code
stringlengths 22
1.05M
| apis
listlengths 1
3.31k
| extract_api
stringlengths 75
3.25M
|
|---|---|---|
""" Simple Example using coreali to access a register model. Needs no h^ardware"""
# Import dependencies and compile register model with systemrdl-compiler
from systemrdl import RDLCompiler
import coreali
import numpy as np
import os
from coreali import RegisterModel
rdlc = RDLCompiler()
rdlc.compile_file(os.path.dirname(__file__)+"/../systemrdl/logger.rdl")
root = rdlc.elaborate()
# Generate hierarchical register model
rio = coreali.registerio.RegIoNoHW(np.zeros([256], np.uint8()))
logger = RegisterModel(root, rio)
# Use the generated register model
logger.Ctrl.read()
logger.LogMem.write(0,[1,2,3])
logger.LogMem.read()
logger.LogMem[1].write(0,[11,12,13])
print(logger)
| [
"os.path.dirname",
"systemrdl.RDLCompiler",
"coreali.RegisterModel",
"numpy.uint8"
]
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import json
import requests
from enum import Enum
from typing import Dict
from ..exceptions import JsonDecodeError, UnexpectedResponse, RequestError, BaseApiRequestError
class MockRequesterMixin:
"""
Набор методов для моков реквестеров
"""
class ERRORS(Enum):
ERROR_TOKEN = 'error'
BAD_CODE_400_TOKEN = 'bad<PASSWORD>00'
BAD_CODE_401_TOKEN = '<PASSWORD>'
BAD_CODE_403_TOKEN = '<PASSWORD>'
BAD_CODE_404_TOKEN = '<PASSWORD>'
class ERRORS_KEYS(Enum):
AUTH = 'auth_error'
APP_AUTH = 'app_auth_error'
USERS = 'users_error'
AWARDS = 'awards_error'
PLACES = 'places_error'
STATS = 'stats_error'
MEDIA = 'media_error'
class ROLES(Enum):
ANON = 'anon'
USER = 'user'
MODERATOR = 'moderator'
SUPERUSER = 'superuser'
@classmethod
def get_all_roles_tuple(cls):
return tuple([x.value for x in cls.ROLES])
@classmethod
def get_all_registered_roles_tuple(cls):
all_roles = list(cls.get_all_roles_tuple())
all_roles.remove(cls.ROLES.ANON.value)
return tuple(all_roles)
@classmethod
def get_all_errors_tuple(cls):
return tuple([x.value for x in cls.ERRORS])
def get_token_dict(self, token: str) -> Dict[str, str]:
return json.loads(token)
def get_role_part(self, token: str) -> str:
return self.get_token_dict(token)['role']
def get_auth_error_part(self, token: str) -> str:
return self.get_token_dict(token)[self.ERRORS_KEYS.AUTH.value]
def get_app_auth_error_part(self, token: str) -> str:
return self.get_token_dict(token)[self.ERRORS_KEYS.APP_AUTH.value]
def get_awards_error_part(self, token: str) -> str:
return self.get_token_dict(token)[self.ERRORS_KEYS.AWARDS.value]
def get_places_error_part(self, token: str) -> str:
return self.get_token_dict(token)[self.ERRORS_KEYS.PLACES.value]
def get_users_error_part(self, token: str) -> str:
return self.get_token_dict(token)[self.ERRORS_KEYS.USERS.value]
def get_stats_error_part(self, token: str) -> str:
return self.get_token_dict(token)[self.ERRORS_KEYS.STATS.value]
def get_media_error_part(self, token: str) -> str:
return self.get_token_dict(token)[self.ERRORS_KEYS.MEDIA.value]
# Этот метод оверрайдить во всех классах-моках для выборки нужной ошибки из токена
def get_mine_error_part(self, token):
raise NotImplementedError
# Этот метод оверрайдить во всех классах-моках для отправки джосн-ответа
def get_object_on_success(self, token=None):
raise NotImplementedError
# Этот оверрайдить, если дсоны на GET/POST отличаются
def get_list_object_on_success(self, token=None):
return self.get_object_on_success(token)
def get_coded_response(self, code: int) -> requests.Response:
resp = requests.Response()
resp.status_code = code
return resp
def raise_coded_error(self, code: int):
resp = self.get_coded_response(code)
raise UnexpectedResponse(resp)
def _handle_errors(self, token):
"""
Обработка ошибок, переданных в с токеном
"""
token = self.get_mine_error_part(token)
if token == self.ERRORS.ERROR_TOKEN.value:
raise BaseApiRequestError()
elif token == self.ERRORS.BAD_CODE_400_TOKEN.value:
self.raise_coded_error(400)
elif token == self.ERRORS.BAD_CODE_401_TOKEN.value:
self.raise_coded_error(401)
elif token == self.ERRORS.BAD_CODE_403_TOKEN.value:
self.raise_coded_error(403)
elif token == self.ERRORS.BAD_CODE_404_TOKEN.value:
self.raise_coded_error(404)
def _mock_token_handler(self, token: str, list_object=False):
"""
Базовый метод обработки моковых токенов
"""
self._handle_errors(token)
if list_object:
return requests.Response(), self.get_list_object_on_success(token)
else:
return requests.Response(), self.get_object_on_success(token)
| [
"json.loads",
"requests.Response"
]
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from filelock import FileLock, Timeout
import os
import time
class ProcessFileLock(FileLock):
"""
FileLock that is unique per path in each process (for, eg., reentrance)
"""
locks = {}
def __new__(cls, path, *args, **kwargs):
if path in ProcessFileLock.locks:
return ProcessFileLock.locks[path]
else:
lock = super().__new__(cls, path, *args, **kwargs)
lock.__new_init__(path, *args, **kwargs)
ProcessFileLock.locks[path] = lock
return lock
def __new_init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def __init__(self, *args, **kwargs):
pass
class ExplosiveFileLock(ProcessFileLock):
def acquire(self, *args, **kwargs):
r = super().acquire(*args, **kwargs)
if self._lock_counter > 1:
raise BlockingIOError(f"Process attempted to reacquire lock for {self._lock_file}")
return r
class HistoriesLock(FileLock):
def __init__(self, dir, ensemble=None):
super().__init__(os.path.join(dir, "histories.lock"))
self.ensemble = ensemble
def release(self, **kwargs):
super().release()
if self.ensemble and self._lock_counter == 0:
self.ensemble.close_histories()
class SamplesLock(FileLock):
def __init__(self, dir, ensemble=None):
super().__init__(os.path.join(dir, "samples.lock"))
self.ensemble = ensemble
def release(self, **kwargs):
if self.ensemble and self._lock_counter == 1:
self.ensemble._test_samples.close()
self.ensemble._test_samples = None
super().release()
def __enter__(self):
print("Acquiring samples lock... ", end='')
super().__enter__()
if self.ensemble._test_samples is None:
from sample_hyperparameters import TrainableSampleGenerator
self.ensemble._test_samples = TrainableSampleGenerator(self.ensemble.config.exploration.sample_file, configs=self.ensemble.config_files, stub=self.ensemble.stub)
print("Done.")
return self.ensemble._test_samples
class ExistLock:
"""
Locks on the existence of the given file.
No guarantees of atomicity!
Unique per process, for reentry
"""
locks={}
def __new__(cls, path, *args, **kwargs):
if path in ExistLock.locks:
lock = ExistLock.locks[path]
#print(f"Reloading ExistLock('{path}')")
#print(f" Lock counter = {lock._lock_counter}")
return lock
else:
#print(f"Creating new ExistLock('{path}')")
lock = super().__new__(cls)
lock.__new_init__(path, *args, **kwargs)
ExistLock.locks[path] = lock
return lock
def __new_init__(self, path, block=True, timeout=None, polling_interval=.05):
self.path = path
if not block:
timeout == 0.0
else:
self.timeout=timeout
self.polling_interval=polling_interval
self._lock_counter = 0
def acquire(self, block=None, timeout=None):
"""
Not atomic. Should probably happen within the context of an
atomic lock.
"""
if block == False:
timeout = 0.0
if timeout is None:
timeout = self.timeout
#print(f"Trying to acquire ExistLock('{self.path}')...")
#print(f" Lock counter = {self._lock_counter}")
start_time = time.time()
while os.path.isfile(self.path):
if self._lock_counter > 0:
self._lock_counter += 1
#print(f"Acquired, lock counter = {self._lock_counter}")
return True
if timeout is None or time.time() - start_time < timeout:
time.sleep(self.polling_interval)
else:
return False
with open(self.path, 'w'):
self._lock_counter = 1
#print(f"Acquired, lock counter = {self._lock_counter}")
return True
def release(self):
self._lock_counter = min(0, self._lock_counter - 1)
if self._lock_counter == 0 and os.path.isfile(self.path):
os.remove(self.path)
def __enter__(self):
if self.acquire():
return self
else:
raise Timeout(f"Failed to acquire ExistLock for file {self.path}")
def __exit__(self, type, value, traceback):
self.release()
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"os.path.join",
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"sample_hyperparameters.TrainableSampleGenerator",
"os.path.isfile",
"filelock.Timeout",
"time.time",
"os.remove"
]
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# Django REST Framework
from rest_framework import serializers
# Model
from todo.management.models import Task
# Utils
from todo.utils.tasks import TaskMetrics
from todo.utils.serializer_fields import CompleteNameUser
class TaskModelSerializer(serializers.ModelSerializer):
"""Modelo serializer del circulo"""
user = CompleteNameUser(many=False)
class Meta:
"""Meta class"""
model = Task
fields = (
'id', 'user', 'title',
'date_to_finish', 'is_finalize',
'description', 'created',
'priority', 'color'
)
read_only_fields = (
'id', 'user',
'created',
)
def create(self, data):
"""Creacion de la tarea"""
# Sacamos los datos que ya tenemos en el context
user = self.context['request'].user
data['is_finalize'] = False
# Creamos la tarea
task = Task.objects.create(
user=user,
**data
)
# Puntos al perfil
TaskMetrics(action='Create', user=user)
return task
def update(self, instance, data):
"""Actualizacion de la tarea"""
# Extraemos el user del contexto y mandamos la funcion update
user = self.context['request'].user
new_is_finalize = data.get('is_finalize', instance.is_finalize)
if new_is_finalize != instance.is_finalize:
TaskMetrics(action='Update', user=user, is_finalize=new_is_finalize)
# Actualizamos los datos normales
super(TaskModelSerializer, self).update(instance, data)
return instance
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"todo.management.models.Task.objects.create",
"todo.utils.serializer_fields.CompleteNameUser",
"todo.utils.tasks.TaskMetrics"
]
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#Negative Indexes
spam = ['cat', 'bat', 'rat', 'elephant']
spam[-1] # elepant
spam[-3] # bat
# Getting a List from another List with Slices
spam = ['cat', 'bat', 'rat', 'elephant']
spam[0:4] # ['cat', 'bat', 'rat', 'elephant']
spam[1:3] # ['bat', 'rat']
spam[0:-1] # ['cat', 'bat', 'rat']
spam[:2] # ['cat', 'bat']
spam[1:] # ['bat', 'rat', 'elephant']
spam[:] # ['cat', 'bat', 'rat', 'elephant']
# Getting a List's length with the len() Function
spam = ['cat', 'dog', 'moose']
len(spam) # 3
# Changing Values in a List with Indexes
spam = ['cat', 'bat', 'rat', 'elephant']
spam[1] = 'aardvark'
spam # ['cat', 'aardvark', 'rat', 'elephant']
spam[2]=spam[1]
spam # ['cat', 'aardvark', 'aardvark', 'elephant']
spam[-1] = 12345
spam # ['cat', 'aardvark', 'aardvark', 12345]
# List Concatenation and List Replication
[1, 2, 3] + ['A', 'B', 'C']
# [1, 2, 3, 'A', 'B', 'C']
['X', 'Y', 'Z'] * 3
#['X', 'Y', 'Z', 'X', 'Y', 'Z', 'X', 'Y', 'Z']
spam = [1, 2, 3]
spam = spam + ['A', 'B', 'C']
# [1, 2, 3, 'A', 'B', 'C']
# Removing Values From Lists with del Statements
spam = ['cat', 'bat', 'rat', 'elephant']
del spam[2]
spam # ['cat', 'bat', 'elephant']
del spam[2]
spam # ['cat', 'bat']
# Using for Loops with Lists
for i in range(4):
print(i)
supplies = ['pens', 'staplers', 'flamethrowers', 'binders']
for i in range(len(supplies)):
print('Index ' + str(i) + ' in supplies is: ' + supplies[i])
# The in and not in Operators
'howdy' in ['hello', 'hi', 'howdy', 'heyas'] # True
spam = ['hello', 'hi', 'howdy', 'heyas']
'cat' in spam # False
'howdy' not in spam # False
# Type in a pet name and then check wether the name is in a list of pets
myPets = ['Zophie', 'Pooka', 'Fat-tail']
print('Enter a pet name:')
name = input()
if name not in myPets:
print('I do not have a pet named ' + name)
else:
print(name + ' is my pet.')
# The Multiple Assignment Trick
cat = ['fat', 'gray', 'loud']
size = cat[0]
color = cat[1]
disposition = cat[2]
# type this line
cat = ['fat', 'gray', 'loud']
size, color, disposition = cat
# Using the enumerate() Function with Lists
# enumerate() Function is useful when you need both the item and item's index in loop's block
supplies = ['pens', 'staplers', 'flamethrowers', 'binders']
for index, item in enumerate(supplies):
print('Index ' + str(index) + ' in supplies is: ' + item)
# Using the random.choice() and random.shuffle() Function with Lists
import random
pets = ['Dog', 'Cat', 'Moose']
random.choice(pets)
random.choice(pets)
random.choice(pets)
# random.choice(someList) to be a shorter form of someList[random.randint(0, len(someList)-1)]
import random
people = ['Alice', 'Bob', 'Carol', 'David']
random.shuffle(people)
people # ['Bob', 'Carol', 'David', 'Alice']
random.shuffle(people)
people # random list of people
#Augmented Assignment Operators
spam += 1 # spam = spam + 1
spam -= 1 # spam = spam - 1
spam *= 1 # spam = spam * 1
spam /= 1 #spam = spam / 1
spam %= 1 #spam = spam % 1
| [
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]
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import pandas as pd
path = "Resources/cities.csv"
data = pd.read_csv(path)
data_html = data.to_html("data.html", bold_rows = True) | [
"pandas.read_csv"
]
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# Copyright (C) 2020 Red Hat Inc.
#
# Authors:
# <NAME> <<EMAIL>>
#
# This work is licensed under the terms of the GNU GPL, version 2. See
# the COPYING file in the top-level directory.
from tempfile import NamedTemporaryFile
from .patching import FileInfo, FileMatch, Patch, FileList
from .regexps import *
class BasicPattern(FileMatch):
regexp = '[abc]{3}'
@property
def name(self):
return self.group(0)
def replacement(self) -> str:
# replace match with the middle character repeated 5 times
return self.group(0)[1].upper()*5
def test_pattern_patching():
of = NamedTemporaryFile('wt')
of.writelines(['one line\n',
'this pattern will be patched: defbbahij\n',
'third line\n',
'another pattern: jihaabfed'])
of.flush()
files = FileList()
f = FileInfo(files, of.name)
f.load()
matches = f.matches_of_type(BasicPattern)
assert len(matches) == 2
p2 = matches[1]
# manually add patch, to see if .append() works:
f.patches.append(p2.append('XXX'))
# apply all patches:
f.gen_patches(matches)
patched = f.get_patched_content()
assert patched == ('one line\n'+
'this pattern will be patched: defBBBBBhij\n'+
'third line\n'+
'another pattern: jihAAAAAXXXfed')
class Function(FileMatch):
regexp = S(r'BEGIN\s+', NAMED('name', RE_IDENTIFIER), r'\n',
r'(.*\n)*?END\n')
class Statement(FileMatch):
regexp = S(r'^\s*', NAMED('name', RE_IDENTIFIER), r'\(\)\n')
def test_container_match():
of = NamedTemporaryFile('wt')
of.writelines(['statement1()\n',
'statement2()\n',
'BEGIN function1\n',
' statement3()\n',
' statement4()\n',
'END\n',
'BEGIN function2\n',
' statement5()\n',
' statement6()\n',
'END\n',
'statement7()\n'])
of.flush()
files = FileList()
f = FileInfo(files, of.name)
f.load()
assert len(f.matches_of_type(Function)) == 2
print(' '.join(m.name for m in f.matches_of_type(Statement)))
assert len(f.matches_of_type(Statement)) == 7
f1 = f.find_match(Function, 'function1')
f2 = f.find_match(Function, 'function2')
st1 = f.find_match(Statement, 'statement1')
st2 = f.find_match(Statement, 'statement2')
st3 = f.find_match(Statement, 'statement3')
st4 = f.find_match(Statement, 'statement4')
st5 = f.find_match(Statement, 'statement5')
st6 = f.find_match(Statement, 'statement6')
st7 = f.find_match(Statement, 'statement7')
assert not f1.contains(st1)
assert not f1.contains(st2)
assert not f1.contains(st2)
assert f1.contains(st3)
assert f1.contains(st4)
assert not f1.contains(st5)
assert not f1.contains(st6)
assert not f1.contains(st7)
assert not f2.contains(st1)
assert not f2.contains(st2)
assert not f2.contains(st2)
assert not f2.contains(st3)
assert not f2.contains(st4)
assert f2.contains(st5)
assert f2.contains(st6)
assert not f2.contains(st7)
| [
"tempfile.NamedTemporaryFile"
]
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import sys
def readInput():
labels, features, all_features, labelCount = [], [], [], {}
l = sys.stdin.readline().strip().split(' ')
while len(l)> 1:
label = l[0]
if label not in labelCount:
labelCount[label] = 0
labelCount[label] += 1
labels.append(label)
currFeat = set()
for key in l[1:]:
feature, _ = key.split(':')
all_features.append(feature)
currFeat.add(feature)
features.append(currFeat)
l = sys.stdin.readline().strip().split(' ')
return [labels, features] , set(all_features), labelCount
def rankByChiSquared(data, features, labelCount):
labels = labelCount.keys()
dataLength = len(data[0])
n = sum(labelCount.values())
results, featureOccourences, featureNonOccourences = [], {}, {}
for feature in features:
for label in labels:
featureOccourences[label] = 0 #Initialize
for i in range(dataLength):
if feature in data[1][i]:
featureOccourences[data[0][i]] += 1 # We could how many times the feature occours in the data for each label
for label in labels:
featureNonOccourences[label] = labelCount[label] - featureOccourences[label] #count of the times it doesnt appear for each label
totalFeatureOccourences = sum(featureOccourences.values())
totalFeatureNonOccourences = sum(featureNonOccourences.values())
chi = sum([((featureOccourences[label]-(labelCount[label]*totalFeatureOccourences/n))**2/(labelCount[label]*totalFeatureOccourences/n) +(featureNonOccourences[label] - (labelCount[label] * totalFeatureNonOccourences/n))**2/(labelCount[label] * totalFeatureNonOccourences/n)) for label in labels]) #Chi squared calc
results.append([feature, chi, totalFeatureOccourences]) #save the re
[print('{} {:.5f} {}'.format(*score)) for score in sorted(results, key = lambda x:(-x[1], -x[2], x[0]), reverse=False)] #print features sorted by chi^2 value, count in text, alphabetically
if __name__ == "__main__":
data, all_features, labelCount= readInput()
results = rankByChiSquared(data, all_features, labelCount) | [
"sys.stdin.readline"
]
| [((100, 120), 'sys.stdin.readline', 'sys.stdin.readline', ([], {}), '()\n', (118, 120), False, 'import sys\n'), ((525, 545), 'sys.stdin.readline', 'sys.stdin.readline', ([], {}), '()\n', (543, 545), False, 'import sys\n')] |
# Importamos la librería para leer archivos json
import json
# Abrimos el archivo master en modo lectura ('r') con todos los id de los archivos descargados
with open('master.json', 'r') as f:
# Guardamos en la variable lista el contenido de master
lista = json.load(f)
# En este ejemplo se representa cómo se asignaría a la lista archivos específicos
#lista = ['2095303', '2169202']
# Abrimos el archivo tryall.json en modo lectura ('w'), si no está creado previamente
# se crea en este momento, se puede cambiar nombre a este archivo
with open('tryall.json', 'w') as outfile:
# Iniciamos un contador para ir marcando cuántos archivos llevamos unidos
contador = 0
# Esta variable ayuda a guardar el nombre del archivo anterior para
# corroborar si no se está repitiendo con el anterior
helper = 0
# Esta variable nos indica que tenemos que escribir dentro del documento lo que hay
# dentro del archivo actual
update = True
# Recorremos toda la lista de archivos descargados
for names in lista:
# Abrimos cada archivo
with open(f'{names}.json') as infile:
# Leemos los primeras 3 líneas
infile.readline()
infile.readline()
infile.readline()
# Guardamos el contenido de la 4° que tiene el número del thread
# en una variable temportal
temp = infile.readline()
# Comprobamos si helper tiene el mismo contenido que temp
if helper != temp:
# Si es diferente se puede hacer la actualización ya que no se va
# a tener threads repetidos
update = True
# asignamos el nuevo contenido a la variable persistente
helper = temp
# Si tienen el mismo contenido entonces no se hace la actualización
else:
update = False
# Abrimos nuevamente el archivo
with open(f'{names}.json') as infile:
# Si el post no está repetido entra
if update == True:
# Se escribe el contenido completo del thread en el archivo de salida
outfile.write(infile.read())
# Se aumenta el contador ya que se escribió un documento nuevo
contador+=1
# Se imporime el contador con el nombre del archivo leído
print(contador, names)
# Se pone un salto de página para escribir el contenido del archivo siguiente
outfile.write("\n") | [
"json.load"
]
| [((265, 277), 'json.load', 'json.load', (['f'], {}), '(f)\n', (274, 277), False, 'import json\n')] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This file contains the generators and their inverses for common archimedean copulas.
"""
import numpy as np
def boundsConditions(x):
if x < 0 or x > 1:
raise ValueError("Unable to compute generator for x equals to {}".format(x))
def claytonGenerator(x, theta):
boundsConditions(x)
if theta == 0:
raise ValueError("The parameter of a Clayton copula must not be equal to 0.")
if theta < -1:
raise ValueError("The parameter of a Clayton copula must be greater than -1 and different from 0.")
return (1. / theta) * (x**(-theta) - 1.)
def claytonGeneratorInvert(x, theta):
if theta == 0:
raise ValueError("The parameter of a Clayton copula must not be equal to 0.")
if theta < -1:
raise ValueError("The parameter of a Clayton copula must be greater than -1 and different from 0.")
return (1. + theta * x)**(-1. / max(theta,1e-6))
def gumbelGenerator(x, theta):
boundsConditions(x)
if theta < 1:
raise ValueError("The parameter of a Gumbel copula must be greater than 1.")
return (-np.log(x))**theta
def gumbelGeneratorInvert(x, theta):
if len(theta) > 1:
theta = theta[0]
if theta < 1:
raise ValueError("The parameter of a Gumbel copula must be greater than 1.")
if (x < 1 and theta != 1):
raise(ValueError("The inverse Gumbel generator cannot be evaluated for negative input and theta > 1"))
return np.exp(-np.power(x,np.divide(1, theta)))
def frankGenerator(x, theta):
boundsConditions(x)
if theta == 0:
raise ValueError("The parameter of a Frank copula must not be equal to 0.")
return -np.log((np.exp(-theta[0] * x) - 1) / (np.exp(-theta[0]) - 1))
def frankGeneratorInvert(x, theta):
if theta == 0:
raise ValueError("The parameter of a Frank copula must not be equal to 0.")
return -1. / theta * np.log(1. + np.exp(-x) * (np.exp(-theta) - 1.))
def joeGenerator(x, theta):
boundsConditions(x)
if theta < 1:
raise ValueError("The parameter of a Joe copula must be greater than 1.")
return -np.log(1. - (1. - x)**theta)
def joeGeneratorInvert(x, theta):
if theta < 1:
raise ValueError("The parameter of a Joe copula must be greater than 1.")
return 1. - (1. - np.exp(-x))**(1. / max(theta,1e-6))
def aliMikhailHaqGenerator(x, theta):
boundsConditions(x)
if theta < -1 or theta >= 1:
raise ValueError("The parameter of an Ali-Mikhail-Haq copula must be between -1 included and 1 excluded.")
return np.log((1. - theta * (1. - x)) / x)
def aliMikhailHaqGeneratorInvert(x, theta):
if theta < -1 or theta >= 1:
raise ValueError("The parameter of an Ali-Mikhail-Haq copula must be between -1 included and 1 excluded.")
return (1. - theta) / (np.exp(x) - theta)
| [
"numpy.exp",
"numpy.log",
"numpy.divide"
]
| [((2585, 2622), 'numpy.log', 'np.log', (['((1.0 - theta * (1.0 - x)) / x)'], {}), '((1.0 - theta * (1.0 - x)) / x)\n', (2591, 2622), True, 'import numpy as np\n'), ((2141, 2173), 'numpy.log', 'np.log', (['(1.0 - (1.0 - x) ** theta)'], {}), '(1.0 - (1.0 - x) ** theta)\n', (2147, 2173), True, 'import numpy as np\n'), ((1128, 1137), 'numpy.log', 'np.log', (['x'], {}), '(x)\n', (1134, 1137), True, 'import numpy as np\n'), ((2841, 2850), 'numpy.exp', 'np.exp', (['x'], {}), '(x)\n', (2847, 2850), True, 'import numpy as np\n'), ((1508, 1527), 'numpy.divide', 'np.divide', (['(1)', 'theta'], {}), '(1, theta)\n', (1517, 1527), True, 'import numpy as np\n'), ((2327, 2337), 'numpy.exp', 'np.exp', (['(-x)'], {}), '(-x)\n', (2333, 2337), True, 'import numpy as np\n'), ((1709, 1730), 'numpy.exp', 'np.exp', (['(-theta[0] * x)'], {}), '(-theta[0] * x)\n', (1715, 1730), True, 'import numpy as np\n'), ((1739, 1756), 'numpy.exp', 'np.exp', (['(-theta[0])'], {}), '(-theta[0])\n', (1745, 1756), True, 'import numpy as np\n'), ((1940, 1950), 'numpy.exp', 'np.exp', (['(-x)'], {}), '(-x)\n', (1946, 1950), True, 'import numpy as np\n'), ((1954, 1968), 'numpy.exp', 'np.exp', (['(-theta)'], {}), '(-theta)\n', (1960, 1968), True, 'import numpy as np\n')] |
# -*- coding:utf-8 -*-
# Author: hankcs
# Date: 2021-03-02 13:32
from typing import Optional, Union, Dict, Any
import torch
from torch import nn
from transformers import PreTrainedTokenizer
from elit.components.mtl.attn.attn import TaskAttention
from elit.components.mtl.attn.transformer import JointEncoder
from elit.layers.embeddings.contextual_word_embedding import ContextualWordEmbeddingModule, ContextualWordEmbedding
from elit.layers.scalar_mix import ScalarMixWithDropoutBuilder
from elit.layers.transformers.utils import pick_tensor_for_each_token
class JointContextualWordEmbeddingModule(ContextualWordEmbeddingModule):
def __init__(self, field: str, transformer: str, transformer_tokenizer: PreTrainedTokenizer, average_subwords=False,
scalar_mix: Union[ScalarMixWithDropoutBuilder, int] = None, word_dropout=None,
max_sequence_length=None, ret_raw_hidden_states=False, transformer_args: Dict[str, Any] = None,
trainable=True, training=True) -> None:
super().__init__(field, transformer, transformer_tokenizer, average_subwords, scalar_mix, word_dropout,
max_sequence_length, ret_raw_hidden_states, transformer_args, trainable, training)
self.adapter: TaskAttention = None
def forward(self, batch: dict, mask=None, **kwargs):
input_ids: torch.LongTensor = batch[f'{self.field}_input_ids']
if self.max_sequence_length and input_ids.size(-1) > self.max_sequence_length:
raise NotImplementedError('Sentence length exceeded and sliding window has not been implemented yet')
token_span: torch.LongTensor = batch.get(f'{self.field}_token_span', None)
token_type_ids: torch.LongTensor = batch.get(f'{self.field}_token_type_ids', None)
attention_mask = input_ids.ne(0)
if self.word_dropout:
input_ids = self.word_dropout(input_ids)
# noinspection PyTypeChecker
transformer: JointEncoder = self.transformer
encoder_outputs = transformer(input_ids, attention_mask, token_type_ids)
outputs = dict()
for task_name, encoder_output in encoder_outputs.items():
encoder_output = encoder_output[0]
outputs[task_name] = pick_tensor_for_each_token(encoder_output, token_span, self.average_subwords)
return outputs
class JointContextualWordEmbedding(ContextualWordEmbedding):
def module(self, training=True, **kwargs) -> Optional[nn.Module]:
return JointContextualWordEmbeddingModule(self.field,
self.transformer,
self._transformer_tokenizer,
self.average_subwords,
self.scalar_mix,
self.word_dropout,
self.max_sequence_length,
self.ret_raw_hidden_states,
self.transformer_args,
self.trainable,
training=training)
| [
"elit.layers.transformers.utils.pick_tensor_for_each_token"
]
| [((2256, 2333), 'elit.layers.transformers.utils.pick_tensor_for_each_token', 'pick_tensor_for_each_token', (['encoder_output', 'token_span', 'self.average_subwords'], {}), '(encoder_output, token_span, self.average_subwords)\n', (2282, 2333), False, 'from elit.layers.transformers.utils import pick_tensor_for_each_token\n')] |
"""Implementations of algorithms for continuous control."""
import functools
from typing import Optional, Sequence, Tuple
import jax
import jax.numpy as jnp
import numpy as np
import optax
from jaxrl.agents.sac import temperature
from jaxrl.agents.sac.actor import update as update_actor
from jaxrl.agents.sac.critic import target_update
from jaxrl.agents.sac_v1.critic import update_q, update_v
from jaxrl.datasets import Batch
from jaxrl.networks import critic_net, policies
from jaxrl.networks.common import InfoDict, Model, PRNGKey
@functools.partial(jax.jit, static_argnames=('update_target'))
def _update_jit(
rng: PRNGKey, actor: Model, critic: Model, value: Model,
target_value: Model, temp: Model, batch: Batch, discount: float,
tau: float, target_entropy: float, update_target: bool
) -> Tuple[PRNGKey, Model, Model, Model, Model, Model, InfoDict]:
new_critic, critic_info = update_q(critic, target_value, batch, discount)
rng, key = jax.random.split(rng)
new_actor, actor_info = update_actor(key, actor, new_critic, temp, batch)
rng, key = jax.random.split(rng)
new_value, value_info = update_v(key, new_actor, new_critic, value, temp,
batch, True)
if update_target:
new_target_value = target_update(new_value, target_value, tau)
else:
new_target_value = target_value
new_temp, alpha_info = temperature.update(temp, actor_info['entropy'],
target_entropy)
return rng, new_actor, new_critic, new_value, new_target_value, new_temp, {
**critic_info,
**value_info,
**actor_info,
**alpha_info
}
class SACV1Learner(object):
def __init__(self,
seed: int,
observations: jnp.ndarray,
actions: jnp.ndarray,
actor_lr: float = 3e-4,
value_lr: float = 3e-4,
critic_lr: float = 3e-4,
temp_lr: float = 3e-4,
hidden_dims: Sequence[int] = (256, 256),
discount: float = 0.99,
tau: float = 0.005,
target_update_period: int = 1,
target_entropy: Optional[float] = None,
init_temperature: float = 1.0):
"""
An implementation of the version of Soft-Actor-Critic described in https://arxiv.org/abs/1801.01290
"""
action_dim = actions.shape[-1]
if target_entropy is None:
self.target_entropy = -action_dim / 2
else:
self.target_entropy = target_entropy
self.tau = tau
self.target_update_period = target_update_period
self.discount = discount
rng = jax.random.PRNGKey(seed)
rng, actor_key, critic_key, temp_key = jax.random.split(rng, 4)
actor_def = policies.NormalTanhPolicy(hidden_dims, action_dim)
actor = Model.create(actor_def,
inputs=[actor_key, observations],
tx=optax.adam(learning_rate=actor_lr))
critic_def = critic_net.DoubleCritic(hidden_dims)
critic = Model.create(critic_def,
inputs=[critic_key, observations, actions],
tx=optax.adam(learning_rate=critic_lr))
value_def = critic_net.ValueCritic(hidden_dims)
value = Model.create(value_def,
inputs=[critic_key, observations],
tx=optax.adam(learning_rate=value_lr))
target_value = Model.create(value_def,
inputs=[critic_key, observations])
temp = Model.create(temperature.Temperature(init_temperature),
inputs=[temp_key],
tx=optax.adam(learning_rate=temp_lr))
self.actor = actor
self.critic = critic
self.value = value
self.target_value = target_value
self.temp = temp
self.rng = rng
self.step = 1
def sample_actions(self,
observations: np.ndarray,
temperature: float = 1.0) -> jnp.ndarray:
rng, actions = policies.sample_actions(self.rng, self.actor.apply_fn,
self.actor.params, observations,
temperature)
self.rng = rng
actions = np.asarray(actions)
return np.clip(actions, -1, 1)
def update(self, batch: Batch) -> InfoDict:
self.step += 1
new_rng, new_actor, new_critic, new_value, new_target_value, new_temp, info = _update_jit(
self.rng, self.actor, self.critic, self.value, self.target_value,
self.temp, batch, self.discount, self.tau, self.target_entropy,
self.step % self.target_update_period == 0)
self.rng = new_rng
self.actor = new_actor
self.critic = new_critic
self.value = new_value
self.target_value = new_target_value
self.temp = new_temp
return info
| [
"jaxrl.networks.critic_net.ValueCritic",
"numpy.clip",
"optax.adam",
"jax.random.PRNGKey",
"jaxrl.agents.sac.temperature.update",
"jaxrl.networks.policies.sample_actions",
"jaxrl.networks.common.Model.create",
"jaxrl.agents.sac_v1.critic.update_q",
"numpy.asarray",
"jaxrl.agents.sac.actor.update",
"jaxrl.agents.sac.critic.target_update",
"jaxrl.agents.sac.temperature.Temperature",
"jaxrl.networks.policies.NormalTanhPolicy",
"functools.partial",
"jaxrl.agents.sac_v1.critic.update_v",
"jaxrl.networks.critic_net.DoubleCritic",
"jax.random.split"
]
| [((542, 601), 'functools.partial', 'functools.partial', (['jax.jit'], {'static_argnames': '"""update_target"""'}), "(jax.jit, static_argnames='update_target')\n", (559, 601), False, 'import functools\n'), ((907, 954), 'jaxrl.agents.sac_v1.critic.update_q', 'update_q', (['critic', 'target_value', 'batch', 'discount'], {}), '(critic, target_value, batch, discount)\n', (915, 954), False, 'from jaxrl.agents.sac_v1.critic import update_q, update_v\n'), ((971, 992), 'jax.random.split', 'jax.random.split', (['rng'], {}), '(rng)\n', (987, 992), False, 'import jax\n'), ((1021, 1070), 'jaxrl.agents.sac.actor.update', 'update_actor', (['key', 'actor', 'new_critic', 'temp', 'batch'], {}), '(key, actor, new_critic, temp, batch)\n', (1033, 1070), True, 'from jaxrl.agents.sac.actor import update as update_actor\n'), ((1087, 1108), 'jax.random.split', 'jax.random.split', (['rng'], {}), '(rng)\n', (1103, 1108), False, 'import jax\n'), ((1137, 1199), 'jaxrl.agents.sac_v1.critic.update_v', 'update_v', (['key', 'new_actor', 'new_critic', 'value', 'temp', 'batch', '(True)'], {}), '(key, new_actor, new_critic, value, temp, batch, True)\n', (1145, 1199), False, 'from jaxrl.agents.sac_v1.critic import update_q, update_v\n'), ((1409, 1472), 'jaxrl.agents.sac.temperature.update', 'temperature.update', (['temp', "actor_info['entropy']", 'target_entropy'], {}), "(temp, actor_info['entropy'], target_entropy)\n", (1427, 1472), False, 'from jaxrl.agents.sac import temperature\n'), ((1287, 1330), 'jaxrl.agents.sac.critic.target_update', 'target_update', (['new_value', 'target_value', 'tau'], {}), '(new_value, target_value, tau)\n', (1300, 1330), False, 'from jaxrl.agents.sac.critic import target_update\n'), ((2763, 2787), 'jax.random.PRNGKey', 'jax.random.PRNGKey', (['seed'], {}), '(seed)\n', (2781, 2787), False, 'import jax\n'), ((2835, 2859), 'jax.random.split', 'jax.random.split', (['rng', '(4)'], {}), '(rng, 4)\n', (2851, 2859), False, 'import jax\n'), ((2881, 2931), 'jaxrl.networks.policies.NormalTanhPolicy', 'policies.NormalTanhPolicy', (['hidden_dims', 'action_dim'], {}), '(hidden_dims, action_dim)\n', (2906, 2931), False, 'from jaxrl.networks import critic_net, policies\n'), ((3125, 3161), 'jaxrl.networks.critic_net.DoubleCritic', 'critic_net.DoubleCritic', (['hidden_dims'], {}), '(hidden_dims)\n', (3148, 3161), False, 'from jaxrl.networks import critic_net, policies\n'), ((3369, 3404), 'jaxrl.networks.critic_net.ValueCritic', 'critic_net.ValueCritic', (['hidden_dims'], {}), '(hidden_dims)\n', (3391, 3404), False, 'from jaxrl.networks import critic_net, policies\n'), ((3601, 3659), 'jaxrl.networks.common.Model.create', 'Model.create', (['value_def'], {'inputs': '[critic_key, observations]'}), '(value_def, inputs=[critic_key, observations])\n', (3613, 3659), False, 'from jaxrl.networks.common import InfoDict, Model, PRNGKey\n'), ((4244, 4348), 'jaxrl.networks.policies.sample_actions', 'policies.sample_actions', (['self.rng', 'self.actor.apply_fn', 'self.actor.params', 'observations', 'temperature'], {}), '(self.rng, self.actor.apply_fn, self.actor.params,\n observations, temperature)\n', (4267, 4348), False, 'from jaxrl.networks import critic_net, policies\n'), ((4481, 4500), 'numpy.asarray', 'np.asarray', (['actions'], {}), '(actions)\n', (4491, 4500), True, 'import numpy as np\n'), ((4516, 4539), 'numpy.clip', 'np.clip', (['actions', '(-1)', '(1)'], {}), '(actions, -1, 1)\n', (4523, 4539), True, 'import numpy as np\n'), ((3725, 3766), 'jaxrl.agents.sac.temperature.Temperature', 'temperature.Temperature', (['init_temperature'], {}), '(init_temperature)\n', (3748, 3766), False, 'from jaxrl.agents.sac import temperature\n'), ((3067, 3101), 'optax.adam', 'optax.adam', ([], {'learning_rate': 'actor_lr'}), '(learning_rate=actor_lr)\n', (3077, 3101), False, 'import optax\n'), ((3311, 3346), 'optax.adam', 'optax.adam', ([], {'learning_rate': 'critic_lr'}), '(learning_rate=critic_lr)\n', (3321, 3346), False, 'import optax\n'), ((3541, 3575), 'optax.adam', 'optax.adam', ([], {'learning_rate': 'value_lr'}), '(learning_rate=value_lr)\n', (3551, 3575), False, 'import optax\n'), ((3846, 3879), 'optax.adam', 'optax.adam', ([], {'learning_rate': 'temp_lr'}), '(learning_rate=temp_lr)\n', (3856, 3879), False, 'import optax\n')] |
"""Example revision
Revision ID: fdf0cf6487a3
Revises:
Create Date: 2021-08-09 17:55:19.491713
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = "<KEY>"
down_revision = None
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table(
"example",
sa.Column("example_id", sa.Integer(), nullable=False),
)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table("measurements")
# ### end Alembic commands ###
| [
"alembic.op.drop_table",
"sqlalchemy.Integer"
]
| [((586, 615), 'alembic.op.drop_table', 'op.drop_table', (['"""measurements"""'], {}), "('measurements')\n", (599, 615), False, 'from alembic import op\n'), ((425, 437), 'sqlalchemy.Integer', 'sa.Integer', ([], {}), '()\n', (435, 437), True, 'import sqlalchemy as sa\n')] |
# -*- Python -*-
import os
# Setup config name.
config.name = 'MemorySanitizer' + getattr(config, 'name_suffix', 'default')
# Setup source root.
config.test_source_root = os.path.dirname(__file__)
# Setup default compiler flags used with -fsanitize=memory option.
clang_msan_cflags = (["-fsanitize=memory",
"-mno-omit-leaf-frame-pointer",
"-fno-omit-frame-pointer",
"-fno-optimize-sibling-calls"] +
[config.target_cflags] +
config.debug_info_flags)
# Some Msan tests leverage backtrace() which requires libexecinfo on FreeBSD.
if config.host_os == 'FreeBSD':
clang_msan_cflags += ["-lexecinfo", "-fPIC"]
clang_msan_cxxflags = config.cxx_mode_flags + clang_msan_cflags
# Flags for KMSAN invocation. This is C-only, we're not interested in C++.
clang_kmsan_cflags = (["-fsanitize=kernel-memory"] +
[config.target_cflags] +
config.debug_info_flags)
def build_invocation(compile_flags):
return " " + " ".join([config.clang] + compile_flags) + " "
config.substitutions.append( ("%clang_msan ", build_invocation(clang_msan_cflags)) )
config.substitutions.append( ("%clangxx_msan ", build_invocation(clang_msan_cxxflags)) )
config.substitutions.append( ("%clang_kmsan ", build_invocation(clang_kmsan_cflags)) )
# Default test suffixes.
config.suffixes = ['.c', '.cc', '.cpp']
if config.host_os not in ['Linux', 'NetBSD', 'FreeBSD']:
config.unsupported = True
# For mips64, mips64el we have forced store_context_size to 1 because these
# archs use slow unwinder which is not async signal safe. Therefore we only
# check the first frame since store_context size is 1.
if config.host_arch in ['mips64', 'mips64el']:
config.substitutions.append( ('CHECK-%short-stack', 'CHECK-SHORT-STACK'))
else:
config.substitutions.append( ('CHECK-%short-stack', 'CHECK-FULL-STACK'))
| [
"os.path.dirname"
]
| [((174, 199), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (189, 199), False, 'import os\n')] |
#
# nuna_sql_tools: Copyright 2022 Nuna Inc
#
# 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.
#
"""Utilityes for checking and."""
import dataclasses
import datetime
import decimal
from types import ModuleType
from typing import NewType, Union
# In your data declaration python modules define a JAVA_PACKAGE
# variable at top level to specify the corresponding Java package of generated
# classes.
JAVA_PACKAGE = 'JAVA_PACKAGE'
def GetJavaPackage(module: ModuleType) -> str:
if hasattr(module, JAVA_PACKAGE):
return getattr(module, JAVA_PACKAGE)
else:
return module.__name__
_SCHEMA_ANNOTATIONS = '__schema_annotations__'
_EXPECTED_DICT_KEYS = set([
'__module__', '__annotations__', '__doc__', '__dict__', '__weakref__',
'__dataclass_params__', '__dataclass_fields__', _SCHEMA_ANNOTATIONS
])
_EXPECTED_FUNCTIONS = ['__init__', '__repr__', '__eq__', '__hash__']
_BASE_TYPES = set([
int, bytes, str, float, bool, datetime.date, datetime.datetime,
decimal.Decimal
])
_SCHEMA_ANNOTATIONS = '__schema_annotations__'
_CLASS_ID = 0
def _Annotate(cls=None, annotation=None):
"""Annotates a class or a type. `annotation` should from annotation.py"""
def Wrap(cls):
schema_annotations = []
if hasattr(cls, _SCHEMA_ANNOTATIONS):
schema_annotations.extend(getattr(cls, _SCHEMA_ANNOTATIONS))
if isinstance(annotation, list):
schema_annotations.extend(annotation)
else:
schema_annotations.append(annotation)
global _CLASS_ID
_CLASS_ID += 1
supertype = cls
if hasattr(cls, '__supertype__'):
supertype = cls.__supertype__
annotated_type = NewType(f'Annotated_{_CLASS_ID}', supertype)
setattr(annotated_type, _SCHEMA_ANNOTATIONS, schema_annotations)
return annotated_type
if cls is None:
return Wrap
return Wrap(cls)
def Annotate(cls, annotation):
"""Annotates a field type with the provided annotation."""
return _Annotate(cls, annotation=annotation)
def IsAnnotatedType(field_cls: type):
"""If provided field_cls is an annotated type."""
return hasattr(field_cls, _SCHEMA_ANNOTATIONS)
def GetAnnotatedType(field_cls: type):
"""Returns the original type behind the annotation (if any)."""
if IsAnnotatedType(field_cls) and hasattr(field_cls, '__supertype__'):
return field_cls.__supertype__
return field_cls
def IsOptionalType(field_cls: type):
"""If the field_cls looks like an Optional[...] type."""
return (hasattr(field_cls, '__origin__')
# pylint: disable=comparison-with-callable
and field_cls.__origin__ == Union and len(field_cls.__args__) == 2
and field_cls.__args__[1] == type(None))
def GetOptionalType(field_cls: type):
"""Returns the type of optional & annotation or None if not optional."""
field_cls = GetAnnotatedType(field_cls)
if IsOptionalType(field_cls):
return field_cls.__args__[0]
return None
def GetOriginalType(field_cls: type):
"""Returns the type of field_cls, behind annotations and Optional."""
field_cls = GetAnnotatedType(field_cls)
if IsOptionalType(field_cls):
return field_cls.__args__[0]
return field_cls
def GetStructuredTypeName(field_cls: type):
"""Returns the structure type name for a type, behind annotation."""
field_cls = GetAnnotatedType(field_cls)
if not hasattr(field_cls, '__origin__'):
return None
if field_cls.__origin__ is dict:
return 'dict'
elif field_cls.__origin__ is list:
return 'list'
elif field_cls.__origin__ is set:
return 'set'
return None
def IsBasicType(field_cls: type):
"""If the type field_cls looks like one of the basic field types."""
if GetAnnotatedType(field_cls) in _BASE_TYPES:
return True
_MAX_DEPTH = 30
class FieldTypeChecker:
"""Checks the type of a fields in a dataclass."""
def __init__(self, field_name, field_cls):
self.field_name = field_name
self.field_cls = field_cls
self.checked = set()
def _check(self, field_cls, depth):
"""Check if the type of a field is acceptable."""
if field_cls in self.checked:
return True
if depth > _MAX_DEPTH:
raise ValueError(f'Recursive field type found at {field_cls} '
f'for field `{self.field_name}`')
field_cls = GetAnnotatedType(field_cls)
if IsBasicType(field_cls):
return True
if hasattr(field_cls, '__origin__'):
if field_cls.__origin__ is dict:
self._check(field_cls.__args__[0], depth)
self._check(field_cls.__args__[1], depth)
elif field_cls.__origin__ is list:
self._check(field_cls.__args__[0], depth)
elif field_cls.__origin__ is set:
self._check(field_cls.__args__[0], depth)
elif ( # pylint: disable=comparison-with-callable
field_cls.__origin__ == Union and
len(field_cls.__args__) == 2 and
field_cls.__args__[1] == type(None)):
if GetStructuredTypeName(field_cls) is not None:
raise ValueError('Cannot have Optional structured fields.'
