\n return red_wine;\n}\n\nfunction wine_position(game_board, red_wine){\n var position = random_position(game_board, red_wine);\n\n red_wine.css({\n top: position[0] + 'px', left: position[1] + 'px'\n });\n \n}\n\nfunction append_red_wine(){\n var red_wine = create_red_wine();\n var game_board = $('#game_board');\n game_board.append(red_wine);\n wine_position(game_board, red_wine);\n red_wine.interval_time = window.setInterval(function(){\n wine_position(game_board, red_wine)\n }, 1200);\n}\n\nfunction add_red_wine(){\n for (var i=0; i<number; i++){\n append_red_wine();\n }\n}\n\nfunction game_over(){\n setTimeout(function(){\n var score = $('#score').data('score');\n $('#final_score').text(score);\n $('#game_board').addClass('hidden');\n $('#game_over').removeClass('hidden');\n }, 8000);\n}"
},
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"avg_line_length": 30.615385055541992,
"blob_id": "f36662943df14d89f5b620f635d19d292cb75241",
"content_id": "5d62e59f14c44990c445c0a1e2b0800f14dc1fcb",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,
"language": "Python",
"length_bytes": 411,
"license_type": "no_license",
"max_line_length": 77,
"num_lines": 13,
"path": "/gameApps/urls.py",
"repo_name": "meshuai/crash_wine",
"src_encoding": "UTF-8",
"text": "from django.conf.urls import include, url\nfrom django.contrib import admin\nfrom django.conf.urls.static import static\nfrom django.conf import settings\n\nurlpatterns = [\n # Examples:\n # url(r'^$', 'gameApps.views.home', name='home'),\n # url(r'^blog/', include('blog.urls')),\n\n# url(r'^admin/', include(admin.site.urls)),\n url(r'^crash_wine/$', 'crash_wine.views.index', name=\"crash_wine_index\"),\n]\n"
}
] | 4 |
centralpark/IntegrationPipeline
|
https://github.com/centralpark/IntegrationPipeline
|
e6b1dcd60ab2a8f1ea74f1049b3963c7c7105cf0
|
f73afd4ee43efadd690836b6964b4eeb60103567
|
cb29ae757e4c57bf44980cf057f86945b3734cb3
|
refs/heads/master
| 2020-03-30T17:25:00.972611 | 2015-08-06T00:34:16 | 2015-08-06T00:34:16 | 40,275,921 | 0 | 0 | null | null | null | null | null |
[
{
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"alphanum_fraction": 0.6500488519668579,
"avg_line_length": 24.575000762939453,
"blob_id": "b9f9a5ff5982fb4440e4174b374750c151fba9eb",
"content_id": "1c11c2d74b4c9bbe111e62f359888e813d91f1f0",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,
"language": "Python",
"length_bytes": 1023,
"license_type": "no_license",
"max_line_length": 119,
"num_lines": 40,
"path": "/debug.py",
"repo_name": "centralpark/IntegrationPipeline",
"src_encoding": "UTF-8",
"text": "import re\nfrom Bio import Entrez\nfrom StringIO import StringIO\ngene = 'CSDE1'\n\n\nEntrez.email = '[email protected]'\nquery = '('+gene+'[Gene Name]) AND human[Organism]'\nhandle = Entrez.esearch(db = 'gene',term = query)\nrecord = Entrez.read(handle)\nhandle.close()\nID = record['IdList'][0]\nhandle = Entrez.efetch(db = 'gene', id = ID, rettype = 'gene_table', retmode = 'text')\nrecord = handle.read()\nhandle.close()\n\nind = record.find('annotated AA length:')\naa_len = 0\nindex = 0\nwhile ind > 0:\n matchObj = re.search('annotated AA length: ([0-9]*)',record[ind:ind+30])\n temp = matchObj.group(1)\n if temp > aa_len:\n aa_len = temp\n index = ind\n ind = record.find('annotated AA length:',ind+30)\n\nf = StringIO(record)\nf.seek(index)\nfor i in range(5):\n f.readline()\n\nline = f.readline()\nwhile line and line!='\\n':\n print line\n [genom_inter_exon,genom_inter_code,gene_inter_exon,gene_inter_code,exon_len,code_len,intron_len] = line.split('\\t')\n print gene_inter_code\n line = f.readline()\n \nf.close()\n"
},
{
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"alphanum_fraction": 0.4870039224624634,
"avg_line_length": 31.456790924072266,
"blob_id": "a6b89c51d0398c3bb9c96615cfa87d46214c5919",
"content_id": "f59859a874c2acd48526d12f43f6260ff2e0056d",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,
"language": "Python",
"length_bytes": 7887,
"license_type": "no_license",
"max_line_length": 99,
"num_lines": 243,
"path": "/GeneFusion_bakcup.py",
"repo_name": "centralpark/IntegrationPipeline",
"src_encoding": "UTF-8",
"text": "import urllib\nimport string\nimport re\nfrom bs4 import BeautifulSoup\n\ndef FindKinaseDomain(Entry):\n \n url = string.join(['http://www.uniprot.org/uniprot/',Entry,'.txt'],'')\n\n f = urllib.urlopen(url)\n\n load_file = open(Entry,'w')\n load_file.write(f.read())\n load_file.close()\n f.close()\n\n info = open(Entry,'r')\n text = info.read()\n loc = text.find('FT DOMAIN')\n if loc>=0: \n info.seek(loc)\n\n FT_end = True\n found = False\n while FT_end:\n line = info.readline()\n params = line.split()\n Description = string.join(params[4:],' ')\n if not (params[0]=='FT' and params[1]=='DOMAIN'):\n break\n if 'kinase' in Description:\n print 'kinase domain found!'\n print 'Description:',Description\n print 'Position(s):',params[2],'-',params[3]\n found = True\n\n if not found:\n print 'kinase domain not found!'\n\n else:\n print 'kinase domain not found!'\n\n\ndef UniprotInfo(GeneName):\n \n urls = 'http://www.uniprot.org/uniprot/?query='+GeneName+\\\n '+AND+organism:Human+reviewed:yes&sort=score'\n query = urllib.urlopen(urls)\n info = {}\n \n try:\n results_page = BeautifulSoup(query.read())\n results = results_page.find('table',attrs={'id':'results'})\n result = results.tr.next_sibling\n entry = result.find('a',href=re.compile('/uniprot/')).string.encode('ascii')\n\n query.close()\n \n info['pEntry'] = entry\n\n url = 'http://www.uniprot.org/uniprot/'+entry\n html = urllib.urlopen(url)\n page = BeautifulSoup(html.read())\n html.close()\n\n comments = page.find('div', attrs={'id':'content-comments'})\n row_locs = comments.find_all('a',href=re.compile('/locations'))\n temp = []\n for loc in row_locs:\n temp.append(loc.string.encode('ascii'))\n info['pLoc'] = '.'.join(temp)\n ontologies = page.find('div',attrs={'id':'content-terms'})\n for row in ontologies.table.tbody.children:\n if not row.find('td'):\n continue\n if re.search('Molecular function',row.td.string.encode('ascii','ignore')):\n temp = []\n for func in row.find_all('a',href=True):\n temp.append(func.string.encode('ascii'))\n info['pFunction'] = '.'.join(temp)\n break\n\n \n \n return info\n except:\n info = {}\n return info\n\ndef integrateFile(filename,tool,ofile):\n # define header columns\n Tool = 0\n sampleID = 1\n UNC_ID = 2\n chrom1 = 3\n gene1 = 4\n gene1_start = 5\n gene1_end = 6\n exon1 = 7\n gene1_ori = 8\n chrom2 = 9\n gene2 = 10\n gene2_start = 11\n gene2_end = 12\n exon2 = 13\n gene2_ori = 14\n Type = 15\n total_fragments = 16\n spanning_fragments = 17\n chimera_cluster_id = 18\n score = 19\n transcript_id_5 = 20\n transcript_id_3 = 21\n\n N_col = 22\n\n\n f = open(filename,'r')\n output = open(ofile,'a')\n\n if tool=='ChimeraScan':\n for line in f:\n line_out = [' ']*N_col\n columns = line.split('\\t')\n line_out[Tool] = 'ChimeraScan'\n line_out[sampleID] = columns[0]\n line_out[UNC_ID] = columns[1]\n line_out[chrom1] = columns[2]\n line_out[gene1] = columns[14]\n line_out[gene1_start] = columns[3]\n line_out[gene1_end] = columns[4]\n line_out[gene1_ori] = columns[10]\n line_out[chrom2] = columns[5]\n line_out[gene2] = columns[15]\n line_out[gene2_start] = columns[6]\n line_out[gene2_end] = columns[7]\n line_out[gene2_ori] = columns[11]\n line_out[Type] = columns[16]\n if columns[16] =='Read_Through':\n line_out[Type] = 'Read-through'\n line_out[total_fragments] = columns[18]\n line_out[spanning_fragments] = columns[19]\n line_out[chimera_cluster_id] = columns[8]\n line_out[score] = columns[9]\n line_out[transcript_id_5] = columns[12]\n line_out[transcript_id_3] = columns[13]\n output.write('\\t'.join(line_out)+'\\n')\n\n elif tool=='BEDA':\n for line in f:\n line_out = [' ']*N_col\n columns = line.split('\\t')\n line_out[Tool] = 'BEDA'\n line_out[UNC_ID] = columns[0]\n line_out[gene1] = columns[1]\n line_out[gene2] = columns[2]\n line_out[Type] = columns[7]\n if line_out[Type] == 'intra':\n line_out[Type] = 'Intrachromosomal'\n elif line_out[Type] =='inter':\n line_out[Type] = 'Interchromosomal'\n else:\n pass\n output.write('\\t'.join(line_out)+'\\n')\n\n elif tool=='BreakFusion':\n for line in f:\n line_out = [' ']*N_col\n columns = line.split('\\t')\n line_out[Tool] = 'BreakFusion'\n line_out[sampleID] = columns[0]\n line_out[chrom1] = columns[2]\n line_out[gene1_start] = columns[3]\n line_out[chrom2] = columns[5]\n line_out[gene2_start] = columns[6]\n\n matchObj = re.match('Gene:(.*?)\\|(.*?),.*?\\|.*?:(.*?)-.*?\\|.*?:(.*?),(.*)',columns[14])\n line_out[gene1] = matchObj.group(1)\n line_out[gene2] = matchObj.group(2)\n if matchObj.group(3) == 'NA':\n line_out[exon1] = 'NA'\n line_out[gene1_start] = 'NA'\n line_out[gene1_end] = 'NA'\n else:\n temp = re.split(':|/',matchObj.group(3))\n line_out[exon1] = temp[0]\n line_out[gene1_start] = temp[1]\n line_out[gene1_end] = temp[2]\n if matchObj.group(4) == 'NA':\n line_out[exon2] = 'NA'\n line_out[gene2_start] = 'NA'\n line_out[gene2_end] = 'NA'\n else:\n temp = re.split(':|/',matchObj.group(4))\n line_out[exon2] = temp[0]\n line_out[gene2_start] = temp[1]\n line_out[gene2_end] = temp[2]\n temp = re.match('Fusion,(.*)',matchObj.group(5))\n if temp:\n line_out[Type] = temp.group(1)\n if line_out[Type] == 'Readthrough':\n line_out[Type] = 'Read-through'\n output.write('\\t'.join(line_out)+'\\n')\n else:\n print 'Gene fusion tool not supported!'\n\n f.close()\n output.close()\n\n\n\ndef integrate():\n ofile = '/Users/HSH/Desktop/Rearrangement'\n f = open(ofile,'w')\n f.write('Gene Fusion Tool\\t'\n 'Sample ID\\t'\n 'UNC_ID\\t'\n 'Chromosome 1\\t'\n 'Gene 1\\t'\n 'Gene 1 Start\\t'\n 'Gene 1 End\\t'\n 'Exon 1\\t'\n 'Gene 1 Orientation\\t'\n 'Chromosome 2\\t'\n 'Gene 2\\t'\n 'Gene 2 Start\\t'\n 'Gene 2 End\\t'\n 'Exon 2\\t'\n 'Gene 2 Orientation\\t'\n 'Fusion Type\\t'\n 'Total Fragments\\t'\n 'Spanning Fragments\\t'\n 'Chimera Cluster ID\\t'\n 'Score\\t'\n '5\\' transcript ids\\t'\n '3\\' transcript ids\\n')\n\n GeneFusion.integrateFile('/Users/HSH/Desktop/Data/all_chimerascan.txt',\\\n 'ChimeraScan',ofile)\n GeneFusion.integrateFile('/Users/HSH/Desktop/Data/beda_fusions.txt',\\\n 'BEDA',ofile)\n GeneFusion.integrateFile('/Users/HSH/Desktop/Data/all_breakfuse.tsv',\\\n 'BreakFusion',ofile)\n"
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"text": "import string\n\noutput_col = open('/Users/HSH/Desktop/Output_ColNames.txt','w')\n\nf = open('/Users/HSH/Desktop/output.txt','r')\n\nline = f.readline()\n\nwhile line:\n words = line.split(':')\n word = words[0]\n output_col.write(word.rstrip()+'\\n')\n line = f.readline()\n\noutput_col.close()\nf.close()\n"
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"text": "from time import time\nimport pickle\nimport sys\nsys.path.append('/Users/HSH/Documents')\nimport GeneFusion\n\nt0 = time()\n\nifile = '/Users/HSH/Desktop/tmp_fusions_ov_chimerascan_integrate.txt'\nofile = '/Users/HSH/Desktop/tmp_fusions_ov_chimerascan_annotate.txt'\n\nf = open(ifile,'r')\nof = open(ofile,'w')\nheader = f.readline()\ncolName = header.split('\\t')\ncolName_new = colName[:9] + \\\n ['Protein 1 Uniprot Entry','Protein 1 Subcellular Location','Protein 1 Function'] + \\\n colName[9:15] + \\\n ['Protein 2 Uniprot Entry','Protein 2 Subcellular Location','Protein 2 Function'] + \\\n colName[15:]\nheader_new = '\\t'.join(colName_new)\nof.write(header_new)\n\nN = 0\n\n# import saved Uniprot information database\npkl_file = open('/Users/HSH/Documents/protInfoDict.pkl','rb')\nprotInfoDict = pickle.load(pkl_file)\n\nfor line in f:\n col = line.split('\\t')\n tool = col[0]\n if tool == 'ChimeraScan':\n gene1 = col[4].split(',')[0]\n gene2 = col[10].split(',')[0]\n else:\n gene1 = col[4]\n gene2 = col[10]\n # If the gene is searched before, no need to search again\n # saves run time on large input file\n b1 = (gene1 in protInfoDict)\n b2 = (gene2 in protInfoDict)\n if b1:\n protInfo1 = protInfoDict[gene1]\n else:\n protInfo1 = GeneFusion.UniprotInfo(gene1)\n if b2:\n protInfo2 = protInfoDict[gene2]\n else:\n protInfo2 = GeneFusion.UniprotInfo(gene2)\n \n # Some protein information does not exist\n p_keys = ['pEntry','pLoc','pFunction']\n p_value1 = []\n p_value2 = []\n for p_key in p_keys:\n try:\n p_value1.append(protInfo1[p_key])\n except KeyError:\n p_value1.append('')\n try:\n p_value2.append(protInfo2[p_key])\n except KeyError:\n p_value2.append('')\n if not b1:\n protInfoDict[gene1] = {'pEntry':p_value1[0],'pLoc':p_value1[1],'pFunction':p_value1[2]}\n if not b2:\n protInfoDict[gene2] = {'pEntry':p_value2[0],'pLoc':p_value2[1],'pFunction':p_value2[2]}\n \n col_new = col[:9] + p_value1 + col[9:15] + p_value2 + col[15:]\n line_new = '\\t'.join(col_new)\n of.write(line_new)\n\n N+=1\n if N > 10:\n break\n \nf.close()\nof.close()\n\nt = time()\n"
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"text": "import string\r\n\r\ndef inputFile(filename,tool):\r\n \r\n f = open(filename,'r')\r\n output = open('Rearrangement','a')\r\n header = f.readline()\r\n names = header.split('\\t')\r\n if names[0]=='Sample ID from TCGA':\r\n col_5pChr = 1\r\n\r\n line = f.readline()\r\n if tool=='ChimeraScan':\r\n while line:\r\n words = line.split('\\t')\r\n five_Chr = words[0].split(':')[1]\r\n five_start = words[1]\r\n five_end = words[2]\r\n five_gene = words[12]\r\n five_ori = words[8]\r\n three_Chr = words[3]\r\n three_start = words[4]\r\n three_end = words[5]\r\n three_gene = words[13]\r\n three_ori = words[9]\r\n fusion_type = words[14]\r\n spanning_reads = words[21]\r\n score = words[7]\r\n line_out = string.join([five_Chr,five_start,five_end,five_gene,\r\n five_ori,three_Chr,three_start,three_end,\r\n three_gene,three_ori,fusion_type],'\\t')\r\n output.write(line_out+'\\n')\r\n line = f.readline()\r\n elif tool=='BEDA':\r\n while line:\r\n words = line.split('\\t')\r\n five_Chr = 'N/A'\r\n five_start = 'N/A'\r\n five_end = 'N/A'\r\n five_gene = words[1]\r\n five_ori = 'N/A'\r\n three_Chr = 'N/A'\r\n three_start = 'N/A'\r\n three_end = 'N/A'\r\n three_gene = words[2]\r\n three_ori = 'N/A'\r\n fusion_type = words[7]\r\n spanning_reads = words[11]\r\n \r\n line_out = string.join([five_Chr,five_start,five_end,five_gene,\r\n five_ori,three_Chr,three_start,three_end,\r\n three_gene,three_ori,fusion_type],'\\t')\r\n output.write(line_out+'\\n')\r\n line = f.readline()\r\n elif tool=='BreakFusion':\r\n while line:\r\n words = line.split('\\t')\r\n five_Chr = words[0].split(':')[1]\r\n five_start = words[1]\r\n five_end = 'N/A'\r\n five_gene = 'N/A'\r\n five_ori = words[2]\r\n three_Chr = words[3]\r\n three_start = words[4]\r\n three_end = 'N/A'\r\n three_gene = 'N/A'\r\n three_ori = words[5]\r\n fusion_type = words[6]\r\n spanning_reads = 'N/A'\r\n score = words[8]\r\n line_out = string.join([five_Chr,five_start,five_end,five_gene,\r\n five_ori,three_Chr,three_start,three_end,\r\n three_gene,three_ori,fusion_type],'\\t')\r\n output.write(line_out+'\\n')\r\n line = f.readline()\r\n else:\r\n print 'The specified gene fusion tool is not supported!'\r\n \r\n f.close()\r\n output.close()\r\n\r\n\r\n"
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"text": "# Transform files into formats that other programs need\n\nimport string\nimport re\n\ndef format_1(ifile,ofile):\n\n f = open(ifile,'r')\n of = open(ofile,'w')\n for line in f:\n if '>' in line:\n fields = line.split()\n info = string.join(fields[:2],' ')\n of.write(info+'\\n')\n of.write(fields[-1]+'\\n')\n else:\n of.write(line)\n f.close()\n of.close()\n\ndef BreakFusion_clean(ifile,ofile):\n f = open(ifile,'r')\n of = open(ofile,'w')\n for line in f:\n columns = line.split('\\t')\n if re.match('Gene',columns[14]):\n of.write(line)\n else:\n newline = '\\t'.join(columns[:13])+'\\t'+' '+'\\t'+'\\t'.join(columns[13:])\n of.write(newline)\n\n\ndef ChimeraScan_clean(ifile,ofile):\n f = open(ifile,'r')\n of = open(ofile,'w')\n line_1 = f.readline()\n if line_1[0] == '#':\n pass\n else:\n of.write(line_1)\n for line in f:\n of.write(line)\n \n\n\ndef overlapRatio(start,end,txStart,txEnd):\n if end<txStart or start>txEnd:\n return 0.0\n elif start<txStart:\n return float(end-txStart+1)/float(end-start+1)\n elif end>txEnd:\n return float(txEnd-start+1)/float(end-start+1)\n else:\n return 1.0\n\ndef mapGeneOnChrom(chrom,start,end,allGenes):\n score_high = 0\n mapGene = 'null'\n try:\n allGenesOnChrom = allGenes[chrom]\n except:\n # Chromosome not listed\n return ''\n for gene in allGenesOnChrom:\n txStart = allGenesOnChrom[gene]['start']\n txEnd = allGenesOnChrom[gene]['end']\n score = overlapRatio(start,end,txStart,txEnd)\n if score > 0.5:\n return gene\n elif score > score_high:\n score_high = score\n mapGene = gene\n else:\n pass\n return mapGene\n\n\n\ndef GeneAnnot(ifile,ofile,RefSeqFile):\n allGenes = {}\n f = open(RefSeqFile,'r')\n f.readline()\n chromList = ['chr1','chr2','chr3','chr4','chr5','chr6','chr7','chr8','chr9',\\\n 'chr10','chr11','chr12','chr13','chr14','chr15','chr16','chr17',\\\n 'chr18','chr19','chr20','chr21','chr22','chrX','chrY']\n for c in chromList:\n allGenes[c] = {}\n\n for line in f:\n chrom = line.split()[2]\n name2 = line.split()[12]\n start = int(line.split()[4])\n end = int(line.split()[5])\n for c in chromList:\n if chrom == c:\n allGenesOnChrom = allGenes[chrom]\n if name2 not in allGenesOnChrom.keys():\n allGenesOnChrom[name2] = {'start':start,'end':end}\n\n f.close()\n\n # Scan integrated gene fusion detection result for genes and replace with\n # official gene names\n f = open(ifile,'r')\n of = open(ofile,'w')\n of.write(f.readline())\n\n for line in f:\n cols = line.split('\\t')\n chrom = cols[3]\n # deal with irregular ifile\n if chrom[0] == '#':\n continue\n start = int(cols[5])\n end = int(cols[6])\n mapGene = mapGeneOnChrom(chrom,start,end,allGenes)\n if mapGene:\n cols[4] = mapGene\n chrom = cols[9]\n start = int(cols[11])\n end = int(cols[12])\n mapGene = mapGeneOnChrom(chrom,start,end,allGenes)\n if mapGene:\n cols[10] = mapGene\n newline = '\\t'.join(cols)\n of.write(newline)\n\n t = time()\n f.close()\n of.close()\n"
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"text": "import string\r\n\r\ndef inputFile(filename,tool):\r\n # define header columns\r\n sampleID = 0\r\n UNC_ID = 1\r\n chrom1 = 2\r\n gene1 = 3\r\n gene1_start = 4\r\n gene1_end = 5\r\n gene1_ori = 6\r\n chrom2 = 7\r\n gene2 = 8\r\n gene2_start = 9\r\n gene2_end = 10\r\n gene2_ori = 11\r\n Type = 12\r\n N_col = 13\r\n \r\n f = open(filename,'r')\r\n output = open('Rearrangement','a')\r\n \r\n if tool=='ChimeraScan':\r\n for line in f:\r\n line_out = ['N\\A']*N_col\r\n columns = line.split('\\t')\r\n line_out[sampleID] = columns[0]\r\n line_out[UNC_ID] = columns[1]\r\n line_out[chrom1] = columns[2]\r\n line_out[gene1] = columns[14]\r\n line_out[gene1_start] = columns[3]\r\n line_out[gene1_end] = columns[4]\r\n line_out[gene1_ori] = columns[10]\r\n line_out[chrom2] = columns[5]\r\n line_out[gene2] = columns[15]\r\n line_out[gene2_start] = columns[6]\r\n line_out[gene2_end] = columns[7]\r\n line_out[gene2_ori] = columns[11]\r\n line_out[Type] = columns[16]\r\n output.write(string.join(line_out,'\\t')+'\\n')\r\n\r\n elif tool=='BEDA':\r\n for line in f:\r\n line_out = ['N\\A']*N_col\r\n columns = line.split('\\t')\r\n line_out[UNC_ID] = columns[0]\r\n line_out[gene1] = columns[1]\r\n line_out[gene2] = columns[2]\r\n line_out[Type] = columns[7]\r\n output.write(string.join(line_out,'\\t')+'\\n')\r\n\r\n elif tool=='BreakFusion':\r\n for line in f:\r\n line_out = ['N\\A']*N_col\r\n columns = line.split('\\t')\r\n line_out[sampleID] = columns[0]\r\n line_out[chrom1] = columns[2]\r\n line_out[gene1_start] = columns[3]\r\n line_out[chrom2] = columns[5]\r\n line_out[gene2_start] = columns[6]\r\n line_out[Type] = columns[8]\r\n output.write(string.join(line_out,'\\t')+'\\n')\r\n\r\n else:\r\n print 'Gene fusion tool not supported!'\r\n \r\n f.close()\r\n output.close()\r\n \r\n"
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"text": "from time import time\nfrom Bio import Entrez\n\n\nEntrez.email = '[email protected]'\nhandle = Entrez.esearch(db = 'pubmed', term = 'biopython')\nrecord = Entrez.read(handle)\nrecord['IdList']"
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"text": "import urllib\nimport string\nimport re\nimport pickle\nfrom os import listdir\nfrom os.path import join\nfrom bs4 import BeautifulSoup\nimport fileFormat\nfrom Bio import Entrez\n\n\ndef FindKinaseDomain(Entry):\n \n url = string.join(['http://www.uniprot.org/uniprot/',Entry,'.txt'],'')\n\n f = urllib.urlopen(url)\n\n load_file = open(Entry,'w')\n load_file.write(f.read())\n load_file.close()\n f.close()\n\n info = open(Entry,'r')\n text = info.read()\n loc = text.find('FT DOMAIN')\n if loc>=0: \n info.seek(loc)\n\n FT_end = True\n found = False\n while FT_end:\n line = info.readline()\n params = line.split()\n Description = string.join(params[4:],' ')\n if not (params[0]=='FT' and params[1]=='DOMAIN'):\n break\n if 'kinase' in Description:\n print 'kinase domain found!'\n print 'Description:',Description\n print 'Position(s):',params[2],'-',params[3]\n found = True\n\n if not found:\n print 'kinase domain not found!'\n\n else:\n print 'kinase domain not found!'\n\n\ndef UniprotInfo(GeneName):\n \n info = {}\n # Dealing with null gene name annotation\n if GeneName.lower()=='null':\n return info\n \n urls = 'http://www.uniprot.org/uniprot/?query='+GeneName+\\\n '+AND+organism:Human+reviewed:yes&sort=score'\n query = urllib.urlopen(urls)\n\n try:\n results_page = BeautifulSoup(query.read())\n results = results_page.find('table',attrs={'id':'results'})\n if not results:\n # The protein may not be reviewed\n query.close()\n urls = 'http://www.uniprot.org/uniprot/?query='+GeneName+\\\n '+AND+organism:Human&sort=score'\n query = urllib.urlopen(urls)\n results_page = BeautifulSoup(query.read())\n results = results_page.find('table',attrs={'id':'results'})\n \n result = results.tr.next_sibling\n entry = result.find('a',href=re.compile('/uniprot/')).string.encode('ascii')\n\n query.close()\n \n info['pEntry'] = entry\n\n url = 'http://www.uniprot.org/uniprot/'+entry\n html = urllib.urlopen(url)\n page = BeautifulSoup(html.read())\n html.close()\n url = 'http://www.uniprot.org/uniprot/'+entry\n html = urllib.urlopen(url)\n page = BeautifulSoup(html.read())\n html.close()\n\n try:\n comments = page.find('div', attrs={'id':'content-comments'})\n row_locs = comments.find_all('a',href=re.compile('/locations'))\n temp = []\n for loc in row_locs:\n temp.append(loc.string.encode('ascii'))\n info['pLoc'] = '.'.join(temp)\n except:\n info['pLoc'] = ''\n\n try:\n info['pFunction'] = ''\n ontologies = page.find('div',attrs={'id':'content-terms'})\n for row in ontologies.table.tbody.children:\n if not row.find('td'):\n continue\n if re.search('Molecular function',row.td.string.encode('ascii','ignore')):\n temp = []\n for func in row.find_all('a',href=True):\n temp.append(func.string.encode('ascii'))\n info['pFunction'] = '.'.join(temp)\n break \n except:\n pass\n \n return info\n except:\n # No Uniprot entry found, probably non-coding gene\n info['pEntry'] = 'NA'\n return info\n\n\ndef integrateFile(filename,tool,ofile):\n # define header columns\n Tool = 0\n sampleID = 1\n UNC_ID = 2\n chrom1 = 3\n gene1 = 4\n gene1_start = 5\n gene1_end = 6\n exon1 = 7\n gene1_ori = 8\n chrom2 = 9\n gene2 = 10\n gene2_start = 11\n gene2_end = 12\n exon2 = 13\n gene2_ori = 14\n Type = 15\n total_fragments = 16\n spanning_fragments = 17\n chimera_cluster_id = 18\n score = 19\n transcript_id_5 = 20\n transcript_id_3 = 21\n\n N_col = 22\n\n\n f = open(filename,'r')\n output = open(ofile,'a')\n\n if tool=='ChimeraScan':\n for line in f:\n line_out = [' ']*N_col\n columns = line.split('\\t')\n line_out[Tool] = 'ChimeraScan'\n line_out[sampleID] = ''\n line_out[UNC_ID] = ''\n line_out[chrom1] = columns[0]\n line_out[gene1] = columns[12]\n line_out[gene1_start] = columns[1]\n line_out[gene1_end] = columns[2]\n line_out[gene1_ori] = columns[8]\n line_out[chrom2] = columns[3]\n line_out[gene2] = columns[13]\n line_out[gene2_start] = columns[4]\n line_out[gene2_end] = columns[5]\n line_out[gene2_ori] = columns[9]\n line_out[Type] = columns[14]\n if columns[14] =='Read_Through':\n line_out[Type] = 'Read-through'\n line_out[total_fragments] = columns[16]\n line_out[spanning_fragments] = columns[17]\n line_out[chimera_cluster_id] = columns[6]\n line_out[score] = columns[7]\n line_out[transcript_id_5] = columns[10]\n line_out[transcript_id_3] = columns[11]\n output.write('\\t'.join(line_out)+'\\n')\n\n elif tool=='BEDA':\n for line in f:\n line_out = [' ']*N_col\n columns = line.split('\\t')\n line_out[Tool] = 'BEDA'\n line_out[UNC_ID] = columns[0]\n line_out[gene1] = columns[1]\n line_out[gene2] = columns[2]\n line_out[Type] = columns[7]\n if line_out[Type] == 'intra':\n line_out[Type] = 'Intrachromosomal'\n elif line_out[Type] =='inter':\n line_out[Type] = 'Interchromosomal'\n else:\n pass\n output.write('\\t'.join(line_out)+'\\n')\n\n elif tool=='BreakFusion':\n for line in f:\n line_out = [' ']*N_col\n columns = line.split('\\t')\n line_out[Tool] = 'BreakFusion'\n line_out[sampleID] = columns[0]\n line_out[chrom1] = columns[2]\n line_out[gene1_start] = columns[3]\n line_out[chrom2] = columns[5]\n line_out[gene2_start] = columns[6]\n\n matchObj = re.match('Gene:(.*?)\\|(.*?),.*?\\|.*?:(.*?)-.*?\\|.*?:(.*?),(.*)',columns[14])\n line_out[gene1] = matchObj.group(1)\n line_out[gene2] = matchObj.group(2)\n if matchObj.group(3) == 'NA':\n line_out[exon1] = 'NA'\n line_out[gene1_start] = 'NA'\n line_out[gene1_end] = 'NA'\n else:\n temp = re.split(':|/',matchObj.group(3))\n line_out[exon1] = temp[0]\n line_out[gene1_start] = temp[1]\n line_out[gene1_end] = temp[2]\n if matchObj.group(4) == 'NA':\n line_out[exon2] = 'NA'\n line_out[gene2_start] = 'NA'\n line_out[gene2_end] = 'NA'\n else:\n temp = re.split(':|/',matchObj.group(4))\n line_out[exon2] = temp[0]\n line_out[gene2_start] = temp[1]\n line_out[gene2_end] = temp[2]\n temp = re.match('Fusion,(.*)',matchObj.group(5))\n if temp:\n line_out[Type] = temp.group(1)\n if line_out[Type] == 'Readthrough':\n line_out[Type] = 'Read-through'\n output.write('\\t'.join(line_out)+'\\n')\n else:\n print 'Gene fusion tool not supported!'\n\n f.close()\n output.close()\n\n\n\ndef integrate(directory,ofile):\n # integrate all gene fusion results file in the directory\n f = open(ofile,'w')\n f.write('Gene Fusion Tool\\t'\n 'Sample ID\\t'\n 'UNC_ID\\t'\n 'Chromosome 1\\t'\n 'Gene 1\\t'\n 'Gene 1 Start\\t'\n 'Gene 1 End\\t'\n 'Exon 1\\t'\n 'Gene 1 Orientation\\t'\n 'Chromosome 2\\t'\n 'Gene 2\\t'\n 'Gene 2 Start\\t'\n 'Gene 2 End\\t'\n 'Exon 2\\t'\n 'Gene 2 Orientation\\t'\n 'Fusion Type\\t'\n 'Total Fragments\\t'\n 'Spanning Fragments\\t'\n 'Chimera Cluster ID\\t'\n 'Score\\t'\n '5\\' transcript ids\\t'\n '3\\' transcript ids\\n')\n f.close()\n\n for ifile in listdir(directory):\n if re.search('chimera',ifile,re.I):\n integrateFile(join(directory,ifile),'ChimeraScan',ofile)\n elif re.search('beda',ifile,re.I):\n integrateFile(join(directory,ifile),'BEDA',ofile)\n elif re.search('break',ifile,re.I):\n filename = ifile+'_CLEAN.txt'\n fileFormat.BreakFusion_clean(join(directory,ifile),join(directory,filename))\n integrateFile(join(directory,filename),'BreakFusion',ofile)\n else:\n pass\n \n\n\ndef EncodeAnnot(gene):\n try:\n Entrez.email = '[email protected]'\n query = '('+gene+'[Gene Name]) AND human[Organism]'\n handle = Entrez.esearch(db = 'gene',term = query)\n record = Entrez.read(handle)\n handle.close()\n ID = record['IdList'][0]\n handle = Entrez.efetch(db = 'gene', id = ID, retmode = 'xml')\n record = Entrez.read(handle)\n handle.close()\n encode = record[0]['Entrezgene_type'].attributes['value'].encode('ascii')\n return encode\n except:\n return ''\n\n\n \ndef AddUniprotAnnotation(ifile,ofile,encodeInfoDict={}):\n f = open(ifile,'r')\n of = open(ofile,'w')\n header = f.readline()\n colName = header.split('\\t')\n colName_new = colName[:9] + \\\n ['Encode 1','Protein 1 Uniprot Entry','Protein 1 Subcellular Location','Protein 1 Function'] + \\\n colName[9:15] + \\\n ['Encode 2','Protein 2 Uniprot Entry','Protein 2 Subcellular Location','Protein 2 Function'] + \\\n colName[15:]\n header_new = '\\t'.join(colName_new)\n of.write(header_new)\n\n # Pre-fill for non-gene\n encodeInfoDict['null'] = ['',{}]\n\n for line in f:\n col = line.split('\\t')\n tool = col[0]\n if tool == 'ChimeraScan':\n gene1 = col[4].split(',')[0]\n gene2 = col[10].split(',')[0]\n else:\n gene1 = col[4]\n gene2 = col[10]\n # If the gene is searched before, no need to search again\n # saves run time on large input file\n b1 = (gene1 in encodeInfoDict)\n b2 = (gene2 in encodeInfoDict)\n if b1:\n encode1 = encodeInfoDict[gene1][0]\n protInfo1 = encodeInfoDict[gene1][1]\n else:\n encode1 = EncodeAnnot(gene1)\n protInfo1 = {}\n if encode1 == 'protein-coding':\n protInfo1 = UniprotInfo(gene1)\n encodeInfoDict[gene1] = [encode1,protInfo1]\n if b2:\n encode2 = encodeInfoDict[gene2][0]\n protInfo2 = encodeInfoDict[gene2][1]\n else:\n encode2 = EncodeAnnot(gene2)\n protInfo2 = {}\n if encode2 == 'protein-coding':\n protInfo2 = UniprotInfo(gene2)\n encodeInfoDict[gene2] = [encode2,protInfo2]\n \n # Some protein information does not exist\n p_keys = ['pEntry','pLoc','pFunction']\n p_value1 = []\n p_value2 = []\n for p_key in p_keys:\n try:\n p_value1.append(protInfo1[p_key])\n except KeyError:\n p_value1.append('')\n try:\n p_value2.append(protInfo2[p_key])\n except KeyError:\n p_value2.append('')\n \n col_new = col[:9] + [encode1] + p_value1 + col[9:15] + [encode2] + p_value2 + col[15:]\n line_new = '\\t'.join(col_new)\n of.write(line_new)\n\n\n f.close()\n of.close()\n return encodeInfoDict\n"
}
] | 9 |
beiyue/aws-rekognition-costdown
|
https://github.com/beiyue/aws-rekognition-costdown
|
4079da1466b05769b4ecf5286d46f1a9f40443b9
|
250ebf671f2c3bd9737036c57b9189d1758d8453
|
94537be63ef7709844ed791d1e9160da1117e710
|
refs/heads/master
| 2023-03-27T10:21:43.758853 | 2021-03-31T08:34:31 | 2021-03-31T08:34:31 | 347,879,695 | 13 | 0 | null | null | null | null | null |
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"text": "#!/bin/bash\n\nsudo ffmpeg -i $1 -an -vf select='eq(pict_type\\,I)' -vsync 2 -s 720*480 -f image2 $2/image-%03d.jpg\n"
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"text": "# AWS Rekognition成本优化方案\n使用图片拼接降低Rekognition API 调用次数从而优化成本\n\n#### 从视频文件中抽取关键帧并转为图片\n\nsudo ./generate_thumbnails.sh videoplayback.mp4 <THUMBNAILS_PATH>\n\n\n\n抽帧图片：\n\n \n\n#### 将4张图片横向拼接成一张大图\n\nsudo python3 mergeImages.py \"<THUMBNAILS_PATH>\"\n\n\n\n拼接图例：\n\n\n#### 将拼接后的大图请求AWS Rekogntion detect_labels API， 可用于人形/人脸/宠物/口罩检测等\n\nsudo python3 rek_person_detect_test.py \"<MERGE_PATH>\"\n\n\n\n效果展示：\n\n\n"
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"text": "import json \nimport boto3 \nimport logging \nimport json \nimport base64 \nimport cgi \nimport json \nfrom io import BytesIO \nimport uuid\nimport os\nimport sys\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\nclient = boto3.client('rekognition')\n\npath = sys.argv[1]\nimages = []\nimagefile = []\n\n#存储所有图片文件名称\nfor root, dirs, files in os.walk(path):\n\tfor f in files:\n\t\timages.append(f)\n\njsonstr=''\nfor i in range(len(images)):\n\tprint(images[i])\n\ttmpf = open(path+'/'+images[i], \"rb\")\n\tresponse = client.detect_labels(Image={'Bytes':tmpf.read()},MaxLabels=5,MinConfidence=99)\n\tfor obj in response['Labels']:\n\t\tif obj['Name'] == 'Person':\n\t\t\tjsonstr = json.dumps(obj)\n\tprint(jsonstr+'\\n')\n\n\n"
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"text": "import os\nimport random\nimport sys\n\nfrom PIL import Image\n \n#单个图片的大小为320*240\nUNIT_SIZE = 720\nTARGET_WIDTH = 4 * UNIT_SIZE\n \npath = sys.argv[1]\n\nimages = []\nimagefile = []\n\n#存储所有图片文件名称\nfor root, dirs, files in os.walk(path):\n for f in files:\n images.append(f)\n print(f)\n\n#我这里是将4张图片横向拼接\nfor i in range(len(images)):\n imagefile.append(path+'/'+images[i])\n\nleft = 0\nright = 0\ntarget = Image.new('RGB',(TARGET_WIDTH, 480))\n\nfor image in imagefile:\n #print(image)\n #将现有图片复制到新的上面 参数分别为图片文件和复制的位置(左上角, 右下角)\n x,y = Image.open(image).size\n right = y\n target.paste(Image.open(image), (left, 0, (left+x), right))\n left += x\n \n #图片的质量 0~100\n quantity_value = 100\n if imagefile.index(image) % 4 == 0 or (len(imagefile) -1 == imagefile.index(image)) : \n target.save(path+'/merge/merge'+str(random.randint(1,1000)).rjust(4,'0') +'.jpg', quantity = quantity_value)\n left = 0\n right = 0\n target = Image.new('RGB',(TARGET_WIDTH, 480))\n\n'''\nfor i in range(5):\n imagefile.append(path+'/'+images[i])\n target = Image.new('RGB',(TARGET_WIDTH, UNIT_SIZE))\n left = 0\n right = UNIT_SIZE\nprint(imagefile)\nfor image in imagefile:\n #print(image)\n #将现有图片复制到新的上面 参数分别为图片文件和复制的位置(左上角, 右下角)\n target.paste(Image.open(image), (left, 0, right, UNIT_SIZE))\n left += UNIT_SIZE\n right += UNIT_SIZE\n #图片的质量 0~100\n quantity_value = 100\n target.save(path+'/end.jpg', quantity = quantity_value)\n'''\n\n\n"
}
] | 4 |
DeveloperJose/Vision-Rat-Brain
|
https://github.com/DeveloperJose/Vision-Rat-Brain
|
4b8d783e0fd29b126900779a66f1381af29cafc9
|
612ef498cf2d6e6ee33ac849216f87e38517d463
|
53b8e745c0c0094a17c5a904bc0ca155852bba89
|
refs/heads/master
| 2021-04-06T13:25:39.189621 | 2019-04-08T18:55:34 | 2019-04-08T18:55:34 | 83,465,992 | 1 | 1 | null | null | null | null | null |
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"repo_name": "DeveloperJose/Vision-Rat-Brain",
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"text": "# Vision Rat Brain\nThis repository contains the supporting code for publications and projects done by Jose G. Perez\n\nIt is not ready for public use yet.\nExpect it to be complete by the end of May 2018 with an installation guide and explanations.\n\nAny questions may be directed to me via pull requests.\n\n## Getting Started\n\nHere's a breakdown of all the projects included in this repository\n\n### Main projects \n* dataset/\n * Used to preprocess (crop, resize, compress) atlas images into NPZ files for use in other projects \n* feature_matching_v1/\n * Proof of concept used for region matching using SIFT and RANSAC\n * http://www.abstractsonline.com/pp8/#!/4376/presentation/4332\n* feature_matching_v2/\n * Region matching improved with custom RANSAC algorithm\n* feature_matching_v3/\n * Whole image matching using SIFT and dynamic programming to derive plate correspondences\n * https://www.frontiersin.org/articles/10.3389/fnsys.2018.00007/abstract\n### Side projects\n* auto_encoder/\n * Deriving atlas correspondences using AE \n* matching_networks/\n * Deriving atlas correspondences through image similarities using matching networks\n* siamese_networks/\n * Deriving atlas correspondences through image similarities using matching networks\n\n### Prerequisites\n\n### Installing\n\n## Contributing\n\nPlease read [CONTRIBUTING.md](http://github.com/DeveloperJose/Vision-Rat-Brain) for details on our code of conduct, and the process for submitting pull requests to us.\n\n## Versioning\n\nWe use [SemVer](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://github.com/DeveloperJose/Vision-Rat-Brain/tags). \n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details\n\n## Acknowledgments\n\n"
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"text": "# Author: Jose G Perez\n# Version 1.0\n# Last Modified: January 31, 2018\nimport numpy as np\nimport cv2\nimport cProfile\nRADIUS = 25 # 25\nRADIUS_SQUARED = RADIUS ** 2\nSCALE_THRESHOLD = 3 # 3\nDISTANCE_THRESHOLD = 200 # 200\nRESPONSE_THRESHOLD = 0.01 # 0.01\n\ndef perform_match(s_idx):\n global s_kp, s_des, pw_kp, pw_des\n matches = []\n print('s_idx', s_idx)\n for pw_idx in range(pw_kp.shape[0]):\n matches.append(match(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]))\n\n np.savez_compressed(str(s_idx) + '-M', m=matches)\n\ndef match(kp1, des1, kp2, des2):\n matches = []\n for idx1 in range(len(kp1)):\n kpa = kp1[idx1]\n kpa_response = kpa[4]\n if kpa_response < RESPONSE_THRESHOLD:\n continue\n\n kpa_x = kpa[0]\n kpa_y = kpa[1]\n kpa_size = kpa[2]\n #kpa_angle = kpa[3]\n dpa = des1[idx1]\n\n best_distance = float('inf')\n best_idx2 = -1\n\n for idx2 in range(len(kp2)):\n kpb = kp2[idx2]\n # 0: Response Strength\n kpb_response = kpb[4]\n if kpb_response < RESPONSE_THRESHOLD:\n continue\n\n # 1: Distance/Radius Check\n kpb_x = kpb[0]\n kpb_y = kpb[1]\n d_pt_squared = (kpb_x - kpa_x) ** 2 + (kpb_y - kpa_y) ** 2\n if d_pt_squared >= RADIUS_SQUARED:\n continue\n\n # 2: Scale Difference\n kpb_size = kpb[2]\n scale_diff = abs(kpa_size - kpb_size)\n if scale_diff > SCALE_THRESHOLD:\n continue\n\n # 3: Descriptor L2 Norm\n dpb = des2[idx2]\n d_des = np.linalg.norm(dpb - dpa)\n if d_des < best_distance:\n best_distance = d_des\n best_idx2 = idx2\n\n # 4: ?? Angle ??\n #kpb_angle = kpb[3]\n\n if best_idx2 == -1 or best_distance > DISTANCE_THRESHOLD:\n continue\n\n match = np.array([idx1, best_idx2, best_distance], dtype=np.int32)\n matches.append(match)\n\n return np.asarray(matches)\n\ndef match_details(kp1, des1, kp2, des2):\n matches = []\n matches_cv = []\n stat_s_diff = set()\n stat_d_dist = set()\n stat_resp_kp1 = set()\n stat_resp_kp2 = set()\n stat_rad = set()\n for idx1 in range(len(kp1)):\n kpa = kp1[idx1]\n kpa_response = kpa[4]\n stat_resp_kp1.add(kpa_response)\n if kpa_response < RESPONSE_THRESHOLD:\n continue\n\n kpa_x = kpa[0]\n kpa_y = kpa[1]\n kpa_size = kpa[2]\n #kpa_angle = kpa[3]\n dpa = des1[idx1]\n\n best_distance = float('inf')\n best_idx2 = -1\n\n for idx2 in range(len(kp2)):\n kpb = kp2[idx2]\n # 0: Response Strength\n kpb_response = kpb[4]\n stat_resp_kp2.add(kpb_response)\n if kpb_response < RESPONSE_THRESHOLD:\n continue\n\n # 1: Distance/Radius Check\n kpb_x = kpb[0]\n kpb_y = kpb[1]\n d_pt_squared = (kpb_x - kpa_x) ** 2 + (kpb_y - kpa_y) ** 2\n stat_rad.add(np.linalg.norm(np.array([kpb_x,kpb_y]) - np.array([kpa_x,kpa_y])))\n if d_pt_squared >= RADIUS_SQUARED:\n continue\n\n # 2: Scale Difference\n kpb_size = kpb[2]\n scale_diff = abs(kpa_size - kpb_size)\n stat_s_diff.add(scale_diff)\n if scale_diff > SCALE_THRESHOLD:\n continue\n\n # 3: Descriptor L2 Norm\n dpb = des2[idx2]\n d_des = np.linalg.norm(dpb - dpa)\n stat_d_dist.add(d_des)\n if d_des < best_distance:\n best_distance = d_des\n best_idx2 = idx2\n\n # 4: ?? Angle ??\n # kpb_angle = kpb[3]\n\n if best_idx2 == -1 or best_distance > DISTANCE_THRESHOLD:\n continue\n\n match = np.array([idx1, best_idx2, best_distance], dtype=np.int32)\n matches.append(match)\n\n match = cv2.DMatch()\n match.queryIdx = idx1\n match.imgIdx = idx1\n match.trainIdx = best_idx2\n match.distance = best_distance\n matches_cv.append(match)\n\n return np.asarray(matches), np.asarray(matches_cv), \\\n np.asarray(stat_s_diff), np.asarray(stat_d_dist), \\\n np.asarray(stat_resp_kp1), np.asarray(stat_resp_kp2), \\\n np.asarray(stat_rad)"
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"text": "import numpy as np\nimport pylab as plt\nfrom PIL import Image\nimport random\nfrom svgpathtools import svg2paths2, wsvg\npaths, attributes, svg_attributes = svg2paths2('Level33.svg')\n\nfor i in range(len(attributes)):\n attributes[i]['fill'] = \"#%06x\" % random.randint(0, 0xFFFFFF)\n\n#%%\nwsvg(paths[0:5], attributes=attributes[0:5], svg_attributes=svg_attributes, filename='output.svg')\n\n#%%\nfor i in range(len(paths)):\n wsvg(paths[i], attributes=[attributes[i]], svg_attributes=svg_attributes, filename='output'+str(i)+'.svg')"
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"text": "import numpy as np\n\ndef normalize(dataset):\n mean = np.mean(dataset)\n std = np.std(dataset)\n return (dataset - mean) / std\n\ndef get_training():\n # X Shape: (Plates, Height, Width)\n sw_data = np.load('atlas_sw.npz')\n x_train = normalize(sw_data['images'])\n y_train = sw_data['labels']\n return x_train, y_train\n\ndef get_testing():\n pw_data = np.load('atlas_pw.npz')\n x_test = pw_data['images']\n y_test = pw_data['labels']\n return x_test, y_test\n\n\n"
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"text": "# Author: Jose G Perez\n# Version 1.0\n# Last Modified: January 31, 2018\nimport pylab as plt\nimport numpy as np\ndef imshow(im, title=''):\n figure = plt.figure()\n plt.axis('off')\n plt.tick_params(axis='both',\n left='off', top='off', right='off', bottom='off',\n labelleft='off', labeltop='off', labelright='off', labelbottom='off')\n\n plt.title(title)\n plt.imshow(im)\n return figure\n\ndef imshow_matches__(im, title):\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.set_xlabel('PW Level')\n ax.set_ylabel('S Level')\n ax.set_title(title)\n plt.set_cmap(plt.get_cmap('hot'))\n plt.imshow(im)\n\n\ndef imshow_matches(im, title):\n fig = plt.figure()\n ax = fig.add_subplot(111)\n # ax.set_xlabel('PW Level')\n # ax.set_ylabel('S Level')\n # ax.set_xticks(s_label)\n # ax.set_yticks(pw_label)\n ax.set_title(title)\n plt.set_cmap(plt.get_cmap('hot'))\n plt.imshow(im)\n"
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"text": "import numpy as np\nimport os\nimport pylab as plt\nimport cv2\nfrom skimage.transform import warp, PiecewiseAffineTransform\nfrom PIL import Image\nfrom timeit import default_timer as timer\n\nUSE_SIFT = False\nUSE_OPTICALFLOW = False\n\ndef load(filename):\n WIDTH = 800\n HEIGHT = 400\n im = Image.open(filename)\n im = im.resize((WIDTH, HEIGHT), Image.LANCZOS)\n P = np.array(im, dtype=np.uint8)\n return P[:, 0:P.shape[1] // 2, :]\n\ndef to_gray(P):\n return P.mean(axis=2)\n\ndef get_controls(P):\n LEFT_PAD = 0\n RIGHT_PAD = 0\n COL_INTERVALS = 5\n CTRL_THRESHOLD = 200\n w = P.shape[0]\n h = P.shape[1]\n ctrl_pts = [[0, 0], [w, 0], [0, h], [w, h]]\n # Top to bottom\n for col in range(LEFT_PAD, P.shape[1] - RIGHT_PAD, COL_INTERVALS):\n for row in range(0, P.shape[0], 1):\n if P[row, col] <= CTRL_THRESHOLD:\n ctrl_pts.append([row, col])\n break\n\n # Bottom to top\n for col in range(LEFT_PAD, P.shape[1] - RIGHT_PAD, COL_INTERVALS):\n for row in range(P.shape[0] - 1, 0, -1):\n if P[row, col] <= CTRL_THRESHOLD:\n ctrl_pts.append([row, col])\n break\n\n return ctrl_pts\n\n#%% Load images\nprint(\"Loading images\")\ndir1 = 'C:/Users/xeroj/Dropbox/Training data sets - Khan-Fuentes/Paxinos and Watson, 2014 (7th Edition) Image set/'\ndir2 = 'C:/Users/xeroj/Downloads/Processed'\n\nP1 = load(os.path.join(dir1, 'RBSC7-068.jpg'))\nP2 = load(os.path.join(dir1, 'RBSC7-070.jpg'))\n\nPM1 = load(os.path.join(dir2, '18-016 LHA s4t2.tif'))\nPM2 = load(os.path.join(dir2, '18-016 LHA s4t3.tif'))\nPM3 = load(os.path.join(dir2, '18-016 LHA s4t4.tif'))\n\n#%% Plate selection\nS1 = P1\nS2 = P2\n\n#%% SIFT Stuff\nif USE_SIFT:\n #%% SIFT Features\n print(\"Computing SIFT features\")\n # SIFT = cv2.xfeatures2d.SIFT_create(contrastThreshold=0.02, edgeThreshold=100, sigma=2)\n SIFT = cv2.xfeatures2d.SIFT_create()\n kp1, des1 = SIFT.detectAndCompute(S1, None)\n kp2, des2 = SIFT.detectAndCompute(S2, None)\n\n #%% Matching (SIFT + Homography)\n # bf = cv2.BFMatcher()\n # matches = bf.knnMatch(des1,des2, k=2)\n # matches = np.asarray([m for m in matches if m[0].distance < 0.9*m[1].distance])\n # if len(matches[:,0]) >= 4:\n # src = np.float32([ kp1[m.queryIdx].pt for m in matches[:,0] ]).reshape(-1,1,2)\n # dst = np.float32([ kp2[m.trainIdx].pt for m in matches[:,0] ]).reshape(-1,1,2)\n # H, masked = cv2.findHomography(src, dst, cv2.RANSAC, 50.0)\n # im_match = cv2.drawMatches(P1, kp1, P2, kp2, matches[:,0], None, flags=2)\n # plt.figure()\n # plt.imshow(im_match)\n #\n # for m in matches[:,0]:\n # pt1 = kp1[m.queryIdx].pt\n # pt2 = kp2[m.trainIdx].pt\n #\n # (x1, y1) = (pt1[1], pt1[0])\n # (x2, y2) = (pt2[1], pt2[0])\n #\n # x1 = (x1 + x2) / 2\n # y1 = (y1 + y2) / 2\n # # x2 = (x1 + x2) / 2\n # # y2 = (y1 + y2) / 2\n #\n # src_pts.append([x1, y1])\n # dst_pts.append([x2, y2])\n #\n # src_pts = np.array(src_pts, dtype=np.float32)\n # dst_pts = np.array(dst_pts, dtype=np.float32)\n\n #%% Matching (Mine)\n import util_matching, util_cv, util_sift\n src_pts = [[0, 0], [S1.shape[0], 0], [0, S1.shape[1]], [S1.shape[0], S1.shape[1]]]\n dst_pts = [[0, 0], [S2.shape[0], 0], [0, S2.shape[1]], [S2.shape[0], S2.shape[1]]]\n print(\"Performing my matching algorithm\")\n matches2 = util_matching.match(util_sift.kp_to_array(kp1), des1, util_sift.kp_to_array(kp2), des2)\n matches2 = util_cv.match_to_cv(matches2)\n im_match2 = cv2.drawMatches(S1, kp1, S1, kp2, matches2, None, flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)\n plt.figure(6)\n plt.imshow(im_match2)\n\n print(\"Adding matching points for warping\")\n for m in matches2:\n pt1 = kp1[m.queryIdx].pt\n pt2 = kp2[m.trainIdx].pt\n\n (x1, y1) = (pt1[1], pt1[0])\n (x2, y2) = (pt2[1], pt2[0])\n\n # x1 = (x1 + x2) / 2\n # y1 = (y1 + y2) / 2\n x2 = (x1 + x2) / 2\n y2 = (y1 + y2) / 2\n\n src_pts.append([x1, y1])\n dst_pts.append([x2, y2])\n\n src_pts = np.array(src_pts, dtype=np.float32)\n dst_pts = np.array(dst_pts, dtype=np.float32)\n\n #%% Warping\n print(\"Warping\")\n tform = PiecewiseAffineTransform()\n e1 = tform.estimate(dst_pts, src_pts)\n im_warp1 = warp(S1, tform)\n im_warp1 = to_gray((im_warp1 * 255).astype(np.uint8))\n\n tform = PiecewiseAffineTransform()\n e2 = tform.estimate(src_pts, dst_pts)\n im_warp2 = warp(S2, tform)\n im_warp2 = to_gray((im_warp2 * 255).astype(np.uint8))\n\n print(\"Cross-Dissolve\")\n im_gen = (im_warp1 * 0.5) + (im_warp2 * 0.5)\n plt.figure(7)\n plt.suptitle(\"SIFT Generated\")\n plt.imshow(im_gen, cmap='gray')\n\n#%% OF\nif USE_OPTICALFLOW:\n print(\"Optical Flow\")\n # params for ShiTomasi corner detection\n feature_params = dict(maxCorners=500,\n qualityLevel=0.3,\n minDistance=7,\n blockSize=7)\n # Parameters for lucas kanade optical flow\n lk_params = dict(winSize=(15, 15),\n maxLevel=3,\n criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 25, 0.03))\n\n P1_C = cv2.imread('P1.png')\n P2_C = cv2.imread('P2.png')\n\n P1_CG = cv2.cvtColor(P1_C, cv2.COLOR_BGR2GRAY)\n P2_CG = cv2.cvtColor(P2_C, cv2.COLOR_BGR2GRAY)\n\n p0 = cv2.goodFeaturesToTrack(P1_CG, mask=None, **feature_params)\n mask = np.zeros_like(P1)\n # Create some random colors\n color = np.random.randint(0, 255, (100, 3))\n\n # calculate optical flow\n p1, st, err = cv2.calcOpticalFlowPyrLK(P1_CG, P2_CG, p0, None, **lk_params)\n # Select good points\n good_new = p1[st == 1]\n good_old = p0[st == 1]\n # draw the tracks\n for i, (new, old) in enumerate(zip(good_new, good_old)):\n a, b = new.ravel()\n c, d = old.ravel()\n mask = cv2.line(mask, (a, b), (c, d), color[i].tolist(), 2)\n frame = cv2.circle(P2, (a, b), 5, color[i].tolist(), -1)\n img = cv2.add(frame, mask)\n cv2.imshow('frame', img)\n\n src_pts = good_old\n dst_pts = good_new\n\n #%% Warping\n print(\"Warping\")\n tform = PiecewiseAffineTransform()\n e1 = tform.estimate(dst_pts, src_pts)\n im_warp1 = warp(P1, tform)\n im_warp1 = to_gray((im_warp1 * 255).astype(np.uint8))\n\n tform = PiecewiseAffineTransform()\n e2 = tform.estimate(src_pts, dst_pts)\n im_warp2 = warp(P2, tform)\n im_warp2 = to_gray((im_warp2 * 255).astype(np.uint8))\n\n print(\"Cross-Dissolve\")\n im_gen = (im_warp1 * 0.5) + (im_warp2 * 0.5)\n plt.figure(7)\n plt.suptitle(\"SIFT Generated\")\n plt.imshow(im_gen, cmap='gray')\n\n\n#%% Stack\n\nS1 = np.array(Image.open('face.jpg'))\nS2 = S1\nplt.figure(8)\nplt.suptitle(\"Intermediate PM2\")\nplt.imshow(PM2)\n\nS1_pts = [[0, 0], [S1.shape[0], 0], [0, S1.shape[1]], [S1.shape[0], S1.shape[1]]]\nS2_pts = [[0, 0], [S2.shape[0], 0], [0, S2.shape[1]], [S2.shape[0], S2.shape[1]]]\n\nHS1 = ((S1 * 0.8) + (S2 * 0.2)).astype(np.uint8)\nHS2 = ((S1 * 0.2) + (S2 * 0.8)).astype(np.uint8)\n\nplt.figure(1)\nplt.imshow(np.hstack((HS1, HS2)))\nwhile True:\n plt.figure(1)\n plt.suptitle('Select point on left plate or press enter to generate warp with current points')\n p1 = plt.ginput(n=1, timeout=0)\n\n if len(p1) == 0:\n plt.suptitle(\"Generating warp with current points...\")\n\n tform = PiecewiseAffineTransform()\n e1 = tform.estimate(np.array(S2_pts), np.array(S1_pts))\n im_warp1 = warp(S1, tform)\n im_warp1 = to_gray((im_warp1 * 255).astype(np.uint8))\n\n tform = PiecewiseAffineTransform()\n e2 = tform.estimate(np.array(S1_pts), np.array(S2_pts))\n im_warp2 = warp(S2, tform)\n im_warp2 = to_gray((im_warp2 * 255).astype(np.uint8))\n\n # plt.figure(2)\n # plt.suptitle(\"Warps\")\n # plt.imshow(np.hstack((im_warp1, im_warp2)), cmap='gray')\n\n plt.figure(3)\n plt.suptitle(\"Cross-dissolve\")\n im_gen = (im_warp1 * 0.5) + (im_warp2 * 0.5)\n plt.imshow(im_gen, cmap='gray')\n\n continue\n\n c = np.random.uniform(0, 1, 3)\n (x1, y1) = (p1[0][0], p1[0][1])\n l1 = plt.plot(x1, y1, marker='x', markersize=15, color=c)\n plt.suptitle('Select point on right plate')\n\n p2 = plt.ginput(n=1, timeout=0)\n if len(p2) == 0:\n l1.pop(0).remove()\n plt.suptitle(\"Breaking out of infinite loop\")\n break\n\n # Translate\n (x2, y2) = (p2[0][0], p2[0][1])\n\n # If you click on the right side\n if x2 > S1.shape[0]:\n x2 = x2 - S1.shape[0]\n print(\"Right side click\")\n else:\n print(\"Left side click\")\n\n x2 = (x2 + x1) / 2\n y2 = (y2 + y1) / 2\n plt.plot(x2, y2, marker='x', markersize=15, color=c)\n\n S1_pts.append([x1, y1])\n S2_pts.append([x2, y2])\n print(\"Total points so far: \", len(S1_pts))\n # plt.figure(2)\n # plt.imshow(S1)\n # plt.plot(x1, y1, marker='x', markersize=5, color='red')\n #\n # plt.figure(3)\n # plt.imshow(S2)\n # plt.plot(x2, y2, marker='x', markersize=5, color='red')\n\n\n#%% Z-Plane using built-in warping\n\n\n#%%\n# print(\"Blending\")\n# plt.figure()\n# im_b1 = im_warp1\n# im_b2 = im_warp2\n# for w in np.linspace(0, 1, 20):\n# b1 = im_b1 * w\n# b2 = im_b2 * (1-w)\n# gen = b1 + b2\n# plt.suptitle(\"Im1 Weight \" + str(w))\n# plt.imshow(gen, cmap='gray')\n# plt.waitforbuttonpress()"
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"text": "import cProfile\nimport numpy as np\nfrom timeit import default_timer as timer\nimport multiprocessing\nfrom multiprocessing.pool import Pool\nfrom util_sift import precompute_sift, load_sift\nimport itertools\n\nRADIUS = 25\nRADIUS_SQUARED = RADIUS ** 2\nSCALE_THRESHOLD = 3\nDISTANCE_THRESHOLD = 200\nRESPONSE_THRESHOLD = 0.01\nprecompute_sift('S_BB_V1', 'PW_BB_V1')\ns_im, s_label, s_kp, s_des = load_sift('S_BB_V1_SIFT.npz')\npw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V1_SIFT.npz')\n\n(best_distance, best_idx2) = (float('inf'), -1)\ndef match_v3_helper2(kpa, dpa, kp2, des2, idx2):\n global best_distance\n kpb = kp2[idx2]\n # (kpa_x, kpa_y, kpa_size, kpa_response, dpa) = (kpa[0], kpa[1], kpa[2], kpa[4], des1[idx1])\n # (kpb_x, kpb_y, kpb_size, kpb_response, dpb) = (kpb[0], kpb[1], kpb[2], kpb[4], des2[idx2])\n if kpb[4] < RESPONSE_THRESHOLD \\\n or (kpb[0] - kpa[0]) ** 2 + (kpb[1] - kpa[1]) ** 2 >= RADIUS_SQUARED \\\n or abs(kpa[2] - kpb[2]) > SCALE_THRESHOLD:\n return\n\n # 3: Descriptor L2 Norm\n d_des = np.linalg.norm(des2[idx2] - dpa)\n if(d_des < best_distance):\n best_distance = d_des\n best_idx2 = idx2\n\ndef match_v3_helper(kp1, des1, kp2, des2, idx1):\n global best_distance, best_idx2\n kpa = kp1[idx1]\n if kpa[4] < RESPONSE_THRESHOLD:\n return\n\n [match_v3_helper2(kpa, des1[idx1], kp2, des2, idx2) for idx2 in range(len(kp2))]\n\n if best_idx2 == -1 or best_distance > DISTANCE_THRESHOLD:\n return\n\n return np.array([idx1, best_idx2, best_distance], dtype=np.int32)\n\ndef match_v3(kp1, des1, kp2, des2):\n matches = [match_v3_helper(kp1,des1,kp2,des2,idx1) for idx1 in range(len(kp1))]\n\ndef match_v2_helper(kp1, des1, kp2, des2, idx1):\n kpa = kp1[idx1]\n (kpa_x, kpa_y, kpa_size, kpa_response, dpa) = (kpa[0], kpa[1], kpa[2], kpa[4], des1[idx1])\n (best_distance, best_idx2) = (float('inf'), -1)\n\n if kpa_response < RESPONSE_THRESHOLD:\n return\n\n for idx2 in range(len(kp2)):\n kpb = kp2[idx2]\n (kpb_x, kpb_y, kpb_size, kpb_response, dpb) = (kpb[0], kpb[1], kpb[2], kpb[4], des2[idx2])\n if kpb_response < RESPONSE_THRESHOLD \\\n or (kpb_x - kpa_x) ** 2 + (kpb_y - kpa_y) ** 2 >= RADIUS_SQUARED \\\n or abs(kpa_size - kpb_size) > SCALE_THRESHOLD:\n continue\n\n # 3: Descriptor L2 Norm\n d_des = np.linalg.norm(dpb - dpa)\n if d_des < best_distance:\n best_distance = d_des\n best_idx2 = idx2\n\n if best_idx2 == -1 or best_distance > DISTANCE_THRESHOLD:\n return\n\n return np.array([idx1, best_idx2, best_distance], dtype=np.int32)\n\ndef match_v2(kp1, des1, kp2, des2):\n matches = (match_v2_helper(kp1,des1,kp2,des2,idx1) for idx1 in range(len(kp1)))\n\ndef match(kp1, des1, kp2, des2):\n matches = []\n for idx1 in range(len(kp1)):\n kpa = kp1[idx1]\n (kpa_x, kpa_y, kpa_size, kpa_response, dpa) = (kpa[0], kpa[1], kpa[2], kpa[4], des1[idx1])\n (best_distance, best_idx2) = (float('inf'), -1)\n\n if kpa_response < RESPONSE_THRESHOLD:\n return\n\n for idx2 in range(len(kp2)):\n kpb = kp2[idx2]\n (kpb_x, kpb_y, kpb_size, kpb_response, dpb) = (kpb[0], kpb[1], kpb[2], kpb[4], des2[idx2])\n if kpb_response < RESPONSE_THRESHOLD \\\n or (kpb_x - kpa_x) ** 2 + (kpb_y - kpa_y) ** 2 >= RADIUS_SQUARED \\\n or abs(kpa_size - kpb_size) > SCALE_THRESHOLD:\n continue\n\n # 3: Descriptor L2 Norm\n d_des = np.linalg.norm(dpb - dpa)\n if d_des < best_distance:\n best_distance = d_des\n best_idx2 = idx2\n\n if best_idx2 == -1 or best_distance > DISTANCE_THRESHOLD:\n return\n\n matches.append(np.array([idx1, best_idx2, best_distance], dtype=np.int32))\n\n return np.asarray(matches)\n\ndef perform_match(s_idx):\n global s_kp, s_des, pw_kp, pw_des\n matches = []\n print('s_idx', s_idx)\n for pw_idx in range(5):\n matches.append(match(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]))\n\ndef perform_match_v2(s_idx):\n global s_kp, s_des, pw_kp, pw_des\n matches = []\n print('s_idx', s_idx)\n matches = [match(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]) for pw_idx in range(5)]\n\ndef test():\n s_idx = 0\n return [match_v3(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]) for pw_idx in range(pw_kp.shape[0])]\n\ndef perform_match_v3(s_idx):\n global s_kp, s_des, pw_kp, pw_des\n print('s_idx', s_idx)\n # matches = (match(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]) for pw_idx in range(5))\n # matches = (match_v2(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]) for pw_idx in range(5))\n matches = (match_v3(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]) for pw_idx in range(pw_kp.shape[0]))\n # np.savez_compressed(str(s_idx) + '-M', m=np.asarray(matches2))\n\nif __name__ == '__main__':\n print('Begin pool work')\n pool = Pool()\n # s_idx = range(2)\n s_idx = range(s_kp.shape[0])\n time_start = timer()\n # pool.map(perform_match, s_idx)\n # pool.map(perform_match_v2, s_idx)\n pool.map(perform_match_v3, s_idx)\n time_end = timer()\n pool.close()\n pool.join()\n duration = time_end - time_start\n print(\"Program took %.3fs\" % duration)"
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"text": "# '''Train a Siamese MLP on pairs of digits from the MNIST dataset.\n#\n# It follows Hadsell-et-al.'06 [1] by computing the Euclidean distance on the\n# output of the shared network and by optimizing the contrastive loss (see paper\n# for mode details).\n#\n# [1] \"Dimensionality Reduction by Learning an Invariant Mapping\"\n# http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n#\n# Gets to 99.5% test accuracy after 20 epochs.\n# 3 seconds per epoch on a Titan X GPU\n# '''\n# import numpy as np\n# np.random.seed(1337) # for reproducibility\n#\n# import pylab as plt\n# from keras.models import Sequential, Model\n# from keras.layers import Dense, Dropout, Input, Lambda, Conv2D, MaxPooling2D, Activation, Flatten\n# from keras.layers.normalization import BatchNormalization\n# from keras.optimizers import RMSprop\n# from keras import backend as K\n# from keras.callbacks import EarlyStopping, ModelCheckpoint\n#\n# from timeit import default_timer as timer\n#\n# EPOCHS = 10\n# BATCH_SIZE = 32\n#\n# #INPUT_SHAPE = (x_train.shape[1], x_train.shape[2], 1)\n# #INPUT_SHAPE = (x_train.shape[1],)\n# INPUT_SHAPE = (x_train.shape[1]*x_train.shape[2],)\n#\n# def euclidean_distance(vects):\n# x, y = vects\n# return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True))\n#\n# def eucl_dist_output_shape(shapes):\n# shape1, shape2 = shapes\n# return (shape1[0], 1)\n#\n# def contrastive_loss(y_true, y_pred):\n# '''Contrastive loss from Hadsell-et-al.'06\n# http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n# '''\n# margin = 1\n# return K.mean(y_true * K.square(y_pred) + (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))\n#\n# def create_base_network():\n# '''Base network to be shared (eq. to feature extraction).\n# '''\n# model = Sequential()\n# #model.add(Conv2D(32, (7, 7), input_shape=INPUT_SHAPE))\n# #model.add(BatchNormalization())\n# #model.add(Activation('relu'))\n# #model.add(MaxPooling2D(pool_size=(2, 2)))\n# #model.add(Dropout(0.1))\n#\n# # model.add(Conv2D(64, (5, 5)))\n# # model.add(BatchNormalization())\n# # model.add(Activation('relu'))\n# # model.add(MaxPooling2D(pool_size=(2, 2)))\n# #\n# # model.add(Conv2D(128, (3, 3)))\n# # model.add(BatchNormalization())\n# # model.add(Activation('relu'))\n# # model.add(MaxPooling2D(pool_size=(2, 2)))\n#\n# #model.add(Conv2D(256, (1, 1)))\n# #model.add(BatchNormalization())\n# #model.add(Activation('relu'))\n# #model.add(MaxPooling2D(pool_size=(2, 2)))\n#\n# #model.add(Conv2D(2, (1, 1)))\n# #model.add(MaxPooling2D(pool_size=(2, 2)))\n# #model.add(Flatten())\n#\n# model.add(Dense(1024, input_shape=INPUT_SHAPE))\n# model.add(BatchNormalization())\n# model.add(Activation('relu'))\n# #model.add(Dropout(0.1))\n#\n# model.add(Dense(2048))\n# model.add(BatchNormalization())\n# model.add(Activation('relu'))\n# #model.add(Dropout(0.1))\n#\n# model.add(Dense(2))\n# #model.add(BatchNormalization())\n# #model.add(Activation('relu'))\n#\n# return model\n#\n#\n# def compute_accuracy(predictions, labels):\n# '''Compute classification accuracy with a fixed threshold on distances.\n# '''\n# return labels[predictions.ravel() < 0.5].mean()\n#\n#\n# tr_pairs = []\n# tr_y = []\n# start_time = timer()\n# print(\"X Shape: \", x_train[0].shape)\n# for i in range(73):\n# x = x_train[i]\n# x_reg = x.reshape((200, 100))\n#\n# #tr_pairs += [[x, x]]\n# #tr_y += [1.0]\n#\n# # Transformations\n# #import pdb\n# #pdb.set_trace()\n# #plt.savefig()\n# tr_pairs += [[x, np.roll(x_reg, (1, 0))]] # Right\n# tr_y += [1.0]\n# tr_pairs += [[x, np.roll(x_reg, (1, 1))]] # Right-Up\n# tr_y += [1.0]\n# tr_pairs += [[x, np.roll(x_reg, (-1, 0))]] # Left\n# tr_y += [1.0]\n# tr_pairs += [[x, np.roll(x_reg, (-1, 1))]] # Left-Up\n# tr_y += [1.0]\n# tr_pairs += [[x, np.roll(x_reg, (1, -1))]] # Right-Down\n# tr_y += [1.0]\n# tr_pairs += [[x, np.roll(x_reg, (-1, -1))]] # Left-Down\n# tr_y += [1.0]\n# tr_pairs += [[x, np.roll(x_reg, (0, 1))]] # Up\n# tr_y += [1.0]\n# tr_pairs += [[x, np.roll(x_reg, (0, -1))]] # Down\n# tr_y += [1.0]\n# tr_pairs += [[x, np.fliplr(x_reg)]] # Left/Right Flip\n# tr_y += [1.0]\n# tr_pairs += [[x, np.flipud(x_reg)]] # Up/Down Flip\n# tr_y += [1.0]\n# #tr_pairs += [[x, np.rot90(x, 2)]] # 90 Rotation\n# #tr_y += [1.0]\n# #tr_pairs += [[x, np.rot90(x, 2)]] # 180 Rotation\n# #tr_y += [1.0]\n# #tr_pairs += [[x, np.rot90(x, 2)]] # 270 Rotation\n# #tr_y += [1.0]\n#\n# indices = np.ones(73, dtype=np.bool)\n# indices[i] = False\n# # Plates above and below this one\n# if (i + 1) < 73:\n# x2 = x_train[i+1] # 0.75\n# tr_pairs += [[x, x2]]\n# tr_y += [0.75]\n# indices[i+1] = False\n#\n# if (i - 1) >= 0:\n# x3 = x_train[i-1] # 0.75\n# tr_pairs += [[x, x3]]\n# tr_y += [0.75]\n# indices[i-1] = False\n#\n# # Plate two above or two below\n# if (i + 2) < 73:\n# x2 = x_train[i + 2]\n# tr_pairs += [[x, x2]]\n# tr_y += [0.50]\n# indices[i+2] = False\n#\n# if (i - 2) >= 0:\n# x3 = x_train[i - 2]\n# tr_pairs += [[x, x3]]\n# tr_y += [0.50]\n# indices[i-2] = False\n#\n# # Plates three above or three below\n# if (i + 3) < 73:\n# x2 = x_train[i + 1] # 0.25\n# tr_pairs += [[x, x2]]\n# tr_y += [0.25]\n# indices[i+3] = False\n#\n# if (i - 3) >= 0:\n# x3 = x_train[i - 1]\n# tr_pairs += [[x, x3]]\n# tr_y += [0.25]\n# indices[i-3] = False\n#\n# # Add the rest as negative examples\n# rest = x_train[indices]\n# for x2 in rest:\n# tr_pairs += [[x, x2]]\n# tr_y += [0]\n#\n# tr_pairs = np.array(tr_pairs)\n# tr_y = np.array(tr_y)\n#\n# te_pairs = []\n# te_y = []\n#\n# # Dr. Khan's table\n# te_pairs += [[x_test[8-1], x_train[6-1]]]\n# te_pairs += [[x_test[11-1], x_train[11-1]]]\n# te_pairs += [[x_test[26], x_train[23-1]]]\n# te_y += [1]\n# te_y += [1]\n# te_y += [1]\n#\n# # PW #68 and SW #33\n# te_pairs += [[x_test[39], x_train[33-1]]]\n# te_y += [1]\n#\n# # PW #68 and SW #32/34\n# te_pairs += [[x_test[39], x_train[32-1]]]\n# te_pairs += [[x_test[39], x_train[34-1]]]\n# te_y += [0.75]\n# te_y += [0.75]\n#\n# te_pairs += [[x_test[39], x_train[31-1]]]\n# te_pairs += [[x_test[39], x_train[33-1]]]\n# te_y += [0.5]\n# te_y += [0.5]\n#\n#\n# te_y = np.array(te_y)\n# te_pairs = np.array(te_pairs)\n#\n# ti_pairs = []\n# ti_pairs += [[x_test[39], x_train[31]]]\n# ti_pairs += [[x_test[39], x_train[30]]]\n# ti_pairs += [[x_test[39], x_train[33]]]\n# ti_pairs += [[x_test[39], x_train[34]]]\n# ti_pairs = np.array(ti_pairs)\n#\n# duration = timer() - start_time\n# print(\"Creating pairs took %.2fs\" % duration)\n# print(\"Pair shape\", tr_pairs.shape)\n#\n# # For CNNs\n#\n# # network definition\n# base_network = create_base_network()\n#\n# left_input = Input(INPUT_SHAPE)\n# right_input = Input(INPUT_SHAPE)\n# # because we re-use the same instance `base_network`,\n# # the weights of the network\n# # will be shared across the two branches\n# encoded_left = base_network(left_input)\n# encoded_right = base_network(right_input)\n#\n# distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([encoded_left, encoded_right])\n# model = Model(inputs=[left_input, right_input], outputs=distance)\n#\n# # train\n# rms = RMSprop()\n# model.compile(loss=contrastive_loss, optimizer=rms, metrics=['accuracy'])\n#\n# model_check = ModelCheckpoint('train_net.epoch{epoch:02d}-valacc{val_acc:.2f}.hdf5', monitor='val_acc')\n#\n# from keras.models import load_model\n# #model2 = load_model('siamese.hdf5', custom_objects={'contrastive_loss':contrastive_loss})\n#\n# from keras.utils import plot_model\n# # plot_model(model2, to_file='model.png')\n#\n# #import pdb\n# #pdb.set_trace()\n#\n# start_time = timer()\n# history = model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,\n# validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),\n# batch_size=BATCH_SIZE,\n# epochs=EPOCHS,\n# callbacks=[model_check],\n# verbose=1)\n#\n# duration = timer() - start_time\n# print(\"Training took %.2fs\" % duration)\n# model.save('siamese.hdf5')\n#\n# # compute final accuracy on training and test sets\n# # Pred is closer to 0 if the numbers are similar\n# pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])\n# tr_acc = compute_accuracy(pred, tr_y)\n#\n# #pred2 = model.predict([te_pairs[:, 0], te_pairs[:, 1]])\n# #te_acc = compute_accuracy(pred2, te_y)\n#\n# print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))\n# #print('* Accuracy on testing set: %0.2f%%' % (100 * te_acc))\n#\n# plt.figure(1)\n# plt.plot(history.history['loss'])\n# plt.plot(history.history['val_loss'])\n# plt.title('model loss')\n# plt.ylabel('loss')\n# plt.xlabel('epoch')\n# plt.legend(['train', 'test'], loc='upper left')\n# plt.show()\n#\n# plt.figure(2)\n# plt.plot(history.history['acc'])\n# plt.title('model accuracy')\n# plt.ylabel('accuracy')\n# plt.xlabel('epoch')\n# plt.legend(['train'], loc='upper left')\n# plt.show()\n#\n# plt.figure(3)\n# plt.plot(history.history['val_acc'])\n# plt.title('model accuracy')\n# plt.ylabel('accuracy')\n# plt.xlabel('epoch')\n# plt.legend(['test'], loc='upper left')\n# plt.show()"
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"text": "# Author: Jose G Perez\n# Version 1.0\n# Last Modified: January 31, 2018\nimport cv2\nimport numpy as np\nimport pylab as plt\nfrom multiprocessing.pool import ThreadPool\nfrom timeit import default_timer as timer\n\nfrom util_sift import load_sift\nfrom util_matching import match_details\nINDIVIDUAL = False\n\nnp.random.seed(1)\nnp.set_printoptions(threshold=np.nan, linewidth=1000)\n# Default: contrast 0.04, edge 10, sigma 1.6\n# Experiment: 0.08, 30, 2\n# CT: The larger the threshold, the less features are produced by the detector\n# ET: The larger the threshold, the more features that are retained\nSIFT = cv2.xfeatures2d.SIFT_create(contrastThreshold=0.05, edgeThreshold=100, sigma=2)\n\nRADIUS = 50\nRADIUS_SQUARED = RADIUS ** 2\nSCALE_THRESHOLD = 3\nDISTANCE_THRESHOLD = 200\nRESPONSE_THRESHOLD = 0.01\n\nDISTANCE_RATIO = 0.95\nRANSAC_REPROJ_TRESHHOLD = 10 # The higher the threshold, the lower the inliers\nRANSAC_MAX_ITERS = 2000\nRANSAC_CONFIDENCE = 0.99\n\nFIGURE_IDX = 0\ndef imshow(im, title=''):\n global FIGURE_IDX\n plt.figure(FIGURE_IDX)\n plt.axis('off')\n plt.tick_params(axis='both',\n left='off', top='off', right='off', bottom='off',\n labelleft='off', labeltop='off', labelright='off', labelbottom='off')\n plt.title(title)\n plt.imshow(im)\n\n FIGURE_IDX += 1\n\ndef gallery(array, ncols=3):\n # Grayscale\n if len(array.shape) == 3:\n nindex, height, width = array.shape\n nrows = nindex//ncols\n result = (array.reshape(nrows, ncols, height, width)\n .swapaxes(1,2)\n .reshape(height*nrows, width*ncols))\n return result\n # Color\n else:\n nindex, height, width, intensity = array.shape\n nrows = nindex//ncols\n # want result.shape = (height*nrows, width*ncols, intensity)\n result = (array.reshape(nrows, ncols, height, width, intensity)\n .swapaxes(1,2)\n .reshape(height*nrows, width*ncols, intensity))\n return result\n\ns_im, s_label, s_kp, s_des = load_sift('S_BB_V1_SIFT.npz')\npw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V1_SIFT.npz')\n\ntime_start = timer()\n# ==== MATCHING SYSTEM BEGIN\nif INDIVIDUAL:\n # im1 = s_im[np.where(s_label == 67)[0][0]]\n # im2 = pw_im[np.where(pw_label == 145)[0][0]]\n\n # im1 = s_im[np.where(s_label == 6)[0][0]]\n # im2 = pw_im[np.where(pw_label == 8)[0][0]]\n\n im1 = s_im[np.where(s_label == 33)[0][0]]\n im2 = pw_im[np.where(pw_label == 68)[0][0]]\n\n kp1, des1 = SIFT.detectAndCompute(im1, None)\n kp2, des2 = SIFT.detectAndCompute(im2, None)\n\n m, matches, stat_s_diff, stat_d_dist, stat_resp_kp1, stat_resp_kp2, stat_rad = match_details(kp1, des1, kp2, des2)\n print(\"\\t[ScaleD] Max %.3f, Min %.3f, Avg %.3f\" % (max(stat_s_diff) , min(stat_s_diff), sum(stat_s_diff) / len(stat_s_diff)))\n print(\"\\t[DescD] Max %.3f, Min %.3f, Avg %.3f\" % (max(stat_d_dist), min(stat_d_dist), sum(stat_d_dist) / len(stat_d_dist)))\n print(\"\\t[RespKp1] Max %.3f, Min %.3f, Avg %.3f\" % (max(stat_resp_kp1), min(stat_resp_kp1), sum(stat_resp_kp1) / len(stat_resp_kp1)))\n print(\"\\t[RespKp2] Max %.3f, Min %.3f, Avg %.3f\" % (max(stat_resp_kp2), min(stat_resp_kp2), sum(stat_resp_kp2) / len(stat_resp_kp2)))\n print(\"\\t[Rad] Max %.3f, Min %.3f, Avg %.3f\" % (max(stat_rad), min(stat_rad), sum(stat_rad) / len(stat_rad)))\n\n # For comparison, the selected SIFT keypoints\n # BF = cv2.BFMatcher(normType=cv2.NORM_L2, crossCheck=True)\n # bf_matches = BF.match(des1, des2)\n # im_bf_matches = cv2.drawMatches(im1,kp1,im2,kp2,bf_matches,None,flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)\n # metric_bf = len(bf_matches)/ (len(kp1) + len(kp2) - len(bf_matches))\n BF = cv2.BFMatcher(normType=cv2.NORM_L2)\n bf_matches = BF.knnMatch(des1, des2, k=2)\n\n # For comparison, RANSAC\n good_matches = [m[0] for m in bf_matches if len(m) == 2 and m[0].distance < m[1].distance * DISTANCE_RATIO]\n src_pts = np.float32([kp1[m.queryIdx].pt for m in good_matches])\n dst_pts = np.float32([kp2[m.trainIdx].pt for m in good_matches])\n H, mask = cv2.findHomography(src_pts, dst_pts, method=cv2.RANSAC, ransacReprojThreshold=RANSAC_REPROJ_TRESHHOLD, maxIters=RANSAC_MAX_ITERS, confidence=RANSAC_CONFIDENCE)\n matchesMask = mask.ravel().tolist()\n drawParameters = dict(matchColor=None, singlePointColor=None, matchesMask=matchesMask,\n flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)\n\n # Draw the lines between the 2 images connecting matches\n im_ransac = cv2.drawMatches(im1, kp1, im2, kp2, good_matches, None, **drawParameters)\n\n # Images\n im1_kp = cv2.drawKeypoints(im1, kp1, None, None, cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n im2_kp = cv2.drawKeypoints(im2, kp2, None, None, cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n im_matches = cv2.drawMatches(im1,kp1,im2,kp2,matches,None,flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)\n\n metric = len(matches) / (len(kp1) + len(kp2) - len(matches))\n print(\"Metric\", metric)\n print(\"Inliers\", mask.sum())\n\n plt.gray()\n imshow(im1_kp, 'S Keypoint Data')\n imshow(im2_kp, 'PW Keypoint Data')\n #imshow(im_bf_matches, 'BF Matches')\n imshow(im_matches, 'New Matching')\n imshow(im_ransac, 'RANSAC')\nelse:\n # im2 = pw_im[np.where(pw_label == 68)[0][0]]\n pw_idx = 33\n im2 = pw_im[pw_idx]\n (kp2, des2) = (pw_kp[pw_idx], pw_des[pw_idx])\n\n all_matches = []\n all_im = []\n all_kp = []\n all_des = []\n\n cidx = 0\n for sidx in range(25, 40):\n # if sidx == 32:\n # print('=[P]', end='')\n print(\"S\", (sidx + 1), \"CIDX\", cidx)\n\n (im1, kp1, des1) = (s_im[sidx], s_kp[sidx], s_des[sidx])\n m, matches, stat_s_diff, stat_d_dist, stat_resp_kp1, stat_resp_kp2, stat_rad = match_details(kp1, des1, kp2,\n des2)\n # print(\"\\t[ScaleD] Max %.3f, Min %.3f, Avg %.3f\" % (\n # max(stat_s_diff), min(stat_s_diff), sum(stat_s_diff) / len(stat_s_diff)))\n # print(\"\\t[DescD] Max %.3f, Min %.3f, Avg %.3f\" % (\n # max(stat_d_dist), min(stat_d_dist), sum(stat_d_dist) / len(stat_d_dist)))\n # print(\"\\t[RespKp1] Max %.3f, Min %.3f, Avg %.3f\" % (\n # max(stat_resp_kp1), min(stat_resp_kp1), sum(stat_resp_kp1) / len(stat_resp_kp1)))\n # print(\"\\t[RespKp2] Max %.3f, Min %.3f, Avg %.3f\" % (\n # max(stat_resp_kp2), min(stat_resp_kp2), sum(stat_resp_kp2) / len(stat_resp_kp2)))\n # print(\"\\t[Rad] Max %.3f, Min %.3f, Avg %.3f\" % (max(stat_rad), min(stat_rad), sum(stat_rad) / len(stat_rad)))\n\n metric = len(matches) / (len(kp1) + len(kp2) - len(matches))\n\n print(\"\\tMetric w/PW %s is %.3f, Matches %d\" % (pw_idx, metric, len(matches)))\n\n all_matches.append(matches)\n all_im.append(im1)\n all_kp.append(kp1)\n all_des.append(des1)\n cidx += 1\n\n # index = 7\n # imshow(cv2.drawMatches(all_im[index], all_kp[index], im2, kp2, all_matches[index], None, flags=2), '[P]SW33')\n # index = 8\n # imshow(cv2.drawMatches(all_im[index], all_kp[index], im2, kp2, all_matches[index], None, flags=2), 'SW34')\n\nduration = timer() - time_start\nprint(\"Program took %.3fs\" % duration)"
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"text": "import tensorflow as tf\nimport tensorflow.contrib.rnn as rnn\nfrom tensorflow.python.ops.nn_ops import max_pool\n\n\nclass BidirectionalLSTM:\n def __init__(self, layer_sizes, batch_size):\n \"\"\"\n Initializes a multi layer bidirectional LSTM\n :param layer_sizes: A list containing the neuron numbers per layer e.g. [100, 100, 100] returns a 3 layer, 100\n neuron bid-LSTM\n :param batch_size: The experiments batch size\n \"\"\"\n self.reuse = False\n self.batch_size = batch_size\n self.layer_sizes = layer_sizes\n\n def __call__(self, inputs, name, training=False):\n \"\"\"\n Runs the bidirectional LSTM, produces outputs and saves both forward and backward states as well as gradients.\n :param inputs: The inputs should be a list of shape [sequence_length, batch_size, 64]\n :param name: Name to give to the tensorflow op\n :param training: Flag that indicates if this is a training or evaluation stage\n :return: Returns the LSTM outputs, as well as the forward and backward hidden states.\n \"\"\"\n with tf.name_scope('bid-lstm' + name), tf.variable_scope('bid-lstm', reuse=self.reuse):\n with tf.variable_scope(\"encoder\"):\n fw_lstm_cells_encoder = [rnn.LSTMCell(num_units=self.layer_sizes[i], activation=tf.nn.tanh)\n for i in range(len(self.layer_sizes))]\n bw_lstm_cells_encoder = [rnn.LSTMCell(num_units=self.layer_sizes[i], activation=tf.nn.tanh)\n for i in range(len(self.layer_sizes))]\n\n\n\n outputs, output_state_fw, output_state_bw = rnn.stack_bidirectional_rnn(\n fw_lstm_cells_encoder,\n bw_lstm_cells_encoder,\n inputs,\n dtype=tf.float32\n )\n print(\"out shape\", tf.stack(outputs, axis=0).get_shape().as_list())\n with tf.variable_scope(\"decoder\"):\n fw_lstm_cells_decoder = [rnn.LSTMCell(num_units=self.layer_sizes[i], activation=tf.nn.tanh)\n for i in range(len(self.layer_sizes))]\n bw_lstm_cells_decoder = [rnn.LSTMCell(num_units=self.layer_sizes[i], activation=tf.nn.tanh)\n for i in range(len(self.layer_sizes))]\n outputs, output_state_fw, output_state_bw = rnn.stack_bidirectional_rnn(\n fw_lstm_cells_decoder,\n bw_lstm_cells_decoder,\n outputs,\n dtype=tf.float32\n )\n\n\n self.reuse = True\n self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='bid-lstm')\n return outputs, output_state_fw, output_state_bw\n\n\nclass DistanceNetwork:\n def __init__(self):\n self.reuse = False\n\n def __call__(self, support_set, input_image, name, training=False):\n \"\"\"\n This module calculates the cosine distance between each of the support set embeddings and the target\n image embeddings.\n :param support_set: The embeddings of the support set images, tensor of shape [sequence_length, batch_size, 64]\n :param input_image: The embedding of the target image, tensor of shape [batch_size, 64]\n :param name: Name of the op to appear on the graph\n :param training: Flag indicating training or evaluation (True/False)\n :return: A tensor with cosine similarities of shape [batch_size, sequence_length, 1]\n \"\"\"\n with tf.name_scope('distance-module' + name), tf.variable_scope('distance-module', reuse=self.reuse):\n eps = 1e-10\n similarities = []\n for support_image in tf.unstack(support_set, axis=0):\n sum_support = tf.reduce_sum(tf.square(support_image), 1, keep_dims=True)\n support_magnitude = tf.rsqrt(tf.clip_by_value(sum_support, eps, float(\"inf\")))\n dot_product = tf.matmul(tf.expand_dims(input_image, 1), tf.expand_dims(support_image, 2))\n dot_product = tf.squeeze(dot_product, [1, ])\n cosine_similarity = dot_product * support_magnitude\n similarities.append(cosine_similarity)\n\n similarities = tf.concat(axis=1, values=similarities)\n self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='distance-module')\n\n return similarities\n\n\nclass AttentionalClassify:\n def __init__(self):\n self.reuse = False\n\n def __call__(self, similarities, support_set_y, name, training=False):\n \"\"\"\n Produces pdfs over the support set classes for the target set image.\n :param similarities: A tensor with cosine similarities of size [sequence_length, batch_size, 1]\n :param support_set_y: A tensor with the one hot vectors of the targets for each support set image\n [sequence_length, batch_size, num_classes]\n :param name: The name of the op to appear on tf graph\n :param training: Flag indicating training or evaluation stage (True/False)\n :return: Softmax pdf\n \"\"\"\n with tf.name_scope('attentional-classification' + name), tf.variable_scope('attentional-classification',\n reuse=self.reuse):\n softmax_similarities = tf.nn.softmax(similarities)\n preds = tf.squeeze(tf.matmul(tf.expand_dims(softmax_similarities, 1), support_set_y))\n self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='attentional-classification')\n return preds\n\n\nclass Classifier:\n def __init__(self, batch_size, layer_sizes, num_channels=1):\n \"\"\"\n Builds a CNN to produce embeddings\n :param batch_size: Batch size for experiment\n :param layer_sizes: A list of length 4 containing the layer sizes\n :param num_channels: Number of channels of images\n \"\"\"\n self.reuse = False\n self.batch_size = batch_size\n self.num_channels = num_channels\n self.layer_sizes = layer_sizes\n assert len(self.layer_sizes)==4, \"layer_sizes should be a list of length 4\"\n\n def __call__(self, image_input, training=False, keep_prob=1.0):\n \"\"\"\n Runs the CNN producing the embeddings and the gradients.\n :param image_input: Image input to produce embeddings for. [batch_size, 28, 28, 1]\n :param training: A flag indicating training or evaluation\n :param keep_prob: A tf placeholder of type tf.float32 indicating the amount of dropout applied\n :return: Embeddings of size [batch_size, 64]\n \"\"\"\n\n def leaky_relu(x, leak=0.2, name=''):\n return tf.maximum(x, x * leak, name=name)\n\n with tf.variable_scope('g', reuse=self.reuse):\n\n with tf.variable_scope('conv_layers'):\n with tf.variable_scope('g_conv1'):\n g_conv1_encoder = tf.layers.conv2d(image_input, self.layer_sizes[0], [3, 3], strides=(1, 1),\n padding='SAME')\n g_conv1_encoder = leaky_relu(g_conv1_encoder, name='outputs')\n g_conv1_encoder = tf.contrib.layers.batch_norm(g_conv1_encoder, updates_collections=None, decay=0.99,\n scale=True, center=True, is_training=training)\n g_conv1_encoder = max_pool(g_conv1_encoder, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],\n padding='SAME')\n g_conv1_encoder = tf.nn.dropout(g_conv1_encoder, keep_prob=keep_prob)\n\n with tf.variable_scope('g_conv2'):\n g_conv2_encoder = tf.layers.conv2d(g_conv1_encoder, self.layer_sizes[1], [3, 3], strides=(1, 1),\n padding='SAME')\n g_conv2_encoder = leaky_relu(g_conv2_encoder, name='outputs')\n g_conv2_encoder = tf.contrib.layers.batch_norm(g_conv2_encoder, updates_collections=None,\n decay=0.99,\n scale=True, center=True, is_training=training)\n g_conv2_encoder = max_pool(g_conv2_encoder, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],\n padding='SAME')\n g_conv2_encoder = tf.nn.dropout(g_conv2_encoder, keep_prob=keep_prob)\n\n with tf.variable_scope('g_conv3'):\n g_conv3_encoder = tf.layers.conv2d(g_conv2_encoder, self.layer_sizes[2], [3, 3], strides=(1, 1),\n padding='SAME')\n g_conv3_encoder = leaky_relu(g_conv3_encoder, name='outputs')\n g_conv3_encoder = tf.contrib.layers.batch_norm(g_conv3_encoder, updates_collections=None,\n decay=0.99,\n scale=True, center=True, is_training=training)\n g_conv3_encoder = max_pool(g_conv3_encoder, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],\n padding='SAME')\n g_conv3_encoder = tf.nn.dropout(g_conv3_encoder, keep_prob=keep_prob)\n\n with tf.variable_scope('g_conv4'):\n g_conv4_encoder = tf.layers.conv2d(g_conv3_encoder, self.layer_sizes[3], [2, 2], strides=(1, 1),\n padding='SAME')\n g_conv4_encoder = leaky_relu(g_conv4_encoder, name='outputs')\n g_conv4_encoder = tf.contrib.layers.batch_norm(g_conv4_encoder, updates_collections=None,\n decay=0.99,\n scale=True, center=True, is_training=training)\n g_conv4_encoder = max_pool(g_conv4_encoder, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],\n padding='SAME')\n g_conv4_encoder = tf.nn.dropout(g_conv4_encoder, keep_prob=keep_prob)\n\n g_conv_encoder = g_conv4_encoder\n g_conv_encoder = tf.contrib.layers.flatten(g_conv_encoder)\n\n self.reuse = True\n self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='g')\n return g_conv_encoder\n\n\nclass MatchingNetwork:\n def __init__(self, support_set_images, support_set_labels, target_image, target_label, keep_prob,\n batch_size=100, num_channels=1, is_training=False, learning_rate=0.001, rotate_flag=False, fce=False, num_classes_per_set=5,\n num_samples_per_class=1):\n\n \"\"\"\n Builds a matching network, the training and evaluation ops as well as data augmentation routines.\n :param support_set_images: A tensor containing the support set images [batch_size, sequence_size, 28, 28, 1]\n :param support_set_labels: A tensor containing the support set labels [batch_size, sequence_size, 1]\n :param target_image: A tensor containing the target image (image to produce label for) [batch_size, 28, 28, 1]\n :param target_label: A tensor containing the target label [batch_size, 1]\n :param keep_prob: A tf placeholder of type tf.float32 denotes the amount of dropout to be used\n :param batch_size: The batch size for the experiment\n :param num_channels: Number of channels of the images\n :param is_training: Flag indicating whether we are training or evaluating\n :param rotate_flag: Flag indicating whether to rotate the images\n :param fce: Flag indicating whether to use full context embeddings (i.e. apply an LSTM on the CNN embeddings)\n :param num_classes_per_set: Integer indicating the number of classes per set\n :param num_samples_per_class: Integer indicating the number of samples per class\n \"\"\"\n self.batch_size = batch_size\n self.fce = fce\n self.g = Classifier(self.batch_size, num_channels=num_channels, layer_sizes=[64, 64, 64 ,64])\n if fce:\n self.lstm = BidirectionalLSTM(layer_sizes=[32], batch_size=self.batch_size)\n self.dn = DistanceNetwork()\n self.classify = AttentionalClassify()\n self.support_set_images = support_set_images\n self.support_set_labels = support_set_labels\n self.target_image = target_image\n self.target_label = target_label\n self.keep_prob = keep_prob\n self.is_training = is_training\n self.k = None\n self.rotate_flag = rotate_flag\n self.num_classes_per_set = num_classes_per_set\n self.num_samples_per_class = num_samples_per_class\n self.learning_rate = learning_rate\n\n def loss(self):\n \"\"\"\n Builds tf graph for Matching Networks, produces losses and summary statistics.\n :return:\n \"\"\"\n with tf.name_scope(\"losses\"):\n [b, num_classes, spc] = self.support_set_labels.get_shape().as_list()\n self.support_set_labels = tf.reshape(self.support_set_labels, shape=(b, num_classes * spc))\n self.support_set_labels = tf.one_hot(self.support_set_labels, self.num_classes_per_set) # one hot encode\n encoded_images = []\n [b, num_classes, spc, h, w, c] = self.support_set_images.get_shape().as_list()\n self.support_set_images = tf.reshape(self.support_set_images, shape=(b, num_classes*spc, h, w, c))\n for image in tf.unstack(self.support_set_images, axis=1): #produce embeddings for support set images\n gen_encode = self.g(image_input=image, training=self.is_training, keep_prob=self.keep_prob)\n encoded_images.append(gen_encode)\n\n target_image = self.target_image #produce embedding for target images\n gen_encode = self.g(image_input=target_image, training=self.is_training, keep_prob=self.keep_prob)\n\n encoded_images.append(gen_encode)\n\n if self.fce: # Apply LSTM on embeddings if fce is enabled\n encoded_images, output_state_fw, output_state_bw = self.lstm(encoded_images, name=\"lstm\",\n training=self.is_training)\n outputs = tf.stack(encoded_images)\n\n similarities = self.dn(support_set=outputs[:-1], input_image=outputs[-1], name=\"distance_calculation\",\n training=self.is_training) #get similarity between support set embeddings and target\n\n preds = self.classify(similarities,\n support_set_y=self.support_set_labels, name='classify', training=self.is_training)\n # produce predictions for target probabilities\n\n correct_prediction = tf.equal(tf.argmax(preds, 1), tf.cast(self.target_label, tf.int64))\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n targets = tf.one_hot(self.target_label, self.num_classes_per_set)\n crossentropy_loss = tf.reduce_mean(-tf.reduce_sum(targets * tf.log(preds),\n reduction_indices=[1]))\n\n tf.add_to_collection('crossentropy_losses', crossentropy_loss)\n tf.add_to_collection('accuracy', accuracy)\n\n return {\n self.classify: tf.add_n(tf.get_collection('crossentropy_losses'), name='total_classification_loss'),\n self.dn: tf.add_n(tf.get_collection('accuracy'), name='accuracy')\n }\n\n def train(self, losses):\n\n \"\"\"\n Builds the train op\n :param losses: A dictionary containing the losses\n :param learning_rate: Learning rate to be used for Adam\n :param beta1: Beta1 to be used for Adam\n :return:\n \"\"\"\n c_opt = tf.train.AdamOptimizer(beta1=0.9, learning_rate=self.learning_rate)\n update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) # Needed for correct batch norm usage\n with tf.control_dependencies(update_ops): # Needed for correct batch norm usage\n if self.fce:\n train_variables = self.lstm.variables + self.g.variables\n else:\n train_variables = self.g.variables\n c_error_opt_op = c_opt.minimize(losses[self.classify],\n var_list=train_variables)\n\n return c_error_opt_op\n\n def init_train(self):\n \"\"\"\n Get all ops, as well as all losses.\n :return:\n \"\"\"\n losses = self.loss()\n c_error_opt_op = self.train(losses)\n summary = tf.summary.merge_all()\n return summary, losses, c_error_opt_op\n"
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"text": "# Author: Jose G Perez\n# Version 1.0\n# Last Modified: January 31, 2018\nimport numpy as np\nimport os\n\ndef load_sm(folder, s_kp, pw_kp):\n sm_match = np.zeros((s_kp.shape[0],pw_kp.shape[0]))\n sm_metric = np.zeros((s_kp.shape[0],pw_kp.shape[0]))\n for sidx in range(sm_match.shape[0]):\n path = os.path.join(folder, str(sidx) + '-M.npz')\n m = np.load(path)['m']\n count = []\n metric = []\n for pidx in range(sm_match.shape[1]):\n pw_matches = m[pidx]\n count.append(len(pw_matches))\n metric.append(len(pw_matches) / (len(s_kp[sidx]) + len(pw_kp[pidx]) - len(pw_matches)))\n sm_match[sidx] = np.asarray(count)\n sm_metric[sidx] = np.asarray(metric)\n return sm_match, sm_metric\n\ndef norm2_sm(sm, max_value=255):\n im_result = np.zeros_like(sm)\n for idx in range(sm.shape[0]):\n norm = sm[idx] / np.max(sm[idx])\n im_result[idx] = (norm*max_value).reshape(1, sm.shape[1])\n return im_result\n\ndef norm_prob_sm(sm):\n norm = np.zeros_like(sm)\n for idx in range(sm.shape[0]):\n norm[idx] = sm[idx] / np.sum(sm[idx])\n return norm\n\ndef norm_sm(sm, max_value=255, min_value=0):\n im_result = np.zeros_like(sm)\n for idx in range(sm.shape[0]):\n x = sm[idx]\n norm = ((x - np.min(x))*(max_value-min_value)) / (np.max(x)-np.min(x)) + min_value\n im_result[idx] = (norm).reshape(1, sm.shape[1])\n return im_result\n"
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"text": "import numpy as np\nimport pylab as plt\n\ns_data = np.load('S_BB_V.npz')\ns_im = s_data['images']\ns_label = s_data['labels']\ns_orig = s_data['originals']\n\npw_data = np.load('PW_BB_V.npz')\npw_im = pw_data['images']\npw_label = pw_data['labels']\npw_orig = pw_data['originals']\n\n"
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"text": "from util_sift import load_sift, array_to_kp, kp_to_array\nfrom util_matching import match\nfrom util_sm import load_sm\nfrom util_cv import match_to_cv\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nimport cv2\n\ndef display(s_idx, pw_idx):\n plt.figure()\n (im1, kp1, des1) = (s_im[s_idx], s_kp[s_idx], s_des[s_idx])\n (im2, kp2, des2) = (pw_im[pw_idx], pw_kp[pw_idx], pw_des[pw_idx])\n matches = match(kp1, des1, kp2, des2)\n # Convert to OpenCV objects for viewing\n matches = match_to_cv(matches)\n kp1 = array_to_kp(kp1)\n kp2 = array_to_kp(kp2)\n s_plate = s_label[s_idx]\n pw_plate = pw_label[pw_idx]\n im_matches = cv2.drawMatches(im1, kp1, im2, kp2, matches, None, flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)\n s = '[CV] Matches between S%s & PW%s = %s' % (s_plate, pw_plate, str(len(matches)))\n print(s)\n plt.title(s)\n plt.imshow(im_matches)\n\n#%% Load data\ns_im, s_label, s_kp, s_des = load_sift('S_BB_V2_SIFT.npz')\npw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V2_SIFT.npz')\npwi_im, pwi_label, pwi_kp, pwi_des = load_sift('PW_BB_V1_SIFT.npz')\nsm_matches, sm_metric = load_sm('sm_v3', s_kp, pw_kp)\n\n#%% Only use Nissl PWs=[''=/=\nidxs = pw_label[pwi_label-1]-1\npw_im = pw_im[idxs]\npw_label = pw_label[idxs]\npw_kp = pw_kp[idxs]\npw_des = pw_des[idxs]\nsm_matches = sm_matches[:,idxs]\nsm_metric = sm_metric[:,idxs]\n\nsm_matches = sm_matches.astype(np.uint8)\n\n#%% Figure\nfig, ax = plt.subplots()\nax.set_title('Similarity Matrix Visualization (v2)')\nax.imshow(sm_matches, cmap=plt.get_cmap('hot'))\n\nfig2, ax2 = plt.subplots()\nwhile True:\n plt.figure(1)\n p = plt.ginput(n=1, timeout=0)\n\n if len(p) == 0:\n break\n\n p = p[0]\n (x, y) = (p[0], p[1])\n rect = patches.Rectangle((np.floor(x)+0.5, np.floor(y)-0.5), 0.5, 0.5, color='blue')\n ax.add_patch(rect)\n\n s_idx = int(y)\n pw_idx = int(x)\n s_plate = s_label[s_idx]\n pw_plate = pw_label[pw_idx]\n\n s = 'Matches between S%s & PW%s = %s' % (s_plate, pw_plate, sm_matches[s_idx,pw_idx])\n ax.set_title(s)\n print(s, 'index', s_idx, pw_idx)\n\n plt.figure(2)\n (im1, kp1, des1) = (s_im[s_idx], s_kp[s_idx], s_des[s_idx])\n (im2, kp2, des2) = (pw_im[pw_idx], pw_kp[pw_idx], pw_des[pw_idx])\n matches = match(kp1, des1, kp2, des2)\n # Convert to OpenCV objects for viewing\n matches = match_to_cv(matches)\n kp1 = array_to_kp(kp1)\n kp2 = array_to_kp(kp2)\n im_matches = cv2.drawMatches(im1, kp1, im2, kp2, matches, None, flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)\n s = '[CV] Matches between S%s & PW%s = %s' % (s_plate, pw_plate, str(len(matches)))\n print(s)\n ax2.set_title(s)\n ax2.imshow(im_matches)\n\nplt.title(\"Finished\")"
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"text": "# Author: Jose G Perez\n# Version 1.0\n# Last Modified: January 31, 2018\nfrom util_im import imshow_matches\nfrom util_sm import load_sm, norm_sm\nfrom util_sift import precompute_sift, load_sift\nimport pylab as plt\nimport numpy as np\nprecompute_sift('S_BB_V4', 'PW_BB_V4')\ns_im, s_label, s_kp, s_des = load_sift('S_BB_V4_SIFT.npz')\npw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V4_SIFT.npz')\n\n# sm_v1_match, sm_v1_metric = load_sm('sm_v1', s_kp, pw_kp)\nsm_matches, sm_metric = load_sm('sm_v4', s_kp, pw_kp)\n\n# imshow_matches(norm_sm(sm_v1_match), 'Experiment 1: Matches Count')\n# imshow_matches(norm_sm(sm_v1_metric), 'Experiment 1: Metric')\n#\n# imshow_matches(norm_sm(sm_v2_match), 'Experiment 2: Matches Count')\n# imshow_matches(norm_sm(sm_v2_metric), 'Experiment 2: Metric')\n\n#%%\npw_ticks_idxs = [0]\npw_ticks_vals = [pw_label[0]]\nfor x in range(len(pw_label)):\n try:\n diff = pw_label[x+1] - pw_label[x]\n if diff > 1:\n pw_ticks_idxs.append(x)\n pw_ticks_vals.append(pw_label[x])\n # print(\"IDX: \", x, \"DIFF:\", diff)\n except:\n continue\n\npw_ticks_idxs.append(len(pw_label)-1)\npw_ticks_vals.append(pw_label[-1])\n\n#%%\nplt.figure()\nax = plt.gca()\nax.set_title('Similarity Matrix (Matches)')\nplt.setp(ax.get_xticklabels(), rotation=90, horizontalalignment='right')\nplt.imshow(sm_matches)\nplt.xticks(pw_ticks_idxs, pw_ticks_vals)\nplt.yticks(np.arange(0, len(s_label)), np.arange(1, len(s_label)+1))\n\nfor tick in ax.xaxis.get_major_ticks():\n tick.label.set_fontsize(8)\n\nfor tick in ax.yaxis.get_major_ticks():\n tick.label.set_fontsize(8)\n # tick.label.set_rotation('vertical')\n\nplt.xlabel('PW Level')\nplt.ylabel('S Level')\n# heatmap = plt.pcolor(sm_matches)\ncolorbar = plt.colorbar()\ncolorbar.set_label('# of Matches')\n\n#%%\nplt.figure()\nax = plt.gca()\nax.set_title('Similarity Matrix (Metric)')\nplt.setp(ax.get_xticklabels(), rotation=90, horizontalalignment='right')\nplt.imshow(sm_metric)\nplt.xticks(pw_ticks_idxs, pw_ticks_vals)\nplt.yticks(np.arange(0, len(s_label)), np.arange(1, len(s_label)+1))\n\nfor tick in ax.xaxis.get_major_ticks():\n tick.label.set_fontsize(8)\n\nfor tick in ax.yaxis.get_major_ticks():\n tick.label.set_fontsize(8)\n # tick.label.set_rotation('vertical')\n\nplt.xlabel('PW Level')\nplt.ylabel('S Level')\n# heatmap = plt.pcolor(sm_matches)\ncolorbar = plt.colorbar()\ncolorbar.set_label('Metric Value')"
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"text": "#%% Load Bregma Data\nimport numpy as np\nimport pylab as plt\nfrom skimage import color\nimport sys\n\nfrom util_im import imshow_matches\nfrom util_sm import load_sm, norm_sm, norm_prob_sm\nfrom util_sift import precompute_sift, load_sift\n\nBREG = True\n\ns_im, s_label, s_kp, s_des = load_sift('S_BB_V2_SIFT.npz')\npw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V2_SIFT.npz')\npwi_im, pwi_label, pwi_kp, pwi_des = load_sift('PW_BB_V1_SIFT.npz')\nsm_matches, sm_metric = load_sm('sm_v3', s_kp, pw_kp)\n\ndef norm_sm(sm, max_value=255, min_value=0):\n sm[sm == np.inf] = sys.maxsize\n im_result = np.zeros_like(sm)\n for idx in range(sm.shape[0]):\n x = sm[idx]\n norm = ((x - np.min(x))*(max_value-min_value)) / (np.max(x)-np.min(x)) + min_value\n im_result[idx] = (norm).reshape(1, sm.shape[1])\n return im_result\n\nnp.set_printoptions(edgeitems=5)\nb_s4 = np.loadtxt('Bregma_S4.csv', dtype=np.float, delimiter=',')\nb_pw3 = np.loadtxt('Bregma_PW3.csv', dtype=np.float, delimiter=',')\n\n\n\nbreg_1 = b_s4\nbreg_2 = b_pw3\n# M = np.zeros((breg_1.shape[0]+1, breg_2.shape[0]))\nM = np.zeros((breg_1.shape[0]+1, breg_2.shape[0]))\nM[1:,:] = np.inf\nDIR = np.zeros_like(M)\nDIR[1:,:] = np.inf\n\n\n\n#%% Dynamic programming\nfor row in range(1, M.shape[0]):\n for col in range(M.shape[1]):\n if row > col:\n continue\n if BREG:\n choices = [M[row,col-1], # Left\n M[row-1,col-1]+abs(breg_1[row-1] - breg_2[col])] # Diagonal\n M[row][col] = min(choices)\n DIR[row][col] = np.argmin(choices)\n else:\n choices = [M[row,col-1], # Left\n M[row-1,col-1] + sm_matches[row-1][col-1], # Diagonal\n M[row-1][col-1] # Right\n ]\n M[row][col] = max(choices)\n DIR[row][col] = np.argmax(choices)\n\n\n\n#%% Overlap\nbg = norm_sm(M, 255).astype(np.uint8)\ncolor_mask = np.zeros((DIR.shape[0], DIR.shape[1], 3))\nfor row in range(1, M.shape[0]):\n for col in range(M.shape[1]):\n if row > col:\n color_mask[row][col] = [255, 0, 0]\nd_row = DIR.shape[0] - 1\nd_col = DIR.shape[1] - 1\ncount = 0\npath = ['START']\npairs = []\nif BREG:\n while d_row != 0 and d_col != 0:\n color_mask[d_row, d_col] = [0, 0, 255]\n bg[d_row, d_col] = 255\n next_dir = DIR[d_row, d_col]\n pairs.append([d_row, d_col])\n if next_dir == 0:\n d_col -= 1\n path.append('L')\n elif next_dir == 1:\n d_row -= 1\n d_col -= 1\n path.append('D')\nelse:\n while d_row != 0 and d_col != 0:\n color_mask[d_row, d_col] = [0, 0, 255]\n bg[d_row, d_col] = 255\n next_dir = DIR[d_row, d_col]\n pairs.append([d_row, d_col])\n if next_dir == 0:\n d_col -= 1\n path.append('L')\n elif next_dir == 1:\n d_row -= 1\n d_col -= 1\n path.append('D')\n else:\n d_row -= 1\n path.append(\"U\")\n\n#%% Path Figure\nprint(\"path\", count, path)\nimg_color = np.stack((bg,) * 3, axis=2)\nimg_hsv = color.rgb2hsv(img_color)\ncolor_mask_hsv = color.rgb2hsv(color_mask)\nimg_hsv[..., 0] = color_mask_hsv[..., 0]\nimg_hsv[..., 1] = color_mask_hsv[..., 1]\nim_overlay = color.hsv2rgb(img_hsv)\nplt.figure()\nplt.title(\"Bregma Overlay: \" + str(BREG))\nplt.imshow(im_overlay)\nplt.show()"
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"text": "# Author: Jose G Perez\n# Version 1.0\n# Last Modified: January 31, 2018\nimport numpy as np\nimport pylab as plt\nimport cv2\n\nfrom util_sift import array_to_kp, precompute_sift, load_sift\nfrom util_matching import match\nfrom util_cv import match_to_cv\nfrom util_im import imshow\n\nprecompute_sift('S_BB_V4', 'PW_BB_V4')\ns_im, s_label, s_kp, s_des = load_sift('S_BB_V4_SIFT.npz')\npw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V4_SIFT.npz')\n\n#%%\ns_idx = 32 # np.where(s_label == 33)[0][0]\npw_idx = 36 # np.where(pw_label == 50)[0][0]\n(im1, kp1, des1) = (s_im[s_idx], s_kp[s_idx], s_des[s_idx])\n(im2, kp2, des2) = (pw_im[pw_idx], pw_kp[pw_idx], pw_des[pw_idx])\nmatches = match(kp1,des1,kp2,des2)\n# Convert to OpenCV objects for viewing\nmatches = match_to_cv(matches)\nkp1 = array_to_kp(kp1)\nkp2 = array_to_kp(kp2)\nim_matches = cv2.drawMatches(im1, kp1, im2, kp2, matches, None, flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)\n# plt.gray()\nstr = '%s Matches Between S%s and PW%s' % (len(matches), s_label[s_idx], pw_label[pw_idx])\nimshow(im_matches, str)"
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"text": "# Author: Jose G Perez\n# Version 1.0\n# Last Modified: January 31, 2018\nimport numpy as np\nimport pylab as plt\n\nfrom util_matching import match\nfrom util_sift import precompute_sift, load_sift\n\nprecompute_sift('S_BB_V1', 'PW_BB_V1')\ns_im, s_label, s_kp, s_des = load_sift('S_BB_V1_SIFT.npz')\npw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V1_SIFT.npz')\n# =======******* Row Testing [S33 and all PW]\n# PW68 is IDX:39\n# matches = []\n# s_idx = np.where(s_label == 33)[0][0]\n# (im1, kp1, des1) = (s_im[s_idx], s_kp[s_idx], s_des[s_idx])\n# for pw_idx in range(pw_im.shape[0]):\n# print(pw_idx, '/', pw_im.shape[0])\n# (im2, kp2, des2) = (pw_im[pw_idx], pw_kp[pw_idx], pw_des[pw_idx])\n# m = match(kp1, des1, kp2, des2)\n# matches.append(m)\n#\n# count = []\n# for match in matches:\n# count.append(len(match))\nmatches = []\npw_idx = 33\n(im2, kp2, des2) = (pw_im[pw_idx], pw_kp[pw_idx], pw_des[pw_idx])\nfor s_idx in range(s_im.shape[0]):\n print(s_idx, '/', s_im.shape[0])\n (im1, kp1, des1) = (s_im[s_idx], s_kp[s_idx], s_des[s_idx])\n m = match(kp1, des1, kp2, des2)\n matches.append(m)\n\ncount = []\nfor match in matches:\n count.append(len(match))\ncount_norm = np.array(count) / np.max(count)\ncount_im = (count_norm * 255).reshape(1, 73)\nplt.gray()\nplt.imshow(count_im)"
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"text": "import numpy as np\nimport cv2\n\nDISTANCE_RATIO = 0.95\nRANSAC_REPROJ_TRESHHOLD = 10 # The higher the threshold, the lower the inliers\nRANSAC_MAX_ITERS = 2000\nRANSAC_CONFIDENCE = 0.99\nBF = cv2.BFMatcher(normType=cv2.NORM_L2)\n\ndef perform_ransac(s_idx):\n global s_kp, s_des, pw_kp, pw_des\n matches = []\n print('s_idx', s_idx)\n for pw_idx in range(pw_kp.shape[0]):\n mat = BF.knnMatch(s_des[s_idx], pw_des[pw_idx], k=2)\n mat = [m[0] for m in mat if len(m) == 2 and m[0].distance < m[1].distance * DISTANCE_RATIO]\n src_pts = np.float32([(s_kp[s_idx][m.queryIdx][0],s_kp[s_idx][m.queryIdx][1]) for m in mat])\n dst_pts = np.float32([(pw_kp[pw_idx][m.trainIdx][0],pw_kp[pw_idx][m.trainIdx][1]) for m in mat])\n H, mask = cv2.findHomography(src_pts, dst_pts, method=cv2.RANSAC, ransacReprojThreshold=RANSAC_REPROJ_TRESHHOLD,maxIters=RANSAC_MAX_ITERS, confidence=RANSAC_CONFIDENCE)\n matches.append(np.array([mask.sum()]))\n\n np.savez_compressed(str(s_idx) + '-R', m=matches)\n\ndef imshow_ransac():\n import os\n import pylab as plt\n im_inliers = np.zeros((73, 89))\n for sidx in range(73):\n path = os.path.join('sm_ransac', str(sidx) + '-R.npz')\n m = np.load(path)['m']\n inliers = []\n for pidx in range(89):\n pw_matches = m[pidx]\n inliers.append(pw_matches[0])\n inlier_norm = np.array(inliers) / np.max(inliers)\n im_inliers[sidx] = (inlier_norm * 255).reshape(1, 89)\n\n fig = plt.figure(0)\n ax = fig.add_subplot(111)\n ax.set_xlabel('PW Level')\n ax.set_ylabel('S Level')\n ax.set_xticks(np.arange(0,89,5))\n ax.set_yticks(np.arange(0,72,5))\n ax.set_title('Inliers')\n plt.set_cmap(plt.get_cmap('hot'))\n plt.imshow(im_inliers)"
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"text": "#%% Load Data\nimport numpy as np\nimport pylab as plt\nimport sys\nfrom util_im import imshow_matches\nfrom util_sm import load_sm, norm_sm, norm_prob_sm\nfrom util_sift import precompute_sift, load_sift\nfrom skimage import color\n\nb_s4 = np.loadtxt('Bregma_S4.csv', dtype=np.float, delimiter=',')\nb_pw3 = np.loadtxt('Bregma_PW3_M.csv', dtype=np.float, delimiter=',')\n\ns_im, s_label, s_kp, s_des = load_sift('S_BB_V2_SIFT.npz')\npw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V2_SIFT.npz')\npwi_im, pwi_label, pwi_kp, pwi_des = load_sift('PW_BB_V1_SIFT.npz')\nsm_matches, sm_metric = load_sm('sm_v3', s_kp, pw_kp)\n\n# Only Nissl but using V2 dataset\nb_pw3_im = []\nb_pw3_label = []\nb_pw3_kp = []\nb_pw3_des = []\nnop = [12, 14, 56, 76, 15, 17, 19, 21, 22, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59,\n 61, 63, 65, 67, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91,93,95,97,99,101,103,105,107,109,111,113,115,117,119,\n 121,123,125,127,129,131,133,135,137,139,141,143,144,145,147,149,151,153,155]\nfor plate in pw_label:\n if plate in nop:\n continue\n idx = plate - 1\n b_pw3_im.append(pw_im[idx])\n b_pw3_label.append(pw_label[idx])\n b_pw3_kp.append(pw_kp[idx])\n b_pw3_des.append(pw_des[idx])\n\npw_im = np.array(b_pw3_im)\npw_label = np.array(b_pw3_label)\npw_kp = np.array(b_pw3_kp)\npw_des = np.array(b_pw3_des)\n\nb_sm_matches = []\nb_sm_metric = []\nfor plate in pw_label:\n idx = plate - 1\n b_sm_matches.append(sm_matches[:,idx])\n b_sm_metric.append(sm_metric[:,idx])\n\nsm_matches = np.array(b_sm_matches)\nsm_metric = np.array(b_sm_metric)\n# norm = norm_sm(sm_metric, 100)\nnorm = norm_prob_sm(sm_matches)\n\n#%% Bregma Algorithm\natlas1 = b_s4\natlas2 = b_pw3\nM = np.zeros((atlas1.shape[0]+1, atlas2.shape[0]))\nM[1:,:] = np.inf\nDIR = np.zeros_like(M)\nDIR[:,:] = 2\nfor row in range(1, M.shape[0]):\n for col in range(M.shape[1]):\n if row > col:\n continue\n\n choices = [M[row,col-1], # Left\n M[row-1,col-1]+abs(atlas1[row-1] - atlas2[col])] # Diagonal\n M[row][col] = min(choices)\n DIR[row][col] = np.argmin(choices)\n\nM = M[1:,:]\nDIR = DIR[1:,:]\n#%% Path Backtracking\nim = np.zeros((DIR.shape[0], DIR.shape[1], 3), dtype=np.uint8)\nfor row in range(DIR.shape[0]):\n for col in range(DIR.shape[1]):\n if row > col:\n im[row, col] = [0, 100, 50] # Dark Green\n elif DIR[row][col] == 0: # Left\n im[row,col] = [200, 0, 0] # Red\n elif DIR[row][col] == 1: # Diagonal\n im[row, col] = [0, 255, 0] # Green\n elif DIR[row][col] == 2: # Unused\n im[row, col] = [0, 100, 50] # Dark Green\n else:\n im[row, col] = [0, 0, 0] # Black\n\nc = [148, 0, 211] # Purple\nim[6-1][8-1] = c\nim[11-1][11-1] = c\nim[23-1][42-1] = c\nim[33-1][68-1] = c\nfor row in range(DIR.shape[0]):\n col = np.argmin(M[row])\n # PW8 S6, PW11 S11, PW42 S23, PW68 S33,\n if (row == 6-1 and col == 8-1) or (row == 11-1 and col == 11-1) or (row == 23-1 and col == 42-1) or (row == 33-1 and col == 68-1):\n im[row][col] = [255, 255, 255] # White\n else:\n im[row][col] = [0, 0, 255] # Blue\n\nfig, ax = plt.subplots()\nax.set_title(\"DIR - Best Path\")\nax.imshow(im)"
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"text": "import numpy as np\nfrom keras.models import Model\nfrom keras.datasets import mnist\nfrom keras.models import load_model\nfrom sklearn.metrics import label_ranking_average_precision_score\nimport time\nimport cv2\n\nt0 = time.time()\n\n#(x_train, y_train), (x_test, y_test) = mnist.load_data()\nsw_data = np.load('atlas_sw.npz')\nx_train = sw_data['images'].astype('float32') / 255.\nx_shape = (x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)\nx_train = np.reshape(x_train, x_shape)\ny_train = sw_data['labels']\nprint(\"X_Train: \", x_train.shape)\n\nprint(\"===--- Paxinos/Watson Atlas\")\npw_data = np.load('atlas_pw.npz')\npw_y = pw_data['labels']\npw_im = pw_data['images'].astype('float32') / 255.\npw_shape = pw_im.shape[0], pw_im.shape[1], pw_im.shape[2], 1\npw_im = np.reshape(pw_im, pw_shape)\n\nx_test = np.array([pw_im[7], pw_im[10], pw_im[26], pw_im[39]])\ny_test = np.array([pw_y[7], pw_y[10], pw_y[26], pw_y[39]])\n\nx_test = pw_im\ny_test = pw_y\n\nprint(\"X_Test: \", x_test.shape)\n\nnoise_factor = 0.4\nx_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)\nx_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)\n\nx_train_noisy = np.clip(x_train_noisy, 0., 1.)\nx_test_noisy = np.clip(x_test_noisy, 0., 1.)\nt1 = time.time()\nprint('Dataset loaded in: ', t1-t0)\n\nprint('Loading model :')\nt0 = time.time()\nautoencoder = load_model('autoencoder.h5')\nencoder = Model(inputs=autoencoder.input, outputs=autoencoder.get_layer('encoder').output)\nt1 = time.time()\nprint('Model loaded in: ', t1-t0)\n\nscores = []\n\n\ndef retrieve_closest_elements(test_code, test_label, learned_codes):\n distances = []\n for code in learned_codes:\n distance = np.linalg.norm(code - test_code)\n distances.append(distance)\n nb_elements = learned_codes.shape[0]\n distances = np.array(distances)\n learned_code_index = np.arange(nb_elements)\n labels = np.copy(y_train).astype('float32')\n labels[labels != test_label] = -1\n labels[labels == test_label] = 1\n labels[labels == -1] = 0\n distance_with_labels = np.stack((distances, labels, learned_code_index), axis=-1)\n sorted_distance_with_labels = distance_with_labels[distance_with_labels[:, 0].argsort()]\n\n sorted_distances = 28 - sorted_distance_with_labels[:, 0]\n sorted_labels = sorted_distance_with_labels[:, 1]\n sorted_indexes = sorted_distance_with_labels[:, 2]\n return sorted_distances, sorted_labels, sorted_indexes\n\ndef compute_average_precision_score(test_codes, test_labels, learned_codes, n_samples):\n out_labels = []\n out_distances = []\n retrieved_elements_indexes = []\n for i in range(len(test_codes)):\n sorted_distances, sorted_labels, sorted_indexes = retrieve_closest_elements(test_codes[i], test_labels[i], learned_codes)\n out_distances.append(sorted_distances[:n_samples])\n out_labels.append(sorted_labels[:n_samples])\n retrieved_elements_indexes.append(sorted_indexes[:n_samples])\n\n out_labels = np.array(out_labels)\n out_labels_file_name = 'computed_data/out_labels_{}'.format(n_samples)\n np.save(out_labels_file_name, out_labels)\n\n out_distances_file_name = 'computed_data/out_distances_{}'.format(n_samples)\n out_distances = np.array(out_distances)\n np.save(out_distances_file_name, out_distances)\n score = label_ranking_average_precision_score(out_labels, out_distances)\n scores.append(score)\n return score\n\nINDEX = 0\ndef retrieve_closest_images(test_element, test_label, n_samples=10):\n global INDEX\n learned_codes = encoder.predict(x_train)\n learned_codes = learned_codes.reshape(learned_codes.shape[0],\n learned_codes.shape[1] * learned_codes.shape[2] * learned_codes.shape[3])\n\n test_code = encoder.predict(np.array([test_element]))\n test_code = test_code.reshape(test_code.shape[1] * test_code.shape[2] * test_code.shape[3])\n\n distances = []\n\n for code in learned_codes:\n distance = np.linalg.norm(code - test_code)\n distances.append(distance)\n nb_elements = learned_codes.shape[0]\n distances = np.array(distances)\n learned_code_index = np.arange(nb_elements)\n labels = np.copy(y_train).astype('float32')\n labels[labels != test_label] = -1\n labels[labels == test_label] = 1\n labels[labels == -1] = 0\n distance_with_labels = np.stack((distances, labels, learned_code_index), axis=-1)\n sorted_distance_with_labels = distance_with_labels[distance_with_labels[:, 0].argsort()]\n\n sorted_distances = 28 - sorted_distance_with_labels[:, 0]\n sorted_labels = sorted_distance_with_labels[:, 1]\n sorted_indexes = sorted_distance_with_labels[:, 2]\n kept_indexes = sorted_indexes[:n_samples]\n\n score = label_ranking_average_precision_score(np.array([sorted_labels[:n_samples]]), np.array([sorted_distances[:n_samples]]))\n \n kept_indexes = kept_indexes.astype(np.uint16)\n result_y = y_train[kept_indexes]\n result_distances = sorted_distances[kept_indexes]\n \n print(\"Plate {} - \".format(test_label), end='')\n for i in range(n_samples):\n match_y = result_y[i]\n match_d = result_distances[i]\n print(\"[{},{:.4f}] \".format(match_y, match_d), end='')\n \n print(\"\")\n \n #print(\"Average precision ranking score for tested element is {}\".format(score))\n\n original_image = test_element\n #cv2.imshow('original_image_' + str(INDEX), original_image)\n retrieved_images = x_train[int(kept_indexes[0]), :]\n for i in range(1, n_samples):\n retrieved_images = np.hstack((retrieved_images, x_train[int(kept_indexes[i]), :]))\n \n #cv2.imshow('Results_' + str(INDEX), retrieved_images)\n\n cv2.imwrite('test_results/plate_' + str(test_label) + '.jpg', 255 * cv2.resize(original_image, (0,0), fx=3, fy=3))\n cv2.imwrite('test_results/results' + str(test_label) + '.jpg', 255 * cv2.resize(retrieved_images, (0,0), fx=2, fy=2))\n\n #import pdb\n #pdb.set_trace()\n \n INDEX += 1\n return result_y\n\ndef test_model(n_test_samples, n_train_samples):\n learned_codes = encoder.predict(x_train)\n learned_codes = learned_codes.reshape(learned_codes.shape[0], learned_codes.shape[1] * learned_codes.shape[2] * learned_codes.shape[3])\n test_codes = encoder.predict(x_test)\n test_codes = test_codes.reshape(test_codes.shape[0], test_codes.shape[1] * test_codes.shape[2] * test_codes.shape[3])\n indexes = np.arange(len(y_test))\n np.random.shuffle(indexes)\n indexes = indexes[:n_test_samples]\n\n print('Start computing score for {} train samples'.format(n_train_samples))\n t1 = time.time()\n score = compute_average_precision_score(test_codes[indexes], y_test[indexes], learned_codes, n_train_samples)\n t2 = time.time()\n print('Score computed in: ', t2-t1)\n print('Model score:', score)\n\n\ndef plot_denoised_images():\n denoised_images = autoencoder.predict(x_test_noisy.reshape(x_test_noisy.shape[0], x_test_noisy.shape[1], x_test_noisy.shape[2], 1))\n test_img = x_test_noisy[0]\n resized_test_img = cv2.resize(test_img, (280, 280))\n cv2.imshow('input', resized_test_img)\n output = denoised_images[0]\n resized_output = cv2.resize(output, (280, 280))\n cv2.imshow('output', resized_output)\n cv2.imwrite('test_results/noisy_image.jpg', 255 * resized_test_img)\n cv2.imwrite('test_results/denoised_image.jpg', 255 * resized_output)\n\n\n# To test the whole model\nn_test_samples = 1000\nn_train_samples = [10, 50, 100, 200, 300, 400, 500, 750, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000,\n 20000, 30000, 40000, 50000, 60000]\n\n\n#for n_train_sample in n_train_samples:\n# test_model(n_test_samples, n_train_sample)\n\nnp.save('computed_data/scores', np.array(scores))\n\nimport pylab as plt\nplt.xkcd()\nplt.figure()\nplt.title('SW Matching')\nplt.xlabel('PW Plate')\nplt.ylabel('SW Plate')\n# To retrieve closest images\nx = []\ny = []\nfor i in range(len(x_test)):\n#for i in range(3):\n x.append(y_test[i]) # Plate #\n predictions = retrieve_closest_images(x_test[i], y_test[i])\n y.append(predictions[0]) # Top Prediction\n \nplt.plot(x, y)\nplt.savefig('results.png')\nplt.show(block=True)\n\n\n# To plot a denoised image\n#plot_denoised_images()"
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"text": "import cv2\ndef match_to_cv(matches):\n cv = []\n for i in range(matches.shape[0]):\n m = matches[i]\n temp = cv2.DMatch()\n temp.queryIdx = int(m[0])\n temp.imgIdx = int(m[0])\n temp.trainIdx = int(m[1])\n temp.distance = int(m[2])\n cv.append(temp)\n return cv"
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"text": "import numpy as np\nimport os\nfrom PIL import Image\nimport pylab as plt\nimport cv2\n\nnp.set_printoptions(threshold=np.nan)\nplt.gray()\n\n#%% Generation function\ndef to_gray(P):\n return P.mean(axis=2)\n\ndef generate_color(P1, P2, rstrides, cstrides, threshold):\n P3 = np.zeros_like(P1)\n rows = P1.shape[0] // rstrides\n cols = P1.shape[1] // cstrides\n\n for ridx in range(rows):\n rstart = ridx * rstrides\n if ridx == rows - 1:\n rend = P1.shape[0]\n else:\n rend = rstart + rstrides\n\n for cidx in range(cols):\n cstart = cidx * cstrides\n if cidx == cols - 1:\n cend = P1.shape[1]\n else:\n cend = cstart + cstrides\n\n # Extract plate quadrants\n q1 = P1[rstart:rend, cstart:cend, :]\n q1 = q1.reshape(q1.shape[0] * q1.shape[1], 3)\n\n q2 = P2[rstart:rend, cstart:cend, :]\n q2 = q2.reshape(q2.shape[0] * q2.shape[1], 3)\n\n # Calculate quadrant coverage\n px_total = q1.shape[0]\n z1 = np.sum(q1 < threshold)\n z2 = np.sum(q2 < threshold)\n coverage1 = z1 / px_total\n coverage2 = z2 / px_total\n coverage3 = (coverage1 + coverage2) / 2\n\n # Create blank quadrant\n q3 = np.zeros_like(q1)\n q3[:, :] = 255\n\n # Figure out how much we need to cover it\n q3_pixels = int(px_total * coverage3)\n\n # Randomly select pixels to paint\n idxs = np.random.choice(q3.shape[0], q3_pixels, True)\n\n # Paint by averaging the two image quadrants\n q3[idxs, :] = (q1[idxs, :] + q2[idxs, :]) / 2\n # q3[idxs, :] = 0\n\n # Reshape back into original form, add to output image\n q3 = q3.reshape(rend-rstart, cend-cstart, 3)\n P3[rstart:rend, cstart:cend, :] = q3\n\n return P3\n\n\ndef generate_gray(P1, P2, rstrides, cstrides, threshold, w1=0.5):\n P3 = np.zeros_like(P1)\n rows = P1.shape[0] // rstrides\n cols = P1.shape[1] // cstrides\n\n for ridx in range(rows):\n rstart = ridx * rstrides\n if ridx == rows - 1:\n rend = P1.shape[0]\n else:\n rend = rstart + rstrides\n\n for cidx in range(cols):\n cstart = cidx * cstrides\n if cidx == cols - 1:\n cend = P1.shape[1]\n else:\n cend = cstart + cstrides\n\n # Extract plate quadrants\n q1 = P1[rstart:rend, cstart:cend]\n q1 = q1.reshape(q1.shape[0] * q1.shape[1])\n\n q2 = P2[rstart:rend, cstart:cend]\n q2 = q2.reshape(q2.shape[0] * q2.shape[1])\n\n # Calculate quadrant coverage\n px_total = q1.shape[0]\n z1 = np.sum(q1 < threshold)\n z2 = np.sum(q2 < threshold)\n coverage1 = z1 / px_total\n coverage2 = z2 / px_total\n coverage3 = (coverage1 + coverage2) / 2\n\n # Create blank quadrant\n q3 = np.zeros_like(q1)\n q3[:] = 255\n\n # Figure out how much we need to cover it\n q3_pixels = int(px_total * coverage3)\n\n # Randomly select pixels to paint\n idxs = np.random.choice(q3.shape[0], q3_pixels, True)\n\n # Paint by averaging the two image quadrants\n w2 = 1 - w1\n # q3[idxs] = (w1 * q1[idxs] + w2 * q2[idxs]) / 2\n q3[idxs] = 0\n\n # Reshape back into original form, add to output image\n q3 = q3.reshape(rend-rstart, cend-cstart)\n P3[rstart:rend, cstart:cend] = q3\n\n return P3\n#%% Load Image Files\nprint(\"Loading images\")\nWIDTH = 800\nHEIGHT = 400\ndir = 'C:/Users/xeroj/Dropbox/Training data sets - Khan-Fuentes/Paxinos and Watson, 2014 (7th Edition) Image set/'\n\nfilename1 = os.path.join(dir, 'RBSC7-068.jpg')\nfilename2 = os.path.join(dir, 'RBSC7-070.jpg')\n\nim1 = Image.open(filename1)\nim1 = im1.resize((WIDTH, HEIGHT), Image.LANCZOS)\nP1 = np.array(im1, dtype=np.uint8)\nP1 = P1[:, 0:P1.shape[1]//2, :]\n\nim2 = Image.open(filename2)\nim2 = im2.resize((WIDTH, HEIGHT), Image.LANCZOS)\nP2 = np.array(im2, dtype=np.uint8)\nP2 = P2[:, 0:P2.shape[1]//2, :]\n\nP1_g = to_gray(P1)\nP2_g = to_gray(P2)\n\n#%% Control points\nprint(\"Computing control points\")\nLEFT_PAD = 0\nRIGHT_PAD = 0\nCOL_INTERVALS = 5\nCTRL_THRESHOLD = 200\ndef get_controls(P):\n w = P.shape[0]\n h = P.shape[1]\n ctrl_pts = [[0, 0], [w, 0], [0, h], [w, h]]\n # Top to bottom\n for col in range(LEFT_PAD, P.shape[1]-RIGHT_PAD, COL_INTERVALS):\n for row in range(0, P.shape[0], 1):\n if P[row,col] <= CTRL_THRESHOLD:\n ctrl_pts.append([row, col])\n break\n\n # Bottom to top\n for col in range(LEFT_PAD, P.shape[1]-RIGHT_PAD, COL_INTERVALS):\n for row in range(P.shape[0]-1, 0, -1):\n if P[row,col] <= CTRL_THRESHOLD:\n ctrl_pts.append([row, col])\n break\n\n\n return ctrl_pts\n\nsrc_pts = get_controls(P1_g)\ndst_pts = get_controls(P2_g)\n\nsize = min(len(src_pts), len(dst_pts))\nsrc_pts = src_pts[:size]\ndst_pts = dst_pts[:size]\n\n#%% Visualize Control Points\nprint(\"Visualizing control points\")\nfig, ax = plt.subplots(nrows=1,ncols=2)\ntitle = 'Control Points'\nfig.suptitle(title, fontsize=22)\n\nax[0].set_title('PW68')\nax[0].imshow(P1)\n\nax[1].set_title('PW72')\nax[1].imshow(P2)\n\nfor pt in src_pts:\n ax[0].plot(pt[1], pt[0], marker='x', color='red', markersize=5)\n\nfor pt in dst_pts:\n ax[1].plot(pt[1], pt[0], marker='x', color='red', markersize=5)\n\n#%% SIFT computing\nprint(\"Computing SIFT features\")\n# SIFT = cv2.xfeatures2d.SIFT_create(contrastThreshold=0.02, edgeThreshold=100, sigma=2)\nSIFT = cv2.xfeatures2d.SIFT_create()\nkp1, des1 = SIFT.detectAndCompute(P1, None)\nkp2, des2 = SIFT.detectAndCompute(P2, None)\n\nfig, ax = plt.subplots(nrows=1,ncols=2)\ntitle = 'SIFT KeyPoints'\nfig.suptitle(title, fontsize=22)\n\nax[0].set_title('Plate 1')\nax[0].imshow(cv2.drawKeypoints(P1, kp1, None))\n\nax[1].set_title('Plate 2')\nax[1].imshow(cv2.drawKeypoints(P2, kp2, None))\n\n#%% SIFT + Homography\n# bf = cv2.BFMatcher()\n# matches = bf.knnMatch(des1,des2, k=2)\n# matches = np.asarray([m for m in matches if m[0].distance < 0.8*m[1].distance])\n# if len(matches[:,0]) >= 4:\n# src = np.float32([ kp1[m.queryIdx].pt for m in matches[:,0] ]).reshape(-1,1,2)\n# dst = np.float32([ kp2[m.trainIdx].pt for m in matches[:,0] ]).reshape(-1,1,2)\n# H, masked = cv2.findHomography(src, dst, cv2.RANSAC, 50.0)\n# im_match = cv2.drawMatches(P1, kp1, P2, kp2, matches[:,0], None, flags=2)\n# plt.figure(1)\n# plt.imshow(im_match)\n# # dst = cv2.warpPerspective(P1,H,(P1.shape[1] + P2.shape[1], P2.shape[0]))\n\n#%% SIFT for warping\n# for m in matches[:,0]:\n# src_pts.append(kp1[m.queryIdx].pt)\n# dst_pts.append(kp2[m.trainIdx].pt)\n#\n# src_pts = np.array(src_pts, dtype=np.float32)\n# dst_pts = np.array(dst_pts, dtype=np.float32)\n\n#%% My Method\nprint(\"Performing my matching algorithm\")\nimport util_matching, util_cv, util_sift\nmatches2 = util_matching.match(util_sift.kp_to_array(kp1), des1, util_sift.kp_to_array(kp2), des2)\nmatches2 = util_cv.match_to_cv(matches2)\nim_match2 = cv2.drawMatches(P1, kp1, P1, kp2, matches2, None, flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)\n# plt.imshow(im_match2)\n\n#%% Mine for warping\nprint(\"Adding matching points for warping\")\nfor m in matches2:\n pt1 = kp1[m.queryIdx].pt\n pt2 = kp2[m.trainIdx].pt\n src_pts.append([pt1[1], pt1[0]])\n dst_pts.append([pt2[1], pt2[0]])\n\nsrc_pts = np.array(src_pts, dtype=np.float32)\ndst_pts = np.array(dst_pts, dtype=np.float32)\n\n#%% Warping\nprint(\"Warping\")\nfrom skimage.transform import warp, PiecewiseAffineTransform\ntform = PiecewiseAffineTransform()\ntform.estimate(src_pts, dst_pts)\nim_warp1 = warp(P1_g, tform)\n\ntform = PiecewiseAffineTransform()\ntform.estimate(dst_pts, src_pts)\nim_warp2 = warp(P2_g, tform)\n#\n# pts1 = np.float32([[56, 65], [368, 52], [28, 387], [389, 390]])\n# pts2 = np.float32([[0, 0], [300, 0], [0, 300], [300, 300]])\n# M = cv2.getPerspectiveTransform(src_pts, dst_pts)\n# wrp = cv2.warpAffine(P1.astype(np.int32), M, dsize=(P1.shape[0], P1.shape[1]), flags=cv2.INTER_LINEAR)\n\n#%% Warp Visualization\n# fig, ax = plt.subplots(nrows=3,ncols=2)\n# title = 'Warping'\n# fig.suptitle(title, fontsize=22)\n#\n# ax[0,0].set_title('PW68')\n# ax[0,0].imshow(cv2.drawKeypoints(P1, kp1, None))\n#\n# ax[0,1].set_title('PW72')\n# ax[0,1].imshow(cv2.drawKeypoints(P2, kp2, None))\n#\n# ax[1,0].set_title('Match (SIFT + RANSAC)')\n# ax[1,0].imshow(None)\n#\n# ax[1,1].set_title(\"Match (Mine)\")\n# ax[1,1].imshow(im_match2)\n#\n# ax[2,0].set_title('WARP1')\n# ax[2,0].imshow(im_warp1)\n#\n# ax[2,1].set_title('WARP2')\n# ax[2,1].imshow(im_warp2)\n\n#%% Calculation of Baseline & Figure\nprint(\"Generating P3 baseline\")\nrstrides = 2\ncstrides = 2\nthreshold = 200\nim_src = im_warp1\nim_dst = im_warp2\nP3 = generate_gray(im_src, im_dst, rstrides, cstrides, threshold, 0.5)\nplt.figure()\nplt.imshow(P3)\n\n#%% P3 Visualization\nprint(\"Visualizing P3 results\")\nfig, ax = plt.subplots(nrows=1,ncols=3)\ntitle = 'Intermediate Plate Generation Baseline'\nfig.suptitle(title, fontsize=22)\n\nax[0].set_title('Source')\nax[0].imshow(im_src)\n\nax[1].set_title('Destination')\nax[1].imshow(im_dst)\n\nax[2].set_title(str(rstrides) + ' by ' + str(cstrides))\nax[2].imshow(P3)\n\n#%%\nprint(\"Blending\")\nplt.figure()\nim_b1 = im_warp1\nim_b2 = im_warp2\n# im_b1 = to_gray((im_warp1 * 255).astype(np.uint8))\n# im_b2 = to_gray((im_warp2 * 255).astype(np.uint8))\nfor w in np.linspace(0, 1, 10):\n b1 = im_b1 * w\n b2 = im_b2 * (1-w)\n gen = b1 + b2\n plt.suptitle(\"Im1 Weight \" + str(w))\n plt.imshow(gen)\n plt.waitforbuttonpress()\n\n#%% Optical Flow\nprint(\"Optical Flow\")\n# params for ShiTomasi corner detection\nfeature_params = dict( maxCorners = 500,\n qualityLevel = 0.3,\n minDistance = 7,\n blockSize = 7 )\n# Parameters for lucas kanade optical flow\nlk_params = dict( winSize = (15,15),\n maxLevel = 3,\n criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 25, 0.03))\n\nP1_C = cv2.imread('P1.png')\nP2_C = cv2.imread('P2.png')\n\nP1_CG = cv2.cvtColor(P1_C, cv2.COLOR_BGR2GRAY)\nP2_CG = cv2.cvtColor(P2_C, cv2.COLOR_BGR2GRAY)\n\np0 = cv2.goodFeaturesToTrack(P1_CG, mask = None, **feature_params)\nmask = np.zeros_like(P1)\n# Create some random colors\ncolor = np.random.randint(0,255,(100,3))\n\n# calculate optical flow\np1, st, err = cv2.calcOpticalFlowPyrLK(P1_CG, P2_CG, p0, None, **lk_params)\n# Select good points\ngood_new = p1[st == 1]\ngood_old = p0[st == 1]\n# draw the tracks\nfor i, (new, old) in enumerate(zip(good_new, good_old)):\n a, b = new.ravel()\n c, d = old.ravel()\n mask = cv2.line(mask, (a, b), (c, d), color[i].tolist(), 2)\n frame = cv2.circle(P2, (a, b), 5, color[i].tolist(), -1)\nimg = cv2.add(frame, mask)\ncv2.imshow('frame', img)\n\n\n\n#%% Generate many intermediate baselines and save as files\n# t = 150\n# for r in range(1, 20, 1):\n# for c in range(1, 20, 1):\n# P = generate(P1, P2, r, c, t)\n# filename = 'baseline/' + str(r) + ' by ' + str(c) + '.png'\n# Image.fromarray(P).save(filename)\n"
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"text": "# Author: Jose G Perez\n# Version 1.0\n# Last Modified: January 31, 2018\nimport numpy as np\nimport pylab as plt\nfrom skimage import color\nfrom util_im import imshow_matches\nfrom util_sm import load_sm, norm_sm, norm_prob_sm\nfrom util_sift import precompute_sift, load_sift\n\nprecompute_sift('S_BB_V4', 'PW_BB_V4')\ns_im, s_label, s_kp, s_des = load_sift('S_BB_V4_SIFT.npz')\npw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V4_SIFT.npz')\nsm_matches, sm_metric = load_sm('sm_v4', s_kp, pw_kp)\n\ndef idx_to_plate(labels, plate):\n return np.where(labels == plate)\n\ndef dynamic_prog(sm, pw_penalty, s_penalty):\n ed = np.zeros((sm.shape[0]+1, sm.shape[1]+1))\n dir = np.zeros_like(ed)\n ed[:,0] = np.arange(ed.shape[0]) * -s_penalty\n ed[0,:] = np.arange(ed.shape[1]) * -pw_penalty\n # ed[:,0] = ed[0,:] = 0\n\n for i in range(1,ed.shape[0]):\n for j in range(1,ed.shape[1]):\n choices = [ed[i,j-1] - pw_penalty, # 0 = top\n ed[i-1,j-1] + sm[i-1,j-1], # 1 = diagonal\n ed[i-1,j] - s_penalty] # 2 = left\n idx = np.argmax(choices)\n dir[i,j]=idx\n ed[i,j]=choices[idx]\n return ed, dir.astype(np.uint8)\n # return ed, dir.astype(np.uint8)\n\ndef get_pairs(dir):\n sidx = dir.shape[0]-1\n pwidx = dir.shape[1]-1\n pairs = []\n while sidx > 0 and pwidx > 0:\n next_dir = dir[sidx, pwidx]\n pairs.append([sidx, pwidx])\n if next_dir == 0:\n sidx -= 1\n elif next_dir == 1:\n sidx -= 1\n pwidx -= 1\n else:\n pwidx -= 1\n return np.array(pairs)\n\ndef pair_metric(sm_metric, pairs):\n best6_pw = get_best_pw(sm_metric, pairs, 6)\n best11_pw = get_best_pw(sm_metric, pairs, 11)\n best23_pw = get_best_pw(sm_metric, pairs, 23)\n best33_pw = get_best_pw(sm_metric, pairs, 33)\n\n # PW8 S6, PW11 S11, PW42 S23, PW68 S33,\n # m += np.count_nonzero(best6_pw == np.where(pw_label == 8))\n # m += np.count_nonzero(best11_pw == np.where(pw_label == 11))\n # m += np.count_nonzero(best23_pw == np.where(pw_label == 42))\n # m += np.count_nonzero(best33_pw == np.where(pw_label == 68))\n return np.min(abs(best6_pw - np.where(pw_label == 8))) + \\\n np.min(abs(best11_pw - np.where(pw_label == 11))) + \\\n np.min(abs(best23_pw - np.where(pw_label == 42))) + \\\n np.min(abs(best33_pw - np.where(pw_label == 68)))\n\ndef overlay(dir, sm):\n # bg = norm_sm(sm, 255).astype(np.uint8)\n bg = sm.astype(np.uint8)\n color_mask = np.zeros((dir.shape[0],dir.shape[1],3))\n\n sidx = sm.shape[0]-1\n pwidx = sm.shape[1]-1\n count = 0\n path = ['START']\n pairs = []\n while sidx >= 0 and pwidx >= 0:\n count += 1\n color_mask[sidx, pwidx] = [0, 0, 255]\n bg[sidx, pwidx] = 255\n next_dir = dir[sidx, pwidx]\n pairs.append([sidx, pwidx])\n if next_dir == 0: # Left\n pwidx -= 1\n path.append('L')\n elif next_dir == 1: # Diagonal\n sidx -= 1\n pwidx -= 1\n path.append('D')\n else: # Up\n sidx -= 1\n path.append('U')\n\n # Remove penalty row/col\n dir = dir[1:,1:]\n color_mask = color_mask[1:,1:,:]\n\n # PW8 S6, PW11 S11, PW42 S23, PW68 S33,\n color_mask[np.where(s_label == 6), np.where(pw_label == 8)] = [255, 0, 0]\n bg[np.where(s_label == 6), np.where(pw_label == 8)] = 255\n\n color_mask[np.where(s_label == 11), np.where(pw_label == 11)] = [255, 0, 0]\n bg[np.where(s_label == 11), np.where(pw_label == 11)] = 255\n\n color_mask[np.where(s_label == 23), np.where(pw_label == 42)] = [255, 0, 0]\n bg[np.where(s_label == 23), np.where(pw_label == 42)] = 255\n\n color_mask[np.where(s_label == 33), np.where(pw_label == 68)] = [255, 0, 0]\n bg[np.where(s_label == 33), np.where(pw_label == 68)] = 255\n\n print(\"path\", count, path)\n img_color = np.stack((bg,)*3,axis=2)\n img_hsv = color.rgb2hsv(img_color)\n color_mask_hsv = color.rgb2hsv(color_mask)\n img_hsv[..., 0] = color_mask_hsv[..., 0]\n img_hsv[..., 1] = color_mask_hsv[..., 1]\n im_overlay = color.hsv2rgb(img_hsv)\n return im_overlay, np.array(pairs)\n\ndef error(best_pw, pw_plate, s_plate):\n # s_idx = int(np.argwhere(s_label == s_plate))\n pw_idx = int(np.argwhere(pw_label == pw_plate))\n pred_sidx = best_pw[pw_idx]\n pred_s = int(np.argwhere(s_label == pred_sidx))\n\n return abs(pred_s - s_plate)\n\ndef get_best_pw(sm_metric, pairs, s_plate):\n # Indices start at 0, plates start at 1\n sidx = s_plate-1\n\n pidx = np.where(pairs[:, 0] == sidx)\n matches = pairs[pidx, 1].flatten()\n # return pw_label[matches] if len(matches >= 1) else -1\n return pw_label[matches] if len(matches >= 1) else np.array([np.inf])\n # if len(matches) > 1:\n # metrics = sm_metric[sidx,matches]\n # best_idx = np.argmax(metrics)\n # return int(pw_label[matches[best_idx]])\n # elif len(matches) == 1:\n # # Convert from PW Indices to PW Labels\n # return int(pw_label[matches])\n # else:\n # return -1\n\nif __name__ == '__main__':\n # lowest_error = np.inf\n # best_pw = -1\n # best_s = -1\n # for pw_penalty in np.arange(0.4, 0.5, 0.001):\n # for s_penalty in np.arange(0.4, 0.5, 0.001):\n # ed, dir = dynamic_prog(norm, pw_penalty=pw_penalty, s_penalty=s_penalty)\n # pairs = get_pairs(dir)\n # metric = pair_metric(sm_metric, pairs)\n # if metric < lowest_error:\n # print(\"New error\", metric, pw_penalty, s_penalty)\n # lowest_error = metric\n # best_pw = pw_penalty\n # best_s = s_penalty\n # ed, dir = dynamic_prog(norm, pw_penalty=best_pw, s_penalty=best_s)\n # im_overlay, pairs = overlay(dir, sm_metric)\n # best6_pw = get_best_pw(sm_metric,pairs,6)\n # best11_pw = get_best_pw(sm_metric,pairs,11)\n # best23_pw = get_best_pw(sm_metric,pairs,23)\n # best33_pw = get_best_pw(sm_metric,pairs,33)\n # print(\"[PW8=%s], [PW11=%s], [PW42=%s [PW68=%s]\" % (best6_pw, best11_pw, best23_pw, best33_pw))\n #\n # imshow_matches(im_overlay, 'Dynamic Programming')\n\n # import pylab as plt\n # best_pw = 200\n # best_s = 220\n # ed, dir = dynamic_prog(norm, pw_penalty=best_pw, s_penalty=best_s)\n # pairs = get_pairs(dir)\n # metric = pair_metric(sm_metric, pairs)\n # im_overlay, pairs = overlay(dir, sm_metric)\n # best6_pw = get_best_pw(sm_metric,pairs,6)\n # best11_pw = get_best_pw(sm_metric,pairs,11)\n # best23_pw = get_best_pw(sm_metric,pairs,23)\n # best33_pw = get_best_pw(sm_metric,pairs,33)\n # print(\"[PW8=%s], [PW11=%s], [PW42=%s [PW68=%s]\" % (best6_pw, best11_pw, best23_pw, best33_pw))\n #\n # imshow_matches(im_overlay, 'Dynamic Programming')\n # plt.show()\n\n # mat = sm_matches\n #\n # pw_penalty = 50\n # s_penalty = 50\n # ed, dir = dynamic_prog(mat, pw_penalty=pw_penalty, s_penalty=s_penalty)\n # im_overlay, pairs = overlay(dir, mat)\n # norm = norm_sm(mat)\n #\n # import pylab as plt\n # fig, axes = plt.subplots(nrows=2, ncols=2)\n # plt.subplots_adjust(left=0.25, bottom=0.25)\n # plt.set_cmap(plt.get_cmap('hot'))\n # # axes.set_title('Dynamic')\n #\n # axes[0,0].set_title('Similarity Matrix')\n # axes[0,0].imshow(mat)\n #\n # axes[0,1].set_title('SM Norm')\n # axes[0,1].imshow(norm_prob_sm(sm_matches))\n #\n # axes[1,0].set_title('ED')\n # axes[1,1].set_title('Overlay')\n #\n # # Sliders\n # axcolor = 'lightgoldenrodyellow'\n # axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)\n # axamp = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)\n # # s_pwp = plt.Slider(axfreq, 'PW Penalty', 0, 1, .0001, valfmt='%.8f')\n # # s_sp = plt.Slider(axamp, 'S Penalty', 0, 1, .0001, valfmt='%.8f')\n # s_pwp = plt.Slider(axfreq, 'PW Penalty', 0, 400, 10, valfmt='%.8f')\n # s_sp = plt.Slider(axamp, 'S Penalty', 0, 400, 10, valfmt='%.8f')\n #\n # def update(val):\n # pw_penalty = s_pwp.val\n # s_penalty = s_sp.val\n #\n # ed, dir = dynamic_prog(mat, pw_penalty=pw_penalty, s_penalty=s_penalty)\n # im_overlay, pairs = overlay(dir, mat)\n #\n # best6_pw = get_best_pw(sm_metric,pairs,6)\n # best11_pw = get_best_pw(sm_metric,pairs,11)\n # best23_pw = get_best_pw(sm_metric,pairs,23)\n # best33_pw = get_best_pw(sm_metric,pairs,33)\n # print(\"[PW8=%s], [PW11=%s], [PW42=%s [PW68=%s]\" % (best6_pw, best11_pw, best23_pw, best33_pw))\n #\n # axes[1,0].imshow(ed)\n # axes[1,1].imshow(im_overlay)\n # fig.canvas.draw_idle()\n #\n # s_pwp.on_changed(update)\n # s_sp.on_changed(update)\n # plt.show()\n\n #%% Runtime Experiments\n mat = sm_matches\n pw_penalty = 50\n s_penalty = 50\n ed, dir = dynamic_prog(mat, pw_penalty=pw_penalty, s_penalty=s_penalty)\n im_overlay, pairs = overlay(dir, mat)\n\n # Figure prep\n pw_ticks_idxs = [0]\n pw_ticks_vals = [pw_label[0]]\n for x in range(len(pw_label)):\n try:\n diff = pw_label[x + 1] - pw_label[x]\n if diff > 1:\n pw_ticks_idxs.append(x)\n pw_ticks_vals.append(pw_label[x])\n # print(\"IDX: \", x, \"DIFF:\", diff)\n except:\n continue\n\n pw_ticks_idxs.append(len(pw_label) - 1)\n pw_ticks_vals.append(pw_label[-1])\n\n # Figure\n plt.figure()\n ax = plt.gca()\n ax.set_title('Dynamic Programming Back-Tracing')\n plt.setp(ax.get_xticklabels(), rotation=90, horizontalalignment='right')\n plt.imshow(im_overlay)\n plt.xticks(pw_ticks_idxs, pw_ticks_vals)\n plt.yticks(np.arange(0, len(s_label)), np.arange(1, len(s_label) + 1))\n\n for tick in ax.xaxis.get_major_ticks():\n tick.label.set_fontsize(8)\n\n for tick in ax.yaxis.get_major_ticks():\n tick.label.set_fontsize(8)\n\n plt.xlabel('PW Level')\n plt.ylabel('S Level')\n\n # best_pwp = 0\n # best_sps = 0\n # best_total = np.inf\n # for pw_penalty in range(0, 200):\n # for s_penalty in range(0, 200):\n # ed, ed2 = dynamic_prog(norm, pw_penalty=pw_penalty, s_penalty=s_penalty)\n # best_pw = s_label[np.argmin(ed, axis=0)]\n #\n # # PW8 S6, PW11 S11, PW42 S23, PW68 S33,\n # e = error(best_pw, 68, 33) + \\\n # error(best_pw, 11, 11) + \\\n # error(best_pw, 42, 23) + \\\n # error(best_pw, 68, 33)\n #\n # if e < best_total:\n # print(\"New best total\", e)\n # best_total = e\n # best_pwp = pw_penalty\n # best_sps = s_penalty\n\n # best_pwp = 200\n # best_sps = 200\n # ed, ed2 = dynamic_prog(norm, pw_penalty=best_pwp, s_penalty=best_sps)\n # im_overlay = overlay(ed, norm)\n\n # imshow_matches(dynamic_prog(norm, pw_penalty=1, s_penalty=1)[1], '')\n # imshow_matches(overlay(dynamic_prog(sm_matches, 0.9, 0.1)[0], sm_matches), '')\n\n # aoi = ed[32:35, 38:41]\n # best_s = pw_label[np.argmin(ed,axis=1)]\n # print(\"PW68 best match\", best_pw[np.where(pw_label==68)])\n # print(\"S33 best match\", best_s[np.where(s_label==33)])\n\n"
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"text": "import numpy as np\nfrom timeit import default_timer as timer\nfrom multiprocessing.pool import Pool\n\nfrom util_ransac import perform_ransac\nfrom util_matching import match\nfrom util_sift import precompute_sift, load_sift\n\nRADIUS = 25\nRADIUS_SQUARED = RADIUS ** 2\nSCALE_THRESHOLD = 3\nDISTANCE_THRESHOLD = 200\nRESPONSE_THRESHOLD = 0.01\nprecompute_sift('S_BB_V4', 'PW_BB_V4')\ns_im, s_label, s_kp, s_des = load_sift('S_BB_V4_SIFT.npz')\npw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V4_SIFT.npz')\n\ndef perform_match(s_idx):\n global s_kp, s_des, pw_kp, pw_des\n matches = []\n print('s_idx', s_idx)\n for pw_idx in range(pw_kp.shape[0]):\n matches.append(match(s_kp[s_idx], s_des[s_idx], pw_kp[pw_idx], pw_des[pw_idx]))\n\n np.savez_compressed(str(s_idx) + '-M', m=matches)\n\nif __name__ == '__main__':\n time_start = timer()\n\n pool = Pool()\n s_idx = range(s_kp.shape[0])\n\n print('Begin pool work')\n pool.map(perform_match, s_idx)\n # pool.map(perform_ransac, s_idx)\n pool.close()\n pool.join()\n\n duration = timer() - time_start\n duration_m = duration / 60\n print(\"Program took %.3fs %.3fm\" % (duration, duration_m))"
