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# coding: utf-8
# import models into model package
from .error_enveloped import ErrorEnveloped
from .health_check_enveloped import HealthCheckEnveloped
from .inline_response200 import InlineResponse200
from .inline_response200_data import InlineResponse200Data
from .inline_response201 import InlineResponse201
from .inline_response2001 import InlineResponse2001
from .inline_response2001_authors import InlineResponse2001Authors
from .inline_response2001_badges import InlineResponse2001Badges
from .inline_response2002 import InlineResponse2002
from .inline_response2002_data import InlineResponse2002Data
from .inline_response2002_data_node_requirements import (
InlineResponse2002DataNodeRequirements,
)
from .inline_response2002_data_service_build_details import (
InlineResponse2002DataServiceBuildDetails,
)
from .inline_response2003 import InlineResponse2003
from .inline_response2003_data import InlineResponse2003Data
from .inline_response_default import InlineResponseDefault
from .inline_response_default_error import InlineResponseDefaultError
from .running_service_enveloped import RunningServiceEnveloped
from .running_services_enveloped import RunningServicesEnveloped
from .service_extras_enveloped import ServiceExtrasEnveloped
from .services_enveloped import ServicesEnveloped
from .simcore_node import SimcoreNode
|
python
|
__author__ = 'Bohdan Mushkevych'
from odm.document import BaseDocument
from odm.fields import StringField, ObjectIdField, DateTimeField
TIMEPERIOD = 'timeperiod'
START_TIMEPERIOD = 'start_timeperiod'
END_TIMEPERIOD = 'end_timeperiod'
FLOW_NAME = 'flow_name'
STATE = 'state'
CREATED_AT = 'created_at'
STARTED_AT = 'started_at'
FINISHED_AT = 'finished_at'
RUN_MODE = 'run_mode'
RUN_MODE_NOMINAL = 'run_mode_nominal'
RUN_MODE_RECOVERY = 'run_mode_recovery'
# Flow can get into STATE_INVALID if:
# a. related Job was marked for reprocessing via MX
# b. have failed with an exception at the step level
# NOTICE: FlowDriver changes STATE_INVALID -> STATE_IN_PROGRESS during re-posting
STATE_INVALID = 'state_invalid'
# given Flow was successfully executed
# This is a final state
STATE_PROCESSED = 'state_processed'
# given Flow had no steps to process
# This is a final state
STATE_NOOP = 'state_noop'
# FlowDriver triggers the flow execution.
# Next valid states: STATE_NOOP, STATE_PROCESSED, STATE_INVALID
STATE_IN_PROGRESS = 'state_in_progress'
# Flow record created in the DB
# Next valid states: STATE_IN_PROGRESS
STATE_EMBRYO = 'state_embryo'
class Flow(BaseDocument):
""" class presents status for a Flow run """
db_id = ObjectIdField('_id', null=True)
flow_name = StringField(FLOW_NAME)
timeperiod = StringField(TIMEPERIOD)
start_timeperiod = StringField(START_TIMEPERIOD)
end_timeperiod = StringField(END_TIMEPERIOD)
state = StringField(STATE, choices=[STATE_EMBRYO, STATE_IN_PROGRESS, STATE_PROCESSED, STATE_NOOP, STATE_INVALID])
# run_mode override rules:
# - default value is read from ProcessEntry.arguments['run_mode']
# - if the ProcessEntry.arguments['run_mode'] is None then run_mode is assumed `run_mode_nominal`
# - Flow.run_mode, if specified, overrides ProcessEntry.arguments['run_mode']
# - UOW.arguments['run_mode'] overrides Flow.run_mode
run_mode = StringField(RUN_MODE, choices=[RUN_MODE_NOMINAL, RUN_MODE_RECOVERY])
created_at = DateTimeField(CREATED_AT)
started_at = DateTimeField(STARTED_AT)
finished_at = DateTimeField(FINISHED_AT)
@BaseDocument.key.getter
def key(self):
return self.flow_name, self.timeperiod
@key.setter
def key(self, value):
""" :param value: tuple (name of the flow, timeperiod as string in Synergy Data format) """
self.flow_name = value[0]
self.timeperiod = value[1]
|
python
|
''' This module generates charts from cohort.pickle rather than from the cumulative csvs.
'''
import os
import pandas as pd
import matplotlib.pyplot as plt
from plot_practice_charts import *
from ethnicities import high_level_ethnicities
wave_column_headings = {
"total": "All",
"all_priority": "Priority groups",
"1": "In care home",
"2": "80+",
"3": "70-79",
"4": "CEV",
"5": "65-69",
"6": "At risk",
"7": "60-64",
"8": "55-59",
"9": "50-54",
"0": "Other",
}
group_names = {
"vacc_group":"Vaccinated",
"decline_group":"Declined",
"decline_total_group":"Declined - all",
"other_reason_group":"Other reason",
"declined_accepted_group": "Declined then received",
"patient_id":"total"
}
backend = os.getenv("OPENSAFELY_BACKEND", "expectations")
out_path = f"output/{backend}/additional_figures"
os.makedirs(out_path, exist_ok=True)
def compute_uptake_percent(uptake):#, labels):
uptake_pc = 100 * uptake / uptake.loc["total"]
uptake_pc.drop("total", inplace=True)
uptake_pc.fillna(0, inplace=True)
if set(uptake_pc.columns) == {"True", "False"}:
# This ensures that chart series are always same colour.
uptake_pc = uptake_pc[["True", "False"]]
else:
# Sort DataFrame columns so that legend is in the same order as chart series.
uptake_pc.sort_values(
uptake_pc.last_valid_index(), axis=1, ascending=False, inplace=True
)
#uptake_pc.rename(columns=labels, inplace=True)
return uptake_pc
def practice_variation(input_path="output/cohort.pickle", output_dir=out_path):
''' Calculates total patients per practice and of whom how many have had a vaccine to date, or declined
Note: those declining only include those who have not later received a vaccine.
'''
cohort = pd.read_pickle(input_path)
# limit to priority groups (ages 50+ and clinical priority groups)
cohort = cohort.loc[cohort["wave"]!=0]
practice_figures = cohort[["practice", "vacc_group", "decline_group", "patient_id"]]\
.groupby("practice").agg({"vacc_group":"sum",
"decline_group":"sum",
"patient_id":"nunique"})
practice_figures = practice_figures.rename(columns={"patient_id":"patient_count"})
# remove tiny practices by setting a minimum no of people in the priority groups
# and ensure that at least 10 patients have been vaccinated in each practice
if backend=="expectations":
practice_figures = practice_figures.loc[(practice_figures["patient_count"]>10)&(practice_figures["vacc_group"]>0)]
else:
practice_figures = practice_figures.loc[(practice_figures["patient_count"]>250)&(practice_figures["vacc_group"]>10)]
# summarise data
practice_count = len(practice_figures.index)
counts = practice_figures.loc[practice_figures["decline_group"]>0]["decline_group"].count()
d = {"practices with declines":counts, "total practices":practice_count}
out = pd.Series(d, index=["practices with declines", "total practices"])
out.to_csv(f"{output_dir}/practice_decline_summary.csv")
practice_figures = practice_figures.assign(
decline_per_1000 = 1000*practice_figures["decline_group"]/practice_figures["patient_count"],
decline_per_1000_vacc = 1000*practice_figures["decline_group"]/practice_figures["vacc_group"],
vacc_per_1000 = 1000*practice_figures["vacc_group"]/practice_figures["patient_count"]
)
plot_hist(df=practice_figures, output_dir=output_dir)
plot_boxplot(df=practice_figures, backend=backend, output_dir=output_dir)
plot_heatmap(df=practice_figures, backend=backend, output_dir=output_dir)
def declined_vaccinated(input_path="output/cohort.pickle", output_dir=out_path):
''' Counts patients who went from "Declined" to "Vaccinated".
Creates a chart.
'''
cohort = pd.read_pickle(input_path)
cohort["wave"] = cohort["wave"].astype(str)
cohort = cohort[["wave", "vacc_group", "declined_accepted_group", "decline_total_group", "patient_id"]]\
.groupby("wave").agg({"vacc_group":"sum",
"declined_accepted_group":"sum",
"decline_total_group":"sum",
"patient_id":"nunique"})
cohort = cohort.rename(columns=group_names, index=wave_column_headings)
cohort = cohort.assign(
per_1000 = 1000*cohort["Declined then received"]/cohort["total"],
per_1000_vacc = 1000*cohort["Declined then received"]/cohort["Vaccinated"],
converted = 1000*cohort["Declined then received"]/cohort["Declined - all"]
)
fig, axs = plt.subplots(3, 1, sharex=True, tight_layout=True, figsize=(6,12))
for n, x in enumerate(["per_1000", "per_1000_vacc", "converted"]):
cohort[x].plot(kind='bar', stacked=True, ax=axs[n])
if x=="per_1000_vacc":
title = "Patients Declining and later Receiving COVID Vaccines\n per 1000 vaccinated patients"
elif x=="converted":
title = "Patients Declining and later Receiving COVID Vaccines\n per 1000 patients who declined"
else:
title = "Patients Declining and later Receiving COVID Vaccines\n per 1000 patients"
axs[n].set_ylabel("Rate per 1000")
axs[n].set_title(title)
axs[1].set_xlabel("Priority group")
fig.savefig(f"{output_dir}/all_declined_then_accepted_by_wave.png")
def decl_acc_time_delay(input_path="output/cohort.pickle", output_dir=out_path):
'''
Measures the time between recorded decline and vaccination for each pt in the declined-then-accepted group,
and groups to number of weeks.
'''
cohort = pd.read_pickle(input_path)
# limit to priority groups (ages 50+ and clinical priority groups)
cohort = cohort.loc[cohort["wave"]!=0]
# filter to the declined-then-accepted group
cohort = cohort.loc[cohort["declined_accepted_group"]==1]
cohort = cohort[["wave", "declined_accepted_group", "patient_id", "vacc1_dat", "decl_first_dat", "high_level_ethnicity"]]
cohort["wave"] = cohort["wave"].astype(str)
# calculate no of days between recorded decline and vaccination.
cohort["date_diff"] = cohort["vacc1_dat"] - cohort['decl_first_dat']
# bin the data
bins = [
pd.Timedelta(days = 0),
pd.Timedelta(days = 14),
pd.Timedelta(days = 28),
pd.Timedelta(weeks = 8),
pd.Timedelta(weeks = 120)
]
labels = ["0-<2 weeks", "2-<4 weeks", "1-<2 months", ">=2 months"]
cohort["weeks_diff"] = pd.cut(cohort["date_diff"], bins=bins, labels=labels, retbins=False, include_lowest=True, right=False)
# summarise for each group
cohort_a = cohort.groupby(["wave","weeks_diff"])["patient_id"].count()
cohort_a = cohort_a.rename(index=wave_column_headings)
# low number suppression and rounding
cohort_a = cohort_a.replace([1,2,3,4,5,6], 0)
cohort_a = ((cohort_a // 7) * 7).astype(int)
cohort_a.to_csv(f"{output_dir}/declined_accepted_weeks_by_wave.csv")
# look at priority groups split by demographics
cohort_b = cohort.copy()
# group by wave and ethnicity
cohort_b = cohort_b.groupby(["wave", "high_level_ethnicity","weeks_diff"])["patient_id"].count()
# rename column headers and indices (2 levels)
cohort_b = cohort_b.rename(index=high_level_ethnicities)
cohort_b = cohort_b.rename(index=wave_column_headings)
# low number suppression and rounding
cohort_b = cohort_b.replace([1,2,3,4,5,6], 0)
cohort_b = ((cohort_b // 7) * 7).astype(int)
cohort_b.to_csv(f"{output_dir}/declined_accepted_weeks_by_wave_and_ethnicity.csv")
def invert_df(df, group="all"):
''' "Inverts" df:
calculates the difference between the total population ("total" row) and
each other row in turn, so if `df` counts "patients vaccinated", the resulting
df counts "patients NOT vaccinated".
'''
for i in df.index.drop("total"):
df.loc[i] = df.loc["total"] - df.loc[i]
return df
|
python
|
# nxpy_svn --------------------------------------------------------------------
# Copyright Nicola Musatti 2010 - 2018
# Use, modification, and distribution are subject to the Boost Software
# License, Version 1.0. (See accompanying file LICENSE.txt or copy at
# http://www.boost.org/LICENSE_1_0.txt)
# See https://github.com/nmusatti/nxpy/tree/master/libs/svn. ------------------
r"""
Subversion administration tool wrapper.
"""
from __future__ import absolute_import
import os
import nxpy.command.command
import nxpy.command.option
_config = nxpy.command.option.Config()
class SvnAdmin(nxpy.command.command.Command):
def __init__(self, debug=None):
super(SvnAdmin, self).__init__("svnadmin", debug)
def create(self, path, debug=None):
op = nxpy.command.option.Parser(_config, "create", ( path, ), {})
self.run(op, debug)
return "file:///" + path.replace(os.sep, "/").lstrip("/")
|
python
|
# -*- Python -*-
# license
# license.
"""
Files opening and reading/writing functions.
"""
import sys, os, bz2, lzma, json
import logging; module_logger = logging.getLogger(__name__)
# ======================================================================
def open_for_reading_binary(filename):
"""Opens binary file for reading. Handles compressed files transparently."""
return FileBinaryReader(filename)
# ======================================================================
def open_for_reading_text(filename):
"""Opens text file for reading. Handles compressed files transparently."""
return FileTextReader(filename)
# ======================================================================
def read_binary(filename):
"""Reads and returns entire binary file content as bytes. Handles
compressed files transparently."""
with FileBinaryReader(filename) as f:
return f.read()
# ======================================================================
def read_or_get_binary(data, try_reading_from_file=True):
"""If data is a filename, reads it. Uncompresses data, if necessary."""
try:
if try_reading_from_file and isinstance(data, str) and len(data) < 1024:
data = read_binary(data)
except Exception as err:
raise RuntimeError('Unable to open file {!r}'.format(data))
# check if data is compressed
try:
data = lzma.decompress(data)
except Exception as err:
try:
data = bz2.decompress(data)
except Exception as err:
pass
# do not convert to str!
return data
# ======================================================================
def write_json(filename, data, indent=None, sort_keys=False, backup=True):
if indent is None:
s = json.dumps(data, separators=[',', ':'], indent=indent, sort_keys=sort_keys)
else:
s = json_dumps(data, indent=indent, indent_increment=indent)
with open_for_writing_binary(filename, backup=backup) as fd:
fd.write(s.encode('utf-8'))
# ======================================================================
def read_json(filename):
return json.loads(open_for_reading_text(filename).read())
# ======================================================================
def json_dumps(data, indent=2, indent_increment=2):
"""More compact dumper with wide lines."""
def simple(d):
r = True
if isinstance(d, dict):
r = not any(isinstance(v, (list, tuple, set, dict)) for v in d.values())
elif isinstance(d, (tuple, list)):
r = not any(isinstance(v, (list, tuple, set, dict)) for v in d)
return r
def end(symbol, indent):
if indent > indent_increment:
r = "{:{}s}{}".format("", indent - indent_increment, symbol)
else:
r = symbol
return r
r = []
if simple(data):
if isinstance(data, set):
r.append(json.dumps(sorted(data), sort_keys=True))
else:
r.append(json.dumps(data, sort_keys=True))
else:
if isinstance(data, dict):
r.append("{")
for no, k in enumerate(sorted(data), start=1):
comma = "," if no < len(data) else ""
r.append("{:{}s}{}: {}{}".format("", indent, json.dumps(k), json_dumps(data[k], indent + indent_increment, indent_increment), comma))
r.append(end("}", indent))
elif isinstance(data, (tuple, list)):
r.append("[")
for no, v in enumerate(data, start=1):
comma = "," if no < len(data) else ""
r.append("{:{}s}{}{}".format("", indent, json_dumps(v, indent + indent_increment, indent_increment), comma))
r.append(end("]", indent))
return "\n".join(r)
# ----------------------------------------------------------------------
def open_for_writing_binary(filename, compressed=None, backup=True, makedirs=True):
"""Opens binary file for writing. If compressed is None, autodetects if data should be compressed by filename suffix."""
if filename == '-' or filename is None:
f = sys.stdout.buffer
else:
if compressed is None:
if filename[-4:] == '.bz2':
compressed = 'bz2'
elif filename[-3:] == '.xz':
compressed = 'xz'
elif compressed is True:
compressed = 'xz'
if backup:
backup_file(filename, backup)
if makedirs and '/' in filename:
try:
os.makedirs(os.path.dirname(filename))
except:
pass
if compressed == 'bz2':
f = bz2.BZ2File(filename, mode='w')
elif compressed == 'xz':
f = lzma.open(filename, mode='wb', preset=9 | lzma.PRESET_EXTREME)
else:
f = open(filename, mode='wb')
return f
# ======================================================================
def write_binary(filename, data, compressed=None, backup=True, makedirs=True):
"""Writes data (bytes) into a binary file. If compressed is None,
autodetects if data should be compressed by filename suffix."""
with open_for_writing_binary(filename, compressed=compressed, backup=backup, makedirs=makedirs) as f:
f.write(data)
# ======================================================================
def backup_file(filename, backup_dir=None):
"""Backup the file, if it exists. Backups versioning is supported."""
if isinstance(backup_dir, str):
newname = os.path.join(backup_dir, os.path.basename(filename))
else:
newname = filename
version = 1
while os.access(newname, os.F_OK):
newname = '{}.~{:02d}~'.format(filename, version)
version += 1
if newname != filename:
#module_logger.debug("Backing up file: {} --> {}".format(filename, newname))
try:
os.rename(filename, newname)
except Exception as err:
module_logger.warning('Cannot create backup copy of {}: {}'.format(filename, err), exc_info=True)
# ======================================================================
class FileBinaryReader:
def __init__(self, filename):
self.filename = filename
self.name = filename
self.open_xz()
def read(self, size=-1):
try:
return self.f.read(size)
except (IOError, EOFError, lzma.LZMAError):
self.open()
return self.read(size)
def readline(self):
try:
return self.f.readline()
except (IOError, EOFError, lzma.LZMAError):
self.open()
return self.readline()
def open_plain(self):
self.f = open(self.filename, mode='rb')
self.open = self.open_fail
def open_bz2(self):
self.f = bz2.BZ2File(self.filename)
self.open = self.open_plain
def open_xz(self):
self.f = lzma.LZMAFile(self.filename)
self.open = self.open_bz2
def open_fail(self):
raise IOError('Unable to open/read ' + repr(self.filename))
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
try:
self.f.close()
except:
pass
def __iter__(self):
return self
def __next__(self):
s = self.readline()
if not len(s):
raise StopIteration()
return s
# ======================================================================
class FileTextReader (FileBinaryReader):
def read(self, size=-1):
s = super().read(size=size)
if isinstance(s, bytes):
s = s.decode('utf-8')
return s
def readline(self):
s = super().readline()
if isinstance(s, bytes):
s = s.decode('utf-8')
return s
# ======================================================================
### Local Variables:
### eval: (if (fboundp 'eu-rename-buffer) (eu-rename-buffer))
### End:
|
python
|
# get largest continues sum
def pair_sum(arr, target):
seen = set()
output = set()
for num in arr:
diff = target - num
if diff not in seen:
print('adding diff',diff)
seen.add(num)
else:
print('adding output',diff, seen)
output.add((diff, num))
return output
#print(pair_sum([1,2,3,9],8)) #False
#print(pair_sum([1,2,4,4],8)) #True
print(pair_sum([3,1,2,2],4)) #True
|
python
|
#!/usr/bin/env python
"""Archive Now for python"""
# import pyhesity wrapper module
from pyhesity import *
from datetime import datetime
# command line arguments
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-v', '--vip', type=str, required=True) # cluster to connect to
parser.add_argument('-u', '--username', type=str, required=True) # username
parser.add_argument('-d', '--domain', type=str, default='local') # (optional) domain - defaults to local
parser.add_argument('-j', '--jobname', action='append', type=str)
parser.add_argument('-l', '--joblist', type=str)
parser.add_argument('-k', '--keepfor', type=int, required=True) # (optional) will use policy retention if omitted
parser.add_argument('-t', '--target', type=str, required=True) # (optional) will use policy target if omitted
parser.add_argument('-f', '--fromtoday', action='store_true') # (optional) keepfor x days from today instead of from snapshot date
parser.add_argument('-c', '--commit', action='store_true')
args = parser.parse_args()
vip = args.vip
username = args.username
domain = args.domain
jobnames = args.jobname
joblist = args.joblist
keepfor = args.keepfor
target = args.target
fromtoday = args.fromtoday
commit = args.commit
# authenticate
apiauth(vip, username, domain)
# gather server list
def gatherList(param=None, filename=None, name='items', required=True):
items = []
if param is not None:
for item in param:
items.append(item)
if filename is not None:
f = open(filename, 'r')
items += [s.strip() for s in f.readlines() if s.strip() != '']
f.close()
if required is True and len(items) == 0:
print('no %s specified' % name)
exit()
return items
jobnames = gatherList(jobnames, joblist, name='jobs', required=True)
jobs = api('get', 'protectionJobs')
# catch invalid job names
notfoundjobs = [n for n in jobnames if n.lower() not in [j['name'].lower() for j in jobs]]
if len(notfoundjobs) > 0:
print('Jobs not found: %s' % ', '.join(notfoundjobs))
daysToKeep = None
vault = [vault for vault in api('get', 'vaults') if vault['name'].lower() == target.lower()]
if len(vault) > 0:
vault = vault[0]
target = {
"vaultId": vault['id'],
"vaultName": vault['name'],
"vaultType": "kCloud"
}
else:
print('No archive target named %s' % target)
exit()
if keepfor:
daysToKeep = keepfor
finishedStates = ['kCanceled', 'kSuccess', 'kFailure', 'kWarning']
for job in sorted(jobs, key=lambda job: job['name'].lower()):
if job['name'].lower() in [j.lower() for j in jobnames]:
print('\n%s' % job['name'])
runs = api('get', 'protectionRuns?jobId=%s&runTypes=kRegular&runTypes=kFull&numRuns=10&excludeTasks=true' % job['id'])
for run in runs:
if run['backupRun']['snapshotsDeleted'] is False and run['backupRun']['status'] in ['kSuccess', 'kWarning']:
# check for active copy tasks
activeCopyTasks = [t for t in run['copyRun'] if t['status'] not in finishedStates]
if activeCopyTasks is None or len(activeCopyTasks) == 0:
# check for already completed archive tasks to this target
copyTasks = [t for t in run['copyRun'] if t['target']['type'] == 'kArchival' and t['status'] == 'kSuccess' and t['target']['archivalTarget']['vaultName'].lower() == target['vaultName'].lower()]
if copyTasks is None or len(copyTasks) == 0:
thisrun = api('get', '/backupjobruns?allUnderHierarchy=true&exactMatchStartTimeUsecs=%s&excludeTasks=true&id=%s' % (run['backupRun']['stats']['startTimeUsecs'], run['jobId']))
jobUid = thisrun[0]['backupJobRuns']['protectionRuns'][0]['backupRun']['base']['jobUid']
currentExpiry = None
# configure archive task
archiveTask = {
"jobRuns": [
{
"copyRunTargets": [
{
"archivalTarget": target,
"type": "kArchival"
}
],
"runStartTimeUsecs": run['copyRun'][0]['runStartTimeUsecs'],
"jobUid": {
"clusterId": jobUid['clusterId'],
"clusterIncarnationId": jobUid['clusterIncarnationId'],
"id": jobUid['objectId']
}
}
]
}
# if fromtoday is not set, calculate days to keep from snapshot date
if fromtoday is False:
daysToKeep = daysToKeep - dayDiff(dateToUsecs(datetime.now().strftime("%Y-%m-%d %H:%M:%S")), run['copyRun'][0]['runStartTimeUsecs'])
archiveTask['jobRuns'][0]['copyRunTargets'][0]['daysToKeep'] = int(daysToKeep)
# update run
if((daysToKeep > 0 and currentExpiry is None) or (daysToKeep != 0 and currentExpiry is not None)):
if commit:
print(' archiving snapshot from %s...' % usecsToDate(run['copyRun'][0]['runStartTimeUsecs']))
result = api('put', 'protectionRuns', archiveTask)
else:
print(' would archive snapshot from %s' % usecsToDate(run['copyRun'][0]['runStartTimeUsecs']))
break
else:
print(' skipping archive snapshot from %s' % usecsToDate(run['copyRun'][0]['runStartTimeUsecs']))
break
else:
print(' already archived snapshot from %s' % usecsToDate(run['copyRun'][0]['runStartTimeUsecs']))
break
else:
# check if currently archiving to this target
copyTasks = [t for t in run['copyRun'] if t['target']['type'] == 'kArchival' and t['target']['archivalTarget']['vaultName'].lower() == target['vaultName'].lower()]
if copyTasks is not None and len(copyTasks) > 0:
print(' already archiving snapshot from %s' % usecsToDate(run['copyRun'][0]['runStartTimeUsecs']))
break
|
python
|
# https://www.hackerrank.com/challenges/sherlock-and-anagrams/problem
def anagramPairs(s):
# Write your code here
dic = {}
res = 0
for k in range(1, len(s)):
for i in range(len(s)-k+1):
j = i+k
strr = "".join(sorted(s[i:j]))
dic[strr] = dic.get(strr,0)+1
for v in dic.values():
if v>1:
res += (v*(v-1))//2
return res
|
python
|
"""
/******************************************************************************
* *
* Name: mylogger.py *
* *
* Description: A module to setup a custom logger with default options *
* Can setup multiple logs with different levels of info *
* in each, and an email log for errors, as well as handling*
* all uncaught exceptions. *
* *
* Creation Date: 19 05 2021 *
* *
* Created By: Michael Walshe *
* Amadeus Software Ltd *
* [email protected] *
* +44 (0) 1993 848010 *
* *
* Edit History: *
* +------------------+------------+---------------------------------------+ *
* | Programmer | Date | Description | *
* +------------------+------------+---------------------------------------+ *
* | Michael Walshe | 25NOV2021 | Original. | *
* +------------------+------------+---------------------------------------+ *
******************************************************************************/
"""
import logging
import logging.handlers
import sys
# Get things for type hinting
from typing import Optional, Type
from types import TracebackType
import autologging
def setup_logger(
file_name: Optional[str] = None,
trace_log: bool = False,
catch_errors: bool = True,
**kwargs,
) -> logging.Logger:
"""Create instance of overall logger
Args:
file_name: Optional, the name/path to the output logs, without a file extension
trace_log: Optional, twhether to output a detailed TRACE log
catch_errors: Replace python standard sys.excepthook with a new exception
handler that sends them to the log.
**kwargs: Arguments for logging.handlers.SMTPHandler, see logging documentation
for details
"""
# Format and extended format to use in logger output
basic_format = "%(asctime)s:%(levelname)s:%(name)s:%(funcName)s:%(message)s"
trace_format = (
"%(asctime)s:%(process)s:%(levelname)s:%(filename)s"
":%(lineno)s:%(name)s:%(funcName)s:%(message)s"
)
# Setup basic logger and console output, note that level here is the minimum
# that will be output
logging.basicConfig(
format=basic_format,
handlers=[logging.StreamHandler(sys.stdout)],
level=autologging.TRACE,
)
# Create the logging object
logger = logging.getLogger()
# Create an email handler for warnings, this will only output when
# a warning occurs
if kwargs:
email_hdlr = logging.handlers.SMTPHandler(**kwargs)
formatter = logging.Formatter(trace_format)
email_hdlr.setFormatter(formatter)
email_hdlr.setLevel(logging.WARNING)
logger.addHandler(email_hdlr)
# If passed a filename, setup file logs
if file_name:
log_hdlr = logging.FileHandler(f"{file_name}.log")
formatter = logging.Formatter(basic_format)
log_hdlr.setFormatter(formatter)
log_hdlr.setLevel(logging.DEBUG)
logger.addHandler(log_hdlr)
# If setting up a TRACE log then add that handler
if trace_log:
trace_hdlr = logging.FileHandler(f"{file_name}_trace.log")
formatter = logging.Formatter(trace_format)
trace_hdlr.setFormatter(formatter)
trace_hdlr.setLevel(autologging.TRACE)
logger.addHandler(trace_hdlr)
if catch_errors:
# Replaces excepthook with our own exception handler
sys.excepthook = handle_exception
return logger
def handle_exception(
exc_type: Type[BaseException],
exc_value: BaseException,
exc_traceback: TracebackType,
) -> None:
"""Sends uncaught exceptions to the log"""
# Allow ending program using Ctrl + C
if issubclass(exc_type, KeyboardInterrupt):
sys.__excepthook__(exc_type, exc_value, exc_traceback)
return
logger = logging.getLogger()
logger.error("Uncaught exception", exc_info=(exc_type, exc_value, exc_traceback))
|
python
|
#!/usr/bin/env python
"""Celery worker to be run as follows:
(venv) $ celery worker -A pili.entrypoints.celery.celery --loglevel=info
Environment variables (such as MAIL_SERVER, MAIL_USERNAME, etc.)
should be set using export:
(venv) $ export MAIL_SERVER=smtp.youserver.com
'export' keyword in bash sets a variable to current shell and all
processes started from current shell.
If environmental variables are not set properly celery may raise
SMTPServerDisconnected('please run connect() first'), as email
settings are effectively absent.
"""
import os
from pili.app import celery, create_app # noqa
app = create_app(config_name=os.getenv('PILI_CONFIG', 'development'))
app.app_context().push()
|
python
|
import asyncio
import http
import aiohttp.client
import urllib.robotparser
import datetime
import collections
import logging
import abc
logger = logging.getLogger(__name__)
import site_graph
class RequestQueue:
"""A queue of pending HTTP requests, that maintains a minimum interval between outgoing requests
It supports determining the rate by """
RequestContext = collections.namedtuple("RequestContext", ["time_enqueued", "times_retried"])
class ResponseHandler(metaclass = abc.ABCMeta):
"""An abstract base class for handles of HTTP responses"""
@abc.abstractmethod
def on_response(self, url_requested: site_graph.URL, url_served: site_graph.URL, status: int, text: str) -> None:
"""Abstract method representing an HTTP response. Note that the URL served could differ from the one requested, due to reedirects"""
pass
def __init__(self, handler: ResponseHandler):
self.handler = handler
self.seconds_interval = 0.5
self.queue = collections.OrderedDict()
self.robots_file = None
# TODO: separate this from the class to make it more reusable
self.req_headers = { "Accept": "text/html,application/xhtml+xml;q=0.9" }
def enqueue(self, url: site_graph.URL):
if url in self.queue:
return
now = asyncio.get_running_loop().time()
self.queue[url] = self.RequestContext(now, 0)
async def load_robots_file(self, site_origin: site_graph.URL):
"""Load the configuration in the site's robots.txt. site_origin should give the site's top-level URL"""
robots_url = site_origin.with_path("/robots.txt")
async with aiohttp.client.ClientSession() as session:
async with session.get(robots_url, headers = self.req_headers) as resp:
if resp.status != http.HTTPStatus.OK.value:
if resp.status != http.HTTPStatus.NOT_FOUND.value:
logger.warning("Unexpected http status {} while trying to get robots.txt.".format(resp.status))
return
content = await resp.text()
rp = urllib.robotparser.RobotFileParser(str(robots_url))
rp.parse(content.splitlines())
crawl_delay = None
try:
crawl_delay = rp.crawl_delay("*")
except Exception as ex:
logger.warning("Failed to read crawl delay from robots.txt: {}.".format(ex))
if crawl_delay:
self.seconds_interval = crawl_delay
else:
rate = None
try:
rate = rp.request_rate("*")
except Exception as ex:
logger.warning("Failed to read request rate from robots.txt: {}.".format(ex))
if rate is not None:
self.seconds_interval = rate.seconds / rate.requests
self.robots_file = rp
async def run(self):
# A session that will serve all the requests from this queue. Since we don't expect to make
# more than 2 requests per second, this would likely suffice.
async with aiohttp.client.ClientSession() as session:
self.session = session
loop = asyncio.get_running_loop()
next_time_to_send = loop.time()
while self.queue:
now = loop.time()
if now < next_time_to_send:
await asyncio.sleep(next_time_to_send - now)
now = loop.time()
next_time_to_send = now + self.seconds_interval
url, context = self.queue.popitem(last = False)
await self._send_http_request(url, context)
self.session = None
return
async def _send_http_request(self, url: site_graph.URL, request_context: RequestContext):
async with self.session.get(url, headers = self.req_headers) as resp:
if resp.status == http.HTTPStatus.OK.value:
html = await resp.text()
self.handler.on_response(url, resp.url, resp.status, html)
elif resp.status == http.HTTPStatus.SERVICE_UNAVAILABLE.value:
logger.warning("Request for {} failed temporarily with HTTP status {} and will be retried".format(url, resp.status))
# TODO: Give up after a certain number of retries.
self.queue[url] = self.RequestContext(asyncio.get_running_loop().time(), request_context.times_retried + 1)
self.handler
else:
if resp.status != http.HTTPStatus.NOT_ACCEPTABLE.value: # No HTML available for this URL
logger.error("Request for the URL {} gave unexpected status {}".format(url, resp.status))
self.handler.on_response(url, resp.url, resp.status, None)
|
python
|
import numpy as np
from scratch_ml.utils import covariance_matrix
class PCA():
"""A method for doing dimensionality reduction by transforming the feature
space to a lower dimensionality, removing correlation between features and
maximizing the variance along each feature axis."""
def transform(self, x, n_components):
covariance = covariance_matrix(x)
eigenvalues, eigenvectors = np.linalg.eig(covariance)
# sort the eigenvalues and corresponding eigenvectors from largest
# to smallest eigenvalue and select the first n components
idx = eigenvalues.argsort()[::-1]
eigenvalues = eigenvalues[idx][:n_components]
eigenvectors = np.atleast_1d(eigenvectors[:, idx])[:, :n_components]
x_transformed = x.dot(eigenvectors)
return x_transformed
|
python
|
import logging
import argparse
from s3push.connector import resource
from s3push.uploader import upload
from s3push.eraser import erase
parser = argparse.ArgumentParser(
description='Upload directories and files to AWS S3')
parser.add_argument(
'path',
type=str,
help='Path to a directory of file that needs to be uploaded')
parser.add_argument(
'bucket',
type=str,
help='AWS S3 bucket name')
parser.add_argument(
'-k', '--aws_access_key_id',
dest='AWS_ACCESS_KEY_ID',
type=str,
default=None,
help='AWS IAM user API key')
parser.add_argument(
'-s', '--aws-secret-access-key',
dest='AWS_SECRET_ACCESS_KEY',
type=str,
default=None,
help='AWS IAM user secret API key')
parser.add_argument(
'-p', '--profile-name',
dest='PROFILE_NAME',
type=str,
default=None,
help='Preconfigured AWS CLI profile')
parser.add_argument(
'-e', '--erase',
action='store_true',
help='Erase bucket before uploading to it')
parser.add_argument(
'--progress',
action='store_true',
help='Show upload progress bar')
parser.add_argument(
'--log',
type=str,
default=logging.getLevelName(logging.WARNING),
help='Show upload progress bar')
def main():
params = parser.parse_args()
logging.basicConfig(
level=getattr(logging, params.log.upper()),
format='%(asctime)s %(filename)20s %(levelname)8s %(message)s')
# Connect and get the S3 resource from API.
# Resource is a high level API to manipulate the service
# in boto3.
s3 = resource(
's3',
params.AWS_ACCESS_KEY_ID,
params.AWS_SECRET_ACCESS_KEY,
params.PROFILE_NAME)
# Get bucket from the API
bucket = s3.Bucket(params.bucket)
# Erase the bucket, if asked to
if params.erase:
erase(bucket)
# Upload
upload(
params.path,
bucket,
show_progress=params.progress)
|
python
|
# parses vcontrold serial data
import struct
from scapy.packet import Packet, bind_layers
from scapy.fields import *
START_BYTE = 0x41
TYPES = {
0x00: "request",
0x01: "response",
0x03: "error"
}
COMMANDS = {
0x01: "readdata",
0x02: "writedata",
0x07: "functioncall"
}
class VS2Header(Packet):
name = 'VS2 Header'
fields_desc = [
XByteField("startbyte", START_BYTE),
ByteField("length", None),
XByteField("checksum", None)
]
@staticmethod
def compute_checksum(data):
checksum = 0x00
for byte in data:
checksum += byte
checksum &= 0xFF
return checksum
def post_build(self, p, pay):
# Switch payload and crc
length = p[1:2] if self.length is not None else struct.pack('B', len(pay))
checksum = p[-1:]
p = p[:1] + length + pay
p += checksum if self.checksum is not None else struct.pack('B', self.compute_checksum(length+pay))
return p
def post_dissect(self, s):
self.raw_packet_cache = None # Reset packet to allow post_build
return s
def pre_dissect(self, s):
# Switch payload and checksum
start_byte = s[:1]
length_byte = s[1:2]
length = struct.unpack('B', s[1:2])[0]
payload, checksum_byte, s = s[2:length+2], s[length+2:length+3], s[length+3:]
checksum = struct.unpack('B', checksum_byte)[0]
calc_checksum = self.compute_checksum(length_byte + payload)
if checksum != calc_checksum:
raise Scapy_Exception("Wrong checksum: %d != %d" % (checksum, calc_checksum))
return start_byte + length_byte + checksum_byte + payload + s
class VS2Data(Packet):
name = 'VS2 Data'
fields_desc = [
ByteEnumField("type", 0, TYPES),
ByteEnumField("command", 0, COMMANDS),
XShortField("address", 0),
FieldLenField('data_len', None, length_of='data', fmt='B'),
XStrLenField('data', '', max_length=10, length_from=lambda pkt: pkt.data_len)
]
def answers(self, other):
if (other.__class__ == self.__class__) and \
(other.address == self.address) and \
(other.type == 0x00 and (self.type == 0x01 or self.type == 0x03)) and \
(other.data_len == self.data_len):
return self.payload.answers(other.payload)
return 0
bind_layers(VS2Header, VS2Data)
|
python
|
import os
def exists_or_create_directory(temp_path: str) -> None:
directory = os.path.dirname(temp_path)
try:
os.stat(directory)
except Exception as exp:
os.mkdir(directory)
print(str(exp) + f" -> {directory} created...")
|
python
|
from collections import defaultdict
raw_template = []
rules = []
with open("input") as file:
raw_template = list(next(file).strip())
for line in file:
line = line.strip()
if not line:
continue
left, right = line.split("->")
left = left.strip()
right = right.strip()
rules.append(((left[0], left[1]), right))
rules = dict(rules)
template = defaultdict(int)
for i, v in enumerate(raw_template):
if i == 0:
continue
key = raw_template[i - 1], v
if key in rules:
template[key] += 1
def insertion(polymer):
changes = defaultdict(int)
for k, v in polymer.items():
if not v:
continue
if k in rules:
left = k[0], rules[k]
right = rules[k], k[1]
changes[k] -= v
changes[left] += v
changes[right] += v
for k, v in changes.items():
if not v:
continue
polymer[k] += v
def summarise(polymer):
summary = defaultdict(int)
for k, v in polymer.items():
summary[k[1]] += v
return max(summary.values()) - min(summary.values())
for i in range(40):
insertion(template)
if i == 9:
print("part 1")
print(summarise(template))
print("part 2")
print(summarise(template))
|
python
|
# 쿼드압축 후 개수 세기
def quadtree(arr):
l = len(arr)
if l == 1: return [1,0] if arr[0][0] == 0 else [0,1]
lu = quadtree([a[:l//2] for a in arr[:l//2]])
ru = quadtree([a[l//2:] for a in arr[:l//2]])
ld = quadtree([a[:l//2] for a in arr[l//2:]])
rd = quadtree([a[l//2:] for a in arr[l//2:]])
if lu == ru == ld == rd == [1,0] or \
lu == ru == ld == rd == [0,1]: return lu
else: return list(map(sum,zip(lu,ru,ld,rd)))
def solution(arr):
return quadtree(arr)
'''
정확성 테스트
테스트 1 〉 통과 (1.44ms, 10.3MB)
테스트 2 〉 통과 (1.31ms, 10.3MB)
테스트 3 〉 통과 (1.36ms, 10.3MB)
테스트 4 〉 통과 (0.38ms, 10.3MB)
테스트 5 〉 통과 (265.86ms, 12.6MB)
테스트 6 〉 통과 (238.92ms, 12.6MB)
테스트 7 〉 통과 (233.99ms, 12.7MB)
테스트 8 〉 통과 (230.72ms, 12.8MB)
테스트 9 〉 통과 (209.33ms, 12.6MB)
테스트 10 〉 통과 (950.55ms, 20.7MB)
테스트 11 〉 통과 (0.37ms, 10.3MB)
테스트 12 〉 통과 (0.35ms, 10.2MB)
테스트 13 〉 통과 (206.64ms, 12.7MB)
테스트 14 〉 통과 (891.77ms, 20.7MB)
테스트 15 〉 통과 (894.10ms, 20.8MB)
테스트 16 〉 통과 (239.40ms, 12.6MB)
채점 결과
정확성: 100.0
합계: 100.0 / 100.0
'''
|
python
|
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 11 16:31:58 2021
@author: snoone
"""
import os
import glob
import pandas as pd
pd.options.mode.chained_assignment = None # default='warn'
OUTDIR3= "D:/Python_CDM_conversion/monthly/cdm_out/cdm_head"
OUTDIR2= "D:/Python_CDM_conversion/monthly/cdm_out/cdm_obs"
OUTDIR = "D:/Python_CDM_conversion/monthly/cdm_out/cdm_lite"
os.chdir("D:/Python_CDM_conversion/monthly/.csv/")
extension = 'csv'
#my_file = open("D:/Python_CDM_conversion/hourly/qff/ls1.txt", "r")
#all_filenames = my_file.readlines()
#print(all_filenames)
##use a list of file names to run 5000 parallel
#with open("D:/Python_CDM_conversion/hourly/qff/ls1.txt", "r") as f:
#all_filenames = f.read().splitlines()
#print(all_filenames)
all_filenames = [i for i in glob.glob('*.{}'.format(extension))]
##to start at begining of files
for filename in all_filenames:
##to start at next file after last processe
#for filename in all_filenames[all_filenames.index('SWM00002338.qff'):] :
usecols = ["STATION","LATITUDE","LONGITUDE","ELEVATION","DATE","NAME", "PRCP", "TMIN", "TMAX", "TAVG", "SNOW", "AWND"]
df=pd.read_csv(filename, sep=",",usecols=lambda c: c in set(usecols))
#add required columnns
df["report_type"]="2"
df["units"]=""
df["minute"]= "00"
df["day"]= "01"
df["hour"]= "00"
df["seconds"]="00"
df[['year', 'month']] = df['DATE'].str.split('-', expand=True)
df["observation_height_above_station_surface"]=""
df["date_time_meaning"]="1"
df["latitude"]=df["LATITUDE"]
df["longitude"]=df["LONGITUDE"]
df["observed_variable"]=""
df["value_significance"]="13"
df["observation_duration"]="14"
df["observation_value"]=""
df["platform_type"]=""
df["station_type"]="1"
df["observation_id"]=""
df["data_policy_licence"]="0"
df["primary_station_id"]=df["STATION"]
df["station_name"]=df["NAME"]
df["quality_flag"]="0"
df["latitude"] = pd.to_numeric(df["latitude"],errors='coerce')
df["longitude"] = pd.to_numeric(df["longitude"],errors='coerce')
df["latitude"]= df["latitude"].round(3)
df["longitude"]= df["longitude"].round(3)
df["Timestamp2"] = df["year"].map(str) + "-" + df["month"].map(str)+ "-" + df["day"].map(str)
df["offset"]="+00"
df["date_time"] = df["Timestamp2"].map(str)+ " " + df["hour"].map(str)+":"+df["minute"].map(str)+":"+df["seconds"].map(str)
df['date_time'] = df['date_time'].astype('str')
df.date_time = df.date_time + '+00'
###extract precip
try:
dfprc = df[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence"]]
##change for each variable to convertto cdm compliant values
dfprc["observation_value"]=df["PRCP"]
#change for each variable if required
dfprc["observation_height_above_station_surface"]="1"
dfprc["units"]="710"
dfprc["observed_variable"]="44"
#set up matching values for merging with record_id_month to add information source_id for first configuration only due yto lack of information
dfprc["record_number"]="1"
dfprc['primary_station_id_2']=dfprc['primary_station_id'].astype(str)+'-'+dfprc['record_number'].astype(str)
dfprc["report_id"]=dfprc["date_time"]
##merge with record_id_mnth.csv to add source id
df2=pd.read_csv("D:/Python_CDM_conversion/monthly/config_files/record_id_mnth.csv")
dfprc = dfprc.astype(str)
df2 = df2.astype(str)
dfprc= df2.merge(dfprc, on=['primary_station_id_2'])
dfprc['observation_id']=dfprc['primary_station_id'].astype(str)+'-'+dfprc['record_number'].astype(str)+'-'+dfprc['date_time'].astype(str)
dfprc['observation_id'] = dfprc['observation_id'].str.replace(r' ', '-')
##remove unwanted last twpo characters
dfprc['observation_id'] = dfprc['observation_id'].str[:-12]
dfprc["observation_id"]=dfprc["observation_id"]+'-'+dfprc['observed_variable'].astype(str)+'-'+dfprc['value_significance'].astype(str)
#reorder columns and drop unwanted columns
dfprc = dfprc[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence","source_id","primary_station_id_2"]]
except:
pass
###extract SNOW
try:
dfsnow = df[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence"]]
##change for each variable to convert to cdm compliant values
dfsnow["observation_value"]=df["SNOW"]
dfsnow = dfsnow.fillna("Null")
dfsnow = dfsnow[dfsnow.observation_value != "Null"]
#change for each variable if required
dfsnow["observation_height_above_station_surface"]="0"
dfsnow["units"]="710"
dfsnow["observed_variable"]="45"
#set up matching values for merging with record_id_month to add information source_id for first configuration only due yto lack of information
dfsnow["record_number"]="1"
dfsnow['primary_station_id_2']=dfsnow['primary_station_id'].astype(str)+'-'+dfsnow['record_number'].astype(str)
dfsnow["report_id"]=dfsnow["date_time"]
##merge with record_id_mnth.csv to add source id
df2=pd.read_csv("D:/Python_CDM_conversion/monthly/config_files/record_id_mnth.csv")
dfsnow = dfsnow.astype(str)
df2 = df2.astype(str)
dfsnow= df2.merge(dfsnow, on=['primary_station_id_2'])
dfsnow['observation_id']=dfsnow['primary_station_id'].astype(str)+'-'+dfsnow['record_number'].astype(str)+'-'+dfsnow['date_time'].astype(str)
dfsnow['observation_id'] = dfsnow['observation_id'].str.replace(r' ', '-')
##remove unwanted last twpo characters
dfsnow['observation_id'] = dfsnow['observation_id'].str[:-12]
dfsnow["observation_id"]=dfsnow["observation_id"]+'-'+dfsnow['observed_variable'].astype(str)+'-'+dfsnow['value_significance'].astype(str)
#reorder columns and drop unwanted columns
dfsnow = dfsnow[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence","source_id","primary_station_id_2"]]
except:
pass
###extract tmax
try:
dftmax = df[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence"]]
##change for each variable to convert to cdm compliant values
dftmax["observation_value"]=df["TMAX"]
dftmax = dftmax.fillna("Null")
dftmax = dftmax[dftmax.observation_value != "Null"]
dftmax["observation_value"] = pd.to_numeric(dftmax["observation_value"],errors='coerce')
dftmax["observation_value"]=dftmax["observation_value"]+273.15
dftmax["observation_value"] = pd.to_numeric(dftmax["observation_value"],errors='coerce')
dftmax["observation_value"]=dftmax["observation_value"].round(2)
#change for each variable if required
dftmax["observation_height_above_station_surface"]="2"
dftmax["units"]="5"
dftmax["observed_variable"]="85"
dftmax["value_significance"]="0"
#set up matching values for merging with record_id_month to add information source_id for first configuration only due yto lack of information
dftmax["record_number"]="1"
dftmax['primary_station_id_2']=dftmax['primary_station_id'].astype(str)+'-'+dftmax['record_number'].astype(str)
dftmax["report_id"]=dftmax["date_time"]
##merge with record_id_mnth.csv to add source id
df2=pd.read_csv("D:/Python_CDM_conversion/monthly/config_files/record_id_mnth.csv")
dftmax = dftmax.astype(str)
df2 = df2.astype(str)
dftmax= df2.merge(dftmax, on=['primary_station_id_2'])
dftmax['observation_id']=dftmax['primary_station_id'].astype(str)+'-'+dftmax['record_number'].astype(str)+'-'+dftmax['date_time'].astype(str)
dftmax['observation_id'] = dftmax['observation_id'].str.replace(r' ', '-')
##remove unwanted last twpo characters
dftmax['observation_id'] = dftmax['observation_id'].str[:-12]
dftmax["observation_id"]=dftmax["observation_id"]+'-'+dftmax['observed_variable'].astype(str)+'-'+dftmax['value_significance'].astype(str)
#reorder columns and drop unwanted columns
dftmax = dftmax[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence","source_id","primary_station_id_2"]]
except:
pass
###extract tmin
try:
dftmin = df[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence"]]
##change for each variable to convert to cdm compliant values
dftmin["observation_value"]=df["TMIN"]
dftmin = dftmin.fillna("Null")
dftmin = dftmin[dftmin.observation_value != "Null"]
dftmin["observation_value"] = pd.to_numeric(dftmin["observation_value"],errors='coerce')
dftmin["observation_value"]=dftmin["observation_value"]+273.15
dftmin["observation_value"] = pd.to_numeric(dftmin["observation_value"],errors='coerce')
dftmin["observation_value"]=dftmin["observation_value"].round(2)
#change for each variable if required
dftmin["observation_height_above_station_surface"]="2"
dftmin["units"]="5"
dftmin["observed_variable"]="85"
dftmin["value_significance"]="1"
#set up matching values for merging with record_id_month to add information source_id for first configuration only due yto lack of information
dftmin["record_number"]="1"
dftmin['primary_station_id_2']=dftmin['primary_station_id'].astype(str)+'-'+dftmin['record_number'].astype(str)
dftmin["report_id"]=dftmin["date_time"]
##merge with record_id_mnth.csv to add source id
df2=pd.read_csv("D:/Python_CDM_conversion/monthly/config_files/record_id_mnth.csv")
dftmin = dftmin.astype(str)
df2 = df2.astype(str)
dftmin= df2.merge(dftmin, on=['primary_station_id_2'])
dftmin['observation_id']=dftmin['primary_station_id'].astype(str)+'-'+dftmin['record_number'].astype(str)+'-'+dftmin['date_time'].astype(str)
dftmin['observation_id'] = dftmin['observation_id'].str.replace(r' ', '-')
##remove unwanted last twpo characters
dftmin['observation_id'] = dftmin['observation_id'].str[:-12]
dftmin["observation_id"]=dftmin["observation_id"]+'-'+dftmin['observed_variable'].astype(str)+'-'+dftmin['value_significance'].astype(str)
#reorder columns and drop unwanted columns
dftmin = dftmin[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence","source_id","primary_station_id_2"]]
except:
pass
###extract tavg
try:
dftavg = df[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence"]]
##change for each variable to convert to cdm compliant values
dftavg["observation_value"]=df["TAVG"]
dftavg = dftavg.fillna("Null")
dftavg = dftavg[dftavg.observation_value != "Null"]
dftavg["observation_value"] = pd.to_numeric(dftavg["observation_value"],errors='coerce')
dftavg["observation_value"]=dftavg["observation_value"]+273.15
dftavg["observation_value"] = pd.to_numeric(dftavg["observation_value"],errors='coerce')
dftavg["observation_value"]=dftavg["observation_value"].round(2)
#change for each variable if required
dftavg["observation_height_above_station_surface"]="2"
dftavg["units"]="5"
dftavg["observed_variable"]="85"
dftavg["value_significance"]="2"
#set up matching values for merging with record_id_month to add information source_id for first configuration only due yto lack of information
dftavg["record_number"]="1"
dftavg['primary_station_id_2']=dftavg['primary_station_id'].astype(str)+'-'+dftavg['record_number'].astype(str)
dftavg["report_id"]=dftavg["date_time"]
##merge with record_id_mnth.csv to add source id
df2=pd.read_csv("D:/Python_CDM_conversion/monthly/config_files/record_id_mnth.csv")
dftavg = dftavg.astype(str)
df2 = df2.astype(str)
dftavg= df2.merge(dftavg, on=['primary_station_id_2'])
dftavg['observation_id']=dftavg['primary_station_id'].astype(str)+'-'+dftavg['record_number'].astype(str)+'-'+dftavg['date_time'].astype(str)
dftavg['observation_id'] = dftavg['observation_id'].str.replace(r' ', '-')
##remove unwanted last twpo characters
dftavg['observation_id'] = dftavg['observation_id'].str[:-12]
dftavg["observation_id"]=dftavg["observation_id"]+'-'+dftavg['observed_variable'].astype(str)+'-'+dftavg['value_significance'].astype(str)
#reorder columns and drop unwanted columns
dftavg = dftavg[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence","source_id","primary_station_id_2"]]
except:
pass
###extract wind speed avge
try:
dftws = df[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence"]]
##change for each variable to convert to cdm compliant values
dftws["observation_value"]=df["AWND"]
dftws = dftws.fillna("Null")
dftws = dftws[dftws.observation_value != "Null"]
dftws["observation_value"] = pd.to_numeric(dftws["observation_value"],errors='coerce')
dftws["observation_value"]=dftws["observation_value"]
dftws["observation_value"] = pd.to_numeric(dftws["observation_value"],errors='coerce')
dftws["observation_value"]=dftws["observation_value"].round(2)
#change for each variable if required
dftws["observation_height_above_station_surface"]="2"
dftws["units"]="732"
dftws["observed_variable"]="107"
dftws["value_significance"]="2"
#set up matching values for merging with record_id_month to add information source_id for first configuration only due yto lack of information
dftws["record_number"]="1"
dftws['primary_station_id_2']=dftws['primary_station_id'].astype(str)+'-'+dftws['record_number'].astype(str)
dftws["report_id"]=dftws["date_time"]
##merge with record_id_mnth.csv to add source id
df2=pd.read_csv("D:/Python_CDM_conversion/monthly/config_files/record_id_mnth.csv")
dftws = dftws.astype(str)
df2 = df2.astype(str)
dftws= df2.merge(dftws, on=['primary_station_id_2'])
dftws['observation_id']=dftws['primary_station_id'].astype(str)+'-'+dftws['record_number'].astype(str)+'-'+dftws['date_time'].astype(str)
dftws['observation_id'] = dftws['observation_id'].str.replace(r' ', '-')
##remove unwanted last twpo characters
dftws['observation_id'] = dftws['observation_id'].str[:-12]
dftws["observation_id"]=dftws["observation_id"]+'-'+dftws['observed_variable'].astype(str)+'-'+dftws['value_significance'].astype(str)
#reorder columns and drop unwanted columns
dftws = dftws[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence","source_id","primary_station_id_2"]]
except:
pass
merged_df=pd.concat([dftmax,dftavg,dftmin,dftws,dfprc], axis=0)
merged_df.sort_values("date_time")
merged_df["latitude"] = pd.to_numeric(merged_df["latitude"],errors='coerce')
merged_df["longitude"] = pd.to_numeric(merged_df["longitude"],errors='coerce')
merged_df["latitude"]= merged_df["latitude"].round(3)
merged_df["longitude"]= merged_df["longitude"].round(3)
merged_df = merged_df[merged_df.observation_value != "nan"]
merged_df["observation_value"] = pd.to_numeric(merged_df["observation_value"],errors='coerce')
merged_df.dropna(subset = ["observation_value"], inplace=True)
merged_df.dropna(subset = ["observation_id"], inplace=True)
df_lite_out = merged_df[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence","source_id"]]
dfobs=merged_df[["observation_id","report_type","date_time","date_time_meaning",
"latitude","longitude","observation_height_above_station_surface"
,"observed_variable","units","observation_value",
"value_significance","observation_duration","platform_type",
"station_type","primary_station_id","station_name","quality_flag"
,"data_policy_licence","source_id","primary_station_id_2"]]
##add region and sub region
df2=pd.read_csv("D:/Python_CDM_conversion/monthly/config_files/record_id_mnth.csv")
dfobs = dfobs.astype(str)
df2 = df2.astype(str)
dfobs= df2.merge(dfobs, on=['primary_station_id_2'])
dfobs["numerical_precision"]=""
dfobs.loc[dfobs['observed_variable'] == "85", 'numerical_precision'] = '0.01'
dfobs.loc[dfobs['observed_variable'] == "44", 'numerical_precision'] = '0.1'
dfobs.loc[dfobs['observed_variable'] == "45", 'numerical_precision'] = '0.1'
dfobs.loc[dfobs['observed_variable'] == "55", 'numerical_precision'] = '0.1'
dfobs.loc[dfobs['observed_variable'] == "106", 'numerical_precision'] = '0.1'
dfobs.loc[dfobs['observed_variable'] == "107", 'numerical_precision'] = "0.1"
dfobs.loc[dfobs['observed_variable'] == "53", 'numerical_precision'] = "1"
dfobs["original_precision"]=""
dfobs.loc[dfobs['observed_variable'] == "85", 'original_precision'] = '0.01'
dfobs.loc[dfobs['observed_variable'] == "44", 'original_precision'] = '0.1'
dfobs.loc[dfobs['observed_variable'] == "45", 'original_precision'] = '0.1'
dfobs.loc[dfobs['observed_variable'] == "55", "original_precision"] = '0.1'
dfobs.loc[dfobs['observed_variable'] == "106", 'original_precision'] = '1'
dfobs.loc[dfobs['observed_variable'] == "107", 'original_precision'] = "0.1"
dfobs.loc[dfobs['observed_variable'] == "53", 'original_precision'] = "1"
##add conversion flags
dfobs["conversion_flag"]=""
dfobs.loc[dfobs['observed_variable'] == "85", 'conversion_flag'] = '0'
dfobs.loc[dfobs['observed_variable'] == "44", 'conversion_flag'] = '2'
dfobs.loc[dfobs['observed_variable'] == "45", 'conversion_flag'] = '2'
dfobs.loc[dfobs['observed_variable'] == "55", 'conversion_flag'] = '2'
dfobs.loc[dfobs['observed_variable'] == "106", 'conversion_flag'] = '2'
dfobs.loc[dfobs['observed_variable'] == "107", 'conversion_flag'] = "2"
dfobs.loc[dfobs['observed_variable'] == "53", 'conversion_flag'] = "2"
##set conversion method for variables
dfobs["conversion_method"]=""
dfobs.loc[dfobs['observed_variable'] == "85", 'conversion_method'] = '1'
#add all columns for obs table
dfobs["date_time_meaning"]="1"
dfobs["crs"]=""
dfobs["z_coordinate"]=""
dfobs["z_coordinate_type"]=""
dfobs["secondary_variable"]=""
dfobs["secondary_value"]=""
dfobs["code_table"]=""
dfobs["sensor_id"]=""
dfobs["sensor_automation_status"]=""
dfobs["exposure_of_sensor"]=""
dfobs["processing_code"]=""
dfobs["processing_level"]="0"
dfobs["adjustment_id"]=""
dfobs["traceability"]=""
dfobs["advanced_qc"]=""
dfobs["advanced_uncertainty"]=""
dfobs["advanced_homogenisation"]=""
dfobs["advanced_assimilation_feedback"]=""
dfobs["source_record_id"]=""
dfobs["location_method"]=""
dfobs["location_precision"]=""
dfobs["z_coordinate_method"]=""
dfobs["bbox_min_longitude"]=""
dfobs["bbox_max_longitude"]=""
dfobs["bbox_min_latitude"]=""
dfobs["bbox_max_latitude"]=""
dfobs["spatial_representativeness"]=""
dfobs["original_code_table"]=""
dfobs["source_id"]=dfobs["source_id_x"]
dfobs['date1'] = dfobs["date_time"].str[:-11]
dfobs['date1'] = dfobs['date1'].str.strip()
dfobs["observation_value"] = pd.to_numeric(dfobs["observation_value"],errors='coerce')
dfobs["report_id"]=dfobs["station_id"].astype(str)+'-'+dfobs["record_id"].astype(str)+'-'+dfobs["date1"].astype(str)
dfobs["original_value"]=dfobs["observation_value"]
dfobs["original_units"]=dfobs["units"]
dfobs["onversion_method"]=""
dfobs.loc[dfobs['observed_variable'] == "85", 'original_units'] = '350'
dfobs.loc[dfobs['observed_variable'] == "85", 'original_value'] = dfobs["observation_value"]-273.15
dfobs.loc[dfobs['observed_variable'] == "85", 'conversion_method'] ="1"
dfobs=dfobs[["observation_id","report_id","data_policy_licence","date_time",
"date_time_meaning","observation_duration","longitude","latitude",
"crs","z_coordinate","z_coordinate_type","observation_height_above_station_surface",
"observed_variable","secondary_variable","observation_value",
"value_significance","secondary_value","units","code_table",
"conversion_flag","location_method","location_precision",
"z_coordinate_method","bbox_min_longitude","bbox_max_longitude",
"bbox_min_latitude","bbox_max_latitude","spatial_representativeness",
"quality_flag","numerical_precision","sensor_id","sensor_automation_status",
"exposure_of_sensor","original_precision","original_units",
"original_code_table","original_value","conversion_method",
"processing_code","processing_level","adjustment_id","traceability",
"advanced_qc","advanced_uncertainty","advanced_homogenisation",
"advanced_assimilation_feedback","source_id"]]
##set up the header table from the obs table
try:
col_list=dfobs [["observation_id","latitude","longitude","report_id","source_id","date_time"]]
hdf=col_list.copy()
##add required columns and set up values etc
hdf[['primary_station_id', 'station_record_number', '1',"2,","3"]] = hdf['report_id'].str.split('-', expand=True)
#hdf["observation_id"]=merged_df["observation_id"]
hdf["report_id"]=dfobs["report_id"]
hdf["application_area"]=""
hdf["observing_programme"]=""
hdf["report_type"]="3"
hdf["station_type"]="1"
hdf["platform_type"]=""
hdf["primary_station_id_scheme"]="13"
hdf["location_accuracy"]="0.1"
hdf["location_method"]=""
hdf["location_quality"]="3"
hdf["crs"]="0"
hdf["station_speed"]=""
hdf["station_course"]=""
hdf["station_heading"]=""
hdf["height_of_station_above_local_ground"]=""
hdf["height_of_station_above_sea_level_accuracy"]=""
hdf["sea_level_datum"]=""
hdf["report_meaning_of_timestamp"]="1"
hdf["report_timestamp"]=""
hdf["report_duration"]="13"
hdf["report_time_accuracy"]=""
hdf["report_time_quality"]=""
hdf["report_time_reference"]="0"
hdf["platform_subtype"]=""
hdf["profile_id"]=""
hdf["events_at_station"]=""
hdf["report_quality"]=""
hdf["duplicate_status"]="4"
hdf["duplicates"]=""
hdf["source_record_id"]=""
hdf ["processing_codes"]=""
hdf['record_timestamp'] = pd.to_datetime('now').strftime("%Y-%m-%d %H:%M:%S")
hdf.record_timestamp = hdf.record_timestamp + '+00'
hdf["history"]=""
hdf["processing_level"]="0"
hdf["report_timestamp"]=dfobs["date_time"]
hdf['primary_station_id_2']=hdf['primary_station_id'].astype(str)+'-'+hdf['source_id'].astype(str)
hdf["duplicates_report"]=hdf["report_id"]+'-'+hdf["station_record_number"].astype(str)
df2=pd.read_csv("D:/Python_CDM_conversion/monthly/config_files/record_id_mnth_head.csv")
hdf = hdf.astype(str)
df2 = df2.astype(str)
hdf= df2.merge(hdf, on=['primary_station_id_2'])
hdf['height_of_station_above_sea_level'] = hdf['height_of_station_above_sea_level'].astype(str).apply(lambda x: x.replace('.0',''))
hdf["source_id"]=hdf["source_id_x"]
hdf["latitude"] = pd.to_numeric(hdf["latitude"],errors='coerce')
hdf["longitude"] = pd.to_numeric(hdf["longitude"],errors='coerce')
hdf["latitude"]= hdf["latitude"].round(3)
hdf["longitude"]= hdf["longitude"].round(3)
hdf = hdf[["report_id","region","sub_region","application_area",
"observing_programme","report_type","station_name",
"station_type","platform_type","platform_subtype","primary_station_id","station_record_number",
"primary_station_id_scheme","longitude","latitude","location_accuracy","location_method",
"location_quality","crs","station_speed","station_course",
"station_heading","height_of_station_above_local_ground",
"height_of_station_above_sea_level",
"height_of_station_above_sea_level_accuracy",
"sea_level_datum","report_meaning_of_timestamp","report_timestamp",
"report_duration","report_time_accuracy","report_time_quality",
"report_time_reference","profile_id","events_at_station","report_quality",
"duplicate_status","duplicates","record_timestamp","history",
"processing_level","processing_codes","source_id","source_record_id",
"primary_station_id_2", "duplicates_report"]]
hdf=hdf.drop_duplicates(subset=['duplicates_report'])
hdf = hdf[["report_id","region","sub_region","application_area",
"observing_programme","report_type","station_name",
"station_type","platform_type","platform_subtype","primary_station_id",
"station_record_number","primary_station_id_scheme",
"longitude","latitude","location_accuracy","location_method",
"location_quality","crs","station_speed","station_course",
"station_heading","height_of_station_above_local_ground",
"height_of_station_above_sea_level","height_of_station_above_sea_level_accuracy",
"sea_level_datum","report_meaning_of_timestamp","report_timestamp",
"report_duration","report_time_accuracy","report_time_quality",
"report_time_reference","profile_id","events_at_station","report_quality",
"duplicate_status","duplicates","record_timestamp","history",
"processing_level","processing_codes","source_id","source_record_id"]]
hdf.sort_values("report_timestamp")
hdf['report_id'] = hdf['report_id'].str.strip()
hdf['region'] = hdf['region'].astype(str).apply(lambda x: x.replace('.0',''))
hdf['sub_region'] = hdf['sub_region'].astype(str).apply(lambda x: x.replace('.0',''))
except:
# Continue to next iteration.
continue
##output merged cdmlite file
try:
station_id=df_lite_out.iloc[1]["primary_station_id"]
cdm_type=("cdm_lite_202111_test_")
print(station_id+"_lite")
a = df_lite_out['observed_variable'].unique()
print (a)
outname = os.path.join(OUTDIR,cdm_type)
df_lite_out.to_csv(outname+ station_id+ ".psv", index=False, sep="|")
except:
# Continue to next iteration.
continue
##output of cdm observations files
try:
station_id=merged_df.iloc[1]["primary_station_id"]
cdm_type=("cdm_obs_202111_test_")
print(station_id+"_obs")
a = dfobs['observed_variable'].unique()
print (a)
outname = os.path.join(OUTDIR2,cdm_type)
dfobs.to_csv(outname+ station_id+ ".psv", index=False, sep="|")
except:
# Continue to next iteration.
continue
##output of cdm header files
try:
## table output
##header table output
station_id=hdf.iloc[1]["primary_station_id"]
cdm_type=("cdm_head_202111_test_")
print(station_id+"_header")
outname = os.path.join(OUTDIR3,cdm_type)
#with open(filename, "w") as outfile:
hdf.to_csv(outname+ station_id+ ".psv", index=False, sep="|")
except:
# Continue to next iteration.
continue
#dfobs.to_csv("D:/Python_CDM_conversion/monthly/dfobs.csv", index=False, sep=",")
# qct.to_csv("D:/Python_CDM_conversion/daily/.csv/qcdy.csv", index=False, sep=",")
|
python
|
#EsmeeEllson
#Help
#Class Attributes
#You can instantiate with AsciiTable(table_data) or AsciiTable(table_data, 'Table Title').
