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third_party/logging.py
sweeneyb/iot-core-micropython
50
9600
# MIT License # # Copyright (c) 2019 <NAME> # # 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. import sys CRITICAL = 50 ERROR = 40 WARNING = 30 INFO = 20 DEBUG = 10 NOTSET = 0 _level_dict = { CRITICAL: "CRIT", ERROR: "ERROR", WARNING: "WARN", INFO: "INFO", DEBUG: "DEBUG", } _stream = sys.stderr class Logger: level = NOTSET def __init__(self, name): self.name = name def _level_str(self, level): l = _level_dict.get(level) if l is not None: return l return "LVL%s" % level def setLevel(self, level): self.level = level def isEnabledFor(self, level): return level >= (self.level or _level) def log(self, level, msg, *args): if level >= (self.level or _level): _stream.write("%s:%s:" % (self._level_str(level), self.name)) if not args: print(msg, file=_stream) else: print(msg % args, file=_stream) def debug(self, msg, *args): self.log(DEBUG, msg, *args) def info(self, msg, *args): self.log(INFO, msg, *args) def warning(self, msg, *args): self.log(WARNING, msg, *args) def error(self, msg, *args): self.log(ERROR, msg, *args) def critical(self, msg, *args): self.log(CRITICAL, msg, *args) def exc(self, e, msg, *args): self.log(ERROR, msg, *args) sys.print_exception(e, _stream) def exception(self, msg, *args): self.exc(sys.exc_info()[1], msg, *args) _level = INFO _loggers = {} def getLogger(name): if name in _loggers: return _loggers[name] l = Logger(name) _loggers[name] = l return l def info(msg, *args): getLogger(None).info(msg, *args) def debug(msg, *args): getLogger(None).debug(msg, *args) def basicConfig(level=INFO, filename=None, stream=None, format=None): global _level, _stream _level = level if stream: _stream = stream if filename is not None: print("logging.basicConfig: filename arg is not supported") if format is not None: print("logging.basicConfig: format arg is not supported")
1.992188
2
assessments/migrations/0003_auto_20210212_1943.py
acounsel/django_msat
0
9601
# Generated by Django 3.1.6 on 2021-02-12 19:43 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('assessments', '0002_auto_20210212_1904'), ] operations = [ migrations.AlterField( model_name='country', name='region', field=models.CharField(blank=True, choices=[('america', 'Americas'), ('europe', 'Europe'), ('africa', 'Africa'), ('asia', 'Asia'), ('oceania', 'Oceania')], max_length=100, null=True), ), ]
1.632813
2
noxfile.py
sethmlarson/workplace-search-python
5
9602
<gh_stars>1-10 import nox SOURCE_FILES = ( "setup.py", "noxfile.py", "elastic_workplace_search/", "tests/", ) @nox.session(python=["2.7", "3.4", "3.5", "3.6", "3.7", "3.8"]) def test(session): session.install(".") session.install("-r", "dev-requirements.txt") session.run("pytest", "--record-mode=none", "tests/") @nox.session() def blacken(session): session.install("black") session.run("black", *SOURCE_FILES) lint(session) @nox.session() def lint(session): session.install("flake8", "black") session.run("black", "--check", *SOURCE_FILES) session.run("flake8", "--select=E,W,F", "--max-line-length=88", *SOURCE_FILES)
1.742188
2
komodo2_rl/src/environments/Spawner.py
osheraz/komodo
5
9603
<reponame>osheraz/komodo # !/usr/bin/env python import rospy import numpy as np from gazebo_msgs.srv import SpawnModel, SpawnModelRequest, SpawnModelResponse from copy import deepcopy from tf.transformations import quaternion_from_euler sdf_cube = """<?xml version="1.0" ?> <sdf version="1.4"> <model name="MODELNAME"> <static>0</static> <link name="link"> <inertial> <mass>1.0</mass> <inertia> <ixx>0.01</ixx> <ixy>0.0</ixy> <ixz>0.0</ixz> <iyy>0.01</iyy> <iyz>0.0</iyz> <izz>0.01</izz> </inertia> </inertial> <collision name="stairs_collision0"> <pose>0 0 0 0 0 0</pose> <geometry> <box> <size>SIZEXYZ</size> </box> </geometry> <surface> <bounce /> <friction> <ode> <mu>1.0</mu> <mu2>1.0</mu2> </ode> </friction> <contact> <ode> <kp>10000000.0</kp> <kd>1.0</kd> <min_depth>0.0</min_depth> <max_vel>0.0</max_vel> </ode> </contact> </surface> </collision> <visual name="stairs_visual0"> <pose>0 0 0 0 0 0</pose> <geometry> <box> <size>SIZEXYZ</size> </box> </geometry> <material> <script> <uri>file://media/materials/scripts/gazebo.material</uri> <name>Gazebo/Wood</name> </script> </material> </visual> <velocity_decay> <linear>0.000000</linear> <angular>0.000000</angular> </velocity_decay> <self_collide>0</self_collide> <kinematic>0</kinematic> <gravity>1</gravity> </link> </model> </sdf> """ sdf_sand = """<?xml version='1.0'?> <sdf version='1.6'> <model name="MODELNAME"> <link name='link'> <pose frame=''>0 0 0.01 0 0 0 </pose> <inertial> <mass>1</mass> <inertia> <ixx>0.1</ixx> <ixy>0</ixy> <ixz>0</ixz> <iyy>0.1</iyy> <iyz>0</iyz> <izz>0.1</izz> </inertia> </inertial> <visual name='visual'> <pose frame=''>0 0 0 0 -0 0</pose> <geometry> <mesh> <scale>SIZEXYZ</scale> <uri>model://sand/sand_particle.stl</uri> </mesh> </geometry> <material> <lighting>1</lighting> <script> <uri>file://media/materials/scripts/gazebo.material</uri> <name>Gazebo/Yellow</name> </script> <ambient>0.3 0.25 0.1 1</ambient> <diffuse>0.7 0.6 0.4 1</diffuse> <specular>0.01 0.005 0.001 1</specular> <emissive>0 0 0 1</emissive> </material> <transparency>0</transparency> <cast_shadows>1</cast_shadows> </visual> <collision name='collision'> <laser_retro>0</laser_retro> <max_contacts>10</max_contacts> <pose frame=''>0 0 0 0 -0 0</pose> <geometry> <mesh> <scale>SIZEXYZ</scale> <uri>model://sand/sand_particle.stl</uri> </mesh> </geometry> <surface> <friction> <ode> <mu>1</mu> <mu2>1</mu2> <fdir1>0 0 0</fdir1> <slip1>0</slip1> <slip2>0</slip2> </ode> <torsional> <coefficient>1</coefficient> <patch_radius>0</patch_radius> <surface_radius>0</surface_radius> <use_patch_radius>1</use_patch_radius> <ode> <slip>0</slip> </ode> </torsional> </friction> <bounce> <restitution_coefficient>0.2</restitution_coefficient> <threshold>1.01</threshold> </bounce> <contact> <collide_without_contact>0</collide_without_contact> <collide_without_contact_bitmask>1</collide_without_contact_bitmask> <collide_bitmask>1</collide_bitmask> <ode> <soft_cfm>0</soft_cfm> <soft_erp>0.2</soft_erp> <kp>1e+13</kp> <kd>1</kd> <max_vel>0.01</max_vel> <min_depth>0</min_depth> </ode> <bullet> <split_impulse>1</split_impulse> <split_impulse_penetration_threshold>-0.01</split_impulse_penetration_threshold> <soft_cfm>0</soft_cfm> <soft_erp>0.2</soft_erp> <kp>1e+13</kp> <kd>1</kd> </bullet> </contact> </surface> </collision> </link> <static>0</static> <allow_auto_disable>1</allow_auto_disable> </model> </sdf> """ sdf_sand_box = """<sdf version='1.6'> <model name='sand_box_osher'> <link name='sand_box_osher'> <pose frame=''>0 0 0 0 -0 0</pose> <inertial> <pose frame=''>-0.35285 -0.305 0.11027 0 -0 0</pose> <mass>2000.892</mass> <inertia> <ixx>130.2204</ixx> <ixy>-220.5538e-15</ixy> <ixz>-4.85191</ixz> <iyy>276.363</iyy> <iyz>-77.9029e-15</iyz> <izz>135.62</izz> </inertia> </inertial> <collision name='sand_box_osher_collision'> <pose frame=''>0 0 0 1.5708 -0 0</pose> <geometry> <mesh> <scale>1 0.8 1</scale> <uri>model://sand_box_osher/meshes/sand_box_osher.STL</uri> </mesh> </geometry> </collision> <visual name='sand_box_osher_visual'> <pose frame=''>0 0 0 1.5708 -0 0</pose> <geometry> <mesh> <scale>1 0.8 1</scale> <uri>model://sand_box_osher/meshes/sand_box_osher.STL</uri> </mesh> </geometry> <material> <ambient>0.3 0.25 0.1 1</ambient> <diffuse>0.7 0.6 0.4 1</diffuse> <specular>0.01 0.005 0.001 1</specular> <emissive>0 0 0 1</emissive> </material> <transparency>0.5</transparency> </visual> </link> </model> </sdf> """ sdf_unit_sphere = """<?xml version='1.0'?> <sdf version='1.6'> <model name="MODELNAME"> <link name='link'> <pose frame=''>0 0 0 0 -0 0</pose> <inertial> <mass>0.1</mass> <inertia> <ixx>0.0000490147</ixx> <ixy>0</ixy> <ixz>0</ixz> <iyy>0.000049147</iyy> <iyz>0</iyz> <izz>0.000049147</izz> </inertia> <pose frame=''>0 0 0 0 -0 0</pose> </inertial> <self_collide>0</self_collide> <kinematic>0</kinematic> <visual name='visual'> <geometry> <sphere> <radius>RADIUS</radius> </sphere> </geometry> <material> <lighting>1</lighting> <script> <uri>file://media/materials/scripts/gazebo.material</uri> <name>Gazebo/Yellow</name> </script> <ambient>0.3 0.25 0.1 1</ambient> <diffuse>0.7 0.6 0.4 1</diffuse> <specular>0.01 0.005 0.001 1</specular> <emissive>0 0 0 1</emissive> </material> <pose frame=''>0 0 0 0 -0 0</pose> <transparency>0</transparency> <cast_shadows>1</cast_shadows> </visual> <collision name='collision'> <laser_retro>0</laser_retro> <max_contacts>10</max_contacts> <pose frame=''>0 0 0 0 -0 0</pose> <geometry> <sphere> <radius>RADIUS</radius> </sphere> </geometry> <surface> <friction> <ode> <mu>1</mu> <mu2>1</mu2> <fdir1>0 0 0</fdir1> <slip1>0</slip1> <slip2>0</slip2> </ode> <torsional> <coefficient>1</coefficient> <patch_radius>0</patch_radius> <surface_radius>0</surface_radius> <use_patch_radius>1</use_patch_radius> <ode> <slip>0</slip> </ode> </torsional> </friction> <bounce> <restitution_coefficient>0</restitution_coefficient> <threshold>1e+06</threshold> </bounce> <contact> <collide_without_contact>0</collide_without_contact> <collide_without_contact_bitmask>1</collide_without_contact_bitmask> <collide_bitmask>1</collide_bitmask> <ode> <soft_cfm>0</soft_cfm> <soft_erp>0.2</soft_erp> <kp>1e+13</kp> <kd>1</kd> <max_vel>0.01</max_vel> <min_depth>0</min_depth> </ode> <bullet> <split_impulse>1</split_impulse> <split_impulse_penetration_threshold>-0.01</split_impulse_penetration_threshold> <soft_cfm>0</soft_cfm> <soft_erp>0.2</soft_erp> <kp>1e+13</kp> <kd>1</kd> </bullet> </contact> </surface> </collision> </link> <static>0</static> <allow_auto_disable>1</allow_auto_disable> </model> </sdf> """ sdf_sand2 = """<?xml version='1.0'?> <sdf version='1.6'> <model name="MODELNAME"> <link name='link'> <pose frame=''>0 0 0.01 0 0 0 </pose> <inertial> <mass>1</mass> <inertia> <ixx>0.1</ixx> <ixy>0</ixy> <ixz>0</ixz> <iyy>0.1</iyy> <iyz>0</iyz> <izz>0.1</izz> </inertia> </inertial> <visual name='visual'> <pose frame=''>0 0 0 0 -0 0</pose> <geometry> <mesh> <scale>SIZEXYZ</scale> <uri>model://sand/sand_particle.stl</uri> </mesh> </geometry> <material> <lighting>1</lighting> <script> <uri>file://media/materials/scripts/gazebo.material</uri> <name>Gazebo/Yellow</name> </script> <ambient>0.3 0.25 0.1 1</ambient> <diffuse>0.7 0.6 0.4 1</diffuse> <specular>0.01 0.005 0.001 1</specular> <emissive>0 0 0 1</emissive> </material> <transparency>0</transparency> <cast_shadows>1</cast_shadows> </visual> <collision name='collision'> <laser_retro>0</laser_retro> <max_contacts>10</max_contacts> <pose frame=''>0 0 0 0 -0 0</pose> <geometry> <mesh> <scale>SIZEXYZ</scale> <uri>model://sand/sand_particle.stl</uri> </mesh> </geometry> <surface> <friction> <ode> <mu>1</mu> <mu2>1</mu2> <fdir1>0 0 0</fdir1> <slip1>0</slip1> <slip2>0</slip2> </ode> <torsional> <coefficient>1</coefficient> <patch_radius>0</patch_radius> <surface_radius>0</surface_radius> <use_patch_radius>1</use_patch_radius> <ode> <slip>0</slip> </ode> </torsional> </friction> <bounce> <restitution_coefficient>0</restitution_coefficient> <threshold>1e+06</threshold> </bounce> <contact> <collide_without_contact>0</collide_without_contact> <collide_without_contact_bitmask>1</collide_without_contact_bitmask> <collide_bitmask>1</collide_bitmask> <ode> <soft_cfm>0</soft_cfm> <soft_erp>0.2</soft_erp> <kp>1e+13</kp> <kd>1</kd> <max_vel>0.01</max_vel> <min_depth>0</min_depth> </ode> <bullet> <split_impulse>1</split_impulse> <split_impulse_penetration_threshold>-0.01</split_impulse_penetration_threshold> <soft_cfm>0</soft_cfm> <soft_erp>0.2</soft_erp> <kp>1e+13</kp> <kd>1</kd> </bullet> </contact> </surface> </collision> </link> <static>0</static> <allow_auto_disable>1</allow_auto_disable> </model> </sdf> """ class Spawner: def __init__(self): self.px = 0 self.py = 0 self.pz = 0 self.rr = 0 self.rp = 0 self.rz = 0 self.sx = 0 self.sy = 0 self.sz = 0 def create_cube_request(self,modelname, px, py, pz, rr, rp, ry, sx, sy, sz): """Create a SpawnModelRequest with the parameters of the cube given. modelname: name of the model for gazebo px py pz: position of the cube (and it's collision cube) rr rp ry: rotation (roll, pitch, yaw) of the model sx sy sz: size of the cube""" cube = deepcopy(sdf_sand2) # Replace size of model size_str = str(round(sx, 3)) + " " + \ str(round(sy, 3)) + " " + str(round(sz, 3)) cube = cube.replace('SIZEXYZ', size_str) # Replace modelname cube = cube.replace('MODELNAME', str(modelname)) req = SpawnModelRequest() req.model_name = modelname req.model_xml = cube req.initial_pose.position.x = px req.initial_pose.position.y = py req.initial_pose.position.z = pz q = quaternion_from_euler(rr, rp, ry) req.initial_pose.orientation.x = q[0] req.initial_pose.orientation.y = q[1] req.initial_pose.orientation.z = q[2] req.initial_pose.orientation.w = q[3] return req def create_sphere_request(self,modelname, px, py, pz, rr, rp, ry, r): """Create a SpawnModelRequest with the parameters of the cube given. modelname: name of the model for gazebo px py pz: position of the cube (and it's collision cube) rr rp ry: rotation (roll, pitch, yaw) of the model sx sy sz: size of the cube""" cube = deepcopy(sdf_unit_sphere) # Replace size of model cube = cube.replace('RADIUS', str(r)) # Replace modelname cube = cube.replace('MODELNAME', str(modelname)) req = SpawnModelRequest() req.model_name = modelname req.model_xml = cube req.initial_pose.position.x = px req.initial_pose.position.y = py req.initial_pose.position.z = pz q = quaternion_from_euler(rr, rp, ry) req.initial_pose.orientation.x = q[0] req.initial_pose.orientation.y = q[1] req.initial_pose.orientation.z = q[2] req.initial_pose.orientation.w = q[3] return req def create_box_request(self,modelname, px, py, pz, rr, rp, ry): """Create a SpawnModelRequest with the parameters of the cube given. modelname: name of the model for gazebo px py pz: position of the cube (and it's collision cube) rr rp ry: rotation (roll, pitch, yaw) of the model""" cube = deepcopy(sdf_sand_box) req = SpawnModelRequest() req.model_name = modelname req.model_xml = cube req.initial_pose.position.x = px req.initial_pose.position.y = py req.initial_pose.position.z = pz q = quaternion_from_euler(rr, rp, ry) req.initial_pose.orientation.x = q[0] req.initial_pose.orientation.y = q[1] req.initial_pose.orientation.z = q[2] req.initial_pose.orientation.w = q[3] return req
2.109375
2
output/models/ms_data/regex/re_l32_xsd/__init__.py
tefra/xsdata-w3c-tests
1
9604
<reponame>tefra/xsdata-w3c-tests<filename>output/models/ms_data/regex/re_l32_xsd/__init__.py<gh_stars>1-10 from output.models.ms_data.regex.re_l32_xsd.re_l32 import ( Regex, Doc, ) __all__ = [ "Regex", "Doc", ]
1.023438
1
sdk/python/tests/dsl/metadata_tests.py
ConverJens/pipelines
6
9605
<reponame>ConverJens/pipelines # Copyright 2018 Google LLC # # 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. from kfp.components.structures import ComponentSpec, InputSpec, OutputSpec import unittest class TestComponentMeta(unittest.TestCase): def test_to_dict(self): component_meta = ComponentSpec(name='foobar', description='foobar example', inputs=[InputSpec(name='input1', description='input1 desc', type={'GCSPath': { 'bucket_type': 'directory', 'file_type': 'csv' }}, default='default1' ), InputSpec(name='input2', description='input2 desc', type={'TFModel': { 'input_data': 'tensor', 'version': '1.8.0' }}, default='default2' ), InputSpec(name='input3', description='input3 desc', type='Integer', default='default3' ), ], outputs=[OutputSpec(name='output1', description='output1 desc', type={'Schema': { 'file_type': 'tsv' }}, ) ] ) golden_meta = { 'name': 'foobar', 'description': 'foobar example', 'inputs': [ { 'name': 'input1', 'description': 'input1 desc', 'type': { 'GCSPath': { 'bucket_type': 'directory', 'file_type': 'csv' } }, 'default': 'default1' }, { 'name': 'input2', 'description': 'input2 desc', 'type': { 'TFModel': { 'input_data': 'tensor', 'version': '1.8.0' } }, 'default': 'default2' }, { 'name': 'input3', 'description': 'input3 desc', 'type': 'Integer', 'default': 'default3' } ], 'outputs': [ { 'name': 'output1', 'description': 'output1 desc', 'type': { 'Schema': { 'file_type': 'tsv' } }, } ] } self.assertEqual(component_meta.to_dict(), golden_meta)
1.875
2
challenges/python-solutions/day-25.py
elifloresch/thirty-days-challenge
0
9606
<gh_stars>0 import math def is_prime_number(number): if number < 2: return False if number == 2 or number == 3: return True if number % 2 == 0 or number % 3 == 0: return False number_sqrt = math.sqrt(number) int_number_sqrt = int(number_sqrt) + 1 for d in range(6, int_number_sqrt, 6): if number % (d - 1) == 0 or number % (d + 1) == 0: return False return True test_cases = int(input()) numbers = [] for test_case in range(test_cases): numbers.append(int(input())) for n in numbers: if is_prime_number(n): print('Prime') else: print('Not prime')
3.890625
4
examples/path_config.py
rnixx/garden.cefpython
13
9607
<reponame>rnixx/garden.cefpython #!/usr/bin/env python # -*- coding: UTF-8 -*- """ Minimal example of the CEFBrowser widget use. Here you don't have any controls (back / forth / reload) or whatsoever. Just a kivy app displaying the chromium-webview. In this example we demonstrate how the cache path of CEF can be set. """ import os from kivy.app import App from kivy.garden.cefpython import CEFBrowser from kivy.logger import Logger if __name__ == '__main__': class SimpleBrowserApp(App): def build(self): # Set runtime data paths CEFBrowser.set_data_path(os.path.realpath("./cef_data")) # CEFBrowser.set_caches_path(os.path.realpath("./cef_caches")) # CEFBrowser.set_cookies_path(os.path.realpath("./cef_cookies")) # CEFBrowser.set_logs_path(os.path.realpath("./cef_logs")) Logger.info("Example: The CEF pathes have been set to") Logger.info("- Cache %s", CEFBrowser._caches_path) Logger.info("- Cookies %s", CEFBrowser._cookies_path) Logger.info("- Logs %s", CEFBrowser._logs_path) # Create CEFBrowser instance. Go to test-site. cb = CEFBrowser(url="http://jegger.ch/datapool/app/test.html") return cb SimpleBrowserApp().run()
2.703125
3
simple-systems/and_xor_shift.py
laserbat/random-projects
3
9608
#!/usr/bin/python3 # If F(a) is any function that can be defined as composition of bitwise XORs, ANDs and left shifts # Then the dynac system x_(n+1) = F(x_n) is Turing complete # Proof by simulation (rule110) a = 1 while a: print(bin(a)) a = a ^ (a << 1) ^ (a & (a << 1)) ^ (a & (a << 1) & (a << 2))
3.34375
3
trinity/protocol/common/peer_pool_event_bus.py
Gauddel/trinity
0
9609
from abc import ( abstractmethod, ) from typing import ( Any, Callable, cast, FrozenSet, Generic, Type, TypeVar, ) from cancel_token import ( CancelToken, ) from p2p.exceptions import ( PeerConnectionLost, ) from p2p.kademlia import Node from p2p.peer import ( BasePeer, PeerSubscriber, ) from p2p.peer_pool import ( BasePeerPool, ) from p2p.protocol import ( Command, PayloadType, ) from p2p.service import ( BaseService, ) from trinity.endpoint import ( TrinityEventBusEndpoint, ) from .events import ( ConnectToNodeCommand, DisconnectPeerEvent, HasRemoteEvent, PeerCountRequest, PeerCountResponse, ) TPeer = TypeVar('TPeer', bound=BasePeer) TStreamEvent = TypeVar('TStreamEvent', bound=HasRemoteEvent) class PeerPoolEventServer(BaseService, PeerSubscriber, Generic[TPeer]): """ Base class to create a bridge between the ``PeerPool`` and the event bus so that peer messages become available to external processes (e.g. isolated plugins). In the opposite direction, other processes can also retrieve information or execute actions on the peer pool by sending specific events through the event bus that the ``PeerPoolEventServer`` answers. This class bridges all common APIs but protocol specific communication can be enabled through subclasses that add more handlers. """ msg_queue_maxsize: int = 2000 subscription_msg_types: FrozenSet[Type[Command]] = frozenset({}) def __init__(self, event_bus: TrinityEventBusEndpoint, peer_pool: BasePeerPool, token: CancelToken = None) -> None: super().__init__(token) self.peer_pool = peer_pool self.event_bus = event_bus async def _run(self) -> None: self.logger.debug("Running %s", self.__class__.__name__) self.run_daemon_event( DisconnectPeerEvent, lambda peer, event: peer.disconnect_nowait(event.reason) ) self.run_daemon_task(self.handle_peer_count_requests()) self.run_daemon_task(self.handle_connect_to_node_requests()) self.run_daemon_task(self.handle_native_peer_messages()) await self.cancellation() def run_daemon_event(self, event_type: Type[TStreamEvent], event_handler_fn: Callable[[TPeer, TStreamEvent], Any]) -> None: """ Register a handler to be run every time that an event of type ``event_type`` appears. """ self.run_daemon_task(self.handle_stream(event_type, event_handler_fn)) @abstractmethod async def handle_native_peer_message(self, remote: Node, cmd: Command, msg: PayloadType) -> None: """ Process every native peer message. Subclasses should overwrite this to forward specific peer messages on the event bus. The handler is called for every message that is defined in ``self.subscription_msg_types``. """ pass def get_peer(self, remote: Node) -> TPeer: """ Look up and return a peer from the ``PeerPool`` that matches the given node. Raise ``PeerConnectionLost`` if the peer is no longer in the pool or is winding down. """ try: peer = self.peer_pool.connected_nodes[remote] except KeyError: self.logger.debug("Peer with remote %s does not exist in the pool anymore", remote) raise PeerConnectionLost() else: if not peer.is_operational: self.logger.debug("Peer %s is not operational when selecting from pool", peer) raise PeerConnectionLost() else: return cast(TPeer, peer) async def handle_connect_to_node_requests(self) -> None: async for command in self.wait_iter(self.event_bus.stream(ConnectToNodeCommand)): self.logger.debug('Received request to connect to %s', command.remote) self.run_task(self.peer_pool.connect_to_node(command.remote)) async def handle_peer_count_requests(self) -> None: async for req in self.wait_iter(self.event_bus.stream(PeerCountRequest)): await self.event_bus.broadcast( PeerCountResponse(len(self.peer_pool)), req.broadcast_config() ) async def handle_stream(self, event_type: Type[TStreamEvent], event_handler_fn: Callable[[TPeer, TStreamEvent], Any]) -> None: async for event in self.wait_iter(self.event_bus.stream(event_type)): try: peer = self.get_peer(event.remote) except PeerConnectionLost: pass else: event_handler_fn(peer, event) async def handle_native_peer_messages(self) -> None: with self.subscribe(self.peer_pool): while self.is_operational: peer, cmd, msg = await self.wait(self.msg_queue.get()) await self.handle_native_peer_message(peer.remote, cmd, msg) class DefaultPeerPoolEventServer(PeerPoolEventServer[BasePeer]): async def handle_native_peer_message(self, remote: Node, cmd: Command, msg: PayloadType) -> None: pass
2.1875
2
tests/e2e/performance/csi_tests/test_pvc_creation_deletion_performance.py
annagitel/ocs-ci
1
9610
<gh_stars>1-10 """ Test to verify performance of PVC creation and deletion for RBD, CephFS and RBD-Thick interfaces """ import time import logging import datetime import pytest import ocs_ci.ocs.exceptions as ex import threading import statistics from concurrent.futures import ThreadPoolExecutor from uuid import uuid4 from ocs_ci.framework.testlib import performance from ocs_ci.ocs.perftests import PASTest from ocs_ci.helpers import helpers, performance_lib from ocs_ci.ocs import constants from ocs_ci.helpers.helpers import get_full_test_logs_path from ocs_ci.ocs.perfresult import PerfResult from ocs_ci.framework import config log = logging.getLogger(__name__) class ResultsAnalyse(PerfResult): """ This class generates results for all tests as one unit and saves them to an elastic search server on the cluster """ def __init__(self, uuid, crd, full_log_path): """ Initialize the object by reading some of the data from the CRD file and by connecting to the ES server and read all results from it. Args: uuid (str): the unique uid of the test crd (dict): dictionary with test parameters - the test yaml file that modify it in the test itself. full_log_path (str): the path of the results files to be found """ super(ResultsAnalyse, self).__init__(uuid, crd) self.new_index = "pvc_create_delete_fullres" self.full_log_path = full_log_path # make sure we have connection to the elastic search server self.es_connect() @performance class TestPVCCreationDeletionPerformance(PASTest): """ Test to verify performance of PVC creation and deletion """ def setup(self): """ Setting up test parameters """ log.info("Starting the test setup") super(TestPVCCreationDeletionPerformance, self).setup() self.benchmark_name = "PVC_Creation-Deletion" self.uuid = uuid4().hex self.crd_data = { "spec": { "test_user": "Homer simpson", "clustername": "test_cluster", "elasticsearch": { "server": config.PERF.get("production_es_server"), "port": config.PERF.get("production_es_port"), "url": f"http://{config.PERF.get('production_es_server')}:{config.PERF.get('production_es_port')}", }, } } if self.dev_mode: self.crd_data["spec"]["elasticsearch"] = { "server": config.PERF.get("dev_es_server"), "port": config.PERF.get("dev_es_port"), "url": f"http://{config.PERF.get('dev_es_server')}:{config.PERF.get('dev_es_port')}", } @pytest.fixture() def base_setup(self, interface_type, storageclass_factory, pod_factory): """ A setup phase for the test Args: interface_type: A fixture to iterate over ceph interfaces storageclass_factory: A fixture to create everything needed for a storageclass pod_factory: A fixture to create new pod """ self.interface = interface_type if self.interface == constants.CEPHBLOCKPOOL_THICK: self.sc_obj = storageclass_factory( interface=constants.CEPHBLOCKPOOL, new_rbd_pool=True, rbd_thick_provision=True, ) else: self.sc_obj = storageclass_factory(self.interface) self.pod_factory = pod_factory @pytest.fixture() def namespace(self, project_factory): """ Create a new project """ proj_obj = project_factory() self.namespace = proj_obj.namespace def init_full_results(self, full_results): """ Initialize the full results object which will send to the ES server Args: full_results (obj): an empty FIOResultsAnalyse object Returns: FIOResultsAnalyse (obj): the input object fill with data """ for key in self.environment: full_results.add_key(key, self.environment[key]) full_results.add_key("storageclass", self.sc) full_results.add_key("index", full_results.new_index) return full_results @pytest.mark.parametrize( argnames=["interface_type", "pvc_size"], argvalues=[ pytest.param( *[constants.CEPHBLOCKPOOL, "5Gi"], marks=[pytest.mark.performance], ), pytest.param( *[constants.CEPHBLOCKPOOL, "15Gi"], marks=[pytest.mark.performance], ), pytest.param( *[constants.CEPHBLOCKPOOL, "25Gi"], marks=[pytest.mark.performance], ), pytest.param( *[constants.CEPHFILESYSTEM, "5Gi"], marks=[pytest.mark.performance], ), pytest.param( *[constants.CEPHFILESYSTEM, "15Gi"], marks=[pytest.mark.performance], ), pytest.param( *[constants.CEPHFILESYSTEM, "25Gi"], marks=[pytest.mark.performance], ), pytest.param( *[constants.CEPHBLOCKPOOL_THICK, "5Gi"], marks=[pytest.mark.performance_extended], ), pytest.param( *[constants.CEPHBLOCKPOOL_THICK, "15Gi"], marks=[pytest.mark.performance_extended], ), pytest.param( *[constants.CEPHBLOCKPOOL_THICK, "25Gi"], marks=[pytest.mark.performance_extended], ), ], ) @pytest.mark.usefixtures(base_setup.__name__) def test_pvc_creation_deletion_measurement_performance( self, teardown_factory, pvc_size ): """ Measuring PVC creation and deletion times for pvc samples Verifying that those times are within the required limits """ # Getting the full path for the test logs self.full_log_path = get_full_test_logs_path(cname=self) if self.interface == constants.CEPHBLOCKPOOL: self.sc = "RBD" elif self.interface == constants.CEPHFILESYSTEM: self.sc = "CephFS" elif self.interface == constants.CEPHBLOCKPOOL_THICK: self.sc = "RBD-Thick" self.full_log_path += f"-{self.sc}-{pvc_size}" log.info(f"Logs file path name is : {self.full_log_path}") self.start_time = time.strftime("%Y-%m-%dT%H:%M:%SGMT", time.gmtime()) self.get_env_info() # Initialize the results doc file. self.full_results = self.init_full_results( ResultsAnalyse(self.uuid, self.crd_data, self.full_log_path) ) self.full_results.add_key("pvc_size", pvc_size) num_of_samples = 5 accepted_creation_time = ( 600 if self.interface == constants.CEPHBLOCKPOOL_THICK else 1 ) # accepted deletion time for RBD is 1 sec, for CephFS is 2 secs and for RBD Thick is 5 secs if self.interface == constants.CEPHFILESYSTEM: accepted_deletion_time = 2 elif self.interface == constants.CEPHBLOCKPOOL: accepted_deletion_time = 1 else: accepted_deletion_time = 5 self.full_results.add_key("samples", num_of_samples) accepted_creation_deviation_percent = 50 accepted_deletion_deviation_percent = 50 creation_time_measures = [] deletion_time_measures = [] msg_prefix = f"Interface: {self.interface}, PVC size: {pvc_size}." for i in range(num_of_samples): logging.info(f"{msg_prefix} Start creating PVC number {i + 1}.") start_time = datetime.datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ") pvc_obj = helpers.create_pvc(sc_name=self.sc_obj.name, size=pvc_size) timeout = 600 if self.interface == constants.CEPHBLOCKPOOL_THICK else 60 helpers.wait_for_resource_state( pvc_obj, constants.STATUS_BOUND, timeout=timeout ) pvc_obj.reload() creation_time = performance_lib.measure_pvc_creation_time( self.interface, pvc_obj.name, start_time ) logging.info( f"{msg_prefix} PVC number {i + 1} was created in {creation_time} seconds." ) if creation_time > accepted_creation_time: raise ex.PerformanceException( f"{msg_prefix} PVC creation time is {creation_time} and is greater than " f"{accepted_creation_time} seconds." ) creation_time_measures.append(creation_time) pv_name = pvc_obj.backed_pv pvc_reclaim_policy = pvc_obj.reclaim_policy pod_obj = self.write_file_on_pvc(pvc_obj) pod_obj.delete(wait=True) teardown_factory(pvc_obj) logging.info(f"{msg_prefix} Start deleting PVC number {i + 1}") if pvc_reclaim_policy == constants.RECLAIM_POLICY_DELETE: pvc_obj.delete() pvc_obj.ocp.wait_for_delete(pvc_obj.name) helpers.validate_pv_delete(pvc_obj.backed_pv) deletion_time = helpers.measure_pvc_deletion_time( self.interface, pv_name ) logging.info( f"{msg_prefix} PVC number {i + 1} was deleted in {deletion_time} seconds." ) if deletion_time > accepted_deletion_time: raise ex.PerformanceException( f"{msg_prefix} PVC deletion time is {deletion_time} and is greater than " f"{accepted_deletion_time} seconds." ) deletion_time_measures.append(deletion_time) else: logging.info( f"Reclaim policy of the PVC {pvc_obj.name} is not Delete;" f" therefore not measuring deletion time for this PVC." ) creation_average = self.process_time_measurements( "creation", creation_time_measures, accepted_creation_deviation_percent, msg_prefix, ) self.full_results.add_key("creation-time", creation_average) deletion_average = self.process_time_measurements( "deletion", deletion_time_measures, accepted_deletion_deviation_percent, msg_prefix, ) self.full_results.add_key("deletion-time", deletion_average) self.full_results.all_results["creation"] = creation_time_measures self.full_results.all_results["deletion"] = deletion_time_measures self.end_time = time.strftime("%Y-%m-%dT%H:%M:%SGMT", time.gmtime()) self.full_results.add_key( "test_time", {"start": self.start_time, "end": self.end_time} ) self.full_results.es_write() log.info(f"The Result can be found at : {self.full_results.results_link()}") def process_time_measurements( self, action_name, time_measures, accepted_deviation_percent, msg_prefix ): """ Analyses the given time measured. If the standard deviation of these times is bigger than the provided accepted deviation percent, fails the test Args: action_name (str): Name of the action for which these measurements were collected; used for the logging time_measures (list of floats): A list of time measurements accepted_deviation_percent (int): Accepted deviation percent to which computed standard deviation may be compared msg_prefix (str) : A string for comprehensive logging Returns: (float) The average value of the provided time measurements """ average = statistics.mean(time_measures) log.info( f"{msg_prefix} The average {action_name} time for the sampled {len(time_measures)} " f"PVCs is {average} seconds." ) if self.interface == constants.CEPHBLOCKPOOL_THICK: st_deviation = statistics.stdev(time_measures) st_deviation_percent = st_deviation / average * 100.0 if st_deviation_percent > accepted_deviation_percent: log.error( f"{msg_prefix} The standard deviation percent for {action_name} of {len(time_measures)} sampled " f"PVCs is {st_deviation_percent}% which is bigger than accepted {accepted_deviation_percent}." ) else: log.info( f"{msg_prefix} The standard deviation percent for {action_name} of {len(time_measures)} sampled " f"PVCs is {st_deviation_percent}% and is within the accepted range." ) self.full_results.add_key( f"{action_name}_deviation_pct", st_deviation_percent ) return average def write_file_on_pvc(self, pvc_obj, filesize=1): """ Writes a file on given PVC Args: pvc_obj: PVC object to write a file on filesize: size of file to write (in GB - default is 1GB) Returns: Pod on this pvc on which the file was written """ pod_obj = self.pod_factory( interface=self.interface, pvc=pvc_obj, status=constants.STATUS_RUNNING ) # filesize to be written is always 1 GB file_size = f"{int(filesize * 1024)}M" log.info(f"Starting IO on the POD {pod_obj.name}") # Going to run only write IO pod_obj.fillup_fs(size=file_size, fio_filename=f"{pod_obj.name}_file") # Wait for the fio to finish fio_result = pod_obj.get_fio_results() err_count = fio_result.get("jobs")[0].get("error") assert ( err_count == 0 ), f"IO error on pod {pod_obj.name}. FIO result: {fio_result}" log.info("IO on the PVC has finished") return pod_obj @pytest.mark.parametrize( argnames=["interface_type"], argvalues=[ pytest.param( *[constants.CEPHBLOCKPOOL], marks=[pytest.mark.performance], ), pytest.param( *[constants.CEPHFILESYSTEM], marks=[pytest.mark.performance], ), pytest.param( *[constants.CEPHBLOCKPOOL_THICK], marks=[pytest.mark.performance_extended], ), ], ) @pytest.mark.usefixtures(base_setup.__name__) @pytest.mark.usefixtures(namespace.__name__) @pytest.mark.polarion_id("OCS-2618") def test_multiple_pvc_deletion_measurement_performance(self, teardown_factory): """ Measuring PVC deletion time of 120 PVCs in 180 seconds Args: teardown_factory: A fixture used when we want a new resource that was created during the tests to be removed in the teardown phase. Returns: """ number_of_pvcs = 120 pvc_size = "1Gi" msg_prefix = f"Interface: {self.interface}, PVC size: {pvc_size}." log.info(f"{msg_prefix} Start creating new 120 PVCs") pvc_objs, _ = helpers.create_multiple_pvcs( sc_name=self.sc_obj.name, namespace=self.namespace, number_of_pvc=number_of_pvcs, size=pvc_size, burst=True, ) for pvc_obj in pvc_objs: pvc_obj.reload() teardown_factory(pvc_obj) timeout = 600 if self.interface == constants.CEPHBLOCKPOOL_THICK else 60 with ThreadPoolExecutor(max_workers=5) as executor: for pvc_obj in pvc_objs: executor.submit( helpers.wait_for_resource_state, pvc_obj, constants.STATUS_BOUND, timeout=timeout, ) executor.submit(pvc_obj.reload) pod_objs = [] for pvc_obj in pvc_objs: pod_obj = self.write_file_on_pvc(pvc_obj, 0.3) pod_objs.append(pod_obj) # Get pvc_name, require pvc_name to fetch deletion time data from log threads = list() for pvc_obj in pvc_objs: process = threading.Thread(target=pvc_obj.reload) process.start() threads.append(process) for process in threads: process.join() pvc_name_list, pv_name_list = ([] for i in range(2)) threads = list() for pvc_obj in pvc_objs: process1 = threading.Thread(target=pvc_name_list.append(pvc_obj.name)) process2 = threading.Thread(target=pv_name_list.append(pvc_obj.backed_pv)) process1.start() process2.start() threads.append(process1) threads.append(process2) for process in threads: process.join() log.info(f"{msg_prefix} Preparing to delete 120 PVC") # Delete PVC for pvc_obj, pod_obj in zip(pvc_objs, pod_objs): pod_obj.delete(wait=True) pvc_obj.delete() pvc_obj.ocp.wait_for_delete(pvc_obj.name) # Get PVC deletion time pvc_deletion_time = helpers.measure_pv_deletion_time_bulk( interface=self.interface, pv_name_list=pv_name_list ) log.info( f"{msg_prefix} {number_of_pvcs} bulk deletion time is {pvc_deletion_time}" ) # accepted deletion time is 2 secs for each PVC accepted_pvc_deletion_time = number_of_pvcs * 2 for del_time in pvc_deletion_time.values(): if del_time > accepted_pvc_deletion_time: raise ex.PerformanceException( f"{msg_prefix} {number_of_pvcs} PVCs deletion time is {pvc_deletion_time.values()} and is " f"greater than {accepted_pvc_deletion_time} seconds" ) logging.info(f"{msg_prefix} {number_of_pvcs} PVCs deletion times are:") for name, a_time in pvc_deletion_time.items(): logging.info(f"{name} deletion time is: {a_time} seconds")
2.3125
2
templates/t/searchresult_withnone.py
MikeBirdsall/food-log
0
9611
<reponame>MikeBirdsall/food-log #!/usr/bin/python3 from jinja2 import Environment, FileSystemLoader def spacenone(value): return "" if value is None else str(value) results = [ dict( description="Noodles and Company steak Stromboli", comment="", size="small", cals=530, carbs=50, fat=25, protein=27, score=30), dict( description="Steak sandwich", comment="", size="4 oz and bun", cals=480, carbs=44, fat=20, protein=27, score=30), dict( description="chipotle tacos", comment="Steak, no beans, gu...", size="", cals=285, carbs=None, fat=16, protein=None, score=30), dict( description="Steak Sandwich", comment="", size="", cals=380, carbs=45, fat=3.5, protein=34, score=30), ] input_ = dict( title="Search for Courses", h1="Full Text Search: steak NOT shake", results=results, ) env = Environment(loader=FileSystemLoader("..")) env.filters['spacenone'] = spacenone template = env.get_template("searchresult.html") output = template.render(input_) print(output)
2.640625
3
payments/views.py
aman-roy/pune.pycon.org
0
9612
from django.http import JsonResponse from django.views.decorators.csrf import csrf_exempt from payments.models import Invoice, RazorpayKeys from payments.razorpay.razorpay_payments import RazorpayPayments from payments.models import Payment, Order import json @csrf_exempt def webhook(request): if request.method == 'POST': keys = RazorpayKeys.objects.first() payment = RazorpayPayments(keys.api_key, keys.api_secret) data = json.loads(request.body) if 'payload' not in data or 'invoice' not in data['payload']: return JsonResponse({"message": "Invalid Data"}) invoice_entity = data['payload']['invoice']['entity'] order_entity = data['payload']['order']['entity'] payment_entity = data['payload']['payment']['entity'] invoice = Invoice.objects.get(invoice_id=invoice_entity['id']) invoice.status = invoice_entity['status'] invoice.save() payment.save_payment(payment_entity) payment.save_order(order_entity) return JsonResponse({"message": "Success"}) return JsonResponse({"message": "Method Not Allowed"}) def sync(request): keys = RazorpayKeys.objects.first() payment = RazorpayPayments(keys.api_key, keys.api_secret) invoices = Invoice.objects.all() for invoice in invoices: invoice_details = payment.fetch_invoices(invoice.invoice_id) invoice.status = invoice_details['status'] invoice.save() if invoice.status == 'paid': orders = Order.objects.filter(order_id=invoice_details['order_id']) if len(orders) == 0: order_details = payment.fetch_orders( invoice_details['order_id']) payment.save_order(order_details) if invoice_details['payment_id']: payments = Payment.objects.filter(payment_id=invoice_details['payment_id']) if len(payments) == 0: payment_details = payment.fetch_payment(invoice_details['payment_id']) payment.save_payment(payment_details) return JsonResponse({"message": "synced"})
2.03125
2
src/convnet/image_classifier.py
danschef/gear-detector
1
9613
import configparser import os import sys from time import localtime, strftime, mktime import torch import torch.nn as nn import torch.optim as optim from torch.autograd import Variable from net import Net from geo_helper import store_image_bounds from image_helper import CLASSES from image_helper import save_image from image_helper import test_set_loader from image_helper import train_set_loader from image_helper import validation_set_loader CONFIG = configparser.ConfigParser() CONFIG.read('./src/config.ini') ########################################### # Training Stage ########################################### def train(net, epochs=50, learning_rate=0.001): start_time = strftime('%H:%M:%S', localtime()) print(f"Started training at: {start_time}") datetime = strftime("%Y%m%d_%H%M", localtime()) logfile = f"{CONFIG['CNN Paths']['accuracy_log_path']}/{datetime}.log" ########################################### # Loss Function ########################################### criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9) for epoch in range(epochs): # loop over the dataset multiple times running_loss = 0.0 for i, (images, labels) in enumerate(train_set_loader(), 0): # Wrap images and labels into Variables images, labels = Variable(images), Variable(labels) # Clear all accumulated gradients optimizer.zero_grad() # Predict classes using images from the test set outputs = net(images) # Compute the loss based on the predictions and actual labels loss = criterion(outputs, labels) # Backpropagate the loss loss.backward() # Adjust parameters according to the computed gradients optimizer.step() # print statistics running_loss += loss.item() if i % 100 == 99: # print every 100 mini-batches print('[%d, %5d] loss: %.3f, accuracy: %.3f' % (epoch + 1, i + 1, running_loss / 100, validate(logfile, net))) running_loss = 0.0 end_time = strftime('%H:%M:%S', localtime()) print(f"Finished Training: {end_time}") ##################################### # Validation stage ##################################### def validate(logfile, net): dataiter = iter(validation_set_loader()) hits = 0.0 for idx, item in enumerate(dataiter): images, labels = item outputs = net(Variable(images)) _, predicted = torch.max(outputs.data, 1) if (labels == predicted[0]).all(): hits += 1 accuracy = hits / (idx + 1) log_accuracy(logfile, accuracy) return accuracy def log_accuracy(filename, accuracy): with open(filename, "a") as file: file.write(str(accuracy)+ '\n') ##################################### # Prediction stage ##################################### def predict(net): print(f"Prediction started at: {strftime('%H:%M:%S', localtime())}") dataiter = iter(test_set_loader()) prediction_cnt = { 'cloud': 0, 'edge': 0, 'land': 0, 'nets': 0, 'rock': 0, 'vessel': 0, 'water': 0 } datetime = strftime("%Y%m%d_%H%M", localtime()) prediction_log = f"{CONFIG['CNN Paths']['predicted_geodata_path']}/{datetime}.json" prediction_img_folder = f"{CONFIG['CNN Paths']['predicted_imagery_path']}/{datetime}" for idx, item in enumerate(dataiter): if idx > int(CONFIG['CNN Prediction']['batch_size']): break if idx % 100 == 0: print('.', end='', flush=True) images, _labels = item ########################################################## # Feed the images into the CNN and check what it predicts ########################################################## outputs = net(Variable(images)) _, predicted = torch.max(outputs.data, 1) # Save images from prediction for visual check if CLASSES[predicted[0]] == 'nets': image_path = dataiter._dataset.imgs[idx][0] save_image(image_path, prediction_img_folder) store_image_bounds(image_path, prediction_log) prediction_cnt[CLASSES[predicted[0]]] += 1 print(f"\nPrediction ended at: {strftime('%H:%M:%S', localtime())}") print(f"\nPredicted: {prediction_cnt}") def model_full_path(path, checkpoint): return f"{path}_{checkpoint}.pt" ################################################################ # Train network or use existing one for prediction ################################################################ def main(mode=''): image_bands = int(CONFIG['CNN Training']['image_bands']) training_epochs = int(CONFIG['CNN Training']['epochs']) resume_epochs = int(CONFIG['CNN Resume Training']['epochs']) learning_rate = float(CONFIG['CNN Training']['learning_rate']) batch_size = CONFIG['CNN Prediction']['batch_size'] if len(sys.argv) > 1: mode = sys.argv[1] net = Net(in_channels=image_bands) model_path = CONFIG['CNN Paths']['model_path'] checkpoint = CONFIG['CNN Prediction']['checkpoint'] # Use network for prediction if mode == 'predict' and os.path.exists(model_full_path(model_path, checkpoint)): print(f"Use trained network {checkpoint} for prediction of max {batch_size} images") # Load existing model model = torch.load(model_full_path(model_path, checkpoint)) net.load_state_dict(model) predict(net) # Start training elif mode == 'train': print(f"Start network training for {training_epochs} epochs") train(net, training_epochs, learning_rate) # Save model after training checkpoint = strftime("%Y%m%d_%H%M", localtime()) torch.save(net.state_dict(), model_full_path(model_path, checkpoint)) # Resume training elif mode == 'resume': checkpoint = CONFIG['CNN Resume Training']['checkpoint'] print(f"Resume training on Model {checkpoint} for {resume_epochs} epochs") # Load existing model and resume training model = torch.load(model_full_path(model_path, checkpoint)) net.load_state_dict(model) train(net, resume_epochs, learning_rate) torch.save(net.state_dict(), model_full_path(model_path, checkpoint)) else: print('No mode provided.') main()
2.21875
2
src/modules/AlphabetPlotter.py
aaanh/duplicated_accelcamp
0
9614
import tkinter as tk from tkinter import filedialog import csv import matplotlib.pyplot as plt root = tk.Tk(screenName=':0.0') root.withdraw() file_path = filedialog.askopenfilename() lastIndex = len(file_path.split('/')) - 1 v0 = [0, 0, 0] x0 = [0, 0, 0] fToA = 1 error = 0.28 errorZ = 3 t = [] time = [] m = [[] for i in range(3)] magnitude = [[] for i in range(3)] shift_x = 0 shift_y = 0 # For when the data starts at (2,1) if file_path.split('/')[lastIndex].split('.')[2] == "pocket": shift_x = 2 shift_y = 1 error = 0.3 fToA = 1 # For when the data starts at (0,0) elif file_path.split('/')[lastIndex].split('.')[2] == "pocket_mobile": shift_x = 0 shift_y = 0 error = 0.3 fToA = 1 # For when the data starts at (1,0) elif file_path.split('/')[lastIndex].split('.')[2] == "android": shift_x = 0 shift_y = 1 error = 0.02 fToA = 9.81 errorZ = 100 shift = 0 uselessboolean = True with open(file_path, 'r+') as csvfile: readCSV = csv.reader(csvfile, delimiter=',') for row in readCSV: if shift < shift_y: shift += 1 else: t = row[shift_x] m[0] = row[1 + shift_x] m[1] = row[2 + shift_x] m[2] = row[3 + shift_x] time.append(float(t)) for i in range(0, 3): magnitude[i].append(float(m[i]) if abs(float(m[i])) > error else 0) acceleration = [[(j * fToA) for j in i] for i in magnitude] acceleration[2] = [i - 9.805 for i in acceleration[2]] # Translates Data into Position velocity = [[0 for i in time] for i in range(3)] position = [[0 for i in time] for i in range(3)] for j in range(3): velocity[j][0] = v0[j] for i in range(1, len(time)): velocity[j][i] = velocity[j][i - 1] + acceleration[j][i - 1] * (time[i] - time[i - 1]) for j in range(3): position[j][0] = x0[j] for i in range(1, len(time)): position[j][i] = position[j][i - 1] + velocity[j][i - 1] * (time[i] - time[i - 1]) for i in range(len(acceleration[2])): if abs(velocity[2][i]) > errorZ: position[0][i] = 0 position[1][i] = 0 fig, axs = plt.subplots(2) axs[0].plot(time, acceleration[0]) axs[0].set_xlabel('Time (s)') axs[0].set_ylabel('AccelerationX (m/s^2)') axs[1].plot(time, acceleration[1]) axs[1].set_xlabel('Time (s)') axs[1].set_ylabel('AccelerationY (m/s^2)') ''' axs[2].scatter(time, acceleration[2]) axs[2].set_xlabel('Time (s)') axs[2].set_ylabel('AccelerationZ (m/s^2)') axs[3].scatter(time, velocity[2]) axs[3].set_xlabel('Time (s)') axs[3].set_ylabel('VelocityZ (m/s)') axs[4].scatter(time, position[2]) axs[4].set_xlabel('Time (s)') axs[4].set_ylabel('PositionZ (m)') axs.scatter(position[0], position[1], marker = "_", linewidth = 70) axs.set_xlabel('PositionX') axs.set_ylabel('PositionY') plt.plot(position[0], position[1], marker = '_', markersize = 30, linewidth = 3, markeredgewidth = 10)''' plt.show()
3.234375
3
users/migrations/0008_profile_fields_optional.py
mitodl/mit-xpro
10
9615
# Generated by Django 2.2.3 on 2019-07-15 19:24 from django.db import migrations, models def backpopulate_incomplete_profiles(apps, schema): """Backpopulate users who don't have a profile record""" User = apps.get_model("users", "User") Profile = apps.get_model("users", "Profile") for user in User.objects.annotate( has_profile=models.Exists(Profile.objects.filter(user=models.OuterRef("pk"))) ).filter(has_profile=False): Profile.objects.get_or_create(user=user) def remove_incomplete_profiles(apps, schema): """Delete records that will cause rollbacks on nullable/blankable fields to fail""" Profile = apps.get_model("users", "Profile") Profile.objects.filter( models.Q(birth_year__isnull=True) | models.Q(gender__exact="") | models.Q(job_title__exact="") | models.Q(company__exact="") ).delete() class Migration(migrations.Migration): dependencies = [("users", "0007_validate_country_and_state")] operations = [ migrations.AlterField( model_name="profile", name="birth_year", field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name="profile", name="company", field=models.CharField(blank=True, default="", max_length=128), ), migrations.AlterField( model_name="profile", name="gender", field=models.CharField( blank=True, choices=[ ("m", "Male"), ("f", "Female"), ("o", "Other/Prefer Not to Say"), ], default="", max_length=10, ), ), migrations.AlterField( model_name="profile", name="industry", field=models.CharField(blank=True, default="", max_length=60), ), migrations.AlterField( model_name="profile", name="job_function", field=models.CharField(blank=True, default="", max_length=60), ), migrations.AlterField( model_name="profile", name="job_title", field=models.CharField(blank=True, default="", max_length=128), ), migrations.AlterField( model_name="profile", name="leadership_level", field=models.CharField(blank=True, default="", max_length=60), ), migrations.RunPython( backpopulate_incomplete_profiles, reverse_code=remove_incomplete_profiles ), ]
2.484375
2
test/unit_testing/grid/element_linear_dx_data/test_element_linearC/element/geom_element_AD.py
nwukie/ChiDG
36
9616
from __future__ import division import torch import torch.autograd as autograd import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import numpy as np import sys import os import time # # TORCH INSTALLATION: refer to https://pytorch.org/get-started/locally/ # def update_progress(job_title, progress): length = 20 # modify this to change the length block = int(round(length*progress)) msg = "\r{0}: [{1}] {2}%".format(job_title, "#"*block + "-"*(length-block), round(progress*100, 2)) if progress >= 1: msg += " DONE\r\n" sys.stdout.write(msg) sys.stdout.flush() def cls(): os.system('cls' if os.name=='nt' else 'clear') cls() ################################################################################################################ # Initialize torch tensor for coordiantes coords_data = [[ 0.0 , 0.0 , 0.0 ], [ 1.0/(2.0**0.5), 0.0 , 1.0/(2.0**0.5)], [ 1.0/(2.0**0.5), 0.0 ,-1.0/(2.0**0.5)], [ 2.0**0.5 , 0.0 , 0.0 ], [ 0.0 , 1.0 , 0.0 ], [ 1.0/(2.0**0.5), 1.0 , 1.0/(2.0**0.5)], [ 1.0/(2.0**0.5), 1.0 ,-1.0/(2.0**0.5)], [ 2.0**0.5 , 1.0 , 0.0 ], ] coords = torch.tensor(coords_data,requires_grad=True,dtype=torch.float64) nnodes_r = coords.size(0) nnodes_ie = 8 nnodes_if = 4 nterms_s = 8 ndirs = 3 coord_sys = 'CARTESIAN' # Define matrix of polynomial basis terms at support nodes val_r_data = [[ 1.0,-1.0,-1.0,-1.0, 1.0, 1.0, 1.0,-1.0], [ 1.0,-1.0,-1.0, 1.0,-1.0,-1.0, 1.0, 1.0], [ 1.0, 1.0,-1.0,-1.0,-1.0, 1.0,-1.0, 1.0], [ 1.0, 1.0,-1.0, 1.0, 1.0,-1.0,-1.0,-1.0], [ 1.0,-1.0, 1.0,-1.0, 1.0,-1.0,-1.0, 1.0], [ 1.0,-1.0, 1.0, 1.0,-1.0, 1.0,-1.0,-1.0], [ 1.0, 1.0, 1.0,-1.0,-1.0,-1.0, 1.0,-1.0], [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ] val_r = torch.tensor(val_r_data,requires_grad=False,dtype=torch.float64) # Define matrices at interpolation nodes (quadrature, level = 1) val_i_data = [[ 1.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), 1.0/3.0, 1.0/3.0, 1.0/3.0,-1.0/3.0*np.sqrt(1.0/3.0)], [ 1.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-1.0/3.0,-1.0/3.0, 1.0/3.0, 1.0/3.0*np.sqrt(1.0/3.0)], [ 1.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-1.0/3.0, 1.0/3.0,-1.0/3.0, 1.0/3.0*np.sqrt(1.0/3.0)], [ 1.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), 1.0/3.0,-1.0/3.0,-1.0/3.0,-1.0/3.0*np.sqrt(1.0/3.0)], [ 1.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), 1.0/3.0,-1.0/3.0,-1.0/3.0, 1.0/3.0*np.sqrt(1.0/3.0)], [ 1.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-1.0/3.0, 1.0/3.0,-1.0/3.0,-1.0/3.0*np.sqrt(1.0/3.0)], [ 1.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-1.0/3.0,-1.0/3.0, 1.0/3.0,-1.0/3.0*np.sqrt(1.0/3.0)], [ 1.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), 1.0/3.0, 1.0/3.0, 1.0/3.0, 1.0/3.0*np.sqrt(1.0/3.0)], ] val_i = torch.tensor(val_i_data,requires_grad=False,dtype=torch.float64) ddxi_i_data = [[ 0.0,0.0,0.0,1.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),0.0, 1.0/3.0], [ 0.0,0.0,0.0,1.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),0.0, 1.0/3.0], [ 0.0,0.0,0.0,1.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),0.0,-1.0/3.0], [ 0.0,0.0,0.0,1.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),0.0,-1.0/3.0], [ 0.0,0.0,0.0,1.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),0.0,-1.0/3.0], [ 0.0,0.0,0.0,1.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),0.0,-1.0/3.0], [ 0.0,0.0,0.0,1.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),0.0, 1.0/3.0], [ 0.0,0.0,0.0,1.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),0.0, 1.0/3.0], ] ddxi_i = torch.tensor(ddxi_i_data,requires_grad=False,dtype=torch.float64) ddeta_i_data = [[ 0.0,1.0,0.0,0.0,-np.sqrt(1.0/3.0),0.0,-np.sqrt(1.0/3.0), 1.0/3.0], [ 0.0,1.0,0.0,0.0, np.sqrt(1.0/3.0),0.0,-np.sqrt(1.0/3.0),-1.0/3.0], [ 0.0,1.0,0.0,0.0,-np.sqrt(1.0/3.0),0.0,-np.sqrt(1.0/3.0), 1.0/3.0], [ 0.0,1.0,0.0,0.0, np.sqrt(1.0/3.0),0.0,-np.sqrt(1.0/3.0),-1.0/3.0], [ 0.0,1.0,0.0,0.0,-np.sqrt(1.0/3.0),0.0, np.sqrt(1.0/3.0),-1.0/3.0], [ 0.0,1.0,0.0,0.0, np.sqrt(1.0/3.0),0.0, np.sqrt(1.0/3.0), 1.0/3.0], [ 0.0,1.0,0.0,0.0,-np.sqrt(1.0/3.0),0.0, np.sqrt(1.0/3.0),-1.0/3.0], [ 0.0,1.0,0.0,0.0, np.sqrt(1.0/3.0),0.0, np.sqrt(1.0/3.0), 1.0/3.0], ] ddeta_i = torch.tensor(ddeta_i_data,requires_grad=False,dtype=torch.float64) ddzeta_i_data= [[ 0.0,0.0,1.0,0.0,0.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), 1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), 1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), 1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-1.0/3.0], [ 0.0,0.0,1.0,0.0,0.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), 1.0/3.0], ] ddzeta_i = torch.tensor(ddzeta_i_data,requires_grad=False,dtype=torch.float64) # Define element interpolation nodes weights for linear element weights_e_data = [1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0] weights_e = torch.tensor(weights_e_data,requires_grad=False,dtype=torch.float64) # Define val_f for each face # Face 1, XI_MIN val_1_data = [[ 1.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-1.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), 1.0/3.0,-1.0/3.0], [ 1.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-1.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-1.0/3.0, 1.0/3.0], [ 1.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-1.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-1.0/3.0, 1.0/3.0], [ 1.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-1.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), 1.0/3.0,-1.0/3.0], ] val_1 = torch.tensor(val_1_data,requires_grad=False,dtype=torch.float64) # Face 2, XI_MAX val_2_data = [[ 1.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),1.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), 1.0/3.0, 1.0/3.0], [ 1.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),1.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-1.0/3.0,-1.0/3.0], [ 1.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),1.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-1.0/3.0,-1.0/3.0], [ 1.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),1.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), 1.0/3.0, 1.0/3.0], ] val_2 = torch.tensor(val_2_data,requires_grad=False,dtype=torch.float64) # Face 3, ETA_MIN val_3_data = [[ 1.0,-1.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), 1.0/3.0, np.sqrt(1.0/3.0),-1.0/3.0], [ 1.0,-1.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-1.0/3.0, np.sqrt(1.0/3.0), 1.0/3.0], [ 1.0,-1.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-1.0/3.0,-np.sqrt(1.0/3.0), 1.0/3.0], [ 1.0,-1.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), 1.0/3.0,-np.sqrt(1.0/3.0),-1.0/3.0], ] val_3 = torch.tensor(val_3_data,requires_grad=False,dtype=torch.float64) # Face 4, ETA_MAX val_4_data = [[ 1.0,1.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), 1.0/3.0,-np.sqrt(1.0/3.0), 1.0/3.0], [ 1.0,1.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-1.0/3.0,-np.sqrt(1.0/3.0),-1.0/3.0], [ 1.0,1.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-1.0/3.0, np.sqrt(1.0/3.0),-1.0/3.0], [ 1.0,1.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), 1.0/3.0, np.sqrt(1.0/3.0), 1.0/3.0], ] val_4 = torch.tensor(val_4_data,requires_grad=False,dtype=torch.float64) # Face 5, ZETA_MIN val_5_data = [[ 1.0,-np.sqrt(1.0/3.0),-1.0,-np.sqrt(1.0/3.0), 1.0/3.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-1.0/3.0], [ 1.0,-np.sqrt(1.0/3.0),-1.0, np.sqrt(1.0/3.0),-1.0/3.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), 1.0/3.0], [ 1.0, np.sqrt(1.0/3.0),-1.0,-np.sqrt(1.0/3.0),-1.0/3.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), 1.0/3.0], [ 1.0, np.sqrt(1.0/3.0),-1.0, np.sqrt(1.0/3.0), 1.0/3.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-1.0/3.0], ] val_5 = torch.tensor(val_5_data,requires_grad=False,dtype=torch.float64) # Face 6, ZETA_MAX val_6_data = [[ 1.0,-np.sqrt(1.0/3.0),1.0,-np.sqrt(1.0/3.0), 1.0/3.0,-np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0), 1.0/3.0], [ 1.0,-np.sqrt(1.0/3.0),1.0, np.sqrt(1.0/3.0),-1.0/3.0, np.sqrt(1.0/3.0),-np.sqrt(1.0/3.0),-1.0/3.0], [ 1.0, np.sqrt(1.0/3.0),1.0,-np.sqrt(1.0/3.0),-1.0/3.0,-np.sqrt(1.0/3.0), np.sqrt(1.0/3.0),-1.0/3.0], [ 1.0, np.sqrt(1.0/3.0),1.0, np.sqrt(1.0/3.0), 1.0/3.0, np.sqrt(1.0/3.0), np.sqrt(1.0/3.0), 1.0/3.0], ] val_6 = torch.tensor(val_6_data,requires_grad=False,dtype=torch.float64) #-------------------------------------------------------------------- # Matrix modes_to_nodes val_r_inv = torch.inverse(val_r) # Computes coordiantes modes coords_modes = torch.mm(val_r_inv,coords) # Initialized coordiantes interp_coords = torch.mm(val_i,coords_modes) # Initialized jacobian jacobian = torch.empty(3,3,nnodes_ie, dtype=torch.float64) for inode in range(0,nnodes_ie): jacobian[0,0,inode] = torch.dot(ddxi_i[inode,:] , coords_modes[:,0]) jacobian[0,1,inode] = torch.dot(ddeta_i[inode,:] , coords_modes[:,0]) jacobian[0,2,inode] = torch.dot(ddzeta_i[inode,:] , coords_modes[:,0]) jacobian[1,0,inode] = torch.dot(ddxi_i[inode,:] , coords_modes[:,1]) jacobian[1,1,inode] = torch.dot(ddeta_i[inode,:] , coords_modes[:,1]) jacobian[1,2,inode] = torch.dot(ddzeta_i[inode,:] , coords_modes[:,1]) jacobian[2,0,inode] = torch.dot(ddxi_i[inode,:] , coords_modes[:,2]) jacobian[2,1,inode] = torch.dot(ddeta_i[inode,:] , coords_modes[:,2]) jacobian[2,2,inode] = torch.dot(ddzeta_i[inode,:] , coords_modes[:,2]) update_progress("Computing Jacobian ", inode/(nnodes_ie-1)) if coord_sys == 'CYLINDRICAL': scaling_factor = torch.mm(val_i,coords_modes[:,0]) for inode in range(0,nnodes_ie): jacobian[1,0,inode] = jacobian[1,0,inode] * scaling_factor[inode] jacobian[1,1,inode] = jacobian[1,1,inode] * scaling_factor[inode] jacobian[1,2,inode] = jacobian[1,2,inode] * scaling_factor[inode] # Matrics and Determinant metrics = torch.empty(3,3,nnodes_ie, dtype=torch.float64) jinv = torch.empty(nnodes_ie, dtype=torch.float64) for inode in range(0,nnodes_ie): ijacobian = torch.empty(3,3, dtype=torch.float64) imetric = torch.empty(3,3, dtype=torch.float64) for irow in range(0,3): for icol in range(0,3): ijacobian[irow,icol] = jacobian[irow,icol,inode] # Compute jacobian for the ith node update_progress("Computing Jinv and Metric ", inode/(nnodes_ie-1)) jinv[inode] = torch.det(ijacobian) imetric = torch.inverse(ijacobian) for irow in range(0,3): for icol in range(0,3): metrics[irow,icol,inode] = imetric[irow,icol] # Compute inverse Mass matrix invmass = torch.empty(nterms_s,nterms_s,nnodes_ie, dtype=torch.float64) mass = torch.empty(nterms_s,nterms_s,nnodes_ie, dtype=torch.float64) val_tmp = torch.empty(nterms_s,nnodes_ie, dtype=torch.float64) i = 1 for iterm in range(0,nterms_s): for inode in range(0,nnodes_ie): val_tmp[inode,iterm] = val_i[inode,iterm] * weights_e[inode] * jinv[inode] update_progress("Computing invmass ", i/(nterms_s*nnodes_ie)) i += 1 mass = torch.mm(torch.t(val_tmp),val_i) invmass = torch.inverse(mass) # Compute BR2_VOL for each face br2_vol_face1 = torch.mm(val_i,torch.mm(invmass,torch.t(val_1))) br2_vol_face2 = torch.mm(val_i,torch.mm(invmass,torch.t(val_2))) br2_vol_face3 = torch.mm(val_i,torch.mm(invmass,torch.t(val_3))) br2_vol_face4 = torch.mm(val_i,torch.mm(invmass,torch.t(val_4))) br2_vol_face5 = torch.mm(val_i,torch.mm(invmass,torch.t(val_5))) br2_vol_face6 = torch.mm(val_i,torch.mm(invmass,torch.t(val_6))) update_progress("Computing br2_vol ", 1) # Compute BR2_FACE for each face br2_face_face1 = torch.mm(val_1,torch.mm(invmass,torch.t(val_1))) br2_face_face2 = torch.mm(val_2,torch.mm(invmass,torch.t(val_2))) br2_face_face3 = torch.mm(val_3,torch.mm(invmass,torch.t(val_3))) br2_face_face4 = torch.mm(val_4,torch.mm(invmass,torch.t(val_4))) br2_face_face5 = torch.mm(val_5,torch.mm(invmass,torch.t(val_5))) br2_face_face6 = torch.mm(val_6,torch.mm(invmass,torch.t(val_6))) update_progress("Computing br2_face ", 1) # Grad1, Grad2, and Grad3 grad1 = torch.empty(nnodes_ie,nterms_s, dtype=torch.float64) grad2 = torch.empty(nnodes_ie,nterms_s, dtype=torch.float64) grad3 = torch.empty(nnodes_ie,nterms_s, dtype=torch.float64) i = 1 for iterm in range(0,nterms_s): for inode in range(0,nnodes_ie): grad1[inode,iterm] = metrics[0,0,inode] * ddxi_i[inode,iterm] + metrics[1,0,inode] * ddeta_i[inode,iterm] + metrics[2,0,inode] * ddzeta_i[inode,iterm] grad2[inode,iterm] = metrics[0,1,inode] * ddxi_i[inode,iterm] + metrics[1,1,inode] * ddeta_i[inode,iterm] + metrics[2,1,inode] * ddzeta_i[inode,iterm] grad3[inode,iterm] = metrics[0,2,inode] * ddxi_i[inode,iterm] + metrics[1,2,inode] * ddeta_i[inode,iterm] + metrics[2,2,inode] * ddzeta_i[inode,iterm] update_progress("Computing grad1, grad2, grad3 ", i/(nnodes_ie*nterms_s)) i += 1 #WRITE_____________________ # # Metrics # f = open("metrics.txt","w") i = 1 for inode in range (0,nnodes_ie): f.write("Metric interpolation node %d \n" % (inode+1)) array = np.zeros([3, 3]) for irow in range(0,3): for icol in range(0,3): array[irow,icol] = metrics[irow,icol,inode].item() update_progress("Writing metrics to file ", i/(nnodes_ie*9)) i += 1 np.savetxt(f,array) f.close() # # jinv # f = open("jinv.txt","w") array = np.zeros([1]) i = 1 for inode in range (0,nnodes_ie): f.write("Jinv interpolation node %d \n" % (inode+1)) array[0] = jinv[inode].item() np.savetxt(f,array) update_progress("Writing jinv to file ", i/(nnodes_ie)) i += 1 f.close() # # Grad1 # f = open("grad1.txt","w") f.write("Grad1 \n") array = np.zeros([nnodes_ie,nterms_s]) i = 1 for inode in range (0,nnodes_ie): for iterm in range(0,nterms_s): array[inode,iterm] = grad1[inode,iterm].item() update_progress("Writing grad1 to file ", i/(nnodes_ie*nterms_s)) i += 1 np.savetxt(f,array) f.close() # # Grad2 # f = open("grad2.txt","w") f.write("Grad2 \n") array = np.zeros([nnodes_ie,nterms_s]) i = 1 for inode in range (0,nnodes_ie): for iterm in range(0,nterms_s): array[inode,iterm] = grad2[inode,iterm].item() update_progress("Writing grad2 to file ", i/(nnodes_ie*nterms_s)) i += 1 np.savetxt(f,array) f.close() # # Grad3 # f = open("grad3.txt","w") f.write("Grad3 \n") array = np.zeros([nnodes_ie,nterms_s]) i = 1 for inode in range (0,nnodes_ie): for iterm in range(0,nterms_s): array[inode,iterm] = grad3[inode,iterm].item() update_progress("Writing grad3 to file ", i/(nnodes_ie*nterms_s)) i += 1 np.savetxt(f,array) f.close() # # dmetric_dx # f = open("dmetric_dx.txt","w") i = 1 for inode in range (0,nnodes_ie): for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): array = np.zeros([3,3]) f.write("dmetric_dx interpolation node %s, diff_node %s, diff_dir %s \n" % (inode+1,inode_diff+1,idir+1)) for irow in range(0,3): for icol in range(0,3): data = metrics[irow,icol,inode] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dmetric_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*3*3)) # This avoid to accumulate derivatives dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() # # interp_coords_dx # f = open("dinterp_xcoords_dx.txt","w") i = 1 f.write("xcoord interpolation, coord 1, row=node, col=nnodes_r*dir \n") array = np.zeros([nnodes_ie,nnodes_r*ndirs]) for inode in range (0,nnodes_ie): data = interp_coords[inode,0] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): if idir == 0: index = inode_diff elif idir == 1: index = nnodes_r + inode_diff elif idir == 2: index = 2*nnodes_r + inode_diff array[inode,index] = ddata_np[inode_diff,idir] update_progress("Writing interp_xcoords_dx to file ", i/(nnodes_ie*nnodes_r*3)) i += 1 # This avoid to accumulate derivatives dummy = coords.grad.data.zero_() np.savetxt(f,array) f.close() f = open("dinterp_ycoords_dx.txt","w") i = 1 f.write("ycoord interpolation, coord 2, row=node, col=nnodes_r*dir \n") array = np.zeros([nnodes_ie,nnodes_r*ndirs]) for inode in range (0,nnodes_ie): data = interp_coords[inode,1] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): if idir == 0: index = inode_diff elif idir == 1: index = nnodes_r + inode_diff elif idir == 2: index = 2*nnodes_r + inode_diff array[inode,index] = ddata_np[inode_diff,idir] update_progress("Writing interp_ycoords_dx to file ", i/(nnodes_ie*nnodes_r*3)) i += 1 # This avoid to accumulate derivatives dummy = coords.grad.data.zero_() np.savetxt(f,array) f.close() f = open("dinterp_zcoords_dx.txt","w") i = 1 f.write("zcoord interpolation, coord 3, row=node, col=nnodes_r*dir \n") array = np.zeros([nnodes_ie,nnodes_r*ndirs]) for inode in range (0,nnodes_ie): data = interp_coords[inode,2] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): if idir == 0: index = inode_diff elif idir == 1: index = nnodes_r + inode_diff elif idir == 2: index = 2*nnodes_r + inode_diff array[inode,index] = ddata_np[inode_diff,idir] update_progress("Writing interp_zcoords_dx to file ", i/(nnodes_ie*nnodes_r*3)) i += 1 # This avoid to accumulate derivatives dummy = coords.grad.data.zero_() np.savetxt(f,array) f.close() # # djinv_dx # f = open("djinv_dx.txt","w") i = 1 for inode in range (0,nnodes_ie): array = np.zeros([nnodes_r,ndirs]) f.write("djinv_dx interpolation node %s, row=inode_diff, col=dir \n" % (inode+1)) for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): data = jinv[inode] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[inode_diff,idir] = ddata_np[inode_diff,idir] update_progress("Writing djinv_dx to file ", i/(nnodes_ie*nnodes_r*ndirs)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() # # dmass_dx # f = open("dmass_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dmass_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nterms_s,nterms_s]) for irow in range(0,nterms_s): for icol in range(0,nterms_s): data = mass[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dmass_dx to file ", i/(nterms_s*nnodes_r*ndirs*nterms_s)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() # # dinvmass_dx # f = open("dinvmass_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dinvmass_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nterms_s,nterms_s]) for irow in range(0,nterms_s): for icol in range(0,nterms_s): data = invmass[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dinvmass_dx to file ", i/(nterms_s*nnodes_r*ndirs*nterms_s)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() # # dbr2_vol_dx # # f = open("dbr2_vol_face1_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_vol_face1_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_ie,nnodes_if]) for irow in range(0,nnodes_ie): for icol in range(0,nnodes_if): data = br2_vol_face1[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dbr2_vol_face1_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nnodes_if)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() f = open("dbr2_vol_face2_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_vol_face2_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_ie,nnodes_if]) for irow in range(0,nnodes_ie): for icol in range(0,nnodes_if): data = br2_vol_face2[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dbr2_vol_face2_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nnodes_if)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() f = open("dbr2_vol_face3_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_vol_face3_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_ie,nnodes_if]) for irow in range(0,nnodes_ie): for icol in range(0,nnodes_if): data = br2_vol_face3[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dbr2_vol_face3_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nnodes_if)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() f = open("dbr2_vol_face4_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_vol_face4_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_ie,nnodes_if]) for irow in range(0,nnodes_ie): for icol in range(0,nnodes_if): data = br2_vol_face4[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dbr2_vol_face4_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nnodes_if)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() f = open("dbr2_vol_face5_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_vol_face5_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_ie,nnodes_if]) for irow in range(0,nnodes_ie): for icol in range(0,nnodes_if): data = br2_vol_face5[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dbr2_vol_face5_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nnodes_if)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() f = open("dbr2_vol_face6_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_vol_face6_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_ie,nnodes_if]) for irow in range(0,nnodes_ie): for icol in range(0,nnodes_if): data = br2_vol_face6[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dbr2_vol_face6_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nnodes_if)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() # # dbr2_face_dx # # f = open("dbr2_face_face1_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_face_face1_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_if,nnodes_if]) for irow in range(0,nnodes_if): for icol in range(0,nnodes_if): data = br2_face_face1[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dbr2_face_face1_dx to file ", i/(nnodes_if*nnodes_r*ndirs*nnodes_if)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() f = open("dbr2_face_face2_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_face_face2_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_if,nnodes_if]) for irow in range(0,nnodes_if): for icol in range(0,nnodes_if): data = br2_face_face2[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dbr2_face_face2_dx to file ", i/(nnodes_if*nnodes_r*ndirs*nnodes_if)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() f = open("dbr2_face_face3_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_face_face3_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_if,nnodes_if]) for irow in range(0,nnodes_if): for icol in range(0,nnodes_if): data = br2_face_face3[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dbr2_face_face3_dx to file ", i/(nnodes_if*nnodes_r*ndirs*nnodes_if)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() f = open("dbr2_face_face4_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_face_face4_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_if,nnodes_if]) for irow in range(0,nnodes_if): for icol in range(0,nnodes_if): data = br2_face_face4[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dbr2_face_face4_dx to file ", i/(nnodes_if*nnodes_r*ndirs*nnodes_if)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() f = open("dbr2_face_face5_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_face_face5_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_if,nnodes_if]) for irow in range(0,nnodes_if): for icol in range(0,nnodes_if): data = br2_face_face5[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dbr2_face_face5_dx to file ", i/(nnodes_if*nnodes_r*ndirs*nnodes_if)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() f = open("dbr2_face_face6_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dbr2_face_face6_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_if,nnodes_if]) for irow in range(0,nnodes_if): for icol in range(0,nnodes_if): data = br2_face_face6[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dbr2_face_face6_dx to file ", i/(nnodes_if*nnodes_r*ndirs*nnodes_if)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() # # dgrad1_dx # f = open("dgrad1_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dgrad1_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_ie,nterms_s]) for irow in range(0,nnodes_ie): for icol in range(0,nterms_s): data = grad1[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dgrad1_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nterms_s)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() # # dgrad2_dx # f = open("dgrad2_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dgrad2_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_ie,nterms_s]) for irow in range(0,nnodes_ie): for icol in range(0,nterms_s): data = grad2[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dgrad2_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nterms_s)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close() # # dgrad3_dx # f = open("dgrad3_dx.txt","w") i = 1 for inode_diff in range(0,nnodes_r): for idir in range(0,ndirs): f.write("dgrad3_dx => diff_node %s, diff_dir %s \n" % (inode_diff+1,idir+1)) array = np.zeros([nnodes_ie,nterms_s]) for irow in range(0,nnodes_ie): for icol in range(0,nterms_s): data = grad3[irow,icol] data.backward(retain_graph=True) ddata = coords.grad ddata_np = ddata.numpy() array[irow,icol] = ddata_np[inode_diff,idir] update_progress("Writing dgrad3_dx to file ", i/(nnodes_ie*nnodes_r*ndirs*nterms_s)) dummy = coords.grad.data.zero_() i += 1 np.savetxt(f,array) f.close()