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"text": "import tensorflow as tf\nimport tqdm\nfrom one_shot_learning_network import MatchingNetwork\n\n\nclass ExperimentBuilder:\n\n def __init__(self, data):\n \"\"\"\n Initializes an ExperimentBuilder object. The ExperimentBuilder object takes care of setting up our experiment\n and provides helper functions such as run_training_epoch and run_validation_epoch to simplify out training\n and evaluation procedures.\n :param data: A data provider class\n \"\"\"\n self.data = data\n\n def build_experiment(self, batch_size, classes_per_set, samples_per_class, fce):\n\n \"\"\"\n\n :param batch_size: The experiment batch size\n :param classes_per_set: An integer indicating the number of classes per support set\n :param samples_per_class: An integer indicating the number of samples per class\n :param channels: The image channels\n :param fce: Whether to use full context embeddings or not\n :return: a matching_network object, along with the losses, the training ops and the init op\n \"\"\"\n height, width, channels = self.data.x.shape[2], self.data.x.shape[3], self.data.x.shape[4]\n self.support_set_images = tf.placeholder(tf.float32, [batch_size, classes_per_set, samples_per_class, height, width,\n channels], 'support_set_images')\n self.support_set_labels = tf.placeholder(tf.int32, [batch_size, classes_per_set, samples_per_class], 'support_set_labels')\n self.target_image = tf.placeholder(tf.float32, [batch_size, height, width, channels], 'target_image')\n self.target_label = tf.placeholder(tf.int32, [batch_size], 'target_label')\n self.training_phase = tf.placeholder(tf.bool, name='training-flag')\n self.rotate_flag = tf.placeholder(tf.bool, name='rotate-flag')\n self.keep_prob = tf.placeholder(tf.float32, name='dropout-prob')\n self.current_learning_rate = 1e-03\n self.learning_rate = tf.placeholder(tf.float32, name='learning-rate-set')\n self.one_shot_omniglot = MatchingNetwork(batch_size=batch_size, support_set_images=self.support_set_images,\n support_set_labels=self.support_set_labels,\n target_image=self.target_image, target_label=self.target_label,\n keep_prob=self.keep_prob, num_channels=channels,\n is_training=self.training_phase, fce=fce, rotate_flag=self.rotate_flag,\n num_classes_per_set=classes_per_set,\n num_samples_per_class=samples_per_class, learning_rate=self.learning_rate)\n\n summary, self.losses, self.c_error_opt_op = self.one_shot_omniglot.init_train()\n init = tf.global_variables_initializer()\n self.total_train_iter = 0\n return self.one_shot_omniglot, self.losses, self.c_error_opt_op, init\n\n def run_training_epoch(self, total_train_batches, sess):\n \"\"\"\n Runs one training epoch\n :param total_train_batches: Number of batches to train on\n :param sess: Session object\n :return: mean_training_categorical_crossentropy_loss and mean_training_accuracy\n \"\"\"\n total_c_loss = 0.\n total_accuracy = 0.\n with tqdm.tqdm(total=total_train_batches) as pbar:\n\n for i in range(total_train_batches): # train epoch\n x_support_set, y_support_set, x_target, y_target = self.data.get_train_batch(augment=True)\n _, c_loss_value, acc = sess.run(\n [self.c_error_opt_op, self.losses[self.one_shot_omniglot.classify], self.losses[self.one_shot_omniglot.dn]],\n feed_dict={self.keep_prob: 1.0, self.support_set_images: x_support_set,\n self.support_set_labels: y_support_set, self.target_image: x_target, self.target_label: y_target,\n self.training_phase: True, self.rotate_flag: False, self.learning_rate: self.current_learning_rate})\n\n iter_out = \"train_loss: {}, train_accuracy: {}\".format(c_loss_value, acc)\n pbar.set_description(iter_out)\n\n pbar.update(1)\n total_c_loss += c_loss_value\n total_accuracy += acc\n self.total_train_iter += 1\n if self.total_train_iter % 2000 == 0:\n self.current_learning_rate /= 2\n print(\"change learning rate\", self.current_learning_rate)\n\n total_c_loss = total_c_loss / total_train_batches\n total_accuracy = total_accuracy / total_train_batches\n return total_c_loss, total_accuracy\n\n def run_validation_epoch(self, total_val_batches, sess):\n \"\"\"\n Runs one validation epoch\n :param total_val_batches: Number of batches to train on\n :param sess: Session object\n :return: mean_validation_categorical_crossentropy_loss and mean_validation_accuracy\n \"\"\"\n total_val_c_loss = 0.\n total_val_accuracy = 0.\n\n with tqdm.tqdm(total=total_val_batches) as pbar:\n for i in range(total_val_batches): # validation epoch\n x_support_set, y_support_set, x_target, y_target = self.data.get_val_batch(augment=True)\n c_loss_value, acc = sess.run(\n [self.losses[self.one_shot_omniglot.classify], self.losses[self.one_shot_omniglot.dn]],\n feed_dict={self.keep_prob: 1.0, self.support_set_images: x_support_set,\n self.support_set_labels: y_support_set, self.target_image: x_target, self.target_label: y_target,\n self.training_phase: False, self.rotate_flag: False})\n\n iter_out = \"val_loss: {}, val_accuracy: {}\".format(c_loss_value, acc)\n pbar.set_description(iter_out)\n pbar.update(1)\n\n total_val_c_loss += c_loss_value\n total_val_accuracy += acc\n\n total_val_c_loss = total_val_c_loss / total_val_batches\n total_val_accuracy = total_val_accuracy / total_val_batches\n\n return total_val_c_loss, total_val_accuracy\n\n def run_testing_epoch(self, total_test_batches, sess):\n \"\"\"\n Runs one testing epoch\n :param total_test_batches: Number of batches to train on\n :param sess: Session object\n :return: mean_testing_categorical_crossentropy_loss and mean_testing_accuracy\n \"\"\"\n total_test_c_loss = 0.\n total_test_accuracy = 0.\n with tqdm.tqdm(total=total_test_batches) as pbar:\n for i in range(total_test_batches):\n x_support_set, y_support_set, x_target, y_target = self.data.get_test_batch(augment=True)\n c_loss_value, acc = sess.run(\n [self.losses[self.one_shot_omniglot.classify], self.losses[self.one_shot_omniglot.dn]],\n feed_dict={self.keep_prob: 1.0, self.support_set_images: x_support_set,\n self.support_set_labels: y_support_set, self.target_image: x_target,\n self.target_label: y_target,\n self.training_phase: False, self.rotate_flag: False})\n\n iter_out = \"test_loss: {}, test_accuracy: {}\".format(c_loss_value, acc)\n pbar.set_description(iter_out)\n pbar.update(1)\n\n total_test_c_loss += c_loss_value\n total_test_accuracy += acc\n total_test_c_loss = total_test_c_loss / total_test_batches\n total_test_accuracy = total_test_accuracy / total_test_batches\n return total_test_c_loss, total_test_accuracy\n"
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"text": "# Author: Jose G Perez\n# Version 1.0\n# Last Modified: January 31, 2018\nimport numpy as np\nimport cv2\nimport os\nSIFT = cv2.xfeatures2d.SIFT_create(contrastThreshold=0.05, edgeThreshold=100, sigma=2)\n\ndef kp_to_array(kp):\n array = np.zeros((len(kp), 7), dtype=np.float32)\n for idx in range(array.shape[0]):\n k = kp[idx]\n array[idx] = np.array([k.pt[0], k.pt[1], k.size,k.angle,k.response,k.octave,k.class_id])\n return array\n\ndef array_to_kp(array):\n kp = []\n for idx in range(array.shape[0]):\n k = array[idx]\n kp.append(cv2.KeyPoint(k[0],k[1],k[2],k[3],k[4],k[5],k[6]))\n return kp\n\ndef __precompute_atlas(name):\n if not os.path.isfile(name + '_SIFT.npz'):\n print('Precomputing SIFT for ', name)\n atlas_data = np.load(name + \".npz\")\n atlas_im = atlas_data['images']\n atlas_labels = atlas_data['labels']\n atlas_kp = []\n atlas_des = []\n\n for i in range(0, atlas_im.shape[0]):\n kp, des = SIFT.detectAndCompute(atlas_im[i], None)\n kp = kp_to_array(kp)\n atlas_kp.append(kp)\n atlas_des.append(des)\n\n atlas_kp = np.asarray(atlas_kp)\n atlas_des = np.asarray(atlas_des)\n\n np.savez_compressed(name + '_SIFT', images=atlas_im, labels=atlas_labels, kp=atlas_kp, des=atlas_des)\n\ndef precompute_sift(S_NAME, PW_NAME):\n __precompute_atlas(S_NAME)\n __precompute_atlas(PW_NAME)\n\ndef load_sift(path):\n data = np.load(path)\n return data['images'], data['labels'], data['kp'], data['des']"
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"text": "import numpy as np\nfrom scipy.ndimage import rotate\nclass OmniglotNShotDataset():\n def __init__(self, batch_size, classes_per_set=10, samples_per_class=1, seed=2591, shuffle_classes=True):\n\n \"\"\"\n Constructs an N-Shot omniglot Dataset\n :param batch_size: Experiment batch_size\n :param classes_per_set: Integer indicating the number of classes per set\n :param samples_per_class: Integer indicating samples per class\n e.g. For a 20-way, 1-shot learning task, use classes_per_set=20 and samples_per_class=1\n For a 5-way, 10-shot learning task, use classes_per_set=5 and samples_per_class=10\n \"\"\"\n np.random.seed(seed)\n self.x = np.load(\"data.npy\")\n self.x = np.reshape(self.x, newshape=(1622, 20, 28, 28, 1))\n if shuffle_classes:\n class_ids = np.arange(self.x.shape[0])\n np.random.shuffle(class_ids)\n self.x = self.x[class_ids]\n self.x_train, self.x_test, self.x_val = self.x[:1200], self.x[1200:1411], self.x[1411:]\n self.mean = np.mean(list(self.x_train) + list(self.x_val))\n self.std = np.std(list(self.x_train) + list(self.x_val))\n self.batch_size = batch_size\n self.n_classes = self.x.shape[0]\n self.classes_per_set = classes_per_set\n self.samples_per_class = samples_per_class\n print(\"train_shape\", self.x_train.shape, \"test_shape\", self.x_test.shape, \"val_shape\", self.x_val.shape)\n self.indexes = {\"train\": 0, \"val\": 0, \"test\": 0}\n self.datasets = {\"train\": self.x_train, \"val\": self.x_val, \"test\": self.x_test} #original data cached\n\n def preprocess_batch(self, x_batch):\n \"\"\"\n Normalizes our data, to have a mean of 0 and sd of 1\n \"\"\"\n x_batch = (x_batch - self.mean) / self.std\n\n return x_batch\n def sample_new_batch(self, data_pack):\n \"\"\"\n Collects 1000 batches data for N-shot learning\n :param data_pack: Data pack to use (any one of train, val, test)\n :return: A list with [support_set_x, support_set_y, target_x, target_y] ready to be fed to our networks\n \"\"\"\n support_set_x = np.zeros((self.batch_size, self.classes_per_set, self.samples_per_class, data_pack.shape[2],\n data_pack.shape[3], data_pack.shape[4]), dtype=np.float32)\n support_set_y = np.zeros((self.batch_size, self.classes_per_set, self.samples_per_class), dtype=np.float32)\n target_x = np.zeros((self.batch_size, data_pack.shape[2], data_pack.shape[3], data_pack.shape[4]),\n dtype=np.float32)\n target_y = np.zeros((self.batch_size,), dtype=np.float32)\n for i in range(self.batch_size):\n classes_idx = np.arange(data_pack.shape[0])\n samples_idx = np.arange(data_pack.shape[1])\n choose_classes = np.random.choice(classes_idx, size=self.classes_per_set, replace=False)\n choose_label = np.random.choice(self.classes_per_set, size=1)\n choose_samples = np.random.choice(samples_idx, size=self.samples_per_class+1, replace=False)\n\n x_temp = data_pack[choose_classes]\n x_temp = x_temp[:, choose_samples]\n y_temp = np.arange(self.classes_per_set)\n support_set_x[i] = x_temp[:, :-1]\n support_set_y[i] = np.expand_dims(y_temp[:], axis=1)\n target_x[i] = x_temp[choose_label, -1]\n target_y[i] = y_temp[choose_label]\n\n return support_set_x, support_set_y, target_x, target_y\n\n def get_batch(self, dataset_name, augment=False):\n \"\"\"\n Gets next batch from the dataset with name.\n :param dataset_name: The name of the dataset (one of \"train\", \"val\", \"test\")\n :return:\n \"\"\"\n x_support_set, y_support_set, x_target, y_target = self.sample_new_batch(self.datasets[dataset_name])\n if augment:\n k = np.random.randint(0, 4, size=(self.batch_size, self.classes_per_set))\n x_augmented_support_set = []\n x_augmented_target_set = []\n for b in range(self.batch_size):\n temp_class_support = []\n\n for c in range(self.classes_per_set):\n x_temp_support_set = self.rotate_batch(x_support_set[b, c], axis=(1, 2), k=k[b, c])\n if y_target[b] == y_support_set[b, c, 0]:\n x_temp_target = self.rotate_batch(x_target[b], axis=(0, 1), k=k[b, c])\n\n temp_class_support.append(x_temp_support_set)\n\n x_augmented_support_set.append(temp_class_support)\n x_augmented_target_set.append(x_temp_target)\n\n x_support_set = np.array(x_augmented_support_set)\n x_target = np.array(x_augmented_target_set)\n x_support_set = self.preprocess_batch(x_support_set)\n x_target = self.preprocess_batch(x_target)\n\n return x_support_set, y_support_set, x_target, y_target\n\n def rotate_batch(self, x_batch, axis, k):\n x_batch = rotate(x_batch, k*90, reshape=False, axes=axis, mode=\"nearest\")\n return x_batch\n\n def get_train_batch(self, augment=False):\n\n \"\"\"\n Get next training batch\n :return: Next training batch\n \"\"\"\n return self.get_batch(\"train\", augment)\n\n def get_test_batch(self, augment=False):\n\n \"\"\"\n Get next test batch\n :return: Next test_batch\n \"\"\"\n return self.get_batch(\"test\", augment)\n\n def get_val_batch(self, augment=False):\n\n \"\"\"\n Get next val batch\n :return: Next val batch\n \"\"\"\n return self.get_batch(\"val\", augment)\n"
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"text": "import numpy as np\nimport os\nimport pylab as plt\nimport cv2\nfrom skimage.transform import warp, PiecewiseAffineTransform\nfrom PIL import Image\nfrom timeit import default_timer as timer\n\ndef load(filename):\n WIDTH = 800\n HEIGHT = 400\n im = Image.open(filename)\n im = im.resize((WIDTH, HEIGHT), Image.LANCZOS)\n P = np.array(im, dtype=np.uint8)\n return P[:, 0:P.shape[1] // 2, :]\n\ndef to_gray(P):\n return P.mean(axis=2)\n\n\n#%% Load images\nprint(\"Loading images\")\ndir1 = 'C:/Users/xeroj/Dropbox/Training data sets - Khan-Fuentes/Paxinos and Watson, 2014 (7th Edition) Image set/'\ndir2 = 'C:/Users/xeroj/Downloads/Processed'\n\nP1 = load(os.path.join(dir1, 'RBSC7-068.jpg'))\nP2 = load(os.path.join(dir1, 'RBSC7-070.jpg'))\nFACE = np.array(Image.open('face.jpg'))\n\nPM1 = load(os.path.join(dir2, '18-016 LHA s4t2.tif'))\nPM2 = load(os.path.join(dir2, '18-016 LHA s4t3.tif'))\nPM3 = load(os.path.join(dir2, '18-016 LHA s4t4.tif'))\n\n# data = np.load('P1P2.npz')\n# S1_pts = data['src']\n# S2_pts = data['dst']\n\ndata = np.load('face.npz')\nS1_pts = data['src']\nS2_pts = data['dst']\nS1 = FACE\nS2 = FACE\n\n#%% Scipy Warping\ntform = PiecewiseAffineTransform()\ne1 = tform.estimate(np.array(S2_pts), np.array(S1_pts))\nim_warp1 = warp(S1, tform)\nim_warp1 = to_gray((im_warp1 * 255).astype(np.uint8))\n\nplt.figure()\nplt.suptitle('warp')\nplt.imshow(im_warp1)\n#%% Inverse Warping with KNN Interpolation\nsrc = S1\ndst = np.zeros_like(src)\nfor x in range(dst.shape[0]):\n for y in range(dst.shape[1]):\n cp_idx = np.argmin(np.sum((S1_pts - [x,y]) ** 2, axis=1))\n diff = [x,y] - S2_pts[cp_idx]\n disp = S1_pts[cp_idx] - S2_pts[cp_idx]\n dist = np.linalg.norm(diff)\n w = np.exp(-dist/100)\n (dx,dy) = (disp[0]*w, disp[1]*w)\n u = min(int(round(x+dy)),dst.shape[0]-1)\n v = min(int(round(y+dx)),dst.shape[1]-1)\n dst[x,y] = src[u,v]\n\nplt.figure()\nplt.imshow(dst)"
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"text": "import pylab as plt\nimport numpy as np\nfrom PIL import Image\nimport time\n\nim = np.array(Image.open('face.jpg'), dtype=np.uint8)\n(rows, cols, colors) = im.shape\n\n#%% Control point loading\ndata = np.load('face.npz')\nsrc_pts = data['src']\ndst_pts = data['dst']\n\nsrc_pts[0] = [0,0]\nsrc_pts[1] = [im.shape[1], 0]\nsrc_pts[2] = [0,im.shape[0]]\nsrc_pts[3] = [im.shape[1], im.shape[0]]\n\ndst_pts[0] = [0,0]\ndst_pts[1] = [im.shape[1], 0]\ndst_pts[2] = [0,im.shape[0]]\ndst_pts[3] = [im.shape[1], im.shape[0]]\n\n\nplt.figure(1)\nplt.imshow(im)\nfor i in range(len(src_pts)):\n (x1,y1) = src_pts[i]\n (x2,y2) = dst_pts[i]\n plt.plot([x1,x2], [y1,y2], color='purple')\n plt.plot(x1, y1, marker='x', markersize=3, color='blue')\n plt.plot(x2, y2, marker='o', markersize=3, color='red')\n\nstart = time.time()\n\n#%% Generate pixels coordinates in the destination image\ndest_im = np.zeros(im.shape, dtype=np.uint8)\nmax_row = im.shape[0] - 1\nmax_col = im.shape[1] - 1\ndest_rows = dest_im.shape[0]\ndest_cols = dest_im.shape[1]\n\n# Painting outline of source image black, so out of bounds pixels can be painted black\nim[0] = 0\nim[max_row] = 0\nim[:, 0] = 0\nim[:, max_col] = 0\n\n# Generate pixel coordinates in the destination image\nind = np.arange(dest_rows * dest_cols)\nrow_vect = ind // dest_cols\ncol_vect = ind % dest_cols\ncoords = np.vstack((row_vect, col_vect))\n\n# Computing pixel weights, pixels close to p[1] will have higher weights\ndist = np.sqrt(np.square(p[1][1] - row_vect) + np.square(p[1][0] - col_vect))\nweight = np.exp(-dist / 100) # Constant needs to be tweaked depending on image size\n\n# Computing pixel weights, pixels close to p[1] will have higher weights\nsource_coords = np.zeros(coords.shape, dtype=np.int)\ndisp_r = (weight * (p[0][1] - p[1][1])).astype(int)\ndisp_c = (weight * (p[0][0] - p[1][0])).astype(int)\nsource_coords[0] = coords[0] + disp_r\nsource_coords[1] = coords[1] + disp_c\n\n# Fixing out-of-bounds coordinates\nsource_coords[source_coords < 0] = 0\nsource_coords[0, source_coords[0] > max_row] = max_row\nsource_coords[1, source_coords[1] > max_col] = max_col\n\ndest_im = source_im[source_coords[0], source_coords[1], :].reshape(dest_rows, dest_cols, 3)\n\nplt.figure(2)\nplt.imshow(dest_im)\nplt.show()\n\nelapsed_time = time.time() - start\nprint('Elapsed time: {0:.2f} '.format(elapsed_time))\n\n"
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"text": "from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, ZeroPadding2D\nfrom keras.models import Model\nfrom keras.callbacks import TensorBoard\nfrom keras.datasets import mnist\nfrom keras.wrappers.scikit_learn import KerasRegressor\nfrom keras.wrappers.scikit_learn import KerasClassifier\nfrom sklearn.model_selection import GridSearchCV\nimport numpy as np\n\nnp.random.seed(0)\n\nsw_data = np.load('atlas_sw.npz')\nx_train = sw_data['images'].astype('float32') / 255.\nx_shape = (x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)\nx_train = np.reshape(x_train, x_shape)\n\nprint(\"X_Train: \", x_train.shape)\n\npw_data = np.load('atlas_pw.npz')\npw_im = pw_data['images'].astype('float32') / 255.\npw_shape = pw_im.shape[0], pw_im.shape[1], pw_im.shape[2], 1\npw_im = np.reshape(pw_im, pw_shape)\n\n#x_train = x_train.append(pw_im)\n\nx_test = pw_im\n#x_test = np.array([pw_im[7],pw_im[10],pw_im[26],pw_im[39]])\nprint(\"X_Test: \", x_test.shape)\n#x_train = x_train.astype('float32') / 255.\n#x_test = x_test.astype('float32') / 255.\n#x_train = np.reshape(x_train, (len(x_train), 28, 28, 1)) # adapt this if using `channels_first` image data format\n#x_test = np.reshape(x_test, (len(x_test), 28, 28, 1)) # adapt this if using `channels_first` image data format\n\n#np.save('x_train', x_train)\n\nnoise_factor = 0.4\nx_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)\nx_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)\n\nx_train_noisy = np.clip(x_train_noisy, 0., 1.)\nx_test_noisy = np.clip(x_test_noisy, 0., 1.)\nprint(\"X_Train_Noisy\", x_train_noisy.shape, \"Test\", x_test_noisy.shape)\n\ndef create_network(optimizer='rmsprop'):\n input_img = Input(shape=(200, 120, 1)) # adapt this if using `channels_first` image data format\n x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n x = MaxPooling2D((2, 2), padding='same')(x)\n x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n x = MaxPooling2D((2, 2), padding='same')(x)\n x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n encoded = MaxPooling2D((2, 2), padding='valid', name='encoder')(x)\n\n # at this point the representation is (4, 4, 8) i.e. 128-dimensional\n\n x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)\n x = UpSampling2D((2, 2))(x)\n x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n x = UpSampling2D((2, 2))(x)\n x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)\n x = UpSampling2D((2, 2))(x)\n decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n autoencoder = Model(input_img, decoded)\n autoencoder.compile(optimizer=optimizer, loss='binary_crossentropy')\n return autoencoder\n\ndef train_model():\n autoencoder = create_network('adadelta')\n #epochs = [50, 60, 70, 80, 90, 100]\n #batches = [16, 32]\n #optimizers = ['rmsprop', 'adam', 'adadelta']\n \n autoencoder.fit(x_train_noisy, x_train,\n epochs=100,\n batch_size=16,\n shuffle=True,\n validation_data=(x_test_noisy, x_test),\n callbacks=[TensorBoard(log_dir='/tmp/ae_both', histogram_freq=0, write_graph=False)]\n )\n\n autoencoder.save('autoencoder.h5')\n\ntrain_model()\n"
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"text": "# Author: Jose G Perez\n# Version: 1.2\n# Last Modified: Jan 31st, 2018\nfrom timeit import default_timer as timer\nfrom PIL import Image\nimport numpy as np\nimport os\n\nWIDTH = 240\nHEIGHT = 300\nWHITE_THRESHOLD = 235\n\ndef process_plate(filename, split=False):\n im = Image.open(filename).convert(\"L\")\n\n # Split in half if required\n if split:\n box = (0, 0, im.width // 2, im.height)\n crop = im.crop(box)\n crop.load()\n im = crop\n\n im = im.resize((WIDTH, HEIGHT), Image.LANCZOS)\n im = np.array(im, dtype=np.uint8)\n\n # Convert values very close to white to white for cropping\n im[im >= WHITE_THRESHOLD] = 255\n\n # Bounding box cropping\n # https://stackoverflow.com/questions/9396312/use-python-pil-or-similar-to-shrink-whitespace\n idx = np.where(im - 255)\n box = list(map(min, idx))[::-1] + list(map(max, idx))[::-1]\n region = Image.fromarray(im).crop(box)\n region = region.resize((WIDTH, HEIGHT), Image.LANCZOS)\n im_cropped = np.array(region, dtype=np.uint8)\n\n return im, im_cropped\n\ndef process_atlas(folder, prefix, ext, zfill, plate_min, plate_max, split):\n atlas_im = []\n atlas_label = []\n atlas_original = []\n print('[', end='', flush=True)\n for plate in range(plate_min, plate_max+1):\n filename = prefix + str(plate).zfill(zfill) + ext\n filename = os.path.join(folder, filename)\n\n print(plate, ',', end='', flush=True)\n if not os.path.exists(filename):\n print(\"Couldn't find \", filename, \", skipping\")\n continue\n\n im, im_cropped = process_plate(filename, split)\n\n atlas_im.append(im_cropped)\n atlas_label.append(plate)\n atlas_original.append(im)\n\n print(']\\n', end='', flush=True)\n return np.asarray(atlas_im), np.asarray(atlas_label), np.asarray(atlas_original)\n\nif __name__ == '__main__':\n print(\"===== Starting timer\")\n time_start = timer()\n\n print(\"Processing S...\")\n s_im, s_label, s_original = process_atlas('atlas_s', 'Level-', '.jpg', 2, 1, 73, False)\n\n print(\"Processing PW...\")\n pw_im, pw_label, pw_original = process_atlas('atlas_pw', 'RBSC7-', '.jpg', 3, 1, 161, True)\n\n print(\"Saving...\")\n np.savez_compressed('S_BB_V', images=s_im, labels=s_label, originals=s_original)\n np.savez_compressed('PW_BB_V', images=pw_im, labels=pw_label, originals=pw_original)\n\n duration = timer() - time_start\n print(\"Program took %.3fs\" % duration)"
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"text": "from one_shot_learning_network import *\nfrom experiment_builder import ExperimentBuilder\nimport tensorflow.contrib.slim as slim\nimport data as dataset\nimport tqdm\nfrom storage import *\n\ntf.reset_default_graph()\n\n# Experiment Setup\nbatch_size = 32\nfce = False\nclasses_per_set = 20\nsamples_per_class = 1\ncontinue_from_epoch = -1 # use -1 to start from scratch\nepochs = 200\nlogs_path = \"one_shot_outputs/\"\nexperiment_name = \"one_shot_learning_embedding_{}_{}\".format(samples_per_class, classes_per_set)\n\n# Experiment builder\ndata = dataset.OmniglotNShotDataset(batch_size=batch_size,\n classes_per_set=classes_per_set, samples_per_class=samples_per_class)\nexperiment = ExperimentBuilder(data)\none_shot_omniglot, losses, c_error_opt_op, init = experiment.build_experiment(batch_size,\n classes_per_set,\n samples_per_class, fce)\ntotal_epochs = 300\ntotal_train_batches = 1000\ntotal_val_batches = 250\ntotal_test_batches = 250\n\nsave_statistics(experiment_name, [\"epoch\", \"train_c_loss\", \"train_c_accuracy\", \"val_loss\", \"val_accuracy\",\n \"test_c_loss\", \"test_c_accuracy\"])\n\n# Experiment initialization and running\nwith tf.Session() as sess:\n sess.run(init)\n saver = tf.train.Saver()\n if continue_from_epoch != -1: #load checkpoint if needed\n checkpoint = \"saved_models/{}_{}.ckpt\".format(experiment_name, continue_from_epoch)\n variables_to_restore = []\n for var in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):\n print(var)\n variables_to_restore.append(var)\n\n tf.logging.info('Fine-tuning from %s' % checkpoint)\n\n fine_tune = slim.assign_from_checkpoint_fn(\n checkpoint,\n variables_to_restore,\n ignore_missing_vars=True)\n fine_tune(sess)\n\n best_val = 0.\n with tqdm.tqdm(total=total_epochs) as pbar_e:\n for e in range(0, total_epochs):\n total_c_loss, total_accuracy = experiment.run_training_epoch(total_train_batches=total_train_batches,\n sess=sess)\n print(\"Epoch {}: train_loss: {}, train_accuracy: {}\".format(e, total_c_loss, total_accuracy))\n\n total_val_c_loss, total_val_accuracy = experiment.run_validation_epoch(\n total_val_batches=total_val_batches,\n sess=sess)\n print(\"Epoch {}: val_loss: {}, val_accuracy: {}\".format(e, total_val_c_loss, total_val_accuracy))\n\n if total_val_accuracy >= best_val: #if new best val accuracy -> produce test statistics\n best_val = total_val_accuracy\n total_test_c_loss, total_test_accuracy = experiment.run_testing_epoch(\n total_test_batches=total_test_batches, sess=sess)\n print(\"Epoch {}: test_loss: {}, test_accuracy: {}\".format(e, total_test_c_loss, total_test_accuracy))\n else:\n total_test_c_loss = -1\n total_test_accuracy = -1\n\n save_statistics(experiment_name,\n [e, total_c_loss, total_accuracy, total_val_c_loss, total_val_accuracy, total_test_c_loss,\n total_test_accuracy])\n\n save_path = saver.save(sess, \"saved_models/{}_{}.ckpt\".format(experiment_name, e))\n pbar_e.update(1)\n"
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"text": "#%% Load Data\nimport numpy as np\nimport pylab as plt\nimport sys\nfrom util_im import imshow_matches\nfrom util_sm import load_sm, norm_sm, norm_prob_sm\nfrom util_sift import precompute_sift, load_sift\nfrom skimage import color\n\nb_s4 = np.loadtxt('Bregma_S4.csv', dtype=np.float, delimiter=',')\nb_pw3 = np.loadtxt('Bregma_PW3_M.csv', dtype=np.float, delimiter=',')\n\ns_im, s_label, s_kp, s_des = load_sift('S_BB_V2_SIFT.npz')\npw_im, pw_label, pw_kp, pw_des = load_sift('PW_BB_V2_SIFT.npz')\npwi_im, pwi_label, pwi_kp, pwi_des = load_sift('PW_BB_V1_SIFT.npz')\nsm_matches, sm_metric = load_sm('sm_v3', s_kp, pw_kp)\n\nb_pw3_m = []\nfor plate in pwi_label:\n idx = plate - 1\n b_pw3_m.append(b_pw3[idx])\n\n# Only Nissl bregmas\nb_pw3 = np.array(b_pw3_m)\n\n#%% Bregma Algorithm\natlas1 = b_s4\natlas2 = b_pw3\nM = np.zeros((atlas1.shape[0]+1, atlas2.shape[0]+1))\nM[1:,:] = np.inf\nDIR = np.zeros_like(M)\nDIR[:,:] = 2\nfor row in range(1, M.shape[0]):\n for col in range(1, M.shape[1]):\n if row > col:\n continue\n\n choices = [M[row,col-1], # Left\n M[row-1,col-1]+abs(atlas1[row-1] - atlas2[col])] # Diagonal\n M[row][col] = min(choices)\n DIR[row][col] = np.argmin(choices)\n\nM = M[1:,:]\nDIR = DIR[1:,:]\n#%% Path Backtracking\nim = np.zeros((DIR.shape[0], DIR.shape[1], 3), dtype=np.uint8)\nfor row in range(DIR.shape[0]):\n for col in range(DIR.shape[1]):\n if row > col:\n im[row, col] = [0, 100, 50] # Dark Green\n elif DIR[row][col] == 0: # Left\n im[row,col] = [200, 0, 0] # Red\n elif DIR[row][col] == 1: # Diagonal\n im[row, col] = [0, 255, 0] # Green\n elif DIR[row][col] == 2: # Unused\n im[row, col] = [0, 100, 50] # Dark Green\n else:\n im[row, col] = [0, 0, 0] # Black\n\nc = [148, 0, 211] # Purple\nim[6-1][8-1] = c\nim[11-1][11-1] = c\nim[23-1][42-1] = c\nim[33-1][68-1] = c\nfor row in range(DIR.shape[0]):\n col = np.argmin(M[row])\n # PW8 S6, PW11 S11, PW42 S23, PW68 S33,\n if (row == 6-1 and col == 8-1) or (row == 11-1 and col == 11-1) or (row == 23-1 and col == 42-1) or (row == 33-1 and col == 68-1):\n im[row][col] = [255, 255, 255] # White\n else:\n im[row][col] = [0, 0, 255] # Blue\n\nfig, ax = plt.subplots()\nax.set_title(\"DIR - Best Path\")\nax.imshow(im)"
}
] | 33 |
jeffreycoen/microcontroller-adventures
|
https://github.com/jeffreycoen/microcontroller-adventures
|
2880501644a76aa5ecd992f014d984cec3a8801d
|
a4e9b25e733fc6e0e5ffb996cb6e9b69a71fc9e2
|
53fc367cca778a3d445d2cfd40c571154e3bf838
|
refs/heads/master
| 2020-03-22T10:35:58.690254 | 2018-12-08T17:44:16 | 2018-12-08T17:44:16 | 139,913,887 | 0 | 0 | null | null | null | null | null |
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"text": "# Inspired by: Artronix Jam - War of the Worlds: Rise of the micro:bits Script 1\n# Tested with SG90 servo @ 3.3v\n# Copy and paste this into the compiler at http://python.microbit.org/editor.html\n\nfrom microbit import *\n\nclass Servo:\n\n \"\"\"\n A simple class for controlling hobby servos.\n Args:\n pin (pin0 .. pin3): The pin where servo is connected.\n freq (int): The frequency of the signal, in hertz.\n min_us (int): The minimum signal length supported by the servo.\n max_us (int): The maximum signal length supported by the servo.\n angle (int): The angle between minimum and maximum positions.\n Usage:\n SG90 @ 3.3v servo connected to pin0\n = Servo(pin0).write_angle(90)\n \"\"\"\n\n def __init__(self, pin, freq=50, min_us=600, max_us=2400, angle=180):\n self.min_us = min_us\n self.max_us = max_us\n self.us = 0\n self.freq = freq\n self.angle = angle\n self.analog_period = 0\n self.pin = pin\n analog_period = round((1/self.freq) * 1000) # hertz to miliseconds\n self.pin.set_analog_period(analog_period)\n\n def write_us(self, us):\n us = min(self.max_us, max(self.min_us, us))\n duty = round(us * 1024 * self.freq // 1000000)\n self.pin.write_analog(duty)\n self.pin.write_digital(0) # turn the pin off\n\n def write_angle(self, degrees=None):\n degrees = degrees % 360\n total_range = self.max_us - self.min_us\n us = self.min_us + total_range * degrees // self.angle\n self.write_us(us)\n\n# loop to check accelerometer position then move servos\nwhile True:\n \n # rescale accelerometer x axis to between 0 and 180\n rescaled_angle = ((accelerometer.get_x() /12)+90)\n\n # pan servo to the rescaled angle\n Servo(pin0).write_angle(rescaled_angle) # write rescaled angle\n \n # rescale accelerometer y axis to between 0 and 180\n rescaled_angle_y = ((accelerometer.get_y() /12)+90)\n\n # tilt servo to rescaled angle\n Servo(pin1).write_angle(rescaled_angle_y) # write rescaled angle\n\n # provide 0.1 sec pause\n sleep(100)\n \n \n"
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"text": "# microbit fishing game\n\n\n**micro:bit + micropython + servos**\n\nservos are controlled through micro:bit accelerometer\n\n\n![Screenshot](https://github.com/jeffreycoen/microcontroller-adventures/blob/master/fishing.png \"fishing\")\n\n[See the video on youtube](https://www.youtube.com/watch?v=YFliOMC9EQA)\n\n\npan/tilt servos link: https://www.amazon.com/gp/product/B0775R6JFF/ref=oh_aui_detailpage_o03_s00?ie=UTF8&psc=1\n\nbbc microbit amazon link: https://www.amazon.com/gp/product/B01G8WUGWU/ref=oh_aui_detailpage_o00_s00?ie=UTF8&psc=1\n\n\n\n\n"
}
] | 2 |
TheLateOne/CodingBat
|
https://github.com/TheLateOne/CodingBat
|
797945c70673498e6cd23e7bd157a5299e4ebe69
|
7d5fcbd9d29a1cb6e4f96071ad56ea9994accd58
|
e76b3431f86bf812122c38121eb10afe749fe6e1
|
refs/heads/master
| 2021-01-10T15:12:46.196060 | 2016-04-09T17:55:48 | 2016-04-09T17:55:48 | 53,616,393 | 0 | 0 | null | null | null | null | null |
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"text": "def sum67(nums):\n count = 0\n for a in nums:\n if a == 6:\n while True:\n nums.remove(nums[count])\n if nums[count] == 7:\n nums.remove(nums[count])\n break\n count += 1\n return sum(nums)\n\n"
}
] | 1 |
GrumpyOldSkeleton/pong
|
https://github.com/GrumpyOldSkeleton/pong
|
c58c33180328f94ff83bacfe22c8c8f32306b611
|
b59231340fee967da8e1cbc8477ba32ca52b56d1
|
91f5f8ad997cd99e3d339d0b26c99a2bfa14438d
|
refs/heads/main
| 2023-02-19T04:26:46.933570 | 2021-01-20T17:55:08 | 2021-01-20T17:55:08 | 329,047,533 | 0 | 0 | null | null | null | null | null |
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"text": "import pygame\nimport math\nimport random\nimport pathlib\nfrom noiseengine import NoiseEngine1D\nfrom vector import Vector2\n \n# ======================================================================\n# constants to help code readability\n# ======================================================================\n\nGAME_STATE_INTRO = 0\nGAME_STATE_IN_PROGRESS = 1\nGAME_STATE_SCORED = 2\nGAME_STATE_OVER = 3\nSCREEN_WIDTH = 1200\nSCREEN_HEIGHT = 600\nORIGINX = SCREEN_WIDTH // 2\nORIGINY = SCREEN_HEIGHT // 2\nCOLOUR_BLACK = [0,0,0]\nCOLOUR_WHITE = [255,255,255]\nCOLOUR_STARS = [100,50,255]\nCOLOUR_YELLOW = [255,255,0]\nCOLOUR_RED = [255,0,0]\n\nparticles_SPAWN_FROM_OPPONENT = 180\nparticles_SPAWN_FROM_PLAYER = 0\n\n# ======================================================================\n# setup pygame\n# ======================================================================\n# set mixer to 512 value to stop buffering causing sound delay\n# this must be called before anything else using mixer.pre_init()\npygame.mixer.pre_init(44100, -16, 2, 512)\npygame.init()\npygame.mixer.init()\npygame.display.set_caption(\"Pong 2020\")\nscreen = pygame.display.set_mode([SCREEN_WIDTH, SCREEN_HEIGHT])\nclock = pygame.time.Clock()\n\n# ======================================================================\n# load images and sounds\n# ======================================================================\n# pngs are saved with a black background layer in gimp with no transparency\n# note - use convert() and not convert_alpha()\n# instead I use set_colorkey to make black pixels transparent\n# and now I can set the alpha value of each image\n# using pathlib functions to make this cross platform (hopefully)\n# get the path that this script is running from (current working dir)\nFILEPATH = pathlib.Path().cwd() \nsound_blip = pygame.mixer.Sound(str(FILEPATH.joinpath('sounds' ,'blip.ogg')))\nsound_blip2 = pygame.mixer.Sound(str(FILEPATH.joinpath('sounds' ,'blip2.ogg')))\nsound_score = pygame.mixer.Sound(str(FILEPATH.joinpath('sounds' ,'score.ogg')))\nsound_boom = pygame.mixer.Sound(str(FILEPATH.joinpath('sounds' ,'boom.ogg')))\nimage_pong_title = pygame.image.load(str(FILEPATH.joinpath('png' ,'pong_title.png'))).convert()\nimage_pong_numbers = pygame.image.load(str(FILEPATH.joinpath('png' ,'pong_numbers.png'))).convert()\nimage_pong_game = pygame.image.load(str(FILEPATH.joinpath('png' ,'pong_game.png'))).convert()\nimage_pong_over = pygame.image.load(str(FILEPATH.joinpath('png' ,'pong_over.png'))).convert()\nimage_pong_you = pygame.image.load(str(FILEPATH.joinpath('png' ,'pong_you.png'))).convert()\nimage_pong_won = pygame.image.load(str(FILEPATH.joinpath('png' ,'pong_won.png'))).convert()\n\n# set the transparent colour, in my case black\nimage_pong_title.set_colorkey(COLOUR_BLACK)\nimage_pong_numbers.set_colorkey(COLOUR_BLACK)\nimage_pong_game.set_colorkey(COLOUR_BLACK)\nimage_pong_over.set_colorkey(COLOUR_BLACK)\nimage_pong_you.set_colorkey(COLOUR_BLACK)\nimage_pong_won.set_colorkey(COLOUR_BLACK)\n\n# ======================================================================\n# chop the scoreboard numbers into individual surfaces\n# ======================================================================\n\n# set the alpha value\nimage_pong_numbers.set_alpha(100)\n\n# the numbers are all stored as a single image. \n# I use subsurface() to create a new image for each number\n# and store them in a list for later use\nimage_offsets = [(0 ,0 ,110, 96),\n (130 ,0 ,90 , 96),\n (215 ,0 ,110, 96),\n (340 ,0 ,110, 96),\n (460 ,0 ,110, 96),\n (580 ,0 ,110, 96),\n (705 ,0 ,110, 96),\n (825 ,0 ,110, 96),\n (938 ,0 ,110, 96),\n (1063,0 ,110, 96)]\n\nimage_numbers = []\n\nfor loc in image_offsets:\n img = image_pong_numbers.subsurface(loc)\n image_numbers.append(img)\n \n\n#=======================================================================\n# some utility functions\n#=======================================================================\n\ndef maprange( a, b, val):\n # map val from range a to range b\n (a1, a2), (b1, b2) = a, b\n return b1 + ((val - a1) * (b2 - b1) / (a2 - a1)) \n \ndef clamp(n, minn, maxn):\n \n if n < minn:\n return minn\n elif n > maxn:\n return maxn\n else:\n return n\n \n#=======================================================================\n# Partical class\n#=======================================================================\n\nclass Partical():\n \n def __init__(self, pos, angle, speed, size, colour):\n \n self.pos = Vector2(pos.x, pos.y)\n self.vel = Vector2(0, 0)\n self.acc = Vector2(0,0) \n self.size = size\n self.alpha = 255 \n self.acc.setFromAngle(angle)\n self.acc.mult(speed)\n self.image = pygame.Surface([self.size, self.size])\n self.image.fill(colour)\n self.image.set_alpha(self.alpha)\n \n def update(self):\n \n self.vel.add(self.acc)\n self.pos.add(self.vel)\n self.alpha -= abs(self.vel.y)\n self.alpha = max(0,self.alpha)\n self.image.set_alpha(self.alpha)\n \n def draw(self):\n \n screen.blit(self.image, (self.pos.x, self.pos.y))\n \n def isOffScreen(self):\n \n return (self.pos.x < 0) or (self.pos.x > SCREEN_WIDTH) or (self.pos.y < 0) or (self.pos.y > SCREEN_HEIGHT)\n \n def isDead(self):\n \n return (self.alpha <= 0) or (self.isOffScreen())\n \n\n#=======================================================================\n# particlesystem class\n#=======================================================================\n\nclass particlesystem():\n \n def __init__(self, x, y, mx = 20):\n \n self.pos = Vector2(x, y)\n self.particles = []\n self.max_particles = mx\n \n def killAll(self):\n \n self.particles = []\n \n def burstDirection(self, angle, spread):\n \n self.killAll()\n for n in range(0, self.max_particles):\n # vary the angle a little bit\n angle = (angle + random.uniform(-spread, spread)) % 360\n speed = random.uniform(0.1, 0.7)\n size = random.randint(1, 4)\n p = Partical(self.pos, angle, speed, size, COLOUR_YELLOW)\n self.particles.append(p)\n \n def burstCircle(self):\n \n self.killAll()\n step = 360 // self.max_particles\n for n in range(0, self.max_particles):\n angle = n * step\n speed = random.uniform(0.1, 0.7)\n size = random.randint(1, 4)\n colour = COLOUR_RED\n p = Partical(self.pos, angle, speed, size, colour)\n self.particles.append(p)\n \n def update(self):\n \n cp = [p for p in self.particles if not p.isDead()]\n self.particles = cp\n for p in self.particles:\n p.update()\n p.draw()\n \n def isDead(self):\n \n return len(self.particles) == 0\n \n\n#=======================================================================\n# particlesystemController class\n#=======================================================================\n\nclass particlesystemController():\n \n def __init__(self):\n \n self.systems = []\n \n def spawn(self, x, y, mx):\n \n system = particlesystem(x, y, mx)\n self.systems.append(system)\n return system\n \n def spawnBurstDirection(self, x, y, angle, spread, max_particles = 20):\n \n system = self.spawn(x, y, max_particles)\n system.burstDirection(angle, spread)\n \n def spawnBurstCircle(self, x, y, max_particles = 20):\n \n system = self.spawn(x, y, max_particles)\n system.burstCircle()\n \n def killAll(self):\n \n self.systems = []\n \n def update(self):\n \n cp = [ps for ps in self.systems if not ps.isDead()]\n self.systems = cp\n for s in self.systems:\n s.update() \n\n \n#=======================================================================\n# Star class\n#=======================================================================\n\nclass Star():\n \n def __init__(self):\n \n self.position = Vector2(random.randint(0, SCREEN_WIDTH), random.randint(0, SCREEN_HEIGHT))\n self.velocity = Vector2(0.0, 1 + random.random() * 10)\n self.size = random.randint(1,4)\n self.image = pygame.Surface([self.size, self.size])\n self.rect = self.image.get_rect()\n self.image.fill(COLOUR_STARS)\n\n def reset(self):\n \n self.position.y = 0\n self.position.x = random.randint(0, SCREEN_WIDTH)\n self.velocity.y = 1 + random.random() * 10\n \n def update(self):\n \n # add a little to vel each frame to make it look a bit \n # like gravity is pulling it down like rain\n # reset() will set vel back to a baseline\n \n self.velocity.y += 0.05\n self.position.add(self.velocity)\n self.rect.x = self.position.x\n self.rect.y = self.position.y\n \n def draw(self):\n \n screen.blit(self.image, self.rect)\n\n\n#=======================================================================\n# Starfield class\n#=======================================================================\n\nclass StarField():\n \n def __init__(self):\n \n self.stars = []\n self.max_stars = 40\n \n for i in range(0, self.max_stars):\n star = Star()\n self.stars.append(star)\n \n def update(self):\n \n for star in self.stars:\n star.update()\n \n if star.position.y > SCREEN_HEIGHT:\n star.reset()\n \n def draw(self):\n \n for star in self.stars:\n star.draw()\n\n\n#=======================================================================\n# Player class\n#=======================================================================\n\nclass Player():\n \n def __init__(self, x, y, w, h, maxspeed):\n \n self.width = w\n self.height = h\n self.maxspeedy = maxspeed\n self.maxposition_y = SCREEN_HEIGHT - self.height\n self.start_position = Vector2(x, y)\n self.position = Vector2(self.start_position.x, self.start_position.y)\n self.velocity = Vector2(0,0)\n self.acceleration = Vector2(0,0)\n self.acceleration_step = 1.5\n self.rect = pygame.Rect([self.position.x, self.position.y, self.width, self.height])\n self.image = pygame.Surface([self.width, self.height])\n self.image.fill(COLOUR_WHITE)\n \n def reset(self):\n \n self.position = Vector2(self.start_position.x, self.start_position.y)\n self.velocity.mult(0)\n self.acceleration.mult(0)\n \n def up(self):\n \n self.acceleration.y -= self.acceleration_step\n \n def down(self):\n \n self.acceleration.y += self.acceleration_step\n \n def constrain(self):\n \n # constrain movement to screen bounds\n if self.position.y < 0:\n self.position.y = 0\n self.velocity.y = 0\n elif self.position.y > self.maxposition_y:\n self.position.y = self.maxposition_y\n self.velocity.y = 0\n \n def update(self):\n \n self.velocity.add(self.acceleration)\n \n # limit the speed\n self.velocity.y = clamp(self.velocity.y, -self.maxspeedy, self.maxspeedy)\n\n # add velocity to position \n self.position.add(self.velocity)\n # clear out the accumulated acceleration \n self.acceleration.mult(0)\n \n self.constrain()\n \n self.rect.x = self.position.x\n self.rect.y = self.position.y\n \n def draw(self):\n \n screen.blit(self.image, self.rect)\n\n\n#=======================================================================\n# Balltrail class\n#=======================================================================\n\nclass Balltrail():\n \n def __init__(self, size):\n \n self.size = size\n self.pad = self.size // 2\n self.current_frame = 0\n self.last_frame = 0\n self.max_length = 30\n self.trail = []\n \n def reset(self):\n \n self.trail.clear()\n \n def update(self, x, y):\n \n self.current_frame += 1\n \n # record a ball position every 3 frames\n if self.current_frame - self.last_frame > 2:\n \n self.last_frame = self.current_frame\n \n if len(self.trail) > self.max_length:\n # remove the oldest item\n self.trail.pop(0)\n \n # and add the current position of the ball \n self.trail.append( (x, y) )\n \n def draw(self):\n \n alpha = 0\n\n for r in self.trail:\n pygame.draw.rect(screen, [100, alpha, 100 + alpha], [r[0] + self.pad,r[1] + self.pad, 1 , 1])\n alpha += 2\n \n \n#=======================================================================\n# Ball class\n#=======================================================================\n\nclass Ball():\n \n def __init__(self, size):\n \n self.mass = 8\n self.width = size\n self.height = size\n self.position = Vector2(SCREEN_WIDTH // 2 - self.width // 2, SCREEN_HEIGHT // 2 - self.height // 2)\n self.velocity = Vector2(-5,0)\n self.acceleration = Vector2(0,0)\n self.rect = pygame.Rect([self.position.x,self.position.y, self.width, self.height])\n self.image = pygame.Surface([self.width, self.height])\n self.image.fill(COLOUR_WHITE)\n self.balltrail = Balltrail(size)\n\n def reset(self):\n \n self.balltrail.reset()\n \n bally = random.randint(-1,1)\n ballx = 4\n \n if game.playerserve:\n ballx = -ballx\n \n self.position = Vector2(SCREEN_WIDTH // 2 - self.width // 2, SCREEN_HEIGHT // 2 - self.height // 2)\n self.velocity = Vector2(ballx,bally) \n \n def applyForce(self, f):\n \n # make a copy to preserve the original vector values\n fcopy = f.getCopy()\n # divide the force by our mass\n fcopy.div(self.mass)\n self.acceleration.add(fcopy)\n\n def update(self):\n \n # add acceleration to velocity\n self.velocity.add(self.acceleration)\n \n # add it to our position vector and we move a bit towards target\n self.position.add(self.velocity)\n \n # important to clear out the accumulated acceleration each frame\n self.acceleration.mult(0)\n \n self.rect.x = self.position.x\n self.rect.y = self.position.y\n \n self.balltrail.update(self.position.x,self.position.y)\n\n def draw(self):\n \n self.balltrail.draw()\n screen.blit(self.image, self.rect)\n \n\n#=======================================================================\n# Arena class draws the game borders and scoreboard\n#=======================================================================\n\nclass Arena():\n \n def __init__(self):\n \n self.width = 2\n self.height = SCREEN_HEIGHT\n self.position = Vector2(SCREEN_WIDTH // 2 - self.width // 2, 0)\n self.player_score_position = Vector2((SCREEN_WIDTH // 2) - 300, (SCREEN_HEIGHT // 2)-52)\n self.opponent_score_position = Vector2((SCREEN_WIDTH // 2) + 180, (SCREEN_HEIGHT // 2)-52)\n\n def update(self):\n \n pass\n \n def draw(self):\n \n pygame.draw.rect(screen,[100,100,100],[self.position.x,self.position.y, self.width, self.height])\n \n screen.blit(image_numbers[game.player_score % 10 ], (self.player_score_position.x, self.player_score_position.y))\n screen.blit(image_numbers[game.opponent_score % 10 ], (self.opponent_score_position.x, self.opponent_score_position.y))\n\n\n#=======================================================================\n# Game class \n# Handles collisions and constraints and player movement\n#=======================================================================\n\nclass Game():\n\n def __init__(self):\n \n # player size and limits\n playerwidth = 20\n playerheight = 80\n playerspeed = 3.0\n opponentspeed = 2.8\n ballsize = 8\n player_edge_offset = 10\n\n # ball limits\n self.ball_max_speed_x = 8.0\n self.ball_max_speed_y = 8.0\n self.ball_speed_step = 1.2\n \n # these are the x positions that the ball is reset to following\n # a rectscollide with either bat to prevent ball going through bat\n self.ball_rebound_player_x = player_edge_offset + playerwidth + ballsize\n self.ball_rebound_opponent_x = SCREEN_WIDTH - (player_edge_offset + playerwidth) - ballsize\n \n self.playerserve = True # this toggles who serves\n \n self.gamestate = GAME_STATE_INTRO\n self.scored_frames_elapsed = 0\n self.player_score = 0\n self.opponent_score = 0\n self.wind = Vector2(0,0)\n self.wind_strength = 0.4\n \n self.player = Player(player_edge_offset, (SCREEN_HEIGHT // 2) - playerheight // 2, playerwidth, playerheight, playerspeed)\n self.opponent = Player(SCREEN_WIDTH - (player_edge_offset + playerwidth), (SCREEN_HEIGHT // 2) - playerheight // 2, playerwidth, playerheight, opponentspeed)\n self.ball = Ball(ballsize)\n self.arena = Arena()\n self.noiseengine = NoiseEngine1D(random.randint(1,100))\n self.starfield = StarField()\n self.psc = particlesystemController()\n \n def checkcollisionBallEdges(self):\n \n # if the ball is at or past either the top or bottom edges of\n # the court, reverse the y velocity and bring its position back\n # within the bounds of the court. This 'bounces' the ball\n # back off the edges\n \n if (self.ball.position.y < 0) or (self.ball.position.y > SCREEN_HEIGHT-self.ball.height):\n if self.ball.position.y < 0:\n self.ball.position.y = 0\n self.ball.velocity.y = -self.ball.velocity.y\n else:\n self.ball.position.y = SCREEN_HEIGHT-self.ball.height\n self.ball.velocity.y = -self.ball.velocity.y\n \n sound_blip2.play()\n self.psc.spawnBurstCircle(self.ball.position.x, self.ball.position.y)\n \n def checkcollisionBats(self):\n \n # check if ball is colliding with either bat. \n # if true reflect ball velocity x\n # use the reflectAngle function to work out how much\n # to add to the y velocity.\n\n if self.ball.rect.colliderect(self.player.rect):\n \n # work out where on the bat the ball hit here\n self.ball.velocity.y = self.reflectAngle(self.player)\n self.ball.velocity.x = -self.ball.velocity.x \n \n # a fudge to stop ball penetrating bat at higher ball speeds\n self.ball.position.x = self.ball_rebound_player_x\n self.batHit()\n # spawn a partical system\n self.psc.spawnBurstDirection(30, self.ball.position.y, particles_SPAWN_FROM_PLAYER, 4)\n \n elif self.ball.rect.colliderect(self.opponent.rect):\n \n self.ball.velocity.y = self.reflectAngle(self.opponent)\n self.ball.velocity.x = -self.ball.velocity.x\n self.ball.position.x = self.ball_rebound_opponent_x\n self.batHit()\n self.psc.spawnBurstDirection(SCREEN_WIDTH-30, self.ball.position.y, particles_SPAWN_FROM_OPPONENT, 4)\n \n def batHit(self):\n \n # called when the ball has hit either bat\n sound_blip.play()\n self.setWind()\n \n # increase the ball x velocity each hit\n # but keep it limited to x max speed\n if self.ball.velocity.x < self.ball_max_speed_x:\n self.ball.velocity.x *= self.ball_speed_step\n else:\n self.ball.velocity.x = self.ball_max_speed_x\n \n # reflectangle has already been applied so just\n # keep vel y within bounds\n if self.ball.velocity.y > self.ball_max_speed_y:\n self.ball.velocity.y = self.ball_max_speed_y\n \n def setWind(self):\n \n # gets a random direction and strength for the wind effect\n # the effect is mostly on the balls vertical movement\n # called when the ball hits a bat\n self.wind.x = self.noiseengine.next(100) * (self.wind_strength / 4)\n self.wind.y = self.noiseengine.next() * self.wind_strength\n \n def reflectAngle(self, player):\n \n # returns an y velocity to reflect the ball at. \n # subtract paddle y pos from ball y pos to get the position that\n # the ball is on the paddle \n # then / by player height to get a normalised 0 to 1.0 value\n # then add -0.5 to shift the range from -0.5 to 0.5 which\n # can then be multiplied to produce a y velocity for the ball\n \n return (((self.ball.position.y - player.position.y) / player.height) + -0.5) * 8\n \n def moveOpponent(self):\n \n # TODO:\n # Make the enemy more intelligent.\n # seek to position towards one end of bat for more angle\n \n if self.opponent.position.y < self.ball.position.y - self.opponent.height // 2:\n self.opponent.down()\n elif self.opponent.position.y > self.ball.position.y - self.opponent.height // 2:\n self.opponent.up()\n \n def checkBallInScorePosition(self):\n \n # if true score and reset game\n \n if self.ball.position.x < 0:\n self.opponent_score += 1\n self.psc.spawnBurstDirection(1, self.ball.position.y, particles_SPAWN_FROM_PLAYER, 20, 100)\n sound_score.play()\n self.gamestate = GAME_STATE_SCORED\n elif self.ball.position.x > SCREEN_WIDTH:\n self.player_score += 1\n self.psc.spawnBurstDirection(SCREEN_WIDTH-1, self.ball.position.y, particles_SPAWN_FROM_OPPONENT, 20, 100)\n sound_score.play()\n self.gamestate = GAME_STATE_SCORED\n \n def resetFromScore(self):\n \n # called after a score has been made\n # reset the player positions and \n # zero the wind effect and\n # toggle the server\n \n self.playerserve = not self.playerserve\n self.resetPositions()\n \n # check if either player has won the game\n # and switch the gamestate to gameover if it has\n \n if self.player_score == 5 or self.opponent_score == 5:\n self.gamestate = GAME_STATE_OVER\n else:\n self.gamestate = GAME_STATE_IN_PROGRESS\n \n def resetFromWin(self):\n \n # called after a game win\n # resets score and positions etc\n self.player_score = 0\n self.opponent_score = 0\n self.resetPositions()\n \n def resetPositions(self):\n \n # called at each serve\n self.ball.reset()\n self.player.reset()\n self.opponent.reset()\n self.wind.mult(0)\n self.psc.spawnBurstCircle(ORIGINX, ORIGINY, 100)\n sound_boom.play()\n \n def switchGameState(self):\n \n if self.gamestate == GAME_STATE_INTRO:\n \n self.gamestate = GAME_STATE_IN_PROGRESS\n image_pong_title.set_alpha(75)\n self.resetPositions()\n \n elif self.gamestate == GAME_STATE_OVER:\n \n self.resetFromWin()\n self.gamestate = GAME_STATE_INTRO\n \n def drawGameOver(self):\n \n randoff1 = self.noiseengine.next()\n randoff2 = self.noiseengine.next(1000)\n jitter = 10\n \n image_pong_game.set_alpha(150 + randoff1 * 50)\n image_pong_over.set_alpha(150 + randoff2 * 50)\n \n screen.blit(image_pong_game, (200 + randoff1 * jitter, 150 + randoff2 * jitter))\n screen.blit(image_pong_over, (400 + randoff2 * jitter, 250 + randoff1 * jitter))\n \n def drawGameWon(self):\n \n randoff1 = self.noiseengine.next()\n randoff2 = self.noiseengine.next(1000)\n jitter = 10\n \n image_pong_you.set_alpha(100 + randoff1 * 50)\n image_pong_won.set_alpha(100 + randoff2 * 50)\n \n screen.blit(image_pong_you, (200 + randoff1 * jitter, 150 + randoff2 * jitter))\n screen.blit(image_pong_won, (400 + randoff2 * jitter, 250 + randoff1 * jitter))\n \n def drawGameIntro(self):\n \n randoff1 = self.noiseengine.next()\n randoff2 = self.noiseengine.next(1000)\n jitter = 10\n \n image_pong_title.set_alpha(200 + randoff1 * 50)\n \n screen.blit(image_pong_title, (200 + randoff1 * jitter, 200 + randoff2 * jitter))\n\n def draw(self):\n \n if self.gamestate == GAME_STATE_INTRO:\n \n self.starfield.update()\n self.starfield.draw()\n self.drawGameIntro()\n \n elif self.gamestate == GAME_STATE_IN_PROGRESS:\n \n self.checkcollisionBallEdges()\n self.checkcollisionBats()\n self.moveOpponent()\n self.checkBallInScorePosition()\n self.arena.update()\n self.ball.applyForce(self.wind)\n self.ball.update()\n self.player.update()\n self.opponent.update()\n self.starfield.update()\n self.starfield.draw()\n self.arena.draw()\n self.player.draw()\n self.opponent.draw()\n self.ball.draw()\n self.psc.update()\n \n elif self.gamestate == GAME_STATE_SCORED:\n \n self.arena.draw()\n self.starfield.update()\n self.starfield.draw()\n self.psc.update()\n self.scored_frames_elapsed += 1\n \n if self.scored_frames_elapsed > 180:\n self.scored_frames_elapsed = 0\n self.resetFromScore()\n \n elif self.gamestate == GAME_STATE_OVER:\n \n self.starfield.update()\n self.starfield.draw()\n \n if self.player_score > self.opponent_score:\n self.drawGameWon()\n else:\n self.drawGameOver()\n \n def run(self):\n \n done = False\n \n while not done:\n \n for event in pygame.event.get(): \n if event.type == pygame.QUIT: \n done = True\n if event.type == pygame.KEYDOWN:\n if (event.key == pygame.K_ESCAPE):\n done = True\n elif (event.key == pygame.K_SPACE):\n game.switchGameState()\n elif (event.key == pygame.K_UP):\n game.player.up()\n elif (event.key == pygame.K_DOWN):\n game.player.down()\n \n screen.fill(COLOUR_BLACK)\n game.draw()\n clock.tick(60)\n pygame.display.flip()\n \n \n\ngame = Game()\ngame.run()\npygame.quit()\n"
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"text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# noiseengine.py\n# \n# NOTE!:\n# pip3 install opensimplex \n#\n# implements a simple 1D noise generator.\n# \n# call next to get a perlin random in range -1..1\n# call next with optional offset to get value ahead/behind the next val\n# this is good for random x/y position value\n# call nextMapped to get that value mapped to any range.\n\ntry:\n from opensimplex import OpenSimplex\nexcept:\n print ('opensimplex not found.')\n print('To install opensimplex: pip3 install opensimplex')\n\n\nclass NoiseEngine1D():\n \n def __init__(self, seed=1):\n \n self.engine = OpenSimplex(seed)\n self.smoothness = 20\n self.x = 1\n \n def maprange(self, a, b, val):\n \n # map val from range a to range b\n (a1, a2), (b1, b2) = a, b\n return b1 + ((val - a1) * (b2 - b1) / (a2 - a1))\n \n def next(self, offset=0):\n \n # return next value of noise\n self.x += 1\n return self.engine.noise2d(1, (self.x + offset) / self.smoothness)\n \n def nextMapped(self, mn, mx, offset=0):\n \n n = self.next(offset)\n return self.maprange((-1,1),(mn,mx),n)\n \n \n\n\n\n\n \n"
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"text": "# Pong\n\nClassic Pong game with starfield background and using OpenSimplex random noise to create a 'wind effect' on ball movement that changes smoothly with each hit of the ball on either paddle.\n\nA partical system class handles some nice-looking partical effects.\n\nWritten using Python3 and PyGame.\n\nRequires OpenSimplex and PyGame libs. \n\n>pip install opensimplex\n>pip install pygame\n\nor\n\n>pip3 install opensimplex\n>pip3 install pygame\n\n## Screenshot\n\n\n\n\n## Controls:\n\nSPACE to start the game.\n\nTap UP and DOWN keys to accelerate the player paddle up and down.\n"
}
] | 3 |
gary576193/homework
|
https://github.com/gary576193/homework
|
aedbcb11a850c57d6a1438d449876db49fd7043b
|
208e99bcdea46bcb00cadadea37574decf7eeb60
|
c81bd58282b964c61e23d0c78aee7587fdb5bead
|
refs/heads/master
| 2023-01-22T11:24:43.677052 | 2020-12-05T08:29:27 | 2020-12-05T08:29:27 | 318,730,063 | 0 | 0 | null | null | null | null | null |
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"text": "# homework\n執行方式:在cmd中輸入 python hw.py 12345678 (\"12345678\"可以改成自己想要測試的字串)\n具有加分題:1.字母與數字不可以連續\n 2.輸入後可以告知錯誤的型別\n"
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"text": "import re\nimport sys\n\nflag=1\n#n=input()\nn=sys.argv[1]\ns=set()\nlist1=[]\n\nif re.search('[0-9]',n) is None:\n x='缺少數字'\n s.add(x)\n flag=0\n\nif re.search('\\W',n) is None:\n if re.search('_',n) is None:\n x='缺少符號'\n s.add(x)\n flag=0\n\nif re.search('[a-z]',n) is None: \n x='缺少字母'\n s.add(x)\n flag=0\n \nif len(n)<8:\n x='長度少於8'\n s.add(x)\n flag=0\n\nif len(n)>16:\n x='長度大於16'\n s.add(x)\n flag=0 \n\nif re.search('[A-Z]',n) is None: \n x='缺少字母大寫'\n s.add(x)\n flag=0 \n\nfor i in range(len(n)-1):\n if ord(n[i])+1 == ord(n[i+1]) or ord(n[i])+33 == ord(n[i+1]):\n flag=0\n x='數字或字母連續'\n s.add(x)\n flag=0 \n \nif flag==1:\n print('Success')\nelse:\n print('Fail')\n for i in s:\n print(i)\n"
}
] | 2 |
pbraunstein/comp116-pbraunstein
|
https://github.com/pbraunstein/comp116-pbraunstein
|
c5ece54a3f4256b4521573a14607fb5dd0ad6941
|
6c6fa3baef98c021b00e81507e1a44ea656afcab
|
60a4988934a4156be63ab60f27412b5f2abc7e5b
|
refs/heads/master
| 2021-01-18T11:12:47.084745 | 2015-12-11T15:20:05 | 2015-12-11T15:20:05 | null | 0 | 0 | null | null | null | null | null |
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"text": "#!/usr/bin/env python\n#\n# humanPass.py\n# Philip Braunstein\n# COMP116: Making Secure Easy-to-Remember Passwords\n#\n# Generates 10 modified XKCD style passwords from noun, verb, and adjective\n# word lists. Prints all passwords (each word separated by spaces for\n# readability) to screen.\n#\n\n# CONSTANTS\nNOUN_FILE = \"../wordlists/nouns.txt\"\nVERB_FILE = \"../wordlists/verbs.txt\"\nADJ_FILE = \"../wordlists/adjectives.txt\"\nNUM_PASSES = 10\n\nfrom sys import exit\nimport random\n\ndef main():\n nouns = readIn(NOUN_FILE)\n verbs = readIn(VERB_FILE)\n adjs = readIn(ADJ_FILE)\n\n for x in range(NUM_PASSES):\n passWord = ''\n passWord += random.choice(adjs) + ' '\n passWord += random.choice(nouns) + ' '\n passWord += random.choice(verbs) + ' '\n passWord += random.choice(adjs) + ' '\n passWord += random.choice(nouns) + ' '\n print passWord\n\n exit(0)\n\n\n# Reads in a line separated file into one long list. Controls for\n# capitalization\ndef readIn(inputFile):\n toReturn = []\n with open(inputFile, 'r') as filer:\n for line in filer:\n toReturn.append(line.strip().lower())\n\n return toReturn\n\n\nif __name__ == '__main__':\n main()\n"
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"text": "#!/usr/bin/env python\n#\n# charFracSim.py\n# Philip Braunstein\n# COMP116: Making Secure Easy-to-Remember Passwords\n#\n# Determines a perent similarity metric of character count similarity. Where\n# the percentage of each character of a string in the other string is\n# calculated.\n#\n\nfrom sys import exit\nfrom sys import argv\n\ndef main():\n if len(argv) != 3:\n usage()\n\n dicts1 = makeDict(argv[1])\n dicts2 = makeDict(argv[2])\n\n charPercList = compareDicts(dicts1, dicts2)\n\n charPerc = float(sum(charPercList)) / float(len(charPercList))\n\n print \"Character Percent Similarity:\", round(charPerc, 2)\n\n exit(0)\n\n\n# Prints the usage of the script then exits nonzero\ndef usage():\n print \"USAGE:\", argv[0], \"STRING_1 STRING_2\"\n exit(1)\n\n\n# Makes a dictionary of character counts from string s and returns this dict\ndef makeDict(s):\n toReturn = {}\n\n for c in s:\n try:\n toReturn[c] += 1\n except KeyError:\n toReturn[c] = 1\n\n return toReturn\n\n\n# Constructs a list of the percentage of each charater from one string in the\n# other. To get this number the smaller is divided into the bigger. For\n# example, if one string has 3 of one character and the other has 4 of that\n# character, the percentage is always 0.75 rather than 1.33. Returns this list.\ndef compareDicts(dicts1, dicts2):\n toReturn = []\n allKeys = set(dicts1.keys() + dicts2.keys()) # Set for no duplicate keys\n\n for key in allKeys:\n try:\n toReturn.append(float(min(dicts1[key], dicts2[key])) /\\\n float(max(dicts1[key], dicts2[key])))\n except KeyError:\n toReturn.append(0.0) # If letter not found in a string, perc is 0\n\n return toReturn\n\n\nif __name__ == '__main__':\n main()\n"
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"text": "#!/usr/bin/env ruby\nrequire 'packetfu'\nrequire 'base64'\nrequire 'apachelogregex'\n\ndef main()\n # Get right number of arguments from command line\n if ARGV.length != 0 && ARGV.length != 2\n puts \"ERROR: Script invoked incorrectly\"\n puts \"USAGE: Alarm -- ruby alarm.rb\"\n puts \"USAGE: Analyze Log -- ruby alarm.rb -r <logfile>\"\n exit 1\n end\n\n if ARGV.length == 0\n liveCapture()\n else # That is, argv.length == 2\n if ARGV[0] != '-r'\n print \"Unrecognized Option \", ARGV[0], \"\\n\"\n exit 1\n else # Go ahead and analyze file\n analyzeLog(ARGV[1])\n end\n end\n exit 0\nend\n\ndef analyzeLog(inputFile)\n parser = ApacheLogRegex.new('%h %l %u %t \\\"%r\\\" %>s %b \\\"%{Referer}i\\\" \\\"%{User-agent}i\\\"')\n parser2 = ApacheLogRegex.new('%h %l %u %t \\\"%r\\\" %>s %b\"')\n count = 0\n File.open(inputFile, \"r\") do |f|\n f.each_line do |line|\n incident = nil\n # Try parsers for both formats and if neither work, skip that line\n dictio = parser.parse(line)\n if dictio.nil?\n dictio = parser2.parse(line)\n end\n if dictio.nil?\n next\n end\n if isNmapScan(dictio[\"%r\"]) || isNmapScan(dictio[\"%{User-agent}i\"])\n incident = \"Nmap Scan\"\n elsif isNiktoScan(dictio[\"%r\"]) || isNiktoScan(dictio[\"%{User-agent}i\"])\n incident = \"Nikto Scan\"\n elsif isBadPHP(dictio[\"%r\"]) || isBadPHP(dictio[\"%{User-agent}i\"])\n incident = \"phpMyAdmin Use\"\n elsif isShellShock(dictio[\"%r\"]) || isShellShock(dictio[\"%{User-agent}i\"])\n incident = \"ShellShock Exploitation\"\n elsif isHasX(dictio[\"%r\"]) || isHasX(dictio[\"%{User-agent}i\"])\n incident = \"Injection of Shell Code\"\n end\n\n if !incident.nil?\n count += 1\n print count, \". ALERT: \", incident, \" is detected from \"\n print dictio[\"%h\"], \" (\", dictio[\"%r\"].split.last, \") (\"\n print dictio[\"%r\"], \")\\n\"\n end\n end\n end\nend\n\ndef liveCapture()\n stream = PacketFu::Capture.new(:start => true, \n :iface => 'eth0', :promisc => false)\n\n me = PacketFu::Utils.whoami?()[:ip_saddr]\n counter = 0\n stream.stream.each do |raw|\n pkt = PacketFu::Packet.parse raw\n \n # Reset these each times so no \"dead squirrls\"\n scanType = nil\n if pkt.is_tcp?\n # If I sent the packet, don't report it\n if pkt.ip_saddr == me\n next\n end\n\n # Check for known scans\n if isNullScan(pkt.tcp_flags)\n scanType = \"NULL\"\n elsif isFinScan(pkt.tcp_flags)\n scanType = \"FIN\"\n elsif isXmasScan(pkt.tcp_flags)\n scanType = \"Xmas\"\n elsif isNmapScan(pkt.payload)\n scanType = \"Nmap\"\n elsif isNiktoScan(pkt.payload)\n scanType = \"Nikto\"\n elsif isSynScan(pkt.tcp_flags)\n scanType = \"SYN\"\n end\n\n # Only print out inident report if recognized intrusion\n if (scanType != nil)\n counter += 1 \n print counter,\". \",\"ALERT \", scanType\n print \" scan is detected from \", pkt.ip_saddr\n print \" (\", pkt.proto.last, \") \"\n print \"\\n\"\n end\n if (isCreditCard(pkt.payload))\n counter += 1\n cc = getCreditCard(pkt.payload)\n print counter,\". \", \"ALERT credit card \", cc\n print \" leaked in the clear from \"\n print pkt.ip_saddr\n print \" (\", pkt.proto.last, \") \"\n print \"\\n\"\n end\n end\n end\nend\n\n# If the sum of all flags is 0, then it is a null scan, otherwise it is \n# something else\ndef isNullScan(fls)\n sumFlags = fls.ack + fls.psh + fls.urg + fls.rst + fls.fin + fls.syn\n if (sumFlags == 0)\n return true\n else\n return false\n end\nend\n\n\ndef isFinScan(fls)\n # If fin bit not set, just get out\n if (fls.fin == 0)\n return false\n end\n\n sumOthers = fls.ack + fls.psh + fls.urg + fls.rst + fls.syn\n\n # At this point, we know that fin bit was set, if others not\n # then it is a fin scan \n if (sumOthers == 0)\n return true\n else\n return false\n end\nend\n\ndef isXmasScan(fls)\n sumOthers = fls.ack + fls.syn + fls.rst\n\n # If anything else lit up, return false\n if (sumOthers != 0)\n return false\n end\n\n if (fls.fin == 1 && fls.psh == 1 && fls.urg == 1)\n return true\n else\n return false\n end\nend\n\ndef isSynScan(fls)\n sumOthers = fls.ack + fls.rst + fls.fin + fls.psh + fls.urg\n if (sumOthers != 0)\n return false\n end\n\n if (fls.syn == 1)\n return true\n else\n return false\n end\nend\n\n# Looks for 2 different credit card formats in the payload\n# Returns the number if found, otherwise, returns nil\ndef getCreditCard(payload)\n # VISA\n matchList = payload.scan(/(4\\d{3}(\\s|-)?\\d{4}(\\s|-)?\\d{4}(\\s|-)?\\d{4})\\D/) \n if matchList.length > 0\n return matchList[0][0]\n end\n\n # MASTER CARD\n #form with dashes\n matchList = payload.scan(/(5\\d{3}(\\s|-)?\\d{4}(\\s|-)?\\d{4}(\\s|-)?\\d{4})\\D/) \n if matchList.length > 0\n return matchList[0][0]\n end\n\n # DISCOVER\n #form with dashes\n matchList = payload.scan(/(6011(\\s|-)?\\d{4}(\\s|-)?\\d{4}(\\s|-)?\\d{4})\\D/) \n if matchList.length > 0\n return matchList[0][0]\n end\n\n # AMERICAN EXPRESS\n #form with dashes\n matchList = payload.scan(/(3\\d{3}(\\s|-)?\\d{4}(\\s|-)?\\d{4}(\\s|-)?\\d{4})\\D/) \n if matchList.length > 0\n return matchList[0][0]\n end\n\n return nil\n\nend\n\n# Takes in a payload and returns true if there is a credit card number,\n# false otherwise\ndef isCreditCard(payload)\n cardNo = getCreditCard(payload)\n if cardNo.nil? # indicates no card found\n return false\n else\n return true\n end\nend\n\n\n# Accepts a payload from a tcp packet and searches for nmap in binary and\n# plain text ignoring case\ndef isNmapScan(payload)\n if payload.scan(/Nmap/i).length > 0\n return true\n elsif payload.scan(/\\x4E\\x6D\\x61\\x70/).length > 0\n return true\n else\n return false\n end\nend\n\n\n# Accepts a payload from a tcp packet and searches for nikto in binary and\n# plain text ignoring case\ndef isNiktoScan(payload)\n if payload.scan(/Nikto/i).length > 0\n return true\n elsif payload.scan(/\\x4E\\x69\\x6B\\x74\\x6F/).length > 0\n return true\n else\n return false\n end\nend\n\n# Pulls out phpMyAdmin badness\ndef isBadPHP(payload)\n if payload.include?(\"phpMyAdmin\")\n return true\n else\n return false\n end\nend\n\ndef isShellShock(payload)\n if payload.include?(\"() { :;};\")\n return true\n else\n return false\n end\nend\n\n# Look for anything that looks like shell code\ndef isHasX(payload)\n if payload.include?(\"\\\\x\")\n return true\n else\n return false\n end\nend\n\nmain\n"
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"text": "### Alarm\n##### Philip Braunstein (philb)\n\nI believe all aspects of the program have been implemented successfully. I discussed this assignment with Evan Parton. I spent around 12 hours on this assignment (longer than I imagined myself needing).\n\n1. The heuristics are okay, but they are clearly easily evadable. Detecting NMAP and NIKTO scans is done by string matching payload to those terms, but this is easily evadable. The most reliable heuristics are the ones that actually look at the flags that are on in the packet. For example, I am fairly confident that my alarm would notice an XMAS scan, but not as confident that it would recognize any generic NMAP scan. The credit card detection is also pretty good. The heuristic for finding code injected into shells just looks for anything containing \\x in it. This might lead to some false positives, but better safe than sorry. However, I wonder if there is a better heuristic for this.\n\n2. If I had more time, I would hard code the rest of the types of NMAP scans in with respect to the flags they have on instead of just looking for NMAP in the text. \n\n#### Changes / (possible) Improvements\n1. I added a bit to the suggested regex for credit cards in the article the assignment links to. I added a \\D character at the end, to make sure that there weren’t more numbers, which would indicate some other kind of number, (not a credit card number).\n\n2. I have the alarm actually print out the credit card number discovered. I would imagine that a user of this alarm may want to cancel or at least closely monitor card numbers leaked in the plain text to prevent suspicious activity. To do this, it is helpful for the alarm to tell the user what the credit card numbers were, that were leaked.\n\n3. I do not print the payload when analyzing live traffic. I found that whether or not using Base64 decoding, printing the payload often resulted in gross unprintable characters. I decided for usability sake to not print the payload. This is not a problem in the log files, so the log files print the payload."
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"text": "#!/usr/bin/env python\n#\n# smartFracSim.py\n# Philip Braunstein\n# COMP116: Making Secure Easy-to-Remember Passwords\n#\n# Evaluates the percent similarity of two strings passed to the script on the\n# command line by averaging the forward and backgward percent similarities\n#\n\nfrom sys import exit\nfrom sys import argv\n\ndef main():\n if len(argv) != 3:\n usage()\n\n forPercSim = percSim(argv[1], argv[2])\n revPercSim = percSim(argv[1][::-1], argv[2][::-1])\n\n\n avgPercSim = (forPercSim + revPercSim) / 2.0\n\n print \"Forward Percent Similarity:\", round(forPercSim, 2)\n print \"Reverse Percent Similarity:\", round(revPercSim, 2)\n print \"Average Percent Similarity:\", round(avgPercSim, 2)\n \n exit(0)\n\n\n# Prints the usage of the script then exits nonzero\ndef usage():\n print \"USAGE:\", argv[0], \"STRING_1 STRING_2\"\n exit(1)\n\n\n# Returns the percent similarity of the strings s1 and s2\n# If the strings are of unequal lengths, spaces are inserted at the end of the\n# shorter string\ndef percSim(s1, s2):\n numSims = 0\n\n if len(s1) < len(s2):\n s1 = addNSpaces(s1, len(s2) - len(s1))\n elif len(s2) < len(s1):\n s2 = addNSpaces(s2, len(s1) - len(s2))\n\n # INVARIANT: Both strings must be the same length at this point\n for i in range(len(s1)):\n if s1[i] == s2[i]:\n numSims += 1\n\n\n return float(numSims) / float(len(s1))\n\n\n\n\n\n# Returns a string s with spacesToAdd spaces appended to the end of it\ndef addNSpaces(s, spacesToAdd):\n for i in range(spacesToAdd):\n s += ' '\n\n return s\n\n\nif __name__ == '__main__':\n main()\n"
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"text": "#!/usr/bin/env python\n#\n# makeHashes.py\n# Philip Braunstein\n# COMP116: Making Secure Easy-to-Remember Passwords\n#\n# Hashes each line of the first file on the command line and writes out the\n# results to the second file provided on the command line. This script uses an\n# unsalted MD5 hash/\n#\n\nfrom sys import argv\nfrom sys import exit\nimport hashlib\n\ndef main():\n if len(argv) != 3:\n usage()\n\n filew = open(argv[2], 'w') \n\n with open(argv[1]) as filer:\n for line in filer:\n line = line.strip()\n p = hashlib.md5()\n p.update(line)\n filew.write(p.hexdigest() + \"\\n\")\n\n filew.close()\n\n exit(0)\n\n\ndef usage():\n print \"USAGE:\", argv[0], \"INPUT_FILE OUTPUT_FILE\"\n exit(1)\n\n\nif __name__ == '__main__':\n main()\n"
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"text": "### Comp 116 Assignment 1\n#### Philip Braunstein ([email protected])\n###### September 23, 2015\n\n\n\n1. 861 packets\n\n2. FTP\n\n3. FTP is an unencrypted service. Therefore, all the data is transmitted in plaintext / in the clear,\nand anyone capturing traffic can construct a copy of the file.\n\n4. sFTP\n\n5. 192.168.1.8.\n\n6. defcon:m1ngisablowhard (username:password)\n\n7. 6 files\n\n8. CDkv69qUsAAq8zN.jpg, CLu-m0MWoAAgjkr.jpg, CJoWmoOUkAAAYpx.jpg, COaqQWnU8AAwX3K.jpg, CNsAEaYUYAARuaj.jpg, CKBXgmOWcAAtc4u.jpg\n\n\n10. 77982 packets\n\n11. 1 pair\n\n12. This was insanenly easy using GUI ettercap. I just loaded the file and scanned the output to see any username or passwords.\n\n13. [email protected]:Z3lenzmej, PROTOCOL: IMAP, SOURCE: 10.125.15.197, DESTINATION: 87.120.13.118, PORT: 143, DOMAIN_NAME:neterra.net\n\n14. This username password pair was granted access successfully.\n\n\n15. 3 pairs\n\n16. seymore:butts, PROTOCOL: HTTP, SOURCE: 10.134.15.231, DESTINATION: 162.222.171.208, PORT: 80, DOMAIN_NAME: cascadelink.com\n\n [email protected]:Nifty->takirin1, PROTOCOL: IMAP, SOURCE: 10.115.15.213, DESTINATION: 210.131.4.155, PORT: 143, DOMAIN_NAME: nifty.com\n\n jeff:asdasdasd, PROTOCOL: HTTP, Source: 10.139.15.225, DESTINATION: 54.191.109.23, PORT: 80, DOMAIN_NAME: aws.amazon.com\n\n17. The last two pairs listed in 16. were granted access successfully.\n\n18. The most popular hostnames include amazon web services (a number of IP addresses including 54.193.4.196 and 54.219.162.53), twitter api (199.16.156.xxx block), and various google services (216.58.216.xxx and 216.58.217.xxx).\n\n\n19. To verify successful username-password pairs, I followed the TCP stream in Wireshark to see if either 200 OK and/or LOGIN SUCCESSFUL messages were sent back by the server, or there was some kind of PERMISSION DENIED message. It was generally pretty clear what happened.\n\n20. Whenever you are using any kind of passwords or dealing with any kind of sensitive data, only only only use encrypted protocols (sFTP, HTTPS, etc.)\n\n**Cool hack I came up with**\n\nIn order to correlate the names of the jpg files with the actual files pulled from the pcap files, I noticed that also transferred by ftp was a list of the file names including their sizes (looked like ls -l). Since each file had a unique size, it was possible to figure out from the names of the files.\n"
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"text": "#!/usr/bin/env python\n#\n# determineOrder.py\n# Philip Braunstein\n# COMP116: Making Secure Easy-to-Remember Passwords\n#\n# Randomizes the order of experiments for each participant.\n# RS - random string\n# MPN - memorable phrase and number\n# MX - modified XKCD\n#\n\nfrom sys import exit\nimport random\n\ndef main():\n opts = ['RS', 'MPM', 'MX']\n\n random.shuffle(opts)\n\n for method in opts:\n print method\n\n exit(0)\n\n\nif __name__ == '__main__':\n main()\n"
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"text": "#!/usr/bin/env python\n#\n# randomPass.py\n# Philip Braunstein\n# COMP116: Making Secure Easy-to-Remember Passwords\n#\n# Generates 10 random password PW_LENGTH long, containing 3 of the 4\n# following character types: lower case letter, upper case letter, symbol,\n# number. All passwords printed to screen.\n#\n\n# CONSTANTS\nNUM_PASSES = 10\nPW_LENGTH = 10\n\nfrom sys import exit\nimport string\nimport random\nimport re\n\ndef main():\n passes = []\n\n while len(passes) < NUM_PASSES:\n passWord = makePW()\n\n if isValid(passWord):\n passes.append(passWord)\n\n for pw in passes:\n print pw\n\n\n# Makes a password of length PW_LENGTH using letters, digits, and punctuation\ndef makePW():\n toReturn = ''\n pool = string.letters + string.digits + string.punctuation\n for i in range(PW_LENGTH):\n toReturn += random.choice(pool)\n\n return toReturn\n\n# Checks if a password is valid (has three of four of the following: lower case\n# letter, upper case letter, number, and punctuation). Returns True if valid,\n# False otherwise.\ndef isValid(pw):\n checks = 0\n if re.search('\\d', pw) is not None:\n checks += 1\n if re.search('[a-z]', pw) is not None:\n checks += 1\n if re.search('[A-Z]', pw) is not None:\n checks += 1\n if re.search('\\W|_', pw) is not None:\n checks += 1\n\n # Made at least 3 of 4\n if checks >= 3:\n return True\n else:\n return False\n\nif __name__ == '__main__':\n main()\n"
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"text": "### Making Secure Easy-to-Remember Passwords\n##### Philip Braunstein ([email protected])\n##### Advised by Ming Chow\n##### Fall 2015\n\n#### Overview\nThis repository contains my final project for the COMP116 security class. The\n`scripts` folder contains all of the scripts described in the report, and the\n`wordlists` folder contains the word lists used to make the MX passwords. The\nfinal write up of the report is the `Better_Passwords.pdf` document.\nThe scripts and wordlists are publically available in this github repository:\nhttps://github.com/pbraunstein/Security-Final-Project\n\n#### Scripts\n`charFracSim.py`: This script deterimines the fraction of the characters common\nbetween two passwords provided on the command line.\n\n`determineOrder.py`: This script randomizes the order in which the password\ntypes should be tested.\n\n`humanPass.py`: This script uses the noun, verb, and adjective word lists to\ngenerate 10 of the MX passwords.\n\n`makeHashes.py`: This script takes a file name as input and writes out a new\nfile with each of the lines of the file hashed using MD5 without salt.\n\n`randomPass.py`: This script generates 10 of the RS passwords.\n\n`smartFracSim.py`: This script determines the average of the forward and\nreverse percent similarities of two passwords.\n"
}
] | 10 |
FullmetalNeverCore/sunrise-sunset_module
|
https://github.com/FullmetalNeverCore/sunrise-sunset_module
|
34f582c2403624225770885c3f53d20992f59a11
|
5a84b6c9c412eb8a27f092f5e4956cd558c4370a
|
21ccc02999703444162e5157864059a691eece83
|
refs/heads/main
| 2023-08-15T00:44:44.463664 | 2021-09-18T09:53:18 | 2021-09-18T09:53:18 | 406,878,525 | 0 | 0 | null | null | null | null | null |
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"text": "from selenium import webdriver\r\nfrom selenium.webdriver.chrome.options import Options\r\nfrom selenium.webdriver.common.keys import Keys \r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as EC \r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.common.exceptions import StaleElementReferenceException\r\nfrom selenium.common.exceptions import ElementNotInteractableException\r\nfrom pyvirtualdisplay import Display\r\nimport pickle \r\nimport colorama,random\r\nimport datetime \r\nfrom datetime import date\r\nfrom colorama import Back,Fore,Style\r\nimport time\r\nimport psutil,subprocess\r\nimport os,sys,string\r\n\r\n\r\nclass SunData:\r\n def __init__(self):\r\n user_city = str(input('Название города: ')).lower()\r\n self.opts = Options()\r\n #self.display = Display(visible=0, size=(800, 600))\r\n # self.display.start()\r\n self.opts.add_argument(\"--disable-extensions\")\r\n self.opts.add_argument(\"--proxy-server='direct://'\")\r\n self.opts.add_argument(\"--proxy-bypass-list=*\")\r\n self.opts.add_argument(\"--start-maximized\")\r\n self.opts.add_argument('--headless')\r\n self.opts.add_argument('--disable-gpu')\r\n self.opts.add_argument('--disable-dev-shm-usage')\r\n self.opts.add_argument('--no-sandbox')\r\n self.opts.add_argument('--ignore-certificate-errors') \r\n self.driver = webdriver.Chrome(options=self.opts)\r\n if not ' '.join(user_city).split() in ' '.join(string.ascii_lowercase).split():\r\n self.url = \"https://voshod-solnca.ru/sun/\"+str(user_city)\r\n else:\r\n print('Err - write city in latin alphabet'),os.execv(sys.argv[0], sys.argv)\r\n def main(self):\r\n self.driver.get(self.url)\r\n def sunset():\r\n time.sleep(4)\r\n ss = self.driver.find_element_by_id('sunset').get_attribute('value')\r\n return ss \r\n def sunrise():\r\n time.sleep(4)\r\n sr = self.driver.find_element_by_id('sunrise').get_attribute('value')\r\n return sr\r\n print(sunset()),print(sunrise())\r\n self.driver.close()\r\n\r\n\r\nSunData().main()\r\n"
}
] | 1 |
pirobot/pedsim_ros
|
https://github.com/pirobot/pedsim_ros
|
8c232879801db138c0ae84b02b20f2c630f47a20
|
f8901fecf02e77f787ca7be929a315331af2502f
|
15b95de06e11b5206ce5d6602f494b86a4570278
|
refs/heads/master
| 2021-01-19T15:12:52.391571 | 2017-10-27T17:29:43 | 2017-10-27T17:29:43 | 100,950,780 | 5 | 0 | null | null | null | null | null |
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"path": "/visualization/spencer_tracking_rviz_plugin/src/detected_persons_display.cpp",
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"text": "#include <rviz/visualization_manager.h>\n#include <rviz/frame_manager.h>\n#include \"rviz/selection/selection_manager.h\"\n\n#include \"detected_persons_display.h\"\n\n#include <boost/foreach.hpp>\n#define foreach BOOST_FOREACH\n\nnamespace spencer_tracking_rviz_plugin\n{\n\n// The constructor must have no arguments, so we can't give the\n// constructor the parameters it needs to fully initialize.\nvoid DetectedPersonsDisplay::onInitialize()\n{\n PersonDisplayCommon::onInitialize();\n\n QObject::connect(m_commonProperties->style, SIGNAL(changed()), this, SLOT(personVisualTypeChanged()) );\n\n m_render_covariances_property = new rviz::BoolProperty( \"Render covariances\", true, \"Render track covariance ellipses\", this, SLOT(stylesChanged()) );\n m_render_detection_ids_property = new rviz::BoolProperty( \"Render detection IDs\", true, \"Render IDs of the detection that a track was matched against, if any\", this, SLOT(stylesChanged()));\n m_render_confidences_property = new rviz::BoolProperty( \"Render confidences\", false, \"Render detection confidences\", this, SLOT(stylesChanged()));\n m_render_orientations_property = new rviz::BoolProperty( \"Render orientation arrows\", true, \"Render orientation arrows (only if orientation covariances are finite!)\", this, SLOT(stylesChanged()));\n m_render_modality_text_property = new rviz::BoolProperty( \"Render modality text\", false, \"Render detection modality as text below detected person\", this, SLOT(stylesChanged()));\n\n m_text_spacing_property = new rviz::FloatProperty( \"Text spacing\", 1.0, \"Factor for vertical spacing betweent texts\", this, SLOT(stylesChanged()), this );\n \n m_low_confidence_threshold_property = new rviz::FloatProperty( \"Low-confidence threshold\", 0.5, \"Detection confidence below which alpha will be reduced\", this, SLOT(stylesChanged()));\n m_low_confidence_alpha_property = new rviz::FloatProperty( \"Low-confidence alpha\", 0.5, \"Alpha multiplier for detections with confidence below low-confidence threshold\", this, SLOT(stylesChanged()));\n\n m_covariance_line_width_property = new rviz::FloatProperty( \"Line width\", 0.1, \"Line width of covariance ellipses\", m_render_covariances_property, SLOT(stylesChanged()), this );\n}\n\nDetectedPersonsDisplay::~DetectedPersonsDisplay()\n{\n m_previousDetections.clear();\n}\n\n// Clear the visuals by deleting their objects.\nvoid DetectedPersonsDisplay::reset()\n{\n PersonDisplayCommon::reset();\n m_previousDetections.clear();\n}\n\n// Set the rendering style (cylinders, meshes, ...) of detected persons\nvoid DetectedPersonsDisplay::personVisualTypeChanged()\n{\n foreach(shared_ptr<DetectedPersonVisual>& detectedPersonVisual, m_previousDetections)\n {\n detectedPersonVisual->personVisual.reset();\n createPersonVisualIfRequired(detectedPersonVisual->sceneNode.get(), detectedPersonVisual->personVisual);\n }\n stylesChanged();\n}\n\n// Update dynamically adjustable properties of all existing detections\nvoid DetectedPersonsDisplay::stylesChanged()\n{\n foreach(shared_ptr<DetectedPersonVisual> detectedPersonVisual, m_previousDetections)\n {\n bool personHidden = isPersonHidden(detectedPersonVisual->detectionId);\n\n // Update common styles to person visual, such as line width\n applyCommonStyles(detectedPersonVisual->personVisual);\n\n // Get current detection color\n Ogre::ColourValue detectionColor = getColorFromId(detectedPersonVisual->detectionId);\n detectionColor.a *= m_commonProperties->alpha->getFloat(); // general alpha\n if(personHidden) detectionColor.a = 0.0;\n if(detectedPersonVisual->confidence < m_low_confidence_threshold_property->getFloat()) detectionColor.a *= m_low_confidence_alpha_property->getFloat();\n\n if(detectedPersonVisual->personVisual) {\n detectedPersonVisual->personVisual->setColor(detectionColor);\n }\n\n // Update texts\n Ogre::ColourValue fontColor = m_commonProperties->font_color_style->getOptionInt() == FONT_COLOR_CONSTANT ? m_commonProperties->constant_font_color->getOgreColor() : detectionColor;\n fontColor.a = m_commonProperties->alpha->getFloat();\n if(personHidden) fontColor.a = 0.0;\n \n float textOffset = 0.0f;\n detectedPersonVisual->detectionIdText->setCharacterHeight(0.18 * m_commonProperties->font_scale->getFloat());\n detectedPersonVisual->detectionIdText->setPosition(Ogre::Vector3(0,0, -0.5*detectedPersonVisual->detectionIdText->getCharacterHeight() - textOffset));\n detectedPersonVisual->detectionIdText->setVisible(m_render_detection_ids_property->getBool());\n detectedPersonVisual->detectionIdText->setColor(fontColor);\n if(m_render_detection_ids_property->getBool()) textOffset += m_text_spacing_property->getFloat() * detectedPersonVisual->detectionIdText->getCharacterHeight();\n\n detectedPersonVisual->modalityText->setCharacterHeight(0.18 * m_commonProperties->font_scale->getFloat());\n detectedPersonVisual->modalityText->setPosition(Ogre::Vector3(textOffset, 0, -0.5*detectedPersonVisual->modalityText->getCharacterHeight() - textOffset));\n detectedPersonVisual->modalityText->setVisible(m_render_modality_text_property->getBool());\n detectedPersonVisual->modalityText->setColor(fontColor);\n if(m_render_modality_text_property->getBool()) textOffset += m_text_spacing_property->getFloat() * detectedPersonVisual->modalityText->getCharacterHeight();\n\n detectedPersonVisual->confidenceText->setCharacterHeight(0.13 * m_commonProperties->font_scale->getFloat());\n detectedPersonVisual->confidenceText->setPosition(Ogre::Vector3(textOffset, 0, -0.5*detectedPersonVisual->confidenceText->getCharacterHeight() - textOffset));\n detectedPersonVisual->confidenceText->setVisible(m_render_confidences_property->getBool());\n detectedPersonVisual->confidenceText->setColor(fontColor);\n if(m_render_confidences_property->getBool()) textOffset += m_text_spacing_property->getFloat() * detectedPersonVisual->confidenceText->getCharacterHeight();\n\n // Set color of covariance visualization\n Ogre::ColourValue covarianceColor = detectionColor;\n if(!m_render_covariances_property->getBool()) covarianceColor.a = 0.0;\n detectedPersonVisual->covarianceVisual->setColor(covarianceColor);\n detectedPersonVisual->covarianceVisual->setLineWidth(m_covariance_line_width_property->getFloat());\n\n // Update orientation arrow\n double arrowAlpha = m_render_orientations_property->getBool() && detectedPersonVisual->hasValidOrientation ? detectionColor.a : 0.0;\n detectedPersonVisual->orientationArrow->setColor(Ogre::ColourValue(detectionColor.r, detectionColor.g, detectionColor.b, arrowAlpha));\n const double shaftLength = 0.5, shaftDiameter = 0.05, headLength = 0.2, headDiameter = 0.2;\n detectedPersonVisual->orientationArrow->set(shaftLength, shaftDiameter, headLength, headDiameter);\n }\n}\n\n// This is our callback to handle an incoming message.\nvoid DetectedPersonsDisplay::processMessage(const spencer_tracking_msgs::DetectedPersons::ConstPtr& msg)\n{\n // Get transforms into fixed frame etc.\n if(!preprocessMessage(msg)) return;\n\n const Ogre::Quaternion shapeQuaternion( Ogre::Degree(90), Ogre::Vector3(1,0,0) ); // required to fix orientation of any Cylinder shapes\n stringstream ss;\n\n // Clear previous detections, this will also delete them from the scene graph\n m_previousDetections.clear();\n\n //\n // Iterate over all detections in this message and create a visual representation\n //\n for (vector<spencer_tracking_msgs::DetectedPerson>::const_iterator detectedPersonIt = msg->detections.begin(); detectedPersonIt != msg->detections.end(); ++detectedPersonIt)\n {\n shared_ptr<DetectedPersonVisual> detectedPersonVisual;\n\n // Create a new visual representation of the detected person\n detectedPersonVisual = shared_ptr<DetectedPersonVisual>(new DetectedPersonVisual);\n m_previousDetections.push_back(detectedPersonVisual);\n\n // This scene node is the parent of all visualization elements for the detected person\n detectedPersonVisual->sceneNode = shared_ptr<Ogre::SceneNode>(scene_node_->createChildSceneNode());\n detectedPersonVisual->detectionId = detectedPersonIt->detection_id;\n detectedPersonVisual->confidence = detectedPersonIt->confidence;\n Ogre::SceneNode* currentSceneNode = detectedPersonVisual->sceneNode.get();\n\n\n //\n // Person visualization\n //\n\n // Create new visual for the person itself, if needed\n shared_ptr<PersonVisual> &personVisual = detectedPersonVisual->personVisual;\n createPersonVisualIfRequired(currentSceneNode, personVisual);\n\n const double personHeight = personVisual ? personVisual->getHeight() : 0;\n const double halfPersonHeight = personHeight / 2.0;\n\n\n //\n // Position & visibility of entire detection\n //\n\n const Ogre::Matrix3 covXYZinTargetFrame = covarianceXYZIntoTargetFrame(detectedPersonIt->pose);\n setPoseOrientation(currentSceneNode, detectedPersonIt->pose, covXYZinTargetFrame, personHeight);\n\n //\n // Texts\n //\n {\n // Detection ID\n if (!detectedPersonVisual->detectionIdText) {\n detectedPersonVisual->detectionIdText.reset(new TextNode(context_->getSceneManager(), currentSceneNode));\n detectedPersonVisual->detectionIdText->showOnTop();\n }\n\n ss.str(\"\"); ss << \"det \" << detectedPersonIt->detection_id;\n detectedPersonVisual->detectionIdText->setCaption(ss.str());\n\n // Confidence value\n if (!detectedPersonVisual->confidenceText) {\n detectedPersonVisual->confidenceText.reset(new TextNode(context_->getSceneManager(), currentSceneNode));\n }\n\n ss.str(\"\"); ss << fixed << setprecision(2) << detectedPersonIt->confidence;\n detectedPersonVisual->confidenceText->setCaption(ss.str());\n detectedPersonVisual->confidenceText->showOnTop();\n\n // Modality text\n if (!detectedPersonVisual->modalityText) {\n detectedPersonVisual->modalityText.reset(new TextNode(context_->getSceneManager(), currentSceneNode));\n }\n\n ss.str(\"\"); ss << detectedPersonIt->modality;\n detectedPersonVisual->modalityText->setCaption(ss.str());\n detectedPersonVisual->modalityText->showOnTop();\n }\n\n //\n // Covariance visualization\n //\n if(!detectedPersonVisual->covarianceVisual) {\n detectedPersonVisual->covarianceVisual.reset(new ProbabilityEllipseCovarianceVisual(context_->getSceneManager(), currentSceneNode));\n }\n\n // Update covariance ellipse\n {\n Ogre::Vector3 covarianceMean(0,0,0); // zero mean because parent node is already centered at pose mean\n detectedPersonVisual->covarianceVisual->setOrientation(currentSceneNode->getOrientation().Inverse());\n detectedPersonVisual->covarianceVisual->setMeanCovariance(covarianceMean, covXYZinTargetFrame);\n }\n\n\n //\n // Orientation arrows\n //\n if (!detectedPersonVisual->orientationArrow) {\n detectedPersonVisual->orientationArrow.reset(new rviz::Arrow(context_->getSceneManager(), currentSceneNode));\n }\n\n // Update orientation arrow\n {\n const Ogre::Vector3 forwardVector(1,0,0);\n\n const double personRadius = 0.2;\n const Ogre::Vector3 arrowAttachPoint(personRadius, 0, halfPersonHeight); // relative to tracked person's scene node\n detectedPersonVisual->orientationArrow->setPosition(arrowAttachPoint);\n detectedPersonVisual->orientationArrow->setOrientation(Ogre::Vector3::NEGATIVE_UNIT_Z.getRotationTo(forwardVector));\n detectedPersonVisual->hasValidOrientation = hasValidOrientation(detectedPersonIt->pose);\n }\n\n } // end for loop over all detected persons\n\n // Set all properties that can dynamically be adjusted in the GUI\n stylesChanged();\n\n //\n // Update status (shown in property pane)\n //\n ss.str(\"\");\n ss << msg->detections.size() << \" detections received\";\n setStatusStd(rviz::StatusProperty::Ok, \"Tracks\", ss.str());\n}\n\n} // end namespace spencer_tracking_rviz_plugin\n\n// Tell pluginlib about this class. It is important to do this in\n// global scope, outside our package's namespace.\n#include <pluginlib/class_list_macros.h>\nPLUGINLIB_EXPORT_CLASS(spencer_tracking_rviz_plugin::DetectedPersonsDisplay, rviz::Display)\n"
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"text": "#!/usr/bin/env python\n\nimport rospy\nimport tf\n\nfrom spencer_tracking_msgs.msg import TrackedPersons\nfrom spencer_tracking_msgs.msg import TrackedGroup\nfrom spencer_tracking_msgs.msg import TrackedGroups\n\n\ndef groups_sender():\n global pub_groups\n global listener\n global group_id\n\n pub_groups = rospy.Publisher(\n '/spencer/perception/tracked_groups', TrackedGroups, queue_size=1)\n sub_agents_poses = rospy.Subscriber(\n '/spencer/perception/tracked_persons', TrackedPersons, ReadAgents, queue_size=1)\n listener = tf.TransformListener()\n r = rospy.Rate(10) # 10hz\n\n readagents = 0\n while not rospy.is_shutdown():\n # rospy.loginfo(\"#Sending Groups\")\n r.sleep()\n\n# Reading the Agents, associate them to a single group, and send\n# the groups msgs to use only for a toy example where only a\n# single group exists\n\n\ndef ReadAgents(arg):\n global listener\n global groups\n global group_id\n\n alltrack_ids = [tracked_person.track_id for tracked_person in arg.tracks]\n # rospy.logwarn(str(alltrack_ids))\n # createGroup(arg.tracks,alltrack_ids)\n groups = TrackedGroups()\n groups.header.frame_id = \"odom\"\n groups.header.stamp = rospy.Time.now()\n\n group_id = 0\n\n # createGroup(arg.tracks, [1, 2, 3, 4])\n\n createGroup(arg.tracks, [1, 2])\n createGroup(arg.tracks, [1, 3])\n createGroup(arg.tracks, [1, 4])\n createGroup(arg.tracks, [1, 5])\n\n # createGroup(arg.tracks, [5, 6])\n # createGroup(arg.tracks, [7, 8])\n # createGroup(arg.tracks, [9, 10])\n # createGroup(arg.tracks, [11, 12])\n pub_groups.publish(groups)\n\n\ndef createGroup(allTracks, tracksInGroup):\n global pub_groups\n global group_id\n global groups\n\n group = TrackedGroup()\n group.group_id = group_id\n group.age = rospy.Duration.from_sec(10)\n x = 0\n y = 0\n nagents = 0\n for tracked_person in allTracks:\n if(tracked_person.track_id in tracksInGroup):\n quat = (tracked_person.pose.pose.orientation.x, tracked_person.pose.pose.orientation.y,\n tracked_person.pose.pose.orientation.z, tracked_person.pose.pose.orientation.w)\n euler = tf.transformations.euler_from_quaternion(quat)\n tracked_person_theta = euler[2]\n x = x + tracked_person.pose.pose.position.x\n y = y + tracked_person.pose.pose.position.y\n nagents = nagents + 1\n group.track_ids.append(tracked_person.track_id)\n\n group.centerOfGravity.pose.position.x = x / nagents\n group.centerOfGravity.pose.position.y = y / nagents\n groups.groups.append(group)\n\n group_id += 1\n\n\nif __name__ == '__main__':\n rospy.init_node('mockgroups_info_screen')\n try:\n groups_sender()\n except rospy.ROSInterruptException:\n pass\n"
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"text": "#!/usr/bin/env python\n# Author: Timm Linder, [email protected]\n#\n# Publishes fake tracked persons and the corresponding detections (if not occluded) at\n# /spencer/perception/tracked_persons and /spencer/perception/detected_persons.\n\nimport rospy, yaml, tf\nfrom spencer_tracking_msgs.msg import TrackedPersons, TrackedPerson\nfrom nav_msgs.msg import GridCells\nfrom math import cos, sin, tan, pi, radians\n\ndef createTrackedPerson(track_id, x, y, theta):\n trackedPerson = TrackedPerson()\n\n theta = radians(theta) + pi/2.0\n\n trackedPerson.track_id = track_id\n quaternion = tf.transformations.quaternion_from_euler(0, 0, theta)\n\n trackedPerson.pose.pose.position.x = x\n trackedPerson.pose.pose.position.y = y\n\n trackedPerson.pose.pose.orientation.x = quaternion[0]\n trackedPerson.pose.pose.orientation.y = quaternion[1]\n trackedPerson.pose.pose.orientation.z = quaternion[2]\n trackedPerson.pose.pose.orientation.w = quaternion[3]\n\n trackedPerson.pose.covariance[0 + 0 * 6] = 0.001 # x\n trackedPerson.pose.covariance[1 + 1 * 6] = 0.001 # y\n trackedPerson.pose.covariance[2 + 2 * 6] = 999999 # z\n trackedPerson.pose.covariance[3 + 3 * 6] = 999999 # x rotation\n trackedPerson.pose.covariance[4 + 5 * 6] = 999999 # y rotation\n trackedPerson.pose.covariance[4 + 5 * 6] = 999999 # z rotation\n\n trackedPerson.twist.twist.linear.x = cos(theta)\n trackedPerson.twist.twist.linear.y = sin(theta)\n\n for i in range(0, 3):\n trackedPerson.twist.covariance[i + i * 6] = 1.0 # linear velocity\n for i in range(3, 6):\n trackedPerson.twist.covariance[i + i * 6] = float(\"inf\") # rotational velocity\n\n return trackedPerson\n\ndef main():\n # Main code\n trackPublisher = rospy.Publisher('/spencer/perception/tracked_persons', TrackedPersons )\n #obstaclesPublisher = rospy.Publisher('/pedsim/static_obstacles', GridCells )\n\n rospy.init_node( 'mock_tracked_persons' )\n rate = rospy.Rate(10)\n\n #obstacles = yaml.load(OBSTACLE_YAML)\n #obstacles = [ d for d in obstacles]\n\n seqCounter = 0\n while not rospy.is_shutdown():\n\n trackedPersons = TrackedPersons()\n trackedPersons.header.seq = seqCounter\n trackedPersons.header.frame_id = \"odom\"\n trackedPersons.header.stamp = rospy.Time.now()\n\n #trackedPersons.tracks.append( createTrackedPerson( trackId, x, y, theta ) )\n trackedPersons.tracks.append( createTrackedPerson( 01, 5, 4, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 02, 6, 5.45878, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 03, 7.22, 5.70, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 04, 2+7.22, 7.33, 90 ) )\n\n trackedPersons.tracks.append( createTrackedPerson( 05, 2+8.92, 8.42, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 06, 2+7.92, 10.41, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 07, 2+7.2, 9.44, 90 ) )\n\n trackedPersons.tracks.append( createTrackedPerson( 8, 2+7, 14-2, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 9, 2+6, 15.4123-2, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 10, 5-1, 18.595-5, 280 ) )\n trackedPersons.tracks.append( createTrackedPerson( 11, 5-1, 20-5, 270 ) )\n trackedPersons.tracks.append( createTrackedPerson( 12, 6-1, 21.5491-5, 240 ) )\n trackedPersons.tracks.append( createTrackedPerson( 13, 7.48044-1, 19-5, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 14, 6, 24.5463, 45 ) )\n trackedPersons.tracks.append( createTrackedPerson( 15, 8, 28, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 16, 10.4458, 23, 68 ) )\n trackedPersons.tracks.append( createTrackedPerson( 17, 11.5004, 27, 88 ) )\n trackedPersons.tracks.append( createTrackedPerson( 18, 14, 25.4389, 20 ) )\n trackedPersons.tracks.append( createTrackedPerson( 19, 15, 21, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 20, 15, 22.4308, 92 ) )\n trackedPersons.tracks.append( createTrackedPerson( 21, 15.4676, 24, 91 ) )\n trackedPersons.tracks.append( createTrackedPerson( 22, 16.5423, 25.4178, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 23, 18, 20, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 24, 18.5532, 21.5011, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 25, 15.4739, 16.5314, 45 ) )\n trackedPersons.tracks.append( createTrackedPerson( 26, 20, 25.5746, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 27, 21.5327, 24, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 28, 22, 26.4632, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 29, 21, 18, 45 ) )\n trackedPersons.tracks.append( createTrackedPerson( 30, 23, 20.4335, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 31, 23.4972, 21.4055, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 32, 23.4025, 22.4749, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 33, 24.5281, 18.5868, 54 ) )\n trackedPersons.tracks.append( createTrackedPerson( 34, 16.554, 3.40568-2, 94 ) )\n trackedPersons.tracks.append( createTrackedPerson( 35, 16, 6-1, 94 ) )\n trackedPersons.tracks.append( createTrackedPerson( 36, 20, 4, 0 ) )\n trackedPersons.tracks.append( createTrackedPerson( 37, 19, 12, 25 ) )\n trackedPersons.tracks.append( createTrackedPerson( 38, 23, 8, 50 ) )\n trackedPersons.tracks.append( createTrackedPerson( 39, 24, 10, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 40, 25, 12, 120 ) )\n trackedPersons.tracks.append( createTrackedPerson( 41, 7.51, 22.41, 80 ) )\n trackedPersons.tracks.append( createTrackedPerson( 42, 8.21, 25.7, 81 ) )\n trackedPersons.tracks.append( createTrackedPerson( 43, 3.31, 27.7, 81 ) )\n trackedPersons.tracks.append( createTrackedPerson( 44, 11.421, 18.7, 75 ) )\n trackedPersons.tracks.append( createTrackedPerson( 45, 25.21, 27.0, 85 ) )\n trackedPersons.tracks.append( createTrackedPerson( 46, 18.23, 6.87, -91 ) )\n trackedPersons.tracks.append( createTrackedPerson( 47, 18.6, 8.90, -90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 48, 20.4, 7.87, 85 ) )\n trackedPersons.tracks.append( createTrackedPerson( 49, 15.684, 10.74, 75 ) )\n trackedPersons.tracks.append( createTrackedPerson( 50, 15.72,14.51 , 70 ) )\n\n\n trackPublisher.publish( trackedPersons )\n\n #obstacles['header'] = trackedPersons.header\n #obstaclesPublisher.publish( obstacles )\n\n seqCounter += 1\n rate.sleep()\n\n\n# Constants\n\nOBSTACLE_YAML= \"\"\"\nheader:\n seq: 48860\n stamp:\n secs: 0\n nsecs: 0\n frame_id: world\ncell_width: 1.0\ncell_height: 1.0\ncells:\n -\n x: -0.5\n y: -0.5\n z: 0.0\n -\n x: 0.5\n y: -0.5\n z: 0.0\n -\n x: 1.5\n y: -0.5\n z: 0.0\n -\n x: 2.5\n y: -0.5\n z: 0.0\n -\n x: 3.5\n y: -0.5\n z: 0.0\n -\n x: 4.5\n y: -0.5\n z: 0.0\n -\n x: 5.5\n y: -0.5\n z: 0.0\n -\n x: 6.5\n y: -0.5\n z: 0.0\n -\n x: 7.5\n y: -0.5\n z: 0.0\n -\n x: 8.5\n y: -0.5\n z: 0.0\n -\n x: 9.5\n y: -0.5\n z: 0.0\n -\n x: 10.5\n y: -0.5\n z: 0.0\n -\n x: 11.5\n y: -0.5\n z: 0.0\n -\n x: 12.5\n y: -0.5\n z: 0.0\n -\n x: 13.5\n y: -0.5\n z: 0.0\n -\n x: 14.5\n 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"text": "/**\n* Copyright 2014-2016 Social Robotics Lab, University of Freiburg\n*\n* Redistribution and use in source and binary forms, with or without\n* modification, are permitted provided that the following conditions are met:\n*\n* # Redistributions of source code must retain the above copyright\n* notice, this list of conditions and the following disclaimer.\n* # Redistributions in binary form must reproduce the above copyright\n* notice, this list of conditions and the following disclaimer in the\n* documentation and/or other materials provided with the distribution.\n* # Neither the name of the University of Freiburg nor the names of its\n* contributors may be used to endorse or promote products derived from\n* this software without specific prior written permission.\n*\n* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n* POSSIBILITY OF SUCH DAMAGE.\n*\n* \\author Billy Okal <[email protected]>\n*/\n\n#ifndef SIMULATOR_H\n#define SIMULATOR_H\n\n// ros and big guys\n#include <ros/console.h>\n#include <ros/ros.h>\n\n#include <functional>\n#include <memory>\n#include <tf/transform_listener.h>\n\n#include <pedsim_msgs/AgentState.h>\n#include <pedsim_msgs/AllAgentsState.h>\n#include <pedsim_msgs/SocialActivities.h>\n#include <pedsim_msgs/SocialActivity.h>\n#include <pedsim_msgs/TrackedGroup.h>\n#include <pedsim_msgs/TrackedGroups.h>\n#include <spencer_tracking_msgs/TrackedPerson.h>\n#include <spencer_tracking_msgs/TrackedPersons.h>\n\n// other ROS-sy messages\n#include <animated_marker_msgs/AnimatedMarker.h>\n#include <animated_marker_msgs/AnimatedMarkerArray.h>\n#include <geometry_msgs/Point.h>\n#include <geometry_msgs/PoseStamped.h>\n#include <geometry_msgs/PoseWithCovariance.h>\n#include <geometry_msgs/TwistWithCovariance.h>\n#include <nav_msgs/GridCells.h>\n#include <nav_msgs/Odometry.h>\n#include <std_msgs/ColorRGBA.h>\n#include <std_msgs/Header.h>\n#include <std_srvs/Empty.h>\n#include <visualization_msgs/Marker.h>\n#include <visualization_msgs/MarkerArray.h>\n\n#include <pedsim_simulator/agentstatemachine.h>\n#include <pedsim_simulator/agentstatemachine.h>\n#include <pedsim_simulator/config.h>\n#include <pedsim_simulator/element/agent.h>\n#include <pedsim_simulator/element/agentgroup.h>\n#include <pedsim_simulator/element/attractionarea.h>\n#include <pedsim_simulator/element/waitingqueue.h>\n#include <pedsim_simulator/element/waypoint.h>\n#include <pedsim_simulator/orientationhandler.h>\n#include <pedsim_simulator/scenarioreader.h>\n#include <pedsim_simulator/scene.h>\n\n#include <dynamic_reconfigure/server.h>\n#include <pedsim_simulator/PedsimSimulatorConfig.h>\n\nusing SimConfig = pedsim_simulator::PedsimSimulatorConfig;\n\n/// -----------------------------------------------------------------\n/// \\class Simulator\n/// \\brief Simulation wrapper\n/// \\details ROS interface to the scene object provided by pedsim\n/// -----------------------------------------------------------------\nclass Simulator {\npublic:\n explicit Simulator(const ros::NodeHandle& node);\n virtual ~Simulator();\n\n bool initializeSimulation();\n void loadConfigParameters();\n void runSimulation();\n void updateAgentActivities();\n\n /// publishers\n void publishAgents();\n void publishData();\n void publishSocialActivities();\n void publishGroupVisuals();\n void publishObstacles();\n void publishWalls();\n void publishAttractions();\n void publishRobotPosition();\n\n // callbacks\n bool onPauseSimulation(std_srvs::Empty::Request& request,\n std_srvs::Empty::Response& response);\n bool onUnpauseSimulation(std_srvs::Empty::Request& request,\n std_srvs::Empty::Response& response);\n\n // update robot position based upon data from TF\n void updateRobotPositionFromTF();\n\nprotected:\n void reconfigureCB(SimConfig& config, uint32_t level);\n void robotPositionCallback(const nav_msgs::Odometry& odom);\n dynamic_reconfigure::Server<SimConfig> server_;\n\nprivate:\n ros::NodeHandle nh_;\n bool paused_; // simulation state\n\n /// publishers\n // - data messages\n ros::Publisher pub_obstacles_; // grid cells\n ros::Publisher pub_all_agents_; // positions and velocities (old msg)\n ros::Publisher pub_tracked_persons_; // in spencer format\n ros::Publisher pub_tracked_groups_;\n ros::Publisher pub_social_activities_;\n // - visualization related messages (e.g. markers)\n ros::Publisher pub_attractions_;\n ros::Publisher pub_agent_visuals_;\n ros::Publisher pub_group_lines_;\n ros::Publisher pub_walls_;\n ros::Publisher pub_queues_;\n ros::Publisher pub_waypoints_;\n ros::Publisher pub_agent_arrows_;\n ros::Publisher pub_robot_position_;\n\n // Subscribers\n ros::Subscriber sub_robot_position_;\n nav_msgs::Odometry gazebo_robot_odom_;\n\n // provided services\n ros::ServiceServer srv_pause_simulation_;\n ros::ServiceServer srv_unpause_simulation_;\n\n // agent id <-> activity map\n std::map<int, std::string> agent_activities_;\n\n // pointers and additional data\n std::unique_ptr<tf::TransformListener> transform_listener_;\n std::unique_ptr<OrientationHandler> orientation_handler_;\n Agent* robot_; // robot agent\n tf::StampedTransform last_robot_pose_; // pose of robot in previous timestep\n geometry_msgs::Quaternion last_robot_orientation_;\n\n inline Eigen::Quaternionf computePose(Agent* a);\n inline std::string agentStateToActivity(AgentStateMachine::AgentState state);\n inline std_msgs::ColorRGBA getColor(int agent_id);\n};\n\n#endif\n"
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"text": "#ifndef HUMAN_ATTRIBUTES_DISPLAY_H\n#define HUMAN_ATTRIBUTES_DISPLAY_H\n\n#include <map>\n#include <boost/circular_buffer.hpp>\n\n#include <spencer_human_attribute_msgs/HumanAttributes.h>\n\n#include \"person_display_common.h\"\n#include \"tracked_persons_cache.h\"\n#include \"visuals/mesh_node.h\"\n\n\nnamespace spencer_tracking_rviz_plugin\n{\n /// The display which can be added in RViz to display human attributes.\n class HumanAttributesDisplay: public PersonDisplayCommon<spencer_human_attribute_msgs::HumanAttributes>\n {\n Q_OBJECT\n public:\n // Constructor. pluginlib::ClassLoader creates instances by calling\n // the default constructor, so make sure you have one.\n HumanAttributesDisplay() {};\n virtual ~HumanAttributesDisplay();\n\n // Overrides of protected virtual functions from Display. As much\n // as possible, when Displays are not enabled, they should not be\n // subscribed to incoming data and should not show anything in the\n // 3D view. These functions are where these connections are made\n // and broken.\n\n // Called after the constructors have run\n virtual void onInitialize();\n\n // Called periodically by the visualization manager\n virtual void update(float wall_dt, float ros_dt);\n\n protected:\n // A helper to clear this display back to the initial state.\n virtual void reset();\n\n // Must be implemented by derived classes because MOC doesn't work in templates\n virtual rviz::DisplayContext* getContext() {\n return context_;\n }\n\n private:\n struct HumanAttributeVisual {\n shared_ptr<Ogre::SceneNode> sceneNode;\n shared_ptr<MeshNode> genderMesh;\n unsigned int trackId;\n shared_ptr<TextNode> ageGroupText;\n shared_ptr<TextNode> personHeightText;\n };\n\n // Functions to handle an incoming ROS message.\n void processMessage(const spencer_human_attribute_msgs::HumanAttributes::ConstPtr& msg);\n \n // Helper functions\n void updateVisualStyles(shared_ptr<HumanAttributeVisual>& humanAttributeVisual);\n shared_ptr<HumanAttributeVisual> createVisualIfNotExists(track_id trackId);\n\n // User-editable property variables.\n rviz::BoolProperty* m_render_gender_property;\n rviz::BoolProperty* m_render_person_height_property;\n rviz::BoolProperty* m_render_age_group_property;\n \n rviz::FloatProperty* m_occlusion_alpha_property; \n\n // State variables\n map<track_id, shared_ptr<HumanAttributeVisual> > m_humanAttributeVisuals;\n\n Ogre::Matrix4 m_frameTransform;\n TrackedPersonsCache m_trackedPersonsCache;\n\n private Q_SLOTS:\n virtual void stylesChanged();\n };\n\n} // end namespace spencer_tracking_rviz_plugin\n\n#endif // HUMAN_ATTRIBUTES_DISPLAY_H\n"
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"text": "#!/usr/bin/env python\n# Author: Timm Linder, [email protected]\n#\n# Publishes fake tracked persons and the corresponding detections (if not occluded) at\n# /spencer/perception/tracked_persons and /spencer/perception/detected_persons.\n\nimport rospy, yaml, tf\nfrom spencer_tracking_msgs.msg import TrackedPersons, TrackedPerson\nfrom nav_msgs.msg import GridCells\nfrom math import cos, sin, tan, pi, radians\n\ndef createTrackedPerson(track_id, x, y, theta):\n trackedPerson = TrackedPerson()\n\n theta = radians(theta) + pi/2.0\n\n trackedPerson.track_id = track_id\n quaternion = tf.transformations.quaternion_from_euler(0, 0, theta)\n\n trackedPerson.pose.pose.position.x = x\n trackedPerson.pose.pose.position.y = y\n\n trackedPerson.pose.pose.orientation.x = quaternion[0]\n trackedPerson.pose.pose.orientation.y = quaternion[1]\n trackedPerson.pose.pose.orientation.z = quaternion[2]\n trackedPerson.pose.pose.orientation.w = quaternion[3]\n\n trackedPerson.pose.covariance[0 + 0 * 6] = 0.001 # x\n trackedPerson.pose.covariance[1 + 1 * 6] = 0.001 # y\n trackedPerson.pose.covariance[2 + 2 * 6] = 999999 # z\n trackedPerson.pose.covariance[3 + 3 * 6] = 999999 # x rotation\n trackedPerson.pose.covariance[4 + 5 * 6] = 999999 # y rotation\n trackedPerson.pose.covariance[4 + 5 * 6] = 999999 # z rotation\n\n trackedPerson.twist.twist.linear.x = cos(theta)\n trackedPerson.twist.twist.linear.y = sin(theta)\n\n for i in range(0, 3):\n trackedPerson.twist.covariance[i + i * 6] = 1.0 # linear velocity\n for i in range(3, 6):\n trackedPerson.twist.covariance[i + i * 6] = float(\"inf\") # rotational velocity\n\n return trackedPerson\n\ndef main():\n # Main code\n trackPublisher = rospy.Publisher('/spencer/perception/tracked_persons', TrackedPersons )\n #obstaclesPublisher = rospy.Publisher('/pedsim/static_obstacles', GridCells )\n\n rospy.init_node( 'mock_tracked_persons' )\n rate = rospy.Rate(10)\n\n #obstacles = yaml.load(OBSTACLE_YAML)\n #obstacles = [ d for d in obstacles]\n\n seqCounter = 0\n while not rospy.is_shutdown():\n\n trackedPersons = TrackedPersons()\n trackedPersons.header.seq = seqCounter\n trackedPersons.header.frame_id = \"odom\"\n trackedPersons.header.stamp = rospy.Time.now()\n\n #trackedPersons.tracks.append( createTrackedPerson( trackId, x, y, theta ) )\n\n trackedPersons.tracks.append( createTrackedPerson( 1, 2, 5, 270 ) )\n trackedPersons.tracks.append( createTrackedPerson( 2, 5, 3.5, 109 ) )\n trackedPersons.tracks.append( createTrackedPerson( 3, 6, 5, 90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 4, 5, 7.2, 109 ) )\n trackedPersons.tracks.append( createTrackedPerson( 5, 9.2, 1.2, 71.56-90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 6, 7.1, 2.5, 80.9097 -90) )\n trackedPersons.tracks.append( createTrackedPerson( 7, 8.2, 7.6, 8) )\n trackedPersons.tracks.append( createTrackedPerson( 8, 7.1, 6.5, 10) )\n trackedPersons.tracks.append( createTrackedPerson( 9, 2.2, 1.8, 85.2364 -90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 10, 4.1, 1.9, 93.8141 -90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 11, 1.7, 9.3, 78.6901 -90 ) )\n trackedPersons.tracks.append( createTrackedPerson( 12, 2.2, 7.5, 63.4349 -90) )\n\n trackPublisher.publish( trackedPersons )\n\n #obstacles['header'] = trackedPersons.header\n #obstaclesPublisher.publish( obstacles )\n\n seqCounter += 1\n rate.sleep()\n\n\nif __name__ == '__main__':\n main()\n"
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"text": "#!/usr/bin/env python\n\n__author__ = \"Luigi Palmieri\"\n__copyright__ = \"Social Robotics Lab, University of Freiburg\"\n__license__ = \"BSD\"\n__version__ = \"0.0.1\"\n__email__ = \"[email protected]\"\n\nimport roslib\nimport time\nimport math\nimport numpy as np\nimport rospy\nimport tf\nimport sys\nimport itertools\nimport os\nfrom std_msgs.msg import String\nfrom std_msgs.msg import Bool\nfrom geometry_msgs.msg import PoseStamped\nfrom geometry_msgs.msg import PoseWithCovariance\nfrom nav_msgs.msg import GridCells\nfrom spencer_tracking_msgs.msg import TrackedPersons\nfrom spencer_tracking_msgs.msg import TrackedPerson\nfrom spencer_tracking_msgs.msg import TrackedGroup\nfrom spencer_tracking_msgs.msg import TrackedGroups\n\n\ndef groups_sender():\n global pub_groups\n global listener\n global group_id\n\n pub_groups = rospy.Publisher(\n '/spencer/perception/tracked_groups', TrackedGroups, queue_size=1)\n sub_agents_poses = rospy.Subscriber(\n '/spencer/perception/tracked_persons', TrackedPersons, ReadAgents, queue_size=1)\n listener = tf.TransformListener()\n r = rospy.Rate(10) # 10hz\n\n readagents = 0\n while not rospy.is_shutdown():\n # rospy.loginfo(\"#Sending Groups\")\n r.sleep()\n\n# Reading the Agents, associate them to a single group, and send the groups msgs\n# to use only for a toy example where only a single group exists\n\n\ndef ReadAgents(arg):\n global listener\n global groups\n global group_id\n\n alltrack_ids = [tracked_person.track_id for tracked_person in arg.tracks]\n # rospy.logwarn(str(alltrack_ids))\n # createGroup(arg.tracks,alltrack_ids)\n groups = TrackedGroups()\n groups.header.frame_id = \"odom\"\n groups.header.stamp = rospy.Time.now()\n\n group_id = 0\n\n createGroup(arg.tracks, [1, 2])\n createGroup(arg.tracks, [3, 4])\n\n createGroup(arg.tracks, [5, 6, 7])\n\n createGroup(arg.tracks, [8, 9])\n createGroup(arg.tracks, [10, 11, 12, 13])\n createGroup(arg.tracks, [34, 35])\n createGroup(arg.tracks, [30, 31, 32])\n createGroup(arg.tracks, [23, 24])\n createGroup(arg.tracks, [19, 20, 21, 22])\n createGroup(arg.tracks, [41, 42])\n createGroup(arg.tracks, [46, 47, 48])\n createGroup(arg.tracks, [49, 50])\n\n pub_groups.publish(groups)\n\n\ndef createGroup(allTracks, tracksInGroup):\n global pub_groups\n global group_id\n global groups\n\n group = TrackedGroup()\n group.group_id = group_id\n group.age = rospy.Duration.from_sec(10)\n x = 0\n y = 0\n nagents = 0\n for tracked_person in allTracks:\n if(tracked_person.track_id in tracksInGroup):\n quat = (tracked_person.pose.pose.orientation.x, tracked_person.pose.pose.orientation.y,\n tracked_person.pose.pose.orientation.z, tracked_person.pose.pose.orientation.w)\n euler = tf.transformations.euler_from_quaternion(quat)\n tracked_person_theta = euler[2]\n x = x + tracked_person.pose.pose.position.x\n y = y + tracked_person.pose.pose.position.y\n nagents = nagents + 1\n group.track_ids.append(tracked_person.track_id)\n\n group.centerOfGravity.pose.position.x = x / nagents\n group.centerOfGravity.pose.position.y = y / nagents\n groups.groups.append(group)\n\n group_id += 1\n\n\nif __name__ == '__main__':\n rospy.init_node('mockgroups_rss_scenario_one')\n try:\n groups_sender()\n except rospy.ROSInterruptException:\n pass\n"
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"text": "/*\n * Copyright (c) 2012, Willow Garage, Inc.\n * All rights reserved.\n *\n * Redistribution and use in source and binary forms, with or without\n * modification, are permitted provided that the following conditions are met:\n *\n * * Redistributions of source code must retain the above copyright\n * notice, this list of conditions and the following disclaimer.\n * * Redistributions in binary form must reproduce the above copyright\n * notice, this list of conditions and the following disclaimer in the\n * documentation and/or other materials provided with the distribution.\n * * Neither the name of the Willow Garage, Inc. nor the names of its\n * contributors may be used to endorse or promote products derived from\n * this software without specific prior written permission.\n *\n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n * POSSIBILITY OF SUCH DAMAGE.\n */\n#ifndef ADDITIONAL_TOPIC_SUBSCRIBER_H\n#define ADDITIONAL_TOPIC_SUBSCRIBER_H\n\n#include <OGRE/OgreSceneManager.h>\n#include <OGRE/OgreSceneNode.h>\n\n#ifndef Q_MOC_RUN\n#include <message_filters/subscriber.h>\n#include <tf/message_filter.h>\n#endif\n\n#include <rviz/display.h>\n#include <rviz/display_context.h>\n#include <rviz/frame_manager.h>\n#include <rviz/properties/ros_topic_property.h>\n\n#include <boost/bind.hpp>\n#include <boost/function.hpp>\n\n#include <iostream>\nusing namespace boost;\n\nnamespace rviz {\n\n/** @brief Helper superclass for AdditionalTopicSubscriber, needed because\n * Qt's moc and c++ templates don't work nicely together. Not\n * intended to be used directly. */\nclass _AdditionalTopicSubscriber : QObject {\n Q_OBJECT\npublic:\n void initialize(Display *display, FrameManager *frameManager) {\n QObject::connect(display, SIGNAL(changed()), this,\n SLOT(displayEnableChanged()));\n QObject::connect(frameManager, SIGNAL(fixedFrameChanged()), this,\n SLOT(fixedFrameChanged()));\n additional_topic_property_ = new RosTopicProperty(\n \"Additional topic\", \"\", \"\", \"\", display, SLOT(updateTopic()), this);\n }\n\nprotected Q_SLOTS:\n virtual void updateTopic() = 0;\n virtual void displayEnableChanged() = 0;\n virtual void fixedFrameChanged() = 0;\n\nprotected:\n RosTopicProperty *additional_topic_property_;\n};\n\n/** @brief Display subclass using a tf::MessageFilter, templated on the ROS\n * message type.\n *\n * This class brings together some common things used in many Display\n * types. It has a tf::MessageFilter to filter incoming messages, and\n * it handles subscribing and unsubscribing when the display is\n * enabled or disabled. It also has an Ogre::SceneNode which */\ntemplate <class MessageType>\nclass AdditionalTopicSubscriber : public _AdditionalTopicSubscriber {\n // No Q_OBJECT macro here, moc does not support Q_OBJECT in a templated class.\npublic:\n /** @brief Convenience typedef so subclasses don't have to use\n * the long templated class name to refer to their super class. */\n typedef AdditionalTopicSubscriber<MessageType> ATSClass;\n\n AdditionalTopicSubscriber(\n const QString &propertyName, Display *display, DisplayContext *context,\n ros::NodeHandle &update_nh,\n const function<void(shared_ptr<const MessageType>)> &messageCallback)\n : tf_filter(NULL), m_messagesReceived(0), m_display(display),\n m_context(context), m_updateNodeHandle(update_nh), m_enabled(false),\n m_messageCallback(messageCallback) {\n _AdditionalTopicSubscriber::initialize(display, context->getFrameManager());\n\n additional_topic_property_->setName(propertyName);\n QString message_type =\n QString::fromStdString(ros::message_traits::datatype<MessageType>());\n additional_topic_property_->setMessageType(message_type);\n additional_topic_property_->setDescription(message_type +\n \" topic to subscribe to.\");\n\n tf_filter = new tf::MessageFilter<MessageType>(\n *m_context->getTFClient(), \"map\", 10, m_updateNodeHandle);\n\n tf_filter->connectInput(m_subscriber);\n tf_filter->registerCallback(boost::bind(\n &AdditionalTopicSubscriber<MessageType>::incomingMessage, this, _1));\n m_context->getFrameManager()->registerFilterForTransformStatusCheck(\n tf_filter, display);\n\n setEnabled(m_display->isEnabled());\n updateTopic();\n fixedFrameChanged();\n }\n\n virtual ~AdditionalTopicSubscriber() {\n unsubscribe();\n delete tf_filter;\n }\n\n virtual void reset() {\n tf_filter->clear();\n m_messagesReceived = 0;\n }\n\n void setEnabled(bool enabled) {\n m_enabled = enabled;\n if (enabled)\n subscribe();\n }\n\nprotected:\n virtual void updateTopic() {\n ROS_DEBUG_STREAM_NAMED(\"AdditionalTopicSubscriber\",\n \"AdditionalTopicSubscriber: Topic was changed to \"\n << additional_topic_property_->getTopicStd());\n unsubscribe();\n reset();\n subscribe();\n m_context->queueRender();\n }\n\n virtual void displayEnableChanged() {\n ROS_DEBUG_STREAM_NAMED(\"AdditionalTopicSubscriber\",\n \"AdditionalTopicSubscriber: Display enabled = \"\n << m_display->getBool());\n setEnabled(m_display->getBool());\n }\n\n virtual void fixedFrameChanged() {\n ROS_DEBUG_STREAM_NAMED(\n \"AdditionalTopicSubscriber\",\n \"AdditionalTopicSubscriber: Fixed frame has changed for topic \"\n << additional_topic_property_->getTopicStd());\n tf_filter->setTargetFrame(m_context->getFixedFrame().toStdString());\n reset();\n }\n\n virtual void subscribe() {\n if (!m_display->isEnabled()) {\n return;\n }\n\n try {\n ROS_DEBUG_STREAM_NAMED(\"AdditionalTopicSubscriber\",\n \"AdditionalTopicSubscriber: Subscribing to topic \"\n << additional_topic_property_->getTopicStd());\n m_subscriber.subscribe(m_updateNodeHandle,\n additional_topic_property_->getTopicStd(), 10);\n m_display->setStatus(StatusProperty::Ok,\n additional_topic_property_->getName(), \"OK\");\n } catch (ros::Exception &e) {\n m_display->setStatus(StatusProperty::Error,\n additional_topic_property_->getName(),\n QString(\"Error subscribing: \") + e.what());\n }\n }\n\n virtual void unsubscribe() { m_subscriber.unsubscribe(); }\n\n /** @brief Incoming message callback. Checks if the message pointer\n * is valid, increments m_messagesReceived, then calls\n * processMessage(). */\n void incomingMessage(const typename MessageType::ConstPtr &msg) {\n if (!msg) {\n return;\n }\n\n ++m_messagesReceived;\n m_display->setStatus(\n StatusProperty::Ok, additional_topic_property_->getName(),\n QString::number(m_messagesReceived) + \" messages received\");\n\n // Callback for further processing\n m_messageCallback(msg);\n }\n\n tf::MessageFilter<MessageType> *tf_filter;\n\nprivate:\n std::string m_topic;\n bool m_enabled;\n Display *m_display;\n DisplayContext *m_context;\n ros::NodeHandle m_updateNodeHandle;\n message_filters::Subscriber<MessageType> m_subscriber;\n uint32_t m_messagesReceived;\n\n const function<void(shared_ptr<const MessageType>)> m_messageCallback;\n};\n\n} // end namespace rviz\n\n#endif // ADDITIONAL_TOPIC_SUBSCRIBER_H\n"
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"text": "#include <rviz/visualization_manager.h>\n#include <rviz/frame_manager.h>\n#include \"rviz/selection/selection_manager.h\"\n\n#include \"social_relations_display.h\"\n\n#include <boost/foreach.hpp>\n#define foreach BOOST_FOREACH\n\nnamespace spencer_tracking_rviz_plugin\n{\n\nvoid SocialRelationsDisplay::onInitialize()\n{\n m_trackedPersonsCache.initialize(this, context_, update_nh_);\n PersonDisplayCommon::onInitialize();\n \n m_relation_type_filter_property = new rviz::StringProperty( \"Relation type filter\", \"\", \"Type of social relations to display (see \\\"type\\\" field in message). No wildcards allowed. Leave empty to allow any type of relation.\", this, SLOT(stylesChanged()));\n \n m_positive_person_relation_threshold = new rviz::FloatProperty( \"Positive relation threshold\", 0.5, \"Above which probability threshold a social relation between tracks is considered as positive\", this, SLOT(stylesChanged()));\n\n m_positive_person_relations_color = new rviz::ColorProperty( \"Positive relation color\", QColor(0,255,0), \"Color for positive track relations\", this, SLOT(stylesChanged()));\n m_negative_person_relations_color = new rviz::ColorProperty( \"Negative relation color\", QColor(255,0,0), \"Color for negative track relations\", this, SLOT(stylesChanged()));\n\n m_render_positive_person_relations_property = new rviz::BoolProperty( \"Render positive relations\", true, \"Render positive person relations\", this, SLOT(stylesChanged()));\n m_render_negative_person_relations_property = new rviz::BoolProperty( \"Render negative relations\", false, \"Render negative person relations\", this, SLOT(stylesChanged()));\n \n // Create a scene node for visualizing social relations\n m_socialRelationsSceneNode = shared_ptr<Ogre::SceneNode>(scene_node_->createChildSceneNode());\n}\n\nSocialRelationsDisplay::~SocialRelationsDisplay()\n{\n}\n\n// Clear the visuals by deleting their objects.\nvoid SocialRelationsDisplay::reset()\n{\n PersonDisplayCommon::reset();\n m_trackedPersonsCache.reset();\n m_relationVisuals.clear();\n}\n\nvoid SocialRelationsDisplay::processMessage(const spencer_social_relation_msgs::SocialRelations::ConstPtr& msg)\n{\n // Get transforms into fixed frame etc.\n if(!preprocessMessage(msg)) return;\n\n // Loop over all social relations between tracks\n m_relationVisuals.clear();\n m_socialRelationsSceneNode->removeAndDestroyAllChildren();\n\n foreach(const spencer_social_relation_msgs::SocialRelation& socialRelation, msg->elements)\n {\n shared_ptr<CachedTrackedPerson> personTrack1 = m_trackedPersonsCache.lookup(socialRelation.track1_id);\n shared_ptr<CachedTrackedPerson> personTrack2 = m_trackedPersonsCache.lookup(socialRelation.track2_id);\n\n // Cannot draw relations for tracks with unknown position\n if(!personTrack1 || !personTrack2) continue;\n\n // Create a new visual representation of the tracked person\n shared_ptr<RelationVisual> relationVisual = shared_ptr<RelationVisual>(new RelationVisual);\n relationVisual->type = socialRelation.type;\n relationVisual->relationStrength = socialRelation.strength;\n m_relationVisuals.push_back(relationVisual);\n\n // Get positions. These are already in fixed frame coordinates!\n const Ogre::Vector3 verticalShift(0,0, 1.0 + m_commonProperties->z_offset->getFloat()), textShift(0,0, 0.3);\n const Ogre::Vector3& position1 = verticalShift + personTrack1->center;\n const Ogre::Vector3& position2 = verticalShift + personTrack2->center;\n const Ogre::Vector3& centerPosition = (position1 + position2) / 2.0;\n\n // Add line connecting the two tracks\n shared_ptr<rviz::BillboardLine> relationLine(new rviz::BillboardLine(context_->getSceneManager(), m_socialRelationsSceneNode.get()));\n relationLine->setMaxPointsPerLine(2);\n relationLine->addPoint(position1);\n relationLine->addPoint(position2);\n relationVisual->relationLine = relationLine;\n\n // Add relationship strength text\n stringstream ss;\n shared_ptr<TextNode> relationText(new TextNode(context_->getSceneManager(), m_socialRelationsSceneNode.get()));\n ss.str(\"\"); ss << std::fixed << std::setprecision(0) << socialRelation.strength * 100 << \"%\";\n relationText->setCaption(ss.str());\n relationText->setPosition(centerPosition + textShift);\n relationText->showOnTop();\n relationVisual->relationText = relationText;\n\n // Remember to which groups the tracks belong, to be able to hide certain groups and their track-to-track relations\n relationVisual->trackId1 = socialRelation.track1_id;\n relationVisual->trackId2 = socialRelation.track2_id;\n\n // Update adjustable styles\n updateRelationVisualStyles(relationVisual);\n }\n}\n\nvoid SocialRelationsDisplay::stylesChanged()\n{\n foreach(shared_ptr<RelationVisual> relationVisual, m_relationVisuals) {\n updateRelationVisualStyles(relationVisual);\n }\n}\n\nvoid SocialRelationsDisplay::updateRelationVisualStyles(shared_ptr<RelationVisual>& relationVisual)\n{\n std::string typeFilter = m_relation_type_filter_property->getStdString();\n bool validRelationType = relationVisual->type.find(typeFilter) != std::string::npos;\n bool hideRelation = !validRelationType || isPersonHidden(relationVisual->trackId1) || isPersonHidden(relationVisual->trackId2);\n\n // Determine type of the relationship\n bool isPositiveRelation = relationVisual->relationStrength > m_positive_person_relation_threshold->getFloat();\n\n // Get color\n Ogre::ColourValue relationColor = isPositiveRelation ? m_positive_person_relations_color->getOgreColor() : m_negative_person_relations_color->getOgreColor();\n relationColor.a *= m_commonProperties->alpha->getFloat(); // general alpha\n if(hideRelation) relationColor.a = 0;\n\n if(isPositiveRelation && !m_render_positive_person_relations_property->getBool()) relationColor.a = 0;\n if(!isPositiveRelation && !m_render_negative_person_relations_property->getBool()) relationColor.a = 0;\n\n\n relationVisual->relationLine->setLineWidth(0.03 * (isPositiveRelation ? 1.0 : 0.3));\n relationVisual->relationLine->setColor(relationColor.r, relationColor.g, relationColor.b, relationColor.a);\n\n relationVisual->relationText->setCharacterHeight(0.15 * m_commonProperties->font_scale->getFloat());\n relationVisual->relationText->setColor(relationColor);\n}\n\n\n} // end namespace spencer_tracking_rviz_plugin\n\n// Tell pluginlib about this class. It is important to do this in\n// global scope, outside our package's namespace.\n#include <pluginlib/class_list_macros.h>\nPLUGINLIB_EXPORT_CLASS(spencer_tracking_rviz_plugin::SocialRelationsDisplay, rviz::Display)\n"
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"text": "#ifndef COVARIANCE_VISUAL_H\n#define COVARIANCE_VISUAL_H\n\n#include <rviz/ogre_helpers/shape.h>\n#include <rviz/ogre_helpers/billboard_line.h>\n#include <cmath>\n\n\nnamespace spencer_tracking_rviz_plugin {\n // Visualization of a covariance matrix\n class CovarianceVisual {\n public:\n CovarianceVisual(Ogre::SceneManager* sceneManager, Ogre::SceneNode* parentNode) : m_sceneManager(sceneManager)\n {\n m_sceneNode = parentNode->createChildSceneNode();\n }\n\n virtual ~CovarianceVisual() {\n m_sceneManager->destroySceneNode(m_sceneNode->getName());\n };\n\n void setPosition(const Ogre::Vector3& position) {\n m_sceneNode->setPosition(position);\n }\n\n void setOrientation(const Ogre::Quaternion& orientation) {\n m_sceneNode->setOrientation(orientation);\n }\n\n void setVisible(bool visible) {\n m_sceneNode->setVisible(visible, true);\n }\n\n virtual void setColor(const Ogre::ColourValue& c) = 0;\n\n virtual void setLineWidth(float lineWidth) = 0;\n\n /// NOTE: It is assumed that the covariance matrix is already rotated into the target frame of the sceneNode!\n virtual void setMeanCovariance(const Ogre::Vector3& mean, const Ogre::Matrix3& cov) = 0;\n\n protected:\n Ogre::SceneManager* m_sceneManager;\n Ogre::SceneNode* m_sceneNode;\n };\n\n\n // 2D ellipse visualization of a covariance matrix\n class ProbabilityEllipseCovarianceVisual : public CovarianceVisual {\n public:\n ProbabilityEllipseCovarianceVisual(Ogre::SceneManager* sceneManager, Ogre::SceneNode* parentNode) : CovarianceVisual(sceneManager, parentNode)\n {\n m_line = new rviz::BillboardLine(m_sceneManager, m_sceneNode);\n }\n\n virtual ~ProbabilityEllipseCovarianceVisual() {\n delete m_line;\n }\n\n virtual void setLineWidth(float lineWidth) {\n m_line->setLineWidth(lineWidth);\n }\n\n virtual void setColor(const Ogre::ColourValue& c) {\n m_line->setColor(c.r, c.g, c.b, c.a);\n }\n\n virtual void setMeanCovariance(const Ogre::Vector3& mean, const Ogre::Matrix3& cov) {\n int numberOfPoints;\n double *xs, *ys;\n double determinant = cov[0][0]*cov[1][1] - cov[1][0]*cov[0][1];\n\n m_line->clear();\n\n if(!std::isfinite(determinant)) {\n ROS_WARN_STREAM_THROTTLE(5.0, \"Covariance matrix has non-finite values in ProbabilityEllipseCovarianceVisual::setMeanCovariance(): \" << cov);\n return;\n }\n\n if(std::abs(cov[0][1] - cov[1][0]) > 0.00001)\n {\n ROS_WARN_STREAM_THROTTLE(5.0, \"Covariance matrix is not symmetric in ProbabilityEllipseCovarianceVisual::setMeanCovariance(): \" << cov);\n return;\n }\n\n if(determinant > 0 && cov[0][0] > 0 /* positive definite? */ || std::abs(determinant-0.00001) == 0.0 && (cov[0][0] > 0 || cov[1][1] > 0) /* positive semidefinite? */)\n {\n calc_prob_elli_99(mean.x, mean.y, cov[0][0], cov[1][1], cov[0][1], numberOfPoints, xs, ys);\n\n m_line->setMaxPointsPerLine(numberOfPoints);\n\n for(int i = 0; i < numberOfPoints; i++) {\n Ogre::Vector3 vertex(xs[i], ys[i], mean.z);\n m_line->addPoint(vertex);\n }\n }\n else {\n ROS_WARN_STREAM_THROTTLE(5.0, \"Covariance matrix is not positive (semi-)definite in ProbabilityEllipseCovarianceVisual::setMeanCovariance(): \" << cov);\n }\n \n }\n\n private:\n rviz::BillboardLine* m_line;\n\n // Puts angle alpha into the interval [min..min+2*pi[\n double set_angle_to_range(double alpha, double min)\n {\n\n while (alpha >= min + 2.0 * M_PI) {\n alpha -= 2.0 * M_PI;\n }\n while (alpha < min) {\n alpha += 2.0 * M_PI;\n }\n return alpha;\n }\n\n // Calculates the points on a rotated ellipse given by center xc, yc, half axes a, b and angle phi.\n // Returns number of points np and points in Cart. coordinates\n void calc_ellipse(double xc, double yc, double a, double b, double phi, int& np, double*& xvec, double*& yvec)\n {\n const int N_ELLI_POINTS = 40;\n int i, offset;\n double t, cr, sr, ca, sa, xi, yi, reso;\n static double x[N_ELLI_POINTS + 1];\n static double y[N_ELLI_POINTS + 1];\n reso = 2 * M_PI / N_ELLI_POINTS;\n offset = N_ELLI_POINTS / 2;\n ca = cos(phi);\n sa = sin(phi);\n i = 0;\n t = 0;\n while (t < M_PI) {\n cr = cos(t);\n sr = sin(t);\n xi = a * cr * ca - b * sr * sa;\n yi = a * cr * sa + b * sr * ca;\n x[i] = xi + xc;\n y[i] = yi + yc;\n x[offset + i] = -xi + xc;\n y[offset + i] = -yi + yc;\n t = t + reso;\n i++;\n }\n x[N_ELLI_POINTS] = x[0]; // Close contour\n y[N_ELLI_POINTS] = y[0]; // Close contour\n np = N_ELLI_POINTS + 1;\n xvec = x;\n yvec = y;\n }\n\n // Calculates the points on a 95%-iso-probability ellipse given by the bivarate RV with mean xc, yc\n // and covariance matrix sxx, syy, sxy. Returns number of points np and points in Cart. coordinates\n void calc_prob_elli_95(double xc, double yc, double sxx, double syy, double sxy, int& np, double*& x, double*& y)\n {\n double la, lb, a, b, phi;\n la = (sxx + syy + sqrt((sxx - syy) * (sxx - syy) + 4 * sxy * sxy)) / 2;\n lb = (sxx + syy - sqrt((sxx - syy) * (sxx - syy) + 4 * sxy * sxy)) / 2;\n a = sqrt(5.991464 * la);\n b = sqrt(5.991464 * lb);\n phi = set_angle_to_range(atan2(2 * sxy, sxx - syy) / 2, 0);\n calc_ellipse(xc, yc, a, b, phi, np, x, y);\n }\n\n // Calculates the points on a 99%-iso-probability ellipse given by the bivarate RV with mean xc, yc\n // and covariance matrix sxx, syy, sxy. Returns number of points np and points in Cart. coordinates\n void calc_prob_elli_99(double xc, double yc, double sxx, double syy, double sxy, int& np, double*& x, double*& y)\n {\n double la, lb, a, b, phi;\n la = (sxx + syy + sqrt((sxx - syy) * (sxx - syy) + 4 * sxy * sxy)) / 2;\n lb = (sxx + syy - sqrt((sxx - syy) * (sxx - syy) + 4 * sxy * sxy)) / 2;\n a = sqrt(9.210340 * la);\n b = sqrt(9.210340 * lb);\n phi = set_angle_to_range(atan2(2 * sxy, sxx - syy) / 2, 0);\n calc_ellipse(xc, yc, a, b, phi, np, x, y);\n }\n };\n\n}\n\n#endif // COVARIANCE_VISUAL_H\n\n"