#These are available after instantiating AsciiTable.
Name Description/Notes
table_data = List of list of strings. Same object passed to __init__().
title = Table title string. Default is None for no title.
inner_column_border = Default is True. Separates columns.
inner_heading_row_border = Default is True. This is what makes the first row a “header row”.
inner_row_border = Default is False. This adds lines between rows.
justify_columns = Dictionary. Keys are column numbers (0 base), values are ‘left’, ‘right’, or ‘center’.
outer_border = Default is True. Toggles the top, bottom, left, and right table borders.
padding_left = Default is 1. Number of spaces to add to the left of the cell.
padding_right = Default is 1. Number of spaces to add to the right of the cell.
#Class Methods
#These are regular methods available in either class.
Name Description/Notes
column_max_width = Takes one argument, column number (0 base). Returns The maximum size it will fit in the the therminal without breaking the table. Takes other columns into account.
#Class Properties
#These are read-only properties after you instantiate either class. They are “real-time”. You do not have to re-instantiate if you change any of the class attributes, including table_data.
Name Description/Notes
column_widths = Returns a list with the current column widths (one int per column) without padding.
ok = Returns True if the table fits within the terminal width, False if the table breaks.
padded_table_data = Returns the padding table data. With spaces and newlines. Does not include borders.
table = Returns a large string, the whole table. This may be printed to the terminal.
table_width = Returns the width of the table including padding and borders.
|
python
|
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 21 18:32:33 2021
@author: User
"""
# Expresiones generadoras
# El módulo itertools
itertools.chain(s1,s2)
itertools.count(n)
itertools.cycle(s)
itertools.dropwhile(predicate, s)
itertools.groupby(s)
itertools.ifilter(predicate, s)
itertools.imap(function, s1, ... sN)
itertools.repeat(s, n)
itertools.tee(s, ncopies)
itertools.izip(s1, ... , sN)
#%%
|
python
|
"""Implementation of the Unet in torch.
Author: zhangfan
Email: [email protected]
data: 2020/09/09
"""
import torch
import torch.nn as nn
from network.blocks.residual_blocks import ResFourLayerConvBlock
class UNet(nn.Module):
"""UNet network"""
def __init__(self, net_args={"num_class": 1,
"nb_filter": [8, 16, 32, 64, 128]}):
super().__init__()
# UNet parameter.
num_class = net_args["num_class"]
nb_filter = net_args["nb_filter"]
self.pool = nn.MaxPool3d(kernel_size=3, stride=2, padding=1)
self.up = nn.Upsample(scale_factor=2, mode='trilinear', align_corners=True)
self.conv0_0 = ResFourLayerConvBlock(1, nb_filter[0], nb_filter[0])
self.conv1_0 = ResFourLayerConvBlock(nb_filter[0], nb_filter[1], nb_filter[1])
self.conv2_0 = ResFourLayerConvBlock(nb_filter[1], nb_filter[2], nb_filter[2])
self.conv3_0 = ResFourLayerConvBlock(nb_filter[2], nb_filter[3], nb_filter[3])
self.conv4_0 = ResFourLayerConvBlock(nb_filter[3], nb_filter[4], nb_filter[4])
self.conv3_1 = ResFourLayerConvBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3])
self.conv2_2 = ResFourLayerConvBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2])
self.conv1_3 = ResFourLayerConvBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1])
self.conv0_4 = ResFourLayerConvBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0])
self.final = nn.Conv3d(nb_filter[0], num_class, kernel_size=1, bias=False)
self.initialize_weights()
def initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv3d):
nn.init.kaiming_normal_(m.weight, nonlinearity='relu')
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
x0_0 = self.conv0_0(x)
x1_0 = self.conv1_0(self.pool(x0_0))
x2_0 = self.conv2_0(self.pool(x1_0))
x3_0 = self.conv3_0(self.pool(x2_0))
x4_0 = self.conv4_0(self.pool(x3_0))
x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))
x2_2 = self.conv2_2(torch.cat([x2_0, self.up(x3_1)], 1))
x1_3 = self.conv1_3(torch.cat([x1_0, self.up(x2_2)], 1))
x0_4 = self.conv0_4(torch.cat([x0_0, self.up(x1_3)], 1))
output = self.final(x0_4)
return [output]
if __name__ == '__main__':
data = torch.randn([1, 1, 32, 32, 32]).float().cuda()
model = UNet().cuda()
with torch.no_grad():
outputs = model(data)
for predict in outputs:
print(predict.shape)
|
python
|
import re
from textwrap import dedent
import black
import autopep8
from pkg_resources import resource_string
def get_snippet(name, decorator=True):
'Get a Python snippet function (as a string) from the snippets directory.'
out = resource_string('matflow_defdap', f'snippets/{name}').decode()
if not decorator:
# Remove the `@main_func` decorator and import.
remove_lns = ['from matflow_defdap import main_func', '@main_func']
for i in remove_lns:
out = ''.join(out.split(i))
return out
def parse_python_func_return(func_str):
"""Get a list of the variable names in a Python function return statement.
The return statement may return a tuple (with parenthesis or not) or a single variable.
"""
out = []
match = re.search(r'return \(*([\S\s][^\)]+)\)*', func_str)
if match:
match_clean = match.group(1).strip().strip(',')
out = [i.strip() for i in match_clean.split(',')]
return out
def parse_python_func_imports(func_str):
"""Get a list of import statement lines from a (string) Python function."""
import_lines = func_str.split('def ')[0].strip()
match = re.search(r'((?:import|from)[\S\s]*)', import_lines)
out = []
if match:
out = match.group(1).splitlines()
return out
def extract_snippet_main(snippet_str):
"""Extract only the snippet main function (plus imports), as annotated by the
`@mainfunc` decorator."""
func_start_pat = r'((?:@main_func\n)?def\s(?:.*)\((?:[\s\S]*?)\):)'
func_split_snip = re.split(func_start_pat, snippet_str)
imports = func_split_snip[0]
main_func_dec_str = '@main_func'
main_func_str = None
for idx in range(1, len(func_split_snip[1:]), 2):
func_str = func_split_snip[idx] + func_split_snip[idx + 1]
if main_func_dec_str in func_str:
if main_func_str:
msg = (f'`{main_func_dec_str}` should decorate only one function within '
f'the snippet.')
raise ValueError(msg)
else:
main_func_str = func_str.lstrip(f'{main_func_dec_str}\n')
imports = ''.join(imports.split('from matflow_defdap import main_func'))
return imports + '\n' + main_func_str
def get_snippet_signature(script_name):
'Get imports, inputs and outputs of a Python snippet function.'
snippet_str = get_snippet(script_name)
snippet_str = extract_snippet_main(snippet_str)
def_line = re.search(r'def\s(.*)\(([\s\S]*?)\):', snippet_str).groups()
func_name = def_line[0]
func_ins = [i.strip() for i in def_line[1].split(',')]
if script_name != func_name + '.py':
msg = ('For simplicity, the snippet main function name should be the same as the '
'snippet file name.')
raise ValueError(msg)
func_outs = parse_python_func_return(snippet_str)
func_imports = parse_python_func_imports(snippet_str)
out = {
'name': func_name,
'imports': func_imports,
'inputs': func_ins,
'outputs': func_outs,
}
return out
def get_snippet_call(script_name):
sig = get_snippet_signature(script_name)
outs_fmt = ', '.join(sig['outputs'])
ins_fmt = ', '.join(sig['inputs'])
ret = f'{sig["name"]}({ins_fmt})'
if outs_fmt:
ret = f'{outs_fmt} = {ret}'
return ret
def get_wrapper_script(script_name, snippets, outputs):
ind = ' '
sigs = [get_snippet_signature(i['name']) for i in snippets]
all_ins = [j for i in sigs for j in i['inputs']]
all_outs = [j for i in sigs for j in i['outputs']]
for i in outputs:
if i not in all_outs:
raise ValueError(f'Cannot output "{i}". No functions return this name.')
# Required inputs are those that are not output by any snippet
req_ins = list(set(all_ins) - set(all_outs))
req_ins_fmt = ', '.join(req_ins)
main_sig = [f'def main({req_ins_fmt}):']
main_body = [ind + get_snippet_call(i['name']) for i in snippets]
main_outs = ['\n' + ind + f'return {", ".join([i for i in outputs])}']
main_func = main_sig + main_body + main_outs
req_imports = [
'import sys',
'import hickle',
'from pathlib import Path',
]
out = req_imports
out += main_func
snippet_funcs = '\n'.join([get_snippet(i['name'], decorator=False) for i in snippets])
out = '\n'.join(out) + '\n' + snippet_funcs + '\n'
out += dedent('''\
if __name__ == '__main__':
inputs = hickle.load(sys.argv[1])
outputs = main(**inputs)
hickle.dump(outputs, 'outputs.hdf5')
''')
out = autopep8.fix_code(out)
out = black.format_str(out, mode=black.FileMode())
return out
|
python
|
# -*- coding:utf-8 -*-
from timeit import default_timer
import numpy as np
import matplotlib.pyplot as plt
from scipy.misc import face
# Importing global thresholding algorithms
from .global_th import otsu_threshold, p_tile_threshold,\
two_peaks_threshold, min_err_threshold
# Importing global entropy thresholding algorithms
from .global_th.entropy import pun_threshold, kapur_threshold,\
johannsen_threshold
# Importing local thresholding algorithms
from .local_th import sauvola_threshold, niblack_threshold, wolf_threshold,\
nick_threshold, lmean_threshold, bradley_roth_threshold,\
bernsen_threshold, contrast_threshold, singh_threshold, feng_threshold
__copyright__ = 'Copyright 2017'
__author__ = u'BSc. Manuel Aguado Martínez'
def apply_threshold(img, threshold=128, wp_val=255):
"""Obtain a binary image based on a given global threshold or
a set of local thresholds.
@param img: The input image.
@type img: ndarray
@param threshold: The global or local thresholds corresponding
to each pixel of the image.
@type threshold: Union[int, ndarray]
@param wp_val: The value assigned to foreground pixels (white pixels).
@type wp_val: int
@return: A binary image.
@rtype: ndarray
"""
return ((img >= threshold) * wp_val).astype(np.uint8)
def test_thresholds(img=None):
"""Runs all the package thresholding algorithms on the input
image with default parameters and plot the results.
@param img: The input gray scale image
@type img: ndarray
"""
# Loading image if needed
if img is None:
img = face(gray=True)
# Plotting test image
plt.figure('image')
plt.imshow(img, cmap='gray')
# Plotting test image histogram
plt.figure('Histogram')
plt.hist(img.ravel(), range=(0, 255), bins=255)
# Applying Otsu method
start = default_timer()
th = otsu_threshold(img)
stop = default_timer()
print('========Otsu==========')
print('Threshold: {0}'.format(th))
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Otsu method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying p_tile method
start = default_timer()
th = p_tile_threshold(img, 0.5)
stop = default_timer()
print('========P-tile [p=0.5]==========')
print('Threshold: {0}'.format(th))
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('p_tile method [pct=0.5]')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying two peaks method
start = default_timer()
th = two_peaks_threshold(img)
stop = default_timer()
print('========Two peaks==========')
print('Threshold: {0}'.format(th))
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Tow peaks method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying minimum error method
start = default_timer()
th = min_err_threshold(img)
stop = default_timer()
print('========Minimum Error==========')
print('Threshold: {0}'.format(th))
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Minimum error method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying global entropy Pun method
start = default_timer()
th = pun_threshold(img)
stop = default_timer()
print('========Global entropy Pun==========')
print('Threshold: {0}'.format(th))
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Global entropy Pun method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying global entropy Kapur method
start = default_timer()
th = kapur_threshold(img)
stop = default_timer()
print('========Global entropy Kapur==========')
print('Threshold: {0}'.format(th))
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Global entropy Kapur method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying global entropy Johannsen method
start = default_timer()
th = johannsen_threshold(img)
stop = default_timer()
print('========Global entropy Johannsen==========')
print('Threshold: {0}'.format(th))
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Global entropy Johannsen method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying local Sauvola method
start = default_timer()
th = sauvola_threshold(img)
stop = default_timer()
print('========Local Sauvola==========')
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Local Sauvola method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying local Niblack method
start = default_timer()
th = niblack_threshold(img)
stop = default_timer()
print('========Local Niblack==========')
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Local Niblack method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying local Wolf method
start = default_timer()
th = wolf_threshold(img)
stop = default_timer()
print('========Local Wolf==========')
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Local Wolf method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying local NICK method
start = default_timer()
th = nick_threshold(img)
stop = default_timer()
print('========Local NICK==========')
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Local NICK method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying local mean method
start = default_timer()
th = lmean_threshold(img)
stop = default_timer()
print('========Local mean==========')
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Local mean method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying local Bradley-Roth method
start = default_timer()
th = bradley_roth_threshold(img)
stop = default_timer()
print('========Local Bradley-Roth==========')
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Local Bradley-Roth method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying local Bernsen method
start = default_timer()
th = bernsen_threshold(img)
stop = default_timer()
print('========Local Bernsen==========')
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Local Bernsen method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying local contrast method
start = default_timer()
th = contrast_threshold(img)
stop = default_timer()
print('========Local contrast==========')
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Local contrast method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying local Singh method
start = default_timer()
th = singh_threshold(img)
stop = default_timer()
print('========Local Singh==========')
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Local Singh method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Applying local Feng method
start = default_timer()
th = feng_threshold(img)
stop = default_timer()
print('========Local Feng==========')
print('Execution time: {0}'.format(stop - start))
print('====================================')
# Plotting results
plt.figure('Local Feng method')
plt.imshow(apply_threshold(img, th), cmap='gray')
# Showing plots
plt.show()
|
python
|
#!/usr/bin/python
import paho.mqtt.publish as publish
import paho.mqtt.client as mqtt
import ssl
auth = {
'username':"ciscohackhub.azure-devices.net/lora1",
'password':"SharedAccessSignature sr=ciscohackhub.azure-devices.net%2Fdevices%2Flora1&sig=xxxx&se=1463048772"
}
tls = {
'ca_certs':"/etc/ssl/certs/ca-certificates.crt",
'tls_version':ssl.PROTOCOL_TLSv1
}
publish.single("devices/lora1/messages/events/",
payload="hello world",
hostname="ciscohackhub.azure-devices.net",
client_id="lora1",
auth=auth,
tls=tls,
port=8883,
protocol=mqtt.MQTTv311)
|
python
|
def input1(type = int):
return type(input())
def input2(type = int):
[a, b] = list(map(type, input().split()))
return a, b
def input3(type = int):
[a, b, c] = list(map(type, input().split()))
return a, b, c
def input_array(type = int):
return list(map(type, input().split()))
def input_string():
s = input()
return list(s)
def main():
t = input1(int)
for ci in range(t):
n, m = input2(int)
if n == 1:
print(0)
continue
if n == 2:
print(m)
continue
pos_value_boshbe = n // 2
na = n - 1
if n % 2 == 0:
pos_value_boshbe -= 1
na -= 1
half = m // pos_value_boshbe
res = 0
if pos_value_boshbe > 0:
res += (half * (na - 2))
if m % pos_value_boshbe == 0:
res += (half * 2)
else:
rem = m // pos_value_boshbe + m % pos_value_boshbe
res += (rem * 2)
print(res)
return
main()
|
python
|
from sklearn.base import BaseEstimator, TransformerMixin
import re
class TextCleaner(BaseEstimator, TransformerMixin):
''' text cleaning :
input can be str, list of sring, or pandas Series
a minimal version, repacing only '\n' with ' '
'''
def __init__(self):
print('')
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
''' text cleaning : input can be str, list of sring, or pandas Series '''
if isinstance(X, str): # e.g. "hello darkness my old friend"
X_ = re.sub(r'\n', ' ', X.lower())
elif isinstance(X, list): # e.g. Xtrain['lyric'].tolist()
X_ = [x.replace('\n', ' ') for x in X]
else: # e.g. Xtrain['lyric']
X_ = [re.sub(r'\n', ' ', x.lower()) for x in X]
return X_
|
python
|
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR
from allenact.algorithms.onpolicy_sync.losses import PPO
from allenact.algorithms.onpolicy_sync.losses.ppo import PPOConfig
from allenact.utils.experiment_utils import (
Builder,
PipelineStage,
TrainingPipeline,
LinearDecay,
)
from projects.objectnav_baselines.experiments.objectnav_base import ObjectNavBaseConfig
class ObjectNavMixInPPOConfig(ObjectNavBaseConfig):
def training_pipeline(self, **kwargs):
ppo_steps = int(300000000)
lr = 3e-4
num_mini_batch = 1
update_repeats = 4
num_steps = 128
save_interval = 100000
log_interval = 10000 if torch.cuda.is_available() else 1
gamma = 0.99
use_gae = True
gae_lambda = 0.95
max_grad_norm = 0.5
return TrainingPipeline(
save_interval=save_interval,
metric_accumulate_interval=log_interval,
optimizer_builder=Builder(optim.Adam, dict(lr=lr)),
num_mini_batch=num_mini_batch,
update_repeats=update_repeats,
max_grad_norm=max_grad_norm,
num_steps=num_steps,
named_losses={"ppo_loss": PPO(**PPOConfig)},
gamma=gamma,
use_gae=use_gae,
gae_lambda=gae_lambda,
advance_scene_rollout_period=self.ADVANCE_SCENE_ROLLOUT_PERIOD,
pipeline_stages=[
PipelineStage(loss_names=["ppo_loss"], max_stage_steps=ppo_steps)
],
lr_scheduler_builder=Builder(
LambdaLR, {"lr_lambda": LinearDecay(steps=ppo_steps)}
),
)
|
python
|
import py.path
import pytest
from .conftest import TEST_PLAYBOOKS
MODULES_PATH = py.path.local(__file__).realpath() / '..' / '..' / 'plugins' / 'modules'
def find_all_modules():
for module in MODULES_PATH.listdir(sort=True):
module = module.basename
if module.endswith('.py') and not module.startswith('_'):
yield module.replace('.py', '')
ALL_MODULES = list(find_all_modules())
def _module_file_path(module):
module_file_name = "{}.py".format(module)
return MODULES_PATH / module_file_name
def _module_is_tested(module):
return module in TEST_PLAYBOOKS
@pytest.mark.parametrize('module', ALL_MODULES)
def test_module_is_tested(module):
assert _module_is_tested(module)
|
python
|
#!/usr/bin/env python
# Copyright 2014-2018 The PySCF Developers. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Author: Qiming Sun <[email protected]>
#
# MOLDEN format:
# http://www.cmbi.ru.nl/molden/molden_format.html
import sys
import re
import numpy
import pyscf
from pyscf import lib
from pyscf import gto
from pyscf.lib import logger
from pyscf import __config__
IGNORE_H = getattr(__config__, 'molden_ignore_h', True)
def orbital_coeff(mol, fout, mo_coeff, spin='Alpha', symm=None, ene=None,
occ=None, ignore_h=IGNORE_H):
from pyscf.symm import label_orb_symm
if ignore_h:
mol, mo_coeff = remove_high_l(mol, mo_coeff)
aoidx = order_ao_index(mol)
nmo = mo_coeff.shape[1]
if symm is None:
symm = ['A']*nmo
if mol.symmetry:
try:
symm = label_orb_symm(mol, mol.irrep_name, mol.symm_orb,
mo_coeff, tol=1e-5)
except ValueError as e:
logger.warn(mol, str(e))
if ene is None:
ene = numpy.arange(nmo)
assert(spin == 'Alpha' or spin == 'Beta')
if occ is None:
occ = numpy.zeros(nmo)
neleca, nelecb = mol.nelec
if spin == 'Alpha':
occ[:neleca] = 1
else:
occ[:nelecb] = 1
fout.write('[MO]\n')
for imo in range(nmo):
fout.write(' Sym= %s\n' % symm[imo])
fout.write(' Ene= %15.10g\n' % ene[imo])
fout.write(' Spin= %s\n' % spin)
fout.write(' Occup= %10.5f\n' % occ[imo])
for i,j in enumerate(aoidx):
fout.write(' %3d %18.14g\n' % (i+1, mo_coeff[j,imo]))
def from_mo(mol, filename, mo_coeff, spin='Alpha', symm=None, ene=None,
occ=None, ignore_h=IGNORE_H):
'''Dump the given MOs in Molden format'''
with open(filename, 'w') as f:
header(mol, f, ignore_h)
orbital_coeff(mol, f, mo_coeff, spin, symm, ene, occ, ignore_h)
def from_scf(mf, filename, ignore_h=IGNORE_H):
'''Dump the given SCF object in Molden format'''
dump_scf(mf, filename, ignore_h)
def dump_scf(mf, filename, ignore_h=IGNORE_H):
import pyscf.scf
mol = mf.mol
mo_coeff = mf.mo_coeff
with open(filename, 'w') as f:
header(mol, f, ignore_h)
if isinstance(mf, pyscf.scf.uhf.UHF) or 'UHF' == mf.__class__.__name__:
orbital_coeff(mol, f, mo_coeff[0], spin='Alpha',
ene=mf.mo_energy[0], occ=mf.mo_occ[0],
ignore_h=ignore_h)
orbital_coeff(mol, f, mo_coeff[1], spin='Beta',
ene=mf.mo_energy[1], occ=mf.mo_occ[1],
ignore_h=ignore_h)
else:
orbital_coeff(mf.mol, f, mf.mo_coeff,
ene=mf.mo_energy, occ=mf.mo_occ, ignore_h=ignore_h)
def from_mcscf(mc, filename, ignore_h=IGNORE_H, cas_natorb=False):
mol = mc.mol
dm1 = mc.make_rdm1()
if cas_natorb:
mo_coeff, ci, mo_energy = mc.canonicalize(sort=True, cas_natorb=cas_natorb)
else:
mo_coeff, ci, mo_energy = mc.mo_coeff, mc.ci, mc.mo_energy
mo_inv = numpy.dot(mc._scf.get_ovlp(), mo_coeff)
occ = numpy.einsum('pi,pq,qi->i', mo_inv, dm1, mo_inv)
with open(filename, 'w') as f:
header(mol, f, ignore_h)
orbital_coeff(mol, f, mo_coeff, ene=mo_energy, occ=occ, ignore_h=ignore_h)
def from_chkfile(filename, chkfile, key='scf/mo_coeff', ignore_h=IGNORE_H):
import pyscf.scf
with open(filename, 'w') as f:
if key == 'scf/mo_coeff':
mol, mf = pyscf.scf.chkfile.load_scf(chkfile)
header(mol, f, ignore_h)
ene = mf['mo_energy']
occ = mf['mo_occ']
mo = mf['mo_coeff']
else:
mol = pyscf.scf.chkfile.load_mol(chkfile)
header(mol, f, ignore_h)
dat = pyscf.scf.chkfile.load(chkfile, key.split('/')[0])
if 'mo_energy' in dat:
ene = dat['mo_energy']
else:
ene = None
occ = dat['mo_occ']
mo = dat['mo_coeff']
if isinstance(ene, str) and ene == 'None':
ene = None
if isinstance(ene, str) and occ == 'None':
occ = None
if occ.ndim == 2:
orbital_coeff(mol, f, mo[0], spin='Alpha', ene=ene[0], occ=occ[0],
ignore_h=ignore_h)
orbital_coeff(mol, f, mo[1], spin='Beta', ene=ene[1], occ=occ[1],
ignore_h=ignore_h)
else:
orbital_coeff(mol, f, mo, ene=ene, occ=occ, ignore_h=ignore_h)
_SEC_RE = re.compile(r'\[[^]]+\]')
def _read_one_section(molden_fp):
sec = [None]
while True:
line = molden_fp.readline()
if not line:
break
line = line.strip()
if line == '' or line[0] == '#': # comment or blank line
continue
mo = _SEC_RE.match(line)
if mo:
if sec[0] is None:
sec[0] = line
else:
# Next section? rewind the fp pointer
molden_fp.seek(last_pos)
break
else:
sec.append(line)
last_pos = molden_fp.tell()
return sec
def _parse_natoms(lines, envs):
envs['natm'] = natm = int(lines[1])
return natm
def _parse_atoms(lines, envs):
if 'ANG' in lines[0].upper():
envs['unit'] = 1
unit = envs['unit']
envs['atoms'] = atoms = []
for line in lines[1:]:
dat = line.split()
symb, atmid, chg = dat[:3]
coord = numpy.array([float(x) for x in dat[3:]])*unit
atoms.append((gto.mole._std_symbol(symb)+atmid, coord))
if envs['natm'] is not None and envs['natm'] != len(atoms):
sys.stderr.write('Number of atoms in section ATOMS does not equal to N_ATOMS\n')
return atoms
def _parse_charge(lines, envs):
mulliken_charges = [float(_d2e(x)) for x in lines[1:]]
return mulliken_charges
def _parse_gto(lines, envs):
mol = envs['mol']
atoms = envs['atoms']
basis = {}
lines_iter = iter(lines)
next(lines_iter) # skip section header
# * Do not use iter() here. Python 2 and 3 are different in iter()
def read_one_bas(lsym, nb, fac=1):
fac = float(fac)
bas = [lib.param.ANGULARMAP[lsym.lower()],]
for i in range(int(nb)):
dat = _d2e(next(lines_iter)).split()
bas.append((float(dat[0]), float(dat[1])*fac))
return bas
# * Be careful with the atom sequence in [GTO] session, it does not correspond
# to the atom sequence in [Atoms] session.
atom_seq = []
for line in lines_iter:
dat = line.split()
if dat[0].isdigit():
atom_seq.append(int(dat[0])-1)
symb = atoms[int(dat[0])-1][0]
basis[symb] = []
elif dat[0].upper() in 'SPDFGHIJ':
basis[symb].append(read_one_bas(*dat))
mol.basis = envs['basis'] = basis
mol.atom = [atoms[i] for i in atom_seq]
return mol
def _parse_mo(lines, envs):
mol = envs['mol']
atoms = envs['atoms']
if not mol._built:
try:
mol.build(0, 0)
except RuntimeError:
mol.build(0, 0, spin=1)
irrep_labels = []
mo_energy = []
spins = []
mo_occ = []
mo_coeff = []
norb_alpha = -1
for line in lines[1:]:
line = line.upper()
if 'SYM' in line:
irrep_labels.append(line.split('=')[1].strip())
orb = []
mo_coeff.append(orb)
elif 'ENE' in line:
mo_energy.append(float(_d2e(line).split('=')[1].strip()))
elif 'SPIN' in line:
spins.append(line.split('=')[1].strip())
elif 'OCC' in line:
mo_occ.append(float(_d2e(line.split('=')[1].strip())))
else:
orb.append(float(_d2e(line.split()[1])))
mo_energy = numpy.array(mo_energy)
mo_occ = numpy.array(mo_occ)
if mol.cart:
aoidx = numpy.argsort(order_ao_index(mol, cart=True))
mo_coeff = (numpy.array(mo_coeff).T)[aoidx]
# AO are assumed to be normalized in molpro molden file
s = mol.intor('int1e_ovlp')
mo_coeff = numpy.einsum('i,ij->ij', numpy.sqrt(1/s.diagonal()), mo_coeff)
else:
aoidx = numpy.argsort(order_ao_index(mol))
mo_coeff = (numpy.array(mo_coeff).T)[aoidx]
return mol, mo_energy, mo_coeff, mo_occ, irrep_labels, spins
def _parse_core(lines, envs):
mol = envs['mol']
atoms = envs['atoms']
line_id = 1
max_lines = len(lines)
for line in lines[1:]:
dat = line.split(':')
if dat[0].strip().isdigit():
atm_id = int(dat[0].strip()) - 1
nelec_core = int(dat[1].strip())
mol.ecp[atoms[atm_id][0]] = [nelec_core, []]
if mol.ecp:
sys.stderr.write('\nECP were dectected in the molden file.\n'
'Note Molden format does not support ECP data. '
'ECP information was lost when saving to molden format.\n\n')
return mol.ecp
_SEC_PARSER = {'GTO' : _parse_gto,
'N_ATOMS' : _parse_natoms,
'ATOMS' : _parse_atoms,
'CHARGE' : _parse_charge,
'MO' : _parse_mo,
'CORE' : _parse_core,
'MOLDEN FORMAT' : lambda *args: None,
}
def load(moldenfile, verbose=0):
'''Extract mol and orbitals from molden file
'''
with open(moldenfile, 'r') as f:
mol = gto.Mole()
mol.cart = True
tokens = {'natm' : None,
'unit' : lib.param.BOHR,
'mol' : mol,
'atoms' : None,
'basis' : None,
}
mo_section_count = 0
while True:
lines = _read_one_section(f)
sec_title = lines[0]
if sec_title is None:
break
sec_title = sec_title[1:sec_title.index(']')].upper()
if sec_title == 'MO':
res = _parse_mo(lines, tokens)
if mo_section_count == 0: # Alpha orbitals
mol, mo_energy, mo_coeff, mo_occ, irrep_labels, spins = res
else:
mo_energy = mo_energy , res[1]
mo_coeff = mo_coeff , res[2]
mo_occ = mo_occ , res[3]
irrep_labels = irrep_labels, res[4]
spins = spins , res[5]
mo_section_count += 1
elif sec_title in _SEC_PARSER:
_SEC_PARSER[sec_title.upper()](lines, tokens)
elif sec_title in ('5D', '7F', '9G'):
mol.cart = False
else:
sys.stderr.write('Unknown section %s\n' % sec_title)
if mo_section_count == 0:
if spins[-1][0] == 'B': # If including beta orbitals
offset = spins.index(spins[-1])
mo_energy = mo_energy [:offset], mo_energy [offset:]
mo_coeff = mo_coeff [:offset], mo_coeff [offset:]
mo_occ = mo_occ [:offset], mo_occ [offset:]
irrep_labels = irrep_labels[:offset], irrep_labels[offset:]
spins = spins [:offset], spins [offset:]
if isinstance(mo_occ, tuple):
mol.spin = int(mo_occ[0].sum() - mo_occ[1].sum())
return mol, mo_energy, mo_coeff, mo_occ, irrep_labels, spins
parse = read = load
def _d2e(token):
return token.replace('D', 'e').replace('d', 'e')
def header(mol, fout, ignore_h=IGNORE_H):
if ignore_h:
mol = remove_high_l(mol)[0]
fout.write('[Molden Format]\n')
fout.write('made by pyscf v[%s]\n' % pyscf.__version__)
fout.write('[Atoms] (AU)\n')
for ia in range(mol.natm):
symb = mol.atom_pure_symbol(ia)
chg = mol.atom_charge(ia)
fout.write('%s %d %d ' % (symb, ia+1, chg))
coord = mol.atom_coord(ia)
fout.write('%18.14f %18.14f %18.14f\n' % tuple(coord))
fout.write('[GTO]\n')
for ia, (sh0, sh1, p0, p1) in enumerate(mol.offset_nr_by_atom()):
fout.write('%d 0\n' %(ia+1))
for ib in range(sh0, sh1):
l = mol.bas_angular(ib)
nprim = mol.bas_nprim(ib)
nctr = mol.bas_nctr(ib)
es = mol.bas_exp(ib)
cs = mol.bas_ctr_coeff(ib)
for ic in range(nctr):
fout.write(' %s %2d 1.00\n' % (lib.param.ANGULAR[l], nprim))
for ip in range(nprim):
fout.write(' %18.14g %18.14g\n' % (es[ip], cs[ip,ic]))
fout.write('\n')
fout.write('[5d]\n[9g]\n\n')
if mol.has_ecp(): # See https://github.com/zorkzou/Molden2AIM
fout.write('[core]\n')
for ia in range(mol.natm):
nelec_ecp_core = mol.atom_nelec_core(ia)
if nelec_ecp_core != 0:
fout.write('%s : %d\n' % (ia+1, nelec_ecp_core))
fout.write('\n')
def order_ao_index(mol, cart=False):
# reorder d,f,g fucntion to
# 5D: D 0, D+1, D-1, D+2, D-2
# 6D: xx, yy, zz, xy, xz, yz
#
# 7F: F 0, F+1, F-1, F+2, F-2, F+3, F-3
# 10F: xxx, yyy, zzz, xyy, xxy, xxz, xzz, yzz, yyz, xyz
#
# 9G: G 0, G+1, G-1, G+2, G-2, G+3, G-3, G+4, G-4
# 15G: xxxx yyyy zzzz xxxy xxxz yyyx yyyz zzzx zzzy xxyy xxzz yyzz xxyz yyxz zzxy
idx = []
off = 0
if cart:
for ib in range(mol.nbas):
l = mol.bas_angular(ib)
for n in range(mol.bas_nctr(ib)):
if l == 2:
idx.extend([off+0,off+3,off+5,off+1,off+2,off+4])
elif l == 3:
idx.extend([off+0,off+6,off+9,off+3,off+1,
off+2,off+5,off+8,off+7,off+4])
elif l == 4:
idx.extend([off+0 , off+10, off+14, off+1 , off+2 ,
off+6 , off+11, off+9 , off+13, off+3 ,
off+5 , off+12, off+4 , off+7 , off+8 ,])
elif l > 4:
raise RuntimeError('l=5 is not supported')
else:
idx.extend(range(off,off+(l+1)*(l+2)//2))
off += (l+1)*(l+2)//2
else: # spherical orbitals
for ib in range(mol.nbas):
l = mol.bas_angular(ib)
for n in range(mol.bas_nctr(ib)):
if l == 2:
idx.extend([off+2,off+3,off+1,off+4,off+0])
elif l == 3:
idx.extend([off+3,off+4,off+2,off+5,off+1,off+6,off+0])
elif l == 4:
idx.extend([off+4,off+5,off+3,off+6,off+2,
off+7,off+1,off+8,off+0])
elif l == 5:
idx.extend(off+odx for odx in [5, 6, 4, 7, 3, 8, 2, 9, 1, 10, 0])
elif l == 6:
idx.extend(off+odx for odx in [6, 7, 5, 8, 4, 9, 3, 10, 2, 11, 1, 12, 0])
elif l == 7:
idx.extend(off+odx for odx in [7, 8, 6, 9, 5, 10, 4, 11, 3, 12, 2, 13, 1, 14, 0])
elif l >= 8:
raise RuntimeError('l>=8 is not supported')
else:
idx.extend(range(off,off+l*2+1))
off += l * 2 + 1
return idx
def remove_high_l(mol, mo_coeff=None):
'''Remove high angular momentum (l >= 5) functions before dumping molden file.
If molden function raised error message ``RuntimeError l=5 is not supported``,
you can use this function to format orbitals.
Note the formated orbitals may have normalization problem. Some visualization
tool will complain about the orbital normalization error.
Examples:
>>> mol1, orb1 = remove_high_l(mol, mf.mo_coeff)
>>> molden.from_mo(mol1, outputfile, orb1)
'''
pmol = mol.copy()
pmol.basis = {}
for symb, bas in mol._basis.items():
pmol.basis[symb] = [b for b in bas if b[0] <= 4]
pmol.build(0, 0)
if mo_coeff is None:
return pmol, None
else:
k = 0
idx = []
for ib in range(mol.nbas):
l = mol.bas_angular(ib)
nc = mol.bas_nctr(ib)
if l <= 4:
idx.append(range(k, k+(l*2+1)*nc))
k += (l*2+1) * nc
idx = numpy.hstack(idx)
return pmol, mo_coeff[idx]
if __name__ == '__main__':
from pyscf import scf
import tempfile
mol = gto.Mole()
mol.verbose = 5
mol.output = None#'out_gho'
mol.atom = [['C', (0.,0.,0.)],
['H', ( 1, 1, 1)],
['H', (-1,-1, 1)],
['H', ( 1,-1,-1)],
['H', (-1, 1,-1)], ]
mol.basis = {
'C': 'sto-3g',
'H': 'sto-3g'}
mol.build(dump_input=False)
m = scf.RHF(mol)
m.scf()
header(mol, mol.stdout)
print(order_ao_index(mol))
orbital_coeff(mol, mol.stdout, m.mo_coeff)
ftmp = tempfile.NamedTemporaryFile(dir=lib.param.TMPDIR)
from_mo(mol, ftmp.name, m.mo_coeff)
print(parse(ftmp.name))
|
python
|
import os
import sys
import time
import glob
import numpy as np
import pandas as pd
import torch
import utils
import logging
import argparse
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from copy import deepcopy
from numpy import linalg as LA
import matplotlib.pyplot as plt
from utils import _data_transforms_focuspath
from plcc_loss import PLCCLoss
from torch.autograd import Variable
from dartsfqa_search import Network
from architect import Architect
from analyze import Analyzer
from dataset import FocusDataset
from adas import Adas
from adas.metrics import Metrics
from adas.adaptive_stop import StopChecker
def parse_config():
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default='../data', help='location of the data corpus')
parser.add_argument('--batch_size', type=int, default=50, help='batch size')
parser.add_argument('--learning_rate', type=float, default=0.175, help='init learning rate')
parser.add_argument('--learning_rate_min', type=float, default=0.001, help='min learning rate')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('--weight_decay', type=float, default=3e-4, help='weight decay')
parser.add_argument('--report_freq', type=float, default=50, help='report frequency')
parser.add_argument('--gpu', type=int, default=0, help='gpu device id')
parser.add_argument('--epochs', type=int, default=60, help='num of training epochs')
parser.add_argument('--init_channels', type=int, default=8, help='num of init channels')
parser.add_argument('--layers', type=int, default=3, help='total number of layers')
parser.add_argument('--model_path', type=str, default='saved_models', help='path to save the model')
parser.add_argument('--cutout', action='store_true', default=False, help='use cutout')
parser.add_argument('--cutout_length', type=int, default=16, help='cutout length')
parser.add_argument('--drop_path_prob', type=float, default=0.0, help='drop path probability')
# Change to FOCUSPATH or DEEPFOCUS depending on the dataset.
parser.add_argument('--save', type=str, default='BIOIMAGE_exp', help='experiment name, options: BIOIMAGE_exp, FOCUSPATH_exp, DEEPFOCUS_exp')
parser.add_argument('--seed', type=int, default=0, help='random seed')
parser.add_argument('--grad_clip', type=float, default=5, help='gradient clipping')
parser.add_argument('--train_portion', type=float, default=0.50, help='portion of training data')
parser.add_argument('--unrolled', action='store_true', default=False, help='use one-step unrolled validation loss')
parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')
# BN
parser.add_argument('--learnable_bn', action='store_true', default=False, help='learnable parameters in batch normalization')
# Gumbel-softmax
parser.add_argument('--gumbel', action='store_true', default=False, help='use or not Gumbel-softmax trick')
parser.add_argument('--tau_max', type=float, default=10.0, help='initial tau')
parser.add_argument('--tau_min', type=float, default=1.0, help='minimum tau')
# Dataset
parser.add_argument('--dataset', type=str, default='FocusPath', help='choose dataset, options: BioImage, DeepFocus, FocusPath')
# Adas optimizer
parser.add_argument('--adas', action='store_true', default=False, help='whether or not to use adas optimizer')
parser.add_argument('--scheduler_beta', type=float, default=0.97, help='beta for lr scheduler')
parser.add_argument('--step_size', type=int, default=None, help='step_size for dropping zeta')
parser.add_argument('--gamma', type=float, default=0.5, help='zeta dropping rate in Adas')
# Save file name
parser.add_argument('--file_name', type=str, default='focuspath', help='metrics and weights data file name')
# Hessian
parser.add_argument('--compute_hessian', action='store_true', default=False, help='compute or not Hessian')
parser.add_argument('--report_freq_hessian', type=int, default=2, help='frequency to report Hessian')
# Local stopping criterion
parser.add_argument('--adaptive_stop', action='store_true', default=False, help='local stopping criterion')
parser.add_argument('--as_start_epoch', type=int, default=10, help='start epoch for local stopping criterion')
parser.add_argument('--num_normal_cell_stop', type=int, default=2, help='param for local stopping criterion')
parser.add_argument('--num_reduce_cell_stop', type=int, default=1, help='param for local stopping criterion')
# Global stopping criterion
parser.add_argument('--global_stop', action='store_true', default=False, help='global stopping criterion')
parser.add_argument('--gs_factor', type=float, default=1.3, help='factor for global stopping criterion')
parser.add_argument('--gs_start_epoch', type=int, default=20, help='start epoch for global stopping criterion')
parser.add_argument('--gs_delta', type=int, default=2, help='delta for global stopping criterion')
# Intermediate nodes in a cell
parser.add_argument('--node', type=int, default=2, help='number of nodes in a cell')
# Path Information
parser.add_argument('--result_path', type=str, default='./search_results', metavar='PATH', help='Path for save loss plots.')
parser.add_argument('--dataset_path', type=str, default='./data', metavar='PATH', help='Path at which the dataset is stored.')
parser.add_argument('--csv_path', type=str, default='./data', metavar='PATH', help='Path at which the CSV is stored.')
args = parser.parse_args()
args.save = 'search-{}-{}-{}'.format(args.save, args.file_name, time.strftime("%Y%m%d-%H%M%S"))
save_folder_parent = args.result_path
if not os.path.exists(save_folder_parent):
os.makedirs(save_folder_parent)
save_folder = save_folder_parent + "%flr_%s" % (args.learning_rate, args.dataset)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
args.save = os.path.join(save_folder, args.save)
utils.create_exp_dir(args.save, scripts_to_save=None)
# utils.create_exp_dir(args.save, scripts_to_save=glob.glob(*.py))
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
format=log_format, datefmt='%m/%d %I:%M:%S %p')
fh = logging.FileHandler(os.path.join(args.save, 'log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
return args
def main(args):
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
torch.cuda.empty_cache()
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
if args.dataset == 'cifar100':
n_classes = 100
data_folder = 'cifar-100-python'
elif args.dataset.lower() == 'focuspath':
n_classes = 15
elif args.dataset.lower() == 'deepfocus':
n_classes = 6
elif args.dataset.lower() == 'bioimage':
n_classes = 11
else:
n_classes = 10
data_folder = 'cifar-10-batches-py'
# Want to use the PLCC loss, not Cross Entropy.
# criterion = nn.CrossEntropyLoss()
# criterion = criterion.cuda()
criterion = PLCCLoss()
criterion = criterion.cuda()
model = Network(args.init_channels, n_classes, args.layers, criterion, args.dataset, args.batch_size, learnable_bn=args.learnable_bn, steps=args.node)
model = model.cuda()
num_param = sum([p.numel() for p in model.parameters()])
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
logging.info("num of params = %d", num_param)
################################################################################
# AdaS: optimizer and scheduler
if args.adas:
optimizer = Adas(params=list(model.parameters()),
lr=args.learning_rate,
beta=args.scheduler_beta,
step_size=args.step_size,
gamma=args.gamma,
momentum=args.momentum,
weight_decay=args.weight_decay)
################################################################################
# original DARTS: SGD optimizer with cosine_annealing lr scheduler
# else:
# optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
# scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.decay_interval, gamma=args.decay_ratio)
else:
optimizer = torch.optim.SGD(
model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
# optimizer, float(args.epochs), eta_min=args.learning_rate_min)
################################################################################
if args.dataset == 'cifar100':
train_transform, valid_transform = utils._data_transforms_cifar100(args)
train_data = dset.CIFAR100(root=args.data, train=True, download=True, transform=train_transform)
elif args.dataset.lower() == 'focuspath':
train_transform, valid_transform = utils._data_transforms_focuspath(args)
train_csv = args.csv_path
trainset = args.dataset_path
elif args.dataset.lower() == 'bioimage':
train_transform, valid_transform = utils._data_transforms_bioimage(args)
train_csv = args.csv_path
trainset = args.dataset_path
elif args.dataset.lower() == 'deepfocus':
train_transform, valid_transformm = utils._data_transforms_deepfocus(args)
train_csv = args.csv_path
trainset = args.dataset_path
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
if args.dataset == 'cifar100' or args.dataset == 'cifar10':
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True, num_workers=0)
valid_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]),
pin_memory=True, num_workers=0)
elif args.dataset.lower() == 'focuspath':
train_data = FocusDataset(csv_file=train_csv, root_dir=trainset, transform=train_transform, dataset=args.dataset)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True, num_workers=0, drop_last=True)
valid_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]),
pin_memory=True, num_workers=0, drop_last=True)
elif args.dataset.lower() == 'bioimage':
train_data = FocusDataset(csv_file=train_csv, root_dir=trainset, transform=train_transform, dataset=args.dataset)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True, num_workers=1, drop_last=True)
valid_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]),
pin_memory=True, num_workers=1, drop_last=True)
elif args.dataset.lower() == 'deepfocus':
train_data = FocusDataset(csv_file=train_csv, root_dir=trainset, transform=train_transform, dataset=args.dataset)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True, num_workers=0, drop_last=True)
valid_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]),
pin_memory=True, num_workers=0, drop_last=True)
architect = Architect(model, args)
"""Hessian"""
analyser = Analyzer(model, args)
"""adaptive stopping based on knowledge gains"""
stop_checker = StopChecker(args)
"""global stopping based on eigen values"""
ev_tracker = utils.EVLocalAvg(args)
if not args.adas:
# adas has already called this
metrics = Metrics(params=list(model.parameters()))
performance_statistics = {}
arch_statistics = {}
genotype_statistics = {}
save_folder = args.result_path + "%flr_%s" % (args.learning_rate, args.dataset)
metrics_path = save_folder + 'metrics_stat_' + args.file_name + '.xlsx'
weights_path = save_folder + 'weights_stat_' + args.file_name + '.xlsx'
genotypes_path = save_folder + 'genotypes_stat_' + args.file_name + '.xlsx'
edge_num = np.sum([2+i for i in range(args.node)])
normal_edge_stop_epoch = np.zeros(edge_num)
reduce_edge_stop_epoch = np.zeros(edge_num)
local_stop_epoch = None
errors_dict = {'train_acc': [], 'train_loss': [], 'valid_acc': [], 'valid_loss': []}
for epoch in range(args.epochs):
if args.adas:
lr = optimizer.lr_vector
else:
logging.info('epoch %d lr %e', epoch, args.learning_rate)
genotype = model.genotype()
logging.info('epoch: %d', epoch)
logging.info('genotype = %s', genotype)
# Training for DARTS-FQA
train_obj = train(args, epoch, train_queue, valid_queue,
model, architect, criterion,
optimizer, args.learning_rate,
analyser, ev_tracker)
print('\n')
logging.info('train_loss %f', train_obj)
# Validation for DARTS-FQA
valid_obj = infer(args, valid_queue, model, criterion)
print('\n')
logging.info('valid_loss %f', valid_obj)
# update the errors dictionary
errors_dict['train_loss'].append(train_obj)
errors_dict['valid_loss'].append(valid_obj)
losses = [train_obj, valid_obj]
# update network io metrics (knowledge gain, condition mapping, etc)
if args.adas:
# AdaS: update learning rates
optimizer.epoch_step(epoch)
io_metrics = optimizer.KG
lr_metrics = optimizer.velocity
else:
metrics()
io_metrics = metrics.KG(epoch)
lr_metrics = None
# weights
weights_normal = F.softmax(model.alphas_normal, dim=-1).detach().cpu().numpy()
weights_reduce = F.softmax(model.alphas_reduce, dim=-1).detach().cpu().numpy()
# write data to excel files
loss_list = write_data(args, epoch, io_metrics, lr_metrics, weights_normal, weights_reduce, genotype,
performance_statistics, arch_statistics, genotype_statistics,
metrics_path, weights_path, genotypes_path, losses, loss_list)
# save model parameters
utils.save(model, os.path.join(args.save, 'weights.pt'))
# adaptive stopping criterion (local, based on knowledge gain)
if args.adaptive_stop and epoch >= args.as_start_epoch:
# apply local stopping criterion
stop_checker.local_stop(optimizer.metrics, epoch)
# freeze some edges based on their knowledge gains
iteration_p = 0
for p in model.parameters():
if ~optimizer.metrics.layers_index_todo[iteration_p]:
p.requires_grad = False
p.grad = None
iteration_p += 1
for i in range(edge_num):
if stop_checker.normal_edge_index_stop[i] & (normal_edge_stop_epoch[i] == 0):
normal_edge_stop_epoch[i] = epoch
if stop_checker.reduce_edge_index_stop[i] & (reduce_edge_stop_epoch[i] == 0):
reduce_edge_stop_epoch[i] = epoch
logging.info(
'Epoch: %d, normal edge stop epoch: %s',
epoch, str(normal_edge_stop_epoch)
)
logging.info(
'Epoch: %d, reduce edge stop epoch: %s',
epoch, str(reduce_edge_stop_epoch)
)
if min(normal_edge_stop_epoch) > 0 and min(reduce_edge_stop_epoch) > 0:
if local_stop_epoch is None:
logging.info(
'All edges are frozen at epoch: %d', epoch
)
local_stop_epoch = epoch + 2
# if all edges are frozen, we should
# stop searching the whole net after 2 epochs
elif epoch == local_stop_epoch:
logging.info(
'Based on the local criterion, decide to stop the search at epoch %d', epoch
)
# FocusPath
logging.info(
'Validation loss at stop epoch: %f' , valid_obj
)
# logging.info(
# 'Validation accuracy at stop epoch: %f', valid_acc
# )
logging.info(
'Genotype at stop epoch: %s', genotype
)
break
else:
logging.info(
'Waiting for the searching to stop in %d epochs', local_stop_epoch - epoch
)
# global stop criterion (global, based on Hessian matrix)
if args.global_stop:
if ev_tracker.stop_search:
# set the following to the values they had at stop_epoch
errors_dict['valid_acc'] = errors_dict['valid_acc'][:ev_tracker.stop_epoch + 1]
stop_genotype = ev_tracker.stop_genotype
stop_valid_acc = errors_dict['valid_acc'][ev_tracker.stop_epoch]
logging.info(
'Based on the global criterion, decide to stop the search at epoch %d (Current epoch: %d)',
ev_tracker.stop_epoch, epoch
)
logging.info(
'Validation accuracy at stop epoch: %f', stop_valid_acc
)
logging.info(
'Genotype at stop epoch: %s', stop_genotype
)
break
def train(args, epoch, train_queue, valid_queue, model, architect, criterion, optimizer, lr, analyser, ev_tracker):
objs = utils.AvgrageMeter()
train_loss = 0
if args.adas:
layers_todo = optimizer.metrics.layers_index_todo
else:
layers_todo = None
for step, sample_batched in enumerate(train_queue, 0):
input, target = sample_batched['image'], sample_batched['score']
# one mini-batch
print('\rtrain mini batch {:03d}'.format(step), end=' ')
model.train()
if args.gumbel:
model.set_tau(args.tau_max - epoch * 1.0 / args.epochs * (args.tau_max - args.tau_min))
input = Variable(input, requires_grad=False).cuda()
target = Variable(target.float(), requires_grad=False).cuda(non_blocking=True)
# get a random minibatch from the search queue with replacement
sample_batched_search = next(iter(valid_queue))
input_search, target_search = sample_batched_search['image'], sample_batched_search['score']
input_search = Variable(input_search, requires_grad=False).cuda()
target_search = Variable(target_search.float(), requires_grad=False).cuda(non_blocking=True)