2.453125
2
osr_stat_generator/generator.py
brian-thomas/osr_stat_generator
0
9617
""" OSR (LOTFP) stat generator. """ import random def d(num_sides): """ Represents rolling a die of size 'num_sides'. Returns random number from that size die """ return random.randint(1, num_sides) def xdy(num_dice, num_sides): """ represents rolling num_dice of size num_sides. Returns random number from that many dice being 'rolled'. """ return sum(d(num_sides) for i in range(num_dice)) class LotFP_Stat (object): def _get_bonus(attribute): if attribute <= 3: return -3 if attribute >= 4 and attribute <= 5: return -2 if attribute >= 6 and attribute <= 8: return -1 if attribute >= 13 and attribute <= 15: return 1 if attribute >= 16 and attribute <= 17: return 2 if attribute >= 18: return 3 # the default return 0 @property def bonus(self): return self._bonus @property def name(self): return self._name @property def value(self): return self._value def __str__(self): return (f"%s : %s(%s)" % (self.name, self.value, self.bonus)) def __init__(self, name, value): self._name = name self._value = value self._bonus = LotFP_Stat._get_bonus(value) class Stat_Set(object): """ Define a package of OSR/DnD stats """ _Stat_Name = ["CON", "DEX", "INT", "WIS", "STR", "CHA"] @property def stats(self)->list: return self._stats def sum(self)->int: # get a summed value for all stats in this set ssum = 0 for s in self.stats: ssum += s.value return ssum @property def is_hopeless(self)->bool: """ Determine if the character is so poorly stated they have bonus sum less than 1. """ bonuses = [s.bonus for s in self._stats] if sum(bonuses) < 1: return True return False def __str__(self)->str: string = "" for stat in stats: string = string + " " + str(stat.value) + " ("+str(stat.bonus) + ")" return string def __init__(self, stats): self._stats = [] for i in range(0,len(stats)): self._stats.append(LotFP_Stat(Stat_Set._Stat_Name[i], stats[i])) def generate_stats (nrof_sets:int=1, no_hopeless_char:bool=True)->list: """ Generate stats for a character. """ stat_sets = [] while (nrof_sets > 0): stats = [] for i in range (0, 6): stats.append(xdy(3,6)) stat_set = Stat_Set(stats) # no "hopeless" characters if no_hopeless_char and stat_set.is_hopeless: continue stat_sets.append(stat_set) nrof_sets -= 1 return stat_sets
3.515625
4
cohesity_management_sdk/models/health_tile.py
nick6655/management-sdk-python
18
9618
# -*- coding: utf-8 -*- # Copyright 2021 Cohesity Inc. import cohesity_management_sdk.models.alert class HealthTile(object): """Implementation of the 'HealthTile' model. Health for Dashboard. Attributes: capacity_bytes (long|int): Raw Cluster Capacity in Bytes. This is not usable capacity and does not take replication factor into account. cluster_cloud_usage_bytes (long|int): Usage in Bytes on the cloud. last_day_alerts (list of Alert): Alerts in last 24 hours. last_day_num_criticals (long|int): Number of Critical Alerts. last_day_num_warnings (long|int): Number of Warning Alerts. num_nodes (int): Number of nodes in the cluster. num_nodes_with_issues (int): Number of nodes in the cluster that are unhealthy. percent_full (float): Percent the cluster is full. raw_used_bytes (long|int): Raw Bytes used in the cluster. """ # Create a mapping from Model property names to API property names _names = { "capacity_bytes":'capacityBytes', "cluster_cloud_usage_bytes":'clusterCloudUsageBytes', "last_day_alerts":'lastDayAlerts', "last_day_num_criticals":'lastDayNumCriticals', "last_day_num_warnings":'lastDayNumWarnings', "num_nodes":'numNodes', "num_nodes_with_issues":'numNodesWithIssues', "percent_full":'percentFull', "raw_used_bytes":'rawUsedBytes' } def __init__(self, capacity_bytes=None, cluster_cloud_usage_bytes=None, last_day_alerts=None, last_day_num_criticals=None, last_day_num_warnings=None, num_nodes=None, num_nodes_with_issues=None, percent_full=None, raw_used_bytes=None): """Constructor for the HealthTile class""" # Initialize members of the class self.capacity_bytes = capacity_bytes self.cluster_cloud_usage_bytes = cluster_cloud_usage_bytes self.last_day_alerts = last_day_alerts self.last_day_num_criticals = last_day_num_criticals self.last_day_num_warnings = last_day_num_warnings self.num_nodes = num_nodes self.num_nodes_with_issues = num_nodes_with_issues self.percent_full = percent_full self.raw_used_bytes = raw_used_bytes @classmethod def from_dictionary(cls, dictionary): """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class. """ if dictionary is None: return None # Extract variables from the dictionary capacity_bytes = dictionary.get('capacityBytes') cluster_cloud_usage_bytes = dictionary.get('clusterCloudUsageBytes') last_day_alerts = None if dictionary.get('lastDayAlerts') != None: last_day_alerts = list() for structure in dictionary.get('lastDayAlerts'): last_day_alerts.append(cohesity_management_sdk.models.alert.Alert.from_dictionary(structure)) last_day_num_criticals = dictionary.get('lastDayNumCriticals') last_day_num_warnings = dictionary.get('lastDayNumWarnings') num_nodes = dictionary.get('numNodes') num_nodes_with_issues = dictionary.get('numNodesWithIssues') percent_full = dictionary.get('percentFull') raw_used_bytes = dictionary.get('rawUsedBytes') # Return an object of this model return cls(capacity_bytes, cluster_cloud_usage_bytes, last_day_alerts, last_day_num_criticals, last_day_num_warnings, num_nodes, num_nodes_with_issues, percent_full, raw_used_bytes)
2.296875
2
TextRank/textrank.py
nihanjali/PageRank
0
9619
<reponame>nihanjali/PageRank<gh_stars>0 import os import sys import copy import collections import nltk import nltk.tokenize sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) import pagerank ''' textrank.py ----------- This module implements TextRank, an unsupervised keyword significance scoring algorithm. TextRank builds a weighted graph representation of a document using words as nodes and coocurrence frequencies between pairs of words as edge weights. It then applies PageRank to this graph, and treats the PageRank score of each word as its significance. The original research paper proposing this algorithm is available here: https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf ''' ## TextRank ##################################################################################### def __preprocessDocument(document, relevantPosTags): ''' This function accepts a string representation of a document as input, and returns a tokenized list of words corresponding to that document. ''' words = __tokenizeWords(document) posTags = __tagPartsOfSpeech(words) # Filter out words with irrelevant POS tags filteredWords = [] for index, word in enumerate(words): word = word.lower() tag = posTags[index] if not __isPunctuation(word) and tag in relevantPosTags: filteredWords.append(word) return filteredWords def textrank(document, windowSize=2, rsp=0.15, relevantPosTags=["NN", "ADJ"]): ''' This function accepts a string representation of a document and three hyperperameters as input. It returns Pandas matrix (that can be treated as a dictionary) that maps words in the document to their associated TextRank significance scores. Note that only words that are classified as having relevant POS tags are present in the map. ''' # Tokenize document: words = __preprocessDocument(document, relevantPosTags) # Build a weighted graph where nodes are words and # edge weights are the number of times words cooccur # within a window of predetermined size. In doing so # we double count each coocurrence, but that will not # alter relative weights which ultimately determine # TextRank scores. edgeWeights = collections.defaultdict(lambda: collections.Counter()) for index, word in enumerate(words): for otherIndex in range(index - windowSize, index + windowSize + 1): if otherIndex >= 0 and otherIndex < len(words) and otherIndex != index: otherWord = words[otherIndex] edgeWeights[word][otherWord] += 1.0 # Apply PageRank to the weighted graph: wordProbabilities = pagerank.powerIteration(edgeWeights, rsp=rsp) wordProbabilities.sort_values(ascending=False) return wordProbabilities ## NLP utilities ################################################################################ def __asciiOnly(string): return "".join([char if ord(char) < 128 else "" for char in string]) def __isPunctuation(word): return word in [".", "?", "!", ",", "\"", ":", ";", "'", "-"] def __tagPartsOfSpeech(words): return [pair[1] for pair in nltk.pos_tag(words)] def __tokenizeWords(sentence): return nltk.tokenize.word_tokenize(sentence) ## tests ######################################################################################## def applyTextRank(fileName, title="a document"): print print "Reading \"%s\" ..." % title filePath = os.path.join(os.path.dirname(__file__), fileName) document = open(filePath).read() document = __asciiOnly(document) print "Applying TextRank to \"%s\" ..." % title keywordScores = textrank(document) print header = "Keyword Significance Scores for \"%s\":" % title print header print "-" * len(header) print keywordScores print def main(): applyTextRank("Cinderalla.txt", "Cinderalla") applyTextRank("Beauty_and_the_Beast.txt", "Beauty and the Beast") applyTextRank("Rapunzel.txt", "Rapunzel") if __name__ == "__main__": main()
3.078125
3
tests/test_exploration.py
lionelkusch/neurolib
0
9620
import logging import os import random import string import time import unittest import neurolib.utils.paths as paths import neurolib.utils.pypetUtils as pu import numpy as np import pytest import xarray as xr from neurolib.models.aln import ALNModel from neurolib.models.fhn import FHNModel from neurolib.models.multimodel import MultiModel from neurolib.models.multimodel.builder.fitzhugh_nagumo import FitzHughNagumoNetwork from neurolib.optimize.exploration import BoxSearch from neurolib.utils.loadData import Dataset from neurolib.utils.parameterSpace import ParameterSpace def randomString(stringLength=10): """Generate a random string of fixed length""" letters = string.ascii_lowercase return "".join(random.choice(letters) for i in range(stringLength)) class TestBoxSearch(unittest.TestCase): """ Basic tests. """ def test_assertions(self): parameters = ParameterSpace( {"mue_ext_mean": np.linspace(0, 3, 2), "mui_ext_mean": np.linspace(0, 3, 2)}, kind="sequence" ) with pytest.raises(AssertionError): _ = BoxSearch(model=None, parameterSpace=parameters) with pytest.raises(AssertionError): _ = BoxSearch(model=None, parameterSpace=None) with pytest.raises(AssertionError): _ = BoxSearch(model=None, parameterSpace=parameters, evalFunction=None) def test_fillin_default_parameters_for_sequential(self): in_dict = {"a": [None, None, 1, 2], "b": [4, 5, None, None]} SHOULD_BE = {"a": [0, 0, 1, 2], "b": [4, 5, 12, 12]} model_params = {"a": 0, "b": 12} parameters = ParameterSpace({"mue_ext_mean": [1.0, 2.0]}) search = BoxSearch(model=ALNModel(), parameterSpace=parameters) out_dict = search._fillin_default_parameters_for_sequential(in_dict, model_params) self.assertDictEqual(out_dict, SHOULD_BE) class TestExplorationSingleNode(unittest.TestCase): """ ALN single node exploration. """ def test_single_node(self): start = time.time() model = ALNModel() parameters = ParameterSpace({"mue_ext_mean": np.linspace(0, 3, 2), "mui_ext_mean": np.linspace(0, 3, 2)}) search = BoxSearch(model, parameters, filename="test_single_nodes.hdf") search.run() search.loadResults() dataarray = search.xr() self.assertTrue(isinstance(dataarray, xr.DataArray)) self.assertFalse(dataarray.attrs) for i in search.dfResults.index: search.dfResults.loc[i, "max_r"] = np.max( search.results[i]["rates_exc"][:, -int(1000 / model.params["dt"]) :] ) end = time.time() logging.info("\t > Done in {:.2f} s".format(end - start)) class TestExplorationBrainNetwork(unittest.TestCase): """ FHN brain network simulation with BOLD simulation. """ def test_fhn_brain_network_exploration(self): ds = Dataset("hcp") model = FHNModel(Cmat=ds.Cmat, Dmat=ds.Dmat) model.params.duration = 10 * 1000 # ms model.params.dt = 0.2 model.params.bold = True parameters = ParameterSpace( { "x_ext": [np.ones((model.params["N"],)) * a for a in np.linspace(0, 2, 2)], "K_gl": np.linspace(0, 2, 2), "coupling": ["additive", "diffusive"], }, kind="grid", ) search = BoxSearch(model=model, parameterSpace=parameters, filename="test_fhn_brain_network_exploration.hdf") search.run(chunkwise=True, bold=True) pu.getTrajectorynamesInFile(os.path.join(paths.HDF_DIR, "test_fhn_brain_network_exploration.hdf")) search.loadDfResults() search.getRun(0, pypetShortNames=True) search.getRun(0, pypetShortNames=False) search.loadResults() # firing rate xr dataarray = search.xr() self.assertTrue(isinstance(dataarray, xr.DataArray)) self.assertFalse(dataarray.attrs) # bold xr dataarray = search.xr(bold=True) self.assertTrue(isinstance(dataarray, xr.DataArray)) self.assertFalse(dataarray.attrs) search.info() class TestExplorationBrainNetworkPostprocessing(unittest.TestCase): """ ALN brain network simulation with custom evaluation function. """ @classmethod def setUpClass(cls): # def test_brain_network_postprocessing(self): ds = Dataset("hcp") model = ALNModel(Cmat=ds.Cmat, Dmat=ds.Dmat) # Resting state fits model.params["mue_ext_mean"] = 1.57 model.params["mui_ext_mean"] = 1.6 model.params["sigma_ou"] = 0.09 model.params["b"] = 5.0 model.params["signalV"] = 2 model.params["dt"] = 0.2 model.params["duration"] = 0.2 * 60 * 1000 # multi stage evaluation function def evaluateSimulation(traj): model = search.getModelFromTraj(traj) model.randomICs() model.params["dt"] = 0.2 model.params["duration"] = 4 * 1000.0 model.run(bold=True) result_dict = {"outputs": model.outputs} search.saveToPypet(result_dict, traj) # define and run exploration parameters = ParameterSpace({"mue_ext_mean": np.linspace(0, 3, 2), "mui_ext_mean": np.linspace(0, 3, 2)}) search = BoxSearch( evalFunction=evaluateSimulation, model=model, parameterSpace=parameters, filename=f"test_brain_postprocessing_{randomString(20)}.hdf", ) search.run() cls.model = model cls.search = search cls.ds = ds def test_getRun(self): self.search.getRun(0) def test_loadResults(self): self.search.loadResults() def test_loadResults_all_False(self): self.search.loadResults(all=False) class TestCustomParameterExploration(unittest.TestCase): """Exploration with custom function""" def test_circle_exploration(self): def explore_me(traj): pars = search.getParametersFromTraj(traj) # let's calculate the distance to a circle computation_result = abs((pars["x"] ** 2 + pars["y"] ** 2) - 1) result_dict = {"scalar_result": computation_result, "list_result": [1, 2, 3, 4], "array_result": np.ones(3)} search.saveToPypet(result_dict, traj) parameters = ParameterSpace({"x": np.linspace(-2, 2, 2), "y": np.linspace(-2, 2, 2)}) search = BoxSearch(evalFunction=explore_me, parameterSpace=parameters, filename="test_circle_exploration.hdf") search.run() search.loadResults(pypetShortNames=False) # call the result dataframe search.dfResults # test integrity of dataframe for i in search.dfResults.index: self.assertEqual(search.dfResults.loc[i, "scalar_result"], search.results[i]["scalar_result"]) self.assertListEqual(search.dfResults.loc[i, "list_result"], search.results[i]["list_result"]) np.testing.assert_array_equal(search.dfResults.loc[i, "array_result"], search.results[i]["array_result"]) class TestExplorationMultiModel(unittest.TestCase): """ MultiModel exploration test - uses FHN network. """ def test_multimodel_explore(self): start = time.time() DELAY = 13.0 fhn_net = FitzHughNagumoNetwork(np.random.rand(2, 2), np.array([[0.0, DELAY], [DELAY, 0.0]])) model = MultiModel(fhn_net) parameters = ParameterSpace({"*input*sigma": [0.0, 0.05], "*epsilon*": [0.5, 0.6]}, allow_star_notation=True) search = BoxSearch(model, parameters, filename="test_multimodel.hdf") search.run() search.loadResults() dataarray = search.xr() self.assertTrue(isinstance(dataarray, xr.DataArray)) self.assertTrue(isinstance(dataarray.attrs, dict)) self.assertListEqual( list(dataarray.attrs.keys()), [k.replace("*", "_").replace(".", "_").replace("|", "_") for k in parameters.dict().keys()], ) end = time.time() logging.info("\t > Done in {:.2f} s".format(end - start)) class TestExplorationMultiModelSequential(unittest.TestCase): """ MultiModel exploration test with sequential exploration - uses FHN network. """ def test_multimodel_explore(self): start = time.time() DELAY = 13.0 fhn_net = FitzHughNagumoNetwork(np.random.rand(2, 2), np.array([[0.0, DELAY], [DELAY, 0.0]])) model = MultiModel(fhn_net) parameters = ParameterSpace( {"*input*sigma": [0.0, 0.05], "*epsilon*": [0.5, 0.6, 0.7]}, allow_star_notation=True, kind="sequence" ) search = BoxSearch(model, parameters, filename="test_multimodel.hdf") search.run() search.loadResults() dataarray = search.xr() self.assertTrue(isinstance(dataarray, xr.DataArray)) self.assertTrue("run_no" in dataarray.dims) self.assertEqual(len(dataarray["run_no"]), 5) self.assertTrue(isinstance(dataarray.attrs, dict)) self.assertListEqual( list(dataarray.attrs.keys()), [k.replace("*", "_").replace(".", "_").replace("|", "_") for k in parameters.dict().keys()], ) end = time.time() logging.info("\t > Done in {:.2f} s".format(end - start)) class TestExplorationSingleNodeSequential(unittest.TestCase): """ ALN single node test with sequential exploration. """ def test_single_node(self): start = time.time() model = ALNModel() parameters = ParameterSpace({"mue_ext_mean": [0.0, 1.5, 3.0], "mui_ext_mean": [1.5, 3.0]}, kind="sequence") search = BoxSearch(model, parameters, filename="test_single_nodes.hdf") search.run() search.loadResults() dataarray = search.xr() self.assertTrue(isinstance(dataarray, xr.DataArray)) self.assertTrue("run_no" in dataarray.dims) self.assertEqual(len(dataarray["run_no"]), 5) self.assertFalse(dataarray.attrs) for i in search.dfResults.index: search.dfResults.loc[i, "max_r"] = np.max( search.results[i]["rates_exc"][:, -int(1000 / model.params["dt"]) :] ) end = time.time() logging.info("\t > Done in {:.2f} s".format(end - start)) if __name__ == "__main__": unittest.main()
2.1875
2
irc3/tags.py
belst/irc3
0
9621
<reponame>belst/irc3 # -*- coding: utf-8 -*- ''' Module offering 2 functions, encode() and decode(), to transcode between IRCv3.2 tags and python dictionaries. ''' import re import random import string _escapes = ( ("\\", "\\\\"), (";", r"\:"), (" ", r"\s"), ("\r", r"\r"), ("\n", r"\n"), ) # make the possibility of the substitute actually appearing in the text # negligible. Even for targeted attacks _substitute = (";TEMP:%s;" % ''.join(random.choice(string.ascii_letters) for i in range(20))) _unescapes = ( ("\\\\", _substitute), (r"\:", ";"), (r"\s", " "), (r"\r", "\r"), (r"\n", "\n"), (_substitute, "\\"), ) # valid tag-keys must contain of alphanumerics and hyphens only. # for vendor-tagnames: TLD with slash appended _valid_key = re.compile("^([\w.-]+/)?[\w-]+$") # valid escaped tag-values must not contain # NUL, CR, LF, semicolons or spaces _valid_escaped_value = re.compile("^[^ ;\n\r\0]*$") def _unescape(string): for a, b in _unescapes: string = string.replace(a, b) return string def _escape(string): for a, b in _escapes: string = string.replace(a, b) return string def encode(tags): '''Encodes a dictionary of tags to fit into an IRC-message. See IRC Message Tags: http://ircv3.net/specs/core/message-tags-3.2.html >>> from collections import OrderedDict >>> encode({'key': 'value'}) 'key=value' >>> d = {'aaa': 'bbb', 'ccc': None, 'example.com/ddd': 'eee'} >>> d_ordered = OrderedDict(sorted(d.items(), key=lambda t: t[0])) >>> encode(d_ordered) 'aaa=bbb;ccc;example.com/ddd=eee' >>> d = {'key': 'value;with special\\\\characters', 'key2': 'with=equals'} >>> d_ordered = OrderedDict(sorted(d.items(), key=lambda t: t[0])) >>> print(encode(d_ordered)) key=value\\:with\\sspecial\\\characters;key2=with=equals >>> print(encode({'key': r'\\something'})) key=\\\\something ''' tagstrings = [] for key, value in tags.items(): if not _valid_key.match(key): raise ValueError("dictionary key is invalid as tag key: " + key) # if no value, just append the key if value: tagstrings.append(key + "=" + _escape(value)) else: tagstrings.append(key) return ";".join(tagstrings) def decode(tagstring): '''Decodes a tag-string from an IRC-message into a python dictionary. See IRC Message Tags: http://ircv3.net/specs/core/message-tags-3.2.html >>> from pprint import pprint >>> pprint(decode('key=value')) {'key': 'value'} >>> pprint(decode('aaa=bbb;ccc;example.com/ddd=eee')) {'aaa': 'bbb', 'ccc': None, 'example.com/ddd': 'eee'} >>> s = r'key=value\\:with\\sspecial\\\\characters;key2=with=equals' >>> pprint(decode(s)) {'key': 'value;with special\\\\characters', 'key2': 'with=equals'} >>> print(decode(s)['key']) value;with special\\characters >>> print(decode(r'key=\\\\something')['key']) \\something ''' if not tagstring: # None/empty = no tags return {} tags = {} for tag in tagstring.split(";"): # value is either everything after "=", or None key, value = (tag.split("=", 1) + [None])[:2] if not _valid_key.match(key): raise ValueError("invalid tag key: " + key) if value: if not _valid_escaped_value.match(value): raise ValueError("invalid escaped tag value: " + value) value = _unescape(value) tags[key] = value return tags
2.890625
3
app/forms.py
Rahmatullina/FinalYearProject
0
9622
from django import forms from django.contrib.auth.forms import PasswordResetForm, SetPasswordForm # from .models import RegionModel # from .models import SERVICE_CHOICES, REGION_CHOICES from django.contrib.auth import authenticate # from django.contrib.auth.forms import UserCreationForm, UserChangeForm # from .models import CustomUser class LoginForm(forms.Form): username = forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control'}), max_length=100) password = forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control','type':'password'}),max_length=100) def clean(self): username = self.cleaned_data.get('username') password = self.cleaned_data.get('password') user = authenticate(username=username, password=password) if not user or not user.is_active: raise forms.ValidationError("Sorry, that login was invalid or user is inactive. Please try again.") return self.cleaned_data def login(self, request): username = self.cleaned_data.get('username') password = self.cleaned_data.get('password') user = authenticate(username=username, password=password) return user # class PassResetForm(PasswordResetForm): # email = forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Enter email', # 'type':'email'}), max_length=100) # # # class PassResetConfirmForm(SetPasswordForm): # new_password1 = forms.CharField(widget=forms.TextInput(attrs={'class':'form-control', # 'placeholder':'Enter new password', # 'type':'password'}), max_length=100) # new_password2 = forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control', # 'placeholder': 'Enter new password again', # 'type': 'password'}), max_length=100) # class CustomUserCreationForm(UserCreationForm): # # class Meta(UserCreationForm): # model = CustomUser # fields = UserCreationForm.Meta.fields + ('region_name',) # # # class CustomUserChangeForm(UserChangeForm): # email = forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control'}), max_length=100) # username = forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control'}), max_length=254) # # class Meta: # model = CustomUser # fields = ('email','username')
2.453125
2
src/fedavg_trainer.py
MrZhang1994/mobile-federated-learning
0
9623
# newly added libraries import copy import wandb import time import math import csv import shutil from tqdm import tqdm import torch import numpy as np import pandas as pd from client import Client from config import * import scheduler as sch class FedAvgTrainer(object): def __init__(self, dataset, model, device, args): self.device = device self.args = args [client_num, _, _, train_data_global, _, train_data_local_num_dict, train_data_local_dict, test_data_local_dict, class_num] = dataset # record the client number of the dataset self.client_num = client_num self.class_num = class_num # setup dataset self.data_shape = list(train_data_global[0][0].size()) self.train_data_local_num_dict = train_data_local_num_dict self.test_data_local_dict = test_data_local_dict self.train_data_local_dict = train_data_local_dict if args.partition_method == "noniid": logger.info("-----------non-i.i.d transform----------") # generate the non i.i.d dataset self.gene_non_iid_dataset(train_data_global, "tmp") # read the non i.i.d dataset self.read_non_iid_dataset("tmp") # rm the tmp directory shutil.rmtree(os.path.join('.', 'tmp')) self.client_list = [] self.setup_clients(train_data_local_num_dict, train_data_local_dict, test_data_local_dict) # initialize the recorder of invalid dataset self.invalid_datasets = dict() # time counter starts from the first line self.time_counter = channel_data['Time'][0] # initialize the cycle_num here self.cycle_num = 0 # initialize the scheduler function if self.args.method == "sch_pn_method_1" or self.args.method == "sch_pn_method_1_empty": for _ in range(100): self.scheduler = sch.Scheduler_PN_method_1() client_indexes, _ = self.scheduler.sch_pn_test(1, 2002) if len(client_indexes) > 5: break elif self.args.method == "sch_pn_method_2" or self.args.method == "sch_pn_method_2_empty": for _ in range(100): self.scheduler = sch.Scheduler_PN_method_2() client_indexes, _ = self.scheduler.sch_pn_test(1, 2002) if len(client_indexes) > 5: break elif self.args.method == "sch_pn_method_3" or self.args.method == "sch_pn_method_3_empty": for _ in range(100): self.scheduler = sch.Scheduler_PN_method_3() client_indexes, _ = self.scheduler.sch_pn_test(1, 2002) if len(client_indexes) > 5: break elif self.args.method == "sch_random": self.scheduler = sch.sch_random elif self.args.method == "sch_channel": self.scheduler = sch.sch_channel elif self.args.method == "sch_rrobin": self.scheduler = sch.sch_rrobin elif self.args.method == "sch_loss": self.scheduler = sch.sch_loss else: self.scheduler = sch.sch_random self.model = model self.model_global = model(self.args, model_name=self.args.model, output_dim=self.class_num) self.model_global.train() def setup_clients(self, train_data_local_num_dict, train_data_local_dict, test_data_local_dict): logger.debug("############setup_clients (START)#############") for client_idx in range(client_num_per_round): c = Client(client_idx, train_data_local_dict[client_idx], test_data_local_dict[client_idx], train_data_local_num_dict[client_idx], self.args, self.device) self.client_list.append(c) logger.debug("############setup_clients (END)#############") def train(self): """ Global initialized values """ # maintain a lst for local losses local_loss_lst = np.zeros((1, client_num_in_total)) # maintain a lst for local acc _, dataset_acc_lst = self.local_test_on_all_clients(self.model_global, 0, True, False) local_acc_lst = dataset_acc_lst[np.arange(client_num_in_total) % self.client_num] # counting days counting_days, reward = 0, 0 # initialize values for calculating iteration num delta, rho, beta, rho_flag, beta_flag = np.random.rand(1)[0], np.random.rand(1)[0], np.random.rand(1)[0], True, True # Initialize values for calculating FPF2 index local_itr_lst = torch.zeros(self.args.comm_round, int(client_num_in_total)).to(self.device) # historical local iterations. G_mat = torch.zeros(int(client_num_in_total)).to(self.device) # initial the value of G with zero # if weight size is larger than THRESHOLD_WEIGHT_SIZE we will use a simpler method to calculate FPF weight_size = sum([self.model_global.cpu().state_dict()[para].numpy().ravel().shape[0] for para in self.model_global.state_dict().keys()]) if weight_size < THRESHOLD_WEIGHT_SIZE: A_mat = torch.ones(weight_size).to(self.device) # initial the value of A with ones. local_w_diffs = torch.zeros((int(client_num_in_total), weight_size)).to(self.device) else: logger.warning("The weight size of the model {} is too large. Thus, we turn to use a more simple method to calculate FPF.".format(self.args.model)) LRU_itr_lst = torch.zeros(int(client_num_in_total)).to(self.device) # store the iteration gap for each client. # show weight size for the model. logger.debug("weight size: {}".format(weight_size)) """ starts training, entering the loop of command round. """ Inform = {} traffic = 0 for round_idx in range(self.args.comm_round): logger.info("################Communication round : {}".format(round_idx)) # set the time_counter self.time_counter = np.array(channel_data['Time'][channel_data['Time'] >= self.time_counter])[0] logger.info("time_counter: {}".format(self.time_counter)) self.model_global.train() # get client_indexes from scheduler reward, loss_a, loss_c = 0, 0, 0 if (self.args.method)[:6] == "sch_pn": if self.args.method[-5:] == "empty" or round_idx == 0: client_indexes, local_itr = self.scheduler.sch_pn_empty(round_idx, self.time_counter) else: client_indexes, local_itr, (reward, loss_a, loss_c) = self.scheduler.sch_pn(round_idx, self.time_counter, loss_locals, FPF2_idx_lst, local_loss_lst, ) else: if self.args.method == "sch_loss": if round_idx == 0: loss_locals = [] client_indexes, local_itr = self.scheduler(round_idx, self.time_counter, loss_locals) else: client_indexes, local_itr = self.scheduler(round_idx, self.time_counter) # write to the scheduler csv with open(scheduler_csv, mode = "a+", encoding='utf-8', newline='') as file: csv_writer = csv.writer(file) if round_idx == 0: csv_writer.writerow(['time counter', 'client index', 'iteration']) csv_writer.writerow([self.time_counter, str(client_indexes), local_itr]) file.flush() logger.info("client_indexes = " + str(client_indexes)) traffic += len(client_indexes) # write one line to trainer_csv trainer_csv_line = [round_idx, self.time_counter, str(client_indexes), traffic] # contribute to time counter self.tx_time(list(client_indexes)) # transmit time # store the last model's training parameters. last_w = copy.deepcopy(self.model_global.cpu().state_dict()) # local Initialization w_locals, loss_locals, beta_locals, rho_locals, cycle_locals = [], [], [], [], [] """ for scalability: following the original FedAvg algorithm, we uniformly sample a fraction of clients in each round. Instead of changing the 'Client' instances, our implementation keeps the 'Client' instances and then updates their local dataset """ for idx in range(len(client_indexes)): # update dataset client = self.client_list[idx] client_idx = client_indexes[idx] dataset_idx = client_idx % self.client_num if dataset_idx in self.invalid_datasets.keys(): current_idx = self.invalid_datasets[dataset_idx] else: current_idx = dataset_idx while True: client.update_local_dataset(current_idx, self.train_data_local_dict[current_idx], self.test_data_local_dict[current_idx], self.train_data_local_num_dict[current_idx]) # train on new dataset # add a new parameter "local_itr" to the funciton "client.train()" # add a new return value "time_interval" which is the time consumed for training model in client. w, loss, local_beta, local_rho, local_acc, local_cycle = client.train(net=copy.deepcopy(self.model_global).to(self.device), local_iteration = local_itr) if loss != None and local_beta != None and local_rho != None and local_acc != None: if dataset_idx != current_idx: self.invalid_datasets[dataset_idx] = current_idx break current_idx = np.random.randint(self.class_num) logger.warning("changing dataset for {} to {}".format(client_idx, current_idx)) # record current cycle cycle_locals.append([client.get_sample_number(), local_cycle]) # record current w into w_locals w_locals.append((client.get_sample_number(), copy.deepcopy(w))) # record current loss into loss_locals loss_locals.append(loss) # record local beta into beta_locals beta_locals.append(local_beta) # record local beta into rho_locals rho_locals.append(local_rho) # update the local_loss_lst local_loss_lst[0, client_idx] = loss # update local_w_diffs if weight_size < THRESHOLD_WEIGHT_SIZE: local_w_diffs[client_idx, :] = torch.cat([w[para].reshape((-1, )) - last_w[para].reshape((-1, )) for para in self.model_global.state_dict().keys()]).to(self.device) # update local_acc_lst local_acc_lst[client_idx] = local_acc # loss logger.info('Client {:3d}, loss {:.3f}'.format(client_idx, loss)) # update global weights w_glob = self.aggregate(w_locals) # copy weight to net_glob self.model_global.load_state_dict(w_glob) # update the time counter if list(client_indexes): self.time_counter += math.ceil(LOCAL_TRAINING_TIME) logger.debug("time_counter after training: {}".format(self.time_counter)) trainer_csv_line += [self.time_counter-trainer_csv_line[1], np.var(local_loss_lst), str(loss_locals), np.var(loss_locals), np.var(local_acc_lst)] # print loss if not loss_locals: logger.info('Round {:3d}, Average loss None'.format(round_idx)) trainer_csv_line.append('None') else: loss_avg = sum(loss_locals) / len(loss_locals) logger.info('Round {:3d}, Average loss {:.3f}'.format(round_idx, loss_avg)) trainer_csv_line.append(loss_avg) if cycle_locals: cycle_locals = np.asarray(cycle_locals) logger.info('Elapsed cycles {:.3f}'.format(np.sum(cycle_locals[:, 0] * cycle_locals[:, 1]) / np.sum(cycle_locals[:, 0]))) # local test on all client. if round_idx % self.args.frequency_of_the_test == 0 or round_idx == self.args.comm_round - 1: test_acc, _ = self.local_test_on_all_clients(self.model_global, round_idx, EVAL_ON_TRAIN, True) trainer_csv_line.append(test_acc) # write headers for csv with open(trainer_csv, mode = "a+", encoding='utf-8', newline='') as file: csv_writer = csv.writer(file) if round_idx == 0: csv_writer.writerow(['round index', 'time counter', 'client index', 'traffic', 'train time', 'fairness', 'local loss', "local loss var", "local acc var", 'global loss', 'test accuracy']) csv_writer.writerow(trainer_csv_line) file.flush() # log on wandb Inform["reward"] = reward wandb.log(Inform) Inform = { "reward": reward, "loss_a": loss_a, "loss_c": loss_c, "round": round_idx, "traffic": traffic, "beta": beta, "rho": rho, "delta": delta, "cum_time": trainer_csv_line[1]+self.cycle_num*59361, "local_itr": local_itr, "client_num": len(client_indexes), "C3": (rho*delta)/beta, "local_loss_var": np.var(loss_locals), "local_acc_var": np.var(local_acc_lst) } # update FPF index list if weight_size < THRESHOLD_WEIGHT_SIZE: FPF2_idx_lst = torch.norm(local_w_diffs * A_mat, dim = 1) / G_mat else: FPF2_idx_lst = LRU_itr_lst / G_mat FPF2_idx_lst = FPF2_idx_lst.cpu().numpy() FPF2_idx_lst[np.bitwise_or(np.isnan(FPF2_idx_lst), np.isinf(FPF2_idx_lst))] = 0 # FPF2_idx_lst = FPF2_idx_lst / max(FPF2_idx_lst) FPF2_idx_lst[np.bitwise_or(np.isnan(FPF2_idx_lst), np.isinf(FPF2_idx_lst))] = 0 # write FPF index list to csv with open(FPF_csv, mode = "a+", encoding='utf-8', newline='') as file: csv_writer = csv.writer(file) if round_idx == 0: csv_writer.writerow(['time counter'] + ["car_"+str(i) for i in range(client_num_in_total)]) csv_writer.writerow([trainer_csv_line[1]]+FPF2_idx_lst.tolist()) file.flush() # update beta & delta & rho if w_locals and loss_locals: sample_nums = np.array([sample_num for sample_num, _ in w_locals]) local_w_diff_norms = np.array([torch.norm(torch.cat([w[para].reshape((-1, )) - w_glob[para].reshape((-1, )) for para in self.model_global.state_dict().keys()])).item() for _, w in w_locals]) # calculate delta delta_tmp = np.sum(sample_nums * local_w_diff_norms) / np.sum(sample_nums) / self.args.lr if (not np.isnan(delta_tmp) and not np.isinf(delta_tmp)): delta = delta_tmp # update rho rho_tmp = np.sum(sample_nums * np.array(rho_locals)) / np.sum(sample_nums) if rho_tmp > rho or rho_flag: if (not np.isnan(rho_tmp) and not np.isinf(rho_tmp)) and rho_tmp < THRESHOLD_RHO: rho, rho_flag = rho_tmp, False # update beta beta_tmp = np.sum(sample_nums * np.array(beta_locals)) / np.sum(sample_nums) if beta_tmp > beta or beta_flag: if (not np.isnan(beta_tmp) and not np.isinf(beta_tmp)) and beta_tmp < THRESHOLD_BETA: beta, beta_flag = beta_tmp, False if self.args.method == "sch_pn_method_1" or self.args.method == "sch_pn_method_1_empty": self.scheduler.calculate_itr_method_1(delta) elif self.args.method == "sch_pn_method_2" or self.args.method == "sch_pn_method_2_empty": self.scheduler.calculate_itr_method_2(rho, beta, delta) elif self.args.method == "sch_pn_method_3" or self.args.method == "sch_pn_method_3_empty": self.scheduler.calculate_itr_method_3(rho, beta, delta) if weight_size < THRESHOLD_WEIGHT_SIZE: # update local_w_diffs global_w_diff = torch.cat([w_glob[para].reshape((-1, )) - last_w[para].reshape((-1, )) for para in self.model_global.state_dict().keys()]).to(self.device) local_w_diffs[list(set(list(range(client_num_in_total))) - set(list(client_indexes))), :] -= global_w_diff # update A_mat A_mat = A_mat * (1 - 1/G2) + (global_w_diff) / G2 / global_w_diff.mean() # Update local_itr_lst if list(client_indexes) and local_itr > 0: # only if client_idx is not empty and local_iter > 0, then I will update following values local_itr_lst[round_idx, list(client_indexes)] = float(local_itr) if weight_size >= THRESHOLD_WEIGHT_SIZE: LRU_itr_lst += float(local_itr) LRU_itr_lst[list(client_indexes)] = 0 # update G_mat G_mat = G_mat * (1 - 1 / G1) + local_itr_lst[round_idx, :] / G1 # if current time_counter has exceed the channel table, I will simply stop early if self.time_counter >= time_cnt_max[counting_days]: counting_days += 1 if counting_days % RESTART_DAYS == 0: if self.args.method == "find_constant" and loss_locals: w_optimal, loss_optimal = self.central_train() w = torch.cat([param.view(-1) for param in self.model_global.parameters()]) w_diff_optimal = torch.norm(w.cpu() - w_optimal.cpu()) logger.info("The norm of difference between w_optmal & w: {}".format(w_diff_optimal.item())) logger.info("The norm of difference between loss & loss_optimal: {}".format(loss_avg - loss_optimal)) break logger.info("################reinitialize model") self.model_global = self.model(self.args, model_name=self.args.model, output_dim=self.class_num) delta, rho, beta, rho_flag, beta_flag = np.random.rand(1)[0], np.random.rand(1)[0], np.random.rand(1)[0], True, True traffic = 0 if counting_days >= DATE_LENGTH: logger.info("################training restarts") counting_days = 0 self.time_counter = 0 self.cycle_num = self.cycle_num+1 def central_train(self): logger.info("################global optimal weights calculation") model = self.model(self.args, model_name=self.args.model, output_dim=self.class_num) criterion = torch.nn.CrossEntropyLoss().to(self.device) model.to(self.device) if self.args.client_optimizer == "sgd": optimizer = torch.optim.SGD(model.parameters(), lr=self.args.lr) else: optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=self.args.lr, weight_decay=self.args.wd, amsgrad=True) for _ in tqdm(range(self.args.central_round)): for client_idx in range(self.client_num): x, labels = next(iter(self.train_data_local_dict[client_idx])) x, labels = x.to(self.device), labels.to(self.device) model.train() model.zero_grad() log_probs = model(x) loss = criterion(log_probs, labels) loss.backward() loss = loss.item() optimizer.step() wandb.log({"central_training/loss": loss}) w_optimal = torch.cat([param.view(-1) for param in model.parameters()]) loss_optimal = loss return w_optimal, loss_optimal def gene_non_iid_dataset(self, train_global, directory): """ changing self.train_data_local_dict to non-i.i.d. dataset. And change self.train_data_local_num_dict correspondingly. """ data, labels = train_global[0][0], train_global[0][1] # read the tensor from train_global. # transform shape data = data.view(data.shape[0], -1) labels = labels.view(labels.shape[0], -1) # get full_df full_df = pd.DataFrame(np.concatenate((data.numpy(), labels.numpy()), axis=1)).sample(frac=1, random_state=self.args.seed) # temporary store the data in dir save_dir = os.path.join(".", directory) if not os.path.exists(save_dir): os.mkdir(save_dir) for client_idx in tqdm(range(self.client_num)): # get selected classes try: selected_classes = set(list(np.random.choice(list(set(full_df.iloc[:, -1])), CLASS_NUM))) except: selected_classes = set(full_df.iloc[:, -1]) # got valid data valid_data = full_df[full_df.iloc[:, -1].isin(selected_classes)] # get number of data on the local client local_num = self.train_data_local_num_dict[client_idx] # got selected data # remember to shuffle the data try: selected_data = valid_data[0:local_num] except: selected_data = valid_data self.train_data_local_dict[client_idx] = len(selected_data) # update the local client data np.save(os.path.join(save_dir, "client_{}_data.npy".format(client_idx)), selected_data.iloc[:, 0:-1].values) np.save(os.path.join(save_dir, "client_{}_labels.npy".format(client_idx)), selected_data.iloc[:, -1].values) # remove the data from the full_df full_df = full_df.drop(index=selected_data.index) def read_non_iid_dataset(self, directory): for client_idx in tqdm(range(self.client_num)): data_shape = [self.train_data_local_num_dict[client_idx]] + self.data_shape[1:] data_path = os.path.join(".", directory, "client_{}_data.npy".format(client_idx)) labels_path = os.path.join(".", directory, "client_{}_labels.npy".format(client_idx)) self.train_data_local_dict[client_idx] = [(torch.from_numpy(np.load(data_path)).view(tuple(data_shape)).float(), torch.from_numpy(np.load(labels_path)).long())] def tx_time(self, client_indexes): if not client_indexes: self.time_counter += 1 return # read the channel condition for corresponding cars. channel_res = np.reshape(np.array(channel_data[channel_data['Time'] == self.time_counter * channel_data['Car'].isin(client_indexes)]["Distance to BS(4982,905)"]), (1, -1)) logger.debug("channel_res: {}".format(channel_res)) # linearly resolve the optimazation problem tmp_t = 1 if self.args.radio_alloc == "optimal": while np.sum(RES_WEIGHT * channel_res * RES_RATIO / tmp_t) > 1: tmp_t += 1 elif self.args.radio_alloc == "uniform": while np.max(channel_res) * RES_WEIGHT * RES_RATIO * len(channel_res) / tmp_t > 1: tmp_t += 1 self.time_counter += math.ceil(TIME_COMPRESSION_RATIO*tmp_t) logger.debug("time_counter after tx_time: {}".format(self.time_counter)) def aggregate(self, w_locals): if not w_locals: return copy.deepcopy(self.model_global.cpu().state_dict()) training_num = 0 for idx in range(len(w_locals)): (sample_num, averaged_params) = w_locals[idx] training_num += sample_num (sample_num, averaged_params) = w_locals[0] for k in averaged_params.keys(): for i in range(0, len(w_locals)): local_sample_number, local_model_params = w_locals[i] w = local_sample_number / training_num if i == 0: averaged_params[k] = local_model_params[k] * w else: averaged_params[k] += local_model_params[k] * w return averaged_params def local_test_on_all_clients(self, model_global, round_idx, eval_on_train=False, if_log=True): logger.info("################local_test_on_all_clients : {}".format(round_idx)) train_metrics = { 'num_samples': [], 'num_correct': [], 'losses': [] } test_metrics = { 'num_samples': [], 'num_correct': [], 'losses': [] } client = self.client_list[0] for client_idx in tqdm(range(min(int(client_num_in_total), self.client_num))): """ Note: for datasets like "fed_CIFAR100" and "fed_shakespheare", the training client number is larger than the testing client number """ if self.test_data_local_dict[client_idx] is None or client_idx in self.invalid_datasets.keys(): continue client.update_local_dataset(client_idx, self.train_data_local_dict[client_idx], self.test_data_local_dict[client_idx], self.train_data_local_num_dict[client_idx]) # test data test_local_metrics = client.local_test(model_global, True) test_metrics['num_samples'].append(copy.deepcopy(test_local_metrics['test_total'])) test_metrics['num_correct'].append(copy.deepcopy(test_local_metrics['test_correct'])) test_metrics['losses'].append(copy.deepcopy(test_local_metrics['test_loss'])) # train data if eval_on_train: train_local_metrics = client.local_test(model_global, False) train_metrics['num_samples'].append(copy.deepcopy(train_local_metrics['test_total'])) train_metrics['num_correct'].append(copy.deepcopy(train_local_metrics['test_correct'])) train_metrics['losses'].append(copy.deepcopy(train_local_metrics['test_loss'])) # test on test dataset test_acc = sum(test_metrics['num_correct']) / sum(test_metrics['num_samples']) test_loss = sum(test_metrics['losses']) / sum(test_metrics['num_samples']) stats = { "Test/Acc": test_acc, "Test/Loss": test_loss, "round": round_idx, "cum_time": self.time_counter+self.cycle_num*59361, } # test on training dataset if eval_on_train: train_acc = sum(train_metrics['num_correct']) / sum(train_metrics['num_samples']) train_loss = sum(train_metrics['losses']) / sum(train_metrics['num_samples']) stats.update({ 'Train/Acc': train_acc, 'Train/Loss': train_loss, "round": round_idx, "cum_time": self.time_counter+self.cycle_num*59361, }) if if_log: logger.info(stats) wandb.log(stats) return test_acc, np.array(train_metrics['num_correct']) / np.array(train_metrics['num_samples']) if if_log: logger.info(stats) wandb.log(stats) return test_acc, None
2.1875
2
src/test.py
jfparentledartech/DEFT
0
9624
<reponame>jfparentledartech/DEFT from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import time import cv2 import matplotlib.pyplot as plt import numpy as np from progress.bar import Bar import torch import pickle import motmetrics as mm from lib.opts import opts from lib.logger import Logger from lib.utils.utils import AverageMeter from lib.dataset.dataset_factory import dataset_factory from lib.utils.pixset_metrics import compute_metrics pixset_categories = [ 'car', 'truck', 'bus', 'pedestrian', 'motorcyclist', 'cyclist', 'van' ] opt = opts().parse() filename = '../options/test_opt_pixset.txt' with open(filename, 'wb') as f: pickle.dump(opt, f) # # print('dataset -> ', opt.dataset) # print('lstm -> ', opt.lstm) # print(f'saved {filename}') # with open(filename, 'rb') as f: # opt = pickle.load(f) # print('use pixell ->', opt.use_pixell) from lib.detector import Detector from lib.utils.image import plot_tracking, plot_tracking_ddd import json min_box_area = 20 _vehicles = ["car", "truck", "bus", "van"] _cycles = ["motorcyclist", "cyclist"] _pedestrians = ["pedestrian"] attribute_to_id = { "": 0, "cycle.with_rider": 1, "cycle.without_rider": 2, "pedestrian.moving": 3, "pedestrian.standing": 4, "pedestrian.sitting_lying_down": 5, "vehicle.moving": 6, "vehicle.parked": 7, "vehicle.stopped": 8, } id_to_attribute = {v: k for k, v in attribute_to_id.items()} nuscenes_att = np.zeros(8, np.float32) class PrefetchDataset(torch.utils.data.Dataset): def __init__(self, opt, dataset, pre_process_func): self.images = dataset.images self.load_image_func = dataset.coco.loadImgs self.get_ann_ids = dataset.coco.getAnnIds self.load_annotations = dataset.coco.loadAnns self.img_dir = dataset.img_dir self.pre_process_func = pre_process_func self.get_default_calib = dataset.get_default_calib self.opt = opt def __getitem__(self, index): self.images.sort() # TODO remove img_id = self.images[index] img_info = self.load_image_func(ids=[img_id])[0] img_path = os.path.join(self.img_dir, img_info["file_name"]) image = cv2.imread(img_path) annotation_ids = self.get_ann_ids(imgIds=[img_id]) annotations = self.load_annotations(ids=annotation_ids) images, meta = {}, {} for scale in opt.test_scales: input_meta = {} calib = ( img_info["calib"] if "calib" in img_info else self.get_default_calib(image.shape[1], image.shape[0]) ) input_meta["calib"] = calib images[scale], meta[scale] = self.pre_process_func(image, scale, input_meta) ret = { "images": images, "image": image, "meta": meta, "frame_id": img_info["frame_id"], "annotations": annotations } if "frame_id" in img_info and img_info["frame_id"] == 1: ret["is_first_frame"] = 1 ret["video_id"] = img_info["video_id"] return img_id, ret, img_info def __len__(self): return len(self.images) def prefetch_test(opt): start_time = time.time() show_image = True if not opt.not_set_cuda_env: os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpus_str Dataset = dataset_factory[opt.test_dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) # split = "val" if not opt.trainval else "test" split = "test" # split = "val" dataset = Dataset(opt, split) detector = Detector(opt) if opt.load_results != "": load_results = json.load(open(opt.load_results, "r")) for img_id in load_results: for k in range(len(load_results[img_id])): if load_results[img_id][k]["class"] - 1 in opt.ignore_loaded_cats: load_results[img_id][k]["score"] = -1 else: load_results = {} data_loader = torch.utils.data.DataLoader( PrefetchDataset(opt, dataset, detector.pre_process), batch_size=1, shuffle=False, num_workers=0, pin_memory=True, ) results = {} num_iters = len(data_loader) if opt.num_iters < 0 else opt.num_iters bar = Bar("{}".format(opt.exp_id), max=num_iters) time_stats = ["tot", "load", "pre", "net", "dec", "post", "merge", "track"] avg_time_stats = {t: AverageMeter() for t in time_stats} if opt.use_loaded_results: for img_id in data_loader.dataset.images: results[img_id] = load_results["{}".format(img_id)] num_iters = 0 final_results = [] out_path = "" if opt.dataset in ["nuscenes", "pixset"]: ret = { "meta": { "use_camera": True, "use_lidar": False, "use_radar": False, "use_map": False, "use_external": False, }, "results": {}, } accumulators = [mm.MOTAccumulator(auto_id=True) for _ in pixset_categories] for ind, (img_id, pre_processed_images, img_info) in enumerate(data_loader): bar.next() if ind >= num_iters: break if opt.dataset == "nuscenes": sample_token = img_info["sample_token"][0] sensor_id = img_info["sensor_id"].numpy().tolist()[0] if opt.dataset == "pixset": sample_token = img_info["sample_token"][0] sensor_id = img_info["sensor_id"].numpy().tolist()[0] if opt.tracking and ("is_first_frame" in pre_processed_images): if "{}".format(int(img_id.numpy().astype(np.int32)[0])) in load_results: pre_processed_images["meta"]["pre_dets"] = load_results[ "{}".format(int(img_id.numpy().astype(np.int32)[0])) ] else: print( "No pre_dets for", int(img_id.numpy().astype(np.int32)[0]), ". Use empty initialization.", ) pre_processed_images["meta"]["pre_dets"] = [] if final_results and opt.dataset not in ["nuscenes", "pixset"]: write_results(out_path, final_results, opt.dataset) final_results = [] img0 = pre_processed_images["image"][0].numpy() h, w, _ = img0.shape detector.img_height = h detector.img_width = w if opt.dataset in ["nuscenes", "pixset"]: save_video_name = os.path.join( opt.dataset + "_videos/", "MOT" + str(int(pre_processed_images["video_id"])) + "_" + str(int(img_info["sensor_id"])) + str(int(img_info["video_id"])) + ".avi", ) elif opt.dataset == "kitti_tracking": save_video_name = os.path.join( opt.dataset + "_videos/", "KITTI_" + str(int(pre_processed_images["video_id"])) + ".avi", ) else: save_video_name = os.path.join( opt.dataset + "_videos/", "MOT" + str(int(pre_processed_images["video_id"])) + ".avi", ) results_dir = opt.dataset + "_results" if not os.path.exists(opt.dataset + "_videos/"): os.mkdir(opt.dataset + "_videos/") if not os.path.exists(results_dir): os.mkdir(results_dir) for video in dataset.coco.dataset["videos"]: video_id = video["id"] file_name = video["file_name"] if pre_processed_images[ "video_id" ] == video_id and opt.dataset not in ["nuscenes", "pixset"]: out_path = os.path.join(results_dir, "{}.txt".format(file_name)) break detector.reset_tracking(opt) vw = cv2.VideoWriter( save_video_name, cv2.VideoWriter_fourcc("M", "J", "P", "G"), 10, (w, h) ) print("Start tracking video", int(pre_processed_images["video_id"])) if opt.public_det: if "{}".format(int(img_id.numpy().astype(np.int32)[0])) in load_results: pre_processed_images["meta"]["cur_dets"] = load_results[ "{}".format(int(img_id.numpy().astype(np.int32)[0])) ] else: print("No cur_dets for", int(img_id.numpy().astype(np.int32)[0])) pre_processed_images["meta"]["cur_dets"] = [] online_targets = detector.run(pre_processed_images, image_info=img_info) online_tlwhs = [] online_ids = [] online_ddd_boxes = [] sample_results = [] classes = [] image = pre_processed_images["image"][0].numpy() for acc_i in range(len(accumulators)): gt_list, hyp_list, distances = compute_metrics(pre_processed_images['annotations'], online_targets, eval_type='distance', im=image, category=pixset_categories[acc_i]) accumulators[acc_i].update(gt_list, hyp_list, distances) idx = 0 print(ind) print(accumulators[idx].mot_events.loc[ind]) mh = mm.metrics.create() summary = mh.compute(accumulators[idx], metrics=['num_frames', 'mota', 'precision', 'recall'], name=f'acc {pixset_categories[idx]}') print(summary) print('-----------------------------------------') for t in online_targets: tlwh = t.tlwh tid = t.track_id if tlwh[2] * tlwh[3] > min_box_area: online_tlwhs.append(tlwh) online_ids.append(tid) classes.append(t.classe) if opt.dataset in ["nuscenes", "pixset"]: online_ddd_boxes.append(t.org_ddd_box) class_name = t.classe if class_name in _cycles: att = id_to_attribute[np.argmax(nuscenes_att[0:2]) + 1] elif class_name in _pedestrians: att = id_to_attribute[np.argmax(nuscenes_att[2:5]) + 3] elif class_name in _vehicles: att = id_to_attribute[np.argmax(nuscenes_att[5:8]) + 6] ddd_box = t.ddd_bbox.copy() ddd_box_submission = t.ddd_submission.tolist() translation, size, rotation = ( ddd_box_submission[:3], ddd_box_submission[3:6], ddd_box_submission[6:], ) result = { "sample_token": sample_token, "translation": translation, "size": size, "rotation": rotation, "velocity": [0, 0], "detection_name": t.classe, # "attribute_name": att, "attribute_name": None, "detection_score": t.score, "tracking_name": t.classe, "tracking_score": t.score, "tracking_id": tid, "sensor_id": sensor_id, "det_id": -1, } sample_results.append(result.copy()) if opt.dataset in ["nuscenes", "pixset"]: if sample_token in ret["results"]: ret["results"][sample_token] = ( ret["results"][sample_token] + sample_results ) else: ret["results"][sample_token] = sample_results final_results.append( (pre_processed_images["frame_id"].cpu().item(), online_tlwhs, online_ids) ) if show_image: img0 = pre_processed_images["image"][0].numpy() if opt.dataset in ["nuscenes", "pixset"]: online_im = plot_tracking_ddd( img0, online_tlwhs, online_ddd_boxes, online_ids, frame_id=pre_processed_images["frame_id"], calib=img_info["calib"], trans_matrix=img_info["trans_matrix"], camera_matrix=img_info["camera_matrix"], distortion_coeffs=img_info["distortion_coefficients"], classes=classes, ) else: online_im = plot_tracking( img0, online_tlwhs, online_ids, frame_id=pre_processed_images["frame_id"], ) vw.write(online_im) if opt.dataset not in ["nuscenes", "pixset"] and final_results: write_results(out_path, final_results, opt.dataset) final_results = [] if opt.dataset in ["nuscenes", "pixset"]: for sample_token in ret["results"].keys(): confs = sorted( [ (-d["detection_score"], ind) for ind, d in enumerate(ret["results"][sample_token]) ] ) ret["results"][sample_token] = [ ret["results"][sample_token][ind] for _, ind in confs[: min(500, len(confs))] ] mh = mm.metrics.create() metrics = ['num_frames', 'mota', 'motp', 'precision', 'recall'] summary = mh.compute_many( accumulators, names=pixset_categories, metrics=metrics, generate_overall=True ) print(summary) save_summary(summary, 'overall') print('total test time', time.time() - start_time) def save_summary(summary, acc_name): with open(f"../pixset_results/test/{acc_name}.txt", "w") as text_file: text_file.write(summary.to_string()) def _to_list(results): for img_id in results: for t in range(len(results[img_id])): for k in results[img_id][t]: if isinstance(results[img_id][t][k], (np.ndarray, np.float32)): results[img_id][t][k] = results[img_id][t][k].tolist() return results def write_results(filename, results, data_type): if data_type == "mot": save_format = "{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n" elif data_type == "kitti_tracking": save_format = "{frame} {id} Car 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n" else: raise ValueError(data_type) with open(filename, "w") as f: for frame_id, tlwhs, track_ids in results: if data_type == "kitti_tracking": frame_id -= 1 for tlwh, track_id in zip(tlwhs, track_ids): if track_id < 0: continue x1, y1, w, h = tlwh x2, y2 = x1 + w, y1 + h line = save_format.format( frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h ) f.write(line) if __name__ == "__main__": # opt = opts().parse() prefetch_test(opt)
1.859375
2
compiler_gym/envs/gcc/datasets/csmith.py
AkillesAILimited/CompilerGym
0
9625
<reponame>AkillesAILimited/CompilerGym # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import logging import shutil import subprocess import tempfile from pathlib import Path from threading import Lock from typing import Iterable, Optional, Union import numpy as np from fasteners import InterProcessLock from compiler_gym.datasets import Benchmark, BenchmarkSource, Dataset from compiler_gym.datasets.benchmark import BenchmarkWithSource from compiler_gym.envs.gcc.gcc import Gcc from compiler_gym.util.decorators import memoized_property from compiler_gym.util.runfiles_path import runfiles_path from compiler_gym.util.shell_format import plural from compiler_gym.util.truncate import truncate # The maximum value for the --seed argument to csmith. UINT_MAX = (2 ** 32) - 1 _CSMITH_BIN = runfiles_path("compiler_gym/third_party/csmith/csmith/bin/csmith") _CSMITH_INCLUDES = runfiles_path( "compiler_gym/third_party/csmith/csmith/include/csmith-2.3.0" ) _CSMITH_INSTALL_LOCK = Lock() # TODO(github.com/facebookresearch/CompilerGym/issues/325): This can be merged # with the LLVM implementation. class CsmithBenchmark(BenchmarkWithSource): """A CSmith benchmark.""" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._src = None @classmethod def create(cls, uri: str, bitcode: bytes, src: bytes) -> Benchmark: """Create a benchmark from paths.""" benchmark = cls.from_file_contents(uri, bitcode) benchmark._src = src # pylint: disable=protected-access return benchmark @memoized_property def sources(self) -> Iterable[BenchmarkSource]: return [ BenchmarkSource(filename="source.c", contents=self._src), ] @property def source(self) -> str: """Return the single source file contents as a string.""" return self._src.decode("utf-8") class CsmithDataset(Dataset): """A dataset which uses Csmith to generate programs. Csmith is a tool that can generate random conformant C99 programs. It is described in the publication: <NAME>, <NAME>, <NAME>, and <NAME>. "Finding and understanding bugs in C compilers." In Proceedings of the 32nd ACM SIGPLAN conference on Programming Language Design and Implementation (PLDI), pp. 283-294. 2011. For up-to-date information about Csmith, see: https://embed.cs.utah.edu/csmith/ Note that Csmith is a tool that is used to find errors in compilers. As such, there is a higher likelihood that the benchmark cannot be used for an environment and that :meth:`env.reset() <compiler_gym.envs.CompilerEnv.reset>` will raise :class:`BenchmarkInitError <compiler_gym.datasets.BenchmarkInitError>`. """ def __init__( self, gcc_bin: Union[Path, str], site_data_base: Path, sort_order: int = 0, csmith_bin: Optional[Path] = None, csmith_includes: Optional[Path] = None, ): """Constructor. :param site_data_base: The base path of a directory that will be used to store installed files. :param sort_order: An optional numeric value that should be used to order this dataset relative to others. Lowest value sorts first. :param csmith_bin: The path of the Csmith binary to use. If not provided, the version of Csmith shipped with CompilerGym is used. :param csmith_includes: The path of the Csmith includes directory. If not provided, the includes of the Csmith shipped with CompilerGym is used. """ super().__init__( name="generator://csmith-v0", description="Random conformant C99 programs", references={ "Paper": "http://web.cse.ohio-state.edu/~rountev.1/5343/pdf/pldi11.pdf", "Homepage": "https://embed.cs.utah.edu/csmith/", }, license="BSD", site_data_base=site_data_base, sort_order=sort_order, benchmark_class=CsmithBenchmark, ) self.gcc_bin = gcc_bin self.csmith_bin_path = csmith_bin or _CSMITH_BIN self.csmith_includes_path = csmith_includes or _CSMITH_INCLUDES self._install_lockfile = self.site_data_path / ".install.LOCK" @property def size(self) -> int: # Actually 2^32 - 1, but practically infinite for all intents and # purposes. return 0 @memoized_property def gcc(self): # Defer instantiation of Gcc from the constructor as it will fail if the # given Gcc is not available. Memoize the result as initialization is # expensive. return Gcc(bin=self.gcc_bin) def benchmark_uris(self) -> Iterable[str]: return (f"{self.name}/{i}" for i in range(UINT_MAX)) def benchmark(self, uri: str) -> CsmithBenchmark: return self.benchmark_from_seed(int(uri.split("/")[-1])) def _random_benchmark(self, random_state: np.random.Generator) -> Benchmark: seed = random_state.integers(UINT_MAX) return self.benchmark_from_seed(seed) @property def installed(self) -> bool: return super().installed and (self.site_data_path / "includes").is_dir() def install(self) -> None: super().install() if self.installed: return with _CSMITH_INSTALL_LOCK, InterProcessLock(self._install_lockfile): if (self.site_data_path / "includes").is_dir(): return # Copy the Csmith headers into the dataset's site directory path because # in bazel builds this includes directory is a symlink, and we need # actual files that we can use in a docker volume. shutil.copytree( self.csmith_includes_path, self.site_data_path / "includes.tmp", ) # Atomic directory rename to prevent race on install(). (self.site_data_path / "includes.tmp").rename( self.site_data_path / "includes" ) def benchmark_from_seed( self, seed: int, max_retries: int = 3, retry_count: int = 0 ) -> CsmithBenchmark: """Get a benchmark from a uint32 seed. :param seed: A number in the range 0 <= n < 2^32. :return: A benchmark instance. :raises OSError: If Csmith fails. :raises BenchmarkInitError: If the C program generated by Csmith cannot be lowered to LLVM-IR. """ if retry_count >= max_retries: raise OSError( f"Csmith failed after {retry_count} {plural(retry_count, 'attempt', 'attempts')} " f"with seed {seed}" ) self.install() # Run csmith with the given seed and pipe the output to clang to # assemble a bitcode. self.logger.debug("Exec csmith --seed %d", seed) csmith = subprocess.Popen( [str(self.csmith_bin_path), "--seed", str(seed)], stdout=subprocess.PIPE, stderr=subprocess.PIPE, ) # Generate the C source. src, stderr = csmith.communicate(timeout=300) if csmith.returncode: try: stderr = "\n".join( truncate(stderr.decode("utf-8"), max_line_len=200, max_lines=20) ) logging.warning("Csmith failed with seed %d: %s", seed, stderr) except UnicodeDecodeError: # Failed to interpret the stderr output, generate a generic # error message. logging.warning("Csmith failed with seed %d", seed) return self.benchmark_from_seed( seed, max_retries=max_retries, retry_count=retry_count + 1 ) # Pre-process the source. with tempfile.TemporaryDirectory() as tmpdir: src_file = f"{tmpdir}/src.c" with open(src_file, "wb") as f: f.write(src) preprocessed_src = self.gcc( "-E", "-I", str(self.site_data_path / "includes"), "-o", "-", src_file, cwd=tmpdir, timeout=60, volumes={ str(self.site_data_path / "includes"): { "bind": str(self.site_data_path / "includes"), "mode": "ro", } }, ) return self.benchmark_class.create( f"{self.name}/{seed}", preprocessed_src.encode("utf-8"), src )
