code stringlengths 22 1.05M | apis listlengths 1 3.31k | extract_api stringlengths 75 3.25M |
|---|---|---|
from pathlib import Path
def read(filename='in'):
file_path = Path(__file__).parent / filename
with file_path.open('r') as file:
return read_lines(file.readlines())
def read_lines(lines):
cells = {}
for y in range(len(lines)):
line = lines[y].strip()
for x in range(len(line)):
cells[(x, y)] = line[x]
return cells
| [
"pathlib.Path"
] | [((68, 82), 'pathlib.Path', 'Path', (['__file__'], {}), '(__file__)\n', (72, 82), False, 'from pathlib import Path\n')] |
## Copyright 2014 Cognitect. All Rights Reserved.
##
## Licensed under the Apache License, Version 2.0 (the "License");
## you may not use this file except in compliance with the License.
## You may obtain a copy of the License at
##
## http://www.apache.org/licenses/LICENSE-2.0
##
## Unless required by applicable law or agreed to in writing, software
## distributed under the License is distributed on an "AS-IS" BASIS,
## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
## See the License for the specific language governing permissions and
## limitations under the License.
import itertools
from transit.pyversion import imap, izip
def mapcat(f, i):
return itertools.chain.from_iterable(imap(f, i))
def pairs(i):
return izip(*[iter(i)] * 2)
cycle = itertools.cycle
def take(n, i):
return itertools.islice(i, 0, n)
| [
"itertools.islice",
"transit.pyversion.imap"
] | [((844, 869), 'itertools.islice', 'itertools.islice', (['i', '(0)', 'n'], {}), '(i, 0, n)\n', (860, 869), False, 'import itertools\n'), ((729, 739), 'transit.pyversion.imap', 'imap', (['f', 'i'], {}), '(f, i)\n', (733, 739), False, 'from transit.pyversion import imap, izip\n')] |
import pandas as pd
import matplotlib.pyplot as plt
#한글 폰트 오류 제거
from matplotlib import font_manager,rc
font_path ="D:/5674-833_4th/part4/malgun.ttf"
font_name = font_manager.FontProperties(fname=font_path).get_name()
rc('font',family = font_name)
df = pd.read_excel('D:/5674-833_4th/part4/시도별 전출입 인구수.xlsx',engine = 'openpyxl',header =0)
df = df.fillna(method='ffill') #누락값을 앞 데이터로 채움
#서울에서 다른 지역으로 이동한 데이터만 추출하여 정리
mask = (df['전출지별'] == '서울특별시') & (df['전입지별'] != '서울특별시')
df_seoul = df[mask]
df_seoul = df_seoul.drop(['전출지별'],axis= 1) #전출지별 column 삭제
df_seoul.rename({'전입지별':'전입지'},axis=1,inplace=True) #전입지별 column을 전입지로 바꿔줌
df_seoul.set_index('전입지',inplace = True)
print(df_seoul)
#서울에서 충청남도 , 경상북도 ,강원도 로 이동한 인구 데이터 값 선택
col_years = list(map(str,range(1970,2018)))
df_3 = df_seoul.loc[['충청남도','경상북도','강원도'],col_years]
#스타일 지정
plt.style.use('ggplot')
#그래프 객체 생성(figure에 1개의 서브 플롯생성)
fig = plt.figure(figsize=(20,5))
ax =fig.add_subplot(1,1,1)
#axe 객체에 plot 함수로 그래프 출력
ax.plot(col_years,df_3.loc['충청남도',:],marker = 'o',markerfacecolor = 'green',
markersize = 10,color = 'olive',linewidth = 2, label = '서울 -> 충남')
ax.plot(col_years,df_3.loc['경상북도',:],marker = 'o',markerfacecolor = 'blue',
markersize = 10, color = 'skyblue', linewidth = 2 , label = '서울 -> 경북')
ax.plot(col_years,df_3.loc['강원도',:],marker = 'o',markerfacecolor = 'red',
markersize =10, color = 'magenta',linewidth = 2, label = '서울 -> 강원')
#범례표시
ax.legend(loc = 'best')
#차트 제목 추가
ax.set_title('서울 -> 충남, 경북 , 강원 인구 이동',size = 20 )
#축 이름 추가
ax.set_xlabel('기간',size =12)
ax.set_ylabel('이동 인구수',size =12)
#축 눈금 라벨 지정 및 90 도 회전
ax.set_xticklabels(col_years,rotation = 90)
#축 눈금 라벨 크기
ax.tick_params(axis = "x", labelsize =10)
ax.tick_params(axis = "y", labelsize= 10)
plt.show() | [
"matplotlib.font_manager.FontProperties",
"matplotlib.pyplot.style.use",
"matplotlib.pyplot.figure",
"matplotlib.rc",
"pandas.read_excel",
"matplotlib.pyplot.show"
] | [((218, 246), 'matplotlib.rc', 'rc', (['"""font"""'], {'family': 'font_name'}), "('font', family=font_name)\n", (220, 246), False, 'from matplotlib import font_manager, rc\n'), ((254, 342), 'pandas.read_excel', 'pd.read_excel', (['"""D:/5674-833_4th/part4/시도별 전출입 인구수.xlsx"""'], {'engine': '"""openpyxl"""', 'header': '(0)'}), "('D:/5674-833_4th/part4/시도별 전출입 인구수.xlsx', engine='openpyxl',\n header=0)\n", (267, 342), True, 'import pandas as pd\n'), ((835, 858), 'matplotlib.pyplot.style.use', 'plt.style.use', (['"""ggplot"""'], {}), "('ggplot')\n", (848, 858), True, 'import matplotlib.pyplot as plt\n'), ((898, 925), 'matplotlib.pyplot.figure', 'plt.figure', ([], {'figsize': '(20, 5)'}), '(figsize=(20, 5))\n', (908, 925), True, 'import matplotlib.pyplot as plt\n'), ((1769, 1779), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (1777, 1779), True, 'import matplotlib.pyplot as plt\n'), ((162, 206), 'matplotlib.font_manager.FontProperties', 'font_manager.FontProperties', ([], {'fname': 'font_path'}), '(fname=font_path)\n', (189, 206), False, 'from matplotlib import font_manager, rc\n')] |
import os
import time
import pyautogui
# from voiceassmain import play_voice_assistant_speech
from vacore import VACore
# based on EnjiRouz realization https://github.com/EnjiRouz/Voice-Assistant-App/blob/master/app.py
# функция на старте
def start(core: VACore):
manifest = {
"name": "Википедия (поиск)",
"version": "1.0",
"require_online": True,
"commands": {
"википедия|вики": run_wiki,
},
}
return manifest
def run_wiki(core: VACore, phrase: str):
# if core != None:
# core.play_voice_assistant_speech("Ищу на вики {}".format(phrase))
import wikipediaapi
wiki = wikipediaapi.Wikipedia("ru")
# поиск страницы по запросу, чтение summary, открытие ссылки на страницу для получения подробной информации
wiki_page = wiki.page(phrase)
try:
if wiki_page.exists():
core.play_voice_assistant_speech(
"Вот что я нашла для {} в википедии".format(phrase)
)
# webbrowser.get().open(wiki_page.fullurl)
# чтение ассистентом первых двух предложений summary со страницы Wikipedia
# (могут быть проблемы с мультиязычностью)
core.play_voice_assistant_speech(wiki_page.summary.split(".")[:2])
else:
# открытие ссылки на поисковик в браузере в случае, если на Wikipedia не удалось найти ничего по запросу
# play_voice_assistant_speech(translator.get(
# "Can't find {} on Wikipedia. But here is what I found on google").format(search_term))
# url = "https://google.com/search?q=" + search_term
# webbrowser.get().open(url)
core.play_voice_assistant_speech("Не нашла {} в википедии".format(phrase))
# поскольку все ошибки предсказать сложно, то будет произведен отлов с последующим выводом без остановки программы
except:
import traceback
core.play_voice_assistant_speech("Проблемы с поиском в Википедии")
traceback.print_exc()
return
| [
"traceback.print_exc",
"wikipediaapi.Wikipedia"
] | [((658, 686), 'wikipediaapi.Wikipedia', 'wikipediaapi.Wikipedia', (['"""ru"""'], {}), "('ru')\n", (680, 686), False, 'import wikipediaapi\n'), ((2009, 2030), 'traceback.print_exc', 'traceback.print_exc', ([], {}), '()\n', (2028, 2030), False, 'import traceback\n')] |
# Django
from django.urls import include, path
# Django REST Framework
from rest_framework.routers import DefaultRouter
# Views
from .views import tasks as task_views
router = DefaultRouter()
router.register(r'tasks', task_views.TaskViewSet, basename='task')
urlpatterns = [
path('', include(router.urls))
]
| [
"rest_framework.routers.DefaultRouter",
"django.urls.include"
] | [((179, 194), 'rest_framework.routers.DefaultRouter', 'DefaultRouter', ([], {}), '()\n', (192, 194), False, 'from rest_framework.routers import DefaultRouter\n'), ((292, 312), 'django.urls.include', 'include', (['router.urls'], {}), '(router.urls)\n', (299, 312), False, 'from django.urls import include, path\n')] |
import importlib.metadata
import logging
import os
import shutil
from typing import Dict, Any, List
import click
from sqlalchemy import text
from dbd.log.dbd_exception import DbdException
from dbd.config.dbd_profile import DbdProfile
from dbd.config.dbd_project import DbdProject
from dbd.executors.model_executor import ModelExecutor, InvalidModelException
from dbd.log.dbd_logger import setup_logging
log = logging.getLogger(__name__)
this_script_dir = os.path.dirname(__file__)
class Dbd(object):
"""
Top level CLI object
"""
def __init__(self, debug: bool = False, logfile: str = 'dbd.log', profile: str = 'dbd.profile',
project: str = 'dbd.project'):
"""
Constructor
:param bool debug: debug flag
:param str logfile: log file
:param str profile: profile file
:param str project: project file
"""
self.__debug = debug
self.__logfile = logfile
self.__profile = profile
self.__project = project
def debug(self) -> bool:
"""
Debug flag getter
:return: debug flag
:rtype: bool
"""
return self.__debug
def logfile(self) -> str:
"""
Logfile getter
:return: logfile
:rtype: str
"""
return self.__logfile
def profile(self) -> str:
"""
Profile getter
:return: profile
:rtype: str
"""
return self.__profile
def project(self) -> str:
"""
Project getter
:return: project
:rtype: str
"""
return self.__project
def print_version():
"""
Prints DBD version
"""
click.echo(f"You're using DBD version {importlib.metadata.version('dbd')}.")
@click.group(invoke_without_command=True)
@click.option('--debug/--no-debug', envvar='DBD_DEBUG', default=False, help='Sets debugging on/off')
@click.option('--version', help="Print the DBD version and exit.", is_flag=True, is_eager=True)
@click.option('--logfile', envvar='DBD_LOG_FILE', default='dbd.log', help='Log file location')
@click.option('--profile', envvar='DBD_PROFILE', default='dbd.profile', help='Profile configuration file')
@click.option('--project', envvar='DBD_PROJECT', default='dbd.project', help='Project configuration file')
@click.pass_context
def cli(ctx, debug, logfile, version, profile, project):
if debug:
click.echo(f"Logging DEBUG info to '{logfile}'")
setup_logging(logging.DEBUG, logfile)
if version:
print_version()
ctx.exit(0)
ctx.obj = Dbd(debug, logfile, profile, project)
# noinspection PyUnusedLocal
@cli.command(help='Initializes a new DBD project.')
@click.argument('dest', required=False, default='my_new_dbd_project')
@click.pass_obj
def init(dbd, dest):
try:
src = os.path.join(this_script_dir, '..', 'resources', 'template')
if os.path.exists(dest):
log.error(f"Can't overwrite directory '{dest}'")
raise DbdException(f"Can't overwrite directory '{dest}'")
shutil.copytree(src, dest)
click.echo(f"New project {dest} generated. Do cd {dest}; dbd run .")
except DbdException as d:
click.echo(f"ERROR: '{d}'")
@cli.command(help='Executes project.')
@click.option('--only', envvar='DBD_ONLY', default=None, help='Comma separated list of fully qualified table names '
'(<schema>.<table-name-no suffix>) to execute.')
@click.option('--deps/--no-deps', envvar='DBD_DEPS', default=True, help='Ignores dependencies for the --only list.')
@click.argument('dest', required=False, default='.')
@click.pass_obj
def run(dbd, only, deps, dest):
try:
log.debug("Loading configuration.")
prf = DbdProfile.load(os.path.join('.', dbd.profile()))
prj = DbdProject.load(prf, os.path.join(dest, dbd.project()))
log.debug("Creating model.")
model = ModelExecutor(prj)
log.debug("Connecting database.")
engine = prj.alchemy_engine_from_project()
# engine.execution_options(supports_statement_cache=False)
log.debug("Executing model.")
if not deps and only is None:
log.error("You must specify --only list for --no-deps.")
raise DbdException("You must specify --only list for --no-deps.")
if only is not None:
only_list = only.split(',')
try:
model.execute(engine, only_list, deps)
except InvalidModelException as e:
log.error(f"Can't run {only_list}: {e}")
raise DbdException(f"Can't run {only_list}: {e}")
else:
model.execute(engine)
log.debug("Finished.")
click.echo("All tasks finished!")
except DbdException as d:
click.echo(f"ERROR: '{d}'")
@cli.command(help='Validates project.')
@click.argument('dest', required=False, default='.')
@click.pass_obj
def validate(dbd, dest):
try:
prf = DbdProfile.load(os.path.join('.', dbd.profile()))
prj = DbdProject.load(prf, os.path.join(dest, dbd.project()))
model = ModelExecutor(prj)
engine = prj.alchemy_engine_from_project()
# noinspection PyBroadException
try:
engine.execute(text("SELECT 1"))
except Exception:
click.echo(
f"Can't connect to the target database. Check profile configuration in "
f"'{os.path.normpath(os.path.join(dest, dbd.profile()))}'.")
validation_result, validation_errors = model.validate()
if validation_result:
click.echo("No errors found. Model is valid.")
else:
click.echo("Model isn't valid. Please fix the following errors:")
__echo_validation_errors(validation_errors)
except DbdException as d:
click.echo(f"ERROR: '{d}'")
def __echo_validation_errors(validation_errors: Dict[str, Any]):
"""
Top level function for printing validation errors
:param validation_errors:
:return:
"""
__echo_validation_level(validation_errors)
class InvalidValidationErrorStructure(DbdException):
pass
def __echo_validation_level(level_validation_errors: Dict[str, Any], indent: int = 0):
"""
Echo validation error line (called recursively on all Dict values)
:param level_validation_errors: Dict with validation result
:param indent: indentation level
"""
for (k, v) in level_validation_errors.items():
if isinstance(v, str):
msg = f"{k}:{v}"
click.echo(msg.rjust(indent * 2 + len(msg), ' '))
elif isinstance(v, Dict):
msg = f"{k}:"
click.echo(msg.rjust(indent * 2 + len(msg), ' '))
__echo_validation_level(v, indent + 1)
elif isinstance(v, List):
msg = f"{k}:{str(v)}"
click.echo(msg.rjust(indent * 2 + len(msg), ' '))
else:
raise InvalidValidationErrorStructure(f"Invalid validation result: '{v}' isn't supported type.")
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"sqlalchemy.text",
"click.group",
"click.option",
"os.path.join",
"dbd.executors.model_executor.ModelExecutor",
"shutil.copytree",
"os.path.dirname",
"click.echo",
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
import os
import logging
ENV_VAR_ROOT = "IG_SERVICE"
CONFIG_FILE_NAME = "trading_ig_config.py"
logger = logging.getLogger(__name__)
class ConfigEnvVar(object):
def __init__(self, env_var_base):
self.ENV_VAR_BASE = env_var_base
def _env_var(self, key):
return(self.ENV_VAR_BASE + "_" + key.upper())
def get(self, key, default_value=None):
env_var = self._env_var(key)
return(os.environ.get(env_var, default_value))
def __getattr__(self, key):
env_var = self._env_var(key)
try:
return(os.environ[env_var])
except KeyError:
raise Exception("Environment variable '%s' doesn't exist"
% env_var)
try:
from trading_ig_config import config
logger.info("import config from %s" % CONFIG_FILE_NAME)
except Exception:
logger.warning("can't import config from config file")
try:
config = ConfigEnvVar(ENV_VAR_ROOT)
logger.info("import config from environment variables '%s_...'"
% ENV_VAR_ROOT)
except Exception:
logger.warning("can't import config from environment variables")
raise("""Can't import config - you might create a '%s' filename or use
environment variables such as '%s_...'""" % (CONFIG_FILE_NAME, ENV_VAR_ROOT))
| [
"logging.getLogger",
"os.environ.get"
] | [((152, 179), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (169, 179), False, 'import logging\n'), ((470, 508), 'os.environ.get', 'os.environ.get', (['env_var', 'default_value'], {}), '(env_var, default_value)\n', (484, 508), False, 'import os\n')] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
episode, season, disc, episode_count, season_count and episode_details properties
"""
import copy
from collections import defaultdict
from rebulk import Rebulk, RemoveMatch, Rule, AppendMatch, RenameMatch
from rebulk.match import Match
from rebulk.remodule import re
from rebulk.utils import is_iterable
from .title import TitleFromPosition
from ..common import dash, alt_dash, seps, seps_no_fs
from ..common.formatters import strip
from ..common.numeral import numeral, parse_numeral
from ..common.pattern import is_disabled
from ..common.validators import compose, seps_surround, seps_before, int_coercable
from ...reutils import build_or_pattern
def episodes(config):
"""
Builder for rebulk object.
:param config: rule configuration
:type config: dict
:return: Created Rebulk object
:rtype: Rebulk
"""
# pylint: disable=too-many-branches,too-many-statements,too-many-locals
def is_season_episode_disabled(context):
"""Whether season and episode rules should be enabled."""
return is_disabled(context, 'episode') or is_disabled(context, 'season')
rebulk = Rebulk().regex_defaults(flags=re.IGNORECASE).string_defaults(ignore_case=True)
rebulk.defaults(private_names=['episodeSeparator', 'seasonSeparator', 'episodeMarker', 'seasonMarker'])
episode_max_range = config['episode_max_range']
season_max_range = config['season_max_range']
def episodes_season_chain_breaker(matches):
"""
Break chains if there's more than 100 offset between two neighbor values.
:param matches:
:type matches:
:return:
:rtype:
"""
eps = matches.named('episode')
if len(eps) > 1 and abs(eps[-1].value - eps[-2].value) > episode_max_range:
return True
seasons = matches.named('season')
if len(seasons) > 1 and abs(seasons[-1].value - seasons[-2].value) > season_max_range:
return True
return False
rebulk.chain_defaults(chain_breaker=episodes_season_chain_breaker)
def season_episode_conflict_solver(match, other):
"""
Conflict solver for episode/season patterns
:param match:
:param other:
:return:
"""
if match.name != other.name:
if match.name == 'episode' and other.name == 'year':
return match
if match.name in ('season', 'episode'):
if other.name in ('video_codec', 'audio_codec', 'container', 'date'):
return match
if (other.name == 'audio_channels' and 'weak-audio_channels' not in other.tags
and not match.initiator.children.named(match.name + 'Marker')) or (
other.name == 'screen_size' and not int_coercable(other.raw)):
return match
if other.name in ('season', 'episode') and match.initiator != other.initiator:
if (match.initiator.name in ('weak_episode', 'weak_duplicate')
and other.initiator.name in ('weak_episode', 'weak_duplicate')):
return '__default__'
for current in (match, other):
if 'weak-episode' in current.tags or 'x' in current.initiator.raw.lower():
return current
return '__default__'
season_words = config['season_words']
episode_words = config['episode_words']
of_words = config['of_words']
all_words = config['all_words']
season_markers = config['season_markers']
season_ep_markers = config['season_ep_markers']
disc_markers = config['disc_markers']
episode_markers = config['episode_markers']
range_separators = config['range_separators']
weak_discrete_separators = list(sep for sep in seps_no_fs if sep not in range_separators)
strong_discrete_separators = config['discrete_separators']
discrete_separators = strong_discrete_separators + weak_discrete_separators
max_range_gap = config['max_range_gap']
def ordering_validator(match):
"""
Validator for season list. They should be in natural order to be validated.
episode/season separated by a weak discrete separator should be consecutive, unless a strong discrete separator
or a range separator is present in the chain (1.3&5 is valid, but 1.3-5 is not valid and 1.3.5 is not valid)
"""
values = match.children.to_dict()
if 'season' in values and is_iterable(values['season']):
# Season numbers must be in natural order to be validated.
if not list(sorted(values['season'])) == values['season']:
return False
if 'episode' in values and is_iterable(values['episode']):
# Season numbers must be in natural order to be validated.
if not list(sorted(values['episode'])) == values['episode']:
return False
def is_consecutive(property_name):
"""
Check if the property season or episode has valid consecutive values.
:param property_name:
:type property_name:
:return:
:rtype:
"""
previous_match = None
valid = True
for current_match in match.children.named(property_name):
if previous_match:
match.children.previous(current_match,
lambda m: m.name == property_name + 'Separator')
separator = match.children.previous(current_match,
lambda m: m.name == property_name + 'Separator', 0)
if separator.raw not in range_separators and separator.raw in weak_discrete_separators:
if not 0 < current_match.value - previous_match.value <= max_range_gap + 1:
valid = False
if separator.raw in strong_discrete_separators:
valid = True
break
previous_match = current_match
return valid
return is_consecutive('episode') and is_consecutive('season')
# S01E02, 01x02, S01S02S03
rebulk.chain(formatter={'season': int, 'episode': int},
tags=['SxxExx'],
abbreviations=[alt_dash],
children=True,
private_parent=True,
validate_all=True,
validator={'__parent__': ordering_validator},
conflict_solver=season_episode_conflict_solver,
disabled=is_season_episode_disabled) \
.regex(build_or_pattern(season_markers, name='seasonMarker') + r'(?P<season>\d+)@?' +
build_or_pattern(episode_markers + disc_markers, name='episodeMarker') + r'@?(?P<episode>\d+)',
validate_all=True,
validator={'__parent__': seps_before}).repeater('+') \
.regex(build_or_pattern(episode_markers + disc_markers + discrete_separators + range_separators,
name='episodeSeparator',
escape=True) +
r'(?P<episode>\d+)').repeater('*') \
.chain() \
.regex(r'(?P<season>\d+)@?' +
build_or_pattern(season_ep_markers, name='episodeMarker') +
r'@?(?P<episode>\d+)',
validate_all=True,
validator={'__parent__': seps_before}) \
.chain() \
.regex(r'(?P<season>\d+)@?' +
build_or_pattern(season_ep_markers, name='episodeMarker') +
r'@?(?P<episode>\d+)',
validate_all=True,
validator={'__parent__': seps_before}) \
.regex(build_or_pattern(season_ep_markers + discrete_separators + range_separators,
name='episodeSeparator',
escape=True) +
r'(?P<episode>\d+)').repeater('*') \
.chain() \
.regex(build_or_pattern(season_markers, name='seasonMarker') + r'(?P<season>\d+)',
validate_all=True,
validator={'__parent__': seps_before}) \
.regex(build_or_pattern(season_markers + discrete_separators + range_separators,
name='seasonSeparator',
escape=True) +
r'(?P<season>\d+)').repeater('*')
# episode_details property
for episode_detail in ('Special', 'Pilot', 'Unaired', 'Final'):
rebulk.string(episode_detail, value=episode_detail, name='episode_details',
disabled=lambda context: is_disabled(context, 'episode_details'))
def validate_roman(match):
"""
Validate a roman match if surrounded by separators
:param match:
:type match:
:return:
:rtype:
"""
if int_coercable(match.raw):
return True
return seps_surround(match)
rebulk.defaults(private_names=['episodeSeparator', 'seasonSeparator', 'episodeMarker', 'seasonMarker'],
validate_all=True, validator={'__parent__': seps_surround}, children=True, private_parent=True,
conflict_solver=season_episode_conflict_solver)
rebulk.chain(abbreviations=[alt_dash],
formatter={'season': parse_numeral, 'count': parse_numeral},
validator={'__parent__': compose(seps_surround, ordering_validator),
'season': validate_roman,
'count': validate_roman},
disabled=lambda context: context.get('type') == 'movie' or is_disabled(context, 'season')) \
.defaults(validator=None) \
.regex(build_or_pattern(season_words, name='seasonMarker') + '@?(?P<season>' + numeral + ')') \
.regex(r'' + build_or_pattern(of_words) + '@?(?P<count>' + numeral + ')').repeater('?') \
.regex(r'@?' + build_or_pattern(range_separators + discrete_separators + ['@'],
name='seasonSeparator', escape=True) +
r'@?(?P<season>\d+)').repeater('*')
rebulk.regex(build_or_pattern(episode_words, name='episodeMarker') + r'-?(?P<episode>\d+)' +
r'(?:v(?P<version>\d+))?' +
r'(?:-?' + build_or_pattern(of_words) + r'-?(?P<count>\d+))?', # Episode 4
abbreviations=[dash], formatter={'episode': int, 'version': int, 'count': int},
disabled=lambda context: context.get('type') == 'episode' or is_disabled(context, 'episode'))
rebulk.regex(build_or_pattern(episode_words, name='episodeMarker') + r'-?(?P<episode>' + numeral + ')' +
r'(?:v(?P<version>\d+))?' +
r'(?:-?' + build_or_pattern(of_words) + r'-?(?P<count>\d+))?', # Episode 4
abbreviations=[dash],
validator={'episode': validate_roman},
formatter={'episode': parse_numeral, 'version': int, 'count': int},
disabled=lambda context: context.get('type') != 'episode' or is_disabled(context, 'episode'))
rebulk.regex(r'S?(?P<season>\d+)-?(?:xE|Ex|E|x)-?(?P<other>' + build_or_pattern(all_words) + ')',
tags=['SxxExx'],
abbreviations=[dash],
validator=None,
formatter={'season': int, 'other': lambda match: 'Complete'},
disabled=lambda context: is_disabled(context, 'season'))
# 12, 13
rebulk.chain(tags=['weak-episode'], formatter={'episode': int, 'version': int},
disabled=lambda context: context.get('type') == 'movie' or is_disabled(context, 'episode')) \
.defaults(validator=None) \
.regex(r'(?P<episode>\d{2})') \
.regex(r'v(?P<version>\d+)').repeater('?') \
.regex(r'(?P<episodeSeparator>[x-])(?P<episode>\d{2})').repeater('*')
# 012, 013
rebulk.chain(tags=['weak-episode'], formatter={'episode': int, 'version': int},
disabled=lambda context: context.get('type') == 'movie' or is_disabled(context, 'episode')) \
.defaults(validator=None) \
.regex(r'0(?P<episode>\d{1,2})') \
.regex(r'v(?P<version>\d+)').repeater('?') \
.regex(r'(?P<episodeSeparator>[x-])0(?P<episode>\d{1,2})').repeater('*')
# 112, 113
rebulk.chain(tags=['weak-episode'],
formatter={'episode': int, 'version': int},
name='weak_episode',
disabled=lambda context: context.get('type') == 'movie' or is_disabled(context, 'episode')) \
.defaults(validator=None) \
.regex(r'(?P<episode>\d{3,4})') \
.regex(r'v(?P<version>\d+)').repeater('?') \
.regex(r'(?P<episodeSeparator>[x-])(?P<episode>\d{3,4})').repeater('*')
# 1, 2, 3
rebulk.chain(tags=['weak-episode'], formatter={'episode': int, 'version': int},
disabled=lambda context: context.get('type') != 'episode' or is_disabled(context, 'episode')) \
.defaults(validator=None) \
.regex(r'(?P<episode>\d)') \
.regex(r'v(?P<version>\d+)').repeater('?') \
.regex(r'(?P<episodeSeparator>[x-])(?P<episode>\d{1,2})').repeater('*')
# e112, e113, 1e18, 3e19
# TODO: Enhance rebulk for validator to be used globally (season_episode_validator)
rebulk.chain(formatter={'season': int, 'episode': int, 'version': int},
disabled=lambda context: is_disabled(context, 'episode')) \
.defaults(validator=None) \
.regex(r'(?P<season>\d{1,2})?(?P<episodeMarker>e)(?P<episode>\d{1,4})') \
.regex(r'v(?P<version>\d+)').repeater('?') \
.regex(r'(?P<episodeSeparator>e|x|-)(?P<episode>\d{1,4})').repeater('*')
# ep 112, ep113, ep112, ep113
rebulk.chain(abbreviations=[dash], formatter={'episode': int, 'version': int},
disabled=lambda context: is_disabled(context, 'episode')) \
.defaults(validator=None) \
.regex(r'ep-?(?P<episode>\d{1,4})') \
.regex(r'v(?P<version>\d+)').repeater('?') \
.regex(r'(?P<episodeSeparator>ep|e|x|-)(?P<episode>\d{1,4})').repeater('*')
# cap 112, cap 112_114
rebulk.chain(abbreviations=[dash],
tags=['see-pattern'],
formatter={'season': int, 'episode': int},
disabled=is_season_episode_disabled) \
.defaults(validator=None) \
.regex(r'(?P<seasonMarker>cap)-?(?P<season>\d{1,2})(?P<episode>\d{2})') \
.regex(r'(?P<episodeSeparator>-)(?P<season>\d{1,2})(?P<episode>\d{2})').repeater('?')
# 102, 0102
rebulk.chain(tags=['weak-episode', 'weak-duplicate'],
formatter={'season': int, 'episode': int, 'version': int},
name='weak_duplicate',
conflict_solver=season_episode_conflict_solver,
disabled=lambda context: (context.get('episode_prefer_number', False) or
context.get('type') == 'movie') or is_season_episode_disabled(context)) \
.defaults(validator=None) \
.regex(r'(?P<season>\d{1,2})(?P<episode>\d{2})') \
.regex(r'v(?P<version>\d+)').repeater('?') \
.regex(r'(?P<episodeSeparator>x|-)(?P<episode>\d{2})').repeater('*')
rebulk.regex(r'v(?P<version>\d+)', children=True, private_parent=True, formatter=int,
disabled=lambda context: is_disabled(context, 'version'))
rebulk.defaults(private_names=['episodeSeparator', 'seasonSeparator'])
# TODO: List of words
# detached of X count (season/episode)
rebulk.regex(r'(?P<episode>\d+)-?' + build_or_pattern(of_words) +
r'-?(?P<count>\d+)-?' + build_or_pattern(episode_words) + '?',
abbreviations=[dash], children=True, private_parent=True, formatter=int,
disabled=lambda context: is_disabled(context, 'episode'))
rebulk.regex(r'Minisodes?', name='episode_format', value="Minisode",
disabled=lambda context: is_disabled(context, 'episode_format'))
rebulk.rules(WeakConflictSolver, RemoveInvalidSeason, RemoveInvalidEpisode,
SeePatternRange(range_separators + ['_']),
EpisodeNumberSeparatorRange(range_separators),
SeasonSeparatorRange(range_separators), RemoveWeakIfMovie, RemoveWeakIfSxxExx,
RemoveWeakDuplicate, EpisodeDetailValidator, RemoveDetachedEpisodeNumber, VersionValidator,
RemoveWeak, RenameToAbsoluteEpisode, CountValidator, EpisodeSingleDigitValidator, RenameToDiscMatch)
return rebulk
class WeakConflictSolver(Rule):
"""
Rule to decide whether weak-episode or weak-duplicate matches should be kept.
If an anime is detected:
- weak-duplicate matches should be removed
- weak-episode matches should be tagged as anime
Otherwise:
- weak-episode matches are removed unless they're part of an episode range match.
"""
priority = 128
consequence = [RemoveMatch, AppendMatch]
def enabled(self, context):
return context.get('type') != 'movie'
@classmethod
def is_anime(cls, matches):
"""Return True if it seems to be an anime.
Anime characteristics:
- version, crc32 matches
- screen_size inside brackets
- release_group at start and inside brackets
"""
if matches.named('version') or matches.named('crc32'):
return True
for group in matches.markers.named('group'):
if matches.range(group.start, group.end, predicate=lambda m: m.name == 'screen_size'):
return True
if matches.markers.starting(group.start, predicate=lambda m: m.name == 'path'):
hole = matches.holes(group.start, group.end, index=0)
if hole and hole.raw == group.raw:
return True
return False
def when(self, matches, context):
to_remove = []
to_append = []
anime_detected = self.is_anime(matches)
for filepart in matches.markers.named('path'):
weak_matches = matches.range(filepart.start, filepart.end, predicate=(
lambda m: m.initiator.name == 'weak_episode'))
weak_dup_matches = matches.range(filepart.start, filepart.end, predicate=(
lambda m: m.initiator.name == 'weak_duplicate'))
if anime_detected:
if weak_matches:
to_remove.extend(weak_dup_matches)
for match in matches.range(filepart.start, filepart.end, predicate=(
lambda m: m.name == 'episode' and m.initiator.name != 'weak_duplicate')):
episode = copy.copy(match)
episode.tags = episode.tags + ['anime']
to_append.append(episode)
to_remove.append(match)
elif weak_dup_matches:
episodes_in_range = matches.range(filepart.start, filepart.end, predicate=(
lambda m:
m.name == 'episode' and m.initiator.name == 'weak_episode'
and m.initiator.children.named('episodeSeparator')
))
if not episodes_in_range and not matches.range(filepart.start, filepart.end,
predicate=lambda m: 'SxxExx' in m.tags):
to_remove.extend(weak_matches)
else:
for match in episodes_in_range:
episode = copy.copy(match)
episode.tags = []
to_append.append(episode)
to_remove.append(match)
if to_append:
to_remove.extend(weak_dup_matches)
return to_remove, to_append
class CountValidator(Rule):
"""
Validate count property and rename it
"""
priority = 64
consequence = [RemoveMatch, RenameMatch('episode_count'), RenameMatch('season_count')]
properties = {'episode_count': [None], 'season_count': [None]}
def when(self, matches, context):
to_remove = []
episode_count = []
season_count = []
for count in matches.named('count'):
previous = matches.previous(count, lambda match: match.name in ['episode', 'season'], 0)
if previous:
if previous.name == 'episode':
episode_count.append(count)
elif previous.name == 'season':
season_count.append(count)
else:
to_remove.append(count)
return to_remove, episode_count, season_count
class SeePatternRange(Rule):
"""
Create matches for episode range for SEE pattern. E.g.: Cap.102_104
"""
priority = 128
consequence = [RemoveMatch, AppendMatch]
def __init__(self, range_separators):
super(SeePatternRange, self).__init__()
self.range_separators = range_separators
def when(self, matches, context):
to_remove = []
to_append = []
for separator in matches.tagged('see-pattern', lambda m: m.name == 'episodeSeparator'):
previous_match = matches.previous(separator, lambda m: m.name == 'episode' and 'see-pattern' in m.tags, 0)
next_match = matches.next(separator, lambda m: m.name == 'season' and 'see-pattern' in m.tags, 0)
if not next_match:
continue
next_match = matches.next(next_match, lambda m: m.name == 'episode' and 'see-pattern' in m.tags, 0)
if previous_match and next_match and separator.value in self.range_separators:
to_remove.append(next_match)
for episode_number in range(previous_match.value + 1, next_match.value + 1):
match = copy.copy(next_match)
match.value = episode_number
to_append.append(match)
to_remove.append(separator)
return to_remove, to_append
class AbstractSeparatorRange(Rule):
"""
Remove separator matches and create matches for season range.
"""
priority = 128
consequence = [RemoveMatch, AppendMatch]
def __init__(self, range_separators, property_name):
super(AbstractSeparatorRange, self).__init__()
self.range_separators = range_separators
self.property_name = property_name
def when(self, matches, context):
to_remove = []
to_append = []
for separator in matches.named(self.property_name + 'Separator'):
previous_match = matches.previous(separator, lambda m: m.name == self.property_name, 0)
next_match = matches.next(separator, lambda m: m.name == self.property_name, 0)
initiator = separator.initiator
if previous_match and next_match and separator.value in self.range_separators:
to_remove.append(next_match)
for episode_number in range(previous_match.value + 1, next_match.value):
match = copy.copy(next_match)
match.value = episode_number
initiator.children.append(match)
to_append.append(match)
to_append.append(next_match)
to_remove.append(separator)
previous_match = None
for next_match in matches.named(self.property_name):
if previous_match:
separator = matches.input_string[previous_match.initiator.end:next_match.initiator.start]
if separator not in self.range_separators:
separator = strip(separator)
if separator in self.range_separators:
initiator = previous_match.initiator
for episode_number in range(previous_match.value + 1, next_match.value):
match = copy.copy(next_match)
match.value = episode_number
initiator.children.append(match)
to_append.append(match)
to_append.append(Match(previous_match.end, next_match.start - 1,
name=self.property_name + 'Separator',
private=True,
input_string=matches.input_string))
to_remove.append(next_match) # Remove and append match to support proper ordering
to_append.append(next_match)
previous_match = next_match
return to_remove, to_append
class RenameToAbsoluteEpisode(Rule):
"""
Rename episode to absolute_episodes.
Absolute episodes are only used if two groups of episodes are detected:
S02E04-06 25-27
25-27 S02E04-06
2x04-06 25-27
28. Anime Name S02E05
The matches in the group with higher episode values are renamed to absolute_episode.
"""
consequence = RenameMatch('absolute_episode')
def when(self, matches, context): # pylint:disable=inconsistent-return-statements
initiators = {match.initiator for match in matches.named('episode')
if len(match.initiator.children.named('episode')) > 1}
if len(initiators) != 2:
ret = []
for filepart in matches.markers.named('path'):
if matches.range(filepart.start + 1, filepart.end, predicate=lambda m: m.name == 'episode'):
ret.extend(
matches.starting(filepart.start, predicate=lambda m: m.initiator.name == 'weak_episode'))
return ret
initiators = sorted(initiators, key=lambda item: item.end)
if not matches.holes(initiators[0].end, initiators[1].start, predicate=lambda m: m.raw.strip(seps)):
first_range = matches.named('episode', predicate=lambda m: m.initiator == initiators[0])
second_range = matches.named('episode', predicate=lambda m: m.initiator == initiators[1])
if len(first_range) == len(second_range):
if second_range[0].value > first_range[0].value:
return second_range
if first_range[0].value > second_range[0].value:
return first_range
class EpisodeNumberSeparatorRange(AbstractSeparatorRange):
"""
Remove separator matches and create matches for episoderNumber range.
"""
def __init__(self, range_separators):
super(EpisodeNumberSeparatorRange, self).__init__(range_separators, "episode")
class SeasonSeparatorRange(AbstractSeparatorRange):
"""
Remove separator matches and create matches for season range.
"""
def __init__(self, range_separators):
super(SeasonSeparatorRange, self).__init__(range_separators, "season")
class RemoveWeakIfMovie(Rule):
"""
Remove weak-episode tagged matches if it seems to be a movie.
"""
priority = 64
consequence = RemoveMatch
def enabled(self, context):
return context.get('type') != 'episode'
def when(self, matches, context):
to_remove = []
to_ignore = set()
remove = False
for filepart in matches.markers.named('path'):
year = matches.range(filepart.start, filepart.end, predicate=lambda m: m.name == 'year', index=0)
if year:
remove = True
next_match = matches.range(year.end, filepart.end, predicate=lambda m: m.private, index=0)
if (next_match and not matches.holes(year.end, next_match.start, predicate=lambda m: m.raw.strip(seps))
and not matches.at_match(next_match, predicate=lambda m: m.name == 'year')):
to_ignore.add(next_match.initiator)
to_ignore.update(matches.range(filepart.start, filepart.end,
predicate=lambda m: len(m.children.named('episode')) > 1))
to_remove.extend(matches.conflicting(year))
if remove:
to_remove.extend(matches.tagged('weak-episode', predicate=(
lambda m: m.initiator not in to_ignore and 'anime' not in m.tags)))
return to_remove
class RemoveWeak(Rule):
"""
Remove weak-episode matches which appears after video, source, and audio matches.
"""
priority = 16
consequence = RemoveMatch
def when(self, matches, context):
to_remove = []
for filepart in matches.markers.named('path'):
weaks = matches.range(filepart.start, filepart.end, predicate=lambda m: 'weak-episode' in m.tags)
if weaks:
previous = matches.previous(weaks[0], predicate=lambda m: m.name in (
'audio_codec', 'screen_size', 'streaming_service', 'source', 'video_profile',
'audio_channels', 'audio_profile'), index=0)
if previous and not matches.holes(
previous.end, weaks[0].start, predicate=lambda m: m.raw.strip(seps)):
to_remove.extend(weaks)
return to_remove
class RemoveWeakIfSxxExx(Rule):
"""
Remove weak-episode tagged matches if SxxExx pattern is matched.
Weak episodes at beginning of filepart are kept.
"""
priority = 64
consequence = RemoveMatch
def when(self, matches, context):
to_remove = []
for filepart in matches.markers.named('path'):
if matches.range(filepart.start, filepart.end,
predicate=lambda m: not m.private and 'SxxExx' in m.tags):
for match in matches.range(filepart.start, filepart.end, predicate=lambda m: 'weak-episode' in m.tags):
if match.start != filepart.start or match.initiator.name != 'weak_episode':
to_remove.append(match)
return to_remove
class RemoveInvalidSeason(Rule):
"""
Remove invalid season matches.
"""
priority = 64
consequence = RemoveMatch
def when(self, matches, context):
to_remove = []
for filepart in matches.markers.named('path'):
strong_season = matches.range(filepart.start, filepart.end, index=0,
predicate=lambda m: m.name == 'season'
and not m.private and 'SxxExx' in m.tags)
if strong_season:
if strong_season.initiator.children.named('episode'):
for season in matches.range(strong_season.end, filepart.end,
predicate=lambda m: m.name == 'season' and not m.private):
# remove weak season or seasons without episode matches
if 'SxxExx' not in season.tags or not season.initiator.children.named('episode'):
if season.initiator:
to_remove.append(season.initiator)
to_remove.extend(season.initiator.children)
else:
to_remove.append(season)
return to_remove
class RemoveInvalidEpisode(Rule):
"""
Remove invalid episode matches.
"""
priority = 64
consequence = RemoveMatch
def when(self, matches, context):
to_remove = []
for filepart in matches.markers.named('path'):
strong_episode = matches.range(filepart.start, filepart.end, index=0,
predicate=lambda m: m.name == 'episode'
and not m.private and 'SxxExx' in m.tags)
if strong_episode:
strong_ep_marker = RemoveInvalidEpisode.get_episode_prefix(matches, strong_episode)
for episode in matches.range(strong_episode.end, filepart.end,
predicate=lambda m: m.name == 'episode' and not m.private):
ep_marker = RemoveInvalidEpisode.get_episode_prefix(matches, episode)
if strong_ep_marker and ep_marker and strong_ep_marker.value.lower() != ep_marker.value.lower():
if episode.initiator:
to_remove.append(episode.initiator)
to_remove.extend(episode.initiator.children)
else:
to_remove.append(ep_marker)
to_remove.append(episode)
return to_remove
@staticmethod
def get_episode_prefix(matches, episode):
"""
Return episode prefix: episodeMarker or episodeSeparator
"""
return matches.previous(episode, index=0,
predicate=lambda m: m.name in ('episodeMarker', 'episodeSeparator'))
class RemoveWeakDuplicate(Rule):
"""
Remove weak-duplicate tagged matches if duplicate patterns, for example The 100.109
"""
priority = 64
consequence = RemoveMatch
def when(self, matches, context):
to_remove = []
for filepart in matches.markers.named('path'):
patterns = defaultdict(list)
for match in reversed(matches.range(filepart.start, filepart.end,
predicate=lambda m: 'weak-duplicate' in m.tags)):
if match.pattern in patterns[match.name]:
to_remove.append(match)
else:
patterns[match.name].append(match.pattern)
return to_remove
class EpisodeDetailValidator(Rule):
"""
Validate episode_details if they are detached or next to season or episode.
"""
priority = 64
consequence = RemoveMatch
def when(self, matches, context):
ret = []
for detail in matches.named('episode_details'):
if not seps_surround(detail) \
and not matches.previous(detail, lambda match: match.name in ['season', 'episode']) \
and not matches.next(detail, lambda match: match.name in ['season', 'episode']):
ret.append(detail)
return ret
class RemoveDetachedEpisodeNumber(Rule):
"""
If multiple episode are found, remove those that are not detached from a range and less than 10.
Fairy Tail 2 - 16-20, 2 should be removed.
"""
priority = 64
consequence = RemoveMatch
dependency = [RemoveWeakIfSxxExx, RemoveWeakDuplicate]
def when(self, matches, context):
ret = []
episode_numbers = []
episode_values = set()
for match in matches.named('episode', lambda m: not m.private and 'weak-episode' in m.tags):
if match.value not in episode_values:
episode_numbers.append(match)
episode_values.add(match.value)
episode_numbers = list(sorted(episode_numbers, key=lambda m: m.value))
if len(episode_numbers) > 1 and \
episode_numbers[0].value < 10 and \
episode_numbers[1].value - episode_numbers[0].value != 1:
parent = episode_numbers[0]
while parent: # TODO: Add a feature in rebulk to avoid this ...
ret.append(parent)
parent = parent.parent
return ret
class VersionValidator(Rule):
"""
Validate version if previous match is episode or if surrounded by separators.
"""
priority = 64
dependency = [RemoveWeakIfMovie, RemoveWeakIfSxxExx]
consequence = RemoveMatch
def when(self, matches, context):
ret = []
for version in matches.named('version'):
episode_number = matches.previous(version, lambda match: match.name == 'episode', 0)
if not episode_number and not seps_surround(version.initiator):
ret.append(version)
return ret
class EpisodeSingleDigitValidator(Rule):
"""
Remove single digit episode when inside a group that doesn't own title.
"""
dependency = [TitleFromPosition]
consequence = RemoveMatch
def when(self, matches, context):
ret = []
for episode in matches.named('episode', lambda match: len(match.initiator) == 1):
group = matches.markers.at_match(episode, lambda marker: marker.name == 'group', index=0)
if group:
if not matches.range(*group.span, predicate=lambda match: match.name == 'title'):
ret.append(episode)
return ret
class RenameToDiscMatch(Rule):
"""
Rename episodes detected with `d` episodeMarkers to `disc`.
"""
consequence = [RenameMatch('disc'), RenameMatch('discMarker'), RemoveMatch]
def when(self, matches, context):
discs = []
markers = []
to_remove = []
disc_disabled = is_disabled(context, 'disc')
for marker in matches.named('episodeMarker', predicate=lambda m: m.value.lower() == 'd'):
if disc_disabled:
to_remove.append(marker)
to_remove.extend(marker.initiator.children)
continue
markers.append(marker)
discs.extend(sorted(marker.initiator.children.named('episode'), key=lambda m: m.value))
return discs, markers, to_remove
| [
"rebulk.Rebulk",
"rebulk.utils.is_iterable",
"rebulk.RenameMatch",
"collections.defaultdict",
"copy.copy",
"rebulk.match.Match"
] | [((25216, 25247), 'rebulk.RenameMatch', 'RenameMatch', (['"""absolute_episode"""'], {}), "('absolute_episode')\n", (25227, 25247), False, 'from rebulk import Rebulk, RemoveMatch, Rule, AppendMatch, RenameMatch\n'), ((20193, 20221), 'rebulk.RenameMatch', 'RenameMatch', (['"""episode_count"""'], {}), "('episode_count')\n", (20204, 20221), False, 'from rebulk import Rebulk, RemoveMatch, Rule, AppendMatch, RenameMatch\n'), ((20223, 20250), 'rebulk.RenameMatch', 'RenameMatch', (['"""season_count"""'], {}), "('season_count')\n", (20234, 20250), False, 'from rebulk import Rebulk, RemoveMatch, Rule, AppendMatch, RenameMatch\n'), ((36881, 36900), 'rebulk.RenameMatch', 'RenameMatch', (['"""disc"""'], {}), "('disc')\n", (36892, 36900), False, 'from rebulk import Rebulk, RemoveMatch, Rule, AppendMatch, RenameMatch\n'), ((36902, 36927), 'rebulk.RenameMatch', 'RenameMatch', (['"""discMarker"""'], {}), "('discMarker')\n", (36913, 36927), False, 'from rebulk import Rebulk, RemoveMatch, Rule, AppendMatch, RenameMatch\n'), ((4577, 4606), 'rebulk.utils.is_iterable', 'is_iterable', (["values['season']"], {}), "(values['season'])\n", (4588, 4606), False, 'from rebulk.utils import is_iterable\n'), ((4814, 4844), 'rebulk.utils.is_iterable', 'is_iterable', (["values['episode']"], {}), "(values['episode'])\n", (4825, 4844), False, 'from rebulk.utils import is_iterable\n'), ((33411, 33428), 'collections.defaultdict', 'defaultdict', (['list'], {}), '(list)\n', (33422, 33428), False, 'from collections import defaultdict\n'), ((1170, 1178), 'rebulk.Rebulk', 'Rebulk', ([], {}), '()\n', (1176, 1178), False, 'from rebulk import Rebulk, RemoveMatch, Rule, AppendMatch, RenameMatch\n'), ((22070, 22091), 'copy.copy', 'copy.copy', (['next_match'], {}), '(next_match)\n', (22079, 22091), False, 'import copy\n'), ((23302, 23323), 'copy.copy', 'copy.copy', (['next_match'], {}), '(next_match)\n', (23311, 23323), False, 'import copy\n'), ((18914, 18930), 'copy.copy', 'copy.copy', (['match'], {}), '(match)\n', (18923, 18930), False, 'import copy\n'), ((24129, 24150), 'copy.copy', 'copy.copy', (['next_match'], {}), '(next_match)\n', (24138, 24150), False, 'import copy\n'), ((24346, 24485), 'rebulk.match.Match', 'Match', (['previous_match.end', '(next_match.start - 1)'], {'name': "(self.property_name + 'Separator')", 'private': '(True)', 'input_string': 'matches.input_string'}), "(previous_match.end, next_match.start - 1, name=self.property_name +\n 'Separator', private=True, input_string=matches.input_string)\n", (24351, 24485), False, 'from rebulk.match import Match\n'), ((19775, 19791), 'copy.copy', 'copy.copy', (['match'], {}), '(match)\n', (19784, 19791), False, 'import copy\n')] |
import flask
import functools
import bs4
import urllib.parse
from .. import auth
from .. import themes
from .. import settings
INVALID_PERMISSIONS_FLASH_MESSAGE = "Sorry, you don't have permission to view that page."
def checkForSession():
if "uid" in flask.session:
session = auth.validateSession(flask.session["uid"])
if session is not None:
return session
return None
def redirectAndSave(path):
flask.session["donePage"] = urllib.parse.urlparse(flask.request.url).path
return flask.redirect(path)
def canRecoverFromRedirect():
if "donePage" in flask.session:
return flask.session["donePage"]
return None
#Decorator which checks if a user logged in and capable of using the specified permissions.
#If redirectPage is equal to none,
#the target funciton MUST have the arguments authed and authMessage defined.
def checkUserPermissions(redirectPage=None, saveRedirect=True, redirectMessage=INVALID_PERMISSIONS_FLASH_MESSAGE, requiredPermissions=None):
def decorator(function):
def decorated(*args, **kwargs):
session = checkForSession()
if session is not None:
username = session.user.username
result = True
#If we don't have any permissions necessary, a login is enough.
#Otherwise, we're going to check to make sure that all necessary permissions are in place.
if requiredPermissions is not None:
if type(requiredPermissions) == str:
result = auth.userHasPermission(username, requiredPermissions)
else:
for permission in requiredPermissions:
if not auth.userHasPermission(username, permission):
result = False
#If all permissions is valid, redirect as needed.
if result:
if redirectPage is not None:
return function(*args, **kwargs)
else:
return function(authed=True, authMessage=redirectMessage, *args, **kwargs)
else:
#We don't want to flash on thigns like ajax routes, so we use redirectPage is not None
willFlash = redirectPage is not None
return _permissionRedirect(redirectPage, saveRedirect, redirectMessage, willFlash, function, *args, **kwargs)
else:
return _permissionRedirect(redirectPage, saveRedirect, redirectMessage, False, function, *args, **kwargs)
return functools.update_wrapper(decorated, function)
return decorator
def _permissionRedirect(redirectPage, saveRedirect, redirectMessage, flash, function, *args, **kwargs):
if flash:
flask.flash(redirectMessage)
if redirectPage is not None:
if not saveRedirect:
return flask.redirect(redirectPage)
else:
return redirectAndSave(redirectPage)
else:
return function(authed=False, authMessage=redirectMessage, *args, **kwargs)
#Will return all information that is needed to render a post.
#Prevents fragmentation in various post display methods
def getPostsParameters():
title = settings.getSettingValue("title")
subtitle = settings.getSettingValue("subtitle")
displayName = settings.getSettingValue("display_name")
return {
"blogTitle": title,
"blogSubtitle": subtitle,
"displayName": displayName,
}
#Renders the theme's template if the theme contains one
#Otherwise, it renders the default template
def renderPosts(defaultPath, pageTitle, pageNumber, pageCount, nextPageExists, basePageUrl="", *args, **kwargs):
theme = themes.getCurrentTheme()
template = theme["template"]
postParams = getPostsParameters()
#Merge postParams and kwargs
#Anything in kwargs will overwrite postParams (which is why we use these two lines)
postParams.update(kwargs)
kwargs = postParams
if template is None:
templateFile = open(defaultPath, "r")
template = templateFile.read()
templateFile.close()
return flask.render_template_string(template, pageTitle=pageTitle,
pageNumber=pageNumber, pageCount=pageCount,
nextPageExists=nextPageExists, basePageUrl=basePageUrl,
*args, **kwargs)
def xssFilter(postBody):
whitelistedTags = ["div", "span", "b", "i", "u", "a", "p", "img", "code",
"ul", "li", "h1", "h2", "h3", "h4", "h5", "h6", "pre",
"br"]
#src and href must be checked seperately
whitelistedAttributes = ["id", "class", "style"]
soupedBody = bs4.BeautifulSoup(postBody, "html.parser")
blockedTags = soupedBody.findAll(lambda tag: tag.name not in whitelistedTags)
#Check if element has any attriutes that are not allowed, but only if
#they are not already in blockedTags. Those will be escaped, anyway.
blockedAttrs = soupedBody.findAll(lambda tag:
len(set(tag.attrs.keys()) - set(whitelistedAttributes)) != 0
and tag.name in whitelistedTags)
for tag in blockedTags:
#Beautiful soup will escape HTML strings
tag.replace_with(str(tag))
for tag in blockedAttrs:
allowedAttrs = {}
for attr in tag.attrs:
if attr in whitelistedAttributes:
allowedAttrs[attr] = tag.attrs[attr]
elif attr == "src" or attr == "href":
scheme = urllib.parse.urlparse(tag.attrs[attr]).scheme
if scheme != "data" and scheme != "javascript":
allowedAttrs[attr] = tag.attrs[attr]
tag.attrs = allowedAttrs
return str(soupedBody)
| [
"flask.flash",
"flask.redirect",
"flask.render_template_string",
"bs4.BeautifulSoup",
"functools.update_wrapper"
] | [((526, 546), 'flask.redirect', 'flask.redirect', (['path'], {}), '(path)\n', (540, 546), False, 'import flask\n'), ((4194, 4379), 'flask.render_template_string', 'flask.render_template_string', (['template', '*args'], {'pageTitle': 'pageTitle', 'pageNumber': 'pageNumber', 'pageCount': 'pageCount', 'nextPageExists': 'nextPageExists', 'basePageUrl': 'basePageUrl'}), '(template, *args, pageTitle=pageTitle,\n pageNumber=pageNumber, pageCount=pageCount, nextPageExists=\n nextPageExists, basePageUrl=basePageUrl, **kwargs)\n', (4222, 4379), False, 'import flask\n'), ((4723, 4765), 'bs4.BeautifulSoup', 'bs4.BeautifulSoup', (['postBody', '"""html.parser"""'], {}), "(postBody, 'html.parser')\n", (4740, 4765), False, 'import bs4\n'), ((2636, 2681), 'functools.update_wrapper', 'functools.update_wrapper', (['decorated', 'function'], {}), '(decorated, function)\n', (2660, 2681), False, 'import functools\n'), ((2830, 2858), 'flask.flash', 'flask.flash', (['redirectMessage'], {}), '(redirectMessage)\n', (2841, 2858), False, 'import flask\n'), ((2940, 2968), 'flask.redirect', 'flask.redirect', (['redirectPage'], {}), '(redirectPage)\n', (2954, 2968), False, 'import flask\n')] |
#!/usr/bin/env python3
# This is bot coded by <NAME> and used for educational purposes only
# https://github.com/AbhijithNT
# Copyright <NAME>
# Thank you https://github.com/pyrogram/pyrogram
from pyrogram.types import (
InlineKeyboardMarkup,
InlineKeyboardButton
)
def server_select():
upload_selection = [
[
InlineKeyboardButton(
"transfer.sh",
callback_data="transfersh"
),
InlineKeyboardButton(
"File.io",
callback_data="File.io"
)
],
[
InlineKeyboardButton(
"gofile.io",
callback_data="gofileio"
),
InlineKeyboardButton(
"anonymfiles.com",
callback_data="anonymfiles"
)
],
[
InlineKeyboardButton(
"aparat",
callback_data="aparat"
),
InlineKeyboardButton(
"splus",
callback_data="splus"
)
]
]
return InlineKeyboardMarkup(upload_selection)
def completedKeyboard(dl):
replayMarkup = InlineKeyboardMarkup(
[[
InlineKeyboardButton(
"DOWNLOAD URL",
url=f"{dl}"
)
],
[
InlineKeyboardButton(
"🗂 SOURCE",
url="https://github.com/AbhijithNT/"
)
]])
return replayMarkup
| [
"pyrogram.types.InlineKeyboardButton",
"pyrogram.types.InlineKeyboardMarkup"
] | [((1111, 1149), 'pyrogram.types.InlineKeyboardMarkup', 'InlineKeyboardMarkup', (['upload_selection'], {}), '(upload_selection)\n', (1131, 1149), False, 'from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n'), ((345, 408), 'pyrogram.types.InlineKeyboardButton', 'InlineKeyboardButton', (['"""transfer.sh"""'], {'callback_data': '"""transfersh"""'}), "('transfer.sh', callback_data='transfersh')\n", (365, 408), False, 'from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n'), ((468, 524), 'pyrogram.types.InlineKeyboardButton', 'InlineKeyboardButton', (['"""File.io"""'], {'callback_data': '"""File.io"""'}), "('File.io', callback_data='File.io')\n", (488, 524), False, 'from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n'), ((604, 663), 'pyrogram.types.InlineKeyboardButton', 'InlineKeyboardButton', (['"""gofile.io"""'], {'callback_data': '"""gofileio"""'}), "('gofile.io', callback_data='gofileio')\n", (624, 663), False, 'from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n'), ((723, 791), 'pyrogram.types.InlineKeyboardButton', 'InlineKeyboardButton', (['"""anonymfiles.com"""'], {'callback_data': '"""anonymfiles"""'}), "('anonymfiles.com', callback_data='anonymfiles')\n", (743, 791), False, 'from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n'), ((871, 925), 'pyrogram.types.InlineKeyboardButton', 'InlineKeyboardButton', (['"""aparat"""'], {'callback_data': '"""aparat"""'}), "('aparat', callback_data='aparat')\n", (891, 925), False, 'from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n'), ((985, 1037), 'pyrogram.types.InlineKeyboardButton', 'InlineKeyboardButton', (['"""splus"""'], {'callback_data': '"""splus"""'}), "('splus', callback_data='splus')\n", (1005, 1037), False, 'from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n'), ((1243, 1292), 'pyrogram.types.InlineKeyboardButton', 'InlineKeyboardButton', (['"""DOWNLOAD URL"""'], {'url': 'f"""{dl}"""'}), "('DOWNLOAD URL', url=f'{dl}')\n", (1263, 1292), False, 'from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n'), ((1376, 1446), 'pyrogram.types.InlineKeyboardButton', 'InlineKeyboardButton', (['"""🗂 SOURCE"""'], {'url': '"""https://github.com/AbhijithNT/"""'}), "('🗂 SOURCE', url='https://github.com/AbhijithNT/')\n", (1396, 1446), False, 'from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n')] |
import pickle
from collections import OrderedDict
from datetime import datetime
from .chunk import Chunk
from .review import Review
from .tokenizer import LineTokenizer
from .utils import norm_path
from .database.snippet import maybe_init, Snippet as DataSnippet
class Snippet(object):
def __init__(self, snippet_id, merged, chunks, source, target):
self.snippet_id = snippet_id
self.merged = merged
self._chunks = chunks
self._chunk_ids = []
self.start = chunks[0].start
self.length = self.total_len(chunks[0].start, chunks[-1].end)
self.source_file = norm_path(str(source))
self.target_file = norm_path(str(target))
self._target_lines = []
self._source_lines = []
self._target_tokens = []
self._source_tokens = []
def __str__(self):
return '\n-------------------------\n'.join(self.to_text())
def to_json(self):
snippet = OrderedDict()
snippet['snippet_id'] = self.snippet_id
reviews = Review.load(self.pr_number(self.snippet_id),
self.repo_id(self.snippet_id))
snippet['reviews'] = [review.to_json() for review in reviews]
snippet['chunk_ids'] = self.chunk_ids
return snippet
@property
def chunks(self):
return self._chunks
@property
def chunk_ids(self):
if not self._chunk_ids:
self._chunk_ids = [c.chunk_id for c in self._chunks]
return self._chunk_ids
@staticmethod
def repo_id(snippet_id):
return snippet_id.split('-')[3]
@staticmethod
def pr_number(snippet_id):
return snippet_id.split('-')[2]
@classmethod
def make_id(cls, hunk_no, file_no, pr_number, repo_id):
return '-'.join([str(hunk_no), str(file_no),
str(pr_number), str(repo_id)])
@staticmethod
def total_len(start, end):
length = end - start + 1
return length
def to_tokens(self):
chunks = []
for chunk in self._chunks:
chunks.append(chunk.as_tokens())
return chunks
def to_text(self):
chunks = []
for chunk in self._chunks:
chunks.append(chunk.as_text(pretty=True))
return chunks
@classmethod
def as_tokens(cls, code):
if not isinstance(code, list):
code = [code]
tokens = LineTokenizer(code).tokens
lines = []
for line in tokens:
lines += line
return lines
@classmethod
def as_elements(cls, code):
if not isinstance(code, list):
code = [code]
tokens = LineTokenizer(code).elements
lines = []
for line in tokens:
lines += line
return lines
@classmethod
def load(cls, snippet_id, path=None):
repo_id = cls.repo_id(snippet_id)
maybe_init(repo_id, path=path)
db_snippet = DataSnippet.get_or_none(snippet_id=snippet_id)
if db_snippet:
chunks = []
chunk_ids = pickle.loads(db_snippet.chunk_ids)
for chunk_id in chunk_ids:
chunks.append(Chunk.load(chunk_id))
merged = db_snippet.merged
source = db_snippet.source
target = db_snippet.target
snippet = cls(snippet_id, merged, chunks, source, target)
return snippet
@classmethod
def load_all(cls, repo_id, merged_only=False, path=None):
maybe_init(repo_id, path=path)
query = DataSnippet.select(
DataSnippet.snippet_id,
DataSnippet.chunk_ids,
DataSnippet.source,
DataSnippet.target)
if merged_only:
query = query.where(DataSnippet.merged == 1)
query = query.order_by(DataSnippet.last_mod.desc())
for db_snippet in query:
snippet_id = db_snippet.snippet_id
chunks = []
chunk_ids = pickle.loads(db_snippet.chunk_ids)
for chunk_id in chunk_ids:
chunks.append(Chunk.load(chunk_id))
merged = db_snippet.merged
source = db_snippet.source
target = db_snippet.target
snippet = cls(snippet_id, merged, chunks, source, target)
print('Finished loading snippet with ID: {0}'.format(snippet_id))
yield snippet
def _serialize_ids(self):
return pickle.dumps(self.chunk_ids, pickle.HIGHEST_PROTOCOL)
def exists(self):
repo_id = self.repo_id(self.snippet_id)
maybe_init(repo_id)
snippet = DataSnippet.get_or_none(snippet_id=self.snippet_id)
return bool(snippet)
def save(self):
repo_id = self.repo_id(self.snippet_id)
maybe_init(repo_id)
snippet = DataSnippet.get_or_none(snippet_id=self.snippet_id)
if snippet:
(DataSnippet
.update(snippet_id=self.snippet_id,
merged=self.merged,
last_mod=datetime.now(),
start=self.start,
length=self.length,
source=self.source_file,
target=self.target_file,
chunk_ids=self._serialize_ids())
.where(DataSnippet.snippet_id == self.snippet_id)
.execute())
else:
(DataSnippet
.create(snippet_id=self.snippet_id,
merged=self.merged,
last_mod=datetime.now(),
start=self.start,
length=self.length,
source=self.source_file,
target=self.target_file,
chunk_ids=self._serialize_ids()))
| [
"pickle.dumps",
"pickle.loads",
"collections.OrderedDict",
"datetime.datetime.now"
] | [((955, 968), 'collections.OrderedDict', 'OrderedDict', ([], {}), '()\n', (966, 968), False, 'from collections import OrderedDict\n'), ((4455, 4508), 'pickle.dumps', 'pickle.dumps', (['self.chunk_ids', 'pickle.HIGHEST_PROTOCOL'], {}), '(self.chunk_ids, pickle.HIGHEST_PROTOCOL)\n', (4467, 4508), False, 'import pickle\n'), ((3084, 3118), 'pickle.loads', 'pickle.loads', (['db_snippet.chunk_ids'], {}), '(db_snippet.chunk_ids)\n', (3096, 3118), False, 'import pickle\n'), ((3990, 4024), 'pickle.loads', 'pickle.loads', (['db_snippet.chunk_ids'], {}), '(db_snippet.chunk_ids)\n', (4002, 4024), False, 'import pickle\n'), ((5530, 5544), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (5542, 5544), False, 'from datetime import datetime\n'), ((5041, 5055), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (5053, 5055), False, 'from datetime import datetime\n')] |
# Copyright (c) 2015 Presslabs SRL
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from rest_framework.pagination import PageNumberPagination
from rest_framework.response import Response
from rest_framework.settings import api_settings
from rest_framework.utils.urls import replace_query_param, remove_query_param
class LinkHeaderPagination(PageNumberPagination):
page_size = api_settings.PAGE_SIZE or 30
page_size_query_param = 'page_size'
max_page_size = 100
def get_last_link(self):
url = self.request.build_absolute_uri()
page_number = self.page.paginator.num_pages
return replace_query_param(url, self.page_query_param, page_number)
def get_first_link(self, display_page_query_param=True):
url = self.request.build_absolute_uri()
if display_page_query_param:
page_number = self.page.paginator.validate_number(1)
return replace_query_param(url, self.page_query_param, page_number)
else:
return remove_query_param(url, self.page_query_param)
def get_paginated_response(self, data):
next_url = self.get_next_link()
previous_url = self.get_previous_link()
first_url = self.get_first_link()
last_url = self.get_last_link()
if next_url is not None and previous_url is not None:
link = '<{next_url}>; rel="next", <{previous_url}>; rel="prev"'
elif next_url is not None:
link = '<{next_url}>; rel="next"'
elif previous_url is not None:
link = '<{previous_url}>; rel="prev"'
else:
link = ''
if link:
link += ', '
link += '<{first_url}>; rel="first", <{last_url}> rel="last"'
link = link.format(next_url=next_url, previous_url=previous_url,
first_url=first_url, last_url=last_url)
headers = {'Link': link} if link else {}
return Response(data, headers=headers)
| [
"rest_framework.utils.urls.replace_query_param",
"rest_framework.utils.urls.remove_query_param",
"rest_framework.response.Response"
] | [((1158, 1218), 'rest_framework.utils.urls.replace_query_param', 'replace_query_param', (['url', 'self.page_query_param', 'page_number'], {}), '(url, self.page_query_param, page_number)\n', (1177, 1218), False, 'from rest_framework.utils.urls import replace_query_param, remove_query_param\n'), ((2471, 2502), 'rest_framework.response.Response', 'Response', (['data'], {'headers': 'headers'}), '(data, headers=headers)\n', (2479, 2502), False, 'from rest_framework.response import Response\n'), ((1450, 1510), 'rest_framework.utils.urls.replace_query_param', 'replace_query_param', (['url', 'self.page_query_param', 'page_number'], {}), '(url, self.page_query_param, page_number)\n', (1469, 1510), False, 'from rest_framework.utils.urls import replace_query_param, remove_query_param\n'), ((1544, 1590), 'rest_framework.utils.urls.remove_query_param', 'remove_query_param', (['url', 'self.page_query_param'], {}), '(url, self.page_query_param)\n', (1562, 1590), False, 'from rest_framework.utils.urls import replace_query_param, remove_query_param\n')] |
import os
import fecfile
import json
import csv
import sys
from settings import RAW_ELECTRONIC_DIR, MASTER_HEADER_ROW, HEADER_DUMP_FILE
START_YEAR = 2019
ERROR_HEADERS = ['path', 'error', ]
def readfile(filepath, writer):
filename = os.path.basename(filepath)
filename = filename.replace(".fec", "")
file_number = int(filename)
file = open(filepath, encoding = "ISO-8859-1")
#file = open(filepath)
firstline = file.readline()
secondline = file.readline()
firstline = firstline.replace("\n", "")
raw_results = fecfile.parse_header(firstline)
results = raw_results[0]
results["filing_number"] = file_number
version = raw_results[1]
lines = None
if len(raw_results)==3:
lines = raw_results[1]
original_report = results.get('report_id', None)
report_number = results.get('report_number', None)
if original_report:
original_report = original_report.replace("FEC-", "")
original_report_number = int(original_report)
results["amends"] = original_report_number
#print("Found amended filing %s amends %s # %s" % (file_number, original_report_number, report_number))
secondlineparsed = fecfile.parse_line(secondline, version)
#print(secondlineparsed)
results["form_type"] = secondlineparsed.get('form_type', '')
results["filer_committee_id_number"] = secondlineparsed.get('filer_committee_id_number', '')
results["committee_name"] = secondlineparsed.get('committee_name', '')
results["date_signed"] = secondlineparsed.get('date_signed', '')
results["coverage_from_date"] = secondlineparsed.get('coverage_from_date', '')
results["coverage_through_date"] = secondlineparsed.get('coverage_through_date', '')
writer.writerow(results)
if __name__ == '__main__':
outfile = open(HEADER_DUMP_FILE, 'w')
dw = csv.DictWriter(outfile, fieldnames=MASTER_HEADER_ROW, extrasaction='ignore')
dw.writeheader()
print("Writing output to %s" % HEADER_DUMP_FILE)
errorfile = open("header_read_errors.csv", 'w')
error_writer = csv.DictWriter(errorfile, fieldnames=ERROR_HEADERS, extrasaction='ignore')
error_writer.writeheader()
for dirName, subdirList, fileList in os.walk(RAW_ELECTRONIC_DIR, topdown=False):
try:
directory_year = int(dirName.split("/")[-1][0:4])
if directory_year < START_YEAR:
print("Ignoring directory %s" % dirName)
continue
except ValueError:
continue
for fname in fileList:
if fname.endswith(".fec"):
full_path = os.path.join(dirName, fname)
#readfile(full_path, dw)
#print("Found file %s" % full_path)
try:
readfile(full_path, dw)
except Exception as e:
print("error reading %s: %s" % (full_path, e))
error_writer.writerow({
'path':full_path,
'error':e
})
| [
"csv.DictWriter",
"fecfile.parse_line",
"fecfile.parse_header",
"os.path.join",
"os.path.basename",
"os.walk"
] | [((244, 270), 'os.path.basename', 'os.path.basename', (['filepath'], {}), '(filepath)\n', (260, 270), False, 'import os\n'), ((555, 586), 'fecfile.parse_header', 'fecfile.parse_header', (['firstline'], {}), '(firstline)\n', (575, 586), False, 'import fecfile\n'), ((1200, 1239), 'fecfile.parse_line', 'fecfile.parse_line', (['secondline', 'version'], {}), '(secondline, version)\n', (1218, 1239), False, 'import fecfile\n'), ((1859, 1935), 'csv.DictWriter', 'csv.DictWriter', (['outfile'], {'fieldnames': 'MASTER_HEADER_ROW', 'extrasaction': '"""ignore"""'}), "(outfile, fieldnames=MASTER_HEADER_ROW, extrasaction='ignore')\n", (1873, 1935), False, 'import csv\n'), ((2082, 2156), 'csv.DictWriter', 'csv.DictWriter', (['errorfile'], {'fieldnames': 'ERROR_HEADERS', 'extrasaction': '"""ignore"""'}), "(errorfile, fieldnames=ERROR_HEADERS, extrasaction='ignore')\n", (2096, 2156), False, 'import csv\n'), ((2230, 2272), 'os.walk', 'os.walk', (['RAW_ELECTRONIC_DIR'], {'topdown': '(False)'}), '(RAW_ELECTRONIC_DIR, topdown=False)\n', (2237, 2272), False, 'import os\n'), ((2632, 2660), 'os.path.join', 'os.path.join', (['dirName', 'fname'], {}), '(dirName, fname)\n', (2644, 2660), False, 'import os\n')] |
# -*- coding: utf-8 -*-
from django.conf.urls.defaults import patterns, url
urlpatterns = patterns('collector.views',
url(r'^blob404/$', 'blob404'),
url(r'^deleted/$', 'deleted'),
url(r'^$', 'create'),
url(r'^(?P<uid>\w+)/$', 'delete'),
)
# Local Variables:
# indent-tabs-mode: nil
# End:
# vim: ai et sw=4 ts=4
| [
"django.conf.urls.defaults.url"
] | [((124, 152), 'django.conf.urls.defaults.url', 'url', (['"""^blob404/$"""', '"""blob404"""'], {}), "('^blob404/$', 'blob404')\n", (127, 152), False, 'from django.conf.urls.defaults import patterns, url\n'), ((159, 187), 'django.conf.urls.defaults.url', 'url', (['"""^deleted/$"""', '"""deleted"""'], {}), "('^deleted/$', 'deleted')\n", (162, 187), False, 'from django.conf.urls.defaults import patterns, url\n'), ((194, 213), 'django.conf.urls.defaults.url', 'url', (['"""^$"""', '"""create"""'], {}), "('^$', 'create')\n", (197, 213), False, 'from django.conf.urls.defaults import patterns, url\n'), ((220, 253), 'django.conf.urls.defaults.url', 'url', (['"""^(?P<uid>\\\\w+)/$"""', '"""delete"""'], {}), "('^(?P<uid>\\\\w+)/$', 'delete')\n", (223, 253), False, 'from django.conf.urls.defaults import patterns, url\n')] |
"""
categories: Modules,array
description: Array deletion not implemented
cause: Unknown
workaround: Unknown
"""
import array
a = array.array('b', (1, 2, 3))
del a[1]
print(a)
| [
"array.array"
] | [((130, 157), 'array.array', 'array.array', (['"""b"""', '(1, 2, 3)'], {}), "('b', (1, 2, 3))\n", (141, 157), False, 'import array\n')] |
# !/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@Time : 2019/9/17 15:07
@Author : <NAME>
@FileName: models.py
@GitHub : https://github.com/cRiii
"""
from datetime import datetime
from werkzeug.security import generate_password_hash, check_password_hash
from jaysblog.extensions import db
from flask_login import UserMixin
class BaseModel(object):
# 模型基类 为所有模型添加创建和更新的时间
create_time = db.Column(db.DateTime, default=datetime.utcnow)
update_time = db.Column(db.DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
class User(BaseModel, db.Model, UserMixin):
"""
UserMixin表示通过认证的用户
is_authenticated 表示用户已通过认证 返回True 否则False
is_active 表示允许用户登陆 返回True 否则False
is_anonymous 表示如果当前未用户登陆(匿名用户) 返回True 否则False
get_id() 以unicode形式返回用户唯一标识
"""
__tablename__ = 'b_users'
id = db.Column(db.Integer, primary_key=True) # 用户id
nick_name = db.Column(db.String(32), nullable=False) # 用户名
password_hash = db.Column(db.String(128), nullable=False) # 用户密码
mobile = db.Column(db.String(11), unique=True) # 手机号码
email = db.Column(db.String(64), unique=True, nullable=True) # 邮箱
desc = db.Column(db.Text) # 个人简介
location = db.Column(db.String(128)) # 地址
avatar_url = db.Column(db.String(256)) # 用户头像路径
is_admin = db.Column(db.Boolean, default=False) # 是否为管理员
last_login_time = db.Column(db.DateTime, default=datetime.utcnow) # 最后一次登陆时间
is_delete = db.Column(db.Integer, default=1) # 用户是否被删除 1正常 0被删除
gender = db.Column(
db.Enum(
'MAN', # 男
'WOMAN' # 女
), default='MAN'
)
@property
def password(self):
raise AttributeError(u'该属性不可读')
@password.setter
def password(self, value):
"""
generate_password_hash（password，method='pbkdf2：sha256'，salt_length=8）
method指定计算散列值的方法
salt_length 指定盐长度
"""
self.password_hash = generate_password_hash(value)
def check_password(self, password):
"""
接收散列值 和 密码作比较 返回布尔类型
check_password_hash（<PASSWORD>，password）
"""
return check_password_hash(self.password_hash, password)
def to_dict(self):
res_dict = {
"id": self.id,
"nick_name": self.nick_name,
"email": self.email,
"desc": self.desc,
"avatar_url": self.avatar_url,
"gender": self.gender,
"is_admin": self.is_admin,
}
return res_dict
class Post(BaseModel, db.Model):
__tablename__ = 'b_posts'
id = db.Column(db.Integer, primary_key=True) # 文章编号
post_title = db.Column(db.String(256), nullable=False) # 文章标题
post_user_id = db.Column(db.Integer, nullable=False) # 创建文章用户
post_digest = db.Column(db.String(512), nullable=True) # 文章简介
post_content = db.Column(db.Text, nullable=False) # 文章内容
post_clicks = db.Column(db.Integer, default=0) # 点击量
post_like_num = db.Column(db.Integer, default=0) # 点赞数量
post_index_image_url = db.Column(db.String(256)) # 主页面列表图片地址
post_status = db.Column(db.Integer, default=1) # 文章状态
post_can_comment = db.Column(db.Integer, default=1) # 当前文章是否可以被评论
post_comments = db.relationship('Comment', backref='comment_post') # 当前文章的评论
post_category = db.relationship('Category', back_populates='cg_posts')
post_category_id = db.Column(db.Integer, db.ForeignKey('b_category.id'), nullable=False) # 文章类型
def get_comment_length(self):
comments = []
if self.post_comments is not []:
for comment in self.post_comments:
if comment.comment_status == 1:
comments.append(comment)
return len(comments)
def to_dict(self):
res_dict = {
"id": self.id,
"post_title": self.post_title,
"post_user_id": self.post_user_id,
"post_digest": self.post_digest if self.post_digest else "",
"post_clicks": self.post_clicks,
"post_like_num": self.post_like_num,
"post_index_image_url": self.post_index_image_url if self.post_index_image_url else "",
"post_category": self.post_category.to_dict() if self.post_category else None,
"post_comments_count": self.get_comment_length(),
"post_create_time": self.create_time,
"post_update_time": self.update_time,
}
return res_dict
def to_dict_details(self):
res_dict = {
"id": self.id,
"post_title": self.post_title,
"post_user_id": self.post_user_id,
"post_content": self.post_content,
"post_clicks": self.post_clicks,
"post_like_num": self.post_like_num,
"post_can_comment": self.post_can_comment,
"post_create_time": self.create_time,
"post_category": self.post_category.to_dict() if self.post_category else None,
"post_comments_count": self.get_comment_length(),
}
return res_dict
class Category(BaseModel, db.Model):
__tablename__ = 'b_category'
id = db.Column(db.Integer, primary_key=True) # 分类编号
cg_name = db.Column(db.String(64), nullable=False, unique=True) # 分类名称
cg_posts = db.relationship('Post', back_populates='post_category') # 分类下的文章
def to_dict(self):
res_dict = {
"id": self.id,
"cg_name": self.cg_name,
"cg_posts_count": len(self.cg_posts) if self.cg_posts else 0
}
return res_dict
class Comment(BaseModel, db.Model):
__tablename__ = 'b_comments'
id = db.Column(db.Integer, primary_key=True) # 评论编号
comment_user_id = db.Column(db.Integer, nullable=False) # 评论用户ID
comment_content = db.Column(db.Text, nullable=False) # 评论内容
comment_from_admin = db.Column(db.Integer, default=0) # 是否为管理员评论
comment_status = db.Column(db.Integer, default=0) # 评论是否通过审核 -1不可用 0:审核中 1:审核通过
comment_post_id = db.Column(db.Integer, db.ForeignKey('b_posts.id'), nullable=False) # 当前评论属于的文章id
comment_reply = db.relationship('Reply', backref='reply_comment') # 当前评论下的回复
def to_dict(self):
comment_replies = []
if self.comment_reply is not []:
for reply in self.comment_reply:
if reply.reply_status == 1:
comment_replies.append(reply.to_dict())
user = User.query.filter_by(id=self.comment_user_id).first()
res_dict = {
"id": self.id,
"comment_user_name": user.nick_name,
"comment_user_avatar_url": user.avatar_url,
"comment_content": self.comment_content,
"comment_from_admin": user.is_admin,
"comment_post_id": self.comment_post_id,
"comment_replies": comment_replies,
"comment_create_time": self.create_time,
"comment_update_time": self.update_time,
}
return res_dict
class Reply(BaseModel, db.Model):
__tablename__ = 'b_reply'
id = db.Column(db.Integer, primary_key=True) # 回复的id
reply_from_user = db.Column(db.String(32), nullable=False) # 谁回复的
reply_to_user = db.Column(db.String(32), nullable=False) # 回复给谁的
reply_content = db.Column(db.Text, nullable=False) # 回复的内容
reply_status = db.Column(db.Integer, default=0) # 回复是否通过审核 -1不可用 0:审核中 1:审核通过
reply_comment_id = db.Column(db.Integer, db.ForeignKey('b_comments.id'), nullable=False) # 当前回复属于的评论id
def to_dict(self):
user = User.query.filter_by(nick_name=self.reply_from_user).first()
res_dict = {
"id": self.id,
"reply_from_user": self.reply_from_user,
"reply_to_user": self.reply_to_user,
"reply_content": self.reply_content,
"reply_comment_id": self.reply_comment_id,
"reply_create_time": self.create_time,
"reply_update_time": self.update_time,
"reply_user_is_admin": user.is_admin,
"reply_user_avatar_url": user.avatar_url,
}
return res_dict
class Journey(BaseModel, db.Model):
__tablename__ = 'b_journey'
id = db.Column(db.Integer, primary_key=True) # 历程id
journey_title = db.Column(db.String(32), nullable=False) # 历程标题
journey_desc = db.Column(db.Text, nullable=False) # 历程详情
journey_time = db.Column(db.DateTime, default=datetime.utcnow) # 历程时间
def to_dict(self):
res_dict = {
"id": self.id,
"journey_title": self.journey_title,
"journey_desc": self.journey_desc,
"journey_time": self.journey_time
}
return res_dict
class MessageBoard(BaseModel, db.Model):
__tablename__ = 'b_board'
id = db.Column(db.Integer, primary_key=True) # 留言板id
board_user = db.Column(db.String(32), nullable=False) # 留言用户
board_desc = db.Column(db.Text, nullable=False) # 留言内容
board_status = db.Column(db.Integer, nullable=False, default=0) # 留言状态 -1不可用 0:审核中 1:审核通过
board_email = db.Column(db.String(50), nullable=False) # 留言回复邮箱
def to_dict(self):
res_dict = {
"id": self.id,
"board_user": self.board_user,
"board_desc": self.board_desc,
"board_status": self.board_status,
"board_create_time": self.create_time,
"board_update_time": self.update_time,
"board_email": self.board_email,
}
return res_dict
class UsersLikePosts(BaseModel, db.Model):
__tablename__ = 'b_users_like_posts'
id = db.Column(db.Integer, primary_key=True) # 主键
user_id = db.Column(db.Integer, nullable=False)
user_like_post_id = db.Column(db.Integer, nullable=False)
def to_dict(self):
res_dict = {
"id": self.id,
"user_id": self.user_id,
"user_like_post_id": self.user_like_post_id,
}
return res_dict
| [
"jaysblog.extensions.db.String",
"jaysblog.extensions.db.Column",
"jaysblog.extensions.db.ForeignKey",
"jaysblog.extensions.db.Enum",
"jaysblog.extensions.db.relationship",
"werkzeug.security.generate_password_hash",
"werkzeug.security.check_password_hash"
] | [((408, 455), 'jaysblog.extensions.db.Column', 'db.Column', (['db.DateTime'], {'default': 'datetime.utcnow'}), '(db.DateTime, default=datetime.utcnow)\n', (417, 455), False, 'from jaysblog.extensions import db\n'), ((474, 547), 'jaysblog.extensions.db.Column', 'db.Column', (['db.DateTime'], {'default': 'datetime.utcnow', 'onupdate': 'datetime.utcnow'}), '(db.DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)\n', (483, 547), False, 'from jaysblog.extensions import db\n'), ((875, 914), 'jaysblog.extensions.db.Column', 'db.Column', (['db.Integer'], {'primary_key': '(True)'}), '(db.Integer, primary_key=True)\n', (884, 914), False, 'from jaysblog.extensions import db\n'), ((1198, 1216), 'jaysblog.extensions.db.Column', 'db.Column', (['db.Text'], {}), '(db.Text)\n', (1207, 1216), False, 'from jaysblog.extensions import db\n'), ((1340, 1376), 'jaysblog.extensions.db.Column', 'db.Column', (['db.Boolean'], {'default': '(False)'}), '(db.Boolean, default=False)\n', (1349, 1376), 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#!/usr/bin/env python
# coding: utf-8
import yaml
import json
import sys
import os
sys.path.append('../venv/lib/python3.5/site-packages')
from elasticsearch import Elasticsearch
sys.path.append('../telemanom')
class Config:
'''Loads parameters from config.yaml into global object'''
def __init__(self, path_to_config):
if os.path.isfile(path_to_config):
pass
else:
path_to_config = '../%s' %path_to_config
setattr(self, "path_to_config", path_to_config)
dictionary = None
with open(path_to_config, "r") as f:
dictionary = yaml.load(f.read())
try:
for k,v in dictionary.items():
setattr(self, k, v)
except:
for k,v in dictionary.iteritems():
setattr(self, k, v)
def build_group_lookup(self, path_to_groupings):
channel_group_lookup = {}
with open(path_to_groupings, "r") as f:
groupings = json.loads(f.read())
for subsystem in groupings.keys():
for subgroup in groupings[subsystem].keys():
for chan in groupings[subsystem][subgroup]:
channel_group_lookup[chan["key"]] = {}
channel_group_lookup[chan["key"]]["subsystem"] = subsystem
channel_group_lookup[chan["key"]]["subgroup"] = subgroup
return channel_group_lookup | [
"os.path.isfile",
"sys.path.append"
] | [((85, 139), 'sys.path.append', 'sys.path.append', (['"""../venv/lib/python3.5/site-packages"""'], {}), "('../venv/lib/python3.5/site-packages')\n", (100, 139), False, 'import sys\n'), ((181, 212), 'sys.path.append', 'sys.path.append', (['"""../telemanom"""'], {}), "('../telemanom')\n", (196, 212), False, 'import sys\n'), ((353, 383), 'os.path.isfile', 'os.path.isfile', (['path_to_config'], {}), '(path_to_config)\n', (367, 383), False, 'import os\n')] |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
__all__ = [
'GetNetworkResult',
'AwaitableGetNetworkResult',
'get_network',
]
@pulumi.output_type
class GetNetworkResult:
"""
A collection of values returned by getNetwork.