# logging.info('update arch...')
architect.step(input, target, input_search, target_search, lr, layers_todo, optimizer, unrolled=args.unrolled)
# logging.info('update weights...')
optimizer.zero_grad()
logits = model.forward(input, gumbel=args.gumbel)
logits_avg = logits.view(args.batch_size, 1).mean(1)
loss = criterion(logits_avg, target)
loss.backward()
# nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
train_loss += loss.item()
print_train_loss = train_loss / (step + 1)
if step % args.report_freq == 0:
print('\n')
logging.info('train %03d %f', step, print_train_loss)
# logging.info('train %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
if args.compute_hessian:
if (epoch % args.report_freq_hessian == 0) or (epoch == (args.epochs - 1)):
_data_loader = deepcopy(train_queue)
sample_batched = next(iter(_data_loader))
input, target = sample_batched['image'], sample_batched['score']
input = Variable(input, requires_grad=False).cuda()
target = Variable(target.float(), requires_grad=False).cuda(non_blocking=True)
# get gradient information
# param_grads = [p.grad for p in model.parameters() if p.grad is not None]
# param_grads = torch.cat([x.view(-1) for x in param_grads])
# param_grads = param_grads.cpu().data.numpy()
# grad_norm = np.linalg.norm(param_grads)
# gradient_vector = torch.cat([x.view(-1) for x in gradient_vector])
# grad_norm = LA.norm(gradient_vector.cpu())
# logging.info('\nCurrent grad norm based on Train Dataset: %.4f',
# grad_norm)
# logging.info('Compute Hessian start')
H = analyser.compute_Hw(input, target, input_search, target_search,
lr, layers_todo, optimizer, unrolled=False)
# g = analyser.compute_dw(input, target, input_search, target_search,
# lr, layers_todo, optimizer, unrolled=False)
# g = torch.cat([x.view(-1) for x in g])
del _data_loader
hessian_file = "../save_data/hessian_{0}_epoch_{1}".format(args.file_name, epoch)
np.save(hessian_file, H.cpu().data.numpy())
# early stopping
ev = max(LA.eigvals(H.cpu().data.numpy()))
ev_tracker.update(epoch, ev, model.genotype())
if args.global_stop and epoch != (args.epochs - 1):
ev_tracker.early_stop(epoch,
factor=args.gs_factor,
es_start_epoch=args.gs_start_epoch,
delta=args.gs_delta)
return print_train_loss
# return top1.avg, objs.avg
def infer(args, valid_queue, model, criterion):
objs = utils.AvgrageMeter()
# top1 = utils.AvgrageMeter()
# top5 = utils.AvgrageMeter()
model.eval()
valid_loss = 0
with torch.no_grad():
for step, sample_batched in enumerate(valid_queue):
print('\rinfer mini batch {:03d}'.format(step), end=' ')
input, target = sample_batched['image'], sample_batched['score']
input = Variable(input).cuda()
target = Variable(target.float()).cuda(non_blocking=True)
logits = model(input)
logits_avg = logits.view(args.batch_size, 1).mean(1)
loss = criterion(logits_avg, target)
valid_loss += loss.item()
print_valid_loss = valid_loss / (step + 1)
if step % args.report_freq == 0:
print('\n')
logging.info('valid %03d %f', step, print_valid_loss)
return print_valid_loss
def write_data(args, epoch, net_metrics, lr_metrics, weights_normal, weights_reduce, genotype,
perform_stat, arch_stat, genotype_stat, metrics_path, weights_path, genotypes_path, losses, loss_list):
# io metrics
perform_stat['S_epoch_{}'.format(epoch)] = net_metrics
if args.adas:
# lr metrics
perform_stat['learning_rate_epoch_{}'.format(epoch)] = lr_metrics
learning_rate_print = np.array(perform_stat['learning_rate_epoch_{}'.format(epoch)])
learning_rate_print = np.mean(learning_rate_print)
loss_list[0, epoch] = losses[0]
loss_list[1, epoch] = losses[1]
result_path = args.result_path
valid_summary_file = "%flr_valid_loss.txt" % args.learning_rate
train_summary_file = "%flr_train_loss.txt" % args.learning_rate
if not os.path.exists(result_path):
os.makedirs(result_path)
valid_result = os.path.join(result_path, valid_summary_file)
train_result = os.path.join(result_path, train_summary_file)
# genotype
if epoch % 5 == 0 or epoch == args.epochs - 1:
genotype_stat['epoch_{}'.format(epoch)] = [genotype]
genotypes_df = pd.DataFrame(data=genotype_stat)
genotypes_df.to_excel(metrics_path)
valid_result_file = open(valid_result, 'a')
format_out = '(E:%d) [loss = %.4f, lr = %.6e]'
print_out = format_out % (epoch, losses[1], args.learning_rate)
valid_result_file.write(print_out + '\n')
valid_result_file.write(str(genotype) + '\n')
print(str(genotype))
valid_result_file.close()
train_result_file = open(train_result, 'a')
format_out = '(E:%d) [loss = %.4f, lr = %.6e]'
print_out = format_out % (epoch, losses[0], args.learning_rate)
train_result_file.write(print_out + '\n')
train_result_file.write(str(genotype) + '\n')
print(str(genotype))
train_result_file.close()
plt.ion()
plt.figure()
plt.plot(range(epoch), loss_list[0, 0:epoch], label='Train Loss')
plt.plot(range(epoch), loss_list[1, 0:epoch], label='Validation Loss')
plt.legend()
plt.title('Architecture Search Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss (PLCC)')
loss_plot = "loss_%flr.png" % args.learning_rate
loss_plot_file = os.path.join(result_path, loss_plot)
plt.savefig(loss_plot_file)
plt.show(block=False)
plt.pause(3)
print("Loss plot finished for epoch %d" % epoch)
plt.ioff()
plt.close('all')
# write metrics data to xls file
metrics_df = pd.DataFrame(data=perform_stat)
metrics_df.to_excel(metrics_path)
# weights
# normal
arch_stat['normal_none_epoch{}'.format(epoch)] = weights_normal[:, 0]
arch_stat['normal_max_epoch{}'.format(epoch)] = weights_normal[:, 1]
arch_stat['normal_avg_epoch{}'.format(epoch)] = weights_normal[:, 2]
arch_stat['normal_skip_epoch{}'.format(epoch)] = weights_normal[:, 3]
arch_stat['normal_sep_3_epoch{}'.format(epoch)] = weights_normal[:, 4]
arch_stat['normal_sep_5_epoch{}'.format(epoch)] = weights_normal[:, 5]
arch_stat['normal_dil_3_epoch{}'.format(epoch)] = weights_normal[:, 6]
arch_stat['normal_dil_5_epoch{}'.format(epoch)] = weights_normal[:, 7]
# reduce
arch_stat['reduce_none_epoch{}'.format(epoch)] = weights_reduce[:, 0]
arch_stat['reduce_max_epoch{}'.format(epoch)] = weights_reduce[:, 1]
arch_stat['reduce_avg_epoch{}'.format(epoch)] = weights_reduce[:, 2]
arch_stat['reduce_skip_epoch{}'.format(epoch)] = weights_reduce[:, 3]
arch_stat['reduce_sep_3_epoch{}'.format(epoch)] = weights_reduce[:, 4]
arch_stat['reduce_sep_5_epoch{}'.format(epoch)] = weights_reduce[:, 5]
arch_stat['reduce_dil_3_epoch{}'.format(epoch)] = weights_reduce[:, 6]
arch_stat['reduce_dil_5_epoch{}'.format(epoch)] = weights_reduce[:, 7]
# write weights data to xls file
weights_df = pd.DataFrame(data=arch_stat)
weights_df.to_excel(weights_path)
return loss_list
if __name__ == '__main__':
args = parse_config()
main(args)
|
python
|
"""
hhpy
~~~~~~
The hhpy package - a Python package developed by Henrik Hanssen centered around the idea of providing
unified and convenient tools for Data Science
"""
from hhpy.main import *
from hhpy.ds import *
from hhpy.ipython import *
from hhpy.modelling import *
from hhpy.plotting import *
from hhpy.regression import *
|
python
|
"""
Copyright 2017 Nikolay Stanchev
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import unittest
from DataStructures.AbstractDataStructures import PriorityQueue
from DataStructures.Errors import *
class PriorityQueueTest(unittest.TestCase):
def test_size(self):
priority_queue = PriorityQueue()
self.assertEqual(len(priority_queue), 0, "Priority queue is not initialised as empty")
priority_queue = PriorityQueue(float, True)
self.assertEqual(priority_queue.size, 0, "Priority queue is not initialised as empty")
priority_queue = PriorityQueue(elements_type=int, reverse=False)
self.assertEqual(len(priority_queue), 0, "Priority queue is not initialised as empty")
self.assertTrue(priority_queue.size == 0, "Priority queue is not initialised as empty")
self.assertEqual(priority_queue.size, 0, "Priority queue is not initialised as empty")
priority_queue.enqueue(5, 10)
priority_queue.enqueue(20, 3)
self.assertEqual(priority_queue.size, 2, "Wrong size implementation")
self.assertEqual(len(priority_queue), 2, "Wrong len implementation")
priority_queue = PriorityQueue(reverse=False)
priority_queue.enqueue(20, 3)
priority_queue.peek()
priority_queue.enqueue(2, 2)
priority_queue.enqueue(10, 3)
self.assertEqual(priority_queue.size, 2, "Wrong size implementation")
priority_queue.dequeue()
priority_queue.get_element(1024)
self.assertEqual(len(priority_queue), 1, "Wrong len implementation")
def test_type(self):
with self.assertRaises(PriorityQueueTypeError):
PriorityQueue(elements_type=5.4)
priority_queue = PriorityQueue()
self.assertEqual(priority_queue.type, None, "Wrong type at initialization")
priority_queue.enqueue(5, 5)
priority_queue.enqueue("word", 10)
priority_queue = PriorityQueue(str, True)
self.assertEqual(priority_queue.type, str, "Wrong type at initialization")
with self.assertRaises(PriorityQueueTypeError):
priority_queue.enqueue(2, 3)
with self.assertRaises(PriorityQueueTypeError):
priority_queue.enqueue("2", "3")
with self.assertRaises(PriorityQueueTypeError):
priority_queue.contains_priority(5.43)
with self.assertRaises(PriorityQueueTypeError):
priority_queue.get_element("7")
def test_reverse(self):
priority_queue = PriorityQueue()
self.assertFalse(priority_queue.reversed, "Wrong reverse implementation")
priority_queue.enqueue(1, 10)
priority_queue.enqueue("word", 5)
self.assertEqual(priority_queue.peek(), 1, "Wrong reverse implementation")
self.assertEqual(priority_queue.dequeue(), 1, "Wrong reverse implementation")
priority_queue = PriorityQueue(int, False)
self.assertFalse(priority_queue.reversed, "Wrong reverse implementation")
priority_queue.enqueue(1, 10)
priority_queue.enqueue(2, 5)
self.assertEqual(priority_queue.peek(), 1, "Wrong reverse implementation")
self.assertEqual(priority_queue.dequeue(), 1, "Wrong reverse implementation")
priority_queue = PriorityQueue(elements_type=str, reverse=True)
self.assertTrue(priority_queue.reversed, "Wrong reverse implementation")
priority_queue.enqueue("python", 1)
priority_queue.enqueue("word", 2)
self.assertEqual(priority_queue.peek(), "python", "Wrong reverse implementation")
self.assertEqual(priority_queue.dequeue(), "python", "Wrong reverse implementation")
priority_queue = PriorityQueue(reverse=True)
self.assertTrue(priority_queue.reversed, "Wrong reverse implementation")
priority_queue.enqueue(1, 10)
priority_queue.enqueue("word", 5)
self.assertEqual(priority_queue.peek(), "word", "Wrong reverse implementation")
self.assertEqual(priority_queue.dequeue(), "word", "Wrong reverse implementation")
def test_str(self):
priority_queue = PriorityQueue()
self.assertEqual(str(priority_queue), "{}", "Wrong str implementation")
priority_queue = PriorityQueue(float, True)
self.assertEqual(str(priority_queue), "{}", "Wrong str implementation")
priority_queue.enqueue(1.2, 2)
self.assertEqual(str(priority_queue), "{2: 1.2}", "Wrong str implementation")
priority_queue.enqueue(2.5, 2)
self.assertEqual(str(priority_queue), "{2: 2.5}", "Wrong str implementation")
def test_contains(self):
priority_queue = PriorityQueue()
self.assertFalse(priority_queue.contains_priority(5), "Contains fails with empty queue")
with self.assertRaises(PriorityQueueTypeError):
priority_queue.contains_priority("7")
self.assertFalse(priority_queue.contains_element("7"), "Contains_element fails")
priority_queue = PriorityQueue(int, True)
for i in range(10):
priority_queue.enqueue(i**2, i)
for j in range(10):
self.assertTrue(priority_queue.contains_priority(j), "Wrong contains implementation")
self.assertTrue(priority_queue.contains_element(j**2), "Contains_element fails")
self.assertTrue(j ** 2 in priority_queue, "Contains_element fails")
with self.assertRaises(PriorityQueueTypeError):
priority_queue.contains_element("word")
def test_enqueue(self):
priority_queue = PriorityQueue(float, False)
with self.assertRaises(PriorityQueueTypeError):
priority_queue.enqueue(5, 5)
with self.assertRaises(PriorityQueueTypeError):
priority_queue.enqueue(5.25, "5")
d = {5: 10.5, 1: 2.7, 3: 4.90, 11: 3.14}
for key in d:
priority_queue.enqueue(d[key], key)
self.assertEqual(len(priority_queue), len(d))
for priority in d:
self.assertFalse(priority_queue.get_element(priority) is None)
self.assertEqual(d[priority], priority_queue.get_element(priority))
def test_dequeue(self):
priority_queue = PriorityQueue(str, True)
with self.assertRaises(EmptyPriorityQueueError):
priority_queue.dequeue()
priority_queue.enqueue("word", 2)
priority_queue.enqueue("python", 10)
priority_queue.enqueue("another_word", 1)
self.assertEqual(priority_queue.dequeue(), "another_word", "Wrong dequeue implementation")
self.assertEqual(len(priority_queue), 2)
priority_queue = PriorityQueue(int, False)
with self.assertRaises(EmptyPriorityQueueError):
priority_queue.dequeue()
priority_queue.enqueue(15, 2)
priority_queue.enqueue(423, 10)
priority_queue.enqueue(20, 1)
self.assertEqual(priority_queue.dequeue(), 423, "Wrong dequeue implementation")
self.assertEqual(priority_queue.dequeue(), 15, "Wrong deuque implementation")
self.assertEqual(len(priority_queue), 1)
def test_iterator(self):
priority_queue = PriorityQueue()
with self.assertRaises(StopIteration):
iter(priority_queue).__next__()
for p in range(0, 41, 2):
priority_queue.enqueue(p*2, p)
list2 = [p*2 for p in range(0, 41, 2)]
for value in priority_queue:
self.assertTrue(value in list2, "Wrong iterator implementation")
self.assertEqual(value, max(list2))
list2.remove(value)
self.assertEqual(len(priority_queue), 0)
self.assertTrue(priority_queue.size == 0)
def test_peek(self):
priority_queue = PriorityQueue()
self.assertTrue(priority_queue.peek() is None, "Wrong peek implementation")
priority_queue = PriorityQueue(float, True)
self.assertTrue(priority_queue.peek() is None, "Wrong peek implementation")
priority_queue.enqueue(15.25, 2)
priority_queue.enqueue(423.56, 10)
priority_queue.enqueue(20.02, 1)
priority_queue.enqueue(33.5, 5)
self.assertEqual(priority_queue.peek(), 20.02, "Wrong dequeue implementation")
self.assertEqual(priority_queue.dequeue(), 20.02, "Wrong dequeue implementation")
self.assertEqual(priority_queue.peek(), 15.25, "Wrong deuque implementation")
self.assertEqual(len(priority_queue), 3)
def test_get_element(self):
priority_queue = PriorityQueue()
for k in range(10):
self.assertTrue(priority_queue.get_element(k) is None, "Wrong get implementation")
priority_queue.enqueue(k*3, k)
for n in range(9, -1, -1):
self.assertEqual(priority_queue.get_element(n), n*3, "Wrong peek implementation")
self.assertEqual(len(priority_queue), 10)
def test_replace_priority(self):
priority_queue = PriorityQueue(int, True)
with self.assertRaises(PriorityQueueTypeError):
priority_queue.replace_priority("element", 10.5)
with self.assertRaises(PriorityQueueElementError):
priority_queue.replace_priority(10, 5)
with self.assertRaises(PriorityQueueTypeError):
priority_queue.replace_priority(5, 5.5)
priority_queue.enqueue(0, 10)
priority_queue.enqueue(1, 1)
priority_queue.enqueue(2, 2)
priority_queue.enqueue(3, 5)
self.assertFalse(priority_queue.replace_priority(1, 0, comparison=1), "Wrong replace_priority implementation")
self.assertEqual(priority_queue.get_element(0), None, "Wrong comparison replacement")
self.assertEqual(priority_queue.get_element(1), 1, "Wrong comparison replacement")
with self.assertRaises(ValueError):
priority_queue.replace_priority(3, 5, "comparison")
self.assertEqual(priority_queue.peek(), 1)
self.assertEqual(priority_queue.get_element(0), None)
self.assertEqual(priority_queue.get_element(10), 0)
self.assertTrue(priority_queue.replace_priority(0, 0, comparison=-1), "Wrong replace_priority implementation")
self.assertEqual(priority_queue.get_element(10), None, "Wrong replace_priority implementation")
self.assertEqual(priority_queue.get_element(0), 0, "Wrong replace_priority implementation")
self.assertEqual(priority_queue.peek(), 0, "Wrong replace_priority implementation")
self.assertTrue(priority_queue.replace_priority(3, -1, comparison=-1), "Wrong replace_priority implementation")
self.assertFalse(priority_queue.replace_priority(3, -5, comparison=1), "Wrong replace_priority implementation")
self.assertEqual(priority_queue.get_element(5), None, "Wrong replace_priority implementation")
self.assertEqual(priority_queue.get_element(-1), 3, "Wrong replace_priority implementation")
self.assertEqual(priority_queue.peek(), 3, "Wrong replace_priority implementation")
self.assertEqual(priority_queue.get_element(20), None)
self.assertTrue(priority_queue.replace_priority(2, 20), "Wrong replace_priority implementation")
self.assertEqual(priority_queue.get_element(20), 2)
priority_queue = PriorityQueue()
priority_queue.enqueue("str", 5)
priority_queue.enqueue(5.5, 0)
self.assertTrue(priority_queue.replace_priority("str", 10), "Wrong replace_priority implementation")
self.assertEqual(priority_queue.get_element(5), None)
self.assertEqual(priority_queue.get_element(10), "str")
self.assertEqual(priority_queue.peek(), "str")
self.assertTrue(priority_queue.replace_priority(5.5, 10, comparison=1), "Wrong replace_priority implementation")
self.assertEqual(len(priority_queue), 1, "Wrong replace_priority implementation")
self.assertEqual(priority_queue.get_element(10), 5.5, "Wrong replace_priority implementation")
priority_queue = PriorityQueue(reverse=True)
for i in range(1, 6):
priority_queue.enqueue(i, i*10)
self.assertEqual(len(priority_queue), 5)
self.assertTrue(priority_queue.replace_priority(2, 10), "Wrong replace_priority implementation")
self.assertEqual(len(priority_queue), 4, "Wrong replace_priority implementation")
self.assertEqual(priority_queue.dequeue(), 2, "Wrong replace_priority implementation")
def test_remove(self):
priority_queue = PriorityQueue()
with self.assertRaises(PriorityQueueElementError):
priority_queue.remove_element(5)
priority_queue.enqueue("word", 1)
priority_queue.enqueue(1, 5)
priority_queue.enqueue(3.14, 10)
priority_queue.remove_element(3.14)
self.assertFalse(priority_queue.contains_element(3.14), "Wrong remove implementation")
self.assertEqual(priority_queue.peek(), 1, "Wrong remove implementation")
with self.assertRaises(PriorityQueueElementError):
priority_queue.remove_element(3.14)
self.assertEqual(priority_queue.size, 2, "Wrong remove implementation")
priority_queue = PriorityQueue(int, reverse=True)
with self.assertRaises(PriorityQueueTypeError):
priority_queue.remove_element(5.5)
for i in range(10):
priority_queue.enqueue(i, i**2)
self.assertEqual(len(priority_queue), 10)
self.assertTrue(priority_queue.contains_element(5))
priority_queue.remove_element(5)
self.assertEqual(len(priority_queue), 9, "Wrong remove implementation")
self.assertFalse(priority_queue.contains_element(5), "Wrong remove implementation")
priority_queue.remove_element(0)
self.assertEqual(priority_queue.dequeue(), 1, "Wrong remove implementation")
if __name__ == "__main__":
unittest.main()
|
python
|
import importlib
from pathlib import Path
from airflow.hooks.base import BaseHook
from astro.databases.base import BaseDatabase
from astro.utils.path import get_class_name, get_dict_with_module_names_to_dot_notations
DEFAULT_CONN_TYPE_TO_MODULE_PATH = get_dict_with_module_names_to_dot_notations(
Path(__file__)
)
CUSTOM_CONN_TYPE_TO_MODULE_PATH = {
"gcpbigquery": DEFAULT_CONN_TYPE_TO_MODULE_PATH["bigquery"],
"google_cloud_platform": DEFAULT_CONN_TYPE_TO_MODULE_PATH["bigquery"],
}
CONN_TYPE_TO_MODULE_PATH = {
**DEFAULT_CONN_TYPE_TO_MODULE_PATH,
**CUSTOM_CONN_TYPE_TO_MODULE_PATH,
}
SUPPORTED_DATABASES = set(DEFAULT_CONN_TYPE_TO_MODULE_PATH.keys())
def create_database(conn_id: str) -> BaseDatabase:
"""
Given a conn_id, return the associated Database class.
:param conn_id: Database connection ID in Airflow
"""
conn_type = BaseHook.get_connection(conn_id).conn_type
module_path = CONN_TYPE_TO_MODULE_PATH[conn_type]
module = importlib.import_module(module_path)
class_name = get_class_name(module_ref=module, suffix="Database")
database: BaseDatabase = getattr(module, class_name)(conn_id)
return database
|
python
|
from PySide2 import QtWidgets
import maya.cmds as cmds
class MyWindow(QtWidgets.QDialog):
def __init__(self, parent=None):
super(MyWindow, self).__init__(parent)
lay = QtWidgets.QHBoxLayout(self)
self._line = QtWidgets.QLineEdit('', self)
self._line.setPlaceholderText('Enter Object Name')
lay.addWidget(self._line,100)
btn = QtWidgets.QPushButton('<')
btn.clicked.connect( self.onClicked )
lay.addWidget(btn,1)
def onClicked(self):
selection = cmds.ls(sl=True)
if selection:
self._line.setText(selection[0])
win = MyWindow()
win.show()
|
python
|
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import os
import tempfile
from functools import partial, reduce
import threading
from absl.testing import parameterized
import numpy as np
from tensorflow.compiler.plugin.poplar.tests import test_utils as tu
from tensorflow.python.client import session as sl
from tensorflow.python.framework import test_util
from tensorflow.python.framework import versions
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.ops import metrics
from tensorflow.python.ops import nn, nn_ops
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.platform import googletest
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops, dtypes
from tensorflow.compiler.plugin.poplar.ops import gen_application_runtime
from tensorflow.compiler.plugin.poplar.tests import test_utils as tu
from tensorflow.python.ipu import ipu_compiler, scopes, loops, ipu_infeed_queue, ipu_outfeed_queue
from tensorflow.python.ipu import dataset_benchmark
from tensorflow.python.ipu import rand_ops
from tensorflow.python.ipu import config
from tensorflow.python.ipu.ops import application_compile_op
from tensorflow.compat.v1 import disable_v2_behavior
from tensorflow.python.training import momentum
from tensorflow.python.keras.datasets import mnist
from tensorflow.compat.v1 import train
from tensorflow.python.ipu import embedded_runtime
from tensorflow.python.ipu import pipelining_ops
ops.disable_eager_execution()
disable_v2_behavior()
L1_SIZE = 320
L2_SIZE = 72
L3_SIZE = 10
NUM_PIXELS = 784
BATCH_SIZE = 16
NUM_ITERATIONS = 8
NUM_ENGINE_ITERATIONS = 4
NUM_ENGINES = 2
NUM_THREADS_PER_ENGINE = 4
NUM_SESSIONS = 4
NUM_TEST_ITERATIONS = NUM_ENGINES * NUM_SESSIONS * NUM_THREADS_PER_ENGINE \
* NUM_ENGINE_ITERATIONS * NUM_ITERATIONS
NUM_OUTER_ITERATIONS = NUM_ENGINES * NUM_SESSIONS * NUM_THREADS_PER_ENGINE \
* NUM_ENGINE_ITERATIONS
def dense_layer(hiddenSize, input_, scope_name):
with variable_scope.variable_scope(scope_name,
reuse=variable_scope.AUTO_REUSE,
use_resource=True):
w = variable_scope.get_variable(
"weight",
shape=[input_.shape[-1], hiddenSize],
initializer=init_ops.glorot_uniform_initializer())
b = variable_scope.get_variable(
"bias",
shape=[hiddenSize],
initializer=init_ops.glorot_uniform_initializer())
return nn.relu_layer(input_, w, b)
def test_model(outqueue, inputs):
relu1 = dense_layer(L1_SIZE, inputs, "d1")
relu2 = dense_layer(L2_SIZE, relu1, "d2")
relu3 = dense_layer(L3_SIZE, relu2, "d3")
return outqueue.enqueue({'predictions': relu3})
def test_model_pipelined(infeed_queue, outfeed_queue):
pipeline_depth = 2
def stage1(images):
relu1 = dense_layer(L1_SIZE, images, "d1")
return relu1
def stage2(relu1):
relu2 = dense_layer(L2_SIZE, relu1, "d2")
relu3 = dense_layer(L3_SIZE, relu2, "d3")
return relu3
return pipelining_ops.pipeline(
[stage1, stage2],
gradient_accumulation_count=pipeline_depth,
repeat_count=NUM_ITERATIONS / pipeline_depth,
infeed_queue=infeed_queue,
outfeed_queue=outfeed_queue,
pipeline_schedule=pipelining_ops.PipelineSchedule.Interleaved)
def loop_builder(iterations, builder_func, infeed):
return loops.repeat(iterations, builder_func, [], infeed)
def run_and_export_model(tmp_dir,
poplar_exec_output_path,
pipelined,
freeze_variables=True,
images=None):
n_test = BATCH_SIZE * NUM_TEST_ITERATIONS
if images is None:
images = np.random.rand(n_test, NUM_PIXELS).astype(np.float32)
test_dataset = dataset_ops.Dataset.from_tensor_slices((images,))
test_dataset = test_dataset.cache().repeat().batch(BATCH_SIZE,
drop_remainder=True)
infeed_test_queue = ipu_infeed_queue.IPUInfeedQueue(test_dataset)
outfeed_test_queue = ipu_outfeed_queue.IPUOutfeedQueue()
if pipelined:
bound_test_loop = partial(test_model_pipelined, infeed_test_queue,
outfeed_test_queue)
else:
bound_test_model = partial(test_model, outfeed_test_queue)
bound_test_loop = partial(loop_builder, NUM_ITERATIONS, bound_test_model,
infeed_test_queue)
# Use the bound builder functions to place the model on the IPU:
with scopes.ipu_scope("/device:IPU:0"):
test_loop = ipu_compiler.compile(bound_test_loop)
# Initialisers should go on the CPU:
with ops.device("cpu"):
saver = train.Saver()
# Setup and acquire an IPU device:
cfg = config.IPUConfig()
cfg.auto_select_ipus = 2 if pipelined else 1
tu.add_hw_ci_connection_options(cfg)
cfg.configure_ipu_system()
# These allow us to retrieve the results of IPU feeds:
dequeue_test_outfeed = outfeed_test_queue.dequeue()
# Run the model:
with sl.Session() as sess:
sess.run(variables.global_variables_initializer())
model_save_path = f'{tmp_dir}/model'
saver.save(sess, model_save_path)
print(f" Testing...")
output = np.empty(
[NUM_OUTER_ITERATIONS, NUM_ITERATIONS, BATCH_SIZE, L3_SIZE],
dtype='float32')
sess.run(infeed_test_queue.initializer)
for ei in range(NUM_OUTER_ITERATIONS):
sess.run(test_loop)
result = sess.run(dequeue_test_outfeed,)
if pipelined:
output[ei, :, :, :] = result[0] if versions.VERSION.startswith(
'1') else result
else:
output[ei, :, :, :] = result['predictions']
d1_bias = train.load_variable(model_save_path, 'd1/bias')
d1_weight = train.load_variable(model_save_path, 'd1/weight')
d2_bias = train.load_variable(model_save_path, 'd2/bias')
d2_weight = train.load_variable(model_save_path, 'd2/weight')
d3_bias = train.load_variable(model_save_path, 'd3/bias')
d3_weight = train.load_variable(model_save_path, 'd3/weight')
model_ref = dict(d1_bias=d1_bias,
d1_weight=d1_weight,
d2_bias=d2_bias,
d2_weight=d2_weight,
d3_bias=d3_bias,
d3_weight=d3_weight,
images=images,
output=output)
# Use a new graph and session for the compilation.
with ops.Graph().as_default(), sl.Session() as sess:
compile_op = application_compile_op.experimental_application_compile_op(
bound_test_loop,
output_path=poplar_exec_output_path,
freeze_variables=freeze_variables)
# Load the weights into the new session.
train.Saver().restore(sess, model_save_path)
print(f" Compiling and exporting...")
sess.run(compile_op)
config.reset_ipu_configuration()
return model_ref
def _build_executable(tmp_dir_obj,
pipelined=False,
freeze_variables=True,
poplar_exec_filepath=None,
images=None):
tmp_dir = tmp_dir_obj.name
if poplar_exec_filepath is None:
poplar_exec_filepath = os.path.join(tmp_dir, "application.poplar_exec")
model_ref = run_and_export_model(tmp_dir,
poplar_exec_filepath,
pipelined,
freeze_variables=freeze_variables,
images=images)
return (model_ref, poplar_exec_filepath)
TESTCASES = [{
'testcase_name':
(f'_pipelined_{pipelined}_multiple_sessions_{multiple_sessions}_' +
f'multiple_threads_{multiple_threads}_' +
f'multiple_engines_{multiple_engines}'),
'pipelined':
pipelined,
'multiple_sessions':
multiple_sessions,
'multiple_threads':
multiple_threads,
'multiple_engines':
multiple_engines,
} for pipelined in [False, True] for multiple_sessions in [False, True]
for multiple_threads in [False, True]
for multiple_engines in [False, True]]
class ApplicationRuntimeTest(test_util.TensorFlowTestCase,
parameterized.TestCase):
reference_cache_initiazed = False
initializer_lock = threading.Lock()
@staticmethod
def __init_reference_data():
with ApplicationRuntimeTest.initializer_lock:
if not ApplicationRuntimeTest.reference_cache_initiazed:
ApplicationRuntimeTest.tmp_dir_obj = tempfile.TemporaryDirectory()
tmp_dir_obj = ApplicationRuntimeTest.tmp_dir_obj
tmp_dir = tmp_dir_obj.name
ApplicationRuntimeTest.non_pipelined_poplar_exec_filepath = \
os.path.join(tmp_dir,
"non_pipelined_application.poplar_exec")
ApplicationRuntimeTest.pipelined_poplar_exec_filepath = \
os.path.join(tmp_dir,
"pipelined_application.poplar_exec")
n_test = NUM_TEST_ITERATIONS * BATCH_SIZE
ApplicationRuntimeTest.images = np.random.rand(
n_test, NUM_PIXELS).astype(np.float32)
images = ApplicationRuntimeTest.images
ApplicationRuntimeTest.non_pipelined_model_ref, _ = _build_executable(
tmp_dir_obj,
pipelined=False,
freeze_variables=True,
poplar_exec_filepath=ApplicationRuntimeTest.
non_pipelined_poplar_exec_filepath,
images=images)
ApplicationRuntimeTest.pipelined_model_ref, _ = _build_executable(
tmp_dir_obj,
pipelined=True,
freeze_variables=True,
poplar_exec_filepath=ApplicationRuntimeTest.
pipelined_poplar_exec_filepath,
images=images)
ApplicationRuntimeTest.reference_cache_initiazed = True
@parameterized.named_parameters(*TESTCASES)
@tu.test_uses_ipus(num_ipus=2)
@test_util.deprecated_graph_mode_only
def test(self, pipelined, multiple_sessions, multiple_threads,
multiple_engines):
ApplicationRuntimeTest.__init_reference_data()
if (multiple_sessions or multiple_engines
or pipelined) and not multiple_threads:
return
if multiple_engines and not multiple_sessions:
return
if pipelined:
poplar_exec_filepath = \
ApplicationRuntimeTest.pipelined_poplar_exec_filepath
ref_output = ApplicationRuntimeTest.pipelined_model_ref['output']
else:
poplar_exec_filepath = \
ApplicationRuntimeTest.non_pipelined_poplar_exec_filepath
ref_output = ApplicationRuntimeTest.non_pipelined_model_ref['output']
images = ApplicationRuntimeTest.images
num_engines = NUM_ENGINES if multiple_engines else 1
num_threads_per_engine = NUM_THREADS_PER_ENGINE if multiple_threads else 1
num_threads = num_engines * num_threads_per_engine
images_ph = array_ops.placeholder(dtypes.float32,
shape=[BATCH_SIZE, NUM_PIXELS],
name='images')
test_shape = (num_threads, NUM_ENGINE_ITERATIONS, NUM_ITERATIONS)
n_test = reduce(lambda p, x: p * x, test_shape)
images_local = images.reshape((-1, BATCH_SIZE, NUM_PIXELS))
images_local = images_local[0:n_test, :, :]
images_local = images_local.reshape(*(test_shape +
(BATCH_SIZE, NUM_PIXELS)))
ref_output = ref_output.reshape((-1, BATCH_SIZE, L3_SIZE))
ref_output = ref_output[0:n_test, :, :]
ref_output = ref_output.reshape(*(test_shape + (BATCH_SIZE, L3_SIZE)))
output = np.empty(ref_output.shape, dtype='float32')
engine_name_prefix = f'engine_pipelined_{pipelined}'
def run_loops(sess, result, infeeds, t):
for ei in range(NUM_ENGINE_ITERATIONS):
for li in range(NUM_ITERATIONS):
images_host = images_local[t, ei, li, :, :]
results = sess.run(
result,
feed_dict={
infeeds: (images_host,), # pylint: disable=cell-var-from-loop
})
output[t, ei, li, :, :] = results[0]
def inference_thread(sess, res, infeeds_, t):
if multiple_engines:
engine_name = f'{engine_name_prefix}_{t % NUM_ENGINES}'
else:
engine_name = engine_name_prefix
if multiple_sessions:
with sl.Session() as session:
run_app = gen_application_runtime.application_runtime(
inputs=(),
filename=poplar_exec_filepath,
engine_name=engine_name)
images_ph = array_ops.placeholder(dtypes.float32,
shape=[BATCH_SIZE, NUM_PIXELS],
name='images')
infeeds = (images_ph,)
result = gen_application_runtime.application_call(
infeeds,
anchor=run_app,
outfeed_types=[dtypes.float32],
engine_name=engine_name)
session.graph.finalize()
run_loops(session, result, infeeds, t)
else:
with sess.graph.as_default():
with sess.as_default():
if multiple_engines:
run_app = gen_application_runtime.application_runtime(
inputs=(),
filename=poplar_exec_filepath,
engine_name=engine_name)
images_ph = array_ops.placeholder(dtypes.float32,
shape=[BATCH_SIZE, NUM_PIXELS],
name=f'images')
infeeds = (images_ph,)
result = gen_application_runtime.application_call(
infeeds,
anchor=run_app,
outfeed_types=[dtypes.float32],
engine_name=engine_name)
run_loops(sess, result, infeeds, t)
else:
run_loops(sess, res, infeeds_, t)
def run_across_threads(session=None, result=None, infeeds=None):
thread_list = []
for t in range(num_threads):
if multiple_threads:
thread = threading.Thread(target=inference_thread,
args=(session, result, infeeds, t))
thread_list.append(thread)
thread.start()
else:
inference_thread(session, result, infeeds, t)
for thread in thread_list:
thread.join()
if multiple_sessions:
run_across_threads()
elif multiple_engines:
with sl.Session() as session:
run_across_threads(session, None, None)
else:
with sl.Session() as session:
run_app = gen_application_runtime.application_runtime(
inputs=(),
filename=poplar_exec_filepath,
engine_name=engine_name_prefix)
images_ph = array_ops.placeholder(dtypes.float32,
shape=[BATCH_SIZE, NUM_PIXELS],
name='images')
infeeds = (images_ph,)
result = gen_application_runtime.application_call(
infeeds,
anchor=run_app,
outfeed_types=[dtypes.float32],
engine_name=engine_name_prefix)
session.graph.finalize()
run_across_threads(session, result, infeeds)
self.assertAllClose(ref_output, output)
class EmbeddedRuntimeTest(test_util.TensorFlowTestCase,
parameterized.TestCase):
@tu.test_uses_ipus(num_ipus=1)
@test_util.deprecated_graph_mode_only
def test_embedded_runtime_wrapper(self):
tmp_dir_obj = tempfile.TemporaryDirectory()
model_ref, poplar_exec_filepath = _build_executable(tmp_dir_obj,
pipelined=False,
freeze_variables=False)
input_descs = [
('XLA_Args/d1/weight', [NUM_PIXELS, L1_SIZE], dtypes.float32, 0),
('XLA_Args/d1/bias', [L1_SIZE], dtypes.float32, 1),
('XLA_Args/d2/weight', [L1_SIZE, L2_SIZE], dtypes.float32, 2),
('XLA_Args/d2/bias', [L2_SIZE], dtypes.float32, 3),
('XLA_Args/d3/weight', [L2_SIZE, L3_SIZE], dtypes.float32, 4),
('XLA_Args/d3/bias', [L3_SIZE], dtypes.float32, 5),
]
inputs = {
'XLA_Args/d1/weight': model_ref['d1_weight'],
'XLA_Args/d1/bias': model_ref['d1_bias'],
'XLA_Args/d2/weight': model_ref['d2_weight'],
'XLA_Args/d2/bias': model_ref['d2_bias'],
'XLA_Args/d3/weight': model_ref['d3_weight'],
'XLA_Args/d3/bias': model_ref['d3_bias'],
}
input_placeholders = []
input_list = [None] * len(input_descs)
for name, shape, dtype, order in input_descs:
input_ph = array_ops.placeholder(dtype, shape=shape, name=name)
input_placeholders.append(input_ph)
input_list[order] = inputs[name]
input_tuple = tuple(input_list)
input_placeholders = tuple(input_placeholders)
n_test = NUM_TEST_ITERATIONS
images = array_ops.placeholder(dtypes.float32,
shape=[BATCH_SIZE, NUM_PIXELS],
name='images')
images_all = model_ref['images'].reshape((-1, BATCH_SIZE, NUM_PIXELS))
images_all = images_all[0:n_test, :, :]
labels_all = np.ones([n_test, BATCH_SIZE, L3_SIZE], dtype='float32')
labels_ref = model_ref['output'].reshape((-1, BATCH_SIZE, L3_SIZE))
labels_ref = labels_ref[0:n_test, :, :]
with sl.Session() as session:
engine_name = f'engine_{self.id()}'
ctx = embedded_runtime.embedded_runtime_start(poplar_exec_filepath,
inputs, engine_name)
infeeds = (images,)
result = embedded_runtime.embedded_runtime_call(infeeds, ctx)
for j in range(n_test):
images_host = images_all[j, :, :]
results = session.run(result,
feed_dict={
infeeds: (images_host,),
input_placeholders: input_tuple,
})
labels_all[j, :, :] = results[0]
self.assertAllClose(labels_ref, labels_all)
@tu.test_uses_ipus(num_ipus=1)
@test_util.deprecated_graph_mode_only
def test_embedded_runtime_input_error(self):
tmp_dir_obj = tempfile.TemporaryDirectory()
model_ref, poplar_exec_filepath = _build_executable(tmp_dir_obj,
pipelined=False,
freeze_variables=False)
inputs = {
'XLA_Args/d1/bias': model_ref['d1_bias'],
'XLA_Args/d1/weight': model_ref['d1_weight'],
'XLA_Args/d2/bias': model_ref['d2_bias'],
'XLA_Args/d3/bias': model_ref['d3_bias'],
'XLA_Args/d3/weight': model_ref['d3_weight'],
}
with sl.Session():
engine_name = f'engine_{self.id()}'
with self.assertRaisesRegex(
Exception,
"Failed to find input tensor with name 'XLA_Args/d2/weight' in "
"input dictionary."):
embedded_runtime.embedded_runtime_start(poplar_exec_filepath, inputs,
engine_name)
@tu.test_uses_ipus(num_ipus=1)
@test_util.deprecated_graph_mode_only
def test_embedded_runtime_no_list(self):
tmp_dir_obj = tempfile.TemporaryDirectory()
_, poplar_exec_filepath = _build_executable(tmp_dir_obj,
freeze_variables=False)
with sl.Session():
engine_name = f'engine_{self.id()}'
with self.assertRaisesRegex(Exception,
"Expected the inputs to be a list."):
embedded_runtime.embedded_runtime_start(poplar_exec_filepath, 4,
engine_name)
@tu.test_uses_ipus(num_ipus=1)
@test_util.deprecated_graph_mode_only
def test_embedded_runtime_too_many_inputs(self):
tmp_dir_obj = tempfile.TemporaryDirectory()
mnist_ref, poplar_exec_filepath = _build_executable(tmp_dir_obj,
freeze_variables=False)
inputs = [
mnist_ref['d1_bias'], mnist_ref['d1_weight'], mnist_ref['d2_bias'],
mnist_ref['d2_weight'], mnist_ref['d3_bias'], mnist_ref['d1_weight'],
mnist_ref['d1_weight']
]
with sl.Session():
engine_name = f'engine_{self.id()}'
with self.assertRaisesRegex(
Exception,
"Embedded application runtime expects 6 inputs, but 7 were "
"provided."):
embedded_runtime.embedded_runtime_start(poplar_exec_filepath, inputs,
engine_name)
@tu.test_uses_ipus(num_ipus=1)
@test_util.deprecated_graph_mode_only
def test_embedded_runtime_too_few_inputs(self):
tmp_dir_obj = tempfile.TemporaryDirectory()
mnist_ref, poplar_exec_filepath = _build_executable(tmp_dir_obj,
freeze_variables=False)
inputs = [
mnist_ref['d1_bias'], mnist_ref['d1_weight'], mnist_ref['d2_bias']
]
with sl.Session():
engine_name = f'engine_{self.id()}'
with self.assertRaisesRegex(
Exception,
"Embedded application runtime expects 6 inputs, but 3 were "
"provided."):
embedded_runtime.embedded_runtime_start(poplar_exec_filepath, inputs,
engine_name)
@tu.test_uses_ipus(num_ipus=1)
@test_util.deprecated_graph_mode_only
def test_embedded_runtime_wrong_shape(self):
tmp_dir_obj = tempfile.TemporaryDirectory()
model_ref, poplar_exec_filepath = _build_executable(tmp_dir_obj,
freeze_variables=False)
inputs = [
model_ref['d1_weight'], model_ref['d1_bias'], model_ref['d2_bias'],
model_ref['d1_weight'], model_ref['d3_bias'], model_ref['d3_weight']
]
with sl.Session():
engine_name = f'engine_{self.id()}'
with self.assertRaisesRegex(
Exception,
"Mismatched input shape at position 0 \\('XLA_Args/d1/bias'\\). "
"Expected \\[320\\], but input 0 has shape \\[784, 320\\]."):
embedded_runtime.embedded_runtime_start(poplar_exec_filepath, inputs,
engine_name)
@tu.test_uses_ipus(num_ipus=1)
@test_util.deprecated_graph_mode_only
def test_embedded_runtime_wrong_type(self):
tmp_dir_obj = tempfile.TemporaryDirectory()
mnist_ref, poplar_exec_filepath = _build_executable(tmp_dir_obj,
freeze_variables=False)
inputs = [
np.ones((320), dtype=np.int32),
mnist_ref['d1_weight'],
mnist_ref['d2_bias'],
mnist_ref['d2_weight'],
mnist_ref['d3_bias'],
mnist_ref['d3_weight'],
]
with sl.Session():
engine_name = f'engine_{self.id()}'
with self.assertRaisesRegex(
Exception,
"Mismatched input dtype at position 0 \\('XLA_Args/d1/bias'\\). "
"Expected <dtype: 'float32'>, but input 0 has dtype int32."):
embedded_runtime.embedded_runtime_start(poplar_exec_filepath, inputs,
engine_name)
@tu.test_uses_ipus(num_ipus=2)
@test_util.deprecated_graph_mode_only
def test_pipeline_flush(self):
dataset = tu.create_single_increasing_dataset(5, shape=[2])
dataset = dataset.batch(batch_size=2, drop_remainder=True)
infeed_queue = ipu_infeed_queue.IPUInfeedQueue(dataset)
outfeed_queue = ipu_outfeed_queue.IPUOutfeedQueue()
def stage1(x):
return x @ constant_op.constant(1.0, shape=[2, 2])
def stage2(x):
return math_ops.reduce_sum(x)
def my_net():
return pipelining_ops.pipeline([stage1, stage2],
12,
infeed_queue=infeed_queue,
outfeed_queue=outfeed_queue)
with tu.ipu_session() as sess:
cfg = config.IPUConfig()
cfg.auto_select_ipus = 2
tu.add_hw_ci_connection_options(cfg)
cfg.configure_ipu_system()
with tempfile.TemporaryDirectory() as tmp_dir:
poplar_exec_filepath = os.path.join(tmp_dir, "application.poplar_exec")
compile_op = application_compile_op.experimental_application_compile_op(
my_net, output_path=poplar_exec_filepath)
sess.run(compile_op)
config.reset_ipu_configuration()
ctx = embedded_runtime.embedded_runtime_start(poplar_exec_filepath, [],
"pipeline_flush")
input_data = array_ops.placeholder(np.float32, shape=[2, 2])
result = embedded_runtime.embedded_runtime_call([input_data], ctx)
outputs1 = sess.run(
result,
feed_dict={input_data: np.full([2, 2], 1.0, dtype=np.float32)})
self.assertAllClose(outputs1[0], 8.)
outputs2 = sess.run(
result,
feed_dict={input_data: np.full([2, 2], 2.0, dtype=np.float32)})
self.assertAllClose(outputs2[0], 16.)
@tu.test_uses_ipus(num_ipus=1)
@test_util.deprecated_graph_mode_only
def test_io_overlap_flush(self):
dataset = tu.create_single_increasing_dataset(5, shape=[2])
dataset = dataset.batch(batch_size=2, drop_remainder=True)
infeed_queue = ipu_infeed_queue.IPUInfeedQueue(dataset)
outfeed_queue = ipu_outfeed_queue.IPUOutfeedQueue()
def body(x):
x = x @ constant_op.constant(1.0, shape=[2, 2])
x = math_ops.reduce_sum(x)
return outfeed_queue.enqueue(x)
def my_net():
return loops.repeat(10, body, [], infeed_queue)
with tu.ipu_session() as sess:
cfg = config.IPUConfig()
cfg.auto_select_ipus = 1
cfg.io_tiles.num_io_tiles = 32
cfg.io_tiles.place_ops_on_io_tiles = True
tu.add_hw_ci_connection_options(cfg)
cfg.configure_ipu_system()
with tempfile.TemporaryDirectory() as tmp_dir:
poplar_exec_filepath = os.path.join(tmp_dir, "application.poplar_exec")
compile_op = application_compile_op.experimental_application_compile_op(
my_net, output_path=poplar_exec_filepath)
sess.run(compile_op)
config.reset_ipu_configuration()
ctx = embedded_runtime.embedded_runtime_start(poplar_exec_filepath, [],
"io_overlap_flush")
input_data = array_ops.placeholder(np.float32, shape=[2, 2])
result = embedded_runtime.embedded_runtime_call([input_data], ctx)
outputs1 = sess.run(
result,
feed_dict={input_data: np.full([2, 2], 1.0, dtype=np.float32)})
self.assertAllClose(outputs1[0], 8.)
outputs2 = sess.run(
result,
feed_dict={input_data: np.full([2, 2], 2.0, dtype=np.float32)})
self.assertAllClose(outputs2[0], 16.)
@tu.test_uses_ipus(num_ipus=1)
@test_util.deprecated_graph_mode_only
def test_multiple_infeeds(self):
a = np.array([2, 3], dtype='float32')
b = np.array([4, 5], dtype='float32')
c = np.array([6, 7], dtype='float32')
ds = dataset_ops.Dataset.from_tensor_slices((a, b, c))
ds = ds.cache().repeat().batch(2, drop_remainder=True)
infeed_queue = ipu_infeed_queue.IPUInfeedQueue(ds)
outfeed_queue = ipu_outfeed_queue.IPUOutfeedQueue()
def my_net(outq, a, b, c):
return outq.enqueue({'result': a * b + c})
model = partial(my_net, outfeed_queue)
model = partial(loop_builder, 1, model, infeed_queue)
a_ph = array_ops.placeholder(dtypes.float32, shape=(2), name='a')
b_ph = array_ops.placeholder(dtypes.float32, shape=(2), name='b')
c_ph = array_ops.placeholder(dtypes.float32, shape=(2), name='c')
with tu.ipu_session() as sess, tempfile.TemporaryDirectory() as tmp_dir:
cfg = config.IPUConfig()
cfg.auto_select_ipus = 1
tu.add_hw_ci_connection_options(cfg)
cfg.configure_ipu_system()
poplar_exec_filepath = os.path.join(
tmp_dir, f'application_{self.id()}.poplar_exec')
compile_op = application_compile_op.experimental_application_compile_op(
model, output_path=poplar_exec_filepath)
sess.run(compile_op)
config.reset_ipu_configuration()
ctx = embedded_runtime.embedded_runtime_start(poplar_exec_filepath, [],
"multiple_infeeds")
result = embedded_runtime.embedded_runtime_call([a_ph, b_ph, c_ph], ctx)
outputs = sess.run(result, feed_dict={a_ph: a, b_ph: b, c_ph: c})
self.assertAllClose(outputs[0], [14, 22])
@tu.test_uses_ipus(num_ipus=1)
@test_util.deprecated_graph_mode_only
def test_embedded_runtime_exception(self):
# The dataset for feeding the graphs.
ds = dataset_ops.Dataset.from_tensors(constant_op.constant(1.0, shape=[1]))
ds = ds.repeat()
# The host side queues.
infeed_queue = ipu_infeed_queue.IPUInfeedQueue(ds)
outfeed_queue = ipu_outfeed_queue.IPUOutfeedQueue()
def body(x):
return outfeed_queue.enqueue(12.0 / x)
# Wrap in a loop.
def my_net():
r = loops.repeat(16, body, [], infeed_queue)
return r
def exception_executable(tmp_dir):
poplar_exec_filepath = os.path.join(tmp_dir.name,
"application.poplar_exec")
cfg = config.IPUConfig()
cfg.auto_select_ipus = 1
cfg.floating_point_behaviour.div0 = True
tu.add_hw_ci_connection_options(cfg)
cfg.configure_ipu_system()
# Compile the application.
compile_op = application_compile_op.experimental_application_compile_op(
my_net, output_path=poplar_exec_filepath)
with sl.Session() as sess:
sess.run(compile_op)
config.reset_ipu_configuration()
return poplar_exec_filepath
tmp_dir_obj = tempfile.TemporaryDirectory()
poplar_exec_filepath = exception_executable(tmp_dir_obj)
engine_name = f'engine_{self.id()}'
ctx = embedded_runtime.embedded_runtime_start(poplar_exec_filepath, [],
engine_name)
result = embedded_runtime.embedded_runtime_call(
[np.zeros((1), dtype=np.float32)], ctx)
with sl.Session() as sess:
with self.assertRaisesRegex(
errors.InternalError,
r"\[Poplar\]\[Execute engine\] application_runtime_error: \[Recovery "
r"action: IPU_RESET\] Tiles in excepted state [\s\S]* IPU will be "
r"reset the next time a program is executed."):
sess.run(result)
@tu.test_uses_ipus(num_ipus=1)
@test_util.deprecated_graph_mode_only
def test_reset_engine(self):
# The dataset for feeding the graphs.
ds = dataset_ops.Dataset.from_tensors(constant_op.constant(1.0, shape=[1]))
ds = ds.repeat()
# The host side queues.
infeed_queue = ipu_infeed_queue.IPUInfeedQueue(ds)
outfeed_queue = ipu_outfeed_queue.IPUOutfeedQueue()
def body(x):
return outfeed_queue.enqueue(12.0 / x)
# Wrap in a loop.
def my_net():
r = loops.repeat(16, body, [], infeed_queue)
return r
def exception_executable(tmp_dir):
poplar_exec_filepath = os.path.join(tmp_dir.name,
"application.poplar_exec")
cfg = config.IPUConfig()
cfg.auto_select_ipus = 1
cfg.floating_point_behaviour.div0 = True
tu.add_hw_ci_connection_options(cfg)
cfg.configure_ipu_system()
# Compile the application.
compile_op = application_compile_op.experimental_application_compile_op(
my_net, output_path=poplar_exec_filepath)
with sl.Session() as sess:
sess.run(compile_op)
config.reset_ipu_configuration()
return poplar_exec_filepath
tmp_dir_obj = tempfile.TemporaryDirectory()
poplar_exec_filepath = exception_executable(tmp_dir_obj)
engine_name = f'engine_{self.id()}'
ctx = embedded_runtime.embedded_runtime_start(poplar_exec_filepath, [],
engine_name)
input_data = array_ops.placeholder(np.float32, shape=[1])
result = embedded_runtime.embedded_runtime_call([input_data], ctx)
with sl.Session() as sess:
# Second to last execution will fail, the last execution should pass.
inputs = [1.0] * 3 + [0.0, 1.0]
failures = [False] * 3 + [True, False]
for val, should_fail in zip(inputs, failures):
failed = False
try:
x = sess.run(result, {input_data: [val]})
self.assertEqual(x, [12.0])
except: # pylint: disable=bare-except
failed = True
self.assertEqual(failed, should_fail)
if __name__ == "__main__":
googletest.main()
|
python
|
"""AVMATH
Avmath is a Python module for mathematical purposes.
It contains functionalities for simple mathematical
usage as sine or logarithm functions and submodules
for more advanced applied math in the topics of ana-
lysis and linear algebra.
The module uses a mathematical syntax. The classes
and functions are named in a mathematical manner and
the class operations enable a mathematical workflow.
Its issue is to calculate as accurate as possible and
not firstly the aspect of time.
Documentation: https://github.com/ballandt/avmath/wiki/documentation
GitHub: https://www.github.com/ballandt/avmath
PyPi: https://www.pypi.org/project/avmath
"""
__author__ = "Camillo Ballandt"
__version__ = "3.1.1"
__date__ = "2022/01/08"
__all__ = ["Fraction",
"sin", "cos", "tan",
"arcsin", "arccos", "arctan",
"sinh", "cosh", "tanh",
"arsinh", "arcosh", "artanh",
"ln", "log", "is_even", "fac", "sgn",
"pi", "e", "phi", "gamma"]
import time
from typing import Union as _Union, Iterable as _Iterable
_TAYLOR_DIFFERENCE = 1e-16
_MAX_CALCULATION_TIME = 5
REAL = _Union[int, float, 'Fraction']
e: float = 2.718_281_828_459_045_235_360
pi: float = 3.141_592_653_589_793_238_463
phi: float = 1.618_033_988_749_894_848_205
gamma: float = 0.577_215_664_901_532_860_607
two_digit_primes: list[int] = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41,
43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]
class ArgumentError(Exception):
"""Raised if false argument is given."""
def __init__(self, got, want):
self.got = got
self.want = want
def __str__(self):
return f"False argument given. Expected {self.want}, got {self.got}."
class DimensionError(Exception):
"""Raised if arguments have different amount of dimensions."""
def __init__(self, got=None, want=None, other=None):
if other is None:
self.got = got
self.want = want
else:
self.other = other
def __str__(self):
try:
return f"Wrong amount of dimensions." \
f"Expected {self.want}; got {self.got}"
except AttributeError:
return self.other
class Fraction:
"""Mathematical fraction"""
def __init__(self, numerator: REAL, denominator: REAL):
"""Initializes fraction. Denominator will be made positive.
Insert
Fraction(a, b)
for
a/b
and
Fraction(a, Fraction(b, c))
for
a/(b/c)
"""
if numerator == 0 and type(denominator) == Fraction:
self.a = 0
self.b = denominator.b
elif (type(numerator) == Fraction or type(denominator) == Fraction)\
and numerator != 0:
self.a = (numerator / denominator).a
self.b = (numerator / denominator).b
else:
self.a = numerator * sgn(denominator)
self.b = abs(denominator)
if self.b == 0:
raise ZeroDivisionError("Zero inserted as denominator.")
def __repr__(self) -> str:
"""Returns string representation. Always reduced."""
if self.b == 1:
ret_str = f"{self.a}"
elif self.a / self.b == self.a // self.b:
ret_str = f"{self.a // self.b}"
elif self.int_args():
ret_str = f"{self.reduce().a}/{self.reduce().b}"
else:
ret_str = f"{self.a}/{self.b}" if not self.b == 1 else f"{self.a}"
return ret_str
def __neg__(self) -> 'Fraction':
"""Returns negative fraction."""
return Fraction(-self.a, self.b)
def __eq__(self, other: REAL) -> bool:
"""Verifies the equality of two fractions."""
if type(other) == Fraction and self.int_args() and other.int_args():
return self.reduce().a == other.reduce().a \
and self.reduce().b == other.reduce().b
else:
return float(self) == float(other)
def __lt__(self, other: REAL) -> bool:
"""Less than."""
return float(self) < float(other)
def __gt__(self, other: REAL) -> bool:
"""Greater than."""
return float(other) < float(self)
def __add__(self, other: REAL | complex) -> 'Fraction':
"""Adds either two fractions or fractions and numbers."""
if type(other) in (int, float):
a = self.a + other * self.b
return Fraction(a, self.b)
elif type(other) == complex:
return complex(self) + other
elif type(other) == Fraction and self.int_args() and other.int_args():
reduced_self = self.reduce()
reduced_other = other.reduce()
factor = lcm(reduced_self.b, reduced_other.b)
summand1 = Fraction(reduced_self.a*int(factor/reduced_self.b),
reduced_self.b*int(factor/reduced_self.b))
summand2 = Fraction(reduced_other.a*int(factor/reduced_other.b),
reduced_other.b*int(factor/reduced_other.b))
return Fraction(summand1.a + summand2.a, summand1.b)
__radd__ = __add__
def __sub__(self, other: REAL) -> 'Fraction':
"""Subtracts either real from Fraction or Fraction from Fraction."""
return self + -other
__rsub__ = __sub__
def __mul__(self, other: REAL) -> 'Fraction':
"""Multiplies REALS."""
if type(other) in (int, float):
return Fraction(self.a * other, self.b)
if type(other) == Fraction:
if self.int_args() and other.int_args:
return Fraction(self.a * other.a, self.b * other.b).reduce()
else:
return Fraction(self.a * other.a, self.b * other.b)
__rmul__ = __mul__
def __truediv__(self, other: REAL) -> 'Fraction':
if type(other) == Fraction:
res = self * other ** -1
else:
res = Fraction(self.a, self.b * other)
if type(res) == Fraction and res.int_args():
res = res.reduce()
return res
__rtruediv__ = __truediv__
def __pow__(self, power: REAL) -> 'Fraction':
if power == -1:
return Fraction(self.b, self.a)
else:
return Fraction(self.a ** float(power), self.b ** float(power))
def __rpow__(self, other: REAL) -> float:
return (other ** self.a) ** (1 / self.b)
def __mod__(self, other: REAL) -> float:
return float(self) % float(other)
__rmod__ = __mod__
def __int__(self) -> int:
return int(float(self))
def __float__(self) -> float:
return self.a / self.b
def __complex__(self):
return float(self) + 0j
def __abs__(self) -> 'Fraction':
return Fraction(abs(self.a), self.b)
def reduce(self) -> 'Fraction':
if not self.int_args():
raise ArgumentError("float values", "integer values")
divisor = gcd(self.a, self.b)
return Fraction(int(self.a / divisor), int(self.b / divisor))
def int_args(self) -> bool:
return type(self.a) == int and type(self.b) == int
def _check_types(arg: _Iterable, *types):
"""Checks if the elements of the argument belong to the given types."""
for ele in arg:
if not type(ele) in types:
raise ArgumentError(type(ele), types)
return True
def is_even(x: int) -> bool:
"""Checks if x is an even number"""
return x/2 == x // 2
def is_prime(x: int) -> bool:
"""Checks if integer is prime."""
int_root = int(x ** 0.5)
for i in range(2, int_root):
if x / i == x // i:
return False
return True
def gcd(x: int, y: int) -> int:
"""Greatest common divisor."""
while x % y != 0:
r = x % y
x = y
y = r
return abs(y)
def lcm(x: int, y: int) -> int:
"""Least common multiply."""
return int(abs(x * y) / gcd(x, y))
def sgn(x: REAL) -> int:
"""Returns signum of x."""
if x < 0:
return -1
elif x == 0:
return 0
elif x > 0:
return 1
def fac(x: REAL, opt: str = None):
"""Returns faculty of x.
fac(x) is x!
fac(x, opt="double") is x!!