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"text": "# Pedestrian Simulator\n<img src=https://github.com/srl-freiburg/pedsim_ros/blob/master/pedsim_simulator/images/crowd1.png width=400/> | <img src=https://github.com/srl-freiburg/pedsim_ros/blob/master/pedsim_simulator/images/costmap.png width=400/>\n\nA ROS meta package for a pedestrian simulator based on social force\nmodel of [Helbing et. al](http://arxiv.org/pdf/cond-mat/9805244.pdf). The implementation is based on a modified version of Christian Gloor's [libpedsim](http://pedsim.silmaril.org/) library which has been extended to include additional behaviors and activities. All visualization is done via [Rviz](http://wiki.ros.org/rviz). The package is useful for robot navigation experiments with crowded scenes which are hard to acquire in practice.\n\n### Features\n- Individual walking using social force model for very large crowds in real time\n- Group walking using the extended social force model\n- Social activities simulation\n- Sensors simulation (point clouds in robot frame for people and walls)\n- XML based scene design\n- Extensive visualization using Rviz\n- Option to connect with gazebo for physics reasoning\n\n### Requirements\n- ROS with the visualization stack (currently tested on `hydro`, `indigo` )\n- C++11 compiler\n- Qt4\n- Eigen3\n\n### Dependencies\n* (**Optional**) Our rviz fork with additional costmap visualization colors (jet, hot, etc). Installable from [https://github.com/srl-freiburg/rviz](https://github.com/srl-freiburg/rviz).\n\n\n### Installation\n\n```\ncd [workspace]/src\ngit clone https://github.com/srl-freiburg/pedsim_ros.git\n# remaining clones are optional\ngit clone https://github.com/srl-freiburg/rviz.git\ncd ..\ncatkin build -c\n```\n\n### Sample usage\n```\nroslaunch pedsim_simulator simple_pedestrians.launch\n```\n\n#### TODO\n- [ ] Add additional crowd behaviours\n- [ ] Scenario build tool (GUI)\n\n\n### Developers\n* Billy Okal\n* Omar Islas\n* Timm Linder\n\n\n### Contributors\n* Dizan Vasquez\n* Sven Wehner\n\nThe package is a **work in progress** used in research prototyping. Pull requests and/or issues are highly encouraged.\n\n\n"
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"text": "cmake_minimum_required(VERSION 2.8)\nproject(pedsim_simulator)\nadd_definitions(-Wall -Wunused -std=c++0x -pipe) # C++ 11 is required\nset(PEDSIM_SIMULATOR_DEPENDENCIES\n roscpp\n rospy\n std_msgs\n pedsim\n pedsim_msgs\n pedsim_srvs\n std_srvs\n visualization_msgs\n animated_marker_msgs\n nav_msgs\n geometry_msgs\n tf\n cmake_modules\n dynamic_reconfigure\n)\n\n## Find catkin macros and libraries\nfind_package(catkin REQUIRED COMPONENTS ${PEDSIM_SIMULATOR_DEPENDENCIES})\nfind_package(Boost REQUIRED)\nfind_package(Qt4 REQUIRED)\nfind_package(Eigen REQUIRED)\n\n# dynamic reconfigure parameters\ngenerate_dynamic_reconfigure_options(config/PedsimSimulator.cfg)\n\ncatkin_package(\n CATKIN_DEPENDS ${PEDSIM_SIMULATOR_DEPENDENCIES}\n INCLUDE_DIRS include\n)\n\ninclude_directories(include)\ninclude_directories(${Eigen_INCLUDE_DIRS})\ninclude_directories(${catkin_INCLUDE_DIRS})\ninclude(${QT_USE_FILE})\n\nset(SOURCES\n src/simulator_node.cpp\n\tsrc/simulator.cpp\n src/scene.cpp\n src/config.cpp\n src/orientationhandler.cpp\n src/agentstatemachine.cpp\n src/scenarioreader.cpp\n\tsrc/rng.cpp\n\n\t# elements\n\tsrc/element/agent.cpp\n\tsrc/element/agentgroup.cpp\n\tsrc/element/agentcluster.cpp\n\tsrc/element/areawaypoint.cpp\n\tsrc/element/attractionarea.cpp\n\tsrc/element/queueingwaypoint.cpp\n\tsrc/element/waitingqueue.cpp\n\tsrc/element/waypoint.cpp\n\tsrc/element/obstacle.cpp\n\tsrc/element/scenarioelement.cpp\n\n\t# forces\n\tsrc/force/alongwallforce.cpp\n\tsrc/force/force.cpp\n\tsrc/force/groupcoherenceforce.cpp\n\tsrc/force/groupgazeforce.cpp\n\tsrc/force/grouprepulsionforce.cpp\n\tsrc/force/randomforce.cpp\n\n\t# waypointplanner\n\tsrc/waypointplanner/waypointplanner.cpp\n\tsrc/waypointplanner/individualwaypointplanner.cpp\n\tsrc/waypointplanner/queueingplanner.cpp\n\tsrc/waypointplanner/shoppingplanner.cpp\n\tsrc/waypointplanner/groupwaypointplanner.cpp\n)\n\n\nset(MOC_FILES\n\tinclude/pedsim_simulator/config.h\n\tinclude/pedsim_simulator/scene.h\n\tinclude/pedsim_simulator/agentstatemachine.h\n\n\tinclude/pedsim_simulator/element/scenarioelement.h\n\tinclude/pedsim_simulator/element/agent.h\n\tinclude/pedsim_simulator/element/agentcluster.h\n\tinclude/pedsim_simulator/element/agentgroup.h\n\tinclude/pedsim_simulator/element/attractionarea.h\n\tinclude/pedsim_simulator/element/obstacle.h\n\tinclude/pedsim_simulator/element/waypoint.h\n\tinclude/pedsim_simulator/element/areawaypoint.h\n\tinclude/pedsim_simulator/element/waitingqueue.h\n\tinclude/pedsim_simulator/element/queueingwaypoint.h\n\n\tinclude/pedsim_simulator/force/force.h\n\tinclude/pedsim_simulator/force/randomforce.h\n\tinclude/pedsim_simulator/force/groupgazeforce.h\n\tinclude/pedsim_simulator/force/groupcoherenceforce.h\n\tinclude/pedsim_simulator/force/grouprepulsionforce.h\n\tinclude/pedsim_simulator/force/alongwallforce.h\n\n\tinclude/pedsim_simulator/waypointplanner/waypointplanner.h\n\tinclude/pedsim_simulator/waypointplanner/individualwaypointplanner.h\n\tinclude/pedsim_simulator/waypointplanner/groupwaypointplanner.h\n\tinclude/pedsim_simulator/waypointplanner/shoppingplanner.h\n\tinclude/pedsim_simulator/waypointplanner/queueingplanner.h\n)\nQT4_WRAP_CPP(MOC_SRCS_UI ${MOC_FILES})\n\nadd_executable(pedsim_simulator ${SOURCES} ${MOC_SRCS_UI})\nadd_dependencies(pedsim_simulator ${catkin_EXPORTED_TARGETS})\nadd_dependencies(pedsim_simulator ${PROJECT_NAME}_gencfg)\ntarget_link_libraries(pedsim_simulator\n ${QT_LIBRARIES} ${BOOST_LIBRARIES} ${catkin_LIBRARIES}\n)\n\nadd_executable(simulate_diff_drive_robot src/simulate_diff_drive_robot.cpp)\nadd_dependencies(simulate_diff_drive_robot ${catkin_EXPORTED_TARGETS})\ntarget_link_libraries(simulate_diff_drive_robot ${BOOST_LIBRARIES} ${catkin_LIBRARIES})\n\ninstall(\n TARGETS\n pedsim_simulator\n simulate_diff_drive_robot\n ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}\n LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}\n RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}\n)\n\n\n## Unit Tests\n"
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"text": "/**\n* Copyright 2014 Social Robotics Lab, University of Freiburg\n*\n* Redistribution and use in source and binary forms, with or without\n* modification, are permitted provided that the following conditions are met:\n*\n* # Redistributions of source code must retain the above copyright\n* notice, this list of conditions and the following disclaimer.\n* # Redistributions in binary form must reproduce the above copyright\n* notice, this list of conditions and the following disclaimer in the\n* documentation and/or other materials provided with the distribution.\n* # Neither the name of the University of Freiburg nor the names of its\n* contributors may be used to endorse or promote products derived from\n* this software without specific prior written permission.\n*\n* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n* POSSIBILITY OF SUCH DAMAGE.\n*\n* \\author Billy Okal <[email protected]>\n* \\author Sven Wehner <[email protected]>\n*/\n\n#include <pedsim_simulator/element/obstacle.h>\n\n\nObstacle::Obstacle ( double pax, double pay, double pbx, double pby )\n : Tobstacle ( pax, pay, pbx, pby )\n{\n};\n\nObstacle::~Obstacle()\n{\n}\n\n/// moves the obstacle to a new position\nvoid Obstacle::setPosition ( double pax, double pay, double pbx, double pby )\n{\n Tobstacle::setPosition ( pax, pay, pbx, pby );\n\n // inform users\n emit positionChanged();\n}\n\nvoid Obstacle::setPosition ( const QPointF& startIn, const QPointF& endIn )\n{\n setPosition ( startIn.x(), startIn.y(), endIn.x(), endIn.y() );\n}\n\nvoid Obstacle::setX1 ( double xIn )\n{\n // update x1, keep the other values\n setPosition ( xIn, getay(), getbx(), getby() );\n}\n\nvoid Obstacle::setY1 ( double yIn )\n{\n // update y1, keep the other values\n setPosition ( getax(), yIn, getbx(), getby() );\n}\n\nvoid Obstacle::setX2 ( double xIn )\n{\n // update y2, keep the other values\n setPosition ( getax(), getay(), xIn, getby() );\n}\n\nvoid Obstacle::setY2 ( double yIn )\n{\n // update x2, keep the other values\n setPosition ( getax(), getay(), getbx(), yIn );\n}\n\nQPointF Obstacle::getVisiblePosition() const\n{\n return QPointF ( getax(), getay() );\n}\n\nvoid Obstacle::setVisiblePosition ( const QPointF& positionIn )\n{\n // compute new end position\n QPointF deltaPos ( positionIn.x() - getax(), positionIn.y() - getay() );\n QPointF endPos = QPointF ( getbx(), getby() ) + deltaPos;\n\n // set new position\n setPosition ( positionIn.x(), positionIn.y(), endPos.x(), endPos.y() );\n\n // inform users\n emit positionChanged();\n}\n\nQString Obstacle::toString() const\n{\n return tr ( \"Obstacle (%1,%2 - %3,%4)\" )\n .arg ( getax() ).arg ( getay() )\n .arg ( getbx() ).arg ( getby() );\n}\n"
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"text": "#include <rviz/frame_manager.h>\n#include <rviz/selection/selection_manager.h>\n\n#include \"human_attributes_display.h\"\n\n#include <boost/lexical_cast.hpp>\n#include <boost/tokenizer.hpp>\n#include <boost/foreach.hpp>\n#include <boost/range/adaptor/map.hpp>\n\n#define foreach BOOST_FOREACH\n\nnamespace spencer_tracking_rviz_plugin\n{\n\nvoid HumanAttributesDisplay::onInitialize()\n{\n m_trackedPersonsCache.initialize(this, context_, update_nh_);\n PersonDisplayCommon::onInitialize();\n \n m_render_gender_property = new rviz::BoolProperty( \"Render gender\", true, \"Render gender visual\", this, SLOT(stylesChanged()));\n m_render_age_group_property = new rviz::BoolProperty( \"Render age group\", true, \"Render age group visual\", this, SLOT(stylesChanged()));\n m_render_person_height_property = new rviz::BoolProperty( \"Render person height\", true, \"Render person height\", this, SLOT(stylesChanged()));\n \n m_occlusion_alpha_property = new rviz::FloatProperty( \"Occlusion alpha\", 0.5, \"Alpha multiplier for occluded tracks\", this, SLOT(stylesChanged()) );\n m_occlusion_alpha_property->setMin( 0.0 );\n\n m_commonProperties->z_offset->setFloat(2.7f);\n m_commonProperties->style->setHidden(true);\n}\n\nHumanAttributesDisplay::~HumanAttributesDisplay()\n{\n}\n\n// Clear the visuals by deleting their objects.\nvoid HumanAttributesDisplay::reset()\n{\n PersonDisplayCommon::reset();\n m_trackedPersonsCache.reset();\n m_humanAttributeVisuals.clear();\n scene_node_->removeAndDestroyAllChildren(); // not sure if required?\n}\n\nvoid HumanAttributesDisplay::update(float wall_dt, float ros_dt)\n{}\n\nvoid HumanAttributesDisplay::stylesChanged()\n{\n foreach(shared_ptr<HumanAttributeVisual> humanAttributeVisual, m_humanAttributeVisuals | boost::adaptors::map_values) {\n updateVisualStyles(humanAttributeVisual);\n }\n}\n\nvoid HumanAttributesDisplay::updateVisualStyles(shared_ptr<HumanAttributeVisual>& humanAttributeVisual)\n{\n track_id trackId = humanAttributeVisual->trackId;\n bool personHidden = isPersonHidden(trackId);\n\n shared_ptr<CachedTrackedPerson> trackedPerson = m_trackedPersonsCache.lookup(trackId);\n float occlusionAlpha = trackedPerson->isOccluded ? m_occlusion_alpha_property->getFloat() : 1.0;\n\n // Update text colors, size and visibility\n Ogre::ColourValue fontColor = m_commonProperties->font_color_style->getOptionInt() == FONT_COLOR_CONSTANT ? m_commonProperties->constant_font_color->getOgreColor() : getColorFromId(trackId);\n fontColor.a = m_commonProperties->alpha->getFloat() * occlusionAlpha;\n if(personHidden) fontColor.a = 0;\n\n humanAttributeVisual->ageGroupText->setVisible(m_render_age_group_property->getBool());\n humanAttributeVisual->ageGroupText->setCharacterHeight(0.17 * m_commonProperties->font_scale->getFloat());\n humanAttributeVisual->ageGroupText->setColor(fontColor);\n humanAttributeVisual->ageGroupText->setPosition(Ogre::Vector3(0, 0, 0.17 * m_commonProperties->font_scale->getFloat()) );\n\n humanAttributeVisual->personHeightText->setVisible(m_render_person_height_property->getBool());\n humanAttributeVisual->personHeightText->setCharacterHeight(0.17 * m_commonProperties->font_scale->getFloat());\n humanAttributeVisual->personHeightText->setColor(fontColor);\n humanAttributeVisual->personHeightText->setPosition(Ogre::Vector3(0, 0, 0) );\n\n if(humanAttributeVisual->genderMesh) {\n humanAttributeVisual->genderMesh->setPosition(Ogre::Vector3(0, 0, 0.17 * 2 * m_commonProperties->font_scale->getFloat() + 0.3));\n humanAttributeVisual->genderMesh->setVisible(m_render_gender_property->getBool());\n }\n}\n\nshared_ptr<HumanAttributesDisplay::HumanAttributeVisual> HumanAttributesDisplay::createVisualIfNotExists(track_id trackId)\n{\n if(m_humanAttributeVisuals.find(trackId) == m_humanAttributeVisuals.end()) {\n shared_ptr<HumanAttributeVisual> humanAttributeVisual = shared_ptr<HumanAttributeVisual>(new HumanAttributeVisual);\n\n humanAttributeVisual->sceneNode = shared_ptr<Ogre::SceneNode>(scene_node_->createChildSceneNode());\n\n humanAttributeVisual->ageGroupText = shared_ptr<TextNode>(new TextNode(context_->getSceneManager(), humanAttributeVisual->sceneNode.get()));\n humanAttributeVisual->ageGroupText->showOnTop();\n humanAttributeVisual->ageGroupText->setCaption(\" \");\n\n humanAttributeVisual->personHeightText = shared_ptr<TextNode>(new TextNode(context_->getSceneManager(), humanAttributeVisual->sceneNode.get()));\n humanAttributeVisual->personHeightText->showOnTop();\n humanAttributeVisual->personHeightText->setCaption(\" \");\n\n humanAttributeVisual->trackId = trackId;\n\n m_humanAttributeVisuals[trackId] = humanAttributeVisual;\n }\n\n\n return m_humanAttributeVisuals[trackId];\n}\n\n// This is our callback to handle an incoming group message.\nvoid HumanAttributesDisplay::processMessage(const spencer_human_attribute_msgs::HumanAttributes::ConstPtr& msg)\n{\n // Get transforms into fixed frame etc.\n if(!preprocessMessage(msg)) return;\n\n // Transform into Rviz fixed frame\n m_frameTransform = Ogre::Matrix4(m_frameOrientation);\n m_frameTransform.setTrans(m_framePosition);\n\n const Ogre::Quaternion shapeQuaternion( Ogre::Degree(90), Ogre::Vector3(1,0,0) ); // required to fix orientation of any Cylinder shapes\n stringstream ss;\n\n //\n // Iterate over all categorical attributes in this message\n //\n foreach (const spencer_human_attribute_msgs::CategoricalAttribute& categoricalAttribute, msg->categoricalAttributes)\n {\n // Check if there is already a visual for this particular track\n track_id trackId = categoricalAttribute.subject_id; // assumes subject_id is a track_id (not detection_id)\n shared_ptr<HumanAttributeVisual> humanAttributeVisual = createVisualIfNotExists(trackId);\n\n if(categoricalAttribute.values.empty()) {\n ROS_ERROR_STREAM(\"categoricalAttribute.values.empty() for track ID \" << trackId << \", attribute \" << categoricalAttribute.type);\n continue;\n }\n if(categoricalAttribute.confidences.size() != categoricalAttribute.values.size()) {\n ROS_WARN_STREAM(\"categoricalAttribute.confidences.size() != categoricalAttribute.values.size() for track ID \" << trackId << \", attribute \" << categoricalAttribute.type);\n }\n\n // Find highest-ranking attribute\n size_t highestRankingIndex = 0; float highestConfidence = -999999;\n for(size_t i = 0; i < categoricalAttribute.confidences.size(); i++) {\n if(categoricalAttribute.confidences[i] > highestConfidence) {\n highestConfidence = categoricalAttribute.confidences[i];\n highestRankingIndex = i;\n }\n }\n\n std::string valueWithHighestConfidence = categoricalAttribute.values[highestRankingIndex];\n\n // Age group\n if(categoricalAttribute.type == spencer_human_attribute_msgs::CategoricalAttribute::AGE_GROUP) {\n ss.str(\"\"); ss << valueWithHighestConfidence << \"yrs\";\n humanAttributeVisual->ageGroupText->setCaption(ss.str());\n }\n\n // Gender\n else if(categoricalAttribute.type == spencer_human_attribute_msgs::CategoricalAttribute::GENDER) {\n ss.str(\"\"); ss << \"package://\" ROS_PACKAGE_NAME \"/media/\" << valueWithHighestConfidence << \"_symbol.dae\";\n std::string meshResource = ss.str();\n \n humanAttributeVisual->genderMesh = shared_ptr<MeshNode>(new MeshNode(context_, humanAttributeVisual->sceneNode.get(), meshResource));\n \n Ogre::ColourValue meshColor(1, 1, 1, 1);\n if(valueWithHighestConfidence == spencer_human_attribute_msgs::CategoricalAttribute::GENDER_MALE) meshColor = Ogre::ColourValue(0, 1, 1, 1);\n if(valueWithHighestConfidence == spencer_human_attribute_msgs::CategoricalAttribute::GENDER_FEMALE) meshColor = Ogre::ColourValue(1, 0, 1, 1);\n humanAttributeVisual->genderMesh->setColor(meshColor);\n\n humanAttributeVisual->genderMesh->setScale(0.5);\n humanAttributeVisual->genderMesh->setCameraFacing(true); \n }\n }\n\n\n //\n // Iterate over all scalar attributes in this message\n //\n foreach (const spencer_human_attribute_msgs::ScalarAttribute& scalarAttribute, msg->scalarAttributes)\n {\n // Check if there is already a visual for this particular track\n track_id trackId = scalarAttribute.subject_id; // assumes subject_id is a track_id (not detection_id)\n shared_ptr<HumanAttributeVisual> humanAttributeVisual = createVisualIfNotExists(trackId);\n \n if(scalarAttribute.values.empty()) {\n ROS_ERROR_STREAM(\"scalarAttribute.values.empty() for track ID \" << trackId << \", attribute \" << scalarAttribute.type);\n continue;\n }\n if(scalarAttribute.confidences.size() != scalarAttribute.values.size()) {\n ROS_WARN_STREAM(\"scalarAttribute.confidences.size() != scalarAttribute.values.size() for track ID \" << trackId << \", attribute \" << scalarAttribute.type);\n }\n\n // Find highest-ranking attribute\n size_t highestRankingIndex = 0; float highestConfidence = -999999;\n for(size_t i = 0; i < scalarAttribute.confidences.size(); i++) {\n if(scalarAttribute.confidences[i] > highestConfidence) {\n highestConfidence = scalarAttribute.confidences[i];\n highestRankingIndex = i;\n }\n }\n\n float valueWithHighestConfidence = scalarAttribute.values[highestRankingIndex];\n\n // Person height\n if(scalarAttribute.type == spencer_human_attribute_msgs::ScalarAttribute::PERSON_HEIGHT) {\n ss.str(\"\"); ss << std::fixed << std::setprecision(2) << valueWithHighestConfidence << \"m\";\n humanAttributeVisual->personHeightText->setCaption(ss.str());\n } \n }\n\n\n //\n // Update position and style of all existing person visuals\n //\n set<track_id> tracksWithUnknownPosition;\n foreach(shared_ptr<HumanAttributeVisual> humanAttributeVisual, m_humanAttributeVisuals | boost::adaptors::map_values)\n {\n shared_ptr<CachedTrackedPerson> trackedPerson = m_trackedPersonsCache.lookup(humanAttributeVisual->trackId);\n\n // Get current track position\n if(!trackedPerson) {\n tracksWithUnknownPosition.insert(humanAttributeVisual->trackId);\n }\n else\n { // Track position is known\n humanAttributeVisual->sceneNode->setPosition(trackedPerson->center + Ogre::Vector3(0, 0, m_commonProperties->z_offset->getFloat()));\n\n // Update styles\n updateVisualStyles(humanAttributeVisual);\n }\n }\n\n\n // Remove visuals for tracks with unknown position\n foreach(track_id trackId, tracksWithUnknownPosition) {\n m_humanAttributeVisuals.erase(trackId);\n }\n\n\n //\n // Update display status (shown in property pane)\n //\n\n ss.str(\"\");\n ss << msg->categoricalAttributes.size() << \" categorical attribute(s)\";\n setStatusStd(rviz::StatusProperty::Ok, \"Categorical attributes\", ss.str());\n\n ss.str(\"\");\n ss << msg->scalarAttributes.size() << \" scalar attribute(s)\";\n setStatusStd(rviz::StatusProperty::Ok, \"Scalar attributes\", ss.str());\n\n ss.str(\"\");\n ss << tracksWithUnknownPosition.size() << \" track(s) with unknown position\";\n setStatusStd(0 == tracksWithUnknownPosition.size() ? rviz::StatusProperty::Ok : rviz::StatusProperty::Warn, \"Attribute-to-track assignment\", ss.str());\n}\n\n} // end namespace spencer_tracking_rviz_plugin\n\n// Tell pluginlib about this class. It is important to do this in\n// global scope, outside our package's namespace.\n#include <pluginlib/class_list_macros.h>\nPLUGINLIB_EXPORT_CLASS(spencer_tracking_rviz_plugin::HumanAttributesDisplay, rviz::Display)\n"
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"text": "#ifndef MESH_NODE_H\n#define MESH_NODE_H\n\n#include <rviz/mesh_loader.h>\n#include <resource_retriever/retriever.h>\n\n#include <rviz/visualization_manager.h>\n#include <rviz/render_panel.h> // hack to get camera position \n\n#include <OgreSceneManager.h>\n#include <OgreSubEntity.h>\n#include <OgreMaterialManager.h>\n#include <OgreTextureManager.h>\n#include <OgreTechnique.h>\n#include <OgreRoot.h>\n#include <OgreFrameListener.h>\n\n\nnamespace spencer_tracking_rviz_plugin {\n class MeshNode : public Ogre::FrameListener {\n public:\n MeshNode(rviz::DisplayContext* displayContext, Ogre::SceneNode* parentNode, const std::string& meshResource, Ogre::Vector3 position = Ogre::Vector3::ZERO)\n : m_sceneManager(displayContext->getSceneManager()), m_displayContext(displayContext), m_meshResource(meshResource)\n {\n m_cameraFacing = false;\n m_sceneNode = parentNode->createChildSceneNode();\n m_sceneNode->setVisible(false);\n\n // Load mesh\n assert(!rviz::loadMeshFromResource(meshResource).isNull());\n\n // Create scene entity\n std::stringstream ss;\n static int counter = 0;\n ss << \"gender_symbol_\" << counter++;\n std::string id = ss.str();\n\n m_entity = m_sceneManager->createEntity(id, meshResource);\n m_sceneNode->attachObject(m_entity);\n\n // set up material\n ss << \"Material\";\n Ogre::MaterialPtr default_material = Ogre::MaterialManager::getSingleton().create( ss.str(), \"rviz\" );\n default_material->setReceiveShadows(false);\n default_material->getTechnique(0)->setLightingEnabled(true);\n default_material->getTechnique(0)->setAmbient( 0.5, 0.5, 0.5 );\n m_materials.insert( default_material );\n m_entity->setMaterial( default_material );\n\n setPosition(position);\n setVisible(true);\n\n // For camera-facing meshes\n Ogre::Root::getSingleton().addFrameListener(this);\n }\n\n virtual ~MeshNode() {\n Ogre::Root::getSingleton().removeFrameListener(this);\n m_sceneManager->destroyEntity(m_entity);\n\n // destroy all the materials we've created\n std::set<Ogre::MaterialPtr>::iterator it;\n for(it = m_materials.begin(); it != m_materials.end(); it++ )\n {\n Ogre::MaterialPtr material = *it;\n if (!material.isNull())\n {\n material->unload();\n Ogre::MaterialManager::getSingleton().remove(material->getName());\n }\n }\n m_materials.clear();\n m_sceneManager->destroySceneNode(m_sceneNode->getName());\n }\n\n void setOrientation(const Ogre::Quaternion& orientation) {\n m_orientation = orientation;\n }\n\n void setPosition(const Ogre::Vector3& position) {\n m_sceneNode->setPosition(position);\n }\n\n void setScale(const float scaleFactor) {\n m_sceneNode->setScale(Ogre::Vector3(scaleFactor, scaleFactor, scaleFactor));\n }\n\n void setVisible(bool visible) {\n m_sceneNode->setVisible(visible, true);\n }\n\n void setCameraFacing(bool cameraFacing) {\n m_cameraFacing = cameraFacing;\n }\n\n void setColor(const Ogre::ColourValue& c) {\n Ogre::SceneBlendType blending;\n bool depth_write;\n\n if ( c.a < 0.9998 )\n {\n blending = Ogre::SBT_TRANSPARENT_ALPHA;\n depth_write = false;\n }\n else\n {\n blending = Ogre::SBT_REPLACE;\n depth_write = true;\n }\n\n std::set<Ogre::MaterialPtr>::iterator it;\n for(it = m_materials.begin(); it != m_materials.end(); it++)\n {\n Ogre::Technique* technique = (*it)->getTechnique( 0 );\n\n technique->setAmbient( c.r*0.5, c.g*0.5, c.b*0.5 );\n technique->setDiffuse( c.r, c.g, c.b, c.a );\n technique->setSceneBlending( blending );\n technique->setDepthWriteEnabled( depth_write );\n technique->setLightingEnabled( true );\n }\n }\n\n const std::string& getMeshResource() const {\n return m_meshResource;\n }\n\n // We are using this FrameListener callback to orient the mesh towards the camera.\n // Using a SceneManager::Listener and its preUpdateSceneGraph(SceneManager, Camera) method doesn't work because\n // it is apparently never invoked by the Rviz render system.\n virtual bool frameStarted(const Ogre::FrameEvent &evt)\n {\n Ogre::Quaternion cameraQuat;\n if(m_cameraFacing) {\n // Align with camera view direction\n // FIXME: The following way of retrieving the camera and its position is a bit hacky, don't try this at home!\n rviz::VisualizationManager* visualizationManager = dynamic_cast<rviz::VisualizationManager*>(m_displayContext);\n assert(visualizationManager != NULL);\n cameraQuat = visualizationManager->getRenderPanel()->getCamera()->getOrientation();\n }\n m_sceneNode->setOrientation(cameraQuat * m_orientation);\n return true;\n }\n\n private:\n Ogre::SceneManager* m_sceneManager;\n Ogre::SceneNode* m_sceneNode;\n rviz::DisplayContext* m_displayContext;\n\n Ogre::Quaternion m_orientation;\n Ogre::Entity* m_entity;\n std::set<Ogre::MaterialPtr> m_materials;\n std::string m_meshResource;\n bool m_cameraFacing;\n };\n\n}\n\n#endif // MESH_NODE_H\n"
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"text": "/**\n* Copyright 2014 Social Robotics Lab, University of Freiburg\n*\n* Redistribution and use in source and binary forms, with or without\n* modification, are permitted provided that the following conditions are met:\n*\n* # Redistributions of source code must retain the above copyright\n* notice, this list of conditions and the following disclaimer.\n* # Redistributions in binary form must reproduce the above copyright\n* notice, this list of conditions and the following disclaimer in the\n* documentation and/or other materials provided with the distribution.\n* # Neither the name of the University of Freiburg nor the names of its\n* contributors may be used to endorse or promote products derived from\n* this software without specific prior written permission.\n*\n* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n* POSSIBILITY OF SUCH DAMAGE.\n*\n* \\author Billy Okal <[email protected]>\n* \\author Sven Wehner <[email protected]>\n*/\n\n#include <pedsim_simulator/element/waitingqueue.h>\n#include <pedsim_simulator/rng.h>\n#include <pedsim_simulator/scene.h>\n#include <pedsim_simulator/element/agent.h>\n#include <pedsim_simulator/config.h>\n\n\nWaitingQueue::WaitingQueue ( const QString& nameIn, Ped::Tvector positionIn, Ped::Tangle directionIn )\n : Waypoint ( nameIn, positionIn ), direction ( directionIn )\n{\n // initialize values\n dequeueTime = INFINITY;\n waitDurationLambda = CONFIG.wait_time_beta;\n\n // connect signals\n connect ( &SCENE, SIGNAL ( sceneTimeChanged ( double ) ), this, SLOT ( onTimeChanged ( double ) ) );\n}\n\nWaitingQueue::~WaitingQueue()\n{\n}\n\nvoid WaitingQueue::onTimeChanged ( double timeIn )\n{\n // skip when there is none\n if ( queuedAgents.empty() )\n {\n return;\n }\n\n Agent* firstInLine = queuedAgents.first();\n\n // check whether waiting started\n if ( std::isinf ( dequeueTime ) )\n {\n if ( hasReachedWaitingPosition() )\n {\n // set the time when to dequeue leading agent\n startDequeueTime();\n }\n }\n\n // let first agent in line pass\n if ( dequeueTime <= timeIn )\n {\n // dequeue agent and inform users\n emit agentMayPass ( firstInLine->getId() );\n dequeueAgent ( firstInLine );\n }\n}\n\nvoid WaitingQueue::onLastAgentPositionChanged ( double xIn, double yIn )\n{\n emit queueEndPositionChanged ( xIn, yIn );\n}\n\nPed::Tangle WaitingQueue::getDirection() const\n{\n return direction;\n}\n\nvoid WaitingQueue::setDirection ( const Ped::Tangle& angleIn )\n{\n direction = angleIn;\n\n // inform users\n emit directionChanged ( direction.toRadian() );\n}\n\nvoid WaitingQueue::setDirection ( double xIn, double yIn )\n{\n setDirection ( Ped::Tvector ( xIn, yIn ) );\n}\n\nvoid WaitingQueue::setDirection ( const Ped::Tvector& directionIn )\n{\n direction = directionIn.polarAngle();\n\n // inform users\n emit directionChanged ( direction.toRadian() );\n}\n\nbool WaitingQueue::isEmpty() const\n{\n return queuedAgents.isEmpty();\n}\n\nPed::Tvector WaitingQueue::getQueueEndPosition() const\n{\n if ( queuedAgents.isEmpty() )\n return position;\n else\n return queuedAgents.last()->getPosition();\n}\n\nconst Agent* WaitingQueue::enqueueAgent ( Agent* agentIn )\n{\n // determine output\n const Agent* aheadAgent = ( queuedAgents.isEmpty() ) ?nullptr:queuedAgents.last();\n\n // add agent to queue\n queuedAgents.append ( agentIn );\n\n // inform about new first in line\n if ( aheadAgent == nullptr )\n {\n emit queueLeaderChanged ( agentIn->getId() );\n }\n\n // stay informed about updates on queue end\n connect ( agentIn, SIGNAL ( positionChanged ( double,double ) ),\n this, SLOT ( onLastAgentPositionChanged ( double,double ) ) );\n // ignore updates from previous queue end\n if ( aheadAgent != nullptr )\n {\n disconnect ( aheadAgent, SIGNAL ( positionChanged ( double,double ) ),\n this, SLOT ( onLastAgentPositionChanged ( double,double ) ) );\n }\n\n // inform users\n emit queueEndChanged();\n informAboutEndPosition();\n\n // return agent ahead of the new agent\n return aheadAgent;\n}\n\nbool WaitingQueue::dequeueAgent ( Agent* agentIn )\n{\n // sanity checks\n if ( queuedAgents.isEmpty() )\n {\n ROS_DEBUG ( \"Cannot dequeue agent from empty waiting queue!\" );\n return false;\n }\n\n // remove agent from queue\n bool dequeueSuccess;\n bool dequeuedWasFirst = ( queuedAgents.first() == agentIn );\n bool dequeuedWasLast = ( queuedAgents.last() == agentIn );\n if ( dequeuedWasFirst )\n {\n queuedAgents.removeFirst();\n dequeueSuccess = true;\n }\n else\n {\n ROS_DEBUG ( \"Dequeueing agent from queue (%s), not in front of the queue\",\nagentIn->toString().toStdString().c_str() );\n int removedCount = queuedAgents.removeAll ( agentIn );\n dequeueSuccess = ( removedCount >= 1 );\n\n if ( dequeueSuccess == false )\n {\n ROS_DEBUG ( \"Agent isn't waiting in queue! (Agent: %s, Queue: %s)\",\nagentIn->toString().toStdString().c_str(), this->toString().toStdString().c_str() );\n return false;\n }\n }\n\n // inform other agents\n emit agentDequeued ( agentIn->getId() );\n\n // update leading position\n if ( dequeuedWasFirst )\n {\n // determine new first agent in line\n const Agent* newFront = ( queuedAgents.isEmpty() ) ? nullptr : queuedAgents.first();\n\n // reset time for next agent\n resetDequeueTime();\n\n // inform users about changed front position\n int frontId = ( newFront != nullptr ) ? newFront->getId() : -1;\n emit queueLeaderChanged ( frontId );\n }\n\n // update queue end\n if ( dequeuedWasLast )\n {\n disconnect ( agentIn, SIGNAL ( positionChanged ( double,double ) ),\n this, SLOT ( onLastAgentPositionChanged ( double,double ) ) );\n\n emit queueEndChanged();\n informAboutEndPosition();\n }\n\n return dequeueSuccess;\n}\n\nbool WaitingQueue::hasReachedWaitingPosition()\n{\n if ( queuedAgents.isEmpty() )\n return false;\n\n // const double waitingRadius = 0.7;\n const double waitingRadius = 0.3;\n\n // compute distance from where queue starts\n const Agent* leadingAgent = queuedAgents.first();\n Ped::Tvector diff = leadingAgent->getPosition() - position;\n return ( diff.length() < waitingRadius );\n}\n\nvoid WaitingQueue::resetDequeueTime()\n{\n dequeueTime = INFINITY;\n}\n\nvoid WaitingQueue::startDequeueTime()\n{\n // draw random waiting period\n // exponential_distribution<> distribution ( waitDurationLambda );\n\n // construct an Erlang distribution from a Gamma\n const int alpha = 2;\n const double beta = 0.5;\n gamma_distribution<> distribution ( alpha, beta );\n\n double waitDuration = distribution ( RNG() );\n dequeueTime = SCENE.getTime() + waitDuration;\n}\n\nvoid WaitingQueue::informAboutEndPosition()\n{\n // inform users\n if ( queuedAgents.isEmpty() )\n {\n emit queueEndPositionChanged ( position.x, position.y );\n }\n else\n {\n Agent* lastAgent = queuedAgents.last();\n Ped::Tvector endPosition = lastAgent->getPosition();\n emit queueEndPositionChanged ( endPosition.x, endPosition.y );\n }\n}\n\nPed::Tvector WaitingQueue::closestPoint ( const Ped::Tvector& p, bool* withinWaypoint ) const\n{\n return getQueueEndPosition();\n}\n\nQPointF WaitingQueue::getVisiblePosition() const\n{\n return QPointF ( position.x, position.y );\n}\n\nvoid WaitingQueue::setVisiblePosition ( const QPointF& positionIn )\n{\n setPosition ( positionIn.x(), positionIn.y() );\n}\n\nQString WaitingQueue::toString() const\n{\n QStringList waitingIDs;\n foreach ( const Agent* agent, queuedAgents )\n waitingIDs.append ( QString::number ( agent->getId() ) );\n QString waitingString = waitingIDs.join ( \",\" );\n\n return tr ( \"WaitingQueue '%1' (@%2,%3; queue: %4)\" )\n .arg ( name )\n .arg ( position.x ).arg ( position.y )\n .arg ( waitingString );\n}\n"
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"text": "#ifndef DETECTED_PERSONS_DISPLAY_H\n#define DETECTED_PERSONS_DISPLAY_H\n\n#include <map>\n#include <boost/circular_buffer.hpp>\n\n#include <spencer_tracking_msgs/DetectedPersons.h>\n\n#include \"person_display_common.h\"\n\nnamespace spencer_tracking_rviz_plugin\n{\n /// The visual of a tracked person.\n struct DetectedPersonVisual\n {\n shared_ptr<Ogre::SceneNode> sceneNode;\n\n shared_ptr<PersonVisual> personVisual;\n shared_ptr<TextNode> detectionIdText, confidenceText, modalityText;\n shared_ptr<rviz::Arrow> orientationArrow;\n shared_ptr<CovarianceVisual> covarianceVisual;\n\n float confidence;\n bool hasValidOrientation;\n unsigned int detectionId;\n };\n\n // The DetectedPersonsDisplay class itself just implements a circular buffer,\n // editable parameters, and Display subclass machinery.\n class DetectedPersonsDisplay: public PersonDisplayCommon<spencer_tracking_msgs::DetectedPersons>\n {\n Q_OBJECT\n public:\n // Constructor. pluginlib::ClassLoader creates instances by calling\n // the default constructor, so make sure you have one.\n DetectedPersonsDisplay() {};\n virtual ~DetectedPersonsDisplay();\n\n // Overrides of protected virtual functions from Display. As much\n // as possible, when Displays are not enabled, they should not be\n // subscribed to incoming data and should not show anything in the\n // 3D view. These functions are where these connections are made\n // and broken.\n \n virtual void onInitialize();\n\n protected:\n // A helper to clear this display back to the initial state.\n virtual void reset();\n\n // Must be implemented by derived classes because MOC doesn't work in templates\n virtual rviz::DisplayContext* getContext() {\n return context_;\n }\n\n private Q_SLOTS:\n void personVisualTypeChanged();\n\n // Called whenever one of the properties in PersonDisplayCommonProperties has been changed\n virtual void stylesChanged();\n\n private:\n // Function to handle an incoming ROS message.\n void processMessage(const spencer_tracking_msgs::DetectedPersons::ConstPtr& msg);\n \n // All currently active tracks, with unique track ID as map key\n vector<shared_ptr<DetectedPersonVisual> > m_previousDetections;\n\n // Properties\n rviz::BoolProperty* m_render_covariances_property;\n rviz::BoolProperty* m_render_detection_ids_property;\n rviz::BoolProperty* m_render_confidences_property;\n rviz::FloatProperty* m_low_confidence_threshold_property;\n rviz::FloatProperty* m_low_confidence_alpha_property;\n rviz::BoolProperty* m_render_orientations_property;\n rviz::BoolProperty* m_render_modality_text_property;\n\n rviz::FloatProperty* m_text_spacing_property;\n rviz::FloatProperty* m_covariance_line_width_property;\n };\n\n} // end namespace spencer_tracking_rviz_plugin\n\n#endif // DETECTED_PERSONS_DISPLAY_H\n"
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"text": "#ifndef TRACKED_GROUPS_DISPLAY_H\n#define TRACKED_GROUPS_DISPLAY_H\n\n#include <map>\n#include <boost/circular_buffer.hpp>\n\n#include <spencer_tracking_msgs/TrackedGroups.h>\n\n#include \"person_display_common.h\"\n#include \"tracked_persons_cache.h\"\n\nnamespace spencer_tracking_rviz_plugin\n{\n typedef unsigned int group_id;\n\n /// A single entry in the history of a tracked person, to show group affiliation.\n struct GroupAffiliationHistoryEntry\n {\n group_id groupId;\n shared_ptr<rviz::Shape> shape;\n bool wasOccluded, wasSinglePersonGroup;\n };\n\n /// History of a tracked person.\n typedef circular_buffer<shared_ptr<GroupAffiliationHistoryEntry> > GroupAffiliationHistory;\n\n /// The display which can be added in RViz to display tracked groups.\n class TrackedGroupsDisplay: public PersonDisplayCommon<spencer_tracking_msgs::TrackedGroups>\n {\n Q_OBJECT\n public:\n // Constructor. pluginlib::ClassLoader creates instances by calling\n // the default constructor, so make sure you have one.\n TrackedGroupsDisplay() {};\n virtual ~TrackedGroupsDisplay();\n\n // Overrides of protected virtual functions from Display. As much\n // as possible, when Displays are not enabled, they should not be\n // subscribed to incoming data and should not show anything in the\n // 3D view. These functions are where these connections are made\n // and broken.\n\n // Called after the constructors have run\n virtual void onInitialize();\n\n // Called periodically by the visualization manager\n virtual void update(float wall_dt, float ros_dt);\n\n protected:\n // A helper to clear this display back to the initial state.\n virtual void reset();\n\n // Must be implemented by derived classes because MOC doesn't work in templates\n virtual rviz::DisplayContext* getContext() {\n return context_;\n }\n\n private:\n struct GroupVisual {\n vector<shared_ptr<rviz::Shape> > groupAssignmentCircles;\n vector<shared_ptr<PersonVisual> > personVisuals;\n vector<shared_ptr<Ogre::SceneNode> > personVisualSceneNodes;\n vector<shared_ptr<rviz::BillboardLine> > connectionLines;\n shared_ptr<TextNode> idText;\n group_id groupId;\n geometry_msgs::Point groupCenter;\n size_t personCount;\n };\n\n // Functions to handle an incoming ROS message.\n void processMessage(const spencer_tracking_msgs::TrackedGroups::ConstPtr& msg);\n \n // Helper functions\n void updateGroupVisualStyles(shared_ptr<GroupVisual>& groupVisual);\n void updateHistoryStyles();\n bool isGroupHidden(group_id groupId);\n\n // Scene node for group affiliation history visualization\n shared_ptr<Ogre::SceneNode> m_groupAffiliationHistorySceneNode, m_groupsSceneNode;\n\n std::string m_realFixedFrame;\n\n // User-editable property variables.\n rviz::StringProperty* m_excluded_group_ids_property;\n rviz::StringProperty* m_included_group_ids_property;\n\n rviz::BoolProperty* m_render_intragroup_connections_property;\n rviz::BoolProperty* m_render_ids_property;\n rviz::BoolProperty* m_render_history_property;\n rviz::BoolProperty* m_single_person_groups_in_constant_color_property;\n rviz::BoolProperty* m_hide_ids_of_single_person_groups_property;\n\n rviz::IntProperty* m_history_length_property;\n\n rviz::FloatProperty* m_occlusion_alpha_property; \n rviz::FloatProperty* m_group_id_offset; // z offset of the group ID text\n\n // State variables\n vector<shared_ptr<GroupVisual> > m_groupVisuals;\n \n map<track_id, group_id> m_groupAffiliations;\n GroupAffiliationHistory m_groupAffiliationHistory;\n set<group_id> m_excludedGroupIDs, m_includedGroupIDs;\n\n Ogre::Matrix4 m_frameTransform;\n TrackedPersonsCache m_trackedPersonsCache;\n\n private Q_SLOTS:\n void personVisualTypeChanged();\n virtual void stylesChanged();\n };\n\n} // end namespace spencer_tracking_rviz_plugin\n\n#endif // TRACKED_GROUPS_DISPLAY_H\n"
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"text": "#ifndef TRACKED_PERSONS_CACHE_H\n#define TRACKED_PERSONS_CACHE_H\n\n#include <map>\n#include <geometry_msgs/PoseWithCovariance.h>\n#include <geometry_msgs/TwistWithCovariance.h>\n#include <spencer_tracking_msgs/TrackedPersons.h>\n\n#include \"additional_topic_subscriber.h\"\n\nnamespace spencer_tracking_rviz_plugin\n{ \n typedef unsigned int track_id;\n\n /// Data structure for storing information about individual person tracks\n struct CachedTrackedPerson\n {\n Ogre::Vector3 center;\n geometry_msgs::PoseWithCovariance pose;\n geometry_msgs::TwistWithCovariance twist;\n bool isOccluded;\n };\n\n /// Subscribes to a TrackedPersons topic and caches all TrackedPersons of the current cycle, so that\n /// the owning rviz::Display can look up track positions etc for visualization.\n class TrackedPersonsCache {\n public:\n typedef std::map<track_id, shared_ptr<CachedTrackedPerson> > CachedTrackedPersonsMap;\n\n // Destructor\n ~TrackedPersonsCache();\n\n /// Create TrackedPersons subscriber and setup RViz properties.\n void initialize(rviz::Display* display, rviz::DisplayContext* context, ros::NodeHandle update_nh);\n\n /// Clear internal state, including all cached track positions.\n void reset();\n\n /// Lookup information for the given tracked person ID. Returns a null pointer if no information is available.\n const shared_ptr<CachedTrackedPerson> lookup(track_id trackId);\n\n /// Return internal map\n const CachedTrackedPersonsMap& getMap() {\n return m_cachedTrackedPersons;\n }\n\n private:\n // Callback when a new TrackedPersons message has arrived\n void processTrackedPersonsMessage(const spencer_tracking_msgs::TrackedPersons::ConstPtr& msg);\n\n rviz::AdditionalTopicSubscriber<spencer_tracking_msgs::TrackedPersons>* m_tracked_person_subscriber;\n rviz::Display* m_display;\n rviz::DisplayContext* m_context;\n\n // Our TrackedPerson memory\n CachedTrackedPersonsMap m_cachedTrackedPersons;\n };\n \n\n}\n\n#endif // TRACKED_PERSONS_CACHE_H"
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"text": "/**\n* Copyright 2014 Social Robotics Lab, University of Freiburg\n*\n* Redistribution and use in source and binary forms, with or without\n* modification, are permitted provided that the following conditions are met:\n*\n* # Redistributions of source code must retain the above copyright\n* notice, this list of conditions and the following disclaimer.\n* # Redistributions in binary form must reproduce the above copyright\n* notice, this list of conditions and the following disclaimer in the\n* documentation and/or other materials provided with the distribution.\n* # Neither the name of the University of Freiburg nor the names of its\n* contributors may be used to endorse or promote products derived from\n* this software without specific prior written permission.\n*\n* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n* POSSIBILITY OF SUCH DAMAGE.\n*\n* \\author Billy Okal <[email protected]>\n* \\author Sven Wehner <[email protected]>\n*/\n\n#include <pedsim_simulator/force/alongwallforce.h>\n#include <pedsim_simulator/config.h>\n#include <pedsim_simulator/scene.h>\n#include <pedsim_simulator/element/agent.h>\n#include <pedsim_simulator/element/obstacle.h>\n\n#include <ros/ros.h>\n\n\nAlongWallForce::AlongWallForce ( Agent* agentIn )\n : Force ( agentIn )\n{\n // initialize values\n // TODO - put these magic values into a yaml parameter file\n speedThreshold = 0.2;\n distanceThreshold = 1.5;\n angleThresholdDegree = 20;\n setFactor ( CONFIG.forceAlongWall );\n\n // connect signals\n connect ( &CONFIG, SIGNAL ( forceFactorAlongWallChanged ( double ) ),\n this, SLOT ( onForceFactorChanged ( double ) ) );\n}\n\nvoid AlongWallForce::onForceFactorChanged ( double valueIn )\n{\n setFactor ( valueIn );\n}\n\nPed::Tvector AlongWallForce::getForce ( Ped::Tvector walkingDirection )\n{\n if ( agent == nullptr )\n {\n\t\tROS_DEBUG(\"Cannot compute AlongWallForce for null agent!\");\n return Ped::Tvector();\n }\n\n // check whether the agent is stuck\n // → doesn't move\n if ( agent->getVelocity().length() > speedThreshold )\n return Ped::Tvector();\n\n // → walks against an obstacle\n Ped::Tvector force;\n Ped::Tvector agentPosition = agent->getPosition();\n const QList<Obstacle*>& obstacles = SCENE.getObstacles();\n // → find closest obstacle\n double minDistance = INFINITY;\n Ped::Tvector minDiff;\n Obstacle* minObstacle = nullptr;\n foreach ( Obstacle* currentObstacle, obstacles )\n {\n Ped::Tvector closestPoint = currentObstacle->closestPoint ( agentPosition );\n Ped::Tvector diff = closestPoint - agentPosition;\n double distance = diff.length();\n if ( distance < minDistance )\n {\n minObstacle = currentObstacle;\n minDiff = diff;\n minDistance = distance;\n }\n }\n\n // check distance to closest obstacle\n if ( minDistance > distanceThreshold )\n return Ped::Tvector();\n\n // check whether closest point is in walking direction\n const Ped::Tangle angleThreshold = Ped::Tangle::fromDegree ( angleThresholdDegree );\n Ped::Tangle angle = walkingDirection.angleTo ( minDiff );\n if ( angle > angleThreshold )\n return Ped::Tvector();\n\n\tROS_DEBUG(\"Found Agent %d to be stuck!\", agent->getId());\n\n // set force\n // → project to find walking direction\n Ped::Tvector obstacleDirection = minObstacle->getEndPoint() - minObstacle->getStartPoint();\n bool projectionPositive = ( Ped::Tvector::dotProduct ( walkingDirection, obstacleDirection ) >= 0 );\n\n Ped::Tvector forceDirection = ( projectionPositive ) ? obstacleDirection : -obstacleDirection;\n forceDirection.normalize();\n\n // scale force\n force = factor * forceDirection;\n return force;\n}\n\nQString AlongWallForce::toString() const\n{\n return tr ( \"AlongWallForce (factor: %2)\" )\n .arg ( factor );\n}\n"
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"text": "cmake_minimum_required(VERSION 2.8.3)\nproject(pedsim_msgs)\n\n# message and service dependencies\nset( MESSAGE_DEPENDENCIES\n std_msgs\n geometry_msgs\n sensor_msgs\n nav_msgs\n)\n\nfind_package(catkin REQUIRED COMPONENTS message_generation ${MESSAGE_DEPENDENCIES})\n\ninclude_directories(${catkin_INCLUDE_DIRS})\nlink_directories(${catkin_LIBRARY_DIRS})\n\n## Generate messages in the 'msg' folder\nadd_message_files( DIRECTORY msg\n FILES\n AgentState.msg\n AllAgentsState.msg\n TrackedPerson.msg\n TrackedPersons.msg\n TrackedGroup.msg\n TrackedGroups.msg\n SocialRelation.msg\n SocialRelations.msg\n SocialActivity.msg\n SocialActivities.msg\n)\n\n# generate the messages\ngenerate_messages(DEPENDENCIES ${MESSAGE_DEPENDENCIES})\n\n\n#Declare package run-time dependencies\ncatkin_package(\n CATKIN_DEPENDS roscpp rospy message_runtime ${MESSAGE_DEPENDENCIES}\n)"
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"text": "#!/usr/bin/env python\n#\n# Publishes fake tracked persons and the corresponding detections\n# (if not occluded) at\n# /pedsim/tracked_persons and /pedsim/detected_persons.\n\nimport rospy\nimport tf\nfrom spencer_tracking_msgs.msg import TrackedPersons, TrackedPerson\nfrom math import cos, sin, pi, radians\n\n\ndef createTrackedPerson(track_id, x, y, theta):\n trackedPerson = TrackedPerson()\n\n theta = radians(theta) + pi / 2.0\n\n trackedPerson.track_id = track_id\n quaternion = tf.transformations.quaternion_from_euler(0, 0, theta)\n\n trackedPerson.pose.pose.position.x = x\n trackedPerson.pose.pose.position.y = y\n\n trackedPerson.pose.pose.orientation.x = quaternion[0]\n trackedPerson.pose.pose.orientation.y = quaternion[1]\n trackedPerson.pose.pose.orientation.z = quaternion[2]\n trackedPerson.pose.pose.orientation.w = quaternion[3]\n\n trackedPerson.pose.covariance[0 + 0 * 6] = 0.001 # x\n trackedPerson.pose.covariance[1 + 1 * 6] = 0.001 # y\n trackedPerson.pose.covariance[2 + 2 * 6] = 999999 # z\n trackedPerson.pose.covariance[3 + 3 * 6] = 999999 # x rotation\n trackedPerson.pose.covariance[4 + 5 * 6] = 999999 # y rotation\n trackedPerson.pose.covariance[4 + 5 * 6] = 999999 # z rotation\n\n trackedPerson.twist.twist.linear.x = cos(theta)\n trackedPerson.twist.twist.linear.y = sin(theta)\n\n for i in range(0, 3):\n trackedPerson.twist.covariance[i + i * 6] = 1.0 # linear velocity\n for i in range(3, 6):\n trackedPerson.twist.covariance[\n i + i * 6] = float(\"inf\") # rotational velocity\n\n return trackedPerson\n\n\ndef main():\n # Main code\n trackPublisher = rospy.Publisher('/spencer/perception/tracked_persons', TrackedPersons)\n\n rospy.init_node('mocktracks_info_screen')\n rate = rospy.Rate(10)\n\n seqCounter = 0\n while not rospy.is_shutdown():\n\n trackedPersons = TrackedPersons()\n trackedPersons.header.seq = seqCounter\n trackedPersons.header.frame_id = \"odom\"\n trackedPersons.header.stamp = rospy.Time.now()\n\n # trackedPersons.tracks.append(\n # createTrackedPerson( trackId, x, y, theta ) )\n\n trackedPersons.tracks.append(createTrackedPerson(1, 3, 7, 270))\n trackedPersons.tracks.append(createTrackedPerson(2, 7, 5.5, 109))\n trackedPersons.tracks.append(createTrackedPerson(3, 8, 6.5, 90))\n trackedPersons.tracks.append(createTrackedPerson(4, 7, 9.2, 109))\n trackedPersons.tracks.append(createTrackedPerson(5, 7.5, 8.0, 109))\n\n # trackedPersons.tracks.append(\n # createTrackedPerson(5, 9.2, 1.2, 71.56 - 90))\n # trackedPersons.tracks.append(\n # createTrackedPerson(6, 7.1, 2.5, 80.9097 - 90))\n # trackedPersons.tracks.append(createTrackedPerson(7, 8.2, 7.6, 8))\n # trackedPersons.tracks.append(createTrackedPerson(8, 7.1, 6.5, 10))\n # trackedPersons.tracks.append(\n # createTrackedPerson(9, 2.2, 1.8, 85.2364 - 90))\n # trackedPersons.tracks.append(\n # createTrackedPerson(10, 4.1, 1.9, 93.8141 - 90))\n # trackedPersons.tracks.append(\n # createTrackedPerson(11, 1.7, 9.3, 78.6901 - 90))\n # trackedPersons.tracks.append(\n # createTrackedPerson(12, 2.2, 7.5, 63.4349 - 90))\n\n trackPublisher.publish(trackedPersons)\n\n seqCounter += 1\n rate.sleep()\n\n\nif __name__ == '__main__':\n main()\n"
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"text": "/**\n* Copyright 2014 Social Robotics Lab, University of Freiburg\n*\n* Redistribution and use in source and binary forms, with or without\n* modification, are permitted provided that the following conditions are met:\n*\n* # Redistributions of source code must retain the above copyright\n* notice, this list of conditions and the following disclaimer.\n* # Redistributions in binary form must reproduce the above copyright\n* notice, this list of conditions and the following disclaimer in the\n* documentation and/or other materials provided with the distribution.\n* # Neither the name of the University of Freiburg nor the names of its\n* contributors may be used to endorse or promote products derived from\n* this software without specific prior written permission.\n*\n* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n* POSSIBILITY OF SUCH DAMAGE.\n*\n* \\author Billy Okal <[email protected]>\n* \\author Sven Wehner <[email protected]>\n*/\n\n#include <pedsim_simulator/force/groupcoherenceforce.h>\n#include <pedsim_simulator/config.h>\n#include <pedsim_simulator/element/agent.h>\n\n#include <ros/ros.h>\n\nGroupCoherenceForce::GroupCoherenceForce ( Agent* agentIn )\n : Force ( agentIn )\n{\n // initialize values\n setFactor ( CONFIG.forceGroupCoherence );\n usePaperVersion = true;\n\n // connect signals\n connect ( &CONFIG, SIGNAL ( forceFactorGroupCoherenceChanged ( double ) ),\n this, SLOT ( onForceFactorGroupCoherenceChanged ( double ) ) );\n}\n\nvoid GroupCoherenceForce::onForceFactorGroupCoherenceChanged ( double valueIn )\n{\n setFactor ( valueIn );\n}\n\nvoid GroupCoherenceForce::setGroup ( AgentGroup* groupIn )\n{\n group = groupIn;\n}\n\nconst AgentGroup& GroupCoherenceForce::getGroup() const\n{\n return *group;\n}\n\nPed::Tvector GroupCoherenceForce::getForce ( Ped::Tvector walkingDirection )\n{\n // sanity checks\n if ( group->isEmpty() )\n {\n\t\tROS_DEBUG(\"Computing GroupCoherenceForce for empty group!\");\n return Ped::Tvector();\n }\n\n // compute group coherence force\n // → compute relative position of center of mass\n Ped::Tvector com = group->getCenterOfMass();\n Ped::Tvector relativeCoM = com - agent->getPosition();\n // → distance to center of mass\n double distance = relativeCoM.length();\n // → approximate maximal distance, according to paper\n const double maxDistance = ( ( double ) group->memberCount() - 1 ) / 2;\n\n // compute force\n Ped::Tvector force;\n // → switch between definitions\n if ( usePaperVersion )\n {\n // force according to paper\n // → check whether maximal distance has been exceeded\n if ( distance >= maxDistance )\n {\n // compute force\n force = relativeCoM.normalized();\n\n // there is no factor for myForce, hence we have to do it\n force *= factor;\n\n return force;\n }\n else\n {\n // there is no force\n return Ped::Tvector();\n }\n }\n else\n {\n // modified force\n force = relativeCoM;\n\n // there is no factor for myForce, hence we have to do it\n //HACK: use smooth transition\n // this doesn't follow the Moussaid paper, but it creates less abrupt changes\n double softenedFactor = factor * ( tanh ( distance - maxDistance ) +1 ) / 2;\n force *= softenedFactor;\n\n ROS_DEBUG(\"softenedFactor = %f = %f * (tanh(%f - %f)+1) / 2\", softenedFactor, factor, distance, maxDistance);\n\n return force;\n }\n}\n\nQString GroupCoherenceForce::toString() const\n{\n return tr ( \"GroupCoherenceForce (factor: %1)\" ).arg ( factor );\n}\n"
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"text": "#ifndef SOCIAL_RELATIONS_DISPLAY_H\n#define SOCIAL_RELATIONS_DISPLAY_H\n\n#include <spencer_social_relation_msgs/SocialRelations.h>\n\n#include \"person_display_common.h\"\n#include \"tracked_persons_cache.h\"\n\nnamespace spencer_tracking_rviz_plugin\n{\n /// The display which can be added in RViz to display social relations.\n class SocialRelationsDisplay: public PersonDisplayCommon<spencer_social_relation_msgs::SocialRelations>\n {\n Q_OBJECT\n public:\n // Constructor. pluginlib::ClassLoader creates instances by calling\n // the default constructor, so make sure you have one.\n SocialRelationsDisplay() {};\n virtual ~SocialRelationsDisplay();\n\n // Overrides of protected virtual functions from Display. As much\n // as possible, when Displays are not enabled, they should not be\n // subscribed to incoming data and should not show anything in the\n // 3D view. These functions are where these connections are made\n // and broken.\n\n // Called after the constructors have run\n virtual void onInitialize();\n\n protected:\n // A helper to clear this display back to the initial state.\n virtual void reset();\n\n // Must be implemented by derived classes because MOC doesn't work in templates\n virtual rviz::DisplayContext* getContext() {\n return context_;\n }\n\n private:\n struct RelationVisual {\n std::string type;\n double relationStrength;\n shared_ptr<rviz::BillboardLine> relationLine;\n shared_ptr<TextNode> relationText;\n track_id trackId1, trackId2; // required to hide certain tracks\n };\n\n // Functions to handle an incoming ROS message.\n void processMessage(const spencer_social_relation_msgs::SocialRelations::ConstPtr& msg);\n \n // Helper functions\n void updateRelationVisualStyles(shared_ptr<RelationVisual>& relationVisual);\n \n // Scene node for group affiliation history visualization\n shared_ptr<Ogre::SceneNode> m_socialRelationsSceneNode;\n\n // User-editable property variables.\n rviz::StringProperty* m_relation_type_filter_property;\n \n rviz::BoolProperty* m_render_positive_person_relations_property;\n rviz::BoolProperty* m_render_negative_person_relations_property;\n\n rviz::FloatProperty* m_positive_person_relation_threshold;\n rviz::ColorProperty* m_positive_person_relations_color;\n rviz::ColorProperty* m_negative_person_relations_color;\n\n // State variables\n vector<shared_ptr<RelationVisual> > m_relationVisuals;\n TrackedPersonsCache m_trackedPersonsCache;\n\n private Q_SLOTS:\n virtual void stylesChanged();\n };\n\n} // end namespace spencer_tracking_rviz_plugin\n\n#endif // SOCIAL_RELATIONS_DISPLAY_H\n"
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"text": "#include <rviz/visualization_manager.h>\n#include <rviz/frame_manager.h>\n#include \"rviz/selection/selection_manager.h\"\n\n#include \"social_activities_display.h\"\n\n#include <boost/lexical_cast.hpp>\n#include <boost/tokenizer.hpp>\n#include <boost/algorithm/string.hpp>\n#include <boost/range/adaptor/map.hpp>\n#include <boost/foreach.hpp>\n\n#define foreach BOOST_FOREACH\n\n// required to fix orientation of any Cylinder shapes\nconst Ogre::Quaternion shapeQuaternion( Ogre::Degree(90), Ogre::Vector3(1,0,0) );\n\n\nnamespace sr = spencer_social_relation_msgs;\nnamespace spencer_tracking_rviz_plugin\n{\n\nvoid SocialActivitiesDisplay::onInitialize()\n{\n m_trackedPersonsCache.initialize(this, context_, update_nh_);\n PersonDisplayCommon::onInitialize();\n\n QObject::connect(m_commonProperties->style, SIGNAL(changed()), this, SLOT(personVisualTypeChanged()) );\n\n m_excluded_activity_types_property = new rviz::StringProperty( \"Excluded activity types\", \"\", \"Comma-separated list of activity types whose visualization should be hidden\", this, SLOT(stylesChanged()) );\n m_included_activity_types_property = new rviz::StringProperty( \"Included activity types\", \"\", \"Comma-separated list of activity types whose visualization should be visible, overrides excluded\", this, SLOT(stylesChanged()) );\n\n m_min_confidence_property = new rviz::FloatProperty( \"Min. confidence\", 0.0, \"Minimum confidence for a social activity to be shown\", this, SLOT(stylesChanged()) );\n m_min_confidence_property->setMin( 0.0 );\n\n m_hide_with_no_activity_property = new rviz::BoolProperty( \"Hide tracks with no activity\", false, \"Hide all tracks which do not have at least one social activity assigned\", this, SLOT(stylesChanged()));\n\n m_render_intraactivity_connections_property = new rviz::BoolProperty( \"Connect tracks sharing the same activity\", true, \"Connect all tracks that share the same activity\", this, SLOT(stylesChanged()));\n m_line_width_property = new rviz::FloatProperty( \"Line width\", 0.05, \"Line width of connecting lines\", m_render_intraactivity_connections_property, SLOT(stylesChanged()), this );\n m_line_width_property->setMin( 0.0 );\n\n m_render_activity_types_property = new rviz::BoolProperty( \"Render activity type texts\", true, \"Render activity types as text\", this, SLOT(stylesChanged()));\n m_activity_type_per_track_property = new rviz::BoolProperty( \"Activity type per track\", false, \"Show activity type for each individual track\", this, SLOT(stylesChanged()));\n m_render_confidences_property = new rviz::BoolProperty( \"Render confidences\", true, \"Render confidence values next to activity type\", this, SLOT(stylesChanged()));\n\n m_render_circles_property = new rviz::BoolProperty( \"Render circles below person\", true, \"Render circles below person\", this, SLOT(stylesChanged()));\n m_circle_radius_property = new rviz::FloatProperty( \"Radius\", 0.45, \"Radius of circles below person in meters\", m_render_circles_property, SLOT(stylesChanged()), this );\n m_circle_radius_property->setMin( 0.0 );\n\n m_circle_alpha_property = new rviz::FloatProperty( \"Alpha\", 1.0, \"Alpha value (opacity) of circles below person\", m_render_circles_property, SLOT(stylesChanged()), this );\n m_circle_alpha_property->setMin( 0.0 );\n m_circle_alpha_property->setMax( 1.0 );\n\n m_occlusion_alpha_property = new rviz::FloatProperty( \"Occlusion alpha\", 0.5, \"Alpha multiplier for history of occluded tracks\", this, SLOT(stylesChanged()) );\n m_occlusion_alpha_property->setMin( 0.0 );\n\n m_activity_type_offset = new rviz::FloatProperty( \"Activity type Z offset\", 2.0, \"Offset in z position (height) of the activity type text\", this, SLOT(stylesChanged()) );\n\n m_activity_colors = new rviz::Property( \"Activity colors\", \"\", \"Colors of different social activity types\", this );\n\n // Add colors for new activity types here, also adjust header file!\n m_activity_color_unknown = new rviz::ColorProperty( \"(Unknown activity)\", QColor(255,255,255), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n m_activity_color_none = new rviz::ColorProperty( \"(No activity)\", QColor(200,200,200), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n m_activity_color_shopping = new rviz::ColorProperty( sr::SocialActivity::TYPE_SHOPPING.c_str(), QColor(0,0,255), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n m_activity_color_standing = new rviz::ColorProperty( sr::SocialActivity::TYPE_STANDING.c_str(), QColor(0,0,0), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n m_activity_color_individual_moving = new rviz::ColorProperty( sr::SocialActivity::TYPE_INDIVIDUAL_MOVING.c_str(), QColor(128,128,128), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n m_activity_color_waiting_in_queue = new rviz::ColorProperty( sr::SocialActivity::TYPE_WAITING_IN_QUEUE.c_str(), QColor(255,0,255), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n m_activity_color_looking_at_information_screen = new rviz::ColorProperty( sr::SocialActivity::TYPE_LOOKING_AT_INFORMATION_SCREEN.c_str(), QColor(255,255,0), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n m_activity_color_looking_at_kiosk = new rviz::ColorProperty( sr::SocialActivity::TYPE_LOOKING_AT_KIOSK.c_str(), QColor(255,128,0), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n m_activity_color_group_assembling = new rviz::ColorProperty( sr::SocialActivity::TYPE_GROUP_ASSEMBLING.c_str(), QColor(0,128,255), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n m_activity_color_group_moving = new rviz::ColorProperty( sr::SocialActivity::TYPE_GROUP_MOVING.c_str(), QColor(0,255,255), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n m_activity_color_flow = new rviz::ColorProperty( sr::SocialActivity::TYPE_FLOW_WITH_ROBOT.c_str(), QColor(0,255,0), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n m_activity_color_antiflow = new rviz::ColorProperty( sr::SocialActivity::TYPE_ANTIFLOW_AGAINST_ROBOT.c_str(), QColor(255,0,0), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n m_activity_color_waiting_for_others = new rviz::ColorProperty( sr::SocialActivity::TYPE_WAITING_FOR_OTHERS.c_str(), QColor(255,170,255), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n m_activity_color_looking_for_help = new rviz::ColorProperty( sr::SocialActivity::TYPE_LOOKING_FOR_HELP.c_str(), QColor(155,170,255), \"\", m_activity_colors, SLOT(stylesChanged()), this);\n\n m_commonProperties->color_transform->setHidden(true);\n m_commonProperties->color_map_offset->setHidden(true);\n\n // Create a scene node for visualizing group affiliation history\n m_socialActivitiesSceneNode = shared_ptr<Ogre::SceneNode>(scene_node_->createChildSceneNode());\n}\n\nSocialActivitiesDisplay::~SocialActivitiesDisplay()\n{\n}\n\n// Clear the visuals by deleting their objects.\nvoid SocialActivitiesDisplay::reset()\n{\n PersonDisplayCommon::reset();\n m_trackedPersonsCache.reset();\n m_socialActivityVisuals.clear();\n m_personVisualMap.clear();\n m_highestConfidenceActivityPerTrack.clear();\n}\n\nvoid SocialActivitiesDisplay::update(float wall_dt, float ros_dt)\n{\n // Update animations\n foreach(PersonVisualContainer& personVisualContainer, m_personVisualMap | boost::adaptors::map_values) {\n if(personVisualContainer.personVisual) {\n personVisualContainer.personVisual->update(ros_dt);\n }\n }\n}\n\nvoid SocialActivitiesDisplay::stylesChanged()\n{\n m_commonProperties->color_map_offset->setHidden(true);\n\n // Get list of group IDs belonging to tracks that shall be hidden or visible\n m_excludedActivityTypes.clear();\n {\n string inputString = m_excluded_activity_types_property->getStdString();\n char_separator<char> separator(\",\");\n tokenizer< char_separator<char> > tokens(inputString, separator);\n foreach(const string& token, tokens) {\n string tmp = token;\n boost::algorithm::to_lower(tmp);\n m_excludedActivityTypes.insert(tmp);\n }\n }\n m_includedActivityTypes.clear();\n {\n string inputString = m_included_activity_types_property->getStdString();\n char_separator<char> separator(\",\");\n tokenizer< char_separator<char> > tokens(inputString, separator);\n foreach(const string& token, tokens) {\n string tmp = token;\n boost::algorithm::to_lower(tmp);\n m_includedActivityTypes.insert(tmp);\n }\n }\n\n foreach(shared_ptr<SocialActivityVisual> socialActivityVisual, m_socialActivityVisuals) {\n updateSocialActivityVisualStyles(socialActivityVisual);\n }\n\n foreach(PersonVisualContainer& personVisualContainer, m_personVisualMap | boost::adaptors::map_values) {\n if(personVisualContainer.personVisual) {\n // Update common styles to person visual, such as line width\n applyCommonStyles(personVisualContainer.personVisual);\n\n // Update color according to highest-ranking social activity for this person\n Ogre::ColourValue activityColor;\n\n activity_type activityType = \"\";\n float confidence = 1.0f;\n if(m_highestConfidenceActivityPerTrack.find(personVisualContainer.trackId) != m_highestConfidenceActivityPerTrack.end()) {\n activityType = m_highestConfidenceActivityPerTrack[personVisualContainer.trackId].type;\n confidence = m_highestConfidenceActivityPerTrack[personVisualContainer.trackId].confidence;\n }\n else {\n if(m_hide_with_no_activity_property->getBool()) confidence = -999;\n }\n activityColor = getActivityColor(activityType, confidence);\n\n personVisualContainer.personVisual->setColor(activityColor);\n }\n }\n}\n\nbool SocialActivitiesDisplay::isActivityTypeHidden(activity_type activityType) {\n boost::algorithm::to_lower(activityType);\n\n bool isIncluded = m_includedActivityTypes.find(activityType) != m_includedActivityTypes.end();\n if(isIncluded) return false;\n if(!m_includedActivityTypes.empty()) return true;\n\n return m_excludedActivityTypes.find(activityType) != m_excludedActivityTypes.end();\n}\n\nOgre::ColourValue SocialActivitiesDisplay::getActivityColor(activity_type activityType, float confidence) {\n bool hideActivityType = isActivityTypeHidden(activityType);\n\n // Determine color\n rviz::ColorProperty* colorProperty = NULL;\n\n // Add new social activity types here, and also add a property in constructor at top of file!\n if(activityType.empty())\n colorProperty = m_activity_color_none;\n else if(activityType == sr::SocialActivity::TYPE_SHOPPING)\n colorProperty = m_activity_color_shopping;\n else if(activityType == sr::SocialActivity::TYPE_STANDING)\n colorProperty = m_activity_color_standing;\n else if(activityType == sr::SocialActivity::TYPE_INDIVIDUAL_MOVING)\n colorProperty = m_activity_color_individual_moving;\n else if(activityType == sr::SocialActivity::TYPE_WAITING_IN_QUEUE)\n colorProperty = m_activity_color_waiting_in_queue;\n else if(activityType == sr::SocialActivity::TYPE_LOOKING_AT_INFORMATION_SCREEN)\n colorProperty = m_activity_color_looking_at_information_screen;\n else if(activityType == sr::SocialActivity::TYPE_LOOKING_AT_KIOSK)\n colorProperty = m_activity_color_looking_at_kiosk;\n else if(activityType == sr::SocialActivity::TYPE_GROUP_ASSEMBLING)\n colorProperty = m_activity_color_group_assembling;\n else if(activityType == sr::SocialActivity::TYPE_GROUP_MOVING)\n colorProperty = m_activity_color_group_moving;\n else if(activityType == sr::SocialActivity::TYPE_FLOW_WITH_ROBOT)\n colorProperty = m_activity_color_flow;\n else if(activityType == sr::SocialActivity::TYPE_ANTIFLOW_AGAINST_ROBOT)\n colorProperty = m_activity_color_antiflow;\n else if(activityType == sr::SocialActivity::TYPE_WAITING_FOR_OTHERS)\n colorProperty = m_activity_color_waiting_for_others;\n else if(activityType == sr::SocialActivity::TYPE_LOOKING_FOR_HELP)\n colorProperty = m_activity_color_looking_for_help;\n else\n colorProperty = m_activity_color_unknown;\n\n Ogre::ColourValue activityColor = colorProperty->getOgreColor();\n activityColor.a = 1.0f;\n\n activityColor.a *= m_commonProperties->alpha->getFloat(); // general alpha\n if(hideActivityType) activityColor.a = 0;\n\n if(confidence < m_min_confidence_property->getFloat()) activityColor.a = 0;\n\n return activityColor;\n}\n\n// Set the rendering style (cylinders, meshes, ...) of tracked persons\nvoid SocialActivitiesDisplay::personVisualTypeChanged()\n{\n m_personVisualMap.clear();\n foreach(PersonVisualContainer& personVisualContainer, m_personVisualMap | boost::adaptors::map_values) {\n personVisualContainer.personVisual.reset();\n createPersonVisualIfRequired(personVisualContainer.sceneNode.get(), personVisualContainer.personVisual);\n }\n stylesChanged();\n}\n\nvoid SocialActivitiesDisplay::updateSocialActivityVisualStyles(shared_ptr<SocialActivityVisual>& socialActivityVisual)\n{\n std::stringstream ss;\n\n Ogre::ColourValue activityColor = getActivityColor(socialActivityVisual->activityType, socialActivityVisual->confidence);\n bool hideActivity = isActivityTypeHidden(socialActivityVisual->activityType) || socialActivityVisual->confidence < m_min_confidence_property->getFloat();\n bool showCircles = m_render_circles_property->getBool();\n\n foreach(shared_ptr<rviz::Shape> circle, socialActivityVisual->socialActivityAssignmentCircles) {\n circle->setColor(activityColor.r, activityColor.g, activityColor.b, activityColor.a * m_circle_alpha_property->getFloat() * (showCircles ? 1.0f : 0.0f));\n const double circleDiameter = m_circle_radius_property->getFloat() * 2, circleHeight = 0;\n circle->setScale(shapeQuaternion * Ogre::Vector3(circleDiameter, circleDiameter, circleHeight));\n }\n\n double connectionLineVisibilityAlpha = m_render_intraactivity_connections_property->getBool() ? 1.0 : 0.0;\n foreach(shared_ptr<rviz::BillboardLine> connectionLine, socialActivityVisual->connectionLines) {\n connectionLine->setColor(activityColor.r, activityColor.g, activityColor.b, activityColor.a * connectionLineVisibilityAlpha);\n connectionLine->setLineWidth(m_line_width_property->getFloat());\n }\n\n // Update text colors, size and visibility\n ss.str(\"\"); ss << socialActivityVisual->activityType;\n if(m_render_confidences_property->getBool()) ss << fixed << setprecision(1) << \" (\" << 100*socialActivityVisual->confidence << \"%)\";\n\n for(int i = 0; i < socialActivityVisual->typeTexts.size(); i++) {\n shared_ptr<TextNode>& typeText = socialActivityVisual->typeTexts[i];\n\n if(typeText) { // might be not set if center of activity could not be determined\n typeText->setCaption(ss.str());\n\n Ogre::Vector3 centerAt;\n if(m_activity_type_per_track_property->getBool()) {\n shared_ptr<CachedTrackedPerson> trackedPerson = m_trackedPersonsCache.lookup(socialActivityVisual->trackIds[i]);\n if(!trackedPerson) continue;\n centerAt = Ogre::Vector3(trackedPerson->center.x, trackedPerson->center.y, m_commonProperties->z_offset->getFloat());\n }\n else centerAt = Ogre::Vector3(socialActivityVisual->socialActivityCenter.x, socialActivityVisual->socialActivityCenter.y, socialActivityVisual->socialActivityCenter.z);\n\n Ogre::ColourValue fontColor = m_commonProperties->font_color_style->getOptionInt() == FONT_COLOR_CONSTANT ? m_commonProperties->constant_font_color->getOgreColor() : activityColor;\n fontColor.a = m_commonProperties->alpha->getFloat();\n if(hideActivity) fontColor.a = 0;\n float characterHeight = 0.23 * m_commonProperties->font_scale->getFloat();\n typeText->setVisible(m_render_activity_types_property->getBool());\n typeText->setCharacterHeight(characterHeight);\n typeText->setColor(fontColor);\n typeText->setPosition(m_frameTransform * Ogre::Vector3(\n centerAt.x,\n centerAt.y,\n centerAt.z + m_activity_type_offset->getFloat() + m_commonProperties->z_offset->getFloat()\n + socialActivityVisual->declutteringOffset * characterHeight /* this is for decluttering of overlapping labels */));\n }\n }\n}\n\n// Helper function for guaranteeing consistent ordering of activity labels\nbool CompareActivityByType (const SocialActivitiesDisplay::ActivityWithConfidence& first, const SocialActivitiesDisplay::ActivityWithConfidence& second) {\n return first.type < second.type;\n}\n\n// This is our callback to handle an incoming group message.\nvoid SocialActivitiesDisplay::processMessage(const spencer_social_relation_msgs::SocialActivities::ConstPtr& msg)\n{\n // Get transforms into fixed frame etc.\n if(!preprocessMessage(msg)) return;\n\n // Transform into Rviz fixed frame\n m_frameTransform = Ogre::Matrix4(m_frameOrientation);\n m_frameTransform.setTrans(m_framePosition);\n stringstream ss;\n\n // Clear previous visualization (not very efficient, but easier to implement)\n // Note that person visuals are not cleared to allow walking animations to function properly\n m_socialActivityVisuals.clear();\n m_socialActivitiesSceneNode->removeAndDestroyAllChildren();\n\n m_highestConfidenceActivityPerTrack.clear(); // used later on to determine color of person visuals\n m_allActivitiesPerTrack.clear();\n\n unsigned int numTracksWithUnknownPosition = 0;\n\n\n //\n // Iterate over all social activities in this message\n //\n foreach (const spencer_social_relation_msgs::SocialActivity& socialActivity, msg->elements)\n {\n // Create a new visual representation of the social activity\n shared_ptr<SocialActivityVisual> socialActivityVisual = shared_ptr<SocialActivityVisual>(new SocialActivityVisual);\n socialActivityVisual->activityType = socialActivity.type;\n socialActivityVisual->confidence = socialActivity.confidence;\n socialActivityVisual->personCount = socialActivity.track_ids.size();\n m_socialActivityVisuals.push_back(socialActivityVisual);\n\n geometry_msgs::Point socialActivityCenter;\n size_t numGoodTracksInActivity = 0;\n\n //\n // Assignment circles, person visuals (if enabled) + connections between social activity members\n //\n\n for(size_t trackIndex = 0; trackIndex < socialActivity.track_ids.size(); trackIndex++)\n {\n const track_id trackId = socialActivity.track_ids[trackIndex];\n shared_ptr<CachedTrackedPerson> trackedPerson = m_trackedPersonsCache.lookup(trackId);\n\n ActivityWithConfidence activityWithConfidence;\n activityWithConfidence.type = socialActivity.type;\n activityWithConfidence.confidence = socialActivity.confidence;\n\n // Update map of highest-confidence activity per person\n if(m_highestConfidenceActivityPerTrack.find(trackId) == m_highestConfidenceActivityPerTrack.end()\n || socialActivity.confidence > m_highestConfidenceActivityPerTrack[trackId].confidence) {\n m_highestConfidenceActivityPerTrack[trackId] = activityWithConfidence;\n }\n\n // Update map of all activities per person\n if(m_allActivitiesPerTrack.find(trackId) == m_allActivitiesPerTrack.end()) {\n m_allActivitiesPerTrack[trackId] = vector<ActivityWithConfidence>();\n }\n m_allActivitiesPerTrack[trackId].push_back(activityWithConfidence);\n\n\n // Get current track position\n if(!trackedPerson) {\n numTracksWithUnknownPosition++;\n }\n else\n {\n socialActivityVisual->trackIds.push_back(trackId);\n numGoodTracksInActivity++;\n\n Ogre::Vector3 trackCenterAtGroundPlane(trackedPerson->center.x, trackedPerson->center.y, m_commonProperties->z_offset->getFloat());\n socialActivityCenter.x += trackCenterAtGroundPlane.x;\n socialActivityCenter.y += trackCenterAtGroundPlane.y;\n socialActivityCenter.z += trackCenterAtGroundPlane.z;\n\n\n //\n // Social activity assignment circles (below tracks)\n //\n\n if(m_render_circles_property->getBool()) // only create circles if they are enabled, for better performance\n {\n shared_ptr<rviz::Shape> circle = shared_ptr<rviz::Shape>(new rviz::Shape(rviz::Shape::Cylinder, context_->getSceneManager(), m_socialActivitiesSceneNode.get()));\n\n const double circleHeight = 0;\n circle->setOrientation(shapeQuaternion);\n\n Ogre::Vector3 circlePos = trackCenterAtGroundPlane + Ogre::Vector3(0, 0, -0.5*circleHeight - 0.01);\n circle->setPosition(circlePos);\n\n socialActivityVisual->socialActivityAssignmentCircles.push_back(circle);\n }\n\n //\n // Intra-activity connections\n //\n\n if(m_render_intraactivity_connections_property->getBool()) // only create circles if they are enabled, for better performance\n {\n // Iterate over all tracks sharing the same activity to render intra-activity connections\n for(size_t otherTrackIndex = trackIndex + 1; otherTrackIndex < socialActivity.track_ids.size(); otherTrackIndex++)\n {\n const track_id otherTrackId = socialActivity.track_ids[otherTrackIndex];\n shared_ptr<CachedTrackedPerson> otherTrackedPerson = m_trackedPersonsCache.lookup(otherTrackId);\n\n // Get other track's position\n if(otherTrackedPerson) {\n // Get positions. These are already in fixed frame coordinates!\n const Ogre::Vector3 verticalShift(0,0, 0.5 + m_commonProperties->z_offset->getFloat());\n const Ogre::Vector3& position1 = verticalShift + trackedPerson->center;\n const Ogre::Vector3& position2 = verticalShift + otherTrackedPerson->center;\n\n // Add line connecting the two tracks\n shared_ptr<rviz::BillboardLine> connectionLine(new rviz::BillboardLine(context_->getSceneManager(), m_socialActivitiesSceneNode.get()));\n connectionLine->setMaxPointsPerLine(2);\n connectionLine->addPoint(position1);\n connectionLine->addPoint(position2);\n socialActivityVisual->connectionLines.push_back(connectionLine);\n }\n } // end loop over other tracks sharing same activity\n }\n\n } // end if track found\n } // end for loop over tracks\n\n //\n // Texts\n //\n socialActivityCenter.x /= (double) numGoodTracksInActivity;\n socialActivityCenter.y /= (double) numGoodTracksInActivity;\n socialActivityCenter.z /= (double) numGoodTracksInActivity;\n socialActivityVisual->socialActivityCenter = socialActivityCenter;\n\n // Social activity type\n if(numGoodTracksInActivity > 0) {\n for(int i = 0; i < (m_activity_type_per_track_property->getBool() ? socialActivityVisual->trackIds.size() : 1); i++) {\n shared_ptr<TextNode> typeText(new TextNode(context_->getSceneManager(), m_socialActivitiesSceneNode.get()));\n typeText->showOnTop();\n socialActivityVisual->typeTexts.push_back(typeText);\n }\n }\n } // end for loop over all social activities in msg\n\n\n //\n // Second iteration over all social activities and member tracks, required for decluttering\n //\n ROS_ASSERT(msg->elements.size() == m_socialActivityVisuals.size());\n for(size_t i = 0; i < msg->elements.size(); i++)\n {\n const spencer_social_relation_msgs::SocialActivity& socialActivity = msg->elements[i];\n shared_ptr<SocialActivityVisual> socialActivityVisual = m_socialActivityVisuals[i];\n size_t maxIndexOfThisActivity = 0;\n\n for(size_t trackIndex = 0; trackIndex < socialActivity.track_ids.size(); trackIndex++)\n {\n const track_id trackId = socialActivity.track_ids[trackIndex];\n vector<ActivityWithConfidence> activitiesOfTrack(m_allActivitiesPerTrack[trackId]);\n\n // Sort to ensure consistency across multiple runs, even if msg->elements order changes\n std::sort(activitiesOfTrack.begin(), activitiesOfTrack.end(), CompareActivityByType);\n\n for(size_t j = 0; j < activitiesOfTrack.size(); j++) {\n if(activitiesOfTrack[j].type == socialActivityVisual->activityType) {\n maxIndexOfThisActivity = std::max(maxIndexOfThisActivity, j);\n break;\n }\n }\n }\n\n socialActivityVisual->declutteringOffset = maxIndexOfThisActivity; // got it\n }\n\n\n //\n // Create person visuals for all tracked persons (colored in color of activity with highest confidence)\n //\n set<track_id> seenTrackIds;\n foreach(const TrackedPersonsCache::CachedTrackedPersonsMap::value_type& entry, m_trackedPersonsCache.getMap()) {\n const track_id trackId = entry.first;\n const shared_ptr<CachedTrackedPerson> trackedPerson = entry.second;\n\n PersonVisualContainer personVisualContainer;\n if(m_personVisualMap.find(trackId) != m_personVisualMap.end()) {\n personVisualContainer = m_personVisualMap[trackId];\n }\n else {\n personVisualContainer.trackId = trackId;\n personVisualContainer.sceneNode = shared_ptr<Ogre::SceneNode>(scene_node_->createChildSceneNode()); // This scene node is the parent of all visualization elements for the tracked person\n }\n\n // Create new visual for the person itself, if needed\n createPersonVisualIfRequired(personVisualContainer.sceneNode.get(), personVisualContainer.personVisual);\n\n const double personHeight = personVisualContainer.personVisual ? personVisualContainer.personVisual->getHeight() : 0;\n const Ogre::Matrix3 covXYZinTargetFrame = covarianceXYZIntoTargetFrame(trackedPerson->pose);\n setPoseOrientation(personVisualContainer.sceneNode.get(), trackedPerson->pose, covXYZinTargetFrame, personHeight);\n\n // Update walking animation if required\n const Ogre::Vector3 velocityVector = getVelocityVector(trackedPerson->twist);\n shared_ptr<MeshPersonVisual> meshPersonVisual = boost::dynamic_pointer_cast<MeshPersonVisual>(personVisualContainer.personVisual);\n if(meshPersonVisual) {\n meshPersonVisual->setWalkingSpeed(velocityVector.length());\n }\n\n m_personVisualMap[trackId] = personVisualContainer; // to keep visuals alive across multiple frames, for walking animation\n seenTrackIds.insert(trackId);\n }\n\n // Delete obsolete track visuals of tracks that have disappeared\n set<track_id> trackIdsToDelete;\n foreach(track_id trackId, m_personVisualMap | boost::adaptors::map_keys) {\n if(seenTrackIds.find(trackId) == seenTrackIds.end()) trackIdsToDelete.insert(trackId);\n }\n foreach(track_id trackIdToDelete, trackIdsToDelete) {\n m_personVisualMap.erase(trackIdToDelete);\n }\n\n //\n // Update all styles (colors etc. which can also be reconfigured at runtime, even if no new messages are received)\n //\n stylesChanged();\n\n\n //\n // Update status (shown in property pane)\n //\n\n ss.str(\"\");\n ss << msg->elements.size() << \" activities(s)\";\n setStatusStd(rviz::StatusProperty::Ok, \"Social activities\", ss.str());\n\n ss.str(\"\");\n ss << numTracksWithUnknownPosition << \" track(s) with unknown position\";\n setStatusStd(0 == numTracksWithUnknownPosition ? rviz::StatusProperty::Ok : rviz::StatusProperty::Warn, \"Track-to-activity assignment\", ss.str());\n}\n\n} // end namespace spencer_tracking_rviz_plugin\n\n// Tell pluginlib about this class. It is important to do this in\n// global scope, outside our package's namespace.\n#include <pluginlib/class_list_macros.h>\nPLUGINLIB_EXPORT_CLASS(spencer_tracking_rviz_plugin::SocialActivitiesDisplay, rviz::Display)\n"
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"text": "#include \"person_display_common.h\"\n\n#include <boost/lexical_cast.hpp>\n#include <boost/tokenizer.hpp>\n#include <boost/foreach.hpp>\n#define foreach BOOST_FOREACH\n\n\nnamespace spencer_tracking_rviz_plugin\n{\n// The constructor must have no arguments, so we can't give the\n// constructor the parameters it needs to fully initialize.\nPersonDisplayCommonProperties::PersonDisplayCommonProperties(rviz::Display* display, StylesChangedSubscriber* stylesChangedSubscriber)\n : m_display(display), m_stylesChangedSubscriber(stylesChangedSubscriber)\n{\n style = new rviz::EnumProperty( \"Style\", \"Cylinders\", \"Rendering mode to use, in order of computational complexity.\", m_display, SLOT(stylesChanged()), this );\n style->addOption( \"Simple\", STYLE_SIMPLE );\n style->addOption( \"Cylinders\", STYLE_CYLINDER );\n style->addOption( \"Person meshes\", STYLE_PERSON_MESHES );\n style->addOption( \"Bounding boxes\", STYLE_BOUNDING_BOXES );\n style->addOption( \"Crosshairs\", STYLE_CROSSHAIRS );\n\n color_transform = new rviz::EnumProperty( \"Color transform\", \"Rainbow\", \"How to color the tracked persons\", m_display, SLOT(stylesChanged()), this );\n color_transform->addOption( \"SRL Tracking Colors\", COLORS_SRL );\n color_transform->addOption( \"Alternative SRL colors\", COLORS_SRL_ALTERNATIVE );\n color_transform->addOption( \"Rainbow\", COLORS_RAINBOW );\n color_transform->addOption( \"Rainbow + B/W\", COLORS_RAINBOW_BW );\n color_transform->addOption( \"Flat\", COLORS_FLAT );\n color_transform->addOption( \"Vintage\", COLORS_VINTAGE );\n color_transform->addOption( \"Constant color\", COLORS_CONSTANT );\n\n constant_color = new rviz::ColorProperty(\"Color\", QColor( 130, 130, 130 ), \"Color for tracked persons if using constant color transform.\", m_display, SLOT(stylesChanged()), this );\n\n color_map_offset = new rviz::IntProperty( \"Color map offset\", 0, \"By how many indices to shift the offset in the color map (useful if not happy with the current colors)\", m_display, SLOT(stylesChanged()), this);\n color_map_offset->setMin( 0 );\n\n alpha = new rviz::FloatProperty( \"Alpha\", 1.0, \"0 is fully transparent, 1.0 is fully opaque.\", m_display, SLOT(stylesChanged()), this);\n alpha->setMin( 0.0 );\n alpha->setMax( 1.0 );\n\n line_width = new rviz::FloatProperty( \"Line width\", 0.05, \"Line width for person visual\", style, SLOT(stylesChanged()), this);\n line_width->setMin( 0.0 );\n line_width->setMax( 1.0 );\n \n scaling_factor = new rviz::FloatProperty( \"Scaling factor\", 1.0, \"Scaling factor for person visual\", style);\n scaling_factor->setMin( 0.0 );\n scaling_factor->setMax( 100.0 );\n\n font_color_style = new rviz::EnumProperty( \"Font color style\", \"Same color\", \"Which type of font coloring to use\", m_display, SLOT(stylesChanged()), this );\n font_color_style->addOption( \"Same color\", FONT_COLOR_FROM_PERSON );\n font_color_style->addOption( \"Constant color\", FONT_COLOR_CONSTANT );\n\n constant_font_color = new rviz::ColorProperty(\"Font color\", QColor( 255, 255, 255 ), \"Font color if using a constant color\", m_display, SLOT(stylesChanged()), this );\n\n font_scale = new rviz::FloatProperty( \"Font scale\", 2.0, \"Larger values mean bigger font\", m_display);\n font_scale->setMin( 0.0 );\n\n z_offset = new rviz::FloatProperty( \"Z offset\", 0.0, \"Offset of all visualizations on the z (height) axis\", m_display, SLOT(stylesChanged()), this);\n\n use_actual_z_position = new rviz::BoolProperty( \"Use Z position from message\", false, \"Use Z position from message (otherwise place above ground plane)\", z_offset, SLOT(stylesChanged()), this);\n\n m_excluded_person_ids_property = new rviz::StringProperty( \"Excluded person IDs\", \"\", \"Comma-separated list of person IDs whose visualization should be hidden\", m_display, SLOT(stylesChanged()), this );\n m_included_person_ids_property = new rviz::StringProperty( \"Included person IDs\", \"\", \"Comma-separated list of person IDs whose visualization should be visible. Overrides excluded IDs.\", m_display, SLOT(stylesChanged()), this );\n\n hideIrrelevantProperties();\n}\n\nvoid PersonDisplayCommonProperties::hideIrrelevantProperties()\n{\n constant_color->setHidden(color_transform->getOptionInt() != COLORS_CONSTANT);\n color_map_offset->setHidden(color_transform->getOptionInt() == COLORS_CONSTANT);\n constant_font_color->setHidden(font_color_style->getOptionInt() != FONT_COLOR_CONSTANT);\n\n line_width->setHidden(style->getOptionInt() != STYLE_BOUNDING_BOXES && style->getOptionInt() != STYLE_CROSSHAIRS);\n}\n\n// Callback for any changed style\nvoid PersonDisplayCommonProperties::stylesChanged()\n{\n hideIrrelevantProperties();\n\n // Update list of person IDs that shall be hidden or visible\n m_excludedPersonIDs.clear();\n {\n string personIDString = m_excluded_person_ids_property->getStdString();\n char_separator<char> separator(\",\");\n tokenizer< char_separator<char> > tokens(personIDString, separator);\n foreach(const string& token, tokens) {\n try { m_excludedPersonIDs.insert(lexical_cast<person_id>(token)); }\n catch(bad_lexical_cast &) {}\n }\n }\n m_includedPersonIDs.clear();\n {\n string personIDString = m_included_person_ids_property->getStdString();\n char_separator<char> separator(\",\");\n tokenizer< char_separator<char> > tokens(personIDString, separator);\n foreach(const string& token, tokens) {\n try { m_includedPersonIDs.insert(lexical_cast<person_id>(token)); }\n catch(bad_lexical_cast &) {}\n }\n }\n\n // Relay change to other subscribers\n m_stylesChangedSubscriber->stylesChanged();\n}\n\n\n} // end namespace spencer_tracking_rviz_plugin\n"
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"text": "#include <rviz/visualization_manager.h>\n#include <rviz/frame_manager.h>\n#include \"rviz/selection/selection_manager.h\"\n\n#include \"tracked_groups_display.h\"\n\n#include <boost/lexical_cast.hpp>\n#include <boost/tokenizer.hpp>\n#include <boost/foreach.hpp>\n#define foreach BOOST_FOREACH\n\nnamespace spencer_tracking_rviz_plugin\n{\n\nvoid TrackedGroupsDisplay::onInitialize()\n{\n m_realFixedFrame = \"map\";\n\n m_trackedPersonsCache.initialize(this, context_, update_nh_);\n PersonDisplayCommon::onInitialize();\n \n QObject::connect(m_commonProperties->style, SIGNAL(changed()), this, SLOT(personVisualTypeChanged()) );\n\n m_excluded_group_ids_property = new rviz::StringProperty( \"Excluded group IDs\", \"\", \"Comma-separated list of group IDs whose group visualization should be hidden\", this, SLOT(stylesChanged()) );\n m_included_group_ids_property = new rviz::StringProperty( \"Included group IDs\", \"\", \"Comma-separated list of group IDs whose group visualization should be visible, overrides excluded\", this, SLOT(stylesChanged()) );\n\n m_render_intragroup_connections_property = new rviz::BoolProperty( \"Connect group members\", true, \"Connect all members of a group by lines\", this, SLOT(stylesChanged()));\n m_render_ids_property = new rviz::BoolProperty( \"Render group IDs\", true, \"Render group IDs as text\", this, SLOT(stylesChanged()));\n m_render_history_property = new rviz::BoolProperty( \"Render history\", false, \"Render group affiliation history\", this, SLOT(stylesChanged()));\n \n m_single_person_groups_in_constant_color_property = new rviz::BoolProperty( \"Single-person groups in constant color\", true, \"Render single-person groups in constant color\", this, SLOT(stylesChanged()));\n m_hide_ids_of_single_person_groups_property = new rviz::BoolProperty( \"Hide IDs of single-person groups\", false, \"Hide IDs of single-person groups\", m_render_ids_property, SLOT(stylesChanged()), this);\n\n m_history_length_property = new rviz::IntProperty( \"Global history size\", 1000, \"Global number of group affiliation history entries to display.\", this, SLOT(stylesChanged()));\n m_history_length_property->setMin( 1 );\n m_history_length_property->setMax( 10000000 );\n\n m_occlusion_alpha_property = new rviz::FloatProperty( \"Occlusion alpha\", 0.5, \"Alpha multiplier for history of occluded tracks\", this, SLOT(stylesChanged()) );\n m_occlusion_alpha_property->setMin( 0.0 );\n\n m_group_id_offset = new rviz::FloatProperty( \"Group ID Z offset\", 2.0, \"Offset in z position (height) of the group ID text\", this, SLOT(stylesChanged()) );\n\n // Create a scene node for visualizing group affiliation history\n m_groupAffiliationHistorySceneNode = shared_ptr<Ogre::SceneNode>(scene_node_->createChildSceneNode());\n m_groupsSceneNode = shared_ptr<Ogre::SceneNode>(scene_node_->createChildSceneNode());\n}\n\nTrackedGroupsDisplay::~TrackedGroupsDisplay()\n{\n}\n\n// Clear the visuals by deleting their objects.\nvoid TrackedGroupsDisplay::reset()\n{\n PersonDisplayCommon::reset();\n m_trackedPersonsCache.reset();\n m_groupVisuals.clear();\n m_groupAffiliationHistory.clear();\n m_groupAffiliations.clear();\n}\n\nvoid TrackedGroupsDisplay::update(float wall_dt, float ros_dt)\n{\n // Move map scene node\n Ogre::Vector3 mapFramePosition; Ogre::Quaternion mapFrameOrientation;\n getContext()->getFrameManager()->getTransform(m_realFixedFrame, ros::Time(0), mapFramePosition, mapFrameOrientation);\n Ogre::Matrix4 mapFrameTransform(mapFrameOrientation); mapFrameTransform.setTrans(mapFramePosition);\n m_groupAffiliationHistorySceneNode->setPosition(mapFramePosition);\n m_groupAffiliationHistorySceneNode->setOrientation(mapFrameOrientation);\n}\n\nbool TrackedGroupsDisplay::isGroupHidden(group_id groupId) {\n bool isIncluded = m_includedGroupIDs.find(groupId) != m_includedGroupIDs.end();\n if(isIncluded) return false;\n if(!m_includedGroupIDs.empty()) return true;\n\n return m_excludedGroupIDs.find(groupId) != m_excludedGroupIDs.end();\n}\n\nvoid TrackedGroupsDisplay::stylesChanged()\n{\n // Get list of group IDs belonging to tracks that shall be hidden or visible\n m_excludedGroupIDs.clear();\n {\n string groupIDString = m_excluded_group_ids_property->getStdString();\n char_separator<char> separator(\",\");\n tokenizer< char_separator<char> > tokens(groupIDString, separator);\n foreach(const string& token, tokens) {\n try { m_excludedGroupIDs.insert(lexical_cast<track_id>(token)); }\n catch(bad_lexical_cast &) {}\n }\n }\n m_includedGroupIDs.clear();\n {\n string groupIDString = m_included_group_ids_property->getStdString();\n char_separator<char> separator(\",\");\n tokenizer< char_separator<char> > tokens(groupIDString, separator);\n foreach(const string& token, tokens) {\n try { m_includedGroupIDs.insert(lexical_cast<track_id>(token)); }\n catch(bad_lexical_cast &) {}\n }\n }\n\n foreach(shared_ptr<GroupVisual> groupVisual, m_groupVisuals) {\n updateGroupVisualStyles(groupVisual);\n }\n\n // Update history size\n m_groupAffiliationHistory.rset_capacity(m_history_length_property->getInt());\n \n // Update history color etc.\n updateHistoryStyles();\n}\n\n\n// Set the rendering style (cylinders, meshes, ...) of tracked persons\nvoid TrackedGroupsDisplay::personVisualTypeChanged()\n{\n foreach(shared_ptr<GroupVisual> groupVisual, m_groupVisuals) {\n foreach(shared_ptr<PersonVisual>& personVisual, groupVisual->personVisuals) {\n Ogre::SceneNode* parentSceneNode = personVisual->getParentSceneNode();\n personVisual.reset();\n createPersonVisualIfRequired(parentSceneNode, personVisual);\n }\n }\n stylesChanged();\n}\n\nvoid TrackedGroupsDisplay::updateGroupVisualStyles(shared_ptr<GroupVisual>& groupVisual)\n{\n bool hideGroup = isGroupHidden(groupVisual->groupId);\n\n // Apply current group color\n Ogre::ColourValue groupColor = m_commonProperties->constant_color->getOgreColor();\n\n if(groupVisual->personCount > 1 || !m_single_person_groups_in_constant_color_property->getBool()) {\n groupColor = getColorFromId(groupVisual->groupId);\n } \n\n groupColor.a *= m_commonProperties->alpha->getFloat(); // general alpha\n if(hideGroup) groupColor.a = 0;\n\n foreach(shared_ptr<rviz::Shape> groupAssignmentCircle, groupVisual->groupAssignmentCircles) {\n groupAssignmentCircle->setColor(groupColor.r, groupColor.g, groupColor.b, groupColor.a);\n }\n\n foreach(shared_ptr<PersonVisual>& personVisual, groupVisual->personVisuals) {\n if(personVisual) {\n // Update common styles to person visual, such as line width\n applyCommonStyles(personVisual);\n \n personVisual->setColor(groupColor);\n }\n }\n\n double connectionLineVisibilityAlpha = m_render_intragroup_connections_property->getBool() ? 1.0 : 0.0;\n foreach(shared_ptr<rviz::BillboardLine> connectionLine, groupVisual->connectionLines) {\n connectionLine->setColor(groupColor.r, groupColor.g, groupColor.b, groupColor.a * connectionLineVisibilityAlpha);\n connectionLine->setLineWidth(0.05);\n }\n\n // Update text colors, size and visibility\n Ogre::ColourValue fontColor = m_commonProperties->font_color_style->getOptionInt() == FONT_COLOR_CONSTANT ? m_commonProperties->constant_font_color->getOgreColor() : groupColor;\n fontColor.a = m_commonProperties->alpha->getFloat();\n if(hideGroup) fontColor.a = 0;\n bool groupIdVisible = groupVisual->personCount > 1 ? true : !m_hide_ids_of_single_person_groups_property->getBool();\n groupVisual->idText->setVisible(m_render_ids_property->getBool() && groupIdVisible);\n groupVisual->idText->setCharacterHeight(0.23 * m_commonProperties->font_scale->getFloat());\n groupVisual->idText->setColor(fontColor);\n groupVisual->idText->setPosition(m_frameTransform * Ogre::Vector3(\n groupVisual->groupCenter.x,\n groupVisual->groupCenter.y,\n groupVisual->groupCenter.z + m_group_id_offset->getFloat() + m_commonProperties->z_offset->getFloat()));\n}\n\nvoid TrackedGroupsDisplay::updateHistoryStyles()\n{\n const Ogre::Quaternion shapeQuaternion( Ogre::Degree(90), Ogre::Vector3(1,0,0) );\n foreach(shared_ptr<GroupAffiliationHistoryEntry> groupAffiliationHistoryEntry, m_groupAffiliationHistory) {\n bool hideGroup = isGroupHidden(groupAffiliationHistoryEntry->groupId);\n const double historyShapeDiameter = 0.1;\n\n Ogre::ColourValue historyColor = m_commonProperties->constant_color->getOgreColor();\n\n if(!groupAffiliationHistoryEntry->wasSinglePersonGroup || !m_single_person_groups_in_constant_color_property->getBool()) {\n historyColor = getColorFromId(groupAffiliationHistoryEntry->groupId);\n } \n\n\n historyColor.a = m_commonProperties->alpha->getFloat();\n\n if(groupAffiliationHistoryEntry->wasOccluded) historyColor.a *= m_occlusion_alpha_property->getFloat();\n if(hideGroup) historyColor.a = 0;\n if(!m_render_history_property->getBool()) historyColor.a = 0;\n\n groupAffiliationHistoryEntry->shape->setColor(historyColor);\n groupAffiliationHistoryEntry->shape->setScale(shapeQuaternion * Ogre::Vector3(historyShapeDiameter, historyShapeDiameter, 0.05));\n }\n}\n\n// This is our callback to handle an incoming group message.\nvoid TrackedGroupsDisplay::processMessage(const spencer_tracking_msgs::TrackedGroups::ConstPtr& msg)\n{\n // Get transforms into fixed frame etc.\n if(!preprocessMessage(msg)) return;\n\n // Transform from map/odometry frame into fixed frame, required to display track history if the fixed frame is not really \"fixed\" (e.g. base_link)\n Ogre::Vector3 mapFramePosition; Ogre::Quaternion mapFrameOrientation;\n getContext()->getFrameManager()->getTransform(m_realFixedFrame, msg->header.stamp, mapFramePosition, mapFrameOrientation);\n Ogre::Matrix4 mapFrameTransform(mapFrameOrientation); mapFrameTransform.setTrans(mapFramePosition);\n\n // Transform into Rviz fixed frame\n m_frameTransform = Ogre::Matrix4(m_frameOrientation);\n m_frameTransform.setTrans(m_framePosition);\n\n const Ogre::Quaternion shapeQuaternion( Ogre::Degree(90), Ogre::Vector3(1,0,0) ); // required to fix orientation of any Cylinder shapes\n stringstream ss;\n\n // Clear previous visualization\n m_groupVisuals.clear();\n m_groupsSceneNode->removeAndDestroyAllChildren();\n\n unsigned int numTracksWithUnknownPosition = 0;\n m_groupAffiliations.clear();\n\n //\n // Iterate over all groups in this message\n //\n foreach (const spencer_tracking_msgs::TrackedGroup& trackedGroup, msg->groups)\n {\n // Create a new visual representation of the tracked group\n shared_ptr<GroupVisual> groupVisual = shared_ptr<GroupVisual>(new GroupVisual);\n groupVisual->groupId = trackedGroup.group_id;\n groupVisual->personCount = trackedGroup.track_ids.size();\n m_groupVisuals.push_back(groupVisual);\n\n //\n // Group visualization circles, person visuals (if enabled) + connections between group members\n //\n\n for(size_t trackIndex = 0; trackIndex < trackedGroup.track_ids.size(); trackIndex++)\n {\n const track_id trackId = trackedGroup.track_ids[trackIndex];\n shared_ptr<CachedTrackedPerson> trackedPerson = m_trackedPersonsCache.lookup(trackId);\n\n // Get current track position\n if(!trackedPerson) {\n numTracksWithUnknownPosition++;\n }\n else\n {\n Ogre::Vector3 trackCenterAtGroundPlane(trackedPerson->center.x, trackedPerson->center.y, m_commonProperties->z_offset->getFloat());\n\n m_groupAffiliations[trackId] = trackedGroup.group_id; // required to hide certain groups later on\n\n\n //\n // Group visualization circles (below tracks)\n //\n\n const double groupAssignmentCircleHeight = 0;\n const double groupAssignmentCircleDiameter = 0.9;\n shared_ptr<rviz::Shape> groupAssignmentCircle = shared_ptr<rviz::Shape>(new rviz::Shape(rviz::Shape::Cylinder, context_->getSceneManager(), m_groupsSceneNode.get()));\n\n groupAssignmentCircle->setScale(shapeQuaternion * Ogre::Vector3(groupAssignmentCircleDiameter, groupAssignmentCircleDiameter, groupAssignmentCircleHeight));\n groupAssignmentCircle->setOrientation(shapeQuaternion);\n\n Ogre::Vector3 groupAssignmentCirclePos = trackCenterAtGroundPlane + Ogre::Vector3(0, 0, -0.5*groupAssignmentCircleHeight - 0.01);\n groupAssignmentCircle->setPosition(groupAssignmentCirclePos);\n\n groupVisual->groupAssignmentCircles.push_back(groupAssignmentCircle);\n\n\n //\n // Person visuals (colored in group color)\n //\n\n // This scene node is the parent of all visualization elements for the tracked person\n shared_ptr<Ogre::SceneNode> sceneNode = shared_ptr<Ogre::SceneNode>(scene_node_->createChildSceneNode());\n groupVisual->personVisualSceneNodes.push_back(sceneNode);\n\n // Create new visual for the person itself, if needed\n shared_ptr<PersonVisual> personVisual;\n createPersonVisualIfRequired(sceneNode.get(), personVisual);\n groupVisual->personVisuals.push_back(personVisual);\n\n const double personHeight = personVisual ? personVisual->getHeight() : 0;\n const Ogre::Matrix3 covXYZinTargetFrame = covarianceXYZIntoTargetFrame(trackedPerson->pose);\n setPoseOrientation(sceneNode.get(), trackedPerson->pose, covXYZinTargetFrame, personHeight);\n\n\n //\n // Intra-group connections\n //\n\n // Iterate over all neighbouring group tracks to render intra-group connections\n for(size_t otherTrackIndex = trackIndex + 1; otherTrackIndex < trackedGroup.track_ids.size(); otherTrackIndex++)\n {\n const track_id otherTrackId = trackedGroup.track_ids[otherTrackIndex];\n shared_ptr<CachedTrackedPerson> otherTrackedPerson = m_trackedPersonsCache.lookup(otherTrackId);\n\n // Get other track's position\n if(otherTrackedPerson) {\n // Get positions. These are already in fixed frame coordinates!\n const Ogre::Vector3 verticalShift(0,0, 0.5 + m_commonProperties->z_offset->getFloat());\n const Ogre::Vector3& position1 = verticalShift + trackedPerson->center;\n const Ogre::Vector3& position2 = verticalShift + otherTrackedPerson->center;\n\n // Add line connecting the two tracks\n shared_ptr<rviz::BillboardLine> connectionLine(new rviz::BillboardLine(context_->getSceneManager(), m_groupsSceneNode.get()));\n connectionLine->setMaxPointsPerLine(2);\n connectionLine->addPoint(position1);\n connectionLine->addPoint(position2);\n groupVisual->connectionLines.push_back(connectionLine);\n }\n } // end loop over neighbouring group tracks\n\n\n //\n // Group affiliation history\n //\n \n shared_ptr<GroupAffiliationHistoryEntry> newHistoryEntry(new GroupAffiliationHistoryEntry);\n newHistoryEntry->shape = shared_ptr<rviz::Shape>(new rviz::Shape(rviz::Shape::Cylinder, context_->getSceneManager(), m_groupAffiliationHistorySceneNode.get()));\n newHistoryEntry->shape->setPosition(mapFrameTransform.inverse() * trackCenterAtGroundPlane);\n newHistoryEntry->shape->setOrientation(shapeQuaternion);\n newHistoryEntry->wasOccluded = trackedPerson->isOccluded;\n newHistoryEntry->wasSinglePersonGroup = trackedGroup.track_ids.size() <= 1;\n newHistoryEntry->groupId = trackedGroup.group_id;\n m_groupAffiliationHistory.push_back(newHistoryEntry);\n\n\n } // end if track found\n } // end for loop over tracks\n\n\n //\n // Texts\n //\n const geometry_msgs::Point& groupCenter = trackedGroup.centerOfGravity.pose.position;\n groupVisual->groupCenter = groupCenter;\n\n // Group ID\n shared_ptr<TextNode> idText(new TextNode(context_->getSceneManager(), m_groupsSceneNode.get()));\n ss.str(\"\"); ss << \"group \" << trackedGroup.group_id;\n idText->setCaption(ss.str());\n idText->showOnTop();\n groupVisual->idText = idText;\n\n // Set adjustable styles such as color etc.\n updateGroupVisualStyles(groupVisual);\n updateHistoryStyles();\n } // end for loop over all tracked groups\n\n\n //\n // Update status (shown in property pane)\n //\n\n ss.str(\"\");\n ss << msg->groups.size() << \" group(s)\";\n setStatusStd(rviz::StatusProperty::Ok, \"Groups\", ss.str());\n\n ss.str(\"\");\n ss << numTracksWithUnknownPosition << \" track(s) with unknown position\";\n setStatusStd(0 == numTracksWithUnknownPosition ? rviz::StatusProperty::Ok : rviz::StatusProperty::Warn, \"Track-to-group assignment\", ss.str());\n}\n\n} // end namespace spencer_tracking_rviz_plugin\n\n// Tell pluginlib about this class. 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"text": "/**\n* Copyright 2014 Social Robotics Lab, University of Freiburg\n*\n* Redistribution and use in source and binary forms, with or without\n* modification, are permitted provided that the following conditions are met:\n*\n* # Redistributions of source code must retain the above copyright\n* notice, this list of conditions and the following disclaimer.\n* # Redistributions in binary form must reproduce the above copyright\n* notice, this list of conditions and the following disclaimer in the\n* documentation and/or other materials provided with the distribution.\n* # Neither the name of the University of Freiburg nor the names of its\n* contributors may be used to endorse or promote products derived from\n* this software without specific prior written permission.\n*\n* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n* POSSIBILITY OF SUCH DAMAGE.\n*\n* \\author Billy Okal <[email protected]>\n* \\author Sven Wehner <[email protected]>\n*/\n\n#include <pedsim_simulator/force/randomforce.h>\n#include <pedsim_simulator/config.h>\n#include <pedsim_simulator/rng.h>\n#include <pedsim_simulator/scene.h>\n\n#include <ros/ros.h>\n\nRandomForce::RandomForce ( Agent* agentIn )\n : Force ( agentIn )\n{\n // initialize values\n setFactor ( CONFIG.forceRandom );\n fadingDuration = 1;\n nextDeviation = computeNewDeviation();\n\n // connect signals\n connect ( &CONFIG, SIGNAL ( forceFactorRandomChanged ( double ) ),\n this, SLOT ( onForceFactorChanged ( double ) ) );\n}\n\nvoid RandomForce::onForceFactorChanged ( double valueIn )\n{\n setFactor ( valueIn );\n}\n\nvoid RandomForce::setFadingTime ( double durationIn )\n{\n // sanity checks\n if ( durationIn < 0 )\n {\n\t\tROS_DEBUG(\"Cannot set fading time to invalid value: %f\", durationIn);\n return;\n }\n\n fadingDuration = durationIn;\n}\n\ndouble RandomForce::getFadingTime() const\n{\n return fadingDuration;\n}\n\nPed::Tvector RandomForce::computeNewDeviation()\n{\n // set up random distributions\n uniform_real_distribution<double> angleDistribution ( 0, 360 );\n double deviationAngle = angleDistribution ( RNG() );\n normal_distribution<double> distanceDistribution ( 0, 1 );\n double deviationDistance = distanceDistribution ( RNG() );\n\n // create deviation from polar coordinates\n Ped::Tvector deviation = Ped::Tvector::fromPolar ( Ped::Tangle::fromDegree ( deviationAngle ), deviationDistance );\n return deviation;\n}\n\nPed::Tvector RandomForce::getForce ( Ped::Tvector walkingDirection )\n{\n // use the current time to compute the fading progress\n double time = SCENE.getTime();\n double progress = fmod ( time, fadingDuration );\n\n // create a new fading goal when necessary\n if ( progress < CONFIG.getTimeStepSize() )\n {\n lastDeviation = nextDeviation;\n nextDeviation = computeNewDeviation();\n }\n\n // compute the force\n Ped::Tvector force = ( 1-progress ) *lastDeviation + progress*nextDeviation;\n\n // scale force\n force *= factor;\n\n return force;\n}\n\nQString RandomForce::toString() const\n{\n return tr ( \"RandomForce (fading duration: %1; factor: %2)\" )\n .arg ( fadingDuration )\n .arg ( factor );\n}\n"
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"text": "/*\n * Copyright (c) Social Robotics Laboratory\n * All rights reserved.\n *\n * Redistribution and use in source and binary forms, with or without\n * modification, are permitted provided that the following conditions\n * are met:\n *\n * 1. Redistributions of source code must retain the above copyright\n * notice, this list of conditions and the following disclaimer.\n *\n * 2. Redistributions in binary form must reproduce the above\n * copyright notice, this list of conditions and the following\n * disclaimer in the documentation and/or other materials provided\n * with the distribution.\n *\n * 3. Neither the name of the copyright holder nor the names of its\n * contributors may be used to endorse or promote products derived\n * from this software without specific prior written permission.\n *\n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n * \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\n * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE\n * COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,\n * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\n * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN\n * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n * POSSIBILITY OF SUCH DAMAGE.\n *\n * \\author Billy Okal <[email protected]>\n */\n\n#include <tf/transform_listener.h> // must come first due to conflict with Boost signals\n\n/// ros\n#include <ros/ros.h>\n\n/// meta\n#include <random>\n#include <cstdlib>\n#include <cmath>\n\n/// data\n#include <sensor_msgs/PointCloud.h>\n#include <nav_msgs/GridCells.h>\n#include <nav_msgs/Odometry.h>\n#include <geometry_msgs/Point.h>\n#include <geometry_msgs/PoseStamped.h>\n#include <spencer_tracking_msgs/TrackedPerson.h>\n#include <spencer_tracking_msgs/TrackedPersons.h>\n\n/// -----------------------------------------------------------\n/// \\class PedsimCloud\n/// \\brief Receives data from pedsim containing obstacles and\n/// persons and published it as point clouds\n/// -----------------------------------------------------------\nclass PedsimCloud {\npublic:\n PedsimCloud(const ros::NodeHandle& node)\n : nh_(node)\n {\n // set up subscribers\n sub_grid_cells_ = nh_.subscribe(\"/pedsim/static_obstacles\", 1, &PedsimCloud::callbackGridCells, this);\n sub_tracked_persons_ = nh_.subscribe(\"/pedsim/tracked_persons\", 1, &PedsimCloud::callbackTrackedPersons, this);\n sub_robot_odom_ = nh_.subscribe(\"/pedsim/robot_position\", 1, &PedsimCloud::callbackRobotOdom, this);\n\n // set up publishers\n // publisher for static obstacles as point clouds\n pub_point_cloud_global_ = nh_.advertise<sensor_msgs::PointCloud>(\"/pedsim/obstacle_cloud_global\", 1);\n pub_point_cloud_local_ = nh_.advertise<sensor_msgs::PointCloud>(\"/pedsim/obstacle_cloud_local\", 1);\n // publisher for dynamic obstacles (people) as point clouds\n pub_people_cloud_global_ = nh_.advertise<sensor_msgs::PointCloud>(\"/pedsim/people_cloud_global\", 1);\n pub_people_cloud_local_ = nh_.advertise<sensor_msgs::PointCloud>(\"/pedsim/people_cloud_local\", 1);\n\n // setup TF listener for obtaining robot position\n transform_listener_ = boost::make_shared<tf::TransformListener>();\n\n robot_position_.clear();\n robot_position_.resize(2);\n robot_position_ = { 0, 0 };\n\n robot_frame_ = \"odom\";\n\n // read local map dimensions\n nh_.param(\"/pedsim_point_clouds/local_width\", local_width_, 3.0);\n nh_.param(\"/pedsim_point_clouds/local_height\", local_height_, 3.0);\n }\n virtual ~PedsimCloud()\n {\n sub_grid_cells_.shutdown();\n pub_point_cloud_global_.shutdown();\n pub_point_cloud_local_.shutdown();\n pub_people_cloud_global_.shutdown();\n pub_people_cloud_local_.shutdown();\n }\n\n // control\n void run();\n\n // subscriber callbacks\n void callbackGridCells(const nav_msgs::GridCells::ConstPtr& msg);\n void callbackTrackedPersons(const spencer_tracking_msgs::TrackedPersons::ConstPtr& msg);\n void callbackRobotOdom(const nav_msgs::Odometry::ConstPtr& msg);\n\nprivate:\n ros::NodeHandle nh_;\n\n // robot position\n std::vector<double> robot_position_;\n\n // local zone around robot (used in local costmaps)\n double local_width_;\n double local_height_;\n std::string robot_frame_;\n\n // publishers\n ros::Publisher pub_point_cloud_global_;\n ros::Publisher pub_point_cloud_local_;\n ros::Publisher pub_people_cloud_global_;\n ros::Publisher pub_people_cloud_local_;\n\n // subscribers\n ros::Subscriber sub_grid_cells_;\n ros::Subscriber sub_tracked_persons_;\n ros::Subscriber sub_robot_odom_;\n\n // Transform listener coverting people poses to be relative to the robot\n boost::shared_ptr<tf::TransformListener> transform_listener_;\n\nprotected:\n // check if a point is in the local zone of the robot\n bool inLocalZone(const std::array<double, 2>& point);\n};\n\n/// -----------------------------------------------------------\n/// \\function run\n/// \\brief Run the node\n/// -----------------------------------------------------------\nvoid PedsimCloud::run()\n{\n ros::Rate r(30); // Hz\n while (ros::ok()) {\n ros::spinOnce();\n r.sleep();\n }\n}\n\n/// -----------------------------------------------------------\n/// \\function inLocalZone\n/// \\brief Check is a point (e.g. center of person or obstacle)\n/// is within the local zone of the robot to be included in the\n/// robot's local costmap for planning and other higher level\n/// cognition\n/// -----------------------------------------------------------\nbool PedsimCloud::inLocalZone(const std::array<double, 2>& point)\n{\n // NOTE - this hack expands the local map, but that should not cause any\n // issue since we are mainly interested in not missing anythin in the\n // local region\n const double r = std::max(local_width_, local_height_);\n const double dist = std::hypot(robot_position_[0] - point[0], robot_position_[1] - point[1]);\n\n if (dist <= r)\n return true;\n else\n return false;\n}\n\n/// -----------------------------------------------------------\n/// \\function callbackGridCells\n/// \\brief Receives grid cells fro pedsim to convert into pcs\n/// -----------------------------------------------------------\nvoid PedsimCloud::callbackGridCells(const nav_msgs::GridCells::ConstPtr& msg)\n{\n const unsigned int mplex = 200;\n int num_points = msg->cells.size() * mplex;\n\n // global cloud\n sensor_msgs::PointCloud cloud_global;\n cloud_global.header.stamp = ros::Time::now();\n cloud_global.header.frame_id = \"odom\";\n cloud_global.points.resize(num_points);\n cloud_global.channels.resize(1);\n cloud_global.channels[0].name = \"intensities\";\n cloud_global.channels[0].values.resize(num_points);\n\n // local obstacle cloud\n sensor_msgs::PointCloud cloud_local;\n cloud_local.header.stamp = ros::Time::now();\n cloud_local.header.frame_id = robot_frame_;\n cloud_local.points.resize(num_points);\n cloud_local.channels.resize(1);\n cloud_local.channels[0].name = \"intensities\";\n cloud_local.channels[0].values.resize(num_points);\n\n // processing\n std::default_random_engine generator;\n std::uniform_real_distribution<float> float_dist(0, 2);\n std::uniform_real_distribution<float> wide_dist(0, 1);\n\n // Get the positions of people relative to the robot via TF transform\n tf::StampedTransform tfTransform;\n try {\n transform_listener_->lookupTransform(robot_frame_, msg->header.frame_id, ros::Time(0), tfTransform);\n }\n catch (tf::TransformException& e) {\n ROS_WARN_STREAM_THROTTLE(5.0, \"TF lookup from base_footprint to odom failed. Reason: \" << e.what());\n return;\n }\n\n int index = 0;\n for (unsigned int i = 0; i < msg->cells.size(); i++) {\n geometry_msgs::Point cell = msg->cells[i];\n std::array<double, 2> obstacle = { cell.x, cell.y };\n const bool inside = inLocalZone(obstacle);\n\n for (unsigned int j = 0; j < mplex; j++) {\n // positions relative the robot (local)\n if (inside) {\n tf::Pose source;\n source.setOrigin(tf::Vector3(cell.x + wide_dist(generator), cell.y + wide_dist(generator), 0));\n tf::Matrix3x3 identity;\n identity.setIdentity();\n source.setBasis(identity);\n /// Apply the proper transform\n tf::Pose result = tfTransform * source;\n\n cloud_local.points[index].x = result.getOrigin().x();\n cloud_local.points[index].y = result.getOrigin().y();\n cloud_local.points[index].z = cell.z + float_dist(generator); // random points in a line\n cloud_local.channels[0].values[index] = 80;\n }\n\n // global positions\n cloud_global.points[index].x = cell.x + wide_dist(generator);\n cloud_global.points[index].y = cell.y + wide_dist(generator);\n cloud_global.points[index].z = cell.z + float_dist(generator); // random points in a line\n cloud_global.channels[0].values[index] = 50;\n\n index++;\n }\n }\n\n // avoid publishing empty local clouds\n if (cloud_local.channels[0].values.size() > 1)\n pub_point_cloud_local_.publish(cloud_local);\n\n pub_point_cloud_global_.publish(cloud_global);\n}\n\n/// -----------------------------------------------------------\n/// \\function callbackTrackedPersons\n/// \\brief Receives tracked persons messages and saves them\n/// -----------------------------------------------------------\nvoid PedsimCloud::callbackTrackedPersons(const spencer_tracking_msgs::TrackedPersons::ConstPtr& msg)\n{\n const unsigned int mplex = 100;\n int num_points = msg->tracks.size() * mplex;\n\n // global cloud\n sensor_msgs::PointCloud cloud_global;\n cloud_global.header.stamp = ros::Time::now();\n cloud_global.header.frame_id = \"odom\";\n cloud_global.points.resize(num_points);\n cloud_global.channels.resize(1);\n cloud_global.channels[0].name = \"intensities\";\n cloud_global.channels[0].values.resize(num_points);\n\n // local obstacle cloud\n sensor_msgs::PointCloud cloud_local;\n cloud_local.header.stamp = ros::Time::now();\n cloud_local.header.frame_id = robot_frame_;\n cloud_local.points.resize(num_points);\n cloud_local.channels.resize(1);\n cloud_local.channels[0].name = \"intensities\";\n cloud_local.channels[0].values.resize(num_points);\n // make some random intensities for the persons\n std::default_random_engine generator;\n std::uniform_int_distribution<int> int_dist(10, 255);\n std::uniform_real_distribution<float> float_dist(0, 1.8);\n std::uniform_real_distribution<float> wide_dist(0, 0.24);\n\n // Get the positions of people relative to the robot via TF transform\n tf::StampedTransform tfTransform;\n try {\n transform_listener_->lookupTransform(robot_frame_, msg->header.frame_id, ros::Time(0), tfTransform);\n }\n catch (tf::TransformException& e) {\n ROS_WARN_STREAM_THROTTLE(5.0, \"TFP lookup from base_footprint to odom failed. Reason: \" << e.what());\n return;\n }\n\n int index = 0;\n for (unsigned int i = 0; i < msg->tracks.size(); i++) {\n spencer_tracking_msgs::TrackedPerson p = msg->tracks[i];\n std::array<double, 2> person = { p.pose.pose.position.x, p.pose.pose.position.y };\n const bool inside = inLocalZone(person);\n\n for (unsigned int j = 0; j < mplex; j++) {\n // positions relative the robot (local)\n if (inside) {\n tf::Pose source;\n source.setOrigin(tf::Vector3(p.pose.pose.position.x + wide_dist(generator),\n p.pose.pose.position.y + wide_dist(generator),\n 0));\n tf::Matrix3x3 identity;\n identity.setIdentity();\n source.setBasis(identity);\n /// Apply the proper transform\n tf::Pose result = tfTransform * source;\n\n cloud_local.points[index].x = result.getOrigin().x();\n cloud_local.points[index].y = result.getOrigin().y();\n cloud_local.points[index].z = float_dist(generator); // random points in a line\n cloud_local.channels[0].values[index] = int_dist(generator);\n }\n\n // global\n cloud_global.points[index].x = p.pose.pose.position.x + wide_dist(generator);\n cloud_global.points[index].y = p.pose.pose.position.y + wide_dist(generator);\n cloud_global.points[index].z = float_dist(generator); // random points in a line\n cloud_global.channels[0].values[index] = int_dist(generator);\n\n index++;\n }\n }\n\n // avoid publishing empty local clouds\n if (cloud_local.channels[0].values.size() > 1)\n pub_people_cloud_local_.publish(cloud_local);\n\n pub_people_cloud_global_.publish(cloud_global);\n}\n\n/// -----------------------------------------------------------\n/// \\function callbackRobotOdom\n/// \\brief Receives robot position and cache it for use later\n/// -----------------------------------------------------------\nvoid PedsimCloud::callbackRobotOdom(const nav_msgs::Odometry::ConstPtr& msg)\n{\n robot_position_[0] = msg->pose.pose.position.x;\n robot_position_[1] = msg->pose.pose.position.y;\n\n robot_frame_ = msg->header.frame_id;\n}\n\n/// -----------------------------------------------------------\n/// main\n/// -----------------------------------------------------------\nint main(int argc, char** argv)\n{\n ros::init(argc, argv, \"pedsim_point_clouds\");\n\n ros::NodeHandle n;\n\n PedsimCloud g(n);\n g.run();\n\n return EXIT_SUCCESS;\n}\n"
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"text": "#include \"tracked_persons_cache.h\"\n\n#include <sstream>\n\n#include <boost/foreach.hpp>\n#define foreach BOOST_FOREACH\n\n\nnamespace spencer_tracking_rviz_plugin\n{\n\nTrackedPersonsCache::~TrackedPersonsCache()\n{\n m_cachedTrackedPersons.clear();\n delete m_tracked_person_subscriber;\n}\n\nvoid TrackedPersonsCache::initialize(rviz::Display* display, rviz::DisplayContext* context, ros::NodeHandle update_nh)\n{\n m_display = display;\n m_context = context;\n\n m_tracked_person_subscriber = new rviz::AdditionalTopicSubscriber<spencer_tracking_msgs::TrackedPersons>(\"Tracked persons topic\", display, context, update_nh,\n boost::bind(&TrackedPersonsCache::processTrackedPersonsMessage, this, _1));\n}\n\nvoid TrackedPersonsCache::reset()\n{\n m_cachedTrackedPersons.clear();\n}\n\nconst shared_ptr<CachedTrackedPerson> TrackedPersonsCache::lookup(track_id trackId)\n{\n CachedTrackedPersonsMap::const_iterator entry = m_cachedTrackedPersons.find(trackId);\n if(entry == m_cachedTrackedPersons.end()) return shared_ptr<CachedTrackedPerson>();\n else return entry->second;\n}\n\nvoid TrackedPersonsCache::processTrackedPersonsMessage(const spencer_tracking_msgs::TrackedPersons::ConstPtr& msg)\n{\n // Get transform of person tracks into fixed frame\n Ogre::Vector3 frameOrigin; Ogre::Quaternion frameOrientation;\n m_context->getFrameManager()->getTransform(msg->header, frameOrigin, frameOrientation);\n Ogre::Matrix4 transform(frameOrientation);\n transform.setTrans(frameOrigin);\n\n // Now iterate over all tracks and store their positions\n m_cachedTrackedPersons.clear();\n foreach(spencer_tracking_msgs::TrackedPerson trackedPerson, msg->tracks)\n {\n m_cachedTrackedPersons[trackedPerson.track_id] = shared_ptr<CachedTrackedPerson>(new CachedTrackedPerson);\n CachedTrackedPerson& cachedTrackedPerson = *m_cachedTrackedPersons[trackedPerson.track_id];\n\n const geometry_msgs::Point& position = trackedPerson.pose.pose.position;\n cachedTrackedPerson.center = transform * Ogre::Vector3(position.x, position.y, position.z);\n cachedTrackedPerson.pose = trackedPerson.pose;\n cachedTrackedPerson.twist = trackedPerson.twist;\n cachedTrackedPerson.isOccluded = trackedPerson.is_occluded;\n }\n\n std::stringstream ss;\n ss << msg->tracks.size() << \" track(s)\";\n m_display->setStatusStd(rviz::StatusProperty::Ok, \"Tracks\", ss.str());\n}\n\n\n} // end namespace spencer_tracking_rviz_plugin"