1.765625
2
dans_pymodules/power_of_two.py
DanielWinklehner/dans_pymodules
0
9626
<reponame>DanielWinklehner/dans_pymodules __author__ = "<NAME>" __doc__ = "Find out if a number is a power of two" def power_of_two(number): """ Function that checks if the input value (data) is a power of 2 (i.e. 2, 4, 8, 16, 32, ...) """ res = 0 while res == 0: res = number % 2 number /= 2.0 print("res: {}, data: {}".format(res, number)) if number == 1 and res == 0: return True return False
3.828125
4
examples/index/context.py
rmorshea/viewdom
0
9627
from viewdom import html, render, use_context, Context expected = '<h1>My Todos</h1><ul><li>Item: first</li></ul>' # start-after title = 'My Todos' todos = ['first'] def Todo(label): prefix = use_context('prefix') return html('<li>{prefix}{label}</li>') def TodoList(todos): return html('<ul>{[Todo(label) for label in todos]}</ul>') result = render(html(''' <{Context} prefix="Item: "> <h1>{title}</h1> <{TodoList} todos={todos} /> <//> ''')) # '<h1>My Todos</h1><ul><li>Item: first</li></ul>'
2.546875
3
biblioteca/views.py
Dagmoores/ProjetoIntegradorIUnivesp
0
9628
from django.views.generic import DetailView, ListView, TemplateView from .models import Books class BooksListView(ListView): model = Books class BooksDeitalView(DetailView): model = Books class Home(TemplateView): template_name = './biblioteca/index.html' class TermsOfService(TemplateView): template_name = './biblioteca/termsOfService.html'
2.078125
2
choir/evaluation/__init__.py
scwangdyd/large_vocabulary_hoi_detection
9
9629
<filename>choir/evaluation/__init__.py<gh_stars>1-10 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from .evaluator import DatasetEvaluator, DatasetEvaluators, inference_context, inference_on_dataset from .testing import print_csv_format, verify_results from .hico_evaluation import HICOEvaluator from .swig_evaluation import SWIGEvaluator # from .doh_evaluation import DOHDetectionEvaluator __all__ = [k for k in globals().keys() if not k.startswith("_")]
1.304688
1
api_yamdb/reviews/models.py
LHLHLHE/api_yamdb
0
9630
<reponame>LHLHLHE/api_yamdb<filename>api_yamdb/reviews/models.py import datetime as dt from django.db import models from django.core.validators import MinValueValidator, MaxValueValidator from django.core.exceptions import ValidationError from users.models import CustomUser def validate_year(value): """ Год выпуска произведения не может быть больше текущего. """ if value > dt.datetime.now().year: raise ValidationError( 'Год выпуска превышает текущий!') return value class Category(models.Model): """Модель категорий.""" name = models.CharField(max_length=256, verbose_name='Название') slug = models.SlugField( max_length=50, unique=True, verbose_name='Идентификатор') class Meta: ordering = ('name',) verbose_name = 'Категория' verbose_name_plural = 'Категории' def __str__(self): return self.slug class Genre(models.Model): """Модель жанров.""" name = models.CharField(max_length=256, verbose_name='Название') slug = models.SlugField( max_length=50, unique=True, verbose_name='Идентификатор') class Meta: ordering = ('name',) verbose_name = 'Жанр' verbose_name_plural = 'Жанры' def __str__(self): return self.slug class Title(models.Model): """Модель произведений.""" name = models.TextField(verbose_name='Название') year = models.IntegerField( validators=[validate_year], verbose_name='Год выпуска') description = models.TextField( blank=True, verbose_name='Описание') genre = models.ManyToManyField( Genre, through='GenreTitle', verbose_name='Жанры') category = models.ForeignKey( Category, on_delete=models.SET_NULL, blank=True, null=True, related_name='titles', verbose_name='Категория') class Meta: ordering = ('name',) verbose_name = 'Произведение' verbose_name_plural = 'Произведения' def __str__(self): return ( f'name: {self.name}, ' f'year: {self.year}, ' ) class GenreTitle(models.Model): """Модель для связи произведений и жанров отношением многие ко многим.""" genre = models.ForeignKey( Genre, on_delete=models.SET_NULL, blank=True, null=True) title = models.ForeignKey(Title, on_delete=models.CASCADE) def __str__(self): return f'{self.genre} --- {self.title}' class Review(models.Model): title = models.ForeignKey( Title, on_delete=models.CASCADE, verbose_name='Произведение', ) text = models.TextField( verbose_name='текст', ) author = models.ForeignKey( CustomUser, on_delete=models.CASCADE, verbose_name='Автор' ) score = models.IntegerField( validators=[ MinValueValidator(1), MaxValueValidator(10) ], verbose_name='Оценка' ) pub_date = models.DateTimeField( auto_now_add=True, verbose_name='Дата публикации' ) class Meta: constraints = [ models.UniqueConstraint( fields=['author', 'title'], name='unique review' ) ] verbose_name = 'Отзыв' verbose_name_plural = 'Отзывы' default_related_name = 'reviews' def __str__(self): return self.text[:60] class Comment(models.Model): review = models.ForeignKey( Review, on_delete=models.CASCADE, related_name='comments', verbose_name='Отзыв', ) text = models.TextField(verbose_name='Текст') author = models.ForeignKey( CustomUser, on_delete=models.CASCADE, related_name='comments', verbose_name='Автор' ) pub_date = models.DateTimeField( auto_now_add=True, verbose_name='Дата публикации' ) class Meta: verbose_name = 'Комментарий' verbose_name_plural = 'Комментарии' def __str__(self): return self.text
2.40625
2
angr/engines/pcode/arch/ArchPcode_PowerPC_LE_32_QUICC.py
matthewpruett/angr
6,132
9631
### ### This file was automatically generated ### from archinfo.arch import register_arch, Endness, Register from .common import ArchPcode class ArchPcode_PowerPC_LE_32_QUICC(ArchPcode): name = 'PowerPC:LE:32:QUICC' pcode_arch = 'PowerPC:LE:32:QUICC' description = 'PowerQUICC-III 32-bit little endian family' bits = 32 ip_offset = 0x780 sp_offset = 0x4 bp_offset = sp_offset instruction_endness = Endness.LE register_list = [ Register('r0', 4, 0x0), Register('r1', 4, 0x4), Register('r2', 4, 0x8), Register('r3', 4, 0xc), Register('r4', 4, 0x10), Register('r5', 4, 0x14), Register('r6', 4, 0x18), Register('r7', 4, 0x1c), Register('r8', 4, 0x20), Register('r9', 4, 0x24), Register('r10', 4, 0x28), Register('r11', 4, 0x2c), Register('r12', 4, 0x30), Register('r13', 4, 0x34), Register('r14', 4, 0x38), Register('r15', 4, 0x3c), Register('r16', 4, 0x40), Register('r17', 4, 0x44), Register('r18', 4, 0x48), Register('r19', 4, 0x4c), Register('r20', 4, 0x50), Register('r21', 4, 0x54), Register('r22', 4, 0x58), Register('r23', 4, 0x5c), Register('r24', 4, 0x60), Register('r25', 4, 0x64), Register('r26', 4, 0x68), Register('r27', 4, 0x6c), Register('r28', 4, 0x70), Register('r29', 4, 0x74), Register('r30', 4, 0x78), Register('r31', 4, 0x7c), Register('xer_so', 1, 0x400), Register('xer_ov', 1, 0x401), Register('xer_ov32', 1, 0x402), Register('xer_ca', 1, 0x403), Register('xer_ca32', 1, 0x404), Register('xer_count', 1, 0x405), Register('fp_fx', 1, 0x500), Register('fp_fex', 1, 0x501), Register('fp_vx', 1, 0x502), Register('fp_ox', 1, 0x503), Register('fp_ux', 1, 0x504), Register('fp_zx', 1, 0x505), Register('fp_xx', 1, 0x506), Register('fp_vxsnan', 1, 0x507), Register('fp_vxisi', 1, 0x508), Register('fp_vxidi', 1, 0x509), Register('fp_vxzdz', 1, 0x50a), Register('fp_vximz', 1, 0x50b), Register('fp_vxvc', 1, 0x50c), Register('fp_fr', 1, 0x50d), Register('fp_fi', 1, 0x50e), Register('fp_c', 1, 0x50f), Register('fp_cc0', 1, 0x510), Register('fp_cc1', 1, 0x511), Register('fp_cc2', 1, 0x512), Register('fp_cc3', 1, 0x513), Register('fp_reserve1', 1, 0x514), Register('fp_vxsoft', 1, 0x515), Register('fp_vxsqrt', 1, 0x516), Register('fp_vxcvi', 1, 0x517), Register('fp_ve', 1, 0x518), Register('fp_oe', 1, 0x519), Register('fp_ue', 1, 0x51a), Register('fp_ze', 1, 0x51b), Register('fp_xe', 1, 0x51c), Register('fp_ni', 1, 0x51d), Register('fp_rn0', 1, 0x51e), Register('fp_rn1', 1, 0x51f), Register('msr', 4, 0x700), Register('reserve_address', 4, 0x720), Register('reserve', 1, 0x728), Register('reserve_length', 1, 0x730), Register('pc', 4, 0x780, alias_names=('ip',)), Register('sr0', 4, 0x800), Register('sr1', 4, 0x804), Register('sr2', 4, 0x808), Register('sr3', 4, 0x80c), Register('sr4', 4, 0x810), Register('sr5', 4, 0x814), Register('sr6', 4, 0x818), Register('sr7', 4, 0x81c), Register('sr8', 4, 0x820), Register('sr9', 4, 0x824), Register('sr10', 4, 0x828), Register('sr11', 4, 0x82c), Register('sr12', 4, 0x830), Register('sr13', 4, 0x834), Register('sr14', 4, 0x838), Register('sr15', 4, 0x83c), Register('crall', 8, 0x900), Register('cr0', 1, 0x900), Register('cr1', 1, 0x901), Register('cr2', 1, 0x902), Register('cr3', 1, 0x903), Register('cr4', 1, 0x904), Register('cr5', 1, 0x905), Register('cr6', 1, 0x906), Register('cr7', 1, 0x907), Register('tea', 4, 0x980), Register('r2save', 4, 0x988), Register('spr000', 4, 0x1000), Register('xer', 4, 0x1004), Register('spr002', 4, 0x1008), Register('spr003', 4, 0x100c), Register('spr004', 4, 0x1010), Register('spr005', 4, 0x1014), Register('spr006', 4, 0x1018), Register('spr007', 4, 0x101c), Register('lr', 4, 0x1020), Register('ctr', 4, 0x1024), Register('spr00a', 4, 0x1028), Register('spr00b', 4, 0x102c), Register('spr00c', 4, 0x1030), Register('spr00d', 4, 0x1034), Register('spr00e', 4, 0x1038), Register('spr00f', 4, 0x103c), Register('spr010', 4, 0x1040), Register('spr011', 4, 0x1044), Register('spr012', 4, 0x1048), Register('spr013', 4, 0x104c), Register('spr014', 4, 0x1050), Register('spr015', 4, 0x1054), Register('spr016', 4, 0x1058), Register('spr017', 4, 0x105c), Register('spr018', 4, 0x1060), Register('spr019', 4, 0x1064), Register('srr0', 4, 0x1068), Register('srr1', 4, 0x106c), Register('spr01c', 4, 0x1070), Register('spr01d', 4, 0x1074), Register('spr01e', 4, 0x1078), Register('spr01f', 4, 0x107c), Register('spr020', 4, 0x1080), Register('spr021', 4, 0x1084), Register('spr022', 4, 0x1088), Register('spr023', 4, 0x108c), Register('spr024', 4, 0x1090), Register('spr025', 4, 0x1094), Register('spr026', 4, 0x1098), Register('spr027', 4, 0x109c), Register('spr028', 4, 0x10a0), Register('spr029', 4, 0x10a4), Register('spr02a', 4, 0x10a8), Register('spr02b', 4, 0x10ac), Register('spr02c', 4, 0x10b0), Register('spr02d', 4, 0x10b4), Register('spr02e', 4, 0x10b8), Register('spr02f', 4, 0x10bc), Register('spr030', 4, 0x10c0), Register('spr031', 4, 0x10c4), Register('spr032', 4, 0x10c8), Register('spr033', 4, 0x10cc), Register('spr034', 4, 0x10d0), Register('spr035', 4, 0x10d4), Register('spr036', 4, 0x10d8), Register('spr037', 4, 0x10dc), Register('spr038', 4, 0x10e0), Register('spr039', 4, 0x10e4), Register('spr03a', 4, 0x10e8), Register('spr03b', 4, 0x10ec), Register('spr03c', 4, 0x10f0), Register('spr03d', 4, 0x10f4), Register('spr03e', 4, 0x10f8), Register('spr03f', 4, 0x10fc), Register('spr040', 4, 0x1100), Register('spr041', 4, 0x1104), Register('spr042', 4, 0x1108), Register('spr043', 4, 0x110c), Register('spr044', 4, 0x1110), Register('spr045', 4, 0x1114), Register('spr046', 4, 0x1118), Register('spr047', 4, 0x111c), Register('spr048', 4, 0x1120), Register('spr049', 4, 0x1124), Register('spr04a', 4, 0x1128), Register('spr04b', 4, 0x112c), Register('spr04c', 4, 0x1130), Register('spr04d', 4, 0x1134), Register('spr04e', 4, 0x1138), Register('spr04f', 4, 0x113c), Register('spr050', 4, 0x1140), Register('spr051', 4, 0x1144), Register('spr052', 4, 0x1148), Register('spr053', 4, 0x114c), Register('spr054', 4, 0x1150), Register('spr055', 4, 0x1154), Register('spr056', 4, 0x1158), Register('spr057', 4, 0x115c), Register('spr058', 4, 0x1160), Register('spr059', 4, 0x1164), Register('spr05a', 4, 0x1168), Register('spr05b', 4, 0x116c), Register('spr05c', 4, 0x1170), Register('spr05d', 4, 0x1174), Register('spr05e', 4, 0x1178), Register('spr05f', 4, 0x117c), Register('spr060', 4, 0x1180), Register('spr061', 4, 0x1184), Register('spr062', 4, 0x1188), Register('spr063', 4, 0x118c), Register('spr064', 4, 0x1190), Register('spr065', 4, 0x1194), Register('spr066', 4, 0x1198), Register('spr067', 4, 0x119c), Register('spr068', 4, 0x11a0), Register('spr069', 4, 0x11a4), Register('spr06a', 4, 0x11a8), Register('spr06b', 4, 0x11ac), Register('spr06c', 4, 0x11b0), Register('spr06d', 4, 0x11b4), Register('spr06e', 4, 0x11b8), Register('spr06f', 4, 0x11bc), Register('spr070', 4, 0x11c0), Register('spr071', 4, 0x11c4), Register('spr072', 4, 0x11c8), Register('spr073', 4, 0x11cc), Register('spr074', 4, 0x11d0), Register('spr075', 4, 0x11d4), Register('spr076', 4, 0x11d8), Register('spr077', 4, 0x11dc), Register('spr078', 4, 0x11e0), Register('spr079', 4, 0x11e4), Register('spr07a', 4, 0x11e8), Register('spr07b', 4, 0x11ec), Register('spr07c', 4, 0x11f0), Register('spr07d', 4, 0x11f4), Register('spr07e', 4, 0x11f8), Register('spr07f', 4, 0x11fc), Register('spr080', 4, 0x1200), Register('spr081', 4, 0x1204), Register('spr082', 4, 0x1208), Register('spr083', 4, 0x120c), Register('spr084', 4, 0x1210), Register('spr085', 4, 0x1214), Register('spr086', 4, 0x1218), Register('spr087', 4, 0x121c), Register('spr088', 4, 0x1220), Register('spr089', 4, 0x1224), Register('spr08a', 4, 0x1228), Register('spr08b', 4, 0x122c), Register('spr08c', 4, 0x1230), Register('spr08d', 4, 0x1234), Register('spr08e', 4, 0x1238), Register('spr08f', 4, 0x123c), Register('spr090', 4, 0x1240), Register('spr091', 4, 0x1244), Register('spr092', 4, 0x1248), Register('spr093', 4, 0x124c), Register('spr094', 4, 0x1250), Register('spr095', 4, 0x1254), Register('spr096', 4, 0x1258), Register('spr097', 4, 0x125c), Register('spr098', 4, 0x1260), Register('spr099', 4, 0x1264), Register('spr09a', 4, 0x1268), Register('spr09b', 4, 0x126c), Register('spr09c', 4, 0x1270), Register('spr09d', 4, 0x1274), Register('spr09e', 4, 0x1278), Register('spr09f', 4, 0x127c), Register('spr0a0', 4, 0x1280), Register('spr0a1', 4, 0x1284), Register('spr0a2', 4, 0x1288), Register('spr0a3', 4, 0x128c), Register('spr0a4', 4, 0x1290), Register('spr0a5', 4, 0x1294), Register('spr0a6', 4, 0x1298), Register('spr0a7', 4, 0x129c), Register('spr0a8', 4, 0x12a0), Register('spr0a9', 4, 0x12a4), Register('spr0aa', 4, 0x12a8), Register('spr0ab', 4, 0x12ac), Register('spr0ac', 4, 0x12b0), Register('spr0ad', 4, 0x12b4), Register('spr0ae', 4, 0x12b8), Register('spr0af', 4, 0x12bc), Register('spr0b0', 4, 0x12c0), Register('spr0b1', 4, 0x12c4), Register('spr0b2', 4, 0x12c8), Register('spr0b3', 4, 0x12cc), Register('spr0b4', 4, 0x12d0), Register('spr0b5', 4, 0x12d4), Register('spr0b6', 4, 0x12d8), Register('spr0b7', 4, 0x12dc), Register('spr0b8', 4, 0x12e0), Register('spr0b9', 4, 0x12e4), Register('spr0ba', 4, 0x12e8), Register('spr0bb', 4, 0x12ec), Register('spr0bc', 4, 0x12f0), Register('spr0bd', 4, 0x12f4), Register('spr0be', 4, 0x12f8), Register('spr0bf', 4, 0x12fc), Register('spr0c0', 4, 0x1300), Register('spr0c1', 4, 0x1304), Register('spr0c2', 4, 0x1308), Register('spr0c3', 4, 0x130c), Register('spr0c4', 4, 0x1310), Register('spr0c5', 4, 0x1314), Register('spr0c6', 4, 0x1318), Register('spr0c7', 4, 0x131c), Register('spr0c8', 4, 0x1320), Register('spr0c9', 4, 0x1324), Register('spr0ca', 4, 0x1328), Register('spr0cb', 4, 0x132c), Register('spr0cc', 4, 0x1330), Register('spr0cd', 4, 0x1334), Register('spr0ce', 4, 0x1338), Register('spr0cf', 4, 0x133c), Register('spr0d0', 4, 0x1340), Register('spr0d1', 4, 0x1344), Register('spr0d2', 4, 0x1348), Register('spr0d3', 4, 0x134c), Register('spr0d4', 4, 0x1350), Register('spr0d5', 4, 0x1354), Register('spr0d6', 4, 0x1358), Register('spr0d7', 4, 0x135c), Register('spr0d8', 4, 0x1360), Register('spr0d9', 4, 0x1364), Register('spr0da', 4, 0x1368), Register('spr0db', 4, 0x136c), Register('spr0dc', 4, 0x1370), Register('spr0dd', 4, 0x1374), Register('spr0de', 4, 0x1378), Register('spr0df', 4, 0x137c), Register('spr0e0', 4, 0x1380), Register('spr0e1', 4, 0x1384), Register('spr0e2', 4, 0x1388), Register('spr0e3', 4, 0x138c), Register('spr0e4', 4, 0x1390), Register('spr0e5', 4, 0x1394), Register('spr0e6', 4, 0x1398), Register('spr0e7', 4, 0x139c), Register('spr0e8', 4, 0x13a0), Register('spr0e9', 4, 0x13a4), Register('spr0ea', 4, 0x13a8), Register('spr0eb', 4, 0x13ac), Register('spr0ec', 4, 0x13b0), Register('spr0ed', 4, 0x13b4), Register('spr0ee', 4, 0x13b8), Register('spr0ef', 4, 0x13bc), Register('spr0f0', 4, 0x13c0), Register('spr0f1', 4, 0x13c4), Register('spr0f2', 4, 0x13c8), Register('spr0f3', 4, 0x13cc), Register('spr0f4', 4, 0x13d0), Register('spr0f5', 4, 0x13d4), Register('spr0f6', 4, 0x13d8), Register('spr0f7', 4, 0x13dc), Register('spr0f8', 4, 0x13e0), Register('spr0f9', 4, 0x13e4), Register('spr0fa', 4, 0x13e8), Register('spr0fb', 4, 0x13ec), Register('spr0fc', 4, 0x13f0), Register('spr0fd', 4, 0x13f4), Register('spr0fe', 4, 0x13f8), Register('spr0ff', 4, 0x13fc), Register('spr100', 4, 0x1400), Register('spr101', 4, 0x1404), Register('spr102', 4, 0x1408), Register('spr103', 4, 0x140c), Register('spr104', 4, 0x1410), Register('spr105', 4, 0x1414), Register('spr106', 4, 0x1418), Register('spr107', 4, 0x141c), Register('spr108', 4, 0x1420), Register('spr109', 4, 0x1424), Register('spr10a', 4, 0x1428), Register('spr10b', 4, 0x142c), Register('tblr', 4, 0x1430), Register('tbur', 4, 0x1434), Register('spr10e', 4, 0x1438), Register('spr10f', 4, 0x143c), Register('spr110', 4, 0x1440), Register('spr111', 4, 0x1444), Register('spr112', 4, 0x1448), Register('spr113', 4, 0x144c), Register('spr114', 4, 0x1450), Register('spr115', 4, 0x1454), Register('spr116', 4, 0x1458), Register('spr117', 4, 0x145c), Register('spr118', 4, 0x1460), Register('spr119', 4, 0x1464), Register('spr11a', 4, 0x1468), Register('spr11b', 4, 0x146c), Register('tblw', 4, 0x1470), Register('tbuw', 4, 0x1474), Register('spr11e', 4, 0x1478), Register('spr11f', 4, 0x147c), Register('spr120', 4, 0x1480), Register('spr121', 4, 0x1484), Register('spr122', 4, 0x1488), Register('spr123', 4, 0x148c), Register('spr124', 4, 0x1490), Register('spr125', 4, 0x1494), Register('spr126', 4, 0x1498), Register('spr127', 4, 0x149c), Register('spr128', 4, 0x14a0), Register('spr129', 4, 0x14a4), Register('spr12a', 4, 0x14a8), Register('spr12b', 4, 0x14ac), Register('spr12c', 4, 0x14b0), Register('spr12d', 4, 0x14b4), Register('spr12e', 4, 0x14b8), Register('spr12f', 4, 0x14bc), Register('spr130', 4, 0x14c0), Register('spr131', 4, 0x14c4), Register('spr132', 4, 0x14c8), Register('spr133', 4, 0x14cc), Register('spr134', 4, 0x14d0), Register('spr135', 4, 0x14d4), Register('spr136', 4, 0x14d8), Register('spr137', 4, 0x14dc), Register('spr138', 4, 0x14e0), Register('spr139', 4, 0x14e4), Register('spr13a', 4, 0x14e8), Register('spr13b', 4, 0x14ec), Register('spr13c', 4, 0x14f0), Register('spr13d', 4, 0x14f4), Register('spr13e', 4, 0x14f8), Register('spr13f', 4, 0x14fc), Register('spr140', 4, 0x1500), Register('spr141', 4, 0x1504), Register('spr142', 4, 0x1508), Register('spr143', 4, 0x150c), Register('spr144', 4, 0x1510), Register('spr145', 4, 0x1514), Register('spr146', 4, 0x1518), Register('spr147', 4, 0x151c), Register('spr148', 4, 0x1520), Register('spr149', 4, 0x1524), Register('spr14a', 4, 0x1528), Register('spr14b', 4, 0x152c), Register('spr14c', 4, 0x1530), Register('spr14d', 4, 0x1534), Register('spr14e', 4, 0x1538), Register('spr14f', 4, 0x153c), Register('spr150', 4, 0x1540), Register('spr151', 4, 0x1544), Register('spr152', 4, 0x1548), Register('spr153', 4, 0x154c), Register('spr154', 4, 0x1550), Register('spr155', 4, 0x1554), Register('spr156', 4, 0x1558), Register('spr157', 4, 0x155c), Register('spr158', 4, 0x1560), Register('spr159', 4, 0x1564), Register('spr15a', 4, 0x1568), Register('spr15b', 4, 0x156c), Register('spr15c', 4, 0x1570), Register('spr15d', 4, 0x1574), Register('spr15e', 4, 0x1578), Register('spr15f', 4, 0x157c), Register('spr160', 4, 0x1580), Register('spr161', 4, 0x1584), Register('spr162', 4, 0x1588), Register('spr163', 4, 0x158c), Register('spr164', 4, 0x1590), Register('spr165', 4, 0x1594), Register('spr166', 4, 0x1598), Register('spr167', 4, 0x159c), Register('spr168', 4, 0x15a0), Register('spr169', 4, 0x15a4), Register('spr16a', 4, 0x15a8), Register('spr16b', 4, 0x15ac), Register('spr16c', 4, 0x15b0), Register('spr16d', 4, 0x15b4), Register('spr16e', 4, 0x15b8), Register('spr16f', 4, 0x15bc), Register('spr170', 4, 0x15c0), Register('spr171', 4, 0x15c4), Register('spr172', 4, 0x15c8), Register('spr173', 4, 0x15cc), Register('spr174', 4, 0x15d0), Register('spr175', 4, 0x15d4), Register('spr176', 4, 0x15d8), Register('spr177', 4, 0x15dc), Register('spr178', 4, 0x15e0), Register('spr179', 4, 0x15e4), Register('spr17a', 4, 0x15e8), Register('spr17b', 4, 0x15ec), Register('spr17c', 4, 0x15f0), Register('spr17d', 4, 0x15f4), Register('spr17e', 4, 0x15f8), Register('spr17f', 4, 0x15fc), Register('spr180', 4, 0x1600), Register('spr181', 4, 0x1604), Register('spr182', 4, 0x1608), Register('spr183', 4, 0x160c), Register('spr184', 4, 0x1610), Register('spr185', 4, 0x1614), Register('spr186', 4, 0x1618), Register('spr187', 4, 0x161c), Register('spr188', 4, 0x1620), Register('spr189', 4, 0x1624), Register('spr18a', 4, 0x1628), Register('spr18b', 4, 0x162c), Register('spr18c', 4, 0x1630), Register('spr18d', 4, 0x1634), Register('spr18e', 4, 0x1638), Register('spr18f', 4, 0x163c), Register('spr190', 4, 0x1640), Register('spr191', 4, 0x1644), Register('spr192', 4, 0x1648), Register('spr193', 4, 0x164c), Register('spr194', 4, 0x1650), Register('spr195', 4, 0x1654), Register('spr196', 4, 0x1658), Register('spr197', 4, 0x165c), Register('spr198', 4, 0x1660), Register('spr199', 4, 0x1664), Register('spr19a', 4, 0x1668), Register('spr19b', 4, 0x166c), Register('spr19c', 4, 0x1670), Register('spr19d', 4, 0x1674), Register('spr19e', 4, 0x1678), Register('spr19f', 4, 0x167c), Register('spr1a0', 4, 0x1680), Register('spr1a1', 4, 0x1684), Register('spr1a2', 4, 0x1688), Register('spr1a3', 4, 0x168c), Register('spr1a4', 4, 0x1690), Register('spr1a5', 4, 0x1694), Register('spr1a6', 4, 0x1698), Register('spr1a7', 4, 0x169c), Register('spr1a8', 4, 0x16a0), Register('spr1a9', 4, 0x16a4), Register('spr1aa', 4, 0x16a8), Register('spr1ab', 4, 0x16ac), Register('spr1ac', 4, 0x16b0), Register('spr1ad', 4, 0x16b4), Register('spr1ae', 4, 0x16b8), Register('spr1af', 4, 0x16bc), Register('spr1b0', 4, 0x16c0), Register('spr1b1', 4, 0x16c4), Register('spr1b2', 4, 0x16c8), Register('spr1b3', 4, 0x16cc), Register('spr1b4', 4, 0x16d0), Register('spr1b5', 4, 0x16d4), Register('spr1b6', 4, 0x16d8), Register('spr1b7', 4, 0x16dc), Register('spr1b8', 4, 0x16e0), Register('spr1b9', 4, 0x16e4), Register('spr1ba', 4, 0x16e8), Register('spr1bb', 4, 0x16ec), Register('spr1bc', 4, 0x16f0), Register('spr1bd', 4, 0x16f4), Register('spr1be', 4, 0x16f8), Register('spr1bf', 4, 0x16fc), Register('spr1c0', 4, 0x1700), Register('spr1c1', 4, 0x1704), Register('spr1c2', 4, 0x1708), Register('spr1c3', 4, 0x170c), Register('spr1c4', 4, 0x1710), Register('spr1c5', 4, 0x1714), Register('spr1c6', 4, 0x1718), Register('spr1c7', 4, 0x171c), Register('spr1c8', 4, 0x1720), Register('spr1c9', 4, 0x1724), Register('spr1ca', 4, 0x1728), Register('spr1cb', 4, 0x172c), Register('spr1cc', 4, 0x1730), Register('spr1cd', 4, 0x1734), Register('spr1ce', 4, 0x1738), Register('spr1cf', 4, 0x173c), Register('spr1d0', 4, 0x1740), Register('spr1d1', 4, 0x1744), Register('spr1d2', 4, 0x1748), Register('spr1d3', 4, 0x174c), Register('spr1d4', 4, 0x1750), Register('spr1d5', 4, 0x1754), Register('spr1d6', 4, 0x1758), Register('spr1d7', 4, 0x175c), Register('spr1d8', 4, 0x1760), Register('spr1d9', 4, 0x1764), Register('spr1da', 4, 0x1768), Register('spr1db', 4, 0x176c), Register('spr1dc', 4, 0x1770), Register('spr1dd', 4, 0x1774), Register('spr1de', 4, 0x1778), Register('spr1df', 4, 0x177c), Register('spr1e0', 4, 0x1780), Register('spr1e1', 4, 0x1784), Register('spr1e2', 4, 0x1788), Register('spr1e3', 4, 0x178c), Register('spr1e4', 4, 0x1790), Register('spr1e5', 4, 0x1794), Register('spr1e6', 4, 0x1798), Register('spr1e7', 4, 0x179c), Register('spr1e8', 4, 0x17a0), Register('spr1e9', 4, 0x17a4), Register('spr1ea', 4, 0x17a8), Register('spr1eb', 4, 0x17ac), Register('spr1ec', 4, 0x17b0), Register('spr1ed', 4, 0x17b4), Register('spr1ee', 4, 0x17b8), Register('spr1ef', 4, 0x17bc), Register('spr1f0', 4, 0x17c0), Register('spr1f1', 4, 0x17c4), Register('spr1f2', 4, 0x17c8), Register('spr1f3', 4, 0x17cc), Register('spr1f4', 4, 0x17d0), Register('spr1f5', 4, 0x17d4), Register('spr1f6', 4, 0x17d8), Register('spr1f7', 4, 0x17dc), Register('spr1f8', 4, 0x17e0), Register('spr1f9', 4, 0x17e4), Register('spr1fa', 4, 0x17e8), Register('spr1fb', 4, 0x17ec), Register('spr1fc', 4, 0x17f0), Register('spr1fd', 4, 0x17f4), Register('spr1fe', 4, 0x17f8), Register('spr1ff', 4, 0x17fc), Register('spr200', 4, 0x1800), Register('spr201', 4, 0x1804), Register('spr202', 4, 0x1808), Register('spr203', 4, 0x180c), Register('spr204', 4, 0x1810), Register('spr205', 4, 0x1814), Register('spr206', 4, 0x1818), Register('spr207', 4, 0x181c), Register('spr208', 4, 0x1820), Register('spr209', 4, 0x1824), Register('spr20a', 4, 0x1828), Register('spr20b', 4, 0x182c), Register('spr20c', 4, 0x1830), Register('spr20d', 4, 0x1834), Register('spr20e', 4, 0x1838), Register('spr20f', 4, 0x183c), Register('spr210', 4, 0x1840), Register('spr211', 4, 0x1844), Register('spr212', 4, 0x1848), Register('spr213', 4, 0x184c), Register('spr214', 4, 0x1850), Register('spr215', 4, 0x1854), Register('spr216', 4, 0x1858), Register('spr217', 4, 0x185c), Register('spr218', 4, 0x1860), Register('spr219', 4, 0x1864), Register('spr21a', 4, 0x1868), Register('spr21b', 4, 0x186c), Register('spr21c', 4, 0x1870), Register('spr21d', 4, 0x1874), Register('spr21e', 4, 0x1878), Register('spr21f', 4, 0x187c), Register('spr220', 4, 0x1880), Register('spr221', 4, 0x1884), Register('spr222', 4, 0x1888), Register('spr223', 4, 0x188c), Register('spr224', 4, 0x1890), Register('spr225', 4, 0x1894), Register('spr226', 4, 0x1898), Register('spr227', 4, 0x189c), Register('spr228', 4, 0x18a0), Register('spr229', 4, 0x18a4), Register('spr22a', 4, 0x18a8), Register('spr22b', 4, 0x18ac), Register('spr22c', 4, 0x18b0), Register('spr22d', 4, 0x18b4), Register('spr22e', 4, 0x18b8), Register('spr22f', 4, 0x18bc), Register('spr230', 4, 0x18c0), Register('spr231', 4, 0x18c4), Register('spr232', 4, 0x18c8), Register('spr233', 4, 0x18cc), Register('spr234', 4, 0x18d0), Register('spr235', 4, 0x18d4), Register('spr236', 4, 0x18d8), Register('spr237', 4, 0x18dc), Register('spr238', 4, 0x18e0), Register('spr239', 4, 0x18e4), Register('spr23a', 4, 0x18e8), Register('spr23b', 4, 0x18ec), Register('spr23c', 4, 0x18f0), Register('spr23d', 4, 0x18f4), Register('spr23e', 4, 0x18f8), Register('spr23f', 4, 0x18fc), Register('spr240', 4, 0x1900), Register('spr241', 4, 0x1904), Register('spr242', 4, 0x1908), Register('spr243', 4, 0x190c), Register('spr244', 4, 0x1910), Register('spr245', 4, 0x1914), Register('spr246', 4, 0x1918), Register('spr247', 4, 0x191c), Register('spr248', 4, 0x1920), Register('spr249', 4, 0x1924), Register('spr24a', 4, 0x1928), Register('spr24b', 4, 0x192c), Register('spr24c', 4, 0x1930), Register('spr24d', 4, 0x1934), Register('spr24e', 4, 0x1938), Register('spr24f', 4, 0x193c), Register('spr250', 4, 0x1940), Register('spr251', 4, 0x1944), Register('spr252', 4, 0x1948), Register('spr253', 4, 0x194c), Register('spr254', 4, 0x1950), Register('spr255', 4, 0x1954), Register('spr256', 4, 0x1958), Register('spr257', 4, 0x195c), Register('spr258', 4, 0x1960), Register('spr259', 4, 0x1964), Register('spr25a', 4, 0x1968), Register('spr25b', 4, 0x196c), Register('spr25c', 4, 0x1970), Register('spr25d', 4, 0x1974), Register('spr25e', 4, 0x1978), Register('spr25f', 4, 0x197c), Register('spr260', 4, 0x1980), Register('spr261', 4, 0x1984), Register('spr262', 4, 0x1988), Register('spr263', 4, 0x198c), Register('spr264', 4, 0x1990), Register('spr265', 4, 0x1994), Register('spr266', 4, 0x1998), Register('spr267', 4, 0x199c), Register('spr268', 4, 0x19a0), Register('spr269', 4, 0x19a4), Register('spr26a', 4, 0x19a8), Register('spr26b', 4, 0x19ac), Register('spr26c', 4, 0x19b0), Register('spr26d', 4, 0x19b4), Register('spr26e', 4, 0x19b8), Register('spr26f', 4, 0x19bc), Register('spr270', 4, 0x19c0), Register('spr271', 4, 0x19c4), Register('spr272', 4, 0x19c8), Register('spr273', 4, 0x19cc), Register('spr274', 4, 0x19d0), Register('spr275', 4, 0x19d4), Register('spr276', 4, 0x19d8), Register('spr277', 4, 0x19dc), Register('spr278', 4, 0x19e0), Register('spr279', 4, 0x19e4), Register('spr27a', 4, 0x19e8), Register('spr27b', 4, 0x19ec), Register('spr27c', 4, 0x19f0), Register('spr27d', 4, 0x19f4), Register('spr27e', 4, 0x19f8), Register('spr27f', 4, 0x19fc), Register('spr280', 4, 0x1a00), Register('spr281', 4, 0x1a04), Register('spr282', 4, 0x1a08), Register('spr283', 4, 0x1a0c), Register('spr284', 4, 0x1a10), Register('spr285', 4, 0x1a14), Register('spr286', 4, 0x1a18), Register('spr287', 4, 0x1a1c), Register('spr288', 4, 0x1a20), Register('spr289', 4, 0x1a24), Register('spr28a', 4, 0x1a28), Register('spr28b', 4, 0x1a2c), Register('spr28c', 4, 0x1a30), Register('spr28d', 4, 0x1a34), Register('spr28e', 4, 0x1a38), Register('spr28f', 4, 0x1a3c), Register('spr290', 4, 0x1a40), Register('spr291', 4, 0x1a44), Register('spr292', 4, 0x1a48), Register('spr293', 4, 0x1a4c), Register('spr294', 4, 0x1a50), Register('spr295', 4, 0x1a54), Register('spr296', 4, 0x1a58), Register('spr297', 4, 0x1a5c), Register('spr298', 4, 0x1a60), Register('spr299', 4, 0x1a64), Register('spr29a', 4, 0x1a68), Register('spr29b', 4, 0x1a6c), Register('spr29c', 4, 0x1a70), Register('spr29d', 4, 0x1a74), Register('spr29e', 4, 0x1a78), Register('spr29f', 4, 0x1a7c), Register('spr2a0', 4, 0x1a80), Register('spr2a1', 4, 0x1a84), Register('spr2a2', 4, 0x1a88), Register('spr2a3', 4, 0x1a8c), Register('spr2a4', 4, 0x1a90), Register('spr2a5', 4, 0x1a94), Register('spr2a6', 4, 0x1a98), Register('spr2a7', 4, 0x1a9c), Register('spr2a8', 4, 0x1aa0), Register('spr2a9', 4, 0x1aa4), Register('spr2aa', 4, 0x1aa8), Register('spr2ab', 4, 0x1aac), Register('spr2ac', 4, 0x1ab0), Register('spr2ad', 4, 0x1ab4), Register('spr2ae', 4, 0x1ab8), Register('spr2af', 4, 0x1abc), Register('spr2b0', 4, 0x1ac0), Register('spr2b1', 4, 0x1ac4), Register('spr2b2', 4, 0x1ac8), Register('spr2b3', 4, 0x1acc), Register('spr2b4', 4, 0x1ad0), Register('spr2b5', 4, 0x1ad4), Register('spr2b6', 4, 0x1ad8), Register('spr2b7', 4, 0x1adc), Register('spr2b8', 4, 0x1ae0), Register('spr2b9', 4, 0x1ae4), Register('spr2ba', 4, 0x1ae8), Register('spr2bb', 4, 0x1aec), Register('spr2bc', 4, 0x1af0), Register('spr2bd', 4, 0x1af4), Register('spr2be', 4, 0x1af8), Register('spr2bf', 4, 0x1afc), Register('spr2c0', 4, 0x1b00), Register('spr2c1', 4, 0x1b04), Register('spr2c2', 4, 0x1b08), Register('spr2c3', 4, 0x1b0c), Register('spr2c4', 4, 0x1b10), Register('spr2c5', 4, 0x1b14), Register('spr2c6', 4, 0x1b18), Register('spr2c7', 4, 0x1b1c), Register('spr2c8', 4, 0x1b20), Register('spr2c9', 4, 0x1b24), Register('spr2ca', 4, 0x1b28), Register('spr2cb', 4, 0x1b2c), Register('spr2cc', 4, 0x1b30), Register('spr2cd', 4, 0x1b34), Register('spr2ce', 4, 0x1b38), Register('spr2cf', 4, 0x1b3c), Register('spr2d0', 4, 0x1b40), Register('spr2d1', 4, 0x1b44), Register('spr2d2', 4, 0x1b48), Register('spr2d3', 4, 0x1b4c), Register('spr2d4', 4, 0x1b50), Register('spr2d5', 4, 0x1b54), Register('spr2d6', 4, 0x1b58), Register('spr2d7', 4, 0x1b5c), Register('spr2d8', 4, 0x1b60), Register('spr2d9', 4, 0x1b64), Register('spr2da', 4, 0x1b68), Register('spr2db', 4, 0x1b6c), Register('spr2dc', 4, 0x1b70), Register('spr2dd', 4, 0x1b74), Register('spr2de', 4, 0x1b78), Register('spr2df', 4, 0x1b7c), Register('spr2e0', 4, 0x1b80), Register('spr2e1', 4, 0x1b84), Register('spr2e2', 4, 0x1b88), Register('spr2e3', 4, 0x1b8c), Register('spr2e4', 4, 0x1b90), Register('spr2e5', 4, 0x1b94), Register('spr2e6', 4, 0x1b98), Register('spr2e7', 4, 0x1b9c), Register('spr2e8', 4, 0x1ba0), Register('spr2e9', 4, 0x1ba4), Register('spr2ea', 4, 0x1ba8), Register('spr2eb', 4, 0x1bac), Register('spr2ec', 4, 0x1bb0), Register('spr2ed', 4, 0x1bb4), Register('spr2ee', 4, 0x1bb8), Register('spr2ef', 4, 0x1bbc), Register('spr2f0', 4, 0x1bc0), Register('spr2f1', 4, 0x1bc4), Register('spr2f2', 4, 0x1bc8), Register('spr2f3', 4, 0x1bcc), Register('spr2f4', 4, 0x1bd0), Register('spr2f5', 4, 0x1bd4), Register('spr2f6', 4, 0x1bd8), Register('spr2f7', 4, 0x1bdc), Register('spr2f8', 4, 0x1be0), Register('spr2f9', 4, 0x1be4), Register('spr2fa', 4, 0x1be8), Register('spr2fb', 4, 0x1bec), Register('spr2fc', 4, 0x1bf0), Register('spr2fd', 4, 0x1bf4), Register('spr2fe', 4, 0x1bf8), Register('spr2ff', 4, 0x1bfc), Register('spr300', 4, 0x1c00), Register('spr301', 4, 0x1c04), Register('spr302', 4, 0x1c08), Register('spr303', 4, 0x1c0c), Register('spr304', 4, 0x1c10), Register('spr305', 4, 0x1c14), Register('spr306', 4, 0x1c18), Register('spr307', 4, 0x1c1c), Register('spr308', 4, 0x1c20), Register('spr309', 4, 0x1c24), Register('spr30a', 4, 0x1c28), Register('spr30b', 4, 0x1c2c), Register('spr30c', 4, 0x1c30), Register('spr30d', 4, 0x1c34), Register('spr30e', 4, 0x1c38), Register('spr30f', 4, 0x1c3c), Register('spr310', 4, 0x1c40), Register('spr311', 4, 0x1c44), Register('spr312', 4, 0x1c48), Register('spr313', 4, 0x1c4c), Register('spr314', 4, 0x1c50), Register('spr315', 4, 0x1c54), Register('spr316', 4, 0x1c58), Register('spr317', 4, 0x1c5c), Register('spr318', 4, 0x1c60), Register('spr319', 4, 0x1c64), Register('spr31a', 4, 0x1c68), Register('spr31b', 4, 0x1c6c), Register('spr31c', 4, 0x1c70), Register('spr31d', 4, 0x1c74), Register('spr31e', 4, 0x1c78), Register('spr31f', 4, 0x1c7c), Register('spr320', 4, 0x1c80), Register('spr321', 4, 0x1c84), Register('spr322', 4, 0x1c88), Register('spr323', 4, 0x1c8c), Register('spr324', 4, 0x1c90), Register('spr325', 4, 0x1c94), Register('spr326', 4, 0x1c98), Register('spr327', 4, 0x1c9c), Register('spr328', 4, 0x1ca0), Register('spr329', 4, 0x1ca4), Register('spr32a', 4, 0x1ca8), Register('spr32b', 4, 0x1cac), Register('spr32c', 4, 0x1cb0), Register('spr32d', 4, 0x1cb4), Register('spr32e', 4, 0x1cb8), Register('tar', 4, 0x1cbc), Register('spr330', 4, 0x1cc0), Register('spr331', 4, 0x1cc4), Register('spr332', 4, 0x1cc8), Register('spr333', 4, 0x1ccc), Register('spr334', 4, 0x1cd0), Register('spr335', 4, 0x1cd4), Register('spr336', 4, 0x1cd8), Register('spr337', 4, 0x1cdc), Register('spr338', 4, 0x1ce0), Register('spr339', 4, 0x1ce4), Register('spr33a', 4, 0x1ce8), Register('spr33b', 4, 0x1cec), Register('spr33c', 4, 0x1cf0), Register('spr33d', 4, 0x1cf4), Register('spr33e', 4, 0x1cf8), Register('spr33f', 4, 0x1cfc), Register('spr340', 4, 0x1d00), Register('spr341', 4, 0x1d04), Register('spr342', 4, 0x1d08), Register('spr343', 4, 0x1d0c), Register('spr344', 4, 0x1d10), Register('spr345', 4, 0x1d14), Register('spr346', 4, 0x1d18), Register('spr347', 4, 0x1d1c), Register('spr348', 4, 0x1d20), Register('spr349', 4, 0x1d24), Register('spr34a', 4, 0x1d28), Register('spr34b', 4, 0x1d2c), Register('spr34c', 4, 0x1d30), Register('spr34d', 4, 0x1d34), Register('spr34e', 4, 0x1d38), Register('spr34f', 4, 0x1d3c), Register('spr350', 4, 0x1d40), Register('spr351', 4, 0x1d44), Register('spr352', 4, 0x1d48), Register('spr353', 4, 0x1d4c), Register('spr354', 4, 0x1d50), Register('spr355', 4, 0x1d54), Register('spr356', 4, 0x1d58), Register('spr357', 4, 0x1d5c), Register('spr358', 4, 0x1d60), Register('spr359', 4, 0x1d64), Register('spr35a', 4, 0x1d68), Register('spr35b', 4, 0x1d6c), Register('spr35c', 4, 0x1d70), Register('spr35d', 4, 0x1d74), Register('spr35e', 4, 0x1d78), Register('spr35f', 4, 0x1d7c), Register('spr360', 4, 0x1d80), Register('spr361', 4, 0x1d84), Register('spr362', 4, 0x1d88), Register('spr363', 4, 0x1d8c), Register('spr364', 4, 0x1d90), Register('spr365', 4, 0x1d94), Register('spr366', 4, 0x1d98), Register('spr367', 4, 0x1d9c), Register('spr368', 4, 0x1da0), Register('spr369', 4, 0x1da4), Register('spr36a', 4, 0x1da8), Register('spr36b', 4, 0x1dac), Register('spr36c', 4, 0x1db0), Register('spr36d', 4, 0x1db4), Register('spr36e', 4, 0x1db8), Register('spr36f', 4, 0x1dbc), Register('spr370', 4, 0x1dc0), Register('spr371', 4, 0x1dc4), Register('spr372', 4, 0x1dc8), Register('spr373', 4, 0x1dcc), Register('spr374', 4, 0x1dd0), Register('spr375', 4, 0x1dd4), Register('spr376', 4, 0x1dd8), Register('spr377', 4, 0x1ddc), Register('spr378', 4, 0x1de0), Register('spr379', 4, 0x1de4), Register('spr37a', 4, 0x1de8), Register('spr37b', 4, 0x1dec), Register('spr37c', 4, 0x1df0), Register('spr37d', 4, 0x1df4), Register('spr37e', 4, 0x1df8), Register('spr37f', 4, 0x1dfc), Register('spr380', 4, 0x1e00), Register('spr381', 4, 0x1e04), Register('spr382', 4, 0x1e08), Register('spr383', 4, 0x1e0c), Register('spr384', 4, 0x1e10), Register('spr385', 4, 0x1e14), Register('spr386', 4, 0x1e18), Register('spr387', 4, 0x1e1c), Register('spr388', 4, 0x1e20), Register('spr389', 4, 0x1e24), Register('spr38a', 4, 0x1e28), Register('spr38b', 4, 0x1e2c), Register('spr38c', 4, 0x1e30), Register('spr38d', 4, 0x1e34), Register('spr38e', 4, 0x1e38), Register('spr38f', 4, 0x1e3c), Register('spr390', 4, 0x1e40), Register('spr391', 4, 0x1e44), Register('spr392', 4, 0x1e48), Register('spr393', 4, 0x1e4c), Register('spr394', 4, 0x1e50), Register('spr395', 4, 0x1e54), Register('spr396', 4, 0x1e58), Register('spr397', 4, 0x1e5c), Register('spr398', 4, 0x1e60), Register('spr399', 4, 0x1e64), Register('spr39a', 4, 0x1e68), Register('spr39b', 4, 0x1e6c), Register('spr39c', 4, 0x1e70), Register('spr39d', 4, 0x1e74), Register('spr39e', 4, 0x1e78), Register('spr39f', 4, 0x1e7c), Register('spr3a0', 4, 0x1e80), Register('spr3a1', 4, 0x1e84), Register('spr3a2', 4, 0x1e88), Register('spr3a3', 4, 0x1e8c), Register('spr3a4', 4, 0x1e90), Register('spr3a5', 4, 0x1e94), Register('spr3a6', 4, 0x1e98), Register('spr3a7', 4, 0x1e9c), Register('spr3a8', 4, 0x1ea0), Register('spr3a9', 4, 0x1ea4), Register('spr3aa', 4, 0x1ea8), Register('spr3ab', 4, 0x1eac), Register('spr3ac', 4, 0x1eb0), Register('spr3ad', 4, 0x1eb4), Register('spr3ae', 4, 0x1eb8), Register('spr3af', 4, 0x1ebc), Register('spr3b0', 4, 0x1ec0), Register('spr3b1', 4, 0x1ec4), Register('spr3b2', 4, 0x1ec8), Register('spr3b3', 4, 0x1ecc), Register('spr3b4', 4, 0x1ed0), Register('spr3b5', 4, 0x1ed4), Register('spr3b6', 4, 0x1ed8), Register('spr3b7', 4, 0x1edc), Register('spr3b8', 4, 0x1ee0), Register('spr3b9', 4, 0x1ee4), Register('spr3ba', 4, 0x1ee8), Register('spr3bb', 4, 0x1eec), Register('spr3bc', 4, 0x1ef0), Register('spr3bd', 4, 0x1ef4), Register('spr3be', 4, 0x1ef8), Register('spr3bf', 4, 0x1efc), Register('spr3c0', 4, 0x1f00), Register('spr3c1', 4, 0x1f04), Register('spr3c2', 4, 0x1f08), Register('spr3c3', 4, 0x1f0c), Register('spr3c4', 4, 0x1f10), Register('spr3c5', 4, 0x1f14), Register('spr3c6', 4, 0x1f18), Register('spr3c7', 4, 0x1f1c), Register('spr3c8', 4, 0x1f20), Register('spr3c9', 4, 0x1f24), Register('spr3ca', 4, 0x1f28), Register('spr3cb', 4, 0x1f2c), Register('spr3cc', 4, 0x1f30), Register('spr3cd', 4, 0x1f34), Register('spr3ce', 4, 0x1f38), Register('spr3cf', 4, 0x1f3c), Register('spr3d0', 4, 0x1f40), Register('spr3d1', 4, 0x1f44), Register('spr3d2', 4, 0x1f48), Register('spr3d3', 4, 0x1f4c), Register('spr3d4', 4, 0x1f50), Register('spr3d5', 4, 0x1f54), Register('spr3d6', 4, 0x1f58), Register('spr3d7', 4, 0x1f5c), Register('spr3d8', 4, 0x1f60), Register('spr3d9', 4, 0x1f64), Register('spr3da', 4, 0x1f68), Register('spr3db', 4, 0x1f6c), Register('spr3dc', 4, 0x1f70), Register('spr3dd', 4, 0x1f74), Register('spr3de', 4, 0x1f78), Register('spr3df', 4, 0x1f7c), Register('spr3e0', 4, 0x1f80), Register('spr3e1', 4, 0x1f84), Register('spr3e2', 4, 0x1f88), Register('spr3e3', 4, 0x1f8c), Register('spr3e4', 4, 0x1f90), Register('spr3e5', 4, 0x1f94), Register('spr3e6', 4, 0x1f98), Register('spr3e7', 4, 0x1f9c), Register('spr3e8', 4, 0x1fa0), Register('spr3e9', 4, 0x1fa4), Register('spr3ea', 4, 0x1fa8), Register('spr3eb', 4, 0x1fac), Register('spr3ec', 4, 0x1fb0), Register('spr3ed', 4, 0x1fb4), Register('spr3ee', 4, 0x1fb8), Register('spr3ef', 4, 0x1fbc), Register('spr3f0', 4, 0x1fc0), Register('spr3f1', 4, 0x1fc4), Register('spr3f2', 4, 0x1fc8), Register('spr3f3', 4, 0x1fcc), Register('spr3f4', 4, 0x1fd0), Register('spr3f5', 4, 0x1fd4), Register('spr3f6', 4, 0x1fd8), Register('spr3f7', 4, 0x1fdc), Register('spr3f8', 4, 0x1fe0), Register('spr3f9', 4, 0x1fe4), Register('spr3fa', 4, 0x1fe8), Register('spr3fb', 4, 0x1fec), Register('spr3fc', 4, 0x1ff0), Register('spr3fd', 4, 0x1ff4), Register('spr3fe', 4, 0x1ff8), Register('spr3ff', 4, 0x1ffc), Register('vs0', 16, 0x4000), Register('f0', 8, 0x4008), Register('vs1', 16, 0x4010), Register('f1', 8, 0x4018), Register('vs2', 16, 0x4020), Register('f2', 8, 0x4028), Register('vs3', 16, 0x4030), Register('f3', 8, 0x4038), Register('vs4', 16, 0x4040), Register('f4', 8, 0x4048), Register('vs5', 16, 0x4050), Register('f5', 8, 0x4058), Register('vs6', 16, 0x4060), Register('f6', 8, 0x4068), Register('vs7', 16, 0x4070), Register('f7', 8, 0x4078), Register('vs8', 16, 0x4080), Register('f8', 8, 0x4088), Register('vs9', 16, 0x4090), Register('f9', 8, 0x4098), Register('vs10', 16, 0x40a0), Register('f10', 8, 0x40a8), Register('vs11', 16, 0x40b0), Register('f11', 8, 0x40b8), Register('vs12', 16, 0x40c0), Register('f12', 8, 0x40c8), Register('vs13', 16, 0x40d0), Register('f13', 8, 0x40d8), Register('vs14', 16, 0x40e0), Register('f14', 8, 0x40e8), Register('vs15', 16, 0x40f0), Register('f15', 8, 0x40f8), Register('vs16', 16, 0x4100), Register('f16', 8, 0x4108), Register('vs17', 16, 0x4110), Register('f17', 8, 0x4118), Register('vs18', 16, 0x4120), Register('f18', 8, 0x4128), Register('vs19', 16, 0x4130), Register('f19', 8, 0x4138), Register('vs20', 16, 0x4140), Register('f20', 8, 0x4148), Register('vs21', 16, 0x4150), Register('f21', 8, 0x4158), Register('vs22', 16, 0x4160), Register('f22', 8, 0x4168), Register('vs23', 16, 0x4170), Register('f23', 8, 0x4178), Register('vs24', 16, 0x4180), Register('f24', 8, 0x4188), Register('vs25', 16, 0x4190), Register('f25', 8, 0x4198), Register('vs26', 16, 0x41a0), Register('f26', 8, 0x41a8), Register('vs27', 16, 0x41b0), Register('f27', 8, 0x41b8), Register('vs28', 16, 0x41c0), Register('f28', 8, 0x41c8), Register('vs29', 16, 0x41d0), Register('f29', 8, 0x41d8), Register('vs30', 16, 0x41e0), Register('f30', 8, 0x41e8), Register('vs31', 16, 0x41f0), Register('f31', 8, 0x41f8), Register('vs32', 16, 0x4200), Register('vr0_64_1', 8, 0x4200), Register('vr0_32_3', 4, 0x4200), Register('vr0_16_7', 2, 0x4200), Register('vr0_8_15', 1, 0x4200), Register('vr0_8_14', 1, 0x4201), Register('vr0_16_6', 2, 0x4202), Register('vr0_8_13', 1, 0x4202), Register('vr0_8_12', 1, 0x4203), Register('vr0_32_2', 4, 0x4204), Register('vr0_16_5', 2, 0x4204), Register('vr0_8_11', 1, 0x4204), Register('vr0_8_10', 1, 0x4205), Register('vr0_16_4', 2, 0x4206), Register('vr0_8_9', 1, 0x4206), Register('vr0_8_8', 1, 0x4207), Register('vr0_64_0', 8, 0x4208), Register('vr0_32_1', 4, 0x4208), Register('vr0_16_3', 2, 0x4208), Register('vr0_8_7', 1, 0x4208), Register('vr0_8_6', 1, 0x4209), Register('vr0_16_2', 2, 0x420a), Register('vr0_8_5', 1, 0x420a), Register('vr0_8_4', 1, 0x420b), Register('vr0_32_0', 4, 0x420c), Register('vr0_16_1', 2, 0x420c), Register('vr0_8_3', 1, 0x420c), Register('vr0_8_2', 1, 0x420d), Register('vr0_16_0', 2, 0x420e), Register('vr0_8_1', 1, 0x420e), Register('vr0_8_0', 1, 0x420f), Register('vs33', 16, 0x4210), Register('vr1_64_1', 8, 0x4210), Register('vr1_32_3', 4, 0x4210), Register('vr1_16_7', 2, 0x4210), Register('vr1_8_15', 1, 0x4210), Register('vr1_8_14', 1, 0x4211), Register('vr1_16_6', 2, 0x4212), Register('vr1_8_13', 1, 0x4212), Register('vr1_8_12', 1, 0x4213), Register('vr1_32_2', 4, 0x4214), Register('vr1_16_5', 2, 0x4214), Register('vr1_8_11', 1, 0x4214), Register('vr1_8_10', 1, 0x4215), Register('vr1_16_4', 2, 0x4216), Register('vr1_8_9', 1, 0x4216), Register('vr1_8_8', 1, 0x4217), Register('vr1_64_0', 8, 0x4218), Register('vr1_32_1', 4, 0x4218), Register('vr1_16_3', 2, 0x4218), Register('vr1_8_7', 1, 0x4218), Register('vr1_8_6', 1, 0x4219), Register('vr1_16_2', 2, 0x421a), Register('vr1_8_5', 1, 0x421a), Register('vr1_8_4', 1, 0x421b), Register('vr1_32_0', 4, 0x421c), Register('vr1_16_1', 2, 0x421c), Register('vr1_8_3', 1, 0x421c), Register('vr1_8_2', 1, 0x421d), Register('vr1_16_0', 2, 0x421e), Register('vr1_8_1', 1, 0x421e), Register('vr1_8_0', 1, 0x421f), Register('vs34', 16, 0x4220), Register('vr2_64_1', 8, 0x4220), Register('vr2_32_3', 4, 0x4220), Register('vr2_16_7', 2, 0x4220), Register('vr2_8_15', 1, 0x4220), Register('vr2_8_14', 1, 0x4221), Register('vr2_16_6', 2, 0x4222), Register('vr2_8_13', 1, 0x4222), Register('vr2_8_12', 1, 0x4223), Register('vr2_32_2', 4, 0x4224), Register('vr2_16_5', 2, 0x4224), Register('vr2_8_11', 1, 0x4224), Register('vr2_8_10', 1, 0x4225), Register('vr2_16_4', 2, 0x4226), Register('vr2_8_9', 1, 0x4226), Register('vr2_8_8', 1, 0x4227), Register('vr2_64_0', 8, 0x4228), Register('vr2_32_1', 4, 0x4228), Register('vr2_16_3', 2, 0x4228), Register('vr2_8_7', 1, 0x4228), Register('vr2_8_6', 1, 0x4229), Register('vr2_16_2', 2, 0x422a), Register('vr2_8_5', 1, 0x422a), Register('vr2_8_4', 1, 0x422b), Register('vr2_32_0', 4, 0x422c), Register('vr2_16_1', 2, 0x422c), Register('vr2_8_3', 1, 0x422c), Register('vr2_8_2', 1, 0x422d), Register('vr2_16_0', 2, 0x422e), Register('vr2_8_1', 1, 0x422e), Register('vr2_8_0', 1, 0x422f), Register('vs35', 16, 0x4230), Register('vr3_64_1', 8, 0x4230), Register('vr3_32_3', 4, 0x4230), Register('vr3_16_7', 2, 0x4230), Register('vr3_8_15', 1, 0x4230), Register('vr3_8_14', 1, 0x4231), Register('vr3_16_6', 2, 0x4232), Register('vr3_8_13', 1, 0x4232), Register('vr3_8_12', 1, 0x4233), Register('vr3_32_2', 4, 0x4234), Register('vr3_16_5', 2, 0x4234), Register('vr3_8_11', 1, 0x4234), Register('vr3_8_10', 1, 0x4235), Register('vr3_16_4', 2, 0x4236), Register('vr3_8_9', 1, 0x4236), Register('vr3_8_8', 1, 0x4237), Register('vr3_64_0', 8, 0x4238), Register('vr3_32_1', 4, 0x4238), Register('vr3_16_3', 2, 0x4238), Register('vr3_8_7', 1, 0x4238), Register('vr3_8_6', 1, 0x4239), Register('vr3_16_2', 2, 0x423a), Register('vr3_8_5', 1, 0x423a), Register('vr3_8_4', 1, 0x423b), Register('vr3_32_0', 4, 0x423c), Register('vr3_16_1', 2, 0x423c), Register('vr3_8_3', 1, 0x423c), Register('vr3_8_2', 1, 0x423d), Register('vr3_16_0', 2, 0x423e), Register('vr3_8_1', 1, 0x423e), Register('vr3_8_0', 1, 0x423f), Register('vs36', 16, 0x4240), Register('vr4_64_1', 8, 0x4240), Register('vr4_32_3', 4, 0x4240), Register('vr4_16_7', 2, 0x4240), Register('vr4_8_15', 1, 0x4240), Register('vr4_8_14', 1, 0x4241), Register('vr4_16_6', 2, 0x4242), Register('vr4_8_13', 1, 0x4242), Register('vr4_8_12', 1, 0x4243), Register('vr4_32_2', 4, 0x4244), Register('vr4_16_5', 2, 0x4244), Register('vr4_8_11', 1, 0x4244), Register('vr4_8_10', 1, 0x4245), Register('vr4_16_4', 2, 0x4246), Register('vr4_8_9', 1, 0x4246), Register('vr4_8_8', 1, 0x4247), Register('vr4_64_0', 8, 0x4248), Register('vr4_32_1', 4, 0x4248), Register('vr4_16_3', 2, 0x4248), Register('vr4_8_7', 1, 0x4248), Register('vr4_8_6', 1, 0x4249), Register('vr4_16_2', 2, 0x424a), Register('vr4_8_5', 1, 0x424a), Register('vr4_8_4', 1, 0x424b), Register('vr4_32_0', 4, 0x424c), Register('vr4_16_1', 2, 0x424c), Register('vr4_8_3', 1, 0x424c), Register('vr4_8_2', 1, 0x424d), Register('vr4_16_0', 2, 0x424e), Register('vr4_8_1', 1, 0x424e), Register('vr4_8_0', 1, 0x424f), Register('vs37', 16, 0x4250), Register('vr5_64_1', 8, 0x4250), Register('vr5_32_3', 4, 0x4250), Register('vr5_16_7', 2, 0x4250), Register('vr5_8_15', 1, 0x4250), Register('vr5_8_14', 1, 0x4251), Register('vr5_16_6', 2, 0x4252), Register('vr5_8_13', 1, 0x4252), Register('vr5_8_12', 1, 0x4253), Register('vr5_32_2', 4, 0x4254), Register('vr5_16_5', 2, 0x4254), Register('vr5_8_11', 1, 0x4254), Register('vr5_8_10', 1, 0x4255), Register('vr5_16_4', 2, 0x4256), Register('vr5_8_9', 1, 0x4256), Register('vr5_8_8', 1, 0x4257), Register('vr5_64_0', 8, 0x4258), Register('vr5_32_1', 4, 0x4258), Register('vr5_16_3', 2, 0x4258), Register('vr5_8_7', 1, 0x4258), Register('vr5_8_6', 1, 0x4259), Register('vr5_16_2', 2, 0x425a), Register('vr5_8_5', 1, 0x425a), Register('vr5_8_4', 1, 0x425b), Register('vr5_32_0', 4, 0x425c), Register('vr5_16_1', 2, 0x425c), Register('vr5_8_3', 1, 0x425c), Register('vr5_8_2', 1, 0x425d), Register('vr5_16_0', 2, 0x425e), Register('vr5_8_1', 1, 0x425e), Register('vr5_8_0', 1, 0x425f), Register('vs38', 16, 0x4260), Register('vr6_64_1', 8, 0x4260), Register('vr6_32_3', 4, 0x4260), Register('vr6_16_7', 2, 0x4260), Register('vr6_8_15', 1, 0x4260), Register('vr6_8_14', 1, 0x4261), Register('vr6_16_6', 2, 0x4262), Register('vr6_8_13', 1, 0x4262), Register('vr6_8_12', 1, 0x4263), Register('vr6_32_2', 4, 0x4264), Register('vr6_16_5', 2, 0x4264), Register('vr6_8_11', 1, 0x4264), Register('vr6_8_10', 1, 0x4265), Register('vr6_16_4', 2, 0x4266), Register('vr6_8_9', 1, 0x4266), Register('vr6_8_8', 1, 0x4267), Register('vr6_64_0', 8, 0x4268), Register('vr6_32_1', 4, 0x4268), Register('vr6_16_3', 2, 0x4268), Register('vr6_8_7', 1, 0x4268), Register('vr6_8_6', 1, 0x4269), Register('vr6_16_2', 2, 0x426a), Register('vr6_8_5', 1, 0x426a), Register('vr6_8_4', 1, 0x426b), Register('vr6_32_0', 4, 0x426c), Register('vr6_16_1', 2, 0x426c), Register('vr6_8_3', 1, 0x426c), Register('vr6_8_2', 1, 0x426d), Register('vr6_16_0', 2, 0x426e), Register('vr6_8_1', 1, 0x426e), Register('vr6_8_0', 1, 0x426f), Register('vs39', 16, 0x4270), Register('vr7_64_1', 8, 0x4270), Register('vr7_32_3', 4, 0x4270), Register('vr7_16_7', 2, 0x4270), Register('vr7_8_15', 1, 0x4270), Register('vr7_8_14', 1, 0x4271), Register('vr7_16_6', 2, 0x4272), Register('vr7_8_13', 1, 0x4272), Register('vr7_8_12', 1, 0x4273), Register('vr7_32_2', 4, 0x4274), Register('vr7_16_5', 2, 0x4274), Register('vr7_8_11', 1, 0x4274), Register('vr7_8_10', 1, 0x4275), Register('vr7_16_4', 2, 0x4276), Register('vr7_8_9', 1, 0x4276), Register('vr7_8_8', 1, 0x4277), Register('vr7_64_0', 8, 0x4278), Register('vr7_32_1', 4, 0x4278), Register('vr7_16_3', 2, 0x4278), Register('vr7_8_7', 1, 0x4278), Register('vr7_8_6', 1, 0x4279), Register('vr7_16_2', 2, 0x427a), Register('vr7_8_5', 1, 0x427a), Register('vr7_8_4', 1, 0x427b), Register('vr7_32_0', 4, 0x427c), Register('vr7_16_1', 2, 0x427c), Register('vr7_8_3', 1, 0x427c), Register('vr7_8_2', 1, 0x427d), Register('vr7_16_0', 2, 0x427e), Register('vr7_8_1', 1, 0x427e), Register('vr7_8_0', 1, 0x427f), Register('vs40', 16, 0x4280), Register('vr8_64_1', 8, 0x4280), Register('vr8_32_3', 4, 0x4280), Register('vr8_16_7', 2, 0x4280), Register('vr8_8_15', 1, 0x4280), Register('vr8_8_14', 1, 0x4281), Register('vr8_16_6', 2, 0x4282), Register('vr8_8_13', 1, 0x4282), Register('vr8_8_12', 1, 0x4283), Register('vr8_32_2', 4, 0x4284), Register('vr8_16_5', 2, 0x4284), Register('vr8_8_11', 1, 0x4284), Register('vr8_8_10', 1, 0x4285), Register('vr8_16_4', 2, 0x4286), Register('vr8_8_9', 1, 0x4286), Register('vr8_8_8', 1, 0x4287), Register('vr8_64_0', 8, 0x4288), Register('vr8_32_1', 4, 0x4288), Register('vr8_16_3', 2, 0x4288), Register('vr8_8_7', 1, 0x4288), Register('vr8_8_6', 1, 0x4289), Register('vr8_16_2', 2, 0x428a), Register('vr8_8_5', 1, 0x428a), Register('vr8_8_4', 1, 0x428b), Register('vr8_32_0', 4, 0x428c), Register('vr8_16_1', 2, 0x428c), Register('vr8_8_3', 1, 0x428c), Register('vr8_8_2', 1, 0x428d), Register('vr8_16_0', 2, 0x428e), Register('vr8_8_1', 1, 0x428e), Register('vr8_8_0', 1, 0x428f), Register('vs41', 16, 0x4290), Register('vr9_64_1', 8, 0x4290), Register('vr9_32_3', 4, 0x4290), Register('vr9_16_7', 2, 0x4290), Register('vr9_8_15', 1, 0x4290), Register('vr9_8_14', 1, 0x4291), Register('vr9_16_6', 2, 0x4292), Register('vr9_8_13', 1, 0x4292), Register('vr9_8_12', 1, 0x4293), Register('vr9_32_2', 4, 0x4294), Register('vr9_16_5', 2, 0x4294), Register('vr9_8_11', 1, 0x4294), Register('vr9_8_10', 1, 0x4295), Register('vr9_16_4', 2, 0x4296), Register('vr9_8_9', 1, 0x4296), Register('vr9_8_8', 1, 0x4297), Register('vr9_64_0', 8, 0x4298), Register('vr9_32_1', 4, 0x4298), Register('vr9_16_3', 2, 0x4298), Register('vr9_8_7', 1, 0x4298), Register('vr9_8_6', 1, 0x4299), Register('vr9_16_2', 2, 0x429a), Register('vr9_8_5', 1, 0x429a), Register('vr9_8_4', 1, 0x429b), Register('vr9_32_0', 4, 0x429c), Register('vr9_16_1', 2, 0x429c), Register('vr9_8_3', 1, 0x429c), Register('vr9_8_2', 1, 0x429d), Register('vr9_16_0', 2, 0x429e), Register('vr9_8_1', 1, 0x429e), Register('vr9_8_0', 1, 0x429f), Register('vs42', 16, 0x42a0), Register('vr10_64_1', 8, 0x42a0), Register('vr10_32_3', 4, 0x42a0), Register('vr10_16_7', 2, 0x42a0), Register('vr10_8_15', 1, 0x42a0), Register('vr10_8_14', 1, 0x42a1), Register('vr10_16_6', 2, 0x42a2), Register('vr10_8_13', 1, 0x42a2), Register('vr10_8_12', 1, 0x42a3), Register('vr10_32_2', 4, 0x42a4), Register('vr10_16_5', 2, 0x42a4), Register('vr10_8_11', 1, 0x42a4), Register('vr10_8_10', 1, 0x42a5), Register('vr10_16_4', 2, 0x42a6), Register('vr10_8_9', 1, 0x42a6), Register('vr10_8_8', 1, 0x42a7), Register('vr10_64_0', 8, 0x42a8), Register('vr10_32_1', 4, 0x42a8), Register('vr10_16_3', 2, 0x42a8), Register('vr10_8_7', 1, 0x42a8), Register('vr10_8_6', 1, 0x42a9), Register('vr10_16_2', 2, 0x42aa), Register('vr10_8_5', 1, 0x42aa), Register('vr10_8_4', 1, 0x42ab), Register('vr10_32_0', 4, 0x42ac), Register('vr10_16_1', 2, 0x42ac), Register('vr10_8_3', 1, 0x42ac), Register('vr10_8_2', 1, 0x42ad), Register('vr10_16_0', 2, 0x42ae), Register('vr10_8_1', 1, 0x42ae), Register('vr10_8_0', 1, 0x42af), Register('vs43', 16, 0x42b0), Register('vr11_64_1', 8, 0x42b0), Register('vr11_32_3', 4, 0x42b0), Register('vr11_16_7', 2, 0x42b0), Register('vr11_8_15', 1, 0x42b0), Register('vr11_8_14', 1, 0x42b1), Register('vr11_16_6', 2, 0x42b2), Register('vr11_8_13', 1, 0x42b2), Register('vr11_8_12', 1, 0x42b3), Register('vr11_32_2', 4, 0x42b4), Register('vr11_16_5', 2, 0x42b4), Register('vr11_8_11', 1, 0x42b4), Register('vr11_8_10', 1, 0x42b5), Register('vr11_16_4', 2, 0x42b6), Register('vr11_8_9', 1, 0x42b6), Register('vr11_8_8', 1, 0x42b7), Register('vr11_64_0', 8, 0x42b8), Register('vr11_32_1', 4, 0x42b8), Register('vr11_16_3', 2, 0x42b8), Register('vr11_8_7', 1, 0x42b8), Register('vr11_8_6', 1, 0x42b9), Register('vr11_16_2', 2, 0x42ba), Register('vr11_8_5', 1, 0x42ba), Register('vr11_8_4', 1, 0x42bb), Register('vr11_32_0', 4, 0x42bc), Register('vr11_16_1', 2, 0x42bc), Register('vr11_8_3', 1, 0x42bc), Register('vr11_8_2', 1, 0x42bd), Register('vr11_16_0', 2, 0x42be), Register('vr11_8_1', 1, 0x42be), Register('vr11_8_0', 1, 0x42bf), Register('vs44', 16, 0x42c0), Register('vr12_64_1', 8, 0x42c0), Register('vr12_32_3', 4, 0x42c0), Register('vr12_16_7', 2, 0x42c0), Register('vr12_8_15', 1, 0x42c0), Register('vr12_8_14', 1, 0x42c1), Register('vr12_16_6', 2, 0x42c2), Register('vr12_8_13', 1, 0x42c2), Register('vr12_8_12', 1, 0x42c3), Register('vr12_32_2', 4, 0x42c4), Register('vr12_16_5', 2, 0x42c4), Register('vr12_8_11', 1, 0x42c4), Register('vr12_8_10', 1, 0x42c5), Register('vr12_16_4', 2, 0x42c6), Register('vr12_8_9', 1, 0x42c6), Register('vr12_8_8', 1, 0x42c7), Register('vr12_64_0', 8, 0x42c8), Register('vr12_32_1', 4, 0x42c8), Register('vr12_16_3', 2, 0x42c8), Register('vr12_8_7', 1, 0x42c8), Register('vr12_8_6', 1, 0x42c9), Register('vr12_16_2', 2, 0x42ca), Register('vr12_8_5', 1, 0x42ca), Register('vr12_8_4', 1, 0x42cb), Register('vr12_32_0', 4, 0x42cc), Register('vr12_16_1', 2, 0x42cc), Register('vr12_8_3', 1, 0x42cc), Register('vr12_8_2', 1, 0x42cd), Register('vr12_16_0', 2, 0x42ce), Register('vr12_8_1', 1, 0x42ce), Register('vr12_8_0', 1, 0x42cf), Register('vs45', 16, 0x42d0), Register('vr13_64_1', 8, 0x42d0), Register('vr13_32_3', 4, 0x42d0), Register('vr13_16_7', 2, 0x42d0), Register('vr13_8_15', 1, 0x42d0), Register('vr13_8_14', 1, 0x42d1), Register('vr13_16_6', 2, 0x42d2), Register('vr13_8_13', 1, 0x42d2), Register('vr13_8_12', 1, 0x42d3), Register('vr13_32_2', 4, 0x42d4), Register('vr13_16_5', 2, 0x42d4), Register('vr13_8_11', 1, 0x42d4), Register('vr13_8_10', 1, 0x42d5), Register('vr13_16_4', 2, 0x42d6), Register('vr13_8_9', 1, 0x42d6), Register('vr13_8_8', 1, 0x42d7), Register('vr13_64_0', 8, 0x42d8), Register('vr13_32_1', 4, 0x42d8), Register('vr13_16_3', 2, 0x42d8), Register('vr13_8_7', 1, 0x42d8), Register('vr13_8_6', 1, 0x42d9), Register('vr13_16_2', 2, 0x42da), Register('vr13_8_5', 1, 0x42da), Register('vr13_8_4', 1, 0x42db), Register('vr13_32_0', 4, 0x42dc), Register('vr13_16_1', 2, 0x42dc), Register('vr13_8_3', 1, 0x42dc), Register('vr13_8_2', 1, 0x42dd), Register('vr13_16_0', 2, 0x42de), Register('vr13_8_1', 1, 0x42de), Register('vr13_8_0', 1, 0x42df), Register('vs46', 16, 0x42e0), Register('vr14_64_1', 8, 0x42e0), Register('vr14_32_3', 4, 0x42e0), Register('vr14_16_7', 2, 0x42e0), Register('vr14_8_15', 1, 0x42e0), Register('vr14_8_14', 1, 0x42e1), Register('vr14_16_6', 2, 0x42e2), Register('vr14_8_13', 1, 0x42e2), Register('vr14_8_12', 1, 0x42e3), Register('vr14_32_2', 4, 0x42e4), Register('vr14_16_5', 2, 0x42e4), Register('vr14_8_11', 1, 0x42e4), Register('vr14_8_10', 1, 0x42e5), Register('vr14_16_4', 2, 0x42e6), Register('vr14_8_9', 1, 0x42e6), Register('vr14_8_8', 1, 0x42e7), Register('vr14_64_0', 8, 0x42e8), Register('vr14_32_1', 4, 0x42e8), Register('vr14_16_3', 2, 0x42e8), Register('vr14_8_7', 1, 0x42e8), Register('vr14_8_6', 1, 0x42e9), Register('vr14_16_2', 2, 0x42ea), Register('vr14_8_5', 1, 0x42ea), Register('vr14_8_4', 1, 0x42eb), Register('vr14_32_0', 4, 0x42ec), Register('vr14_16_1', 2, 0x42ec), Register('vr14_8_3', 1, 0x42ec), Register('vr14_8_2', 1, 0x42ed), Register('vr14_16_0', 2, 0x42ee), Register('vr14_8_1', 1, 0x42ee), Register('vr14_8_0', 1, 0x42ef), Register('vs47', 16, 0x42f0), Register('vr15_64_1', 8, 0x42f0), Register('vr15_32_3', 4, 0x42f0), Register('vr15_16_7', 2, 0x42f0), Register('vr15_8_15', 1, 0x42f0), Register('vr15_8_14', 1, 0x42f1), Register('vr15_16_6', 2, 0x42f2), Register('vr15_8_13', 1, 0x42f2), Register('vr15_8_12', 1, 0x42f3), Register('vr15_32_2', 4, 0x42f4), Register('vr15_16_5', 2, 0x42f4), Register('vr15_8_11', 1, 0x42f4), Register('vr15_8_10', 1, 0x42f5), Register('vr15_16_4', 2, 0x42f6), Register('vr15_8_9', 1, 0x42f6), Register('vr15_8_8', 1, 0x42f7), Register('vr15_64_0', 8, 0x42f8), Register('vr15_32_1', 4, 0x42f8), Register('vr15_16_3', 2, 0x42f8), Register('vr15_8_7', 1, 0x42f8), Register('vr15_8_6', 1, 0x42f9), Register('vr15_16_2', 2, 0x42fa), Register('vr15_8_5', 1, 0x42fa), Register('vr15_8_4', 1, 0x42fb), Register('vr15_32_0', 4, 0x42fc), Register('vr15_16_1', 2, 0x42fc), Register('vr15_8_3', 1, 0x42fc), Register('vr15_8_2', 1, 0x42fd), Register('vr15_16_0', 2, 0x42fe), Register('vr15_8_1', 1, 0x42fe), Register('vr15_8_0', 1, 0x42ff), Register('vs48', 16, 0x4300), Register('vr16_64_1', 8, 0x4300), Register('vr16_32_3', 4, 0x4300), Register('vr16_16_7', 2, 0x4300), Register('vr16_8_15', 1, 0x4300), Register('vr16_8_14', 1, 0x4301), Register('vr16_16_6', 2, 0x4302), Register('vr16_8_13', 1, 0x4302), Register('vr16_8_12', 1, 0x4303), Register('vr16_32_2', 4, 0x4304), Register('vr16_16_5', 2, 0x4304), Register('vr16_8_11', 1, 0x4304), Register('vr16_8_10', 1, 0x4305), Register('vr16_16_4', 2, 0x4306), Register('vr16_8_9', 1, 0x4306), Register('vr16_8_8', 1, 0x4307), Register('vr16_64_0', 8, 0x4308), Register('vr16_32_1', 4, 0x4308), Register('vr16_16_3', 2, 0x4308), Register('vr16_8_7', 1, 0x4308), Register('vr16_8_6', 1, 0x4309), Register('vr16_16_2', 2, 0x430a), Register('vr16_8_5', 1, 0x430a), Register('vr16_8_4', 1, 0x430b), Register('vr16_32_0', 4, 0x430c), Register('vr16_16_1', 2, 0x430c), Register('vr16_8_3', 1, 0x430c), Register('vr16_8_2', 1, 0x430d), Register('vr16_16_0', 2, 0x430e), Register('vr16_8_1', 1, 0x430e), Register('vr16_8_0', 1, 0x430f), Register('vs49', 16, 0x4310), Register('vr17_64_1', 8, 0x4310), Register('vr17_32_3', 4, 0x4310), Register('vr17_16_7', 2, 0x4310), Register('vr17_8_15', 1, 0x4310), Register('vr17_8_14', 1, 0x4311), Register('vr17_16_6', 2, 0x4312), Register('vr17_8_13', 1, 0x4312), Register('vr17_8_12', 1, 0x4313), Register('vr17_32_2', 4, 0x4314), Register('vr17_16_5', 2, 0x4314), Register('vr17_8_11', 1, 0x4314), Register('vr17_8_10', 1, 0x4315), Register('vr17_16_4', 2, 0x4316), Register('vr17_8_9', 1, 0x4316), Register('vr17_8_8', 1, 0x4317), Register('vr17_64_0', 8, 0x4318), Register('vr17_32_1', 4, 0x4318), Register('vr17_16_3', 2, 0x4318), Register('vr17_8_7', 1, 0x4318), Register('vr17_8_6', 1, 0x4319), Register('vr17_16_2', 2, 0x431a), Register('vr17_8_5', 1, 0x431a), Register('vr17_8_4', 1, 0x431b), Register('vr17_32_0', 4, 0x431c), Register('vr17_16_1', 2, 0x431c), Register('vr17_8_3', 1, 0x431c), Register('vr17_8_2', 1, 0x431d), Register('vr17_16_0', 2, 0x431e), Register('vr17_8_1', 1, 0x431e), Register('vr17_8_0', 1, 0x431f), Register('vs50', 16, 0x4320), Register('vr18_64_1', 8, 0x4320), Register('vr18_32_3', 4, 0x4320), Register('vr18_16_7', 2, 0x4320), Register('vr18_8_15', 1, 0x4320), Register('vr18_8_14', 1, 0x4321), Register('vr18_16_6', 2, 0x4322), Register('vr18_8_13', 1, 0x4322), Register('vr18_8_12', 1, 0x4323), Register('vr18_32_2', 4, 0x4324), Register('vr18_16_5', 2, 0x4324), Register('vr18_8_11', 1, 0x4324), Register('vr18_8_10', 1, 0x4325), Register('vr18_16_4', 2, 0x4326), Register('vr18_8_9', 1, 0x4326), Register('vr18_8_8', 1, 0x4327), Register('vr18_64_0', 8, 0x4328), Register('vr18_32_1', 4, 0x4328), Register('vr18_16_3', 2, 0x4328), Register('vr18_8_7', 1, 0x4328), Register('vr18_8_6', 1, 0x4329), Register('vr18_16_2', 2, 0x432a), Register('vr18_8_5', 1, 0x432a), Register('vr18_8_4', 1, 0x432b), Register('vr18_32_0', 4, 0x432c), Register('vr18_16_1', 2, 0x432c), Register('vr18_8_3', 1, 0x432c), Register('vr18_8_2', 1, 0x432d), Register('vr18_16_0', 2, 0x432e), Register('vr18_8_1', 1, 0x432e), Register('vr18_8_0', 1, 0x432f), Register('vs51', 16, 0x4330), Register('vr19_64_1', 8, 0x4330), Register('vr19_32_3', 4, 0x4330), Register('vr19_16_7', 2, 0x4330), Register('vr19_8_15', 1, 0x4330), Register('vr19_8_14', 1, 0x4331), Register('vr19_16_6', 2, 0x4332), Register('vr19_8_13', 1, 0x4332), Register('vr19_8_12', 1, 0x4333), Register('vr19_32_2', 4, 0x4334), Register('vr19_16_5', 2, 0x4334), Register('vr19_8_11', 1, 0x4334), Register('vr19_8_10', 1, 0x4335), Register('vr19_16_4', 2, 0x4336), Register('vr19_8_9', 1, 0x4336), Register('vr19_8_8', 1, 0x4337), Register('vr19_64_0', 8, 0x4338), Register('vr19_32_1', 4, 0x4338), Register('vr19_16_3', 2, 0x4338), Register('vr19_8_7', 1, 0x4338), Register('vr19_8_6', 1, 0x4339), Register('vr19_16_2', 2, 0x433a), Register('vr19_8_5', 1, 0x433a), Register('vr19_8_4', 1, 0x433b), Register('vr19_32_0', 4, 0x433c), Register('vr19_16_1', 2, 0x433c), Register('vr19_8_3', 1, 0x433c), Register('vr19_8_2', 1, 0x433d), Register('vr19_16_0', 2, 0x433e), Register('vr19_8_1', 1, 0x433e), Register('vr19_8_0', 1, 0x433f), Register('vs52', 16, 0x4340), Register('vr20_64_1', 8, 0x4340), Register('vr20_32_3', 4, 0x4340), Register('vr20_16_7', 2, 0x4340), Register('vr20_8_15', 1, 0x4340), Register('vr20_8_14', 1, 0x4341), Register('vr20_16_6', 2, 0x4342), Register('vr20_8_13', 1, 0x4342), Register('vr20_8_12', 1, 0x4343), Register('vr20_32_2', 4, 0x4344), Register('vr20_16_5', 2, 0x4344), Register('vr20_8_11', 1, 0x4344), Register('vr20_8_10', 1, 0x4345), Register('vr20_16_4', 2, 0x4346), Register('vr20_8_9', 1, 0x4346), Register('vr20_8_8', 1, 0x4347), Register('vr20_64_0', 8, 0x4348), Register('vr20_32_1', 4, 0x4348), Register('vr20_16_3', 2, 0x4348), Register('vr20_8_7', 1, 0x4348), Register('vr20_8_6', 1, 0x4349), Register('vr20_16_2', 2, 0x434a), Register('vr20_8_5', 1, 0x434a), Register('vr20_8_4', 1, 0x434b), Register('vr20_32_0', 4, 0x434c), Register('vr20_16_1', 2, 0x434c), Register('vr20_8_3', 1, 0x434c), Register('vr20_8_2', 1, 0x434d), Register('vr20_16_0', 2, 0x434e), Register('vr20_8_1', 1, 0x434e), Register('vr20_8_0', 1, 0x434f), Register('vs53', 16, 0x4350), Register('vr21_64_1', 8, 0x4350), Register('vr21_32_3', 4, 0x4350), Register('vr21_16_7', 2, 0x4350), Register('vr21_8_15', 1, 0x4350), Register('vr21_8_14', 1, 0x4351), Register('vr21_16_6', 2, 0x4352), Register('vr21_8_13', 1, 0x4352), Register('vr21_8_12', 1, 0x4353), Register('vr21_32_2', 4, 0x4354), Register('vr21_16_5', 2, 0x4354), Register('vr21_8_11', 1, 0x4354), Register('vr21_8_10', 1, 0x4355), Register('vr21_16_4', 2, 0x4356), Register('vr21_8_9', 1, 0x4356), Register('vr21_8_8', 1, 0x4357), Register('vr21_64_0', 8, 0x4358), Register('vr21_32_1', 4, 0x4358), Register('vr21_16_3', 2, 0x4358), Register('vr21_8_7', 1, 0x4358), Register('vr21_8_6', 1, 0x4359), Register('vr21_16_2', 2, 0x435a), Register('vr21_8_5', 1, 0x435a), Register('vr21_8_4', 1, 0x435b), Register('vr21_32_0', 4, 0x435c), Register('vr21_16_1', 2, 0x435c), Register('vr21_8_3', 1, 0x435c), Register('vr21_8_2', 1, 0x435d), Register('vr21_16_0', 2, 0x435e), Register('vr21_8_1', 1, 0x435e), Register('vr21_8_0', 1, 0x435f), Register('vs54', 16, 0x4360), Register('vr22_64_1', 8, 0x4360), Register('vr22_32_3', 4, 0x4360), Register('vr22_16_7', 2, 0x4360), Register('vr22_8_15', 1, 0x4360), Register('vr22_8_14', 1, 0x4361), Register('vr22_16_6', 2, 0x4362), Register('vr22_8_13', 1, 0x4362), Register('vr22_8_12', 1, 0x4363), Register('vr22_32_2', 4, 0x4364), Register('vr22_16_5', 2, 0x4364), Register('vr22_8_11', 1, 0x4364), Register('vr22_8_10', 1, 0x4365), Register('vr22_16_4', 2, 0x4366), Register('vr22_8_9', 1, 0x4366), Register('vr22_8_8', 1, 0x4367), Register('vr22_64_0', 8, 0x4368), Register('vr22_32_1', 4, 0x4368), Register('vr22_16_3', 2, 0x4368), Register('vr22_8_7', 1, 0x4368), Register('vr22_8_6', 1, 0x4369), Register('vr22_16_2', 2, 0x436a), Register('vr22_8_5', 1, 0x436a), Register('vr22_8_4', 1, 0x436b), Register('vr22_32_0', 4, 0x436c), Register('vr22_16_1', 2, 0x436c), Register('vr22_8_3', 1, 0x436c), Register('vr22_8_2', 1, 0x436d), Register('vr22_16_0', 2, 0x436e), Register('vr22_8_1', 1, 0x436e), Register('vr22_8_0', 1, 0x436f), Register('vs55', 16, 0x4370), Register('vr23_64_1', 8, 0x4370), Register('vr23_32_3', 4, 0x4370), Register('vr23_16_7', 2, 0x4370), Register('vr23_8_15', 1, 0x4370), Register('vr23_8_14', 1, 0x4371), Register('vr23_16_6', 2, 0x4372), Register('vr23_8_13', 1, 0x4372), Register('vr23_8_12', 1, 0x4373), Register('vr23_32_2', 4, 0x4374), Register('vr23_16_5', 2, 0x4374), Register('vr23_8_11', 1, 0x4374), Register('vr23_8_10', 1, 0x4375), Register('vr23_16_4', 2, 0x4376), Register('vr23_8_9', 1, 0x4376), Register('vr23_8_8', 1, 0x4377), Register('vr23_64_0', 8, 0x4378), Register('vr23_32_1', 4, 0x4378), Register('vr23_16_3', 2, 0x4378), Register('vr23_8_7', 1, 0x4378), Register('vr23_8_6', 1, 0x4379), Register('vr23_16_2', 2, 0x437a), Register('vr23_8_5', 1, 0x437a), Register('vr23_8_4', 1, 0x437b), Register('vr23_32_0', 4, 0x437c), Register('vr23_16_1', 2, 0x437c), Register('vr23_8_3', 1, 0x437c), Register('vr23_8_2', 1, 0x437d), Register('vr23_16_0', 2, 0x437e), Register('vr23_8_1', 1, 0x437e), Register('vr23_8_0', 1, 0x437f), Register('vs56', 16, 0x4380), Register('vr24_64_1', 8, 0x4380), Register('vr24_32_3', 