"""
if x < 0:
raise ArgumentError("x < 0", "x >= 0")
if int(x) != x:
raise ArgumentError("real x", "natural x")
x = int(x)
res = 1
if opt == "double":
if not is_even(x):
for i in range(int(x / 2) + 1):
res *= 2 * i + 1
else:
for i in range(1, int(x / 2)+1):
res *= 2*i
else:
for i in range(1, x + 1):
res *= i
return res
def ln(x: REAL) -> float:
"""Natural logarithm."""
if x <= 0:
raise ArgumentError(x, "x >= 0")
summand = 0
while x > e:
x /= e
summand += 1
while x < 1/e:
x *= e
summand -= 1
res = 0
k = 0
while True:
mem_res = res
res += (x - 1) ** (2 * k + 1) / ((2 * k + 1) * (x + 1) ** (2 * k + 1))
if abs(mem_res - res) < _TAYLOR_DIFFERENCE:
break
k += 1
return 2 * res + summand
def log(x: REAL, base: REAL) -> float:
"""Logarithm."""
return ln(x) / ln(base)
def sin(x: REAL) -> float:
"""Sine."""
x %= 2 * pi
res = 0
k = 0
while True:
mem_res = res
res += (-1) ** k * x ** (2 * k + 1) / fac(2 * k + 1)
if abs(mem_res - res) < _TAYLOR_DIFFERENCE:
return res
k += 1
def cos(x: REAL) -> float:
"""Cosine."""
x %= 2 * pi
res = 0
k = 0
while True:
mem_res = res
res += (-1) ** k * (x**(2 * k) / fac(2 * k))
if abs(mem_res - res) < _TAYLOR_DIFFERENCE:
return res
k += 1
def tan(x: REAL) -> float:
"""Tangent."""
return sin(x) / cos(x)
def arcsin(x: REAL) -> float:
"""Arc sine."""
if abs(x) > 1:
raise ArgumentError("x > 1", "x <= 1")
if abs(x) == 1:
return sgn(x) * pi / 2
res = x
k = 1
start_time = time.time()
while (time.time() - start_time) < _MAX_CALCULATION_TIME:
mem_res = res
res += fac(2 * k - 1, opt="double") /\
(fac(2 * k, opt="double") * (2 * k + 1)) * x ** (2 * k + 1)
if abs(mem_res - res) < _TAYLOR_DIFFERENCE:
break
k += 1
return res
def arccos(x: REAL) -> float:
"""Arc cosine."""
return pi/2 - arcsin(x)
def arctan(x: REAL) -> float:
"""Arc tangent."""
res = 0
k = 0
if abs(x) <= 1:
start_time = time.time()
while (time.time() - start_time) < _MAX_CALCULATION_TIME:
mem_res = res
res += (-1)**k * x**(2*k + 1) / (2*k + 1)
if abs(mem_res - res) < _TAYLOR_DIFFERENCE:
break
k += 1
else:
if x >= 0:
res = pi / 2
else:
res = -pi / 2
start_time = time.time()
while (time.time() - start_time) < _MAX_CALCULATION_TIME:
mem_res = res
res += (-1)**(k + 1) / ((2*k + 1) * x**(2*k + 1))
if abs(mem_res - res) < _TAYLOR_DIFFERENCE:
break
k += 1
return res
def sinh(x: REAL) -> float:
"""Hyperbolic sine."""
if abs(x) > 710:
raise ArgumentError(x, "argument |x| < 710")
res = 0
k = 0
while True:
mem_res = res
res += x ** (2 * k + 1) / fac(2 * k + 1)
if abs(mem_res - res) < _TAYLOR_DIFFERENCE:
return res
k += 1
def cosh(x: REAL) -> float:
"""Hyperbolic cosine."""
if abs(x) > 710:
raise ArgumentError(x, "argument |x| < 710")
res = 0
k = 0
while True:
mem_res = res
res += x**(2*k) / fac(2*k)
if abs(mem_res - res) < _TAYLOR_DIFFERENCE:
return res
k += 1
def tanh(x: REAL) -> float:
"""Hyperbolic tangent."""
return sinh(x) / cosh(x) if abs(x) <= 20 else sgn(x) * 1.0
def arsinh(x: REAL) -> float:
"""Inverse hyperbolic sine."""
return sgn(x) * ln(abs(x) + (x**2 + 1)**0.5)
def arcosh(x: REAL) -> float:
"""Inverse hyperbolic cosine."""
if x < 1:
raise ArgumentError(x, "x >= 1")
return ln(x + (x**2 - 1)**0.5)
def artanh(x: REAL) -> float:
"""Inverse hyperbolic tangent."""
if abs(x) >= 1:
raise ArgumentError(x, "|x| < 1")
return 0.5 * ln((1 + x) / (1 - x))
scope = {
"sin": sin, "arcsin": arcsin,
"cos": cos, "arccos": arccos,
"tan": tan, "arctan": arctan,
"sinh": sinh, "arsinh": arsinh,
"cosh": cosh, "arccosh": arcosh,
"tanh": tanh, "artanh": artanh,
"log": log,
"ln": ln,
"fac": fac,
"sgn": sgn,
"e": e,
"pi": pi,
}
|
python
|
"""
This script deploys all the AWS instances required for a single distributed experiment and runs the docker container
on the instances.
The experiment is one of inner_product / quadratic / kld / dnn.
The user could choose whether to run the coordinator on a strong EC2 instance, or on an ECS Fargate instance.
"""
import sys
import time
import boto3
import json
import os
import argparse
from aws_experiments.aws_ec2_coordinator import run_coordinator_on_ec2_instance
from aws_experiments.utils import read_credentials_file, num_completed_experiments
def get_service_ips(ecs_client, cluster, tasks, region):
tasks_detail = ecs_client.describe_tasks(cluster=cluster, tasks=tasks)
enis = []
for task in tasks_detail.get("tasks", []):
for attachment in task.get("attachments", []):
for detail in attachment.get("details", []):
if detail.get("name") == "networkInterfaceId":
enis.append(detail.get("value"))
ips = []
for eni in enis:
eni_resource = session.resource("ec2", region).NetworkInterface(eni)
ips.append(eni_resource.association_attribute.get("PublicIp"))
return ips
def run_task(ecs_client, automon_task, cluster_name, node_idx, host, node_type, error_bound, subnet_id, sg_id, command):
task_name = "node-" + str(node_idx) + "_" + cluster_name
if node_idx == -1:
task_name = "coordinator_" + cluster_name
response = ecs_client.list_task_definitions(familyPrefix=task_name)
for task_definition in response["taskDefinitionArns"]:
# De-register old task definitions
print("Deregister existing task definition", task_name)
_ = ecs_client.deregister_task_definition(taskDefinition=task_definition)
# Register the new task definition
automon_task["family"] = task_name
automon_task["containerDefinitions"][0]["logConfiguration"]["options"]["awslogs-stream-prefix"] = task_name
if task_name == "coordinator":
automon_task["cpu"] = "4096" # 4 vCPU
automon_task["memory"] = "16384" # 16 GB
else:
automon_task["cpu"] = "1024" # 1 vCPU
automon_task["memory"] = "4096" # 4 GB
_ = ecs_client.register_task_definition(**automon_task)
# Could also override LS_LATENCY (lazy sync latency) and FS_LATENCY (full sync latency) according to the function and network latency
response = ecs_client.run_task(
taskDefinition=task_name,
cluster=cluster_name,
launchType='FARGATE',
count=1,
networkConfiguration={
'awsvpcConfiguration': {
'subnets': [
subnet_id,
],
'securityGroups': [sg_id],
'assignPublicIp': 'ENABLED'
}
},
overrides={
'containerOverrides': [
{
'name': automon_task["containerDefinitions"][0]["name"],
'command': command.split(),
'environment': [
{
'name': 'NODE_IDX',
'value': str(node_idx)
},
{
'name': 'HOST',
'value': host
},
{
'name': 'NODE_TYPE',
'value': node_type
},
{
'name': 'ERROR_BOUND',
'value': str(error_bound)
}
],
}
]
}
)
if len(response['tasks']) == 0:
# Something went wrong
print("Error: node", node_idx, "run_task() failure reason:", response['failures'][0]['reason'])
print("Stop coordinator and nodes manually")
raise Exception
def get_default_security_group(region):
ec2_client = session.client('ec2', region_name=region)
response = ec2_client.describe_security_groups()
for sg in response['SecurityGroups']:
if sg['Description'] == 'default VPC security group':
sg_id = sg['GroupId']
# _ = ec2_client.describe_vpcs()
response = ec2_client.describe_subnets()
subnet_id = response['Subnets'][0]['SubnetId']
print('Found sg and subnet for region', region, ': sg_id:', sg_id, 'subnet_id:', subnet_id)
return sg_id, subnet_id
def create_log_group(automon_task, node_type, error_bound):
log_group_name = node_type.replace("_", "-") + "_" + str(error_bound).replace(".", "-")
automon_task["containerDefinitions"][0]["logConfiguration"]["options"]["awslogs-group"] = log_group_name
logs_client = session.client('logs', region_name=automon_task["containerDefinitions"][0]["logConfiguration"]["options"]["awslogs-region"])
response = logs_client.describe_log_groups()
for log_group in response["logGroups"]:
if log_group["logGroupName"] == log_group_name:
# Log group is already exist
print("Found log group", log_group_name)
return
logs_client.create_log_group(logGroupName=log_group_name)
def run_coordinator_on_ecs_fargate(coordinator_region, node_type, error_bound, automon_task, command):
ecs_client = session.client('ecs', region_name=coordinator_region)
if error_bound:
cluster_name = node_type.replace("_", "-") + "_" + str(error_bound).replace(".", "-") + "_" + coordinator_region
else:
cluster_name = node_type.replace("_", "-") + "_" + str(error_bound).replace(".", "-") + "_" + coordinator_region
print("Cluster:", cluster_name)
_ = ecs_client.create_cluster(clusterName=cluster_name) # Use different cluster for every experiment
sg_id, subnet_id = get_default_security_group(coordinator_region)
run_task(ecs_client, automon_task, cluster_name, -1, '0.0.0.0', node_type, error_bound, subnet_id, sg_id, command)
time.sleep(10) # Wait until the task obtains its public IP
tasks = ecs_client.list_tasks(cluster=cluster_name)
ips = get_service_ips(ecs_client, cluster_name, tasks['taskArns'], coordinator_region)
coordinator_ip = ips[0]
print("Coordinator public IP:", coordinator_ip)
return coordinator_ip
def run_nodes_on_ecs_fargate(nodes_region, node_type, error_bound, automon_task, coordinator_ip, command):
ecs_client = session.client('ecs', region_name=nodes_region)
cluster_name = node_type.replace("_", "-") + "_" + str(error_bound).replace(".", "-") + "_" + nodes_region
print("Cluster:", cluster_name)
_ = ecs_client.create_cluster(clusterName=cluster_name) # Use different cluster for every experiment
sg_id, subnet_id = get_default_security_group(nodes_region)
for node_idx in range(NUM_NODES):
run_task(ecs_client, automon_task, cluster_name, node_idx, coordinator_ip, node_type, error_bound, subnet_id, sg_id, command)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--node_type", type=str, dest="node_type", help="node type. Could be one of: inner_product / quadratic / kld / dnn", default='inner_product')
parser.add_argument("--coordinator_aws_instance_type", type=str, dest="coordinator_aws_instance_type",
help="coordinator AWS instance type. Could be one of: ec2 / fargate. Use ECS Fargate coordinator for cases that do not require strong coordinator "
"(e.g., inner_product), or use strong EC2 coordinator.",
default='ec2')
parser.add_argument("--centralized", dest="b_centralized",
help="if --centralized is specified, a centralization (not AutoMon) experiment ie deployed",
action='store_true')
parser.add_argument("--block", dest="b_block",
help="if --block is specified, the script first checks if the experiment output files are already exist in AWS S3. If not, if runs the experiment "
"and waits until it finds the output files in S3.",
action='store_true')
args = parser.parse_args()
if args.b_centralized:
command = 'python /app/aws_experiments/start_distributed_centralization_object_remote.py'
node_name = 'centralization_' + args.node_type
# Centralized node simply sends all its data to the coordinator. It is not an adaptive algorithm, hence not impacted by error bound.
# Therefore, we use a single dummy error_bound in the list of error_bounds.
error_bounds = ['centralization'] # Used for cluster name and log group name
else:
command = 'python /app/aws_experiments/start_distributed_object_remote.py'
node_name = args.node_type
# These values were taken from the test_max_error_vs_communication_xxx experiments
if args.node_type == "inner_product":
error_bounds = [0.02, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]
if args.node_type == "quadratic":
error_bounds = [0.015, 0.02, 0.03, 0.04, 0.05, 0.08, 0.1, 1.0]
if args.node_type == "kld":
# Use only 8 error bounds to have less than 100 ECS instances in the same region, as Amazon has 100 ECS instance limit for a single region (8 error
# bounds x 12 nodes = 96 ECS instance, plus 8 EC2/ECS coordinator instances in a different region). The original list is:
# [0.003, 0.004, 0.005, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.12, 0.14]
error_bounds = [0.005, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.14]
if args.node_type == "dnn":
# Use only 6 error bounds to save time and money. The original list is:
# [0.001, 0.002, 0.0027, 0.003, 0.005, 0.007, 0.01, 0.016, 0.025, 0.05]
error_bounds = [0.002, 0.003, 0.005, 0.007, 0.016, 0.05]
if args.node_type == "inner_product":
NUM_NODES = 10
if args.node_type == "quadratic":
NUM_NODES = 10
if args.node_type == "kld":
NUM_NODES = 12
if args.node_type == "dnn":
NUM_NODES = 9
if args.b_block:
num_experiments = num_completed_experiments(node_name)
if num_experiments >= len(error_bounds):
print("Found existing remote AWS S3 test folder for " + node_name + ". Skipping.")
sys.exit()
username, access_key_id, secret_access_key = read_credentials_file()
session = boto3.session.Session(aws_access_key_id=access_key_id, aws_secret_access_key=secret_access_key)
account_id = session.client('sts').get_caller_identity().get('Account')
with open(os.path.abspath(os.path.dirname(__file__)) + "/automon_aws_task.json", 'r') as f:
automon_task = f.read()
automon_task = automon_task.replace("<account_id>", account_id)
automon_task = json.loads(automon_task)
for error_bound in error_bounds:
create_log_group(automon_task, args.node_type, error_bound)
# Coordinator
coordinator_region = "us-west-2"
if args.coordinator_aws_instance_type == "fargate":
coordinator_ip = run_coordinator_on_ecs_fargate(coordinator_region, args.node_type, error_bound, automon_task, command) # Using ECS Fargate coordinator is limited to 4 vCPU and 16GB of memory on an Intel Xeon CPU at 2.2–2.5 GHz.
else:
coordinator_ip = run_coordinator_on_ec2_instance(coordinator_region, args.node_type, error_bound, command) # Run the coordinator on EC2 c5.4xlarge instance (16 vCPU and 32GB of memory on an Intel Xeon CPU at 3.4–3.9 GHz).
# Nodes
nodes_region = "us-east-2"
run_nodes_on_ecs_fargate(nodes_region, args.node_type, error_bound, automon_task, coordinator_ip, command)
if args.b_block:
# Wait for the experiment to finish by checking the result folders in S3, and then collect the result from S3.
while True:
num_experiments = num_completed_experiments(node_name)
if num_experiments >= len(error_bounds):
break
print("AWS experiment", node_name, "is still running. Checking status again in 5 minutes.")
sys.stdout.flush()
time.sleep(5 * 60)
# Wait one more minute to let all S3 writes to finish
time.sleep(60)
|
python
|
from .models import Answer, Landscape
def landscape_boundary(landscape_name):
"""
Return landscape boundary as GeoJSON
:param landscape_name:
:return:
"""
landscape_boundaries = Landscape.objects.raw("""SELECT
id,
landscape,
ST_AsGeoJSON(geom) as geojson
FROM bns_landscape
WHERE landscape = '{}' LIMIT 1""".format(landscape_name))
landscape_geojson = '{"type" : "FeatureCollection", "features" :['
if len(landscape_boundaries):
landscape_geojson += '{"type": "Feature", "properties": {"landscape": "%s"}, "geometry": %s }' % \
(landscape_name, landscape_boundaries[0].geojson)
landscape_geojson += ']}'
return landscape_geojson
def landscape_villages(landscape_name):
"""
Return landscape villages as GeoJSON
:param landscape_name:
:return:
"""
landscape_villages = Answer.objects.raw("""SELECT row_number() OVER () as answer_id,
dataset_name,
village,
ST_AsGeoJSON(ST_SetSRID(ST_MakePoint(avg(long), avg(lat)),4326)) as geojson
FROM bns_answer a
JOIN kobo_kobodata k ON a.dataset_uuid_id = k.dataset_uuid
JOIN bns_answergps g ON a.answer_id = g.answer_id
WHERE landscape = '{}' AND lat != 0 AND long != 0
GROUP BY dataset_name, village""".format(landscape_name))
village_geojson = '{"type" : "FeatureCollection", "features" :['
if len(landscape_villages):
i = 0
for village in landscape_villages:
if i > 0:
village_geojson += ','
village_geojson += '{"type": "Feature", "properties": {"landscape": "%s", "survey": "%s", "village": "%s"}, "geometry": %s }' % \
(landscape_name, village.dataset_name, village.village, village.geojson)
i += 1
village_geojson += ']}'
return village_geojson
def survey_villages(survey):
"""
Return survey villages as GeoJSON
:param survey:
:return:
"""
survey_villages = Answer.objects.raw("""SELECT row_number() OVER () as answer_id,
dataset_name,
village,
ST_AsGeoJSON(ST_SetSRID(ST_MakePoint(avg(long), avg(lat)),4326)) as geojson
FROM bns_answer a
JOIN kobo_kobodata k ON a.dataset_uuid_id = k.dataset_uuid
JOIN bns_answergps g ON a.answer_id = g.answer_id
WHERE dataset_name = '{}' AND lat != 0 AND long != 0
GROUP BY dataset_name, village""".format(survey))
village_geojson = '{"type" : "FeatureCollection", "features" :['
if len(survey_villages):
i = 0
for village in survey_villages:
if i > 0:
village_geojson += ','
village_geojson += '{"type": "Feature", "properties": {"survey": "%s", "village": "%s"}, "geometry": %s }' % \
(village.dataset_name, village.village, village.geojson)
i += 1
village_geojson += ']}'
return village_geojson
|
python
|
import random, string
from datetime import datetime
import pytz
import json
import types
from unittest import TestCase
from unittest.mock import patch
from plaw.wrapper import Plaw, InvalidGrant, InvalidToken
class TestPlaw(TestCase):
# helper
def generate_random_token(self, length=16):
return ''.join(random.choice(string.ascii_uppercase + string.ascii_lowercase
+ string.digits) for _ in range(length))
def setUp(self):
self.test_api = Plaw(client_id=self.generate_random_token(),
client_secret=self.generate_random_token(),
account_id=self.generate_random_token(length=5),
refresh_token=self.generate_random_token(),
access_token=self.generate_random_token())
@patch('plaw.wrapper.request')
def test_refresh_access_token_successfully_saves_new_token(self, mock_request):
mock_request.return_value.status_code = 200
mocked_response = {
'access_token': self.generate_random_token(),
'expires_in': 3600,
'token_type': 'bearer',
'scope': 'employee:all systemuserid:152663'
}
mock_request.return_value.json.return_value = mocked_response
new_refresh_token = self.test_api._refresh_access_token()
self.assertEqual(new_refresh_token, mocked_response['access_token'])
@patch('plaw.wrapper.request')
def test_refresh_access_token_raises_on_revoked_access(self, mock_request):
mock_request.return_value.status_code = 400
with self.assertRaises(InvalidGrant):
self.test_api._refresh_access_token()
@patch('plaw.wrapper.request')
def test_call_returns_decoded_json(self, mock_request):
mock_request.return_value.status_code = 200
mocked_response = {
'@attributes': {
'count': '1'
},
'Account': {
'accountID': '12345',
'name': 'Test Store for API Testing',
'link': {
'@attributes': {
'href': '/API/Account/12345'
}
}
}
}
mock_request.return_value.json.return_value = mocked_response
decoded_response = self.test_api._call('/API/Account.json', params=None)
self.assertEqual(decoded_response, mocked_response)
@patch('plaw.wrapper.request')
def test_call_raises_on_invalid_token(self, mock_request):
mock_request.return_value.status_code = 401
with self.assertRaises(InvalidToken):
self.test_api._call('/API/Account.json', params=None)
@patch('plaw.wrapper.Plaw._call')
@patch('plaw.wrapper.Plaw._refresh_access_token')
def test_call_api_refreshes_access_token_if_necessary(self, mock_refresh, mock_call):
new_access_token = self.generate_random_token()
mock_refresh.return_value = new_access_token
refreshed_call_response = {
'@attributes': {
'count': '1'
},
'Account': {
'accountID': '12345',
'name': 'Test Store for API Testing',
'link': {
'@attributes': {
'href': '/API/Account/12345T'
}
}
}
}
mock_call.side_effect = [InvalidToken, refreshed_call_response]
response_gen = self.test_api._call_api('/API/Account.json')
decoded_response = next(response_gen)
self.assertEqual(new_access_token, self.test_api.access_token)
self.assertEqual(decoded_response, refreshed_call_response)
@patch('plaw.wrapper.Plaw._call')
def test_call_api_converts_datetimes_to_iso(self, mock_call):
test_date = pytz.timezone('America/Boise').localize(datetime(2021, 1, 1, 10, 58), is_dst=None)
# without query op
next(self.test_api._call_api(f'API/Account/{self.test_api.account_id}/EmployeeHours.json',
params={
'checkIn': test_date
}))
mock_call.assert_called_with(f'API/Account/{self.test_api.account_id}/EmployeeHours.json',
{
'checkIn': f'{test_date.isoformat()}'
})
# with query op
next(self.test_api._call_api(f'API/Account/{self.test_api.account_id}/EmployeeHours.json',
params={
'checkIn': ['>', test_date]
}))
mock_call.assert_called_with(f'API/Account/{self.test_api.account_id}/EmployeeHours.json',
{
'checkIn': f'>,{test_date.isoformat()}'
})
@patch('plaw.wrapper.Plaw._call')
def test_call_api_handles_query_ops(self, mocked_call):
# the default operator is =
# so if the user intends equals they don't pass in a query op
# if they intend another op they pass in a list, with the op first
equals_params = {
'shopID': '1'
}
next(self.test_api._call_api(f'API/Account/{self.test_api.account_id}/Shop.json',
equals_params))
mocked_call.assert_called_with(f'API/Account/{self.test_api.account_id}/Shop.json',
{
'shopID': '1'
})
less_than_params = {
'shopID': ['<', '3']
}
next(self.test_api._call_api(f'API/Account/{self.test_api.account_id}/Shop.json',
less_than_params))
mocked_call.assert_called_with(f'API/Account/{self.test_api.account_id}/Shop.json',
{
'shopID': '<,3'
})
@patch('plaw.wrapper.Plaw._call')
def test_call_api_handles_pagination(self, mock_call):
with open('pagination_test_file.json') as jf:
mocked_responses = json.load(jf)
mock_call.side_effect = mocked_responses
test_date = pytz.timezone('America/Boise').localize(datetime(2021, 2, 1, 1), is_dst=None)
shifts_since_february = self.test_api._call_api(f'API/Account/{self.test_api.account_id}/EmployeeHours.json',
params={
'checkIn': ['>', test_date]
})
self.assertTrue(isinstance(shifts_since_february, types.GeneratorType))
first_page = next(shifts_since_february)
self.assertEqual('0', first_page['@attributes']['offset'])
self.assertEqual(first_page, mocked_responses[0])
second_page = next(shifts_since_february)
self.assertEqual('100', second_page['@attributes']['offset'])
self.assertEqual(second_page, mocked_responses[1])
third_page = next(shifts_since_february)
self.assertEqual('200', third_page['@attributes']['offset'])
self.assertEqual(third_page, mocked_responses[2])
with self.assertRaises(StopIteration):
next(shifts_since_february)
def test_call_api_handles_rate_limiting(self):
# so
# LS uses a leaky bucket algorithm to handle rate limiting
# The current bucket use is given in the X-LS-API-Bucket-Level header
# and the current drip rate is given in X-LS-API-Drip-Rate
# LS will send a 429 response if we are being rate limited
# tabling this for now
pass
@patch('plaw.wrapper.request')
def test_get_tokens_saves_new_tokens(self, mocked_request):
test_access_token = self.generate_random_token()
test_refresh_token = self.generate_random_token()
test_code = self.generate_random_token()
mocked_request.return_value.json.return_value = {
'access_token': test_access_token,
'expires_in': 1800,
'token_type': 'bearer',
'scope': f'employee:all systemuserid:{self.generate_random_token(length=5)}',
'refresh_token': test_refresh_token
}
self.test_api.get_tokens(test_code)
self.assertEqual(self.test_api.access_token, test_access_token)
self.assertEqual(self.test_api.refresh_token, test_refresh_token)
mocked_request.assert_called_with('POST', self.test_api.AUTH_URL, data={
'client_id': self.test_api.client_id,
'client_secret': self.test_api.client_secret,
'code': test_code,
'grant_type': 'authorization_code'
})
@patch('plaw.wrapper.Plaw.account')
def test_fetch_account_id_saves_account_id(self, mocked_account):
mocked_account.return_value = {
"accountID": "67890",
"name": "Test Account",
"link": {
"@attributes": {
"href": "/API/Account/67890"
}
}
}
self.test_api.fetch_account_id()
self.assertTrue(self.test_api.account_id, '67890')
@patch('plaw.wrapper.Plaw._call')
def test_account_returns_account_info(self, mocked_call):
# mocked call is necessary because it tries to evaluate before _strip_attributes does
mocked_call.return_value = {
'@attributes': {
'count': '1'
},
'Account': {
'accountID': '12345',
'name': 'Test Account',
'link': {
'@attributes': {
'href': '/API/Account/12345'
}
}
}
}
account_info = self.test_api.account()
self.assertTrue(isinstance(account_info, dict))
self.assertFalse(isinstance(account_info, types.GeneratorType))
@patch('plaw.wrapper.Plaw._call')
def test_shop_returns_shop_info(self, mocked_call):
with open('shop_test_file.json') as jf:
test_shop_info = json.load(jf)
mocked_call.return_value = test_shop_info
shop_info = self.test_api.shop()
self.assertTrue(isinstance(shop_info, types.GeneratorType))
self.assertEqual(next(shop_info), test_shop_info)
@patch('plaw.wrapper.Plaw._call')
def test_employee_returns_employee_info(self, mocked_call):
with open('employee_test_file.json') as jf:
test_employee_info = json.load(jf)[0]
mocked_call.return_value = test_employee_info
employee_info = self.test_api.employee()
self.assertTrue(isinstance(employee_info, types.GeneratorType))
self.assertEqual(next(employee_info), test_employee_info)
@patch('plaw.wrapper.Plaw._call')
def test_employee_loads_contact_relation(self, mocked_call):
with open('employee_test_file.json') as jf:
test_employee_info = json.load(jf)[1]
mocked_call.return_value = test_employee_info
employee_info = self.test_api.employee(load_contact=True)
self.assertTrue(isinstance(employee_info, types.GeneratorType))
self.assertEqual(next(employee_info), test_employee_info)
mocked_call.assert_called_with(f'API/Account/{self.test_api.account_id}/Employee.json',
{
'load_relations': json.dumps(['Contact'])
})
@patch('plaw.wrapper.Plaw._call')
def test_employee_hours_returns_employee_hours_info(self, mocked_call):
with open('employee_hours_test_file.json') as jf:
test_employee_hours_info = json.load(jf)
mocked_call.return_value = test_employee_hours_info
employee_hours_info = self.test_api.employee_hours()
self.assertTrue(isinstance(employee_hours_info, types.GeneratorType))
self.assertEqual(next(employee_hours_info), test_employee_hours_info)
|
python
|
# encoding: utf-8
# Duke the dog
# Este código va destinado al reconocimiento de un perro por su color de pelaje,
# eliminando todo ruido causado por el ambiente.
# Todo mediante la librería open cv en Python..subl
# Programador Sergio Luis Beleño Díaz
# Enero.2019
'''
Para empezar se importan las librerías de Open cv para visión
Artificial y se utiliza la librería numpy para la optimización
de datos al trabajar con las matrices que componen a la imagen
obtenida pixel por pixel
'''
import cv2
import numpy as np
# Asignamos la camara ingresando cv2.VideoCapture(0)
cap = cv2.VideoCapture('Duke the dog.mp4')
# funtion
def centroide(imagen_binarizada):
# Obtenemos lo momentos de la imagen
Moments = cv2.moments(imagen_binarizada)
if (Moments["m00"] != 0):
# Se calcula los centroides XY con el fin de ubicar el objeto
centrox = int(Moments["m10"] / Moments["m00"])
centroy = int(Moments["m01"] / Moments["m00"])
else:
centrox, centroy = 0,0
return(centrox, centroy)
def captura(colorrgb_bajo = [175,100,60], colorrgb_alto = [195,115,75]):
#(colorrgb_bajo = [165,40,11], colorrgb_alto = [195,115,100]):
# Se toma una Captura de la imagen de la Camara
[rec, camara] = cap.read()
if rec == True:
camara = cv2.resize(camara, (1040,680))
# Se combierten los colores de BGR a rgb (Rojo, Verde y Azul)
rgb = cv2.cvtColor(camara, cv2.COLOR_BGR2RGB)
# Colores:
bajos = np.array(colorrgb_bajo, dtype=np.uint8)
altos = np.array(colorrgb_alto, dtype=np.uint8)
# Binarización de Color
img_binarizada = cv2.inRange(rgb, bajos, altos)
# Filtros
# Centroides
[x, y] = centroide(img_binarizada)
try: x2
except NameError: i = None
if (i == None):
x2 = x
y2 = y
x = (x + x2)/2
y = (y + y2)/2
x2 = x
y2 = y
cv2.line(camara,(x-100 , y-50),(x+100, y-50),(75, 255, 50),15)
cv2.line(camara,(x+100 , y-50),(x+100, y+150),(75, 255, 50),15)
cv2.line(camara,(x-100 , y-50),(x-100, y+150),(75, 255, 50),15)
cv2.line(camara,(x-100 , y+150),(x+100, y+150),(75, 255, 50),15)
cv2.putText(camara,"Duke the dog",(x-100,y+200),cv2.FONT_HERSHEY_DUPLEX,1,(75, 255, 50),2)
print(x,y)
# Muestra las Capturas de la camara en ventanas de visualización
#cv2.imshow('Mascara', img_binarizada)
cv2.imshow('Camara', camara)
return 1
else:
return 0
#########################################################################
# Se crea un ciclo while para hacer captura por captura y
# tomar la posición del objeto en sus ejes cartesianos (x,y)
while (cap.isOpened()):
# Captura(ValorHSVbajo,ValorHSValto)
cond = captura()
# Sí se pulsa una tecla y la tecla es la letra "q"
# minuscula se rompe el bucle en el que se encuentre
if cv2.waitKey(15) & 0xFF == ord('q') or cond == 0:
break
cap.release()
cv2.destroyAllWindows()
|
python
|
from chemlib.chemistry import Combustion, Compound, Reaction
from chemlib.utils import reduce_list
# TODO: TEMP IMPLEMENTATIONS, NEED TO CONSIDER ADDITIONAL METHODS/CALCULATIONS
# INTERFACE?
def combustion_analysis(CO2, H2O) -> str:
molesC = Compound("CO2").get_amounts(grams = CO2)["moles"]
molesH = (Compound("H2O").get_amounts(grams = H2O)['moles'])*2
moles = reduce_list([molesC, molesH])
moles = ["" if x == 1 else x for x in moles] #Remove all 1's
return (f"C{moles[0]}H{moles[1]}")
class Calorimeter:
def reaction_heat(self):
raise NotImplementedError('Implemented in CoffeeCup and Bomb objects')
class CoffeeCup(Calorimeter, Reaction):
def __init__(self, reactants, products):
pass
@staticmethod
def reaction_heat(mass: float, spec_heat: float, d_temp: float):
return mass * spec_heat * d_temp
class Bomb(Calorimeter, Combustion):
def __init__(self, compound):
Combustion.__init__(compound)
@staticmethod
def reaction_heat(spec_heat_calorimeter: float, d_temp: float):
return spec_heat_calorimeter * d_temp
|
python
|
__all__ = [ 'assign','smart_assign','LCA','linkable',
'Assign','SmartAssign','Linkable',
'join_name',
'Component',
'Input','Output','UInt','SInt','IOGroup','Parameter','Wire','Reg',
'And','Or','Greater','Less','GreaterEqual','LessEqual','NotEqual','Equal',
'BitXnor','BitXor','BitAnd','BitOr',
'Add','Sub','Mul',
'SelfXnor','SelfXor','SelfAnd','SelfOr','Inverse','Not',
'Combine','BitXnorList','BitXorList','BitOrList','BitAndList','OrList','AndList',
'Cut','Case','when','When','EmptyWhen',
'Circuit','get_circuit','set_circuit',
'when']
from .Function import Assign,SmartAssign,LCA,Linkable
from .BasicFunction import join_name
from .Component import Component
from .Variable import Input,Output,UInt,SInt,IOGroup,Parameter,Wire,Reg
from .Variable import And,Or,Greater,Less,GreaterEqual,LessEqual,NotEqual,Equal
from .Variable import BitXnor,BitXor,BitAnd,BitOr
from .Variable import Add,Sub,Mul
from .Variable import SelfXnor,SelfXor,SelfAnd,SelfOr,Inverse,Not
from .Variable import Combine,BitXnorList,BitXorList,BitOrList,BitAndList,OrList,AndList
from .Variable import Case,Cut,When,EmptyWhen
#from .Value import Combine
#from .Expression import
from .Root import Root,get_circuit,set_circuit
Circuit = Root
when = When
assign = Assign
smart_assign = SmartAssign
linkable = Linkable
from .Exception import *
|
python
|
import numpy as np
import torch
import torch.nn as nn
from .anchor_head_template import AnchorHeadTemplate
class GradReverse(torch.autograd.Function):
def __init__(self, lambd):
self.lambd = lambd
def forward(self, x):
return x.view_as(x)
def backward(self, grad_output):
return (grad_output * self.lambd)
def grad_reverse(x, lambd):
return GradReverse(lambd)(x)
class AnchorHeadSingleRangeGuidance(AnchorHeadTemplate):
def __init__(self, model_cfg, input_channels, num_class, class_names, grid_size, point_cloud_range,
predict_boxes_when_training=True, nusc=False, fpn_layers=[], **kwargs):
super().__init__(
model_cfg=model_cfg, num_class=num_class, class_names=class_names, grid_size=grid_size, point_cloud_range=point_cloud_range,
predict_boxes_when_training=predict_boxes_when_training, nusc=nusc, fpn_layers=fpn_layers
)
self.num_anchors_per_location = sum(self.num_anchors_per_location)
if self.range_guidance:
if self.range_guidance_dom_only:
input_channels_dom = input_channels + 2
else:
input_channels = input_channels + 2 + 256
input_channels_dom = input_channels - 256
else:
input_channels_dom = input_channels
self.conv_cls = nn.Conv2d(
input_channels, self.num_anchors_per_location * self.num_class,
kernel_size=1
)
self.conv_box = nn.Conv2d(
input_channels, self.num_anchors_per_location * self.box_coder.code_size,
kernel_size=1
)
self.rangeinv = self.model_cfg.get('RANGE_INV', False)
self.keep_x = self.model_cfg.get('KEEP_X', False)
self.keep_y = self.model_cfg.get('KEEP_Y', False)
self.keep_xy = self.model_cfg.get('KEEP_XY', False)
self.rm_thresh = self.model_cfg.get('RM_THRESH', 0)
if self.rangeinv:
self.conv_range = nn.Conv2d(
input_channels, 1,
kernel_size=1
)
#nn.Sequential(
if self.model_cfg.get('USE_DIRECTION_CLASSIFIER', None) is not None:
self.conv_dir_cls = nn.Conv2d(
input_channels,
self.num_anchors_per_location * self.model_cfg.NUM_DIR_BINS,
kernel_size=1
)
else:
self.conv_dir_cls = None
# if self.model_cfg.get('USE_DOMAIN_CLASSIFIER', None) is not None:
if self.range_da > 0:
self.domain_pool = nn.AdaptiveAvgPool2d(1)
self.domain_classifier_range = nn.ModuleDict()
for n in range(0+self.remove_near_range, self.range_da-self.remove_far_range):
self.domain_classifier_range[str(n)] = nn.Sequential(nn.Linear(input_channels, 1024),
nn.ReLU(True), nn.Dropout(),
nn.Linear(1024, 256), nn.ReLU(True),
nn.Dropout(), nn.Linear(256, 1))
if self.keep_xy:
self.domain_classifier_range2 = nn.ModuleDict()
for n in range(0+self.remove_near_range2, self.range_da-self.remove_far_range2):
self.domain_classifier_range2[str(n)] = nn.Sequential(nn.Linear(input_channels, 1024),
nn.ReLU(True), nn.Dropout(),
nn.Linear(1024, 256), nn.ReLU(True),
nn.Dropout(), nn.Linear(256, 1))
elif self.interval_da > 0:
self.domain_pool = nn.AdaptiveAvgPool2d(1)
self.domain_classifier_interval = nn.ModuleDict()
for n in range(self.interval_da):
self.domain_classifier_interval[str(n)] = nn.Sequential(nn.Linear(input_channels, 1024),
nn.ReLU(True), nn.Dropout(),
nn.Linear(1024, 256), nn.ReLU(True),
nn.Dropout(), nn.Linear(256, 1))
else:
self.domain_pool = nn.AdaptiveAvgPool2d(1)
self.domain_classifier = nn.Sequential(nn.Linear(input_channels_dom, 1024),
nn.ReLU(True), nn.Dropout(),
nn.Linear(1024, 256), nn.ReLU(True),
nn.Dropout(), nn.Linear(256, 1))
self.init_weights()
def init_weights(self):
pi = 0.01
nn.init.constant_(self.conv_cls.bias, -np.log((1 - pi) / pi))
nn.init.normal_(self.conv_box.weight, mean=0, std=0.001)
def forward(self, data_dict):
t_mode = data_dict['t_mode']
l = data_dict['l']
# print("t_mode", t_mode)
if 'pseudo' in t_mode:
pseudo = True
else:
pseudo = False
spatial_features_2d = data_dict['spatial_features_2d']
# print("spatial_features_2d", spatial_features_2d.shape) 126
# print('range ctx',self.range_guidance)
if t_mode == 'tsne':
if self.range_da > 0:
mid_dim = int(spatial_features_2d.shape[-1]/2.)
range_interval = int(spatial_features_2d.shape[-1]/(2*self.range_da))
start_dim = {}
mid1_dim = {}
mid2_dim = {}
end_dim = {}
interval_idx = {}
interval_feat = {}
if self.keep_xy:
interval_feat2 = {}
# for each range 0,1,2,3 (4)
for n in range(0+self.remove_near_range, self.range_da-self.remove_far_range): # no0,1
start_dim[n] = mid_dim - range_interval*(n+1) # 2-1=1, 2-2=0
mid1_dim[n] = mid_dim - range_interval*n # 2-0=2 2-1=1 #int(spatial_features_2d.shape[-1]/2.)
mid2_dim[n] = mid_dim + range_interval*n # 2+0=2 2+1=3
end_dim[n] = mid_dim + range_interval*(n+1) # 2+1=3 2+2=4
interval_idx[n] = torch.LongTensor([i for i in range(start_dim[n], mid1_dim[n])]+[i for i in range(mid2_dim[n], end_dim[n])])
feat1 = spatial_features_2d[:,:,:,interval_idx[n]]
feat1 = self.domain_pool(feat1).view(feat1.size(0), -1)
data_dict[f'spatial_features_2d_x_{n}'] = feat1
feat2 = spatial_features_2d[:,:,interval_idx[n],:]
feat2 = self.domain_pool(feat2).view(feat2.size(0), -1)
data_dict[f'spatial_features_2d_y_{n}'] = feat2
if self.range_guidance and not self.range_guidance_dom_only:
total_range = spatial_features_2d.shape[-1]
half_range = int(spatial_features_2d.shape[-1] * 0.5)
# x_range = torch.zeros((total_range, total_range)).cuda()
# y_range = torch.zeros((total_range, total_range)).cuda()
# for i in range(-half_range, half_range):
# for j in range(-half_range, half_range):
# x_range[i+half_range,j+half_range] = abs(i+0.5)
# y_range[i+half_range,j+half_range] = abs(j+0.5)
x_range = torch.abs(torch.arange(-half_range, half_range, 1).float() + 0.5).unsqueeze(0).unsqueeze(0).unsqueeze(0).repeat(spatial_features_2d.shape[0],1, total_range, 1).cuda()
# print("x_range", x_range)
y_range = torch.abs(torch.arange(-half_range, half_range, 1).float() + 0.5).unsqueeze(-1).unsqueeze(0).unsqueeze(0).repeat(spatial_features_2d.shape[0],1,1,total_range).cuda()
# print('x_range',x_range[0,-1])
# print('y_range',y_range[0,-1])
# print("spatial_features_2d 0", spatial_features_2d.shape)
# x_range = x_range.unsqueeze(0).unsqueeze(0).repeat((spatial_features_2d.shape[0],1,1,1))
# y_range = y_range.unsqueeze(0).unsqueeze(0).repeat((spatial_features_2d.shape[0],1,1,1))
# print('x_range',x_range.shape)
# print('y_range',y_range.shape)
spatial_features_2d = torch.cat((spatial_features_2d, x_range, y_range), dim=1)
# print("spatial_features_2d", spatial_features_2d.shape)
# print("t_mode", t_mode)
if 'dom_img' in t_mode:
if t_mode == 'dom_img_src':
dom_src = True
elif t_mode == 'dom_img_tgt':
dom_src = False
else:
dom_src = None
#
if self.range_da > 0:
mid_dim = int(spatial_features_2d.shape[-1]/2.)
range_interval = int(spatial_features_2d.shape[-1]/(2*self.range_da))
start_dim = {}
mid1_dim = {}
mid2_dim = {}
end_dim = {}
interval_idx = {}
interval_feat = {}
if self.keep_xy:
interval_feat2 = {}
# for each range 0,1,2,3 (4)
for n in range(0+self.remove_near_range, self.range_da-self.remove_far_range): # no0,1
start_dim[n] = mid_dim - range_interval*(n+1) # 2-1=1, 2-2=0
mid1_dim[n] = mid_dim - range_interval*n # 2-0=2 2-1=1 #int(spatial_features_2d.shape[-1]/2.)
mid2_dim[n] = mid_dim + range_interval*n # 2+0=2 2+1=3
end_dim[n] = mid_dim + range_interval*(n+1) # 2+1=3 2+2=4
# print("range n", n)
# print("start_dim[n]", start_dim[n])
# print("mid1_dim[n]", mid1_dim[n])
# print("mid2_dim[n]", mid2_dim[n])
# print("end_dim[n]", end_dim[n])
interval_idx[n] = torch.LongTensor([i for i in range(start_dim[n], mid1_dim[n])]+[i for i in range(mid2_dim[n], end_dim[n])])
if self.keep_x:
interval_feat[n] = spatial_features_2d[:,:,:,interval_idx[n]]
# self.forward_ret_dict[f'spatial_features_2d_x_{n}'] = interval_feat[n]
elif self.keep_y:
interval_feat[n] = spatial_features_2d[:,:,interval_idx[n],:]
# self.forward_ret_dict[f'spatial_features_2d_y_{n}'] = interval_feat[n]
elif self.keep_xy:
interval_feat[n] = spatial_features_2d[:,:,:,interval_idx[n]]
# self.forward_ret_dict[f'spatial_features_2d_x_{n}'] = interval_feat[n]
x_pool = self.domain_pool(interval_feat[n]).view(interval_feat[n].size(0), -1)
x_reverse = grad_reverse(x_pool, l*-1)
# dom_img_preds = self.domain_classifier_range[str(n)](x_reverse).squeeze(-1)
dom_head_context = self.domain_classifier_range[str(n)][:-2](x_reverse)#.squeeze(-1)
if 'dom_img_det' in t_mode:
data_dict['dom_head_context'] = dom_head_context
dom_img_preds = self.domain_classifier_range[str(n)][-2:](dom_head_context)#.squeeze(-1)
self.forward_ret_dict[f'dom_img_preds_range{n}'] = dom_img_preds
if self.keep_xy:
interval_feat2[n] = spatial_features_2d[:,:,interval_idx[n],:]
self.forward_ret_dict[f'spatial_features_2d_y_{n}'] = interval_feat2[n]
x_pool2 = self.domain_pool(interval_feat2[n]).view(interval_feat2[n].size(0), -1)
x_reverse2 = grad_reverse(x_pool2, l*-1)
# dom_img_preds2 = self.domain_classifier_range2[str(n)](x_reverse2).squeeze(-1)
dom_head_context2 = self.domain_classifier_range2[str(n)][:-2](x_reverse2)#.squeeze(-1)
if 'dom_img_det' in t_mode:
data_dict['dom_head_context2'] = dom_head_context2
dom_img_preds2 = self.domain_classifier_range2[str(n)][-2:](dom_head_context2)#.squeeze(-1)
self.forward_ret_dict[f'dom_img_preds_range{n}_2'] = dom_img_preds2
if self.training:
targets_dict_dom = self.assign_targets(
gt_boxes=data_dict['gt_boxes'],
dom_src=dom_src,
pseudo=pseudo
)
self.forward_ret_dict.update(targets_dict_dom)
elif self.interval_da > 0:
# mid_dim = int(spatial_features_2d.shape[-1]/2.)
range_interval = int(spatial_features_2d.shape[-1]/self.interval_da)
start_dim = {}
# mid1_dim = {}
# mid2_dim = {}
end_dim = {}
interval_idx = {}
interval_feat = {}
# for each range 0,1,2,3 (4)
for n in range(self.interval_da): # 0,1
start_dim[n] = range_interval*n # 2-1=1, 2-2=0
# mid1_dim[n] = mid_dim - range_interval*n # 2-0=2 2-1=1 #int(spatial_features_2d.shape[-1]/2.)
# mid2_dim[n] = mid_dim + range_interval*n # 2+0=2 2+1=3
end_dim[n] = range_interval*(n+1) # 2+1=3 2+2=4
interval_idx[n] = torch.LongTensor([i for i in range(start_dim[n], end_dim[n])])
# print("spatial_features_2d", spatial_features_2d.shape)
if self.keep_x:
interval_feat[n] = spatial_features_2d[:,:,:,interval_idx[n]]
elif self.keep_y:
interval_feat[n] = spatial_features_2d[:,:,interval_idx[n],:]
# print("interval_feat[n]", interval_feat[n].shape)
x_pool = self.domain_pool(interval_feat[n]).view(interval_feat[n].size(0), -1)
# print("x_pool[n]", x_pool.shape)
x_reverse = grad_reverse(x_pool, l*-1)
# dom_img_preds = self.domain_classifier_interval[str(n)](x_reverse).squeeze(-1)
dom_head_context = self.domain_classifier_interval[str(n)][:-2](x_reverse)#.squeeze(-1)
if 'dom_img_det' in t_mode:
data_dict['dom_head_context'] = dom_head_context
dom_img_preds = self.domain_classifier_interval[str(n)][-2:](dom_head_context)#.squeeze(-1)
self.forward_ret_dict[f'dom_img_preds_interval{n}'] = dom_img_preds
if self.training:
targets_dict_dom = self.assign_targets(
gt_boxes=data_dict['gt_boxes'],
dom_src=dom_src,
pseudo=pseudo
)
self.forward_ret_dict.update(targets_dict_dom)
else:
if self.range_guidance and self.range_guidance_dom_only:
total_range = spatial_features_2d.shape[-1]
half_range = int(spatial_features_2d.shape[-1] * 0.5)
x_range = torch.abs(torch.arange(-half_range, half_range, 1).float() + 0.5).unsqueeze(0).unsqueeze(0).unsqueeze(0).repeat(spatial_features_2d.shape[0],1, total_range, 1).cuda()
y_range = torch.abs(torch.arange(-half_range, half_range, 1).float() + 0.5).unsqueeze(-1).unsqueeze(0).unsqueeze(0).repeat(spatial_features_2d.shape[0],1,1,total_range).cuda()
spatial_features_2d = torch.cat((spatial_features_2d, x_range, y_range), dim=1)
x_pool = self.domain_pool(spatial_features_2d).view(spatial_features_2d.size(0), -1)
x_reverse = grad_reverse(x_pool, l*-1)
# dom_img_preds = self.domain_classifier(x_reverse).squeeze(-1)
dom_head_context = self.domain_classifier[:-2](x_reverse)
if 'dom_img_det' in t_mode:
data_dict['dom_head_context'] = dom_head_context
dom_img_preds = self.domain_classifier[-2:](dom_head_context)
self.forward_ret_dict['dom_img_preds'] = dom_img_preds
if self.training:
targets_dict_dom = self.assign_targets(
gt_boxes=data_dict['gt_boxes'],
dom_src=dom_src,
pseudo=pseudo
)
self.forward_ret_dict.update(targets_dict_dom)
if 'det' not in t_mode:
return data_dict
dom_head_context = data_dict[f'dom_head_context']
dom_head_context_reshape = dom_head_context.unsqueeze(-1).unsqueeze(-1).repeat(1,1,spatial_features_2d.shape[-2],spatial_features_2d.shape[-1])
spatial_features_2d = torch.cat((spatial_features_2d, dom_head_context_reshape), dim=1)
cls_preds = self.conv_cls(spatial_features_2d)
box_preds = self.conv_box(spatial_features_2d)
cls_preds = cls_preds.permute(0, 2, 3, 1).contiguous() # [N, H, W, C]
box_preds = box_preds.permute(0, 2, 3, 1).contiguous() # [N, H, W, C]
self.forward_ret_dict['cls_preds'] = cls_preds
self.forward_ret_dict['box_preds'] = box_preds
if self.conv_dir_cls is not None:
dir_cls_preds = self.conv_dir_cls(spatial_features_2d)
dir_cls_preds = dir_cls_preds.permute(0, 2, 3, 1).contiguous()
self.forward_ret_dict['dir_cls_preds'] = dir_cls_preds
else:
dir_cls_preds = None
if self.training:
if pseudo:
pseudo_weights = data_dict['pseudo_weights']
else:
pseudo_weights = None
targets_dict = self.assign_targets(
gt_boxes=data_dict['gt_boxes'],
pseudo=pseudo,
pseudo_weights=pseudo_weights
)
self.forward_ret_dict.update(targets_dict)
if not self.training or self.predict_boxes_when_training:
batch_cls_preds, batch_box_preds = self.generate_predicted_boxes(
batch_size=data_dict['batch_size'],
cls_preds=cls_preds, box_preds=box_preds, dir_cls_preds=dir_cls_preds
)
data_dict['batch_cls_preds'] = batch_cls_preds
data_dict['batch_box_preds'] = batch_box_preds
data_dict['cls_preds_normalized'] = False
if self.rangeinv:
# print("spatial_features_2d", spatial_features_2d.shape) #512,128,128
thresh = self.rm_thresh
start_dim = int(spatial_features_2d.shape[-1]/4.)
mid_dim = int(spatial_features_2d.shape[-1]/2.)
end_dim = start_dim+int(spatial_features_2d.shape[-1]/2.)