'(e.g. Optional[List or Set or Dict])')
# Optional[...]
self._check(field_cls.__args__[0], depth)
else:
raise ValueError(f'Invalid origin class for {field_cls}: '
f'`{field_cls.__origin__}`')
else:
checker = DataclassChecker(field_cls)
if checker.check_is_dataclass() is not None:
raise ValueError(
f'Invalid type surfaced for field `{self.field_name}`: '
f'`{self.field_cls}` - {field_cls} is not acceptable')
err = checker.check()
if err:
errors = '; '.join(err)
raise ValueError(
f'Subfield entity class of field `{self.field_name}` '
f'({field_cls}) has type errors: {errors}')
self.checked.add(field_cls)
return True
def check(self):
return self._check(self.field_cls, 0)
class DataclassChecker:
"""Checks if a python type and its structure conforms to Dataclass specs."""
def __init__(self, cls: type):
self.cls = cls
self.nested = []
def _err_class(self):
return f'dataclass class `{self.cls}` in module `{self.cls.__module__}`'
def _err_field(self, field: str):
return (f'field `{field}` of dataclass class `{self.cls.__name__}` '
f'in module `{self.cls.__module__}`')
def check_is_dataclass(self):
if not dataclasses.is_dataclass(self.cls):
return f'{self._err_class()} is not a dataclass'
return None
def _check_type(self, field_name, field_cls):
try:
FieldTypeChecker(field_name, field_cls).check()
return None
except ValueError as e:
return f'{e.args[0]} for {self._err_field(field_name)}'
def _check_field_type(self, field_name, field_cls):
return self._check_type(GetOriginalType(field_name), field_cls)
def _check_dataclass_members(self):
err = []
for key in self.cls.__dict__:
# pylint: disable=comparison-with-callable,unidiomatic-typecheck
if type(self.cls.__dict__[key]) == type:
self.nested.append(
(key, DataclassChecker(self.cls.__dict__[key])))
elif callable(
self.cls.__dict__[key]) and key not in _EXPECTED_FUNCTIONS:
err.append(f'{self._err_class()} has unexpected function '
f'member `{key}`')
elif (key not in _EXPECTED_DICT_KEYS and
key not in _EXPECTED_FUNCTIONS and
key not in self.cls.__annotations__):
err.append(f'{self._err_class()} has unexpected / non annotated'
f' member `{key}`: {self.cls.__dict__[key]}')
for field in dataclasses.fields(self.cls):
field_err = self._check_field_type(field.name, field.type)
if field_err is not None:
err.append(field_err)
for nested in self.nested:
for nested_err in nested[1].check():
err.append(f'{nested_err}; for nested sub-class '
f'{nested[0]} of {self._err_class()}')
return err
def check(self):
err_dataclass = self.check_is_dataclass()
if err_dataclass is not None:
return [err_dataclass]
return self._check_dataclass_members()
def SchemaAnnotations(cls: type):
"""Returns the schema annotations of a type."""
annotations = []
if hasattr(cls, _SCHEMA_ANNOTATIONS):
annotations.extend(cls.__schema_annotations__)
return annotations
| [
"dataclasses.is_dataclass",
"dataclasses.fields",
"typing.NewType"
]
| [((2196, 2240), 'typing.NewType', 'NewType', (['f"""Annotated_{_CLASS_ID}"""', 'supertype'], {}), "(f'Annotated_{_CLASS_ID}', supertype)\n", (2203, 2240), False, 'from typing import NewType, Union\n'), ((8773, 8801), 'dataclasses.fields', 'dataclasses.fields', (['self.cls'], {}), '(self.cls)\n', (8791, 8801), False, 'import dataclasses\n'), ((7383, 7417), 'dataclasses.is_dataclass', 'dataclasses.is_dataclass', (['self.cls'], {}), '(self.cls)\n', (7407, 7417), False, 'import dataclasses\n')] |
#!/usr/bin/python
# Classification (U)
"""Program: slaverep_isslverror.py
Description: Unit testing of SlaveRep.is_slv_error in mysql_class.py.
Usage:
test/unit/mysql_class/slaverep_isslverror.py
Arguments:
"""
# Libraries and Global Variables
# Standard
import sys
import os
if sys.version_info < (2, 7):
import unittest2 as unittest
else:
import unittest
# Third-party
# Local
sys.path.append(os.getcwd())
import mysql_class
import lib.machine as machine
import version
__version__ = version.__version__
class UnitTest(unittest.TestCase):
"""Class: UnitTest
Description: Class which is a representation of a unit testing.
Methods:
setUp -> Initialize testing environment.
test_slv_both_true -> Test with all attrs set to True.
test_sql_err_true -> Test with sql_err set to True.
test_io_err_true -> Test with io_err set to True.
test_default -> Test show_slv_state method.
"""
def setUp(self):
"""Function: setUp
Description: Initialization for unit testing.
Arguments:
"""
self.name = "Mysql_Server"
self.server_id = 10
self.sql_user = "mysql_user"
self.sql_pass = "<PASSWORD>"
self.machine = getattr(machine, "Linux")()
self.host = "host_server"
self.port = 3307
self.defaults_file = "def_cfg_file"
self.extra_def_file = "extra_cfg_file"
def test_slv_both_true(self):
"""Function: test_slv_both_true
Description: Test with all attrs set to True.
Arguments:
"""
mysqlrep = mysql_class.SlaveRep(self.name, self.server_id,
self.sql_user, self.sql_pass,
self.machine,
defaults_file=self.defaults_file)
mysqlrep.sql_err = "Yes"
mysqlrep.io_err = "Yes"
self.assertTrue(mysqlrep.is_slv_error())
def test_sql_err_true(self):
"""Function: test_sql_err_true
Description: Test with sql_err set to True.
Arguments:
"""
mysqlrep = mysql_class.SlaveRep(self.name, self.server_id,
self.sql_user, self.sql_pass,
self.machine,
defaults_file=self.defaults_file)
mysqlrep.sql_err = "Yes"
mysqlrep.io_err = None
self.assertTrue(mysqlrep.is_slv_error())
def test_io_err_true(self):
"""Function: test_io_err_true
Description: Test with io_err set to True.
Arguments:
"""
mysqlrep = mysql_class.SlaveRep(self.name, self.server_id,
self.sql_user, self.sql_pass,
self.machine,
defaults_file=self.defaults_file)
mysqlrep.sql_err = None
mysqlrep.io_err = "Yes"
self.assertTrue(mysqlrep.is_slv_error())
def test_default(self):
"""Function: test_default
Description: Test is_slv_error method.
Arguments:
"""
mysqlrep = mysql_class.SlaveRep(self.name, self.server_id,
self.sql_user, self.sql_pass,
self.machine,
defaults_file=self.defaults_file)
mysqlrep.sql_err = None
mysqlrep.io_err = None
self.assertFalse(mysqlrep.is_slv_error())
if __name__ == "__main__":
unittest.main()
| [
"unittest.main",
"mysql_class.SlaveRep",
"os.getcwd"
]
| [((435, 446), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (444, 446), False, 'import os\n'), ((3651, 3666), 'unittest.main', 'unittest.main', ([], {}), '()\n', (3664, 3666), False, 'import unittest\n'), ((1649, 1779), 'mysql_class.SlaveRep', 'mysql_class.SlaveRep', (['self.name', 'self.server_id', 'self.sql_user', 'self.sql_pass', 'self.machine'], {'defaults_file': 'self.defaults_file'}), '(self.name, self.server_id, self.sql_user, self.\n sql_pass, self.machine, defaults_file=self.defaults_file)\n', (1669, 1779), False, 'import mysql_class\n'), ((2192, 2322), 'mysql_class.SlaveRep', 'mysql_class.SlaveRep', (['self.name', 'self.server_id', 'self.sql_user', 'self.sql_pass', 'self.machine'], {'defaults_file': 'self.defaults_file'}), '(self.name, self.server_id, self.sql_user, self.\n sql_pass, self.machine, defaults_file=self.defaults_file)\n', (2212, 2322), False, 'import mysql_class\n'), ((2731, 2861), 'mysql_class.SlaveRep', 'mysql_class.SlaveRep', (['self.name', 'self.server_id', 'self.sql_user', 'self.sql_pass', 'self.machine'], {'defaults_file': 'self.defaults_file'}), '(self.name, self.server_id, self.sql_user, self.\n sql_pass, self.machine, defaults_file=self.defaults_file)\n', (2751, 2861), False, 'import mysql_class\n'), ((3258, 3388), 'mysql_class.SlaveRep', 'mysql_class.SlaveRep', (['self.name', 'self.server_id', 'self.sql_user', 'self.sql_pass', 'self.machine'], {'defaults_file': 'self.defaults_file'}), '(self.name, self.server_id, self.sql_user, self.\n sql_pass, self.machine, defaults_file=self.defaults_file)\n', (3278, 3388), False, 'import mysql_class\n')] |
"""
108. 将有序数组转换为二叉搜索树
"""
from TreeNode import TreeNode
class Solution:
def sortedArrayToBST(self, nums: [int]) -> TreeNode:
def dfs(left, right):
if left > right:
return None
mid = left + (right - left) // 2
root = TreeNode(nums[mid])
root.left = dfs(left, mid-1)
root.right = dfs(mid+1, right)
return root
return dfs(0, len(nums)-1)
t = [-10,-3,0,5,9]
obj = Solution()
node = obj.sortedArrayToBST(t)
node.preorderTraversal()
| [
"TreeNode.TreeNode"
]
| [((285, 304), 'TreeNode.TreeNode', 'TreeNode', (['nums[mid]'], {}), '(nums[mid])\n', (293, 304), False, 'from TreeNode import TreeNode\n')] |
import sys
from os.path import dirname, abspath, join
cur_folder = dirname(abspath(__file__))
sys.path.insert(0, join(dirname(cur_folder), 'src'))
sys.path.insert(0, dirname(cur_folder))
print(cur_folder) | [
"os.path.abspath",
"os.path.dirname"
]
| [((76, 93), 'os.path.abspath', 'abspath', (['__file__'], {}), '(__file__)\n', (83, 93), False, 'from os.path import dirname, abspath, join\n'), ((167, 186), 'os.path.dirname', 'dirname', (['cur_folder'], {}), '(cur_folder)\n', (174, 186), False, 'from os.path import dirname, abspath, join\n'), ((119, 138), 'os.path.dirname', 'dirname', (['cur_folder'], {}), '(cur_folder)\n', (126, 138), False, 'from os.path import dirname, abspath, join\n')] |
##############################################################################
#
# Copyright (c) 2006 Zope Foundation and Contributors.
# All Rights Reserved.
#
# This software is subject to the provisions of the Zope Public License,
# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.
# THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED
# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS
# FOR A PARTICULAR PURPOSE.
#
##############################################################################
"""Viewlet.
"""
import os
import zope.viewlet.viewlet
from Products.Five.browser.pagetemplatefile import ViewPageTemplateFile
class ViewletBase(zope.viewlet.viewlet.ViewletBase):
pass
class SimpleAttributeViewlet(zope.viewlet.viewlet.SimpleAttributeViewlet):
pass
class simple(zope.viewlet.viewlet.simple):
# We need to ensure that the proper __init__ is called.
__init__ = ViewletBase.__init__
def SimpleViewletClass(template, bases=(), attributes=None, name=''):
"""A function that can be used to generate a viewlet from a set of
information.
"""
# Create the base class hierarchy
bases += (simple, ViewletBase)
attrs = {'index': ViewPageTemplateFile(template),
'__name__': name}
if attributes:
attrs.update(attributes)
# Generate a derived view class.
class_ = type("SimpleViewletClass from %s" % template, bases, attrs)
return class_
class ResourceViewletBase(zope.viewlet.viewlet.ResourceViewletBase):
pass
def JavaScriptViewlet(path):
"""Create a viewlet that can simply insert a javascript link."""
src = os.path.join(os.path.dirname(__file__), 'javascript_viewlet.pt')
klass = type('JavaScriptViewlet',
(ResourceViewletBase, ViewletBase),
{'index': ViewPageTemplateFile(src), '_path': path})
return klass
class CSSResourceViewletBase(zope.viewlet.viewlet.CSSResourceViewletBase):
pass
def CSSViewlet(path, media="all", rel="stylesheet"):
"""Create a viewlet that can simply insert a javascript link."""
src = os.path.join(os.path.dirname(__file__), 'css_viewlet.pt')
klass = type('CSSViewlet',
(CSSResourceViewletBase, ViewletBase),
{'index': ViewPageTemplateFile(src),
'_path': path,
'_media': media,
'_rel': rel})
return klass
| [
"os.path.dirname",
"Products.Five.browser.pagetemplatefile.ViewPageTemplateFile"
]
| [((1320, 1350), 'Products.Five.browser.pagetemplatefile.ViewPageTemplateFile', 'ViewPageTemplateFile', (['template'], {}), '(template)\n', (1340, 1350), False, 'from Products.Five.browser.pagetemplatefile import ViewPageTemplateFile\n'), ((1768, 1793), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (1783, 1793), False, 'import os\n'), ((2233, 2258), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (2248, 2258), False, 'import os\n'), ((1939, 1964), 'Products.Five.browser.pagetemplatefile.ViewPageTemplateFile', 'ViewPageTemplateFile', (['src'], {}), '(src)\n', (1959, 1964), False, 'from Products.Five.browser.pagetemplatefile import ViewPageTemplateFile\n'), ((2393, 2418), 'Products.Five.browser.pagetemplatefile.ViewPageTemplateFile', 'ViewPageTemplateFile', (['src'], {}), '(src)\n', (2413, 2418), False, 'from Products.Five.browser.pagetemplatefile import ViewPageTemplateFile\n')] |
#!/usr/bin/env python3
"""Unpack a MIME message into a directory of files."""
import os
import email
import mimetypes
from email.policy import default
from argparse import ArgumentParser
def main():
parser = ArgumentParser(description="""\
Unpack a MIME message into a directory of files.
""")
parser.add_argument('-d', '--directory', required=True,
help="""Unpack the MIME message into the named
directory, which will be created if it doesn't already
exist.""")
parser.add_argument('msgfile')
args = parser.parse_args()
with open(args.msgfile, 'rb') as fp:
msg = email.message_from_binary_file(fp, policy=default)
try:
os.mkdir(args.directory)
except FileExistsError:
pass
counter = 1
for part in msg.walk():
# multipart/* are just containers
if part.get_content_maintype() == 'multipart':
continue
# Applications should really sanitize the given filename so that an
# email message can't be used to overwrite important files
filename = part.get_filename()
if not filename:
ext = mimetypes.guess_extension(part.get_content_type())
if not ext:
# Use a generic bag-of-bits extension
ext = '.bin'
filename = 'part-%03d%s' % (counter, ext)
counter += 1
with open(os.path.join(args.directory, filename), 'wb') as fp:
fp.write(part.get_payload(decode=True))
if __name__ == '__main__':
main()
| [
"email.message_from_binary_file",
"os.path.join",
"os.mkdir",
"argparse.ArgumentParser"
]
| [((218, 303), 'argparse.ArgumentParser', 'ArgumentParser', ([], {'description': '"""Unpack a MIME message into a directory of files.\n"""'}), "(description='Unpack a MIME message into a directory of files.\\n'\n )\n", (232, 303), False, 'from argparse import ArgumentParser\n'), ((671, 721), 'email.message_from_binary_file', 'email.message_from_binary_file', (['fp'], {'policy': 'default'}), '(fp, policy=default)\n', (701, 721), False, 'import email\n'), ((740, 764), 'os.mkdir', 'os.mkdir', (['args.directory'], {}), '(args.directory)\n', (748, 764), False, 'import os\n'), ((1445, 1483), 'os.path.join', 'os.path.join', (['args.directory', 'filename'], {}), '(args.directory, filename)\n', (1457, 1483), False, 'import os\n')] |
import sys, os
import logging
import datetime
module_name = 'Streetview_Module'
debug_mode = True
class LoggingWrapper(object):
def __init__(self, log_folder_path=None):
self.debug_mode = debug_mode
# Create logger with module name
logger = logging.getLogger(module_name)
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
now = datetime.datetime.now()
log_file = '{}{}{}{}{}{}.log'.format(now.year, now.month, now.day,
now.hour, now.minute,
now.second)
# If no folder provided, output to stderr
if log_folder_path == None:
fh = logging.StreamHandler(sys.stderr)
else:
log_file = os.path.join(log_folder_path, log_file)
fh = logging.FileHandler(log_file)
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
| [
"logging.getLogger",
"logging.StreamHandler",
"logging.Formatter",
"os.path.join",
"datetime.datetime.now",
"logging.FileHandler"
]
| [((273, 303), 'logging.getLogger', 'logging.getLogger', (['module_name'], {}), '(module_name)\n', (290, 303), False, 'import logging\n'), ((419, 442), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (440, 442), False, 'import datetime\n'), ((1020, 1043), 'logging.StreamHandler', 'logging.StreamHandler', ([], {}), '()\n', (1041, 1043), False, 'import logging\n'), ((1154, 1227), 'logging.Formatter', 'logging.Formatter', (['"""%(asctime)s - %(name)s - %(levelname)s - %(message)s"""'], {}), "('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n", (1171, 1227), False, 'import logging\n'), ((755, 788), 'logging.StreamHandler', 'logging.StreamHandler', (['sys.stderr'], {}), '(sys.stderr)\n', (776, 788), False, 'import logging\n'), ((826, 865), 'os.path.join', 'os.path.join', (['log_folder_path', 'log_file'], {}), '(log_folder_path, log_file)\n', (838, 865), False, 'import sys, os\n'), ((883, 912), 'logging.FileHandler', 'logging.FileHandler', (['log_file'], {}), '(log_file)\n', (902, 912), False, 'import logging\n')] |
# asandbox.py
#
# Authors:
# - <NAME> <<EMAIL>>
"""An asynchronous implementation of the Sandbox API."""
import io
import json
import os
from contextlib import AbstractAsyncContextManager
from typing import BinaryIO, Optional, Union
import aiohttp
from .exceptions import status_exceptions
from .utils import ENDPOINTS
class ASandbox(AbstractAsyncContextManager):
"""Interface a Sandbox server asynchronously."""
def __init__(self, url: str, total: Optional[float] = 60, connect: Optional[float] = None,
sock_connect: Optional[float] = None, sock_read: Optional[float] = None):
"""Initialize a sandbox with the given URL.
Default timeout for the whole operation is one minute, use the following
argument to override :
* total : The whole operation time including connection
establishment, request sending and response reading.
* connect : The time consists connection establishment for a new
connection or waiting for a free connection from a pool if
pool connection limits are exceeded.
* sock_connect : A timeout for connecting to a peer for a new
connection, not given from a pool.
* sock_read : The maximum allowed timeout for period between reading
a new data portion from a peer.
"""
self.url = url
self.session = aiohttp.ClientSession(
timeout=aiohttp.ClientTimeout(total, connect, sock_connect, sock_read)
)
async def __aexit__(self, exc_type, exc_val, exc_tb):
await self.close()
async def close(self):
"""Close the aiohttp ClientSession."""
await self.session.close()
async def _build_url(self, endpoint: str, *args: str):
"""Build the url corresponding to <endpoint> with the given <args>."""
return os.path.join(self.url, ENDPOINTS[endpoint] % tuple(args))
async def libraries(self) -> dict:
"""Asynchronously retrieve libraries installed in the containers of the
sandbox."""
async with self.session.get(await self._build_url("libraries")) as response:
if response.status != 200:
raise status_exceptions(response)
return await response.json()
async def specifications(self) -> dict:
"""Asynchronously retrieve specifications of the sandbox."""
async with self.session.get(await self._build_url("specifications")) as response:
if response.status != 200:
raise status_exceptions(response)
return await response.json()
async def usage(self) -> dict:
"""Asynchronously retrieve current usage stats of the sandbox."""
async with self.session.get(await self._build_url("usages")) as response:
if response.status != 200:
raise status_exceptions(response)
return await response.json()
async def download(self, uuid: str, path: str = None) -> BinaryIO:
"""Asynchronously download an environment or a specific file inside an
environment."""
if path is None:
url = await self._build_url("environments", uuid)
else:
url = await self._build_url("files", uuid, path)
async with self.session.get(url) as response:
if response.status != 200:
raise status_exceptions(response)
return io.BytesIO(await response.read())
async def check(self, uuid: str, path: str = None) -> int:
"""Asynchronously check if an environment or a specific file inside an
environment exists."""
if path is None:
url = await self._build_url("environments", uuid)
else:
url = await self._build_url("files", uuid, path)
async with self.session.head(url) as response:
if response.status not in [200, 404]: # pragma: no cover
raise status_exceptions(response)
return 0 if response.status == 404 else response.headers["Content-Length"]
async def execute(self, config: Union[dict], environ: Optional[BinaryIO] = None) -> dict:
"""Asynchronously execute commands on the sandbox according to <config>
and <environ>, returning the response's json as a dict.
<environ>, if not None, will be consumed and closed and shall not be
used further."""
data = aiohttp.FormData()
data.add_field("config", json.dumps(config))
if environ is not None:
data.add_field("environment", environ)
async with self.session.post(await self._build_url("execute"), data=data) as response:
if response.status != 200:
raise status_exceptions(response)
return await response.json()
async def load(self, environ: dict) -> dict:
"""Asynchronously execute commands on the sandbox according to <config>
and <environ>, returning the response's json as a dict.
<environ>, if not None, will be consumed and closed and shall not be
used further."""
data = aiohttp.FormData()
data.add_field("data", json.dumps(environ))
async with self.session.post(await self._build_url("load/fr"), data=data) as response:
if response.status != 200:
raise status_exceptions(response)
return await response.json()
async def demo(self, environ: dict) -> dict:
"""Asynchronously execute commands on the sandbox according to <config>
and <environ>, returning the response's json as a dict.
<environ>, if not None, will be consumed and closed and shall not be
used further."""
data = aiohttp.FormData()
data.add_field("data", json.dumps(environ))
data.add_field("demo", True)
async with self.session.post(await self._build_url("demo"), data=data) as response:
if response.status != 200:
raise status_exceptions(response)
return await response.json()
async def playexo(self, config: dict, environ: dict) -> dict:
"""Asynchronously execute commands on the sandbox according to <config>
and <environ>, returning the response's json as a dict.
<environ>, if not None, will be consumed and closed and shall not be
used further."""
data = aiohttp.FormData()
data.add_field("data", json.dumps(environ))
data.add_field("config", json.dumps(config))
async with self.session.post(await self._build_url("exo"), data=data) as response:
if response.status != 200:
raise status_exceptions(response)
return await response.json()
async def exec(self, datas: dict = {}) -> dict:
"""Asynchronously execute commands on the sandbox according to <config>
and <environ>, returning the response's json as a dict.
<environ>, if not None, will be consumed and closed and shall not be
used further."""
data = aiohttp.FormData()
data.add_field("data", json.dumps(datas))
for key, value in datas.items():
data.add_field(str(key), value)
async with self.session.post(await self._build_url("exec"), data=data) as response:
if response.status != 200:
raise status_exceptions(response)
return await response.json()
| [
"aiohttp.ClientTimeout",
"json.dumps",
"aiohttp.FormData"
]
| [((4665, 4683), 'aiohttp.FormData', 'aiohttp.FormData', ([], {}), '()\n', (4681, 4683), False, 'import aiohttp\n'), ((5388, 5406), 'aiohttp.FormData', 'aiohttp.FormData', ([], {}), '()\n', (5404, 5406), False, 'import aiohttp\n'), ((6024, 6042), 'aiohttp.FormData', 'aiohttp.FormData', ([], {}), '()\n', (6040, 6042), False, 'import aiohttp\n'), ((6714, 6732), 'aiohttp.FormData', 'aiohttp.FormData', ([], {}), '()\n', (6730, 6732), False, 'import aiohttp\n'), ((7404, 7422), 'aiohttp.FormData', 'aiohttp.FormData', ([], {}), '()\n', (7420, 7422), False, 'import aiohttp\n'), ((4717, 4735), 'json.dumps', 'json.dumps', (['config'], {}), '(config)\n', (4727, 4735), False, 'import json\n'), ((5438, 5457), 'json.dumps', 'json.dumps', (['environ'], {}), '(environ)\n', (5448, 5457), False, 'import json\n'), ((6074, 6093), 'json.dumps', 'json.dumps', (['environ'], {}), '(environ)\n', (6084, 6093), False, 'import json\n'), ((6764, 6783), 'json.dumps', 'json.dumps', (['environ'], {}), '(environ)\n', (6774, 6783), False, 'import json\n'), ((6818, 6836), 'json.dumps', 'json.dumps', (['config'], {}), '(config)\n', (6828, 6836), False, 'import json\n'), ((7454, 7471), 'json.dumps', 'json.dumps', (['datas'], {}), '(datas)\n', (7464, 7471), False, 'import json\n'), ((1525, 1587), 'aiohttp.ClientTimeout', 'aiohttp.ClientTimeout', (['total', 'connect', 'sock_connect', 'sock_read'], {}), '(total, connect, sock_connect, sock_read)\n', (1546, 1587), False, 'import aiohttp\n')] |
import datetime
from uuid import UUID
from api.actions import storage
from fastapi import HTTPException
from api.models.usuario import Usuario
from starlette.requests import Request
from api.dependencies import validar_email, validar_formato_fecha,validar_edad
FORMATO_FECHA = "%Y-%m-%d"
EDAD_MINIMA = 18
EDAD_MAXIMA = 100
class Usuarios_Services:
""" Sección de servicios para el manejo de la logica de negocio
Attributes:
FORMATO_FECHA (str): Formato de fecha para validar
EDAD_MINIMA (int): Edad minima para validar
EDAD_MAXIMA (int): Edad maxima para validar
"""
def agregar_usuario(self, usuario: Usuario, request: Request) -> dict:
""" Agrega un usuario a la base de datos.
:param usuario: Usuario a agregar
:param request: Request de FastAPI
"""
try:
if not validar_email(getattr(usuario, "email")):
raise HTTPException(
status_code=400,
detail="El email no es válido"
)
fecha_nacimiento = usuario.fecha_nacimiento
if not validar_formato_fecha(fecha_nacimiento, FORMATO_FECHA):
raise HTTPException(
status_code=400,
detail="El formato de la fecha de nacimiento no es válida"
)
usuario.fecha_nacimiento = datetime.datetime.strptime(fecha_nacimiento, FORMATO_FECHA)
if not validar_edad(usuario.fecha_nacimiento, EDAD_MINIMA, EDAD_MAXIMA):
raise HTTPException(
status_code=400,
detail="La edad no es válida"
)
usuario_id = storage.add(usuario, request)
return { "ID": usuario_id }
except Exception as e:
print("Error al agregar usuario: {}".format(str(e)))
raise e
def editar_usuario(self, usuario_id: UUID, usuario: Usuario, request: Request) -> dict:
""" Edita un usuario de la base de datos.
:param usuario_id: ID del usuario a editar
:param usuario: Usuario a editar
:param request: Request de FastAPI
"""
try:
if not validar_email(getattr(usuario, "email")):
raise HTTPException(
status_code=400,
detail="El email no es válido"
)
fecha_nacimiento = usuario.fecha_nacimiento
if not validar_formato_fecha(fecha_nacimiento, FORMATO_FECHA):
raise HTTPException(
status_code=400,
detail="El formato de la fecha de nacimiento no es válida"
)
usuario.fecha_nacimiento = datetime.datetime.strptime(fecha_nacimiento, FORMATO_FECHA)
if not validar_edad(usuario.fecha_nacimiento, EDAD_MINIMA, EDAD_MAXIMA):
raise HTTPException(
status_code=400,
detail="La edad no es válida"
)
storage.update(usuario_id, usuario, request)
return { "ID": usuario_id }
except Exception as e:
print("Error al editar usuario: {}".format(str(e)))
raise e
def eliminar_usuario(self, usuario_id: UUID, request: Request) -> dict:
""" Elimina un usuario de la base de datos.
:param usuario_id: ID del usuario a eliminar
:param request: Request de FastAPI
"""
try:
storage.delete(Usuario, usuario_id, request)
return { "ID": usuario_id }
except Exception as e:
print("Error al eliminar usuario: {}".format(str(e)))
raise e
def listar_usuarios(self, pagina: int, cantidad: int, order_by: str, sort: str, request: Request)-> dict:
""" Obtiene una lista de usuarios de la base de datos.
:param pagina: Pagina a retornar
:param cantidad: Cantidad de usuarios a retornar
:param order_by: Campo por el cual se ordenará la lista
:param sort: Orden ascendente o descendente
:param request: Request de FastAPI
"""
try:
return storage.get_all(Usuario, pagina, cantidad, request, order_by, sort)
except Exception as e:
print("Error al listar usuarios: {}".format(str(e)))
raise e
def obtener_usuario(self, usuario_id: UUID, request: Request) -> Usuario:
""" Retorna un usuario por su ID
:param usuario_id: ID del usuario a consultar
:param request: Request de FastAPI
"""
try:
usuario = storage.get_by_id(Usuario, usuario_id, request)
return usuario
except Exception as e:
print("Error al obtener usuario: {}".format(str(e)))
raise e | [
"fastapi.HTTPException",
"datetime.datetime.strptime",
"api.actions.storage.get_all",
"api.actions.storage.update",
"api.actions.storage.add",
"api.actions.storage.delete",
"api.actions.storage.get_by_id",
"api.dependencies.validar_edad",
"api.dependencies.validar_formato_fecha"
]
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# Copyright 2021 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
from recipe_engine import post_process
from PB.recipes.infra.windows_image_builder import windows_image_builder as wib
from PB.recipes.infra.windows_image_builder import actions
from PB.recipes.infra.windows_image_builder import sources
from recipe_engine.post_process import DropExpectation, StatusSuccess
from RECIPE_MODULES.infra.windows_scripts_executor import test_helper as t
DEPS = [
'depot_tools/gitiles',
'recipe_engine/platform',
'recipe_engine/properties',
'recipe_engine/raw_io',
'recipe_engine/json',
'windows_adk',
'windows_scripts_executor',
]
PYTHON_VERSION_COMPATIBILITY = 'PY3'
PROPERTIES = wib.Image
def RunSteps(api, image):
""" This recipe executes offline_winpe_customization."""
if not api.platform.is_win:
raise AssertionError('This recipe can only run on windows')
# this recipe will only execute the offline winpe customizations
for cust in image.customizations:
assert (cust.WhichOneof('customization') == 'offline_winpe_customization')
# initialize the image to scripts executor
api.windows_scripts_executor.init()
custs = api.windows_scripts_executor.init_customizations(image)
# pinning all the refs and generating unique keys
custs = api.windows_scripts_executor.process_customizations(custs)
# download all the required refs
api.windows_scripts_executor.download_all_packages(custs)
# download and install the windows ADK and WinPE packages
api.windows_adk.ensure()
# execute the customizations given
api.windows_scripts_executor.execute_customizations(custs)
wpe_image = 'wpe_image'
wpe_cust = 'generic'
arch = 'x86'
key = '9055a3e678be47d58bb860d27b85adbea41fd2ef3e22c5b7cb3180edf358de90'
def GenTests(api):
# actions for adding files from git
ACTION_ADD_STARTNET = actions.Action(
add_file=actions.AddFile(
name='add_startnet_file',
src=sources.Src(
git_src=sources.GITSrc(
repo='chromium.dev',
ref='HEAD',
src='windows/artifacts/startnet.cmd'),),
dst='Windows\\System32',
))
STARTNET_URL = 'chromium.dev/+/ef70cb069518e6dc3ff24bfae7f195de5099c377/' +\
'windows/artifacts/startnet.cmd'
yield (api.test('not_run_on_windows', api.platform('linux', 64)) +
api.expect_exception('AssertionError') +
api.post_process(DropExpectation))
yield (api.test('happy path', api.platform('win', 64)) + api.properties(
t.WPE_IMAGE(wpe_image, wib.ARCH_X86, wpe_cust, 'happy test',
[ACTION_ADD_STARTNET])) +
# mock all the init and deinit steps
t.MOCK_WPE_INIT_DEINIT_SUCCESS(api, key, arch, wpe_image, wpe_cust) +
# mock git pin file
t.GIT_PIN_FILE(api, wpe_cust, 'HEAD', 'windows/artifacts/startnet.cmd',
'HEAD') +
# mock add file to wpe_image mount dir step
t.ADD_FILE(api, wpe_image, wpe_cust, STARTNET_URL) +
# assert that the generated wpe_image was uploaded
t.CHECK_GCS_UPLOAD(
api, wpe_image, wpe_cust,
'\[CLEANUP\]\\\\{}\\\\workdir\\\\gcs.zip'.format(wpe_cust),
'gs://chrome-gce-images/WIB-WIM/{}.zip'.format(key)) +
api.post_process(StatusSuccess) + api.post_process(DropExpectation))
| [
"RECIPE_MODULES.infra.windows_scripts_executor.test_helper.ADD_FILE",
"RECIPE_MODULES.infra.windows_scripts_executor.test_helper.WPE_IMAGE",
"RECIPE_MODULES.infra.windows_scripts_executor.test_helper.GIT_PIN_FILE",
"RECIPE_MODULES.infra.windows_scripts_executor.test_helper.MOCK_WPE_INIT_DEINIT_SUCCESS",
"PB.recipes.infra.windows_image_builder.sources.GITSrc"
]
| [((3084, 3134), 'RECIPE_MODULES.infra.windows_scripts_executor.test_helper.ADD_FILE', 't.ADD_FILE', (['api', 'wpe_image', 'wpe_cust', 'STARTNET_URL'], {}), '(api, wpe_image, wpe_cust, STARTNET_URL)\n', (3094, 3134), True, 'from RECIPE_MODULES.infra.windows_scripts_executor import test_helper as t\n'), ((2084, 2174), 'PB.recipes.infra.windows_image_builder.sources.GITSrc', 'sources.GITSrc', ([], {'repo': '"""chromium.dev"""', 'ref': '"""HEAD"""', 'src': '"""windows/artifacts/startnet.cmd"""'}), "(repo='chromium.dev', ref='HEAD', src=\n 'windows/artifacts/startnet.cmd')\n", (2098, 2174), False, 'from PB.recipes.infra.windows_image_builder import sources\n'), ((2916, 2995), 'RECIPE_MODULES.infra.windows_scripts_executor.test_helper.GIT_PIN_FILE', 't.GIT_PIN_FILE', (['api', 'wpe_cust', '"""HEAD"""', '"""windows/artifacts/startnet.cmd"""', '"""HEAD"""'], {}), "(api, wpe_cust, 'HEAD', 'windows/artifacts/startnet.cmd', 'HEAD')\n", (2930, 2995), True, 'from RECIPE_MODULES.infra.windows_scripts_executor import test_helper as t\n'), ((2808, 2875), 'RECIPE_MODULES.infra.windows_scripts_executor.test_helper.MOCK_WPE_INIT_DEINIT_SUCCESS', 't.MOCK_WPE_INIT_DEINIT_SUCCESS', (['api', 'key', 'arch', 'wpe_image', 'wpe_cust'], {}), '(api, key, arch, wpe_image, wpe_cust)\n', (2838, 2875), True, 'from RECIPE_MODULES.infra.windows_scripts_executor import test_helper as t\n'), ((2648, 2736), 'RECIPE_MODULES.infra.windows_scripts_executor.test_helper.WPE_IMAGE', 't.WPE_IMAGE', (['wpe_image', 'wib.ARCH_X86', 'wpe_cust', '"""happy test"""', '[ACTION_ADD_STARTNET]'], {}), "(wpe_image, wib.ARCH_X86, wpe_cust, 'happy test', [\n ACTION_ADD_STARTNET])\n", (2659, 2736), True, 'from RECIPE_MODULES.infra.windows_scripts_executor import test_helper as t\n')] |