"""
def __init__(__self__, default=None, id=None, label=None, name=None, region=None):
if default and not isinstance(default, bool):
raise TypeError("Expected argument 'default' to be a bool")
pulumi.set(__self__, "default", default)
if id and not isinstance(id, str):
raise TypeError("Expected argument 'id' to be a str")
pulumi.set(__self__, "id", id)
if label and not isinstance(label, str):
raise TypeError("Expected argument 'label' to be a str")
pulumi.set(__self__, "label", label)
if name and not isinstance(name, str):
raise TypeError("Expected argument 'name' to be a str")
pulumi.set(__self__, "name", name)
if region and not isinstance(region, str):
raise TypeError("Expected argument 'region' to be a str")
pulumi.set(__self__, "region", region)
@property
@pulumi.getter
def default(self) -> bool:
"""
If is the default network.
"""
return pulumi.get(self, "default")
@property
@pulumi.getter
def id(self) -> Optional[str]:
"""
A unique ID that can be used to identify and reference a Network.
"""
return pulumi.get(self, "id")
@property
@pulumi.getter
def label(self) -> Optional[str]:
"""
The label used in the configuration.
"""
return pulumi.get(self, "label")
@property
@pulumi.getter
def name(self) -> str:
"""
The name of the network.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def region(self) -> Optional[str]:
return pulumi.get(self, "region")
class AwaitableGetNetworkResult(GetNetworkResult):
# pylint: disable=using-constant-test
def __await__(self):
if False:
yield self
return GetNetworkResult(
default=self.default,
id=self.id,
label=self.label,
name=self.name,
region=self.region)
def get_network(id: Optional[str] = None,
label: Optional[str] = None,
region: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetNetworkResult:
"""
Use this data source to access information about an existing resource.
:param str id: The unique identifier of an existing Network.
:param str label: The label of an existing Network.
:param str region: The region of an existing Network.
"""
__args__ = dict()
__args__['id'] = id
__args__['label'] = label
__args__['region'] = region
if opts is None:
opts = pulumi.InvokeOptions()
if opts.version is None:
opts.version = _utilities.get_version()
__ret__ = pulumi.runtime.invoke('civo:index/getNetwork:getNetwork', __args__, opts=opts, typ=GetNetworkResult).value
return AwaitableGetNetworkResult(
default=__ret__.default,
id=__ret__.id,
label=__ret__.label,
name=__ret__.name,
region=__ret__.region)
| [
"pulumi.InvokeOptions",
"pulumi.set",
"pulumi.runtime.invoke",
"pulumi.get"
] | [((755, 795), 'pulumi.set', 'pulumi.set', (['__self__', '"""default"""', 'default'], {}), "(__self__, 'default', default)\n", (765, 795), False, 'import pulumi\n'), ((913, 943), 'pulumi.set', 'pulumi.set', (['__self__', '"""id"""', 'id'], {}), "(__self__, 'id', id)\n", (923, 943), False, 'import pulumi\n'), ((1070, 1106), 'pulumi.set', 'pulumi.set', (['__self__', '"""label"""', 'label'], {}), "(__self__, 'label', label)\n", (1080, 1106), False, 'import pulumi\n'), ((1230, 1264), 'pulumi.set', 'pulumi.set', (['__self__', '"""name"""', 'name'], {}), "(__self__, 'name', name)\n", (1240, 1264), False, 'import pulumi\n'), ((1394, 1432), 'pulumi.set', 'pulumi.set', (['__self__', '"""region"""', 'region'], {}), "(__self__, 'region', region)\n", (1404, 1432), False, 'import pulumi\n'), ((1572, 1599), 'pulumi.get', 'pulumi.get', (['self', '"""default"""'], {}), "(self, 'default')\n", (1582, 1599), False, 'import pulumi\n'), ((1782, 1804), 'pulumi.get', 'pulumi.get', (['self', '"""id"""'], {}), "(self, 'id')\n", (1792, 1804), False, 'import pulumi\n'), ((1961, 1986), 'pulumi.get', 'pulumi.get', (['self', '"""label"""'], {}), "(self, 'label')\n", (1971, 1986), False, 'import pulumi\n'), ((2120, 2144), 'pulumi.get', 'pulumi.get', (['self', '"""name"""'], {}), "(self, 'name')\n", (2130, 2144), False, 'import pulumi\n'), ((2233, 2259), 'pulumi.get', 'pulumi.get', (['self', '"""region"""'], {}), "(self, 'region')\n", (2243, 2259), False, 'import pulumi\n'), ((3243, 3265), 'pulumi.InvokeOptions', 'pulumi.InvokeOptions', ([], {}), '()\n', (3263, 3265), False, 'import pulumi\n'), ((3357, 3462), 'pulumi.runtime.invoke', 'pulumi.runtime.invoke', (['"""civo:index/getNetwork:getNetwork"""', '__args__'], {'opts': 'opts', 'typ': 'GetNetworkResult'}), "('civo:index/getNetwork:getNetwork', __args__, opts=\n opts, typ=GetNetworkResult)\n", (3378, 3462), False, 'import pulumi\n')] |
'''
Flask Application Config
'''
import os
from logging.config import dictConfig
BASEDIR = os.path.abspath(os.path.dirname(__file__))
class Config:
'''공통 Config'''
JWT_SECRET_KEY = os.environ.get('FLASK_JWT_SECRET_KEY')
# test only
TEST_ACCESS_TOKEN = os.environ.get('FLASK_TEST_ACCESS_TOKEN')
ADMIN_ID = os.environ.get('FLASK_ADMIN_ID', "iml")
ADMIN_PW = os.environ.get('FLASK_ADMIN_PW', "iml")
# DB_PROXY: basic, mysql, mongodb, redis, all
DB_PROXY = os.environ.get('FLASK_DB_PROXY')
if DB_PROXY in ['mysql', 'all']:
MYSQL_URI = os.environ.get('FLASK_MYSQL_URI')
if DB_PROXY in ['mongodb', 'all']:
MONGO_URI = os.environ.get('FLASK_MONGO_URI')
MONGO_DB_NAME = os.environ.get('FLASK_MONGO_DB_NAME')
if DB_PROXY == ['reids', 'all']:
REDIS_HOST = os.environ.get('FLASK_REDIS_HOST')
REDIS_PORT = os.environ.get('FLASK_REDIS_PORT')
REDIS_PW = os.environ.get('FLASK_REDIS_PW')
ALLOWED_EXTENSION = {'txt', 'docs', 'md', 'hwp', 'ppt', 'pptx'}
SLOW_API_TIME = 0.5
@staticmethod
def init_app(app):
'''전역 init_app 함수'''
class TestingConfig(Config):
'''Test 전용 Config'''
DEBUG = True
TESTING = True
class DevelopmentConfig(Config):
'''개발 환경 전용 Config'''
DEBUG = True
TESTING = False
class ProductionConfig(Config):
''' 상용환경 전용 Config'''
DEBUG = False
TESTING = False
@staticmethod
def init_app(app):
'''로거 등록 및 설정'''
dictConfig({
'version': 1,
'formatters': {
'default': {
'format': '[%(asctime)s] %(levelname)s in %(module)s: %(message)s',
}
},
'handlers': {
'file': {
'level': 'INFO',
'class': 'logging.handlers.RotatingFileHandler',
'filename': './server_error.log',
'maxBytes': 1024 * 1024 * 5,
'backupCount': 5,
'formatter': 'default',
},
},
'root': {
'level': 'INFO',
'handlers': ['file']
}
})
config = {
'development':DevelopmentConfig,
'production':ProductionConfig,
'testing':TestingConfig,
'default':DevelopmentConfig,
}
| [
"os.path.dirname",
"os.environ.get",
"logging.config.dictConfig"
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from django.db import models
from django.core.validators import RegexValidator, ValidationError
from django.utils.translation import ugettext_lazy as _
from django.contrib.auth.models import User
from smart_selects.db_fields import ChainedForeignKey, ChainedManyToManyField
from ordered_model.models import OrderedModel
# Create your models here.
class Agency(models.Model):
name = models.CharField(max_length=100, blank=False)
abbreviation = models.CharField(max_length=10, null=True, blank=True)
department = models.CharField(max_length=100, null=True, blank=True)
omb_agency_code = models.IntegerField(null=True, blank=True)
omb_bureau_code = models.IntegerField(null=True, blank=True)
treasury_agency_code = models.IntegerField(null=True, blank=True)
cgac_agency_code = models.IntegerField(null=True, blank=True)
def __str__(self):
return self.name
class Meta:
verbose_name_plural = "Agencies"
ordering = ('name',)
class Subagency(models.Model):
name = models.CharField(max_length=100, blank=False)
abbreviation = models.CharField(max_length=10, null=True, blank=True)
agency = models.ForeignKey(Agency)
def __str__(self):
return "%s - %s" % (self.agency, self.name)
class Meta:
ordering = ('name',)
verbose_name_plural = "Subagencies"
class ContractingOffice(models.Model):
name = models.CharField(max_length=100)
def __str__(self):
return self.name
class Meta:
verbose_name = "Contracting Office"
verbose_name_plural = "Contracting Offices"
class ContractingOfficer(models.Model):
name = models.CharField(max_length=100)
contracting_office = models.ForeignKey(ContractingOffice)
def __str__(self):
return "%s - %s" % (self.name, self.contracting_office)
class Meta:
ordering = ('name',)
verbose_name = "Contracting Officer"
verbose_name_plural = "Contracting Officers"
class COR(models.Model):
name = models.CharField(max_length=100)
def __str__(self):
return self.name
class Meta:
ordering = ('name',)
verbose_name = "Contracting Officer Representative"
verbose_name_plural = "Contracting Officer Representatives"
# Is the acquisition internal or external?
class Track(models.Model):
name = models.CharField(max_length=50)
def __str__(self):
return "%s" % (self.name)
class Stage(OrderedModel):
name = models.CharField(max_length=50)
wip_limit = models.IntegerField(default=0, verbose_name="WIP Limit")
def __str__(self):
return "%s" % (self.name)
class Meta(OrderedModel.Meta):
pass
class Actor(models.Model):
name = models.CharField(max_length=200, blank=False)
def __str__(self):
return "%s" % (self.name)
class Step(models.Model):
actor = models.ForeignKey(
Actor,
blank=False
)
track = models.ManyToManyField(
Track,
blank=False,
through="StepTrackThroughModel"
)
stage = models.ForeignKey(
Stage,
blank=False
)
def __str__(self):
return "%s - %s" % (self.stage, self.actor,)
class Meta:
ordering = ('steptrackthroughmodel__order',)
class StepTrackThroughModel(OrderedModel):
track = models.ForeignKey(Track)
step = models.ForeignKey(Step)
wip_limit = models.IntegerField(default=0, verbose_name="WIP Limit")
order_with_respect_to = 'track'
class Meta(OrderedModel.Meta):
unique_together = ('track', 'step')
ordering = ('track', 'order')
class Vendor(models.Model):
name = models.CharField(max_length=200, blank=False)
email = models.EmailField(blank=False)
duns = models.CharField(max_length=9, blank=False, validators=[
RegexValidator(regex='^\d{9}$', message="DUNS number must be 9 digits")
])
def __str__(self):
return self.name
class Role(models.Model):
description = models.CharField(max_length=100, choices=(
('P', 'Product Lead'),
('A', 'Acquisition Lead'),
('T', 'Technical Lead')
), null=True, blank=True)
teammate = models.ForeignKey(User, blank=True, null=True)
def __str__(self):
return "%s - %s" % (self.get_description_display(), self.teammate)
class Acquisition(models.Model):
SET_ASIDE_CHOICES = (
("AbilityOne", "AbilityOne"),
("HUBZone Small Business", "HUBZone Small Business"),
("Multiple Small Business Categories",
"Multiple Small Business Categories"),
("Other Than Small", "Other Than Small"),
("Service Disabled Veteran-owned Small Business",
"Service Disabled Veteran-owned Small Business"),
("Small Business", "Small Business"),
("Small Disadvantaged Business (includes Section 8a)",
"Small Disadvantaged Business (includes Section 8a)"),
("To Be Determined-BPA", "To Be Determined-BPA"),
("To Be Determined-IDIQ", "To Be Determined-IDIQ"),
("Veteran-Owned Small Business", "Veteran-Owned Small Business"),
("Woman-Owned Small Business", "Woman-Owned Small Business"),
)
CONTRACT_TYPE_CHOICES = (
("Cost No Fee", "Cost No Fee"),
("Cost Plus Award Fee", "Cost Plus Award Fee"),
("Cost Plus Fixed Fee", "Cost Plus Fixed Fee"),
("Cost Plus Incentive Fee", "Cost Plus Incentive Fee"),
("Cost Sharing", "Cost Sharing"),
("Fixed Price Award Fee", "Fixed Price Award Fee"),
("Fixed Price Incentive", "Fixed Price Incentive"),
("Fixed Price Labor Hours", "Fixed Price Labor Hours"),
("Fixed Price Level of Effort", "Fixed Price Level of Effort"),
("Fixed Price Time and Materials", "Fixed Price Time and Materials"),
("Fixed Price with Economic Price Adjustment",
"Fixed Price with Economic Price Adjustment"),
("Fixed Price", "Fixed Price"),
("Interagency Agreement", "Interagency Agreement"),
("Labor Hours and Time and Materials",
"Labor Hours and Time and Materials"),
("Labor Hours", "Labor Hours"),
("Order Dependent", "Order Dependent"),
("Time and Materials", "Time and Materials"),
)
COMPETITION_STRATEGY_CHOICES = (
("A/E Procedures", "A/E Procedures"),
("Competed under SAP", "Competed under SAP"),
("Competitive Delivery Order Fair Opportunity Provided",
"Competitive Delivery Order Fair Opportunity Provided"),
("Competitive Schedule Buy", "Competitive Schedule Buy"),
("Fair Opportunity", "Fair Opportunity"),
("Follow On to Competed Action (FAR 6.302-1)",
"Follow On to Competed Action (FAR 6.302-1)"),
("Follow On to Competed Action", "Follow On to Competed Action"),
("Full and Open after exclusion of sources (competitive small business \
set-asides, competitive 8a)",
"Full and Open after exclusion of sources (competitive small \
business set-asides, competitive 8a)"),
("Full and Open Competition Unrestricted",
"Full and Open Competition Unrestricted"),
("Full and Open Competition", "Full and Open Competition"),
("Limited Sources FSS Order", "Limited Sources FSS Order"),
("Limited Sources", "Limited Sources"),
("Non-Competitive Delivery Order", "Non-Competitive Delivery Order"),
("Not Available for Competition (e.g., 8a sole source, HUBZone & \
SDVOSB sole source, Ability One, all > SAT)",
"Not Available for Competition (e.g., 8a sole source, HUBZone & \
SDVOSB sole source, Ability One, all > SAT)"),
("Not Competed (e.g., sole source, urgency, etc., all > SAT)",
"Not Competed (e.g., sole source, urgency, etc., all > SAT)"),
("Not Competed under SAP (e.g., Urgent, Sole source, Logical \
Follow-On, 8a, HUBZone & SDVOSB sole source, all < SAT)",
"Not Competed under SAP (e.g., Urgent, Sole source, Logical \
Follow-On, 8a, HUBZone & SDVOSB sole source, all < SAT)"),
("Partial Small Business Set-Aside",
"Partial Small Business Set-Aside"),
("Set-Aside", "Set-Aside"),
("Sole Source", "Sole Source"),
)
PROCUREMENT_METHOD_CHOICES = (
("Ability One", "Ability One"),
("Basic Ordering Agreement", "Basic Ordering Agreement"),
("Blanket Purchase Agreement-BPA", "Blanket Purchase Agreement-BPA"),
("BPA Call", "BPA Call"),
("Call Order under GSA Schedules BPA",
"Call Order under GSA Schedules BPA"),
("Commercial Item Contract", "Commercial Item Contract"),
("Contract modification", "Contract modification"),
("Contract", "Contract"),
("Definitive Contract other than IDV",
"Definitive Contract other than IDV"),
("Definitive Contract", "Definitive Contract"),
("Government-wide Agency Contract-GWAC",
"Government-wide Agency Contract-GWAC"),
("GSA Schedule Contract", "GSA Schedule Contract"),
("GSA Schedule", "GSA Schedule"),
("GSA Schedules Program BPA", "GSA Schedules Program BPA"),
("Indefinite Delivery Indefinite Quantity-IDIQ",
"Indefinite Delivery Indefinite Quantity-IDIQ"),
("Indefinite Delivery Vehicle (IDV)",
"Indefinite Delivery Vehicle (IDV)"),
("Indefinite Delivery Vehicle Base Contract",
"Indefinite Delivery Vehicle Base Contract"),
("Multi-Agency Contract", "Multi-Agency Contract"),
("Negotiated", "Negotiated"),
("Order under GSA Federal Supply Schedules Program",
"Order under GSA Federal Supply Schedules Program"),
("Order under GSA Schedules Program BPA",
"Order under GSA Schedules Program BPA"),
("Order under GSA Schedules Program",
"Order under GSA Schedules Program"),
("Order under IDV", "Order under IDV"),
("Purchase Order", "Purchase Order"),
("Sealed Bid", "Sealed Bid"),
)
subagency = models.ForeignKey(Subagency)
task = models.CharField(max_length=100, blank=False)
description = models.TextField(max_length=500, null=True, blank=True)
track = models.ForeignKey(
Track,
blank=False,
related_name="%(class)s_track"
)
step = ChainedForeignKey(
Step,
chained_field="track",
chained_model_field="track",
blank=False
)
dollars = models.DecimalField(decimal_places=2, max_digits=14, null=True,
blank=True)
period_of_performance = models.DateField(null=True, blank=True)
product_owner = models.CharField(max_length=50, null=True, blank=True)
roles = models.ManyToManyField(Role, blank=True)
contracting_officer = models.ForeignKey(ContractingOfficer, null=True,
blank=True)
contracting_officer_representative = models.ForeignKey(COR, null=True,
blank=True)
contracting_office = models.ForeignKey(ContractingOffice, null=True,
blank=True)
vendor = models.ForeignKey(Vendor, null=True, blank=True)
rfq_id = models.IntegerField(null=True, blank=True, verbose_name="RFQ ID")
naics = models.IntegerField(
null=True,
blank=True,
verbose_name="NAICS Code"
)
set_aside_status = models.CharField(max_length=100, null=True, blank=True,
choices=SET_ASIDE_CHOICES)
amount_of_competition = models.IntegerField(null=True, blank=True)
contract_type = models.CharField(max_length=100, null=True, blank=True,
choices=CONTRACT_TYPE_CHOICES)
competition_strategy = models.CharField(
max_length=100,
null=True,
blank=True,
choices=COMPETITION_STRATEGY_CHOICES)
procurement_method = models.CharField(
max_length=100,
null=True,
blank=True,
choices=PROCUREMENT_METHOD_CHOICES)
award_date = models.DateField(null=True, blank=True)
delivery_date = models.DateField(null=True, blank=True)
def clean(self):
print(self.step.track.all())
print(self.track)
if self.track not in self.step.track.all():
raise ValidationError(_('Tracks are not equal.'))
def __str__(self):
return "%s (%s)" % (self.task, self.subagency)
class Evaluator(models.Model):
name = models.CharField(max_length=100)
acquisition = models.ManyToManyField(Acquisition)
def __str__(self):
return self.name
class Meta:
ordering = ('name',)
class Release(models.Model):
acquisition = models.ForeignKey(Acquisition)
def __str__(self):
return self.id
class Meta:
ordering = ('id',)
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"(null=True, blank=True, verbose_name='RFQ ID')\n", (11479, 11525), False, 'from django.db import models\n'), ((11538, 11607), 'django.db.models.IntegerField', 'models.IntegerField', ([], {'null': '(True)', 'blank': '(True)', 'verbose_name': '"""NAICS Code"""'}), "(null=True, blank=True, verbose_name='NAICS Code')\n", (11557, 11607), False, 'from django.db import models\n'), ((11661, 11748), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(100)', 'null': '(True)', 'blank': '(True)', 'choices': 'SET_ASIDE_CHOICES'}), '(max_length=100, null=True, blank=True, choices=\n SET_ASIDE_CHOICES)\n', (11677, 11748), False, 'from django.db import models\n'), ((11812, 11854), 'django.db.models.IntegerField', 'models.IntegerField', ([], {'null': '(True)', 'blank': '(True)'}), '(null=True, blank=True)\n', (11831, 11854), False, 'from django.db import models\n'), ((11875, 11966), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(100)', 'null': '(True)', 'blank': '(True)', 'choices': 'CONTRACT_TYPE_CHOICES'}), '(max_length=100, null=True, blank=True, choices=\n CONTRACT_TYPE_CHOICES)\n', (11891, 11966), False, 'from django.db import models\n'), ((12026, 12124), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(100)', 'null': '(True)', 'blank': '(True)', 'choices': 'COMPETITION_STRATEGY_CHOICES'}), '(max_length=100, null=True, blank=True, choices=\n COMPETITION_STRATEGY_CHOICES)\n', (12042, 12124), False, 'from django.db import models\n'), ((12194, 12290), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(100)', 'null': '(True)', 'blank': '(True)', 'choices': 'PROCUREMENT_METHOD_CHOICES'}), '(max_length=100, null=True, blank=True, choices=\n PROCUREMENT_METHOD_CHOICES)\n', (12210, 12290), False, 'from django.db import models\n'), ((12352, 12391), 'django.db.models.DateField', 'models.DateField', ([], {'null': '(True)', 'blank': '(True)'}), '(null=True, blank=True)\n', (12368, 12391), False, 'from django.db import models\n'), ((12412, 12451), 'django.db.models.DateField', 'models.DateField', ([], {'null': '(True)', 'blank': '(True)'}), '(null=True, blank=True)\n', (12428, 12451), False, 'from django.db import models\n'), ((12774, 12806), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(100)'}), '(max_length=100)\n', (12790, 12806), False, 'from django.db import models\n'), ((12825, 12860), 'django.db.models.ManyToManyField', 'models.ManyToManyField', (['Acquisition'], {}), '(Acquisition)\n', (12847, 12860), False, 'from django.db import models\n'), ((13005, 13035), 'django.db.models.ForeignKey', 'models.ForeignKey', (['Acquisition'], {}), '(Acquisition)\n', (13022, 13035), False, 'from django.db import models\n'), ((3829, 3901), 'django.core.validators.RegexValidator', 'RegexValidator', ([], {'regex': '"""^\\\\d{9}$"""', 'message': '"""DUNS number must be 9 digits"""'}), "(regex='^\\\\d{9}$', message='DUNS number must be 9 digits')\n", (3843, 3901), False, 'from django.core.validators import RegexValidator, ValidationError\n'), ((12623, 12649), 'django.utils.translation.ugettext_lazy', '_', (['"""Tracks are not equal."""'], {}), "('Tracks are not equal.')\n", (12624, 12649), True, 'from django.utils.translation import ugettext_lazy as _\n')] |
from django import forms
from .models import Connection, KoboUser, KoboData
from django.contrib.admin.widgets import FilteredSelectMultiple
from django.db.models import Q
class ConnectionForm(forms.ModelForm):
class Meta:
model = Connection
exclude = []
widgets = {
'auth_pass': forms.PasswordInput(),
}
class KoboUserForm(forms.ModelForm):
class Meta:
model = KoboUser
exclude = []
surveys = forms.ModelMultipleChoiceField(queryset=KoboData.objects.filter(Q(tags__contains=['bns']) | Q(tags__contains=['nrgt'])), widget=FilteredSelectMultiple(
'Surveys', is_stacked=False), label='')
| [
"django.contrib.admin.widgets.FilteredSelectMultiple",
"django.db.models.Q",
"django.forms.PasswordInput"
] | [((321, 342), 'django.forms.PasswordInput', 'forms.PasswordInput', ([], {}), '()\n', (340, 342), False, 'from django import forms\n'), ((602, 653), 'django.contrib.admin.widgets.FilteredSelectMultiple', 'FilteredSelectMultiple', (['"""Surveys"""'], {'is_stacked': '(False)'}), "('Surveys', is_stacked=False)\n", (624, 653), False, 'from django.contrib.admin.widgets import FilteredSelectMultiple\n'), ((538, 563), 'django.db.models.Q', 'Q', ([], {'tags__contains': "['bns']"}), "(tags__contains=['bns'])\n", (539, 563), False, 'from django.db.models import Q\n'), ((566, 592), 'django.db.models.Q', 'Q', ([], {'tags__contains': "['nrgt']"}), "(tags__contains=['nrgt'])\n", (567, 592), False, 'from django.db.models import Q\n')] |
from operator import attrgetter
import pyangbind.lib.xpathhelper as xpathhelper
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType
from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType
from pyangbind.lib.base import PybindBase
from decimal import Decimal
from bitarray import bitarray
import __builtin__
import hops
class lsp(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module brocade-mpls-operational - based on the path /mpls-state/rsvp/igp-sync/link/lsp. Each member element of
the container is represented as a class variable - with a specific
YANG type.
"""
__slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__lsp_name','__lsp_instance_id','__path_name','__cspf_enabled','__rro_enabled','__frr_enabled','__nbr_down_enabled','__link_count','__nbr_down_inprogress','__cspf_hop_count','__rro_hop_count','__hops',)
_yang_name = 'lsp'
_rest_name = 'lsp'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
path_helper_ = kwargs.pop("path_helper", None)
if path_helper_ is False:
self._path_helper = False
elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper):
self._path_helper = path_helper_
elif hasattr(self, "_parent"):
path_helper_ = getattr(self._parent, "_path_helper", False)
self._path_helper = path_helper_
else:
self._path_helper = False
extmethods = kwargs.pop("extmethods", None)
if extmethods is False:
self._extmethods = False
elif extmethods is not None and isinstance(extmethods, dict):
self._extmethods = extmethods
elif hasattr(self, "_parent"):
extmethods = getattr(self._parent, "_extmethods", None)
self._extmethods = extmethods
else:
self._extmethods = False
self.__path_name = YANGDynClass(base=unicode, is_leaf=True, yang_name="path-name", rest_name="path-name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='string', is_config=False)
self.__cspf_hop_count = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="cspf-hop-count", rest_name="cspf-hop-count", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
self.__hops = YANGDynClass(base=YANGListType("index hop_type",hops.hops, yang_name="hops", rest_name="hops", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='index hop-type', extensions={u'tailf-common': {u'callpoint': u'mpls-rsvp-igp-sync-hop-data', u'cli-suppress-show-path': None}}), is_container='list', yang_name="hops", rest_name="hops", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-rsvp-igp-sync-hop-data', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='list', is_config=False)
self.__lsp_name = YANGDynClass(base=unicode, is_leaf=True, yang_name="lsp-name", rest_name="lsp-name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='string', is_config=False)
self.__nbr_down_enabled = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="nbr-down-enabled", rest_name="nbr-down-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
self.__rro_enabled = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="rro-enabled", rest_name="rro-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
self.__cspf_enabled = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="cspf-enabled", rest_name="cspf-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
self.__nbr_down_inprogress = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="nbr-down-inprogress", rest_name="nbr-down-inprogress", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
self.__lsp_instance_id = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="lsp-instance-id", rest_name="lsp-instance-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
self.__rro_hop_count = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="rro-hop-count", rest_name="rro-hop-count", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
self.__frr_enabled = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="frr-enabled", rest_name="frr-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
self.__link_count = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="link-count", rest_name="link-count", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return [u'mpls-state', u'rsvp', u'igp-sync', u'link', u'lsp']
def _rest_path(self):
if hasattr(self, "_parent"):
if self._rest_name:
return self._parent._rest_path()+[self._rest_name]
else:
return self._parent._rest_path()
else:
return [u'mpls-state', u'rsvp', u'igp-sync', u'link', u'lsp']
def _get_lsp_name(self):
"""
Getter method for lsp_name, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/lsp_name (string)
YANG Description: LSP name
"""
return self.__lsp_name
def _set_lsp_name(self, v, load=False):
"""
Setter method for lsp_name, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/lsp_name (string)
If this variable is read-only (config: false) in the
source YANG file, then _set_lsp_name is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_lsp_name() directly.
YANG Description: LSP name
"""
parent = getattr(self, "_parent", None)
if parent is not None and load is False:
raise AttributeError("Cannot set keys directly when" +
" within an instantiated list")
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name="lsp-name", rest_name="lsp-name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='string', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """lsp_name must be of a type compatible with string""",
'defined-type': "string",
'generated-type': """YANGDynClass(base=unicode, is_leaf=True, yang_name="lsp-name", rest_name="lsp-name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='string', is_config=False)""",
})
self.__lsp_name = t
if hasattr(self, '_set'):
self._set()
def _unset_lsp_name(self):
self.__lsp_name = YANGDynClass(base=unicode, is_leaf=True, yang_name="lsp-name", rest_name="lsp-name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='string', is_config=False)
def _get_lsp_instance_id(self):
"""
Getter method for lsp_instance_id, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/lsp_instance_id (uint32)
YANG Description: Instance id of the lsp instance
"""
return self.__lsp_instance_id
def _set_lsp_instance_id(self, v, load=False):
"""
Setter method for lsp_instance_id, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/lsp_instance_id (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_lsp_instance_id is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_lsp_instance_id() directly.
YANG Description: Instance id of the lsp instance
"""
parent = getattr(self, "_parent", None)
if parent is not None and load is False:
raise AttributeError("Cannot set keys directly when" +
" within an instantiated list")
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="lsp-instance-id", rest_name="lsp-instance-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """lsp_instance_id must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="lsp-instance-id", rest_name="lsp-instance-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__lsp_instance_id = t
if hasattr(self, '_set'):
self._set()
def _unset_lsp_instance_id(self):
self.__lsp_instance_id = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="lsp-instance-id", rest_name="lsp-instance-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_path_name(self):
"""
Getter method for path_name, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/path_name (string)
YANG Description: LSP Path name
"""
return self.__path_name
def _set_path_name(self, v, load=False):
"""
Setter method for path_name, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/path_name (string)
If this variable is read-only (config: false) in the
source YANG file, then _set_path_name is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_path_name() directly.
YANG Description: LSP Path name
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name="path-name", rest_name="path-name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='string', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """path_name must be of a type compatible with string""",
'defined-type': "string",
'generated-type': """YANGDynClass(base=unicode, is_leaf=True, yang_name="path-name", rest_name="path-name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='string', is_config=False)""",
})
self.__path_name = t
if hasattr(self, '_set'):
self._set()
def _unset_path_name(self):
self.__path_name = YANGDynClass(base=unicode, is_leaf=True, yang_name="path-name", rest_name="path-name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='string', is_config=False)
def _get_cspf_enabled(self):
"""
Getter method for cspf_enabled, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/cspf_enabled (boolean)
YANG Description: CSPF enabled for LSP
"""
return self.__cspf_enabled
def _set_cspf_enabled(self, v, load=False):
"""
Setter method for cspf_enabled, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/cspf_enabled (boolean)
If this variable is read-only (config: false) in the
source YANG file, then _set_cspf_enabled is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_cspf_enabled() directly.
YANG Description: CSPF enabled for LSP
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="cspf-enabled", rest_name="cspf-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """cspf_enabled must be of a type compatible with boolean""",
'defined-type': "boolean",
'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="cspf-enabled", rest_name="cspf-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)""",
})
self.__cspf_enabled = t
if hasattr(self, '_set'):
self._set()
def _unset_cspf_enabled(self):
self.__cspf_enabled = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="cspf-enabled", rest_name="cspf-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
def _get_rro_enabled(self):
"""
Getter method for rro_enabled, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/rro_enabled (boolean)
YANG Description: RRO enabled for LSP
"""
return self.__rro_enabled
def _set_rro_enabled(self, v, load=False):
"""
Setter method for rro_enabled, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/rro_enabled (boolean)
If this variable is read-only (config: false) in the
source YANG file, then _set_rro_enabled is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_rro_enabled() directly.
YANG Description: RRO enabled for LSP
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="rro-enabled", rest_name="rro-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """rro_enabled must be of a type compatible with boolean""",
'defined-type': "boolean",
'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="rro-enabled", rest_name="rro-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)""",
})
self.__rro_enabled = t
if hasattr(self, '_set'):
self._set()
def _unset_rro_enabled(self):
self.__rro_enabled = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="rro-enabled", rest_name="rro-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
def _get_frr_enabled(self):
"""
Getter method for frr_enabled, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/frr_enabled (boolean)
YANG Description: FRR enabled for LSP
"""
return self.__frr_enabled
def _set_frr_enabled(self, v, load=False):
"""
Setter method for frr_enabled, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/frr_enabled (boolean)
If this variable is read-only (config: false) in the
source YANG file, then _set_frr_enabled is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_frr_enabled() directly.
YANG Description: FRR enabled for LSP
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="frr-enabled", rest_name="frr-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """frr_enabled must be of a type compatible with boolean""",
'defined-type': "boolean",
'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="frr-enabled", rest_name="frr-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)""",
})
self.__frr_enabled = t
if hasattr(self, '_set'):
self._set()
def _unset_frr_enabled(self):
self.__frr_enabled = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="frr-enabled", rest_name="frr-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
def _get_nbr_down_enabled(self):
"""
Getter method for nbr_down_enabled, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/nbr_down_enabled (boolean)
YANG Description: LSP Neighbour down is enabled
"""
return self.__nbr_down_enabled
def _set_nbr_down_enabled(self, v, load=False):
"""
Setter method for nbr_down_enabled, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/nbr_down_enabled (boolean)
If this variable is read-only (config: false) in the
source YANG file, then _set_nbr_down_enabled is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_nbr_down_enabled() directly.
YANG Description: LSP Neighbour down is enabled
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="nbr-down-enabled", rest_name="nbr-down-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """nbr_down_enabled must be of a type compatible with boolean""",
'defined-type': "boolean",
'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="nbr-down-enabled", rest_name="nbr-down-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)""",
})
self.__nbr_down_enabled = t
if hasattr(self, '_set'):
self._set()
def _unset_nbr_down_enabled(self):
self.__nbr_down_enabled = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="nbr-down-enabled", rest_name="nbr-down-enabled", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
def _get_link_count(self):
"""
Getter method for link_count, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/link_count (uint32)
YANG Description: Total links used by the LSP
"""
return self.__link_count
def _set_link_count(self, v, load=False):
"""
Setter method for link_count, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/link_count (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_link_count is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_link_count() directly.
YANG Description: Total links used by the LSP
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="link-count", rest_name="link-count", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """link_count must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="link-count", rest_name="link-count", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__link_count = t
if hasattr(self, '_set'):
self._set()
def _unset_link_count(self):
self.__link_count = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="link-count", rest_name="link-count", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_nbr_down_inprogress(self):
"""
Getter method for nbr_down_inprogress, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/nbr_down_inprogress (boolean)
YANG Description: Neighbor down processing is in progress
"""
return self.__nbr_down_inprogress
def _set_nbr_down_inprogress(self, v, load=False):
"""
Setter method for nbr_down_inprogress, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/nbr_down_inprogress (boolean)
If this variable is read-only (config: false) in the
source YANG file, then _set_nbr_down_inprogress is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_nbr_down_inprogress() directly.
YANG Description: Neighbor down processing is in progress
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="nbr-down-inprogress", rest_name="nbr-down-inprogress", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """nbr_down_inprogress must be of a type compatible with boolean""",
'defined-type': "boolean",
'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="nbr-down-inprogress", rest_name="nbr-down-inprogress", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)""",
})
self.__nbr_down_inprogress = t
if hasattr(self, '_set'):
self._set()
def _unset_nbr_down_inprogress(self):
self.__nbr_down_inprogress = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="nbr-down-inprogress", rest_name="nbr-down-inprogress", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)
def _get_cspf_hop_count(self):
"""
Getter method for cspf_hop_count, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/cspf_hop_count (uint32)
YANG Description: CSPF hop count
"""
return self.__cspf_hop_count
def _set_cspf_hop_count(self, v, load=False):
"""
Setter method for cspf_hop_count, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/cspf_hop_count (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_cspf_hop_count is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_cspf_hop_count() directly.
YANG Description: CSPF hop count
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="cspf-hop-count", rest_name="cspf-hop-count", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """cspf_hop_count must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="cspf-hop-count", rest_name="cspf-hop-count", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__cspf_hop_count = t
if hasattr(self, '_set'):
self._set()
def _unset_cspf_hop_count(self):
self.__cspf_hop_count = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="cspf-hop-count", rest_name="cspf-hop-count", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_rro_hop_count(self):
"""
Getter method for rro_hop_count, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/rro_hop_count (uint32)
YANG Description: RRO hop rout
"""
return self.__rro_hop_count
def _set_rro_hop_count(self, v, load=False):
"""
Setter method for rro_hop_count, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/rro_hop_count (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_rro_hop_count is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_rro_hop_count() directly.
YANG Description: RRO hop rout
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="rro-hop-count", rest_name="rro-hop-count", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """rro_hop_count must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="rro-hop-count", rest_name="rro-hop-count", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""",
})
self.__rro_hop_count = t
if hasattr(self, '_set'):
self._set()
def _unset_rro_hop_count(self):
self.__rro_hop_count = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="rro-hop-count", rest_name="rro-hop-count", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)
def _get_hops(self):
"""
Getter method for hops, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/hops (list)
YANG Description: MPLS Rsvp IGP Synchronization Hop information
"""
return self.__hops
def _set_hops(self, v, load=False):
"""
Setter method for hops, mapped from YANG variable /mpls_state/rsvp/igp_sync/link/lsp/hops (list)
If this variable is read-only (config: false) in the
source YANG file, then _set_hops is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_hops() directly.
YANG Description: MPLS Rsvp IGP Synchronization Hop information
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=YANGListType("index hop_type",hops.hops, yang_name="hops", rest_name="hops", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='index hop-type', extensions={u'tailf-common': {u'callpoint': u'mpls-rsvp-igp-sync-hop-data', u'cli-suppress-show-path': None}}), is_container='list', yang_name="hops", rest_name="hops", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-rsvp-igp-sync-hop-data', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='list', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """hops must be of a type compatible with list""",
'defined-type': "list",
'generated-type': """YANGDynClass(base=YANGListType("index hop_type",hops.hops, yang_name="hops", rest_name="hops", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='index hop-type', extensions={u'tailf-common': {u'callpoint': u'mpls-rsvp-igp-sync-hop-data', u'cli-suppress-show-path': None}}), is_container='list', yang_name="hops", rest_name="hops", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-rsvp-igp-sync-hop-data', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='list', is_config=False)""",
})
self.__hops = t
if hasattr(self, '_set'):
self._set()
def _unset_hops(self):
self.__hops = YANGDynClass(base=YANGListType("index hop_type",hops.hops, yang_name="hops", rest_name="hops", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='index hop-type', extensions={u'tailf-common': {u'callpoint': u'mpls-rsvp-igp-sync-hop-data', u'cli-suppress-show-path': None}}), is_container='list', yang_name="hops", rest_name="hops", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'mpls-rsvp-igp-sync-hop-data', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='list', is_config=False)
lsp_name = __builtin__.property(_get_lsp_name)
lsp_instance_id = __builtin__.property(_get_lsp_instance_id)
path_name = __builtin__.property(_get_path_name)
cspf_enabled = __builtin__.property(_get_cspf_enabled)
rro_enabled = __builtin__.property(_get_rro_enabled)
frr_enabled = __builtin__.property(_get_frr_enabled)
nbr_down_enabled = __builtin__.property(_get_nbr_down_enabled)
link_count = __builtin__.property(_get_link_count)
nbr_down_inprogress = __builtin__.property(_get_nbr_down_inprogress)
cspf_hop_count = __builtin__.property(_get_cspf_hop_count)
rro_hop_count = __builtin__.property(_get_rro_hop_count)
hops = __builtin__.property(_get_hops)
_pyangbind_elements = {'lsp_name': lsp_name, 'lsp_instance_id': lsp_instance_id, 'path_name': path_name, 'cspf_enabled': cspf_enabled, 'rro_enabled': rro_enabled, 'frr_enabled': frr_enabled, 'nbr_down_enabled': nbr_down_enabled, 'link_count': link_count, 'nbr_down_inprogress': nbr_down_inprogress, 'cspf_hop_count': cspf_hop_count, 'rro_hop_count': rro_hop_count, 'hops': hops, }
| [
"pyangbind.lib.yangtypes.YANGDynClass",
"__builtin__.property",
"pyangbind.lib.yangtypes.RestrictedClassType",
"pyangbind.lib.yangtypes.YANGListType"
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# -*- coding:utf-8 -*-
# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Data parallel callback."""
import logging
import vega
from vega.common import ClassFactory, ClassType
from .callback import Callback
logger = logging.getLogger(__name__)
@ClassFactory.register(ClassType.CALLBACK)
class Horovod(Callback):
"""Callback that saves the evaluated Performance."""
def __init__(self):
"""Initialize ModelCheckpoint callback."""
super(Horovod, self).__init__()
self.priority = 260
def before_train(self, logs=None):
"""Be called before the training process."""
if not self.trainer.horovod:
return
if vega.is_torch_backend():
self._init_torch()
def _init_torch(self):
import torch
import horovod.torch as hvd
hvd.broadcast_parameters(self.trainer.model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(self.trainer.optimizer, root_rank=0)
self.trainer._average_metrics = self._average_metrics
def _average_metrics(self, metrics_results):
import torch
import horovod.torch as hvd
for key, value in metrics_results.items():
tensor = torch.tensor(value)
avg_tensor = hvd.allreduce(tensor, name=key)
metrics_results[key] = avg_tensor.item()
return metrics_results
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"horovod.torch.allreduce",
"torch.tensor",
"vega.common.ClassFactory.register",
"vega.is_torch_backend"
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"""
<NAME>
Skeleton of https://github.com/pyhoneybot/honeybot/
"""
import time
import os
import socket
directory = "irc"
if not os.path.exists(directory):
os.makedirs(directory)
target = open(os.path.join(directory, "log.txt"), "w")
def message_checker(msgLine):
sendvar = ""
global mute
mute = False
completeLine = str(msgLine[1:]).replace("'b", "").split(":", 1)
info = completeLine[0].split()
message = (completeLine[1].split("\\r")[0]).replace("'b", "")
sender = info[0][2:].split("!", 1)[0]
refinedmsg = str(message.lower())
refinedmsgl = len(refinedmsg)
print("Complete Line-->" + str(completeLine))
print("Info-->" + str(info))
print("Message-->" + str(message))
print("Sender-->" + str(sender) + "\n")
def ping_checker(pingLine):
if pingLine.find(bytes("PING", "utf8")) != -1:
pingLine = pingLine.rstrip().split()
if pingLine[0] == bytes("PING", "utf8"):
irc.send(bytes("PONG ", "utf8") + pingLine[1] + bytes("\r\n", "utf8"))
BOT_IRC_SERVER = "chat.freenode.net"
BOT_IRC_CHANNEL = "##bottestingmu"
# BOT_IRC_CHANNEL = "#python"
BOT_IRC_PORT = 6667
BOT_NICKNAME = "appinventormuBot"
# BOT_PASSWORD = ''
irc = socket.socket()
irc.connect((BOT_IRC_SERVER, BOT_IRC_PORT))
irc.recv(4096)
irc.send(bytes("NICK " + BOT_NICKNAME + "\r\n", "utf8"))
ping_checker(irc.recv(4096))
irc.send(
bytes(
"USER appinventormuBot appinventormuBot appinventormuBot : appinventormuBot IRC\r\n",
"utf8",
)
)
ping_checker(irc.recv(4096))
# irc.send(bytes('msg NickServ identify ' + BOT_PASSWORD + " \r\n" ,'utf8') )
# ping_checker(irc.recv(4096))
# irc.send(bytes('NICKSERV identify ' + BOT_NICKNAME+' '+BOT_PASSWORD+ '\r\n','utf8' ) )
# ping_checker(irc.recv(4096))
time.sleep(3)
irc.send(bytes("JOIN " + BOT_IRC_CHANNEL + "\r\n", "utf8"))
while 1:
pass
line = irc.recv(4096)
print(line)
ping_checker(line)
if (
line.find(bytes("PRIVMSG", "utf8")) != -1
or line.find(bytes("NOTICE", "utf8")) != -1
):
message_checker(line)
target.write(str(line))
target.flush()
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"os.path.exists",
"os.makedirs",
"socket.socket",
"os.path.join",
"time.sleep"
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# coding: utf-8
"""
Xenia Python Client Library
Python Client Library to interact with the Xenia API. # noqa: E501
The version of the OpenAPI document: v2.1
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import six
from xenia_python_client_library.configuration import Configuration
class AttachmentsList(object):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
"""
Attributes:
openapi_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
openapi_types = {
'id': 'str',
'source_name': 'str',
'destination_name': 'str',
'mapping': 'list[AttachmentFieldsList]'
}
attribute_map = {
'id': 'id',
'source_name': 'source_name',
'destination_name': 'destination_name',
'mapping': 'mapping'
}
def __init__(self, id=None, source_name=None, destination_name=None, mapping=None, local_vars_configuration=None): # noqa: E501
"""AttachmentsList - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._id = None
self._source_name = None
self._destination_name = None
self._mapping = None
self.discriminator = None
if id is not None:
self.id = id
if source_name is not None:
self.source_name = source_name
if destination_name is not None:
self.destination_name = destination_name
if mapping is not None:
self.mapping = mapping
@property
def id(self):
"""Gets the id of this AttachmentsList. # noqa: E501
:return: The id of this AttachmentsList. # noqa: E501
:rtype: str
"""
return self._id
@id.setter
def id(self, id):
"""Sets the id of this AttachmentsList.
:param id: The id of this AttachmentsList. # noqa: E501
:type: str
"""
self._id = id
@property
def source_name(self):
"""Gets the source_name of this AttachmentsList. # noqa: E501
:return: The source_name of this AttachmentsList. # noqa: E501
:rtype: str
"""
return self._source_name
@source_name.setter
def source_name(self, source_name):
"""Sets the source_name of this AttachmentsList.
:param source_name: The source_name of this AttachmentsList. # noqa: E501
:type: str
"""
self._source_name = source_name
@property
def destination_name(self):
"""Gets the destination_name of this AttachmentsList. # noqa: E501
:return: The destination_name of this AttachmentsList. # noqa: E501
:rtype: str
"""
return self._destination_name
@destination_name.setter
def destination_name(self, destination_name):
"""Sets the destination_name of this AttachmentsList.
:param destination_name: The destination_name of this AttachmentsList. # noqa: E501
:type: str
"""
self._destination_name = destination_name
@property
def mapping(self):
"""Gets the mapping of this AttachmentsList. # noqa: E501
:return: The mapping of this AttachmentsList. # noqa: E501
:rtype: list[AttachmentFieldsList]
"""
return self._mapping
@mapping.setter
def mapping(self, mapping):
"""Sets the mapping of this AttachmentsList.
:param mapping: The mapping of this AttachmentsList. # noqa: E501
:type: list[AttachmentFieldsList]
"""
self._mapping = mapping
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, AttachmentsList):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, AttachmentsList):
return True
return self.to_dict() != other.to_dict()
| [
"six.iteritems",
"xenia_python_client_library.configuration.Configuration"
] | [((4145, 4178), 'six.iteritems', 'six.iteritems', (['self.openapi_types'], {}), '(self.openapi_types)\n', (4158, 4178), False, 'import six\n'), ((1417, 1432), 'xenia_python_client_library.configuration.Configuration', 'Configuration', ([], {}), '()\n', (1430, 1432), False, 'from xenia_python_client_library.configuration import Configuration\n')] |
################################################################################
# Create a Registration with the UI for a Role.
# Each module's aushadha.py is screened for this
#
# Each Class is registered for a Role in UI
# These can be used to generate Role based UI elements later.
#
# As of now string base role assignement is done.
# This can be later extended to class based role
################################################################################
from .models import Chapter, Section,Diagnosis
from AuShadha.apps.ui.ui import ui as UI
UI.register('RegistryApp',Chapter )
UI.register('DiseaseCodes',Chapter)
UI.register('ReferenceApp',Chapter)
| [
"AuShadha.apps.ui.ui.ui.register"
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from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, Flatten, Dense, Dropout
import tensorflow.keras as keras
import os
import cv2
import numpy as np
from sklearn.model_selection import train_test_split
def data_prep(path, img_rows, img_cols, color):
"""
A function to preprocess the input data for a CNN.
The images are resized, normalised to have pixel values between 0-1, converted into greyscale if required and put into a numpy array.
Each class label is turned into a one hot pixel array and added to an ordered numpy array such that the order for the labels is the same as the images.
The data is shuffled to make sure each batch is representative of the overall data during training which will reduce overfitting to each batch.
This function requires that the images for each class are in a seperate directory.
param:
- path, a string of the path to the directory containing the images
- img_rows, an integer for the number of rows the resized image should have
- img_cols, an integer for the number of columns the resized image should have
- color, a boolean that is set to true if the image should be in RGB colour space or false for greyscale
return:
- images, a numpy array of images with pixel values normalised to be between 0 and 1.
numpy array dimensions are [number of images, number of rows, number of columns, number of chanels]
- labels, a numpy array of labels associated with each image (labels are a one hot pixel numpy array [1, 0, 0, ...] or [0, 1, 0, ...], etc)
"""
images = []
labels = []
for image_class in os.listdir(path):
print('image_class =', image_class)
path_to_class_directory = os.path.join(path, image_class)
for img_name in os.listdir(path_to_class_directory):
true_path = os.path.join(path_to_class_directory, img_name)
if color:
images.append(cv2.imread(true_path, 1)/255.0)
else:
images.append(cv2.imread(true_path, 0)/255.0) # greyscale
labels.append(os.listdir(path).index(image_class))
data = list(zip(images, labels))
np.random.shuffle(data)
images, labels = zip(*data)
images = [cv2.resize(img, (img_rows, img_cols), cv2.INTER_AREA) for img in images] # resize images to all be the same
if color:
images = np.array(images).reshape(len(images), img_rows, img_cols, 3)
else:
images = np.array(images).reshape(len(images), img_rows, img_cols, 1)
labels = keras.utils.to_categorical(labels, num_classes=len(os.listdir(path)))
return images, labels
def build_CNN(img_rows, img_cols, color=False):
model = Sequential()
if color:
model.add(Conv2D(20, kernel_size=(3, 3), strides=1, activation='relu', input_shape=(img_rows, img_cols, 3)))
else:
model.add(Conv2D(20, kernel_size=(3, 3), strides=1, activation='relu', input_shape=(img_rows, img_cols, 1)))
model.add(Conv2D(20, kernel_size=(3, 3), strides=1, activation='relu'))
model.add(Flatten())
#model.add(Dropout(0.25))
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy, optimizer='adam', metrics=['accuracy'])
return model
def decode_labels(coded, class_names):
"""
A funtion to get the name of the class by decoding a one hot pixel array.
Uses a list comprehension and boolean indexing.
The list comprehension returns the index of the variable with the highest value in each one hot pixel array.
That list is then used for boolean indexing with a numpy array to get a list of class_names for each label in coded.
Param:
- coded, a numpy array of coded labels
- class_names, a list of the class_names in the same order they were coded (alphabetical)
Return:
- numpy array of class names for each label in coded
"""
return np.array(class_names)[[np.argmax(example) for example in coded]]
def calc_accuracy(pred, real):
"""
A function to calculate the accuracy of a CNN when given a list of predicted classes and a list of the real classes
Param:
- pred, a numpy array of predicted classes
- real, a numpy array of the real classes
Return:
- Accuracy as a decimal
"""
return sum(pred==real) / len(pred)
if __name__ == '__main__':
path = 'data'
img_rows = 150
img_cols = 150
is_color = True
model_filename = 'flare_cnn'
print('\nloading training data\n')
num_classes = len(os.listdir(path))
x, y = data_prep(path, img_rows, img_cols, color=is_color)
x_train, x_test, y_train, y_test = train_test_split(x, y)
print('\nbuilding model\n')
cnn = build_CNN(img_rows, img_cols, color=is_color)
print('\ntraining model\n')
cnn.fit(x_train, y_train, batch_size=50, epochs=1, validation_split=0.2)
print('\nsaving model\n')
if is_color:
model_filename = model_filename + '_RGB' + '.h5'
else:
model_filename = model_filename + '_grey' + '.h5'
cnn.save(model_filename)
print('\nsaved model to file {}\n'.format(model_filename))
print('\nloading model\n')
loaded_cnn = keras.models.load_model(model_filename)
print('\ngenerating predictions\n')
predictions = loaded_cnn.predict(x_test)
dec_preds = decode_labels(predictions, os.listdir(path))
dec_ytest = decode_labels(y_test, os.listdir(path))
# F1 score would probably be a better metric due to skew of training expample (num B > num C)
print('\naccuracy =', calc_accuracy(dec_preds, dec_ytest)) | [
"os.listdir",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.Sequential",
"sklearn.model_selection.train_test_split",
"os.path.join",
"numpy.argmax",
"numpy.array",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.models.load_model",
"tensorflow.keras.layers.Flatten",
"cv2.resize",
"cv2... | [((1704, 1720), 'os.listdir', 'os.listdir', (['path'], {}), '(path)\n', (1714, 1720), False, 'import os\n'), ((2245, 2268), 'numpy.random.shuffle', 'np.random.shuffle', (['data'], {}), '(data)\n', (2262, 2268), True, 'import numpy as np\n'), ((2773, 2785), 'tensorflow.keras.Sequential', 'Sequential', ([], {}), '()\n', (2783, 2785), False, 'from tensorflow.keras import Sequential\n'), ((4835, 4857), 'sklearn.model_selection.train_test_split', 'train_test_split', (['x', 'y'], {}), '(x, y)\n', (4851, 4857), False, 'from sklearn.model_selection import train_test_split\n'), ((5379, 5418), 'tensorflow.keras.models.load_model', 'keras.models.load_model', (['model_filename'], {}), '(model_filename)\n', (5402, 5418), True, 'import tensorflow.keras as keras\n'), ((1800, 1831), 'os.path.join', 'os.path.join', (['path', 'image_class'], {}), '(path, image_class)\n', (1812, 1831), False, 'import os\n'), ((1856, 1891), 'os.listdir', 'os.listdir', (['path_to_class_directory'], {}), '(path_to_class_directory)\n', (1866, 1891), False, 'import os\n'), ((2315, 2368), 'cv2.resize', 'cv2.resize', (['img', '(img_rows, img_cols)', 'cv2.INTER_AREA'], {}), '(img, (img_rows, img_cols), cv2.INTER_AREA)\n', (2325, 2368), False, 'import cv2\n'), ((3058, 3118), 'tensorflow.keras.layers.Conv2D', 'Conv2D', (['(20)'], {'kernel_size': '(3, 3)', 'strides': '(1)', 'activation': '"""relu"""'}), "(20, kernel_size=(3, 3), strides=1, activation='relu')\n", (3064, 3118), False, 'from tensorflow.keras.layers import Conv2D, Flatten, Dense, Dropout\n'), ((3134, 3143), 'tensorflow.keras.layers.Flatten', 'Flatten', ([], {}), '()\n', (3141, 3143), False, 'from tensorflow.keras.layers import Conv2D, Flatten, Dense, Dropout\n'), ((3189, 3218), 'tensorflow.keras.layers.Dense', 'Dense', (['(128)'], {'activation': '"""relu"""'}), "(128, activation='relu')\n", (3194, 3218), False, 'from tensorflow.keras.layers import Conv2D, Flatten, Dense, Dropout\n'), ((3234, 3274), 'tensorflow.keras.layers.Dense', 'Dense', (['num_classes'], {'activation': '"""softmax"""'}), "(num_classes, activation='softmax')\n", (3239, 3274), False, 'from tensorflow.keras.layers import Conv2D, Flatten, Dense, Dropout\n'), ((4071, 4092), 'numpy.array', 'np.array', (['class_names'], {}), '(class_names)\n', (4079, 4092), True, 'import numpy as np\n'), ((4715, 4731), 'os.listdir', 'os.listdir', (['path'], {}), '(path)\n', (4725, 4731), False, 'import os\n'), ((5548, 5564), 'os.listdir', 'os.listdir', (['path'], {}), '(path)\n', (5558, 5564), False, 'import os\n'), ((5604, 5620), 'os.listdir', 'os.listdir', (['path'], {}), '(path)\n', (5614, 5620), False, 'import os\n'), ((1917, 1964), 'os.path.join', 'os.path.join', (['path_to_class_directory', 'img_name'], {}), '(path_to_class_directory, img_name)\n', (1929, 1964), False, 'import os\n'), ((2818, 2920), 'tensorflow.keras.layers.Conv2D', 'Conv2D', (['(20)'], {'kernel_size': '(3, 3)', 'strides': '(1)', 'activation': '"""relu"""', 'input_shape': '(img_rows, img_cols, 3)'}), "(20, kernel_size=(3, 3), strides=1, activation='relu', input_shape=(\n img_rows, img_cols, 3))\n", (2824, 2920), False, 'from tensorflow.keras.layers import Conv2D, Flatten, Dense, Dropout\n'), ((2945, 3047), 'tensorflow.keras.layers.Conv2D', 'Conv2D', (['(20)'], {'kernel_size': '(3, 3)', 'strides': '(1)', 'activation': '"""relu"""', 'input_shape': '(img_rows, img_cols, 1)'}), "(20, kernel_size=(3, 3), strides=1, activation='relu', input_shape=(\n img_rows, img_cols, 1))\n", (2951, 3047), False, 'from tensorflow.keras.layers import Conv2D, Flatten, Dense, Dropout\n'), ((4094, 4112), 'numpy.argmax', 'np.argmax', (['example'], {}), '(example)\n', (4103, 4112), True, 'import numpy as np\n'), ((2454, 2470), 'numpy.array', 'np.array', (['images'], {}), '(images)\n', (2462, 2470), True, 'import numpy as np\n'), ((2542, 2558), 'numpy.array', 'np.array', (['images'], {}), '(images)\n', (2550, 2558), True, 'import numpy as np\n'), ((2667, 2683), 'os.listdir', 'os.listdir', (['path'], {}), '(path)\n', (2677, 2683), False, 'import os\n'), ((2017, 2041), 'cv2.imread', 'cv2.imread', (['true_path', '(1)'], {}), '(true_path, 1)\n', (2027, 2041), False, 'import cv2\n'), ((2097, 2121), 'cv2.imread', 'cv2.imread', (['true_path', '(0)'], {}), '(true_path, 0)\n', (2107, 2121), False, 'import cv2\n'), ((2167, 2183), 'os.listdir', 'os.listdir', (['path'], {}), '(path)\n', (2177, 2183), False, 'import os\n')] |
"""
Oracle library
"""
import lib_common
from lib_properties import pc
def Graphic_colorbg():
return "#CC99FF"
def EntityOntology():
return ( ["Db", "Schema", "Library"], )
# Ambiguity with tables, oracle or normal users.
def MakeUri(dbName,schemaName,libraryName):
return lib_common.gUriGen.UriMakeFromDict("oracle/library", { "Db" : dbName, "Schema" : schemaName, "Library" : libraryName } )
def AddInfo(grph,node,entity_ids_arr):
# TODO: SPECIAL. Imported here to avoid circular inclusions, see oracle/package_body/__init__.py
from sources_types.oracle import schema as oracle_schema
argDb = entity_ids_arr[0]
argSchema = entity_ids_arr[1]
node_oraschema = oracle_schema.MakeUri( argDb, argSchema )
grph.add( ( node_oraschema, pc.property_oracle_library, node ) )
def EntityName(entity_ids_arr):
return entity_ids_arr[0] + "." + entity_ids_arr[1] + "." + entity_ids_arr[2]
| [
"sources_types.oracle.schema.MakeUri",
"lib_common.gUriGen.UriMakeFromDict"
] | [((281, 399), 'lib_common.gUriGen.UriMakeFromDict', 'lib_common.gUriGen.UriMakeFromDict', (['"""oracle/library"""', "{'Db': dbName, 'Schema': schemaName, 'Library': libraryName}"], {}), "('oracle/library', {'Db': dbName,\n 'Schema': schemaName, 'Library': libraryName})\n", (315, 399), False, 'import lib_common\n'), ((676, 715), 'sources_types.oracle.schema.MakeUri', 'oracle_schema.MakeUri', (['argDb', 'argSchema'], {}), '(argDb, argSchema)\n', (697, 715), True, 'from sources_types.oracle import schema as oracle_schema\n')] |
from myhdl import Signal, intbv, block, always_comb, ConcatSignal
import myhdl
from collections import OrderedDict
import keyword
def _is_valid_name(ident: str) -> bool:
'''Determine if ident is a valid register or bitfield name.