near_idx = torch.LongTensor([i for i in range(start_dim, mid_dim-thresh)]+[i for i in range(mid_dim+thresh, end_dim)])
far_idx = torch.LongTensor([i for i in range(start_dim)]+[i for i in range(end_dim, spatial_features_2d.shape[-1])])
if self.keep_x:
near_feat_2d = spatial_features_2d[:,:,:,near_idx]
far_feat_2d = spatial_features_2d[:,:,:, far_idx]
elif self.keep_y:
near_feat_2d = spatial_features_2d[:,:,near_idx,:]
far_feat_2d = spatial_features_2d[:,:,far_idx,:]
near_feat_2d_reverse = grad_reverse(near_feat_2d, l*-1)
range_pred_near = self.conv_range(near_feat_2d_reverse)
# print("near_range_pred", near_range_pred.shape)
far_feat_2d_reverse = grad_reverse(far_feat_2d, l*-1)
range_pred_far = self.conv_range(far_feat_2d_reverse)
# print("far_range_pred", far_range_pred.shape)
range_labels_near = torch.ones((range_pred_near.shape), dtype=torch.float32, device=spatial_features_2d.device)
range_labels_far = torch.zeros((range_pred_far.shape), dtype=torch.float32, device=spatial_features_2d.device)
targets_dict_range = {
'range_pred_near': range_pred_near,
'range_pred_far': range_pred_far,
'range_labels_near': range_labels_near,
'range_labels_far': range_labels_far,
}
self.forward_ret_dict.update(targets_dict_range)
return data_dict
|
python
|
# coding= utf-8
# author= Administrator
# date= 2021/3/15 17:38
from bs4 import BeautifulSoup
# 创建html字符串
html_doc = """
<!DOCTYPE html><!--STATUS OK-->
<html><head><meta http-equiv="Content-Type" content="text/html;charset=utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"><meta content="always" name="referrer"><meta name="theme-color" content="#2932e1"><meta name="description" content="全球最大的中文搜索引擎、致力于让网民更便捷地获取信息，找到所求。百度超过千亿的中文网页数据库，可以瞬间找到相关的搜索结果。"><link rel="shortcut icon" href="/favicon.ico" type="image/x-icon" /><link rel="search" type="application/opensearchdescription+xml" href="/content-search.xml" title="百度搜索" /><link rel="icon" sizes="any" mask href="//www.baidu.com/img/baidu_85beaf5496f291521eb75ba38eacbd87.svg"><link rel="dns-prefetch" href="//dss0.bdstatic.com"/><link rel="dns-prefetch" href="//dss1.bdstatic.com"/><link rel="dns-prefetch" href="//ss1.bdstatic.com"/><link rel="dns-prefetch" href="//sp0.baidu.com"/><link rel="dns-prefetch" href="//sp1.baidu.com"/><link rel="dns-prefetch" href="//sp2.baidu.com"/><title>百度一下，你就知道</title>
<style index="newi" type="text/css">#form .bdsug{top:39px}.bdsug{display:none;position:absolute;width:535px;background:#fff;border:1px solid #ccc!important;_overflow:hidden;box-shadow:1px 1px 3px #ededed;-webkit-box-shadow:1px 1px 3px #ededed;-moz-box-shadow:1px 1px 3px #ededed;-o-box-shadow:1px 1px 3px #ededed}.bdsug li{width:519px;color:#000;font:14px arial;line-height:25px;padding:0 8px;position:relative;cursor:default}.bdsug li.bdsug-s{background:#f0f0f0}.bdsug-store span,.bdsug-store b{color:#7A77C8}.bdsug-store-del{font-size:12px;color:#666;text-decoration:underline;position:absolute;right:8px;top:0;cursor:pointer;display:none}.bdsug-s .bdsug-store-del{display:inline-block}.bdsug-ala{display:inline-block;border-bottom:1px solid #e6e6e6}.bdsug-ala h3{line-height:14px;background:url(//www.baidu.com/img/sug_bd.png?v=09816787.png) no-repeat left center;margin:6px 0 4px;font-size:12px;font-weight:400;color:#7B7B7B;padding-left:20px}.bdsug-ala p{font-size:14px;font-weight:700;padding-left:20px}#m .bdsug .bdsug-direct p{color:#00c;font-weight:700;line-height:34px;padding:0 8px;margin-top:0;cursor:pointer;white-space:nowrap;overflow:hidden}#m .bdsug .bdsug-direct p img{width:16px;height:16px;margin:7px 6px 9px 0;vertical-align:middle}#m .bdsug .bdsug-direct p span{margin-left:8px}#form .bdsug .bdsug-direct{width:auto;padding:0;border-bottom:1px solid #f1f1f1}#form .bdsug .bdsug-direct p i{font-size:12px;line-height:100%;font-style:normal;font-weight:400;color:#fff;background-color:#2b99ff;display:inline;text-align:center;padding:1px 5px;*padding:2px 5px 0;margin-left:8px;overflow:hidden}.bdsug .bdsug-pcDirect{color:#000;font-size:14px;line-height:30px;height:30px;background-color:#f8f8f8}.bdsug .bdsug-pc-direct-tip{position:absolute;right:15px;top:8px;width:55px;height:15px;display:block;background:url(https://ss1.bdstatic.com/5eN1bjq8AAUYm2zgoY3K/r/www/cache/static/protocol/https/global/img/pc_direct_42d6311.png) no-repeat 0 0}.bdsug li.bdsug-pcDirect-s{background-color:#f0f0f0}.bdsug .bdsug-pcDirect-is{color:#000;font-size:14px;line-height:22px;background-color:#f5f5f5}.bdsug .bdsug-pc-direct-tip-is{position:absolute;right:15px;top:3px;width:55px;height:15px;display:block;background:url(https://ss1.bdstatic.com/5eN1bjq8AAUYm2zgoY3K/r/www/cache/static/protocol/https/global/img/pc_direct_42d6311.png) no-repeat 0 0}.bdsug li.bdsug-pcDirect-is-s{background-color:#f0f0f0}.bdsug .bdsug-pcDirect-s .bdsug-pc-direct-tip,.bdsug .bdsug-pcDirect-is-s .bdsug-pc-direct-tip-is{background-position:0 -15px}.bdsug .bdsug-newicon{color:#929292;opacity:.7;font-size:12px;display:inline-block;line-height:22px;letter-spacing:2px}.bdsug .bdsug-s .bdsug-newicon{opacity:1}.bdsug .bdsug-newicon i{letter-spacing:0;font-style:normal}.bdsug .bdsug-feedback-wrap{display:none}.toggle-underline{text-decoration:none}.toggle-underline:hover{text-decoration:underline}.bdpfmenu,.usermenu{border:1px solid #d1d1d1;position:absolute;width:105px;top:36px;z-index:302;box-shadow:1px 1px 5px #d1d1d1;-webkit-box-shadow:1px 1px 5px #d1d1d1;-moz-box-shadow:1px 1px 5px #d1d1d1;-o-box-shadow:1px 1px 5px #d1d1d1}.bdpfmenu{font-size:12px;background-color:#fff}.bdpfmenu a,.usermenu a{display:block;text-align:left;margin:0!important;padding:0 9px;line-height:26px;text-decoration:none}.briiconsbg{background-repeat:no-repeat;background-size:300px 18px;background-image:url(https://ss1.bdstatic.com/5eN1bjq8AAUYm2zgoY3K/r/www/cache/static/protocol/https/home/img/icons_0c37e9b.png);background-image:url(https://ss1.bdstatic.com/5eN1bjq8AAUYm2zgoY3K/r/www/cache/static/protocol/https/home/img/icons_809ae65.gif)\9}.bdpfmenu a:link,.bdpfmenu a:visited,#u .usermenu a:link,#u .usermenu a:visited{background:#fff;color:#333}.bdpfmenu a:hover,.bdpfmenu a:active,#u .usermenu a:hover,#u .usermenu a:active{background:#38f;text-decoration:none;color:#fff}.bdpfmenu{width:70px}#wrapper .bdnuarrow{width:0;height:0;font-size:0;line-height:0;display:block;position:absolute;top:-10px;left:50%;margin-left:-5px}#wrapper .bdnuarrow em,#wrapper .bdnuarrow i{width:0;height:0;font-size:0;line-height:0;display:block;position:absolute;border:5px solid transparent;border-style:dashed dashed solid}#wrapper .bdnuarrow em{border-bottom-color:#d8d8d8;top:-1px}#wrapper .bdnuarrow i{border-bottom-color:#fff;top:0}#gxszHead .prefpanelclose{cursor:pointer;width:16px;height:16px;float:right;margin-top:7px;background-position:-248px 0}#gxszHead .prefpanelclose:hover{background-position:-264px 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7px;background:0 0;border-top:2px solid #f5f5f6}#head_wrapper #form .bdsug-new ul li{padding:0;color:#626675;line-height:28px;background:0 0;font-family:Arial,"PingFang SC","Microsoft YaHei",sans-serif}#head_wrapper #form .bdsug-new ul li span{color:#626675}#head_wrapper #form .bdsug-new ul li b{font-weight:400;color:#222}#head_wrapper #form .bdsug-new .bdsug-store-del{font-size:13px;text-decoration:none;color:#9195A3;right:3px}#head_wrapper #form .bdsug-new .bdsug-store-del:hover{color:#315EFB;cursor:pointer}#head_wrapper #form .bdsug-new ul li:hover,#head_wrapper #form .bdsug-new ul li:hover span,#head_wrapper #form .bdsug-new ul li:hover b{cursor:pointer}#head .s-down #form .bdsug-new{top:32px}.s-skin-hasbg #head_wrapper #form .bdsug-new{border-color:#E4E4E5!important;border-top:0!important}.s-skin-hasbg #head_wrapper.s-down #form .bdsug-new{border-color:#4e6ef2!important;border-top:0!important}#head_wrapper #form .bdsug-new .bdsug-s,#head_wrapper #form .bdsug-new .bdsug-s span,#head_wrapper #form .bdsug-new .bdsug-s b{color:#315EFB}#head_wrapper #form .bdsug-new>div span:hover,#head_wrapper #form .bdsug-new>div a:hover{color:#315EFB!important}#head_wrapper #form #kw.new-ipt-focus{border-color:#4e6ef2}</style>
<style type="text/css" index="head">blockquote,body,button,dd,dl,dt,fieldset,form,h1,h2,h3,h4,h5,h6,hr,input,legend,li,ol,p,pre,td,textarea,th,ul{margin:0;padding:0}
html{color:#000;overflow-y:scroll;overflow:-moz-scrollbars}
body,button,input,select,textarea{font-size:12px;font-family:"PingFang SC",Arial,"Microsoft YaHei",sans-serif}
h1,h2,h3,h4,h5,h6{font-size:100%}
em{font-style:normal}
small{font-size:12px}
ol,ul{list-style:none}
a{text-decoration:none}
a:hover{text-decoration:underline}
legend{color:#000}
fieldset,img{border:0}
button,input,select,textarea{font-size:100%}
table{border-collapse:collapse;border-spacing:0}
img{-ms-interpolation-mode:bicubic}
textarea{resize:vertical}
.left{float:left}
.right{float:right}
.overflow{overflow:hidden}
.hide{display:none}
.block{display:block}
.inline{display:inline}
.error{color:red;font-size:12px}
button,label{cursor:pointer}
.clearfix:after{content:'\20';display:block;height:0;clear:both}
.clearfix{zoom:1}
.clear{clear:both;height:0;line-height:0;font-size:0;visibility:hidden;overflow:hidden}
.wordwrap{word-break:break-all;word-wrap:break-word}
.s-yahei{font-family:arial,'Microsoft Yahei','微软雅黑'}
pre.wordwrap{white-space:pre-wrap}
body{text-align:center;background:#fff}
body,form{position:relative;z-index:0}
td{text-align:left}
img{border:0}
#s_wrap{position:relative;z-index:0;min-width:1000px}
#wrapper{height:100%}
#head .s-ps-islite{_padding-bottom:370px}
#head_wrapper.s-ps-islite{padding-bottom:370px}
#head_wrapper.s-ps-islite #s_lm_wrap{bottom:298px;background:0 0!important;filter:none!important}
#head_wrapper.s-ps-islite .s_form{position:relative;z-index:1}
#head_wrapper.s-ps-islite .fm{position:absolute;bottom:0}
#head_wrapper.s-ps-islite .s-p-top{position:absolute;bottom:40px;width:100%;height:181px}
#head_wrapper.s-ps-islite #s_lg_img,#head_wrapper.s-ps-islite #s_lg_img_new{position:static;margin:33px auto 0 auto}
.s_lm_hide{display:none!important}
#head_wrapper.s-down #s_lm_wrap{display:none}
.s-lite-version #m{padding-top:125px}
#s_lg_img,#s_lg_img_new{position:absolute;bottom:10px;left:50%;margin-left:-135px}
#form{z-index:1}
#s_lm_wrap{position:absolute;margin-left:-447px;bottom:0;left:50%;z-index:0;height:30px;width:895px;line-height:30px;text-align:center}
.s-skin-hasbg #s_lm_wrap{background:0 0;background-image:-webkit-gradient(linear,left top,left bottom,from(rgba(0,0,0,.3)),to(rgba(0,0,0,.3)));background-image:-moz-linear-gradient(rgba(0,0,0,.3) 0,rgba(0,0,0,.3) 100%);background-image:-ms-linear-gradient(rgba(0,0,0,.3) 0,rgba(0,0,0,.3) 100%);background-image:-o-linear-gradient(rgba(0,0,0,.3) 0,rgba(0,0,0,.3) 100%);background-image:linear-gradient(rgba(0,0,0,.3) 0,rgba(0,0,0,.3) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=#66000000, endColorstr=#66000000)}
#s_lm_wrap.s-down{display:none}
#lm{color:#666;height:15px;line-height:16px;padding:7px 0}
#lm a{text-decoration:underline;color:#666}
#nv{margin:0 0 5px;_margin-bottom:4px;padding:2px 0 0;text-align:left;text-indent:50px}
#nv a,#nv b{margin-left:19px}
#lk,#nv a,#nv b,.btn{font-size:14px}
.s-down .s_form{padding-left:0;margin-top:0;min-height:0}
.s_form .tools{position:absolute;right:-55px}
.s_form_wrapper{height:100%}
#head_wrapper.s-down #mCon span{color:#000}
#lk{margin:33px 0}
#lk span{font:14px "\5b8b\4f53"}
#lh{margin:16px 0 5px;word-spacing:3px}
#mCon{height:15px;line-height:15px;width:28px;padding:10px 8px 0 0;cursor:pointer;background:url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/img/spis7-d578e7ff4b.png) no-repeat -684px -605px}
#mCon span{color:#333;cursor:default;display:block}
#mCon .hw{text-decoration:underline;cursor:pointer}
#mMenu{width:56px;border:1px solid #9b9b9b;list-style:none;position:absolute;right:-9px;top:30px;display:none;background:#fff;box-shadow:1px 1px 2px #ccc;-moz-box-shadow:1px 1px 2px #ccc;-webkit-box-shadow:1px 1px 2px #ccc;filter:progid:DXImageTransform.Microsoft.Shadow(Strength=2, Direction=135, Color="#cccccc")\9}
#mMenu a,#mMenu a:visited{color:#00c;width:100%;height:100%;display:block;line-height:22px;text-indent:6px;text-decoration:none;filter:none\9}
#mMenu a:hover{background:#ebebeb}
#mMenu .ln{height:1px;background:#ebebeb;overflow:hidden;font-size:1px;line-height:1px;margin-top:-1px}
#cp,#cp a{color:#77c}
#tb_mr{color:#00c;cursor:pointer;position:relative;z-index:200}
#tb_mr b{font-weight:400}
#nv a,#tb_mr b{text-decoration:underline}
#nv a{color:#00c}
#hwr_div,#loading{z-index:3000}
.bd_bear_home{display:none}
#mHolder{display:none}
#mHolder .c-icon{right:0;top:0;position:absolute;float:right;width:15px;height:15px}
.main{display:none}
#s_feed{display:none}
.s-ps-sug{border:1px solid #ccc!important;box-shadow:1px 1px 3px #ededed;-webkit-box-shadow:1px 1px 3px #ededed;-moz-box-shadow:1px 1px 3px #ededed;-o-box-shadow:1px 1px 3px #ededed;position:absolute;top:32px;left:0}
.s-ps-sug table{width:100%;background:#fff;cursor:default}
.s-ps-sug td{color:#000;font:14px arial;height:25px;line-height:25px;padding:0 8px}
.s-ps-sug td b{color:#000}
.s-ps-sug .mo{background:#ebebeb;cursor:pointer}
.s-ps-sug .ml{background:#fff}
.s-ps-sug td.sug_storage{color:#7a77c8}
.s-ps-sug td.sug_storage b{color:#7a77c8}
.s-ps-sug .sug_del{font-size:12px;color:#666;text-decoration:underline;float:right;cursor:pointer;display:none}
.s-ps-sug .sug_del{font-size:12px;color:#666;text-decoration:underline;float:right;cursor:pointer;display:none}
.s-ps-sug .mo .sug_del{display:block}
.s-ps-sug .sug_ala{border-bottom:1px solid #e6e6e6}
.s-ps-sug td h3{line-height:14px;margin:6px 0 4px 0;font-size:12px;font-weight:400;color:#7b7b7b;padding-left:20px;background:url(img/sug_bd.png) no-repeat left center}
.s-ps-sug td p{font-size:14px;font-weight:700;padding-left:20px}
.s-ps-sug td p span{font-size:12px;font-weight:400;color:#7b7b7b}
#s_user_center{font-weight:400;background-position:right -223px\9}
#s_user_center_menu{right:131px}
.s-ps-islite #nv{padding-top:22px;line-height:16px;height:16px;margin-bottom:13px}
#form .bdsug .bdsug-direct{width:auto;padding:0;border-bottom:1px solid #f1f1f1}
#head_wrapper .bdsug .bdsug-direct p{color:#00c;font-weight:700;line-height:34px;padding:0 8px;margin-top:0;cursor:pointer;white-space:nowrap;overflow:hidden}
#head_wrapper .bdsug .bdsug-direct p img{width:16px;height:16px;margin:7px 6px 9px 0;vertical-align:middle}
#head_wrapper .bdsug .bdsug-direct p span{margin-left:8px}
#head_wrapper .bdsug .bdsug-direct p i{font-size:12px;line-height:100%;font-style:normal;font-weight:400;color:#fff;background-color:#2b99ff;display:inline;text-align:center;padding:1px 5px;*padding:2px 5px 0 5px;margin-left:8px;overflow:hidden}
#result_logo,#s_tab,#u,#wrapper_wrapper{display:none}
#prefpanel{background:#fafafa;display:none;opacity:0;position:fixed;_position:absolute;top:-359px;z-index:500;width:100%;min-width:960px;border-bottom:1px solid #ebebeb;*left:0!important;text-align:left}
#prefpanel form{_width:850px}
@font-face{font-family:cIconfont;src:url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/font/iconfont-03f7028492.eot);src:url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/font/iconfont-03f7028492.eot?#iefix) format('embedded-opentype'),url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/font/iconfont-d312d35c5b.woff2) format('woff2'),url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/font/iconfont-d187c4be30.woff) format('woff'),url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/font/iconfont-81527e9464.ttf) format('truetype'),url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/font/iconfont-4816f3b73b.svg#iconfont) format('svg')}
.c-gap-top-small{margin-top:3px}
.c-gap-top{margin-top:7px}
.c-gap-top-large{margin-top:11px}
.c-gap-top-mini{margin-top:2px}
.c-gap-top-xsmall{margin-top:4px}
.c-gap-top-middle{margin-top:10px}
.c-gap-bottom-small{margin-bottom:3px}
.c-gap-bottom{margin-bottom:7px}
.c-gap-bottom-large{margin-bottom:11px}
.c-gap-bottom-mini{margin-bottom:2px}
.c-gap-bottom-xsmall{margin-bottom:4px}
.c-gap-bottom-middle{margin-bottom:10px}
.c-gap-left{margin-left:12px}
.c-gap-left-small{margin-left:8px}
.c-gap-left-xsmall{margin-left:4px}
.c-gap-left-mini{margin-left:2px}
.c-gap-left-large{margin-left:16px}
.c-gap-left-middle{margin-left:10px}
.c-gap-right{margin-right:12px}
.c-gap-right-small{margin-right:8px}
.c-gap-right-xsmall{margin-right:4px}
.c-gap-right-mini{margin-right:2px}
.c-gap-right-large{margin-right:16px}
.c-gap-right-middle{margin-right:10px}
.c-gap-icon-right-small{margin-right:5px}
.c-gap-icon-right{margin-right:10px}
.c-gap-icon-left-small{margin-left:5px}
.c-gap-icon-left{margin-left:10px}
.c-row{*zoom:1}
.c-row:after{display:block;height:0;content:"";clear:both;visibility:hidden}
.c-span1{width:32px}
.c-span2{width:80px}
.c-span3{width:128px}
.c-span4{width:176px}
.c-span5{width:224px}
.c-span6{width:272px}
.c-span7{width:320px}
.c-span8{width:368px}
.c-span9{width:416px}
.c-span10{width:464px}
.c-span11{width:512px}
.c-span12{width:560px}
.c-span10,.c-span11,.c-span12,.c-span2,.c-span3,.c-span4,.c-span5,.c-span6,.c-span7,.c-span8,.c-span9{float:left;_display:inline;margin-right:16px;list-style:none}
.c-span-last{margin-right:0}
.c-span-last-s{margin-right:0}
.c-feed-box .c-span1{width:43px}
.c-feed-box .c-span2{width:90px}
.c-feed-box .c-span3{width:137px}
.c-feed-box .c-span4{width:184px}
.c-feed-box .c-span5{width:231px}
.c-feed-box .c-span6{width:278px}
.c-feed-box .c-span7{width:325px}
.c-feed-box .c-span8{width:372px}
.c-feed-box .c-span9{width:419px}
.c-feed-box .c-span10{width:466px}
.c-feed-box .c-span11{width:513px}
.c-feed-box .c-span12{width:560px}
.c-feed-box .c-span10,.c-feed-box .c-span11,.c-feed-box .c-span12,.c-feed-box .c-span2,.c-feed-box .c-span3,.c-feed-box .c-span4,.c-feed-box .c-span5,.c-feed-box .c-span6,.c-feed-box .c-span7,.c-feed-box .c-span8,.c-feed-box .c-span9{margin-right:4px}
.c-feed-box .c-span-last{margin-right:0}
.c-index{display:inline-block;width:14px;padding:1px 0;line-height:100%;text-align:center;color:#fff;background-color:#8eb9f5;font-size:12px}
.c-index-hot,.c-index-hot1{background-color:#f54545}
.c-index-hot2{background-color:#ff8547}
.c-index-hot3{background-color:#ffac38}
.c-index-single{display:inline-block;background:0 0;color:#9195a3;width:18px;font-size:15px;letter-spacing:-1px}
.c-index-single-hot,.c-index-single-hot1{color:#fe2d46}
.c-index-single-hot2{color:#f60}
.c-index-single-hot3{color:#faa90e}
.c-font-sigma{font:36px/60px Arial,sans-serif}
.c-font-large{font:20px/30px Arial,sans-serif}
.c-font-big{font:20px/30px Arial,sans-serif}
.c-font-special{font:16px/26px Arial,sans-serif}
.c-font-medium{font:14px/24px Arial,sans-serif}
.c-font-middle{font:14px/24px Arial,sans-serif}
.c-font-normal{font:13px/23px Arial,sans-serif}
.c-font-small{font:12px/20px Arial,sans-serif}
.c-font-family{font-family:Arial,sans-serif}
.c-color-t{color:#222}
.c-color-text{color:#333}
.c-color-gray{color:#626675}
.c-color-gray2{color:#9195a3}
.c-color-visited{color:#626675}
.c-color-link{color:#222}
.c-color-orange{color:#fa4901}
.c-color-green{color:#0ebe90}
.c-color-ad{color:#77a9f9}
.c-color-red{color:#f63051}
.c-color-red:visited{color:#f63051}
.c-color-warn{color:#ff7900}
.c-color-warn:visited{color:#ff7900}
.c-color-link{color:#3951b3}
.c-btn,.c-btn:visited{color:#333!important}
.c-btn{display:inline-block;overflow:hidden;font-family:inherit;font-weight:400;text-align:center;vertical-align:middle;outline:0;border:0;height:30px;width:80px;line-height:30px;font-size:13px;border-radius:6px;padding:0;background-color:#f5f5f6;*zoom:1;cursor:pointer}
.c-btn:hover{background-color:#315efb;color:#fff!important}
a.c-btn{text-decoration:none}
button.c-btn{*overflow:visible;border:0}
button.c-btn::-moz-focus-inner{padding:0;border:0}
.c-btn-disable{color:#c4c7ce!important}
.c-btn-disable:visited{color:#c4c7ce!important}
.c-btn-disable:hover{cursor:default;color:#c4c7ce!important;background-color:#f5f5f6}
.c-btn-mini{height:24px;width:48px;line-height:24px}
.c-btn-mini .c-icon{margin-top:2px}
.c-btn-large{height:30px;line-height:30px;font-size:14px}
button.c-btn-large{height:30px;_line-height:24px}
.c-btn-large .c-icon{margin-top:7px;_margin-top:6px}
.c-btn-primary,.c-btn-primary:visited{color:#fff!important}
.c-btn-primary{background-color:#4e6ef2}
.c-btn-primary:hover{background-color:#315efb}
.c-btn-weak{height:24px;line-height:24px;border-radius:4px;font-size:12px}
.c-btn-add{width:32px;height:32px;line-height:32px;text-align:center;color:#9195a3!important}
.c-btn-add:hover{background-color:#4e6ef2;color:#fff!important}
.c-btn-add .c-icon{float:none}
.c-btn-add-disable:hover{cursor:default;color:#c4c7ce!important;background-color:#f5f5f6}
.c-select{position:relative;display:inline-block;width:96px;box-sizing:border-box;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;vertical-align:middle;color:#222;font:13px/23px Arial,sans-serif}
.c-select-selection{display:block;height:30px;line-height:29px;box-sizing:border-box;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;padding:0 26px 0 10px;background-color:#fff;border-radius:6px;border:1px solid #d7d9e0;outline:0;user-select:none;cursor:pointer;position:relative;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}
.c-select-arrow,.c-select-arrow-up{position:absolute;top:-1px;right:10px;color:#9195a3;font-size:16px}
.c-select-dropdown{display:none;position:absolute;padding-top:4px;top:25px;z-index:999;left:0;width:94px;box-sizing:content-box;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;background:#fff;border-radius:0 0 6px 6px;border:1px solid #d7d9e0;border-top:0;zoom:1}
.c-select-split{border-top:1px solid #f5f5f5;margin:0 5px}
.c-select-dropdown-list{padding:0;margin:5px 0 0;list-style:none}
.c-select-dropdown-list.c-select-scroll{max-height:207px;overflow-y:auto;overflow-x:hidden;margin-right:5px;margin-bottom:9px}
.c-select-dropdown-list.c-select-scroll::-webkit-scrollbar{width:2px}
.c-select-dropdown-list.c-select-scroll::-webkit-scrollbar-track{width:2px;background:#f5f5f6;border-radius:1px}
.c-select-dropdown-list.c-select-scroll::-webkit-scrollbar-thumb{width:2px;height:58px;background-color:#4e71f2;border-radius:1px}
.c-select-dropdown-list.c-select-scroll .c-select-item:last-child{margin:0}
.c-select-item{margin:0 0 4px;padding:0 10px;clear:both;white-space:nowrap;list-style:none;cursor:pointer;box-sizing:border-box;-webkit-box-sizing:border-box;-moz-box-sizing:border-box}
.c-select-item:hover{color:#315efb}
.c-select-item-selected{color:#315efb}
.c-select-arrow-up{display:none}
.c-select-visible .c-select-selection{border-radius:6px 6px 0 0}
.c-select-visible .c-select-dropdown{display:block}
.c-select-visible .c-select-arrow{display:none}
.c-select-visible .c-select-arrow-up{display:inline-block}
.c-img{position:relative;display:block;min-height:1px;border:0;line-height:0;background:#f5f5f6;overflow:hidden}
.c-img img{width:100%}
.c-img1{width:32px}
.c-img2{width:80px}
.c-img3{width:128px}
.c-img4{width:176px}
.c-img6{width:272px}
.c-img12{width:560px}
.c-feed-box .c-img1{width:43px}
.c-feed-box .c-img2{width:90px}
.c-feed-box .c-img3{width:137px}
.c-feed-box .c-img4{width:184px}
.c-feed-box .c-img6{width:278px}
.c-feed-box .c-img12{width:560px}
.c-img-l,.c-img-s,.c-img-v,.c-img-w,.c-img-x,.c-img-y,.c-img-z{height:0;overflow:hidden}
.c-img-s{padding-bottom:100%}
.c-img-l{padding-bottom:133.33333333%}
.c-img-w{padding-bottom:56.25%}
.c-img-x{padding-bottom:75%}
.c-img-y{padding-bottom:66.66666667%}
.c-img-v{padding-bottom:116.66666667%}
.c-img-z{padding-bottom:62.5%}
.c-img-radius{border-radius:6px}
.c-img-radius-s{border-radius:2px}
.c-img-radius-small{border-radius:2px}
.c-img-radius-large{border-radius:12px}
.c-img-radius-middle{border-radius:4px}
.c-img-radius-left{border-top-left-radius:6px;border-bottom-left-radius:6px}
.c-img-radius-right{border-top-right-radius:6px;border-bottom-right-radius:6px}
.c-img-radius-left-s{border-top-left-radius:2px;border-bottom-left-radius:2px}
.c-img-radius-right-s{border-top-right-radius:2px;border-bottom-right-radius:2px}
.c-img-radius-left-l{border-top-left-radius:12px;border-bottom-left-radius:12px}
.c-img-radius-right-l{border-top-right-radius:12px;border-bottom-right-radius:12px}
.c-img-mask{position:absolute;top:0;left:0;z-index:2;width:100%;height:100%;background-image:radial-gradient(circle,rgba(0,0,0,0),rgba(0,0,0,.04));background-image:-ms-radial-gradient(circle,rgba(0,0,0,0),rgba(0,0,0,.04))}
.c-img-border{content:'';position:absolute;top:0;left:0;bottom:0;right:0;border:1px solid rgba(0,0,0,.05)}
.c-img-circle{border-radius:100%;overflow:hidden}
.c-input{display:inline-block;font:13px/23px Arial,sans-serif;color:#333;padding:0 10px;border:1px solid #d7d9e0;border-radius:6px;height:28px;line-height:28px\9;font-size:13px;outline:0;box-sizing:content-box;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;vertical-align:top;overflow:hidden}
.c-input .c-icon{float:right;margin-top:6px;font-size:16px;color:#9195a3}
.c-input .c-icon-left{float:left;margin-right:4px}
.c-input input{float:left;font-size:13px;border:0;outline:0}
.c-input input::-webkit-input-placeholder{color:#9195a3}
.c-input input::-ms-input-placeholder{color:#9195a3}
.c-input input::-moz-placeholder{color:#9195a3}
.c-input::-webkit-input-placeholder{color:#9195a3}
.c-input::-ms-input-placeholder{color:#9195a3}
.c-input::-moz-placeholder{color:#9195a3}
.c-input{width:394px}
.c-input input{width:374px}
.c-input-xmini{width:154px}
.c-input-xmini input{width:134px}
.c-input-mini{width:202px}
.c-input-mini input{width:182px}
.c-input-small{width:346px}
.c-input-small input{width:326px}
.c-input-large{width:442px}
.c-input-large input{width:422px}
.c-input-xlarge{width:730px}
.c-input-xlarge input{width:710px}
.c-input12{width:538px}
.c-input12 input{width:518px}
.c-input20{width:922px}
.c-input20 input{width:902px}
.c-checkbox,.c-radio{display:inline-block;position:relative;white-space:nowrap;outline:0;line-height:1;vertical-align:middle;cursor:pointer;width:16px;height:16px}
.c-checkbox-inner,.c-radio-inner{display:inline-block;position:relative;width:16px;height:16px;line-height:16px;text-align:center;top:0;left:0;background-color:#fff;color:#d7d9e0}
.c-checkbox-input,.c-radio-input{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1;opacity:0;filter:alpha(opacity=0)\9;user-select:none;margin:0;padding:0;width:100%;height:100%;cursor:pointer;zoom:1}
.c-checkbox-inner-i,.c-radio-inner-i{display:none;font-size:16px}
.c-checkbox-inner-bg,.c-radio-inner-bg{font-size:16px;position:absolute;top:0;left:0;z-index:1}
.c-checkbox-checked .c-checkbox-inner-i,.c-radio-checked .c-radio-inner-i{color:#4e71f2;display:inline-block}
.c-textarea{font:13px/23px Arial,sans-serif;color:#333;padding:0 10px;border:1px solid #d7d9e0;border-radius:6px;padding:5px 10px;resize:none;outline:0}
.c-textarea::-webkit-input-placeholder{color:#9195a3}
.c-textarea::-ms-input-placeholder{color:#9195a3}
.c-textarea::-moz-placeholder{color:#9195a3}
.c-icon{font-family:cIconfont!important;font-style:normal;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}
.c-line-clamp1{overflow:hidden;text-overflow:ellipsis;white-space:nowrap}
.c-text{display:inline-block;padding:0 2px;text-align:center;vertical-align:middle;font-style:normal;color:#fff;overflow:hidden;line-height:16px;height:16px;font-size:12px;border-radius:4px;font-weight:200}
a.c-text{text-decoration:none!important}
.c-text-info{padding-left:0;padding-right:0;font-weight:700;color:#2b99ff;vertical-align:text-bottom}
.c-text-info span{padding:0 2px;font-weight:400}
.c-text-important{background-color:#1cb7fd}
.c-text-public{background-color:#4e6ef2}
.c-text-warning{background-color:#f60}
.c-text-prompt{background-color:#ffc20d}
.c-text-danger{background-color:#f73131}
.c-text-safe{background-color:#39b362}
.c-text-mult{padding:0 4px;line-height:18px;height:18px;border-radius:4px;font-weight:400}
.c-text-blue{background-color:#4e6ef2}
.c-text-blue-border{border:1px solid #cbd2ff;padding:0 8px;border-radius:4px;font-weight:400;color:#4e6ef2!important}
.c-text-green{background-color:#39b362}
.c-text-green-border{border:1px solid #c9e7cd;padding:0 8px;border-radius:4px;font-weight:400;color:#39b362!important}
.c-text-red{background-color:#f73131}
.c-text-red-border{border:1px solid #f0c8bd;padding:0 8px;border-radius:4px;font-weight:400;color:#f73131!important}
.c-text-yellow{background-color:#ffc20d}
.c-text-yellow-border{border:1px solid #fcedb1;padding:0 8px;border-radius:4px;font-weight:400;color:#ffc20d!important}
.c-text-orange{background-color:#f60}
.c-text-orange-border{border:1px solid #f8d2b0;padding:0 8px;border-radius:4px;font-weight:400;color:#f60!important}
.c-text-pink{background-color:#fc3274}
.c-text-pink-border{border:1px solid #f6c4d7;padding:0 8px;border-radius:4px;font-weight:400;color:#fc3274!important}
.c-text-gray{background-color:#858585}
.c-text-gray-border{border:1px solid #dbdbdb;padding:0 8px;border-radius:4px;font-weight:400;color:#858585!important}
.c-text-dark-red{background-color:#cc2929}
.c-text-gray-opacity{background-color:rgba(0,0,0,.3)}
.c-text-white-border{border:1px solid rgba(255,255,255,.8);padding:0 8px;border-radius:4px;font-weight:400;color:#fff!important}
.c-text-hot{background-color:#ff9812}
.c-text-new{background-color:#ff455b}
.c-text-fei{background-color:#ff7413}
.c-text-bao{background-color:#d61a6e}
.c-text-rec{background-color:#3ca6ff}
.c-text-time{background-color:rgba(0,0,0,.3)}
.c-wrapper{word-wrap:break-word;word-break:break-all;font:14px/24px Arial,sans-serif;color:#222}
.c-wrapper:after{display:block;height:0;content:"";clear:both;visibility:hidden}
.c-container{width:560px}
.c-wrapper-l{width:1040px}
.c-wrapper-l .c-container-r{width:368px}
.c-wrapper-s{width:896px}
.c-wrapper-s .c-container-r{width:272px}
@media screen and (max-width:1340px){
.c-wrapper{width:896px}
.c-wrapper .c-container-r{width:272px}
}
.c-dialog-box{display:none;position:absolute;z-index:999;box-shadow:0 2px 10px 0 rgba(0,0,0,.1);-webkit-box-shadow:0 2px 10px 0 rgba(0,0,0,.1);-moz-box-shadow:0 2px 10px 0 rgba(0,0,0,.1);-o-box-shadow:0 2px 10px 0 rgba(0,0,0,.1);border-radius:16px;background:#fff;padding:19px 24px}
.c-dialog-box .c-dialog-close{position:absolute;cursor:pointer;top:12px;right:12px;height:14px;width:14px;line-height:1;color:#d7d9e0}
.c-dialog-box .c-dialog-close:hover{color:#315efb}
.c-floating-box{background:#fff;box-shadow:0 2px 10px 0 rgba(0,0,0,.15);-webkit-box-shadow:0 2px 10px 0 rgba(0,0,0,.15);-moz-box-shadow:0 2px 10px 0 rgba(0,0,0,.15);-o-box-shadow:0 2px 10px 0 rgba(0,0,0,.15);border-radius:12px;*border:1px solid #d7d9e0}
.c-link{color:#222;text-decoration:none}
.c-link:visited{color:#626675}
.c-link:hover{color:#315efb;text-decoration:none}
.c-capsule-tip{display:inline-block;background:#f63051;border-radius:7px;padding:0 4px;height:13px;font-size:11px;line-height:14px;color:#fff;text-align:center}</style>
<style type="text/css" index="common">#head_wrapper{position:relative;height:40%;min-height:314px;max-height:510px;width:1000px;margin:0 auto}
#head_wrapper .s-p-top{height:60%;min-height:185px;max-height:310px;position:relative;z-index:0;text-align:center}
#head_wrapper #s_lg_img,#head_wrapper #s_lg_img_new{bottom:15px!important}
#head_wrapper input{outline:0;-webkit-appearance:none}
#head_wrapper input::-webkit-input-placeholder{color:#9195a3}
#head_wrapper .s_btn_wr,#head_wrapper .s_ipt_wr{display:inline-block;*display:inline;zoom:1;background:0 0;vertical-align:top;*vertical-align:middle}
#head_wrapper .s_ipt_wr{position:relative;width:546px}
#head_wrapper .s_btn_wr{width:108px;height:44px;position:relative;z-index:2}
#head_wrapper .s_ipt_wr:hover #kw{border-color:#a7aab5}
#head_wrapper #kw{width:512px;height:16px;padding:12px 16px;font-size:16px;margin:0;vertical-align:top;outline:0;box-shadow:none;border-radius:10px 0 0 10px;border:2px solid #c4c7ce;background:#fff;color:#222;overflow:hidden;box-sizing:content-box}
#head_wrapper #kw:focus{border-color:#4e6ef2!important;opacity:1;filter:alpha(opacity=100)\9}
#head_wrapper .soutu-env-mac #form #kw{width:450px!important;padding-right:78px!important}
#head_wrapper.s-down .soutu-env-mac #form #kw{width:450px!important}
#head_wrapper .soutu-env-nomac #form #kw{width:480px!important;padding-right:48px!important}
#head_wrapper.s-down .soutu-env-nomac #form #kw{width:480px!important}
#head_wrapper .s_form{width:654px;height:100%;margin:0 auto;text-align:left;z-index:100}
#head_wrapper .s_btn{cursor:pointer;width:108px;height:44px;line-height:45px;line-height:44px\9;padding:0;background:0 0;background-color:#4e6ef2;border-radius:0 10px 10px 0;font-size:17px;color:#fff;box-shadow:none;font-weight:400;border:none;outline:0}
#head_wrapper .s_btn:hover{background-color:#4662d9}
#head_wrapper .s_btn:active{background-color:#4662d9}
#head_wrapper.s-down{position:fixed;_position:static;top:0;left:0;height:50px;min-height:50px;z-index:20;width:100%;padding-top:15px;_margin:0 auto}
#head_wrapper.s-down .s_form{width:100%;min-width:1250px;margin:0 auto;height:100%;padding-left:0;margin-top:0;min-height:0}
#head_wrapper.s-down .s_form .s_form_wrapper{margin:0 auto}
#head_wrapper.s-down .s-p-top{display:none}
#head_wrapper.s-down #result_logo,#head_wrapper.s-down .fm{display:inline-block;*display:inline;zoom:1;vertical-align:middle;margin-left:-119px}
@-webkit-keyframes fadein{
from{opacity:0}
to{opacity:1}
}
#head_wrapper.s-down #result_logo{-webkit-animation:fadein 1s}
#head_wrapper.s-down .fm{margin:0 0 0 18px}
#head_wrapper.s-down #result_logo img{width:101px}
#head_wrapper.s-down #kw{padding:10px 16px;width:512px}
#head_wrapper.s-down .s_ipt_wr{width:546px}
#head_wrapper.s-down .s_btn,#head_wrapper.s-down .s_btn_wr{height:40px}
#head_wrapper.s-down .s_btn{line-height:41px;line-height:40px\9}
#head_wrapper .ipt_rec,#head_wrapper .soutu-btn{background:#fff url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/img/searchbox/nicon-10750f3f7d.png) no-repeat;width:24px;height:20px}
@media only screen and (-webkit-min-device-pixel-ratio:2){
#head_wrapper .ipt_rec,#head_wrapper .soutu-btn{background-image:url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/img/searchbox/nicon-2x-6258e1cf13.png);background-size:24px 96px}
}
#head_wrapper .soutu-btn{background-position:0 -51px;right:16px;margin-top:-9px}
#head_wrapper .soutu-btn:hover{background-position:0 -75px}
#head_wrapper .ipt_rec{background-position:0 -2px;top:50%;right:52px!important;margin-top:-10px}
#head_wrapper .ipt_rec:hover{background-position:0 -26px}
#head_wrapper .ipt_rec:after{display:none}
#head_wrapper .under-tips{font-size:13px;color:#222;text-align:center}
#head_wrapper .under-tips .links-link{color:#222;display:inline-block}
#head_wrapper .under-tips .links-link:hover{color:#315efb}
#head_wrapper .under-tips .links-link--image{display:inline-block;width:176px;height:30px;border-radius:6px;overflow:hidden;margin-top:8px}
#head_wrapper .under-tips .links-emphasize-link{margin-top:2px;margin-right:20px;padding:0 8px;font-size:14px;text-decoration:none;line-height:30px;display:inline-block;color:#2027b4;border-radius:6px;background:#f5f7fe}
#head_wrapper .under-tips .links-emphasize-link:hover{color:#315efb}
#head_wrapper .under-tips .links-emphasize-link.last{margin-right:0}
#head_wrapper .under-tips .icon{display:inline-block;background-color:#dadde2;width:4px;height:4px;border-radius:50%;margin:0 20px;line-height:18px;vertical-align:top}
#head_wrapper #m{margin:38px auto 0 auto;width:100%}
#head_wrapper #m .icon{margin-top:8px}
#head_wrapper #s_lm_wrap{position:static;margin:32px auto 0 auto;width:100%}
#head_wrapper #s_lm_wrap .links-wrap{display:inline-block;margin:0 auto}
#head_wrapper #s_lm_wrap .links-wrap .links-link:hover{text-decoration:none}
#head_wrapper #s_lm_wrap .links-wrap .icon{margin-top:13px}
#head_wrapper.s-ps-islite #m{position:absolute;bottom:-56px}
#head_wrapper.s-ps-islite #s_lm_wrap{position:absolute;margin-bottom:0;bottom:-62px;left:0;box-sizing:border-box}
.s-skin-hasbg #head_wrapper .s_btn{background:#e4e4e5;color:#222}
.s-skin-hasbg #head_wrapper .s_btn:hover{background-color:#cdcdce}
.s-skin-hasbg #head_wrapper .s_btn:active{background-color:#cdcdce}
.s-skin-hasbg #head_wrapper #form #kw{border-color:#e4e4e5}
.s-skin-hasbg #head_wrapper #form #kw:hover{border-color:#e4e4e5;opacity:1;filter:alpha(opacity=100)\9}
.s-skin-hasbg #head_wrapper #form #kw:focus{border-color:#e4e4e5!important;opacity:1;filter:alpha(opacity=100)\9}
.s-skin-hasbg #head_wrapper #form #kw.new-ipt-focus{border-color:#e4e4e5}
.s-skin-hasbg #head_wrapper #s_lm_wrap{background-image:none;filter:none}
.s-skin-hasbg #head_wrapper #s_lm_wrap .links-wrap{background-color:rgba(255,255,255,.65)!important;padding:0 12px;border-radius:6px}
.s-skin-hasbg #head_wrapper #s_lm_wrap .links-wrap .icon{margin-top:13px}
.s-skin-hasbg #head_wrapper.s-down #form #kw{border-color:#c4c7ce}
.s-skin-hasbg #head_wrapper.s-down #form #kw:hover{border-color:#a7aab5;opacity:.8;filter:alpha(opacity=80)\9}
.s-skin-hasbg #head_wrapper.s-down #form #kw:focus{border-color:#4e6ef2!important;opacity:1;filter:alpha(opacity=100)\9}
.s-skin-hasbg #head_wrapper.s-down #form #kw.new-ipt-focus{border-color:#4e6ef2}
.s-skin-hasbg #head_wrapper.s-down .s_btn{background:#4e6ef2;color:#fff}
.s-skin-hasbg #head_wrapper.s-down .s_btn:hover{background-color:#4662d9}
.s-skin-hasbg #head_wrapper.s-down .s_btn:active{background-color:#4662d9}
#s_top_wrap{position:absolute;z-index:99;min-width:1000px;width:100%}
#s_top_wrap.s-down{position:fixed;_position:absolute;top:0;left:0;height:70px;z-index:10;width:100%}
#s_top_wrap .s-center-box{position:relative;z-index:1;width:100%;_width:1000px;height:100%}
#s_top_wrap.s-down .s-center-box{box-shadow:0 2px 10px 0 rgba(0,0,0,.1);background-color:#fff;border-bottom:1px solid #888\9;_border-bottom:0}
#s_top_wrap .s-top-nav{position:absolute;top:70px;width:100%;min-width:1250px;_width:1000px;height:40px;overflow:hidden;display:none}
.s-top-wrap{border-bottom:0;height:60px;background:#fff}
.s-top-left{position:absolute;left:0;top:0;z-index:100;height:60px;padding-left:24px}
.s-top-left .mnav{margin-right:31px;margin-top:19px;display:inline-block;position:relative}
.s-top-left .mnav:hover .s-bri,.s-top-left a:hover{color:#315efb;text-decoration:none}
.s-top-left .s-top-more-btn{padding-bottom:19px}
.s-top-left .s-top-more{display:none;position:absolute;top:29px;right:-12px;width:304px;height:223px;background:#fff;box-shadow:0 2px 10px 0 rgba(0,0,0,.15);border-radius:12px}
.s-top-left .s-top-more .s-top-more-content.row-1{padding-top:16px}
.s-top-left .s-top-more .s-top-more-content.row-2{padding-top:19px}
.s-top-left .s-top-more .s-top-more-content a{width:76px;height:70px;float:left}
.s-top-left .s-top-more .s-top-more-content img{width:42px;height:42px;margin:auto;border:1px solid rgba(0,0,0,.03);border-radius:12px;display:block}
.s-top-left .s-top-more .s-top-more-content .s-top-more-title{width:76px;text-align:center;margin-top:3px}
.s-top-left .s-top-more .s-top-more-content>a:hover .s-top-more-title{color:#315efb}
.s-top-left .s-top-more .s-top-tomore{margin-top:10px}
.s-top-left .s-top-more-btn:hover .s-top-more{display:block}
.s-top-right{position:absolute;right:0;top:0;z-index:100;height:60px;padding-right:24px}
.s-top-right .s-top-right-text{margin-left:32px;margin-top:19px;display:inline-block;position:relative;vertical-align:top;cursor:pointer}
.s-top-right .s-top-right-text:hover{color:#315efb}
.s-top-right .s-top-username{margin-left:32px;margin-top:15px;display:inline-block;height:30px}
.s-top-right .s-top-username .s-top-img-wrapper{position:relative;width:28px;height:28px;border:1px solid #4e71f2;display:inline-block;border-radius:50%}
.s-top-right .s-top-username img{padding:2px;width:24px;height:24px;border-radius:50%}
.s-top-right .s-top-username:hover .user-name{color:#315efb}
.s-top-right .s-top-username .user-name{display:inline-block;max-width:100px;overflow:hidden;white-space:nowrap;text-overflow:ellipsis;-o-text-overflow:ellipsis;vertical-align:top;margin-top:3px;margin-left:6px}
.s-top-right .s-top-username.s-hasmsg-tip .s-top-img-wrapper::after{content:'';position:absolute;top:-1px;right:0;width:6px;height:6px;border:1px solid #fff;border-radius:6px;background:#f63051}
.s-top-right .s-top-login-btn{display:inline-block;margin-top:18px;margin-left:32px;font-size:13px}
.s-top-right a:hover{text-decoration:none}
.s-top-userset-menu{display:none;width:84px;padding:8px 0;top:48px;position:absolute;right:10px;float:right;z-index:999;text-align:left}
.s-top-userset-menu a{display:block;margin:3px 16px 3px 16px;color:#333}
.s-top-userset-menu a:hover{color:#315efb;text-decoration:none}
.s-top-userset-menu .split-line{display:block;margin:8px 16px;background:#d7d9e0;height:1px}
.s-top-userset-menu .s-msg-count{display:none;margin-left:4px}
.s-top-userset-menu .hide-feed{display:inline-block}
.s-top-userset-menu .show-feed{display:none}
.s-top-userset-menu.hiding-feed .hide-feed{display:none}
.s-top-userset-menu.hiding-feed .show-feed{display:inline-block}
.s-skin-hasbg .s-top-wrap{background:rgba(0,0,0,.2)}
.s-skin-hasbg .s-top-left .mnav,.s-skin-hasbg .s-top-left .mnav .s-bri{color:rgba(255,255,255,.85)}
.s-skin-hasbg .s-top-left .mnav:hover,.s-skin-hasbg .s-top-left .mnav:hover .s-bri{color:#fff;text-decoration:none}
.s-skin-hasbg .s-top-right .s-top-right-text.c-color-t{color:rgba(255,255,255,.85)}
.s-skin-hasbg .s-top-right .s-top-right-text.c-color-t:hover{color:#fff}
.s-skin-hasbg .s-top-right .s-top-username .user-name{color:rgba(255,255,255,.85)}
.s-skin-hasbg .s-top-right .s-top-username:hover .user-name{color:#fff}
.s-top-right.s-down{position:fixed;left:0;top:5px;min-width:1250px;width:100%;height:0;text-align:right;padding-right:0}
.s-top-right.s-down>*{display:none}
.s-top-right.s-down>#s-top-username,.s-top-right.s-down>#s-usersetting-top{display:inline-block}
.s-top-right.s-down #s-top-username{margin-right:24px}
.s-top-right.s-down .s-top-right-text.c-color-t{color:#222}
.s-top-right.s-down .s-top-right-text.c-color-t:hover{color:#315efb}
.s-top-right.s-down .s-top-username .user-name{color:#222}
.s-top-right.s-down .s-top-username:hover .user-name{color:#315efb}
.guide-info{background-color:#fff;box-shadow:0 2px 10px 0 rgba(0,0,0,.1);border-radius:12px 2px 12px 12px;height:36px;width:258px;text-align:left;position:absolute;margin-top:6px;padding:5px 0 5px 10px;display:none}
.guide-info .guide-icon{color:#4e6ef2;font-size:15px;display:inline-block;line-height:36px;vertical-align:top;margin-right:6px}
.guide-info span{display:inline-block;line-height:36px;vertical-align:top;font-size:13px;font-family:Arial,sans-serif;color:#333}
.guide-info .guide-close{color:#c4c7ce;margin-left:11px;display:inline-block;width:20px;height:36px;text-align:center;line-height:36px;vertical-align:top;font-size:13px;cursor:pointer}
.guide-info .guide-close:hover{color:#4e6ef2}
.guide-info-login{width:244px}
.s-ie8-hack .s-top-userset-menu{margin-right:-20px}
.s-ie8-hack .s-top-userset-menu{border:1px solid #f5f5f6}
#bottom_layer{width:100%;position:fixed;z-index:302;bottom:0;left:0;height:39px;padding-top:1px;overflow:hidden;zoom:1;margin:0;line-height:39px;background:#fff}
#bottom_layer .lh{display:inline;margin-right:18px}
#bottom_layer .lh .emphasize{text-decoration:underline;font-weight:700}
#bottom_layer .lh:last-child{margin-left:-2px;margin-right:0}
#bottom_layer .lh.activity{font-weight:700;text-decoration:underline}
#bottom_layer a{font-size:12px;text-decoration:none}
#bottom_layer .text-color{color:#bbb}
#bottom_layer a:hover{color:#222}
#bottom_layer .s-bottom-layer-content{text-align:center}
.s-bottom-space{position:static;width:100%;height:40px;margin:23px auto 12px}
#blind-box{position:fixed;right:24px;bottom:185px;height:44px;width:44px;border-radius:22px}
#blind-box .blind-search-box{position:absolute;bottom:-17px;right:0;min-width:208px;height:80px;box-sizing:border-box;padding:17px 0 17px 6px;overflow:hidden;text-align:right}
#blind-box .blind-search-box .blind-search-area{background-color:#fff;text-align:left;display:inline-block;height:46px;max-width:100%;width:fit-content;white-space:nowrap;overflow:hidden;box-sizing:border-box;padding-right:56px;border-radius:12px 2px 12px 12px;font-size:13px;cursor:pointer;color:#333;transform:translateX(110%);line-height:46px;position:relative}
#blind-box .blind-search-box .blind-search-area .blind-text,#blind-box .blind-search-box .blind-search-area .i{display:inline-block;vertical-align:top}
#blind-box .blind-search-box .blind-search-area i{color:#4e71f2;font-size:14px;margin-left:10px}
#blind-box .blind-search-box .blind-search-area .blind-text{height:100%;min-width:50px;position:relative;line-height:46px;transition:all .3s;overflow:hidden}
#blind-box .blind-search-box .blind-search-area .blind-text:hover{color:#315efb}
#blind-box .blind-search-box .blind-search-area .blind-text .blind-span{line-height:46px;position:absolute;white-space:nowrap;left:0;top:0;opacity:0;transition:all .3s}
#blind-box .blind-search-box .blind-search-area .blind-text .span-now{opacity:1}
#blind-box .blind-search-box .blind-search-area .blind-text .span-next{opacity:0;transform:translateX(-40%)}
#blind-box .blind-search-box .blind-search-area .blind-text .span-last{opacity:0;transform:translateX(40%)}
#blind-box .blind-search-box .blind-box-hover{transform:translateX(0);box-shadow:0 2px 10px 0 rgba(0,0,0,.1)}
#blind-box .blind-search-img{height:80px;width:80px;position:absolute;left:-12px;bottom:0;transform-origin:bottom center;transform:scale(.55);border-radius:40px;cursor:pointer;background-color:#fbfbfb;overflow:hidden}
#blind-box .blind-search-img .blind-img{height:100%;width:100%;position:absolute;top:0;left:0;object-fit:contain;object-position:center;opacity:0;transition:all .3s}
#blind-box .blind-search-img .blind-img-show{opacity:1}
#blind-box .blind-img-hover{transform:scale(1) translateZ(0);border-radius:0;background-color:transparent}
#blind-box .blind-img-ie,#blind-box .blind-title-ie{cursor:pointer;position:absolute;bottom:0;right:0}
#blind-box .blind-title-ie{background-color:#fff;height:46px;width:191px;line-height:46px;box-sizing:border-box;padding:0 10px;font-size:13px;color:#333;visibility:hidden}
#blind-box .blind-title-ie:hover{color:#315efb}
#blind-box .blind-img-ie{height:44px;width:44px;object-fit:cover;object-position:center}
#blind-box:hover .blind-img-ie{height:80px;width:80px;right:-24px}
#blind-box:hover .blind-img-ie,#blind-box:hover .blind-title-ie{visibility:visible}
@media screen and (max-width:1158px){
#blind-box{display:none}
}
#s_popup_advert{position:absolute}
#s_popup_advert .popup-advert{display:none;position:fixed;right:0;bottom:0;z-index:303;width:100%;text-align:center}
#s_popup_advert .advert-link{display:block;width:100%}
#s_popup_advert .advert{display:block;width:100%;height:auto}
#s_popup_advert .right-wrap{position:absolute;right:24px;top:24px;width:152px;height:30px;border-radius:6px;line-height:30px;font-size:13px;color:#9195a3}
#s_popup_advert .popup-count-down{float:left;padding-left:10px}
#s_popup_advert .close-wrap{float:right;padding-left:10px;padding-right:10px;cursor:pointer}
#s_popup_advert .close-icon{vertical-align:middle;color:#c0c2c8}
#s_popup_advert .close-text{padding-left:8px}
#s_popup_advert .close-wrap:hover .close-icon{color:#9195a3}
#s_popup_advert .close-wrap:hover .close-text{color:#626675}
#s_popup_advert .advert-shrink{transform:scale(0);-ms-transform:scale(0);-moz-transform:scale(0);-webkit-transform:scale(0);-o-transform:scale(0);opacity:0;position:fixed;right:24px;bottom:140px;z-index:303;width:44px}
#s_popup_advert .close-shrink{cursor:pointer;position:absolute;left:41px;top:-5px;color:#c0c2c8;font-size:12px}
#s_popup_advert .shrink-link{display:block;height:44px}
#s_popup_advert .shrink{display:block;width:100%;height:100%;border-radius:22px}
#s_popup_advert .replay{cursor:pointer;display:block;margin-top:6px;border-radius:4px;text-align:center;line-height:20px;font-size:13px;color:#9195a3}
#s_popup_advert .close-shrink:hover{color:#9195a3}
#s_popup_advert .replay:hover{color:#626675}
@media screen and (max-width:1158px){
#s_popup_advert{display:none}
}
.guide-info-new{z-index:999;height:34px;padding:0 15px;min-width:120px;background-color:rgba(98,102,117,.8);box-shadow:0 2px 10px 0 rgba(0,0,0,.1);border-radius:6px 6px 6px 6px;text-align:left;position:absolute;line-height:35px}
.guide-info-new span{display:inline-block;vertical-align:top;font-size:13px;font-family:Arial,sans-serif;color:#fff;margin-right:-5px}
.guide-info-new .guide-close{color:#d7d9e0;margin-left:8px;display:inline-block;height:34px;text-align:center;vertical-align:top;font-size:13px!important;cursor:pointer}
.guide-info-new .guide-close:hover{color:#fff!important}
.guide-info-new .guide-arrow-bottom,.guide-info-new .guide-arrow-left,.guide-info-new .guide-arrow-right,.guide-info-new .guide-arrow-top{position:absolute;opacity:.8}
.guide-info-new .guide-arrow-bottom{top:-11px;right:0;height:22px;width:22px;background:url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/img/guide_new/arrow-bottom-a44a0c6a30.png) no-repeat 0 0;background-image:-webkit-image-set(url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/img/guide_new/arrow-bottom-a44a0c6a30.png) 2x)}
.guide-info-new .guide-arrow-left{right:-22px;top:10px;height:22px;width:22px;background:url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/img/guide_new/arrow-left-a7b272965a.png) no-repeat 0 0;background-image:-webkit-image-set(url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/img/guide_new/arrow-left-a7b272965a.png) 2x)}
.guide-info-new .guide-arrow-top{bottom:-22px;left:10px;height:22px;width:22px;background:url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/img/guide_new/arrow-top-d81f5f8843.png) no-repeat 0 0;background-image:-webkit-image-set(url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/img/guide_new/arrow-top-d81f5f8843.png) 2x)}
.guide-info-new .guide-arrow-right{left:-11px;top:10px;height:22px;width:22px;background:url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/img/guide_new/arrow-right-69f7969669.png) no-repeat 0 0;background-image:-webkit-image-set(url(https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/img/guide_new/arrow-right-69f7969669.png) 2x)}
.guide-info-new :hover .guide-close{color:#d7d9e0}
.red-point{position:relative}
.red-point::before{content:" ";border:3px solid #f73131;border-radius:3px;position:absolute;z-index:1000;right:0;margin-right:-5px;margin-top:-2px}
.color222{color:#222!important}</style>
<style type="text/css" index="weather">.s-weather-wrapper{float:none;display:inline-block;margin-left:32px;margin-top:19px;height:23px;vertical-align:top}
.s-weather-wrapper .s-mod-weather{margin:0;height:23px;line-height:23px;display:inline-block}
.s-weather-wrapper .s-mod-weather .weather-mod{display:none}
.s-weather-wrapper .s-mod-weather .weather-mod .city-wather{display:inline-block}
.s-weather-wrapper .s-mod-weather .weather-mod .city-wather .show-city{height:23px;display:inline-block;vertical-align:top;margin-right:8px}
.s-weather-wrapper .s-mod-weather .weather-mod .city-wather .show-icon{margin-right:8px}
.s-weather-wrapper .s-mod-weather .weather-mod .city-wather .show-icon .weather-icon{width:23px;height:23px;display:inline-block;background-size:100% 100%;margin-right:4px}
.s-weather-wrapper .s-mod-weather .weather-mod .city-wather .show-icon .show-icon-temp{vertical-align:top}
.s-weather-wrapper .s-mod-weather .weather-mod .show-pollution{display:inline-block;vertical-align:top;height:23px;line-height:23px}
.s-weather-wrapper .s-mod-weather .weather-mod .show-pollution .show-polution-name{height:16px;color:#fff;background:#4e71f2;border-radius:3px;font-size:11px;display:inline-block;line-height:17px;text-align:center;padding:0 2px}
.s-weather-wrapper .s-mod-weather .unknown-city .unknown-icon{color:#4e71f2;font-size:20px;background:0 0;margin-right:4px}
.s-weather-wrapper .s-mod-weather .unknown-city .unknown-setting{vertical-align:top;color:#222}
.s-weather-wrapper .s-mod-weather.hide-unknow-city .weather-mod{display:block}
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<li class="news-meta-item clearfix" data-index="0"><a class="title-content c-link c-font-medium c-line-ellipsis" href="https://www.baidu.com/s?cl=3&tn=baidutop10&fr=top1000&wd=%E4%B8%AD%E5%9B%BD%E6%98%AF%E5%90%A6%E5%AE%89%E6%8E%92%E4%BB%8E%E7%BC%85%E7%94%B8%E6%92%A4%E4%BE%A8%3F%E4%B8%AD%E6%96%B9%E5%9B%9E%E5%BA%94&rsv_idx=2&rsv_dl=fyb_n_homepage&hisfilter=1" target="_blank" ><span class="title-content-index c-index-single c-index-single-hot1">1</span><span class="title-content-title">中国是否安排从缅甸撤侨?中方回应</span><span class="title-content-mark c-text c-text-hot">热</span></a><div class="news-count c-font-normal c-color-gray2" title="搜索指数466万">466万</div></li>
<li class="news-meta-item clearfix" data-index="1"><a class="title-content c-link c-font-medium c-line-ellipsis" href="https://www.baidu.com/s?cl=3&tn=baidutop10&fr=top1000&wd=%E4%BD%8E%E9%A3%8E%E9%99%A9%E5%9C%B0%E5%8C%BA%E5%A8%B1%E4%B9%90%E5%9C%BA%E6%89%80%E4%BA%BA%E6%95%B0%E4%B8%8D%E5%86%8D%E9%99%90%E5%88%B6&rsv_idx=2&rsv_dl=fyb_n_homepage&hisfilter=1" target="_blank" ><span class="title-content-index c-index-single c-index-single-hot2">2</span><span class="title-content-title">低风险地区娱乐场所人数不再限制</span><span class="title-content-mark c-text"></span></a><div class="news-count c-font-normal c-color-gray2" title="搜索指数450万">450万</div></li>
<li class="news-meta-item clearfix" data-index="2"><a class="title-content c-link c-font-medium c-line-ellipsis" href="https://www.baidu.com/s?cl=3&tn=baidutop10&fr=top1000&wd=%E4%BB%8A%E5%B9%B43%C2%B715%E8%BF%99%E5%85%AD%E7%A7%8D%E4%BA%A7%E5%93%81%E8%A2%AB%E7%82%B9%E5%90%8D&rsv_idx=2&rsv_dl=fyb_n_homepage&hisfilter=1" target="_blank" ><span class="title-content-index c-index-single c-index-single-hot3">3</span><span class="title-content-title">今年3·15这六种产品被点名</span><span class="title-content-mark c-text"></span></a><div class="news-count c-font-normal c-color-gray2" title="搜索指数434万">434万</div></li>
<li class="news-meta-item clearfix" data-index="3"><a class="title-content c-link c-font-medium c-line-ellipsis" href="https://www.baidu.com/s?cl=3&tn=baidutop10&fr=top1000&wd=%E5%8C%97%E4%BA%AC%E6%B2%99%E5%B0%98%E6%9D%A5%E4%B8%B4+%E6%BC%AB%E5%A4%A9%E6%A9%98%E9%BB%84%E8%89%B2&rsv_idx=2&rsv_dl=fyb_n_homepage&hisfilter=1" target="_blank" ><span class="title-content-index c-index-single c-index-single-hot4">4</span><span class="title-content-title">北京沙尘来临 漫天橘黄色</span><span class="title-content-mark c-text"></span></a><div class="news-count c-font-normal c-color-gray2" title="搜索指数419万">419万</div></li>
<li class="news-meta-item clearfix" data-index="4"><a class="title-content c-link c-font-medium c-line-ellipsis" href="https://www.baidu.com/s?cl=3&tn=baidutop10&fr=top1000&wd=%E4%B8%AD%E5%9B%BD%E5%85%B6%E4%BB%96%E6%96%B0%E5%86%A0%E7%96%AB%E8%8B%97%E7%9A%84%E5%BC%80%E5%8F%91%E8%BF%9B%E5%B1%95%E5%A6%82%E4%BD%95%3F&rsv_idx=2&rsv_dl=fyb_n_homepage&hisfilter=1" target="_blank" ><span class="title-content-index c-index-single c-index-single-hot5">5</span><span class="title-content-title">中国其他新冠疫苗的开发进展如何?</span><span class="title-content-mark c-text "></span></a><div class="news-count c-font-normal c-color-gray2" title="搜索指数404万">404万</div></li>
<li class="news-meta-item clearfix" data-index="5"><a class="title-content c-link c-font-medium c-line-ellipsis" href="https://www.baidu.com/s?cl=3&tn=baidutop10&fr=top1000&wd=%E7%94%A8%E6%89%AB%E5%B8%9A%E6%8D%A3%E5%88%B6%E5%86%B0%E6%9C%BA%3F%E5%B0%8F%E9%BE%99%E5%9D%8E%E7%81%AB%E9%94%85%E8%87%B4%E6%AD%89&rsv_idx=2&rsv_dl=fyb_n_homepage&hisfilter=1" target="_blank" ><span class="title-content-index c-index-single c-index-single-hot6">6</span><span class="title-content-title">用扫帚捣制冰机?小龙坎火锅致歉</span><span class="title-content-mark c-text"></span></a><div class="news-count c-font-normal c-color-gray2" title="搜索指数390万">390万</div></li>
<li class="news-meta-item clearfix" data-index="6"><a class="title-content c-link c-font-medium c-line-ellipsis" href="https://www.baidu.com/s?cl=3&tn=baidutop10&fr=top1000&wd=%E5%8F%98%E5%BC%82%E7%97%85%E6%AF%92%E5%AF%B9%E6%88%91%E5%9B%BD4%E6%AC%BE%E7%96%AB%E8%8B%97%E6%97%A0%E6%98%8E%E6%98%BE%E5%BD%B1%E5%93%8D&rsv_idx=2&rsv_dl=fyb_n_homepage&hisfilter=1" target="_blank" ><span class="title-content-index c-index-single c-index-single-hot7">7</span><span class="title-content-title">变异病毒对我国4款疫苗无明显影响</span><span class="title-content-mark c-text c-text-new">新</span></a><div class="news-count c-font-normal c-color-gray2" title="搜索指数376万">376万</div></li>
<li class="news-meta-item clearfix" data-index="7"><a class="title-content c-link c-font-medium c-line-ellipsis" href="https://www.baidu.com/s?cl=3&tn=baidutop10&fr=top1000&wd=%E5%85%AC%E5%AE%89%E9%83%A8%E5%9B%9E%E5%BA%94%E5%90%B8%E6%AF%92%E7%BB%88%E8%BA%AB%E7%A6%81%E6%BC%94%E5%BB%BA%E8%AE%AE&rsv_idx=2&rsv_dl=fyb_n_homepage&hisfilter=1" target="_blank" ><span class="title-content-index c-index-single c-index-single-hot8">8</span><span class="title-content-title">公安部回应吸毒终身禁演建议</span><span class="title-content-mark c-text"></span></a><div class="news-count c-font-normal c-color-gray2" title="搜索指数363万">363万</div></li>
<li class="news-meta-item clearfix" data-index="8"><a class="title-content c-link c-font-medium c-line-ellipsis" href="https://www.baidu.com/s?cl=3&tn=baidutop10&fr=top1000&wd=%E7%AC%AC63%E5%B1%8A%E6%A0%BC%E8%8E%B1%E7%BE%8E%E8%8E%B7%E5%A5%96%E5%90%8D%E5%8D%95%E6%8F%AD%E6%99%93&rsv_idx=2&rsv_dl=fyb_n_homepage&hisfilter=1" target="_blank" ><span class="title-content-index c-index-single c-index-single-hot9">9</span><span class="title-content-title">第63届格莱美获奖名单揭晓</span><span class="title-content-mark c-text"></span></a><div class="news-count c-font-normal c-color-gray2" title="搜索指数350万">350万</div></li>
<li class="news-meta-item clearfix" data-index="9"><a class="title-content c-link c-font-medium c-line-ellipsis" href="https://www.baidu.com/s?cl=3&tn=baidutop10&fr=top1000&wd=%E8%8B%A6%E7%AD%899%E4%B8%AA%E6%9C%88%E7%9A%84%E5%A9%9A%E7%A4%BC%E8%A7%86%E9%A2%91%E5%85%A8%E6%98%AF%E8%83%8C%E5%BD%B1&rsv_idx=2&rsv_dl=fyb_n_homepage&hisfilter=1" target="_blank" ><span class="title-content-index c-index-single c-index-single-hot10">10</span><span class="title-content-title">苦等9个月的婚礼视频全是背影</span><span class="title-content-mark c-text"></span></a><div class="news-count c-font-normal c-color-gray2" title="搜索指数338万">338万</div></li>
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clearfix"><p class=title>推荐电台(<span class=count>48</span>)</p><p class=mylike><a class=js-formy href="http://play.baidu.com/?__t=list_allfavor&from=bd_card" target=_blank>我的收藏 (<span class="like-count js-like-count"></span>)</a></p></div> <div class="s-list-wrapper clearfix slider-wrapper"> <div class="prev-page-btn control-left" title="上一页"><span class="icon-pic prev-page-icon"></span></div> <div class="next-page-btn control-right" title="下一页"><span class="icon-pic next-page-icon"></span></div> <div style="position:relative;overflow:hidden;width:100%;"> <div style="position:relative;left:0px;width:60000px;" class="clearfix mui-slider-scroll-container"> <ul class="s-list clearfix" data-index="0""> <li data-id="public_tuijian_suibiantingting" data-type="other" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px 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class="icon-pic s-playing-wrapper"> </div> <div class=desc>成名曲</div> <li data-id="public_shiguang_jingdianlaoge" data-type="other" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/jingdianlaoge.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>经典老歌</div> <li data-id="public_tuijian_yingshi" data-type="other" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/yingshi.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>影视</div> <li data-id="public_fengge_qingyinyue" data-type="other" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/qingyinyue.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>轻音乐</div> <li data-id="public_xinqing_qingge" data-type="other" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/danshen.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>单身情歌</div> <li data-id="156" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/wangluogequ.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>网络歌曲</div> <li data-id="42" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/chinese.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>国语</div> </ul> <ul class="s-list clearfix" data-index="1""> <li data-id="71" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/2000nian.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>2000年代</div> <li data-id="public_fengge_yaogun" data-type="other" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/yaogun.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>摇滚</div> <li data-id="44" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/english.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>英文</div> <li data-id="public_tuijian_ktv" data-type="other" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/KTV.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>KTV金曲</div> <li data-id="public_shiguang_90hou" data-type="other" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/90.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>90后</div> <li data-id="public_fengge_zhongguofeng" data-type="other" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/zhongguofeng.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>中国风</div> <li data-id="public_shiguang_80hou" data-type="other" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/80.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>80后</div> <li data-id="public_fengge_xiaoqingxin" data-type="other" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/xiaoqingxin.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>小清新</div> <li data-id="public_yuzhong_hanyu" data-type="other" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/hanyu.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>韩语</div> </ul> <ul class="s-list clearfix" data-index="2""> <li data-id="public_fengge_dj" data-type="other" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss1.bdstatic.com/kvoZeXSm1A5BphGlnYG/newmusic/DJ.png?v=md5"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>DJ舞曲</div> <li data-id="10" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/single.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>独处</div> <li data-id="5" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/morining.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>清晨</div> <li data-id="40" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/relax.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>轻松时刻</div> <li data-id="15" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/afternoon.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>慵懒午后</div> <li data-id="33" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/newsong.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>新歌试听</div> <li data-id="38" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/happy.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>欢乐旋律</div> <li data-id="41" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/miss.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>思念</div> <li data-id="37" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/zhiyu.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>治愈系</div> </ul> <ul class="s-list clearfix" data-index="3""> <li data-id="81" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/slience.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>安静</div> <li data-id="36" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/sad.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>伤感</div> <li data-id="35" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/lonly.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>寂寞电波</div> <li data-id="43" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/yueyu.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>粤语</div> <li data-id="39" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/zuile.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>醉了</div> <li data-id="9" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/sleep.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>睡眠</div> <li data-id="16" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/study.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>学习</div> <li data-id="1" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/sport.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>运动</div> <li data-id="13" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/evening.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>傍晚</div> </ul> <ul class="s-list clearfix" data-index="4""> <li data-id="12" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/work.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>工作</div> <li data-id="48" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/pop.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>流行风</div> <li data-id="31" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/household.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>家务</div> <li data-id="23" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/coffee.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>咖啡时光</div> <li data-id="28" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/home.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>宅</div> <li data-id="2" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/party.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>聚会</div> <li data-id="29" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/net.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>上网</div> <li data-id="21" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/haichuang.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>赖床</div> <li data-id="20" data-type="default" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/wake.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> <div class="icon-pic s-playing-wrapper"> </div> <div class=desc>懒洋洋</div> </ul> <ul class="s-list clearfix" data-index="5""> <li data-id="34" data-type="" class="s-item js-play-songlist"> <span class=sence-block-hover></span> <span href="javascript:void(0);" class=sence-block> <img width=70px height=70px data-src="https://dss0.bdstatic.com/k4oZeXSm1A5BphGlnYG/newmusic/jidong.png"> </span> <div class="icon-pic s-play-btn js-splay-btn"></div> 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var LogoColor = $("#head");
if(LogoColor.hasClass('red-blue-logo')) {
if (s_session.logoCode) {
change(skinOn,skinOnRe);
}else {
change(userSetLogoRed, userSetLogoRed);
}
}else if(LogoColor.hasClass('white-logo')){
if(!s_session.userLogoIsupload || s_session.logoCode) {
change(skinOn,skinOnRe);
}else {
change(userSetLogoWrite,userSetLogoWrite);
}
}else {
change(skinOff,skinOffRe);
}
});
})()
</script>
<script type="text/javascript" id="s_js_setting" data-src="https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/superman/js/min_setting-c3f73c1e26.js"></script>
<script id=tipsplus-js data-src="https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/tipsplus/js/min_tips_f870f501.js"></script><script data-onload=true data-src="https://dss0.bdstatic.com/5aV1bjqh_Q23odCf/static/activity/js/activity_start_31d46e4f.js"></script>
<script>
setTimeout(function(){if(document.getElementById("s_main").offsetWidth==0 && typeof(F)=='undefined'){new Image().src=s_domain.baseuri+'/page/data/pageserver?errno=2015&msg=cdn_failed'}},2000);
if(typeof ns_c == "undefined"){var ns_c=function(){}}
</script>
<script defer src="//hectorstatic.baidu.com/cd37ed75a9387c5b.js"></script>
</body>
</html>
"""
# 使用soup对象解析
soup = BeautifulSoup(html_doc, features= 'lxml')
print(soup)
|
python
|
# Generated by Django 3.0.7 on 2020-06-30 17:03
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('portfolio', '0005_auto_20200630_1658'),
]
operations = [
migrations.AddField(
model_name='collection',
name='spotlight',
field=models.BooleanField(default=True),
preserve_default=False,
),
]
|
python
|
from tool.runners.python import SubmissionPy
class DivSubmission(SubmissionPy):
def match(self, x):
s = str(x)
n = len(s)
if n != 6:
return False
# always increasing (or equal)
for i in range(n-1):
if s[i] > s[i+1]:
return False
# should have two identical adjacent digits
# and only two
i = 0
while i < n:
j = i
while j < n and s[j] == s[i]:
j += 1
if j-i == 2:
return True
i = j
return False
def run(self, s):
min_value, max_value = [int(x) for x in s.split("-")]
return sum(1 for x in range(min_value, max_value+1) if self.match(x))
|
python
|
#take file called numbers.txt to start
fileName = "numbers.txt"
def main():
global fileName
fileInput = open(fileName, 'r')
numAry = []
for line in fileInput.readlines():
numAry.append(eval(line))
maxInt = findMax(numAry, 0, len(numAry)-1)
print("The Max Value Is " + str(maxInt))
def findMax(numAry, start, end):
if (end-start == 0):
return numAry[start]
else:
midpoint = ((end-start)//2)+start
l = findMax(numAry, start, midpoint) #recursive call to left half
r = findMax(numAry, midpoint+1, end) #recursive call to right half
if (l > r):
return l
else:
return r
main()
|
python
|
#!/usr/bin/python2.7
# coding:utf-8
import logging
from time import sleep
from RPi import GPIO
from cmdtree import INT
from cmdtree import command
from cmdtree import entry
from cmdtree import group
from cmdtree import option
GPIO_CONTROL_PORT = 12
FAN_ON_TEMPERATURE = 55
def get_cpu_temp():
with open("/sys/class/thermal/thermal_zone0/temp", 'r') as f:
temp = float(f.read()) / 1000
return temp
def init():
GPIO.setmode(GPIO.BOARD)
def set_debug():
logging.basicConfig(level=logging.DEBUG)
@group(name="port", help="Turn a pin port to `IN` or `OUT`")
def port_manage():
pass
@option("port", help="Port on GPIO Physical Port", type=INT, default=GPIO_CONTROL_PORT)
@port_manage.command("turn-out", help="Turn a port into `OUT` mode")
def turn_port_out(port):
GPIO.setup(port, GPIO.OUT)
GPIO.output(port, 1)
return True
@option("port", help="Port number of GPIO Physical Port", type=INT, default=GPIO_CONTROL_PORT)
@port_manage.command("turn-in", help="Turn a port into `IN` mode")
def turn_port_in(port):
GPIO.setup(port, GPIO.IN)
return False
@option("loop", help="loop until user interrupting execution.", is_flag=True)
@command("cpu-show")
def show_cpu_temperature(loop):
def show_temperature():
print("Cpu Temperature is: {0}".format(get_cpu_temp()))
if loop:
while True:
show_temperature()
sleep(1)
else:
show_temperature()
@option(
"on-t",
help="Temperature which triggers the fan on.",
type=INT,
default=FAN_ON_TEMPERATURE,
)
@option(
"port",
help="Port number of GPIO Physical Port",
type=INT,
default=GPIO_CONTROL_PORT,
)
@group(name="fan", help="Auto tune the cpu fan in `simple` or `auto` mode.")