# -*- coding: utf-8 -*-
# Copyright 2020 Green Valley Belgium NV
#
# 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.
#
# @@license_version:1.7@@
from google.appengine.api import users as gusers
from mcfw.cache import CachedModelMixIn
from mcfw.consts import MISSING
from mcfw.restapi import register_postcall_hook, INJECTED_FUNCTIONS
from mcfw.rpc import serialize_value, get_type_details
from rogerthat.rpc import users
from rogerthat.utils import OFFLOAD_TYPE_WEB, offload
from rogerthat.utils.transactions import on_trans_committed
dummy = lambda: None
def log_restapi_call_result(function, success, kwargs, result_or_error):
if function.meta['silent']:
request_data = "****"
else:
kwarg_types = function.meta[u"kwarg_types"]
request_data = dict()
for arg, value in kwargs.iteritems():
if arg == 'accept_missing':
continue
if value == MISSING:
continue
request_data[arg] = serialize_value(value, *get_type_details(kwarg_types[arg], value), skip_missing=True)
if function.meta['silent_result']:
result = "****"
elif isinstance(result_or_error, Exception):
result = unicode(result_or_error)
else:
result = result_or_error
offload(users.get_current_user() or gusers.get_current_user(), OFFLOAD_TYPE_WEB, request_data,
result, function.meta['uri'], success)
register_postcall_hook(log_restapi_call_result)
INJECTED_FUNCTIONS.get_current_session = users.get_current_session
del log_restapi_call_result
CachedModelMixIn.on_trans_committed = lambda self, f, *args, **kwargs: on_trans_committed(f, *args, **kwargs)
| [
"mcfw.restapi.register_postcall_hook",
"rogerthat.utils.transactions.on_trans_committed",
"mcfw.rpc.get_type_details",
"rogerthat.rpc.users.get_current_user",
"google.appengine.api.users.get_current_user"
]
| [((1919, 1966), 'mcfw.restapi.register_postcall_hook', 'register_postcall_hook', (['log_restapi_call_result'], {}), '(log_restapi_call_result)\n', (1941, 1966), False, 'from mcfw.restapi import register_postcall_hook, INJECTED_FUNCTIONS\n'), ((2135, 2173), 'rogerthat.utils.transactions.on_trans_committed', 'on_trans_committed', (['f', '*args'], {}), '(f, *args, **kwargs)\n', (2153, 2173), False, 'from rogerthat.utils.transactions import on_trans_committed\n'), ((1779, 1803), 'rogerthat.rpc.users.get_current_user', 'users.get_current_user', ([], {}), '()\n', (1801, 1803), False, 'from rogerthat.rpc import users\n'), ((1807, 1832), 'google.appengine.api.users.get_current_user', 'gusers.get_current_user', ([], {}), '()\n', (1830, 1832), True, 'from google.appengine.api import users as gusers\n'), ((1507, 1548), 'mcfw.rpc.get_type_details', 'get_type_details', (['kwarg_types[arg]', 'value'], {}), '(kwarg_types[arg], value)\n', (1523, 1548), False, 'from mcfw.rpc import serialize_value, get_type_details\n')] |
# -*- encoding: utf-8 -*-
"""
Copyright (c) 2021 <EMAIL>
"""
from django.contrib import admin
from django.contrib.auth import views as auth_views
from django.urls import path, include
from django.conf import settings
from django.conf.urls.static import static
from apps.user import views as user_views
from.views import EditProfilePage
urlpatterns = [
#User
path('admin/', admin.site.urls),
path('register/', user_views.register, name='register'),
path('login/', auth_views.LoginView.as_view(template_name='registration/login.html'), name='login'),
path('profile/', user_views.profile, name='profile'),
path('edit_profile/', user_views.edit_profile, name='edit_profile'),
path("myprofile/", user_views.myprofile, name="Myprofile"),
path('logout/', auth_views.LogoutView.as_view(template_name='users/logout.html'), name='logout'),
#path('tinymce/', include('tinymce.urls')),
path('edit_profile_page/', user_views.EditProfilePage.as_view(template_name='registration/edit_profile_page.html'), name='edit_profile_page'),
# For PasswordPresset
path('admin/password_reset/',auth_views.PasswordResetView.as_view(),name='admin_password_reset',),
path('admin/password_reset/done/',auth_views.PasswordResetDoneView.as_view(),name='password_reset_done',),
path('reset/<uidb64>/<token>/',auth_views.PasswordResetConfirmView.as_view(),name='password_reset_confirm',),
path('reset/done/',auth_views.PasswordResetCompleteView.as_view(),name='password_reset_complete',),
] | [
"django.contrib.auth.views.PasswordResetDoneView.as_view",
"django.contrib.auth.views.PasswordResetCompleteView.as_view",
"apps.user.views.EditProfilePage.as_view",
"django.contrib.auth.views.LogoutView.as_view",
"django.contrib.auth.views.PasswordResetConfirmView.as_view",
"django.contrib.auth.views.LoginView.as_view",
"django.urls.path",
"django.contrib.auth.views.PasswordResetView.as_view"
]
| [((369, 400), 'django.urls.path', 'path', (['"""admin/"""', 'admin.site.urls'], {}), "('admin/', admin.site.urls)\n", (373, 400), False, 'from django.urls import path, include\n'), ((406, 461), 'django.urls.path', 'path', (['"""register/"""', 'user_views.register'], {'name': '"""register"""'}), "('register/', user_views.register, name='register')\n", (410, 461), False, 'from django.urls import path, include\n'), ((572, 624), 'django.urls.path', 'path', (['"""profile/"""', 'user_views.profile'], {'name': '"""profile"""'}), "('profile/', user_views.profile, name='profile')\n", (576, 624), False, 'from django.urls import path, include\n'), ((630, 697), 'django.urls.path', 'path', (['"""edit_profile/"""', 'user_views.edit_profile'], {'name': '"""edit_profile"""'}), "('edit_profile/', user_views.edit_profile, name='edit_profile')\n", (634, 697), False, 'from django.urls import path, include\n'), ((703, 761), 'django.urls.path', 'path', (['"""myprofile/"""', 'user_views.myprofile'], {'name': '"""Myprofile"""'}), "('myprofile/', user_views.myprofile, name='Myprofile')\n", (707, 761), False, 'from django.urls import path, include\n'), ((482, 551), 'django.contrib.auth.views.LoginView.as_view', 'auth_views.LoginView.as_view', ([], {'template_name': '"""registration/login.html"""'}), "(template_name='registration/login.html')\n", (510, 551), True, 'from django.contrib.auth import views as auth_views\n'), ((783, 847), 'django.contrib.auth.views.LogoutView.as_view', 'auth_views.LogoutView.as_view', ([], {'template_name': '"""users/logout.html"""'}), "(template_name='users/logout.html')\n", (812, 847), True, 'from django.contrib.auth import views as auth_views\n'), ((944, 1036), 'apps.user.views.EditProfilePage.as_view', 'user_views.EditProfilePage.as_view', ([], {'template_name': '"""registration/edit_profile_page.html"""'}), "(template_name=\n 'registration/edit_profile_page.html')\n", (978, 1036), True, 'from apps.user import views as user_views\n'), ((1120, 1158), 'django.contrib.auth.views.PasswordResetView.as_view', 'auth_views.PasswordResetView.as_view', ([], {}), '()\n', (1156, 1158), True, 'from django.contrib.auth import views as auth_views\n'), ((1228, 1270), 'django.contrib.auth.views.PasswordResetDoneView.as_view', 'auth_views.PasswordResetDoneView.as_view', ([], {}), '()\n', (1268, 1270), True, 'from django.contrib.auth import views as auth_views\n'), ((1336, 1381), 'django.contrib.auth.views.PasswordResetConfirmView.as_view', 'auth_views.PasswordResetConfirmView.as_view', ([], {}), '()\n', (1379, 1381), True, 'from django.contrib.auth import views as auth_views\n'), ((1438, 1484), 'django.contrib.auth.views.PasswordResetCompleteView.as_view', 'auth_views.PasswordResetCompleteView.as_view', ([], {}), '()\n', (1482, 1484), True, 'from django.contrib.auth import views as auth_views\n')] |
#! /usr/bin/env python
import cv2
import matplotlib.pyplot as plt
import skimage
import skimage.io
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.pyplot import cm
from mpl_toolkits.axes_grid1 import make_axes_locatable
from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, \
fromstring, ceil, dtype, float32, sqrt, dot, zeros
from misc import WithTimer
def norm01(arr):
arr = arr.copy()
arr -= arr.min()
arr /= arr.max() + 1e-10
return arr
def norm01c(arr, center):
'''Maps the input range to [0,1] such that the center value maps to .5'''
arr = arr.copy()
arr -= center
arr /= max(2 * arr.max(), -2 * arr.min()) + 1e-10
arr += .5
assert arr.min() >= 0
assert arr.max() <= 1
return arr
def norm0255(arr):
'''Maps the input range to [0,255] as dtype uint8'''
arr = arr.copy()
arr -= arr.min()
arr *= 255.0 / (arr.max() + 1e-10)
arr = array(arr, 'uint8')
return arr
def cv2_read_cap_rgb(cap, saveto=None):
rval, frame = cap.read()
if saveto:
cv2.imwrite(saveto, frame)
if len(frame.shape) == 2:
# Upconvert single channel grayscale to color
frame = frame[:, :, newaxis]
if frame.shape[2] == 1:
frame = tile(frame, (1, 1, 3))
if frame.shape[2] > 3:
# Chop off transparency
frame = frame[:, :, :3]
frame = frame[:, :, ::-1] # Convert native OpenCV BGR -> RGB
return frame
def plt_plot_signal(data, labels, zoom_level=-1, offset=0, markers=None, title=None):
fig = Figure(figsize=(5, 5))
canvas = FigureCanvas(fig)
ax = None
if len(data.shape) == 1:
data = expand_dims(data, axis=1)
if zoom_level == -1:
zoom_level = data.shape[0]
color = iter(cm.rainbow(linspace(0, 1, data.shape[1])))
s = offset
e = s + zoom_level
x = arange(s, e)
for i in range(data.shape[1]):
c = next(color)
label = labels[i] if labels is not None else 'Signal {}'.format(i + 1)
ax = fig.add_subplot(data.shape[1], 1, (i + 1), sharex=ax)
ax.plot(x, data[s:e, i], lw=1, label=label, c=c)
# # ax.set_adjustable('box-forced')
# ax.set_xlim(left=0, right=zoom_level)
# ax.get_xaxis().set_visible(i == data.shape[1] - 1)
# ax.xaxis.set_ticks(arange(s, e + 1, (e - s) / 10.0))
# ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
ax.legend(loc='lower right')
if markers is not None and i in markers:
for val in markers[i]:
if val >= s and val < e:
ax.axvline(x=val)
if title is not None:
fig.suptitle(title)
fig.tight_layout()
fig.subplots_adjust(hspace=0)
canvas.draw() # draw the canvas, cache the renderer
l, b, w, h = fig.bbox.bounds
w, h = int(w), int(h)
im = fromstring(canvas.tostring_rgb(), dtype='uint8')
im.shape = h, w, 3
return im
def plt_plot_heatmap(data,
shape,
rows,
cols,
title=None,
x_axis_label=None,
y_axis_label=None,
x_axis_values=None,
y_axis_values=None,
hide_axis=True,
vmin=None,
vmax=None):
res = []
shape = (max(2, ceil(shape[1] / 80 / cols)), max(2, ceil(shape[0] / 80 / rows)))
fig, ax = plt.subplots(1, 1, figsize=shape)
canvas = FigureCanvas(fig)
# for i in xrange(y.shape[0]):
# sns.heatmap(y[i], ax=ax, vmin=minn, vmax=maxx)
# canvas.draw() # draw the canvas, cache the renderer
#
# l, b, w, h = fig.bbox.bounds
# w, h = int(w), int(h)
# im = fromstring(canvas.tostring_rgb(), dtype='uint8')
# im.shape = h, w, 3
# res.append(im)
img = ax.imshow(
zeros((data.shape[1], data.shape[2])),
cmap='viridis',
vmin=vmin if vmin is not None else data.min(),
vmax=vmax if vmax is not None else data.max(),
interpolation='none',
aspect='auto'
)
# get rid of spines and fix range of axes, rotate x-axis labels
ax.spines['left'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
if hide_axis:
ax.set_xticks([])
ax.set_yticks([])
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95, hspace=0, wspace=0)
else:
if title is not None:
plt.title(title)
if x_axis_label is not None:
ax.set_xlabel(x_axis_label)
if y_axis_label is not None:
ax.set_ylabel(y_axis_label)
if x_axis_values is not None:
a = arange(0, x_axis_values.shape[0], 3) + 0.5
b = arange(x_axis_values.min(), x_axis_values.max() + 1.5, 1.5)
ax.set_xticks(a)
ax.set_xticklabels(b, rotation=90)
if y_axis_values is not None:
a = arange(0, y_axis_values.shape[0], 3) + 0.5
# c = roundup((y_axis_values.max() - y_axis_values.min()) / 11)
# b = arange(y_axis_values.min(), y_axis_values.max(), c)
b = linspace(y_axis_values.min(), y_axis_values.max(), num=10, dtype=int)
ax.set_yticks(a)
ax.set_yticklabels(b)
# for tick in ax.get_xticklabels():
# tick.set_rotation(90)
if not hide_axis:
divider = make_axes_locatable(ax)
# colorbar on the right of ax. Colorbar width in % of ax and space between them is defined by pad in inches
cax = divider.append_axes('right', size='5%', pad=0.07)
cb = fig.colorbar(img, cax=cax)
# remove colorbar frame/spines
cb.outline.set_visible(False)
# don't stop after each subfigure change
plt.show(block=False)
if not hide_axis:
fig.tight_layout()
canvas.draw() # draw the canvas, cache the renderer
# keep bg in memory
background = fig.canvas.copy_from_bbox(ax.bbox)
# start = time.time()
for i in xrange(data.shape[0]):
img.set_array(data[i])
# restore background
fig.canvas.restore_region(background)
ax.draw_artist(img)
# fill in the axes rectangle
fig.canvas.blit(ax.bbox)
# loop through array
# for i in xrange(data.shape[0]):
# time.sleep(0.005)
# img.set_array(data[i])
# canvas.draw()
l, b, w, h = fig.bbox.bounds
w, h = int(w), int(h)
im = fromstring(canvas.tostring_rgb(), dtype='uint8')
im.shape = h, w, 3
res.append(im)
fig.clf()
plt.clf()
plt.close()
return array(res)
def plt_plot_filter(x, y, title, x_axis_label, y_axis_label, log_scale):
fig, ax = plt.subplots(1, 1, figsize=(4, 4))
canvas = FigureCanvas(fig)
x = arange(0, y.shape[0]) if x is None else x
if log_scale == 1:
ax.semilogy(x, y, lw=2)
else:
ax.plot(x, y, lw=2)
ax.set(xlabel=x_axis_label, ylabel=y_axis_label, title=title)
fig.tight_layout()
canvas.draw() # draw the canvas, cache the renderer
l, b, w, h = fig.bbox.bounds
w, h = int(w), int(h)
im = fromstring(canvas.tostring_rgb(), dtype='uint8')
im.shape = h, w, 3
fig.clf()
plt.clf()
plt.close()
return im
def plt_plot_filters_blit(y, x, shape, rows, cols,
title=None,
x_axis_label=None,
y_axis_label=None,
log_scale=0,
hide_axis=False):
res = []
x = arange(0, y.shape[1]) if x is None else x
# if log_scale == 1:
# y = log(y)
# elif log_scale == 2:
# x = log(x)
# elif log_scale == 3:
# x = log(x)
# y = log(y)
shape = (max(2, ceil(shape[1] / 80 / cols)), max(2, ceil(shape[0] / 80 / rows)))
fig, ax = plt.subplots(1, 1, figsize=shape)
canvas = FigureCanvas(fig)
ax.set_xlim(min(x), max(x))
ax.set_ylim(y.min(), y.max())
if hide_axis:
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95, hspace=0, wspace=0)
else:
if x_axis_label is not None:
ax.set_xlabel(x_axis_label)
if y_axis_label is not None:
ax.set_ylabel(y_axis_label)
if title is not None:
plt.title(title)
line, = ax.plot([], [], lw=2)
if not hide_axis:
fig.tight_layout()
canvas.draw() # draw the canvas, cache the renderer
# keep bg in memory
background = fig.canvas.copy_from_bbox(ax.bbox)
for i in xrange(y.shape[0]):
line.set_data(x, y[i])
# line.set_color()
# restore background
fig.canvas.restore_region(background)
# redraw just the points
ax.draw_artist(line)
# fill in the axes rectangle
fig.canvas.blit(ax.bbox)
l, b, w, h = fig.bbox.bounds
w, h = int(w), int(h)
im = fromstring(canvas.tostring_rgb(), dtype='uint8')
im.shape = h, w, 3
res.append(im)
fig.clf()
plt.clf()
plt.close()
return array(res)
def plt_plot_filters_fast(y, x, shape, rows, cols,
title=None,
x_axis_label=None,
y_axis_label=None,
share_axes=True,
log_scale=0):
res = []
shape = (ceil(shape[1] / 80 / cols), ceil(shape[0] / 80 / rows))
fig, ax = plt.subplots(1, 1, figsize=shape)
canvas = FigureCanvas(fig)
# ax.set_aspect('equal')
if share_axes:
if x is not None:
min_x, max_x = min(x), max(x)
else:
min_x, max_x = 0, y.shape[1]
min_y, max_y = y.min(), y.max()
ax.set_xlim(min_x, max_x)
ax.set_ylim(min_y, max_y)
# ax.hold(True)
plt.subplots_adjust(left=0.185, bottom=0.125, right=0.98, top=0.98)
# plt.show(False)
# plt.draw()
# background = fig.canvas.copy_from_bbox(ax.bbox)
# points = ax.plot(x[0], linewidth=1)[0]
for i in xrange(y.shape[0]):
if x is not None:
if log_scale == 1:
ax.semilogy(x, y[i], linewidth=1)
else:
ax.plot(x, y[i], linewidth=1)
else:
if log_scale == 1:
ax.semilogy(y[i], linewidth=1)
else:
ax.plot(y[i], linewidth=1)
if x_axis_label is not None:
ax.set_xlabel(x_axis_label)
if y_axis_label is not None:
ax.set_ylabel(y_axis_label)
if title is not None:
plt.title(title)
# plt.autoscale(enable=True, axis='y', tight=True)
# plt.tight_layout()
# Turn off axes and set axes limits
# ax.axis('off')
canvas.draw() # draw the canvas, cache the renderer
l, b, w, h = fig.bbox.bounds
w, h = int(w), int(h)
im = fromstring(canvas.tostring_rgb(), dtype='uint8')
im.shape = h, w, 3
res.append(im)
# ax.cla()
fig.clf()
return array(res)
def plt_plot_filters(x, y, shape, rows, cols,
selected_unit=None,
selected_unit_color=None,
title=None,
x_axis_label=None,
y_axis_label=None,
share_axes=True,
log_scale=0):
shape = (ceil(shape[1] / 80), ceil(shape[0] / 80))
fig = Figure(figsize=shape)
canvas = FigureCanvas(fig)
ax, highlighted_ax, right_ax, bottom_ax, curr, right, bottom = None, None, None, None, None, None, None
if selected_unit is not None:
row = selected_unit / cols
col = selected_unit % cols
curr = selected_unit
bottom = (selected_unit + cols) if row < rows - 1 else None
right = (selected_unit + 1) if col < cols - 1 else None
for i in xrange(x.shape[0]):
if share_axes:
ax = fig.add_subplot(rows, cols, (i + 1), axisbelow=False, sharex=ax, sharey=ax)
else:
ax = fig.add_subplot(rows, cols, (i + 1), axisbelow=False)
if y is not None:
if log_scale == 1:
ax.semilogy(y, x[i], linewidth=1)
else:
ax.plot(y, x[i], linewidth=1)
else:
if log_scale == 1:
ax.semilogy(x[i], linewidth=1)
else:
ax.plot(x[i], linewidth=1)
ax.set_xlim(left=0, right=x.shape[1] - 1)
ax.get_xaxis().set_visible(i >= ((rows - 1) * cols))
ax.get_yaxis().set_visible(i % cols == 0)
if i == curr:
highlighted_ax = ax
if i == bottom:
bottom_ax = ax
if i == right:
right_ax = ax
if x_axis_label is not None:
ax.set_xlabel(x_axis_label)
if y_axis_label is not None:
ax.set_ylabel(y_axis_label)
if highlighted_ax is not None:
for axis in ['top', 'bottom', 'left', 'right']:
highlighted_ax.spines[axis].set_linewidth(2.5)
highlighted_ax.spines[axis].set_color(selected_unit_color)
if bottom_ax is not None:
bottom_ax.spines['top'].set_linewidth(2)
bottom_ax.spines['top'].set_color(selected_unit_color)
if right_ax is not None:
right_ax.spines['left'].set_linewidth(2)
right_ax.spines['left'].set_color(selected_unit_color)
if title is not None:
fig.suptitle(title)
fig.tight_layout()
fig.subplots_adjust(hspace=0, wspace=0)
canvas.draw() # draw the canvas, cache the renderer
l, b, w, h = fig.bbox.bounds
w, h = int(w), int(h)
im = fromstring(canvas.tostring_rgb(), dtype='uint8')
im.shape = h, w, 3
return im
def cv2_read_file_rgb(filename):
'''Reads an image from file. Always returns (x,y,3)'''
im = cv2.imread(filename)
if len(im.shape) == 2:
# Upconvert single channel grayscale to color
im = im[:, :, newaxis]
if im.shape[2] == 1:
im = tile(im, (1, 1, 3))
if im.shape[2] > 3:
# Chop off transparency
im = im[:, :, :3]
return cv2.cvtColor(im, cv2.COLOR_BGR2RGB) # Convert native OpenCV BGR -> RGB
def crop_to_square(frame):
i_size, j_size = frame.shape[0], frame.shape[1]
if j_size > i_size:
# landscape
offset = (j_size - i_size) / 2
return frame[:, offset:offset + i_size, :]
else:
# portrait
offset = (i_size - j_size) / 2
return frame[offset:offset + j_size, :, :]
def cv2_imshow_rgb(window_name, img):
# Convert native OpenCV BGR -> RGB before displaying
cv2.imshow(window_name, cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
def caffe_load_image(filename, color=True, as_uint=False):
'''
Copied from Caffe to simplify potential import problems.
Load an image converting from grayscale or alpha as needed.
Take
filename: string
color: flag for color format. True (default) loads as RGB while False
loads as intensity (if image is already grayscale).
Give
image: an image with type float32 in range [0, 1]
of size (H x W x 3) in RGB or
of size (H x W x 1) in grayscale.
'''
with WithTimer('imread', quiet=True):
if as_uint:
img = skimage.io.imread(filename)
else:
img = skimage.img_as_float(skimage.io.imread(filename)).astype(float32)
if img.ndim == 2:
img = img[:, :, newaxis]
if color:
img = tile(img, (1, 1, 3))
elif img.shape[2] == 4:
img = img[:, :, :3]
return img
def get_tiles_height_width(n_tiles, desired_width=None):
'''Get a height x width size that will fit n_tiles tiles.'''
if desired_width == None:
# square
width = int(ceil(sqrt(n_tiles)))
height = width
else:
assert isinstance(desired_width, int)
width = desired_width
height = int(ceil(float(n_tiles) / width))
return height, width
def get_tiles_height_width_ratio(n_tiles, width_ratio=1.0):
'''Get a height x width size that will fit n_tiles tiles.'''
width = int(ceil(sqrt(n_tiles * width_ratio)))
return get_tiles_height_width(n_tiles, desired_width=width)
def tile_images_normalize(data, c01=False, boost_indiv=0.0, boost_gamma=1.0, single_tile=False, scale_range=1.0,
neg_pos_colors=None):
data = data.copy()
if single_tile:
# promote 2D image -> 3D batch (01 -> b01) or 3D image -> 4D batch (01c -> b01c OR c01 -> bc01)
data = data[newaxis]
if c01:
# Convert bc01 -> b01c
assert len(data.shape) == 4, 'expected bc01 data'
data = data.transpose(0, 2, 3, 1)
if neg_pos_colors:
neg_clr, pos_clr = neg_pos_colors
neg_clr = array(neg_clr).reshape((1, 3))
pos_clr = array(pos_clr).reshape((1, 3))
# Keep 0 at 0
data /= max(data.max(), -data.min()) + 1e-10 # Map data to [-1, 1]
# data += .5 * scale_range # now in [0, scale_range]
# assert data.min() >= 0
# assert data.max() <= scale_range
if len(data.shape) == 3:
data = data.reshape(data.shape + (1,))
assert data.shape[3] == 1, 'neg_pos_color only makes sense if color data is not provided (channels should be 1)'
data = dot((data > 0) * data, pos_clr) + dot((data < 0) * -data, neg_clr)
data -= data.min()
data *= scale_range / (data.max() + 1e-10)
# sqrt-scale (0->0, .1->.3, 1->1)
assert boost_indiv >= 0 and boost_indiv <= 1, 'boost_indiv out of range'
# print 'using boost_indiv:', boost_indiv
if boost_indiv > 0:
if len(data.shape) == 4:
mm = (data.max(-1).max(-1).max(-1) + 1e-10) ** -boost_indiv
else:
mm = (data.max(-1).max(-1) + 1e-10) ** -boost_indiv
data = (data.T * mm).T
if boost_gamma != 1.0:
data = data ** boost_gamma
# Promote single-channel data to 3 channel color
if len(data.shape) == 3:
# b01 -> b01c
data = tile(data[:, :, :, newaxis], 3)
return data
def tile_images_make_tiles(data, padsize=1, padval=0, hw=None, highlights=None):
if hw:
height, width = hw
else:
height, width = get_tiles_height_width(data.shape[0])
assert height * width >= data.shape[0], '{} rows x {} columns cannot fit {} tiles'.format(height, width,
data.shape[0])
# First iteration: one-way padding, no highlights
# padding = ((0, width*height - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)
# data = pad(data, padding, mode='constant', constant_values=(padval, padval))
# Second iteration: padding with highlights
# padding = ((0, width*height - data.shape[0]), (padsize, padsize), (padsize, padsize)) + ((0, 0),) * (data.ndim - 3)
# print 'tile_images: data min,max =', data.min(), data.max()
# padder = SmartPadder()
##data = pad(data, padding, mode=jy_pad_fn)
# data = pad(data, padding, mode=padder.pad_function)
# print 'padder.calls =', padder.calls
# Third iteration: two-way padding with highlights
if highlights is not None:
assert len(highlights) == data.shape[0]
padding = ((0, width * height - data.shape[0]), (padsize, padsize), (padsize, padsize)) + ((0, 0),) * (
data.ndim - 3)
# First pad with constant vals
try:
len(padval)
except:
padval = tuple((padval,))
assert len(padval) in (1, 3), 'padval should be grayscale (len 1) or color (len 3)'
if len(padval) == 1:
data = pad(data, padding, mode='constant', constant_values=(padval, padval))
else:
data = pad(data, padding, mode='constant', constant_values=(0, 0))
for cc in (0, 1, 2):
# Replace 0s with proper color in each channel
data[:padding[0][0], :, :, cc] = padval[cc]
if padding[0][1] > 0:
data[-padding[0][1]:, :, :, cc] = padval[cc]
data[:, :padding[1][0], :, cc] = padval[cc]
if padding[1][1] > 0:
data[:, -padding[1][1]:, :, cc] = padval[cc]
data[:, :, :padding[2][0], cc] = padval[cc]
if padding[2][1] > 0:
data[:, :, -padding[2][1]:, cc] = padval[cc]
if highlights is not None:
# Then highlight if necessary
for ii, highlight in enumerate(highlights):
if highlight is not None:
data[ii, :padding[1][0], :, :] = highlight
if padding[1][1] > 0:
data[ii, -padding[1][1]:, :, :] = highlight
data[ii, :, :padding[2][0], :] = highlight
if padding[2][1] > 0:
data[ii, :, -padding[2][1]:, :] = highlight
# tile the filters into an image
data = data.reshape((height, width) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))
data = data.reshape((height * data.shape[1], width * data.shape[3]) + data.shape[4:])
data = data[0:-padsize, 0:-padsize] # remove excess padding
return (height, width), data
def to_255(vals_01):
'''Convert vals in [0,1] to [0,255]'''
try:
ret = [v * 255 for v in vals_01]
if type(vals_01) is tuple:
return tuple(ret)
else:
return ret
except TypeError:
# Not iterable (single int or float)
return vals_01 * 255
def ensure_uint255_and_resize_to_fit(img, out_max_shape,
shrink_interpolation=cv2.INTER_LINEAR,
grow_interpolation=cv2.INTER_NEAREST):
as_uint255 = ensure_uint255(img)
return resize_to_fit(as_uint255, out_max_shape,
dtype_out='uint8',
shrink_interpolation=shrink_interpolation,
grow_interpolation=grow_interpolation)
def ensure_uint255(arr):
'''If data is float, multiply by 255 and convert to uint8. Else leave as uint8.'''
if arr.dtype == 'uint8':
return arr
elif arr.dtype in ('float32', 'float64'):
# print 'extra check...'
# assert arr.max() <= 1.1
return array(arr * 255, dtype='uint8')
else:
raise Exception('ensure_uint255 expects uint8 or float input but got %s with range [%g,%g,].' % (
arr.dtype, arr.min(), arr.max()))
def ensure_float01(arr, dtype_preference='float32'):
'''If data is uint, convert to float and divide by 255. Else leave at float.'''
if arr.dtype == 'uint8':
# print 'extra check...'
# assert arr.max() <= 256
return array(arr, dtype=dtype_preference) / 255
elif arr.dtype in ('float32', 'float64'):
return arr
else:
raise Exception('ensure_float01 expects uint8 or float input but got %s with range [%g,%g,].' % (
arr.dtype, arr.min(), arr.max()))
def resize_to_fit(img, out_max_shape,
dtype_out=None,
shrink_interpolation=cv2.INTER_LINEAR,
grow_interpolation=cv2.INTER_NEAREST):
'''Resizes to fit within out_max_shape. If ratio is different,
returns an image that fits but is smaller along one of the two
dimensions.
If one of the out_max_shape dimensions is None, then use only the other dimension to perform resizing.
Timing info on MBP Retina with OpenBlas:
- conclusion: uint8 is always tied or faster. float64 is slower.
Scaling down:
In [79]: timeit.Timer('resize_to_fit(aa, (200,200))', setup='from kerasvis.app import resize_to_fit; import numpy as np; aa = array(np.random.uniform(0,255,(1000,1000,3)), dtype="uint8")').timeit(100)
Out[79]: 0.04950380325317383
In [77]: timeit.Timer('resize_to_fit(aa, (200,200))', setup='from kerasvis.app import resize_to_fit; import numpy as np; aa = array(np.random.uniform(0,255,(1000,1000,3)), dtype="float32")').timeit(100)
Out[77]: 0.049156904220581055
In [76]: timeit.Timer('resize_to_fit(aa, (200,200))', setup='from kerasvis.app import resize_to_fit; import numpy as np; aa = array(np.random.uniform(0,255,(1000,1000,3)), dtype="float64")').timeit(100)
Out[76]: 0.11808204650878906
Scaling up:
In [68]: timeit.Timer('resize_to_fit(aa, (2000,2000))', setup='from kerasvis.app import resize_to_fit; import numpy as np; aa = array(np.random.uniform(0,255,(1000,1000,3)), dtype="uint8")').timeit(100)
Out[68]: 0.4357950687408447
In [70]: timeit.Timer('resize_to_fit(aa, (2000,2000))', setup='from kerasvis.app import resize_to_fit; import numpy as np; aa = array(np.random.uniform(0,255,(1000,1000,3)), dtype="float32")').timeit(100)
Out[70]: 1.3411099910736084
In [73]: timeit.Timer('resize_to_fit(aa, (2000,2000))', setup='from kerasvis.app import resize_to_fit; import numpy as np; aa = array(np.random.uniform(0,255,(1000,1000,3)), dtype="float64")').timeit(100)
Out[73]: 2.6078310012817383
'''
if dtype_out is not None and img.dtype != dtype_out:
dtype_in_size = img.dtype.itemsize
dtype_out_size = dtype(dtype_out).itemsize
convert_early = (dtype_out_size < dtype_in_size)
convert_late = not convert_early
else:
convert_early = False
convert_late = False
if img.shape[0] == 0 and img.shape[1] == 0:
scale = 1
elif out_max_shape[0] is None or img.shape[0] == 0:
scale = float(out_max_shape[1]) / img.shape[1]
elif out_max_shape[1] is None or img.shape[1] == 0:
scale = float(out_max_shape[0]) / img.shape[0]
else:
scale = min(float(out_max_shape[0]) / img.shape[0],
float(out_max_shape[1]) / img.shape[1])
if convert_early:
img = array(img, dtype=dtype_out)
out = cv2.resize(img,
(int(img.shape[1] * scale), int(img.shape[0] * scale)), # in (c,r) order
interpolation=grow_interpolation if scale > 1 else shrink_interpolation)
if convert_late:
out = array(out, dtype=dtype_out)
return out
class FormattedString(object):
def __init__(self, string, defaults, face=None, fsize=None, clr=None, thick=None, align=None, width=None):
self.string = string
self.face = face if face else defaults['face']
self.fsize = fsize if fsize else defaults['fsize']
self.clr = clr if clr else defaults['clr']
self.thick = thick if thick else defaults['thick']
self.width = width # if None: calculate width automatically
self.align = align if align else defaults.get('align', 'left')
def cv2_typeset_text(data, lines, loc, between=' ', string_spacing=0, line_spacing=0, wrap=False):
'''Typesets mutliple strings on multiple lines of text, where each string may have its own formatting.
Given:
data: as in cv2.putText
loc: as in cv2.putText
lines: list of lists of FormattedString objects, may be modified by this function!
between: what to insert between each string on each line, ala str.join
string_spacing: extra spacing to insert between strings on a line
line_spacing: extra spacing to insert between lines
wrap: if true, wraps words to next line
Returns:
locy: new y location = loc[1] + y-offset resulting from lines of text
'''
data_width = data.shape[1]
# lines_modified = False
# lines = lines_in # will be deepcopied if modification is needed later
if isinstance(lines, FormattedString):
lines = [lines]
assert isinstance(lines,
list), 'lines must be a list of lines or list of FormattedString objects or a single FormattedString object'
if len(lines) == 0:
return loc[1]
if not isinstance(lines[0], list):
# If a single line of text is given as a list of strings, convert to multiline format
lines = [lines]
locy = loc[1]
line_num = 0
while line_num < len(lines):
line = lines[line_num]
maxy = 0
locx = loc[0]
for ii, fs in enumerate(line):
last_on_line = (ii == len(line) - 1)
if not last_on_line:
fs.string += between
boxsize, _ = cv2.getTextSize(fs.string, fs.face, fs.fsize, fs.thick)
if fs.width is not None:
if fs.align == 'right':
locx += fs.width - boxsize[0]
elif fs.align == 'center':
locx += (fs.width - boxsize[0]) / 2
# print 'right boundary is', locx + boxsize[0], '(%s)' % fs.string
# print 'HERE'
right_edge = locx + boxsize[0]
if wrap and ii > 0 and right_edge > data_width:
# Wrap rest of line to the next line
# if not lines_modified:
# lines = deepcopy(lines_in)
# lines_modified = True
new_this_line = line[:ii]
new_next_line = line[ii:]
lines[line_num] = new_this_line
lines.insert(line_num + 1, new_next_line)
break
###line_num += 1
###continue
cv2.putText(data, fs.string, (locx, locy), fs.face, fs.fsize, fs.clr, fs.thick)
maxy = max(maxy, boxsize[1])
if fs.width is not None:
if fs.align == 'right':
locx += boxsize[0]
elif fs.align == 'left':
locx += fs.width
elif fs.align == 'center':
locx += fs.width - (fs.width - boxsize[0]) / 2
else:
locx += boxsize[0]
locx += string_spacing
line_num += 1
locy += maxy + line_spacing
return locy
def saveimage(filename, im):
'''Saves an image with pixel values in [0,1]'''
# matplotlib.image.imsave(filename, im)
if len(im.shape) == 3:
# Reverse RGB to OpenCV BGR order for color images
cv2.imwrite(filename, 255 * im[:, :, ::-1])
else:
cv2.imwrite(filename, 255 * im)
def saveimagesc(filename, im):
saveimage(filename, norm01(im))
def saveimagescc(filename, im, center):
saveimage(filename, norm01c(im, center))
| [
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"numpy.dot",
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"numpy.tile",
"numpy.ceil",
"cv2.putText",
"skimage.io.imread",
"cv2.cvtColor",
"matplotlib.pyplot.title",
"cv2.getTextSize",
"cv2.imread",
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"misc.WithTimer",
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"matplotlib.pyplot.clf",
"numpy.zeros",
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"numpy.expand_dims",
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"matplotlib.pyplot.subplots"
]
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'"""constant"""', 'constant_values': '(0, 0)'}), "(data, padding, mode='constant', constant_values=(0, 0))\n", (20298, 20354), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((26336, 26363), 'numpy.array', 'array', (['img'], {'dtype': 'dtype_out'}), '(img, dtype=dtype_out)\n', (26341, 26363), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((26615, 26642), 'numpy.array', 'array', (['out'], {'dtype': 'dtype_out'}), '(out, dtype=dtype_out)\n', (26620, 26642), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((30594, 30637), 'cv2.imwrite', 'cv2.imwrite', (['filename', '(255 * im[:, :, ::-1])'], {}), '(filename, 255 * im[:, :, ::-1])\n', (30605, 30637), False, 'import cv2\n'), ((30656, 30687), 'cv2.imwrite', 'cv2.imwrite', (['filename', '(255 * im)'], {}), '(filename, 255 * im)\n', (30667, 30687), False, 'import cv2\n'), ((1862, 1891), 'numpy.linspace', 'linspace', (['(0)', '(1)', 'data.shape[1]'], {}), '(0, 1, data.shape[1])\n', (1870, 1891), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((3477, 3503), 'numpy.ceil', 'ceil', (['(shape[1] / 80 / cols)'], {}), '(shape[1] / 80 / cols)\n', (3481, 3503), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((3513, 3539), 'numpy.ceil', 'ceil', (['(shape[0] / 80 / rows)'], {}), '(shape[0] / 80 / rows)\n', (3517, 3539), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((4840, 4856), 'matplotlib.pyplot.title', 'plt.title', (['title'], {}), '(title)\n', (4849, 4856), True, 'import 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expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((16651, 16678), 'numpy.sqrt', 'sqrt', (['(n_tiles * width_ratio)'], {}), '(n_tiles * width_ratio)\n', (16655, 16678), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((17848, 17879), 'numpy.dot', 'dot', (['((data > 0) * data)', 'pos_clr'], {}), '((data > 0) * data, pos_clr)\n', (17851, 17879), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((17882, 17914), 'numpy.dot', 'dot', (['((data < 0) * -data)', 'neg_clr'], {}), '((data < 0) * -data, neg_clr)\n', (17885, 17914), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((22799, 22830), 'numpy.array', 'array', (['(arr * 255)'], {'dtype': '"""uint8"""'}), "(arr * 255, dtype='uint8')\n", (22804, 22830), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((23243, 23277), 'numpy.array', 'array', (['arr'], {'dtype': 'dtype_preference'}), '(arr, dtype=dtype_preference)\n', (23248, 23277), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((25687, 25703), 'numpy.dtype', 'dtype', (['dtype_out'], {}), '(dtype_out)\n', (25692, 25703), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((28793, 28848), 'cv2.getTextSize', 'cv2.getTextSize', (['fs.string', 'fs.face', 'fs.fsize', 'fs.thick'], {}), '(fs.string, fs.face, fs.fsize, fs.thick)\n', (28808, 28848), False, 'import cv2\n'), ((29785, 29864), 'cv2.putText', 'cv2.putText', (['data', 'fs.string', '(locx, locy)', 'fs.face', 'fs.fsize', 'fs.clr', 'fs.thick'], {}), '(data, fs.string, (locx, locy), fs.face, fs.fsize, fs.clr, fs.thick)\n', (29796, 29864), False, 'import cv2\n'), ((5068, 5104), 'numpy.arange', 'arange', (['(0)', 'x_axis_values.shape[0]', '(3)'], {}), '(0, x_axis_values.shape[0], 3)\n', (5074, 5104), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((5318, 5354), 'numpy.arange', 'arange', (['(0)', 'y_axis_values.shape[0]', '(3)'], {}), '(0, y_axis_values.shape[0], 3)\n', (5324, 5354), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((16302, 16315), 'numpy.sqrt', 'sqrt', (['n_tiles'], {}), '(n_tiles)\n', (16306, 16315), False, 'from numpy import arange, array, newaxis, tile, linspace, pad, expand_dims, fromstring, ceil, dtype, float32, sqrt, dot, zeros\n'), ((17311, 17325), 'numpy.array', 'array', (['neg_clr'], {}), '(neg_clr)\n', (17316, 17325), False, 'from 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import pygame.font
import copy
class Text:
"""Draws a text to the screen."""
def __init__(self, rect, size, color, screen, text):
self.screen = screen
self.rect = copy.deepcopy(rect)
self.text = text
self.color = color
self.font = pygame.font.SysFont(None, size)
self.text_image = None
self.text_image_rect = None
self.prep_img()
def prep_img(self):
"""Turn msg into a rendered image, and center text on the button."""
self.text_image = self.font.render(self.text, True,
self.color)
self.text_image_rect = self.text_image.get_rect()
self.text_image_rect.center = self.rect.center
def render(self):
self.screen.blit(self.text_image, self.text_image_rect)
| [
"copy.deepcopy"
]
| [((190, 209), 'copy.deepcopy', 'copy.deepcopy', (['rect'], {}), '(rect)\n', (203, 209), False, 'import copy\n')] |
# Licensed to the Software Freedom Conservancy (SFC) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The SFC licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
import pytest
from selenium.common.exceptions import WebDriverException
from selenium.webdriver.support.wait import WebDriverWait
# @pytest.mark.xfail_ie
# @pytest.mark.xfail_chromiumedge(reason="Fails on Travis")
# @pytest.mark.xfail_firefox(reason="Fails on Travis")
# @pytest.mark.xfail_remote(reason="Fails on Travis")
# def testShouldMaximizeTheWindow(driver):
# resize_timeout = 5
# wait = WebDriverWait(driver, resize_timeout)
# old_size = driver.get_window_size()
# driver.set_window_size(200, 200)
# wait.until(
# lambda dr: dr.get_window_size() != old_size if old_size["width"] != 200 and old_size["height"] != 200 else True)
# size = driver.get_window_size()
# driver.maximize_window()
# wait.until(lambda dr: dr.get_window_size() != size)
# new_size = driver.get_window_size()
# assert new_size["width"] > size["width"]
# assert new_size["height"] > size["height"]
def test_should_get_the_size_of_the_current_window(driver):
size = driver.get_window_size()
assert size.get('width') > 0
assert size.get('height') > 0
def test_should_set_the_size_of_the_current_window(driver):
size = driver.get_window_size()
target_width = size.get('width') - 20
target_height = size.get('height') - 20
driver.set_window_size(width=target_width, height=target_height)
new_size = driver.get_window_size()
assert new_size.get('width') == target_width
assert new_size.get('height') == target_height
def test_should_get_the_position_of_the_current_window(driver):
position = driver.get_window_position()
assert position.get('x') >= 0
assert position.get('y') >= 0
def test_should_set_the_position_of_the_current_window(driver):
position = driver.get_window_position()
target_x = position.get('x') + 10
target_y = position.get('y') + 10
driver.set_window_position(x=target_x, y=target_y)
WebDriverWait(driver, 2)\
.until(lambda d: d.get_window_position()['x'] != position['x'] and d.get_window_position()['y'] != position['y'])
new_position = driver.get_window_position()
assert new_position.get('x') == target_x
assert new_position.get('y') == target_y
@pytest.mark.xfail_safari(raises=WebDriverException,
reason='Get Window Rect command not implemented')
def test_should_get_the_rect_of_the_current_window(driver):
rect = driver.get_window_rect()
assert rect.get('x') >= 0
assert rect.get('y') >= 0
assert rect.get('width') >= 0
assert rect.get('height') >= 0
@pytest.mark.xfail_safari(raises=WebDriverException,
reason='Get Window Rect command not implemented')
def test_should_set_the_rect_of_the_current_window(driver):
rect = driver.get_window_rect()
target_x = rect.get('x') + 10
target_y = rect.get('y') + 10
target_width = rect.get('width') + 10
target_height = rect.get('height') + 10
driver.set_window_rect(x=target_x, y=target_y, width=target_width, height=target_height)
WebDriverWait(driver, 2)\
.until(lambda d: d.get_window_position()['x'] != rect['x'] and d.get_window_position()['y'] != rect['y'])
new_rect = driver.get_window_rect()
assert new_rect.get('x') == target_x
assert new_rect.get('y') == target_y
assert new_rect.get('width') == target_width
assert new_rect.get('height') == target_height
# @pytest.mark.xfail_safari(raises=WebDriverException,
# reason='Fullscreen command not implemented')
# @pytest.mark.skipif(os.environ.get('TRAVIS') == 'true',
# reason='Fullscreen command causes Travis to hang')
# @pytest.mark.no_driver_after_test
# def test_should_fullscreen_the_current_window(driver):
# start_width = driver.execute_script('return window.innerWidth;')
# start_height = driver.execute_script('return window.innerHeight;')
# driver.fullscreen_window()
# WebDriverWait(driver, 2)\
# .until(lambda d: driver.execute_script('return window.innerWidth;') > start_width)
# end_width = driver.execute_script('return window.innerWidth;')
# end_height = driver.execute_script('return window.innerHeight;')
# driver.quit() # Kill driver so we aren't running fullscreen after
# assert end_width > start_width
# assert end_height > start_height
# @pytest.mark.xfail_safari(raises=WebDriverException,
# reason='Minimize command not implemented')
# @pytest.mark.skipif(os.environ.get('TRAVIS') == 'true',
# reason='Minimize command causes Travis to hang')
# @pytest.mark.no_driver_after_test
# def test_should_minimize_the_current_window(driver):
# driver.minimize_window()
# minimized = driver.execute_script('return document.hidden;')
# driver.quit() # Kill driver so we aren't running minimized after
# assert minimized is True
| [
"pytest.mark.xfail_safari",
"selenium.webdriver.support.wait.WebDriverWait"
]
| [((3004, 3110), 'pytest.mark.xfail_safari', 'pytest.mark.xfail_safari', ([], {'raises': 'WebDriverException', 'reason': '"""Get Window Rect command not implemented"""'}), "(raises=WebDriverException, reason=\n 'Get Window Rect command not implemented')\n", (3028, 3110), False, 'import pytest\n'), ((3360, 3466), 'pytest.mark.xfail_safari', 'pytest.mark.xfail_safari', ([], {'raises': 'WebDriverException', 'reason': '"""Get Window Rect command not implemented"""'}), "(raises=WebDriverException, reason=\n 'Get Window Rect command not implemented')\n", (3384, 3466), False, 'import pytest\n'), ((2714, 2738), 'selenium.webdriver.support.wait.WebDriverWait', 'WebDriverWait', (['driver', '(2)'], {}), '(driver, 2)\n', (2727, 2738), False, 'from selenium.webdriver.support.wait import WebDriverWait\n'), ((3838, 3862), 'selenium.webdriver.support.wait.WebDriverWait', 'WebDriverWait', (['driver', '(2)'], {}), '(driver, 2)\n', (3851, 3862), False, 'from selenium.webdriver.support.wait import WebDriverWait\n')] |
__author__ = '<NAME>'
__email__ = '<EMAIL>'
__version__= '2.2'
__status__ = "Research"
__date__ = "28/1/2018"
__license__= "MIT License"
import os
import numpy as np
import glob
import subprocess
import platform
import sys
import pkg_resources
import torch
import PIL as Image
try:
import cv2
except:
print("WARNING: Could not load OpenCV python package. Some functionality may not be available.")