4, 0x4380), Register('vr24_16_7', 2, 0x4380), Register('vr24_8_15', 1, 0x4380), Register('vr24_8_14', 1, 0x4381), Register('vr24_16_6', 2, 0x4382), Register('vr24_8_13', 1, 0x4382), Register('vr24_8_12', 1, 0x4383), Register('vr24_32_2', 4, 0x4384), Register('vr24_16_5', 2, 0x4384), Register('vr24_8_11', 1, 0x4384), Register('vr24_8_10', 1, 0x4385), Register('vr24_16_4', 2, 0x4386), Register('vr24_8_9', 1, 0x4386), Register('vr24_8_8', 1, 0x4387), Register('vr24_64_0', 8, 0x4388), Register('vr24_32_1', 4, 0x4388), Register('vr24_16_3', 2, 0x4388), Register('vr24_8_7', 1, 0x4388), Register('vr24_8_6', 1, 0x4389), Register('vr24_16_2', 2, 0x438a), Register('vr24_8_5', 1, 0x438a), Register('vr24_8_4', 1, 0x438b), Register('vr24_32_0', 4, 0x438c), Register('vr24_16_1', 2, 0x438c), Register('vr24_8_3', 1, 0x438c), Register('vr24_8_2', 1, 0x438d), Register('vr24_16_0', 2, 0x438e), Register('vr24_8_1', 1, 0x438e), Register('vr24_8_0', 1, 0x438f), Register('vs57', 16, 0x4390), Register('vr25_64_1', 8, 0x4390), Register('vr25_32_3', 4, 0x4390), Register('vr25_16_7', 2, 0x4390), Register('vr25_8_15', 1, 0x4390), Register('vr25_8_14', 1, 0x4391), Register('vr25_16_6', 2, 0x4392), Register('vr25_8_13', 1, 0x4392), Register('vr25_8_12', 1, 0x4393), Register('vr25_32_2', 4, 0x4394), Register('vr25_16_5', 2, 0x4394), Register('vr25_8_11', 1, 0x4394), Register('vr25_8_10', 1, 0x4395), Register('vr25_16_4', 2, 0x4396), Register('vr25_8_9', 1, 0x4396), Register('vr25_8_8', 1, 0x4397), Register('vr25_64_0', 8, 0x4398), Register('vr25_32_1', 4, 0x4398), Register('vr25_16_3', 2, 0x4398), Register('vr25_8_7', 1, 0x4398), Register('vr25_8_6', 1, 0x4399), Register('vr25_16_2', 2, 0x439a), Register('vr25_8_5', 1, 0x439a), Register('vr25_8_4', 1, 0x439b), Register('vr25_32_0', 4, 0x439c), Register('vr25_16_1', 2, 0x439c), Register('vr25_8_3', 1, 0x439c), Register('vr25_8_2', 1, 0x439d), Register('vr25_16_0', 2, 0x439e), Register('vr25_8_1', 1, 0x439e), Register('vr25_8_0', 1, 0x439f), Register('vs58', 16, 0x43a0), Register('vr26_64_1', 8, 0x43a0), Register('vr26_32_3', 4, 0x43a0), Register('vr26_16_7', 2, 0x43a0), Register('vr26_8_15', 1, 0x43a0), Register('vr26_8_14', 1, 0x43a1), Register('vr26_16_6', 2, 0x43a2), Register('vr26_8_13', 1, 0x43a2), Register('vr26_8_12', 1, 0x43a3), Register('vr26_32_2', 4, 0x43a4), Register('vr26_16_5', 2, 0x43a4), Register('vr26_8_11', 1, 0x43a4), Register('vr26_8_10', 1, 0x43a5), Register('vr26_16_4', 2, 0x43a6), Register('vr26_8_9', 1, 0x43a6), Register('vr26_8_8', 1, 0x43a7), Register('vr26_64_0', 8, 0x43a8), Register('vr26_32_1', 4, 0x43a8), Register('vr26_16_3', 2, 0x43a8), Register('vr26_8_7', 1, 0x43a8), Register('vr26_8_6', 1, 0x43a9), Register('vr26_16_2', 2, 0x43aa), Register('vr26_8_5', 1, 0x43aa), Register('vr26_8_4', 1, 0x43ab), Register('vr26_32_0', 4, 0x43ac), Register('vr26_16_1', 2, 0x43ac), Register('vr26_8_3', 1, 0x43ac), Register('vr26_8_2', 1, 0x43ad), Register('vr26_16_0', 2, 0x43ae), Register('vr26_8_1', 1, 0x43ae), Register('vr26_8_0', 1, 0x43af), Register('vs59', 16, 0x43b0), Register('vr27_64_1', 8, 0x43b0), Register('vr27_32_3', 4, 0x43b0), Register('vr27_16_7', 2, 0x43b0), Register('vr27_8_15', 1, 0x43b0), Register('vr27_8_14', 1, 0x43b1), Register('vr27_16_6', 2, 0x43b2), Register('vr27_8_13', 1, 0x43b2), Register('vr27_8_12', 1, 0x43b3), Register('vr27_32_2', 4, 0x43b4), Register('vr27_16_5', 2, 0x43b4), Register('vr27_8_11', 1, 0x43b4), Register('vr27_8_10', 1, 0x43b5), Register('vr27_16_4', 2, 0x43b6), Register('vr27_8_9', 1, 0x43b6), Register('vr27_8_8', 1, 0x43b7), Register('vr27_64_0', 8, 0x43b8), Register('vr27_32_1', 4, 0x43b8), Register('vr27_16_3', 2, 0x43b8), Register('vr27_8_7', 1, 0x43b8), Register('vr27_8_6', 1, 0x43b9), Register('vr27_16_2', 2, 0x43ba), Register('vr27_8_5', 1, 0x43ba), Register('vr27_8_4', 1, 0x43bb), Register('vr27_32_0', 4, 0x43bc), Register('vr27_16_1', 2, 0x43bc), Register('vr27_8_3', 1, 0x43bc), Register('vr27_8_2', 1, 0x43bd), Register('vr27_16_0', 2, 0x43be), Register('vr27_8_1', 1, 0x43be), Register('vr27_8_0', 1, 0x43bf), Register('vs60', 16, 0x43c0), Register('vr28_64_1', 8, 0x43c0), Register('vr28_32_3', 4, 0x43c0), Register('vr28_16_7', 2, 0x43c0), Register('vr28_8_15', 1, 0x43c0), Register('vr28_8_14', 1, 0x43c1), Register('vr28_16_6', 2, 0x43c2), Register('vr28_8_13', 1, 0x43c2), Register('vr28_8_12', 1, 0x43c3), Register('vr28_32_2', 4, 0x43c4), Register('vr28_16_5', 2, 0x43c4), Register('vr28_8_11', 1, 0x43c4), Register('vr28_8_10', 1, 0x43c5), Register('vr28_16_4', 2, 0x43c6), Register('vr28_8_9', 1, 0x43c6), Register('vr28_8_8', 1, 0x43c7), Register('vr28_64_0', 8, 0x43c8), Register('vr28_32_1', 4, 0x43c8), Register('vr28_16_3', 2, 0x43c8), Register('vr28_8_7', 1, 0x43c8), Register('vr28_8_6', 1, 0x43c9), Register('vr28_16_2', 2, 0x43ca), Register('vr28_8_5', 1, 0x43ca), Register('vr28_8_4', 1, 0x43cb), Register('vr28_32_0', 4, 0x43cc), Register('vr28_16_1', 2, 0x43cc), Register('vr28_8_3', 1, 0x43cc), Register('vr28_8_2', 1, 0x43cd), Register('vr28_16_0', 2, 0x43ce), Register('vr28_8_1', 1, 0x43ce), Register('vr28_8_0', 1, 0x43cf), Register('vs61', 16, 0x43d0), Register('vr29_64_1', 8, 0x43d0), Register('vr29_32_3', 4, 0x43d0), Register('vr29_16_7', 2, 0x43d0), Register('vr29_8_15', 1, 0x43d0), Register('vr29_8_14', 1, 0x43d1), Register('vr29_16_6', 2, 0x43d2), Register('vr29_8_13', 1, 0x43d2), Register('vr29_8_12', 1, 0x43d3), Register('vr29_32_2', 4, 0x43d4), Register('vr29_16_5', 2, 0x43d4), Register('vr29_8_11', 1, 0x43d4), Register('vr29_8_10', 1, 0x43d5), Register('vr29_16_4', 2, 0x43d6), Register('vr29_8_9', 1, 0x43d6), Register('vr29_8_8', 1, 0x43d7), Register('vr29_64_0', 8, 0x43d8), Register('vr29_32_1', 4, 0x43d8), Register('vr29_16_3', 2, 0x43d8), Register('vr29_8_7', 1, 0x43d8), Register('vr29_8_6', 1, 0x43d9), Register('vr29_16_2', 2, 0x43da), Register('vr29_8_5', 1, 0x43da), Register('vr29_8_4', 1, 0x43db), Register('vr29_32_0', 4, 0x43dc), Register('vr29_16_1', 2, 0x43dc), Register('vr29_8_3', 1, 0x43dc), Register('vr29_8_2', 1, 0x43dd), Register('vr29_16_0', 2, 0x43de), Register('vr29_8_1', 1, 0x43de), Register('vr29_8_0', 1, 0x43df), Register('vs62', 16, 0x43e0), Register('vr30_64_1', 8, 0x43e0), Register('vr30_32_3', 4, 0x43e0), Register('vr30_16_7', 2, 0x43e0), Register('vr30_8_15', 1, 0x43e0), Register('vr30_8_14', 1, 0x43e1), Register('vr30_16_6', 2, 0x43e2), Register('vr30_8_13', 1, 0x43e2), Register('vr30_8_12', 1, 0x43e3), Register('vr30_32_2', 4, 0x43e4), Register('vr30_16_5', 2, 0x43e4), Register('vr30_8_11', 1, 0x43e4), Register('vr30_8_10', 1, 0x43e5), Register('vr30_16_4', 2, 0x43e6), Register('vr30_8_9', 1, 0x43e6), Register('vr30_8_8', 1, 0x43e7), Register('vr30_64_0', 8, 0x43e8), Register('vr30_32_1', 4, 0x43e8), Register('vr30_16_3', 2, 0x43e8), Register('vr30_8_7', 1, 0x43e8), Register('vr30_8_6', 1, 0x43e9), Register('vr30_16_2', 2, 0x43ea), Register('vr30_8_5', 1, 0x43ea), Register('vr30_8_4', 1, 0x43eb), Register('vr30_32_0', 4, 0x43ec), Register('vr30_16_1', 2, 0x43ec), Register('vr30_8_3', 1, 0x43ec), Register('vr30_8_2', 1, 0x43ed), Register('vr30_16_0', 2, 0x43ee), Register('vr30_8_1', 1, 0x43ee), Register('vr30_8_0', 1, 0x43ef), Register('vs63', 16, 0x43f0), Register('vr31_64_1', 8, 0x43f0), Register('vr31_32_3', 4, 0x43f0), Register('vr31_16_7', 2, 0x43f0), Register('vr31_8_15', 1, 0x43f0), Register('vr31_8_14', 1, 0x43f1), Register('vr31_16_6', 2, 0x43f2), Register('vr31_8_13', 1, 0x43f2), Register('vr31_8_12', 1, 0x43f3), Register('vr31_32_2', 4, 0x43f4), Register('vr31_16_5', 2, 0x43f4), Register('vr31_8_11', 1, 0x43f4), Register('vr31_8_10', 1, 0x43f5), Register('vr31_16_4', 2, 0x43f6), Register('vr31_8_9', 1, 0x43f6), Register('vr31_8_8', 1, 0x43f7), Register('vr31_64_0', 8, 0x43f8), Register('vr31_32_1', 4, 0x43f8), Register('vr31_16_3', 2, 0x43f8), Register('vr31_8_7', 1, 0x43f8), Register('vr31_8_6', 1, 0x43f9), Register('vr31_16_2', 2, 0x43fa), Register('vr31_8_5', 1, 0x43fa), Register('vr31_8_4', 1, 0x43fb), Register('vr31_32_0', 4, 0x43fc), Register('vr31_16_1', 2, 0x43fc), Register('vr31_8_3', 1, 0x43fc), Register('vr31_8_2', 1, 0x43fd), Register('vr31_16_0', 2, 0x43fe), Register('vr31_8_1', 1, 0x43fe), Register('vr31_8_0', 1, 0x43ff), Register('contextreg', 4, 0x6000), Register('dcr000', 4, 0x7000), Register('dcr001', 4, 0x7004), Register('dcr002', 4, 0x7008), Register('dcr003', 4, 0x700c), Register('dcr004', 4, 0x7010), Register('dcr005', 4, 0x7014), Register('dcr006', 4, 0x7018), Register('dcr007', 4, 0x701c), Register('dcr008', 4, 0x7020), Register('dcr009', 4, 0x7024), Register('dcr00a', 4, 0x7028), Register('dcr00b', 4, 0x702c), Register('dcr00c', 4, 0x7030), Register('dcr00d', 4, 0x7034), Register('dcr00e', 4, 0x7038), Register('dcr00f', 4, 0x703c), Register('dcr010', 4, 0x7040), Register('dcr011', 4, 0x7044), Register('dcr012', 4, 0x7048), Register('dcr013', 4, 0x704c), Register('dcr014', 4, 0x7050), Register('dcr015', 4, 0x7054), Register('dcr016', 4, 0x7058), Register('dcr017', 4, 0x705c), Register('dcr018', 4, 0x7060), Register('dcr019', 4, 0x7064), Register('dcr01a', 4, 0x7068), Register('dcr01b', 4, 0x706c), Register('dcr01c', 4, 0x7070), Register('dcr01d', 4, 0x7074), Register('dcr01e', 4, 0x7078), Register('dcr01f', 4, 0x707c), Register('dcr020', 4, 0x7080), Register('dcr021', 4, 0x7084), Register('dcr022', 4, 0x7088), Register('dcr023', 4, 0x708c), Register('dcr024', 4, 0x7090), Register('dcr025', 4, 0x7094), Register('dcr026', 4, 0x7098), Register('dcr027', 4, 0x709c), Register('dcr028', 4, 0x70a0), Register('dcr029', 4, 0x70a4), Register('dcr02a', 4, 0x70a8), Register('dcr02b', 4, 0x70ac), Register('dcr02c', 4, 0x70b0), Register('dcr02d', 4, 0x70b4), Register('dcr02e', 4, 0x70b8), Register('dcr02f', 4, 0x70bc), Register('dcr030', 4, 0x70c0), Register('dcr031', 4, 0x70c4), Register('dcr032', 4, 0x70c8), Register('dcr033', 4, 0x70cc), Register('dcr034', 4, 0x70d0), Register('dcr035', 4, 0x70d4), Register('dcr036', 4, 0x70d8), Register('dcr037', 4, 0x70dc), Register('dcr038', 4, 0x70e0), Register('dcr039', 4, 0x70e4), Register('dcr03a', 4, 0x70e8), Register('dcr03b', 4, 0x70ec), Register('dcr03c', 4, 0x70f0), Register('dcr03d', 4, 0x70f4), Register('dcr03e', 4, 0x70f8), Register('dcr03f', 4, 0x70fc), Register('dcr040', 4, 0x7100), Register('dcr041', 4, 0x7104), Register('dcr042', 4, 0x7108), Register('dcr043', 4, 0x710c), Register('dcr044', 4, 0x7110), Register('dcr045', 4, 0x7114), Register('dcr046', 4, 0x7118), Register('dcr047', 4, 0x711c), Register('dcr048', 4, 0x7120), Register('dcr049', 4, 0x7124), Register('dcr04a', 4, 0x7128), Register('dcr04b', 4, 0x712c), Register('dcr04c', 4, 0x7130), Register('dcr04d', 4, 0x7134), Register('dcr04e', 4, 0x7138), Register('dcr04f', 4, 0x713c), Register('dcr050', 4, 0x7140), Register('dcr051', 4, 0x7144), Register('dcr052', 4, 0x7148), Register('dcr053', 4, 0x714c), Register('dcr054', 4, 0x7150), Register('dcr055', 4, 0x7154), Register('dcr056', 4, 0x7158), Register('dcr057', 4, 0x715c), Register('dcr058', 4, 0x7160), Register('dcr059', 4, 0x7164), Register('dcr05a', 4, 0x7168), Register('dcr05b', 4, 0x716c), Register('dcr05c', 4, 0x7170), Register('dcr05d', 4, 0x7174), Register('dcr05e', 4, 0x7178), Register('dcr05f', 4, 0x717c), Register('dcr060', 4, 0x7180), Register('dcr061', 4, 0x7184), Register('dcr062', 4, 0x7188), Register('dcr063', 4, 0x718c), Register('dcr064', 4, 0x7190), Register('dcr065', 4, 0x7194), Register('dcr066', 4, 0x7198), Register('dcr067', 4, 0x719c), Register('dcr068', 4, 0x71a0), Register('dcr069', 4, 0x71a4), Register('dcr06a', 4, 0x71a8), Register('dcr06b', 4, 0x71ac), Register('dcr06c', 4, 0x71b0), Register('dcr06d', 4, 0x71b4), Register('dcr06e', 4, 0x71b8), Register('dcr06f', 4, 0x71bc), Register('dcr070', 4, 0x71c0), Register('dcr071', 4, 0x71c4), Register('dcr072', 4, 0x71c8), Register('dcr073', 4, 0x71cc), Register('dcr074', 4, 0x71d0), Register('dcr075', 4, 0x71d4), Register('dcr076', 4, 0x71d8), Register('dcr077', 4, 0x71dc), Register('dcr078', 4, 0x71e0), Register('dcr079', 4, 0x71e4), Register('dcr07a', 4, 0x71e8), Register('dcr07b', 4, 0x71ec), Register('dcr07c', 4, 0x71f0), Register('dcr07d', 4, 0x71f4), Register('dcr07e', 4, 0x71f8), Register('dcr07f', 4, 0x71fc), Register('dcr080', 4, 0x7200), Register('dcr081', 4, 0x7204), Register('dcr082', 4, 0x7208), Register('dcr083', 4, 0x720c), Register('dcr084', 4, 0x7210), Register('dcr085', 4, 0x7214), Register('dcr086', 4, 0x7218), Register('dcr087', 4, 0x721c), Register('dcr088', 4, 0x7220), Register('dcr089', 4, 0x7224), Register('dcr08a', 4, 0x7228), Register('dcr08b', 4, 0x722c), Register('dcr08c', 4, 0x7230), Register('dcr08d', 4, 0x7234), Register('dcr08e', 4, 0x7238), Register('dcr08f', 4, 0x723c), Register('dcr090', 4, 0x7240), Register('dcr091', 4, 0x7244), Register('dcr092', 4, 0x7248), Register('dcr093', 4, 0x724c), Register('dcr094', 4, 0x7250), Register('dcr095', 4, 0x7254), Register('dcr096', 4, 0x7258), Register('dcr097', 4, 0x725c), Register('dcr098', 4, 0x7260), Register('dcr099', 4, 0x7264), Register('dcr09a', 4, 0x7268), Register('dcr09b', 4, 0x726c), Register('dcr09c', 4, 0x7270), Register('dcr09d', 4, 0x7274), Register('dcr09e', 4, 0x7278), Register('dcr09f', 4, 0x727c), Register('dcr0a0', 4, 0x7280), Register('dcr0a1', 4, 0x7284), Register('dcr0a2', 4, 0x7288), Register('dcr0a3', 4, 0x728c), Register('dcr0a4', 4, 0x7290), Register('dcr0a5', 4, 0x7294), Register('dcr0a6', 4, 0x7298), Register('dcr0a7', 4, 0x729c), Register('dcr0a8', 4, 0x72a0), Register('dcr0a9', 4, 0x72a4), Register('dcr0aa', 4, 0x72a8), Register('dcr0ab', 4, 0x72ac), Register('dcr0ac', 4, 0x72b0), Register('dcr0ad', 4, 0x72b4), Register('dcr0ae', 4, 0x72b8), Register('dcr0af', 4, 0x72bc), Register('dcr0b0', 4, 0x72c0), Register('dcr0b1', 4, 0x72c4), Register('dcr0b2', 4, 0x72c8), Register('dcr0b3', 4, 0x72cc), Register('dcr0b4', 4, 0x72d0), Register('dcr0b5', 4, 0x72d4), Register('dcr0b6', 4, 0x72d8), Register('dcr0b7', 4, 0x72dc), Register('dcr0b8', 4, 0x72e0), Register('dcr0b9', 4, 0x72e4), Register('dcr0ba', 4, 0x72e8), Register('dcr0bb', 4, 0x72ec), Register('dcr0bc', 4, 0x72f0), Register('dcr0bd', 4, 0x72f4), Register('dcr0be', 4, 0x72f8), Register('dcr0bf', 4, 0x72fc), Register('dcr0c0', 4, 0x7300), Register('dcr0c1', 4, 0x7304), Register('dcr0c2', 4, 0x7308), Register('dcr0c3', 4, 0x730c), Register('dcr0c4', 4, 0x7310), Register('dcr0c5', 4, 0x7314), Register('dcr0c6', 4, 0x7318), Register('dcr0c7', 4, 0x731c), Register('dcr0c8', 4, 0x7320), Register('dcr0c9', 4, 0x7324), Register('dcr0ca', 4, 0x7328), Register('dcr0cb', 4, 0x732c), Register('dcr0cc', 4, 0x7330), Register('dcr0cd', 4, 0x7334), Register('dcr0ce', 4, 0x7338), Register('dcr0cf', 4, 0x733c), Register('dcr0d0', 4, 0x7340), Register('dcr0d1', 4, 0x7344), Register('dcr0d2', 4, 0x7348), Register('dcr0d3', 4, 0x734c), Register('dcr0d4', 4, 0x7350), Register('dcr0d5', 4, 0x7354), Register('dcr0d6', 4, 0x7358), Register('dcr0d7', 4, 0x735c), Register('dcr0d8', 4, 0x7360), Register('dcr0d9', 4, 0x7364), Register('dcr0da', 4, 0x7368), Register('dcr0db', 4, 0x736c), Register('dcr0dc', 4, 0x7370), Register('dcr0dd', 4, 0x7374), Register('dcr0de', 4, 0x7378), Register('dcr0df', 4, 0x737c), Register('dcr0e0', 4, 0x7380), Register('dcr0e1', 4, 0x7384), Register('dcr0e2', 4, 0x7388), Register('dcr0e3', 4, 0x738c), Register('dcr0e4', 4, 0x7390), Register('dcr0e5', 4, 0x7394), Register('dcr0e6', 4, 0x7398), Register('dcr0e7', 4, 0x739c), Register('dcr0e8', 4, 0x73a0), Register('dcr0e9', 4, 0x73a4), Register('dcr0ea', 4, 0x73a8), Register('dcr0eb', 4, 0x73ac), Register('dcr0ec', 4, 0x73b0), Register('dcr0ed', 4, 0x73b4), Register('dcr0ee', 4, 0x73b8), Register('dcr0ef', 4, 0x73bc), Register('dcr0f0', 4, 0x73c0), Register('dcr0f1', 4, 0x73c4), Register('dcr0f2', 4, 0x73c8), Register('dcr0f3', 4, 0x73cc), Register('dcr0f4', 4, 0x73d0), Register('dcr0f5', 4, 0x73d4), Register('dcr0f6', 4, 0x73d8), Register('dcr0f7', 4, 0x73dc), Register('dcr0f8', 4, 0x73e0), Register('dcr0f9', 4, 0x73e4), Register('dcr0fa', 4, 0x73e8), Register('dcr0fb', 4, 0x73ec), Register('dcr0fc', 4, 0x73f0), Register('dcr0fd', 4, 0x73f4), Register('dcr0fe', 4, 0x73f8), Register('dcr0ff', 4, 0x73fc), Register('dcr100', 4, 0x7400), Register('dcr101', 4, 0x7404), Register('dcr102', 4, 0x7408), Register('dcr103', 4, 0x740c), Register('dcr104', 4, 0x7410), Register('dcr105', 4, 0x7414), Register('dcr106', 4, 0x7418), Register('dcr107', 4, 0x741c), Register('dcr108', 4, 0x7420), Register('dcr109', 4, 0x7424), Register('dcr10a', 4, 0x7428), Register('dcr10b', 4, 0x742c), Register('dcr10c', 4, 0x7430), Register('dcr10d', 4, 0x7434), Register('dcr10e', 4, 0x7438), Register('dcr10f', 4, 0x743c), Register('dcr110', 4, 0x7440), Register('dcr111', 4, 0x7444), Register('dcr112', 4, 0x7448), Register('dcr113', 4, 0x744c), Register('dcr114', 4, 0x7450), Register('dcr115', 4, 0x7454), Register('dcr116', 4, 0x7458), Register('dcr117', 4, 0x745c), Register('dcr118', 4, 0x7460), Register('dcr119', 4, 0x7464), Register('dcr11a', 4, 0x7468), Register('dcr11b', 4, 0x746c), Register('dcr11c', 4, 0x7470), Register('dcr11d', 4, 0x7474), Register('dcr11e', 4, 0x7478), Register('dcr11f', 4, 0x747c), Register('dcr120', 4, 0x7480), Register('dcr121', 4, 0x7484), Register('dcr122', 4, 0x7488), Register('dcr123', 4, 0x748c), Register('dcr124', 4, 0x7490), Register('dcr125', 4, 0x7494), Register('dcr126', 4, 0x7498), Register('dcr127', 4, 0x749c), Register('dcr128', 4, 0x74a0), Register('dcr129', 4, 0x74a4), Register('dcr12a', 4, 0x74a8), Register('dcr12b', 4, 0x74ac), Register('dcr12c', 4, 0x74b0), Register('dcr12d', 4, 0x74b4), Register('dcr12e', 4, 0x74b8), Register('dcr12f', 4, 0x74bc), Register('dcr130', 4, 0x74c0), Register('dcr131', 4, 0x74c4), Register('dcr132', 4, 0x74c8), Register('dcr133', 4, 0x74cc), Register('dcr134', 4, 0x74d0), Register('dcr135', 4, 0x74d4), Register('dcr136', 4, 0x74d8), Register('dcr137', 4, 0x74dc), Register('dcr138', 4, 0x74e0), Register('dcr139', 4, 0x74e4), Register('dcr13a', 4, 0x74e8), Register('dcr13b', 4, 0x74ec), Register('dcr13c', 4, 0x74f0), Register('dcr13d', 4, 0x74f4), Register('dcr13e', 4, 0x74f8), Register('dcr13f', 4, 0x74fc), Register('dcr140', 4, 0x7500), Register('dcr141', 4, 0x7504), Register('dcr142', 4, 0x7508), Register('dcr143', 4, 0x750c), Register('dcr144', 4, 0x7510), Register('dcr145', 4, 0x7514), Register('dcr146', 4, 0x7518), Register('dcr147', 4, 0x751c), Register('dcr148', 4, 0x7520), Register('dcr149', 4, 0x7524), Register('dcr14a', 4, 0x7528), Register('dcr14b', 4, 0x752c), Register('dcr14c', 4, 0x7530), Register('dcr14d', 4, 0x7534), Register('dcr14e', 4, 0x7538), Register('dcr14f', 4, 0x753c), Register('dcr150', 4, 0x7540), Register('dcr151', 4, 0x7544), Register('dcr152', 4, 0x7548), Register('dcr153', 4, 0x754c), Register('dcr154', 4, 0x7550), Register('dcr155', 4, 0x7554), Register('dcr156', 4, 0x7558), Register('dcr157', 4, 0x755c), Register('dcr158', 4, 0x7560), Register('dcr159', 4, 0x7564), Register('dcr15a', 4, 0x7568), Register('dcr15b', 4, 0x756c), Register('dcr15c', 4, 0x7570), Register('dcr15d', 4, 0x7574), Register('dcr15e', 4, 0x7578), Register('dcr15f', 4, 0x757c), Register('dcr160', 4, 0x7580), Register('dcr161', 4, 0x7584), Register('dcr162', 4, 0x7588), Register('dcr163', 4, 0x758c), Register('dcr164', 4, 0x7590), Register('dcr165', 4, 0x7594), Register('dcr166', 4, 0x7598), Register('dcr167', 4, 0x759c), Register('dcr168', 4, 0x75a0), Register('dcr169', 4, 0x75a4), Register('dcr16a', 4, 0x75a8), Register('dcr16b', 4, 0x75ac), Register('dcr16c', 4, 0x75b0), Register('dcr16d', 4, 0x75b4), Register('dcr16e', 4, 0x75b8), Register('dcr16f', 4, 0x75bc), Register('dcr170', 4, 0x75c0), Register('dcr171', 4, 0x75c4), Register('dcr172', 4, 0x75c8), Register('dcr173', 4, 0x75cc), Register('dcr174', 4, 0x75d0), Register('dcr175', 4, 0x75d4), Register('dcr176', 4, 0x75d8), Register('dcr177', 4, 0x75dc), Register('dcr178', 4, 0x75e0), Register('dcr179', 4, 0x75e4), Register('dcr17a', 4, 0x75e8), Register('dcr17b', 4, 0x75ec), Register('dcr17c', 4, 0x75f0), Register('dcr17d', 4, 0x75f4), Register('dcr17e', 4, 0x75f8), Register('dcr17f', 4, 0x75fc), Register('dcr180', 4, 0x7600), Register('dcr181', 4, 0x7604), Register('dcr182', 4, 0x7608), Register('dcr183', 4, 0x760c), Register('dcr184', 4, 0x7610), Register('dcr185', 4, 0x7614), Register('dcr186', 4, 0x7618), Register('dcr187', 4, 0x761c), Register('dcr188', 4, 0x7620), Register('dcr189', 4, 0x7624), Register('dcr18a', 4, 0x7628), Register('dcr18b', 4, 0x762c), Register('dcr18c', 4, 0x7630), Register('dcr18d', 4, 0x7634), Register('dcr18e', 4, 0x7638), Register('dcr18f', 4, 0x763c), Register('dcr190', 4, 0x7640), Register('dcr191', 4, 0x7644), Register('dcr192', 4, 0x7648), Register('dcr193', 4, 0x764c), Register('dcr194', 4, 0x7650), Register('dcr195', 4, 0x7654), Register('dcr196', 4, 0x7658), Register('dcr197', 4, 0x765c), Register('dcr198', 4, 0x7660), Register('dcr199', 4, 0x7664), Register('dcr19a', 4, 0x7668), Register('dcr19b', 4, 0x766c), Register('dcr19c', 4, 0x7670), Register('dcr19d', 4, 0x7674), Register('dcr19e', 4, 0x7678), Register('dcr19f', 4, 0x767c), Register('dcr1a0', 4, 0x7680), Register('dcr1a1', 4, 0x7684), Register('dcr1a2', 4, 0x7688), Register('dcr1a3', 4, 0x768c), Register('dcr1a4', 4, 0x7690), Register('dcr1a5', 4, 0x7694), Register('dcr1a6', 4, 0x7698), Register('dcr1a7', 4, 0x769c), Register('dcr1a8', 4, 0x76a0), Register('dcr1a9', 4, 0x76a4), Register('dcr1aa', 4, 0x76a8), Register('dcr1ab', 4, 0x76ac), Register('dcr1ac', 4, 0x76b0), Register('dcr1ad', 4, 0x76b4), Register('dcr1ae', 4, 0x76b8), Register('dcr1af', 4, 0x76bc), Register('dcr1b0', 4, 0x76c0), Register('dcr1b1', 4, 0x76c4), Register('dcr1b2', 4, 0x76c8), Register('dcr1b3', 4, 0x76cc), Register('dcr1b4', 4, 0x76d0), Register('dcr1b5', 4, 0x76d4), Register('dcr1b6', 4, 0x76d8), Register('dcr1b7', 4, 0x76dc), Register('dcr1b8', 4, 0x76e0), Register('dcr1b9', 4, 0x76e4), Register('dcr1ba', 4, 0x76e8), Register('dcr1bb', 4, 0x76ec), Register('dcr1bc', 4, 0x76f0), Register('dcr1bd', 4, 0x76f4), Register('dcr1be', 4, 0x76f8), Register('dcr1bf', 4, 0x76fc), Register('dcr1c0', 4, 0x7700), Register('dcr1c1', 4, 0x7704), Register('dcr1c2', 4, 0x7708), Register('dcr1c3', 4, 0x770c), Register('dcr1c4', 4, 0x7710), Register('dcr1c5', 4, 0x7714), Register('dcr1c6', 4, 0x7718), Register('dcr1c7', 4, 0x771c), Register('dcr1c8', 4, 0x7720), Register('dcr1c9', 4, 0x7724), Register('dcr1ca', 4, 0x7728), Register('dcr1cb', 4, 0x772c), Register('dcr1cc', 4, 0x7730), Register('dcr1cd', 4, 0x7734), Register('dcr1ce', 4, 0x7738), Register('dcr1cf', 4, 0x773c), Register('dcr1d0', 4, 0x7740), Register('dcr1d1', 4, 0x7744), Register('dcr1d2', 4, 0x7748), Register('dcr1d3', 4, 0x774c), Register('dcr1d4', 4, 0x7750), Register('dcr1d5', 4, 0x7754), Register('dcr1d6', 4, 0x7758), Register('dcr1d7', 4, 0x775c), Register('dcr1d8', 4, 0x7760), Register('dcr1d9', 4, 0x7764), Register('dcr1da', 4, 0x7768), Register('dcr1db', 4, 0x776c), Register('dcr1dc', 4, 0x7770), Register('dcr1dd', 4, 0x7774), Register('dcr1de', 4, 0x7778), Register('dcr1df', 4, 0x777c), Register('dcr1e0', 4, 0x7780), Register('dcr1e1', 4, 0x7784), Register('dcr1e2', 4, 0x7788), Register('dcr1e3', 4, 0x778c), Register('dcr1e4', 4, 0x7790), Register('dcr1e5', 4, 0x7794), Register('dcr1e6', 4, 0x7798), Register('dcr1e7', 4, 0x779c), Register('dcr1e8', 4, 0x77a0), Register('dcr1e9', 4, 0x77a4), Register('dcr1ea', 4, 0x77a8), Register('dcr1eb', 4, 0x77ac), Register('dcr1ec', 4, 0x77b0), Register('dcr1ed', 4, 0x77b4), Register('dcr1ee', 4, 0x77b8), Register('dcr1ef', 4, 0x77bc), Register('dcr1f0', 4, 0x77c0), Register('dcr1f1', 4, 0x77c4), Register('dcr1f2', 4, 0x77c8), Register('dcr1f3', 4, 0x77cc), Register('dcr1f4', 4, 0x77d0), Register('dcr1f5', 4, 0x77d4), Register('dcr1f6', 4, 0x77d8), Register('dcr1f7', 4, 0x77dc), Register('dcr1f8', 4, 0x77e0), Register('dcr1f9', 4, 0x77e4), Register('dcr1fa', 4, 0x77e8), Register('dcr1fb', 4, 0x77ec), Register('dcr1fc', 4, 0x77f0), Register('dcr1fd', 4, 0x77f4), Register('dcr1fe', 4, 0x77f8), Register('dcr1ff', 4, 0x77fc), Register('dcr200', 4, 0x7800), Register('dcr201', 4, 0x7804), Register('dcr202', 4, 0x7808), Register('dcr203', 4, 0x780c), Register('dcr204', 4, 0x7810), Register('dcr205', 4, 0x7814), Register('dcr206', 4, 0x7818), Register('dcr207', 4, 0x781c), Register('dcr208', 4, 0x7820), Register('dcr209', 4, 0x7824), Register('dcr20a', 4, 0x7828), Register('dcr20b', 4, 0x782c), Register('dcr20c', 4, 0x7830), Register('dcr20d', 4, 0x7834), Register('dcr20e', 4, 0x7838), Register('dcr20f', 4, 0x783c), Register('dcr210', 4, 0x7840), Register('dcr211', 4, 0x7844), Register('dcr212', 4, 0x7848), Register('dcr213', 4, 0x784c), Register('dcr214', 4, 0x7850), Register('dcr215', 4, 0x7854), Register('dcr216', 4, 0x7858), Register('dcr217', 4, 0x785c), Register('dcr218', 4, 0x7860), Register('dcr219', 4, 0x7864), Register('dcr21a', 4, 0x7868), Register('dcr21b', 4, 0x786c), Register('dcr21c', 4, 0x7870), Register('dcr21d', 4, 0x7874), Register('dcr21e', 4, 0x7878), Register('dcr21f', 4, 0x787c), Register('dcr220', 4, 0x7880), Register('dcr221', 4, 0x7884), Register('dcr222', 4, 0x7888), Register('dcr223', 4, 0x788c), Register('dcr224', 4, 0x7890), Register('dcr225', 4, 0x7894), Register('dcr226', 4, 0x7898), Register('dcr227', 4, 0x789c), Register('dcr228', 4, 0x78a0), Register('dcr229', 4, 0x78a4), Register('dcr22a', 4, 0x78a8), Register('dcr22b', 4, 0x78ac), Register('dcr22c', 4, 0x78b0), Register('dcr22d', 4, 0x78b4), Register('dcr22e', 4, 0x78b8), Register('dcr22f', 4, 0x78bc), Register('dcr230', 4, 0x78c0), Register('dcr231', 4, 0x78c4), Register('dcr232', 4, 0x78c8), Register('dcr233', 4, 0x78cc), Register('dcr234', 4, 0x78d0), Register('dcr235', 4, 0x78d4), Register('dcr236', 4, 0x78d8), Register('dcr237', 4, 0x78dc), Register('dcr238', 4, 0x78e0), Register('dcr239', 4, 0x78e4), Register('dcr23a', 4, 0x78e8), Register('dcr23b', 4, 0x78ec), Register('dcr23c', 4, 0x78f0), Register('dcr23d', 4, 0x78f4), Register('dcr23e', 4, 0x78f8), Register('dcr23f', 4, 0x78fc), Register('dcr240', 4, 0x7900), Register('dcr241', 4, 0x7904), Register('dcr242', 4, 0x7908), Register('dcr243', 4, 0x790c), Register('dcr244', 4, 0x7910), Register('dcr245', 4, 0x7914), Register('dcr246', 4, 0x7918), Register('dcr247', 4, 0x791c), Register('dcr248', 4, 0x7920), Register('dcr249', 4, 0x7924), Register('dcr24a', 4, 0x7928), Register('dcr24b', 4, 0x792c), Register('dcr24c', 4, 0x7930), Register('dcr24d', 4, 0x7934), Register('dcr24e', 4, 0x7938), Register('dcr24f', 4, 0x793c), Register('dcr250', 4, 0x7940), Register('dcr251', 4, 0x7944), Register('dcr252', 4, 0x7948), Register('dcr253', 4, 0x794c), Register('dcr254', 4, 0x7950), Register('dcr255', 4, 0x7954), Register('dcr256', 4, 0x7958), Register('dcr257', 4, 0x795c), Register('dcr258', 4, 0x7960), Register('dcr259', 4, 0x7964), Register('dcr25a', 4, 0x7968), Register('dcr25b', 4, 0x796c), Register('dcr25c', 4, 0x7970), Register('dcr25d', 4, 0x7974), Register('dcr25e', 4, 0x7978), Register('dcr25f', 4, 0x797c), Register('dcr260', 4, 0x7980), Register('dcr261', 4, 0x7984), Register('dcr262', 4, 0x7988), Register('dcr263', 4, 0x798c), Register('dcr264', 4, 0x7990), Register('dcr265', 4, 0x7994), Register('dcr266', 4, 0x7998), Register('dcr267', 4, 0x799c), Register('dcr268', 4, 0x79a0), Register('dcr269', 4, 0x79a4), Register('dcr26a', 4, 0x79a8), Register('dcr26b', 4, 0x79ac), Register('dcr26c', 4, 0x79b0), Register('dcr26d', 4, 0x79b4), Register('dcr26e', 4, 0x79b8), Register('dcr26f', 4, 0x79bc), Register('dcr270', 4, 0x79c0), Register('dcr271', 4, 0x79c4), Register('dcr272', 4, 0x79c8), Register('dcr273', 4, 0x79cc), Register('dcr274', 4, 0x79d0), Register('dcr275', 4, 0x79d4), Register('dcr276', 4, 0x79d8), Register('dcr277', 4, 0x79dc), Register('dcr278', 4, 0x79e0), Register('dcr279', 4, 0x79e4), Register('dcr27a', 4, 0x79e8), Register('dcr27b', 4, 0x79ec), Register('dcr27c', 4, 0x79f0), Register('dcr27d', 4, 0x79f4), Register('dcr27e', 4, 0x79f8), Register('dcr27f', 4, 0x79fc), Register('dcr280', 4, 0x7a00), Register('dcr281', 4, 0x7a04), Register('dcr282', 4, 0x7a08), Register('dcr283', 4, 0x7a0c), Register('dcr284', 4, 0x7a10), Register('dcr285', 4, 0x7a14), Register('dcr286', 4, 0x7a18), Register('dcr287', 4, 0x7a1c), Register('dcr288', 4, 0x7a20), Register('dcr289', 4, 0x7a24), Register('dcr28a', 4, 0x7a28), Register('dcr28b', 4, 0x7a2c), Register('dcr28c', 4, 0x7a30), Register('dcr28d', 4, 0x7a34), Register('dcr28e', 4, 0x7a38), Register('dcr28f', 4, 0x7a3c), Register('dcr290', 4, 0x7a40), Register('dcr291', 4, 0x7a44), Register('dcr292', 4, 0x7a48), Register('dcr293', 4, 0x7a4c), Register('dcr294', 4, 0x7a50), Register('dcr295', 4, 0x7a54), Register('dcr296', 4, 0x7a58), Register('dcr297', 4, 0x7a5c), Register('dcr298', 4, 0x7a60), Register('dcr299', 4, 0x7a64), Register('dcr29a', 4, 0x7a68), Register('dcr29b', 4, 0x7a6c), Register('dcr29c', 4, 0x7a70), Register('dcr29d', 4, 0x7a74), Register('dcr29e', 4, 0x7a78), Register('dcr29f', 4, 0x7a7c), Register('dcr2a0', 4, 0x7a80), Register('dcr2a1', 4, 0x7a84), Register('dcr2a2', 4, 0x7a88), Register('dcr2a3', 4, 0x7a8c), Register('dcr2a4', 4, 0x7a90), Register('dcr2a5', 4, 0x7a94), Register('dcr2a6', 4, 0x7a98), Register('dcr2a7', 4, 0x7a9c), Register('dcr2a8', 4, 0x7aa0), Register('dcr2a9', 4, 0x7aa4), Register('dcr2aa', 4, 0x7aa8), Register('dcr2ab', 4, 0x7aac), Register('dcr2ac', 4, 0x7ab0), Register('dcr2ad', 4, 0x7ab4), Register('dcr2ae', 4, 0x7ab8), Register('dcr2af', 4, 0x7abc), Register('dcr2b0', 4, 0x7ac0), Register('dcr2b1', 4, 0x7ac4), Register('dcr2b2', 4, 0x7ac8), Register('dcr2b3', 4, 0x7acc), Register('dcr2b4', 4, 0x7ad0), Register('dcr2b5', 4, 0x7ad4), Register('dcr2b6', 4, 0x7ad8), Register('dcr2b7', 4, 0x7adc), Register('dcr2b8', 4, 0x7ae0), Register('dcr2b9', 4, 0x7ae4), Register('dcr2ba', 4, 0x7ae8), Register('dcr2bb', 4, 0x7aec), Register('dcr2bc', 4, 0x7af0), Register('dcr2bd', 4, 0x7af4), Register('dcr2be', 4, 0x7af8), Register('dcr2bf', 4, 0x7afc), Register('dcr2c0', 4, 0x7b00), Register('dcr2c1', 4, 0x7b04), Register('dcr2c2', 4, 0x7b08), Register('dcr2c3', 4, 0x7b0c), Register('dcr2c4', 4, 0x7b10), Register('dcr2c5', 4, 0x7b14), Register('dcr2c6', 4, 0x7b18), Register('dcr2c7', 4, 0x7b1c), Register('dcr2c8', 4, 0x7b20), Register('dcr2c9', 4, 0x7b24), Register('dcr2ca', 4, 0x7b28), Register('dcr2cb', 4, 0x7b2c), Register('dcr2cc', 4, 0x7b30), Register('dcr2cd', 4, 0x7b34), Register('dcr2ce', 4, 0x7b38), Register('dcr2cf', 4, 0x7b3c), Register('dcr2d0', 4, 0x7b40), Register('dcr2d1', 4, 0x7b44), Register('dcr2d2', 4, 0x7b48), Register('dcr2d3', 4, 0x7b4c), Register('dcr2d4', 4, 0x7b50), Register('dcr2d5', 4, 0x7b54), Register('dcr2d6', 4, 0x7b58), Register('dcr2d7', 4, 0x7b5c), Register('dcr2d8', 4, 0x7b60), Register('dcr2d9', 4, 0x7b64), Register('dcr2da', 4, 0x7b68), Register('dcr2db', 4, 0x7b6c), Register('dcr2dc', 4, 0x7b70), Register('dcr2dd', 4, 0x7b74), Register('dcr2de', 4, 0x7b78), Register('dcr2df', 4, 0x7b7c), Register('dcr2e0', 4, 0x7b80), Register('dcr2e1', 4, 0x7b84), Register('dcr2e2', 4, 0x7b88), Register('dcr2e3', 4, 0x7b8c), Register('dcr2e4', 4, 0x7b90), Register('dcr2e5', 4, 0x7b94), Register('dcr2e6', 4, 0x7b98), Register('dcr2e7', 4, 0x7b9c), Register('dcr2e8', 4, 0x7ba0), Register('dcr2e9', 4, 0x7ba4), Register('dcr2ea', 4, 0x7ba8), Register('dcr2eb', 4, 0x7bac), Register('dcr2ec', 4, 0x7bb0), Register('dcr2ed', 4, 0x7bb4), Register('dcr2ee', 4, 0x7bb8), Register('dcr2ef', 4, 0x7bbc), Register('dcr2f0', 4, 0x7bc0), Register('dcr2f1', 4, 0x7bc4), Register('dcr2f2', 4, 0x7bc8), Register('dcr2f3', 4, 0x7bcc), Register('dcr2f4', 4, 0x7bd0), Register('dcr2f5', 4, 0x7bd4), Register('dcr2f6', 4, 0x7bd8), Register('dcr2f7', 4, 0x7bdc), Register('dcr2f8', 4, 0x7be0), Register('dcr2f9', 4, 0x7be4), Register('dcr2fa', 4, 0x7be8), Register('dcr2fb', 4, 0x7bec), Register('dcr2fc', 4, 0x7bf0), Register('dcr2fd', 4, 0x7bf4), Register('dcr2fe', 4, 0x7bf8), Register('dcr2ff', 4, 0x7bfc), Register('dcr300', 4, 0x7c00), Register('dcr301', 4, 0x7c04), Register('dcr302', 4, 0x7c08), Register('dcr303', 4, 0x7c0c), Register('dcr304', 4, 0x7c10), Register('dcr305', 4, 0x7c14), Register('dcr306', 4, 0x7c18), Register('dcr307', 4, 0x7c1c), Register('dcr308', 4, 0x7c20), Register('dcr309', 4, 0x7c24), Register('dcr30a', 4, 0x7c28), Register('dcr30b', 4, 0x7c2c), Register('dcr30c', 4, 0x7c30), Register('dcr30d', 4, 0x7c34), Register('dcr30e', 4, 0x7c38), Register('dcr30f', 4, 0x7c3c), Register('dcr310', 4, 0x7c40), Register('dcr311', 4, 0x7c44), Register('dcr312', 4, 0x7c48), Register('dcr313', 4, 0x7c4c), Register('dcr314', 4, 0x7c50), Register('dcr315', 4, 0x7c54), Register('dcr316', 4, 0x7c58), Register('dcr317', 4, 0x7c5c), Register('dcr318', 4, 0x7c60), Register('dcr319', 4, 0x7c64), Register('dcr31a', 4, 0x7c68), Register('dcr31b', 4, 0x7c6c), Register('dcr31c', 4, 0x7c70), Register('dcr31d', 4, 0x7c74), Register('dcr31e', 4, 0x7c78), Register('dcr31f', 4, 0x7c7c), Register('dcr320', 4, 0x7c80), Register('dcr321', 4, 0x7c84), Register('dcr322', 4, 0x7c88), Register('dcr323', 4, 0x7c8c), Register('dcr324', 4, 0x7c90), Register('dcr325', 4, 0x7c94), Register('dcr326', 4, 0x7c98), Register('dcr327', 4, 0x7c9c), Register('dcr328', 4, 0x7ca0), Register('dcr329', 4, 0x7ca4), Register('dcr32a', 4, 0x7ca8), Register('dcr32b', 4, 0x7cac), Register('dcr32c', 4, 0x7cb0), Register('dcr32d', 4, 0x7cb4), Register('dcr32e', 4, 0x7cb8), Register('dcr32f', 4, 0x7cbc), Register('dcr330', 4, 0x7cc0), Register('dcr331', 4, 0x7cc4), Register('dcr332', 4, 0x7cc8), Register('dcr333', 4, 0x7ccc), Register('dcr334', 4, 0x7cd0), Register('dcr335', 4, 0x7cd4), Register('dcr336', 4, 0x7cd8), Register('dcr337', 4, 0x7cdc), Register('dcr338', 4, 0x7ce0), Register('dcr339', 4, 0x7ce4), Register('dcr33a', 4, 0x7ce8), Register('dcr33b', 4, 0x7cec), Register('dcr33c', 4, 0x7cf0), Register('dcr33d', 4, 0x7cf4), Register('dcr33e', 4, 0x7cf8), Register('dcr33f', 4, 0x7cfc), Register('dcr340', 4, 0x7d00), Register('dcr341', 4, 0x7d04), Register('dcr342', 4, 0x7d08), Register('dcr343', 4, 0x7d0c), Register('dcr344', 4, 0x7d10), Register('dcr345', 4, 0x7d14), Register('dcr346', 4, 0x7d18), Register('dcr347', 4, 0x7d1c), Register('dcr348', 4, 0x7d20), Register('dcr349', 4, 0x7d24), Register('dcr34a', 4, 0x7d28), Register('dcr34b', 4, 0x7d2c), Register('dcr34c', 4, 0x7d30), Register('dcr34d', 4, 0x7d34), Register('dcr34e', 4, 0x7d38), Register('dcr34f', 4, 0x7d3c), Register('dcr350', 4, 0x7d40), Register('dcr351', 4, 0x7d44), Register('dcr352', 4, 0x7d48), Register('dcr353', 4, 0x7d4c), Register('dcr354', 4, 0x7d50), Register('dcr355', 4, 0x7d54), Register('dcr356', 4, 0x7d58), Register('dcr357', 4, 0x7d5c), Register('dcr358', 4, 0x7d60), Register('dcr359', 4, 0x7d64), Register('dcr35a', 4, 0x7d68), Register('dcr35b', 4, 0x7d6c), Register('dcr35c', 4, 0x7d70), Register('dcr35d', 4, 0x7d74), Register('dcr35e', 4, 0x7d78), Register('dcr35f', 4, 0x7d7c), Register('dcr360', 4, 0x7d80), Register('dcr361', 4, 0x7d84), Register('dcr362', 4, 0x7d88), Register('dcr363', 4, 0x7d8c), Register('dcr364', 4, 0x7d90), Register('dcr365', 4, 0x7d94), Register('dcr366', 4, 0x7d98), Register('dcr367', 4, 0x7d9c), Register('dcr368', 4, 0x7da0), Register('dcr369', 4, 0x7da4), Register('dcr36a', 4, 0x7da8), Register('dcr36b', 4, 0x7dac), Register('dcr36c', 4, 0x7db0), Register('dcr36d', 4, 0x7db4), Register('dcr36e', 4, 0x7db8), Register('dcr36f', 4, 0x7dbc), Register('dcr370', 4, 0x7dc0), Register('dcr371', 4, 0x7dc4), Register('dcr372', 4, 0x7dc8), Register('dcr373', 4, 0x7dcc), Register('dcr374', 4, 0x7dd0), Register('dcr375', 4, 0x7dd4), Register('dcr376', 4, 0x7dd8), Register('dcr377', 4, 0x7ddc), Register('dcr378', 4, 0x7de0), Register('dcr379', 4, 0x7de4), Register('dcr37a', 4, 0x7de8), Register('dcr37b', 4, 0x7dec), Register('dcr37c', 4, 0x7df0), Register('dcr37d', 4, 0x7df4), Register('dcr37e', 4, 0x7df8), Register('dcr37f', 4, 0x7dfc), Register('dcr380', 4, 0x7e00), Register('dcr381', 4, 0x7e04), Register('dcr382', 4, 0x7e08), Register('dcr383', 4, 0x7e0c), Register('dcr384', 4, 0x7e10), Register('dcr385', 4, 0x7e14), Register('dcr386', 4, 0x7e18), Register('dcr387', 4, 0x7e1c), Register('dcr388', 4, 0x7e20), Register('dcr389', 4, 0x7e24), Register('dcr38a', 4, 0x7e28), Register('dcr38b', 4, 0x7e2c), Register('dcr38c', 4, 0x7e30), Register('dcr38d', 4, 0x7e34), Register('dcr38e', 4, 0x7e38), Register('dcr38f', 4, 0x7e3c), Register('dcr390', 4, 0x7e40), Register('dcr391', 4, 0x7e44), Register('dcr392', 4, 0x7e48), Register('dcr393', 4, 0x7e4c), Register('dcr394', 4, 0x7e50), Register('dcr395', 4, 0x7e54), Register('dcr396', 4, 0x7e58), Register('dcr397', 4, 0x7e5c), Register('dcr398', 4, 0x7e60), Register('dcr399', 4, 0x7e64), Register('dcr39a', 4, 0x7e68), Register('dcr39b', 4, 0x7e6c), Register('dcr39c', 4, 0x7e70), Register('dcr39d', 4, 0x7e74), Register('dcr39e', 4, 0x7e78), Register('dcr39f', 4, 0x7e7c), Register('dcr3a0', 4, 0x7e80), Register('dcr3a1', 4, 0x7e84), Register('dcr3a2', 4, 0x7e88), Register('dcr3a3', 4, 0x7e8c), Register('dcr3a4', 4, 0x7e90), Register('dcr3a5', 4, 0x7e94), Register('dcr3a6', 4, 0x7e98), Register('dcr3a7', 4, 0x7e9c), Register('dcr3a8', 4, 0x7ea0), Register('dcr3a9', 4, 0x7ea4), Register('dcr3aa', 4, 0x7ea8), Register('dcr3ab', 4, 0x7eac), Register('dcr3ac', 4, 0x7eb0), Register('dcr3ad', 4, 0x7eb4), Register('dcr3ae', 4, 0x7eb8), Register('dcr3af', 4, 0x7ebc), Register('dcr3b0', 4, 0x7ec0), Register('dcr3b1', 4, 0x7ec4), Register('dcr3b2', 4, 0x7ec8), Register('dcr3b3', 4, 0x7ecc), Register('dcr3b4', 4, 0x7ed0), Register('dcr3b5', 4, 0x7ed4), Register('dcr3b6', 4, 0x7ed8), Register('dcr3b7', 4, 0x7edc), Register('dcr3b8', 4, 0x7ee0), Register('dcr3b9', 4, 0x7ee4), Register('dcr3ba', 4, 0x7ee8), Register('dcr3bb', 4, 0x7eec), Register('dcr3bc', 4, 0x7ef0), Register('dcr3bd', 4, 0x7ef4), Register('dcr3be', 4, 0x7ef8), Register('dcr3bf', 4, 0x7efc), Register('dcr3c0', 4, 0x7f00), Register('dcr3c1', 4, 0x7f04), Register('dcr3c2', 4, 0x7f08), Register('dcr3c3', 4, 0x7f0c), Register('dcr3c4', 4, 0x7f10), Register('dcr3c5', 4, 0x7f14), Register('dcr3c6', 4, 0x7f18), Register('dcr3c7', 4, 0x7f1c), Register('dcr3c8', 4, 0x7f20), Register('dcr3c9', 4, 0x7f24), Register('dcr3ca', 4, 0x7f28), Register('dcr3cb', 4, 0x7f2c), Register('dcr3cc', 4, 0x7f30), Register('dcr3cd', 4, 0x7f34), Register('dcr3ce', 4, 0x7f38), Register('dcr3cf', 4, 0x7f3c), Register('dcr3d0', 4, 0x7f40), Register('dcr3d1', 4, 0x7f44), Register('dcr3d2', 4, 0x7f48), Register('dcr3d3', 4, 0x7f4c), Register('dcr3d4', 4, 0x7f50), Register('dcr3d5', 4, 0x7f54), Register('dcr3d6', 4, 0x7f58), Register('dcr3d7', 4, 0x7f5c), Register('dcr3d8', 4, 0x7f60), Register('dcr3d9', 4, 0x7f64), Register('dcr3da', 4, 0x7f68), Register('dcr3db', 4, 0x7f6c), Register('dcr3dc', 4, 0x7f70), Register('dcr3dd', 4, 0x7f74), Register('dcr3de', 4, 0x7f78), Register('dcr3df', 4, 0x7f7c), Register('dcr3e0', 4, 0x7f80), Register('dcr3e1', 4, 0x7f84), Register('dcr3e2', 4, 0x7f88), Register('dcr3e3', 4, 0x7f8c), Register('dcr3e4', 4, 0x7f90), Register('dcr3e5', 4, 0x7f94), Register('dcr3e6', 4, 0x7f98), Register('dcr3e7', 4, 0x7f9c), Register('dcr3e8', 4, 0x7fa0), Register('dcr3e9', 4, 0x7fa4), Register('dcr3ea', 4, 0x7fa8), Register('dcr3eb', 4, 0x7fac), Register('dcr3ec', 4, 0x7fb0), Register('dcr3ed', 4, 0x7fb4), Register('dcr3ee', 4, 0x7fb8), Register('dcr3ef', 4, 0x7fbc), Register('dcr3f0', 4, 0x7fc0), Register('dcr3f1', 4, 0x7fc4), Register('dcr3f2', 4, 0x7fc8), Register('dcr3f3', 4, 0x7fcc), Register('dcr3f4', 4, 0x7fd0), Register('dcr3f5', 4, 0x7fd4), Register('dcr3f6', 4, 0x7fd8), Register('dcr3f7', 4, 0x7fdc), Register('dcr3f8', 4, 0x7fe0), Register('dcr3f9', 4, 0x7fe4), Register('dcr3fa', 4, 0x7fe8), Register('dcr3fb', 4, 0x7fec), Register('dcr3fc', 4, 0x7ff0), Register('dcr3fd', 4, 0x7ff4), Register('dcr3fe', 4, 0x7ff8), Register('dcr3ff', 4, 0x7ffc), Register('acc', 8, 0x10000) ] register_arch(['powerpc:le:32:quicc'], 32, Endness.LE, ArchPcode_PowerPC_LE_32_QUICC)
1.617188
2
makesense/graph.py
sieben/makesense
5
9632
# -*- coding: utf-8 -*- import json import pdb import os from os.path import join as pj import networkx as nx import pandas as pd from networkx.readwrite.json_graph import node_link_data def chain(): g = nx.Graph() # Horizontal for i in range(11, 15): g.add_edge(i, i + 1) for i in range(7, 10): g.add_edge(i, i + 1) for i in range(4, 6): g.add_edge(i, i + 1) for i in range(2, 3): g.add_edge(i, i + 1) g.add_node(1) # Trans height g.add_edge(1, 2) g.add_edge(1, 3) g.add_edge(2, 4) g.add_edge(2, 5) g.add_edge(3, 5) g.add_edge(3, 6) g.add_edge(4, 7) g.add_edge(4, 8) g.add_edge(5, 8) g.add_edge(5, 9) g.add_edge(6, 9) g.add_edge(6, 10) g.add_edge(7, 11) g.add_edge(7, 12) g.add_edge(8, 12) g.add_edge(8, 13) g.add_edge(9, 13) g.add_edge(9, 14) g.add_edge(10, 14) g.add_edge(10, 15) def tree(): with open("graph_radio.json", "w") as f: f.write(json_graph.dumps(g,sort_keys=True, indent=4, separators=(',', ': ') )) # Drawing pos = nx.spectral_layout(g) nx.draw(g, pos, node_color="g") nx.draw_networkx_nodes(g, pos, nodelist=[1], node_color="b") plt.savefig("topology_tree.pdf", format="pdf") plt.show() def plot_graph_chain(folder): g = nx.DiGraph() N = 7 for i in range(1, N): g.add_edge(i + 1, i) g.add_node(1, root=True) with open("radio_tree.json", "w") as f: f.write(json_graph.dumps(g, sort_keys=True, indent=4, separators=(',', ': '))) pos = nx.circular_layout(g) nx.draw(g, pos=pos) nx.draw_networkx_nodes(g, pos, node_color='g') nx.draw_networkx_nodes(g, pos, nodelist=[1], node_color='b') nx.draw_networkx_edges(g, pos, edge_color="r", arrows=True) plt.savefig(pj(folder, "topology_chain.pdf"), format="pdf") def flower(): g = wheel_graph(7) g.add_edge(6, 1) g.add_edge(7, 6) g.add_edge(8, 7) with open("radio_graph.json", "w") as f: f.write(json_graph.dumps(g, sort_keys=True, indent=4, separators=(',', ': '))) pos = nx.spring_layout(g) nx.draw(g, pos=pos) nx.draw_networkx_nodes(g,pos, node_color='g') nx.draw_networkx_nodes(g,pos, nodelist=[8], node_color='b') #nx.draw_networkx_edges(g, pos, edge_color="r", arrows=True) plt.savefig("topology_fleur.pdf", format="pdf") plt.show() def plot_graph(self): """ Plot the transmission graph of the simulation. TODO: Draw arrows and have a directed graph. http://goo.gl/Z697dH TODO: Graph with big nodes for big transmissions """ fig = plt.figure() ax1 = fig.add_subplot(111) ax1.set_title("Transmission / RPL tree") ax1.axis("off") val_color = {"udp_server": 0.5714285714285714} pos = {node: data["pos"] for node, data in self.radio_tree.nodes(data=True)} # color for all nodes node_color = [val_color.get(data["mote_type"], 0.25) for node, data in self.radio_tree.nodes(data=True)] # Drawing the nodes nx.draw_networkx_nodes(self.radio_tree, pos, node_color=node_color, ax=ax1) nx.draw_networkx_labels(self.radio_tree, pos, ax=ax1) # Drawing radio edges nx.draw_networkx_edges(self.radio_tree, pos, edgelist=self.radio_tree.edges(), width=8, alpha=0.5, ax=ax1) # Adding the depth of each node. with open(PJ(self.result_dir, "depth.csv")) as depth_f: reader = DictReader(depth_f) for row in reader: node = int(row["node"]) depth = row["depth"] ax1.text(pos[node][0] + 5, pos[node][1] + 5, depth, bbox=dict(facecolor='red', alpha=0.5), horizontalalignment='center') # Drawing RPL edges nx.draw_networkx_edges( self.rpl_tree, pos, edge_color='r', nodelist=[], arrows=True, ax=ax1) img_path = PJ(self.img_dir, "graph.pdf") fig.savefig(img_path, format="pdf") update_report(self.result_dir, "plot_graph", { "img_src": "img/graph.pdf", "comment": """ When the edge is thick it means edges are in an RPL instance. Otherwise it means that the two nodes can see each others. """, "text": """ We generate a random geometric graph then use information coming to the RPL root to construct the gateway representation of the RPL tree. We add into this tree representation the traffic generated. """}) def transmission_graph(self): """ Plot the transmission graph of the simulation. """ settings = self.settings["transmission_graph"] output_path = pj(self.result_folder_path, *settings["output_path"]) fig_rplinfo, ax_transmission_graph = plt.subplots() net = nx.Graph() # nodes mote_types = self.settings["mote_types"] motes = self.settings["motes"] position = {} for mote in motes: mote_type = mote["mote_type"] mote_id = mote["mote_id"] position[mote_id] = (mote["x"], mote["y"]) mote_types[mote_type] \ .setdefault("nodes", []) \ .append(mote["mote_id"]) # edges transmitting_range = self.settings["transmitting_range"] for couple in itertools.product(motes, motes): if 0 < distance(couple) <= transmitting_range: net.add_edge(couple[0]["mote_id"], couple[1]["mote_id"]) for mote_type in mote_types: color = mote_types[mote_type]["color"] nodelist = mote_types[mote_type]["nodes"] nx.draw_networkx_nodes(net, position, nodelist=nodelist, node_color=color, ax=ax_transmission_graph) nx.draw_networkx_edges(net, pos=position, ax=ax_transmission_graph) # labels nx.draw_networkx_labels(net, position, ax=ax_transmission_graph) plt.axis('off') plt.savefig(output_path) # save as PNG return ax_transmission_graph def rpl_graph(folder): """ Build up the RPL representation at the gateway """ output_folder = pj(folder, "results", "graph") if not os.path.exists(output_folder): os.makedirs(output_folder) df = pd.read_csv(pj(folder, "results", "messages.csv")) parent_df = df[df.message_type == "parent"] rpl_graph = nx.DiGraph() for c, p in parent_df.iterrows(): rpl_graph.add_edge(p["mote_id"], p["node"]) with open(pj(output_folder, "rpl_graph.json"), "w") as f: f.write(json.dumps(node_link_data(rpl_graph), sort_keys=True, indent=4))
2.78125
3
recipes/Python/52228_Remote_control_with_telnetlib/recipe-52228.py
tdiprima/code
2,023
9633
<reponame>tdiprima/code<filename>recipes/Python/52228_Remote_control_with_telnetlib/recipe-52228.py # auto_telnet.py - remote control via telnet import os, sys, string, telnetlib from getpass import getpass class AutoTelnet: def __init__(self, user_list, cmd_list, **kw): self.host = kw.get('host', 'localhost') self.timeout = kw.get('timeout', 600) self.command_prompt = kw.get('command_prompt', "$ ") self.passwd = {} for user in user_list: self.passwd[user] = getpass("Enter user '%s' password: " % user) self.telnet = telnetlib.Telnet() for user in user_list: self.telnet.open(self.host) ok = self.action(user, cmd_list) if not ok: print "Unable to process:", user self.telnet.close() def action(self, user, cmd_list): t = self.telnet t.write("\n") login_prompt = "login: " response = t.read_until(login_prompt, 5) if string.count(response, login_prompt): print response else: return 0 password_prompt = "Password:" t.write("%s\n" % user) response = t.read_until(password_prompt, 3) if string.count(response, password_prompt): print response else: return 0 t.write("%s\n" % self.passwd[user]) response = t.read_until(self.command_prompt, 5) if not string.count(response, self.command_prompt): return 0 for cmd in cmd_list: t.write("%s\n" % cmd) response = t.read_until(self.command_prompt, self.timeout) if not string.count(response, self.command_prompt): return 0 print response return 1 if __name__ == '__main__': basename = os.path.splitext(os.path.basename(sys.argv[0]))[0] logname = os.environ.get("LOGNAME", os.environ.get("USERNAME")) host = 'localhost' import getopt optlist, user_list = getopt.getopt(sys.argv[1:], 'c:f:h:') usage = """ usage: %s [-h host] [-f cmdfile] [-c "command"] user1 user2 ... -c command -f command file -h host (default: '%s') Example: %s -c "echo $HOME" %s """ % (basename, host, basename, logname) if len(sys.argv) < 2: print usage sys.exit(1) cmd_list = [] for (opt, optarg) in optlist: if opt == '-f': for r in open(optarg).readlines(): if string.rstrip(r): cmd_list.append(r) elif opt == '-c': command = optarg if command[0] == '"' and command[-1] == '"': command = command[1:-1] cmd_list.append(command) elif opt == '-h': host = optarg autoTelnet = AutoTelnet(user_list, cmd_list, host=host)
2.890625
3
FFTNet_dilconv.py
mimbres/FFTNet
0
9634
<filename>FFTNet_dilconv.py #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon May 7 09:46:10 2018 @author: sungkyun FFTNet model using 2x1 dil-conv """ import numpy as np import torch import torch.nn as nn import torch.nn.functional as F # Models with Preset (for convenience) ''' dim_input: dimension of input (256 for 8-bit mu-law input) num_layer: number of layers (11 in paper). receptive field = 2^11 (2,048) io_ch: number of input(=output) channels in each fft layers skip_ch: number of skip-channels, only required for fft-residual net. Annotations: B: batch dimension C: channel dimension L: length dimension ''' def fftnet_base(input_dim=256, num_layer=11, io_ch=256): return FFTNet(input_dim=input_dim, num_layer=num_layer, io_ch=io_ch, skip_ch=0, bias=True) def fftnet_residual(input_dim=256, num_layer=11, io_ch=256, skip_ch=256): return FFTNet(input_dim=input_dim, num_layer=num_layer, io_ch=io_ch, skip_ch=skip_ch, bais=True) # FFT_Block: define a basic FFT Block ''' FFT_Block: - using 2x1 dilated-conv, instead of LR split 1x1 conv. - described in the paper, section 2.2. - in case of the first layer used in the first FFT_Block, we use nn.embedding layer for one-hot index(0-255) entries. ''' class FFT_Block(nn.Module): def __init__(self, cond_dim=26, io_ch=int, recep_sz=int, bias=True): super(FFT_Block, self).__init__() self.cond_dim=cond_dim # Number of dimensions of condition input self.io_ch = io_ch self.recep_sz = recep_sz # Size of receptive field: i.e., the 1st layer has receptive field of 2^11(=2,048). 2nd has 2^10. self.bias = bias # If True, use bias in 1x1 conv. self.dilation = int(recep_sz / 2) self.conv_2x1_LR = nn.Conv1d(in_channels=self.io_ch, out_channels=self.io_ch, kernel_size=2, stride=1, dilation=self.dilation, bias=self.bias) self.conv_2x1_VLR = nn.Conv1d(in_channels=self.cond_dim, out_channels=self.io_ch, kernel_size=2, stride=1, dilation=self.dilation, bias=self.bias) self.conv_1x1_last = nn.Conv1d(in_channels=self.io_ch, out_channels=self.io_ch, kernel_size=1, stride=1, bias=self.bias) return None def forward(self, x, cond): z = self.conv_2x1_LR(x) # Eq(1), z = w_L*x_L + w_R*x_R z = z + self.conv_2x1_VLR(cond) # Eq(2), z = (WL ∗ xL + WR ∗ xR) + (VL ∗ hL + VR ∗ hR) x = F.relu(self.conv_1x1_last(F.relu(z))) # x = ReLU(conv1x1(ReLU(z))) return x ''' FFTNet: - [11 FFT_blocks] --> [FC_layer] --> [softmax] ''' class FFTNet(nn.Module): def __init__(self, input_dim=256, cond_dim=26, num_layer=11, io_ch=256, skip_ch=0, bias=True): super(FFTNet, self).__init__() self.input_dim = input_dim # 256 (=num_classes) self.cond_dim = cond_dim # 26 self.num_layer = num_layer # 11 self.io_ch = io_ch # 256 ch. in the paper self.skip_ch = skip_ch # Not implemented yet (no skip channel in the paper) self.bias = bias # If True, use bias in 2x1 conv. self.max_recep_sz = int(pow(2, self.num_layer)) # 2^11, max receptive field size # Embedding layer: one-hot_index -> embedding -> 256ch output self.input_embedding_layer = nn.Embedding(num_embeddings=self.input_dim, embedding_dim=self.io_ch) # Constructing FFT Blocks: blocks = nn.ModuleList() for l in range(self.num_layer): recep_sz = int(pow(2, self.num_layer-l)) # 1024, 512, ... 2 blocks.append( FFT_Block(cond_dim=self.cond_dim, io_ch=self.io_ch, recep_sz=recep_sz, bias=self.bias) ) self.fft_blocks=blocks # Final FC layer: self.fc = nn.Linear(in_features=self.io_ch, out_features=self.io_ch) return None def forward(self, x, cond, gen_mod=False): # Padding x: zpad_sz = int(self.max_recep_sz) x = F.pad(x, (zpad_sz, 0), 'constant', 128) # 128? or 0? # Embedding(x): x = self.input_embedding_layer(x) # In : BxL, Out: BxLxC x = x.permute(0,2,1) # Out: BxCxL # FFT_Blocks: for l in range(self.num_layer): # Padding cond: zpad_sz = int(self.max_recep_sz/pow(2, l)) padded_cond = F.pad(cond, (zpad_sz, 0), 'constant', 0) x = self.fft_blocks[l](x, padded_cond) if gen_mod is True: x = x[:,:,-1] # In generator mode, take the last one sample only. x = x.reshape(-1, 1, self.io_ch) # (BxC) --> (Bx1xC) else: x = x[:,:,:-1] # In training mode, right-omit 1 is required. x = x.permute(0,2,1) # (BxCxL) --> (BxLxC) x = self.fc(x) # (BxLxC) # NOTE: in PyTorch, softmax() is included in CE loss. return x
2.34375
2
snewpdag/plugins/Copy.py
SNEWS2/snewpdag
0
9635
<filename>snewpdag/plugins/Copy.py """ Copy - copy fields into other (possibly new) fields configuration: on: list of 'alert', 'revoke', 'report', 'reset' (optional: def 'alert' only) cp: ( (in,out), ... ) Field names take the form of dir1/dir2/dir3, which in the payload will be data[dir1][dir2][dir3] """ import logging from snewpdag.dag import Node class Copy(Node): def __init__(self, cp, **kwargs): self.cp = [] for op in cp: src = op[0].split('/') dst = op[1].split('/') self.cp.append( [src, dst[:-1], dst[-1]] ) self.on = kwargs.pop('on', [ 'alert' ]) super().__init__(**kwargs) def copy(self, data): for op in self.cp: v = data # should just follow references for k in op[0]: if k in v: v = v[k] else: logging.warning('Field {} not found from source {}'.format(k, op[0])) continue # v should now hold the value to be copied d = data for k in op[1]: if k not in d: d[k] = {} d = d[k] d[op[2]] = v return data def alert(self, data): return self.copy(data) if 'alert' in self.on else True def revoke(self, data): return self.copy(data) if 'revoke' in self.on else True def reset(self, data): return self.copy(data) if 'reset' in self.on else True def report(self, data): return self.copy(data) if 'report' in self.on else True
2.359375
2
pages/aboutus.py
BuildWeek-AirBnB-Optimal-Price/application
0
9636
<gh_stars>0 ''' houses each team member's link to personal GitHub io or website or blog space RJProctor ''' # Imports from 3rd party libraries import dash import dash_bootstrap_components as dbc import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output from app import app # 1 column layout # https://dash-bootstrap-components.opensource.faculty.ai/l/components/layout column1 = dbc.Col( [ dcc.Markdown( """ ## The Team: Select a link to learn more about each of our team members. """ ), ], ) # create footer column2 = dbc.Col( [ dcc.Markdown( """ **<NAME>** https://github.com/dscohen75/dscohen75.github.io https://medium.com/@debbiecohen_22419 **<NAME>** --- **<NAME>** https://medium.com/@eprecendez --- **<NAME>** https://jproctor-rebecca.github.io/ https://medium.com/@jproctor.m.ed.tn --- **Code Review Team Members:** <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME> """ ), ], ) layout = dbc.Row([column1, column2])
2.28125
2
Codes/Python32/Lib/importlib/test/extension/test_path_hook.py
eyantra/FireBird_Swiss_Knife
319
9637
from importlib import _bootstrap from . import util import collections import imp import sys import unittest class PathHookTests(unittest.TestCase): """Test the path hook for extension modules.""" # XXX Should it only succeed for pre-existing directories? # XXX Should it only work for directories containing an extension module? def hook(self, entry): return _bootstrap._file_path_hook(entry) def test_success(self): # Path hook should handle a directory where a known extension module # exists. self.assertTrue(hasattr(self.hook(util.PATH), 'find_module')) def test_main(): from test.support import run_unittest run_unittest(PathHookTests) if __name__ == '__main__': test_main()
2.515625
3
3. count_words/solution.py
dcragusa/WeeklyPythonExerciseB2
0
9638
import os from glob import iglob from concurrent.futures import ThreadPoolExecutor def count_words_file(path): if not os.path.isfile(path): return 0 with open(path) as file: return sum(len(line.split()) for line in file) def count_words_sequential(pattern): return sum(map(count_words_file, iglob(pattern))) def count_words_threading(pattern): with ThreadPoolExecutor() as pool: return sum(pool.map(count_words_file, iglob(pattern)))
3.078125
3
kafka-connect-azblob/docs/autoreload.py
cirobarradov/kafka-connect-hdfs-datalab
0
9639
<reponame>cirobarradov/kafka-connect-hdfs-datalab<filename>kafka-connect-azblob/docs/autoreload.py #!/usr/bin/env python from livereload import Server, shell server = Server() server.watch('*.rst', shell('make html')) server.serve()
1.234375
1
keras_textclassification/conf/path_config.py
atom-zh/Keras-TextClassification
0
9640
# -*- coding: UTF-8 -*- # !/usr/bin/python # @time :2019/6/5 21:04 # @author :Mo # @function :file of path import os import pathlib import sys # 项目的根目录 path_root = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir)) path_root = path_root.replace('\\', '/') path_top = str(pathlib.Path(os.path.abspath(__file__)).parent.parent.parent) path_top = path_top.replace('\\', '/') # path of embedding path_embedding_user_dict = path_root + '/data/embeddings/user_dict.txt' path_embedding_random_char = path_root + '/data/embeddings/term_char.txt' path_embedding_random_word = path_root + '/data/embeddings/term_word.txt' path_embedding_bert = path_root + '/data/embeddings/chinese_L-12_H-768_A-12/' path_embedding_xlnet = path_root + '/data/embeddings/chinese_xlnet_mid_L-24_H-768_A-12/' path_embedding_albert = path_root + '/data/embeddings/albert_base_zh' path_embedding_vector_word2vec_char = path_root + '/data/embeddings/multi_label_char.vec' path_embedding_vector_word2vec_word = path_root + '/data/embeddings/multi_label_word.vec' path_embedding_vector_word2vec_char_bin = path_root + '/data/embeddings/multi_label_char.bin' path_embedding_vector_word2vec_word_bin = path_root + '/data/embeddings/multi_label_word.bin' # classify data of baidu qa 2019 path_baidu_qa_2019_train = path_root + '/data/baidu_qa_2019/baike_qa_train.csv' path_baidu_qa_2019_valid = path_root + '/data/baidu_qa_2019/baike_qa_valid.csv' # 今日头条新闻多标签分类 path_byte_multi_news_train = path_root + '/data/byte_multi_news/train.csv' path_byte_multi_news_valid = path_root + '/data/byte_multi_news/valid.csv' path_byte_multi_news_label = path_root + '/data/byte_multi_news/labels.csv' # classify data of baidu qa 2019 path_sim_webank_train = path_root + '/data/sim_webank/train.csv' path_sim_webank_valid = path_root + '/data/sim_webank/valid.csv' path_sim_webank_test = path_root + '/data/sim_webank/test.csv' # classfiy multi labels 2021 path_multi_label_train = path_root + '/data/multi_label/train.csv' path_multi_label_valid = path_root + '/data/multi_label/valid.csv' path_multi_label_labels = path_root + '/data/multi_label/labels.csv' path_multi_label_tests = path_root + '/data/multi_label/tests.csv' # 路径抽象层 path_label = path_multi_label_labels path_train = path_multi_label_train path_valid = path_multi_label_valid path_tests = path_multi_label_tests path_edata = path_root + "/../out/error_data.csv" # fast_text config path_out = path_top + "/out/" # 模型目录 path_model_dir = path_root + "/data/model/fast_text/" # 语料地址 path_model = path_root + '/data/model/fast_text/model_fast_text.h5' # 超参数保存地址 path_hyper_parameters = path_root + '/data/model/fast_text/hyper_parameters.json' # embedding微调保存地址 path_fineture = path_root + "/data/model/fast_text/embedding_trainable.h5" # 保持 分类-标签 索引 path_category = path_root + '/data/multi_label/category2labels.json' # l2i_i2l path_l2i_i2l = path_root + '/data/multi_label/l2i_i2l.json'
2.25
2
tests/test_apyhgnc.py
robertopreste/apyhgnc
0
9641
#!/usr/bin/env python # -*- coding: UTF-8 -*- # Created by <NAME> import pytest import asyncio from pandas.testing import assert_frame_equal from apyhgnc import apyhgnc # apyhgnc.info def test_info_searchableFields(searchable_fields): result = apyhgnc.info().searchableFields assert result == searchable_fields def test_info_storedFields(stored_fields): result = apyhgnc.info().storedFields assert result == stored_fields def test_info_url(): result = apyhgnc.info().url assert result == "http://rest.genenames.org/info" # apyhgnc.fetch def test_fetch_symbol_znf3(df_fetch_symbol_znf3): result = apyhgnc.fetch("symbol", "ZNF3") assert_frame_equal(result, df_fetch_symbol_znf3) def test_fetch_symbol_znf3_async(df_fetch_symbol_znf3): loop = asyncio.get_event_loop() result = loop.run_until_complete( apyhgnc.afetch("symbol", "ZNF3") ) assert_frame_equal(result, df_fetch_symbol_znf3) # apyhgnc.search def test_search_all_braf(df_search_all_braf): result = apyhgnc.search("BRAF") assert_frame_equal(result, df_search_all_braf) def test_search_all_braf_async(df_search_all_braf): loop = asyncio.get_event_loop() result = loop.run_until_complete( apyhgnc.asearch("BRAF") ) assert_frame_equal(result, df_search_all_braf) def test_search_symbol_braf(df_search_symbol_braf): result = apyhgnc.search("symbol", "BRAF") assert_frame_equal(result, df_search_symbol_braf) def test_search_symbol_braf_async(df_search_symbol_braf): loop = asyncio.get_event_loop() result = loop.run_until_complete( apyhgnc.asearch("symbol", "BRAF") ) assert_frame_equal(result, df_search_symbol_braf) def test_search_symbols_braf_znf3(df_search_symbols_braf_znf3): result = apyhgnc.search(symbol=["BRAF", "ZNF3"]) assert_frame_equal(result, df_search_symbols_braf_znf3) def test_search_symbols_braf_znf3_async(df_search_symbols_braf_znf3): loop = asyncio.get_event_loop() result = loop.run_until_complete( apyhgnc.asearch(symbol=["BRAF", "ZNF3"]) ) assert_frame_equal(result, df_search_symbols_braf_znf3) def test_search_symbol_and_status(df_search_symbol_and_status): result = apyhgnc.search(symbol="BRAF", status="Approved") assert_frame_equal(result, df_search_symbol_and_status) def test_search_symbol_and_status_async(df_search_symbol_and_status): loop = asyncio.get_event_loop() result = loop.run_until_complete( apyhgnc.asearch(symbol="BRAF", status="Approved") ) assert_frame_equal(result, df_search_symbol_and_status)
2.3125
2
bdaq/tools/extract_enums.py
magnium/pybdaq
0
9642
import os.path import argparse from xml.etree import ElementTree as ET class ExtractedEnum(object): def __init__(self, tag_name, value_names): self.tag_name = tag_name self.value_names = value_names def write_pxd(self, file_): file_.write("\n ctypedef enum {}:\n".format(self.tag_name)) for name in self.value_names: file_.write(" " * 8 + "{}\n".format(name)) def write_pyx(self, file_): file_.write("\nclass {}(enum.Enum):\n".format(self.tag_name)) for name in self.value_names: file_.write(" " * 4 + "{0} = _c.{0}\n".format(name)) @staticmethod def from_xml(element, typedefs): value_names = [v.attrib["name"] for v in element.findall("EnumValue")] return ExtractedEnum( typedefs[element.attrib["id"]], value_names) def find_enums(file_or_path): # parse XML tree = ET.parse(file_or_path) # extract typedefs typedefs = {} for element in tree.findall("Typedef"): typedefs[element.attrib["type"]] = element.attrib["name"] # extract enums enums = [] for element in tree.findall("Enumeration"): enums.append(ExtractedEnum.from_xml(element, typedefs)) print "Found {} enums to extract.".format(len(enums)) return enums def write_cython(pyx_file, pxd_file, enums): # write pxd file header pxd_file.write("# GENERATED FILE; DO NOT MODIFY\n\n") pxd_file.write( 'cdef extern from "bdaqctrl.h" namespace "Automation::BDaq":') # write pyx file header pyx_file.write("# GENERATED FILE; DO NOT MODIFY\n\n") pyx_file.write("import enum\n\n") pyx_file.write("cimport wrapper_enums_c as _c\n\n") # write enums for extracted in enums: print "Extracting definition of {}...".format(extracted.tag_name) extracted.write_pyx(pyx_file) extracted.write_pxd(pxd_file) print "Done extracting definitions." def main(): # parse script arguments parser = argparse.ArgumentParser( description="Extract enum definitions from header.") parser.add_argument( "--xml-in", default="bdaqctrl.h.xml", help="path to gccxml result") parser.add_argument( "--path-out", default=".", help="path to output directory") args = parser.parse_args() # extract enums enums = find_enums(args.xml_in) out_pyx_path = os.path.join(args.path_out, "wrapper_enums.pyx") out_pxd_path = os.path.join(args.path_out, "wrapper_enums_c.pxd") with open(out_pyx_path, "wb") as out_pyx_file: with open(out_pxd_path, "wb") as out_pxd_file: write_cython(out_pyx_file, out_pxd_file, enums) if __name__ == "__main__": main()
2.859375
3
Objetos/biblioteca.py
SebaB29/Python
0
9643
<filename>Objetos/biblioteca.py<gh_stars>0 class Libro: def __init__(self, titulo, autor): """...""" self.titulo = titulo self.autor = autor def obtener_titulo(self): """...""" return str(self.titulo) def obtener_autor(self): """...""" return str(self.autor) class Biblioteca: def __init__(self): """...""" self.coleccion = set() def agregar_libro(self, libro): """...""" self.coleccion.add((libro.titulo, libro.autor)) def sacar_libro(self, titulo, autor): """...""" if not (titulo, autor) in self.coleccion: raise Exception("El libro no esta en la colección") self.coleccion.remove((titulo, autor)) return f"Libro: {titulo}, Autor: {autor}" def contiene_libro(self, titulo, autor): """...""" return (titulo, autor) in self.coleccion libro = Libro("HyP", "JK") libro1 = Libro("La Isla M", "JCortazar") libro2 = Libro("El tunel", "Sabato") biblio = Biblioteca() biblio.agregar_libro(libro) biblio.agregar_libro(libro1) biblio.agregar_libro(libro2) print(biblio.contiene_libro("HyP", "JK")) print(biblio.sacar_libro("HyP", "JK")) print(biblio.contiene_libro("HyP", "JK"))
3.40625
3
parser_tool/tests/test_htmlgenerator.py
Harvard-ATG/visualizing_russian_tools
2
9644
<filename>parser_tool/tests/test_htmlgenerator.py<gh_stars>1-10 # -*- coding: utf-8 -*- import unittest from xml.etree import ElementTree as ET from parser_tool import tokenizer from parser_tool import htmlgenerator class TestHtmlGenerator(unittest.TestCase): def _maketokendict(self, **kwargs): token_text = kwargs.get("token", "") token_dict = { "token": token_text, "index": kwargs.get("index", 0), "offset": kwargs.get("offset", 0), "tokentype": kwargs.get("tokentype", tokenizer.TOKEN_WORD), "canonical": kwargs.get("canonical", tokenizer.canonical(token_text)), "form_ids": kwargs.get("form_ids", []), "level": kwargs.get("level", ""), } return token_dict def test_render_token_russian_word(self): token_text = "п<PASSWORD>" token_dict = self._maketokendict(token=token_text, tokentype=tokenizer.TOKEN_RUS, level="3A", form_ids=["174128"]) rendered = htmlgenerator.render_token(token_dict) node_type, el = rendered['node_type'], rendered['element'] self.assertEqual(htmlgenerator.ELEMENT_NODE, node_type) self.assertEqual("span", el.tag) self.assertEqual({ "class": "word parsed level3", "data-form-ids": ",".join(token_dict['form_ids']), "data-level": token_dict['level'] }, el.attrib) self.assertEqual(token_text, el.text) def test_render_token_english_word(self): token_text = "<PASSWORD>" token_dict = self._maketokendict(token=token_text, tokentype=tokenizer.TOKEN_WORD) rendered = htmlgenerator.render_token(token_dict) node_type, el = rendered['node_type'], rendered['element'] self.assertEqual(htmlgenerator.ELEMENT_NODE, node_type) self.assertEqual("span", el.tag) self.assertEqual({"class": "word"}, el.attrib) self.assertEqual(token_text, el.text) def test_render_token_with_multiple_spaces(self): token_text = " " * 3 expected_text = token_text.replace(" ", "\u00A0\u00A0") token_dict = self._maketokendict(token=token_text, tokentype=tokenizer.TOKEN_SPACE) rendered = htmlgenerator.render_token(token_dict) self.assertEqual(htmlgenerator.TEXT_NODE, rendered['node_type']) self.assertEqual(expected_text, rendered['text']) def test_render_token_with_punctuation(self): token_text = "')." expected_text = token_text token_dict = self._maketokendict(token=token_text, tokentype=tokenizer.TOKEN_SPACE) rendered = htmlgenerator.render_token(token_dict) self.assertEqual(htmlgenerator.TEXT_NODE, rendered['node_type']) self.assertEqual(expected_text, rendered['text']) def test_tokens_with_leading_punct_to_html(self): # (собака) dog tokens = [ self._maketokendict(token="(", tokentype=tokenizer.TOKEN_PUNCT), self._maketokendict(token="собака", tokentype=tokenizer.TOKEN_RUS, level="1E", form_ids=["7599"]), self._maketokendict(token=")", tokentype=tokenizer.TOKEN_RUS), self._maketokendict(token=" ", tokentype=tokenizer.TOKEN_SPACE), self._maketokendict(token="dog", tokentype=tokenizer.TOKEN_WORD), ] html = htmlgenerator.tokens2html(tokens) expected_html = '<pre class="words">(<span data-form-ids="7599" data-level="1E" class="word parsed level1">собака</span><span class="word">)</span> <span class="word">dog</span></pre>' self.assertEqual(expected_html, html) def test_tokens2html(self): tokens = [ self._maketokendict(token="A", tokentype=tokenizer.TOKEN_WORD), self._maketokendict(token=" ", tokentype=tokenizer.TOKEN_SPACE), self._maketokendict(token="первоку́рсник", tokentype=tokenizer.TOKEN_RUS, level="3A", form_ids=["174128"]), self._maketokendict(token=" ", tokentype=tokenizer.TOKEN_SPACE), self._maketokendict(token="|", tokentype=tokenizer.TOKEN_PUNCT), self._maketokendict(token="первоку́рсница", tokentype=tokenizer.TOKEN_RUS, level="3A", form_ids=["174128"]), self._maketokendict(token=" ", tokentype=tokenizer.TOKEN_SPACE), ] html = htmlgenerator.tokens2html(tokens) root = ET.fromstring(html) # Check the root element (e.g. container) self.assertEqual("pre", root.tag) self.assertEqual({"class": "words"}, root.attrib) # Check that we have the expected number of child elements (1 element for each word or russian token) expected_word_elements = sum([1 for t in tokens if t['tokentype'] in (tokenizer.TOKEN_WORD, tokenizer.TOKEN_RUS)]) self.assertEqual(expected_word_elements, len(root)) # Now check the first few tokens... # 1) Check that the first child contains the text of the first token self.assertEqual(tokens[0]['token'], root[0].text) self.assertEqual("span", root[0].tag) self.assertEqual({"class": "word"}, root[0].attrib) # 2) Check that the first child's tail contains the text of the second token since it's a space token self.assertEqual(tokens[1]['token'], root[0].tail) # 3) Check that the second child contains the text of the third token self.assertEqual(tokens[2]['token'], root[1].text) self.assertEqual("span", root[1].tag) self.assertEqual({'class': 'word parsed level3', 'data-form-ids': '174128', 'data-level': '3A'}, root[1].attrib)