def auto_fan():
pass
@auto_fan.command("on", help="Turn one the fan.")
def on_fan(port, **kwargs):
turn_port_out(port)
@auto_fan.command("off", help="Turn off the fan.")
def off_fan(port, **kwargs):
turn_port_in(port)
@option("debug", is_flag=True, default=False)
@auto_fan.command("simple", help="Simply turn on or off the fan in given temperature range.")
def simple_on_of(debug, port, on_t):
if debug:
set_debug()
fan_on = turn_port_in(port)
try:
while True:
temperature = get_cpu_temp()
if temperature >= on_t:
if not fan_on:
logging.debug("Temperature {0} CPU fan on.".format(temperature))
fan_on = turn_port_out(port)
else:
if fan_on:
logging.debug("Temperature {0} CPU fan off.".format(temperature))
fan_on = turn_port_in(port)
sleep(10)
except Exception:
logging.exception("Error occurs while tune fan status:")
GPIO.cleanup()
def main():
init()
entry()
if __name__ == '__main__':
main()
|
python
|
from copy import deepcopy
from torch import nn
import numpy as np
import math
import torch
from torch.utils.data import dataset
from torch.utils.data.dataset import Subset
from torch.utils.data import DataLoader
# def compare_models(model: nn.Module, clients: 'list[Client]', num: int):
# def print_params(model: nn.Module, num: int):
# counter = 1
# for name, param in model.state_dict().items():
# if counter > num:
# break
# else:
# print(param[0][0], end="")
# counter += 1
# print("")
# print_params(model, num)
# print_params(clients[0].model, num)
# print_params(clients[len(clients)//2].model, num)
# print_params(clients[-1].model, num)
# def regroup(G: np.ndarray, A: np.ndarray, s: int) -> 'tuple[np.ndarry, np.ndarry]':
# "s: each new group contains s old groups"
# group_num: int = G.shape[1]
# new_group_size: int = math.ceil(group_num / s)
# A_T = A.transpose()
# group2server: list[int] = []
# # get new groups as list
# new_groups: 'list[list[int]]' = []
# for i, server in enumerate(A_T):
# new_group: 'list[int]' = []
# for j, group in server:
# if A_T[i][j] == 1:
# if len(new_group) < new_group_size:
# new_group.append(j)
# else:
# new_groups.append(new_group)
# new_group = []
# # construct new A
# new_A = np.zeros((len(new_groups), A.shape[1],))
# for i, new_group in enumerate(new_groups):
# one_group = new_group[0]
# belong_to_server = 0
# for j, to_server in enumerate(A[one_group]):
# if to_server == 1:
# belong_to_server = j
# break
# new_A[i][belong_to_server] = 1
# # construct new G
# new_G = np.zeros((G.shape[0], len(new_groups),))
# for i, new_group in enumerate(new_groups):
# for old_group in new_group:
# G_T = G.transpose()
# for k, contain_client in enumerate(G_T[old_group]):
# if contain_client == 1:
# new_G[k][i] = 1
# return new_G, new_A
|
python
|
#!/usr/bin/env python3
# Copyright 2018 Brocade Communications Systems LLC. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may also obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
:mod:`password_cfg_set` - PyFOS util to change password \
configuration parameters.
********************************************************\
**************************
This module is a stand-alone script and API that can be used to change
password configuration paramters.
* Input:
| Infrastructure Options:
| -i,--ipaddr=IPADDR The IP address of FOS switch.
| -L,--login=LOGIN The login name.
| -P,--password=PASSWORD The password.
| -f,--vfid=VFID The VFID to which the request is directed [OPTIONAL].
| -s,--secured=MODE The HTTPS mode "self" or "CA" [OPTIONAL].
| -v,--verbose The Verbose mode [OPTIONAL].
* Util Script Options:
--password-action The actions to be performed.
--minimum-length The minumum length of the password.
--character-set The minimum criteria of the \
character set.
--user-name-allowed Whether the username can be used in \
the password.
--min-lower-case-char The minimum number of lowercase \
alphabetic characters.
--min-upper-case-char The minimum number of uppercase \
alphabetic characters.
--min-numeric-char The minimum number of numeric digits.
--min-special-char The minimum number of special \
characters.
--past-password-history The number of past password values \
that are disallowed.
--min-password-age Sets the minimum number of days before \
which the
password cannot be modified.
--max-password-age Sets the maximum number of days after \
which the
password should be modified.
--warn-on-expire The number of days to display warning \
messages until
password expiration.
--lock-out-threshold The maximum number of login attempts \
before the
account is locked.
--lock-out-duration The duration, in minutes, to wait and \
unlock the locked
account.
--admin-lock-out-enabled Enables or disables admin lockout.
--repeat-char-limit The maximum length of repeated character \
sequences that
is disallowed.
--sequence-character-limit The length of sequential character \
sequences that
is disallowed.
--reverse-user-name-allowed Enables or disables a reverse string of \
the username
as the password.
--hash-type Sets the hash type.
--manual-hash-enabled Manually enforces a password change \
due to hash change.
--enforce-expire Enforces password expiration.
--min-diff The minimum difference between the old \
and new password.
* Output:
* A success response or a dictionary in case of error.
"""
import sys
from pyfos import pyfos_auth
from pyfos import pyfos_util
from pyfos.pyfos_brocade_security import password_cfg
from pyfos.utils import brcd_util
def main(argv):
filters = ["minimum_length", "character_set", "user_name_allowed",
"minimum_lower_case_character",
"minimum_upper_case_character",
"minimum_numeric_character",
"minimum_special_character",
"past_password_history",
"minimum_password_age",
"maximum_password_age",
"warn_on_expire",
"lock_out_threshold",
"lock_out_duration",
"admin_lock_out_enabled",
"repeat_character_limit",
"sequence_character_limit",
"reverse_user_name_allowed",
"hash_type",
"manual_hash_enabled",
"enforce_expire",
"password_action", "minimum_difference"]
inputs = brcd_util.parse(argv, password_cfg, filters)
password_cfg_obj = inputs['utilobject']
if (password_cfg_obj.peek_password_action() is None and
password_cfg_obj.peek_minimum_length() is None and
password_cfg_obj.peek_character_set() is None and
password_cfg_obj.peek_user_name_allowed() is None and
password_cfg_obj.peek_minimum_lower_case_character() is None and
password_cfg_obj.peek_minimum_upper_case_character() is None and
password_cfg_obj.peek_minimum_numeric_character() is None and
password_cfg_obj.peek_minimum_special_character() is None and
password_cfg_obj.peek_past_password_history() is None and
password_cfg_obj.peek_lock_out_threshold() is None and
password_cfg_obj.peek_lock_out_duration() is None and
password_cfg_obj.peek_admin_lock_out_enabled() is None and
password_cfg_obj.peek_repeat_character_limit() is None and
password_cfg_obj.peek_sequence_character_limit() is None and
password_cfg_obj.peek_reverse_user_name_allowed() is None and
password_cfg_obj.peek_minimum_password_age() is None and
password_cfg_obj.peek_maximum_password_age() is None and
password_cfg_obj.peek_warn_on_expire() is None and
password_cfg_obj.peek_minimum_difference() is None and
password_cfg_obj.peek_enforce_expire() is None):
print("Missing command line options")
print(inputs['utilusage'])
exit(1)
elif (password_cfg_obj.peek_password_action() == "hash-config" and
password_cfg_obj.peek_hash_type() is None and
password_cfg_obj.peek_manual_hash_enabled() is None):
print("Missing command line options")
print(inputs['utilusage'])
exit(1)
elif (password_cfg_obj.peek_password_action() == "default" or
password_cfg_obj.peek_password_action() == "delete-all"):
pass
session = brcd_util.getsession(inputs)
result = password_cfg_obj.patch(session)
pyfos_util.response_print(result)
pyfos_auth.logout(session)
if __name__ == "__main__":
main(sys.argv[1:])
|
python
|
import json
from django.contrib.auth import authenticate, login, logout
from django.contrib.auth.decorators import login_required
from django.core.paginator import Paginator
from django.db import IntegrityError
from django.http import HttpResponse, HttpResponseRedirect, JsonResponse
from django.shortcuts import render
from django.urls import reverse
from .models import POST_MAX_LENGTH, Following, Post, User
def index(request):
post_objects = Post.objects.all().order_by("-created_on")
paginator = Paginator(post_objects, 10) # show 10 posts per page.
page_number = request.GET.get("page", 1)
posts = paginator.get_page(page_number)
return render(
request,
"network/index.html",
{
"posts": posts,
"num_page_range": range(posts.paginator.num_pages),
"current_page": page_number,
"post_max_length": POST_MAX_LENGTH,
},
)
# return render(request, "network/index.html", {"posts": posts})
@login_required
def get_following_posts(request):
user = User.objects.get(id=request.user.id)
following = user.following.all()
following_users = [follow.user for follow in following]
post_objects = Post.objects.filter(user__in=following_users).order_by("-created_on")
paginator = Paginator(post_objects, 10) # show 10 posts per page.
page_number = request.GET.get("page", 1)
posts = paginator.get_page(page_number)
return render(
request,
"network/following.html",
{
"posts": posts,
"num_page_range": range(posts.paginator.num_pages),
"current_page": page_number,
},
)
def login_view(request):
if request.method == "POST":
# Attempt to sign user in
username = request.POST["username"]
password = request.POST["password"]
user = authenticate(request, username=username, password=password)
# Check if authentication successful
if user is not None:
login(request, user)
return HttpResponseRedirect(reverse("index"))
else:
return render(
request,
"network/login.html",
{"message": "Invalid username and/or password."},
)
else:
return render(request, "network/login.html")
def logout_view(request):
logout(request)
return HttpResponseRedirect(reverse("index"))
def register(request):
if request.method == "POST":
username = request.POST["username"]
email = request.POST["email"]
# Ensure password matches confirmation
password = request.POST["password"]
confirmation = request.POST["confirmation"]
if password != confirmation:
return render(
request, "network/register.html", {"message": "Passwords must match."}
)
# Attempt to create new user
try:
user = User.objects.create_user(username, email, password)
user.save()
except IntegrityError:
return render(
request, "network/register.html", {"message": "Username already taken."}
)
login(request, user)
return HttpResponseRedirect(reverse("index"))
else:
return render(request, "network/register.html")
@login_required
def like_post(request, post_id):
if request.method == "POST":
post = Post.objects.get(id=post_id)
user = request.user
if user in post.likes.all():
post.likes.remove(user)
post.save()
return JsonResponse({"liked": "False", "post_id": post_id}, status=200)
post.likes.add(user)
post.save()
return JsonResponse({"liked": "True", "post_id": post_id}, status=200)
@login_required
def post(request, post_id=None):
if request.method == "POST":
if not post_id:
user = User.objects.get(id=request.user.id)
user_post = Post.objects.create(
user=user, message=request.POST["post-message"]
)
user_post.likes.add(user)
user_post.save()
return HttpResponseRedirect(reverse("index"))
post = Post.objects.get(id=post_id)
if request.user.id != post.user.id:
return HttpResponseRedirect(reverse("index"))
data = json.loads(request.body)
if len(data.get("message", "")) <= 0:
return JsonResponse({"message": "Posts cannot be empty"}, status=406)
if len(data.get("message")) > POST_MAX_LENGTH:
return JsonResponse(
{"message": "Posts cannot be over 240 characters"}, status=406
)
post.message = data.get("message")
post.save()
return JsonResponse({"saved": True}, status=200)
return render(request, "network/post.html", {"post_max_length": POST_MAX_LENGTH})
def get_user(request, user_id):
user_profile = User.objects.get(id=user_id)
followed_user, _ = Following.objects.get_or_create(user=user_profile)
is_following = request.user in followed_user.followers.all()
numbers_of_followers = followed_user.followers.count()
number_of_users_followed = user_profile.following.count()
post_objects = Post.objects.filter(user=user_profile).order_by("-created_on")
paginator = Paginator(post_objects, 10) # show 10 posts per page.
page_number = request.GET.get("page", 1)
posts = paginator.get_page(page_number)
return render(
request,
"network/profile.html",
{
"user": user_profile,
"following": is_following,
"number_of_followers": numbers_of_followers,
"number_of_users_followed": number_of_users_followed,
"posts": posts,
"num_page_range": range(posts.paginator.num_pages),
"current_page": page_number,
"post_max_length": POST_MAX_LENGTH,
},
)
def follow_user(request, user_id):
if request.method == "POST":
user = User.objects.get(id=user_id)
if request.user == user:
return JsonResponse({"message": "Can not follow yourself"}, status=400)
user_following, _ = Following.objects.get_or_create(user=user)
if request.user not in user_following.followers.all():
user_following.followers.add(request.user)
user_following.save()
return JsonResponse(
{"followed": True, "user_id": user.id, "follower": request.user.id},
status=200,
)
user_following.followers.remove(request.user)
user_following.save()
return JsonResponse(
{"followed": False, "user": user.id, "follower": request.user.id},
status=200,
)
return JsonResponse({"message": "Can only post to method"}, status=400)
|
python
|
nome = str(input('Digite seu nome completo: ')).strip()
print('Silva' in nome.title())
|
python
|
from __future__ import print_function
from optparse import make_option
from datetime import datetime, timedelta
import sys
from textwrap import dedent
from django.core.mail import mail_admins
from django.contrib.auth.models import User
from reversion.models import Revision
from cobl.utilities import LexDBManagementCommand
from cobl.lexicon.models import LanguageListOrder
class Command(LexDBManagementCommand):
help = """Report the changes to the database over the previous N days"""
option_list = (
make_option(
"-d", "--dry-run", dest="dry_run",
action="store_true", default=False,
help="Report daily summary to stdout "
"[default: email it to admins]"),
make_option(
"-e", "--no-empty", dest="include_empty",
action="store_false", default=True,
help="Suppress reports when there have been no changes "
"[default: show empty]"),
make_option(
"-n", "--num-days", dest="num_days",
action="store", default=1, type=int,
metavar="N",
help="Report the activity for the previous N days "
"[default: 1]"),
)
def handle(self, **options):
if options["dry_run"]:
io = sys.stdout
else:
import StringIO
import codecs
buffer = StringIO.StringIO()
codecinfo = codecs.lookup("utf8")
io = codecs.StreamReaderWriter(
buffer, codecinfo.streamreader, codecinfo.streamwriter)
activity_flag = False
print_report = get_printer(io)
end_date = datetime.now()
start_date = end_date - timedelta(days=options["num_days"])
print_report("== Report period ==")
print_report("Start:", strftime(start_date))
print_report(" End:", strftime(end_date))
print_report()
recent_changes = Revision.objects.filter(
date_created__gt=start_date).order_by("date_created")
# Report logins
print_report("== Logins ==")
for user in User.objects.filter(
last_login__gt=start_date).order_by("last_name"):
print_report(strftime(user.last_login), strfuser(user))
activity_flag = True
print_report()
print_report("== Active editors ==")
user_ids = set(recent_changes.values_list("user", flat=True))
if None in user_ids:
print_report(dedent("""\
***********************************************
WARNING: revisions made by unauthenticated user
(indicates a missing login_required constraint)
***********************************************"""))
for user in User.objects.filter(
id__in=user_ids):
print_report(strftime(user.last_login), strfuser(user))
print_report()
# Report database changes
boring_models = [LanguageListOrder]
print_report("== Activity report ==")
for revision in recent_changes:
timestamp = strftime(revision.date_created)
user = revision.user
if user:
print_report(timestamp, user)
else:
print_report(timestamp, "** UNAUTHENTICATED USER **")
for version in revision.version_set.all():
model = version.content_type.model_class()
if model not in boring_models:
try:
print_report(
" %s %s#%s <%s>" % (
version.get_type_display(),
model.__name__, version.object_id,
version.object_repr))
except:
print_report(" %s OBJECT UNAVAILABLE",
version.get_type_display())
activity_flag = True
# Send email
if not options["dry_run"]:
if activity_flag:
io.seek(0)
subject = ("Activity report %s" % strftime(end_date))
mail_admins(subject, io.read())
else:
if options["include_empty"]:
subject = ("No activity %s" % strftime(end_date))
msg = "No activity from %s to %s" % \
(strftime(start_date), strftime(end_date))
mail_admins(subject, msg)
# standard report formatters
def get_printer(fileobj):
def printer(*args, **kwargs):
kwargs["file"] = fileobj
print(*args, **kwargs)
return printer
def strfuser(userobj):
if userobj.last_name:
return "%s %s (%s)" % \
(userobj.first_name, userobj.last_name, userobj.username)
else:
return userobj.username
def strftime(datetimeobj):
return datetimeobj.strftime("%Y-%m-%d %H:%M:%S")
|
python
|
def load_STNTable(fobj, kwArgCheck = None, debug = False, errors = "strict", indent = 0):
# NOTE: see https://github.com/lw/BluRay/wiki/STNTable
# Import standard modules ...
import struct
# Import sub-functions ...
from .load_StreamAttributes import load_StreamAttributes
from .load_StreamEntry import load_StreamEntry
# Check keyword arguments ...
if kwArgCheck is not None:
print(f"WARNING: \"{__name__}\" has been called with an extra positional argument")
# Initialize answer and find it current position ...
ans = {}
pos = fobj.tell() # [B]
if debug:
print("DEBUG:{:s} {:s}() called at {:,d} bytes".format(indent * " ", __name__, pos), end = "")
# Read the binary data ...
ans["Length"], = struct.unpack(">H", fobj.read(2)) # [B]
if debug:
print(" and is {:,d} bytes long".format(ans["Length"] + 2))
if ans["Length"] != 0:
fobj.read(2)
ans["NumberOfPrimaryVideoStreamEntries"], = struct.unpack(">B", fobj.read(1))
ans["NumberOfPrimaryAudioStreamEntries"], = struct.unpack(">B", fobj.read(1))
ans["NumberOfPrimaryPGStreamEntries"], = struct.unpack(">B", fobj.read(1))
ans["NumberOfPrimaryIGStreamEntries"], = struct.unpack(">B", fobj.read(1))
ans["NumberOfSecondaryAudioStreamEntries"], = struct.unpack(">B", fobj.read(1))
ans["NumberOfSecondaryVideoStreamEntries"], = struct.unpack(">B", fobj.read(1))
ans["NumberOfSecondaryPGStreamEntries"], = struct.unpack(">B", fobj.read(1))
ans["NumberOfDVStreamEntries"], = struct.unpack(">B", fobj.read(1))
fobj.read(4)
# Loop over stream list names ...
for name in ["PrimaryVideoStreamEntries", "PrimaryAudioStreamEntries", "PrimaryPGStreamEntries", "SecondaryPGStreamEntries", "PrimaryIGStreamEntries", "SecondaryAudioStreamEntries", "SecondaryVideoStreamEntries", "DVStreamEntries"]:
# Loop over entries and add to list ...
ans[name] = []
for i in range(ans["NumberOf{:s}".format(name)]):
tmp = {}
tmp["StreamEntry"] = load_StreamEntry(fobj, debug = debug, errors = errors, indent = indent + 1)
tmp["StreamAttributes"] = load_StreamAttributes(fobj, debug = debug, errors = errors, indent = indent + 1)
ans[name].append(tmp)
# Skip ahead to the end of the data structure ...
fobj.seek(pos + ans["Length"] + 2)
# Return answer ...
return ans
|
python
|
import unicodedata
def parse(dict_file):
with open(dict_file, 'r', encoding='utf-8') as r:
for line in r:
for word in line.split("#")[1].split(","):
word = unicodedata.normalize('NFKC', word.strip())
yield word
def parse_lexemes(dict_file):
with open(dict_file, 'r', encoding='utf-8') as r:
for line in r:
yield [unicodedata.normalize('NFKC', word.strip()) for word in line.split("#")[1].split(",")]
|
python
|
#!/usr/bin/env python
# Copyright (c) 2014, Stanford University
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import sys
import os
import argparse
# from cgi import logfile
import datetime
import time
source_dir = [os.path.join(os.path.dirname(os.path.abspath(__file__)), "../json_to_relation/")]
source_dir.extend(sys.path)
sys.path = source_dir
from json_to_relation import JSONToRelation
from output_disposition import OutputDisposition, OutputFile
from input_source import InURI
from edxTrackLogJSONParser import EdXTrackLogJSONParser
if __name__ == "__main__":
parser = argparse.ArgumentParser(prog='json2sql.py')
parser.add_argument('-x', '--expungeTables',
help='DROP all tables in database before beginning transform',
dest='dropTables',
action='store_true',
default=False)
# parser.add_argument('-l', '--logFile',
# help='fully qualified log file name. Default: no logging.',
# dest='logFile',
# default='/tmp/j2s.sql');
parser.add_argument('-v', '--verbose',
help='print operational info to console.',
dest='verbose',
action='store_true');
parser.add_argument('destDir',
help='file path for the destination .sql file')
parser.add_argument('inFilePath',
help='json file path to be converted to sql.')
args = parser.parse_args();
# Output file is name of input file with the
# .json extension replaced by .sql, and a unique
# timestamp/pid added to avoid name collisions during
# parallel processing:
dt = datetime.datetime.fromtimestamp(time.time())
fileStamp = dt.isoformat().replace(':','_') + '_' + str(os.getpid())
outFullPath = os.path.join(args.destDir, os.path.basename(args.inFilePath)) + '.' + fileStamp + '.sql'
# Log file will go to <destDir>/../TransformLogs, the file being named j2s_<inputFileName>.log:
logDir = os.path.join(args.destDir, '..') + '/TransformLogs'
if not os.access(logDir, os.W_OK):
os.makedirs(logDir)
logFile = os.path.join(logDir, 'j2s_%s_%s.log' % (os.path.basename(args.inFilePath), fileStamp))
# print('xpunge: %s' % args.dropTables)
# print('verbose: %s' % args.verbose)
# print('destDir: %s' % args.destDir)
# print('in=FilePath: %s' % args.inFilePath)
# print('outFullPath: %s' % outFullPath)
# print('logFile: %s' % logFile)
# Create an instance of JSONToRelation, taking input from the given file:
# and pumping output to the given output path:
jsonConverter = JSONToRelation(InURI(args.inFilePath),
OutputFile(outFullPath,
OutputDisposition.OutputFormat.SQL_INSERT_STATEMENTS,
options='wb'), # overwrite any sql file that's there
mainTableName='EdxTrackEvent',
logFile=logFile
)
jsonConverter.setParser(EdXTrackLogJSONParser(jsonConverter,
'EdxTrackEvent',
replaceTables=args.dropTables,
dbName='Edx'
))
jsonConverter.convert()
|
python
|
import pyperclip
class User:
'''
class for passing all instances of the user
'''
user_list = []
def __init__(self,user_name,account_name,email,password):
'''
initiates properties of my objects
Args:
user_name:New username of the account
'''
self.user_name = user_name
self.account_name = account_name
self.email = email
self.password = password
def save_user(self):
'''
save_contact method saves contact objects into contact_list
'''
User.user_list.append(self)
def delete_user(self):
'''
delete_user method delete a saved user from user_list
'''
User.user_list.remove(self)
@classmethod
def find_by_account_name(cls,account_name):
'''
Method takes account name and returns password that matches that account
Args:
number: Phone number to search for
Returns:
Contact of person that matches the number
'''
for user in cls.user_list:
if user.account_name == account_name :
return user
@classmethod
def user_exist(cls,account_name):
'''
Method that checks if a contact exists from contact lists.
Args:
number: account name to search if it exists
Returns:
Boolean: True or False depending if the contact exists
'''
for user in cls.user_list:
if user.account_name == account_name:
return True
return False
@classmethod
def display_user(cls):
'''
method that returns the user list
'''
return cls.user_list
@classmethod
def copy_email(cls,account_name):
user_found = User.find_by_account_name(account_name)
pyperclip.copy(user_found.email)
|
python
|
from ecco.attribution import gradient_x_inputs_attribution
import torch
import pytest
@pytest.fixture
def simpleNNModel():
class simpleNNModel(torch.nn.Module):
def __init__(self):
super(simpleNNModel, self).__init__()
self.w = torch.tensor([[10., 10.]])
def forward(self, x):
return x * self.w
yield simpleNNModel()
class TestAttribution:
def test_grad_x_input(self, simpleNNModel):
input = torch.tensor([[9., 9.]], requires_grad=True)
output = simpleNNModel(input)
expected = torch.tensor([1.])
actual = gradient_x_inputs_attribution(output[0][0], input)
assert torch.all(torch.eq(actual, expected))
|
python
|
from sklearn import ensemble
from model_man import get_models
def create():
models = get_models()
v_models = [(key, models[key].create()) for key in models.keys()]
return ensemble.VotingClassifier(
estimators=v_models,
voting="hard"
)
|
python
|
# -*- coding: utf-8 -*-
"""
A nested dict with both attribute and item access.
NA stands for Nested and Attribute.
"""
import collections
import copy
class NADict:
"""A nested dict with both attribute and item access.
It is intended to be used with keys that are valid Python
identifiers. However, except for string keys containing a dot,
there are actually no hard limitations. If a key equals an existing
attribute name, attribute access is of cause not possible.
Nested items can be accessed via a dot notation, as shown in the
example below.
Examples
--------
>>> n = NADict(a=1, b=NADict(c=3, d=4))
>>> n['a']
1
>>> n.a
1
>>> n['b.c']
3
>>> n.b.c
3
>>> n['b.e'] = 5
>>> n.b.e
5
Attributes
----------
_dict : dict
Dictionary holding the actial items.
"""
def __init__(self, *args, **kw):
object.__setattr__(self, '_dict', {})
self.update(*args, **kw)
def __getitem__(self, key):
if '.' in key:
k1, k2 = key.split('.', 1)
return self._dict[k1][k2]
else:
return self._dict[key]
def __setitem__(self, key, value):
if key in ('clear', 'copy', 'fromkeys', 'get', 'items', 'keys',
'pop', 'popitem', 'setdefault', 'update', 'values'):
raise ValueError('invalid key "%s": must not override supported '
'dict method names' % key)
elif '.' in key:
k1, k2 = key.split('.', 1)
if k1 not in self._dict:
self._dict[k1] = NADict()
self._dict[k1][k2] = value
elif key in self._dict:
if isinstance(self._dict[key], NADict):
self._dict[key].update(value)
else:
self._dict[key] = value
else:
if isinstance(value, collections.abc.Mapping):
self._dict[key] = NADict(value)
else:
self._dict[key] = value
def __delitem__(self, key):
if '.' in key:
k1, k2 = key.split('.', 1)
del self._dict[k1][k2]
else:
del self._dict[key]
def __getattr__(self, key):
if key not in self._dict:
raise AttributeError('No such key: %s' % (key, ))
return self._dict[key]
def __setattr__(self, key, value):
if key in self._dict:
self._dict[key] = value
else:
object.__setattr__(self, key, value)
def __delattr__(self, key):
if key in self._dict:
del self._dict[key]
else:
object.__delattr__(self, key)
def __len__(self):
return len(self._dict)
def __contains__(self, key):
if '.' in key:
k1, k2 = key.split('.', 1)
return k2 in self._dict[k1]
else:
return key in self._dict
def __iter__(self, prefix=''):
for k, v in self._dict.items():
key = '%s.%s' % (prefix, k) if prefix else k
if isinstance(v, NADict):
yield from v.__iter__(key)
else:
yield key
def __repr__(self):
return '%s(%s)' % (
self.__class__.__name__,
', '.join('%s=%s' % (k, repr(v)) for k, v in self._dict.items()))
def clear(self):
"""Clear all keys."""
self._dict.clear()
def copy(self):
"""Returns a deep copy of self."""
return copy.deepcopy(self)
@staticmethod
def fromkeys(self, iterable, value=None):
"""Returns a new NADict with keys from `iterable` and values
set to `value`."""
n = NADict()
for key in iterable:
n[key] = value
return n
def get(self, key, default=None):
"""Returns the value for `key` if `key` is in self, else return
`default`."""
if '.' in key:
k1, k2 = key.split('.', 1)
return self._dict[k1].get(k2, default)
else:
return self._dict.get(key, default)
def items(self, prefix=''):
"""Returns an iterator over all items as (key, value) pairs."""
for k, v in self._dict.items():
key = '%s.%s' % (prefix, k) if prefix else k
if isinstance(v, NADict):
yield from v.items(key)
else:
yield (key, v)
def keys(self, prefix=''):
"""Returns an iterator over all keys."""
for k, v in self._dict.items():
key = '%s.%s' % (prefix, k) if prefix else k
if isinstance(v, NADict):
yield from v.keys(key)
else:
yield key
def pop(self, key, default=None):
"""Removed `key` and returns corresponding value. If `key` is not
found, `default` is returned if given, otherwise KeyError is
raised."""
if '.' in key:
k1, k2 = key.split('.', 1)
return self._dict[k1].pop(k2, default)
else:
return self._dict.pop(key, default)
def popitem(self, prefix=''):
"""Removes and returns some (key, value). Raises KeyError if empty."""
item = self._dict.popitem()
if isinstance(item, NADict):
k, v = item
item2 = item.popitem(k)
self._dict[k] = v
return item2
else:
k, v = self._dict.popitem()
key = '%s.%s' % (prefix, k) if prefix else k
return (key, v)
def setdefault(self, key, value=None):
"""Inserts `key` and `value` pair if key is not found.
Returns the new value for `key`."""
if '.' in key:
k1, k2 = key.split('.', 1)
return self._dict[k1].setdefault(k2, value)
else:
return self._dict.setdefault(key, value)
def update(self, *args, **kw):
"""Updates self with dict/iterable from `args` and keyword arguments
from `kw`."""
for arg in args:
if hasattr(arg, 'keys'):
for k in arg:
self[k] = arg[k]
else:
for k, v in arg:
self[k] = v
for k, v in kw.items():
self[k] = v
def values(self):
"""Returns a set-like providing a view of all style values."""
return self._dict.values()
|
python
|
# -*- coding: utf-8 -*-
# FreeCAD macro for woodworking
# Author: Darek L (aka dprojects)
# Version: 5.0
# Latest version: https://github.com/dprojects/getDimensions
import FreeCAD
# #######################################################
# SETTINGS ( SET HERE )
# #######################################################
# set language:
# "pl" - Polish
# "en" - English
sLang = "pl"
# set metric system:
# "mm" - millimeter
# "m" - meter
# "in" - inch
sUnits = "mm"
# set square area units:
# "m" - meter
# "mm" - millimeter
# "in" - inch
sSquareArea = "m"
# toggle visibility:
# "on" - the feature is on and hidden items (visibility = false) will be skipped
# "off" - all items are calculated, like it was before
sVisible = "on"
# #######################################################
# MAIN CODE ( NOT CHANGE HERE )
# #######################################################
# setting variables - autoconfig
if sLang == "pl":
vLang1 = "Element"
vLang2 = "Wymiary"
vLang3 = "Grubość"
vLang4 = "Sztuki"
if sSquareArea == "mm":
vLang5 = "Milimetry kwadratowe"
if sSquareArea == "m":
vLang5 = "Metry kwadratowe"
if sSquareArea == "in":
vLang5 = "Cale kwadratowe"
vLang6 = "Suma"
else:
vLang1 = "Name"
vLang2 = "Dimensions"
vLang3 = "Thickness"
vLang4 = "Quantity"
if sSquareArea == "mm":
vLang5 = "Square millimeters"
if sSquareArea == "m":
vLang5 = "Square meters"
if sSquareArea == "in":
vLang5 = "Square inches"
vLang6 = "Summary"
# create spreadsheet and prepere it for data
if FreeCAD.ActiveDocument.getObject("toCut"):
FreeCAD.ActiveDocument.removeObject("toCut")
result = FreeCAD.ActiveDocument.addObject("Spreadsheet::Sheet", "toCut")
result.mergeCells("B1:D1")
result.set("A1", vLang1)
result.set("B1", vLang2)
result.set("E1", vLang3)
result.set("F1", vLang4)
result.set("G1", vLang5)
result.setForeground("A1:G1", (0, 0, 0))
result.setBackground("A1:G1", (1, 1, 1))
result.setStyle("A1:G1", "bold", "add")
result.setAlignment("A1:G1", "top", "keep")
result.setAlignment("A1:G1", "center", "keep")
# scan all objects and count chipboards (cubes)
objs = FreeCAD.ActiveDocument.Objects
quantity = dict()
sqmSum = dict()
for obj in objs:
if sVisible == "on":
if FreeCADGui.ActiveDocument.getObject(obj.Name).Visibility is False:
continue
# support for cube objects
if obj.isDerivedFrom("Part::Box"):
keyArr = [str(obj.Length), str(obj.Width), str(obj.Height)]
keyArr.sort()
key = "x".join(keyArr)
if key in quantity:
quantity[key] = quantity[key] + 1
else:
quantity[key] = 1
# support for array objects with cube as base
elif obj.isDerivedFrom("Part::FeaturePython") and obj.Base.isDerivedFrom("Part::Box"):
# the main box cube will be added in next loop
if obj.ArrayType == "polar":
arrayQuantity = obj.NumberPolar - 1
else:
arrayQuantity = obj.NumberX * obj.NumberY * obj.NumberZ - 1
keyArr = [str(obj.Base.Length), str(obj.Base.Width), str(obj.Base.Height)]
keyArr.sort()
key = "x".join(keyArr)
if key in quantity:
quantity[key] = quantity[key] + arrayQuantity
else:
quantity[key] = arrayQuantity
# check what we have...
sqm = 0
i = 1
# calculate rows for later TechDraw print, first row for header
vRows = 1
for obj in objs:
if obj.isDerivedFrom("Part::Box"):
keyArr = [str(obj.Length), str(obj.Width), str(obj.Height)]
keyArr.sort()
key = "x".join(keyArr)
if key not in quantity:
continue
i = i + 1
if obj.Length.Value < 30:
size1 = obj.Width
size2 = obj.Height
thick = obj.Length
elif obj.Width.Value < 30:
size1 = obj.Length
size2 = obj.Height
thick = obj.Width
else:
size1 = obj.Length
size2 = obj.Width
thick = obj.Height
# calculate square area
sqm = quantity[key] * size1.getValueAs(sSquareArea) * size2.getValueAs(sSquareArea)
# ...and add to spreadsheet
result.set("A" + str(i), "'" + str(obj.Label))
result.set("B" + str(i), "'" + str(size1))
result.set("C" + str(i), "x")
result.set("D" + str(i), "'" + str(size2))
result.set("E" + str(i), "'" + str(thick))
result.set("F" + str(i), "'" + str(quantity[key]))
result.set("G" + str(i), "'" + str(sqm))
vRows = vRows + 1
# set metric system
result.setDisplayUnit("B" + str(i), sUnits)
result.setDisplayUnit("D" + str(i), sUnits)
result.setDisplayUnit("E" + str(i), sUnits)
# recalculate and add partial square meters
del quantity[key]
key = str(thick)
if key in sqmSum:
sqmSum[key] = sqmSum[key] + sqm
else:
sqmSum[key] = sqm
# add to spreadsheet summary for square meters
i = i + 1
# add empty line separator
vRows = vRows + 1
for key in sqmSum.keys():
i = i + 1
result.set("A" + str(i), vLang6)
result.set("E" + str(i), "'" + str(key))
result.set("G" + str(i), "'" + str(sqmSum[key]))
result.setDisplayUnit("E" + str(i), sUnits)
vRows = vRows + 1
# final decoration
result.setForeground("A2:G" + str(i), (0, 0, 0))
result.setBackground("A2:G" + str(i), (1, 1, 1))
# result.setStyle( 'A2:A'+str(i), 'bold', 'add')
result.setColumnWidth("A", 135)
result.setColumnWidth("B", 80)
result.setColumnWidth("C", 40)
result.setColumnWidth("D", 80)
result.setColumnWidth("E", 100)
result.setColumnWidth("F", 100)
result.setColumnWidth("G", 180)
result.setAlignment("A1:A" + str(i), "left", "keep")
result.setAlignment("B2:B" + str(i), "right", "keep")
result.setAlignment("C1:C" + str(i), "center", "keep")
result.setAlignment("D1:D" + str(i), "right", "keep")
result.setAlignment("E1:E" + str(i), "right", "keep")
result.setAlignment("F1:F" + str(i), "right", "keep")
result.setAlignment("G1:G" + str(i), "right", "keep")
result.setAlignment("B1:B1", "center", "keep")
result.setAlignment("C1:C1", "center", "keep")
result.setAlignment("D1:D1", "center", "keep")
# refresh document
App.ActiveDocument.recompute()
# remove existing toPrint page
if FreeCAD.ActiveDocument.getObject("toPrint"):
App.getDocument("Index").removeObject("toPrint")
# create TechDraw page for print
App.activeDocument().addObject("TechDraw::DrawPage", "toPrint")
App.activeDocument().addObject("TechDraw::DrawSVGTemplate", "Template")
App.activeDocument().Template.Template = App.getResourceDir() + "Mod/TechDraw/Templates/A4_Portrait_blank.svg"
App.activeDocument().toPrint.Template = App.activeDocument().Template
# add spreadsheet to TechDraw page
App.activeDocument().addObject("TechDraw::DrawViewSpreadsheet", "Sheet")
App.activeDocument().Sheet.Source = App.activeDocument().toCut
App.activeDocument().toPrint.addView(App.activeDocument().Sheet)
# add decoration to the table
FreeCAD.getDocument("Index").getObject("Sheet").X = 105.00
FreeCAD.getDocument("Index").getObject("Sheet").Y = 260.00
FreeCAD.getDocument("Index").getObject("Sheet").CellEnd = "G" + str(vRows)
# reload to see changes
App.ActiveDocument.recompute()
|
python
|
def get_length_of_missing_array(arr):
if len(arr) == 0:
return 0
else:
lens = []
for i in arr:
if i == None or len(i) == 0:
return 0
lens.append(len(i))
lens.sort()
prevInt = 0
for i in lens:
if (prevInt == 0):
prevInt = i
continue
if (i - 1 > prevInt):
return prevInt + 1
prevInt = i
return 0
print(get_length_of_missing_array([[5, 2, 9], [4, 5, 1, 1], [1], [69, 69], [5, 6, 7, 8, 9]]))
|
python
|
"""
Given an array of positive integers nums and a positive integer target, return the minimal length of a contiguous
subarray [numsl, numsl+1, ..., numsr-1, numsr] of which the sum is greater than or equal to target. If there is no
such subarray, return 0 instead.
Example 1:
Input: target = 7, nums = [2,3,1,2,4,3]
Output: 2
Explanation: The subarray [4,3] has the minimal length under the problem constraint.
Example 2:
Input: target = 4, nums = [1,4,4]
Output: 1
Example 3:
Input: target = 11, nums = [1,1,1,1,1,1,1,1]
Output: 0
Constraints:
1 <= target <= 109
1 <= nums.length <= 105
1 <= nums[i] <= 105
Follow up: If you have figured out the O(n) solution, try coding another solution of which the time complexity
is O(n log(n)).
"""
from typing import List
class Solution:
"""
Time Limit Exceeded (TLE).
"""
def minSubArrayLen(self, s: int, nums: List[int]) -> int:
min_len = len(nums)
sum_found = False
for i in range(len(nums)):
for j in range(i + 1, len(nums) + 1):
subarray_sum = sum(nums[i:j])
subarray_len = j - i
if subarray_sum >= s:
sum_found = True
min_len = min(subarray_len, min_len)
return min_len if sum_found else 0
class Solution2:
"""
Another TLE. Trying to put two-pointer approach.
Works ok on small arrays. LC fails on long array (with len 33168).
Time complexity: O(n**3) - two cycles plus sum calculation on each subarray
"""
def minSubArrayLen(self, s: int, nums: List[int]) -> int:
i, k = 0, 0
min_len = None
while i < len(nums):
k = i
while k < len(nums):
if sum(nums[i:k + 1]) >= s:
min_len = min((k + 1) - i, min_len) if min_len is not None else (k + 1) - i
break
k += 1
i += 1
return min_len or 0
class Solution3:
"""
LC proposed solution (ported from C++). Also got TLE, LOL :)
Algorithm idea:
In 1st/2nd solution sum is calculated for every subarray in O(n) time.
But, we could easily find the sum in O(1) time by storing the cumulative sum from the beginning(Memoization).
After we have stored the cumulative sum in sums, we could easily find the sum of any subarray from i to j.
Time complexity: O(n**2)
"""
def minSubArrayLen(self, s: int, nums: List[int]) -> int:
len_n = len(nums)
if len_n == 0 or len_n == 1:
return len_n
int32max = (1 << 31)
ans = int32max
sums = list()
sums.append(nums[0])
for i in range(1, len_n):
sums.append(sums[i - 1] + nums[i])
for i in range(len_n):
for j in range(i, len_n):
subarray_sum = sums[j] - sums[i] + nums[i]
if subarray_sum >= s:
ans = min(ans, (j - i + 1))
# found the smallest subarray with sum >= s starting with current index i, move to the next index i.
break
return ans if ans != int32max else 0
class Solution4:
"""
Algorithm: scan from left to right, 'total' tracks the sum of the subarray.
If the sum is less than s, right moves forward one step, else left moves forward one step.
Left (tail) and right (head) form a window.
Runtime: 72 ms, faster than 67.25% of Python3
Memory Usage: 16.7 MB, less than 28.44% of Python3
Time complexity: O(n)
"""
def minSubArrayLen(self, s: int, nums: List[int]) -> int:
total = left = right = 0
res = len(nums) + 1 # found seq will never exceed that
while right < len(nums):
total += nums[right]
while total >= s:
res = min(res, right - left + 1)
total -= nums[left]
left += 1
right += 1
return res if res <= len(nums) else 0
if __name__ == '__main__':
tc = [
(11, [1, 2, 3, 4, 5], 3),
(7, [2, 3, 1, 2, 4, 3], 2),
]
solutions = [Solution(), Solution2(), Solution3(), Solution4()]
for sol in solutions:
for inp_s, inp_nums, expected in tc:
assert sol.minSubArrayLen(inp_s, inp_nums) == expected
|
python
|
# -*- coding: utf-8 -*-
# Copyright 2021 Jacob M. Graving <[email protected]>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch.nn.functional as F
def logmeanexp(x, dim=-1):
return x.logsumexp(dim) - np.log(x.shape[dim])
def categorical_cross_entropy(pos_logits, neg_logits):
attract = -pos_logits
repel = logmeanexp(neg_logits)
return attract + repel
def binary_cross_entropy(pos_logits, neg_logits):
attract = -F.logsigmoid(pos_logits)
# use numerically stable repulsion term
# Shi et al. 2022 (https://arxiv.org/abs/2111.08851)
# log(1 - sigmoid(logits)) = log(sigmoid(logits)) - logits
repel = -(F.logsigmoid(neg_logits) - neg_logits).mean(-1)
return attract + repel
def cauchy_schwarz_divergence(pos_logits, neg_logits):
attract = -pos_logits
repel = 0.5 * logmeanexp(2 * neg_logits)
return attract + repel
# discriminators from Qin et al. (2020) https://arxiv.org/abs/1811.09567
def wasserstein_logits(pos_logits, neg_logits):
attract = -pos_logits
repel = neg_logits.mean(-1)
return attract + repel
def wasserstein(pos_logits, neg_logits):
attract = -pos_logits.exp()
repel = logmeanexp(neg_logits).exp()
return attract + repel
def least_squares(pos_logits, neg_logits):
attract = pos_logits.expm1().pow(2)
repel = neg_logits.exp().pow(2).mean(-1)
return attract + repel
def zero_centered_least_squares(pos_logits, neg_logits):
attract = pos_logits.expm1().pow(2)
repel = F.softplus(neg_logits).exp().pow(2).mean(-1)
return attract + repel
def cosine(pos_logits, neg_logits):
attract = -pos_logits.expm1().cos()
repel = -neg_logits.exp().cos().add(1).mean(-1)
return attract + repel
# discriminators from Nowozin et al. (2016) https://arxiv.org/abs/1606.00709
def jensen_shannon_divergence(pos_logits, neg_logits):
def activation(v):
return np.log(2) + F.logsigmoid(v)
def conjugate(t):
return -(2 - t.exp()).log()
attract = -activation(pos_logits)
repel = conjugate(activation(neg_logits)).mean(-1)
return attract + repel
def kullback_leibler_divergence(pos_logits, neg_logits):
def activation(v):
return v
def conjugate(t):
return (t - 1).exp()
attract = -activation(pos_logits)
repel = conjugate(activation(neg_logits)).mean(-1)
return attract + repel
def reverse_kullback_leibler_divergence(pos_logits, neg_logits):
def activation(v):
return -torch.exp(-v)
def conjugate(t):
return -1 - torch.log(-t)
attract = -activation(pos_logits)
repel = conjugate(activation(neg_logits)).mean(-1)
return attract + repel
def pearson_chi_sq(pos_logits, neg_logits):
def activation(v):
return v
def conjugate(t):
return 0.25 * t ** 2 + t
attract = -activation(pos_logits)
repel = conjugate(activation(neg_logits)).mean(-1)
return attract + repel
def squared_hellinger(pos_logits, neg_logits):
def activation(v):
return 1 - (-v).exp()
def conjugate(t):
return t / (1 - t)
attract = -activation(pos_logits)
repel = conjugate(activation(neg_logits)).mean(-1)
return attract + repel
DISCRIMINATORS = {
"categorical": categorical_cross_entropy,
"binary": binary_cross_entropy,
"bernoulli": binary_cross_entropy,
"jsd": jensen_shannon_divergence,
"reverse_kld": reverse_kullback_leibler_divergence,
"kld": kullback_leibler_divergence,
"pearson": pearson_chi_sq,
"squared_hellinger": squared_hellinger,
"wasserstein": wasserstein,
"wasserstein_logits": wasserstein_logits,
"least_squares": least_squares,
"centered_least_squares": zero_centered_least_squares,
"cosine": cosine,
"cauchy_schwarz": cauchy_schwarz_divergence,
}
|
python
|
from typing import Sequence, Hashable
def find_field(d, candidates):
"""
Given a dict and a list of possible keys, find the first key
which is included into this dict. Throws ValueError if not found.