def list_files(path, extensions=[], sort=True, max_len=-1):
if os.path.isdir(path):
filenames = [os.path.join(path, fn) for fn in os.listdir(path) if
any([fn.endswith(ext) for ext in extensions])]
else:
print("ERROR. ", path,' is not a directory!')
return []
if sort:
filenames.sort()
if max_len>-1:
filenames = filenames[:max_len]
return filenames
def get_video_list(video_path, max_len=-1):
return list_files(video_path, extensions=['avi', 'flv', 'mpg', 'mp4'], sort=True, max_len=max_len)
def get_image_list(video_path, max_len=-1):
return list_files(video_path, extensions=['jpg', 'jpeg', 'png'], sort=True, max_len=max_len)
def get_split_files(dataset_path, splits_path, split_name, absolute_path=False):
path = os.path.join(dataset_path, splits_path, split_name)
files = glob.glob(path)
files.sort()
if not absolute_path:
files_out = []
for file in files:
_,filename = os.path.split(file)
files_out.append(filename)
return files_out
return files
def get_max_rc_weights(experiment_path):
log_filename = 'train_log_0.csv'
try:
f = open(os.path.join(experiment_path, log_filename), 'rt')
max_rc = 0
max_epoch = -1
max_mse = -1
for line in f:
toks = line.split(',')
if toks[0] == 'val':
epoch = toks[1]
try:
rc = float(toks[4])
if rc > max_rc:
max_rc = rc
max_epoch = int(epoch)
max_mse = float(toks[6])
except:
pass
f.close()
chkpt_file = experiment_path + '/' + 'weights_' + str(max_epoch) + '.pkl'
if not os.path.isfile(chkpt_file):
print("WARNING: File ",chkpt_file," does not exists!")
return '', 0, 0, 0
return chkpt_file, max_rc, max_mse, max_epoch
except:
print('WARNING: Could not open ' + os.path.join(experiment_path, log_filename))
return '', 0, 0, 0
def get_split_index(split_filename):
filename, _ = os.path.splitext(split_filename)
id = int(filename.split('_')[-1])
return id
def get_weight_files(split_files, experiment_name, max_rc_checkpoints=True):
data_dir = 'data'
weight_files = []
for split_filename in split_files:
split_name,_ = os.path.splitext(split_filename)
_, split_id = split_name.split('_')
weight_files_all = os.path.join(data_dir, experiment_name+'_train_'+split_id+'/*.pkl')
files = glob.glob(weight_files_all)
if len(files) == 0:
# No trained model weights for this split
weight_files.append('')
continue
elif len(files) == 1:
weight_files.append(files[0])
else:
# Multiple weights
if max_rc_checkpoints:
weights_dir = os.path.join(data_dir, experiment_name + '_train_' + split_id)
print("Selecting model weights with the highest RC on validation set in ",weights_dir)
weight_file, max_rc, max_mse, max_epoch= get_max_rc_weights(weights_dir)
if weight_file != '':
print('Found: ',weight_file, ' RC=', max_rc, ' MSE=', max_rc, ' epoch=', max_epoch)
weight_files.append(weight_file)
continue
# Get the weights from the last training epoch
files.sort(key=lambda x: get_split_index(x), reverse=True)
weight_file=files[0]
weight_files.append(weight_file)
return weight_files
def run_command(command):
p = subprocess.Popen(command.split(),
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
return '\n'.join([ '\t'+line.decode("utf-8").strip() for line in p.stdout.readlines()])
def ge_pkg_versions():
dep_versions = {}
cmd = 'cat /proc/driver/nvidia/version'
display_driver = run_command(cmd)
dep_versions['display'] = display_driver
dep_versions['cuda'] = 'NA'
cuda_home = '/usr/local/cuda/'
if 'CUDA_HOME' in os.environ:
cuda_home = os.environ['CUDA_HOME']
cmd = cuda_home+'/version.txt'
if os.path.isfile(cmd):
cuda_version = run_command('cat '+cmd)
dep_versions['cuda'] = cuda_version
dep_versions['cudnn'] = torch.backends.cudnn.version()
dep_versions['platform'] = platform.platform()
dep_versions['python'] = sys.version_info[0]
dep_versions['torch'] = torch.__version__
dep_versions['numpy'] = np.__version__
dep_versions['PIL'] = Image.VERSION
dep_versions['OpenCV'] = 'NA'
if 'cv2' in sys.modules:
dep_versions['OpenCV'] = cv2.__version__
dep_versions['torchvision'] = pkg_resources.get_distribution("torchvision").version
return dep_versions
def print_pkg_versions():
print("Packages & system versions:")
print("----------------------------------------------------------------------")
versions = ge_pkg_versions()
for key, val in versions.items():
print(key,": ",val)
print("")
return
if __name__ == "__main__":
print_pkg_versions()
split_files = get_split_files('datasets/lamem', 'splits', 'test_*.txt')
print(split_files)
weight_files = get_weight_files(split_files, experiment_name='lamem_ResNet50FC_lstm3_last', max_rc_checkpoints=True)
# weight_files = get_weight_files(split_files, experiment_name='lamem_ResNet50FC_lstm3')
print(weight_files) | [
"os.listdir",
"platform.platform",
"os.path.splitext",
"os.path.join",
"os.path.split",
"os.path.isfile",
"os.path.isdir",
"pkg_resources.get_distribution",
"glob.glob",
"torch.backends.cudnn.version"
]
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# automatically generated by the FlatBuffers compiler, do not modify
# namespace: aghast_generated
import flatbuffers
class Slice(object):
__slots__ = ["_tab"]
# Slice
def Init(self, buf, pos):
self._tab = flatbuffers.table.Table(buf, pos)
# Slice
def Start(self):
return self._tab.Get(
flatbuffers.number_types.Int64Flags,
self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(0),
)
# Slice
def Stop(self):
return self._tab.Get(
flatbuffers.number_types.Int64Flags,
self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(8),
)
# Slice
def Step(self):
return self._tab.Get(
flatbuffers.number_types.Int32Flags,
self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(16),
)
# Slice
def HasStart(self):
return self._tab.Get(
flatbuffers.number_types.BoolFlags,
self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(20),
)
# Slice
def HasStop(self):
return self._tab.Get(
flatbuffers.number_types.BoolFlags,
self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(21),
)
# Slice
def HasStep(self):
return self._tab.Get(
flatbuffers.number_types.BoolFlags,
self._tab.Pos + flatbuffers.number_types.UOffsetTFlags.py_type(22),
)
def CreateSlice(builder, start, stop, step, hasStart, hasStop, hasStep):
builder.Prep(8, 24)
builder.Pad(1)
builder.PrependBool(hasStep)
builder.PrependBool(hasStop)
builder.PrependBool(hasStart)
builder.PrependInt32(step)
builder.PrependInt64(stop)
builder.PrependInt64(start)
return builder.Offset()
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"flatbuffers.table.Table",
"flatbuffers.number_types.UOffsetTFlags.py_type"
]
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import concurrent
import os
import re
import shutil
import xml.etree.ElementTree as ET # TODO do we have this as requirement?
from concurrent.futures import as_completed
from concurrent.futures._base import as_completed
from pathlib import Path
import ffmpeg
import pandas as pd
import webrtcvad
from audio_korpora_pipeline.baseobjects import FileHandlingObject
from audio_korpora_pipeline.inputadapter.audiosplit.splitter import Splitter
from audio_korpora_pipeline.metamodel.mediasession import MediaAnnotationBundle, \
MediaAnnotationBundleWithoutTranscription, WrittenResource, MediaFile, \
MediaSessionActor, Sex, \
MediaSessionActors, MediaSession
class Adapter(FileHandlingObject):
def __init__(self, config):
super(Adapter, self).__init__()
def toMetamodel(self) -> MediaSession:
raise NotImplementedError("Please use a subclass")
def skipAlreadyProcessedFiles(self):
skip = self.config['global']['skipAlreadyProcessedFiles']
if not (skip):
self.logger.warn("No config setting for skipAlreadyProcessedFiles set. Assuming True")
return True
return skip
class UntranscribedMediaSplittingAdapter(Adapter):
AUDIO_SPLIT_AGRESSIVENESS = 3 # webrtcvad 1 (low), 3 (max)
ADAPTERNAME = "MediaSplittingAdapter"
mediaAnnotationBundles = []
mediaSessionActors = set() # using a set so we don't have duplets
def __init__(self, config):
super(UntranscribedMediaSplittingAdapter, self).__init__(config=config)
self.config = config
self.mediaSessionActors.add(MediaSessionActor("UNKNOWN", Sex.UNKNOWN, None))
def _splitMonoRawAudioToVoiceSectionsThread(self, file, outputpath):
self.logger.debug("Splitting file into chunks: {}".format(self._getFilenameWithExtension(file)))
splitter = Splitter()
vad = webrtcvad.Vad(int(self.AUDIO_SPLIT_AGRESSIVENESS))
basename = self._getFilenameWithoutExtension(file)
audiochunkPathsForThisfile = []
try:
audio, sample_rate = splitter.read_wave(file)
frames = splitter.frame_generator(30, audio, sample_rate)
frames = list(frames)
segments = splitter.vad_collector(sample_rate, 30, 300, vad, frames)
for i, segment in enumerate(segments):
path = os.path.join(outputpath, basename + '_chunk_{:05d}.wav'.format(i))
self.logger.debug("Write chunk {} of file {}".format(i, file))
splitter.write_wave(path, segment, sample_rate)
audiochunkPathsForThisfile.append(path)
# write staging complete file
stagingPath = os.path.join(outputpath, basename + ".stagingComplete")
with open(stagingPath, 'a'):
os.utime(stagingPath, None)
self.logger.debug("Finished splitting file {}".format(file))
except Exception as excep:
self.logger.warn("Could split file into chunks {}. Skipping".format(file), exc_info=excep)
return (False, str(file), []) # returning an empty list, as no success here
return (True, str(file), audiochunkPathsForThisfile)
def _convertMediafileToMonoAudioThread(self, filenumber, totalNumberOfFiles, singleFilepathToProcess, outputPath):
self.logger.debug(
"Processing file {}/{} on path {}".format(filenumber + 1, totalNumberOfFiles, singleFilepathToProcess))
nextFilename = os.path.join(outputPath, self._getFilenameWithoutExtension(singleFilepathToProcess) + ".wav")
try:
(ffmpeg
.input(singleFilepathToProcess)
.output(nextFilename, format='wav', acodec='pcm_s16le', ac=1, ar='16k')
.overwrite_output()
.run()
)
except ffmpeg.Error as ffmpgError:
self.logger.warn("Ffmpeg rose an error", exc_info=ffmpgError)
self.logger.warn("Due to error of ffmpeg skipped file {}".format(singleFilepathToProcess))
return (False, str(singleFilepathToProcess), str(nextFilename))
except Exception as e:
self.logger.warn("Got an error while using ffmpeg for file {}".format(singleFilepathToProcess), exc_info=e)
return (False, str(singleFilepathToProcess), str(nextFilename))
return (True, str(singleFilepathToProcess), str(nextFilename))
def createMediaSession(self, bundles):
session = MediaSession(self.ADAPTERNAME, self.mediaSessionActors, bundles)
return session
def createMediaAnnotationBundles(self, audiochunks):
annotationBundles = []
for index, filepath in enumerate(audiochunks):
bundle = MediaAnnotationBundleWithoutTranscription(identifier=filepath) # we do not have any written ressources
bundle.setMediaFile(filepath)
annotationBundles.append(bundle)
return annotationBundles
def splitAudioToChunks(self, filesToChunk, outputPath):
if ((filesToChunk == None) or (len(filesToChunk) == 0)):
self.logger.info("Nothing to split, received empty wav-filenamelist")
return []
successfullyChunkedFiles = []
with concurrent.futures.ThreadPoolExecutor(max_workers=None) as executor:
futures = []
for filenumber, file in enumerate(filesToChunk):
futures.append(
executor.submit(self._splitMonoRawAudioToVoiceSectionsThread, file, outputPath))
for future in as_completed(futures):
if (future.result()[0] == False):
self.logger.warning("Couldnt split audiofile {}, removing from list".format(future.result()[1]))
else:
successfullyChunkedFiles.extend(future.result()[2])
self.logger.debug("Splitting Audio is done {}".format(future.result()))
self.logger.debug("Finished splitting {} wav files".format(len(filesToChunk)))
return successfullyChunkedFiles
def determineWavFilesToChunk(self, baseFilesToChunk, stagingChunkPath):
allStageIndicatorFilesFullpath = set(self._getAllMediaFilesInBasepath(stagingChunkPath, {".stagingComplete"}))
allExistingChunkedFilesFullpath = set(self._getAllMediaFilesInBasepath(stagingChunkPath, {".wav"}))
allStageIndicatorFilesDictionary = self._toFilenameDictionary(allStageIndicatorFilesFullpath)
allBaseFilesDictionary = self._toFilenameDictionary(baseFilesToChunk)
stagingCompleteCorrectKeys = set(allBaseFilesDictionary.keys()).intersection(
set(allStageIndicatorFilesDictionary.keys()))
stagingIncompleteCorrectKeys = set(allBaseFilesDictionary.keys()).difference(
set(allStageIndicatorFilesDictionary.keys()))
stagingComplete = []
for fullpath in allExistingChunkedFilesFullpath:
if any(self._getFilenameWithoutExtension(fullpath).startswith(cm) for cm in stagingCompleteCorrectKeys):
stagingComplete.append(fullpath)
stagingIncomplete = [allBaseFilesDictionary[key] for key in stagingIncompleteCorrectKeys]
self.logger.debug("Got {} files not yet chunked".format(len(stagingIncomplete)))
self.logger.debug("Got {} files chunked".format(len(stagingComplete)))
return stagingIncomplete, stagingComplete
def convertMediaFilesToMonoAudio(self, filesToProcess, outputpath, adapterName):
if (filesToProcess == None or len(filesToProcess) == 0):
self.logger.debug("No files to convert for {}, skipping".format(adapterName))
return []
successfulFilenames = []
with concurrent.futures.ThreadPoolExecutor(max_workers=None) as executor:
futures = []
for filenumber, currentFile in enumerate(filesToProcess):
futures.append(
executor.submit(self._convertMediafileToMonoAudioThread, filenumber, len(filesToProcess),
currentFile, outputpath))
for future in as_completed(futures):
if (future.result()[0] == False):
self.logger.warning("Couldnt process audiofile {}, removing from list".format(future.result()[1]))
else:
successfulFilenames.append(future.result()[2])
self.logger.debug("Processing Audio is done {} for Converter {}".format(future.result(), adapterName))
return successfulFilenames
def _toFilenameDictionary(self, list):
if (list == None or len(list) == 0):
self.logger.debug("Got nothing in list, returning empty dictionary")
return dict()
listDict = dict()
for fullpath in list:
listDict[self._getFilenameWithoutExtension(fullpath)] = fullpath
self.logger.debug("Created dictionary of files of length {}".format(len(listDict)))
return listDict
def determineFilesToConvertToMonoFromGivenLists(self, alreadyStagedFiles, originalFiles, adaptername):
dictionaryOfOriginalFilepaths = self._toFilenameDictionary(originalFiles)
dictionaryOfStagedFilepaths = self._toFilenameDictionary(alreadyStagedFiles)
notYetProcessedKeys = set(dictionaryOfOriginalFilepaths.keys()).difference(set(dictionaryOfStagedFilepaths.keys()))
alreadyProcessedKeys = set(dictionaryOfOriginalFilepaths.keys()).intersection(
set(dictionaryOfStagedFilepaths.keys()))
fullpathsToNotYetProcessed = [dictionaryOfOriginalFilepaths[key] for key in notYetProcessedKeys]
fullpathsProcessed = [dictionaryOfStagedFilepaths[key] for key in alreadyProcessedKeys]
self.logger.debug("Got {} files not yet processed for corpus {}".format(len(notYetProcessedKeys), adaptername))
self.logger.debug("Got {} files already processed for corpus {}".format(len(alreadyProcessedKeys), adaptername))
return fullpathsToNotYetProcessed, fullpathsProcessed
def _preprocess_workflow_with_splitting(self, filesAlreadyProcessed, filesToProcess, monoPath, chunkPath,
adaptername):
filesSuccessfullyProcessed = self.convertMediaFilesToMonoAudio(filesToProcess, monoPath, adaptername)
baseFilesToChunk = []
baseFilesToChunk = baseFilesToChunk + filesSuccessfullyProcessed + filesAlreadyProcessed
# split mono audio to chunks
filesToChunk, filesAlreadyChunked = self.determineWavFilesToChunk(baseFilesToChunk,
chunkPath)
filesSuccessfullyChunked = self.splitAudioToChunks(filesToChunk, chunkPath)
# add chunks to media session
mediaBundleFiles = [] + filesSuccessfullyChunked + filesAlreadyChunked
mediaAnnotationbundles = self.createMediaAnnotationBundles(mediaBundleFiles)
mediaSession = self.createMediaSession(mediaAnnotationbundles)
return mediaSession
class UntranscribedVideoAdapter(UntranscribedMediaSplittingAdapter):
ADAPTERNAME = "UntranscribedVideoAdapter"
def __init__(self, config):
super(UntranscribedVideoAdapter, self).__init__(config=config)
self.config = config
def toMetamodel(self):
self.logger.debug("Untranscribed Video Korpus")
# convert video to mono audio
filesToProcess, filesAlreadyProcessed = self._determineVideoFilesToConvertToMono()
return self._preprocess_workflow_with_splitting(filesAlreadyProcessed, filesToProcess,
self._validateStagingMonoPath(), self._validateStagingChunksPath(),
self.ADAPTERNAME)
def _validateKorpusPath(self):
korpus_path = self.config['untranscribed_videos_input_adapter']['korpus_path']
if not os.path.isdir(korpus_path):
raise IOError("Could not read korpus path" + korpus_path)
return korpus_path
def _validateStagingMonoPath(self):
workdir = self.config['global']['workdir']
if not os.path.isdir(workdir):
raise IOError("Could not read workdir path" + workdir)
workdir = Path(workdir).joinpath("untranscribed_video_staging_mono")
workdir.mkdir(parents=True, exist_ok=True)
return str(workdir)
def _validateStagingChunksPath(self):
workdir = self.config['global']['workdir']
if not os.path.isdir(workdir):
raise IOError("Could not read workdir path" + workdir)
workdir = Path(workdir).joinpath("untranscribed_video_staging_chunks")
workdir.mkdir(parents=True, exist_ok=True)
return str(workdir)
def _determineVideoFilesToConvertToMono(self):
originalFiles = set(self._getAllMediaFilesInBasepath(self._validateKorpusPath(), {".mp4"}))
alreadyStagedFiles = set(self._getAllMediaFilesInBasepath(self._validateStagingMonoPath(), {".wav"}))
self.logger.debug("Got {} original untranscribed mp4 files to process".format(len(originalFiles)))
return self.determineFilesToConvertToMonoFromGivenLists(alreadyStagedFiles, originalFiles, self.ADAPTERNAME)
class ChJugendspracheAdapter(UntranscribedMediaSplittingAdapter):
ADAPTERNAME = "CHJugendspracheAdapter"
def __init__(self, config):
super(ChJugendspracheAdapter, self).__init__(config=config)
self.config = config
def toMetamodel(self):
self.logger.debug("CH-Jugendsprache Korpus")
# convert audio to mono audio
filesToProcess, filesAlreadyProcessed = self._determineChJugendspracheFilesToConvertToMono()
return self._preprocess_workflow_with_splitting(filesAlreadyProcessed, filesToProcess,
self._validateStagingMonoPath(), self._validateStagingChunksPath(),
self.ADAPTERNAME)
def _determineChJugendspracheFilesToConvertToMono(self):
originalFiles = set(self._getAllMediaFilesInBasepath(self._validateKorpusPath(), {".WAV", ".wav"}))
alreadyStagedFiles = set(self._getAllMediaFilesInBasepath(self._validateStagingMonoPath(), {".wav"}))
self.logger.debug("Got {} original jugendsprache files to process".format(len(originalFiles)))
return self.determineFilesToConvertToMonoFromGivenLists(alreadyStagedFiles, originalFiles, self.ADAPTERNAME)
def _validateStagingMonoPath(self):
workdir = self.config['global']['workdir']
if not os.path.isdir(workdir):
raise IOError("Could not read workdir path" + workdir)
workdir = Path(workdir).joinpath("ch_jugensprache_staging_mono")
workdir.mkdir(parents=True, exist_ok=True)
return str(workdir)
def _validateStagingChunksPath(self):
workdir = self.config['global']['workdir']
if not os.path.isdir(workdir):
raise IOError("Could not read workdir path" + workdir)
workdir = Path(workdir).joinpath("ch_jugensprache_staging_chunks")
workdir.mkdir(parents=True, exist_ok=True)
return str(workdir)
def _validateKorpusPath(self):
korpus_path = self.config['ch_jugendsprache_input_adapter']['korpus_path']
if not os.path.isdir(korpus_path):
raise IOError("Could not read korpus path" + korpus_path)
return korpus_path
class ArchimobAdapter(UntranscribedMediaSplittingAdapter):
"""
ArchimobAdapter
"""
ADAPTERNAME = "Archimob"
def __init__(self, config):
super(ArchimobAdapter, self).__init__(config=config)
self.config = config
def _validateKorpusPath(self):
korpus_path = self.config['archimob_input_adapter']['korpus_path']
if not os.path.isdir(korpus_path):
raise IOError("Could not read korpus path" + korpus_path)
return korpus_path
def _transcription_pause_tag_symbol(self):
symbol = self.config['archimob_input_adapter']['transcription_pause_tag_symbol']
if not symbol:
self.logger.warn("No symbol for transcription pause tag configured, falling back to default, which is '@'-Symbol")
symbol = '@'
return symbol
def _transcription_vocal_tag_symbol(self):
symbol = self.config['archimob_input_adapter']['transcription_vocal_tag_symbol']
if not symbol:
self.logger.warn("No symbol for transcription pause tag configured, falling back to default, which is '#'-Symbol")
symbol = '#'
return symbol
def _validateWorkdir(self):
workdir = self.config['global']['workdir']
if not os.path.isdir(workdir):
raise IOError("Could not read workdir path" + workdir)
workdir = Path(workdir).joinpath("archimob_staging")
workdir.mkdir(parents=True, exist_ok=True)
return str(workdir)
def _determineArchimobFilesToProcess(self):
originalFiles = set(self._getAllMediaFilesInBasepath(self._validateKorpusPath(), {".wav"}))
originalFiles = self._fixOriginalDatasetFlawsIfNecessary(originalFiles)
alreadyStagedFiles = set(self._getAllMediaFilesInBasepath(self._validateWorkdir(), {".wav"}))
self.logger.debug("Got {} original archimob files to process".format(len(originalFiles)))
return self.determineFilesToConvertToMonoFromGivenLists(alreadyStagedFiles, originalFiles, self.ADAPTERNAME)
def toMetamodel(self):
self.logger.debug("Archimob V2 Korpus")
# convert chunks to mono audio
filesToProcess, filesAlreadyProcessed = self._determineArchimobFilesToProcess()
filesSuccessfullyProcessed = self.convertMediaFilesToMonoAudio(filesToProcess, self._validateWorkdir(),
self.ADAPTERNAME)
filesForMediaBundle = []
filesForMediaBundle = filesForMediaBundle + filesSuccessfullyProcessed + filesAlreadyProcessed
# add chunks to media session
mediaAnnotationbundles = self.createMediaAnnotationBundles(filesForMediaBundle)
mediaSession = self.createMediaSession(mediaAnnotationbundles)
return mediaSession
def createMediaSession(self, bundles):
actors = self._createMediaSessionActorsFromBundles(bundles)
session = MediaSession(self.ADAPTERNAME, actors, bundles)
return session
def createMediaAnnotationBundles(self, filesForMediaBundle):
allXmlOriginalTranscriptionFiles = self._archimobOriginalTranscriptionFiles(self._validateKorpusPath())
transcriptionsPerSpeaker = self._extract(allXmlOriginalTranscriptionFiles)
mediaFilesAndTranscription = self._onlyTranscriptionsWithMediaFilesAndViceVersa(transcriptionsPerSpeaker,
filesForMediaBundle)
mediaAnnotationBundles = self._createActualMediaAnnotationBundles(mediaFilesAndTranscription)
return mediaAnnotationBundles
def _fixOriginalDatasetFlawsIfNecessary(self, originalFiles):
# As of Archimobe release V2 there are some minor flaws in the data, which are treated sequentially
if (self._fixForDuplicateWavs1063Necessary(originalFiles)):
originalFiles = self._fixForDuplicateWavs1063(originalFiles)
if (self._fixForWrongFilenames1082Necessary(originalFiles)):
originalFiles = self._fixForWrongFilenames1082(originalFiles)
return originalFiles
def _fixForDuplicateWavs1063Necessary(self, originalFiles):
# This flaw is simply, that within 1063 there exists another folder 1063 containing all files again
existingPathsForDoubled1063 = list(
filter(lambda file: os.path.sep + "1063" + os.path.sep + "1063" + os.path.sep in file, originalFiles))
fixNecessary = len(existingPathsForDoubled1063) > 0
self.logger.info("Found {} files of speaker 1063 which are duplicates. They will be ignored".format(
len(existingPathsForDoubled1063)))
return fixNecessary
def _fixForDuplicateWavs1063(self, originalFiles):
# fix is simply by removing the files in question from list
pathsWithout1063duplicates = list(
filter(lambda file: not (os.path.sep + "1063" + os.path.sep + "1063" + os.path.sep in file), originalFiles))
originalFiles = pathsWithout1063duplicates
return originalFiles
def _fixForWrongFilenames1082Necessary(self, originalFiles):
regexForFindingWrongNames = "(^\d{4}_\d)(d\d{4}_.*\.wav)" # like 1082_2d1082_2_TLI_3.wav
onlyFilenames = [os.path.basename(filename) for filename in originalFiles]
for filename in onlyFilenames:
m = re.search(regexForFindingWrongNames, filename)
if (not (m is None)):
return True
return False
def _fixForWrongFilenames1082(self, originalFiles):
fixedFiles = originalFiles.copy()
regexForFindingWrongFullpaths = "(.*\\" + os.path.sep + ")(\d{4}_\d)(d\d{4}_.*\.wav)" # like /home/somebody/files/1082/1082_2d1082_2_TLI_3.wav
for filename in originalFiles:
m = re.search(regexForFindingWrongFullpaths, filename)
if (not (m is None)):
newFilename = m.group(1) + m.group(3)
self.logger.debug(
"Fix 1082: Renaming file {} from {} to {}".format(m.group(2) + m.group(3), filename, newFilename))
try:
shutil.move(filename, newFilename)
fixedFiles.append(newFilename)
except Exception as inst:
self.logger.warn(
"Could not move file {} to {}, skipping and just removing from usable filenames".format(filename,
newFilename),
exc_info=inst)
fixedFiles.remove(filename)
return fixedFiles
def _archimobOriginalTranscriptionFiles(self, path):
xmlOriginalFiles = list(Path(path).glob("**/*.xml"))
self.logger.debug("Found {} original xml files for archimob".format(len(xmlOriginalFiles)))
return xmlOriginalFiles
def _extract(self, allXmlOriginalTranscriptionFiles):
transcriptionsPerSpeaker = []
with concurrent.futures.ThreadPoolExecutor(max_workers=None) as executor:
futures = []
for filenumber, file in enumerate(allXmlOriginalTranscriptionFiles):
futures.append(executor.submit(self._extractSingleXmlFileThread, file))
for future in as_completed(futures):
if (future.result()[0] == False):
self.logger.warning("Couldnt extract metadata for file {}, removing from list".format(future.result()[1]))
else:
transcriptionsPerSpeaker.append(
(future.result()[1], future.result()[2])) # tuple of original file and transcription dataframe
self.logger.debug("Extracting metadata for speaker finished {}".format(future.result()))
self.logger.debug("Finished metadata extraction for all {} xml files".format(len(allXmlOriginalTranscriptionFiles)))
return transcriptionsPerSpeaker
def _extractSingleXmlFileThread(self, xmlFile):
namespaceprefix = "{http://www.tei-c.org/ns/1.0}"
try:
tree = ET.parse(xmlFile)
root = tree.getroot()
ch_datacolumns = pd.DataFrame(columns=['Filename', 'transcript'])
transcriptionForSpeaker = pd.DataFrame(columns=ch_datacolumns.columns)
tagsToIgnore = set([namespaceprefix + tag for tag in {"gap", "incident", "kinesic", "other"}])
for utteranceTag in root.iter(namespaceprefix + 'u'):
media = utteranceTag.attrib['start']
filename = media.split('#')[1]
ch_transcript = [""]
for element in utteranceTag:
extractedWord = ""
if (namespaceprefix + "w" == element.tag):
extractedWord = self._extractWordTag(element)
if (namespaceprefix + "pause" == element.tag):
extractedWord = self._extractPauseTag(element)
if (namespaceprefix + "vocal" == element.tag):
extractedWord = self._extractVocalTag(namespaceprefix, element)
if (namespaceprefix + "del" == element.tag):
extractedWord = self._extractDeletionTag(element)
if (namespaceprefix + "unclear" == element.tag):
extractedWord = self._extractUnclearTag(namespaceprefix, element)
if (element.tag in tagsToIgnore):
self.logger.debug(
"Found tag {} which is in ignore list, ignoring the whole utterance {}".format(element.tag, filename))
break
if (extractedWord):
cleanedWord = self._cleanExtractedWord(extractedWord)
if (cleanedWord):
ch_transcript.append(cleanedWord)
try:
actualTranscript = " ".join(ch_transcript).strip()
if (not actualTranscript or (self._transcription_pause_tag_symbol() == actualTranscript)):
self.logger.debug("Skipping empty transcription for filename {}".format(filename))
continue
transcriptionForSpeaker = transcriptionForSpeaker.append(
{'Filename': filename, 'transcript': actualTranscript}, ignore_index=True)
transcriptionForSpeaker = self._cleanSpecialCaseWhereTwoSentencesPerFileExist(transcriptionForSpeaker)
except Exception as e:
self.logger.warn("Couldn't append single utterance for filename {}".format(filename), exc_info=e)
continue
# writing is just for manual checking
transcriptionForSpeaker.to_csv(
os.path.join(self._getFullFilenameWithoutExtension(xmlFile) + "_transcript_CH.csv"),
header=True, index=False, encoding='utf-8')
return True, xmlFile, transcriptionForSpeaker
except Exception as e:
self.logger.warn("Couldn't extract metadata for xml file {}".format(xmlFile), exc_info=e)
return False, xmlFile, None
def _extractWordTag(self, element):
return element.text
def _extractPauseTag(self, element):
return self._transcription_pause_tag_symbol()
def _extractVocalTag(self, namespaceprefix, element):
desc = element.find(namespaceprefix + "desc")
if desc is not None:
return self._transcription_vocal_tag_symbol() + desc.text
return ""
def _extractDeletionTag(self, element):
truncatedTextWithPotentialSlash = element.text
if truncatedTextWithPotentialSlash:
truncatedText = truncatedTextWithPotentialSlash.replace("/", "")
return truncatedText
return ""
def _extractUnclearTag(self, namespaceprefix, element):
if element is not None:
wordsWithinUnclearTag = element.findall(namespaceprefix + 'w')
unclearText = []
for word in wordsWithinUnclearTag:
unclearText.append(word.text)
return " ".join(unclearText)
return ""
def _cleanExtractedWord(self, extractedWord):
# replace all tokens with gravis with their counterpart
# remove all chars not in allowed list
# Note: q,x and y are not allowed, as thos are not existing within transcription of archimob!
allowed_chars = {
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', 'z',
'ä', 'ö', 'ü',
' '
}
allowed_chars.add(self._transcription_pause_tag_symbol())
allowed_chars.add(self._transcription_vocal_tag_symbol())
whitespace_regex = re.compile(r'[ \t]+')
extractedWord = extractedWord.lower()
extractedWord = extractedWord.replace('á', 'a')
extractedWord = extractedWord.replace('à', 'a')
extractedWord = extractedWord.replace('â', 'a')
extractedWord = extractedWord.replace('ç', 'c')
extractedWord = extractedWord.replace('é', 'e')
extractedWord = extractedWord.replace('è', 'e')
extractedWord = extractedWord.replace('ê', 'e')
extractedWord = extractedWord.replace('í', 'i')
extractedWord = extractedWord.replace('ì', 'i')
extractedWord = extractedWord.replace('î', 'i')
extractedWord = extractedWord.replace('ñ', 'n')
extractedWord = extractedWord.replace('ó', 'o')
extractedWord = extractedWord.replace('ò', 'o')
extractedWord = extractedWord.replace('ô', 'o')
extractedWord = extractedWord.replace('ú', 'u')
extractedWord = extractedWord.replace('ù', 'u')
extractedWord = extractedWord.replace('ǜ', 'u')
extractedWord = extractedWord.replace('û', 'u')
extractedWord = extractedWord.replace('ș', 's')
extractedWord = extractedWord.replace('ş', 's')
extractedWord = extractedWord.replace('ß', 'ss')
extractedWord = extractedWord.replace('-', ' ')
# Those should not exist anymore, however, be safe
extractedWord = extractedWord.replace('–', ' ')
extractedWord = extractedWord.replace('/', ' ')
extractedWord = whitespace_regex.sub(' ', extractedWord)
extractedWord = ''.join([char for char in extractedWord if char in allowed_chars])
extractedWord = whitespace_regex.sub(' ', extractedWord)
extractedWord = extractedWord.strip()
return extractedWord
def _onlyTranscriptionsWithMediaFilesAndViceVersa(self, transcriptionsPerSpeaker, filesForMediaBundle):
if not transcriptionsPerSpeaker or not filesForMediaBundle:
return []
existingMediaFilesTuples = [(self._getFilenameWithoutExtension(mediafile), mediafile) for mediafile in
filesForMediaBundle]
existingMediaFiles, existingMediaFilesFullpath = zip(*existingMediaFilesTuples)
# combine all transcriptions
allTranscriptions = pd.concat([transcription[1] for transcription in transcriptionsPerSpeaker])
if any("-" in filename for filename in allTranscriptions.Filename) \
and not any("-" in filename for filename in existingMediaFiles):
self.logger.debug(
"Found filenames with dash (-) instead of underscore (_) but only filenames with underscore. Automatically fixing this...")