2.671875
3
taiga/hooks/gitlab/migrations/0002_auto_20150703_1102.py
threefoldtech/Threefold-Circles
1
9645
<reponame>threefoldtech/Threefold-Circles # -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations from django.core.files import File def update_gitlab_system_user_photo_to_v2(apps, schema_editor): # We get the model from the versioned app registry; # if we directly import it, it'll be the wrong version User = apps.get_model("users", "User") db_alias = schema_editor.connection.alias try: user = User.objects.using(db_alias).get(username__startswith="gitlab-", is_active=False, is_system=True) f = open("taiga/hooks/gitlab/migrations/logo-v2.png", "rb") user.photo.save("logo.png", File(f)) user.save() except User.DoesNotExist: pass def update_gitlab_system_user_photo_to_v1(apps, schema_editor): # We get the model from the versioned app registry; # if we directly import it, it'll be the wrong version User = apps.get_model("users", "User") db_alias = schema_editor.connection.alias try: user = User.objects.using(db_alias).get(username__startswith="gitlab-", is_active=False, is_system=True) f = open("taiga/hooks/gitlab/migrations/logo.png", "rb") user.photo.save("logo.png", File(f)) user.save() except User.DoesNotExist: pass class Migration(migrations.Migration): dependencies = [ ('gitlab', '0001_initial'), ('users', '0011_user_theme'), ] operations = [ migrations.RunPython(update_gitlab_system_user_photo_to_v2, update_gitlab_system_user_photo_to_v1), ]
2.03125
2
external_plugin_deps.bzl
michalgagat/plugins_oauth
143
9646
<reponame>michalgagat/plugins_oauth load("//tools/bzl:maven_jar.bzl", "maven_jar") def external_plugin_deps(omit_commons_codec = True): JACKSON_VERS = "2.10.2" maven_jar( name = "scribejava-core", artifact = "com.github.scribejava:scribejava-core:6.9.0", sha1 = "ed761f450d8382f75787e8fee9ae52e7ec768747", ) maven_jar( name = "jackson-annotations", artifact = "com.fasterxml.jackson.core:jackson-annotations:" + JACKSON_VERS, sha1 = "3a13b6105946541b8d4181a0506355b5fae63260", ) maven_jar( name = "jackson-databind", artifact = "com.fasterxml.jackson.core:jackson-databind:" + JACKSON_VERS, sha1 = "0528de95f198afafbcfb0c09d2e43b6e0ea663ec", deps = [ "@jackson-annotations//jar", ], ) if not omit_commons_codec: maven_jar( name = "commons-codec", artifact = "commons-codec:commons-codec:1.4", sha1 = "4216af16d38465bbab0f3dff8efa14204f7a399a", )
1.578125
2
11.-Operaciones_entero_con_float_python.py
emiliocarcanobringas/11.-Operaciones_entero_con_float_python
0
9647
<filename>11.-Operaciones_entero_con_float_python.py # Este programa muestra la suma de dos variables, de tipo int y float print("Este programa muestra la suma de dos variables, de tipo int y float") print("También muestra que la variable que realiza la operación es de tipo float") numero1 = 7 numero2 = 3.1416 sumadeambos = numero1 + numero2 print("El resultado de la suma es: ") print(sumadeambos) print(type(sumadeambos)) # Este programa fue escrito por <NAME>
3.90625
4
main_gat.py
basiralab/RG-Select
1
9648
# -*- coding: utf-8 -*- from sklearn import preprocessing from torch.autograd import Variable from models_gat import GAT import os import torch import numpy as np import argparse import pickle import sklearn.metrics as metrics import cross_val import time import random torch.manual_seed(0) np.random.seed(0) random.seed(0) device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') def evaluate(dataset, model_GAT, args, threshold_value, model_name): """ Parameters ---------- dataset : dataloader (dataloader for the validation/test dataset). model_GCN : nn model (GAT model). args : arguments threshold_value : float (threshold for adjacency matrices). Description ---------- This methods performs the evaluation of the model on test/validation dataset Returns ------- test accuracy. """ model_GAT.eval() labels = [] preds = [] for batch_idx, data in enumerate(dataset): adj = Variable(data['adj'].float(), requires_grad=False).to(device) labels.append(data['label'].long().numpy()) adj = torch.squeeze(adj) features = np.identity(adj.shape[0]) features = Variable(torch.from_numpy(features).float(), requires_grad=False).cpu() if args.threshold in ["median", "mean"]: adj = torch.where(adj > threshold_value, torch.tensor([1.0]), torch.tensor([0.0])) ypred = model_GAT(features, adj) _, indices = torch.max(ypred, 1) preds.append(indices.cpu().data.numpy()) labels = np.hstack(labels) preds = np.hstack(preds) simple_r = {'labels':labels,'preds':preds} with open("./gat/Labels_and_preds/"+model_name+".pickle", 'wb') as f: pickle.dump(simple_r, f) result = {'prec': metrics.precision_score(labels, preds, average='macro'), 'recall': metrics.recall_score(labels, preds, average='macro'), 'acc': metrics.accuracy_score(labels, preds), 'F1': metrics.f1_score(labels, preds, average="micro")} if args.evaluation_method == 'model assessment': name = 'Test' if args.evaluation_method == 'model selection': name = 'Validation' print(name, " accuracy:", result['acc']) return result['acc'] def minmax_sc(x): min_max_scaler = preprocessing.MinMaxScaler() x = min_max_scaler.fit_transform(x) return x def train(args, train_dataset, val_dataset, model_GAT, threshold_value, model_name): """ Parameters ---------- args : arguments train_dataset : dataloader (dataloader for the validation/test dataset). val_dataset : dataloader (dataloader for the validation/test dataset). model_GAT : nn model (GAT model). threshold_value : float (threshold for adjacency matrices). Description ---------- This methods performs the training of the model on train dataset and calls evaluate() method for evaluation. Returns ------- test accuracy. """ params = list(model_GAT.parameters()) optimizer = torch.optim.Adam(params, lr=args.lr, weight_decay=args.weight_decay) test_accs = [] train_loss=[] val_acc=[] for epoch in range(args.num_epochs): print("Epoch ",epoch) print("Size of Training Set:" + str(len(train_dataset))) print("Size of Validation Set:" + str(len(val_dataset))) model_GAT.train() total_time = 0 avg_loss = 0.0 preds = [] labels = [] for batch_idx, data in enumerate(train_dataset): begin_time = time.time() adj = Variable(data['adj'].float(), requires_grad=False).to(device) label = Variable(data['label'].long()).to(device) #adj_id = Variable(data['id'].int()).to(device) adj = torch.squeeze(adj) features = np.identity(adj.shape[0]) features = Variable(torch.from_numpy(features).float(), requires_grad=False).cpu() if args.threshold in ["median", "mean"]: adj = torch.where(adj > threshold_value, torch.tensor([1.0]), torch.tensor([0.0])) ypred = model_GAT(features, adj) _, indices = torch.max(ypred, 1) preds.append(indices.cpu().data.numpy()) labels.append(data['label'].long().numpy()) loss = model_GAT.loss(ypred, label) model_GAT.zero_grad() loss.backward() #nn.utils.clip_grad_norm_(model_DIFFPOOL.parameters(), args.clip) optimizer.step() avg_loss += loss elapsed = time.time() - begin_time total_time += elapsed if epoch == args.num_epochs-1: model_GAT.is_trained = True preds = np.hstack(preds) labels = np.hstack(labels) print("Train accuracy : ", np.mean( preds == labels )) test_acc = evaluate(val_dataset, model_GAT, args, threshold_value, model_name) print('Avg loss: ', avg_loss, '; epoch time: ', total_time) test_accs.append(test_acc) train_loss.append(avg_loss) val_acc.append(test_acc) path = './gat/weights/W_'+model_name+'.pickle' if os.path.exists(path): os.remove(path) os.rename('GAT_W.pickle',path) los_p = {'loss':train_loss} with open("./gat/training_loss/Training_loss_"+model_name+".pickle", 'wb') as f: pickle.dump(los_p, f) torch.save(model_GAT,"./gat/models/GAT_"+model_name+".pt") return test_acc def load_data(args): """ Parameters ---------- args : arguments Description ---------- This methods loads the adjacency matrices representing the args.view -th view in dataset Returns ------- List of dictionaries{adj, label, id} """ #Load graphs and labels with open('data/'+args.dataset+'/'+args.dataset+'_edges','rb') as f: multigraphs = pickle.load(f) with open('data/'+args.dataset+'/'+args.dataset+'_labels','rb') as f: labels = pickle.load(f) adjacencies = [multigraphs[i][:,:,args.view] for i in range(len(multigraphs))] #Normalize inputs if args.NormalizeInputGraphs==True: for subject in range(len(adjacencies)): adjacencies[subject] = minmax_sc(adjacencies[subject]) #Create List of Dictionaries G_list=[] for i in range(len(labels)): G_element = {"adj": adjacencies[i],"label": labels[i],"id": i,} G_list.append(G_element) return G_list def arg_parse(dataset, view, num_shots=2, cv_number=5): """ arguments definition method """ parser = argparse.ArgumentParser(description='Graph Classification') parser.add_argument('--mode', type=str, default='train', choices=['train', 'test']) parser.add_argument('--v', type=str, default=1) parser.add_argument('--data', type=str, default='Sample_dataset', choices = [ f.path[5:] for f in os.scandir("data") if f.is_dir() ]) parser.add_argument('--dataset', type=str, default=dataset, help='Dataset') parser.add_argument('--view', type=int, default=view, help = 'view index in the dataset') parser.add_argument('--num_epochs', type=int, default=1, #50 help='Training Epochs') parser.add_argument('--num_shots', type=int, default=num_shots, #100 help='number of shots') parser.add_argument('--cv_number', type=int, default=cv_number, help='number of validation folds.') parser.add_argument('--NormalizeInputGraphs', default=False, action='store_true', help='Normalize Input adjacency matrices of graphs') parser.add_argument('--evaluation_method', type=str, default='model assessment', help='evaluation method, possible values : model selection, model assessment') parser.add_argument('--threshold', dest='threshold', default='mean', help='threshold the graph adjacency matrix. Possible values: no_threshold, median, mean') parser.add_argument('--no-cuda', action='store_true', default=False, help='Disables CUDA training.') parser.add_argument('--num-classes', dest='num_classes', type=int, default=2, help='Number of label classes') parser.add_argument('--lr', type=float, default=0.001, help='Initial learning rate.') parser.add_argument('--weight_decay', type=float, default=5e-4, help='Weight decay (L2 loss on parameters).') parser.add_argument('--hidden', type=int, default=8, help='Number of hidden units.') parser.add_argument('--nb_heads', type=int, default=8, help='Number of head attentions.') parser.add_argument('--dropout', type=float, default=0.8, help='Dropout rate (1 - keep probability).') parser.add_argument('--alpha', type=float, default=0.2, help='Alpha for the leaky_relu.') return parser.parse_args() def benchmark_task(args, model_name): """ Parameters ---------- args : Arguments Description ---------- Initiates the model and performs train/test or train/validation splits and calls train() to execute training and evaluation. Returns ------- test_accs : test accuracies (list) """ G_list = load_data(args) num_nodes = G_list[0]['adj'].shape[0] test_accs = [] folds = cross_val.stratify_splits(G_list,args) [random.shuffle(folds[i]) for i in range(len(folds))] for i in range(args.cv_number): train_set, validation_set, test_set = cross_val.datasets_splits(folds, args, i) if args.evaluation_method =='model selection': train_dataset, val_dataset, threshold_value = cross_val.model_selection_split(train_set, validation_set, args) if args.evaluation_method =='model assessment': train_dataset, val_dataset, threshold_value = cross_val.model_assessment_split(train_set, validation_set, test_set, args) print("CV : ",i) model_GAT = GAT(nfeat=num_nodes, nhid=args.hidden, nclass=args.num_classes, dropout=args.dropout, nheads=args.nb_heads, alpha=args.alpha) test_acc = train(args, train_dataset, val_dataset, model_GAT, threshold_value, model_name+"_CV_"+str(i)+"_view_"+str(args.view)) test_accs.append(test_acc) return test_accs def test_scores(dataset, view, model_name, cv_number): args = arg_parse(dataset, view, cv_number=cv_number) print("Main : ",args) test_accs = benchmark_task(args, model_name) print("test accuracies ",test_accs) return test_accs def two_shot_trainer(dataset, view, num_shots): args = arg_parse(dataset, view, num_shots=num_shots) torch.manual_seed(0) np.random.seed(0) random.seed(0) start = time.time() for i in range(args.num_shots): model = "gat" model_name = "Few_Shot_"+dataset+"_"+model + str(i) print("Shot : ",i) with open('./Two_shot_samples_views/'+dataset+'_view_'+str(view)+'_shot_'+str(i)+'_train','rb') as f: train_set = pickle.load(f) with open('./Two_shot_samples_views/'+dataset+'_view_'+str(view)+'_shot_'+str(i)+'_test','rb') as f: test_set = pickle.load(f) num_nodes = train_set[0]['adj'].shape[0] model_GAT = GAT(nfeat=num_nodes, nhid=args.hidden, nclass=args.num_classes, dropout=args.dropout, nheads=args.nb_heads, alpha=args.alpha) train_dataset, val_dataset, threshold_value = cross_val.two_shot_loader(train_set, test_set, args) test_acc = train(args, train_dataset, val_dataset, model_GAT, threshold_value, model_name+"_view_"+str(view)) print("Test accuracy:"+str(test_acc)) print('load data using ------>', time.time()-start)
2.5
2
dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Python/piexif/_dump.py
jeikabu/lumberyard
8
9649
import copy import numbers import struct from ._common import * from ._exif import * TIFF_HEADER_LENGTH = 8 def dump(exif_dict_original): """ py:function:: piexif.load(data) Return exif as bytes. :param dict exif: Exif data({"0th":dict, "Exif":dict, "GPS":dict, "Interop":dict, "1st":dict, "thumbnail":bytes}) :return: Exif :rtype: bytes """ exif_dict = copy.deepcopy(exif_dict_original) header = b"Exif\x00\x00\x4d\x4d\x00\x2a\x00\x00\x00\x08" exif_is = False gps_is = False interop_is = False first_is = False if "0th" in exif_dict: zeroth_ifd = exif_dict["0th"] else: zeroth_ifd = {} if (("Exif" in exif_dict) and len(exif_dict["Exif"]) or ("Interop" in exif_dict) and len(exif_dict["Interop"]) ): zeroth_ifd[ImageIFD.ExifTag] = 1 exif_is = True exif_ifd = exif_dict["Exif"] if ("Interop" in exif_dict) and len(exif_dict["Interop"]): exif_ifd[ExifIFD. InteroperabilityTag] = 1 interop_is = True interop_ifd = exif_dict["Interop"] elif ExifIFD. InteroperabilityTag in exif_ifd: exif_ifd.pop(ExifIFD.InteroperabilityTag) elif ImageIFD.ExifTag in zeroth_ifd: zeroth_ifd.pop(ImageIFD.ExifTag) if ("GPS" in exif_dict) and len(exif_dict["GPS"]): zeroth_ifd[ImageIFD.GPSTag] = 1 gps_is = True gps_ifd = exif_dict["GPS"] elif ImageIFD.GPSTag in zeroth_ifd: zeroth_ifd.pop(ImageIFD.GPSTag) if (("1st" in exif_dict) and ("thumbnail" in exif_dict) and (exif_dict["thumbnail"] is not None)): first_is = True exif_dict["1st"][ImageIFD.JPEGInterchangeFormat] = 1 exif_dict["1st"][ImageIFD.JPEGInterchangeFormatLength] = 1 first_ifd = exif_dict["1st"] zeroth_set = _dict_to_bytes(zeroth_ifd, "0th", 0) zeroth_length = (len(zeroth_set[0]) + exif_is * 12 + gps_is * 12 + 4 + len(zeroth_set[1])) if exif_is: exif_set = _dict_to_bytes(exif_ifd, "Exif", zeroth_length) exif_length = len(exif_set[0]) + interop_is * 12 + len(exif_set[1]) else: exif_bytes = b"" exif_length = 0 if gps_is: gps_set = _dict_to_bytes(gps_ifd, "GPS", zeroth_length + exif_length) gps_bytes = b"".join(gps_set) gps_length = len(gps_bytes) else: gps_bytes = b"" gps_length = 0 if interop_is: offset = zeroth_length + exif_length + gps_length interop_set = _dict_to_bytes(interop_ifd, "Interop", offset) interop_bytes = b"".join(interop_set) interop_length = len(interop_bytes) else: interop_bytes = b"" interop_length = 0 if first_is: offset = zeroth_length + exif_length + gps_length + interop_length first_set = _dict_to_bytes(first_ifd, "1st", offset) thumbnail = _get_thumbnail(exif_dict["thumbnail"]) thumbnail_max_size = 64000 if len(thumbnail) > thumbnail_max_size: raise ValueError("Given thumbnail is too large. max 64kB") else: first_bytes = b"" if exif_is: pointer_value = TIFF_HEADER_LENGTH + zeroth_length pointer_str = struct.pack(">I", pointer_value) key = ImageIFD.ExifTag key_str = struct.pack(">H", key) type_str = struct.pack(">H", TYPES.Long) length_str = struct.pack(">I", 1) exif_pointer = key_str + type_str + length_str + pointer_str else: exif_pointer = b"" if gps_is: pointer_value = TIFF_HEADER_LENGTH + zeroth_length + exif_length pointer_str = struct.pack(">I", pointer_value) key = ImageIFD.GPSTag key_str = struct.pack(">H", key) type_str = struct.pack(">H", TYPES.Long) length_str = struct.pack(">I", 1) gps_pointer = key_str + type_str + length_str + pointer_str else: gps_pointer = b"" if interop_is: pointer_value = (TIFF_HEADER_LENGTH + zeroth_length + exif_length + gps_length) pointer_str = struct.pack(">I", pointer_value) key = ExifIFD.InteroperabilityTag key_str = struct.pack(">H", key) type_str = struct.pack(">H", TYPES.Long) length_str = struct.pack(">I", 1) interop_pointer = key_str + type_str + length_str + pointer_str else: interop_pointer = b"" if first_is: pointer_value = (TIFF_HEADER_LENGTH + zeroth_length + exif_length + gps_length + interop_length) first_ifd_pointer = struct.pack(">L", pointer_value) thumbnail_pointer = (pointer_value + len(first_set[0]) + 24 + 4 + len(first_set[1])) thumbnail_p_bytes = (b"\x02\x01\x00\x04\x00\x00\x00\x01" + struct.pack(">L", thumbnail_pointer)) thumbnail_length_bytes = (b"\x02\x02\x00\x04\x00\x00\x00\x01" + struct.pack(">L", len(thumbnail))) first_bytes = (first_set[0] + thumbnail_p_bytes + thumbnail_length_bytes + b"\x00\x00\x00\x00" + first_set[1] + thumbnail) else: first_ifd_pointer = b"\x00\x00\x00\x00" zeroth_bytes = (zeroth_set[0] + exif_pointer + gps_pointer + first_ifd_pointer + zeroth_set[1]) if exif_is: exif_bytes = exif_set[0] + interop_pointer + exif_set[1] return (header + zeroth_bytes + exif_bytes + gps_bytes + interop_bytes + first_bytes) def _get_thumbnail(jpeg): segments = split_into_segments(jpeg) while (b"\xff\xe0" <= segments[1][0:2] <= b"\xff\xef"): segments.pop(1) thumbnail = b"".join(segments) return thumbnail def _pack_byte(*args): return struct.pack("B" * len(args), *args) def _pack_signed_byte(*args): return struct.pack("b" * len(args), *args) def _pack_short(*args): return struct.pack(">" + "H" * len(args), *args) def _pack_signed_short(*args): return struct.pack(">" + "h" * len(args), *args) def _pack_long(*args): return struct.pack(">" + "L" * len(args), *args) def _pack_slong(*args): return struct.pack(">" + "l" * len(args), *args) def _pack_float(*args): return struct.pack(">" + "f" * len(args), *args) def _pack_double(*args): return struct.pack(">" + "d" * len(args), *args) def _value_to_bytes(raw_value, value_type, offset): four_bytes_over = b"" value_str = b"" if value_type == TYPES.Byte: length = len(raw_value) if length <= 4: value_str = (_pack_byte(*raw_value) + b"\x00" * (4 - length)) else: value_str = struct.pack(">I", offset) four_bytes_over = _pack_byte(*raw_value) elif value_type == TYPES.Short: length = len(raw_value) if length <= 2: value_str = (_pack_short(*raw_value) + b"\x00\x00" * (2 - length)) else: value_str = struct.pack(">I", offset) four_bytes_over = _pack_short(*raw_value) elif value_type == TYPES.Long: length = len(raw_value) if length <= 1: value_str = _pack_long(*raw_value) else: value_str = struct.pack(">I", offset) four_bytes_over = _pack_long(*raw_value) elif value_type == TYPES.SLong: length = len(raw_value) if length <= 1: value_str = _pack_slong(*raw_value) else: value_str = struct.pack(">I", offset) four_bytes_over = _pack_slong(*raw_value) elif value_type == TYPES.Ascii: try: new_value = raw_value.encode("latin1") + b"\x00" except: try: new_value = raw_value + b"\x00" except TypeError: raise ValueError("Got invalid type to convert.") length = len(new_value) if length > 4: value_str = struct.pack(">I", offset) four_bytes_over = new_value else: value_str = new_value + b"\x00" * (4 - length) elif value_type == TYPES.Rational: if isinstance(raw_value[0], numbers.Integral): length = 1 num, den = raw_value new_value = struct.pack(">L", num) + struct.pack(">L", den) elif isinstance(raw_value[0], tuple): length = len(raw_value) new_value = b"" for n, val in enumerate(raw_value): num, den = val new_value += (struct.pack(">L", num) + struct.pack(">L", den)) value_str = struct.pack(">I", offset) four_bytes_over = new_value elif value_type == TYPES.SRational: if isinstance(raw_value[0], numbers.Integral): length = 1 num, den = raw_value new_value = struct.pack(">l", num) + struct.pack(">l", den) elif isinstance(raw_value[0], tuple): length = len(raw_value) new_value = b"" for n, val in enumerate(raw_value): num, den = val new_value += (struct.pack(">l", num) + struct.pack(">l", den)) value_str = struct.pack(">I", offset) four_bytes_over = new_value elif value_type == TYPES.Undefined: length = len(raw_value) if length > 4: value_str = struct.pack(">I", offset) try: four_bytes_over = b"" + raw_value except TypeError: raise ValueError("Got invalid type to convert.") else: try: value_str = raw_value + b"\x00" * (4 - length) except TypeError: raise ValueError("Got invalid type to convert.") elif value_type == TYPES.SByte: # Signed Byte length = len(raw_value) if length <= 4: value_str = (_pack_signed_byte(*raw_value) + b"\x00" * (4 - length)) else: value_str = struct.pack(">I", offset) four_bytes_over = _pack_signed_byte(*raw_value) elif value_type == TYPES.SShort: # Signed Short length = len(raw_value) if length <= 2: value_str = (_pack_signed_short(*raw_value) + b"\x00\x00" * (2 - length)) else: value_str = struct.pack(">I", offset) four_bytes_over = _pack_signed_short(*raw_value) elif value_type == TYPES.Float: length = len(raw_value) if length <= 1: value_str = _pack_float(*raw_value) else: value_str = struct.pack(">I", offset) four_bytes_over = _pack_float(*raw_value) elif value_type == TYPES.DFloat: # Double length = len(raw_value) value_str = struct.pack(">I", offset) four_bytes_over = _pack_double(*raw_value) length_str = struct.pack(">I", length) return length_str, value_str, four_bytes_over def _dict_to_bytes(ifd_dict, ifd, ifd_offset): tag_count = len(ifd_dict) entry_header = struct.pack(">H", tag_count) if ifd in ("0th", "1st"): entries_length = 2 + tag_count * 12 + 4 else: entries_length = 2 + tag_count * 12 entries = b"" values = b"" for n, key in enumerate(sorted(ifd_dict)): if (ifd == "0th") and (key in (ImageIFD.ExifTag, ImageIFD.GPSTag)): continue elif (ifd == "Exif") and (key == ExifIFD.InteroperabilityTag): continue elif (ifd == "1st") and (key in (ImageIFD.JPEGInterchangeFormat, ImageIFD.JPEGInterchangeFormatLength)): continue raw_value = ifd_dict[key] key_str = struct.pack(">H", key) value_type = TAGS[ifd][key]["type"] type_str = struct.pack(">H", value_type) four_bytes_over = b"" if isinstance(raw_value, numbers.Integral) or isinstance(raw_value, float): raw_value = (raw_value,) offset = TIFF_HEADER_LENGTH + entries_length + ifd_offset + len(values) try: length_str, value_str, four_bytes_over = _value_to_bytes(raw_value, value_type, offset) except ValueError: raise ValueError( '"dump" got wrong type of exif value.\n' + '{0} in {1} IFD. Got as {2}.'.format(key, ifd, type(ifd_dict[key])) ) entries += key_str + type_str + length_str + value_str values += four_bytes_over return (entry_header + entries, values)
2.46875
2
portal.py
mrahman4782/portalhoop
0
9650
import pygame import random from pygame import * pygame.init() width, height = 740, 500 screen = pygame.display.set_mode((width, height)) player = [pygame.transform.scale(pygame.image.load("Resources/Balljump-1(2).png"), (100,100)), pygame.transform.scale(pygame.image.load("Resources/Balljump-1.png"),(100,100))] launch = [pygame.transform.scale(pygame.image.load("Resources/Balljump-1.png"), (100,100)), pygame.transform.scale(pygame.image.load("Resources/Balljump-1(2).png"), (100,100)),pygame.transform.scale(pygame.image.load("Resources/Balljump-2.png"), (100,100)),pygame.transform.scale(pygame.image.load("Resources/Balljump-3.png"), (100,100)), pygame.transform.scale(pygame.image.load("Resources/Balljump-4.png"),(100,100))] shoot = [pygame.transform.scale(pygame.image.load("Resources/Balljump-5.png"), (100, 100)), pygame.transform.scale(pygame.image.load("Resources/Balljump-6.png"), (100, 100))] ball = pygame.transform.scale(pygame.image.load("Resources/ball.png"), (100,100)) blue = (0, 0, 128) white = (255, 255, 255) janimation, danimation, movable, motionactivate, limit_reached, nojump = False, False, False, False, False, False jumplock = True ballrelease, ballregain = False, False fr = pygame.time.Clock() c = 0 i = 0 p = 0 x, y = 0, 300 score = 0 a, b, rpos = 0, 0, 0 xpos, ypos = 17, 313 # Background image source: https://www.freepik.com/free-vector/floral-ornamental-abstract-background_6189902.htm#page=1&query=black%20background&position=40 background = pygame.image.load("Resources/back.jpg") gamestart = False def basketball(): #Draw basketball global rpos, xpos, ypos, ballregain if gamestart == True and ballrelease == False: if nojump == True: if c % 2 == 0: screen.blit(ball, (xpos, ypos + 24)) if c % 2 == 1: screen.blit(ball, (xpos + 2 , ypos )) if nojump == False and motionactivate == True: if p // 4 == 0: screen.blit(ball, (xpos, ypos)) if p // 4 == 1: screen.blit(ball, (xpos-2, ypos-5)) if p // 4 == 2: screen.blit(ball, (xpos-2, ypos-7)) if p // 4 == 3: screen.blit(ball, (xpos-2, ypos-11)) if p// 4 == 4: screen.blit(ball, (xpos-2, ypos-13)) if janimation == True: rpos = y -13 screen.blit(ball, (xpos, rpos)) rposNew = 400 - rpos if gamestart == True and ballrelease == True: if rpos <= 325: screen.blit(ball, (xpos, rpos)) if xpos <= 700: ballregain = False xpos += (rposNew / 20) print("rpos is: " + str(rpos) + " xpos is: " + str(xpos)) rpos = (-1*((xpos/600)**2))+((xpos)/150)+rpos if xpos > 700 or rpos > 325: xpos = 17 ballregain = True def player_animations(): # Animations while the user makes no input global c global player global i if nojump == True: if c % 2 == 0 and i<= 10: if i<10: screen.blit(player[c], (0, 300)) i += 1 if i == 10: c += 1 i += 1 elif c % 2 == 1 and i<= 20: if i>10 and i<20: screen.blit(player[c], (0, 300)) i += 1 if i == 20: c -= 1 i += 1 elif i>20: i = 0 screen.blit(player[c], (0, 300)) if nojump == False: screen.fill(0) def screen_text(): global score global nojump global movable if nojump == True: font = pygame.font.Font("Resources/android.ttf", 16) text2 = font.render("Hold space to throw the ball", True, white) textRect2 = text2.get_rect() textRect2.center = (width // 2, height // 2 + 200) screen.blit(text2, textRect2) movable = True font = pygame.font.Font("Resources/android.ttf", 16) text2 = font.render("Score: "+ str(score), True, white) textRect2 = text2.get_rect() textRect2.center = (width // 2 - 300, height // 2 - 200) screen.blit(text2, textRect2) def player_jump(): # Initial animations before the player jumps global p, nojump, movable, x, y, janimation, danimation, a, b, motionactivate, limit_reached global jumplock, ballrelease, ballregain if movable == True and keypress[K_SPACE]: #print(pygame.time.get_ticks()) motionactivate = True #print(nojump) #if p >= 19: # p = 0 if motionactivate == True: #screen.fill(0) nojump = False if p < 21: screen.blit(launch[p // 4], (0, 300)) p += 1 if p == 20: a = pygame.time.get_ticks() janimation = True p += 1 #elif keypress[K_SPACE]: # what to do when jump is completed if janimation == True and limit_reached == False: if keypress[K_SPACE] and pygame.KEYDOWN and jumplock == True: b = pygame.time.get_ticks() if y > 239: y = ((b - a) / -25) + 310 if y >= 305: screen.fill(0) screen.blit(shoot[0], (x, y)) if y < 305 and y > 240: screen.blit(shoot[1], (x,y)) if y <= 239: screen.blit(shoot[0], (x, y)) danimation = True limit_reached = True #print(danimation) if event.type == pygame.KEYUP: if event.key == K_SPACE: danimation = True motionactivate = False ballrelease = True if danimation == True: jumplock = False if danimation == True or limit_reached == True: #print("poopc "+ str(y)) if y < 310: screen.blit(shoot[0], (x, y)) y += 2 # # print("zag") #print("poop: " + str(pygame.KEYUP) + " key down is: " + str(pygame.KEYDOWN)) if y >= 310: nojump = True danimation = False janimation = False movable = False limit_reached = False p = 0 jumplock = True if ballregain == True: ballrelease = False #print("y value is: "+ str(y)+ " a is: "+ str(a) + " b is: "+ str(b)) while 1: keypress = pygame.key.get_pressed() fr.tick(30) screen.fill(0) if keypress[K_RETURN]: gamestart = True if gamestart == False: #screen.fill(0) screen.blit(background, (0,0)) # Draw opening texts font = pygame.font.Font("Resources/android.ttf", 64) text = font.render("Portal Hoop", True, white) textRect = text.get_rect() textRect.center = (width // 2, height // 2 - 100) screen.blit(text, textRect) font = pygame.font.Font("Resources/android.ttf", 18) text2 = font.render("Press Return to start", True, white) textRect2 = text2.get_rect() textRect2.center = (width // 2, height // 2 + 100) screen.blit(text2, textRect2) nojump = True # Check if any if gamestart == True: #screen.fill(0) player_animations() player_jump() basketball() screen_text() pygame.display.flip() pygame.display.set_caption("Portal Hoop") for event in pygame.event.get(): if event.type == pygame.QUIT: pygame.quit() exit(0)
2.78125
3
datadog_cluster_agent/tests/test_datadog_cluster_agent.py
tdimnet/integrations-core
1
9651
<gh_stars>1-10 # (C) Datadog, Inc. 2021-present # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) from typing import Any, Dict from datadog_checks.base.stubs.aggregator import AggregatorStub from datadog_checks.datadog_cluster_agent import DatadogClusterAgentCheck from datadog_checks.dev.utils import get_metadata_metrics NAMESPACE = 'datadog.cluster_agent' METRICS = [ 'admission_webhooks.certificate_expiry', 'admission_webhooks.mutation_attempts', 'admission_webhooks.mutation_errors', 'admission_webhooks.reconcile_errors', 'admission_webhooks.reconcile_success', 'admission_webhooks.webhooks_received', 'aggregator.flush', 'aggregator.processed', 'api_requests', 'cluster_checks.busyness', 'cluster_checks.configs_dangling', 'cluster_checks.configs_dispatched', 'cluster_checks.failed_stats_collection', 'cluster_checks.nodes_reporting', 'cluster_checks.rebalancing_decisions', 'cluster_checks.rebalancing_duration_seconds', 'cluster_checks.successful_rebalancing_moves', 'cluster_checks.updating_stats_duration_seconds', 'datadog.rate_limit_queries.limit', 'datadog.rate_limit_queries.period', 'datadog.rate_limit_queries.remaining', 'datadog.rate_limit_queries.reset', 'datadog.requests', 'external_metrics', 'external_metrics.datadog_metrics', 'external_metrics.delay_seconds', 'external_metrics.processed_value', 'secret_backend.elapsed', 'go.goroutines', 'go.memstats.alloc_bytes', 'go.threads', ] def test_check(aggregator, instance, mock_metrics_endpoint): # type: (AggregatorStub, Dict[str, Any]) -> None check = DatadogClusterAgentCheck('datadog_cluster_agent', {}, [instance]) # dry run to build mapping for label joins check.check(instance) check.check(instance) for metric in METRICS: aggregator.assert_metric(NAMESPACE + '.' + metric) aggregator.assert_metric_has_tag_prefix(NAMESPACE + '.' + metric, 'is_leader:') aggregator.assert_all_metrics_covered() aggregator.assert_metrics_using_metadata(get_metadata_metrics())
1.75
2
prev_ob_models/KaplanLansner2014/plotting_and_analysis/plot_results.py
fameshpatel/olfactorybulb
5
9652
<gh_stars>1-10 import pylab import numpy import sys if (len(sys.argv) < 2): fn = raw_input("Please enter data file to be plotted\n") else: fn = sys.argv[1] data = np.loadtxt(fn) # if the first line contains crap use skiprows=1 #data = np.loadtxt(fn, skiprows=1) fig = pylab.figure() ax = fig.add_subplot(111) # if you want to use multiple figures in one, use #ax1 = fig.add_subplot(211) #ax2 = fig.add_subplot(212) # and if (data.ndim == 1): x_axis = numpy.arange(data.size) ax.plot(x_axis, data) else: # ax.errorbar(data[:,0], data[:,1], yerr=data[:, 2]) # print 'mean y-value:', data[:, 1].mean() ax.plot(data[:, 0], data[:, 1], ls='-', lw=3, c='b') # ax.scatter(data[:,0], data[:,2]) # ax.plot(data[:,3], data[:,6]) # saving: # fig.savefig('output_figure.png') # otherwise nothing is shown pylab.show()
2.78125
3
pyeccodes/defs/grib2/tables/15/3_11_table.py
ecmwf/pyeccodes
7
9653
def load(h): return ({'abbr': 0, 'code': 0, 'title': 'There is no appended list'}, {'abbr': 1, 'code': 1, 'title': 'Numbers define number of points corresponding to full coordinate ' 'circles (i.e. parallels), coordinate values on each circle are ' 'multiple of the circle mesh, and extreme coordinate values given ' 'in grid definition (i.e. extreme longitudes) may not be reached in ' 'all rows'}, {'abbr': 2, 'code': 2, 'title': 'Numbers define number of points corresponding to coordinate lines ' 'delimited by extreme coordinate values given in grid definition ' '(i.e. extreme longitudes) which are present in each row'}, {'abbr': 3, 'code': 3, 'title': 'Numbers define the actual latitudes for each row in the grid. The ' 'list of numbers are integer values of the valid latitudes in ' 'microdegrees (scaled by 10-6) or in unit equal to the ratio of the ' 'basic angle and the subdivisions number for each row, in the same ' 'order as specified in the scanning mode flag', 'units': 'bit no. 2'}, {'abbr': None, 'code': 255, 'title': 'Missing'})
2.34375
2
lib/take2/main.py
zacharyfrederick/deep_q_gaf
0
9654
<filename>lib/take2/main.py<gh_stars>0 from __future__ import division from lib import env_config from lib.senior_env import BetterEnvironment from keras.optimizers import Adam from rl.agents.dqn import DQNAgent from rl.policy import LinearAnnealedPolicy, BoltzmannQPolicy, EpsGreedyQPolicy from rl.memory import SequentialMemory from lib import models import random choices = [0,1,2] def gen_action(): return random.choice(choices) if __name__ == '__main__': config_ = env_config.EnvConfig('config/debug.json') env = BetterEnvironment(config_) INPUT_SHAPE = (30, 180) WINDOW_LENGTH = 4 model = models.build_paper_model() # Get the environment and extract the number of actions. nb_actions = 3 # Next, we build our model. We use the same model that was described by Mnih et al. (2015). input_shape = (WINDOW_LENGTH,) + INPUT_SHAPE # Finally, we configure and compile our agent. You can use every built-in Keras optimizer and # even the metrics! memory = SequentialMemory(limit=10000000, window_length=WINDOW_LENGTH) # Select a policy. We use eps-greedy action selection, which means that a random action is selected # with probability eps. We anneal eps from 1.0 to 0.1 over the course of 1M steps. This is done so that # the agent initially explores the environment (high eps) and then gradually sticks to what it knows # (low eps). We also set a dedicated eps value that is used during testing. Note that we set it to 0.05 # so that the agent still performs some random actions. This ensures that the agent cannot get stuck. policy = LinearAnnealedPolicy(EpsGreedyQPolicy(), attr='eps', value_max=1., value_min=.1, value_test=.05, nb_steps=1000000) # The trade-off between exploration and exploitation is difficult and an on-going research topic. # If you want, you can experiment with the parameters or use a different policy. Another popular one # is Boltzmann-style exploration: # policy = BoltzmannQPolicy(tau=1.) # Feel free to give it a try! dqn = DQNAgent(model=model, nb_actions=nb_actions, policy=policy, memory=memory, nb_steps_warmup=50000, gamma=.99, target_model_update=10000, train_interval=4, delta_clip=1.) dqn.compile(Adam(lr=.00025), metrics=['mae']) # Okay, now it's time to learn something! We capture the interrupt exception so that training # can be prematurely aborted. Notice that now you can use the built-in Keras callbacks! weights_filename = 'dqn_{}_weights.h5f'.format('god_help_me.weights') dqn.fit(env, nb_steps=100000, log_interval=10000) print(env.portfolio.print_portfolio_results())
2.34375
2
src/clcore.py
ShepardPower/PyMCBuilder
1
9655
<filename>src/clcore.py # I'm just the one that executes the instructions! import sys, math, json, operator, time import mcpi.minecraft as minecraft from PIL import Image as pillow from blockid import get_block import mcpi.block as block import functions as pymc from tqdm import tqdm import tkinter as tk # Functions # Main code mc = minecraft.Minecraft.create() try: json_file = open("blocks.json") json_put = json.load(json_file) except: pymc.chat(mc, "blocks.json not found, exiting!", 0) sys.exit(1) try: rim = pillow.open(sys.argv[1]) except: pymc.chat(mc, "bad image, exiting!", 0) sys.exit(1) orders = [] used = [] imwid, imhei = rim.size if imhei > 200: maxheight = 200 rim.thumbnail((200, maxheight), pillow.ANTIALIAS) imwid, imhei = rim.size pymc.chat(mc, "image is over 200 pixels, reducing height.", 1) rim.convert('RGB') im = rim.load() pbar = tqdm(total=imhei*imwid) for hei in range(imhei): for wid in range(imwid): smal = pymc.comp_pixel((im[wid, hei][0], im[wid, hei][1], im[wid, hei][2]), json_put) im[wid, hei] = smal[1] used.append(str(smal[2])) pbar.update(1) pbar.close() rim.save("result.GIF") # The result json_file.close() oldPos = mc.player.getPos() playerPos = [round(oldPos.x), round(oldPos.y), round(oldPos.z)] pymc.chat(mc, "Ready!") pbar = tqdm(total=imhei*imwid) num_temp = imhei*imwid-1 for hei in range(imhei): for wid in range(imwid): #print(used[wid + (imhei * hei)]) gblock = get_block(used[num_temp]) mc.setBlock(playerPos[0]+wid, playerPos[1]+hei, playerPos[2], gblock) num_temp -= 1 pbar.update(1) pbar.close() pymc.chat(mc, "Done!!") pymc.chat(mc, "Please star us on github if you like the result!", 2)
2.4375
2
gaphor/tools/gaphorconvert.py
987Frogh/project-makehuman
1
9656
<reponame>987Frogh/project-makehuman<gh_stars>1-10 #!/usr/bin/python import optparse import os import re import sys import cairo from gaphas.painter import Context, ItemPainter from gaphas.view import View import gaphor.UML as UML from gaphor.application import Application from gaphor.storage import storage def pkg2dir(package): """ Return directory path from UML package class. """ name = [] while package: name.insert(0, package.name) package = package.package return "/".join(name) def paint(view, cr): view.painter.paint(Context(cairo=cr, items=view.canvas.get_all_items(), area=None)) def main(argv=sys.argv[1:]): def message(msg): """ Print message if user set verbose mode. """ if options.verbose: print(msg, file=sys.stderr) usage = "usage: %prog [options] file1 file2..." parser = optparse.OptionParser(usage=usage) parser.add_option( "-v", "--verbose", dest="verbose", action="store_true", help="verbose output" ) parser.add_option( "-u", "--use-underscores", dest="underscores", action="store_true", help="use underscores instead of spaces for output filenames", ) parser.add_option( "-d", "--dir", dest="dir", metavar="directory", help="output to directory" ) parser.add_option( "-f", "--format", dest="format", metavar="format", help="output file format, default pdf", default="pdf", choices=["pdf", "svg", "png"], ) parser.add_option( "-r", "--regex", dest="regex", metavar="regex", help="process diagrams which name matches given regular expresion;" " name includes package name; regular expressions are case insensitive", ) (options, args) = parser.parse_args(argv) if not args: parser.print_help() Application.init( services=["event_manager", "component_registry", "element_factory"] ) factory = Application.get_service("element_factory") name_re = None if options.regex: name_re = re.compile(options.regex, re.I) # we should have some gaphor files to be processed at this point for model in args: message(f"loading model {model}") storage.load(model, factory) message("ready for rendering") for diagram in factory.select(lambda e: e.isKindOf(UML.Diagram)): odir = pkg2dir(diagram.package) # just diagram name dname = diagram.name # full diagram name including package path pname = f"{odir}/{dname}" if options.underscores: odir = odir.replace(" ", "_") dname = dname.replace(" ", "_") if name_re and not name_re.search(pname): message(f"skipping {pname}") continue if options.dir: odir = f"{options.dir}/{odir}" outfilename = f"{odir}/{dname}.{options.format}" if not os.path.exists(odir): message(f"creating dir {odir}") os.makedirs(odir) message(f"rendering: {pname} -> {outfilename}...") view = View(diagram.canvas) view.painter = ItemPainter() tmpsurface = cairo.ImageSurface(cairo.FORMAT_ARGB32, 0, 0) tmpcr = cairo.Context(tmpsurface) view.update_bounding_box(tmpcr) tmpcr.show_page() tmpsurface.flush() w, h = view.bounding_box.width, view.bounding_box.height if options.format == "pdf": surface = cairo.PDFSurface(outfilename, w, h) elif options.format == "svg": surface = cairo.SVGSurface(outfilename, w, h) elif options.format == "png": surface = cairo.ImageSurface( cairo.FORMAT_ARGB32, int(w + 1), int(h + 1) ) else: assert False, f"unknown format {options.format}" cr = cairo.Context(surface) view.matrix.translate(-view.bounding_box.x, -view.bounding_box.y) paint(view, cr) cr.show_page() if options.format == "png": surface.write_to_png(outfilename) surface.flush() surface.finish()
2.390625
2
zeta_python_sdk/exceptions.py
prettyirrelevant/zeta-python-sdk
2
9657
<reponame>prettyirrelevant/zeta-python-sdk class InvalidSideException(Exception): """Invalid side""" class NotSupportedException(Exception): """Not supported by dummy wallet""" class InvalidProductException(Exception): """Invalid product type""" class OutOfBoundsException(Exception): """Attempt to access memory outside buffer bounds"""
2.015625
2
test/test_ID.py
a-buntjer/tsib
14
9658
<gh_stars>10-100 # -*- coding: utf-8 -*- """ Created on Fri Apr 08 11:33:01 2016 @author: <NAME> """ import tsib def test_get_ID(): # parameterize a building bdgcfg = tsib.BuildingConfiguration( { "refurbishment": False, "nightReduction": False, "occControl": False, "capControl": True, "n_persons": 2, "roofOrientation": 0.0, "n_apartments": 1, "latitude": 49., "longitude": 12., } ) bdgObj = tsib.Building(configurator=bdgcfg) print('ID is : ' + str(bdgObj.ID)) return def test_set_ID(): # parameterize a building bdgcfg = tsib.BuildingConfiguration( { "buildingYear": 1980, "n_persons": 2, "roofOrientation": 0.0, "n_apartments": 2, "a_ref": 300., "surrounding": "Detached", "latitude": 52., "longitude": 13., } ) bdgObj = tsib.Building(configurator=bdgcfg) bdgObj.ID = 'custom' if not bdgObj.ID == 'custom': raise ValueError() return
2.453125
2
dislib/model_selection/_search.py
alexbarcelo/dislib
36
9659
<gh_stars>10-100 from abc import ABC, abstractmethod from collections import defaultdict from collections.abc import Sequence from functools import partial from itertools import product import numpy as np from pycompss.api.api import compss_wait_on from scipy.stats import rankdata from sklearn import clone from sklearn.model_selection import ParameterGrid, ParameterSampler from numpy.ma import MaskedArray from dislib.model_selection._split import infer_cv from dislib.model_selection._validation import check_scorer, fit_and_score, \ validate_score, aggregate_score_dicts class BaseSearchCV(ABC): """Abstract base class for hyper parameter search with cross-validation.""" def __init__(self, estimator, scoring=None, cv=None, refit=True): self.estimator = estimator self.scoring = scoring self.cv = cv self.refit = refit @abstractmethod def _run_search(self, evaluate_candidates): """Abstract method to perform the search. The parameter `evaluate_candidates` is a function that evaluates a ParameterGrid at a time """ pass def fit(self, x, y=None, **fit_params): """Run fit with all sets of parameters. Parameters ---------- x : ds-array Training data samples. y : ds-array, optional (default = None) Training data labels or values. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of the estimator """ estimator = self.estimator cv = infer_cv(self.cv) scorers, refit_metric = self._infer_scorers() base_estimator = clone(estimator) n_splits = None all_candidate_params = [] all_out = [] def evaluate_candidates(candidate_params): """Evaluate some parameters""" candidate_params = list(candidate_params) out = [fit_and_score(clone(base_estimator), train, validation, scorer=scorers, parameters=parameters, fit_params=fit_params) for parameters, (train, validation) in product(candidate_params, cv.split(x, y))] nonlocal n_splits n_splits = cv.get_n_splits() all_candidate_params.extend(candidate_params) all_out.extend(out) self._run_search(evaluate_candidates) for params_result in all_out: scores = params_result[0] for scorer_name, score in scores.items(): score = compss_wait_on(score) scores[scorer_name] = validate_score(score, scorer_name) results = self._format_results(all_candidate_params, scorers, n_splits, all_out) # For multi-metric evaluation, store the best_index_, best_params_ and # best_score_ iff refit is one of the scorer names # In single metric evaluation, refit_metric is "score" if self.refit or not self.multimetric_: # If callable, refit is expected to return the index of the best # parameter set. if callable(self.refit): self.best_index_ = self.refit(results) if not isinstance(self.best_index_, (int, np.integer)): raise TypeError('best_index_ returned is not an integer') if (self.best_index_ < 0 or self.best_index_ >= len(results["params"])): raise IndexError('best_index_ index out of range') else: self.best_index_ = results["rank_test_%s" % refit_metric].argmin() self.best_score_ = results["mean_test_%s" % refit_metric][ self.best_index_] self.best_params_ = results["params"][self.best_index_] if self.refit: self.best_estimator_ = clone(base_estimator).set_params( **self.best_params_) self.best_estimator_.fit(x, y, **fit_params) # Store the only scorer not as a dict for single metric evaluation self.scorer_ = scorers if self.multimetric_ else scorers['score'] self.cv_results_ = results self.n_splits_ = n_splits return self @staticmethod def _format_results(candidate_params, scorers, n_splits, out): n_candidates = len(candidate_params) (test_score_dicts,) = zip(*out) test_scores = aggregate_score_dicts(test_score_dicts) results = {} def _store(key_name, array, splits=False, rank=False): """A small helper to store the scores/times to the cv_results_""" array = np.array(array, dtype=np.float64).reshape(n_candidates, n_splits) if splits: for split_i in range(n_splits): # Uses closure to alter the results results["split%d_%s" % (split_i, key_name)] = array[:, split_i] array_means = np.mean(array, axis=1) results['mean_%s' % key_name] = array_means array_stds = np.std(array, axis=1) results['std_%s' % key_name] = array_stds if rank: results["rank_%s" % key_name] = np.asarray( rankdata(-array_means, method='min'), dtype=np.int32) # Use one MaskedArray and mask all the places where the param is not # applicable for that candidate. Use defaultdict as each candidate may # not contain all the params param_results = defaultdict(partial(MaskedArray, np.empty(n_candidates, ), mask=True, dtype=object)) for cand_i, params in enumerate(candidate_params): for name, value in params.items(): # An all masked empty array gets created for the key # `"param_%s" % name` at the first occurrence of `name`. # Setting the value at an index also unmasks that index param_results["param_%s" % name][cand_i] = value results.update(param_results) # Store a list of param dicts at the key 'params' results['params'] = candidate_params for scorer_name in scorers.keys(): _store('test_%s' % scorer_name, test_scores[scorer_name], splits=True, rank=True) return results def _infer_scorers(self): estimator = self.estimator scoring = self.scoring refit = self.refit if scoring is None or callable(scoring): scorers = {"score": check_scorer(estimator, scoring)} refit_metric = 'score' self.multimetric_ = False elif isinstance(scoring, dict): scorers = {key: check_scorer(estimator, scorer) for key, scorer in scoring.items()} if refit is not False and ( not isinstance(refit, str) or refit not in scorers) and not callable(refit): raise ValueError("For multi-metric scoring, the parameter " "refit must be set to a scorer key or a " "callable to refit an estimator with the " "best parameter setting on the whole " "data and make the best_* attributes " "available for that metric. If this is " "not needed, refit should be set to " "False explicitly. %r was passed." % refit) refit_metric = refit self.multimetric_ = True else: raise ValueError('scoring is not valid') return scorers, refit_metric class GridSearchCV(BaseSearchCV): """Exhaustive search over specified parameter values for an estimator. GridSearchCV implements a "fit" and a "score" method. The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid. Parameters ---------- estimator : estimator object. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a ``score`` function, or ``scoring`` must be passed. param_grid : dict or list of dictionaries Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings. scoring : callable, dict or None, optional (default=None) A callable to evaluate the predictions on the test set. It should take 3 parameters, estimator, x and y, and return a score (higher meaning better). For evaluating multiple metrics, give a dict with names as keys and callables as values. If None, the estimator's score method is used. cv : int or cv generator, optional (default=None) Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - integer, to specify the number of folds in a `KFold`, - custom cv generator. refit : boolean, string, or callable, optional (default=True) Refit an estimator using the best found parameters on the whole dataset. For multiple metric evaluation, this needs to be a string denoting the scorer that would be used to find the best parameters for refitting the estimator at the end. Where there are considerations other than maximum score in choosing a best estimator, ``refit`` can be set to a function which returns the selected ``best_index_`` given ``cv_results_``. The refitted estimator is made available at the ``best_estimator_`` attribute and permits using ``predict`` directly on this ``GridSearchCV`` instance. Also for multiple metric evaluation, the attributes ``best_index_``, ``best_score_`` and ``best_params_`` will only be available if ``refit`` is set and all of them will be determined w.r.t this specific scorer. ``best_score_`` is not returned if refit is callable. See ``scoring`` parameter to know more about multiple metric evaluation. Examples -------- >>> import dislib as ds >>> from dislib.model_selection import GridSearchCV >>> from dislib.classification import RandomForestClassifier >>> import numpy as np >>> from sklearn import datasets >>> >>> >>> if __name__ == '__main__': >>> x_np, y_np = datasets.load_iris(return_X_y=True) >>> x = ds.array(x_np, (30, 4)) >>> y = ds.array(y_np[:, np.newaxis], (30, 1)) >>> param_grid = {'n_estimators': (2, 4), 'max_depth': range(3, 5)} >>> rf = RandomForestClassifier() >>> searcher = GridSearchCV(rf, param_grid) >>> searcher.fit(x, y) >>> searcher.cv_results_ Attributes ---------- cv_results_ : dict of numpy (masked) ndarrays A dict with keys as column headers and values as columns, that can be imported into a pandas ``DataFrame``. For instance the below given table: +------------+------------+-----------------+---+---------+ |param_kernel|param_degree|split0_test_score|...|rank_t...| +============+============+=================+===+=========+ | 'poly' | 2 | 0.80 |...| 2 | +------------+------------+-----------------+---+---------+ | 'poly' | 3 | 0.70 |...| 4 | +------------+------------+-----------------+---+---------+ | 'rbf' | -- | 0.80 |...| 3 | +------------+------------+-----------------+---+---------+ | 'rbf' | -- | 0.93 |...| 1 | +------------+------------+-----------------+---+---------+ will be represented by a ``cv_results_`` dict of:: { 'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'], mask = [False False False False]...), 'param_degree': masked_array(data = [2.0 3.0 -- --], mask = [False False True True]...), 'split0_test_score' : [0.80, 0.70, 0.80, 0.93], 'split1_test_score' : [0.82, 0.50, 0.68, 0.78], 'split2_test_score' : [0.79, 0.55, 0.71, 0.93], ... 'mean_test_score' : [0.81, 0.60, 0.75, 0.85], 'std_test_score' : [0.01, 0.10, 0.05, 0.08], 'rank_test_score' : [2, 4, 3, 1], 'params' : [{'kernel': 'poly', 'degree': 2}, ...], } NOTES: The key ``'params'`` is used to store a list of parameter settings dicts for all the parameter candidates. The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and ``std_score_time`` are all in seconds. For multi-metric evaluation, the scores for all the scorers are available in the ``cv_results_`` dict at the keys ending with that scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown above ('split0_test_precision', 'mean_train_precision' etc.). best_estimator_ : estimator or dict Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if ``refit=False``. See ``refit`` parameter for more information on allowed values. best_score_ : float Mean cross-validated score of the best_estimator For multi-metric evaluation, this is present only if ``refit`` is specified. best_params_ : dict Parameter setting that gave the best results on the hold out data. For multi-metric evaluation, this is present only if ``refit`` is specified. best_index_ : int The index (of the ``cv_results_`` arrays) which corresponds to the best candidate parameter setting. The dict at ``search.cv_results_['params'][search.best_index_]`` gives the parameter setting for the best model, that gives the highest mean score (``search.best_score_``). For multi-metric evaluation, this is present only if ``refit`` is specified. scorer_ : function or a dict Scorer function used on the held out data to choose the best parameters for the model. For multi-metric evaluation, this attribute holds the validated ``scoring`` dict which maps the scorer key to the scorer callable. n_splits_ : int The number of cross-validation splits (folds/iterations). """ def __init__(self, estimator, param_grid, scoring=None, cv=None, refit=True): super().__init__(estimator=estimator, scoring=scoring, cv=cv, refit=refit) self.param_grid = param_grid self._check_param_grid(param_grid) def _run_search(self, evaluate_candidates): evaluate_candidates(ParameterGrid(self.param_grid)) @staticmethod def _check_param_grid(param_grid): if hasattr(param_grid, 'items'): param_grid = [param_grid] for p in param_grid: for name, v in p.items(): if isinstance(v, np.ndarray) and v.ndim > 1: raise ValueError("Parameter array should be " "one-dimensional.") if (isinstance(v, str) or not isinstance(v, (np.ndarray, Sequence))): raise ValueError( "Parameter values for parameter ({0}) need " "to be a sequence (but not a string) or" " np.ndarray.".format(name)) if len(v) == 0: raise ValueError( "Parameter values for parameter ({0}) need " "to be a non-empty sequence.".format(name)) class RandomizedSearchCV(BaseSearchCV): """Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings. In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. Parameters ---------- estimator : estimator object. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a ``score`` function, or ``scoring`` must be passed. param_distributions : dict Dictionary with parameters names (string) as keys and distributions or lists of parameters to try. Distributions must provide a ``rvs`` method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. n_iter : int, optional (default=10) Number of parameter settings that are sampled. scoring : callable, dict or None, optional (default=None) A callable to evaluate the predictions on the test set. It should take 3 parameters, estimator, x and y, and return a score (higher meaning better). For evaluating multiple metrics, give a dict with names as keys and callables as values. If None, the estimator's score method is used. cv : int or cv generator, optional (default=None) Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - integer, to specify the number of folds in a `KFold`, - custom cv generator. refit : boolean, string, or callable, optional (default=True) Refit an estimator using the best found parameters on the whole dataset. For multiple metric evaluation, this needs to be a string denoting the scorer that would be used to find the best parameters for refitting the estimator at the end. Where there are considerations other than maximum score in choosing a best estimator, ``refit`` can be set to a function which returns the selected ``best_index_`` given ``cv_results_``. The refitted estimator is made available at the ``best_estimator_`` attribute and permits using ``predict`` directly on this ``GridSearchCV`` instance. Also for multiple metric evaluation, the attributes ``best_index_``, ``best_score_`` and ``best_params_`` will only be available if ``refit`` is set and all of them will be determined w.r.t this specific scorer. ``best_score_`` is not returned if refit is callable. See ``scoring`` parameter to know more about multiple metric evaluation. random_state : int, RandomState instance or None, optional, default=None Pseudo random number generator state used for random sampling of params in param_distributions. This is not passed to each estimator. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Examples -------- >>> import dislib as ds >>> from dislib.model_selection import RandomizedSearchCV >>> from dislib.classification import CascadeSVM >>> import numpy as np >>> import scipy.stats as stats >>> from sklearn import datasets >>> >>> >>> if __name__ == '__main__': >>> x_np, y_np = datasets.load_iris(return_X_y=True) >>> # Pre-shuffling required for CSVM >>> p = np.random.permutation(len(x_np)) >>> x = ds.array(x_np[p], (30, 4)) >>> y = ds.array((y_np[p] == 0)[:, np.newaxis], (30, 1)) >>> param_distributions = {'c': stats.expon(scale=0.5), >>> 'gamma': stats.expon(scale=10)} >>> csvm = CascadeSVM() >>> searcher = RandomizedSearchCV(csvm, param_distributions, n_iter=10) >>> searcher.fit(x, y) >>> searcher.cv_results_ Attributes ---------- cv_results_ : dict of numpy (masked) ndarrays A dict with keys as column headers and values as columns, that can be imported into a pandas ``DataFrame``. For instance the below given table +---------+-------------+-------------------+---+---------------+ | param_c | param_gamma | split0_test_score |...|rank_test_score| +=========+=============+===================+===+===============+ | 0.193 | 1.883 | 0.82 |...| 3 | +---------+-------------+-------------------+---+---------------+ | 1.452 | 0.327 | 0.81 |...| 2 | +---------+-------------+-------------------+---+---------------+ | 0.926 | 3.452 | 0.94 |...| 1 | +---------+-------------+-------------------+---+---------------+ will be represented by a ``cv_results_`` dict of:: { 'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'], mask = False), 'param_gamma' : masked_array(data = [0.1 0.2 0.3], mask = False), 'split0_test_score' : [0.82, 0.81, 0.94], 'split1_test_score' : [0.66, 0.75, 0.79], 'split2_test_score' : [0.82, 0.87, 0.84], ... 'mean_test_score' : [0.76, 0.84, 0.86], 'std_test_score' : [0.01, 0.20, 0.04], 'rank_test_score' : [3, 2, 1], 'params' : [{'c' : 0.193, 'gamma' : 1.883}, ...], } NOTE The key ``'params'`` is used to store a list of parameter settings dicts for all the parameter candidates. The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and ``std_score_time`` are all in seconds. For multi-metric evaluation, the scores for all the scorers are available in the ``cv_results_`` dict at the keys ending with that scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown above. ('split0_test_precision', 'mean_train_precision' etc.) best_estimator_ : estimator or dict Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if ``refit=False``. For multi-metric evaluation, this attribute is present only if ``refit`` is specified. See ``refit`` parameter for more information on allowed values. best_score_ : float Mean cross-validated score of the best_estimator. For multi-metric evaluation, this is not available if ``refit`` is ``False``. See ``refit`` parameter for more information. best_params_ : dict Parameter setting that gave the best results on the hold out data. For multi-metric evaluation, this is not available if ``refit`` is ``False``. See ``refit`` parameter for more information. best_index_ : int The index (of the ``cv_results_`` arrays) which corresponds to the best candidate parameter setting. The dict at ``search.cv_results_['params'][search.best_index_]`` gives the parameter setting for the best model, that gives the highest mean score (``search.best_score_``). For multi-metric evaluation, this is not available if ``refit`` is ``False``. See ``refit`` parameter for more information. scorer_ : function or a dict Scorer function used on the held out data to choose the best parameters for the model. For multi-metric evaluation, this attribute holds the validated ``scoring`` dict which maps the scorer key to the scorer callable. n_splits_ : int The number of cross-validation splits (folds/iterations). """ def __init__(self, estimator, param_distributions, n_iter=10, scoring=None, cv=None, refit=True, random_state=None): super().__init__(estimator=estimator, scoring=scoring, cv=cv, refit=refit) self.param_distributions = param_distributions self.n_iter = n_iter self.random_state = random_state def _run_search(self, evaluate_candidates): """Search n_iter candidates from param_distributions""" ps = ParameterSampler(self.param_distributions, self.n_iter, random_state=self.random_state) evaluate_candidates(ps)
2.734375
3
webapp/ui/tests/test_parse_search_results.py
robseed/botanist
0
9660
<filename>webapp/ui/tests/test_parse_search_results.py import os from django.test import TestCase from mock import patch from ui.views import parse_search_results FIXTURES_ROOT = os.path.join(os.path.dirname(__file__), 'fixtures') FX = lambda *relpath: os.path.join(FIXTURES_ROOT, *relpath) @patch('ui.views.get_repo_type') @patch('ui.views.CODE_ROOT', '/opt/botanist/repos') class ParseSearchResults(TestCase): def test_duplicate_repositories_in_github_and_bitbucket(self, get_repo_type): def se(filepath): if 'bitbucket' in filepath: return 'hg' elif 'github' in filepath: return 'git' else: raise Exception('thats odd') get_repo_type.side_effect = se with open(FX('duplicate_repositories_in_github_and_bitbucket.results.txt')) as f: output = f.read() results, count = parse_search_results(output, 'AbstractSendTimeJob', True) self.assertEqual(2, count) self.assertListEqual(['bitbucket', 'github'], results['sproutjobs'].keys()) self.assertEqual('public abstract class AbstractJob implements Job {', results['sproutjobs']['bitbucket']['files']['src/main/java/com/sproutsocial/AbstractJob.java'][0]['srcline']) self.assertEqual('public abstract class AbstractJob implements Job {', results['sproutjobs']['github']['files']['src/main/java/com/sproutsocial/AbstractJob.java'][0]['srcline'])
2.28125
2
minos/api_gateway/common/exceptions.py
Clariteia/api_gateway_common
3
9661
""" Copyright (C) 2021 Clariteia SL This file is part of minos framework. Minos framework can not be copied and/or distributed without the express permission of Clariteia SL. """ from typing import ( Any, Type, ) class MinosException(Exception): """Exception class for import packages or modules""" __slots__ = "_message" def __init__(self, error_message: str): self._message = error_message def __repr__(self): return f"{type(self).__name__}(message={repr(self._message)})" def __str__(self) -> str: """represent in a string format the error message passed during the instantiation""" return self._message class MinosImportException(MinosException): pass class MinosProtocolException(MinosException): pass class MinosMessageException(MinosException): pass class MinosConfigException(MinosException): """Base config exception.""" class MinosConfigDefaultAlreadySetException(MinosConfigException): """Exception to be raised when some config is already set as default.""" class MinosRepositoryException(MinosException): """Base repository exception.""" class MinosRepositoryAggregateNotFoundException(MinosRepositoryException): """Exception to be raised when some aggregate is not found on the repository.""" class MinosRepositoryDeletedAggregateException(MinosRepositoryException): """Exception to be raised when some aggregate is already deleted from the repository.""" class MinosRepositoryManuallySetAggregateIdException(MinosRepositoryException): """Exception to be raised when some aggregate is trying to be created with a manually set id.""" class MinosRepositoryManuallySetAggregateVersionException(MinosRepositoryException): """Exception to be raised when some aggregate is trying to be created with a manually set version.""" class MinosRepositoryUnknownActionException(MinosRepositoryException): """Exception to be raised when some entry tries to perform an unknown action.""" class MinosRepositoryNonProvidedException(MinosRepositoryException): """Exception to be raised when a repository is needed but none is set.""" class MinosModelException(MinosException): """Exception to be raised when some mandatory condition is not satisfied by a model.""" pass class EmptyMinosModelSequenceException(MinosModelException): """Exception to be raised when a sequence must be not empty, but it is empty.""" pass class MultiTypeMinosModelSequenceException(MinosModelException): """Exception to be raised when a sequence doesn't satisfy the condition to have the same type for each item.""" pass class MinosModelAttributeException(MinosException): """Base model attributes exception.""" pass class MinosReqAttributeException(MinosModelAttributeException): """Exception to be raised when some required attributes are not provided.""" pass class MinosTypeAttributeException(MinosModelAttributeException): """Exception to be raised when there are any mismatching between the expected and observed attribute type.""" def __init__(self, name: str, target_type: Type, value: Any): self.name = name self.target_type = target_type self.value = value super().__init__( f"The {repr(target_type)} expected type for {repr(name)} does not match with " f"the given data type: {type(value)}" ) class MinosMalformedAttributeException(MinosModelAttributeException): """Exception to be raised when there are any kind of problems with the type definition.""" pass class MinosParseAttributeException(MinosModelAttributeException): """Exception to be raised when there are any kind of problems with the parsing logic.""" def __init__(self, name: str, value: Any, exception: Exception): self.name = name self.value = value self.exception = exception super().__init__(f"{repr(exception)} was raised while parsing {repr(name)} field with {repr(value)} value.") class MinosAttributeValidationException(MinosModelAttributeException): """Exception to be raised when some fields are not valid.""" def __init__(self, name: str, value: Any): self.name = name self.value = value super().__init__(f"{repr(value)} value does not pass the {repr(name)} field validation.")