"""
for c in candidates:
if c in d:
return c
raise ValueError(f"Can't find any of: {candidates}")
def find_value(d, candidates):
"""
Given a dict and a list of possible keys, find the first key
which is included into this dict and return its value.
Throws ValueError if not found.
"""
fieldname = find_field(d, candidates)
return d[fieldname]
def normalize_dict(source, replacements, allow_original_key=True):
"""
Given a dict and a map: reference key -> [] possible replacements,
return a new dict where all keys are guaranteed to be reference keys
with values taken from any of replacement keys.
Typically used to normalize a dataset where the same info can be listed
under different names.
Example map:
{
"artist": ["Artist", "ARTIST"],
"year": ["Year", "yr", "yob"],
}
"""
ret = {}
for key in replacements:
candidates = replacements[key]
if allow_original_key:
candidates += (key,)
value = find_value(source, candidates)
ret[key] = value
return ret
def filter_dict(src: dict, keys_to_filter: Sequence[Hashable]) -> dict:
"""
Filters dictionary by keys_to_filter set.
Parameters
----------
src: dict
Source dictionary.
keys_to_filter: Sequence[Hashable]
Set of keys that should be in the final dictionary.
Returns
-------
dict
Filtered source dictionary.
"""
return {key: value for key, value in src.items() if key in keys_to_filter}
|
python
|
from zmodulo.plot.properties.color.color import Color
class LineColor:
""" A Z-Tree plot line color """
def __init__(self, color=None):
"""
Initializes the LineColor object
:param color: line color
:type color: Color
"""
if color is None:
self.color = Color()
else:
self.color = color
self.template = '\tlinecolor = {line_color};\n'
def to_str(self):
"""
Converts the LineColor instance to a z-tree plot line property declaration
:return: Z-Tree linecolor property declaration
:rtype: str
"""
return self.template.format(line_color=self.color.to_str())
|
python
|
from lin import db
from lin.core import File
from lin.exception import NotFound
from pydash import uniq_by
from sqlalchemy import Column, Integer, String
from sqlalchemy.orm import aliased
from app.models.base import Base
class Category(Base):
id = Column(Integer, primary_key=True)
name = Column(String(50), nullable=False, unique=True, comment='分类名称')
img_id = Column(Integer, comment='关联图片ID')
mini_img_id = Column(Integer, comment='关联小图片ID')
summary = Column(String(100), comment='描述')
@classmethod
def get_all_with_mini_img(cls, soft=True, *, throw=False):
res = db.session.query(cls, File.path).filter_by(soft=soft).filter(
cls.mini_img_id == File.id
).all()
if not res:
if not throw:
return []
else:
raise NotFound(msg='相关种类不存在')
models = cls._combine_data_for_get_all_with_mini_img(res)
return models
@classmethod
def _combine_data_for_get_all_with_mini_img(cls, data):
models = []
for item in data:
category, mini_path = item
category.mini_image = cls.get_file_url(mini_path)
category._fields.append('mini_image')
models.append(category)
return models
@classmethod
def get_pagiante(cls, start, count, q=None, soft=True, *, throw=False):
Image = aliased(File)
MiniImage = aliased(File)
statement = db.session.query(cls, Image.path, MiniImage.path).join(
Image, cls.img_id == Image.id,
).join(
MiniImage, cls.mini_img_id == MiniImage.id
).filter_by(soft=soft).filter(
).order_by(cls.id.desc()).offset(start).limit(count)
if q:
q = '%{}%'.format(q)
statement = statement.filter(cls.name.ilike(q))
total = statement.count()
res = statement.all()
if not res:
if not throw:
return []
else:
raise NotFound(msg='相关种类不存在')
models = cls._combine_data_for_get_paginate(res)
return {
'start': start,
'count': count,
'models': models,
'total': total
}
@classmethod
def _combine_data_for_get_paginate(cls, data):
models = []
for item in data:
model = cls._combine_single_data_for_get_paginate(*item)
models.append(model)
return models
@classmethod
def _combine_single_data_for_get_paginate(cls, category, path, mini_path):
category.image = cls.get_file_url(path)
category.mini_image = cls.get_file_url(mini_path)
category._fields.extend(['image', 'mini_image'])
return category
@classmethod
def get_with_products(cls, cid, count=12, soft=True, *, throw=False):
from app.models.product import Product
cate_img = aliased(File)
prod_img = aliased(File)
res = db.session.query(cls, Product, cate_img.path, prod_img.path).filter_by(soft=soft).filter(
cls.id == cid,
cls.id == Product.category_id,
cls.img_id == cate_img.id,
Product.img_id == prod_img.id
).offset(0).limit(count).all()
if not res:
if not throw:
return []
else:
raise NotFound(msg='相关种类不存在')
model = cls._combine_data(res)[0]
return model
@classmethod
def get_all_with_products(cls, count=12, soft=True, *, throw=False):
from app.models.product import Product
cate_img = aliased(File)
prod_img = aliased(File)
res = db.session.query(cls, Product, cate_img.path, prod_img.path).filter_by(soft=soft).filter(
cls.id == Product.category_id,
cls.img_id == cate_img.id,
Product.img_id == prod_img.id
).order_by(cls.id.desc()).all()
if not res:
if not throw:
return []
else:
raise NotFound(msg='相关种类和产品不存在')
models = cls._combine_data(res)
for model in models:
model.products = model.products[0:count]
return models
@classmethod
def _combine_data(cls, data):
res = []
for category, product, cate_path, prod_path in data:
category.products = getattr(category, 'products', [])
product.image = cls.get_file_url(prod_path)
product._fields.append('image')
category.products.append(product)
category.image = cls.get_file_url(cate_path)
res.append(category)
res = uniq_by(res, 'id')
for category in res:
category._fields.extend(['products', 'image'])
return res
|
python
|
swaps=[]
def heapify(data,n,vert):
min=vert
l=2*vert+1
r=2*vert+2
if l<n and data[l]<data[min]:
min=l
if r<n and data[r]<data[min]:
min=r
if min!=vert:
data[vert],data[min]=data[min],data[vert]
swaps.append([vert,min])
heapify(data,n,min)
def main():
n = int(input())
data = list(map(int, input().split()))
for i in range(n//2-1,-1,-1):
heapify(data,n,i)
print(len(swaps))
for i, j in swaps:
print(i, j)
print(data)
if __name__ == "__main__":
main()
|
python
|
from django.contrib import admin
from .models import Report, ReportFiles, ReportIndex
admin.site.register(Report)
admin.site.register(ReportFiles)
admin.site.register(ReportIndex)
|
python
|
#!/usr/bin/env python
# encoding: utf-8
"""
@author: anly_jun
@file: github_trending
@time: 16/10/17 下午2:23
"""
import requests
from bs4 import BeautifulSoup
GITHUB = 'https://github.com'
TRENDING_URL = GITHUB + "/trending"
TRENDING_DEV_URL = TRENDING_URL + "/developers"
USER_AGENT_BY_MOBILE = 'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.23 Mobile Safari/537.36'
def get_trending_repos(opts):
repos = []
language = opts.get('language', None)
since = opts.get('since', None)
url = TRENDING_URL
if language:
url = url + '/' + language
if since:
url += '?since={}'.format(since)
response, code = read_page(url)
if code == 200:
repos = parser_repos(response)
return repos
def get_trending_developers(opts):
developers = []
language = opts.get('language', None)
since = opts.get('since', None)
url = TRENDING_DEV_URL
if language:
url = url + '/' + language
if since:
url += '?since={}'.format(since)
print url
response, code = read_page(url)
if code == 200:
developers = parser_developers(response)
return developers
def parser_repos(response):
repos = []
soup = BeautifulSoup(response.text, "lxml")
for li in soup.find_all('li', {'class': 'col-12 d-block width-full py-4 border-bottom'}):
avatar_img = li.find('img', {'class': 'avatar mb-1'})
if avatar_img:
avatar = avatar_img['src']
name_div = li.find('div', {'class': 'd-inline-block col-9 mb-1'})
name_string = name_div.find('a', href=True)['href']
# name_string = li.find('span', {'class': 'text-normal'}).string
owner = name_string.split('/')[1]
repo = name_string.split('/')[2]
link = GITHUB + name_string
meta = li.find('div', {'class': 'f6 text-gray mt-2'})
if meta:
stars = li.find('a', {'class': 'muted-link d-inline-block mr-3'}).text.replace('\n', '').strip(' ')
else:
stars = "0"
desc = parser_desc(li.find('div', {'class': 'py-1'}))
repos.append({
'owner': owner,
'avatar': avatar,
'repo': repo,
'stars': stars,
'desc': desc,
'link': link
})
return repos
def parser_desc(desc):
repo_desc = ""
if desc:
for each in desc.stripped_strings:
repo_desc += " " + each
return repo_desc.lstrip(" ")
def parser_developers(response):
developers = []
soup = BeautifulSoup(response.text, "lxml")
for li in soup.find_all('li', {'class': 'd-sm-flex flex-justify-between border-bottom border-gray-light py-3'}):
avatar_img = li.find('img', {'class': 'rounded-1'})['src']
if avatar_img:
avatar = avatar_img['src']
href = li.find('a', {'class': 'd-inline-block'})['href']
link = GITHUB + href
name = href[1:]
developers.append({
'avatar': avatar,
'name': name,
'full_name': "",
'link': link
})
return developers
def parser_developer_name(name):
full_name = ""
if name:
for each in name.stripped_strings:
full_name += " " + each
return full_name.lstrip(" ")
def read_page(url, timeout=5):
header = {'User-Agent': USER_AGENT_BY_MOBILE}
try:
response = requests.get(url=url, timeout=timeout, headers=header)
except requests.exceptions.ConnectionError as e:
print e
return None, False
return response, response.status_code
if __name__ == '__main__':
response, code = read_page(TRENDING_DEV_URL)
if code == 200:
parser_developers(response)
|
python
|
import requests
from bs4 import BeautifulSoup as bs
import time
import random
import os
import pandas as pd
from pathlib import Path
pwd = os.getcwd()
# Creating /details_page folder
Path(pwd + '/details_pages').mkdir(parents=True, exist_ok=True)
df = pd.read_csv(pwd + '\\dataframes\\Data - IT_Companies_Algiers.csv')
URL = "https://www.example.com/profil/?id="
page_counter = 0
for i in df.index:
page = requests.get(URL + str(df['company_id'][i]))
soup = bs(page.content, "html.parser")
# setting a timeout each 10 pages of 8 to 13 seconds
if(page_counter % 10 == 0 and page_counter != 0):
time.sleep(random.randrange(8, 13))
print(page_counter)
with open('details_pages/'+str(df['company_id'][i])+".html", "w", encoding='utf-8') as file:
file.write(str(soup))
print('Finished with page :' + str(page_counter))
page_counter += 1
|
python
|
def make_local_image( embedded_cid , filename, payload):
"""helper func that creates an image object. others may replace this with a specific solution.
the important point is that it returns a link
"""
import models #my models module. you will probably replace this entire function with your own code
image_model = models.EmbeddedImageModel(filename=filename,
data = payload)
image_model.put()
return image_model.link
def cid_2_images(message):
'''this replaces the <img src="<cid:SOMETHING>"/> tags with <img src="SOME URL"/> tags in the message'''
from BeautifulSoup import BeautifulSoup
import re
cid_to_link = {}
for part in message.walk():
if part.get('Content-ID') :
logging.info('found cid:%s', part.get('Content-ID'))
cid_to_link[part.get('Content-ID')] = \
make_local_image(part.get('Content-ID'),
part.get_filename(),
part.get_payload(decode=True)
)
for part in message.walk():
if str(part.get_content_type()) == 'text/html':
soup = BeautifulSoup(part.get_payload(decode=True))
images_with_cid = soup('img', attrs = {'src' : re.compile('cid:.*')})
for image_tag in images_with_cid:
cid = '<%s>'% image_tag['src'][4:]
image_tag['src'] = cid_to_link[cid]
part.set_payload(soup.renderContents())
|
python
|
import json
from mock import patch
import jenkins
from tests.base import JenkinsTestBase
class JenkinsCancelQueueTest(JenkinsTestBase):
@patch.object(jenkins.Jenkins, 'jenkins_open')
def test_simple(self, jenkins_mock):
job_name_to_return = {u'name': 'TestJob'}
jenkins_mock.return_value = json.dumps(job_name_to_return)
self.j.cancel_queue(52)
self.assertEqual(
jenkins_mock.call_args[0][0].url,
self.make_url('queue/cancelItem?id=52'))
self._check_requests(jenkins_mock.call_args_list)
@patch.object(jenkins.Jenkins, 'jenkins_open',
side_effect=jenkins.NotFoundException('not found'))
def test_notfound(self, jenkins_mock):
job_name_to_return = {u'name': 'TestJob'}
jenkins_mock.return_value = json.dumps(job_name_to_return)
self.j.cancel_queue(52)
self.assertEqual(
jenkins_mock.call_args[0][0].url,
self.make_url('queue/cancelItem?id=52'))
self._check_requests(jenkins_mock.call_args_list)
class JenkinsQueueInfoTest(JenkinsTestBase):
@patch.object(jenkins.Jenkins, 'jenkins_open')
def test_simple(self, jenkins_mock):
queue_info_to_return = {
'items': {
u'task': {
u'url': u'http://your_url/job/my_job/',
u'color': u'aborted_anime',
u'name': u'my_job'
},
u'stuck': False,
u'actions': [
{
u'causes': [
{
u'shortDescription': u'Started by timer',
},
],
},
],
u'buildable': False,
u'params': u'',
u'buildableStartMilliseconds': 1315087293316,
u'why': u'Build #2,532 is already in progress (ETA:10 min)',
u'blocked': True,
}
}
jenkins_mock.return_value = json.dumps(queue_info_to_return)
queue_info = self.j.get_queue_info()
self.assertEqual(queue_info, queue_info_to_return['items'])
self.assertEqual(
jenkins_mock.call_args[0][0].url,
self.make_url('queue/api/json?depth=0'))
self._check_requests(jenkins_mock.call_args_list)
class JenkinsQueueItemTest(JenkinsTestBase):
@patch.object(jenkins.Jenkins, 'jenkins_open')
def test_simple(self, jenkins_mock):
queue_item_to_return = {
u'_class': u'hudson.model.Queue$LeftItem',
u'actions': [{u'_class': u'hudson.model.CauseAction',
u'causes': [{u'_class': u'hudson.model.Cause$UserIdCause',
u'shortDescription': u'Started by user Bob',
u'userId': u'bsmith',
u'userName': u'Bob'}]}],
u'blocked': False,
u'buildable': False,
u'cancelled': False,
u'executable': {u'_class': u'hudson.model.FreeStyleBuild',
u'number': 198,
u'url': u'http://your_url/job/my_job/198/'},
u'id': 25,
u'inQueueSince': 1507914654469,
u'params': u'',
u'stuck': False,
u'task': {u'_class': u'hudson.model.FreeStyleProject',
u'color': u'red',
u'name': u'my_job',
u'url': u'http://your_url/job/my_job/'},
u'url': u'queue/item/25/',
u'why': None,
}
jenkins_mock.return_value = json.dumps(queue_item_to_return)
queue_item = self.j.get_queue_item(25)
self.assertEqual(queue_item, queue_item_to_return)
self.assertEqual(
jenkins_mock.call_args[0][0].url,
self.make_url('queue/item/25/api/json?depth=0'))
self._check_requests(jenkins_mock.call_args_list)
|
python
|
from itertools import permutations
l=list(map(int,input().split()))
r=list(permutations(l))
print(r)
|
python
|
import typing
import datetime
import pandas as pd
from .make_df import ComicDataFrame
from lib.aws_util.s3.upload import upload_to_s3
from lib.aws_util.s3.download import download_from_s3
def store(df: ComicDataFrame) -> typing.NoReturn:
dt = datetime.datetime.now()
bucket = 'av-adam-store'
save_dir = '/tmp/'
upload_dir = f'ruijianime/comic/'
meta_path = f'{save_dir}meta.csv'
meta_obj = f'{upload_dir}meta.csv'
tag_path = f'{save_dir}tag.csv'
tag_obj = f'{upload_dir}tag.csv'
author_path = f'{save_dir}author.csv'
author_obj = f'{upload_dir}author.csv'
def add_timestamp() -> typing.NoReturn:
df.meta['updated_at'] = dt
df.tag['updated_at'] = dt
df.author['updated_at'] = dt
def download() -> typing.NoReturn:
download_from_s3(bucket, meta_obj, meta_path)
download_from_s3(bucket, tag_obj, tag_path)
download_from_s3(bucket, author_obj, author_path)
def merge() -> typing.NoReturn:
meta_old = pd.read_csv(meta_path)
meta = pd.concat((meta_old, df.meta), ignore_index=True)
meta.drop_duplicates(
subset=['comic_id'],
keep='last',
inplace=True,
)
print(meta)
meta.to_csv(meta_path, index=False)
tag_old = pd.read_csv(tag_path)
tag = pd.concat((tag_old, df.tag), ignore_index=True)
tag.drop_duplicates(
subset=['comic_id', 'tag_id'],
keep='last',
inplace=True,
)
print(tag)
tag.to_csv(tag_path, index=False)
author_old = pd.read_csv(author_path)
author = pd.concat(
(author_old, df.author),
ignore_index=True,
)
author.drop_duplicates(
subset=['comic_id', 'author'],
keep='last',
inplace=True,
)
print(author)
author.to_csv(author_path, index=False)
def upload() -> typing.NoReturn:
upload_to_s3(bucket, meta_obj, meta_path)
upload_to_s3(bucket, tag_obj, tag_path)
upload_to_s3(bucket, author_obj, author_path)
add_timestamp()
download()
merge()
upload()
|
python
|
from .charge import Charge
|
python
|
from telegram.ext import Updater, CommandHandler, ConversationHandler, MessageHandler, Filters, CallbackQueryHandler
from telegram import InlineKeyboardMarkup, InlineKeyboardButton, ChatAction
#Google
import gspread
from oauth2client.service_account import ServiceAccountCredentials
import os
import pandas as pd
INPUT_TEXT=0
# Google
nSHEET='publi'
SPREAD_KEY='SPREAD'
CREDS_JSON = 'access-key.json'
scope = ["https://spreadsheets.google.com/feeds",
"https://www.googleapis.com/auth/spreadsheets",
"https://www.googleapis.com/auth/drive.file",
"https://www.googleapis.com/auth/drive"]
creds = ServiceAccountCredentials.from_json_keyfile_name(
CREDS_JSON,
scope
)
client = gspread.authorize(creds)
gsheet = client.open_by_key(SPREAD_KEY)
#termina Google
def start(update, context):
user = update.message.from_user.username
button1 = InlineKeyboardButton(
text='Autor',
#url='https://app.proofofhumanity.id/profile/0xEc5E23454b8Efe59E990e7AC2e443B8d980EEa18',
callback_data = 'autorcb'
#Devolver nombre y wallet
)
button2=InlineKeyboardButton(
text='Publicar',
callback_data='publicarcb'
)
button3=InlineKeyboardButton(
text='Borrar',
url='https://app.proofofhumanity.id/profile/0xEc5E23454b8Efe59E990e7AC2e443B8d980EEa18'
#Tomar datos del usuario y borrar en la lista de publicaciones
)
button4 = InlineKeyboardButton(
text='Lista',
callback_data='listacb'
)
update.message.reply_text(
f'Bienvenido {update.message.from_user.username}, \nSelecciona una opcion:',
reply_markup=InlineKeyboardMarkup([
[button4],
[button2, button3],
[button1]
])
)
#Enviar lista
def lista_command (update, context):
# tomar id del grupo de telegram P2P
# mirar como pasar persona
# user_id:update.effective_user['id']
# context.bot.send_message(
# chat_id='@ubip2p',
'''
columna 1
itemss = get_items()
for a in itemss:
update.message.reply_text(a)
'''
items = get_items()
update.message.reply_text(f'{items}')
#TO-DO: Sacar id de pandas y primer fila.
#TO-DO: Poner @ antes del nombre
def lista_callback(update, context):
query = update.callback_query
query.answer()
items = get_items()
query.edit_message_text('Lista:')
query.message.reply_text(
f'{items}'
)
#TO-DO: Sacar id de pandas y primer fila.
# Publicar
def publicar_command(update, context):
update.message.reply_text(
'Publica tu anuncio con los siguientes datos: \n\nCantidad de UBIS, COMRPO|VENDO, comentario')
return INPUT_TEXT
def publicar_callback(update, context):
query = update.callback_query
query.answer()
query.edit_message_text(
text='Publica tu anuncio con los siguientes datos: \n\nCantidad de UBIS, COMRPO|VENDO, comentario'
)
return INPUT_TEXT
#Autor
def autor_command(update, context):
update.message.reply_text(
'Autor del bot:\nhttps://app.proofofhumanity.id/profile/0xEc5E23454b8Efe59E990e7AC2e443B8d980EEa18\nSi queres ayudar, podes contribuir con UBIS o con el token que puedas, en la red que quieras.\nWallet:\n0xEc5E23454b8Efe59E990e7AC2e443B8d980EEa18')
return INPUT_TEXT
def autor_callback(update, context):
query = update.callback_query
query.answer()
query.edit_message_text(
text='Autor del bot:\nhttps://app.proofofhumanity.id/profile/0xEc5E23454b8Efe59E990e7AC2e443B8d980EEa18\nSi queres ayudar, podes contribuir con UBIS o con el token que puedas, en la red que quieras.\nWallet:\n0xEc5E23454b8Efe59E990e7AC2e443B8d980EEa18'
)
#Borrar
#Fin
def fin_conv(update, context):
return ConversationHandler.END
#Google
def get_items():
items = get_sheet(nSHEET)
return items
#Toma los valores de la segunda columna
def get_sheetcol(sheet_name):
sheet = gsheet.worksheet(sheet_name)
items = sheet.col_values(2)
return items
#anterior (toma todos los valores como lsita de listas)
def get_sheet(sheet_name):
sheet = gsheet.worksheet(sheet_name)
items = pd.DataFrame(sheet.get_all_records())
return items
def store_publi(update,context):
text = update.message.text
user = update.message.from_user.username
chat = update.message.chat
sheet = gsheet.worksheet(nSHEET)
clients = pd.DataFrame(get_sheet(nSHEET))
cond = sheet.findall(user)
if (cond == []):
sheet.add_rows(1)
sheet.append_row([text,user])
update.message.reply_text(f'El anuncio ha sido creado.')
else:
update.message.reply_text(f'Ya existe un anuncio hecho al nombre de @{user}.\nBorralo para publicar uno nuevo.')
return ConversationHandler.END
if __name__== '__main__':
updater = Updater(token='TOKEN', use_context=True)
dp= updater.dispatcher
#manejadores
dp.add_handler(CommandHandler('start', start))
dp.add_handler(ConversationHandler(
entry_points=[
CommandHandler('autor', autor_command),
CallbackQueryHandler(pattern='autorcb', callback=autor_callback),
CommandHandler('lista', lista_command),
CallbackQueryHandler(pattern='listacb', callback=lista_callback),
],
states={
INPUT_TEXT: [MessageHandler(Filters.text, fin_conv)]
},
fallbacks=[]
))
dp.add_handler(ConversationHandler(
entry_points=[
CommandHandler('publicar', publicar_command),
CallbackQueryHandler(pattern='publicarcb', callback=publicar_callback),
],
states = {
INPUT_TEXT: [MessageHandler(Filters.text, store_publi)]
},
fallbacks = []
))
#add handdler
updater.start_polling()
updater.idle()
|
python
|
"""
Manages creation of POVMs and geneartion of outcomes from measuring these POVM given a state
Author: Akshay Seshadri
"""
import numpy as np
import scipy as sp
from scipy import stats
import project_root # noqa
from src.utilities.qi_utilities import generate_random_state, generate_POVM, generate_Pauli_operator
from src.utilities.noise_process import depolarizing_channel
class Measurement_Manager():
"""
Manages creation of different types of POVMs, and generating measurement outcomes from these POVMs (for which a state is created)
"""
def __init__(self, random_seed = 1):
"""
Initializes some of the parameters
"""
# store the random seed for access by other functions (mainly for including it in parameters to be saved)
self.random_seed = random_seed
# set the random seed only once when the class is invoked, and nowhere else
if random_seed:
np.random.seed(int(random_seed))
def create_measurements(self, n = 2, N = 2, num_povm_list = 2, pauli = True, rho = None, projective_list = False, return_POVM = True):
"""
Generates POVM using the specified parameters. The actual POVM generation is done by qi_utilities.generate_POVM; create_measurements
generates appropriate parameters to pass to this function and does the necessary post-processing.
'pauli' can either be True, False/None, or a list of pauli operators (represented by a string of the form 'i_1i_2...i_nq', where each i_k is between 0 & 3)
If pauli is True, then a state "rho" must be provided; Pauli weights are obtained using this state
"""
### store the parameters for later use
# dimension of the system
self.n = n
# number of (types of) measurements
self.N = N
# list of number of POVM elements for each (type of) measurement
# if a number is provided, a list (of integers) is created from it
if type(num_povm_list) != list:
num_povm_list = [int(num_povm_list)]*N
else:
num_povm_list = [int(num_povm) for num_povm in num_povm_list]
# list specifying whether to use projective measurements
if type(projective_list) != list:
projective_list = [projective_list]*N
self.projective_list = projective_list
else:
projective_list = [bool(is_projective) for is_projective in projective_list]
self.projective_list = projective_list
# whether to use Pauli measurements
self.pauli = pauli
# generate the POVM
# list of POVMs
POVM_list = [0]*N
# generate a POVM
if not (pauli is None or pauli is False):
# ensure that the systems is comprised of qubits
# number of qubits
nq = int(np.log2(n))
if 2**nq != n:
raise ValueError("Pauli weighting possible only in systems of qubits, i.e., the dimension should be a power of 2")
# pauli can either be True, False/None, or a list of pauli operators (represented by a string of the form 'i_1i_2...i_nq', where each i_k is between 0 & 3)
# if pauli is not a list, do weighted Pauli measurements
if type(pauli) not in [tuple, list, np.ndarray]:
if rho is None:
raise ValueError("A state must be provided to find the Pauli weights")
# find Tr(rho W) for each Pauli operator W; this is only a heuristic weight if rho is not pure
self.pauli_weight_list = [(count + 1, (np.abs(np.conj(rho).dot(W))/np.sqrt(n))**2) for (count, W) in\
enumerate(generate_Pauli_operator(nq, list(range(1, 4**nq)), flatten = True))]
# find the largest 'N' weights, and measure the corresponding Pauli operators
self.pauli_measurements = sorted(self.pauli_weight_list, key = lambda x: x[1], reverse = True)[:N]
# Note: if the entries of num_povm_list are different, it is ambiguous which entry corresponds to the Pauli operator after the above re-ordering,
# so we don't allow for this; one can specify the pauli operators as well as num_povm_list explicitly (as a list) for using this option
# see https://stackoverflow.com/questions/3844801/check-if-all-elements-in-a-list-are-identical
if [num_povm_list[0]] * len(num_povm_list) != num_povm_list:
raise ValueError("When Pauli operators are to be inferred using weights, every element of num_povm_list must be the same")
else:
num_povm = num_povm_list[0]
# build a POVM for each selected Pauli measurement
POVM_list = [generate_POVM(n, num_povm = num_povm, projective = True, flatten = True, isComplex = True, verify = False,\
pauli = np.base_repr(i, base = 4), random_seed = None) for (i, _) in self.pauli_measurements]
else:
if len(pauli) != N:
raise ValueError("Number of measurements (N) provided and the number of pauli operators listed do not match")
# use the strings specified in pauli to measure those Pauli operators
POVM_list = [generate_POVM(n, num_povm = num_povm, projective = True, flatten = True, isComplex = True, verify = False,\
pauli = pauli_string, random_seed = None) for (num_povm, pauli_string) in zip(num_povm_list, pauli)]
else:
for i in range(N):
num_povm = num_povm_list[i]
# when projective is True, ensure that num_povm = n
if projective_list[i]:
num_povm = n
POVM_list[i] = generate_POVM(n, num_povm = num_povm, projective = projective_list[i], flatten = True, isComplex = True, verify = False,\
random_seed = None)
# list of POVM elements corresponding to each measurement
self.POVM_list = POVM_list
# list of POVM elements flattened and stacked to be stored as one large matrix for each measurement
self.POVM_mat_list = [np.vstack(POVM) for POVM in POVM_list]
# we also store N_list (which is basically num_povm_list), but this naming convention is used for other parts of the code
self.N_list = [len(POVM) for POVM in POVM_list]
if return_POVM:
return POVM_list
def perform_measurements(self, sigma = None, R_list = 1000, epsilon_o = 1e-5, outcome_eigvals = None, num_sets_outcomes = 1, pure = True, noise = False,\
noise_type = 'depolarizing', return_outcomes = True):
"""
Performs measurements specified by generate_POVM. The number of repetitions of each type of measurement is specified by R_list.
For performing the measurement, a state (of the system) 'sigma' can be specified.
If this is not specified, a state is generated. This can either be pure or mixed (speified by 'pure').
The state can optionally be passed through a noise channel. The type of noise channel is specified by 'noise_type', and
whether or how much noise to add is specified by 'noise'.
sigma : state of the system
R_list : a list of number of repetitions of each type of measurement
epsilon_o : constant incorporated in Born's rule to avoid zero division (a positive number much smaller than 1)
outcome_eigvals : The eigenvalues of the outcomes described by the POVM (each eigenvalue should correspond to the
appropriate POVM element).
Default is None, which means instead of the eigenvalue, the index of the POVM is returned.
Should have the format [[Ni elts] for Ni in range(N_list)]. Checks are not performed to ensure this is the case.
num_sets_outcomes : one set of outcomes is each of the measurements repeated a number of times specified by R_list;
'num_sets_outcomes' specifies how many such sets are generated
pure : specifies whether the state of the system is pure or mixed
noise : if True/False, specifies whether to pass the generated state through a noise channel
if True, a noise value of 0.1 is used
if noise is a number between 0 and 1, that noise level is used
noise_type : specifies which noise channel to use
TODO: If num_sets_outcomes = 1, return the outcomes directly, instead of nested them in a list.
Not changing this behaviour now as it would break some other code.
"""
# constant in modified Born's rule that prevents zero-division
self.epsilon_o = epsilon_o
# list of number of repetitions of each (type of) measurement
if type(R_list) not in [tuple, list, np.ndarray]:
self.R_list = [int(R_list)]*self.N
else:
# convert R to an integer if it already isn't one
self.R_list = [int(R) for R in R_list]
num_sets_outcomes = int(num_sets_outcomes)
### parameters related to the state
# whether to create a pure or mixed state
self.pure = pure
# whether to pass the prepared state through a noisy channel
# note that noise can be True, False or any number between 0 and 1
self.noise = noise
# type of noise channel
self.noise_type = noise_type
# generate the state
# create the state ("prepared in the lab")
if sigma is None:
sigma = generate_random_state(self.n, pure = pure, density_matrix = True, flatten = True, isComplex = True, verify = False,\
random_seed = None)
if noise:
# the state decoheres due to noise
if type(noise) == float:
if not (noise >= 0 and noise <= 1):
raise ValueError("noise level must be between 0 and 1")
sigma = depolarizing_channel(sigma, p = noise)
else:
sigma = depolarizing_channel(sigma, p = 0.1)
### perform the measurements
# check if we have POVM_list to perform the measurement
try:
POVM_mat_list = self.POVM_mat_list
except AttributeError:
raise ValueError("Measurements to be performed have not been specified. Use Measurement_Manager.create_measurements to generate the necessary measurements.")
# assign eigenvalues the POVM outcomes if provided, else return the index of the POVM
if outcome_eigvals is None or outcome_eigvals is False:
# point to the index of the outcome
self.outcome_indicator = [list(range(Ni)) for Ni in self.N_list]
else:
# if eigenvalues are provided, generate them
self.outcome_indicator = outcome_eigvals
# a (discrete) random variable present with probabilities from Born's rule for each measurement
self.drv_list = [None]*self.N
self.p_sigma_list = [np.zeros(Ni) for Ni in self.N_list]
# first obtain the probability distribution corresponding to each POVM measurement, and create a random variable with these
# probability distributions for repeated use
for i in range(self.N):
# matrix for ith POVM
POVM_mat_i = POVM_mat_list[i]
# number of repetitions for ith measurement
Ri = self.R_list[i]
# number of possible outcomes for ith measurement
Ni = self.N_list[i]
# outcomes to be generated for the ith measurement
outcome_indicator_i = self.outcome_indicator[i]
# the probability distribution corresponding to the ith POVM in the actual state sigma
p_sigma_i = np.real(np.conj(POVM_mat_i).dot(sigma) + epsilon_o/Ni) / (1. + epsilon_o)
self.p_sigma_list[i] = p_sigma_i
# create a list discrete random variable distributed as per p_sigma and taking values in outcome_indicator_i
drv = sp.stats.rv_discrete(values = (outcome_indicator_i, p_sigma_i))
self.drv_list[i] = drv
# use the random variables to generate measurement outcomes
self.num_sets_outcomes = num_sets_outcomes
# Caution: don't do [[list]]*num in the following because this duplicates the inner list [list], and
# changing the ith inner list will change all of the inner list
self.data_list = [[np.zeros(Ri) for Ri in self.R_list] for _ in range(num_sets_outcomes)]
for i_set in range(num_sets_outcomes):
for i in range(self.N):
# generate Ri samples from {0, ..., N - 1} as per the probability distribution p_sigma_i
data_i = self.drv_list[i].rvs(size = self.R_list[i])
# raw data is used to estimate fidelity directly
self.data_list[i_set][i] = data_i
if return_outcomes:
return self.data_list
|
python
|
#!/usr/bin/env python3
import logging
import resource
import sys
logging.basicConfig(level=logging.INFO)
import teether.project
if __name__ == '__main__':
if len(sys.argv) < 2:
print('Usage: %s [flags] <code>' % sys.argv[0])
exit(-1)
mem_limit = 4 * 1024 * 1024 * 1024 # 4GB
resource.setrlimit(resource.RLIMIT_AS, (mem_limit, mem_limit))
infile = sys.argv[-1]
if infile.endswith('.json'):
import json
with open(infile, 'rb') as f:
jd = json.load(f)
p = teether.project.Project.from_json(jd)
else:
p = teether.project.load(infile)
print(p.cfg.to_dot(minimal='-m' in sys.argv or '--minimal' in sys.argv))
|
python
|
#!/usr/bin/python3
from PyQt5.QtWidgets import *
class ModulPU(QWidget):
def __init__(self):
super().__init__()
self.layout = QGridLayout()
self.setLayout(self.layout)
#################################################################################################################
#################################################################################### Przykladowa klasa modulu ####
#################################################################################################################
#class Przyklad(ModulPU): #Dziedziczenie po klasie glownej "Modul"
# def __init__(self,config):
# super().__init__()
# butt = QPushButton("Click ME",self)
# butt.clicked.connect(self.wcisniety)
# butt.show()
# self.layout.addWidget(butt,0,0)
# self.cn = config
# def wcisniety(self):
# qm = QMessageBox(self)
# qm.setText(self.cn["LOGIN"])
# qm.show()
|
python
|
# encoding: utf-8
# Copyright 2011 California Institute of Technology. ALL RIGHTS
# RESERVED. U.S. Government Sponsorship acknowledged.
'''Service Binding: implementation'''
from Acquisition import aq_inner, aq_parent
from ipdasite.services import ProjectMessageFactory as _
from ipdasite.services.config import PROJECTNAME
from ipdasite.services.interfaces import IServiceBinding
from Products.Archetypes import atapi
from Products.ATContentTypes.content import folder
from Products.ATContentTypes.content import schemata
from zope.interface import implements
import pds.registry.model, registryobject
ServiceBindingSchema = registryobject.RegistryObjectSchema.copy() + folder.ATFolderSchema.copy() + atapi.Schema((
atapi.StringField(
'accessURI',
required=False,
searchable=False,
storage=atapi.AnnotationStorage(),
widget=atapi.StringWidget(
label=_(u'Access URI'),
description=_(u'URI that identifies the endpoint where the service may be accessed'),
size=50,
),
),
))
ServiceBindingSchema['title'].storage = atapi.AnnotationStorage()
ServiceBindingSchema['description'].storage = atapi.AnnotationStorage()
schemata.finalizeATCTSchema(ServiceBindingSchema, folderish=True, moveDiscussion=False)
class ServiceBinding(folder.ATFolder, registryobject.RegistryObject):
'''A service binding identifies the endpoint and specifications for a service.'''
implements(IServiceBinding)
schema = ServiceBindingSchema
portal_type = 'Service Binding'
accessURI = atapi.ATFieldProperty('accessURI')
description = atapi.ATFieldProperty('description')
title = atapi.ATFieldProperty('title')
def toPDSRegistry(self):
service = aq_parent(aq_inner(self))
b = pds.registry.model.ServiceBinding(
self.guid, self.lid, service.guid, self.home, set(), self.title, 'accepted', self.description,
versionName=self.versionID, accessURI=self.accessURI, specificationLinks=None, targetBinding=None
)
for i in self.keys():
specificationLink = self[i]
b.specificationLinks.add(specificationLink.toPDSRegistry())
return b
atapi.registerType(ServiceBinding, PROJECTNAME)
|
python
|
from pathlib import Path
from conanRunner import conanRunner
def conanInstall(conanfile, installFolder):
print("\n")
conanfile = str(conanfile)
installFolder = str(installFolder)
args = ["install", conanfile, "--install-folder", installFolder]
for s in conanRunner(args):
print(s)
|
python
|
# coding: utf-8
"""
Telstra Event Detection API
# Introduction Telstra's Event Detection API provides the ability to subscribe to and receive mobile network events for registered mobile numbers associated with Telstra's mobile network, such as; SIM swap, port-in, port-out, new MSIDN, new mobile service and cancelled mobile service, as well as carrier-detection. ## Features Event Detection API provides these features | Feature | Description | |---|---| |`SIM swap` | Returns timestamped event data when any of the following network events occurs in connection with a registered mobile number associated with Telstra’s mobile network: SIM swap, port-in, port-out, new MSISDN, new mobile service or cancelled mobile service | |`Carrier Detection` | Find out what Australian carrier a mobile number is subscribed to | |`International Roaming` | *Coming soon.* Will indicate if a mobile number is operaing in Australia or outside of Australia. | ## Getting access to the API The Event Detection API is available on our Enterprise Plans only. Please submit your [sales enquiry](https://dev.telstra.com/content/sales-enquiry-contact-form) . Or contact your Telstra Account Executive. We're available Monday to Friday 9am - 5pm. ## Frequently asked questions **Q: What is the Telstra Event Detection (TED) API?** A: The Telstra Event Detection (TED) API is a subscription based service from Telstra that enables a customer to be alerted when a particular network event is detected in connection with a registered mobile number that may indicate that a fraudulent misuse of an end user’s mobility service is about to occur. **Q: What are the network events that the TED API can detect?** A: Currently the TED API is able to detect a bundle of events associated with Telstra SIM swaps. **Q: Can TED API detect number porting between operators other than Telstra? E.g. Optus to Vodafone?** A: No, we don’t report these type of events at present. **Q: How quickly are the network events detected?** A: This will vary depending on the event being detected, but generally we detect the event within a couple of seconds of it occurring and notify subscribers within near real time via the API. **Q: How long does Telstra store the event data for?** A: Event data is stored for 90 days from the occurrence of a network event and then securely purged. **Q: Is there a limit to the number of registered mobile numbers I can have for the Telstra Event Detection API?** A: No. You may have as many Telstra Event Detection API registered mobile numbers as you require within practical limits. **Q: Why is monitoring for SIM SWAP events important?** A: Criminals are becoming much more savvy and will often try to circumvent two factor authentication protocols by swapping the SIM card for a particular mobile number in order to gain fraudulent access to the end user’s service. Monitoring for SIM swap events may provide early detection that this is occurring and help prevent criminals from being successful in their endeavours. **Q: If an end user is currently a customer of a Telstra Reseller that still utilises the Telstra Network, am I able to detect their Network events?** A: No. Telstra resellers such as Aldi Mobile are Mobile Virtual Network Operators (MVNO) that operate as totally independent businesses to Telstra. The Telstra SIM swap API does not monitor MNVO network events at present. **Q: How do I purchase Telstra Event Detection API?** A: At the moment, the Telstra Event Detection API is only available through your Telstra Account Manager. If you don't have a Telstra Account Manager, or are not sure who they are, please submit a [sales enquiry](https://dev.telstra.com/content/sales-enquiry-contact-form). **Q: What support options are available for the Telstra Event Detection API?** A: We provide 24/7 telephone based technical support (for paid plans) along with email support and an online community forum. **Q: Do you detect network events from another carrier?** A: The Telstra Event Detection API detects network events associated with the Telstra network and Telstra mobile services. **Q: Which Telstra personnel have access to the event detection data?** A: Access to Telstra Event Detection data is restricted to only Telstra personnel that require access for the purposes of providing the service. **Q: Why should I purchase the Telstra Event Detection API from Telstra?** A: As the network events are occurring on the Telstra network, Telstra is in a position to be able to provide fast notification of an event as it is occurring, helping subscribers to prevent fraudulent activity from occurring and to minimise the resulting financial losses. **Q: If I require assistance setting up my Telstra Event Detection API, are there any Professional Services options available to me?** A: At the current time, the Telstra Event Detection API does not have any Professional Service options available. **Q: What subscription options are available for Telstra Event Detection API?** A: There is a month-by-month Pay As You Go (PAYG) plan or 12 Month contract option available. **Q: Do Early Termination Charges (ETC’s) apply?** A: If you have subscribed to a 12 month contract and want to terminate the plan or downgrade to a lower plan before the expiry of your existing 12 month term, we may charge you ETCs. **Q: What privacy requirements apply to my use of the Telstra Event Detection API?** A: Before registering an end user’s mobile number with Telstra Event Detection API, you must: 1. prepare an “End User Notification” for our approval, which sets out what end user information will be disclosed via the API, the purposes for which that information will be disclosed, and to which third parties that information will be disclosed; 2. provide each of your end user with the End User Notification; and 3. obtain express, informed consent from each end user to the use and disclosure of their event data via the API for the purposes set out in the notification. **Q: What terms and conditions apply to my use of the Telstra Event Detection API?** A: Before using the Telstra Event Detection API, you must agree to the TED API [“Our Customer Terms”](https://www.telstra.com.au/customer-terms/business-government#cloud-services). # Getting Started First step is to create an `App`. After you've created an `App`, follow these steps 1. Authenticate by getting an Oauth token 2. Use the Event Detection API ## Run in Postman To get started quickly and easily with all the features of the Event Detection API, download the Postman collection here <a href=\"https://app.getpostman.com/run-collection/8ab2273e066e5c6fd653#?env%5BEvent%20Detection%20API%5D=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\"><img alt=\"Run in Postman\" src=\"https://run.pstmn.io/button.svg\" /></a> ## Authentication To get an OAuth 2.0 Authentication token, pass through your Consumer Key and Consumer Secret that you received when you registered for the Event Detection API key. The `grant_type` should be left as `client_credentials` and the scope as v1_eventdetection_simswap. The token will expire in one hour. Get your keys by creating an `App`. # Request ` CONSUMER_KEY=\"your consumer key\" CONSUMER_SECRET=\"your consumer secret\" curl -X POST -H 'Content-Type: application/x-www-form-urlencoded' \\ -d 'grant_type=client_credentials&client_id=$CONSUMER_KEY&client_secret=$CONSUMER_SECRET&scope=v1_eventdetection_simswap' \\ 'https://tapi.telstra.com/v2/oauth/token' ` # Response `{ \"access_token\" : \"1234567890123456788901234567\", \"token_type\" : \"Bearer\", \"expires_in\" : \"3599\" }` ## Subscribe mobile numbers Subscribing end user mobile numbers informs the API to register that mobile number so that you can poll those numbers for particular events. You can subscribe and unsubscribe numbers (opt in and opt out) against this service. Only numbers that are opted in (i.e. subscribed) can be polled for events. You must have obtained your end customer’s consent before you can opt them into the Event Detection service. # Request `curl -X POST -H 'content-type: application/json' \\ -H 'Authorization: Bearer $TOKEN' \\ -d '{ \"msisdns\": [ \"61467754783\" ], \"eventType\": \"simswap\", \"notificationUrl\": \"https://requestb.in/161r14g1\" }' \\ 'https://tapi.telstra.com/v1/eventdetection/events'` | Parameter | Description | |---|---| |`msisdns` | list of mobile numbers that has to be registered for the event | |`eventType` | event Type to be subscribed to | |`notificationUrl` | URL where the event notifications has to be posted (Optional) | # Response `{ \"msisdns\": [ { \"msisdn\": \"61467754783\", \"description\": \"opt-in status updated for this MSISDN\", \"carrierName\": \"Telstra\" } ] }` | Parameter | Description | |---|---| |`msisdn` | msisdn | |`description` | status description indicating if the msisdn was opted-in| |`carrierName` | carrier name for the msisdn | ## Unsubscribe mobile numbers Unsubscribe mobile numbers against a particular event # Request `curl -X DELETE -H 'content-type: application/json' \\ -H 'Authorization: Bearer $token' \\ -d '{\"msisdns\": [\"61467754783\"]}' \\ 'https://tapi.telstra.com/v1/eventdetection/events/{event-type}'` | Parameter | Description | |---|---| |`msisdns` | list of mobile numbers that has to be unsubscribed from the event | |`eventType` | event Type to be unsubscribed from | |`notificationUrl` | notification URL that has to be removed (Optional) | # Response ` { \"msisdns\": [ { \"msisdn\": \"61467754783\", \"description\": \"opt-out status updated for this MSISDN\", \"carrierName\": \"Telstra\" } ] } ` | Parameter | Description | |---|---| |`msisdn` | msisdn | |`description` | status description indicating if the msisdn was opted-out | |`carrierName` | carrier name for the msisdn | ## Get event subscriptions Get the list of events subscribed for # Request `curl -X POST -H 'content-type: application/json' \\ -H 'Authorization: Bearer $TOKEN' \\ -d '{ \"msisdns\": [ \"61467754783\" ] }' \\ 'https://tapi.telstra.com/v1/eventdetection/events/subscriptions'` | Parameter | Description | |---|---| |`msisdns` | list of msisdns to get the subscription details | # Response ` { \"notificationURL\": \"https://requestb.in/161r14g1\", \"subscriptions\": [ { \"msisdn\": \"61467754783\", \"events\": [ \"SIM_SWAP\" ], \"carrierName\": \"Telstra\" } ] } ` | Parameter | Description | |---|---| |`notificationURL` | notification URL configured while registering msisdns | |`msisdn` | msisdn | |`events` | list of subscribed events for that msisdn | |`carrierName` | carrier name for the msisdn | ## Poll events Poll events for a given set of msisdns # Request `curl -X POST -H 'content-type: application/json' \\ -H 'Authorization: Bearer $token' \\ -d '{ \"msisdns\": [ \"61467754783\", \"61467984007\" ] }' \\ 'https://tapi.telstra.com/v1/eventdetection/events/{event_type}'` Parameter | Description | |---|---| |`msisdns` | list of msisdns to be polled for events | |`eventType` | event Type to be polled for | # Response ` { \"eventType\": \"simswap\", \"msisdns\": [ { \"msisdn\": \"+61467754783\", \"mobileServiceEvents\": [ { \"eventId\": \"NEW_SIM\", \"eventDate\": \"2018-01-19T14:40:34\" } ] }, { \"msisdn\": \"+61467984007\", \"mobileServiceEvents\": [ { \"eventId\": \"PORTOUT_SVC\", \"eventDate\": \"2018-02-21T15:20:01\", \"carrierName\": \"Telstra\" } ] } ] } ` | Parameter | Description | |---|---| |`eventType` | event type requested | |`msisdn` | msisdn | |`mobileServiceEvents` | list of service events | |`eventId` | Id of the event occured. Event Id can be any one of the following - NEW_MSISDN, PORTIN_SVC, PORTOUT_SVC, NEW_SIM, CREATE_SVC, DELETE_SVC | |`eventDate` | timestamp indicating when the event occured | |`carrierName` | carrier name for the msisdn. Carrier name will be returned only for port out events | ## Push notifications Push event notifications to the URL are configured with the parameter `notificationUrl` while subscribing mobile numbers. # Event notification format ` { \"eventId\": \"NEW_SIM\", \"msisdn\" : \"61467754783\", \"eventDate\" : \"2018-01-19T14:40:34\" } ` | Parameter | Description | |---|---| |`eventId` | event Id indicating the event occured. Event Id can be any one of the following - NEW_MSISDN, PORTIN_SVC, PORTOUT_SVC, NEW_SIM, CREATE_SVC, DELETE_SVC | |`msisdn` | msisdn for which the event occured | |`eventDate` | timestamp indicating when the event occured | ## SIMswap sub-features The following is a list of the sub-features for SIM swap and the description for that sub-feature. These will appear in the 'eventId' parameter in the API response payload for SIMswap events. | SIM swap Sub-Feature | Description | |---|---| |`NEW_MSISDN` | The MSISDN of a service changes. The SIM card is not changed. Results in two events being created: 1) CREATE_SVC/PORT_IN_SVC for the new number, and 2) a NEW_MSISDN for the old MSISDN | |`PORTIN_SVC` | A MSISDN registered for event detection is created as a mobile service on the Telstra network (note: if the MSISDN was not already registered by at least one customer for at least one event type, this event would be interpreted as a CREATE_SVC) | |`PORTOUT_SVC` | The MSISDN is ported out from Telstra to another domestic operator | |`NEW_SIM` | An existing Telstra MSISDN is moved onto a new SIM | |`CREATE_SVC` | A new mobile service is created on the Telstra network (a new SIM and a new MSISDN) | |`DELETE_SVC` | A mobile service (MSISDN and SIM) on the Telstra network is cancelled outright (as opposed to ported out to another domestic network) | ## SDK repos * [Event Detection API - Java SDK](https://github.com/telstra/EventDetectionAPI-SDK-java) * [Event Detection API - .Net2 SDK](https://github.com/telstra/EventDetectionAPI-SDK-dotnet) * [Event Detection API - NodeJS SDK](https://github.com/telstra/EventDetectionAPI-SDK-node) * [Event Detection API - PHP SDK](https://github.com/telstra/EventDetectionAPI-SDK-php) * [Event Detection API - Python SDK](https://github.com/telstra/EventDetectionAPI-SDK-python) * [Event Detection API - Ruby SDK](https://github.com/telstra/EventDetectionAPI-SDK-ruby) # noqa: E501
OpenAPI spec version: 1.0.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import io
import json
import logging
import re
import ssl
import certifi
# python 2 and python 3 compatibility library
import six
from six.moves.urllib.parse import urlencode
try:
import urllib3
except ImportError:
raise ImportError('Swagger python client requires urllib3.')
logger = logging.getLogger(__name__)
class RESTResponse(io.IOBase):
def __init__(self, resp):
self.urllib3_response = resp
self.status = resp.status
self.reason = resp.reason
self.data = resp.data
def getheaders(self):
"""Returns a dictionary of the response headers."""
return self.urllib3_response.getheaders()
def getheader(self, name, default=None):
"""Returns a given response header."""
return self.urllib3_response.getheader(name, default)
class RESTClientObject(object):
def __init__(self, configuration, pools_size=4, maxsize=None):
# urllib3.PoolManager will pass all kw parameters to connectionpool
# https://github.com/shazow/urllib3/blob/f9409436f83aeb79fbaf090181cd81b784f1b8ce/urllib3/poolmanager.py#L75 # noqa: E501
# https://github.com/shazow/urllib3/blob/f9409436f83aeb79fbaf090181cd81b784f1b8ce/urllib3/connectionpool.py#L680 # noqa: E501
# maxsize is the number of requests to host that are allowed in parallel # noqa: E501
# Custom SSL certificates and client certificates: http://urllib3.readthedocs.io/en/latest/advanced-usage.html # noqa: E501
# cert_reqs
if configuration.verify_ssl:
cert_reqs = ssl.CERT_REQUIRED
else:
cert_reqs = ssl.CERT_NONE
# ca_certs
if configuration.ssl_ca_cert:
ca_certs = configuration.ssl_ca_cert
else:
# if not set certificate file, use Mozilla's root certificates.
ca_certs = certifi.where()
addition_pool_args = {}
if configuration.assert_hostname is not None:
addition_pool_args['assert_hostname'] = configuration.assert_hostname # noqa: E501
if maxsize is None:
if configuration.connection_pool_maxsize is not None:
maxsize = configuration.connection_pool_maxsize
else:
maxsize = 4
# https pool manager
if configuration.proxy:
self.pool_manager = urllib3.ProxyManager(
num_pools=pools_size,
maxsize=maxsize,
cert_reqs=cert_reqs,
ca_certs=ca_certs,
cert_file=configuration.cert_file,
key_file=configuration.key_file,
proxy_url=configuration.proxy,
**addition_pool_args
)
else:
self.pool_manager = urllib3.PoolManager(
num_pools=pools_size,
maxsize=maxsize,
cert_reqs=cert_reqs,
ca_certs=ca_certs,
cert_file=configuration.cert_file,
key_file=configuration.key_file,
**addition_pool_args
)
def request(self, method, url, query_params=None, headers=None,
body=None, post_params=None, _preload_content=True,
_request_timeout=None):
"""Perform requests.
:param method: http request method
:param url: http request url
:param query_params: query parameters in the url
:param headers: http request headers
:param body: request json body, for `application/json`
:param post_params: request post parameters,
`application/x-www-form-urlencoded`
and `multipart/form-data`
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
"""
method = method.upper()
assert method in ['GET', 'HEAD', 'DELETE', 'POST', 'PUT',
'PATCH', 'OPTIONS']
if post_params and body:
raise ValueError(
"body parameter cannot be used with post_params parameter."