'''
if not isinstance(ident, str):
raise TypeError("expected str, but got {!r}".format(type(ident)))
if not ident.isidentifier():
return False
if keyword.iskeyword(ident):
return False
return True
@block
def assign_bitfield_from_register(reg, bitfield, offset):
if isinstance(bitfield.val, bool):
@always_comb
def assignment():
bitfield.next = reg[offset]
else:
start = offset
stop = offset + len(bitfield)
@always_comb
def assignment():
bitfield.next = reg[stop:start]
return assignment
class Bitfields:
def __eq__(self, other):
if not ((self._bitfields_config == other._bitfields_config) and
(self._initial_values == other._initial_values) and
(self._register_width == other._register_width) and
(self._reg_type == other._reg_type)):
return False
else:
# The values also need to be the same
for bf_name in self._bitfields_config:
if getattr(self, bf_name) != getattr(other, bf_name):
return False
if self.register != other.register:
return False
return True
def __init__(
self, register_width, register_type, bitfields_config,
initial_values=None):
'''
Creates a MyHDL interface representing a series of bitfields.
`register_width` is the width of the register that the bitfields sit
on top of.
`register_type` is one of `axi_read_write`, `axi_read_only` or
`axi_write_only`.
`initial_values` is an optional lookup for each bitfield when the
register type is `axis_read_write`. If a bitfield
has an initial value set, then, assuming the register_type is
`axis_read_write`, the bitfield will be set to the initial value. If
the register type is not `axis_read_write`, then a ValueError will be
raised if this argument is not `None`.
`bitfields_config` is a dictionary that provides the configuration
for each bitfield on a register. The keys are the names of the
bitfields and each key should point to a configuration dict.
Each configution should have the `type` key, which should have data
which is one of:
- `uint`
- `bool`
- `const-uint`
- `const-bool`
In addition, it should also have keys which depend on the type, as
follows:
- `uint`:
- `length` giving the length in bits of the uint
- `offset` giving the offset of the bitfield.
- `bool`:
- `offset` giving the offset of the boolean value.
- `const-uint`:
- `length` giving the length in bits of the uint
- `offset` giving the offset of the bitfield.
- `const-value` giving the value of the constant.
- `const-bool`:
- `offset` giving the offset of the boolean balue.
- `const-value` giving the value of the constant.
Extra keys are ignored.
Other constraints are enforced and will cause an error:
- All bitfields must fit within the register width.
- A `const-uint` and `const-bool` can only be set on a read-only
register.
- Overlapping bitfields are invalid.
- No bitfield can be called 'register'. This is reserved for the
full register representation.
- Only read-write registers can have an initial value.
An example bitfield entry might look something like:
{'foo':{'type': 'uint',
'length': 6,
'offset': 0},
'bar': {'type': 'bool',
'offset': 6},
'baz': {'type': 'const-uint',
'length': 5,
'offset': 7,
'const-value': 15}}
'''
if len(bitfields_config) == 0:
raise ValueError('bitfields_config cannot be empty')
if register_type not in (
'axi_read_write', 'axi_read_only', 'axi_write_only'):
raise ValueError(
'The register type must be one of `axi_read_write`, '
'`axi_read_only` or `axi_write_only`')
if initial_values != None and register_type != 'axi_read_write':
raise ValueError(
'`initial_values` must be `None` if the register type '
'is not `axi_read_write`')
if initial_values is None:
initial_values = {}
# We always create a register attribute
register_initial_val = 0
for bitfield in bitfields_config:
offset = bitfields_config[bitfield]['offset']
try:
init_val = initial_values[bitfield]
except KeyError:
init_val = 0
register_initial_val += init_val << offset
self._reg_type = register_type
self._register_width = register_width
self._bitfields_config = bitfields_config
self._initial_values = initial_values
bitfield_masks = {}
bitfield_starts = {}
bitfield_stops = {}
self._constant_vals = {}
for bitfield in bitfields_config:
if not _is_valid_name(bitfield):
raise ValueError(
'Bitfield names must be valid python identifiers: '
'{}'.format(bitfield))
if bitfield[0] == '_':
raise ValueError(
'Bitfield names cannot begin with an underscore: '
'{}'.format(bitfield))
if bitfield == 'register':
raise ValueError('Bitfields cannot be named `register`.')
if bitfields_config[bitfield]['type'] == 'uint':
length = bitfields_config[bitfield]['length']
offset = bitfields_config[bitfield]['offset']
bf_signal = Signal(intbv(0)[length:])
mask = (2**length - 1) << offset
bitfield_starts[offset] = bitfield
bitfield_stops[bitfield] = offset + length
elif bitfields_config[bitfield]['type'] == 'bool':
offset = bitfields_config[bitfield]['offset']
bf_signal = Signal(False)
mask = 1 << offset
bitfield_starts[offset] = bitfield
bitfield_stops[bitfield] = offset + 1
elif bitfields_config[bitfield]['type'] == 'const-uint':
if register_type != 'axi_read_only':
raise ValueError(
'The bitfield `{}` is of type `const-uint` which '
'requires the register is read-only, but the register '
'has been configured to be `{}`'.format(
bitfield, register_type))
length = bitfields_config[bitfield]['length']
offset = bitfields_config[bitfield]['offset']
const_val = int(bitfields_config[bitfield]['const-value'])
if (const_val >= 2**length or const_val < 0):
raise ValueError(
'The bitfield const value, {}, is invalid for '
'bitfield {}'.format(const_val, bitfield))
bf_signal = intbv(const_val)[length:]
self._constant_vals[bitfield] = const_val
# We also set the initial value for constants
register_initial_val += const_val << offset
mask = (2**length - 1) << offset
bitfield_starts[offset] = bitfield
bitfield_stops[bitfield] = offset + length
elif bitfields_config[bitfield]['type'] == 'const-bool':
if register_type != 'axi_read_only':
raise ValueError(
'The bitfield `{}` is of type `const-bool` which '
'requires the register is read-only, but the register '
'has been configured to be `{}`'.format(
bitfield, register_type))
offset = bitfields_config[bitfield]['offset']
const_val = bitfields_config[bitfield]['const-value']
if not isinstance(const_val, bool):
raise ValueError(
'The bitfield const value, {}, is invalid for '
'bitfield {}'.format(const_val, bitfield))
bf_signal = const_val
self._constant_vals[bitfield] = const_val
# We also set the initial value for constants
register_initial_val += const_val << offset
mask = 1 << offset
bitfield_starts[offset] = bitfield
bitfield_stops[bitfield] = offset + 1
else:
raise ValueError('A bitfield type must be one of `uint`, '
'`bool`, `const-uint` or `const-bool`: '
'{}'.format(bitfield))
if mask >= 2**register_width:
raise ValueError(
'The bitfield `{}` is out of range for a register of '
'width {}'.format(bitfield, register_width))
# Check the bitfield doesn't overlap with any others
for other_bf in bitfield_masks:
if (bitfield_masks[other_bf] & mask) != 0:
raise ValueError(
'Bitfield `{}` overlaps with bitfield `{}`'.format(
bitfield, other_bf))
bitfield_masks[bitfield] = mask
setattr(self, bitfield, bf_signal)
# We now need to construct the packed version of the bitfields,
# including padding.
rev_concat_list = []
bitfield_starts_list = list(bitfield_starts.keys())
bitfield_starts_list.sort()
if bitfield_starts_list[0] != 0:
padding = intbv(0)[bitfield_starts_list[0]:]
rev_concat_list.append(padding)
for i, start in enumerate(bitfield_starts_list):
bitfield = bitfield_starts[start]
rev_concat_list.append(getattr(self, bitfield))
try:
next_start = bitfield_starts_list[i + 1]
# The higher up checks make sure padding_len should never be
# negative.
padding_len = next_start - bitfield_stops[bitfield]
if padding_len > 0:
padding = intbv(0)[padding_len:]
rev_concat_list.append(padding)
except IndexError:
if bitfield_stops[bitfield] < register_width:
padding = intbv(0)[
register_width - bitfield_stops[bitfield]:]
rev_concat_list.append(padding)
self.register = Signal(intbv(register_initial_val)[register_width:])
self._concat_list = rev_concat_list[::-1]
self._bitfield_starts = bitfield_starts
self._bitfield_masks = bitfield_masks
@block
def bitfield_connector(self):
if self._reg_type in ('axi_read_write', 'axi_write_only'):
instances = []
for bitfield_start in self._bitfield_starts:
bitfield = getattr(self, self._bitfield_starts[bitfield_start])
instances.append(
assign_bitfield_from_register(
self.register, bitfield, bitfield_start))
return instances
elif self._reg_type in ('axi_read_only'):
if len(self._concat_list) == 1:
# This is a hack to allow a concat signal to work in
# all cases. An alternative would be to special case single
# signals, but that doesn't work well with constants, which
# themselves would require a special case, and some hackery to
# have the constant read (and requiring initial_values to be
# turned on).
keep = Signal(True)
keep.driven = True
reg_signal = ConcatSignal(keep, self._concat_list[0])
else:
reg_signal = ConcatSignal(*self._concat_list)
@always_comb
def assign_register():
self.register.next = reg_signal[self._register_width:]
return assign_register
class RegisterSet(object):
pass
class Registers(object):
''' A general purpose register definition.
'''
@property
def register_types(self):
return self._register_types
def __eq__(self, other):
return (self._bitfields == other._bitfields and
self._register_types == other._register_types and
self._register_width == other._register_width)
def __init__(
self, register_list, register_types=None, register_width=32,
initial_values=None, bitfields=None):
'''
Constructs a MyHDL interface that encapsulates each register name
given in `register_list`. The order of the registers in the list is
kept.
If `register_types` is set, it should be a dictionary like object
that provides data of the form `axi_read_write`, `axi_read_only` or
`axi_write_only` for the register name given by its key. If a register
name is missing from `register_types`, then the register type defaults
to `axi_read_write`. If `register_types` is `None`, then all the
registers are `axi_read_write`.
`register_width` gives the width in bits of each register that is
created, defaulting to 32.
`initial_values` is an optional dictionary that sets the initial
value of a read-write register. A `ValueError` will be raised if an
initial value is set for a non read-write register. The default is
for the initial values to be zero. If a register has bitfields set
(see below), then the dictionary entry should itself be a dictionary
to the initial values for each bitfield.
`bitfields` is an optional dictionary argument in which each register
that is included in the dictionary is populated as a Bitfield interface
rather than a signal. Each data in bitfields is passed directly as the
bitfields_config argument to the initialisation of a `Bitfield` class.
See the documentation for that class to see what form the data should
be.
'''
for name in register_list:
if not _is_valid_name(name):
raise ValueError('Invalid register name: {}'.format(name))
if register_types is None:
# Create a register types dictionary so that the system can handle
# an empty register types argument.
register_types = {}
self._register_width = register_width
# Create an ordered dictionary
self._register_types = OrderedDict()
for each in register_types:
if each not in register_list:
# Check that the register types have a corresponding register
# in the register list. If not error.
raise ValueError(
'Invalid register in register_types: %s' % each)
if initial_values is None:
initial_values = {}
if bitfields is None:
bitfields = {}
for initial_val_key in initial_values:
if (register_types.get(initial_val_key, 'axi_read_write') !=
'axi_read_write'):
raise ValueError(
'Only read-write registers can take initial values: %s' %
initial_val_key + ': ' +
str(register_types[initial_val_key]))
for name in register_list:
register_type = register_types.get(name, 'axi_read_write')
if name in bitfields:
initial_vals = initial_values.get(name, None)
setattr(
self, name,
Bitfields(register_width, register_type, bitfields[name],
initial_values=initial_vals))
else:
# Create the registers
setattr(self, name, Signal(
intbv(initial_values.get(name, 0))[register_width:]))
# Populate the ordered dictionary with the appropriate
# register types, defaulting to 'axi_read_write'
self._register_types[name] = (
register_types.get(name, 'axi_read_write'))
self._bitfields = bitfields
| [
"collections.OrderedDict",
"myhdl.Signal",
"keyword.iskeyword",
"myhdl.intbv",
"myhdl.ConcatSignal"
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# Copyright (c) 2014 Simplistix Ltd
# See license.txt for license details.
from decimal import Decimal
from testfixtures import RoundComparison as R, compare, ShouldRaise
from unittest import TestCase
from ..compat import PY2, PY3
class Tests(TestCase):
def test_equal_yes_rhs(self):
self.assertTrue(0.123457 == R(0.123456, 5))
def test_equal_yes_lhs(self):
self.assertTrue(R(0.123456, 5) == 0.123457)
def test_equal_no_rhs(self):
self.assertFalse(0.123453 == R(0.123456, 5))
def test_equal_no_lhs(self):
self.assertFalse(R(0.123456, 5) == 0.123453)
def test_not_equal_yes_rhs(self):
self.assertFalse(0.123457 != R(0.123456, 5))
def test_not_equal_yes_lhs(self):
self.assertFalse(R(0.123456, 5) != 0.123457)
def test_not_equal_no_rhs(self):
self.assertTrue(0.123453 != R(0.123456, 5))
def test_not_equal_no_lhs(self):
self.assertTrue(R(0.123456, 5) != 0.123453)
def test_equal_in_sequence_rhs(self):
self.assertEqual((1, 2, 0.123457),
(1, 2, R(0.123456, 5)))
def test_equal_in_sequence_lhs(self):
self.assertEqual((1, 2, R(0.123456, 5)),
(1, 2, 0.123457))
def test_not_equal_in_sequence_rhs(self):
self.assertNotEqual((1, 2, 0.1236),
(1, 2, R(0.123456, 5)))
def test_not_equal_in_sequence_lhs(self):
self.assertNotEqual((1, 2, R(0.123456, 5)),
(1, 2, 0.1236))
def test_not_numeric_rhs(self):
with ShouldRaise(TypeError):
'abc' == R(0.123456, 5)
def test_not_numeric_lhs(self):
with ShouldRaise(TypeError):
R(0.123456, 5) == 'abc'
def test_repr(self):
compare('<R:0.12346 to 5 digits>',
repr(R(0.123456, 5)))
def test_str(self):
compare('<R:0.12346 to 5 digits>',
repr(R(0.123456, 5)))
def test_str_negative(self):
if PY3:
expected = '<R:123500 to -2 digits>'
else:
expected = '<R:123500.0 to -2 digits>'
compare(expected, repr(R(123456, -2)))
TYPE_ERROR_DECIMAL = TypeError(
"Cannot compare <R:0.12346 to 5 digits> with <class 'decimal.Decimal'>"
)
def test_equal_yes_decimal_to_float_rhs(self):
with ShouldRaise(self.TYPE_ERROR_DECIMAL, unless=PY2):
self.assertTrue(Decimal("0.123457") == R(0.123456, 5))
def test_equal_yes_decimal_to_float_lhs(self):
with ShouldRaise(self.TYPE_ERROR_DECIMAL, unless=PY2):
self.assertTrue(R(0.123456, 5) == Decimal("0.123457"))
def test_equal_no_decimal_to_float_rhs(self):
with ShouldRaise(self.TYPE_ERROR_DECIMAL, unless=PY2):
self.assertFalse(Decimal("0.123453") == R(0.123456, 5))
def test_equal_no_decimal_to_float_lhs(self):
with ShouldRaise(self.TYPE_ERROR_DECIMAL, unless=PY2):
self.assertFalse(R(0.123456, 5) == Decimal("0.123453"))
TYPE_ERROR_FLOAT = TypeError(
"Cannot compare <R:0.12346 to 5 digits> with <class 'float'>"
)
def test_equal_yes_float_to_decimal_rhs(self):
with ShouldRaise(self.TYPE_ERROR_FLOAT, unless=PY2):
self.assertTrue(0.123457 == R(Decimal("0.123456"), 5))
def test_equal_yes_float_to_decimal_lhs(self):
with ShouldRaise(self.TYPE_ERROR_FLOAT, unless=PY2):
self.assertTrue(R(Decimal("0.123456"), 5) == 0.123457)
def test_equal_no_float_to_decimal_rhs(self):
with ShouldRaise(self.TYPE_ERROR_FLOAT, unless=PY2):
self.assertFalse(0.123453 == R(Decimal("0.123456"), 5))
def test_equal_no_float_to_decimal_lhs(self):
with ShouldRaise(self.TYPE_ERROR_FLOAT, unless=PY2):
self.assertFalse(R(Decimal("0.123456"), 5) == 0.123453)
def test_integer_float(self):
with ShouldRaise(TypeError, unless=PY2):
1 == R(1.000001, 5)
def test_float_integer(self):
with ShouldRaise(TypeError, unless=PY2):
R(1.000001, 5) == 1
def test_equal_yes_integer_other_rhs(self):
self.assertTrue(10 == R(11, -1))
def test_equal_yes_integer_lhs(self):
self.assertTrue(R(11, -1) == 10)
def test_equal_no_integer_rhs(self):
self.assertFalse(10 == R(16, -1))
def test_equal_no_integer_lhs(self):
self.assertFalse(R(16, -1) == 10)
def test_equal_integer_zero_precision(self):
self.assertTrue(1 == R(1, 0))
def test_equal_yes_negative_precision(self):
self.assertTrue(149.123 == R(101.123, -2))
def test_equal_no_negative_precision(self):
self.assertFalse(149.123 == R(150.001, -2))
def test_decimal_yes_rhs(self):
self.assertTrue(Decimal('0.123457') == R(Decimal('0.123456'), 5))
def test_decimal_yes_lhs(self):
self.assertTrue(R(Decimal('0.123456'), 5) == Decimal('0.123457'))
def test_decimal_no_rhs(self):
self.assertFalse(Decimal('0.123453') == R(Decimal('0.123456'), 5))
def test_decimal_no_lhs(self):
self.assertFalse(R(Decimal('0.123456'), 5) == Decimal('0.123453'))
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"decimal.Decimal",
"testfixtures.ShouldRaise",
"testfixtures.RoundComparison"
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# -*- coding=utf-8 -*-
# author: dongrixinyu
# contact: <EMAIL>
# blog: https://github.com/dongrixinyu/
# file: bare_embedding.py
# time: 2020-06-12 11:27
import os
import pdb
import logging
from typing import Union, Optional, Dict, Any, Tuple
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from jiotc.embeddings.base_embedding import BaseEmbedding
from .base_model import BaseModel
# Bidirectional LSTM neural network (many-to-one)
class BiLSTMModel(BaseModel):
@classmethod
def get_default_hyper_parameters(cls) -> Dict[str, Dict[str, Any]]:
return {
'layer_bi_lstm': {
'hidden_size': 128,
'num_layers': 1,
'dropout': 0.2, # 当 num_layers == 1 时失效
'bidirectional': True
},
'layer_dense': {
'activation': 'softmax'
}
}
def __init__(self, embed_model: Optional[BaseEmbedding] = None,
device: Union['cuda', 'cpu'] = None,
hyper_parameters: Optional[Dict[str, Dict[str, Any]]] = None):
'''
self.device
self.embedding_layer
self.embedding
self.embedding_size
self.num_classes
参数已知，可以直接使用
'''
super(BiLSTMModel, self).__init__(embed_model, device=device)
self.hidden_size = hyper_parameters['layer_bi_lstm']['hidden_size']
self.num_layers = hyper_parameters['layer_bi_lstm']['num_layers']
self.dropout = hyper_parameters['layer_bi_lstm']['dropout']
self.lstm = nn.LSTM(
self.embedding_size, self.hidden_size, self.num_layers,
batch_first=True, bidirectional=True)
self.fc = nn.Linear(self.hidden_size * 2,
self.num_classes) # 2 for bidirection
def forward(self, samples):
masks = samples.gt(0)
embeds = self.embedding_layer(samples) #.to(self.device)
# 按长短调整样本顺序
seq_length = masks.sum(1)
sorted_seq_length, perm_idx = seq_length.sort(descending=True)
embeds = embeds[perm_idx, :] # 重新排序
pack_sequence = pack_padded_sequence(
embeds, lengths=sorted_seq_length, batch_first=True)
# Set initial states, involved with batch_size
'''
h0 = torch.autograd.Variable(torch.randn(
self.num_layers * 2, embeds.shape[0],
self.hidden_size)).to(self.device) # 2 for bidirection
c0 = torch.autograd.Variable(torch.randn(
self.num_layers * 2, embeds.shape[0],
self.hidden_size)).to(self.device)
#'''
# Forward propagate LSTM
packed_output, _ = self.lstm(pack_sequence) #, (h0, c0))
# out: tensor of shape (batch_size, seq_length, hidden_size * 2)
lstm_out, _ = pad_packed_sequence(packed_output, batch_first=True)
_, unperm_idx = perm_idx.sort()
lstm_out = lstm_out[unperm_idx, :]
# dropout_layer
lstm_out = lstm_out.permute(1, 0, 2) # [batch_size, seq_len, hidden_size * 2] => [seq_len, batch_size, hidden_size * 2]
# disabled when not training
lstm_out = F.dropout2d(lstm_out, p=self.dropout, training=self.training)
lstm_out = lstm_out.permute(1, 0, 2) # [seq_len, batch_size, hidden_size * 2] => [batch_size, seq_len, hidden_size * 2]
lstm_out_sum = torch.mean(lstm_out, dim=1)
output = self.fc(lstm_out_sum)
return output
| [
"torch.nn.LSTM",
"torch.mean",
"torch.nn.utils.rnn.pack_padded_sequence",
"torch.nn.Linear",
"torch.nn.utils.rnn.pad_packed_sequence"
] | [((1650, 1756), 'torch.nn.LSTM', 'nn.LSTM', (['self.embedding_size', 'self.hidden_size', 'self.num_layers'], {'batch_first': '(True)', 'bidirectional': '(True)'}), '(self.embedding_size, self.hidden_size, self.num_layers, batch_first\n =True, bidirectional=True)\n', (1657, 1756), True, 'import torch.nn as nn\n'), ((1796, 1845), 'torch.nn.Linear', 'nn.Linear', (['(self.hidden_size * 2)', 'self.num_classes'], {}), '(self.hidden_size * 2, self.num_classes)\n', (1805, 1845), True, 'import torch.nn as nn\n'), ((2245, 2318), 'torch.nn.utils.rnn.pack_padded_sequence', 'pack_padded_sequence', (['embeds'], {'lengths': 'sorted_seq_length', 'batch_first': '(True)'}), '(embeds, lengths=sorted_seq_length, batch_first=True)\n', (2265, 2318), False, 'from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n'), ((2941, 2993), 'torch.nn.utils.rnn.pad_packed_sequence', 'pad_packed_sequence', (['packed_output'], {'batch_first': '(True)'}), '(packed_output, batch_first=True)\n', (2960, 2993), False, 'from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n'), ((3536, 3563), 'torch.mean', 'torch.mean', (['lstm_out'], {'dim': '(1)'}), '(lstm_out, dim=1)\n', (3546, 3563), False, 'import torch\n')] |
from collections import namedtuple
from dagster import check
from dagster.config.config_type import ConfigType, ConfigTypeKind
from dagster.config.field import Field
from dagster.core.serdes import whitelist_for_serdes
@whitelist_for_serdes
class NonGenericTypeRefMeta(namedtuple('_NonGenericTypeRefMeta', 'key')):
def __new__(cls, key):
return super(NonGenericTypeRefMeta, cls).__new__(cls, check.str_param(key, 'key'))
@whitelist_for_serdes
class ConfigTypeMeta(
namedtuple(
'_ConfigTypeMeta',
'kind key given_name description '
'type_param_refs ' # only valid for closed generics (Set, Tuple, List, Optional)
'enum_values ' # only valid for enums
'fields', # only valid for dicts and selectors
)
):
def __new__(
cls, kind, key, given_name, type_param_refs, enum_values, fields, description,
):
return super(ConfigTypeMeta, cls).__new__(
cls,
kind=check.inst_param(kind, 'kind', ConfigTypeKind),
key=check.str_param(key, 'key'),
given_name=check.opt_str_param(given_name, 'given_name'),
type_param_refs=None
if type_param_refs is None
else check.list_param(type_param_refs, 'type_param_refs', of_type=TypeRef),
enum_values=None
if enum_values is None
else check.list_param(enum_values, 'enum_values', of_type=ConfigEnumValueMeta),
fields=None
if fields is None
else check.list_param(fields, 'field', of_type=ConfigFieldMeta),
description=check.opt_str_param(description, 'description'),
)
@property
def inner_type_refs(self):
'''
This recurses through the type references with non-generic types as leaves.
'''
def _doit():
next_level_refs = _get_next_level_refs(self)
if next_level_refs:
for next_level in next_level_refs:
for inner_ref in _recurse_through_generics(next_level):
yield inner_ref
# there might be duplicate keys (esp for scalars)
refs_by_key = {}
for ref in _doit():
if ref.key not in refs_by_key:
refs_by_key[ref.key] = ref
return list(refs_by_key.values())
# This function is used by the recursive descent
# through all the inner types. This does *not*
# recursively descend through the type parameters
# of generic types. It just gets the next level of
# types. Either the direct type parameters of a
# generic type. Or the type refs of all the fields
# if it is a type with fields.
def _get_next_level_refs(ref):
# if a generic type, get type params
# if a type with fields, get refs of the fields
if ConfigTypeKind.is_closed_generic(ref.kind):
return ref.type_param_refs
elif (
ConfigTypeKind.has_fields(ref.kind) and ref.fields
): # still check fields because permissive
return [field_meta.type_ref for field_meta in ref.fields]
def _recurse_through_generics(ref):
yield ref
if isinstance(ref, ConfigTypeMeta) and ConfigTypeKind.is_closed_generic(ref.kind):
for type_param_ref in ref.type_param_refs:
for inner_ref in _recurse_through_generics(type_param_ref):
yield inner_ref
# A type reference in these serializable data structures are one of two things
# 1) A closed generic type (e.g. List[Int] of Optional[Set[str]])
# 2) Or a reference to a non-generic type, such as Dict, Selector, or a Scalar.
# Upon deserialization and when hydrated back to the graphql query, it will
# be the responsibility of that module to maintain a dictionary of the
# non-generic types and then do lookups into the dictionary in order to
# to explode the entire type hierarchy requested by the client
TypeRef = (ConfigTypeMeta, NonGenericTypeRefMeta)
@whitelist_for_serdes
class ConfigEnumValueMeta(namedtuple('_ConfigEnumValueMeta', 'value description')):
def __new__(cls, value, description):
return super(ConfigEnumValueMeta, cls).__new__(
cls,
value=check.str_param(value, 'value'),
description=check.opt_str_param(description, 'description'),
)
@whitelist_for_serdes
class ConfigFieldMeta(
namedtuple(
'_ConfigFieldMeta',
'name type_ref is_required default_provided default_value_as_str description',
)
):
def __new__(
cls, name, type_ref, is_required, default_provided, default_value_as_str, description
):
return super(ConfigFieldMeta, cls).__new__(
cls,
name=check.opt_str_param(name, 'name'),
type_ref=check.inst_param(type_ref, 'type_ref', TypeRef),
is_required=check.bool_param(is_required, 'is_required'),
default_provided=check.bool_param(default_provided, 'default_provided'),
default_value_as_str=check.opt_str_param(default_value_as_str, 'default_value_as_str'),
description=check.opt_str_param(description, 'description'),
)
def meta_from_field(name, field):
check.str_param(name, 'name')
check.inst_param(field, 'field', Field)
return ConfigFieldMeta(
name=name,
type_ref=type_ref_of(field.config_type),
is_required=field.is_required,
default_provided=field.default_provided,
default_value_as_str=field.default_value_as_str if field.default_provided else None,
description=field.description,
)
def type_ref_of(config_type):
check.inst_param(config_type, 'config_type', ConfigType)
if ConfigTypeKind.is_closed_generic(config_type.kind):
return meta_from_config_type(config_type)
else:
return NonGenericTypeRefMeta(key=config_type.key)
def type_refs_of(type_list):
return list(map(type_ref_of, type_list)) if type_list is not None else None
def meta_from_config_type(config_type):
check.inst_param(config_type, 'config_type', ConfigType)
return ConfigTypeMeta(
key=config_type.key,
given_name=config_type.given_name,
kind=config_type.kind,
description=config_type.description,
type_param_refs=type_refs_of(config_type.type_params),
enum_values=[
ConfigEnumValueMeta(ev.config_value, ev.description) for ev in config_type.enum_values
]
if config_type.kind == ConfigTypeKind.ENUM
else None,
fields=[meta_from_field(name, field) for name, field in config_type.fields.items()]
if ConfigTypeKind.has_fields(config_type.kind)
else None,
)
| [
"dagster.check.list_param",
"dagster.check.inst_param",
"collections.namedtuple",
"dagster.config.config_type.ConfigTypeKind.has_fields",
"dagster.check.str_param",
"dagster.check.opt_str_param",
"dagster.check.bool_param",
"dagster.config.config_type.ConfigTypeKind.is_closed_generic"
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#########################################################
# 母比率の差の検定／タイプ1
#########################################################
import sys
import math
def error_usage():
sys.stderr.write("usage: " + sys.argv[0] + "\n")
sys.stderr.write("\tこのプログラムは、4つの引数が必要です。\n\n")
sys.stderr.write(
"\t1.属性1のn数 2.属性1における比率p 3.属性2のn数 4.属性2における比率p\n")
sys.stderr.write("\t例： 200 0.6 100 0.48\n\n")
sys.stderr.write("\tただし、それぞれn数は30以上かつ比率pは[0<=p<=1]を満たすこと\n")
sys.exit(1)
# 引数がちょうど4つあるか？
if len(sys.argv[1:]) != 4:
error_usage()
n1,p1,n2,p2 = map(float, sys.argv[1:])
p = ((n1*p1) + (n2*p2))/(n1+n2)
# n数が30以上か？
if (n1 < 30) or (n2 < 30):
error_usage()
# 比率は0から1の間か？
if not (0 <= p1 <= 1) or not (0 <= p2 <= 1):
error_usage()
T = math.fabs(p1 - p2) / math.sqrt((p * (1-p)) * ((1/n1) + (1/n2)))
if T >= 2.58:
print("1%有意 (検定統計量:" + str(T) + "）")
elif T >= 1.96:
print("5%有意 (検定統計量:" + str(T) + "）")
elif T >= 1.65:
print("10%有意 (検定統計量:" + str(T) + "）")
else:
print("有意差なし (検定統計量:" + str(T) + "）")
| [
"sys.stderr.write",
"math.fabs",
"math.sqrt",
"sys.exit"
] | [((180, 228), 'sys.stderr.write', 'sys.stderr.write', (["('usage: ' + sys.argv[0] + '\\n')"], {}), "('usage: ' + sys.argv[0] + '\\n')\n", (196, 228), False, 'import sys\n'), ((233, 279), 'sys.stderr.write', 'sys.stderr.write', (['"""\tこのプログラムは、4つの引数が必要です。\n\n"""'], {}), "('\\tこのプログラムは、4つの引数が必要です。\\n\\n')\n", (249, 279), False, 'import sys\n'), ((284, 351), 'sys.stderr.write', 'sys.stderr.write', (['"""\t1.属性1のn数 2.属性1における比率p 3.属性2のn数 4.属性2における比率p\n"""'], {}), "('\\t1.属性1のn数 2.属性1における比率p 3.属性2のn数 4.属性2における比率p\\n')\n", (300, 351), False, 'import sys\n'), ((365, 410), 'sys.stderr.write', 'sys.stderr.write', (['"""\t例： 200 0.6 100 0.48\n\n"""'], {}), "('\\t例： 200 0.6 100 0.48\\n\\n')\n", (381, 410), False, 'import sys\n'), ((415, 475), 'sys.stderr.write', 'sys.stderr.write', (['"""\tただし、それぞれn数は30以上かつ比率pは[0<=p<=1]を満たすこと\n"""'], {}), "('\\tただし、それぞれn数は30以上かつ比率pは[0<=p<=1]を満たすこと\\n')\n", (431, 475), False, 'import sys\n'), ((480, 491), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (488, 491), False, 'import sys\n'), ((767, 785), 'math.fabs', 'math.fabs', (['(p1 - p2)'], {}), '(p1 - p2)\n', (776, 785), False, 'import math\n'), ((788, 830), 'math.sqrt', 'math.sqrt', (['(p * (1 - p) * (1 / n1 + 1 / n2))'], {}), '(p * (1 - p) * (1 / n1 + 1 / n2))\n', (797, 830), False, 'import math\n')] |
# coding=utf-8
import functools
from flask import Flask, session
from flask import redirect
from flask import request, make_response
from flask import render_template
from flask import url_for
from flask_bootstrap import Bootstrap
# 数据库处理
from db import *
# json
import json
# 生成一个app
app = Flask(__name__, instance_relative_config=True)
bootstrap=Bootstrap(app)
app.secret_key = 'lab3'
# 对app执行请求页面地址到函数的绑定
@app.route("/", methods=("GET", "POST"))
@app.route("/login", methods=("GET", "POST"))
def login():
"""Log in a registered user by adding the user id to the session."""
if request.method == "POST":
# 客户端在login页面发起的POST请求
username = request.form["username"]
password = request.form["password"]
ipaddr = request.form["ipaddr"]
database = request.form["database"]
db = MyDefSQL(username, password, ipaddr, database)
err = db.login()
if err != '0':
return render_template("login_fail.html", err=err)
else:
#print(err)
session['username'] = username
session['password'] = password
session['ipaddr'] = ipaddr
session['database'] = database
return redirect(url_for('home'))
else :
# 客户端GET 请求login页面时
return render_template("login.html")
# 主页面
@app.route("/home", methods=(["GET", "POST"]))
def home():
return render_template("home.html")
# 请求url为host/table的页面返回结果
@app.route("/table", methods=(["GET", "POST"]))
def table():
# 出于简单考虑，每次请求都需要连接数据库，可以尝试使用其它context保存数据库连接
if 'username' in session:
db = MyDefSQL(session['username'], session['password'],
session['ipaddr'], session['database'])
err = db.login()
else:
return redirect(url_for('login'))
tabs = db.showtablecnt()
if request.method == "POST":
if 'clear' in request.form:
return render_template("table.html", rows = '', dbname=session['database'])
elif 'search' in request.form:
return render_template("table.html", rows = tabs, dbname=session['database'])
else:
return render_template("table.html", rows = tabs, dbname=session['database'])
# 客户管理页面
@app.route("/customer", methods=(["GET", "POST"]))
def customer():
if 'username' in session:
db = MyDefSQL(session['username'], session['password'],
session['ipaddr'], session['database'])
err = db.login()
else:
return redirect(url_for('login'))
tabs = db.showcustomer()
if tabs==None:
tabs=""
if request.method == "POST":
if 'search' in request.form:
# 是由search表单提交的post请求
searchinfo = {}
# print(len(request.form[u"客户身份证号"]))
for key,value in request.form.items():
# 注意这里key和value仍然是unicode编码，统一在db.py中处理！
if len(value) != 0 and key!='search':
# 做第一层过滤，使得可以表单中某块信息不填
searchinfo[key] = value
tabs = db.customer_search(searchinfo)
return render_template("customer.html", rows = tabs, dbname=session['database'])
# 其它删改查需求，是由Ajax提交的post
datas = json.loads(request.get_data(as_text=True))
function = datas["function"]
datas = datas["inputdata"]
# print(function)
# print(datas[0][u"客户身份证号"])
if function == "delete":
res = {'info':'删除成功！', 'errs':[]}
for data in datas:
err = db.customer_del(data)
if err != '0':
res['errs'].append([data[u"客户身份证号"],err])
if len(res['errs']) != 0:
res['info'] = "删除失败！"
return json.dumps(res)
elif function == "insert":
res = {'info':'插入成功！', 'errs':[]}
for data in datas:
err = db.customer_insert(data)
if err != '0':
res['errs'].append([data[u"客户身份证号"],err])
if len(res['errs']) != 0:
res['info'] = "插入失败！"
return json.dumps(res)
elif function == "update":
res = {'info':'修改成功！', 'errs':[]}
for data in datas:
err = db.customer_update(data)
if err != '0':
res['errs'].append([data[u"客户身份证号"],err])
if len(res['errs']) != 0:
res['info'] = "修改失败！"
return json.dumps(res)
else:
return render_template("customer.html", rows = tabs, dbname=session['database'])
# 账户管理页面
# 储蓄账户
@app.route("/account/saving", methods=(["GET", "POST"]))
def saving():
if 'username' in session:
db = MyDefSQL(session['username'], session['password'],
session['ipaddr'], session['database'])
err = db.login()
else:
return redirect(url_for('login'))
tabs = db.showaccount(True)
if tabs==None:
tabs=""
if request.method == "POST":
if 'search' in request.form:
# 是由search表单提交的post请求
searchinfo = {}
for key,value in request.form.items():
# 注意这里key和value仍然是unicode编码，统一在db.py中处理！
if len(value) != 0 and key!='search':
# 做第一层过滤，使得可以表单中某块信息不填
searchinfo[key] = value
tabs = db.account_search(searchinfo, True)
return render_template("account_saving.html", rows = tabs, dbname=session['database'])
# 其它删改查需求，是由Ajax提交的post
datas = json.loads(request.get_data(as_text=True))
function = datas["function"]
datas = datas["inputdata"]
# print(function)
if function == "delete":
res = {'info':'删除成功！', 'errs':[]}
for data in datas:
err = db.account_del(data, True)
if err != '0':
res['errs'].append([data[u"账户.账户号"],err])
if len(res['errs']) != 0:
res['info'] = "删除失败！"
return json.dumps(res)
elif function == "insert":
res = {'info':'插入成功！', 'errs':[]}
for data in datas:
err = db.account_insert(data, True)
if err != '0':
res['errs'].append([data[u"账户.账户号"],err])
if len(res['errs']) != 0:
res['info'] = "插入失败！"
return json.dumps(res)
elif function == "update":
res = {'info':'修改成功！', 'errs':[]}
for data in datas:
err = db.account_update(data, True)
if err != '0':
res['errs'].append([data[u"账户.账户号"],err])
if len(res['errs']) != 0:
res['info'] = "修改失败！"
return json.dumps(res)
else:
return render_template("account_saving.html", rows = tabs, dbname=session['database'])
# 支票账户
@app.route("/account/checking", methods=(["GET", "POST"]))
def checking():
if 'username' in session:
db = MyDefSQL(session['username'], session['password'],
session['ipaddr'], session['database'])
err = db.login()
else:
return redirect(url_for('login'))
tabs = db.showaccount(False)
if tabs==None:
tabs=""
if request.method == "POST":
if 'search' in request.form:
# 是由search表单提交的post请求
searchinfo = {}
for key,value in request.form.items():
# 注意这里key和value仍然是unicode编码，统一在db.py中处理！
if len(value) != 0 and key!='search':
# 做第一层过滤，使得可以表单中某块信息不填
searchinfo[key] = value
tabs = db.account_search(searchinfo, False)
return render_template("account_checking.html", rows = tabs, dbname=session['database'])
# 其它删改查需求，是由Ajax提交的post
datas = json.loads(request.get_data(as_text=True))
function = datas["function"]
datas = datas["inputdata"]
# print(function)
if function == "delete":
res = {'info':'删除成功！', 'errs':[]}
for data in datas:
err = db.account_del(data, False)
if err != '0':
res['errs'].append([data[u"账户.账户号"],err])
if len(res['errs']) != 0:
res['info'] = "删除失败！"
return json.dumps(res)
elif function == "insert":
res = {'info':'插入成功！', 'errs':[]}
for data in datas:
err = db.account_insert(data, False)
if err != '0':
res['errs'].append([data[u"账户.账户号"],err])
if len(res['errs']) != 0:
res['info'] = "插入失败！"
return json.dumps(res)
elif function == "update":
res = {'info':'修改成功！', 'errs':[]}
for data in datas:
err = db.account_update(data, False)
if err != '0':
res['errs'].append([data[u"账户.账户号"],err])
if len(res['errs']) != 0:
res['info'] = "修改失败！"
return json.dumps(res)
else:
return render_template("account_checking.html", rows = tabs, dbname=session['database'])
# 贷款管理页面
@app.route("/loan", methods=(["GET", "POST"]))
def loan():
if 'username' in session:
db = MyDefSQL(session['username'], session['password'],
session['ipaddr'], session['database'])
err = db.login()
else:
return redirect(url_for('login'))
tabs = db.showloan()
if tabs==None:
tabs=""
if request.method == "POST":
if 'search' in request.form:
# 是由search表单提交的post请求
searchinfo = {}
for key,value in request.form.items():
# 注意这里key和value仍然是unicode编码，统一在db.py中处理！
if len(value) != 0 and key!='search':
# 做第一层过滤，使得可以表单中某块信息不填
searchinfo[key] = value
tabs = db.loan_search(searchinfo)
return render_template("loan.html", rows = tabs, dbname=session['database'])
# 其它删改查需求，是由Ajax提交的post
datas = json.loads(request.get_data(as_text=True))
function = datas["function"]
datas = datas["inputdata"]
# print(function)
if function == "delete":
res = {'info':'删除成功！', 'errs':[]}
for data in datas:
err = db.loan_del(data)
if err != '0':
res['errs'].append([data[u"贷款号"],err])
if len(res['errs']) != 0:
res['info'] = "删除失败！"
return json.dumps(res)
elif function == "insert":
res = {'info':'插入成功！', 'errs':[]}
for data in datas:
err = db.loan_insert(data)
if err != '0':
res['errs'].append([data[u"贷款号"],err])
if len(res['errs']) != 0:
res['info'] = "插入失败！"
return json.dumps(res)
elif function == "release":
res = {'info':'贷款发放成功！', 'errs':[]}
for data in datas:
err = db.loan_release(data)
if err != '0':
res['errs'].append([data[u"贷款号"],err])
if len(res['errs']) != 0:
res['info'] = "贷款发放失败！"
return json.dumps(res)
else:
return render_template("loan.html", rows = tabs, dbname=session['database'])
# 业务统计
# 按月
@app.route("/statistic/month")
def month():
if 'username' in session:
db = MyDefSQL(session['username'], session['password'],
session['ipaddr'], session['database'])
err = db.login()
else:
return redirect(url_for('login'))
tabs = db.statistic_month()
return render_template("statistic.html", how = u'月份', rows = tabs, dbname=session['database'])
# 按季度
@app.route("/statistic/quarter")
def quarter():
if 'username' in session:
db = MyDefSQL(session['username'], session['password'],
session['ipaddr'], session['database'])
err = db.login()
else:
return redirect(url_for('login'))
tabs = db.statistic_quarter()
return render_template("statistic.html", how = u'季度', rows = tabs, dbname=session['database'])
# 按年
@app.route("/statistic/year")
def year():
if 'username' in session:
db = MyDefSQL(session['username'], session['password'],
session['ipaddr'], session['database'])
err = db.login()
else:
return redirect(url_for('login'))
tabs = db.statistic_year()
return render_template("statistic.html", how = u'年份', rows = tabs, dbname=session['database'])
# 测试新html页面
@app.route("/test")
def test():
if 'username' in session:
db = MyDefSQL(session['username'], session['password'],
session['ipaddr'], session['database'])
err = db.login()
else:
return redirect(url_for('login'))
tabs = db.showtablecnt()
return render_template("test.html", rows = tabs)
# 测试URL下返回html page
@app.route("/hello")
def hello():
return "hello world!"
#返回不存在页面的处理
@app.errorhandler(404)
def not_found(e):
return render_template("404.html")
if __name__ == "__main__":
app.run(host = "0.0.0.0", debug=True) | [
"flask.render_template",
"flask.Flask",
"flask.request.get_data",
"flask.request.form.items",
"json.dumps",
"flask.url_for",
"flask_bootstrap.Bootstrap"
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how=u'季度', rows=tabs, dbname=session[\n 'database'])\n", (12644, 12717), False, 'from flask import render_template\n'), ((13063, 13151), 'flask.render_template', 'render_template', (['"""statistic.html"""'], {'how': 'u"""年份"""', 'rows': 'tabs', 'dbname': "session['database']"}), "('statistic.html', how=u'年份', rows=tabs, dbname=session[\n 'database'])\n", (13078, 13151), False, 'from flask import render_template\n'), ((13496, 13535), 'flask.render_template', 'render_template', (['"""test.html"""'], {'rows': 'tabs'}), "('test.html', rows=tabs)\n", (13511, 13535), False, 'from flask import render_template\n'), ((13694, 13721), 'flask.render_template', 'render_template', (['"""404.html"""'], {}), "('404.html')\n", (13709, 13721), False, 'from flask import render_template\n'), ((1347, 1376), 'flask.render_template', 'render_template', (['"""login.html"""'], {}), "('login.html')\n", (1362, 1376), False, 'from flask import render_template\n'), ((2232, 2300), 'flask.render_template', 'render_template', (['"""table.html"""'], {'rows': 'tabs', 'dbname': "session['database']"}), "('table.html', rows=tabs, dbname=session['database'])\n", (2247, 2300), False, 'from flask import render_template\n'), ((4661, 4732), 'flask.render_template', 'render_template', (['"""customer.html"""'], {'rows': 'tabs', 'dbname': "session['database']"}), "('customer.html', rows=tabs, dbname=session['database'])\n", (4676, 4732), False, 'from flask import render_template\n'), ((7045, 7122), 'flask.render_template', 'render_template', (['"""account_saving.html"""'], {'rows': 'tabs', 'dbname': "session['database']"}), "('account_saving.html', rows=tabs, dbname=session['database'])\n", (7060, 7122), False, 'from flask import render_template\n'), ((9438, 9517), 'flask.render_template', 'render_template', (['"""account_checking.html"""'], {'rows': 'tabs', 'dbname': "session['database']"}), "('account_checking.html', rows=tabs, dbname=session['database'])\n", (9453, 9517), False, 'from flask import render_template\n'), ((11754, 11821), 'flask.render_template', 'render_template', (['"""loan.html"""'], {'rows': 'tabs', 'dbname': "session['database']"}), "('loan.html', rows=tabs, dbname=session['database'])\n", (11769, 11821), False, 'from flask import render_template\n'), ((986, 1029), 'flask.render_template', 'render_template', (['"""login_fail.html"""'], {'err': 'err'}), "('login_fail.html', err=err)\n", (1001, 1029), False, 'from flask import render_template\n'), ((1856, 1872), 'flask.url_for', 'url_for', (['"""login"""'], {}), "('login')\n", (1863, 1872), False, 'from flask import url_for\n'), ((2003, 2069), 'flask.render_template', 'render_template', (['"""table.html"""'], {'rows': '""""""', 'dbname': "session['database']"}), "('table.html', rows='', dbname=session['database'])\n", (2018, 2069), False, 'from flask import render_template\n'), ((2610, 2626), 'flask.url_for', 'url_for', (['"""login"""'], {}), "('login')\n", (2617, 2626), False, 'from flask import url_for\n'), 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'flask.render_template', 'render_template', (['"""account_saving.html"""'], {'rows': 'tabs', 'dbname': "session['database']"}), "('account_saving.html', rows=tabs, dbname=session['database'])\n", (5631, 5693), False, 'from flask import render_template\n'), ((5757, 5787), 'flask.request.get_data', 'request.get_data', ([], {'as_text': '(True)'}), '(as_text=True)\n', (5773, 5787), False, 'from flask import request, make_response\n'), ((6246, 6261), 'json.dumps', 'json.dumps', (['res'], {}), '(res)\n', (6256, 6261), False, 'import json\n'), ((7440, 7456), 'flask.url_for', 'url_for', (['"""login"""'], {}), "('login')\n", (7447, 7456), False, 'from flask import url_for\n'), ((7703, 7723), 'flask.request.form.items', 'request.form.items', ([], {}), '()\n', (7721, 7723), False, 'from flask import request, make_response\n'), ((8004, 8083), 'flask.render_template', 'render_template', (['"""account_checking.html"""'], {'rows': 'tabs', 'dbname': "session['database']"}), "('account_checking.html', 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'(res)\n', (11719, 11724), False, 'import json\n')] |
# STL imports
import random
import logging
import string
import time
import datetime
import random
import struct
import sys
from functools import wraps
# Third party imports
import numpy as np
import faker
from faker.providers import BaseProvider
logging.getLogger('faker').setLevel(logging.ERROR)
sys.path.append('.')
# grpc
from milvus.grpc_gen import milvus_pb2
def gen_vectors(num, dim):
return [[random.random() for _ in range(dim)] for _ in range(num)]
def gen_single_vector(dim):
return [[random.random() for _ in range(dim)]]
def gen_vector(nb, d, seed=np.random.RandomState(1234)):
xb = seed.rand(nb, d).astype("float32")
return xb.tolist()
def gen_unique_str(str=None):
prefix = "".join(random.choice(string.ascii_letters + string.digits) for _ in range(8))
return prefix if str is None else str + "_" + prefix
def get_current_day():
return time.strftime('%Y-%m-%d', time.localtime())
def get_last_day(day):
tmp = datetime.datetime.now() - datetime.timedelta(days=day)
return tmp.strftime('%Y-%m-%d')
def get_next_day(day):
tmp = datetime.datetime.now() + datetime.timedelta(days=day)
return tmp.strftime('%Y-%m-%d')
def gen_long_str(num):
string = ''
for _ in range(num):
char = random.choice('tomorrow')
string += char
def gen_one_binary(topk):
ids = [random.randrange(10000000, 99999999) for _ in range(topk)]
distances = [random.random() for _ in range(topk)]
return milvus_pb2.TopKQueryResult(struct.pack(str(topk) + 'l', *ids), struct.pack(str(topk) + 'd', *distances))
def gen_nq_binaries(nq, topk):
return [gen_one_binary(topk) for _ in range(nq)]
def fake_query_bin_result(nq, topk):
return gen_nq_binaries(nq, topk)
class FakerProvider(BaseProvider):
def collection_name(self):
return 'collection_names' + str(random.randint(1000, 9999))
def name(self):
return 'name' + str(random.randint(1000, 9999))
def dim(self):
return random.randint(0, 999)
fake = faker.Faker()
fake.add_provider(FakerProvider)
def collection_name_factory():
return fake.collection_name()
def records_factory(dimension, nq):
return [[random.random() for _ in range(dimension)] for _ in range(nq)]
def binary_records_factory(dimension, nq):
def binary_record(bsize):
s_m = "abcdefghijklmnopqrstuvwxyz"
s_list = [s_m[random.randint(0, 25)] for _ in range(bsize)]
s = "".join(s_list)
return bytes(s, encoding="ASCII")
bs = dimension // 8
return [binary_record(bs) for _ in range(nq)]
def integer_factory(nq):
return [random.randint(0, 128) for _ in range(nq)]
def time_it(func):
@wraps(func)
def inner(*args, **kwrgs):
pref = time.perf_counter()
result = func(*args, **kwrgs)
delt = time.perf_counter() - pref
print(f"[{func.__name__}][{delt:.4}s]")
return result
return inner
| [
"logging.getLogger",
"random.choice",
"random.randrange",
"time.perf_counter",
"functools.wraps",
"datetime.timedelta",
"faker.Faker",
"datetime.datetime.now",
"random.random",
"time.localtime",
"sys.path.append",
"random.randint",
"numpy.random.RandomState"
] | [((301, 321), 'sys.path.append', 'sys.path.append', (['"""."""'], {}), "('.')\n", (316, 321), False, 'import sys\n'), ((2030, 2043), 'faker.Faker', 'faker.Faker', ([], {}), '()\n', (2041, 2043), False, 'import faker\n'), ((578, 605), 'numpy.random.RandomState', 'np.random.RandomState', (['(1234)'], {}), '(1234)\n', (599, 605), True, 'import numpy as np\n'), ((2697, 2708), 'functools.wraps', 'wraps', (['func'], {}), '(func)\n', (2702, 2708), False, 'from functools import wraps\n'), ((249, 275), 'logging.getLogger', 'logging.getLogger', (['"""faker"""'], {}), "('faker')\n", (266, 275), False, 'import logging\n'), ((918, 934), 'time.localtime', 'time.localtime', ([], {}), '()\n', (932, 934), False, 'import time\n'), ((971, 994), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (992, 994), False, 'import datetime\n'), ((997, 1025), 'datetime.timedelta', 'datetime.timedelta', ([], {'days': 'day'}), '(days=day)\n', (1015, 1025), False, 'import datetime\n'), ((1097, 1120), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (1118, 1120), False, 'import datetime\n'), ((1123, 1151), 'datetime.timedelta', 'datetime.timedelta', ([], {'days': 'day'}), '(days=day)\n', (1141, 1151), False, 'import datetime\n'), ((1269, 1294), 'random.choice', 'random.choice', (['"""tomorrow"""'], {}), "('tomorrow')\n", (1282, 1294), False, 'import random\n'), ((1357, 1393), 'random.randrange', 'random.randrange', (['(10000000)', '(99999999)'], {}), '(10000000, 99999999)\n', (1373, 1393), False, 'import random\n'), ((1433, 1448), 'random.random', 'random.random', ([], {}), '()\n', (1446, 1448), False, 'import random\n'), ((1998, 2020), 'random.randint', 'random.randint', (['(0)', '(999)'], {}), '(0, 999)\n', (2012, 2020), False, 'import random\n'), ((2628, 2650), 'random.randint', 'random.randint', (['(0)', '(128)'], {}), '(0, 128)\n', (2642, 2650), False, 'import random\n'), ((2755, 2774), 'time.perf_counter', 'time.perf_counter', ([], {}), '()\n', (2772, 2774), False, 'import time\n'), ((410, 425), 'random.random', 'random.random', ([], {}), '()\n', (423, 425), False, 'import random\n'), ((511, 526), 'random.random', 'random.random', ([], {}), '()\n', (524, 526), False, 'import random\n'), ((728, 779), 'random.choice', 'random.choice', (['(string.ascii_letters + string.digits)'], {}), '(string.ascii_letters + string.digits)\n', (741, 779), False, 'import random\n'), ((2195, 2210), 'random.random', 'random.random', ([], {}), '()\n', (2208, 2210), False, 'import random\n'), ((2828, 2847), 'time.perf_counter', 'time.perf_counter', ([], {}), '()\n', (2845, 2847), False, 'import time\n'), ((1858, 1884), 'random.randint', 'random.randint', (['(1000)', '(9999)'], {}), '(1000, 9999)\n', (1872, 1884), False, 'import random\n'), ((1935, 1961), 'random.randint', 'random.randint', (['(1000)', '(9999)'], {}), '(1000, 9999)\n', (1949, 1961), False, 'import random\n'), ((2398, 2419), 'random.randint', 'random.randint', (['(0)', '(25)'], {}), '(0, 25)\n', (2412, 2419), False, 'import random\n')] |
import math
import os
import random
import re
import sys
first_multiple_input = input().rstrip().split()
n = int(first_multiple_input[0])
m = int(first_multiple_input[1])
matrix = []
if (n>0 and m>0 and n<100 and m< 100):
for _ in range(n):
matrix_item = input()
matrix.append(matrix_item)
for _ in range(m):
string = ""
for cols in range (m):
for rows in range (n):
string += matrix[rows][cols]
output = re.sub(r"\b[!@#$%& ]+\b"," ", string)
print(output)
| [
"re.sub"
] | [((468, 507), 're.sub', 're.sub', (['"""\\\\b[!@#$%& ]+\\\\b"""', '""" """', 'string'], {}), "('\\\\b[!@#$%& ]+\\\\b', ' ', string)\n", (474, 507), False, 'import re\n')] |
import torch
import torch.nn.functional as F
import os.path as osp
import json
from torch_geometric.utils import precision, recall
from torch_geometric.utils import f1_score, accuracy
from torch.utils.tensorboard import SummaryWriter
def train_epoch_classifier(model, train_loader, len_train, optimizer, device):
model.train()
loss_all = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
output, _ = model(data.x, data.edge_index, batch=data.batch)
loss = F.nll_loss(F.log_softmax(output, dim=-1), data.y)
loss.backward()
loss_all += data.num_graphs * loss.item()
optimizer.step()
return loss_all / len_train
def test_classifier(model, loader, device):
model.eval()
y = torch.tensor([]).long().to(device)
yp = torch.tensor([]).long().to(device)
loss_all = 0
for data in loader:
data = data.to(device)
pred, _ = model(data.x, data.edge_index, batch=data.batch)
loss = F.nll_loss(F.log_softmax(pred, dim=-1), data.y)
pred = pred.max(dim=1)[1]
y = torch.cat([y, data.y])
yp = torch.cat([yp, pred])
loss_all += data.num_graphs * loss.item()
return (
accuracy(y, yp),
precision(y, yp, model.num_output).mean().item(),
recall(y, yp, model.num_output).mean().item(),
f1_score(y, yp, model.num_output).mean().item(),
loss_all
)
def train_cycle_classifier(task, train_loader, val_loader, test_loader, len_train, len_val, len_test,
model, optimizer, device, base_path, epochs):
best_acc = (0, 0)
writer = SummaryWriter(base_path + '/plots')
for epoch in range(epochs):
loss = train_epoch_classifier(model, train_loader, len_train, optimizer, device)
writer.add_scalar('Loss/train', loss, epoch)
train_acc, train_prec, train_rec, train_f1, _ = test_classifier(model, train_loader, device)
val_acc, val_prec, val_rec, val_f1, l = test_classifier(model, val_loader, device)
writer.add_scalar('Accuracy/train', train_acc, epoch)
writer.add_scalar('Accuracy/val', val_acc, epoch)
writer.add_scalar('Loss/val', l / len_val, epoch)
print(f'Epoch: {epoch}, Loss: {loss:.5f}')
print(f'Train -> Acc: {train_acc:.5f} Rec: {train_rec:.5f} \
Prec: {train_prec:.5f} F1: {train_f1:.5f}')
print(f'Val -> Acc: {val_acc:.5f} Rec: {val_rec:.5f} \
Prec: {val_prec:.5f} F1: {val_f1:.5f}')
if best_acc[1] < val_acc:
best_acc = train_acc, val_acc
torch.save(
model.state_dict(),
osp.join(base_path + '/ckpt/',
model.__class__.__name__ + ".pth")
)
print("New best model saved!")
with open(base_path + '/best_result.json', 'w') as outfile:
json.dump({'train_acc': train_acc,
'val_acc': val_acc,
'train_rec': train_rec,
'val_rec': val_rec,
'train_f1': train_f1,
'val_f1': val_f1,
'train_prec': train_prec,
'val_prec': val_prec}, outfile)
def train_epoch_regressor(model, train_loader, len_train, optimizer, device):
model.train()
loss_all = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
output, _ = model(data.x.float(), data.edge_index, batch=data.batch)
loss = F.mse_loss(output, data.y)
loss.backward()
loss_all += data.num_graphs * loss.item()
optimizer.step()
return loss_all / len_train
def test_regressor(model, loader, len_loader, device):
model.eval()
loss_all = 0
for data in loader:
data = data.to(device)
pred, _ = model(data.x.float(), data.edge_index, batch=data.batch)
loss = F.mse_loss(pred, data.y).detach()
loss_all += data.num_graphs * loss.item()
return loss_all / len_loader
def train_cycle_regressor(task, train_loader, val_loader, test_loader,
len_train, len_val, len_test, model,
optimizer, device, base_path, epochs):
best_acc = (0, 0)
writer = SummaryWriter(base_path + '/plots')
best_error = (+10000, +10000)
for epoch in range(epochs):
loss = train_epoch_regressor(model, train_loader, len_train, optimizer, device)
writer.add_scalar('Loss/train', loss, epoch)
train_error = test_regressor(model, train_loader, len_train, device)
val_error = test_regressor(model, val_loader, len_val, device)
writer.add_scalar('MSE/train', train_error, epoch)
writer.add_scalar('MSE/test', val_error, epoch)
print(f'Epoch: {epoch}, Loss: {loss:.5f}')
print(f'Training Error: {train_error:.5f}')
print(f'Val Error: {val_error:.5f}')
if best_error[1] > val_error:
best_error = train_error, val_error
torch.save(
model.state_dict(),
osp.join(base_path + '/ckpt/',
model.__class__.__name__ + ".pth")
)
print("New best model saved!")
with open(base_path + '/best_result.json', 'w') as outfile:
json.dump({'train_error': train_error,
'val_error': val_error}, outfile)
| [
"torch.utils.tensorboard.SummaryWriter",
"torch.nn.functional.mse_loss",
"json.dump",
"os.path.join",
"torch.tensor",
"torch_geometric.utils.f1_score",
"torch.nn.functional.log_softmax",
"torch_geometric.utils.recall",
"torch_geometric.utils.precision",
"torch_geometric.utils.accuracy",
"torch.c... | [((1661, 1696), 'torch.utils.tensorboard.SummaryWriter', 'SummaryWriter', (["(base_path + '/plots')"], {}), "(base_path + '/plots')\n", (1674, 1696), False, 'from torch.utils.tensorboard import SummaryWriter\n'), ((4366, 4401), 'torch.utils.tensorboard.SummaryWriter', 'SummaryWriter', (["(base_path + '/plots')"], {}), "(base_path + '/plots')\n", (4379, 4401), False, 'from torch.utils.tensorboard import SummaryWriter\n'), ((1108, 1130), 'torch.cat', 'torch.cat', (['[y, data.y]'], {}), '([y, data.y])\n', (1117, 1130), False, 'import torch\n'), ((1144, 1165), 'torch.cat', 'torch.cat', (['[yp, pred]'], {}), '([yp, pred])\n', (1153, 1165), False, 'import torch\n'), ((1239, 1254), 'torch_geometric.utils.accuracy', 'accuracy', (['y', 'yp'], {}), '(y, yp)\n', (1247, 1254), False, 'from torch_geometric.utils import f1_score, accuracy\n'), ((3612, 3638), 'torch.nn.functional.mse_loss', 'F.mse_loss', (['output', 'data.y'], {}), '(output, data.y)\n', (3622, 3638), True, 'import torch.nn.functional as F\n'), ((537, 566), 'torch.nn.functional.log_softmax', 'F.log_softmax', (['output'], {'dim': '(-1)'}), '(output, dim=-1)\n', (550, 566), True, 'import torch.nn.functional as F\n'), ((1024, 1051), 'torch.nn.functional.log_softmax', 'F.log_softmax', (['pred'], {'dim': '(-1)'}), '(pred, dim=-1)\n', (1037, 1051), True, 'import torch.nn.functional as F\n'), ((2689, 2754), 'os.path.join', 'osp.join', (["(base_path + '/ckpt/')", "(model.__class__.__name__ + '.pth')"], {}), "(base_path + '/ckpt/', model.__class__.__name__ + '.pth')\n", (2697, 2754), True, 'import os.path as osp\n'), ((2926, 3130), 'json.dump', 'json.dump', (["{'train_acc': train_acc, 'val_acc': val_acc, 'train_rec': train_rec,\n 'val_rec': val_rec, 'train_f1': train_f1, 'val_f1': val_f1,\n 'train_prec': train_prec, 'val_prec': val_prec}", 'outfile'], {}), "({'train_acc': train_acc, 'val_acc': val_acc, 'train_rec':\n train_rec, 'val_rec': val_rec, 'train_f1': train_f1, 'val_f1': val_f1,\n 'train_prec': train_prec, 'val_prec': val_prec}, outfile)\n", (2935, 3130), False, 'import json\n'), ((4010, 4034), 'torch.nn.functional.mse_loss', 'F.mse_loss', (['pred', 'data.y'], {}), '(pred, data.y)\n', (4020, 4034), True, 'import torch.nn.functional as F\n'), ((5187, 5252), 'os.path.join', 'osp.join', (["(base_path + '/ckpt/')", "(model.__class__.__name__ + '.pth')"], {}), "(base_path + '/ckpt/', model.__class__.__name__ + '.pth')\n", (5195, 5252), True, 'import os.path as osp\n'), ((5424, 5496), 'json.dump', 'json.dump', (["{'train_error': train_error, 'val_error': val_error}", 'outfile'], {}), "({'train_error': train_error, 'val_error': val_error}, outfile)\n", (5433, 5496), False, 'import json\n'), ((779, 795), 'torch.tensor', 'torch.tensor', (['[]'], {}), '([])\n', (791, 795), False, 'import torch\n'), ((823, 839), 'torch.tensor', 'torch.tensor', (['[]'], {}), '([])\n', (835, 839), False, 'import torch\n'), ((1264, 1298), 'torch_geometric.utils.precision', 'precision', (['y', 'yp', 'model.num_output'], {}), '(y, yp, model.num_output)\n', (1273, 1298), False, 'from torch_geometric.utils import precision, recall\n'), ((1322, 1353), 'torch_geometric.utils.recall', 'recall', (['y', 'yp', 'model.num_output'], {}), '(y, yp, model.num_output)\n', (1328, 1353), False, 'from torch_geometric.utils import precision, recall\n'), ((1377, 1410), 'torch_geometric.utils.f1_score', 'f1_score', (['y', 'yp', 'model.num_output'], {}), '(y, yp, model.num_output)\n', (1385, 1410), False, 'from torch_geometric.utils import f1_score, accuracy\n')] |
# nuScenes dev-kit.
# Code written by <NAME>, 2020.
import argparse
import gc
import os
import random
from typing import List
from collections import defaultdict
import cv2
import tqdm
from nuimages.nuimages import NuImages
def render_images(nuim: NuImages,
mode: str = 'all',
cam_name: str = None,
log_name: str = None,
sample_limit: int = 50,
filter_categories: List[str] = None,
out_type: str = 'image',
out_dir: str = '~/Downloads/nuImages',
cleanup: bool = True) -> None:
"""
Render a random selection of images and save them to disk.
Note: The images rendered here are keyframes only.
:param nuim: NuImages instance.
:param mode: What to render:
"image" for the image without annotations,
"annotated" for the image with annotations,
"trajectory" for a rendering of the trajectory of the vehice,
"all" to render all of the above separately.
:param cam_name: Only render images from a particular camera, e.g. "CAM_BACK'.
:param log_name: Only render images from a particular log, e.g. "n013-2018-09-04-13-30-50+0800".
:param sample_limit: Maximum number of samples (images) to render. Note that the mini split only includes 50 images.
:param filter_categories: Specify a list of object_ann category names. Every sample that is rendered must
contain annotations of any of those categories.
:param out_type: The output type as one of the following:
'image': Renders a single image for the image keyframe of each sample.
'video': Renders a video for all images/pcls in the clip associated with each sample.
:param out_dir: Folder to render the images to.
:param cleanup: Whether to delete images after rendering the video. Not relevant for out_type == 'image'.
"""
# Check and convert inputs.
assert out_type in ['image', 'video'], ' Error: Unknown out_type %s!' % out_type
all_modes = ['image', 'annotated', 'trajectory']
assert mode in all_modes + ['all'], 'Error: Unknown mode %s!' % mode
assert not (out_type == 'video' and mode == 'trajectory'), 'Error: Cannot render "trajectory" for videos!'
if mode == 'all':
if out_type == 'image':
modes = all_modes
elif out_type == 'video':
modes = [m for m in all_modes if m not in ['annotated', 'trajectory']]
else:
raise Exception('Error" Unknown mode %s!' % mode)
else:
modes = [mode]
if filter_categories is not None:
category_names = [c['name'] for c in nuim.category]
for category_name in filter_categories:
assert category_name in category_names, 'Error: Invalid object_ann category %s!' % category_name
# Create output folder.
out_dir = os.path.expanduser(out_dir)
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
# Filter by camera.
sample_tokens = [s['token'] for s in nuim.sample]
if cam_name is not None:
sample_tokens_cam = []
for sample_token in sample_tokens:
sample = nuim.get('sample', sample_token)
key_camera_token = sample['key_camera_token']
sensor = nuim.shortcut('sample_data', 'sensor', key_camera_token)
if sensor['channel'] == cam_name:
sample_tokens_cam.append(sample_token)
sample_tokens = sample_tokens_cam
# Filter by log.
if log_name is not None:
sample_tokens_cleaned = []
for sample_token in sample_tokens:
sample = nuim.get('sample', sample_token)
log = nuim.get('log', sample['log_token'])
if log['logfile'] == log_name:
sample_tokens_cleaned.append(sample_token)
sample_tokens = sample_tokens_cleaned
# Filter samples by category.
if filter_categories is not None:
# Get categories in each sample.
sd_to_object_cat_names = defaultdict(lambda: set())
for object_ann in nuim.object_ann:
category = nuim.get('category', object_ann['category_token'])
sd_to_object_cat_names[object_ann['sample_data_token']].add(category['name'])
# Filter samples.
sample_tokens_cleaned = []
for sample_token in sample_tokens:
sample = nuim.get('sample', sample_token)
key_camera_token = sample['key_camera_token']
category_names = sd_to_object_cat_names[key_camera_token]
if any([c in category_names for c in filter_categories]):
sample_tokens_cleaned.append(sample_token)
sample_tokens = sample_tokens_cleaned
# Get a random selection of samples.
random.shuffle(sample_tokens)
# Limit number of samples.
sample_tokens = sample_tokens[:sample_limit]
print('Rendering %s for mode %s to folder %s...' % (out_type, mode, out_dir))
for sample_token in tqdm.tqdm(sample_tokens):
sample = nuim.get('sample', sample_token)
log = nuim.get('log', sample['log_token'])
log_name = log['logfile']
key_camera_token = sample['key_camera_token']
sensor = nuim.shortcut('sample_data', 'sensor', key_camera_token)
sample_cam_name = sensor['channel']
sd_tokens = nuim.get_sample_content(sample_token)
# We cannot render a video if there are missing camera sample_datas.
if len(sd_tokens) < 13 and out_type == 'video':
print('Warning: Skipping video for sample token %s, as not all 13 frames exist!' % sample_token)
continue
for mode in modes:
out_path_prefix = os.path.join(out_dir, '%s_%s_%s_%s' % (log_name, sample_token, sample_cam_name, mode))
if out_type == 'image':
write_image(nuim, key_camera_token, mode, '%s.jpg' % out_path_prefix)
elif out_type == 'video':
write_video(nuim, sd_tokens, mode, out_path_prefix, cleanup=cleanup)
def write_video(nuim: NuImages,
sd_tokens: List[str],
mode: str,
out_path_prefix: str,
cleanup: bool = True) -> None:
"""
Render a video by combining all the images of type mode for each sample_data.
:param nuim: NuImages instance.
:param sd_tokens: All sample_data tokens in chronological order.
:param mode: The mode - see render_images().
:param out_path_prefix: The file prefix used for the images and video.
:param cleanup: Whether to delete images after rendering the video.