2.25
2
tsdl/tools/extensions.py
burgerdev/hostload
0
9662
<reponame>burgerdev/hostload """ Extensions for pylearn2 training algorithms. Those are either reimplemented to suit the execution model of this package, or new ones for recording metrics. """ import os import cPickle as pkl import numpy as np from pylearn2.train_extensions import TrainExtension from .abcs import Buildable class BuildableTrainExtension(TrainExtension, Buildable): """ makes a pylearn2 TrainExtension buildable """ @classmethod def build(cls, config, parent=None, graph=None, workingdir=None): """ build an instance of this class with given configuration dict """ config_copy = config.copy() if "wd" not in config_copy: config_copy["wd"] = workingdir obj = super(BuildableTrainExtension, cls).build(config_copy) return obj def __init__(self, **kwargs): if "workingdir" in kwargs: self._wd = kwargs["workingdir"] super(BuildableTrainExtension, self).__init__() @classmethod def get_default_config(cls): """ override to provide your own default configuration """ conf = super(BuildableTrainExtension, cls).get_default_config() conf["wd"] = None return conf class PersistentTrainExtension(BuildableTrainExtension): """ abstract extension that can store its results (on disk, probably) """ def store(self): """ store the findings of this extension """ pass class WeightKeeper(PersistentTrainExtension): """ keeps track of the model's weights at each monitor step This model stores weights *per monitor step* - the list grows large pretty quickly. """ _weights = [] def on_monitor(self, model, dataset, algorithm): """ save the model's weights """ self._weights.append(model.get_param_values()) def setup(self, model, dataset, algorithm): """ initialize the weight list """ self._weights = [] def get_weights(self): """ get weights history """ return self._weights def store(self): path = os.path.join(self._wd, "weightkeeper.pkl") with open(path, "w") as file_: pkl.dump(self._weights, file_) class ProgressMonitor(PersistentTrainExtension): """ Makes the monitor channel's history accessible to us. """ _progress = np.NaN @classmethod def get_default_config(cls): config = super(ProgressMonitor, cls).get_default_config() config["channel"] = "valid_objective" return config def on_monitor(self, model, dataset, algorithm): """ save the desired channel """ monitor = model.monitor channels = monitor.channels channel = channels[self._channel] self._progress = channel.val_record def get_progress(self): """ get the value's history """ return self._progress def store(self): filename = "progress_{}.pkl".format(self._channel) path = os.path.join(self._wd, filename) with open(path, "w") as file_: pkl.dump(self._progress, file_) class MonitorBasedSaveBest(BuildableTrainExtension): """ similar to pylearn2's MonitorBasedSaveBest, but avoids memory hogging (see https://github.com/lisa-lab/pylearn2/issues/1567) """ best_cost = np.inf best_params = None @classmethod def get_default_config(cls): config = super(MonitorBasedSaveBest, cls).get_default_config() config["channel"] = "valid_objective" return config def setup(self, model, dataset, algorithm): self.best_cost = np.inf self.best_params = model.get_param_values() def on_monitor(self, model, dataset, algorithm): """ Looks whether the model performs better than earlier. If it's the case, saves the model. Parameters ---------- model : pylearn2.models.model.Model model.monitor must contain a channel with name given by self.channel_name dataset : pylearn2.datasets.dataset.Dataset Not used algorithm : TrainingAlgorithm Not used """ monitor = model.monitor channels = monitor.channels channel = channels[self._channel] val_record = channel.val_record new_cost = val_record[-1] if new_cost < self.best_cost: self.best_cost = new_cost self.best_params = model.get_param_values()
2.25
2
nogi/utils/post_extractor.py
Cooomma/nogi-backup-blog
0
9663
import asyncio from io import BytesIO import logging import os import random import time from typing import List from urllib.parse import urlparse import aiohttp from aiohttp import ClientSession, TCPConnector import requests from requests import Response from tqdm import tqdm from nogi import REQUEST_HEADERS from nogi.db.nogi_blog_content import NogiBlogContent from nogi.db.nogi_blog_summary import NogiBlogSummary from nogi.storages.gcs import GCS from nogi.utils.parsers import PostParser, generate_post_key logger = logging.getLogger() HEADERS = { 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36' } class PostExecutor: def __init__(self, member: dict, summary_db: NogiBlogSummary, content_db: NogiBlogContent, gcs_client: GCS, bucket: str, concurrent: int = 4): self._waiting_limit = concurrent self.member = member # DB self.summary_db = summary_db self.content_db = content_db # GCS Storage self.bucket = bucket self.storage = gcs_client self.storage_blog_post_prefix = os.path.join(member['roma_name'], 'post') self.storage_blog_image_prefix = os.path.join(member['roma_name'], 'img') # Tasks self.todos = self.summary_db.get_missing_blog_url(member['id']) @staticmethod def db_transform(post_url: str, obj: dict, **kwargs) -> dict: return dict( member_id=kwargs.get('member_id'), blog_key=generate_post_key(post_url), url=post_url, title=obj['title'], content=obj['content'], image_gcs_paths=kwargs.get('image_gcs_paths'), post_gcs_path=kwargs.get('post_gcs_path'), blog_created_at=int(obj['created_at'].timestamp())) @staticmethod def _get_hd_image(url: str) -> BytesIO: first_layer_response: Response = requests.get(url, headers=HEADERS) logger.debug(first_layer_response.cookies) resp = requests.get( url=url.replace('http://dcimg.awalker.jp/v/', 'http://dcimg.awalker.jp/i/'), cookies=first_layer_response.cookies) logger.debug(resp.status_code) logger.debug(resp.headers) return BytesIO(resp.content) if resp.status_code == 200 else BytesIO(bytes=b'') def backup_images(self, image_urls: List[dict]) -> List[str]: downloaded_image_urls = list() for url in image_urls: image_gcs_path = os.path.join(self.storage_blog_image_prefix, '/'.join(urlparse(url['image_url']).path.split('/')[-5:])) if url['high_resolution_url'] != url['image_url']: hd_image = self._get_hd_image(url['high_resolution_url']) if hd_image: self.storage.upload_stream( bucket=self.bucket, blob_name=image_gcs_path, content=hd_image.read(), content_type='image/jpeg' ) else: image = requests.get(url=url['image_url']) if image.status_code != 200: logger.warning('Image Request Fail: %s', url) continue self.storage.upload_stream( bucket=self.bucket, blob_name=image_gcs_path, content=image.content, content_type='image/jpeg' ) downloaded_image_urls.append(url) return downloaded_image_urls async def backup_content(self, session: ClientSession, post_url: str) -> str: post_gcs_path = os.path.join(self.storage_blog_post_prefix, '/'.join(urlparse(post_url).path.split('/')[-3:])) try: async with session.get(url=post_url, headers=REQUEST_HEADERS) as response: self.storage.upload_stream( bucket=self.bucket, blob_name=post_gcs_path, content=await response.read(), content_type='text/html') return post_gcs_path except aiohttp.client_exceptions.InvalidURL: print('Invalid URL: %s' % post_url) except aiohttp.client_exceptions.ClientConnectorError: print('Client Connector Error: %s' % post_url) @staticmethod def crawl_post(url: str) -> None: return PostParser(requests.get(url, headers=REQUEST_HEADERS).text).to_dict() async def _run(self, url: str): try: async with aiohttp.ClientSession(connector=TCPConnector(verify_ssl=False)) as session: post_gcs_path = await self.backup_content(session, url) post = self.crawl_post(url) images_gcs_paths = self.backup_images(post['image_urls']) result = self.db_transform( post_url=url, obj=post, member_id=self.member['id'], image_gcs_paths=images_gcs_paths, post_gcs_path=post_gcs_path) self.content_db.upsert_crawled_post(result) self.summary_db.update_crawled_result(result) except aiohttp.client_exceptions.InvalidURL: print('Invalid URL: %s' % url) except aiohttp.client_exceptions.ClientConnectorError: print('Client Connector Error: %s' % url) except Exception: import traceback print('Error URL: %s' % url) print(traceback.format_exc()) def run(self): loop = asyncio.get_event_loop() if self.todos: tasks = [] for url in tqdm(self.todos, desc='Current Member: {}'.format(self.member['kanji_name']), ncols=120): tasks.append(asyncio.ensure_future(self._run(url))) if len(tasks) > self._waiting_limit: loop.run_until_complete(asyncio.gather(*tasks)) tasks = [] if tasks: loop.run_until_complete(asyncio.gather(*tasks)) slepp_second = random.randint(1, 15) print('Sleep for %s second' % slepp_second) time.sleep(slepp_second)
2.015625
2
sandbox/lib/jumpscale/JumpscaleLibsExtra/sal_zos/gateway/dhcp.py
threefoldtech/threebot_prebuilt
1
9664
from Jumpscale import j import signal from .. import templates DNSMASQ = "/bin/dnsmasq --conf-file=/etc/dnsmasq.conf -d" class DHCP: def __init__(self, container, domain, networks): self.container = container self.domain = domain self.networks = networks def apply_config(self): dnsmasq = templates.render("dnsmasq.conf", domain=self.domain, networks=self.networks) self.container.upload_content("/etc/dnsmasq.conf", dnsmasq) dhcp = templates.render("dhcp", networks=self.networks) self.container.upload_content("/etc/dhcp", dhcp) self.stop() self.container.client.system(DNSMASQ, id="dhcp.{}".format(self.container.name)) # check if command is listening for dhcp if not j.tools.timer.execute_until(self.is_running, 10): raise j.exceptions.Base("Failed to run dnsmasq") def is_running(self): for port in self.container.client.info.port(): if port["network"] == "udp" and port["port"] == 53: return True def stop(self): for process in self.container.client.process.list(): if "dnsmasq" in process["cmdline"]: self.container.client.process.kill(process["pid"], signal.SIGKILL) if not j.tools.timer.execute_until(lambda: not self.is_running(), 10): raise j.exceptions.Base("Failed to stop DNSMASQ")
2.234375
2
answer/a4_type.py
breeze-shared-inc/python_training_01
0
9665
hensu_int = 17 #数字 hensu_float = 1.7 #小数点(浮動小数点) hensu_str = "HelloWorld" #文字列 hensu_bool = True #真偽 hensu_list = [] #リスト hensu_tuple = () #タプル hensu_dict = {} #辞書(ディクト)型 print(type(hensu_int)) print(type(hensu_float)) print(type(hensu_str)) print(type(hensu_bool)) print(type(hensu_list)) print(type(hensu_tuple)) print(type(hensu_dict))
3.375
3
LEDdebug/examples/led-demo.py
UrsaLeo/LEDdebug
0
9666
#!/usr/bin/env python3 """UrsaLeo LEDdebug board LED demo Turn the LED's on one at a time, then all off""" import time ON = 1 OFF = 0 DELAY = 0.5 # seconds try: from LEDdebug import LEDdebug except ImportError: try: import sys import os sys.path.append("..") sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'LEDdebug')) from LEDdebug import LEDdebug except ImportError: print('LEDdebug import failed') exit(0) def main(): # Create device device = LEDdebug() # Turn on each LED in succession for led in range(1, 7): device.set_led(led, ON) print(f'Turning LED{led} on') time.sleep(DELAY) print('Turning all LEDs off') # Turn all the lights of before leaving! device.set_leds(OFF) if __name__ == '__main__': main()
2.9375
3
modules/server.py
Nitin-Mane/SARS-CoV-2-xDNN-Classifier
0
9667
<reponame>Nitin-Mane/SARS-CoV-2-xDNN-Classifier #!/usr/bin/env python ################################################################################### ## ## Project: COVID -19 xDNN Classifier 2020 ## Version: 1.0.0 ## Module: Server ## Desription: The COVID -19 xDNN Classifier 2020 server. ## License: MIT ## Copyright: 2021, Asociacion De Investigacion En Inteligencia Artificial Para ## La Leucemia Peter Moss. ## Author: <NAME> ## Maintainer: <NAME> ## ## Modified: 2021-2-19 ## ################################################################################### ## ## 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. ## ################################################################################### import cv2 import json import jsonpickle import os import requests import time import numpy as np import tensorflow as tf from modules.AbstractServer import AbstractServer from flask import Flask, request, Response from io import BytesIO from PIL import Image from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import preprocess_input class server(AbstractServer): """ COVID 19 xDNN Classifier 2020 Server. This object represents the COVID 19 xDNN Classifier 2020 Server. """ def predict(self, req): """ Classifies an image sent via HTTP. """ if len(req.files) != 0: img = Image.open(req.files['file'].stream).convert('RGB') else: img = Image.open(BytesIO(req.data)).convert('RGB') img = img.resize((224, 224), Image.ANTIALIAS) np_img = tf.keras.preprocessing.image.img_to_array(img) np_img.transpose(1, 2, 0) #img = keras.preprocessing.image.img_to_array(img) #img = np.array([img]) # Convert single image to a batch. img = np.expand_dims(np_img, axis=0) img = preprocess_input(img) #prediction = self.predict(img) #img = img.resize((224, 224), Image.ANTIALIAS) #img = image.img_to_array(img) #img = np.expand_dims(img, axis=0) #img = preprocess_input(img) #img = img.reshape((1,224,224,3)) return self.model.predict(img) def request(self, img_path): """ Sends image to the inference API endpoint. """ self.helpers.logger.info("Sending request for: " + img_path) _, img_encoded = cv2.imencode('.png', cv2.imread(img_path)) response = requests.post( self.addr, data=img_encoded.tostring(), headers=self.headers) response = json.loads(response.text) return response def start(self): """ Starts the server. """ app = Flask(self.helpers.credentials["iotJumpWay"]["name"]) @app.route('/Inference', methods=['POST']) def Inference(): """ Responds to HTTP POST requests. """ self.mqtt.publish("States", { "Type": "Prediction", "Name": self.helpers.credentials["iotJumpWay"]["name"], "State": "Processing", "Message": "Processing data" }) message = "" prediction = self.predict(request) print(prediction) if prediction == 1: message = "Acute Lymphoblastic Leukemia detected!" diagnosis = "Positive" elif prediction == 0: message = "Acute Lymphoblastic Leukemia not detected!" diagnosis = "Negative" self.mqtt.publish("States", { "Type": "Prediction", "Name": self.helpers.credentials["iotJumpWay"]["name"], "State": diagnosis, "Message": message }) resp = jsonpickle.encode({ 'Response': 'OK', 'Message': message, 'Diagnosis': diagnosis }) return Response(response=resp, status=200, mimetype="application/json") app.run(host=self.helpers.credentials["server"]["ip"], port=self.helpers.credentials["server"]["port"]) def test(self): """ Tests the trained model via HTTP. """ totaltime = 0 files = 0 tp = 0 fp = 0 tn = 0 fn = 0 self.addr = "http://" + self.helpers.credentials["server"]["ip"] + \ ':'+str(self.helpers.credentials["server"]["port"]) + '/Inference' self.headers = {'content-type': 'image/jpeg'} for testFile in os.listdir(self.model.testing_dir): if os.path.splitext(testFile)[1] in self.model.valid: start = time.time() prediction = self.request(self.model.testing_dir + "/" + testFile) print(prediction) end = time.time() benchmark = end - start totaltime += benchmark msg = "" status = "" outcome = "" if prediction["Diagnosis"] == "Positive" and "Non-Covid" in testFile: fp += 1 status = "incorrectly" outcome = "(False Positive)" elif prediction["Diagnosis"] == "Negative" and "Non-Covid" in testFile: tn += 1 status = "correctly" outcome = "(True Negative)" elif prediction["Diagnosis"] == "Positive" and "Covid" in testFile: tp += 1 status = "correctly" outcome = "(True Positive)" elif prediction["Diagnosis"] == "Negative" and "Covid" in testFile: fn += 1 status = "incorrectly" outcome = "(False Negative)" files += 1 self.helpers.logger.info("COVID-19 xDNN Classifier " + status + " detected " + outcome + " in " + str(benchmark) + " seconds.") self.helpers.logger.info("Images Classified: " + str(files)) self.helpers.logger.info("True Positives: " + str(tp)) self.helpers.logger.info("False Positives: " + str(fp)) self.helpers.logger.info("True Negatives: " + str(tn)) self.helpers.logger.info("False Negatives: " + str(fn)) self.helpers.logger.info("Total Time Taken: " + str(totaltime))
1.265625
1
rdr_service/lib_fhir/fhirclient_3_0_0/models/allergyintolerance_tests.py
all-of-us/raw-data-repository
39
9668
<reponame>all-of-us/raw-data-repository #!/usr/bin/env python # -*- coding: utf-8 -*- # # Generated from FHIR 3.0.0.11832 on 2017-03-22. # 2017, SMART Health IT. import io import json import os import unittest from . import allergyintolerance from .fhirdate import FHIRDate class AllergyIntoleranceTests(unittest.TestCase): def instantiate_from(self, filename): datadir = os.environ.get('FHIR_UNITTEST_DATADIR') or '' with io.open(os.path.join(datadir, filename), 'r', encoding='utf-8') as handle: js = json.load(handle) self.assertEqual("AllergyIntolerance", js["resourceType"]) return allergyintolerance.AllergyIntolerance(js) def testAllergyIntolerance1(self): inst = self.instantiate_from("allergyintolerance-example.json") self.assertIsNotNone(inst, "Must have instantiated a AllergyIntolerance instance") self.implAllergyIntolerance1(inst) js = inst.as_json() self.assertEqual("AllergyIntolerance", js["resourceType"]) inst2 = allergyintolerance.AllergyIntolerance(js) self.implAllergyIntolerance1(inst2) def implAllergyIntolerance1(self, inst): self.assertEqual(inst.assertedDate.date, FHIRDate("2014-10-09T14:58:00+11:00").date) self.assertEqual(inst.assertedDate.as_json(), "2014-10-09T14:58:00+11:00") self.assertEqual(inst.category[0], "food") self.assertEqual(inst.clinicalStatus, "active") self.assertEqual(inst.code.coding[0].code, "227493005") self.assertEqual(inst.code.coding[0].display, "Cashew nuts") self.assertEqual(inst.code.coding[0].system, "http://snomed.info/sct") self.assertEqual(inst.criticality, "high") self.assertEqual(inst.id, "example") self.assertEqual(inst.identifier[0].system, "http://acme.com/ids/patients/risks") self.assertEqual(inst.identifier[0].value, "49476534") self.assertEqual(inst.lastOccurrence.date, FHIRDate("2012-06").date) self.assertEqual(inst.lastOccurrence.as_json(), "2012-06") self.assertEqual(inst.note[0].text, "The criticality is high becasue of the observed anaphylactic reaction when challenged with cashew extract.") self.assertEqual(inst.onsetDateTime.date, FHIRDate("2004").date) self.assertEqual(inst.onsetDateTime.as_json(), "2004") self.assertEqual(inst.reaction[0].description, "Challenge Protocol. Severe reaction to subcutaneous cashew extract. Epinephrine administered") self.assertEqual(inst.reaction[0].exposureRoute.coding[0].code, "34206005") self.assertEqual(inst.reaction[0].exposureRoute.coding[0].display, "Subcutaneous route") self.assertEqual(inst.reaction[0].exposureRoute.coding[0].system, "http://snomed.info/sct") self.assertEqual(inst.reaction[0].manifestation[0].coding[0].code, "39579001") self.assertEqual(inst.reaction[0].manifestation[0].coding[0].display, "Anaphylactic reaction") self.assertEqual(inst.reaction[0].manifestation[0].coding[0].system, "http://snomed.info/sct") self.assertEqual(inst.reaction[0].onset.date, FHIRDate("2012-06-12").date) self.assertEqual(inst.reaction[0].onset.as_json(), "2012-06-12") self.assertEqual(inst.reaction[0].severity, "severe") self.assertEqual(inst.reaction[0].substance.coding[0].code, "1160593") self.assertEqual(inst.reaction[0].substance.coding[0].display, "cashew nut allergenic extract Injectable Product") self.assertEqual(inst.reaction[0].substance.coding[0].system, "http://www.nlm.nih.gov/research/umls/rxnorm") self.assertEqual(inst.reaction[1].manifestation[0].coding[0].code, "64305001") self.assertEqual(inst.reaction[1].manifestation[0].coding[0].display, "Urticaria") self.assertEqual(inst.reaction[1].manifestation[0].coding[0].system, "http://snomed.info/sct") self.assertEqual(inst.reaction[1].note[0].text, "The patient reports that the onset of urticaria was within 15 minutes of eating cashews.") self.assertEqual(inst.reaction[1].onset.date, FHIRDate("2004").date) self.assertEqual(inst.reaction[1].onset.as_json(), "2004") self.assertEqual(inst.reaction[1].severity, "moderate") self.assertEqual(inst.text.status, "generated") self.assertEqual(inst.type, "allergy") self.assertEqual(inst.verificationStatus, "confirmed")
2.375
2
jsparse/meijiexia/meijiexia.py
PyDee/Spiders
6
9669
import time import random import requests from lxml import etree import pymongo from .url_file import mjx_weibo, mjx_dy, mjx_ks, mjx_xhs class DBMongo: def __init__(self): self.my_client = pymongo.MongoClient("mongodb://localhost:27017/") # 连接数据库 self.db = self.my_client["mcn"] def insert_2_xt(self, success_item, collection_name): try: collection = self.db[collection_name] collection.insert_one(success_item) # 数据写入mongoDB print('success!!!') except: print('写入数据失败') class MJX: def __init__(self): self.db = DBMongo() self.headers = { 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh;q=0.9', 'Cache-Control': 'max-age=0', 'Connection': 'keep-alive', 'Cookie': 'accessId=defba4d0-9ab2-11e8-b156-7b8f577687be; qimo_seokeywords_defba4d0-9ab2-11e8-b156-7b8f577687be=; href=https%3A%2F%2Fwww.meijiexia.com%2Fmedias-118.html; ci_session=ccb97bb846cd5e0ce6538c2cc8f11ca7abc296ee; Hm_lvt_c96abf7da979015953d1d22702db6de8=1591685037,1592274339,1592278224; qimo_seosource_defba4d0-9ab2-11e8-b156-7b8f577687be=%E7%AB%99%E5%86%85; Hm_lpvt_c96abf7da979015953d1d22702db6de8=1592278238; pageViewNum=34', 'Host': 'www.meijiexia.com', 'Referer': 'https://www.meijiexia.com/medias-118.html', 'Sec-Fetch-Dest': 'document', 'Sec-Fetch-Mode': 'navigate', 'Sec-Fetch-Site': 'same-origin', 'Sec-Fetch-User': '?1', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36', } def get_response(self, url, collection): proxy = self.get_proxy() proxies = { "http": "http://{}:{}".format(proxy.get('IP'), proxy.get('Port')), "https": "http://{}:{}".format(proxy.get('IP'), proxy.get('Port')), } ret = requests.get(url, headers=self.headers, proxies=proxies) response = etree.HTML(ret.text) tr_list = response.xpath('//tbody[@id="qu-con"]/tr') for tr in tr_list: item = dict() user_id = tr.xpath('./td[@class="td1"]/input/@value')[0] nick_name = tr.xpath('./td[@class="td2"]/div[@class="itemMsg"]//a/text()')[0] place = tr.xpath('./td[@class="td3"]/text()')[0] fans_num = tr.xpath('./td[@class="td6"]/p[@class="num"]/text()')[0] price_list = tr.xpath('./td[@class="td4"]/p') for price_element in price_list: classify = price_element.xpath( './span[@class="money"]/preceding-sibling::span[1]/text()')[0] price = price_element.xpath('./span[@class="money"]/text()')[0] item[classify.strip()] = price.strip() item['fans_num'] = fans_num.strip() item['user_id'] = user_id.strip() item['nick_name'] = nick_name.strip() item['place'] = place.strip() item['plant'] = collection.split('mjx_')[1] self.db.insert_2_xt(item, collection) @staticmethod def get_proxy(): proxy = [{"IP": "192.168.3.11", "Port": 21730}] return random.choice(proxy) def run(self): urls = '' for item in {'mjx_weibo': mjx_weibo, 'mjx_dy': mjx_dy, 'mjx_ks': mjx_ks, 'mjx_xhs': mjx_xhs}.keys(): if item == 'mjx_weibo': urls = mjx_weibo if item == 'mjx_dy': urls = mjx_dy if item == 'mjx_ks': urls = mjx_ks if item == 'mjx_xhs': urls = mjx_xhs for url in urls: time.sleep(3) print(url) self.get_response(url, item) if __name__ == '__main__': mjx = MJX() mjx.run()
2.515625
3
MLModules/ABD/B_PCAQDA.py
jamster112233/ICS_IDS
0
9670
import numpy as np from keras.utils import np_utils import pandas as pd import sys from sklearn.preprocessing import LabelEncoder from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA from sklearn.decomposition import PCA import os from sklearn.externals import joblib from sklearn.metrics import f1_score trainName = sys.argv[1] testName = sys.argv[2] # Create an object called iris with the iris Data dftrain = pd.read_csv(filepath_or_buffer=trainName, header=None, sep=',') dftest = pd.read_csv(filepath_or_buffer=testName, header=None, sep=',') cols = ['Proto'] for i in range(1,dftrain.shape[1]): cols.append('Byte' + str(i)) dftrain.columns=cols dftrain.dropna(how="all", inplace=True) dftrain.tail() dftest.columns=cols dftest.dropna(how="all", inplace=True) dftest.tail() Xtrain = dftrain.ix[:,1:dftrain.shape[1]].values Ytrain = dftrain.ix[:,0].values Xtest = dftest.ix[:,1:dftrain.shape[1]].values Ytest = dftest.ix[:,0].values encoder = LabelEncoder() encoder.fit(Ytrain) encYtrain = encoder.transform(Ytrain) encoder = LabelEncoder() encoder.fit(Ytest) encYtest = encoder.transform(Ytest) directory = "models/ABD/QDA/" if not os.path.exists(directory): os.makedirs(directory) logfile = directory + "log-0.csv" with open(logfile, "w") as file: file.write("PCAlevel,acc,val_acc,f1\n") fscores = [] accs = [] for q in xrange(1,151): pca = PCA(n_components=q) Xtrain_pca = pca.fit_transform(Xtrain) Xtest_pca = pca.transform(Xtest) clf = QDA(priors=None, reg_param=0.0) clf.fit(Xtrain_pca, encYtrain) trainPred = clf.predict(Xtrain_pca) testPred = clf.predict(Xtest_pca) score = 0.0 for i in xrange(0, len(trainPred)): if trainPred[i] == encYtrain[i]: score += 1 trainAcc = float(score) / len(trainPred) score = 0.0 for i in xrange(0, len(testPred)): if testPred[i] == encYtest[i]: score += 1 testAcc = float(score) / len(testPred) f1 = f1_score(encYtest, testPred) accs.append(testAcc) fscores.append(f1) print("Train " + str(trainAcc)) print("Test " + str(testAcc)) print("F1 " + str(f1)) with open(logfile, "a") as file: file.write(str(q) + "," + str(trainAcc) + "," + str(testAcc) + "," + str(f1) + "\n") if q == 2: joblib.dump(clf, 'QDA2.pkl') print("Val Acc max" + str(max(accs))) print("FMAX " + str(max(fscores))) # print(str(q) + ":" + str((float(score)/len(classesPred)*100)) + "%") # # preds = classesPred # if(len(preds) > 0): # preds = np.array(list(encoder.inverse_transform(preds))) # # df = pd.crosstab(dftest['Proto'], preds, rownames=['Actual Protocol'], colnames=['Predicted Protocol']) # df.to_csv('ConfusionMatrixLDA.csv')
2.328125
2
GR2-Save-Loader.py
203Null/Gravity-Rush-2-Save-Loader
2
9671
<reponame>203Null/Gravity-Rush-2-Save-Loader import struct import json from collections import OrderedDict file_path = "data0002.bin" show_offset = True show_hash = False loaded_data = 0 def unpack(upstream_data_set): global loaded_data loaded_data = loaded_data + 1 currentCursor = file.tell() print(hex(file.tell())) file.seek(int.from_bytes(file.read(4), byteorder='little'), 0) variable_name = file.read(200).split(b'\x00')[0].decode('UTF8') #Use UTF8 because some strings are in Japanese print(hex(file.tell())) print(variable_name) file.seek(currentCursor + 4, 0) type = int.from_bytes(file.read(4), byteorder='little') data_location = file.tell() if type == 0x08: # List list_length = int.from_bytes(file.read(4), byteorder='little') name_hash = file.read(4).hex() data_location = file.tell() value = {} for i in range(0, list_length): unpack(value) value = OrderedDict(sorted(value.items())) else: if type % 0x10 == 0x0b: # String string_length = int.from_bytes(file.read(4), byteorder='little') - 1 data_location = type // 0x10 file.seek(data_location, 0) try: value = file.read(string_length).decode('UTF8') except: value = "ERROR EXTRACTING STRING" file.seek(currentCursor + 0x0c, 0) elif type == 0x09: # Float value = struct.unpack('f', file.read(4))[0] elif type == 0x0C: # Boolean value = int.from_bytes(file.read(1), byteorder='little') > 0 file.seek(3, 1) else: value = file.read(4).hex() print("Warring!!! Unknow type!!! %s at %s with value %s" % (hex(type), hex(file.tell()-8), value)) print() name_hash = file.read(4).hex() if variable_name == None: variable_name = hex(data_location) else: if show_hash: variable_name = variable_name = "%s %s" % (variable_name, name_hash) if show_offset: variable_name = variable_name = "%s %s" % (variable_name, hex(data_location)) print(value) upstream_data_set[variable_name] = value file = open(file_path, mode='rb') data = file.read() data_set = OrderedDict() if len(data) > 0x40 and data[0:4] == b'ggdL': file.seek(0x0c, 0) numOfData = int.from_bytes(file.read(4), byteorder='little') while loaded_data < numOfData: unpack(data_set) print() print(data_set) print() print("Complete with %i/%i data" % (loaded_data, numOfData)) with open(r"%s.txt" % (file_path.split('.')[0]), 'w', encoding='utf-8') as json_file: json.dump(data_set, json_file, indent=4, ensure_ascii=False) else: print("File Incorrect")
2.546875
3
python/Recursion.py
itzsoumyadip/vs
1
9672
## to change recursion limit import sys print(sys.getrecursionlimit()) #Return the current value of the recursion limit #1000 ## change the limit sys.setrecursionlimit(2000) # change value of the recursion limit #2000 i=0 def greet(): global i i+=1 print('hellow',i) greet() greet() # hellow 1996 then error
3.765625
4
pages/tests/test_views.py
andywar65/starter-fullstack
0
9673
from django.test import TestCase, override_settings from django.urls import reverse from pages.models import Article, HomePage @override_settings(USE_I18N=False) class PageViewTest(TestCase): @classmethod def setUpTestData(cls): print("\nTest page views") # Set up non-modified objects used by all test methods HomePage.objects.create(title="Title") Article.objects.create(title="First", date="2022-04-09") def test_homepage_view(self): response = self.client.get(reverse("home")) self.assertEqual(response.status_code, 200) print("\n-Test Homepage status 200") self.assertTemplateUsed(response, "pages/home.html") print("\n-Test Homepage template") def test_no_homepage(self): HomePage.objects.all().delete() response = self.client.get(reverse("home")) self.assertEqual(response.status_code, 404) print("\n-Test Homepage status 404") def test_article_template(self): response = self.client.get( reverse( "pages:article_detail", kwargs={"year": 2022, "month": 4, "day": 9, "slug": "first"}, ) ) self.assertEqual(response.status_code, 200) print("\n-Test Article status 200") self.assertTemplateUsed(response, "pages/article_detail.html") print("\n-Test Article template")
2.25
2
poco/services/batch/server.py
sunliwen/poco
0
9674
<gh_stars>0 #!/usr/bin/env python import logging import sys sys.path.append("../../") sys.path.append("pylib") import time import datetime import pymongo import uuid import os import subprocess import os.path import settings from common.utils import getSiteDBCollection sys.path.insert(0, "../../") class LoggingManager: def __init__(self): self.h_console = None self.h_file = None logging.getLogger('').setLevel(logging.INFO) def reconfig_h_console(self, site_id, calculation_id): if self.h_console is not None: self.h_console.flush() logging.getLogger('').removeHandler(self.h_console) self.h_console = logging.StreamHandler() self.h_console.setLevel(logging.INFO) formatter = logging.Formatter("%(asctime)s|" + calculation_id + "|%(levelname)s|%(name)s|%(message)s", datefmt="%Y-%m-%d %H:%M:%S") self.h_console.setFormatter(formatter) logging.getLogger('').addHandler(self.h_console) def getLogFilePath(self, site_id, calculation_id): site_log_dir = os.path.join(settings.log_dir, site_id) if not os.path.isdir(site_log_dir): os.makedirs(site_log_dir) formatted_date_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") log_file_name = "%s_%s.log" % (formatted_date_time, calculation_id) log_file_path = os.path.join(site_log_dir, log_file_name) return log_file_path def reconfig_h_file(self, site_id, calculation_id): if self.h_file is not None: self.h_file.flush() self.h_file.close() logging.getLogger('').removeHandler(self.h_file) self.h_file = logging.FileHandler( self.getLogFilePath(site_id, calculation_id)) self.h_file.setLevel(logging.INFO) formatter = logging.Formatter( "%(asctime)s|%(levelname)s|%(name)s|%(message)s", datefmt="%Y-%m-%d %H:%M:%S") self.h_file.setFormatter(formatter) logging.getLogger('').addHandler(self.h_file) def reconfig(self, site_id, calculation_id): self.reconfig_h_console(site_id, calculation_id) self.reconfig_h_file(site_id, calculation_id) logging_manager = LoggingManager() def getLogger(): return logging.getLogger("Batch Server") def getBaseWorkDir(site_id, calculation_id): site_work_dir = os.path.join(settings.work_dir, site_id) formatted_date_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") calculation_work_dir_name = "%s_%s" % (formatted_date_time, calculation_id) calculation_work_dir_path = os.path.join( site_work_dir, calculation_work_dir_name) os.makedirs(calculation_work_dir_path) return calculation_work_dir_path def getConnection(): if(settings.replica_set): return pymongo.MongoReplicaSetClient(settings.mongodb_host, replicaSet=settings.replica_set) else: return pymongo.Connection(settings.mongodb_host) connection = getConnection() class ShellExecutionError(Exception): pass class BaseFlow: def __init__(self, name): self.name = name self.jobs = [] self.dependencies = [] def dependOn(self, flow): self.parent = flow flow.dependencies.append(self) def getWorkDir(self): work_dir = os.path.join(BASE_WORK_DIR, self.name) if not os.path.exists(work_dir): os.makedirs(work_dir) return work_dir def getWorkFile(self, file_name): return os.path.join(self.getWorkDir(), file_name) def __call__(self): global CALC_SUCC writeFlowBegin(SITE_ID, self.__class__.__name__) if self.__class__.__name__ in DISABLEDFLOWS: getLogger().info("Flow Skipped: %s" % self.__class__.__name__) writeFlowEnd(SITE_ID, self.__class__.__name__, is_successful=True, is_skipped=True) return True else: for job_callable in self.jobs: if not self._execJob(job_callable): writeFlowEnd( SITE_ID, self.__class__.__name__, is_successful=False, is_skipped=False, err_msg="SOME_JOBS_FAILED") CALC_SUCC = False return False writeFlowEnd(SITE_ID, self.__class__.__name__, is_successful=True, is_skipped=False) # execute downlines for dependency in self.dependencies: dependency() return True def _exec_shell(self, command): getLogger().info("Execute %s" % command) #ret_code = os.system(command) # if ret_code != 0: # raise ShellExecutionError("Shell Execution Failed, ret_code=%s" % ret_code) ret_code = subprocess.call(command, shell=True) if ret_code != 0: getLogger().error("Failed %s" % sys.stderr) raise ShellExecutionError( "Shell Execution Failed, ret_code=%s" % ret_code) def _execJob(self, callable): try: getLogger().info("Start Job: %s.%s" % (self.__class__.__name__, callable.__name__)) callable() getLogger().info("Job Succ: %s.%s" % (self.__class__.__name__, callable.__name__)) return True except: getLogger( ).critical("An Exception happened while running Job: %s" % callable, exc_info=True) # TODO: send message (email, sms) # TODO: record exception info. writeFailedJob(SITE_ID, self.__class__.__name__, callable.__name__) return False class PreprocessingFlow(BaseFlow): def __init__(self): BaseFlow.__init__(self, "preprocessing") self.jobs += [self.do_backfill, self.do_reverse_reversed_backfilled_raw_logs] def do_backfill(self): from preprocessing import backfiller last_ts = None # FIXME: load correct last_ts from somewhere bf = backfiller.BackFiller(connection, SITE_ID, last_ts, self.getWorkFile("reversed_backfilled_raw_logs")) last_ts = bf.start() # FIXME: save last_ts somewhere def do_reverse_reversed_backfilled_raw_logs(self): input_path = self.getWorkFile("reversed_backfilled_raw_logs") output_path = self.getWorkFile("backfilled_raw_logs") self._exec_shell("%s <%s >%s" % (settings.tac_command, input_path, output_path)) class HiveBasedStatisticsFlow(BaseFlow): def __init__(self): BaseFlow.__init__(self, "hive-based-statistics") self.jobs += [self.do_hive_based_calculations] # Begin Hive Based Calculations def do_hive_based_calculations(self): from statistics.hive_based_calculations import hive_based_calculations backfilled_raw_logs_path = self.parent.getWorkFile( "backfilled_raw_logs") hive_based_calculations( connection, SITE_ID, self.getWorkDir(), backfilled_raw_logs_path) # # End Hive Based Calculations class BaseSimilarityCalcFlow(BaseFlow): def __init__(self, type): BaseFlow.__init__(self, "similarities-calc:%s" % type) self.type = type self.jobs += self.getExtractUserItemMatrixJobs( ) + [self.do_sort_user_item_matrix, self.do_calc_item_prefer_count, self.do_calc_user_count, self.do_emit_cooccurances, self.do_sort_cooccurances, self.do_count_cooccurances, self.do_format_cooccurances_counts, self.do_calc_item_similarities, self.do_make_item_similarities_bi_directional, self.do_sort_item_similarities_bi_directional, self.do_extract_top_n, self.do_upload_item_similarities_result] def do_sort_user_item_matrix(self): input_path = self.getWorkFile("user_item_matrix") output_path = self.getWorkFile("user_item_matrix_sorted") self._exec_shell("sort -T /cube/services/batch/temp %s > %s" % (input_path, output_path)) def do_calc_item_prefer_count(self): if SITE["algorithm_type"] == "llh": input_path = self.getWorkFile("user_item_matrix_sorted") output_path = self.getWorkFile("item_prefer_count") self._exec_shell( "cut -d , -f 2 %s | sort -T /cube/services/batch/temp | uniq -c > %s" % (input_path, output_path)) def do_calc_user_count(self): if SITE["algorithm_type"] == "llh": input_path = self.getWorkFile("user_item_matrix_sorted") output_path = self.getWorkFile("user_count") self._exec_shell("cut -d , -f 1 %s | uniq | wc -l > %s" % (input_path, output_path)) def do_emit_cooccurances(self): from similarity_calculation.amazon.emit_cooccurances import emit_cooccurances input_path = self.getWorkFile("user_item_matrix_sorted") output_path = self.getWorkFile("cooccurances_not_sorted") emit_cooccurances(input_path, output_path) def do_sort_cooccurances(self): input_path = self.getWorkFile("cooccurances_not_sorted") output_path = self.getWorkFile("cooccurances_sorted") self._exec_shell("sort -T /cube/services/batch/temp %s > %s" % (input_path, output_path)) def do_count_cooccurances(self): input_path = self.getWorkFile("cooccurances_sorted") output_path = self.getWorkFile("cooccurances_counts_raw") self._exec_shell("uniq -c %s > %s" % (input_path, output_path)) def do_format_cooccurances_counts(self): from similarity_calculation.amazon.format_item_similarities import format_item_similarities input_path = self.getWorkFile("cooccurances_counts_raw") output_path = self.getWorkFile("cooccurances_counts_formatted") format_item_similarities(input_path, output_path) def do_calc_item_similarities(self): if SITE["algorithm_type"] == "llh": from similarity_calculation.loglikelihood.calc_loglikelihood import calc_loglikelihood cooccurances_counts_path = self.getWorkFile( "cooccurances_counts_formatted") user_counts_path = self.getWorkFile("user_count") item_prefer_count_path = self.getWorkFile("item_prefer_count") output_path = self.getWorkFile("item_similarities_formatted") calc_loglikelihood(cooccurances_counts_path, user_counts_path, item_prefer_count_path, output_path) else: input_path = self.getWorkFile("cooccurances_counts_formatted") output_path = self.getWorkFile("item_similarities_formatted") self._exec_shell("mv %s %s" % (input_path, output_path)) def do_make_item_similarities_bi_directional(self): from similarity_calculation.make_similarities_bidirectional import make_similarities_bidirectional input_path = self.getWorkFile("item_similarities_formatted") output_path = self.getWorkFile("item_similarities_bi_directional") make_similarities_bidirectional(input_path, output_path) def do_sort_item_similarities_bi_directional(self): input_path = self.getWorkFile("item_similarities_bi_directional") output_path = self.getWorkFile( "item_similarities_bi_directional_sorted") self._exec_shell("sort -T /cube/services/batch/temp %s > %s" % (input_path, output_path)) def do_extract_top_n(self): from similarity_calculation.extract_top_n import extract_top_n input_path = self.getWorkFile( "item_similarities_bi_directional_sorted") output_path = self.getWorkFile("item_similarities_top_n") n = 20 extract_top_n(input_path, output_path, n) def do_upload_item_similarities_result(self): from common.utils import UploadItemSimilarities input_path = self.getWorkFile("item_similarities_top_n") uis = UploadItemSimilarities(connection, SITE_ID, self.type) uis(input_path) class VSimiliarityCalcFlow(BaseSimilarityCalcFlow): def __init__(self): BaseSimilarityCalcFlow.__init__(self, "V") def getExtractUserItemMatrixJobs(self): return [self.do_extract_user_item_matrix, self.do_de_duplicate_user_item_matrix] def do_extract_user_item_matrix(self): from preprocessing.extract_user_item_matrix import v_extract_user_item_matrix input_path = self.parent.getWorkFile("backfilled_raw_logs") output_path = self.getWorkFile("user_item_matrix_maybe_dup") v_extract_user_item_matrix(input_path, output_path) def do_de_duplicate_user_item_matrix(self): input_path = self.getWorkFile("user_item_matrix_maybe_dup") output_path = self.getWorkFile("user_item_matrix") self._exec_shell("sort -T /cube/services/batch/temp < %s | uniq > %s" % (input_path, output_path)) class PLOSimilarityCalcFlow(BaseSimilarityCalcFlow): def __init__(self): BaseSimilarityCalcFlow.__init__(self, "PLO") def getExtractUserItemMatrixJobs(self): return [self.do_extract_user_item_matrix, self.do_de_duplicate_user_item_matrix] def do_extract_user_item_matrix(self): from preprocessing.extract_user_item_matrix import plo_extract_user_item_matrix input_path = self.parent.getWorkFile("backfilled_raw_logs") output_path = self.getWorkFile("user_item_matrix_maybe_dup") plo_extract_user_item_matrix(input_path, output_path) def do_de_duplicate_user_item_matrix(self): input_path = self.getWorkFile("user_item_matrix_maybe_dup") output_path = self.getWorkFile("user_item_matrix") self._exec_shell("sort -T /cube/services/batch/temp < %s | uniq > %s" % (input_path, output_path)) class BuyTogetherSimilarityFlow(BaseSimilarityCalcFlow): def __init__(self): BaseSimilarityCalcFlow.__init__(self, "BuyTogether") def getExtractUserItemMatrixJobs(self): return [self.do_extract_user_item_matrix, self.do_de_duplicate_user_item_matrix] def do_extract_user_item_matrix(self): from preprocessing.extract_user_item_matrix import buytogether_extract_user_item_matrix input_path = self.parent.getWorkFile("backfilled_raw_logs") output_path = self.getWorkFile("user_item_matrix_maybe_dup") buytogether_extract_user_item_matrix(input_path, output_path) def do_de_duplicate_user_item_matrix(self): input_path = self.getWorkFile("user_item_matrix_maybe_dup") output_path = self.getWorkFile("user_item_matrix") self._exec_shell("sort -T /cube/services/batch/temp < %s | uniq > %s" % (input_path, output_path)) class ViewedUltimatelyBuyFlow(BaseFlow): def __init__(self): BaseFlow.__init__(self, "ViewedUltimatelyBuy") self.jobs += [self.do_extract_user_view_buy_logs, self.do_sort_user_view_buy_logs, self.do_pair_view_buy, self.count_pairs, self.do_extract_user_item_matrix, self.do_de_duplicate_user_item_matrix, self.count_item_view, self.upload_viewed_ultimately_buy] def do_extract_user_view_buy_logs(self): from viewed_ultimately_buy.extract_user_view_buy_logs import extract_user_view_buy_logs input_path = self.parent.getWorkFile("backfilled_raw_logs") output_path = self.getWorkFile("user_view_buy_logs") extract_user_view_buy_logs(input_path, output_path) def do_sort_user_view_buy_logs(self): input_path = self.getWorkFile("user_view_buy_logs") output_path = self.getWorkFile("user_view_buy_logs_sorted") self._exec_shell("sort -T /cube/services/batch/temp <%s >%s" % (input_path, output_path)) def do_pair_view_buy(self): from viewed_ultimately_buy.pair_view_buy import pair_view_buy input_path = self.getWorkFile("user_view_buy_logs_sorted") output_path = self.getWorkFile("view_buy_pairs") pair_view_buy(input_path, output_path) def count_pairs(self): input_path = self.getWorkFile("view_buy_pairs") output_path = self.getWorkFile("view_buy_pairs_counted") self._exec_shell("sort -T /cube/services/batch/temp <%s | uniq -c >%s" % (input_path, output_path)) def do_extract_user_item_matrix(self): from preprocessing.extract_user_item_matrix import v_extract_user_item_matrix input_path = self.parent.getWorkFile("backfilled_raw_logs") output_path = self.getWorkFile("user_item_matrix_maybe_dup") v_extract_user_item_matrix(input_path, output_path) def do_de_duplicate_user_item_matrix(self): input_path = self.getWorkFile("user_item_matrix_maybe_dup") output_path = self.getWorkFile("user_item_matrix") self._exec_shell("sort -T /cube/services/batch/temp < %s | uniq > %s" % (input_path, output_path)) def count_item_view(self): # FIXME a hack input_path = self.getWorkFile("user_item_matrix") output_path = self.getWorkFile("item_view_times") self._exec_shell( "cut -d , -f 2 <%s | sort -T /cube/services/batch/temp | uniq -c >%s" % (input_path, output_path)) def upload_viewed_ultimately_buy(self): from viewed_ultimately_buy.upload_viewed_ultimately_buy import upload_viewed_ultimately_buy item_view_times_path = self.getWorkFile("item_view_times") view_buy_pairs_counted_path = self.getWorkFile( "view_buy_pairs_counted") upload_viewed_ultimately_buy( connection, SITE_ID, item_view_times_path, view_buy_pairs_counted_path) class EDMRelatedPreprocessingFlow(BaseFlow): def __init__(self): BaseFlow.__init__(self, "ViewedUltimatelyBuy") self.jobs += [self.do_update_user_orders_collection, self.do_generate_edm_emailing_list] def do_update_user_orders_collection(self): from edm_calculations import doUpdateUserOrdersCollection doUpdateUserOrdersCollection(connection, SITE_ID) def do_generate_edm_emailing_list(self): from edm_calculations import generateEdmEmailingList generateEdmEmailingList(connection, SITE_ID) class BeginFlow(BaseFlow): def __init__(self): BaseFlow.__init__(self, "Root") self.jobs += [self.begin] def begin(self): pass # TODO: removed items' similarities should also be removed. begin_flow = BeginFlow() preprocessing_flow = PreprocessingFlow() preprocessing_flow.dependOn(begin_flow) hive_based_statistics_flow = HiveBasedStatisticsFlow() hive_based_statistics_flow.dependOn(preprocessing_flow) v_similarity_calc_flow = VSimiliarityCalcFlow() v_similarity_calc_flow.dependOn(preprocessing_flow) plo_similarity_calc_flow = PLOSimilarityCalcFlow() plo_similarity_calc_flow.dependOn(preprocessing_flow) buy_together_similarity_flow = BuyTogetherSimilarityFlow() buy_together_similarity_flow.dependOn(preprocessing_flow) viewed_ultimately_buy_flow = ViewedUltimatelyBuyFlow() viewed_ultimately_buy_flow.dependOn(preprocessing_flow) #edm_related_preprocessing_flow = EDMRelatedPreprocessingFlow() # edm_related_preprocessing_flow.dependOn(preprocessing_flow) def createCalculationRecord(site_id): calculation_id = str(uuid.uuid4()) record = { "calculation_id": calculation_id, "begin_datetime": datetime.datetime.now(), "flows": {}} calculation_records = getSiteDBCollection( connection, site_id, "calculation_records") calculation_records.save(record) return calculation_id def getCalculationRecord(site_id, calculation_id): calculation_records = getSiteDBCollection( connection, site_id, "calculation_records") return calculation_records.find_one({"calculation_id": calculation_id}) def updateCalculationRecord(site_id, record): calculation_records = getSiteDBCollection( connection, site_id, "calculation_records") calculation_records.save(record) def writeFailedJob(site_id, flow_name, failed_job_name): record = getCalculationRecord(SITE_ID, CALCULATION_ID) flow_record = record["flows"][flow_name] flow_record["failed_job_name"] = failed_job_name updateCalculationRecord(SITE_ID, record) def writeFlowBegin(site_id, flow_name): record = getCalculationRecord(SITE_ID, CALCULATION_ID) logging.info("FlowBegin: %s" % (flow_name, )) record["flows"][flow_name] = {"begin_datetime": datetime.datetime.now()} updateCalculationRecord(SITE_ID, record) def writeFlowEnd(site_id, flow_name, is_successful, is_skipped, err_msg=None): record = getCalculationRecord(SITE_ID, CALCULATION_ID) logging.info("FlowEnd: %s" % (flow_name, )) flow_record = record["flows"][flow_name] flow_record["end_datetime"] = datetime.datetime.now() flow_record["is_successful"] = is_successful flow_record["is_skipped"] = is_skipped if not is_successful: flow_record["err_msg"] = err_msg updateCalculationRecord(SITE_ID, record) def writeCalculationEnd(site_id, is_successful, err_msg=None): record = getCalculationRecord(SITE_ID, CALCULATION_ID) record["end_datetime"] = datetime.datetime.now() record["is_successful"] = is_successful if not is_successful: record["err_msg"] = err_msg updateCalculationRecord(SITE_ID, record) def getManualCalculationSites(): result = [] for site in loadSites(connection): manual_calculation_list = connection[ "tjb-db"]["manual_calculation_list"] record_in_db = manual_calculation_list.find_one( {"site_id": site["site_id"]}) if record_in_db is not None: result.append(site) return result def updateSiteLastUpdateTs(site_id): sites = connection["tjb-db"]["sites"] sites.update({"site_id": site_id}, {"$set": {"last_update_ts": time.time()}}) def is_time_okay_for_automatic_calculation(): now = datetime.datetime.now() return now.hour >= 0 and now.hour < 6 def loadSites(connection, site_ids=None): c_sites = connection["tjb-db"]["sites"] if site_ids: return [site for site in c_sites.find({'available': 'on'}) if site["site_id"] in site_ids] else: return [site for site in c_sites.find({'available': 'on'})] def workOnSite(site, is_manual_calculation=False): calculation_result = None # Pop a job manual_calculation_list = connection["tjb-db"]["manual_calculation_list"] record_in_db = manual_calculation_list.find_one( {"site_id": site["site_id"]}) if record_in_db is not None: manual_calculation_list.remove(record_in_db) # Proceed the job now = time.time() is_time_interval_okay_for_auto = (site.get("last_update_ts", None) is None or now - site.get("last_update_ts") > site["calc_interval"]) # print site["site_id"], is_time_interval_okay_for_auto, # is_time_okay_for_automatic_calculation() is_automatic_calculation_okay = is_time_okay_for_automatic_calculation( ) and is_time_interval_okay_for_auto if is_manual_calculation or is_automatic_calculation_okay: global SITE global SITE_ID global DISABLEDFLOWS global CALCULATION_ID global CALC_SUCC global BASE_WORK_DIR SITE = site SITE_ID = site["site_id"] DISABLEDFLOWS = site.get("disabledFlows", []) CALC_SUCC = True CALCULATION_ID = createCalculationRecord(SITE_ID) logging_manager.reconfig(SITE_ID, CALCULATION_ID) BASE_WORK_DIR = getBaseWorkDir(SITE_ID, CALCULATION_ID) try: try: getLogger().info("BEGIN CALCULATION ON:%s, CALCULATION_ID:%s" % (SITE_ID, CALCULATION_ID)) # Begin workflow to do calculations begin_flow() writeCalculationEnd( SITE_ID, CALC_SUCC, err_msg="SOME_FLOWS_FAILED") if CALC_SUCC: calculation_result = "SUCC" else: calculation_result = "FAIL" except: getLogger().critical("Unexpected Exception:", exc_info=True) writeCalculationEnd(SITE_ID, False, "UNEXPECTED_EXCEPTION") calculation_result = "FAIL" finally: getLogger( ).info("END CALCULATION ON:%s, RESULT:%s, CALCULATION_ID:%s" % (SITE_ID, calculation_result, CALCULATION_ID)) # FIXME: save last_update_ts updateSiteLastUpdateTs(site["site_id"]) return calculation_result def workOnSiteWithRetries(site, is_manual_calculation=False, max_attempts=2): current_attempts = 0 while current_attempts < max_attempts: calculation_result = workOnSite(site, is_manual_calculation) if calculation_result != "FAIL": break current_attempts += 1 if __name__ == "__main__": os.environ["PATH"] = "%s:%s" % (getattr(settings, "extra_shell_path", ""), os.environ["PATH"]) while True: #site_ids = ["test_with_gdian_data"] for site in loadSites(connection): for site in getManualCalculationSites(): workOnSiteWithRetries(site, is_manual_calculation=True) workOnSiteWithRetries(site) sleep_seconds = 1 time.sleep(sleep_seconds)
2.15625
2
tests/integration/basket/model_tests.py
makielab/django-oscar
0
9675
from decimal import Decimal as D from django.test import TestCase from oscar.apps.basket.models import Basket from oscar.apps.partner import strategy from oscar.test import factories from oscar.apps.catalogue.models import Option class TestAddingAProductToABasket(TestCase): def setUp(self): self.basket = Basket() self.basket.strategy = strategy.Default() self.product = factories.create_product() self.record = factories.create_stockrecord( currency='GBP', product=self.product, price_excl_tax=D('10.00')) self.stockinfo = factories.create_stockinfo(self.record) self.basket.add(self.product) def test_creates_a_line(self): self.assertEqual(1, self.basket.num_lines) def test_sets_line_prices(self): line = self.basket.lines.all()[0] self.assertEqual(line.price_incl_tax, self.stockinfo.price.incl_tax) self.assertEqual(line.price_excl_tax, self.stockinfo.price.excl_tax) def test_means_another_currency_product_cannot_be_added(self): product = factories.create_product() factories.create_stockrecord( currency='USD', product=product, price_excl_tax=D('20.00')) with self.assertRaises(ValueError): self.basket.add(product) class TestANonEmptyBasket(TestCase): def setUp(self): self.basket = Basket() self.basket.strategy = strategy.Default() self.product = factories.create_product() self.record = factories.create_stockrecord( self.product, price_excl_tax=D('10.00')) self.stockinfo = factories.create_stockinfo(self.record) self.basket.add(self.product, 10) def test_can_be_flushed(self): self.basket.flush() self.assertEqual(self.basket.num_items, 0) def test_returns_correct_product_quantity(self): self.assertEqual(10, self.basket.product_quantity( self.product)) def test_returns_correct_line_quantity_for_existing_product_and_stockrecord(self): self.assertEqual(10, self.basket.line_quantity( self.product, self.record)) def test_returns_zero_line_quantity_for_alternative_stockrecord(self): record = factories.create_stockrecord( self.product, price_excl_tax=D('5.00')) self.assertEqual(0, self.basket.line_quantity( self.product, record)) def test_returns_zero_line_quantity_for_missing_product_and_stockrecord(self): product = factories.create_product() record = factories.create_stockrecord( product, price_excl_tax=D('5.00')) self.assertEqual(0, self.basket.line_quantity( product, record)) def test_returns_correct_quantity_for_existing_product_and_stockrecord_and_options(self): product = factories.create_product() record = factories.create_stockrecord( product, price_excl_tax=D('5.00')) option = Option.objects.create(name="Message") options = [{"option": option, "value": "2"}] self.basket.add(product, options=options) self.assertEqual(0, self.basket.line_quantity( product, record)) self.assertEqual(1, self.basket.line_quantity( product, record, options)) class TestMergingTwoBaskets(TestCase): def setUp(self): self.product = factories.create_product() self.record = factories.create_stockrecord( self.product, price_excl_tax=D('10.00')) self.stockinfo = factories.create_stockinfo(self.record) self.main_basket = Basket() self.main_basket.strategy = strategy.Default() self.main_basket.add(self.product, quantity=2) self.merge_basket = Basket() self.merge_basket.strategy = strategy.Default() self.merge_basket.add(self.product, quantity=1) self.main_basket.merge(self.merge_basket) def test_doesnt_sum_quantities(self): self.assertEquals(1, self.main_basket.num_lines) def test_changes_status_of_merge_basket(self): self.assertEquals(Basket.MERGED, self.merge_basket.status) class TestASubmittedBasket(TestCase): def setUp(self): self.basket = Basket() self.basket.strategy = strategy.Default() self.basket.submit() def test_has_correct_status(self): self.assertTrue(self.basket.is_submitted) def test_can_be_edited(self): self.assertFalse(self.basket.can_be_edited)
2.3125
2
tests/fixtures/db/sqlite.py
code-watch/meltano
8
9676
import pytest import os import sqlalchemy import contextlib @pytest.fixture(scope="session") def engine_uri(test_dir): database_path = test_dir.joinpath("pytest_meltano.db") try: database_path.unlink() except FileNotFoundError: pass return f"sqlite:///{database_path}"
1.859375
2
experiments/render-tests-avg.py
piotr-karon/realworld-starter-kit
0
9677
<reponame>piotr-karon/realworld-starter-kit #!/usr/bin/env python3 import json import os from pathlib import Path import numpy as np from natsort import natsorted try: from docopt import docopt from marko.ext.gfm import gfm import pygal from pygal.style import Style, DefaultStyle except ImportError as e: raise Exception('Some external dependencies not found, install them using: pip install -r requirements.txt') from e def render(): suffix = '.avg.checks.bench.json' suites = {} for filepath in Path('').glob(f'*{suffix}'): name = filepath.name[:-len(suffix)] print(f'Loading {filepath} as {name}.') with open(filepath) as fp: suites[name] = json.load(fp) names = natsorted(suites.keys()) figure_filenames = render_figures(names, suites) out_filename = Path('bench-results.md') with open(out_filename, 'w') as out: cwd = os.getcwd().split(os.sep)[-2:] print(f'# Benchmark of {", ".join(names)} in {cwd}', file=out) notes_file = Path('notes.md') if notes_file.exists(): print(f'Including {notes_file} in resulting Markdown.') with notes_file.open() as fp: out.write(fp.read()) else: print(f'File {notes_file} does not exist, create it to include it in resulting Markdown.') # print('## General Info & Checks', file=out) # render_checks(names, suites, out) print('## Graphs', file=out) print('*The graphs are interactive, view the rendered HTML locally to enjoy it.*\n', file=out) for filename in figure_filenames: # Use HTML instead of Markdown image to specify the width print(f'<img type="image/svg+xml" src="{filename}" alt="{filename}" width="49%"/>', file=out) print(f'Markdown output written to {out_filename}.') render_html(out_filename, Path('bench-results.html')) def render_checks(names, suites, out): print(f'|Check|{"|".join(names)}|', file=out) print(f'|{"|".join(["---"] * (len(names) + 1))}|', file=out) per_impl_checks = {name: suite['checks'] for name, suite in suites.items()} check_names = sorted(set().union(*(checks.keys() for checks in per_impl_checks.values()))) def sanitize(value): if type(value) is float: value = float(f'{value:.3g}') # round to 3 significant figures return str(int(value) if value >= 100 else value) return str(value) for check_name in check_names: values = [sanitize(per_impl_checks[name].get(check_name)) for name in names] if len(values) > 1 and len(set(values)) > 1: values = [f'**{value}**' for value in values] print(f'|{check_name}|{"|".join(values)}|', file=out) FIGURE_FUNCS = [] def figure(func): """Simple decorator to mark a function as a figure generator.""" FIGURE_FUNCS.append(func) return func def render_figures(names, suites): filenames = [] config = pygal.Config(legend_at_bottom=True, style=DefaultStyle) for figure_func in FIGURE_FUNCS: chart = figure_func(names, suites, config.copy()) filename = f'bench-results.{figure_func.__name__}.svg' chart.render_to_file(filename) filenames.append(filename) return filenames @figure def startup_time_figure(names, suites, config): all_vals = [suites[name]['startup_max'] for name in names] mx = np.max(all_vals) config.range = (0, mx + 0.1) chart = pygal.Bar(config, value_formatter=lambda x: "{:0.2f}s".format(x)) chart.title = 'Czas uruchomienia (s)' for name in names: vals = [{'value': suites[name]['startup_avg'], 'ci': {'low': suites[name]['startup_min'], 'high': suites[name]['startup_max']}}] # print(vals) chart.add(name, vals) return chart @figure def errors_vs_connections_figure(names, suites, config): all_vals = [suites[name]['stats'] for name in names] flat = [item for sublist in all_vals for item in sublist] print(flat) all_rates = [ div_or_none(s['request_errors_new_avg'], s['request_errors_new_avg'] + s['requests_new_avg'], scale=100) for s in flat] mx = np.max(all_rates) config.range = (0, mx + mx * 0.1) chart = pygal.Line(config, value_formatter=lambda x: "{:0.2f}%".format(x)) chart.title = 'Współczynnik liczby błędów względem liczby połączeń (%)' connections_x_labels(chart, suites, skip=0) for name in names: chart.add(name, [ div_or_none(s['request_errors_new_avg'], s['request_errors_new_avg'] + s['requests_new_avg'], scale=100) for s in suites[name]['stats'][0:]]) return chart @figure def requests_vs_connections_figure(names, suites, config): vals = [[x['requests_per_s_avg'] for x in suites[name]['stats']] for name in names] print(vals) mx = np.max(vals) config.range = (0, mx + mx * 0.1) config.min_scale = 6 chart = pygal.Line(config, value_formatter=lambda x: "{:0.0f}".format(x)) chart.title = 'Liczba sukcesów na sekundę względem liczby połączeń (Zapytań/s)' connections_x_labels(chart, suites, skip=0) for name in names: # print(suites[name]['stats']) # vals = [{'value': x['requests_per_s_avg'], 'ci': {'low': x['requests_per_s_min'], 'high': x['requests_per_s_max']}} for x in suites[name]['stats']] vals = [{'value': x['requests_per_s_avg']} for x in suites[name]['stats']] chart.add(name, vals) return chart @figure def latency_vs_connections_50_figure(names, suites, config): return latency_vs_connections_figure(50, names, suites, config) @figure def latency_vs_connections_90_figure(names, suites, config): return latency_vs_connections_figure(90, names, suites, config) @figure def latency_vs_connections_99_figure(names, suites, config): return latency_vs_connections_figure(99, names, suites, config) def latency_vs_connections_figure(percentile, names, suites, config): all_vals = [[s[f'latency_{percentile}p_ms_avg'] for s in suites[name]['stats'][0:]] for name in names] mx = np.max(all_vals) mn = np.min(all_vals) config.range = (mn - mn * .5, mx + mx * .5) chart = pygal.Line(config, logarithmic=True, value_formatter=lambda x: "{:0.0f}".format(x)) chart.title = f'{percentile}. centyl czasu odpowiedzi względem liczby połączeń (ms)' connections_x_labels(chart, suites, skip=0) for name in names: chart.add(name, [s[f'latency_{percentile}p_ms_avg'] for s in suites[name]['stats'][0:]]) return chart @figure def max_mem_usage_figure(names, suites, config): all_vals = [[s['mem_usage_mb_avg'] for s in suites[name]['stats']] for name in names] mx = np.max(all_vals) config.range = (0, mx + .1 * mx) chart = pygal.Line(config, value_formatter=lambda x: "{:0.0f}".format(x)) chart.title = 'Maksymalne zużycie pamięci względem liczby połączeń (MiB)' connections_x_labels(chart, suites) for name in names: chart.add(name, [s['mem_usage_mb_avg'] for s in suites[name]['stats']]) return chart @figure def max_mem_usage_per_requests_figure(names, suites, config): all_vals = [[div_or_none(s['mem_usage_mb_avg'], s['requests_per_s_avg']) for s in suites[name]['stats'][0:]] for name in names] mx = np.max(all_vals) config.range = (0, mx + .1 * mx) config.min_scale = 6 chart = pygal.Line(config, value_formatter=lambda x: "{:0.3f}".format(x)) chart.title = 'Maksymalne zużycie pamięci per liczba sukcesów na sekundę (MiB-sekunda/Zapytanie)' connections_x_labels(chart, suites, skip=0) for name in names: chart.add(name, [div_or_none(s['mem_usage_mb_avg'], s['requests_per_s_avg']) for s in suites[name]['stats'][0:]]) return chart @figure def cpu_figure(names, suites, config): mx = np.max([[s['cpu_new_s_avg'] for s in suites[name]['stats'][0:]] for name in names]) config.range = (0, mx + mx * 0.1) chart = pygal.Line(config, value_formatter=lambda x: "{:0.3f}".format(x)) chart.title = 'Wykorzystanie czasu procesora w czasie rundy testów (sekundy CPU)' connections_x_labels(chart, suites, skip=0) for name in names: chart.add(name, [s['cpu_new_s_avg'] for s in suites[name]['stats'][0:]]) return chart @figure def cpu_per_request_figure(names, suites, config): mx = np.max([[div_or_none(s['cpu_new_s_avg'], s['requests_new_avg'], scale=1000) for s in suites[name]['stats'][0:]] for name in names]) config.range = (0, mx + mx * 0.1) chart = pygal.Line(config, value_formatter=lambda x: "{:0.3f}".format(x)) chart.title = 'Wykorzystanie czasu procesora per poprawna odpowiedź (milisekundy CPU/Req)' connections_x_labels(chart, suites, skip=0) for name in names: chart.add(name, [div_or_none(s['cpu_new_s_avg'], s['requests_new_avg'], scale=1000) for s in suites[name]['stats'][0:]]) return chart @figure def cpu_vs_requests_figure(names, suites, config): all_vls = [[s['requests_total_avg'] for s in suites[name]['stats']] for name in names] mx = np.max(all_vls) config.range = (0, mx + mx * 0.1) config.min_scale = 6 chart = pygal.XY(config, value_formatter=lambda x: "{:0.0f}".format(x), series_formatter=lambda x: "{:0.2f}".format(x)) chart.title = 'Skumulowana liczba poprawnych odpowiedzi względem skumulowanego czasu CPU' chart.x_title = 'sekundy CPU' chart.y_title = 'skumulowana liczba poprawnych odpowiedzi' for name in names: chart.add(name, [ {'value': (s['cpu_total_s_avg'], s['requests_total_avg']), 'label': f'After {s["connections"]} connections round.'} for s in suites[name]['stats'] ]) return chart def connections_x_labels(chart, suites, skip=0): chart.x_labels = [f"{s['connections']} conn's" if s['connections'] else s['message'] for s in next(iter(suites.values()))['stats']][skip:] chart.x_label_rotation = -30 def div_or_none(numerator, denominator, scale=1): if not denominator: return None return scale * numerator / denominator HTML_PREFIX = '''<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"> <title>Benchmark Report</title> </head> <body> ''' HTML_SUFFIX = ''' </body> </html> ''' def render_html(md_file, html_file): with open(md_file) as in_fp, open(html_file, 'w') as out_fp: rs = in_fp.read() html = gfm(rs) # Replace <img> by <embed> for pygal interactivity, http://www.pygal.org/en/latest/documentation/web.html html = html.replace('<img', '<embed') # Replace link to md with link to .html for better browsability at HTML level. html = html.replace('/README.md">full benchmark', '/README.html">full benchmark') out_fp.write(HTML_PREFIX) out_fp.write(html) out_fp.write(HTML_SUFFIX) print(f'HTML output written to {html_file.resolve().as_uri()}.') if __name__ == '__main__': # args = docopt(__doc__) render()
2.25
2
litex/build/altera/quartus.py
osterwood/litex
1,501
9678
# # This file is part of LiteX. # # Copyright (c) 2014-2019 <NAME> <<EMAIL>> # Copyright (c) 2019 msloniewski <<EMAIL>> # Copyright (c) 2019 vytautasb <<EMAIL>> # SPDX-License-Identifier: BSD-2-Clause import os import subprocess import sys import math from shutil import which from migen.fhdl.structure import _Fragment from litex.build.generic_platform import Pins, IOStandard, Misc from litex.build import tools # IO/Placement Constraints (.qsf) ------------------------------------------------------------------ def _format_constraint(c, signame, fmt_r): # IO location constraints if isinstance(c, Pins): tpl = "set_location_assignment -comment \"{name}\" -to {signame} Pin_{pin}" return tpl.format(signame=signame, name=fmt_r, pin=c.identifiers[0]) # IO standard constraints elif isinstance(c, IOStandard): tpl = "set_instance_assignment -name io_standard -comment \"{name}\" \"{std}\" -to {signame}" return tpl.format(signame=signame, name=fmt_r, std=c.name) # Others constraints elif isinstance(c, Misc): if not isinstance(c.misc, str) and len(c.misc) == 2: tpl = "set_instance_assignment -comment \"{name}\" -name {misc[0]} \"{misc[1]}\" -to {signame}" return tpl.format(signame=signame, name=fmt_r, misc=c.misc) else: tpl = "set_instance_assignment -comment \"{name}\" -name {misc} -to {signame}" return tpl.format(signame=signame, name=fmt_r, misc=c.misc) def _format_qsf_constraint(signame, pin, others, resname): fmt_r = "{}:{}".format(*resname[:2]) if resname[2] is not None: fmt_r += "." + resname[2] fmt_c = [_format_constraint(c, signame, fmt_r) for c in ([Pins(pin)] + others)] return '\n'.join(fmt_c) def _build_qsf_constraints(named_sc, named_pc): qsf = [] for sig, pins, others, resname in named_sc: if len(pins) > 1: for i, p in enumerate(pins): qsf.append(_format_qsf_constraint("{}[{}]".format(sig, i), p, others, resname)) else: qsf.append(_format_qsf_constraint(sig, pins[0], others, resname)) if named_pc: qsf.append("\n\n".join(named_pc)) return "\n".join(qsf) # Timing Constraints (.sdc) ------------------------------------------------------------------------ def _build_sdc(clocks, false_paths, vns, named_sc, build_name, additional_sdc_commands): sdc = [] # Clock constraints for clk, period in sorted(clocks.items(), key=lambda x: x[0].duid): is_port = False for sig, pins, others, resname in named_sc: if sig == vns.get_name(clk): is_port = True if is_port: tpl = "create_clock -name {clk} -period {period} [get_ports {{{clk}}}]" sdc.append(tpl.format(clk=vns.get_name(clk), period=str(period))) else: tpl = "create_clock -name {clk} -period {period} [get_nets {{{clk}}}]" sdc.append(tpl.format(clk=vns.get_name(clk), period=str(period))) # False path constraints for from_, to in sorted(false_paths, key=lambda x: (x[0].duid, x[1].duid)): tpl = "set_false_path -from [get_clocks {{{from_}}}] -to [get_clocks {{{to}}}]" sdc.append(tpl.format(from_=vns.get_name(from_), to=vns.get_name(to))) # Add additional commands sdc += additional_sdc_commands # Generate .sdc tools.write_to_file("{}.sdc".format(build_name), "\n".join(sdc)) # Project (.qsf) ----------------------------------------------------------------------------------- def _build_qsf(device, ips, sources, vincpaths, named_sc, named_pc, build_name, additional_qsf_commands): qsf = [] # Set device qsf.append("set_global_assignment -name DEVICE {}".format(device)) # Add sources for filename, language, library in sources: if language == "verilog": language = "systemverilog" # Enforce use of SystemVerilog tpl = "set_global_assignment -name {lang}_FILE {path} -library {lib}" # Do not add None type files if language is not None: qsf.append(tpl.format(lang=language.upper(), path=filename.replace("\\", "/"), lib=library)) # Check if the file is a header. Those should not be explicitly added to qsf, # but rather included in include search_path else: if filename.endswith(".svh") or filename.endswith(".vh"): fpath = os.path.dirname(filename) if fpath not in vincpaths: vincpaths.append(fpath) # Add ips for filename in ips: tpl = "set_global_assignment -name QSYS_FILE {filename}" qsf.append(tpl.replace(filename=filename.replace("\\", "/"))) # Add include paths for path in vincpaths: qsf.append("set_global_assignment -name SEARCH_PATH {}".format(path.replace("\\", "/"))) # Set top level qsf.append("set_global_assignment -name top_level_entity " + build_name) # Add io, placement constraints qsf.append(_build_qsf_constraints(named_sc, named_pc)) # Set timing constraints qsf.append("set_global_assignment -name SDC_FILE {}.sdc".format(build_name)) # Add additional commands qsf += additional_qsf_commands # Generate .qsf tools.write_to_file("{}.qsf".format(build_name), "\n".join(qsf)) # Script ------------------------------------------------------------------------------------------- def _build_script(build_name, create_rbf): if sys.platform in ["win32", "cygwin"]: script_contents = "REM Autogenerated by LiteX / git: " + tools.get_litex_git_revision() script_file = "build_" + build_name + ".bat" else: script_contents = "# Autogenerated by LiteX / git: " + tools.get_litex_git_revision() script_file = "build_" + build_name + ".sh" script_contents += """ quartus_map --read_settings_files=on --write_settings_files=off {build_name} -c {build_name} quartus_fit --read_settings_files=off --write_settings_files=off {build_name} -c {build_name} quartus_asm --read_settings_files=off --write_settings_files=off {build_name} -c {build_name} quartus_sta {build_name} -c {build_name}""" if create_rbf: script_contents += """ if [ -f "{build_name}.sof" ] then quartus_cpf -c {build_name}.sof {build_name}.rbf fi """ script_contents = script_contents.format(build_name=build_name) tools.write_to_file(script_file, script_contents, force_unix=True) return script_file def _run_script(script): if sys.platform in ["win32", "cygwin"]: shell = ["cmd", "/c"] else: shell = ["bash"] if which("quartus_map") is None: msg = "Unable to find Quartus toolchain, please:\n" msg += "- Add Quartus toolchain to your $PATH." raise OSError(msg) if subprocess.call(shell + [script]) != 0: raise OSError("Error occured during Quartus's script execution.") # AlteraQuartusToolchain --------------------------------------------------------------------------- class AlteraQuartusToolchain: attr_translate = {} def __init__(self): self.clocks = dict() self.false_paths = set() self.additional_sdc_commands = [] self.additional_qsf_commands = [] def build(self, platform, fragment, build_dir = "build", build_name = "top", run = True, **kwargs): # Create build directory cwd = os.getcwd() os.makedirs(build_dir, exist_ok=True) os.chdir(build_dir) # Finalize design if not isinstance(fragment, _Fragment): fragment = fragment.get_fragment() platform.finalize(fragment) # Generate verilog v_output = platform.get_verilog(fragment, name=build_name, **kwargs) named_sc, named_pc = platform.resolve_signals(v_output.ns) v_file = build_name + ".v" v_output.write(v_file) platform.add_source(v_file) # Generate design timing constraints file (.sdc) _build_sdc( clocks = self.clocks, false_paths = self.false_paths, vns = v_output.ns, named_sc = named_sc, build_name = build_name, additional_sdc_commands = self.additional_sdc_commands) # Generate design project and location constraints file (.qsf) _build_qsf( device = platform.device, ips = platform.ips, sources = platform.sources, vincpaths = platform.verilog_include_paths, named_sc = named_sc, named_pc = named_pc, build_name = build_name, additional_qsf_commands = self.additional_qsf_commands) # Generate build script script = _build_script(build_name, platform.create_rbf) # Run if run: _run_script(script) os.chdir(cwd) return v_output.ns def add_period_constraint(self, platform, clk, period): clk.attr.add("keep") period = math.floor(period*1e3)/1e3 # round to lowest picosecond if clk in self.clocks: if period != self.clocks[clk]: raise ValueError("Clock already constrained to {:.2f}ns, new constraint to {:.2f}ns" .format(self.clocks[clk], period)) self.clocks[clk] = period def add_false_path_constraint(self, platform, from_, to): from_.attr.add("keep") to.attr.add("keep") if (to, from_) not in self.false_paths: self.false_paths.add((from_, to))
2.046875
2
arxiv/canonical/util.py
arXiv/arxiv-canonical
5
9679
<gh_stars>1-10 """Various helpers and utilities that don't belong anywhere else.""" from typing import Dict, Generic, TypeVar KeyType = TypeVar('KeyType') ValueType = TypeVar('ValueType') class GenericMonoDict(Dict[KeyType, ValueType]): """A dict with specific key and value types.""" def __getitem__(self, key: KeyType) -> ValueType: ...