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"text": "/**\n* Copyright 2014-2016 Social Robotics Lab, University of Freiburg\n*\n* Redistribution and use in source and binary forms, with or without\n* modification, are permitted provided that the following conditions are met:\n*\n* # Redistributions of source code must retain the above copyright\n* notice, this list of conditions and the following disclaimer.\n* # Redistributions in binary form must reproduce the above copyright\n* notice, this list of conditions and the following disclaimer in the\n* documentation and/or other materials provided with the distribution.\n* # Neither the name of the University of Freiburg nor the names of its\n* contributors may be used to endorse or promote products derived from\n* this software without specific prior written permission.\n*\n* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n* POSSIBILITY OF SUCH DAMAGE.\n*\n* \\author Billy Okal <[email protected]>\n* \\author Sven Wehner <[email protected]>\n*/\n\n#include <QApplication>\n\n#include <pedsim_simulator/element/agentcluster.h>\n#include <pedsim_simulator/scene.h>\n#include <pedsim_simulator/simulator.h>\n\nconst double PERSON_MESH_SCALE = 2.0 / 8.5 * 1.8;\n\nSimulator::Simulator(const ros::NodeHandle& node)\n : nh_(node)\n{\n dynamic_reconfigure::Server<SimConfig>::CallbackType f;\n f = boost::bind(&Simulator::reconfigureCB, this, _1, _2);\n server_.setCallback(f);\n}\n\nSimulator::~Simulator()\n{\n // shutdown service servers and publishers\n pub_agent_visuals_.shutdown();\n pub_agent_arrows_.shutdown();\n pub_group_lines_.shutdown();\n pub_walls_.shutdown();\n pub_attractions_.shutdown();\n pub_queues_.shutdown();\n pub_waypoints_.shutdown();\n pub_obstacles_.shutdown();\n pub_all_agents_.shutdown();\n pub_tracked_persons_.shutdown();\n pub_tracked_groups_.shutdown();\n pub_social_activities_.shutdown();\n pub_robot_position_.shutdown();\n\n srv_pause_simulation_.shutdown();\n srv_unpause_simulation_.shutdown();\n\n delete robot_;\n\n int returnValue = 0;\n QCoreApplication::exit(returnValue);\n}\n\nvoid Simulator::robotPositionCallback(const nav_msgs::Odometry& odom) {\n gazebo_robot_odom_ = odom;\n}\n\nbool Simulator::initializeSimulation()\n{\n ros::NodeHandle private_nh(\"~\");\n\n int queue_size = 0;\n private_nh.param<int>(\"default_queue_size\", queue_size, 0);\n ROS_INFO_STREAM(\"Using default queue size of \"\n << queue_size << \" for publisher queues... \"\n << (queue_size == 0\n ? \"NOTE: This means the queues are of infinite size!\"\n : \"\"));\n\n /// setup ros publishers\n // visualizations\n pub_agent_visuals_ = nh_.advertise<animated_marker_msgs::AnimatedMarkerArray>(\n \"/pedsim/agents_markers\", queue_size);\n pub_agent_arrows_ = nh_.advertise<visualization_msgs::MarkerArray>(\n \"/pedsim/agent_directions\", queue_size);\n pub_group_lines_ = nh_.advertise<visualization_msgs::MarkerArray>(\n \"/pedsim/group_relations\", queue_size);\n pub_walls_ = nh_.advertise<visualization_msgs::Marker>(\n \"/pedsim/walls\", queue_size, true);\n pub_attractions_ = nh_.advertise<visualization_msgs::Marker>(\n \"/pedsim/attractions\", queue_size, true);\n pub_queues_ = nh_.advertise<visualization_msgs::Marker>(\n \"/pedsim/queues\", queue_size, true);\n pub_waypoints_ = nh_.advertise<visualization_msgs::Marker>(\n \"/pedsim/waypoints\", queue_size, true);\n\n // informative topics (data)\n pub_obstacles_ = nh_.advertise<nav_msgs::GridCells>(\n \"/pedsim/static_obstacles\", queue_size);\n pub_all_agents_ = nh_.advertise<pedsim_msgs::AllAgentsState>(\n \"/pedsim/dynamic_obstacles\", queue_size);\n pub_tracked_persons_ = nh_.advertise<spencer_tracking_msgs::TrackedPersons>(\n \"/pedsim/tracked_persons\", queue_size);\n pub_tracked_groups_ = nh_.advertise<pedsim_msgs::TrackedGroups>(\n \"/pedsim/tracked_groups\", queue_size);\n pub_social_activities_ = nh_.advertise<pedsim_msgs::SocialActivities>(\n \"/pedsim/social_activities\", queue_size);\n pub_robot_position_ = nh_.advertise<nav_msgs::Odometry>(\n \"/pedsim/robot_position\", queue_size);\n\n // services\n srv_pause_simulation_ = nh_.advertiseService(\n \"/pedsim/pause_simulation\", &Simulator::onPauseSimulation, this);\n srv_unpause_simulation_ = nh_.advertiseService(\n \"/pedsim/unpause_simulation\", &Simulator::onUnpauseSimulation, this);\n\n /// setup TF listener and other pointers\n transform_listener_.reset(new tf::TransformListener());\n orientation_handler_.reset(new OrientationHandler());\n robot_ = nullptr;\n\n /// Subscribers\n sub_robot_position_ = nh_.subscribe(\"odom\", 1, &Simulator::robotPositionCallback, this);\n\n /// Frame parameters\n std::string global_frame;\n std::string robot_base_link;\n private_nh.param<std::string>(\"global_frame\", global_frame, \"map\");\n private_nh.param<std::string>(\"robot_base_link\", robot_base_link, \"sibot/base_link\");\n CONFIG.global_frame = global_frame;\n CONFIG.robot_base_link = robot_base_link;\n //CONFIG.global_frame = \"map\";\n //CONFIG.robot_base_link = \"sibot/base_link\";\n \n /// load additional parameters\n std::string scene_file_param;\n private_nh.param<std::string>(\"scene_file\", scene_file_param,\n \"package://pedsim_simulator/scenarios/singleagent.xml\");\n\n QString scenefile = QString::fromStdString(scene_file_param);\n ScenarioReader scenario_reader;\n bool read_result = scenario_reader.readFromFile(scenefile);\n if (read_result == false) {\n ROS_ERROR(\"Could not load the scene file, please check the paths and param names\");\n return false;\n }\n\n private_nh.param<bool>(\"enable_groups\", CONFIG.groups_enabled, true);\n private_nh.param<double>(\"max_robot_speed\", CONFIG.max_robot_speed, 2.5);\n\n int op_mode = 1;\n private_nh.param<int>(\"robot_mode\", op_mode, 1); // teleop\n CONFIG.robot_mode = static_cast<RobotMode>(op_mode);\n\n int vis_mode = 1;\n private_nh.param<int>(\"visual_mode\", vis_mode, 1);\n CONFIG.visual_mode = static_cast<VisualMode>(vis_mode);\n\n bool show_robot = false;\n bool show_robot_direction = false;\n private_nh.param<bool>(\"show_robot\", show_robot, false);\n private_nh.param<bool>(\"show_robot_direction\", show_robot_direction, false);\n CONFIG.show_robot = show_robot;\n CONFIG.show_robot_direction = show_robot_direction;\n\n agent_activities_.clear();\n paused_ = false;\n\n return true;\n}\n\n/// -----------------------------------------------------------------\n/// \\brief runSimulation\n/// \\details Hub of the application\n/// -----------------------------------------------------------------\nvoid Simulator::runSimulation()\n{\n ros::Rate r(CONFIG.updateRate); // Hz\n\n while (ros::ok()) {\n if (SCENE.getTime() < 0.1) {\n \t // setup the robot\n for (Agent* a : SCENE.getAgents()) {\n if (a->getType() == Ped::Tagent::ROBOT) {\n robot_ = a;\n\n // init default pose of robot\n Eigen::Quaternionf q = computePose(robot_);\n last_robot_orientation_.x = q.x();\n last_robot_orientation_.y = q.y();\n last_robot_orientation_.z = q.z();\n last_robot_orientation_.w = q.w();\n }\n }\n }\n\n updateRobotPositionFromTF(); // move robot\n if (!paused_)\n SCENE.moveAllAgents(); // move all the pedestrians\n\n // mandatory data stream\n publishData();\n publishRobotPosition();\n publishObstacles();\n publishWalls();\n\tpublishAttractions();\n\n if (CONFIG.visual_mode == VisualMode::MINIMAL) {\n publishAgents(); // animated markers\n\n\t /*\n if (SCENE.getTime() < 20) {\n publishWalls();\n }\n\t */\n }\n\n if (CONFIG.visual_mode == VisualMode::FULL) {\n publishAgents(); // animated markers\n publishSocialActivities();\n publishGroupVisuals();\n updateAgentActivities();\n\n /*\n if (SCENE.getTime() < 20) {\n publishAttractions();\n publishWalls();\n }\n */\n\n }\n\n ros::spinOnce();\n r.sleep();\n }\n}\n\n/**\n * @brief reconfigure call back\n * @details Callback function that receives parameters from the dynamic\n * parameter\n * server to run the simulation. Useful for experimentation with the model\n * parameters\n */\nvoid Simulator::reconfigureCB(pedsim_simulator::PedsimSimulatorConfig& config,\n uint32_t level)\n{\n CONFIG.updateRate = config.update_rate;\n CONFIG.simulationFactor = config.simulation_factor;\n\n // update force scaling factors\n CONFIG.setObstacleForce(config.force_obstacle);\n CONFIG.setObstacleSigma(config.sigma_obstacle);\n CONFIG.setSocialForce(config.force_social);\n CONFIG.setGroupGazeForce(config.force_group_gaze);\n CONFIG.setGroupCoherenceForce(config.force_group_coherence);\n CONFIG.setGroupRepulsionForce(config.force_group_repulsion);\n CONFIG.setRandomForce(config.force_random);\n CONFIG.setAlongWallForce(config.force_wall);\n\n // puase or unpause the simulation\n if (paused_ != config.paused) {\n paused_ = config.paused;\n }\n}\n\n/// -----------------------------------------------------------------\n/// \\brief onPauseSimulation\n/// \\details Pause the simulation\n/// -----------------------------------------------------------------\nbool Simulator::onPauseSimulation(std_srvs::Empty::Request& request,\n std_srvs::Empty::Response& response)\n{\n paused_ = true;\n return true;\n}\n\n/// -----------------------------------------------------------------\n/// \\brief onUnpauseSimulation\n/// \\details Unpause the simulation\n/// -----------------------------------------------------------------\nbool Simulator::onUnpauseSimulation(std_srvs::Empty::Request& request,\n std_srvs::Empty::Response& response)\n{\n paused_ = false;\n return true;\n}\n\n/// -----------------------------------------------------------------\n/// \\brief updateAgentActivities\n/// \\details Update the map of activities of each agent for visuals\n/// -----------------------------------------------------------------\nvoid Simulator::updateAgentActivities()\n{\n agent_activities_.clear();\n\n // TODO - add a switch between using simulated activities of showing detected\n // ones\n\n for (Agent* a : SCENE.getAgents()) {\n // activity of the current agent\n AgentStateMachine::AgentState sact = a->getStateMachine()->getCurrentState();\n\n if (sact == AgentStateMachine::AgentState::StateQueueing) {\n agent_activities_.insert(\n std::pair<int, std::string>(a->getId(), \"queueing\"));\n }\n\n if (sact == AgentStateMachine::AgentState::StateShopping) {\n agent_activities_.insert(\n std::pair<int, std::string>(a->getId(), \"shopping\"));\n }\n\n if (a->getType() == Ped::Tagent::ELDER) // Hack for really slow people\n {\n agent_activities_.insert(\n std::pair<int, std::string>(a->getId(), \"standing\"));\n }\n\n if (sact == AgentStateMachine::AgentState::StateGroupWalking) {\n agent_activities_.insert(\n std::pair<int, std::string>(a->getId(), \"group_walking\"));\n }\n\n if (sact == AgentStateMachine::AgentState::StateWalking) {\n agent_activities_.insert(\n std::pair<int, std::string>(a->getId(), \"walking\"));\n }\n }\n}\n\n/// -----------------------------------------------------------------\n/// \\brief updateRobotPositionFromTF\n/// \\details Updates the robot's position, and estimated vx vy, based upon the\n/// TF transform world --> base_footprint.\n/// -----------------------------------------------------------------\nvoid Simulator::updateRobotPositionFromTF()\n{\n if (!robot_)\n return;\n\n if (CONFIG.robot_mode == RobotMode::TELEOPERATION || CONFIG.robot_mode == RobotMode::CONTROLLED) {\n robot_->setTeleop(true);\n robot_->setVmax(\n CONFIG.max_robot_speed); // NOTE - check if this is really necessary\n\n // Get robot position via TF\n tf::StampedTransform tfTransform;\n try {\n transform_listener_->lookupTransform(CONFIG.global_frame, CONFIG.robot_base_link,\n ros::Time(0), tfTransform);\n }\n catch (tf::TransformException& e) {\n ROS_WARN_STREAM_THROTTLE(\n 5.0,\n \"TF lookup from robot base frame to odom failed. Reason: \" << e.what());\n return;\n }\n\n double x = tfTransform.getOrigin().x(), y = tfTransform.getOrigin().y();\n double dx = x - last_robot_pose_.getOrigin().x(),\n dy = y - last_robot_pose_.getOrigin().y();\n double dt = tfTransform.stamp_.toSec() - last_robot_pose_.stamp_.toSec();\n double vx = dx / dt, vy = dy / dt;\n\n if (!std::isfinite(vx))\n vx = 0;\n if (!std::isfinite(vy))\n vy = 0;\n\n robot_->setX(x);\n robot_->setY(y);\n robot_->setvx(vx);\n robot_->setvy(vy);\n\n last_robot_pose_ = tfTransform;\n }\n}\n\n/// -----------------------------------------------------------------\n/// \\brief publishSocialActivities\n/// \\details publish spencer_relation_msgs::SocialActivities\n/// -----------------------------------------------------------------\nvoid Simulator::publishSocialActivities()\n{\n /// Social activities\n pedsim_msgs::SocialActivities social_activities;\n std_msgs::Header social_activities_header;\n social_activities_header.stamp = ros::Time::now();\n social_activities.header = social_activities_header;\n social_activities.header.frame_id = CONFIG.global_frame;\n\n pedsim_msgs::SocialActivity queueing_activity;\n pedsim_msgs::SocialActivity shopping_activity;\n pedsim_msgs::SocialActivity standing_activity;\n pedsim_msgs::SocialActivity group_moving_activity;\n pedsim_msgs::SocialActivity individual_moving_activity;\n\n for (Agent* a : SCENE.getAgents()) {\n /// activity of the current agent\n AgentStateMachine::AgentState sact = a->getStateMachine()->getCurrentState();\n\n if (sact == AgentStateMachine::AgentState::StateQueueing) {\n queueing_activity.type = pedsim_msgs::SocialActivity::TYPE_WAITING_IN_QUEUE;\n queueing_activity.confidence = 1.0;\n queueing_activity.track_ids.push_back(a->getId());\n }\n\n if (sact == AgentStateMachine::AgentState::StateShopping) {\n shopping_activity.type = pedsim_msgs::SocialActivity::TYPE_SHOPPING;\n shopping_activity.confidence = 1.0;\n shopping_activity.track_ids.push_back(a->getId());\n }\n\n if (a->getType() == Ped::Tagent::ELDER) // Hack for really slow people\n {\n standing_activity.type = pedsim_msgs::SocialActivity::TYPE_STANDING;\n standing_activity.confidence = 1.0;\n standing_activity.track_ids.push_back(a->getId());\n }\n\n if (sact == AgentStateMachine::AgentState::StateGroupWalking) {\n group_moving_activity.type = pedsim_msgs::SocialActivity::TYPE_GROUP_MOVING;\n group_moving_activity.confidence = 1.0;\n group_moving_activity.track_ids.push_back(a->getId());\n }\n\n if (sact == AgentStateMachine::AgentState::StateWalking) {\n individual_moving_activity.type = pedsim_msgs::SocialActivity::TYPE_INDIVIDUAL_MOVING;\n individual_moving_activity.confidence = 1.0;\n individual_moving_activity.track_ids.push_back(a->getId());\n }\n }\n\n social_activities.elements.push_back(queueing_activity);\n social_activities.elements.push_back(shopping_activity);\n social_activities.elements.push_back(standing_activity);\n social_activities.elements.push_back(group_moving_activity);\n social_activities.elements.push_back(individual_moving_activity);\n\n pub_social_activities_.publish(social_activities);\n}\n\n/// -----------------------------------------------------------------\n/// \\brief publishData\n/// \\details publish tracked persons and tracked groups messages\n/// -----------------------------------------------------------------\nvoid Simulator::publishData()\n{\n /// Tracked people\n spencer_tracking_msgs::TrackedPersons tracked_people;\n std_msgs::Header tracked_people_header;\n tracked_people_header.stamp = ros::Time::now();\n tracked_people.header = tracked_people_header;\n tracked_people.header.frame_id = CONFIG.global_frame;\n\n for (Agent* a : SCENE.getAgents()) {\n if (a->getType() == Ped::Tagent::ROBOT)\n continue;\n\n spencer_tracking_msgs::TrackedPerson person;\n person.track_id = a->getId();\n person.is_occluded = false;\n person.detection_id = a->getId();\n // person.age = 0; // also not simulated yet, use a distribution from data\n // collected\n\n double theta = atan2(a->getvy(), a->getvx());\n Eigen::Quaternionf q = orientation_handler_->angle2Quaternion(theta);\n\n geometry_msgs::PoseWithCovariance pcov;\n\n pcov.pose.position.x = a->getx();\n pcov.pose.position.y = a->gety();\n pcov.pose.position.z = 0.0;\n pcov.pose.orientation.x = q.x();\n pcov.pose.orientation.y = q.y();\n pcov.pose.orientation.z = q.z();\n pcov.pose.orientation.w = q.w();\n person.pose = pcov;\n\n geometry_msgs::TwistWithCovariance tcov;\n tcov.twist.linear.x = a->getvx();\n tcov.twist.linear.y = a->getvy();\n person.twist = tcov;\n\n tracked_people.tracks.push_back(person);\n }\n\n /// Tracked groups\n pedsim_msgs::TrackedGroups tracked_groups;\n std_msgs::Header tracked_groups_header;\n tracked_groups_header.stamp = ros::Time::now();\n tracked_groups.header = tracked_groups_header;\n tracked_groups.header.frame_id = CONFIG.global_frame;\n\n QList<AgentGroup*> sim_groups = SCENE.getGroups();\n for (AgentGroup* ag : sim_groups) {\n pedsim_msgs::TrackedGroup group;\n group.group_id = ag->getId();\n // group.age = 0; //NOTE not simulated so far\n Ped::Tvector com = ag->getCenterOfMass();\n group.centerOfGravity.pose.position.x = com.x;\n group.centerOfGravity.pose.position.y = com.y;\n\n for (Agent* m : ag->getMembers()) {\n group.track_ids.push_back(m->getId());\n }\n\n tracked_groups.groups.push_back(group);\n }\n\n /// publish the messages\n pub_tracked_persons_.publish(tracked_people);\n pub_tracked_groups_.publish(tracked_groups);\n}\n\n/// -----------------------------------------------------------------\n/// \\brief publishRobotPosition\n/// \\details publish the robot position for use in navigation related\n/// tasks and learning simple behaviors\n/// -----------------------------------------------------------------\nvoid Simulator::publishRobotPosition()\n{\n if (robot_ == nullptr)\n return;\n\n nav_msgs::Odometry robot_location;\n robot_location.header.stamp = ros::Time::now();\n robot_location.header.frame_id = CONFIG.global_frame;\n robot_location.child_frame_id = CONFIG.robot_base_link;\n\n robot_location.pose.pose.position.x = robot_->getx();\n robot_location.pose.pose.position.y = robot_->gety();\n if (hypot(robot_->getvx(), robot_->getvy()) < 0.05) {\n robot_location.pose.pose.orientation = last_robot_orientation_;\n }\n else {\n Eigen::Quaternionf q = computePose(robot_);\n robot_location.pose.pose.orientation.x = q.x();\n robot_location.pose.pose.orientation.y = q.y();\n robot_location.pose.pose.orientation.z = q.z();\n robot_location.pose.pose.orientation.w = q.w();\n\n last_robot_orientation_ = robot_location.pose.pose.orientation;\n }\n\n robot_location.twist.twist.linear.x = robot_->getvx();\n robot_location.twist.twist.linear.y = robot_->getvy();\n\n pub_robot_position_.publish(robot_location);\n}\n\n/// -----------------------------------------------------------------\n/// \\brief publishAgents\n/// \\details publish agent status information and the visual markers\n/// \\note This method is old format and is deprecated\n/// -----------------------------------------------------------------\nvoid Simulator::publishAgents()\n{\n animated_marker_msgs::AnimatedMarkerArray marker_array;\n visualization_msgs::MarkerArray arrow_array;\n\n // status message\n pedsim_msgs::AllAgentsState all_status;\n std_msgs::Header all_header;\n all_header.stamp = ros::Time::now();\n all_status.header = all_header;\n\n for (Agent* a : SCENE.getAgents()) {\n /// walking people message\n animated_marker_msgs::AnimatedMarker marker;\n marker.mesh_use_embedded_materials = true;\n marker.header.frame_id = CONFIG.global_frame;\n marker.header.stamp = ros::Time();\n marker.id = a->getId();\n marker.type = animated_marker_msgs::AnimatedMarker::MESH_RESOURCE;\n marker.mesh_resource = \"package://pedsim_simulator/images/animated_walking_man.mesh\";\n\n marker.pose.position.x = a->getx();\n marker.pose.position.y = a->gety();\n marker.action = 0; // add or modify\n marker.scale.x = PERSON_MESH_SCALE;\n marker.scale.y = PERSON_MESH_SCALE;\n marker.scale.z = PERSON_MESH_SCALE;\n\n /// arrows\n visualization_msgs::Marker arrow;\n arrow.header.frame_id = CONFIG.global_frame;\n arrow.header.stamp = ros::Time();\n arrow.id = a->getId() + 3000;\n\n arrow.pose.position.x = a->getx();\n arrow.pose.position.y = a->gety();\n arrow.pose.position.z = 1.4;\n arrow.action = 0; // add or modify\n arrow.color.a = 1.0;\n arrow.color.r = 1.0;\n arrow.color.g = 0.0;\n arrow.color.b = 0.0;\n arrow.scale.y = 0.05;\n arrow.scale.z = 0.05;\n\n marker.color = getColor(a->getId());\n\n double agentsAlpha = 1.0;\n nh_.getParamCached(\"/pedsim_simulator/agents_alpha\", agentsAlpha);\n marker.color.a *= agentsAlpha;\n\n Eigen::Quaternionf q = computePose(a);\n marker.pose.orientation.x = q.x();\n marker.pose.orientation.y = q.y();\n marker.pose.orientation.z = q.z();\n marker.pose.orientation.w = q.w();\n\n double theta = atan2(a->getvy(), a->getvx());\n Eigen::Quaternionf qa = orientation_handler_->angle2Quaternion(theta);\n\n if (a->getvx() != 0.0) {\n arrow.pose.orientation.x = qa.x();\n arrow.pose.orientation.y = qa.y();\n arrow.pose.orientation.z = qa.z();\n arrow.pose.orientation.w = qa.w();\n\n double xx = sqrt(a->getvx() * a->getvx() + a->getvy() * a->getvy());\n arrow.scale.x = xx > 0.0 ? xx : 0.01;\n\n marker.animation_speed = xx * 0.7;\n }\n else {\n marker.animation_speed = 0.0;\n }\n\n //bool publishMarker = true, publishArrow = true;\n if (robot_ != nullptr && a->getType() == robot_->getType()) {\n marker.type = visualization_msgs::Marker::MESH_RESOURCE;\n // TODO - this should be a configurable parameter via launch file\n marker.mesh_resource = \"package://pedsim_simulator/images/darylbot_rotated_shifted.dae\";\n marker.color.a = 1.0;\n marker.color.r = 1.0;\n marker.color.g = 0.549;\n marker.color.b = 0.0;\n\n marker.scale.x = 0.8;\n marker.scale.y = 0.8;\n marker.scale.z = 1.0;\n\n marker.pose.orientation.x = qa.x();\n marker.pose.orientation.y = qa.y();\n marker.pose.orientation.z = qa.z();\n marker.pose.orientation.w = qa.w();\n\n marker.pose.position.z = 0.7;\n arrow.pose.position.z = 1.0;\n\n\t if (CONFIG.show_robot)\n\t marker_array.markers.push_back(marker);\n\t if (CONFIG.show_robot_direction)\n\t arrow_array.markers.push_back(arrow);\n\n //nh_.getParamCached(\"/pedsim_simulator/show_robot\", publishMarker);\n //nh_.getParamCached(\"/pedsim_simulator/show_robot_direction\", publishArrow);\n }\n\n\t/*\n if (publishMarker)\n marker_array.markers.push_back(marker);\n if (publishArrow)\n arrow_array.markers.push_back(arrow);\n\t*/\n\n\tbool show_animated_agents = true;\n\tbool show_animated_agents_direction = true;\n if (show_animated_agents)\n marker_array.markers.push_back(marker);\n if (show_animated_agents_direction)\n arrow_array.markers.push_back(arrow);\n\n /// status message\n /// TODO - remove this once, we publish internal states using\n /// spencer messages\n pedsim_msgs::AgentState state;\n std_msgs::Header agent_header;\n agent_header.stamp = ros::Time::now();\n state.header = agent_header;\n\n state.id = a->getId();\n state.type = a->getType();\n state.pose.position.x = a->getx();\n state.pose.position.y = a->gety();\n state.pose.position.z = a->getz();\n\n state.twist.linear.x = a->getvx();\n state.twist.linear.y = a->getvy();\n state.twist.linear.z = a->getvz();\n\n AgentStateMachine::AgentState sc = a->getStateMachine()->getCurrentState();\n state.social_state = agentStateToActivity(sc);\n if (a->getType() == Ped::Tagent::ELDER)\n state.social_state = pedsim_msgs::AgentState::TYPE_STANDING;\n\n all_status.agent_states.push_back(state);\n }\n\n // publish the marker array\n pub_agent_visuals_.publish(marker_array);\n pub_agent_arrows_.publish(arrow_array);\n\n pub_all_agents_.publish(all_status);\n}\n\n/// -----------------------------------------------------------------\n/// \\brief publishGroupVisuals\n/// \\details publish visualization of groups within the crowd\n/// -----------------------------------------------------------------\nvoid Simulator::publishGroupVisuals()\n{\n QList<AgentGroup*> groups = SCENE.getGroups();\n\n /// visualize groups (sketchy)\n for (AgentGroup* ag : groups) {\n // skip empty ones\n if (ag->memberCount() < 1)\n continue;\n\n /// members of the group\n geometry_msgs::Point p1;\n Ped::Tvector gcom = ag->getCenterOfMass();\n p1.x = gcom.x;\n p1.y = gcom.y;\n p1.z = 1.4;\n visualization_msgs::MarkerArray lines_array;\n\n for (Agent* m : ag->getMembers()) {\n visualization_msgs::Marker marker;\n marker.header.frame_id = CONFIG.global_frame;\n marker.header.stamp = ros::Time();\n marker.id = m->getId() + 1000;\n\n marker.color.a = 1.0;\n marker.color.r = 1.0;\n marker.color.g = 1.0;\n marker.color.b = 0.0;\n marker.scale.x = 0.1;\n marker.scale.y = 0.1;\n marker.scale.z = 0.1;\n marker.type = visualization_msgs::Marker::ARROW;\n geometry_msgs::Point p2;\n p2.x = m->getx();\n p2.y = m->gety();\n p2.z = 1.4;\n\n marker.points.push_back(p1);\n marker.points.push_back(p2);\n lines_array.markers.push_back(marker);\n }\n\n pub_group_lines_.publish(lines_array);\n }\n}\n\n/// -----------------------------------------------------------------\n/// \\brief publishObstacles\n/// \\details publish obstacle cells with information about their\n/// positions and cell sizes. Useful for path planning.\n/// -----------------------------------------------------------------\nvoid Simulator::publishObstacles()\n{\n nav_msgs::GridCells grid_cells;\n grid_cells.header.frame_id = CONFIG.global_frame;\n grid_cells.cell_width = CONFIG.cell_width;\n grid_cells.cell_height = CONFIG.cell_height;\n\n for (const auto& obstacle : SCENE.obstacle_cells_) {\n geometry_msgs::Point p;\n p.x = obstacle.x + 1.0;\n p.y = obstacle.y + 1.0;\n p.z = 0.0;\n grid_cells.cells.push_back(p);\n }\n\n pub_obstacles_.publish(grid_cells);\n}\n\n/// -----------------------------------------------------------------\n/// \\brief publishWalls\n/// \\details publish visual markers for obstacle given as 3D cells\n/// for visualizing in rviz. Useful for visual plan inspection\n/// -----------------------------------------------------------------\nvoid Simulator::publishWalls()\n{\n visualization_msgs::Marker marker;\n marker.header.frame_id = CONFIG.global_frame;\n marker.header.stamp = ros::Time();\n marker.id = 10000;\n marker.color.a = 1.0;\n marker.color.r = 1.0;\n marker.color.g = 0.0;\n marker.color.b = 0.0;\n marker.scale.x = 1.0;\n marker.scale.y = 1.0;\n marker.scale.z = 1.0;\n marker.pose.position.z = marker.scale.z / 2.0;\n marker.type = visualization_msgs::Marker::CUBE_LIST;\n\n for (const auto& obstacle : SCENE.obstacle_cells_) {\n geometry_msgs::Point p;\n p.x = obstacle.x + 1.0;\n p.y = obstacle.y + 1.0;\n p.z = 0.0;\n marker.points.push_back(p);\n }\n\n pub_walls_.publish(marker);\n}\n\n/// -----------------------------------------------------------------\n/// \\brief publishAttractions\n/// \\details publish visual markers for attractions given as 3D cells\n/// for visualizing in rviz.\n/// -----------------------------------------------------------------\nvoid Simulator::publishAttractions()\n{\n /// waypoints\n for (Waypoint* wp : SCENE.getWaypoints()) {\n // wp->getType()\n visualization_msgs::Marker marker;\n marker.header.frame_id = CONFIG.global_frame;\n marker.header.stamp = ros::Time();\n marker.id = wp->getId();\n\n marker.color.a = 0.4;\n marker.color.r = 0.1;\n marker.color.g = 1.0;\n marker.color.b = 0.1;\n\n // TODO - get radius information from waypoints\n marker.scale.x = 3.0;\n marker.scale.y = 3.0;\n marker.scale.z = 0.02;\n\n marker.pose.position.x = wp->getPosition().x;\n marker.pose.position.y = wp->getPosition().y;\n marker.pose.position.z = marker.scale.z / 2.0;\n\n marker.type = visualization_msgs::Marker::CYLINDER;\n\n pub_waypoints_.publish(marker);\n }\n\n /// publish attractions (shopping areas etc)\n for (AttractionArea* atr : SCENE.getAttractions()) {\n visualization_msgs::Marker marker;\n marker.header.frame_id = CONFIG.global_frame;\n marker.header.stamp = ros::Time();\n marker.id = atr->getId();\n\n marker.color.a = 0.35;\n marker.color.r = 1.0;\n marker.color.g = 1.0;\n marker.color.b = 0.0;\n\n marker.scale.x = atr->getSize().width();\n marker.scale.y = atr->getSize().height();\n marker.scale.z = 3.0;\n\n marker.pose.position.x = atr->getPosition().x;\n marker.pose.position.y = atr->getPosition().y;\n marker.pose.position.z = marker.scale.z / 2.0;\n\n marker.type = visualization_msgs::Marker::CUBE;\n\n pub_attractions_.publish(marker);\n }\n}\n\n/// -----------------------------------------------------------------\n/// \\brief Compute pose of an agent in quaternion format\n/// -----------------------------------------------------------------\nEigen::Quaternionf Simulator::computePose(Agent* a)\n{\n double theta = atan2(a->getvy(), a->getvx());\n\n Eigen::Quaternionf q = orientation_handler_->rpy2Quaternion(\n M_PI / 2.0, theta + (M_PI / 2.0), 0.0);\n\n /*\n Eigen::Quaternionf q = orientation_handler_->rpy2Quaternion(\n M_PI / 2.0, theta, 0.0);\n */\n return q;\n}\n\n/// -----------------------------------------------------------------\n/// \\brief Convert agent state machine state to simulated activity\n/// -----------------------------------------------------------------\nstd::string Simulator::agentStateToActivity(AgentStateMachine::AgentState state)\n{\n std::string activity = \"Unknown\";\n\n switch (state) {\n case AgentStateMachine::AgentState::StateWalking:\n activity = pedsim_msgs::AgentState::TYPE_INDIVIDUAL_MOVING;\n break;\n case AgentStateMachine::AgentState::StateGroupWalking:\n activity = pedsim_msgs::AgentState::TYPE_GROUP_MOVING;\n break;\n case AgentStateMachine::AgentState::StateQueueing:\n activity = pedsim_msgs::AgentState::TYPE_WAITING_IN_QUEUE;\n break;\n case AgentStateMachine::AgentState::StateShopping:\n activity = pedsim_msgs::AgentState::TYPE_SHOPPING;\n break;\n case AgentStateMachine::AgentState::StateNone:\n break;\n case AgentStateMachine::AgentState::StateWaiting:\n break;\n }\n\n // TODO\n // - add standing to the state machine\n // - add waiting at the end of the queue\n\n return activity;\n}\n\n/// -----------------------------------------------------------------\n/// \\brief Find agent color based on id\n/// -----------------------------------------------------------------\nstd_msgs::ColorRGBA Simulator::getColor(int agent_id)\n{\n std::string agent_activity = agent_activities_[agent_id];\n std_msgs::ColorRGBA color;\n color.a = 1.0;\n\n if (agent_activity == \"standing\") {\n color.r = 1.0;\n color.g = 1.0;\n color.b = 1.0;\n }\n else if (agent_activity == \"queueing\") {\n color.r = 1.0;\n color.g = 0.0;\n color.b = 1.0;\n }\n else if (agent_activity == \"shopping\") {\n color.r = 0.0;\n color.g = 0.0;\n color.b = 1.0;\n }\n else {\n color.r = 0.255;\n color.g = 0.412;\n color.b = 0.882;\n }\n\n return color;\n}\n"
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"text": "#include <rviz/visualization_manager.h>\n#include <rviz/frame_manager.h>\n#include \"rviz/selection/selection_manager.h\"\n\n#include \"tracked_persons_display.h\"\n\n#include <boost/lexical_cast.hpp>\n#include <boost/tokenizer.hpp>\n#include <boost/foreach.hpp>\n#define foreach BOOST_FOREACH\n\n\nnamespace spencer_tracking_rviz_plugin\n{\n\n// The constructor must have no arguments, so we can't give the\n// constructor the parameters it needs to fully initialize.\nvoid TrackedPersonsDisplay::onInitialize()\n{\n PersonDisplayCommon::onInitialize();\n\n m_realFixedFrame = \"odom\";\n QObject::connect(m_commonProperties->style, SIGNAL(changed()), this, SLOT(personVisualTypeChanged()) );\n\n m_occlusion_alpha_property = new rviz::FloatProperty( \"Occlusion alpha\", 0.3, \"Alpha multiplier for occluded tracks\", this, SLOT(stylesChanged()) );\n m_occlusion_alpha_property->setMin( 0.0 );\n\n m_missed_alpha_property = new rviz::FloatProperty( \"Missed alpha\", 0.5, \"Alpha multiplier for missed tracks\", this, SLOT(stylesChanged()) );\n m_missed_alpha_property->setMin( 0.0 );\n\n m_history_length_property = new rviz::IntProperty( \"History size\", 100, \"Number of prior track positions to display.\", this, SLOT(stylesChanged()));\n m_history_length_property->setMin( 1 );\n m_history_length_property->setMax( 10000000 );\n\n m_delete_after_ncycles_property = new rviz::IntProperty( \"Delete after no. cycles\", 100, \"After how many time steps to delete an old track that has not been seen again, including its history\", this, SLOT(stylesChanged()));\n m_delete_after_ncycles_property->setMin( 0 );\n m_delete_after_ncycles_property->setMax( 10000000 );\n\n m_show_deleted_property = new rviz::BoolProperty( \"Show DELETED tracks\", false, \"Show tracks which have been marked as deleted\", this, SLOT(stylesChanged()));\n m_show_occluded_property = new rviz::BoolProperty( \"Show OCCLUDED tracks\", true, \"Show tracks which could not be matched to an detection due to sensor occlusion\", this, SLOT(stylesChanged()));\n m_show_missed_property = new rviz::BoolProperty( \"Show MISSED tracks\", true, \"Show tracks which could not be matched to an detection but should be observable by the sensor\", this, SLOT(stylesChanged()));\n m_show_matched_property = new rviz::BoolProperty( \"Show MATCHED tracks\", true, \"Show tracks which could be matched to an detection\", this, SLOT(stylesChanged()));\n\n\n m_render_history_property = new rviz::BoolProperty( \"Render history\", true, \"Render prior track positions\", this, SLOT(stylesChanged()));\n m_render_history_as_line_property = new rviz::BoolProperty( \"History as line\", true, \"Display history as line instead of dots\", this, SLOT(stylesChanged()));\n m_render_person_property = new rviz::BoolProperty( \"Render person visual\", true, \"Render person visualization\", this, SLOT(stylesChanged()));\n m_render_covariances_property = new rviz::BoolProperty( \"Render covariances\", true, \"Render track covariance ellipses\", this, SLOT(stylesChanged()));\n m_render_velocities_property = new rviz::BoolProperty( \"Render velocities\", true, \"Render track velocity arrows\", this, SLOT(stylesChanged()));\n m_render_ids_property = new rviz::BoolProperty( \"Render track IDs\", true, \"Render track IDs as text\", this, SLOT(stylesChanged()));\n m_render_detection_ids_property = new rviz::BoolProperty( \"Render detection IDs\", true, \"Render IDs of the detection that a track was matched against, if any\", this, SLOT(stylesChanged()));\n m_render_track_state_property = new rviz::BoolProperty( \"Render track state\", true, \"Render track state text\", this, SLOT(stylesChanged()));\n\n m_history_min_point_distance_property = new rviz::FloatProperty( \"Min. history point distance\", 0.4, \"Minimum distance between history points before a new one is placed\", this, SLOT(stylesChanged()) );\n m_history_line_width_property = new rviz::FloatProperty( \"Line width\", 0.05, \"Line width of history\", m_render_history_as_line_property, SLOT(stylesChanged()), this );\n m_covariance_line_width_property = new rviz::FloatProperty( \"Line width\", 0.1, \"Line width of covariance ellipses\", m_render_covariances_property, SLOT(stylesChanged()), this );\n\n\n // TODO: Implement functionality\n //m_render_state_prediction_property = new rviz::BoolProperty( \"Render state prediction\", true, \"Render state prediction from Kalman filter\", this, SLOT( updateRenderFlags() ));\n\n // Create a scene node for visualizing track history\n m_trackHistorySceneNode = shared_ptr<Ogre::SceneNode>(scene_node_->createChildSceneNode());\n}\n\nTrackedPersonsDisplay::~TrackedPersonsDisplay()\n{\n m_cachedTracks.clear();\n}\n\n// Clear the visuals by deleting their objects.\nvoid TrackedPersonsDisplay::reset()\n{\n PersonDisplayCommon::reset();\n m_cachedTracks.clear();\n}\n\nvoid TrackedPersonsDisplay::update(float wall_dt, float ros_dt)\n{\n // Move map scene node\n Ogre::Vector3 mapFramePosition; Ogre::Quaternion mapFrameOrientation;\n getContext()->getFrameManager()->getTransform(m_realFixedFrame, ros::Time(0), mapFramePosition, mapFrameOrientation);\n Ogre::Matrix4 mapFrameTransform(mapFrameOrientation); mapFrameTransform.setTrans(mapFramePosition);\n m_trackHistorySceneNode->setPosition(mapFramePosition);\n m_trackHistorySceneNode->setOrientation(mapFrameOrientation);\n\n // Update position of deleted tracks (because they are not being updated by ROS messages any more)\n foreach(const track_map::value_type& entry, m_cachedTracks)\n {\n const shared_ptr<TrackedPersonVisual>& trackedPersonVisual = entry.second;\n if(trackedPersonVisual->isDeleted) {\n Ogre::Matrix4 poseInCurrentFrame = mapFrameTransform * trackedPersonVisual->lastObservedPose;\n Ogre::Vector3 position = poseInCurrentFrame.getTrans(); Ogre::Quaternion orientation = poseInCurrentFrame.extractQuaternion();\n if(!position.isNaN() && !orientation.isNaN()) {\n trackedPersonVisual->sceneNode->setPosition(position);\n trackedPersonVisual->sceneNode->setOrientation(orientation);\n }\n }\n else {\n // Update animation etc.\n if(trackedPersonVisual->personVisual) trackedPersonVisual->personVisual->update(ros_dt);\n }\n }\n}\n\n/// Update all dynamically adjusted visualization properties (colors, font sizes etc.) of all currently tracked persons\nvoid TrackedPersonsDisplay::stylesChanged()\n{\n const Ogre::Quaternion shapeQuaternion( Ogre::Degree(90), Ogre::Vector3(1,0,0) );\n\n // Update each track\n foreach(const track_map::value_type& entry, m_cachedTracks)\n {\n const track_id trackId = entry.first;\n const shared_ptr<TrackedPersonVisual>& trackedPersonVisual = entry.second;\n\n // Update common styles to person visual, such as line width\n applyCommonStyles(trackedPersonVisual->personVisual);\n\n // Update track visibility\n bool trackVisible = !isPersonHidden(trackId);\n\n if (trackedPersonVisual->isDeleted) trackVisible &= m_show_deleted_property->getBool();\n else if(trackedPersonVisual->isOccluded) trackVisible &= m_show_occluded_property->getBool();\n else if(trackedPersonVisual->isMissed) trackVisible &= m_show_missed_property->getBool();\n else trackVisible &= m_show_matched_property->getBool();\n\n trackedPersonVisual->sceneNode->setVisible(trackVisible);\n trackedPersonVisual->historySceneNode->setVisible(trackVisible && !m_render_history_as_line_property->getBool());\n trackedPersonVisual->historyLineSceneNode->setVisible(trackVisible && m_render_history_as_line_property->getBool());\n\n // Get current track color\n Ogre::ColourValue trackColorWithFullAlpha = getColorFromId(trackId);\n Ogre::ColourValue trackColor = getColorFromId(trackId);\n trackColor.a *= m_commonProperties->alpha->getFloat(); // general alpha\n if(trackedPersonVisual->isOccluded) trackColor.a *= m_occlusion_alpha_property->getFloat(); // occlusion alpha\n if(trackedPersonVisual->isMissed) trackColor.a *= m_missed_alpha_property->getFloat(); // occlusion alpha\n\n // Update person color\n Ogre::ColourValue personColor = trackColor;\n if(!m_render_person_property->getBool()) personColor.a = 0.0;\n\n if(trackedPersonVisual->personVisual) {\n trackedPersonVisual->personVisual->setColor(personColor);\n }\n\n // Update history size\n trackedPersonVisual->history.rset_capacity(m_history_length_property->getInt());\n\n // Update history color\n foreach(shared_ptr<TrackedPersonHistoryEntry> historyEntry, trackedPersonVisual->history) {\n const double historyShapeDiameter = 0.1;\n Ogre::ColourValue historyColor = trackColorWithFullAlpha;\n historyColor.a *= m_commonProperties->alpha->getFloat(); // general alpha\n if(historyEntry->wasOccluded) historyColor.a *= m_occlusion_alpha_property->getFloat();\n if(isPersonHidden(trackId) || m_render_history_as_line_property->getBool()) historyColor.a = 0;\n\n if(historyEntry->shape) {\n historyEntry->shape->setColor(historyColor);\n historyEntry->shape->setScale(shapeQuaternion * Ogre::Vector3(historyShapeDiameter, historyShapeDiameter, 0.05));\n }\n }\n\n if(trackedPersonVisual->historyLine) { // history-as-line mode (as opposed to history-as-dots)\n Ogre::ColourValue historyColor = trackColorWithFullAlpha;\n historyColor.a *= m_commonProperties->alpha->getFloat(); // general alpha\n if(isPersonHidden(trackId)) historyColor.a = 0;\n trackedPersonVisual->historyLine->setColor(historyColor.r, historyColor.g, historyColor.b, historyColor.a);\n }\n\n // Update text colors, font size and visibility\n const double personHeight = trackedPersonVisual->personVisual ? trackedPersonVisual->personVisual->getHeight() : 0;\n Ogre::ColourValue fontColor = m_commonProperties->font_color_style->getOptionInt() == FONT_COLOR_CONSTANT ? m_commonProperties->constant_font_color->getOgreColor() : trackColor;\n fontColor.a = m_commonProperties->alpha->getFloat();\n\n trackedPersonVisual->detectionIdText->setCharacterHeight(0.18 * m_commonProperties->font_scale->getFloat());\n trackedPersonVisual->detectionIdText->setVisible(!trackedPersonVisual->isOccluded && m_render_detection_ids_property->getBool() && trackVisible);\n trackedPersonVisual->detectionIdText->setColor(fontColor);\n trackedPersonVisual->detectionIdText->setPosition(Ogre::Vector3(0,0, -trackedPersonVisual->detectionIdText->getCharacterHeight()));\n\n trackedPersonVisual->stateText->setCharacterHeight(0.18 * m_commonProperties->font_scale->getFloat());\n trackedPersonVisual->stateText->setVisible(m_render_track_state_property->getBool() && trackVisible);\n trackedPersonVisual->stateText->setColor(fontColor);\n trackedPersonVisual->stateText->setPosition(Ogre::Vector3(0,0, personHeight + trackedPersonVisual->stateText->getCharacterHeight()));\n \n const double stateTextOffset = m_render_track_state_property->getBool() ? 1.2*trackedPersonVisual->stateText->getCharacterHeight() : 0;\n trackedPersonVisual->idText->setCharacterHeight(0.25 * m_commonProperties->font_scale->getFloat());\n trackedPersonVisual->idText->setVisible(m_render_ids_property->getBool() && trackVisible);\n trackedPersonVisual->idText->setColor(fontColor);\n trackedPersonVisual->idText->setPosition(Ogre::Vector3(0,0, personHeight + trackedPersonVisual->idText->getCharacterHeight() + stateTextOffset));\n\n // Update velocity arrow color\n double arrowAlpha = m_render_velocities_property->getBool() ? trackColor.a : 0.0;\n if(trackedPersonVisual->hasZeroVelocity) arrowAlpha = 0.0;\n trackedPersonVisual->velocityArrow->setColor(Ogre::ColourValue(trackColor.r, trackColor.g, trackColor.b, arrowAlpha));\n\n // Set color of covariance visualization\n Ogre::ColourValue covarianceColor = trackColor;\n if(!m_render_covariances_property->getBool()) covarianceColor.a = 0.0;\n trackedPersonVisual->covarianceVisual->setColor(covarianceColor);\n trackedPersonVisual->covarianceVisual->setLineWidth(m_covariance_line_width_property->getFloat());\n }\n\n // Update global history visibility\n m_trackHistorySceneNode->setVisible(m_render_history_property->getBool());\n}\n\n\n// Set the rendering style (cylinders, meshes, ...) of tracked persons\nvoid TrackedPersonsDisplay::personVisualTypeChanged()\n{\n foreach(const track_map::value_type& entry, m_cachedTracks)\n {\n const shared_ptr<TrackedPersonVisual>& trackedPersonVisual = entry.second;\n trackedPersonVisual->personVisual.reset();\n createPersonVisualIfRequired(trackedPersonVisual->sceneNode.get(), trackedPersonVisual->personVisual);\n }\n stylesChanged();\n}\n\n// This is our callback to handle an incoming message.\nvoid TrackedPersonsDisplay::processMessage(const spencer_tracking_msgs::TrackedPersons::ConstPtr& msg)\n{\n // Get transforms into fixed frame etc.\n if(!preprocessMessage(msg)) return;\n\n // Transform from map/odometry frame into fixed frame, required to display track history if the fixed frame is not really \"fixed\" (e.g. base_link)\n Ogre::Vector3 mapFramePosition; Ogre::Quaternion mapFrameOrientation;\n getContext()->getFrameManager()->getTransform(m_realFixedFrame, msg->header.stamp, mapFramePosition, mapFrameOrientation);\n Ogre::Matrix4 mapFrameTransform(mapFrameOrientation); mapFrameTransform.setTrans(mapFramePosition);\n\n // Transform required to fix orientation of any Cylinder shapes\n const Ogre::Quaternion shapeQuaternion( Ogre::Degree(90), Ogre::Vector3(1,0,0) );\n stringstream ss;\n\n //\n // Iterate over all tracks in this message, see if we have a cached visual (then update it) or create a new one.\n //\n set<unsigned int> encounteredTrackIds;\n for (vector<spencer_tracking_msgs::TrackedPerson>::const_iterator trackedPersonIt = msg->tracks.begin(); trackedPersonIt != msg->tracks.end(); ++trackedPersonIt)\n {\n shared_ptr<TrackedPersonVisual> trackedPersonVisual;\n\n // See if we encountered this track ID before in this loop (means duplicate track ID)\n if (encounteredTrackIds.find(trackedPersonIt->track_id) != encounteredTrackIds.end()) {\n ROS_ERROR_STREAM(\"spencer_tracking_msgs::TrackedPersons contains duplicate track ID \" << trackedPersonIt->track_id << \"! Skipping duplicate track.\");\n continue;\n }\n else {\n encounteredTrackIds.insert(trackedPersonIt->track_id);\n }\n\n // See if we have cached a track with this ID\n if (m_cachedTracks.find(trackedPersonIt->track_id) != m_cachedTracks.end()) {\n trackedPersonVisual = m_cachedTracks[trackedPersonIt->track_id];\n }\n else {\n // Create a new visual representation of the tracked person\n trackedPersonVisual = shared_ptr<TrackedPersonVisual>(new TrackedPersonVisual);\n m_cachedTracks[trackedPersonIt->track_id] = trackedPersonVisual;\n\n // This scene node is the parent of all visualization elements for the tracked person\n trackedPersonVisual->sceneNode = shared_ptr<Ogre::SceneNode>(scene_node_->createChildSceneNode());\n trackedPersonVisual->historySceneNode = shared_ptr<Ogre::SceneNode>(m_trackHistorySceneNode->createChildSceneNode());\n trackedPersonVisual->historyLineSceneNode = shared_ptr<Ogre::SceneNode>(m_trackHistorySceneNode->createChildSceneNode());\n }\n\n // These values need to be remembered for later use in stylesChanged()\n if(trackedPersonIt->is_occluded && !trackedPersonIt->is_matched){\n trackedPersonVisual->isOccluded = true;\n trackedPersonVisual->isMissed = false;\n }\n else if(!trackedPersonIt->is_occluded && !trackedPersonIt->is_matched){\n trackedPersonVisual->isOccluded = false;\n trackedPersonVisual->isMissed = true;\n }\n else {\n trackedPersonVisual->isOccluded = false;\n trackedPersonVisual->isMissed = false;\n }\n\n trackedPersonVisual->isDeleted = false;\n trackedPersonVisual->numCyclesNotSeen = 0;\n\n Ogre::SceneNode* currentSceneNode = trackedPersonVisual->sceneNode.get();\n\n\n //\n // Person visualization\n //\n\n // Create new visual for the person itself, if needed\n shared_ptr<PersonVisual> &personVisual = trackedPersonVisual->personVisual;\n createPersonVisualIfRequired(currentSceneNode, personVisual);\n\n const double personHeight = personVisual ? personVisual->getHeight() : 0;\n const double halfPersonHeight = personHeight / 2.0;\n\n\n //\n // Position of entire track\n //\n\n const Ogre::Matrix3 covXYZinTargetFrame = covarianceXYZIntoTargetFrame(trackedPersonIt->pose);\n setPoseOrientation(currentSceneNode, trackedPersonIt->pose, covXYZinTargetFrame, personHeight);\n\n\n //\n // Track history\n //\n\n Ogre::Vector3 newHistoryEntryPosition = mapFrameTransform.inverse() * currentSceneNode->getPosition();\n\n const float MIN_HISTORY_ENTRY_DISTANCE = m_history_min_point_distance_property->getFloat(); // in meters\n if((trackedPersonVisual->positionOfLastHistoryEntry - newHistoryEntryPosition).length() > MIN_HISTORY_ENTRY_DISTANCE)\n {\n // General history\n shared_ptr<TrackedPersonHistoryEntry> newHistoryEntry(new TrackedPersonHistoryEntry);\n newHistoryEntry->trackId = trackedPersonIt->track_id;\n newHistoryEntry->position = newHistoryEntryPosition; // used by history lines (below) even if no shape is set\n newHistoryEntry->wasOccluded = trackedPersonIt->is_occluded;\n trackedPersonVisual->history.push_back(newHistoryEntry);\n\n // Always need to reset history line since history is like a queue, oldest element has to be removed but BillboardLine doesn't offer that functionality\n trackedPersonVisual->historyLine.reset(new rviz::BillboardLine(context_->getSceneManager(), trackedPersonVisual->historyLineSceneNode.get()) );\n\n if(m_render_history_as_line_property->getBool()) {\n // History lines\n if(trackedPersonVisual->history.size() >= 2) {\n trackedPersonVisual->historyLine->setLineWidth(m_history_line_width_property->getFloat());\n trackedPersonVisual->historyLine->setMaxPointsPerLine(trackedPersonVisual->history.size());\n\n foreach(const shared_ptr<TrackedPersonHistoryEntry>& historyEntry, trackedPersonVisual->history) {\n historyEntry->shape.reset(); // remove existing dot shapes, if any, for better performance\n trackedPersonVisual->historyLine->addPoint(historyEntry->position);\n }\n }\n }\n else {\n // History dots\n newHistoryEntry->shape = shared_ptr<rviz::Shape>(new rviz::Shape(rviz::Shape::Cylinder, context_->getSceneManager(), trackedPersonVisual->historySceneNode.get()));\n newHistoryEntry->shape->setPosition(newHistoryEntryPosition);\n newHistoryEntry->shape->setOrientation(shapeQuaternion);\n }\n\n trackedPersonVisual->positionOfLastHistoryEntry = newHistoryEntryPosition;\n }\n\n\n //\n // Texts\n //\n {\n if (!trackedPersonVisual->idText) {\n trackedPersonVisual->idText.reset(new TextNode(context_->getSceneManager(), currentSceneNode));\n trackedPersonVisual->stateText.reset(new TextNode(context_->getSceneManager(), currentSceneNode));\n trackedPersonVisual->detectionIdText.reset(new TextNode(context_->getSceneManager(), currentSceneNode));\n }\n\n // Detection ID\n ss.str(\"\"); ss << \"det \" << trackedPersonIt->detection_id;\n trackedPersonVisual->detectionIdText->setCaption(ss.str());\n \n // Track state\n ss.str(\"\");\n\n if(trackedPersonIt->is_occluded && !trackedPersonIt->is_matched)\n ss << \"OCCLUDED\";\n else if (!trackedPersonIt->is_occluded && !trackedPersonIt->is_matched)\n ss << \"MISSED\";\n else\n ss << \"MATCHED\";\n\n trackedPersonVisual->stateText->setCaption(ss.str());\n \n // Track ID\n ss.str(\"\"); ss << trackedPersonIt->track_id;\n trackedPersonVisual->idText->setCaption(ss.str());\n }\n\n //\n // Velocity arrows\n //\n if (!trackedPersonVisual->velocityArrow) {\n trackedPersonVisual->velocityArrow.reset(new rviz::Arrow(context_->getSceneManager(), currentSceneNode));\n }\n\n // Update velocity arrow\n {\n const Ogre::Vector3 velocityVector = getVelocityVector(trackedPersonIt->twist);\n\n if(velocityVector.isZeroLength() || velocityVector.length() > 100 || velocityVector.isNaN()) {\n if(!velocityVector.isZeroLength()) { // do not show warning for zero velocity\n ROS_WARN(\"Track %lu has suspicious velocity (%.1f m/s), not showing velocity vector!\", trackedPersonIt->track_id, velocityVector.length());\n }\n }\n else {\n const double personRadius = 0.2;\n const Ogre::Vector3 velocityArrowAttachPoint(personRadius, 0, halfPersonHeight); // relative to tracked person's scene node\n trackedPersonVisual->velocityArrow->setPosition(velocityArrowAttachPoint);\n trackedPersonVisual->velocityArrow->setOrientation(m_frameOrientation * currentSceneNode->getOrientation().Inverse() * Ogre::Vector3::NEGATIVE_UNIT_Z.getRotationTo(velocityVector));\n\n const double shaftLength = velocityVector.length(), shaftDiameter = 0.05, headLength = 0.2, headDiameter = 0.2;\n trackedPersonVisual->velocityArrow->set(shaftLength, shaftDiameter, headLength, headDiameter);\n trackedPersonVisual->hasZeroVelocity = velocityVector.length() < 0.05;\n }\n\n shared_ptr<MeshPersonVisual> meshPersonVisual = boost::dynamic_pointer_cast<MeshPersonVisual>(personVisual);\n if(meshPersonVisual) {\n meshPersonVisual->setWalkingSpeed(velocityVector.length());\n }\n }\n\n\n //\n // Covariance visualization\n //\n if(!trackedPersonVisual->covarianceVisual) {\n trackedPersonVisual->covarianceVisual.reset(new ProbabilityEllipseCovarianceVisual(context_->getSceneManager(), currentSceneNode));\n }\n\n // Update covariance ellipse\n {\n Ogre::Vector3 covarianceMean(0,0,0); // zero mean because parent node is already centered at pose mean\n trackedPersonVisual->covarianceVisual->setOrientation(currentSceneNode->getOrientation().Inverse());\n trackedPersonVisual->covarianceVisual->setMeanCovariance(covarianceMean, covXYZinTargetFrame);\n }\n\n } // end for loop over all tracked persons\n\n // Set all properties which can be dynamically in the GUI. This iterates over all tracks.\n stylesChanged();\n\n //\n // First hide, then delete old cached tracks which have not been seen for a while\n //\n set<unsigned int> trackIdsToDelete;\n for (map<unsigned int, shared_ptr<TrackedPersonVisual> >::const_iterator cachedTrackIt = m_cachedTracks.begin(); cachedTrackIt != m_cachedTracks.end(); ++cachedTrackIt) {\n if (encounteredTrackIds.end() == encounteredTrackIds.find(cachedTrackIt->first)) {\n const shared_ptr<TrackedPersonVisual>& trackedPersonVisual = cachedTrackIt->second;\n\n // Update state and visibility\n if(!trackedPersonVisual->isDeleted) {\n trackedPersonVisual->stateText->setCaption(\"DELETED\");\n trackedPersonVisual->isDeleted = true;\n\n Ogre::Matrix4 lastObservedPose(trackedPersonVisual->sceneNode->getOrientation()); lastObservedPose.setTrans(trackedPersonVisual->sceneNode->getPosition());\n trackedPersonVisual->lastObservedPose = mapFrameTransform.inverse() * lastObservedPose;\n }\n\n if(!m_show_deleted_property->getBool()) trackedPersonVisual->sceneNode->setVisible(false);\n\n // Delete if too old\n if(++trackedPersonVisual->numCyclesNotSeen > m_delete_after_ncycles_property->getInt()) {\n trackIdsToDelete.insert(cachedTrackIt->first);\n }\n }\n }\n\n for (set<unsigned int>::const_iterator setIt = trackIdsToDelete.begin(); setIt != trackIdsToDelete.end(); ++setIt) {\n m_cachedTracks.erase(*setIt);\n }\n\n //\n // Update status (shown in property pane)\n //\n ss.str(\"\");\n ss << msg->tracks.size() << \" tracks received\";\n setStatusStd(rviz::StatusProperty::Ok, \"Tracks\", ss.str());\n}\n\n} // end namespace spencer_tracking_rviz_plugin\n\n// Tell pluginlib about this class. 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"text": "/**\n* Copyright 2014 Social Robotics Lab, University of Freiburg\n*\n* Redistribution and use in source and binary forms, with or without\n* modification, are permitted provided that the following conditions are met:\n*\n* # Redistributions of source code must retain the above copyright\n* notice, this list of conditions and the following disclaimer.\n* # Redistributions in binary form must reproduce the above copyright\n* notice, this list of conditions and the following disclaimer in the\n* documentation and/or other materials provided with the distribution.\n* # Neither the name of the University of Freiburg nor the names of its\n* contributors may be used to endorse or promote products derived from\n* this software without specific prior written permission.\n*\n* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n* POSSIBILITY OF SUCH DAMAGE.\n*\n* \\author Billy Okal <[email protected]>\n* \\author Sven Wehner <[email protected]>\n*/\n\n#include <pedsim_simulator/waypointplanner/shoppingplanner.h>\n#include <pedsim_simulator/rng.h>\n#include <pedsim_simulator/scene.h>\n\n#include <pedsim_simulator/element/agent.h>\n#include <pedsim_simulator/element/attractionarea.h>\n#include <pedsim_simulator/element/areawaypoint.h>\n\n\nShoppingPlanner::ShoppingPlanner()\n{\n // initialize values\n agent = nullptr;\n currentWaypoint = nullptr;\n attraction = nullptr;\n timeReached = 0;\n}\n\nvoid ShoppingPlanner::loseAttraction()\n{\n // reset\n delete currentWaypoint;\n currentWaypoint = nullptr;\n attraction = nullptr;\n timeReached = 0;\n\n // inform users\n emit lostAttraction();\n}\n\nbool ShoppingPlanner::setAgent ( Agent* agentIn )\n{\n /// NOTE - should the robot be allowed to shop?\n /// who funds robot's shopping expenses??\n // if (agentIn->getType() == 2)\n // \treturn false;\n\n agent = agentIn;\n\n // some nice fix to dancing in shops\n agent->disableForce ( \"Social\" );\n agent->disableForce ( \"Random\" );\n agent->disableForce ( \"GroupCoherence\" );\n agent->disableForce ( \"GroupGaze\" );\n agent->disableForce ( \"GroupRepulsion\" );\n\n return true;\n}\n\nAttractionArea* ShoppingPlanner::getAttraction() const\n{\n return attraction;\n}\n\nbool ShoppingPlanner::setAttraction ( AttractionArea* attractionIn )\n{\n attraction = attractionIn;\n\n // reset waypoint\n delete currentWaypoint;\n currentWaypoint = nullptr;\n\n return true;\n}\n\nWaypoint* ShoppingPlanner::getCurrentWaypoint()\n{\n if ( hasCompletedWaypoint() )\n currentWaypoint = getNextWaypoint();\n\n return currentWaypoint;\n}\n\nbool ShoppingPlanner::hasCompletedWaypoint()\n{\n if ( currentWaypoint == nullptr )\n return true;\n\n // check whether agent has reached the waypoint and has been there for a given time\n const double distanceThreshold = 1.0;\n // TODO - make shopping time also random\n const double waitTime = 15.0;\n double distance = ( agent->getPosition() - currentWaypoint->getPosition() ).length();\n if ( distance <= distanceThreshold )\n {\n double sceneTime = SCENE.getTime();\n if ( timeReached == 0 )\n timeReached = sceneTime;\n else if ( timeReached - sceneTime >= waitTime )\n {\n return true;\n }\n }\n\n return false;\n}\n\nbool ShoppingPlanner::hasCompletedDestination() const\n{\n // Note: The shopping planner is never done.\n // Change Planner via StateMachine!\n return false;\n}\n\nWaypoint* ShoppingPlanner::getNextWaypoint()\n{\n bool hadWaypoint = ( currentWaypoint != nullptr );\n Waypoint* oldWaypoint = currentWaypoint;\n\n // set new waypoint\n //TODO: create random attraction point in attraction\n QString name = createWaypointName();\n Ped::Tvector position;\n if ( !hadWaypoint )\n {\n //TODO: closest point or random point?\n // maybe also add some timing here, only change position after some random wait (Erlang dist)\n position = getRandomAttractionPosition();\n }\n else\n {\n position = oldWaypoint->getPosition();\n // → add random offset\n position += createRandomOffset();\n }\n\n // → ensure that the position is within the area\n QRectF area ( QPointF ( 0, 0 ), attraction->getSize() );\n area.moveCenter ( QPointF ( attraction->getPosition().x, attraction->getPosition().y ) );\n position.x = qBound ( area.left(), position.x, area.right() );\n position.y = qBound ( area.top(), position.y, area.bottom() );\n\n // → create new waypoint\n currentWaypoint = new AreaWaypoint ( name, position, 0.5 );\n\n // reset reached time\n timeReached = 0;\n\n // remove previous waypoint\n delete oldWaypoint;\n oldWaypoint = nullptr;\n\n return currentWaypoint;\n}\n\nQString ShoppingPlanner::createWaypointName() const\n{\n return QString ( \"AttractionHelper_A%1_Q%2\" ).arg ( agent->getId() ).arg ( attraction->getName() );\n}\n\nPed::Tvector ShoppingPlanner::getRandomAttractionPosition() const\n{\n Ped::Tvector randomPosition = attraction->getPosition();\n\n // → add random part\n QSizeF size = attraction->getSize();\n std::uniform_real_distribution<double> xDistribution ( -size.width() /2, size.width() /2 );\n std::uniform_real_distribution<double> yDistribution ( -size.height() /2, size.height() /2 );\n\n\tdouble xdiff = xDistribution ( RNG() );\n\tdouble ydiff = yDistribution ( RNG() );\n\n randomPosition += Ped::Tvector ( xdiff, ydiff );\n\n return randomPosition;\n}\n\nPed::Tvector ShoppingPlanner::createRandomOffset() const\n{\n const double radiusStd = 4;\n\tstd::normal_distribution<double> radiusDistribution ( 0, radiusStd );\n\tdouble radius = radiusDistribution ( RNG() );\n\n\tstd::discrete_distribution<int> angleDistribution {0,45,90,135,180,225,270,315,360};\n\tdouble angle = angleDistribution ( RNG() );\n\n Ped::Tvector randomOffset = Ped::Tvector::fromPolar ( Ped::Tangle::fromDegree ( angle ), radius );\n\n\t// only update for significant shopping idea change\n\tif (randomOffset.lengthSquared() < 2.0)\n\t\treturn Ped::Tvector(0, 0, 0);\n\n return randomOffset;\n}\n\nQString ShoppingPlanner::name() const\n{\n return tr ( \"ShoppingPlanner\" );\n}\n"
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"text": "#include <rviz/mesh_loader.h>\n#include <ros/console.h>\n#include <ros/package.h>\n#include <resource_retriever/retriever.h>\n\n#include <boost/algorithm/string/predicate.hpp>\n#include <boost/filesystem.hpp>\n\n#include <OgreSceneManager.h>\n#include <OgreSubEntity.h>\n#include <OgreMaterialManager.h>\n#include <OgreTextureManager.h>\n#include <OgreTechnique.h>\n#include <OgreAnimation.h>\n\n#include \"person_visual.h\"\n\n\nnamespace fs = boost::filesystem;\n\nnamespace spencer_tracking_rviz_plugin {\n\n/** This helper class ensures that skeletons can be loaded from a package:// path **/\nclass RosPackagePathResourceLoadingListener : public Ogre::ResourceLoadingListener\n{\npublic:\n RosPackagePathResourceLoadingListener(const fs::path& parentPath) : _parentPath(parentPath) {\n }\n\n /** This event is called when a resource beings loading. */\n virtual Ogre::DataStreamPtr resourceLoading(const Ogre::String &name, const Ogre::String &group, Ogre::Resource *resource) {\n fs::path absolutePath = _parentPath / name;\n ROS_INFO_STREAM(\"RosPackagePathResourceLoadingListener loading resource: \" << absolutePath.string());\n\n try\n {\n resource_retriever::Retriever retriever;\n _lastResource = retriever.get(absolutePath.string()); // not thread-safe!\n return Ogre::DataStreamPtr(new Ogre::MemoryDataStream(_lastResource.data.get(), _lastResource.size));\n }\n catch (resource_retriever::Exception& e)\n {\n ROS_ERROR(\"In RosPackagePathResourceLoadingListener: %s\", e.what());\n return Ogre::DataStreamPtr();\n }\n }\n\n virtual void resourceStreamOpened(const Ogre::String &name, const Ogre::String &group, Ogre::Resource *resource, Ogre::DataStreamPtr& dataStream) {\n }\n\n virtual bool resourceCollision(Ogre::Resource *resource, Ogre::ResourceManager *resourceManager) {\n return false;\n }\n\nprivate:\n const fs::path& _parentPath;\n resource_retriever::MemoryResource _lastResource;\n};\n\n\n\nMeshPersonVisual::MeshPersonVisual(const PersonVisualDefaultArgs& args) : PersonVisual(args), m_animationState(NULL), m_walkingSpeed(1.0), entity_(NULL)\n{\n m_childSceneNode = m_sceneNode->createChildSceneNode();\n m_childSceneNode->setVisible(false);\n\n std::string meshResource = \"package://\" ROS_PACKAGE_NAME \"/media/animated_walking_man.mesh\";\n\n /// This is required to load referenced skeletons from package:// path\n fs::path model_path(meshResource);\n fs::path parent_path(model_path.parent_path());\n\n Ogre::ResourceLoadingListener *newListener = new RosPackagePathResourceLoadingListener(parent_path), \n *oldListener = Ogre::ResourceGroupManager::getSingleton().getLoadingListener();\n\n Ogre::ResourceGroupManager::getSingleton().setLoadingListener(newListener);\n bool loadFailed = rviz::loadMeshFromResource(meshResource).isNull();\n Ogre::ResourceGroupManager::getSingleton().setLoadingListener(oldListener);\n\n delete newListener;\n\n\n // Create scene entity\n static size_t count = 0;\n std::stringstream ss;\n ss << \"mesh_person_visual\" << count++;\n std::string id = ss.str();\n\n entity_ = m_sceneManager->createEntity(id, meshResource);\n m_childSceneNode->attachObject(entity_);\n\n // set up animation\n setAnimationState(\"\");\n\n // set up material\n ss << \"Material\";\n Ogre::MaterialPtr default_material = Ogre::MaterialManager::getSingleton().create( ss.str(), \"rviz\" );\n default_material->setReceiveShadows(false);\n default_material->getTechnique(0)->setLightingEnabled(true);\n default_material->getTechnique(0)->setAmbient( 0.5, 0.5, 0.5 );\n materials_.insert( default_material );\n entity_->setMaterial( default_material );\n\n // set position\n Ogre::Quaternion quat1; quat1.FromAngleAxis(Ogre::Degree(90), Ogre::Vector3(0,1,0));\n Ogre::Quaternion quat2; quat2.FromAngleAxis(Ogre::Degree(-90), Ogre::Vector3(0,0,1));\n m_childSceneNode->setOrientation(quat1 * quat2);\n\n double scaleFactor = 0.243 * 1.75;\n m_childSceneNode->setScale(Ogre::Vector3(scaleFactor, scaleFactor, scaleFactor));\n m_childSceneNode->setPosition(Ogre::Vector3(0, 0, -1));\n\n m_childSceneNode->setVisible(true);\n}\n\nMeshPersonVisual::~MeshPersonVisual() {\n m_sceneManager->destroyEntity( entity_ );\n\n // destroy all the materials we've created\n std::set<Ogre::MaterialPtr>::iterator it;\n for ( it = materials_.begin(); it!=materials_.end(); it++ )\n {\n Ogre::MaterialPtr material = *it;\n if (!material.isNull())\n {\n material->unload();\n Ogre::MaterialManager::getSingleton().remove(material->getName());\n }\n }\n materials_.clear();\n\n m_sceneManager->destroySceneNode(m_childSceneNode->getName());\n}\n\nvoid MeshPersonVisual::setColor(const Ogre::ColourValue& c) {\n Ogre::SceneBlendType blending;\n bool depth_write;\n\n if ( c.a < 0.9998 )\n {\n blending = Ogre::SBT_TRANSPARENT_ALPHA;\n depth_write = false;\n }\n else\n {\n blending = Ogre::SBT_REPLACE;\n depth_write = true;\n }\n\n std::set<Ogre::MaterialPtr>::iterator it;\n for( it = materials_.begin(); it != materials_.end(); it++ )\n {\n Ogre::Technique* technique = (*it)->getTechnique( 0 );\n\n technique->setAmbient( c.r*0.5, c.g*0.5, c.b*0.5 );\n technique->setDiffuse( c.r, c.g, c.b, c.a );\n technique->setSceneBlending( blending );\n technique->setDepthWriteEnabled( depth_write );\n technique->setLightingEnabled( true );\n }\n}\n\nvoid MeshPersonVisual::setAnimationState(const std::string& nameOfAnimationState) {\n Ogre::AnimationStateSet *animationStates = entity_->getAllAnimationStates();\n if(animationStates != NULL)\n {\n Ogre::AnimationStateIterator animationsIterator = animationStates->getAnimationStateIterator();\n while (animationsIterator.hasMoreElements())\n {\n Ogre::AnimationState *animationState = animationsIterator.getNext();\n if(animationState->getAnimationName() == nameOfAnimationState || nameOfAnimationState.empty()) {\n animationState->setLoop(true);\n animationState->setEnabled(true);\n m_animationState = animationState;\n return;\n } \n }\n\n // Not found. Set first animation state then.\n ROS_WARN_STREAM_ONCE(\"Person mesh animation state \" << nameOfAnimationState << \" does not exist in mesh!\");\n setAnimationState(\"\");\n }\n}\n\nvoid MeshPersonVisual::setWalkingSpeed(float walkingSpeed) {\n m_walkingSpeed = walkingSpeed;\n}\n\n\nvoid MeshPersonVisual::update(float deltaTime) {\n if(m_animationState) {\n m_animationState->addTime(0.7 * deltaTime * m_walkingSpeed);\n }\n}\n\n\n} // end of namespace spencer_tracking_rviz_plugin"
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"text": "#!/usr/bin/env python\n\n__author__ = \"Luigi Palmieri\"\n__copyright__ = \"Social Robotics Lab, University of Freiburg\"\n__license__ = \"BSD\"\n__version__ = \"0.0.1\"\n__email__ = \"[email protected]\"\n\nimport roslib\nimport time\nimport math\nimport numpy as np\nimport rospy\nimport tf\nimport sys\nimport itertools\nimport os\nfrom std_msgs.msg import String\nfrom std_msgs.msg import Bool\nfrom geometry_msgs.msg import PoseStamped\nfrom geometry_msgs.msg import PoseWithCovariance\nfrom nav_msgs.msg import GridCells\nfrom spencer_tracking_msgs.msg import TrackedPersons\nfrom spencer_tracking_msgs.msg import TrackedPerson\nfrom spencer_tracking_msgs.msg import TrackedGroup\nfrom spencer_tracking_msgs.msg import TrackedGroups\n\n\ndef groups_sender():\n global pub_groups\n global listener\n global group_id\n\n pub_groups = rospy.Publisher('/spencer/perception/tracked_groups', TrackedGroups, queue_size=1)\n sub_agents_poses = rospy.Subscriber('/spencer/perception/tracked_persons',TrackedPersons,ReadAgents,queue_size=1)\n listener = tf.TransformListener()\n r = rospy.Rate(10) # 10hz\n\n readagents=0\n while not rospy.is_shutdown():\n # rospy.loginfo(\"#Sending Groups\")\n r.sleep()\n\n# Reading the Agents, associate them to a single group, and send the groups msgs\n# to use only for a toy example where only a single group exists\ndef ReadAgents(arg):\n global listener\n global groups\n global group_id\n\n\n alltrack_ids=[tracked_person.track_id for tracked_person in arg.tracks]\n # rospy.logwarn(str(alltrack_ids))\n # createGroup(arg.tracks,alltrack_ids)\n groups=TrackedGroups()\n groups.header.frame_id=\"odom\"\n groups.header.stamp= rospy.Time.now()\n\n group_id=0\n\n createGroup(arg.tracks,[1,2,3,4])\n\n createGroup(arg.tracks,[5,6])\n createGroup(arg.tracks,[7,8])\n createGroup(arg.tracks,[9,10])\n createGroup(arg.tracks,[11,12])\n pub_groups.publish(groups)\n\n\ndef createGroup(allTracks,tracksInGroup):\n global pub_groups\n global group_id\n global groups\n\n group=TrackedGroup()\n group.group_id=group_id\n group.age=rospy.Duration.from_sec(10)\n x=0\n y=0\n nagents=0\n for tracked_person in allTracks:\n if(tracked_person.track_id in tracksInGroup):\n quat = (tracked_person.pose.pose.orientation.x,tracked_person.pose.pose.orientation.y,tracked_person.pose.pose.orientation.z,tracked_person.pose.pose.orientation.w)\n euler = tf.transformations.euler_from_quaternion(quat)\n tracked_person_theta=euler[2]\n x=x+tracked_person.pose.pose.position.x\n y=y+tracked_person.pose.pose.position.y\n nagents=nagents+1\n group.track_ids.append(tracked_person.track_id)\n\n group.centerOfGravity.pose.position.x=x/nagents\n group.centerOfGravity.pose.position.y=y/nagents\n groups.groups.append(group)\n\n group_id += 1\n\n\nif __name__ == '__main__':\n rospy.init_node('mockgroups_rss_scenario_one')\n try:\n groups_sender()\n except rospy.ROSInterruptException: pass\n"
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"text": "#ifndef TRACKED_PERSONS_DISPLAY_H\n#define TRACKED_PERSONS_DISPLAY_H\n\n#include <map>\n#include <boost/circular_buffer.hpp>\n\n#include <spencer_tracking_msgs/TrackedPersons.h>\n\n#include \"person_display_common.h\"\n\nnamespace spencer_tracking_rviz_plugin\n{\n typedef unsigned int track_id;\n\n /// A single entry in the history of a tracked person.\n struct TrackedPersonHistoryEntry\n {\n Ogre::Vector3 position;\n shared_ptr<rviz::Shape> shape;\n bool wasOccluded;\n track_id trackId;\n };\n\n /// History of a tracked person.\n typedef circular_buffer<shared_ptr<TrackedPersonHistoryEntry> > TrackedPersonHistory;\n\n /// The visual of a tracked person.\n struct TrackedPersonVisual\n {\n TrackedPersonHistory history;\n shared_ptr<rviz::BillboardLine> historyLine;\n Ogre::Vector3 positionOfLastHistoryEntry;\n\n shared_ptr<Ogre::SceneNode> sceneNode, historySceneNode, historyLineSceneNode;\n\n shared_ptr<PersonVisual> personVisual;\n shared_ptr<TextNode> idText, detectionIdText, stateText;\n shared_ptr<rviz::Arrow> velocityArrow;\n shared_ptr<CovarianceVisual> covarianceVisual;\n\n Ogre::Matrix4 lastObservedPose;\n\n bool isOccluded, isDeleted, isMissed, hasZeroVelocity;\n int numCyclesNotSeen;\n };\n\n // The TrackedPersonsDisplay class itself just implements a circular buffer,\n // editable parameters, and Display subclass machinery.\n class TrackedPersonsDisplay: public PersonDisplayCommon<spencer_tracking_msgs::TrackedPersons>\n {\n Q_OBJECT\n public:\n // Constructor. pluginlib::ClassLoader creates instances by calling\n // the default constructor, so make sure you have one.\n TrackedPersonsDisplay() {};\n virtual ~TrackedPersonsDisplay();\n\n // Overrides of protected virtual functions from Display. As much\n // as possible, when Displays are not enabled, they should not be\n // subscribed to incoming data and should not show anything in the\n // 3D view. These functions are where these connections are made\n // and broken.\n\n // Called after the constructors have run\n virtual void onInitialize();\n\n // Called periodically by the visualization manager\n virtual void update(float wall_dt, float ros_dt);\n\n protected:\n // A helper to clear this display back to the initial state.\n virtual void reset();\n\n // Must be implemented by derived classes because MOC doesn't work in templates\n virtual rviz::DisplayContext* getContext() {\n return context_;\n }\n\n private Q_SLOTS:\n void personVisualTypeChanged();\n\n // Called whenever one of the properties in PersonDisplayCommonProperties has been changed\n virtual void stylesChanged();\n\n private:\n // Function to handle an incoming ROS message.\n void processMessage(const spencer_tracking_msgs::TrackedPersons::ConstPtr& msg);\n \n // All currently active tracks, with unique track ID as map key\n typedef map<track_id, shared_ptr<TrackedPersonVisual> > track_map;\n track_map m_cachedTracks;\n\n // Scene node for track history visualization\n shared_ptr<Ogre::SceneNode> m_trackHistorySceneNode;\n std::string m_realFixedFrame;\n\n // User-editable property variables.\n rviz::FloatProperty* m_occlusion_alpha_property;\n rviz::FloatProperty* m_missed_alpha_property;\n rviz::IntProperty* m_history_length_property;\n rviz::IntProperty* m_delete_after_ncycles_property;\n\n rviz::BoolProperty* m_show_deleted_property;\n rviz::BoolProperty* m_show_occluded_property;\n rviz::BoolProperty* m_show_missed_property;\n rviz::BoolProperty* m_show_matched_property;\n \n rviz::BoolProperty* m_render_person_property;\n rviz::BoolProperty* m_render_history_property;\n rviz::BoolProperty* m_render_history_as_line_property;\n rviz::BoolProperty* m_render_covariances_property;\n rviz::BoolProperty* m_render_state_prediction_property;\n rviz::BoolProperty* m_render_velocities_property;\n rviz::BoolProperty* m_render_ids_property;\n rviz::BoolProperty* m_render_detection_ids_property;\n rviz::BoolProperty* m_render_track_state_property;\n\n rviz::FloatProperty* m_history_line_width_property;\n rviz::FloatProperty* m_history_min_point_distance_property;\n rviz::FloatProperty* m_covariance_line_width_property;\n };\n\n} // end namespace spencer_tracking_rviz_plugin\n\n#endif // TRACKED_PERSONS_DISPLAY_H\n"
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"text": "#ifndef SOCIAL_ACTIVITIES_DISPLAY_H\n#define SOCIAL_ACTIVITIES_DISPLAY_H\n\n#include <map>\n#include <vector>\n#include <boost/circular_buffer.hpp>\n\n#include <spencer_social_relation_msgs/SocialActivities.h>\n\n#include \"person_display_common.h\"\n#include \"tracked_persons_cache.h\"\n\nnamespace spencer_tracking_rviz_plugin\n{\n typedef std::string activity_type;\n\n /// The display which can be added in RViz to display social activities.\n class SocialActivitiesDisplay: public PersonDisplayCommon<spencer_social_relation_msgs::SocialActivities>\n {\n Q_OBJECT\n public:\n // To determine, for persons involved in multiple social activities, their most likely one.\n struct ActivityWithConfidence {\n activity_type type;\n float confidence;\n };\n\n // Constructor. pluginlib::ClassLoader creates instances by calling\n // the default constructor, so make sure you have one.\n SocialActivitiesDisplay() {};\n virtual ~SocialActivitiesDisplay();\n\n // Overrides of protected virtual functions from Display. As much\n // as possible, when Displays are not enabled, they should not be\n // subscribed to incoming data and should not show anything in the\n // 3D view. These functions are where these connections are made\n // and broken.\n\n // Called after the constructors have run\n virtual void onInitialize();\n\n // Called periodically by the visualization manager\n virtual void update(float wall_dt, float ros_dt);\n\n protected:\n // A helper to clear this display back to the initial state.\n virtual void reset();\n\n // Must be implemented by derived classes because MOC doesn't work in templates\n virtual rviz::DisplayContext* getContext() {\n return context_;\n }\n\n private:\n struct SocialActivityVisual {\n vector<shared_ptr<rviz::Shape> > socialActivityAssignmentCircles;\n vector<shared_ptr<rviz::BillboardLine> > connectionLines;\n vector< shared_ptr<TextNode> > typeTexts;\n vector<track_id> trackIds;\n activity_type activityType;\n float confidence;\n geometry_msgs::Point socialActivityCenter;\n size_t personCount;\n size_t declutteringOffset; // for decluttering of labels etc. in case of multiple activities per track, e.g. 1 = shift label by 1 row\n };\n\n // Functions to handle an incoming ROS message.\n void processMessage(const spencer_social_relation_msgs::SocialActivities::ConstPtr& msg);\n\n // Helper functions\n void updateSocialActivityVisualStyles(shared_ptr<SocialActivityVisual>& groupVisual);\n bool isActivityTypeHidden(activity_type activityType);\n Ogre::ColourValue getActivityColor(activity_type activityType, float confidence);\n\n // Scene nodes\n shared_ptr<Ogre::SceneNode> m_socialActivitiesSceneNode;\n\n // User-editable property variables.\n rviz::StringProperty* m_excluded_activity_types_property;\n rviz::StringProperty* m_included_activity_types_property;\n\n rviz::BoolProperty* m_render_intraactivity_connections_property;\n rviz::BoolProperty* m_render_activity_types_property;\n rviz::BoolProperty* m_activity_type_per_track_property;\n rviz::BoolProperty* m_render_confidences_property;\n rviz::BoolProperty* m_render_circles_property;\n rviz::BoolProperty* m_hide_with_no_activity_property;\n\n rviz::FloatProperty* m_occlusion_alpha_property;\n rviz::FloatProperty* m_min_confidence_property;\n rviz::FloatProperty* m_circle_radius_property;\n rviz::FloatProperty* m_circle_alpha_property;\n rviz::FloatProperty* m_line_width_property;\n rviz::FloatProperty* m_activity_type_offset; // z offset of the group ID text\n\n rviz::Property* m_activity_colors;\n rviz::ColorProperty* m_activity_color_none;\n rviz::ColorProperty* m_activity_color_unknown;\n rviz::ColorProperty* m_activity_color_shopping;\n rviz::ColorProperty* m_activity_color_standing;\n rviz::ColorProperty* m_activity_color_individual_moving;\n rviz::ColorProperty* m_activity_color_waiting_in_queue;\n rviz::ColorProperty* m_activity_color_looking_at_information_screen;\n rviz::ColorProperty* m_activity_color_looking_at_kiosk;\n rviz::ColorProperty* m_activity_color_group_assembling;\n rviz::ColorProperty* m_activity_color_group_moving;\n rviz::ColorProperty* m_activity_color_flow;\n rviz::ColorProperty* m_activity_color_antiflow;\n rviz::ColorProperty* m_activity_color_waiting_for_others;\n rviz::ColorProperty* m_activity_color_looking_for_help;\n\n\n // State variables\n struct PersonVisualContainer {\n shared_ptr<PersonVisual> personVisual;\n shared_ptr<Ogre::SceneNode> sceneNode;\n track_id trackId;\n };\n\n vector<shared_ptr<SocialActivityVisual> > m_socialActivityVisuals;\n map<track_id, PersonVisualContainer > m_personVisualMap; // to keep person visuals alive across multiple frames, for walking animation\n\n map<track_id, ActivityWithConfidence> m_highestConfidenceActivityPerTrack; // only highest-confidence activity per person\n map<track_id, vector<ActivityWithConfidence> > m_allActivitiesPerTrack; // all activities that a track is involved in\n\n set<activity_type> m_excludedActivityTypes, m_includedActivityTypes;\n\n Ogre::Matrix4 m_frameTransform;\n TrackedPersonsCache m_trackedPersonsCache;\n\n private Q_SLOTS:\n void personVisualTypeChanged();\n virtual void stylesChanged();\n };\n\n} // end namespace spencer_tracking_rviz_plugin\n\n#endif // SOCIAL_ACTIVITIES_DISPLAY_H\n"
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"text": "#!/usr/bin/env python\n# Author: Timm Linder, [email protected]\n#\n# Publishes fake tracked persons and the corresponding detections\n# (if not occluded) at\n# /spencer/perception/tracked_persons and /spencer/perception/detected_persons.\n\nimport rospy\nimport tf\nfrom spencer_tracking_msgs.msg import TrackedPersons, TrackedPerson\nfrom math import cos, sin, radians\nimport numpy as np\n\nnp.random.seed(42) # Use the whole truth\n\n\ndef createTrackedPerson(track_id, x, y, theta, speed):\n tracked_persons = TrackedPerson()\n\n # theta = radians(theta) + pi / 2.0\n theta = radians(theta)\n\n tracked_persons.track_id = track_id\n quaternion = tf.transformations.quaternion_from_euler(0, 0, theta)\n\n tracked_persons.pose.pose.position.x = x\n tracked_persons.pose.pose.position.y = y\n\n tracked_persons.pose.pose.orientation.x = quaternion[0]\n tracked_persons.pose.pose.orientation.y = quaternion[1]\n tracked_persons.pose.pose.orientation.z = quaternion[2]\n tracked_persons.pose.pose.orientation.w = quaternion[3]\n\n tracked_persons.pose.covariance[0 + 0 * 6] = 0.001 # x\n tracked_persons.pose.covariance[1 + 1 * 6] = 0.001 # y\n tracked_persons.pose.covariance[2 + 2 * 6] = 999999 # z\n tracked_persons.pose.covariance[3 + 3 * 6] = 999999 # x rotation\n tracked_persons.pose.covariance[4 + 5 * 6] = 999999 # y rotation\n tracked_persons.pose.covariance[4 + 5 * 6] = 999999 # z rotation\n\n # tracked_persons.twist.twist.linear.x = 0.0\n # tracked_persons.twist.twist.linear.y = 0.0\n\n tracked_persons.twist.twist.linear.x = speed * cos(theta)\n tracked_persons.twist.twist.linear.y = speed * sin(theta)\n\n for i in range(0, 3):\n tracked_persons.twist.covariance[i + i * 6] = 1.0 # linear velocity\n for i in range(3, 6):\n tracked_persons.twist.covariance[\n i + i * 6] = float(\"inf\") # rotational velocity\n\n return tracked_persons\n\n\ndef main():\n # Main code\n trackPublisher = rospy.Publisher(\n '/spencer/perception/tracked_persons', TrackedPersons)\n\n rospy.init_node('mock_tracked_persons')\n rate = rospy.Rate(10)\n\n # create speeds\n speeds = np.random.normal(1.34, 0.26, size=50)\n\n while not rospy.is_shutdown():\n index = 0\n counter = 0\n\n tracked_persons = TrackedPersons()\n tracked_persons.header.seq = counter\n tracked_persons.header.frame_id = \"odom\"\n tracked_persons.header.stamp = rospy.Time.now()\n\n # (createTrackedPerson( trackId, x, y, theta, speed ) )\n tracked_persons.tracks.append(\n createTrackedPerson(01, 5, 4, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(02, 6, 5.45878, 270, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(03, 1.3 + 7.22, 5.70, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(04, 7.22, 7.33, 290, speeds[index]))\n\n tracked_persons.tracks.append(\n createTrackedPerson(05, 2 + 8.92, 8.42, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(06, 2 + 7.92, 10.41, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(07, 2 + 7.2, 9.44, 90, speeds[index]))\n\n tracked_persons.tracks.append(\n createTrackedPerson(8, 2 + 7, 14 - 2, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(9, 2 + 5.5, 14.4123 - 2, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(10, 5 - 1, 18.595 - 5, 280, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(11, 5 - 1, 20 - 5, 270, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(12, 6 - 1, 21.5491 - 5, 240, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(13, 7.48044 - 1, 19 - 5.7, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(14, 6, 24.5463, 45, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(15, 8, 28, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(16, 10.4458, 23, 68, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(17, 11.5004, 27, 88, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(18, 14, 25.4389, 20, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(19, 15, 21, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(20, 15, 22.4308, 92, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(21, 15.4676, 24, 91, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(22, 16.5423, 25.4178, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(23, 18, 20, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(24, 18.5532, 21.5011, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(25, 15.4739, 16.5314, 45, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(26, 20, 25.5746, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(27, 21.5327, 24, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(28, 22, 26.4632, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(29, 21, 18, 45, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(30, 23, 20.4335, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(31, 23.4972, 21.4055, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(32, 23.4025, 22.4749, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(33, 24.5281, 18.5868, 54, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(34, 16.554, 3.40568 - 2, 94, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(35, 14.8, 6 - 3, 94, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(36, 20, 4, 0, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(37, 19, 12, 25, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(38, 23, 8, 50, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(39, 24, 10, 90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(40, 25, 12, 120, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(41, 7.51, 22.41, 80, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(42, 8.21, 25.7, 81, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(43, 3.31, 27.7, 81, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(44, 11.421, 18.7, 75, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(45, 25.21, 27.0, 85, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(46, 18.23, 6.87, -91, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(47, 18.6, 8.90, -90, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(48, 20.4, 7.87, 85, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(49, 15.684, 10.74, 75, speeds[index]))\n tracked_persons.tracks.append(\n createTrackedPerson(50, 15.72, 13.51, 70, speeds[index]))\n\n trackPublisher.publish(tracked_persons)\n\n counter += 1\n index += 1\n rate.sleep()\n\n\n# Constants\n\nOBSTACLE_YAML = \"\"\"\nheader:\n seq: 48860\n stamp:\n secs: 0\n nsecs: 0\n frame_id: world\ncell_width: 1.0\ncell_height: 1.0\ncells:\n -\n x: -0.5\n y: -0.5\n z: 0.0\n -\n x: 0.5\n y: -0.5\n z: 0.0\n -\n x: 1.5\n y: -0.5\n z: 0.0\n -\n x: 2.5\n y: -0.5\n z: 0.0\n -\n x: 3.5\n y: -0.5\n z: 0.0\n -\n x: 4.5\n y: -0.5\n z: 0.0\n -\n x: 5.5\n y: -0.5\n z: 0.0\n -\n x: 6.5\n y: -0.5\n z: 0.0\n -\n x: 7.5\n y: -0.5\n z: 0.0\n -\n x: 8.5\n y: -0.5\n z: 0.0\n -\n x: 9.5\n y: -0.5\n z: 0.0\n -\n x: 10.5\n y: -0.5\n z: 0.0\n -\n x: 11.5\n y: -0.5\n z: 0.0\n -\n x: 12.5\n y: -0.5\n z: 0.0\n -\n x: 13.5\n y: -0.5\n z: 0.0\n -\n x: 14.5\n y: -0.5\n z: 0.0\n -\n x: 15.5\n y: -0.5\n z: 0.0\n -\n x: 16.5\n y: -0.5\n z: 0.0\n -\n x: 17.5\n y: -0.5\n z: 0.0\n -\n x: 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"text": "#ifndef PERSON_DISPLAY_COMMON_H\n#define PERSON_DISPLAY_COMMON_H\n\n#include <map>\n#include <set>\n#include <boost/circular_buffer.hpp>\n\n#include <rviz/properties/bool_property.h>\n#include <rviz/properties/enum_property.h>\n#include <rviz/properties/color_property.h>\n#include <rviz/properties/float_property.h>\n#include <rviz/properties/int_property.h>\n#include <rviz/properties/string_property.h>\n\n#include <rviz/ogre_helpers/shape.h>\n#include <rviz/ogre_helpers/movable_text.h>\n#include <rviz/ogre_helpers/arrow.h>\n\n#include <OGRE/OgreSceneNode.h>\n#include <OGRE/OgreSceneManager.h>\n\n#include <spencer_tracking_msgs/TrackedPersons.h>\n#include <geometry_msgs/Twist.h>\n#include <rviz/message_filter_display.h>\n\n#include \"visuals/person_visual.h\"\n#include \"visuals/text_node.h\"\n#include \"visuals/covariance_visual.h\"\n\nusing namespace std;\nusing namespace boost;\n\nnamespace spencer_tracking_rviz_plugin\n{\n typedef unsigned int person_id;\n\n /// Visualization style for a person\n enum Styles {\n STYLE_SIMPLE,\n STYLE_CYLINDER,\n STYLE_PERSON_MESHES,\n STYLE_BOUNDING_BOXES,\n STYLE_CROSSHAIRS\n };\n\n /// How to color persons\n enum ColorTransforms {\n COLORS_SRL,\n COLORS_SRL_ALTERNATIVE,\n COLORS_RAINBOW,\n COLORS_RAINBOW_BW,\n COLORS_FLAT,\n COLORS_VINTAGE,\n COLORS_CONSTANT,\n };\n\n /// Which font colors to use\n enum FontColorStyle {\n FONT_COLOR_FROM_PERSON,\n FONT_COLOR_CONSTANT\n };\n\n /// Subclasses of PersonDisplayCommon can override stylesChanged() to get notified when one of the properties in PersonDisplayCommonProperties has changed.\n class StylesChangedSubscriber {\n public:\n virtual ~StylesChangedSubscriber() {}\n virtual void stylesChanged() {}\n };\n\n /// Common properties shared by multiple displays.\n class PersonDisplayCommonProperties : public QObject {\n Q_OBJECT\n public:\n PersonDisplayCommonProperties(rviz::Display* display, StylesChangedSubscriber* stylesChangedSubscriber);\n\n // User-editable property variables.\n rviz::EnumProperty* style;\n rviz::EnumProperty* color_transform;\n rviz::IntProperty* color_map_offset;\n\n rviz::ColorProperty* constant_color;\n rviz::FloatProperty* alpha;\n\n rviz::FloatProperty* line_width;\n rviz::FloatProperty* z_offset;\n rviz::FloatProperty* scaling_factor;\n\n rviz::BoolProperty* use_actual_z_position;\n\n rviz::EnumProperty* font_color_style;\n rviz::ColorProperty* constant_font_color;\n rviz::FloatProperty* font_scale;\n\n rviz::StringProperty* m_excluded_person_ids_property;\n rviz::StringProperty* m_included_person_ids_property;\n\n /// These sets get updated automatically whenever the corresponding properties are updated.\n set<person_id> m_excludedPersonIDs, m_includedPersonIDs;\n\n private:\n rviz::Display* m_display;\n StylesChangedSubscriber* m_stylesChangedSubscriber;\n\n void hideIrrelevantProperties();\n\n private Q_SLOTS:\n void stylesChanged();\n };\n\n /// A display with common properties that are shared by multiple specializations.\n template<typename MessageType>\n class PersonDisplayCommon: public rviz::MessageFilterDisplay<MessageType>, public StylesChangedSubscriber\n {\n public:\n /// Constructor. pluginlib::ClassLoader creates instances by calling\n /// the default constructor, so make sure you have one.\n PersonDisplayCommon() : m_commonProperties(0), m_veryLargeVariance(99999) {}\n virtual ~PersonDisplayCommon() {}\n\n /// Overrides base class method\n virtual void onInitialize()\n {\n rviz::MessageFilterDisplay<MessageType>::onInitialize();\n m_commonProperties = new PersonDisplayCommonProperties(this, this);\n }\n\n protected:\n /// Common message processing. This method needs to be called by derived classes\n bool preprocessMessage(const typename MessageType::ConstPtr& msg)\n {\n // Here we call the rviz::FrameManager to get the transform from the\n // fixed frame to the frame in the header of this Imu message. If\n // it fails, we can't do anything else so we return.\n if (!getContext()->getFrameManager()->getTransform(msg->header, m_framePosition, m_frameOrientation)) {\n ROS_ERROR_THROTTLE(5.0, \"Error transforming from frame '%s' into fixed frame!\", msg->header.frame_id.c_str());\n return false;\n }\n\n m_frameOrientation.ToRotationMatrix(m_frameRotationMatrix);\n return true;\n }\n\n /// Create a visual representation of the person itself, if not set yet\n void createPersonVisualIfRequired(Ogre::SceneNode* sceneNode, shared_ptr<PersonVisual> &personVisual)\n {\n if (!personVisual) {\n PersonVisualDefaultArgs defaultArgs(getContext()->getSceneManager(), sceneNode);\n PersonVisual* newPersonVisual = 0;\n\n if (m_commonProperties->style->getOptionInt() == STYLE_CYLINDER) newPersonVisual = new CylinderPersonVisual(defaultArgs);\n if (m_commonProperties->style->getOptionInt() == STYLE_PERSON_MESHES) newPersonVisual = new MeshPersonVisual(defaultArgs);\n if (m_commonProperties->style->getOptionInt() == STYLE_BOUNDING_BOXES) newPersonVisual = new BoundingBoxPersonVisual(defaultArgs);\n if (m_commonProperties->style->getOptionInt() == STYLE_CROSSHAIRS) newPersonVisual = new CrosshairPersonVisual(defaultArgs);\n personVisual.reset(newPersonVisual);\n }\n\n // Update position of the person visual\n if (personVisual) {\n personVisual->setPosition(Ogre::Vector3(0,0, personVisual->getHeight() * 0.5));\n }\n }\n\n /// Applies common styles which apply to person visuals, such as line width etc.\n void applyCommonStyles(shared_ptr<PersonVisual> &personVisual) {\n if(!personVisual) return;\n\n // Set line width of wireframe visualization\n HasLineWidth* hasLineWidth = dynamic_cast<HasLineWidth*>(personVisual.get());\n if(hasLineWidth) {\n hasLineWidth->setLineWidth(m_commonProperties->line_width->getFloat());\n }\n\n // Set scaling factor\n personVisual->setScalingFactor(m_commonProperties->scaling_factor->getFloat());\n }\n\n // Builds velocity vector for a person from a twist message\n Ogre::Vector3 getVelocityVector(const geometry_msgs::TwistWithCovariance& twist) {\n const double zVelocityVariance = twist.covariance[2 * 6 + 2];\n const double zVelocity = (isnan(zVelocityVariance) || isinf(zVelocityVariance)) ? 0.0 : twist.twist.linear.z;\n return Ogre::Vector3(twist.twist.linear.x, twist.twist.linear.y, zVelocity);\n }\n\n /// Returns true if all xyz rotation variances are finite\n bool hasValidOrientation(const geometry_msgs::PoseWithCovariance& pose)\n {\n // Check if quaternion has not been initialized, then it's invalid (all-zero elements)\n if(pose.pose.orientation.x == 0 && pose.pose.orientation.y == 0 && pose.pose.orientation.z == 0 && pose.pose.orientation.w == 0) return false;\n\n // According to ROS conventions, orientation covariance is always fixed-frame\n // so no transform necessary!\n const double xRotVariance = pose.covariance[3 * 6 + 3];\n const double yRotVariance = pose.covariance[4 * 6 + 4];\n const double zRotVariance = pose.covariance[5 * 6 + 5];\n // Using logical OR instead of AND here because the orientation is given as a quaternion, instead of independent\n // RPY angles. We assume if at least one RPY angle is valid (according to its covariance), the other angles are set to a\n // reasonable default (as part of the quaternion) if their variance is non-finite.\n return xRotVariance < m_veryLargeVariance || yRotVariance < m_veryLargeVariance || zRotVariance < m_veryLargeVariance;\n }\n\n /// Rotate the position (xyz) part of a pose covariance matrix into the fixed frame used for visualization\n /// The covariance matrix needs to be transformed from the source (e.g. sensor) into the target (e.g. odometry) frame\n /// This is mainly be a problem if the sensor is rotated vertically compared to the odometry frame, so that axes get swapped\n Ogre::Matrix3 covarianceXYZIntoTargetFrame(const geometry_msgs::PoseWithCovariance& pose) {\n Ogre::Matrix3 xyzcov;\n for(int row = 0; row < 3; row++) for(int col = 0; col < 3; col++) xyzcov[row][col] = pose.covariance[row*6 + col]; // 6 = dimension of ROS covariance matrix\n if(!isfinite(xyzcov.Determinant())) ROS_WARN_STREAM(\"Covariance matrix supplied to covarianceXYZIntoTargetFrame() contains non-finite elements: \" << xyzcov);\n return m_frameRotationMatrix * xyzcov * m_frameRotationMatrix.Transpose(); // cov(AX + a) = A cov(X) A^T\n }\n\n /// Set pose and orientation of person visual\n void setPoseOrientation(Ogre::SceneNode* sceneNode, const geometry_msgs::PoseWithCovariance& pose, const Ogre::Matrix3& covXYZinTargetFrame, double personVisualHeight)\n {\n const geometry_msgs::Point& position = pose.pose.position;\n const geometry_msgs::Quaternion& orientation = pose.pose.orientation;\n\n Ogre::Matrix4 transform(m_frameOrientation);\n transform.setTrans(m_framePosition);\n\n Ogre::Vector3 originalPosition(position.x, position.y, position.z);\n if(!isfinite(originalPosition.x) || !isfinite(originalPosition.y) || !isfinite(originalPosition.z)) {\n ROS_WARN(\"Detected or tracked person has non-finite position! Something is wrong!\");\n return;\n }\n\n Ogre::Vector3 positionInTargetFrame = transform * originalPosition;\n\n if(hasValidOrientation(pose)) {\n Ogre::Quaternion detectionOrientation(orientation.w, orientation.x, orientation.y, orientation.z);\n detectionOrientation.FromAngleAxis(detectionOrientation.getRoll(), Ogre::Vector3(0,0,1)); // only use yaw angle, ignore roll and pitch\n sceneNode->setOrientation(m_frameOrientation * detectionOrientation);\n }\n else {\n Ogre::Quaternion rotateTowardsCamera;\n rotateTowardsCamera.FromAngleAxis(Ogre::Degree(180), Ogre::Vector3(0,0,1));\n sceneNode->setOrientation(rotateTowardsCamera);\n }\n\n const double zVariance = covXYZinTargetFrame[2][2];\n bool useActualZPosition = m_commonProperties->use_actual_z_position->getBool() && isfinite(zVariance) && zVariance >= 0 && zVariance < m_veryLargeVariance;\n\n positionInTargetFrame.z = useActualZPosition ? positionInTargetFrame.z - personVisualHeight/2.0: 0.0;\n positionInTargetFrame.z += m_commonProperties->z_offset->getFloat();\n\n sceneNode->setPosition(positionInTargetFrame);\n }\n\n /// Get a color based upon track / detection ID.\n Ogre::ColourValue getColorFromId(unsigned int object_id)\n {\n Ogre::ColourValue color;\n const int colorScheme = m_commonProperties->color_transform->getOptionInt();\n object_id += max(0, m_commonProperties->color_map_offset->getInt());\n\n if(colorScheme == COLORS_SRL || colorScheme == COLORS_SRL_ALTERNATIVE)\n {\n // SRL People Tracker colors\n const size_t NUM_SRL_COLORS = 6, NUM_SRL_COLOR_SHADES = 4, NUM_SRL_TOTAL_COLORS = NUM_SRL_COLORS * NUM_SRL_COLOR_SHADES;\n const unsigned int spencer_colors[NUM_SRL_TOTAL_COLORS] = {\n 0xC00000, 0xFF0000, 0xFF5050, 0xFFA0A0, // red\n 0x00C000, 0x00FF00, 0x50FF50, 0xA0FFA0, // green\n 0x0000C0, 0x0000FF, 0x5050FF, 0xA0A0FF, // blue\n 0xF20A86, 0xFF00FF, 0xFF50FF, 0xFFA0FF, // magenta\n 0x00C0C0, 0x00FFFF, 0x50FFFF, 0xA0FFFF, // cyan\n 0xF5A316, 0xFFFF00, 0xFFFF50, 0xFFFFA0 // yellow\n };\n\n unsigned int rgb = 0;\n const unsigned int tableId = object_id % NUM_SRL_TOTAL_COLORS;\n if(m_commonProperties->color_transform->getOptionInt() == COLORS_SRL) {\n // Colors in original order (first vary shade, then vary color)\n rgb = spencer_colors[tableId];\n }\n else if(m_commonProperties->color_transform->getOptionInt() == COLORS_SRL_ALTERNATIVE) {\n // Colors in alternative order (first vary color, then vary shade)\n unsigned int shadeIndex = tableId / NUM_SRL_COLORS;\n unsigned int colorIndex = tableId % NUM_SRL_COLORS;\n rgb = spencer_colors[colorIndex * NUM_SRL_COLOR_SHADES + shadeIndex];\n }\n\n float r = ((rgb >> 16) & 0xff) / 255.0f,\n g = ((rgb >> 8) & 0xff) / 255.0f,\n b = ((rgb >> 0) & 0xff) / 255.0f;\n\n color = Ogre::ColourValue(r, g, b, 1.0);\n }\n else if(colorScheme == COLORS_RAINBOW || colorScheme == COLORS_RAINBOW_BW)\n {\n const size_t NUM_COLOR = 10, NUM_BW = 4;\n const unsigned int rainbow_colors[NUM_COLOR + NUM_BW] = {\n 0xaf1f90, 0x000846, 0x00468a, 0x00953d, 0xb2c908, 0xfcd22a, 0xffa800, 0xff4500, 0xe0000b, 0xb22222,\n 0xffffff, 0xb8b8b8, 0x555555, 0x000000\n };\n\n color.setAsARGB(rainbow_colors[object_id % (colorScheme == COLORS_RAINBOW ? NUM_COLOR : (NUM_COLOR+NUM_BW))]);\n }\n else if(colorScheme == COLORS_FLAT)\n {\n const size_t NUM_COLOR = 10;\n const unsigned int flat_colors[NUM_COLOR] = {\n 0x990033, 0xa477c4, 0x3498db, 0x1abc9c, 0x55e08f, 0xfff054, 0xef5523, 0xfe374a, 0xbaa891, 0xad5f43\n };\n\n color.setAsARGB(flat_colors[object_id % NUM_COLOR]);\n }\n else if(colorScheme == COLORS_VINTAGE)\n {\n const size_t NUM_COLOR = 10;\n const unsigned int vintage_colors[NUM_COLOR] = {\n 0xd05e56, 0x797d88, 0x448d7a, 0xa5d1cd, 0x88a764, 0xebe18c, 0xd8a027, 0xffcc66, 0xdc3f1c, 0xff9966\n };\n\n color.setAsARGB(vintage_colors[object_id % NUM_COLOR]);\n }\n else\n {\n // Constant color for all tracks\n color = m_commonProperties->constant_color->getOgreColor();\n }\n\n color.a = 1.0f; // force full opacity\n return color;\n }\n\n /// Checks if a person shall be hidden (can be set using include/exclude person ID properties in GUI)\n bool isPersonHidden(person_id personId)\n {\n bool isIncluded = m_commonProperties->m_includedPersonIDs.find(personId) != m_commonProperties->m_includedPersonIDs.end();\n if(isIncluded) return false;\n if(!m_commonProperties->m_includedPersonIDs.empty()) return true;\n return m_commonProperties->m_excludedPersonIDs.find(personId) != m_commonProperties->m_excludedPersonIDs.end();\n }\n\n /// Must be implemented by derived classes because MOC doesn't work in templates\n virtual rviz::DisplayContext* getContext() = 0;\n\n /// Common properties for the displays in this plugin\n PersonDisplayCommonProperties* m_commonProperties;\n\n protected:\n Ogre::Quaternion m_frameOrientation;\n Ogre::Matrix3 m_frameRotationMatrix;\n Ogre::Vector3 m_framePosition;\n const double m_veryLargeVariance;\n };\n} // end namespace spencer_tracking_rviz_plugin\n\n\n#endif // PERSON_DISPLAY_COMMON_H\n"
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"text": "#ifndef PERSON_VISUAL_H\n#define PERSON_VISUAL_H\n\n#include <rviz/ogre_helpers/shape.h>\n#include <rviz/ogre_helpers/billboard_line.h>\n\n#include <OgreSceneNode.h>\n#include <OgreAnimation.h>\n#include <OgreSharedPtr.h>\n#include <OgreEntity.h>\n\n\nnamespace spencer_tracking_rviz_plugin {\n // Abstract class for visuals which have got an adjustable line width\n class HasLineWidth {\n public:\n virtual void setLineWidth(double lineWidth) = 0;\n };\n\n // Default arguments that need to be supplied to all types of PersonVisual\n struct PersonVisualDefaultArgs {\n PersonVisualDefaultArgs(Ogre::SceneManager* sceneManager, Ogre::SceneNode* parentNode) : sceneManager(sceneManager), parentNode(parentNode) {}\n Ogre::SceneManager* sceneManager;\n Ogre::SceneNode* parentNode;\n };\n\n /// Base class for all person visualization types\n class PersonVisual {\n public:\n PersonVisual(const PersonVisualDefaultArgs& args) :\n m_sceneManager(args.sceneManager),\n m_correctOrientation( Ogre::Degree(90), Ogre::Vector3(1,0,0) )\n {\n m_parentSceneNode = args.parentNode;\n m_sceneNode = args.parentNode->createChildSceneNode();\n\n Ogre::Vector3 scale(1,1,1);\n m_sceneNode->setScale(m_correctOrientation * scale);\n }\n\n virtual ~PersonVisual() {\n m_sceneManager->destroySceneNode(m_sceneNode->getName());\n };\n\n void setPosition(const Ogre::Vector3& position) {\n m_sceneNode->setPosition(position);\n }\n\n const Ogre::Vector3& getPosition() const {\n return m_sceneNode->getPosition();\n }\n\n void setOrientation(const Ogre::Quaternion& orientation) {\n m_sceneNode->setOrientation(orientation * m_correctOrientation);\n }\n\n const Ogre::Quaternion& getOrientation() const {\n return m_sceneNode->getOrientation();\n }\n\n virtual void setScalingFactor(double scalingFactor) {\n m_sceneNode->setScale(scalingFactor, scalingFactor, scalingFactor);\n }\n\n void setVisible(bool visible) {\n m_sceneNode->setVisible(visible, true);\n }\n\n Ogre::SceneNode* getParentSceneNode() {\n return m_parentSceneNode;\n }\n\n virtual void update(float deltaTime) {}\n virtual void setColor(const Ogre::ColourValue& c) = 0;\n virtual double getHeight() = 0;\n\n protected:\n Ogre::SceneManager* m_sceneManager;\n Ogre::SceneNode *m_sceneNode, *m_parentSceneNode;\n Ogre::Quaternion m_correctOrientation;\n };\n\n\n /// Visualization of a person as cylinder (body) + sphere (head)\n class CylinderPersonVisual : public PersonVisual {\n public:\n CylinderPersonVisual(const PersonVisualDefaultArgs& args) : PersonVisual(args)\n {\n m_bodyShape = new rviz::Shape(rviz::Shape::Cylinder, args.sceneManager, m_sceneNode);\n m_headShape = new rviz::Shape(rviz::Shape::Sphere, args.sceneManager, m_sceneNode);\n\n const float headDiameter = 0.4;\n const float totalHeight = getHeight();\n const float cylinderHeight = totalHeight - headDiameter;\n\n m_bodyShape->setScale(Ogre::Vector3(headDiameter, headDiameter, cylinderHeight));\n m_headShape->setScale(Ogre::Vector3(headDiameter, headDiameter, headDiameter));\n\n m_bodyShape->setPosition(Ogre::Vector3(0, 0, cylinderHeight / 2 - totalHeight / 2));\n m_headShape->setPosition(Ogre::Vector3(0, 0, totalHeight / 2 - headDiameter / 2 ));\n }\n\n virtual ~CylinderPersonVisual() {\n delete m_bodyShape;\n delete m_headShape;\n }\n\n virtual void setColor(const Ogre::ColourValue& c) {\n m_bodyShape->setColor(c);\n m_headShape->setColor(c);\n }\n\n virtual double getHeight() {\n return 1.75;\n }\n\n private:\n rviz::Shape *m_bodyShape, *m_headShape;\n };\n\n\n /// Visualization of a person as a wireframe bounding box\n class BoundingBoxPersonVisual : public PersonVisual, public HasLineWidth {\n public:\n BoundingBoxPersonVisual(const PersonVisualDefaultArgs& args, double height = 1.75, double width = 0.6, double scalingFactor = 1.0) : PersonVisual(args)\n {\n m_width = width; m_height = height; m_scalingFactor = scalingFactor; m_lineWidth = 0.03;\n m_wireframe = NULL;\n generateWireframe();\n }\n\n virtual ~BoundingBoxPersonVisual() {\n delete m_wireframe;\n }\n\n virtual void setColor(const Ogre::ColourValue& c) {\n m_wireframe->setColor(c.r, c.g, c.b, c.a);\n }\n\n virtual double getHeight() {\n return m_height;\n }\n\n virtual void setLineWidth(double lineWidth) {\n m_wireframe->setLineWidth(lineWidth);\n }\n\n /*\n virtual void setScalingFactor(double scalingFactor) {\n if(scalingFactor != m_scalingFactor) {\n m_scalingFactor = scalingFactor;\n generateWireframe();\n }\n }\n */\n\n protected:\n virtual void generateWireframe() {\n delete m_wireframe;\n m_wireframe = new rviz::BillboardLine(m_sceneManager, m_sceneNode);\n \n m_wireframe->setLineWidth(m_lineWidth); \n m_wireframe->setMaxPointsPerLine(2);\n m_wireframe->setNumLines(12);\n\n double w = m_width * m_scalingFactor, h = m_height * m_scalingFactor;\n Ogre::Vector3 bottomLeft(0, -w, 0), bottomRight(0, 0, 0), topLeft(0, -w, h), topRight(0, 0, h);\n Ogre::Vector3 rear(w, 0, 0);\n\n // Front quad\n m_wireframe->addPoint(bottomLeft); m_wireframe->addPoint(bottomRight);\n m_wireframe->newLine(); m_wireframe->addPoint(bottomRight); m_wireframe->addPoint(topRight);\n m_wireframe->newLine(); m_wireframe->addPoint(topRight); m_wireframe->addPoint(topLeft);\n m_wireframe->newLine(); m_wireframe->addPoint(topLeft); m_wireframe->addPoint(bottomLeft);\n\n // Rear quad\n m_wireframe->newLine(); m_wireframe->addPoint(bottomLeft + rear); m_wireframe->addPoint(bottomRight + rear);\n m_wireframe->newLine(); m_wireframe->addPoint(bottomRight + rear); m_wireframe->addPoint(topRight + rear);\n m_wireframe->newLine(); m_wireframe->addPoint(topRight + rear); m_wireframe->addPoint(topLeft + rear);\n m_wireframe->newLine(); m_wireframe->addPoint(topLeft + rear); m_wireframe->addPoint(bottomLeft + rear);\n\n // Four connecting lines between front and rear\n m_wireframe->newLine(); m_wireframe->addPoint(bottomLeft); m_wireframe->addPoint(bottomLeft + rear);\n m_wireframe->newLine(); m_wireframe->addPoint(bottomRight); m_wireframe->addPoint(bottomRight + rear);\n m_wireframe->newLine(); m_wireframe->addPoint(topRight); m_wireframe->addPoint(topRight + rear);\n m_wireframe->newLine(); m_wireframe->addPoint(topLeft); m_wireframe->addPoint(topLeft + rear);\n \n m_wireframe->setPosition(Ogre::Vector3(-w/2, w/2, -h/2));\n } \n\n private:\n rviz::BillboardLine *m_wireframe;\n double m_width, m_height, m_scalingFactor, m_lineWidth;\n };\n\n\n /// Visualization of a person as a crosshair\n class CrosshairPersonVisual : public PersonVisual, public HasLineWidth {\n public:\n CrosshairPersonVisual(const PersonVisualDefaultArgs& args, double height = 1.0, double width = 1.0) : PersonVisual(args)\n {\n m_width = width; m_height = height; m_lineWidth = 0.03;\n m_crosshair = NULL;\n generateCrosshair();\n }\n\n virtual ~CrosshairPersonVisual() {\n delete m_crosshair;\n }\n\n virtual void setColor(const Ogre::ColourValue& c) {\n m_crosshair->setColor(c.r, c.g, c.b, c.a);\n }\n\n virtual double getHeight() {\n return m_height;\n }\n\n virtual void setLineWidth(double lineWidth) {\n m_crosshair->setLineWidth(lineWidth);\n }\n\n\n protected:\n virtual void generateCrosshair() {\n delete m_crosshair;\n m_crosshair = new rviz::BillboardLine(m_sceneManager, m_sceneNode);\n \n m_crosshair->setLineWidth(m_lineWidth); \n m_crosshair->setMaxPointsPerLine(2);\n m_crosshair->setNumLines(5);\n\n double w = m_width / 2.0;\n Ogre::Vector3 p1a(-w, 0, 0), p1b(+w, 0, 0);\n Ogre::Vector3 p2a(0, -w, 0), p2b(0, +w, 0);\n Ogre::Vector3 p3a(0, 0, -w), p3b(0, 0, +w);\n\n Ogre::Vector3 arrow_a(0.7*w, -0.2*w, 0), arrow_m(w, 0, 0), arrow_b(0.7*w, +0.2*w, 0);\n\n m_crosshair->addPoint(p1a); m_crosshair->addPoint(p1b);\n m_crosshair->newLine(); m_crosshair->addPoint(p2a); m_crosshair->addPoint(p2b);\n m_crosshair->newLine(); m_crosshair->addPoint(p3a); m_crosshair->addPoint(p3b);\n\n m_crosshair->newLine(); m_crosshair->addPoint(arrow_m); m_crosshair->addPoint(arrow_a);\n m_crosshair->newLine(); m_crosshair->addPoint(arrow_m); m_crosshair->addPoint(arrow_b);\n\n m_crosshair->setPosition(Ogre::Vector3(0, 0, 0));\n } \n\n private:\n rviz::BillboardLine *m_crosshair;\n double m_width, m_height, m_lineWidth;\n };\n\n\n /// Visualization of a person as a mesh (walking human)\n class MeshPersonVisual : public PersonVisual {\n public:\n MeshPersonVisual(const PersonVisualDefaultArgs& args);\n\n virtual ~MeshPersonVisual();\n\n virtual void update(float deltaTime);\n\n virtual void setColor(const Ogre::ColourValue& c);\n\n void setAnimationState(const std::string& nameOfAnimationState);\n\n void setWalkingSpeed(float walkingSpeed);\n\n virtual double getHeight() {\n return 1.75;\n }\n\n virtual void setScalingFactor(double scalingFactor) {\n // Not supported (for some reason causes the mesh to be mirrored vertically).\n }\n\n private:\n Ogre::SceneNode *m_childSceneNode;\n Ogre::Entity* entity_;\n Ogre::AnimationState* m_animationState;\n std::set<Ogre::MaterialPtr> materials_;\n float m_walkingSpeed;\n };\n\n}\n\n#endif // PERSON_VISUAL_H\n"
}
] | 43 |
ankur11/Miscellaneous
|
https://github.com/ankur11/Miscellaneous
|
6d12381fff986be434934bfd4f0b56038dd4b7e2
|
aaf390f164ec24ee624d26fe8f48553ccc643927
|
0394099f2e2e6edff66a690802e4acd11912d993
|
refs/heads/master
| 2021-01-12T08:07:39.212875 | 2017-01-04T04:14:10 | 2017-01-04T04:14:10 | 76,480,434 | 0 | 0 | null | null | null | null | null |
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"text": "#os utilities\nfrom os import listdir, remove\nfrom os.path import isfile, join\nfrom subprocess import call\nimport sys\nimport os\n\n#for files\nfrom shutil import copyfile\n\n#natural languaje proccesing\nimport nltk\nfrom nltk.tag import StanfordNERTagger\nfrom nltk.tokenize import word_tokenize\n\n#regular expr\nimport re\n\n#cvs\nimport csv\n\n#services\nimport requests\nimport json\n\n\n#input values\npath = \"../\"\n\n#retrive all files\nonlyfiles = [f for f in listdir(path) if isfile(join(path, f))]\n\n###\ntext = \"\"\nalltext = \"\"\ni = 0\n\n#load models for nlp\ntry:\n st = StanfordNERTagger('english.all.3class.distsim.crf.ser.gz')\n print('models loaded...')\nexcept ValueError:\n print('program should be closed.')\n sys.exit()\n\n###\n#load technologies\nskills = []\n\nr = requests.get('http://host.ipues.com/RP/Lab/signage/fonts/ipues.php')\nr.headers['content-type']\nif r.status_code == 200:\n r = json.loads(r.text)\n r = r['words']\n for word in r:\n skills.append(str(word))\n print('corpus loaded...')\nelse:\t\n\tprint(\"error fatal, could not connect to server\")\n\tsys.exit()\n\n#load complete corpus by bruteforce (no performance required)\nfor i in range (0,len(skills)):\n skills[i] = \"\\\\b\"+str.lower(skills[i]).strip()+\"\\\\b\"\n\n#compiling regex\nprint('|'.join(skills))\nskillsRgx = re.compile(r'|'.join(skills),re.I) \nprint('regex compiled for corpus.')\n\n###\n#writting cvs\nwith open('profiles.csv', 'w') as csvfile:\n fieldnames = ['hint','first_name', 'last_name','email','phone','experience']\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n writer.writeheader()\n\t\n print('ready to write cvs...')\n\n #info\n name = \"\"\n lastname = \"\"\n email = \"\"\n phone = \"\"\n exp = \"\"\n\n for file in onlyfiles:\n text = \"\"\n alltext = \"\"\n\n k = file.rfind(\".\")\n \n new_string = file[:k]\n extension = file[k+1:]\n \n required = False\n \n #linux test left / rest api\n if extension == \"pdf\":\n #run command for create a tmp file\n #textutil -convert txt balaji_perumal_Chennai_7.10_yrs.doc\n #pdftotext Anusha_Kurian_Kannur_0.00_yrs.pdf text4.txt\n \n call([\"pdftotext\", path + file ,\"tmp\"+str(i)+\".txt\"])\n\n #open a file and save content\n\n with open(\"tmp\"+str(i)+\".txt\") as f:\n text = f.readlines()\n \n #del file\n remove(\"tmp\"+str(i)+\".txt\")\n i = i+ 1\n\n for txt in text:\n alltext += txt\n required = True\n \n elif extension == \"docx\" or extension == \"doc\":\n \n call([\"pandoc\",\"-s\",path + file,\"-o\", path + new_string +\".txt\"])\n\n #open a file and save content\n \n with open(path + new_string+\".txt\") as f:\n text = f.readlines()\n\n #del file\n remove(path + new_string+\".txt\")\n\n for txt in text:\n alltext += txt\n required = True\n\n if required == True:\n\n print(':::::::::::::::processing file:::::::::::::::')\n \n\t hint = \"\"\n phone = \"\"\n name = \"\"\n lastname = \"\"\n email = \"\"\n exp = []\n \n #looking for phone number\n result = re.compile(\"\\d{3}\\d{3}\\d{4}\")\n result = result.search(alltext)\n \n if result:\n phone = result.group(0)\n \t \n \n #looking for email\n result = re.compile(\"[_a-z0-9-]+@[_a-z0-9-]+\\.[_a-z0-9-]+\")\n result = result.search(alltext)\n \n if result:\n email = result.group(0)\n \n #looking for skills\n alltext = str.lower(alltext).strip()\n\n mytuple = [] \n for match in skillsRgx.findall(alltext):\n mytuple.append(match)\n\n newlist = []\n for l in mytuple:\n if l not in newlist:\n newlist.append(l)\n\n mytuple = []\n exp = newlist\n newlist = []\n\n\n #name\n name = str.split(new_string,\"_\")\n for l in name:\n if l != \"\":\n newlist.append(l)\n\n\t hint = ''.join(str(elem) + ' ' for elem in newlist)\n\n while '' in newlist: newlist.remove('') \n\n nregx = re.compile(r\"\\b([a-zA-Z]*)\\b\")\n res1 = nregx.search(newlist[0])\n\n res2 = nregx.search(newlist[1])\n\n if res1 and res2:\n name = res1.group(0)\n lastname = res2.group(0)\n elif res2:\n lastname = res2.group(0)\n else:\n newlist = []\n \n #looking for human name if not\n if len(newlist) == 0:\n r = st.tag(alltext.split())\n if (len(r) > 0):\n for word in r:\n if word[1] == 'PERSON':\n name = word[0]\n \n exp = '|'.join(exp)\n \n writer.writerow({'hint': hint,'first_name':str(name), 'last_name': str(lastname), 'email': str(email), 'phone':str(phone),'experience': exp})\n #print(new_string)\n #print('writting row. ' + str(name) + ' ' + str(lastname) + ' ' + str(email) + ' ' + str(phone) + ' ' + exp)\n\n\n \n"
},
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"text": "# cvs_nlp\nnatural languaje processing and parsing for extracting names and experience from resumes.\n\n# Instructions\n#### 1. Make sure you have python 2.7 +\n#### 2. Install the following dependencies:\n\n pip install -U nltk (natural languaje tool kit)\n\n pip install numpy\n\n pip install csv\n\n pip install requests\n\n\n#### 3. Download the englis model jar file from stanfordnlp.github.io/CoreNLP/download.html\n#### 4. Download the Stanford Named Entity Recognizar 3.6.0 from nlp.stanford.edu/software/CRF-NER.shtml#Download\n#### 5. set the environment variables CLASSPATH and STANFORD_MODELS as follows:\n\n\n CLASSPATH = PATH_NER_3-6-0/ner3.6.0.jar\n \n STANDFORD_MODELS = PATH_ENGLISH_MODEL.jar\n \n#### 6. Install the textutil utility (e.g. for macos $brew install textutils)\n#### 7. Install the pdftotext utility\n#### 8. Create a directory from you have the pdfs and docs resumes and put resumes.py\n#### 9. run by $ python resumes.py\n#### 10. cvs file output will be created\n\n\n# Notes\n \n\n\n\n"
}
] | 2 |
Peesky/network-tutorial
|
https://github.com/Peesky/network-tutorial
|
a260c6ef3a6a6a3eea7c259307fae9ce1019f27e
|
a2a12db7991752c1524508217c10b20aa75ca36c
|
5b9d5b614ea492a072400a2c91138ed7fa6ad80c
|
refs/heads/main
| 2023-04-05T09:24:11.171646 | 2021-04-11T20:21:28 | 2021-04-11T20:21:28 | 356,970,755 | 0 | 0 | null | null | null | null | null |
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"text": "import socket\r\n\r\nhost,port=('`127.0.0.1', 5566)\r\n\r\nsocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n\r\ntry:\r\n socket.connect((host,port))\r\n print(\"client conn\")\r\n data = \"Bonjour a toi!\"\r\n data = data.encode('utf8')\r\n socket.sendall(data)\r\nexcept:\r\n print(\"connection a échoué\")\r\nfinally:\r\n socket.close()"
}
] | 1 |
lixiongzhang/GitDemo
|
https://github.com/lixiongzhang/GitDemo
|
d6e9325f1d220d25320d10c4e87b53a320fe98f8
|
611844dfe3dd41038d2431c7f61b5a4d9c9d6805
|
2ba66f93a80845f4560d78107fdcc38300c97ff5
|
refs/heads/master
| 2022-07-31T13:37:41.868523 | 2020-05-24T12:06:29 | 2020-05-24T12:06:29 | null | 0 | 0 | null | null | null | null | null |
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"text": "import tkinter\nfrom tkinter import filedialog\nimport os\nfrom datetime import datetime\n\n\n\ntoday = datetime.now().date()\nprint('today is ', today)\ndirectory = tkinter.filedialog.askdirectory()\nprint(directory)\nos.chdir(directory)\nfiles = os.listdir(directory)\nfor file in files:\n in_file = open(file, 'rb')\n image = in_file.read()\n in_file.close()\n image = bytearray(image)\n key = 46\n for index, value in enumerate(image):\n image[index] = value ^ key\n save_path = r'C:/test/'\n s = file.split(\"/\")\n file = s[-1]\n# os.chdir(save_path)\n out_filename = os.path.join(save_path, file + ' ' + str(today) + '.enc')\n print(out_filename)\n out_file = open(out_filename, 'wb')\n out_file.write(image)\n# os.startfile(save_path)\n out_file.close()\nprint(directory)\nprint(files)"
}
] | 1 |
ivanwhaf/gta5-auto-driver
|
https://github.com/ivanwhaf/gta5-auto-driver
|
b98d8f78b2e6d43245082d17cb4aeb8425e2b852
|
ff9b466e33c81ee95abb82c7c50cbff8aea432d0
|
2431b63f0ae31758e1dccb654ca119fbffa46643
|
refs/heads/master
| 2022-12-12T10:30:03.448499 | 2020-09-07T11:50:23 | 2020-09-07T11:50:23 | 283,163,590 | 3 | 0 | null | null | null | null | null |
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"text": "# gta5-auto-driver\nA gta5 auto driver program based on deep learning\n\n# Usage\n* run GTAV and resize game window to 800*600 or your customized size\n* select a perfect road and vehicle to drive\n* run `drive_train.py` to get training data,`train_data.npy`is your training data\n* run `train_model.py` to train your model\n* when finishing training,`model.h5` would be added to root path\n* run `auto_drive.py` for auto driving\n\n# Dependency\n* OpenCV\n* Keras\n* Tensorflow-gpu\n* pillow\n* matplotlib\n* numpy\n* pywin32\n\nJust enjoy your AI(shit) auto driver trip~🤭"
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"text": "# @Author: Ivan\n# @LastEdit: 2020/8/6\nimport ctypes\nimport win32api\n\nSendInput = ctypes.windll.user32.SendInput\n\n# C struct redefinitions\nPUL = ctypes.POINTER(ctypes.c_ulong)\n\n\nclass KeyBdInput(ctypes.Structure):\n _fields_ = [(\"wVk\", ctypes.c_ushort),\n (\"wScan\", ctypes.c_ushort),\n (\"dwFlags\", ctypes.c_ulong),\n (\"time\", ctypes.c_ulong),\n (\"dwExtraInfo\", PUL)]\n\n\nclass HardwareInput(ctypes.Structure):\n _fields_ = [(\"uMsg\", ctypes.c_ulong),\n (\"wParamL\", ctypes.c_short),\n (\"wParamH\", ctypes.c_ushort)]\n\n\nclass MouseInput(ctypes.Structure):\n _fields_ = [(\"dx\", ctypes.c_long),\n (\"dy\", ctypes.c_long),\n (\"mouseData\", ctypes.c_ulong),\n (\"dwFlags\", ctypes.c_ulong),\n (\"time\", ctypes.c_ulong),\n (\"dwExtraInfo\", PUL)]\n\n\nclass InputI(ctypes.Union):\n _fields_ = [(\"ki\", KeyBdInput),\n (\"mi\", MouseInput),\n (\"hi\", HardwareInput)]\n\n\nclass Input(ctypes.Structure):\n _fields_ = [(\"type\", ctypes.c_ulong),\n (\"ii\", InputI)]\n\n\nscan_code = {'W': 0x11, 'A': 0x1E, 'S': 0x1f, 'D': 0x20}\n\n\n# Actual Functions\ndef press_key(key_string):\n print('key', key_string, 'down')\n hex_key_code = scan_code[key_string]\n\n extra = ctypes.c_ulong(0)\n ii_ = InputI()\n ii_.ki = KeyBdInput(0, hex_key_code, 0x0008, 0, ctypes.pointer(extra))\n x = Input(ctypes.c_ulong(1), ii_)\n ctypes.windll.user32.SendInput(1, ctypes.pointer(x), ctypes.sizeof(x))\n\n\ndef release_key(key_string):\n print('key', key_string, 'up')\n hex_key_code = scan_code[key_string]\n\n extra = ctypes.c_ulong(0)\n ii_ = InputI()\n ii_.ki = KeyBdInput(0, hex_key_code, 0x0008 | 0x0002,\n 0, ctypes.pointer(extra))\n x = Input(ctypes.c_ulong(1), ii_)\n ctypes.windll.user32.SendInput(1, ctypes.pointer(x), ctypes.sizeof(x))\n\n\nkey_lst = [\"\\b\"]\nfor char in \"WAD\":\n key_lst.append(char)\n\n\ndef check_key():\n keys = []\n for key in key_lst:\n if win32api.GetAsyncKeyState(ord(key)):\n keys.append(key)\n return keys\n"
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"text": "# @Author: Ivan\n# @LastEdit: 2020/8/6\nimport time\nimport cv2\nimport numpy as np\nfrom PIL import ImageGrab\n\n\ndef grab_screen():\n last_time = time.time()\n frame, accum_time, fps = 0, 0, 0\n\n while True:\n # screenshot normalization\n screen = np.array(ImageGrab.grab(bbox=(0, 40, 800, 640)))\n # screen = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)\n screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)\n screen = cv2.GaussianBlur(screen, (3, 3), 0)\n\n # calculate fps\n this_time = time.time()\n print('loop took {} seconds'.format(this_time - last_time))\n accum_time += this_time - last_time\n last_time = time.time()\n\n frame += 1\n if accum_time >= 1:\n fps = frame\n print('fps:', frame)\n frame, accum_time = 0, 0\n\n cv2.putText(screen, 'fps:{}'.format(fps), (10, 30),\n cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 0), 2)\n\n # show screenshot\n cv2.imshow('screen', screen)\n if cv2.waitKey(25) & 0xFF == ord('q'):\n cv2.destoryAllWindows()\n break\n\n\ndef main():\n grab_screen()\n\n\nif __name__ == \"__main__\":\n main()\n"
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"text": "# @Author: Ivan\n# @LastEdit: 2020/8/13\nimport os\nimport cv2 # install\nimport numpy as np # install\nfrom keras import backend as K\nfrom keras.models import Model\nimport matplotlib.pyplot as plt # install\n\n# ----------------Neural Network Visualization----------------\n\n# input shape\nwidth, height, depth = 100, 100, 3\n\n\ndef deprocess_image(x):\n if K.image_data_format() == 'channels_first':\n x = x.transpose(1, 2, 3, 0)\n elif K.image_data_format() == 'channels_last':\n x = x.transpose(3, 1, 2, 0)\n x *= 255\n x = np.clip(x, 0, 255).astype('uint8')\n return x\n\n\ndef get_intermediate_output(model, layer_name, img):\n \"\"\"Get the output of intermediate layer.\n Args:\n model: keras model.\n layer_name: name of layer in the model.\n img: processed input image.\n Returns:\n intermediate_output: feature map.\n \"\"\"\n try:\n # this is the placeholder for the intermediate output\n out_intermediate = model.get_layer(layer_name).output\n except:\n raise Exception('Not layer named {}!'.format(layer_name))\n\n # get the intermediate layer model\n intermediate_layer_model = Model(\n inputs=model.input, outputs=out_intermediate)\n\n # get the output of intermediate layer model\n intermediate_output = intermediate_layer_model.predict(img)\n return intermediate_output[0]\n\n\ndef show_intermediate_output(model, layer_name, image):\n \"\"\"show the output of intermediate layer.\n Args:\n model: keras model.\n layer_name: name of layer in the model.\n image: processed input image.\n Returns:\n display_grid: feature maps grid.\n \"\"\"\n if depth == 1:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n image = cv2.resize(image, (width, height))\n\n img_ndarray = np.asarray(image, dtype='float64') / 255\n test_data = np.ndarray.flatten(img_ndarray)\n test_data = test_data.astype('float32')\n\n if K.image_data_format() == 'channels_first':\n test_data = test_data.reshape(1, depth, height, width)\n else:\n test_data = test_data.reshape(1, height, width, depth)\n\n output = get_intermediate_output(model, layer_name, test_data)\n n = output.shape[-1] # number of feature\n size = output.shape[1] # feature map side length\n display_grid = np.zeros((size * 1, n * size)) # grid\n\n for i in range(n):\n channel_image = output[:, :, i]\n display_grid[0:size, i * size:(i + 1) * size] = channel_image\n\n plt.figure()\n plt.title(layer_name)\n plt.grid(False)\n plt.imshow(display_grid, cmap='viridis')\n plt.savefig('visualization/' + layer_name + '_output.jpg') # save output diagram\n plt.show() # must show after imshow\n\n return display_grid\n\n\ndef show_heatmap(model, layer_name, image):\n \"\"\"show the heatmap of intermediate layer.\n Args:\n model: keras model.\n layer_name: name of layer in the model.\n image: processed input image.\n Returns:\n heatmap: the heatmap of the trained model.\n superimposed_img: heatmap apply on input image\n \"\"\"\n img = image.copy()\n if depth == 1:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n image = cv2.resize(image, (width, height))\n\n img_ndarray = np.asarray(image, dtype='float64') / 255\n test_data = np.ndarray.flatten(img_ndarray)\n test_data = test_data.astype('float32')\n\n if K.image_data_format() == 'channels_first':\n test_data = test_data.reshape(1, depth, height, width)\n else:\n test_data = test_data.reshape(1, height, width, depth)\n\n preds = model.predict(test_data)\n index = np.argmax(preds[0]) # index of output class\n output = model.output[:, index]\n\n layer = model.get_layer(layer_name) # intermediate layer\n\n grads = K.gradients(output, layer.output)[0]\n\n pooled_grads = K.mean(grads, axis=(0, 1, 2))\n iterate = K.function([model.input], [pooled_grads, layer.output[0]])\n\n pooled_grads_value, layer_output_value = iterate([test_data])\n\n for i in range(layer_output_value.shape[-1]):\n layer_output_value[:, :, i\n ] *= pooled_grads_value[i]\n heatmap = np.mean(layer_output_value, axis=-1)\n\n # heatmap = np.maximum(heatmap, 0)\n # heatmap /= np.max(heatmap)\n\n plt.matshow(heatmap)\n plt.savefig('visualize/heatmap.jpg')\n plt.show()\n\n # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n heatmap = np.uint8(255 * heatmap) # convert to rgb\n heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) # heatmap apply to raw image\n superimposed_img = heatmap * 0.4 + img # heatmap intensity factor - 0.4\n cv2.imwrite('visualize/heatmap_apply.jpg', superimposed_img)\n\n return heatmap, superimposed_img\n\n\n# -----------------------------------------------------------------------------------\n\n\n# -------------------------------CV Algorithms--------------------------------------\n\n\ndef face_detect():\n \"\"\"function of detection faces\n\n * use camera to detect faces and use rectangle box to frame\n \"\"\"\n face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n cap = cv2.VideoCapture(0)\n while True:\n ret, fram = cap.read()\n faces = face_cascade.detectMultiScale(fram, 1.1, 7)\n for x, y, w, h in faces:\n cv2.rectangle(fram, (x - 5, y - 25), (x + w, y + h), (0, 255, 0), 2)\n cv2.imshow('fram', fram)\n\n\ndef cut_face(path):\n \"\"\"function of cuting faces\n\n * detect faces,cut faces,cover old image\n\n Args:\n path: images path\n Returns:\n None\n \"\"\"\n face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n img_categories = os.listdir(path)\n for img_category in img_categories:\n img_category_path = os.path.join(path, img_category)\n if os.path.isdir(img_category_path):\n imgs = os.listdir(img_category_path)\n for img in imgs:\n img_path = os.path.join(img_category_path, img)\n face = cv2.imread(img_path) # read image\n faces = face_cascade.detectMultiScale(face, 1.1, 7) # detect faces\n for x, y, w, h in faces[0]:\n # cv2.rectangle(face,(x-5,y-25),(x+w,y+h),(0,255,0),2)\n face = face[y - 60:y + h + 15, x:x + w]\n cv2.imwrite(img_path, face) # cover old images\n print(img_path + '--cuting successfully')\n print('all faces cut successfully!')\n\n\ndef d_hash(img):\n # Different hash algorithm\n img = cv2.resize(img, (9, 8), interpolation=cv2.INTER_AREA)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n hash_ = ''\n for i in range(8):\n for j in range(8):\n if img[i, j] > img[i, j + 1]:\n hash_ = hash_ + '1'\n else:\n hash_ = hash_ + '0'\n print(\"dHash:\" + str(hash_))\n return hash_\n\n\ndef a_hash(img):\n # Average hash algorithm\n img = cv2.resize(img, (8, 8))\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n hash_ = ''\n average = 0\n for i in range(8):\n for j in range(8):\n average = average + img[i, j]\n average = average / 64\n\n for i in range(8):\n for j in range(8):\n if img[i, j] > average:\n hash_ = hash_ + '1'\n else:\n hash_ = hash_ + '0'\n print(\"aHash:\" + str(hash_))\n return hash_\n\n\ndef p_hash(img):\n # Perceptual hash algorithm\n img = cv2.resize(img, (32, 32), interpolation=cv2.INTER_AREA)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n hash_ = ''\n mean = 0.0\n h, w = img.shape[:2]\n vis0 = np.zeros((h, w), np.float32)\n vis0[:h, :w] = img\n vis1 = cv2.dct(vis0)\n for i in range(8):\n for j in range(8):\n mean += vis1[i, j]\n mean = mean / 64\n\n for i in range(8):\n for j in range(8):\n if vis1[i, j] >= mean:\n hash_ = hash_ + '1'\n else:\n hash_ = hash_ + '0'\n print(\"pHash:\" + str(hash_))\n return hash_\n\n\ndef _hamming_distance(hash1, hash2):\n # calculate two values' hamming distance\n hamming = 0\n for i in range(64):\n if hash1[i] != hash2[i]:\n hamming = hamming + 1\n return hamming\n\n\ndef hamming_distance(img1, img2, func):\n # calculate two images' hamming distance\n hamming = None\n if func == 'aHash':\n hamming = _hamming_distance(a_hash(img1), a_hash(img2))\n elif func == 'pHash':\n hamming = _hamming_distance(p_hash(img1), p_hash(img2))\n elif func == 'dHash':\n hamming = _hamming_distance(d_hash(img1), d_hash(img2))\n return hamming\n\n\ndef blur_test():\n img = cv2.imread('1.jpg')\n img = cv2.resize(img, (800, 1000), interpolation=cv2.INTER_AREA)\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n grad_x = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)\n grad_y = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1, ksize=-1)\n gradient = cv2.subtract(grad_x, grad_y)\n gradient = cv2.convertScaleAbs(gradient)\n\n gradient = cv2.blur(gradient, (3, 3))\n ret, binary = cv2.threshold(gradient, 127, 255, cv2.THRESH_BINARY)\n cv2.imshow('img', binary)\n i = cv2.waitKey(0)\n\n\ndef harris(img):\n # cornerHarris角点检测函数\n # img=cv2.imread('6.jpg')\n # img=cv2.resize(img,(800,800),interpolation=cv2.INTER_AREA)\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n gray = cv2.GaussianBlur(gray, (5, 5), 0)\n gray = np.float32(gray)\n dst = cv2.cornerHarris(gray, 2, 3, 0.04)\n dst = cv2.dilate(dst, None)\n img[dst > 0.01 * dst.max()] = [0, 0, 255]\n cv2.imshow('harris', img)\n # i=cv2.waitKey(0)\n\n\ndef draw_faces(img):\n # 检测人脸并框出\n # img=cv2.resize(img,(800,800),interpolation=cv2.INTER_AREA)\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n face_cascade = cv2.CascadeClassifier(\n 'D:/Python36/Lib/site-packages/cv2/data/haarcascade_frontalface_default.xml')\n faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n for (x, y, w, h) in faces:\n img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)\n cv2.imshow('img', img)\n\n\ndef sift_test(img1, img2):\n sift = cv2.xfeatures2d.SIFT_create()\n\n img1 = cv2.resize(img1, (800, 800), interpolation=cv2.INTER_AREA)\n gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n # gray1=cv2.GaussianBlur(gray1,(5,5),0)\n\n img2 = cv2.resize(img2, (800, 800), interpolation=cv2.INTER_AREA)\n gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n # gray2=cv2.GaussianBlur(gray2,(5,5),0)\n\n kp1, des1 = sift.detectAndCompute(gray1, None)\n kp2, des2 = sift.detectAndCompute(gray2, None)\n\n # img=cv2.drawKeypoints(img,kp,img,color=(255,0,255))\n # bf=cv2.BFMatcher(cv2.NORM_HAMMING,crossCheck=True)\n\n FLANN_INDEX_KDTREE = 0\n index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)\n search_params = dict(checks=50)\n flann = cv2.FlannBasedMatcher(index_params, search_params)\n\n # bf=cv2.BFMatcher()\n # matches=bf.knnMatch(des1,des2,k=2)\n matches = flann.knnMatch(des1, des2, k=2)\n print('matches:' + str(len(matches)), end='')\n good = []\n for m, n in matches:\n if m.distance < 0.7 * n.distance:\n good.append([m])\n print(' good:' + str(len(good)))\n # img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None, flags=2)\n # img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,None, flags=2)\n # cv2.imshow('img',img3)\n # i=cv2.waitKey(0)\n cv2.imshow('img', img2)\n\n\ndef surf_test_fast(kp1, des1, img1, img2):\n surf = cv2.xfeatures2d.SURF_create(400)\n img2 = cv2.resize(img2, (800, 800), interpolation=cv2.INTER_AREA)\n gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n # gray2=cv2.GaussianBlur(gray2,(5,5),0)\n # surf.hessianThreshold = 500\n kp2, des2 = surf.detectAndCompute(gray2, None)\n # print(len(des1))\n # img=cv2.drawKeypoints(img,kp,img,color=(255,0,255))\n # bf=cv2.BFMatcher(cv2.NORM_HAMMING,crossCheck=True)\n\n FLANN_INDEX_KDTREE = 0\n index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)\n search_params = dict(checks=50)\n flann = cv2.FlannBasedMatcher(index_params, search_params)\n\n # bf=cv2.BFMatcher()\n # matches=bf.knnMatch(des1,des2,k=2)\n try:\n matches = flann.knnMatch(np.asarray(\n des1, np.float32), np.asarray(des2, np.float32), k=2)\n except:\n return\n # matches=flann.knnMatch(des1,des2,k=2)\n print('matches:' + str(len(matches)), end='')\n good = []\n for m, n in matches:\n if m.distance < 0.65 * n.distance:\n good.append([m])\n print(' good:' + str(len(good)))\n # img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None, flags=2)\n img3 = cv2.drawMatchesKnn(img1, kp1, gray2, kp2, good, None, flags=2)\n cv2.imshow('img3', img3)\n\n\ndef surf_test(img1, img2):\n surf = cv2.xfeatures2d.SURF_create()\n img1 = cv2.resize(img1, (800, 800), interpolation=cv2.INTER_AREA)\n gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n # gray1=cv2.GaussianBlur(gray1,(5,5),0)\n\n img2 = cv2.resize(img2, (800, 800), interpolation=cv2.INTER_AREA)\n gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n # gray2=cv2.GaussianBlur(gray2,(5,5),0)\n # surf.hessianThreshold = 500\n kp1, des1 = surf.detectAndCompute(gray1, None)\n kp2, des2 = surf.detectAndCompute(gray2, None)\n # print(len(des1))\n # img=cv2.drawKeypoints(img,kp,img,color=(255,0,255))\n # bf=cv2.BFMatcher(cv2.NORM_HAMMING,crossCheck=True)\n\n FLANN_INDEX_KDTREE = 0\n index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)\n search_params = dict(checks=50)\n flann = cv2.FlannBasedMatcher(index_params, search_params)\n\n # bf=cv2.BFMatcher()\n # matches=bf.knnMatch(des1,des2,k=2)\n try:\n matches = flann.knnMatch(np.asarray(\n des1, np.float32), np.asarray(des2, np.float32), k=2)\n except:\n return\n # matches=flann.knnMatch(des1,des2,k=2)\n print('matches:' + str(len(matches)), end='')\n good = []\n for m, n in matches:\n if m.distance < 0.7 * n.distance:\n good.append([m])\n print(' good:' + str(len(good)))\n # img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None, flags=2)\n img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=2)\n cv2.imshow('img3', img3)\n # i=cv2.waitKey(0)\n\n\ndef orb_test(img1, img2):\n orb = cv2.ORB_create()\n\n img1 = cv2.resize(img1, (800, 800), interpolation=cv2.INTER_AREA)\n gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n gray1 = cv2.GaussianBlur(gray1, (5, 5), 0)\n\n img2 = cv2.resize(img2, (800, 800), interpolation=cv2.INTER_AREA)\n gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n gray2 = cv2.GaussianBlur(gray2, (5, 5), 0)\n\n kp1, des1 = orb.detectAndCompute(gray1, None)\n kp2, des2 = orb.detectAndCompute(gray2, None)\n\n # img=cv2.drawKeypoints(img,kp,img,color=(255,0,255))\n # bf=cv2.BFMatcher(cv2.NORM_HAMMING,crossCheck=True)\n\n FLANN_INDEX_KDTREE = 0\n # index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)\n search_params = dict(checks=50)\n\n index_params = dict(algorithm=FLANN_INDEX_KDTREE,\n table_number=6, # 12\n key_size=12, # 20\n multi_probe_level=1) # 2\n\n flann = cv2.FlannBasedMatcher(index_params, search_params)\n\n # bf=cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n # f=cv2.BFMatcher()\n\n des1 = np.asarray(des1, np.float32)\n des2 = np.asarray(des2, np.float32)\n # matches=flann.knnMatch(np.asarray(des1,np.float32),np.asarray(des2,np.float32),k=2)\n try:\n matches = flann.knnMatch(des1, des2, k=2)\n except:\n return\n # matches=bf.match(des1,des2)\n # matches=flann.knnMatch(des1,des2,k=2)\n print('matches:' + str(len(matches)), end='')\n good = []\n try:\n for m, n in matches:\n if m.distance < 0.75 * n.distance:\n good.append([m])\n except:\n return\n print(' good:' + str(len(good)))\n # img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None, flags=2)\n img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=2)\n # img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches,None, flags=2)\n cv2.imshow('img', img3)\n # i=cv2.waitKey(0)\n\n\ndef get_max_contour(contours):\n # 获取最大的轮廓\n max_area = 0\n max_cnt = 0\n for i in range(len(contours)):\n cnt = contours[i]\n area = cv2.contourArea(cnt)\n if area > max_area:\n max_area = area\n max_cnt = cnt\n return max_cnt\n\n\ndef get_range_contours(contours, low, high):\n # 获取指定面积范围内的轮廓，返回轮廓列表list\n contours_list = []\n for i in range(len(contours)):\n cnt = contours[i]\n area = cv2.contourArea(cnt)\n if low < area < high:\n contours_list.append(cnt)\n return contours_list\n\n\ndef draw_contours(img):\n # 画轮廓函数\n\n # img=cv2.resize(img,(720,1280),interpolation=cv2.INTER_AREA)\n # img= cv2.blur(img,(3,3)) #进行滤波去掉噪声\n # img= cv2.medianBlur(img,5) #进行滤波去掉噪声\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n # gray=img\n # kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(50, 50))\n # 开闭运算，先开运算去除背景噪声，再继续闭运算填充目标内的孔洞\n # opened = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)\n\n # closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, kernel)\n # cv2.imshow('sa',closed)\n\n # cv2.imshow('gray',gray)\n ret, binary = cv2.threshold(gray, 160, 255, cv2.THRESH_BINARY)\n # cv2.imshow('binary',binary)\n # contours,hierarchy=cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)\n contours, hierarchy = cv2.findContours(\n binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n # contours,hierarchy=cv2.findContours(binary,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n # cv2.drawContours(img,contours,-1,(0,0,0),cv2.FILLED)\n print(len(contours))\n if contours:\n c_max = []\n max_contour = get_max_contour(contours)\n c_max.append(max_contour)\n try:\n cv2.drawContours(img, c_max, -1, (0, 0, 255), 3)\n except Exception as e:\n return\n\n # r1=np.zeros(img.shape[:2],dtype=\"uint8\")#创建黑色图像\n # 将轮廓信息转换成(x, y)坐标，并加上矩形的高度和宽度\n x, y, w, h = cv2.boundingRect(max_contour)\n cv2.rectangle(img, (x, y), (x + w, y + h), (255, 255, 255), 2) # 画出矩形\n\n # mask=r1\n # masked=cv2.bitwise_and(img,img,mask=mask)\n\n # rect = cv2.minAreaRect(max_contour)\n # box = cv2.boxPoints(rect)\n # box =np.int0(box)\n # cv2.drawContours(img, [box], 0, (0, 0, 255), 3) # 画出该矩形\n # 注：OpenCV没有函数能直接从轮廓信息中计算出最小矩形顶点的坐标。所以需要计算出最小矩形区域，\n # 然后计算这个矩形的顶点。由于计算出来的顶点坐标是浮点型，但是所得像素的坐标值是整数（不能获取像素的一部分），\n # 所以需要做一个转换\n\n # (x,y),radius = cv2.minEnclosingCircle(max_contour)\n # center = (int(x),int(y))\n # radius = int(radius)\n # img = cv2.circle(img,center,radius,(0,255,0),2)\n\n # cv2.imshow('final',masked)\n cv2.imshow('final', img)\n # i=cv2.waitKey(0)\n\n\ndef get_video():\n # 获取视频帧测试函数\n cap = cv2.VideoCapture(0)\n # img1=cv2.imread('1.jpg')\n # img1=cv2.imread('5.jpg')\n # surf = cv2.xfeatures2d.SURF_create(400)\n # img1=cv2.resize(img1,(800,800),interpolation=cv2.INTER_AREA)\n # gray1=cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)\n # kp1,des1=surf.detectAndCompute(gray1,None)\n\n # mog2=cv2.createBackgroundSubtractorMOG2()\n # mog2=cv2.bgsegm.createBackgroundSubtractorGMG()\n while True:\n # time.sleep(0.034)\n ret, frame = cap.read()\n if not ret:\n continue\n # hamming=compare_hamming_distance(img1,frame,'dHash')\n # print(hamming)\n # frame=cv2.blur(frame,(3,3))\n # frame=cv2.GaussianBlur(frame,(5,5),0)\n # fgmask=mog2.apply(frame)\n # draw_contours(fgmask)\n # cv2.imshow('1',fgmask)\n draw_contours(frame)\n # surf_test_fast(kp1,des1,img1,frame)\n # draw_faces(frame)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n cap.release()\n cv2.destroyAllWindows()\n\n\n# -------------------------------------------------------------\n\n\ndef main():\n # get_video()\n pass\n\n\nif __name__ == '__main__':\n main()\n"
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"text": "# @Author: Ivan\n# @LastEdit: 2020/8/13\nimport os\nimport time\nimport numpy as np # install\nimport keras # install\nfrom keras.regularizers import l1, l2\nfrom keras import backend as K\nfrom keras.utils import np_utils\nfrom keras.utils import plot_model\nfrom keras.optimizers import SGD\nfrom keras.models import Sequential\nfrom keras.models import load_model\nfrom keras.layers import Conv2D, MaxPooling2D, SeparableConv2D\nfrom keras.layers import Dense, Dropout, Activation, Flatten\nimport matplotlib.pyplot as plt # install\n\n# from image_util import show_intermediate_output, show_heatmap\n\nnp.random.seed(1337)\n# os.environ[\"PATH\"] += os.pathsep + \\\n# 'C:/Program Files (x86)/Graphviz2.38/bin'\n\nepochs = 500\nnb_classes = 3\nnb_per_class = 2000\nbatch_size = 32\nlearning_rate = 0.0001\nactivation = 'relu'\nwidth, height, depth = 400, 90, 1\nnb_filters1, nb_filters2 = 5, 10\ntrain_proportion = 0.8\nvalid_proportion = 0.1\ntest_proportion = 0.1\n\n\ndef set_model(lr=learning_rate, decay=1e-6, momentum=0.9):\n model = Sequential()\n if K.image_data_format() == 'channels_first':\n model.add(SeparableConv2D(nb_filters1, kernel_size=(3, 3), kernel_regularizer=l2(0.01),\n input_shape=(depth, height, width), name='conv1'))\n else:\n model.add(SeparableConv2D(nb_filters1, kernel_size=(3, 3),\n input_shape=(height, width, depth), name='conv1'))\n model.add(Activation(activation))\n model.add(MaxPooling2D(pool_size=(2, 2), name='maxpooling1'))\n model.add(Dropout(0.5))\n\n model.add(SeparableConv2D(nb_filters2, kernel_size=(3, 3),\n kernel_regularizer=l2(0.01), name='conv2'))\n model.add(Activation(activation))\n model.add(MaxPooling2D(pool_size=(2, 2), name='maxpooling2'))\n model.add(Dropout(0.5))\n\n model.add(Flatten())\n model.add(Dense(128, kernel_regularizer=l2(\n 0.01), name='dense1')) # Full connection\n model.add(Activation(activation))\n model.add(Dropout(0.5))\n\n model.add(Dense(nb_classes, name='dense2')) # output\n model.add(Activation('softmax'))\n\n sgd = SGD(lr=learning_rate, decay=decay, momentum=momentum,\n nesterov=True) # optimizer\n model.compile(loss='categorical_crossentropy',\n optimizer=sgd, metrics=['accuracy'])\n model.summary() # 输出模型各层的参数状况\n return model\n\n\nclass LossHistory(keras.callbacks.Callback):\n # 损失历史记录 输出参数变化图像\n def on_train_begin(self, logs={}):\n self.losses = {'batch': [], 'epoch': []}\n self.accuracy = {'batch': [], 'epoch': []}\n self.val_loss = {'batch': [], 'epoch': []}\n self.val_acc = {'batch': [], 'epoch': []}\n\n def on_batch_end(self, batch, logs={}):\n self.losses['batch'].append(logs.get('loss'))\n self.accuracy['batch'].append(logs.get('acc'))\n self.val_loss['batch'].append(logs.get('val_loss'))\n self.val_acc['batch'].append(logs.get('val_acc'))\n\n def on_epoch_end(self, batch, logs={}):\n self.losses['epoch'].append(logs.get('loss')) # train loss\n self.accuracy['epoch'].append(logs.get('acc')) # train acc\n self.val_loss['epoch'].append(logs.get('val_loss'))\n self.val_acc['epoch'].append(logs.get('val_acc'))\n\n def loss_plot(self, loss_type):\n iters = range(len(self.losses[loss_type]))\n plt.figure(num='Change of parameters')\n # train acc\n plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')\n # train loss\n plt.plot(iters, self.losses[loss_type], 'g', label='train loss')\n\n if loss_type == 'epoch':\n # val_acc\n plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')\n # val_loss\n plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')\n\n plt.title('epoch=' + str(epochs) + ',lr=' + str(learning_rate) + ',batch_size=' + str(batch_size)\n + '\\nactivation=' + activation + ',nb_classes=' + str(nb_classes) + ',nb_per_class=' + str(\n nb_per_class))\n plt.grid(True)\n plt.xlabel(loss_type)\n plt.ylabel('acc-loss')\n plt.legend(loc=\"upper right\")\n now = time.strftime('%Y-%m-%d@%H-%M-%S', time.localtime(time.time()))\n plt.savefig('./parameter/' + now + '.jpg')\n plt.show()\n\n\nhistory = LossHistory()\n\n\ndef train_model(model, X_train, Y_train, X_val, Y_val):\n # tensorboard = keras.callbacks.TensorBoard(\n # log_dir='F:/Log/', histogram_freq=1)\n\n model.fit(X_train, Y_train, batch_size=batch_size, epochs=epochs, shuffle=True,\n verbose=1, validation_data=(X_val, Y_val), callbacks=[history])\n model.save('model.h5')\n return model\n\n\ndef test_model(X_test, Y_test):\n model = load_model('model.h5')\n score = model.evaluate(X_test, Y_test, verbose=0)\n return score\n\n\ndef load_data():\n train_data = np.load('train_data.npy')[:nb_per_class]\n train_img = train_data[:, 0]\n train_label = train_data[:, 1]\n\n images = []\n for img in train_img:\n img_ndarray = np.asarray(img, dtype='float64') / 255\n data = np.ndarray.flatten(img_ndarray)\n data = data.astype('float32')\n images.append(data)\n images = np.asarray(images)\n\n labels = []\n for label in train_label:\n if label == [1, 0, 0]:\n labels.append(0)\n elif label == [0, 1, 0]:\n labels.append(1)\n else:\n labels.append(2)\n labels = np.asarray(labels)\n\n train_per_category = int(nb_per_class * train_proportion)\n valid_per_category = int(nb_per_class * valid_proportion)\n\n X_train = images[:train_per_category]\n X_val = images[train_per_category:train_per_category + valid_per_category]\n X_test = images[train_per_category + valid_per_category:]\n\n y_train = labels[:train_per_category]\n y_val = labels[train_per_category:train_per_category + valid_per_category]\n y_test = labels[train_per_category + valid_per_category:]\n rval = [(X_train, y_train), (X_val, y_val), (X_test, y_test)]\n return rval\n\n\ndef main():\n (X_train, y_train), (X_val, y_val), (X_test, y_test) = load_data()\n if K.image_data_format() == 'channels_first':\n X_train = X_train.reshape(X_train.shape[0], depth, height, width)\n X_val = X_val.reshape(X_val.shape[0], depth, height, width)\n X_test = X_test.reshape(X_test.shape[0], depth, height, width)\n else:\n X_train = X_train.reshape(X_train.shape[0], height, width, depth)\n X_val = X_val.reshape(X_val.shape[0], height, width, depth)\n X_test = X_test.reshape(X_test.shape[0], height, width, depth)\n\n print('X_train shape:', X_train.shape)\n print('Class number:', nb_classes)\n print(X_train.shape[0], 'train samples')\n print(X_val.shape[0], 'validate samples')\n print(X_test.shape[0], 'test samples')\n\n # convert class vectors to binary class matrices\n Y_train = np_utils.to_categorical(y_train, nb_classes)\n Y_val = np_utils.to_categorical(y_val, nb_classes)\n Y_test = np_utils.to_categorical(y_test, nb_classes)\n\n model = set_model()\n\n # plot_model(model, to_file='model.png', show_shapes=True)\n\n start = time.clock()\n model = train_model(model, X_train, Y_train, X_val, Y_val)\n end = time.clock()\n\n pred_classes = model.predict_classes(X_test, verbose=0)\n\n test_accuracy = np.mean(np.equal(y_test, pred_classes))\n right = np.sum(np.equal(y_test, pred_classes))\n\n print('Total training time:', end - start)\n print('Test number:', len(Y_test))\n print('Test right:', right)\n print('Test wrong:', len(Y_test) - right)\n print('Test accuracy:', test_accuracy)\n\n history.loss_plot('epoch')\n\n\nif __name__ == '__main__':\n main()\n"