"""
# Loop through each frame to create the video.
out_paths = []
for i, sd_token in enumerate(sd_tokens):
out_path = '%s_%d.jpg' % (out_path_prefix, i)
out_paths.append(out_path)
write_image(nuim, sd_token, mode, out_path)
# Create video.
first_im = cv2.imread(out_paths[0])
freq = 2 # Display frequency (Hz).
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
video_path = '%s.avi' % out_path_prefix
out = cv2.VideoWriter(video_path, fourcc, freq, first_im.shape[1::-1])
# Load each image and add to the video.
for out_path in out_paths:
im = cv2.imread(out_path)
out.write(im)
# Delete temporary image if requested.
if cleanup:
os.remove(out_path)
# Finalize video.
out.release()
def write_image(nuim: NuImages, sd_token: str, mode: str, out_path: str) -> None:
"""
Render a single image of type mode for the given sample_data.
:param nuim: NuImages instance.
:param sd_token: The sample_data token.
:param mode: The mode - see render_images().
:param out_path: The file to write the image to.
"""
if mode == 'annotated':
nuim.render_image(sd_token, annotation_type='all', out_path=out_path)
elif mode == 'image':
nuim.render_image(sd_token, annotation_type='none', out_path=out_path)
elif mode == 'trajectory':
sample_data = nuim.get('sample_data', sd_token)
nuim.render_trajectory(sample_data['sample_token'], out_path=out_path)
else:
raise Exception('Error: Unknown mode %s!' % mode)
# Trigger garbage collection to avoid memory overflow from the render functions.
gc.collect()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Render a random selection of images and save them to disk.')
parser.add_argument('--seed', type=int, default=42) # Set to 0 to disable.
parser.add_argument('--version', type=str, default='v1.0-mini')
parser.add_argument('--dataroot', type=str, default='/data/sets/nuimages')
parser.add_argument('--verbose', type=int, default=1)
parser.add_argument('--mode', type=str, default='all')
parser.add_argument('--cam_name', type=str, default=None)
parser.add_argument('--log_name', type=str, default=None)
parser.add_argument('--sample_limit', type=int, default=50)
parser.add_argument('--filter_categories', action='append')
parser.add_argument('--out_type', type=str, default='image')
parser.add_argument('--out_dir', type=str, default='~/Downloads/nuImages')
args = parser.parse_args()
# Set random seed for reproducible image selection.
if args.seed != 0:
random.seed(args.seed)
# Initialize NuImages class.
nuim_ = NuImages(version=args.version, dataroot=args.dataroot, verbose=bool(args.verbose), lazy=False)
# Render images.
render_images(nuim_, mode=args.mode, cam_name=args.cam_name, log_name=args.log_name, sample_limit=args.sample_limit,
filter_categories=args.filter_categories, out_type=args.out_type, out_dir=args.out_dir)
| [
"random.shuffle",
"argparse.ArgumentParser",
"os.makedirs",
"tqdm.tqdm",
"os.path.join",
"random.seed",
"cv2.VideoWriter",
"os.path.isdir",
"cv2.VideoWriter_fourcc",
"gc.collect",
"cv2.imread",
"os.path.expanduser",
"os.remove"
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import os
import sys
cwd = os.getcwd()
sys.path.append(cwd)
import pickle
import numpy as np
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from plot.helper import plot_task, plot_weights, plot_rf_z_max, plot_rf_quad, plot_vector_traj
tasks = [
'com_pos', 'com_vel', 'chassis_quat', 'chassis_ang_vel', 'toeFL_pos',
'toeFL_vel', 'toeFR_pos', 'toeFR_vel', 'toeRR_pos', 'toeRR_vel',
'toeRL_pos', 'toeRL_vel'
]
weights = [
'w_com', 'w_chassis_ori', 'w_toeFL', 'w_toeFR', 'w_toeRR', 'w_toeRL'
]
rf_z = ['rf_z_max_toeFL', 'rf_z_max_toeFR', 'rf_z_max_toeRR', 'rf_z_max_toeRL']
time = []
phase = []
rf_cmd = []
des, act = dict(), dict()
for topic in tasks:
des[topic] = []
act[topic] = []
w = dict()
for topic in weights:
w[topic] = []
rf_z_max = dict()
for topic in rf_z:
rf_z_max[topic] = []
with open('data/pnc.pkl', 'rb') as file:
while True:
try:
d = pickle.load(file)
time.append(d['time'])
phase.append(d['phase'])
for topic in tasks:
des[topic].append(d[topic + '_des'])
act[topic].append(d[topic])
for topic in weights:
w[topic].append(d[topic])
for topic in rf_z:
rf_z_max[topic].append(d[topic])
rf_cmd.append(d['rf_cmd'])
except EOFError:
break
for k, v in des.items():
des[k] = np.stack(v, axis=0)
for k, v in act.items():
act[k] = np.stack(v, axis=0)
rf_cmd = np.stack(rf_cmd, axis=0)
phase = np.stack(phase, axis=0)
## =============================================================================
## Plot Task
## =============================================================================
plot_task(time, des['com_pos'], act['com_pos'], des['com_vel'], act['com_vel'],
phase, 'com lin')
plot_task(time, des['chassis_quat'], act['chassis_quat'],
des['chassis_ang_vel'], act['chassis_ang_vel'], phase, 'pelvis ori')
plot_task(time, des['toeFL_pos'], act['toeFL_pos'], des['toeFL_vel'],
act['toeFL_vel'], phase, 'left foot lin')
plot_task(time, des['toeFR_pos'], act['toeFR_pos'], des['toeFR_vel'],
act['toeFR_vel'], phase, 'left foot ori')
plot_task(time, des['toeRR_pos'], act['toeRR_pos'], des['toeRR_vel'],
act['toeRR_vel'], phase, 'right foot lin')
plot_task(time, des['toeRL_pos'], act['toeRL_pos'], des['toeRL_vel'],
act['toeRL_vel'], phase, 'right foot ori')
## =============================================================================
## Plot WBC Solutions
## =============================================================================
plot_rf_quad(time, rf_cmd, phase)
## =============================================================================
## Plot Weights and Max Reaction Force Z
## =============================================================================
plot_weights(time, w, phase)
plot_rf_z_max(time, rf_z_max, phase)
plt.show()
| [
"matplotlib.use",
"plot.helper.plot_rf_z_max",
"pickle.load",
"os.getcwd",
"numpy.stack",
"plot.helper.plot_weights",
"plot.helper.plot_task",
"sys.path.append",
"plot.helper.plot_rf_quad",
"matplotlib.pyplot.show"
] | [((27, 38), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (36, 38), False, 'import os\n'), ((39, 59), 'sys.path.append', 'sys.path.append', (['cwd'], {}), '(cwd)\n', (54, 59), False, 'import sys\n'), ((112, 135), 'matplotlib.use', 'matplotlib.use', (['"""TkAgg"""'], {}), "('TkAgg')\n", (126, 135), False, 'import matplotlib\n'), ((1524, 1548), 'numpy.stack', 'np.stack', (['rf_cmd'], {'axis': '(0)'}), '(rf_cmd, axis=0)\n', (1532, 1548), True, 'import numpy as np\n'), ((1557, 1580), 'numpy.stack', 'np.stack', (['phase'], {'axis': '(0)'}), '(phase, axis=0)\n', (1565, 1580), True, 'import numpy as np\n'), ((1758, 1860), 'plot.helper.plot_task', 'plot_task', (['time', "des['com_pos']", "act['com_pos']", "des['com_vel']", "act['com_vel']", 'phase', '"""com lin"""'], {}), "(time, des['com_pos'], act['com_pos'], des['com_vel'], act[\n 'com_vel'], phase, 'com lin')\n", (1767, 1860), False, 'from plot.helper import plot_task, plot_weights, plot_rf_z_max, plot_rf_quad, plot_vector_traj\n'), ((1867, 1998), 'plot.helper.plot_task', 'plot_task', (['time', "des['chassis_quat']", "act['chassis_quat']", "des['chassis_ang_vel']", "act['chassis_ang_vel']", 'phase', '"""pelvis ori"""'], {}), "(time, des['chassis_quat'], act['chassis_quat'], des[\n 'chassis_ang_vel'], act['chassis_ang_vel'], phase, 'pelvis ori')\n", (1876, 1998), False, 'from plot.helper import plot_task, plot_weights, plot_rf_z_max, plot_rf_quad, plot_vector_traj\n'), ((2005, 2121), 'plot.helper.plot_task', 'plot_task', (['time', "des['toeFL_pos']", "act['toeFL_pos']", "des['toeFL_vel']", "act['toeFL_vel']", 'phase', '"""left foot lin"""'], {}), "(time, des['toeFL_pos'], act['toeFL_pos'], des['toeFL_vel'], act[\n 'toeFL_vel'], phase, 'left foot lin')\n", (2014, 2121), False, 'from plot.helper import plot_task, plot_weights, plot_rf_z_max, plot_rf_quad, plot_vector_traj\n'), ((2128, 2244), 'plot.helper.plot_task', 'plot_task', (['time', "des['toeFR_pos']", "act['toeFR_pos']", "des['toeFR_vel']", "act['toeFR_vel']", 'phase', '"""left foot ori"""'], {}), "(time, des['toeFR_pos'], act['toeFR_pos'], des['toeFR_vel'], act[\n 'toeFR_vel'], phase, 'left foot ori')\n", (2137, 2244), False, 'from plot.helper import plot_task, plot_weights, plot_rf_z_max, plot_rf_quad, plot_vector_traj\n'), ((2251, 2368), 'plot.helper.plot_task', 'plot_task', (['time', "des['toeRR_pos']", "act['toeRR_pos']", "des['toeRR_vel']", "act['toeRR_vel']", 'phase', '"""right foot lin"""'], {}), "(time, des['toeRR_pos'], act['toeRR_pos'], des['toeRR_vel'], act[\n 'toeRR_vel'], phase, 'right foot lin')\n", (2260, 2368), False, 'from plot.helper import plot_task, plot_weights, plot_rf_z_max, plot_rf_quad, plot_vector_traj\n'), ((2375, 2492), 'plot.helper.plot_task', 'plot_task', (['time', "des['toeRL_pos']", "act['toeRL_pos']", "des['toeRL_vel']", "act['toeRL_vel']", 'phase', '"""right foot ori"""'], {}), "(time, des['toeRL_pos'], act['toeRL_pos'], des['toeRL_vel'], act[\n 'toeRL_vel'], phase, 'right foot ori')\n", (2384, 2492), False, 'from plot.helper import plot_task, plot_weights, plot_rf_z_max, plot_rf_quad, plot_vector_traj\n'), ((2683, 2716), 'plot.helper.plot_rf_quad', 'plot_rf_quad', (['time', 'rf_cmd', 'phase'], {}), '(time, rf_cmd, phase)\n', (2695, 2716), False, 'from plot.helper import plot_task, plot_weights, plot_rf_z_max, plot_rf_quad, plot_vector_traj\n'), ((2921, 2949), 'plot.helper.plot_weights', 'plot_weights', (['time', 'w', 'phase'], {}), '(time, w, phase)\n', (2933, 2949), False, 'from plot.helper import plot_task, plot_weights, plot_rf_z_max, plot_rf_quad, plot_vector_traj\n'), ((2951, 2987), 'plot.helper.plot_rf_z_max', 'plot_rf_z_max', (['time', 'rf_z_max', 'phase'], {}), '(time, rf_z_max, phase)\n', (2964, 2987), False, 'from plot.helper import plot_task, plot_weights, plot_rf_z_max, plot_rf_quad, plot_vector_traj\n'), ((2989, 2999), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (2997, 2999), True, 'import matplotlib.pyplot as plt\n'), ((1437, 1456), 'numpy.stack', 'np.stack', (['v'], {'axis': '(0)'}), '(v, axis=0)\n', (1445, 1456), True, 'import numpy as np\n'), ((1495, 1514), 'numpy.stack', 'np.stack', (['v'], {'axis': '(0)'}), '(v, axis=0)\n', (1503, 1514), True, 'import numpy as np\n'), ((941, 958), 'pickle.load', 'pickle.load', (['file'], {}), '(file)\n', (952, 958), False, 'import pickle\n')] |
from pyramid.httpexceptions import HTTPConflict
from h.auth.util import client_authority
from h.presenters import TrustedUserJSONPresenter
from h.schemas import ValidationError
from h.schemas.api.user import CreateUserAPISchema, UpdateUserAPISchema
from h.services.user_unique import DuplicateUserError
from h.views.api.config import api_config
from h.views.api.exceptions import PayloadError
@api_config(
versions=["v1", "v2"],
route_name="api.user_read",
request_method="GET",
link_name="user.read",
description="Fetch a user",
permission="read",
)
def read(context, _request):
"""
Fetch a user.
This API endpoint allows authorized clients (those able to provide a valid
Client ID and Client Secret) to read users in their authority.
"""
return TrustedUserJSONPresenter(context.user).asdict()
@api_config(
versions=["v1", "v2"],
route_name="api.users",
request_method="POST",
link_name="user.create",
description="Create a new user",
permission="create",
)
def create(request):
"""
Create a user.
This API endpoint allows authorised clients (those able to provide a valid
Client ID and Client Secret) to create users in their authority. These
users are created pre-activated, and are unable to log in to the web
service directly.
Note: the authority-enforcement logic herein is, by necessity, strange.
The API accepts an ``authority`` parameter but the only valid value for
the param is the client's verified authority. If the param does not
match the client's authority, ``ValidationError`` is raised.
:raises ValidationError: if ``authority`` param does not match client
authority
:raises HTTPConflict: if user already exists
"""
client_authority_ = client_authority(request)
schema = CreateUserAPISchema()
appstruct = schema.validate(_json_payload(request))
# Enforce authority match
if appstruct["authority"] != client_authority_:
raise ValidationError(
"authority '{auth_param}' does not match client authority".format(
auth_param=appstruct["authority"]
)
)
user_unique_service = request.find_service(name="user_unique")
try:
user_unique_service.ensure_unique(appstruct, authority=client_authority_)
except DuplicateUserError as err:
raise HTTPConflict(str(err)) from err
user_signup_service = request.find_service(name="user_signup")
user = user_signup_service.signup(require_activation=False, **appstruct)
presenter = TrustedUserJSONPresenter(user)
return presenter.asdict()
@api_config(
versions=["v1", "v2"],
route_name="api.user",
request_method="PATCH",
link_name="user.update",
description="Update a user",
permission="update",
)
def update(user, request):
"""
Update a user.
This API endpoint allows authorised clients (those able to provide a valid
Client ID and Client Secret) to update users in their authority.
"""
schema = UpdateUserAPISchema()
appstruct = schema.validate(_json_payload(request))
user_update_service = request.find_service(name="user_update")
user = user_update_service.update(user, **appstruct)
presenter = TrustedUserJSONPresenter(user)
return presenter.asdict()
def _json_payload(request):
try:
return request.json_body
except ValueError as err:
raise PayloadError() from err
| [
"h.views.api.exceptions.PayloadError",
"h.views.api.config.api_config",
"h.schemas.api.user.CreateUserAPISchema",
"h.presenters.TrustedUserJSONPresenter",
"h.auth.util.client_authority",
"h.schemas.api.user.UpdateUserAPISchema"
] | [((397, 558), 'h.views.api.config.api_config', 'api_config', ([], {'versions': "['v1', 'v2']", 'route_name': '"""api.user_read"""', 'request_method': '"""GET"""', 'link_name': '"""user.read"""', 'description': '"""Fetch a user"""', 'permission': '"""read"""'}), "(versions=['v1', 'v2'], route_name='api.user_read',\n request_method='GET', link_name='user.read', description='Fetch a user',\n permission='read')\n", (407, 558), False, 'from h.views.api.config import api_config\n'), ((850, 1018), 'h.views.api.config.api_config', 'api_config', ([], {'versions': "['v1', 'v2']", 'route_name': '"""api.users"""', 'request_method': '"""POST"""', 'link_name': '"""user.create"""', 'description': '"""Create a new user"""', 'permission': '"""create"""'}), "(versions=['v1', 'v2'], route_name='api.users', request_method=\n 'POST', link_name='user.create', description='Create a new user',\n permission='create')\n", (860, 1018), False, 'from h.views.api.config import api_config\n'), ((2676, 2840), 'h.views.api.config.api_config', 'api_config', ([], {'versions': "['v1', 'v2']", 'route_name': '"""api.user"""', 'request_method': '"""PATCH"""', 'link_name': '"""user.update"""', 'description': '"""Update a user"""', 'permission': '"""update"""'}), "(versions=['v1', 'v2'], route_name='api.user', request_method=\n 'PATCH', link_name='user.update', description='Update a user',\n permission='update')\n", (2686, 2840), False, 'from h.views.api.config import api_config\n'), ((1823, 1848), 'h.auth.util.client_authority', 'client_authority', (['request'], {}), '(request)\n', (1839, 1848), False, 'from h.auth.util import client_authority\n'), ((1862, 1883), 'h.schemas.api.user.CreateUserAPISchema', 'CreateUserAPISchema', ([], {}), '()\n', (1881, 1883), False, 'from h.schemas.api.user import CreateUserAPISchema, UpdateUserAPISchema\n'), ((2612, 2642), 'h.presenters.TrustedUserJSONPresenter', 'TrustedUserJSONPresenter', (['user'], {}), '(user)\n', (2636, 2642), False, 'from h.presenters import TrustedUserJSONPresenter\n'), ((3083, 3104), 'h.schemas.api.user.UpdateUserAPISchema', 'UpdateUserAPISchema', ([], {}), '()\n', (3102, 3104), False, 'from h.schemas.api.user import CreateUserAPISchema, UpdateUserAPISchema\n'), ((3303, 3333), 'h.presenters.TrustedUserJSONPresenter', 'TrustedUserJSONPresenter', (['user'], {}), '(user)\n', (3327, 3333), False, 'from h.presenters import TrustedUserJSONPresenter\n'), ((799, 837), 'h.presenters.TrustedUserJSONPresenter', 'TrustedUserJSONPresenter', (['context.user'], {}), '(context.user)\n', (823, 837), False, 'from h.presenters import TrustedUserJSONPresenter\n'), ((3480, 3494), 'h.views.api.exceptions.PayloadError', 'PayloadError', ([], {}), '()\n', (3492, 3494), False, 'from h.views.api.exceptions import PayloadError\n')] |
import tkinter as tk
from presentacion.formulario import FormularioPersona
def centrar_ventana(ventana):
aplicacion_ancho = 550
aplicacion_largo = 650
pantall_ancho = ventana.winfo_screenwidth()
pantall_largo = ventana.winfo_screenheight()
x = int((pantall_ancho/2) - (aplicacion_ancho/2))
y = int((pantall_largo/2) - (aplicacion_largo/2))
return ventana.geometry(f"{aplicacion_ancho}x{aplicacion_largo}+{x}+{y}")
try:
ventana=tk.Tk()
centrar_ventana(ventana)
ventana.title("Formulario")
form = FormularioPersona(ventana)
ventana.mainloop()
except Exception as e:
print("Existe un error : ", e)
| [
"presentacion.formulario.FormularioPersona",
"tkinter.Tk"
] | [((466, 473), 'tkinter.Tk', 'tk.Tk', ([], {}), '()\n', (471, 473), True, 'import tkinter as tk\n'), ((581, 607), 'presentacion.formulario.FormularioPersona', 'FormularioPersona', (['ventana'], {}), '(ventana)\n', (598, 607), False, 'from presentacion.formulario import FormularioPersona\n')] |
from django.urls import path
from .views import teams_all, team_vote
urlpatterns = [
path('teams/all', teams_all, name="teams_all"),
path('teams/<int:pk>', team_vote, name="team_vote"),
] | [
"django.urls.path"
] | [((90, 136), 'django.urls.path', 'path', (['"""teams/all"""', 'teams_all'], {'name': '"""teams_all"""'}), "('teams/all', teams_all, name='teams_all')\n", (94, 136), False, 'from django.urls import path\n'), ((142, 193), 'django.urls.path', 'path', (['"""teams/<int:pk>"""', 'team_vote'], {'name': '"""team_vote"""'}), "('teams/<int:pk>', team_vote, name='team_vote')\n", (146, 193), False, 'from django.urls import path\n')] |
from Dataset import *
from Network import *
from Functions import *
import os
from fastai.distributed import *
import argparse
import torch
try:
#from apex.parallel import DistributedDataParallel as DDP
from apex.fp16_utils import *
from apex import amp, optimizers
from apex.multi_tensor_apply import multi_tensor_applier
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this example.")
from tqdm import tqdm
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu_id', type=str, default='0,1', help='which gpu to use')
parser.add_argument('--path', type=str, default='/N/u/soodn/Carbonate/hubmap-kidney-segmentation', help='path of csv file with DNA sequences and labels')
parser.add_argument('--epochs', type=int, default=32, help='number of epochs to train')
parser.add_argument('--batch_size', type=int, default=64, help='size of each batch during training')
parser.add_argument('--weight_decay', type=float, default=1e-5, help='weight dacay used in optimizer')
parser.add_argument('--save_freq', type=int, default=1, help='saving checkpoints per save_freq epochs')
parser.add_argument('--dropout', type=float, default=.1, help='transformer dropout')
parser.add_argument('--lr', type=float, default=1e-3, help='learning rate')
parser.add_argument('--nfolds', type=int, default=4, help='number of cross validation folds')
parser.add_argument('--fold', type=int, default=0, help='which fold to train')
parser.add_argument('--val_freq', type=int, default=1, help='which fold to train')
parser.add_argument('--workers', type=int, default=8, help='number of workers for dataloader')
parser.add_argument('--expansion', type=int, default=64, help='number of expansion pixels')
parser.add_argument('--gradient_accumulation_steps', type=int, default=1, help='gradient_accumulation_steps')
parser.add_argument('--transfer', default=1, help='transfer learning activated')
opts = parser.parse_args()
return opts
opts=get_args()
#set up gpu
os.environ["CUDA_VISIBLE_DEVICES"] = opts.gpu_id
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
os.system('mkdir models')
os.system('mkdir logs')
#dice = Dice_th_pred(np.arange(0.2,0.7,0.01))
#datasets and dataloaders
dataset = HuBMAPDataset(path=opts.path, fold=opts.fold, nfolds=opts.nfolds, train=True, tfms=get_aug())
val_dataset = HuBMAPDataset(path=opts.path, fold=opts.fold, nfolds=opts.nfolds, train=False)
dataloader = DataLoader(dataset, batch_size=opts.batch_size, shuffle=True, num_workers=opts.workers, drop_last=True)
val_dataloader = DataLoader(val_dataset, batch_size=opts.batch_size, shuffle=False, num_workers=opts.workers, drop_last=True)
#model and optimizer
model = UneXt50().cuda()
#optimizer = Ranger(model.parameters(), lr=opts.lr, weight_decay=opts.weight_decay)
optimizer = torch.optim.Adam(model.parameters(), lr=opts.lr, weight_decay=opts.weight_decay)
# scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer=optimizer, pct_start=0.1, div_factor=1e3,
# max_lr=1e-3, epochs=opts.epochs, steps_per_epoch=len(dataloader))
criterion=nn.BCEWithLogitsLoss()
opt_level = 'O1'
model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level)
model = nn.DataParallel(model)
####### Transfer learning #######
if opts.transfer == 1:
best_model_path = "models_scratch/fold4.pth"
state_dict = torch.load(best_model_path)
model.load_state_dict(state_dict)
#some more things
logger=CSVLogger(['epoch','train_loss','val_loss','dice_coef'],f"logs/log_fold{opts.fold}.csv")
metric=Dice_soft()
best_metric=0
#training
scheduler=torch.optim.lr_scheduler.OneCycleLR(optimizer=optimizer, pct_start=0.2, div_factor=1e2, max_lr=1e-4, epochs=opts.epochs, steps_per_epoch=len(dataloader))
for epoch in range(opts.epochs):
train_loss=0
model.train(True)
for data in tqdm(dataloader):
img=data['img'].to(device)
mask=data['mask'].to(device)
img=cutout(img)
output=model(img)
loss=criterion(output,mask)
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1)
#if step%opts.gradient_accumulation_steps==0:
optimizer.step()
scheduler.step()
optimizer.zero_grad()
train_loss+=loss.item()
#break
train_loss/=len(dataloader)
print(f"### validating for epoch {epoch} ###")
val_loss=0
model.eval()
metric.reset()
with torch.no_grad():
for data in tqdm(val_dataloader):
if img.shape[0]%2!=0:
img=img[:-1]
mask=mask[:-1]
img=data['img'].to(device)
mask=data['mask'].to(device)
shape=img.shape
output=model(img)[:,:,opts.expansion//2:-opts.expansion//2,opts.expansion//2:-opts.expansion//2]
output[output != output] = 0
mask=mask[:,:,opts.expansion//2:-opts.expansion//2,opts.expansion//2:-opts.expansion//2]
metric.accumulate(output.detach(), mask)
loss=criterion(output,mask)
val_loss+=loss.item()
val_loss/=len(val_dataloader)
metric_this_epoch=metric.value
# metric_this_epoch=val_loss
logger.log([epoch+1,train_loss,val_loss,metric_this_epoch])
if metric_this_epoch>best_metric:
torch.save(model.state_dict(),f'models/fold{opts.fold}.pth')
best_metric=metric_this_epoch | [
"apex.amp.scale_loss",
"argparse.ArgumentParser",
"torch.load",
"tqdm.tqdm",
"torch.no_grad",
"apex.amp.initialize",
"torch.cuda.is_available",
"os.system"
] | [((2228, 2253), 'os.system', 'os.system', (['"""mkdir models"""'], {}), "('mkdir models')\n", (2237, 2253), False, 'import os\n'), ((2254, 2277), 'os.system', 'os.system', (['"""mkdir logs"""'], {}), "('mkdir logs')\n", (2263, 2277), False, 'import os\n'), ((3298, 3351), 'apex.amp.initialize', 'amp.initialize', (['model', 'optimizer'], {'opt_level': 'opt_level'}), '(model, optimizer, opt_level=opt_level)\n', (3312, 3351), False, 'from apex import amp, optimizers\n'), ((517, 542), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (540, 542), False, 'import argparse\n'), ((3507, 3534), 'torch.load', 'torch.load', (['best_model_path'], {}), '(best_model_path)\n', (3517, 3534), False, 'import torch\n'), ((3984, 4000), 'tqdm.tqdm', 'tqdm', (['dataloader'], {}), '(dataloader)\n', (3988, 4000), False, 'from tqdm import tqdm\n'), ((2190, 2215), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (2213, 2215), False, 'import torch\n'), ((4645, 4660), 'torch.no_grad', 'torch.no_grad', ([], {}), '()\n', (4658, 4660), False, 'import torch\n'), ((4682, 4702), 'tqdm.tqdm', 'tqdm', (['val_dataloader'], {}), '(val_dataloader)\n', (4686, 4702), False, 'from tqdm import tqdm\n'), ((4174, 4205), 'apex.amp.scale_loss', 'amp.scale_loss', (['loss', 'optimizer'], {}), '(loss, optimizer)\n', (4188, 4205), False, 'from apex import amp, optimizers\n')] |
"""
NODE model definition and experiment setup.
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
https://arxiv.org/abs/1909.06312
Model details:
https://pytorch-tabular.readthedocs.io/en/latest/models/#nodemodel
"""
import logging
import os.path
import shutil
from sklearn.metrics import classification_report
from omegaconf import OmegaConf
import optuna
from optuna.samplers import TPESampler
from pytorch_tabular import TabularModel
from pytorch_tabular.models import NodeConfig
from pytorch_tabular.config import (
DataConfig, OptimizerConfig, TrainerConfig, ExperimentConfig)
from pytorch_tabular.utils import get_class_weighted_cross_entropy
from optuna_utils import OptunaExperiments, run_experiments
LOGGER = logging.getLogger(__name__)
LABEL_COL = "retweet_label"
# updated by train.py before running
config = OmegaConf.create(
{"max_epochs": 50,
"lr_exp_min": -4,
"lr_exp_max": -3,
"alpha_exp_min": -4,
"alpha_exp_max": -3,
"batch_exp_min": 7,
"batch_exp_max": 8,
"num_trees_min": 512,
"num_trees_max": 2560,
"num_trees_step": 512,
"depth_min": 4,
"depth_max": 6,
"categorical_cols": [
"entities.urls", "entities.media", "user_in_net",
"has_covid_keyword", "user.followers_isna",
"users_mention_isna", "following_users_isna",
"users_reply_isna"],
"exp_log_freq": 100,
"seed": 1,
"num_workers": 24,
"embed_categorical": True}
)
class Experiments(OptunaExperiments):
def __init__(
self,
train_data,
val_data,
train_labels,
val_labels,
experiment_root,
config):
self.train_data_joined = train_data.copy()
self.train_data_joined[LABEL_COL] = train_labels
self.val_data_joined = val_data.copy()
self.val_data_joined[LABEL_COL] = val_labels
self.experiment_root = experiment_root
self.config = config
self.study = self.create_study()
self.best_score = None
self.cat_col_names = config.categorical_cols
self.num_col_names = [
c for c in train_data.columns if c not in config.categorical_cols]
self.data_config = DataConfig(
target=[LABEL_COL],
continuous_cols=self.num_col_names,
categorical_cols=self.cat_col_names,
normalize_continuous_features=False,
num_workers=config.num_workers)
self.weighted_loss = get_class_weighted_cross_entropy(
train_labels.values.ravel(), mu=0.1)
def create_study(self):
sampler = TPESampler(seed=self.config.study_seed)
study = optuna.create_study(sampler=sampler, direction="maximize")
for trial_dict in self.config.default_trials:
study.enqueue_trial(trial_dict)
return study
def optimize(self):
self.study.optimize(self.objective, n_trials=self.config.n_trials)
self.store_study()
def objective(self, trial):
lr_exp = trial.suggest_int(
"lr_exp", self.config.lr_exp_min, self.config.lr_exp_max)
lr = 10 ** lr_exp
alpha_exp = trial.suggest_int(
"alpha_exp", self.config.alpha_exp_min, self.config.alpha_exp_max)
alpha = 10 ** alpha_exp
batch_exp = trial.suggest_int(
"batch_exp", self.config.batch_exp_min, self.config.batch_exp_max)
batch_size = 2 ** batch_exp
num_trees = trial.suggest_int(
"num_trees",
self.config.num_trees_min, self.config.num_trees_max,
self.config.num_trees_step
)
depth = trial.suggest_int(
"depth", self.config.depth_min, self.config.depth_max)
experiment_path = self.config.experiment_root
checkpoints_path = os.path.join(experiment_path, "checkpoints")
tb_logs = os.path.join(experiment_path, "tb_logs")
run_name = "category_embedding"
# store all just for the current optuna run
if os.path.exists(checkpoints_path):
shutil.rmtree(checkpoints_path)
if os.path.exists(tb_logs):
shutil.rmtree(tb_logs)
trainer_config = TrainerConfig(
auto_lr_find=False,
gpus=1,
deterministic=True,
batch_size=batch_size,
max_epochs=self.config.max_epochs,
checkpoints_path=checkpoints_path,
)
optimizer_config = OptimizerConfig(
optimizer="AdamW",
optimizer_params={"weight_decay": alpha}
)
model_config = NodeConfig(
task="classification",
learning_rate=lr,
loss=self.weighted_loss,
num_trees=num_trees,
depth=depth,
embed_categorical=self.config.embed_categorical,
)
experiment_config = ExperimentConfig(
project_name=tb_logs,
run_name=run_name,
exp_log_freq=self.config.exp_log_freq
)
tabular_model = TabularModel(
data_config=self.data_config,
model_config=model_config,
optimizer_config=optimizer_config,
trainer_config=trainer_config,
experiment_config=experiment_config
)
tabular_model.fit(
train=self.train_data_joined,
validation=self.val_data_joined,
seed=self.config.seed,
loss=self.weighted_loss)
result = tabular_model.evaluate(self.val_data_joined)
LOGGER.info(result)
pred_df = tabular_model.predict(self.val_data_joined)
val_predictions = pred_df.prediction.values
out = classification_report(
self.val_data_joined[LABEL_COL].values, val_predictions,
digits=3, output_dict=True)
LOGGER.info(out)
f1 = out["macro avg"]["f1-score"]
if self.best_score is None or f1 > self.best_score:
self.best_score = f1
self.store_results(tabular_model, out)
self.store_study()
return f1
def run(config):
run_experiments(
config=config,
experiments_class=Experiments)
| [
"logging.getLogger",
"pytorch_tabular.models.NodeConfig",
"pytorch_tabular.config.OptimizerConfig",
"sklearn.metrics.classification_report",
"optuna_utils.run_experiments",
"pytorch_tabular.config.ExperimentConfig",
"pytorch_tabular.config.TrainerConfig",
"shutil.rmtree",
"optuna.samplers.TPESampler... | [((749, 776), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (766, 776), False, 'import logging\n'), ((852, 1394), 'omegaconf.OmegaConf.create', 'OmegaConf.create', (["{'max_epochs': 50, 'lr_exp_min': -4, 'lr_exp_max': -3, 'alpha_exp_min': -4,\n 'alpha_exp_max': -3, 'batch_exp_min': 7, 'batch_exp_max': 8,\n 'num_trees_min': 512, 'num_trees_max': 2560, 'num_trees_step': 512,\n 'depth_min': 4, 'depth_max': 6, 'categorical_cols': ['entities.urls',\n 'entities.media', 'user_in_net', 'has_covid_keyword',\n 'user.followers_isna', 'users_mention_isna', 'following_users_isna',\n 'users_reply_isna'], 'exp_log_freq': 100, 'seed': 1, 'num_workers': 24,\n 'embed_categorical': True}"], {}), "({'max_epochs': 50, 'lr_exp_min': -4, 'lr_exp_max': -3,\n 'alpha_exp_min': -4, 'alpha_exp_max': -3, 'batch_exp_min': 7,\n 'batch_exp_max': 8, 'num_trees_min': 512, 'num_trees_max': 2560,\n 'num_trees_step': 512, 'depth_min': 4, 'depth_max': 6,\n 'categorical_cols': ['entities.urls', 'entities.media', 'user_in_net',\n 'has_covid_keyword', 'user.followers_isna', 'users_mention_isna',\n 'following_users_isna', 'users_reply_isna'], 'exp_log_freq': 100,\n 'seed': 1, 'num_workers': 24, 'embed_categorical': True})\n", (868, 1394), False, 'from omegaconf import OmegaConf\n'), ((6141, 6202), 'optuna_utils.run_experiments', 'run_experiments', ([], {'config': 'config', 'experiments_class': 'Experiments'}), '(config=config, experiments_class=Experiments)\n', (6156, 6202), False, 'from optuna_utils import OptunaExperiments, run_experiments\n'), ((2263, 2444), 'pytorch_tabular.config.DataConfig', 'DataConfig', ([], {'target': '[LABEL_COL]', 'continuous_cols': 'self.num_col_names', 'categorical_cols': 'self.cat_col_names', 'normalize_continuous_features': '(False)', 'num_workers': 'config.num_workers'}), '(target=[LABEL_COL], continuous_cols=self.num_col_names,\n categorical_cols=self.cat_col_names, normalize_continuous_features=\n False, 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'from pytorch_tabular.config import DataConfig, OptimizerConfig, TrainerConfig, ExperimentConfig\n'), ((4499, 4575), 'pytorch_tabular.config.OptimizerConfig', 'OptimizerConfig', ([], {'optimizer': '"""AdamW"""', 'optimizer_params': "{'weight_decay': alpha}"}), "(optimizer='AdamW', optimizer_params={'weight_decay': alpha})\n", (4514, 4575), False, 'from pytorch_tabular.config import DataConfig, OptimizerConfig, TrainerConfig, ExperimentConfig\n'), ((4634, 4802), 'pytorch_tabular.models.NodeConfig', 'NodeConfig', ([], {'task': '"""classification"""', 'learning_rate': 'lr', 'loss': 'self.weighted_loss', 'num_trees': 'num_trees', 'depth': 'depth', 'embed_categorical': 'self.config.embed_categorical'}), "(task='classification', learning_rate=lr, loss=self.weighted_loss,\n num_trees=num_trees, depth=depth, embed_categorical=self.config.\n embed_categorical)\n", (4644, 4802), False, 'from pytorch_tabular.models import NodeConfig\n'), ((4906, 5007), 'pytorch_tabular.config.ExperimentConfig', 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from django.db import models
from django.core.urlresolvers import reverse
from djnfusion import server, key
from django.conf import settings
from jsonfield import JSONField
# TODO: change to this. Currently doesnt work. may have something to do with
# the server not in __init__
# from packages.providers.infusionsoft import server, key
from .managers import InfusionsoftTagManager, PackagePurchaseManager
from packages.managers import PackageManager
def remove_unused(_dict):
return_dict = {}
for _key, _value in _dict.iteritems():
if _value:
return_dict[_key] = _value
return return_dict
def setdictattrs(obj, _dict):
_dict = remove_unused(_dict)
for _key, _value in _dict.iteritems():
setattr(obj, _key, _value)
class Package(models.Model):
"""
Base for package classes
"""
name = models.CharField(max_length=255)
courses = models.ManyToManyField("courses.Course", null=True, blank=True)
lessons = models.ManyToManyField("lessons.Lesson", null=True, blank=True)
groups = models.ManyToManyField("facebook_groups.FacebookGroup", null=True,
blank=True)
journals = models.ManyToManyField("journals.JournalQuestion", null=True,
blank=True)
objects = PackageManager()
def __unicode__(self):
return u'{}'.format(self.name if self.name else 'Package')
def get_absolute_url(self):
return reverse('packages:detail', kwargs={'pk': self.pk})
class PackagePurchase(models.Model):
"""
User's purchased packages.
"""
INACTIVE = 0
ACTIVE = 1
EXPIRED = 2
STATUS_CHOICES = [
[INACTIVE, 'Inactive'],
[ACTIVE, 'Active'],
[EXPIRED, 'Expired'],
]
user = models.ForeignKey(settings.AUTH_USER_MODEL)
package = models.ForeignKey('Package')
status = models.IntegerField(choices=STATUS_CHOICES, default=INACTIVE)
data = JSONField(blank=True, null=True)
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
objects = PackagePurchaseManager()
def __unicode__(self):
return u'{0} => {1}'.format(self.user, self.package)
def set_status(self, status):
self.status = status
self.save()
class InfusionsoftPackage(Package):
"""
Package with infusionsoft api hooks
"""
subscription_id = models.TextField(blank=True, null=True)
product_id = models.TextField(blank=True, null=True)
cycle = models.TextField(blank=True, null=True)
frequency = models.TextField(blank=True, null=True)
pre_authorize_amount = models.TextField(blank=True, null=True)
prorate = models.TextField(blank=True, null=True)
active = models.TextField(blank=True, null=True)
plan_price = models.TextField(blank=True, null=True)
product_price = models.TextField(blank=True, null=True)
description = models.TextField(blank=True, null=True)
status = models.TextField(blank=True, null=True)
action_set_id = models.TextField(blank=True, null=True)
tag = models.OneToOneField("InfusionsoftTag", blank=True, null=True)
purchase_url = models.URLField(blank=True, null=True)
def save(self, *args, **kwargs):
sync_data = self._get_sync_data(product_id=self.product_id) if self.product_id else None
if sync_data:
setdictattrs(self, sync_data)
return super(InfusionsoftPackage, self).save(*args, **kwargs)
def sync(self):
sync_data = self._get_sync_data()
if sync_data:
setdictattrs(self, sync_data)
self.save()
def _get_sync_data(self, product_id=None):
subscription_data = self._get_subscription_data(product_id)
product_data = self._get_product_data(product_id)
if subscription_data and product_data:
package_data = dict({
"id": self.id,
"pk": self.pk,
"action_set_id": self.action_set_id,
"name": product_data.get("ProductName"),
"subscription_id": subscription_data.get("Id"),
"product_id": subscription_data.get("ProductId"),
"cycle": subscription_data.get("Cycle"),
"frequency": subscription_data.get("Frequency"),
"prorate": subscription_data.get("Prorate"),
"active": subscription_data.get("Active"),
"plan_price": subscription_data.get("PlanPrice"),
"product_price": product_data.get("ProductPrice"),
"description": product_data.get("Description"),
"status": product_data.get("Status"),
})
elif product_data:
# product but not subscription
package_data = dict({
"id": self.id,
"pk": self.pk,
"action_set_id": self.action_set_id,
"name": product_data.get("ProductName"),
"product_id": product_data.get("Id"),
"product_price": product_data.get("ProductPrice"),
"description": product_data.get("Description"),
"status": product_data.get("Status"),
})
return package_data if package_data else None
def _get_subscription_data(self, product_id=None):
product_id = product_id if product_id else self.product_id
if product_id:
results = server.DataService.findByField(key, "SubscriptionPlan",
10, 0, "productid", product_id,
["Id", "ProductId", "Cycle", "Frequency", "PreAuthorizeAmount",
"Prorate", "Active", "PlanPrice"]);
return results[0] if len(results) else None
def _get_product_data(self, product_id=None):
product_id = product_id if product_id else self.product_id
if product_id:
results = server.DataService.findByField(key, "Product",
10, 0, "id", product_id,
["Id", "ProductName", "ProductPrice", "Description",
"Status", "IsPackage"]);
return results[0] if len(results) else None
def cancel_subscription(self, contactId, actionSetId):
results = server.ContactService.runActionSequence(key, contactId,
actionSetId)
return results
@property
def price(self):
return self.plan_price if self.plan_price else self.product_price
class InfusionsoftTag(models.Model):
'''
Infusionsoft Tag (ContactGroup)
'''
remote_id = models.TextField()
group_category_id = models.TextField(blank=True, null=True)
group_name = models.TextField(blank=True, null=True)
group_description = models.TextField(blank=True, null=True)
objects = InfusionsoftTagManager()
def __unicode__(self):
return u'{}'.format(self.group_name if self.group_name else u'InfusionsoftTag Object')
def save(self, *args, **kwargs):
remote_id = kwargs.get('remote_id') if kwargs.get('remote_id') else self.remote_id
sync_data = self._get_sync_data(remote_id=remote_id) if remote_id else None
if sync_data:
obj = InfusionsoftTag(**sync_data)
return super(InfusionsoftTag, obj).save(*args, **kwargs)
else:
return super(InfusionsoftTag, self).save(*args, **kwargs)
def sync(self):
sync_data = self._get_sync_data()
if sync_data:
self = InfusionsoftTag(**sync_data)
self.save()
def _get_sync_data(self, remote_id=None):
provider_data = self._get_provider_data(remote_id)
if provider_data:
tag_data = dict({
"id": self.id,
"pk": self.pk,
"remote_id": provider_data.get("Id"),
"group_category_id": provider_data.get("GroupCategoryId"),
"group_name": provider_data.get("GroupName"),
"group_description": provider_data.get("GroupDescription"),
})
return tag_data
def _get_provider_data(self, remote_id=None):
remote_id = remote_id if remote_id else self.remote_id
if remote_id:
results = server.DataService.findByField(key, "ContactGroup",
10, 0, "id", remote_id,
["Id", "GroupCategoryId", "GroupName", "GroupDescription"]);
return results[0] if len(results) else None
| [
"djnfusion.server.ContactService.runActionSequence",
"django.db.models.OneToOneField",
"django.db.models.TextField",
"django.db.models.IntegerField",
"django.db.models.ForeignKey",
"django.db.models.ManyToManyField",
"django.core.urlresolvers.reverse",
"packages.managers.PackageManager",
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'PreAuthorizeAmount', 'Prorate', 'Active', 'PlanPrice'])\n", (5566, 5730), False, 'from djnfusion import server, key\n'), ((5991, 6144), 'djnfusion.server.DataService.findByField', 'server.DataService.findByField', (['key', '"""Product"""', '(10)', '(0)', '"""id"""', 'product_id', "['Id', 'ProductName', 'ProductPrice', 'Description', 'Status', 'IsPackage']"], {}), "(key, 'Product', 10, 0, 'id', product_id, [\n 'Id', 'ProductName', 'ProductPrice', 'Description', 'Status', 'IsPackage'])\n", (6021, 6144), False, 'from djnfusion import server, key\n'), ((8345, 8484), 'djnfusion.server.DataService.findByField', 'server.DataService.findByField', (['key', '"""ContactGroup"""', '(10)', '(0)', '"""id"""', 'remote_id', "['Id', 'GroupCategoryId', 'GroupName', 'GroupDescription']"], {}), "(key, 'ContactGroup', 10, 0, 'id', remote_id,\n ['Id', 'GroupCategoryId', 'GroupName', 'GroupDescription'])\n", (8375, 8484), False, 'from djnfusion import server, key\n')] |
import numpy as np
import matplotlib.pyplot as plt
from tqdm import trange
class CFG:
n = 10
mean = 0.0
variance = 1.0
t = 1000
esp = [0, 0.01, 0.05, 0.1, 0.15, 0.2]
n_try = 2000
class bandit():
def __init__(self, m, v):
self.m = m
self.v = v
self.mean = 0.0
self.cnt = 0
def reset(self):
self.mean = 0.0
self.cnt = 0
def get_reward(self):
reward = self.v * np.random.randn() + self.m
return reward
def update(self, reward):
self.cnt += 1
self.mean = self.mean + 1/self.cnt * (reward - self.mean)
def get_result(e):
bandits = [bandit(np.random.randn(),CFG.variance) for i in range(CFG.n)]
res = []
global cnt
for _ in range(CFG.t):
if (np.random.random()<e):
choose = np.random.choice(CFG.n)
else:
choose = np.argmax([ban.mean for ban in bandits])
val = bandits[choose].get_reward()
res.append(val)
bandits[choose].update(val)
# print(res)
return res
plt.figure(figsize=(20, 10))
for e in CFG.esp:
res = np.zeros(CFG.t)
for tr in trange(CFG.n_try):
res += get_result(e)
print(res.shape)
res /= CFG.n_try
# print(res)
plt.plot(res, label = e)
print(f'done {e}')
plt.xlabel('step')
plt.ylabel('average reward')
plt.legend()
plt.savefig('figure_2_1.png')
plt.show()
| [
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.ylabel",
"numpy.random.random",
"numpy.random.choice",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"numpy.argmax",
"matplotlib.pyplot.figure",
"numpy.zeros",
"numpy.random.randn",
"tqdm.trange",
"matplotlib.pyplot.legend",
"matplotlib... | [((1088, 1116), 'matplotlib.pyplot.figure', 'plt.figure', ([], {'figsize': '(20, 10)'}), '(figsize=(20, 10))\n', (1098, 1116), True, 'import matplotlib.pyplot as plt\n'), ((1336, 1354), 'matplotlib.pyplot.xlabel', 'plt.xlabel', (['"""step"""'], {}), "('step')\n", (1346, 1354), True, 'import matplotlib.pyplot as plt\n'), ((1355, 1383), 'matplotlib.pyplot.ylabel', 'plt.ylabel', (['"""average reward"""'], {}), "('average reward')\n", (1365, 1383), True, 'import matplotlib.pyplot as plt\n'), ((1384, 1396), 'matplotlib.pyplot.legend', 'plt.legend', ([], {}), '()\n', (1394, 1396), True, 'import matplotlib.pyplot as plt\n'), ((1397, 1426), 'matplotlib.pyplot.savefig', 'plt.savefig', (['"""figure_2_1.png"""'], {}), "('figure_2_1.png')\n", (1408, 1426), True, 'import matplotlib.pyplot as plt\n'), ((1427, 1437), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (1435, 1437), True, 'import matplotlib.pyplot as plt\n'), ((1146, 1161), 'numpy.zeros', 'np.zeros', (['CFG.t'], {}), '(CFG.t)\n', (1154, 1161), True, 'import numpy as np\n'), ((1176, 1193), 'tqdm.trange', 'trange', (['CFG.n_try'], {}), '(CFG.n_try)\n', (1182, 1193), False, 'from tqdm import trange\n'), ((1287, 1309), 'matplotlib.pyplot.plot', 'plt.plot', (['res'], {'label': 'e'}), '(res, label=e)\n', (1295, 1309), True, 'import matplotlib.pyplot as plt\n'), ((671, 688), 'numpy.random.randn', 'np.random.randn', ([], {}), '()\n', (686, 688), True, 'import numpy as np\n'), ((794, 812), 'numpy.random.random', 'np.random.random', ([], {}), '()\n', (810, 812), True, 'import numpy as np\n'), ((838, 861), 'numpy.random.choice', 'np.random.choice', (['CFG.n'], {}), '(CFG.n)\n', (854, 861), True, 'import numpy as np\n'), ((897, 937), 'numpy.argmax', 'np.argmax', (['[ban.mean for ban in bandits]'], {}), '([ban.mean for ban in bandits])\n', (906, 937), True, 'import numpy as np\n'), ((460, 477), 'numpy.random.randn', 'np.random.randn', ([], {}), '()\n', (475, 477), True, 'import numpy as np\n')] |
from dataclasses import dataclass
from .base import _MiscOptionBase
from application.src.misc.sampling import PurposesOfSampling
@dataclass
class Editor(_MiscOptionBase):
name = "Purpose of sampling"
id = "purpose_of_sampling"
link = "misc_bp.submit_purpose_of_sampling"
description = "The reason the sample was collected " \
"<em>e.g. diagnostic testing</em>"
@classmethod
def get_values(cls) -> list:
return PurposesOfSampling.fetch_list()
@classmethod
def save(cls, data: list) -> None:
PurposesOfSampling.save_by_procedure(data)
| [
"application.src.misc.sampling.PurposesOfSampling.fetch_list",
"application.src.misc.sampling.PurposesOfSampling.save_by_procedure"
] | [((454, 485), 'application.src.misc.sampling.PurposesOfSampling.fetch_list', 'PurposesOfSampling.fetch_list', ([], {}), '()\n', (483, 485), False, 'from application.src.misc.sampling import PurposesOfSampling\n'), ((552, 594), 'application.src.misc.sampling.PurposesOfSampling.save_by_procedure', 'PurposesOfSampling.save_by_procedure', (['data'], {}), '(data)\n', (588, 594), False, 'from application.src.misc.sampling import PurposesOfSampling\n')] |
from rest_framework import status
from rest_framework.authentication import SessionAuthentication, BasicAuthentication
from rest_framework.permissions import IsAuthenticated
from rest_framework.views import APIView
from apprest.plugins.icat.helpers.complex_encoder import JsonResponse
from apprest.plugins.icat.services.ICAT import ICATService
class GetInvestigationUsers(APIView):
"""
get:
Return: Users involved in an investigation
"""
authentication_classes = (SessionAuthentication, BasicAuthentication)
permission_classes = (IsAuthenticated,)
pagination_class = None
def get(self, request, *args, **kwargs):
service = ICATService()
investigation_id = self.kwargs.get('investigation_id')
investigation_users = service.get_users_involved_in_investigation(investigation_id, request)
return JsonResponse(investigation_users, status=status.HTTP_200_OK)
| [
"apprest.plugins.icat.helpers.complex_encoder.JsonResponse",
"apprest.plugins.icat.services.ICAT.ICATService"
] | [((669, 682), 'apprest.plugins.icat.services.ICAT.ICATService', 'ICATService', ([], {}), '()\n', (680, 682), False, 'from apprest.plugins.icat.services.ICAT import ICATService\n'), ((863, 923), 'apprest.plugins.icat.helpers.complex_encoder.JsonResponse', 'JsonResponse', (['investigation_users'], {'status': 'status.HTTP_200_OK'}), '(investigation_users, status=status.HTTP_200_OK)\n', (875, 923), False, 'from apprest.plugins.icat.helpers.complex_encoder import JsonResponse\n')] |
import unittest
from table_tests.utils import BaseTestNoFlushTable
from evaluator.hashtable8 import NO_FLUSH_8
class TestNoFlush8Table(BaseTestNoFlushTable):
TOCOMPARE = NO_FLUSH_8
TABLE = [0] * len(TOCOMPARE)
VISIT = [0] * len(TOCOMPARE)
NUM_CARDS = 8
def test_noflush8_table(self):
self.assertListEqual(self.TABLE, self.TOCOMPARE)
if __name__ == "__main__":
unittest.main()
| [
"unittest.main"
] | [((380, 395), 'unittest.main', 'unittest.main', ([], {}), '()\n', (393, 395), False, 'import unittest\n')] |
'''
Leetcode problem No 862 Shortest Subarray with Sum at Least K
Solution written by <NAME> on 1 July, 2018
'''
import collections
class Solution(object):
def shortestSubarray(self, A, K):
"""
:type A: List[int]
:type K: int
:rtype: int
"""
n = len(A)
B = [0] * (n + 1)
for i in range(n):
B[i+1] = B[i] + A[i]
d = collections.deque()
ans = n + 1
for i in range(n+1):
while d and B[i] - B[d[0]] >= K:
ans = min(ans, i-d.popleft())
while d and B[i] <= B[d[-1]]:
d.pop()
d.append(i)
return ans if ans <= n else -1
def main():
s = Solution()
print(s.shortestSubarray([2,-1,2], 3))
print(s.shortestSubarray([1,2], 4))
print(s.shortestSubarray([1], 1))
print(s.shortestSubarray([1,2,3,-5,4,-7,5,-8,6,-9,7,8,-4], 5)) #1
print(s.shortestSubarray([1,2,-5,3,-5,4,-7,5,-8,6,-9,7,8,-4], 5))
main()
| [
"collections.deque"
] | [((403, 422), 'collections.deque', 'collections.deque', ([], {}), '()\n', (420, 422), False, 'import collections\n')] |
# coding=utf-8
import logging
import random
import string
import sys
import unittest
from time import time, sleep
import apiritif
import os
import re
from selenium import webdriver
from selenium.common.exceptions import NoSuchElementException, TimeoutException
from selenium.webdriver.common.by import By
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.support.ui import Select
from selenium.webdriver.support import expected_conditions as econd
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.common.keys import Keys
from bzt.resources.selenium_extras import waiter, get_locator
class TestSc1(unittest.TestCase):
def setUp(self):
self.vars = {}
timeout = 2.0
options = webdriver.FirefoxOptions()
profile = webdriver.FirefoxProfile()
profile.set_preference('webdriver.log.file', '/somewhere/webdriver.log')
options.set_capability('unhandledPromptBehavior', 'ignore')
self.driver = webdriver.Firefox(profile, options=options)
self.driver.implicitly_wait(timeout)
apiritif.put_into_thread_store(timeout=timeout, func_mode=False,
driver=self.driver, windows={}, scenario_name='sc1')
def _1_httpsblazedemocomsetup1(self):
with apiritif.smart_transaction('https://blazedemo.com/setup1'):
self.driver.get('https://blazedemo.com/setup1')
def _2_setup2(self):
with apiritif.smart_transaction('setup2'):
self.driver.get('https://blazedemo.com/setup2')
waiter()
def _3_httpsblazedemocommain1(self):
with apiritif.smart_transaction('https://blazedemo.com/main1'):
self.driver.get('https://blazedemo.com/main1')
def _4_main2(self):
with apiritif.smart_transaction('main2'):
self.driver.get('https://blazedemo.com/main2')
waiter()
def _5_httpsblazedemocomteardown1(self):
with apiritif.smart_transaction('https://blazedemo.com/teardown1'):
self.driver.get('https://blazedemo.com/teardown1')
def _6_teardown2(self):
with apiritif.smart_transaction('teardown2'):
self.driver.get('https://blazedemo.com/teardown2')
waiter()
def test_sc1(self):
try:
self._1_httpsblazedemocomsetup1()
self._2_setup2()
self._3_httpsblazedemocommain1()
self._4_main2()
finally:
apiritif.set_stage("teardown") # can't be interrupted
self._5_httpsblazedemocomteardown1()
self._6_teardown2()
def tearDown(self):
if self.driver:
self.driver.quit()
| [
"apiritif.put_into_thread_store",
"selenium.webdriver.Firefox",
"apiritif.set_stage",
"apiritif.smart_transaction",
"selenium.webdriver.FirefoxOptions",
"bzt.resources.selenium_extras.waiter",
"selenium.webdriver.FirefoxProfile"
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__description__ = \
"""
Fitter subclass for performing bayesian (MCMC) fits.
"""
__author__ = "<NAME>"
__date__ = "2017-05-10"
from .base import Fitter
import emcee, corner
import numpy as np
import scipy.optimize as optimize
import multiprocessing
class BayesianFitter(Fitter):
"""
"""
def __init__(self,num_walkers=100,initial_walker_spread=1e-4,ml_guess=True,
num_steps=100,burn_in=0.1,num_threads=1):
"""
Initialize the bayesian fitter
Parameters
----------
num_walkers : int > 0
how many markov chains to have in the analysis
initial_walker_spread : float
each walker is initialized with parameters sampled from normal
distributions with mean equal to the initial guess and a standard
deviation of guess*initial_walker_spread
ml_guess : bool
if true, do an ML optimization to get the initial guess
num_steps:
number of steps to run the markov chains
burn_in : float between 0 and 1
fraction of samples to discard from the start of the run
num_threads : int or `"max"`
number of threads to use. if `"max"`, use the total number of
cpus. [NOT YET IMPLEMENTED]
"""
Fitter.__init__(self)
self._num_walkers = num_walkers
self._initial_walker_spread = initial_walker_spread
self._ml_guess = ml_guess
self._num_steps = num_steps
self._burn_in = burn_in
self._num_threads = num_threads
if self._num_threads == "max":
self._num_threads = multiprocessing.cpu_count()
if not type(self._num_threads) == int and self._num_threads > 0:
err = "num_threads must be 'max' or a positive integer\n"
raise ValueError(err)
if self._num_threads != 1:
err = "multithreading has not yet been (fully) implemented.\n"
raise NotImplementedError(err)
self._success = None
self.fit_type = "bayesian"
def ln_prior(self,param):
"""
Log prior of fit parameters. Priors are uniform between bounds and
set to -np.inf outside of bounds.
Parameters
----------
param : array of floats
parameters to fit
Returns
-------
float value for log of priors.
"""
# If a paramter falls outside of the bounds, make the prior -infinity
if np.sum(param < self._bounds[0,:]) > 0 or np.sum(param > self._bounds[1,:]) > 0:
return -np.inf
# otherwise, uniform
return 0.0
def ln_prob(self,param):
"""
Posterior probability of model parameters.
Parameters
----------
param : array of floats
parameters to fit
Returns
-------
float value for log posterior proability
"""
# Calcualte prior. If not finite, this solution has an -infinity log
# likelihood
ln_prior = self.ln_prior(param)
if not np.isfinite(ln_prior):
return -np.inf
# Calcualte likelihood. If not finite, this solution has an -infinity
# log likelihood
ln_like = self.ln_like(param)
if not np.isfinite(ln_like):
return -np.inf
# log posterior is log prior plus log likelihood
return ln_prior + ln_like
def fit(self,model,parameters,bounds,y_obs,y_err=None,param_names=None):
"""
Fit the parameters.
Parameters
----------
model : callable
model to fit. model should take "parameters" as its only argument.
this should (usually) be GlobalFit._y_calc
parameters : array of floats
parameters to be optimized. usually constructed by GlobalFit._prep_fit
bounds : list
list of two lists containing lower and upper bounds
y_obs : array of floats
observations in an concatenated array
y_err : array of floats or None
standard deviation of each observation. if None, each observation
is assigned an error of 1/num_obs
param_names : array of str
names of parameters. If None, parameters assigned names p0,p1,..pN
"""
self._model = model
self._y_obs = y_obs
# Convert the bounds (list of lower and upper lists) into a 2d numpy array
self._bounds = np.array(bounds)
# If no error is specified, assign the error as 1/N, identical for all
# points
self._y_err = y_err
if y_err is None:
self._y_err = np.array([1/len(self._y_obs) for i in range(len(self._y_obs))])
if param_names is None:
self._param_names = ["p{}".format(i) for i in range(len(parameters))]
else:
self._param_names = param_names[:]
# Make initial guess (ML or just whatever the paramters sent in were)
if self._ml_guess:
fn = lambda *args: -self.weighted_residuals(*args)
ml_fit = optimize.least_squares(fn,x0=parameters,bounds=self._bounds)
self._initial_guess = np.copy(ml_fit.x)
else:
self._initial_guess = np.copy(parameters)
# Create walker positions
# Size of perturbation in parameter depends on the scale of the parameter
perturb_size = self._initial_guess*self._initial_walker_spread
ndim = len(parameters)
pos = [self._initial_guess + np.random.randn(ndim)*perturb_size
for i in range(self._num_walkers)]
# Sample using walkers
self._fit_result = emcee.EnsembleSampler(self._num_walkers, ndim, self.ln_prob,
threads=self._num_threads)
self._fit_result.run_mcmc(pos, self._num_steps)
# Create list of samples
to_discard = int(round(self._burn_in*self._num_steps,0))
self._samples = self._fit_result.chain[:,to_discard:,:].reshape((-1,ndim))
self._lnprob = self._fit_result.lnprobability[:,:].reshape(-1)
# Get mean and standard deviation
self._estimate = np.mean(self._samples,axis=0)
self._stdev = np.std(self._samples,axis=0)
# Calculate 95% confidence intervals
self._ninetyfive = []
lower = int(round(0.025*self._samples.shape[0],0))
upper = int(round(0.975*self._samples.shape[0],0))
for i in range(self._samples.shape[1]):
nf = np.sort(self._samples[:,i])
self._ninetyfive.append([nf[lower],nf[upper]])
self._ninetyfive = np.array(self._ninetyfive)
self._success = True
@property
def fit_info(self):
"""
Information about the Bayesian run.
"""
output = {}
output["Num walkers"] = self._num_walkers
output["Initial walker spread"] = self._initial_walker_spread
output["Use ML guess"] = self._ml_guess
output["Num steps"] = self._num_steps
output["Burn in"] = self._burn_in
output["Final sample number"] = len(self._samples[:,0])
output["Num threads"] = self._num_threads
return output
@property
def samples(self):
"""
Bayesian samples.
"""
return self._samples
| [
"numpy.mean",
"numpy.copy",
"scipy.optimize.least_squares",
"numpy.sort",
"multiprocessing.cpu_count",
"emcee.EnsembleSampler",
"numpy.array",
"numpy.sum",
"numpy.isfinite",
"numpy.std",
"numpy.random.randn"
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# -*- coding: utf-8 -*-
# We must always import the relevant libraries for our problem at hand. NumPy and TensorFlow are required for this example.
# https://www.kaggle.com/c/costa-rican-household-poverty-prediction/data#_=_
import numpy as np
np.set_printoptions(threshold='nan')
import matplotlib.pyplot as plt
import tensorflow as tf
import pandas as pd
def toInt(x):
if x == 'yes':
return 1
else:
if x == 'no':
return 0
else:
return x
costa_rica_household = pd.read_csv('data/train.csv')
#x1 =
costa_rica_household.describe()
#x1["v2a1"]
costa_rica_household.head()
list(costa_rica_household.dtypes)
#costa_rica_household = costa_rica_household.fillna(0)
costa_rica_household = costa_rica_household.fillna(costa_rica_household.mean())
#costa_rica_household["idhogar"] = costa_rica_household["idhogar"].apply(lambda x: int(x, 16))
#costa_rica_household["dependency"] = costa_rica_household["dependency"].apply(lambda x: toInt(x))
#costa_rica_household["edjefe"] = costa_rica_household["edjefe"].apply(lambda x: toInt(x))//edjefa
#costa_rica_household.loc[costa_rica_household['dependency'] == "'<='"]
#v1 = costa_rica_household[costa_rica_household['dependency'].apply(lambda x: type(x) == str)]['dependency']
#col_name = costa_rica_household.columns
#print(list(col_name))
#costa_rica_household[["age", "SQBage", "agesq", "r4h1", "r4h2"]]
cols_to_norm = ['v2a1', 'hacdor', 'rooms', 'hacapo', 'v14a', 'refrig', 'v18q', 'v18q1',
'tamhog', 'tamviv', 'escolari', 'rez_esc', 'hhsize', 'paredblolad', 'paredzocalo', 'paredpreb', 'pareddes', 'paredmad', 'paredzinc', 'paredfibras',
'paredother', 'pisomoscer', 'pisocemento', 'pisoother', 'pisonatur', 'pisonotiene', 'pisomadera', 'techozinc', 'techoentrepiso', 'techocane', 'techootro',
'cielorazo', 'abastaguadentro', 'abastaguafuera', 'abastaguano', 'public', 'planpri', 'noelec', 'coopele', 'sanitario1', 'sanitario2', 'sanitario3',
'sanitario5', 'sanitario6', 'energcocinar1', 'energcocinar2', 'energcocinar3', 'energcocinar4', 'elimbasu1', 'elimbasu2', 'elimbasu3', 'elimbasu4',
'elimbasu5', 'elimbasu6', 'epared1', 'epared2', 'epared3', 'etecho1', 'etecho2', 'etecho3', 'eviv1', 'eviv2', 'eviv3', 'dis', 'male', 'female',
'estadocivil1', 'estadocivil2', 'estadocivil3', 'estadocivil4', 'estadocivil5', 'estadocivil6', 'estadocivil7', 'parentesco1', 'parentesco2',
'parentesco3', 'parentesco4', 'parentesco5', 'parentesco6', 'parentesco7', 'parentesco8', 'parentesco9', 'parentesco10', 'parentesco11',
'parentesco12', 'hogar_nin', 'hogar_adul', 'hogar_mayor', 'hogar_total', 'meaneduc', 'instlevel1',
'instlevel2', 'instlevel3', 'instlevel4', 'instlevel5', 'instlevel6', 'instlevel7', 'instlevel8', 'instlevel9', 'bedrooms', 'overcrowding', 'tipovivi1',
'tipovivi2', 'tipovivi3', 'tipovivi4', 'tipovivi5', 'computer', 'television', 'mobilephone', 'qmobilephone', 'lugar1', 'lugar2', 'lugar3', 'lugar4',
'lugar5', 'lugar6', 'area1', 'area2', 'SQBescolari', 'SQBhogar_total', 'SQBedjefe', 'SQBhogar_nin', 'SQBovercrowding', 'SQBdependency',
'SQBmeaned', 'agesq']
cat_cols_to_norm = ['r4h1', 'r4h2', 'r4h3', 'r4m1', 'r4m2', 'r4m3', 'r4t1', 'r4t2', 'r4t3']
cols_of_interest = ['v2a1', 'hacdor', 'rooms', 'hacapo', 'v14a', 'refrig', 'v18q', 'v18q1', 'r4h1', 'r4h2', 'r4h3', 'r4m1', 'r4m2', 'r4m3', 'r4t1', 'r4t2', 'r4t3',
'tamhog', 'tamviv', 'escolari', 'rez_esc', 'hhsize', 'paredblolad', 'paredzocalo', 'paredpreb', 'pareddes', 'paredmad', 'paredzinc', 'paredfibras',
'paredother', 'pisomoscer', 'pisocemento', 'pisoother', 'pisonatur', 'pisonotiene', 'pisomadera', 'techozinc', 'techoentrepiso', 'techocane', 'techootro',
'cielorazo', 'abastaguadentro', 'abastaguafuera', 'abastaguano', 'public', 'planpri', 'noelec', 'coopele', 'sanitario1', 'sanitario2', 'sanitario3',
'sanitario5', 'sanitario6', 'energcocinar1', 'energcocinar2', 'energcocinar3', 'energcocinar4', 'elimbasu1', 'elimbasu2', 'elimbasu3', 'elimbasu4',
'elimbasu5', 'elimbasu6', 'epared1', 'epared2', 'epared3', 'etecho1', 'etecho2', 'etecho3', 'eviv1', 'eviv2', 'eviv3', 'dis', 'male', 'female',
'estadocivil1', 'estadocivil2', 'estadocivil3', 'estadocivil4', 'estadocivil5', 'estadocivil6', 'estadocivil7', 'parentesco1', 'parentesco2',
'parentesco3', 'parentesco4', 'parentesco5', 'parentesco6', 'parentesco7', 'parentesco8', 'parentesco9', 'parentesco10', 'parentesco11',
'parentesco12', 'hogar_nin', 'hogar_adul', 'hogar_mayor', 'hogar_total', 'meaneduc', 'instlevel1',
'instlevel2', 'instlevel3', 'instlevel4', 'instlevel5', 'instlevel6', 'instlevel7', 'instlevel8', 'instlevel9', 'bedrooms', 'overcrowding', 'tipovivi1',
'tipovivi2', 'tipovivi3', 'tipovivi4', 'tipovivi5', 'computer', 'television', 'mobilephone', 'qmobilephone', 'lugar1', 'lugar2', 'lugar3', 'lugar4',
'lugar5', 'lugar6', 'area1', 'area2', 'SQBescolari', 'SQBhogar_total', 'SQBedjefe', 'SQBhogar_nin', 'SQBovercrowding', 'SQBdependency',
'SQBmeaned', 'agesq']
#costa_rica_household[cols_to_norm] = costa_rica_household[cols_to_norm].apply(lambda x: (x - x.min())/(x.max() - x.min()))
#costa_rica_household[cat_cols_to_norm] = costa_rica_household[cat_cols_to_norm].apply(lambda x: (x - x.min())/(x.max() - x.min()))
costa_rica_household[cols_of_interest] = costa_rica_household[cols_of_interest].apply(lambda x: (x - x.min())/(x.max() - x.min()))
feat_cols = []
for col_name in cols_to_norm:
col_name = tf.feature_column.numeric_column(col_name)
feat_cols.append(col_name)
age_range_count = [1,2,3,4,5,7]
r4h1_bucket = tf.feature_column.bucketized_column(tf.feature_column.numeric_column('r4h1'), boundaries=age_range_count)
r4h2_bucket = tf.feature_column.bucketized_column(tf.feature_column.numeric_column('r4h2'), boundaries=age_range_count)
r4h3_bucket = tf.feature_column.bucketized_column(tf.feature_column.numeric_column('r4h3'), boundaries=age_range_count)
crossed_r4h = tf.feature_column.crossed_column([r4h1_bucket, r4h2_bucket, r4h3_bucket], 100)
#fc = [r4h1_bucket, r4h2_bucket, r4h3_bucket, crossed_r4h]
r4m1_bucket = tf.feature_column.bucketized_column(tf.feature_column.numeric_column('r4m1'), boundaries=age_range_count)
r4m2_bucket = tf.feature_column.bucketized_column(tf.feature_column.numeric_column('r4m2'), boundaries=age_range_count)
r4m3_bucket = tf.feature_column.bucketized_column(tf.feature_column.numeric_column('r4m3'), boundaries=age_range_count)
crossed_r4m = tf.feature_column.crossed_column([r4m1_bucket, r4m2_bucket, r4m3_bucket], 100)
r4t1_bucket = tf.feature_column.bucketized_column(tf.feature_column.numeric_column('r4t1'), boundaries=age_range_count)
r4t2_bucket = tf.feature_column.bucketized_column(tf.feature_column.numeric_column('r4t2'), boundaries=age_range_count)
r4t3_bucket = tf.feature_column.bucketized_column(tf.feature_column.numeric_column('r4t3'), boundaries=age_range_count)
crossed_r4t = tf.feature_column.crossed_column([r4t1_bucket, r4t2_bucket, r4t3_bucket], 100)
feat_cols.extend([r4h1_bucket, r4h2_bucket, r4h3_bucket, crossed_r4h, r4m1_bucket, r4m2_bucket, r4m3_bucket, crossed_r4m, r4t1_bucket, r4t2_bucket, r4t3_bucket, crossed_r4t])
len(feat_cols)
feat_cols[138]
estimator = tf.estimator.LinearClassifier(feature_columns=feat_cols, n_classes=4)
#costa_rica_household[(costa_rica_household.Target == 4)]
x_data = costa_rica_household.drop('Id', axis=1).drop('edjefa', axis=1).drop('idhogar', axis=1).drop('dependency', axis=1).drop('Target', axis=1)
#x_data['idhogar']
#x_data.describe()
#x_data.head()
labels = costa_rica_household['Target']
labels.head()
from sklearn.model_selection import train_test_split
X_train, X_eval, y_train, y_eval = train_test_split(x_data, labels, test_size=0.3, random_state=101)
print(X_train.shape, y_eval.shape)
input_func = tf.estimator.inputs.pandas_input_fn(x=X_train, y=y_train, batch_size=10, num_epochs=100, shuffle=True)
estimator.train(input_fn=input_func,steps=1000)
eval_input_func = tf.estimator.inputs.pandas_input_fn(x=X_eval, y=y_eval, batch_size=10, num_epochs=1, shuffle=False)
eval_metrics = estimator.evaluate(input_fn=eval_input_func)
print('Eval metrics')
print(eval_metrics)
pred_input_func = tf.estimator.inputs.pandas_input_fn(x=X_eval, shuffle=False)
predictions = []
for predict in estimator.predict(input_fn=pred_input_func):
predictions.append(predict)
predictions
#categorical_columun_voc = tf.feature_column.embedding_column(categorical_columun_voc, 4)
dnn_classifier = tf.estimator.DNNClassifier(hidden_units=[10, 10, 10], feature_columns=feat_cols, n_classes=2)
dnn_classifier.train(input_fn=input_func,steps=1000)
dnn_eval_metrics = dnn_classifier.evaluate(input_fn=eval_input_func)
dnn_eval_metrics
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"""JSON implementations of authentication queries."""