2.609375
3
records/urls.py
Glucemy/Glucemy-back
0
9680
from rest_framework.routers import DefaultRouter from records.views import RecordViewSet router = DefaultRouter() router.register('', RecordViewSet, basename='records') urlpatterns = router.urls
1.679688
2
polystores/stores/azure_store.py
polyaxon/polystores
50
9681
# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function import os from rhea import RheaError from rhea import parser as rhea_parser from azure.common import AzureHttpError from azure.storage.blob.models import BlobPrefix from polystores.clients.azure_client import get_blob_service_connection from polystores.exceptions import PolyaxonStoresException from polystores.stores.base_store import BaseStore from polystores.utils import append_basename, check_dirname_exists, get_files_in_current_directory # pylint:disable=arguments-differ class AzureStore(BaseStore): """ Azure store Service. """ STORE_TYPE = BaseStore._AZURE_STORE # pylint:disable=protected-access def __init__(self, connection=None, **kwargs): self._connection = connection self._account_name = kwargs.get('account_name') or kwargs.get('AZURE_ACCOUNT_NAME') self._account_key = kwargs.get('account_key') or kwargs.get('AZURE_ACCOUNT_KEY') self._connection_string = ( kwargs.get('connection_string') or kwargs.get('AZURE_CONNECTION_STRING')) @property def connection(self): if self._connection is None: self.set_connection(account_name=self._account_name, account_key=self._account_key, connection_string=self._connection_string) return self._connection def set_connection(self, account_name=None, account_key=None, connection_string=None): """ Sets a new Blob service connection. Args: account_name: `str`. The storage account name. account_key: `str`. The storage account key. connection_string: `str`. If specified, this will override all other parameters besides request session. Returns: BlockBlobService instance """ self._connection = get_blob_service_connection(account_name=account_name, account_key=account_key, connection_string=connection_string) def set_env_vars(self): if self._account_name: os.environ['AZURE_ACCOUNT_NAME'] = self._account_name if self._account_key: os.environ['AZURE_ACCOUNT_KEY'] = self._account_key if self._connection_string: os.environ['AZURE_CONNECTION_STRING'] = self._connection_string @staticmethod def parse_wasbs_url(wasbs_url): """ Parses and validates a wasbs url. Returns: tuple(container, storage_account, path). """ try: spec = rhea_parser.parse_wasbs_path(wasbs_url) return spec.container, spec.storage_account, spec.path except RheaError as e: raise PolyaxonStoresException(e) def check_blob(self, blob, container_name=None): """ Checks if a blob exists. Args: blob: `str`. Name of existing blob. container_name: `str`. Name of existing container. Returns: bool """ if not container_name: container_name, _, blob = self.parse_wasbs_url(blob) try: return self.connection.get_blob_properties( container_name, blob ) except AzureHttpError: return None def ls(self, path): results = self.list(key=path) return {'files': results['blobs'], 'dirs': results['prefixes']} def list(self, key, container_name=None, path=None, delimiter='/', marker=None): """ Checks if a blob exists. Args: key: `str`. key prefix. container_name: `str`. Name of existing container. path: `str`. an extra path to append to the key. delimiter: `str`. the delimiter marks key hierarchy. marker: `str`. An opaque continuation token. """ if not container_name: container_name, _, key = self.parse_wasbs_url(key) if key and not key.endswith('/'): key += '/' prefix = key if path: prefix = os.path.join(prefix, path) if prefix and not prefix.endswith('/'): prefix += '/' list_blobs = [] list_prefixes = [] while True: results = self.connection.list_blobs(container_name, prefix=prefix, delimiter=delimiter, marker=marker) for r in results: if isinstance(r, BlobPrefix): name = r.name[len(key):] list_prefixes.append(name) else: name = r.name[len(key):] list_blobs.append((name, r.properties.content_length)) if results.next_marker: marker = results.next_marker else: break return { 'blobs': list_blobs, 'prefixes': list_prefixes } def upload_file(self, filename, blob, container_name=None, use_basename=True): """ Uploads a local file to Google Cloud Storage. Args: filename: `str`. the file to upload. blob: `str`. blob to upload to. container_name: `str`. the name of the container. use_basename: `bool`. whether or not to use the basename of the filename. """ if not container_name: container_name, _, blob = self.parse_wasbs_url(blob) if use_basename: blob = append_basename(blob, filename) self.connection.create_blob_from_path(container_name, blob, filename) def upload_dir(self, dirname, blob, container_name=None, use_basename=True): """ Uploads a local directory to to Google Cloud Storage. Args: dirname: `str`. name of the directory to upload. blob: `str`. blob to upload to. container_name: `str`. the name of the container. use_basename: `bool`. whether or not to use the basename of the directory. """ if not container_name: container_name, _, blob = self.parse_wasbs_url(blob) if use_basename: blob = append_basename(blob, dirname) # Turn the path to absolute paths dirname = os.path.abspath(dirname) with get_files_in_current_directory(dirname) as files: for f in files: file_blob = os.path.join(blob, os.path.relpath(f, dirname)) self.upload_file(filename=f, blob=file_blob, container_name=container_name, use_basename=False) def download_file(self, blob, local_path, container_name=None, use_basename=True): """ Downloads a file from Google Cloud Storage. Args: blob: `str`. blob to download. local_path: `str`. the path to download to. container_name: `str`. the name of the container. use_basename: `bool`. whether or not to use the basename of the blob. """ if not container_name: container_name, _, blob = self.parse_wasbs_url(blob) local_path = os.path.abspath(local_path) if use_basename: local_path = append_basename(local_path, blob) check_dirname_exists(local_path) try: self.connection.get_blob_to_path(container_name, blob, local_path) except AzureHttpError as e: raise PolyaxonStoresException(e) def download_dir(self, blob, local_path, container_name=None, use_basename=True): """ Download a directory from Google Cloud Storage. Args: blob: `str`. blob to download. local_path: `str`. the path to download to. container_name: `str`. the name of the container. use_basename: `bool`. whether or not to use the basename of the key. """ if not container_name: container_name, _, blob = self.parse_wasbs_url(blob) local_path = os.path.abspath(local_path) if use_basename: local_path = append_basename(local_path, blob) try: check_dirname_exists(local_path, is_dir=True) except PolyaxonStoresException: os.makedirs(local_path) results = self.list(container_name=container_name, key=blob, delimiter='/') # Create directories for prefix in sorted(results['prefixes']): direname = os.path.join(local_path, prefix) prefix = os.path.join(blob, prefix) # Download files under self.download_dir(blob=prefix, local_path=direname, container_name=container_name, use_basename=False) # Download files for file_key in results['blobs']: file_key = file_key[0] filename = os.path.join(local_path, file_key) file_key = os.path.join(blob, file_key) self.download_file(blob=file_key, local_path=filename, container_name=container_name, use_basename=False) def delete(self, blob, container_name=None): if not container_name: container_name, _, blob = self.parse_wasbs_url(blob) results = self.list(container_name=container_name, key=blob, delimiter='/') if not any([results['prefixes'], results['blobs']]): self.delete_file(blob=blob, container_name=container_name) # Delete directories for prefix in sorted(results['prefixes']): prefix = os.path.join(blob, prefix) # Download files under self.delete(blob=prefix, container_name=container_name) # Delete files for file_key in results['blobs']: file_key = file_key[0] file_key = os.path.join(blob, file_key) self.delete_file(blob=file_key, container_name=container_name) def delete_file(self, blob, container_name=None): """ Deletes if a blob exists. Args: blob: `str`. Name of existing blob. container_name: `str`. Name of existing container. """ if not container_name: container_name, _, blob = self.parse_wasbs_url(blob) try: self.connection.delete_blob(container_name, blob) except AzureHttpError: pass
2.078125
2
analysis/webservice/NexusHandler.py
dataplumber/nexus
23
9682
""" Copyright (c) 2016 Jet Propulsion Laboratory, California Institute of Technology. All rights reserved """ import sys import numpy as np import logging import time import types from datetime import datetime from netCDF4 import Dataset from nexustiles.nexustiles import NexusTileService from webservice.webmodel import NexusProcessingException AVAILABLE_HANDLERS = [] AVAILABLE_INITIALIZERS = [] def nexus_initializer(clazz): log = logging.getLogger(__name__) try: wrapper = NexusInitializerWrapper(clazz) log.info("Adding initializer '%s'" % wrapper.clazz()) AVAILABLE_INITIALIZERS.append(wrapper) except Exception as ex: log.warn("Initializer '%s' failed to load (reason: %s)" % (clazz, ex.message), exc_info=True) return clazz def nexus_handler(clazz): log = logging.getLogger(__name__) try: wrapper = AlgorithmModuleWrapper(clazz) log.info("Adding algorithm module '%s' with path '%s' (%s)" % (wrapper.name(), wrapper.path(), wrapper.clazz())) AVAILABLE_HANDLERS.append(wrapper) except Exception as ex: log.warn("Handler '%s' is invalid and will be skipped (reason: %s)" % (clazz, ex.message), exc_info=True) return clazz DEFAULT_PARAMETERS_SPEC = { "ds": { "name": "Dataset", "type": "string", "description": "One or more comma-separated dataset shortnames" }, "minLat": { "name": "Minimum Latitude", "type": "float", "description": "Minimum (Southern) bounding box Latitude" }, "maxLat": { "name": "Maximum Latitude", "type": "float", "description": "Maximum (Northern) bounding box Latitude" }, "minLon": { "name": "Minimum Longitude", "type": "float", "description": "Minimum (Western) bounding box Longitude" }, "maxLon": { "name": "Maximum Longitude", "type": "float", "description": "Maximum (Eastern) bounding box Longitude" }, "startTime": { "name": "Start Time", "type": "long integer", "description": "Starting time in milliseconds since midnight Jan. 1st, 1970 UTC" }, "endTime": { "name": "End Time", "type": "long integer", "description": "Ending time in milliseconds since midnight Jan. 1st, 1970 UTC" }, "lowPassFilter": { "name": "Apply Low Pass Filter", "type": "boolean", "description": "Specifies whether to apply a low pass filter on the analytics results" }, "seasonalFilter": { "name": "Apply Seasonal Filter", "type": "boolean", "description": "Specified whether to apply a seasonal cycle filter on the analytics results" } } class NexusInitializerWrapper: def __init__(self, clazz): self.__log = logging.getLogger(__name__) self.__hasBeenRun = False self.__clazz = clazz self.validate() def validate(self): if "init" not in self.__clazz.__dict__ or not type(self.__clazz.__dict__["init"]) == types.FunctionType: raise Exception("Method 'init' has not been declared") def clazz(self): return self.__clazz def hasBeenRun(self): return self.__hasBeenRun def init(self, config): if not self.__hasBeenRun: self.__hasBeenRun = True instance = self.__clazz() instance.init(config) else: self.log("Initializer '%s' has already been run" % self.__clazz) class AlgorithmModuleWrapper: def __init__(self, clazz): self.__instance = None self.__clazz = clazz self.validate() def validate(self): if "calc" not in self.__clazz.__dict__ or not type(self.__clazz.__dict__["calc"]) == types.FunctionType: raise Exception("Method 'calc' has not been declared") if "path" not in self.__clazz.__dict__: raise Exception("Property 'path' has not been defined") if "name" not in self.__clazz.__dict__: raise Exception("Property 'name' has not been defined") if "description" not in self.__clazz.__dict__: raise Exception("Property 'description' has not been defined") if "params" not in self.__clazz.__dict__: raise Exception("Property 'params' has not been defined") def clazz(self): return self.__clazz def name(self): return self.__clazz.name def path(self): return self.__clazz.path def description(self): return self.__clazz.description def params(self): return self.__clazz.params def instance(self, algorithm_config=None, sc=None): if "singleton" in self.__clazz.__dict__ and self.__clazz.__dict__["singleton"] is True: if self.__instance is None: self.__instance = self.__clazz() try: self.__instance.set_config(algorithm_config) except AttributeError: pass try: self.__instance.set_spark_context(sc) except AttributeError: pass return self.__instance else: instance = self.__clazz() try: instance.set_config(algorithm_config) except AttributeError: pass try: self.__instance.set_spark_context(sc) except AttributeError: pass return instance def isValid(self): try: self.validate() return True except Exception as ex: return False class CalcHandler(object): def calc(self, computeOptions, **args): raise Exception("calc() not yet implemented") class NexusHandler(CalcHandler): def __init__(self, skipCassandra=False, skipSolr=False): CalcHandler.__init__(self) self.algorithm_config = None self._tile_service = NexusTileService(skipCassandra, skipSolr) def set_config(self, algorithm_config): self.algorithm_config = algorithm_config def _mergeDicts(self, x, y): z = x.copy() z.update(y) return z def _now(self): millis = int(round(time.time() * 1000)) return millis def _mergeDataSeries(self, resultsData, dataNum, resultsMap): for entry in resultsData: #frmtdTime = datetime.fromtimestamp(entry["time"] ).strftime("%Y-%m") frmtdTime = entry["time"] if not frmtdTime in resultsMap: resultsMap[frmtdTime] = [] entry["ds"] = dataNum resultsMap[frmtdTime].append(entry) def _resultsMapToList(self, resultsMap): resultsList = [] for key, value in resultsMap.iteritems(): resultsList.append(value) resultsList = sorted(resultsList, key=lambda entry: entry[0]["time"]) return resultsList def _mergeResults(self, resultsRaw): resultsMap = {} for i in range(0, len(resultsRaw)): resultsSeries = resultsRaw[i] resultsData = resultsSeries[0] self._mergeDataSeries(resultsData, i, resultsMap) resultsList = self._resultsMapToList(resultsMap) return resultsList class SparkHandler(NexusHandler): class SparkJobContext(object): class MaxConcurrentJobsReached(Exception): def __init__(self, *args, **kwargs): Exception.__init__(self, *args, **kwargs) def __init__(self, job_stack): self.spark_job_stack = job_stack self.job_name = None self.log = logging.getLogger(__name__) def __enter__(self): try: self.job_name = self.spark_job_stack.pop() self.log.debug("Using %s" % self.job_name) except IndexError: raise SparkHandler.SparkJobContext.MaxConcurrentJobsReached() return self def __exit__(self, exc_type, exc_val, exc_tb): if self.job_name is not None: self.log.debug("Returning %s" % self.job_name) self.spark_job_stack.append(self.job_name) def __init__(self, **kwargs): import inspect NexusHandler.__init__(self, **kwargs) self._sc = None self.spark_job_stack = [] def with_spark_job_context(calc_func): from functools import wraps @wraps(calc_func) def wrapped(*args, **kwargs1): try: with SparkHandler.SparkJobContext(self.spark_job_stack) as job_context: # TODO Pool and Job are forced to a 1-to-1 relationship calc_func.im_self._sc.setLocalProperty("spark.scheduler.pool", job_context.job_name) calc_func.im_self._sc.setJobGroup(job_context.job_name, "a spark job") return calc_func(*args, **kwargs1) except SparkHandler.SparkJobContext.MaxConcurrentJobsReached: raise NexusProcessingException(code=503, reason="Max concurrent requests reached. Please try again later.") return wrapped for member in inspect.getmembers(self, predicate=inspect.ismethod): if member[0] == "calc": setattr(self, member[0], with_spark_job_context(member[1])) def set_spark_context(self, sc): self._sc = sc def set_config(self, algorithm_config): max_concurrent_jobs = algorithm_config.getint("spark", "maxconcurrentjobs") if algorithm_config.has_section( "spark") and algorithm_config.has_option("spark", "maxconcurrentjobs") else 10 self.spark_job_stack = list(["Job %s" % x for x in xrange(1, max_concurrent_jobs + 1)]) self.algorithm_config = algorithm_config def _setQueryParams(self, ds, bounds, start_time=None, end_time=None, start_year=None, end_year=None, clim_month=None, fill=-9999., spark_master=None, spark_nexecs=None, spark_nparts=None): self._ds = ds self._minLat, self._maxLat, self._minLon, self._maxLon = bounds self._startTime = start_time self._endTime = end_time self._startYear = start_year self._endYear = end_year self._climMonth = clim_month self._fill = fill self._spark_master = spark_master self._spark_nexecs = spark_nexecs self._spark_nparts = spark_nparts def _find_global_tile_set(self): if type(self._ds) in (list,tuple): ds = self._ds[0] else: ds = self._ds ntiles = 0 ################################################################## # Temporary workaround until we have dataset metadata to indicate # temporal resolution. if "monthly" in ds.lower(): t_incr = 2592000 # 30 days else: t_incr = 86400 # 1 day ################################################################## t = self._endTime self._latRes = None self._lonRes = None while ntiles == 0: nexus_tiles = self._tile_service.get_tiles_bounded_by_box(self._minLat, self._maxLat, self._minLon, self._maxLon, ds=ds, start_time=t-t_incr, end_time=t) ntiles = len(nexus_tiles) self.log.debug('find_global_tile_set got {0} tiles'.format(ntiles)) if ntiles > 0: for tile in nexus_tiles: self.log.debug('tile coords:') self.log.debug('tile lats: {0}'.format(tile.latitudes)) self.log.debug('tile lons: {0}'.format(tile.longitudes)) if self._latRes is None: lats = tile.latitudes.data if (len(lats) > 1): self._latRes = abs(lats[1]-lats[0]) if self._lonRes is None: lons = tile.longitudes.data if (len(lons) > 1): self._lonRes = abs(lons[1]-lons[0]) if ((self._latRes is not None) and (self._lonRes is not None)): break if (self._latRes is None) or (self._lonRes is None): ntiles = 0 else: lats_agg = np.concatenate([tile.latitudes.compressed() for tile in nexus_tiles]) lons_agg = np.concatenate([tile.longitudes.compressed() for tile in nexus_tiles]) self._minLatCent = np.min(lats_agg) self._maxLatCent = np.max(lats_agg) self._minLonCent = np.min(lons_agg) self._maxLonCent = np.max(lons_agg) t -= t_incr return nexus_tiles def _find_tile_bounds(self, t): lats = t.latitudes lons = t.longitudes if (len(lats.compressed()) > 0) and (len(lons.compressed()) > 0): min_lat = np.ma.min(lats) max_lat = np.ma.max(lats) min_lon = np.ma.min(lons) max_lon = np.ma.max(lons) good_inds_lat = np.where(lats.mask == False)[0] good_inds_lon = np.where(lons.mask == False)[0] min_y = np.min(good_inds_lat) max_y = np.max(good_inds_lat) min_x = np.min(good_inds_lon) max_x = np.max(good_inds_lon) bounds = (min_lat, max_lat, min_lon, max_lon, min_y, max_y, min_x, max_x) else: self.log.warn('Nothing in this tile!') bounds = None return bounds @staticmethod def query_by_parts(tile_service, min_lat, max_lat, min_lon, max_lon, dataset, start_time, end_time, part_dim=0): nexus_max_tiles_per_query = 100 #print 'trying query: ',min_lat, max_lat, min_lon, max_lon, \ # dataset, start_time, end_time try: tiles = \ tile_service.find_tiles_in_box(min_lat, max_lat, min_lon, max_lon, dataset, start_time=start_time, end_time=end_time, fetch_data=False) assert(len(tiles) <= nexus_max_tiles_per_query) except: #print 'failed query: ',min_lat, max_lat, min_lon, max_lon, \ # dataset, start_time, end_time if part_dim == 0: # Partition by latitude. mid_lat = (min_lat + max_lat) / 2 nexus_tiles = SparkHandler.query_by_parts(tile_service, min_lat, mid_lat, min_lon, max_lon, dataset, start_time, end_time, part_dim=part_dim) nexus_tiles.extend(SparkHandler.query_by_parts(tile_service, mid_lat, max_lat, min_lon, max_lon, dataset, start_time, end_time, part_dim=part_dim)) elif part_dim == 1: # Partition by longitude. mid_lon = (min_lon + max_lon) / 2 nexus_tiles = SparkHandler.query_by_parts(tile_service, min_lat, max_lat, min_lon, mid_lon, dataset, start_time, end_time, part_dim=part_dim) nexus_tiles.extend(SparkHandler.query_by_parts(tile_service, min_lat, max_lat, mid_lon, max_lon, dataset, start_time, end_time, part_dim=part_dim)) elif part_dim == 2: # Partition by time. mid_time = (start_time + end_time) / 2 nexus_tiles = SparkHandler.query_by_parts(tile_service, min_lat, max_lat, min_lon, max_lon, dataset, start_time, mid_time, part_dim=part_dim) nexus_tiles.extend(SparkHandler.query_by_parts(tile_service, min_lat, max_lat, min_lon, max_lon, dataset, mid_time, end_time, part_dim=part_dim)) else: # No exception, so query Cassandra for the tile data. #print 'Making NEXUS query to Cassandra for %d tiles...' % \ # len(tiles) #t1 = time.time() #print 'NEXUS call start at time %f' % t1 #sys.stdout.flush() nexus_tiles = list(tile_service.fetch_data_for_tiles(*tiles)) nexus_tiles = list(tile_service.mask_tiles_to_bbox(min_lat, max_lat, min_lon, max_lon, nexus_tiles)) #t2 = time.time() #print 'NEXUS call end at time %f' % t2 #print 'Seconds in NEXUS call: ', t2-t1 #sys.stdout.flush() #print 'Returning %d tiles' % len(nexus_tiles) return nexus_tiles @staticmethod def _prune_tiles(nexus_tiles): del_ind = np.where([np.all(tile.data.mask) for tile in nexus_tiles])[0] for i in np.flipud(del_ind): del nexus_tiles[i] def _lat2ind(self,lat): return int((lat-self._minLatCent)/self._latRes) def _lon2ind(self,lon): return int((lon-self._minLonCent)/self._lonRes) def _ind2lat(self,y): return self._minLatCent+y*self._latRes def _ind2lon(self,x): return self._minLonCent+x*self._lonRes def _create_nc_file_time1d(self, a, fname, varname, varunits=None, fill=None): self.log.debug('a={0}'.format(a)) self.log.debug('shape a = {0}'.format(a.shape)) assert len(a.shape) == 1 time_dim = len(a) rootgrp = Dataset(fname, "w", format="NETCDF4") rootgrp.createDimension("time", time_dim) vals = rootgrp.createVariable(varname, "f4", dimensions=("time",), fill_value=fill) times = rootgrp.createVariable("time", "f4", dimensions=("time",)) vals[:] = [d['mean'] for d in a] times[:] = [d['time'] for d in a] if varunits is not None: vals.units = varunits times.units = 'seconds since 1970-01-01 00:00:00' rootgrp.close() def _create_nc_file_latlon2d(self, a, fname, varname, varunits=None, fill=None): self.log.debug('a={0}'.format(a)) self.log.debug('shape a = {0}'.format(a.shape)) assert len(a.shape) == 2 lat_dim, lon_dim = a.shape rootgrp = Dataset(fname, "w", format="NETCDF4") rootgrp.createDimension("lat", lat_dim) rootgrp.createDimension("lon", lon_dim) vals = rootgrp.createVariable(varname, "f4", dimensions=("lat","lon",), fill_value=fill) lats = rootgrp.createVariable("lat", "f4", dimensions=("lat",)) lons = rootgrp.createVariable("lon", "f4", dimensions=("lon",)) vals[:,:] = a lats[:] = np.linspace(self._minLatCent, self._maxLatCent, lat_dim) lons[:] = np.linspace(self._minLonCent, self._maxLonCent, lon_dim) if varunits is not None: vals.units = varunits lats.units = "degrees north" lons.units = "degrees east" rootgrp.close() def _create_nc_file(self, a, fname, varname, **kwargs): self._create_nc_file_latlon2d(a, fname, varname, **kwargs) def executeInitializers(config): [wrapper.init(config) for wrapper in AVAILABLE_INITIALIZERS]
2.328125
2
utils/box/metric.py
ming71/SLA
9
9683
import numpy as np from collections import defaultdict, Counter from .rbbox_np import rbbox_iou def get_ap(recall, precision): recall = [0] + list(recall) + [1] precision = [0] + list(precision) + [0] for i in range(len(precision) - 1, 0, -1): precision[i - 1] = max(precision[i - 1], precision[i]) ap = sum((recall[i] - recall[i - 1]) * precision[i] for i in range(1, len(recall)) if recall[i] != recall[i - 1]) return ap * 100 def get_ap_07(recall, precision): ap = 0. for t in np.linspace(0, 1, 11, endpoint=True): mask = recall >= t if np.any(mask): ap += np.max(precision[mask]) / 11 return ap * 100 def get_det_aps(detect, target, num_classes, iou_thresh=0.5, use_07_metric=False): # [[index, bbox, score, label], ...] aps = [] for c in range(num_classes): target_c = list(filter(lambda x: x[3] == c, target)) detect_c = filter(lambda x: x[3] == c, detect) detect_c = sorted(detect_c, key=lambda x: x[2], reverse=True) tp = np.zeros(len(detect_c)) fp = np.zeros(len(detect_c)) target_count = Counter([x[0] for x in target_c]) target_count = {index: np.zeros(count) for index, count in target_count.items()} target_lut = defaultdict(list) for index, bbox, conf, label in target_c: target_lut[index].append(bbox) detect_lut = defaultdict(list) for index, bbox, conf, label in detect_c: detect_lut[index].append(bbox) iou_lut = dict() for index, bboxes in detect_lut.items(): if index in target_lut: iou_lut[index] = rbbox_iou(np.stack(bboxes), np.stack(target_lut[index])) counter = defaultdict(int) for i, (index, bbox, conf, label) in enumerate(detect_c): count = counter[index] counter[index] += 1 iou_max = -np.inf hit_j = 0 if index in iou_lut: for j, iou in enumerate(iou_lut[index][count]): if iou > iou_max: iou_max = iou hit_j = j if iou_max > iou_thresh and target_count[index][hit_j] == 0: tp[i] = 1 target_count[index][hit_j] = 1 else: fp[i] = 1 tp_sum = np.cumsum(tp) fp_sum = np.cumsum(fp) npos = len(target_c) recall = tp_sum / npos precision = tp_sum / (tp_sum + fp_sum) aps.append((get_ap_07 if use_07_metric else get_ap)(recall, precision)) return aps
1.953125
2
app.py
winstonschroeder/setlistmanager
0
9684
<filename>app.py import logging import pygame from app import * from pygame.locals import * from werkzeug.serving import run_simple from web import webapp as w import data_access as da logging.basicConfig(filename='setlistmanager.log', level=logging.DEBUG) SCREEN_WIDTH = 160 SCREEN_HEIGHT = 128 class Button: pass class Text(): """Create a text object.""" def __init__(self, surface, text, pos, **options): self.text = text self.surface = surface self.pos = pos self.bold = True self.italic = False self.underline = False self.background = None # Color('white') self.font = pygame.font.SysFont('Arial', 64) self.fontname = None # 'Free Sans' self.fontsize = 40 self.fontcolor = Color('black') self.set_font() da.connect_db('db.db') songs = da.get_all_songs_as_json() print (songs) # self.words = [word.split(' ') for word in self.text.splitlines()] # 2D array where each row is a list of words. # self.space = self.font.size(' ')[0] # The width of a space. # max_width, max_height = self.surface.get_size() # x, y = self.pos # for line in self.words: # for word in line: # word_surface = self.font.render(word, 0, self.fontcolor) # # print(word) # word_width, word_height = word_surface.get_size() # if x + word_width >= max_width: # x = pos[0] # Reset the x. # y += word_height # Start on new row. # surface.blit(word_surface, (x, y)) # x += word_width + self.space # x = pos[0] # Reset the x. # y += word_height # Start on new row. self.render() def set_font(self): """Set the font from its name and size.""" self.font = pygame.font.Font(self.fontname, self.fontsize) self.font.set_bold(self.bold) self.font.set_italic(self.italic) self.font.set_underline(self.underline) def render(self): """Render the text into an image.""" self.img = self.font.render(self.text, True, self.fontcolor, self.background) self.rect = self.img.get_rect() self.rect.size = self.img.get_size() self.rect.topleft = self.pos def draw(self): """Draw the text image to the screen.""" # Put the center of surf at the center of the display surf_center = ( (SCREEN_WIDTH - self.rect.width)/2, (SCREEN_HEIGHT - self.rect.height)/2 ) App.screen.blit(self.img, surf_center) # App.screen.blit(self.img, self.rect) class App: """Create a single-window app with multiple scenes.""" def __init__(self): """Initialize pygame and the application.""" logging.debug('Initializing App') pygame.init() pygame.mouse.set_cursor((8, 8), (0, 0), (0, 0, 0, 0, 0, 0, 0, 0), (0, 0, 0, 0, 0, 0, 0, 0)) self.shortcuts = { (K_x, KMOD_LMETA): 'print("cmd+X")', (K_x, KMOD_LALT): 'print("alt+X")', (K_x, KMOD_LCTRL): 'print("ctrl+X")', (K_x, KMOD_LMETA + KMOD_LSHIFT): 'print("cmd+shift+X")', (K_x, KMOD_LMETA + KMOD_LALT): 'print("cmd+alt+X")', (K_x, KMOD_LMETA + KMOD_LALT + KMOD_LSHIFT): 'print("cmd+alt+shift+X")', } self.color = Color('green') self.flags = RESIZABLE self.rect = Rect(0, 0, SCREEN_WIDTH, SCREEN_HEIGHT) App.screen = pygame.display.set_mode(self.rect.size, self.flags) App.t = Text(App.screen, 'Chorus', pos=(0, 0)) App.running = True def run(self): """Run the main event loop.""" logging.debug('entering method run') app = w.create_app() run_simple('127.0.0.1', 5000, app, use_debugger=True, use_reloader=True) logging.debug('after start of flask') while App.running: logging.debug('.') for event in pygame.event.get(): if event.type == QUIT: App.running = False if event.type == KEYDOWN: self.do_shortcut(event) App.screen.fill(self.color) App.t.draw() pygame.display.update() logging.debug('exiting setlistmanager') pygame.quit() def do_shortcut(self, event): """Find the the key/mod combination in the dictionary and execute the cmd.""" k = event.key m = event.mod if (k, m) in self.shortcuts: exec(self.shortcuts[k, m]) def toggle_fullscreen(self): """Toggle between full screen and windowed screen.""" self.flags ^= FULLSCREEN pygame.display.set_mode((0, 0), self.flags) def toggle_resizable(self): """Toggle between resizable and fixed-size window.""" self.flags ^= RESIZABLE pygame.display.set_mode(self.rect.size, self.flags) def toggle_frame(self): """Toggle between frame and noframe window.""" self.flags ^= NOFRAME pygame.display.set_mode(self.rect.size, self.flags)
2.96875
3
sim_keypoints.py
Praznat/annotationmodeling
8
9685
import json import pandas as pd import numpy as np from matplotlib import pyplot as plt import simulation from eval_functions import oks_score_multi import utils def alter_location(points, x_offset, y_offset): x, y = points.T return np.array([x + x_offset, y + y_offset]).T def alter_rotation(points, radians): centroid = np.mean(points, axis=0) return utils.rotate_via_numpy((points - centroid).T, radians) + centroid def alter_magnitude(points, percent_diff): centroid = np.mean(points, axis=0) return (points - centroid) * np.exp(percent_diff) + centroid def alter_normal_jump(points, scale): return points + np.random.normal(0, scale, points.shape) def alter_cauchy_jump(points, scale, abs_bound): return points + utils.bounded_cauchy(scale, points.shape, abs_bound) def disappear(points, p_disappear): return None if np.random.uniform() < p_disappear else points def shift_by_uerr(annotation, uerr): shifts = [ alter_rotation(annotation, np.random.normal(0, 0.5 * uerr) * np.pi / 8), alter_magnitude(annotation, np.random.normal(0, 0.3 * uerr)), alter_normal_jump(annotation, 30 * uerr), alter_cauchy_jump(annotation, 30 * uerr, 100), ] return np.mean(shifts, axis=0) * np.abs(np.sign(annotation)) def create_user_data(uid, df, pct_items, u_err, difficulty_dict=None, extraarg=None): items = df["item"].unique() n_items_labeled = int(np.round(pct_items * len(items))) items_labeled = sorted(np.random.choice(items, n_items_labeled, replace=False)) labels = [] for item in items_labeled: gold = df[df["item"] == item]["gold"].values[0] shifted_kpobjs = [shift_by_uerr(kpobj, u_err) for kpobj in gold] kpobjs = [shifted_kpobjs[0]] + [disappear(kp, u_err / 2) for kp in shifted_kpobjs[1:]] kpobjs = [kp for kp in kpobjs if kp is not None] labels.append(kpobjs) dfdict = { "uid": [uid] * len(items_labeled), "item": items_labeled, "annotation": labels, } return pd.DataFrame(dfdict) class KeypointSimulator(simulation.Simulator): def __init__(self, rawdata_dir='data/coco/person_keypoints_train2017.json', max_items=500, minlabelsperitem=4): with open(rawdata_dir) as f: dataset = json.load(f) self.category_id_skeletons = {c["id"]: np.array(c["skeleton"])-1 for c in iter(dataset["categories"])} img_label = {} for dataset_annotation in iter(dataset["annotations"]): v = img_label.setdefault(dataset_annotation["image_id"], []) v.append(dataset_annotation) img_label_minlen = {k: v for k, v in img_label.items() if len(v) >= minlabelsperitem} i = 0 rows = [] item = [] annotation = [] category = [] for dataset_annotations in iter(img_label_minlen.values()): for dataset_annotation in dataset_annotations: kp = np.reshape(dataset_annotation["keypoints"], (-1,3)) kp = kp[kp[:,2]>-90][:,:2] if len(kp) == 0: continue item.append(dataset_annotation["image_id"]) annotation.append(kp) category.append(dataset_annotation["category_id"]) i += 1 if i > max_items: break kp_df = pd.DataFrame({"item":item, "gold":annotation, "category":category}) self.df = kp_df.groupby("item")["gold"].apply(list).reset_index() self.itemdict = utils.make_categorical(self.df, "item") def create_stan_data(self, n_users, pct_items, err_rates, difficulty_dict): self.err_rates = err_rates self.difficulty_dict = difficulty_dict self.sim_df = simulation.create_sim_df(create_user_data, self.df, n_users, pct_items, err_rates, difficulty_dict) stan_data = utils.calc_distances(self.sim_df, (lambda x,y: 1 - oks_score_multi(x, y)), label_colname="annotation", item_colname="item") return stan_data def sim_uerr_fn(self, uerr_a, uerr_b, n_users): z = np.abs(np.random.normal(uerr_a, uerr_b, 10000)) return np.quantile(z, np.linspace(0,1,n_users+2)[1:-1]) def sim_diff_fn(self, difficulty_a, difficulty_b): z = 1 * np.random.beta(difficulty_a, difficulty_b, 10000) n_items = len(self.df["item"].unique()) return dict(zip(np.arange(n_items), np.quantile(z, np.linspace(0,1,n_items+2)[1:-1])))
2.40625
2
local/controller.py
Loptt/home-automation-system
0
9686
<reponame>Loptt/home-automation-system import requests import time import os import sys import json import threading from getpass import getpass import schedule import event as e import configuration as c import RPi.GPIO as GPIO #SERVER_URL = "https://home-automation-289621.uc.r.appspot.com" #SERVER_URL = "http://127.0.0.1:4747" SERVER_URL = "http://192.168.11.117:4747" pins = [2, 3, 4, 7, 8, 9, 10, 11, 14, 15, 17, 18, 22, 23, 24, 27] def calculate_max_duration(time): hours = 23 - time.hour minutes = 60 - time.minute return hours * 60 + minutes def turn_on(pin): print("Turn on " + str(pin)) GPIO.output(pin, GPIO.HIGH) def turn_off(pin): print("Turn off " + str(pin)) GPIO.output(pin, GPIO.LOW) def schedule_off(time, day, duration, pin): new_day = day end_time = e.Time(0, 0) if duration > calculate_max_duration(time): # Next day calculation new_day = day + 1 off_duration = duration - calculate_max_duration(time) end_time.hour = off_duration // 60 end_time.minute = off_duration % 60 else: # Same day calculation end_time.hour = time.hour + \ (duration // 60) + (time.minute + (duration % 60)) // 60 end_time.minute = (time.minute + duration % 60) % 60 if new_day > 7: new_day = 1 if new_day == 1: schedule.every().monday.at(str(end_time)).do(turn_off, pin) elif new_day == 2: schedule.every().tuesday.at(str(end_time)).do(turn_off, pin) elif new_day == 3: schedule.every().wednesday.at(str(end_time)).do(turn_off, pin) elif new_day == 4: schedule.every().thursday.at(str(end_time)).do(turn_off, pin) elif new_day == 5: schedule.every().friday.at(str(end_time)).do(turn_off, pin) elif new_day == 6: schedule.every().saturday.at(str(end_time)).do(turn_off, pin) elif new_day == 7: schedule.every().sunday.at(str(end_time)).do(turn_off, pin) def schedule_job(event): GPIO.setup(event.pin, GPIO.OUT) if len(event.days) == 0 or len(event.days) == 7: schedule.every().day.at(str(event.time)).do(turn_on, event.pin) else: if 1 in event.days: schedule.every().monday.at(str(event.time)).do(turn_on, event.pin) schedule_off(event.time, 1, event.duration, event.pin) if 2 in event.days: schedule.every().tuesday.at(str(event.time)).do(turn_on, event.pin) schedule_off(event.time, 2, event.duration, event.pin) if 3 in event.days: schedule.every().wednesday.at(str(event.time)).do(turn_on, event.pin) schedule_off(event.time, 3, event.duration, event.pin) if 4 in event.days: schedule.every().thursday.at(str(event.time)).do(turn_on, event.pin) schedule_off(event.time, 4, event.duration, event.pin) if 5 in event.days: schedule.every().friday.at(str(event.time)).do(turn_on, event.pin) schedule_off(event.time, 5, event.duration, event.pin) if 6 in event.days: schedule.every().saturday.at(str(event.time)).do(turn_on, event.pin) schedule_off(event.time, 6, event.duration, event.pin) if 7 in event.days: schedule.every().sunday.at(str(event.time)).do(turn_on, event.pin) schedule_off(event.time, 7, event.duration, event.pin) def run_scheduling(): while True: schedule.run_pending() time.sleep(1) def initial_setup(): username = input("Enter your username: ") password = getpass("Enter your password: ") pload = json.dumps({"username": username, "password": password}) r = requests.post(SERVER_URL + "/login", data=pload, headers={'Content-type': 'application/json'}) r_dict = r.json() if not r_dict["valid"]: print("Invalid username/password") print("Run program again to try again") sys.exit() print("Successful login...") print("Saving configuration...") f = open("config.txt", "w") f.write(r_dict["user"]) f.close() return r_dict["user"] def get_user(): f = open("config.txt", "r") user = f.readline() r = requests.get(SERVER_URL + "/users/" + user) if r.status_code == 200: print("Successful login...") return user else: print("Invalid user... Reinitializing configuration") return initial_setup() def get_configuration(user): r = requests.get(SERVER_URL + "/configurations/by-user/" + user) if r.status_code != 200: print("Error retrieving configuration, check internet connection.") sys.exit() r_dict = r.json() return c.Configuration( r_dict["systemStatus"], r_dict["rainPercentage"], r_dict["defaultDuration"], r_dict["update"], r_dict["_id"]) def set_update_off(configuration): r = requests.put( SERVER_URL + "/configurations/set-off-update/" + configuration.id) if r.status_code >= 400: print( "Error updating configuration status... Possible reconfiguration on next cycle") else: print("Update set off.") def update_schedules(user): r = requests.get( SERVER_URL + "/devices/by-user-with-events/" + user) devices = r.json() schedule.clear() GPIO.cleanup() for device in devices: for event in device["events"]: print(event) schedule_job(e.Event( device["pin"], event["days"], e.Time(event["time"]["hour"], event["time"]["minute"]), e.Repetition(event["repetition"]["times"], event["repetition"]["date"], event["repetition"]["current"]), event["duration"])) def setup(): GPIO.setmode(GPIO.BCM) GPIO.setwarnings(False) for pin in pins: GPIO.setup(pin, GPIO.OUT, initial=GPIO.LOW) GPIO.cleanup() def main(): setup() user = "" if not os.path.isfile("./config.txt"): print("No configuration found... Initializing configuration") user = initial_setup() else: print("Validating user...") user = get_user() print("Initializing routine...") # Initialize separate thread to run scheduling jobs thread = threading.Thread(None, run_scheduling, "Schedule") thread.start() print("Schedule running.") while True: configuration = get_configuration(user) if configuration.update: print("Updating schedule...") update_schedules(user) set_update_off(configuration) time.sleep(1) thread.join() if __name__ == "__main__": main()
3.125
3
src/graphql_sqlalchemy/graphql_types.py
gzzo/graphql-sqlalchemy
12
9687
from typing import Dict, Union from graphql import ( GraphQLBoolean, GraphQLFloat, GraphQLInputField, GraphQLInt, GraphQLList, GraphQLNonNull, GraphQLScalarType, GraphQLString, ) from sqlalchemy import ARRAY, Boolean, Float, Integer from sqlalchemy.dialects.postgresql import ARRAY as PGARRAY from sqlalchemy.types import TypeEngine def get_graphql_type_from_column(column_type: TypeEngine) -> Union[GraphQLScalarType, GraphQLList]: if isinstance(column_type, Integer): return GraphQLInt if isinstance(column_type, Float): return GraphQLFloat if isinstance(column_type, Boolean): return GraphQLBoolean if isinstance(column_type, (ARRAY, PGARRAY)): return GraphQLList(get_graphql_type_from_column(column_type.item_type)) return GraphQLString def get_base_comparison_fields(graphql_type: Union[GraphQLScalarType, GraphQLList]) -> Dict[str, GraphQLInputField]: return { "_eq": GraphQLInputField(graphql_type), "_neq": GraphQLInputField(graphql_type), "_in": GraphQLInputField(GraphQLList(GraphQLNonNull(graphql_type))), "_nin": GraphQLInputField(GraphQLList(GraphQLNonNull(graphql_type))), "_lt": GraphQLInputField(graphql_type), "_gt": GraphQLInputField(graphql_type), "_gte": GraphQLInputField(graphql_type), "_lte": GraphQLInputField(graphql_type), "_is_null": GraphQLInputField(GraphQLBoolean), } def get_string_comparison_fields() -> Dict[str, GraphQLInputField]: return {"_like": GraphQLInputField(GraphQLString), "_nlike": GraphQLInputField(GraphQLString)}
2.3125
2
Knapsack.py
byterubpay/mininero1
182
9688
<reponame>byterubpay/mininero1 import Crypto.Random.random as rand import itertools import math #for log import sys def decomposition(i): #from stack exchange, don't think it's uniform while i > 0: n = rand.randint(1, i) yield n i -= n def Decomposition(i): while True: l = list(decomposition(i)) if len(set(l)) == len(l): return l def decomposition2(n, s, d, k): #home-brewed, returns no duplicates, includes the number d s = s - 1 n = n while True: a = [d] nn = n #a.append(d) for i in range(0, s): a.append(rand.randint(0, n)) a.sort() #print("a", a) b = [] c = [] while len(a) > 0: t = a.pop() #print(t, a) if t >= d: b.append(nn - t) else: c.append(nn - t) nn = t c.append(nn) tot = b[:] + c[:] #print("b", b) if sum(set(tot)) == n and len(c) > int(k): return sorted(c), sorted(b) def decomposition3(n, s, d, k): #a combination of both methods, designed to get some smaller values send, change = decomposition2(n, s, d, k) for i in send: if i > n / s: send.remove(i) send = send + list(Decomposition(i)) for i in change: if i > n / (s - 1): change.remove(i) change = change + list(Decomposition(i)) return send, change def divv(l, m): return [a /float( m) for a in l] def frexp10(x): exp = int(math.log10(x)) return x / 10**exp, exp def decideAmounts(totalInputs, toSend, Partitions, k, fuzz): #fuzz is an optional amount to fuzz the transaction by #so if you start with a big obvious number like 2000, it might be fuzzed by up to "fuzz" amount fz = rand.randint(0, int(fuzz * 1000) ) / 1000.0 toSend += fz g, ii =frexp10(totalInputs) ii = 10 ** (-1 * min(ii - 2, 0)) print("ii", ii) M = 10 ** (int(math.log(2 ** Partitions) / math.log(10))) * ii #M = 10 ** M print("multiplier:", M) totalInputs = int(totalInputs * M) toSend = int(toSend * M) change = totalInputs - toSend send_amounts, change_amounts = decomposition3(totalInputs, Partitions, toSend, k) all_amounts = send_amounts[:] + change_amounts[:] rand.shuffle(all_amounts) print("") print("change amounts:", divv(change_amounts, M)) print("send amounts:", divv(send_amounts, M)) print("now from the following, how much is sent?") print("all amounts:", sorted(divv(all_amounts, M))) print("possible sent amounts:") amounts = [] for L in range(0, len(all_amounts)+1): for subset in itertools.combinations(all_amounts, L): amounts.append(sum(subset)) print("number of possible sent amounts:") print(len(amounts)) print("2^N:", 2 ** len(all_amounts)) print("number of possible sent amounts duplicates removed:") print(len(list(set(amounts)))) if len(sys.argv) > 2: kk = 2 parts = 7 kk = rand.randint(1, int(parts / 4)) #how many sends to demand fuzz = 1 decideAmounts(float(sys.argv[1]), float(sys.argv[2]), parts, kk, fuzz)
2.796875
3
drought_impact_forecasting/models/model_parts/Conv_Transformer.py
rudolfwilliam/satellite_image_forecasting
4
9689
import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange from .shared import Conv_Block from ..utils.utils import zeros, mean_cube, last_frame, ENS class Residual(nn.Module): def __init__(self, fn): super().__init__() self.fn = fn def forward(self, x, **kwargs): return self.fn(x, **kwargs) + x class PreNorm(nn.Module): def __init__(self, dim, fn): super().__init__() self.norm = nn.LayerNorm(dim) self.fn = fn def forward(self, x, **kwargs): return self.fn(torch.stack([self.norm(x[..., i]) for i in range(x.size()[-1])], dim=-1), **kwargs) class FeedForward(nn.Module): def __init__(self, kernel_size, num_hidden, dilation_rate, num_conv_layers): super().__init__() self.kernel_size = kernel_size self.num_hidden = num_hidden self.num_conv_layers = num_conv_layers self.dilation_rate = dilation_rate self.conv = Conv_Block(self.num_hidden, self.num_hidden, kernel_size=self.kernel_size, dilation_rate=self.dilation_rate, num_conv_layers=self.num_conv_layers) def forward(self, x): return torch.stack([self.conv(x[..., i]) for i in range(x.size()[-1])], dim=-1) class ConvAttention(nn.Module): def __init__(self, num_hidden, kernel_size, enc=True, mask=False): super(ConvAttention, self).__init__() self.enc = enc self.mask = mask self.kernel_size = kernel_size self.num_hidden = num_hidden # important note: shared convolution is intentional here if self.enc: # 3 times num_hidden for out_channels due to queries, keys & values self.conv1 = nn.Sequential( nn.Conv2d(in_channels=self.num_hidden, out_channels=3*self.num_hidden, kernel_size=1, padding="same", padding_mode="reflect") ) else: # only 2 times num_hidden for keys & values self.conv1 = nn.Sequential( nn.Conv2d(in_channels=self.num_hidden, out_channels=2*self.num_hidden, kernel_size=1, padding="same", padding_mode="reflect") ) self.conv2 = nn.Sequential( nn.Conv2d(in_channels=self.num_hidden*2, out_channels=1, kernel_size=self.kernel_size, padding="same", padding_mode="reflect") ) def forward(self, x, enc_out=None): # s is num queries, t is num keys/values b, _, _, _, s = x.shape if self.enc: t = s qkv_set = torch.stack([self.conv1(x[..., i]) for i in range(t)], dim=-1) Q, K, V = torch.split(qkv_set, self.num_hidden, dim=1) else: # x correspond to queries t = enc_out.size()[-1] kv_set = torch.stack([self.conv1(enc_out[..., i]) for i in range(t)], dim=-1) K, V = torch.split(kv_set, self.num_hidden, dim=1) Q = x K_rep = torch.stack([K] * s, dim=-2) V_rep = torch.stack([V] * s, dim=-1) Q_rep = torch.stack([Q] * t, dim=-1) # concatenate queries and keys for cross-channel convolution Q_K = torch.concat((Q_rep, K_rep), dim=1) if self.mask: # only feed in 'previous' keys & values for computing softmax V_out = [] # for each query for i in range(t): Q_K_temp = rearrange(Q_K[..., :i+1, i], 'b c h w t -> (b t) c h w') extr_feat = rearrange(torch.squeeze(self.conv2(Q_K_temp), dim=1), '(b t) h w -> b h w t', b=b, t=i+1) attn_mask = F.softmax(extr_feat, dim=-1) # convex combination over values using weights from attention mask, per channel c V_out.append(torch.stack([torch.sum(torch.mul(attn_mask, V_rep[:, c, :, :, i, :i+1]), dim=-1) for c in range(V_rep.size()[1])], dim=1)) V_out = torch.stack(V_out, dim=-1) else: Q_K = rearrange(Q_K, 'b c h w s t -> (b s t) c h w') # no convolution across time dim! extr_feat = rearrange(torch.squeeze(self.conv2(Q_K), dim=1), '(b s t) h w -> b h w t s', b=b, t=t) attn_mask = F.softmax(extr_feat, dim=-2) V_out = torch.stack([torch.sum(torch.mul(attn_mask, V_rep[:, c, ...]), dim=-2) for c in range(V_rep.size()[1])], dim=1) return V_out class PositionalEncoding(nn.Module): def __init__(self, num_hidden, img_width): # no differentiation should happen with respect to the params in here! super(PositionalEncoding, self).__init__() self.num_hidden = num_hidden self.img_width = img_width def _get_sinusoid_encoding_table(self, t, device): ''' Sinusoid position encoding table ''' sinusoid_table = torch.stack([self._get_position_angle_vec(pos_i) for pos_i in range(t)], dim=0) sinusoid_table[:, :, 0::2] = torch.sin(sinusoid_table[:, :, 0::2]) # even dim sinusoid_table[:, :, 1::2] = torch.cos(sinusoid_table[:, :, 1::2]) # odd dim return torch.moveaxis(sinusoid_table, 0, -1) def _get_position_angle_vec(self, position): return_list = [torch.ones((1, self.img_width, self.img_width), device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")) * (position / np.power(10000, 2 * (hid_j // 2) / self.num_hidden[-1])) for hid_j in range(self.num_hidden[-1])] return torch.stack(return_list, dim=1) def forward(self, x, t, single=False): """Returns entire positional encoding until step T if not single, otherwise only encoding of time step T.""" if not single: self.register_buffer('pos_table', self._get_sinusoid_encoding_table(t, x.get_device())) return torch.squeeze(x + self.pos_table.clone().detach(), dim=0) else: if t % 2 == 0: return x + torch.unsqueeze(torch.sin(self._get_position_angle_vec(t)), dim=-1).clone().detach() else: return x + torch.unsqueeze(torch.cos(self._get_position_angle_vec(t)), dim=-1).clone().detach() class Encoder(nn.Module): def __init__(self, num_hidden, depth, dilation_rate, num_conv_layers, kernel_size, img_width): super().__init__() self.num_hidden = num_hidden self.depth = depth self.dilation_rate = dilation_rate self.num_conv_layers = num_conv_layers self.kernel_size = kernel_size self.img_width = img_width self.layers = nn.ModuleList([]) self.num_hidden = self.num_hidden for _ in range(self.depth): self.layers.append(nn.ModuleList([ Residual(PreNorm([self.num_hidden[-1], self.img_width, self.img_width], ConvAttention(kernel_size=self.kernel_size, num_hidden=self.num_hidden[-1], enc=True))), Residual(PreNorm([self.num_hidden[-1], self.img_width, self.img_width], FeedForward(kernel_size=self.kernel_size, num_hidden=self.num_hidden[-1], dilation_rate=self.dilation_rate, num_conv_layers=self.num_conv_layers))) ])) def forward(self, x): for attn, ff in self.layers: x = attn(x) x = ff(x) return x class Decoder(nn.Module): def __init__(self, num_hidden, depth, dilation_rate, num_conv_layers, kernel_size, img_width, non_pred_channels): super().__init__() self.layers = nn.ModuleList([]) self.dilation_rate = dilation_rate self.num_conv_layers = num_conv_layers self.depth = depth self.kernel_size = kernel_size self.img_width = img_width self.num_hidden = num_hidden self.num_non_pred_feat = non_pred_channels for _ in range(self.depth): self.layers.append(nn.ModuleList([ # (masked) query self-attention Residual(PreNorm([self.num_hidden[-1], self.img_width, self.img_width], ConvAttention(num_hidden=self.num_hidden[-1], kernel_size=self.kernel_size, mask=True))), # encoder-decoder attention Residual(PreNorm([self.num_hidden[-1], self.img_width, self.img_width], ConvAttention(num_hidden=self.num_hidden[-1], kernel_size=self.kernel_size, enc=False))), # feed forward Residual(PreNorm([self.num_hidden[-1], self.img_width, self.img_width], FeedForward(num_hidden=self.num_hidden[-1], kernel_size=self.kernel_size, dilation_rate=self.dilation_rate, num_conv_layers=self.num_conv_layers))) ])) def forward(self, queries, enc_out): for query_attn, attn, ff in self.layers: queries = query_attn(queries) x = attn(queries, enc_out=enc_out) x = ff(x) return x class Conv_Transformer(nn.Module): """Standard, single-headed ConvTransformer like in https://arxiv.org/pdf/2011.10185.pdf""" def __init__(self, num_hidden, depth, dilation_rate, num_conv_layers, kernel_size, img_width, non_pred_channels, num_layers_query_feat, in_channels): super(Conv_Transformer, self).__init__() self.num_hidden = num_hidden self.depth = depth self.num_layers_query_feat = num_layers_query_feat self.dilation_rate = dilation_rate self.num_conv_layers = num_conv_layers self.kernel_size = kernel_size self.img_width = img_width self.in_channels = in_channels self.non_pred_channels = non_pred_channels self.pos_embedding = PositionalEncoding(self.num_hidden, self.img_width) self.Encoder = Encoder(num_hidden=self.num_hidden, depth=self.depth, dilation_rate=self.dilation_rate, num_conv_layers=self.num_conv_layers, kernel_size=self.kernel_size, img_width=self.img_width) self.Decoder = Decoder(num_hidden=self.num_hidden, depth=self.depth, dilation_rate=self.dilation_rate, num_conv_layers=self.num_conv_layers, kernel_size=self.kernel_size, img_width=self.img_width, non_pred_channels=self.non_pred_channels) self.input_feat_gen = Conv_Block(self.in_channels, self.num_hidden[-1], num_conv_layers=self.num_conv_layers, kernel_size=self.kernel_size) # TODO (optionally): replace this by SFFN self.back_to_pixel = nn.Sequential( nn.Conv2d(self.num_hidden[-1], 4, kernel_size=1) ) def forward(self, frames, n_predictions): _, _, _, _, T = frames.size() feature_map = self.feature_embedding(img=frames, network=self.input_feat_gen) enc_in = self.pos_embedding(feature_map, T) # encode all input values enc_out = torch.concat(self.Encoder(enc_in), dim=-1) out_list = [] queries = self.feature_embedding(img=feature_map[..., -1], network=self.query_feat_gen) for _ in range(n_predictions): dec_out = self.Decoder(queries, enc_out) pred = self.feature_embedding(dec_out) out_list.append(pred) queries = torch.concat((queries, pred), dim=-1) x = torch.stack(out_list, dim=-1) return x def feature_embedding(self, img, network): generator = network gen_img = [] for i in range(img.shape[-1]): gen_img.append(generator(img[..., i])) gen_img = torch.stack(gen_img, dim=-1) return gen_img class ENS_Conv_Transformer(Conv_Transformer): """ConvTransformer that employs delta model and can read in non-pred future features, hence taylored to the ENS challenge.""" def __init__(self, num_hidden, output_dim, depth, dilation_rate, num_conv_layers, kernel_size, img_width, non_pred_channels, num_layers_query_feat, in_channels, baseline): super(ENS_Conv_Transformer, self).__init__(num_hidden, depth, dilation_rate, num_conv_layers, kernel_size, img_width, non_pred_channels, num_layers_query_feat, in_channels - 1) # remove cloud mask self.in_channels = self.in_channels - 1 self.baseline = baseline self.output_dim = output_dim def forward(self, input_tensor, non_pred_feat=None, prediction_count=1): baseline = eval(self.baseline + "(input_tensor[:, 0:5, :, :, :], 4)") b, _, width, height, T = input_tensor.size() pred_deltas = torch.zeros((b, self.output_dim, height, width, prediction_count), device = self._get_device()) preds = torch.zeros((b, self.output_dim, height, width, prediction_count), device = self._get_device()) baselines = torch.zeros((b, self.output_dim, height, width, prediction_count), device = self._get_device()) # remove cloud mask channel for feature embedding feature_map = torch.concat((input_tensor[:, :4, ...], input_tensor[:, 5:, ...]), dim=1) features = self.feature_embedding(img=feature_map, network=self.input_feat_gen) enc_in = torch.stack([self.pos_embedding(features[i, ...], T) for i in range(b)], dim=0) enc_out = self.Encoder(enc_in) # first query stems from last input frame queries = features[..., -1:] baselines[..., 0] = baseline pred_deltas[..., 0] = self.back_to_pixel(self.Decoder(queries, enc_out)[..., 0]) preds[..., 0] = pred_deltas[..., 0] + baselines[..., 0] for t in range(1, prediction_count): if self.baseline == "mean_cube": baselines[..., t] = (preds[..., t - 1] + (baselines[..., t - 1] * (T + t)))/(T + t + 1) if self.baseline == "zeros": pass else: baselines[..., t] = preds[..., t - 1] # concatenate with non-pred features & feature embedding & do positional encoding query = self.pos_embedding(self.feature_embedding(torch.concat((preds[..., t-1:t], non_pred_feat[..., t-1:t]), dim=1), network=self.input_feat_gen), t, single=True) queries = torch.concat((queries, query), dim=-1) pred_deltas[..., :t] = torch.stack([self.back_to_pixel(self.Decoder(queries, enc_out)[..., i]) for i in range(t)], dim=-1) preds[..., t] = pred_deltas[..., t] + baselines[..., t] return preds, pred_deltas, baselines def _get_device(self): return next(self.parameters()).device
2.234375
2
tests/test_clients.py
rodrigoapereira/python-hydra-sdk
0
9690
# Copyright (C) 2017 O.S. Systems Software LTDA. # This software is released under the MIT License import unittest from hydra import Hydra, Client class ClientsTestCase(unittest.TestCase): def setUp(self): self.hydra = Hydra('http://localhost:4444', 'client', 'secret') self.client = Client( name='new-client', secret='client-secret', scopes=['devices', 'products'], redirect_uris=['http://localhost/callback'], ) def test_can_create_client(self): client = self.hydra.clients.create(self.client) self.addCleanup(self.hydra.clients.delete, client_id=client.id) self.assertEqual(client.name, 'new-client') self.assertEqual(client.secret, 'client-secret') self.assertEqual(client.scopes, ['devices', 'products']) self.assertEqual(client.redirect_uris, ['http://localhost/callback']) def test_can_get_client(self): client_id = self.hydra.clients.create(self.client).id self.addCleanup(self.hydra.clients.delete, client_id=client_id) client = self.hydra.clients.get(client_id) self.assertEqual(client.id, client_id) def test_can_update_client(self): client = self.hydra.clients.create(self.client) self.addCleanup(self.hydra.clients.delete, client_id=client.id) self.assertEqual(client.name, 'new-client') client.name = 'new-client-name' self.hydra.clients.update(client) self.assertEqual(client.name, 'new-client-name') def test_can_delete_client(self): client = self.hydra.clients.create(self.client) self.addCleanup(self.hydra.clients.delete, client_id=client.id) self.assertIsNotNone(self.hydra.clients.get(client.id)) self.hydra.clients.delete(client.id) self.assertIsNone(self.hydra.clients.get(client.id)) def test_can_list_all_clients(self): client1 = self.hydra.clients.create(self.client) self.addCleanup(self.hydra.clients.delete, client_id=client1.id) client2 = self.hydra.clients.create(self.client) self.addCleanup(self.hydra.clients.delete, client_id=client2.id) clients = [c.id for c in self.hydra.clients.all()] self.assertIn(client1.id, clients) self.assertIn(client2.id, clients)
2.5625
3
test/PR_test/unit_test/backend/test_binary_crossentropy.py
Phillistan16/fastestimator
0
9691
<gh_stars>0 # Copyright 2020 The FastEstimator 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 unittest import numpy as np import tensorflow as tf import torch import fastestimator as fe class TestBinarayCrossEntropy(unittest.TestCase): @classmethod def setUpClass(cls): cls.tf_true = tf.constant([[1], [0], [1], [0]]) cls.tf_pred = tf.constant([[0.9], [0.3], [0.8], [0.1]]) cls.torch_true = torch.tensor([[1], [0], [1], [0]]) cls.torch_pred = torch.tensor([[0.9], [0.3], [0.8], [0.1]]) def test_binaray_crossentropy_average_loss_true_tf(self): obj1 = fe.backend.binary_crossentropy(y_pred=self.tf_pred, y_true=self.tf_true).numpy() obj2 = 0.19763474 self.assertTrue(np.allclose(obj1, obj2)) def test_binaray_crossentropy_average_loss_false_tf(self): obj1 = fe.backend.binary_crossentropy(y_pred=self.tf_pred, y_true=self.tf_true, average_loss=False).numpy() obj2 = np.array([0.10536041, 0.3566748, 0.22314338, 0.10536041]) self.assertTrue(np.allclose(obj1, obj2)) def test_binaray_crossentropy_average_from_logit_average_loss_true_tf(self): obj1 = fe.backend.binary_crossentropy(y_pred=self.tf_pred, y_true=self.tf_true, from_logits=True, average_loss=True).numpy() obj2 = 0.57775164 self.assertTrue(np.allclose(obj1, obj2)) def test_binaray_crossentropy_average_from_logit_average_loss_false_tf(self): obj1 = fe.backend.binary_crossentropy(y_pred=self.tf_pred, y_true=self.tf_true, from_logits=True, average_loss=False).numpy() obj2 = np.array([0.34115386, 0.8543553, 0.37110066, 0.7443967]) self.assertTrue(np.allclose(obj1, obj2)) def test_binaray_crossentropy_average_loss_true_torch(self): obj1 = fe.backend.binary_crossentropy(y_pred=self.torch_pred, y_true=self.torch_true).numpy() obj2 = 0.19763474 self.assertTrue(np.allclose(obj1, obj2)) def test_binaray_crossentropy_average_loss_false_torch(self): obj1 = fe.backend.binary_crossentropy(y_pred=self.torch_pred, y_true=self.torch_true, average_loss=False).numpy() obj2 = np.array([0.10536041, 0.3566748, 0.22314338, 0.10536041]) self.assertTrue(np.allclose(obj1, obj2)) def test_binaray_crossentropy_average_from_logit_average_loss_true_torch(self): obj1 = fe.backend.binary_crossentropy(y_pred=self.torch_pred, y_true=self.torch_true, from_logits=True, average_loss=True).numpy() obj2 = 0.57775164 self.assertTrue(np.allclose(obj1, obj2)) def test_binaray_crossentropy_average_from_logit_average_loss_false_torch(self): obj1 = fe.backend.binary_crossentropy(y_pred=self.torch_pred, y_true=self.torch_true, from_logits=True, average_loss=False).numpy() obj2 = np.array([0.34115386, 0.8543553, 0.37110066, 0.7443967]) self.assertTrue(np.allclose(obj1, obj2))
2.125
2
ats_hex.py
kyeser/scTools
0
9692
<reponame>kyeser/scTools #!/usr/bin/env python from scTools import interval, primeForm from scTools.rowData import ats from scTools.scData import * count = 1 for w in ats: prime = primeForm(w[0:6]) print '%3d\t' % count, for x in w: print '%X' % x, print ' ', intervals = interval(w) for y in intervals: print '%X' % y, print '\t%2d\t' % sc6.index(prime), if prime == sc6[1] or prime == sc6[7] or prime == sc6[8] or \ prime == sc6[20] or prime == sc6[32] or prime == sc6[35]: print 'AC' elif prime == sc6[17]: print 'AT' else: print count += 1
3.3125
3
src/precon/commands.py
Albert-91/precon
0
9693
import asyncio import click from precon.devices_handlers.distance_sensor import show_distance as show_distance_func from precon.remote_control import steer_vehicle, Screen try: import RPi.GPIO as GPIO except (RuntimeError, ModuleNotFoundError): import fake_rpi GPIO = fake_rpi.RPi.GPIO @click.command(name="rc") def remote_control() -> None: loop = asyncio.get_event_loop() try: with Screen() as screen: loop.run_until_complete(steer_vehicle(screen)) except KeyboardInterrupt: print("Finishing remote control...") except Exception as e: print("Raised unexpected error: %s" % e) finally: GPIO.cleanup() @click.command(name="show-distance") def show_distance() -> None: loop = asyncio.get_event_loop() try: loop.run_until_complete(show_distance_func()) except KeyboardInterrupt: print("Finishing measuring distance...") except Exception as e: print("Raised unexpected error: %s" % e) finally: GPIO.cleanup()