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"text": "# @Author: Ivan\n# @LastEdit: 2020/8/6\nimport time\nimport cv2\nimport numpy as np\nfrom PIL import ImageGrab\nfrom keras.models import load_model\nfrom keras import backend as K\nfrom key_util import press_key, release_key\n\n\ndef straight():\n release_key('A')\n release_key('D')\n press_key('W')\n # time.sleep(0.1)\n\n\ndef left():\n release_key('D')\n press_key('A')\n # time.sleep(0.1)\n\n\ndef right():\n release_key('A')\n press_key('D')\n # time.sleep(0.1)\n\n\ndef keys_to_output(keys):\n output = [0, 0, 0]\n if 'A' in keys:\n output[0] = 1\n elif 'D' in keys:\n output[2] = 1\n else:\n output[1] = 1\n return output\n\n\ndef countdown(sec):\n for i in range(sec, 0, -1):\n print(i)\n time.sleep(1)\n print('start!')\n\n\ndef main():\n last_time = time.time()\n frame, accum_time, fps = 0, 0, 0\n\n model = load_model('model.h5')\n print('Model loading complete!')\n count = 0\n while True:\n # screenshot normalization\n screen = np.array(ImageGrab.grab(bbox=(0, 0, 800, 600)))\n screen = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)\n\n gray = cv2.cvtColor(screen, cv2.COLOR_RGB2GRAY)\n gray = cv2.GaussianBlur(gray, (5, 5), 0)\n\n # image process\n mask = np.zeros_like(gray)\n vertices = np.array([[0, 500], [0, 300], [200, 200], [600, 200], [800, 300], [800, 500]])\n cv2.fillPoly(mask, [vertices], 255)\n gray = cv2.bitwise_and(gray, mask)\n\n # gray = cv2.Sobel(gray, cv2.CV_16S, 1, 1, 5) # Sobel operator filtering\n # gray = cv2.convertScaleAbs(gray)\n gray = cv2.Canny(gray, 50, 150) # edges detection\n # ret, gray = cv2.threshold(qray, 160, 255, cv2.THRESH_BINARY)\n # ret, gray = cv2.threshold(\n # gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) # threshold algorithm\n # gray = cv2.GaussianBlur(gray, (3, 3), 0)\n mask = np.zeros_like(gray)\n vertices = np.array([[0, 400], [0, 300], [270, 220], [530, 220], [800, 300], [800, 400]])\n cv2.fillPoly(mask, [vertices], 255)\n gray = cv2.bitwise_and(gray, mask)\n kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 15)) # kernel of closing operation\n gray = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel) # closing operation\n\n # detect lines\n # left_line, right_line = [], []\n # lines = cv2.HoughLinesP(gray, 1, np.pi / 180, 100, 100, 10)\n # try:\n # for x1, y1, x2, y2 in lines[0]:\n # if (y2 - y1) / (x2 - x1) > 0:\n # left_line.append((x1, y1, x2, y2))\n # else:\n # right_line.append((x1, y1, x2, y2))\n # cv2.line(screen, (x1, y1), (x2, y2), (0, 255, 0), 3)\n # except Exception:\n # pass\n\n cv2.putText(screen, 'fps:{}'.format(fps), (10, 30),\n cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 0), 2)\n\n # calculate fps\n this_time = time.time()\n # print('loop took {} seconds'.format(this_time-last_time))\n accum_time += this_time - last_time\n last_time = time.time()\n\n frame += 1\n if accum_time >= 1:\n fps = frame\n # print('fps:', frame)\n frame, accum_time = 0, 0\n\n # get train image\n test_img = gray[220:400, :]\n test_img = cv2.resize(test_img, (400, 90))\n\n img_ndarray = np.asarray(test_img, dtype='float64') / 255\n test_data = np.ndarray.flatten(img_ndarray)\n test_data = test_data.astype('float32')\n\n if K.image_data_format() == 'channels_first':\n test_data = test_data.reshape(1, 1, 90, 400)\n else:\n test_data = test_data.reshape(1, 90, 400, 1)\n\n preds = model.predict(test_data)\n class_ = np.argmax(preds[0])\n if class_ == 0:\n left()\n elif class_ == 1:\n straight()\n else:\n right()\n\n count += 1\n if count == 100:\n break\n\n # show screenshot\n cv2.imshow('screen', test_img)\n if cv2.waitKey(25) & 0xFF == ord('q'):\n cv2.destoryAllWindows()\n break\n\n\nif __name__ == \"__main__\":\n main()\n"
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"text": "# @Author: Ivan\n# @LastEdit: 2020/8/6\nimport time\nimport cv2\nimport numpy as np\nfrom PIL import ImageGrab\nfrom key_util import press_key, release_key, check_key\n\n\ndef straight():\n release_key('A')\n release_key('D')\n press_key('W')\n time.sleep(0.5)\n\n\ndef left():\n release_key('D')\n press_key('A')\n time.sleep(0.5)\n\n\ndef right():\n release_key('A')\n press_key('D')\n time.sleep(0.5)\n\n\ndef keys_to_output(keys):\n output = [0, 0, 0]\n if 'A' in keys:\n output[0] = 1\n elif 'D' in keys:\n output[2] = 1\n else:\n output[1] = 1\n return output\n\n\ndef countdown(sec):\n for i in range(sec, 0, -1):\n print(i)\n time.sleep(1)\n print('start!')\n\n\ndef main():\n countdown(2)\n\n last_time = time.time()\n frame, accum_time, fps = 0, 0, 0\n train_num = 2000\n\n train_data = []\n while True:\n # screenshot normalization\n screen = np.array(ImageGrab.grab(bbox=(0, 0, 800, 600)))\n screen = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)\n\n gray = cv2.cvtColor(screen, cv2.COLOR_RGB2GRAY)\n gray = cv2.GaussianBlur(gray, (5, 5), 0)\n\n # image process\n mask = np.zeros_like(gray)\n vertices = np.array([[0, 500], [0, 300], [200, 200], [600, 200], [800, 300], [800, 500]])\n cv2.fillPoly(mask, [vertices], 255)\n gray = cv2.bitwise_and(gray, mask)\n\n # gray = cv2.Sobel(gray, cv2.CV_16S, 1, 1, 5) # Sobel operator filtering\n # gray = cv2.convertScaleAbs(gray)\n gray = cv2.Canny(gray, 50, 150) # edges detection\n # ret, gray = cv2.threshold(qray, 160, 255, cv2.THRESH_BINARY)\n # ret, gray = cv2.threshold(\n # gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) # threshold algorithm\n # gray = cv2.GaussianBlur(gray, (3, 3), 0)\n mask = np.zeros_like(gray)\n vertices = np.array([[0, 400], [0, 300], [270, 220], [530, 220], [800, 300], [800, 400]])\n cv2.fillPoly(mask, [vertices], 255)\n gray = cv2.bitwise_and(gray, mask)\n kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 15)) # kernel of closing operation\n gray = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel) # closing operation\n\n # detect lines\n # left_line, right_line = [], []\n # lines = cv2.HoughLinesP(gray, 1, np.pi / 180, 100, 100, 10)\n # try:\n # for x1, y1, x2, y2 in lines[0]:\n # if (y2 - y1) / (x2 - x1) > 0:\n # left_line.append((x1, y1, x2, y2))\n # else:\n # right_line.append((x1, y1, x2, y2))\n # cv2.line(screen, (x1, y1), (x2, y2), (0, 255, 0), 3)\n # except Exception:\n # pass\n\n cv2.putText(screen, 'fps:{}'.format(fps), (10, 30),\n cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 0), 2)\n\n # calculate fps\n this_time = time.time()\n # print('loop took {} seconds'.format(this_time-last_time))\n accum_time += this_time - last_time\n last_time = time.time()\n\n frame += 1\n if accum_time >= 1:\n fps = frame\n # print('fps:', frame)\n frame, accum_time = 0, 0\n\n # get key output\n keys = check_key()\n key_output = keys_to_output(keys)\n print(key_output)\n\n # get train image\n train_img = gray[220:400, :]\n train_img = cv2.resize(train_img, (400, 90))\n\n # adding train data\n train_data.append([train_img, key_output])\n\n print(len(train_data))\n if len(train_data) == train_num:\n np.save('train_data.npy', train_data)\n break\n\n # show screenshot\n cv2.imshow('screen', train_img)\n if cv2.waitKey(25) & 0xFF == ord('q'):\n cv2.destoryAllWindows()\n break\n\n\nif __name__ == \"__main__\":\n main()\n"
}
] | 7 |
ulrichji/Environment_sensor_logger
|
https://github.com/ulrichji/Environment_sensor_logger
|
436ec0f1c672b9ab27cbe68a8897e1018b3ae189
|
0fc383571dee53bc2663d415efe2b45075f3afd5
|
529e1bfc5955c8b99e38c770da606aa4fcf1bb2e
|
refs/heads/master
| 2021-09-06T14:41:28.542132 | 2018-02-07T17:08:35 | 2018-02-07T17:08:35 | 120,337,163 | 0 | 0 | null | null | null | null | null |
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"text": "#include <Time.h>\n#include \"measurement.h\"\n#include <DHT22.h>\n\nint lightSensorIn = A0;\nint soundSensorIn = A2;\nint co2SensorIn = A4;\nint tempHumidSensorIn = 7;\n\nDHT22 temp_humid_sensor(tempHumidSensorIn);\nconst char* last_DHT22_error = \"\";\n\nvoid setup()\n{\n Serial.begin(9600); // open serial port, set the baud rate to 9600 bps\n // Set the default voltage of the reference voltage\n analogReference(DEFAULT);\n}\n\nMeasurement measureCO2(void)\n{\n Measurement measurement;\n measurement.name = \"CO2\";\n measurement.unit = \"ppm\";\n measurement.time = 2000;\n measurement.error = \"\";\n\n int sensor_value = analogRead(co2SensorIn);\n float voltage = sensor_value * (5000 / 1024.0);\n if (voltage == 0)\n {\n measurement.error = \"Measurement error\";\n }\n else if (voltage < 400)\n {\n measurement.error = \"Preaheating\";\n }\n else\n {\n int voltage_difference = voltage - 400;\n float concentration = voltage_difference * 50.0 / 16.0;\n measurement.value = concentration;\n }\n\n return measurement;\n}\n\nvoid readDHT22Data(void)\n{\n last_DHT22_error = \"\";\n DHT22_ERROR_t error_code;\n error_code = temp_humid_sensor.readData();\n \n switch(error_code)\n {\n case DHT_ERROR_CHECKSUM:\n last_DHT22_error = \"DHT22: check sum error\";\n break;\n case DHT_BUS_HUNG:\n last_DHT22_error = \"DHT22: Bus hung error\";\n break;\n case DHT_ERROR_NOT_PRESENT:\n last_DHT22_error = \"DHT22: Not present\";\n break;\n case DHT_ERROR_ACK_TOO_LONG:\n last_DHT22_error = \"ACK time out\";\n break;\n case DHT_ERROR_SYNC_TIMEOUT:\n last_DHT22_error = \"Sync timeout\";\n break;\n case DHT_ERROR_DATA_TIMEOUT:\n last_DHT22_error = \"Data timeout\";\n break;\n case DHT_ERROR_TOOQUICK:\n last_DHT22_error = \"Polled to quick\";\n break;\n }\n}\n\nMeasurement measureTemperature(bool do_read_data=true)\n{\n Measurement measurement;\n\n measurement.name = \"Temp\";\n measurement.unit = \"C\";\n measurement.time = 2000;\n measurement.error = \"\";\n\n if(do_read_data)\n {\n readDHT22Data();\n }\n\n measurement.value = temp_humid_sensor.getTemperatureC();\n measurement.error = last_DHT22_error;\n\n return measurement;\n}\n\nMeasurement measureHumidity(bool do_read_data=true)\n{\n Measurement measurement;\n\n measurement.name = \"Humidity\";\n measurement.unit = \"%\";\n measurement.time = 2000;\n measurement.error = \"\";\n\n if(do_read_data)\n {\n readDHT22Data();\n }\n measurement.value = temp_humid_sensor.getHumidity();\n measurement.error = last_DHT22_error;\n\n return measurement;\n}\n\nMeasurement measureSound(void)\n{\n Measurement measurement;\n measurement.name = \"Sound\";\n measurement.unit = \"V\";\n measurement.time = 2000;\n measurement.error = \"\";\n \n int sensor_value = analogRead(soundSensorIn);\n float voltage = 5.0 * (sensor_value / 1024.0);\n measurement.value = voltage;\n\n return measurement;\n}\n\nMeasurement measureLight(void)\n{\n Measurement measurement;\n measurement.name = \"Light\";\n measurement.unit = \"V\";\n measurement.time = 2000;\n measurement.error = \"\";\n\n int sensor_value = analogRead(lightSensorIn);\n float voltage = 5.0 * (sensor_value / 1024.0);\n measurement.value = voltage;\n\n return measurement;\n}\n\nvoid sendMeasurementOnSerial(Measurement measurement)\n{\n\n Serial.print(\"name:\\\"\"); Serial.print(measurement.name); Serial.print(\"\\\";\");\n Serial.print(\"timestamp:\"); Serial.print(measurement.timestamp); Serial.print(\";\");\n Serial.print(\"sequence:\"); Serial.print(measurement.sequence); Serial.print(\";\");\n Serial.print(\"value:\"); Serial.print(measurement.value); Serial.print(\";\");\n Serial.print(\"unit:\\\"\"); Serial.print(measurement.unit); Serial.print(\"\\\";\");\n Serial.print(\"time:\"); Serial.print(measurement.time); Serial.print(\";\");\n Serial.print(\"Error:\\\"\");Serial.print(measurement.error);Serial.print(\"\\\";\");\n Serial.println(\"\");\n}\n\nvoid loop()\n{\n while (1)\n {\n Measurement co2Measurement = measureCO2();\n Measurement temperatureMeasurement = measureTemperature();\n //The false argument prevents a new read and uses the value obtained from the same sensor in the temperature measurement\n //If not set to false, it will give a \"polled to quickly error\"\n Measurement humidityMeasurement = measureHumidity(false);\n Measurement soundMeasurement = measureSound();\n Measurement lightMeasurement = measureLight();\n \n sendMeasurementOnSerial(co2Measurement);\n sendMeasurementOnSerial(temperatureMeasurement);\n sendMeasurementOnSerial(humidityMeasurement);\n sendMeasurementOnSerial(soundMeasurement);\n sendMeasurementOnSerial(lightMeasurement);\n\n delay(2000);\n }\n}\n"
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"text": "\nfrom measurement import *\n\nclass MeasurementStream:\n\t\n\tdef __init__(self):\n\t\tself.mesurement_callback = None\n\n\tdef setMeasurementCallback(self, callback):\n\t\tself.measurement_callback = callback\n\n\tdef measurementCallback(self, measurement):\n\t\tif(not (self.measurement_callback is None)):\n\t\t\tself.measurement_callback(measurement)\n\t\n\tdef startStream(self):\n\t\tpass\n\n\tdef stopStream(self):\n\t\tpass\n"
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"text": "typedef struct struct_measurement\n{\n const char* name;\n int timestamp;\n int sequence;\n float value;\n const char* unit;\n int time;\n const char* error;\n} Measurement;\n"
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"path": "/logger/example_measurement_stream.py",
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"text": "\nfrom measurement_stream import *\nimport random\nimport time\n\nclass ExampleMeasurementStream(MeasurementStream):\n\t\n\tdef __init__(self):\n\t\tself.running = True\n\t\n\tdef stopStream(self):\n\t\tprint(\"Stopping now\")\n\t\tself.running = False\n\t\n\tdef startStream(self):\n\t\tself.running = True\n\t\tname_list = [\"CO2\", \"Temperature\", \"Humidity\", \"Sound\", \"Light\"]\n\t\tunit_list = [\"ppm\", \"C\", \"%\", \"dB\", \"Lux\"]\n\t\t\n\t\tindex = 0\n\t\twhile(self.running):\n\t\t\tmeasurement = Measurement(\"\")\n\t\t\trandom_index = int(random.random() * len(name_list))\n\t\t\tmeasurement.sensor_name = name_list[random_index]\n\t\t\tmeasurement.unit = unit_list[random_index]\n\t\t\tmeasurement.value = random.random() * 1024\n\t\t\tmeasurement.sequence = index\n\t\t\tmeasurement.next_measurement_time = 1000\n\t\t\t\n\t\t\tself.measurementCallback(measurement)\n\t\t\t\n\t\t\ttime.sleep(random.random())\n\t\t\t\n\t\t\tindex += 1\n\t\t\n\t\tprint(\"Has stopped\")\n"
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"text": "\ndef specialCharConversion(special_char):\n\treturn special_char\n\ndef parseMeasurement(measurement_string):\n\tkey_values = {}\n\tstate = \"key\"\n\tkey = ''\n\tvalue = ''\n\t\n\tfor char in measurement_string:\n\t\tif(state == \"key\"):\n\t\t\tif(char == \":\"):\n\t\t\t\tstate = \"value\"\n\t\t\telse:\n\t\t\t\tkey += char\n\t\t\t\t\n\t\telif(state == \"value\"):\n\t\t\tif(char == \"\\\"\"):\n\t\t\t\tstate = \"string\"\n\t\t\t#We have finished the key\n\t\t\telif(char == \";\"):\n\t\t\t\tkey_values[key] = value\n\t\t\t\tkey = ''\n\t\t\t\tvalue = ''\n\t\t\t\tstate = \"key\"\n\t\t\telse:\n\t\t\t\tvalue += char\n\t\t\t\t\n\t\telif(state == \"string\"):\n\t\t\tif(char == \"\\\\\"):\n\t\t\t\tstate = \"escape\"\n\t\t\telif(char == \"\\\"\"):\n\t\t\t\tstate = \"value\"\n\t\t\telse:\n\t\t\t\tvalue += char\n\t\t\t\t\n\t\telif(state == \"escape\"):\n\t\t\tvalue += specialCharConversion(char)\n\t\n\tif(key != '' or value != ''):\n\t\tkey_values[key] = value\n\n\treturn key_values\n\nclass Measurement:\n\t\n\tdef __init__(self, measurement_string = \"\"):\n\t\tkey_values = parseMeasurement(measurement_string)\n\t\t\n\t\tself.timestamp = None\n\t\tself.sequence = None\n\t\tself.value = None\n\t\tself.unit = None\n\t\tself.sensor_name = None\n\t\tself.next_measurement_time = 1000\n\t\t\n\t\tif(\"timestamp\" in key_values):\n\t\t\tself.timestamp = key_values[\"timestamp\"]\n\t\tif(\"sequence\" in key_values):\n\t\t\tself.sequence = int(key_values[\"sequence\"])\n\t\tif(\"value\" in key_values):\n\t\t\tself.value = float(key_values[\"value\"])\n\t\tif(\"unit\" in key_values):\n\t\t\tself.unit = key_values[\"unit\"]\n\t\tif(\"name\" in key_values):\n\t\t\tself.sensor_name = key_values[\"name\"]\n\t\tif(\"time\" in key_values):\n\t\t\tself.next_measurement_time = int(key_values[\"time\"])\n\n\ndef run_tests():\n\tmeasurement_string = \"timestamp:\\\"31.01.2018:12:48\\\";sequence:1002341;value:440;unit:ppm;name:\\\"CO2\\\";time=2000\"\n\tmeasurement_string2 = \"timestamp:\\\"31.01.2018:12:48\\\";sequence:1002341;value:440;unit:ppm;name:\\\"CO2\\\";time=2000;\"\n\tkey_pairs = parseMeasurement(measurement_string)\n\tkey_pairs2 = parseMeasurement(measurement_string2)\n\tprint(key_pairs)\n\tprint(key_pairs2)\n\t\n\tmeasure1 = Measurement(measurement_string)\n\tmeasure2 = Measurement(measurement_string2)\n\t\n\tprint(measure1)\n\tprint(measure2)\n\n\nif __name__ == \"__main__\":\n\trun_tests()\n"
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"text": "#! python3\n\nimport measurement_stream\nimport measurement\n\nimport serial\n\nclass SerialMeasurementStream(measurement_stream.MeasurementStream):\n\t\n\tdef __init__(self, serial_port):\n\t\tself.running = True\n\t\tself.serial_port = serial_port\n\t\n\tdef startStream(self):\n\t\tself.running = True\n\t\t\n\t\tser = serial.Serial(self.serial_port, timeout=1.0)\n\t\t\n\t\twhile(self.running):\n\t\t\tline = ser.readline().decode(\"utf-8\") \n\t\t\tmeasure = measurement.Measurement(line)\n\t\t\tself.measurementCallback(measure)\n\t\n\tdef stopStream(self):\n\t\tself.running = False\n"
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"text": "#! python3\n\nimport sys\n\nimport example_measurement_stream\nimport gui\nimport tkinter as tk\nimport threading\nimport serial_measurement_stream\n\ndef applicationThread(application):\n\tapplication.mainloop()\n\ndef loggerThread(application, logger):\n\t#Register what function to call when the gui is closing\n\tapplication.addStopCallback(logger.stopStream)\n\t#Set which function the logger should send measurements to\n\tlogger.setMeasurementCallback(application.addMeasurement)\n\t#Now start the actual logger stream\n\tlogger.startStream()\n\ndef main():\n\t#Set default logger as the example logger\n\tlogger_type = \"example\"\n\t\n\tif(len(sys.argv) > 1):\n\t\tlogger_type = sys.argv[1].lower()\n\t\n\tlogger = None\n\t\n\t#It is already lowercase\n\tif(logger_type == \"example\"):\n\t\tlogger = example_measurement_stream.ExampleMeasurementStream()\n\telif(logger_type.startswith(\"com\")):\n\t\tlogger = serial_measurement_stream.SerialMeasurementStream(logger_type.upper())\n\telse:\n\t\traise Exception(\"The logger \"+str(logger_type)+\" is not recognized by the system\")\n\t\n\troot = tk.Tk()\n\tapplication = gui.SensorLoggerApplication(root)\n\t\n\tlogger_thread = threading.Thread(target=loggerThread, args=(application,logger,))\n\tlogger_thread.start()\n\t\n\t#This must be on the main thread\n\tapplicationThread(application)\n\t\n\treturn logger_type\n\nif __name__ == \"__main__\":\n\tmain()\n"
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"text": "#! python3\n\nimport tkinter as tk\nimport measurement\nimport collections\nimport threading\nimport time\nimport example_measurement_stream\n\nclass SensorLoggerApplication(tk.Frame):\n\t\n\tdef __init__(self, master=None):\n\t\tsuper().__init__(master)\n\t\tself.pack()\n\t\tself.value_list = {}\n\t\tself.stop_callbacks = []\n\t\t\n\t\tself.master = master\n\t\tif(not (master is None)):\n\t\t\tmaster.protocol(\"WM_DELETE_WINDOW\", self.stop)\n\t\n\tdef stop(self):\n\t\tfor stop_callback in self.stop_callbacks:\n\t\t\tstop_callback()\n\t\t\n\t\tif(not (self.master is None)):\n\t\t\tself.master.destroy()\n\t\n\tdef addStopCallback(self, stop_callback):\n\t\tself.stop_callbacks.append(stop_callback)\n\t\n\tdef processEvents(self):\n\t\tshortest_time = float(\"inf\")\n\t\tevent_name = \"\"\n\t\tcurrent_time = time.time()\n\t\t\n\t\tfor key in self.value_list:\n\t\t\tlabel_tuple = self.value_list[key]\n\t\t\tlabel_time = label_tuple[7]\n\t\t\t\n\t\t\tif(label_time < shortest_time):\n\t\t\t\tshortest_time = label_time\n\t\t\t\tevent_name = key\n\t\n\tdef addMeasurement(self, measurement):\n\t\tname = measurement.sensor_name\n\t\t\n\t\tif(not (name is None)):\n\t\t\tif(name in self.value_list):\n\t\t\t\tvalue_tuple = self.value_list[name]\n\t\t\t\tvalue_tuple[1].set(measurement.value)\n\t\t\t\tvalue_tuple[2].set(measurement.unit)\n\t\t\t\n\t\t\telse:\n\t\t\t\tname_label_text = tk.StringVar()\n\t\t\t\tvalue_label_text = tk.StringVar()\n\t\t\t\tunit_label_text = tk.StringVar()\n\t\t\t\t\n\t\t\t\tname_label_text.set(measurement.sensor_name)\n\t\t\t\tvalue_label_text.set(measurement.value)\n\t\t\t\tunit_label_text.set(measurement.unit)\n\t\t\t\t\n\t\t\t\tname_label = tk.Label(self, textvariable=name_label_text, font=(\"Calibri\",40))\n\t\t\t\tvalue_label = tk.Label(self, textvariable=value_label_text, font=(\"Calibri\",40))\n\t\t\t\tunit_label = tk.Label(self, textvariable=unit_label_text, font=(\"Calibri\",40))\n\t\t\t\t\n\t\t\t\trow = len(self.value_list)\n\t\t\t\t\n\t\t\t\tname_label.grid(row=row, column=0)\n\t\t\t\tvalue_label.grid(row=row, column=1)\n\t\t\t\tunit_label.grid(row=row, column=2)\n\t\t\t\t\n\t\t\t\tcurrent_time = time.time()\n\t\t\t\tevent_time = current_time + (measurement.next_measurement_time / 1000.0)\n\t\t\t\tself.value_list[name] = [name_label_text, value_label_text, unit_label_text, name_label, value_label, unit_label, row, event_time]\n\t\t\t\tself.processEvents()\n\t\n\tdef reEnumerateElements(self):\n\t\tvalues = sorted(self.value_list.values(), key=lambda tup : tup[6])\n\t\t\n\t\tfor expected_index in range(len(values)):\n\t\t\tactual_index = values[expected_index][6]\n\t\t\tif(actual_index != expected_index):\n\t\t\t\tvalues[expected_index][6] = expected_index\n\t\t\n\t\tfor label_tuple in values:\n\t\t\tname_label = label_tuple[3]\n\t\t\tvalue_label = label_tuple[4]\n\t\t\tunit_label = label_tuple[5]\n\t\t\trow = label_tuple[6]\n\t\t\t\n\t\t\tname_label.grid_forget()\n\t\t\tvalue_label.grid_forget()\n\t\t\tunit_label.grid_forget()\n\t\t\t\n\t\t\tname_label.grid(row=row, column=0)\n\t\t\tvalue_label.grid(row=row, column=1)\n\t\t\tunit_label.grid(row=row, column=2)\n\t\n\tdef removeMeasurement(self, measure):\n\t\tname = \"\"\n\t\tif(type(measure) is measurement.Measurement):\n\t\t\tname = measure.sensor_name\n\t\telse:\n\t\t\tname = measure\n\t\t\n\t\tif(name in self.value_list):\n\t\t\tself.value_list[name][3].grid_forget()\n\t\t\tself.value_list[name][4].grid_forget()\n\t\t\tself.value_list[name][5].grid_forget()\n\t\t\t\n\t\t\tdel self.value_list[name]\n\t\t\tself.reEnumerateElements()\n\t\t\t\n\t\telse:\n\t\t\traise Exception(\"The sensor \"+str(name)+\" cannot be removed because it's not added.\")\n\t\t\n\t\tself.processEvents()\n\ndef exampleMeasurements(application):\n\ttime.sleep(1.0)\n\tex1 = measurement.Measurement(\"\")\n\tex1.sensor_name = \"CO2\"\n\tex1.value = \"2000\"\n\tex1.unit = \"ppm\"\n\tapplication.addMeasurement(ex1)\n\t\n\ttime.sleep(1.0)\n\tex2 = measurement.Measurement(\"\")\n\tex2.sensor_name = \"CO2\"\n\tex2.value = \"2200\"\n\tex2.unit = \"ppm\"\n\tapplication.addMeasurement(ex2)\n\t\n\ttime.sleep(1.0)\n\tex3 = measurement.Measurement(\"\")\n\tex3.sensor_name = \"Temperature\"\n\tex3.value = \"22.2\"\n\tex3.unit = \"C\"\n\tapplication.addMeasurement(ex3)\n\t\n\tex4 = measurement.Measurement(\"\")\n\tex4.sensor_name = \"Humidity\"\n\tex4.value = \"30\"\n\tex4.unit = \"%\"\n\tapplication.addMeasurement(ex4)\n\t\n\ttime.sleep(1.0)\n\tapplication.removeMeasurement(ex1)\n\ttime.sleep(1.0)\n\tapplication.removeMeasurement(\"Temperature\")\n\t\n\ttime.sleep(1.0)\n\tex5 = measurement.Measurement(\"\")\n\tex5.sensor_name = \"Humidity\"\n\tex5.value = \"34\"\n\tex5.unit = \"%\"\n\tapplication.addMeasurement(ex5)\n\n\ttime.sleep(1.0)\n\tex6 = measurement.Measurement(\"\")\n\tex6.sensor_name = \"CO2\"\n\tex6.value = \"2300\"\n\tex6.unit = \"ppm\"\n\tapplication.addMeasurement(ex6)\n\ndef exampleMeasurements2(application):\n\tstream = example_measurement_stream.ExampleMeasurementStream()\n\t\n\tapplication.addStopCallback(stream.stopStream)\n\tstream.setMeasurementCallback(application.addMeasurement)\n\tstream.startStream()\n\ndef main():\n\troot = tk.Tk()\n\tlogger_application = SensorLoggerApplication(master=root)\n\t\n\texample_thread = threading.Thread(target=exampleMeasurements2, args=(logger_application,))\n\texample_thread.start()\n\t\n\tlogger_application.mainloop()\n\t\n\texample_thread.join()\n\t\nif __name__ == \"__main__\":\n\tmain()\n"
}
] | 9 |
dejfowler/389Rfall2019
|
https://github.com/dejfowler/389Rfall2019
|
0bfccc8f80065c91accbd180f8a330f4e33f9af6
|
a8b1ddb8214f7c93533360fb90ca7bf966b80541
|
31f55e5e6e64e167269013cf6017855601b41a32
|
refs/heads/master
| 2022-07-16T03:40:04.326035 | 2019-11-28T04:49:42 | 2019-11-28T04:49:42 | null | 0 | 0 | null | null | null | null | null |
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"text": "#!/usr/bin/env python2\nimport os\nimport sys\nimport struct\nfrom datetime import datetime\n\n# You can use this method to exit on failure conditions.\ndef bork(msg):\n sys.exit(msg)\n\n\n# Some constants. You shouldn't need to change these.\nMAGIC = 0x8BADF00D\nVERSION = 1\n\nif len(sys.argv) < 2:\n sys.exit(\"Usage: python stub.py input_file.fpff\")\n\n# Normally we'd parse a stream to save memory, but the FPFF files in this\n# assignment are relatively small.\nwith open(sys.argv[1], 'rb') as fpff:\n data = fpff.read()\n\n# Hint: struct.unpack will be VERY useful.\n# Hint: you might find it easier to use an index/offset variable than\n# hardcoding ranges like 0:8\nmagic, version = struct.unpack(\"<LL\", data[0:8])\ntimestamp = struct.unpack(\"<L\", data[8:12])\nauthor = struct.unpack(\"<Q\", data[12:20])\nsec_num = struct.unpack(\"<L\",data[20:24])\n\nif magic != MAGIC:\n bork(\"Bad magic! Got %s, expected %s\" % (hex(magic), hex(MAGIC)))\n\nif version != VERSION:\n bork(\"Bad version! Got %d, expected %d\" % (int(version), int(VERSION)))\n\nif sec_num <= 0:\n bork(\"Bad Section Number: Got %d, needs to be above 1\" % int(sec_num))\nelse:\n new_dir = sys.argv[1] + \"_extracted\"\n try:\n\tos.mkdir(new_dir)\n except OSError:\n\tprint (\"Creation of new directory %s failed\" % new_dir)\n else:\n\tprint (\"Creation of new directory %s success\" % new_dir)\n \n#Unpacking the timestamp and converting the time to a unix timestamp\n(x,) = timestamp\ntime_conv = datetime.fromtimestamp(float(x))\n#Unpacking the section number from tuple\n(y,) = sec_num\nsec_num = y\n#Unpacking the author value from tuple, convert it to hex then from hex covert to ASCII. Then reverse the string since the hex was in little endian.\n(r,) = author\ntemp = str(hex(r))\nrev = ''.join(reversed(bytearray.fromhex(temp[2:]).decode()))\nauthor = rev\n\n#Necessary printing of Header Information\nprint(\"------- HEADER -------\")\nprint(\"MAGIC: %s\" % hex(magic))\nprint(\"VERSION: %d\" % int(version))\nprint(\"TIMESTAMP: %s\" % time_conv )\nprint(\"Author: %s\" % author)\nprint(\"Section : %d\" % int(y))\n\ndef sec_type(i):\n\tswitcher={\n\t\t1:'SECTION_ASCII',\n\t\t2:'SECTION_UTF8',\n\t\t3:'SECTION_WORDS',\n\t\t4:'SECTION_DWORDS',\n\t\t5:'SECTION_DOUBLES',\n\t\t6:'SECTION_COORD',\n\t\t7:'SECTION_REFERENCE',\n\t\t8:'SECTION_PNG',\n\t\t9:'SECTION_GIF87',\n\t\t10:'SECTION_GIF89'\n \t }\n\treturn switcher.get(i, \"Invalid\")\n\n#Necessart printing of body information\nprint(\"------- BODY -------\")\nstart = 24\nend = 28\norig_stdout = sys.stdout\nfor x in range(sec_num):\n\t#Reset the stdout after writing a file\n\tsys.stdout = orig_stdout\n\t#Prints the section number\n\tprint(\"Section %d:\" % int(int(x)+int(1)))\n\ttype = struct.unpack(\"<L\",data[start:end])\n\t(stype,) = type\n\tlen = struct.unpack(\"<L\",data[(start+4):(end+4)])\n\t(slen,) = len\n\t#(Above) unpack and retreieve values for stype and slen then (below) print the values accordingly\n\tprint(\"\\tSection Type: %s\" % sec_type(stype))\n\tprint(\"\\tSection Length: %d\" % int(slen))\n\tif len > 0:\n\t\tsvalue = 8 + slen\n\t\tprint(\"\\tSection Value: %d\" % int(svalue))\n\n\t#if sec_type is SECTION_ASCII it parses the information\n\tif (sec_type(stype) == sec_type(1)):\n\t\ttemp = slen/4\n\t\tformat = (\"<\" + (\"L\" * temp))\n\t\tbody = struct.unpack(format,data[(start+8):(end+4+int(slen))])\n\t\tres = \"\"\n\t\tfor var in body:\n\t\t\ttemp1 = str(hex(var))\n\t\t\ttemp2 = ''.join(reversed(bytearray.fromhex(temp1[2:]).decode()))\n\t\t\tres = res + temp2\n\t\tfile_name = \"/SEC_ASCII%d.txt\" % int(int(x)+int(1))\n\t\tfile_name = new_dir + file_name\n\t\tprint(\"You can find the extracted file in: %s\" % file_name)\n\t\t#print(body)\n\t\tf = open(file_name, 'w')\n\t\tsys.stdout = f\n\t\tprint(res)\n\t\tf.close()\n\n #if sec_type is SECTION_UTF8 it parses the information\n if (sec_type(stype) == sec_type(2)):\n temp = slen/4\n format = (\"<\" + (\"L\" * temp))\n body = struct.unpack(format,data[(start+8):(end+4+int(slen))])\n res = \"\"\n for var in body:\n temp1 = str(hex(var))\n temp2 = ''.join(reversed(bytearray.fromhex(temp1[2:]).decode()))\n res = res + temp2.decode(\"utf-8\")\n file_name = \"/SEC_UTF8Decoded%d.txt\" % int(int(x)+int(1))\n file_name = new_dir + file_name\n print(\"You can find the extracted file in: %s\" % file_name)\n #print(body)\n f = open(file_name, 'w')\n sys.stdout = f \n print(res)\n f.close()\n\t#if sec_type is SECTION_WORDS it parses the information\n\tif (sec_type(stype) == sec_type(3)):\n\t\ttemp = slen/4\n\t\tformat = (\"<\" + (\"L\" * temp))\n\t\tbody = struct.unpack(format,data[(start+8):(end+4+int(slen))])\n\t\tres = \"\"\n\t\tfor var in body:\n\t\t\ttemp1 = ''.join(reversed(var))\n\t\t\tres = res+temp1\n\t\tfile_name = \"/SEC_WORDS%d.txt\" % int(int(x)+int(1))\n\t\tfile_name = new_dir + file_name\n\t\tprint(\"You cant find the extracted file in: %s\" % file_name)\n\t\t#print(body)\n\t\tf = open(file_name, 'w')\n\t\tsys.stdout = f\n\t\tf.close()\n\t#if sec_type is SECTION_DWORD it parses the information\n\tif (sec_type(stype) == sec_type(4)):\n\t\ttemp = slen/8\n\t\tformat = (\"<\" + (\"Q\" * temp ))\n\t\tbody = struct.unpack(format,data[(start+8):(end+4+int(slen))])\n res = \"\"\n for var in body:\n temp1 = ''.join(reversed(var))\n res = res+temp1\n file_name = \"/SEC_DWORDS%d.txt\" % int(int(x)+int(1))\n file_name = new_dir + file_name\n print(\"You cant find the extracted file in: %s\" % file_name)\n #print(body)\n f = open(file_name, 'w')\n sys.stdout = f\n f.close()\n\t#if sec-type is SECTION_DOUBLE it parses the information\n if (sec_type(stype) == sec_type(5)):\n temp = slen/8\n format = (\"<\" + (\"d\" * temp ))\n body = struct.unpack(format,data[(start+8):(end+4+int(slen))])\n res = \"\"\n for var in body:\n temp1 = ''.join(reversed(var))\n res = res+temp1\n file_name = \"/SEC_DOUBLE%d.txt\" % int(int(x)+int(1))\n file_name = new_dir + file_name\n print(\"You cant find the extracted file in: %s\" % file_name)\n #print(body)\n f = open(file_name, 'w')\n sys.stdout = f\n f.close()\n\n\t#if sec_type is SECTION_COORD is parses the information\n\tif(sec_type(stype) == sec_type(6)):\n \t\ttemp = slen/8\n\t\tformat = (\"<\" +(\"d\" *temp))\n\t\tbody = struct.unpack(format,data[(start+8):(end+4+int(slen))])\n res = \"\"\n\t\tfile_name = \"/SEC_COORD%d\" % int(int(x)+int(1))\n\t\tfile_name = new_dir + file_name\n\t\tprint(\"You can find the extracted file in: %s.txt\" % file_name)\n\t\tf = open(file_name, 'w')\n\t\tsys.stdout = f\n print(\"(%f, %f)\" % body)\n\t\tf.close()\n\t#if sec_type is SECTION_REFERENCE it parses the information\n\tif(sec_type(stype) == sec_type(7)):\n\t\tif slen != 4:\n\t\t\traise Exception(\"SECTION_REFERENCE slen is not 4\")\n\t\telse:\n\t\t\ttemp = slen/4\n\t\t\tformat = (\"<\" + (\"L\" * temp))\n\t\t\tbody = struct.unpack(format,data[(start+8):(end+4+int(slen))])\n \tres = \"\"\n \tfile_name = \"/SEC_REFERENCE%d\" % int(int(x)+int(1))\n \tfile_name = new_dir + file_name\n \tprint(\"You can find the extracted file in: %s.txt\" % file_name)\n \tf = open(file_name, 'w')\n \t\tsys.stdout = f\n\t print(\"(%f,)\" % body)\n \t f.close()\n\t#if sec_type is SECTION_PNG it parses the information\n\tif(sec_type(stype) == sec_type(8)):\n\t\ttemp = slen/4\n format = (\"<\" + (\"L\" * temp))\n body = struct.unpack(format,data[(start+8):(end+4+int(slen))])\n res = \"89504e470d0a1a0a\"\n for var in body:\n temp1 = str(hex(var))\n temp2 = ''.join(reversed((temp1[2:])))\n res = res + temp2\t\t\n file_name = \"/SEC_PNG%d.png\" % int(int(x)+int(1))\n file_name = new_dir + file_name\n print(\"You can find the extracted file in: %s\" % file_name)\n #print(body)\n f = open(file_name, 'w')\n sys.stdout = f\n print(res)\n f.close()\n\t#if sec_type is SECTION_GIF87 it parses the information\n\tif(sec_type(stype) == sec_type(9)):\n temp = slen/4\n format = (\"<\" + (\"L\" * temp))\n body = struct.unpack(format,data[(start+8):(end+4+int(slen))])\n res = \"4749463837610000\"\n for var in body:\n temp1 = str(hex(var))\n temp2 = ''.join(reversed((temp1[2:])))\n res = res + temp2\n \n file_name = \"/SEC_GIF87_%d.gif87\" % int(int(x)+int(1))\n file_name = new_dir + file_name\n print(\"You can find the extracted file in: %s\" % file_name)\n #print(body)\n f = open(file_name, 'w')\n sys.stdout = f\n print(res)\n f.close()\n #if sec_type is SECTION_GIF89 it parses the information\n if(sec_type(stype) == sec_type(10)):\n temp = slen/4\n format = (\"<\" + (\"L\" * temp))\n body = struct.unpack(format,data[(start+8):(end+4+int(slen))])\n res = \"4749463839610000\"\n for var in body:\n temp1 = str(hex(var))\n temp2 = ''.join(reversed((temp1[2:])))\n res = res + temp2\n \n file_name = \"/SEC_GIF87_%d.gif89\" % int(int(x)+int(1))\n file_name = new_dir + file_name\n print(\"You can find the extracted file in: %s\" % file_name)\n #print(body)\n f = open(file_name, 'w')\n sys.stdout = f\n print(res)\n f.close()\n\n\t#Increments the position where we are reading in the file\n\tstart = start + slen + 8\n\tend = end + slen + 8\n\t\n\n"
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"text": "# Writeup 9 - Forensics II\n\nName: *PUT YOUR NAME HERE*\nSection: *PUT YOUR SECTION NUMBER HERE*\n\nI pledge on my honor that I have not given or received any unauthorized assistance on this assignment or examination.\n\nDigital acknowledgement: *PUT YOUR NAME HERE*\n\n\n## Assignment details\n\n### Part 1 (45 Pts)\n\t1. What IP address is being attacked?\n\t\t142.93.136.81:21\n\t2. What kind of assesment tool(s) were the attackers using against the victim machine? List the name(s) of the tool(s) as well.\n\n\t3. What are the hackers IP addresses and where are they connecting from?\n\t\tThe hackers IP address is 159.203.113.181 connected on port 55914\n\n\t4. What port are they using to steal files?\n\t\tThey are connecting to port 21 on the server (FTP) probably to have the ability to transfer files back and fourth.\n\n\t5. Which file did they steal? What kind of file is it? Do you recognize it?\n\t\tIt looks like they retrieved the 'find_me.jpeg' from the server. This is a jpeg file.\n\n\t6. Which file did the attackers leave behind on the server?\n\t\tThe attackers left behind a file called 'greetz.fpff'\n\n\t7. What is a countermeasure to prevent this kind of intrusion from happening again?\n\t\tI noticed that the password isn't very secure, updating password policies and restrictions would make it harder for the attacker to gain access. You could implement a firewall setting that is implicit-deny, this means that unless the ip address is implicitly whitelisted and allowed than connection from the address will be denied. Other than that you could implement an IPS on the network. This will enable similar capabilities to the implicit-deny I mentioned earlier and go even further since an IPS has additional capabilities.\n### Part 2 (55 Pts)\n\t1. Develop the parser\n\t\tSee stub.py for code\n\t2. Parse greetz.fpff and give the following:\n\t\tI. When was greetz.fpff generated?\n\t\t\t2019-03-27 00:15:05\t\n\t\tII. Who authored greetz.fpff?\n\t\t\tf1nch\n\t\tIII. List each section and give data in it and type.\n\t\t\tSection 1: SECTION_ASCII\n\t\t\tData: Hey you, keep looking :)\n\n\t\t\tSection 2: SECTION_COORD\n\t\t\tData: (52.)\n\n\t\t\tSection 3: SECTION_PNG\n\t\t\tData: Picture\n\n\t\t\tSection 4: SECTION_ASCII\n\t\t\tData: }R983CSMC_perg_tndid_u0y_yllufep0h{-R983CSMC\n\n\t\t\tSection 5: SECTION_ASCII\n\t\t\tData: Q01T1zM4OVIte2hleV9oM3lfeTBVX3lvdV9JX2RvbnRfbGlrZV95b3VyX2Jhc2U2NF9lbmNvZGluZ30=\n \n\t\tIV. Report at least one flag hidden in greetz.fpff\n\t\t\t1.CMSC389R-{h0pefully_y0u_didnt_grep_CMSC389R}\n\t\t\t2.CMSC389R-{hey_h3y_yoU_you_I_dont_like_your_base64_encoding}\n\t\t\t3.Probably in the png but my parser isn't 1337 enough\n"
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"text": "# Writeup 0 - Ethics\n\nName: Dalton Fowler\nSection: 0101\n\nI pledge on my honor that I have not given or received any unauthorized assistance on this assignment or examniation.\n\nDigital acknowledgement: Dalton Fowler\n\n## Assignment Writeup\n\n### Part 1 (25 pts)\n\nThis was done via the ELMS assignment.\n\n### Part 2 (75 pts)\n\nI believe ethically that it is very important to tell someone. Initially tell your boss, if that doesn't work properly than it might be better to go up the food chain and speak to someone more involved. Although it's very possible that these people act to benefit shareholders as most company's do. If this doesn't work than it might actually be better to be a whistleblower, if the company shoots you down and is about to release the ECU which is capable for taking lives, theres is a very real chance that the public deserves to know that this company doesn't care about the consumer. I strongly believe that a company that doesn't worry about the safety of people/consumers isn't a company worth working for. It's very possible to hop to another job that will hopefully value the work you do and listen to the results provided. If the ECU rolls out as is there is a very compelling argument to be made that argues where the fault lies. Of course you did your due dilligence and told the management about the issue. However, was that enough? Should you have gone further as to lose your job after going public? I think there is only so much the auditor can do before the fault should really be on the management. If nothing is done about it, fault is undoubtly on the auditor, but on the other hand should the auditornotify management of the problem and nothing changes I believe that most of the fault transfers from the auditor to the management and the company itself.\n"
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"text": "# Writeup 7 - Forensics I\n\nName: Dalton Fowler\nSection 0101:\n\nI pledge on my honor that I have not given or recieved any unauthorized assistance on this assignment or examination\n\nDigital ackknowledgement: DALTON FOWLER\n\n## Assignment Writeup\n\n### Part 1 (100 pts)\nAnswer the following questions regarding [this](../image) file:\n\n1. What kind of file is it?\n\tThe file type associated with the 'image' file is JPEG\n\n2. Where was this photo taken? Provide a city, state and the name of the building in your answer.\n\tPhoto was taken at 41deg53'54.87\"N, 87deg37'22.53\"W\n\tThese coordiantes relate to the John Hancock Center located in Chicago, Illinois\n\n3. When was this photo taken? Provide a timestamp in your answer.\n\tPhoto was taken on 08/22/2018 at 11:33:24 AM\n\n4. What kind of camera took this photo?\n\tAn iPhone 8 camera took the photo.\n\n5. How high up was this photo taken? Provide an answer in meters.\n\tThis photo was taken 539.5 meters above sea level.\n\n6. Provide any found flags in this file in standard flag format.\n\tAfter doing a binwalk with the option --dd=\"png:png\" I extracted another file which provided a png file that contained a flag: CMSC389R-{abr@cadabra}.\n"
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"text": "# Operational Security and Social Engineering\n\n## Assignment details\n\nThis assignment has two parts. It is due by 9/20 at 11:59 PM.\n\n**There will be a late penalty of 5% off per day late!**\n\n### Part 1\n\nYou have been hired by a penetration testing firm and have been asked to collect some specific information about the Wattsamp employee from HW2, Eric Norman. Some of the information you're looking for seems to be unavailable or unlikely to be found through OSINT:\n\n- What's his mother's maiden name?\n- What browser does she primarily use?\n- What city was she born in?\n- What's her ATM pin number?\n- What was the name of her first pet?\n\nWrite up a pretext that you would use to social engineer this information out of Eric Norman. What approach would you take and how would you present yourself to elicit this information under the radar? Use the slides and what we covered in lecture to come up with a plan to obtain this information.\n\nI would pose as a employee at a bank. Since social engineering is somewhat delicate in the sense that you can blow your cover doing something small. I would start by calling and asking if Eric was a member of the bank and then if Eric banks elsewhere ask where he banks and what you could do to get his business. If Eric uses another bank then it would be wise to note that information and either start the process of signing Eric up and during the process get into a 'Security Questions' section and use these questions as the security questions to elicit the information from Eric, and as one of the final steps ask him to choose a pin to use for the card he will recieve (many people re-use passwords and likely pins aswell so this pin could be the answer to his other banks pin). Suppose that Eric doesn't want to sign up for this bank then it would be in my best interest to hang up and call later (hours, days, week maybe) then call back assuming the role of an employee from the bank he uses, claim that there was a large number of customers that accounts have been compromised due to fraud and you need to check a few things to verify identity before being able to send out a new card. I would take the same route assuming that Eric turns out to be a customer all along. Verifying identity would include asking questions similar to above and finally asking for the PIN of the current card before asking if they want to change the PIN as it could have been compromised as part of the fraud breach. Since we were able to get Eric Normans address I would look up a legitimate bank around him in order to be able to give a real address for a corresponding bank if Eric were to ask. Finding the local banks also provides insight on what bank Eric might actually be using, also knowing local establishments could establish a sense of familiarity with Eric, this would make the converstation more friendly and as a result Eric would be more willing to give up some of this information without thinking. \n\n### Part 2\n\nEric Norman has recently discovered that Watsam's web server has been broken into by the crafty CMSC389R ethical hackers. After reading your published report, he has reached out to you to seek guidance in how he can repair some of the vulnerabilities that you have discovered.\nChoose 3 specific vulnerabilities from homework 2 that you have identified (ie. exposed ports, weak passwords, etc.) and write a brief summary of some suggestions you can provide Eric for the Wattsamp web server and admin server. Be as thorough as possible in your answer, use specific examples and citing online research into security techniques that could be applied to the servers (ie. firewall, IDS/IPS, password managers, etc.).\n\nSome techincal vulnerabilities that I discovered is that Eric was using a weak password. Increasing the password complexities on the server side would mitigate this risk. Using complex password including min/max length, require uppercase, lowercase, numbers, and symbols all increase the security of a password and is best practice. As we discovered it's rather simple to brute force into a system using a wordlist, having a complex password will make this option much more difficult and useless. Additionally, Eric should close port 1337 as no services are running on that port. The only service that the server was using was port 80 to host a website server so it is also best practice to disable all unused ports because keeping ports open that aren't being used can become a huge security risk as Eric found out. On the non-technical side Eric should not promote the website on his social media like that. Although it may be a good way to increase his network, maybe adding customers here or there, its not a great idea to mix personal and professinal business. As we learned in class, one of the biggest vulnerabilities are people themselves. The best practices based on password complexities and port security I learned while reading a Security+ book while pursuing my Sec+ certification. Some ways eric could add to his system would be to add and IPS to the network before traffic reaches the server. The IPS will be able to detect and prevent these brute force attacks because it can see that someone is constantly connecting to and failing to connect. As a result the IPS can begin to block the traffic altogether. This will add an additinal layer of security ontop of the identification/authentication that the server itself needs to permit access. In this scenario it would be best to use an IPS due to the amount of traffic the network takes on. However, it would also behove Eric to disable the port altogether in order to prevent that attack vector altogether. \n\n### Format\n\nThe submission should be answered in bullet form or full, grammatical sentences. It should also be stored in `assignments/3_OPSEC_SE/writeup/README.md`. Push it to your GitHub repository by the deadline.\n\n### Scoring\n\nPart 1 is worth 40 points, part 2 is worth 60 points. The rubric with our expectations can be found on the ELMS assignment posting.\n\nGood luck!\n"
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"text": "# Writeup 1 - Web I\n\nName: *PUT YOUR NAME HERE*\nSection: *PUT YOUR SECTION NUMBER HERE*\n\nI pledge on my honor that I have not given or received any unauthorized assistance on this assignment or examination.\n\nDigital acknowledgement: *PUT YOUR NAME HERE*\n\n\n## Assignment details\nThis assignment has two parts. It is due by 11/27/19 at 11:59PM.\n\n**There will be late penalty of 5% per day late!**\n\n### Part 1 (40 Pts)\n\nSuch a Quick Little, website!\n\n[http://142.93.136.81:5000/](http://142.93.136.81:5000/)\n\t\n\tInititally I tried several LFI techniques such as directory traversal and other things. Then I was stuck until figuring out that there was a SQL database lurking in the backgroung. In hindsight the keywords 'item?id=' should have been a giveaway but know I know better. I tried several injections that don't workand return a \"ERROR: ATTEMPTED SQL INJECTION DETECTED\". I tried variations trying to bypass the filter such as \"item?id=0'%20OR%201=1--\", \"item?id=0%27%20OR%201=1--\", and \"item?id=0'/**/OR/**/1=1--\" among others that have similar variations. All of which have provided the same error. I initially thought this was the result of some firewall blocking the injection however I'm using Firefox on Kali with burp as my proxy, I intercept the GET request with my proxy and modify it in burp before forwarding the request so I don't believe I'm geting blocked by a firewall.\n\n### Part 2 (60 Pts)\nComplete all 6 levels of:\n\n[https://xss-game.appspot.com](https://xss-game.appspot.com)\n\nProduce a writeup. We will not take off points for viewing the source code and/or viewing hints, but we strongly discourage reading online write-ups as that defeats the purpose of the homework.\n\tThe first level was rather simple. The objective is to raise an alert by injecting a script. At first I tried <script>alert</script> and it didn't work so i looked up some syntax things and <script>aler(1)</script> worked!\n\tThe second level was kind of fun to figure out after sitting around a few minutes I tried just posting some html code and it worked so I understand that I write an html line that results in an alert by using some element that can raise an alert for this i used <img src=\"random.jpg\" onerror=\"javascript:alert(1)\"/> to raise an alert\n\tThe third level I took time scrolling through the 'Taget code' to get an idea of what is happening. At some point the code dynamically loads the appropriate image (given comment) and adds a img element to the html line. So we can play around with the formatting and insert another onerror into the img element to raise an alert. Since it gives us the image number (i.e #1,#2,#3) we can hardcode whichever one into this then add ' onerror='alert(1)'; The quotes are important to breaking up the quotes in the current context, adding the onerror, then continuing.\n\tThe fourth level i had to resort to the code and hints for help. The hitns gave away that i should try using decoded values when adding input. I basically seperated the code from timer.html into something different in order to inject the alert() command. I used ')%Balert(1)%3B(' the quotes basically make it so it just inserts nicely into the current code where the startTimer function is called. and %3B are decoded semicolons.\n\tThe fifth level I just added a javascript line to the url so when the email was entered and 'next' was clicked an alert popped up. Once you get to the signup page, remove 'confirm' and add javascript:alert(1) this will make it so when 'Next >>' is clicked the alert is executed because since the program uses next=... you can insert somthing such as the alert to change the next page. Looking at the code was extremely beneficial for this one.\n\tThe last level went over my head. After sifting through the code and get a very general idea of what was going on, I exhausted my hints and still was somewhat lost on what to do. The code kind of gave a hint in a comment which read \"This will totally prevent us from loading evil URLs\" and so that code i took a closer look at. It utilizes a regex in order to match with https:// and its apparent that the regex only checks one case, that is http. so i tried all caps because HTTP won't get caught by a regex looking for http. knowing it would take a website i created a pastebin to host a script that was just utilized the alert(1) function. and provided a remote file inclusion on the pastebin making sure i used HTTPS instead of https.\n### Format\n\nPart 1 and 2 can be answered in bullet form or full, grammatical sentences.\n\n### Scoring\n\n* Part 1 is worth 40 points\n* Part 2 is worth 60 points\n\n### Tips\n\nRemember to document your thought process for maximum credit!\n\nReview the slides for help with using any of the tools or libraries discussed in\nclass.\n"
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"text": "# Writeup 8 - Binaries II\n\nName: *PUT YOUR NAME HERE*\nSection: *PUT YOUR SECTION NUMBER HERE*\n\nI pledge on my honor that I have not given or received any unauthorized assistance on this assignment or examination.\n\nDigital acknowledgement: *PUT YOUR NAME HERE*\n\n## Assignment Writeup\n\n### Part 1 (100 pts)\nAnswer the following questions regarding the server executable (for which source is provided).\n\n1. How is the per-session administrator password generated? Are there any inherent weaknesses in this implementation?\n\tThe password is randomly generated by utilizing a random function based on the system time. Although generally this would make the password unpredictable, in reality it doesn't make sense to have a password you can't remember because most people would resort to writing it down, I don't believe this is what you're looking for. The password is generated with a pointer. This pointer points to the address of the heap and therefore we know the address of the password and can use that to our advantage later.\n\n2. Describe two vulnerabilities in this program. Provide specific line numbers and classifications of the vulnerability. Explain potential ramifications as well as ways to avoid these vulnerabilities when writing code.\n\tThe first vulnerability I discovered was the 'gets()' function within the elevate command on line 68. This allowed me to do a bufferoverflow which I'll go more in depth on in number 4. You can easily avoid this by using the safer 'fgets' function because in this case you can pass a pre-determined buffer size which would prevent this type of attackin this case.\n\tThe second vulnerability after searching around thoroughly is the 'printf(output)' in the cipher function on line 46. Without utilizing expected string formatting when calling the printf method you're able to pass through string formatting into the function itself and it doesn't know any better but to process the string formatting in this case we exploit this vulnerability to find the password along the stack and generally speaking will cause the function to behave unpredictably. You can avoid this my password more arguments into the printf function to prevent this or use a different function altogether. I believe the 'puts' function would have behaved properly as that function only prints the string as is, not falling victim to a string formatting being passed through.\n \n3. What is the flag?\n\tHere is the discovered flag: CMSC389R-{expl017-2-w1n}\n \n4. Describe the process you followed to obtain the flag: vulnerabilities exploited, your inputs to the server in a human-readable format, etc. If you create any helper code please include it.\n\tAt first I thoroughly examined the source code provided and fiddled around in gdb for awhile. I started to play around with string formatting within the printf function and couldn't easily find a way to reach the password. My next step was to try and find where on the stack the password actually was and compare that to the printf function so I know how far to go when scanning the stack for the password. I found the address of the password through gdb (p &password) and also found the general location of the printf function by adding a variable to the stack right before the printf call. This got me the general location of the printf call on the stack relative to the password, after doing some hexadecimal math and dividing that by 4 (32 bit program means that theres about 4bytes allocated to each 'layer' of the stack) and got 29. That was the offset between printf and the password, 29. I provided the printf with the formatted string '%29$s' which gracefully provided the password. This was definetly the hardest part. I took the password to elevate my privileges so I could access the exec_command function. After getting into the exec_function I basically brute forced the buffer overflow. I started with an arbitrary amount of a's until i found the right amount to where the command would be changed to my desired command. At first i got it down to 'grep 389 grep 389' then discovered the arbitrary amount of whitespace would be an issue. Luckily thats easy enough to balance out so then i tried 'grep 389 grep 389 ' which worked! However the grep command itself didn't work. After that it was the homestretch, next I did something similar but with ls. 'ls ls ' provided me with bin,dev,flag,lib,lib32,lib64, etc. so essentially i found the flag file but I needed to extract the flag so I provided the server with a simple 'cat flag cat flag '.\n*NOTE* The whitespace i provided in this README within the commands is not the exact amount i provided the server, its just to give you an idea of how I completed this part using the buffer overflow\n\n*Ignore* nc ec2-18-222-89-163.us-east-2.compute.amazonaws.com 1337\n\n"
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"text": "# Writeup 2 - OSINT\n\nName: *PUT YOUR NAME HERE*\nSection: *PUT YOUR SECTION NUMBER HERE*\n\nI pledge on my honor that I have not given or received any unauthorized assistance on this assignment or examination.\n\nDigital acknowledgement: *PUT YOUR NAME HERE*\n\n## Assignment Writeup\n\n### Part 1 (45 pts)\n\n*Please use this space to writeup your answers and solutions (and how you found them!) for part 1.*\nName: Eric Norman\nEric works for Wattsamp Energy, the website is http://wattsamp.net\nThe website had a link to the umd csec club and the umdcsec twitter. Additionally, I found an easter egg while looking at the html 'inspect element'\nthe easter egg is: *CMSC389R-{html_h@x0r_lulz}, this was on the admin login page inspect element.\nUsed Namechkr to determine what websites the username 'ejnorman84' was used for. I found an account for ejnorman84 on reddit and Instagram\nThe instagram had a picture with an easter egg labeled \"I <3 my power plant\" with the flag *CMSC389R{Looking_Closely_Pays}\nAfter doing '$ whois wattsamp.net' I gathered that the doman was registered under Eric Norma, 1300 Adabel Dr El Paso Texas, 79835 United States\nwith a Phone: +1-202-656-2837 and email: [email protected]\nDid a dns search of wattsamp.net i found a server with an ip of 157.230.179.99\nI did an nmap of this ip address and found that ports 22 (ssh)-disabled 80(http) and 1337 were open.\nI preformed '$ nmap -sV --script=banner 157.230.179.99' to determine that the server was running a Ubuntu linux OS and an apache web server.\n### Part 2 (75 pts)\n\n*Please use this space to detail your approach and solutions for part 2. Don't forget to upload your completed source code to this /writeup directory as well!*\n\nKnowing that the username was most likely 'ejnorman84' I added to the stub.py to go through the wordlist and hopefully find the right password.\nHeres the overall idea of the script:\nwhile there are lines in the wordlist it will create a socket and netcat to the server, it uses pythons re.findall to create a list of the regex\nwhich is just the (digit operand digit). I had to create a try/catch because I got an exception for something based on the list, if the exception got caught\nthe loop would re-iterate without changing the attempted password. The rest is pretty straight forward, send the captcha, recieve the next bytes of data,\nsend, recieve, send, then loop if \"Fail\" was caught in a regular expression, if \"Fail\" wasn't found the loop broke and printed the password.\nAfter gaining access to the server I found the flag almost instantly in /home/flag.txt after preforming a 'grep -r CMSC389R'.\n\n"
}
] | 8 |
nuribabo/djangoRestApi
|
https://github.com/nuribabo/djangoRestApi
|
30386d8e0b5e26ee275a9f34995ac135134a4447
|
eb0a85f4183f871136b16af84be9c38dc387eaa8
|
efea1793443b45f4d38027f61971ff4fc1f92252
|
refs/heads/master
| 2023-06-19T05:34:45.944852 | 2021-07-20T01:27:41 | 2021-07-20T01:27:41 | 387,628,737 | 0 | 0 | null | null | null | null | null |
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"text": "# 프로젝트 기능 설명\n\n + rest api를 post로 요청시 셋팅되어 있는 db에 create\n + rest api를 get으로 요청시 db 목록 가져옴\n \n# 프로젝트 폴더 설명\n\n1. intflowtest\n\n* 기본 앱\n* settings.py -> 외부 database, module(app), server 설정\n* urls.py -> url 관한 페이지 설정\n\n2. api\n* 재사용 가능한 app\n* models.py -> data담는 그릇 정의\n* urls.py -> url 관한 페이지 설정, \n* views.py -> 실제 로직 구현 , controller\n"
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"text": "\nfrom django.http.response import HttpResponse\nfrom .models import Post\nimport json\nfrom django.core import serializers as sl\n\n# Create your views here.\n\ndef test(request):\n if request.method == 'POST':\n data = json.loads(request.body)\n print(data)\n try:\n Post(text = data.get('text','')).save()\n # text = Test.objects.get_\n return HttpResponse(json.dumps({'code':'true'})) \n except:\n return HttpResponse(json.dumps({'code':'fail'}))\n else:\n qs = Post.objects.all()\n qs_json = sl.serialize('json', qs)\n\n return HttpResponse(qs_json, content_type='application/json')"
}
] | 2 |
dongdong841/learning-python3
|
https://github.com/dongdong841/learning-python3
|
592382a434eb68e750a9aaccfb9c3bc2daaf9fc1
|
a504576033bb1e201b914d0dd50f55e430dae0ad
|
95a931019f21449583ae98f2d7947d34b3d624ad
|
refs/heads/master
| 2020-07-09T08:02:15.507094 | 2020-01-25T07:52:29 | 2020-01-25T07:52:29 | 203,921,101 | 0 | 0 | null | null | null | null | null |
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"text": "# 模块名：file_format_dd841\r\n# 功能：提供文件格式相关的操作\r\n# 作者：zhangtianyu\r\n\r\nimport chardet\r\n\r\n# 函数名：get_file_encoding_format\r\n# 函数功能：获取文件的编码格式\r\n# 参数：文件路径\r\n# 返回值：文件的编码格式\r\ndef get_file_encoding_format(path):\r\n f = open(path, 'rb')\r\n c = f.read()\r\n f.close()\r\n info = chardet.detect(c)\r\n return info['encoding']\r\n\r\n# 函数名：chg_file_encoding_format\r\n# 函数功能：将目标文件的编码格式转换到目标格式，结果是生成以_new结尾新文件，源文件不变。\r\n# 注意_new是在文件名称最后面，文件名称是包含扩展名的。\r\n# 例如：源文件：1.txt\r\n# 新文件：1.txt_new\r\n# 源文件：test\r\n# 新文件：test_new\r\n# 参数：path 要转换的文件路径\r\n# tgt_encoding_format 目标编码格式\r\n# 返回值：无\r\ndef chg_file_encoding_format(path, tgt_encoding_format):\r\n src_encoding_format = get_file_encoding_format(path)\r\n with open(path, 'r', encoding=src_encoding_format) as f:\r\n with open(path+'_new', 'w', encoding=tgt_encoding_format) as tgt_f:\r\n for line in f:\r\n tgt_f.write(line)\r\n tgt_f.close()\r\n f.close()\r\n"
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"text": "# Structs\n## dictionary\n## list\n"
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"text": "import element, csv, matrix_row, matrix_column, matrix_square\n\nclass matrix:\n def __init__(self):\n # 1、将初始数据读入到matrix_all列表中\n self.matrix_all = []\n reader = csv.reader(open('data_lvl_four.csv'))\n y = 0\n length = 0\n for line in reader:\n x = 0\n length = len(line)\n for val in line:\n self.matrix_all.append(element.element(x, y, int(val)))\n x = x+1\n y = y+1\n\n # 2、原始数据加工\n # 2-1、创建行列表\n # 2-2、创建列列表\n self.MATRIX_ROW_LENGTH = length\n self.MATRIX_COLUMN_LENGTH = length\n self.MATRIX_SQUARE_LENGTH = length\n if length == 4:\n self.create_four_lvl()\n self.init_row_lvl_four()\n self.init_column_lvl_four()\n self.init_parts_lvl_four()\n elif length == 9:\n self.init_nine_lvl()\n else:\n print('wrong data!')\n quit()\n\n print('原始数独数据：')\n self.matrix_print()\n\n print('块数据：')\n print('0块：')\n string = ''\n for i in self.matrix_parts[0].elements:\n string += ' '\n string += str(i.value)\n string += '('\n string += str(i.x)\n string += str(i.y)\n string += ')'\n print(string)\n print('')\n print('块数据：')\n print('1块：')\n string = ''\n for i in self.matrix_parts[1].elements:\n string += ' '\n string += str(i.value)\n string += '('\n string += str(i.x)\n string += str(i.y)\n string += ')'\n print(string)\n print('')\n print('块数据：')\n print('2块：')\n string = ''\n for i in self.matrix_parts[2].elements:\n string += ' '\n string += str(i.value)\n string += '('\n string += str(i.x)\n string += str(i.y)\n string += ')'\n print(string)\n print('')\n print('块数据：')\n print('3块：')\n string = ''\n for i in self.matrix_parts[3].elements:\n string += ' '\n string += str(i.value)\n string += '('\n string += str(i.x)\n string += str(i.y)\n string += ')'\n print(string)\n print('')\n\n def create_four_lvl(self):\n # 创建行元素\n self.matrix_rows = []\n cnt = 0\n while cnt < self.MATRIX_ROW_LENGTH:\n self.matrix_rows.append(matrix_row.matrix_row())\n cnt += 1\n\n # 创建列元素\n self.matrix_columns = []\n cnt = 0\n while cnt < self.MATRIX_COLUMN_LENGTH:\n self.matrix_columns.append(matrix_column.matrix_column())\n cnt += 1\n\n # 创建正方形元素\n self.matrix_parts = []\n cnt = 0\n while cnt < self.MATRIX_SQUARE_LENGTH:\n self.matrix_parts.append(matrix_square.matrix_square())\n cnt += 1\n\n def init_row_lvl_four(self):\n for i in self.matrix_all:\n self.matrix_rows[i.y].add(i)\n for i in self.matrix_rows:\n i.init_data()\n\n def init_column_lvl_four(self):\n for i in self.matrix_all:\n self.matrix_columns[i.x].add(i)\n for i in self.matrix_columns:\n i.init_data()\n \n def init_parts_lvl_four(self):\n self.matrix_parts[0].add(self.matrix_rows[0].elements[0])\n self.matrix_parts[0].add(self.matrix_rows[0].elements[1])\n self.matrix_parts[0].add(self.matrix_rows[1].elements[0])\n self.matrix_parts[0].add(self.matrix_rows[1].elements[1])\n self.matrix_parts[0].init_data()\n\n self.matrix_parts[1].add(self.matrix_rows[0].elements[2])\n self.matrix_parts[1].add(self.matrix_rows[0].elements[3])\n self.matrix_parts[1].add(self.matrix_rows[1].elements[2])\n self.matrix_parts[1].add(self.matrix_rows[1].elements[3])\n self.matrix_parts[1].init_data()\n \n self.matrix_parts[2].add(self.matrix_rows[2].elements[0])\n self.matrix_parts[2].add(self.matrix_rows[2].elements[1])\n self.matrix_parts[2].add(self.matrix_rows[3].elements[0])\n self.matrix_parts[2].add(self.matrix_rows[3].elements[1])\n self.matrix_parts[2].init_data()\n\n self.matrix_parts[3].add(self.matrix_rows[2].elements[2])\n self.matrix_parts[3].add(self.matrix_rows[2].elements[3])\n self.matrix_parts[3].add(self.matrix_rows[3].elements[2])\n self.matrix_parts[3].add(self.matrix_rows[3].elements[3])\n self.matrix_parts[3].init_data()\n \n def init_nine_lvl(self):\n self.matrix_rows = []\n self.matrix_row_0 = []\n self.matrix_rows.append(self.matrix_row_0)\n self.matrix_row_1 = []\n self.matrix_rows.append(self.matrix_row_1)\n self.matrix_row_2 = []\n self.matrix_rows.append(self.matrix_row_2)\n self.matrix_row_3 = []\n self.matrix_rows.append(self.matrix_row_3)\n self.matrix_row_4 = []\n self.matrix_rows.append(self.matrix_row_4)\n self.matrix_row_5 = []\n self.matrix_rows.append(self.matrix_row_5)\n self.matrix_row_6 = []\n self.matrix_rows.append(self.matrix_row_6)\n self.matrix_row_7 = []\n self.matrix_rows.append(self.matrix_row_7)\n self.matrix_row_8 = []\n self.matrix_rows.append(self.matrix_row_8)\n\n self.matrix_columns = []\n self.matrix_column_0 = []\n self.matrix_columns.append(self.matrix_column_0)\n self.matrix_column_1 = []\n self.matrix_columns.append(self.matrix_column_1)\n self.matrix_column_2 = []\n self.matrix_columns.append(self.matrix_column_2)\n self.matrix_column_3 = []\n self.matrix_columns.append(self.matrix_column_3)\n self.matrix_column_4 = []\n self.matrix_columns.append(self.matrix_column_4)\n self.matrix_column_5 = []\n self.matrix_columns.append(self.matrix_column_5)\n self.matrix_column_6 = []\n self.matrix_columns.append(self.matrix_column_6)\n self.matrix_column_7 = []\n self.matrix_columns.append(self.matrix_column_7)\n self.matrix_column_8 = []\n self.matrix_columns.append(self.matrix_column_8)\n\n def get_one_element(self, x, y):\n if (x < self.MATRIX_ROW_LENGTH and x >= 0) and (y < self.MATRIX_COLUMN_LENGTH and y >= 0):\n return (self.matrix_rows[x])[y]\n else:\n print('error x:'+str(x)+'error y:'+str(y))\n quit()\n\n def get_one_column(self, y):\n if y < self.MATRIX_COLUMN_LENGTH and y >= 0:\n return self.matrix_columns[y]\n else:\n print('error y:'+str(y))\n quit()\n\n def get_all_column(self):\n return self.matrix_columns\n\n def get_one_row(self, x):\n if x < self.MATRIX_ROW_LENGTH and x >= 0:\n return self.matrix_rows[x]\n\n def get_all_rows(self):\n return self.matrix_rows\n\n def get_one_square(self, row, column):\n if (row >= 0 and row <= 1) and (column >= 0 and column <= 1):\n return self.matrix_parts[0]\n elif (row >=0 and row <= 1) and (column > 1 and column <= 3):\n return self.matrix_parts[1]\n elif (row > 1 and row <=3) and (column >=0 and column <= 1):\n return self.matrix_parts[2]\n else:\n return self.matrix_parts[3]\n \n def get_all_squares(self):\n return self.matrix_parts\n\n def is_complete(self):\n ret = True\n for l in self.matrix_rows:\n if l.zero_cnt != 0:\n ret = False\n break\n return ret\n\n def matrix_print(self):\n for line in self.matrix_rows:\n string = ''\n for i in line.elements:\n string += ' '\n string += str(i.value)\n print(string)\n print('')\n\n #def column_print(self, y):\n #print(self.get_one_column(y))\n"
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"text": "import subject\n\nclass element(subject.subject):\n chg_flag = False\n\n def __init__(self, x, y, v):\n subject.subject.__init__(self)\n self.x = x\n self.y = y\n self.value = v\n self.row_possible = []\n self.column_possible =[]\n self.square_possible = []\n element.chg_flag = True\n\n def possible_add_for_row(self, val):\n if val not in self.row_possible:\n self.row_possible.append(val)\n element.chg_flag = True\n\n def possible_minus_for_row(self, val):\n del self.row_possible[self.row_possible.index(val)]\n element.chg_flag = True\n\n def possible_add_for_column(self, val):\n if val not in self.column_possible:\n self.column_possible.append(val)\n element.chg_flag = True\n\n def possible_minus_for_column(self, val):\n del self.column_possible[self.column_possible.index(val)]\n element.chg_flag = True\n\n def possible_add_for_square(self, val):\n if val not in self.square_possible:\n self.square_possible.append(val)\n element.chg_flag = True\n\n def possible_minus_for_square(self, val):\n del self.square_possible[self.square_possible.index(val)]\n element.chg_flag = True\n\n def chg_val(self, val):\n self.value = val\n if val != 0:\n self.row_possible.clear()\n self.column_possible.clear()\n self.square_possible.clear()\n self.notify_observes()\n element.chg_flag = True\n \n"
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"text": "# class\n## 1、类的继承\n实现代码的复用\n\n### 父类和子类的交互方式\n* 子类上的动作完全等同于父类上的动作\n* 子类上的动作完全覆盖了父类上的动作\n* 子类上的动作部分替换了父类上的动作\n\n### 隐式继承\n子类使用父类的方法\n\n#### code\n```python\nclass Parent:\n def inplicit(self):\n print(\"PARENT implicit()\");\n\nclass Child(Parent):\n pass\n\ndad = Parent()\nson = Child()\n\ndad.inplicit()\nson.inplicit()\n```\n#### 结果\n```\nPARENT implicit()\nPARENT implicit()\n```\n### 显示覆盖\n子类方法与父类方法有不同的行为\n\n#### code\n```python\nclass Parent:\n def overrideFunc(self):\n print(\"PARENT overrideFunc()\")\n\nclass Child(Parent):\n def overrideFunc(self):\n print(\"CHILD overrideFunc()\")\n\ndad = Parent()\nson = Child()\n\ndad.overrideFunc()\nson.overrideFunc()\n```\n\n#### 结果\n```\nPARENT overrideFunc()\nCHILD overrideFunc()\n```\n\n### 成员变量的继承\n#### 子类__init__方法空的情况\n##### code\n```python\nclass Parent:\n def __init__(self):\n self.member1 = 10\n self.member2 = \"hello\"\n\nclass Child(Parent):\n def __init__(self):\n pass\n\nson = Child()\nprint(son.member1)\nprint(son.member2)\n```\n\n##### 结果\n```\nTraceback (most recent call last):\n File \"C:/Users/zhangtianyu/PycharmProjects/learn_python_the_hard_way/member_inheritance.py\", line 11, in <module>\n print(son.member1)\nAttributeError: 'Child' object has no attribute 'member1'\n```\n#### 子类__init__方法中初始化父类的情况\n##### code\n```python\nclass Parent:\n def __init__(self):\n self.member1 = 10\n self.member2 = \"hello\"\n\nclass Child(Parent):\n def __init__(self):\n super(Child, self).__init__()\n\nson = Child()\nprint(son.member1)\nprint(son.member2)\n```\n\n##### 结果\n```\n10\nhello\n```\n### super()\n在子类中调用父类的方法,super有两个参数，第一个是子类，第二个参数是self\n\n#### code\n```python\nclass Parent:\n def __init__(self):\n self.member1 = 10\n self.member2 = \"hello\"\n\nclass Child(Parent):\n def __init__(self):\n super(Child, self).__init__()\n```\n### 多重继承\n能避则避吧\n"
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"text": "import time as t\nimport matrix as m\nimport calc as c\nimport element as e\n\n# 输出这个数独\n#for i in sudoku:\n #print(i)\n\nmgr = m.matrix()\ncontroler = c.calc_lvl_four()\ncontroler.init_possible_for_row(mgr)\ncontroler.init_possible_for_column(mgr)\ncontroler.init_possible_for_square(mgr)\n\ne.element.chg_flag = True\nwhile mgr.is_complete() == False:\n # 增加0.5秒的进程挂起，让出cpu，避免本程序运行时其它程序被卡死\n t.sleep(0.5)\n if e.element.chg_flag == True:\n e.element.chg_flag = False\n controler.calc_row_one_less(mgr)\n mgr.matrix_print()\n controler.calc_column_one_less(mgr)\n mgr.matrix_print()\n controler.calc_square_one_less(mgr)\n mgr.matrix_print()\n controler.calc_row_exclude(mgr)\n mgr.matrix_print()\n controler.calc_column_exclude(mgr)\n mgr.matrix_print()\n controler.calc_square_exclude(mgr)\n mgr.matrix_print()\n else:\n # 仍然存在空元素，但是无法确定该填什么的情况\n # 根据行或列的可能值主动选择一个\n controler.choice_and_change(mgr)\n\n\ndel mgr\ndel controler\n"
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"text": "import observe\n\nclass matrix_row(observe.observe):\n def __init__(self):\n observe.observe.__init__(self)\n self.elements = []\n self.zero_cnt = 0\n self.zero_item_list = []\n\n def init_data(self):\n for i in self.elements:\n if i.value == 0:\n self.zero_item_list.append(i)\n self.zero_cnt = len(self.zero_item_list)\n\n def add(self, e):\n self.elements.append(e)\n self.add_listener(e)\n\n def respone(self, obj):\n self.zero_item_list.remove(obj)\n self.zero_cnt = len(self.zero_item_list)\n for i in self.zero_item_list:\n i.possible_minus_for_row(obj.value)\n\n def add_listener(self, obj):\n obj.add(self)\n"
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"text": "# 程序概述\n一个尝试计算数独答案的程序。这个程序并不是采用穷举法野蛮的计算，而是采用数独游戏的玩法规则和技巧，拟人的方式计算数独的答案。\n## 使用说明\n目前支持四阶数独，使用前需要将四阶数独数据放置在data_lvl_four.csv中（替换文件中原有的数独数据），然后在sudoku文件夹下运行sudoku.py"
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"text": "# python3 语法\n## 数据类型\n* string\n\n## 数据结构\n* list\n* dict\n\n## 代码结构\n* 条件判断\n* 循环\n* 函数\n* 模块\n* 包\n* 类\n\n## 内置函数\n"
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"text": "工作、娱乐中随手写的python库，方便使用，节省编码时间\n"
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"text": "project目录下放置着所有正在开发的项目\n# sudoku\n一个尝试计算数独答案的程序。这个程序并不是采用穷举法野蛮的计算，而是采用数独游戏的玩法规则和技巧，拟人的方式计算数独的答案。\n"
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"text": "import random\nimport pics\nimport words\n\ndef get_random_word(word_list):\n index = random.randint(0, len(word_list)-1)\n return word_list[index]\n\ndef display_board(missed_letters, correct_letters, secret_word):\n print(pics.HANGMAN_PICS[len(missed_letters)])\n print('')\n\n print('Missed letters:', end=' ')\n for letter in missed_letters:\n print(letter, end=' ')\n print('')\n\n blanks = '_'*len(secret_word)\n\n for i in range(len(secret_word)):\n if secret_word[i] in correct_letters:\n blanks = blanks[:i] + secret_word[i] + blanks[i+1:]\n\n for letter in blanks:\n print(letter, end=' ')\n print('')\n\ndef get_guess(already_guessed):\n while True:\n print('Guess a letter.')\n guess = input()\n guess = guess.lower()\n if len(guess) != 1:\n print('Please enter a single letter.')\n elif guess in already_guessed:\n print('You have already guessed that letter. Choose again.')\n elif guess not in 'abcdefghijklmnopqrstuvwxyz':\n print('Please enter a LETTER.')\n else:\n return guess\n\ndef play_again():\n print('Do you want to play again? (yes or not)')\n return input().lower().startswith('y')\n\nprint('H A N G M A N')\nmissed_letters = ''\ncorrect_letters = ''\nsecret_word = get_random_word(words.words)\ngame_is_done = False\n\nwhile True:\n display_board(missed_letters, correct_letters, secret_word)\n\n guess = get_guess(missed_letters + correct_letters)\n\n if guess in secret_word:\n correct_letters = correct_letters + guess\n\n found_all_letters = True\n for i in range(len(secret_word)):\n if secret_word[i] not in correct_letters:\n found_all_letters = False\n break\n if found_all_letters:\n print('Yes! The secret word is \"' + secret_word + '\"! You have won!')\n game_is_done = True\n else:\n missed_letters = missed_letters + guess\n\n if len(missed_letters) == len(pics.HANGMAN_PICS) - 1:\n display_board(missed_letters, correct_letters, secret_word)\n print('You have run out of guesses!\\nAfter '+\n str(len(missed_letters)) + ' missed guesses and ' +\n str(len(correct_letters)) + 'correct guesses, the word was \"' +\n secret_word + '\"')\n game_is_done = True\n\n if game_is_done:\n if play_again():\n missed_letters = ''\n correct_letters = ''\n game_is_done = False\n secret_word = get_random_word(words.words)\n else:\n break"
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"text": "# dictionary\n字典是另一种可变容器模型，且可存储任意类型对象。\n字典的每个键值(key=>value)对用冒号(:)分割，每个对之间用逗号(,)分割，整个字典包括在花括号({})中 ,格式如下所示：\n> d = {key1 : value1, key2 : value2 }\n\n键必须是唯一的，但值则不必。\n值可以取任何数据类型，但键必须是不可变的，如字符串，数字或元组。\n一个简单的字典实例：\n> dict = {'Alice': '2341', 'Beth': '9102', 'Cecil': '3258'}\n\n也可如此创建字典：\n> dict1 = { 'abc': 456 } \n> dict2 = { 'abc': 123, 98.6: 37 }\n# 访问字典里的值\n使用索引访问字典里的值\n> #!/usr/bin/python3 \n> dict = {'Name': 'Runoob', 'Age': 7, 'Class': 'First'} \n> print (\"dict['Name']: \", dict['Name']) \n> print (\"dict['Age']: \", dict['Age'])\n# 修改字典里的值\n向字典添加新内容的方法是增加新的键/值对，或通过已有键找到对应的值进行修改：\n> #!/usr/bin/python3 \n> dict = {'Name': 'Runoob', 'Age': 7, 'Class': 'First'} \n> dict['Age'] = 8 # 更新 Age \n> dict['School'] = \"菜鸟教程\" # 添加信息 \n> print (\"dict['Age']: \", dict['Age']) \n> print (\"dict['School']: \", dict['School'])\n# 删除字典里的值\n> #!/usr/bin/python3 \n> dict = {'Name': 'Runoob', 'Age': 7, 'Class': 'First'} \n> del dict['Name'] # 删除键 'Name' \n> dict.clear() # 清空字典 \n> del dict # 删除字典\n# 字典键的特性\n* 不允许同一个键出现两次。创建时如果同一个键被赋值两次，后一个值会被记住 \n* 键必须不可变，所以可以用数字，字符串或元组充当，而用列表就不行\n\n# 字典内置函数\n## 计算字典元素个数\n> dict = {'Name': 'Runoob', 'Age': 7, 'Class': 'First'} \n> len(dict) \n> 结果：3\n## 输出字典，以可打印的字符串表示\n> dict = {'Name': 'Runoob', 'Age': 7, 'Class': 'First'} \n> str(dict) \n> \"{'Name': 'Runoob', 'Class': 'First', 'Age': 7}\"\n## in操作符\n如果键在字典dict里返回true，否则返回false\nPython 字典 in 操作符用于判断键是否存在于字典中，如果键在字典 dict 里返回 true，否则返回 false。\n而 not in 操作符刚好相反，如果键在字典 dict 里返回 false，否则返回 true\n> #!/usr/bin/python3 \ndict = {'Name': 'Runoob', 'Age': 7} \n if 'Age' in dict: \n print(\"键 Age 存在\") \nelse : \n print(\"键 Age 不存在\") \nif 'Sex' in dict: \n print(\"键 Sex 存在\") \nelse : \n print(\"键 Sex 不存在\") \n## not in 操作符\n> if 'Age' not in dict: \n print(\"键 Age 不存在\") \nelse : \n print(\"键 Age 存在\") \n\n# 字典内置方法\n## 删除字典内所有元素\nPython 字典 clear() 函数用于删除字典内所有元素。\n### 语法\ndict.clear()\n### 参数\nNA\n### 返回值\n该函数没有任何返回值。\n### 实例\n> #!/usr/bin/python3 \ndict = {'Name': 'Zara', 'Age': 7} \nprint (\"字典长度 : %d\" % len(dict)) \ndict.clear() \nprint (\"字典删除后长度 : %d\" % len(dict)) \n#### 结果\n> 字典长度 : 2 \n字典删除后长度 : 0\n## 从字典获取值的列表\nPython 字典 values() 方法返回一个迭代器，可以使用 list() 来转换为列表，列表为字典中的所有值。\n### 语法\ndict.values()\n### 参数\nNA\n### 返回值\n返回迭代器。\n### 实例\n> #!/usr/bin/python3 \ndict = {'Sex': 'female', 'Age': 7, 'Name': 'Zara'} \nprint (\"字典所有值为 : \", list(dict.values()))\n#### 结果\n> ['female', 7, 'Zara']\n## 字典浅复制\nPython 字典 copy() 函数返回一个字典的浅复制。\n### 语法\ndict.copy()\n### 参数\nNA\n### 返回值\n返回一个字典的浅复制。\n### 实例\n> #!/usr/bin/python3 \ndict1 = {'Name': 'Runoob', 'Age': 7, 'Class': 'First'} \ndict2 = dict1.copy() \nprint (\"新复制的字典为 : \",dict2)\n#### 结果\n> {'Age': 7, 'Name': 'Runoob', 'Class': 'First'}\n### 直接赋值和 copy 的区别\n> #!/usr/bin/python \n> \\# -*- coding: UTF-8 -* -\ndict1 = {'user':'runoob','num':[1,2,3]} \ndict2 = dict1 # 浅拷贝: 引用对象 \ndict3 = dict1.copy() # 浅拷贝：深拷贝父对象（一级目录），子对象（二级目录）不拷贝，还是引用 \n> \\# 修改 data 数据 \ndict1['user']='root '\ndict1['num'].remove(1) \n> \\# 输出结果 \nprint(dict1) \nprint(dict2) \nprint(dict3) \n#### 结果\n实例中 dict2 其实是 dict1 的引用（别名），所以输出结果都是一致的，dict3 父对象进行了深拷贝，不会随dict1 修改而修改，子对象是浅拷贝所以随 dict1 的修改而修改。\n> {'user': 'root', 'num': [2, 3]} \n{'user': 'root', 'num': [2, 3]} \n{'user': 'runoob', 'num': [2, 3]}\n## 从字典获取索引的列表\nPython3 字典 keys() 方法返回一个可迭代对象，可以使用 list() 来转换为列表。\n### 语法\ndict.keys()\n### 参数\nNA\n### 返回值\n返回一个迭代器。\n### 实例\n> dict = {'Name': 'Runoob', 'Age': 7} \n> dict.keys() \ndict_keys(['Name', 'Age']) \n> list(dict.keys()) # 转换为列表 \n['Name', 'Age']\n## 从字典中删除指定的元素\nPython 字典 pop() 方法删除字典给定键 key 所对应的值，返回值为被删除的值。key值必须给出。 否则，返回default值。\n### 语法\n> pop(key[,default])\n### 参数\n* key 要删除的元素\n* default 如果key不存在，返回default值\n\n### 返回值\n返回被删除的元素的值\n### 实例\n> site= {'name': '菜鸟教程', 'alexa': 10000, 'url': 'www.runoob.com'} \n> pop_obj=site.pop('name') \n> print(pop_obj) \n## 使用元祖序列作为索引创建字典\nPython 字典 fromkeys() 函数用于创建一个新字典，以序列 seq 中元素做字典的键，value 为字典所有键对应的初始值。\n### 语法\n> dict.fromkeys(seq[, value])\n### 参数\n* seq -- 字典键值列表。\n* value -- 可选参数, 设置键序列（seq）对应的值，默认为 None。\n\n### 返回值\n该方法返回一个新字典。\n### 实例\n> #!/usr/bin/python3 \nseq = ('name', 'age', 'sex') \ndict = dict.fromkeys(seq) \nprint (\"新的字典为 : %s\" % str(dict)) \ndict = dict.fromkeys(seq, 10) \nprint (\"新的字典为 : %s\" % str(dict)) \n#### 结果\n> 新的字典为 : {'age': None, 'name': None, 'sex': None} \n新的字典为 : {'age': 10, 'name': 10, 'sex': 10}\n## pop最后一个键值对（删除并返回）\nPython 字典 popitem() 方法返回并删除字典中的最后一对键和值。\n如果字典已经为空，却调用了此方法，就报出KeyError异常。\n### 语法\n> popitem()\n### 参数\n无\n### 返回值\n返回一个键值对(key,value)形式，按照 LIFO（Last In First Out 后进先出法） 顺序规则，即最末尾的键值对。\n### 实例\n> #!/usr/bin/python3 \nsite= {'name': '菜鸟教程', 'alexa': 10000, 'url': 'www.runoob.com'} \npop_obj=site.popitem() \nprint(pop_obj) \nprint(site) \n#### 结果\n> ('url', 'www.runoob.com') \n{'name': '菜鸟教程', 'alexa': 10000}\n"
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"text": "import matrix as d\n\nclass calc_lvl_four:\n def __init_(self):\n pass\n\n def choice_and_change(self, m):\n # 遍历所有行，看看有没有空元素个数为2的\n for line in m.get_all_rows():\n if line.zero_cnt == 2:\n line.zero_item_list[0].chg_val(line.zero_item_list[0].row_possible[0])\n return\n\n # 遍历所有列，看看有没有空元素个数为2的\n for column in m.get_all_column():\n if column.zero_cnt == 2:\n column.zero_item_list[0].chg_val(column.zero_item_list[0].column_possible[0])\n return\n\n def init_possible_for_row(self, m):\n for i in m.matrix_rows:\n if i.zero_cnt != 0:\n # 找出这一行中缺少的值\n less_val_list = [1,2,3,4]\n for e in i.elements:\n if e.value != 0:\n less_val_list.remove(e.value)\n # 为每个空元素初始化可能的值\n for e in i.zero_item_list:\n for val in less_val_list:\n e.possible_add_for_row(val)\n\n def init_possible_for_column(self, m):\n for i in m.matrix_columns:\n if i.zero_cnt != 0:\n # 找出这一行中缺少的值\n less_val_list = [1,2,3,4]\n for e in i.elements:\n if e.value != 0:\n less_val_list.remove(e.value)\n # 为每个空元素初始化可能的值\n for e in i.zero_item_list:\n for val in less_val_list:\n e.possible_add_for_column(val)\n\n def init_possible_for_square(self, m):\n for i in m.matrix_parts:\n if i.zero_cnt != 0:\n # 找出这一行中缺少的值\n less_val_list = [1,2,3,4]\n for e in i.elements:\n if e.value != 0:\n less_val_list.remove(e.value)\n # 为每个空元素初始化可能的值\n for e in i.zero_item_list:\n for val in less_val_list:\n e.possible_add_for_square(val) \n \n def calc_row_one_less(self, m):\n for line in m.get_all_rows():\n # 看看本行是否是只少一个元素\n if line.zero_cnt == 1:\n # 少的那个值就是空元素的possible中仅剩的值\n line.zero_item_list[0].chg_val(line.zero_item_list[0].row_possible[0])\n\n def calc_column_one_less(self, m):\n for line in m.get_all_column():\n # 看看本行是否是只少一个元素\n if line.zero_cnt == 1:\n # 少的那个值就是空元素的possible中仅剩的值\n line.zero_item_list[0].chg_val(line.zero_item_list[0].column_possible[0])\n\n def calc_square_one_less(self, m):\n for line in m.get_all_squares():\n # 看看本行是否是只少一个元素\n if line.zero_cnt == 1:\n # 少的那个值就是空元素的possible中仅剩的值\n line.zero_item_list[0].chg_val(line.zero_item_list[0].square_possible[0]) \n\n def calc_row_exclude(self, m):\n for line in m.get_all_rows():\n # 本行是否缺少多个元素\n if line.zero_cnt > 1:\n for e in line.zero_item_list:\n confirm_list = []\n for v in e.row_possible:\n confirm_list.append(v)\n # 看看这个空元素的可能值是否与所在列和所在块的值冲突，将冲突的值删除\n # 1、获取所在列的已存在的元素值\n column_exsit_list = []\n for i in m.get_one_column(e.x).elements:\n if i.value != 0:\n column_exsit_list.append(i.value)\n # 2、拿掉冲突的值\n for v in confirm_list:\n if v in column_exsit_list:\n confirm_list.remove(v)\n # 3、获取所在块的已存在的元素值\n square_exsit_list = []\n for i in m.get_one_square(e.y, e.x).elements:\n if i.value != 0:\n square_exsit_list.append(i.value)\n # 4、拿掉冲突的值\n for v in confirm_list:\n if v in square_exsit_list:\n confirm_list.remove(v)\n if len(confirm_list) == 1:\n e.chg_val(confirm_list[0])\n\n def calc_column_exclude(self, m):\n for line in m.get_all_column():\n # 本列是否缺少多个元素\n if line.zero_cnt > 1:\n for e in line.zero_item_list:\n confirm_list = []\n for v in e.column_possible:\n confirm_list.append(v)\n # 看看这个空元素的可能值是否与所在行和所在块的值冲突，将冲突的值删除\n # 1、获取所在行的已存在的元素值\n row_exsit_list = []\n for i in m.get_one_row(e.y).elements:\n if i.value != 0:\n row_exsit_list.append(i.value)\n # 2、拿掉冲突的值\n for v in confirm_list:\n if v in row_exsit_list:\n confirm_list.remove(v)\n # 3、获取所在块的已存在的元素值\n square_exsit_list = []\n for i in m.get_one_square(e.y, e.x).elements:\n if i.value != 0:\n square_exsit_list.append(i.value)\n # 4、拿掉冲突的值\n for v in confirm_list:\n if v in square_exsit_list:\n confirm_list.remove(v)\n if len(confirm_list) == 1:\n e.chg_val(confirm_list[0])\n\n def calc_square_exclude(self, m):\n for line in m.get_all_squares():\n # 本块是否缺少多个元素\n if line.zero_cnt > 1:\n for e in line.zero_item_list:\n confirm_list = []\n for v in e.square_possible:\n confirm_list.append(v)\n # 看看这个空元素的可能值是否与所在行和所在列的值冲突，将冲突的值删除\n # 1、获取所在行的已存在的元素值\n row_exsit_list = []\n for i in m.get_one_row(e.y).elements:\n if i.value != 0:\n row_exsit_list.append(i.value)\n # 2、拿掉冲突的值\n for v in confirm_list:\n if v in row_exsit_list:\n confirm_list.remove(v)\n # 3、获取所在列的已存在的元素值\n column_exsit_list = []\n for i in m.get_one_column(e.x).elements:\n if i.value != 0:\n column_exsit_list.append(i.value)\n # 4、拿掉冲突的值\n for v in confirm_list:\n if v in column_exsit_list:\n confirm_list.remove(v)\n"
}
] | 16 |
arp19690/Facebook-Auto-Post
|
https://github.com/arp19690/Facebook-Auto-Post
|
72784aa3f259ea7df89d85105c477d520e2a6dcb
|
9c15753a1a49661c645b188b4cd6eaee1f7a37de
|
d6bdcce086f1b24910708d9a6171d45636519ccc
|
refs/heads/master
| 2021-01-12T05:19:00.521281 | 2017-05-16T13:48:50 | 2017-05-16T13:48:50 | 77,909,360 | 2 | 1 | null | 2017-01-03T11:03:45 | 2017-01-03T11:08:51 | 2017-01-04T05:18:18 |
Python
|
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"text": "import os\n\n\ndef mac_notify(title, message):\n os.system('terminal-notifier -title \"' + str(title) + '\" -message \"' + str(\n message) + '\"')\n os.system(\"say Sir, \" + str(message))\n return True\n\n\ndef dictfetchall(cursor):\n \"Return all rows from a cursor as a dict\"\n columns = [col[0] for col in cursor.description]\n return [\n dict(zip(columns, row))\n for row in cursor.fetchall()\n ]\n"
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"text": "#!/usr/bin/python\n\nfrom datetime import datetime, timedelta\nimport sys\n\nimport os\nimport facebookFunctions as FFS\nimport config\nfrom helpers import mac_notify\n\nreload(sys)\nsys.setdefaultencoding('utf-8')\nos.environ['TZ'] = 'Europe/London'\n\n\ndef get_start_timestamp(filename=config.LAST_RUN_TIME_FILENAME, hours_input=config.DEFAULT_TIMEDELTA_HOURS):\n some_timestamp = datetime.now() - timedelta(hours=int(hours_input))\n last_timestamp = str(some_timestamp.strftime('%Y-%m-%dT%H:%M'))\n if (os.path.isfile(filename)):\n tmp_time = str(open(filename, \"r\").read())\n if tmp_time != \"None\":\n last_timestamp = tmp_time\n\n return last_timestamp\n\n\ndef update_last_run_time(last_timestamp=None, filename=config.LAST_RUN_TIME_FILENAME):\n if (os.path.isfile(filename)):\n os.remove(filename)\n\n if last_timestamp is None:\n last_timestamp = str(datetime.now().strftime('%Y-%m-%dT%H:%M'))\n\n with open(filename, \"w\") as f:\n f.write(str(last_timestamp))\n f.close()\n return True\n\n\ndef start_posting(since_timestamp, data):\n api_url = FFS.create_feed_url(data[\"from_profile_id\"], since_timestamp, data[\"access_token\"])\n new_posts = FFS.fetch_data(api_url, [])\n latest_timestamp = since_timestamp\n if len(new_posts) > 0:\n latest_timestamp = new_posts[0][\"created_time\"][:16]\n # Reverse sorting the dictionary, since we want to post the last photo first so that it looks in an incremental order\n new_posts = sorted(new_posts, reverse=True)\n print(\"Now we will start posting \" + str(len(new_posts)) + \" posts\")\n for json_data in new_posts:\n if \"message\" not in json_data:\n message = data[\"default_message\"]\n else:\n message = json_data[\"message\"].decode('ascii', 'ignore')\n json_data.update({\"message\": message})\n\n try:\n # Posting the article, only if the post was posted from that page and not other users\n if str(json_data[\"from\"][\"id\"]) == str(data[\"from_profile_id\"]):\n if \"source\" in json_data:\n api_status, api_message = FFS.post_video_on_fb(data[\"profile_id\"], data[\"access_token\"],\n message,\n json_data[\"link\"])\n elif \"full_picture\" in json_data:\n api_status, api_message = FFS.post_photo_on_fb(data[\"access_token\"], json_data)\n else:\n api_status, api_message = FFS.post_message_on_fb(data[\"profile_id\"], data[\"access_token\"],\n json_data)\n\n if api_status:\n print(\"Message successfully posted on \" + data[\"name\"] + \"'s Timeline\")\n else:\n raise Exception(api_message)\n except Exception as e:\n print(\"An error occurred: \" + str(e))\n mac_notify(data[\"name\"], e)\n pass\n\n return latest_timestamp\n\n\nstart_timestamp = get_start_timestamp()\n\nmac_notify(\"Facebook Auto Post\", \"I will now be posting on Facebook on your behalf\")\nfor tmpdata in config.ACCESS_TOKENS_LIST:\n last_timestamp = start_posting(start_timestamp, tmpdata)\n\nupdate_last_run_time(last_timestamp)\nmac_notify(\"Facebook Auto Post\", \"I have finished publishing posts on Facebook\")\n"
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"text": "EXCLUDE_PROFILE_IDS = [\"280228608801032\"]\n\nMESSAGE_TEXT_LIST = [\n \"Looking for that perfect gift for your girl? What's better than a Victoria's Secret present. Make her feel special, get them now\",\n \"Gift your girl from an exclusive range of Victoria Secret's products. Get them now\",\n \"Shop from Victoria Secret's line of products and gift here a unique gift this year.\",\n \"Victoria Secret's cosmetics. Shop now and get amazing offers.\"\n]\n\nCHILD_ATTACHMENT_LIST = [\n {\n \"name\": \"Victoria's Secret PURE SEDUCTION Fragrance Mist \",\n \"description\": \"Pure Seduction Refreshing Body Mist\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/51uUoJcZyML._SX522_.jpg\",\n \"link\": \"http://amzn.to/2kBUcPE\",\n },\n {\n \"name\": \"Victoria's Secret Fantasies Aqua Kiss\",\n \"description\": \"Play for a custom scent, with rain-kissed freesia and daisy\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/51t%2B0rlleKL._SY679_.jpg\",\n \"link\": \"http://amzn.to/2kfhGJq\",\n },\n {\n \"name\": \"Victoria's Secret Noir Tease Glitter Train Case\",\n \"description\": \"With a mirror inside lid\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/51VGuJZPF0L.jpg\",\n \"link\": \"http://amzn.to/2jQYTk2\",\n },\n {\n \"name\": \"Victoria's Secret Love Spell Fragrance\",\n \"description\": \"Seductive Amber Fragrance Mist\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/81wP1miPxzL._SX522_.jpg\",\n \"link\": \"http://amzn.to/2lbmdLc\",\n },\n {\n \"name\": \"Victoria's Secret Unapologetic Fragrance Mist\",\n \"description\": \"Fresh maple water and warm vanilla\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/51bgNfeRFPL._SY679_.jpg\",\n \"link\": \"http://amzn.to/2kfk3M4\",\n },\n {\n \"name\": \"Victoria Secret Pure Daydream Body Mist\",\n \"description\": \"Pure daydream mist\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/51hkTTMe1nL._SX522_.jpg\",\n \"link\": \"http://amzn.to/2lbL6qg\",\n },\n {\n \"name\": \"Victoria's Secret Romantic Fragrance Lotion\",\n \"description\": \"Pink Petals & Solar Musk\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/316TazZ80iL.jpg\",\n \"link\": \"http://amzn.to/2lbEU1n\",\n },\n {\n \"name\": \"Victoria's Secret Pure Seduction Lotion\",\n \"description\": \"Pamper your skin to give it a supple feel\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/71yLSWJl2cL._SX522_.jpg\",\n \"link\": \"http://amzn.to/2kfydNt\",\n },\n {\n \"name\": \"Victoria's Secret Endless Love Fragrance Lotion\",\n \"description\": \"Nourishes skin with a light mix of Apple blossom\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/51SNq6UT68L._SY679_.jpg\",\n \"link\": \"http://amzn.to/2kC1vH2\",\n },\n]\n"
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"text": "from config import AMAZON_AFFILIATE_DEALS_ACCESS_TOKENS_LIST\nfrom config import AMAZON_AFFILIATE_URL\nfrom helpers import mac_notify\nfrom amazon_offers import helpers as AMZ_helpers\n\nfrom amazon_offers import general_links as GL\nfrom amazon_offers import victoria_secret_cosmetics as VCS\nfrom amazon_offers.threadaffiliates import functions as TAFunctions\nimport TwitterFunctions\n\n# Posting Amazon Affiliate links - ThreadAffiliate Website Links\nAffiliateProducts = TAFunctions.fetch_products()\n\nprint(\"Current Task: Posting ThreadAffiliate Website Links on Facebook\")\nAMZ_helpers.post_on_fb(AMAZON_AFFILIATE_DEALS_ACCESS_TOKENS_LIST,\n AffiliateProducts,\n TAFunctions.get_post_message_list(),\n TAFunctions.WEBSITE_BASE_URL)\n\nprint(\"Current Task: Posting ThreadAffiliate Website Links on Twitter\")\nTwitterFunctions.post_multiple_tweets(AffiliateProducts)\n\n\n# Posting Amazon Affiliate links - General Categories\nprint(\"Current Task: Posting General Amazon Links\")\nAMZ_helpers.post_on_fb(AMAZON_AFFILIATE_DEALS_ACCESS_TOKENS_LIST,\n GL.CHILD_ATTACHMENT_LIST,\n GL.MESSAGE_TEXT_LIST,\n AMAZON_AFFILIATE_URL)\n\n# Posting Amazon Affiliate links - Victoria Secret Cosmetics\nprint(\"\\nCurrent Task: Victoria Secret Cosmetics Links\")\nAMZ_helpers.post_on_fb(AMAZON_AFFILIATE_DEALS_ACCESS_TOKENS_LIST,\n VCS.CHILD_ATTACHMENT_LIST,\n VCS.MESSAGE_TEXT_LIST,\n AMAZON_AFFILIATE_URL,\n VCS.EXCLUDE_PROFILE_IDS)\n\nmac_notify(\"Affiliate links\",\n \"All promotional links have been posted successfully\")\n"
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"text": "from wordpress_xmlrpc import Client\nfrom wordpress_xmlrpc.methods.posts import GetPosts\nfrom facebookFunctions import post_message_on_fb\nfrom helpers import mac_notify\nimport config\n\n\ndef get_attachments_dict(post_data):\n attachment_dict = {\n \"name\": post_data.title,\n \"caption\": post_data.title,\n \"description\": post_data.excerpt,\n \"picture\": post_data.thumbnail[\"link\"],\n \"link\": post_data.link\n }\n return attachment_dict\n\n\ndef fetch_posts(wp_client, post_status=\"publish\", post_limit=10):\n output_list = []\n posts = wp_client.call(GetPosts({'post_status': post_status, 'number': post_limit}))\n if len(posts) > 0:\n for post_data in posts:\n try:\n data_dict = get_attachments_dict(post_data)\n output_list.append(data_dict)\n except Exception as e:\n print(\"An error occurred: \" + str(e))\n pass\n\n return output_list\n\n\nwp = Client(config.WP_WEBSITE + \"/xmlrpc.php\", config.WP_USERNAME, config.WP_PASSWORD)\nformatted_posts = fetch_posts(wp)\nif len(formatted_posts) > 0 and len(config.WP_ACCESS_TOKENS_LIST) > 0:\n for data in config.WP_ACCESS_TOKENS_LIST:\n for post_data in formatted_posts:\n fb_post_message = post_data[\"name\"]\n if \"appended_message\" in data:\n if data[\"appended_message\"] is not None:\n fb_post_message += \"\\n\\n\" + data[\"appended_message\"]\n\n try:\n api_status, api_message = post_message_on_fb(data[\"profile_id\"], data[\"access_token\"],\n {\"message\": fb_post_message}, post_data)\n\n if api_status:\n print(\"Message successfully posted on \" + data[\"name\"] + \"'s Timeline\")\n else:\n raise Exception, api_message\n except Exception as e:\n print(\"An error occurred: \" + str(e))\n mac_notify(data[\"name\"], e)\n pass\n"
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"text": "import random\nimport json\n\nimport requests\n\n\ndef get_random_but_unique_num_in_list(attachment_list, limit=5):\n output_list = []\n for i in xrange(0, len(attachment_list)):\n random_int = random.randint(0, len(attachment_list) - 1)\n if len(output_list) < limit:\n if random_int not in output_list:\n output_list.append(random_int)\n else:\n output_list = get_random_but_unique_num_in_list(\n attachment_list)\n\n return output_list\n\n\ndef get_child_attachments_list(attachment_list, limit=5):\n output_list = []\n random_num_list = get_random_but_unique_num_in_list(attachment_list, limit)\n for i in random_num_list:\n tmp_dict = {\n \"link\": attachment_list[i][\"link\"],\n \"picture\": attachment_list[i][\"picture\"],\n \"name\": attachment_list[i][\"name\"].encode(\"utf-8\"),\n \"description\": attachment_list[i][\"description\"].encode(\"utf-8\")\n }\n output_list.append(tmp_dict)\n return output_list\n\n\ndef post_on_fb(access_tokens_list, child_attachment_list, message_list,\n post_url_link, exclude_profile_ids=[]):\n for AAD in access_tokens_list:\n if AAD[\"profile_id\"] not in exclude_profile_ids:\n api_url = \"https://graph.facebook.com/v2.8/\" + AAD[\n \"profile_id\"] + \"/feed?access_token=\" + AAD[\"access_token\"]\n\n for data in child_attachment_list:\n post_data_dict = {\n \"name\": data[\"name\"].encode(\"utf-8\"),\n \"link\": data[\n \"threadcrafts_buy_link\"] if \"threadcrafts_buy_link\" in data else\n data[\"link\"],\n \"picture\": data[\"picture\"],\n \"description\": data[\"description\"].encode(\"utf-8\"),\n \"message\": data[\"name\"].encode(\n \"utf-8\") + \"\\n\\n\" + random.choice(message_list),\n }\n\n status = requests.post(api_url, post_data_dict)\n if status.status_code == 200:\n print(\"Affiliate links successfully posted on \" + str(\n AAD[\"name\"]) + \"'s timeline\")\n else:\n print(\"An error occurred\")\n print(status.text)\n"
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"text": "from TwitterAPI import TwitterAPI\nfrom config import TWITTER_CONSUMER_KEY, TWITTER_CONSUMER_SECRET, \\\n TWITTER_ACCESS_TOKEN_KEY, TWITTER_ACCESS_TOKEN_SECRET\nfrom facebookFunctions import download_photo, remove_photo, BASE_DIR\n\n\ndef post_status_on_twitter(tweet_message, media_list=list()):\n api = TwitterAPI(TWITTER_CONSUMER_KEY, TWITTER_CONSUMER_SECRET,\n TWITTER_ACCESS_TOKEN_KEY, TWITTER_ACCESS_TOKEN_SECRET)\n\n media_dict = None\n if len(media_list) > 0:\n media_dict = dict()\n for tmp_media in media_list:\n file = open(tmp_media, 'rb')\n media_data = file.read()\n media_dict.update({'media[]': media_data})\n\n r = api.request('statuses/update_with_media', {'status': tweet_message},\n media_dict)\n return r\n\n\ndef post_multiple_tweets(tweets_list):\n for tweet_dict in tweets_list:\n tweet_message = tweet_dict[\"name\"] + \" @ Rs. \" + tweet_dict[\n \"price\"] + \". Visit \" + tweet_dict[\"threadcrafts_buy_link\"]\n img_destination = BASE_DIR + 'tmpdata/' + tweet_dict[\"name\"] + \".jpg\"\n download_photo(tweet_dict[\"picture\"], img_destination)\n post_status_on_twitter(tweet_message, media_list=[img_destination])\n remove_photo(img_destination)\n\n return True\n"
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"text": "import config\nimport requests\nimport json\n\n\ndef post_now():\n url = \"https://api.bufferapp.com/1/updates/create.json\"\n data_dict = {\n \"access_token\": config.BUFFER_ACCESS_TOKEN,\n \"text\": \"Sample test\",\n \"shorten\": True,\n \"profile_ids\": [\"57b2dc47ec2002a372d671b3\"],\n # \"profile_ids\": [\"5799071fe7e3d76978261b12\"],\n \"media\": {\n \"thumbnail\": \"http://www.technicaltextile.net/businessleads/ProductImages/6017_15_191.jpg\",\n \"picture\": \"http://www.technicaltextile.net/businessleads/ProductImages/6017_15_191.jpg\",\n \"photo\": \"http://www.technicaltextile.net/businessleads/ProductImages/6017_15_191.jpg\",\n \"link\": \"https://www.threadcrafts.in\",\n \"description\": \"test img desc\"\n }\n }\n\n headers = {\"Content-Type\": \"application/x-www-form-urlencoded;\"}\n tmp = requests.post(url, data=data_dict, headers=headers)\n print(tmp.status_code)\n print(tmp.text)\n\n\npost_now()\n"
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"text": "#!/bin/bash\n\nif [ $1 = \"fbautopost\" ];then\n echo \"Activating VirtualENV\"\n . /Applications/XAMPP/htdocs/work/svn/Facebook-Auto-Post/venv/bin/activate\n\n echo \"Running the script now\"\n python /Applications/XAMPP/htdocs/work/svn/Facebook-Auto-Post/run.py\n\n echo \"Deactivating VirtualENV\"\n deactivate\nelif [ $1 = \"amazonaffiliates\" ];then\n echo \"Activating VirtualENV\"\n . /Applications/XAMPP/htdocs/work/svn/Facebook-Auto-Post/venv/bin/activate\n\n echo \"Running the script now\"\n python /Applications/XAMPP/htdocs/work/svn/Facebook-Auto-Post/AmazonAffiliateFunctions.py\n\n echo \"Deactivating VirtualENV\"\n deactivate\nfi\n"
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"text": "MESSAGE_TEXT_LIST = [\n \"Huge offers on Amazon.\\nGreat quality products at affordable prices.\\nGet now\",\n \"The Great Indian Sale\\nAvail surprisingly big discounts on branded products.\\n\\nGet additional offers and discounts when you pay using Amazon Pay Balance.\"\n]\n\nCHILD_ATTACHMENT_LIST = [\n {\n \"name\": \"Digital Cameras, Lenses\",\n \"description\": \"Special offers on digital cameras and lenses. Get now!\",\n \"picture\": \"https://www.bhphotovideo.com/images/images2000x2000/canon_0591c003_eos_rebel_t6i_dslr_1116101.jpg\",\n \"link\": \"http://amzn.to/2jRmt4o\",\n },\n {\n \"name\": \"LED and LCD TVs\",\n \"description\": \"Get upto 50% off on Televisions. Limited Stock.\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/71tgJV3LpML._SL1000_.jpg\",\n \"link\": \"http://amzn.to/2kiluLg\",\n },\n {\n \"name\": \"iPhones at lowest prices. Big savings on Apple products\",\n \"description\": \"iPhones starting at Rs. 10,000. Amazing discounts on exchange. Get Now!\",\n \"picture\": \"http://store.storeimages.cdn-apple.com/4974/as-images.apple.com/is/image/AppleInc/aos/published/images/M/MY/MMY32/MMY32_AV1_SILVER?wid=1000&hei=1000&fmt=jpeg&qlt=95&op_sharpen=0&resMode=bicub&op_usm=0.5,0.5,0,0&iccEmbed=0&layer=comp&.v=1472245951991\",\n \"link\": \"http://amzn.to/2kh8Hod\",\n },\n {\n \"name\": \"Fashion Sale. Big Brands starting @ 499\",\n \"description\": \"Enjoy big cashbacks and offers on Clothing and accessories. Amazon Fashin Sale is here.\",\n \"picture\": \"http://www.thegogle.com/wp-content/uploads/2016/11/fashion13.jpg\",\n \"link\": \"http://amzn.to/2jfp7Qf\",\n },\n {\n \"name\": \"Shoes for Women. Upto 70% off\",\n \"description\": \"Big brands on sale. Offer only for a limited period. Shop now.\",\n \"picture\": \"http://cdn2.secure-e.com/bridalshoes.com.au/prodimg/2015/06/1054_harper-ruby-new_2048_1363.jpg\",\n \"link\": \"http://amzn.to/2kizPHm\",\n },\n {\n \"name\": \"Microwave Ovens starting just @ 4,090\",\n \"description\": \"Buy microwave ovens at super cheap prices. Buy now.\",\n \"picture\": \"http://www.lg.com/in/images/microwave-ovens/md05265523/gallery/Large-940x620_0000146.jpg\",\n \"link\": \"http://amzn.to/2kjoFCv\",\n },\n {\n \"name\": \"Fashion Jewellery & accessories for every occasion\",\n \"description\": \"Get fashionable. Get trendy. Show now.\",\n \"picture\": \"https://picscelb.files.wordpress.com/2014/07/western-wedding-bridal-new-fashion-for-girls-women-by-royal-jewelley-1.jpg\",\n \"link\": \"http://amzn.to/2jIqpln\",\n },\n {\n \"name\": \"Buy Furniture @ Amazon\",\n \"description\": \"Discover the Latest Furniture Designs Online\",\n \"picture\": \"http://www.homezguru.com/wp-content/uploads/2015/05/keens-furniture-beersbridge.jpg\",\n \"link\": \"http://amzn.to/2kjfuSv\",\n },\n\n {\n \"name\": \"Watches\",\n \"description\": \"Special offers on Watches\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/61iSZLm68cL._UL1500_.jpg\",\n \"link\": \"http://amzn.to/2kLDKwf\",\n },\n {\n \"name\": \"Jewellery\",\n \"description\": \"Special offers on Jewellery\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/61VHUeQWymL._UL1500_.jpg\",\n \"link\": \"http://amzn.to/2kLytF9\",\n },\n {\n \"name\": \"Beauty and Grooming\",\n \"description\": \"Special offers on Beauty and Grooming\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/61VHUeQWymL._UL1500_.jpg\",\n \"link\": \"http://amzn.to/2k173v8\",\n },\n {\n \"name\": \"Bags & Sunglasses\",\n \"description\": \"Special offers on Bags & Sunglasses\",\n \"picture\": \"https://mocnikova.com/shop/media/billboard/image/i/n/instagramfrontpagesquare03.jpg\",\n \"link\": \"http://amzn.to/2kLDe1i\",\n },\n {\n \"name\": \"Home Decor\",\n \"description\": \"Special offers on Home Decor\",\n \"picture\": \"http://www.realdealstubblefield.com/wp-content/uploads/2017/01/home-decor-gold-home-dcor-target.jpg\",\n \"link\": \"http://amzn.to/2kLFvta\",\n },\n {\n \"name\": \"Apparel\",\n \"description\": \"Special offers on Apparel\",\n \"picture\": \"https://s-media-cache-ak0.pinimg.com/originals/0f/98/73/0f98732caa99f7d5a70824c98eb37e08.jpg\",\n \"link\": \"http://amzn.to/2k1cTwz\",\n },\n\n {\n \"name\": \"Victoria's Secret PURE SEDUCTION Fragrance Mist \",\n \"description\": \"Pure Seduction Refreshing Body Mist\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/51uUoJcZyML._SX522_.jpg\",\n \"link\": \"http://amzn.to/2kBUcPE\",\n },\n {\n \"name\": \"Victoria's Secret Fantasies Aqua Kiss\",\n \"description\": \"Play for a custom scent, with rain-kissed freesia and daisy\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/51t%2B0rlleKL._SY679_.jpg\",\n \"link\": \"http://amzn.to/2kfhGJq\",\n },\n {\n \"name\": \"Victoria's Secret Noir Tease Glitter Train Case\",\n \"description\": \"With a mirror inside lid\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/51VGuJZPF0L.jpg\",\n \"link\": \"http://amzn.to/2jQYTk2\",\n },\n {\n \"name\": \"Victoria's Secret Love Spell Fragrance\",\n \"description\": \"Seductive Amber Fragrance Mist\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/81wP1miPxzL._SX522_.jpg\",\n \"link\": \"http://amzn.to/2lbmdLc\",\n },\n {\n \"name\": \"Victoria's Secret Unapologetic Fragrance Mist\",\n \"description\": \"Fresh maple water and warm vanilla\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/51bgNfeRFPL._SY679_.jpg\",\n \"link\": \"http://amzn.to/2kfk3M4\",\n },\n {\n \"name\": \"Victoria Secret Pure Daydream Body Mist\",\n \"description\": \"Pure daydream mist\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/51hkTTMe1nL._SX522_.jpg\",\n \"link\": \"http://amzn.to/2lbL6qg\",\n },\n {\n \"name\": \"Victoria's Secret Romantic Fragrance Lotion\",\n \"description\": \"Pink Petals & Solar Musk\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/316TazZ80iL.jpg\",\n \"link\": \"http://amzn.to/2lbEU1n\",\n },\n {\n \"name\": \"Victoria's Secret Pure Seduction Lotion\",\n \"description\": \"Pamper your skin to give it a supple feel\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/71yLSWJl2cL._SX522_.jpg\",\n \"link\": \"http://amzn.to/2kfydNt\",\n },\n {\n \"name\": \"Victoria's Secret Endless Love Fragrance Lotion\",\n \"description\": \"Nourishes skin with a light mix of Apple blossom\",\n \"picture\": \"http://ecx.images-amazon.com/images/I/51SNq6UT68L._SY679_.jpg\",\n \"link\": \"http://amzn.to/2kC1vH2\",\n },\n]\n"
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"text": "import sys\n\nfrom config import THREADAFFILIATES_FB_DETAILS\nimport helpers\nimport pymysql\n\nreload(sys)\nsys.setdefaultencoding('utf8')\n\nWEBSITE_BASE_URL = \"https://www.threadcrafts.in\"\n\n\ndef get_db_connection():\n db = pymysql.connect(THREADAFFILIATES_FB_DETAILS[\"host\"],\n THREADAFFILIATES_FB_DETAILS[\"user\"],\n THREADAFFILIATES_FB_DETAILS[\"pass\"],\n THREADAFFILIATES_FB_DETAILS[\"name\"])\n return db\n\n\ndef execute_query(sql, db=get_db_connection()):\n cursor = db.cursor()\n cursor.execute(sql)\n results = helpers.dictfetchall(cursor)\n cursor.close()\n return results\n\n\ndef get_post_message_list():\n message_list = [\n \"Threadcrafts Store. Products exclusively handpicked for you.\",\n \"Exclusive range of products available only at Threadcrafts Store\",\n \"Amazing offers only on Threadcrafts Store\",\n \"Grab 'em before they are gone. Shop now.\",\n \"Great Indian Sale !!!\",\n ]\n return message_list\n\n\ndef fetch_products(limit=\"0,20\"):\n # Fetching a random category from products table\n product_data_sql = \"SELECT product_category_id FROM products WHERE product_status = 1 ORDER BY rand() LIMIT 0,1\"\n product_data_result = execute_query(product_data_sql)\n category_id = product_data_result[0][\"product_category_id\"]\n\n # Now fetching realted products for that particular category\n sql = \"SELECT * FROM products WHERE product_status = 1 AND product_category_id = \" + str(\n category_id) + \" ORDER BY rand() LIMIT \" + str(limit)\n data = execute_query(sql)\n output_list = list()\n if len(data) > 0:\n for tmpdata in data:\n post_data_dict = {\n \"name\": str(tmpdata[\"product_title\"].decode('string_escape')),\n \"price\": str(int(tmpdata[\"product_price_min\"])),\n \"description\": \"Starts at Rs. \" + str(\n int(tmpdata[\"product_price_min\"])),\n \"picture\": tmpdata[\"product_image_url\"],\n \"link\": tmpdata[\"product_url_long\"],\n \"threadcrafts_buy_link\": str(\n WEBSITE_BASE_URL + \"/buy-now/\" + tmpdata[\n \"product_url_key\"]),\n }\n output_list.append(post_data_dict)\n return output_list\n"
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"text": "#!/usr/bin/python\nimport urllib\n\nimport facebook\nimport warnings\nimport requests\nimport os\nimport re\nfrom config import BASE_DIR, FILTER_KEYWORDS, AMAZON_AFFILIATE_URL\nfrom pyshorteners import Shortener\nfrom config import GOOGLE_URL_SHORTENER_API_KEY\n\n\n# Hide deprecation warnings. The facebook module isn't that up-to-date (facebook.GraphAPIError).\nwarnings.filterwarnings('ignore', category=DeprecationWarning)\n\n\ndef get_app_access_token(fb_app_id, fb_app_secret):\n return facebook.get_app_access_token(fb_app_id, fb_app_secret)\n\n\ndef get_long_lived_access_token(fb_app_id, fb_app_secret, access_token):\n api_url = \"https://graph.facebook.com/v2.2/oauth/access_token?grant_type=fb_exchange_token&client_id=\" + str(\n fb_app_id) + \"&client_secret=\" + str(\n fb_app_secret) + \"&fb_exchange_token=\" + str(access_token)\n response = requests.get(api_url)\n if response.status_code == 200:\n output = response.text.split(\"=\")\n return output[1]\n else:\n return False\n\n\ndef post_message_on_fb(fb_profile_id, oauth_access_token, json_data,\n attachments=None):\n facebook_graph = facebook.GraphAPI(oauth_access_token)\n\n message = filter_text(json_data[\"message\"])\n if attachments is None:\n attachments_dict = get_attachments_dict(json_data, oauth_access_token)\n else:\n attachments_dict = attachments\n\n # Try to post something on the wall.\n try:\n fb_response = facebook_graph.put_wall_post(\n message=filter_text(message),\n attachment=attachments_dict,\n profile_id=fb_profile_id)\n return True, fb_response\n except facebook.GraphAPIError as e:\n return False, 'Something went wrong: ' + str(e.message)\n\n\ndef post_photo_on_fb(oauth_access_token, json_data):\n image_file_path = BASE_DIR + 'tmpdata/' + str(json_data[\"id\"]) + \".jpg\"\n download_photo(json_data[\"full_picture\"], image_file_path)\n fb_response = upload_photo(image_file_path, oauth_access_token,\n filter_text(json_data[\"message\"]))\n remove_photo(image_file_path)\n return True, fb_response\n\n\ndef post_video_on_fb(profile_id, oauth_access_token, message, video_link):\n facebook_graph = facebook.GraphAPI(oauth_access_token)\n fb_response = facebook_graph.put_object(profile_id, \"feed\",\n message=filter_text(message),\n link=video_link)\n return True, fb_response\n\n\ndef upload_photo(image_file_path, oauth_access_token, message=\"\"):\n facebook_graph = facebook.GraphAPI(oauth_access_token)\n fb_response = facebook_graph.put_photo(image=open(image_file_path, 'rb'),\n message=filter_text(message))\n return fb_response\n\n\ndef download_photo(source, destination):\n return urllib.urlretrieve(source, destination)\n\n\ndef remove_photo(destination):\n return os.remove(destination)\n\n\ndef find_and_replace_url(message, replace_with_str=AMAZON_AFFILIATE_URL):\n new_str = re.sub(r'\\w+:\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+)*(?:(?:\\/[^\\s/]*))*',\n replace_with_str, message)\n return new_str\n\n\ndef filter_text(message, replace_with=AMAZON_AFFILIATE_URL):\n message = find_and_replace_url(message, replace_with)\n for keyword in FILTER_KEYWORDS:\n message = message.replace(str(keyword), replace_with)\n return message\n\n\ndef get_image_url_on_id(photo_id, oauth_access_token):\n api_url = \"https://graph.facebook.com/\" + str(\n photo_id) + \"?fields=images&access_token=\" + oauth_access_token\n response = requests.get(api_url)\n if response.status_code == 200:\n return response.json()[\"images\"][0][\"source\"]\n else:\n return False\n\n\ndef create_feed_url(fb_page_id, since_timestamp, oauth_access_token,\n get_fields=\"id,link,picture,source,message,created_time,full_picture,description,from\",\n limit=None, data_format=\"json\"):\n api_url = \"https://graph.facebook.com/v2.8/\" + fb_page_id + \"/feed?fields=\" + get_fields + \"&since=\" + since_timestamp\n if limit is not None:\n api_url += \"&limit=\" + limit\n api_url += \"&format=\" + data_format + \"&access_token=\" + oauth_access_token\n return api_url\n\n\ndef fetch_data(api_url, data_list=[]):\n try:\n response = requests.get(api_url)\n data = response.json()\n data_list += data[\"data\"]\n print(str(len(data_list)) + \" results found\")\n if \"error\" in data:\n raise Exception(data[\"error\"][\"message\"])\n else:\n if 'paging' in data:\n if 'next' in data['paging']:\n next_url = data['paging']['next']\n\n # Running this function again\n data_list = fetch_data(next_url, data_list)\n\n return data_list\n except Exception as e:\n print(\"An error occurred: \" + str(e))\n return []\n\n\ndef get_attachments_dict(json_data, oauth_access_token):\n attachment_dict = {}\n\n if \"full_picture\" in json_data:\n image_file_path = BASE_DIR + 'tmpdata/' + str(json_data[\"id\"]) + \".jpg\"\n download_photo(json_data[\"full_picture\"], image_file_path)\n upload_response = upload_photo(image_file_path, oauth_access_token)\n remove_photo(image_file_path)\n\n fb_image_url = get_image_url_on_id(upload_response[\"id\"],\n oauth_access_token)\n attachment_dict[\"picture\"] = fb_image_url\n\n if \"source\" in json_data:\n attachment_dict[\"link\"] = json_data[\"link\"]\n else:\n attachment_dict[\"link\"] = fb_image_url\n\n if \"message\" in json_data:\n attachment_dict[\"caption\"] = filter_text(json_data[\"message\"])\n attachment_dict[\"name\"] = filter_text(json_data[\"message\"])\n elif \"story\" in json_data:\n attachment_dict[\"caption\"] = filter_text(json_data[\"story\"])\n attachment_dict[\"name\"] = filter_text(json_data[\"story\"][:255])\n\n if \"description\" in json_data:\n attachment_dict[\"description\"] = filter_text(\n json_data[\"description\"])\n\n return attachment_dict\n\n\ndef shorten_url(url):\n shortener = Shortener('Google', api_key=GOOGLE_URL_SHORTENER_API_KEY)\n short_url = shortener.short(url)\n return short_url\n"
}
] | 14 |
peppelorum/django-articles
|
https://github.com/peppelorum/django-articles
|
4e8d37b298ef80b91ea4cd4aac1fc619784a09d6
|
4d040f4e0c508bf911d5fc433b0b010359ca98e6
|
c136161443b349d7bcfa18c68729c7781bfe9417
|
refs/heads/master
| 2021-01-17T22:25:58.998805 | 2012-05-31T10:23:49 | 2012-05-31T10:23:49 | 1,555,173 | 0 | 0 | null | null | null | null | null |
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"text": "from django.db.models import signals\nfrom models import Article, Tag\n#\n#def apply_new_tag(sender, instance, created, **kwargs):\n# \"\"\"\n# Applies new tags to existing articles that are marked for auto-tagging\n# \"\"\"\n#\n# for article in Article.objects.filter(auto_tag=True):\n# article.do_auto_tag()\n#\n#signals.post_save.connect(apply_new_tag, sender=Tag)\n"
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"text": "from django.conf.urls.defaults import *\nfrom django.contrib.syndication.views import feed\nfrom django.views.generic import date_based, list_detail\nfrom articles import views\nfrom articles.feeds import TagFeed, LatestEntries\n\nfeeds = {\n 'latest': LatestEntries,\n 'tags': TagFeed\n}\nfeed_dict = {'feed_dict': feeds}\n\nurlpatterns = patterns('',\n (r'^(?P<year>\\d{4})/(?P<month>.{3})/(?P<day>\\d{1,2})/(?P<slug>.*)/$', views.redirect_to_article),\n url(r'^(?P<year>\\d{4})/(?P<month>\\d{1,2})/page/(?P<page>\\d+)/$', views.display_blog_page, name='articles_in_month_page'),\n url(r'^(?P<year>\\d{4})/(?P<month>\\d{1,2})/$', views.display_blog_page, name='articles_in_month'),\n)\n\nurlpatterns += patterns('',\n url(r'^$', views.display_blog_page, name='articles_archive'),\n url(r'^page/(?P<page>\\d+)/$', views.display_blog_page, name='articles_archive_page'),\n\n url(r'^tag/(?P<tag>.*)/page/(?P<page>\\d+)/$', views.display_blog_page, name='articles_display_tag_page'),\n url(r'^tag/(?P<tag>.*)/$', views.display_blog_page, name='articles_display_tag'),\n \n url(r'^author/(?P<username>.*)/page/(?P<page>\\d+)/$', views.display_blog_page, name='articles_by_author_page'),\n url(r'^author/(?P<username>.*)/$', views.display_blog_page, name='articles_by_author'),\n\n url(r'^(?P<slug>.*)/$', views.display_article, name='articles_display_article'),\n\n # AJAX\n url(r'^ajax/tag/autocomplete/$', views.ajax_tag_autocomplete, name='articles_tag_autocomplete'),\n\n # RSS\n (r'^feeds/(?P<url>.*)/$', feed, feed_dict),\n url(r'^feeds/(?P<url>.*)\\.rss$', feed, feed_dict, name='articles_feed'),\n\n)\n"
}
] | 2 |
wassim1996/yearup_challenge_2
|
https://github.com/wassim1996/yearup_challenge_2
|
a72a3115f95ea1023cb2b69f79ff0e0dbd79a890
|
274f92ab1059061ae987ed8ffcbb710d1223fb85
|
dd5351aae4ac7a63491085a331f40278fb23bc84
|
refs/heads/master
| 2023-05-12T04:21:54.507786 | 2021-01-18T22:45:42 | 2021-01-18T22:45:42 | null | 0 | 0 | null | null | null | null | null |
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"text": "import glob\nimport os\nimport json\nimport re\nimport csv\nimport pprint\nfrom datetime import datetime\n\nTWEET_LOG_DIR_NAME = 'data/yearup_challenge/'\n\n\n# EXAMPLE METRICS FUNCTION\ndef get_weekday_metrics_for_all_tweets(tweet_data_list):\n weekday_metrics = {}\n\n for tweet_data in tweet_data_list:\n weekday = tweet_data['date_weekday']\n\n # Check to see if we've already added this weekday before, otherwise we'll need to initialize it\n if weekday in weekday_metrics: # this means we've already initialized this weekday, so we can just update it\n # += 1 is a short way to say add one to the previous number\n weekday_metrics[weekday] += 1\n else: #initialize metrics for the new weekday\n weekday_metrics[weekday] = 1\n\n return weekday_metrics\n\n\n# EXAMPLE METRICS FUNCTION\ndef get_weekday_metrics_for_by_cve(tweet_data_list):\n weekday_metrics = {}\n\n # Note: well need to make a nested dictionary like this:\n # weekday_metrics[cve][weekday]\n # or\n # { \"cve1\": {\"weekday1: 13, \"weekday2\": 24...} ,\n # \"cve2\": {\"weekday1: 54, \"weekday2\": 12...} ... }\n\n for tweet_data in tweet_data_list:\n cve = tweet_data['cve']\n weekday = tweet_data['date_weekday']\n\n # Check to see if we've already added this cve before, otherwise we'll need to initialize it\n if cve not in weekday_metrics: # this means we've haven't initialized this cve yet so we'll need to\n # initialize cve with an empty dictionary for the weekday metrics\n weekday_metrics[cve] = {}\n\n # Check to see if we've already added this weekday before, otherwise we'll need to initialize it\n if weekday in weekday_metrics[cve]: # this means we've already initialized this weekday, so we can just update it\n # += 1 is a short way to say add one to the previous number\n weekday_metrics[cve][weekday] += 1\n else: # initialize metrics for the new weekday\n weekday_metrics[cve][weekday] = 1\n\n return weekday_metrics\n\n\n# EXAMPLE METRICS FUNCTION\ndef get_tweet_user_count(tweet_data_list):\n user_count_metrics = {}\n\n for tweet_data in tweet_data_list:\n username = tweet_data['user_screen_name']\n\n # Check to see if we've already added this weekday before, otherwise we'll need to initialize it\n if username in user_count_metrics: # this means we've already initialized this weekday, so we can just update it\n # += 1 is a short way to say add one to the previous number\n user_count_metrics[username] += 1\n else: #initialize metrics for the new weekday\n user_count_metrics[username] = 1\n\n return user_count_metrics\n\n\n# TODO: Update print_tweet_data_metrics to include each of the requested challenge metrics\ndef print_tweet_data_metrics(tweet_data_list):\n\n # ----- EXAMPLES -------#\n\n # call your function that computes the metric (you'll need to write the function above)\n weekday_metrics = get_weekday_metrics_for_all_tweets(tweet_data_list)\n # print the metric name/headline\n print(\"DAY OF THE WEEK METRICS (total tweet count for each day\")\n # print the metric file\n pretty_print(weekday_metrics)\n\n # call your function that computes the metric (you'll need to write the function above)\n weekday_metrics_per_tweet = get_weekday_metrics_for_by_cve(tweet_data_list)\n # print the metric name/headline\n print(\"DAY OF THE WEEK METRICS (count per tweet\")\n # print the metric file\n pretty_print(weekday_metrics_per_tweet)\n\n # call your function that computes the metric (you'll need to write the function above)\n tweeter_count = get_tweet_user_count(tweet_data_list)\n # print the metric name/headline\n print(\"USER COUNT METRICS (number of tweets by user)\")\n # print the metric file\n pretty_print(tweeter_count)\n\n # ----- END EXAMPLES -------#\n\n # TODO: insert your code to fill out the metrics report, use the examples above for inspiration\n\n\n# TODO: remove tweets from the list that have cve set to '' (empty), because they didn't have a valid CVE\n# see extract_data_from_tweet_json where cve was extracted from text for reference\ndef filter_tweet_data(tweet_data_list):\n filtered_list = []\n\n for tweet in tweet_data_list:\n tweet_cve = tweet['cve']\n # \"append\" tweet to filtered_list if tweet_cve is not equal to '' (blank)\n # TODO: add your code here to \"append\" ONLY valid _tweets_ to the filtered_list\n # (be sure to append the full tweet, not tweet_cve)\n\n\n return filtered_list\n\n\n# NO NEED TO EDIT BELOW THIS LINE, FEEL FREE TO MAKE CHANGES BUT BE CAREFUL\n# Any \"INFO\" below this line marks areas where you can uncomment print statements for debugging (feel free to add your own)\n\ndef pretty_print(data):\n pp = pprint.PrettyPrinter(indent=4)\n pp.pprint(data)\n\n\ndef convert_weekday_num_to_string(weekday_num):\n weekday_string = \"\"\n if weekday_num == 0:\n weekday_string = 'Monday'\n elif weekday_num == 1:\n weekday_string = 'Tuesday'\n elif weekday_num == 2:\n weekday_string = 'Wednesday'\n elif weekday_num == 3:\n weekday_string = 'Thursday'\n elif weekday_num == 4:\n weekday_string = 'Friday'\n elif weekday_num == 5:\n weekday_string = 'Saturday'\n elif weekday_num == 6:\n weekday_string = 'Sunday'\n return weekday_string\n\n\n# extract_data_from_tweet_json returns a dict (dictionary) object with the extracted values\n#\n# EXAMPLE:\n#\n# { 'cve': 'CVE-2020-16971',\n# 'date': '2020/12/10',\n# 'date_day': 10,\n# 'date_month': 12,\n# 'date_weekday': Wednesday,\n# 'date_year': 2020,\n# 'id': 1336872395802632203,\n# 'text': 'One night, CVE-2020-16971 wished upon a star, and today that wish '\n# 'has been granted. It now has a name, like a real,… '\n# 'https://t.co/6MZm4ARBzg',\n# 'user_followers_count': 953,\n# 'user_friends_count': 0,\n# 'user_screen_name': 'vulnonym',\n# 'user_statuses_count': 18257\n# }\ndef extract_data_from_tweet_json(tweet_json):\n extracted_tweet_data = {}\n extracted_tweet_data['id'] = tweet_json['id']\n\n # Convert create_at string format to our desired format YEAR/MONTH/DAY\n # date_str = datetime.today().strftime('%Y-%m-%d')\n # \"created_at\": \"Wed Oct 10 20:19:24 +0000 2018\"\n created_at_datetime = datetime.strptime(tweet_json['created_at'], '%a %b %d %H:%M:%S +0000 %Y')\n created_at_date = created_at_datetime.date()\n date_str = datetime.strftime(created_at_datetime, '%Y/%m/%d')\n extracted_tweet_data['date'] = date_str\n extracted_tweet_data['date_year'] = created_at_date.year\n extracted_tweet_data['date_month'] = created_at_date.month\n extracted_tweet_data['date_day'] = created_at_date.day\n # Monday is 0 and Sunday is 6 - ref: https://docs.python.org/3/library/datetime.html\n extracted_tweet_data['date_weekday'] = convert_weekday_num_to_string(created_at_date.weekday())\n\n extracted_tweet_data['text'] = tweet_json['text']\n # extract first CVE from text (note: there may be multiple, but to simplify we'll only take the first)\n # ref: https://stackoverflow.com/questions/60178826/extracting-cve-info-with-a-python-3-regular-expression\n\n # CVE regular expression\n cve_pattern = r'CVE-\\d{4}-\\d{4,7}'\n\n # search for CVE references using RegEx\n cves = re.findall(cve_pattern, tweet_json['text'])\n # if no CVEs are found in the text then set it as '' or empty string\n # there are at least two cases where we may not have a CVE:\n # 1) the search function includes anything with \"cve\" so we need to filter tweets with 'cve' but not the full with year and cve id\n # 2) tweets are currently truncated, so long tweets that matched might not have the cve included in the truncated text\n if len(cves) > 0:\n first_cve = cves[0]\n extracted_tweet_data['cve'] = first_cve\n else:\n extracted_tweet_data['cve'] = ''\n\n extracted_tweet_data['user_screen_name'] = tweet_json['user']['screen_name']\n extracted_tweet_data['user_followers_count'] = tweet_json['user']['followers_count']\n extracted_tweet_data['user_friends_count'] = tweet_json['user']['friends_count']\n extracted_tweet_data['user_statuses_count'] = tweet_json['user']['statuses_count']\n\n return extracted_tweet_data\n\n\n# process_tweet_log_file will process a single tweet log file and\n# return a list of dicts in the format that's returned by extract_data_from_tweet_json\n# see notes above extract_data_from_tweet_json for an example of the tweet data format\ndef process_tweet_log_file(tweet_filepath):\n processed_tweet_data = []\n tweet_log_file = open(tweet_filepath, 'r')\n # Note: tweets are stored in the log as one line per tweet, so this for loop will let us process\n # each tweet one by one by processing each line of the file\n for tweet_line in tweet_log_file.readlines():\n tweet_json = json.loads(tweet_line)\n extracted_tweet_data = extract_data_from_tweet_json(tweet_json)\n processed_tweet_data.append(extracted_tweet_data)\n # INFO: uncomment below if you want to see the original tweets that were processed (good for debugging)\n # pretty_print(extracted_tweet_data)\n tweet_log_file.close()\n return processed_tweet_data\n\n\n# process_tweet_log_files will process a list of tweet log files\n# and filter out tweet data that doesn't contain a valid CVE\ndef process_tweet_log_files(list_of_tweet_log_paths):\n clean_and_processed_tweet_data = []\n\n for tweet_log_file_path in list_of_tweet_log_paths:\n processed_tweet_data = process_tweet_log_file(tweet_log_file_path)\n filtered_tweet_data = filter_tweet_data(processed_tweet_data)\n # INFO: uncomment below if you want to see the filtered tweets that were processed (good for debugging)\n # for tweet_data in filtered_tweet_data:\n # pretty_print(tweet_data)\n # INFO: uncomment below if you want to see the size difference between the original and filtered lists (good for debugging)\n # print(\"{file}: size of original tweet data: {size}\".format(file=tweet_log_file_path, size=len(processed_tweet_data)))\n # print(\"{file}: size of filtered tweet data: {size}\".format(file=tweet_log_file_path, size=len(filtered_tweet_data)))\n clean_and_processed_tweet_data.extend(filtered_tweet_data)\n return clean_and_processed_tweet_data\n\n\ndef save_filtered_tweets_to_csv(tweet_data_list):\n # get header (column names) - using the keys of the first in the list as an example\n column_names = tweet_data_list[0].keys()\n\n with open(\"twitter_data.csv\", 'w', encoding='utf-8') as csv_output_file:\n csv_writer = csv.DictWriter(csv_output_file, fieldnames=column_names, quoting=csv.QUOTE_NONNUMERIC)\n\n # write header\n csv_writer.writeheader()\n\n # write tweet data as rows\n for tweet_data in tweet_data_list:\n # remove newlines from tweet text, replace double quotes with single quotes\n tweet_data['text'] = tweet_data['text'].replace('\\n', ' ').replace('\"', '\\'')\n csv_writer.writerow(tweet_data)\n\n\ndef main():\n tweet_logs_path = os.path.join(TWEET_LOG_DIR_NAME, '*.log')\n list_of_tweet_log_paths = glob.glob(tweet_logs_path)\n\n # process all tweet logs in tweet log directory\n tweet_data_list = process_tweet_log_files(list_of_tweet_log_paths)\n print_tweet_data_metrics(tweet_data_list)\n\n # for extra SQL challenge, feel free to comment this out if you don't need the CSV file\n save_filtered_tweets_to_csv(tweet_data_list)\n\nif __name__ == '__main__':\n main()\n"
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"text": "# Main Challenge: Generate Twitter CVE Metrics\n\n## Overview\nGet ready to have some fun with data and explore how we can process it using Python!\n\nThis challenge may look a little daunting, but just have fun with it and do what you can using the starter code provided.\n\nThe goal of this challenge is to generate a number of metrics from Twitter CVE data similar to what we saw used the last challenge.\n\nIn this challenge there's a full month of data, each day in it's own file in the [data/yearup folder](data/yearup_challenge/)\n\nThe starter code will load each of the log files in the [data/yearup folder](data/yearup_challenge/), filter out invalid tweets, and extract certain data fields from each tweet.\n\nThere is also a framework for a simple report generator and example metrics functions to get you started.\n\nFor the bonus challenge, this program will also write out a CSV file \"twitter_data.csv\" to the current directory\n\n:information_source: **Tips** \n- use Python3 to run yearup_challenge2.py (python3 yearup_challenge2.py)\n- run yearup_challenge2.py from inside it's current directory (otherwise you'll have trouble accessing the data)\n\n\n## Main Challenge: Metrics\n\n:information_source: Places were you need to make updates are marked with TODO in the comments, anything marked with \"INFO\" comments are optional for debugging\n\n1. First, I need your help fixing the _filter_tweet_data_ function by adding some code [here](https://github.com/ryanwsmith/yearup_challenge_2/blob/a79b4a30e763400131c30582bb41da18690716ee/yearup_challenge2.py#L113-L116)\n2. Next, I need you to update _print_tweet_data_metrics_ function by writing new functions to compute the metrics and adding a call to that code [here](https://github.com/ryanwsmith/yearup_challenge_2/blob/a79b4a30e763400131c30582bb41da18690716ee/yearup_challenge2.py#L103)\n\nFor #2, I've added 3 examples above the TODO that you can use/copy/modify as you see fit. You should notice a pattern for each section:\n1. call a function to get the computed metrics in a dictionary \n2. print the title of the metric for the report\n3. print the metrics (a pretty_print function has been provided to handle formatting)\n\nEach of your metrics functions should return a dictionary where the KEY is the name of the metrics and the VALUE is the value of the metrics. \nOne example of nested dictionaries is shown in _get_weekday_metrics_for_by_cve_ in case you want to try that.\n\nReview the metrics functions in the examples, you should be able to copy and modify them to create new metrics for the report.\n\nFor the main challenge add as many of the additional metrics as you can: (they get harder further down the list)\n- number of tweets for each cve (like challenge #1)\n- most popular day of the week for all tweets\n- total number (sum) of \"followers\" who could have have seen any tweet for a CVE (use user follower count in tweet)\n- number of cves not from 2020 (remember the year is in the format of the CVE: CVE-YYYY-... date_year is the year the tweet was _sent_)\n- count of CVEs from each CVE release year (using the year in the CVE number)\n- average number of tweets for each user (user_status_count is the number of tweets they've sent)\n- date that the CVE was first seen in a tweet and the date it was last seen in the tweet\n\nFeel free to get creative and add any additional metrics you like. If you get REALLY adventurous, you can extract additional fields in the _extract_data_from_tweet_json_ function\n\nBe sure to review an [example of the extracted tweet data](https://github.com/ryanwsmith/yearup_challenge_2/blob/a79b4a30e763400131c30582bb41da18690716ee/yearup_challenge2.py#L150-L166) to help you find the data you'll need\n\n:warning: Notice that I've marked a comment _\"# NO NEED TO EDIT BELOW THIS LINE, FEEL FREE TO MAKE CHANGES BUT BE CAREFUL\"_\nAs this says, everything below is starter code and there should be no **need** to update it, but you're welcome to make changes if you like.\n\n\n## Bonus Challenge: SQL\n\n:warning: This bonus challenge may be quite difficult and I haven't had the time to provide any starter code for it, check back and I may provide some tips later this week.\nWe may also have a chance to do some demos on Thursday.\n\n## Your Challenge\n\nUse the csv file generated twitter_data.csv\n\n- **Option 1:** If you have access to an AWS account, setup a new Athena data base and table to run SQL commands on the data in S3. \n- **Option 2:** If you have access to an AWS account, you could also setup a free-tier RDS instance.\n- **Option 3:** Setup a SQL database on your laptop (e.g. mysql, sqlite) create a new database and table, then load data from the CSV file to run SQL commands on the data\n\n:warning: If you choose option #1 with Athena be sure to gzip the CSV file (gzip twitter_data.csv) before uploading it to S3 to save on any costs. This will reduce it from ~16MB -> ~3MB, so each Athena query should only cost about 2 cents \n\n## Beyond the Challenge\n\nFeel free to have fun and explore what you can do with this data. \n\nIf you have different ideas than what's in the challenge, feel free to submit and demo them as well."