# pylint: disable=no-init
# Numerous classes don't require __init__.
# pylint: disable=too-many-public-methods,too-few-public-methods
# Number of methods are defined in specification
# pylint: disable=protected-access
# Access to protected methods allowed in package json package scope
# pylint: disable=too-many-ancestors
# Inheritance defined in specification
from .. import utilities
from ..osid import queries as osid_queries
from ..primitives import Id
from ..utilities import get_registry
from dlkit.abstract_osid.authentication import queries as abc_authentication_queries
from dlkit.abstract_osid.osid import errors
class AgentQuery(abc_authentication_queries.AgentQuery, osid_queries.OsidObjectQuery):
"""This is the query for searching agents.
Each method specifies an ``AND`` term while multiple invocations of
the same method produce a nested ``OR``.
The following example returns agents whose display name begins with
"Tom" and whose "login name" is "tom" or "tjcoppet" in an agent
record specified by ``companyAgentType``.
Agent Query query = session.getAgentQuery();
query.matchDisplayName("Tom*", wildcardStringMatchType, true);
companyAgentQuery = query.getAgentQueryRecord(companyAgentType);
companyAgentQuery.matchLoginName("tom");
companyAgentQuery = query.getAgentQueryRecord(companyAgentType);
companyAgentQuery.matchLoginName("tjcoppet");
AgentList agentList = session.getAgentsByQuery(query);
"""
def __init__(self, runtime):
self._namespace = 'authentication.Agent'
self._runtime = runtime
record_type_data_sets = get_registry('AGENT_RECORD_TYPES', runtime)
self._all_supported_record_type_data_sets = record_type_data_sets
self._all_supported_record_type_ids = []
for data_set in record_type_data_sets:
self._all_supported_record_type_ids.append(str(Id(**record_type_data_sets[data_set])))
osid_queries.OsidObjectQuery.__init__(self, runtime)
@utilities.arguments_not_none
def match_resource_id(self, agency_id, match):
"""Sets the resource ``Id`` for this query.
arg: agency_id (osid.id.Id): a resource ``Id``
arg: match (boolean): ``true`` for a positive match,
``false`` for a negative match
raise: NullArgument - ``agency_id`` is ``null``
*compliance: mandatory -- This method must be implemented.*
"""
# Implemented from template for osid.resource.ResourceQuery.match_avatar_id
self._add_match('resourceId', str(agency_id), match)
def clear_resource_id_terms(self):
"""Clears the resource ``Id`` terms.
*compliance: mandatory -- This method must be implemented.*
"""
# Implemented from template for osid.resource.ResourceQuery.clear_avatar_id
self._clear_terms('resourceId')
resource_id_terms = property(fdel=clear_resource_id_terms)
def supports_resource_query(self):
"""Tests if a ``ResourceQuery`` is available.
return: (boolean) - ``true`` if a resource query is available,
``false`` otherwise
*compliance: mandatory -- This method must be implemented.*
"""
raise errors.Unimplemented()
def get_resource_query(self):
"""Gets the query for a resource.
Multiple retrievals produce a nested ``OR`` term.
return: (osid.resource.ResourceQuery) - the resource query
raise: Unimplemented - ``supports_resource_query()`` is
``false``
*compliance: optional -- This method must be implemented if
``supports_resource_query()`` is ``true``.*
"""
raise errors.Unimplemented()
resource_query = property(fget=get_resource_query)
@utilities.arguments_not_none
def match_any_resource(self, match):
"""Matches agents with any resource.
arg: match (boolean): ``true`` if to match agents with a
resource, ``false`` to match agents with no resource
*compliance: mandatory -- This method must be implemented.*
"""
raise errors.Unimplemented()
def clear_resource_terms(self):
"""Clears the resource terms.
*compliance: mandatory -- This method must be implemented.*
"""
raise errors.Unimplemented()
resource_terms = property(fdel=clear_resource_terms)
@utilities.arguments_not_none
def match_agency_id(self, agency_id, match):
"""Sets the agency ``Id`` for this query.
arg: agency_id (osid.id.Id): an agency ``Id``
arg: match (boolean): ``true`` for a positive match,
``false`` for negative match
raise: NullArgument - ``agency_id`` is ``null``
*compliance: mandatory -- This method must be implemented.*
"""
# Implemented from template for osid.resource.ResourceQuery.match_bin_id
self._add_match('assignedAgencyIds', str(agency_id), match)
def clear_agency_id_terms(self):
"""Clears the agency ``Id`` terms.
*compliance: mandatory -- This method must be implemented.*
"""
# Implemented from template for osid.resource.ResourceQuery.clear_bin_id_terms
self._clear_terms('assignedAgencyIds')
agency_id_terms = property(fdel=clear_agency_id_terms)
def supports_agency_query(self):
"""Tests if an ``AgencyQuery`` is available.
return: (boolean) - ``true`` if an agency query is available,
``false`` otherwise
*compliance: mandatory -- This method must be implemented.*
"""
raise errors.Unimplemented()
def get_agency_query(self):
"""Gets the query for an agency.
Multiple retrievals produce a nested ``OR`` term.
return: (osid.authentication.AgencyQuery) - the agency query
raise: Unimplemented - ``supports_agency_query()`` is ``false``
*compliance: optional -- This method must be implemented if
``supports_agency_query()`` is ``true``.*
"""
raise errors.Unimplemented()
agency_query = property(fget=get_agency_query)
def clear_agency_terms(self):
"""Clears the agency terms.
*compliance: mandatory -- This method must be implemented.*
"""
# Implemented from template for osid.resource.ResourceQuery.clear_group_terms
self._clear_terms('agency')
agency_terms = property(fdel=clear_agency_terms)
@utilities.arguments_not_none
def get_agent_query_record(self, agent_record_type):
"""Gets the agent query record corresponding to the given ``Agent`` record ``Type``.
Multiple retrievals produce a nested ``OR`` term.
arg: agent_record_type (osid.type.Type): an agent record type
return: (osid.authentication.records.AgentQueryRecord) - the
agent query record
raise: NullArgument - ``agent_record_type`` is ``null``
raise: OperationFailed - unable to complete request
raise: Unsupported - ``has_record_type(agent_record_type)`` is
``false``
*compliance: mandatory -- This method must be implemented.*
"""
raise errors.Unimplemented()
| [
"dlkit.abstract_osid.osid.errors.Unimplemented"
] | [((3335, 3357), 'dlkit.abstract_osid.osid.errors.Unimplemented', 'errors.Unimplemented', ([], {}), '()\n', (3355, 3357), False, 'from dlkit.abstract_osid.osid import errors\n'), ((3800, 3822), 'dlkit.abstract_osid.osid.errors.Unimplemented', 'errors.Unimplemented', ([], {}), '()\n', (3820, 3822), False, 'from dlkit.abstract_osid.osid import errors\n'), ((4233, 4255), 'dlkit.abstract_osid.osid.errors.Unimplemented', 'errors.Unimplemented', ([], {}), '()\n', (4253, 4255), False, 'from dlkit.abstract_osid.osid import errors\n'), ((4427, 4449), 'dlkit.abstract_osid.osid.errors.Unimplemented', 'errors.Unimplemented', ([], {}), '()\n', (4447, 4449), False, 'from dlkit.abstract_osid.osid import errors\n'), ((5746, 5768), 'dlkit.abstract_osid.osid.errors.Unimplemented', 'errors.Unimplemented', ([], {}), '()\n', (5766, 5768), False, 'from dlkit.abstract_osid.osid import errors\n'), ((6190, 6212), 'dlkit.abstract_osid.osid.errors.Unimplemented', 'errors.Unimplemented', ([], {}), '()\n', (6210, 6212), False, 'from dlkit.abstract_osid.osid import errors\n'), ((7335, 7357), 'dlkit.abstract_osid.osid.errors.Unimplemented', 'errors.Unimplemented', ([], {}), '()\n', (7355, 7357), False, 'from dlkit.abstract_osid.osid import errors\n')] |
# Adapted by <NAME>, 2019
#
# Based on Detectron.pytorch/lib/roi_data/fast_rcnn.py
# Original license text:
# --------------------------------------------------------
# Copyright (c) 2017-present, Facebook, 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.
##############################################################################
"""Construct minibatches for Fast R-CNN training. Handles the minibatch blobs
that are specific to Fast R-CNN. Other blobs that are generic to RPN, etc.
are handled by their respecitive roi_data modules.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import numpy.random as npr
import logging
from core.config import cfg
import utils_rel.boxes_rel as box_utils_rel
import utils.blob as blob_utils
import utils.fpn as fpn_utils
logger = logging.getLogger(__name__)
def add_rel_blobs(blobs, im_scales, roidb):
"""Add blobs needed for training Fast R-CNN style models."""
# Sample training RoIs from each image and append them to the blob lists
for im_i, entry in enumerate(roidb):
frcn_blobs = _sample_pairs(entry, im_scales[im_i], im_i)
for k, v in frcn_blobs.items():
blobs[k].append(v)
# Concat the training blob lists into tensors
for k, v in blobs.items():
if isinstance(v, list) and len(v) > 0:
blobs[k] = np.concatenate(v)
if cfg.FPN.FPN_ON and cfg.FPN.MULTILEVEL_ROIS:
_add_rel_multilevel_rois(blobs)
return True
def _sample_pairs(roidb, im_scale, batch_idx):
"""Generate a random sample of RoIs comprising foreground and background
examples.
"""
fg_pairs_per_image = cfg.TRAIN.FG_REL_SIZE_PER_IM
pairs_per_image = int(cfg.TRAIN.FG_REL_SIZE_PER_IM / cfg.TRAIN.FG_REL_FRACTION) # need much more pairs since it's quadratic
max_pair_overlaps = roidb['max_pair_overlaps']
if cfg.MODEL.MULTI_RELATION:
prd_gt_overlaps = roidb['prd_gt_overlaps'].toarray()
prd_class_num = prd_gt_overlaps.shape[1]
gt_pair_inds, gt_pair_class = np.where(prd_gt_overlaps > 1.0 - 1e-4)
fg_pair_inds, fg_pair_class = np.where((prd_gt_overlaps >= cfg.TRAIN.FG_THRESH) &
(prd_gt_overlaps <= 1.0 - 1e-4))
hash_gt_pair_inds = prd_class_num * gt_pair_inds + gt_pair_class
hash_fg_pair_inds = prd_class_num * fg_pair_inds + fg_pair_class
fg_pairs_per_this_image = np.minimum(fg_pairs_per_image, hash_gt_pair_inds.size + hash_fg_pair_inds.size)
if hash_fg_pair_inds.size > 0 and fg_pairs_per_this_image > hash_gt_pair_inds.size:
hash_fg_pair_inds = npr.choice(
hash_fg_pair_inds, size=(fg_pairs_per_this_image - hash_gt_pair_inds.size), replace=False)
hash_fg_pair_inds = np.append(hash_fg_pair_inds, hash_gt_pair_inds)
elif fg_pairs_per_this_image <= hash_gt_pair_inds.size:
hash_gt_pair_inds = npr.choice(
hash_gt_pair_inds, size=fg_pairs_per_this_image, replace=False)
hash_fg_pair_inds = hash_gt_pair_inds
else:
hash_fg_pair_inds = hash_gt_pair_inds
blob_dict = {}
if cfg.MODEL.USE_BG:
bg_pair_inds, bg_pair_class_inds = np.where((prd_gt_overlaps < cfg.TRAIN.BG_THRESH_HI))
hash_bg_pair_inds = prd_class_num * bg_pair_inds + bg_pair_class_inds
bg_pairs_per_this_image = pairs_per_image - fg_pairs_per_this_image
bg_pairs_per_this_image = np.minimum(bg_pairs_per_this_image, hash_bg_pair_inds.size)
if hash_bg_pair_inds.size > 0:
hash_bg_pair_inds = npr.choice(
hash_bg_pair_inds, size=bg_pairs_per_this_image, replace=False)
hash_keep_pair_inds = np.append(hash_fg_pair_inds, hash_bg_pair_inds)
multi_prd_labels = np.zeros(hash_keep_pair_inds.size, dtype=np.int32)
multi_prd_labels[:hash_fg_pair_inds.size] = 1.0 #fg_multi_prd_labels
keep_pair_inds = np.append(hash_fg_pair_inds // prd_class_num, hash_bg_pair_inds // prd_class_num)
keep_pair_class = np.append(hash_fg_pair_inds % prd_class_num, hash_bg_pair_inds % prd_class_num)
else:
multi_prd_labels = np.ones(fg_multi_prd_labels.size, dtype=np.int32) #fg_multi_prd_labels
keep_pair_inds = np.append(hash_fg_pair_inds // prd_class_num)
keep_pair_class = np.append(hash_fg_pair_inds % prd_class_num)
blob_dict['multi_prd_labels_int32'] = multi_prd_labels.astype(np.int32, copy=False)
blob_dict['keep_pair_class_int32'] = keep_pair_class.astype(np.int32, copy=False)
blob_dict['fg_size'] = np.array([hash_fg_pair_inds.size], dtype=np.int32)
else:
gt_pair_inds = np.where(max_pair_overlaps > 1.0 - 1e-4)[0]
fg_pair_inds = np.where((max_pair_overlaps >= cfg.TRAIN.FG_THRESH) &
(max_pair_overlaps <= 1.0 - 1e-4))[0]
fg_pairs_per_this_image = np.minimum(fg_pairs_per_image, gt_pair_inds.size + fg_pair_inds.size)
# Sample foreground regions without replacement
if fg_pair_inds.size > 0 and fg_pairs_per_this_image > gt_pair_inds.size:
fg_pair_inds = npr.choice(
fg_pair_inds, size=(fg_pairs_per_this_image - gt_pair_inds.size), replace=False)
fg_pair_inds = np.append(fg_pair_inds, gt_pair_inds)
elif fg_pairs_per_this_image <= gt_pair_inds.size:
gt_pair_inds = npr.choice(
gt_pair_inds, size=fg_pairs_per_this_image, replace=False)
fg_pair_inds = gt_pair_inds
else:
fg_pair_inds = gt_pair_inds
# Label is the class each RoI has max overlap with
fg_prd_labels = roidb['max_prd_classes'][fg_pair_inds]
blob_dict = dict(
fg_prd_labels_int32=fg_prd_labels.astype(np.int32, copy=False))
if cfg.MODEL.USE_BG:
bg_pair_inds = np.where((max_pair_overlaps < cfg.TRAIN.BG_THRESH_HI))[0]
# Compute number of background RoIs to take from this image (guarding
# against there being fewer than desired)
bg_pairs_per_this_image = pairs_per_image - fg_pairs_per_this_image
bg_pairs_per_this_image = np.minimum(bg_pairs_per_this_image, bg_pair_inds.size)
# Sample foreground regions without replacement
if bg_pair_inds.size > 0:
bg_pair_inds = npr.choice(
bg_pair_inds, size=bg_pairs_per_this_image, replace=False)
# logger.info('{} : {}'.format(fg_pair_inds.size, bg_pair_inds.size))
keep_pair_inds = np.append(fg_pair_inds, bg_pair_inds)
all_prd_labels = np.zeros(keep_pair_inds.size, dtype=np.int32)
all_prd_labels[:fg_pair_inds.size] = fg_prd_labels + 1 # class should start from 1
else:
keep_pair_inds = fg_pair_inds
all_prd_labels = fg_prd_labels
blob_dict['all_prd_labels_int32'] = all_prd_labels.astype(np.int32, copy=False)
blob_dict['fg_size'] = np.array([fg_pair_inds.size], dtype=np.int32) # this is used to check if there is at least one fg to learn
sampled_sbj_boxes = roidb['sbj_boxes'][keep_pair_inds]
sampled_obj_boxes = roidb['obj_boxes'][keep_pair_inds]
sampled_all_boxes = roidb['all_boxes']
det_labels = roidb['det_labels']
sampled_sbj_inds = roidb['sbj_id'][keep_pair_inds]
sampled_obj_inds = roidb['obj_id'][keep_pair_inds]
# Scale rois and format as (batch_idx, x1, y1, x2, y2)
sampled_sbj_rois = sampled_sbj_boxes * im_scale
sampled_obj_rois = sampled_obj_boxes * im_scale
sampled_all_rois = sampled_all_boxes * im_scale
repeated_batch_idx = batch_idx * blob_utils.ones((keep_pair_inds.shape[0], 1))
all_boxes_repeated_batch_idx = batch_idx * blob_utils.ones((sampled_all_boxes.shape[0], 1))
sampled_sbj_rois = np.hstack((repeated_batch_idx, sampled_sbj_rois))
sampled_obj_rois = np.hstack((repeated_batch_idx, sampled_obj_rois))
sampled_all_rois = np.hstack((all_boxes_repeated_batch_idx, sampled_all_rois))
int_repeated_batch_idx = batch_idx * np.ones((keep_pair_inds.shape[0], 1), dtype=np.int)
blob_dict['sbj_inds'] = np.hstack((repeated_batch_idx, sampled_sbj_inds.reshape(-1, 1)))
blob_dict['obj_inds'] = np.hstack((repeated_batch_idx, sampled_obj_inds.reshape(-1, 1)))
blob_dict['sbj_rois'] = sampled_sbj_rois
blob_dict['obj_rois'] = sampled_obj_rois
blob_dict['det_rois'] = sampled_all_rois
blob_dict['det_labels'] = det_labels
sampled_rel_rois = box_utils_rel.rois_union(sampled_sbj_rois, sampled_obj_rois)
blob_dict['rel_rois'] = sampled_rel_rois
if cfg.MODEL.USE_SPATIAL_FEAT:
sampled_spt_feat = box_utils_rel.get_spt_features(
sampled_sbj_boxes, sampled_obj_boxes, roidb['width'], roidb['height'])
blob_dict['spt_feat'] = sampled_spt_feat
if cfg.MODEL.USE_FREQ_BIAS:
sbj_labels = roidb['max_sbj_classes'][keep_pair_inds]
obj_labels = roidb['max_obj_classes'][keep_pair_inds]
blob_dict['all_sbj_labels_int32'] = sbj_labels.astype(np.int32, copy=False)
blob_dict['all_obj_labels_int32'] = obj_labels.astype(np.int32, copy=False)
if cfg.MODEL.USE_NODE_CONTRASTIVE_LOSS or cfg.MODEL.USE_NODE_CONTRASTIVE_SO_AWARE_LOSS or cfg.MODEL.USE_NODE_CONTRASTIVE_P_AWARE_LOSS:
nodes_per_image = cfg.MODEL.NODE_SAMPLE_SIZE
max_sbj_overlaps = roidb['max_sbj_overlaps']
max_obj_overlaps = roidb['max_obj_overlaps']
# sbj
# Here a naturally existing assumption is, each positive sbj should have at least one positive obj
sbj_pos_pair_pos_inds = np.where((max_pair_overlaps >= cfg.TRAIN.FG_THRESH))[0]
sbj_pos_obj_pos_pair_neg_inds = np.where((max_sbj_overlaps >= cfg.TRAIN.FG_THRESH) &
(max_obj_overlaps >= cfg.TRAIN.FG_THRESH) &
(max_pair_overlaps < cfg.TRAIN.BG_THRESH_HI))[0]
sbj_pos_obj_neg_pair_neg_inds = np.where((max_sbj_overlaps >= cfg.TRAIN.FG_THRESH) &
(max_obj_overlaps < cfg.TRAIN.FG_THRESH) &
(max_pair_overlaps < cfg.TRAIN.BG_THRESH_HI))[0]
if sbj_pos_pair_pos_inds.size > 0:
sbj_pos_pair_pos_inds = npr.choice(
sbj_pos_pair_pos_inds,
size=int(min(nodes_per_image, sbj_pos_pair_pos_inds.size)),
replace=False)
if sbj_pos_obj_pos_pair_neg_inds.size > 0:
sbj_pos_obj_pos_pair_neg_inds = npr.choice(
sbj_pos_obj_pos_pair_neg_inds,
size=int(min(nodes_per_image, sbj_pos_obj_pos_pair_neg_inds.size)),
replace=False)
sbj_pos_pair_neg_inds = sbj_pos_obj_pos_pair_neg_inds
if nodes_per_image - sbj_pos_obj_pos_pair_neg_inds.size > 0 and sbj_pos_obj_neg_pair_neg_inds.size > 0:
sbj_pos_obj_neg_pair_neg_inds = npr.choice(
sbj_pos_obj_neg_pair_neg_inds,
size=int(min(nodes_per_image - sbj_pos_obj_pos_pair_neg_inds.size, sbj_pos_obj_neg_pair_neg_inds.size)),
replace=False)
sbj_pos_pair_neg_inds = np.append(sbj_pos_pair_neg_inds, sbj_pos_obj_neg_pair_neg_inds)
sbj_pos_inds = np.append(sbj_pos_pair_pos_inds, sbj_pos_pair_neg_inds)
binary_labels_sbj_pos = np.zeros(sbj_pos_inds.size, dtype=np.int32)
binary_labels_sbj_pos[:sbj_pos_pair_pos_inds.size] = 1
blob_dict['binary_labels_sbj_pos_int32'] = binary_labels_sbj_pos.astype(np.int32, copy=False)
prd_pos_labels_sbj_pos = roidb['max_prd_classes'][sbj_pos_pair_pos_inds]
prd_labels_sbj_pos = np.zeros(sbj_pos_inds.size, dtype=np.int32)
prd_labels_sbj_pos[:sbj_pos_pair_pos_inds.size] = prd_pos_labels_sbj_pos + 1
blob_dict['prd_labels_sbj_pos_int32'] = prd_labels_sbj_pos.astype(np.int32, copy=False)
sbj_labels_sbj_pos = roidb['max_sbj_classes'][sbj_pos_inds] + 1
# 1. set all obj labels > 0
obj_labels_sbj_pos = roidb['max_obj_classes'][sbj_pos_inds] + 1
# 2. find those negative obj
max_obj_overlaps_sbj_pos = roidb['max_obj_overlaps'][sbj_pos_inds]
obj_neg_inds_sbj_pos = np.where(max_obj_overlaps_sbj_pos < cfg.TRAIN.FG_THRESH)[0]
obj_labels_sbj_pos[obj_neg_inds_sbj_pos] = 0
blob_dict['sbj_labels_sbj_pos_int32'] = sbj_labels_sbj_pos.astype(np.int32, copy=False)
blob_dict['obj_labels_sbj_pos_int32'] = obj_labels_sbj_pos.astype(np.int32, copy=False)
# this is for freq bias in RelDN
blob_dict['sbj_labels_sbj_pos_fg_int32'] = roidb['max_sbj_classes'][sbj_pos_inds].astype(np.int32, copy=False)
blob_dict['obj_labels_sbj_pos_fg_int32'] = roidb['max_obj_classes'][sbj_pos_inds].astype(np.int32, copy=False)
sampled_sbj_boxes_sbj_pos = roidb['sbj_boxes'][sbj_pos_inds]
sampled_obj_boxes_sbj_pos = roidb['obj_boxes'][sbj_pos_inds]
# Scale rois and format as (batch_idx, x1, y1, x2, y2)
sampled_sbj_rois_sbj_pos = sampled_sbj_boxes_sbj_pos * im_scale
sampled_obj_rois_sbj_pos = sampled_obj_boxes_sbj_pos * im_scale
repeated_batch_idx = batch_idx * blob_utils.ones((sbj_pos_inds.shape[0], 1))
sampled_sbj_rois_sbj_pos = np.hstack((repeated_batch_idx, sampled_sbj_rois_sbj_pos))
sampled_obj_rois_sbj_pos = np.hstack((repeated_batch_idx, sampled_obj_rois_sbj_pos))
blob_dict['sbj_rois_sbj_pos'] = sampled_sbj_rois_sbj_pos
blob_dict['obj_rois_sbj_pos'] = sampled_obj_rois_sbj_pos
sampled_rel_rois_sbj_pos = box_utils_rel.rois_union(sampled_sbj_rois_sbj_pos, sampled_obj_rois_sbj_pos)
blob_dict['rel_rois_sbj_pos'] = sampled_rel_rois_sbj_pos
_, inds_unique_sbj_pos, inds_reverse_sbj_pos = np.unique(
sampled_sbj_rois_sbj_pos, return_index=True, return_inverse=True, axis=0)
assert inds_reverse_sbj_pos.shape[0] == sampled_sbj_rois_sbj_pos.shape[0]
blob_dict['inds_unique_sbj_pos'] = inds_unique_sbj_pos
blob_dict['inds_reverse_sbj_pos'] = inds_reverse_sbj_pos
if cfg.MODEL.USE_SPATIAL_FEAT:
sampled_spt_feat_sbj_pos = box_utils_rel.get_spt_features(
sampled_sbj_boxes_sbj_pos, sampled_obj_boxes_sbj_pos, roidb['width'], roidb['height'])
blob_dict['spt_feat_sbj_pos'] = sampled_spt_feat_sbj_pos
# obj
# Here a naturally existing assumption is, each positive obj should have at least one positive sbj
obj_pos_pair_pos_inds = np.where((max_pair_overlaps >= cfg.TRAIN.FG_THRESH))[0]
obj_pos_sbj_pos_pair_neg_inds = np.where((max_obj_overlaps >= cfg.TRAIN.FG_THRESH) &
(max_sbj_overlaps >= cfg.TRAIN.FG_THRESH) &
(max_pair_overlaps < cfg.TRAIN.BG_THRESH_HI))[0]
obj_pos_sbj_neg_pair_neg_inds = np.where((max_obj_overlaps >= cfg.TRAIN.FG_THRESH) &
(max_sbj_overlaps < cfg.TRAIN.FG_THRESH) &
(max_pair_overlaps < cfg.TRAIN.BG_THRESH_HI))[0]
if obj_pos_pair_pos_inds.size > 0:
obj_pos_pair_pos_inds = npr.choice(
obj_pos_pair_pos_inds,
size=int(min(nodes_per_image, obj_pos_pair_pos_inds.size)),
replace=False)
if obj_pos_sbj_pos_pair_neg_inds.size > 0:
obj_pos_sbj_pos_pair_neg_inds = npr.choice(
obj_pos_sbj_pos_pair_neg_inds,
size=int(min(nodes_per_image, obj_pos_sbj_pos_pair_neg_inds.size)),
replace=False)
obj_pos_pair_neg_inds = obj_pos_sbj_pos_pair_neg_inds
if nodes_per_image - obj_pos_sbj_pos_pair_neg_inds.size > 0 and obj_pos_sbj_neg_pair_neg_inds.size:
obj_pos_sbj_neg_pair_neg_inds = npr.choice(
obj_pos_sbj_neg_pair_neg_inds,
size=int(min(nodes_per_image - obj_pos_sbj_pos_pair_neg_inds.size, obj_pos_sbj_neg_pair_neg_inds.size)),
replace=False)
obj_pos_pair_neg_inds = np.append(obj_pos_pair_neg_inds, obj_pos_sbj_neg_pair_neg_inds)
obj_pos_inds = np.append(obj_pos_pair_pos_inds, obj_pos_pair_neg_inds)
binary_labels_obj_pos = np.zeros(obj_pos_inds.size, dtype=np.int32)
binary_labels_obj_pos[:obj_pos_pair_pos_inds.size] = 1
blob_dict['binary_labels_obj_pos_int32'] = binary_labels_obj_pos.astype(np.int32, copy=False)
prd_pos_labels_obj_pos = roidb['max_prd_classes'][obj_pos_pair_pos_inds]
prd_labels_obj_pos = np.zeros(obj_pos_inds.size, dtype=np.int32)
prd_labels_obj_pos[:obj_pos_pair_pos_inds.size] = prd_pos_labels_obj_pos + 1
blob_dict['prd_labels_obj_pos_int32'] = prd_labels_obj_pos.astype(np.int32, copy=False)
obj_labels_obj_pos = roidb['max_obj_classes'][obj_pos_inds] + 1
# 1. set all sbj labels > 0
sbj_labels_obj_pos = roidb['max_sbj_classes'][obj_pos_inds] + 1
# 2. find those negative sbj
max_sbj_overlaps_obj_pos = roidb['max_sbj_overlaps'][obj_pos_inds]
sbj_neg_inds_obj_pos = np.where(max_sbj_overlaps_obj_pos < cfg.TRAIN.FG_THRESH)[0]
sbj_labels_obj_pos[sbj_neg_inds_obj_pos] = 0
blob_dict['sbj_labels_obj_pos_int32'] = sbj_labels_obj_pos.astype(np.int32, copy=False)
blob_dict['obj_labels_obj_pos_int32'] = obj_labels_obj_pos.astype(np.int32, copy=False)
# this is for freq bias in RelDN
blob_dict['sbj_labels_obj_pos_fg_int32'] = roidb['max_sbj_classes'][obj_pos_inds].astype(np.int32, copy=False)
blob_dict['obj_labels_obj_pos_fg_int32'] = roidb['max_obj_classes'][obj_pos_inds].astype(np.int32, copy=False)
sampled_sbj_boxes_obj_pos = roidb['sbj_boxes'][obj_pos_inds]
sampled_obj_boxes_obj_pos = roidb['obj_boxes'][obj_pos_inds]
# Scale rois and format as (batch_idx, x1, y1, x2, y2)
sampled_sbj_rois_obj_pos = sampled_sbj_boxes_obj_pos * im_scale
sampled_obj_rois_obj_pos = sampled_obj_boxes_obj_pos * im_scale
repeated_batch_idx = batch_idx * blob_utils.ones((obj_pos_inds.shape[0], 1))
sampled_sbj_rois_obj_pos = np.hstack((repeated_batch_idx, sampled_sbj_rois_obj_pos))
sampled_obj_rois_obj_pos = np.hstack((repeated_batch_idx, sampled_obj_rois_obj_pos))
blob_dict['sbj_rois_obj_pos'] = sampled_sbj_rois_obj_pos
blob_dict['obj_rois_obj_pos'] = sampled_obj_rois_obj_pos
sampled_rel_rois_obj_pos = box_utils_rel.rois_union(sampled_sbj_rois_obj_pos, sampled_obj_rois_obj_pos)
blob_dict['rel_rois_obj_pos'] = sampled_rel_rois_obj_pos
_, inds_unique_obj_pos, inds_reverse_obj_pos = np.unique(
sampled_obj_rois_obj_pos, return_index=True, return_inverse=True, axis=0)
assert inds_reverse_obj_pos.shape[0] == sampled_obj_rois_obj_pos.shape[0]
blob_dict['inds_unique_obj_pos'] = inds_unique_obj_pos
blob_dict['inds_reverse_obj_pos'] = inds_reverse_obj_pos
if cfg.MODEL.USE_SPATIAL_FEAT:
sampled_spt_feat_obj_pos = box_utils_rel.get_spt_features(
sampled_sbj_boxes_obj_pos, sampled_obj_boxes_obj_pos, roidb['width'], roidb['height'])
blob_dict['spt_feat_obj_pos'] = sampled_spt_feat_obj_pos
return blob_dict
def _add_rel_multilevel_rois(blobs):
"""By default training RoIs are added for a single feature map level only.
When using FPN, the RoIs must be distributed over different FPN levels
according the level assignment heuristic (see: modeling.FPN.
map_rois_to_fpn_levels).
"""
lvl_min = cfg.FPN.ROI_MIN_LEVEL
lvl_max = cfg.FPN.ROI_MAX_LEVEL
def _distribute_rois_over_fpn_levels(rois_blob_names):
"""Distribute rois over the different FPN levels."""
# Get target level for each roi
# Recall blob rois are in (batch_idx, x1, y1, x2, y2) format, hence take
# the box coordinates from columns 1:5
lowest_target_lvls = None
for rois_blob_name in rois_blob_names:
target_lvls = fpn_utils.map_rois_to_fpn_levels(
blobs[rois_blob_name][:, 1:5], lvl_min, lvl_max)
if lowest_target_lvls is None:
lowest_target_lvls = target_lvls
else:
lowest_target_lvls = np.minimum(lowest_target_lvls, target_lvls)
for rois_blob_name in rois_blob_names:
# Add per FPN level roi blobs named like: <rois_blob_name>_fpn<lvl>
fpn_utils.add_multilevel_roi_blobs(
blobs, rois_blob_name, blobs[rois_blob_name], lowest_target_lvls, lvl_min,
lvl_max)
_distribute_rois_over_fpn_levels(['sbj_rois'])
_distribute_rois_over_fpn_levels(['obj_rois'])
_distribute_rois_over_fpn_levels(['rel_rois'])
_distribute_rois_over_fpn_levels(['det_rois'])
if cfg.MODEL.USE_NODE_CONTRASTIVE_LOSS or cfg.MODEL.USE_NODE_CONTRASTIVE_SO_AWARE_LOSS or cfg.MODEL.USE_NODE_CONTRASTIVE_P_AWARE_LOSS:
_distribute_rois_over_fpn_levels(['sbj_rois_sbj_pos'])
_distribute_rois_over_fpn_levels(['obj_rois_sbj_pos'])
_distribute_rois_over_fpn_levels(['rel_rois_sbj_pos'])
_distribute_rois_over_fpn_levels(['sbj_rois_obj_pos'])
_distribute_rois_over_fpn_levels(['obj_rois_obj_pos'])
_distribute_rois_over_fpn_levels(['rel_rois_obj_pos'])
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(21275, 21308), True, 'import numpy as np\n')] |
import time
import re
from src.core.tables import Table, MigrationTable
from src.core.constraints import Index
class MysqlTable(Table):
@staticmethod
def _join_cols(cols):
'''Join and escape a list'''
return ', '.join(['`%s`' % i for i in cols])
@staticmethod
def _join_conditionals(row_dict):
'''Create a joined conditional statement for updates
return escaped string of `key`=val, `key`='val' for dictionary
'''
equalities = []
for key, val in row_dict.items():
temp = '`{}`='.format(key)
if isinstance(val, (int, float)):
temp += '{}'.format(val)
elif isinstance(val, str):
temp += '\'{}\''.format(val)
else:
raise TypeError('Value %s, type %s not recognised as a number or string' % (val, type(val)))
equalities.append(temp)
return ', '.join(equalities)
@staticmethod
def _qualify(table, cols):
'''Qualify, join and escape the list'''
return ', '.join(['`{}`.`{}`'.format(table, c) for c in cols])
@staticmethod
def _equals(cols, new_table, new_cols):
'''Qualify, join and equate'''
return ', '.join('`{}`=`{}`.`{}`'.format(cols[i], new_table, new_cols[i]) for i in range(len(cols)))
def insert_row(self, row_dict):
"""Add a row to the table"""
sql = self.commands.insert_row(
self.name,
self._join_cols(row_dict.keys()),
self._join_values(row_dict.values())
)
self.execute(sql)
return self.db.last_row
def get_column_definition(self, column_name):
'''Get the sql column definition
Selects the column type, and YES or NO from the column, IS NULLABLE.
That's enough information to re-create the column.
'''
sql = self.commands.column_definition(self.db.name, self.name, column_name)
ans = self.execute(sql)[0]
if ans[1] == 'NO':
return '{} NOT NULL'.format(ans[0])
else:
return ans[0]
def rename_column(self, old_name, new_name):
'''Rename a column'''
self.execute(self.commands.rename_column(
self.name,
old_name,
new_name,
self.get_column_definition(old_name))
)
@property
def create_statement(self):
"""Get table create statement"""
query = self.commands.get_table_create_statement(self.name)
if self.db.table_exists(self.name):
statement = self.execute(query)[0][1]
statement = re.sub('\s+', ' ', statement)
return statement
raise ValueError('Table does not exist, no create statement')
@property
def indexes(self):
"""Return list of indexes"""
indexes = self.execute(self.commands.get_indexes(self.name))
return [Index(tup[0], tup[2], tup[1], tup[4]) for tup in indexes]
class MySqlMigrationTable(MysqlTable, MigrationTable):
def create_from_source(self):
"""Create new table like source_table"""
create_statement = self.source.create_statement.replace(
'CREATE TABLE `{}`'.format(self.source.name),
'CREATE TABLE `{}`'
)
self.create_from_statement(create_statement)
def _trigger_name(self, method_type):
'Create trigger name'
name = 'migration_trigger_{}_{}'.format(method_type, self.source.name)
return name[:self.db.config['MAX_LENGTH_NAME']]
def create_insert_trigger(self):
'''Set insert Triggers.
'NEW' and 'OLD' are mysql references
see https://dev.mysql.com/doc/refman/5.0/en/create-trigger.html
'''
sql = self.commands.insert_trigger(
self._trigger_name('insert'),
self.source.name,
self.name,
self._join_cols(self.intersection.dest_columns),
self._qualify('NEW', self.intersection.origin_columns))
import pdb
pdb.set_trace()
print(sql)
self.execute(sql)
def create_delete_trigger(self):
'''Set delete triggers
'NEW' and 'OLD' are mysql references
see https://dev.mysql.com/doc/refman/5.0/en/create-trigger.html
'''
sql = self.commands.delete_trigger(
self._trigger_name('delete'),
self.source.name,
self.name,
self.primary_key_column)
self.execute(sql)
def create_update_trigger(self):
'''Set update triggers
'NEW' and 'OLD' are mysql references
see https://dev.mysql.com/doc/refman/5.0/en/create-trigger.html
'''
sql = self.commands.update_trigger(
self._trigger_name('update'),
self.source.name,
self.name,
self._equals(self.intersection.dest_columns, 'NEW', self.intersection.origin_columns),
self.primary_key_column
)
self.execute(sql)
def rename_tables(self):
'Rename the tables'
self.delete_triggers()
retries = 0
source_name, archive_name, migrate_name = self.source.name, self.source.archive_name, self.name
while True:
try:
self.execute(self.commands.rename_table(source_name, archive_name, migrate_name))
break
except Exception as e:
retries += 1
if retries > self.db.config['MAX_RENAME_RETRIES']:
self.create_triggers()
return False
# TODO: make sure this is a Lock wait timeout error before retrying
print('Rename retry %d, error: %s' % (retries, e))
time.sleep(self.db.donfig['RETRY_SLEEP_TIME'])
self.name, self.source.name = self.source.name, self.archive_name
print("Rename complete!")
return True
| [
"re.sub",
"src.core.constraints.Index",
"time.sleep",
"pdb.set_trace"
] | [((4067, 4082), 'pdb.set_trace', 'pdb.set_trace', ([], {}), '()\n', (4080, 4082), False, 'import pdb\n'), ((2658, 2688), 're.sub', 're.sub', (['"""\\\\s+"""', '""" """', 'statement'], {}), "('\\\\s+', ' ', statement)\n", (2664, 2688), False, 'import re\n'), ((2948, 2985), 'src.core.constraints.Index', 'Index', (['tup[0]', 'tup[2]', 'tup[1]', 'tup[4]'], {}), '(tup[0], tup[2], tup[1], tup[4])\n', (2953, 2985), False, 'from src.core.constraints import Index\n'), ((5782, 5828), 'time.sleep', 'time.sleep', (["self.db.donfig['RETRY_SLEEP_TIME']"], {}), "(self.db.donfig['RETRY_SLEEP_TIME'])\n", (5792, 5828), False, 'import time\n')] |
'''
Local DB Authentication module.
.. moduleauthor:: <NAME> <<EMAIL>>
'''
import logging
from django.contrib.auth.models import User, Group
from django.contrib.auth.backends import ModelBackend
from tardis.tardis_portal.auth.interfaces import AuthProvider, GroupProvider, UserProvider
logger = logging.getLogger(__name__)
auth_key = u'localdb'
auth_display_name = u'Local DB'
_modelBackend = ModelBackend()
class DjangoAuthBackend(AuthProvider):
"""Authenticate against Django's Model Backend.
"""
def authenticate(self, request):
"""authenticate a user, this expect the user will be using
form based auth and the *username* and *password* will be
passed in as **POST** variables.
:param request: a HTTP Request instance
:type request: :class:`django.http.HttpRequest`
"""
username = request.POST['username']
password = request.POST['password']
if not username or not password:
return None
return _modelBackend.authenticate(username, password)
def get_user(self, user_id):
try:
user = User.objects.get(username=user_id)
except User.DoesNotExist:
user = None
return user
class DjangoGroupProvider(GroupProvider):
name = u'django_group'
def getGroups(self, user):
"""return an iteration of the available groups.
"""
groups = user.groups.all()
return [g.id for g in groups]
def getGroupById(self, id):
"""return the group associated with the id::
{"id": 123,
"display": "Group Name",}
"""
groupObj = Group.objects.get(id=id)
if groupObj:
return {'id': id, 'display': groupObj.name}
return None
def searchGroups(self, **filter):
result = []
groups = Group.objects.filter(**filter)
for g in groups:
users = [u.username for u in User.objects.filter(groups=g)]
result += [{'id': g.id,
'display': g.name,
'members': users}]
return result
class DjangoUserProvider(UserProvider):
name = u'django_user'
def getUserById(self, id):
"""
return the user dictionary in the format of::
{"id": 123,
"first_name": "John",
"last_name": "Smith",
"email": "<EMAIL>"}
"""
try:
userObj = User.objects.get(username=id)
return {'id': id,
'first_name': userObj.first_name,
'last_name': userObj.last_name,
'email': userObj.email}
except User.DoesNotExist:
return None
django_user = DjangoUserProvider.name
django_group = DjangoGroupProvider.name
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"logging.getLogger",
"django.contrib.auth.models.Group.objects.get",
"django.contrib.auth.models.Group.objects.filter",
"django.contrib.auth.models.User.objects.filter",
"django.contrib.auth.backends.ModelBackend",
"django.contrib.auth.models.User.objects.get"
] | [((301, 328), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (318, 328), False, 'import logging\n'), ((403, 417), 'django.contrib.auth.backends.ModelBackend', 'ModelBackend', ([], {}), '()\n', (415, 417), False, 'from django.contrib.auth.backends import ModelBackend\n'), ((1669, 1693), 'django.contrib.auth.models.Group.objects.get', 'Group.objects.get', ([], {'id': 'id'}), '(id=id)\n', (1686, 1693), False, 'from django.contrib.auth.models import User, Group\n'), ((1867, 1897), 'django.contrib.auth.models.Group.objects.filter', 'Group.objects.filter', ([], {}), '(**filter)\n', (1887, 1897), False, 'from django.contrib.auth.models import User, Group\n'), ((1131, 1165), 'django.contrib.auth.models.User.objects.get', 'User.objects.get', ([], {'username': 'user_id'}), '(username=user_id)\n', (1147, 1165), False, 'from django.contrib.auth.models import User, Group\n'), ((2478, 2507), 'django.contrib.auth.models.User.objects.get', 'User.objects.get', ([], {'username': 'id'}), '(username=id)\n', (2494, 2507), False, 'from django.contrib.auth.models import User, Group\n'), ((1964, 1993), 'django.contrib.auth.models.User.objects.filter', 'User.objects.filter', ([], {'groups': 'g'}), '(groups=g)\n', (1983, 1993), False, 'from django.contrib.auth.models import User, Group\n')] |
import os
import time
from pathlib import Path
from flask import Flask, jsonify, abort, request, Response, Request
import concurrent.futures
import threading
import requests
import logging
import ast
from urllib.parse import urlparse
from flask import current_app as app
from urllib3.exceptions import InsecureRequestWarning
from yaml import safe_load
# Local modules
from elasticsearch.indexer import Indexer
from libs.assay_type import AssayType
# HuBMAP commons
from hubmap_commons.hm_auth import AuthHelper
# Set logging fromat and level (default is warning)
# All the API logging is forwarded to the uWSGI server and gets written into the log file `uwsgo-entity-api.log`
# Log rotation is handled via logrotate on the host system with a configuration file
# Do NOT handle log file and rotation via the Python logging to avoid issues with multi-worker processes
logging.basicConfig(format='[%(asctime)s] %(levelname)s in %(module)s:%(lineno)d: %(message)s', level=logging.DEBUG, datefmt='%Y-%m-%d %H:%M:%S')
logger = logging.getLogger(__name__)
# Specify the absolute path of the instance folder and use the config file relative to the instance path
app = Flask(__name__, instance_path=os.path.join(os.path.abspath(os.path.dirname(__file__)), 'instance'), instance_relative_config=True)
app.config.from_pyfile('app.cfg')
# load the index configurations and set the default
INDICES = safe_load((Path(__file__).absolute().parent / 'instance/search-config.yaml').read_text())
DEFAULT_INDEX_WITHOUT_PREFIX = INDICES['default_index']
logger.debug("############ INDICES config LOADED")
logger.debug(INDICES)
# Remove trailing slash / from URL base to avoid "//" caused by config with trailing slash
DEFAULT_ELASTICSEARCH_URL = INDICES['indices'][DEFAULT_INDEX_WITHOUT_PREFIX]['elasticsearch']['url'].strip('/')
DEFAULT_ENTITY_API_URL = INDICES['indices'][DEFAULT_INDEX_WITHOUT_PREFIX]['document_source_endpoint'].strip('/')
# Suppress InsecureRequestWarning warning when requesting status on https with ssl cert verify disabled
requests.packages.urllib3.disable_warnings(category = InsecureRequestWarning)
####################################################################################################
## Register error handlers
####################################################################################################
# Error handler for 400 Bad Request with custom error message
@app.errorhandler(400)
def http_bad_request(e):
return jsonify(error=str(e)), 400
# Error handler for 401 Unauthorized with custom error message
@app.errorhandler(401)
def http_unauthorized(e):
return jsonify(error=str(e)), 401
# Error handler for 403 Forbidden with custom error message
@app.errorhandler(403)
def http_forbidden(e):
return jsonify(error=str(e)), 403
# Error handler for 500 Internal Server Error with custom error message
@app.errorhandler(500)
def http_internal_server_error(e):
return jsonify(error=str(e)), 500
####################################################################################################
## AuthHelper initialization
####################################################################################################
# Initialize AuthHelper class and ensure singleton
try:
if AuthHelper.isInitialized() == False:
auth_helper_instance = AuthHelper.create(app.config['APP_CLIENT_ID'],
app.config['APP_CLIENT_SECRET'])
logger.info("Initialized AuthHelper class successfully :)")
else:
auth_helper_instance = AuthHelper.instance()
except Exception:
msg = "Failed to initialize the AuthHelper class"
# Log the full stack trace, prepend a line with our message
logger.exception(msg)
####################################################################################################
## Default route
####################################################################################################
@app.route('/', methods = ['GET'])
def index():
return "Hello! This is HuBMAP Search API service :)"
####################################################################################################
## Assay type API
####################################################################################################
@app.route('/assaytype', methods = ['GET'])
def assaytypes():
primary = None
simple = False
for key, val in request.args.items():
if key == 'primary':
primary = val.lower() == "true"
elif key == 'simple':
simple = val.lower() == "true"
else:
abort(400, f'invalid request parameter {key}')
if primary is None:
name_l = [name for name in AssayType.iter_names()]
else:
name_l = [name for name in AssayType.iter_names(primary=primary)]
if simple:
return jsonify(result=name_l)
else:
return jsonify(result=[AssayType(name).to_json() for name in name_l])
@app.route('/assaytype/<name>', methods = ['GET'])
@app.route('/assayname', methods = ['POST'])
def assayname(name=None):
if name is None:
request_json_required(request)
try:
name = request.json['name']
except Exception:
abort(400, 'request contains no "name" field')
try:
return jsonify(AssayType(name).to_json())
except Exception as e:
abort(400, str(e))
####################################################################################################
## API
####################################################################################################
# Both HTTP GET and HTTP POST can be used to execute search with body against ElasticSearch REST API.
# general search uses the DEFAULT_INDEX
@app.route('/search', methods = ['GET', 'POST'])
def search():
# Always expect a json body
request_json_required(request)
logger.info("======search with no index provided======")
logger.info ("default_index: " + DEFAULT_INDEX_WITHOUT_PREFIX)
# Determine the target real index in Elasticsearch to be searched against
# Use the DEFAULT_INDEX_WITHOUT_PREFIX since /search doesn't take any index
target_index = get_target_index(request, DEFAULT_INDEX_WITHOUT_PREFIX)
# get URL for that index
es_url = INDICES['indices'][DEFAULT_INDEX_WITHOUT_PREFIX]['elasticsearch']['url'].strip('/')
# Return the elasticsearch resulting json data as json string
return execute_query('_search', request, target_index, es_url)
# Both HTTP GET and HTTP POST can be used to execute search with body against ElasticSearch REST API.
# Note: the index in URL is not he real index in Elasticsearch, it's that index without prefix
@app.route('/<index_without_prefix>/search', methods = ['GET', 'POST'])
def search_by_index(index_without_prefix):
# Always expect a json body
request_json_required(request)
# Make sure the requested index in URL is valid
validate_index(index_without_prefix)
logger.info("======requested index_without_prefix======")
logger.info(index_without_prefix)
# Determine the target real index in Elasticsearch to be searched against
target_index = get_target_index(request, index_without_prefix)
# get URL for that index
es_url = INDICES['indices'][index_without_prefix]['elasticsearch']['url'].strip('/')
# Return the elasticsearch resulting json data as json string
return execute_query('_search', request, target_index, es_url)
# HTTP GET can be used to execute search with body against ElasticSearch REST API.
@app.route('/count', methods = ['GET'])
def count():
# Always expect a json body
request_json_required(request)
logger.info("======count with no index provided======")
# Determine the target real index in Elasticsearch to be searched against
target_index = get_target_index(request, DEFAULT_INDEX_WITHOUT_PREFIX)
# get URL for that index
es_url = INDICES['indices'][DEFAULT_INDEX_WITHOUT_PREFIX]['elasticsearch']['url'].strip('/')
# Return the elasticsearch resulting json data as json string
return execute_query('_count', request, target_index, es_url)
# HTTP GET can be used to execute search with body against ElasticSearch REST API.
# Note: the index in URL is not he real index in Elasticsearch, it's that index without prefix
@app.route('/<index_without_prefix>/count', methods = ['GET'])
def count_by_index(index_without_prefix):
# Always expect a json body
request_json_required(request)
# Make sure the requested index in URL is valid
validate_index(index_without_prefix)
logger.info("======requested index_without_prefix======")
logger.info(index_without_prefix)
# Determine the target real index in Elasticsearch to be searched against
target_index = get_target_index(request, index_without_prefix)
# get URL for that index
es_url = INDICES['indices'][index_without_prefix]['elasticsearch']['url'].strip('/')
# Return the elasticsearch resulting json data as json string
return execute_query('_count', request, target_index, es_url)
# Get a list of indices
@app.route('/indices', methods = ['GET'])
def indices():
# Return the resulting json data as json string
result = {
"indices": get_filtered_indices()
}
return jsonify(result)
# Get the status of Elasticsearch cluster by calling the health API
# This shows the connection status and the cluster health status (if connected)
@app.route('/status', methods = ['GET'])
def status():
response_data = {
# Use strip() to remove leading and trailing spaces, newlines, and tabs
'version': ((Path(__file__).absolute().parent.parent / 'VERSION').read_text()).strip(),
'build': ((Path(__file__).absolute().parent.parent / 'BUILD').read_text()).strip(),
'elasticsearch_connection': False
}
target_url = DEFAULT_ELASTICSEARCH_URL + '/_cluster/health'
#target_url = app.config['ELASTICSEARCH_URL'] + '/_cluster/health'
resp = requests.get(url = target_url)
if resp.status_code == 200:
response_data['elasticsearch_connection'] = True
# If connected, we also get the cluster health status
status_dict = resp.json()
# Add new key
response_data['elasticsearch_status'] = status_dict['status']
return jsonify(response_data)
# This reindex function will also reindex Collection and Upload
# in addition to the Dataset, Donor, Sample entities
@app.route('/reindex/<uuid>', methods=['PUT'])
def reindex(uuid):
# Reindex individual document doesn't require the token to belong
# to the HuBMAP-Data-Admin group
# since this is being used by entity-api and ingest-api too
token = get_user_token(request.headers)
try:
indexer = init_indexer(token)
threading.Thread(target=indexer.reindex, args=[uuid]).start()
# indexer.reindex(uuid) # for non-thread
logger.info(f"Started to reindex uuid: {uuid}")
except Exception as e:
logger.exception(e)
internal_server_error(e)
return f"Request of reindexing {uuid} accepted", 202
# Live reindex without first deleting and recreating the indices
# This just deletes the old document and add the latest document of each entity (if still available)
@app.route('/reindex-all', methods=['PUT'])
def reindex_all():
# The token needs to belong to the HuBMAP-Data-Admin group
# to be able to trigger a live reindex for all documents
token = get_user_token(request.headers, admin_access_required = True)
saved_request = request.headers
logger.debug(saved_request)
try:
indexer = init_indexer(token)
threading.Thread(target=reindex_all_uuids, args=[indexer, token]).start()
logger.info('Started live reindex all')
except Exception as e:
logger.exception(e)
internal_server_error(e)
return 'Request of live reindex all documents accepted', 202
####################################################################################################
## Internal Functions Used By API
####################################################################################################
# Throws error for 400 Bad Reqeust with message
def bad_request_error(err_msg):
abort(400, description = err_msg)
# Throws error for 401 Unauthorized with message
def unauthorized_error(err_msg):
abort(401, description = err_msg)
# Throws error for 403 Forbidden with message
def forbidden_error(err_msg):
abort(403, description = err_msg)
# Throws error for 500 Internal Server Error with message
def internal_server_error(err_msg):
abort(500, description = err_msg)
# Get user infomation dict based on the http request(headers)
# `group_required` is a boolean, when True, 'hmgroupids' is in the output
def get_user_info_for_access_check(request, group_required):
return auth_helper_instance.getUserInfoUsingRequest(request, group_required)
"""
Parase the token from Authorization header
Parameters
----------
request_headers: request.headers
The http request headers
admin_access_required : bool
If the token is required to belong to the HuBMAP-Data-Admin group, default to False
Returns
-------
str
The token string if valid
"""
def get_user_token(request_headers, admin_access_required = False):
# Get user token from Authorization header
# getAuthorizationTokens() also handles MAuthorization header but we are not using that here
try:
user_token = auth_helper_instance.getAuthorizationTokens(request_headers)
except Exception:
msg = "Failed to parse the Authorization token by calling commons.auth_helper.getAuthorizationTokens()"
# Log the full stack trace, prepend a line with our message
logger.exception(msg)
internal_server_error(msg)
# The user_token is flask.Response on error
if isinstance(user_token, Response):
# The Response.data returns binary string, need to decode
unauthorized_error(user_token.data.decode())
if admin_access_required:
# By now the token is already a valid token
# But we also need to ensure the user belongs to HuBMAP-Data-Admin group
# in order to execute the live reindex-all
# Return a 403 response if the user doesn't belong to HuBMAP-Data-Admin group
if not user_in_hubmap_data_admin_group(request):
forbidden_error("Access not granted")
return user_token
"""
Check if the user with token belongs to the HuBMAP-Data-Admin group
Parameters
----------
request : falsk.request
The flask http request object that containing the Authorization header
with a valid Globus nexus token for checking group information
Returns
-------
bool
True if the user belongs to HuBMAP-Data-Admin group, otherwise False
"""
def user_in_hubmap_data_admin_group(request):
try:
# The property 'hmgroupids' is ALWASYS in the output with using get_user_info()
# when the token in request is a nexus_token
user_info = get_user_info(request)
hubmap_data_admin_group_uuid = auth_helper_instance.groupNameToId('HuBMAP-Data-Admin')['uuid']
except Exception as e:
# Log the full stack trace, prepend a line with our message
logger.exception(e)
# If the token is not a nexus token, no group information available
# The commons.hm_auth.AuthCache would return a Response with 500 error message
# We treat such cases as the user not in the HuBMAP-Data-Admin group
return False
return (hubmap_data_admin_group_uuid in user_info['hmgroupids'])
"""
Get user infomation dict based on the http request(headers)
The result will be used by the trigger methods
Parameters
----------
request : Flask request object
The Flask request passed from the API endpoint
Returns
-------
dict
A dict containing all the user info
{
"scope": "urn:globus:auth:scope:nexus.api.globus.org:groups",
"name": "<NAME>",
"iss": "https://auth.globus.org",
"client_id": "21f293b0-5fa5-4ee1-9e0e-3cf88bd70114",
"active": True,
"nbf": 1603761442,
"token_type": "Bearer",
"aud": ["nexus.api.globus.org", "21f293b0-5fa5-4ee1-9e0e-3cf88bd70114"],
"iat": 1603761442,
"dependent_tokens_cache_id": "af2d5979090a97536619e8fbad1ebd0afa875c880a0d8058cddf510fc288555c",
"exp": 1603934242,
"sub": "c0f8907a-ec78-48a7-9c85-7da995b05446",
"email": "<EMAIL>",
"username": "<EMAIL>",
"hmscopes": ["urn:globus:auth:scope:nexus.api.globus.org:groups"],
}
"""
def get_user_info(request):
# `group_required` is a boolean, when True, 'hmgroupids' is in the output
user_info = auth_helper_instance.getUserInfoUsingRequest(request, True)
logger.debug("======get_user_info()======")
logger.debug(user_info)
# It returns error response when:
# - invalid header or token
# - token is valid but not nexus token, can't find group info
if isinstance(user_info, Response):
# Bubble up the actual error message from commons
# The Response.data returns binary string, need to decode
msg = user_info.get_data().decode()
# Log the full stack trace, prepend a line with our message
logger.exception(msg)
raise Exception(msg)
return user_info
# Always expect a json body
def request_json_required(request):
if not request.is_json:
bad_request_error("A JSON body and appropriate Content-Type header are required")
# We'll need to verify the requested index in URL is valid
def validate_index(index_without_prefix):
separator = ','
#indices = get_filtered_indices()
indices = INDICES['indices'].keys()
if index_without_prefix not in indices:
bad_request_error(f"Invalid index name. Use one of the following: {separator.join(indices)}")
# Determine the target real index in Elasticsearch bases on the request header and given index (without prefix)
# The Authorization header with globus token is optional
# Case #1: Authorization header is missing, default to use the `hm_public_<index_without_prefix>`.
# Case #2: Authorization header with valid token, but the member doesn't belong to the HuBMAP-Read group, direct the call to `hm_public_<index_without_prefix>`.
# Case #3: Authorization header presents but with invalid or expired token, return 401 (if someone is sending a token, they might be expecting more than public stuff).
# Case #4: Authorization header presents with a valid token that has the group access, direct the call to `hm_consortium_<index_without_prefix>`.
def get_target_index(request, index_without_prefix):
# Case #1 and #2
target_index = INDICES['indices'][index_without_prefix]['public']
# Keys in request.headers are case insensitive
if 'Authorization' in request.headers:
# user_info is a dict
user_info = get_user_info_for_access_check(request, True)
logger.info("======user_info======")
logger.info(user_info)
# Case #3
if isinstance(user_info, Response):
# Notify the client with 401 error message
unauthorized_error("The globus token in the HTTP 'Authorization: Bearer <globus-token>' header is either invalid or expired.")
# Otherwise, we check user_info['hmgroupids'] list
# Key 'hmgroupids' presents only when group_required is True
else:
# Case #4
if app.config['GLOBUS_HUBMAP_READ_GROUP_UUID'] in user_info['hmgroupids']:
#target_index = app.config['PRIVATE_INDEX_PREFIX'] + index_without_prefix
target_index = INDICES['indices'][index_without_prefix]['private']
return target_index
# Make a call to Elasticsearch
def execute_query(query_against, request, index, es_url, query=None):
supported_query_against = ['_search', '_count']
separator = ','
if query_against not in supported_query_against:
bad_request_error(f"Query against '{query_against}' is not supported by Search API. Use one of the following: {separator.join(supported_query_against)}")
# Determine the target real index in Elasticsearch to be searched against
#index = get_target_index(request, index_without_prefix)
#target_url = app.config['ELASTICSEARCH_URL'] + '/' + target_index + '/' + query_against
#es_url = INDICES['indices'][index_without_prefix]['elasticsearch']['url'].strip('/')
logger.debug('es_url')
logger.debug(es_url)
logger.debug(type(es_url))
# use the index es connection
target_url = es_url + '/' + index + '/' + query_against
logger.debug("Target url: " + target_url)
if query is None:
# Parse incoming json string into json data(python dict object)
json_data = request.get_json()
# All we need to do is to simply pass the search json to elasticsearch
# The request json may contain "access_group" in this case
# Will also pass through the query string in URL
target_url = target_url + get_query_string(request.url)
# Make a request with json data
# The use of json parameter converts python dict to json string and adds content-type: application/json automatically
else:
json_data = query
logger.debug(json_data)
resp = requests.post(url=target_url, json=json_data)
logger.debug("==========response==========")
logger.debug(resp)
try:
return jsonify(resp.json())
except Exception as e:
logger.debug(e)
raise e
# Return the elasticsearch resulting json data as json string
return jsonify(resp)
# Get the query string from orignal request
def get_query_string(url):
query_string = ''
parsed_url = urlparse(url)
logger.debug("======parsed_url======")
logger.debug(parsed_url)
# Add the ? at beginning of the query string if not empty
if not parsed_url.query:
query_string = '?' + parsed_url.query
return query_string
# Get a list of entity uuids via entity-api for a given entity type:
# Collection, Donor, Sample, Dataset, Submission. Case-insensitive.
def get_uuids_by_entity_type(entity_type, token):
entity_type = entity_type.lower()
request_headers = create_request_headers_for_auth(token)
# Use different entity-api endpoint for Collection
if entity_type == 'collection':
#url = app.config['ENTITY_API_URL'] + "/collections?property=uuid"
url = DEFAULT_ENTITY_API_URL + "/collections?property=uuid"
else:
#url = app.config['ENTITY_API_URL'] + "/" + entity_type + "/entities?property=uuid"
url = DEFAULT_ENTITY_API_URL + "/" + entity_type + "/entities?property=uuid"
response = requests.get(url, headers = request_headers, verify = False)
if response.status_code != 200:
internal_server_error("get_uuids_by_entity_type() failed to make a request to entity-api for entity type: " + entity_type)
uuids_list = response.json()
return uuids_list
# Create a dict with HTTP Authorization header with Bearer token
def create_request_headers_for_auth(token):
auth_header_name = 'Authorization'
auth_scheme = 'Bearer'
headers_dict = {
# Don't forget the space between scheme and the token value
auth_header_name: auth_scheme + ' ' + token
}
return headers_dict
def get_uuids_from_es(index, es_url):
uuids = []
size = 10_000
query = {
"size": size,
"from": len(uuids),
"_source": ["_id"],
"query": {
"bool": {
"must": [],
"filter": [
{
"match_all": {}
}
],
"should": [],
"must_not": []
}
}
}
end_of_list = False
while not end_of_list:
logger.debug("Searching ES for uuids...")
logger.debug(es_url)
resp = execute_query('_search', None, index, es_url, query)
logger.debug('Got a response from ES...')
ret_obj = resp.get_json()
uuids.extend(hit['_id'] for hit in ret_obj.get('hits').get('hits'))
total = ret_obj.get('hits').get('total').get('value')
if total <= len(uuids):
end_of_list = True
else:
query['from'] = len(uuids)
return uuids
def init_indexer(token):
return Indexer(
INDICES,
app.config['APP_CLIENT_ID'],
app.config['APP_CLIENT_SECRET'],
token
)
def reindex_all_uuids(indexer, token):
with app.app_context():
try:
logger.info("############# Reindex Live Started #############")
start = time.time()
# Make calls to entity-api to get a list of uuids for each entity type
donor_uuids_list = get_uuids_by_entity_type("donor", token)
sample_uuids_list = get_uuids_by_entity_type("sample", token)
dataset_uuids_list = get_uuids_by_entity_type("dataset", token)
upload_uuids_list = get_uuids_by_entity_type("upload", token)
public_collection_uuids_list = get_uuids_by_entity_type("collection", token)
logger.debug("merging sets into a one list...")
# Merge into a big list that with no duplicates
all_entities_uuids = set(donor_uuids_list + sample_uuids_list + dataset_uuids_list + upload_uuids_list + public_collection_uuids_list)
es_uuids = []
#for index in ast.literal_eval(app.config['INDICES']).keys():
logger.debug("looping through the indices...")
logger.debug(INDICES['indices'].keys())
index_names = get_all_indice_names()
logger.debug(index_names)
for index in index_names.keys():
all_indices = index_names[index]
# get URL for that index
es_url = INDICES['indices'][index]['elasticsearch']['url'].strip('/')
for actual_index in all_indices:
es_uuids.extend(get_uuids_from_es(actual_index, es_url))
es_uuids = set(es_uuids)
logger.debug("looping through the UUIDs...")
# Remove entities found in Elasticserach but no longer in neo4j
for uuid in es_uuids:
if uuid not in all_entities_uuids:
logger.debug(f"Entity of uuid: {uuid} found in Elasticserach but no longer in neo4j. Delete it from Elasticserach.")
indexer.delete(uuid)
logger.debug("Starting multi-thread reindexing ...")
# Reindex in multi-treading mode for:
# - each public collection
# - each upload, only add to the hm_consortium_entities index (private index of the default)
# - each donor and its descendants in the tree
futures_list = []
results = []
with concurrent.futures.ThreadPoolExecutor() as executor:
public_collection_futures_list = [executor.submit(indexer.index_public_collection, uuid, reindex = True) for uuid in public_collection_uuids_list]
upload_futures_list = [executor.submit(indexer.index_upload, uuid, reindex = True) for uuid in upload_uuids_list]
donor_futures_list = [executor.submit(indexer.index_tree, uuid) for uuid in donor_uuids_list]
# Append the above three lists into one
futures_list = public_collection_futures_list + upload_futures_list + donor_futures_list
for f in concurrent.futures.as_completed(futures_list):
logger.debug(f.result())
end = time.time()
logger.info(f"############# Live Reindex-All Completed. Total time used: {end - start} seconds. #############")
except Exception as e:
logger.error(e)
# Gets a list of actually public and private indice names
def get_all_indice_names():
all_names = {}
try:
indices = INDICES['indices'].keys()
for i in indices:
index_info = {}
index_names = []
public_index = INDICES['indices'][i]['public']
private_index = INDICES['indices'][i]['private']
index_names.append(public_index)
index_names.append(private_index)
index_info[i] = index_names
all_names.update(index_info)
except Exception as e:
raise e
return all_names
# Get a list of filtered Elasticsearch indices to expose to end users without the prefix
def get_filtered_indices():
# just get all the defined index keys from the yml file
indices = INDICES['indices'].keys()
return list(indices)
# For local development/testing
if __name__ == "__main__":
try:
app.run(host='0.0.0.0', port="5005")
except Exception as e:
print("Error during starting debug server.")
print(str(e))
logger.error(e, exc_info=True)
print("Error during startup check the log file for further information")
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"flask.jsonify",
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"pathlib.Path",
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#!/usr/bin/env python
#
# Creates resources
# This script creates VPC/security group/keypair if not already present
import logging
import os
import sys
import time
from . import aws_util as u
from . import util
DRYRUN = False
DEBUG = True
# Names of Amazon resources that are created. These settings are fixed across
# all runs, and correspond to resources created once per user per region.
PUBLIC_TCP_RANGES = [
22, # ssh
(8888, 8899), # ipython notebook ports
6379, # redis port
(6006, 6016) # tensorboard ports
]
PUBLIC_UDP_RANGES = [(60000, 61000)] # mosh ports
logger = logging.getLogger(__name__)
def network_setup():
"""Creates VPC if it doesn't already exists, configures it for public
internet access, returns vpc, subnet, security_group"""
ec2 = u.get_ec2_resource()
client = u.get_ec2_client()
existing_vpcs = u.get_vpc_dict()
zones = u.get_zones()
# create VPC from scratch. Remove this if default VPC works well enough.
vpc_name = u.get_vpc_name()
if u.get_vpc_name() in existing_vpcs:
logger.info("Reusing VPC " + vpc_name)
vpc = existing_vpcs[vpc_name]
else:
logger.info("Creating VPC " + vpc_name)
vpc = ec2.create_vpc(CidrBlock='192.168.0.0/16')
# enable DNS on the VPC
local_response = vpc.modify_attribute(EnableDnsHostnames={"Value": True})
assert u.is_good_response(local_response)
local_response = vpc.modify_attribute(EnableDnsSupport={"Value": True})
assert u.is_good_response(local_response)
vpc.create_tags(Tags=u.create_name_tags(vpc_name))
vpc.wait_until_available()
gateways = u.get_gateway_dict(vpc)
gateway_name = u.get_gateway_name()
if gateway_name in gateways:
logger.info("Reusing gateways " + gateway_name)
else:
logger.info("Creating internet gateway " + gateway_name)
ig = ec2.create_internet_gateway()
ig.attach_to_vpc(VpcId=vpc.id)
ig.create_tags(Tags=u.create_name_tags(gateway_name))
# check that attachment succeeded
attach_state = u.extract_attr_for_match(ig.attachments, State=-1, VpcId=vpc.id)
assert attach_state == 'available', "vpc %s is in state %s" % (vpc.id, attach_state)
route_table = vpc.create_route_table()
route_table_name = u.get_route_table_name()
route_table.create_tags(Tags=u.create_name_tags(route_table_name))
dest_cidr = '0.0.0.0/0'
route_table.create_route(DestinationCidrBlock=dest_cidr, GatewayId=ig.id)
assert len(zones) <= 16 # for cidr/20 to fit into cidr/16
ip = 0
for zone in zones:
cidr_block = '192.168.%d.0/20' % (ip,)
ip += 16
logging.info("Creating subnet %s in zone %s" % (cidr_block, zone))
subnet = vpc.create_subnet(CidrBlock=cidr_block, AvailabilityZone=zone)
subnet.create_tags(Tags=[{'Key': 'Name', 'Value': f'{vpc_name}-subnet'}, {'Key': 'Region', 'Value': zone}])
local_response = client.modify_subnet_attribute(MapPublicIpOnLaunch={'Value': True}, SubnetId=subnet.id)
assert u.is_good_response(local_response)
u.wait_until_available(subnet)
assert subnet.map_public_ip_on_launch, "Subnet doesn't enable public IP by default, why?"
route_table.associate_with_subnet(SubnetId=subnet.id)
existing_security_groups = u.get_security_group_dict(vpc.id)
security_group_name = u.get_security_group_name()
if security_group_name in existing_security_groups:
logger.info("Reusing security group " + security_group_name)
security_group = existing_security_groups[security_group_name]
assert security_group.vpc_id == vpc.id, f"Found security group {security_group} " \
f"attached to {security_group.vpc_id} but expected {vpc.id}"
else:
logging.info("Creating security group " + security_group_name)
security_group = ec2.create_security_group(
GroupName=security_group_name, Description=security_group_name,
VpcId=vpc.id)
cidr_ip = os.environ.get('SCLUSTER_SECURITY_GROUP_CidrIp', '0.0.0.0/0')
security_group.create_tags(Tags=u.create_name_tags(security_group_name))
# allow ICMP access for public ping
security_group.authorize_ingress(
CidrIp='0.0.0.0/0',
IpProtocol='icmp',
FromPort=-1,
ToPort=-1
)
# open public ports
# always include SSH port which is required for basic functionality
assert 22 in PUBLIC_TCP_RANGES, "Must enable SSH access"
for port in PUBLIC_TCP_RANGES:
if util.is_iterable(port):
assert len(port) == 2
from_port, to_port = port
else:
from_port, to_port = port, port
response = security_group.authorize_ingress(
IpProtocol="tcp",
CidrIp=cidr_ip,
FromPort=from_port,
ToPort=to_port
)
assert u.is_good_response(response)
for port in PUBLIC_UDP_RANGES:
if util.is_iterable(port):
assert len(port) == 2
from_port, to_port = port
else:
from_port, to_port = port, port
response = security_group.authorize_ingress(IpProtocol="udp",
CidrIp=cidr_ip,
FromPort=from_port,
ToPort=to_port)
assert u.is_good_response(response)
return vpc, security_group
def keypair_setup():
"""Creates keypair if necessary, saves private key locally, returns contents
of private key file."""
os.system('mkdir -p ' + u.PRIVATE_KEY_LOCATION)
keypair_name = u.get_keypair_name()
keypair = u.get_keypair_dict().get(keypair_name, None)
keypair_fn = u.get_keypair_fn()
if keypair:
print("Reusing keypair " + keypair_name)
# check that local pem file exists and is readable
assert os.path.exists(
keypair_fn), "Keypair %s exists, but corresponding .pem file %s is not found, delete keypair %s through " \
"console and run again to recreate keypair/.pem together" % (
keypair_name, keypair_fn, keypair_name)
keypair_contents = open(keypair_fn).read()
assert len(keypair_contents) > 0
else:
print("Creating keypair " + keypair_name)
ec2 = u.get_ec2_resource()
assert not os.path.exists(
keypair_fn), "previous keypair exists, delete it with 'sudo rm %s' and also delete corresponding " \
"keypair through console" % (keypair_fn)
keypair = ec2.create_key_pair(KeyName=keypair_name)
open(keypair_fn, 'w').write(keypair.key_material)
os.system('chmod 400 ' + keypair_fn)
return keypair
def placement_group_setup(group_name):
"""Creates placement_group group if necessary. Returns True if new placement_group
group was created, False otherwise."""
existing_placement_groups = u.get_placement_group_dict()
group = existing_placement_groups.get(group_name, None)
if group:
assert group.state == 'available'
assert group.strategy == 'cluster'
print("Reusing group ", group.name)
return group
print("Creating group " + group_name)
ec2 = u.get_ec2_resource()
group = ec2.create_placement_group(GroupName=group_name, Strategy='cluster')
return group
def create_resources():
logger.info(f"Creating {u.get_prefix()} resources in region {u.get_region()}")
vpc, security_group = network_setup()
keypair_setup() # saves private key locally to keypair_fn
# create EFS
efss = u.get_efs_dict()
efs_name = u.get_efs_name()
efs_id = efss.get(efs_name, '')
if not efs_id:
logger.info("Creating EFS " + efs_name)
efs_id = u.create_efs(efs_name)
else:
logger.info("Reusing EFS " + efs_name)
efs_client = u.get_efs_client()
# create mount target for each subnet in the VPC
# added retries because efs is not immediately available
max_failures = 10
retry_interval_sec = 1
for subnet in vpc.subnets.all():
for retry_attempt in range(max_failures):
try:
sys.stdout.write("Creating efs mount target for %s ... " % (subnet.availability_zone,))
sys.stdout.flush()
response = efs_client.create_mount_target(
FileSystemId=efs_id,
SubnetId=subnet.id,
SecurityGroups=[security_group.id]
)
if u.is_good_response(response):
logger.info("success")
break
except Exception as e:
if 'already exists' in str(e): # ignore "already exists" errors
logger.info('already exists')
break
# Takes couple of seconds for EFS to come online, with
# errors like this:
# Creating efs mount target for us-east-1f ... Failed with An error occurred (IncorrectFileSystemLifeCycleState) when calling the CreateMountTarget operation: None, retrying in 1 sec
logger.info("Got %s, retrying in %s sec" % (str(e), retry_interval_sec))
time.sleep(retry_interval_sec)
else:
logger.info("Giving up.")
if __name__ == '__main__':
create_resources()
| [
"logging.getLogger",
"os.path.exists",
"os.environ.get",
"time.sleep",
"os.system",
"sys.stdout.flush",
"logging.info",
"sys.stdout.write"
] | [((595, 622), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (612, 622), False, 'import logging\n'), ((5900, 5947), 'os.system', 'os.system', (["('mkdir -p ' + u.PRIVATE_KEY_LOCATION)"], {}), "('mkdir -p ' + u.PRIVATE_KEY_LOCATION)\n", (5909, 5947), False, 'import os\n'), ((3934, 3996), 'logging.info', 'logging.info', (["('Creating security group ' + security_group_name)"], {}), "('Creating security group ' + security_group_name)\n", (3946, 3996), False, 'import logging\n'), ((4165, 4226), 'os.environ.get', 'os.environ.get', (['"""SCLUSTER_SECURITY_GROUP_CidrIp"""', '"""0.0.0.0/0"""'], {}), "('SCLUSTER_SECURITY_GROUP_CidrIp', '0.0.0.0/0')\n", (4179, 4226), False, 'import os\n'), ((6223, 6249), 'os.path.exists', 'os.path.exists', (['keypair_fn'], {}), '(keypair_fn)\n', (6237, 6249), False, 'import os\n'), ((7016, 7052), 'os.system', 'os.system', (["('chmod 400 ' + keypair_fn)"], {}), "('chmod 400 ' + keypair_fn)\n", (7025, 7052), False, 'import os\n'), ((2745, 2811), 'logging.info', 'logging.info', (["('Creating subnet %s in zone %s' % (cidr_block, zone))"], {}), "('Creating subnet %s in zone %s' % (cidr_block, zone))\n", (2757, 2811), False, 'import logging\n'), ((6698, 6724), 'os.path.exists', 'os.path.exists', (['keypair_fn'], {}), '(keypair_fn)\n', (6712, 6724), False, 'import os\n'), ((8518, 8610), 'sys.stdout.write', 'sys.stdout.write', (["('Creating efs mount target for %s ... ' % (subnet.availability_zone,))"], {}), "('Creating efs mount target for %s ... ' % (subnet.\n availability_zone,))\n", (8534, 8610), False, 'import sys\n'), ((8622, 8640), 'sys.stdout.flush', 'sys.stdout.flush', ([], {}), '()\n', (8638, 8640), False, 'import sys\n'), ((9577, 9607), 'time.sleep', 'time.sleep', (['retry_interval_sec'], {}), '(retry_interval_sec)\n', (9587, 9607), False, 'import time\n')] |
#
# Copyright (c) 2020, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import warnings
try:
from numba import cuda
except ImportError:
cuda = None
try:
import psutil
except ImportError:
psutil = None
def _pynvml_mem_size(kind="total", index=0):
import pynvml
pynvml.nvmlInit()
size = None
if kind == "free":
size = int(pynvml.nvmlDeviceGetMemoryInfo(pynvml.nvmlDeviceGetHandleByIndex(index)).free)
elif kind == "total":
size = int(pynvml.nvmlDeviceGetMemoryInfo(pynvml.nvmlDeviceGetHandleByIndex(index)).total)
else:
raise ValueError("{0} not a supported option for device_mem_size.".format(kind))
pynvml.nvmlShutdown()
return size
def device_mem_size(kind="total", cpu=False):
# Use psutil (if available) for cpu mode
if cpu and psutil:
if kind == "total":
return psutil.virtual_memory().total
elif kind == "free":
return psutil.virtual_memory().free
elif cpu:
warnings.warn("Please install psutil for full cpu=True support.")