)
post_params = post_params or {}
headers = headers or {}
timeout = None
if _request_timeout:
if isinstance(_request_timeout, (int, ) if six.PY3 else (int, long)): # noqa: E501,F821
timeout = urllib3.Timeout(total=_request_timeout)
elif (isinstance(_request_timeout, tuple) and
len(_request_timeout) == 2):
timeout = urllib3.Timeout(
connect=_request_timeout[0], read=_request_timeout[1])
if 'Content-Type' not in headers:
headers['Content-Type'] = 'application/json'
try:
# For `POST`, `PUT`, `PATCH`, `OPTIONS`, `DELETE`
if method in ['POST', 'PUT', 'PATCH', 'OPTIONS', 'DELETE']:
if query_params:
url += '?' + urlencode(query_params)
if re.search('json', headers['Content-Type'], re.IGNORECASE):
request_body = None
if body is not None:
request_body = json.dumps(body)
r = self.pool_manager.request(
method, url,
body=request_body,
preload_content=_preload_content,
timeout=timeout,
headers=headers)
elif headers['Content-Type'] == 'application/x-www-form-urlencoded': # noqa: E501
r = self.pool_manager.request(
method, url,
fields=post_params,
encode_multipart=False,
preload_content=_preload_content,
timeout=timeout,
headers=headers)
elif headers['Content-Type'] == 'multipart/form-data':
# must del headers['Content-Type'], or the correct
# Content-Type which generated by urllib3 will be
# overwritten.
del headers['Content-Type']
r = self.pool_manager.request(
method, url,
fields=post_params,
encode_multipart=True,
preload_content=_preload_content,
timeout=timeout,
headers=headers)
# Pass a `string` parameter directly in the body to support
# other content types than Json when `body` argument is
# provided in serialized form
elif isinstance(body, str):
request_body = body
r = self.pool_manager.request(
method, url,
body=request_body,
preload_content=_preload_content,
timeout=timeout,
headers=headers)
else:
# Cannot generate the request from given parameters
msg = """Cannot prepare a request message for provided
arguments. Please check that your arguments match
declared content type."""
raise ApiException(status=0, reason=msg)
# For `GET`, `HEAD`
else:
r = self.pool_manager.request(method, url,
fields=query_params,
preload_content=_preload_content,
timeout=timeout,
headers=headers)
except urllib3.exceptions.SSLError as e:
msg = "{0}\n{1}".format(type(e).__name__, str(e))
raise ApiException(status=0, reason=msg)
if _preload_content:
r = RESTResponse(r)
# In the python 3, the response.data is bytes.
# we need to decode it to string.
if six.PY3:
r.data = r.data.decode('utf8')
# log response body
logger.debug("response body: %s", r.data)
if not 200 <= r.status <= 299:
raise ApiException(http_resp=r)
return r
def GET(self, url, headers=None, query_params=None, _preload_content=True,
_request_timeout=None):
return self.request("GET", url,
headers=headers,
_preload_content=_preload_content,
_request_timeout=_request_timeout,
query_params=query_params)
def HEAD(self, url, headers=None, query_params=None, _preload_content=True,
_request_timeout=None):
return self.request("HEAD", url,
headers=headers,
_preload_content=_preload_content,
_request_timeout=_request_timeout,
query_params=query_params)
def OPTIONS(self, url, headers=None, query_params=None, post_params=None,
body=None, _preload_content=True, _request_timeout=None):
return self.request("OPTIONS", url,
headers=headers,
query_params=query_params,
post_params=post_params,
_preload_content=_preload_content,
_request_timeout=_request_timeout,
body=body)
def DELETE(self, url, headers=None, query_params=None, body=None,
_preload_content=True, _request_timeout=None):
return self.request("DELETE", url,
headers=headers,
query_params=query_params,
_preload_content=_preload_content,
_request_timeout=_request_timeout,
body=body)
def POST(self, url, headers=None, query_params=None, post_params=None,
body=None, _preload_content=True, _request_timeout=None):
return self.request("POST", url,
headers=headers,
query_params=query_params,
post_params=post_params,
_preload_content=_preload_content,
_request_timeout=_request_timeout,
body=body)
def PUT(self, url, headers=None, query_params=None, post_params=None,
body=None, _preload_content=True, _request_timeout=None):
return self.request("PUT", url,
headers=headers,
query_params=query_params,
post_params=post_params,
_preload_content=_preload_content,
_request_timeout=_request_timeout,
body=body)
def PATCH(self, url, headers=None, query_params=None, post_params=None,
body=None, _preload_content=True, _request_timeout=None):
return self.request("PATCH", url,
headers=headers,
query_params=query_params,
post_params=post_params,
_preload_content=_preload_content,
_request_timeout=_request_timeout,
body=body)
class ApiException(Exception):
def __init__(self, status=None, reason=None, http_resp=None):
if http_resp:
self.status = http_resp.status
self.reason = http_resp.reason
self.body = http_resp.data
self.headers = http_resp.getheaders()
else:
self.status = status
self.reason = reason
self.body = None
self.headers = None
def __str__(self):
"""Custom error messages for exception"""
error_message = "({0})\n"\
"Reason: {1}\n".format(self.status, self.reason)
if self.headers:
error_message += "HTTP response headers: {0}\n".format(
self.headers)
if self.body:
error_message += "HTTP response body: {0}\n".format(self.body)
return error_message
|
python
|
import unittest
import mock
from factor_graph import FactorGraph, FactorGraphService, Node, Edge
class TestFactorGraph(unittest.TestCase):
pass
class TestFactorGraphService(unittest.TestCase):
## mock FG for failure tests
def test_create_success(self):
factor_graph_service = FactorGraphService()
factor_graph = factor_graph_service.create('some path')
self.assertIsInstance(factor_graph, FactorGraph)
def test_run_success(self):
factor_graph_service = FactorGraphService()
factor_graph = factor_graph_service.create('some path')
result = factor_graph_service.run(factor_graph)
self.assertIsInstance(result, dict)
class TestEdge(unittest.TestCase):
pass
class TestNode(unittest.TestCase):
@mock.patch('state.NodeState')
def setUp(self, mock_node_state):
node_id = 0
node_function = lambda x, y: x + y
self.node_state = mock_node_state.return_value
self.node = Node(node_id, node_function, self.node_state)
def test_message_pass_success(self):
incoming_message = 'incoming'
self.assertIsNone(self.node.message_pass(incoming_message))
def test_message_pass_update_state_exception(self):
self.node_state.update.side_effect = Exception()
incoming_message = 'incoming'
self.assertRaises(Exception, self.node.message_pass, incoming_message)
def test_message_pass_compute_outgoing_message_exception(self):
pass
@mock.patch('Pubsub.Publisher.publish')
def test_message_pass_propagate_message_exception(self, mock_publish_call):
mock_publish_call.side_effect = Exception()
incoming_message = 'incoming'
self.assertRaises(Exception, self.node.message_pass, incoming_message)
if __name__ == '__main__':
unittest.main()
|
python
|
import configparser
import inspect
import logging
import os
import typing
import numpy as np
from ConfigSpace.configuration_space import Configuration
from smac.runhistory.runhistory import RunHistory, RunKey
from cave.utils.exceptions import NotApplicable
def get_timeout(rh, conf, cutoff):
"""Check for timeouts. If multiple runs for an inst/config-pair are
available, using the median (not the mean: no fractional timeouts)
Parameters
----------
rh: RunHistory
runhistory to take runs from
conf: Configuration
config to use
cutoff: int
to determine timeouts
Returns
-------
timeouts: Dict(str: bool)
mapping instances to [True, False], where True indicates a timeout
"""
# TODO Possibly inconsistent: median over timeouts is timeout, but mean over
# costs is not. Possible?
if not cutoff:
return {}
# Check if config is in runhistory
conf_id = rh.config_ids[conf]
timeouts = {}
runs = rh.get_runs_for_config(conf, only_max_observed_budget=True)
for run in runs:
# Averaging over seeds, run = (inst, seed)
inst, seed, _git = run
status = rh.data[RunKey(conf_id, inst, seed)].time < cutoff
if inst in timeouts:
timeouts[inst].append(status)
else:
timeouts[inst] = [status]
# Use median
timeouts = {i: np.floor(np.median(timeouts[i])) for i in timeouts.keys()}
return timeouts
def get_cost_dict_for_config(rh: RunHistory,
conf: Configuration,
par: int=1,
cutoff: typing.Union[float, None]=None):
"""
Aggregates loss for configuration on evaluated instances over seeds.
Parameters
----------
rh: RunHistory
runhistory with data
conf: Configuration
configuration to evaluate
par: int
par-factor with which to multiply timeouts
cutoff: float
cutoff of scenario - used to penalize costs if par != 1
Returns
-------
cost: dict(instance->cost)
cost per instance (aggregated or as list per seed)
"""
instance_costs = rh.get_instance_costs_for_config(conf)
if par != 1:
if cutoff:
instance_costs = {k: v if v < cutoff else v * par for k, v in instance_costs.items()}
else:
raise ValueError("To apply penalization of costs, a cutoff needs to be provided.")
return instance_costs
def escape_parameter_name(p):
"""Necessary because:
1. parameters called 'size' or 'origin' might exist in cs
2. '-' not allowed in bokeh's CDS"""
return 'p_' + p.replace('-', '_')
def scenario_sanity_check(s, logger):
"""Check scenario for number of train- and test-instances, (duplicate) features and inconsistencies.
Logs information and raises ValueError if train-features available, but test-features not."""
train, test, feat = [t for t in s.train_insts if t], [t for t in s.test_insts if t], list(s.feature_dict.keys())
train_feat, test_feat = [t for t in feat if t in train], [t for t in feat if t in test]
logger.debug("Instances: train=%d, test=%d, train-features=%d, test-features=%d",
len([t for t in train if t]), len([t for t in test if t]), len(train_feat), len(test_feat))
if (train and train_feat) and (test and not test_feat):
raise ValueError("Detected train- and test-instances, but only train-features. Either\n (a) remove train-"
"features\n (b) add test-features or\n (c) remove test-instances.")
def combine_runhistories(rhs, logger=None):
"""Combine list of given runhistories. interleaving to best approximate execution order"""
combi_rh = RunHistory()
rh_to_runs = {rh : list(rh.data.items()) for rh in rhs}
if logger:
logger.debug("number of elements: " + str({k : len(v) for k, v in rh_to_runs}))
idx = 0
while len(rh_to_runs) > 0:
for rh in list(rh_to_runs.keys()):
try:
k, v = rh_to_runs[rh][idx]
combi_rh.add(config=rh.ids_config[k.config_id],
cost=v.cost,
time=v.time,
status=v.status,
instance_id=k.instance_id,
#TODO budget option
seed=k.seed,
additional_info=v.additional_info)
except IndexError:
rh_to_runs.pop(rh)
idx += 1
if logger:
logger.debug("number of elements in individual rhs: " + str({k : len(v) for k, v in rh_to_runs}))
logger.debug("number of elements in combined rh: " + str(len(combi_rh.data)))
return combi_rh
def combine_trajectories(trajs, logger=None):
"""Combine trajectories. Trajectories are expected as an iterable of sorted lists, which are increasing in time.
A trajectory entry is expected as:
TrajEntry = collections.namedtuple(
'TrajEntry', ['train_perf', 'incumbent_id', 'incumbent',
'ta_runs', 'ta_time_used', 'wallclock_time'])
Parameters
----------
trajs: List[List[TrajEntry]]
trajectories to be combined
Returns
-------
combined_traj: List[TrajEntry]
combined trajectory
"""
# flatten list
flattened_list = [a for b in trajs for a in b]
# Sort by wallclock-time
flattened_list.sort(key=lambda traj_entry: traj_entry['wallclock_time'])
if logger:
logger.debug("{} trajectories combined to one with {} elements".format(len(trajs), len(flattened_list)))
#logger.debug(flattened_list)
# Now add one by one in order of time if better performance than before
combined_traj = [flattened_list[0]]
for entry in flattened_list:
if entry['cost'] < combined_traj[-1]['cost']:
combined_traj.append(entry)
return combined_traj
class MissingInstancesError(Exception):
"""Exception indicating that instances are missing."""
pass
def get_config_origin(c):
"""Return appropriate configuration origin
Parameters
----------
c: Configuration
configuration to be examined
Returns
-------
origin: str
origin of configuration (e.g. "Local", "Random", etc.)
"""
if not c.origin:
origin = "Unknown"
elif c.origin.startswith("Local") or c.origin == 'Model based pick' or "sorted" in c.origin:
origin = "Acquisition Function"
elif c.origin.startswith("Random"):
origin = "Random"
else:
logging.getLogger("cave.utils.helpers").debug("Cannot interpret origin: %s", c.origin)
origin = "Unknown"
return origin
def check_for_features(scenario):
features = scenario.feature_dict
# filter instance features
train = scenario.train_insts
test = scenario.test_insts
train_feats = {k: v for k, v in features.items() if k in train}
test_feats = {k: v for k, v in features.items() if k in test}
if not (train_feats or test_feats):
raise NotApplicable("Could not detect any instances.")
def load_default_options(options=None, file_format=None):
# Load the configuration file
own_folder = os.path.realpath(os.path.abspath(os.path.split(inspect.getfile(inspect.currentframe()))[0]))
default_options = configparser.ConfigParser()
default_options.read(os.path.join(own_folder, 'options/default_analysis_options.ini'))
if options is not None:
if isinstance(options, str):
default_options.read_file(options)
else:
default_options.read_dict(options)
return default_options
def detect_fileformat(folders):
from cave.reader.conversion.csv2smac import CSV2SMAC
from cave.reader.smac2_reader import SMAC2Reader
from cave.reader.smac3_reader import SMAC3Reader
# First check if it's APT, else BOHB
bohb_files = ["configs.json", "results.json", "configspace.json"]
apt_files = ["autonet_config.json", "results_fit.json"]
if all([all([os.path.isfile(os.path.join(f, sub)) for sub in bohb_files]) for f in folders]):
if all([all([os.path.isfile(os.path.join(f, sub)) for sub in apt_files]) for f in folders]):
return "APT"
else:
return "BOHB"
# Check if it's SMAC
if all([SMAC3Reader.check_for_files(f) for f in folders]):
return "SMAC3"
if all([SMAC2Reader.check_for_files(f) for f in folders]):
return "SMAC2"
# Check if it's CSV
if all([CSV2SMAC.check_for_files(f) for f in folders]):
return "CSV"
raise RuntimeError("Autodetection of file-format failed. Please try to specify (using --file_format on cmd-line)")
def get_folder_basenames(folders):
"""Shorten folder-strings as much as possible (always keeping the basename).
["foo/bar/run_1", "foo/bar/run_2/"] will be ["run_1", "run_2"]
["foo/run_1/bar/", "foo/run_2/bar"] will be ["run_1/bar", "run_2/bar"]
"""
throw, keep = folders[:], ['' for _ in range(len(set(folders)))]
max_parts = max([len(f.split('/')) for f in folders])
for _ in range(max_parts):
for idx in range(len(folders)):
throw[idx], new = os.path.split(throw[idx].rstrip('/'))
keep[idx] = os.path.join(new, keep[idx]).rstrip('/')
if len(set(keep)) == len(set(folders)):
break
return keep
|
python
|
# encoding: utf-8
from .default import Config
class DevelopmentConfig(Config):
# App config
DEBUG = True
SQLALCHEMY_DATABASE_URI = "sqlite:///../db/dictionary.sqlite3"
|
python
|
# -*- coding: utf-8 -*-
#
# Copyright (C) 2017 KuraLabs S.R.L
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""
Module implementating Aggregator base class.
All custom Flowbber aggregators must extend from the Aggregator class.
"""
from functools import wraps
from collections import OrderedDict
from .loader import PluginLoader
from ..logging import get_logger
from ..components import Aggregator
log = get_logger(__name__)
class AggregatorsLoader(PluginLoader):
"""
Aggregators plugins loader class.
"""
_base_class = Aggregator
_locally_registered = OrderedDict()
def __init__(self):
super().__init__('aggregators')
@wraps(AggregatorsLoader.register)
def register(key):
return AggregatorsLoader.register(key)
__all__ = ['AggregatorsLoader', 'register']
|
python
|
############################################################
# -*- coding: utf-8 -*-
#
# # # # # # #
# ## ## # ## # #
# # # # # # # # # # #
# # ## # ## ## ######
# # # # # # #
#
# Python-based Tool for interaction with the 10micron mounts
# GUI with PyQT5 for python
#
# written in python3, (c) 2019-2021 by mworion
#
# Licence APL2.0
#
###########################################################
# standard libraries
# external packages
# local imports
from base.alpacaClass import AlpacaClass
class PegasusUPBAlpaca(AlpacaClass):
"""
"""
__all__ = ['PegasusUPBAlpaca',
]
def __init__(self, app=None, signals=None, data=None):
super().__init__(app=app, data=data, threadPool=app.threadPool)
self.signals = signals
self.data = data
def workerPollData(self):
"""
:return: true for test purpose
"""
if not self.deviceConnected:
return False
model = 'UPB' if self.getAlpacaProperty('maxswitch') == 15 else 'UPBv2'
self.data['FIRMWARE_INFO.VERSION'] = '1.4' if model == 'UPB' else '2.1'
if model == 'UPB':
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=0),
'POWER_CONTROL.POWER_CONTROL_1')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=1),
'POWER_CONTROL.POWER_CONTROL_2')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=2),
'POWER_CONTROL.POWER_CONTROL_3')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=3),
'POWER_CONTROL.POWER_CONTROL_4')
self.storePropertyToData(self.getAlpacaProperty('getswitchvalue', Id=4),
'DEW_CURRENT.DEW_CURRENT_A')
self.storePropertyToData(self.getAlpacaProperty('getswitchvalue', Id=5),
'DEW_CURRENT.DEW_CURRENT_B')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=6),
'USB_HUB_CONTROL.INDI_ENABLED')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=7),
'AUTO_DEW.INDI_ENABLED')
self.storePropertyToData(self.getAlpacaProperty('getswitchvalue', Id=11),
'POWER_SENSORS.SENSOR_VOLTAGE')
self.storePropertyToData(self.getAlpacaProperty('getswitchvalue', Id=12),
'POWER_SENSORS.SENSOR_CURRENT')
self.storePropertyToData(self.getAlpacaProperty('getswitchvalue', Id=13),
'POWER_SENSORS.SENSOR_POWER')
if model == 'UPBv2':
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=0),
'POWER_CONTROL.POWER_CONTROL_1')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=1),
'POWER_CONTROL.POWER_CONTROL_2')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=2),
'POWER_CONTROL.POWER_CONTROL_3')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=3),
'POWER_CONTROL.POWER_CONTROL_4')
self.storePropertyToData(self.getAlpacaProperty('getswitchvalue',
Id=4) / 2.55,
'DEW_PWM.DEW_A')
self.storePropertyToData(self.getAlpacaProperty('getswitchvalue',
Id=5) / 2.55,
'DEW_PWM.DEW_B')
self.storePropertyToData(self.getAlpacaProperty('getswitchvalue',
Id=6) / 2.55,
'DEW_PWM.DEW_C')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=7),
'USB_PORT_CONTROL.PORT_1')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=8),
'USB_PORT_CONTROL.PORT_2')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=9),
'USB_PORT_CONTROL.PORT_3')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=10),
'USB_PORT_CONTROL.PORT_4')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=11),
'USB_PORT_CONTROL.PORT_5')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=12),
'USB_PORT_CONTROL.PORT_6')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=13),
'AUTO_DEW.DEW_A')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=13),
'AUTO_DEW.DEW_B')
self.storePropertyToData(self.getAlpacaProperty('getswitch', Id=13),
'AUTO_DEW.DEW_C')
self.storePropertyToData(self.getAlpacaProperty('getswitchvalue', Id=17),
'POWER_SENSORS.SENSOR_VOLTAGE')
self.storePropertyToData(self.getAlpacaProperty('getswitchvalue', Id=18),
'POWER_SENSORS.SENSOR_CURRENT')
self.storePropertyToData(self.getAlpacaProperty('getswitchvalue', Id=19),
'POWER_SENSORS.SENSOR_POWER')
return True
def togglePowerPort(self, port=None):
if not self.deviceConnected:
return False
if port is None:
return False
switchNumber = int(port) - 1
val = self.data.get(f'POWER_CONTROL.POWER_CONTROL_{port}', True)
self.setAlpacaProperty('setswitchvalue', Id=switchNumber, Value=not val)
return True
def togglePowerPortBoot(self, port=None):
if not self.deviceConnected:
return False
return True
def toggleHubUSB(self):
if not self.deviceConnected:
return False
return True
def togglePortUSB(self, port=None):
if not self.deviceConnected:
return False
if port is None:
return False
model = 'UPB' if self.getAlpacaProperty('maxswitch') == 15 else 'UPBv2'
if model == 'UPBv2':
switchNumber = int(port) + 6
val = self.data.get(f'USB_PORT_CONTROL.PORT_{port}', True)
self.setAlpacaProperty('setswitchvalue', Id=switchNumber, Value=val)
return True
def toggleAutoDew(self):
if not self.deviceConnected:
return False
model = 'UPB' if self.getAlpacaProperty('maxswitch') == 15 else 'UPBv2'
if model == 'UPB':
val = self.data.get('AUTO_DEW.INDI_ENABLED', False)
self.setAlpacaProperty('setswitchvalue', Id=7, Value=val)
else:
val = self.data.get('AUTO_DEW.DEW_A', False)
self.setAlpacaProperty('setswitchvalue', Id=13, Value=val)
return True
def sendDew(self, port=None, value=None):
if not self.deviceConnected:
return False
if port is None:
return False
if value is None:
return False
model = 'UPB' if self.getAlpacaProperty('maxswitch') == 15 else 'UPBv2'
switchNumber = ord(port) - ord('A') + 4
val = int(value * 2.55)
if model == 'UPBv2':
self.setAlpacaProperty('setswitchvalue', Id=switchNumber, Value=val)
return True
def sendAdjustableOutput(self, value=None):
if not self.deviceConnected:
return False
return True
def reboot(self):
if not self.deviceConnected:
return False
return True
|
python
|
import sysconfig
import sys
CFLAGS = "{} -I{} -I{} -fno-omit-frame-pointer".format(
sysconfig.get_config_var("CFLAGS"),
sysconfig.get_path("include"),
sysconfig.get_path("platinclude"),
)
if __name__ == "__main__":
print(f'export CFLAGS="{CFLAGS}"')
|
python
|
#!/usr/bin/env python3
# This example demonstrates the interaction between different execution modes
import mavlinkinterface # Needed to use the library
from time import sleep # For waiting between commands
# Create interface object, All calls will be made through this
# execMode="queue" means that the "queue" execution mode will be used when none is given to a function
MLI = mavlinkinterface.mavlinkInterface(execMode="queue")
MLI.arm() # Enable the propellors.
print("synchronous command has started")
MLI.move(0, 1, execMode="synchronous")
print("synchronous command has ended")
# MLI.setFlightMode("STABILIZE") # Sets the drone to stabilize itself. For more info, see docs/active/setFlightMode.md
# Add some commands to the queue
MLI.move(90, 3) # Strafe right for 3 sec, Since no execMode flag is given, reverts to queue (see above)
MLI.move(270, 3) # Strafe left for 3 sec, Since no execMode flag is given, reverts to queue (see above)
MLI.move(90, 3) # Strafe right for 3 sec, Since no execMode flag is given, reverts to queue (see above)
MLI.move(270, 3) # Strafe left for 3 sec, Since no execMode flag is given, reverts to queue (see above)
MLI.move(90, 3) # Strafe right for 3 sec, Since no execMode flag is given, reverts to queue (see above)
MLI.move(270, 3) # Strafe left for 3 sec, Since no execMode flag is given, reverts to queue (see above)
print("all queuing commands have been added to the queue")
# Wait 5 seconds to give the queue a chance to start
sleep(5)
MLI.move(90, 5, execMode="ignore") # Move right for 5 sec
MLI.move(0, 5, execMode="override") # Move forward for 5 sec
input("Test 1 done, press enter to continue")
MLI.move(90, 3) # Strafe right for 3 sec, Since no execMode flag is given, reverts to queue (see above)
MLI.move(270, 3) # Strafe left for 3 sec, Since no execMode flag is given, reverts to queue (see above)
MLI.move(90, 3) # Strafe right for 3 sec, Since no execMode flag is given, reverts to queue (see above)
MLI.move(270, 3) # Strafe left for 3 sec, Since no execMode flag is given, reverts to queue (see above)
# This should wait until the queue finishes
MLI.move(180, 3, execMode="synchronous") # Move backward for 3 sec
|
python
|
from uuid import uuid4
import pytest
from protean import BaseCommand, BaseEventSourcedAggregate
from protean.exceptions import IncorrectUsageError
from protean.fields import String
from protean.fields.basic import Identifier
class User(BaseEventSourcedAggregate):
id = Identifier(identifier=True)
email = String()
name = String()
class Register(BaseCommand):
user_id = Identifier(identifier=True)
email = String()
name = String()
def test_command_definition_without_aggregate_or_stream(test_domain):
test_domain.register(User)
test_domain.register(Register)
with pytest.raises(IncorrectUsageError) as exc:
test_domain.process(
Register(
user_id=str(uuid4()),
email="[email protected]",
name="John Doe",
)
)
assert exc.value.messages == {
"_entity": [
"Command `Register` needs to be associated with an aggregate or a stream"
]
}
def test_that_abstract_commands_can_be_defined_without_aggregate_or_stream(test_domain):
class AbstractCommand(BaseCommand):
foo = String()
class Meta:
abstract = True
try:
test_domain.register(AbstractCommand)
except Exception:
pytest.fail(
"Abstract commands should be definable without being associated with an aggregate or a stream"
)
@pytest.mark.eventstore
def test_command_associated_with_aggregate(test_domain):
test_domain.register(User)
test_domain.register(Register, aggregate_cls=User)
identifier = str(uuid4())
test_domain.process(
Register(
user_id=identifier,
email="[email protected]",
name="John Doe",
)
)
messages = test_domain.event_store.store.read("user:command")
assert len(messages) == 1
messages[0].stream_name == f"user:command-{identifier}"
@pytest.mark.eventstore
def test_command_associated_with_stream_name(test_domain):
test_domain.register(Register, stream_name="foo")
identifier = str(uuid4())
test_domain.process(
Register(
user_id=identifier,
email="[email protected]",
name="John Doe",
)
)
messages = test_domain.event_store.store.read("foo:command")
assert len(messages) == 1
messages[0].stream_name == f"foo:command-{identifier}"
|
python
|
#!/usr/bin/env python3
# MIT License
#
# Copyright (c) 2020 FABRIC Testbed
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
#
# Author: Komal Thareja ([email protected])
import time
from fabric_cf.actor.core.apis.abc_controller_policy import ABCControllerPolicy
from fabric_cf.actor.core.common.constants import Constants
from fabric_cf.actor.core.kernel.reservation_client import ClientReservationFactory
from fabric_cf.actor.core.kernel.resource_set import ResourceSet
from fabric_cf.actor.core.kernel.slice import SliceFactory
from fabric_cf.actor.core.time.term import Term
from fabric_cf.actor.core.util.id import ID
from fabric_cf.actor.core.util.resource_type import ResourceType
from fabric_cf.actor.test.core.policy.controller_simple_policy_test import ControllerSimplePolicyTest
from fabric_cf.actor.test.core.policy.controller_ticket_review_policy_test_wrapper import \
ControllerTicketReviewPolicyTestWrapper
class ControllerTicketReviewPolicyTest(ControllerSimplePolicyTest):
def get_controller_policy(self) -> ABCControllerPolicy:
return ControllerTicketReviewPolicyTestWrapper()
def test_c_fail(self):
controller = self.get_controller()
clock = controller.get_actor_clock()
Term.clock = clock
resources = ResourceSet(units=1, rtype=ResourceType(resource_type="1"))
slice_obj = SliceFactory.create(slice_id=ID(), name="fail")
controller.register_slice(slice_object=slice_obj)
start = 5
end = 10
term = Term(start=clock.cycle_start_date(cycle=start), end=clock.cycle_end_date(cycle=end))
r1 = ClientReservationFactory.create(rid=ID(), resources=resources, term=term, slice_object=slice_obj)
r1.set_renewable(renewable=False)
controller.register(reservation=r1)
controller.demand(rid=r1.get_reservation_id())
r2 = ClientReservationFactory.create(rid=ID(), resources=resources, term=term, slice_object=slice_obj)
r2.set_renewable(renewable=False)
controller.register(reservation=r2)
controller.demand(rid=r2.get_reservation_id())
for i in range(1, end + 3):
controller.external_tick(cycle=i)
while controller.get_current_cycle() != i:
time.sleep(0.001)
if i >= start and (i < (end - 1)):
self.assertTrue(r1.is_closed())
self.assertTrue(r2.is_closed())
self.assertTrue(r2.get_notices().__contains__(Constants.CLOSURE_BY_TICKET_REVIEW_POLICY))
def test_d_nascent(self):
controller = self.get_controller()
clock = controller.get_actor_clock()
Term.clock = clock
resources = ResourceSet(units=1, rtype=ResourceType(resource_type="1"))
slice_obj = SliceFactory.create(slice_id=ID(), name="nascent")
controller.register_slice(slice_object=slice_obj)
start = 5
end = 10
term = Term(start=clock.cycle_start_date(cycle=start), end=clock.cycle_end_date(cycle=end))
r1 = ClientReservationFactory.create(rid=ID(), resources=resources, term=term, slice_object=slice_obj)
r1.set_renewable(renewable=False)
controller.register(reservation=r1)
controller.demand(rid=r1.get_reservation_id())
r2 = ClientReservationFactory.create(rid=ID(), resources=resources, term=term, slice_object=slice_obj)
r2.set_renewable(renewable=False)
controller.register(reservation=r2)
r2demanded = False
for i in range(1, end + 3):
controller.external_tick(cycle=i)
while controller.get_current_cycle() != i:
time.sleep(0.001)
if i == (start -3) and not r2demanded:
self.assertTrue(r1.is_ticketed())
self.assertTrue(r2.is_nascent())
controller.demand(rid=r2.get_reservation_id())
r2demanded = True
if i >= start and (i < end - 1):
self.assertTrue(r1.is_active())
self.assertTrue(r2.is_active())
if i > end:
self.assertTrue(r1.is_closed())
self.assertTrue(r2.is_closed())
def test_e_fail_and_nascent(self):
controller = self.get_controller()
clock = controller.get_actor_clock()
Term.clock = clock
resources = ResourceSet(units=1, rtype=ResourceType(resource_type="1"))
slice_obj = SliceFactory.create(slice_id=ID(), name="fail_nascent")
controller.register_slice(slice_object=slice_obj)
start = 10
end = 15
term = Term(start=clock.cycle_start_date(cycle=start), end=clock.cycle_end_date(cycle=end))
r1 = ClientReservationFactory.create(rid=ID(), resources=resources, term=term, slice_object=slice_obj)
r1.set_renewable(renewable=False)
controller.register(reservation=r1)
controller.demand(rid=r1.get_reservation_id())
r2 = ClientReservationFactory.create(rid=ID(), resources=resources, term=term, slice_object=slice_obj)
r2.set_renewable(renewable=False)
controller.register(reservation=r2)
r2demanded = False
r3 = ClientReservationFactory.create(rid=ID(), resources=resources, term=term, slice_object=slice_obj)
r3.set_renewable(renewable=False)
controller.register(reservation=r3)
controller.demand(rid=r3.get_reservation_id())
for i in range(1, end + 3):
controller.external_tick(cycle=i)
while controller.get_current_cycle() != i:
time.sleep(0.001)
if i > 2 and not r2demanded:
self.assertTrue(r1.is_failed())
self.assertTrue(r2.is_nascent())
self.assertTrue(r3.is_ticketed())
if i > 6:
controller.demand(rid=r2.get_reservation_id())
r2demanded = True
if (i >= start) and (i < (end - 1)):
self.assertTrue(r1.is_closed())
self.assertTrue(r2.is_closed())
self.assertTrue(r3.is_closed())
self.assertTrue(r2.get_notices().__contains__(Constants.CLOSURE_BY_TICKET_REVIEW_POLICY))
self.assertTrue(r3.get_notices().__contains__(Constants.CLOSURE_BY_TICKET_REVIEW_POLICY))
if i > end:
self.assertTrue(r1.is_closed())
self.assertTrue(r2.is_closed())
self.assertTrue(r3.is_closed())
self.assertTrue(r2.get_notices().__contains__(Constants.CLOSURE_BY_TICKET_REVIEW_POLICY))
self.assertTrue(r3.get_notices().__contains__(Constants.CLOSURE_BY_TICKET_REVIEW_POLICY))
|
python
|
import dataclasses
import logging
from dataclasses import dataclass
import tempfile
import os
from typing import Optional
import loaders
import synth
from synth import ( # noqa: F401
grid_dataset,
grid_dataset_path,
dataset_fixtures_dir,
)
import fv3fit
from fv3net.diagnostics.offline import compute
from fv3net.diagnostics.offline.views.create_report import create_report
import pathlib
import pytest
import numpy as np
import yaml
logger = logging.getLogger(__name__)
@pytest.fixture
def data_path(tmpdir):
schema_path = pathlib.Path(__file__).parent / "data.zarr.json"
with open(schema_path) as f:
schema = synth.load(f)
ranges = {"pressure_thickness_of_atmospheric_layer": synth.Range(0.99, 1.01)}
ds = synth.generate(schema, ranges)
ds.to_zarr(str(tmpdir), consolidated=True)
return str(tmpdir)
@dataclass
class ComputeDiagsArgs:
model_path: str
output_path: str
data_yaml: str
snapshot_time: Optional[str] = None
grid: str = None
grid_resolution: str = "c8_random_values"
n_jobs: int = 1
@dataclass
class CreateReportArgs:
input_path: str
output_path: str
commit_sha: str = "commit_sha_placeholder"
training_config: Optional[str] = None
training_data_config: Optional[str] = None
def test_offline_diags_integration(data_path, grid_dataset_path): # noqa: F811
"""
Test the bash endpoint for computing offline diagnostics
"""
batches_kwargs = {
"needs_grid": False,
"res": "c8_random_values",
"timesteps_per_batch": 1,
"timesteps": ["20160801.001500"],
}
trained_model = fv3fit.testing.ConstantOutputPredictor(
input_variables=["air_temperature", "specific_humidity"],
output_variables=["dQ1", "dQ2"],
)
trained_model.set_outputs(dQ1=np.zeros([19]), dQ2=np.zeros([19]))
data_config = loaders.BatchesFromMapperConfig(
loaders.MapperConfig(function="open_zarr", kwargs={"data_path": data_path},),
function="batches_from_mapper",
kwargs=batches_kwargs,
)
with tempfile.TemporaryDirectory() as tmpdir:
model_dir = os.path.join(tmpdir, "trained_model")
fv3fit.dump(trained_model, model_dir)
data_config_filename = os.path.join(tmpdir, "data_config.yaml")
with open(data_config_filename, "w") as f:
yaml.safe_dump(dataclasses.asdict(data_config), f)
compute_diags_args = ComputeDiagsArgs(
model_path=model_dir,
output_path=os.path.join(tmpdir, "offline_diags"),
data_yaml=data_config_filename,
grid=grid_dataset_path,
)
compute.main(compute_diags_args)
if isinstance(data_config, loaders.BatchesFromMapperConfig):
assert "transect_lon0.nc" in os.listdir(
os.path.join(tmpdir, "offline_diags")
)
create_report_args = CreateReportArgs(
input_path=os.path.join(tmpdir, "offline_diags"),
output_path=os.path.join(tmpdir, "report"),
)
create_report(create_report_args)
with open(os.path.join(tmpdir, "report/index.html")) as f:
report = f.read()
if isinstance(data_config, loaders.BatchesFromMapperConfig):
assert "Transect snapshot at" in report
|
python
|
import gym
from agents.random_agent import RandomAgent
def main(episode_count):
env = gym.make('CartPole-v0')
agent = RandomAgent(env.action_space.n)
for i in range(episode_count):
observation = env.reset() # initialize the environment
done = False
step = 0
while not done:
env.render()
action = agent.act(observation)
next_observation, reward, done, info = env.step(action)
if done:
print("Episode finished after {} timesteps".format(step + 1))
observation = next_observation
step += 1
if __name__ == "__main__":
main(episode_count=20)
|
python
|
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
import io
from knack import util
from unittest import mock
import unittest
from azext_ssh import custom
class SshCustomCommandTest(unittest.TestCase):
@mock.patch('azext_ssh.custom._do_ssh_op')
@mock.patch('azext_ssh.custom.ssh_utils')
def test_ssh_vm(self, mock_ssh_utils, mock_do_op):
cmd = mock.Mock()
custom.ssh_vm(cmd, "rg", "vm", "ip", "public", "private", False)
mock_do_op.assert_called_once_with(
cmd, "rg", "vm", "ip", "public", "private", False, mock_ssh_utils.start_ssh_connection)
@mock.patch('azext_ssh.custom._do_ssh_op')
@mock.patch('azext_ssh.ssh_utils.write_ssh_config')
def test_ssh_config(self, mock_ssh_utils, mock_do_op):
cmd = mock.Mock()
def do_op_side_effect(cmd, resource_group, vm_name, ssh_ip, public_key_file, private_key_file, use_private_ip, op_call):
op_call(ssh_ip, "username", "cert_file", private_key_file)
mock_do_op.side_effect = do_op_side_effect
custom.ssh_config(cmd, "path/to/file", "rg", "vm", "ip", "public", "private", False, False)
mock_ssh_utils.assert_called_once_with("path/to/file", "rg", "vm", False, "ip", "username", "cert_file", "private")
mock_do_op.assert_called_once_with(
cmd, "rg", "vm", "ip", "public", "private", False, mock.ANY)
@mock.patch('azext_ssh.ssh_utils.get_ssh_cert_principals')
@mock.patch('os.path.join')
@mock.patch('azext_ssh.custom._assert_args')
@mock.patch('azext_ssh.custom._check_or_create_public_private_files')
@mock.patch('azext_ssh.ip_utils.get_ssh_ip')
@mock.patch('azext_ssh.custom._get_modulus_exponent')
@mock.patch('azure.cli.core._profile.Profile.get_msal_token')
@mock.patch('azext_ssh.custom._write_cert_file')
def test_do_ssh_op(self, mock_write_cert, mock_ssh_creds, mock_get_mod_exp, mock_ip,
mock_check_files, mock_assert, mock_join, mock_principal):
cmd = mock.Mock()
mock_op = mock.Mock()
mock_check_files.return_value = "public", "private"
mock_principal.return_value = ["username"]
mock_get_mod_exp.return_value = "modulus", "exponent"
mock_ssh_creds.return_value = "username", "certificate"
mock_join.return_value = "public-aadcert.pub"
custom._do_ssh_op(cmd, None, None, "1.2.3.4", "publicfile", "privatefile", False, mock_op)
mock_assert.assert_called_once_with(None, None, "1.2.3.4")
mock_check_files.assert_called_once_with("publicfile", "privatefile")
mock_ip.assert_not_called()
mock_get_mod_exp.assert_called_once_with("public")
mock_write_cert.assert_called_once_with("certificate", "public-aadcert.pub")
mock_op.assert_called_once_with(
"1.2.3.4", "username", "public-aadcert.pub", "private")
@mock.patch('azext_ssh.custom._assert_args')
@mock.patch('azext_ssh.custom._check_or_create_public_private_files')
@mock.patch('azext_ssh.ip_utils.get_ssh_ip')
@mock.patch('azext_ssh.custom._get_modulus_exponent')
def test_do_ssh_op_no_public_ip(self, mock_get_mod_exp, mock_ip, mock_check_files, mock_assert):
cmd = mock.Mock()
mock_op = mock.Mock()
mock_check_files.return_value = "public", "private"
mock_get_mod_exp.return_value = "modulus", "exponent"
mock_ip.return_value = None
self.assertRaises(
util.CLIError, custom._do_ssh_op, cmd, "rg", "vm", None,
"publicfile", "privatefile", False, mock_op)
mock_assert.assert_called_once_with("rg", "vm", None)
mock_check_files.assert_called_once_with("publicfile", "privatefile")
mock_ip.assert_called_once_with(cmd, "rg", "vm", False)
def test_assert_args_no_ip_or_vm(self):
self.assertRaises(util.CLIError, custom._assert_args, None, None, None)
def test_assert_args_vm_rg_mismatch(self):
self.assertRaises(util.CLIError, custom._assert_args, "rg", None, None)
self.assertRaises(util.CLIError, custom._assert_args, None, "vm", None)
def test_assert_args_ip_with_vm_or_rg(self):
self.assertRaises(util.CLIError, custom._assert_args, None, "vm", "ip")
self.assertRaises(util.CLIError, custom._assert_args, "rg", "vm", "ip")
@mock.patch('azext_ssh.ssh_utils.create_ssh_keyfile')
@mock.patch('tempfile.mkdtemp')
@mock.patch('os.path.isfile')
@mock.patch('os.path.join')
def test_check_or_create_public_private_files_defaults(self, mock_join, mock_isfile, mock_temp, mock_create):
mock_isfile.return_value = True
mock_temp.return_value = "/tmp/aadtemp"
mock_join.side_effect = ['/tmp/aadtemp/id_rsa.pub', '/tmp/aadtemp/id_rsa']
public, private = custom._check_or_create_public_private_files(None, None)
self.assertEqual('/tmp/aadtemp/id_rsa.pub', public)
self.assertEqual('/tmp/aadtemp/id_rsa', private)
mock_join.assert_has_calls([
mock.call("/tmp/aadtemp", "id_rsa.pub"),
mock.call("/tmp/aadtemp", "id_rsa")
])
mock_isfile.assert_has_calls([
mock.call('/tmp/aadtemp/id_rsa.pub'),
mock.call('/tmp/aadtemp/id_rsa')
])
mock_create.assert_has_calls([
mock.call('/tmp/aadtemp/id_rsa')
])
@mock.patch('os.path.isfile')
@mock.patch('os.path.join')
def test_check_or_create_public_private_files_no_public(self, mock_join, mock_isfile):
mock_isfile.side_effect = [False]
self.assertRaises(
util.CLIError, custom._check_or_create_public_private_files, "public", None)
mock_isfile.assert_called_once_with("public")
@mock.patch('os.path.isfile')
@mock.patch('os.path.join')
def test_check_or_create_public_private_files_no_private(self, mock_join, mock_isfile):
mock_isfile.side_effect = [True, False]
self.assertRaises(
util.CLIError, custom._check_or_create_public_private_files, "public", "private")
mock_join.assert_not_called()
mock_isfile.assert_has_calls([
mock.call("public"),
mock.call("private")
])
@mock.patch('builtins.open')
def test_write_cert_file(self, mock_open):
mock_file = mock.Mock()
mock_open.return_value.__enter__.return_value = mock_file
custom._write_cert_file("cert", "publickey-aadcert.pub")
mock_open.assert_called_once_with("publickey-aadcert.pub", 'w')
mock_file.write.assert_called_once_with("[email protected] cert")
@mock.patch('azext_ssh.rsa_parser.RSAParser')
@mock.patch('os.path.isfile')
@mock.patch('builtins.open')
def test_get_modulus_exponent_success(self, mock_open, mock_isfile, mock_parser):
mock_isfile.return_value = True
mock_open.return_value = io.StringIO('publickey')
modulus, exponent = custom._get_modulus_exponent('file')
self.assertEqual(mock_parser.return_value.modulus, modulus)
self.assertEqual(mock_parser.return_value.exponent, exponent)
mock_isfile.assert_called_once_with('file')
mock_open.assert_called_once_with('file', 'r')
mock_parser.return_value.parse.assert_called_once_with('publickey')
@mock.patch('os.path.isfile')
def test_get_modulus_exponent_file_not_found(self, mock_isfile):
mock_isfile.return_value = False
self.assertRaises(util.CLIError, custom._get_modulus_exponent, 'file')
mock_isfile.assert_called_once_with('file')
@mock.patch('azext_ssh.rsa_parser.RSAParser')
@mock.patch('os.path.isfile')
@mock.patch('builtins.open')
def test_get_modulus_exponent_parse_error(self, mock_open, mock_isfile, mock_parser):
mock_isfile.return_value = True
mock_open.return_value = io.StringIO('publickey')
mock_parser_obj = mock.Mock()
mock_parser.return_value = mock_parser_obj
mock_parser_obj.parse.side_effect = ValueError
self.assertRaises(util.CLIError, custom._get_modulus_exponent, 'file')
if __name__ == '__main__':
unittest.main()
|
python
|
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XXXXXXX XX X XXXXXXXXXXX XXXX XXXXXX XXX XXXXX XXXXXXXXXX XX XXXXXXX XX XXX
XXXXXX XXXXXXXX XX XXXX XX XXX XXXXXXXXX XXXXXX XXX XXXXX
XX XXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXX XXXXXX
XXXXXXXXXXXXXXXXXXX
XXX XXX XXXXXXX XXXXXX XXXX XXXXXXXXX
XXXXXX XX XXX XXXXXXX XXXXXX
XXXXXXX XXXXXX XX XXXXXX
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XXXX XXXXXX XX XXXXXXXXXX X XXXXXXXXXX XXXXXXX XX XXXXXXXXX XXXXX XXXXXX XXX
XXXXXX XXXXXX XXXXXXXX XX XXX XXXXXXXXX XXXXXX XXXXX XX XX XXXXXX XXXX XXX
XXXXXXXX XX X XXXXXX XXXXXXXXXX XX XXXX XXXXXXXXXXX XXXXXXX XXXXXXXX XX XXXX
XXX XXXXXXXXXXX XX XXXXXX XXXXXXXXXX XX X XXXXXXX
XXX X XXXXXXX XXXXXX XXXXXXXX XXXXXXXX XXXXXX XXXXX XXXX XXXXXX
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XXXX XXXX XXXXX XXX XXXXXXX XXXXXXXXX XX XXXXXXXXXXXXXXXXXXX XX XXXXXXXX XXXX
XXXX XXXXXXX XX XX XXX XXXXXX XXXXX XX XXXXXX XXXX XXX XXXX XXXXXXX XXXX XXX
XXXX XXXXXXXXX XX XXXX XXXXXXXXXXXXX XXXXX
|
python
|
"""
zoom.mail
email services
"""
import os
import json
import logging
from smtplib import SMTP
from mimetypes import guess_type
from email import encoders
from email.mime.text import MIMEText
from email.mime.base import MIMEBase
from email.mime.image import MIMEImage
from email.mime.audio import MIMEAudio
from email.mime.multipart import MIMEMultipart
from email.utils import formataddr
import zoom
from zoom.context import context
from zoom.store import EntityStore
from zoom.tools import ensure_listy, now
from zoom.utils import Record
from zoom.tools import get_template
__all__ = (
'send',
'send_as',
'deliver',
'Attachment',
)
class AttachmentDataException(Exception):
"""raised when asked to deliver data in background process"""
pass
class SystemMail(Record):
"""system message"""
pass
class Attachment(object):
"""Email attachment
provide either a pathname, or a filename and a pathname, or if sending
directly a filename and a file-like object.
"""
# pylint: disable=too-few-public-methods
def __init__(self, pathname, data=None, mime_type=None):
self.pathname = pathname
self.data = data
self.mimetype = mime_type
self.filename = os.path.split(pathname)[1]
def as_tuple(self):
"""partilars required for delivery"""
if hasattr(self.data, 'read'):
msg = 'Unable to deliver data directly, use physical file instead'
raise AttachmentDataException(msg)
return self.pathname, self.data, self.mimetype
@property
def read(self):
"""provides a reader for the data
if the data is not open, it will be because the user provided only a
pathanme so we open the file at the pathname and return it"""
if not self.data:
self.data = open(self.pathname)
elif isinstance(self.data, str):
self.data = open(self.data)
return self.data.read
def get_mail_store(site):
"""returns the mail store"""
return EntityStore(site.db, SystemMail)
def format_as_html(text, logo_url):
site = zoom.get_site()
template = get_template('email_template', theme=site.theme)
return template.format(logo_url=logo_url, message=text)
def display_email_address(email):
"""Make a formatted address (eg: "User Name <[email protected]>"),
from a tuple (Display name, email address) or a list of tuples.
If the parameter is a string, it is returned.
>>> recipients = [('Joe','[email protected]'),'[email protected]']
>>> display_email_address(recipients)
'Joe <[email protected]>;[email protected]'
"""
if isinstance(email, (list, tuple)):
result = []
for item in email:
if isinstance(item, (list, tuple)) and len(item) == 2:
result.append(formataddr(item))
else:
result.append(item)
return ';'.join(result)
return email
def get_plain_from_html(html):
"""extract plain text from html
>>> test_html = "<div><h1>Hey<h1><p>This is some text</p></div>"
>>> get_plain_from_html(test_html)
'Hey\\nThis is some text'
"""
# import here to avoid high startup cost
from html.parser import HTMLParser
class MyHTMLParser(HTMLParser):
"""custom HTML parser"""
def __init__(self):
HTMLParser.__init__(self)
self.lines = []
def handle_data(self, data):
self.lines.append(data)
def value(self):
return '\n'.join(self.lines)
parser = MyHTMLParser()
parser.feed(html)
parser.close()
return parser.value()
def compose(sender, reply_to, recipients, subject, body, attachments, style, logo_url):
"""compose an email message"""
email = MIMEMultipart()
email['Subject'] = subject
email['From'] = formataddr(sender)
email['To'] = display_email_address(recipients)
if sender != reply_to:
email['Reply-To'] = formataddr(reply_to)
email.preamble = (
'This message is in MIME format. '
'You will not see this in a MIME-aware mail reader.\n'
)
email.epilogue = '' # To guarantee the message ends with a newline
# Encapsulate the plain and HTML versions of the message body in an
# 'alternative' part, so message agents can decide which they
# want to display.
msg_alternative = MIMEMultipart('alternative')
email.attach(msg_alternative)
# if isinstance(body, str):
# body = body.encode('utf8')
#
# simple encoding test, we will leave as ascii if possible (readable)
_char = 'us-ascii'
try:
body.encode('ascii')
except UnicodeDecodeError:
_char = 'utf8'
except AttributeError:
_char = 'utf8'
# attach a plain text version of the html email
if style == 'html':
msg_alternative.attach(
MIMEText(get_plain_from_html(body), 'plain', _char)
)
body = format_as_html(body, logo_url)
body = MIMEText(body.encode('utf8'), style, _char)
msg_alternative.attach(body)
for attachment in attachments or []:
# Guess the content type based on the file's extension. Encoding
# will be ignored, although we should check for simple things like
# gzip'd or compressed files.
ctype, encoding = guess_type(attachment.filename)
if ctype is None or encoding is not None:
# No guess could be made, or the file is encoded (compressed),
# so use a generic bag-of-bits type.
ctype = 'application/octet-stream'
maintype, subtype = ctype.split('/', 1)
if maintype == 'text' or (
ctype is not None and
attachment.filename[-4:].lower() == '.ini'
):
# Note: we should handle calculating the charset
msg = MIMEText(attachment.read(), _subtype=subtype)
elif maintype == 'image':
msg = MIMEImage(attachment.read(), _subtype=subtype)
elif maintype == 'audio':
msg = MIMEAudio(attachment.read(), _subtype=subtype)
else:
msg = MIMEBase(maintype, subtype)
msg.set_payload(attachment.read())
# Encode the payload using Base64
encoders.encode_base64(msg)
# Set the filename parameter
msg.add_header(
'Content-Disposition',
'attachment',
filename=attachment.filename
)
email.attach(msg)
return email.as_string()
def connect(site, get_server, debug=False):
"""connect to the mail server"""
if site.smtp_host and site.smtp_port:
server = get_server(site.smtp_host, site.smtp_port)
if debug:
server.set_debuglevel(1)
if site.smtp_user and site.smtp_passwd:
server.login(site.smtp_user, site.smtp_passwd)
return server
def disconnect(server):
"""disconnect from the mail server"""
if server:
server.quit()
def deliver():
"""deliver mail"""
# spylint: disable=too-many-locals
count = 0
server = connect(context.site, SMTP)
try:
mail_store = get_mail_store(context.site)
mails = mail_store.find(status='waiting')
for mail in mails:
sender = json.loads(mail.sender)
reply_to = json.loads(mail.reply_to) if mail.reply_to else sender
recipients = json.loads(mail.recipients)
attachments = [
Attachment(name, data, mimetype)
for name, data, mimetype
in json.loads(mail.attachments) or []
]
email = compose(
sender,
reply_to,
recipients,
mail.subject,
mail.body,
attachments,
mail.style,
context.site.mail_logo,
)
try:
sender_address = sender[1]
except IndexError:
sender_address = sender
try:
server.sendmail(
sender_address,
[r[1] for r in recipients],
email
)
mail.status = 'sent'
count += 1
except Exception:
mail.status = 'error'
raise
finally:
mail_store.put(mail)
finally:
disconnect(server)
return count
def expedite(site, sender, recipients, subject, body,
attachments=None, style='plain'):
"""deliver this email now"""
logger = logging.getLogger(__name__)
email = compose(
sender,
sender,
recipients,
subject,
body,
attachments or [],
style,
site.mail_logo,
)
sender_address = formataddr(sender)
server = connect(site, SMTP)
if server:
try:
result = server.sendmail(
sender_address,
[r[1] for r in recipients],
email
)
if result:
msg = 'Unable to send email. Please contact administrator.'
logger.error('Unable to send email: {}'.format(result))
raise Exception(msg)
else:
logger.debug('mail sent successfully')
finally:
disconnect(server)
else:
logger.error('unable to connect to mail server')
def post(put, sender, recipients, subject,
body, attachments=None, style='plain'):
"""post an email message for delivery"""
# pylint: disable=too-many-arguments
dumps = json.dumps
mail = SystemMail(
sender=dumps(sender),
recipients=dumps(recipients),
subject=subject,
body=body,
attachments=dumps([a.as_tuple() for a in attachments or []]),
style=style,
status='waiting',
created=now(),
)
put(mail)
def make_recipients_list(recipients):
"""build a well formed list of recipients
>>> make_recipients_list('[email protected]')
[('[email protected]', '[email protected]')]
>>> v = make_recipients_list('[email protected];[email protected]')
>>> v == [
... ('[email protected]', '[email protected]'),
... ('[email protected]', '[email protected]')
... ]
True
>>> make_recipients_list(['[email protected]'])
[('[email protected]', '[email protected]')]
>>> v = make_recipients_list(
... [
... '[email protected]',
... ('Sally', '[email protected]')
... ])
>>> v == [
... ('[email protected]', '[email protected]'),
... ('Sally', '[email protected]')
... ]
True
"""
if isinstance(recipients, str):
if ';' in recipients:
recipients = list(zip(recipients.split(';'), recipients.split(';')))
else:
recipients = (recipients, recipients)
recipients = isinstance(recipients, list) and recipients or [recipients]
# if it's a list we interpret it as a list of addresses since
# (name, address) pairs are always tuples.
recipients = ensure_listy(recipients)
recipients = [isinstance(x, str) and (x, x) or x for x in recipients]
return recipients
def as_sender_tuple(sender):
"""build a sender tuple
>>> as_sender_tuple('[email protected]')
('[email protected]', '[email protected]')
>>> as_sender_tuple(('[email protected]', '[email protected]'))
('[email protected]', '[email protected]')
>>> as_sender_tuple(['[email protected]', '[email protected]'])
('[email protected]', '[email protected]')
"""
if isinstance(sender, str):
return sender, sender
return tuple(sender)
def send_as(sender, recipients, subject, message, attachments=None):
"""send an email as a specific sender
>>> zoom.system.site = zoom.sites.Site()
>>> send_as('[email protected]', '[email protected]', "test", "This is a test")
This function compares the sender to the default send_from information
configured for the site, and if they differ it includes a Reply-To header
in the email message.
NOTE: Some email providers may flag emails sent using the reply-to header
as spam or phishing emails. To send email reliably when the domains of the
site send_from address differ from the reply-to address domain the system
must be configured properly. There is much written on this topic so we
don't try to cover it here, however here is a useful place to start:
* https://blog.codinghorror.com/so-youd-like-to-send-some-email-through-code/
"""
site = context.site
sender = as_sender_tuple(sender)
recipients = make_recipients_list(recipients)
if site.mail_delivery != 'background':
expedite(site, sender, recipients, subject, message, attachments, 'html')
else:
raise Exception('background email processing not yet implemented')
# put = get_mail_store(site).put
# post(put, sender, recipients, subject, message, attachments, 'html')
def get_default_sender(site):
"""get default sender (name, address) tuple"""
sender = (site.mail_from_name, site.mail_from_addr)
return sender
def send(recipients, subject, message, attachments=None):
"""send an email
>>> zoom.system.site = zoom.sites.Site()
>>> send('[email protected]', "test", "This is a test")
"""
sender = get_default_sender(context.site)
send_as(sender, recipients, subject, message, attachments)
|
python
|
# SPDX-License-Identifier: MIT
# Copyright (c) 2022 MBition GmbH
from ..globals import logger
from .compumethodbase import CompuMethod
class TabIntpCompuMethod(CompuMethod):
def __init__(self,
internal_type,
physical_type):
super().__init__(internal_type, physical_type, "TAB-INTP")
logger.debug("Created table interpolation compu method!")
logger.warning(
"TODO: Implement table interpolation compu method properly!")
def convert_physical_to_internal(self, physical_value):
return self.internal_type.make_from(physical_value)
def convert_internal_to_physical(self, internal_value):
return self.physical_type.make_from(internal_value)
def is_valid_physical_value(self, physical_value):
return True
def is_valid_internal_value(self, internal_value):
return True
|
python
|
# -*- coding: utf-8 -*-
from datetime import datetime, timedelta
from hashlib import md5
from flask import Flask, request, redirect, current_app
from time import gmtime
from xmltodict import parse
import requests
import urllib
app = Flask(__name__)
app.config.from_pyfile('config.py', silent=True)
app.config.from_pyfile('/etc/dim/cas.cfg', silent=True)
@app.after_request
def after_request(response):
if app.config.get('FRONTEND_SERVICE', ''):
header = response.headers
header['Access-Control-Allow-Origin'] = app.config['FRONTEND_SERVICE']
return response
@app.route("/")
def login():
if (not app.config.get('CURRENT_SERVICE', '') or
not app.config.get('CAS_LOGIN_URL', '') or
not app.config.get('CAS_VALIDATE_URL', '')):
return "CAS config incomplete", 500
if (not app.config.get('FRONTEND_SERVICE', '') or
not app.config.get('TOOL_NAME', '') or
not app.config.get('SECRET_KEY', '')):
return "DIM frontend config incomplete", 500
if 'ticket' not in request.args:
return redirect(app.config['CAS_LOGIN_URL'].format(
app.config['CURRENT_SERVICE']))
ticket = request.args['ticket']
cas_response = requests.get(app.config['CAS_VALIDATE_URL'].format(
app.config['CURRENT_SERVICE'], ticket))
tree = parse(cas_response.content)
if not tree.get('cas:serviceResponse', False):
return "CAS response invalid", 500
service_response = tree.get('cas:serviceResponse')
if (not service_response.get('cas:authenticationSuccess', False) and
not service_response.get('cas:authenticationFailure', False)):
return "CAS response invalid", 400
if service_response.get('cas:authenticationFailure', False):
return service_response.get('cas:authenticationFailure').get('#text'), 400
success = service_response.get('cas:authenticationSuccess', False)
if not success.get('cas:user', False):
return "CAS response does not contain user", 400
username = success.get('cas:user')
attributes = success.get('cas:attributes')
last_name = ''
if 'cas:lastName' in attributes:
last_name = attributes['cas:lastName'].encode('latin-1')
first_name = ''
if 'cas:firstName' in attributes:
first_name = attributes['cas:firstName'].encode('latin-1')
full_name = first_name + " " + last_name
salt = str(gmtime())
response = current_app.make_response(redirect(app.config['FRONTEND_SERVICE']))
response.set_cookie(
key='LOGIN_ARGS',
value=urllib.urlencode({
'username': username,
'tool': app.config['TOOL_NAME'],
'salt': salt,
'sign': md5((username + salt + app.config['SECRET_KEY']).encode()).hexdigest()
}),
expires=datetime.now() + timedelta(seconds=24 * 3600)
)
response.set_cookie(
key='FULL_NAME',
value=full_name,
expires=datetime.now() + timedelta(seconds=24 * 3600)
)
return response
@app.route("/logout")
def logout():
if not app.config.get('CAS_LOGOUT_URL', ''):
return "CAS config incomplete", 500
return redirect(app.config['CAS_LOGOUT_URL'])
if __name__ == "__main__":
app.run(ssl_context='adhoc', port=443, host='0.0.0.0')
|
python
|
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from catboost import Pool
from sklearn.model_selection import StratifiedKFold
from tqdm import tqdm
class CatBoostCustomModel:
def __init__(self, model, model_params={}):
self.result = {}
try:
self.model = model
self.model.set_params(**model_params)
except:
raise
def fit(self, X, y, cat_features=[], fit_params={}, n_folds=None, shuffle=False):
if not len(cat_features):
cat_features = np.where(X.dtypes == np.object)[0]
if not n_folds:
df_pool = Pool(X, label=y, cat_features=cat_features)
model_fit = self.model.fit(df_pool, **fit_params)
else:
kf = StratifiedKFold(n_splits=n_folds, random_state=42, shuffle=shuffle)
model_fit = self.model
for train_index, valid_index in tqdm(kf.split(X, y)):
print(len(train_index), len(valid_index))
df_pool = Pool(X.iloc[train_index], label=y.iloc[train_index], cat_features=cat_features)
x_val, y_val = X.iloc[valid_index], y.iloc[valid_index]
model_fit.fit(df_pool, eval_set=(x_val, y_val)
, **fit_params)
self.result = {
"X": X,
"y": y,
"cat_features": cat_features,
"model": self.model,
"fit_params": fit_params,
"model_fit": model_fit
}
return model_fit
def get_features_importance(self, sorted=False):
feature_score = pd.DataFrame(
list(zip(self.result.get("X").dtypes.index, self.result.get("model_fit").get_feature_importance())),
columns=['Feature', 'Score'])
if sorted:
feature_score.sort_values(by='Score', ascending=False, inplace=True, na_position='last')
return feature_score
def plot_features_importance(self, sorted=True):
feature_score = self.get_features_importance(sorted=sorted)
ax = feature_score.plot('Feature', 'Score', kind='bar')
ax.set_title("Catboost Feature Importance Ranking", fontsize=14)
ax.set_xlabel('')
rects = ax.patches
# get feature score as labels round to 2 decimal
labels = feature_score['Score'].round(2)
for rect, label in zip(rects, labels):
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2, height + 0.35, label, ha='center', va='bottom')
plt.show()
def get_score(self):
return self.result["model_fit"].score(self.result["X"], self.result["y"])
def get_crosstab(self):
crosstab = pd.DataFrame()
crosstab['GroundTruth'] = self.result["y"]
crosstab['Predict'] = self.result["model_fit"].predict(self.result["X"])
return pd.crosstab(crosstab['GroundTruth'], crosstab['Predict'], margins=True)
|
python
|
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