}
] | 2 |
atefehmohseni/IoT_secure_distributed_database
|
https://github.com/atefehmohseni/IoT_secure_distributed_database
|
2fb212f0d6466a9ae259b59c8ffd48c25144d9a4
|
b3722b4b5a383ba26f786694772aee433b53e234
|
90aaa85cdf5547a373c7c92c01f3844f24684bb0
|
refs/heads/main
| 2023-05-23T07:36:20.107246 | 2021-06-08T01:11:24 | 2021-06-08T01:11:24 | 358,379,169 | 0 | 0 | null | null | null | null | null |
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"text": "# CS263_project\nThese instructions have been tested on an Ubuntu 18.04 machine. \n\n## Requirements\n`libopenssl` and `libpthread` must be installed on the target machine. The Python implementation of the client (`resources/client.py`) has been tested on Python 3.6 and uses the `requests` library for network communication (`pip install requests`). `pypy` must be installed in the target system in order to collect the profiling data.\n\nAdditionally, the `.json.default` files in the `resources` directory must be copied as `.json` files in the same directory.\n\n```bash\n# install libssl-dev libpthread-dev python3 pypy3\nsudo add-apt-repository ppa:pypy/ppa\nsudo apt update\nsudo apt-get install -y libssl-dev libpthread-stubs0-dev python3 python3-pip pypy3\n\n# create the python/pypy virtualenvs\npython3 -m pip install virtualenv\nvirtualenv -p $(which pypy3) ~/venv_pypy3\nvirtualenv -p $(which python3) ~/venv_python3\n\n# install the python requests module (pypy3)\nsource ~/venv_pypy3/bin/activate\npython3 -m ensurepip\npython3 -m pip install requests\ndeactivate\n\n# install the python requests module (python3)\nsource ~/venv_python3/bin/activate\npython3 -m pip install requests\ndeactivate\n\n# copy the .json.default files\nfor f in resources/*.json.default; do cp $f ${f%%.default}; done\n```\n\nFinally, to parse the profiling files and generate the profiling charts, the `matplotlib` and `numpy` Python modules are required.\n\n## Build\n```bash\n$ mkdir build\n$ cd build\n$ cmake ..\n$ make\n```\n\n## Execute\nYou should separtely execute the master server, edge-server, and client.\n\n### Run the Master Server\n```bash\n$ cd build/\n$ ./master_server\n```\n### Run an Edge Server\n```bash\n$ # within the build directory \n$ ./server\n```\n\n### Run a Client\n```bash\n$ # within the build directory \n$ ./client\n```\n\n### Test cases\nThe test queries in [test directory](https://github.com/atefehmohseni/IoT_secure_distributed_database/tree/main/test) can be used to run write/read/delete queries from a client to an edge server (usage: `cat <test_file> | ./client`).\n\n## Profiling\nTo collect the profiling information run the following script:\n```bash\n$ cd resources\n$ ./run_profiling.sh\n```\nTo parse the profiling information:\n```bash\n# within the resources directory \n python3 parse_profiling.py\n```\n\n## Project Vision\n\nThe high-level goal of this project is to research the topic of data persistence in a distributed programming system, with a focus on data protection and reliability.\n\nA distributed database is a database in which data is stored across different physical locations. In this project, we want to implement a simple but secure distributed database system in C++. Our architecture relies on a centralized server for managing nodes, but read/write operations can happen offline, and only data synchronization requires the nodes to be online. At the very core, our system should guarantee data protection and reliability among different nodes. \n\nReferences we might use for this projects are: \\[[Condensation](https://condensationdb.com/white-paper/), [Data Protection in DDS](https://link.springer.com/chapter/10.1007/11425274_20)].\n"
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"text": "//******************************************************************\n// 0: ERROR only, 1: INFO, 2: DEBUG\n#define LOGLEVEL 2\n\n#define ERROR(x) (std::cout << \"[ERROR] \" << x)\n\n#if LOGLEVEL == 2 // DEBUG\n#define DEBUG(x) (std::cout << \"[DEBUG] \" << x)\n#define INFO(x) (std::cout << \"[INFO] \" << x)\n\n#elif LOGLEVEL == 1 // INFO\n#define DEBUG(x) do{}while(0)\n#define INFO(x) (std::cout << \"[INFO] \" << x)\n\n#else // ERROR\n#define DEBUG(x) do{}while(0)\n#define INFO(x) do{}while(0)\n#endif\n//******************************************************************\n\n#define CPPHTTPLIB_OPENSSL_SUPPORT\n#define SSL_CERT_FILE \"../resources/ssl.debug.crt\"\n#define SSL_KEY_FILE \"../resources/ssl.debug.key\"\n\n#define DATABASE_FILE \"../resources/database.json\"\n#define CREDENTIALS_FILE \"../resources/credentials.json\"\n#define SALTS_FILE \"../resources/salts.json\"\n#define DATABASE_FILE_CLOUD \"../resources/cloud_database.json\"\n\n#define LOCAL_STORE_FILE \"../resources/local_store.json\"\n\n//set backup frequency to enable edge server backup to the cloud server every x write operation\n#define BACKUP_FREQUENCY 1000"
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"text": "#!/bin/bash\n\n# write profiling\necho running pypy write profiling...\nfor i in {1..10}; do _=$(cat ../test/write_testcase.txt | /usr/bin/time -o ./profiling_data/pypy.write.profile --append -f \"%e real,\\t%U user,\\t%S sys,\\t%P CPU,\\t%Mk max mem,\\t%F major pagefaults,\\t%R minor pagefaults\" pypy3 client.py); done\necho running cpython write profiling...\nfor i in {1..10}; do _=$(cat ../test/write_testcase.txt | /usr/bin/time -o ./profiling_data/cpython.write.profile --append -f \"%e real,\\t%U user,\\t%S sys,\\t%P CPU,\\t%Mk max mem,\\t%F major pagefaults,\\t%R minor pagefaults\" python3 client.py); done\necho running cpp write profiling...\nfor i in {1..10}; do _=$(cat ../test/write_testcase.txt | /usr/bin/time -o ./profiling_data/cpp.write.profile --append -f \"%e real,\\t%U user,\\t%S sys,\\t%P CPU,\\t%Mk max mem,\\t%F major pagefaults,\\t%R minor pagefaults\" ../build/client); done\n\n# read profiling\necho running pypy read profiling...\nfor i in {1..10}; do _=$(cat ../test/read_testcase.txt | /usr/bin/time -o ./profiling_data/pypy.read.profile --append -f \"%e real,\\t%U user,\\t%S sys,\\t%P CPU,\\t%Mk max mem,\\t%F major pagefaults,\\t%R minor pagefaults\" pypy3 client.py); done\necho running cpython read profiling...\nfor i in {1..10}; do _=$(cat ../test/read_testcase.txt | /usr/bin/time -o ./profiling_data/cpython.read.profile --append -f \"%e real,\\t%U user,\\t%S sys,\\t%P CPU,\\t%Mk max mem,\\t%F major pagefaults,\\t%R minor pagefaults\" python3 client.py); done\necho running cpp read profiling...\nfor i in {1..10}; do _=$(cat ../test/read_testcase.txt | /usr/bin/time -o ./profiling_data/cpp.read.profile --append -f \"%e real,\\t%U user,\\t%S sys,\\t%P CPU,\\t%Mk max mem,\\t%F major pagefaults,\\t%R minor pagefaults\" ../build/client); done\n\n# delete profiling\necho running pypy delete profiling...\nfor i in {1..10}; do _=$(cat ../test/delete_testcase.txt | /usr/bin/time -o ./profiling_data/pypy.delete.profile --append -f \"%e real,\\t%U user,\\t%S sys,\\t%P CPU,\\t%Mk max mem,\\t%F major pagefaults,\\t%R minor pagefaults\" pypy3 client.py); done\necho running cpython delete profiling...\nfor i in {1..10}; do _=$(cat ../test/delete_testcase.txt | /usr/bin/time -o ./profiling_data/cpython.delete.profile --append -f \"%e real,\\t%U user,\\t%S sys,\\t%P CPU,\\t%Mk max mem,\\t%F major pagefaults,\\t%R minor pagefaults\" python3 client.py); done\necho running cpp delete profiling...\nfor i in {1..10}; do _=$(cat ../test/delete_testcase.txt | /usr/bin/time -o ./profiling_data/cpp.delete.profile --append -f \"%e real,\\t%U user,\\t%S sys,\\t%P CPU,\\t%Mk max mem,\\t%F major pagefaults,\\t%R minor pagefaults\" ../build/client); done\n\n# syscalls (just for the write, no need to repeat)\necho running pypy3 syscalls profiling\ncat ../test/write_testcase.txt | strace -c -o ./profiling_data/pypy.syscalls.write.profile pypy3 client.py\necho running python3 syscalls profiling\ncat ../test/write_testcase.txt | strace -c -o ./profiling_data/cpython.syscalls.write.profile python3 client.py\necho running cpp syscalls profiling\ncat ../test/write_testcase.txt | strace -c -o ./profiling_data/cpp.syscalls.write.profile ../build/client\n\n"
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"text": "/*\nour database query language supports two queries:\n- read key\n- write key value\n*/\n\n#include<iostream>\n#include <iomanip>\n#include<fstream>\n#include <filesystem>\n#include \"common.h\"\n#include \"json.hpp\"\n\nusing json = nlohmann::json;\nusing namespace std;\n\nclass IDataBase {\n public:\n virtual string read_record(string key) = 0;\n virtual void delete_record(string key) = 0;\n virtual void write_record(string key, string value) = 0;\n virtual json get_database() = 0;\n};\n\nclass DataBase : public IDataBase {\n private:\n string filename;\n json database;\n ofstream database_ofstream;\n public:\n explicit DataBase(const string& filename) {\n this->filename = string(filename);\n // read a JSON file into the json database object (see https://github.com/nlohmann/json)\n ifstream file;\n file.open(this->filename, ifstream::in);\n file >> this->database;\n file.close();\n\n // init the ofstream\n this->database_ofstream.open(this->filename, ofstream::out | ofstream::trunc);\n\n // write back the database to disk\n this->database_ofstream.seekp(0);\n this->database_ofstream << setw(4) << this->database << endl;\n }\n ~DataBase() {\n // write back the database to disk\n this->database_ofstream.seekp(0);\n this->database_ofstream << setw(4) << this->database << endl;\n this->database_ofstream.close();\n }\n\n string read_record(string key) override;\n void delete_record(string key) override;\n void write_record(string key, string value) override;\n json get_database() override;\n};\n\nstring DataBase::read_record(string key) {\n //TODO: sharding the database to prevent load the whole database each time!\n //TODO: check database access. Have some authorization mechanism.\n DEBUG(\"Database::read key=\" << key << endl);\n\n if (this->database.contains(key)) {\n return this->database[key];\n } else {\n return \"\";\n }\n}\n\nvoid DataBase::delete_record(string key) {\n DEBUG(\"Database::delete key=\" << key << endl);\n this->database.erase(key);\n\n // write back the database to disk\n std::filesystem::resize_file(this->filename, 0);\n this->database_ofstream.seekp(0);\n this->database_ofstream << setw(4) << this->database << endl;\n}\n\nvoid DataBase::write_record(string key, string value) {\n DEBUG(\"Database::write key=\" << key << \", value=\" << value << endl);\n\n // overwrite any existing value\n this->database[key] = value;\n\n // write back the database to disk\n this->database_ofstream.seekp(0);\n this->database_ofstream << setw(4) << this->database << endl;\n}\n\njson DataBase::get_database(){\n return this->database;\n}"
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"text": "import argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--type', '-t', type=str, choices=['read', 'write', 'delete'],\n required=True, action='store', help='Type of testcase to generate')\nargs = parser.parse_args()\n\nif args.type == 'read':\n with open('../test/read_testcase.txt', 'w') as f:\n for i in range(100):\n f.write('r\\n')\n f.write(f'key{i}\\n')\n f.write('q\\n')\nelif args.type == 'write':\n with open('../test/write_testcase.txt', 'w') as f:\n for i in range(100):\n f.write('w\\n')\n f.write(f'key{i}\\n')\n f.write(f'value{i}\\n')\n f.write('q\\n')\nelif args.type == 'delete':\n with open('../test/delete_testcase.txt', 'w') as f:\n for i in range(100):\n f.write('d\\n')\n f.write(f'key{i}\\n')\n f.write('q\\n')"
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"text": "import argparse\nimport json\nimport requests\nimport time\nimport threading\nfrom requests.packages.urllib3.exceptions import InsecureRequestWarning\n\nSSL_CERT_FILE = \"./ssl.debug.crt\"\nSSL_KEY_FILE = \"./ssl.debug.key\"\n\nrequests.packages.urllib3.disable_warnings(InsecureRequestWarning)\n\nclass LocalStore:\n def __init__(self):\n with open('./local_store.json', 'rb') as f:\n self.data = json.loads(f.read())\n self.ofstream = open('./local_store.json', 'wb', buffering=0)\n # self.ofstream.truncate(0)\n self.ofstream.write(json.dumps(self.data).encode('utf-8'))\n\n def read_record(self, key):\n return self.data[key]\n\n def write_record(self, key, value):\n self.data[key] = value\n self.ofstream.truncate(0)\n self.ofstream.seek(0)\n self.ofstream.write(json.dumps(self.data).encode('utf-8'))\n\n def delete_record(self, key):\n del self.data[key]\n self.ofstream.truncate(0)\n self.ofstream.seek(0)\n self.ofstream.write(json.dumps(self.data).encode('utf-8'))\n\nclass Client:\n def __init__(self, ssl=True):\n self.http_session = requests.Session()\n self.http_session.auth = ('username', 'password')\n # self.http_session.cert = SSL_CERT_FILE\n self.http_session.verify = False\n\n self.action_queue = []\n self.local_store = LocalStore()\n\n self.exit = False\n\n if ssl:\n self.base_url = 'https://localhost:4444'\n else:\n self.base_url = 'http://localhost:4444'\n\n # start secondary thread\n self.local_store_thread = threading.Thread(target=Client.local_store_callable, args=(self, ))\n self.local_store_thread.start()\n\n def read_query(self, key):\n r = self.http_session.get(f'{self.base_url}/get', params={'key': key})\n\n if r.status_code == 200:\n return r.text\n\n def write_query(self, key, value):\n self.action_queue.append(('put', key))\n\n # write value to local value store\n self.local_store.write_record(key, value)\n\n def delete_query(self, key):\n self.action_queue.append(('del', key))\n\n @staticmethod\n def local_store_callable(client):\n while True:\n # print('running', client.action_queue)\n # process action queue\n while len(client.action_queue) > 0:\n status = 500\n type, key = client.action_queue[0]\n\n if type == 'del':\n r = client.http_session.get(f'{client.base_url}/delete', params={'key': key})\n status = r.status_code\n elif type == 'put':\n # read value from local store\n value = client.local_store.read_record(key)\n r = client.http_session.get(f'{client.base_url}/put', params={'key': key, 'value': value})\n status = r.status_code\n pass\n\n # check status\n if status == 200:\n client.action_queue.pop(0)\n if type == 'put':\n # delete key from local data store\n client.local_store.delete_record(key)\n else:\n break\n\n if client.exit:\n break\n time.sleep(5)\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--no-ssl', action='store_true', default=False,\n help='Disable SSL')\n args = parser.parse_args()\n\n client = Client(ssl=(not args.no_ssl))\n\n while True:\n print('r) read query w) write query d) delete query q) quit\\nPlease enter your choice: ')\n choice = input()[0]\n\n if choice == 'r':\n print('Please enter a key to query: ')\n key = input()\n client.read_query(key)\n elif choice == 'w':\n print('Please enter a key to write: ')\n key = input()\n print('Please enter a value to write: ')\n value = input()\n client.write_query(key, value)\n elif choice == 'd':\n print('Please enter a key to delete: ')\n key = input()\n client.delete_query(key)\n elif choice == 'q':\n client.exit = True\n client.local_store_thread.join()\n exit(0)\n"
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"text": "#include <iostream>\n#include<fstream>\n#include \"common.h\"\n#include \"database.h\"\n#include \"httplib.h\" \n\nusing namespace std;\n\nclass IServerCloud {\n public:\n IServerCloud() = default;\n virtual void start() = 0;\n};\n\n//\n// This server is running in teh Cloud\n//\nclass ServerCloud: public IServerCloud {\n public:\n httplib::Server *http_server;\n \n DataBase *database;\n\n ServerCloud(){\n\n this->http_server = new httplib::Server;\n\n this->database = new DataBase(DATABASE_FILE_CLOUD);\n }\n \n void start() override {\n DEBUG(\"Setting up /backup endpoint\" << endl);\n this->http_server->Post(\"/backup\", [&](const auto& req, auto& res) {\n DEBUG(\"MASTER-SERVER: Recieved a backup request\");\n auto files = req.files;\n auto content = res.body;\n\n // auto size = req.files.size();\n // auto file = req.get_file_value(\"backup_100\");\n\n ofstream ofs(\"backup_\"+time(0));\n ofs << content;\n\n });\n \n DEBUG(\"Binding to 0.0.0.0:5555\" << endl);\n if (!this->http_server->listen(\"0.0.0.0\", 5555)) {\n ERROR(\"Cannot bind to 0.0.0.0:5555\" << endl);\n }\n }\n}; \n\nint main() {\n DEBUG(\"Starting the Cloud Server\" << endl);\n auto *master_server = new ServerCloud;\n master_server->start();\n}\n"
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"text": "## Design Centralized server\n\n### Data object:\nKey, value → update the value if the key already existed\nQuery language → supports read/update (key,value) pair\n\n### Server:\nStore data → file (txt, json, xml? preferably a file that supports a structured data format)\nRead data → return value if key exists, otherwise Null\nWrite data → write the value raise an error if key does not exist (requires mutex)\n\n### Client: \nQuery data => submit the query (based on the query language) \nWrite new data \n(Synchronization with other nodes) → when the server will not be centralized\nDo we need a caching mechanism? (ignore for now)\n\n### Communication between server and client:\nREST Api (get,put request?)\n\n\n"
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"text": "## Related Literature\n* [Condensation: a distributed data system with conflict-free synchronization\nand end-to-end security.](https://condensationdb.com/white-paper/)\n * Goal: proposing a hybrid database structure, with mutable documents stored (and merged) locally and immutable objects transferred through one or more (distributed) intermediate servers.\n * The system provides data certification (with user signature), versioning (with transaction history), and conflict free merge (with CRDTs -- Conflict-free Replicated Data Types).\n * Centralized server data flow (one centralized server is used to synchronize the data): \n 1. The data is decrypted in the server \n 2. The data is manipulated by the server \n 3. The data is stored in the server. \n \n This potentially creates a single point of failure and exposes the users to security and privacy issues like a data breach.\n \n * Condensation DB data flow: \n 1. a mutable object on node A is signed and encrypted for node B \n 2. the object is stored in an intermediate server and remains encrypted\n 3. node A sends a message to node B \n 4. node B pulls the new object \n 5. node B decrypts and processes the object, then encrypts its version of the object and stores it in an intermediate server (the fact that objects are immutable naturally leads to a versioning abstraction)"
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"text": "#include <iostream>\n#include \"base64.h\"\n#include \"bcrypt.h\"\n#include \"common.h\"\n#include \"database.h\"\n#include \"httplib.h\"\n\nusing namespace std;\n\nclass IServer {\n public:\n IServer()= default;\n virtual void start() = 0;\n virtual int backup() = 0;\n private:\n virtual bool check_authorization(const httplib::Request &req) = 0;\n};\n//\n// This server is running at the edge\n//\nclass Server: public IServer {\n public:\n #ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n httplib::SSLServer *http_server;\n #else\n httplib::Server *http_server;\n #endif\n\n // define an http client to connect to the server in cloud\n httplib::Client *http_client;\n\n DataBase *database;\n DataBase *credentials;\n DataBase *salts;\n\n string server_unique_id;\n\n int write_counter;\n\n Server() {\n #ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n this->http_server = new httplib::SSLServer(SSL_CERT_FILE, SSL_KEY_FILE);\n #else\n this->http_server = new httplib::Server; //(\"/tmp/test.csr\", \"/tmp/test.key\");\n #endif\n\n // set up an httpclient to connect to the master server (in cloud)\n this->http_client = new httplib::Client(\"http://localhost:5555\");\n\n this->database = new DataBase(DATABASE_FILE);\n this->credentials = new DataBase(CREDENTIALS_FILE);\n this->salts = new DataBase(SALTS_FILE);\n\n this->write_counter = 0; \n\n //TODO: make server IDs unique\n this->server_unique_id=\"100\";\n }\n\n int backup() override {\n DEBUG(\"Backup local data to the cloud\" << endl);\n //this->http_client->set_compress(true);\n //?id=\"+this->server_unique_id).c_str()\n auto res = this->http_client->Post(\"/backup\",this->database->get_database().dump(),\"application/json\");\n if (res != nullptr && res->status == 200) {\n return res->status;\n } else {\n return 0;\n }\n }\n\n void start() override {\n DEBUG(\"Setting up /get endpoint\" << endl);\n this->http_server->Get(\"/get\", [&](const httplib::Request &req, httplib::Response &res) {\n if (!this->check_authorization(req)) {\n res.set_header(\"WWW-Authenticate\", \"Basic\");\n res.status = 401;\n } else if (req.has_param(\"key\")) {\n string key = req.get_param_value(\"key\");\n string value = this->database->read_record(key);\n res.set_content(value, \"text/plain\");\n } else {\n res.set_content(\"\", \"text/plain\");\n }\n });\n\n DEBUG(\"Setting up /put endpoint\" << endl);\n this->http_server->Get(\"/put\", [&](const httplib::Request &req, httplib::Response &res) {\n if (!this->check_authorization(req)) {\n res.set_header(\"WWW-Authenticate\", \"Basic\");\n res.status = 401;\n } else if (req.has_param(\"key\") && req.has_param(\"value\")) {\n string key = req.get_param_value(\"key\");\n string value = req.get_param_value(\"value\");\n this->database->write_record(key, value);\n\n this->write_counter++;\n if(this->write_counter == BACKUP_FREQUENCY){\n int res = this->backup();\n //cout<<res<<endl;\n if(res == 200){\n //after a successful backup, set write-operation counter to zero\n DEBUG(\"Succefuly backed up!\" << endl);\n this->write_counter=0;\n }\n }\n res.set_content(\"success\", \"text/plain\");\n } else {\n res.set_content(\"failure\", \"text/plain\");\n }\n });\n\n DEBUG(\"Setting up /delete endpoint\" << endl);\n this->http_server->Get(\"/delete\", [&](const httplib::Request &req, httplib::Response &res) {\n if (!this->check_authorization(req)) {\n res.set_header(\"WWW-Authenticate\", \"Basic\");\n res.status = 401;\n } else if (req.has_param(\"key\")) {\n string key = req.get_param_value(\"key\");\n this->database->delete_record(key);\n res.set_content(\"success\", \"text/plain\");\n } else {\n res.set_content(\"failure\", \"text/plain\");\n }\n });\n\n DEBUG(\"Binding to 0.0.0.0:4444\" << endl);\n if (!this->http_server->listen(\"0.0.0.0\", 4444)) {\n ERROR(\"Cannot bind to 0.0.0.0:4444\" << endl);\n }\n }\n private:\n bool check_authorization(const httplib::Request &req) override {\n if (!req.has_header(\"Authorization\")) {\n DEBUG(\"Not Authorized: missing Authorization header\" << endl);\n return false;\n }\n\n string auth_header = req.get_header_value(\"Authorization\");\n if ((auth_header.rfind(\"Basic\", 0) == 0)) {\n //Extract credentials (skip \"Basic\")\n string b64_credentials = auth_header.substr(6);\n string credentials(b64_credentials.length() / 4 * 3, '\\00');\n Base64::Decode(b64_credentials, credentials);\n\n // split username:password\n int delimiter = credentials.find_first_of(':');\n string username = credentials.substr(0, delimiter);\n string password = credentials.substr(delimiter+1);\n\n // read stored (hashed) password\n string stored_hash = this->credentials->read_record(username);\n\n if (stored_hash.empty()) {\n // username not in the database\n DEBUG(\"Not Authorized: username not in the database\" << endl);\n return false;\n }\n\n // read the stored salt\n string salt = this->salts->read_record(username);\n assert(!salt.empty()); // this should never happen\n\n // hash with bcrypt (with salt)\n char hash[BCRYPT_HASHSIZE] = {'\\00'};\n bcrypt_hashpw(password.c_str(), salt.c_str(), hash);\n\n // compare the stored hash with the computed hash\n string computed_hash(hash);\n if (stored_hash == hash) {\n return true;\n }\n }\n\n DEBUG(\"Not Authorized: failed to verify username:password\" << endl);\n return false;\n }\n};\n\n\nint main() {\n DEBUG(\"Starting a Server instance\" << endl);\n auto *server = new Server;\n server->start();\n}\n"
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"text": "import ast\nimport re\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# to actually run the profiling, use something like:\n# for i in {1..10}; do _=$(cat ../test/write_testcase.txt | /usr/bin/time -o ./pypy.profiling.txt --append -f \"%e real,\\t%U user,\\t%S sys,\\t%P CPU,\\t%Mk max mem,\\t%F major pagefaults,\\t%R minor pagefaults\" pypy3 client.py); done\n\nREGEX = '^(?P<real>\\d+\\.\\d+) real,\t(?P<user>\\d+\\.\\d+) user,\t(?P<sys>\\d+\\.\\d+) sys,\t(?P<cpu>\\d+)% CPU,\t(?P<maxmem>\\d+)k max mem,\t(?P<Mpagefaults>\\d+) major pagefaults,\t(?P<mpagefaults>\\d+) minor pagefaults'\nregex = re.compile(REGEX)\ngroups = list(regex.groupindex)\n\ntime_dict = {\"real\":0, \"user\":0, \"sys\":0}\nmem_dict = {\"maxmem\" :0, \"Mpagefaults\":0, \"mpagefaults\":0}\ncpu_dict = {\"cpu\":0}\n\ntime_profile_dict = {\n \"cpp\":time_dict.copy(),\n \"cpython\":time_dict.copy(),\n \"pypy\":time_dict.copy(),\n}\nmem_profile_dict = {\n \"cpp\":mem_dict.copy(),\n \"cpython\":mem_dict.copy(),\n \"pypy\":mem_dict.copy(),\n}\ncpu_profile_dict = {\n \"cpp\": cpu_dict.copy(),\n \"cpython\": cpu_dict.copy(),\n \"pypy\": cpu_dict.copy()\n}\n\nwrite_profiles = {\"time\": time_profile_dict.copy(),\"mem\":mem_profile_dict.copy(), \"cpu\": cpu_profile_dict.copy()}\nread_profiles = {\"time\": time_profile_dict.copy(),\"mem\":mem_profile_dict.copy(), \"cpu\": cpu_profile_dict.copy()}\ndelete_profiles = {\"time\": time_profile_dict.copy(),\"mem\":mem_profile_dict.copy(), \"cpu\": cpu_profile_dict.copy()}\n\ndef make_time_plot(profile_dict, profile_type):\n # set width of bars\n barWidth = 0.10\n\n # set heights of bars\n bars1 = [profile_dict[\"time\"][\"cpp\"][\"sys\"], profile_dict[\"time\"][\"cpp\"][\"user\"]]\n bars2 = [profile_dict[\"time\"][\"pypy\"][\"sys\"],profile_dict[\"time\"][\"pypy\"][\"user\"]]\n bars3 = [profile_dict[\"time\"][\"cpython\"][\"sys\"],profile_dict[\"time\"][\"cpython\"][\"user\"]]\n\n # Set position of bar on X axis\n r1 = np.arange(len(bars1))\n r2 = [x + barWidth for x in r1]\n r3 = [x + barWidth for x in r2]\n\n # Make the plot\n plt.bar(r1, bars1, color = \"sandybrown\", width=barWidth, edgecolor='white', label='CPP')\n plt.bar(r2, bars2, color = \"lightseagreen\", width=barWidth, edgecolor='white', label='PyPy')\n plt.bar(r3, bars3, color = \"plum\", width=barWidth, edgecolor='white', label='CPython')\n\n # Add xticks on the middle of the group bars\n plt.xlabel('Time of {0} Query (Seconds)'.format(profile_type), fontweight='bold')\n plt.xticks([r + barWidth for r in range(len(bars1))], ['System', 'User'])\n \n # Create legend & Show graphic\n plt.legend()\n plt.show()\n\n\ndef make_real_time_plot(read_profile, write_profile, delete_profile):\n # set width of bars\n barWidth = 0.15\n \n # set heights of bars \n bars1 = [read_profile[\"time\"][\"cpp\"][\"real\"], write_profile[\"time\"][\"cpp\"][\"real\"],delete_profile[\"time\"][\"cpp\"][\"real\"]]\n bars2 = [read_profile[\"time\"][\"pypy\"][\"real\"],write_profile[\"time\"][\"pypy\"][\"real\"], delete_profile[\"time\"][\"pypy\"][\"real\"]]\n bars3 = [read_profile[\"time\"][\"cpython\"][\"real\"],write_profile[\"time\"][\"cpython\"][\"real\"], delete_profile[\"time\"][\"cpython\"][\"real\"]]\n \n # Set position of bar on X axis\n r1 = np.arange(len(bars1))\n r2 = [x + barWidth for x in r1]\n r3 = [x + barWidth for x in r2]\n\n # Make the plot\n plt.bar(r1, bars1, color= \"sandybrown\", width=barWidth, edgecolor='white', label='CPP')\n plt.bar(r2, bars2, color=\"lightseagreen\", width=barWidth, edgecolor='white', label='PyPy')\n plt.bar(r3, bars3, color= \"plum\", width=barWidth, edgecolor='white', label='CPython')\n\n # Add xticks on the middle of the group bars\n x_label = \"Real time (Seconds)\"\n \n plt.xlabel(x_label, fontweight='bold')\n plt.xticks([r + barWidth for r in range(len(bars1))], ['Read', 'Write', 'Delete'])\n \n # Create legend & Show graphic\n plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.05),\n ncol=3, fancybox=True, shadow=True)\n plt.show()\n\n\n\ndef make_mem_plot(read_profile, write_profile, delete_profile, memory_info):\n # set width of bars\n barWidth = 0.15\n\n # set heights of bars \n bars1 = [read_profile[\"mem\"][\"cpp\"][memory_info], write_profile[\"mem\"][\"cpp\"][memory_info],delete_profile[\"mem\"][\"cpp\"][memory_info]]\n bars2 = [read_profile[\"mem\"][\"pypy\"][memory_info],write_profile[\"mem\"][\"pypy\"][memory_info], delete_profile[\"mem\"][\"pypy\"][memory_info]]\n bars3 = [read_profile[\"mem\"][\"cpython\"][memory_info],write_profile[\"mem\"][\"cpython\"][memory_info], delete_profile[\"mem\"][\"cpython\"][memory_info]]\n \n # Set position of bar on X axis\n r1 = np.arange(len(bars1))\n r2 = [x + barWidth for x in r1]\n r3 = [x + barWidth for x in r2]\n\n # Make the plot\n plt.bar(r1, bars1, color= \"sandybrown\", width=barWidth, edgecolor='white', label='CPP')\n plt.bar(r2, bars2, color=\"lightseagreen\", width=barWidth, edgecolor='white', label='PyPy')\n plt.bar(r3, bars3, color= \"plum\", width=barWidth, edgecolor='white', label='CPython')\n\n\n # Add xticks on the middle of the group bars\n x_label = \"Max Memory Usage (KB)\"\n if memory_info == \"mpagefaults\":\n x_label = \"Minor pagefaults\"\n plt.xlabel(x_label, fontweight='bold')\n plt.xticks([r + barWidth for r in range(len(bars1))], ['Read', 'Write', 'Delete'])\n \n # Create legend & Show graphic\n plt.legend()\n plt.show()\n\ndef make_cpu_plot(read_profile, write_profile, delete_profile, cpu_info=\"cpu\"):\n # set width of bars\n barWidth = 0.15\n\n # set heights of bars\n # bars1 = [read_profile[\"cpu\"][\"cpp\"][cpu_info], read_profile[\"cpu\"][\"cpython\"][cpu_info], read_profile[\"cpu\"][\"pypy\"][cpu_info]]\n # bars2 = [write_profile[\"cpu\"][\"cpp\"][cpu_info], write_profile[\"cpu\"][\"cpython\"][cpu_info], write_profile[\"cpu\"][\"pypy\"][cpu_info]]\n # bars3 = [delete_profile[\"cpu\"][\"cpp\"][cpu_info], delete_profile[\"cpu\"][\"cpython\"][cpu_info], delete_profile[\"cpu\"][\"pypy\"][cpu_info]]\n\n bars1 = [read_profile[\"cpu\"][\"cpp\"][cpu_info], write_profile[\"cpu\"][\"cpp\"][cpu_info],delete_profile[\"cpu\"][\"cpp\"][cpu_info]]\n bars2 = [read_profile[\"cpu\"][\"pypy\"][cpu_info],write_profile[\"cpu\"][\"pypy\"][cpu_info], delete_profile[\"cpu\"][\"pypy\"][cpu_info]]\n bars3 = [read_profile[\"cpu\"][\"cpython\"][cpu_info],write_profile[\"cpu\"][\"cpython\"][cpu_info], delete_profile[\"cpu\"][\"cpython\"][cpu_info]]\n\n # Set position of bar on X axis\n r1 = np.arange(len(bars1))\n r2 = [x + barWidth for x in r1]\n r3 = [x + barWidth for x in r2]\n\n # Make the plot\n plt.bar(r1, bars1, color= \"sandybrown\", width=barWidth, edgecolor='white', label='CPP')\n plt.bar(r2, bars2, color=\"lightseagreen\", width=barWidth, edgecolor='white', label='PyPy')\n plt.bar(r3, bars3, color= \"plum\", width=barWidth, edgecolor='white', label='CPython')\n\n # Add xticks on the middle of the group bars\n plt.xlabel(\"CPU Usage (Percentage)\", fontweight='bold')\n plt.xticks([r + barWidth for r in range(len(bars1))], ['Read', 'Write', 'Delete'])\n \n # Create legend & Show graphic\n plt.legend()\n plt.show()\n\ndef get_runtime_type(filename):\n runtime_type = \"pypy\"\n if \"cpp\" in filename:\n runtime_type = \"cpp\"\n elif \"cpython\" in filename:\n runtime_type = \"cpython\"\n \n return runtime_type\n\n\ndef parse_profile(filename):\n print('-'*20+f'\\n{filename}\\n'+'-'*20)\n with open(filename, 'r') as f:\n total = {group:0 for group in groups}\n lines = f.readlines()\n for line in lines:\n match = regex.match(line)\n for group in groups:\n total[group] += ast.literal_eval(match.group(group))\n \n runtime = get_runtime_type(filename)\n\n #print({k:round(v/len(lines),2) for k,v in total.items()})\n for k,v in total.items():\n v = round(v/len(lines),2)\n print(k,\" : \" ,v) \n if \"write\" in filename:\n if k in time_dict.keys() and write_profiles[\"time\"][runtime][k] == 0:\n write_profiles[\"time\"][runtime][k] = v\n elif k in mem_dict.keys() and write_profiles[\"mem\"][runtime][k] == 0:\n write_profiles[\"mem\"][runtime][k]= v\n elif k in cpu_dict.keys():\n write_profiles[\"cpu\"][runtime][k]= v\n\n elif \"read\" in filename:\n if k in time_dict.keys() and read_profiles[\"time\"][runtime][k] == 0:\n read_profiles[\"time\"][runtime][k] = v\n elif k in mem_dict.keys() and read_profiles[\"mem\"][runtime][k] == 0:\n read_profiles[\"mem\"][runtime][k]= v\n elif k in cpu_dict.keys():\n read_profiles[\"cpu\"][runtime][k]= v\n\n elif \"delete\" in filename:\n if k in time_dict.keys() and delete_profiles[\"time\"][runtime][k] == 0:\n delete_profiles[\"time\"][runtime][k] = v\n elif k in mem_dict.keys() and delete_profiles[\"mem\"][runtime][k] == 0:\n delete_profiles[\"mem\"][runtime][k]= v\n elif k in cpu_dict.keys():\n delete_profiles[\"cpu\"][runtime][k]= v\n\n\nfor filename in ['profiling_data/cpp.write.profile', 'profiling_data/cpython.write.profile', 'profiling_data/pypy.write.profile',\n 'profiling_data/cpp.read.profile', 'profiling_data/cpython.read.profile', 'profiling_data/pypy.read.profile',\n 'profiling_data/cpp.delete.profile', 'profiling_data/cpython.delete.profile', 'profiling_data/pypy.delete.profile']:\n parse_profile(filename)\n\nmake_time_plot(write_profiles, profile_type=\"Write\")\n# make_time_plot(read_profiles, profile_type=\"Read\")\n# make_time_plot(delete_profiles, profile_type=\"Delete\")\n\nmake_real_time_plot(read_profiles, write_profiles, delete_profiles)\n\n# make_mem_plot(read_profiles, write_profiles, delete_profiles, memory_info=\"maxmem\")\n\n# make_mem_plot(read_profiles, write_profiles, delete_profiles, memory_info=\"mpagefaults\")\n\n# make_cpu_plot(read_profiles, write_profiles, delete_profiles)"
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"text": "#include <iostream>\n#include \"common.h\"\n#include \"database.h\"\n#include \"httplib.h\"\n#include <queue>\n#include <thread>\n\nusing namespace std;\n\nclass IClient {\n public:\n IClient()= default;\n virtual void start() = 0;\n virtual string read_query(string key) = 0;\n virtual void write_query(string key, string value) = 0;\n virtual void delete_query(string key) = 0;\n};\n\nclass Client : public IClient {\n public:\n #ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n httplib::SSLClient *http_client;\n #else\n httplib::Client *http_client;\n #endif\n\n DataBase *local_store;\n queue< pair<string,string> > action_queue; // pair<action_type, key>\n\n thread local_store_thread;\n bool exit;\n\n Client() {\n #ifdef CPPHTTPLIB_OPENSSL_SUPPORT\n this->http_client = new httplib::SSLClient(\"localhost\", 4444);\n // Use CA bundle\n this->http_client->set_ca_cert_path(SSL_CERT_FILE);\n // Disable cert verification\n this->http_client->enable_server_certificate_verification(false);\n #else\n this->http_client = new httplib::Client(\"localhost\", 4444);\n #endif\n\n // Basic Authentication\n this->http_client->set_basic_auth(\"username\", \"password\");\n\n this->local_store = new DataBase(LOCAL_STORE_FILE);\n this->exit = false;\n }\n void start() override;\n string read_query(string key) override;\n void write_query(string key, string value) override;\n void delete_query(string key) override;\n\n static void local_store_callable(Client *client);\n};\n\nvoid Client::start() {\n this->local_store_thread = thread(&Client::local_store_callable, this);\n}\n\nstring Client::read_query(string key) {\n DEBUG(\"Client::read_query key=\" << key << endl);\n auto res = this->http_client->Get((\"/get?key=\"+key).c_str());\n if (res != nullptr && res->status == 200) {\n return res->body;\n } else {\n return \"\";\n }\n}\n\nvoid Client::write_query(string key, string value) {\n DEBUG(\"Client::write_query key=\" << key << \"&value=\" << value << endl);\n this->local_store->write_record(key, value);\n this->action_queue.push(make_pair(\"put\", key));\n}\n\nvoid Client::delete_query(string key) {\n DEBUG(\"Client::delete_query key=\" << key << endl);\n this->action_queue.push(make_pair(\"del\", key));\n}\n\nvoid Client::local_store_callable (Client *client) {\n while (true) {\n // process action queue\n while (!client->action_queue.empty()) {\n int status = 500;\n auto[type, key] = client->action_queue.front();\n\n if (type == \"del\") {\n auto res = client->http_client->Get((\"/delete?key=\"+key).c_str());\n status = res->status;\n } else if (type == \"put\") {\n string value = client->local_store->read_record(key);\n auto res = client->http_client->Get((\"/put?key=\" + key + \"&value=\" + value).c_str());\n status = res->status;\n }\n\n if (status == 200) {\n client->action_queue.pop();\n if (type == \"put\") {\n client->local_store->delete_record(key);\n }\n } else {\n break;\n }\n }\n if (client->exit)\n break;\n sleep(5);\n }\n}\n\nstring prompt(const string& prompt) {\n string input;\n cout << prompt;\n getline(cin, input);\n return input;\n}\n\nint main() {\n string MENU_PROMPT = \"r) read query w) write query d) delete query q) quit\\nPlease enter your choice: \";\n string KEY_PROMPT = \"Please enter a key: \";\n string VALUE_PROMPT = \"Please enter a value: \";\n\n auto *client = new Client();\n client->start();\n\n string key, value;\n while(true) {\n char choice = prompt(MENU_PROMPT)[0];\n switch (choice) {\n case 'r':\n key = prompt(KEY_PROMPT);\n value = client->read_query(key);\n break;\n case 'w':\n key = prompt(KEY_PROMPT);\n value = prompt(VALUE_PROMPT);\n client->write_query(key, value);\n break;\n case 'd':\n key = prompt(KEY_PROMPT);\n client->delete_query(key);\n break;\n case 'q':\n client->exit = true;\n client->local_store_thread.join();\n goto END;\n default:\n cout << \"Invalid choice\" << endl;\n }\n }\n\n END:;\n}\n"
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"text": "# cmake version to be used\ncmake_minimum_required( VERSION 3.1 )\n\n# compiler\nset( CMAKE_C_COMPILER gcc-8 )\nset( CMAKE_CXX_COMPILER g++-8 )\n\n# project name\nproject( distDB )\n\n# dependencies\nset( THREADS_PREFER_PTHREAD_FLAG ON )\nfind_package( Threads REQUIRED )\nfind_package( OpenSSL REQUIRED )\nset( SSL TRUE )\n\nadd_custom_command( OUTPUT bcrypt\n COMMAND make\n WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/libs/libbcrypt )\nset_property(SOURCE src/server.cpp APPEND PROPERTY OBJECT_DEPENDS bcrypt)\n\n# flags\n\n# files\n\n# include\ninclude_directories( include src libs/httplib libs/json libs/base64 )\n\n# subdirectories\ninclude_directories( libs/libbcrypt )\n# link_directories( libs/libbcrypt )\n\n# target\n\nadd_executable( client src/client.cpp )\nadd_executable( server src/server.cpp )\nadd_executable( master_server src/server_cloud.cpp )\n\n\n# link with pthread, openssl, filesystem\ntarget_link_libraries( client PUBLIC\n $<$<BOOL:${SSL}>:OpenSSL::SSL>\n $<$<BOOL:${SSL}>:OpenSSL::Crypto>\n stdc++fs\n pthread )\ntarget_link_libraries( server PUBLIC\n ${CMAKE_SOURCE_DIR}/libs/libbcrypt/bcrypt.a\n $<$<BOOL:${SSL}>:OpenSSL::SSL>\n $<$<BOOL:${SSL}>:OpenSSL::Crypto>\n Threads::Threads\n stdc++fs )\n\ntarget_link_libraries( master_server PUBLIC\n ${CMAKE_SOURCE_DIR}/libs/libbcrypt/bcrypt.a\n $<$<BOOL:${SSL}>:OpenSSL::SSL>\n $<$<BOOL:${SSL}>:OpenSSL::Crypto>\n Threads::Threads\n stdc++fs )\n\n# use c++17\nset_target_properties( client PROPERTIES CXX_STANDARD 17 )\nset_target_properties( server PROPERTIES CXX_STANDARD 17 )\nset_target_properties( master_server PROPERTIES CXX_STANDARD 17 )\n\n\n# external libs\n\n"
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"text": "Meeting agenda of May 12th:\n\n- Choose an application: \n - IoT applications for sustainability see examples [here](https://builtin.com/internet-things/iot-environment-sustainability-green-examples)\n\n- Start working on the security aspect of the application. Good reading [here](https://ndy.com/lifecycle/why-cyber-security-risks-should-be-on-the-sustainability-radar).\n\n- Prepare for the presentation: \n - Have diff implementation: \n - Implement the whole system both in python and c++\n - Implement partially client or server in python and check performance\n - Profiling \n\n"
}
] | 14 |
therealAJ/treehacks17
|
https://github.com/therealAJ/treehacks17
|
0a4cd9cba26284baedf4130002101e812e53c471
|
be4e7e643084e1dacad5c572892b6ba90a791c3e
|
55e99ec6fca315820ea9f489cabf6f6cff51df86
|
refs/heads/master
| 2021-01-19T13:18:13.504696 | 2017-02-19T01:07:19 | 2017-02-19T01:07:19 | 82,374,674 | 0 | 0 | null | null | null | null | null |
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"text": "\n# coding: utf-8\n\n# In[67]:\n\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom random import randint\nimport time\n\n\n# #### Create plot + add subplots \n\n# In[68]:\n\nfig = plt.figure()\naxis = fig.add_subplot(1,1,1);\n\n\n# In[80]:\n\ndef genCoords(i):\n xvals = []\n yvals = []\n x = 0\n while(x < 500):\n x+=1\n randX = randint(0,9)\n randY = randint(0,9)\n xvals.append(randX)\n yvals.append(randY)\n axis.clear()\n axis.plot(xvals,yvals) \n\n\n# #### Animation function\n\n# In[81]:\n\ndef animate(i):\n imported = open(\"sample.txt\",\"r\").read()\n data = imported.split('\\n')\n xvals = []\n yvals = []\n for line in data:\n if len(line)>1:\n x,y = line.split(',')\n xvals.append(int(x)) \n yvals.append(int(y))\n axis.clear()\n axis.plot(xvals,yvals)\n\n\n# In[82]:\n\npretty = animation.FuncAnimation(fig,animate,interval=100)\nplt.plot()\n\n\n# In[ ]:\n\n\n\n"
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"text": "\n# coding: utf-8\n\n# In[141]:\n\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom random import randint\nimport time\n\n\n# #### Create plot + add subplots \n\n# In[142]:\n\nfig = plt.figure()\naxis = fig.add_subplot(1,1,1);\n\n\n# In[143]:\n\ndef genCoords(i):\n xvals = []\n yvals = []\n x = 0\n while(x < 10):\n x+=1\n randY = randint(0,9)\n xvals.append(x)\n yvals.append(randY)\n axis.clear()\n axis.plot(xvals,yvals) \n\n\n# #### Animation function\n\n# In[144]:\n\ndef animate(i):\n imported = open(\"sample.txt\",\"r\").read()\n data = imported.split('\\n')\n xvals = []\n yvals = []\n for line in data:\n if len(line)>1:\n x,y = line.split(',')\n xvals.append(int(x)) \n yvals.append(int(y))\n axis.clear()\n axis.plot(xvals,yvals)\n\n\n# In[145]:\n\npretty = animation.FuncAnimation(fig,genCoords,interval=0.1)\n\n\n# In[146]:\n\nplt.show()\n\n\n# In[ ]:\n\n\n\n"
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"text": "import serial\nser = serial.Serial('/dev/cu.usbserial-AI03Y56G', 9600)\n\nwhile True:\n line = ser.readline()\n if 'accleration' in str(line):\n v = []\n for _ in range(3):\n line = ser.readline()\n end = 7 if '-' in str(line) else 6\n val = float(str(line)[2:end])\n v.append(val)\n print(v)\n"
}
] | 3 |
alessandrostockman/ilp-solver
|
https://github.com/alessandrostockman/ilp-solver
|
4e7c52e30491bfd69c8760ee9283198eb05de51b
|
08a4456a0a9cf54f0e1988899bad7432c7d2f790
|
34d58a1b4d859281a57c7e2f4d859e4a1de4d2c8
|
refs/heads/main
| 2023-05-29T23:09:44.906864 | 2021-06-04T09:21:56 | 2021-06-04T09:21:56 | null | 0 | 0 | null | null | null | null | null |
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"text": "\nfrom lib.utils import Parameters\nfrom lib.domain import DomainProblem\nimport numpy as np\nfrom lib.simplex import SimplexProblem\nimport unittest\nfrom fractions import Fraction \nimport os \nimport json\n\nclass TestBase(unittest.TestCase):\n\n def _fract_to_dec(self, fract):\n if isinstance(fract, np.ndarray):\n if fract.dtype.type is np.str_:\n tmp = []\n for x in fract:\n tmp.append(self._fract_to_dec(str(x)))\n val = np.array(tmp)\n else:\n val = fract\n\n val = np.around(val, Parameters.DECIMAL_PRECISION)\n else:\n if type(fract) == str:\n val = Fraction(fract)\n val = val.numerator / val.denominator\n else:\n val = fract\n\n val = round(val, Parameters.DECIMAL_PRECISION)\n return val\n\n def _get_base_dir(self):\n return os.path.join(os.path.dirname(__file__))\n\n def _test_simplex_problem_correctness(self, p):\n self.assertTrue(isinstance(p, SimplexProblem))\n\n self.assertTrue(isinstance(p.c, np.ndarray))\n self.assertTrue(isinstance(p.A, np.ndarray))\n self.assertTrue(isinstance(p.b, np.ndarray))\n self.assertTrue(isinstance(p.in_basis, np.ndarray))\n self.assertTrue(isinstance(p.out_basis, np.ndarray))\n self._test_carry_correctness(p)\n\n def _test_carry_correctness(self, p):\n self.assertTrue(isinstance(p.carry_matrix, np.ndarray))\n self.assertTrue(isinstance(p.get_y(), np.ndarray))\n self.assertTrue(isinstance(p.get_inverse_matrix(), np.ndarray))\n self.assertTrue(isinstance(p.get_xb(), np.ndarray))\n\n self.assertEqual(p.carry_matrix[0,:-1], p.get_y())\n self.assertEqual(p.carry_matrix[0,-1], p.get_z())\n self.assertEqual(p.carry_matrix[1:,:-1] , p.get_inverse_matrix())\n self.assertEqual(p.carry_matrix[1:,-1], p.get_xb())\n\n def _create_problem(self, shape, A=None, b=None, c=None):\n constraints, vars = shape\n\n if A is None:\n A = np.zeros(shape)\n else:\n A = np.array(A)\n \n if b is None:\n b = np.zeros(constraints)\n else:\n b = np.array(b)\n\n if c is None:\n c = np.zeros(vars)\n else:\n c = np.array(c)\n\n return SimplexProblem(c, A, b)\n\n def _load_problems(self, problem_type):\n problems = {\n 'integer': [],\n 'decimal': []\n }\n for p in self._get_problem_files():\n with open(p) as json_file:\n problem = json.load(json_file)\n if problem.get('integer', False):\n problems['integer'].append((DomainProblem.from_json(p), problem['solution']))\n else:\n problems['decimal'].append((DomainProblem.from_json(p), problem['solution']))\n\n return problems[problem_type]\n\n def _get_problem_files(self):\n return [self._get_base_dir()+'/res/problem'+str(i)+'.json' for i in range(1, 20)]"
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"text": "from lib.simplex import simplex_algorithm\nfrom lib.utils import ProblemSolution\nfrom lib.domain import DomainProblem\nfrom tests.test_base import TestBase\nimport numpy as np\n\nclass TestDomainProblem(TestBase):\n\n def test_static_from_json(self):\n for p in self._get_problem_files():\n try:\n DomainProblem.from_json(p)\n except Exception:\n self.fail(\"deserialize_problem raised Exception on \" + p)\n\n def test_get_standard_form(self):\n for p, solution in self._load_problems('decimal'):\n std_problem, chg = p.get_standard_form()\n std_real = np.array(solution['standard']['form'])\n self.assertEqual(std_problem.shape, std_real.shape)\n\n if std_problem.shape == std_real.shape:\n self.assertTrue((std_problem == std_real).all())\n \n def test_get_problem_sol(self):\n #(self, optimum, standard_sol, var_chg_map):\n for p, solution in self._load_problems('decimal'):\n std_problem, chg = p.get_standard_form()\n ret_type, std_opt, std_sol = simplex_algorithm(std_problem[0,:-1], std_problem[1:,:-1], std_problem[1:,-1])\n\n self.assertEqual(ret_type.value, solution['type'])\n\n if ret_type is ProblemSolution.FINITE:\n opt, sol = p.get_problem_sol(std_opt, std_sol, chg)\n self.assertEqual(opt, self._fract_to_dec(solution['optimum']))\n self.assertTrue((sol == self._fract_to_dec(np.array(solution['values']))).all())\n"
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"text": "# Integer Linear Programming Solver\n\nImplementation of a Two-Phase Revised Simplex algorithm and Branch and Bound algorithm for integer linear programming problems solving.\n\n## Modeling\n\nThe project contains some example use cases, including a resource assignment problem aimed at the assignment of electric vehicle recharging stations distributed in different city blocks with several constrains on the placement.\n\n\n\n## Usage\n\nThe class `DomainProblem` implements the method `solve` and returns the solution of the given problem.\n\nThe problem can be created either by instanciating `DomainProblem` and adding constraints to it or by importing a json file which contains the problem definition with `DomainProblem.from_json(filename)`.\n\n### How to run\n\n`python main.py [-L/--logging LOGGING] [-v/--verbosity VERBOSITY] [-P/--plot] [-R/--run RUN]`\n\n- `--logging: Defines logging mode [\"file\", \"console\", \"none\"]`\n- `--verbosity: Level of verbosity of logs [\"low\", \"high\"]`\n- `--plot: If specified plots the resulting image at the end of execution (when running map)`\n- `--run: Chooses the example to run [\"linear\", \"integer\", \"map\"]`\n "
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"text": "from tests.test_base import TestBase\nimport unittest\nimport numpy as np \nfrom fractions import Fraction\nimport os\nimport json\n\nfrom lib.simplex import *\nfrom lib.domain import DomainProblem\n\nclass TestBaseProblem(TestBase):\n\n def test_find_initial_basis(self):\n ps = [\n self._create_problem((4,4), A=[\n [0, 0, 1], \n [1, 0, 0], \n [0, 1, 0]\n ]),\n self._create_problem((4,4), A=[\n [7, 0, 0], \n [0, 0, 1], \n [0, 1, 0]\n ]),\n self._create_problem((4,4), A=[\n [0, 0, 0], \n [0, 0, 0], \n [1, 1, 1]\n ]),\n self._create_problem((4,4), A=[\n [22, 0, -2, 1, 4], \n [3, 0, 4, 0, 3], \n [54, 1, 2, 0, 2]\n ]),\n ]\n\n sols = [\n [2, 0, 1], [-1, 2, 1], [-1, -1, 0], [3, -1, 1]\n ]\n\n for p, sol in zip(ps, sols):\n sol = np.array(sol)\n p.find_initial_basis()\n self.assertEqual(p.in_basis.shape, sol.shape)\n\n if p.in_basis.shape == sol.shape:\n self.assertTrue((p.in_basis == sol).all())\n\n def test_compute_out_of_base(self):\n ps = [\n self._create_problem((4,4), A=[\n [0, 0, 1], \n [1, 0, 0], \n [0, 1, 0]\n ]),\n self._create_problem((4,4), A=[\n [7, 0, 0], \n [0, 0, 1], \n [0, 1, 0]\n ]),\n self._create_problem((4,4), A=[\n [0, 0, 0], \n [0, 0, 0], \n [1, 1, 1]\n ]),\n self._create_problem((4,4), A=[\n [22, 0, -2, 1, 4], \n [3, 0, 4, 0, 3], \n [54, 1, 2, 0, 2]\n ]),\n ]\n\n sols = [\n [], [0], [1, 2], [0, 2, 4]\n ]\n\n for p, sol in zip(ps, sols):\n sol = np.array(sol)\n p.find_initial_basis()\n p.compute_out_of_base()\n self.assertEqual(p.out_basis.shape, sol.shape)\n\n if p.out_basis.shape == sol.shape:\n self.assertTrue((p.out_basis == sol).all())\n\nclass TestBaseFunctions(TestBase):\n \n def test_domain_problem_solve(self):\n for dp, solution in self._load_problems('decimal'):\n #print(\"Solving problem:\", dp.optimization_type, \"z =\",\" + \".join([str(x) + \"x\" + str(i+1) for i, x in enumerate(dp.costs)]))\n ret, opt, sol = dp.solve()\n\n self.assertEqual(ret.value, solution['type'])\n\n if ret is ProblemSolution.FINITE:\n arr = self._fract_to_dec(np.array(solution['values']))\n\n self.assertEqual(sol.shape, arr.shape)\n if sol.shape == arr.shape:\n self.assertTrue((sol == arr).all())\n\n self.assertEqual(opt, self._fract_to_dec(solution['optimum']))\n "
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"text": "from lib import logger\nimport math\nfrom collections import deque\nimport numpy as np\nfrom typing import Tuple\n\nfrom lib.utils import Parameters, ProblemSolution, DomainOptimizationType\nfrom lib.simplex import simplex_algorithm\n\nfrom lib.utils import SortedList\n\nclass BBNode:\n def __init__(self, std_problem, variables, coefficients, val, slack_coeff, var_chg_map, node_name, opt_type: DomainOptimizationType):\n self.std_problem = std_problem.copy()\n self.var_chg_map = var_chg_map\n self.node_name = node_name\n self.opt_type = opt_type\n self.child_left, self.child_right = None, None\n self.sol = None\n self.opt = None\n self.ret_type = None\n\n if variables is not None:\n rows, cols = self.std_problem.shape\n new_row = np.zeros(cols+1)\n new_row[variables] = coefficients\n new_row[-1] = val\n new_row[-2] = slack_coeff\n\n self.std_problem = np.c_[self.std_problem[:,:-1], np.zeros(rows), self.std_problem[:,-1]]\n self.std_problem = np.r_[self.std_problem, [new_row]]\n\n def solve(self):\n ret, std_opt, std_sol = simplex_algorithm(self.std_problem[0,:-1], self.std_problem[1:,:-1], self.std_problem[1:,-1])\n logger.write(\"The problem is \"+ret.name)\n \n if ret is ProblemSolution.FINITE:\n self.sol = np.array([sum([factor['coeff'] * std_sol[factor['var']] for factor in factors]) for factors in self.var_chg_map.values()])\n self.opt = std_opt if self.opt_type is DomainOptimizationType.MIN else -1 * std_opt\n logger.write(\"The variables values are\", self.sol, \"with optimum value\", self.opt,\"\\n\")\n\n else :\n self.opt = np.inf if self.opt_type is DomainOptimizationType.MIN else np.NINF\n \n self.ret_type = ret\n\n def is_int(self):\n return np.all(np.mod(np.around(self.sol, Parameters.DECIMAL_PRECISION), 1) == 0)\n \n def __lt__(self, other):\n if self.opt_type is DomainOptimizationType.MAX:\n return self.opt > other.opt \n else :\n return self.opt < other.opt \n \n def __repr__(self):\n return str(self)+\"(\"+str(self.opt)+\")\" \n \n def __str__(self):\n return 'P'+str(self.node_name) \n\n\nclass BBTree:\n def __init__(self, std_problem, var_chg_map, optimization_type):\n self.std_problem = std_problem\n self.var_chg_map = var_chg_map\n self.optimization_type = optimization_type\n\n self.generated_nodes = 0\n self.root = BBNode(self.std_problem, None, None, None, None, self.var_chg_map,self.generated_nodes,self.optimization_type)\n self.best_node = None\n\n self.working_memory = deque([self.root])\n\n def solve(self) -> Tuple[ProblemSolution,BBNode] :\n logger.write(\"Solving root node \"+str(self.root))\n self.root.solve()\n\n while self.working_memory:\n\n logger.write(\"\\nCurrent nodes stack: \",str(self.working_memory))\n\n node = self.select_next()\n logger.write(\"Considering node \"+str(node))\n \n if node.ret_type is ProblemSolution.UNLIMITED:\n return node.ret_type, None\n\n if node.ret_type is ProblemSolution.IMPOSSIBLE:\n logger.write(\"The problem associated with node \"+str(node)+\" is impossible - Pruning by infeasibility\")\n elif self.is_worse(node): \n logger.write(\"The solution associated with node \"+str(node)+\" is worse than the current best one - Pruning by bound\")\n else:\n if node.is_int():\n logger.write(\"The solution associated with node \"+str(node)+\" is integer (\",node.sol,\") - Pruning by integrality\")\n if self.best_node == None :\n assert isinstance(self.working_memory,deque), 'working memory should have been a deque til this point'\n self.working_memory = SortedList(self.working_memory)\n self.best_node = node # Pruned by integrality\n else:\n logger.write(\"The solution associated with node \"+str(node)+\" is not integer - Branching the tree\")\n self.branch(node) # Branch\n\n return ProblemSolution.FINITE if self.best_node is not None else ProblemSolution.IMPOSSIBLE, self.best_node\n\n def is_worse(self, node : BBNode) -> bool:\n if self.best_node is None:\n return False\n return node.opt <= self.best_node.opt if self.optimization_type is DomainOptimizationType.MAX else node.opt >= self.best_node.opt \n\n def branch(self, node : BBNode):\n f = node.sol - np.trunc(node.sol)\n idx=np.argmax(np.minimum(f, 1-f))\n val = node.sol[idx]\n\n variables = [d['var'] for d in self.var_chg_map[idx]]\n coefficients = [d['coeff'] for d in self.var_chg_map[idx]]\n\n logger.write(\"Branch on variable X\"+str(idx))\n logger.write(\"Adding new costraints : X\"+str(idx)+\" <= \"+str(math.floor(val))+\" for left node and X\"+str(idx)+\" >= \"+str(math.ceil(val))+\" for right node\\n\")\n node.child_left = BBNode(node.std_problem, variables, coefficients, math.floor(val), 1, self.var_chg_map,self.incr_generated_nodes(),self.optimization_type)\n node.child_right = BBNode(node.std_problem, variables, coefficients, math.ceil(val), -1, self.var_chg_map,self.incr_generated_nodes(),self.optimization_type)\n \n #solve it already \n logger.write(\"Solving left node \"+str(node.child_left))\n node.child_left.solve()\n logger.write(\"Solving right node \"+str(node.child_right))\n node.child_right.solve()\n\n self.add_childs_to_memory(node.child_left, node.child_right)\n \n def select_next(self) -> BBNode :\n if isinstance(self.working_memory,deque): \n node = self.working_memory.popleft()\n else :\n node = self.working_memory.pop(0)\n return node \n\n def add_childs_to_memory(self, ch_left : BBNode, ch_right : BBNode ) :\n if self.best_node is None :\n self.working_memory.appendleft(ch_right)\n self.working_memory.appendleft(ch_left)\n else :\n self.working_memory.add(ch_right)\n self.working_memory.add(ch_left)\n \n def incr_generated_nodes(self) -> int:\n self.generated_nodes+=1\n return self.generated_nodes\n\n\ndef bb_algorithm(std_problem, var_chg_map, optimization_type):\n logger.write(\"\\nStarting Branch and Bound algorithm\\n\")\n tree = BBTree(std_problem, var_chg_map, optimization_type)\n ret, best_node = tree.solve()\n\n if ret is ProblemSolution.FINITE:\n return ret, best_node.opt, best_node.sol\n else: \n return ret, None, None\n"
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"text": "import json\nfrom lib import logger\nfrom lib.logger import LogTarget, LogVerbosity\n\nimport numpy as np\nfrom lib.utils import DomainConstraintType, DomainOptimizationType, ProblemSolution, plot_map\nfrom lib.domain import DomainConstraint, DomainProblem\nfrom argparse import ArgumentParser\nimport time\n\ndef run_map_example(blocks_filename, columns_filename, image_filename=None, plot=False, log_target=LogTarget.NONE, log_verbose=LogVerbosity.LOW):\n start_time = time.time()\n logger.set_target(log_target)\n logger.set_verbosity(log_verbose)\n\n budget = 100000\n\n with open(blocks_filename) as json_blocks, open(columns_filename) as json_columns:\n blocks = json.load(json_blocks)\n columns = json.load(json_columns)\n\n blocks_num = len(blocks)\n cols_num = len(columns)\n var_number = blocks_num * cols_num\n c = np.array([columns[i % cols_num]['power'] for i in range(var_number)])\n\n int_probl = DomainProblem(c, DomainOptimizationType.MAX, is_integer=True)\n\n # area\n for index, block in enumerate(blocks):\n int_probl.add_constraint(DomainConstraint([columns[i % cols_num]['area'] if index * cols_num <= i < (index + 1) * cols_num else 0 for i in range(var_number)], block['area'], DomainConstraintType.LESS_EQUAL))\n\n # minim \n for index, block in enumerate(blocks):\n int_probl.add_constraint(DomainConstraint([1 if index * cols_num <= i < (index + 1) * cols_num else 0 for i in range(var_number)], block['min_number'], DomainConstraintType.GREAT_EQUAL))\n\n # price \n int_probl.add_constraint(DomainConstraint([columns[i % cols_num]['cost'] for i in range(var_number)], budget, DomainConstraintType.LESS_EQUAL))\n\n # availability \n for index, column in enumerate(columns):\n int_probl.add_constraint(DomainConstraint([1 if i % cols_num == index else 0 for i in range(var_number)], column['availability'], DomainConstraintType.LESS_EQUAL))\n\n ret, opt, sol = int_probl.solve()\n end_time = time.time()\n \n if plot:\n plot_map(image_filename, blocks, columns, sol)\n \n if ret is ProblemSolution.FINITE: \n print(\"\\nSolution: the variables values are\", sol, \"with optimum value\", opt)\n\n return end_time - start_time\n\ndef run_toy_example(example_filename, plot=False, log_target=LogTarget.NONE, log_verbose=LogVerbosity.LOW):\n start_time = time.time()\n logger.set_target(log_target)\n logger.set_verbosity(log_verbose)\n\n int_probl = DomainProblem.from_json(example_filename)\n ret, opt, sol = int_probl.solve()\n\n end_time = time.time()\n\n if ret is ProblemSolution.FINITE: \n print(\"\\nSolution: the variables values are\", sol, \"with optimum value\", opt)\n \n return end_time - start_time\n\n\nif __name__ == \"__main__\":\n parser = ArgumentParser()\n parser.add_argument(\"-L\", \"--logging\", dest=\"logging\", help=\"Defines logging mode\", default=\"none\", choices=['none', 'console', 'file'])\n parser.add_argument('-V', '--verbosity', dest=\"verbosity\", help=\"Defines verbosity level\", default=\"low\", choices=['low', 'high'])\n parser.add_argument('-P', '--plot', dest=\"plot\", help=\"If specified plots the resulting image at the end of execution\", default=False, action='store_true')\n parser.add_argument('-R', '--run', dest=\"run\", help=\"Chooses the example to run\", default=\"linear\", choices=['linear', 'integer', 'map'])\n args = parser.parse_args()\n\n log_target = {\n \"none\": LogTarget.NONE,\n \"console\": LogTarget.CONSOLE,\n \"file\": LogTarget.FILE,\n }[args.logging]\n\n log_verbose = {\n \"low\": LogVerbosity.LOW,\n \"high\": LogVerbosity.HIGH,\n }[args.verbosity]\n\n if args.run == \"map\":\n execution_time = run_map_example(\"res/rome.json\", \"res/columns.json\", \"res/rome.png\", plot=args.plot, log_target=log_target, log_verbose=log_verbose)\n elif args.run == \"linear\":\n execution_time = run_toy_example(\"res/linear_example.json\", plot=args.plot, log_target=log_target, log_verbose=log_verbose)\n elif args.run == \"integer\":\n execution_time = run_toy_example(\"res/integer_example.json\", plot=args.plot, log_target=log_target, log_verbose=log_verbose)\n\n print(\"Execution finished in \" + str(execution_time) + \"s\")\n"
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"text": "from lib.utils import ProblemSolution\nfrom lib.bb import bb_algorithm\nfrom tests.test_base import TestBase\nimport numpy as np\n\n\nclass TestBranchAndBound(TestBase):\n \n def test_get_problem_sol(self):\n for p, solution in self._load_problems('integer'):\n std_problem, chg = p.get_standard_form()\n ret_type, opt, sol = bb_algorithm(std_problem, chg, p.optimization_type)\n self.assertEqual(ret_type.value, solution['type'])\n\n if ret_type is ProblemSolution.FINITE:\n self.assertEqual(opt, self._fract_to_dec(solution['optimum']))\n # self.assertTrue((sol == self._fract_to_dec(np.array(solution['values']))).all())\n"
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"text": "from datetime import datetime\nfrom enum import Enum\n\nclass LogVerbosity(Enum):\n NOTHING = 1\n LOW = 2\n HIGH = 3\n\nclass LogTarget(Enum):\n FILE = 1\n CONSOLE = 2\n NONE = 3\n\n_verbosity = LogVerbosity.HIGH\n_target = LogTarget.FILE\n_target_initialized = False\n_target_file = None\n\ndef set_verbosity(level: LogVerbosity):\n global _verbosity\n _verbosity = level\n\ndef set_target(target: LogTarget):\n global _target\n _target = target\n\ndef write(*message: list, verbosity: LogVerbosity=LogVerbosity.LOW):\n global _target_initialized, _target_file, _target\n if not _target_initialized:\n if _target is LogTarget.FILE:\n _target_file = open(\"logs/\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\") + \".log\", \"w\")\n\n _target_initialized = True\n if _verbosity.value >= verbosity.value:\n _print_to_target(*message)\n\ndef _print_to_target(*message: list):\n global _target_file, _target\n if _target is LogTarget.CONSOLE:\n print(message)\n elif _target is LogTarget.FILE:\n _target_file.write(\" \".join(str(x) for x in message) + \"\\n\")\n"
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"text": "from enum import Enum\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Rectangle\nimport bisect\n\nclass Parameters:\n DECIMAL_PRECISION = 8\n\nclass ProblemSolution(Enum):\n FINITE = 1\n UNLIMITED = 2\n IMPOSSIBLE = 3\n\nclass DomainConstraintType(Enum):\n LESS_EQUAL = 1\n GREAT_EQUAL = 2\n EQUAL = 3\n\nclass DomainOptimizationType(Enum):\n MAX = 1\n MIN = 2\n\ndef plot_map(image_path, blocks, columns, sols):\n w, h = 100, 80 # Size of the box containing the values to plot\n fontsize = 7 # Size of the font\n padding_left = 5 # Left padding on the box\n\n cols_num = len(columns)\n vert_offset = 2 * h / (cols_num * 2 + 1)\n img = plt.imread(image_path)\n fig, ax = plt.subplots(1, 1, figsize=(12, 10))\n ax.set_aspect('equal')\n ax.imshow(img)\n\n for b_index, block in enumerate(blocks):\n x, y = block['coord'].values()\n left_x, top_y = x - w / 2, y - h / 2\n background = Rectangle((left_x, top_y), w, h, color=\"black\")\n ax.add_patch(background)\n\n offset = vert_offset\n for c_index, column in enumerate(columns):\n ax.text(left_x + padding_left, top_y + offset, column['name'] + \": \" + str(sols[b_index * cols_num + c_index]), fontsize=fontsize, va='center', color=\"white\")\n offset += vert_offset\n \n plt.show()\n\n\nclass SortedList():\n \n def __init__(self, items=[]) -> None:\n self._list = list(items)\n self._list.sort()\n \n def add(self, item):\n bisect.insort(self._list,item)\n\n # self._list.append(item)\n # self._list.sort()\n\n def pop(self, n=0):\n return self._list.pop(n)\n\n def __bool__(self):\n return len(self._list) > 0\n \n def __str__(self):\n return \"SortedList(\"+str(self._list)+\")\"\n \n def __repr__(self):\n return \"SorteListd(\"+str(self._list)+\")\""
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"text": "from enum import Enum\nfrom lib.utils import Parameters, ProblemSolution\nfrom lib import logger \nimport numpy as np\n\nclass SimplexProblem:\n \n def __init__(self, obj_func_coefficent, coefficent_matrix, constant_terms):\n self.c = np.array(obj_func_coefficent)\n self.A = np.array(coefficent_matrix)\n self.b = np.array(constant_terms) \n\n self.in_basis = None\n self.out_basis = None\n\n self.carry_matrix = np.zeros((self.b.size + 1,self.b.size + 1))\n\n def get_y(self):\n return self.carry_matrix[0,:-1]\n def get_z(self):\n return self.carry_matrix[0:1,-1] \n def get_inverse_matrix(self):\n return self.carry_matrix[1:,:-1] \n def get_xb(self):\n return self.carry_matrix[1:,-1]\n \n def set_carry_matrix(self, matrix):\n self.carry_matrix = matrix\n\n def set_y(self, y):\n self.carry_matrix[0,:-1] = y\n \n def set_xb(self, xb):\n self.carry_matrix[1:,-1] = xb\n\n def set_z(self, z):\n self.carry_matrix[0,-1] = z\n\n def set_inverse_matrix(self, inverse_matrix = None):\n self.carry_matrix[1:,:-1] = inverse_matrix or np.identity(self.A.shape[0])\n \n def check_basis(self):\n return -1 in self.in_basis\n \n def find_initial_basis(self):\n base_indexes = []\n id_matrix = np.identity(self.A.shape[0])\n for col in id_matrix:\n idx = np.where((self.A.T == col).all(axis=1))\n base_indexes.append(idx[0][0] if len(idx[0])>0 else -1)\n self.in_basis = np.array(base_indexes) \n \n def init_carry(self):\n self.set_xb(np.dot(self.get_inverse_matrix(),self.b)) \n self.set_y(np.dot(-self.c[self.in_basis],self.get_inverse_matrix()))\n self.set_z(np.dot(self.get_y(),self.b))\n \n def compute_out_of_base(self):\n self.out_basis = np.array(list(set(range(self.A.shape[1])) - set(self.in_basis)))\n self.out_basis.sort() #BLAND rule\n \n def get_Aj(self, j):\n return np.dot(self.get_inverse_matrix(),self.A[:,j])\n \n def swap_vars(self,ext_var,ent_var):\n self.in_basis[ext_var] = ent_var\n \n def determine_entering_var(self):\n for j in self.out_basis :\n cj = self.c[j] + np.dot(self.get_y(),self.A[:,j])\n if cj < 0 : \n logger.write(\"Chosen entering variable: x\" + str(j))\n return cj,j\n \n return None,None\n \n def determine_exiting_var(self,ent_var):\n Aj = self.get_Aj(ent_var)\n if (Aj<=0).all() : #unlimited problem \n return None,None \n\n positives = np.where(Aj > 0, np.divide(self.get_xb(), Aj, out=np.zeros_like(Aj), where=(Aj!=0)), np.inf)\n h = np.where(positives == positives.min())[0]\n\n out_index = h[self.in_basis[h].argmin()] #BLAND rule\n \n logger.write(\"Chosen exiting variable: x\" + str(self.in_basis[out_index])) #TODO Print bland rule\n return Aj,out_index\n\n def update_carry(self,h,Aj,cost=None):\n self.carry_matrix[h+1] = self.carry_matrix[h+1]/Aj[h]\n for i in range(self.carry_matrix.shape[0]):\n if i != h+1:\n if i == 0 and cost is not None:\n self.carry_matrix[i] = self.carry_matrix[i]-self.carry_matrix[h+1]*cost\n else :\n self.carry_matrix[i] = self.carry_matrix[i]-self.carry_matrix[h+1]*Aj[i-1]\n \n\nclass SimplexArtificialProblem(SimplexProblem):\n def __init__(self, obj_func_coefficent, coefficent_matrix, constant_terms,artificial_vars,old_basis):\n self.artificial_vars = artificial_vars\n self.old_basis = old_basis\n super().__init__(obj_func_coefficent,coefficent_matrix,constant_terms)\n \n def find_initial_basis(self):\n in_basis = self.old_basis.copy()\n np.place(in_basis, in_basis == -1, self.artificial_vars)\n self.in_basis = in_basis\n \n def check_basis(self):\n return np.in1d(self.in_basis,self.artificial_vars).any()\n\n def substitute_artificial_vars(self):\n lin_dependent_rows = []\n idxs = np.where(np.in1d(self.in_basis,self.artificial_vars))\n #all artificial var with idx index should leave basis \n for idx in idxs[0]:\n #determine which is entering\n ent_var = None \n for var in self.out_basis[~np.isin(self.out_basis,self.artificial_vars)]:\n Aj = self.get_Aj(var)\n if round(Aj[idx], Parameters.DECIMAL_PRECISION) != 0:\n # var entering\n self.in_basis[idx] = var\n self.update_carry(idx,Aj)\n ent_var = var\n break \n if ent_var == None : #cannot find a substituting out of base var\n lin_dependent_rows.append(idx) #a row of the original problem was redundant\n else :\n self.compute_out_of_base() \n \n return np.array(lin_dependent_rows) if lin_dependent_rows else None\n \n\ndef define_artificial_problem(p):\n r = np.count_nonzero(p.in_basis == -1) #num of var to be replaced by artificial ones \n \n #create obj function with artificial variables \n obj_func = np.zeros_like(p.c) \n obj_func = np.concatenate([obj_func,np.ones(r)]) \n\n #create artificial columns for the coefficents matrix \n id = np.identity(p.A.shape[0])\n coeff_matrix = p.A.copy()\n for i in range(len(p.in_basis)):\n if p.in_basis[i] == -1:\n coeff_matrix = np.c_[coeff_matrix,id[:,i]]\n\n #copy costant terms \n constant_terms = p.b.copy()\n\n #determine artificial vars\n artificial_variables = np.arange(len(p.c),len(p.c)+r)\n \n return obj_func,coeff_matrix,constant_terms,artificial_variables\n\ndef simplex_algorithm(c, A, b): \n logger.write(\"\\nStarting simplex\") \n #create object \n problem = SimplexProblem(c, A, b)\n\n #set starting basis \n problem.find_initial_basis()\n problem.set_inverse_matrix()\n \n #if cannot find starting basis phase1 is needed \n if problem.check_basis():\n logger.write(\"Unable to find an Initial Starting Basis, proceeding with Phase1\")\n ret_type = phase1(problem)\n logger.write(\"End of Phase 1\") \n \n if ret_type is ProblemSolution.IMPOSSIBLE:\n return ret_type, None, None\n else:\n logger.write(\"Starting basis found, switching to Phase 2\") \n \n ret_type = phase2(problem)\n\n if ret_type is ProblemSolution.FINITE:\n solution = np.zeros(problem.c.size)\n solution[problem.in_basis] = problem.get_xb()\n opt = round(-problem.get_z()[0], Parameters.DECIMAL_PRECISION)\n return ret_type, opt, np.around(solution, Parameters.DECIMAL_PRECISION) \n else:\n return ret_type, None, None\n\ndef from_p1_to_p2(p1 : SimplexArtificialProblem,p : SimplexProblem,lin_dep_rows):\n if lin_dep_rows is not None :\n #modify original problem data\n p.A = np.delete(p.A, lin_dep_rows, axis=0)\n p.b = np.delete(p.b,lin_dep_rows)\n #modify phase1 data\n p1.set_carry_matrix(np.delete(p1.carry_matrix, lin_dep_rows+1 , axis=0)) #delete rows from carry\n p1.set_carry_matrix(np.delete(p1.carry_matrix, lin_dep_rows, axis=1)) #delete columns from carry\n p1.in_basis = np.delete(p1.in_basis,lin_dep_rows) #remove not needed in basis variable \n\n p.set_carry_matrix(p1.carry_matrix)\n p.in_basis = p1.in_basis.copy()\n\n\ndef phase1(p : SimplexProblem):\n logger.write(\"\\nStarting Phase 1\")\n #determine changes to make for artificial problem\n c,A,b,art_vars = define_artificial_problem(p)\n logger.write(\"Inserting \",[\"x\"+str(i) for i in art_vars],\" as artificial variables\")\n\n #create object \n p1 = SimplexArtificialProblem(c,A,b,art_vars,p.in_basis.copy())\n\n #set starting basis \n p1.find_initial_basis()\n p1.set_inverse_matrix()\n\n p1.init_carry()\n\n while True :\n logger.write(\"\\nNew basis: \",[\"x\"+str(i) for i in p1.in_basis])\n\n #save out_of_basis vars\n p1.compute_out_of_base()\n\n #compute reduced costs and determine entering var \n cost,ent_var = p1.determine_entering_var()\n lin_dep_rows = None\n if cost == None: #no negative cost found\n if round(p1.get_z()[0], Parameters.DECIMAL_PRECISION) != 0 : \n return ProblemSolution.IMPOSSIBLE\n elif p1.check_basis(): \n lin_dep_rows = p1.substitute_artificial_vars() \n\n from_p1_to_p2(p1,p,lin_dep_rows)\n return ProblemSolution.FINITE\n\n #determine exiting var \n Aj,ext_var_index = p1.determine_exiting_var(ent_var)\n \n if Aj is None:\n # Raising exception as this case should not be achievable\n raise ArithmeticError\n\n #ent_var entering basis , ext_var leaving\n p1.swap_vars(ext_var_index,ent_var) \n\n #modify carry matrix \n p1.update_carry(ext_var_index,Aj,cost)\n\ndef phase2(p : SimplexProblem):\n logger.write(\"\\nStarting Phase 2\")\n\n p.init_carry()\n\n while True :\n logger.write(\"\\nNew basis: \",[\"x\"+str(i) for i in p.in_basis])\n\n #save out_of_basis vars\n p.compute_out_of_base()\n\n #compute reduced costs and determine entering var \n cost,ent_var = p.determine_entering_var()\n if cost == None: \n return ProblemSolution.FINITE\n \n #determine exiting var \n Aj,ext_var_index = p.determine_exiting_var(ent_var)\n if Aj is None:\n return ProblemSolution.UNLIMITED\n\n #ent_var entering basis , ext_var leaving\n p.swap_vars(ext_var_index,ent_var) \n\n #modify carry matrix \n p.update_carry(ext_var_index,Aj,cost)\n"
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"text": "from fractions import Fraction\nfrom lib import logger\nfrom lib.bb import bb_algorithm\nfrom lib.simplex import simplex_algorithm\nfrom lib.utils import DomainConstraintType, DomainOptimizationType, ProblemSolution\nimport numpy as np\nimport json\n\nclass DomainProblem:\n\n def __init__(self, costs, optimization_type, constraints=[], non_negatives=None, non_positives=[], is_integer=False):\n if non_negatives is None:\n non_negatives = np.arange(costs.size)\n\n self.costs = costs\n self.constraints = constraints\n self.non_positives = non_positives\n self.optimization_type = optimization_type\n self.is_integer = is_integer\n self.non_negatives = non_negatives\n\n self._constraint_array = None\n self._constants_array = None\n\n @staticmethod\n def from_matrix(matrix, type=DomainOptimizationType.MIN, non_negatives=None, non_positives=[], is_integer=False):\n A, b, c = matrix[1:,:-1], matrix[:,-1], matrix[0,:]\n\n return DomainProblem.from_abc(A, b, c, type, non_negatives, non_positives, is_integer)\n\n @staticmethod\n def from_abc(A, b, c, type=DomainOptimizationType.MIN, non_negatives=None, non_positives=[], is_integer=False):\n constraints = []\n for coefficients, constant in zip(A, b):\n constraints.append(DomainConstraint(coefficients, constant, DomainConstraintType.EQUAL))\n\n return DomainProblem(np.array(c), type, constraints, non_negatives, non_positives, is_integer)\n\n @staticmethod\n def from_json(filename):\n with open(filename) as json_file:\n problem = json.load(json_file)\n \n constraints = []\n for constraint in problem['constraints']:\n constraints.append(DomainConstraint(constraint['coefficients'], constraint['constant'], {\n 'EQ': DomainConstraintType.EQUAL,\n 'LEQ': DomainConstraintType.LESS_EQUAL,\n 'GEQ': DomainConstraintType.GREAT_EQUAL\n }[constraint['type']]))\n\n #p, A, b, c = problem, np.array(constraints), np.array(constants), np.array(problem['objective']['costs'])\n return DomainProblem(np.array(problem['objective']['costs']), {\n 'MIN': DomainOptimizationType.MIN,\n 'MAX': DomainOptimizationType.MAX\n }[problem['objective']['optimization']], constraints, problem['non-negatives'], problem.get('non-positives', []), problem.get('integer', False))\n\n def add_constraint(self, c):\n self.constraints.append(c)\n\n def get_constraint_array(self):\n if self._constraint_array is None:\n self._constraint_array = np.array([c.coefficients for c in self.constraints])\n return self._constraint_array\n\n def get_constants_array(self):\n if self._constants_array is None:\n self._constants_array = np.array([c.constant for c in self.constraints])\n return self._constants_array\n\n def get_standard_form(self):\n logger.write(\"\\nTurning the problem into standard form\")\n Ac = np.r_[[self.costs], self.get_constraint_array()]\n\n # Sets the variable-change map only if the variable is present in the objective function\n var_chg_map = {i: [{'var': i, 'coeff': 1 if self.costs[i] != 0 else 0}] for i in range(self.costs.size)}\n rows, cols = Ac.shape\n\n # 1. Change objective function to minimization\n if self.optimization_type is DomainOptimizationType.MAX:\n logger.write(\"Changing the objective function into a minimization function\")\n Ac[0,:] *= -1\n\n # 2. Perform variable change over non-positive variables\n positive_variables = np.zeros(cols, dtype=bool)\n\n for i in range(cols):\n if i in self.non_negatives:\n positive_variables[i] = True\n\n for var in np.where(positive_variables == False):\n if var in self.non_positives:\n logger.write(\"Changing the sign of the negative variable/s \" + str(var))\n Ac[:, var] *= -1\n var_chg_map[var[0]][0]['coeff'] = -1\n elif var.size > 0:\n logger.write(\"Perform variable change for variable/s \" + str(var))\n _, Ac_cols = Ac.shape\n var_chg_map[var[0]].append({'var': Ac_cols, 'coeff': -1})\n Ac = np.c_[Ac, Ac[:, var] * -1]\n\n # 3. Add slack variables to change constraints into equations\n for index, constraint in enumerate(self.constraints):\n if constraint.type != DomainConstraintType.EQUAL:\n logger.write(\"Adding slack variable for constraint \" + str(index))\n Ac = np.c_[Ac, np.zeros(rows)]\n \n if constraint.type == DomainConstraintType.LESS_EQUAL:\n Ac[index + 1,-1] = 1\n elif constraint.type == DomainConstraintType.GREAT_EQUAL:\n Ac[index + 1,-1] = -1\n\n matrix = np.c_[Ac, np.insert(self.get_constants_array(), 0, 0)]\n\n # 4. Constant terms should be positive or 0\n for index, const in enumerate(self.get_constants_array()):\n if const < 0:\n logger.write(\"Inverting sign of constraint because of negative constant term\")\n matrix[index + 1, :] *= -1\n\n return matrix, var_chg_map\n\n def get_problem_sol(self, optimum, standard_sol, var_chg_map):\n sol = np.array([sum([factor['coeff'] * standard_sol[factor['var']] for factor in factors]) for factors in var_chg_map.values()])\n return (optimum if self.optimization_type is DomainOptimizationType.MIN else -optimum), sol\n\n def solve(self):\n standard_matrix, var_chg_map = self.get_standard_form()\n\n if self.is_integer:\n ret, std_opt, std_sol = bb_algorithm(standard_matrix, var_chg_map, self.optimization_type)\n opt, sol = std_opt, std_sol \n else:\n ret, std_opt, std_sol = simplex_algorithm(standard_matrix[0,:-1], standard_matrix[1:,:-1], standard_matrix[1:,-1])\n opt, sol = self.get_problem_sol(std_opt, std_sol, var_chg_map) if ret is ProblemSolution.FINITE else (None, None)\n\n logger.write(\"The problem is \"+ret.name)\n if ret is ProblemSolution.FINITE: \n logger.write(\"The variables values are\", sol, \"with optimum value\", opt)\n \n return ret, opt, sol \n\nclass DomainConstraint:\n\n def __init__(self, coefficients, constant, const_type):\n self.coefficients = []\n for c in coefficients:\n if type(c) == str:\n val = Fraction(c)\n val = val.numerator / val.denominator\n else:\n val = c\n self.coefficients.append(val)\n \n self.coefficients = np.array(self.coefficients)\n self.constant = constant\n self.type = const_type\n"
}
] | 11 |
vaculikjan/IZV
|
https://github.com/vaculikjan/IZV
|
e427ae00bbac8ff74743413aecef030b0e034ed2
|
187e76e9766ddbba28c7d8fcedb51f6f8a40cf36
|
d0659e5d4750b887426cd6d82c345e07cd9c056a
|
refs/heads/main
| 2023-02-06T20:13:03.560968 | 2021-09-09T11:38:20 | 2021-09-09T11:38:20 | 310,440,253 | 0 | 0 | null | null | null | null | null |
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"text": "#!/usr/bin/python3.8\n# coding=utf-8\nimport pandas as pd\nimport geopandas\nimport matplotlib.pyplot as plt\nimport contextily as ctx\nimport sklearn.cluster\nimport numpy as np\n# muzeze pridat vlastni knihovny\n\n\ndef make_geo(df: pd.DataFrame) -> geopandas.GeoDataFrame:\n\n \"\"\"Take Pandas DataFrame and return GeoDataFrame.\"\"\"\n\n df = df.loc[df[\"region\"] == \"JHM\"]\n df = df.dropna(subset=['d', 'e'])\n\n gdf = geopandas.GeoDataFrame(\n df,\n geometry=geopandas.points_from_xy(\n df[\"d\"],\n df[\"e\"]),\n crs=\"EPSG:5514\")\n\n return gdf\n\n\ndef plot_geo(\n gdf: geopandas.GeoDataFrame,\n fig_location: str = None,\n show_figure: bool = False):\n\n \"\"\"Plot geographical data of accidents in a chosen region (JHM).\n\n Keyword arguments:\n gdf -- GeoDataFrame with data needed for plotting\n fig_location -- location saying where to store the plotted figure\n show_figure -- whether the plotted figure is shown\n \"\"\"\n\n # change crs to get a less blurry map\n gdf = gdf.to_crs(\"epsg:3857\")\n\n # prep 2 geo data frames for 2 subplots\n vob = gdf.loc[gdf[\"p5a\"] == 1]\n nob = gdf.loc[gdf[\"p5a\"] == 2]\n\n # plotting\n _, ax = plt.subplots(1, 2, figsize=(13, 8))\n\n # setting boundaries for subplots to be the same for a prettier result\n x1, y1, x2, y2 = gdf.geometry.total_bounds\n\n ax[0].set_xlim(x1 + (x2-x1)/5.5, x2)\n ax[0].set_ylim(y1 - (y2-y1)/50, y2)\n\n ax[1].set_xlim(x1 + (x2-x1)/5.5, x2)\n ax[1].set_ylim(y1 - (y2-y1)/50, y2)\n\n # creating subplots and setting params for them\n vob.plot(markersize=0.5, ax=ax[1])\n ctx.add_basemap(\n ax[1],\n crs=gdf.crs.to_string(),\n source=ctx.providers.Stamen.TonerLite)\n\n nob.plot(markersize=0.5, ax=ax[0], color=\"r\")\n ctx.add_basemap(\n ax[0],\n crs=gdf.crs.to_string(),\n source=ctx.providers.Stamen.TonerLite)\n\n for col in ax:\n col.set_xticks([])\n col.set_yticks([])\n\n ax[0].title.set_text('Nehody mimo obec [JHM]')\n ax[1].title.set_text('Nehody v obci [JHM]')\n\n plt.tight_layout()\n\n # checking whather to save and show the plot\n if fig_location is not None:\n plt.savefig(fig_location)\n\n if show_figure:\n plt.show()\n\n\ndef plot_cluster(\n gdf: geopandas.GeoDataFrame,\n fig_location: str = None,\n show_figure: bool = False):\n\n \"\"\"Plot clustered accident data.\n\n Keyword arguments:\n gdf -- GeoDataFrame with data needed for plotting\n fig_location -- location saying where to store the plotted figure\n show_figure -- whether the plotted figure is shown\"\"\"\n\n # drop one accident that is far outside of the region\n # and is messing with clustering\n gdf = gdf.drop(gdf[gdf.geometry.x < -700000].index)\n gdf = gdf.to_crs(\"epsg:3857\")\n\n # cluster accident data\n coords = np.dstack([gdf.geometry.x, gdf.geometry.y]).reshape(-1, 2)\n model = sklearn.cluster.MiniBatchKMeans(n_clusters=20).fit(coords)\n\n # add column to identify clusters\n gdf_clstrd = gdf.copy()\n gdf_clstrd[\"cluster\"] = model.labels_\n\n # aggregate to get count of accidents in each cluster\n gdf_clstrd = gdf_clstrd.dissolve(\n by=\"cluster\",\n aggfunc={\"p1\": \"count\"}).rename(columns={\"p1\": \"cnt\"})\n\n # get center points from cluster polygons\n gdf_coords = geopandas.GeoDataFrame(\n geometry=geopandas.points_from_xy(\n model.cluster_centers_[:, 0],\n model.cluster_centers_[:, 1]))\n gdf_clstrd = gdf_clstrd.merge(\n gdf_coords,\n left_on=\"cluster\",\n right_index=True).set_geometry(\"geometry_y\")\n\n # plotting\n plt.figure(figsize=(13, 8))\n ax = plt.gca()\n\n # 2 plots; first one for clusters and second one for all accidents\n\n gdf.plot(ax=ax, color=\"grey\", markersize=0.1)\n gdf_clstrd.plot(\n ax=ax,\n markersize=gdf_clstrd[\"cnt\"] / 10,\n column=\"cnt\",\n alpha=0.6,\n legend=True)\n\n # add map\n ctx.add_basemap(\n ax,\n crs=gdf.crs.to_string(),\n source=ctx.providers.Stamen.TonerLite)\n\n # set params for the plot\n ax.set_xticks([])\n ax.set_yticks([])\n ax.title.set_text('Nehody v JHM kraji')\n plt.tight_layout()\n\n # checking whather to save and show the plot\n if fig_location is not None:\n plt.savefig(fig_location)\n\n if show_figure:\n plt.show()\n\n\nif __name__ == \"__main__\":\n # zde muzete delat libovolne modifikace\n gdf = make_geo(pd.read_pickle(\"accidents.pkl.gz\"))\n # plot_geo(gdf, \"geo1.png\", True)\n # plot_cluster(gdf, \"geo2.png\", True)\n"
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"text": "# IZV projekt 3\n\nČástečné řešení třetího projektu do předmětu IZV\n"
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"text": "from bs4 import BeautifulSoup\nfrom copy import deepcopy\nimport numpy as np\nimport zipfile as zf\nimport os, sys, requests, re, csv, io, pickle, gzip\n\nclass DataDownloader:\n\n #dictionary used to match a Region with a corresponding .csv file \n region_match = {\n \"PHA\" : \"00.csv\",\n \"STC\" : \"01.csv\",\n \"JHC\" : \"02.csv\",\n \"PLK\" : \"03.csv\",\n \"ULK\" : \"04.csv\",\n \"HKK\" : \"05.csv\",\n \"JHM\" : \"06.csv\",\n \"MSK\" : \"07.csv\",\n \"OLK\" : \"14.csv\",\n \"ZLK\" : \"15.csv\",\n \"VYS\" : \"16.csv\",\n \"PAK\" : \"17.csv\",\n \"LBK\" : \"18.csv\",\n \"KVK\" : \"19.csv\"\n }\n\n #dictionary used to store the column types with their description\n column_types = {\n \"p1\" : \"Identifikační číslo\",\n \"p36\" : \"Druh pozemní komunikace\",\n \"p37\" : \"Číslo pozemní komunikace\",\n \"p2a\" : \"Den, Měsíc, Rok\",\n \"weekday(p2a)\" : \"Den týdne\",\n \"p2b\" : \"Čas\",\n \"p6\" : \"Druh nehody\",\n \"p7\" : \"Druh srážky jedoucích vozidel\",\n \"p8\" : \"Druh pevné překážky\",\n \"p9\": \"Charakter nehody\",\n \"p10\" : \"Zavinění nehody\",\n \"p11\" : \"Alkohol u viníka nehody přítomen\",\n \"p12\" : \"Hlavní příčiny nehody\",\n \"p13a\" : \"Usmrceno osob\",\n \"p13b\" : \"Těžce zraněno osob\",\n \"p13c\" : \"Lehce zraněno osob\",\n \"p14\" : \"Celková hmotná škoda\",\n \"p15\" : \"Druh povrchu vozovky\",\n \"p16\" : \"Stav povrchu vozovky v době nehody\",\n \"p17\" : \"Stav komunikace\",\n \"p18\" : \"Povětrnostní podmínky v době nehody\",\n \"p19\" : \"Viditelnost\",\n \"p20\" : \"Rozhledové poměry\",\n \"p21\" : \"Dělení komunikace\",\n \"p22\" : \"Situování nehody na komunikaci\",\n \"p23\" : \"Řízení provozu v době nehody\",\n \"p24\" : \"Místní úprava přednosti v jízdě\",\n \"p27\" : \"Specifická místa a objekty v místě nehody\",\n \"p28\" : \"Směrové poměry\",\n \"p34\" : \"Počet zúčastněných vozidel\",\n \"p35\" : \"Místo dopravní nehody\",\n \"p39\" : \"Druh křižující komunikace\",\n \"p44\" : \"Druh vozidla\",\n \"p45a\" : \"Výrobní značka motorového vozidla\",\n \"p47\" : \"Rok výroby vozidla\",\n \"p48a\" : \"Charakteristika vozidla\",\n \"p49\" : \"Smyk\",\n \"p50a\" : \"Vozidlo po nehodě\",\n \"p50b\" : \"Únik provozních, přepravovaných hmot\",\n \"p51\" : \"Způsob vyproštění osob z vozidla\",\n \"p52\" : \"Směr jízdy nebo postavení vozidla\",\n \"p53\" : \"Škoda na vozidle\",\n \"p55a\" : \"Kategorie řidiče\",\n \"p57\" : \"Stav řidiče\",\n \"p58\" : \"Vnější ovlivnění řidiče\",\n \"a\" : \"a\",\n \"b\" : \"b\",\n \"d\" : \"Souřadnice X\",\n \"e\" : \"Souřadnice Y\",\n \"f\" : \"f\",\n \"g\" : \"g\",\n \"h\" : \"h\",\n \"i\" : \"i\",\n \"j\" : \"j\",\n \"k\" : \"k\",\n \"l\" : \"l\",\n \"n\" : \"n\",\n \"o\" : \"o\",\n \"p\" : \"p\",\n \"q\" : \"q\",\n \"r\" : \"r\",\n \"s\" : \"s\",\n \"t\" : \"t\",\n \"p5a\" : \"Lokalita nehody\",\n \"p99\" : \"Region\"\n }\n \n def __init__(self,url='https://ehw.fit.vutbr.cz/izv/', folder=\"data\", cache_filename='data_{}.pkl.gz'):\n \n self.url = url\n self.folder = folder\n self.cache_filename = cache_filename\n \n #variables for storing region data\n self.PHA = None\n self.STC = None\n self.JHC = None\n self.PLK = None\n self.KVK = None\n self.ULK = None\n self.LBK = None\n self.HKK = None\n self.PAK = None\n self.OLK = None\n self.MSK = None\n self.JHM = None\n self.ZLK = None\n self.VYS = None \n \n #create dir \"folder\" if it doesn't exist\n if not os.path.exists(folder):\n os.makedirs(folder)\n \n def download_data(self):\n \n #robots don't get access, so we add header\n headers = {'User-Agent' : 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0 Safari/605.1.15'}\n\n #GET website and parse with beautiful soup for download links\n r = requests.get(self.url, headers = headers)\n data = r.text\n soup = BeautifulSoup(data, features='html.parser')\n soup.prettify()\n links = soup.findAll('a', string = \"ZIP\", )\n last_month = -1\n regex =re.compile(r'(1[0-2]|0[1-9])-')\n\n #download items - only download latest sets so we don't have redundant data\n for item in links:\n name = str(item).split('href=', 1)[1].split('\"', 2)[1].split(\"/\", 1)[1]\n path = self.folder + '/' + name\n link = self.url + str(item).split('href=', 1)[1].split('\"', 2)[1]\n \n if \"2020\" in name:\n month = int(name.split('-',1)[1].split('-',1)[0])\n\n if month > last_month:\n last_month = month\n\n else:\n if regex.search(name) is None:\n\n if not os.path.exists(path):\n r = requests.get(link, headers = headers)\n open(path, 'wb').write(r.content)\n\n for item in links:\n name = str(item).split('href=', 1)[1].split('\"', 2)[1].split(\"/\", 1)[1]\n\n if \"2020\" in name:\n month = int(name.split('-',1)[1].split('-',1)[0])\n\n if month == last_month:\n \n #don't download already existing files\n if not os.path.exists(path):\n r = requests.get(link, headers = headers)\n open(path, 'wb').write(r.content)\n\n def parse_region_data(self,region):\n #self.download_data()\n\n parsed_data = (list(), list())\n\n #intialize first list in the tuple and fill it with strings corresponding to column type of csv file\n for value in DataDownloader.column_types.keys():\n parsed_data[0].append(value)\n \n #go through the folder\n for file in os.listdir(self.folder):\n \n #if file is zip we open it and look for specific region file based on region id e.g. \"PHA\"\n if zf.is_zipfile(self.folder + '/' +file):\n \n current_zip = zf.ZipFile(self.folder + '/' +file)\n \n #we open the zip and the corresponding csv file\n with current_zip.open(DataDownloader.region_match[region], 'r') as csvfile:\n current_csv = csv.reader(io.TextIOWrapper(csvfile, encoding=\"windows-1250\"), delimiter=';') \n csv_list = list(current_csv)\n\n #parsing\n\n #if this is the first file we create the array, later we only append to it\n if len(parsed_data[1]) == 0:\n\n self.initialize_nd_list(parsed_data[1], len(csv_list))\n self.parse_csv(parsed_data[1], csv_list, region)\n \n #if this is not a first file, we create temp array and then append to ndarray\n else:\n\n #temp array\n numpy_parsed = list()\n\n #prepare temp array with ndarrays of corresponding types where necessary, elsewhere leave float\n self.initialize_nd_list(numpy_parsed, len(csv_list))\n\n #fill ndarrays with values\n self.parse_csv(numpy_parsed, csv_list, region)\n \n #concat to parent array\n for i, data in enumerate(numpy_parsed):\n parsed_data[1][i] = np.append(parsed_data[1][i], data, axis=0) \n \n #save region data to memory -- save it to instance variable\n return(parsed_data)\n\n def get_list(self, regions = None):\n\n #download data, doesn't download duplicate data\n self.download_data()\n region_list = None\n #return data on all regions\n if regions == None:\n region_list = DataDownloader.region_match\n else:\n if not isinstance(regions, list):\n print(\"Arg has to be list\", file=sys.stderr)\n return\n region_list = regions\n\n full_data = (list(), list())\n for value in DataDownloader.column_types.keys():\n full_data[0].append(value)\n self.initialize_nd_list(full_data[1], 0)\n\n for region in region_list:\n if region not in DataDownloader.region_match:\n print(\"Region {} doesn't exist\".format(region), file=sys.stderr)\n continue\n region_data = None\n\n #try to get data from variable\n if getattr(self, region) is not None:\n region_data = getattr(self, region)\n\n #try to open from file if it fails, start parsing\n else:\n try:\n region_data = self.load_pickle(region)\n #print(region_data)\n except:\n region_data = self.get_region(region)\n #cache data and save to memory\n self.save_pickle(region_data, region)\n setattr(self, region, deepcopy(region_data))\n\n for i, data in enumerate(region_data[1]):\n full_data[1][i] = np.append(full_data[1][i], data, axis=0)\n \n return full_data\n\n def get_region(self, region):\n #get single region data\n region_data = None\n if getattr(self, region) == None:\n region_data = self.parse_region_data(region)\n return region_data\n\n def save_pickle(self, region_data, region):\n with gzip.GzipFile(self.folder + '/' + self.cache_filename.format(region), mode='wb') as f:\n pickle.dump(region_data, f)\n\n def load_pickle(self, region):\n with gzip.open(self.folder + '/' + self.cache_filename.format(region), mode='rb') as f:\n region_data = pickle.load(f)\n return region_data\n\n def initialize_nd_list(self, nd_list, len):\n #create list of ndarrays with correct types)\n for key in DataDownloader.column_types:\n if key == \"p2a\":\n nd_list.append(np.zeros([len], dtype = \"datetime64[D]\"))\n elif key == \"h\" or key == \"i\":\n nd_list.append(np.zeros([len], dtype = \"U64\"))\n elif key == \"k\" or key == \"t\":\n nd_list.append(np.zeros([len], dtype = \"U32\"))\n elif key == \"l\" or key == \"p99\":\n nd_list.append(np.zeros([len], dtype = \"U8\"))\n elif key == \"p\" or key == \"q\":\n nd_list.append(np.zeros([len], dtype = \"U16\"))\n else:\n nd_list.append(np.zeros([len]))\n \n def parse_csv(self, nd_list, csv_list, region):\n #parse csv file and save values to list of ndarrays\n for i, row in enumerate(csv_list):\n for ((j, cell), ct) in zip(enumerate(row), DataDownloader.column_types):\n if ct == \"p2a\":\n nd_list[j][i] = np.datetime64(cell) \n elif ct == \"p47\" and cell.upper() ==\"XX\":\n nd_list[j][i] = -1\n elif ',' in cell and (ct == \"a\" or ct == \"b\" or ct == \"d\" or ct ==\"e\" or ct == \"f\" or ct ==\"g\" or ct == \"o\"):\n nd_list[j][i] = float(cell.replace(',','.',1))\n elif cell == '':\n nd_list[j][i] = np.nan\n else:\n try:\n nd_list[j][i] = cell\n except:\n try:\n nd_list[j][i] = float.fromhex(cell)\n except:\n nd_list[j][i] = np.nan\n nd_list[-1][i] = region\n\n\nif __name__ == \"__main__\":\n download = DataDownloader()\n dataset = download.get_list([\"PHA\", \"JHM\", \"KVK\"])\n print(\"Sloupce:\")\n for data in dataset[0]:\n if data == dataset[0][-1]:\n print(data) \n else:\n print(data, end=', ')\n print(\"\\nPočet záznamů:\")\n print( dataset[1][0].size)\n print(\"\\nRegiony:\\nPHA, JHM, KVK\")\n \n"
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"text": "#!/usr/bin/env python3.8\n# coding=utf-8\n\nfrom matplotlib import pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nimport numpy as np\nimport os\nimport sys\nimport io\n# muzete pridat libovolnou zakladni knihovnu ci knihovnu predstavenou na prednaskach\n# dalsi knihovny pak na dotaz\n\n# Ukol 1: nacteni dat\ndef get_dataframe(filename: str, verbose: bool = False) -> pd.DataFrame: \n\n if not os.path.exists(filename):\n print(\"{} is an invalid path to a file.\".format(filename), file=sys.stderr)\n return\n df = pd.read_pickle(filename)\n\n \n if verbose is True:\n buf = io.StringIO()\n df.info(buf=buf, memory_usage = \"deep\")\n info = buf.getvalue()\n print(\"orig_size=\" + info.split('\\n')[-2].split(': ')[1])\n \n # change types do reduce size, try int for all then float for all, then category for rest\n for column in df:\n try: \n df[column] = df[column].astype(np.int64)\n except:\n try:\n df[column] = df[column].astype(np.float32)\n except:\n try:\n df[column] = df[column].astype(\"category\")\n except:\n pass\n\n df[\"date\"] = pd.to_datetime(df[\"p2a\"]).astype('datetime64[ns]')\n \n if verbose is True:\n buf = io.StringIO()\n df.info(buf=buf, memory_usage = \"deep\")\n info = buf.getvalue()\n print(\"new_size=\" + info.split('\\n')[-2].split(': ')[1])\n\n return df\n\n# Ukol 2: následky nehod v jednotlivých regionech\ndef plot_conseq(df: pd.DataFrame, fig_location: str = None,\n show_figure: bool = False):\n\n # melt to get values needed for plot \n df = df.melt(value_vars = [\"date\"], id_vars = [\"region\", \"p13a\", \"p13b\", \"p13c\", \"p1\"])\n\n region_df = pd.DataFrame()\n \n # sum data \n for region in df[\"region\"].unique().tolist():\n region_df[region] = df.loc[df[\"region\"] == region].agg({\"p13a\" : \"sum\", \"p13b\" : \"sum\", \"p13c\" : \"sum\", \"p1\" :\"size\"})\n\n # plotting\n plt.style.use('fivethirtyeight')\n _, axes = plt.subplots(nrows=4, ncols=1, figsize=(6,8), constrained_layout = True)\n\n for row in axes:\n row.tick_params(axis='x', labelsize = 7)\n row.tick_params(axis='y', labelsize = 7)\n\n #sub plots\n region_df = region_df.sort_values(by = \"p13a\", axis = 1, ascending = False)\n region_df.iloc[0].plot(kind=\"bar\", ax=axes[0])\n axes[0].set_title(\"Úmrtí\", fontsize = 11)\n\n region_df = region_df.sort_values(by = \"p13b\", axis = 1, ascending = False)\n region_df.iloc[1].plot(kind=\"bar\", ax=axes[1])\n axes[1].set_title(\"Těžká zranění\", fontsize = 11)\n\n region_df = region_df.sort_values(by = \"p13c\", axis = 1, ascending = False)\n region_df.iloc[2].plot(kind=\"bar\", ax=axes[2])\n axes[2].set_title(\"Lehká zranění\", fontsize = 11)\n\n region_df = region_df.sort_values(by = \"p1\", axis = 1, ascending = False)\n region_df.iloc[3].plot(kind=\"bar\", ax=axes[3])\n axes[3].set_title(\"Celkový počet nehod\", fontsize = 11)\n\n if fig_location is not None:\n plt.savefig(fig_location)\n \n if show_figure:\n plt.show()\n \n# Ukol3: příčina nehody a škoda\ndef plot_damage(df: pd.DataFrame, fig_location: str = None,\n show_figure: bool = False):\n \n #div by ten to get number in thousands instead of hudnreds\n df[\"p53\"] = (df[\"p53\"]/10).astype(np.int64)\n\n #melt to get values needed; label bins \n df = df.melt(value_vars = [\"p1\"], id_vars = [\"p12\", \"p53\", \"region\"])\n df[\"cause\"] = pd.cut(df[\"p12\"], [np.NINF, 200,300,400,500,600,700], labels=[\"nezaviněná řidičem\", \n \"nepřiměřená rychlost jízdy\", \n \"nesprávné předjíždění\", \n \"nedání přednosti v jízdě\", \n \"nesprávný způsob jízdy\", \n \"technická závada vozidla\"])\n\n df[\"damage\"] = pd.cut(df[\"p53\"], [np.NINF, 49, 199, 499, 999, np.inf])\n \n df_agg = pd.DataFrame()\n\n #get aggregated data for regions\n for price in df[\"damage\"].unique().tolist():\n for cause in df[\"cause\"].unique().tolist():\n count = df.loc[df[\"damage\"] == price].loc[df[\"cause\"] == cause].loc[df[\"region\"] == \"PHA\"].agg({\"damage\" : \"count\"})[0]\n df_agg = df_agg.append({\"damage\" : price, \"cause\" : cause, \"count\" : count, \"region\" : \"PHA\"}, ignore_index=True)\n\n count = df.loc[df[\"damage\"] == price].loc[df[\"cause\"] == cause].loc[df[\"region\"] == \"JHM\"].agg({\"damage\" : \"count\"})[0]\n df_agg = df_agg.append({\"damage\" : price, \"cause\" : cause, \"count\" : count, \"region\" : \"JHM\"}, ignore_index=True)\n\n count = df.loc[df[\"damage\"] == price].loc[df[\"cause\"] == cause].loc[df[\"region\"] == \"OLK\"].agg({\"damage\" : \"count\"})[0]\n df_agg = df_agg.append({\"damage\" : price, \"cause\" : cause, \"count\" : count, \"region\" : \"OLK\"}, ignore_index=True)\n\n count = df.loc[df[\"damage\"] == price].loc[df[\"cause\"] == cause].loc[df[\"region\"] == \"LBK\"].agg({\"damage\" : \"count\"})[0]\n df_agg = df_agg.append({\"damage\" : price, \"cause\" : cause, \"count\" : count, \"region\" : \"LBK\"}, ignore_index=True)\n \n df_agg[\"count\"] = df_agg[\"count\"].astype(np.int64)\n\n \n #plotting using seaborn\n fig, ax = plt.subplots(nrows = 2, ncols=3, figsize=(11,6), constrained_layout = True)\n \n sns.set_theme(style = \"whitegrid\")\n sns.barplot(\n data = df_agg.loc[df_agg[\"region\"] == \"PHA\"].sort_values(by = \"damage\"),\n x = \"damage\", y = \"count\" , hue=\"cause\", ci = \"sd\", palette=\"dark\", alpha = .6,\n ax=ax[0][0]\n ).set_title(\"PHA\", fontsize = 11)\n\n sns.barplot(\n data = df_agg.loc[df_agg[\"region\"] == \"JHM\"].sort_values(by = \"damage\"),\n x = \"damage\", y = \"count\" , hue=\"cause\", ci = \"sd\", palette=\"dark\", alpha = .6,\n ax=ax[1][0]\n ).set_title(\"JHM\", fontsize = 11)\n\n sns.barplot(\n data = df_agg.loc[df_agg[\"region\"] == \"OLK\"].sort_values(by = \"damage\"),\n x = \"damage\", y = \"count\" , hue=\"cause\", ci = \"sd\", palette=\"dark\", alpha = .6,\n ax=ax[0][1]\n ).set_title(\"OLK\", fontsize = 11)\n\n sns.barplot(\n data = df_agg.loc[df_agg[\"region\"] == \"LBK\"].sort_values(by = \"damage\"),\n x = \"damage\", y = \"count\" , hue=\"cause\", ci = \"sd\", palette=\"dark\", alpha = .6,\n ax=ax[1][1]\n ).set_title(\"LBK\", fontsize = 11)\n\n #formatting plot\n for col in ax:\n for row in col:\n row.tick_params(axis='x', labelsize = 8)\n row.tick_params(axis='y', labelsize = 8)\n row.set_yscale(\"log\")\n row.legend().remove()\n row.set(xlabel = \"Škoda [sto Kč]\", ylabel = \"Počet\")\n row.set_xticklabels([\"< 50\", \"50 - 200\", \"200 - 500\", \"500 - 1000\", \"> 1000\"])\n\n handles, labels = ax[1][1].get_legend_handles_labels()\n\n ax[-1, -1].axis('off')\n ax[-2, -1].axis('off')\n \n #legend saved in place of a subplot to look cleaner\n fig.legend(handles, labels, loc = \"center left\", bbox_to_anchor=(0.7, 0.5))\n\n\n if fig_location is not None:\n plt.savefig(fig_location)\n \n if show_figure:\n plt.show()\n\n# Ukol 4: povrch vozovky\n\ndef plot_surface(df: pd.DataFrame, fig_location: str = None,\n show_figure: bool = False):\n \n #table = df.pivot_table(values=\"p1\", index = [\"p16\", \"date\"], aggfunc=\"count\")\n \n #getting required columns\n df = df.melt(value_vars = [\"p1\"], id_vars = [\"p16\", \"region\", \"date\"])\n df_agg = pd.DataFrame()\n \n #aggregating data - very suboptimal time efficiency\n for year in range(2016, 2021):\n for month in range(1,13):\n for acc in range (0,10):\n count = df.loc[df[\"date\"].dt.month == month].loc[df[\"date\"].dt.year == year].loc[df[\"p16\"] == acc].loc[df[\"region\"] == \"KVK\"].agg({\"p16\" : \"count\"})[0]\n df_agg = df_agg.append({\"date\" : str(year) + \"-\" + str(month), \"p16\" : acc, \"count\" : count, \"region\" : \"KVK\"}, ignore_index=True)\n \n count = df.loc[df[\"date\"].dt.month == month].loc[df[\"date\"].dt.year == year].loc[df[\"p16\"] == acc].loc[df[\"region\"] == \"PHA\"].agg({\"p16\" : \"count\"})[0]\n df_agg = df_agg.append({\"date\" : str(year) + \"-\" + str(month), \"p16\" : acc, \"count\" : count, \"region\" : \"PHA\"}, ignore_index=True)\n \n count = df.loc[df[\"date\"].dt.month == month].loc[df[\"date\"].dt.year == year].loc[df[\"p16\"] == acc].loc[df[\"region\"] == \"JHM\"].agg({\"p16\" : \"count\"})[0]\n df_agg = df_agg.append({\"date\" : str(year) + \"-\" + str(month), \"p16\" : acc, \"count\" : count, \"region\" : \"JHM\"}, ignore_index=True)\n \n count = df.loc[df[\"date\"].dt.month == month].loc[df[\"date\"].dt.year == year].loc[df[\"p16\"] == acc].loc[df[\"region\"] == \"OLK\"].agg({\"p16\" : \"count\"})[0]\n df_agg = df_agg.append({\"date\" : str(year) + \"-\" + str(month), \"p16\" : acc, \"count\" : count, \"region\" : \"OLK\"}, ignore_index=True)\n \n \n #changing typos\n df_agg[\"date\"] = df_agg[\"date\"].astype('datetime64[M]')\n df_agg[\"count\"] = df_agg[\"count\"].astype(np.int64)\n df_agg[\"p16\"] = df_agg[\"p16\"].astype(\"category\")\n\n #plotting\n fig, ax = plt.subplots(nrows = 2, ncols=3, figsize=(11,6), constrained_layout = True)\n sns.set_theme(style = \"whitegrid\")\n\n sns.lineplot(data=df_agg.loc[df_agg[\"region\"] == \"KVK\"], \n x = \"date\", y = \"count\", hue = \"p16\",\n ax=ax[0][0]).set_title(\"KVK\", fontsize = 11)\n\n sns.lineplot(data=df_agg.loc[df_agg[\"region\"] == \"PHA\"], \n x = \"date\", y = \"count\", hue = \"p16\",\n ax=ax[1][0]).set_title(\"PHA\", fontsize = 11)\n \n sns.lineplot(data=df_agg.loc[df_agg[\"region\"] == \"JHM\"], \n x = \"date\", y = \"count\", hue = \"p16\",\n ax=ax[0][1]).set_title(\"JHM\", fontsize = 11)\n\n sns.lineplot(data=df_agg.loc[df_agg[\"region\"] == \"OLK\"], \n x = \"date\", y = \"count\", hue = \"p16\",\n ax=ax[1][1]).set_title(\"OLK\", fontsize = 11)\n\n for col in ax:\n for row in col:\n row.tick_params(axis='x', labelsize = 8)\n row.tick_params(axis='y', labelsize = 8)\n row.legend().remove()\n row.set(xlabel = \"Datum\", ylabel = \"Počet\")\n\n handles, _ = ax[1][1].get_legend_handles_labels()\n labels = [\"jiný stav\", \n \"suchý - neznečištěný\", \n \"suchý - znečištěný\", \n \"mokrý\", \n \"na vozovce bláto\", \n \"na vozovce náledí, ujetý sníh - posypané\", \n \"na vozovce náledí, ujetý sníh - neposypané\", \n \"na vozovce rozlitý olej, nafta, apod.\", \n \"souvislá sněhová vrstva, rozbředlý sníh\", \n \"náhlá změna stavu vozovky\"]\n fig.legend(handles, labels, loc = \"center left\", bbox_to_anchor=(0.7, 0.5))\n\n ax[-1, -1].axis('off')\n ax[-2, -1].axis('off')\n\n if fig_location is not None:\n plt.savefig(fig_location)\n \n if show_figure:\n plt.show()\n\n \nif __name__ == \"__main__\":\n pass\n # zde je ukazka pouziti, tuto cast muzete modifikovat podle libosti\n # skript nebude pri testovani pousten primo, ale budou volany konkreni\n # funkce.\n df = get_dataframe(\"accidents.pkl.gz\", verbose=True)\n #plot_conseq(df, fig_location=\"01_nasledky.png\", show_figure=True)\n plot_damage(df, None, True)\n #plot_surface(df, \"03_stav.png\", True)\n"
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"path": "/get_stat.py",
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"text": "from download import DataDownloader\nimport matplotlib.pyplot as plt\nimport os, sys, argparse, collections\n\n#create parser for args from command line\nparser = argparse.ArgumentParser(description=\"Get plot of the number of accidents in all of the regions in Czechia\")\nparser.add_argument('--fig_location',type=str,metavar='directory', default=None)\nparser.add_argument('--show_figure', type=bool,metavar='(True/False)', default=False)\nargs = parser.parse_args()\n\ndef plot_stat(data_source, fig_location = None, show_figure = False):\n\n #if optional arg fig_location given, look for directory and create if doesn't exist\n if fig_location is not None:\n if not os.path.exists(fig_location):\n try:\n os.makedirs(fig_location)\n except:\n print(\"{} is an invalid path and directory couldn't be created\".format(fig_location), file=sys.stderr)\n exit(-1)\n\n #save relevant data for plotting into nested dict\n regions = dict()\n \n for date, region in zip(data_source[1][3], data_source[1][-1]):\n if region not in regions:\n regions[region] = dict()\n if str(date.astype(object).year) not in regions[region]:\n \n regions[region][str(date.astype(object).year)] = 1\n else:\n \n regions[region][str(date.astype(object).year)] += 1\n \n #plotting\n\n plt.style.use('fivethirtyeight')\n \n x = list()\n for region in regions:\n x.append(region)\n \n x_pos = [i for i, _ in enumerate(x)]\n \n _, ax = plt.subplots(nrows = 5, ncols=1, figsize=(6,8), sharey=True)\n\n #creating subplots\n for col, year in zip(ax,sorted(list(list(regions.items())[2][1].keys()))):\n \n accident_values = list()\n order = dict()\n ordered = collections.OrderedDict()\n\n #get order for annotations\n for region in x:\n accident_values.append(regions[region][year])\n order[region] = regions[region][year]\n order = sorted(list(order.items()), key=lambda z:z[1], reverse=True)\n \n for i in order:\n ordered[i[0]] = i[1]\n\n #setting up parameters and appearance of subplot\n values = col.bar(x_pos, accident_values, width = 0.35)\n col.set_title(year, fontsize = 11)\n col.set_xticks(x_pos)\n col.set_xticklabels(x)\n col.tick_params(axis='x', labelsize=7)\n col.tick_params(axis='y', labelsize = 7)\n col.grid(b=None, axis='x')\n\n #annotations\n for value,region in zip(values, regions):\n col.annotate(str(list(ordered.keys()).index(region) + 1) + '.', xy=(value.get_x() + value.get_width() / 2, value.get_height()), xytext = (0,2), textcoords = \"offset points\", ha=\"center\", fontsize=6)\n plt.tight_layout()\n\n #arg handling\n if fig_location is not None:\n plt.savefig(fig_location + '/' + \"figure1.png\")\n if show_figure:\n plt.show()\n\n\nif __name__ == \"__main__\":\n\n plot_stat(data_source = DataDownloader().get_list(),fig_location=args.fig_location, show_figure=args.show_figure)\n \n\n"
}
] | 5 |
fcwu/ibs-cli
|
https://github.com/fcwu/ibs-cli
|
9ce89437ab4e0f5b18bbd9cebe2a3e39edda34ec
|
db5bdf7aad39531862082709228fff1405044cf7
|
7899ef13e77493676db3c2e2b7d936ff6c288d37
|
refs/heads/master
| 2021-01-15T11:29:09.386875 | 2013-11-11T05:18:14 | 2013-11-11T05:18:14 | 9,516,853 | 2 | 0 | null | null | null | null | null |
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"path": "/ibs-cli.completion",
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"src_encoding": "UTF-8",
"text": "# bash completion for ibs-cli\n\nhave ibs-cli &&\n_ibs_cli()\n{\n COMPREPLY=()\n local cur prev\n _get_comp_words_by_ref -n : cur prev\n\n _expand || return 0\n\n case $prev in\n --log-dir|-z|--zsync-input)\n _filedir\n return 0\n ;;\n --log-level)\n COMPREPLY=( $( compgen -W 'debug info warning error critical' -- \"$cur\" ) )\n return 0\n ;;\n esac\n\n case $cur in\n -*)\n COMPREPLY=( $( compgen -W '-h --log-level --log-dir -p --project \\\n -b -z --zsync-input -g --no-download-image' -- \"$cur\" ) )\n ;;\n *)\n COMPREPLY=( $( compgen -W 'list-projects list-builds build \\\n download monitor version ' -- \"$cur\" ) )\n ;;\n\n esac\n\n return 0\n} &&\ncomplete -F _ibs_cli -o nospace ibs-cli\n\n# Local variables:\n# mode: shell-script\n# sh-basic-offset: 4\n# sh-indent-comment: t\n# indent-tabs-mode: nil\n# End:\n# ex: ts=4 sw=4 et filetype=sh\n"
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"repo_name": "fcwu/ibs-cli",
"src_encoding": "UTF-8",
"text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n__version__ = '1.4'\n__all__ = ['IbsCli']\n__author__ = 'DoroWu'\n__home_page__ = ''\n\nimport os\nfrom os.path import join as pjoin\nimport sys\nimport getpass\nimport re\nimport logging\nimport logging.handlers\nfrom argparse import ArgumentParser, SUPPRESS\nimport glob\nimport subprocess\nimport json\nimport time\nimport urllib2\nimport shutil\nimport mechanize\nimport cookielib\nfrom HTMLParser import HTMLParser\n\n\n#The terminal has 8 colors with codes from 0 to 7\nBLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE = range(8)\n\n#These are the sequences need to get colored ouput\nRESET_SEQ = \"\\033[0m\"\nCOLOR_SEQ = \"\\033[1;%dm\"\nBOLD_SEQ = \"\\033[1m\"\n\n#The background is set with 40 plus the number of the color,\n#and the foreground with 30\nCOLORS = {\n 'WARNING': COLOR_SEQ % (30 + YELLOW) + 'WARNING' + RESET_SEQ,\n 'INFO': COLOR_SEQ % (30 + WHITE) + 'INFO' + RESET_SEQ,\n 'DEBUG': COLOR_SEQ % (30 + BLUE) + 'DEBUG' + RESET_SEQ,\n 'CRITICAL': COLOR_SEQ % (30 + YELLOW) + 'CRITICAL' + RESET_SEQ,\n 'ERROR': COLOR_SEQ % (30 + RED) + 'ERROR' + RESET_SEQ,\n}\n\n\nclass MLStripper(HTMLParser):\n def __init__(self):\n self.reset()\n self.fed = []\n\n def handle_data(self, d):\n self.fed.append(d)\n\n def get_data(self):\n return ''.join(self.fed)\n\n\nclass IbsCli(object):\n def __init__(self, args, extra_args):\n self._args = args\n self._extra_args = extra_args\n\n @property\n def project_build_results(self, project_name):\n \"\"\" Return build results\n \"\"\"\n pass\n\n @property\n def cookie(self):\n if hasattr(self, '_cookie'):\n return self._cookie\n self._cookie = cookielib.LWPCookieJar()\n if os.path.exists(os.path.join(self._args.config_dir, 'mycookie')):\n self._cookie.load(os.path.join(self._args.config_dir, 'mycookie'))\n return self.cookie\n\n @property\n def browser(self):\n if hasattr(self, '_br'):\n return self._br\n br = self._br = mechanize.Browser()\n\n # XXX saving the cookie and dynamically prompting\n # for requisite informations\n # Cookie Jar\n br.set_cookiejar(self.cookie)\n\n # Browser options\n br.set_handle_equiv(True)\n br.set_handle_gzip(False) # XXX messes up stdin\n br.set_handle_redirect(True)\n br.set_handle_referer(True)\n br.set_handle_robots(False)\n\n br.addheaders = [('User-agent',\n 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:19.0) '\n 'Gecko/20100101 Firefox/19.0')]\n\n ## Want debugging messages?\n if self._args.log_level <= logging.DEBUG:\n br.set_debug_http(True)\n br.set_debug_redirects(True)\n br.set_debug_responses(True)\n return self._br\n\n @property\n def pysid(self):\n if hasattr(self, '_pysid'):\n return self._pysid\n return None\n\n def download(self, base_path, base_url, url):\n def chunk_report(bytes_so_far, chunk_size, total_size):\n percent = float(bytes_so_far) / total_size\n percent = round(percent * 100, 2)\n sys.stdout.write(\"Downloaded {0} of {1} bytes ({2:.2f})\\r\".format(\n bytes_so_far, total_size, percent))\n\n if bytes_so_far >= total_size:\n sys.stdout.write('\\n')\n\n def chunk_read(response, f, chunk_size=8192, report_hook=None):\n total_size = response.info().getheader('Content-Length').strip()\n total_size = int(total_size)\n bytes_so_far = 0\n\n while 1:\n chunk = response.read(chunk_size)\n bytes_so_far += len(chunk)\n\n if not chunk:\n break\n f.write(chunk)\n\n if report_hook:\n report_hook(bytes_so_far, chunk_size, total_size)\n\n return bytes_so_far\n\n def download_all(base_path, base_url, url):\n def find_records(html):\n regex = re.compile('<tr><td.*<a href=[\"\\']?([^\"\\']*)[\"\\']?>'\n '(.*)</a>.*</td></tr>')\n links = []\n for line in html.split('\\n'):\n line.strip()\n #print line\n m = regex.match(line)\n if not m:\n continue\n #print 'bingo: {0}:{1}'.format(m.group(1), m.group(2))\n links.append((m.group(1), m.group(2)))\n return links\n\n def zsync():\n if self._args.zsync_file is None:\n return False\n compare_link = link[0] + '.zsync'\n for link_check in find_records(html)[1:]:\n if link_check[0] == compare_link:\n logging.info('Trying download via zsync...')\n download_one_zsync(base_path,\n base_url,\n url + link[0])\n break\n else:\n logging.info('No zsync file or error '\n 'during process: ' + url + link[0])\n return True\n\n def rsync():\n return download_one_rsync(base_path, base_url, url + link[0])\n\n logging.info('Open dir {0}'.format(url))\n try:\n r = self.browser.open(base_url + url)\n except Exception as e:\n logging.debug('browser.open: ' + str(e))\n return\n try:\n html = r.read()\n for link in find_records(html)[1:]:\n if any(link[0].endswith(x) for x in ('.iso.zsync',\n '.img.zsync')):\n continue\n if link[0][-1] == '/':\n download_all(base_path, base_url, url + link[0])\n continue\n if any(link[0].endswith(x) for x in ('.iso', '.img')):\n if zsync():\n continue\n if rsync():\n continue\n download_one(base_path, base_url, url + link[0])\n except IndexError:\n pass\n\n def download_one(base_path, base_url, url, not_download=False,\n ifile=False):\n logging.debug('Open file {0}'.format(url))\n\n filename = os.path.join(base_path, url.replace('/', os.sep))\n if url.find('/') != -1:\n dirname = os.path.dirname(filename)\n if not os.path.exists(dirname):\n logging.debug('Create dir: ' + dirname)\n os.makedirs(dirname)\n if ifile:\n try:\n logging.debug('Copy file from {0} to {1}'.format(\n ifile, filename))\n shutil.copyfile(ifile, filename)\n except Exception as e:\n logging.warn('Failed to copy from {0} to {1}: {2}'.format(\n ifile, filename, e))\n if not_download:\n return filename\n\n logging.info('Download {0}'.format(url))\n try:\n r = self.browser.open(base_url + url)\n with open(filename, 'wb+') as f:\n chunk_read(r, f, report_hook=chunk_report)\n except Exception as e:\n logging.warn('Failed to download {0}{1}: {2}'.format(base_url,\n url, e))\n\n def download_one_rsync(base_path, base_url, url):\n if not os.path.exists('/usr/bin/rsync'):\n logging.debug('No rsync found in /usr/bin. Revert')\n return False\n logging.debug('fork rsync process for {0}'.format(url))\n # infile\n ifile = ''\n files = glob.glob(os.path.join(base_path, '..', '..', '*', '*',\n 'images', '*', '*.iso'))\n if len(files) <= 0:\n files = glob.glob(os.path.join(base_path, '..', '..', '*', '*',\n 'images', '*', '*.img'))\n path_prefix = os.path.join(base_path, '..', '..')\n biggest = 0\n for file in files:\n logging.debug('file 1: ' + file)\n file_no_prefix = file[len(path_prefix) + 1:]\n logging.debug('file 2: ' + file_no_prefix)\n file_list = file_no_prefix.split(os.sep)\n logging.debug('pair: ' + str((file_list[0], file_list[1])))\n t = int(file_list[0] + file_list[1])\n if t > biggest:\n biggest = t\n ifile = file\n if not ifile:\n logging.debug('No reference file found. Revert')\n return False\n # outfile\n ofile = download_one(base_path, base_url, url, True, ifile)\n # Assuming username and ssh key has been setup in .ssh/config\n rsync_url = base_url.replace('https://oem-share.canonical.com',\n 'oem-share.canonical.com:/srv/'\n 'oem-share.canonical.com/www') + url\n cmd = 'rsync -Pv {url} {ofile}'.format(url=rsync_url, ofile=ofile)\n logging.info('Start rsync process')\n logging.debug('Run command: ' + cmd)\n result = subprocess.call(cmd, shell=True)\n logging.debug('Command result: ' + str(result))\n if result != 0:\n logging.warn('rsync failed with result ' + str(result))\n return False\n return True\n\n def download_one_zsync(base_path, base_url, url):\n if not os.path.exists('/usr/bin/zsync_curl'):\n logging.error('No zsync_curl found in /usr/bin. Revert')\n return False\n logging.debug('fork zsync process for {0}'.format(url))\n # cookie\n if self.pysid is None:\n logging.error('No cookie pysid found. Revert')\n return False\n cookie_str = self.pysid\n # infile\n ifile = self._args.zsync_file\n if not ifile:\n files = glob.glob(pjoin(base_path, '..', '..', '*', '*',\n 'images', '*', '*.iso'))\n if len(files) <= 0:\n files = glob.glob(pjoin(base_path, '..', '..', '*',\n '*', 'images', '*', '*.img'))\n path_prefix = pjoin(base_path, '..', '..')\n biggest = 0\n for file in files:\n logging.debug('file 1: ' + file)\n file_no_prefix = file[len(path_prefix) + 1:]\n logging.debug('file 2: ' + file_no_prefix)\n file_list = file_no_prefix.split(os.sep)\n logging.debug('pair: ' + str((file_list[0], file_list[1])))\n t = int(file_list[0] + file_list[1])\n if t > biggest:\n biggest = t\n ifile = file\n if not ifile:\n logging.error('No reference file found. Revert')\n return False\n # outfile\n ofile = download_one(base_path, base_url, url, True)\n cmd = 'zsync_curl -c {cookie} {url} -i {ifile} -o {ofile}'.format(\n cookie=cookie_str,\n url=base_url + url + '.zsync',\n ifile=ifile,\n ofile=ofile)\n logging.info('Start zsync process')\n logging.debug('Run command: ' + cmd)\n result = subprocess.call(cmd, shell=True)\n logging.debug('Command result: ' + str(result))\n if result != 0:\n logging.error('zsync failed with result ' + str(result))\n return False\n return True\n\n if url[-1] == ('/'):\n download_all(base_path, base_url, url)\n else:\n download_one(base_path, base_url, url)\n\n def monitor_project_build(self, project_name):\n \"\"\" Monitor status change of specified build of project\n \"\"\"\n pass\n\n def fetch_page(self, page_url):\n br = self.browser\n logging.debug('open ' + page_url)\n r = br.open(page_url)\n html = r.read()\n\n try:\n br.select_form('oid_form')\n logging.debug('open oid_form')\n r = br.submit()\n html = r.read()\n except Exception:\n logging.debug('no oid_form')\n\n try:\n for form in br.forms():\n if 'openid_message' != form.attrs['id']:\n continue\n br.form = form\n logging.debug('open openid_message')\n r = br.submit()\n html = r.read()\n break\n else:\n pass\n except Exception:\n logging.debug('no openid_message')\n\n try:\n br.select_form(name='loginform')\n br['email'] = raw_input('email: ')\n br['password'] = getpass.getpass()\n logging.debug('open loginform')\n r = br.submit()\n html = r.read()\n except Exception:\n logging.debug('no loginform')\n\n try:\n br.select_form(name='loginform')\n logging.debug('open loginform for 2-factor')\n br['oath_token'] = getpass.getpass('2-factor token: ')\n logging.debug('input oath_token')\n r = br.submit()\n html = r.read()\n except Exception:\n logging.debug('no loginform for 2-factor')\n\n try:\n br.select_form(name='decideform')\n logging.debug('open decideform')\n r = br.submit()\n html = r.read()\n except Exception:\n logging.debug('no decideform')\n\n try:\n if br.title().find('in progress') != -1:\n logging.debug('in progress')\n br.select_form(nr=0)\n r = br.submit()\n html = r.read()\n except Exception as e:\n logging.debug('no in progress exception: ' + str(e))\n\n # check last page is not a loginform\n has_last_loginform = False\n try:\n br.select_form(name='loginform')\n has_last_loginform = True\n except Exception:\n # pass\n logging.debug('last loginform check: no')\n if has_last_loginform:\n raise PermissionDenied()\n\n if self._args.log_level <= logging.DEBUG:\n with open(pjoin(self._args.log_dir, 'ibs-cli.html'), 'wb+') as f:\n f.write(html)\n self.cookie.save(pjoin(self._args.config_dir, 'mycookie'),\n ignore_discard=True)\n self.update_pysid()\n return html\n\n def update_pysid(self):\n if hasattr(self, '_pysid'):\n return\n cookie_str = None\n cookie_file = os.path.join(self._args.config_dir, 'mycookie')\n with open(cookie_file, 'r') as f:\n for line in f:\n loc_pysid = line.find('pysid=')\n if loc_pysid < 0:\n continue\n loc_pysid_end = line.find(';', loc_pysid)\n if loc_pysid_end < 0:\n continue\n cookie_str = line[loc_pysid:loc_pysid_end]\n self._pysid = cookie_str\n break\n\n def project_status(self):\n if not self._args.proj_name:\n logging.critical('No project name provided (-p proj_name)')\n return\n page_url = 'https://oem-ibs.canonical.com/projects/{0}/'.format(\n self._args.proj_name)\n html = self.fetch_page(page_url)\n #with open('p3.html', 'wb+') as f:\n #with open('p3.html', 'r') as f:\n # f.write(html)\n # html = f.read()\n loc = html.find('<dt>Build Status:')\n if loc < 0:\n return ''\n loc = html.find('<dd>', loc)\n loc_end = html.find('</dd>', loc)\n if loc < 0 or loc_end < 0:\n return ''\n return html[loc + 4:loc_end]\n\n def act_list_projects(self):\n page_url = 'https://oem-ibs.canonical.com/'\n html = self.fetch_page(page_url + 'projects/')\n #with open('p1.html', 'wb+') as f:\n #with open('p1.html', 'r') as f:\n # #f.write(html)\n # html = f.read()\n projects_regex = re.compile('<a href=\"/projects/(.+)/\">(.+)</a>',\n re.MULTILINE)\n logging.info(' {0:40} {1}'.format('[Codename]', '[Title]'))\n for p in projects_regex.finditer(html):\n logging.info('- {0:40} - {1}'.format(p.group(1), p.group(2)))\n\n def act_list_builds(self):\n if not self._args.proj_name:\n logging.critical('No project name provided (-p proj_name)')\n return\n page_url = 'https://oem-ibs.canonical.com/builds/+api/{0}/'.format(\n self._args.proj_name)\n html = self.fetch_page(page_url)\n #with open('p1.html', 'wb+') as f:\n #with open('p1.html', 'r') as f:\n # f.write(html)\n # html = f.read()\n logging.info(' {0:15} {1:20} {2:20} {3}'.format(\n '[build name]', '[start]', '[finish]', '[result]'))\n for r in json.loads(html):\n logging.info('- {0:15} - {1:20s} - {2:20s} - {3}'.format(\n r['name'],\n r['started_at'],\n r['finished_at'],\n r['result']))\n\n def act_build(self):\n if not self._args.proj_name:\n logging.critical('No project name provided (-p proj_name)')\n return\n page_url = 'https://oem-ibs.canonical.com/projects/{0}/+build'.format(\n self._args.proj_name)\n #logging.info(page_url)\n self.fetch_page(page_url)\n #with open('p2.html', 'wb+') as f:\n #with open('p1.html', 'r') as f:\n # f.write(html)\n # html = f.read()\n logging.info('{0} - {1}'.format(self._args.proj_name,\n self.project_status()))\n\n def act_download(self):\n \"\"\" Download\n \"\"\"\n def find_records(html):\n record_regex = re.compile('<tr><td.*'\n '<a href=[\"\\']?([^\"\\']*)[\"\\']?>(.*)</a>'\n '.*</td></tr>')\n links = []\n for line in html.split('\\n'):\n line.strip()\n #print line\n m = record_regex.match(line)\n if not m:\n continue\n #print 'bingo: {0}:{1}'.format(m.group(1), m.group(2))\n links.append((m.group(1), m.group(2)))\n return links\n\n if not self._args.proj_name:\n logging.critical('No project name provided (-p proj_name)')\n return\n logging.info('Downloading...')\n page_url = ('https://oem-share.canonical.com/'\n 'oem/cesg-builds/{0}/'.format(\n self._args.proj_name))\n html = self.fetch_page(page_url)\n br = self.browser\n\n if self._args.build_name:\n try:\n date_url, nr_in_date_url = self._args.build_name.split('-')\n date_url += '/'\n nr_in_date_url += '/'\n except Exception:\n logging.critical('No build_name: ' + self._args.build_name)\n return\n else:\n logging.info('Find the latest build')\n # project/YYYYMMDD\n links = find_records(html)\n try:\n date_url = links[-1][0]\n if date_url == 'archive/':\n date_url = links[-2][0]\n r = br.open(page_url + date_url)\n html = r.read()\n except IndexError:\n print 'Error follow link'\n return\n except Exception:\n print 'Error follow link {0}{1}YYYYMMDD'.format(page_url,\n date_url)\n return\n\n # project/YYYYMMDD/N\n links = find_records(html)\n try:\n nr_in_date_url = links[-1][0]\n except Exception:\n print 'No link N'\n return\n\n record_url = page_url + date_url + nr_in_date_url\n #urls_to_fetch = ('build-log.txt', 'config/manifest.html')\n #urls_to_fetch = ('build-log.txt', 'config/acubens.manifest',\n # 'images/usb-hdd/')\n urls_to_fetch = ['build-log.txt', 'config/manifest.html',\n 'config/acubens.manifest', 'config.tgz']\n #urls_to_fetch = ('images/iso/',)\n if not self._args.no_image:\n urls_to_fetch.extend(['images/iso/', 'images/usb-hdd/'])\n\n path_to_store = pjoin('download', self._args.proj_name,\n date_url.rstrip('/'),\n nr_in_date_url.rstrip('/'))\n if not os.path.exists(path_to_store):\n print 'Create dir: ' + path_to_store\n os.makedirs(path_to_store)\n\n for url in urls_to_fetch:\n self.download(path_to_store, record_url, url)\n\n def act_monitor(self):\n def strip_tags(html):\n s = MLStripper()\n s.feed(html)\n return s.get_data()\n\n result = None\n while True:\n try:\n tmp = self.project_status()\n if len(tmp) != 0:\n if tmp != result:\n text = strip_tags(tmp).strip()\n logging.info('{0} - {1}'.format(\n self._args.proj_name, text))\n if self._args.osd:\n cmd = 'notify-send \"IBS - {0}\" \"{1}\"'.format(\n self._args.proj_name, text)\n subprocess.call(cmd, shell=True)\n result = tmp\n else:\n logging.debug('No status got')\n time.sleep(10)\n except urllib2.URLError:\n logging.debug('URLError when act_monitor')\n\n def act_version(self):\n print __version__\n\n def act_add(self):\n # ./ibs-cli -a \"name=dell-bto-precise-fish-init-test-4\\\n #&title=Dell precise fish-init-test 3&project_group=So\\\n #merville&arch=amd64&series=precise&launchpad_project=\\\n #&status=devel&config_url=lp:~oem-solutions-engineers/\\\n #bugsy-config/dell-bto-precise-fish-init-test¬es=\" add\n\n args = self._args.add_project_args\n kvs = dict()\n for arg in args.split('&'):\n k, v = arg.split('=', 2)\n kvs[k] = v\n page_url = 'https://oem-ibs.canonical.com/projects/+add/'\n html = self.fetch_page(page_url)\n with open('add-test.html', 'wb+') as f:\n f.write(html)\n br = self.browser\n #logging.info(str(br.forms()))\n #br.form = br.forms().next()\n br.select_form(nr=0)\n logging.info(str(br.form))\n for k in kvs:\n logging.debug('set parameters: {}={}'.format(k, kvs[k]))\n if isinstance(br.form.find_control(k), mechanize.ListControl):\n br[k] = (kvs[k],)\n else:\n br[k] = kvs[k]\n r = br.submit()\n html = r.read()\n with open('add-test-submited.html', 'wb+') as f:\n f.write(html)\n #br['id_title'] = 'Dell precise '\n #br['id_project_group'] = 'Somerville'\n #br['id_arch'] = 'amd64'\n #br['id_series'] = 'precise'\n #br['id_config_url'] = 'Dell precise '\n\n def act_login(self):\n page_url = 'https://oem-ibs.canonical.com/'\n try:\n self.fetch_page(page_url + 'projects/')\n except PermissionDenied:\n sys.exit(1)\n sys.exit(0)\n\n def run(self):\n action_fns = {'list-projects': self.act_list_projects,\n 'list-builds': self.act_list_builds,\n 'build': self.act_build,\n 'download': self.act_download,\n 'monitor': self.act_monitor,\n 'version': self.act_version,\n 'login': self.act_login,\n 'add': self.act_add}\n if not os.path.exists(self._args.config_dir):\n os.makedirs(self._args.config_dir)\n if len(self._extra_args) > 0:\n action_fns[self._args.action](self._extra_args)\n else:\n action_fns[self._args.action]()\n\n\nclass PermissionDenied(Exception):\n pass\n\n\nclass ColoredFormatter(logging.Formatter):\n def __init__(self, msg, use_color=True):\n logging.Formatter.__init__(self, msg)\n self.use_color = use_color\n\n def format(self, record):\n if self.use_color:\n record.levelname = COLORS.get(record.levelname, record.levelname)\n return logging.Formatter.format(self, record)\n\n\nclass LoggingConfiguration(object):\n COLOR_FORMAT = \"[\" + BOLD_SEQ + \"%(asctime)s\" + RESET_SEQ + \\\n \"][%(levelname)s] %(message)s (\" + BOLD_SEQ + \\\n \"%(filename)s\" + RESET_SEQ + \":%(lineno)d)\"\n NO_COLOR_FORMAT = \"[%(asctime)s][%(levelname)s] %(message)s \" \\\n \"(%(filename)s:%(lineno)d)\"\n\n @classmethod\n def set(cls, log_level, log_filename, append):\n \"\"\" Configure a rotating file logging\n \"\"\"\n logger = logging.getLogger()\n logger.setLevel(logging.DEBUG)\n\n # Log to sys.stderr using log level passed through command line\n if log_level != logging.NOTSET:\n log_handler = logging.StreamHandler(sys.stdout)\n if sys.platform.find('linux') >= 0:\n formatter = ColoredFormatter(cls.COLOR_FORMAT)\n else:\n formatter = ColoredFormatter(cls.NO_COLOR_FORMAT, False)\n log_handler.setFormatter(formatter)\n log_handler.setLevel(log_level)\n logger.addHandler(log_handler)\n\n # Log to rotating file using DEBUG log level\n log_handler = logging.handlers.RotatingFileHandler(log_filename,\n mode='a+',\n backupCount=3)\n formatter = logging.Formatter('%(asctime)s %(levelname)-8s '\n '%(message)s')\n log_handler.setFormatter(formatter)\n log_handler.setLevel(logging.DEBUG)\n logger.addHandler(log_handler)\n\n if not append:\n # Create a new log file on every new\n # (i.e. not scheduled) invocation\n log_handler.doRollover()\n\n\nclass MyArgumentParser(object):\n \"\"\"Command-line argument parser\n \"\"\"\n def __init__(self):\n \"\"\"Create parser object\n \"\"\"\n description = ('IBS command line interface. '\n '')\n\n epilog = ('')\n parser = ArgumentParser(description=description, epilog=epilog)\n log_levels = ['notset', 'debug', 'info',\n 'warning', 'error', 'critical']\n parser.add_argument('--log-level', dest='log_level_str',\n default='info', choices=log_levels,\n help=('Log level. '\n 'One of {0} or {1} (%(default)s by default)'\n .format(', '.join(log_levels[:-1]),\n log_levels[-1])))\n parser.add_argument('--log-dir', dest='log_dir', default='/tmp/',\n help=('Path to the directory to store log files'))\n parser.add_argument('-p', '--project', dest='proj_name',\n help=('codename such as '\n 'dell-bto-precise-palm-beach-mlk-precise'))\n parser.add_argument('-b', dest='build_name',\n help=('build name such as 20130412-0'))\n parser.add_argument('-z', '--zsync-input', dest='zsync_file',\n default=None,\n help=('file path of zsync input path'))\n parser.add_argument('--config-dir', dest='config_dir',\n default=os.path.join(os.path.expanduser('~'),\n '.config', 'ibs-cli'),\n help=SUPPRESS)\n parser.add_argument('-g', '--osd', action='store_true', dest=\"osd\",\n default=False,\n help=('show OSD notify during monitor'))\n parser.add_argument('--no-download-image', action='store_true',\n dest=\"no_image\", default=False,\n help=('Not to download image'))\n #parser.add_argument('-a', '--add-project-args', dest=\"add_project_args\",\n # default='',\n # help=('arguments for adding project. It may be'\n # 'name=dell-bto-precise-fish-init-test&'\n # 'title=Dell precise fish-init-test&'\n # 'project_group=Somerville&'\n # 'arch=amd64&'\n # 'series=precise&'\n # 'config_url=lp:~oem-solutions-engineers/bugsy-config/dell-bto-precise-titan'))\n actions = ['list-projects', 'list-builds', 'build', 'download',\n 'monitor', 'version', 'login']\n parser.add_argument('action', choices=actions,\n default='check',\n help=('Action is one of {0}'.\n format(', '.join(actions))))\n # Append to log on subsequent startups\n parser.add_argument('--append', action='store_true',\n default=False, help=SUPPRESS)\n\n self.parser = parser\n\n def parse(self):\n \"\"\"Parse command-line arguments\n \"\"\"\n args, extra_args = self.parser.parse_known_args()\n args.log_level = getattr(logging, args.log_level_str.upper())\n\n # Log filename shows clearly the type of test (pm_operation)\n # and the times it was repeated (repetitions)\n args.log_filename = os.path.join(args.log_dir,\n ('{0}.log'\n .format(os.path.basename(__file__))))\n return args, extra_args\n\n\ndef main():\n args, extra_args = MyArgumentParser().parse()\n\n LoggingConfiguration.set(args.log_level, args.log_filename, args.append)\n logging.debug('Arguments: {0!r}'.format(args))\n logging.debug('Extra Arguments: {0!r}'.format(extra_args))\n\n try:\n IbsCli(args, extra_args).run()\n except PermissionDenied:\n logging.critical('Permission Denied (email, password or 2FA token'\n 'error)')\n except KeyboardInterrupt:\n logging.info('^c')\n\n\nif __name__ == '__main__':\n main()\n"
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"text": "INSTALL = /usr/bin/install\nINSTALL_PROGRAM = ${INSTALL}\nINSTALL_DATA = ${INSTALL} -m 644\n\nall: ibs-cli\n\ninstall: all\n\tmkdir -p $(DESTDIR)/usr/bin/\n\t$(INSTALL_PROGRAM) ibs-cli $(DESTDIR)/usr/bin/\n\tmkdir -p $(DESTDIR)/etc/bash_completion.d/\n\t$(INSTALL_PROGRAM) ibs-cli.completion $(DESTDIR)/etc/bash_completion.d/ibs-cli\n"
}
] | 3 |
diwadd/Tensor_Flow_Examples
|
https://github.com/diwadd/Tensor_Flow_Examples
|
b80f35f8bfc40717dc5c7df7e5f60770eeacb993
|
82855237c6cd4097cedc660aa3cf559c814b0065
|
95ff60eb0e908dd7bbb0251182158dbc2652f13c
|
refs/heads/master
| 2021-01-17T19:59:44.388150 | 2016-12-31T14:55:47 | 2016-12-31T14:56:34 | 61,448,751 | 0 | 0 | null | null | null | null | null |
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"text": "import csv\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom sklearn.model_selection import train_test_split\n\nRANDOM_STATE = 1\nIMAGE_SIZE = 28\nTEST_SIZE = 0.1\n\ntf.set_random_seed(RANDOM_STATE)\n\ndef read_data(file_name):\n\n f = open(file_name, \"r\")\n file_contents = csv.reader(f, delimiter=\",\")\n file_contents = list(file_contents)\n\n file_contents = np.asarray(file_contents[1:], dtype=np.int32)\n return file_contents\n\n\ndef show_image(row_image):\n\n label = row_image[0]\n row_image = row_image[1:]\n\n print(\"Printing: \" + str(label))\n\n square_image = np.resize(row_image, (IMAGE_SIZE, IMAGE_SIZE))\n for i in range(IMAGE_SIZE):\n for j in range(IMAGE_SIZE):\n if square_image[i][j] == 0:\n print(\"0\", end=\"\")\n else:\n print(\"1\", end=\"\")\n print()\n\n\ndef labels_to_vectors(row_of_labels):\n\n N = len(row_of_labels)\n vector_labels = np.zeros((N, 10))\n for i in range(N):\n vector_labels[i][row_of_labels[i]] = 1\n\n return vector_labels\n\n\nclass Network:\n\n def weight_variable(self, shape):\n initial = tf.truncated_normal(shape, stddev=1.0, seed=RANDOM_STATE)\n return tf.Variable(initial)\n\n def bias_variable(self, shape):\n initial = tf.truncated_normal(shape, stddev=1.0, seed=RANDOM_STATE)\n return tf.Variable(initial)\n\n def __init__(self, n_layers = 30):\n self.input = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE*IMAGE_SIZE], name=\"input_image\")\n\n self.w_one = self.weight_variable([IMAGE_SIZE*IMAGE_SIZE, n_layers])\n self.b_one = self.bias_variable([n_layers])\n self.layer_one = tf.sigmoid(tf.matmul(self.input, self.w_one) + self.b_one)\n\n self.w_two = self.weight_variable([n_layers, 10])\n self.b_two = self.bias_variable([10])\n self.output = tf.sigmoid(tf.matmul(self.layer_one, self.w_two) + self.b_two)\n\n print(\"input: \" + str((self.input).get_shape()))\n print(\"w_one: \" + str(self.w_one.get_shape()))\n print(\"b_one: \" + str(self.b_one.get_shape()))\n print(\"layer_one: \" + str((self.layer_one).get_shape()))\n print(\"w_two: \" + str(self.w_two.get_shape()))\n print(\"b_two: \" + str(self.b_two.get_shape()))\n print(\"output: \" + str((self.output).get_shape()))\n\n #print(\"C: \" + str(C.get_shape()))\n\n def setup_loss(self, mini_batch_size):\n\n self.expected_output = tf.placeholder(tf.float32, shape=[None, 10], name=\"expected_output\")\n\n s = tf.subtract(self.output, self.expected_output)\n self.C = tf.reduce_sum(tf.multiply(s, s))\n m = tf.constant( 2.0*mini_batch_size, dtype=tf.float32 )\n self.C = tf.divide(self.C, m)\n\n\n def setup_minimize(self, learning_rate):\n self.train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(self.C)\n\n def evaluate_performance(self,sess, x_test, y_test):\n\n n_train_samples = len(x_test)\n n_pos = 0\n for i in range(n_train_samples):\n parameter_dict = {self.input: np.resize(x_test[i], (1, IMAGE_SIZE*IMAGE_SIZE) )}\n\n output = (self.output).eval(session=sess, feed_dict=parameter_dict)\n output = np.argmax(output)\n expected_output = np.argmax(y_test[i])\n\n if output == expected_output:\n n_pos = n_pos + 1\n\n return n_pos / n_train_samples\n\n def train(self, x_train, x_test, y_train, y_test, n_epochs, mini_batch_size, learning_rate):\n\n sess = tf.InteractiveSession()\n init = tf.initialize_all_variables()\n sess = tf.Session()\n sess.run(init)\n\n self.setup_loss(mini_batch_size)\n self.setup_minimize(learning_rate)\n\n n_batches_per_epoch = int(len(x_train) / mini_batch_size)\n\n for epoch in range(n_epochs):\n ptr = 0\n print(\"Epoch number: %10s\" % (str(epoch)))\n for batch in range(n_batches_per_epoch):\n network_input, expected_output = x_train[ptr:ptr + mini_batch_size], y_train[ptr:ptr + mini_batch_size]\n\n ptr = ptr + mini_batch_size\n parameter_dict = {self.input: network_input, self.expected_output: expected_output}\n\n (self.train_step).run(session=sess, feed_dict=parameter_dict)\n\n c_val = (self.C).eval(session=sess, feed_dict=parameter_dict)\n print(\"chi^2 value: \" + str(c_val))\n acc = self.evaluate_performance(sess, x_test, y_test)\n print(\"Accuracy: \" + str(acc))\n #input(\"Press Enter to continue...\")\n\n\n\nprint(\"Recognize Digits\")\n\nfile_name = \"/home/tadek/Coding_Competitions/Kaggle/DigitRecognizer/train.csv\"\n\ndata = read_data(file_name)\n\nrow_labels = data[:, 0]\nimages = data[:, 1:]/255.0\nvector_labels = labels_to_vectors(row_labels)\n\nx_train, x_test, y_train, y_test = train_test_split(images, vector_labels, test_size=TEST_SIZE, random_state=RANDOM_STATE)\n\nprint(\"=== Data shape ===\")\nprint(data.shape)\nprint(row_labels.shape)\nprint(images.shape)\nprint(\"==================\")\n\n\n\nfor i in range(30):\n print(str(row_labels[i]) + \" \", end=\"\")\n for j in range(len(vector_labels[i])):\n print(str(vector_labels[i][j]) + \" \", end=\"\")\n print()\n\n#show_image(data[3336])\n\nnn = Network(100)\nnn.train(x_train, x_test, y_train, y_test, 30, 10, 0.001)\n"
}
] | 1 |
Dujinkwon/algorithm
|
https://github.com/Dujinkwon/algorithm
|
d61bc5e62badec8779f637a464815d163a390abc
|
3a4b4b7fff4cd9a9ec4c53140e9514794d465b5a
|
d90f8aa53adeee31997b543088bb121788eb1a52
|
refs/heads/main
| 2023-02-28T09:21:15.619244 | 2021-02-03T16:01:07 | 2021-02-03T16:01:07 | 331,678,516 | 0 | 0 | null | null | null | null | null |
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"max_line_length": 41,
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"path": "/practice4.py",
"repo_name": "Dujinkwon/algorithm",
"src_encoding": "UTF-8",
"text": "# N을 입력받기\nn=int(input())\nx,y=1,1\nplans=input().split()\n\n#L,R,U,D에 따른 방향이동.\n\ndx=[0,0,-1,1]\ndy=[-1,1,0,0]\n\nmoves_type=['L','R','U','D']\n\n# 이동계획을 하나씩 확인\n\nfor plan in plans:\n #이동후 좌표 구하기\n for i in range(len(moves_type)):\n if plan == moves_type[i]:\n nx=x+dx[i]\n ny=y+dy[i]\n\n #공간을 벗어나는 경우 무시\n if nx < 1 or ny <1 or nx > n or ny > n:\n continue\n #이동 수행\n x,y=nx, ny\n print(x,y)\n"
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"text": "n,m=map(int, input().split())\n\nresult=0\n\nfor i in range(n):\n data=list(map(int, input().split()))\n min_value=10001\n for a in data:\n min_value=min(min_value, a)\n\n result=max(result, min_value)\n\nprint(result)\n"
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"max_line_length": 52,
"num_lines": 22,
"path": "/practice1.py",
"repo_name": "Dujinkwon/algorithm",
"src_encoding": "UTF-8",
"text": "# N M K를 공백으로 구분하여 입력 받기.\nn,m,k=map(int, input().split()) \ndata=list(map(int,input().split()))\n\ndata.sort() # 입력받은수를 정렬\nfirst =data[n-1] # 가장 큰수\nsecond=data[n-2] # 두번 째로 큰수\n\nresult=0\n\nwhile True:\n for i in range(k): # 가장 큰수를 k번 더하기, range=길이를 뜻한다\n if m==0:\n break\n result += first\n m -= 1\n if m == 0: #m이 0이라면 반복문 탈출\n break\n result += second\n m -= 1\n\nprint(result)\n"
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"path": "/practice3.py",
"repo_name": "Dujinkwon/algorithm",
"src_encoding": "UTF-8",
"text": "# n,k=map(int,input().split())\n# result=0\n# while n > k:\n# # N이 K로 나누어 떨어지지 않는다면 N에서 1씩 빼기.\n# while n%k !=0:\n# n-=1\n# result +=1\n# #K로 나누기\n# n//=k\n# result+=1\n\n# #마지막으로 남은 수에 대하여 1씩 빼기\n# while n>1:\n# n-=1\n# result += 1\n\n# print(result)\n\n\nn,k=map(int,input().split())\nresult = 0\n\nwhile True :\n #N==K가 될때까지 1씩 한번에 빼기\n target=(n//k)*k\n result += (n-target)\n n=target\n\n if(n<k):\n break\n result +=1\n n//=k\n\n #마지막으로 남은 수에 대하여 1씩 빼기\n result +=(n-1)\n\n print(result)\n"
}
] | 4 |
wellkamp/iot-cloud-br-django
|
https://github.com/wellkamp/iot-cloud-br-django
|
01cebbf07d8b09f9c98acfe2354612cfd9377d92
|
0267ea17310a3f553be8d9664f37f2e17ab9c629
|
dedbffbc58e237c58be8e1ca3b93249370e7ec63
|
refs/heads/main
| 2023-02-23T23:23:18.386965 | 2021-03-03T20:11:39 | 2021-03-03T20:11:39 | 333,488,932 | 0 | 0 | null | null | null | null | null |
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"num_lines": 11,
"path": "/sensors/views.py",
"repo_name": "wellkamp/iot-cloud-br-django",
"src_encoding": "UTF-8",
"text": "from django.shortcuts import render\nfrom rest_framework import viewsets, generics\nfrom .models import Sensor\nfrom .serializers import SensorSerializer\n# Create your views here.\n\n\nclass SensorsViewSet(viewsets.ModelViewSet):\n \"\"\"Exibir todos os sensores\"\"\"\n queryset = Sensor.objects.all()\n serializer_class = SensorSerializer\n"
},
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"num_lines": 12,
"path": "/requirements.txt",
"repo_name": "wellkamp/iot-cloud-br-django",
"src_encoding": "UTF-8",
"text": "asgiref==3.3.1\nDjango==3.1.5\ndjango-cors-headers==3.7.0\ndjangorestframework==3.12.2\ndjangorestframework-simplejwt==4.6.0\nMarkdown==3.3.3\nmysql-connector-python==8.0.23\nprotobuf==3.14.0\nPyJWT==2.0.1\npytz==2020.5\nsix==1.15.0\nsqlparse==0.4.1\n"
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"path": "/README.md",
"repo_name": "wellkamp/iot-cloud-br-django",
"src_encoding": "UTF-8",
"text": "# iot-cloud-br-django-drf\n<p> Primeiros estudos com Django Rest Framework\n"
},
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"num_lines": 9,
"path": "/sensors/serializers.py",
"repo_name": "wellkamp/iot-cloud-br-django",
"src_encoding": "UTF-8",
"text": "from rest_framework import serializers\nfrom .models import Sensor\n\n\nclass SensorSerializer(serializers.ModelSerializer):\n class Meta:\n model = Sensor\n fields = ['id', 'user', 'sensor_name',\n 'temperature', 'humidity', 'last_date', 'last_hour']"
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"src_encoding": "UTF-8",
"text": "from django.contrib import admin\nfrom sensors.models import Sensor\n\n# Register your models here.\n\n\nclass Sensors(admin.ModelAdmin):\n list_display = ('id', 'user', 'sensor_name',\n 'temperature', 'humidity', 'last_date', 'last_hour')\n list_display_links = ('id', 'user')\n search_fields = ('user',)\n list_per_page = 10\n\n\nadmin.site.register(Sensor, Sensors)\n"
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"repo_name": "wellkamp/iot-cloud-br-django",
"src_encoding": "UTF-8",
"text": "from django.contrib import admin\nfrom django.urls import path, include\nfrom rest_framework.routers import DefaultRouter\nfrom sensors.views import SensorsViewSet\nfrom auth.views import RegisterView\n\n\nrouter = DefaultRouter()\nrouter.register('sensor', SensorsViewSet, basename='Sensor')\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('auth/', include('auth.urls')),\n path('', include(router.urls)),\n path('register/', RegisterView.as_view(), name='auth_register'),\n]"
},
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"path": "/sensors/models.py",
"repo_name": "wellkamp/iot-cloud-br-django",
"src_encoding": "UTF-8",
"text": "from django.db import models\nfrom django.contrib.auth.models import User\n# Create your models here.\n\n\nclass Sensor(models.Model):\n user = models.ForeignKey(User, on_delete=models.CASCADE)\n sensor_name = models.CharField(max_length=50)\n temperature = models.CharField(max_length=50)\n humidity = models.CharField(max_length=50)\n last_date = models.DateField()\n last_hour = models.TimeField()\n\n def __str__(self):\n return self.user\n\n\n"
}
] | 7 |
lvah/acm-sdk-python
|
https://github.com/lvah/acm-sdk-python
|
95c62effb33ce440af294568a18702a24245580e
|
63388984df7abbb92cec7ffb372a5fcc8ce0774b
|
ce7700653f96d8f3c705f87cc966462a9a19b129
|
refs/heads/master
| 2021-04-26T23:44:31.823437 | 2018-02-08T05:17:29 | 2018-02-08T05:17:29 | null | 0 | 0 | null | null | null | null | null |
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"repo_name": "lvah/acm-sdk-python",
"src_encoding": "UTF-8",
"text": "from .client import ACMClient, ACMException, DEFAULTS\n\n__version__ = client.VERSION\n\n__all__ = [\"ACMClient\", \"ACMException\", \"DEFAULTS\"]\n"
}
] | 1 |
hyy044101331/mkpython
|
https://github.com/hyy044101331/mkpython
|
ba8652f78d1eeb4b33ff8feae7f40738a6fe0207
|
4f8b7e65e6ab99b9b72cf5bca3d4794c793cafbe
|
cb709ebf2eb505ae61763c424d4fdb35be387a7b
|
refs/heads/master
| 2019-01-01T18:14:40.456792 | 2012-08-19T08:56:25 | 2012-08-19T08:56:25 | 5,469,553 | 0 | 0 | null | null | null | null | null |
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"text": "#!/usr/bin/python\n\naa={'host':'zhangsan'}\n\n#=========================\n\naa['port']=8080\n\nprint \"aa = \",aa\n\nprint 'aa[\\'host\\'] = ',aa['host']\n\n#=========================\n\nprint 'aa.keys() = ',aa.keys()\n\nfor kk in aa:\n print kk,aa[kk] \n"
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"repo_name": "hyy044101331/mkpython",
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"text": "#! /usr/bin/env python \n\n#======================\nk='baicai'\n\nprint k\n\nprint \"%s is number %s\" %(\"myPython\",\"baicai\")\n\n#======================\nkk=raw_input('please input a string:')\n\nprint 'mengkaAAA:',kk\n\n#=======================\naa=raw_input('please input a Integer:')\n\nprint 'mengkaBBB:%d' %(int(aa)*2)\n\n"
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"text": "mkpython\n========"
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"text": "#!/usr/bin/python\nfilename =raw_input('enter the file name:')\nfobj=open(filename,'r')\n\nfor aa in fobj:\n print aa,\n\nfobj.close()\n"
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"path": "/mkpython/mk01/python_03.py",
"repo_name": "hyy044101331/mkpython",
"src_encoding": "UTF-8",
"text": "#! /usr/bin/python \n\nprint -2*4+7\n\nprint \"5/2 = \",5/2\n\nprint \"5//2 = \",5//2\n\nprint \"7/3 = \",7/3\n\nprint \"7//3 = \",7//3\n\nprint \"2<4and2==4 : \",2<4 and 2==4\n\nprint \"not 6.2<=6 : \",not 6.2 <= 6\n\n\n"
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"text": "#!/usr/bin/python\n\naa=raw_input('input a Number:')\n\nif int(aa)>10:\n print '111 is a baicai'\nelse:\n print '000 is a baicai'\n\nprint '='*20\n\n\n\nif int(aa)>20:\n print '>20 ...'\nelif int(aa)==20:\n print 'get 20 baicai'\nelse:\n print '<20 ...'\n"
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"text": "#!/usr/bin/python\n\naa=[x**2 for x in range(4)]\n\nfor i in aa:\n print i\n\n\nprint '='*20\n\n\nbb=[x**2 for x in range(8) if not x%2]\n\nfor i in bb:\n print i\n"
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"src_encoding": "UTF-8",
"text": "#!/usr/bin/python\n\ndef mengka(x):return (x+x)\n\n\nprintln mengka('baicia')\n\nprintln mengka([-1,'abc'])\n"
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"repo_name": "hyy044101331/mkpython",
"src_encoding": "UTF-8",
"text": "#! /usr/bin/python \nprint \"baicai aaaaaaaaa!!!\"\n"
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"path": "/mkpython/mk01/string_01.py",
"repo_name": "hyy044101331/mkpython",
"src_encoding": "UTF-8",
"text": "#!/usr/bin/env python\n\n#=======================\n\naa='python'\nbb='is a baicai'\n\n#=======================\n\naa = 'aa is qingcai'\n\n\nprint aa\n"
}
] | 11 |
doansangg/thay_an
|
https://github.com/doansangg/thay_an
|
51d99a69f04e593abb81a0e251d1ce6d1d18ffe9
|
0f85502b762dfdd8f62407973a4b8799ea0c5870
|
bc039392f4a6a7b108cb902cb1d258db769cb207
|
refs/heads/master
| 2023-02-19T17:04:28.890061 | 2021-01-22T15:17:55 | 2021-01-22T15:17:55 | 318,707,440 | 0 | 0 | null | null | null | null | null |
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"num_lines": 55,
"path": "/coor_new.py",
"repo_name": "doansangg/thay_an",
"src_encoding": "UTF-8",