# Assume 1GB of memory
return int(1e9)
if kind not in ["free", "total"]:
raise ValueError("{0} not a supported option for device_mem_size.".format(kind))
try:
if kind == "free":
return int(cuda.current_context().get_memory_info()[0])
else:
return int(cuda.current_context().get_memory_info()[1])
except NotImplementedError:
if kind == "free":
# Not using NVML "free" memory, because it will not include RMM-managed memory
warnings.warn("get_memory_info is not supported. Using total device memory from NVML.")
size = _pynvml_mem_size(kind="total", index=0)
return size
def get_rmm_size(size):
return (size // 256) * 256
| [
"pynvml.nvmlShutdown",
"pynvml.nvmlDeviceGetHandleByIndex",
"pynvml.nvmlInit",
"psutil.virtual_memory",
"numba.cuda.current_context",
"warnings.warn"
] | [((809, 826), 'pynvml.nvmlInit', 'pynvml.nvmlInit', ([], {}), '()\n', (824, 826), False, 'import pynvml\n'), ((1192, 1213), 'pynvml.nvmlShutdown', 'pynvml.nvmlShutdown', ([], {}), '()\n', (1211, 1213), False, 'import pynvml\n'), ((1523, 1588), 'warnings.warn', 'warnings.warn', (['"""Please install psutil for full cpu=True support."""'], {}), "('Please install psutil for full cpu=True support.')\n", (1536, 1588), False, 'import warnings\n'), ((1394, 1417), 'psutil.virtual_memory', 'psutil.virtual_memory', ([], {}), '()\n', (1415, 1417), False, 'import psutil\n'), ((2120, 2212), 'warnings.warn', 'warnings.warn', (['"""get_memory_info is not supported. Using total device memory from NVML."""'], {}), "(\n 'get_memory_info is not supported. Using total device memory from NVML.')\n", (2133, 2212), False, 'import warnings\n'), ((916, 956), 'pynvml.nvmlDeviceGetHandleByIndex', 'pynvml.nvmlDeviceGetHandleByIndex', (['index'], {}), '(index)\n', (949, 956), False, 'import pynvml\n'), ((1472, 1495), 'psutil.virtual_memory', 'psutil.virtual_memory', ([], {}), '()\n', (1493, 1495), False, 'import psutil\n'), ((1040, 1080), 'pynvml.nvmlDeviceGetHandleByIndex', 'pynvml.nvmlDeviceGetHandleByIndex', (['index'], {}), '(index)\n', (1073, 1080), False, 'import pynvml\n'), ((1831, 1853), 'numba.cuda.current_context', 'cuda.current_context', ([], {}), '()\n', (1851, 1853), False, 'from numba import cuda\n'), ((1913, 1935), 'numba.cuda.current_context', 'cuda.current_context', ([], {}), '()\n', (1933, 1935), False, 'from numba import cuda\n')] |
import re
import argparse
import os
import sys
import logging
import traceback
import pysatl
class EtsiTs101955(object):
COMMENT_MARKER = "REM"
COMMAND_MARKER = "CMD"
RESET_MARKER = "RST"
INIT_MARKER = "INI"
OFF_MARKER = "OFF"
def __init__(self, cmdHandler):
self._cmdHandler = cmdHandler
def runStream(self, scriptStream, *, line_cnt = 0):
lineBuf = ""
for line in scriptStream:
line_cnt += 1
if line in ["\n", "\r"]:
line = ""
elif len(line):
while line[-1] in ["\n", "\r"]: # remove end of line characters
line = line[:-1]
if 0 == len(line):
continue
if 0 == len(line):
continue
lineBreak = line[-1] == "\\"
if lineBreak:
lineBuf += line[:-1]
continue
line = lineBuf + line
lineBuf = ""
logging.debug("line %4d: '%s'" % (line_cnt, line))
if 0 == len(line):
continue
if line.startswith(EtsiTs101955.COMMENT_MARKER):
self._cmdHandler.comment(line[len(EtsiTs101955.COMMENT_MARKER):])
continue
tokens = line.split()
if tokens[0] == EtsiTs101955.RESET_MARKER:
self._cmdHandler.reset()
elif tokens[0] == EtsiTs101955.OFF_MARKER:
self._cmdHandler.off()
elif tokens[0] == EtsiTs101955.INIT_MARKER:
datstr = line[len(tokens[0]):]
dat = pysatl.Utils.ba(datstr)
self._cmdHandler.init(dat)
elif tokens[0] == EtsiTs101955.COMMAND_MARKER:
params = line[len(tokens[0]):]
cmd_params_pattern = re.compile(r"(.*)\[(.*)\]\s*\((.*)\)")
matchRes = cmd_params_pattern.match(params)
if matchRes is not None:
capdustr = matchRes.group(1)
leDatStr = matchRes.group(2).replace(" ","").replace("\t","").lower()
swStr = matchRes.group(3).replace(" ","").replace("\t","").lower()
else:
cmd_params_pattern = re.compile(r"(.*)\s*\((.*)\)")
matchRes = cmd_params_pattern.match(params)
capdustr = matchRes.group(1)
leDatStr = ""
swStr = matchRes.group(2)
swStr = swStr.replace(" ","").replace("\t","").lower()
capdu = pysatl.CAPDU.from_hexstr(capdustr)
rapdu = self._cmdHandler.apdu(capdu,leDatStr,swStr)
swlist = swStr.split(",")
swMatch = False
for sw in swlist:
swMatch |= rapdu.matchSW(sw)
if not swMatch:
raise Exception("RAPDU does not match any of the expected status word")
if not rapdu.matchDATA(leDatStr):
raise Exception("RAPDU does not match expected outgoing data")
else:
raise Exception("line %d, syntax not supported: '%s'"%(line_cnt,line))
def runFile(scriptFile, apduHandler):
tool = EtsiTs101955(apduHandler)
with open(scriptFile) as script:
tool.runStream(script)
class CmdHandler(object):
"""Base class for command handlers"""
def __init__(self):
pass
def apdu(self, capdu, leDatStr="", swStr=""):
dat = pysatl.Utils.ba(leDatStr.replace('x','0'))
sw=0
swStr = swStr.split(",")[0]
for i in range(0,len(swStr)):
d = swStr[i]
sw = (sw << 4) | int(d,16)
sw1=sw >> 8
sw2=sw & 0xFF
rapdu = pysatl.RAPDU(SW1=sw1,SW2=sw2, DATA=dat)
line = "CMD "
header_len = 4
lc=len(capdu.DATA)
if lc:
header_len = 5
if lc>255:
header_len = 7
else:
header_len = 5
if capdu.LE>256:
header_len = 7
dat = capdu.to_ba()
line += pysatl.Utils.hexstr(dat[:header_len])
if len(capdu.DATA) > 0:
line += " \\\n "
dat = capdu.DATA
while len(dat) > 16:
line += pysatl.Utils.hexstr(dat[0:16]) + " \\\n "
dat = dat[16:]
line += pysatl.Utils.hexstr(dat)
if len(rapdu.DATA) > 0:
line += " \\\n ["
dat = rapdu.DATA
while len(dat) > 16:
line += pysatl.Utils.hexstr(dat[0:16]) + " \\\n "
dat = dat[16:]
line += pysatl.Utils.hexstr(dat)
line += " ] \\\n"
elif capdu.LE > 0:
line += " []"
line += " ("+ pysatl.Utils.hexstr(rapdu.swBytes()) +")"
logging.info(line)
return rapdu
def reset(self):
logging.info("RST")
def init(self, dat):
logging.info("INIT "+pysatl.Utils.hexstr(dat))
def off(self):
logging.info("OFF")
def comment(self, msg):
logging.info("REM %s" % (msg))
class ApduTool(object):
"""ETSI TS 101 955 script player"""
def __init__(self, argv):
scriptname = os.path.basename(__file__)
parser = argparse.ArgumentParser(scriptname)
#TODO: pass argv to parser.
levels = ('DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL')
parser.add_argument('--log-level', default='INFO', choices=levels)
parser.add_argument('--script', default="stdin", help='path to script', type=str)
options = parser.parse_args()
root = logging.getLogger()
root.setLevel(options.log_level)
if options.script == "stdin":
player = EtsiTs101955(CmdHandler())
player.runStream(sys.stdin)
else:
EtsiTs101955.runFile(options.script,CmdHandler())
if __name__ == "__main__":
ApduTool(sys.argv)
| [
"logging.getLogger",
"logging.debug",
"argparse.ArgumentParser",
"re.compile",
"pysatl.RAPDU",
"pysatl.Utils.ba",
"os.path.basename",
"pysatl.CAPDU.from_hexstr",
"pysatl.Utils.hexstr",
"logging.info"
] | [((3787, 3827), 'pysatl.RAPDU', 'pysatl.RAPDU', ([], {'SW1': 'sw1', 'SW2': 'sw2', 'DATA': 'dat'}), '(SW1=sw1, SW2=sw2, DATA=dat)\n', (3799, 3827), False, 'import pysatl\n'), ((4140, 4177), 'pysatl.Utils.hexstr', 'pysatl.Utils.hexstr', (['dat[:header_len]'], {}), '(dat[:header_len])\n', (4159, 4177), False, 'import pysatl\n'), ((4864, 4882), 'logging.info', 'logging.info', (['line'], {}), '(line)\n', (4876, 4882), False, 'import logging\n'), ((4934, 4953), 'logging.info', 'logging.info', (['"""RST"""'], {}), "('RST')\n", (4946, 4953), False, 'import logging\n'), ((5063, 5082), 'logging.info', 'logging.info', (['"""OFF"""'], {}), "('OFF')\n", (5075, 5082), False, 'import logging\n'), ((5120, 5148), 'logging.info', 'logging.info', (["('REM %s' % msg)"], {}), "('REM %s' % msg)\n", (5132, 5148), False, 'import logging\n'), ((5268, 5294), 'os.path.basename', 'os.path.basename', (['__file__'], {}), '(__file__)\n', (5284, 5294), False, 'import os\n'), ((5312, 5347), 'argparse.ArgumentParser', 'argparse.ArgumentParser', (['scriptname'], {}), '(scriptname)\n', (5335, 5347), False, 'import argparse\n'), ((5669, 5688), 'logging.getLogger', 'logging.getLogger', ([], {}), '()\n', (5686, 5688), False, 'import logging\n'), ((999, 1049), 'logging.debug', 'logging.debug', (['("line %4d: \'%s\'" % (line_cnt, line))'], {}), '("line %4d: \'%s\'" % (line_cnt, line))\n', (1012, 1049), False, 'import logging\n'), ((4418, 4442), 'pysatl.Utils.hexstr', 'pysatl.Utils.hexstr', (['dat'], {}), '(dat)\n', (4437, 4442), False, 'import pysatl\n'), ((4684, 4708), 'pysatl.Utils.hexstr', 'pysatl.Utils.hexstr', (['dat'], {}), '(dat)\n', (4703, 4708), False, 'import pysatl\n'), ((5009, 5033), 'pysatl.Utils.hexstr', 'pysatl.Utils.hexstr', (['dat'], {}), '(dat)\n', (5028, 5033), False, 'import pysatl\n'), ((4325, 4355), 'pysatl.Utils.hexstr', 'pysatl.Utils.hexstr', (['dat[0:16]'], {}), '(dat[0:16])\n', (4344, 4355), False, 'import pysatl\n'), ((4591, 4621), 'pysatl.Utils.hexstr', 'pysatl.Utils.hexstr', (['dat[0:16]'], {}), '(dat[0:16])\n', (4610, 4621), False, 'import pysatl\n'), ((1623, 1646), 'pysatl.Utils.ba', 'pysatl.Utils.ba', (['datstr'], {}), '(datstr)\n', (1638, 1646), False, 'import pysatl\n'), ((1833, 1875), 're.compile', 're.compile', (['"""(.*)\\\\[(.*)\\\\]\\\\s*\\\\((.*)\\\\)"""'], {}), "('(.*)\\\\[(.*)\\\\]\\\\s*\\\\((.*)\\\\)')\n", (1843, 1875), False, 'import re\n'), ((2581, 2615), 'pysatl.CAPDU.from_hexstr', 'pysatl.CAPDU.from_hexstr', (['capdustr'], {}), '(capdustr)\n', (2605, 2615), False, 'import pysatl\n'), ((2262, 2294), 're.compile', 're.compile', (['"""(.*)\\\\s*\\\\((.*)\\\\)"""'], {}), "('(.*)\\\\s*\\\\((.*)\\\\)')\n", (2272, 2294), False, 'import re\n')] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2017 <EMAIL>
# Licensed under the MIT license (http://opensource.org/licenses/MIT)
import cffi
import ctypes.util
import platform
ffi = cffi.FFI()
ffi.cdef("""
struct hid_device_info {
char *path;
unsigned short vendor_id;
unsigned short product_id;
wchar_t *serial_number;
unsigned short release_number;
wchar_t *manufacturer_string;
wchar_t *product_string;
unsigned short usage_page;
unsigned short usage;
int interface_number;
struct hid_device_info *next;
};
typedef struct hid_device_ hid_device;
int hid_init(void);
int hid_exit(void);
struct hid_device_info* hid_enumerate(unsigned short, unsigned short);
void hid_free_enumeration (struct hid_device_info *devs);
hid_device* hid_open (unsigned short vendor_id, unsigned short product_id, const wchar_t *serial_number);
hid_device* hid_open_path (const char *path);
int hid_write (hid_device *device, const unsigned char *data, size_t length);
int hid_read_timeout (hid_device *dev, unsigned char *data, size_t length, int milliseconds);
int hid_read (hid_device *device, unsigned char *data, size_t length);
int hid_set_nonblocking (hid_device *device, int nonblock);
int hid_send_feature_report (hid_device *device, const unsigned char *data, size_t length);
int hid_get_feature_report (hid_device *device, unsigned char *data, size_t length);
void hid_close (hid_device *device);
int hid_get_manufacturer_string (hid_device *device, wchar_t *string, size_t maxlen);
int hid_get_product_string (hid_device *device, wchar_t *string, size_t maxlen);
int hid_get_serial_number_string (hid_device *device, wchar_t *string, size_t maxlen);
int hid_get_indexed_string (hid_device *device, int string_index, wchar_t *string, size_t maxlen);
const wchar_t* hid_error (hid_device *device);
""")
if "Windows" in platform.platform():
try:
hidapi = ffi.dlopen('hidapi.dll')
except:
hidapi = ffi.dlopen(ctypes.util.find_library('hidapi.dll'))
else:
try:
hidapi = ffi.dlopen('hidapi-libusb')
except:
hidapi = ffi.dlopen(ctypes.util.find_library('hidapi-libusb'))
def _c_to_py_str(val):
if val == ffi.NULL:
return None
new_val = ffi.string(val)
if type(new_val) == bytes or type(new_val) == bytearray:
return new_val.decode("utf-8")
else:
return new_val
class HIDException(Exception):
pass
class Device:
def __init__(self, cdata):
"""
"""
if cdata == ffi.NULL:
raise TypeError
self.path = _c_to_py_str(cdata.path)
self.vendor_id = cdata.vendor_id
self.product_id = cdata.product_id
self.release_number = cdata.release_number
self.manufacturer_string = _c_to_py_str(cdata.manufacturer_string)
self.product_string = _c_to_py_str(cdata.product_string)
self.serial_number = _c_to_py_str(cdata.serial_number)
self.usage_page = cdata.usage_page
self.usage = cdata.usage
self.interface_number = cdata.interface_number
self._device = None
self._is_open = False
def __del__(self):
self.close()
def open(self):
if self._is_open:
raise HIDException("Failed to open device: Device already open")
path = self.path.encode('utf-8')
dev = hidapi.hid_open_path(path)
if dev:
self._is_open = True
self._device = dev
else:
raise HIDException("Failed to open device")
def close(self):
"""
Closes the hid device
"""
if self._is_open:
self._is_open = False
hidapi.hid_close(self._device)
def description(self):
return self.info.description()
def write(self, data, report_id=0):
"""
Writes `bytes` to the hid device.
"""
if not self._is_open:
raise HIDException("Device not open")
write_data = bytearray([report_id]) + bytearray(data)
cdata = ffi.new("const unsigned char[]", bytes(write_data))
num_written = hidapi.hid_write(self._device, cdata, len(write_data))
if num_written < 0:
raise HIDException("Failed to write to HID device: " + str(num_written))
else:
return num_written
def read(self, size=64, timeout=None):
"""
Read from the hid device. Returns bytes read or None if no bytes read.
size: number of bytes to read
timeout: length to wait in milliseconds
"""
if not self._is_open:
raise HIDException("Device not open")
data = [0] * size
cdata = ffi.new("unsigned char[]", data)
bytes_read = 0
if timeout == None:
bytes_read = hidapi.hid_read(self._device, cdata, len(cdata))
else:
bytes_read = hidapi.hid_read_timeout(self._device, cdata, len(cdata), timeout)
if bytes_read < 0:
raise HIDException("Failed to read from HID device: " + str(bytes_read))
elif bytes_read == 0:
return None
else:
return bytearray(cdata)
def set_nonblocking(self, enable_nonblocking):
if not self._is_open:
raise HIDException("Device not open")
if type(enable_nonblocking) != bool:
raise TypeError
hidapi.hid_set_nonblocking(self._device, enable_nonblocking)
def is_open(self):
return _is_open
def is_connected(self):
"""
Checks if the USB device is still connected
"""
if self._is_open:
err = hidapi.hid_read_timeout(self._device, ffi.NULL, 0, 0)
if err == -1:
return False
else:
return True
else:
en = Enumeration(vid=self.vendor_id, pid=self.product_id).find(path=self.path)
if len(en) == 0:
return False
else:
return True
# int hid_send_feature_report (hid_device *device, const unsigned char *data, size_t length);
# def send_feature_report(self, data):
# cdata = ffi.new("const unsigned char[]", data)
# hidapi.hid_send_feature_report(self._device, cdata, length)
# pass
# def get_feature_report(self, size=64):
# hid_data = bytes([report_id]) + bytes(data)
# cdata = ffi.new("unsigned char[]", data)
# hidapi.hid_send_feature_report(self._device, cdata, length)
# pass
def get_error(self):
err_str = hidapi.hid_error(self._device)
if err_str == ffi.NULL:
return None
else:
return ffi.string(err_str)
def _get_prod_string_common(self, hid_fn):
max_len = 128
str_buf = ffi.new("wchar_t[]", bytearray(max_len).decode('utf-8'))
ret = hid_fn(self._device, str_buf, max_len)
if ret < 0:
raise HIDException(self._device.get_error())
else:
assert(ret == 0)
return ffi.string(str_buf)
# Probably don't need these excpet for get_indexed_string, since they won't
# change from the values found in the enumeration
def get_manufacture_string(self):
"""
Get the manufacturer string of the device from its device descriptor
"""
return self._get_prod_string_common(hidapi.hid_get_manufacturer_string)
def get_product_string(self):
"""
Get the product string of the device from its device descriptor
"""
return self._get_prod_string_common(hidapi.hid_get_product_string)
def get_serial_number(self):
"""
Get the serial number string of the device from its device descriptor
"""
return self._get_prod_string_common(hidapi.hid_get_serial_number_string)
def get_indexed_string(self, index):
"""
Get the string with the given index from the device
"""
max_len = 128
str_buf = ffi.new("wchar_t[]", str(bytearray(max_len)))
ret = hidapi.hid_get_indexed_string(self._device, index, str_buf, max_len)
if ret < 0:
raise HIDException(self._device.get_error())
elif ret == 0:
return None
else:
return ffi.string(str_buf).encode('utf-8')
def description(self):
return \
"""Device:
{} | {:x}:{:x} | {} | {} | {}
release_number: {}
usage_page: {}
usage: {}
interface_number: {}\
""".format(self.path,
self.vendor_id,
self.product_id,
self.manufacturer_string,
self.product_string,
self.serial_number,
self.release_number,
self.usage_page,
self.usage,
self.interface_number
)
class Enumeration:
def __init__(self, vid=0, pid=0):
self.device_list = _hid_enumerate(vid, pid)
def show(self):
for dev in self.device_list:
print(dev.description())
def find(self, vid=None, pid=None, serial=None, interface=None, \
path=None, release_number=None, manufacturer=None,
product=None, usage=None, usage_page=None):
"""
Attempts to open a device in the HID enumeration list. This function
is only away of devices that were present when the object was created.
"""
result = []
for dev in self.device_list:
if vid and dev.vendor_id != vid:
continue
if pid and dev.product_id != pid:
continue
if serial and dev.serial_number != serial:
continue
if interface and dev.interface_number != interface:
continue
if path and dev.path != path:
continue
if manufacturer and dev.manufacturer_string != manufacturer:
continue
if product and dev.product_string != product:
continue
if release_number and dev.release_number != release_number:
continue
if usage and dev.usage != usage:
continue
if usage_page and dev.usage_page != usage_page:
continue
result.append(dev)
return result
def _hid_enumerate(vendor_id=0, product_id=0):
"""
Enumerates all the hid devices for VID:PID. Returns a list of `DeviceInfo`.
If vid is 0, then match any vendor id. Similarly, if pid is 0, match any
product id. If both are zero, enumerate all HID devices.
"""
start = hidapi.hid_enumerate(vendor_id, product_id)
result = []
cur = ffi.new("struct hid_device_info*");
cur = start
# Copy everything into python list
while cur != ffi.NULL:
result.append(Device(cur))
cur = cur.next
# Free the C memory
hidapi.hid_free_enumeration(start)
return result
# def hid_open(vendor_id, product_id, serial=None):
# """
# """
# if serial == None:
# serial = ffi.NULL
# else:
# if type(serial) == bytes or type(serial) == bytearray:
# serial = serial.decode('utf-8')
# serial = ffi.new("wchar_t[]", serial)
# dev = hidapi.hid_open(vendor_id, product_id, serial)
# if dev:
# return Device(dev)
# else:
# None
if __name__ == "__main__":
# Examples
from easyhid import Enumeration
# Stores an enumertion of all the connected USB HID devices
en = Enumeration()
# return a list of devices based on the search parameters
devices = en.find(manufacturer="Company", product="Widget", interface=3)
# print a description of the devices found
for dev in devices:
print(dev.description())
# open a device
dev.open()
# write some bytes to the device
dev.write(bytearray([0, 1, 2, 3]))
# read some bytes
print(dev.read())
# close a device
dev.close()
| [
"easyhid.Enumeration",
"platform.platform",
"cffi.FFI"
] | [((196, 206), 'cffi.FFI', 'cffi.FFI', ([], {}), '()\n', (204, 206), False, 'import cffi\n'), ((1868, 1887), 'platform.platform', 'platform.platform', ([], {}), '()\n', (1885, 1887), False, 'import platform\n'), ((11515, 11528), 'easyhid.Enumeration', 'Enumeration', ([], {}), '()\n', (11526, 11528), False, 'from easyhid import Enumeration\n'), ((5838, 5890), 'easyhid.Enumeration', 'Enumeration', ([], {'vid': 'self.vendor_id', 'pid': 'self.product_id'}), '(vid=self.vendor_id, pid=self.product_id)\n', (5849, 5890), False, 'from easyhid import Enumeration\n')] |
#!/usr/bin/env python3
import anki_vector
import paho.mqtt.client as mqtt
import time
###############################################################################
def main():
voltage = 0
batlevel = 0
charging = 0
docked = 0
status = "error"
ltime = time.strftime("%d.%m.%Y %H:%M:%S")
try:
# Connect to Vector and get battery info
with anki_vector.Robot(behavior_control_level=None,
cache_animation_lists=False) as robot:
battery_state = robot.get_battery_state()
voltage = battery_state.battery_volts
batlevel = battery_state.battery_level
charging = battery_state.is_charging
docked = battery_state.is_on_charger_platform
status = get_status(robot)
except:
print("couldn't connect to Vector")
# In the openHAB channel, use a jsonpath transform to get specific values like this: JSONPATH:$..voltage
data = {}
data['robots'] = []
data['robots'].append({
'name': 'Vector Green',
'voltage': voltage,
'batlevel': batlevel,
'charging': charging,
'docked': docked,
'time': ltime,
'status': status
})
# Configure and publish data to mqtt
do_mqtt(data)
###############################################################################
def get_status(robot):
status = "error"
if robot.status.are_motors_moving:
status = "Vector is moving"
if robot.status.are_wheels_moving:
status = "Vector's wheels are moving"
if robot.status.is_animating:
status = "Vector is animating"
if robot.status.is_being_held:
status = "Vector is being held"
if robot.status.is_button_pressed:
status = "Vector's button was button pressed"
if robot.status.is_carrying_block:
status = "Vector is carrying his block"
if robot.status.is_charging:
status = "Vector is currently charging"
if robot.status.is_cliff_detected:
status = "Vector has detected a cliff"
if robot.status.is_docking_to_marker:
status = "Vector has found a marker and is docking to it"
if robot.status.is_falling:
status = "Vector is falling"
if robot.status.is_head_in_pos:
status = "Vector's head is in position"
if robot.status.is_in_calm_power_mode:
status = "Vector is in calm power mode"
if robot.status.is_lift_in_pos:
status = "Vector's arm is in position"
if robot.status.is_on_charger:
status = "Vector is on the charger"
if robot.status.is_pathing:
status = "Vector is traversing a path"
if robot.status.is_picked_up:
status = "Vector is picked up"
if robot.status.is_robot_moving:
status = "Vector is in motion"
return status
###############################################################################
def on_publish(client, userdata, mid):
print("Message published to broker")
###############################################################################
def do_mqtt(data):
# define variables for MQTT
MQTT_HOST = "192.168.0.7"
MQTT_TOPIC = "Vector"
MQTT_PORT = 1883
MQTT_KEEPALIVE_INTERVAL = 20
MQTT_USER = "YOUR_MQTT_USER"
MQTT_PW = "<PASSWORD>"
# Convert it to text? Not sure why I did this but it works. Yay, 1am programming.
MQTT_MSG = str(data)
# Initiate MQTT Client
mqttc = mqtt.Client()
# Set username and password for the Broker
mqttc.username_pw_set(MQTT_USER, MQTT_PW)
# Register publish callback function
#mqttc.on_publish = on_publish
# Connect with MQTT Broker
mqttc.connect(MQTT_HOST, MQTT_PORT, MQTT_KEEPALIVE_INTERVAL)
# Publish message to MQTT Broker
mqttc.publish(MQTT_TOPIC,MQTT_MSG)
# Disconnect from MQTT_Broker
mqttc.disconnect()
###############################################################################
if __name__ == "__main__":
main()
| [
"paho.mqtt.client.Client",
"anki_vector.Robot",
"time.strftime"
] | [((287, 321), 'time.strftime', 'time.strftime', (['"""%d.%m.%Y %H:%M:%S"""'], {}), "('%d.%m.%Y %H:%M:%S')\n", (300, 321), False, 'import time\n'), ((3490, 3503), 'paho.mqtt.client.Client', 'mqtt.Client', ([], {}), '()\n', (3501, 3503), True, 'import paho.mqtt.client as mqtt\n'), ((398, 473), 'anki_vector.Robot', 'anki_vector.Robot', ([], {'behavior_control_level': 'None', 'cache_animation_lists': '(False)'}), '(behavior_control_level=None, cache_animation_lists=False)\n', (415, 473), False, 'import anki_vector\n')] |
import time
from beacontools import BeaconScanner, IBeaconFilter
def callback(bt_addr, rssi, packet, additional_info):
print("<%s, %d> %s %s" % (bt_addr, rssi, packet, additional_info))
# scan for all iBeacon advertisements from beacons with the specified uuid
scanner = BeaconScanner(callback,
device_filter=IBeaconFilter(uuid="e2c56db5-dffb-48d2-b060-d0f5a71096e0")
)
scanner.start()
time.sleep(10)
scanner.stop()
| [
"beacontools.IBeaconFilter",
"time.sleep"
] | [((398, 412), 'time.sleep', 'time.sleep', (['(10)'], {}), '(10)\n', (408, 412), False, 'import time\n'), ((321, 379), 'beacontools.IBeaconFilter', 'IBeaconFilter', ([], {'uuid': '"""e2c56db5-dffb-48d2-b060-d0f5a71096e0"""'}), "(uuid='e2c56db5-dffb-48d2-b060-d0f5a71096e0')\n", (334, 379), False, 'from beacontools import BeaconScanner, IBeaconFilter\n')] |
# Desafio 61 Curso em Video Python
# By Rafabr
from estrutura_modelo import cabecalho, rodape
cabecalho(61, "Termos de uma Progressão Aritmética - II")
while True:
try:
p0 = float(input('Digite o Termo inicial da PA: '))
r = float(input('Digite a razão da PA: '))
except ValueError:
print('Voçe digitou um valor indevido!\n')
continue
break
n = 1
print()
while (n <= 10):
print(f'Termo {n}:'.ljust(10) + f'{p0 + (n-1)*r}')
n += 1
rodape()
| [
"estrutura_modelo.rodape",
"estrutura_modelo.cabecalho"
] | [((96, 153), 'estrutura_modelo.cabecalho', 'cabecalho', (['(61)', '"""Termos de uma Progressão Aritmética - II"""'], {}), "(61, 'Termos de uma Progressão Aritmética - II')\n", (105, 153), False, 'from estrutura_modelo import cabecalho, rodape\n'), ((490, 498), 'estrutura_modelo.rodape', 'rodape', ([], {}), '()\n', (496, 498), False, 'from estrutura_modelo import cabecalho, rodape\n')] |
from setuptools import setup
tests_require = [
'cov-core',
'mock',
'nose2',
]
setup(name='steinlib',
version='0.1',
description='Python bindings for Steinlib format.',
url='http://github.com/leandron/steinlib',
author='<NAME>',
author_email='<EMAIL>',
license='MIT',
packages=['steinlib'],
tests_require=tests_require,
test_suite='nose2.collector.collector',
zip_safe=False)
| [
"setuptools.setup"
] | [((108, 424), 'setuptools.setup', 'setup', ([], {'name': '"""steinlib"""', 'version': '"""0.1"""', 'description': '"""Python bindings for Steinlib format."""', 'url': '"""http://github.com/leandron/steinlib"""', 'author': '"""<NAME>"""', 'author_email': '"""<EMAIL>"""', 'license': '"""MIT"""', 'packages': "['steinlib']", 'tests_require': 'tests_require', 'test_suite': '"""nose2.collector.collector"""', 'zip_safe': '(False)'}), "(name='steinlib', version='0.1', description=\n 'Python bindings for Steinlib format.', url=\n 'http://github.com/leandron/steinlib', author='<NAME>', author_email=\n '<EMAIL>', license='MIT', packages=['steinlib'], tests_require=\n tests_require, test_suite='nose2.collector.collector', zip_safe=False)\n", (113, 424), False, 'from setuptools import setup\n')] |
# -*- coding:utf-8 -*-
"""
@File : test_template
@Author : Chen
@Contact : <EMAIL>
@Date : 2021/1/20 20:09
@Desc :
"""
# 导包
import pytest
import requests
from time import sleep
from api.template_api import TemplateAPI
from tools.get_log import GetLog
from tools.read_file import read_json
import allure
# 获取日志器
log = GetLog.get_log()
@allure.feature('测试类模板')
@pytest.skip("参考模板, 不执行")
class TestTemplate:
session = None
# 初始化方法
@classmethod
def setup_class(cls):
cls.session = requests.Session() # 初始化session对象
cls.template = TemplateAPI()
# 结束方法
@classmethod
def teardown_class(cls):
cls.session.close()
@classmethod
def setup(cls):
sleep(1.5)
# 测试方法
@allure.story("测试方法模板-add")
@pytest.mark.parametrize(("attr1", "attr2", "success", "expect"), read_json("test_add"))
def test_add(self, attr1, attr2, success, expect):
# 添加功能API调用
response = self.template.api_add(self.session, attr1, attr2)
# 打印日志
log.info("添加功能-状态码为: {}".format(response.status_code))
# 断言状态码
assert response.status_code == expect, "状态码断言失败"
@allure.story("测试方法模板-upd")
@pytest.mark.parametrize(("attr1", "attr2", "success", "expect"), read_json("test_upd"))
def test_upd(self, attr1, attr2, success, expect):
# 添加功能API调用
response = self.template.api_upd(self.session, attr1, attr2)
# 打印日志
log.info("修改功能-状态码为: {}".format(response.status_code))
# 断言状态码
assert response.status_code == expect, "状态码断言失败"
@allure.story("测试方法模板-get")
@pytest.mark.parametrize(("attr1", "attr2", "success", "expect"), read_json("test_get"))
def test_get(self, attr1, attr2, success, expect):
# 添加功能API调用
response = self.template.api_get(self.session, attr1, attr2)
# 打印日志
log.info("查询功能-状态码为: {}".format(response.status_code))
# 断言状态码
assert response.status_code == expect, "状态码断言失败"
@allure.story("测试方法模板-del")
@pytest.mark.parametrize(("uid", "success", "expect"), read_json("test_del"))
def test_del(self, uid, success, expect):
# 添加功能API调用
response = self.template.api_del(self.session, uid)
# 打印日志
log.info("删除功能-状态码为: {}".format(response.status_code))
# 断言状态码
assert response.status_code == expect, "状态码断言失败"
| [
"requests.Session",
"allure.story",
"api.template_api.TemplateAPI",
"time.sleep",
"allure.feature",
"pytest.skip",
"tools.read_file.read_json",
"tools.get_log.GetLog.get_log"
] | [((321, 337), 'tools.get_log.GetLog.get_log', 'GetLog.get_log', ([], {}), '()\n', (335, 337), False, 'from tools.get_log import GetLog\n'), ((341, 364), 'allure.feature', 'allure.feature', (['"""测试类模板"""'], {}), "('测试类模板')\n", (355, 364), False, 'import allure\n'), ((366, 390), 'pytest.skip', 'pytest.skip', (['"""参考模板, 不执行"""'], {}), "('参考模板, 不执行')\n", (377, 390), False, 'import pytest\n'), ((742, 768), 'allure.story', 'allure.story', (['"""测试方法模板-add"""'], {}), "('测试方法模板-add')\n", (754, 768), False, 'import allure\n'), ((1163, 1189), 'allure.story', 'allure.story', (['"""测试方法模板-upd"""'], {}), "('测试方法模板-upd')\n", (1175, 1189), False, 'import allure\n'), ((1584, 1610), 'allure.story', 'allure.story', (['"""测试方法模板-get"""'], {}), "('测试方法模板-get')\n", (1596, 1610), False, 'import allure\n'), ((2005, 2031), 'allure.story', 'allure.story', (['"""测试方法模板-del"""'], {}), "('测试方法模板-del')\n", (2017, 2031), False, 'import allure\n'), ((508, 526), 'requests.Session', 'requests.Session', ([], {}), '()\n', (524, 526), False, 'import requests\n'), ((568, 581), 'api.template_api.TemplateAPI', 'TemplateAPI', ([], {}), '()\n', (579, 581), False, 'from api.template_api import TemplateAPI\n'), ((714, 724), 'time.sleep', 'sleep', (['(1.5)'], {}), '(1.5)\n', (719, 724), False, 'from time import sleep\n'), ((839, 860), 'tools.read_file.read_json', 'read_json', (['"""test_add"""'], {}), "('test_add')\n", (848, 860), False, 'from tools.read_file import read_json\n'), ((1260, 1281), 'tools.read_file.read_json', 'read_json', (['"""test_upd"""'], {}), "('test_upd')\n", (1269, 1281), False, 'from tools.read_file import read_json\n'), ((1681, 1702), 'tools.read_file.read_json', 'read_json', (['"""test_get"""'], {}), "('test_get')\n", (1690, 1702), False, 'from tools.read_file import read_json\n'), ((2091, 2112), 'tools.read_file.read_json', 'read_json', (['"""test_del"""'], {}), "('test_del')\n", (2100, 2112), False, 'from tools.read_file import read_json\n')] |
# -*- coding: utf-8 -*-
import botocore.exceptions
import logging
import dockerfilegenerator.lib.constants as constants
import dockerfilegenerator.lib.exceptions as exceptions
import dockerfilegenerator.lib.versions as versions
import dockerfilegenerator.lib.jsonstore as jsonstore
import dockerfilegenerator.lib.s3store as s3store
import dockerfilegenerator.lib.github as github
logger = logging.getLogger()
TRACKED_TOOLS = {
"terraform": versions.get_latest_hashicorp_terraform_version,
"packer": versions.get_latest_hashicorp_packer_version,
"go": versions.get_latest_golango_go_version
}
class UtilsMixin:
@property
def tools_current_versions(self):
if not hasattr(self, "_tools_current_versions"):
self._tools_current_versions = None
if self._tools_current_versions is None:
self._tools_current_versions = dict(
(tool_name, self.dockerfile.version(tool_name))
for tool_name in self.dockerfile.json)
return self._tools_current_versions
@property
def tools_next_versions(self):
if not hasattr(self, "_tools_next_versions"):
self._tools_next_versions = None
if self._tools_next_versions is None:
self._tools_next_versions = dict(
(tool_name, TRACKED_TOOLS[tool_name]())
for tool_name in TRACKED_TOOLS)
return self._tools_next_versions
def update_dockerfile_versions(self):
dockerfile_changed = False
for tool in self.tools_current_versions:
# TODO: Refactor this method...
if self.dockerfile.force_version(tool):
logger.info("Update versions: %s has force_version" % tool)
continue
if tool == self.dockerfile.dockerfile_repo_name:
continue
current_version = self.tools_current_versions[tool]
next_version = self.tools_next_versions.get(tool, None)
if next_version is None:
logger.info("Update versions: %s has no next version" % tool)
continue
if current_version == next_version:
logger.info(
"Update versions: %s has no changed version" % tool)
continue
self.dockerfile.set_version(tool, next_version)
logger.info("Update versions: %s has next version %s" %
(tool, next_version))
dockerfile_changed = True
if dockerfile_changed:
self.dockerfile.set_next_version_dockerfile()
return dockerfile_changed
class DockerfileGeneratorLambda(UtilsMixin):
def __init__(self):
self.s3bucket = s3store.get_s3_bucket_manager()
self.dockerfile_repo = github.get_github_repository(
constants.DOCKERFILE_GITHUB_REPO)
self.dockerfile = jsonstore.get_dockerfile(self.dockerfile_repo)
self._internal_state = None
self.exit_code = 0
@property
def internal_state(self):
""" Get the state from AWS S3 json file, or use the one from Github,
if there is none."""
if self._internal_state is None:
internal_state = self.s3bucket.read_object(
constants.INTERNAL_STATE_FILE)
if internal_state is None:
logger.info("Internal state: No state from S3")
internal_state = self.dockerfile.dump
self.save_state_to_s3(internal_state)
self._internal_state = jsonstore.Store(internal_state)
return self._internal_state
def update_files_on_github(self):
template_dockerfile = self.dockerfile_repo.get_file_contents(
constants.TEMPLATE_GITHUB_DOCKERFILE_PATH)
template_readme = self.dockerfile_repo.get_file_contents(
constants.TEMPLATE_GITHUB_README_PATH)
commit_msg = self.dockerfile.update_summary(self.internal_state)
commit_files = [
(constants.INTERNAL_STATE_FILE, self.dockerfile.dump),
("Dockerfile", template_dockerfile.format(
**self.dockerfile.template_variables)),
("README.md", template_readme.format(
**self.dockerfile.template_variables))]
logger.info("Updating files on Github with message:\n\t%s" %
commit_msg)
self.dockerfile_repo.commit(commit_files, commit_msg)
def save_state_to_s3(self, content):
try:
logger.info("Saving state to S3")
self.s3bucket.write_object(constants.INTERNAL_STATE_FILE, content)
except (botocore.exceptions.ClientError, Exception) as e:
raise exceptions.LambdaException(
"Error: Uploading object to s3 bucket: %s" % (str(e)))
def main(self):
if self.update_dockerfile_versions():
self.update_files_on_github()
self.save_state_to_s3(self.dockerfile.dump)
return self.exit_code # Making Lambda Service happy
def lambda_handler():
return DockerfileGeneratorLambda().main()
| [
"logging.getLogger",
"dockerfilegenerator.lib.jsonstore.get_dockerfile",
"dockerfilegenerator.lib.jsonstore.Store",
"dockerfilegenerator.lib.s3store.get_s3_bucket_manager",
"dockerfilegenerator.lib.github.get_github_repository"
] | [((392, 411), 'logging.getLogger', 'logging.getLogger', ([], {}), '()\n', (409, 411), False, 'import logging\n'), ((2721, 2752), 'dockerfilegenerator.lib.s3store.get_s3_bucket_manager', 's3store.get_s3_bucket_manager', ([], {}), '()\n', (2750, 2752), True, 'import dockerfilegenerator.lib.s3store as s3store\n'), ((2784, 2846), 'dockerfilegenerator.lib.github.get_github_repository', 'github.get_github_repository', (['constants.DOCKERFILE_GITHUB_REPO'], {}), '(constants.DOCKERFILE_GITHUB_REPO)\n', (2812, 2846), True, 'import dockerfilegenerator.lib.github as github\n'), ((2886, 2932), 'dockerfilegenerator.lib.jsonstore.get_dockerfile', 'jsonstore.get_dockerfile', (['self.dockerfile_repo'], {}), '(self.dockerfile_repo)\n', (2910, 2932), True, 'import dockerfilegenerator.lib.jsonstore as jsonstore\n'), ((3537, 3568), 'dockerfilegenerator.lib.jsonstore.Store', 'jsonstore.Store', (['internal_state'], {}), '(internal_state)\n', (3552, 3568), True, 'import dockerfilegenerator.lib.jsonstore as jsonstore\n')] |
# coding: utf-8
"""
FreeClimb API
FreeClimb is a cloud-based application programming interface (API) that puts the power of the Vail platform in your hands. FreeClimb simplifies the process of creating applications that can use a full range of telephony features without requiring specialized or on-site telephony equipment. Using the FreeClimb REST API to write applications is easy! You have the option to use the language of your choice or hit the API directly. Your application can execute a command by issuing a RESTful request to the FreeClimb API. The base URL to send HTTP requests to the FreeClimb REST API is: /apiserver. FreeClimb authenticates and processes your request. # noqa: E501
The version of the OpenAPI document: 1.0.0
Contact: <EMAIL>
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import six
from freeclimb.configuration import Configuration
class MessageResult(object):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
"""
Attributes:
openapi_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
openapi_types = {
'uri': 'str',
'date_created': 'str',
'date_updated': 'str',
'revision': 'int',
'account_id': 'str',
'message_id': 'str',
'status': 'str',
'_from': 'str',
'to': 'str',
'text': 'str',
'direction': 'str',
'notification_url': 'str'
}
attribute_map = {
'uri': 'uri',
'date_created': 'dateCreated',
'date_updated': 'dateUpdated',
'revision': 'revision',
'account_id': 'accountId',
'message_id': 'messageId',
'status': 'status',
'_from': 'from',
'to': 'to',
'text': 'text',
'direction': 'direction',
'notification_url': 'notificationUrl'
}
def __init__(self, uri=None, date_created=None, date_updated=None, revision=None, account_id=None, message_id=None, status=None, _from=None, to=None, text=None, direction=None, notification_url=None, local_vars_configuration=None): # noqa: E501
"""MessageResult - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._uri = None
self._date_created = None
self._date_updated = None
self._revision = None
self._account_id = None
self._message_id = None
self._status = None
self.__from = None
self._to = None
self._text = None
self._direction = None
self._notification_url = None
self.discriminator = None
if uri is not None:
self.uri = uri
if date_created is not None:
self.date_created = date_created
if date_updated is not None:
self.date_updated = date_updated
if revision is not None:
self.revision = revision
if account_id is not None:
self.account_id = account_id
if message_id is not None:
self.message_id = message_id
if status is not None:
self.status = status
if _from is not None:
self._from = _from
if to is not None:
self.to = to
if text is not None:
self.text = text
if direction is not None:
self.direction = direction
if notification_url is not None:
self.notification_url = notification_url
@property
def uri(self):
"""Gets the uri of this MessageResult. # noqa: E501
The URI for this resource, relative to /apiserver. # noqa: E501
:return: The uri of this MessageResult. # noqa: E501
:rtype: str
"""
return self._uri
@uri.setter
def uri(self, uri):
"""Sets the uri of this MessageResult.
The URI for this resource, relative to /apiserver. # noqa: E501
:param uri: The uri of this MessageResult. # noqa: E501
:type: str
"""
self._uri = uri
@property
def date_created(self):
"""Gets the date_created of this MessageResult. # noqa: E501
The date that this resource was created (GMT) in RFC 1123 format (e.g., Mon, 15 Jun 2009 20:45:30 GMT). # noqa: E501
:return: The date_created of this MessageResult. # noqa: E501
:rtype: str
"""
return self._date_created
@date_created.setter
def date_created(self, date_created):
"""Sets the date_created of this MessageResult.
The date that this resource was created (GMT) in RFC 1123 format (e.g., Mon, 15 Jun 2009 20:45:30 GMT). # noqa: E501
:param date_created: The date_created of this MessageResult. # noqa: E501
:type: str
"""
self._date_created = date_created
@property
def date_updated(self):
"""Gets the date_updated of this MessageResult. # noqa: E501
The date that this resource was last updated (GMT) in RFC 1123 format (e.g., Mon, 15 Jun 2009 20:45:30 GMT). # noqa: E501
:return: The date_updated of this MessageResult. # noqa: E501
:rtype: str
"""
return self._date_updated
@date_updated.setter
def date_updated(self, date_updated):
"""Sets the date_updated of this MessageResult.
The date that this resource was last updated (GMT) in RFC 1123 format (e.g., Mon, 15 Jun 2009 20:45:30 GMT). # noqa: E501
:param date_updated: The date_updated of this MessageResult. # noqa: E501
:type: str
"""
self._date_updated = date_updated
@property
def revision(self):
"""Gets the revision of this MessageResult. # noqa: E501
Revision count for the resource. This count is set to 1 on creation and is incremented every time it is updated. # noqa: E501
:return: The revision of this MessageResult. # noqa: E501
:rtype: int
"""
return self._revision
@revision.setter
def revision(self, revision):
"""Sets the revision of this MessageResult.
Revision count for the resource. This count is set to 1 on creation and is incremented every time it is updated. # noqa: E501
:param revision: The revision of this MessageResult. # noqa: E501
:type: int
"""
self._revision = revision
@property
def account_id(self):
"""Gets the account_id of this MessageResult. # noqa: E501
String that uniquely identifies this account resource. # noqa: E501
:return: The account_id of this MessageResult. # noqa: E501
:rtype: str
"""
return self._account_id
@account_id.setter
def account_id(self, account_id):
"""Sets the account_id of this MessageResult.
String that uniquely identifies this account resource. # noqa: E501
:param account_id: The account_id of this MessageResult. # noqa: E501
:type: str
"""
self._account_id = account_id
@property
def message_id(self):
"""Gets the message_id of this MessageResult. # noqa: E501
String that uniquely identifies this message resource # noqa: E501
:return: The message_id of this MessageResult. # noqa: E501
:rtype: str
"""
return self._message_id
@message_id.setter
def message_id(self, message_id):
"""Sets the message_id of this MessageResult.
String that uniquely identifies this message resource # noqa: E501
:param message_id: The message_id of this MessageResult. # noqa: E501
:type: str
"""
self._message_id = message_id
@property
def status(self):
"""Gets the status of this MessageResult. # noqa: E501
Indicates the state of the message through the message lifecycle including: new, queued, rejected, sending, sent, failed, received, undelivered, expired, deleted, and unknown # noqa: E501
:return: The status of this MessageResult. # noqa: E501
:rtype: str
"""
return self._status
@status.setter
def status(self, status):
"""Sets the status of this MessageResult.
Indicates the state of the message through the message lifecycle including: new, queued, rejected, sending, sent, failed, received, undelivered, expired, deleted, and unknown # noqa: E501
:param status: The status of this MessageResult. # noqa: E501
:type: str
"""
allowed_values = ["new", "queued", "rejected", "sending", "sent", "failed", "received", "undelivered", "expired", "deleted", "unknown"] # noqa: E501
if self.local_vars_configuration.client_side_validation and status not in allowed_values: # noqa: E501
raise ValueError(
"Invalid value for `status` ({0}), must be one of {1}" # noqa: E501
.format(status, allowed_values)
)
self._status = status
@property
def _from(self):
"""Gets the _from of this MessageResult. # noqa: E501
Phone number in E.164 format that sent the message. # noqa: E501
:return: The _from of this MessageResult. # noqa: E501
:rtype: str
"""
return self.__from
@_from.setter
def _from(self, _from):
"""Sets the _from of this MessageResult.
Phone number in E.164 format that sent the message. # noqa: E501
:param _from: The _from of this MessageResult. # noqa: E501
:type: str
"""
self.__from = _from
@property
def to(self):
"""Gets the to of this MessageResult. # noqa: E501
Phone number in E.164 format that received the message. # noqa: E501
:return: The to of this MessageResult. # noqa: E501
:rtype: str
"""
return self._to
@to.setter
def to(self, to):
"""Sets the to of this MessageResult.
Phone number in E.164 format that received the message. # noqa: E501
:param to: The to of this MessageResult. # noqa: E501
:type: str
"""
self._to = to
@property
def text(self):
"""Gets the text of this MessageResult. # noqa: E501
Message contents # noqa: E501
:return: The text of this MessageResult. # noqa: E501
:rtype: str
"""
return self._text
@text.setter
def text(self, text):
"""Sets the text of this MessageResult.
Message contents # noqa: E501
:param text: The text of this MessageResult. # noqa: E501
:type: str
"""
self._text = text
@property
def direction(self):
"""Gets the direction of this MessageResult. # noqa: E501
Noting whether the message was inbound or outbound # noqa: E501
:return: The direction of this MessageResult. # noqa: E501
:rtype: str
"""
return self._direction
@direction.setter
def direction(self, direction):
"""Sets the direction of this MessageResult.
Noting whether the message was inbound or outbound # noqa: E501
:param direction: The direction of this MessageResult. # noqa: E501
:type: str
"""
self._direction = direction
@property
def notification_url(self):
"""Gets the notification_url of this MessageResult. # noqa: E501
URL invoked when message sent # noqa: E501
:return: The notification_url of this MessageResult. # noqa: E501
:rtype: str
"""
return self._notification_url
@notification_url.setter
def notification_url(self, notification_url):
"""Sets the notification_url of this MessageResult.
URL invoked when message sent # noqa: E501
:param notification_url: The notification_url of this MessageResult. # noqa: E501
:type: str
"""
self._notification_url = notification_url
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
attr = self.to_camel_case(attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
elif value is None:
continue
else:
result[attr] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, MessageResult):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, MessageResult):
return True
return self.to_dict() != other.to_dict()
def to_camel_case(self, snake_str):
components = snake_str.split('_')
return components[0] + ''.join(x.title() for x in components[1:])
| [
"six.iteritems",
"freeclimb.configuration.Configuration"
] | [((12557, 12590), 'six.iteritems', 'six.iteritems', (['self.openapi_types'], {}), '(self.openapi_types)\n', (12570, 12590), False, 'import six\n'), ((2550, 2565), 'freeclimb.configuration.Configuration', 'Configuration', ([], {}), '()\n', (2563, 2565), False, 'from freeclimb.configuration import Configuration\n')] |
from kdbush import KDBush
# test data
points = [
[54,1],[97,21],[65,35],[33,54],[95,39],[54,3],[53,54],[84,72],[33,34],[43,15],[52,83],[81,23],[1,61],[38,74],
[11,91],[24,56],[90,31],[25,57],[46,61],[29,69],[49,60],[4,98],[71,15],[60,25],[38,84],[52,38],[94,51],[13,25],
[77,73],[88,87],[6,27],[58,22],[53,28],[27,91],[96,98],[93,14],[22,93],[45,94],[18,28],[35,15],[19,81],[20,81],
[67,53],[43,3],[47,66],[48,34],[46,12],[32,38],[43,12],[39,94],[88,62],[66,14],[84,30],[72,81],[41,92],[26,4],
[6,76],[47,21],[57,70],[71,82],[50,68],[96,18],[40,31],[78,53],[71,90],[32,14],[55,6],[32,88],[62,32],[21,67],
[73,81],[44,64],[29,50],[70,5],[6,22],[68,3],[11,23],[20,42],[21,73],[63,86],[9,40],[99,2],[99,76],[56,77],
[83,6],[21,72],[78,30],[75,53],[41,11],[95,20],[30,38],[96,82],[65,48],[33,18],[87,28],[10,10],[40,34],
[10,20],[47,29],[46,78]]
ids = [
97, 74, 95, 30, 77, 38, 76, 27, 80, 55, 72, 90, 88, 48, 43, 46, 65, 39, 62, 93, 9, 96, 47, 8, 3, 12, 15, 14, 21, 41, 36, 40, 69, 56, 85, 78, 17, 71, 44,
19, 18, 13, 99, 24, 67, 33, 37, 49, 54, 57, 98, 45, 23, 31, 66, 68, 0, 32, 5, 51, 75, 73, 84, 35, 81, 22, 61, 89, 1, 11, 86, 52, 94, 16, 2, 6, 25, 92,
42, 20, 60, 58, 83, 79, 64, 10, 59, 53, 26, 87, 4, 63, 50, 7, 28, 82, 70, 29, 34, 91]
coords = [
10,20,6,22,10,10,6,27,20,42,18,28,11,23,13,25,9,40,26,4,29,50,30,38,41,11,43,12,43,3,46,12,32,14,35,15,40,31,33,18,
43,15,40,34,32,38,33,34,33,54,1,61,24,56,11,91,4,98,20,81,22,93,19,81,21,67,6,76,21,72,21,73,25,57,44,64,47,66,29,
69,46,61,38,74,46,78,38,84,32,88,27,91,45,94,39,94,41,92,47,21,47,29,48,34,60,25,58,22,55,6,62,32,54,1,53,28,54,3,
66,14,68,3,70,5,83,6,93,14,99,2,71,15,96,18,95,20,97,21,81,23,78,30,84,30,87,28,90,31,65,35,53,54,52,38,65,48,67,
53,49,60,50,68,57,70,56,77,63,86,71,90,52,83,71,82,72,81,94,51,75,53,95,39,78,53,88,62,84,72,77,73,99,76,73,81,88,
87,96,98,96,82]
index = KDBush(points)
result = index.range(20, 30, 50, 70)
print(result) # [60, 20, 45, 3, 17, 71, 44, 19, 18, 15, 69, 90, 62, 96, 47, 8, 77, 72]
for id in result:
p = points[id]
if p[0] < 20 or p[0] > 50 or p[1] < 30 or p[1] > 70:
print("FAIL")
for id in result:
p = points[id]
if id not in result and p[0] >= 20 and p[0] <= 50 and p[1] >= 30 and p[1] <= 70:
print("FAIL: outside point not in range")
def sqDist2(a, b):
dx = a[0] - b[0]
dy = a[1] - b[1]
return dx * dx + dy * dy;
index2 = KDBush(points)
qp = [50, 50]
r = 20
r2 = 20 * 20
result = index.within(qp[0], qp[1], r)
print(result) # [60, 6, 25, 92, 42, 20, 45, 3, 71, 44, 18, 96]
for id in result:
p = points[id]
if (sqDist2(p, qp) > r2): print('FAIL: result point in range')
for id in result:
p = points[id]
if (id not in result and sqDist2(p, qp) <= r2):
print('FAIL: result point not in range')
| [
"kdbush.KDBush"
] | [((1923, 1937), 'kdbush.KDBush', 'KDBush', (['points'], {}), '(points)\n', (1929, 1937), False, 'from kdbush import KDBush\n'), ((2434, 2448), 'kdbush.KDBush', 'KDBush', (['points'], {}), '(points)\n', (2440, 2448), False, 'from kdbush import KDBush\n')] |
#!/usr/bin/env python3
"""
Contains testcases for the individual examination.
"""
import unittest
from io import StringIO
import os
import sys
from unittest.mock import patch
from examiner import ExamTestCase, ExamTestResult, tags
from examiner import import_module, find_path_to_assignment
FILE_DIR = os.path.dirname(os.path.realpath(__file__))
REPO_PATH = find_path_to_assignment(FILE_DIR)
if REPO_PATH not in sys.path:
sys.path.insert(0, REPO_PATH)
# Path to file and basename of the file to import
main = import_module(REPO_PATH, "main")
class Test1Files(ExamTestCase):
"""
Each assignment has 1 testcase with multiple asserts.
The different asserts https://docs.python.org/3.6/library/unittest.html#test-cases
"""
class Test2Counters(ExamTestCase):
"""
Meny options for counting
"""
@classmethod
def setUpClass(cls):
# Otherwise the .txt files will not be found
os.chdir(REPO_PATH)
@tags("count", "lines")
def test_b_lines(self):
"""
Testar att anropa menyval 'lines' i main.py.
Använder följande som input:
{arguments}
Förväntar att följande finns med i utskrift:
{correct}
Fick följande:
{student}
"""
self.norepr = True
self._multi_arguments = ["lines", "", "q"]
with patch('builtins.input', side_effect=self._multi_arguments):
with patch('sys.stdout', new=StringIO()) as fake_out:
main.main()
str_data = fake_out.getvalue()
self.assertIn("17", str_data)
@tags("count", "words")
def test_c_words(self):
"""
Testar att anropa menyval 'words' i main.py.
Använder följande som input:
{arguments}
Förväntar att följande finns med i utskrift:
{correct}
Fick följande:
{student}
"""
self.norepr = True
self._multi_arguments = ["words", "", "q"]
with patch('builtins.input', side_effect=self._multi_arguments):
with patch('sys.stdout', new=StringIO()) as fake_out:
main.main()
str_data = fake_out.getvalue()
self.assertIn("199", str_data)
@tags("count", "letters")
def test_d_letters(self):
"""
Testar att anropa menyval 'letters' i main.py.
Använder följande som input:
{arguments}
Förväntar att följande finns med i utskrift:
{correct}
Fick följande:
{student}
"""
self.norepr = True
self._multi_arguments = ["letters", "", "q"]
self.norepr = True
with patch('builtins.input', side_effect=self._multi_arguments):
with patch('sys.stdout', new=StringIO()) as fake_out:
main.main()
str_data = fake_out.getvalue()
self.assertIn("907", str_data)
class Test3Frequencies(ExamTestCase):
"""
Meny options for frequency
"""
def check_print_contain(self, inp, correct):
"""
One function for testing print input functions.
"""
with patch("builtins.input", side_effect=inp):
with patch("sys.stdout", new=StringIO()) as fake_out:
main.main()
for val in correct:
str_data = fake_out.getvalue()
self.assertIn(val, str_data)
@tags("freq", "word_frequency")
def test_a_word_frequency(self):
"""
Testar att anropa menyval 'word_frequency' i main.py.
Använder följande som input:
{arguments}
Förväntar att följande finns med i utskrift:
{correct}
Fick följande:
{student}
"""
self.norepr = True
self._multi_arguments = ["word_frequency", "", "q"]
self.check_print_contain(self._multi_arguments, [
"the: 12 | 6.0%",
"to: 8 | 4.0%",
"and: 7 | 3.5%",
"of: 6 | 3.0%",
"street: 5 | 2.5%",
"him: 5 | 2.5%",
"he: 5 | 2.5%",
])
@tags("freq", "letter_frequency")
def test_b_letter_frequency(self):
"""
Testar att anropa menyval 'letter_frequency' i main.py.
Använder följande som input:
{arguments}
Förväntar att följande finns med i utskrift:
{correct}
Fick följande:
{student}
"""
self.norepr = True
self._multi_arguments = ["letter_frequency", "", "q"]
self.check_print_contain(self._multi_arguments, [
"e: 108 | 11.9%",
"t: 91 | 10.0%",
"o: 77 | 8.5%",
"h: 67 | 7.4%",
"n: 66 | 7.3%",
"i: 64 | 7.1%",
"a: 64 | 7.1%",
])
class Test4All(ExamTestCase):
"""
Meny options for frequency
"""
def check_print_contain(self, inp, correct):
"""
One function for testing print input functions.
"""
with patch("builtins.input", side_effect=inp):
with patch("sys.stdout", new=StringIO()) as fake_out:
main.main()
for val in correct:
str_data = fake_out.getvalue()
self.assertIn(val, str_data)
@tags("all")
def test_a_all(self):
"""
Testar att anropa menyval 'all' i main.py.
Använder följande som input:
{arguments}
Förväntar att följande finns med i utskrift:
{correct}
Fick följande:
{student}
"""
self.norepr = True
self._multi_arguments = ["all", "", "q"]
self.check_print_contain(self._multi_arguments, [
"17",
"199",
"907",
"the: 12 | 6.0%",
"to: 8 | 4.0%",
"and: 7 | 3.5%",
"of: 6 | 3.0%",
"street: 5 | 2.5%",
"him: 5 | 2.5%",
"he: 5 | 2.5%",
"e: 108 | 11.9%",
"t: 91 | 10.0%",
"o: 77 | 8.5%",
"h: 67 | 7.4%",
"n: 66 | 7.3%",
"i: 64 | 7.1%",
"a: 64 | 7.1%",
])
class Test4Change(ExamTestCase):
"""
Meny options for frequency
"""
@tags("change")
def test_a_change(self):
"""
Testar att anropa menyval 'all' i main.py.