allTranscriptions.Filename = allTranscriptions.Filename.str.replace("-", "_")
# Find all files that exist in both sets
# TODO: Performance not good for 70k files
allMatchingTranscriptions = allTranscriptions[allTranscriptions.Filename.isin(existingMediaFiles)].copy()
allMatchingTranscriptions["FullpathFilename"] = ""
allMatchingTranscriptions.set_index("Filename", inplace=True)
for filenumber, existingFile in enumerate(existingMediaFiles):
allMatchingTranscriptions.loc[existingFile, "FullpathFilename"] = existingMediaFilesFullpath[filenumber]
return allMatchingTranscriptions[["FullpathFilename", "transcript"]].copy()
def _createActualMediaAnnotationBundles(self, mediaFilesAndTranscription):
bundles = []
for fileAndTranscription in mediaFilesAndTranscription.itertuples(index=False):
bundle = MediaAnnotationBundle(fileAndTranscription.FullpathFilename)
speakerId = self._speakerIdFromFullpath(fileAndTranscription.FullpathFilename)
bundle.setMediaFile(MediaFile(speakerId))
written_resource = WrittenResource(fileAndTranscription.transcript, speakerId, languageCode="CH",
annotationType=WrittenResource.DIETH_WITHOUT_GRAVIS)
bundle.setWrittenResource(written_resource)
bundles.append(bundle)
self.logger.debug("Created {} mediaAnnotationBundles out of {} transcriptions".format(len(bundles), len(
mediaFilesAndTranscription)))
return bundles
def _speakerIdFromFullpath(self, fullpathFilename):
return self._getFilenameWithoutExtension(fullpathFilename).split("_")[0]
def _createMediaSessionActorsFromBundles(self, bundles):
speakerIds = set([speaker.writtenResource.actorRef for speaker in bundles])
actors = [MediaSessionActor(speakerId, Sex.UNKNOWN, None) for speakerId in speakerIds]
return MediaSessionActors(actors)
def _cleanSpecialCaseWhereTwoSentencesPerFileExist(self, transcriptionForSpeaker):
if transcriptionForSpeaker is None or len(transcriptionForSpeaker) < 2:
return transcriptionForSpeaker
lastFilename = transcriptionForSpeaker.iloc[-1]["Filename"]
filenameBefore = transcriptionForSpeaker.iloc[-2]["Filename"]
if lastFilename == filenameBefore:
lastTranscription = transcriptionForSpeaker.iloc[-1]["transcript"]
transcriptionBefore = transcriptionForSpeaker.iloc[-2]["transcript"]
newTranscript = transcriptionBefore + " " + lastTranscription
transcriptionForSpeaker.drop(transcriptionForSpeaker.tail(2).index, inplace=True)
transcriptionForSpeaker = transcriptionForSpeaker.append(
{'Filename': lastFilename, 'transcript': newTranscript}, ignore_index=True)
self.logger.info(
"Found a case {} where two sentences '{}' and '{}' are within one audio-file, merging them together".format(
lastFilename,
transcriptionBefore, lastTranscription))
return transcriptionForSpeaker
class CommonVoiceAdapter(Adapter):
RELATIVE_PATH_TO_AUDIO = "clips"
LANGUAGECODE_DE = "de_DE"
ADAPTERNAME = "CommonVoiceDE"
mediaAnnotationBundles = []
mediaSessionActors = set() # using a set so we don't have duplets
def __init__(self, config):
super(CommonVoiceAdapter, self).__init__(config=config)
self.config = config
def toMetamodel(self):
self.logger.debug("Created CommonVoice Adapter")
self.audiofilenames = self._readExistingAudioFiles()
self.speakermetadata = self._readExistingSpeakerMetadata()
self._persistMetamodel()
self._buildMediaSession()
return self.mediaSession
def _validateKorpusPath(self):
korpus_path = self.config['common_voice_input_adapter']['korpus_path']
if not os.path.isdir(korpus_path):
raise IOError("Could not read korpus path" + korpus_path)
return korpus_path
def _existingAudioFileFullpath(self, filename):
return os.path.join(self._validateKorpusPath(), self.RELATIVE_PATH_TO_AUDIO, filename)
def _readExistingAudioFiles(self):
fullpath = os.path.join(self._validateKorpusPath(), self.RELATIVE_PATH_TO_AUDIO)
for file in os.listdir(fullpath):
if file.endswith(".mp3"):
currentfile = MediaAnnotationBundle(self._existingAudioFileFullpath(file))
self.mediaAnnotationBundles.append(currentfile)
self.logger.debug("Found {} audiofiles to process".format(len(self.mediaAnnotationBundles)))
pass
def _readExistingSpeakerMetadata(self, ):
existing_audio_identifier = self._getFilenamesFromMediaAnnotationBundles()
common_voice_valid_metadata = self._getCommonVoiceValidMetadata(
existing_audio_identifier, self._validateKorpusPath())
self._enrichWithTranscription(common_voice_valid_metadata)
self._extractMediaSessionActors(common_voice_valid_metadata)
def _enrichWithTranscription(self, common_voice_valid_metadata):
self.mediaAnnotationBundles_dictionary_withoutExtension = {self._getFilenameWithoutExtension(x.identifier): x for x
in self.mediaAnnotationBundles}
self.mediaAnnotationBundles_dictionary_withExtension = {self._getFilenameWithExtension(x.identifier): x for x in
self.mediaAnnotationBundles}
common_voice_valid_metadata.apply(self._enrichWithTranscriptionInner, axis=1)
pass
def _enrichWithTranscriptionInner(self, row):
currentMediaAnnotationBundle = self.mediaAnnotationBundles_dictionary_withoutExtension.get(row.path,
self.mediaAnnotationBundles_dictionary_withExtension.get(
row.path))
currentMediaAnnotationBundle.setWrittenResource(
WrittenResource(row.sentence, row.client_id, self.LANGUAGECODE_DE))
currentMediaAnnotationBundle.setMediaFile(MediaFile(row.client_id))
self.logger.debug(
"Found matching media-annotation bundle for identifier {} and path {}".format(row.client_id, row.path))
def _extractMediaSessionActors(self, common_voice_valid_metadata):
common_voice_valid_metadata.apply(self._createMediaSessionActorFromRow, axis=1)
self.logger.debug("Found {} Speakers".format(len(self.mediaSessionActors)))
pass
def _createMediaSessionActorFromRow(self, row):
self.mediaSessionActors.add(MediaSessionActor(row.client_id, Sex.toSexEnum(row.gender), row.age))
pass
def _getCommonVoiceValidMetadata(self, existing_audio_identifier,
korpus_path):
commonvoice_valid_metadatafilenames = ["dev.tsv", "test.tsv", "train.tsv", "validated.tsv"]
combined_csv = pd.concat(
[pd.read_csv(os.path.join(korpus_path, f), sep="\t", header=0) for f in commonvoice_valid_metadatafilenames])
common_voice_valid_metadata = combined_csv[combined_csv.path.isin(existing_audio_identifier)]
common_voice_valid_metadata = self._fixChangeInDataFormatCommonVoice(common_voice_valid_metadata, combined_csv)
return common_voice_valid_metadata
def _getFilenamesFromMediaAnnotationBundles(self):
return [os.path.splitext(os.path.basename(base.identifier))[0] for base in
self.mediaAnnotationBundles]
def _getFilenamesFromMediaAnnotationBundlesWithExtension(self):
return [os.path.basename(base.identifier) for base in self.mediaAnnotationBundles]
def _persistMetamodel(self):
# TODO actual persisting of working json
# Actual json output
# print(json.dumps(self.mediaAnnotationBundles, default=lambda o: o.__dict__, sort_keys=True, indent=4))
pass
def _buildMediaSession(self):
actors = MediaSessionActors(self.mediaSessionActors)
session = MediaSession(self.ADAPTERNAME, actors, self.mediaAnnotationBundles)
# TODO Validate
self.mediaSession = session
pass
def _fixChangeInDataFormatCommonVoice(self, common_voice_valid_metadata, combined_csv):
if (len(common_voice_valid_metadata) == 0):
self.logger.debug(
"CommonVoice tsv-files seem to have filename-extension set (new fileformat). Trying matching with extension")
common_voice_valid_metadata = combined_csv[
combined_csv.path.isin(self._getFilenamesFromMediaAnnotationBundlesWithExtension())]
self.logger.debug(
"CommonVoice Valid metadata length is: {}".format(len(common_voice_valid_metadata)))
return common_voice_valid_metadata
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Path\n'), ((11650, 11663), 'pathlib.Path', 'Path', (['workdir'], {}), '(workdir)\n', (11654, 11663), False, 'from pathlib import Path\n'), ((13646, 13659), 'pathlib.Path', 'Path', (['workdir'], {}), '(workdir)\n', (13650, 13659), False, 'from pathlib import Path\n'), ((13970, 13983), 'pathlib.Path', 'Path', (['workdir'], {}), '(workdir)\n', (13974, 13983), False, 'from pathlib import Path\n'), ((15603, 15616), 'pathlib.Path', 'Path', (['workdir'], {}), '(workdir)\n', (15607, 15616), False, 'from pathlib import Path\n'), ((29583, 29603), 'audio_korpora_pipeline.metamodel.mediasession.MediaFile', 'MediaFile', (['speakerId'], {}), '(speakerId)\n', (29592, 29603), False, 'from audio_korpora_pipeline.metamodel.mediasession import MediaAnnotationBundle, MediaAnnotationBundleWithoutTranscription, WrittenResource, MediaFile, MediaSessionActor, Sex, MediaSessionActors, MediaSession\n'), ((35059, 35084), 'audio_korpora_pipeline.metamodel.mediasession.Sex.toSexEnum', 'Sex.toSexEnum', (['row.gender'], {}), '(row.gender)\n', (35072, 35084), False, 'from audio_korpora_pipeline.metamodel.mediasession import MediaAnnotationBundle, MediaAnnotationBundleWithoutTranscription, WrittenResource, MediaFile, MediaSessionActor, Sex, MediaSessionActors, MediaSession\n'), ((20064, 20098), 'shutil.move', 'shutil.move', (['filename', 'newFilename'], {}), '(filename, newFilename)\n', (20075, 20098), False, 'import shutil\n'), ((20601, 20611), 'pathlib.Path', 'Path', (['path'], {}), '(path)\n', (20605, 20611), False, 'from pathlib import Path\n'), ((35341, 35369), 'os.path.join', 'os.path.join', (['korpus_path', 'f'], {}), '(korpus_path, f)\n', (35353, 35369), False, 'import os\n'), ((35776, 35809), 'os.path.basename', 'os.path.basename', (['base.identifier'], {}), '(base.identifier)\n', (35792, 35809), False, 'import os\n'), ((3359, 3396), 'ffmpeg.input', 'ffmpeg.input', (['singleFilepathToProcess'], {}), '(singleFilepathToProcess)\n', (3371, 3396), False, 'import ffmpeg\n')] |
import decimal
from unittest import mock
from django.conf import settings
from django.test import modify_settings
from rest_framework import test
from rest_framework.reverse import reverse
import stripe
from restshop import serializers
from restshop.models import Order
from paymentmethods.stripejs.models import StripeInvoice
import restshop.exceptions
from restshop.tests.test_product import products_and_price
@modify_settings(INSTALLED_APPS={
'append': 'restshop.paymentmethods.stripejs'
})
class StripeTest(test.APITestCase):
def setUp(self):
stripe.api_key = settings.STRIPE_API_KEY
self.order = Order.objects.create(
email='<EMAIL>',
)
self.order.items.create(
description='test purchase',
price='1000',
vat='250',
quantity=3,
product=products_and_price(1000).skus.all()[0]
)
session = self.client.session
session['order_id'] = self.order.pk
session.save()
def get_token(self):
return stripe.Token.create(card={
"number": '4242424242424242',
"exp_month": 12,
"exp_year": 2016,
"cvc": '123'
}).id
def test_pay(self):
response = self.client.post(
reverse(
'order-pay',
args=['stripejs.stripeinvoice']
),
{
'stripeToken': self.get_token(),
'order': serializers.OrderSerializer(instance=self.order).data
}
)
self.assertEqual(201, response.status_code, response.data)
self.assertEqual(0,
decimal.Decimal(response.data['owed']) -
decimal.Decimal(response.data['paid']))
order = Order.objects.get()
self.assertEqual(
Order.STATUS.completed,
order.status
)
self.assertEqual(
decimal.Decimal('3750.00'),
order.invoices.all()[0].paid
)
@mock.patch('stripe.Charge.create')
def test_card_error(self, create_mock):
create_mock.side_effect = stripe.CardError('fail!', '', '402')
si = StripeInvoice.objects.create(
order=self.order,
owed=self.order.amount,
stripeToken=self.get_token(),
)
try:
si.authorize()
except restshop.exceptions.PaymentFailed as e:
self.assertEqual('fail!', e.detail)
else:
self.assertRaises(restshop.exceptions.PaymentFailed, lambda: None)
def test_cancel_auth(self):
si = StripeInvoice.objects.create(
order=self.order,
owed=self.order.amount,
stripeToken=self.get_token(),
)
self.assertRaises(
restshop.exceptions.InvalidOperation,
si.cancel_auth
)
self.assertTrue(si.authorize())
self.assertTrue(si.cancel_auth())
si.refresh_from_db()
self.assertEqual(2, si.events.all().count())
self.assertEqual(StripeInvoice.STATUS.canceled, si.status)
| [
"restshop.models.Order.objects.get",
"stripe.Token.create",
"decimal.Decimal",
"restshop.tests.test_product.products_and_price",
"django.test.modify_settings",
"stripe.CardError",
"restshop.models.Order.objects.create",
"unittest.mock.patch",
"restshop.serializers.OrderSerializer",
"rest_framework.reverse.reverse"
]
| [((418, 496), 'django.test.modify_settings', 'modify_settings', ([], {'INSTALLED_APPS': "{'append': 'restshop.paymentmethods.stripejs'}"}), "(INSTALLED_APPS={'append': 'restshop.paymentmethods.stripejs'})\n", (433, 496), False, 'from django.test import modify_settings\n'), ((2044, 2078), 'unittest.mock.patch', 'mock.patch', (['"""stripe.Charge.create"""'], {}), "('stripe.Charge.create')\n", (2054, 2078), False, 'from unittest import mock\n'), ((631, 668), 'restshop.models.Order.objects.create', 'Order.objects.create', ([], {'email': '"""<EMAIL>"""'}), "(email='<EMAIL>')\n", (651, 668), False, 'from restshop.models import Order\n'), ((1804, 1823), 'restshop.models.Order.objects.get', 'Order.objects.get', ([], {}), '()\n', (1821, 1823), False, 'from restshop.models import Order\n'), ((2157, 2193), 'stripe.CardError', 'stripe.CardError', (['"""fail!"""', '""""""', '"""402"""'], {}), "('fail!', '', '402')\n", (2173, 2193), False, 'import stripe\n'), ((1054, 1163), 'stripe.Token.create', 'stripe.Token.create', ([], {'card': "{'number': '4242424242424242', 'exp_month': 12, 'exp_year': 2016, 'cvc': '123'}"}), "(card={'number': '4242424242424242', 'exp_month': 12,\n 'exp_year': 2016, 'cvc': '123'})\n", (1073, 1163), False, 'import stripe\n'), ((1295, 1348), 'rest_framework.reverse.reverse', 'reverse', (['"""order-pay"""'], {'args': "['stripejs.stripeinvoice']"}), "('order-pay', args=['stripejs.stripeinvoice'])\n", (1302, 1348), False, 'from rest_framework.reverse import reverse\n'), ((1959, 1985), 'decimal.Decimal', 'decimal.Decimal', (['"""3750.00"""'], {}), "('3750.00')\n", (1974, 1985), False, 'import decimal\n'), ((1682, 1720), 'decimal.Decimal', 'decimal.Decimal', (["response.data['owed']"], {}), "(response.data['owed'])\n", (1697, 1720), False, 'import decimal\n'), ((1748, 1786), 'decimal.Decimal', 'decimal.Decimal', (["response.data['paid']"], {}), "(response.data['paid'])\n", (1763, 1786), False, 'import decimal\n'), ((1484, 1532), 'restshop.serializers.OrderSerializer', 'serializers.OrderSerializer', ([], {'instance': 'self.order'}), '(instance=self.order)\n', (1511, 1532), False, 'from restshop import serializers\n'), ((859, 883), 'restshop.tests.test_product.products_and_price', 'products_and_price', (['(1000)'], {}), '(1000)\n', (877, 883), False, 'from restshop.tests.test_product import products_and_price\n')] |
from tnnt.settings import UNIQUE_DEATH_REJECTIONS, UNIQUE_DEATH_NORMALIZATIONS
import re
def normalize(death):
# Given a death string, apply normalizations from settings.
for regtuple in UNIQUE_DEATH_NORMALIZATIONS:
death = re.sub(regtuple[0], regtuple[1], death)
return death
def reject(death):
# Given a death string, return True if it should be excluded as a
# unique death and False if not.
for regex in UNIQUE_DEATH_REJECTIONS:
if re.search(regex, death) is not None:
return True
return False
def compile_unique_deaths(gameQS):
# Given a QuerySet of Game objects, return a set containing strings of all
# the unique deaths from those games after rejections and normalizations are
# applied.
# This is primarily for aggregation, and runs somewhat faster than it would
# if we wanted to return the players who got a death and when. This is a
# post 2021 TODO.
# First, get all unique, un-normalized deaths.
raw_uniq_deaths = \
gameQS.values_list('death', flat=True).distinct()
# Then apply normalizations and rejections, and turn it into a set
# to automatically remove any duplicates produced by normalization.
return set(normalize(d) for d in raw_uniq_deaths if not reject(d))
# post 2021 TODO: showing unique deaths of a player or clan:
# 1. list(Game.objects.values_list('death', 'player__name', 'endtime'))
# 2. iterate through list, filtering any death for which reject is True, and
# normalizing all death strings.
# 3. sort by first death, then endtime.
# 4. filter again by taking only the first player/endtime for each death and
# ignoring later ones.
| [
"re.sub",
"re.search"
]
| [((241, 280), 're.sub', 're.sub', (['regtuple[0]', 'regtuple[1]', 'death'], {}), '(regtuple[0], regtuple[1], death)\n', (247, 280), False, 'import re\n'), ((478, 501), 're.search', 're.search', (['regex', 'death'], {}), '(regex, death)\n', (487, 501), False, 'import re\n')] |
# This file is part of QuTiP: Quantum Toolbox in Python.
#
# Copyright (c) 2011 and later, <NAME> and <NAME>.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the QuTiP: Quantum Toolbox in Python 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.
###############################################################################
"""
This module contains a collection of graph theory routines used mainly
to reorder matrices for iterative steady state solvers.
"""
__all__ = ['graph_degree', 'column_permutation', 'breadth_first_search',
'reverse_cuthill_mckee', 'maximum_bipartite_matching',
'weighted_bipartite_matching']
import numpy as np
import scipy.sparse as sp
from qutip.cy.graph_utils import (
_breadth_first_search, _node_degrees,
_reverse_cuthill_mckee, _maximum_bipartite_matching,
_weighted_bipartite_matching)
def graph_degree(A):
"""
Returns the degree for the nodes (rows) of a symmetric
graph in sparse CSR or CSC format, or a qobj.
Parameters
----------
A : qobj, csr_matrix, csc_matrix
Input quantum object or csr_matrix.
Returns
-------
degree : array
Array of integers giving the degree for each node (row).
"""
if not (sp.isspmatrix_csc(A) or sp.isspmatrix_csr(A)):
raise TypeError('Input must be CSC or CSR sparse matrix.')
return _node_degrees(A.indices, A.indptr, A.shape[0])
def breadth_first_search(A, start):
"""
Breadth-First-Search (BFS) of a graph in CSR or CSC matrix format starting
from a given node (row). Takes Qobjs and CSR or CSC matrices as inputs.
This function requires a matrix with symmetric structure.
Use A+trans(A) if original matrix is not symmetric or not sure.
Parameters
----------
A : csc_matrix, csr_matrix
Input graph in CSC or CSR matrix format
start : int
Staring node for BFS traversal.
Returns
-------
order : array
Order in which nodes are traversed from starting node.
levels : array
Level of the nodes in the order that they are traversed.
"""
if not (sp.isspmatrix_csc(A) or sp.isspmatrix_csr(A)):
raise TypeError('Input must be CSC or CSR sparse matrix.')
num_rows = A.shape[0]
start = int(start)
order, levels = _breadth_first_search(A.indices, A.indptr, num_rows, start)
# since maybe not all nodes are in search, check for unused entires in
# arrays
return order[order != -1], levels[levels != -1]
def column_permutation(A):
"""
Finds the non-symmetric column permutation of A such that the columns
are given in ascending order according to the number of nonzero entries.
This is sometimes useful for decreasing the fill-in of sparse LU
factorization.
Parameters
----------
A : csc_matrix
Input sparse CSC sparse matrix.
Returns
-------
perm : array
Array of permuted row and column indices.
"""
if not sp.isspmatrix_csc(A):
A = sp.csc_matrix(A)
count = np.diff(A.indptr)
perm = np.argsort(count)
return perm
def reverse_cuthill_mckee(A, sym=False):
"""
Returns the permutation array that orders a sparse CSR or CSC matrix
in Reverse-Cuthill McKee ordering. Since the input matrix must be
symmetric, this routine works on the matrix A+Trans(A) if the sym flag is
set to False (Default).
It is assumed by default (*sym=False*) that the input matrix is not
symmetric. This is because it is faster to do A+Trans(A) than it is to
check for symmetry for a generic matrix. If you are guaranteed that the
matrix is symmetric in structure (values of matrix element do not matter)
then set *sym=True*
Parameters
----------
A : csc_matrix, csr_matrix
Input sparse CSC or CSR sparse matrix format.
sym : bool {False, True}
Flag to set whether input matrix is symmetric.
Returns
-------
perm : array
Array of permuted row and column indices.
Notes
-----
This routine is used primarily for internal reordering of Lindblad
superoperators for use in iterative solver routines.
References
----------
<NAME> and <NAME>, "Reducing the Bandwidth of Sparse Symmetric
Matrices", ACM '69 Proceedings of the 1969 24th national conference,
(1969).
"""
if not (sp.isspmatrix_csc(A) or sp.isspmatrix_csr(A)):
raise TypeError('Input must be CSC or CSR sparse matrix.')
nrows = A.shape[0]
if not sym:
A = A + A.transpose()
return _reverse_cuthill_mckee(A.indices, A.indptr, nrows)
def maximum_bipartite_matching(A, perm_type='row'):
"""
Returns an array of row or column permutations that removes nonzero
elements from the diagonal of a nonsingular square CSC sparse matrix. Such
a permutation is always possible provided that the matrix is nonsingular.
This function looks at the structure of the matrix only.
The input matrix will be converted to CSC matrix format if
necessary.
Parameters
----------
A : sparse matrix
Input matrix
perm_type : str {'row', 'column'}
Type of permutation to generate.
Returns
-------
perm : array
Array of row or column permutations.
Notes
-----
This function relies on a maximum cardinality bipartite matching algorithm
based on a breadth-first search (BFS) of the underlying graph[1]_.
References
----------
<NAME>, <NAME>, and <NAME>, "Design, Implementation, and
Analysis of Maximum Transversal Algorithms", ACM Trans. Math. Softw.
38, no. 2, (2011).
"""
nrows = A.shape[0]
if A.shape[0] != A.shape[1]:
raise ValueError(
'Maximum bipartite matching requires a square matrix.')
if sp.isspmatrix_csr(A) or sp.isspmatrix_coo(A):
A = A.tocsc()
elif not sp.isspmatrix_csc(A):
raise TypeError("matrix must be in CSC, CSR, or COO format.")
if perm_type == 'column':
A = A.transpose().tocsc()
perm = _maximum_bipartite_matching(A.indices, A.indptr, nrows)
if np.any(perm == -1):
raise Exception('Possibly singular input matrix.')
return perm
def weighted_bipartite_matching(A, perm_type='row'):
"""
Returns an array of row permutations that attempts to maximize
the product of the ABS values of the diagonal elements in
a nonsingular square CSC sparse matrix. Such a permutation is
always possible provided that the matrix is nonsingular.
This function looks at both the structure and ABS values of the
underlying matrix.
Parameters
----------
A : csc_matrix
Input matrix
perm_type : str {'row', 'column'}
Type of permutation to generate.
Returns
-------
perm : array
Array of row or column permutations.
Notes
-----
This function uses a weighted maximum cardinality bipartite matching
algorithm based on breadth-first search (BFS). The columns are weighted
according to the element of max ABS value in the associated rows and
are traversed in descending order by weight. When performing the BFS
traversal, the row associated to a given column is the one with maximum
weight. Unlike other techniques[1]_, this algorithm does not guarantee the
product of the diagonal is maximized. However, this limitation is offset
by the substantially faster runtime of this method.
References
----------
<NAME> and <NAME>, "The design and use of algorithms for
permuting large entries to the diagonal of sparse matrices", SIAM J.
Matrix Anal. and Applics. 20, no. 4, 889 (1997).
"""
nrows = A.shape[0]
if A.shape[0] != A.shape[1]:
raise ValueError('weighted_bfs_matching requires a square matrix.')
if sp.isspmatrix_csr(A) or sp.isspmatrix_coo(A):
A = A.tocsc()
elif not sp.isspmatrix_csc(A):
raise TypeError("matrix must be in CSC, CSR, or COO format.")
if perm_type == 'column':
A = A.transpose().tocsc()
perm = _weighted_bipartite_matching(
np.asarray(np.abs(A.data), dtype=float),
A.indices, A.indptr, nrows)
if np.any(perm == -1):
raise Exception('Possibly singular input matrix.')
return perm
| [
"scipy.sparse.isspmatrix_csc",
"scipy.sparse.isspmatrix_csr",
"numpy.abs",
"qutip.cy.graph_utils._maximum_bipartite_matching",
"qutip.cy.graph_utils._breadth_first_search",
"qutip.cy.graph_utils._node_degrees",
"numpy.diff",
"qutip.cy.graph_utils._reverse_cuthill_mckee",
"numpy.any",
"numpy.argsort",
"scipy.sparse.csc_matrix",
"scipy.sparse.isspmatrix_coo"
]
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import pytest
from daisy.workflow import build_combinations
def test_one_option():
assert build_combinations(
{"option1": ["value1", "value2"]}) == \
[{'option1': 'value1'},
{'option1': 'value2'}]
def test_two_options():
assert build_combinations(
{'option1': ["value1", "value2"],
'option2': 'valueA'}) == \
[{'option2': 'valueA', 'option1': 'value1'},
{'option2': 'valueA', 'option1': 'value2'}]
def test_two_options():
assert build_combinations(
{'option1': ["value1", "value2"],
'option2': ["valueA", "valueB"]}) == \
[{'option1': 'value1', 'option2': 'valueA'},
{'option1': 'value1', 'option2': 'valueB'},
{'option1': 'value2', 'option2': 'valueA'},
{'option1': 'value2', 'option2': 'valueB'}]
def test_complex_values():
assert build_combinations(
{'option1': [{"value1": [1, 2, 3]},
{"value2": [4, 5, 6]}]}) == \
[{'option1': {'value1': [1, 2, 3]}},
{'option1': {'value2': [4, 5, 6]}}]
def test_groupby_design(tmp_path):
design_file = tmp_path / "design.tsv"
with open(design_file, "w") as outf:
outf.write("label\tc_option1\tc_option2\n")
outf.write("label1\tvalue1\tvalueA\n")
outf.write("label2\tvalue1\tvalueB\n")
outf.write("label3\tvalue2\tvalueA\n")
outf.write("label4\tvalue2\tvalueB\n")
assert build_combinations(
{"groupby": "file",
"label": "label",
"input": design_file,
"option1": "c_option1",
"option2": "c_option2"}) == \
[{'option1': 'value1', 'option2': 'valueA', "name": "label1"},
{'option1': 'value1', 'option2': 'valueB', "name": "label2"},
{'option1': 'value2', 'option2': 'valueA', "name": "label3"},
{'option1': 'value2', 'option2': 'valueB', "name": "label4"}]
def test_groupby_design_with_constant_option(tmp_path):
design_file = tmp_path / "design.tsv"
with open(design_file, "w") as outf:
outf.write("label\tc_option1\tc_option2\n")
outf.write("label1\tvalue1\tvalueA\n")
outf.write("label2\tvalue1\tvalueB\n")
outf.write("label3\tvalue2\tvalueA\n")
outf.write("label4\tvalue2\tvalueB\n")
assert build_combinations(
{"groupby": "file",
"label": "label",
"input": design_file,
"option1": "c_option1",
"option2": "c_option2",
"option3": "valueX"}) == \
[{'option1': 'value1', 'option2': 'valueA', "name": "label1", "option3": "valueX"},
{'option1': 'value1', 'option2': 'valueB', "name": "label2", "option3": "valueX"},
{'option1': 'value2', 'option2': 'valueA', "name": "label3", "option3": "valueX"},
{'option1': 'value2', 'option2': 'valueB', "name": "label4", "option3": "valueX"}]
def test_groupby_design_with_combinatorial_option(tmp_path):
design_file = tmp_path / "design.tsv"
with open(design_file, "w") as outf:
outf.write("label\tc_option1\tc_option2\n")
outf.write("label1\tvalue1\tvalueA\n")
outf.write("label2\tvalue1\tvalueB\n")
outf.write("label3\tvalue2\tvalueA\n")
outf.write("label4\tvalue2\tvalueB\n")
assert build_combinations(
{"groupby": "file",
"label": "label",
"input": design_file,
"option1": "c_option1",
"option2": "c_option2",
"option3": ["valueX", "valueY"]}) == \
[{'option1': 'value1', 'option2': 'valueA', "name": "label1", "option3": "valueX"},
{'option1': 'value1', 'option2': 'valueA', "name": "label1", "option3": "valueY"},
{'option1': 'value1', 'option2': 'valueB', "name": "label2", "option3": "valueX"},
{'option1': 'value1', 'option2': 'valueB', "name": "label2", "option3": "valueY"},
{'option1': 'value2', 'option2': 'valueA', "name": "label3", "option3": "valueX"},
{'option1': 'value2', 'option2': 'valueA', "name": "label3", "option3": "valueY"},
{'option1': 'value2', 'option2': 'valueB', "name": "label4", "option3": "valueX"},
{'option1': 'value2', 'option2': 'valueB', "name": "label4", "option3": "valueY"}]
def test_groupby_regex(tmp_path):
assert build_combinations(
{"groupby": "regex",
"files_a": ["{}/data_0.a".format(tmp_path),
"{}/data_1.a".format(tmp_path)],
"files_b": ["{}/data_0.b".format(tmp_path),
"{}/data_1.b".format(tmp_path)],
"files_a_regex": r"data_(\d+).a",
"files_b_regex": r"data_(\d+).b"}) == \
[{'files_a': "{}/data_0.a".format(tmp_path),
'files_b': "{}/data_0.b".format(tmp_path),
'name': "0"},
{'files_a': "{}/data_1.a".format(tmp_path),
'files_b': "{}/data_1.b".format(tmp_path),
'name': "1"}]
def test_groupby_regex_filters_when_data_point_missing(tmp_path):
assert build_combinations(
{"groupby": "regex",
"files_a": ["{}/data_0.a".format(tmp_path)],
"files_b": ["{}/data_0.b".format(tmp_path),
"{}/data_1.b".format(tmp_path)],
"files_a_regex": r"data_(\d+).a",
"files_b_regex": r"data_(\d+).b"}) == \
[{'files_a': "{}/data_0.a".format(tmp_path),
'files_b': "{}/data_0.b".format(tmp_path),
'name': "0"}]
def test_groupby_regex_with_constant(tmp_path):
assert build_combinations(
{"groupby": "regex",
"files_x": "x.y",
"files_a": ["{}/data_0.a".format(tmp_path),
"{}/data_1.a".format(tmp_path)],
"files_b": ["{}/data_0.b".format(tmp_path),
"{}/data_1.b".format(tmp_path)],
"files_a_regex": r"data_(\d+).a",
"files_b_regex": r"data_(\d+).b"}) == \
[
{'files_a': "{}/data_0.a".format(tmp_path),
'files_b': "{}/data_0.b".format(tmp_path),
'files_x': "x.y",
'name': "0"},
{'files_a': "{}/data_1.a".format(tmp_path),
'files_b': "{}/data_1.b".format(tmp_path),
'files_x': "x.y",
'name': "1"},
]
def test_groupby_regex_with_combinatorial_option(tmp_path):
assert build_combinations(
{"groupby": "regex",
"files_x": ["y.x", "z.x"],
"files_a": ["{}/data_0.a".format(tmp_path),
"{}/data_1.a".format(tmp_path)],
"files_b": ["{}/data_0.b".format(tmp_path),
"{}/data_1.b".format(tmp_path)],
"files_a_regex": r"data_(\d+).a",
"files_b_regex": r"data_(\d+).b"}) == \
[
{'files_a': "{}/data_0.a".format(tmp_path),
'files_b': "{}/data_0.b".format(tmp_path),
'files_x': "y.x",
'name': "0"},
{'files_a': "{}/data_0.a".format(tmp_path),
'files_b': "{}/data_0.b".format(tmp_path),
'files_x': "z.x",
'name': "0"},
{'files_a': "{}/data_1.a".format(tmp_path),
'files_b': "{}/data_1.b".format(tmp_path),
'files_x': "y.x",
'name': "1"},
{'files_a': "{}/data_1.a".format(tmp_path),
'files_b': "{}/data_1.b".format(tmp_path),
'files_x': "z.x",
'name': "1"},
]
def test_groupby_named_regex(tmp_path):
assert build_combinations(
{"groupby": "regex",
"files_a": ["{}/data_0.a".format(tmp_path),
"{}/data_1.a".format(tmp_path)],
"files_b": ["{}/data_0.b".format(tmp_path),
"{}/data_1.b".format(tmp_path)],
"files_a_regex": r"data_(?P<key1>\d+).a",
"files_b_regex": r"data_(?P<key1>\d+).b"}) == \
[{'files_a': "{}/data_0.a".format(tmp_path),
'files_b': "{}/data_0.b".format(tmp_path),
'name': "0"},
{'files_a': "{}/data_1.a".format(tmp_path),
'files_b': "{}/data_1.b".format(tmp_path),
'name': "1"}]
def test_groupby_named_regex_paired(tmp_path):
assert build_combinations(
{"groupby": "regex",
"files_a": ["{}/data_0_2.a".format(tmp_path),
"{}/data_0_3.a".format(tmp_path),
"{}/data_1_2.a".format(tmp_path),
"{}/data_1_3.a".format(tmp_path)],
"files_b": ["{}/data_0.b".format(tmp_path),
"{}/data_1.b".format(tmp_path)],
"files_a_regex": r"data_(?P<key1>\d+)_(?P<key2>\d+).a",
"files_b_regex": r"data_(?P<key1>\d+).b"}) == \
[{'files_a': "{}/data_0_2.a".format(tmp_path),
'files_b': "{}/data_0.b".format(tmp_path),
'name': "0_2"},
{'files_a': "{}/data_0_3.a".format(tmp_path),
'files_b': "{}/data_0.b".format(tmp_path),
'name': "0_3"},
{'files_a': "{}/data_1_2.a".format(tmp_path),
'files_b': "{}/data_1.b".format(tmp_path),
'name': "1_2"},
{'files_a': "{}/data_1_3.a".format(tmp_path),
'files_b': "{}/data_1.b".format(tmp_path),
'name': "1_3"}]
| [
"daisy.workflow.build_combinations"
]
| [((96, 149), 'daisy.workflow.build_combinations', 'build_combinations', (["{'option1': ['value1', 'value2']}"], {}), "({'option1': ['value1', 'value2']})\n", (114, 149), False, 'from daisy.workflow import build_combinations\n'), ((265, 339), 'daisy.workflow.build_combinations', 'build_combinations', (["{'option1': ['value1', 'value2'], 'option2': 'valueA'}"], {}), "({'option1': ['value1', 'value2'], 'option2': 'valueA'})\n", (283, 339), False, 'from daisy.workflow import build_combinations\n'), ((508, 598), 'daisy.workflow.build_combinations', 'build_combinations', (["{'option1': ['value1', 'value2'], 'option2': ['valueA', 'valueB']}"], {}), "({'option1': ['value1', 'value2'], 'option2': ['valueA',\n 'valueB']})\n", (526, 598), False, 'from daisy.workflow import build_combinations\n'), ((874, 953), 'daisy.workflow.build_combinations', 'build_combinations', (["{'option1': [{'value1': [1, 2, 3]}, {'value2': [4, 5, 6]}]}"], {}), "({'option1': [{'value1': [1, 2, 3]}, {'value2': [4, 5, 6]}]})\n", (892, 953), False, 'from daisy.workflow import build_combinations\n'), ((1452, 1583), 'daisy.workflow.build_combinations', 'build_combinations', (["{'groupby': 'file', 'label': 'label', 'input': design_file, 'option1':\n 'c_option1', 'option2': 'c_option2'}"], {}), "({'groupby': 'file', 'label': 'label', 'input':\n design_file, 'option1': 'c_option1', 'option2': 'c_option2'})\n", (1470, 1583), False, 'from daisy.workflow import build_combinations\n'), ((2312, 2468), 'daisy.workflow.build_combinations', 'build_combinations', (["{'groupby': 'file', 'label': 'label', 'input': design_file, 'option1':\n 'c_option1', 'option2': 'c_option2', 'option3': 'valueX'}"], {}), "({'groupby': 'file', 'label': 'label', 'input':\n design_file, 'option1': 'c_option1', 'option2': 'c_option2', 'option3':\n 'valueX'})\n", (2330, 2468), False, 'from daisy.workflow import build_combinations\n'), ((3295, 3463), 'daisy.workflow.build_combinations', 'build_combinations', (["{'groupby': 'file', 'label': 'label', 'input': design_file, 'option1':\n 'c_option1', 'option2': 'c_option2', 'option3': ['valueX', 'valueY']}"], {}), "({'groupby': 'file', 'label': 'label', 'input':\n design_file, 'option1': 'c_option1', 'option2': 'c_option2', 'option3':\n ['valueX', 'valueY']})\n", (3313, 3463), False, 'from daisy.workflow import build_combinations\n')] |
import time
from config import get_password, get_username
from playwright.sync_api import Page
def login(page: Page, url: str, landing_url: str):
raise RuntimeError("default login not supported")
def login_kenan_flagler(page: Page, url: str, landing_url: str) -> None:
page.goto(url)
page.wait_for_load_state('load')
if page.url.startswith(landing_url):
return
with page.expect_navigation():
page.locator("text=ONYEN Login").click()
time.sleep(0.5)
page.locator("input[type=email]").fill(get_username())
with page.expect_navigation():
page.locator("input[type=submit]").click()
time.sleep(1)
page.locator("input[type=password]").fill(get_password())
with page.expect_navigation():
page.click('input[type=submit]')
if page.url.endswith("/login"):
# 2-factor auth
page.locator("div[role=\"button\"]:has-text(\"Text\")").click()
print("Enter code: ", end="")
code = input()
code = code.strip()
page.locator("[aria-label=\"Code\"]").fill(code)
with page.expect_navigation():
page.locator("text=Verify").click()
page.locator("[aria-label=\"Don\\'t\\ show\\ this\\ again\"]").check()
page.locator("text=Yes").click()
time.sleep(0.5)
assert page.url.startswith(landing_url)
| [
"config.get_username",
"config.get_password",
"time.sleep"
]
| [((480, 495), 'time.sleep', 'time.sleep', (['(0.5)'], {}), '(0.5)\n', (490, 495), False, 'import time\n'), ((646, 659), 'time.sleep', 'time.sleep', (['(1)'], {}), '(1)\n', (656, 659), False, 'import time\n'), ((1291, 1306), 'time.sleep', 'time.sleep', (['(0.5)'], {}), '(0.5)\n', (1301, 1306), False, 'import time\n'), ((539, 553), 'config.get_username', 'get_username', ([], {}), '()\n', (551, 553), False, 'from config import get_password, get_username\n'), ((706, 720), 'config.get_password', 'get_password', ([], {}), '()\n', (718, 720), False, 'from config import get_password, get_username\n')] |
import asyncio
import json
import os
import pty
import shutil
import sys
import tty
import termios
import time
import threading
import tornado.iostream
from tornado.ioloop import IOLoop
from tornado.websocket import websocket_connect
ioloop = tornado.ioloop.IOLoop.instance()
SSH_LOGIN = "root"
SSH_PASSWORD = "<PASSWORD>"
SCREEN_TO_SCREEN_0_SEQ = b"ls -l\r\x1bOC" + b"\x010" # ^A 0
async def send_stdin_to_ws_task(ws, on_finish_cb):
print("mangling terminal")
try:
fn = os.dup(sys.stdin.fileno())
inp = tornado.iostream.PipeIOStream(fn)
mode = termios.tcgetattr(sys.stdin.fileno())
tty.setraw(fn)
while True:
try:
print("reading stdin", end="\r\n")
content = await inp.read_bytes(100, partial=True)
print("read stdin", end="\r\n")
# content = await self.inp.read_bytes(100, partial=True)
except tornado.iostream.StreamClosedError:
print("Stdin closed", end="\r\n")
# await self.finish()
ioloop.add_callback(on_finish_cb)
break
print(f"stdin: {content}", end="\r\n")
if content[0] == 3 or not content: # CTRL-C
print("Got a ^C", end="\r\n")
ioloop.add_callback(on_finish_cb)
break
ioloop.add_callback(
ws.write_message,
json.dumps(
{
"client_command": "keystrokes",
"keystrokes": [int(x) for x in content],
}
),
)
print("no exc", end="\r\n")
except asyncio.CancelledError:
print("stdin read task cancelled", end="\r\n")
except Exception as e: # pylint: disable=broad-except
print(f"Exception: {e}")
finally:
inp.close()
termios.tcsetattr(sys.stdin, termios.TCSADRAIN, mode)
print("finally")
async def run_ssh(host, port, login=SSH_LOGIN, password=SSH_PASSWORD):
os.environ["SSHPASS"] = password
ssh_cmd = [
"ssh",
"-o",
"PreferredAuthentications=password",
"-o",
"PubkeyAuthentication=no",
"-o",
"StrictHostKeyChecking=no", # Skip fingerpint warning.
f"{login}@{host}",
"-p",
str(port),
]
sshpass_cmd = [shutil.which("sshpass"), "-e"] + ssh_cmd
args = sshpass_cmd
print(" ".join(args))
e = threading.Event()
def stdin_read(fd):
if not e.is_set():
e.set()
return SCREEN_TO_SCREEN_0_SEQ + os.read(fd, 1024)
b = os.read(fd, 1024)
return b
def master_read(fd):
b = os.read(fd, 1024)
return b
# Let Web UI connect to screen 0 first.
time.sleep(3)
res = pty.spawn(args, master_read=master_read, stdin_read=stdin_read)
print(f"ssh returned {res}")
class Client:
mode: str
def __init__(self, url, timeout):
self.url = url
self.timeout = timeout
self.ioloop = IOLoop.instance()
self.ws = None
self.send_stdin_task = None
async def connect(self):
print("trying to connect")
try:
self.ws = await websocket_connect(self.url)
except Exception as e: # pylint: disable=broad-except
print(f"connection error: {str(e)}")
else:
print("connected")
# await self.ws.write_message({'client': self.i})
self.mode = "idle"
self.ioloop.spawn_callback(self.run_idle)
self.ioloop.spawn_callback(self.run)
def finish_ws(self):
if self.ws:
self.ws.close()
self.ws = None
async def finish(self):
if self.send_stdin_task:
await self.stop_idle()
self.finish_ws()
self.ioloop.stop()
async def run_idle(self):
assert not self.send_stdin_task
print("running idle, spawning task")
self.send_stdin_task = asyncio.create_task(
send_stdin_to_ws_task(self.ws, self.finish)
)
async def stop_idle(self):
assert self.send_stdin_task
self.send_stdin_task.cancel()
await self.send_stdin_task
self.send_stdin_task = None
@staticmethod
async def run_ssh(host, port):
# Blocks ioloop
await run_ssh(host, port)
async def run(self):
while True:
msg = await self.ws.read_message()
if msg is None:
print("server left, terminating", end="\r\n")
self.ioloop.add_callback(self.finish)
return
msg = json.loads(msg)
print(f"got msg: {msg}", end="\r\n")
if "mode" not in msg:
continue
if msg["mode"] == "ssh":
host, port = msg["ssh"]["host"], msg["ssh"]["port"]
print(f"Connecting to ssh {host}:{port}...", end="\r\n")
await self.stop_idle()
await self.run_ssh(host, port)
print("restarting idle task")
self.finish_ws()
await self.connect()
break
| [
"sys.stdin.fileno",
"json.loads",
"pty.spawn",
"shutil.which",
"time.sleep",
"threading.Event",
"termios.tcsetattr",
"tornado.ioloop.IOLoop.instance",
"tornado.websocket.websocket_connect",
"os.read",
"tty.setraw"
]
| [((2503, 2520), 'threading.Event', 'threading.Event', ([], {}), '()\n', (2518, 2520), False, 'import threading\n'), ((2825, 2838), 'time.sleep', 'time.sleep', (['(3)'], {}), '(3)\n', (2835, 2838), False, 'import time\n'), ((2849, 2912), 'pty.spawn', 'pty.spawn', (['args'], {'master_read': 'master_read', 'stdin_read': 'stdin_read'}), '(args, master_read=master_read, stdin_read=stdin_read)\n', (2858, 2912), False, 'import pty\n'), ((631, 645), 'tty.setraw', 'tty.setraw', (['fn'], {}), '(fn)\n', (641, 645), False, 'import tty\n'), ((1913, 1966), 'termios.tcsetattr', 'termios.tcsetattr', (['sys.stdin', 'termios.TCSADRAIN', 'mode'], {}), '(sys.stdin, termios.TCSADRAIN, mode)\n', (1930, 1966), False, 'import termios\n'), ((2668, 2685), 'os.read', 'os.read', (['fd', '(1024)'], {}), '(fd, 1024)\n', (2675, 2685), False, 'import os\n'), ((2741, 2758), 'os.read', 'os.read', (['fd', '(1024)'], {}), '(fd, 1024)\n', (2748, 2758), False, 'import os\n'), ((3091, 3108), 'tornado.ioloop.IOLoop.instance', 'IOLoop.instance', ([], {}), '()\n', (3106, 3108), False, 'from tornado.ioloop import IOLoop\n'), ((502, 520), 'sys.stdin.fileno', 'sys.stdin.fileno', ([], {}), '()\n', (518, 520), False, 'import sys\n'), ((603, 621), 'sys.stdin.fileno', 'sys.stdin.fileno', ([], {}), '()\n', (619, 621), False, 'import sys\n'), ((2404, 2427), 'shutil.which', 'shutil.which', (['"""sshpass"""'], {}), "('sshpass')\n", (2416, 2427), False, 'import shutil\n'), ((4709, 4724), 'json.loads', 'json.loads', (['msg'], {}), '(msg)\n', (4719, 4724), False, 'import json\n'), ((2637, 2654), 'os.read', 'os.read', (['fd', '(1024)'], {}), '(fd, 1024)\n', (2644, 2654), False, 'import os\n'), ((3275, 3302), 'tornado.websocket.websocket_connect', 'websocket_connect', (['self.url'], {}), '(self.url)\n', (3292, 3302), False, 'from tornado.websocket import websocket_connect\n')] |
import abc
import logging
from typing import Any, Dict, List, Optional
from homeassistant.components.water_heater import WaterHeaterEntity
from homeassistant.const import (
TEMP_FAHRENHEIT,
TEMP_CELSIUS
)
from gehomesdk import ErdCode, ErdMeasurementUnits
from ...const import DOMAIN
from .ge_erd_entity import GeEntity
_LOGGER = logging.getLogger(__name__)
class GeWaterHeater(GeEntity, WaterHeaterEntity, metaclass=abc.ABCMeta):
"""Mock temperature/operation mode supporting device as a water heater"""
@property
def heater_type(self) -> str:
raise NotImplementedError
@property
def operation_list(self) -> List[str]:
raise NotImplementedError
@property
def unique_id(self) -> str:
return f"{DOMAIN}_{self.serial_or_mac}_{self.heater_type}"
@property
def name(self) -> Optional[str]:
return f"{self.serial_or_mac} {self.heater_type.title()}"
@property
def temperature_unit(self):
measurement_system = self.appliance.get_erd_value(ErdCode.TEMPERATURE_UNIT)
if measurement_system == ErdMeasurementUnits.METRIC:
return TEMP_CELSIUS
return TEMP_FAHRENHEIT
@property
def supported_features(self):
raise NotImplementedError
| [
"logging.getLogger"
]
| [((340, 367), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (357, 367), False, 'import logging\n')] |
import json
from scrapy.item import BaseItem
from scrapy.http import Request
from scrapy.exceptions import ContractFail
from scrapy.contracts import Contract
# contracts
class UrlContract(Contract):
""" Contract to set the url of the request (mandatory)
@url http://scrapy.org
"""
name = 'url'
def adjust_request_args(self, args):
args['url'] = self.args[0]
return args
class CallbackKeywordArgumentsContract(Contract):
""" Contract to set the keyword arguments for the request.