"text": "import cv2\nimport sys\nimport numpy as np\nimport alphashape\nimport matplotlib.pyplot as plt\nfrom descartes import PolygonPatch\ndef roi(img1):\n kernel= [[11, 4, 17, 1, 5],\n [ 6, 14, 0, 12, 16],\n [24, 19, 13, 18, 23],\n [ 7, 11, 11, 10, 5],\n [10, 13, 23, 3, 0]]\n kernel = np.array(kernel,np.float32)/235\n img = cv2.filter2D(img1,-1,kernel)\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n mask = cv2.GaussianBlur(gray,(5,5),0)\n ret1, output = cv2.threshold(mask, 200, 255, cv2.THRESH_BINARY_INV)\n (conts, _) = cv2.findContours(output, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n cnt = max(conts, key = cv2.contourArea)\n h, w = img.shape[:2]\n mask1 = np.zeros((h, w), np.uint8)\n cv2.drawContours(mask1, [cnt],-1, 255, -1)\n edges = cv2.Canny(mask,100,200)\n (conts, _) = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n cnt = max(conts, key = cv2.contourArea)\n x,y,w,h = cv2.boundingRect(cnt)\n roi=img1[y+15:y+h-15,x+15:x+w-15]\n return ((x,y),roi)\ndef get_coor(img):\n (x,y),img=roi(img)\n median = cv2.GaussianBlur(image,(5,5),0)\n median = cv2.medianBlur(median,15)\n hsv = cv2.cvtColor(median, cv2.COLOR_BGR2HSV)\n lower_blue=np.array([91,111,68])\n upper_blue=np.array([115,255,255])\n mask=cv2.inRange(hsv,lower_blue,upper_blue)\n edges = cv2.Canny(mask,100,200)\n (contours,_) = cv2.findContours(edges.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n #hull = cv2.convexHull(contours,returnPoints = False)\n #print(hull)\n arr=[]\n for contour in contours:\n (x,y,w,h) = cv2.boundingRect(contour)\n arr.append([x,y])\n arr.append([x+w,y])\n arr.append([x,y+h])\n arr.append([x+w,y+h])\n alpha = 0.4 * alphashape.optimizealpha(arr)\n hull = alphashape.alphashape(arr, alpha)\n hull_pts = hull.exterior.coords.xy\n arr=[]\n for i in range(len(hull_pts[0])):\n arr.append([int(hull_pts[0][i]),int(hull_pts[1][i])])\n results=[(np.array(z)+np.array([x,y])).tolist() for z in arr]\n return results\n"
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"path": "/README.md",
"repo_name": "doansangg/thay_an",
"src_encoding": "UTF-8",
"text": "## 1. Install env\n\n ```\npip install -r requirements.txt\npip install python3.6-tk\n ```\n**NOTE**\npython3.6-tk : python version user now ok\n## 2. Run \n ```\npython main_inteface.py\n ```\n\n"
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"path": "/sorfware.py",
"repo_name": "doansangg/thay_an",
"src_encoding": "UTF-8",
"text": "\r\nimport cv2\r\nimport numpy as np\r\nfilename = 'weights/finalized_model_1.sav'\r\ndef load_model(filename):\r\n model=cv2.face.LBPHFaceRecognizer_create()\r\n model.read(filename)\r\n return model\r\ndef predict(img,filename):\r\n model=load_model(filename)\r\n img = cv2.resize(img,(500, 400))\r\n gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\r\n predict = model.predict(gray)\r\n return predict[0]"
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"text": "import cv2\nimport sys\nimport numpy as np\ndef roi(img1):\n kernel= [[11, 4, 17, 1, 5],\n [ 6, 14, 0, 12, 16],\n [24, 19, 13, 18, 23],\n [ 7, 11, 11, 10, 5],\n [10, 13, 23, 3, 0]]\n kernel = np.array(kernel,np.float32)/235\n img = cv2.filter2D(img1,-1,kernel)\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n mask = cv2.GaussianBlur(gray,(5,5),0)\n ret1, output = cv2.threshold(mask, 200, 255, cv2.THRESH_BINARY_INV)\n (conts, _) = cv2.findContours(output, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n cnt = max(conts, key = cv2.contourArea)\n h, w = img.shape[:2]\n mask1 = np.zeros((h, w), np.uint8)\n cv2.drawContours(mask1, [cnt],-1, 255, -1)\n edges = cv2.Canny(mask,100,200)\n (conts, _) = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n cnt = max(conts, key = cv2.contourArea)\n x,y,w,h = cv2.boundingRect(cnt)\n roi=img1[y+15:y+h-15,x+15:x+w-15]\n return ((x,y),roi)\ndef de_hole(img):\n img=roi(img)[1]\n arr_X=[]\n arr_Y=[]\n gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n mask = cv2.GaussianBlur(gray,(5,5),0)\n ret1, output = cv2.threshold(mask, 30, 255, cv2.THRESH_BINARY_INV)\n edges = cv2.Canny(output,100,200,apertureSize = 3)\n minLineLength = 50\n maxLineGap = 10\n lines = cv2.HoughLinesP(edges,2,np.pi/180,15,minLineLength=minLineLength,maxLineGap=maxLineGap)\n for x in range(0, len(lines)):\n for x1,y1,x2,y2 in lines[x]:\n if x1==x2:\n arr_X.append([x1,y1])\n if y1==y2:\n arr_Y.append([x1,y1])\n def get_X(box):\n return box[0]\n def get_Y(box):\n return box[1]\n X=sorted(arr_X,key=get_X,reverse=False)\n Y=sorted(arr_Y,key=get_Y,reverse=False)\n (x_min,x_max)=(X[0],X[-1])\n (y_min,y_max)=(Y[0],Y[-1])\n img_r=img[y_min[1]:y_max[1],x_min[0]:x_max[0]]\n return img_r\n# cv2.namedWindow('image',cv2.WINDOW_NORMAL)\n# cv2.resizeWindow(\"image\", 1000, 800)\n# cv2.imshow(\"image\",img_r)\n# cv2.waitKey(0)\n #cv2.imwrite(\"/home/doan/Documents/data/test_thayan/image11.jpg\",img_r)\n \ndef de_hole1(img):\n img=de_hole(img)\n arr_X=[]\n arr_Y=[]\n gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n mask = cv2.GaussianBlur(gray,(5,5),0)\n ret1, output = cv2.threshold(mask, 10, 255, cv2.THRESH_BINARY_INV)\n# cv2.namedWindow('image',cv2.WINDOW_NORMAL)\n# cv2.resizeWindow(\"image\", 1000, 800)\n# cv2.imshow(\"image\",output)\n# cv2.waitKey(0)\n edges = cv2.Canny(output,100,200,apertureSize = 3)\n minLineLength = 50\n maxLineGap = 10\n lines = cv2.HoughLinesP(edges,2,np.pi/180,15,minLineLength=minLineLength,maxLineGap=maxLineGap)\n for x in range(0, len(lines)):\n for x1,y1,x2,y2 in lines[x]:\n if x1==x2:\n arr_X.append([[x1,y1],[x2,y2]])\n if y1==y2:\n arr_Y.append([[x1,y1],[x2,y2]])\n def sort_X(box):\n return box[1][1]-box[0][1]\n def sort_Y(box):\n return box[1][0]-box[0][0]\n def get_X(box):\n return box[0][0]\n def get_Y(box):\n return box[1][1]\n X_sort=sorted(arr_X,key=sort_X,reverse=True)\n Y_sort=sorted(arr_Y,key=sort_Y,reverse=True)\n X=sorted(X_sort,key=get_X,reverse=False)\n Y=sorted(Y_sort,key=get_Y,reverse=False)\n X_1=[X_sort[0]]\n for i in X:\n if abs(i[0][0]-X_1[-1][0][0])>10:\n X_1.append(i)\n Y_1=[Y_sort[0]]\n for i in Y:\n if abs(i[1][1]-Y_1[-1][1][1])>10:\n Y_1.append(i)\n X=sorted(X_1,key=get_X,reverse=False)\n Y=sorted(Y_1,key=get_Y,reverse=False)\n Z=[[x[0][0],y[1][1]] for x in X for y in Y]\n X1=[i[0] for i in X]\n for i in X:\n X1.append(i[1])\n Y1=[i[1] for i in Y]\n for i in Y:\n Y1.append(i[0])\n print(len(Y1))\n print(len(X1))\n print(len(Z))\n return (X1,Y1,Z)\ndef get_coor(image):\n X,Y,Z=de_hole1(image)\n arr_X=[]\n arr_Y=[]\n for z in Z:\n for x in X:\n if z[0]==x[0] or z[1]==z[1]:\n if check(z,x) :\n arr_X.append(z)\n for z in Z:\n for x in Y:\n if z[0]==x[0] or z[1]==z[1]:\n if check(z,x) :\n arr_Y.append(z)\n for x in arr:\n if x not in output:\n output.append(x)\n print(output)\n alpha = 0.005 * alphashape.optimizealpha(output)\n hull = alphashape.alphashape(output, alpha)\n hull = hull.exterior.coords.xy\n return hull"
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"text": "import tkinter as tk\nfrom tkinter import filedialog\nfrom tkinter import *\nfrom PIL import Image,ImageTk\nimport os\nimport cv2\nimport numpy as np\nfrom main_process import *\nclass ImageClassifyer(tk.Frame):\n def __init__(self, parent, *args, **kwargs):\n\n tk.Frame.__init__(self, parent, *args, **kwargs)\n self.filename =\"\"\n self.root = parent\n self.root.wm_title(\"Find Boundaries\")\n\n # self.next_image()\n claButton = tk.Button(self.root, text='Browser',height=2, width=10, command = self.UploadAction)\n claButton.grid(row=0, column=0, padx=2, pady=2)\n broButton = tk.Button(self.root, text='Next', height=2, width=8, command = self.next_image)\n broButton.grid(row=0, column=1, padx=2, pady=2)\n\n self.frame1 = tk.Frame(self.root, width=1000, height=1000, bd=2)\n self.frame1.grid(row=1, column=0)\n self.frame2 = tk.Frame(self.root, width=1000, height=1000, bd=1)\n self.frame2.grid(row=1, column=1)\n\n\n self.cv1 = tk.Canvas(self.frame1, height=800, width=800, background=\"white\", bd=1, relief=tk.RAISED)\n self.cv1.grid(row=1,column=0)\n self.cv2 = tk.Canvas(self.frame2, height=800, width=800, background=\"white\", bd=1, relief=tk.RAISED)\n self.cv2.grid(row=1, column=0)\n\n\n def UploadAction(self):\n self.filename = filedialog.askdirectory()\n print('Selected:', self.filename)\n src = self.filename\n self.list_images = []\n print(src)\n for d in os.listdir(src):\n self.list_images.append(d)\n print(len(self.list_images))\n self.counter = 0\n self.max_count = len(self.list_images) - 1\n def next_image(self):\n if self.counter > self.max_count:\n self.counter = 0\n self.cv1.create_image(0, 0, anchor='nw', image=self.photo)\n self.cv2.create_image(0, 0, anchor='nw', image = self.imgtk)\n else:\n im = Image.open(\"{}/{}\".format(self.filename, self.list_images[self.counter]))\n \n # if (590-im.size[0])<(490-im.size[1]):\n width = 800\n # height = width*im.size[1]/im.size[0]\n # self.next_step(height, width)\n # else:\n height = 800\n # width = height*im.size[0]/im.size[1]\n self.next_step(height, width)\n\n def next_step(self, height, width):\n self.im = Image.open(\"{}/{}\".format(self.filename, self.list_images[self.counter])).convert('RGB') \n # img = np.asarray(self.im)\n img=cv2.imread(\"{}/{}\".format(self.filename, self.list_images[self.counter]))\n #img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n print(img.shape)\n img = show(img)\n img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n img = cv2.resize(img,(int(width) , int(height) ))\n \n img = Image.fromarray(img)\n\n self.im.thumbnail((width, height), Image.ANTIALIAS)\n self.root.photo = ImageTk.PhotoImage(self.im)\n self.photo = ImageTk.PhotoImage(self.im)\n\n\n self.imgtk = ImageTk.PhotoImage(img)\n if self.counter == 0:\n self.cv1.create_image(0, 0, anchor = 'nw', image = self.photo)\n self.cv2.create_image(0, 0, anchor='nw', image=self.imgtk)\n\n else:\n self.im.thumbnail((width, height), Image.ANTIALIAS)\n self.cv1.delete(\"all\")\n self.cv1.create_image(0, 0, anchor = 'nw', image = self.photo)\n self.cv2.create_image(0, 0, anchor='nw', image=self.imgtk)\n self.counter += 1\n\n\nif __name__ == \"__main__\":\n root = tk.Tk()\n MyApp = ImageClassifyer(root)\n tk.mainloop()\n"
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"text": "from sorfware import predict\nfrom PolluteRecognized import Solve,ResizeWithAspectRatio\nfrom function import detect\nfrom detect_boder import find_max,find_2,find_1,roi\nimport argparse\nimport os\nimport cv2\nimport numpy as np\nfrom poli_1 import poli\n# cv2.namedWindow(\"image\", cv2.WINDOW_NORMAL) # Create window with freedom of dimensions\n# cv2.resizeWindow(\"image\", 1000, 900) \n# ap = argparse.ArgumentParser()\n# ap.add_argument(\"-f\", \"--folder\", required=True,help=\"path forder image\")\n# ap.add_argument(\"-w\", \"--weights\", required=True,help=\"path file weigths\")\n# args = vars(ap.parse_args())\npath_folder=\"/home/doan/Desktop/a\"\npath_weight=\"weights/finalized_model_1.sav\"\ndef process(img):\n coordinates_=[]\n predict_class=predict(img,path_weight)\n if(predict_class==1):\n \n # cv2.imshow(\"image\", img1)\n # cv2.waitKey()\n coordinates=poli(img)\n coordinates_.append(coordinates)\n if(predict_class==2):\n coordinates1=detect(img)\n coordinates_.append(coordinates1)\n if(predict_class==3):\n coor=find_max(img)\n coor1=find_1(img)\n coor2=find_2(img)\n coordinates_.append(coor)\n coordinates_.append(coor1)\n coordinates_.append(coor2)\n if (predict_class==0):\n coordinates_=[]\n return (predict_class,coordinates_)\ndef show(img):\n predict_class,coordinates_=process(img)\n if(predict_class==1): #square\n # img = ResizeWithAspectRatio(img, width=600)\n # img = img[:, :400]\n # blue_color = (0,0, 255)\n # img_draw = img.copy()\n # img_draw = cv2.drawContours(img_draw, coordinates_[0], -1, blue_color, 2)\n img_draw=coordinates_[0][0]\n print(coordinates_[0][1])\n img_draw=cv2.resize(img_draw,(800,800))\n # img_draw = np.hstack([img, img_draw])\n # img_draw=cv2.resize(img_draw,(800,800))\n return img_draw\n\n\n # plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n # plt.show()\n if(predict_class==2): #general\n # img1=cv2.resize(img,(800,800))\n clone=coordinates_[0][1]\n for c in coordinates_[0][0]:\n\n cv2.drawContours(clone, [c], -1, (0,200,0), 3)\n x,y,w,h=coordinates_[0][2]\n # img[y:y+h, x:x+w] = clone\n img=cv2.resize(clone,(800,800))\n return img\n if(predict_class==3): #area\n\n # img1=cv2.resize(img,(800,800))\n img1=roi(img)[1]\n # cv2.imshow(\"image\",img1)\n # cv2.waitKey(0)\n coor=coordinates_[0]\n coor1=coordinates_[1]\n coor2=coordinates_[2]\n h, w = img1.shape[:2]\n mask = np.zeros((h, w), np.uint8)\n mask1=np.zeros((h, w), np.uint8)\n mask2 = np.zeros((h, w), np.uint8)\n mask3=np.zeros((h, w), np.uint8)\n cv2.drawContours(mask, [coor[0]],-1, 255, -1)\n cv2.drawContours(mask1, [coor[1]],-1, 255, -1)\n for cnt in coor1:\n cv2.drawContours(mask2, [cnt],-1, 255, -1)\n for cnt in coor2:\n cv2.drawContours(mask3, [cnt],-1, 255, -1)\n res = cv2.bitwise_and(img1, img1, mask=mask)\n res1 = cv2.bitwise_and(img1, img1, mask=mask1)\n res2 = cv2.bitwise_and(img1, img1, mask=mask2)\n res3 = cv2.bitwise_and(img1, img1, mask=mask3)\n #img[y_min:y_max,x_min:x_max]=res\n res1=cv2.resize(res1,(800,800))\n # cv2.imshow('image',res1)\n # cv2.waitKey(0)\n #img[y_min:y_max,x_min:x_max]=res1\n res2=cv2.resize(res2,(800,800))\n # cv2.imshow('image',res2)\n # cv2.waitKey(0)\n res3=cv2.resize(res3,(800,800))\n #img[y_min:y_max,x_min:x_max]=res2\n # cv2.imshow('image',res3)\n # cv2.waitKey(0)\n res=cv2.resize(res,(800,800))\n img=np.hstack((res1,res))\n img1=np.hstack((res2,res3))\n img=np.vstack((img,img1))\n img=cv2.resize(img,(800,800))\n # cv2.imshow(\"image\",res)\n # cv2.waitKey(0)\n # cv2.imshow(\"image\",res1)\n # cv2.waitKey(0)\n # cv2.imshow(\"image\",res2)\n # cv2.waitKey(0)\n # cv2.imshow(\"image\",res3)\n # cv2.waitKey(0)\n # cv2.imshow(\"image\",img)\n # cv2.waitKey(0)\n #img[y_min:y_max,x_min:x_max]=res3\n # cv2.imshow('image',res)\n # cv2.waitKey(0)\n # image_data =[]\n # image_data.append(res)\n # image_data.append(res1)\n # image_data.append(res2)\n # image_data.append(res3)\n # dst = image_data[0]\n # for i in range(len(image_data)):\n # if i == 0:\n # pass\n # else:\n # alpha = 1.0 / (i + 1)\n # beta = 1.0 - alpha\n # dst = cv2.addWeighted(image_data[i], alpha, dst, beta, 0.0)\n return img\n if(predict_class==0): #wrongImg\n img=cv2.resize(img,(800,800))\n return img\n\n# def test(img):\n# img1 = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)\n# img1 = cv2.Canny(img1,100,200)\n# return img1\n#\ndef main(path_folder):\n for image in os.listdir(path_folder):\n img=cv2.imread(os.path.join(path_folder,image))\n # img = np.asarray(img)\n # print(img.shape)\n show(img)\n\nif __name__ == \"__main__\":\n main(path_folder)"
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"text": "import cv2\nimport imutils\nimport numpy as np\nimport skimage.exposure\n\ndef remove_border(image):\n \n img = cv2.resize(image, (800, 600))\n\n ################################################################ clean border \n img = cv2.resize(img, (800, 600))\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n gradX = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)\n gradY = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1, ksize=-1)\n\n # subtract the y-gradient from the x-gradient\n gradient = cv2.subtract(gradX, gradY)\n gradient = cv2.convertScaleAbs(gradient)\n\n (_, threshold) = cv2.threshold(gradient, 225, 255, cv2.THRESH_BINARY)\n\n contour = cv2.findContours(threshold.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n contour = imutils.grab_contours(contour)\n c = sorted(contour, key=cv2.contourArea, reverse=True)[0]\n (x, y, w, h) = cv2.boundingRect(c)\n\n roi = img[y:y+h, x:x+w] #### ROI\n clone = img.copy()\n\n cv2.drawContours(img, [c], -1, (255, 255, 255), 5)\n return (x, y), img, clone\n\n\ndef detect(image):\n \n X, img, clone = remove_border(image)\n\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n hsv = cv2.cvtColor(clone, cv2.COLOR_BGR2HSV)\n lower_white = np.array([23,41,133])\n upper_white = np.array([40,150,255])\n mask5 = cv2.inRange(hsv, lower_white, upper_white)\n img[mask5 == 255] = 0\n\n ############################### red bounding #############################\n list_contours = []\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n lower_red = np.array([0,120,70])\n upper_red = np.array([10,255,255])\n mask2 = cv2.inRange(hsv, lower_red, upper_red)\n dilated = cv2.dilate(mask2, None, iterations=3)\n erosioned = cv2.erode(dilated, None, iterations=1)\n contours = cv2.findContours(erosioned.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n contours = imutils.grab_contours(contours)\n\n cnt = max(contours, key=cv2.contourArea)\n list_image1 = get_coor(X, cnt)\n print(list_image1)\n\n contours = sorted(contours, key=cv2.contourArea, reverse=True)\n for c in contours:\n area = cv2.contourArea(c)\n # print(area)\n if area < 1200:\n continue\n list_contours.append(c)\n cv2.drawContours(clone, [c], -1, (0,0,255), 3)\n\n ################################ Blue bounding #########################\n lower_blue = np.array([110,50,50])\n upper_blue = np.array([130,255,255])\n mask3 = cv2.inRange(hsv, lower_blue, upper_blue)\n dilated = cv2.dilate(mask3, None, iterations=3)\n erosioned = cv2.erode(dilated, None, iterations=1)\n contours = cv2.findContours(erosioned.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n contours = imutils.grab_contours(contours)\n\n cnt = max(contours, key=cv2.contourArea)\n list_image2 = get_coor(X, cnt)\n print(list_image2)\n\n contours = sorted(contours, key=cv2.contourArea, reverse=True)\n for c in contours:\n area = cv2.contourArea(c)\n # print(area)\n if area < 1200:\n continue\n list_contours.append(c)\n cv2.drawContours(clone, [c], -1, (255,0,0), 3)\n\n ####################### Green bounding ##################\n lower_green = np.array([26,56,46])\n upper_green = np.array([95, 255, 255])\n mask4 = cv2.inRange(hsv, lower_green, upper_green)\n dilated = cv2.dilate(mask4, None, iterations=3)\n erosioned = cv2.erode(dilated, None, iterations=1)\n contours = cv2.findContours(erosioned.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n contours = imutils.grab_contours(contours)\n\n cnt = max(contours, key=cv2.contourArea)\n list_image3 = get_coor(X, cnt)\n print(list_image3)\n\n contours = sorted(contours, key=cv2.contourArea, reverse=True)\n for c in contours:\n area = cv2.contourArea(c)\n # print(area)\n if area < 1200:\n continue\n list_contours.append(c)\n cv2.drawContours(clone, [c], -1, (0,255,0), 3)\n\n ################################## black bounding ##################\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n lower_hue = np.array([0,0,50])\n upper_hue = np.array([50,50,100])\n mask1 = cv2.inRange(hsv, lower_hue, upper_hue)\n dilated = cv2.dilate(mask1, None, iterations=3)\n erosioned = cv2.erode(dilated, None, iterations=1)\n contours = cv2.findContours(erosioned.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n contours = imutils.grab_contours(contours)\n\n cnt = max(contours, key=cv2.contourArea)\n list_image4 = get_coor(X, cnt)\n print(list_image4)\n\n contours = sorted(contours, key=cv2.contourArea, reverse=True)\n for c in contours:\n area = cv2.contourArea(c)\n # print(area)\n if area < 1000:\n continue\n list_contours.append(c)\n cv2.drawContours(clone, [c], -1, (0,0,0), 3)\n\n return clone\n\ndef get_coor(X, cnt):\n list_image1 = [i.tolist()[0] for i in cnt]\n list_image1 = [[img[0]+X[0], img[1]+X[1]] for img in list_image1]\n return list_image1\n\n\n"
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"text": "from detect_boder import roi\ndef de(img):\n\timg=roi(img)[1]\n\tgray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n\tmask = cv2.GaussianBlur(gray,(5,5),0)\n\tret1, output = cv2.threshold(mask, 40, 255, cv2.THRESH_BINARY_INV)\n\tcorners = cv2.goodFeaturesToTrack(output, 27, 0.01, 10) \n\tcorners = np.int0(corners)\n\treturn corners\n"
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"text": "import cv2\nimport sys\nimport numpy as np\ndef poli(img1):\n\tkernel= [[11, 4, 17, 1, 5],\n\t [ 6, 14, 0, 12, 16],\n\t [24, 19, 13, 18, 23],\n\t [ 7, 11, 11, 10, 5],\n\t [10, 13, 23, 3, 0]]\n\tkernel = np.array(kernel,np.float32)/235\n\timg = cv2.filter2D(img1,-1,kernel)\n\tgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\tmask = cv2.GaussianBlur(gray,(5,5),0)\n\tret1, output = cv2.threshold(mask, 200, 255, cv2.THRESH_BINARY_INV)\n\t(conts, _) = cv2.findContours(output, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n\tcnt = max(conts, key = cv2.contourArea)\n\th, w = img.shape[:2]\n\tmask1 = np.zeros((h, w), np.uint8)\n\tcv2.drawContours(mask1, [cnt],-1, 255, -1)\n\tedges = cv2.Canny(mask,100,200)\n\t(conts, _) = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n\tcnt = max(conts, key = cv2.contourArea)\n\tx,y,w,h = cv2.boundingRect(cnt)\n\t# cv2.circle(img, (x,y), 2, [0,0,255], thickness=100)\n\t# cv2.circle(img, (x+w,y+h), 2, [0,0,255], thickness=100)\n\t# cv2.rectangle(img, (x, y), (x + w, y + h), (0,0,255), 10)\n\troi=img1[y+15:y+h-15,x+15:x+w-15]\n\tgray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)\n\tmask = cv2.GaussianBlur(gray,(5,5),0)\n\tret1, output = cv2.threshold(mask, 30, 255, cv2.THRESH_BINARY_INV)\n\tcorners = cv2.goodFeaturesToTrack(output, 27, 0.01, 10) \n\tcorners = np.int0(corners)\n\treturn output,corners "
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"path": "/PolluteRecognized.py",
"repo_name": "doansangg/thay_an",
"src_encoding": "UTF-8",
"text": "import cv2\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\nimport pathlib\r\n\r\ndef ResizeWithAspectRatio(image, width=None, height=None, inter=cv2.INTER_AREA):\r\n dim = None\r\n (h, w) = image.shape[:2]\r\n if width is None and height is None:\r\n return image\r\n if width is None:\r\n r = height / float(h)\r\n dim = (int(w * r), height)\r\n else:\r\n r = width / float(w)\r\n dim = (width, int(h * r))\r\n return cv2.resize(image, dim, interpolation=inter)\r\n\r\ndef DrawContour(image):\r\n img_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\r\n # mask_red1 = cv2.inRange(img_hsv, (170, 100, 20), (180, 255, 255))\r\n # mask_red2 = cv2.inRange(img_hsv, (0, 100, 20), (10, 255, 255))\r\n # mask_red = mask_red1 + mask_red2\r\n # mask_blue = cv2.inRange(img_hsv, (100, 100, 20), (140, 255, 255))\r\n mask_red = cv2.inRange(img_hsv, (-10, 254, 214), (10, 265, 294))\r\n mask_blue = cv2.inRange(img_hsv, (97, 245, 214), (117, 265, 294))\r\n mask = cv2.bitwise_or(mask_red, mask_blue)\r\n target = cv2.bitwise_and(img_hsv, img_hsv, mask=mask)\r\n # cv2.imshow(\"target\", target)\r\n # cv2.waitKey()\r\n\r\n imgray = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY)\r\n ret, thresh = cv2.threshold(imgray, 127, 255, 0)\r\n contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\r\n img_draw = cv2.drawContours(imgray, contours, -1, (0,0,255), 1)\r\n # cv2.imshow(\"img_draw\", img_draw)\r\n # cv2.waitKey()\r\n return contours\r\n\r\ndef Solve(im):\r\n # im = cv2.imread(file_name)\r\n im = ResizeWithAspectRatio(im, width=600)\r\n im = im[:, :400]\r\n contours = DrawContour(im)\r\n if len(contours) == 0:\r\n print(\"doan sang\")\r\n else:\r\n cont = np.vstack(contours[i] for i in range(len(contours)))\r\n hull = cv2. convexHull(cont)\r\n print(hull)\r\n uni_hull = []\r\n uni_hull.append(hull)\r\n return uni_hull\r\n # black_color = (255, 255, 255)\r\n # img_draw = im.copy()\r\n # img_draw = cv2.drawContours(img_draw, uni_hull, -1, black_color, 1)\r\n # cv2.imshow(\"img_draw\", np.hstack([im, img_draw]))\r\n # cv2.waitKey()\r\n\r\n\r\n # plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\r\n # plt.show()\r\n # print(contours)\r\n\r\n\r\n# file_name = 'KSKT_ST_04.jpg'\r\n# file_name = '/home/doan/Desktop/a/KSKT_02.jpg'\r\n# file_name = 'KSKT_04.jpg'\r\n# file_name = 'CHA_008.jpg'\r\n# file_name = 'KSKT_ST_06.jpg'\r\n# file_name = 'CHA_001.png'\r\n# file_name = 'CHA_006.png'\r\n# file_name = 'KSKT_11.png' \r\n# file_name = 'CHA_001.jpg'\r\n# Solve(file_name)\r\n\r\n\r\n"
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"src_encoding": "UTF-8",
"text": "import cv2\r\nimport imutils\r\nimport numpy as np\r\n\r\n\r\ndef detect(image):\r\n ################################################################ clean border \r\n image = cv2.resize(image, (800, 600))\r\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\n gradX = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)\r\n gradY = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1, ksize=-1)\r\n\r\n # subtract the y-gradient from the x-gradient\r\n gradient = cv2.subtract(gradX, gradY)\r\n gradient = cv2.convertScaleAbs(gradient)\r\n\r\n (_, threshold) = cv2.threshold(gradient, 225, 255, cv2.THRESH_BINARY)\r\n\r\n contour = cv2.findContours(threshold.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\r\n contour = imutils.grab_contours(contour)\r\n c = sorted(contour, key=cv2.contourArea, reverse=True)[0]\r\n (x, y, w, h) = cv2.boundingRect(c)\r\n\r\n clone = image[y:y+h, x:x+w] #### ROI\r\n\r\n cv2.drawContours(image, [c], -1, (255, 255, 255), 5)\r\n # cv2.imshow(\"clone\", clone)\r\n\r\n ################################################################################\r\n hsv = cv2.cvtColor(clone, cv2.COLOR_BGR2HSV)\r\n lower_hue = np.array([0,0,0])\r\n upper_hue = np.array([60,60,100])\r\n mask1 = cv2.inRange(hsv, lower_hue, upper_hue)\r\n clone[mask1 == 255] = 0 \r\n hsv = cv2.cvtColor(clone, cv2.COLOR_BGR2HSV)\r\n lower_red = np.array([0,120,70])\r\n upper_red = np.array([10,255,255])\r\n mask2 = cv2.inRange(hsv, lower_red, upper_red)\r\n clone[mask2 == 255] = 0\r\n lower_blue = np.array([110,50,50])\r\n upper_blue = np.array([130,255,255])\r\n mask3 = cv2.inRange(hsv, lower_blue, upper_blue)\r\n clone[mask3 == 255] = 0\r\n lower_white = np.array([23,41,133])\r\n upper_white = np.array([40,150,255])\r\n mask4 = cv2.inRange(hsv, lower_white, upper_white)\r\n clone[mask4 == 255] = 0\r\n lower_green = np.array([26,56,46])\r\n upper_green = np.array([95, 255, 255])\r\n mask5 = cv2.inRange(hsv, lower_green, upper_green)\r\n clone[mask5 == 255] = 0\r\n # cv2.imshow(\"clone2\", clone) \r\n # cv2.waitKey(0)\r\n # cv2.destroyAllWindows()\r\n\r\n ##########################################################################################################\r\n list_contours = []\r\n blurred_2 = cv2.GaussianBlur(mask2, (3, 3), 0)\r\n dilated_2 = cv2.dilate(blurred_2, None, iterations=2)\r\n erosioned_2 = cv2.erode(dilated_2, None, iterations=1)\r\n\r\n gray = cv2.cvtColor(clone, cv2.COLOR_BGR2GRAY)\r\n blurred = cv2.GaussianBlur(gray, (3, 3), 0)\r\n thresh = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]\r\n dilated = cv2.dilate(thresh, None, iterations=2)\r\n erosioned = cv2.erode(dilated, None, iterations=1)\r\n erosioned = erosioned + erosioned_2\r\n # cv2.imshow(\"erosioned\", erosioned)\r\n contours = cv2.findContours(erosioned.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\n contours = imutils.grab_contours(contours)\r\n contours = sorted(contours, key=cv2.contourArea, reverse=True)[:3]\r\n for c in contours:\r\n list_contours.append(c)\r\n # area = cv2.contourArea(c)\r\n # cv2.drawContours(clone, [c], -1, (0,200,0), 3)\r\n \r\n # image[y:y+h, x:x+w] = clone\r\n # cv2.imshow(\"image\", image)\r\n # cv2.waitKey(0)\r\n # cv2.destroyAllWindows()\r\n\r\n return list_contours,clone,(x,y,w,h)\r\n\r\n# if __name__ == \"__main__\":\r\n# image = cv2.imread(\"/home/doan/Desktop/a/ALuoi_BCv2.jpg\")\r\n# list_contour = detect(image)\r\n# print(list_contour)\r\n"
}
] | 11 |
ayaanrizv/djangoblog
|
https://github.com/ayaanrizv/djangoblog
|
0a7f31abcbf6cbcd0adf774953f821c1f1404435
|
03f39e20853926fba4d636d277ba600f385b7fea
|
cbeafc9286eea9d91a8cea8e789f8264394fec8b
|
refs/heads/main
| 2023-04-03T20:57:37.064580 | 2021-05-05T10:24:38 | 2021-05-05T10:24:38 | 364,038,321 | 0 | 0 | null | null | null | null | null |
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"path": "/simpleblog/ablog/members/urls.py",
"repo_name": "ayaanrizv/djangoblog",
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"text": "from django.urls import path\nfrom .views import UserRegisterView\nfrom django.contrib.auth import views as auth_views\nfrom . import views\n\n\n\nurlpatterns = [\n\tpath('register/', UserRegisterView.as_view(), name='register'),\n\tpath('reset_password/', auth_views.PasswordResetView.as_view(template_name=\"registration/password_reset.html\"), name=\"reset_password\"),\n\tpath('reset_password_sent/', auth_views.PasswordResetDoneView.as_view(template_name=\"registration/password_reset_sent.html\"), name=\"password_reset_done\"),\n\tpath('reset_password_complete/done', auth_views.PasswordResetCompleteView.as_view(template_name=\"registration/password_reset_complete.html\"), name=\"password_reset_complete\"),\n\tpath('reset_confirm/<uidb64>/<token>/', auth_views.PasswordResetConfirmView.as_view(template_name=\"registration/password_reset_confirm.html\"), name=\"password_reset_confirm\"),\n]\n"
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"path": "/README.md",
"repo_name": "ayaanrizv/djangoblog",
"src_encoding": "UTF-8",
"text": "# djangoblog\nlink - https://arizstheblog.herokuapp.com/ \nRegister to make sure you can access all features. \nNote - 1.You can only edit/delete posts you have written. Edit and delete options will be visible on posts you've written only! \n2. You need to login to add a post. \n3. You need to login to comment. \n4. dummy account - bob12 - iamawesome7 \n5. for the forgot password feature to work make sure you add the correct email address while registering.\n"
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"num_lines": 44,
"path": "/simpleblog/ablog/theblog/views.py",
"repo_name": "ayaanrizv/djangoblog",
"src_encoding": "UTF-8",
"text": "from django.shortcuts import render\nfrom django.views.generic import ListView, DetailView, CreateView, UpdateView, DeleteView\nfrom .models import Post, Comment\nfrom .forms import PostForm, EditForm, CommentForm\nfrom django.urls import reverse_lazy\n\n#def home(request):\n#\treturn render(request, 'home.html', {})\nclass HomeView(ListView):\n\tmodel = Post\n\ttemplate_name = 'home.html'\n\t#ordering = ['-id']\n\tordering = ['-post_date']\n\nclass ArticleDetailView(DetailView):\n\tmodel = Post\n\ttemplate_name = 'article_details.html'\n\nclass AddPostView(CreateView):\n\tmodel = Post\n\tform_class = PostForm\n\ttemplate_name = 'add_post.html'\n\t#fields = '__all__'\n\nclass UpdatePostView(UpdateView):\n\tmodel = Post\n\tform_class = EditForm\n\ttemplate_name = 'update_post.html'\n\t#fields = ['title', 'title_tag', 'body']\n\nclass DeletePostView(DeleteView):\n\tmodel = Post\n\ttemplate_name = 'delete_post.html'\n\tsuccess_url = reverse_lazy('home')\n\nclass AddCommentView(CreateView):\n\tmodel = Comment\n\tform_class = CommentForm\n\ttemplate_name = 'add_comment.html'\n\t#fields = '__all__'\n\tdef form_valid(self,form):\n\t\tform.instance.post_id = self.kwargs['pk']\n\t\treturn super().form_valid(form)\n\tsuccess_url = reverse_lazy('home')"
}
] | 3 |
QuentinDevPython/Hadopi
|
https://github.com/QuentinDevPython/Hadopi
|
a7c017f3af5f00a25c2891193828c19f8298da18
|
b970310b80211e8459f1c404b2f8ce4e83f2c88b
|
67c6374d1126b7d14c4ad9cacac0c9140c546cbc
|
refs/heads/master
| 2023-04-05T10:51:11.951513 | 2021-04-11T14:22:00 | 2021-04-11T14:22:00 | 356,579,842 | 0 | 0 | null | null | null | null | null |
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"path": "/Api/inserter.py",
"repo_name": "QuentinDevPython/Hadopi",
"src_encoding": "UTF-8",
"text": "from Database.inserter_functions import Inserter_functions\nfrom tqdm import tqdm\n\n\nclass Inserter:\n\n\tdef __init__(self, data, Region, Ville, Foyer, Musique, Consommation, Moment_d_écoute, Plateforme, Personne, Ecoute, Ecoute_habituellement, Utilise):\n\t\tself.data = data\n\t\tself.database_inserter = Inserter_functions()\n\t\tself.Region = Region\n\t\tself.Ville = Ville\n\t\tself.Foyer = Foyer\n\t\tself.Musique = Musique\n\t\tself.Consommation = Consommation\n\t\tself.Moment_d_écoute = Moment_d_écoute\n\t\tself.Plateforme = Plateforme\n\t\tself.Personne = Personne\n\t\tself.Ecoute = Ecoute\n\t\tself.Ecoute_habituellement = Ecoute_habituellement\n\t\tself.Utilise = Utilise\n\n\n\tdef insert(self):\n\t\tfor row in tqdm(range(len(self.data))):\n\t\t\tself.database_inserter.insert_Region(self.data[row], self.Region)\n\t\t\tself.database_inserter.insert_Ville(self.data[row], self.Ville, self.Region)\n\t\t\tself.database_inserter.insert_Foyer(self.data[row], self.Foyer, self.Ville, self.Region)\n\t\t\tself.database_inserter.insert_Musique(self.data[row], self.Musique)\n\t\t\tself.database_inserter.insert_Consommation(self.data[row], self.Consommation)\n\t\t\tself.database_inserter.insert_Moment_d_écoute(self.data[row], self.Moment_d_écoute)\n\t\t\tself.database_inserter.insert_Plateforme(self.data[row], self.Plateforme)\n\t\t\tself.database_inserter.insert_Personne(self.data[row], self.Personne, self.Consommation, self.Foyer, self.Ville, self.Region)\n\t\t\tself.database_inserter.insert_Ecoute(row, self.data[row], self.Ecoute, self.Personne, self.Musique)\n\t\t\tself.database_inserter.insert_Ecoute_habituellement(row, self.data[row], self.Ecoute_habituellement, self.Personne, self.Moment_d_écoute)\n\t\t\tself.database_inserter.insert_Utilise(row, self.data[row], self.Utilise, self.Personne, self.Plateforme)\n\n\n\n"
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"path": "/requete1.sql",
"repo_name": "QuentinDevPython/Hadopi",
"src_encoding": "UTF-8",
"text": "# Regarder le nombre d'hommes et de femmes qui écoutent chaque type de musique\n\nSELECT sexe, style_de_musique, COUNT(style_de_musique)\nFROM personne \nINNER JOIN ecoute ON personne.id_personne = ecoute.id_personne_id\nINNER JOIN musique ON ecoute.id_musique_id = musique.id_musique\nGROUP BY sexe,style_de_musique\nORDER BY style_de_musique;\n"
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"path": "/requirements.txt",
"repo_name": "QuentinDevPython/Hadopi",
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"repo_name": "QuentinDevPython/Hadopi",
"src_encoding": "UTF-8",
"text": "import peewee\n\nmysql_db = peewee.MySQLDatabase(\n\thost = 'localhost',\n\tuser = 'root',\n\tpassword = 'root',\n\tdatabase = 'hadopi')\n\nmysql_db.connect()\n\nclass BaseModel(peewee.Model):\n\tclass Meta:\n\t\tdatabase = mysql_db\n\nclass Region(BaseModel):\n\tid_region = peewee.AutoField(primary_key=True)\n\tnom_region = peewee.CharField(50)\n\nclass Ville(BaseModel):\n\tid_ville = peewee.AutoField(primary_key = True)\n\tnb_habitants_ville = peewee.CharField(30)\n\tid_region = peewee.ForeignKeyField(Region)\n\nclass Foyer(BaseModel):\n\tid_foyer = peewee.AutoField(primary_key = True)\n\tnb_personnes_foyer = peewee.CharField(20)\n\tnb_enfants_foyer = peewee.CharField(30)\n\tid_ville = peewee.ForeignKeyField(Ville)\n\nclass Musique(BaseModel):\n\tid_musique = peewee.AutoField(primary_key=True)\n\tstyle_de_musique = peewee.CharField(250)\n\nclass Consommation(BaseModel):\n\tid_consommation = peewee.AutoField(primary_key=True)\n\tecoute_jours = peewee.CharField(100)\n\tfrequence_musique = peewee.CharField(100)\n\tnb_concert = peewee.CharField(100)\n\tappareil_plus_frequent = peewee.CharField(100)\n\tfrequence_seul = peewee.CharField(100)\n\tfrequence_conso_illegale = peewee.CharField(100)\n\tfrequence_accompagne = peewee.CharField(100)\n\nclass Moment_d_écoute(BaseModel):\n\tid_moment_d_ecoute = peewee.AutoField(primary_key=True)\n\tmoment_d_ecoute = peewee.CharField(350)\n\nclass Plateforme(BaseModel):\n\tid_plateforme = peewee.AutoField(primary_key=True)\n\tplateforme = peewee.CharField(500)\n\nclass Personne(BaseModel):\n\tid_personne = peewee.AutoField(primary_key = True)\n\tsexe = peewee.CharField(5)\n\ttranche_age = peewee.CharField(20)\n\tstatut_social = peewee.CharField(12)\n\tid_consommation = peewee.ForeignKeyField(Consommation)\n\tid_foyer = peewee.ForeignKeyField(Foyer)\n\nclass Ecoute(BaseModel):\n\tid_personne = peewee.ForeignKeyField(Personne)\n\tid_musique = peewee.ForeignKeyField(Musique)\n\n\tclass Meta:\n\t\tprimary_key = peewee.CompositeKey('id_personne', 'id_musique')\n\nclass Ecoute_habituellement(BaseModel):\n\tid_personne = peewee.ForeignKeyField(Personne)\n\tid_moment_d_ecoute = peewee.ForeignKeyField(Moment_d_écoute)\n\n\tclass Meta:\n\t\tprimary_key = peewee.CompositeKey('id_personne', 'id_moment_d_ecoute')\n\nclass Utilise(BaseModel):\n\tid_personne = peewee.ForeignKeyField(Personne)\n\tid_plateforme = peewee.ForeignKeyField(Plateforme)\n\n\tclass Meta:\n\t\tprimary_key = peewee.CompositeKey('id_personne', 'id_plateforme')\n\n\nmysql_db.create_tables([\n\tRegion,\n\tVille,\n\tFoyer,\n\tMusique,\n\tConsommation,\n\tMoment_d_écoute,\n\tPlateforme,\n\tPersonne,\n\tEcoute,\n\tEcoute_habituellement,\n\tUtilise\n\t])\n\nmysql_db.close()\n\n\n"
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"num_lines": 46,
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"repo_name": "QuentinDevPython/Hadopi",
"src_encoding": "UTF-8",
"text": "import Database.sql_connector\nfrom Api.downloader import Downloader\nfrom Api.inserter import Inserter\n\nclass Interface:\n\n\tdef __init__(self, Region, Ville, Foyer, Musique, Consommation, Moment_d_écoute, Plateforme, Personne, Ecoute, Ecoute_habituellement, Utilise):\n\t\tself.Region = Region\n\t\tself.Ville = Ville\n\t\tself.Foyer = Foyer\n\t\tself.Musique = Musique\n\t\tself.Consommation = Consommation\n\t\tself.Moment_d_écoute = Moment_d_écoute\n\t\tself.Plateforme = Plateforme\n\t\tself.Personne = Personne\n\t\tself.Ecoute = Ecoute\n\t\tself.Ecoute_habituellement = Ecoute_habituellement\n\t\tself.Utilise = Utilise\n\n\tdef choose_download(self):\n\t\tdownload = int(\n\t\t\tinput(\n\t\t\t\t\"\\nVoulez-vous télécharger la base de donnée ?\\n\\n\"\n\t\t\t\t\"1 - Oui\\n\"\n\t\t\t\t\"2 - Non\\n \\n\"\n\t\t\t))\n\t\tif download == 1:\n\t\t\tdownloader = Downloader()\n\t\t\tdata = downloader.get_data()\n\t\t\tinserter = Inserter(\n\t\t\t\tdata,\n\t\t\t\tself.Region,\n\t\t\t\tself.Ville,\n\t\t\t\tself.Foyer,\n\t\t\t\tself.Musique,\n\t\t\t\tself.Consommation,\n\t\t\t\tself.Moment_d_écoute,\n\t\t\t\tself.Plateforme,\n\t\t\t\tself.Personne,\n\t\t\t\tself.Ecoute,\n\t\t\t\tself.Ecoute_habituellement,\n\t\t\t\tself.Utilise\n\t\t\t)\n\t\t\tinserter.insert()\n\t\telse:\n\t\t\tprint('Vous avez choisi de ne pas (re)télécharger la base de donnée')\n"
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"text": "import mysql.connector\n\ndatabase = mysql.connector.connect(\n\thost='localhost',\n\tuser = 'root',\n\tpassword = 'root',\n\t)\n\ncur = database.cursor()\n\ncur.execute('DROP DATABASE IF EXISTS hadopi')\ncur.execute('CREATE DATABASE IF NOT EXISTS hadopi CHARACTER SET \\'utf8\\'')\n"
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"max_line_length": 85,
"num_lines": 8,
"path": "/requete2.sql",
"repo_name": "QuentinDevPython/Hadopi",
"src_encoding": "UTF-8",
"text": "# Regarder la fréquence de téléchargement illégal en fonction des tranches d'âge\n\nSELECT tranche_age, frequence_conso_illegale, COUNT(frequence_conso_illegale)\nFROM personne\nINNER JOIN consommation ON consommation.id_consommation = personne.id_consommation_id\nWHERE frequence_conso_illegale != 'NULL'\nGROUP BY tranche_age, frequence_conso_illegale\nORDER BY tranche_age;"
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"is_generated": false,
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"language": "Python",
"length_bytes": 3581,
"license_type": "no_license",
"max_line_length": 103,
"num_lines": 116,
"path": "/Database/inserter_functions.py",
"repo_name": "QuentinDevPython/Hadopi",
"src_encoding": "UTF-8",
"text": "import Database.create_tables\n\nclass Inserter_functions:\n\n\n\tdef insert_Region(self, data, Region):\n\t\tRegion.get_or_create(\n\t\t\tnom_region = data[6]\n\t\t) \n\n\tdef insert_Ville(self, data, Ville, Region):\n\t\tVille.get_or_create(\n\t\t\tnb_habitants_ville = data[5],\n\t\t\tid_region = Region.get(Region.nom_region == data[6])\n\t\t)\n\n\tdef insert_Foyer(self, data, Foyer, Ville, Region):\n\t\tFoyer.get_or_create(\n\t\t\tnb_personnes_foyer = data[3],\n\t\t\tnb_enfants_foyer = data[4],\n\t\t\tid_ville = Ville.select(Ville.id_ville).where(\n\t\t\t\t\t(Ville.nb_habitants_ville == data[5])\n\t\t\t\t\t& (Ville.id_region == Region.select(Region.id_region).where(\n\t\t\t\t\t\t\tRegion.nom_region == data[6]\n\t\t\t\t\t\t)\n\t\t\t\t\t)\n\t\t\t\t).get()\n\t\t)\n\n\tdef insert_Musique(self, data, Musique):\n\t\tdata_dict = data[7].split(',')\n\t\tfor item in data_dict:\n\t\t\tif item != '':\n\t\t\t\tMusique.get_or_create(\n\t\t\t\t\tstyle_de_musique = item\n\t\t\t\t)\n\n\tdef insert_Consommation(self, data, Consommation):\n\t\tConsommation.get_or_create(\n\t\t\tecoute_jours = data[9],\n\t\t\tfrequence_musique = data[10],\n\t\t\tnb_concert = data[11],\n\t\t\tappareil_plus_frequent = data[12],\n\t\t\tfrequence_seul = data[13],\n\t\t\tfrequence_conso_illegale = data[14],\n\t\t\tfrequence_accompagne = data[15]\n\t\t)\n\n\tdef insert_Moment_d_écoute(self, data, Moment_d_écoute):\n\t\tdata_dict = data[8].split(',')\n\t\tfor item in data_dict:\n\t\t\tif item != '':\n\t\t\t\tMoment_d_écoute.get_or_create(\n\t\t\t\t\tmoment_d_ecoute = item\n\t\t\t\t)\n\n\tdef insert_Plateforme(self, data, Plateforme):\n\t\tdata_dict = data[16].split(',')\n\t\tfor item in data_dict:\n\t\t\tif item != '':\n\t\t\t\tPlateforme.get_or_create(\n\t\t\t\t\tplateforme = item\n\t\t\t\t)\n\n\tdef insert_Personne(self, data, Personne, Consommation, Foyer, Ville, Region):\n\t\tPersonne.create(\n\t\t\tsexe = data[0],\n\t\t\ttranche_age = data[1],\n\t\t\tstatut_social = data[2],\n\t\t\tid_consommation = Consommation.select(Consommation.id_consommation).where(\n\t\t\t\t(Consommation.ecoute_jours == data[9])\n\t\t\t\t& (Consommation.frequence_musique == data[10])\n\t\t\t\t& (Consommation.nb_concert == data[11])\n\t\t\t\t& (Consommation.appareil_plus_frequent == data[12])\n\t\t\t\t& (Consommation.frequence_seul == data[13])\n\t\t\t\t& (Consommation.frequence_conso_illegale == data[14])\n\t\t\t\t& (Consommation.frequence_accompagne == data[15])\n\t\t\t\t).get(),\n\t\t\tid_foyer = Foyer.select(Foyer.id_foyer).where(\n\t\t\t\t\t(Foyer.nb_personnes_foyer == data[3])\n\t\t\t\t\t& (Foyer.nb_enfants_foyer == data[4])\n\t\t\t\t\t& (Foyer.id_ville == Ville.select(Ville.id_ville).where(\n\t\t\t\t\t\t\t(Ville.nb_habitants_ville == data[5])\n\t\t\t\t\t\t\t& (Ville.id_region == Region.select(Region.id_region).where(\n\t\t\t\t\t\t\t\tRegion.nom_region == data[6]\n\t\t\t\t\t\t\t))\n\t\t\t\t\t\t)\n\t\t\t\t\t)\n\t\t\t\t).get()\n\t\t)\n\tdef insert_Ecoute(self, index, data, Ecoute, Personne, Musique):\n\t\tdata_dict = data[7].split(',')\n\t\tfor item in data_dict:\n\t\t\tif item != '':\n\t\t\t\tEcoute.get_or_create(\n\t\t\t\t\tid_personne = Personne.get(Personne.id_personne == index+1),\n\t\t\t\t\tid_musique = Musique.get(Musique.style_de_musique == item)\n\t\t\t\t)\n\n\tdef insert_Ecoute_habituellement(self, index, data, Ecoute_habituellement, Personne, Moment_d_écoute):\n\t\tdata_dict = data[8].split(',')\n\t\tfor item in data_dict:\n\t\t\tif item != '':\n\t\t\t\tEcoute_habituellement.get_or_create(\n\t\t\t\t\tid_personne = Personne.get(Personne.id_personne == index+1),\n\t\t\t\t\tid_moment_d_ecoute = Moment_d_écoute.get(Moment_d_écoute.moment_d_ecoute == item)\n\t\t\t\t)\n\n\tdef insert_Utilise(self, index, data, Utilise, Personne, Plateforme):\n\t\tdata_dict = data[16].split(',')\n\t\tfor item in data_dict:\n\t\t\tif item != '':\n\t\t\t\tUtilise.get_or_create(\n\t\t\t\t\tid_personne = Personne.get(Personne.id_personne == index+1),\n\t\t\t\t\tid_plateforme = Plateforme.get(Plateforme.plateforme == item)\n\t\t\t\t)\n"
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"max_line_length": 110,
"num_lines": 14,
"path": "/requete3.sql",
"repo_name": "QuentinDevPython/Hadopi",
"src_encoding": "UTF-8",
"text": "# Regarder le moment d'écoute en fonction de la région\n\nSELECT nom_region, moment_d_ecoute, COUNT(moment_d_ecoute)\nFROM personne \nINNER JOIN foyer ON foyer.id_foyer = personne.id_foyer_id\nINNER JOIN ville ON ville.id_ville = foyer.id_ville_id\nINNER JOIN region ON region.id_region = ville.id_region_id\nINNER JOIN ecoute_habituellement ON ecoute_habituellement.id_personne_id = personne.id_personne\nINNER JOIN moment_d_écoute ON moment_d_écoute.id_moment_d_ecoute = ecoute_habituellement.id_moment_d_ecoute_id\nWHERE moment_d_ecoute = 'En voiture' \nor moment_d_ecoute = 'Au réveil ou avant de vous endormir' \nor moment_d_ecoute = 'En faisant du sport ou des activités récréatives'\nGROUP BY nom_region, moment_d_ecoute\nORDER BY nom_region;"
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"language": "Python",
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"max_line_length": 49,
"num_lines": 19,
"path": "/Api/downloader.py",
"repo_name": "QuentinDevPython/Hadopi",
"src_encoding": "UTF-8",
"text": "import csv \nfrom tqdm import tqdm\n\n\nclass Downloader:\n\n\tdef __init__(self):\n\t\tself.data = list()\n\n\tdef download(self):\n\t\tfile = open(\"excel_hadopi_BDD.csv\", \"r\")\n\t\tdata_reader = csv.reader(file, delimiter = \",\")\n\t\tfor row in data_reader:\n\t\t\tself.data.append(row)\n\t\tfile.close()\n\n\tdef get_data(self):\n\t\tDownloader.download(self)\n\t\treturn self.data[1:]"
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"max_line_length": 41,
"num_lines": 30,
"path": "/main.py",
"repo_name": "QuentinDevPython/Hadopi",
"src_encoding": "UTF-8",
"text": "from Interface.interface import Interface\nfrom Database.create_tables import (\n\tRegion,\n\tVille,\n\tFoyer,\n\tMusique,\n\tConsommation,\n\tMoment_d_écoute,\n\tPlateforme,\n\tPersonne,\n\tEcoute,\n\tEcoute_habituellement,\n\tUtilise\n)\n\nif __name__ == \"__main__\":\n\tinterface = Interface(\n\t\tRegion,\n\t\tVille,\n\t\tFoyer,\n\t\tMusique,\n\t\tConsommation,\n\t\tMoment_d_écoute,\n\t\tPlateforme,\n\t\tPersonne,\n\t\tEcoute,\n\t\tEcoute_habituellement,\n\t\tUtilise\n\t)\n\tinterface.choose_download()\n\n\n\t"
}
] | 11 |
emiwatanabe422/Rango
|
https://github.com/emiwatanabe422/Rango
|
bbbc094dfe797f64c2425a6dc798ac426a537079
|
c4e8c4b3de1c66c58573c14c3d75ac342f192cb3
|
979c50801248bc961f74dc8e20914fa80bc1a652
|
refs/heads/master
| 2020-05-14T22:57:21.564434 | 2019-04-18T23:02:27 | 2019-04-18T23:02:27 | 181,988,151 | 0 | 0 | null | null | null | null | null |
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"length_bytes": 114,
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"num_lines": 5,
"path": "/tango_with_django_project/rango/views.py",
"repo_name": "emiwatanabe422/Rango",
"src_encoding": "UTF-8",
"text": "from django.http import HttpResponse\n\n\ndef index(request):\n return HttpResponse(\"Rango says hey there matey!\")\n"
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"max_line_length": 39,
"num_lines": 3,
"path": "/README.md",
"repo_name": "emiwatanabe422/Rango",
"src_encoding": "UTF-8",
"text": "# Rango\nProject from the book Tango with Django\nA tutorial for learning Django\n"
}
] | 2 |
JetChars/jet4icpc
|
https://github.com/JetChars/jet4icpc
|
d08e915d4942b4d916e6c1cf573140e5071ba75a
|
aaec373111263337e47f09d0d30dc9afa921fce3
|
b0760055eb455c24c7d5673be3a4eb8b0fe412ea
|
refs/heads/master
| 2021-01-01T17:38:02.794388 | 2015-10-22T07:10:02 | 2015-10-22T07:10:02 | 42,428,428 | 1 | 0 | null | null | null | null | null |
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"language": "C++",
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"path": "/stdcode/string.cpp",
"repo_name": "JetChars/jet4icpc",
"src_encoding": "UTF-8",
"text": "#include <iostream>\nusing namespace std;\n\n\n/*==================================================*\\\n| String Hash\n| Notice: mod should be a big enough prime number\n| (bigger than string number)\n\\*==================================================*/\n\n\nunsigned int hasha(char *url, int mod){\n unsigned int n = 0;\n char *b = (char *) &n;\n for (int i = 0; url[i]; ++i) b[i % 4] ^= url[i];\n return n % mod;\n}\n\nint main(){\n cout<<hasha(\"helloworld!\", 20)<<endl;\n}\n"
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"text": "# jet4icpc\nLet's Enjoy The Pure Happiness of Coding\n"
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"text": "\n/*==================================================*\\\n * hardness: \n | 1 star = simple\n | 2 star = can solve within 10 min(think time)\n | 3 star = can solve within 30 min\n | 4 star = can solve within 60 min\n | 5 star = have to ask for help\n\\*==================================================*/\n\n\n/*==================================================*\\\n * title: 二维数组中的查找 *\n * des. : 在一个二维数组中，每一行都按照从左到右递增的顺序排序，\n 每一列都按照从上到下递增的顺序排序。\n 请完成一个函数，输入这样的一个二维数组和一个整数，判断数组中是否含有该整数。\n\\*==================================================*/\nclass Solution {\npublic:\n bool Find(vector<vector<int> > array,int target) {\n if(!array.empty()){\n int i = 0, j = array[0].size()-1;\n while(i<array.size() && j>=0){\n if(array[i][j] == target) return true;\n else if(array[i][j] > target) --j;\n else ++i;\n }//while\n }//if\n return false;\n }\n};\n\n\n/*==================================================*\\\n * title: 从尾到头打印链表 *\n * des. : 输入一个链表，从尾到头打印链表每个节点的值。\n\\*==================================================*/\n/**\n* struct ListNode {\n* int val;\n* struct ListNode *next;\n* ListNode(int x) :\n* val(x), next(NULL) {\n* }\n* };\n*/\nclass Solution {\npublic:\n vector<int> printListFromTailToHead(struct ListNode* head) {\n vector<int> results;\n stack<int> stack;\n while(head != NULL){\n stack.push(head->val);\n head = head->next;\n }\n while(!stack.empty()){\n results.push_back(stack.top());\n stack.pop();\n }\n return results;\n }\n};\n\n\n\n/*==================================================*\\\n * rebuild binary treenode\n * Hint : 回溯法\n * NLR + LNR -> tree\n * 1. NLR的第一个节点为根节点\n * 2. 找到N在LNR中的位置\n * 3. 分别对L和R进行重构生成N的左右子树\n\\*==================================================*/\n/**\n * Definition for binary tree\n * struct TreeNode {\n * int val;\n * TreeNode *left;\n * TreeNode *right;\n * TreeNode(int x) : val(x), left(NULL), right(NULL) {}\n * };\n */\nclass Solution {\npublic:\n struct TreeNode* reConstructBinaryTree(vector<int> pre,vector<int> in) {\n int len = pre.size();\n if (len == 0) return NULL;\n\n int val = pre[0], pos = 0;\n TreeNode* results = new TreeNode(val);\n vector<int> pre_l, pre_r, in_l, in_r;\n\n while (val != in[pos]) pos++;\n for (i = 0; i < pos; i++){\n pre_l.push_back(pre[i+1]);\n in_l.push_back(in[i]);\n }\n for (i = pos+1; i < len; i++){\n pre_r.push_back(pre[i]);\n in_r.push_back(in[i]);\n }\n\n results->left = reConstructBinaryTree(pre_l, in_l);\n results->right = reConstructBinaryTree(pre_r, in_r);\n return results;\n }\n};\n\n\n/*==================================================*\\\n * build queue with 2 stacks\n * Hint : \n * 1. stack1 仅进行push\n * 2. stack2 存放pop队列，若空时将stack1中的数值压到stack2则顺序正好\n * 3. 若stack2非空，直接返回栈顶数值即可\n\\*==================================================*/\nclass Solution\n{\npublic:\n void push(int node) {\n stack1.push(node);\n }\n\n int pop() {\n if (stack2.empty()){\n while (!stack1.empty()){\n stack2.push(stack1.top());\n stack1.pop();\n }\n }\n int result = stack2.top();\n stack2.pop();\n return result;\n }\n\nprivate:\n stack<int> stack1;\n stack<int> stack2;\n};\n\n/*==================================================*\\\n * title: spin the array\n * des. : 把一个数组最开始的若干个元素搬到数组的末尾，我们称之为数组的旋转。\n * 输入一个非递减序列的一个旋转，输出旋转数组的最小元素。\n * 例如数组{3,4,5,1,2}为{1,2,3,4,5}的一个旋转，该数组的最小值为1。\n\\*==================================================*/\nclass Solution {\npublic:\n int minNumberInRotateArray(vector<int> rotateArray) {\n if (rotateArray.size() <= 0) return 0;\n min = rotateArray[0];\n for (int i = 1; i < rotateArray.size(); i++) if (rotateArray[i] < min) min = rotateArray[i];\n return min;\n }\n};\n\n\n\n/*==================================================*\\\n * title: 斐波那契数列\n * des. : 大家都知道斐波那契数列，现在要求输入一个整数n，请你输出斐波那契数列的第n项\n * hint : 因为fib(n)是不会变的，所以可以将已经计算出的数值缓存在一个数组中\n\\*==================================================*/\nclass Solution {\npublic:\n int Fibonacci(int n) {\n if (results.size() < 2){\n results.push_back(0);\n results.push_back(1);\n }\n int len = results.size();\n if (len <= n) for (int i = len; i <= n; i++) results.push_back(results[i-2] + results[i-1]);\n return results[n];\n }\n\nprivate:\n vector<int> results;\n};\n\n\n/*==================================================*\\\n * title: 跳台阶\n * des. : 一只青蛙一次可以跳上1级台阶，也可以跳上2级。\n * 求该青蛙跳上一个n级的台阶总共有多少种跳法\n * hint : 其实就是个fib数列，fib0=0, fib1=1, fib2=2\n\\*==================================================*/\nclass Solution {\npublic:\n int jumpFloor(int n) {\n if (results.size() < 3){\n results.push_back(0);\n results.push_back(1);\n results.push_back(2);\n }\n int len = results.size();\n if (len <= n) for (int i = len; i <= n; i++) results.push_back(results[i-2] + results[i-1]);\n return results[n];\n }\n\nprivate:\n vector<int> results;\n\n\n};\n\n\n/*==================================================*\\\n * title: 变态跳台阶\n * des. : 一只青蛙一次可以跳上1级台阶，也可以跳上2级……它也可以跳上n级。\n * 求该青蛙跳上一个n级的台阶总共有多少种跳法\n * Hint : fibn = fib1 +...+ fib(n-1)\n\\*==================================================*/\nclass Solution {\npublic:\n int jumpFloorII(int n) {\n if (results.size() < 3){\n results.push_back(0);\n results.push_back(1);\n results.push_back(2);\n }\n int len = results.size();\n if (len <= n){\n for (int i = len; i <= n; i++){\n int result = 1;\n for (int j = 1; j < i; j++) result += results[j];\n results.push_back(result);\n }\n }\n return results[n];\n }\n\nprivate:\n vector<int> results;\n\n\n};\n\n\n\n/*==================================================*\\\n * title: 矩形覆盖(同-跳台阶)\n * des. : 我们可以用2*1的小矩形横着或者竖着去覆盖更大的矩形。\n * 请问用n个2*1的小矩形无重叠地覆盖一个2*n的大矩形，总共有多少种方法？\n * Hint : 典型的fib数列，但fib0=1, fib1=1, fib2=2\n\\*==================================================*/\nclass Solution {\npublic:\n int rectCover(int n) {\n if (results.size() < 3){\n results.push_back(1);\n results.push_back(1);\n results.push_back(2);\n }\n int len = results.size();\n if (len <= n) for (int i = len; i <= n; i++) results.push_back(results[i-2] + results[i-1]);\n return results[n];\n }\n\nprivate:\n vector<int> results;\n\n\n};\n\n\n/*==================================================*\\\n * title: 二进制中1的个数\n * des. : 输入一个整数，输出该数二进制表示中1的个数。\n * 其中负数用补码表示\n * hint : 典型的位运算，对正负数都适用\n\\*==================================================*/\nclass Solution {\npublic:\n int NumberOf1(int n) {\n int count = 0;\n while (n != 0){\n count++;\n n &= (n - 1);\n }\n return count;\n }\n};\n\n\n/*==================================================*\\\n * title: 数值的整数次方\n * des. : 给定一个double类型的浮点数base和int类型的整数exponent。\n * 求base的exponent次方\n * Hint : 1. 确保base不为0\n 2. 将负指数换成正指数\n 3. 指数二进制化\n\\*==================================================*/\nclass Solution {\npublic:\n double Power(double base, int exponent) {\n if (exponent == 0) return 1;\n if (((base -0.0)>-0.0000001) &&((base-0.0) <0.0000001)) return 0;\n\n double result = 1;\n bool positive = true;\n if (exponent < 0){\n positive = false;\n exponent = -exponent;\n }\n while (exponent != 0){\n if (exponent & 1 == 1) result *= base;\n base = base * base;\n exponent >>= 1;\n }\n return positive ? result : 1 / result;\n }\n};\n\n\n\n\n/*==================================================*\\\n * title: 调整数组顺序使奇数位于偶数前面 **\n * note : boundary condition\n * des. : 输入一个整数数组，实现一个函数来调整该数组中数字的顺序，\n 使得所有的奇数位于数组的前半部分，所有的偶数位于位于数组的后半部分，\n 并保证奇数和奇数，偶数和偶数之间的相对位置不变\n * hint : 1. create an array to storage even numbers\n 2. mean while, move odd numbers to the front of vector\n 3. put even numbers\n\\*==================================================*/\nclass Solution {\npublic:\n void reOrderArray(vector<int> &array) {\n int len = array.size(), pos = 0;\n if (len <= 1) return;\n\n vector<int> even;\n for (int i = 0; i < len; i++){\n if ((array[i] & 1) == 1) array[pos++] = array[i];\n else even.push_back(array[i]);\n }\n for (int i = 0; i < even.size(); i++){\n array[pos++] = even[i];\n }\n }\n};\n\n\n\n\n/*==================================================*\\\n * title: 链表中倒数第k个结点 **\n * description: 输入一个链表，输出该链表中倒数第k个结点\n * note: check NULL, 0, Invalid\n\\*==================================================*/\n/*\nstruct ListNode {\n int val;\n struct ListNode *next;\n ListNode(int x) :\n val(x), next(NULL) {\n }\n};*/\nclass Solution {\npublic:\n ListNode* FindKthToTail(ListNode* pListHead, unsigned int k) {\n if (pListHead == NULL || k == 0) return NULL;\n\n ListNode *p = pListHead, *q = pListHead;\n for (int i = 1; i < k; i++){\n if (p->next == NULL) return NULL;\n p = p->next;\n }\n while(p->next != NULL){\n p = p->next;\n q = q->next;\n }\n return q;\n }\n};\n\n\n\n/*==================================================*\\\n * title: 反转链表 **\n * description: 输入一个链表，反转链表后，输出链表的所有元素\n * note: head node store val\n\\*==================================================*/\n/*\nstruct ListNode {\n int val;\n struct ListNode *next;\n ListNode(int x) :\n val(x), next(NULL) {\n }\n};*/\nclass Solution {\npublic:\n ListNode* ReverseList(ListNode* pHead) {\n if(pHead == NULL || pHead->next == NULL) return pHead;\n\n ListNode *qHead = pHead, *tmp = NULL;\n pHead = pHead->next;\n qHead->next = NULL;\n\n while(pHead->next != NULL){\n tmp = pHead;\n pHead = pHead->next;\n tmp->next = qHead;\n qHead = tmp;\n }\n\n pHead->next = qHead;\n return pHead;\n }\n};\n\n\n\n/*==================================================*\\\n * title: 合并两个排序的链表 *\n * description: 输入两个单调递增的链表，输出两个链表合成后的链表，当然我们需要合成后的链表满足单调不减规则\n\\*==================================================*/\n/*\nstruct ListNode {\n int val;\n struct ListNode *next;\n ListNode(int x) :\n val(x), next(NULL) {\n }\n};*/\nclass Solution {\npublic:\n ListNode* Merge(ListNode* pHead1, ListNode* pHead2){\n if(pHead1 == NULL) return pHead2;\n if(pHead2 == NULL) return pHead1;\n\n ListNode *head = NULL;\n if(pHead1->val > pHead2->val){\n head = pHead2;\n pHead2 = pHead2->next;\n }else{\n head = pHead1;\n pHead1 = pHead1->next;\n } \n\n head->next = Merge(pHead1, pHead2);\n return head;\n \n }\n};\n\n\n/*==================================================*\\\n * title: 树的子结构 ***\n * description: 输入两颗二叉树A，B，判断B是不是A的子结构\n * notice: \n * 1. empty tree is subtree of any tree, but no in this testcase\n * 2. this issue can be optimize with kmp\n\\*==================================================*/\n/*\nstruct TreeNode {\n int val;\n struct TreeNode *left;\n struct TreeNode *right;\n TreeNode(int x) :\n val(x), left(NULL), right(NULL) {\n }\n};*/\nclass Solution1 {\npublic:\n bool HasSubtree(TreeNode* pRoot1, TreeNode* pRoot2){\n if(pRoot1 == NULL || pRoot2 == NULL) return false;\n return isSubtree(pRoot1, pRoot2) || HasSubtree(pRoot1->left, pRoot2) || HasSubtree(pRoot1->right, pRoot2);\n }\n\n bool isSubtree(TreeNode* pRoot1, TreeNode* pRoot2){\n if(pRoot2 == NULL) return true;\n if(pRoot1 == NULL) return false;\n return pRoot1->val == pRoot2->val && isSubtree(pRoot1->left, pRoot2->left) && isSubtree(pRoot1->right, pRoot2->right);\n }\n};\n\n\nclass Solution2 {\n/* 改进算法，时间复杂度O（m+n）\n * 1.将root1和root2分别按先序遍历序列化。\n * 2.运用KMP算法匹配序列化结果。\n */\n public boolean HasSubtree(TreeNode root1,TreeNode root2) {\n if(root2==null)\n return false;// 空树本应是任意树的子结构，但从测试集来看，应视为false\n if(root1==null)\n return false;\n char[] str = Serialize(root1).toCharArray();\n char[] pattern = Serialize(root2).toCharArray();\n int[] next = new int[pattern.length];\n System.out.println(String.valueOf(str));\n System.out.println(String.valueOf(pattern));\n getNext(pattern,next);\n return KMP(str,pattern,next);\n \n }\n private boolean KMP(char[] str, char[] pattern, int[] next) {\n if(str==null||pattern==null)\n return false;\n if(str.length<pattern.length)\n return false;\n int i=0,j=0,len = str.length;\n while(i<len&&j<pattern.length){\n if(j==-1||str[i]==pattern[j]){\n i++;j++;\n }else{\n j = next[j];\n }\n }\n if(j==pattern.length)// 表示最后一个字符也相等，匹配成功\n return true;\n return false;\n }\n \n private void getNext(char[] pattern, int[] next) {\n if(pattern==null||pattern.length==0)\n return;\n int i=0,j=-1;\n next[0] = -1;\n while(i<pattern.length-1){\n if(j==-1||pattern[i]==pattern[j]){\n ++i;++j; \n if(pattern[i]==pattern[j]){\n next[i] = next[j];\n }else{\n next[i] = j;\n }\n }else{\n j = next[j];\n }\n }\n }\n public String Serialize(TreeNode root) {\n if(root==null)\n return \"\";\n this.buffer = new StringBuffer();\n SerializeF(root);\n int i;\n // 删除序列尾部的$\n for(i = buffer.length()-1;i>=0;i--){\n if(buffer.charAt(i)==','||buffer.charAt(i)=='$'){\n continue;\n }else\n break;\n }\n buffer.delete(i+1,buffer.length());\n return buffer.toString();\n }\n};\n\n\n/*==================================================*\\\n * title: 二叉树的镜像 *\n * description: 操作给定的二叉树，将其变换为源二叉树的镜像\n 二叉树的镜像定义： 源二叉树 镜像二叉树\n 8 8\n / \\ / \\\n 10 6 6 10\n / \\ / \\ / \\ / \\\n 11 9 7 5 5 7 9 11\n\\*==================================================*/\n/*\nstruct TreeNode {\n int val;\n struct TreeNode *left;\n struct TreeNode *right;\n TreeNode(int x) :\n val(x), left(NULL), right(NULL) {\n }\n};*/\nclass Solution {\npublic:\n void Mirror(TreeNode *pRoot) {\n if(pRoot == NULL) return;\n TreeNode *tmp = pRoot->left;\n pRoot->left = pRoot->right;\n pRoot->right = tmp;\n Mirror(pRoot->left);\n Mirror(pRoot->right);\n }\n};\n\n\n/*==================================================*\\\n * title: 顺时针打印矩阵 ****\n * description: 输入一个矩阵，按照从外向里以顺时针的顺序依次打印出每一个数字\n 例如，如果输入如下矩阵： 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 \n 则依次打印出数字1,2,3,4,8,12,16,15,14,13,9,5,6,7,11,10.\n * notice: 边界条件要理清楚，使用易识别的变量名\n\\*==================================================*/\nclass Solution {\npublic:\n vector<int> printMatrix(vector<vector<int>> matrix) {\n int row = matrix.size(), col = matrix[0].size();\n vector<int> results;\n if(col == 0 || row == 0) return results;\n\n int left = 0, right = col - 1, top = 0, down = row - 1;\n while(top <= down && left <= right){\n for(int i = left; i <= right; ++i) results.push_back(matrix[top][i]);\n for(int i = top + 1; i <= down; ++i) results.push_back(matrix[i][right]);\n if(top<down)for(int i = right - 1; i >= left; --i) results.push_back(matrix[down][i]);\n if(left<right)for(int i = down - 1; i > top; --i) results.push_back(matrix[i][left]);\n left++, right--, top++, down--;\n }\n return results;\n }\n};\n\n\n\n/*==================================================*\\\n * title: 包含min函数的栈 ***\n * description: 定义栈的数据结构，请在该类型中实现一个能够得到栈最小元素的min函数。\n * hint: 每个负值之前存放的是相对当前最小的的差值，可通过减去该值获得当前最小值。\n\\*==================================================*/\nclass Solution {\npublic:\n void push(int value) {\n if(stk.empty()){\n stk.push(0);\n mini = value;\n return;\n }\n stk.push(value - mini);\n if(value < mini) mini = value;\n \n }\n void pop() {\n int x = stk.top();\n if(x < 0) mini -= x;\n stk.pop();\n }\n int top() {\n return stk.top() + mini;\n }\n int min() {\n return mini;\n }\n\nprivate:\n stack<int> stk;\n int mini;\n};\n\n\n\n/*==================================================*\\\n * title: 栈的压入、弹出序列 ***\n * description: 输入两个整数序列，第一个序列表示栈的压入顺序，请判断第二个序列是否为该栈的弹出顺序。\n 假设压入栈的所有数字均不相等。例如序列1,2,3,4,5是某栈的压入顺序，\n 序列4，5,3,2,1是该压栈序列对应的一个弹出序列，但4,3,5,1,2就不可能是该压栈序列的弹出序列。\n\\*==================================================*/\nclass Solution {\npublic:\n bool IsPopOrder(vector<int> pushV,vector<int> popV) {\n if(pushV.empty()) return false;\n stack<int> st;\n for(int i = 0, j = 0, len = pushV.size(); i < len; ){\n st.push(pushV[i++]);\n while(!st.empty() && j < len && st.top() == popV[j]){\n st.pop();\n j++;\n }\n }\n return st.empty();\n }\n};\n\n\n\n/*==================================================*\\\n * title: 从上往下打印二叉树 **\n * description: 从上往下打印出二叉树的每个节点，同层节点从左至右打印。\n\\*==================================================*/\n/*\nstruct TreeNode {\n int val;\n struct TreeNode *left;\n struct TreeNode *right;\n TreeNode(int x) :\n val(x), left(NULL), right(NULL) {\n }\n};*/\nclass Solution {\npublic:\n vector<int> PrintFromTopToBottom(TreeNode *root) {\n vector<int> res;\n if(root == NULL) return res;\n\n queue<TreeNode*> qu;\n TreeNode* tmp;\n qu.push(root);\n while(!qu.empty()){\n tmp = qu.front();\n res.push_back(tmp->val);\n qu.pop();\n if(tmp->left != NULL)qu.push(tmp->left);\n if(tmp->right != NULL)qu.push(tmp->right);\n }\n\n return res;\n }\n};\n\n\n\n/*==================================================*\\\n * title: 二叉搜索树(二叉排序、查找树)的后序遍历序列 **\n * description: 输入一个整数数组，判断该数组是不是某二叉搜索树的后序遍历的结果。\n 如果是则输出Yes,否则输出No。假设输入的数组的任意两个数字都互不相同。\n\\*==================================================*/\nclass Solution {\npublic:\n bool VerifySquenceOfBST(vector<int> sequence) {\n int len = sequence.size();\n if(len == 0) return false;\n if(len <= 2) return true;\n\n vector<int> left, right;\n int root = sequence.back(), i = 0, j;\n while(sequence[i] < root) ++i;\n j = i;\n while(j < len - 1)if(sequence[j++] < root) return false;\n left.assign(sequence.begin(), sequence.begin() + i);\n right.assign(sequence.begin() + i, sequence.end() - 1);\n\n bool l = true, r = true;\n if(!left.empty()) l = VerifySquenceOfBST(left);\n if(!right.empty()) r = VerifySquenceOfBST(right);\n return l && r;\n\n }\n};\n\n\ncalss Solution2 {\npublic:\n bool judge(vector<int>& a, int l, int r){\n if(l >= r) return true;\n int i = r;\n while(i > l && a[i - 1] > a[r]) --i;\n for(int j = i - 1; j >= l; --j) if(a[j] > a[r]) return false;\n return judge(a, l, i - 1) && (judge(a, i, r - 1));\n }\n bool VerifySquenceOfBST(vector<int> a) {\n if(!a.size()) return false;\n return judge(a, 0, a.size() - 1);\n }\n};\n}\n\n\n\n/*==================================================*\\\n * title: 二叉树中和为某一值的路径 ****\n * description: 输入一颗二叉树和一个整数，打印出二叉树中结点值的和为输入整数的所有路径。\n 路径定义为从树的根结点开始往下一直到叶结点所经过的结点形成一条路径\n * notice: copy code should really carefully\n\\*==================================================*/\n/*\nstruct TreeNode {\n int val;\n struct TreeNode *left;\n struct TreeNode *right;\n TreeNode(int x) :\n val(x), left(NULL), right(NULL) {\n }\n};*/\nclass Solution {\npublic:\n vector<vector<int> > FindPath(TreeNode* root,int expectNumber) {\n vector<vector<int>> res, ltmp, rtmp;\n if(root == NULL || root->val > expectNumber || expectNumber <= 0) return res;\n\n if(root->left != NULL){\n ltmp = FindPath(root->left, expectNumber - root->val);\n if(ltmp.size()){\n for(int i = 0; i < ltmp.size(); ++i){\n ltmp[i].insert(ltmp[i].begin(), root->val);\n res.push_back(ltmp[i]);\n }\n }\n }\n if(root->right != NULL){\n rtmp = FindPath(root->right, expectNumber - root->val);\n if(rtmp.size()){\n for(int i = 0; i < rtmp.size(); ++i){\n rtmp[i].insert(rtmp[i].begin(), root->val);\n res.push_back(rtmp[i]);\n }\n }\n } \n \n if(expectNumber == root->val && root->left == NULL && root->right == NULL){\n vector<int> re;\n re.push_back(expectNumber);\n res.push_back(re);\n }\n\n return res;\n }\n};\n\n\n/*==================================================*\\\n * title: 复杂链表的复制 *****\n * description: 输入一个复杂链表（每个节点中有节点值，以及两个指针，\n 一个指向下一个节点，另一个特殊指针指向任意一个节点）。\n * solution: A->A->A ==> A->B->A->B->A->B\n 1. add new nodes B(A->next)\n 2. B->random = A->random->next;\n 3. split\n\\*==================================================*/\n/*\nstruct RandomListNode {\n int label;\n struct RandomListNode *next, *random;\n RandomListNode(int x) :\n label(x), next(NULL), random(NULL) {\n }\n};\n*/\nclass Solution {\npublic:\n \n RandomListNode* Clone(RandomListNode* pHead)\n {\n if(!pHead) return NULL;\n RandomListNode *p, *res, *tmp;\n for(p = pHead; p; p = tmp->next){\n tmp = new RandomListNode(p->label);\n tmp->next = p->next;\n p->next = tmp;\n }\n for(p = pHead; p; p = p->next->next)if(p->random) p->next->random = p->random->next;\n for(p = pHead, res = pHead->next; p->next; p = tmp){\n tmp = p->next;\n p->next = tmp->next;\n }\n\n return res;\n }\n};\n\n\n/*==================================================*\\\n * title: 二叉搜索树与双向链表 *****\n * description: 输入一棵二叉搜索树，将该二叉搜索树转换成一个排序的双向链表。\n 要求不能创建任何新的结点，只能调整树中结点指针的指向。\n * HINT: 递归法求解要考虑最原子的操作才才合理\n 还可以考虑使用队列解决该问题\n\\*==================================================*/\n/*\nstruct TreeNode {\n int val;\n struct TreeNode *left;\n struct TreeNode *right;\n TreeNode(int x) :\n val(x), left(NULL), right(NULL) {\n }\n};*/\nclass Solution0 { // now working\npublic:\n bool isLeaf(TreeNode* node){\n return node->left == NULL && node->right == NULL;\n }\n\n TreeNode* Convert(TreeNode* pRootOfTree){\n if(pRootOfTree || isLeaf(pRootOfTree)) return pRootOfTree;\n TreeNode *left = pRootOfTree->left, *right = pRootOfTree->right, *res = pRootOfTree;\n if(left){\n res = left;\n while(left->right) left = left->right;\n if(pRootOfTree->left) res = Convert(pRootOfTree->left);\n left->right = pRootOfTree;\n pRootOfTree->left = left;\n }\n if(right){\n while(right->left) right = right->left;\n if(pRootOfTree->right) Convert(pRootOfTree->right);\n right->left = pRootOfTree;\n pRootOfTree->right = right;\n }\n return res;\n }\n};\n\n\nclass Solution {\npublic:\n TreeNode* Convert(TreeNode* root){\n if(!root) return NULL;\n if(root->left == NULL && root->right == NULL) return root;\n\n TreeNode *left = NULL, *right, *last;\n if(root->left){\n left = Convert(root->left);\n last = left;\n while(last->right)last = last->right;\n last->right = root;\n root->left = last;\n }\n if(root->right){\n right = Convert(root->right);\n right->left = root;\n root->right = right;\n }\n return left ? left : root;\n }\n};\n\n\n/*==================================================*\\\n * title: 字符串的排列\n * description: 输入一个字符串,按字典序打印出该字符串中字符的所有排列。\n 例如输入字符串abc,则打印出由字符a,b,c所能排列出来的所有字符串abc,acb,bac,bca,cab和cba。\n 结果请按字母顺序输出。\n 输入一个字符串,长度不超过9(可能有字符重复),字符只包括大小写字母\n\\*==================================================*/\nclass Solution {\npublic:\n void help(vector<string> &res,int index,int len,string str){\n if(index == len-1){\n res.push_back(str);\n return;\n }\n for(int i = index; i <= len-1; i++){\n if(i != index && str[i] == str[index]) continue; //相同值不交换\n swap(str[i], str[index]);\n help(res, index+1, len, str); //已交换头一个字符\n swap(str[i], str[index]);\n }\n \n }\n vector<string> Permutation(string str) {\n vector<string> res;\n help(res, 0, str.size(), str);\n sort(res.begin(), res.end());\n return res;\n }\n};\n\n\n/*==================================================*\\\n * title: 数组中出现次数超过一半的数字 **\n * description: 数组中有一个数字出现的次数超过数组长度的一半，请找出这个数字。\n 例如输入一个长度为9的数组{1,2,3,2,2,2,5,4,2}。\n 由于数字2在数组中出现了5次，超过数组长度的一半，因此输出2。\n\\*==================================================*/\nclass Solution {\npublic:\n int MoreThanHalfNum_Solution(vector<int> num) {\n int len = num.size();\n if(len == 0) return 0;\n if(len == 1) return num[0];\n int half = len / 2;\n map<int, int> res;\n for(vector<int>::iterator iter = num.begin(); iter != num.end(); ++iter){\n if(res.find(*iter) == res.end()) res[*iter] = 1;\n else if(++res[*iter] > half) return *iter;\n }\n return 0;\n }\n};\n\n\n/*==================================================*\\\n * title: 最小的K个数 ***\n * description: 输入n个整数，找出其中最小的K个数。\n 例如输入4,5,1,6,2,7,3,8这8个数字，\n 则最小的4个数字是1,2,3,4,\n\\*==================================================*/\nclass Solution {\npublic:\n vector<int> GetLeastNumbers_Solution(vector<int> input, int k) {\n vector<int> res;\n if(input.size() < k || k <= 0) return res; //需注意每一个变量的合法性\n\n res.insert(res.begin(), input.begin(), input.begin() + k);\n sort(res.begin(), res.end());\n\n for(vector<int>::iterator iter = input.begin() + k; iter != input.end(); ++iter){\n if(res.back() > *iter){\n vector<int>::iterator it = res.begin();\n while(it != res.end() && *it < *iter) ++it;\n if(it != res.end()){\n res.insert(it, *iter);\n res.pop_back();\n }\n }\n }\n return res;\n }\n};\n\n\n/*==================================================*\\\n * title: 连续子数组的最大和 *\n * description: HZ偶尔会拿些专业问题来忽悠那些非计算机专业的同学。\n 今天测试组开完会后,他又发话了:\n 在古老的一维模式识别中,常常需要计算连续子向量的最大和,当向量全为正数的时候,问题很好解决。\n 但是,如果向量中包含负数,是否应该包含某个负数,并期望旁边的正数会弥补它呢？\n 例如:{6,-3,-2,7,-15,1,2,2},连续子向量的最大和为8(从第0个开始,到第3个为止)。\n 你会不会被他忽悠住？\n\\*==================================================*/\nclass Solution {\npublic:\n int FindGreatestSumOfSubArray(vector<int> array) {\n if(!array.size()) return 0;\n \n int res = array[0], sum = 0;\n for(vector<int>::iterator iter = array.begin(); iter != array.end(); ++iter){\n sum += *iter;\n if(sum > res) res = sum;\n if(sum < 0) sum = 0;\n }\n return res;\n }\n};\n\n\n/*==================================================*\\\n * title: 整数中1出现的次数（从1到n整数中1出现的次数） ****\n * description: 求出1~13的整数中1出现的次数,并算出100~1300的整数中1出现的次数？\n 为此他特别数了一下1~13中包含1的数字有1、10、11、12、13因此共出现6次,但是对于后面问题他就没辙了。\n ACMer希望你们帮帮他,并把问题更加普遍化,可以很快的求出任意非负整数区间中1出现的次数。\n \\*==================================================*/\nclass Solution0 { //写的好渣啊\npublic:\n int NumberOf1Between1AndN_Solution(int n){\n if (n <= 0) return 0;\n \n int dig = int(log(n) / log(10)) - 1, res = 0, multi = 1, i; //还可以使用log10()\n vector<int> ones; // storage results of 1 ~ {n x 9}\n ones.push_back(1);\n for (i = 1; i < dig; ++i) {\n multi *= 10;\n ones.push_back(ones.back()*10 + multi);\n }\n \n for (i = 1, multi *= 10; i <= dig; ++i, n %= multi, multi %= 10) {\n if (n / multi > 1) res += n / multi * ones[dig - i] + multi;\n else res += multi * ones[dig - i] + n % multi;\n }\n if (n > 0) ++res;\n return res;\n }\n};\n\n\nclass Solution { //refer to http://www.cnblogs.com/nailperry/p/4752987.html\npublic:\n int NumberOf1Between1AndN_Solution(int n){\n int count = 0;\n long long i = 1;\n for(i = 1; i <= n; i *= 10){ //i表示当前分析的是哪一个数位, 分割成a,b两部分\n int a = n/i, b = n%i;\n count += (a + 8)/10*i + (a%10 == 1)*(b + 1); //分三种情况，i位为0/1/(2~9), i==1为临界情况\n }\n return count;\n }\n};\n\n\n/*==================================================*\\\n * title: 把数组排成最小的数\n * description: 输入一个正整数数组，把数组里所有数字拼接起来排成一个数，打印能拼接出的所有数字中最小的一个。\n 例如输入数组{3，32，321}，则打印出这三个数字能排成的最小数字为321323。\n \\*==================================================*/\nclass Solution {\npublic:\n static bool cmp(string s1, string s2){\n return s1+s2 < s2+s1;\n }\n string PrintMinNumber(vector<int> num) {\n vector<string> str;\n for (vector<int>::iterator iter = num.begin(); iter != num.end(); iter++)str.push_back(to_string(*iter));\n // 此处到底需要替换成stable_sort()吗？\n sort(str.begin(), str.end(), cmp);\n string res;\n for (vector<string>::iterator iter = str.begin(); iter != str.end(); iter++)res += *iter;\n return res;\n }\n};\n\n\n/*==================================================*\\\n * title: 丑数 *****\n * description: 把只包含因子2、3和5的数称作丑数（Ugly Number）。\n 例如6、8都是丑数，但14不是，因为它包含因子7。\n 习惯上我们把1当做是第一个丑数。求按从小到大的顺序的第N个丑数。\n \\*==================================================*/\nclass Solution { //这是个错误解， GetUglyNumber_Solution(7) != 8, 而是 9， 为啥会跳过8呢？\npublic:\n static vector<int> res;\n inline int min(int a, int b){\n return a < b ? a : b;\n }\n int GetUglyNumber_Solution(int index) {\n if (index <= 0) return 0;\n if (index <= res.size()) return res[index - 1];\n \n\n for(vector<int>::iterator iter2 = res.begin(), iter3 = res.begin(), iter5 = res.begin(); res.size() < index; ){\n while(*iter2 * 2 <= res.back())iter2++;\n while(*iter3 * 3 <= res.back())iter3++;\n while(*iter5 * 5 <= res.back())iter5++;\n res.push_back(min(*iter2 * 2, min(*iter3 * 3, *iter5 * 5)));\n }\n return res.back();\n }\n};\n\nvector<int> Solution::res(1, 1);\n\n\nclass Solution {\npublic:\n int GetUglyNumber_Solution(int index) {\n if(index < 1) return 0;\n\n int count = 1, *arr = new int[index];\n int *num2 = arr, *num3 = arr, *num5 = arr;\n for(*arr = 1; count < index; count++) {\n arr++;\n *arr = min(*num2 * 2, min(*num3 * 3, *num5 * 5));\n while(*num2 * 2 <= *arr)num2++;\n while(*num3 * 3 <= *arr)num3++;\n while(*num5 * 5 <= *arr)num5++;\n }\n return *arr;\n }\n inline int min(int a, int b){\n return a <= b ? a : b;\n }\n};\n\n\n/*==================================================*\\\n * title: 第一个只出现一次的字符位置 **\n * description: 在一个字符串(1<=字符串长度<=10000，全部由字母组成)中找到第一个只出现一次的字符的位置。\n 若为空串，返回-1。位置索引从0开始\n \\*==================================================*/\nclass Solution {\npublic:\n int FirstNotRepeatingChar(string str) {\n if(str.size() == 0) return -1;\n vector<char> seq;\n map<char, int> res;\n char a = '0';\n\n for (string::iterator iter = str.begin(); iter != str.end(); ++iter){\n if(res.find(*iter) == res.end()){\n res[*iter] = 1;\n seq.push_back(*iter);\n }else res[*iter]++;\n }\n\n for(vector<char>::iterator iter = seq.begin(); iter != seq.end(); ++iter){\n if(res.find(*iter) != res.end() && res[*iter] == 1){\n a = *iter;\n break;\n }\n }\n\n for(int i=0; i < str.size(); ++i) if(str[i] == a) return i;\n return -1;\n }\n};\n\n\n/*==================================================*\\\n * title: 数组中的逆序对 * or *****\n * des. : 在数组中的两个数字，如果前面一个数字大于后面的数字，则这两个数字组成一个逆序对。\n 输入一个数组，求出这个数组中的逆序对的总数。\n * input: [1,2,3,4,7,6,5]\n * ouput: 3\n \\*==================================================*/\nclass Solution {\npublic:\n int InversePairs(vector<int> data) {\n if(data.size() <= 1) return 0;\n int count = 0;\n for (int i = 0; i < data.size() - 1; ++i){\n for (int j = i+1; j < data.size(); ++j) if(data[j] < data[i]) count++;\n }\n return count;\n }\n};\n\n\n#define lb(x) ((x) & -(x))\nclass BIT{\n int n;\n map<int, int> d;\npublic:\n BIT(int n_) : n(n_) {}\n void add(int i, int v){\n for(; i <= n; i += lb(i)) d[i] += v;\n }\n int sum(int i){\n int r = 0;\n for(; i; i -= lb(i)) r += d[i];\n return r;\n }\n};\nclass Solution {\npublic:\n int InversePairs(vector<int> d) {\n int mi = 0x7fffffff, mx = 0x80000000;\n for(int i = 0; i < d.size(); ++i) mi = min(mi, d[i]), mx = max(mx, d[i]);\n int r = 0;\n BIT bit(mx - mi + 5);\n for(int i = (int)d.size() - 1; 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"text": "\n#==================================================#\n# title: 二维数组中的查找 *\n# des. : 在一个二维数组中，每一行都按照从左到右递增的顺序排序，\n# 每一列都按照从上到下递增的顺序排序。\n# 请完成一个函数，输入这样的一个二维数组和一个整数，判断数组中是否含有该整数。\n#==================================================#\n# -*- coding:utf-8 -*-\nclass Solution:\n # array 二维列表\n def Find(self, array, target):\n # write code here\n i, j, l = len(array) - 1, 0, len(array[0])\n if (l == 0): return False\n while(j < l and i >= 0):\n \tif(array[i][j] < target): j += 1\n \telif(array[i][j] > target): i -= 1\n \telse: return True\n return False\n\n\n#==================================================#\n# title: 替换空格 *\n# des. : 请实现一个函数，将一个字符串中的空格替换成“%20”。\n#\t\t 例如，当字符串为We Are Happy.则经过替换之后的字符串为We%20Are%20Happy\n#==================================================#\n# -*- coding:utf-8 -*-\nclass Solution:\n # s 源字符串\n def replaceSpace(self, s):\n return s.replace(\" \", \"%20\")\n\n\n#==================================================#\n# title: 从尾到头打印链表 *\n# des. : 输入一个链表，从尾到头打印链表每个节点的值。\n#==================================================#\n# -*- coding:utf-8 -*-\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\ndef iter_n(head):\n\twhile head:\n\t\tyield head.val\n\t\thead = head.next\n\nclass Solution:\n # 返回从尾部到头部的列表值序列，例如[1,2,3]\n def printListFromTailToHead(self, head):\n return list(iter_n(head))[::-1]\n\n\n#==================================================#\n# title: 重建二叉树 *\n# des. : 输入某二叉树的前序遍历和中序遍历的结果，请重建出该二叉树。\n# 假设输入的前序遍历和中序遍历的结果中都不含重复的数字。\n#\t\t 例如输入前序遍历序列{1,2,4,7,3,5,6,8}和中序遍历序列{4,7,2,1,5,3,8,6}，则重建二叉树并返回。\n#==================================================#\n# -*- coding:utf-8 -*-\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\nclass Solution:\n # 返回构造的TreeNode根节点\n def reConstructBinaryTree(self, pre, tin):\n if len(pre) == 0:\n return None\n else:\n res = TreeNode(pre[0])\n for i in range(len(pre)):\n if(tin[i] == pre[0]):\n res.left = self.reConstructBinaryTree(pre[1:i+1], tin[0:i])\n res.right = self.reConstructBinaryTree(pre[i+1:], tin[i+1:])\n break\n return res\n\n\n# Solution2\nclass Solution:\n # 返回构造的TreeNode根节点\n def reConstructBinaryTree(self, pre, tin):\n # write code here\n l = len(pre)\n if l==0:\n return None\n else:\n patitionNum = pre[0]\n i = 0\n while tin[i]!=patitionNum:\n i += 1\n pre_left = pre[1:i+1]\n pre_right = pre[i+1:l]\n tin_left = tin[0:i]\n tin_right = tin[i+1:]\n leftNode = self.reConstructBinaryTree(pre_left,tin_left)\n rightNode = self.reConstructBinaryTree(pre_right,tin_right)\n node = TreeNode(patitionNum)\n node.left = leftNode\n node.right = rightNode\n return node\n\n\n\n\n#==================================================#\n# title: 用两个栈实现队列\n# des. : 用两个栈来实现一个队列，完成队列的Push和Pop操作。\n#\t\t 队列中的元素为int类型\n# Note : 不能tab和空格混用\n#==================================================#\n# -*- coding:utf-8 -*-\nclass Solution:\n def __init__(self):\n self.stack1 = []\n self.stack2 = []\n\n def push(self, node):\n self.stack1.append(node)\n\n def pop(self):\n if self.stack2:\n return self.stack2.pop()\n elif self.stack1:\n while self.stack1:\n self.stack2.append(self.stack1.pop())\n return self.stack2.pop()\n else:\n return None\n\n\n\n#==================================================#\n# title: 两个链表的第一个公共结点 **\n# des. : 输入两个链表，找出它们的第一个公共结点。\n# Note :\n#==================================================#\n\n# -*- coding:utf-8 -*-\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\nclass Solution:\n def FindFirstCommonNode(self, pHead1, pHead2):\n # write code here\n if pHead1 == None or pHead2 == None:\n return None\n\n len1, len2 = 0, 0\n p, q = pHead1, pHead2\n while p:\n len1 += 1\n p = p.next\n while q:\n len2 += 1\n q = q.next\n\n p, q = pHead1, pHead2\n if len1 < len2:\n for i in xrange(len2-len1):\n q = q.next\n elif len1 > len2:\n for i in xrange(len1-len2):\n p = p.next\n\n while p:\n if p == q:\n return p\n p, q = p.next, q.next\n\n return None\n\n\n\n#==================================================#\n# title: 数字在排序数组中出现的次数 *\n# des. : 统计一个数字在排序数组中出现的次数。\n# Note :\n#==================================================#\n\n# -*- coding:utf-8 -*-\nclass Solution:\n def GetNumberOfK(self, data, k):\n # write code here\n if data == None:\n return 0\n\n res = 0\n for i in data:\n if i == k:\n res += 1\n\n return res\n\n\n\n#==================================================#\n# title: 二叉树的深度 *\n# des. : 输入一棵二叉树，求该树的深度。\n# 从根结点到叶结点依次经过的结点（含根、叶结点）\n# 形成树的一条路径，最长路径的长度为树的深度。\n# Note :\n#==================================================#\n# -*- coding:utf-8 -*-\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\nclass Solution:\n def TreeDepth(self, pRoot):\n # write code here\n if pRoot == None:\n return 0\n self.left, self.right = self.TreeDepth(pRoot.left), self.TreeDepth(pRoot.right)\n return self.left+1 if self.left > self.right else self.right+1\n\n\n\n\n#==================================================#\n# title: 平衡二叉树 ***\n# des. : 输入一棵二叉树，判断该二叉树是否是平衡二叉树。\n# Note :\n#==================================================#\n# -*- coding:utf-8 -*-\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\nclass Solution:\n def getDepth(self, pRoot):\n if pRoot == None:\n return 0\n self.left, self.right = self.getDepth(pRoot.left), self.getDepth(pRoot.right)\n if self.left == -1 or self.right == -1 or abs(self.left - self.right) > 1:\n return -1\n return self.left+1 if self.left > self.right else self.right+1\n\n def IsBalanced_Solution(self, pRoot):\n # write code here\n if self.getDepth(pRoot) == -1:\n return False\n return True\n\n\n\n\n#==================================================#\n# title: 数组中只出现一次的数字 **\n# des. : 一个整型数组里除了两个数字之外，\n# 其他的数字都出现了两次。请写程序找出这两个只出现一次的数字。\n# Note :\n#==================================================#\n# -*- coding:utf-8 -*-\nclass Solution:\n # 返回[a,b] 其中ab是出现一次的两个数字\n def FindNumsAppearOnce(self, array):\n # write code here\n if array == None or len(array) < 2:\n return [0, 0]\n\n res = []\n array.sort()\n if array[0] != array[1]:\n res.append(array[0])\n for i in xrange(1, len(array)-1):\n if array[i] != array[i-1] and array[i] != array[i+1]:\n res.append(array[i])\n if array[-1] != array[-2]:\n res.append(array[-1])\n\n return res\n\n\n\n#==================================================#\n# title: 和为S的连续正数序列 ***\n# des. : 小明很喜欢数学,有一天他在做数学作业时,\n# 要求计算出9~16的和,他马上就写出了正确答案是100。\n# 但是他并不满足于此,\n# 他在想究竟有多少种连续的正数序列的和为100(至少包括两个数)。\n# 没多久,他就得到另一组连续正数和为100的序列:18,19,20,21,22。\n# 现在把问题交给你,你能不能也很快的找出所有和为S的连续正数序列? Good Luck!\n# 输出所有和为S的连续正数序列。\n# 序列内按照从小至大的顺序，序列间按照开始数字从小到大的顺序\n# Note :\n#==================================================#\n# -*- coding:utf-8 -*-\nclass Solution:\n def FindContinuousSequence(self, tsum):\n # write code here\n if tsum < 3:\n return []\n\n res = []\n for i in range(tsum-1, 1, -1):\n if not 2*tsum%i and (2*tsum/i-i+1)>0 and not (2*tsum/i-i+1)%2:\n res.append(range((2*tsum/i-i+1)/2, (2*tsum/i-i+1)/2 + i))\n\n return res\n\n\n\n\n#==================================================#\n# title: 和为S的两个数字 **\n# des. : 输入一个递增排序的数组和一个数字S，\n# 在数组中查找两个数，是的他们的和正好是S，\n# 如果有多对数字的和等于S，输出两个数的乘积最小的。\n# 对应每个测试案例，输出两个数，小的先输出。\n# Note : 第一组答案就是想要的答案\n#==================================================#\n# -*- coding:utf-8 -*-\nclass Solution:\n def FindNumbersWithSum(self, a, tsum):\n # write code here\n if a == None:\n return []\n\n i, j = 0, len(a)-1\n while i < j:\n while a[i]+a[j] > tsum:\n j -= 1\n while a[i]+a[j] < tsum:\n i += 1\n if a[i]+a[j] == tsum and i < j:\n return [a[i], a[j]]\n i += 1\n\n return []\n\n\n#==================================================#\n# title: 翻转单词顺序列 **\n# des. : JOBDU最近来了一个新员工Fish，每天早晨总是会拿着一本英文杂志，写些句子在本子上。\n# 同事Cat对Fish写的内容颇感兴趣，有一天他向Fish借来翻看，但却读不懂它的意思。\n# 例如，“student. a am I”。后来才意识到，\n# 这家伙原来把句子单词的顺序翻转了，正确的句子应该是“I am a student.”。\n# Cat对一一的翻转这些单词顺序可不在行，你能帮助他么？\n# Note : we should specifiy the white space ' ', not using the default val\n#==================================================#\n# -*- coding:utf-8 -*-\nclass Solution:\n def ReverseSentence(self, s):\n # write code here\n return ' '.join(s.split(' ')[::-1])\n\n\n#==================================================#\n# title: 扑克牌顺子\n# des. : LL今天心情特别好,因为他去买了一副扑克牌,\n# 发现里面居然有2个大王,2个小王(一副牌原本是54张^_^)...\n# 他随机从中抽出了5张牌,想测测自己的手气,看看能不能抽到顺子,\n# 如果抽到的话,他决定去买体育彩票,嘿嘿！！\n# “红心A,黑桃3,小王,大王,方片5”,“Oh My God!”不是顺子.....\n# LL不高兴了,他想了想,决定大\\小 王可以看成任何数字,\n# 并且A看作1,J为11,Q为12,K为13。\n# 上面的5张牌就可以变成“1,2,3,4,5”(大小王分别看作2和4),“So Lucky!”。\n# LL决定去买体育彩票啦。\n# 现在,要求你使用这幅牌模拟上面的过程,然后告诉我们LL的运气如何。\n# 为了方便起见,你可以认为大小王是0。\n# Note : 1. king == 0, and 4 kings maximum\n# 2. return false, if have same cards other than 0\n#==================================================#\n# -*- coding:utf-8 -*-\nclass Solution:\n def IsContinuous(self, numbers):\n # write code here\n if len(numbers) != 5:\n return False\n\n numbers.sort()\n zeros,cnt = 0,0\n for i in numbers:\n if not i:\n zeros += 1\n else:\n break\n for i in range(zeros+1, len(numbers)):\n if numbers[i] == numbers[i-1]:\n return False\n cnt += numbers[i] - numbers[i-1] - 1\n\n return zeros >= cnt\n\n\n#==================================================#\n# title: 孩子们的游戏(圆圈中最后剩下的数)\n# des. : 每年六一儿童节,NowCoder都会准备一些小礼物去看望孤儿院的小朋友,今年亦是如此。\n# HF作为NowCoder的资深元老,自然也准备了一些小游戏。\n# 其中,有个游戏是这样的:首先,让小朋友们围成一个大圈。\n# 然后,他随机指定一个数m,让编号为0的小朋友开始报数。\n# 每次喊到m的那个小朋友要出列唱首歌,然后可以在礼品箱中任意的挑选礼物,\n# 并且不再回到圈中,从他的下一个小朋友开始,继续0...m-1报数....\n# 这样下去....直到剩下最后一个小朋友,可以不用表演,\n# 并且拿到NowCoder名贵的“名侦探柯南”典藏版(名额有限哦!!^_^)。请你试着想下,哪个小朋友会得到这份礼品呢？\n# Note : k = m%n, 则第一个淘汰的是k-1, 剩余的是 k,k+1,..,k-3,k-2\n# 若将他们重新排序得到新的数列 0,1,...,n-2\n# hence, f(n,m) = (f(n-1,m)+m)%n\n# and, f(n+1,m) = (f(n,m)+m)%(n+1)\n#==================================================#\n# -*- coding:utf-8 -*-\nclass Solution:\n def LastRemaining_Solution(self, n, m):\n # write code here\n if n <=0 and m <=0:\n return -1\n elif n == 0:\n return 0\n\n res = 0;\n for i in xrange(n):\n res = (res+m)%(i+1)\n\n return res\n\n\n#==================================================#\n# title: 不用加减乘除做加法\n# des. : 写一个函数，求两个整数之和，要求在函数体内不得使用+、-、*、/四则运算符号。\n# Note : 分别使用^和&来保存 异或位为1的位置 和 同位1的位置\n#==================================================#\n# 1. stupidest way\n# -*- coding:utf-8 -*-\nclass Solution:\n def Add(self, a, b):\n # write code here\n lst,flag = [],0\n while a!=0 and b!=0:\n x,y = a&1,b&1\n if x^y:\n if flag:\n lst.append(0)\n flag = 1\n else:\n lst.append(1)\n flag = 0\n else:\n if flag:\n lst.append(1)\n else:\n lst.append(0)\n if x&y:\n flag = 1\n else:\n flag = 0\n a >>= 1\n b >>= 1\n\n while a!=0:\n x = a&1\n if x^flag:\n lst.append(1)\n flag = 0\n else:\n lst.append(0)\n if x&flag:\n flag = 1\n else:\n flag = 0\n a >>= 1\n\n while b!=0:\n x = b&1\n if x^flag:\n lst.append(1)\n flag = 0\n elif x&flag:\n lst.append(0)\n flag = 1\n else:\n lst.append(0)\n flag = 0\n b >>= 1\n\n if flag:\n lst.append(1)\n\n lst.reverse()\n res = 0\n for i in lst:\n res <<= 1\n res ^= i\n\n return res\n\n# solution2\n# -*- coding:utf-8 -*-\nclass Solution:\n def Add(self, a, b):\n # write code here\n while b:\n x, y = a^b, a&b\n y <<= 1\n a, b = x, y\n\n return a\n\n\n\n\n#==================================================#\n# title: 把字符串转换成整数\n# des. : 将一个字符串转换成一个整数，要求不能使用字符串转换整数的库函数。\n# Note : 1. can be null\n# 2. can be single '-' or '+'\n#==================================================#\n# -*- coding:utf-8 -*-\nclass Solution:\n def StrToInt(self, s):\n # write code here\n if len(s) <= 0:\n return 0\n res,neg = 0,False\n if s[0] == '-':\n s = s[1:]\n neg = True\n elif s[0] == '+':\n s = s[1:]\n for i in s:\n if i >= '0' and i <= '9':\n res = res*10 + int(i)\n else:\n return 0\n if neg:\n return -res\n return res\n\n\n\n\n#==================================================#\n# title:\n# des. :\n# Note :\n#==================================================#\n\n\n"
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"text": "/*==================================================*\\\n * title: 编写一个线程安全的红包类\n * description: 实现存钱、发钱，确保每次都有钱可发\n\\*==================================================*/\n\n\n\n#include <iostream>\n#include <pthread.h>\n#include <stdlib.h>\nusing namespace std;\n\nclass Solution(){\npublic:\n // put money, specify total number & times\n static void put_money(double number, int times){\n money_pool = number;\n count = times;\n }\n \n static double get_money(){\n // check withdraw times\n pthread_mutex_lock( &m_mutex );\n if (count == 0)return 0;\n else if (count == 1){\n opened.push_back(money_pool);\n count--;\n val = money_pool;\n }else{\n double val = double(rand()%(money_pool*100 - count))/100;\n opened.push_back(val);\n money_pool -= val;\n count--;\n }\n pthread_mutex_unlock( &m_mutex );\n return val;\n }\n \n static vector<double> list_opened(){\n pthread_mutex_lock( &m_mutex );\n vector<double> result = opened;\n pthread_mutex_unlock( &m_mutex );\n return result;\n }\n\nprivate:\n static double money_pool;\n static int count;\n static vector<double> opened;\n static pthread_mutex_t m_mutex;\n}\n"
}
] | 5 |
marsilinou97/AutoSelfieTaker
|
https://github.com/marsilinou97/AutoSelfieTaker
|
954f830271c20ec39cb4dae4615fee25e61351af
|
58caae162c6cca33f9309171d4b9020ff9e727d4
|
de60bb7008b9c746af064d7416a18f4145584d7a
|
refs/heads/master
| 2020-12-09T20:47:16.544017 | 2020-01-15T04:21:02 | 2020-01-15T04:21:02 | 233,413,136 | 1 | 1 | null | null | null | null | null |
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"text": "import enum\nimport numpy\nimport os\n\nclass Likelihood(enum.Enum):\n UNKNOWN = 0\n VERY_UNLIKELY = 1\n UNLIKELY = 2\n POSSIBLE = 3\n LIKELY = 4\n VERY_LIKELY = 5\n\n\nclass Weight(enum.Enum):\n ONE = 1\n TWO = 2\n THREE = 3\n FOUR = 4\n FIVE = 5\n\n\nscore_ranges = {'Good': range(int(5.2650 * 1000),int(6.2651 * 1000)), 'Average': range(int(3.665 * 1000), int(5.2650 * 1000)), 'Bad': range(int(3.665 * 1000))}\n\nmin_detection_confidence = 0.9\nblur_threshold = 35\nframes_per_second = 1/20\njson_path = os.environ.get('GOOGLE_VISION_API_KEY')\nenhancment_median = 127.5\nsharpness_factor = 2\nip_cam_url = \"http://192.168.0.23:8080/shot.jpg\"\nweight = {'One': 1, 'Two': 2, 'Three': 3, 'Four': 4, 'Five': 5}\nmax_pics_saved = 9\nseconds_to_run = 2\nnumber_of_processes = 5\nnumber_of_threads = 5\ntext_size = 100\ntext_color = (255, 0, 0)\ntext_font = \"arial.ttf\"\nsave_path = \"Generated_Images\"\nface_ratio = 0.5"
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"text": "# AutoSelfieTaker\n*This program was developed in two days at SB Hacks*\n\nAutoSelfieTaker is a Python program that uses your smartphone camera to capture the perfect selfie for you. The program utilitzes GCP Vision API to generate image data, and uses it to compute various aspects and measurments to accurately find the best image.\n\n## Features\n* Compute facial features suchs as joy, sorrow, anger, etc\n* Detect faces that are relevant to the image\n* Generates a score based on the abovementiond features\n* Enhances the brightness, contrast, and sharpness, of the taken selfie\n* Displays the selfie overlayed with it's score and rating\n\n"
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"path": "/AutoSelfieTaker.py",
"repo_name": "marsilinou97/AutoSelfieTaker",
"src_encoding": "UTF-8",
"text": "from google.cloud import vision\nfrom google.cloud.vision import types\nimport urllib.request\nimport threading\nfrom queue import Queue\nimport time\nfrom collections import OrderedDict\nimport cv2\nimport numpy as np\nfrom PIL import ImageDraw , ImageStat, Image, ImageEnhance, ImageFont\nimport io\nimport shutil\nimport os\nimport wx\nimport concurrent.futures as cf\nimport Settings\n\n\ndef get_faces_data(faces):\n faces_data = list()\n for face in faces:\n width = face.bounding_poly.vertices[1].x - face.bounding_poly.vertices[0].x \n height = face.bounding_poly.vertices[-1].y - face.bounding_poly.vertices[0].y\n faces_data.append([face, (width, height)])\n\n return faces_data\n\n\ndef get_valid_faces(faces_data):\n faces_data = sorted(faces_data, key=lambda k: k[1], reverse=True)\n valid_faces = [faces_data[0][0]]\n\n main_face = faces_data[0][1]\n for face_data in faces_data[1:]:\n if face_data[1][0] / main_face[0] >= Settings.face_ratio:\n valid_faces.append(face_data[0])\n return valid_faces\n\n\ndef write_to_image(text, image, y_cordinate):\n draw = ImageDraw.Draw(image)\n font = ImageFont.truetype(Settings.text_font, Settings.text_size)\n draw.text((0,y_cordinate),str(text),Settings.text_color,font=font)\n\n\ndef variance_of_laplacian(image):\n try:\n variance = cv2.Laplacian(image, cv2.CV_64F).var()\n return variance\n except Exception:\n print ('Error finding Laplacian variance')\n \ndef detect_faces(image_content):\n global BLUR_THRESHOLD\n image = types.Image(content=image_content)\n response = client.face_detection(image=image)\n faces = response.face_annotations\n \n nparr = np.frombuffer(image_content, np.uint8)\n img_np = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE)\n \n if variance_of_laplacian(img_np) < Settings.blur_threshold: faces = []\n\n return image_content, faces\n \n \ndef check_smile(faces, labels=None):\n pic_valid = False\n for face in faces:\n if face.joy_likelihood >= Settings.Likelihood.POSSIBLE.value and \\\n face.anger_likelihood <= Settings.Likelihood.UNLIKELY.value and \\\n face.sorrow_likelihood <= Settings.Likelihood.UNLIKELY.value and \\\n face.under_exposed_likelihood <= Settings.Likelihood.UNLIKELY.value and \\\n face.blurred_likelihood <= Settings.Likelihood.UNLIKELY.value and \\\n face.detection_confidence >= Settings.min_detection_confidence and \\\n all([abs(angle) <= 15 for angle in [face.roll_angle, face.pan_angle, face.tilt_angle]]):\n pic_valid = True\n else:\n pic_valid = False\n break\n \n return pic_valid\n \n \ndef face_score(face):\n score = (face.joy_likelihood / 5) * Settings.Weight.FIVE.value\n score -= (face.sorrow_likelihood / 5) * Settings.Weight.FIVE.value\n score -= (face.anger_likelihood / 5) * Settings.Weight.ONE.value\n score -= (face.under_exposed_likelihood / 5) * Settings.Weight.ONE.value \n score -= (face.blurred_likelihood / 5) * Settings.Weight.ONE.value\n score += (face.detection_confidence) * Settings.Weight.THREE.value\n score -= (abs(face.roll_angle) / 90) * Settings.Weight.FOUR.value if face.roll_angle else 0 \n score -= (abs(face.pan_angle) / 90) * Settings.Weight.FOUR.value if face.pan_angle else 0 \n score -= (abs(face.tilt_angle) / 90) * Settings.Weight.FOUR.value if face.tilt_angle else 0 \n return score\n\n \ndef score_pic(faces):\n score = [face_score(face) for face in faces]\n return sum(score) / len(score) if score else 0\n \n \ndef captureImages(queue, number_of_images=0, time_limit = 0):\n global PRINT_LOCK, FPS, client, img_counter, start\n img_counter = 0\n if time_limit: number_of_images = time_limit // FPS\n \n url = Settings.ip_cam_url\n \n while True:\n imgResp=urllib.request.urlopen(url).read()\n queue.put(imgResp)\n img_counter+=1\n if img_counter >= number_of_images: break\n time.sleep(FPS)\n \n \ndef threader(queue, img_dict):\n global PRINT_LOCK, FPS, client, img_counter, start\n while True:\n if not queue.empty():\n img = queue.get()\n image_content, faces = detect_faces(img)\n if faces: faces = get_valid_faces(get_faces_data(faces))\n score = score_pic(faces)\n result = check_smile(faces)\n if result and score: img_dict[score] = image_content\n with PRINT_LOCK:\n if score: print(f'Valid faces detected = {len(faces)},\\tScore = {score},\\tResult = {result}')\n \n\ndef get_brightness(image):\n stat = ImageStat.Stat(image)\n return stat.rms[0]\n\n\ndef change_brightness(image, factor):\n enh_bri = ImageEnhance.Brightness(image)\n image_brightened = enh_bri.enhance(factor)\n return image_brightened, get_brightness(image_brightened)\n\n\ndef finalize_image(image):\n try:\n score, content = image\n image = Image.open(io.BytesIO(content))\n\n image_brightness = get_brightness(image)\n factor = Settings.enhancment_median / ((image_brightness - Settings.enhancment_median) / 2.5 + Settings.enhancment_median)\n image, new_brightness = change_brightness(image, factor)\n enh_sha = ImageEnhance.Sharpness(image)\n sharpness = Settings.sharpness_factor\n image_sharped = enh_sha.enhance(sharpness) \n image_sharped.save(f\"{Settings.save_path}/image_{score}.jpg\")\n except Exception as e:\n print(e)\n\n\ndef main():\n wx_app = wx.App(0)\n wx_app.MainLoop()\n pic_message = wx.BusyInfo(\"Taking pictures...\")\n queue = Queue()\n img_dict = dict()\n \n try: shutil.rmtree(Settings.save_path)\n except FileNotFoundError: pass\n finally: os.makedirs(Settings.save_path)\n \n captureImages_thread = threading.Thread(target=captureImages, args=(queue, 0, Settings.seconds_to_run))\n captureImages_thread.daemon = True\n captureImages_thread.start()\n \n for process in range(Settings.number_of_processes):\n thread = threading.Thread(target=threader, args=(queue, img_dict))\n thread.daemon = True\n thread.start()\n \n queue.join()\n captureImages_thread.join()\n\n del pic_message\n\n processing_message = wx.BusyInfo(\"Processing pictures...\")\n\n img_dict = OrderedDict(sorted(img_dict.items(), reverse=True)[:Settings.max_pics_saved])\n\n\n with cf.ProcessPoolExecutor(Settings.number_of_processes) as ex:\n ex.map(finalize_image, img_dict.items())\n\n if len(img_dict.keys()):\n max_score = max(img_dict.keys())\n final_image = Image.open(f\"{Settings.save_path}/image_{max_score}.jpg\")\n write_to_image(max_score, final_image, 0)\n for score_status, score_range in Settings.score_ranges.items():\n if int(max_score * 1000) in score_range:\n write_to_image(score_status, final_image, 100)\n break\n del processing_message\n final_image.show()\n\n del wx_app\n\n\nif __name__ == '__main__':\n global PRINT_LOCK, FPS, client, img_counter, start, BLUR_THRESHOLD\n PRINT_LOCK = threading.Lock()\n BLUR_THRESHOLD = Settings.blur_threshold\n FPS = Settings.frames_per_second\n CREDNTIALS = Settings.json_path\n\n client = vision.ImageAnnotatorClient.from_service_account_json(CREDNTIALS)\n img_counter = 0\n start = 0\n main()"
}
] | 3 |
skye1204/SRT-Reader-JP
|
https://github.com/skye1204/SRT-Reader-JP
|
0894c79f99aa098001e2a85c14bae2878a3673a1
|
3619fcd3c0d68ddd2ef5bc2409e41f0a75b8ae02
|
03003db30ba331b9f6a91d043a268cee3b974e24
|
refs/heads/master
| 2021-08-24T03:12:12.786943 | 2017-11-21T21:39:16 | 2017-11-21T21:39:16 | 110,647,961 | 0 | 0 | null | 2017-11-14T06:06:56 | 2017-11-14T06:08:14 | 2017-11-21T21:39:16 |
Python
|
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"src_encoding": "UTF-8",
"text": "#TODO add labels, descriptions, how to use\n#TODO better the layout\n#TODO make listboxes stretch vertically\n#TODO Wrap text\n#TODO Add 3 modes:\n# One mode for combining and directly editing saved entries\n# one mode for adding entries to save list\n#TODO Add support for more file extensions\n#TODO Search function\n#TODO Go to certain time\n#TODO Change color of already saved lines and only allow one save per line\n\nimport codecs\nimport pyperclip\nfrom appJar import gui\n\ndef move(btn):\n for line in app.getListBox(\"subs\"):\n if(line != \"\"):\n app.setListItem(\"save\", \"\", line, first = True)\n app.addListItem(\"save\", \"\", select = False)\n app.setLabel(\"num_saved_1\", len(app.getAllListItems(\"save\"))-1)\n app.setLabel(\"num_saved_2\", len(app.getAllListItems(\"save\"))-1)\n \n\ndef copy(btn):\n for line in app.getListBox(\"save\"):\n if(line != \"\"):\n pyperclip.copy(line)\n app.removeListItem(\"save\", line)\n app.setLabel(\"num_saved_1\", len(app.getAllListItems(\"save\"))-1)\n app.setLabel(\"num_saved_2\", len(app.getAllListItems(\"save\"))-1)\n\ndef clear_save(btn):\n app.updateListBox(\"save\", [\"\"], select=False)\n app.setLabel(\"num_saved_1\", 0)\n app.setLabel(\"num_saved_2\", 0)\n \ndef open_file(btn):\n file_name = app.openBox(\"Open .srt\", dirName=None, fileTypes=[(\"subtitle files\", '*.srt')],\n asFile=False, parent=None)\n with codecs.open(file_name, encoding = \"utf-8\") as f:\n sub_text = f.read()\n #start = sub_raw.index('0')\n #f.seek(0)\n #sub_raw = f.read()\n #TODO Ensure proper formatting by getting rid of possible excess space on top\n\n sub_list = []\n sub_raw_list = sub_text.splitlines()\n #splitlines() does not add last line break\n sub_raw_list.append(\"\")\n #list of ending indexes of each sub section\n sub_sec = [] \n #get ending indexes of each sub section\n for i in range(len(sub_raw_list)): \n if(sub_raw_list[i] == \"\"):\n sub_sec.append(i)\n #start at 2 because of .srt format\n start = 2\n \n for i in range(len(sub_sec)):\n end = sub_sec[i]\n #temp string for concatenation\n conc = \"\" \n for j in range(start, end):\n conc = conc + sub_raw_list[j]\n \n sub_list.append(conc)\n sub_list.append(\"\")\n start = end + 3\n\n app.updateListBox(\"subs\", sub_list, select=False)\n \n\nwith gui(\"SRT Reader\") as app:\n #holds loaded subs\n sub_list = []\n #holds all sentences user wants to save\n save = [\"\"]\n app.setResizable(canResize=True)\n app.setGeometry(300, 500)\n app.setLocation(0, 0)\n app.setFont(12, font = \"Consolas\")\n \n app.startTabbedFrame(\"TabbedFrame\")\n \n app.startTab(\"Subtitles\")\n app.addLabel(\"subs_title\", \"Subtitles\")\n app.setLabelRelief(\"subs_title\", \"groove\")\n app.addListBox(\"subs\", sub_list)\n app.setListBoxChangeFunction(\"subs\", move)\n app.addLabel(\"num_saved_1\", len(save)-1)\n app.setLabelAlign(\"num_saved_1\", \"right\")\n app.setLabelTooltip(\"num_saved_1\", \"Tracks number of saved lines\")\n app.addButton(\"Open\", open_file)\n #app.setButtonSticky(\"Open\", \"left\")\n app.stopTab()\n \n app.startTab(\"Saved Lines\")\n app.addLabel(\"save_title\", \"Saved Lines\")\n app.setLabelRelief(\"save_title\", \"groove\")\n app.addListBox(\"save\", save)\n app.setListBoxChangeFunction(\"save\", copy)\n app.addLabel(\"num_saved_2\", len(save)-1)\n app.setLabelAlign(\"num_saved_2\", \"right\")\n app.setLabelTooltip(\"num_saved_2\", \"Tracks number of saved lines\")\n app.addButton(\"Clear\", clear_save)\n #app.setButtonSticky(\"Clear\", \"left\")\n app.stopTab()\n app.stopTabbedFrame()\n \n \n\n \n\n \n\n\n\n \n \n\n"
}
] | 1 |
andrewlaikh/NLPLawGUI
|
https://github.com/andrewlaikh/NLPLawGUI
|
0f2bb4ad6fb019435b8f3d1794d551543e8d2ba5
|
70fe55cc48103c698693dac752c3a6b981b8d5c8
|
e8742e04a1badd89f412f5883faadc3da00dfc60
|
refs/heads/master
| 2023-04-16T00:21:00.563951 | 2020-09-13T05:35:05 | 2020-09-13T05:35:05 | 282,791,459 | 0 | 0 | null | null | null | null | null |
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"path": "/requirements.txt",
"repo_name": "andrewlaikh/NLPLawGUI",
"src_encoding": "UTF-8",
"text": "streamlit \npyvis \nnetworkx\n"
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"path": "/source/main.py",
"repo_name": "andrewlaikh/NLPLawGUI",
"src_encoding": "UTF-8",
"text": "import os \nimport re\nimport streamlit as st\nimport streamlit.components.v1 as components\nfrom source.graph import Graph\nfrom source.layout import local_css\nfrom source.layout import set_block_container_style\n\nos.chdir(r\"/app/source\")\nprint('current location is: ' + os.getcwd())\n\ngraph = Graph()\n\nst.title(\"EU Legal documents visualised\")\nst.markdown(\"This application helps to **visualise EU documents** and the **relevant provisions cited **.\")\noption = st.selectbox('Select one piece of lex to visualise: ', ('', 'Example 1', 'Example 2', 'Example 3'))\nif option:\n num = re.search(r\"\\d\", option).group(0)\n graph.createGraph(num)\n graph.defaultGraphText = graph.returnGraphText(num)\ncomponents.html(graph.defaultGraph, width=1000, height=550)\nset_block_container_style()\nst.markdown('**The text below shows relevant sections highlighted by a BERT model.**')\nlocal_css(\"style.css\")\nst.markdown(r'<div>' + graph.defaultGraphText + r'</div>', unsafe_allow_html=True)\n"
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"path": "/source/layout.py",
"repo_name": "andrewlaikh/NLPLawGUI",
"src_encoding": "UTF-8",
"text": "import streamlit as st\n\n\ndef local_css(file_name):\n with open(file_name) as f:\n st.markdown('<style>{}</style>'.format(f.read()), unsafe_allow_html=True)\n\n\ndef set_block_container_style(\n max_width: int = 1000,\n max_width_100_percent: bool = False,\n padding_top: int = 5,\n padding_right: int = 0,\n padding_left: int = 0,\n padding_bottom: int = 10,\n):\n if max_width_100_percent:\n max_width_str = f\"max-width: 100%;\"\n else:\n max_width_str = f\"max-width: {max_width}px;\"\n st.markdown(\n f\"\"\"\n<style>\n .reportview-container .main .block-container{{\n {max_width_str}\n padding-top: {padding_top}rem;\n padding-right: {padding_right}rem;\n padding-left: {padding_left}rem;\n padding-bottom: {padding_bottom}rem;\n }}\n .reportview-container .main {{\n color: {\"black\"};\n background-color: {\"#fff\"};\n }}\n</style>\n\"\"\",\n unsafe_allow_html=True,\n )\n"
},
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"language": "Python",
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"num_lines": 104,
"path": "/source/htmlParser.py",
"repo_name": "andrewlaikh/NLPLawGUI",
"src_encoding": "UTF-8",
"text": "# Utility parser to 1. create string for prediction for NER 2. process prediction into HTML for streamlit\nimport string\nfrom bs4 import BeautifulSoup\n\n\ndef findText(fileName):\n soup = open(fileName, \"r\", encoding=\"UTF-8\")\n soup = BeautifulSoup(soup, from_encoding=\"UTF-8\")\n if not soup.find('div', attrs={'class': 'texte'}):\n return\n else:\n text = soup.find('div', attrs={'class': 'texte'})\n return str(text)\n\n\ndef saveParaMarks(text):\n paraList = []\n for line in text.splitlines():\n paraStartPrefix = (r'<p>', r'</p>')\n if line.startswith(paraStartPrefix):\n if line.startswith(r'<p>'):\n paraList.append([r'<p>', r'</p>'])\n else:\n paraList.append([r'</p>'])\n return paraList\n\n\ndef preprocessText(text):\n outputList = []\n text = text.replace(r'<p>', '')\n text = text.replace(r'</p>', '')\n puncList = string.punctuation.replace(\"/\", '').replace(\"(\", \"\").replace(\")\", \"\").replace(\",\", \"\")\n text.translate(str.maketrans('', '', puncList))\n for line in text.splitlines():\n if line.startswith('<div class') or line.startswith('</div>'):\n continue\n output = []\n line = line.strip()\n line = line.replace(' +', ' ')\n wordsInLine = line.split(' ')\n for word in wordsInLine:\n if not word.isspace() and not word == '':\n output.append(word)\n outputList.append(output)\n return outputList\n\n\ndef createString(textList):\n outputString = ''\n for individualList in textList:\n for item in individualList:\n outputString += item + ' '\n return outputString\n\n\ndef markString(predictionList, textList):\n blueStartSpan = r\"<span class='highlight blue'>\"\n greenStartSpan = r\"<span class='highlight green'>\"\n redStartSpan = r\"<span class='highlight red'>\"\n endSpan = r\"</span>\"\n bigOutputList = []\n smallerList = predictionList[0]\n predictionListCounter = 0\n for textIndivList in textList:\n smallOutputList = []\n for textIndivItem in textIndivList:\n value = list(smallerList[predictionListCounter].values())[0]\n predictionListCounter += 1\n if value == \"Context\":\n textIndivItem = blueStartSpan + textIndivItem + endSpan\n elif value == \"Legislation\":\n textIndivItem = greenStartSpan + textIndivItem + endSpan\n elif value == \"Provision\":\n textIndivItem = redStartSpan + textIndivItem + endSpan\n smallOutputList.append(textIndivItem)\n bigOutputList.append(smallOutputList)\n return bigOutputList\n\n\ndef flattenHtmlAndProcessedText(modList, paraList):\n outputString = ''\n for indivModList, indivParaList in zip(modList, paraList):\n if len(indivParaList) > 1:\n outputString += r'<p>' + \" \".join(indivModList) + r'</p>'\n else:\n outputStirng += paraList[0] + \" \".join(indivModList)\n outputString += r'<p/>'\n return outputString\n\n\nrawText = findText('raw1text.html')\nparaList = saveParaMarks(rawText)\nprint(paraList)\n# assert len(paraList) == 170\noutputList = preprocessText(rawText)\nprint(outputList)\noutputString = createString(outputList)\nprint(outputString)\n# print(outputString)\n# modList = markString(prediction, outputList)\n# modOutput = flattenHtmlAndProcessedText(modList, paraList)\n# htmlOutput = open(\"html2Text.html\", \"w\", errors=\"ignore\")\n# htmlOutput.write(modOutput)\n# htmlOutput.close()\n"
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"repo_name": "andrewlaikh/NLPLawGUI",
"src_encoding": "UTF-8",
"text": "<p>Commission Decision</p><p/><p>of 6 July 2000</p><p/><p>amending <span class='highlight green'>Decision</span> <span class='highlight green'>1999/710/EC</span> on drawing up provisional lists of third country establishments from which the Member States authorise imports of minced meat and meat preparations</p><p/><p>(notified under document number C(2000) 1846)</p><p/><p>(Text with EEA relevance)</p><p/><p><span class='highlight green'>(2000/430/EC)</span></p><p/><p></p><p/><p>THE COMMISSION OF THE EUROPEAN COMMUNITIES,</p><p/><p>Having regard to the <span class='highlight green'>Treaty</span> <span class='highlight green'>establishing</span> <span class='highlight green'>the</span> <span class='highlight green'>European</span> <span class='highlight green'>Community,</span></p><p/><p>Having regard to Council <span class='highlight green'>Decision</span> <span class='highlight green'>95/408/EC</span> of 22 June 1995 on the conditions for drawing up, for an interim period, provisional lists of third country establishments from which Member States are authorised to import certain products of animal origin, fishery products or live bivalve molluscs(1), as amended by <span class='highlight green'>Decision</span> <span class='highlight green'>98/603/EC(2),</span> and in particular <span class='highlight red'>Article</span> <span class='highlight red'>2(1)</span> and <span class='highlight red'>Article</span> <span class='highlight red'>7</span> thereof,</p><p/><p>Whereas:</p><p/><p>(1) A provisional list of establishments producing minced meat and meat preparations has been drawn up by Commission <span class='highlight green'>Decision</span> <span class='highlight green'>1999/710/EC(3).</span></p><p/><p>(2) Romania has sent a list of establishments producing minced meat and meat preparations and for which the responsible authorities certify that the establishments are in accordance with the Community rules.</p><p/><p>(3) A provisional list of establishments producing minced meat and meat preparations can thus be drawn up for Romania in accordance with the procedure laid down in Council <span class='highlight green'>Decision</span> <span class='highlight green'>95/408/EC</span> in respect of certain countries.</p><p/><p>(4) The measures provided for in this Decision are in accordance with the opinion of the Standing Veterinary Committee,</p><p/><p>HAS ADOPTED THIS DECISION:</p><p/><p></p><p/><p>Article 1</p><p/><p>The text of the Annex to this Decision is added to the Annex to <span class='highlight green'>Decision</span> <span class='highlight green'>1999/710/EC.</span></p><p/><p></p><p/><p>Article 2</p><p/><p>This Decision is addressed to the Member States.</p><p/><p></p><p/><p>Done at Brussels, 6 July 2000.</p><p/><p></p><p/><p>For the Commission</p><p/><p>David Byrne</p><p/><p>Member of the Commission</p><p/><p></p><p/><p>(1) OJ L 243, 11.10.1995, p. 17.</p><p/><p>(2) OJ L 289, 28.10.1998, p. 36.</p><p/><p>(3) OJ L 281, 4.11.1999, p. 82.</p><p/><p></p><p/><p></p><p/><p>ANEXO/BILAG/ANHANG/ΠΑΡΑΡΤΗΜΑ/ANNEX/ANNEXE/ALLEGATO/BIJLAGE/ANEXO/LIITE/BILAGA</p><p/><p></p><p/><p></p><p/><p></p><p/><p></p><p/><p></p><p/><p></p><p/>"
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"language": "Python",
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"path": "/source/graph.py",
"repo_name": "andrewlaikh/NLPLawGUI",
"src_encoding": "UTF-8",
"text": "import streamlit as st\nfrom pyvis.network import Network\nimport os\nimport networkx as nx\nimport itertools\n\n\n# Can try to fix for session specific ID later, but leave it for now.\[email protected](allow_output_mutation=True)\nclass Graph:\n def __init__(self):\n self.defaultGraph = self.returnGraph(2)\n self.defaultGraphText = self.returnGraphText(2)\n\n def returnGraph(self, num):\n fileName = 'example' + str(num) + r'.html'\n with open(fileName, 'r') as file:\n text = file.read()\n text = text.replace('border: 1px', 'border: 0px')\n return text\n\n def createGraph(self, num):\n self.defaultGraph = self.returnGraph(num)\n\n def returnGraphText(self, num):\n fileName = 'html' + str(num) + r'Text.html'\n with open(fileName, 'r', errors='ignore') as file:\n text = file.read()\n text = text.replace('border: 1px', 'border: 0px')\n text = text.lower()\n for line in text.splitlines():\n line = line.strip()\n with open(\"copy.txt\", \"w\") as file:\n file.write(text)\n return text\n"
}
] | 6 |
metalerk/boletia-broker
|
https://github.com/metalerk/boletia-broker
|
ca564cc13d52fbbe12a504e152e6400160fad943
|
6485cf9588cf8b520e2e41d3fd295ea0841c0ec8
|
26c8ce95c94932ba0e0765df119c8d9051478cb0
|
refs/heads/master
| 2021-01-21T07:20:57.029984 | 2017-05-17T19:49:54 | 2017-05-17T19:49:54 | 91,612,337 | 0 | 0 | null | null | null | null | null |
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"text": "from flask import Flask\nfrom flask import jsonify\nfrom flask_restful import Api\nfrom flask_restful import Resource\nfrom api.messages import Message\nfrom flask_cors import CORS\n\napp = Flask(__name__)\napi = Api(app)\nCORS(app, methods=[\"GET\", \"HEAD\", \"POST\", \"OPTIONS\", \"PUT\", \"PATCH\", \"DELETE\"])\n\napi.add_resource(Message, '/message')\n\nif __name__ == '__main__':\n app.run(host=\"0.0.0.0\", debug=False)\n"
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"path": "/api/messages.py",
"repo_name": "metalerk/boletia-broker",
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"text": "from flask import request\nfrom flask_restful import Api\nfrom flask_restful import Resource\nfrom api.watson import Conversation\n\nclass Message(Resource):\n\n def post(self):\n\n cors_domains = \"*\"\n\n response_msg = {\n \"msg\": \"\",\n \"error\": False\n }\n\n try:\n req = request.json\n msg = req['message']\n watson_client = Conversation()\n res = watson_client.send_message(message=msg)\n\n if res['output']['text'].__len__() > 0:\n response_msg['msg'] = res['output']['text'][0]\n\n return response_msg, 200, {'Access-Control-Allow-Origin': cors_domains}\n\n except Exception as err:\n response_msg['msg'] = err.__str__()\n response_msg['error'] = True\n return response_msg, 400, {'Access-Control-Allow-Origin': cors_domains}\n"
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"num_lines": 10,
"path": "/test_watson.py",
"repo_name": "metalerk/boletia-broker",
"src_encoding": "UTF-8",
"text": "import unittest\nfrom api.watson import Conversation\n\nclass TestMessage(unittest.TestCase):\n def test_watson(self):\n\n c = Conversation()\n res = c.send_message(message=\"\")\n\n print(res['output']['text'])\n"
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"path": "/api/watson.py",
"repo_name": "metalerk/boletia-broker",
"src_encoding": "UTF-8",
"text": "from watson_developer_cloud import ConversationV1\nimport os\n\n\nclass Conversation:\n def __init__(self):\n\n self.conversation = ConversationV1(\n username=os.environ['WATSON_SERVICE_USER'],\n password=os.environ['WATSON_SERVICE_PASSWORD'],\n version='2016-09-20')\n\n self.workspace_id = os.environ['WATSON_WORKSPACE_ID']\n\n def send_message(self, message):\n\n self.response = self.conversation.message(workspace_id=self.workspace_id, message_input={'text': message})\n return self.response if self.response else {}\n"
}
] | 4 |
039710/Sentiment-Analysis
|
https://github.com/039710/Sentiment-Analysis
|
edf00209c830702ed31a40c6f9ba834f83f39fe1
|
b1eed28441a469f696fc80b8cdae49cc97ab644a
|
2be0449d40095cc3927af683a8b3f522bb187507
|
refs/heads/master
| 2021-07-05T07:38:49.223005 | 2020-11-09T02:57:29 | 2020-11-09T02:57:29 | 196,943,618 | 1 | 0 | null | null | null | null | null |
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"max_line_length": 65,
"num_lines": 29,
"path": "/sentiment.py",
"repo_name": "039710/Sentiment-Analysis",
"src_encoding": "UTF-8",
"text": "from sentiment_logic import preprocessing_input,predict_input\nfrom flask import Flask, render_template, request\n\napp = Flask(__name__)\nsameInput = 0\nisinya =\"\"\n\[email protected](\"/\")\ndef home():\n return render_template(\"index.html\")\n\[email protected](\"/get\")\ndef get_review_response():\n global sameInput\n global isinya\n inputText = request.args.get('msg')\n if (inputText == ''):\n life = False\n return str('Please input the reviewe text to predict')\n if(inputText!=isinya):\n isinya = inputText\n sameInput=0\n return str(predict_input(preprocessing_input(inputText)))\n elif (inputText==isinya):\n sameInput += 1\n return(predict_input(preprocessing_input(inputText)))\n\nif __name__ == \"__main__\":\n app.run()\n"
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"path": "/sentiment_logic.py",
"repo_name": "039710/Sentiment-Analysis",
"src_encoding": "UTF-8",
"text": "import re\nimport string\nimport sklearn\nimport pickle\nimport csv\nfrom csv import DictReader\nfrom nltk.stem import PorterStemmer\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import sent_tokenize, word_tokenize\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer, TfidfTransformer\nfrom sklearn.model_selection import train_test_split\n\n# Load Model\nwith open('model_svm', 'rb') as file:\n model = pickle.load(file)\n\nstop_words = set(stopwords.words('english'))\nps = PorterStemmer()\n\ndef preprocessing(dataset):\n for row in dataset:\n row['review'] = row['review'].casefold()\n row['review'] = re.sub('[^A-Za-z0-9 ]+','', row['review'])\n\n word_tokens = word_tokenize(row['review']) \n\n filtered_sentence = [w for w in word_tokens if not w in stop_words]\n\n for w in word_tokens: \n if w not in stop_words: \n filtered_sentence.append(w)\n\n row['review'] = \" \".join(filtered_sentence)\n\n stop_sentence = [] \n\n for v in filtered_sentence:\n stop_sentence.append(ps.stem(v))\n\n row['review'] = \" \".join(stop_sentence)\n \n\n\n\ndataset = []\nwith open ('data2.csv') as csvfile:\n readCSV = csv.DictReader(csvfile, delimiter=',')\n for row in readCSV:\n rating = row['Rating']\n intent = \"\"\n if rating == \"1\" or rating == \"2\":\n intent = \"Bad\"\n dataset.append(\n {\n 'review': row ['Review Text'],\n 'intent': intent\n }\n )\n elif rating == \"4\" or rating == \"5\" or rating == \"3\":\n intent = \"Good\"\n dataset.append(\n {\n 'review': row ['Review Text'],\n 'intent': intent\n }\n )\n \n \n \n#Remove review text without rating\nfor row in dataset:\n if row['review'] == '' or row['intent'] == '':\n dataset.remove(row)\n \npreprocessing(dataset)\n\n\n\n\ndatatrain, datatest = train_test_split(dataset, test_size=0.4, random_state=40)\nxtrain_counts = CountVectorizer().fit_transform([x['review'] for x in datatrain])\nxtrain_tfidf = TfidfTransformer().fit_transform(xtrain_counts)\ntext_clf_svm = Pipeline([('vect', CountVectorizer()),\n ('tfidf', TfidfTransformer()),\n ('clf-svm', SVC(C=1.0, kernel='linear', gamma=0.5,probability=True)),])\n\ntext_clf_svm = text_clf_svm.fit([row['review'] for row in datatrain], [row['intent'] for row in datatrain])\n\npredicted_svm = text_clf_svm.predict(row['review'] for row in datatest)\n\n#PREPROCESS INPUT\n\n\ndef preprocessing_input(input_text):\n input_text = input_text.casefold()\n input_text = re.sub('[^A-Za-z0-9 ]+','', input_text)\n filtered_sentence = []\n word_tokens = word_tokenize(input_text) \n for w in word_tokens: \n if w not in stop_words: \n filtered_sentence.append(w)\n input_text= \" \".join(filtered_sentence)\n \n return input_text\n\n#Predict\ndef predict_input(input_text):\n print(\"After preprocessing : \",preprocessing_input(input_text))\n pred = text_clf_svm.predict([preprocessing_input(input_text)])\n decision = text_clf_svm.predict_proba([preprocessing_input(input_text)])\n print()\n print(\"Result : \" ,pred)\n print(\"---Bad---- \",\"---Good---\")\n print(decision)\n result = pred \n result = str(\"Predicted as \") + str(result) + str(\" review with probability \")+ str(\" Bad / Good : \") + str(decision)\n \n return result\n\npredict_input(\"very bad,uncomfortable, cheap quality\")"
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"max_line_length": 112,
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"repo_name": "039710/Sentiment-Analysis",
"src_encoding": "UTF-8",
"text": "# Sentiment-Analysis BOT\nSentiment analysis project using dataset from Kaggle.\nThe project build with python and flask for the UI.\nSentiment analysis based on supervised machine learning.\n\n# How to run\n- go to directory where sentiment.py is saved.\n- type : python sentiment.py\n- wait until this text shown on the console\n * Serving Flask app \"sentiment\" (lazy loading)\n * Environment: production\n WARNING: Do not use the development server in a production environment.\n Use a production WSGI server instead.\n * Debug mode: off\n * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n \n \n\n - Open a Browser and insert the url http://127.0.0.1:5000/.\n - Input some reviews text on the text field \n - Click send button to get the analysis.\n\n"
}
] | 3 |
Palkovsky/BasicRecomendationSystem
|
https://github.com/Palkovsky/BasicRecomendationSystem
|
a4f39171fa7448967d3b267fb43d04df8cb91f71
|
e9a492a8412b951f0f8740269f2733c3b1462457
|
1f9f9785ae4d4cfb7873f0cff775f93678eb270f
|
refs/heads/master
| 2015-07-11T19:52:50 | 2015-05-28T12:15:01 | 2015-05-28T12:15:01 | 36,435,544 | 1 | 0 | null | null | null | null | null |
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"repo_name": "Palkovsky/BasicRecomendationSystem",
"src_encoding": "UTF-8",
"text": "Sketch for building bigger recommendation system.\n"
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"path": "/recomendations.py",
"repo_name": "Palkovsky/BasicRecomendationSystem",
"src_encoding": "UTF-8",
"text": "\t\n# This method returns n people who are most similar to you\ndef topMatches(prefs,person,n=5,similarity=sim_pearson):\n\t\n\tscores=[(similarity(prefs,person,other),other)\n\t\tfor other in prefs if other!=person]\n\t\t\n\t# Sort the list so the highest scores appear at the top\n\tscores.sort()\n\tscores.reverse()\n\treturn scores[0:n]\n\t\ndef getRecommendations(prefs,person,similarity=sim_pearson):\n\ttotals={}\n\tsimSums={}\n\tfor other in prefs:\n\t\t# don't compare me to myself\n\t\tif other==person: continue\n\t\tsim=similarity(prefs,person,other)\n\t\t# ignore scores of zero or lower\n\t\tif sim<=0: continue\n\t\tfor item in prefs[other]:\n\t\t# only score movies I haven't seen yet\n\t\t\tif item not in prefs[person] or prefs[person][item]==0:\n\t\t\t\t# Similarity * Score\n\t\t\t\ttotals.setdefault(item,0)\n\t\t\t\ttotals[item]+=prefs[other][item]*sim\n\t\t\t\t# Sum of similarities\n\t\t\t\tsimSums.setdefault(item,0)\n\t\t\t\tsimSums[item]+=sim\n\t\t\t\t\t\n\t# Create the normalized list\n\trankings=[(total/simSums[item],item) for item,total in totals.items( )]\n\t# Return the sorted list\n\trankings.sort()\n\trankings.reverse()\n\treturn rankings\n\t\n"
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"text": "from math import sqrt\n\ndef sim_distance(prefs,person1,person2):\n\t# Get the list of shared_items\n\tsi={}\n\tfor item in prefs[person1]:\n\t\tif item in prefs[person2]:\n\t\t\tsi[item]=1\n\t\t\t# if they have no ratings in common, return 0\n\tif len(si)==0: return 0\n\t\t# Add up the squares of all the differences\n\tsum_of_squares=sum([pow(prefs[person1][item]-prefs[person2][item],2)\n\t\t\tfor item in prefs[person1] if item in prefs[person2]])\n\treturn 1/(1+sum_of_squares)\n\t\ndef sim_pearson(prefs,p1,p2):\n\t\n\t# Get the list of mutually rated items\n\tsi={}\n\tfor item in prefs[p1]:\n\t\tif item in prefs[p2]: si[item]=1\n\t\n\t# Find the number of elements\n\tn=len(si)\n\t# if they are no ratings in common, return 0\n\tif n==0: return 0\n\t\n\t# Add up all the preferences\n\tsum1=sum([prefs[p1][it] for it in si])\n\tsum2=sum([prefs[p2][it] for it in si])\n\t\n\t# Sum up the squares\n\tsum1Sq=sum([pow(prefs[p1][it],2) for it in si])\n\tsum2Sq=sum([pow(prefs[p2][it],2) for it in si])\n\t\n\t# Sum up the products\n\tpSum=sum([prefs[p1][it]*prefs[p2][it] for it in si])\n\t\n\t# Calculate Pearson score\n\tnum=pSum-(sum1*sum2/n)\n\tden=sqrt((sum1Sq-pow(sum1,2)/n)*(sum2Sq-pow(sum2,2)/n))\n\t\n\tif den==0: return 0 # Protection from dividing by 0\n\t\n\tr=num/den\n\t\n\treturn r\n"
}
] | 3 |
nbroadbent/Python-Assignments
|
https://github.com/nbroadbent/Python-Assignments
|
fd303743caac1a47a1f9f0629a688fbfcc95f810
|
5a69bdda638cb91eb3d0b6a117754c339a43793a
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| 2021-08-06T21:08:44.266230 | 2017-11-07T04:54:43 | 2017-11-07T04:54:43 | 108,705,584 | 0 | 0 | null | null | null | null | null |
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"text": "# Family name: Nicholas Broadbent\n# Student number: 8709720\n# Course: IT1 1120 \n# Assignment Number 5\n\nclass Point:\n 'class that represents a point in the plane'\n\n def __init__(self, xcoord=0, ycoord=0):\n ''' (Point,number, number) -> None\n initialize point coordinates to (xcoord, ycoord)'''\n self.x = xcoord\n self.y = ycoord\n\n def setx(self, xcoord):\n ''' (Point,number)->None\n Sets x coordinate of point to xcoord'''\n self.x = xcoord\n\n def sety(self, ycoord):\n ''' (Point,number)->None\n Sets y coordinate of point to ycoord'''\n self.y = ycoord\n\n def get(self):\n '''(Point)->tuple\n Returns a tuple with x and y coordinates of the point'''\n return (self.x, self.y)\n\n def move(self, dx, dy):\n '''(Point,number,number)->None\n changes the x and y coordinates by dx and dy'''\n self.x += dx\n self.y += dy\n\n def __eq__(self, other):\n '''(Point,Point)->bool\n Returns True if self and other have the same coordinates'''\n return self.x == other.x and self.y == other.y\n def __repr__(self):\n '''(Point)->str\n Returns canonical string representation Point(x, y)'''\n return 'Point('+str(self.x)+','+str(self.y)+')'\n def __str__(self):\n '''(Point)->str\n Returns nice string representation Point(x, y).\n In this case we chose the same representation as in __repr__'''\n return 'Point('+str(self.x)+','+str(self.y)+')'\n\nclass Rectangle(Point):\n 'class that represents a rectangle in a plane'\n\n def __init__(self, p1, p2, colour):\n '''(Rectangle, number, number, str)->None\n \n Initialise rectangle.\n '''\n \n self.p1 = p1\n self.p2 = p2\n self.colour = colour\n\n def __eq__(self, y):\n '''(Rectangle, Rectangle)->bool\n\n Returns true if both rectangles are equal\n '''\n \n if self.p1 == y.p1 and self.p2 == y.p2 and self.colour == y.colour:\n return True\n return False\n\n def __str__(self):\n '''(Rectangle)->str\n\n Returns a nice string of rectangle\n '''\n \n return 'I am a '+self.colour+' rectangle with bottom left corner at ('+str(self.p1.x)+', '+str(self.p1.y)+') and top right corner at ('+str(self.p2.x)+', '+str(self.p2.y)+').'\n\n def __repr__(self):\n '''(Rectangle)->str\n\n Returns string of object\n '''\n \n return \"Rectangle(\"+str(self.p1)+\",\"+str(self.p2)+\",'\"+self.colour+\"')\"\n \n def get_bottom_left(self):\n '''(Rectangle)->Point\n\n Returns bottom-left point.\n '''\n\n # Return the point\n return self.p1\n\n def get_top_right(self):\n '''(Rectangle)->Point\n\n Returns top-right point.\n '''\n\n # Return the point\n return self.p2\n\n def get_color(self):\n '''(Rectangle)->str\n\n Returns colour or rectangle.\n '''\n\n # Return the colour\n return self.colour\n\n def reset_color(self, colour):\n '''(Rectangle, str)->None\n\n Sets colour of rectangle\n '''\n\n # Set the colour\n self.colour = colour\n\n def get_perimeter(self):\n '''(Rectangle)->number\n\n Returns perimeter of rectangle\n '''\n\n # Return perimeter\n return (self.p2.x - self.p1.x)*2 + (self.p2.y - self.p1.y)*2\n \n def get_area(self):\n '''(Rectangle)->number\n\n Returns area of rectangle.\n '''\n\n # Return Area\n return (self.p2.x - self.p1.x) * (self.p2.y - self.p1.y)\n \n def move(self, x, y):\n '''(Rectangle, number, number)->None\n\n Moves Rectangle.\n '''\n \n # Point 1\n self.p1.move(x, y)\n \n # Point 2\n self.p2.move(x, y)\n\n def intersects(self, r):\n '''(Rectangle, Rectangle)\n\n This function returns true if the two rectangles intersect\n and false otherwise.\n '''\n\n # Check if rectangle intersects other rectangle\n if self.p1.x <= r.p2.x and r.p1.x <= self.p2.x:\n if self.p1.y <= r.p2.y and r.p1.y <= self.p2.y:\n # The rectangles intersect\n return True\n # The rectangles do not intersect\n return False\n\n ''' Over thought version\n def intersects2(self, r, n = False):\n '(Rectangle, Rectangle, [bool])->bool'\n if self.p1.x <= r.p1.x <= self.p2.x or self.p1.x <= r.p2.x <= self.p2.x:\n # Check for corners inside\n if self.p1.y <= r.p1.y <= self.p2.y or self.p1.y <= r.p2.y <= self.p2.y:\n return True\n # Check for vertical intersect\n elif r.p1.y <= self.p1.y and self.p2.y <= r.p2.y:\n return True\n if self.p1.y <= r.p1.y <= self.p2.y or self.p1.y <= r.p2.y <= self.p2.y:\n # Check for horizontal intersect\n if r.p1.x <= self.p1.x and self.p2.x <= r.p2.x:\n return True\n \n # Check other corners and return\n if not(n):\n r = Rectangle(Point(r.p1.x, r.p1.y), Point(r.p2.x, r.p2.y), \"red\")\n return self.intersects(r, True)\n return False\n '''\n \n def contains(self, x, y):\n '''(Rectangle, number, number)->bool\n\n Returns true if rectangle contains the point with coordinates\n at x and y.\n '''\n\n if self.p1.x <= x <= self.p2.x and self.p1.y <= y <= self.p2.y:\n # Rectangle contains point\n return True\n # Rectangle does not contain point\n return False\n \nclass Canvas(Rectangle):\n 'class that represents the plane'\n\n def __init__(self):\n '(Canvas)-None'\n self.rects = []\n\n def __len__(self):\n '''(Canvas)-int\n\n Returns the number of rectangles on canvas\n '''\n \n return len(self.rects)\n\n def __repr__(self):\n '''(Canvas)->str\n\n Returns a string of object\n '''\n\n return 'Canvas('+str(self.rects)+')'\n \n def add_one_rectangle(self, r):\n '''(Canvas, Rectangle)->None\n\n Adds a rectangle to the canvas\n '''\n \n self.rects.append(r)\n \n def count_same_color(self, colour):\n '''(Canvas, str)->int\n\n Returns the number of rectangles with colour, colour\n '''\n \n num = 0\n\n # loop through list checking for same colour\n for i in self.rects:\n if i.colour == colour:\n num += 1\n return num\n \n def total_perimeter(self):\n '''(Canvas)->int\n\n Returns the total perimeter of each rectangle\n '''\n\n total = 0\n\n # add the perimeters of each rectangle on canvas\n for i in self.rects:\n total += i.get_perimeter()\n return total\n\n def min_enclosing_rectangle(self):\n '''(Canvas)->Rectangle\n\n Returns the smallest rectangle that can enclose all rectangles on the canvas.\n '''\n\n # Starting values\n min_point = [self.rects[0].p1.x, self.rects[0].p1.y]\n max_point = [self.rects[0].p2.x, self.rects[0].p2.y]\n \n for i in self.rects:\n # Check bottom-left point\n if i.p1.x < min_point[0]:\n min_point[0] = i.p1.x\n if i.p1.y < min_point[1]:\n min_point[1] = i.p1.y \n # Check top-right point\n if i.p2.x > max_point[0]:\n max_point[0] = i.p2.x\n if i.p2.y > max_point[1]:\n max_point[1] = i.p2.y\n return Rectangle(Point(min_point[0], min_point[1]), Point(max_point[0], max_point[1]), 'red') \n \n def common_point(self):\n '''(Canvas)->bool\n\n Returns true if there's a common point between all rectangles on canvas\n '''\n\n # Look for common point\n for i in range(len(self.rects)):\n for j in range(i, len(self.rects)):\n if not(self.rects[i].intersects(self.rects[j])):\n return False\n return True\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n"
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"text": "# Family name: Nicholas Broadbent\r\n# Course: IT1 1120 \r\n# Assignment Number 4\r\n\r\ndef ap4(m):\r\n ''' (2d_list)-> 2d_list\r\n\r\n This function takes in a 2d list as input and searches\r\n for an arithmetic progression containing at least 4 numbers,\r\n and returns 4 locations in the matrix of one such sequence.\r\n\r\n If sequence found, returns 2d list.\r\n If sequence not found, returns empty 1d list.\r\n\r\n Precondition: m is a matrix (m will not have rows with different number of elements)\r\n '''\r\n\r\n vert = False\r\n hor = False\r\n\r\n # If the matrix has 4 or more columns then check horizontal\r\n if len(m[0]) >= 4:\r\n hor = True\r\n # If the matrix has 4 or more rows then check vertical\r\n if len(m) >= 4:\r\n vert = True\r\n\r\n # Check all directions\r\n if (vert and hor):\r\n for i in range(len(m)):\r\n for j in range(len(m[i])-3):\r\n # Check horizontal\r\n if m[i][j]-m[i][j+1] == m[i][j+1] - m[i][j+2] == m[i][j+2]-m[i][j+3]:\r\n # Arithmetic progression found\r\n return print_indices(i, j)\r\n # Check diagonal\r\n if i < len(m)-3:\r\n # Top-left to bottom-right\r\n if m[i][j]-m[i+1][j+1] == m[i+1][j+1]-m[i+2][j+2] == m[i+2][j+2]-m[i+3][j+3]:\r\n # Arithmetic progression found\r\n return print_indices(i, j, 3)\r\n # Top-right to bottom-left\r\n if m[i][j+3]-m[i+1][j+2] == m[i+1][j+2]-m[i+2][j+1] == m[i+2][j+1]-m[i+3][j]:\r\n return print_indices(i, j+3, 2)\r\n # Check vertical\r\n if i < len(m)-3:\r\n for j in range(len(m[i])):\r\n if m[i][j]-m[i+1][j] == m[i+1][j] - m[i+2][j] == m[i+2][j]-m[i+3][j]:\r\n # Arithmetic progression found\r\n return print_indices(i, j, 1)\r\n # Check horizontal only\r\n elif hor:\r\n for i in range(len(m)):\r\n for j in range(len(m[i])-3):\r\n if m[i][j]-m[i][j+1] == m[i][j+1] - m[i][j+2] == m[i][j+2]-m[i][j+3]:\r\n # Arithmetic progression found\r\n return print_indices(i, j)\r\n # Check vertical only\r\n elif vert:\r\n for i in range(len(m)-3):\r\n for j in range(len(m[i])):\r\n if m[i][j]-m[i+1][j] == m[i+1][j] - m[i+2][j] == m[i+2][j]-m[i+3][j]:\r\n # Arithmetic progression found\r\n return print_indices(i, j, 1)\r\n # No arithmetic progression patterns found. Return empty list\r\n return []\r\n \r\n\r\ndef print_indices(i, j, direction = 0):\r\n ''' (int, int, [int])-> 2d_list\r\n\r\n Takes in the starting index of a 2d list and prints 4 indices\r\n from starting index, vertically, horizontally or diagonally.\r\n\r\n If no 3rd argument passed, the function assumes horizontally.\r\n\r\n Otherwise:\r\n direction is 1 for vertical and 2 for diagonal (bottom-left to top-right)\r\n and any integer for diagonal (top-left to bottom-right).\r\n '''\r\n \r\n if direction == 0:\r\n return [[i, j+k] for k in range(4)]\r\n elif direction == 1:\r\n return [[i+k, j] for k in range(4)]\r\n elif direction == 2:\r\n return [[i+k, j-k] for k in range(4)]\r\n else:\r\n return [[i+k, j+k] for k in range(4)]\r\n"
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"text": "# Family name: Nicholas Broadbent\r\n# Course: IT1 1120 \r\n# Assignment Number 4\r\n\r\ndef valid_input(x):\r\n ''' (str)->str\r\n\r\n This function takes in a string as input and checks if it is valid input\r\n (a number or it is Q or q) and returns it if it's valid. Otherwise it will\r\n keep asking for input.\r\n '''\r\n if str(x).isdigit():\r\n if 0 < int(x) < 7:\r\n return x\r\n print(\"\\nInvalid input, try again\\n\")\r\n elif x == 'Q' or x == 'q':\r\n return x\r\n else:\r\n print(\"\\nIf you answer is not Q or q, then it must be an integer.\\nInvalid input, try again\\n\")\r\n \r\n return valid_input(input(\"Answer (1, 2, 3, 4, 5, 6, Q or q): \"))\r\n\r\ndef menu():\r\n ''' (None)->None\r\n\r\n Prints a menu to the user, and calls the right functions to do what the user wants.\r\n '''\r\n \r\n books = []\r\n f = []\r\n \r\n while True:\r\n # Print menu\r\n print(\"===================================================\")\r\n print(\"What would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\")\r\n print(\"1: Look up year range\")\r\n print(\"2: Look up month/year\")\r\n print(\"3: Search for author\")\r\n print(\"4: Search for title\")\r\n print(\"5: Number of authors with at least x bestsellers\")\r\n print(\"6: List y authors with the most bestsellers\")\r\n print(\"Q: Quit\")\r\n print(\"===================================================\")\r\n\r\n # Get and validate input\r\n choice = valid_input(input(\"Answer (1, 2, 3, 4, 5, 6, Q or q): \"))\r\n\r\n # Check if user wants to quit\r\n if choice == 'Q' or choice == 'q':\r\n break\r\n \r\n # Check if choice is a number\r\n if choice.isdigit():\r\n choice = int(choice)\r\n\r\n # Read file once\r\n if books == []:\r\n books = read__parse_file('bestsellers.txt')\r\n \r\n # Change dates\r\n for i in range(len(books)):\r\n j = books[i][3].strip().split('/')\r\n\r\n # Make months 2 digits\r\n try:\r\n if int(j[0]) < 10:\r\n j[0] = '0'+j[0]\r\n if int(j[1]) < 10:\r\n j[1] = '0'+j[1]\r\n except ValueError:\r\n pass\r\n \r\n # Make date (year-month-day)\r\n books[i][3] = j[2]+'-'+j[0]+'-'+j[1]\r\n \r\n # Sort the list by date\r\n books.sort(key=lambda x: x[3])\r\n\r\n #[print(books[i][3]) for i in range(len(books)-1) if books[i][3][:4] == books[i+1][3][:4] and books[i][3][5:7] == books[i+1][3][5:7]]\r\n\r\n # Do choice\r\n if choice == 1:\r\n find_year_range(books)\r\n elif choice == 2:\r\n find_month_year(books)\r\n elif choice == 3:\r\n search_author(books)\r\n elif choice == 4:\r\n search_title(books)\r\n elif choice == 5:\r\n f = min_num_bestsellers(books, f)\r\n elif choice == 6:\r\n f = y_most_bestsellers(books, f)\r\n\r\n # Wait for user\r\n input(\"\\nPress Enter to continue...\\n\\n\\n\")\r\n\r\n\r\ndef read__parse_file(path):\r\n ''' (str)-> 2D_list\r\n\r\n This function takes in a string, path,\r\n and reads from the file that the path leads to.\r\n \r\n Returns a 2D list of the file where each list is\r\n one line from the file and each element of a list is\r\n parsed by tab in the file.\r\n\r\n Preconditions: path must include the file extension.\r\n '''\r\n return [line.strip().split('\\t') for line in open(path, 'r')]\r\n \r\n# Choice 1\r\ndef find_year_range(books):\r\n ''' (2D_list)-> None\r\n\r\n Prompts the user for two years (a starting year and an ending year),\r\n then display all books which reached the #1 spot between those two years (inclusive).\r\n\r\n Preconditions: books is a sorted list.\r\n '''\r\n start_year = ''\r\n end_year = ''\r\n\r\n #Search algorithm for choice 1\r\n def two_year_search():\r\n ''' (None)->None\r\n\r\n This function does a binary search to find the index of the starting year,\r\n then does an O(k) loop to find and print the books until it reaches\r\n the end year (inclusively).\r\n '''\r\n\r\n # Check years\r\n if start_year > end_year:\r\n print(\"No books found in that range of years.\")\r\n return None\r\n\r\n # Variables\r\n t = 0\r\n b = len(books)\r\n x = b//2\r\n found = False\r\n\r\n # Binary search to find starting index\r\n while True:\r\n if books[x][3][:4] > start_year:\r\n # Ignore the part of the list below books[x-1]\r\n b = x-1\r\n elif books[x][3][:4] < start_year:\r\n #Ignore the part of the list above books[x+1]\r\n t = x+1\r\n elif books[x][3][:4] == start_year:\r\n # Ensure this index is the beginning\r\n if books[x-1][3][:4] == start_year:\r\n b = x-1\r\n else:\r\n # Starting position found!\r\n break\r\n x = (t+b)//2\r\n\r\n # Check for end of search\r\n if t == b:\r\n break\r\n \r\n # Linear search to display books from starting year to end year\r\n while x < len(books):\r\n # Check if we've gone out of year range\r\n if end_year < books[x][3][:4]:\r\n break\r\n print('\\n'+books[x][0] + ', ' + books[x][1] + ' (' + books[x][3] + ')\\n')\r\n x += 1\r\n found = True\r\n\r\n # Check if we found any books\r\n if not(found):\r\n print(\"No books found in that range of years.\")\r\n\r\n # Get years\r\n while True:\r\n start_year = input(\"Enter beginning year: \")\r\n if start_year.isdigit() and len(start_year) == 4:\r\n break\r\n print(\"\\nPlease give a four digit integer for the year.\\n\")\r\n while True:\r\n end_year = input(\"Enter ending year: \")\r\n if end_year.isdigit() and len(end_year) == 4:\r\n break\r\n print(\"\\nPlease give a four digit integer for the year.\\n\")\r\n\r\n # Search\r\n two_year_search()\r\n \r\n# Choice 2\r\ndef find_month_year(books):\r\n ''' (2d_list)- None\r\n\r\n Prompts the user for a month and a year, then prints all of the books\r\n in the 2d list that are found in that month of the year.\r\n '''\r\n \r\n month = ''\r\n year = ''\r\n\r\n # Search algorith for choice 2\r\n def month_year_search():\r\n ''' (None)->None\r\n\r\n This function does a binary search to find the index starting year,\r\n the uses linear search to find and print the books until it reaches\r\n the end year (inclusively).\r\n '''\r\n \r\n t = 0\r\n b = len(books)\r\n x = b//2\r\n found = False\r\n\r\n # Binary search to find starting index\r\n while True:\r\n if books[x][3][:4] > year:\r\n # Ignore the part of the list below books[x-1]\r\n b = x-1\r\n elif books[x][3][:4] < year:\r\n #Ignore the part of the list above books[x+1]\r\n t = x+1\r\n elif books[x][3][:4] == year:\r\n # Ensure this index is the beginning\r\n if books[x][3][5:7] > month:\r\n b = x-1\r\n elif books[x][3][5:7] < month:\r\n #Ignore the part of the list above books[x+1]\r\n t = x+1\r\n elif books[x][3][5:7] == month:\r\n # Check before it for same month\r\n if books[x-1][3][5:7] == month:\r\n b = x-1\r\n else:\r\n # Starting position found!\r\n break\r\n x = (t+b)//2\r\n\r\n # Check for end of search\r\n if t == b:\r\n break\r\n \r\n # Linear search to display books from entire month of year\r\n while x < len(books):\r\n # Check if we've gone out of month range\r\n if month < books[x][3][5:7] or year < books[x][3][:4]:\r\n break\r\n print('\\n'+books[x][0] + ', ' + books[x][1] + ' (' + books[x][3] + ')\\n')\r\n x += 1\r\n found = True\r\n\r\n # Check if we found any books\r\n if not(found):\r\n print(\"No books found in that month of year.\") \r\n \r\n # Get valid month\r\n while True:\r\n month = input('Enter month (as an integer, 1-12): ')\r\n try:\r\n if 12 < int(month) or int(month) < 1:\r\n print('\\nInvalid month. Must be interger, 1-12. Try again\\n')\r\n else:\r\n break\r\n except ValueError:\r\n print('\\nMonth must be an integer.\\nInvalid month. Must be interger, 1-12. Try again\\n')\r\n # Get valid year\r\n while True:\r\n year = input('Enter year: ')\r\n try:\r\n x = int(year)\r\n if len(year) != 4 or int(year) < 0:\r\n print('\\nPlease give a four digit integer for the year.\\n')\r\n else:\r\n break\r\n except ValueError:\r\n print('\\nYear must be an integer.\\nPlease give a four digit integer for the year.\\n')\r\n \r\n if int(month) < 10:\r\n month = '0'+month\r\n \r\n month_year_search() \r\n \r\n#Choice 3\r\ndef search_author(books):\r\n ''' (2D_list)->None\r\n\r\n Prompts the user for a string, then display all books whose\r\n author’s namecontains that string (regardless of case). \r\n '''\r\n found = False\r\n \r\n # Get name to be searched\r\n name = input(\"Enter an author's name (or part of a name): \").lower()\r\n\r\n # Search for name\r\n for i in range(len(books)):\r\n if name in books[i][1].lower():\r\n print('\\n'+books[i][0]+', by ' + books[i][1] + ' (' + books[i][3]+')\\n')\r\n found = True\r\n if not(found):\r\n print('\\nNo books found by an author whose name contains: '+name)\r\n\r\n# Choice 4\r\ndef search_title(books):\r\n ''' (2D_list)->None\r\n\r\n Prompts the user for a string, then display all books whose\r\n title contains that string (regardless of case). \r\n '''\r\n found = False\r\n \r\n # Get title to be searched\r\n title = input(\"Enter a title (or part of a title): \").lower()\r\n \r\n # Search for title\r\n for i in range(len(books)):\r\n if title in books[i][0].lower():\r\n print('\\n'+books[i][0]+', by ' + books[i][1] + ' (' + books[i][3]+')\\n')\r\n found = True\r\n if not(found):\r\n print('\\nNo books found with title that contains: '+title)\r\n \r\n# Choice 5\r\ndef min_num_bestsellers(books, f=[]):\r\n ''' (2D_list, [2D_list])->2D_list\r\n\r\n Prompts the user for a string, then displays all of the books\r\n whose title contains that string (regardless of case).\r\n\r\n Takes up to 2 arguments.\r\n\r\n If only 1 argument is passed, then the function will find the\r\n frequency of authors in the 2d list books. Otherwise, if another\r\n 2d list for f is passed, then the function will use that list as\r\n the frequency list instead of finding it.\r\n\r\n Preconditions:\r\n\r\n f is a 2d list where element 1 and 2 of each list in f\r\n is a string and int, repsectively.\r\n '''\r\n x = 0\r\n\r\n # Get valid input\r\n while True:\r\n x = input(\"Enter an integer bigger than zero: \")\r\n if x.isdigit():\r\n x = int(x)\r\n if x > 0:\r\n break\r\n try:\r\n int(x)\r\n print(\"\\nNumber must be at least one. Try again\\n\")\r\n except ValueError:\r\n print(\"\\nNumber must be an integer.\\nNumber must be at least one. Try again\\n\")\r\n\r\n # Don't find frequency of of authors if we already did it\r\n if f == []:\r\n f = frequency(books)\r\n\r\n # Print authors\r\n print(\"\\nThe list of authors with at least \"+ str(x) +\" NYT bestsellers is:\")\r\n [print(str(i+1)+'.', f[i][0] + ' with ' + str(f[i][1]) + ' bestsellers') for i in range(len(f)) if f[i][1] >= x]\r\n\r\n # Keep the frequency list\r\n return f\r\n\r\n# Choice 6\r\ndef y_most_bestsellers(books, f=[]):\r\n ''' (2D_list)->2D_list\r\n\r\n Takes in a 2d list of books and prompts the user for an integer y\r\n and prints y authors with the most best sellers.\r\n\r\n Optional: second argument, 2d list, frequency: where f = [[authors, frequency]]\r\n\r\n returns a 2d list of authors and their frequency.\r\n '''\r\n y = 0\r\n\r\n # Get valid input\r\n while True:\r\n y = input(\"Enter an integer bigger than zero: \")\r\n if y.isdigit():\r\n y = int(y)\r\n if y > 0:\r\n break\r\n try:\r\n int(y)\r\n print(\"\\nNumber must be at least one. Try again\\n\")\r\n except ValueError:\r\n print(\"\\nNumber must be an integer.\\nNumber must be at least one. Try again\\n\")\r\n\r\n # Don't find frequency of of authors if we already did it\r\n if f == []:\r\n f = frequency(books)\r\n\r\n # Print authors\r\n print('Top ', y,' authors by the number of NYT bestsellers is: \\n')\r\n \r\n for i in range(y):\r\n if i >= len(f):\r\n break\r\n print(str(i+1)+'.', f[i][0])\r\n\r\n # Keep the frequency list\r\n return f\r\n\r\ndef frequency(books):\r\n '''(2D_list)->2D_list\r\n\r\n Counts the frequency of every author in a 2d list\r\n and returns a sorted 2d list of authors and their frequency.\r\n\r\n Sorted from highest to lowest.\r\n\r\n [author, frequency]\r\n '''\r\n \r\n f=[]\r\n\r\n # Find the frequency of authors\r\n for i in range(len(books)):\r\n flag=False\r\n for j in range(len(f)):\r\n if books[i][1].strip() == f[j][0]:\r\n f[j][1] = f[j][1]+1\r\n flag = True\r\n if not(flag):\r\n f.append([books[i][1], 1])\r\n # Let's sort this list by descending order of frequency\r\n f.sort(key=lambda x: x[1], reverse=True)\r\n return f\r\n\r\n# MAIN\r\nmenu()\r\n"