Använder följande som input:
{arguments}
Förväntar att följande finns med i utskrift:
{correct}
Fick följande:
{student}
"""
self.norepr = True
self._multi_arguments = ["change", "lorum.txt", "", "all", "", "q"]
with patch('builtins.input', side_effect=self._multi_arguments):
with patch('sys.stdout', new=StringIO()) as fake_out:
main.main()
str_data = fake_out.getvalue()
self.assertIn("23", str_data)
self.assertIn("3", str_data)
self.assertIn("140", str_data)
self.assertIn("dolor: 2 | 8.0%", str_data)
self.assertIn("vivamus: 1 | 4.0%", str_data)
self.assertIn("vitae: 1 | 4.0%", str_data)
self.assertIn("varius: 1 | 4.0%", str_data)
self.assertIn("urna: 1 | 4.0%", str_data)
self.assertIn("sit: 1 | 4.0%", str_data)
self.assertIn("pellentesque: 1 | 4.0%", str_data)
self.assertIn("i: 18 | 12.9%", str_data)
self.assertIn("e: 16 | 11.4%", str_data)
self.assertIn("u: 12 | 8.6%", str_data)
self.assertIn("a: 12 | 8.6%", str_data)
self.assertIn("t: 10 | 7.1%", str_data)
self.assertIn("l: 10 | 7.1%", str_data)
self.assertIn("s: 9 | 6.4%", str_data)
if __name__ == '__main__':
runner = unittest.TextTestRunner(resultclass=ExamTestResult, verbosity=2)
unittest.main(testRunner=runner, exit=False)
| [
"sys.path.insert",
"examiner.tags",
"examiner.import_module",
"examiner.find_path_to_assignment",
"os.path.realpath",
"os.chdir",
"unittest.main",
"io.StringIO",
"unittest.mock.patch",
"unittest.TextTestRunner"
] | [((360, 393), 'examiner.find_path_to_assignment', 'find_path_to_assignment', (['FILE_DIR'], {}), '(FILE_DIR)\n', (383, 393), False, 'from examiner import import_module, find_path_to_assignment\n'), ((517, 549), 'examiner.import_module', 'import_module', (['REPO_PATH', '"""main"""'], {}), "(REPO_PATH, 'main')\n", (530, 549), False, 'from examiner import import_module, find_path_to_assignment\n'), ((320, 346), 'os.path.realpath', 'os.path.realpath', (['__file__'], {}), '(__file__)\n', (336, 346), False, 'import os\n'), ((429, 458), 'sys.path.insert', 'sys.path.insert', (['(0)', 'REPO_PATH'], {}), '(0, REPO_PATH)\n', (444, 458), False, 'import sys\n'), ((961, 983), 'examiner.tags', 'tags', (['"""count"""', '"""lines"""'], {}), "('count', 'lines')\n", (965, 983), False, 'from examiner import ExamTestCase, ExamTestResult, tags\n'), ((1604, 1626), 'examiner.tags', 'tags', (['"""count"""', '"""words"""'], {}), "('count', 'words')\n", (1608, 1626), False, 'from examiner import ExamTestCase, ExamTestResult, tags\n'), ((2249, 2273), 'examiner.tags', 'tags', (['"""count"""', '"""letters"""'], {}), "('count', 'letters')\n", (2253, 2273), False, 'from examiner import ExamTestCase, ExamTestResult, tags\n'), ((3430, 3460), 'examiner.tags', 'tags', (['"""freq"""', '"""word_frequency"""'], {}), "('freq', 'word_frequency')\n", (3434, 3460), False, 'from examiner import ExamTestCase, ExamTestResult, tags\n'), ((4122, 4154), 'examiner.tags', 'tags', (['"""freq"""', '"""letter_frequency"""'], {}), "('freq', 'letter_frequency')\n", (4126, 4154), False, 'from examiner import ExamTestCase, ExamTestResult, tags\n'), ((5310, 5321), 'examiner.tags', 'tags', (['"""all"""'], {}), "('all')\n", (5314, 5321), False, 'from examiner import ExamTestCase, ExamTestResult, tags\n'), ((6286, 6300), 'examiner.tags', 'tags', (['"""change"""'], {}), "('change')\n", (6290, 6300), False, 'from examiner import ExamTestCase, ExamTestResult, tags\n'), ((7888, 7952), 'unittest.TextTestRunner', 'unittest.TextTestRunner', ([], {'resultclass': 'ExamTestResult', 'verbosity': '(2)'}), '(resultclass=ExamTestResult, verbosity=2)\n', (7911, 7952), False, 'import unittest\n'), ((7957, 8001), 'unittest.main', 'unittest.main', ([], {'testRunner': 'runner', 'exit': '(False)'}), '(testRunner=runner, exit=False)\n', (7970, 8001), False, 'import unittest\n'), ((934, 953), 'os.chdir', 'os.chdir', (['REPO_PATH'], {}), '(REPO_PATH)\n', (942, 953), False, 'import os\n'), ((1349, 1407), 'unittest.mock.patch', 'patch', (['"""builtins.input"""'], {'side_effect': 'self._multi_arguments'}), "('builtins.input', side_effect=self._multi_arguments)\n", (1354, 1407), False, 'from unittest.mock import patch\n'), ((1993, 2051), 'unittest.mock.patch', 'patch', (['"""builtins.input"""'], {'side_effect': 'self._multi_arguments'}), "('builtins.input', side_effect=self._multi_arguments)\n", (1998, 2051), False, 'from unittest.mock import patch\n'), ((2672, 2730), 'unittest.mock.patch', 'patch', (['"""builtins.input"""'], {'side_effect': 'self._multi_arguments'}), "('builtins.input', side_effect=self._multi_arguments)\n", (2677, 2730), False, 'from unittest.mock import patch\n'), ((3151, 3191), 'unittest.mock.patch', 'patch', (['"""builtins.input"""'], {'side_effect': 'inp'}), "('builtins.input', side_effect=inp)\n", (3156, 3191), False, 'from unittest.mock import patch\n'), ((5032, 5072), 'unittest.mock.patch', 'patch', (['"""builtins.input"""'], {'side_effect': 'inp'}), "('builtins.input', side_effect=inp)\n", (5037, 5072), False, 'from unittest.mock import patch\n'), ((6690, 6748), 'unittest.mock.patch', 'patch', (['"""builtins.input"""'], {'side_effect': 'self._multi_arguments'}), "('builtins.input', side_effect=self._multi_arguments)\n", (6695, 6748), False, 'from unittest.mock import patch\n'), ((1450, 1460), 'io.StringIO', 'StringIO', ([], {}), '()\n', (1458, 1460), False, 'from io import StringIO\n'), ((2094, 2104), 'io.StringIO', 'StringIO', ([], {}), '()\n', (2102, 2104), False, 'from io import StringIO\n'), ((2773, 2783), 'io.StringIO', 'StringIO', ([], {}), '()\n', (2781, 2783), False, 'from io import StringIO\n'), ((3234, 3244), 'io.StringIO', 'StringIO', ([], {}), '()\n', (3242, 3244), False, 'from io import StringIO\n'), ((5115, 5125), 'io.StringIO', 'StringIO', ([], {}), '()\n', (5123, 5125), False, 'from io import StringIO\n'), ((6791, 6801), 'io.StringIO', 'StringIO', ([], {}), '()\n', (6799, 6801), False, 'from io import StringIO\n')] |
from django.contrib import admin
from olha_boca.infratores.models import Infratores
# Register your models here.
class InfratoresAdmin(admin.ModelAdmin):
list_display = ('nome', 'infracoes_a_pagar', 'total_infracoes', 'valor_a_pagar')
@admin.display(empty_value='???')
def total_infracoes(self, obj):
return obj.infracoes.count()
@admin.display(empty_value='???')
def infracoes_a_pagar(self, obj):
return obj.infracoes.filter(paga=False).count()
@admin.display(empty_value='???')
def valor_a_pagar(self, obj):
total = 0
infracoes_a_pagar = obj.infracoes.filter(paga=False).all()
for inf in infracoes_a_pagar:
total += (inf.tipo.vibs * inf.tipo.multiplicador_vibs)
return f'R$ {total:.2f}'
admin.site.register(Infratores, InfratoresAdmin) | [
"django.contrib.admin.site.register",
"django.contrib.admin.display"
] | [((783, 831), 'django.contrib.admin.site.register', 'admin.site.register', (['Infratores', 'InfratoresAdmin'], {}), '(Infratores, InfratoresAdmin)\n', (802, 831), False, 'from django.contrib import admin\n'), ((247, 279), 'django.contrib.admin.display', 'admin.display', ([], {'empty_value': '"""???"""'}), "(empty_value='???')\n", (260, 279), False, 'from django.contrib import admin\n'), ((359, 391), 'django.contrib.admin.display', 'admin.display', ([], {'empty_value': '"""???"""'}), "(empty_value='???')\n", (372, 391), False, 'from django.contrib import admin\n'), ((492, 524), 'django.contrib.admin.display', 'admin.display', ([], {'empty_value': '"""???"""'}), "(empty_value='???')\n", (505, 524), False, 'from django.contrib import admin\n')] |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import functools
import torch.nn as nn
from torch.nn import init
import torch.functional as F
from torch.autograd import Variable
print('ok')
def weights_init_normal(m):
classname = m.__class__.__name__
print(classname)
if classname.find('Conv') != -1:
init.normal(m.weight.data, 0.0, 0.02)
elif classname.find('Linear') != -1:
init.normal(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm2d') != -1:
init.normal(m.weight.data, 1.0, 0.02)
init.constant(m.bias.data, 0.0)
def init_weight(net,init_type='normal'):
print('initialization method [%s]' % init_type)
if init_type == 'normal':
net.apply(weights_init_normal)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
class ResnetBlock(nn.Module):
def __init__(self, dim, use_dropout, use_bias):
super(ResnetBlock, self).__init__()
self.conv_block = self.build_conv_block(dim, use_dropout, use_bias)
def build_conv_block(self,dim,use_dropout,use_bias):
conv_block = []
conv_block += [nn.ReflectionPad2d(1)]
conv_block += [nn.Conv2d(dim,dim,kernel_size=3,padding=0,bias=use_bias),
nn.InstanceNorm2d(dim),
nn.ReLU(True)]
if use_dropout:
conv_block += [nn.Dropout(0.5)]
conv_block += [nn.ReflectionPad2d(1)]
conv_block += [nn.Conv2d(dim,dim,kernel_size=3,padding=0,bias=use_bias),
nn.InstanceNorm2d(dim)]
return nn.Sequential(*conv_block)
def forward(self,x):
out = x + self.conv_block(x)
return out
class G(nn.Module):
def __init__(self,dim=64,device_ids=[]):
super(G,self).__init__()
self.device_ids = device_ids
model = [nn.ReflectionPad2d(3),
nn.Conv2d(3, dim, kernel_size=7, padding=0,bias=False),
nn.InstanceNorm2d(dim),
nn.ReLU(True)]
for i in range(2):
mult = 2 ** i
model += [nn.Conv2d(dim * mult, dim * mult * 2, kernel_size=3,
stride=2, padding=1, bias=False),
nn.InstanceNorm2d(dim * mult * 2),
nn.ReLU(True)]
for i in range(9):
model += [ResnetBlock(dim*4,use_dropout=False,use_bias=False)]
for i in range(2):
mult = 2**(2 - i)
model += [nn.ConvTranspose2d(dim * mult, int(dim * mult / 2),
kernel_size=3, stride=2,
padding=1, output_padding=1,
bias=False),
nn.InstanceNorm2d(int(dim * mult / 2)),
nn.ReLU(True)]
model += [nn.ReflectionPad2d(3)]
model += [nn.Conv2d(dim,3,kernel_size=7,padding=0)]
model += [nn.Tanh()]
self.model = nn.Sequential(*model)
def forward(self, input):
use_gpu = len(self.device_ids) > 0
if (use_gpu):
assert (torch.cuda.is_available())
if len(self.device_ids)and isinstance(input.data, torch.cuda.FloatTensor):
print('Train on GPU...')
return nn.parallel.data_parallel(self.model, input, self.device_ids)
else:
print('Train on CPU...')
return self.model(input)
class D(nn.Module):
def __init__(self,dim=64,device_ids=[]):
super(D,self).__init__()
self.device_ids = device_ids
model = [nn.Conv2d(3,dim,kernel_size=4,stride=2,padding=1),
nn.LeakyReLU(0.2,True)]
model += [nn.Conv2d(dim,dim*2,kernel_size=4,stride=2,padding=1,bias=False),
nn.InstanceNorm2d(dim*2),
nn.LeakyReLU(0.2,True)]
model += [nn.Conv2d(dim*2, dim*4, kernel_size=4, stride=2, padding=1, bias=False),
nn.InstanceNorm2d(dim*4),
nn.LeakyReLU(0.2,True)]
model += [nn.Conv2d(dim*4, dim*8, kernel_size=4, stride=1, padding=1, bias=False),
nn.InstanceNorm2d(dim*8),
nn.LeakyReLU(0.2,True)]
model += [nn.Conv2d(dim*8,1,kernel_size=4,stride=1,padding=1)]
self.model = nn.Sequential(*model)
def forward(self, input):
use_gpu = len(self.device_ids) > 0
if (use_gpu):
assert (torch.cuda.is_available())
if len(self.device_ids)and isinstance(input.data, torch.cuda.FloatTensor):
print('Train on GPU...')
return nn.parallel.data_parallel(self.model, input, self.device_ids)
else:
print('Train on CPU...')
return self.model(input)
print ('kkk')
# class te(nn.Module):
# def __init__(self):
# super(te,self).__init__()
# norm_layer=nn.InstanceNorm2d
# kw = 4
# padw = 1
# input_nc=3
# n_layers=3
# ndf=64
# use_bias = False
# sequence = [
# nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
# nn.LeakyReLU(0.2, True)
# ]
#
# nf_mult = 1
# nf_mult_prev = 1
# for n in range(1, n_layers):
# nf_mult_prev = nf_mult
# nf_mult = min(2**n, 8)
# sequence += [
# nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult,
# kernel_size=kw, stride=2, padding=padw, bias=use_bias),
# norm_layer(ndf * nf_mult),
# nn.LeakyReLU(0.2, True)
# ]
#
# nf_mult_prev = nf_mult
# nf_mult = min(2**n_layers, 8)
# sequence += [
# nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult,
# kernel_size=kw, stride=1, padding=padw, bias=use_bias),
# norm_layer(ndf * nf_mult),
# nn.LeakyReLU(0.2, True)
# ]
#
# sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]
#
# self.model1 = nn.Sequential(*sequence)
# def forward(self,x):
# return self.model1(x)
| [
"torch.nn.ReLU",
"torch.nn.parallel.data_parallel",
"torch.nn.Dropout",
"torch.nn.Tanh",
"torch.nn.LeakyReLU",
"torch.nn.Sequential",
"torch.nn.init.normal",
"torch.nn.ReflectionPad2d",
"torch.nn.InstanceNorm2d",
"torch.nn.Conv2d",
"torch.cuda.is_available",
"torch.nn.init.constant"
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# Generated by Django 2.0.13 on 2019-06-27 17:04
import django.contrib.postgres.fields.jsonb
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('research', '0001_initial'),
('datasets', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Bottleneck',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('homology', models.PositiveIntegerField()),
('kind', models.CharField(choices=[('consecutive', 'consecutive'), ('one_to_all', 'one_to_all'), ('all_to_all', 'all_to_all')], max_length=20)),
],
),
migrations.CreateModel(
name='Diagram',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('image', models.TextField()),
('bottleneck_distance', models.FloatField()),
('bottleneck', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='analysis.Bottleneck')),
],
),
migrations.CreateModel(
name='FiltrationAnalysis',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(help_text='Name this analysis', max_length=100)),
('slug', models.SlugField(max_length=110)),
('description', models.TextField(blank=True, help_text='Write a brief description of the analysis', max_length=500)),
('creation_date', models.DateTimeField(auto_now_add=True)),
('precomputed_distance_matrix_json', django.contrib.postgres.fields.jsonb.JSONField(default='"[]"')),
('window_size', models.PositiveIntegerField(blank=True, default=None, help_text="Leave window size blank to not use windows. Window parameter\n is ignored when dealing with precomputed distance matrix. Always check\n the dimensions of the dataset your are operating on and plan your windows\n accordingly; eventual data that won't fit into the final window will be\n discarded.", null=True)),
('window_overlap', models.PositiveIntegerField(default=0, help_text='How many columns of overlap to have in\n consequent windows, if windows are being used. It must be at most 1\n less than window size.')),
('filtration_type', models.CharField(choices=[('VRF', 'Vietoris Rips Filtration'), ('CWRF', 'Clique Weighted Rank Filtration')], help_text='Choose the type of analysis.', max_length=50)),
('distance_matrix_metric', models.CharField(blank=True, choices=[('braycurtis', 'Braycurtis'), ('canberra', 'Canberra'), ('chebyshev', 'Chebyshev'), ('cityblock', 'City block'), ('correlation', 'Correlation'), ('cosine', 'Cosine'), ('dice', 'Dice'), ('euclidean', 'Euclidean'), ('hamming', 'Hamming'), ('jaccard', 'Jaccard'), ('jensenshannon', '<NAME>'), ('kulsinski', 'Kulsinski'), ('mahalanobis', 'Mahalonobis'), ('matching', 'Matching'), ('minkowski', 'Minkowski'), ('rogerstanimoto', 'Rogers-Tanimoto'), ('russellrao', '<NAME>'), ('seuclidean', 'Seuclidean'), ('sokalmichener', 'Sojal-Michener'), ('sokalsneath', 'Sokal-Sneath'), ('sqeuclidean', 'Sqeuclidean'), ('yule', 'Yule')], help_text='If Vietoris-Rips filtration is selected and not using a precomputed distance matrix, choose the\n distance metric to use on the selected dataset. This parameter is ignored in all other cases.', max_length=20)),
('max_homology_dimension', models.PositiveIntegerField(default=1, help_text='Maximum homology dimension computed. Will compute all dimensions lower than and equal to this value.\n For 1, H_0 and H_1 will be computed.')),
('max_distances_considered', models.FloatField(blank=True, default=None, help_text='Maximum distances considered when constructing filtration.\n If blank, compute the entire filtration.', null=True)),
('coeff', models.PositiveIntegerField(default=2, help_text='Compute homology with coefficients in the prime field Z/pZ for\n p=coeff.')),
('do_cocycles', models.BooleanField(default=False, help_text='Indicator of whether to compute cocycles.')),
('n_perm', models.IntegerField(blank=True, default=None, help_text='The number of points to subsample in\n a “greedy permutation,” or a furthest point sampling of the points. These points will\n be used in lieu of the full point cloud for a faster computation, at the expense of\n some accuracy, which can be bounded as a maximum bottleneck distance to all diagrams\n on the original point set', null=True)),
('entropy_normalized_graph', models.TextField(blank=True, null=True)),
('entropy_unnormalized_graph', models.TextField(blank=True, null=True)),
('dataset', models.ForeignKey(blank=True, default=None, help_text='Select the source dataset from the loaded datasets', null=True, on_delete=django.db.models.deletion.CASCADE, to='datasets.Dataset')),
('research', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='research.Research')),
],
options={
'verbose_name': 'filtration analysis',
'verbose_name_plural': 'filtration analyses',
'abstract': False,
},
),
migrations.CreateModel(
name='FiltrationWindow',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.PositiveIntegerField()),
('slug', models.SlugField(max_length=150)),
('creation_date', models.DateTimeField(auto_now_add=True)),
('start', models.PositiveIntegerField(blank=True, null=True)),
('end', models.PositiveIntegerField(blank=True, null=True)),
('result_matrix', django.contrib.postgres.fields.jsonb.JSONField(blank=True, null=True)),
('diagrams', django.contrib.postgres.fields.jsonb.JSONField(blank=True, null=True)),
('result_entropy_normalized', django.contrib.postgres.fields.jsonb.JSONField(blank=True, null=True)),
('result_entropy_unnormalized', django.contrib.postgres.fields.jsonb.JSONField(blank=True, null=True)),
('analysis', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='analysis.FiltrationAnalysis')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='MapperAnalysis',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(help_text='Name this analysis', max_length=100)),
('slug', models.SlugField(max_length=110)),
('description', models.TextField(blank=True, help_text='Write a brief description of the analysis', max_length=500)),
('creation_date', models.DateTimeField(auto_now_add=True)),
('precomputed_distance_matrix_json', django.contrib.postgres.fields.jsonb.JSONField(default='"[]"')),
('window_size', models.PositiveIntegerField(blank=True, default=None, help_text="Leave window size blank to not use windows. Window parameter\n is ignored when dealing with precomputed distance matrix. Always check\n the dimensions of the dataset your are operating on and plan your windows\n accordingly; eventual data that won't fit into the final window will be\n discarded.", null=True)),
('window_overlap', models.PositiveIntegerField(default=0, help_text='How many columns of overlap to have in\n consequent windows, if windows are being used. It must be at most 1\n less than window size.')),
('distance_matrix_metric', models.CharField(blank=True, choices=[('braycurtis', 'Braycurtis'), ('canberra', 'Canberra'), ('chebyshev', 'Chebyshev'), ('cityblock', 'City block'), ('correlation', 'Correlation'), ('cosine', 'Cosine'), ('dice', 'Dice'), ('euclidean', 'Euclidean'), ('hamming', 'Hamming'), ('jaccard', 'Jaccard'), ('jensenshannon', '<NAME>'), ('kulsinski', 'Kulsinski'), ('mahalanobis', 'Mahalonobis'), ('matching', 'Matching'), ('minkowski', 'Minkowski'), ('rogerstanimoto', 'Rogers-Tanimoto'), ('russellrao', '<NAME>'), ('seuclidean', 'Seuclidean'), ('sokalmichener', 'Sojal-Michener'), ('sokalsneath', 'Sokal-Sneath'), ('sqeuclidean', 'Sqeuclidean'), ('yule', 'Yule')], help_text='If not using a precomputed matrix, choose the distance metric to use on the dataset.', max_length=20)),
('projection', models.CharField(choices=[('sum', 'Sum'), ('mean', 'Mean'), ('median', 'Median'), ('max', 'Max'), ('min', 'Min'), ('std', 'Std'), ('dist_mean', 'Dist_mean'), ('l2norm', 'L2norm'), ('knn_distance_n', 'knn_distance_n')], default='sum', help_text='Specify a projection/lens type.', max_length=50)),
('knn_n_value', models.PositiveIntegerField(blank=True, help_text='Specify the value of n in knn_distance_n', null=True)),
('scaler', models.CharField(choices=[('None', 'None'), ('MinMaxScaler', 'MinMaxScaler'), ('MaxAbsScaler', 'MaxAbsScaler'), ('RobustScaler', 'RobustScaler'), ('StandardScaler', 'StandardScaler')], default='MinMaxScaler', help_text='Scaler of the data applied after mapping. Use None for no scaling.', max_length=50)),
('use_original_data', models.BooleanField(default=False, help_text='If ticked, clustering is run on the original data,\n else it will be run on the lower dimensional projection.')),
('clusterer', models.CharField(choices=[('k-means', 'K-Means'), ('affinity_propagation', 'Affinity propagation'), ('mean-shift', 'Mean-shift'), ('spectral_clustering', 'Spectral clustering'), ('agglomerative_clustering', 'StandardScaler'), ('DBSCAN(min_samples=1)', 'DBSCAN(min_samples=1)'), ('DBSCAN', 'DBSCAN'), ('gaussian_mixtures', 'Gaussian mixtures'), ('birch', 'Birch')], default='DBSCAN', help_text='Select the clustering algorithm.', max_length=50)),
('cover_n_cubes', models.PositiveIntegerField(default=10, help_text='Number of hypercubes along each dimension.\n Sometimes referred to as resolution.')),
('cover_perc_overlap', models.FloatField(default=0.5, help_text='Amount of overlap between adjacent cubes calculated\n only along 1 dimension.')),
('graph_nerve_min_intersection', models.IntegerField(default=1, help_text='Minimum intersection considered when\n computing the nerve. An edge will be created only when the\n intersection between two nodes is greater than or equal to\n min_intersection')),
('precomputed', models.BooleanField(default=False, help_text='Tell Mapper whether the data that you are clustering on\n is a precomputed distance matrix. If set to True, the assumption is that you are\n also telling your clusterer that metric=’precomputed’ (which is an argument for\n DBSCAN among others), which will then cause the clusterer to expect a square\n distance matrix for each hypercube. precomputed=True will give a square matrix\n to the clusterer to fit on for each hypercube.')),
('remove_duplicate_nodes', models.BooleanField(default=False, help_text='Removes duplicate nodes before edges are\n determined. A node is considered to be duplicate if it has exactly\n the same set of points as another node.')),
('dataset', models.ForeignKey(blank=True, default=None, help_text='Select the source dataset from the loaded datasets', null=True, on_delete=django.db.models.deletion.CASCADE, to='datasets.Dataset')),
('research', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='research.Research')),
],
options={
'verbose_name': 'mapper algorithm analysis',
'verbose_name_plural': 'mapper algoritm analyses',
'abstract': False,
},
),
migrations.CreateModel(
name='MapperWindow',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.PositiveIntegerField()),
('slug', models.SlugField(max_length=150)),
('creation_date', models.DateTimeField(auto_now_add=True)),
('start', models.PositiveIntegerField(blank=True, null=True)),
('end', models.PositiveIntegerField(blank=True, null=True)),
('graph', models.TextField(blank=True, null=True)),
('analysis', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='windows', related_query_name='window', to='analysis.MapperAnalysis')),
],
options={
'abstract': False,
},
),
migrations.AddField(
model_name='diagram',
name='window1',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='window1', to='analysis.FiltrationWindow'),
),
migrations.AddField(
model_name='diagram',
name='window2',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='window2', to='analysis.FiltrationWindow'),
),
migrations.AddField(
model_name='bottleneck',
name='analysis',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='analysis.FiltrationAnalysis'),
),
migrations.AddField(
model_name='bottleneck',
name='window',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='analysis.FiltrationWindow'),
),
migrations.AlterUniqueTogether(
name='mapperanalysis',
unique_together={('slug', 'research')},
),
migrations.AlterUniqueTogether(
name='filtrationanalysis',
unique_together={('slug', 'research')},
),
]
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"django.db.models.TextField",
"django.db.models.ForeignKey",
"django.db.models.IntegerField",
"django.db.models.BooleanField",
"django.db.models.SlugField",
"django.db.models.AutoField",
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django.db import migrations, models\n'), ((1368, 1461), 'django.db.models.AutoField', 'models.AutoField', ([], {'auto_created': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'verbose_name': '"""ID"""'}), "(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')\n", (1384, 1461), False, 'from django.db import migrations, models\n'), ((1485, 1549), 'django.db.models.CharField', 'models.CharField', ([], {'help_text': '"""Name this analysis"""', 'max_length': '(100)'}), "(help_text='Name this analysis', max_length=100)\n", (1501, 1549), False, 'from django.db import migrations, models\n'), ((1577, 1609), 'django.db.models.SlugField', 'models.SlugField', ([], {'max_length': '(110)'}), '(max_length=110)\n', (1593, 1609), False, 'from django.db import migrations, models\n'), ((1644, 1748), 'django.db.models.TextField', 'models.TextField', ([], {'blank': '(True)', 'help_text': '"""Write a brief description of the analysis"""', 'max_length': '(500)'}), "(blank=True, help_text=\n 'Write a brief description of the analysis', max_length=500)\n", (1660, 1748), False, 'from django.db import migrations, models\n'), ((1780, 1819), 'django.db.models.DateTimeField', 'models.DateTimeField', ([], {'auto_now_add': '(True)'}), '(auto_now_add=True)\n', (1800, 1819), False, 'from django.db import migrations, models\n'), ((1972, 2536), 'django.db.models.PositiveIntegerField', 'models.PositiveIntegerField', ([], {'blank': '(True)', 'default': 'None', 'help_text': '"""Leave window size blank to not use windows. Window parameter\n is ignored when dealing with precomputed distance matrix. Always check\n the dimensions of the dataset your are operating on and plan your windows\n accordingly; eventual data that won\'t fit into the final window will be\n discarded."""', 'null': '(True)'}), '(blank=True, default=None, help_text=\n """Leave window size blank to not use windows. Window parameter\n is ignored when dealing with precomputed distance matrix. Always check\n the dimensions of the dataset your are operating on and plan your windows\n accordingly; eventual data that won\'t fit into the final window will be\n discarded."""\n , null=True)\n', (1999, 2536), False, 'from django.db import migrations, models\n'), ((2564, 2857), 'django.db.models.PositiveIntegerField', 'models.PositiveIntegerField', ([], {'default': '(0)', 'help_text': '"""How many columns of overlap to have in\n consequent windows, if windows are being used. It must be at most 1\n less than window size."""'}), '(default=0, help_text=\n """How many columns of overlap to have in\n consequent windows, if windows are being used. It must be at most 1\n less than window size."""\n )\n', (2591, 2857), False, 'from django.db import migrations, models\n'), ((2884, 3058), 'django.db.models.CharField', 'models.CharField', ([], {'choices': "[('VRF', 'Vietoris Rips Filtration'), ('CWRF',\n 'Clique Weighted Rank Filtration')]", 'help_text': '"""Choose the type of analysis."""', 'max_length': '(50)'}), "(choices=[('VRF', 'Vietoris Rips Filtration'), ('CWRF',\n 'Clique Weighted Rank Filtration')], help_text=\n 'Choose the type of analysis.', max_length=50)\n", (2900, 3058), False, 'from django.db import migrations, models\n'), ((3095, 4042), 'django.db.models.CharField', 'models.CharField', ([], {'blank': '(True)', 'choices': "[('braycurtis', 'Braycurtis'), ('canberra', 'Canberra'), ('chebyshev',\n 'Chebyshev'), ('cityblock', 'City block'), ('correlation',\n 'Correlation'), ('cosine', 'Cosine'), ('dice', 'Dice'), ('euclidean',\n 'Euclidean'), ('hamming', 'Hamming'), ('jaccard', 'Jaccard'), (\n 'jensenshannon', '<NAME>'), ('kulsinski', 'Kulsinski'), ('mahalanobis',\n 'Mahalonobis'), ('matching', 'Matching'), ('minkowski', 'Minkowski'), (\n 'rogerstanimoto', 'Rogers-Tanimoto'), ('russellrao', '<NAME>'), (\n 'seuclidean', 'Seuclidean'), ('sokalmichener', 'Sojal-Michener'), (\n 'sokalsneath', 'Sokal-Sneath'), ('sqeuclidean', 'Sqeuclidean'), ('yule',\n 'Yule')]", 'help_text': '"""If Vietoris-Rips filtration is selected and not using a precomputed distance matrix, choose the\n distance metric to use on the selected dataset. This parameter is ignored in all other cases."""', 'max_length': '(20)'}), '(blank=True, choices=[(\'braycurtis\', \'Braycurtis\'), (\n \'canberra\', \'Canberra\'), (\'chebyshev\', \'Chebyshev\'), (\'cityblock\',\n \'City block\'), (\'correlation\', \'Correlation\'), (\'cosine\', \'Cosine\'), (\n \'dice\', \'Dice\'), (\'euclidean\', \'Euclidean\'), (\'hamming\', \'Hamming\'), (\n \'jaccard\', \'Jaccard\'), (\'jensenshannon\', \'<NAME>\'), (\'kulsinski\',\n \'Kulsinski\'), (\'mahalanobis\', \'Mahalonobis\'), (\'matching\', \'Matching\'),\n (\'minkowski\', \'Minkowski\'), (\'rogerstanimoto\', \'Rogers-Tanimoto\'), (\n \'russellrao\', \'<NAME>\'), (\'seuclidean\', \'Seuclidean\'), (\'sokalmichener\',\n \'Sojal-Michener\'), (\'sokalsneath\', \'Sokal-Sneath\'), (\'sqeuclidean\',\n \'Sqeuclidean\'), (\'yule\', \'Yule\')], help_text=\n """If Vietoris-Rips filtration is selected and not using a precomputed distance matrix, choose the\n distance metric to use on the selected dataset. This parameter is ignored in all other cases."""\n , max_length=20)\n', (3111, 4042), False, 'from django.db import migrations, models\n'), ((4035, 4287), 'django.db.models.PositiveIntegerField', 'models.PositiveIntegerField', ([], {'default': '(1)', 'help_text': '"""Maximum homology dimension computed. Will compute all dimensions lower than and equal to this value.\n For 1, H_0 and H_1 will be computed."""'}), '(default=1, help_text=\n """Maximum homology dimension computed. Will compute all dimensions lower than and equal to this value.\n For 1, H_0 and H_1 will be computed."""\n )\n', (4062, 4287), False, 'from django.db import migrations, models\n'), ((4322, 4552), 'django.db.models.FloatField', 'models.FloatField', ([], {'blank': '(True)', 'default': 'None', 'help_text': '"""Maximum distances considered when constructing filtration.\n If blank, compute the entire filtration."""', 'null': '(True)'}), '(blank=True, default=None, help_text=\n """Maximum distances considered when constructing filtration.\n If blank, compute the entire filtration."""\n , null=True)\n', (4339, 4552), False, 'from django.db import migrations, models\n'), ((4568, 4737), 'django.db.models.PositiveIntegerField', 'models.PositiveIntegerField', ([], {'default': '(2)', 'help_text': '"""Compute homology with coefficients in the prime field Z/pZ for\n p=coeff."""'}), '(default=2, help_text=\n """Compute homology with coefficients in the prime field Z/pZ for\n p=coeff."""\n )\n', (4595, 4737), False, 'from django.db import migrations, models\n'), ((4759, 4853), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'default': '(False)', 'help_text': '"""Indicator of whether to compute cocycles."""'}), "(default=False, help_text=\n 'Indicator of whether to compute cocycles.')\n", (4778, 4853), False, 'from django.db import migrations, models\n'), ((4878, 5411), 'django.db.models.IntegerField', 'models.IntegerField', ([], {'blank': '(True)', 'default': 'None', 'help_text': '"""The number of points to subsample in\n a “greedy permutation,” or a furthest point sampling of the points. These points will\n be used in lieu of the full point cloud for a faster computation, at the expense of\n some accuracy, which can be bounded as a maximum bottleneck distance to all diagrams\n on the original point set"""', 'null': '(True)'}), '(blank=True, default=None, help_text=\n """The number of points to subsample in\n a “greedy permutation,” or a furthest point sampling of the points. 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Window parameter\n is ignored when dealing with precomputed distance matrix. Always check\n the dimensions of the dataset your are operating on and plan your windows\n accordingly; eventual data that won\'t fit into the final window will be\n discarded."""', 'null': '(True)'}), '(blank=True, default=None, help_text=\n """Leave window size blank to not use windows. Window parameter\n is ignored when dealing with precomputed distance matrix. Always check\n the dimensions of the dataset your are operating on and plan your windows\n accordingly; eventual data that won\'t fit into the final window will be\n discarded."""\n , null=True)\n', (8097, 8634), False, 'from django.db import migrations, models\n'), ((8662, 8955), 'django.db.models.PositiveIntegerField', 'models.PositiveIntegerField', ([], {'default': '(0)', 'help_text': '"""How many columns of overlap to have in\n consequent windows, if windows are being used. 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Use None for no scaling."""', 'max_length': '(50)'}), "(choices=[('None', 'None'), ('MinMaxScaler', 'MinMaxScaler'\n ), ('MaxAbsScaler', 'MaxAbsScaler'), ('RobustScaler', 'RobustScaler'),\n ('StandardScaler', 'StandardScaler')], default='MinMaxScaler',\n help_text=\n 'Scaler of the data applied after mapping. Use None for no scaling.',\n max_length=50)\n", (10271, 10580), False, 'from django.db import migrations, models\n'), ((10599, 10812), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'default': '(False)', 'help_text': '"""If ticked, clustering is run on the original data,\n else it will be run on the lower dimensional projection."""'}), '(default=False, help_text=\n """If ticked, clustering is run on the original data,\n else it will be run on the lower dimensional projection."""\n )\n', (10618, 10812), False, 'from django.db import migrations, models\n'), ((10832, 11302), 'django.db.models.CharField', 'models.CharField', ([], {'choices': "[('k-means', 'K-Means'), ('affinity_propagation', 'Affinity propagation'),\n ('mean-shift', 'Mean-shift'), ('spectral_clustering',\n 'Spectral clustering'), ('agglomerative_clustering', 'StandardScaler'),\n ('DBSCAN(min_samples=1)', 'DBSCAN(min_samples=1)'), ('DBSCAN', 'DBSCAN'\n ), ('gaussian_mixtures', 'Gaussian mixtures'), ('birch', 'Birch')]", 'default': '"""DBSCAN"""', 'help_text': '"""Select the clustering algorithm."""', 'max_length': '(50)'}), "(choices=[('k-means', 'K-Means'), ('affinity_propagation',\n 'Affinity propagation'), ('mean-shift', 'Mean-shift'), (\n 'spectral_clustering', 'Spectral clustering'), (\n 'agglomerative_clustering', 'StandardScaler'), ('DBSCAN(min_samples=1)',\n 'DBSCAN(min_samples=1)'), ('DBSCAN', 'DBSCAN'), ('gaussian_mixtures',\n 'Gaussian mixtures'), ('birch', 'Birch')], default='DBSCAN', help_text=\n 'Select the clustering algorithm.', max_length=50)\n", (10848, 11302), False, 'from django.db import migrations, models\n'), ((11312, 11498), 'django.db.models.PositiveIntegerField', 'models.PositiveIntegerField', ([], {'default': '(10)', 'help_text': '"""Number of hypercubes along each dimension.\n Sometimes referred to as resolution."""'}), '(default=10, help_text=\n """Number of hypercubes along each dimension.\n Sometimes referred to as resolution."""\n )\n', (11339, 11498), False, 'from django.db import migrations, models\n'), ((11527, 11703), 'django.db.models.FloatField', 'models.FloatField', ([], {'default': '(0.5)', 'help_text': '"""Amount of overlap between adjacent cubes calculated\n only along 1 dimension."""'}), '(default=0.5, help_text=\n """Amount of overlap between adjacent cubes calculated\n only along 1 dimension."""\n )\n', (11544, 11703), False, 'from django.db import migrations, models\n'), ((11742, 12136), 'django.db.models.IntegerField', 'models.IntegerField', ([], {'default': '(1)', 'help_text': '"""Minimum intersection considered when\n computing the nerve. An edge will be created only when the\n intersection between two nodes is greater than or equal to\n min_intersection"""'}), '(default=1, help_text=\n """Minimum intersection considered when\n computing the nerve. An edge will be created only when the\n intersection between two nodes is greater than or equal to\n min_intersection"""\n )\n', (11761, 12136), False, 'from django.db import migrations, models\n'), ((12160, 12831), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'default': '(False)', 'help_text': '"""Tell Mapper whether the data that you are clustering on\n is a precomputed distance matrix. If set to True, the assumption is that you are\n also telling your clusterer that metric=’precomputed’ (which is an argument for\n DBSCAN among others), which will then cause the clusterer to expect a square\n distance matrix for each hypercube. precomputed=True will give a square matrix\n to the clusterer to fit on for each hypercube."""'}), '(default=False, help_text=\n """Tell Mapper whether the data that you are clustering on\n is a precomputed distance matrix. If set to True, the assumption is that you are\n also telling your clusterer that metric=’precomputed’ (which is an argument for\n DBSCAN among others), which will then cause the clusterer to expect a square\n distance matrix for each hypercube. precomputed=True will give a square matrix\n to the clusterer to fit on for each hypercube."""\n )\n', (12179, 12831), False, 'from django.db import migrations, models\n'), ((12868, 13175), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'default': '(False)', 'help_text': '"""Removes duplicate nodes before edges are\n determined. A node is considered to be duplicate if it has exactly\n the same set of points as another node."""'}), '(default=False, help_text=\n """Removes duplicate nodes before edges are\n determined. A node is considered to be duplicate if it has exactly\n the same set of points as another node."""\n )\n', (12887, 13175), False, 'from django.db import migrations, models\n'), ((13194, 13389), 'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'blank': '(True)', 'default': 'None', 'help_text': '"""Select the source dataset from the loaded datasets"""', 'null': '(True)', 'on_delete': 'django.db.models.deletion.CASCADE', 'to': '"""datasets.Dataset"""'}), "(blank=True, default=None, help_text=\n 'Select the source dataset from the loaded datasets', null=True,\n on_delete=django.db.models.deletion.CASCADE, to='datasets.Dataset')\n", (13211, 13389), False, 'from django.db import migrations, models\n'), ((13412, 13503), 'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'on_delete': 'django.db.models.deletion.CASCADE', 'to': '"""research.Research"""'}), "(on_delete=django.db.models.deletion.CASCADE, to=\n 'research.Research')\n", (13429, 13503), False, 'from django.db import migrations, models\n'), ((13836, 13929), 'django.db.models.AutoField', 'models.AutoField', ([], {'auto_created': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'verbose_name': '"""ID"""'}), "(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')\n", (13852, 13929), False, 'from django.db import migrations, models\n'), ((13953, 13982), 'django.db.models.PositiveIntegerField', 'models.PositiveIntegerField', ([], {}), '()\n', (13980, 13982), False, 'from django.db import migrations, models\n'), ((14010, 14042), 'django.db.models.SlugField', 'models.SlugField', ([], {'max_length': '(150)'}), '(max_length=150)\n', (14026, 14042), False, 'from django.db import migrations, models\n'), ((14079, 14118), 'django.db.models.DateTimeField', 'models.DateTimeField', ([], {'auto_now_add': '(True)'}), '(auto_now_add=True)\n', (14099, 14118), False, 'from django.db import migrations, models\n'), ((14147, 14197), 'django.db.models.PositiveIntegerField', 'models.PositiveIntegerField', ([], {'blank': '(True)', 'null': '(True)'}), '(blank=True, null=True)\n', (14174, 14197), False, 'from django.db import migrations, models\n'), ((14224, 14274), 'django.db.models.PositiveIntegerField', 'models.PositiveIntegerField', ([], {'blank': '(True)', 'null': '(True)'}), '(blank=True, null=True)\n', (14251, 14274), False, 'from django.db import migrations, models\n'), ((14303, 14342), 'django.db.models.TextField', 'models.TextField', ([], {'blank': '(True)', 'null': '(True)'}), '(blank=True, null=True)\n', (14319, 14342), False, 'from django.db import migrations, models\n'), ((14374, 14524), 'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'on_delete': 'django.db.models.deletion.CASCADE', 'related_name': '"""windows"""', 'related_query_name': '"""window"""', 'to': '"""analysis.MapperAnalysis"""'}), "(on_delete=django.db.models.deletion.CASCADE, related_name\n ='windows', related_query_name='window', to='analysis.MapperAnalysis')\n", (14391, 14524), False, 'from django.db import migrations, models\n')] |
import FWCore.ParameterSet.Config as cms
def reEmulateLayer2(process):
process.load('L1Trigger/L1TCalorimeter/simCaloStage2Digis_cfi')
process.load('L1Trigger.L1TCalorimeter.caloStage2Params_2017_v1_7_excl30_cfi')
process.simCaloStage2Digis.towerToken = cms.InputTag("caloStage2Digis", "CaloTower")
process.caloLayer2 = cms.Path(process.simCaloStage2Digis)
process.schedule.append(process.caloLayer2)
return process
def hwEmulCompHistos(process):
process.TFileService = cms.Service("TFileService",
fileName = cms.string("l1tCalo_2016_simHistos.root"),
closeFileFast = cms.untracked.bool(True)
)
# histograms
process.load('L1Trigger.L1TCalorimeter.l1tStage2CaloAnalyzer_cfi')
process.l1tStage2CaloAnalyzer.doEvtDisp = False
process.l1tStage2CaloAnalyzer.mpBx = 0
process.l1tStage2CaloAnalyzer.dmxBx = 0
process.l1tStage2CaloAnalyzer.allBx = False
process.l1tStage2CaloAnalyzer.towerToken = cms.InputTag("simCaloStage2Digis", "MP")
process.l1tStage2CaloAnalyzer.clusterToken = cms.InputTag("None")
process.l1tStage2CaloAnalyzer.mpEGToken = cms.InputTag("simCaloStage2Digis", "MP")
process.l1tStage2CaloAnalyzer.mpTauToken = cms.InputTag("simCaloStage2Digis", "MP")
process.l1tStage2CaloAnalyzer.mpJetToken = cms.InputTag("simCaloStage2Digis", "MP")
process.l1tStage2CaloAnalyzer.mpEtSumToken = cms.InputTag("simCaloStage2Digis", "MP")
process.l1tStage2CaloAnalyzer.egToken = cms.InputTag("simCaloStage2Digis")
process.l1tStage2CaloAnalyzer.tauToken = cms.InputTag("simCaloStage2Digis")
process.l1tStage2CaloAnalyzer.jetToken = cms.InputTag("simCaloStage2Digis")
process.l1tStage2CaloAnalyzer.etSumToken = cms.InputTag("simCaloStage2Digis")
import L1Trigger.L1TCalorimeter.l1tStage2CaloAnalyzer_cfi
process.l1tCaloStage2HwHistos = L1Trigger.L1TCalorimeter.l1tStage2CaloAnalyzer_cfi.l1tStage2CaloAnalyzer.clone()
process.l1tCaloStage2HwHistos.doEvtDisp = False
process.l1tCaloStage2HwHistos.mpBx = 0
process.l1tCaloStage2HwHistos.dmxBx = 0
process.l1tCaloStage2HwHistos.allBx = False
process.l1tCaloStage2HwHistos.towerToken = cms.InputTag("caloStage2Digis", "CaloTower")
process.l1tCaloStage2HwHistos.clusterToken = cms.InputTag("None")
process.l1tCaloStage2HwHistos.mpEGToken = cms.InputTag("caloStage2Digis", "MP")
process.l1tCaloStage2HwHistos.mpTauToken = cms.InputTag("caloStage2Digis","MP")
process.l1tCaloStage2HwHistos.mpJetToken = cms.InputTag("caloStage2Digis", "MP")
process.l1tCaloStage2HwHistos.mpEtSumToken = cms.InputTag("caloStage2Digis", "MP")
process.l1tCaloStage2HwHistos.egToken = cms.InputTag("caloStage2Digis", "EGamma")
process.l1tCaloStage2HwHistos.tauToken = cms.InputTag("caloStage2Digis", "Tau")
process.l1tCaloStage2HwHistos.jetToken = cms.InputTag("caloStage2Digis", "Jet")
process.l1tCaloStage2HwHistos.etSumToken = cms.InputTag("caloStage2Digis", "EtSum")
process.hwEmulHistos = cms.Path(
process.l1tStage2CaloAnalyzer
+process.l1tCaloStage2HwHistos
)
process.schedule.append(process.hwEmulHistos)
return process
def reEmulateLayer2ValHistos(process):
process.load('EventFilter.L1TRawToDigi.caloTowersFilter_cfi')
reEmulateLayer2(process)
hwEmulCompHistos(process)
#process.l1ntupleraw.insert(0,process.caloTowersFilter)
#process.l1ntuplesim.insert(0,process.caloTowersFilter)
process.caloLayer2.insert(0,process.caloTowersFilter)
process.hwEmulHistos.insert(0,process.caloTowersFilter)
return process
| [
"FWCore.ParameterSet.Config.Path",
"FWCore.ParameterSet.Config.string",
"FWCore.ParameterSet.Config.untracked.bool",
"FWCore.ParameterSet.Config.InputTag"
] | [((270, 314), 'FWCore.ParameterSet.Config.InputTag', 'cms.InputTag', (['"""caloStage2Digis"""', '"""CaloTower"""'], {}), "('caloStage2Digis', 'CaloTower')\n", (282, 314), True, 'import FWCore.ParameterSet.Config as cms\n'), ((345, 381), 'FWCore.ParameterSet.Config.Path', 'cms.Path', (['process.simCaloStage2Digis'], {}), '(process.simCaloStage2Digis)\n', (353, 381), True, 'import FWCore.ParameterSet.Config as cms\n'), ((1088, 1128), 'FWCore.ParameterSet.Config.InputTag', 'cms.InputTag', (['"""simCaloStage2Digis"""', '"""MP"""'], {}), "('simCaloStage2Digis', 'MP')\n", (1100, 1128), True, 'import FWCore.ParameterSet.Config as cms\n'), ((1178, 1198), 'FWCore.ParameterSet.Config.InputTag', 'cms.InputTag', (['"""None"""'], {}), "('None')\n", (1190, 1198), True, 'import FWCore.ParameterSet.Config as cms\n'), ((1245, 1285), 'FWCore.ParameterSet.Config.InputTag', 'cms.InputTag', (['"""simCaloStage2Digis"""', '"""MP"""'], {}), "('simCaloStage2Digis', 'MP')\n", (1257, 1285), True, 'import 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"""
ethereum.py
ethereum.py contains an EthereumClient class that provides functions for interacting
with the Coverage.sol solidity contract on an Ethereum blockchain network.
"""
import asyncio
import datetime
import json
import logging
import os
from ethereum.clients.nats import get_nats_client
from ethereum.config import get_settings, nats_eligibility_subject
from ethereum.exceptions import EthereumNetworkConnectionError
from hexbytes import HexBytes
from typing import Optional, Any, List
from web3 import Web3
logger = logging.getLogger(__name__)
# client instance
eth_client = None
class EthereumClient:
"""
Ethereum client for LFH that utilizes the Web3 library for interacting
with an Ethereum blockchain network.
"""
def __init__(self, **qwargs):
logger.debug("Initializing EthereumClient")
self.eth_network_uri = qwargs["eth_network_uri"]
logger.debug("Initializing Web3")
self.client: Optional[Web3] = Web3(Web3.HTTPProvider(self.eth_network_uri))
self.from_acct = {"from": self.client.eth.accounts[0]}
if (self.client and self.client.isConnected()):
logger.info(f"Connected to the Ethereum network at: {self.eth_network_uri}")
self.contract = self.client.eth.contract(address=qwargs["contract_address"],
abi=qwargs["contract_abi"])
event_filter = self.contract.events.EligibilityResult.createFilter(fromBlock="latest")
self.cancelled = False
contract_event_loop = asyncio.get_event_loop()
contract_event_loop.create_task(self.event_loop(event_filter, qwargs["event_poll_interval"]))
logger.info(f"Connected to the contract at: {qwargs['contract_address']}")
else:
error_msg = f"Failed to connect to the Ethereum network at: {self.eth_network_uri}"
logger.error(error_msg)
raise EthereumNetworkConnectionError(error_msg)
def add_coverage_resource(self, path: str, fhir_json: Any, payor_ref: str,
subscriber_ref: str, coverage_start: int, coverage_end: int):
"""
Send a Coverage FHIR resource to the Coverage.sol contract.
:param path: FHIR path of the resource, e.g. /Coverage/001
:param fhir_json: The string representation of the FHIR resource
:param payor_ref: coverage.payor[0].reference
:param subscriber_ref: coverage.subscriber.reference
:param coverage_start: coverage.period.start converted to a timestamp
:param coverage_end: coverage.period.end converted to a timestamp
:return: The hash of the submitted transaction or None
"""
if not self.client.isConnected():
error = f"Not connected to the Ethereum network"
logger.error(error)
return {"error": error}
try:
tx_hash = self.contract.functions.add_coverage_resource(path,
json.dumps(fhir_json),
payor_ref,
subscriber_ref,
coverage_start,
coverage_end).transact(self.from_acct)
tx_receipt = self.client.eth.waitForTransactionReceipt(tx_hash)
receipt_dict = dict(tx_receipt)
hash_str = receipt_dict["transactionHash"].hex()
logger.info(f"tx hash: {hash_str}")
return {"result": hash_str}
except Exception as ex:
error = f"Transaction error {ex}"
logger.error(error)
return {"error": error}
def check_eligibility(self, path: str, fhir_json: Any, insurer_ref: str,
patient_ref: str, coverage_ref: str, coverage_date: int):
"""
Send a CoverageEligibilityRequest FHIR resource to the Coverage.sol contract.
:param path: FHIR path of the resource, e.g. /CoverageEligibilityRequest/001
:param fhir_json: The string representation of the FHIR resource
:param insurer_ref: coverageeligibilityrequest.insurer.reference
:param patient_ref: coverageeligibilityrequest.patient.reference
:param coverage_ref: coverageeligibilityrequest.insurance[0].coverage
:param coverage_date: coverageeligibilityrequest.created converted to a timestamp
:return: The hash of the submitted transaction or None
"""
if not self.client.isConnected():
error = f"Not connected to the Ethereum network"
logger.error(error)
return {"error": error}
try:
tx_hash = self.contract.functions.check_eligibility(path,
json.dumps(fhir_json),
insurer_ref,
patient_ref,
coverage_ref,
coverage_date).transact(self.from_acct)
tx_receipt = self.client.eth.waitForTransactionReceipt(tx_hash)
receipt_dict = dict(tx_receipt)
hash_str = receipt_dict["transactionHash"].hex()
logger.info(f"tx hash: {hash_str}")
return {"result": hash_str}
except Exception as ex:
error = f"Transaction error {ex}"
logger.error(error)
return {"error": error}
def add_fhir_resource(self, fhir_type: str, path: str, fhir_json: Any):
"""
Send a Patient or Organization FHIR resource to the Coverage.sol contract.
:param fhir_type: FHIR type of the resource, e.g. Patient
:param path: FHIR path of the resource, e.g. /Patient/001
:param fhir_json: The string representation of the FHIR resource
:return: The hash of the submitted transaction or None
"""
if not self.client.isConnected():
error = f"Not connected to the Ethereum network"
logger.error(error)
return {"error": error}
try:
tx_hash = self.contract.functions.add_fhir_resource(fhir_type,
path,
json.dumps(fhir_json)).transact(self.from_acct)
tx_receipt = self.client.eth.waitForTransactionReceipt(tx_hash)
receipt_dict = dict(tx_receipt)
hash_str = receipt_dict["transactionHash"].hex()
logger.info(f"tx hash: {hash_str}")
return {"result": hash_str}
except Exception as ex:
error = f"Transaction error {ex}"
logger.error(error)
return {"error": error}
def close(self):
self.cancelled = True
async def event_loop(self, event_filter, poll_interval: int):
while not self.cancelled:
for event in event_filter.get_new_entries():
await self.handle_event(json.loads(Web3.toJSON(event)))
await asyncio.sleep(poll_interval)
async def handle_event(self, event: dict):
"""
Send a FHIR CoverageEligibilityResponse based on the eligibility decision from the contract.
:param event: The JSON contract event containing the eligibility decision and supporting info.
"""
logger.trace(f"Received contract event: {event}")
path: List[str] = event["args"]["path"].split("/")
request_id: str = path[1]
result: bool = event["args"]["result"]
disposition: str = "Policy is currently in effect."
if not result:
disposition = "Policy is not in effect."
today: str = datetime.date.today().isoformat()
message: Any = {
"resourceType": "CoverageEligibilityResponse",
"id": request_id,
"text": {
"status": "generated",
"div": "<div xmlns=\"http://www.w3.org/1999/xhtml\">A human-readable rendering of the CoverageEligibilityResponse.</div>"
},
"identifier": [
{
"system": "http://localhost:5000/fhir/coverageeligibilityresponse/" + request_id,
"value": request_id
}
],
"status": "active",
"purpose": [
"validation"
],
"patient": {
"reference": event["args"]["patient_ref"]
},
"created": today,
"request": {
"reference": "http://www.BenefitsInc.com/fhir/coverageeligibilityrequest/" + request_id
},
"outcome": "complete",
"disposition": disposition,
"insurer": {
"reference": event["args"]["insurer_ref"]
},
"insurance": [
{
"coverage": {
"reference": event["args"]["coverage_ref"]
},
"inforce": result
}
]
};
nats_client = await get_nats_client()
msg_str = json.dumps(message)
logger.info(f"CoverageEligibilityResponse: {msg_str}")
await nats_client.publish(nats_eligibility_subject, bytearray(msg_str, "utf-8"))
logger.trace("Sent CoverageEligibilityResponse via NATS")
def get_ethereum_client() -> Optional[EthereumClient]:
"""
:return: a connected EthereumClient instance
"""
global eth_client
if not eth_client:
settings = get_settings()
# load ABI file
abi_file: str = os.path.join(settings.ethereum_config_directory, settings.ethereum_contract_abi)
contract_info = json.load(open(abi_file))
eth_client = EthereumClient(
eth_network_uri=settings.ethereum_network_uri,
contract_address=settings.ethereum_contract_address,
contract_abi=contract_info["abi"],
event_poll_interval=settings.ethereum_event_poll_seconds
)
return eth_client
def stop_ethereum_client():
client = get_ethereum_client()
client.close()
class HexJsonEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, HexBytes):
return obj.hex()
return super().default(obj)
| [
"logging.getLogger",
"json.dumps",
"os.path.join",
"web3.Web3.toJSON",
"asyncio.sleep",
"ethereum.clients.nats.get_nats_client",
"datetime.date.today",
"asyncio.get_event_loop",
"ethereum.exceptions.EthereumNetworkConnectionError",
"ethereum.config.get_settings",
"web3.Web3.HTTPProvider"
] | [((530, 557), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (547, 557), False, 'import logging\n'), ((9462, 9481), 'json.dumps', 'json.dumps', (['message'], {}), '(message)\n', (9472, 9481), False, 'import json\n'), ((9886, 9900), 'ethereum.config.get_settings', 'get_settings', ([], {}), '()\n', (9898, 9900), False, 'from ethereum.config import get_settings, nats_eligibility_subject\n'), ((9950, 10035), 'os.path.join', 'os.path.join', (['settings.ethereum_config_directory', 'settings.ethereum_contract_abi'], {}), '(settings.ethereum_config_directory, settings.ethereum_contract_abi\n )\n', (9962, 10035), False, 'import os\n'), ((979, 1018), 'web3.Web3.HTTPProvider', 'Web3.HTTPProvider', (['self.eth_network_uri'], {}), '(self.eth_network_uri)\n', (996, 1018), False, 'from web3 import Web3\n'), ((1567, 1591), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (1589, 1591), False, 'import asyncio\n'), ((1949, 1990), 'ethereum.exceptions.EthereumNetworkConnectionError', 'EthereumNetworkConnectionError', (['error_msg'], {}), '(error_msg)\n', (1979, 1990), False, 'from ethereum.exceptions import EthereumNetworkConnectionError\n'), ((9426, 9443), 'ethereum.clients.nats.get_nats_client', 'get_nats_client', ([], {}), '()\n', (9441, 9443), False, 'from ethereum.clients.nats import get_nats_client\n'), ((7356, 7384), 'asyncio.sleep', 'asyncio.sleep', (['poll_interval'], {}), '(poll_interval)\n', (7369, 7384), False, 'import asyncio\n'), ((8017, 8038), 'datetime.date.today', 'datetime.date.today', ([], {}), '()\n', (8036, 8038), False, 'import datetime\n'), ((3053, 3074), 'json.dumps', 'json.dumps', (['fhir_json'], {}), '(fhir_json)\n', (3063, 3074), False, 'import json\n'), ((4972, 4993), 'json.dumps', 'json.dumps', (['fhir_json'], {}), '(fhir_json)\n', (4982, 4993), False, 'import json\n'), ((6593, 6614), 'json.dumps', 'json.dumps', (['fhir_json'], {}), '(fhir_json)\n', (6603, 6614), False, 'import json\n'), ((7317, 7335), 'web3.Web3.toJSON', 'Web3.toJSON', (['event'], {}), '(event)\n', (7328, 7335), False, 'from web3 import Web3\n')] |
# -*- coding: utf-8 -*-
from django.shortcuts import resolve_url
from django.template.loader import render_to_string
from django_jinja import library
from jinja2 import contextfunction
@contextfunction
@library.global_function
def breadcrumb(context, contents, *args, **kwargs):
class_name = kwargs.pop('class', False)
url = kwargs.pop('url', False)
if url is not False:
url = resolve_url(url, *args, **kwargs)
breadcrumb_context = {
'contents': contents,
'url': url,
'class': class_name
}
context['breadcrumbs'].append(breadcrumb_context)
return ''
@library.global_function
def render_breadcrumbs(breadcrumbs):
breadcrumbs.reverse()
ctx = {'breadcrumbs': breadcrumbs}
return render_to_string('breadcrumbs/breadcrumbs.html', ctx)
| [
"django.shortcuts.resolve_url",
"django.template.loader.render_to_string"
] | [((699, 752), 'django.template.loader.render_to_string', 'render_to_string', (['"""breadcrumbs/breadcrumbs.html"""', 'ctx'], {}), "('breadcrumbs/breadcrumbs.html', ctx)\n", (715, 752), False, 'from django.template.loader import render_to_string\n'), ((384, 417), 'django.shortcuts.resolve_url', 'resolve_url', (['url', '*args'], {}), '(url, *args, **kwargs)\n', (395, 417), False, 'from django.shortcuts import resolve_url\n')] |
import torchvision
import torch
import torch.nn as nn
import torch.nn.functional as F
class Vgg_Deeplab(nn.Module):
def __init__(self,*args, **kwargs):
super(Vgg_Deeplab, self).__init__()
vgg16 = torchvision.models.vgg16()
layers = []
layers.append(nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.MaxPool2d(3, stride=2, padding=1))
layers.append(nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.MaxPool2d(3, stride=2, padding=1))
layers.append(nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.MaxPool2d(3, stride=2, padding=1))
layers.append(nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.MaxPool2d(3, stride=1, padding=1))
layers.append(nn.Conv2d(512,
512,
kernel_size=3,
stride=1,
padding=2,
dilation=2))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.Conv2d(512,
512,
kernel_size=3,
stride=1,
padding=2,
dilation=2))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.Conv2d(512,
512,
kernel_size=3,
stride=1,
padding=2,
dilation=2))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.MaxPool2d(3, stride=1, padding=1))
self.features = nn.Sequential(*layers)
classifier = []
classifier.append(nn.AvgPool2d(3, stride=1, padding=1))
classifier.append(nn.Conv2d(512,
1024,
kernel_size=3,
stride=1,
padding=12,
dilation=12))
classifier.append(nn.ReLU(inplace=True))
classifier.append(nn.Dropout(p=0.5))
self.classifier = nn.Sequential(*classifier)
self.init_weights()
def forward(self, x):
x = self.features(x)
x = self.classifier(x)
return x
def init_weights(self):
vgg = torchvision.models.vgg16(pretrained=True)
state_vgg = vgg.features.state_dict()
self.features.load_state_dict(state_vgg)
for ly in self.classifier.children():
if isinstance(ly, nn.Conv2d):
nn.init.kaiming_normal_(ly.weight, a=1)
nn.init.constant_(ly.bias, 0)
def get_1x_lr_params(self):
"""
This generator returns all the parameters for the last layer of the net,
which does the classification of pixel into classes
"""
# b = []
#
# b.append(self.conv1)
# b.append(self.bn1)
# b.append(self.layer1)
# b.append(self.layer2)
# b.append(self.layer3)
# b.append(self.layer4)
for i in self.features:
#for j in self.features[i].modules():
jj = 0
for k in i.parameters():
jj += 1
if k.requires_grad:
yield k
def optim_parameters_1x(self, args):
return [{"params": self.get_1x_lr_params(), "lr": 1 * args.learning_rate}]
def get_10x_lr_params(self):
"""
This generator returns all the parameters for the last layer of the net,
which does the classification of pixel into classes
"""
# b = []
# b.append(self.layer.parameters())
for i in self.classifier:
#for j in self.classifier[i].modules():
jj = 0
for k in i.parameters():
jj += 1
if k.requires_grad:
yield k
def optim_parameters_10x(self, args):
return [{"params": self.get_10x_lr_params(), "lr": 10 * args.learning_rate}]
if __name__ == "__main__":
net = Vgg_Deeplab(3, 10)
in_ten = torch.randn(1, 3, 224, 224)
out = net(in_ten)
print(net)
print(out.size())
in_ten = torch.randn(1, 3, 64, 64)
mod = nn.Conv2d(3,
512,
kernel_size=3,
stride=1,
padding=2,
dilation=2)
out = mod(in_ten)
print(out.shape)
| [
"torch.nn.ReLU",
"torch.nn.Dropout",
"torch.nn.init.constant_",
"torch.nn.Sequential",
"torch.nn.init.kaiming_normal_",
"torch.nn.Conv2d",
"torch.nn.MaxPool2d",
"torch.nn.AvgPool2d",
"torchvision.models.vgg16",
"torch.randn"
] | [((5212, 5239), 'torch.randn', 'torch.randn', (['(1)', '(3)', '(224)', '(224)'], {}), '(1, 3, 224, 224)\n', (5223, 5239), False, 'import torch\n'), ((5313, 5338), 'torch.randn', 'torch.randn', (['(1)', '(3)', '(64)', '(64)'], {}), '(1, 3, 64, 64)\n', (5324, 5338), False, 'import torch\n'), ((5349, 5414), 'torch.nn.Conv2d', 'nn.Conv2d', (['(3)', '(512)'], {'kernel_size': '(3)', 'stride': '(1)', 'padding': '(2)', 'dilation': '(2)'}), '(3, 512, kernel_size=3, stride=1, padding=2, dilation=2)\n', (5358, 5414), True, 'import torch.nn as nn\n'), ((218, 244), 'torchvision.models.vgg16', 'torchvision.models.vgg16', ([], {}), '()\n', (242, 244), False, 'import torchvision\n'), ((2716, 2738), 'torch.nn.Sequential', 'nn.Sequential', (['*layers'], {}), '(*layers)\n', (2729, 2738), True, 'import torch.nn as nn\n'), ((3226, 3252), 'torch.nn.Sequential', 'nn.Sequential', (['*classifier'], {}), '(*classifier)\n', (3239, 3252), True, 'import torch.nn as nn\n'), ((3429, 3470), 'torchvision.models.vgg16', 'torchvision.models.vgg16', ([], {'pretrained': '(True)'}), '(pretrained=True)\n', (3453, 3470), False, 'import torchvision\n'), ((288, 340), 'torch.nn.Conv2d', 'nn.Conv2d', (['(3)', '(64)'], {'kernel_size': '(3)', 'stride': '(1)', 'padding': '(1)'}), '(3, 64, kernel_size=3, stride=1, padding=1)\n', (297, 340), True, 'import torch.nn as nn\n'), ((364, 385), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (371, 385), True, 'import torch.nn as nn\n'), ((409, 462), 'torch.nn.Conv2d', 'nn.Conv2d', (['(64)', '(64)'], {'kernel_size': '(3)', 'stride': '(1)', 'padding': '(1)'}), '(64, 64, kernel_size=3, stride=1, padding=1)\n', (418, 462), True, 'import torch.nn as nn\n'), ((486, 507), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (493, 507), True, 'import torch.nn as nn\n'), ((531, 567), 'torch.nn.MaxPool2d', 'nn.MaxPool2d', (['(3)'], {'stride': '(2)', 'padding': '(1)'}), '(3, stride=2, padding=1)\n', (543, 567), True, 'import torch.nn as nn\n'), ((592, 646), 'torch.nn.Conv2d', 'nn.Conv2d', (['(64)', '(128)'], {'kernel_size': '(3)', 'stride': '(1)', 'padding': '(1)'}), '(64, 128, kernel_size=3, stride=1, padding=1)\n', (601, 646), True, 'import torch.nn as nn\n'), ((670, 691), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (677, 691), True, 'import torch.nn as nn\n'), ((715, 770), 'torch.nn.Conv2d', 'nn.Conv2d', (['(128)', '(128)'], {'kernel_size': '(3)', 'stride': '(1)', 'padding': '(1)'}), '(128, 128, kernel_size=3, stride=1, padding=1)\n', (724, 770), True, 'import torch.nn as nn\n'), ((794, 815), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (801, 815), True, 'import torch.nn as nn\n'), ((839, 875), 'torch.nn.MaxPool2d', 'nn.MaxPool2d', (['(3)'], {'stride': '(2)', 'padding': '(1)'}), '(3, stride=2, padding=1)\n', (851, 875), True, 'import torch.nn as nn\n'), ((900, 955), 'torch.nn.Conv2d', 'nn.Conv2d', (['(128)', '(256)'], {'kernel_size': '(3)', 'stride': '(1)', 'padding': '(1)'}), '(128, 256, kernel_size=3, stride=1, padding=1)\n', (909, 955), True, 'import torch.nn as nn\n'), ((979, 1000), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (986, 1000), True, 'import torch.nn as nn\n'), ((1024, 1079), 'torch.nn.Conv2d', 'nn.Conv2d', (['(256)', '(256)'], {'kernel_size': '(3)', 'stride': '(1)', 'padding': '(1)'}), '(256, 256, kernel_size=3, stride=1, padding=1)\n', (1033, 1079), True, 'import torch.nn as nn\n'), ((1103, 1124), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (1110, 1124), True, 'import torch.nn as nn\n'), ((1148, 1203), 'torch.nn.Conv2d', 'nn.Conv2d', (['(256)', '(256)'], {'kernel_size': '(3)', 'stride': '(1)', 'padding': '(1)'}), '(256, 256, kernel_size=3, stride=1, padding=1)\n', (1157, 1203), True, 'import torch.nn as nn\n'), ((1227, 1248), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (1234, 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'nn.Conv2d', (['(512)', '(512)'], {'kernel_size': '(3)', 'stride': '(1)', 'padding': '(1)'}), '(512, 512, kernel_size=3, stride=1, padding=1)\n', (1590, 1636), True, 'import torch.nn as nn\n'), ((1660, 1681), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (1667, 1681), True, 'import torch.nn as nn\n'), ((1705, 1741), 'torch.nn.MaxPool2d', 'nn.MaxPool2d', (['(3)'], {'stride': '(1)', 'padding': '(1)'}), '(3, stride=1, padding=1)\n', (1717, 1741), True, 'import torch.nn as nn\n'), ((1766, 1833), 'torch.nn.Conv2d', 'nn.Conv2d', (['(512)', '(512)'], {'kernel_size': '(3)', 'stride': '(1)', 'padding': '(2)', 'dilation': '(2)'}), '(512, 512, kernel_size=3, stride=1, padding=2, dilation=2)\n', (1775, 1833), True, 'import torch.nn as nn\n'), ((2017, 2038), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (2024, 2038), True, 'import torch.nn as nn\n'), ((2062, 2129), 'torch.nn.Conv2d', 'nn.Conv2d', (['(512)', '(512)'], {'kernel_size': 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stride=1, padding=1)\n', (2802, 2826), True, 'import torch.nn as nn\n'), ((2854, 2924), 'torch.nn.Conv2d', 'nn.Conv2d', (['(512)', '(1024)'], {'kernel_size': '(3)', 'stride': '(1)', 'padding': '(12)', 'dilation': '(12)'}), '(512, 1024, kernel_size=3, stride=1, padding=12, dilation=12)\n', (2863, 2924), True, 'import torch.nn as nn\n'), ((3132, 3153), 'torch.nn.ReLU', 'nn.ReLU', ([], {'inplace': '(True)'}), '(inplace=True)\n', (3139, 3153), True, 'import torch.nn as nn\n'), ((3181, 3198), 'torch.nn.Dropout', 'nn.Dropout', ([], {'p': '(0.5)'}), '(p=0.5)\n', (3191, 3198), True, 'import torch.nn as nn\n'), ((3671, 3710), 'torch.nn.init.kaiming_normal_', 'nn.init.kaiming_normal_', (['ly.weight'], {'a': '(1)'}), '(ly.weight, a=1)\n', (3694, 3710), True, 'import torch.nn as nn\n'), ((3727, 3756), 'torch.nn.init.constant_', 'nn.init.constant_', (['ly.bias', '(0)'], {}), '(ly.bias, 0)\n', (3744, 3756), True, 'import torch.nn as nn\n')] |
# -*- coding: utf-8 -*-
"""
This module defines a connexion app object and configures the API
endpoints based the swagger.yml configuration file.
copyright: © 2019 by <NAME>.
license: MIT, see LICENSE for more details.
"""
import connexion
app = connexion.App(__name__, specification_dir="./")
app.app.url_map.strict_slashes = False
app.add_api("swagger.yml")
if __name__ == "__main__":
# FLASK_ENV=development & FLASK_DEBUG=1 w/ Docker don't seem to enable debug mode.
app.run(debug=True)
| [
"connexion.App"
] | [((250, 297), 'connexion.App', 'connexion.App', (['__name__'], {'specification_dir': '"""./"""'}), "(__name__, specification_dir='./')\n", (263, 297), False, 'import connexion\n')] |
from django import forms
from django.test import TestCase
from template_forms import bs3
def startswith_a(value):
if value.startswith('a'):
return value
raise forms.ValidationError('Value must start with "a".')
def not_now(value):
if value:
raise forms.ValidationError('I cannot let you do that right now.')
class StandardFieldTests(TestCase):
class Form(bs3.BlockForm, forms.Form):
field = forms.CharField(required=False, validators=[startswith_a], help_text='Example text.', )
def get_attrs(self, bf):
return {
'name': bf.html_name,
'id': bf.auto_id,
'label': bf.label,
}
def test_field(self):
form = self.Form()
field = form['field']
template = """
<div class="form-group">
<label for="{id}" class="control-label">{label}:</label>
<input id="{id}" name="{name}" type="text" class="form-control">
<small class="help-block">Example text.</small>
</div>
"""
self.assertHTMLEqual(
template.format(**self.get_attrs(field)),
form.render_field(field, field.errors)
)
def test_field_bound(self):
form = self.Form({'field': 'a value'})
field = form['field']
template = """
<div class="form-group">
<label for="{id}" class="control-label">{label}:</label>
<input id="{id}" name="{name}" type="text" class="form-control" value="a value">
<small class="help-block">Example text.</small>
</div>
"""
self.assertHTMLEqual(
template.format(**self.get_attrs(field)),
form.render_field(field, field.errors)
)
def test_field_error(self):
form = self.Form({'field': 'error'})
field = form['field']
template = """
<div class="form-group has-error">
<label for="{id}" class="control-label">{label}:</label>
<input id="{id}" name="{name}" type="text" class="form-control has-error" value="error">
<small class="help-block">Value must start with "a".</small>
<small class="help-block">Example text.</small>
</div>
"""
self.assertHTMLEqual(
template.format(**self.get_attrs(field)),
form.render_field(field, field.errors)
)
class CheckboxFieldTests(TestCase):
class Form(bs3.BlockForm, forms.Form):
field = forms.BooleanField(required=False, validators=[not_now], help_text='Example text.')
def get_attrs(self, bf):
return {
'name': bf.html_name,
'id': bf.auto_id,
'label': bf.label,
}
def test_field(self):
form = self.Form()
field = form['field']
template = """
<div class="form-group">
<div class="checkbox">
<label>
<input id="{id}" name="{name}" type="checkbox"> {label}
</label>
</div>
<small class="help-block">Example text.</small>
</div>
"""
self.assertHTMLEqual(
template.format(**self.get_attrs(field)),
form.render_field(field, field.errors)
)
def test_field_error(self):
form = self.Form({'field': 'on'})
field = form['field']
template = """
<div class="form-group has-error">
<div class="checkbox">
<label>
<input id="{id}" name="{name}" type="checkbox" checked> {label}
</label>
</div>
<small class="help-block">I cannot let you do that right now.</small>
<small class="help-block">Example text.</small>
</div>
"""
self.assertHTMLEqual(
template.format(**self.get_attrs(field)),
form.render_field(field, field.errors)
)
| [
"django.forms.BooleanField",
"django.forms.CharField",
"django.forms.ValidationError"
] | [((179, 230), 'django.forms.ValidationError', 'forms.ValidationError', (['"""Value must start with "a"."""'], {}), '(\'Value must start with "a".\')\n', (200, 230), False, 'from django import forms\n'), ((281, 341), 'django.forms.ValidationError', 'forms.ValidationError', (['"""I cannot let you do that right now."""'], {}), "('I cannot let you do that right now.')\n", (302, 341), False, 'from django import forms\n'), ((439, 529), 'django.forms.CharField', 'forms.CharField', ([], {'required': '(False)', 'validators': '[startswith_a]', 'help_text': '"""Example text."""'}), "(required=False, validators=[startswith_a], help_text=\n 'Example text.')\n", (454, 529), False, 'from django import forms\n'), ((2516, 2604), 'django.forms.BooleanField', 'forms.BooleanField', ([], {'required': '(False)', 'validators': '[not_now]', 'help_text': '"""Example text."""'}), "(required=False, validators=[not_now], help_text=\n 'Example text.')\n", (2534, 2604), False, 'from django import forms\n')] |
from snakemake import shell
input, output, params, threads, w, config = snakemake.input, snakemake.output, snakemake.params, snakemake.threads, snakemake.wildcards, snakemake.config
genome = w.genome
params.hybrid = config['x'][genome]['hybrid']
opt = params.opt
shell("""
rm -rf {output.fna}* {output.fai}*
rm -rf {output.chrom_bed} {output.chrom_size} {output.gap}
mkdir -p {params.wdir}/{params.odir}
cd {params.wdir}/{params.odir}
rm -rf raw.fna.* renamed* map* raw.sizes
""")
merge_tag = '--merge_short' if w.genome != 'Mt_R108' else ''
if params.hybrid:
shell("""
cat {input} > {params.wdir}/{params.odir}/renamed.fna
cd {params.wdir}/{params.odir}
fasta.py size renamed.fna > renamed.sizes
touch mapf.chain mapb.chain
""")
else:
params.gap = int(config['x'][genome]['gap'])
params.prefix = config['x'][genome]['prefix']
shell("""
cd {params.wdir}/{params.odir}
ln -sf ../download/raw.fna raw.fna
fasta.py size raw.fna > raw.sizes
fasta.py rename raw.fna renamed.fna mapf.bed mapb.bed \
--opt {params.opt} {merge_tag} \
--gap {params.gap} --prefix_chr {params.prefix}
fasta.py size renamed.fna > renamed.sizes
chain.py fromBed mapf.bed raw.sizes renamed.sizes > mapf.chain
chainSwap mapf.chain mapb.chain
""")
shell("""
cd {params.wdir}
ln -sf {params.odir}/renamed.fna 10_genome.fna
cd ..
samtools faidx {output.fna}
fasta.py size --bed {output.fna} > {output.chrom_bed}
cut -f1,3 {output.chrom_bed} > {output.chrom_size}
fasta.py gaps {output.fna} > {output.gap}
""")
| [
"snakemake.shell"
] | [((265, 513), 'snakemake.shell', 'shell', (['"""\n rm -rf {output.fna}* {output.fai}*\n rm -rf {output.chrom_bed} {output.chrom_size} {output.gap}\n\n mkdir -p {params.wdir}/{params.odir}\n cd {params.wdir}/{params.odir}\n rm -rf raw.fna.* renamed* map* raw.sizes\n"""'], {}), '(\n """\n rm -rf {output.fna}* {output.fai}*\n rm -rf {output.chrom_bed} {output.chrom_size} {output.gap}\n\n mkdir -p {params.wdir}/{params.odir}\n cd {params.wdir}/{params.odir}\n rm -rf raw.fna.* renamed* map* raw.sizes\n"""\n )\n', (270, 513), False, 'from snakemake import shell\n'), ((1379, 1677), 'snakemake.shell', 'shell', (['"""\n cd {params.wdir}\n ln -sf {params.odir}/renamed.fna 10_genome.fna\n cd ..\n\n samtools faidx {output.fna}\n fasta.py size --bed {output.fna} > {output.chrom_bed}\n cut -f1,3 {output.chrom_bed} > {output.chrom_size}\n fasta.py gaps {output.fna} > {output.gap}\n"""'], {}), '(\n """\n cd {params.wdir}\n ln -sf {params.odir}/renamed.fna 10_genome.fna\n cd ..\n\n samtools faidx {output.fna}\n fasta.py size --bed {output.fna} > {output.chrom_bed}\n cut -f1,3 {output.chrom_bed} > {output.chrom_size}\n fasta.py gaps {output.fna} > {output.gap}\n"""\n )\n', (1384, 1677), False, 'from snakemake import shell\n'), ((588, 803), 'snakemake.shell', 'shell', (['"""\n cat {input} > {params.wdir}/{params.odir}/renamed.fna\n cd {params.wdir}/{params.odir}\n fasta.py size renamed.fna > renamed.sizes\n touch mapf.chain mapb.chain\n """'], {}), '(\n """\n cat {input} > {params.wdir}/{params.odir}/renamed.fna\n cd {params.wdir}/{params.odir}\n fasta.py size renamed.fna > renamed.sizes\n touch mapf.chain mapb.chain\n """\n )\n', (593, 803), False, 'from snakemake import shell\n'), ((903, 1383), 'snakemake.shell', 'shell', (['"""\n cd {params.wdir}/{params.odir}\n ln -sf ../download/raw.fna raw.fna\n fasta.py size raw.fna > raw.sizes\n\n fasta.py rename raw.fna renamed.fna mapf.bed mapb.bed --opt {params.opt} {merge_tag} --gap {params.gap} --prefix_chr {params.prefix}\n\n fasta.py size renamed.fna > renamed.sizes\n chain.py fromBed mapf.bed raw.sizes renamed.sizes > mapf.chain\n chainSwap mapf.chain mapb.chain\n """'], {}), '(\n """\n cd {params.wdir}/{params.odir}\n ln -sf ../download/raw.fna raw.fna\n fasta.py size raw.fna > raw.sizes\n\n fasta.py rename raw.fna renamed.fna mapf.bed mapb.bed --opt {params.opt} {merge_tag} --gap {params.gap} --prefix_chr {params.prefix}\n\n fasta.py size renamed.fna > renamed.sizes\n chain.py fromBed mapf.bed raw.sizes renamed.sizes > mapf.chain\n chainSwap mapf.chain mapb.chain\n """\n )\n', (908, 1383), False, 'from snakemake import shell\n')] |
import io
import pytest
from requests import get
from urllib.parse import urljoin
def test_my_uploads_page(wait_for_api, login_user):
"""
GIVEN a user has logged in (login_user)
WHEN the '/my/uploads' page is navigated to (GET)
THEN check the response is valid and page title is correct
"""
request_session, api_url = wait_for_api
response = request_session.get(urljoin(api_url, '/my/uploads'))
assert response.status_code == 200
assert '<h1>My uploads</h1>' in response.text
def test_valid_new_upload_page(wait_for_api, login_user):
"""
GIVEN a user has logged in (login_user)
WHEN the '/media/newupload' page is navigated to (GET)
THEN check the response is valid and page title is correct
"""
request_session, api_url = wait_for_api
response = request_session.get(urljoin(api_url, '/media/newupload'))
assert response.status_code == 200
assert '<h1>New upload</h1>' in response.text
def test_invalid_new_upload_page(wait_for_api):
"""
GIVEN a user has not logged in
WHEN the '/media/newupload' page is navigated to (GET)
THEN check the response is valid and page title is correct
"""
request_session, api_url = wait_for_api
response = request_session.get(urljoin(api_url, '/media/newupload'))
assert response.status_code == 200
assert '<div class="flash">Please login first</div>' in response.text
def test_new_upload(wait_for_api, login_user):
"""
GIVEN a user has logged in (login_user)
WHEN the '/media/newupload' page is posted an example image (POST)
THEN check the response is valid and the page title is correct
"""
example_file=open("./app/static/gfx/example.png","rb")
files = { 'file': example_file }
request_session, api_url = wait_for_api
response = request_session.post(urljoin(api_url, '/media/newupload'), files=files, allow_redirects=True)
assert response.status_code == 200
assert '<h1>My uploads</h1>' in response.text
#def test_remove_upload(wait_for_api, login_user):
# """
# GIVEN a user has logged in (login_user)
# WHEN the '/blob/delete' page is posted (POST)
# THEN check the response is valid and the user is logged in
# """
# valid_blob = dict(blob_path='images/*example.png', upload_id=2)
# request_session, api_url = wait_for_api
# response = request_session.post(urljoin(api_url, '/blob/delete'), data=valid_blob, allow_redirects=True)
# assert response.status_code == 200
# assert 'example.png was deleted successfully' in response.text
| [
"urllib.parse.urljoin"
] | [((391, 422), 'urllib.parse.urljoin', 'urljoin', (['api_url', '"""/my/uploads"""'], {}), "(api_url, '/my/uploads')\n", (398, 422), False, 'from urllib.parse import urljoin\n'), ((833, 869), 'urllib.parse.urljoin', 'urljoin', (['api_url', '"""/media/newupload"""'], {}), "(api_url, '/media/newupload')\n", (840, 869), False, 'from urllib.parse import urljoin\n'), ((1261, 1297), 'urllib.parse.urljoin', 'urljoin', (['api_url', '"""/media/newupload"""'], {}), "(api_url, '/media/newupload')\n", (1268, 1297), False, 'from urllib.parse import urljoin\n'), ((1834, 1870), 'urllib.parse.urljoin', 'urljoin', (['api_url', '"""/media/newupload"""'], {}), "(api_url, '/media/newupload')\n", (1841, 1870), False, 'from urllib.parse import urljoin\n')] |
import justpy as jp
from .group import Group
class Row(Group):
def __init__(self):
'''Row Element
Provides a container which arranges its child in a row.