The value should be a JSON-encoded dictionary, e.g.:
@cb_kwargs {"arg1": "some value"}
"""
name = 'cb_kwargs'
def adjust_request_args(self, args):
args['cb_kwargs'] = json.loads(' '.join(self.args))
return args
class ReturnsContract(Contract):
""" Contract to check the output of a callback
general form:
@returns request(s)/item(s) [min=1 [max]]
e.g.:
@returns request
@returns request 2
@returns request 2 10
@returns request 0 10
"""
name = 'returns'
objects = {
'request': Request,
'requests': Request,
'item': (BaseItem, dict),
'items': (BaseItem, dict),
}
def __init__(self, *args, **kwargs):
super(ReturnsContract, self).__init__(*args, **kwargs)
assert len(self.args) in [1, 2, 3]
self.obj_name = self.args[0] or None
self.obj_type = self.objects[self.obj_name]
try:
self.min_bound = int(self.args[1])
except IndexError:
self.min_bound = 1
try:
self.max_bound = int(self.args[2])
except IndexError:
self.max_bound = float('inf')
def post_process(self, output):
occurrences = 0
for x in output:
if isinstance(x, self.obj_type):
occurrences += 1
assertion = (self.min_bound <= occurrences <= self.max_bound)
if not assertion:
if self.min_bound == self.max_bound:
expected = self.min_bound
else:
expected = '%s..%s' % (self.min_bound, self.max_bound)
raise ContractFail("Returned %s %s, expected %s" % \
(occurrences, self.obj_name, expected))
class ScrapesContract(Contract):
""" Contract to check presence of fields in scraped items
@scrapes page_name page_body
"""
name = 'scrapes'
def post_process(self, output):
for x in output:
if isinstance(x, (BaseItem, dict)):
missing = [arg for arg in self.args if arg not in x]
if missing:
raise ContractFail(
"Missing fields: %s" % ", ".join(missing))
| [
"scrapy.exceptions.ContractFail"
]
| [((2209, 2297), 'scrapy.exceptions.ContractFail', 'ContractFail', (["('Returned %s %s, expected %s' % (occurrences, self.obj_name, expected))"], {}), "('Returned %s %s, expected %s' % (occurrences, self.obj_name,\n expected))\n", (2221, 2297), False, 'from scrapy.exceptions import ContractFail\n')] |
import click
import pickle
import numpy as np
from collections import defaultdict
from utils import reset_seeds, get_dataset, load_embeddings
from mlp_multilabel_wrapper import PowersetKerasWrapper, MultiOutputKerasWrapper
from mlp_utils import CrossLabelDependencyLoss
def get_random_sample(dataset_name='bbc', train_frac=0.25):
# get model runner specific dataset
_, _, y_train, y_test = get_dataset(dataset_name)
X_train, X_test = load_embeddings(dataset_name)
grps = y_train.apply(lambda v: ''.join(map(str, v)), axis=1).to_frame(0).groupby(0)[0]
train_idx = grps.apply(lambda g: g.sample(frac=train_frac)).index.get_level_values(1)
X_train_sample = X_train.loc[train_idx, :]
y_train_sample = y_train.loc[train_idx, :]
return X_train_sample, X_test, y_train_sample, y_test
def _get_label_set(y):
return set(y.apply(lambda v: ''.join(map(str, v)), axis=1).values)
@click.command()
@click.option('--n-samples', default=10)
@click.option('--dataset-name', default='moral-dataset-MeToo')
def run(n_samples, dataset_name):
mlp_cld_bootstrap_results = defaultdict(lambda: defaultdict(list))
mlp_powerset_bootstrap_results = defaultdict(lambda: defaultdict(list))
mlp_labels_bootstrap_results = defaultdict(lambda: defaultdict(list))
reset_seeds()
for i in range(n_samples):
print('Running bootstrap sample: {}'.format(i + 1))
for f in np.arange(0.1, 1.1, 0.1):
X_train, X_test, y_train, y_test = get_random_sample(dataset_name, train_frac=f)
print('Training set size: {}'.format(X_train.shape))
print('Test set size: {}'.format(X_test.shape))
mlp_powerset_model = PowersetKerasWrapper(columns=y_train.columns)
mlp_powerset_model.fit(X_train.values, y_train.values)
y_pred_mlp = mlp_powerset_model.predict(X_test.values)
mlp_powerset_bootstrap_results[i][f].append(y_pred_mlp)
cld_loss = CrossLabelDependencyLoss(alpha=0.2)
mlp_cld_model = MultiOutputKerasWrapper(columns=y_train.columns, loss=cld_loss)
mlp_cld_model.fit(X_train.values, y_train.values)
y_pred_cld = mlp_cld_model.predict(X_test.values)
mlp_cld_bootstrap_results[i][f].append(y_pred_cld)
mlp_labels_bootstrap_results[i][f].append((_get_label_set(y_train), _get_label_set(y_test)))
with open('training_size_bootstrap_{}.pkl'.format(dataset_name), 'wb') as f:
pickle.dump({'cld': dict(mlp_cld_bootstrap_results),
'powerset': dict(mlp_powerset_bootstrap_results),
'labels': dict(mlp_labels_bootstrap_results)}, f)
if __name__ == '__main__':
run()
| [
"mlp_multilabel_wrapper.MultiOutputKerasWrapper",
"click.option",
"utils.get_dataset",
"mlp_utils.CrossLabelDependencyLoss",
"collections.defaultdict",
"mlp_multilabel_wrapper.PowersetKerasWrapper",
"utils.load_embeddings",
"utils.reset_seeds",
"click.command",
"numpy.arange"
]
| [((912, 927), 'click.command', 'click.command', ([], {}), '()\n', (925, 927), False, 'import click\n'), ((929, 968), 'click.option', 'click.option', (['"""--n-samples"""'], {'default': '(10)'}), "('--n-samples', default=10)\n", (941, 968), False, 'import click\n'), ((970, 1031), 'click.option', 'click.option', (['"""--dataset-name"""'], {'default': '"""moral-dataset-MeToo"""'}), "('--dataset-name', default='moral-dataset-MeToo')\n", (982, 1031), False, 'import click\n'), ((400, 425), 'utils.get_dataset', 'get_dataset', (['dataset_name'], {}), '(dataset_name)\n', (411, 425), False, 'from utils import reset_seeds, get_dataset, load_embeddings\n'), ((448, 477), 'utils.load_embeddings', 'load_embeddings', (['dataset_name'], {}), '(dataset_name)\n', (463, 477), False, 'from utils import reset_seeds, get_dataset, load_embeddings\n'), ((1292, 1305), 'utils.reset_seeds', 'reset_seeds', ([], {}), '()\n', (1303, 1305), False, 'from utils import reset_seeds, get_dataset, load_embeddings\n'), ((1414, 1438), 'numpy.arange', 'np.arange', (['(0.1)', '(1.1)', '(0.1)'], {}), '(0.1, 1.1, 0.1)\n', (1423, 1438), True, 'import numpy as np\n'), ((1118, 1135), 'collections.defaultdict', 'defaultdict', (['list'], {}), '(list)\n', (1129, 1135), False, 'from collections import defaultdict\n'), ((1194, 1211), 'collections.defaultdict', 'defaultdict', (['list'], {}), '(list)\n', (1205, 1211), False, 'from collections import defaultdict\n'), ((1268, 1285), 'collections.defaultdict', 'defaultdict', (['list'], {}), '(list)\n', (1279, 1285), False, 'from collections import defaultdict\n'), ((1693, 1738), 'mlp_multilabel_wrapper.PowersetKerasWrapper', 'PowersetKerasWrapper', ([], {'columns': 'y_train.columns'}), '(columns=y_train.columns)\n', (1713, 1738), False, 'from mlp_multilabel_wrapper import PowersetKerasWrapper, MultiOutputKerasWrapper\n'), ((1965, 2000), 'mlp_utils.CrossLabelDependencyLoss', 'CrossLabelDependencyLoss', ([], {'alpha': '(0.2)'}), '(alpha=0.2)\n', (1989, 2000), False, 'from mlp_utils import CrossLabelDependencyLoss\n'), ((2029, 2092), 'mlp_multilabel_wrapper.MultiOutputKerasWrapper', 'MultiOutputKerasWrapper', ([], {'columns': 'y_train.columns', 'loss': 'cld_loss'}), '(columns=y_train.columns, loss=cld_loss)\n', (2052, 2092), False, 'from mlp_multilabel_wrapper import PowersetKerasWrapper, MultiOutputKerasWrapper\n')] |
#!/usr/bin/env python
# encoding: utf-8
"""
evaluate.py
Created by Shuailong on 2016-12-2.
Evaluate model accuracy on test set.
"""
from __future__ import print_function
from time import time
from keras.models import load_model
import os
from utils import true_accuracy
from dataset import get_data
from train import MODEL_FILE, MODEL_DIR
from train import data_generator
def main():
start_time = time()
print('\nGetting data...')
data = get_data(force=False)
X_test = data['X_test']
X_test_feats = data['X_test_feats']
y_test = data['y_test']
tag_size = len(data['tag_index'])
print('\nLoading models...')
model = load_model(os.path.join(MODEL_DIR, MODEL_FILE), custom_objects={'true_accuracy': true_accuracy})
print('\nEvaluating...')
_, true_acc = model.evaluate_generator(data_generator(X_test, X_test_feats, y_test, tag_size),
val_samples=len(X_test))
print('Test accuracy: {}.'.format(true_acc))
seconds = time() - start_time
minutes = seconds / 60
print('[Finished in {} seconds ({} minutes)]'.format(str(round(seconds, 1)),
str(round(minutes, 1))))
if __name__ == '__main__':
main()
| [
"os.path.join",
"dataset.get_data",
"time.time",
"train.data_generator"
]
| [((410, 416), 'time.time', 'time', ([], {}), '()\n', (414, 416), False, 'from time import time\n'), ((459, 480), 'dataset.get_data', 'get_data', ([], {'force': '(False)'}), '(force=False)\n', (467, 480), False, 'from dataset import get_data\n'), ((672, 707), 'os.path.join', 'os.path.join', (['MODEL_DIR', 'MODEL_FILE'], {}), '(MODEL_DIR, MODEL_FILE)\n', (684, 707), False, 'import os\n'), ((831, 885), 'train.data_generator', 'data_generator', (['X_test', 'X_test_feats', 'y_test', 'tag_size'], {}), '(X_test, X_test_feats, y_test, tag_size)\n', (845, 885), False, 'from train import data_generator\n'), ((1019, 1025), 'time.time', 'time', ([], {}), '()\n', (1023, 1025), False, 'from time import time\n')] |
#!/usr/bin/env python
# Copyright (c) 2013, Digium, Inc.
# Copyright (c) 2014-2016, Yelp, Inc.
import os
from setuptools import setup
import bravado
setup(
name="bravado",
# cloudlock version, no twisted dependency
version=bravado.version + "cl",
license="BSD 3-Clause License",
description="Library for accessing Swagger-enabled API's",
long_description=open(os.path.join(os.path.dirname(__file__),
"README.rst")).read(),
author="Digium, Inc. and Yelp, Inc.",
author_email="<EMAIL>",
url="https://github.com/Yelp/bravado",
packages=["bravado"],
classifiers=[
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Topic :: Software Development :: Libraries :: Python Modules",
"License :: OSI Approved :: BSD License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 2.6",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3.4",
],
install_requires=[
"bravado-core >= 4.2.2",
"yelp_bytes",
"python-dateutil",
"pyyaml",
"requests",
"six",
],
extras_require={
},
)
| [
"os.path.dirname"
]
| [((402, 427), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (417, 427), False, 'import os\n')] |
# -*- coding: utf-8 -*-
# Generated by Django 1.10.1 on 2018-08-02 07:58
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('website', '0002_enable_compression'),
]
operations = [
migrations.AddField(
model_name='workload',
name='status',
field=models.IntegerField(choices=[(1, 'MODIFIED'), (2, 'PROCESSING'), (3, 'PROCESSED')], default=1, editable=False),
)
]
| [
"django.db.models.IntegerField"
]
| [((434, 548), 'django.db.models.IntegerField', 'models.IntegerField', ([], {'choices': "[(1, 'MODIFIED'), (2, 'PROCESSING'), (3, 'PROCESSED')]", 'default': '(1)', 'editable': '(False)'}), "(choices=[(1, 'MODIFIED'), (2, 'PROCESSING'), (3,\n 'PROCESSED')], default=1, editable=False)\n", (453, 548), False, 'from django.db import migrations, models\n')] |
# for normalization we need to have the maxima of x and y values with the help of which
# we can normalise the given values
import csv
filename = "values.csv"
fields = []
rows = []
with open(filename,'r') as csvfile:
reader = csv.reader(csvfile)
fields = next(reader)
for row in reader:
rows.append(row)
for row in rows:
for col in row:
a = col[0]
norm=50
#a = float(input("enter the x cordinate:"))
#b = float(input("enter the y cordinate:"))
if (a>norm or b>norm or a<-(norm) or b<-(norm)):
print("the value given is invalid/out of bound")
else:
a = a/norm
b = b/norm
print("the normalized values are "+str(a)+","+str(b)) | [
"csv.reader"
]
| [((238, 257), 'csv.reader', 'csv.reader', (['csvfile'], {}), '(csvfile)\n', (248, 257), False, 'import csv\n')] |
from pygdp import _execute_request
from pygdp import _get_geotype
from owslib.util import log
def submitFeatureWeightedGridStatistics(geoType, dataSetURI, varID, startTime, endTime, attribute, value, gmlIDs,
verbose, coverage, delim, stat, grpby, timeStep, summAttr, weighted, WFS_URL, outputfname, sleepSecs):
"""
Makes a featureWeightedGridStatistics algorithm call.
The web service interface implemented is summarized here:
https://my.usgs.gov/confluence/display/GeoDataPortal/Generating+Area+Weighted+Statistics+Of+A+Gridded+Dataset+For+A+Set+Of+Vector+Polygon+Features
Note that varID and stat can be a list of strings.
"""
# test for dods:
dataSetURI = _execute_request.dodsReplace(dataSetURI)
log.info('Generating feature collection.')
featureCollection = _get_geotype._getFeatureCollectionGeoType(geoType, attribute, value, gmlIDs, WFS_URL)
if featureCollection is None:
return
processid = 'gov.usgs.cida.gdp.wps.algorithm.FeatureWeightedGridStatisticsAlgorithm'
if weighted==False:
processid = 'gov.usgs.cida.gdp.wps.algorithm.FeatureGridStatisticsAlgorithm'
solo_inputs = [("FEATURE_ATTRIBUTE_NAME",attribute),
("DATASET_URI", dataSetURI),
("TIME_START",startTime),
("TIME_END",endTime),
("REQUIRE_FULL_COVERAGE",str(coverage).lower()),
("DELIMITER",delim),
("GROUP_BY", grpby),
("SUMMARIZE_TIMESTEP", str(timeStep).lower()),
("SUMMARIZE_FEATURE_ATTRIBUTE",str(summAttr).lower()),
("FEATURE_COLLECTION", featureCollection)]
if isinstance(stat, list):
num_stats=len(stat)
if num_stats > 7:
raise Exception('Too many statistics were submitted.')
else:
num_stats=1
if isinstance(varID, list):
num_varIDs=len(varID)
else:
num_varIDs=1
inputs = [('','')]*(len(solo_inputs)+num_varIDs+num_stats)
count=0
rmvCnt=0
for solo_input in solo_inputs:
if solo_input[1]!=None:
inputs[count] = solo_input
count+=1
else:
rmvCnt+=1
del inputs[count:count+rmvCnt]
if num_stats > 1:
for stat_in in stat:
if stat_in not in ["MEAN", "MINIMUM", "MAXIMUM", "VARIANCE", "STD_DEV", "SUM", "COUNT"]:
raise Exception('The statistic %s is not in the allowed list: "MEAN", "MINIMUM", "MAXIMUM", "VARIANCE", "STD_DEV", "SUM", "COUNT"' % stat_in)
inputs[count] = ("STATISTICS",stat_in)
count+=1
elif num_stats == 1:
if stat not in ["MEAN", "MINIMUM", "MAXIMUM", "VARIANCE", "STD_DEV", "SUM", "COUNT"]:
raise Exception('The statistic %s is not in the allowed list: "MEAN", "MINIMUM", "MAXIMUM", "VARIANCE", "STD_DEV", "SUM", "COUNT"' % stat)
inputs[count] = ("STATISTICS",stat)
count+=1
if num_varIDs > 1:
for var in varID:
inputs[count] = ("DATASET_ID",var)
count+=1
elif num_varIDs == 1:
inputs[count] = ("DATASET_ID",varID)
output = "OUTPUT"
return _execute_request._executeRequest(processid, inputs, output, verbose, outputfname, sleepSecs)
| [
"pygdp._execute_request._executeRequest",
"pygdp._get_geotype._getFeatureCollectionGeoType",
"owslib.util.log.info",
"pygdp._execute_request.dodsReplace"
]
| [((744, 784), 'pygdp._execute_request.dodsReplace', '_execute_request.dodsReplace', (['dataSetURI'], {}), '(dataSetURI)\n', (772, 784), False, 'from pygdp import _execute_request\n'), ((794, 836), 'owslib.util.log.info', 'log.info', (['"""Generating feature collection."""'], {}), "('Generating feature collection.')\n", (802, 836), False, 'from owslib.util import log\n'), ((866, 955), 'pygdp._get_geotype._getFeatureCollectionGeoType', '_get_geotype._getFeatureCollectionGeoType', (['geoType', 'attribute', 'value', 'gmlIDs', 'WFS_URL'], {}), '(geoType, attribute, value, gmlIDs,\n WFS_URL)\n', (907, 955), False, 'from pygdp import _get_geotype\n'), ((3384, 3480), 'pygdp._execute_request._executeRequest', '_execute_request._executeRequest', (['processid', 'inputs', 'output', 'verbose', 'outputfname', 'sleepSecs'], {}), '(processid, inputs, output, verbose,\n outputfname, sleepSecs)\n', (3416, 3480), False, 'from pygdp import _execute_request\n')] |
import sys
import socket
conn = socket.create_connection(('0.0.0.0', 8080))
msgs = [
# 0 Keep-Alive, Transfer-Encoding chunked
'GET / HTTP/1.1\r\nConnection: Keep-Alive\r\n\r\n',
# 1,2,3 Close, EOF "encoding"
'GET / HTTP/1.1\r\n\r\n',
'GET / HTTP/1.1\r\nConnection: close\r\n\r\n',
'GET / HTTP/1.0\r\nConnection: Keep-Alive\r\n\r\n',
# 4 Bad Request
'GET /%20%20% HTTP/1.1\r\n\r\n',
# 5 Bug #14
'GET /%20abc HTTP/1.0\r\n\r\n',
# 6 Content-{Length, Type}
'GET / HTTP/1.0\r\nContent-Length: 11\r\n'
'Content-Type: text/blah\r\nContent-Fype: bla\r\n'
'Content-Tength: bla\r\n\r\nhello world',
# 7 POST memory leak
'POST / HTTP/1.0\r\nContent-Length: 1000\r\n\r\n%s' % ('a'*1000),
# 8,9 CVE-2015-0219
'GET / HTTP/1.1\r\nFoo_Bar: bad\r\n\r\n',
'GET / HTTP/1.1\r\nFoo-Bar: good\r\nFoo_Bar: bad\r\n\r\n'
]
conn.send(msgs[int(sys.argv[1])].encode())
while 1:
data = conn.recv(100)
if not data: break
print(repr(data))
if data.endswith(b'0\r\n\r\n'):
if raw_input('new request? Y/n') == 'n':
exit()
conn.send(b'GET / HTTP/1.1\r\nConnection: Keep-Alive\r\n\r\n')
| [
"socket.create_connection"
]
| [((33, 76), 'socket.create_connection', 'socket.create_connection', (["('0.0.0.0', 8080)"], {}), "(('0.0.0.0', 8080))\n", (57, 76), False, 'import socket\n')] |
"""
Django settings for TusPachangas project.
For more information on this file, see
https://docs.djangoproject.com/en/1.7/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.7/ref/settings/
"""
import django
import dj_database_url
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
import os
BASE_DIR = os.path.dirname(os.path.dirname(__file__))
TEMPLATE_PATH = os.path.join(BASE_DIR, 'templates')
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/1.7/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = '<KEY>'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
TEMPLATE_DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = (
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'registration', #add in the registration package
'rest_framework',
'restaurante',
'easy_maps',
)
if django.VERSION < (1, 7):
INSTALLED_APPS += (
'south',
)
MIDDLEWARE_CLASSES = (
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.auth.middleware.SessionAuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
)
ROOT_URLCONF = 'ProyectoDAI.urls'
WSGI_APPLICATION = 'ProyectoDAI.wsgi.application'
TEMPLATE_DIRS = (TEMPLATE_PATH,)
# Database
# https://docs.djangoproject.com/en/1.7/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
ON_HEROKU = os.environ.get('PORT')
if ON_HEROKU:
DATABASE_URL='postgres://kytzveedsclzaf:eIJAAuElYvSxPK-vmSdXG9Hjv8@ec2-107-21-219-235.compute-1.amazonaws.com:5432/df9sfr7a9b8vjf'
DATABASES = {'default': dj_database_url.config(default=DATABASE_URL)}
# Internationalization
# https://docs.djangoproject.com/en/1.7/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.7/howto/static-files/
STATIC_PATH = os.path.join(BASE_DIR,'static')
STATIC_URL = '/static/'
STATICFILES_DIRS = (
STATIC_PATH,
)
#Media
MEDIA_URL = '/media/'
MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
| [
"os.path.dirname",
"os.path.join",
"os.environ.get",
"dj_database_url.config"
]
| [((438, 473), 'os.path.join', 'os.path.join', (['BASE_DIR', '"""templates"""'], {}), "(BASE_DIR, 'templates')\n", (450, 473), False, 'import os\n'), ((2029, 2051), 'os.environ.get', 'os.environ.get', (['"""PORT"""'], {}), "('PORT')\n", (2043, 2051), False, 'import os\n'), ((2555, 2587), 'os.path.join', 'os.path.join', (['BASE_DIR', '"""static"""'], {}), "(BASE_DIR, 'static')\n", (2567, 2587), False, 'import os\n'), ((2696, 2727), 'os.path.join', 'os.path.join', (['BASE_DIR', '"""media"""'], {}), "(BASE_DIR, 'media')\n", (2708, 2727), False, 'import os\n'), ((394, 419), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (409, 419), False, 'import os\n'), ((1971, 2007), 'os.path.join', 'os.path.join', (['BASE_DIR', '"""db.sqlite3"""'], {}), "(BASE_DIR, 'db.sqlite3')\n", (1983, 2007), False, 'import os\n'), ((2223, 2267), 'dj_database_url.config', 'dj_database_url.config', ([], {'default': 'DATABASE_URL'}), '(default=DATABASE_URL)\n', (2245, 2267), False, 'import dj_database_url\n')] |
# -*- coding: utf-8 -*-
from gi.repository.GdkPixbuf import Pixbuf
from os import makedirs
def main():
for size in (16, 22, 24, 32, 48, 64, 128, 256, 512):
icon = Pixbuf.new_from_file_at_scale("formiko.svg", size, size, True)
makedirs("%dx%d" % (size, size))
icon.savev("%dx%d/formiko.png" % (size, size), "png", [], [])
if __name__ == "__main__":
main()
| [
"gi.repository.GdkPixbuf.Pixbuf.new_from_file_at_scale",
"os.makedirs"
]
| [((178, 240), 'gi.repository.GdkPixbuf.Pixbuf.new_from_file_at_scale', 'Pixbuf.new_from_file_at_scale', (['"""formiko.svg"""', 'size', 'size', '(True)'], {}), "('formiko.svg', size, size, True)\n", (207, 240), False, 'from gi.repository.GdkPixbuf import Pixbuf\n'), ((249, 281), 'os.makedirs', 'makedirs', (["('%dx%d' % (size, size))"], {}), "('%dx%d' % (size, size))\n", (257, 281), False, 'from os import makedirs\n')] |
from typing import Optional
import pandas as pd
from ruptures import Binseg
from ruptures.base import BaseCost
from sklearn.linear_model import LinearRegression
from etna.transforms.base import PerSegmentWrapper
from etna.transforms.decomposition.change_points_trend import BaseEstimator
from etna.transforms.decomposition.change_points_trend import TDetrendModel
from etna.transforms.decomposition.change_points_trend import _OneSegmentChangePointsTrendTransform
class _OneSegmentTrendTransform(_OneSegmentChangePointsTrendTransform):
"""_OneSegmentTrendTransform adds trend as a feature."""
def __init__(
self,
in_column: str,
out_column: str,
change_point_model: BaseEstimator,
detrend_model: TDetrendModel,
**change_point_model_predict_params,
):
"""Init _OneSegmentTrendTransform.
Parameters
----------
in_column:
name of column to apply transform to
out_column:
name of added column
change_point_model:
model to get trend change points
detrend_model:
model to get trend from data
change_point_model_predict_params:
params for change_point_model predict method
"""
self.out_column = out_column
super().__init__(
in_column=in_column,
change_point_model=change_point_model,
detrend_model=detrend_model,
**change_point_model_predict_params,
)
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
"""Add column with trend, got from the detrend_model.
Parameters
----------
df:
data to get trend from
Returns
-------
pd.DataFrame:
df with trend column
"""
df._is_copy = False
series = df[self.in_column]
trend_series = self._predict_per_interval_model(series=series)
df[self.out_column] = trend_series
return df
def inverse_transform(self, df: pd.DataFrame) -> pd.DataFrame:
"""Inverse transform dataframe.
Parameters
----------
df:
one segment dataframe
Returns
-------
pd.DataFrame:
given dataframe
"""
return df
class _TrendTransform(PerSegmentWrapper):
"""_TrendTransform adds trend as a feature. Creates column 'regressor_<in_column>_trend'."""
def __init__(
self,
in_column: str,
out_column: str,
change_point_model: BaseEstimator,
detrend_model: TDetrendModel,
**change_point_model_predict_params,
):
"""Init _TrendTransform.
Parameters
----------
in_column:
name of column to apply transform to
out_column:
name of added column
change_point_model:
model to get trend change points
detrend_model:
model to get trend in data
change_point_model_predict_params:
params for change_point_model predict method
"""
super().__init__(
transform=_OneSegmentTrendTransform(
in_column=in_column,
out_column=out_column,
change_point_model=change_point_model,
detrend_model=detrend_model,
**change_point_model_predict_params,
)
)
class TrendTransform(_TrendTransform):
"""TrendTransform adds trend as a feature.
TrendTransform uses Binseg model as a change point detection model in _TrendTransform.
"""
def __init__(
self,
in_column: str,
out_column: Optional[str] = None,
detrend_model: TDetrendModel = LinearRegression(),
model: str = "ar",
custom_cost: Optional[BaseCost] = None,
min_size: int = 2,
jump: int = 1,
n_bkps: int = 5,
pen: Optional[float] = None,
epsilon: Optional[float] = None,
):
"""Init TrendTransform.
Parameters
----------
in_column:
name of column to apply transform to
out_column:
name of added column. Don't forget to add regressor prefix if necessary.
If not given, use 'regressor_{self.__repr__()}'
detrend_model:
model to get trend in data
model:
binseg segment model, ["l1", "l2", "rbf",...]. Not used if 'custom_cost' is not None.
custom_cost:
binseg custom cost function
min_size:
minimum segment length necessary to decide it is a stable trend segment
jump:
jump value can speed up computations: if jump==k, the algo will use every k-th value for change points search.
n_bkps:
number of change points to find
pen:
penalty value (>0)
epsilon:
reconstruction budget (>0)
"""
self.in_column = in_column
self.out_column = out_column
self.detrend_model = detrend_model
self.model = model
self.custom_cost = custom_cost
self.min_size = min_size
self.jump = jump
self.n_bkps = n_bkps
self.pen = pen
self.epsilon = epsilon
super().__init__(
in_column=self.in_column,
out_column=self.out_column if self.out_column is not None else f"regressor_{self.__repr__()}",
change_point_model=Binseg(
model=self.model, custom_cost=self.custom_cost, min_size=self.min_size, jump=self.jump
),
detrend_model=self.detrend_model,
n_bkps=self.n_bkps,
pen=self.pen,
epsilon=self.epsilon,
)
| [
"sklearn.linear_model.LinearRegression",
"ruptures.Binseg"
]
| [((3774, 3792), 'sklearn.linear_model.LinearRegression', 'LinearRegression', ([], {}), '()\n', (3790, 3792), False, 'from sklearn.linear_model import LinearRegression\n'), ((5504, 5603), 'ruptures.Binseg', 'Binseg', ([], {'model': 'self.model', 'custom_cost': 'self.custom_cost', 'min_size': 'self.min_size', 'jump': 'self.jump'}), '(model=self.model, custom_cost=self.custom_cost, min_size=self.\n min_size, jump=self.jump)\n', (5510, 5603), False, 'from ruptures import Binseg\n')] |
import xarray as xr
import pytest
import warnings
import argopy
from argopy import IndexFetcher as ArgoIndexFetcher
from argopy.errors import InvalidFetcherAccessPoint, InvalidFetcher, ErddapServerError, DataNotFound
from . import (
AVAILABLE_INDEX_SOURCES,
requires_fetcher_index,
requires_connected_erddap_index,
requires_localftp_index,
requires_connection,
safe_to_server_errors
)
class Test_Facade:
src = list(AVAILABLE_INDEX_SOURCES.keys())[0]
def test_invalid_fetcher(self):
with pytest.raises(InvalidFetcher):
ArgoIndexFetcher(src="invalid_fetcher").to_xarray()
@requires_fetcher_index
def test_invalid_accesspoint(self):
# Use the first valid data source
with pytest.raises(InvalidFetcherAccessPoint):
ArgoIndexFetcher(
src=self.src
).invalid_accesspoint.to_xarray() # Can't get data if access point not defined first
with pytest.raises(InvalidFetcherAccessPoint):
ArgoIndexFetcher(
src=self.src
).to_xarray() # Can't get data if access point not defined first
@requires_fetcher_index
def test_invalid_dataset(self):
with pytest.raises(ValueError):
ArgoIndexFetcher(src=self.src, ds='dummy_ds')
@requires_connection
@requires_fetcher_index
class Test_AllBackends:
""" Test main API facade for all available index fetching backends """
local_ftp = argopy.tutorial.open_dataset("localftp")[0]
# todo Determine the list of output format to test
# what else beyond .to_xarray() ?
fetcher_opts = {}
# Define API entry point options to tests:
# These should be available online and with the argopy-data dummy gdac ftp
args = {}
args["float"] = [[2901623], [6901929, 2901623]]
args["region"] = [
[-60, -40, 40.0, 60.0],
[-60, -40, 40.0, 60.0, "2007-08-01", "2007-09-01"],
]
args["profile"] = [[2901623, 2], [6901929, [5, 45]]]
def __test_float(self, bk, **ftc_opts):
""" Test float index fetching for a given backend """
for arg in self.args["float"]:
options = {**self.fetcher_opts, **ftc_opts}
f = ArgoIndexFetcher(src=bk, **options).float(arg)
assert isinstance(f.to_xarray(), xr.Dataset)
def __test_profile(self, bk, **ftc_opts):
""" Test profile index fetching for a given backend """
for arg in self.args["profile"]:
options = {**self.fetcher_opts, **ftc_opts}
f = ArgoIndexFetcher(src=bk, **options).profile(*arg)
assert isinstance(f.to_xarray(), xr.Dataset)
def __test_region(self, bk, **ftc_opts):
""" Test float index fetching for a given backend """
for arg in self.args["region"]:
options = {**self.fetcher_opts, **ftc_opts}
f = ArgoIndexFetcher(src=bk, **options).region(arg)
assert isinstance(f.to_xarray(), xr.Dataset)
@pytest.mark.skip(reason="Waiting for https://github.com/euroargodev/argopy/issues/16")
@requires_connected_erddap_index
@safe_to_server_errors
def test_float_erddap(self):
self.__test_float("erddap")
@requires_localftp_index
def test_float_localftp(self):
with argopy.set_options(local_ftp=self.local_ftp):
self.__test_float("localftp", index_file="ar_index_global_prof.txt")
@requires_localftp_index
def test_profile_localftp(self):
with argopy.set_options(local_ftp=self.local_ftp):
self.__test_profile("localftp", index_file="ar_index_global_prof.txt")
@pytest.mark.skip(reason="Waiting for https://github.com/euroargodev/argopy/issues/16")
@requires_connected_erddap_index
def test_region_erddap(self):
self.__test_region("erddap")
@requires_localftp_index
def test_region_localftp(self):
with argopy.set_options(local_ftp=self.local_ftp):
self.__test_region("localftp", index_file="ar_index_global_prof.txt")
| [
"pytest.mark.skip",
"argopy.IndexFetcher",
"pytest.raises",
"argopy.tutorial.open_dataset",
"argopy.set_options"
]
| [((2985, 3076), 'pytest.mark.skip', 'pytest.mark.skip', ([], {'reason': '"""Waiting for https://github.com/euroargodev/argopy/issues/16"""'}), "(reason=\n 'Waiting for https://github.com/euroargodev/argopy/issues/16')\n", (3001, 3076), False, 'import pytest\n'), ((3625, 3716), 'pytest.mark.skip', 'pytest.mark.skip', ([], {'reason': '"""Waiting for https://github.com/euroargodev/argopy/issues/16"""'}), "(reason=\n 'Waiting for https://github.com/euroargodev/argopy/issues/16')\n", (3641, 3716), False, 'import pytest\n'), ((1469, 1509), 'argopy.tutorial.open_dataset', 'argopy.tutorial.open_dataset', (['"""localftp"""'], {}), "('localftp')\n", (1497, 1509), False, 'import argopy\n'), ((532, 561), 'pytest.raises', 'pytest.raises', (['InvalidFetcher'], {}), '(InvalidFetcher)\n', (545, 561), False, 'import pytest\n'), ((752, 792), 'pytest.raises', 'pytest.raises', (['InvalidFetcherAccessPoint'], {}), '(InvalidFetcherAccessPoint)\n', (765, 792), False, 'import pytest\n'), ((964, 1004), 'pytest.raises', 'pytest.raises', (['InvalidFetcherAccessPoint'], {}), '(InvalidFetcherAccessPoint)\n', (977, 1004), False, 'import pytest\n'), ((1221, 1246), 'pytest.raises', 'pytest.raises', (['ValueError'], {}), '(ValueError)\n', (1234, 1246), False, 'import pytest\n'), ((1260, 1305), 'argopy.IndexFetcher', 'ArgoIndexFetcher', ([], {'src': 'self.src', 'ds': '"""dummy_ds"""'}), "(src=self.src, ds='dummy_ds')\n", (1276, 1305), True, 'from argopy import IndexFetcher as ArgoIndexFetcher\n'), ((3283, 3327), 'argopy.set_options', 'argopy.set_options', ([], {'local_ftp': 'self.local_ftp'}), '(local_ftp=self.local_ftp)\n', (3301, 3327), False, 'import argopy\n'), ((3490, 3534), 'argopy.set_options', 'argopy.set_options', ([], {'local_ftp': 'self.local_ftp'}), '(local_ftp=self.local_ftp)\n', (3508, 3534), False, 'import argopy\n'), ((3899, 3943), 'argopy.set_options', 'argopy.set_options', ([], {'local_ftp': 'self.local_ftp'}), '(local_ftp=self.local_ftp)\n', (3917, 3943), False, 'import argopy\n'), ((575, 614), 'argopy.IndexFetcher', 'ArgoIndexFetcher', ([], {'src': '"""invalid_fetcher"""'}), "(src='invalid_fetcher')\n", (591, 614), True, 'from argopy import IndexFetcher as ArgoIndexFetcher\n'), ((1018, 1048), 'argopy.IndexFetcher', 'ArgoIndexFetcher', ([], {'src': 'self.src'}), '(src=self.src)\n', (1034, 1048), True, 'from argopy import IndexFetcher as ArgoIndexFetcher\n'), ((2219, 2254), 'argopy.IndexFetcher', 'ArgoIndexFetcher', ([], {'src': 'bk'}), '(src=bk, **options)\n', (2235, 2254), True, 'from argopy import IndexFetcher as ArgoIndexFetcher\n'), ((2547, 2582), 'argopy.IndexFetcher', 'ArgoIndexFetcher', ([], {'src': 'bk'}), '(src=bk, **options)\n', (2563, 2582), True, 'from argopy import IndexFetcher as ArgoIndexFetcher\n'), ((2874, 2909), 'argopy.IndexFetcher', 'ArgoIndexFetcher', ([], {'src': 'bk'}), '(src=bk, **options)\n', (2890, 2909), True, 'from argopy import IndexFetcher as ArgoIndexFetcher\n'), ((806, 836), 'argopy.IndexFetcher', 'ArgoIndexFetcher', ([], {'src': 'self.src'}), '(src=self.src)\n', (822, 836), True, 'from argopy import IndexFetcher as ArgoIndexFetcher\n')] |
import voluptuous as vol
from homeassistant.config_entries import ConfigFlow
from homeassistant.const import CONF_HOST, CONF_NAME
from .acthor import test_connection
from .const import DEVICE_NAME, DOMAIN
class ACThorConfigFlow(ConfigFlow, domain=DOMAIN):
async def async_step_user(self, user_input: dict = None) -> dict:
errors = {}
if user_input is not None:
ok = await test_connection(user_input[CONF_HOST], timeout=5)
if ok:
return self.async_create_entry(
title=user_input[CONF_NAME],
data=user_input,
)
else:
errors["base"] = "connection_failed"
return self.async_show_form(
step_id="user",
data_schema=vol.Schema({
vol.Required(CONF_NAME, default=DEVICE_NAME): str,
vol.Required(CONF_HOST): str,
}),
errors=errors,
)
| [
"voluptuous.Required"
]
| [((818, 862), 'voluptuous.Required', 'vol.Required', (['CONF_NAME'], {'default': 'DEVICE_NAME'}), '(CONF_NAME, default=DEVICE_NAME)\n', (830, 862), True, 'import voluptuous as vol\n'), ((885, 908), 'voluptuous.Required', 'vol.Required', (['CONF_HOST'], {}), '(CONF_HOST)\n', (897, 908), True, 'import voluptuous as vol\n')] |
""" docnado.py
A rapid documentation tool that will blow you away.
"""
import os
import re
import sys
import csv
import glob
import time
import signal
import shutil
import urllib
import base64
import hashlib
import argparse
import tempfile
import datetime
import threading
import traceback
import subprocess
import platform
import requests
from bs4 import BeautifulSoup
from multiprocessing import Pool
from urllib.parse import urlparse
from watchdog.observers import Observer
from watchdog.events import PatternMatchingEventHandler
from xml.etree import ElementTree
from flask import Flask, url_for, abort, send_from_directory, \
render_template, Markup, make_response, render_template_string
import markdown
import markdown.util
from markdown.extensions import Extension
from markdown.postprocessors import Postprocessor
from markdown.inlinepatterns import LinkPattern, IMAGE_LINK_RE, dequote, handleAttributes
from markdown.blockprocessors import HashHeaderProcessor
from http.client import responses
if __package__:
from .navtree import NavItem, parse_nav_string
else:
from navtree import NavItem, parse_nav_string
class MultiPurposeLinkPattern(LinkPattern):
""" Embed image, video, youtube, csv or file download links
by extending the typical image tag pattern.
#  or 
If the link has "DOWNLOAD" in the alt text, treat it as a download.
Otherwise, see if its a YouTube video. Otherwise, see if its a
csv that can be turned into a table, otherwise if the link cannot be parsed
as a video, it will always be treated as an image.
"""
SUPPORTED_VIDEO = ('ogv', 'ogg', 'avi', 'mp4', 'webm', )
SUPPORTED_TABLES = ('csv', )
SUPPORTED_PDF = ('pdf', )
def get_src(self, m):
""" Get the source and parts from the matched groups: src, parts """
src_parts = m.group(9).split()
if src_parts:
src = src_parts[0]
if src[0] == "<" and src[-1] == ">":
src = src[1:-1]
return self.sanitize_url(self.unescape(src)), src_parts
else:
return '', src_parts
@staticmethod
def youtube_url_validation(url):
""" Given a YouTube URL, return the ID component.
https://stackoverflow.com/questions/4705996
"""
youtube_regex = (r'(https?://)?(www\.)?'
r'(youtube|youtu|youtube-nocookie)\.(com|be)/'
r'(watch\?v=|embed/|v/|.+\?v=)?([^&=%\?]{11})')
youtube_regex_match = re.match(youtube_regex, url)
return youtube_regex_match.group(6) if youtube_regex_match else None
@staticmethod
def as_youtube(m, video_id):
""" Return a DOM element that embeds a YouTube video. """
el = ElementTree.Element('iframe')
el.set('class', 'video')
el.set('src', f'https://www.youtube.com/embed/{video_id}?rel=0')
el.set('frameborder', '0')
el.set('allow', 'autoplay; encrypted-media')
el.set('allowfullscreen', '1')
return el
def as_pdf(self, m):
""" Return a DOM element that embeds a PDF document using an embed. """
src, parts = self.get_src(m)
wrapper = ElementTree.Element('aside')
wrapper.set('class', 'pdf-embed-wrapper')
el = ElementTree.SubElement(wrapper, 'embed')
el.set('class', 'pdf-embed')
el.set('src', src)
el.set('width', '100%')
el.set('type', 'application/pdf')
el.set('height', '100%') # width * 1.4142 (aspect ratio of a4)
el.set('pluginspage', 'http://www.adobe.com/products/acrobat/readstep2.html')
if len(parts) > 1:
el.set('alt', dequote(self.unescape(" ".join(parts[1:]))))
return wrapper
def as_video(self, m):
""" Return a video element """
src, parts = self.get_src(m)
el = ElementTree.Element('video')
el.set('src', src)
el.set("controls", "true")
handleAttributes(m.group(2), el)
return el
def as_image(self, m):
""" Return an image element """
el = ElementTree.Element('img')
src, parts = self.get_src(m)
el.set('src', src)
# Set the title if present.
if len(parts) > 1:
el.set('title', dequote(self.unescape(" ".join(parts[1:]))))
# Set the attributes on the element, if enabled.
# Set the 'alt' attribute with whatever is left from `handleAttributes`.
attrs = self.markdown.enable_attributes
alt_text = handleAttributes(m.group(2), el) if attrs else m.group(2)
el.set('alt', self.unescape(alt_text))
return el
def as_csv(self, m):
src, parts = self.get_src(m)
root = ElementTree.Element('table')
root.set('source', src)
root.set('class', 'csv-table table thead-light table-hover')
file_path = os.path.join(self.markdown.page_root, src)
with open(file_path, 'r', encoding='utf-8') as f:
reader = csv.reader(f)
headers = next(reader)
rows = [r for r in reader]
thead = ElementTree.SubElement(root, 'thead')
for col in headers:
ElementTree.SubElement(thead, 'th').text = col
for row in rows:
tr = ElementTree.SubElement(root, 'tr')
for col in row:
ElementTree.SubElement(tr, 'td').text = col
return root
def as_download(self, m):
""" Create card layers used to make a download button. """
src, parts = self.get_src(m)
# Returns a human readable string representation of bytes
def _human_size(byte_number, units=(' bytes', 'KB', 'MB', 'GB', 'TB', 'PB', 'EB')):
return str(byte_number) + units[0] if byte_number < 1024 else _human_size(byte_number >> 10, units[1:])
# Get information required for card.
split_src = os.path.split(src)
file_path = os.path.join(self.markdown.page_root, *split_src)
file_size = os.path.getsize(file_path)
file_basename = os.path.basename(file_path)
card_text = dequote(self.unescape(" ".join(parts[1:]))) if len(parts) > 1 else ''
# If its a pptx, extract the thumbnail previews.
# NOTE: This works, but is is removed until we support other
# file types, which for now is not a priority.
# preview_uri = None
# import zipfile
# if (file_path.endswith('pptx')):
# with zipfile.ZipFile(file_path) as zipper:
# with zipper.open('docProps/thumbnail.jpeg', 'r') as fp:
# mime = 'image/jpeg'
# data64 = base64.b64encode(fp.read()).decode('utf-8')
# preview_uri = u'data:%s;base64,%s' % (mime, data64)
# Card and structure.
card = ElementTree.Element("div")
card.set('class', 'card download-card')
header = ElementTree.SubElement(card, 'div')
header.set('class', 'download-card-header')
body = ElementTree.SubElement(card, 'div')
body.set('class', 'download-card-body')
# Add preview image.
# if preview_uri:
# img = ET.SubElement(header, 'img')
# img.set('src', preview_uri)
# Filename link heading.
heading = ElementTree.SubElement(body, 'a')
heading.set('class', 'download-card-title')
heading.set('href', src)
download_icon = ElementTree.SubElement(heading, 'i')
download_icon.set('class', 'fa fa-download')
download_text = ElementTree.SubElement(heading, 'span')
download_text.text = file_basename
# Title element from the "quote marks" part.
body_desc = ElementTree.SubElement(body, 'span')
body_desc.text = card_text
# File size span at the bottom.
body_size = ElementTree.SubElement(body, 'span')
body_size.set('class', 'small text-muted')
body_size.text = f'{_human_size(file_size)}'
return card
@staticmethod
def _is_inject(m):
""" Determine if the ALT text [] part of the link says 'INJECT'. """
alt = m.group(2)
return alt.lower() == 'inject'
def as_raw(self, m):
""" Load the HTML document specified in the link, parse it to HTML elements and return it.