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"text": "# Family name: Nicholas Broadbent\r\n# Student number: 8709720\r\n# Course: IT1 1120 \r\n# Assignment Number 5\r\n\r\n### 1a ###\r\ndef largest_34(a):\r\n '''(list)->int\r\n\r\n Returns the sum of the 3rd and 4th largest values in a list, a.\r\n '''\r\n \r\n l = [0]*4\r\n \r\n for i in range(len(a)):\r\n # Check if current item is larger than the current 4 largest\r\n # Insert item and move others if necessary\r\n if a[i] > l[0]:\r\n l[3] = l[2]\r\n l[2] = l[1]\r\n l[1] = l[0]\r\n l[0] = a[i]\r\n elif a[i] > l[1]:\r\n l[3] = l[2]\r\n l[2] = l[1]\r\n l[1] = a[i]\r\n elif a[i] > l[2]:\r\n l[3] = l[2]\r\n l[2] = a[i]\r\n elif a[i] > l[3]:\r\n l[3] = a[i]\r\n\r\n # Return the sum of the 3rd and 4th largest values\r\n return l[2]+l[3] \r\n\r\n### 1b ###\r\ndef largest_third(a):\r\n '''(list)->int\r\n\r\n Returns the sum of the largest len(a)//3 number of elements in list a\r\n '''\r\n\r\n # Sort this list\r\n # n log n\r\n a.sort(reverse = True)\r\n\r\n # Return the sum\r\n return sum(a[:(len(a)//3)])\r\n\r\n### 1c ###\r\ndef third_at_least(a):\r\n '''(list)->bool\r\n\r\n Return true of false if a value is in the list at least\r\n len(a)//3 + 1 times.\r\n '''\r\n\r\n # Initialise variables\r\n x = len(a)//3 + 1\r\n l = []\r\n flag = False\r\n\r\n for i in a:\r\n flag = True\r\n for j in l:\r\n # Check if value is in the list\r\n if i == j[0]:\r\n j[1] += 1\r\n # Check if frequency is great enough\r\n if j[1] >= x:\r\n return j[0]\r\n flag = False\r\n if flag:\r\n # Add value to the list\r\n l.append([i, 1])\r\n flag = False\r\n # None found\r\n return None \r\n \r\n### 1d ###\r\ndef sum_tri(a, x):\r\n '''(list, int)->bool\r\n\r\n Returns true if 3 numbers add up to equal c\r\n '''\r\n\r\n # Move from left to right\r\n for i in range(len(a)):\r\n # Move j\r\n for j in range(len(a)):\r\n # Move k\r\n for k in range(len(a)):\r\n # Check if found\r\n if a[i] + a[j] + a[k] == x:\r\n return True\r\n return False\r\n"
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"text": "Python 3.4.3 (v3.4.3:9b73f1c3e601, Feb 23 2015, 02:52:03) \n[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin\nType \"copyright\", \"credits\" or \"license()\" for more information.\n>>> ================================ RESTART ================================\n>>> \n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): aha\nIf you answer is not Q or q, then it must be an integer.\nInvalid input, try again\nAnswer (1,2,3, 4, 5, 6, Q or q): 10\nInvalid input, try again\nAnswer (1,2,3, 4, 5, 6, Q or q): jooj\nIf you answer is not Q or q, then it must be an integer.\nInvalid input, try again\nAnswer (1,2,3, 4, 5, 6, Q or q): 1\nEnter beginning year: 1972\nEnter ending year: 1973\nThe Winds of War, by Herman Wouk (1972-01-23)\nThe Game of the Foxes, by Ladislas Farago (1972-03-19)\nThe Word, by Irving Wallace (1972-05-14)\nThe Boys of Summer, by Roger Kahn (1972-05-28)\nI'm OK, You're OK, by Thomas Harris (1972-06-25)\nJonathan Livingston Seagull, by Richard Bach (1972-07-02)\nO Jerusalem!, by Larry Collins (1972-07-23)\nHarry S. Truman, by Margaret Truman (1973-01-14)\nThe Best and the Brightest, by David Halberstam (1973-01-21)\nDr. Atkins' Diet Revolution, by Robert C. Atkins (1973-02-18)\nThe Odessa File, by Frederick Forsyth (1973-03-25)\nOnce Is Not Enough, by Jacqueline Susann (1973-05-06)\nBreakfast of Champions, by Kurt Vonnegut (1973-07-01)\nThe Joy of Sex, by Alex Comfort (1973-08-05)\nThe Hollow Hills, by Mary Stewart (1973-09-09)\nHow to Be Your Own Best Friend, by Mildred Newman (1973-10-14)\nThe Honorary Consul, by Graham Greene (1973-11-25)\nAlistair Cooke's America, by Alistair Cooke (1973-12-09)\nBurr, by Gore Vidal (1973-12-09)\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 1\nEnter beginning year: lasj\nPlease give a four digit integer for the year.\n999\nPlease give a four digit integer for the year.\n-100\nPlease give a four digit integer for the year.\n1000\nEnter ending year: 1001\nNo books found in that range of years.\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 1\nEnter beginning year: 1999\nEnter ending year: 1997\nNo books found in that range of years.\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 2\nEnter month (as an integer, 1-12): 0\nInvalid month. Must be interger, 1-12. Try again\nEnter month (as an integer, 1-12): jooooj\nMonth must be an integer.\nInvalid month. Must be interger, 1-12. Try again\nEnter month (as an integer, 1-12): -10\nInvalid month. Must be interger, 1-12. Try again\nEnter month (as an integer, 1-12): 4.5\nMonth must be an integer.\nInvalid month. Must be interger, 1-12. Try again\nEnter month (as an integer, 1-12): 5\nEnter year: 999\nPlease give a four digit integer for the year.\n1999\nAll Titles in month 5 of 1999:\nThe Girl Who Loved Tom Gordon, by Stephen King (1999-05-02)\nWe'll Meet Again, by Mary Higgins Clark (1999-05-09)\nStar Wars Episode IThe Phantom Menace, by Terry Brooks (1999-05-23)\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 2\nEnter month (as an integer, 1-12): 12\nEnter year: 2012\nAll Titles in month 12 of 2012:\nThe Last Man, by Vince Flynn (2012-12-02)\nNotorious Nineteen, by Janet Evanovich (2012-12-09)\nCold Days, by Jim Butcher (2012-12-16)\nThreat Vector, by Tom Clancy with Mark Greaney (2012-12-23)\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 3\nEnter an author's name (or part of a name): King\nMine Enemy Grows Older, by Alexander King (1959-04-12)\nMay This House Be Safe from Tigers, by Alexander King (1960-03-13)\nThe Dead Zone, by Stephen King (1979-10-14)\nFirestarter, by Stephen King (1980-09-28)\nCujo, by Stephen King (1981-08-23)\nDifferent Seasons, by Stephen King (1982-08-15)\nPet Sematary, by Stephen King (1983-11-13)\nThe Talisman, by Stephen King (1984-10-28)\nThinner, by Stephen King (1985-04-28)\nSkeleton Crew, by Stephen King (1985-06-23)\nIt, by Stephen King (1986-09-14)\nThe Eyes of the Dragon, by Stephen King (1987-02-01)\nMisery, by Stephen King (1987-06-07)\nThe Tommyknockers, by Stephen King (1987-11-29)\nA Brief History of Time, by Stephen Hawking (1988-06-26)\nThe Dark Half, by Stephen King (1989-11-05)\nThe Stand, by Stephen King (1990-05-13)\nFour Past Midnight, by Stephen King (1990-09-16)\nGerald's Game, by Stephen King (1992-07-19)\nDolores Claiborne, by Stephen King (1992-12-06)\nInsomnia, by Stephen King (1994-10-23)\nDesperation, by Stephen King (1996-10-13)\nBag of Bones, by Stephen King (1998-10-11)\nThe Girl Who Loved Tom Gordon, by Stephen King (1999-05-02)\nDreamcatcher, by Stephen King (2001-04-08)\nBlack House, by Stephen King (2001-09-30)\nEverything's Eventual, by Stephen King (2002-04-07)\nFrom a Buick 8, by Stephen King (2002-10-13)\nSong of Susannah, by Stephen King (2004-06-27)\nThe Dark Tower, by Stephen King (2004-10-10)\nCell, by Stephen King (2006-02-12)\nLisey's Story, by Stephen King (2006-11-12)\nDuma Key, by Stephen King (2008-02-10)\nUnder the Dome, by Stephen King (2009-11-29)\nThe Grand Design, by Stephen Hawking (2010-09-26)\n23337, by Stephen King (2011-11-27)\nThe Wind Through the Keyhole, by Stephen King (2012-05-13)\nDoctor Sleep, by Stephen King (2013-10-13)\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): ah\nIf you answer is not Q or q, then it must be an integer.\nInvalid input, try again\nAnswer (1,2,3, 4, 5, 6, Q or q): 3\nEnter an author's name (or part of a name): ah\nEarth and High Heaven, by Gwethalyn Graham (1945-04-22)\nKon-Tiki, by Thor Heyerdahl (1950-10-08)\nTallulah, by Tallulah Bankhead (1952-10-26)\nAku-Aku, by Thor Heyerdahl (1958-10-19)\nThe Boys of Summer, by Roger Kahn (1972-05-28)\nThe Honorary Consul, by Graham Greene (1973-11-25)\nDonahue, by Phil Donahue (1980-03-23)\nHeir to the Empire, by Timothy Zahn (1991-06-30)\nPersonal History, by Katharine Graham (1997-02-23)\nJust As I Am, by Billy Graham (1997-06-29)\nIndwelling, by Tim LaHaye (2000-06-11)\nThe Mark, by Tim LaHaye (2000-12-03)\nDesecration, by Tim LaHaye (2001-11-18)\nThe Remnant, by Tim LaHaye (2002-07-21)\nArmageddon, by Tim LaHaye (2003-04-27)\nGlorious Appearing, by Tim LaHaye (2004-04-18)\nThe Rising, by Tim LaHaye (2005-04-03)\nA Long Way Gone, by Ishmael Beah (2007-04-15)\nPower to the People, by Laura Ingraham (2007-09-30)\nLiberal Fascism, by Jonah Goldberg (2008-03-09)\nUnaccustomed Earth, by Jhumpa Lahiri (2008-04-20)\nGoing Rogue, by Sarah Palin (2009-12-06)\nThe Obama Diaries, by Laura Ingraham (2010-08-01)\nHome Front, by Kristin Hannah (2012-02-19)\nThe Storm, by Clive Cussler and Graham Brown (2012-06-17)\nShadow of Night, by Deborah Harkness (2012-07-29)\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 3\nEnter an author's name (or part of a name): lasjdfl\nNo books found by an author whose name contains: lasjdfl\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 4\nEnter a title (or part of a title): secret\nThe Secret of Santa Vittoria, by Robert Crichton (1966-11-20)\nThe Secret Pilgrim, by John le Carre (1991-01-20)\nHarry Potter and the Chamber of Secrets, by J. K. Rowling (1999-06-20)\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 4\nEnter a title (or part of a title): scary\nNo books found with title that contains: scary\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 5\nEnter an integer bigger than zero: 4.5\nNumber must be an integer.\nNumber must be at least one. Try again\nEnter an integer bigger than zero: 88.99\nNumber must be an integer.\nNumber must be at least one. Try again\nEnter an integer bigger than zero: asljf\nNumber must be an integer.\nNumber must be at least one. Try again\nEnter an integer bigger than zero: -10\nNumber must be at least one. Try again\nEnter an integer bigger than zero: -10000\nNumber must be at least one. Try again\nEnter an integer bigger than zero: 14\nThe list of authors with at least 14 NYT bestsellers is: \n1. James Patterson with 42 bestsellers\n2. Stephen King with 34 bestsellers\n3. Danielle Steel with 28 bestsellers\n4. John Grisham with 25 bestsellers\n5. Janet Evanovich with 19 bestsellers\n6. Mary Higgins Clark with 18 bestsellers\n7. Patricia Cornwell with 17 bestsellers\n8. Tom Clancy with 16 bestsellers\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 5\nEnter an integer bigger than zero: 18\nThe list of authors with at least 18 NYT bestsellers is: \n1. James Patterson with 42 bestsellers\n2. Stephen King with 34 bestsellers\n3. Danielle Steel with 28 bestsellers\n4. John Grisham with 25 bestsellers\n5. Janet Evanovich with 19 bestsellers\n6. Mary Higgins Clark with 18 bestsellers\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 5\nEnter an integer bigger than zero: 30\nThe list of authors with at least 30 NYT bestsellers is: \n1. James Patterson with 42 bestsellers\n2. Stephen King with 34 bestsellers\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 5\nEnter an integer bigger than zero: 100\nThe list of authors with at least 100 NYT bestsellers is: \n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 6\nEnter an integer bigger than zero: 0\nNumber must be at least one. Try again\nEnter an integer bigger than zero: 4.5\nNumber must be an integer.\nNumber must be at least one. Try again\nEnter an integer bigger than zero: 3\nTop 3 authors by the number of NYT bestsellers is: \n1. James Patterson\n2. Stephen King\n3. Danielle Steel\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): 6\nEnter an integer bigger than zero: 20\nTop 20 authors by the number of NYT bestsellers is: \n1. James Patterson\n2. Stephen King\n3. Danielle Steel\n4. John Grisham\n5. Janet Evanovich\n6. Mary Higgins Clark\n7. Patricia Cornwell\n8. Tom Clancy\n9. David Baldacci\n10. Bob Woodward\n11. Nora Roberts\n12. Dean R. Koontz\n13. Sue Grafton\n14. James Michener\n15. Sidney Sheldon\n16. Robert Ludlum\n17. Tim LaHaye\n18. Nicholas Sparks\n19. Michael Connelly\n20. Lee Child\n\nPress enter to continue. \n\n\n\n\n===================================================\nWhat would you like to do? Enter 1, 2, 3, 4, 5, 6 or Q for answer.\n1: Look up year range\n2: Look up month/year\n3: Search for author\n4: Search for title\n5: Number of authors with at least x bestsellers\n6: List y authors with the most bestsellers\nQ: Quit\n===================================================\nAnswer (1,2,3, 4, 5, 6, Q or q): q\n>>> \n"
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"text": "# Family name: Nicholas Broadbent\r\n# Student number: 8709720\r\n# Course: IT1 1120 \r\n# Assignment Number 5\r\n\r\ndef digit_sum(n):\r\n ''' (int)->int\r\n\r\n This function returns the sum of all of the digits\r\n of the integer n.\r\n '''\r\n \r\n if n > 0:\r\n # Take the last number, remove it and add it\r\n x = n/10\r\n n = n//10\r\n return digit_sum(n) + round((x-n)*10)\r\n return 0\r\n\r\ndef digital_root(n):\r\n ''' (int)->int\r\n\r\n Returns the sum of the sum of digits of an integer n,\r\n or the digital roots of integer n.\r\n '''\r\n \r\n # Get sum of digits\r\n n = digit_sum(n)\r\n\r\n # Get digital root\r\n if n > 10:\r\n return digital_root(n)\r\n return n\r\n"
}
] | 7 |
xueqing/hikvision
|
https://github.com/xueqing/hikvision
|
526984d59481751c24f36270e2cb0e8cd4470880
|
3f21015b79817bbe441f80472425f814363eed5b
|
f77bbca2bf91b676c741fe7ced3f5c5c5f6cf3c9
|
refs/heads/master
| 2017-03-26T14:29:00.310487 | 2011-11-09T16:53:25 | 2011-11-09T16:53:25 | null | 0 | 0 | null | null | null | null | null |
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"text": "// hikdump.cpp : Defines the entry point for the console application.\n//\n\n#include \"stdafx.h\"\n#include \"hikdump.h\"\n#include \"../SDK/PlayM4.h\"\n#include <cstdio>\n\n#ifdef _DEBUG\n#define new DEBUG_NEW\n#endif\n\nCWinApp theApp;\nbool isIndexed = false;\n\nusing namespace std;\n\nvoid _stdcall pFileRefDone(DWORD nPort, DWORD nUser) {\n\tisIndexed = true;\n\tcout << \"Callback called, file is indexed (\" << nPort << \",\" << nUser<< \")\" << endl;\n}\n\nint main(int argc, char* argv[], char* envp[])\n{\n\tint nRetCode = 0;\n\n\tHMODULE hModule = ::GetModuleHandle(NULL);\n\n\tif (hModule == NULL)\n\t{\n\t\tprintf(\"Fatal Error: GetModuleHandle failed\\n\");\n\t\tnRetCode = 1;\n\t}\n\n\tif (argc != 2) {\n\t\tcout << \"Called with invalid number of parameters. You need to specify the video filename.\";\n\t\treturn -1;\n\t}\n\n\tHINSTANCE dll;\n\tdll = LoadLibrary(\"./PlayCtrl.dll\"); // STEP-1\n \n\tif (dll == 0)\n\t{\n\t\tcout << \"Unable to load HISDK player library!\" << endl;\n\t\treturn -1;\n\t}\n\n\tlong port = 0;\n\t\n\tprintf(\"%.8x <- PlayM4_GetPort()\\n\\r\", PlayM4_GetSdkVersion());\n\tcout << PlayM4_GetPort(&port) << \" <- PlayM4_GetPort() \" << endl; // STEP-2\n\n\tcout << PlayM4_SetFileRefCallBack(port, pFileRefDone, 0) << \" <- PlayM4_SetFileRefCallBack() \" << endl; // STEP-3\n\t\n\tint ret = PlayM4_OpenFile(port, argv[1]);\n\tif (ret==0) { // STEP-4\n\t\tcout << \"Unable to open video file \" << argv[1];\n\t\treturn -1;\n\t} else cout << ret << \" <- PlayM4_OpenFile()\" << endl;\n\t\n\twhile(!isIndexed) { // STEP-5\n\t\tSleep(500);\n\t}\n\t\n\tlong width = 0, height = 0;\n\tPlayM4_GetPictureSize(port,&width,&height); \n\tcout << width << \"x\" << height << \" <- PlayM4_GetPictureSize()\" << endl;\n \n\tint frames = PlayM4_GetFileTotalFrames(port); // STEP-6\n cout << frames << \" <- PlayM4_GetFileTotalFrames()\" << endl;\n\t\n\tcout << PlayM4_Play(port, 0) << \" <- PlayM4_Play(port,0)\" << endl; // STEP-7\n cout << PlayM4_Pause(port, 1) << \" <- PlayM4_Pause(port,1)\" << endl; // STEP-8\n\t//cout << PlayM4_OneByOne(port) << \" <- PlayM4_OneByOne()\" << endl;\n\t\n\tint range = min(100, frames);\n\tint failures = 0;\n\tint successes = 0;\n\n\tfor(int i=0;i<range;i++) {\n\t\t// note, do not assume what was the previous frame, this should work for any frame in the video\n\n\t\tint ret = PlayM4_SetCurrentFrameNum(port, i); // STEP-9\n\t\t//Sleep(100);\n\t\tint got = PlayM4_GetCurrentFrameNum(port); // STEP-10\n\t\tif (!ret || i!=got) {\n\t\t\tcout << \"Expect \" << i << \" but got \" << got << \"\" << endl;\n\t\t\tfailures++;\n\t\t}\n\t\telse successes++;\n \n\t}\n\n\tcout << \"Test ended with \" << failures << \" failures and \" << successes << \" successes.\";\n\n\treturn nRetCode;\n}\n"
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"text": "// VolumeCtrl.cpp : implementation file\n//\n\n#include \"stdafx.h\"\n#include \"Player.h\"\n#include \"VolumeCtrl.h\"\n\n#ifdef _DEBUG\n#define new DEBUG_NEW\n#undef THIS_FILE\nstatic char THIS_FILE[] = __FILE__;\n#endif\n\n/////////////////////////////////////////////////////////////////////////////\n// CVolumeCtrl\n\nCVolumeCtrl::CVolumeCtrl() : m_brush(GetSysColor(COLOR_3DFACE)),\n\t\t\t\t\t\t\t m_LightPen(PS_SOLID,1,RGB(255,255,255)),\n\t\t\t\t\t\t\t m_ShadowPen(PS_SOLID,1,RGB(128,128,128))\n{\n}\n\nCVolumeCtrl::~CVolumeCtrl()\n{\n}\n\n\nBEGIN_MESSAGE_MAP(CVolumeCtrl, CSliderCtrl)\n\t//{{AFX_MSG_MAP(CVolumeCtrl)\n\tON_WM_PAINT()\n\tON_WM_ERASEBKGND()\n\t//}}AFX_MSG_MAP\nEND_MESSAGE_MAP()\n\n/////////////////////////////////////////////////////////////////////////////\n// CVolumeCtrl message handlers\n\nvoid CVolumeCtrl::OnPaint() \n{\n\t// 调用默认的窗口过程Default()画出控件的标准形状\n\tDefault();\n\n\t// 抹掉除滑动块的其它东西\n\tCWindowDC dc(this);\n\tCRect rcThumb, rcClient;\n\tGetThumbRect(&rcThumb);\n\tdc.ExcludeClipRect(&rcThumb);\n\n\tGetClientRect(&rcClient);\n\tdc.FillRect(&rcClient, &m_brush);\n\n\t// 画三角型\n\tint half = rcThumb.Width() >> 1;\n\trcClient.left += 12 + half;\n\trcClient.right -= 12 + half;\n\trcClient.top += 3;\n\trcClient.bottom -= 3;\n\n\tDrawTriangle(&dc, &rcClient);\n\n\t// Do not call CSliderCtrl::OnPaint() for painting messages\n}\n\n\nvoid CVolumeCtrl::DrawTriangle(CDC *pDC, CRect *prcDraw)\n{\n\t// 笔移动左下\n\tpDC->MoveTo(prcDraw->left, prcDraw->bottom);\n\n\t// 选择高亮画笔,并保存原来的画笔\n\tCPen *pOldPen = pDC->SelectObject(&m_LightPen);\n \n\t// 画下边\n\tpDC->LineTo(prcDraw->BottomRight());\n\n\t// 画右边\n\tpDC->LineTo(prcDraw->right, prcDraw->top);\n\n\t// 选择阴影画笔\n\tpDC->SelectObject(&m_ShadowPen);\n\n\t// 画斜边\n\tpDC->LineTo(prcDraw->left, prcDraw->bottom); \n\n\t// 恢复原来的画笔\n\tpDC->SelectObject(pOldPen);\n}\n\n\nBOOL CVolumeCtrl::OnEraseBkgnd(CDC* pDC) \n{\n\t// 直接返回, 因为OnPaint里我们有一个FillRect操作, 可以理解为擦除背景\n // 没有必要擦除两次\n\tUNREFERENCED_PARAMETER(pDC);\n\t\n\treturn TRUE; // CSliderCtrl::OnEraseBkgnd(pDC);\n}\n\nBOOL CVolumeCtrl::PreTranslateMessage(MSG* pMsg)\n{\n\t// TODO: Add your specialized code here and/or call the base class\n\tCRect m_SliderRect;\n\tGetWindowRect(m_SliderRect);\n\tif(pMsg->message == WM_LBUTTONDOWN)\n\t{\n\t\tm_MousePt.x = pMsg->pt.x;\n\t}\n\tif(pMsg->message == WM_LBUTTONUP)\n\t{\n\t\tif(m_MousePt.x == pMsg->pt.x)\n\t\t{\n\t\t\tint max, min;\n\t\t\tthis->GetRange(min, max);\n\t\t\tthis->SetPos( min + (m_MousePt.x-m_SliderRect.left)*(max-min)/(m_SliderRect.right - m_SliderRect.left) );\n\t\t\tInvalidate();\n\t\t}\t\t\t\n\t}\n\n\treturn CSliderCtrl::PreTranslateMessage(pMsg);\n}"
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"text": "import sys, time, logging, os, tempfile\nimport ctypes, ctypes.wintypes\nfrom ctypes import *\nfrom ctypes.wintypes import DWORD\n\nindexed = False\n\ndef pyCallbackFileRefDone(port, user_data):\n global indexed\n indexed = True\n print(\"Callback called, file is indexed (%s,%s)\" % (port, user_data))\n\nif len(sys.argv) != 2:\n sys.exit(\"Called with invalid number of parameters. You need to specify the video filename.\")\n\nMY_CALLBACK = WINFUNCTYPE(None, DWORD, DWORD)\ncbFunc = MY_CALLBACK(pyCallbackFileRefDone)\n\ntry:\n dll = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"PlayCtrl.dll\") # the directory with the .py file\n dll2 = os.path.join(\"PlayCtrl.dll\") # currnet directory\n if not os.path.isfile(dll):\n if not os.path.isfile(dll2):\n raise Exception(\"File not found: %s or %s\" % (dll,dll2))\n else:\n hsdk = ctypes.WinDLL(dll2) # STEP-1\n else:\n hsdk = ctypes.WinDLL(dll) # STEP-1\nexcept Exception, e:\n print(\"Unable to load HISDK player library (PlayCtrl.dll).\\n%s\" % e)\n raise e\n\nport = c_long(0)\n\nversion = \"%.8x\" % hsdk.PlayM4_GetSdkVersion()\nprint version, \" <- PlayM4_GetSdkVersion()\"\n\nwidth = c_long(0)\nheight = c_long(0)\nif not hsdk.PlayM4_GetPictureSize(port, byref(width), byref(height)):\n sys.exit(\"PlayM4_GetPictureSize() returned False.\")\nprint \"%sx%s <- PlayM4_GetPictureSize()\" % (width.value, height.value)\n\nport = hsdk.PlayM4_GetPort(byref(port))\nprint port, \" <- PlayM4_GetPort()\" # STEP-2\n\nsample_video_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), sys.argv[1])\n\nret = hsdk.PlayM4_SetFileRefCallBack(port, cbFunc, DWORD(0)) # STEP-3\nprint ret, \" <- PlayM4_SetFileRefCallBack()\"\nif not ret:\n sys.exit(\"Unable to set callback for indexing\")\n\nret = hsdk.PlayM4_OpenFile(port, sample_video_file) # STEP-4\nprint ret, \" <- PlayM4_OpenFile()\"\nif not ret: \n sys.exit(\"Unable to open video file %s\" % sys.argv[1])\n\nwhile not indexed:\n logging.info(\"Waiting for indexing to finish...\") # STEP-5\n time.sleep(0.5)\n\ntotal_frames = hsdk.PlayM4_GetFileTotalFrames(port) # STEP-6\nprint total_frames, \" <- PlayM4_GetFileTotalFrames()\"\n\nprint hsdk.PlayM4_Play(port, 0), \" <- PlayM4_Play(port,0)\" # STEP-7\nprint hsdk.PlayM4_Pause(port, 1), \" <- PlayM4_Pause(port,1)\" # STEP-8\n\n# putting first and last 5 frames into `frames` list\nlimit = min(100, total_frames)\nframes = range(0,limit)\n#frames.extend(range(total_frames-limit, total_frames))\n\nerrors = 0\nsuccesses = 0\n\nfor i in frames:\n ret = hsdk.PlayM4_SetCurrentFrameNum (port, i) # STEP-9\n got = hsdk.PlayM4_GetCurrentFrameNum(port) # STEP-10\n if not ret or not got==i:\n print(\"Expect %s but got %s\" % (i, got))\n errors += 1\n else:\n successes += 1\n\nprint(\"Test ended with %s failures and %s successes.\" % (errors, successes))\n"
}
] | 3 |
AIDrug/VHTS
|
https://github.com/AIDrug/VHTS
|
02aec2a1c3f1520a41936fd5b8a59c30413919d0
|
dd23cbb0216dd628bb95bf4bb7e93ce49514c89d
|
e2b0de6be78dd4423a8c45e5a3c3a7f0e3e07c1a
|
refs/heads/main
| 2023-06-21T13:22:55.577512 | 2021-07-27T09:30:03 | 2021-07-27T09:30:03 | 449,812,012 | 1 | 0 |
MIT
| 2022-01-19T18:36:41 | 2021-07-27T09:30:24 | 2021-07-27T09:30:21 | null |
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"text": "#!/usr/bin/env python\nimport sys\nimport os\nimport numpy as np\nimport argparse\nimport time\nfrom filelock import FileLock\nimport pandas as pd\nimport subprocess\n\n\ndef get_job_list(list_dir):\n job_idx_list = list()\n list_file = list_dir + '/list.txt'\n if not os.path.exists(list_file):\n return job_idx_list\n freeze_lock = FileLock('{}.lock'.format(list_file))\n with freeze_lock.acquire(timeout=30):\n with open(list_file, 'r') as fp:\n lines = fp.readlines()\n for line in lines:\n job_idx_list += [line.strip()]\n job_idx_list2 = list()\n for job_idx in job_idx_list:\n x = job_idx.split('_')\n job_idx_list2 += [[int(x[0]), int(x[1])]]\n job_idx_list2 = sorted(job_idx_list2)\n job_idx_list3 = ['%d_%d' % (x[0], x[1]) for x in job_idx_list2]\n\n return job_idx_list3\n\n\ndef set_job_list(job_idx_list, list_dir):\n list_file = list_dir + '/list.txt'\n freeze_lock = FileLock('{}.lock'.format(list_file))\n with freeze_lock.acquire(timeout=30):\n if os.path.exists(list_file):\n with open(list_file, 'r') as fp:\n lines = fp.readlines()\n else:\n lines = list()\n with open(list_file, 'w') as fp:\n for line in lines:\n fp.write(line)\n for job_idx in job_idx_list:\n line = job_idx + '\\n'\n fp.write(line)\n\n return\n\n\ndef data_sampling(params_dict, iteration):\n\n field_separator = params_dict['field_separator']\n smi_file_format = params_dict['smi_file_format']\n sampling_method = params_dict['sampling_method']\n num_sample = params_dict['num_sample']\n file_size = params_dict['file_size']\n master_dir = params_dict['master_dir']\n todo_dir = params_dict['todo_dir']\n\n remain_smi_file = master_dir + '/' + 'remain.' + smi_file_format\n\n try:\n if smi_file_format == 'pkl':\n df_remain = pd.read_pickle(remain_smi_file)\n else:\n df_remain = pd.read_csv(\n remain_smi_file, sep=field_separator, header=0)\n\n except pd.errors.EmptyDataError:\n return False\n# 0: 'MOL_IDX', 1: 'MOL_ID', 2: 'SMILES' 3:, 'Pred', 4: 'uncertainty'\n fkey = df_remain.keys()[0]\n if fkey.startswith('#'):\n df_remain.rename(columns={fkey: fkey[1:]}, inplace=True)\n\n num_remain = df_remain.shape[0]\n if num_remain == 0:\n return False, False\n if sampling_method == 'sequential':\n df_sample = df_remain[0:num_sample]\n df_remain_new = df_remain[num_sample:]\n elif sampling_method == 'random' or iteration == 0:\n index = df_remain.index.values\n np.random.shuffle(index)\n idx_sam = np.sort(index[0:num_sample])\n idx_rem = np.sort(index[num_sample])\n df_sample = df_remain.loc[idx_sam]\n df_remain_new = df_remain.loc[idx_rem]\n elif sampling_method == 'score':\n index = df_remain['Pred'].sort_values().index\n idx_sam = np.sort(index[0:num_sample])\n idx_rem = np.sort(index[num_sample])\n df_sample = df_remain.loc[idx_sam]\n df_remain_new = df_remain.loc[idx_rem]\n elif sampling_method == 'uncertainty':\n index = df_remain['Uncertainty'].sort_values(ascending=False).index\n idx_sam = np.sort(index[0:num_sample])\n idx_rem = np.sort(index[num_sample])\n df_sample = df_remain.loc[idx_sam]\n df_remain_new = df_remain.loc[idx_rem]\n\n sample_smi_file = '%s/sample_%d.%s' % (master_dir, iteration,\n smi_file_format)\n sep = field_separator\n if sep == '\\s+':\n sep = ' '\n\n if smi_file_format == 'pkl':\n df_sample.to_pickle(sample_smi_file)\n df_remain_new.to_pickle(remain_smi_file)\n\n else:\n df_sample.to_csv(sample_smi_file, sep=sep, float_format='%.3f',\n header=True, index=False)\n df_remain_new.to_csv(remain_smi_file, sep=sep,\n float_format='%.3f', index=False)\n\n todo_list = list()\n\n num_data = df_sample.shape[0]\n num_file = int(np.ceil(num_data/file_size))\n for idx in range(0, num_file):\n job_idx = '%d_%d' % (iteration, idx)\n ini = idx * file_size\n fin = (idx+1) * file_size\n df_todo = df_sample[ini:fin]\n job_todo_file = todo_dir + '/' + job_idx + '.' + smi_file_format\n\n if smi_file_format == 'pkl':\n df_todo.to_pickle(job_todo_file)\n else:\n df_todo.to_csv(job_todo_file, sep=sep, float_format='%.3f',\n header=True, index=False)\n todo_list += [job_idx]\n\n set_job_list(todo_list, master_dir)\n set_job_list(todo_list, todo_dir)\n num_remain_new = df_remain_new.shape[0]\n check_remain = True\n if num_remain_new == 0:\n check_remain = False\n\n return True, check_remain\n\n\ndef write_stage(stage_list, stage_file):\n fp_stage = open(stage_file, 'w')\n for stage in stage_list:\n iteration, state = stage\n line_out = '%d %s\\n' % (iteration, state)\n fp_stage.write(line_out)\n fp_stage.close()\n\n\ndef check_done(params_dict, iteration):\n\n check = False\n master_dir = params_dict['master_dir']\n done_dir = params_dict['done_dir']\n# mlsync = params_dict['mlsync']\n\n master_job_idx_list = get_job_list(master_dir)\n done_job_idx_list = get_job_list(done_dir)\n master_job_i = list()\n done_job_i = list()\n\n for job_idx in master_job_idx_list:\n lis = job_idx.strip().split('_')\n idx = int(lis[1])\n iteration0 = int(lis[0])\n if iteration == iteration0:\n master_job_i += [idx]\n for job_idx in done_job_idx_list:\n lis = job_idx.strip().split('_')\n idx = int(lis[1])\n iteration0 = int(lis[0])\n if iteration == iteration0:\n done_job_i += [idx]\n\n master_job = set(master_job_i)\n done_job = set(done_job_i)\n running_job = master_job - done_job\n if len(running_job) == 0:\n check = True\n return check\n\n\ndef gather_result(params_dict, iteration):\n\n master_dir = params_dict['master_dir']\n done_dir = params_dict['done_dir']\n field_separator = params_dict['field_separator']\n smi_file_format = params_dict['smi_file_format']\n\n# head_dict = {0: 'MOL_IDX', 1: 'MOL_ID',\n# 2: 'SMILES', 3: 'Docking1', 4: 'Docking'}\n docking_smi_file = master_dir + '/' + 'docking.' + smi_file_format\n\n all_data = list()\n if os.path.exists(docking_smi_file):\n if smi_file_format == 'pkl':\n df_docking = pd.read_pickle(docking_smi_file)\n else:\n df_docking = pd.read_csv(\n docking_smi_file, sep=field_separator, header=0)\n all_data.append(df_docking)\n done_job_idx_list = get_job_list(done_dir)\n for job_idx in done_job_idx_list:\n lis = job_idx.strip().split('_')\n iteration0 = int(lis[0])\n if iteration != iteration0:\n continue\n job_done_file = done_dir + '/' + job_idx + '.' + smi_file_format\n if smi_file_format == 'pkl':\n df_done = pd.read_pickle(job_done_file)\n else:\n df_done = pd.read_csv(job_done_file, sep=field_separator, header=0)\n# df_done.rename(columns=head_dict, inplace=True)\n all_data.append(df_done)\n df = pd.concat(all_data, axis=0, ignore_index=True)\n sep = field_separator\n if sep == '\\s+':\n sep = ' '\n if smi_file_format == 'pkl':\n df.to_pickle(docking_smi_file)\n else:\n df.to_csv(docking_smi_file, sep=sep, float_format='%.3f', index=False)\n return\n\n\ndef gapjil(params_dict):\n '''\n master gapjil\n '''\n sleep_cycle = params_dict['sleep_cycle']\n# sampling_method = params_dict['sampling_method']\n mlsync = params_dict['mlsync']\n master_dir = params_dict['master_dir']\n auto_sub = params_dict['auto_sub']\n sub_dock_script = params_dict['sub_dock_script']\n num_sub = params_dict['num_sub']\n\n stage_file = master_dir + '/' + 'stage.txt'\n stage_list = list()\n iteration = 0\n\n check_docking = True\n check_remain = True\n if os.path.exists(stage_file):\n stage_list = [x.strip().split() for x in open(stage_file)]\n if len(stage_list) != 0:\n iteration = int(stage_list[-1][0])\n if stage_list[-1][1] != 'done':\n check_docking = False\n line_out = 'restart iteration: %d docking_done: %s' % (\n iteration, check_docking)\n print(line_out, flush=True)\n while True:\n if not check_docking:\n check_docking = check_done(params_dict, iteration)\n if check_docking:\n stage_list[iteration] = [iteration, 'done']\n write_stage(stage_list, stage_file)\n gather_result(params_dict, iteration)\n line_out = 'end iteration: %d docking_done: %s' % (\n iteration, check_docking)\n print(line_out, flush=True)\n iteration += 1\n # do ml\n if check_docking and not check_remain:\n line_out = 'End simulation'\n print(line_out, flush=True)\n break\n if check_docking and check_remain:\n check_sampling, check_remain = data_sampling(\n params_dict, iteration)\n if not check_sampling:\n line_out = 'End simulation'\n print(line_out, flush=True)\n break\n check_docking = False\n stage_list += [[iteration, 'current']]\n write_stage(stage_list, stage_file)\n line_out = 'get_sample iteration: %d docking_done: %s' % (\n iteration, check_docking)\n print(line_out, flush=True)\n\n if auto_sub == 'shell':\n run_line = 'bash ' + sub_dock_script\n for i in range(num_sub):\n subprocess.Popen(run_line.split())\n line_out = '%d sub_dock is generation' % num_sub\n print(line_out, flush=True)\n\n elif auto_sub == 'batch':\n run_line = 'sbatch ' + sub_dock_script\n for i in range(num_sub):\n subprocess.Popen(run_line.split())\n line_out = '%d sub_dock_batch is submitted' % num_sub\n print(line_out, flush=True)\n\n if sleep_cycle is None:\n line_out = 'stop master: sleep_cycle is None '\n print(line_out, flush=True)\n break\n time.sleep(sleep_cycle)\n\n return\n\n\nclass LoadFromConfig(argparse.Action):\n def __call__(self, parser, namespace, values, option_string=None):\n with values as f:\n parser.parse_args(f.read().split(), namespace)\n\n\nclass ExtendAction(argparse.Action):\n\n def __call__(self, parser, namespace, values, option_string=None):\n items = getattr(namespace, self.dest) or []\n items.extend(values)\n setattr(namespace, self.dest, items)\n\n\ndef main():\n\n parser = argparse.ArgumentParser(description='Master of puppets')\n parser.register('action', 'extend', ExtendAction)\n parser.add_argument('--arg_file', type=open, required=False, default=None,\n action=LoadFromConfig, help='argment file')\n parser.add_argument('--work_dir', type=str, required=False,\n default='workflow', help='workflow directory')\n parser.add_argument('-s', '--smi_file_format', type=str, required=False,\n default='pkl', help='pkl (default), txt, csv, tsv')\n parser.add_argument('--sleep_cycle', type=int, required=False, default=None,\n help='sleep master for xx seconds per loop\\n' +\n 'if None (default), escape without repeating the loop')\n parser.add_argument('--num_sample', type=int, required=False,\n default='100000', help='number of samples')\n parser.add_argument('--file_size', type=int, required=False,\n default='1000', help='smiles per file')\n parser.add_argument('--sampling_method', type=str, required=False,\n default='sequential',\n help='sequential, random, score, uncertainty')\n parser.add_argument('--mlsync', type=int, required=False, default=0,\n help='0: sequential (alternative), 1: parallel')\n\n parser.add_argument('--auto_sub', type=str, required=False,\n default=None,\n help='shell (bash), batch (slurm) default is None')\n parser.add_argument('--sub_dock_script', type=str, required=False,\n default='run_sub_dock.sh',\n help='run_sub_dock.sh or slurm_sub_dock.sh')\n parser.add_argument('--num_sub', type=int, required=False, default=1,\n help='number of docking subprocess')\n\n if len(sys.argv) < 2:\n parser.print_usage()\n sys.exit()\n args = parser.parse_args()\n\n work_dir = args.work_dir\n master_dir = work_dir + '/master'\n todo_dir = work_dir + '/todo'\n current_dir = work_dir + '/current'\n done_dir = work_dir + '/done'\n smi_file_format = args.smi_file_format\n if smi_file_format == 'txt':\n field_separator = '\\s+'\n elif smi_file_format == 'csv':\n field_separator = ','\n elif smi_file_format == 'tsv':\n field_separator = '\\t'\n else:\n field_separator = None\n\n num_sample = args.num_sample\n sampling_method = args.sampling_method\n file_size = args.file_size\n sleep_cycle = args.sleep_cycle\n mlsync = args.mlsync\n\n auto_sub = args.auto_sub\n sub_dock_script = args.sub_dock_script\n num_sub = args.num_sub\n\n params_dict = dict()\n params_dict['work_dir'] = work_dir\n params_dict['master_dir'] = master_dir\n params_dict['todo_dir'] = todo_dir\n params_dict['current_dir'] = current_dir\n params_dict['done_dir'] = done_dir\n params_dict['field_separator'] = field_separator\n params_dict['smi_file_format'] = smi_file_format\n params_dict['num_sample'] = num_sample\n params_dict['sampling_method'] = sampling_method\n params_dict['file_size'] = file_size\n params_dict['sleep_cycle'] = sleep_cycle\n params_dict['mlsync'] = mlsync\n params_dict['auto_sub'] = auto_sub\n params_dict['sub_dock_script'] = sub_dock_script\n params_dict['num_sub'] = num_sub\n\n gapjil(params_dict)\n\n\nif __name__ == \"__main__\":\n main()\n"
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"text": "#!/usr/bin/env python\nimport sys\nimport os\nimport argparse\nfrom filelock import FileLock\n\n\ndef set_job_from_list(job_idx_list, list_dir):\n list_file = list_dir + '/list.txt'\n freeze_lock = FileLock('{}.lock'.format(list_file))\n with freeze_lock.acquire(timeout=30):\n if os.path.exists(list_file):\n with open(list_file, 'r') as fp:\n lines = fp.readlines()\n else:\n lines = list()\n with open(list_file, 'w') as fp:\n for job_idx in job_idx_list:\n line = job_idx + '\\n'\n fp.write(line)\n for line in lines:\n fp.write(line)\n return\n\n\ndef remove_job_from_list(restart_list, list_dir):\n job_idx_list = list()\n list_file = list_dir + '/list.txt'\n freeze_lock = FileLock('{}.lock'.format(list_file))\n with freeze_lock.acquire(timeout=30):\n if os.path.exists(list_file):\n with open(list_file, 'r') as fp:\n lines = fp.readlines()\n with open(list_file, 'w') as fp:\n for line in lines:\n job_idx = line.strip()\n if restart_list != 'all':\n if job_idx not in restart_list:\n fp.write(line)\n else:\n job_idx_list += [job_idx]\n else:\n job_idx_list += [job_idx]\n return job_idx_list\n\n\ndef move_current_to_todo(params_dict):\n restart_list = params_dict['restart_list']\n todo_dir = params_dict['todo_dir']\n current_dir = params_dict['current_dir']\n smi_file_format = params_dict['smi_file_format']\n job_idx_list = remove_job_from_list(restart_list, current_dir)\n print('restart job_idx list', job_idx_list, flush=True)\n for job_idx in job_idx_list:\n job_todo_file = todo_dir + '/' + job_idx + '.' + smi_file_format\n job_current_file = current_dir + '/' + job_idx + '.' + smi_file_format\n os.replace(job_current_file, job_todo_file)\n\n set_job_from_list(job_idx_list, todo_dir)\n\n return\n\n\ndef main():\n\n parser = argparse.ArgumentParser(description='worker for docking')\n parser.add_argument('--work_dir', type=str, required=False,\n default='workflow', help='workflow directory')\n parser.add_argument('-s', '--smi_file_format', type=str, required=False,\n default='pkl', help='pkl (default), txt, csv, tsv')\n parser.add_argument('--restart_list', type=str, required=True,\n nargs='+',\n help='example: --restart_list 0_0 0_1 0_2 ...' +\n 'or --restart_list all')\n\n args = parser.parse_args()\n\n work_dir = args.work_dir\n todo_dir = work_dir + '/todo'\n current_dir = work_dir + '/current'\n done_dir = work_dir + '/done'\n smi_file_format = args.smi_file_format\n restart_list = args.restart_list\n if 'all' in restart_list:\n restart_list = 'all'\n params_dict = dict()\n params_dict['work_dir'] = work_dir\n params_dict['todo_dir'] = todo_dir\n params_dict['current_dir'] = current_dir\n params_dict['done_dir'] = done_dir\n params_dict['smi_file_format'] = smi_file_format\n params_dict['restart_list'] = restart_list\n\n move_current_to_todo(params_dict)\n\n\nif __name__ == \"__main__\":\n main()\n"
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"text": "from setuptools import setup, find_packages\n\nsetup(name='VHTS',\n version='0.1',\n packages=['vhts'],\n# packages=find_packages(),\n url='https://github.com/gicsaw/VHTS',\n license='MIT LICENSE',\n author='Seung Hwan Hong',\n author_email='[email protected]',\n description='',\n scripts=['bin/master_dock.py',\n 'bin/pydock_run.py',\n 'bin/sub_dock.py',\n 'bin/vhts_check_restart.py'\n ]\n)\n\n\n"
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"text": "import pandas as pd\nimport numpy as np\n\ndef cal_metric(df, num_active,num_data, value='Docking1'):\n df2 = df.sort_values(value, ascending=False)\n #print(df2)\n for num_select in range(100, df2.shape[0], 100):\n #num_select = 1000\n df3 = df2[0:num_select]\n ratio_a = (num_active/num_data)\n TP = (df3['activity']==True).sum()\n precision = TP/num_select\n print(precision/ratio_a, TP, num_select, num_active, num_data, precision, ratio_a)\n\nfile_name = 'workflow/master/docking.pkl'\n#file_name = 'docking.pkl'\n\ndf1 = pd.read_pickle(file_name)\nfile_name2 = 'prediction.csv'\ndf2 = pd.read_csv(file_name2)\ndf = pd.merge(df1, df2[['MOL_ID', 'prediction']], on='MOL_ID')\n\nnum_data = df.shape[0]\nprint(num_data)\n#num_active = 714\nnum_active = (df['activity']==True).sum()\nprint(num_active)\n#cal_metric(df, num_active, value='Docking1')\n#docking = df['Docking']\n#pinfo = df['Pinfo']\n#print(df[['MOL_ID', 'Pinfo']])\n#pinfo2 = list()\n#for i in range(num_data) :\n# pin = pinfo.loc[i]\n# pin2 = pin.clip(0,2)\n# a = np.argsort(-pin2)\n# pinfo2 += [pin2]\n\ndf_b1 = df[df['prediction']==True]\nnum_select = df_b1.shape[0]\nprint(num_select)\ncal_metric(df_b1, num_active, num_data, value='VPIscore1')\n\n#print(df_b1.loc[0])\ndf_b2 = df_b1.sort_values('VPIscore1', ascending=False)\ndf_b3 = df_b2[['MOL_IDX', 'MOL_ID', 'SMILES', 'activity', 'VPIscore_info1', 'VPIscore1', 'Docking1', 'Pinfo']]\nfile_out = 'docking_metal_rule.csv'\ndf_b3.to_csv(file_out, index=False)\n\n\n"
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"text": "find_pf_receptor.py -v config.txt -r pdb/receptor.pdb -p pdb/pf_receptor.txt -t pdb/crystal_ligand.pdb -u pdb/pf_receptor_info.txt\n\n# vhts for slurm\nmkdir workflow\ncd workflow\nmkdir current done master todo\ncd ..\ncp smiles_list.pkl workflow/master/remain.pkl\nnohup master_dock.py --arg_file master_config.txt > mm.txt 2>&1 &\nsbatch slurm_sub_dock.sh\n\n\n"
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"text": "import numpy as np\nfrom pifinder import pifinder\n\n\nclass my_class(object):\n def __init__(self, docking_params):\n self.use_piscore = docking_params['use_piscore']\n if self.use_piscore:\n self.pharma = docking_params['pharma']\n self.use_pinfo = self.pharma.use_pinfo\n self.vpiscore_w1 = docking_params['vpiscore_w1']\n self.vpiscore_w2 = docking_params['vpiscore_w2']\n\n def simulation_process(self, idx, mol_id, smi, pid, smi_p,\n out_dock_dir1, docking_pdb_file, result_dict):\n if self.use_piscore:\n if self.output_save:\n pf_ligand_file = '%s/pf_%s.txt' % (out_dock_dir1, mol_id)\n interaction_file = '%s/pinter_%s.txt' % (out_dock_dir1, mol_id)\n else:\n pf_ligand_file = None\n interaction_file = None\n try:\n result_piscore = self.pharma.cal_piscore(\n docking_pdb_file, pf_ligand_file, interaction_file)\n total_count_dict, total_count_info_dict = result_piscore\n keys = list(total_count_dict.keys())\n piscore = list()\n if self.use_pinfo:\n pinfo = list()\n for key in keys:\n count_dict = total_count_dict[key]\n pis = count_dict['Score']\n piscore += [pis]\n if self.use_pinfo:\n count_info_dict = total_count_info_dict[key]\n pin = count_info_dict['Score']\n pinfo += [pin]\n piscore = np.array(piscore, dtype=np.float32)\n if self.use_pinfo:\n pinfo = np.array(pinfo, dtype=np.float32)\n\n except Exception as e:\n print(e, 'piscore', idx, mol_id, smi_p, flush=True)\n piscore = np.array([0], dtype=np.float32)\n if self.use_pinfo:\n pinfo = np.array([0], dtype=np.float32)\n\n result_dict['piscore'] = piscore\n if self.rescoring:\n docking_rescore = result_dict['docking_re']\n vpiscore = (self.vpiscore_w1 * (-docking_rescore)\n + (1.0 - self.vpiscore_w1) * piscore)\n else:\n docking_score = result_dict['docking']\n vpiscore = (self.vpiscore_w1 * (-docking_score)\n + (1.0 - self.vpiscore_w1) * piscore)\n\n result_dict['vpiscore'] = vpiscore\n\n if self.use_pinfo:\n result_dict['pinfo'] = pinfo\n if self.rescoring:\n docking_rescore = result_dict['docking_re']\n vpiscore_info = (self.vpiscore_w1 * (-docking_rescore)\n + (1.0 - self.vpiscore_w1)\n * (self.vpiscore_w2 * piscore\n + (1.0 - self.vpiscore_w1) * pinfo))\n else:\n docking_score = result_dict['docking']\n vpiscore_info = (self.vpiscore_w1 * (-docking_score)\n + (1.0 - self.vpiscore_w1)\n * (self.vpiscore_w2 * piscore\n + (1.0 - self.vpiscore_w1) * pinfo))\n\n result_dict['vpiscore_info'] = vpiscore_info\n\n def predict(self, smiles_list, result_dict, return_dict):\n data = list(enumerate(smiles_list))\n num_data = len(data)\n\n keys = sorted(return_dict.keys())\n if self.use_piscore:\n piscore_list = list()\n vpiscore_list = list()\n if self.use_pinfo:\n pinfo_list = list()\n vpiscore_info_list = list()\n\n for key in range(num_data):\n if key in keys:\n result_dict0 = return_dict[key]\n if self.use_piscore:\n if 'piscore' in result_dict0:\n piscore = result_dict0['piscore']\n else:\n piscore = np.array([0], dtype=np.float32)\n\n if 'vpiscore' in result_dict0:\n vpiscore = result_dict0['vpiscore']\n else:\n vpiscore = np.array([0], dtype=np.float32)\n if self.use_pinfo:\n if 'pinfo' in result_dict0:\n pinfo = result_dict0['pinfo']\n else:\n pinfo = np.array([0], dtype=np.float32)\n if 'vpiscore_info' in result_dict0:\n vpiscore_info = result_dict0['vpiscore_info']\n else:\n vpiscore_info = np.array([0], dtype=np.float32)\n else:\n if self.use_piscore:\n piscore = np.array([0], dtype=np.float32)\n vpiscore = np.array([0], dtype=np.float32)\n if self.use_pinfo:\n pinfo = np.array([0], dtype=np.float32)\n vpiscore_info = np.array([0], dtype=np.float32)\n if self.use_piscore:\n piscore_list += [piscore]\n vpiscore_list += [vpiscore]\n if self.use_pinfo:\n pinfo_list += [pinfo]\n vpiscore_info_list += [vpiscore_info]\n if self.use_piscore:\n result_dict['piscore_list'] = piscore_list\n result_dict['vpiscore_list'] = vpiscore_list\n if self.use_pinfo:\n result_dict['pinfo_list'] = pinfo_list\n result_dict['vpiscore_info_list'] = vpiscore_info_list\n return result_dict\n\n\ndef parser_arg(parser):\n parser.add_argument('--piscore_receptor', required=False,\n default=None,\n help='input receptor pdb file for piscore')\n parser.add_argument('--pf_receptor', required=False,\n default=None,\n help='output for pharmacophoric feature of receptor')\n parser.add_argument('--pinfo_ligand', required=False,\n default=None, help='template ligand file for pinfo')\n parser.add_argument('--pf_receptor_info', required=False, default=None,\n help='output for template feature of receptor')\n parser.add_argument('--pi_cutoff', type=float, default=6.5, required=False,\n help='pharmacophoric interaction cutoff distance')\n parser.add_argument('--vpiscore_w1', type=float, required=False,\n default=0.2,\n help='weight_1 of VPIscore\\n' +\n 'VPIscore = w1*vina_score + (1-w1)*piscore')\n parser.add_argument('--vpiscore_w2', type=float, required=False,\n default=0.8,\n help='weight_2 of VPIscore_info\\n' +\n 'VPIscore_info = w1*vina_score + (1-w1)*(w2*piscore'\n + '(1-w2)*pinfo)')\n parser.add_argument('--include_hydrophobic', action='store_true',\n required=False,\n help='include hydrophobic feature for template')\n\n\ndef arg_to_params(parser, docking_params):\n\n args = parser.parse_args()\n use_piscore = False\n if args.pinfo_ligand is not None or args.pf_receptor_info is not None:\n use_piscore = True\n pharma = pifinder.set_pifinder(args)\n docking_params['use_piscore'] = use_piscore\n docking_params['pharma'] = pharma\n docking_params['use_pinfo'] = pharma.use_pinfo\n docking_params['vpiscore_w1'] = args.vpiscore_w1\n docking_params['vpiscore_w2'] = args.vpiscore_w2\n return docking_params\n\n\ndef my_score_to_df(df, docking_params, result_dict):\n if docking_params['use_piscore']:\n piscore = result_dict['piscore_list']\n vpiscore = result_dict['vpiscore_list']\n vpiscore_1 = [x.max() for x in vpiscore]\n df['VPIscore1'] = vpiscore_1\n\n if docking_params['use_pinfo']:\n pinfo = result_dict['pinfo_list']\n vpiscore_info = result_dict['vpiscore_info_list']\n vpiscore_info_1 = [x.max() for x in vpiscore_info]\n\n df['VPIscore_info1'] = vpiscore_info_1\n\n df['VPIscore'] = vpiscore\n if docking_params['use_pinfo']:\n df['VPIscore_info'] = vpiscore_info\n df['PIscore'] = piscore\n if docking_params['use_pinfo']:\n df['Pinfo'] = pinfo\n"
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"text": "#!/bin/bash\n#SBATCH --job-name=docking # Job name\n#SBATCH --partition=docking # Job name\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=256\n#SBATCH --time=72:00:00 # Time limit hrs:min:sec\n#SBATCH --output=subdock_%j.log # Standard output and error log\n\n## --ntasks=256 # Run on a single CPU\n## [email protected]\n## --mail-type=All\n\npwd; hostname; date\n\nNPROCS=$SLURM_NPROCS\necho NPROCS=$NPROCS\n#NPROCS=$SLURM_NTASKS\n#echo NPROCS=$NPROCS\n#NPROCS=`srun --nodes=${SLURM_NNODES} bash -c 'hostname' |wc -l`\n#echo NPROCS=$NPROCS\n\nsource activate ml\nsub_dock.py --arg_file subdock_config.txt --dock_config config.txt --my_module ./my_module.py --out_log print --num_sub_proc=$NPROCS\n\ndate\n"
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"text": "import sys\nimport numpy as np\nfrom vhts import metal_bind\n\n\nclass my_class(object):\n def __init__(self, docking_params):\n self.check_metal_bind = docking_params['check_metal_bind']\n if self.check_metal_bind:\n self.metal_coor = docking_params['metal_coor']\n self.metal_bind_cutoff = docking_params['metal_bind_cutoff']\n\n def simulation_process(self, idx, mol_id, smi, pid, smi_p,\n out_dock_dir1, docking_pdb_file, result_dict):\n\n if self.check_metal_bind:\n try:\n result = metal_bind.find_neighbor_metal(\n docking_pdb_file, self.metal_coor,\n dist_cutoff=self.metal_bind_cutoff,\n skip_neighbor_hydrogen=True)\n num_metal_bind_atom_list = np.array(result, dtype=np.float32)\n except Exception as e:\n num_metal_bind_atom_list = np.array([0], dtype=np.float32)\n print(e, 'check metal bind', idx, mol_id, smi_p, flush=True)\n result_dict['num_metal_bind_atom'] = num_metal_bind_atom_list\n\n def predict(self, smiles_list, result_dict, return_dict):\n data = list(enumerate(smiles_list))\n num_data = len(data)\n\n keys = sorted(return_dict.keys())\n\n if self.check_metal_bind:\n num_metal_bind_atom_list = list()\n\n for key in range(num_data):\n if key in keys:\n result_dict0 = return_dict[key]\n\n if self.check_metal_bind:\n if 'num_metal_bind_atom' in result_dict0:\n num_metal_bind_atom = result_dict0['num_metal_bind_atom']\n else:\n num_metal_bind_atom = np.array([0], dtype=np.float32)\n else:\n if self.check_metal_bind:\n num_metal_bind_atom = np.array([0], dtype=np.float32)\n if self.check_metal_bind:\n num_metal_bind_atom_list += [num_metal_bind_atom]\n if self.check_metal_bind:\n result_dict['num_metal_bind_atom'] = num_metal_bind_atom_list\n return result_dict\n\n\ndef parser_arg(parser):\n parser.add_argument('--metal_coor', type=str, default=None, required=False,\n help='position of metal_ion,' +\n ' example: --metal_coor=\"1.0,-1.0,0.0\" default: None')\n parser.add_argument('--metal_cutoff', type=float, required=False,\n default=3.0,\n help='metal ion - HDA cutoff distance,')\n\n\ndef arg_to_params(parser, docking_params):\n\n args = parser.parse_args()\n\n check_metal_bind = False\n if args.metal_coor is not None:\n check_metal_bind = True\n\n metal_coor = np.array(args.metal_coor.strip(\n '\"').split(','), dtype=np.float32)\n if metal_coor.shape[0] != 3:\n print('metal coordinate is strange', args.metal_coor)\n sys.exit()\n metal_bind_cutoff = args.metal_cutoff\n\n docking_params['check_metal_bind'] = check_metal_bind\n docking_params['metal_coor'] = metal_coor\n docking_params['metal_bind_cutoff'] = metal_bind_cutoff\n return docking_params\n\n\ndef my_score_to_df(df, docking_params, result_dict):\n\n if docking_params['check_metal_bind']:\n num_metal_bind_atom_list = result_dict['num_metal_bind_atom']\n df['Metal_bind'] = num_metal_bind_atom_list\n"
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"text": "#!/usr/bin/env python\nimport sys\nimport os\nimport argparse\nimport vhts.pydock as pydock\nimport pandas as pd\n\n\nclass LoadFromConfig(argparse.Action):\n def __call__(self, parser, namespace, values, option_string=None):\n with values as f:\n parser.parse_args(f.read().split(), namespace)\n\n\nclass ExtendAction(argparse.Action):\n\n def __call__(self, parser, namespace, values, option_string=None):\n items = getattr(namespace, self.dest) or []\n items.extend(values)\n setattr(namespace, self.dest, items)\n\n\ndef parser_arg(parser):\n # vina parameter\n\n parser.register('action', 'extend', ExtendAction)\n parser.add_argument('--arg_file', type=open, required=False, default=None,\n action=LoadFromConfig, help='argment file')\n parser.add_argument('--dock_config', type=str, required=False,\n default=None, help='docking config file ')\n parser.add_argument('-v', '--vina_program', type=str, required=False,\n default='qvina02',\n help='select vina, qvina02, or smina')\n parser.add_argument('--my_module', type=str, required=False,\n default=None,\n help='set user python module path (for pifinder)')\n parser.add_argument('--neutralize', action='store_true',\n required=False, help='neutralize smiles ')\n parser.add_argument('--pH', type=float, default=None,\n required=False, help='protonate state for pH 7.4 ')\n parser.add_argument('--output_save', action='store_true', required=False,\n help='default output pdbqt is temp file ')\n parser.add_argument('--gen3d_dir', type=str, required=False, default='tmp',\n help='3d initial conformation directory')\n parser.add_argument('--dock_dir', type=str, required=False,\n default='tmp', help='binding conformation directory')\n parser.add_argument('--num_sub_proc', type=int, required=False,\n default=10, help=' --num_sub_proc 10')\n parser.add_argument('--timeout_gen3d', type=int, required=False,\n default=1, help=' --timeout_gen3d 1')\n parser.add_argument('--timeout_dock', type=int, required=False,\n default=120, help=' --timeout_dock 120')\n parser.add_argument('--tlen', type=int, default='7', required=False,\n help='lenth of sub directory name, default: 7')\n parser.add_argument('--pout', type=int, default='0', required=False,\n help='print processing out: 0 or number, default: 0')\n parser.add_argument('--rescoring_program', type=str, required=False,\n default='smina', help='smina path')\n parser.add_argument('--rescoring_config', type=str, required=False,\n default=None, help='docking config file for rescoring')\n\n return\n\n\ndef arg_to_params(parser):\n\n use_my_module = False\n for i, m in enumerate(sys.argv):\n if m == '--my_module':\n my_module_path = sys.argv[i+1]\n use_my_module = True\n my_module_dir = os.path.dirname(my_module_path)\n sys.path.append(my_module_dir)\n import my_module\n my_module.parser_arg(parser)\n\n args = parser.parse_args()\n\n vina_program = args.vina_program\n num_sub_proc = args.num_sub_proc\n timeout_gen3d = args.timeout_gen3d\n timeout_dock = args.timeout_dock\n output_save = args.output_save\n gen3d_dir = args.gen3d_dir\n dock_dir = args.dock_dir\n dock_config_file = args.dock_config\n\n tlen = args.tlen\n pout = args.pout\n neutralize = args.neutralize\n pH = args.pH\n\n rescoring = False\n rescoring_config_file = args.rescoring_config\n rescoring_program = args.rescoring_program\n if rescoring_config_file is not None:\n rescoring = True\n\n docking_params = dict()\n docking_params['vina_program'] = vina_program\n docking_params['gen3d_dir'] = gen3d_dir\n docking_params['dock_dir'] = dock_dir\n docking_params['num_sub_proc'] = num_sub_proc\n docking_params['timeout_gen3d'] = timeout_gen3d\n docking_params['timeout_dock'] = timeout_dock\n docking_params['output_save'] = output_save\n docking_params['tlen'] = tlen\n docking_params['pout'] = pout\n docking_params['neutralize'] = neutralize\n docking_params['pH'] = pH\n docking_params['dock_config_file'] = dock_config_file\n docking_params['rescoring'] = rescoring\n docking_params['rescoring_program'] = rescoring_program\n docking_params['rescoring_config_file'] = rescoring_config_file\n\n my_module_path = args.my_module\n docking_params['use_my_module'] = use_my_module\n docking_params['my_module_path'] = my_module_path\n\n if use_my_module:\n docking_params = my_module.arg_to_params(parser, docking_params)\n\n return args, docking_params\n\n\ndef main():\n\n parser = argparse.ArgumentParser(description='docking with multi process')\n parser.add_argument('-l', '--ligand_list_file', type=str, required=False,\n default='smiles.txt',\n help=' --ligand_list_file smiles.txt')\n parser.add_argument('-o', '--output_file', type=str, required=False,\n default='docking.txt',\n help=' --output_file docking.txt')\n\n parser_arg(parser)\n\n args, docking_params = arg_to_params(parser)\n ligand_list_file = args.ligand_list_file\n output_file = args.output_file\n\n if len(sys.argv) < 2:\n parser.print_usage()\n sys.exit()\n if args.dock_config is None:\n parser.print_usage()\n print('dock_config is missing')\n sys.exit()\n\n ligand_file_format = ligand_list_file.strip().split('.')[-1]\n if ligand_file_format == 'txt':\n field_separator = '\\s+'\n elif ligand_file_format == 'csv':\n field_separator = ','\n elif ligand_file_format == 'tsv':\n field_separator = '\\t'\n else:\n field_separator = None\n\n if ligand_file_format == 'pkl':\n df = pd.read_pickle(ligand_list_file)\n else:\n df = pd.read_csv(ligand_list_file, sep=field_separator)\n\n# num_data = df.shape[0]\n fkey = df.keys()[0]\n if fkey.startswith('#'):\n df.rename(columns={fkey: fkey[1:]}, inplace=True)\n smiles_list = df[['MOL_ID', 'SMILES']].values.tolist()\n\n# smiles_list = smiles_list[0:10]\n docking_vina = pydock.DockingVina(docking_params)\n\n result_dict = docking_vina.predict(smiles_list)\n docking_score_list = result_dict['docking']\n docking_min = [x[0] for x in docking_score_list]\n df['Docking1'] = docking_min\n df['Docking'] = docking_score_list\n if docking_params['rescoring']:\n rescoring = result_dict['docking_re']\n df['Docking_re'] = rescoring\n\n use_my_module = docking_params['use_my_module']\n my_module_path = docking_params['my_module_path']\n\n if use_my_module:\n my_module_dir = os.path.dirname(my_module_path)\n sys.path.append(my_module_dir)\n import my_module\n my_module.my_score_to_df(df, docking_params, result_dict)\n\n sep = field_separator\n if sep == '\\s+':\n sep = ' '\n\n if output_file.strip().split('.')[-1] == 'pkl':\n df.to_pickle(output_file)\n else:\n df.to_csv(output_file, sep=sep, float_format='%.3f', index=False)\n\n\nif __name__ == \"__main__\":\n main()\n"
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"text": "import os\nimport pandas as pd\nimport numpy as np\nfrom rdkit import Chem\nfrom multiprocessing import Manager\nfrom multiprocessing import Process\nfrom multiprocessing import Queue\nfrom openbabel import pybel\nimport subprocess\n\n\ndef ligand_preperation(smi, pH=7.4):\n run_line = 'obabel -:%s -osmi --neutralize' % (smi)\n result = subprocess.check_output(run_line.split(),\n stderr=subprocess.STDOUT,\n universal_newlines=True)\n for line in result.split('\\n'):\n if line.startswith('1 molecule converted'):\n continue\n if line.startswith('0 molecule converted'):\n smi0 = smi\n break\n if len(line.strip()) < 1:\n continue\n smi0 = line.strip()\n try:\n m = pybel.readstring(\"smi\", smi0)\n m.OBMol.AddHydrogens(False, True, pH)\n smi_p = m.write(\"smi\").strip()\n except Exception:\n smi_p = smi0\n return smi_p\n\n\ndef creator(q, data, num_sub_proc):\n for d in data:\n idx = d[0]\n q.put((idx, d[1]))\n for i in range(0, num_sub_proc):\n q.put('DONE')\n\n\ndef worker(q, return_dict):\n smarts_pattern_list = [\n '[#7;H0&R]~*~*~[#7;H0&R]',\n# '[#7;H0&R]~*~*~[#8R0]',\n '[#7;H0&R]~*~*~[#8R0;D1,D2&H1]',\n '[#8R0;D1,D2&H1]~*~*~[#8R0;D1,D2&H1]',\n '[#8R0;D1,D2&H1]~*~*~*~[#8R0;D1,D2&H1]',\n\n# '[#8R0]~*~*~[#8R0]',\n# '[#8R0]~*~*~*~[#8R0]',\n\n# '[#7;H0&R]~*~*~*~[#8R0]',\n# '[#8R0]~*~*~*~*~[#8R0]'\n ]\n smarts_pattern_ring_list = ['[*r]1[*r][*r][*r]1', '[*r]1[*r][*r][*r][*r]1',\n '[*r]1[*r][*r][*r][*r][*r]1', '[*r]1[*r][*r][*r][*r][*r][*r]1',\n '[*r]1[*r][*r][*r][*r][*r][*r][*r]1']\n smarts_list = list()\n for smarts_pattern in smarts_pattern_list:\n smarts = pybel.Smarts(smarts_pattern)\n smarts_list += [smarts]\n smarts_ring_list = list()\n for smarts_pattern in smarts_pattern_ring_list:\n smarts = pybel.Smarts(smarts_pattern)\n smarts_ring_list += [smarts]\n\n pout = 1000\n pid = os.getpid()\n while True:\n qqq = q.get()\n if qqq == 'DONE':\n # print('proc =', os.getpid())\n break\n\n (idx, d) = qqq\n mol_id = d[0]\n smi = d[1]\n # print screening processing in every pout step\n if idx % pout == pout-1:\n print(\"processing: \", idx+1, flush=True)\n\n prediction = False\n smi_p = ligand_preperation(smi, pH=7.4)\n try:\n m = pybel.readstring('smi', smi)\n except:\n return_dict[idx] = prediction\n continue\n\n m.addh()\n Nfeatures = 0\n features_m_list = list()\n for smarts in smarts_list:\n features = smarts.findall(m)\n Nfeatures += len(features)\n features_m_list += [features]\n if Nfeatures < 1:\n return_dict[idx] = prediction\n continue\n ring_list = list()\n for smarts in smarts_ring_list:\n ring_l0 = smarts.findall(m)\n for ring in ring_l0:\n ring_list += [set(ring)]\n\n Nfeatures_new = 0\n for ii, features in enumerate(features_m_list):\n for feature in features:\n check_subset = False\n if ii == 0:\n aa = set((feature[0], feature[2]))\n aa2 = set((feature[1], feature[3]))\n elif ii == 1 or ii == 2:\n aa = set((feature[1], feature[2]))\n elif ii == 3 : # or ii == 4:\n aa = set((feature[1], feature[3]))\n for ratoms in ring_list:\n if aa.issubset(ratoms):\n check_subset = True\n break\n if ii==0:\n if aa2.issubset(ratoms):\n check_subset = True\n break\n\n if not check_subset:\n Nfeatures_new += 1\n if Nfeatures_new > 0:\n prediction = True\n\n return_dict[idx] = prediction\n\n\ndef main():\n\n file_name = 'smiles_list.csv'\n# file_name = 'active.csv'\n\n df = pd.read_csv(file_name)\n# df = df[0:2000]\n num_data = df.shape[0]\n\n num_sub_proc = 30\n smiles_list = df[['MOL_IDX', 'SMILES']].values.tolist()\n data = list(enumerate(smiles_list))\n num_data = len(data)\n num_sub_proc = min(num_sub_proc, num_data)\n\n q1 = Queue()\n manager = Manager()\n return_dict = manager.dict()\n proc_master = Process(target=creator,\n args=(q1, data, num_sub_proc))\n proc_master.start()\n\n procs = []\n for sub_id in range(0, num_sub_proc):\n proc = Process(target=worker, args=(q1, return_dict))\n procs.append(proc)\n proc.start()\n\n q1.close()\n q1.join_thread()\n proc_master.join()\n for proc in procs:\n proc.join()\n keys = sorted(return_dict.keys())\n\n prediction_list = list()\n for key in keys:\n prediction = return_dict[key]\n prediction_list += [prediction]\n df['prediction'] = prediction_list\n\n\n out_file = 'prediction.csv'\n# out_file = 'prediction_active.csv'\n\n df.to_csv(out_file, index=False)\n\n prediction = np.array(prediction_list, dtype=np.bool)\n activity = np.array(df['activity'], dtype=np.bool)\n NA = activity.sum()\n NP = prediction.sum()\n NTP = (activity*prediction).sum()\n print(NTP, NP, NA)\n print(NTP/NP, NTP/NA)\n\n\nif __name__ == \"__main__\":\n main()\n"
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"text": "\nexport PYTHONPATH=\"${YOUR_PATH}/VHTS:$PYTHONPATH\"\nexport PATH=\"${YOUR_PATH}/VHTS/bin:$PATH\"\n# PIFinder\nexport PYTHONPATH=\"${YOUR_PATH}/PIFinder:$PYTHONPATH\nexport PATH=\"{YOUR_PATH}/PIFinder/bin:$PATH\"\n\n# prepare \nfind_pf_receptor.py -v config.txt -r pdb/2G1MA_receptor.pdb -p pdb/pf_receptor.txt -t pdb/2G1MA_4HG.pdb -u pdb/pf_receptor_info.txt\n\n# fix pdb/pf_receptor_info.txt to below (only remain metal and change weight)\nvi pdb/pf_receptor_info.txt\n\n$ echo pdb/pf_receptor_info.txt\nfeature_type:atom_idx_group:pattern_idx:pseudo_atom_coor:atom_type:etc:chain_id:residue_name:residue_num:weight\nMetal:(3315):3:(39.864,22.370,13.612):Fe::A:FE2:404:5.000\n\n\n### VHTS for batch system (slurm)\n\nmkdir workflow\ncd workflow\nmkdir current done master todo\ncd ..\n\ncp smiles_list.csv workflow/master/remain.csv\n\n# master\nnohup master_dock.py --arg_file master_config.txt > master_log.txt 2>&1 &\n# sub_dock \nsbatch slurm_sub_dock.sh\n# \n\n\n### non VHTS \npydock_run.py --arg_file pydock_config.txt --my_module ./my_module.py\n\n"
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"text": "#! /usr/bin/env python\n#\n# Substitute for rbtether of rDock. Will align input molecules to a reference fragment defined by a smarts string, \n# it will add a TETHERED ATOM property field to the output SDF that is correctly understood by rDock \n# rDock will restrain the matching atom positions to the reference molecule coordinates.\n#\n# Initially implemented with a conformational search algorithm to better match target coordinates.\n# But had problems with OBabel FF generating non-sense conformers. So in this version the conformer search is commented out.\n# Now if the input molecule do not have a good conformation, might not align well with the target. This effect will be \n# dimished or even vanish if the SMARTS string is defined for a rigid region (like a ring).\n# I'm still trying to incorporate somehow this conformational search.\n#\n# Script distributed under GNU LGPL 3.0 along rDock software.\n# \n# Author: Daniel Alvarez-Garcia\n# Date: 08-11-2013\n\nimport math\nimport numpy as npy\ntry:\n # Open Babel 3.0\n from openbabel import pybel\nexcept ModuleNotFoundError:\n # Open Babel 2.x\n import pybel\n\ndef superpose3D(ref, target, weights=None,refmask=None,targetmask=None,returnRotMat=False):\n \"\"\"superpose3D performs 3d superposition using a weighted Kabsch algorithm : http://dx.doi.org/10.1107%2FS0567739476001873 & doi: 10.1529/biophysj.105.066654\n definition : superpose3D(ref, target, weights,refmask,targetmask)\n @parameter 1 : ref - xyz coordinates of the reference structure (the ligand for instance)\n @type 1 : float64 numpy array (nx3)\n ---\n @parameter 2 : target - theoretical target positions to which we should move (does not need to be physically relevant.\n @type 2 : float 64 numpy array (nx3)\n ---\n @parameter 3: weights - numpy array of atom weights (usuallly between 0 and 1)\n @type 3 : float 64 numpy array (n)\n @parameter 4: mask - a numpy boolean mask for designating atoms to include\n Note ref and target positions must have the same dimensions -> n*3 numpy arrays where n is the number of points (or atoms)\n Returns a set of new coordinates, aligned to the target state as well as the rmsd\n \"\"\"\n if weights == None :\n weights=1.0\n if refmask == None :\n refmask=npy.ones(len(ref),\"bool\")\n if targetmask == None :\n targetmask=npy.ones(len(target),\"bool\")\n #first get the centroid of both states\n ref_centroid = npy.mean(ref[refmask]*weights,axis=0)\n #print ref_centroid\n refCenteredCoords=ref-ref_centroid\n #print refCenteredCoords\n target_centroid=npy.mean(target[targetmask]*weights,axis=0)\n targetCenteredCoords=target-target_centroid\n #print targetCenteredCoords\n #the following steps come from : http://www.pymolwiki.org/index.php/OptAlign#The_Code and http://en.wikipedia.org/wiki/Kabsch_algorithm\n # Initial residual, see Kabsch.\n E0 = npy.sum( npy.sum(refCenteredCoords[refmask] * refCenteredCoords[refmask]*weights,axis=0),axis=0) + npy.sum( npy.sum(targetCenteredCoords[targetmask] * targetCenteredCoords[targetmask]*weights,axis=0),axis=0)\n reftmp=npy.copy(refCenteredCoords[refmask])\n targettmp=npy.copy(targetCenteredCoords[targetmask])\n #print refCenteredCoords[refmask]\n #single value decomposition of the dotProduct of both position vectors\n try:\n dotProd = npy.dot( npy.transpose(reftmp), targettmp* weights)\n V, S, Wt = npy.linalg.svd(dotProd )\n except Exception:\n try:\n dotProd = npy.dot( npy.transpose(reftmp), targettmp)\n V, S, Wt = npy.linalg.svd(dotProd )\n except Exception:\n print(\"Couldn't perform the Single Value Decomposition, skipping alignment\", file=sys.stderr)\n return ref, 0\n # we already have our solution, in the results from SVD.\n # we just need to check for reflections and then produce\n # the rotation. V and Wt are orthonormal, so their det's\n # are +/-1.\n reflect = float(str(float(npy.linalg.det(V) * npy.linalg.det(Wt))))\n if reflect == -1.0:\n S[-1] = -S[-1]\n V[:,-1] = -V[:,-1]\n rmsd = E0 - (2.0 * sum(S))\n rmsd = npy.sqrt(abs(rmsd / len(ref[refmask]))) #get the rmsd\n #U is simply V*Wt\n U = npy.dot(V, Wt) #get the rotation matrix\n # rotate and translate the molecule\n new_coords = npy.dot((refCenteredCoords), U)+ target_centroid #translate & rotate\n #new_coords=(refCenteredCoords + target_centroid)\n #print U\n if returnRotMat : \n return U, ref_centroid, target_centroid, rmsd\n return new_coords,rmsd\n\n\ndef squared_distance(coordsA, coordsB):\n \"\"\"Find the squared distance between two 3-tuples\"\"\"\n sqrdist = sum( (a-b)**2 for a, b in zip(coordsA, coordsB) )\n return sqrdist\n \ndef rmsd(allcoordsA, allcoordsB):\n \"\"\"Find the RMSD between two lists of 3-tuples\"\"\"\n deviation = sum(squared_distance(atomA, atomB) for\n (atomA, atomB) in zip(allcoordsA, allcoordsB))\n return math.sqrt(deviation / float(len(allcoordsA)))\n \ndef mapToCrystal(xtal, pose):\n \"\"\"Some docking programs might alter the order of the atoms in the output (like Autodock Vina does...)\n this will mess up the rmsd calculation with OpenBabel\"\"\"\n query = pybel.ob.CompileMoleculeQuery(xtal.OBMol) \n mapper=pybel.ob.OBIsomorphismMapper.GetInstance(query)\n mappingpose = pybel.ob.vvpairUIntUInt()\n exit=mapper.MapUnique(pose.OBMol,mappingpose)\n return mappingpose[0]\n\ndef takeCoords(obmol):\n \"\"\"Take coordinates of an OBMol as a npy array\"\"\"\n return npy.array([atom.coords for atom in obmol])\n\ndef updateCoords(obmol, newcoords):\n \"Update OBMol coordinates. newcoords is a numpy array\"\n for i,atom in enumerate(obmol):\n atom.OBAtom.SetVector(*newcoords[i])\n\ndef prepareAtomString(idlist):\n s = \"\"\n n = len(idlist)\n for i, id in enumerate(idlist):\n s += \"{:d}\".format(id)\n if (i+1) == n: s+=\"\\n\"\n elif (i+1)%35 == 0: s+=\",\\n\"\n else: s+=\",\"\n return s\n\n\ndef find_tether_ref_coords(tether_ref_lig, tether_SMARTS, removeh=True):\n smarts = pybel.Smarts(tether_SMARTS)\n file_format = tether_ref_lig.split('.')[-1]\n ref = next(pybel.readfile(file_format, tether_ref_lig))\n if removeh:\n ref.removeh()\n refMatchIds = smarts.findall(ref)\n numRefMatchs = len(refMatchIds)\n\n refIndxPerMatch = [npy.array(rmi) - 1 for rmi in refMatchIds]\n\n # Take coordinates for the reference matched atoms\n refCoords = takeCoords(ref)\n refMatchCoords = [npy.take(refCoords, refIndx, axis=0)\n for refIndx in refIndxPerMatch]\n return refMatchCoords\n\n\ndef gen_smarts(tether_SMARTS):\n smarts = pybel.Smarts(tether_SMARTS)\n return smarts\n\ndef tether_ligand(ligand_file, smarts, refMatchCoords):\n numRefMatchs = len(refMatchCoords)\n molSupp = pybel.readfile(\"sdf\", ligand_file)\n# ff = pybel.ob.OBForceField_FindForceField('MMFF94')\n out = pybel.Outputfile('sdf', ligand_file, overwrite=True)\n count = 0\n e = None\n for i, mol in enumerate(molSupp):\n mol.OBMol.DeleteNonPolarHydrogens()\n molMatchAllIds = smarts.findall(mol)\n numMatchs = len(molMatchAllIds)\n if numMatchs == 0:\n continue\n # If more than one match, write an output of the same molecule for each match\n # Start a default bestcoord and rmsd for later looping for each pose\n bestCoordPerMatch = [\n [0 for i in range(numMatchs)] for i in range(numRefMatchs)]\n bestRMSPerMatch = [\n [999 for i in range(numMatchs)] for i in range(numRefMatchs)]\n\n molCoords = takeCoords(mol)\n for imatch, molMatchIds in enumerate(molMatchAllIds):\n molMatchIndx = npy.array(molMatchIds) - 1\n molMatchCoords = npy.take(molCoords, molMatchIndx, axis=0)\n\n for ir, refMatchCoord in enumerate(refMatchCoords):\n rotMat, targetCentroid, refCentroid, rmsd = superpose3D(\n molMatchCoords, refMatchCoord, returnRotMat=True)\n if rmsd < bestRMSPerMatch[ir][imatch]:\n newcoords = npy.dot(\n (molCoords - targetCentroid), rotMat) + refCentroid\n bestRMSPerMatch[ir][imatch] = rmsd\n bestCoordPerMatch[ir][imatch] = newcoords\n\n for imatch in range(numMatchs):\n for irefmatch in range(numRefMatchs):\n bestCoord = bestCoordPerMatch[irefmatch][imatch]\n bestRMS = bestRMSPerMatch[irefmatch][imatch]\n molMatchID = molMatchAllIds[imatch]\n updateCoords(mol, bestCoord)\n newData = pybel.ob.OBPairData()\n newData.SetAttribute(\"TETHERED ATOMS\")\n newData.SetValue(prepareAtomString(molMatchID))\n\n # Remove Previous DATA\n mol.OBMol.DeleteData(\"TETHERED ATOMS\")\n mol.OBMol.CloneData(newData) # Add new data\n out.write(mol)\n count += 1\n out.close()\n if count == 0:\n e = 'number of SMARTS match for tether is 0'\n return e\n\n\ndef read_tether_coords(tether_ref_coor_file, sep=None):\n fp = open(tether_ref_coor_file)\n lines = fp.readlines()\n fp.close()\n ref_match_coords = list()\n ref_match_coords_dict = dict()\n\n for line in lines[1:]:\n lis = line.strip().split(sep)\n match_id = int(lis[0])\n# idx = int(lis[1])\n x = float(lis[2])\n y = float(lis[3])\n z = float(lis[4])\n if match_id not in ref_match_coords_dict:\n ref_match_coords_dict[match_id] = list()\n ref_match_coords_dict[match_id] += [(x, y, z)]\n\n for match_id in ref_match_coords_dict:\n ref_match_coords += [npy.array(ref_match_coords_dict[match_id])]\n\n return ref_match_coords\n\n\ndef write_tether_coords(ref_match_coords, tether_ref_coor_file, sep=' '):\n fp = open(tether_ref_coor_file, 'w')\n line_out = 'match_id%sidx%sx%sy%sz\\n' % (sep, sep, sep, sep)\n fp.write(line_out)\n for match_id, ref_match_coord in enumerate(ref_match_coords):\n num_match_atom = ref_match_coord.shape[0]\n for idx in range(num_match_atom):\n x = ref_match_coord[idx][0]\n y = ref_match_coord[idx][1]\n z = ref_match_coord[idx][2]\n line_out = '%d%s%d%s%.3f%s%.3f%s%.3f\\n' % (match_id, sep, idx, sep,\n x, sep, y, sep, z)\n fp.write(line_out)\n fp.close()\n return\n\nif __name__ == \"__main__\":\n import sys\n\n if len(sys.argv) != 5:\n sys.exit(\"USAGE: {} reference.sdf input.sdf output.sdf 'SMARTS'\".format(sys.argv[0]))\n\n refsdf = sys.argv[1]\n molsdf = sys.argv[2]\n outsdf = sys.argv[3]\n smarts = pybel.Smarts(sys.argv[4])\n\n # Read reference pose and get atom list matching smarts query\n # if more than 1 match, take the first one\n ref = next(pybel.readfile(\"sdf\", refsdf))\n# ref.removeh()\n refMatchIds = smarts.findall(ref)\n numRefMatchs = len(refMatchIds)\n\n if not numRefMatchs:\n sys.exit(\"No match found in the reference structure and the SMARTS string given. Please check it.\")\n\n if numRefMatchs > 1: \n print(\"More than one match in the reference molecule for the SMARTS string given. Will tether each input molecule all possible ways.\")\n\n refIndxPerMatch = [npy.array(rmi) - 1 for rmi in refMatchIds]\n\n print(refIndxPerMatch)\n # Take coordinates for the reference matched atoms\n refCoords = takeCoords(ref)\n refMatchCoords = [npy.take(refCoords, refIndx, axis=0) for refIndx in refIndxPerMatch]\n print(refMatchCoords)\n\n # Do the same for molecule in molsdf\n out=pybel.Outputfile('sdf', outsdf, overwrite=True)\n molSupp = pybel.readfile(\"sdf\", molsdf)\n ff = pybel.ob.OBForceField_FindForceField('MMFF94')\n for i,mol in enumerate(molSupp):\n print(\"## Molecule {:d}\".format(i+1), end=\" \")\n mol.OBMol.DeleteNonPolarHydrogens()\n molMatchAllIds = smarts.findall(mol)\n numMatchs = len(molMatchAllIds)\n\n if numMatchs == 0:\n print(\"No_Match\", end=\" \")\n continue\n elif numMatchs == 1:\n print(\"Match\", end=\" \")\n elif numMatchs > 1:\n print(\"Multiple_Match SMART Matches for this molecule {:d}\".format(numMatchs), end=\" \")\n\n # If more than one match, write an output of the same molecule for each match\n # Start a default bestcoord and rmsd for later looping for each pose\n bestCoordPerMatch = [[0 for i in range(numMatchs)] for i in range(numRefMatchs)]\n bestRMSPerMatch = [[999 for i in range(numMatchs)] for i in range(numRefMatchs)]\n\n # Will do a randomrotorsearch to find conformer with the lower rmsd when superposing\n # At least 20 when possible\n #ff.Setup(mol.OBMol)\n #numats = mol.OBMol.NumAtoms()\n #numrot = mol.OBMol.NumRotors()\n #print \"Atoms: %i, Rotors: %i\"%(numats, numrot)\n #geomopt = 300\n #genconf = 100\n # increase iterations if bigger molecule or bigger number of rotatable bonds\n # for allowing better sampling\n #if numats > 40 and numrot > 5:\n # geomopt = 300\n # genconf = 150\n #if numats > 55 and numrot > 7:\n # genconf = 100\n # geomopt = 500\n #print \"\\tDoing conformational search with WeightedRotorSearch (%i, %i)...\"%(genconf, geomopt),\n #ff.SteepestDescent(500, 1.0e-4)\n #ff.WeightedRotorSearch(genconf,geomopt)\n #ff.ConjugateGradients(500, 1.0e-6)\n #ff.GetConformers(mol.OBMol)\n #numconf = mol.OBMol.NumConformers()\n numconf = 1\n #print \"%i conformers generated\"%numconf\n if numconf > 1:\n # Doing conf search\n #for i in range(numconf):\n # mol.OBMol.SetConformer(i)\n # confCoords = takeCoords(mol)\n # print 'coord:',confCoords[0,:]\n # \n # for imatch, molMatchIds in enumerate(molMatchAllIds):\n # molMatchIndx = npy.array(molMatchIds) - 1\n # confMatchCoords = npy.take(confCoords, molMatchIndx, axis=0)\n # \n # # Align: Get rotation matrix between the two sets of coords\n # # Apply rotation to the whole target molecule\n # rotMat, targetCentroid, refCentroid, rmsd = superpose3D(confMatchCoords, refMatchCoords, returnRotMat=True)\n # if rmsd < bestRMSPerMatch[imatch]: \n # newcoords = npy.dot((confCoords - targetCentroid), rotMat) + refCentroid\n # bestRMSPerMatch[imatch] = rmsd\n # bestCoordPerMatch[imatch] = newcoords\n # #if bestrms < 0.01: break\n pass\n else:\n molCoords = takeCoords(mol)\n for imatch, molMatchIds in enumerate(molMatchAllIds):\n # loop in each matching way for the input molecule\n molMatchIndx = npy.array(molMatchIds) - 1\n molMatchCoords = npy.take(molCoords, molMatchIndx, axis=0)\n\n # Loop over the reference matches\n # Align: Get rotation matrix between the two sets of coords\n # Apply rotation to the whole target molecule\n for ir, refMatchCoord in enumerate(refMatchCoords):\n rotMat, targetCentroid, refCentroid, rmsd = superpose3D(molMatchCoords, refMatchCoord, returnRotMat=True)\n if rmsd < bestRMSPerMatch[ir][imatch]:\n newcoords = npy.dot((molCoords - targetCentroid), rotMat) + refCentroid\n bestRMSPerMatch[ir][imatch] = rmsd\n bestCoordPerMatch[ir][imatch] = newcoords\n \n # Finally update molecule coordinates with the best matching coordinates found\n # change molecule coordinates, set TETHERED ATOMS property and save\n for imatch in range(numMatchs):\n for irefmatch in range(numRefMatchs):\n bestCoord = bestCoordPerMatch[irefmatch][imatch]\n bestRMS = bestRMSPerMatch[irefmatch][imatch]\n print(\"\\tBest RMSD reached (match {:d}, refmatch {:d}): {}\".format(imatch, irefmatch, bestRMS))\n molMatchID = molMatchAllIds[imatch]\n updateCoords(mol, bestCoord)\n newData = pybel.ob.OBPairData()\n newData.SetAttribute(\"TETHERED ATOMS\")\n newData.SetValue(prepareAtomString(molMatchID))\n\n mol.OBMol.DeleteData(\"TETHERED ATOMS\") # Remove Previous DATA\n mol.OBMol.CloneData(newData) # Add new data\n out.write(mol)\n \n out.close()\n \n print(\"DONE\")\n sys.stdout.close()\n sys.stderr.close()\n"
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"text": "import pandas as pd\nimport numpy as np\n\ndef cal_metric(df, num_active,num_data, value='Docking1'):\n df2 = df.sort_values(value, ascending=False)\n #print(df2)\n for num_select in range(100, df2.shape[0], 100):\n #num_select = 1000\n df3 = df2[0:num_select]\n ratio_a = (num_active/num_data)\n TP = (df3['activity']==True).sum()\n precision = TP/num_select\n print(precision/ratio_a, TP, num_select, num_active, num_data, precision, ratio_a)\n\nfile_name = 'workflow/master/docking.pkl'\n#file_name = 'docking.pkl'\n\ndf = pd.read_pickle(file_name)\nnum_data = df.shape[0]\nprint(num_data)\n#num_active = 714\nnum_active = (df['activity']==True).sum()\nprint(num_active)\ncal_metric(df, num_active, num_data, value='VPIscore1')\n#df_b2 = df_b1.sort_values('VPIscore1', ascending=False)\n\n#file_out = 'docking_bond.csv'\n#df_b2.to_csv(file_out, index=False)\n\n\n"
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"text": "# setup path\nexport PYTHONPATH=\"${YOUR_PATH}/vhts:$PYTHONPATH\"\nexport PATH=\"${YOUR_PATH}/vhts/bin:$PATH\"\n\n# find docking box from template ligand file\ncal_box.py --arg_file box_config.txt\n# single docking \npydock.py --arg_file pydock_config.txt --dock_config config.txt\n\n\n\n# for batch system\n# prepare \nmkdir workflow\ncd workflow\nmkdir current done master todo\ncd ..\n\ncp smiles_list.txt workflow/master/remain.txt\n# master\nnohup master_dock.py --arg_file master_config.txt > mm.txt 2>&1 &\n# sub_dock \nnohup sub_dock.py --arg_file subdock_config.txt --dock_config config.txt --out_log file > a.txt 2>&1 &\nnohup sub_dock.py --arg_file subdock_config.txt --dock_config config.txt --out_log file > b.txt 2>&1 &\n\n\n"
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"text": "find_pf_receptor.py -v config.txt -r pdb/receptor.pdb -p pdb/pf_receptor.txt -t pdb/crystal_ligand.pdb -u pdb/pf_receptor_info.txt\n\nsbatch slurm_pydock.sh\n"
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"text": "#!/usr/bin/env python\nimport sys\nimport os\nimport numpy as np\nfrom multiprocessing import Manager\nfrom multiprocessing import Process\nfrom multiprocessing import Queue\nimport subprocess\nimport argparse\nimport pandas as pd\nfrom pdbtools import ligand_tools\nfrom vhts import sdtether\n\n\nclass Docking(object):\n \"\"\"\n python module for Docking\n \"\"\"\n\n def __init__(self, docking_params):\n \"\"\"\n Construction Docking object\n \"\"\"\n self.docking_program = docking_params['docking_program']\n dp_tmp = self.docking_program.lower()\n if (dp_tmp.find('vina') != -1) or (dp_tmp.find('smina') != -1):\n self.dp = 'vina'\n elif (dp_tmp.find('rbdock') != -1):\n self.dp = 'rdock'\n else:\n self.dp = 'etc'\n if 'exhaustiveness' not in docking_params:\n self.exhaustiveness = 10\n else:\n self.exhaustiveness = docking_params['exhaustiveness']\n self.num_sub_proc = docking_params['num_sub_proc']\n self.timeout_gen3d = docking_params['timeout_gen3d']\n self.timeout_dock = docking_params['timeout_dock']\n self.neutralize = docking_params['neutralize']\n self.pH = docking_params['pH']\n self.dock_config_file = docking_params['dock_config_file']\n\n self.output_save = docking_params['output_save']\n if self.output_save:\n self.tlen = docking_params['tlen']\n\n self.pout = docking_params['pout']\n\n self.gen3d_dir = docking_params['gen3d_dir']\n if not os.path.exists(self.gen3d_dir):\n try:\n os.makedirs(self.gen3d_dir)\n except FileExistsError as e:\n print(e, flush=True)\n\n self.dock_dir = docking_params['dock_dir']\n if not os.path.exists(self.dock_dir):\n try:\n os.makedirs(self.dock_dir)\n except FileExistsError as e:\n print(e, flush=True)\n\n self.use_my_module = docking_params['use_my_module']\n if self.use_my_module:\n my_module_path = docking_params['my_module_path']\n my_module_dir = os.path.dirname(my_module_path)\n sys.path.append(my_module_dir)\n import my_module\n self.my_class = my_module.my_class\n self.my_class.__init__(self, docking_params)\n\n self.rescoring = docking_params['rescoring']\n self.rescoring_program = docking_params['rescoring_program']\n self.rescoring_config_file = docking_params['rescoring_config_file']\n\n self.tether_SMARTS = docking_params['tether_SMARTS']\n self.tether_ref_lig = docking_params['tether_ref_lig']\n self.tether_ref_coor_file = docking_params['tether_ref_coor_file']\n\n self.tether_docking = False\n if self.tether_SMARTS is not None:\n if self.dp != 'rdock':\n print('Tether scaffold docking is only supported by rdock.')\n sys.exit()\n self.smarts = sdtether.gen_smarts(self.tether_SMARTS)\n self.ref_match_coords = list()\n if self.tether_ref_coor_file is not None:\n self.ref_match_coords = sdtether.read_tether_coords(\n self.tether_ref_coor_file)\n elif self.tether_ref_lig is not None:\n self.ref_match_coords = sdtether.find_tether_ref_coords(\n self.tether_ref_ligand, self.tether_SMARTS)\n\n if len(self.ref_match_coords) >= 1:\n self.tether_docking = True\n else:\n print('No reference matching coordinates.')\n sys.exit()\n\n def docking_vina(self, ligand_file, docking_pdbqt_file, docking_log_file):\n \"\"\"\n run_docking program using subprocess\n input :\n ligand_file\n docking_pdbqt_file\n output :\n affinity list for a input molecule\n \"\"\"\n\n run_line = '%s' % self.docking_program\n run_line += ' --config %s' % self.dock_config_file\n run_line += ' --ligand %s' % ligand_file\n run_line += ' --out %s' % docking_pdbqt_file\n if self.output_save:\n run_line += ' --log %s' % (docking_log_file)\n e = None\n try:\n result = subprocess.check_output(run_line.split(),\n stderr=subprocess.STDOUT,\n timeout=self.timeout_dock,\n universal_newlines=True)\n except Exception as e:\n return [99.999], e\n\n result_lines = result.split('\\n')\n\n check_result = False\n affinity_list = list()\n for result_line in result_lines:\n if result_line.startswith('-----+'):\n check_result = True\n continue\n if not check_result:\n continue\n if result_line.startswith('Writing output'):\n break\n if result_line.startswith('Refine time'):\n break\n lis = result_line.strip().split()\n if not lis[0].isdigit():\n break\n# mode = int(lis[0])\n affinity = float(lis[1])\n affinity_list += [affinity]\n if len(affinity_list) == 0:\n e = 'WARNING: Could not find any conformations.'\n return [99.999], e\n return affinity_list, e\n\n def docking_vina_score_only(self, ligand_file):\n \"\"\"\n run docking program with score_only using subprocess\n input :\n ligand_file\n output :\n affinity list for a input molecule\n \"\"\"\n\n run_line = '%s' % self.rescoring_program\n run_line += ' --config %s' % self.rescoring_config_file\n run_line += ' --ligand %s' % ligand_file\n run_line += ' --score_only'\n\n e = None\n try:\n result = subprocess.check_output(run_line.split(),\n stderr=subprocess.STDOUT,\n timeout=self.timeout_dock,\n universal_newlines=True)\n except Exception as e:\n return [99.999], e\n\n result_lines = result.split('\\n')\n\n# weight_list = list()\n# check_weight = False\n affinity_list = list()\n for result_line in result_lines:\n # if result_line.startswith('Weights'):\n # check_weight = True\n # continue\n # if check_weight:\n # lis = result_line.strip().split()\n # if len(lis) <2:\n # check_weight = False\n # continue\n # weight_list += [[float(lis[0]), lis[1]]]\n # continue\n if result_line.startswith('Affinity:'):\n lis = result_line.strip().split()\n affinity = float(lis[1])\n affinity_list += [affinity]\n if len(affinity_list) == 0:\n return [99.999], e\n return affinity_list, e\n\n def docking_rdock(self, ligand_file, docking_file, docking_log_file):\n \"\"\"\n run_docking program using subprocess\n input :\n ligand_file\n docking_pdbqt_file\n output :\n affinity list for a input molecule\n \"\"\"\n\n docking_prefix = '.'.join(docking_file.strip().split('.')[:-1])\n run_line = '%s' % self.docking_program\n run_line += ' -r %s' % self.dock_config_file\n run_line += ' -p dock.prm'\n run_line += ' -n %d' % self.exhaustiveness\n run_line += ' -i %s' % ligand_file\n run_line += ' -o %s' % docking_prefix\n\n# run_line2 = 'sdsort -n -fSCORE %s.sd' % (docking_prefix)\n run_line2 = 'sdsort -n -fSCORE.INTER %s.sd' % (docking_prefix)\n\n e = None\n try:\n result = subprocess.check_output(run_line.split(),\n stderr=subprocess.STDOUT,\n timeout=self.timeout_dock,\n universal_newlines=True)\n if self.output_save:\n fp = open(docking_log_file, 'w')\n fp.write(result)\n fp.close()\n\n result2 = subprocess.check_output(run_line2.split(),\n universal_newlines=True)\n fp = open(docking_file, 'w')\n fp.write(result2)\n fp.close()\n\n except Exception as e:\n return [99.999], e\n\n affinity_list = list()\n out_lines = result2.split('\\n')\n check_score = False\n for line in out_lines:\n if line[0:16] == '> <SCORE.INTER>':\n# if line[0:10] == '> <SCORE>':\n check_score = True\n continue\n if check_score is True:\n affinity = float(line)\n affinity_list += [affinity]\n check_score = False\n continue\n if len(affinity_list) == 0:\n e = 'WARNING: Could not find any conformations.'\n return [99.999], e\n return affinity_list, e\n\n def creator(self, q, data, num_sub_proc):\n \"\"\"\n put data to queue\n input: queue\n data = [(idx1, molid1, smi1), (idx2, molid2, smi2), ...]\n num_sub_proc (for end signal)\n \"\"\"\n for d in data:\n idx = d[0]\n q.put((idx, d[1]))\n\n for i in range(0, num_sub_proc):\n q.put('DONE')\n\n def simulation_process(self, idx, mol_id, smi, pid):\n\n result_dict = dict()\n if self.neutralize or (self.pH is not None):\n smi_p = ligand_tools.ligand_preparation(smi, self.neutralize,\n self.pH)\n else:\n smi_p = smi\n if not self.output_save:\n ligand_sdf_file = '%s/ligand_%d.sdf' % (self.gen3d_dir, pid)\n ligand_pdb_file = '%s/ligand_%d.pdb' % (self.gen3d_dir, pid)\n ligand_pdbqt_file = '%s/ligand_%s.pdbqt' % (self.gen3d_dir, pid)\n docking_pdbqt_file = '%s/dock_%d.pdbqt' % (\n self.dock_dir, pid)\n docking_log_file = '%s/dock_%d.log' % (self.dock_dir, pid)\n docking_pdb_file = '%s/dock_%s.pdb' % (self.dock_dir, pid)\n docking_sdf_file = '%s/dock_%s.sdf' % (self.dock_dir, pid)\n\n out_dock_dir1 = None\n else:\n mol_id2 = mol_id[0:self.tlen]\n out_gen3d_dir1 = self.gen3d_dir + \"/\" + mol_id2\n if not os.path.exists(out_gen3d_dir1):\n try:\n os.makedirs(out_gen3d_dir1)\n except FileExistsError as e:\n print(e, flush=True)\n ligand_sdf_file = '%s/ligand_%s.sdf' % (out_gen3d_dir1, mol_id)\n ligand_pdb_file = '%s/ligand_%s.pdb' % (out_gen3d_dir1, mol_id)\n ligand_pdbqt_file = '%s/ligand_%s.pdbqt' % (out_gen3d_dir1, mol_id)\n\n out_dock_dir1 = self.dock_dir + \"/\" + mol_id2\n if not os.path.exists(out_dock_dir1):\n try:\n os.makedirs(out_dock_dir1)\n except FileExistsError as e:\n print(e, flush=True)\n\n docking_pdbqt_file = '%s/dock_%s.pdbqt' % (out_dock_dir1, mol_id)\n docking_pdb_file = '%s/dock_%s.pdb' % (out_dock_dir1, mol_id)\n docking_log_file = '%s/dock_%s.log' % (out_dock_dir1, mol_id)\n docking_sdf_file = '%s/dock_%s.sdf' % (out_dock_dir1, mol_id)\n\n if self.dp == 'vina':\n e = ligand_tools.gen_3d(smi_p, ligand_pdb_file, mol_id=mol_id,\n timeout=self.timeout_gen3d)\n if e is not None:\n e2 = ligand_tools.gen_3d(smi_p, ligand_pdb_file, mol_id=mol_id,\n timeout=self.timeout_gen3d)\n if e2 is not None:\n print(e2, 'gen_3d', idx, mol_id, smi_p, flush=True)\n docking_score = np.array([99.999], dtype=np.float32)\n result_dict['docking'] = docking_score\n return result_dict\n\n e = ligand_tools.pdb_to_pdbqt(ligand_pdb_file, ligand_pdbqt_file)\n if e is not None:\n print(e, 'pdb_to_pdbqt', idx, mol_id, smi_p, flush=True)\n docking_score = np.array([99.999], dtype=np.float32)\n result_dict['docking'] = docking_score\n return result_dict\n\n elif self.dp == 'rdock':\n e = ligand_tools.gen_3d(smi_p, ligand_sdf_file, mol_id=mol_id,\n timeout=self.timeout_gen3d)\n if e is not None:\n e2 = ligand_tools.gen_3d(smi_p, ligand_sdf_file, mol_id=mol_id,\n timeout=self.timeout_gen3d)\n if e2 is not None:\n print(e2, 'gen_3d', idx, mol_id, smi_p, flush=True)\n docking_score = np.array([99.999], dtype=np.float32)\n result_dict['docking'] = docking_score\n return result_dict\n\n if self.tether_docking:\n e = sdtether.tether_ligand(ligand_sdf_file, self.smarts,\n self.ref_match_coords)\n if e is not None:\n docking_score = np.array([99.999], dtype=np.float32)\n result_dict['docking'] = docking_score\n return result_dict\n\n if self.dp == 'vina':\n docking_score, e = self.docking_vina(ligand_pdbqt_file,\n docking_pdbqt_file,\n docking_log_file)\n elif self.dp == 'rdock':\n docking_score, e = self.docking_rdock(ligand_sdf_file,\n docking_sdf_file,\n docking_log_file)\n\n docking_score = np.array(docking_score, dtype=np.float32)\n if e is not None:\n docking_score = [99.999]\n result_dict['docking'] = docking_score\n print(e, 'docking', idx, mol_id, smi_p, flush=True)\n return result_dict\n result_dict['docking'] = docking_score\n if self.output_save or self.rescoring or self.use_my_module:\n if self.dp == 'vina':\n ligand_tools.pdbqt_to_pdb_ref(docking_pdbqt_file,\n docking_pdb_file,\n ligand_pdb_file)\n elif self.dp == 'rdock':\n ligand_tools.obabel_rewrite(docking_sdf_file,\n docking_pdb_file, option=' -h')\n\n if self.rescoring:\n docking_rescore, e = self.docking_vina_score_only(docking_pdb_file)\n docking_rescore = np.array(docking_rescore, dtype=np.float32)\n if e is not None:\n docking_rescore = np.array([99.999], dtype=np.float32)\n print(e, 're-scoring', idx, mol_id, smi_p, flush=True)\n result_dict['docking_re'] = docking_rescore\n\n if self.use_my_module:\n self.my_class.simulation_process(self, idx, mol_id, smi, smi_p,\n pid, out_dock_dir1,\n docking_pdb_file, result_dict)\n\n return result_dict\n\n def worker(self, q, return_dict):\n \"\"\"\n generate subprocess for docking\n input\n q (queue)\n return_dict\n \"\"\"\n pid = os.getpid()\n while True:\n qqq = q.get()\n if qqq == 'DONE':\n # print('proc =', os.getpid())\n break\n\n (idx, d) = qqq\n mol_id = d[0]\n smi = d[1]\n # print screening processing in every pout step\n if self.pout != 0:\n if idx % self.pout == self.pout-1:\n print(\"processing: \", idx+1, flush=True)\n result_dict = self.simulation_process(idx, mol_id, smi, pid)\n return_dict[idx] = result_dict\n\n def predict(self, smiles_list):\n \"\"\"\n input SMILES list\n output result_dict\n result_dict include affinity list (and other scores)\n corresponding to the SMILES list\n if docking is fail, docking score is [99.999]\n \"\"\"\n data = list(enumerate(smiles_list))\n num_data = len(data)\n num_sub_proc = min(self.num_sub_proc, num_data)\n\n q1 = Queue()\n manager = Manager()\n return_dict = manager.dict()\n proc_master = Process(target=self.creator,\n args=(q1, data, num_sub_proc))\n proc_master.start()\n\n # create slave process\n procs = []\n for sub_id in range(0, num_sub_proc):\n proc = Process(target=self.worker, args=(q1, return_dict))\n procs.append(proc)\n proc.start()\n\n q1.close()\n q1.join_thread()\n proc_master.join()\n for proc in procs:\n proc.join()\n keys = sorted(return_dict.keys())\n\n result_dict = dict()\n docking_score_list = list()\n if self.rescoring:\n docking_re_list = list()\n\n for key in range(num_data):\n if key in keys:\n result_dict0 = return_dict[key]\n if 'docking' in result_dict0:\n docking_score = result_dict0['docking']\n else:\n docking_score = np.array([99.999], dtype=np.float32)\n\n if self.rescoring:\n if 'docking_re' in result_dict0:\n docking_re = result_dict0['docking_re']\n else:\n docking_re = np.array([99.999], dtype=np.float32)\n\n else:\n docking_score = np.array([99.999], dtype=np.float32)\n if self.rescoring:\n docking_re = np.array([99.999], dtype=np.float32)\n\n docking_score_list += [docking_score]\n if self.rescoring:\n docking_re_list += [docking_re]\n\n result_dict['docking'] = docking_score_list\n if self.rescoring:\n result_dict['docking_re'] = docking_re_list\n\n if self.use_my_module:\n self.my_class.predict(self, smiles_list, result_dict, return_dict)\n\n return result_dict\n\n\nclass LoadFromConfig(argparse.Action):\n def __call__(self, parser, namespace, values, option_string=None):\n with values as f:\n parser.parse_args(f.read().split(), namespace)\n\n\nclass ExtendAction(argparse.Action):\n\n def __call__(self, parser, namespace, values, option_string=None):\n items = getattr(namespace, self.dest) or []\n items.extend(values)\n setattr(namespace, self.dest, items)\n\n\ndef parser_arg(parser):\n # docking parameter\n\n parser.register('action', 'extend', ExtendAction)\n parser.add_argument('--arg_file', type=open, required=False, default=None,\n action=LoadFromConfig, help='argment file')\n parser.add_argument('--dock_config', type=str, required=False,\n default=None, help='docking config file ')\n parser.add_argument('-v', '--docking_program', type=str, required=False,\n default='rbdock',\n help='select rdock, rbdock')\n parser.add_argument('--my_module', type=str, required=False,\n default=None,\n help='set user python module path (for pifinder)')\n parser.add_argument('--neutralize', action='store_true',\n required=False, help='neutralize smiles ')\n parser.add_argument('--pH', type=float, default=None,\n required=False, help='protonate state for pH 7.4 ')\n parser.add_argument('--output_save', action='store_true', required=False,\n help='default output pdbqt is temp file ')\n parser.add_argument('--gen3d_dir', type=str, required=False, default='tmp',\n help='3d initial conformation directory')\n parser.add_argument('--dock_dir', type=str, required=False,\n default='tmp', help='binding conformation directory')\n parser.add_argument('--num_sub_proc', type=int, required=False,\n default=10, help=' --num_sub_proc 10')\n parser.add_argument('--timeout_gen3d', type=int, required=False,\n default=1, help=' --timeout_gen3d 1')\n parser.add_argument('--timeout_dock', type=int, required=False,\n default=120, help=' --timeout_dock 120')\n parser.add_argument('--tlen', type=int, default='7', required=False,\n help='lenth of sub directory name, default: 7')\n parser.add_argument('--pout', type=int, default='0', required=False,\n help='print processing out: 0 or number, default: 0')\n parser.add_argument('--rescoring_program', type=str, required=False,\n default='smina', help='smina path')\n parser.add_argument('--rescoring_config', type=str, required=False,\n default=None, help='docking config file for rescoring')\n\n parser.add_argument('--tether_ref_lig', type=str, required=False,\n default=None,\n help='reference ligand for tether docking')\n parser.add_argument('--tether_SMARTS', type=str, required=False,\n default=None, help='SMARTS pattern for tether docking')\n parser.add_argument('--tether_ref_coor_file', type=str, required=False,\n default=None,\n help='reference coordinate file for tether docking')\n\n parser.add_argument('--exhaustiveness', type=int, required=False,\n default=10,\n help='exhaustiveness for rdock')\n\n return\n\n\ndef arg_to_params(parser):\n\n use_my_module = False\n for i, m in enumerate(sys.argv):\n if m == '--my_module':\n my_module_path = sys.argv[i+1]\n use_my_module = True\n my_module_dir = os.path.dirname(my_module_path)\n sys.path.append(my_module_dir)\n import my_module\n my_module.parser_arg(parser)\n\n args = parser.parse_args()\n\n docking_program = args.docking_program\n num_sub_proc = args.num_sub_proc\n timeout_gen3d = args.timeout_gen3d\n timeout_dock = args.timeout_dock\n output_save = args.output_save\n gen3d_dir = args.gen3d_dir\n dock_dir = args.dock_dir\n dock_config_file = args.dock_config\n\n tlen = args.tlen\n pout = args.pout\n neutralize = args.neutralize\n pH = args.pH\n\n tether_ref_lig = args.tether_ref_lig\n tether_SMARTS = args.tether_SMARTS\n tether_ref_coor_file = args.tether_ref_coor_file\n\n exhaustiveness = args.exhaustiveness\n\n rescoring = False\n rescoring_config_file = args.rescoring_config\n rescoring_program = args.rescoring_program\n if rescoring_config_file is not None:\n rescoring = True\n\n docking_params = dict()\n docking_params['docking_program'] = docking_program\n docking_params['gen3d_dir'] = gen3d_dir\n docking_params['dock_dir'] = dock_dir\n docking_params['num_sub_proc'] = num_sub_proc\n docking_params['timeout_gen3d'] = timeout_gen3d\n docking_params['timeout_dock'] = timeout_dock\n docking_params['output_save'] = output_save\n docking_params['tlen'] = tlen\n docking_params['pout'] = pout\n docking_params['neutralize'] = neutralize\n docking_params['pH'] = pH\n docking_params['dock_config_file'] = dock_config_file\n docking_params['rescoring'] = rescoring\n docking_params['rescoring_program'] = rescoring_program\n docking_params['rescoring_config_file'] = rescoring_config_file\n docking_params['tether_ref_lig'] = tether_ref_lig\n docking_params['tether_SMARTS'] = tether_SMARTS\n docking_params['tether_ref_coor_file'] = tether_ref_coor_file\n docking_params['exhaustiveness'] = exhaustiveness\n\n my_module_path = args.my_module\n docking_params['use_my_module'] = use_my_module\n docking_params['my_module_path'] = my_module_path\n\n if use_my_module:\n docking_params = my_module.arg_to_params(parser, docking_params)\n\n return args, docking_params\n\n\ndef main():\n\n parser = argparse.ArgumentParser(description='docking with multi process')\n parser.add_argument('-l', '--ligand_list_file', type=str, required=False,\n default='smiles.txt',\n help=' --ligand_list_file smiles.txt')\n parser.add_argument('-o', '--output_file', type=str, required=False,\n default='docking.txt',\n help=' --output_file docking.txt')\n\n parser_arg(parser)\n\n args, docking_params = arg_to_params(parser)\n ligand_list_file = args.ligand_list_file\n output_file = args.output_file\n\n if len(sys.argv) < 2:\n parser.print_usage()\n sys.exit()\n if args.dock_config is None:\n parser.print_usage()\n print('dock_config is missing')\n sys.exit()\n\n ligand_file_format = ligand_list_file.strip().split('.')[-1]\n if ligand_file_format == 'txt':\n field_separator = '\\s+'\n elif ligand_file_format == 'csv':\n field_separator = ','\n elif ligand_file_format == 'tsv':\n field_separator = '\\t'\n else:\n field_separator = None\n\n if ligand_file_format == 'pkl':\n df = pd.read_pickle(ligand_list_file)\n else:\n df = pd.read_csv(ligand_list_file, sep=field_separator)\n\n# num_data = df.shape[0]\n fkey = df.keys()[0]\n if fkey.startswith('#'):\n df.rename(columns={fkey: fkey[1:]}, inplace=True)\n smiles_list = df[['MOL_ID', 'SMILES']].values.tolist()\n\n# smiles_list = smiles_list[0:10]\n docking = Docking(docking_params)\n\n result_dict = docking.predict(smiles_list)\n docking_score_list = result_dict['docking']\n docking_min = [x[0] for x in docking_score_list]\n df['Docking1'] = docking_min\n df['Docking'] = docking_score_list\n if docking_params['rescoring']:\n rescoring = result_dict['docking_re']\n df['Docking_re'] = rescoring\n\n use_my_module = docking_params['use_my_module']\n my_module_path = docking_params['my_module_path']\n\n if use_my_module:\n my_module_dir = os.path.dirname(my_module_path)\n sys.path.append(my_module_dir)\n import my_module\n my_module.my_score_to_df(df, docking_params, result_dict)\n\n sep = field_separator\n if sep == '\\s+':\n sep = ' '\n\n if output_file.strip().split('.')[-1] == 'pkl':\n df.to_pickle(output_file)\n else:\n df.to_csv(output_file, sep=sep, float_format='%.3f', index=False)\n\n\nif __name__ == \"__main__\":\n main()\n"
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"text": "#!/usr/bin/env python\nimport sys\nimport os\nimport argparse\nimport vhts.pyrdock as pyrdock\nfrom filelock import FileLock\nimport pandas as pd\n\n\ndef get_job_from_list(list_dir):\n list_file = list_dir + '/list.txt'\n if not os.path.exists(list_file):\n job_idx = None\n return job_idx\n freeze_lock = FileLock('{}.lock'.format(list_file))\n with freeze_lock.acquire(timeout=30):\n with open(list_file, 'r') as fp:\n lines = fp.readlines()\n if len(lines) == 0:\n job_idx = None\n else:\n job_idx = lines[0].strip()\n with open(list_file, 'w') as fp:\n for line in lines[1:]:\n fp.write(line)\n return job_idx\n\n\ndef set_job_from_list(job_idx, list_dir):\n list_file = list_dir + '/list.txt'\n freeze_lock = FileLock('{}.lock'.format(list_file))\n with freeze_lock.acquire(timeout=30):\n if os.path.exists(list_file):\n with open(list_file, 'r') as fp:\n lines = fp.readlines()\n else:\n lines = list()\n with open(list_file, 'w') as fp:\n for line in lines:\n fp.write(line)\n line = job_idx + '\\n'\n fp.write(line)\n return\n\n\ndef remove_job_from_list(job_idx, list_dir):\n list_file = list_dir + '/list.txt'\n freeze_lock = FileLock('{}.lock'.format(list_file))\n with freeze_lock.acquire(timeout=30):\n if os.path.exists(list_file):\n with open(list_file, 'r') as fp:\n lines = fp.readlines()\n with open(list_file, 'w') as fp:\n for line in lines:\n if line.strip() != job_idx:\n fp.write(line)\n return\n\n\ndef get_and_set_job(params_dict):\n todo_dir = params_dict['todo_dir']\n current_dir = params_dict['current_dir']\n job_idx = get_job_from_list(todo_dir)\n if job_idx is None:\n return job_idx\n set_job_from_list(job_idx, current_dir)\n smi_file_format = params_dict['smi_file_format']\n job_todo_file = todo_dir + '/' + job_idx + '.' + smi_file_format\n job_current_file = current_dir + '/' + job_idx + '.' + smi_file_format\n\n os.replace(job_todo_file, job_current_file)\n return job_idx\n\n\ndef run_docking(job_idx, docking, params_dict):\n current_dir = params_dict['current_dir']\n done_dir = params_dict['done_dir']\n field_separator = params_dict['field_separator']\n smi_file_format = params_dict['smi_file_format']\n job_current_file = current_dir + '/' + job_idx + '.' + smi_file_format\n job_done_file = done_dir + '/' + job_idx + '.' + smi_file_format\n if smi_file_format == 'pkl':\n df = pd.read_pickle(job_current_file)\n else:\n df = pd.read_csv(job_current_file, sep=field_separator, header=0)\n\n# num_data = df.shape[0]\n# df.rename(columns={0: 'MOL_IDX', 1: 'MOL_ID', 2: 'SMILES'}, inplace=True)\n smiles_list = df[['MOL_ID', 'SMILES']].values.tolist()\n result_dict = docking.predict(smiles_list)\n affinity_list = result_dict['docking']\n docking_min = [x[0] for x in affinity_list]\n# docking = [x for x in affinity_list]\n docking = affinity_list\n df['Docking1'] = docking_min\n df['Docking'] = docking\n if params_dict['rescoring']:\n rescoring = result_dict['docking_re']\n df['Docking_re'] = rescoring\n\n use_my_module = params_dict['use_my_module']\n my_module_path = params_dict['my_module_path']\n docking_params = params_dict['docking_params']\n\n if use_my_module:\n my_module_dir = os.path.dirname(my_module_path)\n sys.path.append(my_module_dir)\n import my_module\n my_module.my_score_to_df(df, docking_params, result_dict)\n\n sep = field_separator\n if sep == '\\s+':\n sep = ' '\n\n if smi_file_format == 'pkl':\n df.to_pickle(job_done_file)\n else:\n df.to_csv(job_done_file, sep=sep, float_format='%.3f',\n header=True, index=False)\n\n return\n\n\ndef move_done(job_idx, params_dict):\n current_dir = params_dict['current_dir']\n done_dir = params_dict['done_dir']\n remove_job_from_list(job_idx, current_dir)\n set_job_from_list(job_idx, done_dir)\n smi_file_format = params_dict['smi_file_format']\n job_current_file = current_dir + '/' + job_idx + '.' + smi_file_format\n os.remove(job_current_file)\n return job_idx\n\n\ndef working(docking, params_dict):\n pid = os.getpid()\n out_log = params_dict['out_log']\n log_file = params_dict['log_file']\n\n line_out = 'Start sub_dock pid: %d' % (pid)\n if out_log == 'file':\n fp_log = open(log_file, 'w')\n fp_log.write(line_out + '\\n')\n fp_log.flush()\n elif out_log == 'print':\n print(line_out, flush=True)\n\n while True:\n job_idx = get_and_set_job(params_dict)\n line_out = 'get a job: %s' % job_idx\n if out_log == 'file':\n fp_log.write(line_out + '\\n')\n fp_log.flush()\n\n elif out_log == 'print':\n print(line_out, flush=True)\n if job_idx is None:\n line_out = 'End sub_dock pid %d' % pid\n if out_log == 'file':\n fp_log.write(line_out + '\\n')\n fp_log.flush()\n\n elif out_log == 'print':\n print(line_out, flush=True)\n break\n run_docking(job_idx, docking, params_dict)\n move_done(job_idx, params_dict)\n line_out = 'done job: %s' % job_idx\n if out_log == 'file':\n fp_log.write(line_out + '\\n')\n fp_log.flush()\n\n elif out_log == 'print':\n print(line_out, flush=True)\n if out_log == 'file':\n fp_log.close()\n\n return\n\n\nclass LoadFromConfig(argparse.Action):\n def __call__(self, parser, namespace, values, option_string=None):\n with values as f:\n parser.parse_args(f.read().split(), namespace)\n\n\nclass ExtendAction(argparse.Action):\n\n def __call__(self, parser, namespace, values, option_string=None):\n items = getattr(namespace, self.dest) or []\n items.extend(values)\n setattr(namespace, self.dest, items)\n\n\ndef parser_arg(parser):\n # docking parameter\n\n parser.register('action', 'extend', ExtendAction)\n parser.add_argument('--arg_file', type=open, required=False, default=None,\n action=LoadFromConfig, help='argment file')\n parser.add_argument('--dock_config', type=str, required=False,\n default=None, help='docking config file ')\n parser.add_argument('-v', '--docking_program', type=str, required=False,\n default='rbdock',\n help='select rdock, rbdock')\n parser.add_argument('--my_module', type=str, required=False,\n default=None,\n help='set user python module path (for pifinder)')\n parser.add_argument('--neutralize', action='store_true',\n required=False, help='neutralize smiles ')\n parser.add_argument('--pH', type=float, default=None,\n required=False, help='protonate state for pH 7.4 ')\n parser.add_argument('--output_save', action='store_true', required=False,\n help='default output pdbqt is temp file ')\n parser.add_argument('--gen3d_dir', type=str, required=False, default='tmp',\n help='3d initial conformation directory')\n parser.add_argument('--dock_dir', type=str, required=False,\n default='tmp', help='binding conformation directory')\n parser.add_argument('--num_sub_proc', type=int, required=False,\n default=10, help=' --num_sub_proc 10')\n parser.add_argument('--timeout_gen3d', type=int, required=False,\n default=1, help=' --timeout_gen3d 1')\n parser.add_argument('--timeout_dock', type=int, required=False,\n default=120, help=' --timeout_dock 120')\n parser.add_argument('--tlen', type=int, default='7', required=False,\n help='lenth of sub directory name, default: 7')\n parser.add_argument('--pout', type=int, default='0', required=False,\n help='print processing out: 0 or number, default: 0')\n parser.add_argument('--rescoring_program', type=str, required=False,\n default='smina', help='smina path')\n parser.add_argument('--rescoring_config', type=str, required=False,\n default=None, help='docking config file for rescoring')\n\n parser.add_argument('--tether_ref_lig', type=str, required=False,\n default=None,\n help='reference ligand for tether docking')\n parser.add_argument('--tether_SMARTS', type=str, required=False,\n default=None, help='SMARTS pattern for tether docking')\n parser.add_argument('--tether_ref_coor_file', type=str, required=False,\n default=None,\n help='reference coordinate file for tether docking')\n\n parser.add_argument('--exhaustiveness', type=int, required=False,\n default=10,\n help='exhaustiveness for rdock')\n\n return\n\n\ndef arg_to_params(parser):\n\n use_my_module = False\n for i, m in enumerate(sys.argv):\n if m == '--my_module':\n my_module_path = sys.argv[i+1]\n use_my_module = True\n my_module_dir = os.path.dirname(my_module_path)\n sys.path.append(my_module_dir)\n import my_module\n my_module.parser_arg(parser)\n\n args = parser.parse_args()\n\n docking_program = args.docking_program\n num_sub_proc = args.num_sub_proc\n timeout_gen3d = args.timeout_gen3d\n timeout_dock = args.timeout_dock\n output_save = args.output_save\n gen3d_dir = args.gen3d_dir\n dock_dir = args.dock_dir\n dock_config_file = args.dock_config\n\n tlen = args.tlen\n pout = args.pout\n neutralize = args.neutralize\n pH = args.pH\n\n tether_ref_lig = args.tether_ref_lig\n tether_SMARTS = args.tether_SMARTS\n tether_ref_coor_file = args.tether_ref_coor_file\n\n exhaustiveness = args.exhaustiveness\n\n rescoring = False\n rescoring_config_file = args.rescoring_config\n rescoring_program = args.rescoring_program\n if rescoring_config_file is not None:\n rescoring = True\n\n docking_params = dict()\n docking_params['docking_program'] = docking_program\n docking_params['gen3d_dir'] = gen3d_dir\n docking_params['dock_dir'] = dock_dir\n docking_params['num_sub_proc'] = num_sub_proc\n docking_params['timeout_gen3d'] = timeout_gen3d\n docking_params['timeout_dock'] = timeout_dock\n docking_params['output_save'] = output_save\n docking_params['tlen'] = tlen\n docking_params['pout'] = pout\n docking_params['neutralize'] = neutralize\n docking_params['pH'] = pH\n docking_params['dock_config_file'] = dock_config_file\n docking_params['rescoring'] = rescoring\n docking_params['rescoring_program'] = rescoring_program\n docking_params['rescoring_config_file'] = rescoring_config_file\n docking_params['tether_ref_lig'] = tether_ref_lig\n docking_params['tether_SMARTS'] = tether_SMARTS\n docking_params['tether_ref_coor_file'] = tether_ref_coor_file\n docking_params['exhaustiveness'] = exhaustiveness\n\n my_module_path = args.my_module\n docking_params['use_my_module'] = use_my_module\n docking_params['my_module_path'] = my_module_path\n\n if use_my_module:\n docking_params = my_module.arg_to_params(parser, docking_params)\n\n return args, docking_params\n\n\ndef main():\n\n parser = argparse.ArgumentParser(description='worker for docking')\n parser.add_argument('--work_dir', type=str, required=False,\n default='workflow', help='workflow directory')\n parser.add_argument('-s', '--smi_file_format', type=str, required=False,\n default='pkl', help='pkl (default), txt, csv, tsv')\n parser.add_argument('--out_log', type=str, required=False,\n default=None,\n help='print : screen, or file : sub_dock_$PID.log' +\n 'default: do not print output')\n\n parser_arg(parser)\n\n args, docking_params = arg_to_params(parser)\n if len(sys.argv) < 2:\n parser.print_usage()\n sys.exit()\n if args.dock_config is None:\n parser.print_usage()\n print('dock_config is missing')\n sys.exit()\n\n work_dir = args.work_dir\n todo_dir = work_dir + '/todo'\n current_dir = work_dir + '/current'\n done_dir = work_dir + '/done'\n smi_file_format = args.smi_file_format\n if smi_file_format == 'txt':\n field_separator = '\\s+'\n elif smi_file_format == 'csv':\n field_separator = ','\n elif smi_file_format == 'tsv':\n field_separator = '\\t'\n else:\n field_separator = None\n\n out_log = args.out_log\n pid = os.getpid()\n log_file = 'sub_dock_%d.log' % (pid)\n\n docking = pyrdock.Docking(docking_params)\n\n params_dict = dict()\n params_dict['work_dir'] = work_dir\n params_dict['todo_dir'] = todo_dir\n params_dict['current_dir'] = current_dir\n params_dict['done_dir'] = done_dir\n params_dict['field_separator'] = field_separator\n params_dict['smi_file_format'] = smi_file_format\n params_dict['out_log'] = out_log\n params_dict['log_file'] = log_file\n\n params_dict['rescoring'] = docking_params['rescoring']\n params_dict['use_my_module'] = docking_params['use_my_module']\n params_dict['my_module_path'] = docking_params['my_module_path']\n params_dict['docking_params'] = docking_params\n\n working(docking, params_dict)\n\n\nif __name__ == \"__main__\":\n main()\n"
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"text": "#!/usr/bin/env python\nimport argparse\nimport numpy as np\nfrom openbabel import pybel\nimport pandas as pd\n\n\ndef get_bond_info(m):\n bond_dict = dict()\n mol = m.OBMol\n\n for i in range(mol.NumBonds()):\n bb = mol.GetBondById(i)\n if bb is None:\n continue\n begin = bb.GetBeginAtomIdx()\n end = bb.GetEndAtomIdx()\n if begin not in bond_dict:\n bond_dict[begin] = [end]\n else:\n bond_dict[begin] += [end]\n if end not in bond_dict:\n bond_dict[end] = [begin]\n else:\n bond_dict[end] += [begin]\n return bond_dict\n\n\ndef find_neighbor_metal(ligand_file, metal_coor, dist_cutoff=2.5, skip_neighbor_hydrogen=True):\n\n ms = pybel.readfile('pdb', ligand_file)\n ms= list(ms)\n bond_dict = get_bond_info(ms[0])\n\n num_conformers = len(ms)\n if num_conformers>1:\n num_conformers = num_conformers - 1\n\n num_bind_atom_list = list()\n num_bind_max= 0\n model_num = 0\n\n for i in range(num_conformers):\n m = ms[i]\n atoms = m.atoms\n bind_list = list()\n for atom in atoms:\n atom_idx = atom.idx\n atomic_num = atom.atomicnum\n neighbor_hydrogen = False\n if atomic_num==7 or atomic_num==8:\n# if skip_neighbor_hydrogen:\n if skip_neighbor_hydrogen and atomic_num==7:\n neighbor_atoms_idx = bond_dict[atom_idx]\n for neighbor_atom_idx in neighbor_atoms_idx:\n neighbor_atom = atoms[neighbor_atom_idx - 1]\n if neighbor_atom.atomicnum == 1:\n neighbor_hydrogen = True\n if neighbor_hydrogen :\n continue\n coor = np.array(atom.coords)\n dist = np.linalg.norm(metal_coor-coor)\n if dist < dist_cutoff:\n bind_list += [[atom_idx, atomic_num, dist]]\n\n num_bind_atom = 0\n batom_idx = list()\n for bind in bind_list:\n atom_idx, atomic_num, dist = bind\n atom = atoms[atom_idx-1]\n neighbor_atoms_idx = bond_dict[atom_idx]\n neighbor_check = False\n for neighbor_atom_idx in neighbor_atoms_idx:\n if neighbor_atom_idx in batom_idx:\n neighbor_check = True\n if not neighbor_check :\n num_bind_atom += 1\n batom_idx += [atom_idx]\n num_bind_atom_list += [num_bind_atom]\n return num_bind_atom_list\n\n\ndef main():\n\n# ligand_file = 'pdb/2G1MA_4HG.pdb'\n\n metal_coor = np.array([39.864, 22.370, 13.612])\n# ligand_list = ['2G1MA_4HG', '4JZRA_4JR', '4KBZA_1QA',\n# '5V18A_8UY', '6ST3A_LUW', '6YVTA_PW2']\n\n df = pd.read_csv('docking.csv')\n num_data = df.shape[0]\n size = (400,400)\n num_d = 100\n df2 = df.sort_values('Docking1')[0:num_d]\n for i in range(num_d):\n mdata = df2.iloc[i]\n ligand = mdata['MOL_ID']\n docking1 = mdata['Docking1']\n docking = mdata['Docking'].strip('[').strip(']').split(',')\n dir1 = ligand[0:4]\n ligand_file = 'docking/%s/dock_%s.pdb' % (dir1, ligand)\n\n num_bind_atom_list = find_neighbor_metal(ligand_file, metal_coor)\n line_out = '%s %s %7.3f %s' % (ligand, num_bind_atom_list, docking1, docking)\n print(line_out)\n\nif __name__ == '__main__':\n main()\n\n"
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] | 20 |
maciek226/GradeBook
|
https://github.com/maciek226/GradeBook
|
8d00f11629583f0f17daadbe0be105758ce1883a
|
0693d3dd22dad66a393b7baa23f3dc5e82577f9a
|
de7f5ce748ae31943ef4a8e8285c3f7d0900246c
|
refs/heads/master
| 2020-09-15T12:41:09.231319 | 2020-03-02T17:22:13 | 2020-03-02T17:22:13 | 223,447,856 | 0 | 0 | null | null | null | null | null |
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"text": "# GradeBook\n# Maciej Lacki\n# 2019\n\n# Use at your own risk\n\n# Version 0.70\n# Spelling Error Fixes \n# Version 0.65\n# Fixed issue with reopening a file saved using the GradeBook\n# Fixed overwrite settings\n# Fixed file corruption on save\n# Improvment to saving file- the files names will iterate and won't overwrite\n# Version 0.62\n# Fixed issue with added columns not working correctly\n# Version 0.6\n# Fixed inability to cancle selection in grade input\n# Improved the config file creation wizard\n# Config files now have save overwrite preferences\n# Moved all methods to the GradeBook Class\n# Fixed the naming scheme - now using python naming convention\n# Commented the code\n# Added the ability to add a new column to a sheet with command ADD\n\n# Version 0.55\n# Fixed -1 after using CNC\n# Added the path to the selected sheet in the grade selection path\n\n# limitations: error detection\n# It is possible for user to not select any grade column even multiple times\n\n# TODO:\n# Loger - gather all the data in case something crashes\n# Restore - from the logs\n# Menu - Make it easier to select the columns, adjust their order\n# Add select save location\n# Ability to assign the same grades to more then one person\n# Add Doc Strings\n\n# Possibly make the input take letter ?\n\n\n\nimport os\nimport csv\nimport datetime\n\n\nclass GradeBook:\n \"\"\"Opens Spreadsheets from downloaded from blackboard and aids in\n inputing the grades\n\n The class contains all the data and methods used to interacts with\n the spreadsheets\n \"\"\"\n version = \"0.62\"\n dev = 0\n\n # Current root directory\n working_dir = str\n\n # Assumed config file name/location\n config_dir = str\n\n # Input Files\n input_files = list()\n output_files = list()\n\n file_format = csv.Sniffer()\n\n # Name of the config file\n config_name = 'GradesConf.txt'\n\n # Student Data\n s_num_col = 2\n first_name_col = 1\n last_name_col = 0\n number_sheets = 0\n grading_columns = list()\n\n sheets = list()\n added_columns = int()\n\n # Commands\n exit_command = 'EXT'\n cancel_command = 'CNC'\n add_sheet_command = 'ADD'\n\n # Logger\n log_path = str\n logger_setup = 0\n\n # Settings\n # Overwrite grades\n overwrite = 1\n\n # Options used in main menu\n initialized = 0\n menu_options = [\"Start\",\n \"Settings\",\n \"Spreadsheet Setup\",\n \"Exit and save\",\n \"Cancel\"]\n ready = 0\n\n def __init__(self):\n print('Welcome to GradeBook V%s \\n' % self.version)\n\n###############################################################################\n# Setup #\n###############################################################################\n # Determines current working path, attempts to read the config file\n # Creates a config file if one is not present, or not working\n def setup(self):\n self.working_dir = os.getcwd()\n self.config_dir = os.path.join(self.working_dir, self.config_name)\n\n if os.path.isfile(self.config_dir):\n # Config Exists, read it\n print('Config file found\\n\\t Loading Setting...\\n')\n self.import_settings()\n else:\n # Config does not exist...\n print('Config file not found')\n self.get_settings()\n self.export_settings()\n\n if len(self.input_files) > 0:\n # There is more then 0 input files defined - read them\n self.read_sheets()\n else:\n # No sheets were loaded, assume the config is broken, make new one\n print('no sheets added')\n self.get_settings()\n self.export_settings()\n self.read_sheets()\n\n # Read the settings file\n def import_settings(self):\n with open(self.config_dir, newline='') as book:\n bookType = csv.Sniffer().sniff(book.read(1024))\n\n book.seek(0)\n reader = csv.reader(book, bookType)\n\n print('Opening file(s):')\n for row in reader: # Check each line\n if row[0] == 'in': # If the first item in the line is in\n if os.path.isfile(row[1]): # If the file exists\n # Add the input file to the list\n self.input_files.append(row[1])\n print('\\t%s' % (str(row[1])))\n else:\n # The file does not exist\n print('File ', row[1], ' does not exist')\n elif row[0] == 'out': # If the first item in the line is in\n if os.path.isfile(row[1]): # If the file exists\n # Add the input file to the list\n self.output_files.append(row[1])\n print('Opening file \\n \\t ', row[1])\n else:\n # The file does not exist\n print('File ', row[1], ' does not exist')\n elif row[0] == 'overwrite':\n self.overwrite = row[1]\n elif row[0] == 'dev':\n self.dev = row[1]\n print('\\n')\n\n # Save or overwrite the settings\n def export_settings(self):\n print('\\n\\t Saving settings...\\n')\n with open(self.config_dir, 'w+', newline='') as book:\n writer = csv.writer(book, delimiter='\\t')\n\n for file in self.input_files:\n writer.writerow(['in', file])\n\n writer.writerow([\"overwrite\", self.overwrite])\n writer.writerow([\"dev\", self.dev])\n\n # Get the neccesserry user preferences\n def get_settings(self):\n # If the config file does not exist, get the requiered paths\n while 1:\n while 1:\n file = str(input('Enter path to the sheet\\n'))\n if os.path.isfile(file):\n self.input_files.append(file)\n break\n elif 'cancel' in file:\n break\n\n if len(self.input_files) > 0 and str(input('Add more sheets? (y/n)\\n\\t')) == 'n':\n self.overwrite = 1\n break\n while 1:\n overwrite = str(input(\"Would you like to overwrite the sheets?(y/n)\\n\\t\"))\n if overwrite == 'y':\n self.overwrite = 1\n break\n elif overwrite == 'n':\n self.overwrite = 0\n break\n else:\n print(\"Invalid Input\")\n\n # Collects all the data from the sheets found\n # in the config file and puts them in a list of lists\n def read_sheets(self):\n # Counter of the sheets\n cc = 0\n\n # Open each sheet\n for sheet in self.input_files:\n\n with open(self.input_files[cc], 'r',\n encoding='utf-16', newline='') as book:\n\n # Check the type of the sheet; skip the first\n # line due to strange formatting \n book.readline()\n bookType = csv.Sniffer().sniff(book.readline()\n )\n self.file_format = bookType\n # Bring the cursor back to 0\n book.seek(0)\n reader = csv.reader(book, bookType)\n\n dd = 0\n\n # Add a new list to for each sheet\n self.sheets.append(list())\n for row in reader:\n # Add to the newly created list a\n # new list to contain the user data\n self.sheets[cc].append(list())\n\n # Add the data from each row\n for item in row:\n self.sheets[cc][dd].append(item)\n dd = dd + 1\n cc = cc + 1\n\n self.number_sheets = cc\n\n # Select the grading columns\n self.column_selection()\n\n\n###############################################################################\n# User Interatction #\n###############################################################################\n\n # Select the spreadsheet columns which will be overwriten\n def column_selection(self):\n\n cc = 0\n print('The Columns in the sheet to add a new column type %s:\\n' % self.add_sheet_command)\n # print the first column of the sheet\n for sheet in self.sheets:\n\n self.grading_columns.append(list())\n dd = 0\n print((\"\\t%s\\n\") % str(self.input_files[cc]))\n\n for entery in sheet[0]:\n\n print(\"\\t%d \\t%s\" % (dd, entery))\n dd = dd + 1\n\n while 1:\n\n selection = self.select_column(dd-1, cc)\n\n if selection != -1:\n self.grading_columns[cc].append(selection)\n\n more = str(input('\\nWould you like to add more grade columns? (y/n)\\n\\t'))\n \n if more == 'n':\n break\n cc = cc + 1\n\n # Make a selection of the sheet\n def select_column(self, number, sheet):\n while 1:\n selection = str(input('Select the grading column (or type %s) \\n\\t' % self.add_sheet_command))\n try:\n \n if int(selection) >= 0 and int(selection) <= number:\n\n return (int(selection))\n else:\n print('\\nSelection out of range')\n except(ValueError):\n\n if selection == self.add_sheet_command:\n self.added_columns = self.added_columns+1\n new_column_name = str(input('Enter the name of the new collumn: '))\n self.add_column(sheet, new_column_name)\n return(number+self.added_columns) #number is 1+the total number of cols\n elif selection == str(self.cancel_command):\n return (-1)\n else:\n print('\\nInput is invalid')\n\n # Add a column to the spreadsheet\n def add_column(self, sheet, column_name):\n\n cc = 0\n for student in self.sheets[sheet]:\n self.sheets[sheet][cc].append('')\n cc = cc + 1\n\n self.sheets[sheet][0][-1] = column_name\n\n # Main interaction loop\n def enter_student(self):\n\n print(\"\\nYou can now search for the studentsusing their student\",\n \"number or name \\nYou do not need to provide full student\",\n \" number or full name \\nIf you select wrong person type CNC\",\n \" to cancel the selection \\nTo save all the data and\",\n \" exit the application type EXT\\n\")\n\n # On until broken\n while 1:\n # Get the user input\n userIn = str(input('Enter Name or Student Number\\n\\t'))\n\n # If exit command - leave, else pass the input into search\n if str(userIn) == self.exit_command:\n break\n elif str(userIn) == 'Settings':\n print('\\tFeature coming in future version... maybe')\n else:\n student = self.search(userIn)\n\n if student != -1:\n self.enter_grades(student)\n # Once the search loop is initiated, save the sheets\n while 1:\n # Check if the sheets are writable\n status = self.check_sheet()\n if all(status):\n if self.overwrite == '0':\n self.write_sheet()\n elif self.overwrite == '1':\n self.overwrite_sheet()\n break\n else:\n # If sheets are not writable let the user know\n saveas = str(input(\"One of the sheets is open\"\n \" by another program, please close it,\"\n \" and press enter or type saveas\\n\"))\n if saveas == \"saveas\":\n self.write_sheet()\n break\n\n\n###############################################################################\n# Searching #\n###############################################################################\n\n # Searches the list of students\n def search(self, phrase):\n\n # Deafult serach mode - by number\n sMode = 0\n\n # If the entery is not an intiger change to name search mode\n try:\n int(phrase)\n except ValueError:\n sMode = 1\n\n # Search using one of the modes\n if sMode == 0:\n student = self.search_number(phrase)\n else:\n student = self.search_name(phrase)\n\n if student != -1:\n return (student)\n else:\n return (-1)\n\n def search_name(self, name):\n matches = list()\n\n # Check each sheet\n for sheet in range(0, self.number_sheets):\n index = 0\n flag = 0\n\n # In each sheet find matches\n for entery in self.sheets[sheet]:\n # Skip the first row\n if flag == 0:\n flag = 1\n else:\n # If a match is found, append the info, and the index\n if str.lower(name) in str.lower(entery[self.first_name_col]):\n matches.append([entery[self.first_name_col],\n entery[self.last_name_col],\n entery[self.s_num_col], sheet, index])\n\n if str.lower(name) in str.lower(entery[self.last_name_col]):\n matches.append([entery[self.first_name_col],\n entery[self.last_name_col],\n entery[self.s_num_col], sheet, index])\n index = index + 1\n\n # Check if any matches were found\n if len(matches) == 1:\n # If one match is found return\n return (matches[0])\n\n elif len(matches) > 1:\n # If multiple are found - refine the selection\n selection = self.refine_search(matches)\n if selection == -1:\n return (-1)\n else:\n return (matches[int(selection)])\n\n elif len(matches) == 0:\n # If non are found - let the user know\n print('\\n\\tNo Matches Found')\n return (-1)\n\n def search_number(self, number):\n matches = list()\n\n # In each sheet find matches\n for sheet in range(0, self.number_sheets):\n index = 0\n flag = 0\n for entery in self.sheets[sheet]:\n # Skip the first row\n if flag == 0:\n flag = 1\n else:\n # If a match is found, append the info, and the index\n if str(number) in entery[self.s_num_col]:\n matches.append([entery[self.first_name_col],\n entery[self.last_name_col],\n entery[self.s_num_col],\n sheet,\n index])\n index = index + 1\n\n # Check if any matches were found\n if len(matches) == 1:\n # If one match is found return\n return (matches[0])\n\n elif len(matches) > 1:\n # If multiple are found - refine the selection\n selection = self.refine_search(matches)\n # print(selection)\n if selection == -1:\n return (-1)\n else:\n return (matches[int(selection)])\n\n elif len(matches) == 0:\n # If non are found - let the user know\n print('\\n\\tNo Matches Found')\n return (-1)\n\n # Gets a list of potnetial matches, and gets the user to select one\n def refine_search(self, matches):\n\n # print all the matches\n print(\"\\nFound following matches:\")\n cc = 0\n for student in matches:\n print(\"\\t%d - %s\" % (cc, str(student[self.s_num_col])),\n student[self.last_name_col],\n student[self.first_name_col], '\\n')\n\n cc = cc + 1\n\n # Get user to select one\n while 1:\n selection = str(input(\"Select the student:\\n\\t\"))\n\n # Check if user wants to cancel\n if str(selection) == str(self.cancel_command):\n return (-1)\n\n # attempt to use the input to select the user, if invalid try again\n try:\n if int(selection) >= 0 and int(selection) < len(matches):\n return (selection)\n else:\n print(\"Please enter a valid number\")\n except(ValueError):\n print(\"Please enter a valid number\")\n\n # Print the selected student, and gets the grade input\n # for each selected grading column\n def enter_grades(self, student):\n # Print the student info\n print('Selected')\n print(\"\\t%s %s %s\"\n % (student[self.last_name_col],\n student[self.first_name_col],\n student[self.s_num_col]))\n\n cc = 1\n print('\\tCurrent Grades:')\n\n for entery in self.grading_columns[student[-2]]:\n print(\"\\t\\tG#%d:\" % (cc),\n self.sheets[student[-2]][student[-1]][entery])\n\n cc = cc + 1\n\n # Get the grade for each selected column\n cc = 1\n for entery in self.grading_columns[student[-2]]:\n # Runs until a correct input is recived - float or CNC\n while 1:\n grade = str(input('\\n\\tEnter Grade #%d\\n\\t\\t' % (cc)))\n try:\n self.sheets[student[-2]][student[-1]][entery] = float(grade)\n break\n except(ValueError):\n if grade == str(self.cancel_command):\n break\n else:\n print('Invalid Input')\n cc = cc + 1\n\n###############################################################################\n# Write Sheets #\n###############################################################################\n\n # Writes a new sheet in the working directory\n def write_sheet(self):\n cc = 0\n for sheet in self.sheets:\n dd = 1\n input('press enter')\n #Check if the file exists. Iterate the name by one if it does\n while 1:\n name = 'file' + str(dd)+ '-' +str(cc + 1) + '.xls'\n if os.path.exists(os.path.join(self.working_dir, name)):\n dd = dd+1\n else:\n break\n \n #name = 'file' +str(cc + 1) + '.xls'\n with open(os.path.join(self.working_dir, name), 'w+',\n encoding='utf-16', newline='') as book:\n\n writer = csv.writer(book, self.file_format)\n book.seek(0)\n\n for row in self.sheets[cc]:\n writer.writerow(row)\n cc = cc + 1\n\n # Opens the sheet and prints the updated data\n def overwrite_sheet(self):\n cc = 0\n for sheet in self.input_files:\n\n with open(self.input_files[cc], 'w',\n encoding='utf-16', newline='') as book:\n\n writer = csv.writer(book, self.file_format)\n book.seek(0)\n\n for row in self.sheets[cc]:\n writer.writerow(row)\n cc = cc + 1\n\n # Test if the spreadsheet is writable\n def check_sheet(self):\n status = list()\n cc = 0\n for sheet in self.input_files:\n try:\n with open(self.input_files[cc], 'a', encoding='utf-16', newline=''):\n status.append(True)\n except:\n status.append(False)\n cc = cc + 1\n return(status)\n\n # Working in progress - logs all the user input and actions\n def logger(self, Log):\n if self.logger_setup == 1:\n with open(self.log_path, 'a') as logger:\n time = datetime.now()\n logger.write(time.strftime(\"%y-%m-%d %H:%M:%S - \"), Log)\n\n def logger_setup(self):\n self.log_path = os.path.join(GradeBook.log_path, \"Log.txt\")\n\n if os.path.exists(GradeBook.log_path):\n self.logger_setup = 1\n\n return True\n else:\n flag = 0\n\n\n# The class holding all the information\ngb = GradeBook()\n\nif gb.dev == 1:\n flag = 0\n #gb.settings()\n # gb.menu()\nelse:\n gb.setup()\n gb.enter_student()\n"
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"text": "# GradeBook\n## Feature Highlights \nThe app was intended for simplifying the grade input process for individuals unlucky enough to have to use blackboard services, however, it may find use in other applications as well.\n- Searching multiple spreadsheets \n- Partial matches of both names and student numbers\n- Entering multiple grades for the same student at the same time \n\n## Overview \nIf your school is using blackboard and you are tasked with entering grades the process will take forever, especially if the course has many sections, this script may reduce the extent of your misery. This app opens the spreadsheets downloadable from blackboard and allows you to quickly search for a student by portion of first name, last name, or student number, and enter the grade. The script supports searching multiple spreadsheets and entering multiple grades for a given student, which is useful for creating mark breakdowns for accreditation. \n**Use the app at your own risk.**\nThe complied version of the app was tested on Windows 10. The pyton script should work on other platforms. \n\n# Quick Start \n1. Download the application executable (GradeBook.exe) from github \n(check repository for the newest version: https://github.com/maciek226/GradeBook)\n2. Download the spreadsheets with students names and desired grades from blackboad\n3. Open GradesConf.txt and add add the path to the spreadsheets you would like to search \n4. For each sheet start input \"in\" followed by a tab and the path to the file \n5. Open GradeBook.exe\n6. Follow instructions in the command line window\n\n## Commands\nThe script operates using written commands that differ slightly depending on the task. Note, the commands in the script are case sensitive\n\n- `EXT` - Exit and save\\\n Available only in the search for student filed \n- `CNC` - Cancel\\\n Available whenever there is a choice such as adding a student grade, selecting student from the list, or adding spreadsheet column\n- `ADD`- Add column\\\n Available only in the column selection screen \n\n## Adding a Column to a Spreadsheet \nThe app can modify the size of the spreadsheet using `ADD` command. This feature is intended to aid in creating mark breakdowns (needed for accreditation). These new fields, however, will not be read by blackboard upon upload of the updated sheet. Therefore, add all the grade fields before downloading the spreadsheet to your computer. Before uploading the updated spreadsheet make a copy of the file for your records and delete the added columns.\n\n## Limitations \n- The script can catch serious errors; however, it does not have any logic checking ability. \n- Make sure that when selecting columns in multiple sheets the columns are selected in the same order for all sheets. \n- It is not possible to change any configurations (set up of columns) after the selection.\n- The grades must be numbers (natural or positive real) \n\n## Configuration File\n- Configuration file is a tab separated file (tab separated the property name from value)\n- Add spreadsheets by writing int followed by tab (`\\t`) and the path to the file\\\n `in \\t C:\\User\\...`\n- The modifications to the file can be either overwritten by setting\\\n `overwrite \\t 1`\n- To write data to a new file set\\\n `overwrite \\t 0` and add an output file path\\\n `out \\t C:User\\`\n- If the configuration file is missing the script will ask for the paths to the desired files. Once complete, a new config file will be made in the root directory\n\n## Troubleshooting \n- Make sure that the spreadsheets you are editing are not open in other applications \\\n The script can detect if other app prevents it from closing and it should give a warning\n\n## Other Information\n- The script is built in Python 3\n"
}
] | 2 |
ecz5032/hw1-python
|
https://github.com/ecz5032/hw1-python
|
27051168d9fcffe04f500daf633556ec343ec2b4
|
02b762b15469e6787b92e3aba904134fceec6e8b
|
47e9fb0cc30525dc02d76eac949fed1d52eb1527
|
refs/heads/master
| 2022-12-13T23:56:05.201377 | 2020-09-05T00:05:47 | 2020-09-05T00:05:47 | 292,969,502 | 0 | 0 | null | null | null | null | null |
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"text": "#author Eric Zhang [email protected]\n\ngradeOneInput = input(\"Enter your course 1 letter grade: \") \ncredit1 = input(\"Enter your course 1 credit: \")\ncredit1 = float(credit1)\n\nif gradeOneInput == \"A\":\n gradeOne = 4\nelif gradeOneInput == \"A-\":\n gradeOne = 3.67\nelif gradeOneInput == \"B+\":\n gradeOne = 3.33\nelif gradeOneInput == \"B\":\n gradeOne = 3\nelif gradeOneInput == \"B-\":\n gradeOne = 2.67\nelif gradeOneInput == \"C+\":\n gradeOne = 2.33\nelif gradeOneInput == \"C\":\n gradeOne = 2\nelif gradeOneInput == \"D\":\n gradeOne = 1\nelse:\n gradeOne = 0\n\nprint(\"Grade point for course 1 is: \" +str(gradeOne))\n\ngradeTwoInput = input(\"Enter your course 2 letter grade: \") \ncredit2 = input(\"Enter your course 2 credit: \")\ncredit2 = float(credit2)\n\nif gradeTwoInput == \"A\":\n gradeTwo = 4\nelif gradeTwoInput == \"A-\":\n gradeTwo = 3.67\nelif gradeTwoInput == \"B+\":\n gradeTwo = 3.33\nelif gradeTwoInput == \"B\":\n gradeTwo = 3\nelif gradeTwoInput == \"B-\":\n gradeTwo = 2.67\nelif gradeTwoInput == \"C+\":\n gradeTwo = 2.33\nelif gradeTwoInput == \"C\":\n gradeTwo = 2\nelif gradeTwoInput == \"D\":\n gradeTwo = 1\nelse:\n gradeTwo = 0\n\nprint(\"Grade point for course 2 is: \" +str(gradeTwo))\n\ngradeThreeInput = input(\"Enter your course 3 letter grade: \") \ncredit3 = input(\"Enter your course 3 credit: \")\ncredit3 = float(credit3)\n\nif gradeThreeInput == \"A\":\n gradeThree = 4\nelif gradeThreeInput == \"A-\":\n gradeThree = 3.67\nelif gradeThreeInput == \"B+\":\n gradeThree = 3.33\nelif gradeThreeInput == \"B\":\n gradeThree = 3\nelif gradeThreeInput == \"B-\":\n gradeThree = 2.67\nelif gradeThreeInput == \"C+\":\n gradeThree = 2.33\nelif gradeThreeInput == \"C\":\n gradeThree = 2\nelif gradeThreeInput == \"D\":\n gradeThree = 1\nelse:\n gradeThree = 0\n\nprint(\"Grade point for course 3 is: \" +str(gradeThree))\n\nGPA = (gradeOne * credit1 + gradeTwo * credit2+gradeThree * credit3) / (credit1+credit2+credit3)\nprint(\"Your GPA is: \"+str(GPA))\n"
}
] | 1 |
huynhminhtruong/django-rest-framework
|
https://github.com/huynhminhtruong/django-rest-framework
|
6053c6dd81c4ccfd2511ea6543249aa2b6b831d3
|
ec9bf3530cfabcb231f23cd81b6405d536eb0f4d
|
3b4e8cf68eb31eadf0b79fcc5f297e96afbe7011
|
refs/heads/master
| 2020-04-14T09:00:44.683348 | 2019-01-02T17:01:10 | 2019-01-02T17:01:10 | 163,749,546 | 0 | 0 | null | null | null | null | null |
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"path": "/todo/music/views.py",
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"text": "from django.shortcuts import render, get_object_or_404\nfrom django.http import HttpResponse\nfrom django.urls import reverse\nfrom .models import Song\nfrom django.views.generic import ListView, DetailView\nfrom rest_framework import viewsets\nfrom .api.serializer import SongSerializer\n\n# Create your views here.\n\nclass SongListView(ListView):\n model = Song\n template = \"music/template/list_view.html\"\n\nclass SongDetailsView(DetailView):\n model = Song\n template = \"music/template/details_view.html\"\n"
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"text": "from music.api.viewset import SongViewSet\nfrom rest_framework import routers\n\nrouter = routers.DefaultRouter()\nrouter.register(\"music\", SongViewSet, base_name=\"music\")"
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"text": "from django.urls import path, include\nfrom rest_framework import routers\nfrom . import views\n\napp_name = \"music\"\n\nurlpatterns = [\n path('', views.SongListView.as_view(), name=\"list\"),\n path('<int:pk>/', views.SongDetailsView.as_view(), name=\"details\")\n]"
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"text": "from music.models import Song\nfrom .serializer import SongSerializer\nfrom rest_framework import viewsets\nfrom rest_framework.decorators import action\nfrom rest_framework.response import Response\n\n\n# class SongViewSet(viewsets.ViewSet):\n#\n# # list, create, retrieve, update, partial_update, destroy\n#\n# def list(self, request):\n# queryset = Song.objects.all()\n# serializer = SongSerializer(queryset, many=True)\n# return Response(serializer.data)\n\n\nclass SongViewSet(viewsets.ModelViewSet):\n queryset = Song.objects.all()\n serializer_class = SongSerializer\n\n @action(methods=['get'], detail=False)\n def newest(self, request):\n newest = self.get_queryset().order_by('created').last()\n serializer = self.get_serializer_class()(newest)\n return Response(serializer.data)"
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"text": "from rest_framework import serializers\nfrom music.models import Song\n\nclass SongSerializer(serializers.Serializer):\n class Meta:\n model = Song\n fields = (\"title\", \"created\")"
}
] | 5 |
beewhoo/fanx-coding-challenge
|
https://github.com/beewhoo/fanx-coding-challenge
|
2b0da70a2fab933513005710cec85a44a1b1af67
|
3316ac379691727c5357dcd430ecb31dcea5b1bf
|
4edcde5af03bfabdfc46a7a498c8aad209aa05ee
|
refs/heads/master
| 2020-03-28T13:37:14.765771 | 2018-09-16T00:52:59 | 2018-09-16T00:52:59 | 148,411,370 | 0 | 0 | null | null | null | null | null |
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"repo_name": "beewhoo/fanx-coding-challenge",
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"text": "import sys\nfrom word_counter_methods import *\n\n\n'''Program accepts file in command line if none- program refers to the file test_most_common_words.txt'''\n\nif __name__ == '__main__':\n if len(sys.argv) > 1:\n text_file = sys.argv[1]\n else:\n text_file = './data/input.txt'\n occuring_words = ten_most_common_words(text_file)\n word_count = word_count(text_file)\n print('-------------program starting---------------')\n print(\"Loading file: %s\" % (text_file))\n print ('------------word count---------------------')\n print \"Word Count: %s\" % (word_count)\n print ('------------most occuring words ordered------------')\n print \"Top words: %s\" %(occuring_words)\n print ('-------------------------------------------')\n"
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"text": "import unittest\n\nfrom word_counter_methods import word_count,remove_punctuation_and_split, ten_most_common_words\n\nclass test_word_counter(unittest.TestCase):\n\n def test_word_count(self):\n self.assertEqual(word_count('./data/word_count.txt'),8)\n\n def test_remove_punctuation_and_split(self):\n self.assertEqual(remove_punctuation_and_split(\"Hello, I'm a string!\"),[\"Hello\", \"Im\", 'a', 'string'])\n\n def test_most_common_words(self):\n self.assertEqual(ten_most_common_words('./data/most_common_words.txt'),[('barcelonafc', 10), ('Raptors', 8), ('Torontofc', 6), ('Leafs', 4),('Argonauts', 2)])\n\n\n\n\n\nif __name__ == '__main__':\n unittest.main()\n"
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"text": "\nimport collections\nfrom collections import OrderedDict\nimport string\n\n\n'''Outputs the word count after removing punctuation'''\ndef word_count(text_file):\n counter = 0\n with open(text_file) as file:\n for line in file:\n counter += len(remove_punctuation_and_split(line))\n return counter\n\n''' Remove punctuation and split words'''\ndef remove_punctuation_and_split(line):\n return line.translate(None,string.punctuation).split()\n\n''' Top 10 words ordered'''\ndef ten_most_common_words(text_file):\n words = collections.Counter()\n with open(text_file, \"r\") as text_file:\n for line in text_file:\n words.update(remove_punctuation_and_split(line))\n return OrderedDict(words.most_common(10)).items()\n"
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"path": "/README.md",
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"text": "\n# Word Counter\n\nProgram accepts <file_name.txt> on command line (optional). If none provided program will run './data/input.txt' from this repo.\n\n## Getting Started\n\n```\npython word_counter.py ./data/input.txt \n```\n\n## Run test \n\n```\npython word_counter_test.py\n```\n\n\n\n# Festival names\n\nProgram accepts <file_name.txt> on command line (optional). If none provided program will run './data/data.txt' from this repo.\n\n## Getting Started\n\n```\npython festival_names.py ./data/data.txt\n```\n## Run test \n\n```\npython festival_names_test.py\n```\n\n\n\n\n\n\n\n\n\n"
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"path": "/festival_names_methods.py",
"repo_name": "beewhoo/fanx-coding-challenge",
"src_encoding": "UTF-8",
"text": "''' Unique list without loosing order '''\n\ndef unique_ordered(list):\n seen = []\n for f in list:\n if f not in seen:\n seen.append(f)\n return seen\n\n\n\n''' Compares two lists - breaks when not matched - returns matched list'''\n\ndef compare_lists(list1, list2):\n match = []\n smaller_line = min(len(list1),len(list2))\n for i in range(0, smaller_line):\n if list1[i] == list2[i]:\n match.append(list1[i])\n else:\n break\n return ' '.join(match)\n\n\n'''Opens file two lines at a time - splits two lines into two lists -calls compare lists functions - returns matched festivals list'''\n\ndef open_file(text_file):\n with open(text_file, 'r') as f:\n festivals = []\n previous = next(f)\n for line in f:\n festival = compare_lists(previous.split(' '), line.split(' '))\n previous = line\n if festival != '':\n festivals.append(festival)\n return festivals\n"
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"path": "/festival_names.py",
"repo_name": "beewhoo/fanx-coding-challenge",
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"text": "import sys\nfrom festival_names_methods import open_file, unique_ordered\n\n\n\n'''Program accepts file in command line if 'none'- program refers to the file data.txt'''\n\nif __name__ == '__main__':\n if len(sys.argv) > 1:\n text_file = sys.argv[1]\n else:\n text_file = './data/data.txt'\nfestivals = open_file(text_file)\nunique_festivals = unique_ordered(festivals)\n\n\nprint('----------FESTIVALS--------------')\n\nfor f in unique_festivals:\n print f\nprint('---------------------------------')\n"
},
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"path": "/festival_names_test.py",
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"text": "import unittest\n\nfrom festival_names_methods import *\n\nclass festival_names_test(unittest.TestCase):\n\n\n def test_unique_ordered(self):\n self.assertEqual(unique_ordered(['a','b','b','c']),['a','b','c'])\n\n def test_compare_lists(self):\n a =['a','b','c']\n b =['a','b','c','d']\n self.assertEqual(compare_lists(a,b),'a b c')\n\n def test_open_file(self):\n self.assertEqual(open_file('./data/test.txt'),['ab\\n','cd\\n'])\n\n\n\n\n\nif __name__ == '__main__':\n unittest.main()\n"
}
] | 7 |
jdrkanchana/java_projects
|
https://github.com/jdrkanchana/java_projects
|
e6d83fec9de9ce9e4ed5129a59da49e58f8bf593
|
e61042877588b98af2898127a28d18f63f2ae6fd
|
1c21afbf7c8555bed9f33f8d8f0cbe547ef3b730
|
refs/heads/main
| 2023-06-02T05:45:48.116590 | 2021-06-21T02:33:37 | 2021-06-21T02:33:37 | 320,820,511 | 0 | 0 | null | null | null | null | null |
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"num_lines": 6,
"path": "/println/HelloPrint.java",
"repo_name": "jdrkanchana/java_projects",
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"text": "class HelloPrint {\npublic static void main(String[] args) {\n System.out.print( \"Welcome to java programming!\");\n System.out.println( \"This is the third lecture!\");\n}\n}\n"
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"num_lines": 9,
"path": "/variables/summation/SumOfNumber.java",
"repo_name": "jdrkanchana/java_projects",
"src_encoding": "UTF-8",
"text": "class SumOfNumber{\npublic static void main(String[] args) {\n int x=10;\n int y=11;\n int sum;\n sum=x+y;\n System.out.println(sum);\n}\n}\n"
},
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"path": "/comments/ManyComment2.py",
"repo_name": "jdrkanchana/java_projects",
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"text": "class ManyComment2{\npublic static void main(String[] args) {\n /*\n x-number of students participate to lesson 1\n y-number of students participate to lesson 2\n */\n int x=10, y=11,sum;\n sum=x+y;\n System.out.println(sum);\n}\n}\n"
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"path": "/variables/sensible_names/SensibleNames.java",
"repo_name": "jdrkanchana/java_projects",
"src_encoding": "UTF-8",
"text": "class SensibleNames{\npublic static void main(String[] args) {\n int no_of_students_lec1=10; \n int no_of_students_lec2=11;\n int sum;\n sum=no_of_students_lec1+no_of_students_lec2;\n System.out.println( sum);\n}\n}\n"
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}
] | 5 |
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