2.75
3
midway.py
sjtichenor/midway-ford
0
9694
<reponame>sjtichenor/midway-ford import csv import string import ftplib import math import time from selenium import webdriver from selenium.webdriver.common.keys import Keys from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC import sqlite3 from lxml import html import requests import sys import midwords import facebook import hd_images import adwords_feeds import sheets import random import sales_specials import scrape from pprint import pprint from pyvirtualdisplay import Display import locale locale.setlocale(locale.LC_ALL, 'en_US.utf8') # Misc stuff def isNumber(s): try: float(s) return True except ValueError: return False def start_chromedriver(): display = Display(visible=0, size=(800, 800)) display.start() path_to_chromedriver = 'chromedriver' browser = webdriver.Chrome(executable_path=path_to_chromedriver) return browser # FMC Dealer Scrapes def randomInterval(): #returns random float roughly between 1.5 and 2.75 return 1.75+1*random.random()-.25*random.random() def switchDefaultSearch(browser) : # Switch between MyLot/States #Switch default search back to Dealership Proximity print('Switching default search...') browser.get('https://www.vlplus.dealerconnection.com/Search?&searchType=quicksearch') time.sleep(3) browser.find_element_by_xpath('//a[@id="ActivateSettings"]').click() time.sleep(3) browser.find_element_by_xpath('//a[text()="Search Settings"]').click() time.sleep(3) # Check what default is currently set to tree = html.fromstring(browser.page_source) currentSetting = tree.xpath('//option[@selected]/text()') print('Setting Before:', currentSetting) if 'My Lot' in currentSetting: print('Switching default search from My Lot to Proximity') browser.find_element_by_xpath('//select[@id="searchSettingsDefaultSearchMode"]').click() time.sleep(2) browser.find_element_by_xpath('//option[@value="6"]').click() time.sleep(2) elif 'States' in currentSetting : print('Switching default search from States to My Lot') browser.find_element_by_xpath('//select[@id="searchSettingsDefaultSearchMode"]').click() time.sleep(2) browser.find_element_by_xpath('//option[@value="1"]').click() time.sleep(2) currentSetting = tree.xpath('//option[@selected]/text()') #print('Setting After:', currentSetting) This doesn't work.. browser.find_element_by_xpath('//a[@id="saveSearchSettings"]').click() time.sleep(2) browser.get('https://www.vlplus.dealerconnection.com/Search?&searchType=quicksearch') time.sleep(2) print('Finished switching default search...') return browser def getVinList() : conn = sqlite3.connect('data/inventory.db') c = conn.cursor() vinList = [] c.execute('SELECT vin FROM masterInventory where invType = ?', ('New',)) vinTupleList = c.fetchall() for vinTuple in vinTupleList : vin = vinTuple[0] vinList.append(vin) numVehicles = len(vinList) conn.commit() conn.close() return vinList def fmcLogin(browser) : #Logs into fmcdealer and returns browser # Fire up ChomeDriver # path_to_chromedriver = '/Users/spencertichenor/PycharmProjects/midway/chromedriver' # browser = webdriver.Chrome(executable_path = path_to_chromedriver) # Log into FMC Dealer url = 'https://fmcdealer.com' browser.get(url) username = browser.find_element_by_id('DEALER-WSLXloginUserIdInput') password = browser.find_element_by_id('DEALER-WSLXloginPasswordInput') username.send_keys('t-spen29') password.send_keys('<PASSWORD>') browser.find_element_by_xpath('//div[@id="DEALER-WSLXloginWSLSubmitButton"]/input').click() time.sleep(5) return browser def navigateToVincent(browser, vin) : print('\nNavigating to Vincent page for VIN: ' + vin + '...\n\n') #print('\nSearching for rebate info for vehicle ' + str(k+1) + '/' + str(len(vinList)) + '...') #print('\n\tVIN: ' + vin + '\n') browser.get('https://www.vlplus.dealerconnection.com/Search?&searchType=quicksearch') time.sleep(3) try : vinField = browser.find_element_by_id('txtVIN') vinField.send_keys(vin) browser.find_element_by_xpath('//input[@value="Search"]').click() time.sleep(2) except : print('VIN FIELD ERROR:') print(sys.exc_info()[0]) #errorList.append(vin) #pass this was pass but i think it should be return return browser source = browser.page_source if 'Please broaden your search.' not in source : # Check if vehicle was not found in dealership proximity search # Click on Vincent button #source = browser.page_source try : vincentUrl = vincentUrl[0] browser.get(vincentUrl) time.sleep(4) except : print('Vincent Url Error:') print(sys.exc_info()[0]) #errorList.append(vin) #pass return browser source = browser.page_source tree = html.fromstring(source) if 'Please click the "Close" button to continue with the Sales Process.' in source : # Check for recall warning browser.find_element_by_xpath('//input[@value="Close"]').click() time.sleep(2) if 'value="Certificate Inquiry"' not in source : # Check if vehicle already sold # Enter ZIP code and click next try : zipField = browser.find_element_by_xpath('//div/input[@name="customerZip"]') zipField.send_keys('55113') browser.find_element_by_id('primaryButtonId').click() time.sleep(2) except : print('ZIP FIELD ERROR:') print(sys.exc_info()[0]) #errorList.append(vin) pass # Get rebate info #rebateInfo = scrapeRebateInfo(browser) else : #soldList.append(vin) print('\tIt looks like this vehicle has already been sold.\n\n') else : # Vehicle not found in Dealership Proximity search print('\tVehicle not found after searching Dealership Proximity.') #Switch default search to My Lot browser = switchDefaultSearch(browser) try : vinField = browser.find_element_by_id('txtVIN') vinField.send_keys(vin) browser.find_element_by_xpath('//input[@value="Search"]').click() time.sleep(2) except : #errorList.append(vin) print('VIN FIELD ERROR:') print(sys.exc_info()[0]) #switchToProximity(browser) return browser # Click on Vincent button source = browser.page_source tree = html.fromstring(source) vincentUrl = tree.xpath('//a[@title="Smart Vincent"]/@href') try : vincentUrl = vincentUrl[0] browser.get(vincentUrl) time.sleep(4) except : #errorList.append(vin) print('Vincent Url Error:') print(sys.exc_info()[0]) #switchToProximity(browser) #return browser source = browser.page_source tree = html.fromstring(source) if 'Please click the "Close" button to continue with the Sales Process.' in source : # Check for recall warning browser.find_element_by_xpath('//input[@value="Close"]').click() time.sleep(2) if 'value="Certificate Inquiry"' not in source : # Check if vehicle already sold # Enter ZIP code and click next try : zipField = browser.find_element_by_xpath('//div/input[@name="customerZip"]') zipField.send_keys('55113') browser.find_element_by_id('primaryButtonId').click() time.sleep(2) except : #errorList.append(vin) print('ZIP FIELD ERROR:') print(sys.exc_info()[0]) #switchToProximity(browser) #return browser # Get rebate info #rebateInfo = scrapeRebateInfo(browser) else : #soldList.append(vin) print('\tIt looks like this vehicle has already been sold.\n\n') #Switch default search back to Dealership Proximity #switchToProximity(browser) #pass return browser # print('\nNumber of vehicles appear to have been sold: ' + str(len(soldList))) # print('Sold List:') # print(soldList) # print('\nNumber of vehicles that ran into errors: ' + str(len(errorList))) # print('Error List:') # print(errorList) #print('\n\nFinished getting rebate information.') def scrapeRebateInfo(page_source) : #input browser of vincent page, return tuple with unconditional rebate info # Get rebate info #source = browser.page_source tree = html.fromstring(page_source) vin = tree.xpath('//dt[.="VIN:"]/following-sibling::dd/text()') vin = vin[0].replace('\xa0', ' ').replace('\t', '').replace('\n', '') rowspans = tree.xpath('//table[@summary="This table displays and lets you choose public program bundles."]/tbody/tr/td[@class="textC altRow"]/@rowspan | //table[@summary="This table displays and lets you choose public program bundles."]/tbody/tr/td[@class="textC "]/@rowspan') conditions = tree.xpath('//table[@summary="This table displays and lets you choose public program bundles."]/tbody/tr[@class="programTableHeader"]/td[@style="{border-right:none;}"]/text()') nums = tree.xpath('//table[@summary="This table displays and lets you choose public program bundles."]/tbody/tr/td[@class="textL txtCol "]/a/text() | //table[@summary="This table displays and lets you choose public program bundles."]/tbody/tr/td[@class="textL txtCol altRow "]/a/text()') names = tree.xpath('//table[@summary="This table displays and lets you choose public program bundles."]/tbody/tr/td[@class="textL txtCol "]/text() | //table[@summary="This table displays and lets you choose public program bundles."]/tbody/tr/td[@class="textL txtCol altRow "]/text()') amounts = tree.xpath('//table[@summary="This table displays and lets you choose public program bundles."]/tbody/tr/td[@class="textR "]/text() | //table[@summary="This table displays and lets you choose public program bundles."]/tbody/tr/td[@class="textR altRow"]/text()') expirations = tree.xpath('//table[@summary="This table displays and lets you choose public program bundles."]/tbody/tr/td[@class="textC highlight noWrap"]/text()') to_db = (vin,) if rowspans == [] : # No unconditional rebates print('No rebates found for this vehicle.\n') print('Updating rebate info...') while len(to_db) < 43 : to_db += (None,) else : # Yah, it has unconditional rebates # Clean up Condition info condition = conditions[0] condition = condition.replace('\n', '').replace('\t', '').replace(' ', '').replace(':C', ': C') condition = condition[1:] condition = removeWeirdChars(condition) if 'Cash Payment' in condition : print('\tUnconditional Rebates:\n') i=0 for i in range(i, int(rowspans[0])) : num = nums[i].replace('\n', '').replace('\t', '').replace(' ', '') name = names[i*2+1].replace('\n', '').replace('\t', '').replace(' - ', '').replace('s C', 's C').replace(' ', '').replace('"', '') amount = amounts[i].replace('\n', '').replace('\t', '').replace(' ', '') expiration = expirations[i].replace('\n', '').replace('\t', '').replace(' ', '') if 'SIRIUS' in name : #Fix for the stupid 6-month extra Sirius incentive amount = '$0' if ' - ' not in amount and 'Amount Not Available' not in amount : # stupid fix for Oct 2016 rebate and anotehr fix for Dec 2016 rebate print('\t\tProgram: #' + num) print('\t\tName: ' + name) print('\t\tAmount: ' + amount) print('\t\tExpiration: ' + expiration + '\n') to_db += (num,) + (name,) + (condition,) + (amount,) + (expiration,) + (condition,) #fix double header while len(to_db) < 43 : to_db += (None,) return to_db time.sleep(2) def scrapeLeaseInfo(page_source) : # Connect to database conn = sqlite3.connect('data/inventory.db') c = conn.cursor() to_db = () # Get rebate info tree = html.fromstring(page_source) vin = tree.xpath('//dt[.="VIN:"]/following-sibling::dd/text()') vin = vin[0].replace('\xa0', ' ').replace('\t', '').replace('\n', '') vehDesc = tree.xpath('//dt[.="Description:"]/following-sibling::dd/text()') residualTable = tree.xpath('//table[@class="rateTable"]/tbody/tr/td/text() | //table[@class="rateTable"]/thead/tr/th/text()') #rclRebateRow = tree.xpath('//tr[td[contains(., "RCL Customer Cash")]]/td/text()') rclFactorsRow = tree.xpath('//tr[td[contains(., "RCL Factors")]]/td/text()') rclTermLengths = tree.xpath('//tr[td[contains(., "RCL Factors")]]//th/text()') rclFactors = tree.xpath('//tr[td[contains(., "RCL Factors")]]//td/text()') rebateCells = tree.xpath('//tr[td[contains(., "LEASE")]]/following-sibling::*/td/text()') #print('rebateCells:', rebateCells) #print('length of rebateCells:', len(rebateCells)) if rebateCells != [] : print('Lease Rebates:') rebateDict = {} for i, cell in enumerate(rebateCells) : if 'Cash' in cell and 'Fast Cash Certificate' not in cell: rebateName = cell.replace('\t', '').replace('\n', '').replace(' - ', '') if '$' in rebateCells[i+2] : rebateAmount = int(rebateCells[i+2].replace('\t', '').replace('\n', '').replace(' ', '').replace('$', '').replace(',', '')) rebateExpiration = rebateCells[i+3].replace('\t', '').replace('\n', '').replace(' ', '') elif '$' in rebateCells[i+3] : rebateAmount = int(rebateCells[i+3].replace('\t', '').replace('\n', '').replace(' ', '').replace('$', '').replace(',', '')) rebateExpiration = rebateCells[i+4].replace('\t', '').replace('\n', '').replace(' ', '') rebateDict[rebateName] = [rebateAmount, rebateExpiration] print('\tRebate Name:', rebateName) print('\tRebate Amount:', rebateAmount) print('\tRebate Expiration:', rebateExpiration) print('\n') print('rebateDict:', rebateDict) totalRebates = 0 for rebateName in rebateDict : totalRebates += rebateDict[rebateName][0] vehDesc = vehDesc[0].replace('\xa0', ' ').replace('\t', '').replace('\n', '') rclResiduals = {} for i, leaseTerm in enumerate(residualTable[0:4]) : rclResiduals[leaseTerm + ' Month'] = float(residualTable[i+5])/100 #rclRebateName = rclRebateRow[5].replace('\t', '').replace('\n', '').replace(' - ', '') #rclRebateAmount = rclRebateRow[8].replace('\t', '').replace('\n', '').replace(' ', '').replace('$', '').replace(',', '') #rclRebateExpiration = rclRebateRow[9].replace('\t', '').replace('\n', '').replace(' ', '') rclTermLengths = rclTermLengths[:-1] for i, termLength in enumerate(rclTermLengths) : rclTermLengths[i] = int(termLength) rclFactorsExpiration = rclFactorsRow[8].replace('\t', '').replace('\n', '').replace(' ', '') factors = {} for e in rclFactors : if 'Tier' in e : tierIndex = rclFactors.index(e) tier = rclFactors[tierIndex] tierFactors = rclFactors[tierIndex+1:tierIndex+5] for i, factor in enumerate(tierFactors) : tierFactors[i] = float(factor) factors[tier] = tierFactors print('VIN:', vin) print('Vehicle Description:', vehDesc) #print('RCL Rebate Name:', rclRebateName) print('Total Rebates:', totalRebates) #print('RCL Rebate Expiration:', rclRebateExpiration) print('RCL Lengths:', rclTermLengths) print('RCL Factors: ', factors) #used to be factors but too hard to deal with everything print('RCL Factors Expiration:', rclFactorsExpiration) print('RCL Residual:', rclResiduals) c.execute('SELECT stock, year, model, vehTrim FROM masterInventory WHERE vin = ?', (vin,)) vehInfo = c.fetchall() vehInfo = vehInfo[0] print('vehInfo:', vehInfo) to_db = (vin,) + vehInfo + (str(rebateDict), totalRebates, str(rclTermLengths), str(factors), rclFactorsExpiration, str(rclResiduals)) #to_db = (vin, str(rebateDict), totalRebates, str(rclTermLengths), str(factors), rclFactorsExpiration, str(rclResiduals)) else : print('No lease info found.') to_db = (vin, None, None, None, None, None, None, None, None, None, None) # Close connection to database conn.commit() conn.close() return to_db time.sleep(2) def calculateLeasePayment(vin, termLength, mileage, tier) : # outputs monthly payments. input example: ('1FAHP1231', 36, 15000, 'Tier 0-1') print('Calculating lease payments for VIN: ' + vin) leaseParameters = getLeaseParameters(vin) #print('leaseParameters:', leaseParameters) if leaseParameters[5] == None : # if there are no lease deals paymentOptions = (None, None, None, None, None, vin) else : msrp = leaseParameters[0] dealerDiscount = leaseParameters[1] rebateAmount = leaseParameters[2] termLengths = leaseParameters[3] interestRates = leaseParameters[4] residuals = leaseParameters[5] termLengthIndex = termLengths.index(termLength) apr = interestRates[tier][termLengthIndex] apr += 1 # Juicing the apr by 1% residual = residuals[str(termLength) + ' Month'] # Adjust residual for mileage residual += (15000 - mileage)/1500 * .01 residual = round(residual, 2) taxRate = .07125 # plus any local taxes i guess aquisitionFee = 645 # need to figure out better way moneyFactor = apr/2400 salesTax = round(msrp * taxRate, 2) # dunno if this should be here salesTax = 0 signAndDrive = 0 - aquisitionFee - salesTax downPayments = [signAndDrive, 0, 1000, 2000, 3000] print('MSRP:', msrp) print('Dealer Discount:', dealerDiscount) print('Rebate Amount:', rebateAmount) print('Term Length:', str(termLength) + ' Month') print('APR:', apr) print('Money Factor:', moneyFactor) print('Residual:', residual) print('\n\n') paymentOptions = () for downPayment in downPayments : sellingPrice = msrp - dealerDiscount - rebateAmount #taxableAmount = sellingPrice - residualValue - downPayment + rentCharge # not accurate #salesTax = msrp * taxRate #salesTax = 0 grossCapCost = msrp - dealerDiscount + aquisitionFee + salesTax capCostReduction = rebateAmount + downPayment netCapCost = round(grossCapCost - capCostReduction, 2) residualValue = round(msrp * residual, 2) depreciation = round(netCapCost - residualValue, 2) basePayment = round(depreciation/termLength, 2) rentPayment = round((netCapCost + residualValue) * moneyFactor, 2) rentCharge = rentPayment*termLength totalPayment = round(basePayment + rentPayment, 2) print('Down Payment:', downPayment) print('\n') print('Gross Cap. Cost:', grossCapCost) print('Cap. Cost Reduction:', capCostReduction) print('Net Cap. Cost:', netCapCost) print('Residual Value:', residualValue) print('Depreciation:', depreciation) print('Base Payment:', basePayment) print('Rent Payment:', rentPayment) print('Total Monthly Payment:', totalPayment) print('\n\n\n') paymentOptions += (totalPayment,) paymentOptions += (vin,) #print('Payment Options:', paymentOptions) return paymentOptions def scrapeFMC(): # Gets rebate and lease info from FMC Dealer vinList = getVinList() #vinList = ['3FA6P0VP1HR195216', '3FA6P0H77HR187150'] #path_to_chromedriver = 'chromedriver' #browser = webdriver.Chrome(executable_path=path_to_chromedriver) browser = start_chromedriver() browser = fmcLogin(browser) errorList = [] for i, vin in enumerate(vinList) : print('Vehicle ' + str(i+1) + '/' + str(len(vinList)) + ':\n') browser = navigateToVincent(browser, vin) try : to_db = scrapeRebateInfo(browser.page_source) updateRebateTable(to_db) to_db = scrapeLeaseInfo(browser.page_source) updateLeaseTable(to_db) #to_db = calculateLeasePayment(vin, 36, 10500, 'Tier 0-1') #updateLeaseTable(to_db) except Exception as e: template = "An exception of type {0} occurred. Arguments:\n{1!r}" message = template.format(type(e).__name__, e.args) message += '\nError on line {}'.format(sys.exc_info()[-1].tb_lineno) print(message) errorList.append(vin) continue print('Error List:', errorList) print('Number of Errors:', len(errorList)) doubleErrorList = [] for i, vin in enumerate(errorList) : # Re-run all VINs that had errors print('Vehicle ' + str(i+1) + '/' + str(len(errorList)) + ':\n') browser = navigateToVincent(browser, vin) try : to_db = scrapeRebateInfo(browser.page_source) updateRebateTable(to_db) to_db = scrapeLeaseInfo(browser.page_source) updateLeaseTable(to_db) except Exception as e: template = "An exception of type {0} occurred. Arguments:\n{1!r}" message = template.format(type(e).__name__, e.args) message += '\nError on line {}'.format(sys.exc_info()[-1].tb_lineno) print(message) doubleErrorList.append(vin) continue print('Double Error List:', errorList) print('Number of Double Errors:', len(errorList)) print(20*'\n') def updateVLPlusInventoryTable(): print('Scraping Vehicle Locator..') # Open connection to database conn = sqlite3.connect('data/inventory.db') c = conn.cursor() # Delete old data query = 'DELETE FROM VLPlusInventory' c.execute(query) # all the xpath that we're gonna need vin_list_xpath = '//tr[contains(@class, "vehiclerow")]/@vin' msrp_list_xpath = '//td[contains(@class, "price")]/a[@class="pdfWindowSticker"]/span/text()' invoice_list_xpath = '//tr[contains(@class, "vehiclerow")]/td[11]/a/span/text()' pep_list_xpath = '//tr[contains(@class, "vehiclerow")]/td[7]/span[3]/text()' order_type_list_xpath = '//a[@onclick="showOrderTypeInfo();"]/span/text()' engine_list_xpath = '//tr[contains(@class, "vehiclerow")]/td[8]/span[1]/text()' status_list_xpath = '//tr[contains(@class, "vehiclerow")]/td[1]/@class' # Log into FMC Dealer #path_to_chromedriver = 'chromedriver' #browser = webdriver.Chrome(executable_path=path_to_chromedriver) browser = start_chromedriver() browser = fmcLogin(browser) wait = WebDriverWait(browser, 10) browser.get('https://www.vlplus.dealerconnection.com/InvMgt/') time.sleep(randomInterval() * 2) source = browser.page_source tree = html.fromstring(source) vehicle_count = tree.xpath('//th[@class="resultcount"]/text()') print(vehicle_count) vehicle_count = vehicle_count[1].split(' ') vehicle_count_index = vehicle_count.index('vehicles') - 1 vehicle_count = vehicle_count[vehicle_count_index] vehicle_count = int(vehicle_count) page_count = math.ceil(vehicle_count/25) print('Total pages:', page_count) for j in range(0, page_count-1): tree = html.fromstring(browser.page_source) vin_list = tree.xpath(vin_list_xpath) ugly_msrp_list = tree.xpath(msrp_list_xpath) ugly_invoice_list = tree.xpath(invoice_list_xpath) ugly_pep_list = tree.xpath(pep_list_xpath) ugly_order_type_list = tree.xpath(order_type_list_xpath) ugly_engine_list = tree.xpath(engine_list_xpath) ugly_status_list = tree.xpath(status_list_xpath) # Clean up PEP Codes msrp_list = [] invoice_list = [] pep_list = [] order_type_list = [] engine_list = [] status_list = [] for k in range(0, len(vin_list)): msrp_list.append(ugly_msrp_list[k].replace('$', '').replace(',', '')) if msrp_list[k] != 'n/a': msrp_list[k] = int(msrp_list[k]) else: msrp_list[k] = '' invoice_list.append(ugly_invoice_list[k].replace('$', '').replace(',', '')) if invoice_list[k] != 'n/a': invoice_list[k] = int(invoice_list[k]) else: invoice_list[k] = '' for pep_code in ugly_pep_list: pep_list.append(pep_code) for order_type in ugly_order_type_list: order_type_list.append(order_type) for engine in ugly_engine_list: engine = engine.split('<br>')[0].replace(' ', '').replace('\n', '') if 'L ' in engine and 'SPD' not in engine and 'SPEED' not in engine: engine_list.append(engine) for status in ugly_status_list: if 'transit' in status: status_list.append('In Transit') elif 'plant' in status: status_list.append('In Plant') else: status_list.append('In Stock') if len(msrp_list) != len(invoice_list): print('len msrp != invoice') raise ValueError if len(pep_list) != len(msrp_list): print('len pep != msrp') print(msrp_list) print(ugly_pep_list) raise ValueError print('msrp_list len: ', len(msrp_list)) print('msrp_list: ', msrp_list) print('invoice_list: ', invoice_list) print('pep_list: ', pep_list) print('order_type_list: ', order_type_list) print('engine_list: ', engine_list) print('status_list: ', status_list) to_db = [] for k, vin in enumerate(vin_list): print('VIN: ', vin) print('msrp: ', msrp_list[k]) print('invoice: ', invoice_list[k]) print('pep: ', pep_list[k]) print('order_type: ', order_type_list[k]) print('engine: ', engine_list[k], '\n') if msrp_list[k] < invoice_list[k]: raise ValueError to_db.append((vin, msrp_list[k], invoice_list[k], pep_list[k], order_type_list[k], engine_list[k], status_list[k])) query = 'INSERT OR REPLACE INTO VLPlusInventory (vin, msrp, invoice, pepCode, orderType, engine, status) VALUES (?, ?, ?, ?, ?, ?, ?)' c.executemany(query, to_db) conn.commit() time.sleep(randomInterval()) next_page_xpath = '//a[@page="{}"]'.format(str(j+2)) next_page_link = wait.until(EC.element_to_be_clickable((By.XPATH, next_page_xpath))) next_page_link.click() #browser.find_element_by_xpath(next_page_xpath).click() time.sleep(randomInterval()*2) conn.close() def updateMasterInventoryStockStatus(): # Open connection to database conn = sqlite3.connect('data/inventory.db') c = conn.cursor() # Get all vin in master inv query = 'SELECT vin FROM masterInventory' c.execute(query) master_results = c.fetchall() master_vin_list = [] for r in master_results: master_vin_list.append(r[0]) # Get all retail veh in vlplus inv query = 'SELECT vin, status FROM VLPlusInventory WHERE orderType = ? OR orderType = ?' to_db = ('1', '2') c.execute(query, to_db) vlplus_results = c.fetchall() for r in vlplus_results: vin = r[0] vlpus_status = r[1] print('\n', vin, ':\n\n') if vin in master_vin_list: query = 'SELECT status, dateInStock FROM masterInventory WHERE vin = ?' to_db = (vin,) c.execute(query, to_db) result = c.fetchall() master_status = result[0][0] date_in_stock = result[0][1] print(master_status) if date_in_stock and master_status == 'In Stock': print('Stock status already set') continue elif date_in_stock and master_status != 'In Stock': print('Updating stock status') query = 'UPDATE masterInventory SET status = ? WHERE vin = ?' to_db = ('In Stock', vin) c.execute(query, to_db) else: print('Adding veh to master') query = 'INSERT OR REPLACE INTO masterInventory (vin, status, invType) VALUES (?, ?, ?)' to_db = (vin, vlpus_status, 'New') c.execute(query, to_db) conn.commit() conn.close() # Data stuff def get_incoming_homenet_file(): # Logs into Homenet FTP server and downloads inventory file print('Getting CSV file from Homenet feed...') #autouplinkFilePath = 'spencertichenor.com/home/sjtichenor/public_ftp/incoming/RosevilleMidwayFord' + YEAR + MO + DAY ftp = ftplib.FTP('spencertichenor.com') ftp.login(user='<EMAIL>', passwd='<PASSWORD>') homenetFileName = 'homenet_feed.csv' localFilePath = 'data/local_homenet_file.csv' localFile = open(localFilePath, 'wb') ftp.retrbinary('RETR ' + homenetFileName, localFile.write, 1024) print('CSV file from Homenet feed saved at: data/local_homenet_file.csv') ftp.quit() localFile.close() def update_incoming_homenet_table(): # Gets data from local_homenet_file.csv then updates homenetInventory and masterInventory tables conn = sqlite3.connect('data/inventory.db') c = conn.cursor() print('Updating homenetInventory table with data sent from Homenet FTP feed...') with open('data/local_homenet_file.csv', 'r') as homenetFile: # csv.DictReader uses first line in file for column headings by default dr = csv.DictReader(homenetFile) # comma is default delimiter to_db = [] homenetVinList = [] ## Clean out weird characters valid_chars = string.ascii_letters + string.digits + ' ' + ':' + '-' + ',' + '&' + '$' + '/' + '.' + '_' + '!' for i in dr: for key in i.keys(): s = i[key] clean = ''.join(c for c in s if c in valid_chars) i[key] = clean #print(key + ': ' + i[key]) #print('\n' + 50*'*' + '\n') to_db.append(( i['VIN'], i['Stock'], i['Type'], i['Year'], i['Make'], i['Model'], i['Trim'], i['Body'], i['MSRP'], i['SellingPrice'], i['InternetPrice'], i['Invoice'], i['BookValue'], i['Certified'], i['ModelNumber'], i['Doors'], i['ExteriorColor'], i['InteriorColor'], i['EngineCylinders'], i['EngineDisplacement'], i['Transmission'], i['Miles'], i['DateInStock'], i['Description'], i['Options'], i['Categorized Options'], i['ImageList'], i['Style Description'], i['Drive type'], i['Wheelbase Code'], i['Engine Description'], i['Market Class'], i['Factory_Codes'] )) homenetVinList.append(i['VIN']) #used later to delete vehicles that aren't in stock anymore, index of 0 because it is a tuple query = (""" INSERT OR REPLACE INTO homenetInventory (vin, stock, invType, year, make, model, vehTrim, cabStyle, intMSRP, intPrice, intInternetPrice, intInvoice, intGeneralLedger, cpo, modelNumber, doors, exteriorColor, interiorColor, engineCylinders, engineDisplacement, transmission, miles, dateInStock, description, options, optionsCategorized, imageUrls, style, drive, wheelbase, engine, marketClass, factCodes) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) """) c.executemany(query, to_db) #c.executemany("INSERT OR REPLACE INTO masterInventory (vin, stock, invType, year, make, model, vehTrim, cabStyle, intMSRP, intPrice, intInternetPrice, intInvoice, intGeneralLedger, cpo, modelNumber, doors, exteriorColor, interiorColor, engineCylinders, engineDisplacement, transmission, miles, dateInStock, description, options, optionsCategorized, imageUrls, style, drive, wheelbase, engine, marketClass, factCodes) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", to_db) # that was redundent i think ^^ # Delete vehicles that aren't in stock anymore from Homenet table currentVinList = [] c.execute('SELECT vin FROM homenetInventory') tupleVinList = c.fetchall() for tupleVin in tupleVinList: # Convert tuples to strings in order to compare later vin = tupleVin[0] currentVinList.append(vin) for vin in currentVinList: if vin not in homenetVinList: c.execute('DELETE FROM homenetInventory WHERE vin = ?', (vin,)) print('Deleted VIN ' + vin + ' from Homenet Inventory Table.') conn.commit() print('Finished updating homenetInventory table.\n') # Update masterInventory table print('Updating masterInventory table with data from homenetInventory table...') query = 'INSERT OR REPLACE INTO masterInventory (vin, stock, invType, year, make, model, vehTrim, cabStyle, intMSRP, intPrice, intInternetPrice, intInvoice, intGeneralLedger, cpo, modelNumber, doors, exteriorColor, interiorColor, engineCylinders, engineDisplacement, transmission, miles, dateInStock, description, options, optionsCategorized, imageUrls, style, drive, wheelbase, engine, marketClass, factCodes) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)' c.executemany(query, to_db) c.execute('SELECT vin from masterInventory') masterVinTupleList = c.fetchall() for vinTuple in masterVinTupleList: vin = vinTuple[0] if vin not in homenetVinList: c.execute('DELETE FROM masterInventory WHERE vin = ?', (vin,)) print('Deleted VIN ' + vin + ' from Master Inventory Table.') conn.commit() conn.close() def updateMasterTable() : conn = sqlite3.connect('data/inventory.db') c = conn.cursor() c.execute('SELECT * FROM homenetInventory') vehTupleList = c.fetchall() to_db = vehTupleList print(to_db) print(len(to_db)) for i in to_db: print(i) print(len(i)) c.executemany("INSERT OR REPLACE INTO masterInventory (vin, stock, invType, year, make, model, vehTrim, bodyStyle, intMSRP, intPrice, intInternetPrice, intInvoice, intGeneralLedger, cpo, modelNumber, doors, exteriorColor, interiorColor, engineCylinders, engineDisplacement, transmission, miles, dateinStock, description, options, optionsCategorized, imageUrls) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?);", to_db) conn.commit() # Delete vehicles that are no longer in stock from masterInventory homenetVinList = [] c.execute('SELECT vin from homenetInventory') homenetVinTupleList = c.fetchall() for homenetVinTuple in homenetVinTupleList : homenetVin = homenetVinTuple[0] homenetVinList.append(homenetVin) c.execute('SELECT vin from masterInventory') masterVinTupleList = c.fetchall() for vinTuple in masterVinTupleList : vin = vinTuple[0] if vin not in homenetVinList : c.execute('DELETE FROM masterInventory WHERE vin = ?', (vin,)) print('Deleted VIN ' + vin + ' from Master Inventory Table.') conn.commit() conn.close() def removeOldVins(table): #DOES NOT WORK removes VINs that are no longer in masterInventory from supplied table conn = sqlite3.connect('data/inventory.db') c = conn.cursor() masterVinList = [] c.execute('SELECT vin FROM masterInventory') masterVinTupleList = c.fetchall() for masterVinTuple in masterVinTupleList : vin = masterVinTuple[0] masterVinList.append(vin) c.execute('SELECT vin FROM ?', (table,)) rebateVinTupleList = c.fetchall() for rebateVinTuple in rebateVinTupleList : vin = rebateVinTuple[0] if vin not in masterVinList : c.execute('DELETE FROM rebateInfo WHERE vin = ?', (vin,)) print('\t' + vin + ' deleted from rebateInfo table.') conn.commit() conn.close() def compute_highlights(): # Gets masterInventory 'options' field for each veh then finds highlights then adds them to highlights column separated by commas conn = sqlite3.connect('data/inventory.db') c = conn.cursor() c.execute('SELECT vin, options, year, invType, description, cpo, engine, drive, stock, make, model, marketClass FROM masterInventory') optionsTupleList = c.fetchall() for optionsTuple in optionsTupleList: highlightList = [] highlightStr = '' vin = optionsTuple[0] options = optionsTuple[1].lower() year = optionsTuple[2] invType = optionsTuple[3] description = optionsTuple[4].lower() cpo = optionsTuple[5] engine = optionsTuple[6] drive = optionsTuple[7] stock = optionsTuple[8] make = optionsTuple[9] model = optionsTuple[10] marketClass = optionsTuple[11] # Get coolest options if cpo == 'True': highlightList.append('Certified Pre-Owned') highlightList.append('100,000-Mile Warranty') #if year == 2017 and invType == 'New' : #highlightList.append('Apple CarPlay') #highlightList.append('Android Auto') # Highlight Idicators - List of dictionaries where the key is the highlight name and the value is a list of indicator phrases indicatorList = [ {'One-Owner': ['one owner', 'one-owner']}, {'Low Miles': ['low mile']}, {'Remote Start': ['remote start', 'remote engine start', 'remote auto start']}, {'Technology Package': ['technology package', 'technology pkg']}, {'Cold Weather Package': ['cold weather package']}, {'Appearance Package': ['appearance package']}, {'Moonroof': ['vista roof', 'moonroof', 'glass roof', 'panoramic roof']}, {'Rear Camera': ['rear view camera', 'back-up camera', 'rear-view camera']}, {'Rear Camera w/ Hitch Assist': ['rear view camera w/dynamic hitch assist']}, {'Heated Seats': ['heated leather', 'heated front seats', 'heated bucket']}, {'Heated/Cooled Seats': ['heated & cooled', 'heated and cooled', 'heated/cooled']}, {'Heated Steering Wheel': ['heated steering wheel']}, {'Heated Mirrors': ['heated mirrors']}, {'Tow Package': ['tow package', 'Towing', 'Trailer Hitch']}, {'Trailer Brake Controller': ['trailer brake controller']}, {'Premium Audio System': ['premium audio system', 'premium 9 speaker']}, {'Leather Interior': ['leather seats', 'leather-trimmed', 'leather trimmed']}, {'Bluetooth': ['bluetooth']}, {'USB Connectivity': ['usb']}, {'Apple CarPlay': ['apple carplay']}, {'Android Auto': ['android auto']}, {'Snow Plow Package': ['snow plow package']}, {'Lane-Keeping System': ['lane-keeping system']}, {'Rain-Sensing Wipers': ['rain-sensing wipers']}, {'Park Assist System': ['park assist system']}, {'Sirius': ['sirius', 'satellite radio']}, {'Power Liftgate': ['pwr liftgate', 'power liftgate']}, {'Remote Tailgate': ['remote tailgate']}, {'Push Button Start': ['push button start']}, {'Navigation': ['navigation']}, {'Bedliner': ['bedliner']}, {'Extended Range Fuel Tank': ['extended range']}, {'2nd Row Bucket Seats': ['2nd row bucket seats']}, {'3rd Row Seat': ['3rd row seat', '3rd seat']}, {'Touchscreen': ['touchscreen', 'touch-screen', 'myford touch', 'sync 3']}, {'Keyless Entry': ['keyless', 'keypad entry']}, {'Cruise Control': ['cruise control']}, {'Auto Start-Stop Technology': ['auto start-stop technology']}, {'LED Box Lighting': ['led box lighting']}, ] for i in indicatorList: highlight = list(i.keys())[0] phraseList = list(i.values())[0] for phrase in phraseList: if phrase in options or phrase in description: highlightList.append(highlight) break highlightList.append(engine) highlightList.append(drive) # Remove redundant highlights redundantList = [ ['Heated Seats', 'Heated/Cooled Seats'], ['Rear Camera', 'Rear Camera w/ Hitch Assist'], ['USB Connectivity', 'Bluetooth'], ['Bluetooth', 'Apple CarPlay'], ['Tow Package', 'Trailer Brake Controller'] ] for i in redundantList: if i[0] in highlightList and i[1] in highlightList: highlightList.remove(i[0]) for highlight in highlightList: highlightStr += highlight + ',' if len(highlightStr) > 0: # Get rid of unnecessary comma on end of string highlightStr = highlightStr[:-1] # Set Body Style (not really a highlight) - Had to switch to ghetto version below because vans were getting marked as cars because iterating throguh dict is not ordered # indicatorDict = { # 'Car': ['Car'], # 'Truck': ['Truck'], # 'Van': ['Van', 'van'], # 'SUV': ['Sport Utility Vehicles'] # } # bodyStyles = indicatorDict.keys() # for bodyStyle in bodyStyles : # for indicator in indicatorDict[bodyStyle] : # if indicator in marketClass : # style = bodyStyle if 'Car' in marketClass: # has to come first so cargo van gets listed as Van style = 'Car' if 'Truck' in marketClass: style = 'Truck' if 'Van' in marketClass or 'van' in marketClass : style = 'Van' if 'Sport Utility Vehicles' in marketClass : style = 'SUV' # Clean up Model model = model.replace(' Commercial Cutaway', '').replace(' Sport Fleet', '').replace(' Cutaway', '') # Clean up Engine engine = engine.replace(' L', 'L') print('Vehicle: ' + stock + ' ' + make + ' ' + model) print('Highlights:', highlightList) print('BodyStyle:', style) print('\n') # Set Status to In Stock status = 'In Stock' # Update database c.execute('UPDATE masterInventory SET highlights = ?, bodyStyle = ?, model = ?, engine = ?, status = ? WHERE vin = ?', (highlightStr, style, model, engine, status, vin,)) conn.commit() conn.close() def calculate_pricing(): print('Calculating max discount for each vehicle...\n') conn = sqlite3.connect('data/inventory.db') c = conn.cursor() # Set dealer discount and total discount query = ('SELECT vin, intMSRP, intInternetPrice, intTotalRebates, totalConditionalRebates ' 'FROM masterInventory ' 'WHERE invType = "New" AND intMSRP != 0') c.execute(query) results = c.fetchall() for r in results: print('r:', r) vin = r[0] msrp = r[1] price_before_rebates = r[2] unconditional_rebates = r[3] conditional_rebates = r[4] dealer_discount = msrp - price_before_rebates if unconditional_rebates: best_discount = dealer_discount + unconditional_rebates + conditional_rebates else: best_discount = dealer_discount # Print results print('\t\tVIN:', vin) print('\t\tMSRP:', msrp) print('\t\tPrice before rebates:', price_before_rebates) print('\t\tDealer Discount:', dealer_discount) print('\t\tUnconditional Rebates:', unconditional_rebates) print('\t\tConditional Rebates:', conditional_rebates) print('\t\tBest Discount:', best_discount, '\n\n') # Update database query = 'UPDATE masterInventory SET intTotalDiscount = ? WHERE vin = ?' to_db = (best_discount, vin) c.execute(query, to_db) conn.commit() conn.close() print('Finished calculating max discount for each vehicle.\n') def create_outgoing_homenet_table(): conn = sqlite3.connect('data/inventory.db') c = conn.cursor() query = (""" CREATE TABLE IF NOT EXISTS outgoingHomenet (VIN TEXT UNIQUE, comment1 TEXT, misc_price1 INTEGER, comment2 TEXT, misc_price2 INTEGER, comment3 TEXT, misc_price3 INTEGER, comment5 TEXT) """) c.execute(query) conn.commit() conn.close() def update_outgoing_homenet_table(): conn = sqlite3.connect('data/inventory.db') c = conn.cursor() c.execute('DELETE FROM outgoingHomenet') to_db = [] c.execute('SELECT vin, highlights, intTotalRebates, totalConditionalRebates FROM masterInventory') results = c.fetchall() for r in results: vin = r[0] highlights = r[1] unconditional_rebates = r[2] conditional_rebates = r[3] if not unconditional_rebates: unconditional_rebates = 0 if not conditional_rebates: conditional_rebates = 0 to_db.append((vin, highlights, 0, None, unconditional_rebates, None, conditional_rebates, '')) print('\n\nVIN:', vin) print('Highlights:', highlights) print('Unconditional Rebates:', unconditional_rebates) print('Conditional Rebates:', conditional_rebates) query = (""" INSERT OR REPLACE INTO outgoingHomenet (vin, comment1, misc_price1, comment2, misc_price2, comment3, misc_price3, comment5) VALUES (?, ?, ?, ?, ?, ?, ?, ?) """) c.executemany(query, to_db) conn.commit() conn.close() def update_outgoing_homenet_file(): conn = sqlite3.connect('data/inventory.db') c = conn.cursor() c.execute('SELECT vin, comment1, misc_price1, comment2, misc_price2, comment3, misc_price3, comment5 FROM outgoingHomenet') with open('data/homenet-incentive-feed.csv', 'w') as csv_file: csv_writer = csv.writer(csv_file, dialect='excel') csv_writer.writerow([i[0] for i in c.description]) # write headers csv_writer.writerows(c) conn.commit() conn.close() def upload_outgoing_homenet_file(): print('\nUploading inventory to FTP server for Homenet...') file_path = 'data/homenet-incentive-feed.csv' file_name = file_path.split('/') file_name = file_name[-1] print('Uploading ' + file_name + ' to FTP server...\n') file = open(file_path, 'rb') ftp = ftplib.FTP('iol.homenetinc.com') ftp.login('hndatafeed', 'gx8m6') ftp.storbinary('STOR ' + file_name, file, 1024) file.close() ftp.quit() print('Successfully uploaded ' + file_name + ' to homenet folder on FTP server.\n') def send_feeds_from_homenet(): print('Navigating to Homenet.com and send out feeds to cars.com, cargurus, etc..') # Fire up ChromeDriver browser = start_chromedriver() wait = WebDriverWait(browser, 10) # Log into Homenet #url = 'https://www.homenetiol.com/marketplace/overview' url = 'https://www.homenetiol.com/login?RedirectUrl=%2fmarketplace%2foverview' browser.get(url) username = browser.find_element_by_xpath('//input[@class="username text-value"]') password = browser.find_element_by_xpath('//input[@class="password text-value"]') username.send_keys('<EMAIL>') password.send_keys('<PASSWORD>') wait.until(EC.element_to_be_clickable((By.XPATH, '//a[@class="login-action button"]'))).click() wait.until(EC.element_to_be_clickable((By.XPATH, '//a[@class="run-all-button button"]'))).click() time.sleep(10) print('Finished sending out feeds.') def vacuum_db(): conn = sqlite3.connect('data/inventory.db') c = conn.cursor() c.execute("VACUUM") conn.close() def figureManagerSpecials(): # Open connection to database conn = sqlite3.connect('data/inventory.db') c = conn.cursor() url = 'http://www.rosevillemidwayford.com/new-car-sales-roseville-mn' page = requests.get(url) tree = html.fromstring(page.content) stockResults = tree.xpath('//span[contains(@class, "spec-value-stocknumber")]/text()') specialStockList = [] for specialStock in stockResults : specialStock = specialStock.replace('#', '') specialStockList.append(specialStock) print(specialStockList) c.execute('SELECT stock FROM masterInventory') results = c.fetchall() for r in results: stock = r[0] if stock in specialStockList : print('looks like stock #' + stock + ' is a special!') query = 'UPDATE masterInventory SET managerSpecial = ? WHERE stock = ?' to_db = ('True', stock) c.execute(query, to_db) else : print('looks like stock #' + stock + ' is NOT a special!') query = 'UPDATE masterInventory SET managerSpecial = ? WHERE stock = ?' to_db = ('False', stock) c.execute(query, to_db) conn.commit() conn.close() def figureLeaseSpecials(): # Open connection to database conn = sqlite3.connect('data/inventory.db') c = conn.cursor() lease_specials = [] c.execute('SELECT DISTINCT year FROM masterInventory') year_results = c.fetchall() for y in year_results: year = y[0] # print(year) query = 'SELECT DISTINCT model FROM masterInventory WHERE year = ? AND leasePayment != ?' to_db = (year, '') c.execute(query, to_db) model_results = c.fetchall() for m in model_results: model = m[0] query = 'SELECT min(leasePayment) FROM masterInventory WHERE year = ? AND model = ?' to_db = (year, model) c.execute(query, to_db) payment_results = c.fetchall() min_payment = payment_results[0][0] query = 'SELECT vin, stock, vehTrim, intMSRP, intPrice, leaseRebateExpiration FROM masterInventory WHERE year = ? AND model = ? AND leasePayment = ?' to_db = (year, model, minPayment) c.execute(query, to_db) veh_results = c.fetchall() v = veh_results[0] # Just get first vehicle even if there are many print(v) vin = v[0] stock = v[1] vehTrim = v[2] msrp = v[3] price = v[4] term = 36 residual = v[5] downPayment = v[6] totalLeaseRebates = v[7] dueAtSigning = v[8] expiration = v[9] # Get data from masterInventory table for rest of required info c.execute('SELECT bodyStyle, imageUrls, imageUrlsHD, vdp_url, drive FROM masterInventory WHERE vin = ?', (vin,)) # add option codes to this later master_results = c.fetchall() if not master_results: continue bodyStyle = master_results[0][0] # just getting that matched the else query, could maybe hone this to get one with pic later?? imageUrls = master_results[0][1] imageUrlsHD = master_results[0][2] vdp = master_results[0][3] drive = master_results[0][4] # option_codes = masterResults[0][4] # Set image to HD version if available if imageUrlsHD: imageUrl = imageUrlsHD elif imageUrls: imageUrl = imageUrls.split(',')[0] else: continue minPayment = locale.currency(minPayment, grouping=True).replace('.00', '') #downPayment = locale.currency(downPayment, grouping=True).replace('.00', '') #dueAtSigning = locale.currency(dueAtSigning, grouping=True).replace('.00', '') msrp = locale.currency(msrp, grouping=True).replace('.00', '') price = locale.currency(price, grouping=True).replace('.00', '') # offer = '<p>' + minPayment + '/month with ' + downPayment + ' down payment.<br><br>Just ' + dueAtSigning + ' due at signing.<br><br>Based on MSRP of ' + msrp + '.</p>' # title = minPayment + '/month with {} down.'.format(downPayment) # description = 'Lease term of {} months. Based on MSRP of {} and selling price of {}. Requires {} due at signing.'.format(term, msrp, price, dueAtSigning) # disclaimer = 'Must take new retail delivery from dealer stock by {}. Requires {} due at signing. Based on MSRP of {} and selling price of {}. See Subject to credit approval. Assumes 10,500 miles/year and Tier 0-1 credit. Tax, title, and license not included. Some restrictions apply. See sales representative for details.'.format(expiration, minPayment, msrp, price) lease_specials.append({ 'vin': vin, 'stock': stock, 'year': year, 'model': model, 'vehTrim': vehTrim, # 'title': title, # 'description': description, 'expiration': expiration, 'monthlyPayment': minPayment, 'dueAtSigning': dueAtSigning, 'vdp': vdp, 'imageUrl': imageUrl, 'bodyStyle': bodyStyle, 'msrp': msrp, 'price': price, # 'disclaimer': disclaimer, 'drive': drive, # 'option_codes': option_codes }) print('\nFresh Specials:') for s in lease_specials: print('\n') # print('\n\n', s, '\n') for k in s.keys(): print(k + ': ' + str(s[k])) print('\n\n') # Close connection to database conn.close() return lease_specials def wait_for_next_run(minutes_to_wait): print('Finished running program. Waiting 30 minutes to rerun.') minutes_to_wait = int(minutes_to_wait) for i in range(minutes_to_wait, 1, -1): time.sleep(60) print('Waiting {} minutes until next run.'.format(i)) def main(): while True: get_incoming_homenet_file() update_incoming_homenet_table() scrape.scrape_cdk() calculate_pricing() compute_highlights() create_outgoing_homenet_table() update_outgoing_homenet_table() update_outgoing_homenet_file() upload_outgoing_homenet_file() send_feeds_from_homenet() sales_specials.main() midwords.main() hd_images.main() facebook.main() #adwords_feeds.main() sheets.main() # maybe add something to check if any dealer discounts are negative then re run (and if model isnt raptor) vacuum_db() wait_for_next_run(30) if __name__ == '__main__': main()
2.421875
2
monolithe/generators/sdkgenerator.py
edwinfeener/monolithe
18
9695
# -*- coding: utf-8 -*- # # Copyright (c) 2015, Alcatel-Lucent Inc # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * 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. # * 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. from __future__ import unicode_literals import os import shutil from monolithe.lib import Printer from monolithe.generators.lib import Generator from monolithe.generators.managers import MainManager, CLIManager, VanillaManager from .sdkapiversiongenerator import SDKAPIVersionGenerator class SDKGenerator(Generator): def cleanup(self): output = self.config.get_option("output", "transformer") language = self.config.language overrides_path = "%s/%s/__overrides" % (output, language) if os.path.exists(overrides_path): shutil.rmtree(overrides_path) attrs_defaults_path = "%s/%s/__attributes_defaults" % (output, language) if os.path.exists(attrs_defaults_path): shutil.rmtree(attrs_defaults_path) code_header_path = "%s/%s/__code_header" % (output, language) if os.path.exists(code_header_path): os.remove(code_header_path) def generate(self, specification_info): user_vanilla = self.config.get_option("user_vanilla", "transformer") output = self.config.get_option("output", "transformer") name = self.config.get_option("name", "transformer") lang = self.config.language if not os.path.exists(os.path.join(output, lang)): os.makedirs(os.path.join(output, lang)) vanilla_manager = VanillaManager(monolithe_config=self.config) vanilla_manager.execute(output_path="%s/%s" % (output, lang)) self.install_user_vanilla(user_vanilla_path=user_vanilla, output_path="%s/%s" % (output, lang)) version_generator = SDKAPIVersionGenerator(self.config) apiversions = [] for info in specification_info: Printer.log("transforming specifications into %s for version %s..." % (lang, info["api"]["version"])) apiversions.append(info["api"]["version"]) version_generator.generate(specification_info=specification_info) Printer.log("assembling...") manager = MainManager(monolithe_config=self.config) manager.execute(apiversions=apiversions) cli_manager = CLIManager(monolithe_config=self.config) cli_manager.execute() self.cleanup() Printer.success("%s generation complete and available in \"%s/%s\"" % (name, output, self.config.language))
1.234375
1
rllab-taewoo/rllab/plotter/plotter.py
kyuhoJeong11/GrewRL
0
9696
import atexit import sys if sys.version_info[0] == 2: from Queue import Empty else: from queue import Empty from multiprocessing import Process, Queue from rllab.sampler.utils import rollout import numpy as np __all__ = [ 'init_worker', 'init_plot', 'update_plot' ] process = None queue = None def _worker_start(): env = None policy = None max_length = None try: while True: msgs = {} # Only fetch the last message of each type while True: try: msg = queue.get_nowait() msgs[msg[0]] = msg[1:] except Empty: break if 'stop' in msgs: break elif 'update' in msgs: env, policy = msgs['update'] # env.start_viewer() elif 'demo' in msgs: param_values, max_length = msgs['demo'] policy.set_param_values(param_values) rollout(env, policy, max_path_length=max_length, animated=True, speedup=5) else: if max_length: rollout(env, policy, max_path_length=max_length, animated=True, speedup=5) except KeyboardInterrupt: pass def _shutdown_worker(): if process: queue.put(['stop']) queue.close() process.join() def init_worker(): print("####################init_worker") global process, queue queue = Queue() process = Process(target=_worker_start) process.start() atexit.register(_shutdown_worker) def init_plot(env, policy): queue.put(['update', env, policy]) def update_plot(policy, max_length=np.inf): queue.put(['demo', policy.get_param_values(), max_length])
2.390625
2
OpenCV/bookIntroCV_008_binarizacao.py
fotavio16/PycharmProjects
0
9697
<gh_stars>0 ''' Livro-Introdução-a-Visão-Computacional-com-Python-e-OpenCV-3 Repositório de imagens https://github.com/opencv/opencv/tree/master/samples/data ''' import cv2 import numpy as np from matplotlib import pyplot as plt #import mahotas VERMELHO = (0, 0, 255) VERDE = (0, 255, 0) AZUL = (255, 0, 0) AMARELO = (0, 255, 255) BRANCO = (255,255,255) CIANO = (255, 255, 0) PRETO = (0, 0, 0) img = cv2.imread('ponte2.jpg') # Flag 1 = Color, 0 = Gray, -1 = Unchanged img = img[::2,::2] # Diminui a imagem #Binarização com limiar img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) suave = cv2.GaussianBlur(img, (7, 7), 0) # aplica blur (T, bin) = cv2.threshold(suave, 160, 255, cv2.THRESH_BINARY) (T, binI) = cv2.threshold(suave, 160, 255, cv2.THRESH_BINARY_INV) ''' resultado = np.vstack([ np.hstack([suave, bin]), np.hstack([binI, cv2.bitwise_and(img, img, mask = binI)]) ]) ''' resultado = np.vstack([ np.hstack([img, suave]), np.hstack([bin, binI]) ]) cv2.imshow("Binarização da imagem", resultado) cv2.waitKey(0) #Threshold adaptativo bin1 = cv2.adaptiveThreshold(suave, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 21, 5) bin2 = cv2.adaptiveThreshold(suave, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 21, 5) resultado = np.vstack([ np.hstack([img, suave]), np.hstack([bin1, bin2]) ]) cv2.imshow("Binarização adaptativa da imagem", resultado) cv2.waitKey(0) #Threshold com Otsu e Riddler-Calvard ''' T = mahotas.thresholding.otsu(suave) temp = img.copy() temp[temp > T] = 255 temp[temp < 255] = 0 temp = cv2.bitwise_not(temp) T = mahotas.thresholding.rc(suave) temp2 = img.copy() temp2[temp2 > T] = 255 temp2[temp2 < 255] = 0 temp2 = cv2.bitwise_not(temp2) resultado = np.vstack([ np.hstack([img, suave]), np.hstack([temp, temp2]) ]) cv2.imshow("Binarização com método Otsu e Riddler-Calvard", resultado) cv2.waitKey(0) '''
2.703125
3
djangito/backends.py
mechanicbuddy/djangito
0
9698
<reponame>mechanicbuddy/djangito import base64 import json import jwt import requests from django.conf import settings from django.contrib.auth import get_user_model from django.contrib.auth.backends import ModelBackend USER_MODEL = get_user_model() class ALBAuth(ModelBackend): def authenticate(self, request, **kwargs): if request: self.encoded_jwt = request.META.get('HTTP_X_AMZN_OIDC_DATA') if self.encoded_jwt: self.payload = self.decode_alb_jwt() return self.get_or_create_for_alb() def decode_alb_jwt(self): # Step 1: Get the key id from JWT headers (the kid field) jwt_headers = self.encoded_jwt.split('.')[0] decoded_jwt_headers = base64.b64decode(jwt_headers) decoded_jwt_headers = decoded_jwt_headers.decode("utf-8") decoded_json = json.loads(decoded_jwt_headers) kid = decoded_json['kid'] # Step 2: Get the public key from regional endpoint url = f'https://public-keys.auth.elb.us-east-1.amazonaws.com/{kid}' req = requests.get(url) pub_key = req.text # Step 3: Get the payload return jwt.decode( self.encoded_jwt, pub_key, algorithms=['ES256'] ) def get_or_create_for_alb(self): user_info = {'username': self.payload['sub'][:150]} if 'given_name' in self.payload: user_info['first_name'] = self.payload['given_name'][:30] elif 'name' in self.payload: user_info['first_name'] = self.payload['name'][:30] if 'family_name' in self.payload: user_info['last_name'] = self.payload['family_name'][:30] self.user, created = USER_MODEL.objects.get_or_create( email=self.payload['email'], defaults=user_info ) if created: self.setup_user_profile() return self.user def setup_user_profile(self): pass
2.375
2
data_profiler/labelers/regex_model.py
gme5078/data-profiler
0
9699
<reponame>gme5078/data-profiler<gh_stars>0 import json import os import sys import re import copy import numpy as np from data_profiler.labelers.base_model import BaseModel from data_profiler.labelers.base_model import AutoSubRegistrationMeta _file_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.append(_file_dir) class RegexModel(BaseModel, metaclass=AutoSubRegistrationMeta): def __init__(self, label_mapping=None, parameters=None): """ Regex Model Initializer. Example regex_patterns: regex_patterns = { "LABEL_1": [ "LABEL_1_pattern_1", "LABEL_1_pattern_2", ... ], "LABEL_2": [ "LABEL_2_pattern_1", "LABEL_2_pattern_2", ... ], ... } Example encapsulators: encapsulators = { 'start': r'(?<![\w.\$\%\-])', 'end': r'(?:(?=(\b|[ ]))|(?=[^\w\%\$]([^\w]|$))|$)', } :param label_mapping: maps labels to their encoded integers :type label_mapping: dict :param parameters: Contains all the appropriate parameters for the model. Possible parameters are: max_length, max_num_chars, dim_embed :type parameters: dict :return: None """ # parameter initialization if not parameters: parameters = {} parameters.setdefault('regex_patterns', {}) parameters.setdefault('encapsulators', {'start': '', 'end': ''}) parameters.setdefault('ignore_case', True) parameters.setdefault('default_label', 'BACKGROUND') self._epoch_id = 0 # initialize class self.set_label_mapping(label_mapping) self._validate_parameters(parameters) self._parameters = parameters def _validate_parameters(self, parameters): """ Validate the parameters sent in. Raise error if invalid parameters are present. :param parameters: parameter dict containing the following parameters: regex_patterns: patterns associated with each label_mapping Example regex_patterns: regex_patterns = { "LABEL_1": [ "LABEL_1_pattern_1", "LABEL_1_pattern_2", ... ], "LABEL_2": [ "LABEL_2_pattern_1", "LABEL_2_pattern_2", ... ], ... } encapsulators: regex to add to start and end of each regex (used to capture entities inside of text). Example encapsulators: encapsulators = { 'start': r'(?<![\w.\$\%\-])', 'end': r'(?:(?=(\b|[ ]))|(?=[^\w\%\$]([^\w]|$))|$)', } ignore_case: whether or not to set the regex ignore case flag default_label: default label to assign when no regex found :type parameters: dict :return: None """ _retype = type(re.compile('pattern for py 3.6 & 3.7')) errors = [] list_of_necessary_params = ['encapsulators', 'regex_patterns', 'ignore_case', 'default_label'] # Make sure the necessary parameters are present and valid. for param in parameters: value = parameters[param] if param == 'encapsulators' and ( not isinstance(value, dict) or 'start' not in value or 'end' not in value): errors.append( "`{}` must be a dict with keys 'start' and 'end'".format( param )) elif param == 'regex_patterns': if not isinstance(value, dict): errors.append('`{}` must be a dict of regex pattern lists.'. format(param)) continue for key in value: if key not in self.label_mapping: errors.append( "`{}` was a regex pattern not found in the " "label_mapping".format(key)) elif not isinstance(value[key], list): errors.append( "`{}` must be a list of regex patterns, i.e." "[pattern_1, pattern_2, ...]".format(key)) else: for i in range(len(value[key])): if not isinstance(value[key][i], (_retype, str)): errors.append( "`{}`, pattern `{}' was not a valid regex " "pattern (re.Pattern, str)".format(key, i)) elif isinstance(value[key][i], str): try: re.compile(value[key][i]) except re.error as e: errors.append( "`{}`, pattern {} was not a valid regex" " pattern: {}".format(key, i, str(e))) elif param == 'ignore_case' \ and not isinstance(parameters[param], bool): errors.append("`{}` must be a bool.".format(param)) elif param == 'default_label' \ and not isinstance(parameters[param], str): errors.append("`{}` must be a string.".format(param)) elif param not in list_of_necessary_params: errors.append("`{}` is not an accepted parameter.".format( param)) if errors: raise ValueError('\n'.join(errors)) def _construct_model(self): pass def _reconstruct_model(self): pass def _need_to_reconstruct_model(self): pass def reset_weights(self): pass def predict(self, data, batch_size=None, show_confidences=False, verbose=True): """ Applies the regex patterns (within regex_model) to the input_string, create predictions for all matching patterns. Each pattern has an associated entity and the predictions of each character within the string are given a True or False identification for each entity. All characters not identified by ANY of the regex patterns in the pattern_dict are considered background characters, and are replaced with the default_label value. :param data: list of strings to predict upon :type data: iterator :param batch_size: does not impact this model and should be fixed to not be required. :type batch_size: N/A :param show_confidences: whether user wants prediction confidences :type show_confidences: :param verbose: Flag to determine whether to print status or not :type verbose: bool :return: char level predictions and confidences :rtype: dict """ start_pattern = '' end_pattern = '' regex_patterns = self._parameters['regex_patterns'] default_ind = self.label_mapping[self._parameters['default_label']] encapsulators = self._parameters['encapsulators'] re_flags = re.IGNORECASE if self._parameters['ignore_case'] else 0 if encapsulators: start_pattern = encapsulators['start'] end_pattern = encapsulators['end'] pre_compiled_patterns = copy.deepcopy(regex_patterns) for entity_label, entity_patterns in pre_compiled_patterns.items(): for i in range(len(entity_patterns)): pattern = (start_pattern + pre_compiled_patterns[entity_label][i] + end_pattern) pre_compiled_patterns[entity_label][i] = re.compile( pattern, flags=re_flags) # Construct array initial regex predictions where background is # predicted. predictions = [np.empty((0,))] * 100 i = 0 for i, input_string in enumerate(data): # Double array size if len(predictions) <= i: predictions.extend([np.empty((0,))] * len(predictions)) pred = np.zeros((len(input_string), self.num_labels), dtype=int) pred[:, default_ind] = 1 for entity_label, entity_patterns in pre_compiled_patterns.items(): entity_id = self.label_mapping[entity_label] for re_pattern in entity_patterns: for each_find in re_pattern.finditer(input_string): indices = each_find.span(0) pred[indices[0]:indices[1], default_ind] = 0 pred[indices[0]:indices[1], entity_id] = 1 if verbose: sys.stdout.flush() sys.stdout.write( "\rData Samples Processed: {:d} ".format(i)) predictions[i] = pred if verbose: print() # Trim array size to number of samples if len(predictions) > i+1: del predictions[i+1:] if show_confidences: conf = copy.deepcopy(predictions) for i in range(len(conf)): conf[i] = conf[i] / \ np.linalg.norm(conf[i], axis=1, ord=1, keepdims=True) return {"pred": predictions, 'conf': conf} return {"pred": predictions} @classmethod def load_from_disk(cls, dirpath): """ Loads whole model from disk with weights :param dirpath: directory path where you want to load the model from :type dirpath: str :return: None """ # load parameters model_param_dirpath = os.path.join(dirpath, "model_parameters.json") with open(model_param_dirpath, 'r') as fp: parameters = json.load(fp) # load label_mapping labels_dirpath = os.path.join(dirpath, "label_mapping.json") with open(labels_dirpath, 'r') as fp: label_mapping = json.load(fp) loaded_model = cls(label_mapping, parameters) return loaded_model def save_to_disk(self, dirpath): """ Saves whole model to disk with weights. :param dirpath: directory path where you want to save the model to :type dirpath: str :return: None """ if not os.path.isdir(dirpath): os.makedirs(dirpath) model_param_dirpath = os.path.join(dirpath, "model_parameters.json") with open(model_param_dirpath, 'w') as fp: json.dump(self._parameters, fp) labels_dirpath = os.path.join(dirpath, "label_mapping.json") with open(labels_dirpath, 'w') as fp: json.dump(self.label_mapping, fp)
2.390625
2