"""
src, parts = self.get_src(m)
# Find the path to the HTML document, relative to the current markdown page.
file_path = os.path.join(self.markdown.page_root, src)
raw_html_string = read_html_for_injection(file_path)
if len(parts) < 2:
parts.append("nothing_one=1||nothing_two=2")
# Helper function.
def _argify(args):
if '=' not in args:
raise ValueError('injection template requires named arguments split by ||')
left, right = args.split('=')
return left.strip(), right.strip()
# Split arg string on double pipe. Joins them to undo automattic splitting from the markdown.
arg_strings = " ".join(parts[1:]).strip('\"').split("||")
# Parse into dictionary of key-value pairs based on the '=' notation.
try:
named_args = dict([_argify(args) for args in arg_strings])
except Exception as e:
raise Exception(f"Error parsing ![INJECT] arguments in {self.markdown.page_file} {repr(e)}")
# Take the template renderer and give it our string, and named args.
# Capture the output as a string.
try:
injectable_templated_str = render_template_string(raw_html_string, **named_args)
except Exception as e:
raise Exception(f"Error rendering ![INJECT] template for file {file_path} {repr(e)}")
# Feed that string to the XML parser.
try:
return ElementTree.fromstring(injectable_templated_str)
except Exception as e:
raise Exception(f"Error parsing ![INJECT] template for file {file_path} {repr(e)}")
@staticmethod
def _is_download(m):
""" Determine if the ALT text [] part of the link says 'DOWNLOAD'. """
alt = m.group(2)
return alt.lower() == 'download'
def handleMatch(self, m):
""" Use the URL extension to render the link. """
src, parts = self.get_src(m)
if self._is_download(m):
return self.as_download(m)
elif self._is_inject(m):
return self.as_raw(m)
youtube = self.youtube_url_validation(src)
if youtube:
return self.as_youtube(m, youtube)
src_lower = src.lower()
if src_lower.endswith(self.SUPPORTED_TABLES):
return self.as_csv(m)
elif src_lower.endswith(self.SUPPORTED_PDF):
return self.as_pdf(m)
elif src_lower.endswith(self.SUPPORTED_VIDEO):
return self.as_video(m)
return self.as_image(m)
class OffsetHashHeaderProcessor(HashHeaderProcessor):
""" Process hash headers with an offset to control the type of heading
DOM element that is generated. """
HEADING_LEVEL_OFFSET = 1
def run(self, parent, blocks):
block = blocks.pop(0)
m = self.RE.search(block)
if m:
before = block[:m.start()]
after = block[m.end():]
if before:
self.parser.parseBlocks(parent, [before])
heading_level = len(m.group('level'))
h = ElementTree.SubElement(parent, 'h%d' % (heading_level + self.HEADING_LEVEL_OFFSET))
h.text = m.group('header').strip()
if after:
blocks.insert(0, after)
class ChecklistPostprocessor(Postprocessor):
"""
Adds checklist class to list element.
Adapted from: `markdown_checklist.extension`
"""
pattern = re.compile(r'<li>\[([ Xx])\]')
def run(self, html):
html = re.sub(self.pattern, self._convert_checkbox, html)
before = '<ul>\n<li><input type="checkbox"'
after = before.replace('<ul>', '<ul class="checklist">')
html = html.replace(before, after)
return html
@staticmethod
def _convert_checkbox(match):
state = match.group(1)
checked = ' checked' if state != ' ' else ''
return '<li><input type="checkbox" disabled%s>' % checked
# Remove the `video`, `iframe`, `aside`, and `table` elements as block elements.
markdown.util.BLOCK_LEVEL_ELEMENTS = re.compile(
r"^(p|div|h[1-6]|blockquote|pre|dl|ol|ul"
r"|script|noscript|form|fieldset|math"
r"|hr|hr/|style|li|dt|dd|thead|tbody"
r"|tr|th|td|section|footer|header|group|figure"
r"|figcaption|article|canvas|output"
r"|progress|nav|main)$",
re.IGNORECASE
)
class MultiExtension(Extension):
""" Markdown `Extension` that adds our new components and
overrides some that we are not using.
"""
def extendMarkdown(self, md, md_globals):
""" Configure markdown by disabling elements and replacing them with
others. """
# Add checklist processing extension based on: 'markdown_checklist.extension'.
md.postprocessors.add('checklist', ChecklistPostprocessor(md), '>raw_html')
# Remove default patterns.
del md.inlinePatterns['image_link']
# Create a new one and insert into pipeline.
multi_purpose_pattern = MultiPurposeLinkPattern(IMAGE_LINK_RE, md)
md.inlinePatterns['multi_purpose_pattern'] = multi_purpose_pattern
# Remove line headers.
del md.parser.blockprocessors['setextheader']
# Swap hash headers for one that can change the DOM h1, h2 level.
md.parser.blockprocessors['hashheader'] = OffsetHashHeaderProcessor(md.parser)
# https://python-markdown.github.io/extensions/
mdextensions = [MultiExtension(),
'markdown.extensions.tables',
'markdown.extensions.meta',
'markdown.extensions.def_list',
'markdown.extensions.headerid',
'markdown.extensions.fenced_code',
'markdown.extensions.attr_list']
def build_meta_cache(root):
""" Recursively search for Markdown files and build a cache of `Meta`
from metadata in the Markdown.
:param root: str: The path to search for files from.
"""
doc_files = glob.iglob(root + '/**/*.md', recursive=True)
def _meta(path):
with open(path, 'r', encoding='utf-8') as f:
md = markdown.Markdown(extensions=mdextensions)
md.page_root = os.path.dirname(path)
Markup(md.convert(f.read()))
return md.Meta if hasattr(md, 'Meta') else None
doc_files_meta = {os.path.relpath(path, start=root): _meta(path) for path in doc_files}
doc_files_meta = {path: value for path, value in doc_files_meta.items() if value is not None}
# If a nav filter is set, exclude relevant documents.
# This takes the comma separated string supplied to `nav_limit`
# and excludes certain documents if they are NOT in this list.
global CMD_ARGS
if CMD_ARGS.nav_limit:
nav_filters = CMD_ARGS.nav_limit.split(',')
nav_filters = [nav_filter.strip().lower() for nav_filter in nav_filters]
nav_filters = [nav_filter for nav_filter in nav_filters if nav_filter]
def _should_include(doc_meta):
nav_strings = [nav.lower() for nav in doc_meta.get('nav', [])]
return any([y.startswith(x) for x in nav_filters for y in nav_strings])
doc_files_meta = {path: value for path, value in doc_files_meta.items() if _should_include(value)}
return doc_files_meta
def build_nav_menu(meta_cache):
""" Given a cache of Markdown `Meta` data, compile a structure that can be
used to generate the NAV menu.
This uses the `nav: Assembly>Bench>Part` variable at the top of the Markdown file.
"""
root = NavItem('root', 0)
# Pre-sort the nav-items alphabetically by nav-string. This will get overridden with the arange()
# function, but this avoids-un arranged items moving round between page refreshes due to Dicts being
# unordered.
sorted_meta_cache = sorted(
meta_cache.items(),
key = lambda items: items[1].get('nav', [''])[0].split('>')[-1] # Sort by the last part of the nav string for each page.
)
for path, meta in sorted_meta_cache:
nav_str = meta.get('nav', [None])[0]
nav_chunks = parse_nav_string(nav_str)
node = root
for name, weight in nav_chunks:
n = NavItem(name, weight)
node = node.add(n)
node.bind(meta=meta, link=path)
root.arrange()
return root
def build_reload_files_list(extra_dirs):
""" Given a list of directories, return a list of files to watch for modification
and subsequent server reload. """
extra_files = extra_dirs[:]
for extra_dir in extra_dirs:
for dirname, dirs, files in os.walk(extra_dir):
for filename in files:
filename = os.path.join(dirname, filename)
if os.path.isfile(filename):
extra_files.append(filename)
return extra_files
def read_html_for_injection(path):
""" Open an HTML file at the given path and return the contents
as a string. If the file does not exist, we raise an exception.
"""
# TODO: In the future, consider adding some caching here. However,
# beware of reloading / refereshing the page UX implications.
with open(path) as file:
return file.read()
def _render_markdown(file_path, **kwargs):
""" Given a `file_path` render the Markdown and return the result of `render_template`.
"""
global NAV_MENU, PROJECT_LOGO, PDF_GENERATION_ENABLED
default_template = 'document'
with open(file_path, 'r', encoding='utf-8') as f:
md = markdown.Markdown(extensions=mdextensions)
md.page_root = os.path.dirname(file_path)
md.page_file = file_path
markup = Markup(md.convert(f.read()))
# Fetch the template defined in the metadata.
template = md.Meta.get('template', None)
template = template[0] if template else default_template
if not template:
raise Exception('no template found for document')
template = f'{template}.html'
# Load any HTML to be injected from the meta-data.
injections = md.Meta.get('inject', [])
injections = [os.path.join(md.page_root, file) for file in injections]
injections = [read_html_for_injection(file) for file in injections]
# Render it out with all the prepared data.
return render_template(template,
content=markup,
nav_menu=NAV_MENU,
project_logo=PROJECT_LOGO,
pdf_enabled=PDF_GENERATION_ENABLED,
injections=injections,
**md.Meta,
**kwargs)
def configure_flask(app, root_dir):
""" Setup the flask application within this scope. """
@app.before_first_request
def build_navigation_cache():
""" Build an in-memory cache of document meta-data.
NOTE: The design choice is made to crash the application if any
of the markdown files cannot be opened and parsed. In the
future when it becomes more stable, this will probably change.
"""
# This is called each time the server restarts.
global NAV_MENU
meta_cache = build_meta_cache(root_dir)
# Build the nav menu data-structure.
NAV_MENU = build_nav_menu(meta_cache)
# Store the reference to the function that rebuilds the navigation cache.
app.build_navigation_cache = build_navigation_cache
@app.template_filter('gravatar')
def gravatar(email, size=100, rating='g', default='retro', use_ssl=False):
""" Return a gravatar link for a given email address. """
url = "https://secure.gravatar.com/avatar/" if use_ssl else "http://www.gravatar.com/avatar/"
email = email.strip().lower().encode('utf-8')
hash_email = hashlib.md5(email).hexdigest()
return f'{url}{hash_email}?s={size}&r={rating}&d={default}'
@app.template_filter()
def url_unquote(url):
""" Removes encoding around a URL. """
return urllib.parse.unquote(url)
@app.route('/favicon.ico')
def favicon():
return send_from_directory(os.path.join(app.root_path, 'static'),
'favicon.ico', mimetype='image/vnd.microsoft.icon')
@app.route("/print_header")
def print_header():
""" Render the template for the header used when printing with WKPDFTOHTML. """
global PROJECT_LOGO
return render_template('print_header.html', project_logo=PROJECT_LOGO)
@app.route("/print_footer")
def print_footer():
""" Render the template for the footer used when printing with WKPDFTOHTML. """
global PROJECT_LOGO
return render_template('print_footer.html', project_logo=PROJECT_LOGO)
@app.errorhandler(404)
def page_not_found(e):
global NAV_MENU, PROJECT_LOGO
return render_template('404.html', nav_menu=NAV_MENU, project_logo=PROJECT_LOGO), 404
@app.route("/w/<path:page>")
def wiki(page):
""" Render the page. """
file_path = os.path.abspath(os.path.join(root_dir, page))
if not os.path.isfile(file_path):
abort(404)
if '.md' in [ext.lower() for ext in os.path.splitext(file_path)]:
return _render_markdown(file_path, current_page=page)
else:
return send_from_directory(os.path.dirname(file_path), os.path.basename(file_path))
@app.route("/")
@app.route("/w/")
def homepage():
return wiki('home.md')
@app.route("/pdf/<path:page>")
def wiki_pdf(page):
file_path = os.path.abspath(os.path.join(root_dir, page))
if not os.path.isfile(file_path):
abort(404)
if '.md' not in [ext.lower() for ext in os.path.splitext(file_path)]:
return send_from_directory(os.path.dirname(file_path), os.path.basename(file_path))
# Configure the different paths.
pdf_temp = f'{tempfile.mktemp()}.pdf'
input_url = url_for('wiki', page=page, _external=True)
header_url = url_for('print_header', _external=True)
footer_url = url_for('print_footer', _external=True)
args = f'{WKHTMLTOPDF_BINARY} --header-html {header_url} --footer-html {footer_url} \
--print-media-type --header-spacing 2 {input_url} {pdf_temp}'
# Invoke WkHTMLtoPDF
result = subprocess.check_output(args, shell=True)
if not result:
pass
# Write the newly generated temp pdf into a response.
with open(pdf_temp, 'rb') as f:
binary_pdf = f.read()
target_file_name = page.replace("/", "_").replace("\\", "_")
response = make_response(binary_pdf)
response.headers['Content-Type'] = 'application/pdf'
# response.headers['Content-Disposition'] = f'attachment; filename={target_file_name}.pdf'
response.headers['Content-Disposition'] = f'inline; filename={target_file_name}.pdf'
# Delete the temp file and return the response.
os.remove(pdf_temp)
return response
def generate_static_pdf(app, root_dir, output_dir, nav_filter=None):
""" Generate a static PDF directory for the documentation in `root_dir`
into `output_dir`.
"""
global PORT_NUMBER
# Find all markdown document paths that are in the nav.
documents = build_meta_cache(root_dir)
markdown_docs_urls = ['pdf/' + file.replace('\\', '/') for file in documents.keys()]
# Generate URl to file pairs.
pairs = [(f'http://localhost:{PORT_NUMBER}/{url}',
f'{os.path.join(output_dir, *os.path.split(url))}.pdf')
for url in markdown_docs_urls]
# Download each pair.
for source, target in pairs:
os.makedirs(os.path.dirname(target), exist_ok=True)
print(f'Source: {source} \n Target: {target}')
urllib.request.urlretrieve(source, target)
# Helper function to return the domain if present.
def is_absolute(url):
""" Returns True if the passed url string is an absolute path.
False if not
"""
links = urlparse(url)
return bool(links.netloc)
def generate_static_html(app, root_dir, output_dir):
""" Generate a static HTML site for the documentation in `root_dir`
into `output_dir`.
"""
from flask_frozen import Freezer, MissingURLGeneratorWarning
import warnings
warnings.filterwarnings("ignore", category=MissingURLGeneratorWarning)
# Update the flask config.
app.config['FREEZER_RELATIVE_URLS'] = True
app.config['FREEZER_IGNORE_MIMETYPE_WARNINGS'] = True
app.config['FREEZER_DESTINATION'] = output_dir
# Create the freezer app. Make it use specific URLs.
freezer = Freezer(app, with_no_argument_rules=False, log_url_for=False)
# Register a generator that passes ALL files in the docs directory into the
# `wiki` flask route.
@freezer.register_generator
def wiki():
all_docs = [file.replace(f'{root_dir}', '/w').replace(f'{os.path.sep}', '/')
for file in glob.iglob(f'{root_dir}/**/*', recursive=True)
if os.path.isfile(file)]
for doc in all_docs:
yield doc
# Save all the URLs using the correct extension and MIME type.
freezer.freeze()
# For each `.md` file in the output directory:
for markdown_file in glob.iglob(f'{output_dir}/**/*.md', recursive=True):
# Rewrite all relative links to other `.md` files to `.html.`
output = ''
with open(markdown_file, 'r', encoding="utf-8") as f:
html = f.read()
def _href_replace(m):
href = m.group()
if is_absolute(href[6:-1]):
return href
return href.replace('.md', '.html')
output = re.sub('href="(.*md)"', _href_replace, html)
# Rename the file from `.md` to HTML.
with open(markdown_file[:-3] + '.html', 'w', encoding="utf-8") as f:
f.write(output)
# Delete the Markdown file.
os.remove(markdown_file)
def load_project_logo(logo_file=None):
""" Attempt to load the project logo from the specified path.
If this fails, return None. If this succeeds, convert it to a data-uri.
"""
if not logo_file:
return None
if not os.path.exists(logo_file):
return None
with open(logo_file, 'rb') as fp:
mime = 'image/png'
data64 = base64.b64encode(fp.read()).decode('utf-8')
preview_uri = u'data:%s;base64,%s' % (mime, data64)
return preview_uri
def check_pdf_generation_cap():
""" Check to see if we can use PDF generation by attempting to use the binary. """
global WKHTMLTOPDF_BINARY
retcode = subprocess.call(f'{WKHTMLTOPDF_BINARY} --version',
shell=True,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL)
return retcode == 0
def copy_local_project(force=False):
""" Copy the sample docs and style into the local working directory.
Note: This will overwrite anything currently in those folders.
"""
source_root = os.path.dirname(__file__)
target_root = os.getcwd()
targets = ['docs', 'style', 'logo.png']
pairs = [(os.path.join(source_root, path), os.path.join(target_root, path))
for path in targets]
for source, target in pairs:
if os.path.isdir(source):
if os.path.exists(target):
if force:
print(f'Deleting existing {target} and replacing it with {target}')
shutil.rmtree(target)
shutil.copytree(source, target)
else:
print(f'Warning: {target} already exists.')
else:
print(f'Copying: {source} -> {target}')
shutil.copytree(source, target)
else:
if os.path.exists(target):
if force:
print(f'Deleting existing {target} and replacing it with {target}')
os.remove(target)
shutil.copyfile(source, target)
else:
print(f'Warning: {target} already exists.')
else:
print(f'Copying: {source} -> {target}')
shutil.copyfile(source, target)
def find_references(document_path):
""" Search through the markdown 'document_path' and make a list of referenced files
with paths that are relative to the directory containing the `document_path`.
"""
# Open the file to search.
with open(document_path, 'r', encoding='utf-8') as f:
markdown_raw_data = f.read()
# Render as HTML.
md = markdown.Markdown(extensions=mdextensions)
document_dir = os.path.dirname(document_path)
md.page_root = document_dir
# Interpret with the BeautifulSoup HTML scraping library.
soup = BeautifulSoup(md.convert(markdown_raw_data), 'html.parser')
tags_to_search = {
'img': 'src',
'a': 'href',
'video': 'src',
'table': 'source',
'embed': 'src',
}
# For each entry in the `tags_to_search` table, extract the tag attribute value.
references = set()
for k, v in tags_to_search.items():
for tag in soup.find_all(k):
val = tag.get(v)
if val:
references.add(val)
# Normalise the referenced assets (to take into account relative paths).
references = [os.path.join(document_dir, urllib.request.url2pathname(ref)) for ref in references]
# Make unique.
return set(references)
def has_nav(markdown_text):
""" Returns True if the passed string of text contains navbar metadata.
Returns False if it does not.
"""
expression = re.compile(r'(?=\n|)nav:\s+\w+(?=\n |)')
return True if expression.search(markdown_text) else False
def find_orphans(files):
""" Searches all files and folders recursively in the given path for image and video assets
that are unused by markdown files.
"""
# Find all references in
pages = {}
for file in files:
if file.endswith('.md'):
pages[file] = find_references(file)
# Remove the markdown documents that have a navbar metadata.
md_with_nav = []
for file in files:
if file.endswith('.md'):
with open(file, encoding='utf-8') as f:
if has_nav(f.read().lower()):
md_with_nav.append(file)
files = [x for x in files if x not in md_with_nav]
# Create a flat list of all references in the markdown files
all_references = []
for i in pages.values():
all_references += [k for k in i]
# Output unused assets
return [i for i in files if i not in all_references]
class DocumentLinks:
""" A helper class to process the `<a href.../>` links from a single
markdown document that is rendered using our own renderer.
"""
def __init__(self, md_file):
""" Open a Markdown document and find all links in `<a href .../>`.
"""
# Store important information about this document.
self.md_file = md_file
self.md_dir = os.path.dirname(md_file)
# Read in Markdown and generate HTML with our parser.
with open(md_file, 'r', encoding='utf-8') as f:
markdown_raw_data = f.read()
md = markdown.Markdown(extensions=mdextensions)
md.page_root = self.md_dir
html = md.convert(markdown_raw_data)
# Interpret with the BeautifulSoup HTML scraping library.
soup = BeautifulSoup(html, 'html.parser')
tags_to_search = {
'img': 'src',
'a': 'href',
'video': 'src',
'table': 'source',
'embed': 'src',
}
self.references = set()
for k, v in tags_to_search.items():
links = soup.find_all(k)
for link in links:
if link.get('href'):
if link.get('href').find('http:') > -1 or link.get('href').find('https:') > -1:
val = link.get(v)
if val:
self.references.add(val)
else:
val = link.get(v)
if val:
self.references.add(val)
@property
def web_links(self):
""" Generate a list of web links from our cached links.
"""
return [link for link in self.references if is_absolute(link)]
@property
def relative_links(self):
""" Generate a list of relative file system links from our cached links.
This converts from a web path to a path on disk then normalises the path to the current directory.
"""
def _norm(path):
return os.path.join(self.md_dir, urllib.request.url2pathname(path))
return [_norm(link) for link in self.references if not is_absolute(link)]
@staticmethod
def validate_url(address):
""" Returns `True` if page at address returns with status code 200 (ok) otherwise returns `False`.
"""
try:
request = requests.head(address)
return request.status_code, address
except requests.exceptions.RequestException:
return False, address
def detect_broken_links(self, process_pool):
""" Go through all the `web_links` and the `relative_links` and report
which are broken (i.e. do not resolve to HTTP200OK or a file on disk).
"""
result = process_pool.map(self.validate_url, self.web_links)
for response, url in result:
if not response == 200:
yield url + ' Status: ' + (responses[response] if response is int else "Exception")
for file in self.relative_links:
if not os.path.exists(file):
yield file
def generate_metadata(path):
""" Add relevant metadata to the top of the markdown file at the passed path.
Title is drawn from the filename, Date from the last modified timestamp, Version defaults at 1.0.0,
Nav is generated from the filepath, and Authors are generated from the git contributors (if applicable) and
are otherwise left blank.
Warning: Does not check if there is existing metadata.
"""
s = subprocess.getoutput(f"git log -p {path}")
lines = s.split(os.linesep)
authors = set([re.search(r'<(.*)>', line).group(1)for line in lines if 'Author:' in line])
file_status = os.stat(path)
nav_path = os.path.sep.join(path.split(os.path.sep)[1:])
metadata = {
'title': ' '.join(
path
.split('.')[0]
.split(os.path.sep)[-1]
.replace('_', ' ')
.replace('-', ' ')
.title()
.split()
),
'desc': '',
'date': datetime.datetime.utcfromtimestamp(file_status.st_mtime).strftime('%Y/%m/%d'),
'version': '1.0.0',
'template': '',
'nav': nav_path.replace(os.path.sep, '>').title().split('.')[0],
'percent': '100',
'authors': ' '.join(authors),
}
result = ""
for key in metadata.keys():
result += ('{}:{}{}\n'.format(key, '\t' if len(key) > 6 else '\t\t', metadata[key]))
with open(path, 'r+', encoding='utf-8') as f:
content = f.read()
f.seek(0, 0)
f.write(result)
f.write(content)
class ReloadHandler(PatternMatchingEventHandler):
""" Rebuild the document metadata / navigation cache when markdown files are updated
in the documents directory. """
def __init__(self, app):
super(ReloadHandler, self).__init__(patterns=['*.md'], ignore_directories=False, case_sensitive=False)
self.flask_app = app
def on_any_event(self, event):
self.flask_app.build_navigation_cache()
global CMD_ARGS, NAV_MENU, PROJECT_LOGO, WKHTMLTOPDF_BINARY, PDF_GENERATION_ENABLED, PORT_NUMBER
CMD_ARGS = None
NAV_MENU = {}
PROJECT_LOGO = None
WKHTMLTOPDF_BINARY = None
PDF_GENERATION_ENABLED = False
def main():
""" Application entrypoint. """
global PORT_NUMBER
PORT_NUMBER = 5000
# Parse the command line arguments.
parser = argparse.ArgumentParser(description='docnado: Lightweight tool for rendering \
Markdown documentation with different templates.')
parser.add_argument('--html', action='store', dest='html_output_dir',
help='Generate a static site from the server and output to the \
specified directory.')
parser.add_argument('--pdf', action='store', dest='pdf_output_dir',
help='Generate static PDFs from the server and output to the \
specified directory.')
parser.add_argument('--nav-limit', action='store', dest='nav_limit',
default=None,
help='Include certain document trees only based on a comma separated \
list of nav strings. e.g. Tooling,Document')
parser.add_argument('--new', action="store_true", dest='new_project',
default=False,
help='Copy the `docs` and `styles` folder into the working directory \
and output a config file that addresses them. Does not overwrite existing files.')
parser.add_argument('--new-force', action="store_true", dest='new_project_force',
default=False,
help='Copy the `docs` and `styles` folder into the working directory \
and output a config file that addresses them. Force deletion of existing files.')
parser.add_argument('--dirs', action="store_true", dest='show_dirs',
default=False,
help='Display the different directories the software is using \
to search for documentation and styles.')
parser.add_argument('--generate-meta', action="store", dest='generate_meta',
default=False,
help='Generate metadata for markdown files in the specified directory.')
parser.add_argument('--find-orphans', action="store_true", dest='find_orphans',
default=False,
help='Identify unused media assets (orphans)')
parser.add_argument('--find-broken-links', action="store_true", dest='find_broken_links',
default=False,
help='Identify broken external links.')
parser.add_argument('--port', action="store", dest='new_port_number',
default=False,
help='Specify a port for the docnado server')
parser.add_argument('--host', action="store", dest='set_host',
default=False,
help='Set the docnado development server to listen on IP addresses.')
# Import the command line args and make them application global.
global CMD_ARGS
args = parser.parse_args()
CMD_ARGS = args
# Load config from the environment and validate it.
global PROJECT_LOGO, PDF_GENERATION_ENABLED, NAV_MENU, WKHTMLTOPDF_BINARY
TRUE = 'TRUE'
FALSE = 'FALSE'
flask_debug = os.environ.get('DN_FLASK_DEBUG', FALSE) == TRUE
watch_changes = os.environ.get('DN_RELOAD_ON_CHANGES', TRUE) == TRUE
WKHTMLTOPDF_BINARY = ('wkhtmltopdf_0.12.5.exe' if platform.system() == 'Windows' else 'wkhtmltopdf')
PDF_GENERATION_ENABLED = check_pdf_generation_cap()
dir_documents = os.environ.get('DN_DOCS_DIR', os.path.join(os.getcwd(), 'docs'))
dir_style = os.environ.get('DN_STYLE_DIR', os.path.join(os.getcwd(), 'style'))
logo_location = os.environ.get('DN_PROJECT_LOGO', os.path.join(os.getcwd(), 'logo.png'))
# If `style` folder does not exist, use the one in site-packages.
if not os.path.exists(dir_style) and not os.path.isdir(dir_style):
dir_style = os.path.join(os.path.dirname(__file__), 'style')
# Attempt to load the project logo into a base64 data uri.
PROJECT_LOGO = load_project_logo(logo_location)
# Compute the static and template directories.
dir_static = os.path.join(dir_style, 'static')
dir_templates = os.path.join(dir_style, 'templates')
# If the user is asking to create a new project.
if args.new_project:
copy_local_project()
sys.exit()
if args.new_project_force:
copy_local_project(force=True)
return 0
if args.new_port_number:
PORT_NUMBER = int(args.new_port_number)
if args.generate_meta:
doc_files = glob.iglob(args.generate_meta + '/**/*.md', recursive=True)
for i in doc_files:
generate_metadata(i)
return 0
if args.find_orphans:
# Find all the assets in the directory/subdirectories recursively and append their file path to a list.
files = glob.glob((dir_documents + '/**/*.*'), recursive=True)
files = [f for f in files if not os.path.isdir(f)]
orphans = find_orphans(files)
if orphans:
print(f'{len(orphans)} Unused assets (orphans):\n\t' + '\n\t'.join(orphans))
return -1
return 0
if args.find_broken_links:
process_pool = Pool(processes=10)
md_files = glob.glob((dir_documents + '/**/*.md'), recursive=True)
md_reports = tuple((md, list(DocumentLinks(md).detect_broken_links(process_pool))) for md in md_files)
num_broken = 0
for file, report in md_reports:
if report:
num_broken += len(report)
print(f'{file}\n\t' + '\n\t'.join(report))
return -1 if num_broken else 0
if args.show_dirs:
print('The following directories are being used: ')
print('\t', f'Documents -> {dir_documents}')
print('\t', f'Logo -> {logo_location}')
print('\t', f'Style -> {dir_style}')
print('\t', f' Static -> {dir_static}')
print('\t', f' Templates -> {dir_templates}')
sys.exit()
if not os.path.exists(dir_documents) and not os.path.isdir(dir_documents):
print(f'Error: Documents directory "{dir_documents}" does not exist. \
Create one called `docs` and fill it with your documentation.', file=sys.stderr)
sys.exit(-1)
if not os.path.exists(dir_static) and not os.path.isdir(dir_static):
print(f'Error: Static directory "{dir_static}" does not exist.', file=sys.stderr)
sys.exit(-1)
if not os.path.exists(dir_templates) and not os.path.isdir(dir_templates):
print(f'Error: Templates directory "{dir_templates}" does not exist.', file=sys.stderr)
sys.exit(-1)
# Create the server.
app = Flask(__name__,
static_url_path='',
template_folder=dir_templates,
static_folder=dir_static)
# Attach routes and filters.
configure_flask(app, dir_documents)
# Output PDF files.
if args.pdf_output_dir:
if not check_pdf_generation_cap():
print(f'Error: PDF generation requires WkHTMLtoPDF.', file=sys.stderr)
sys.exit(-1)
def gen_pdfs():
time.sleep(2)
generate_static_pdf(
app, dir_documents, os.path.join(os.getcwd(), args.pdf_output_dir)
)
time.sleep(5)
os.kill(os.getpid(), signal.SIGTERM)
t1 = threading.Thread(target=gen_pdfs)
t1.start()
app.run(debug=flask_debug, threaded=True, port=PORT_NUMBER)
sys.exit()
# Output a static site.
if args.html_output_dir:
PDF_GENERATION_ENABLED = False
try:
generate_static_html(app, dir_documents, os.path.join(os.getcwd(), args.html_output_dir))
index_html = """ <!DOCTYPE html>
<html>
<head>
<meta http-equiv="refresh" content="0; url=./w/">
</head>
<body>
</body>
</html>"""
with open(os.path.join(os.getcwd(), args.html_output_dir, 'index.html'), 'w') as f:
f.write(index_html)
except Exception:
traceback.print_exc(file=sys.stderr)
sys.exit(-1)
sys.exit()
# Watch for any changes in the docs or style directories.
dn_watch_files = []
observer = None
if watch_changes:
observer = Observer()
observer.schedule(ReloadHandler(app), path=dir_documents, recursive=True)
observer.start()
dn_watch_files = build_reload_files_list([__name__, dir_style])
# Run the server.
if args.set_host:
try:
print('Attempting set sevelopment server listen on public IP address: ' + args.set_host)
print('WARNING: The Docnado development environment is intended to be used as a development tool ONLY, '
'and is not recommended for use in a production environment.')
app.run(debug=flask_debug, port=PORT_NUMBER, extra_files=dn_watch_files, host=args.set_host)
except OSError as e:
print(e)
print(f'Error initialising server.')
except KeyboardInterrupt:
pass
finally:
if observer:
observer.stop()
observer.join()
else:
try:
app.run(debug=flask_debug, port=PORT_NUMBER, extra_files=dn_watch_files)
except OSError as e:
print(e)
print(f'Error initialising server.')
except KeyboardInterrupt:
pass
finally:
if observer:
observer.stop()
observer.join()
# if running brainerd directly, boot the app
if __name__ == "__main__":
main()
| [
"flask.render_template",
"subprocess.getoutput",
"datetime.datetime.utcfromtimestamp",
"glob.iglob",
"re.compile",
"flask.Flask",
"time.sleep",
"sys.exit",
"os.walk",
"os.remove",
"re.search",
"os.path.exists",
"flask_frozen.Freezer",
"argparse.ArgumentParser",
"urllib.request.urlretrieve",
"os.path.split",
"navtree.parse_nav_string",
"os.path.isdir",
"platform.system",
"subprocess.call",
"os.getpid",
"flask.abort",
"xml.etree.ElementTree.fromstring",
"traceback.print_exc",
"csv.reader",
"urllib.request.url2pathname",
"urllib.parse.unquote",
"os.path.relpath",
"subprocess.check_output",
"glob.glob",
"os.path.getsize",
"hashlib.md5",
"re.match",
"os.path.splitext",
"shutil.rmtree",
"os.path.isfile",
"os.path.dirname",
"requests.head",
"shutil.copyfile",
"re.sub",
"xml.etree.ElementTree.SubElement",
"threading.Thread",
"flask.make_response",
"warnings.filterwarnings",
"markdown.Markdown",
"urllib.parse.urlparse",
"navtree.NavItem",
"os.path.join",
"os.environ.get",
"os.getcwd",
"flask.url_for",
"xml.etree.ElementTree.Element",
"bs4.BeautifulSoup",
"flask.render_template_string",
"tempfile.mktemp",
"os.path.basename",
"multiprocessing.Pool",
"shutil.copytree",
"os.stat",
"watchdog.observers.Observer"
]
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# 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")
| [
"sys.print_exception",
"sys.exc_info"
]
| [((2448, 2479), 'sys.print_exception', 'sys.print_exception', (['e', '_stream'], {}), '(e, _stream)\n', (2467, 2479), False, 'import sys\n'), ((2535, 2549), 'sys.exc_info', 'sys.exc_info', ([], {}), '()\n', (2547, 2549), False, 'import sys\n')] |
# 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),
),
]
| [
"django.db.models.CharField"
]
| [((341, 525), 'django.db.models.CharField', 'models.CharField', ([], {'blank': '(True)', 'choices': "[('america', 'Americas'), ('europe', 'Europe'), ('africa', 'Africa'), (\n 'asia', 'Asia'), ('oceania', 'Oceania')]", 'max_length': '(100)', 'null': '(True)'}), "(blank=True, choices=[('america', 'Americas'), ('europe',\n 'Europe'), ('africa', 'Africa'), ('asia', 'Asia'), ('oceania',\n 'Oceania')], max_length=100, null=True)\n", (357, 525), False, 'from django.db import migrations, models\n')] |
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
| [
"typing.cast",
"p2p.exceptions.PeerConnectionLost",
"typing.TypeVar"
]
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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"}) | [
"payments.models.Payment.objects.filter",
"json.loads",
"django.http.JsonResponse",
"payments.models.Order.objects.filter",
"payments.models.Invoice.objects.get",
"payments.models.Invoice.objects.all",
"payments.models.RazorpayKeys.objects.first",
"payments.razorpay.razorpay_payments.RazorpayPayments"
]
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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()
| [
"net.Net",
"torch.nn.CrossEntropyLoss",
"configparser.ConfigParser",
"image_helper.validation_set_loader",
"image_helper.save_image",
"torch.max",
"image_helper.train_set_loader",
"geo_helper.store_image_bounds",
"image_helper.test_set_loader",
"time.localtime",
"torch.autograd.Variable"
]
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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() | [
"tkinter.Tk",
"csv.reader",
"matplotlib.pyplot.subplots",
"tkinter.filedialog.askopenfilename",
"matplotlib.pyplot.show"
]
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# 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
),
]
| [
"django.db.models.IntegerField",
"django.db.models.OuterRef",
"django.db.migrations.RunPython",
"django.db.models.Q",
"django.db.models.CharField"
]
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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()
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"numpy.savetxt",
"torch.dot",
"os.system",
"sys.stdout.flush",
"torch.empty",
"torch.inverse",
"sys.stdout.write"
]
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16798), 'numpy.savetxt', 'np.savetxt', (['f', 'array'], {}), '(f, array)\n', (16788, 16798), True, 'import numpy as np\n')] |
"""
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
| [
"random.randint"
]
| [((198, 226), 'random.randint', 'random.randint', (['(1)', 'num_sides'], {}), '(1, num_sides)\n', (212, 226), False, 'import random\n')] |
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()
| [
"neurolib.optimize.exploration.BoxSearch",
"random.choice",
"numpy.random.rand",
"numpy.ones",
"neurolib.models.multimodel.MultiModel",
"os.path.join",
"neurolib.utils.parameterSpace.ParameterSpace",
"neurolib.utils.loadData.Dataset",
"neurolib.models.aln.ALNModel",
"numpy.array",
"numpy.linspace",
"neurolib.models.fhn.FHNModel",
"pytest.raises",
"unittest.main",
"time.time",
"numpy.testing.assert_array_equal"
]
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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')
| [
"django.contrib.auth.authenticate",
"django.forms.ValidationError",
"django.forms.TextInput"
]
| [((736, 786), 'django.contrib.auth.authenticate', 'authenticate', ([], {'username': 'username', 'password': 'password'}), '(username=username, password=password)\n', (748, 786), False, 'from django.contrib.auth import authenticate\n'), ((1127, 1177), 'django.contrib.auth.authenticate', 'authenticate', ([], {'username': 'username', 'password': 'password'}), '(username=username, password=password)\n', (1139, 1177), False, 'from django.contrib.auth import authenticate\n'), ((406, 454), 'django.forms.TextInput', 'forms.TextInput', ([], {'attrs': "{'class': 'form-control'}"}), "(attrs={'class': 'form-control'})\n", (421, 454), False, 'from django import forms\n'), ((510, 578), 'django.forms.TextInput', 'forms.TextInput', ([], {'attrs': "{'class': 'form-control', 'type': 'password'}"}), "(attrs={'class': 'form-control', 'type': 'password'})\n", (525, 578), False, 'from django import forms\n'), ((848, 946), 'django.forms.ValidationError', 'forms.ValidationError', (['"""Sorry, that login was invalid or user is inactive. Please try again."""'], {}), "(\n 'Sorry, that login was invalid or user is inactive. Please try again.')\n", (869, 946), False, 'from django import forms\n')] |
# 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 | [
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(10390, 10393), False, 'import copy\n'), ((15393, 15433), 'numpy.sum', 'np.sum', (['(sample_nums * local_w_diff_norms)'], {}), '(sample_nums * local_w_diff_norms)\n', (15399, 15433), True, 'import numpy as np\n'), ((15436, 15455), 'numpy.sum', 'np.sum', (['sample_nums'], {}), '(sample_nums)\n', (15442, 15455), True, 'import numpy as np\n'), ((15495, 15514), 'numpy.isnan', 'np.isnan', (['delta_tmp'], {}), '(delta_tmp)\n', (15503, 15514), True, 'import numpy as np\n'), ((15523, 15542), 'numpy.isinf', 'np.isinf', (['delta_tmp'], {}), '(delta_tmp)\n', (15531, 15542), True, 'import numpy as np\n'), ((12407, 12454), 'numpy.sum', 'np.sum', (['(cycle_locals[:, 0] * cycle_locals[:, 1])'], {}), '(cycle_locals[:, 0] * cycle_locals[:, 1])\n', (12413, 12454), True, 'import numpy as np\n'), ((12457, 12483), 'numpy.sum', 'np.sum', (['cycle_locals[:, 0]'], {}), '(cycle_locals[:, 0])\n', (12463, 12483), True, 'import numpy as np\n'), ((15659, 15679), 'numpy.array', 'np.array', (['rho_locals'], {}), 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(['beta_tmp'], {}), '(beta_tmp)\n', (16141, 16151), True, 'import numpy as np\n'), ((23099, 23119), 'numpy.load', 'np.load', (['labels_path'], {}), '(labels_path)\n', (23106, 23119), True, 'import numpy as np\n'), ((9636, 9668), 'copy.deepcopy', 'copy.deepcopy', (['self.model_global'], {}), '(self.model_global)\n', (9649, 9668), False, 'import copy\n'), ((23857, 23876), 'numpy.max', 'np.max', (['channel_res'], {}), '(channel_res)\n', (23863, 23876), True, 'import numpy as np\n'), ((23029, 23047), 'numpy.load', 'np.load', (['data_path'], {}), '(data_path)\n', (23036, 23047), True, 'import numpy as np\n')] |
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>'
| [
"viewdom.html",
"viewdom.use_context"
]
| [((200, 221), 'viewdom.use_context', 'use_context', (['"""prefix"""'], {}), "('prefix')\n", (211, 221), False, 'from viewdom import html, render, use_context, Context\n'), ((233, 265), 'viewdom.html', 'html', (['"""<li>{prefix}{label}</li>"""'], {}), "('<li>{prefix}{label}</li>')\n", (237, 265), False, 'from viewdom import html, render, use_context, Context\n'), ((300, 351), 'viewdom.html', 'html', (['"""<ul>{[Todo(label) for label in todos]}</ul>"""'], {}), "('<ul>{[Todo(label) for label in todos]}</ul>')\n", (304, 351), False, 'from viewdom import html, render, use_context, Context\n'), ((370, 492), 'viewdom.html', 'html', (['"""\n <{Context} prefix="Item: ">\n <h1>{title}</h1>\n <{TodoList} todos={todos} />\n <//> \n"""'], {}), '(\n """\n <{Context} prefix="Item: ">\n <h1>{title}</h1>\n <{TodoList} todos={todos} />\n <//> \n"""\n )\n', (374, 492), False, 'from viewdom import html, render, use_context, Context\n')] |
###
### 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)
| [
"archinfo.arch.register_arch",
"archinfo.arch.Register"
]
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'archinfo.arch.Register', 'Register', (['"""acc"""', '(8)', '(65536)'], {}), "('acc', 8, 65536)\n", (127349, 127366), False, 'from archinfo.arch import register_arch, Endness, Register\n')] |
# -*- 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))
| [
"os.path.exists",
"networkx.spectral_layout",
"os.makedirs",
"networkx.circular_layout",
"networkx.DiGraph",
"networkx.spring_layout",
"networkx.Graph",
"os.path.join",
"networkx.draw_networkx_nodes",
"networkx.draw_networkx_labels",
"networkx.readwrite.json_graph.node_link_data",
"networkx.draw_networkx_edges",
"networkx.draw"
]
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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()
| [
"test.support.run_unittest",
"importlib._bootstrap._file_path_hook"
]
| [((682, 709), 'test.support.run_unittest', 'run_unittest', (['PathHookTests'], {}), '(PathHookTests)\n', (694, 709), False, 'from test.support import run_unittest\n'), ((389, 422), 'importlib._bootstrap._file_path_hook', '_bootstrap._file_path_hook', (['entry'], {}), '(entry)\n', (415, 422), False, 'from importlib import _bootstrap\n')] |
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)))
| [
"os.path.isfile",
"concurrent.futures.ThreadPoolExecutor",
"glob.iglob"
]
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# -*- 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'
| [
"os.path.abspath",
"os.path.dirname"
]
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#!/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)
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"pandas.testing.assert_frame_equal",
"asyncio.get_event_loop",
"apyhgnc.apyhgnc.fetch"
]
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# -*- 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) | [
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"sklearn.metrics.f1_score",
"pickle.dump",
"cross_val.model_assessment_split",
"random.seed",
"torch.tensor",
"models_gat.GAT"
]
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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)
| [
"struct.pack",
"copy.deepcopy"
]
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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)
| [
"pygame.display.set_caption",
"pygame.init",
"pygame.time.get_ticks",
"pygame.event.get",
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"pygame.key.get_pressed",
"pygame.time.Clock",
"pygame.image.load",
"pygame.font.Font"
]
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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)
| [
"logging.getLogger",
"traceback.format_exc",
"urllib.parse.urlparse",
"os.path.join",
"io.BytesIO",
"requests.get",
"time.sleep",
"nogi.utils.parsers.generate_post_key",
"asyncio.gather",
"aiohttp.TCPConnector",
"asyncio.get_event_loop",
"random.randint"
]
| [((526, 545), 'logging.getLogger', 'logging.getLogger', ([], {}), '()\n', (543, 545), False, 'import logging\n'), ((1157, 1198), 'os.path.join', 'os.path.join', (["member['roma_name']", '"""post"""'], {}), "(member['roma_name'], 'post')\n", (1169, 1198), False, 'import os\n'), ((1240, 1280), 'os.path.join', 'os.path.join', (["member['roma_name']", '"""img"""'], {}), "(member['roma_name'], 'img')\n", (1252, 1280), False, 'import os\n'), ((1949, 1983), 'requests.get', 'requests.get', (['url'], {'headers': 'HEADERS'}), '(url, headers=HEADERS)\n', (1961, 1983), False, 'import requests\n'), ((5556, 5580), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (5578, 5580), False, 'import asyncio\n'), ((2292, 2313), 'io.BytesIO', 'BytesIO', (['resp.content'], {}), '(resp.content)\n', (2299, 2313), False, 'from io import BytesIO\n'), ((2346, 2364), 'io.BytesIO', 'BytesIO', ([], {'bytes': "b''"}), "(bytes=b'')\n", (2353, 2364), False, 'from io import BytesIO\n'), ((6074, 6095), 'random.randint', 'random.randint', (['(1)', '(15)'], {}), '(1, 15)\n', (6088, 6095), False, 'import random\n'), ((6164, 6188), 'time.sleep', 'time.sleep', (['slepp_second'], {}), '(slepp_second)\n', (6174, 6188), False, 'import time\n'), ((1544, 1571), 'nogi.utils.parsers.generate_post_key', 'generate_post_key', (['post_url'], {}), '(post_url)\n', (1561, 1571), False, 'from nogi.utils.parsers import PostParser, generate_post_key\n'), ((3148, 3182), 'requests.get', 'requests.get', ([], {'url': "url['image_url']"}), "(url=url['image_url'])\n", (3160, 3182), False, 'import requests\n'), ((5497, 5519), 'traceback.format_exc', 'traceback.format_exc', ([], {}), '()\n', (5517, 5519), False, 'import traceback\n'), ((6022, 6044), 'asyncio.gather', 'asyncio.gather', (['*tasks'], {}), '(*tasks)\n', (6036, 6044), False, 'import asyncio\n'), ((4470, 4512), 'requests.get', 'requests.get', (['url'], {'headers': 'REQUEST_HEADERS'}), '(url, headers=REQUEST_HEADERS)\n', (4482, 4512), False, 'import requests\n'), ((4634, 4664), 'aiohttp.TCPConnector', 'TCPConnector', ([], {'verify_ssl': '(False)'}), '(verify_ssl=False)\n', (4646, 4664), False, 'from aiohttp import ClientSession, TCPConnector\n'), ((5905, 5927), 'asyncio.gather', 'asyncio.gather', (['*tasks'], {}), '(*tasks)\n', (5919, 5927), False, 'import asyncio\n'), ((3803, 3821), 'urllib.parse.urlparse', 'urlparse', (['post_url'], {}), '(post_url)\n', (3811, 3821), False, 'from urllib.parse import urlparse\n'), ((2627, 2653), 'urllib.parse.urlparse', 'urlparse', (["url['image_url']"], {}), "(url['image_url'])\n", (2635, 2653), False, 'from urllib.parse import urlparse\n')] |
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")
| [
"Jumpscale.j.tools.timer.execute_until",
"Jumpscale.j.exceptions.Base"
]
| [((774, 822), 'Jumpscale.j.tools.timer.execute_until', 'j.tools.timer.execute_until', (['self.is_running', '(10)'], {}), '(self.is_running, 10)\n', (801, 822), False, 'from Jumpscale import j\n'), ((842, 884), 'Jumpscale.j.exceptions.Base', 'j.exceptions.Base', (['"""Failed to run dnsmasq"""'], {}), "('Failed to run dnsmasq')\n", (859, 884), False, 'from Jumpscale import j\n'), ((1385, 1428), 'Jumpscale.j.exceptions.Base', 'j.exceptions.Base', (['"""Failed to stop DNSMASQ"""'], {}), "('Failed to stop DNSMASQ')\n", (1402, 1428), False, 'from Jumpscale import j\n')] |
#!/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()
| [
"os.path.dirname",
"LEDdebug.LEDdebug",
"sys.path.append",
"time.sleep"
]
| [((570, 580), 'LEDdebug.LEDdebug', 'LEDdebug', ([], {}), '()\n', (578, 580), False, 'from LEDdebug import LEDdebug\n'), ((725, 742), 'time.sleep', 'time.sleep', (['DELAY'], {}), '(DELAY)\n', (735, 742), False, 'import time\n'), ((276, 297), 'sys.path.append', 'sys.path.append', (['""".."""'], {}), "('..')\n", (291, 297), False, 'import sys\n'), ((335, 360), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (350, 360), False, 'import os\n')] |
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()
| [
"random.choice",
"time.sleep",
"requests.get",
"lxml.etree.HTML",
"pymongo.MongoClient"
]
| [((203, 252), 'pymongo.MongoClient', 'pymongo.MongoClient', (['"""mongodb://localhost:27017/"""'], {}), "('mongodb://localhost:27017/')\n", (222, 252), False, 'import pymongo\n'), ((2201, 2257), 'requests.get', 'requests.get', (['url'], {'headers': 'self.headers', 'proxies': 'proxies'}), '(url, headers=self.headers, proxies=proxies)\n', (2213, 2257), False, 'import requests\n'), ((2278, 2298), 'lxml.etree.HTML', 'etree.HTML', (['ret.text'], {}), '(ret.text)\n', (2288, 2298), False, 'from lxml import etree\n'), ((3485, 3505), 'random.choice', 'random.choice', (['proxy'], {}), '(proxy)\n', (3498, 3505), False, 'import random\n'), ((3959, 3972), 'time.sleep', 'time.sleep', (['(3)'], {}), '(3)\n', (3969, 3972), False, 'import time\n')] |
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')
| [
"os.path.exists",
"sklearn.preprocessing.LabelEncoder",
"sklearn.metrics.f1_score",
"os.makedirs",
"pandas.read_csv",
"sklearn.decomposition.PCA",
"sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis",
"sklearn.externals.joblib.dump"
]
| [((440, 503), 'pandas.read_csv', 'pd.read_csv', ([], {'filepath_or_buffer': 'trainName', 'header': 'None', 'sep': '""","""'}), "(filepath_or_buffer=trainName, header=None, sep=',')\n", (451, 503), True, 'import pandas as pd\n'), ((513, 575), 'pandas.read_csv', 'pd.read_csv', ([], {'filepath_or_buffer': 'testName', 'header': 'None', 'sep': '""","""'}), "(filepath_or_buffer=testName, header=None, sep=',')\n", (524, 575), True, 'import pandas as pd\n'), ((984, 998), 'sklearn.preprocessing.LabelEncoder', 'LabelEncoder', ([], {}), '()\n', (996, 998), False, 'from sklearn.preprocessing import LabelEncoder\n'), ((1068, 1082), 'sklearn.preprocessing.LabelEncoder', 'LabelEncoder', ([], {}), '()\n', (1080, 1082), False, 'from sklearn.preprocessing import LabelEncoder\n'), ((1176, 1201), 'os.path.exists', 'os.path.exists', (['directory'], {}), '(directory)\n', (1190, 1201), False, 'import os\n'), ((1207, 1229), 'os.makedirs', 'os.makedirs', (['directory'], {}), '(directory)\n', (1218, 1229), False, 'import os\n'), ((1400, 1419), 'sklearn.decomposition.PCA', 'PCA', ([], {'n_components': 'q'}), '(n_components=q)\n', (1403, 1419), False, 'from sklearn.decomposition import PCA\n'), ((1511, 1542), 'sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'QDA', ([], {'priors': 'None', 'reg_param': '(0.0)'}), '(priors=None, reg_param=0.0)\n', (1514, 1542), True, 'from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA\n'), ((1994, 2022), 'sklearn.metrics.f1_score', 'f1_score', (['encYtest', 'testPred'], {}), '(encYtest, testPred)\n', (2002, 2022), False, 'from sklearn.metrics import f1_score\n'), ((2324, 2352), 'sklearn.externals.joblib.dump', 'joblib.dump', (['clf', '"""QDA2.pkl"""'], {}), "(clf, 'QDA2.pkl')\n", (2335, 2352), False, 'from sklearn.externals import joblib\n')] |
## 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
| [
"sys.setrecursionlimit",
"sys.getrecursionlimit"
]
| [((152, 179), 'sys.setrecursionlimit', 'sys.setrecursionlimit', (['(2000)'], {}), '(2000)\n', (173, 179), False, 'import sys\n'), ((49, 72), 'sys.getrecursionlimit', 'sys.getrecursionlimit', ([], {}), '()\n', (70, 72), False, 'import sys\n')] |
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")
| [
"pages.models.HomePage.objects.all",
"pages.models.Article.objects.create",
"django.test.override_settings",
"pages.models.HomePage.objects.create",
"django.urls.reverse"
]
| [((131, 164), 'django.test.override_settings', 'override_settings', ([], {'USE_I18N': '(False)'}), '(USE_I18N=False)\n', (148, 164), False, 'from django.test import TestCase, override_settings\n'), ((346, 384), 'pages.models.HomePage.objects.create', 'HomePage.objects.create', ([], {'title': '"""Title"""'}), "(title='Title')\n", (369, 384), False, 'from pages.models import Article, HomePage\n'), ((393, 449), 'pages.models.Article.objects.create', 'Article.objects.create', ([], {'title': '"""First"""', 'date': '"""2022-04-09"""'}), "(title='First', date='2022-04-09')\n", (415, 449), False, 'from pages.models import Article, HomePage\n'), ((520, 535), 'django.urls.reverse', 'reverse', (['"""home"""'], {}), "('home')\n", (527, 535), False, 'from django.urls import reverse\n'), ((846, 861), 'django.urls.reverse', 'reverse', (['"""home"""'], {}), "('home')\n", (853, 861), False, 'from django.urls import reverse\n'), ((1046, 1143), 'django.urls.reverse', 'reverse', (['"""pages:article_detail"""'], {'kwargs': "{'year': 2022, 'month': 4, 'day': 9, 'slug': 'first'}"}), "('pages:article_detail', kwargs={'year': 2022, 'month': 4, 'day': 9,\n 'slug': 'first'})\n", (1053, 1143), False, 'from django.urls import reverse\n'), ((779, 801), 'pages.models.HomePage.objects.all', 'HomePage.objects.all', ([], {}), '()\n', (799, 801), False, 'from pages.models import Article, HomePage\n')] |
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)
| [
"oscar.test.factories.create_stockinfo",
"oscar.test.factories.create_product",
"oscar.apps.catalogue.models.Option.objects.create",
"oscar.apps.partner.strategy.Default",
"decimal.Decimal",
"oscar.apps.basket.models.Basket"
]
| [((323, 331), 'oscar.apps.basket.models.Basket', 'Basket', ([], {}), '()\n', (329, 331), False, 'from oscar.apps.basket.models import Basket\n'), ((363, 381), 'oscar.apps.partner.strategy.Default', 'strategy.Default', ([], {}), '()\n', (379, 381), False, 'from oscar.apps.partner import strategy\n'), ((405, 431), 'oscar.test.factories.create_product', 'factories.create_product', ([], {}), '()\n', (429, 431), False, 'from oscar.test import factories\n'), ((598, 637), 'oscar.test.factories.create_stockinfo', 'factories.create_stockinfo', (['self.record'], {}), '(self.record)\n', (624, 637), False, 'from oscar.test import factories\n'), ((1083, 1109), 'oscar.test.factories.create_product', 'factories.create_product', ([], {}), '()\n', (1107, 1109), False, 'from oscar.test import factories\n'), ((1384, 1392), 'oscar.apps.basket.models.Basket', 'Basket', ([], {}), '()\n', (1390, 1392), False, 'from oscar.apps.basket.models import Basket\n'), ((1424, 1442), 'oscar.apps.partner.strategy.Default', 'strategy.Default', ([], {}), '()\n', (1440, 1442), False, 'from oscar.apps.partner import strategy\n'), ((1466, 1492), 'oscar.test.factories.create_product', 'factories.create_product', ([], {}), '()\n', (1490, 1492), False, 'from oscar.test import factories\n'), ((1623, 1662), 'oscar.test.factories.create_stockinfo', 'factories.create_stockinfo', (['self.record'], {}), '(self.record)\n', (1649, 1662), False, 'from oscar.test import factories\n'), ((2511, 2537), 'oscar.test.factories.create_product', 'factories.create_product', ([], {}), '()\n', (2535, 2537), False, 'from oscar.test import factories\n'), ((2830, 2856), 'oscar.test.factories.create_product', 'factories.create_product', ([], {}), '()\n', (2854, 2856), False, 'from oscar.test import factories\n'), ((2968, 3005), 'oscar.apps.catalogue.models.Option.objects.create', 'Option.objects.create', ([], {'name': '"""Message"""'}), "(name='Message')\n", (2989, 3005), False, 'from oscar.apps.catalogue.models import Option\n'), ((3375, 3401), 'oscar.test.factories.create_product', 'factories.create_product', ([], {}), '()\n', (3399, 3401), False, 'from oscar.test import factories\n'), ((3532, 3571), 'oscar.test.factories.create_stockinfo', 'factories.create_stockinfo', (['self.record'], {}), '(self.record)\n', (3558, 3571), False, 'from oscar.test import factories\n'), ((3600, 3608), 'oscar.apps.basket.models.Basket', 'Basket', ([], {}), '()\n', (3606, 3608), False, 'from oscar.apps.basket.models import Basket\n'), ((3645, 3663), 'oscar.apps.partner.strategy.Default', 'strategy.Default', ([], {}), '()\n', (3661, 3663), False, 'from oscar.apps.partner import strategy\n'), ((3748, 3756), 'oscar.apps.basket.models.Basket', 'Basket', ([], {}), '()\n', (3754, 3756), False, 'from oscar.apps.basket.models import Basket\n'), ((3794, 3812), 'oscar.apps.partner.strategy.Default', 'strategy.Default', ([], {}), '()\n', (3810, 3812), False, 'from oscar.apps.partner import strategy\n'), ((4223, 4231), 'oscar.apps.basket.models.Basket', 'Basket', ([], {}), '()\n', (4229, 4231), False, 'from oscar.apps.basket.models import Basket\n'), ((4263, 4281), 'oscar.apps.partner.strategy.Default', 'strategy.Default', ([], {}), '()\n', (4279, 4281), False, 'from oscar.apps.partner import strategy\n'), ((561, 571), 'decimal.Decimal', 'D', (['"""10.00"""'], {}), "('10.00')\n", (562, 571), True, 'from decimal import Decimal as D\n'), ((1208, 1218), 'decimal.Decimal', 'D', (['"""20.00"""'], {}), "('20.00')\n", (1209, 1218), True, 'from decimal import Decimal as D\n'), ((1586, 1596), 'decimal.Decimal', 'D', (['"""10.00"""'], {}), "('10.00')\n", (1587, 1596), True, 'from decimal import Decimal as D\n'), ((2308, 2317), 'decimal.Decimal', 'D', (['"""5.00"""'], {}), "('5.00')\n", (2309, 2317), True, 'from decimal import Decimal as D\n'), ((2621, 2630), 'decimal.Decimal', 'D', (['"""5.00"""'], {}), "('5.00')\n", (2622, 2630), True, 'from decimal import Decimal as D\n'), ((2940, 2949), 'decimal.Decimal', 'D', (['"""5.00"""'], {}), "('5.00')\n", (2941, 2949), True, 'from decimal import Decimal as D\n'), ((3495, 3505), 'decimal.Decimal', 'D', (['"""10.00"""'], {}), "('10.00')\n", (3496, 3505), True, 'from decimal import Decimal as D\n')] |
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}"
| [
"pytest.fixture"
]
| [((63, 94), 'pytest.fixture', 'pytest.fixture', ([], {'scope': '"""session"""'}), "(scope='session')\n", (77, 94), False, 'import pytest\n')] |
#
# 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))
| [
"os.makedirs",
"math.floor",
"shutil.which",
"os.getcwd",
"os.chdir",
"litex.build.tools.get_litex_git_revision",
"os.path.dirname",
"subprocess.call",
"litex.build.generic_platform.Pins",
"litex.build.tools.write_to_file"
]
| [((6393, 6459), 'litex.build.tools.write_to_file', 'tools.write_to_file', (['script_file', 'script_contents'], {'force_unix': '(True)'}), '(script_file, script_contents, force_unix=True)\n', (6412, 6459), False, 'from litex.build import tools\n'), ((6627, 6647), 'shutil.which', 'which', (['"""quartus_map"""'], {}), "('quartus_map')\n", (6632, 6647), False, 'from shutil import which\n'), ((6808, 6841), 'subprocess.call', 'subprocess.call', (['(shell + [script])'], {}), '(shell + [script])\n', (6823, 6841), False, 'import subprocess\n'), ((7460, 7471), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (7469, 7471), False, 'import os\n'), ((7480, 7517), 'os.makedirs', 'os.makedirs', (['build_dir'], {'exist_ok': '(True)'}), '(build_dir, exist_ok=True)\n', (7491, 7517), False, 'import os\n'), ((7526, 7545), 'os.chdir', 'os.chdir', (['build_dir'], {}), '(build_dir)\n', (7534, 7545), False, 'import os\n'), ((9088, 9101), 'os.chdir', 'os.chdir', (['cwd'], {}), '(cwd)\n', (9096, 9101), False, 'import os\n'), ((5584, 5614), 'litex.build.tools.get_litex_git_revision', 'tools.get_litex_git_revision', ([], {}), '()\n', (5612, 5614), False, 'from litex.build import tools\n'), ((5741, 5771), 'litex.build.tools.get_litex_git_revision', 'tools.get_litex_git_revision', ([], {}), '()\n', (5769, 5771), False, 'from litex.build import tools\n'), ((9237, 9264), 'math.floor', 'math.floor', (['(period * 1000.0)'], {}), '(period * 1000.0)\n', (9247, 9264), False, 'import math\n'), ((4431, 4456), 'os.path.dirname', 'os.path.dirname', (['filename'], {}), '(filename)\n', (4446, 4456), False, 'import os\n'), ((1721, 1730), 'litex.build.generic_platform.Pins', 'Pins', (['pin'], {}), '(pin)\n', (1725, 1730), False, 'from litex.build.generic_platform import Pins, IOStandard, Misc\n')] |
from rest_framework.routers import DefaultRouter
from records.views import RecordViewSet
router = DefaultRouter()
router.register('', RecordViewSet, basename='records')
urlpatterns = router.urls
| [
"rest_framework.routers.DefaultRouter"
]
| [((100, 115), 'rest_framework.routers.DefaultRouter', 'DefaultRouter', ([], {}), '()\n', (113, 115), False, 'from rest_framework.routers import DefaultRouter\n')] |
# -*- 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
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"""
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]
| [
"logging.getLogger",
"numpy.ma.max",
"inspect.getmembers",
"numpy.flipud",
"numpy.where",
"webservice.webmodel.NexusProcessingException",
"netCDF4.Dataset",
"time.time",
"functools.wraps",
"numpy.max",
"numpy.linspace",
"numpy.min",
"numpy.all",
"numpy.ma.min",
"nexustiles.nexustiles.NexusTileService"
]
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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
| [
"numpy.any",
"numpy.max",
"collections.Counter",
"numpy.stack",
"numpy.linspace",
"numpy.zeros",
"collections.defaultdict",
"numpy.cumsum"
]
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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]))) | [
"numpy.random.normal",
"numpy.mean",
"eval_functions.oks_score_multi",
"numpy.random.beta",
"utils.bounded_cauchy",
"numpy.reshape",
"numpy.random.choice",
"utils.make_categorical",
"numpy.exp",
"numpy.array",
"json.load",
"simulation.create_sim_df",
"numpy.linspace",
"numpy.sign",
"numpy.random.uniform",
"pandas.DataFrame",
"utils.rotate_via_numpy",
"numpy.arange"
]
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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)}
| [
"graphql.GraphQLInputField",
"graphql.GraphQLNonNull"
]
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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 | [
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"torch.nn.Conv2d",
"torch.cos",
"torch.cuda.is_available",
"torch.concat",
"torch.moveaxis",
"torch.nn.functional.softmax"
]
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'einops.rearrange', 'rearrange', (['Q_K', '"""b c h w s t -> (b s t) c h w"""'], {}), "(Q_K, 'b c h w s t -> (b s t) c h w')\n", (3987, 4024), False, 'from einops import rearrange\n'), ((4194, 4222), 'torch.nn.functional.softmax', 'F.softmax', (['extr_feat'], {'dim': '(-2)'}), '(extr_feat, dim=-2)\n', (4203, 4222), True, 'import torch.nn.functional as F\n'), ((10637, 10685), 'torch.nn.Conv2d', 'nn.Conv2d', (['self.num_hidden[-1]', '(4)'], {'kernel_size': '(1)'}), '(self.num_hidden[-1], 4, kernel_size=1)\n', (10646, 10685), True, 'import torch.nn as nn\n'), ((11332, 11369), 'torch.concat', 'torch.concat', (['(queries, pred)'], {'dim': '(-1)'}), '((queries, pred), dim=-1)\n', (11344, 11369), False, 'import torch\n'), ((14235, 14273), 'torch.concat', 'torch.concat', (['(queries, query)'], {'dim': '(-1)'}), '((queries, query), dim=-1)\n', (14247, 14273), False, 'import torch\n'), ((1792, 1923), 'torch.nn.Conv2d', 'nn.Conv2d', ([], {'in_channels': 'self.num_hidden', 'out_channels': '(3 * 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(3629, 3648), True, 'import torch.nn.functional as F\n'), ((5452, 5507), 'numpy.power', 'np.power', (['(10000)', '(2 * (hid_j // 2) / self.num_hidden[-1])'], {}), '(10000, 2 * (hid_j // 2) / self.num_hidden[-1])\n', (5460, 5507), True, 'import numpy as np\n'), ((14098, 14169), 'torch.concat', 'torch.concat', (['(preds[..., t - 1:t], non_pred_feat[..., t - 1:t])'], {'dim': '(1)'}), '((preds[..., t - 1:t], non_pred_feat[..., t - 1:t]), dim=1)\n', (14110, 14169), False, 'import torch\n'), ((4266, 4304), 'torch.mul', 'torch.mul', (['attn_mask', 'V_rep[:, c, ...]'], {}), '(attn_mask, V_rep[:, c, ...])\n', (4275, 4304), False, 'import torch\n'), ((3799, 3849), 'torch.mul', 'torch.mul', (['attn_mask', 'V_rep[:, c, :, :, i, :i + 1]'], {}), '(attn_mask, V_rep[:, c, :, :, i, :i + 1])\n', (3808, 3849), False, 'import torch\n'), ((5363, 5388), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (5386, 5388), False, 'import torch\n')] |
# 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)
| [
"hydra.Client",
"hydra.Hydra"
]
| [((236, 286), 'hydra.Hydra', 'Hydra', (['"""http://localhost:4444"""', '"""client"""', '"""secret"""'], {}), "('http://localhost:4444', 'client', 'secret')\n", (241, 286), False, 'from hydra import Hydra, Client\n'), ((309, 439), 'hydra.Client', 'Client', ([], {'name': '"""new-client"""', 'secret': '"""client-secret"""', 'scopes': "['devices', 'products']", 'redirect_uris': "['http://localhost/callback']"}), "(name='new-client', secret='client-secret', scopes=['devices',\n 'products'], redirect_uris=['http://localhost/callback'])\n", (315, 439), False, 'from hydra import Hydra, Client\n')] |
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()
| [
"RPi.GPIO.cleanup",
"click.command",
"precon.remote_control.steer_vehicle",
"precon.remote_control.Screen",
"asyncio.get_event_loop",
"precon.devices_handlers.distance_sensor.show_distance"
]
| [((305, 329), 'click.command', 'click.command', ([], {'name': '"""rc"""'}), "(name='rc')\n", (318, 329), False, 'import click\n'), ((687, 722), 'click.command', 'click.command', ([], {'name': '"""show-distance"""'}), "(name='show-distance')\n", (700, 722), False, 'import click\n'), ((371, 395), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (393, 395), False, 'import asyncio\n'), ((763, 787), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (785, 787), False, 'import asyncio\n'), ((669, 683), 'RPi.GPIO.cleanup', 'GPIO.cleanup', ([], {}), '()\n', (681, 683), True, 'import RPi.GPIO as GPIO\n'), ((1027, 1041), 'RPi.GPIO.cleanup', 'GPIO.cleanup', ([], {}), '()\n', (1039, 1041), True, 'import RPi.GPIO as GPIO\n'), ((418, 426), 'precon.remote_control.Screen', 'Screen', ([], {}), '()\n', (424, 426), False, 'from precon.remote_control import steer_vehicle, Screen\n'), ((829, 849), 'precon.devices_handlers.distance_sensor.show_distance', 'show_distance_func', ([], {}), '()\n', (847, 849), True, 'from precon.devices_handlers.distance_sensor import show_distance as show_distance_func\n'), ((474, 495), 'precon.remote_control.steer_vehicle', 'steer_vehicle', (['screen'], {}), '(screen)\n', (487, 495), False, 'from precon.remote_control import steer_vehicle, Screen\n')] |
# -*- 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))
| [
"os.path.exists",
"monolithe.lib.Printer.success",
"monolithe.lib.Printer.log",
"monolithe.generators.managers.MainManager",
"os.path.join",
"shutil.rmtree",
"monolithe.generators.managers.VanillaManager",
"monolithe.generators.managers.CLIManager",
"os.remove"
]
| [((2126, 2156), 'os.path.exists', 'os.path.exists', (['overrides_path'], {}), '(overrides_path)\n', (2140, 2156), False, 'import os\n'), ((2293, 2328), 'os.path.exists', 'os.path.exists', (['attrs_defaults_path'], {}), '(attrs_defaults_path)\n', (2307, 2328), False, 'import os\n'), ((2459, 2491), 'os.path.exists', 'os.path.exists', (['code_header_path'], {}), '(code_header_path)\n', (2473, 2491), False, 'import os\n'), ((2956, 3000), 'monolithe.generators.managers.VanillaManager', 'VanillaManager', ([], {'monolithe_config': 'self.config'}), '(monolithe_config=self.config)\n', (2970, 3000), False, 'from monolithe.generators.managers import MainManager, CLIManager, VanillaManager\n'), ((3560, 3588), 'monolithe.lib.Printer.log', 'Printer.log', (['"""assembling..."""'], {}), "('assembling...')\n", (3571, 3588), False, 'from monolithe.lib import Printer\n'), ((3607, 3648), 'monolithe.generators.managers.MainManager', 'MainManager', ([], {'monolithe_config': 'self.config'}), '(monolithe_config=self.config)\n', (3618, 3648), False, 'from monolithe.generators.managers import MainManager, CLIManager, VanillaManager\n'), ((3721, 3761), 'monolithe.generators.managers.CLIManager', 'CLIManager', ([], {'monolithe_config': 'self.config'}), '(monolithe_config=self.config)\n', (3731, 3761), False, 'from monolithe.generators.managers import MainManager, CLIManager, VanillaManager\n'), ((3824, 3933), 'monolithe.lib.Printer.success', 'Printer.success', (['(\'%s generation complete and available in "%s/%s"\' % (name, output, self.\n config.language))'], {}), '(\'%s generation complete and available in "%s/%s"\' % (name,\n output, self.config.language))\n', (3839, 3933), False, 'from monolithe.lib import Printer\n'), ((2170, 2199), 'shutil.rmtree', 'shutil.rmtree', (['overrides_path'], {}), '(overrides_path)\n', (2183, 2199), False, 'import shutil\n'), ((2342, 2376), 'shutil.rmtree', 'shutil.rmtree', (['attrs_defaults_path'], {}), '(attrs_defaults_path)\n', (2355, 2376), False, 'import shutil\n'), ((2505, 2532), 'os.remove', 'os.remove', (['code_header_path'], {}), '(code_header_path)\n', (2514, 2532), False, 'import os\n'), ((3319, 3424), 'monolithe.lib.Printer.log', 'Printer.log', (["('transforming specifications into %s for version %s...' % (lang, info[\n 'api']['version']))"], {}), "('transforming specifications into %s for version %s...' % (lang,\n info['api']['version']))\n", (3330, 3424), False, 'from monolithe.lib import Printer\n'), ((2848, 2874), 'os.path.join', 'os.path.join', (['output', 'lang'], {}), '(output, lang)\n', (2860, 2874), False, 'import os\n'), ((2901, 2927), 'os.path.join', 'os.path.join', (['output', 'lang'], {}), '(output, lang)\n', (2913, 2927), False, 'import os\n')] |
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])
| [
"multiprocessing.Queue",
"rllab.sampler.utils.rollout",
"atexit.register",
"multiprocessing.Process"
]
| [((1499, 1506), 'multiprocessing.Queue', 'Queue', ([], {}), '()\n', (1504, 1506), False, 'from multiprocessing import Process, Queue\n'), ((1521, 1550), 'multiprocessing.Process', 'Process', ([], {'target': '_worker_start'}), '(target=_worker_start)\n', (1528, 1550), False, 'from multiprocessing import Process, Queue\n'), ((1575, 1608), 'atexit.register', 'atexit.register', (['_shutdown_worker'], {}), '(_shutdown_worker)\n', (1590, 1608), False, 'import atexit\n'), ((1018, 1092), 'rllab.sampler.utils.rollout', 'rollout', (['env', 'policy'], {'max_path_length': 'max_length', 'animated': '(True)', 'speedup': '(5)'}), '(env, policy, max_path_length=max_length, animated=True, speedup=5)\n', (1025, 1092), False, 'from rllab.sampler.utils import rollout\n'), ((1162, 1236), 'rllab.sampler.utils.rollout', 'rollout', (['env', 'policy'], {'max_path_length': 'max_length', 'animated': '(True)', 'speedup': '(5)'}), '(env, policy, max_path_length=max_length, animated=True, speedup=5)\n', (1169, 1236), False, 'from rllab.sampler.utils import rollout\n')] |
import re, scrapy
from crawler.items import *
class BenchmarkSpider(scrapy.Spider):
drop_params = True
# Spider name, for use with the scrapy crawl command
name = "benchmarks"
# Constants to get url parts
FULL, PROTOCOL, USER, PASSWORD, SUBDOMAIN, DOMAIN, TOP_LEVEL_DOMAIN, PORT_NUM, PATH, PAGE, GET_PARAMS, HASHTAGS = range(12)
# List of start urls to start crawling
start_urls = [
# 'https://app1.com',
# 'https://app2.com',
# 'https://app3.com',
# 'https://app4.com',
# 'https://app5.com',
# 'https://app6.com',
# 'https://app7.com',
# 'https://app8.com',
# 'https://app9.com',
# 'https://app10.com',
# 'https://app11.com',
'http://ec2-54-255-215-139.ap-southeast-1.compute.amazonaws.com/'
]
allowed_domains = [
"app1.com",
"app2.com",
"app3.com",
"app4.com",
"app5.com",
"app6.com",
"app7.com",
"app8.com",
"app9.com",
"app10.com",
"app11.com",
"app12.com",
"app13.com",
"app14.com",
"app15.com",
"app16.com",
"app17.com",
"app18.com",
"app19.com",
"app20.com",
"app21.com"
]
# Set to keep track of visited urls
visited_urls = set(start_urls)
"""
Uses Regex to split up url into components. Groups and what they are:
0 : the full url
1 : Protocol
2 : User
3 : Password
4 : Subdomain
5 : Domain
6 : Top level domain (.com .net etc)
7 : Port number
8 : Path
9 : Page
10: Get parameters
11: Hashtags
"""
def splitUrlIntoParts(self, url, index):
pattern = '(?:([^\:]*)\:\/\/)?(?:([^\:\@]*)(?:\:([^\@]*))?\@)?(?:([^\/\:]*)\.(?=[^\.\/\:]*\.[^\.\/\:]*))?([^\.\/\:]*)(?:\.([^\/\.\:#]*))?(?:\:([0-9]*))?(\/[^\?#]*(?=.*?\/)\/)?([^\?#]*)?(?:\?([^#]*))?(?:#(.*))?'
match = re.search(pattern, url)
if match:
if match.group(index):
return match.group(index)
return ''
def populateURLItem(self, item, url):
item['url'] = url
item['protocol'] = self.splitUrlIntoParts(url, self.PROTOCOL)
item['domain'] = self.splitUrlIntoParts(url, self.DOMAIN)
item['path'] = self.splitUrlIntoParts(url, self.PATH)
item['page'] = self.splitUrlIntoParts(url, self.PAGE)
item['get_params'] = self.splitUrlIntoParts(url, self.GET_PARAMS)
def getUrlWithoutParams(self, url):
# Pattern looks out for a question mark that marks start of params
# Assumption is that url is already valid
pattern = '([^? ]+).*'
match = re.search(pattern, url)
if match:
if match.group(1):
return match.group(1)
else:
return ''
def isVisited(self, url):
if self.drop_params:
truncated_url = self.getUrlWithoutParams(url)
return truncated_url in self.visited_urls
else :
return url in self.visited_urls
def markAsVisited(self, url):
if self.drop_params:
truncated_url = self.getUrlWithoutParams(url)
self.visited_urls.add(truncated_url)
else:
self.visited_urls.add(url)
# The default method that's called by scrapy for each url in the start_url list
def parse(self, response):
# Get URL item
item = URLItem()
# Get parts of URL item
self.populateURLItem(item, response.url)
yield item
# Look for Forms
# Assumption: forms will have id attribute
# We will be using this id and url to uniquely identify each form
forms = response.css('form')
for form in forms:
formItem = FormItem()
formItem['url'] = response.url
form_id = form.css('::attr(id)').extract_first()
if form_id is None:
form_id = ''
formItem['id_attr'] = form_id
yield formItem
inputs = form.css('input')
for a in inputs:
inputItem = InputItem()
inputItem['url'] = response.url
inputItem['form_id'] = form_id
inputItem['complete'] = a.extract()
inputItem['type_attr'] = a.css('::attr(type)').extract_first()
yield inputItem
# Get url to visit next
links = response.css('a::attr(href)').extract()
for next_page in links:
# Check that url exist
if next_page is not None:
# Handle weirdass cases where hrefs has scheme:///domain
next_page = next_page.replace("///", "//", 1)
next_page = response.urljoin(next_page)
# Check that url is not visited yet
if not self.isVisited(next_page):
self.markAsVisited(next_page)
yield scrapy.Request(next_page, callback=self.parse) | [
"scrapy.Request",
"re.search"
]
| [((2021, 2044), 're.search', 're.search', (['pattern', 'url'], {}), '(pattern, url)\n', (2030, 2044), False, 'import re, scrapy\n'), ((2776, 2799), 're.search', 're.search', (['pattern', 'url'], {}), '(pattern, url)\n', (2785, 2799), False, 'import re, scrapy\n'), ((5064, 5110), 'scrapy.Request', 'scrapy.Request', (['next_page'], {'callback': 'self.parse'}), '(next_page, callback=self.parse)\n', (5078, 5110), False, 'import re, scrapy\n')] |
import re
import logging
import httplib
import view_base
from models import rt_proxy
LOG = logging.getLogger('ryu.gui')
class RtAddrDel(view_base.ViewBase):
def __init__(self, host, port, dpid, address_id, status=None):
super(RtAddrDel, self).__init__()
self.host = host
self.port = port
self.dpid = dpid
self.address_id = address_id
self.status = status
def run(self):
LOG.debug('Router Address Delete Rule running')
if not self.status:
# set rule
return self._delete_address()
def _delete_address(self):
address = '%s:%s' % (self.host, self.port)
res = {'host': self.host,
'port': self.port,
'status': None}
address_no = {}
address_no['address_id'] = self.address_id
status = rt_proxy.delete_router_address(address, address_no, self.dpid)
if status[0]['command_result']:
command_result = status[0]['command_result']
res['status'] = command_result
else:
res['status'] = status
res['status'] = status
return self.json_response(res) | [
"logging.getLogger",
"models.rt_proxy.delete_router_address"
]
| [((93, 121), 'logging.getLogger', 'logging.getLogger', (['"""ryu.gui"""'], {}), "('ryu.gui')\n", (110, 121), False, 'import logging\n'), ((855, 917), 'models.rt_proxy.delete_router_address', 'rt_proxy.delete_router_address', (['address', 'address_no', 'self.dpid'], {}), '(address, address_no, self.dpid)\n', (885, 917), False, 'from models import rt_proxy\n')] |
"""This module contains tests for the helper module.
"""
from pywrangler.util.helper import get_param_names
def test_get_param_names():
def func():
pass
assert get_param_names(func) == []
def func1(a, b=4, c=6):
pass
assert get_param_names(func1) == ["a", "b", "c"]
assert get_param_names(func1, ["a"]) == ["b", "c"]
| [
"pywrangler.util.helper.get_param_names"
]
| [((182, 203), 'pywrangler.util.helper.get_param_names', 'get_param_names', (['func'], {}), '(func)\n', (197, 203), False, 'from pywrangler.util.helper import get_param_names\n'), ((264, 286), 'pywrangler.util.helper.get_param_names', 'get_param_names', (['func1'], {}), '(func1)\n', (279, 286), False, 'from pywrangler.util.helper import get_param_names\n'), ((317, 346), 'pywrangler.util.helper.get_param_names', 'get_param_names', (['func1', "['a']"], {}), "(func1, ['a'])\n", (332, 346), False, 'from pywrangler.util.helper import get_param_names\n')] |
import numpy as np
import photon_stream as ps
import photon_stream_production as psp
import pkg_resources
import os
runinfo_path = pkg_resources.resource_filename(
'photon_stream_production',
os.path.join('tests', 'resources', 'runinfo_20161115_to_20170103.csv')
)
drs_fRunID_for_obs_run = psp.drs_run._drs_fRunID_for_obs_run
def test_drs_run_assignment():
ri = psp.runinfo.read(runinfo_path)
ro = psp.drs_run.assign_drs_runs(ri)
ri = ri[(ri.fNight > 20161229) & (ri.fNight <= 20170102)]
ro = ro[(ro.fNight > 20161229) & (ro.fNight <= 20170102)]
for i, row in ri.iterrows():
assert row.fNight == ro.loc[i, 'fNight']
assert row.fRunID == ro.loc[i, 'fRunID']
if row.fRunTypeKey == psp.runinfo.OBSERVATION_RUN_TYPE_KEY:
first_method_drs_run_id = drs_fRunID_for_obs_run(
runinfo=ri,
fNight=row.fNight,
fRunID=row.fRunID
)
second_method_drs_run_id = ro.loc[i, 'DrsRunID']
if np.isnan(first_method_drs_run_id):
assert np.isnan(second_method_drs_run_id)
else:
assert first_method_drs_run_id == second_method_drs_run_id
| [
"photon_stream_production.drs_run.assign_drs_runs",
"photon_stream_production.runinfo.read",
"os.path.join",
"numpy.isnan"
]
| [((201, 271), 'os.path.join', 'os.path.join', (['"""tests"""', '"""resources"""', '"""runinfo_20161115_to_20170103.csv"""'], {}), "('tests', 'resources', 'runinfo_20161115_to_20170103.csv')\n", (213, 271), False, 'import os\n'), ((379, 409), 'photon_stream_production.runinfo.read', 'psp.runinfo.read', (['runinfo_path'], {}), '(runinfo_path)\n', (395, 409), True, 'import photon_stream_production as psp\n'), ((419, 450), 'photon_stream_production.drs_run.assign_drs_runs', 'psp.drs_run.assign_drs_runs', (['ri'], {}), '(ri)\n', (446, 450), True, 'import photon_stream_production as psp\n'), ((1028, 1061), 'numpy.isnan', 'np.isnan', (['first_method_drs_run_id'], {}), '(first_method_drs_run_id)\n', (1036, 1061), True, 'import numpy as np\n'), ((1086, 1120), 'numpy.isnan', 'np.isnan', (['second_method_drs_run_id'], {}), '(second_method_drs_run_id)\n', (1094, 1120), True, 'import numpy as np\n')] |
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