'''
view = jp.QDiv(classes='row items-start', style='gap: 1em', delete_flag=False)
super().__init__(view)
| [
"justpy.QDiv"
] | [((204, 275), 'justpy.QDiv', 'jp.QDiv', ([], {'classes': '"""row items-start"""', 'style': '"""gap: 1em"""', 'delete_flag': '(False)'}), "(classes='row items-start', style='gap: 1em', delete_flag=False)\n", (211, 275), True, 'import justpy as jp\n')] |
import uvicorn
from fastapi import FastAPI
from database import Base, engine
from routers.user import router as router_user
from routers.product import router as router_product
from routers.authentication import router as router_auth
app = FastAPI(
title="Wish List",
description="Permita que seus clientes acompanhem seus produtos favoritos, adicionando-os a uma lista de desejos.",
version="1.0.0",
)
Base.metadata.create_all(engine)
@app.get('/')
def index():
"""
"welcome": "Wish List",
"documentation": "127.0.0.1:8000/docs ou 127.0.0.1:8000/redoc"
"""
return {
"welcome": "Wish List",
"documentation": "127.0.0.1:8000/docs ou 127.0.0.1:8000/redoc"
}
app.include_router(router_auth)
app.include_router(router_product)
app.include_router(router_user)
if __name__ == "__main__":
uvicorn.run("main:app", host="127.0.0.1", port=8000, reload=True)
| [
"uvicorn.run",
"fastapi.FastAPI",
"database.Base.metadata.create_all"
] | [((244, 413), 'fastapi.FastAPI', 'FastAPI', ([], {'title': '"""Wish List"""', 'description': '"""Permita que seus clientes acompanhem seus produtos favoritos, adicionando-os a uma lista de desejos."""', 'version': '"""1.0.0"""'}), "(title='Wish List', description=\n 'Permita que seus clientes acompanhem seus produtos favoritos, adicionando-os a uma lista de desejos.'\n , version='1.0.0')\n", (251, 413), False, 'from fastapi import FastAPI\n'), ((411, 443), 'database.Base.metadata.create_all', 'Base.metadata.create_all', (['engine'], {}), '(engine)\n', (435, 443), False, 'from database import Base, engine\n'), ((811, 876), 'uvicorn.run', 'uvicorn.run', (['"""main:app"""'], {'host': '"""127.0.0.1"""', 'port': '(8000)', 'reload': '(True)'}), "('main:app', host='127.0.0.1', port=8000, reload=True)\n", (822, 876), False, 'import uvicorn\n')] |
# encoding: utf-8
import torch
import cv2
import numpy as np
import pdb
def detection_collate(batch):
"""Custom collate fn for dealing with batches of images that have a different
number of associated object annotations (bounding boxes).
Arguments:
batch: (tuple) A tuple of tensor images and lists of annotations
Return:
A tuple containing:
1) (tensor) batch of images stacked on their 0 dim
2) (tensor) [batch, num_gt, 5]
batch of annotations stacked on their 0 dim
annotations for a given image are stacked on 1 dim
"""
targets = []
imgs = []
# numpy array
num_gts = [sample[1].shape[0] for sample in batch]
max_num_gt = max(num_gts)
for sample in batch:
imgs.append(sample[0])
size_gt = sample[1].shape
num_gt = size_gt[0]
aug_size = list(size_gt[:])
aug_size[0] = max_num_gt
aug_gt = np.zeros(aug_size, dtype=sample[1].dtype)
aug_gt[:num_gt] = sample[1]
targets.append(torch.FloatTensor(aug_gt))
return torch.stack(imgs, 0), torch.stack(targets, 0)
def base_transform(image, size, mean):
x = cv2.resize(image, (size, size)).astype(np.float32)
x -= mean
x = x.astype(np.float32)
return x
class BaseTransform:
"""
For evaluation and testing.
"""
def __init__(self, size, mean):
self.size = size
self.mean = np.array(mean, dtype=np.float32)
def __call__(self, image, boxes=None, labels=None):
return base_transform(image, self.size, self.mean), boxes, labels
| [
"torch.stack",
"numpy.array",
"numpy.zeros",
"cv2.resize",
"torch.FloatTensor"
] | [((948, 989), 'numpy.zeros', 'np.zeros', (['aug_size'], {'dtype': 'sample[1].dtype'}), '(aug_size, dtype=sample[1].dtype)\n', (956, 989), True, 'import numpy as np\n'), ((1087, 1107), 'torch.stack', 'torch.stack', (['imgs', '(0)'], {}), '(imgs, 0)\n', (1098, 1107), False, 'import torch\n'), ((1109, 1132), 'torch.stack', 'torch.stack', (['targets', '(0)'], {}), '(targets, 0)\n', (1120, 1132), False, 'import torch\n'), ((1441, 1473), 'numpy.array', 'np.array', (['mean'], {'dtype': 'np.float32'}), '(mean, dtype=np.float32)\n', (1449, 1473), True, 'import numpy as np\n'), ((1049, 1074), 'torch.FloatTensor', 'torch.FloatTensor', (['aug_gt'], {}), '(aug_gt)\n', (1066, 1074), False, 'import torch\n'), ((1182, 1213), 'cv2.resize', 'cv2.resize', (['image', '(size, size)'], {}), '(image, (size, size))\n', (1192, 1213), False, 'import cv2\n')] |
# Copyright (c) 2018 Copyright holder of the paper Generative Adversarial Model Learning
# submitted to NeurIPS 2019 for review
# All rights reserved.
import numpy as np
import torch
class Optimizer(object):
def __init__(self, policy, use_gpu=False):
self.networks = self._init_networks(policy.input_dim, policy.output_dim)
networks = self.networks.copy()
networks['policy'] = policy
self.optimizers = self._init_optimizers(networks)
self.use_gpu = use_gpu
if self.use_gpu:
self.networks = {k: v.cuda() for k, v in self.networks.items()}
@classmethod
def _init_networks(cls, obs_dim, action_dim):
raise NotImplementedError
def process_batch(self, policy, batch, update_policy_args):
states, actions, rewards, masks = unpack_batch(batch)
if self.use_gpu:
states, actions, rewards, masks = map(
lambda x: x.cuda(), [states, actions, rewards, masks])
policy = self.update_networks(
policy, actions, masks, rewards, states,
batch["num_episodes"], *update_policy_args)
return policy
def update_networks(self, policy,
actions, masks, rewards, states, num_episodes,
*args, **step_kwargs):
raise NotImplementedError
@staticmethod
def _init_optimizers(networks, lr_rates=None):
return init_optimizers(networks, lr_rates=lr_rates)
def init_optimizers(networks, lr_rates=None):
args = {key: [network] for key, network in networks.items()}
if lr_rates is not None:
for key in args.keys():
args[key].append(lr_rates[key])
optimizers = {key: init_optimizer(*args[key])
for key in networks.keys()}
return optimizers
def unpack_batch(batch):
states = torch.from_numpy(np.array(batch["states"], dtype=np.float32))
rewards = torch.from_numpy(np.array(batch["rewards"], dtype=np.float32))
masks = torch.from_numpy(np.array(batch["masks"], dtype=np.float32))
actions = torch.from_numpy(np.array(batch["actions"]))
return states, actions, rewards, masks
def init_optimizer(network, lr_rate=0.01):
return torch.optim.Adam(network.parameters(), lr=lr_rate)
| [
"numpy.array"
] | [((1868, 1911), 'numpy.array', 'np.array', (["batch['states']"], {'dtype': 'np.float32'}), "(batch['states'], dtype=np.float32)\n", (1876, 1911), True, 'import numpy as np\n'), ((1944, 1988), 'numpy.array', 'np.array', (["batch['rewards']"], {'dtype': 'np.float32'}), "(batch['rewards'], dtype=np.float32)\n", (1952, 1988), True, 'import numpy as np\n'), ((2019, 2061), 'numpy.array', 'np.array', (["batch['masks']"], {'dtype': 'np.float32'}), "(batch['masks'], dtype=np.float32)\n", (2027, 2061), True, 'import numpy as np\n'), ((2094, 2120), 'numpy.array', 'np.array', (["batch['actions']"], {}), "(batch['actions'])\n", (2102, 2120), True, 'import numpy as np\n')] |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import os
import sys
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # 基地址是本项目根目录
sys.path.append(BASE_DIR)
import datetime
from yxf_yixue.xiaochengtu import XiaochengtuApi
if __name__ == '__main__':
string = '1996/02/29 23:16'
obj = datetime.datetime(2012, 3, 7, 17, 40)
a = XiaochengtuApi()
res1 = a.paipan(obj)
print(res1)
a.print_pan()
res2 = a.get_chuantongfenxi()
print(res2)
| [
"datetime.datetime",
"sys.path.append",
"os.path.abspath",
"yxf_yixue.xiaochengtu.XiaochengtuApi"
] | [((149, 174), 'sys.path.append', 'sys.path.append', (['BASE_DIR'], {}), '(BASE_DIR)\n', (164, 174), False, 'import sys\n'), ((311, 348), 'datetime.datetime', 'datetime.datetime', (['(2012)', '(3)', '(7)', '(17)', '(40)'], {}), '(2012, 3, 7, 17, 40)\n', (328, 348), False, 'import datetime\n'), ((357, 373), 'yxf_yixue.xiaochengtu.XiaochengtuApi', 'XiaochengtuApi', ([], {}), '()\n', (371, 373), False, 'from yxf_yixue.xiaochengtu import XiaochengtuApi\n'), ((107, 132), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (122, 132), False, 'import os\n')] |
# -*- coding: UTF-8 -*-
"""
Provides a summary after each test run.
"""
from __future__ import absolute_import, division, print_function
import sys
from time import time as time_now
from behave.model import Rule, ScenarioOutline # MAYBE: Scenario
from behave.model_core import Status
from behave.reporter.base import Reporter
from behave.formatter.base import StreamOpener
# ---------------------------------------------------------------------------
# CONSTANTS:
# ---------------------------------------------------------------------------
# -- DISABLED: OPTIONAL_STEPS = ('untested', 'undefined')
OPTIONAL_STEPS = (Status.untested,) # MAYBE: Status.undefined
STATUS_ORDER = (Status.passed, Status.failed, Status.skipped,
Status.undefined, Status.untested)
# ---------------------------------------------------------------------------
# UTILITY FUNCTIONS:
# ---------------------------------------------------------------------------
def pluralize(word, count=1, suffix="s"):
if count == 1:
return word
# -- OTHERWISE:
return "{0}{1}".format(word, suffix)
def compute_summary_sum(summary):
"""Compute sum of all summary counts (except: all)
:param summary: Summary counts (as dict).
:return: Sum of all counts (as integer).
"""
counts_sum = 0
for name, count in summary.items():
if name == "all":
continue # IGNORE IT.
counts_sum += count
return counts_sum
def format_summary0(statement_type, summary):
parts = []
for status in STATUS_ORDER:
if status.name not in summary:
continue
counts = summary[status.name]
if status in OPTIONAL_STEPS and counts == 0:
# -- SHOW-ONLY: For relevant counts, suppress: untested items, etc.
continue
if not parts:
# -- FIRST ITEM: Add statement_type to counter.
label = statement_type
if counts != 1:
label += 's'
part = u"%d %s %s" % (counts, label, status.name)
else:
part = u"%d %s" % (counts, status.name)
parts.append(part)
return ", ".join(parts) + "\n"
def format_summary(statement_type, summary):
parts = []
for status in STATUS_ORDER:
if status.name not in summary:
continue
counts = summary[status.name]
if status in OPTIONAL_STEPS and counts == 0:
# -- SHOW-ONLY: For relevant counts, suppress: untested items, etc.
continue
name = status.name
if status.name == "passed":
statement = pluralize(statement_type, counts)
name = u"%s passed" % statement
part = u"%d %s" % (counts, name)
parts.append(part)
return ", ".join(parts) + "\n"
# -- PREPARED:
def format_summary2(statement_type, summary, end="\n"):
"""Format the summary line for one statement type.
.. code-block::
6 scenarios (passed: 5, failed: 1, skipped: 0, untested: 0)
:param statement_type:
:param summary:
:return:
"""
parts = []
for status in STATUS_ORDER:
if status.name not in summary:
continue
counts = summary[status.name]
if status in OPTIONAL_STEPS and counts == 0:
# -- SHOW-ONLY: For relevant counts, suppress: untested items, etc.
continue
parts.append((status.name, counts))
counts_sum = summary["all"]
statement = pluralize(statement_type, sum)
parts_text = ", ".join(["{0}: {1}".format(name, value)
for name, value in parts])
return "{count:4} {statement:<9} ({parts}){end}".format(
count=counts_sum, statement=statement, parts=parts_text, end=end)
# ---------------------------------------------------------------------------
# REPORTERS:
# ---------------------------------------------------------------------------
class SummaryReporter(Reporter):
show_failed_scenarios = True
output_stream_name = "stdout"
def __init__(self, config):
super(SummaryReporter, self).__init__(config)
stream = getattr(sys, self.output_stream_name, sys.stderr)
self.stream = StreamOpener.ensure_stream_with_encoder(stream)
summary_zero_data = {
"all": 0,
Status.passed.name: 0,
Status.failed.name: 0,
Status.skipped.name: 0,
Status.untested.name: 0
}
self.feature_summary = summary_zero_data.copy()
self.rule_summary = summary_zero_data.copy()
self.scenario_summary = summary_zero_data.copy()
self.step_summary = {Status.undefined.name: 0}
self.step_summary.update(summary_zero_data)
self.duration = 0.0
self.run_starttime = 0
self.run_endtime = 0
self.failed_scenarios = []
self.show_rules = True
def testrun_started(self, timestamp=None):
if timestamp is None:
timestamp = time_now()
self.run_starttime = timestamp
def testrun_finished(self, timestamp=None):
if timestamp is None:
timestamp = time_now()
self.run_endtime = timestamp
def print_failing_scenarios(self, stream=None):
if stream is None:
stream = self.stream
stream.write("\nFailing scenarios:\n")
for scenario in self.failed_scenarios:
# add the list of tags matching ###-### so we will show the issue identifier with the error OPE-1234
stream.write(u" %s %s %s\n" % (', '.join(t for t in scenario.tags if len(t.split('-'))==2 ), scenario.location, scenario.name))
def compute_summary_sums(self):
"""(Re)Compute summary sum of all counts (except: all)."""
summaries = [
self.feature_summary,
self.rule_summary,
self.scenario_summary,
self.step_summary
]
for summary in summaries:
summary["all"] = compute_summary_sum(summary)
def print_summary(self, stream=None, with_duration=True):
if stream is None:
stream = self.stream
self.compute_summary_sums()
has_rules = (self.rule_summary["all"] > 0)
stream.write(format_summary("feature", self.feature_summary))
if self.show_rules and has_rules:
# -- HINT: Show only rules, if any exists.
self.stream.write(format_summary("rule", self.rule_summary))
stream.write(format_summary("scenario", self.scenario_summary))
stream.write(format_summary("step", self.step_summary))
# -- DURATION:
if with_duration:
timings = (int(self.duration / 60.0), self.duration % 60)
stream.write('Took %dm%02.3fs\n' % timings)
# -- REPORTER-API:
def feature(self, feature):
if self.run_starttime == 0:
# -- DISCOVER: TEST-RUN started.
self.testrun_started()
self.process_feature(feature)
def end(self):
self.testrun_finished()
# -- SHOW FAILED SCENARIOS (optional):
if self.show_failed_scenarios and self.failed_scenarios:
self.print_failing_scenarios()
self.stream.write("\n")
# -- SHOW SUMMARY COUNTS:
self.print_summary()
def process_run_items_for(self, parent):
for run_item in parent:
if isinstance(run_item, Rule):
self.process_rule(run_item)
elif isinstance(run_item, ScenarioOutline):
self.process_scenario_outline(run_item)
else:
# assert isinstance(run_item, Scenario)
self.process_scenario(run_item)
def process_feature(self, feature):
self.duration += feature.duration
self.feature_summary[feature.status.name] += 1
self.process_run_items_for(feature)
def process_rule(self, rule):
self.rule_summary[rule.status.name] += 1
self.process_run_items_for(rule)
def process_scenario(self, scenario):
if scenario.status == Status.failed:
self.failed_scenarios.append(scenario)
self.scenario_summary[scenario.status.name] += 1
for step in scenario:
self.step_summary[step.status.name] += 1
def process_scenario_outline(self, scenario_outline):
for scenario in scenario_outline.scenarios:
self.process_scenario(scenario)
| [
"behave.formatter.base.StreamOpener.ensure_stream_with_encoder",
"time.time"
] | [((4196, 4243), 'behave.formatter.base.StreamOpener.ensure_stream_with_encoder', 'StreamOpener.ensure_stream_with_encoder', (['stream'], {}), '(stream)\n', (4235, 4243), False, 'from behave.formatter.base import StreamOpener\n'), ((4977, 4987), 'time.time', 'time_now', ([], {}), '()\n', (4985, 4987), True, 'from time import time as time_now\n'), ((5130, 5140), 'time.time', 'time_now', ([], {}), '()\n', (5138, 5140), True, 'from time import time as time_now\n')] |
from typing import Dict, Any
import pytest
from checkov.common.bridgecrew.bc_source import SourceType
from checkov.common.bridgecrew.platform_integration import BcPlatformIntegration, bc_integration
@pytest.fixture()
def mock_bc_integration() -> BcPlatformIntegration:
bc_integration.bc_api_key = "<KEY>"
bc_integration.setup_bridgecrew_credentials(
repo_id="bridgecrewio/checkov",
skip_fixes=True,
skip_suppressions=True,
skip_policy_download=True,
source=SourceType("Github", False),
source_version="1.0",
repo_branch="master",
)
return bc_integration
@pytest.fixture()
def scan_result() -> Dict[str, Any]:
return {
"repository": "/abs_path/to/app/requirements.txt",
"passed": True,
"packages": {"type": "python", "name": "django", "version": "1.2", "path": "/abs_path/to/app/requirements.txt"},
"complianceIssues": None,
"complianceDistribution": {"critical": 0, "high": 0, "medium": 0, "low": 0, "total": 0},
"vulnerabilities": [
{
"id": "CVE-2019-19844",
"status": "fixed in 3.0.1, 2.2.9, 1.11.27",
"cvss": 9.8,
"vector": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"description": "Django before 1.11.27, 2.x before 2.2.9, and 3.x before 3.0.1 allows account takeover.",
"severity": "critical",
"packageName": "django",
"packageVersion": "1.2",
"link": "https://nvd.nist.gov/vuln/detail/CVE-2019-19844",
"riskFactors": ["Critical severity", "Has fix", "Attack complexity: low", "Attack vector: network"],
"impactedVersions": ["\u003c1.11.27"],
"publishedDate": "2019-12-18T20:15:00+01:00",
"discoveredDate": "2019-12-18T19:15:00Z",
"fixDate": "2019-12-18T20:15:00+01:00",
}
],
"vulnerabilityDistribution": {"critical": 1, "high": 0, "medium": 0, "low": 0, "total": 0},
}
| [
"pytest.fixture",
"checkov.common.bridgecrew.bc_source.SourceType"
] | [((204, 220), 'pytest.fixture', 'pytest.fixture', ([], {}), '()\n', (218, 220), False, 'import pytest\n'), ((633, 649), 'pytest.fixture', 'pytest.fixture', ([], {}), '()\n', (647, 649), False, 'import pytest\n'), ((509, 536), 'checkov.common.bridgecrew.bc_source.SourceType', 'SourceType', (['"""Github"""', '(False)'], {}), "('Github', False)\n", (519, 536), False, 'from checkov.common.bridgecrew.bc_source import SourceType\n')] |
import argparse
from pathlib import Path
from cv2 import cv2
from trimap import generate_trimap
from trimap_output_utils import save_trimap_output
def main():
args = parse_args()
image_path = args.image
output_directory_path = args.output
image_path = Path(image_path)
if not image_path.is_file():
raise RuntimeError(f'The provided image path "{image_path}" does not exist!')
image_filename = image_path.stem
saliency_image_path = image_path.as_posix()
trimap_image = generate_trimap(saliency_image_path, kernel_size=3, iterations=20)
save_trimap_output(trimap_image, image_filename, output_directory_path)
def parse_args():
parser = argparse.ArgumentParser(description='Trimap Generator Application')
parser.add_argument('-i', '--image', required=True, type=str, help='path to input image')
parser.add_argument('-o', '--output', required=False, default='.', type=str, help='path to output directory')
return parser.parse_args()
if __name__ == "__main__":
main()
| [
"argparse.ArgumentParser",
"trimap.generate_trimap",
"trimap_output_utils.save_trimap_output",
"pathlib.Path"
] | [((273, 289), 'pathlib.Path', 'Path', (['image_path'], {}), '(image_path)\n', (277, 289), False, 'from pathlib import Path\n'), ((515, 581), 'trimap.generate_trimap', 'generate_trimap', (['saliency_image_path'], {'kernel_size': '(3)', 'iterations': '(20)'}), '(saliency_image_path, kernel_size=3, iterations=20)\n', (530, 581), False, 'from trimap import generate_trimap\n'), ((586, 657), 'trimap_output_utils.save_trimap_output', 'save_trimap_output', (['trimap_image', 'image_filename', 'output_directory_path'], {}), '(trimap_image, image_filename, output_directory_path)\n', (604, 657), False, 'from trimap_output_utils import save_trimap_output\n'), ((691, 758), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Trimap Generator Application"""'}), "(description='Trimap Generator Application')\n", (714, 758), False, 'import argparse\n')] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from http.client import OK
from unittest.mock import MagicMock, patch
from urllib.parse import urlencode
import graphene_django.views as views
from django.urls import reverse
from graphql import GraphQLError
from graphql.error import GraphQLLocatedError
class CustomException(Exception):
""" Boom! """
def get_query_string():
path = reverse('graphql')
query = urlencode({'query': 'query {test}'})
path = f'{path}?{query}'
return path
def test_view(client):
result = client.get(
get_query_string(),
HTTP_ACCEPT="application/json;q=0.8, text/html;q=0.9",
)
assert result.status_code == OK
@patch.object(views.GraphQLView, 'execute_graphql_request')
@patch('sentry_sdk.capture_exception')
def test_execute_graphql_request(
mocked_capture_exception,
mocked_method,
client,
):
error = CustomException('Boom')
errors = [GraphQLLocatedError([], error)]
mocked_return_value = MagicMock()
mocked_return_value.errors = errors
mocked_method.return_value = mocked_return_value
result = client.get(
get_query_string(),
HTTP_ACCEPT="application/json;q=0.8, text/html;q=0.9",
)
assert result.status_code == 400
assert result.json()['errors'][0]['message'] == 'Boom'
mocked_capture_exception.assert_called_with(error)
@patch.object(views.GraphQLView, 'execute_graphql_request')
@patch('sentry_sdk.capture_exception')
def test_execute_graphql_request_raises_raw_graphql_exceptions(
mocked_capture_exception,
mocked_method,
client,
):
error = GraphQLError(message='Syntax error in GraphQL query')
mocked_return_value = MagicMock()
mocked_return_value.errors = [error]
mocked_method.return_value = mocked_return_value
result = client.get(
reverse('graphql'),
{'query': '{__schema{types{name}}}'},
)
assert result.status_code == 400
assert result.json()['errors'][0]['message'] == (
'Syntax error in GraphQL query'
)
mocked_capture_exception.assert_called_with(error)
| [
"unittest.mock.MagicMock",
"graphql.GraphQLError",
"graphql.error.GraphQLLocatedError",
"django.urls.reverse",
"urllib.parse.urlencode",
"unittest.mock.patch.object",
"unittest.mock.patch"
] | [((692, 750), 'unittest.mock.patch.object', 'patch.object', (['views.GraphQLView', '"""execute_graphql_request"""'], {}), "(views.GraphQLView, 'execute_graphql_request')\n", (704, 750), False, 'from unittest.mock import MagicMock, patch\n'), ((752, 789), 'unittest.mock.patch', 'patch', (['"""sentry_sdk.capture_exception"""'], {}), "('sentry_sdk.capture_exception')\n", (757, 789), False, 'from unittest.mock import MagicMock, patch\n'), ((1379, 1437), 'unittest.mock.patch.object', 'patch.object', (['views.GraphQLView', '"""execute_graphql_request"""'], {}), "(views.GraphQLView, 'execute_graphql_request')\n", (1391, 1437), False, 'from unittest.mock import MagicMock, patch\n'), ((1439, 1476), 'unittest.mock.patch', 'patch', (['"""sentry_sdk.capture_exception"""'], {}), "('sentry_sdk.capture_exception')\n", (1444, 1476), False, 'from unittest.mock import MagicMock, patch\n'), ((393, 411), 'django.urls.reverse', 'reverse', (['"""graphql"""'], {}), "('graphql')\n", (400, 411), False, 'from django.urls import reverse\n'), ((424, 460), 'urllib.parse.urlencode', 'urlencode', (["{'query': 'query {test}'}"], {}), "({'query': 'query {test}'})\n", (433, 460), False, 'from urllib.parse import urlencode\n'), ((997, 1008), 'unittest.mock.MagicMock', 'MagicMock', ([], {}), '()\n', (1006, 1008), False, 'from unittest.mock import MagicMock, patch\n'), ((1617, 1670), 'graphql.GraphQLError', 'GraphQLError', ([], {'message': '"""Syntax error in GraphQL query"""'}), "(message='Syntax error in GraphQL query')\n", (1629, 1670), False, 'from graphql import GraphQLError\n'), ((1698, 1709), 'unittest.mock.MagicMock', 'MagicMock', ([], {}), '()\n', (1707, 1709), False, 'from unittest.mock import MagicMock, patch\n'), ((938, 968), 'graphql.error.GraphQLLocatedError', 'GraphQLLocatedError', (['[]', 'error'], {}), '([], error)\n', (957, 968), False, 'from graphql.error import GraphQLLocatedError\n'), ((1838, 1856), 'django.urls.reverse', 'reverse', (['"""graphql"""'], {}), "('graphql')\n", (1845, 1856), False, 'from django.urls import reverse\n')] |
'''
(c) University of Liverpool 2020
Licensed under the MIT License.
To view a copy of this license, visit <http://opensource.org/licenses/MIT/>..
@author: neilswainston
'''
# pylint: disable=broad-except
import os.path
import tempfile
from liv_covid19.web.artic import opentrons
from liv_covid19.web.job import JobThread, save_export
class OpentronsThread(JobThread):
'''Runs a Opentrons job.'''
def __init__(self, query, out_dir):
self.__filename, suffix = os.path.splitext(query['file_name'])
tmpfile = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
self.__in_filename = tmpfile.name
with open(self.__in_filename, 'w') as fle:
fle.write(query['file_content'])
self.__temp_deck = query['temp_deck']
self.__vol_scale = float(query['vol_scale'])
self.__out_dir = out_dir
JobThread.__init__(self, query, 1)
def run(self):
'''Run.'''
try:
parent_dir = tempfile.mkdtemp()
iteration = 0
self._fire_job_event('running', iteration, 'Running...')
opentrons.run(in_filename=self.__in_filename,
temp_deck=self.__temp_deck,
vol_scale=self.__vol_scale,
out_dir=parent_dir)
iteration += 1
if self._cancelled:
self._fire_job_event('cancelled', iteration,
message='Job cancelled')
else:
save_export(parent_dir, self.__out_dir, self._job_id)
self._result = self._job_id
self._fire_job_event('finished', iteration,
message='Job completed')
except Exception as err:
self._fire_job_event('error', iteration, message=str(err))
| [
"liv_covid19.web.job.save_export",
"liv_covid19.web.job.JobThread.__init__",
"tempfile.mkdtemp",
"liv_covid19.web.artic.opentrons.run",
"tempfile.NamedTemporaryFile"
] | [((538, 594), 'tempfile.NamedTemporaryFile', 'tempfile.NamedTemporaryFile', ([], {'delete': '(False)', 'suffix': 'suffix'}), '(delete=False, suffix=suffix)\n', (565, 594), False, 'import tempfile\n'), ((876, 910), 'liv_covid19.web.job.JobThread.__init__', 'JobThread.__init__', (['self', 'query', '(1)'], {}), '(self, query, 1)\n', (894, 910), False, 'from liv_covid19.web.job import JobThread, save_export\n'), ((988, 1006), 'tempfile.mkdtemp', 'tempfile.mkdtemp', ([], {}), '()\n', (1004, 1006), False, 'import tempfile\n'), ((1116, 1241), 'liv_covid19.web.artic.opentrons.run', 'opentrons.run', ([], {'in_filename': 'self.__in_filename', 'temp_deck': 'self.__temp_deck', 'vol_scale': 'self.__vol_scale', 'out_dir': 'parent_dir'}), '(in_filename=self.__in_filename, temp_deck=self.__temp_deck,\n vol_scale=self.__vol_scale, out_dir=parent_dir)\n', (1129, 1241), False, 'from liv_covid19.web.artic import opentrons\n'), ((1534, 1587), 'liv_covid19.web.job.save_export', 'save_export', (['parent_dir', 'self.__out_dir', 'self._job_id'], {}), '(parent_dir, self.__out_dir, self._job_id)\n', (1545, 1587), False, 'from liv_covid19.web.job import JobThread, save_export\n')] |
# Copyright 2018 <NAME>.
#
# Licensed under the MIT License
import tensorflow as tf
DEFAULT_VARIABLE_NAMES = ['conv1', 'conv2', 'conv3', 'conv4', 'fc1', 'fc2', 'softmax_linear']
BATCH_SIZE = 200
IMAGE_WIDTH = 32
IMAGE_HEIGHT = 32
IMAGE_DEPTH = 3
NUM_CLASSES = 10
INPUT_PLACEHOLDER = 'X_INPUT'
LABELS_PLACEHOLDER = 'Y_LABELS'
def create_placeholder():
x_placeholder = tf.placeholder(tf.float32, [None, IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_DEPTH], name=INPUT_PLACEHOLDER)
y_placeholder = tf.placeholder(tf.int32, [None], name=LABELS_PLACEHOLDER)
return x_placeholder, y_placeholder
def initialize_parameters():
parameters = {
"w1": tf.get_variable("w1", shape=[3, 3, 3, 64],
initializer=tf.truncated_normal_initializer(stddev=5e-2, dtype=tf.float32)),
"w2": tf.get_variable("w2", shape=[3, 3, 64, 64],
initializer=tf.truncated_normal_initializer(stddev=5e-2, dtype=tf.float32)),
"w3": tf.get_variable("w3", shape=[3, 3, 64, 64],
initializer=tf.truncated_normal_initializer(stddev=5e-2, dtype=tf.float32)),
"w4": tf.get_variable("w4", shape=[3, 3, 64, 64],
initializer=tf.truncated_normal_initializer(stddev=5e-2, dtype=tf.float32)),
"w5": tf.get_variable("w5", shape=[4096, 384],
initializer=tf.truncated_normal_initializer(stddev=5e-2, dtype=tf.float32)),
"w6": tf.get_variable("w6", shape=[384, 192],
initializer=tf.truncated_normal_initializer(stddev=5e-2, dtype=tf.float32)),
"w7": tf.get_variable("w7", shape=[192, 10],
initializer=tf.truncated_normal_initializer(stddev=5e-2, dtype=tf.float32)),
"b1": tf.get_variable('b1', [64], initializer=tf.constant_initializer(0.0), dtype=tf.float32),
"b2": tf.get_variable('b2', [64], initializer=tf.constant_initializer(0.0), dtype=tf.float32),
"b3": tf.get_variable('b3', [64], initializer=tf.constant_initializer(0.0), dtype=tf.float32),
"b4": tf.get_variable('b4', [64], initializer=tf.constant_initializer(0.0), dtype=tf.float32),
"b5": tf.get_variable('b5', [384], initializer=tf.constant_initializer(0.1), dtype=tf.float32),
"b6": tf.get_variable('b6', [192], initializer=tf.constant_initializer(0.1), dtype=tf.float32),
"b7": tf.get_variable('b7', [10], initializer=tf.constant_initializer(0.0), dtype=tf.float32)
}
return parameters
def create_conv2d_layer(inputs, name, weight, bias, strides=[1, 1, 1, 1], padding='SAME'):
with tf.variable_scope(name) as scope:
conv = tf.nn.conv2d(inputs, weight, strides, padding)
pre_activation = tf.nn.bias_add(conv, bias)
activation = tf.nn.relu(pre_activation, name=scope.name)
return activation
def forward_propagation(input, parameters):
conv1 = create_conv2d_layer(input, 'conv1', parameters['w1'], parameters['b1'])
conv2 = create_conv2d_layer(conv1, 'conv2', parameters['w2'], parameters['b2'])
pool1 = tf.nn.max_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
padding='SAME', name='pool1')
conv3 = create_conv2d_layer(pool1, 'conv3', parameters['w3'], parameters['b3'])
conv4 = create_conv2d_layer(conv3, 'conv4', parameters['w4'], parameters['b4'])
pool2 = tf.nn.max_pool(conv4, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
padding='SAME', name='pool2')
flattened_conv = tf.reshape(pool2, shape=[-1, parameters['w5'].get_shape().as_list()[0]])
fc1 = tf.nn.relu(tf.matmul(flattened_conv, parameters['w5']) + parameters['b5'], name='fc1')
fc2 = tf.nn.relu(tf.matmul(fc1, parameters['w6']) + parameters['b6'], name='fc2')
softmax_linear = tf.add(tf.matmul(fc2, parameters['w7']), parameters['b7'], name='softmax')
return softmax_linear
| [
"tensorflow.nn.conv2d",
"tensorflow.nn.max_pool",
"tensorflow.variable_scope",
"tensorflow.nn.relu",
"tensorflow.placeholder",
"tensorflow.truncated_normal_initializer",
"tensorflow.matmul",
"tensorflow.constant_initializer",
"tensorflow.nn.bias_add"
] | [((377, 479), 'tensorflow.placeholder', 'tf.placeholder', (['tf.float32', '[None, IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_DEPTH]'], {'name': 'INPUT_PLACEHOLDER'}), '(tf.float32, [None, IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_DEPTH],\n name=INPUT_PLACEHOLDER)\n', (391, 479), True, 'import tensorflow as tf\n'), ((496, 553), 'tensorflow.placeholder', 'tf.placeholder', (['tf.int32', '[None]'], {'name': 'LABELS_PLACEHOLDER'}), '(tf.int32, [None], name=LABELS_PLACEHOLDER)\n', (510, 553), True, 'import tensorflow as tf\n'), ((3106, 3204), 'tensorflow.nn.max_pool', 'tf.nn.max_pool', (['conv2'], {'ksize': '[1, 3, 3, 1]', 'strides': '[1, 2, 2, 1]', 'padding': '"""SAME"""', 'name': '"""pool1"""'}), "(conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding=\n 'SAME', name='pool1')\n", (3120, 3204), True, 'import tensorflow as tf\n'), ((3410, 3508), 'tensorflow.nn.max_pool', 'tf.nn.max_pool', (['conv4'], {'ksize': '[1, 3, 3, 1]', 'strides': '[1, 2, 2, 1]', 'padding': '"""SAME"""', 'name': '"""pool2"""'}), 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'tensorflow.matmul', 'tf.matmul', (['flattened_conv', "parameters['w5']"], {}), "(flattened_conv, parameters['w5'])\n", (3656, 3690), True, 'import tensorflow as tf\n'), ((3745, 3777), 'tensorflow.matmul', 'tf.matmul', (['fc1', "parameters['w6']"], {}), "(fc1, parameters['w6'])\n", (3754, 3777), True, 'import tensorflow as tf\n'), ((744, 806), 'tensorflow.truncated_normal_initializer', 'tf.truncated_normal_initializer', ([], {'stddev': '(0.05)', 'dtype': 'tf.float32'}), '(stddev=0.05, dtype=tf.float32)\n', (775, 806), True, 'import tensorflow as tf\n'), ((909, 971), 'tensorflow.truncated_normal_initializer', 'tf.truncated_normal_initializer', ([], {'stddev': '(0.05)', 'dtype': 'tf.float32'}), '(stddev=0.05, dtype=tf.float32)\n', (940, 971), True, 'import tensorflow as tf\n'), ((1074, 1136), 'tensorflow.truncated_normal_initializer', 'tf.truncated_normal_initializer', ([], {'stddev': '(0.05)', 'dtype': 'tf.float32'}), '(stddev=0.05, dtype=tf.float32)\n', (1105, 1136), True, 'import tensorflow as tf\n'), ((1239, 1301), 'tensorflow.truncated_normal_initializer', 'tf.truncated_normal_initializer', ([], {'stddev': '(0.05)', 'dtype': 'tf.float32'}), '(stddev=0.05, dtype=tf.float32)\n', (1270, 1301), True, 'import tensorflow as tf\n'), ((1401, 1463), 'tensorflow.truncated_normal_initializer', 'tf.truncated_normal_initializer', ([], {'stddev': '(0.05)', 'dtype': 'tf.float32'}), '(stddev=0.05, dtype=tf.float32)\n', (1432, 1463), True, 'import tensorflow as tf\n'), ((1562, 1624), 'tensorflow.truncated_normal_initializer', 'tf.truncated_normal_initializer', ([], {'stddev': '(0.05)', 'dtype': 'tf.float32'}), '(stddev=0.05, dtype=tf.float32)\n', (1593, 1624), True, 'import tensorflow as tf\n'), ((1722, 1784), 'tensorflow.truncated_normal_initializer', 'tf.truncated_normal_initializer', ([], {'stddev': '(0.05)', 'dtype': 'tf.float32'}), '(stddev=0.05, dtype=tf.float32)\n', (1753, 1784), True, 'import tensorflow as tf\n'), ((1842, 1870), 'tensorflow.constant_initializer', 'tf.constant_initializer', (['(0.0)'], {}), '(0.0)\n', (1865, 1870), True, 'import tensorflow as tf\n'), ((1945, 1973), 'tensorflow.constant_initializer', 'tf.constant_initializer', (['(0.0)'], {}), '(0.0)\n', (1968, 1973), True, 'import tensorflow as tf\n'), ((2048, 2076), 'tensorflow.constant_initializer', 'tf.constant_initializer', (['(0.0)'], {}), '(0.0)\n', (2071, 2076), True, 'import tensorflow as tf\n'), ((2151, 2179), 'tensorflow.constant_initializer', 'tf.constant_initializer', (['(0.0)'], {}), '(0.0)\n', (2174, 2179), True, 'import tensorflow as tf\n'), ((2255, 2283), 'tensorflow.constant_initializer', 'tf.constant_initializer', (['(0.1)'], {}), '(0.1)\n', (2278, 2283), True, 'import tensorflow as tf\n'), ((2359, 2387), 'tensorflow.constant_initializer', 'tf.constant_initializer', (['(0.1)'], {}), '(0.1)\n', (2382, 2387), True, 'import tensorflow as tf\n'), ((2462, 2490), 'tensorflow.constant_initializer', 'tf.constant_initializer', (['(0.0)'], {}), '(0.0)\n', (2485, 2490), True, 'import tensorflow as tf\n')] |
import urwid
from scronsole.config_manager import ConfigManager
from scronsole.plugin_manager import PluginManager
from scronsole.widgets.main_menu import MainMenu
from scronsole.widgets.server_screen import ServerScreen
class MainScreen(urwid.WidgetPlaceholder):
def __init__(self):
super().__init__(urwid.SolidFill(u'/'))
self.config = ConfigManager()
self.show_main_menu()
self.plugins = PluginManager(self)
self.plugins.load_plugins()
def show_server_screen(self, server_data):
self.original_widget = ServerScreen(self, server_data)
def show_main_menu(self):
self.original_widget = MainMenu(self)
| [
"urwid.SolidFill",
"scronsole.config_manager.ConfigManager",
"scronsole.widgets.main_menu.MainMenu",
"scronsole.widgets.server_screen.ServerScreen",
"scronsole.plugin_manager.PluginManager"
] | [((361, 376), 'scronsole.config_manager.ConfigManager', 'ConfigManager', ([], {}), '()\n', (374, 376), False, 'from scronsole.config_manager import ConfigManager\n'), ((430, 449), 'scronsole.plugin_manager.PluginManager', 'PluginManager', (['self'], {}), '(self)\n', (443, 449), False, 'from scronsole.plugin_manager import PluginManager\n'), ((565, 596), 'scronsole.widgets.server_screen.ServerScreen', 'ServerScreen', (['self', 'server_data'], {}), '(self, server_data)\n', (577, 596), False, 'from scronsole.widgets.server_screen import ServerScreen\n'), ((659, 673), 'scronsole.widgets.main_menu.MainMenu', 'MainMenu', (['self'], {}), '(self)\n', (667, 673), False, 'from scronsole.widgets.main_menu import MainMenu\n'), ((316, 337), 'urwid.SolidFill', 'urwid.SolidFill', (['u"""/"""'], {}), "(u'/')\n", (331, 337), False, 'import urwid\n')] |
import re
from csv import reader
def splitEscaped(str, by, escapeChar):
infile = [str]
return reader(infile, delimiter=by, quotechar=escapeChar)
def removeComments(text):
p = r'/\*[^*]*\*+([^/*][^*]*\*+)*/|("(\\.|[^"\\])*"|\'(\\.|[^\'\\])*\'|.[^/"\'\\]*)'
return ''.join(m.group(2) for m in re.finditer(p, text, re.M|re.S) if m.group(2))
def escapeAnnotations(text):
return re.sub(r'(/\*@)(.*)(\*/)',r'@\2',text)
| [
"re.sub",
"csv.reader",
"re.finditer"
] | [((103, 153), 'csv.reader', 'reader', (['infile'], {'delimiter': 'by', 'quotechar': 'escapeChar'}), '(infile, delimiter=by, quotechar=escapeChar)\n', (109, 153), False, 'from csv import reader\n'), ((397, 438), 're.sub', 're.sub', (['"""(/\\\\*@)(.*)(\\\\*/)"""', '"""@\\\\2"""', 'text'], {}), "('(/\\\\*@)(.*)(\\\\*/)', '@\\\\2', text)\n", (403, 438), False, 'import re\n'), ((309, 342), 're.finditer', 're.finditer', (['p', 'text', '(re.M | re.S)'], {}), '(p, text, re.M | re.S)\n', (320, 342), False, 'import re\n')] |
#!/usr/bin/python
from ConfigUtils import getBaseConfig
from LogUtils import getModuleLogger
from StringUtils import isValidUrl, randomString
from urlparse import urlparse
import json
import os
import requests
import sys
cDir = os.path.dirname(os.path.realpath(__file__))
rootDir = os.path.abspath(os.path.join(cDir, os.pardir))
baseConfig = getBaseConfig(rootDir)
logging = getModuleLogger(__name__)
def getMalShareList():
try:
payload = {'action': 'getsourcesraw', 'api_key': baseConfig.malShareApiKey }
userAgent = {'User-agent': baseConfig.userAgent}
logging.info('Fetching latest MalShare list.')
request = requests.get('http://malshare.com/api.php', params=payload, headers=userAgent)
if request.status_code == 200:
mal_list = []
for line in request.content.split('\n'):
url = line.strip()
if isValidUrl(url):
mal_list.append(url)
return mal_list
else:
logging.error('Problem connecting to MalShare. Status code:{0}. Please try again later.'.format(request.status_code))
except requests.exceptions.ConnectionError as e:
logging.warning('Problem connecting to Malshare. Error: {0}'.format(e))
except Exception as e:
logging.warning('Problem connecting to Malshare. Aborting task.')
logging.exception(sys.exc_info())
logging.exception(type(e))
logging.exception(e.args)
logging.exception(e)
return []
| [
"os.path.join",
"requests.get",
"ConfigUtils.getBaseConfig",
"os.path.realpath",
"LogUtils.getModuleLogger",
"StringUtils.isValidUrl",
"sys.exc_info"
] | [((346, 368), 'ConfigUtils.getBaseConfig', 'getBaseConfig', (['rootDir'], {}), '(rootDir)\n', (359, 368), False, 'from ConfigUtils import getBaseConfig\n'), ((379, 404), 'LogUtils.getModuleLogger', 'getModuleLogger', (['__name__'], {}), '(__name__)\n', (394, 404), False, 'from LogUtils import getModuleLogger\n'), ((248, 274), 'os.path.realpath', 'os.path.realpath', (['__file__'], {}), '(__file__)\n', (264, 274), False, 'import os\n'), ((302, 331), 'os.path.join', 'os.path.join', (['cDir', 'os.pardir'], {}), '(cDir, os.pardir)\n', (314, 331), False, 'import os\n'), ((656, 734), 'requests.get', 'requests.get', (['"""http://malshare.com/api.php"""'], {'params': 'payload', 'headers': 'userAgent'}), "('http://malshare.com/api.php', params=payload, headers=userAgent)\n", (668, 734), False, 'import requests\n'), ((909, 924), 'StringUtils.isValidUrl', 'isValidUrl', (['url'], {}), '(url)\n', (919, 924), False, 'from StringUtils import isValidUrl, randomString\n'), ((1418, 1432), 'sys.exc_info', 'sys.exc_info', ([], {}), '()\n', (1430, 1432), False, 'import sys\n')] |
from attempt.ddpg import HERDDPG, DDPG
import gym
import os
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
if __name__ == "__main__":
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
env = gym.make('FetchReach-v1')
agent = HERDDPG(env)
for epoch in range(2):
for cycle in tqdm(range(10)):
agent.gather_cycle()
# target_agent.train()
agent.test_env(10)
env.close()
plt.plot(np.vstack(agent.rewards))
plt.title('Rewards')
plt.show()
plt.plot(np.vstack(agent.policy_losses))
plt.title('Policy Losses')
plt.show()
plt.plot(np.vstack(agent.value_losses))
plt.title('Value Losses')
plt.show() | [
"attempt.ddpg.HERDDPG",
"numpy.vstack",
"matplotlib.pyplot.title",
"gym.make",
"matplotlib.pyplot.show"
] | [((219, 244), 'gym.make', 'gym.make', (['"""FetchReach-v1"""'], {}), "('FetchReach-v1')\n", (227, 244), False, 'import gym\n'), ((257, 269), 'attempt.ddpg.HERDDPG', 'HERDDPG', (['env'], {}), '(env)\n', (264, 269), False, 'from attempt.ddpg import HERDDPG, DDPG\n'), ((488, 508), 'matplotlib.pyplot.title', 'plt.title', (['"""Rewards"""'], {}), "('Rewards')\n", (497, 508), True, 'import matplotlib.pyplot as plt\n'), ((513, 523), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (521, 523), True, 'import matplotlib.pyplot as plt\n'), ((574, 600), 'matplotlib.pyplot.title', 'plt.title', (['"""Policy Losses"""'], {}), "('Policy Losses')\n", (583, 600), True, 'import matplotlib.pyplot as plt\n'), ((605, 615), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (613, 615), True, 'import matplotlib.pyplot as plt\n'), ((665, 690), 'matplotlib.pyplot.title', 'plt.title', (['"""Value Losses"""'], {}), "('Value Losses')\n", (674, 690), True, 'import matplotlib.pyplot as plt\n'), ((695, 705), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (703, 705), True, 'import matplotlib.pyplot as plt\n'), ((458, 482), 'numpy.vstack', 'np.vstack', (['agent.rewards'], {}), '(agent.rewards)\n', (467, 482), True, 'import numpy as np\n'), ((538, 568), 'numpy.vstack', 'np.vstack', (['agent.policy_losses'], {}), '(agent.policy_losses)\n', (547, 568), True, 'import numpy as np\n'), ((630, 659), 'numpy.vstack', 'np.vstack', (['agent.value_losses'], {}), '(agent.value_losses)\n', (639, 659), True, 'import numpy as np\n')] |
#!/usr/bin/env python
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# Written (W) 2008-2009 <NAME>
# Copyright (C) 2008-2009 Fraunhofer Institute FIRST and Max Planck Society
class_str='class'
types=["BOOL", "CHAR", "INT8", "UINT8", "INT16", "UINT16", "INT32", "UINT32",
"INT64", "UINT64", "FLOAT32", "FLOAT64", "FLOATMAX", "COMPLEX128"]
config_tests=["HAVE_HDF5", "HAVE_JSON", "HAVE_XML", "HAVE_LAPACK", "USE_CPLEX",
"USE_SVMLIGHT", "USE_GLPK", "USE_LZO", "USE_GZIP", "USE_BZIP2", "USE_LZMA",
"USE_MOSEK", "HAVE_EIGEN3", "HAVE_COLPACK", "HAVE_NLOPT", "HAVE_PROTOBUF",
"HAVE_VIENNACL"]
SHOGUN_TEMPLATE_CLASS = "SHOGUN_TEMPLATE_CLASS"
SHOGUN_BASIC_CLASS = "SHOGUN_BASIC_CLASS"
def check_class(line):
if not (line.find('public')==-1 and
line.find('private')==-1 and
line.find('protected')==-1):
return True
def check_abstract_class(line):
line=line.replace(' ','').replace('\t','').strip()
return line.endswith('=0;')
def check_is_in_blacklist(c, lines, line_nr, blacklist):
ifdef_cnt=0
for i in range(line_nr,0,-1):
line=lines[i]
if line.find('#endif')!=-1:
ifdef_cnt-=1
if line.find('#ifdef')!=-1:
ifdef_cnt+=1
for b in blacklist.keys():
if line.find(b)!=-1 and ifdef_cnt>0:
return True
if line.find('#ifndef')!=-1:
ifdef_cnt+=1
return False
def extract_class_name(lines, line_nr, line, blacklist):
try:
if not line:
line=lines[line_nr]
c=line[line.index(class_str)+len(class_str):]
if not ':' in c:
return
if not check_class(line):
if not check_class(lines[line_nr+1]):
return
c=c.split()[0]
except:
return
c=c.strip(':').strip()
if not c.startswith('C'):
return
if c.endswith(';'):
return
if '>' in c:
return
if not (len(c)>2 and c[1].isupper()):
return
if check_is_in_blacklist(c[1:], lines, line_nr, blacklist):
return
return c[1:]
def get_includes(classes):
class_headers = []
for c,t in classes:
class_headers.append(c+".h")
import os
result = []
for root, dirs, files in os.walk("."):
for f in files:
if f in class_headers:
result.append(os.path.join(root, f))
includes=[]
for o in result:
includes.append('#include <shogun/%s>' % o.strip().lstrip('./'))
return includes
def get_definitions(classes):
definitions=[]
definitions.append("#define %s" % SHOGUN_TEMPLATE_CLASS)
definitions.append("#define %s" % SHOGUN_BASIC_CLASS)
for c,t in classes:
d="static %s CSGObject* __new_C%s(EPrimitiveType g) { return g == PT_NOT_GENERIC? new C%s(): NULL; }" % (SHOGUN_BASIC_CLASS,c,c)
definitions.append(d)
return definitions
def get_template_definitions(classes, supports_complex):
definitions=[]
for c,t in classes:
d=[]
d.append("static %s CSGObject* __new_C%s(EPrimitiveType g)\n{\n\tswitch (g)\n\t{\n" % (SHOGUN_TEMPLATE_CLASS,c))
for t in types:
if t in ('BOOL','CHAR'):
suffix=''
else:
suffix='_t'
if t=='COMPLEX128' and not supports_complex:
d.append("\t\tcase PT_COMPLEX128: return NULL;\n")
else:
d.append("\t\tcase PT_%s: return new C%s<%s%s>();\n" % (t,c,t.lower(),suffix))
d.append("\t\tcase PT_SGOBJECT:\n")
d.append("\t\tcase PT_UNDEFINED: return NULL;\n\t}\n\treturn NULL;\n}")
definitions.append(''.join(d))
return definitions
def get_struct(classes):
struct=[]
for c,template in classes:
prefix = SHOGUN_BASIC_CLASS
if template:
prefix = SHOGUN_TEMPLATE_CLASS
s='{"%s", %s __new_C%s},' % (c,prefix,c)
struct.append(s)
return struct
def extract_block(c, lines, start_line, stop_line, start_sym, stop_sym):
sym_cnt=0
block_start=-1;
block_stop=-1;
for line_nr in range(start_line, stop_line):
line=lines[line_nr]
if line.find(start_sym)!=-1:
sym_cnt+=1
if block_start==-1:
block_start=line_nr
if line.find(stop_sym)!=-1:
block_stop=line_nr+1
sym_cnt-=1
if sym_cnt==0 and block_start!=-1 and block_stop!=-1:
return block_start,block_stop
return block_start,block_stop
def check_complex_supported_class(line):
l=list(filter(lambda y:y if y!='' else None,\
line.strip().replace('\t',' ').split(' ')))
supported=len(l)==3 and l[0]=='typedef' and l[1]=='bool'\
and l[2]=='supports_complex128_t;'
return supported
def test_candidate(c, lines, line_nr, supports_complex):
start,stop=extract_block(c, lines, line_nr, len(lines), '{','}')
if stop<line_nr:
return False, line_nr+1
complex_supported=False
for line_nr in range(start, stop):
line=lines[line_nr]
if line.find('virtual')!=-1:
if check_abstract_class(line):
return False, stop
else:
vstart,vstop=extract_block(c, lines, line_nr, stop, '(',')')
for line_nr in range(vstart, vstop):
line=lines[line_nr]
if check_abstract_class(line):
return False, stop
if line.find('supports_complex128_t')!=-1:
if check_complex_supported_class(line):
complex_supported=True
if not supports_complex:
return False, stop
if supports_complex and not complex_supported:
return False, stop
return True, stop
def extract_classes(HEADERS, template, blacklist, supports_complex):
"""
Search in headers for non-template/non-abstract class-names starting
with `C'.
Does not support local nor multiple classes and
drops classes with pure virtual functions
"""
classes=list()
for fname in HEADERS:
try:
lines=open(fname).readlines()
except: # python3 workaround
lines=open(fname, encoding='utf-8', errors='ignore').readlines()
line_nr=0
while line_nr<len(lines):
line=lines[line_nr]
if line.find('IGNORE_IN_CLASSLIST')!=-1:
line_nr+=1
continue
c=None
if template:
tp=line.find('template')
if tp!=-1:
line=line[tp:]
cp=line.find('>')
line=line[cp+1:]
cp=line.find(class_str)
if cp!=-1:
c=extract_class_name(lines, line_nr, line, blacklist)
else:
if line.find(class_str)!=-1:
c=extract_class_name(lines, line_nr, None, blacklist)
if c:
ok, line_nr=test_candidate(c, lines, line_nr, supports_complex)
if ok:
classes.append((c,template))
continue
line_nr+=1
return classes
def write_templated_file(fname, substitutes):
template=open(fname).readlines()
f=open(fname,'w')
for line in template:
l=line.strip()
if l.startswith('REPLACE') and l.endswith('THIS'):
l=line.split()[1]
if sys.version_info >= (3,):
for s in substitutes.keys():
if l==s:
f.write('\n'.join(substitutes[s]))
continue
else:
for s in substitutes.iterkeys():
if l==s:
f.write('\n'.join(substitutes[s]))
continue
else:
f.write(line)
def read_config():
config=dict()
for line in open('lib/config.h').readlines():
if line=='\n':
continue
l=[l.strip() for l in line.split()]
config[l[1]]=1
return config
def get_blacklist():
config=read_config()
blacklist=dict()
for cfg in config_tests:
if not cfg in config:
blacklist[cfg]=1
return blacklist
if __name__=='__main__':
import sys
TEMPL_FILE=sys.argv[1]
HEADERS=None
if (sys.argv[2] == "-in"):
# read header file list from file
with open(sys.argv[3]) as f:
content = f.readlines()
HEADERS = [x.strip() for x in content]
else:
HEADERS=sys.argv[2:]
blacklist = get_blacklist()
classes = extract_classes(HEADERS, False, blacklist, False)
template_classes = extract_classes(HEADERS, True, blacklist, False)
complex_template_classes = extract_classes(HEADERS, True, blacklist, True)
includes = get_includes(classes+template_classes+complex_template_classes)
definitions = get_definitions(classes)
template_definitions = get_template_definitions(template_classes, False)
complex_template_definitions = get_template_definitions(complex_template_classes, True)
struct = get_struct(classes+template_classes+complex_template_classes)
substitutes = {'includes': includes,
'definitions' :definitions,
'template_definitions' : template_definitions,
'complex_template_definitions' : complex_template_definitions,
'struct' : struct
}
write_templated_file(TEMPL_FILE, substitutes)
| [
"os.path.join",
"os.walk"
] | [((2195, 2207), 'os.walk', 'os.walk', (['"""."""'], {}), "('.')\n", (2202, 2207), False, 'import os\n'), ((2271, 2292), 'os.path.join', 'os.path.join', (['root', 'f'], {}), '(root, f)\n', (2283, 2292), False, 'import os\n')] |
"""Main farm access."""
from __future__ import annotations
import os
from datetime import datetime
from typing import Dict, Iterable, Iterator, List, Type, Union
from farmos_ext.area import Area
from farmos_ext.asset import Asset, Equipment, Planting
from farmos_ext.log import (Activity, Birth, Harvest, Input, Log, Maintenance,
Medical, Observation, Purchase, Sale, Seeding,
SoilTest, Transplanting)
from farmos_ext.others import Content, Quantity
from farmos_ext.term import Crop, CropFamily, Season, Term, Unit
from farmOS import farmOS # pylint: disable=wrong-import-order
from farmOS.client import BaseAPI # pylint: disable=wrong-import-order
class FarmTypeMissingError(Exception):
pass
def farm():
"""Access to farm with provided credentials."""
return Farm()
class FileAPI(BaseAPI):
def __init__(self, session):
# Define 'log' as the farmOS API entity endpoint
super().__init__(session=session, entity_type='file')
# pylint: disable=too-many-public-methods
class Farm(farmOS):
def __init__(self, local_resources="./resources"):
self._host = None
self._user = None
self._pass = None
self.local_resources = local_resources
if os.path.exists("farmos.cfg"):
with open('farmos.cfg') as cfg:
for line in cfg.readlines():
if line.startswith("HOST"):
self._host = line[line.index("=")+1:].strip()
if line.startswith("USER"):
self._user = line[line.index("=")+1:].strip()
if line.startswith("PASS"):
self._pass = line[line.index("=")+1:].strip()
if not self._host:
raise KeyError("HOST key is not defined in farmos.cfg")
if not self._user:
raise KeyError("USER key is not defined in farmos.cfg")
if not self._pass:
raise KeyError("PASS key is not defined in farmos.cfg")
super().__init__(self._host)
self._token = self.authorize(self._user, self._pass)
else:
raise Exception('farmos.cfg not found.')
self.file = FileAPI(self.session)
def assets(self,
filters: Union[Dict, List[Dict], int, str] = None,
asset_class: Type[Asset] = Asset) -> Iterator[Type[Asset]]:
if isinstance(filters, list):
for filt in filters:
for asset in self.asset.get(filt)['list']:
yield asset_class(self, keys=asset)
else:
for asset in self.asset.get(filters)['list']:
yield asset_class(self, keys=asset)
# def _get_assets(self, items: List[Dict], obj_class):
# retitems = []
# for item in items:
# rets = self.asset.get(item['id'])
# if 'list' in rets:
# self.extract(rets, obj_class)
# else:
# retitems.append(obj_class(self, rets))
# return retitems
def logs(self,
filters: Union[Dict, List[Dict], int, str] = None,
log_class: Type[Log] = Log) -> Iterator[Type[Log]]:
if isinstance(filters, list):
for filt in filters:
for log in self.log.get(filt):
yield log_class(self, keys=log)
elif isinstance(filters, int):
yield log_class(self, keys=self.log.get(filters))
else:
for log in self.log.get(filters):
yield log_class(self, keys=log)
def terms(self, filters: Union[str, List[Dict], Dict] = None,
term_class: Type[Term] = Term) -> Iterator[Type[Term]]:
if isinstance(filters, list):
for item in filters:
for term in self.term.get({"tid": item['id']})['list']:
yield term_class(self, keys=term)
else:
rets = self.term.get(filters)
yield term_class(self, keys=rets)
def areas(self, filters: Union[Dict, List[Dict], int, str] = None) -> Iterator[Area]:
if isinstance(filters, list):
for filt in filters:
for area in self.area.get(filt)['list']:
yield Area(self, keys=area)
else:
for area in self.area.get(filters)['list']:
yield Area(self, keys=area)
def _create_log(self, name: str, date: datetime, category: str, fields: Dict, done=False):
data = {
"name": name,
"timestamp": str(int(datetime.timestamp(date))),
"log_category": [{
"name": category
}],
"type": "farm_observation"
}
data.update(fields)
if 'done' not in data:
data['done'] = '1' if done else '0'
ret = self.log.send(data)
return ret
@property
def content(self) -> Content:
return Content(self, keys=self.info())
@property
def seasons(self) -> Iterator[Season]:
for season in self.term.get("farm_season")['list']:
yield Season(self, season)
@property
def crop_families(self) -> Iterator[CropFamily]:
for fam in self.term.get("farm_crop_families")['list']:
yield CropFamily(self, keys=fam)
@property
def crops(self) -> Iterator[Crop]:
for crop in self.term.get("farm_crops")['list']:
yield Crop(self, crop)
def equipment(self, filters: Dict = None) -> Iterable[Equipment]:
if not filters:
filters = {'type': 'equipment'}
else:
filters.update({'type': 'equipment'})
return self.assets(filters, Equipment)
def plantings(self, filters: Dict = None) -> Iterable[Planting]:
if not filters:
filters = {'type': 'planting'}
else:
filters.update({'type': 'planting'})
return self.assets(filters, Planting)
@property
def units(self) -> Iterable[Unit]:
for unit in self.term.get('farm_quantity_units')['list']:
yield Unit(self, unit)
def harvests(self, filters: Dict = None) -> Iterable[Harvest]:
if 'farm_harvests' in self.content.resources['log']:
if not filters:
filters = {'type': 'farm_harvest'}
else:
filters.update({'type': 'farm_harvest'})
return self.logs(filters, Harvest)
else:
raise FarmTypeMissingError("Harvest logs not supported.")
def seedings(self, filters: Dict = None) -> Iterable[Seeding]:
if 'farm_seedings' in self.content.resources['log']:
if not filters:
filters = {'type': 'farm_seeding'}
else:
filters.update({'type': 'farm_seeding'})
return self.logs(filters, Seeding)
else:
raise FarmTypeMissingError("Seeding logs not supported.")
def transplants(self, filters: Dict = None) -> Iterable[Transplanting]:
if 'farm_transplanting' in self.content.resources['log']:
if not filters:
filters = {'type': 'farm_transplanting'}
else:
filters.update({'type': 'farm_transplanting'})
return self.logs(filters, Transplanting)
else:
raise FarmTypeMissingError("Transplanting logs not supported.")
def observations(self, filters: Dict = None) -> Iterable[Observation]:
if 'farm_observation' in self.content.resources['log']:
if not filters:
filters = {'type': 'farm_observation'}
else:
filters.update({'type': 'farm_observation'})
return self.logs(filters, Observation)
else:
raise FarmTypeMissingError("Observation logs not supported.")
def maintenances(self, filters: Dict = None) -> Iterator[Maintenance]:
if 'farm_maintenance' in self.content.resources['log']:
if not filters:
filters = {'type': 'farm_maintenance'}
else:
filters.update({'type': 'farm_maintenance'})
return self.logs(filters, Maintenance)
else:
raise FarmTypeMissingError("Maintenance logs not supported.")
def purchases(self, filters: Dict = None) -> Iterator[Purchase]:
if 'farm_purchase' in self.content.resources['log']:
if not filters:
filters = {'type': 'farm_purchase'}
else:
filters.update({'type': 'farm_purchase'})
return self.logs(filters, Purchase)
else:
raise FarmTypeMissingError("Purchase logs not supported.")
def sales(self, filters: Dict = None) -> Iterator[Sale]:
if 'farm_sale' in self.content.resources['log']:
if not filters:
filters = {'type': 'farm_sale'}
else:
filters.update({'type': 'farm_sale'})
return self.logs(filters, Sale)
else:
raise FarmTypeMissingError("Sale logs not supported.")
def births(self, filters: Dict = None) -> Iterator[Birth]:
if 'farm_birth' in self.content.resources['log']:
if not filters:
filters = {'type': 'farm_birth'}
else:
filters.update({'type': 'farm_birth'})
return self.logs(filters, Birth)
else:
raise FarmTypeMissingError("Birth logs not supported.")
def inputs(self, filters: Dict = None) -> Iterator[Input]:
if 'farm_input' in self.content.resources['input']:
if not filters:
filters = {'type': 'farm_input'}
else:
filters.update({'type': 'farm_input'})
return self.logs(filters, Input)
else:
raise FarmTypeMissingError("Input logs not supported.")
def soil_tests(self, filters: Dict = None) -> Iterator[SoilTest]:
if 'farm_soil_test' in self.content.resources['log']:
if not filters:
filters = {'type': 'farm_soil_test'}
else:
filters.update({'type': 'farm_soil_test'})
return self.logs(filters, SoilTest)
else:
raise FarmTypeMissingError("Soil test logs not supported.")
def activities(self, filters: Dict = None) -> Iterator[Activity]:
if 'farm_activity' in self.content.resources['log']:
if not filters:
filters = {'type': 'farm_activity'}
else:
filters.update({'type': 'farm_activity'})
return self.logs(filters, Activity)
else:
raise FarmTypeMissingError("Activity logs not supported.")
def medicals(self, filters: Dict = None) -> Iterator[Medical]:
if 'farm_medical' in self.content.resources['log']:
if not filters:
filters = {'type': 'farm_medical'}
else:
filters.update({'type': 'farm_medical'})
return self.logs(filters, Medical)
else:
raise FarmTypeMissingError("Medical logs are not supported.")
def create_planting(self, crop: Crop, season: str, location: str) -> Planting:
ret = self.asset.send({
"name": "{} {} {}".format(season, location, crop.name),
"type": "planting",
"crop": [
{
"id": crop.tid
}
],
"season": [{"name": season}]
})
plant = Planting(self, keys=ret)
return plant
def create_seeding(self, planting: Planting, location: Area, crop: Crop,
date: datetime, seeds: int, source=None, done=False) -> Seeding:
name = "Seed {} {} {}".format(date.year, location.name, crop.name)
fields = {
"type": "farm_seeding",
"asset": [
{
"id": planting.id,
"resource": "taxonomy_term"
}
],
"seed_source": source,
"movement": {
"area": [
{
"id": location.tid,
"resource": "taxonomy_term"
}
]
},
"quantity": [
{
"measure": "count",
"value": str(seeds),
"unit": {
'name': 'Seeds',
"resource": "taxonomy_term"
}
}
]
}
ret = self._create_log(name, date, 'Plantings', fields, done=done)
return Seeding(self, keys=ret)
def create_transplant(self, planting: Planting, location: Area, date: datetime, fields=None, done=False):
name = "Transplant {}".format(planting.name)
data = {
"type": "farm_transplanting",
"movement": {
"area": [
{
"id": location.tid,
"resource": "taxonomy_term"
}
]
},
"asset": [
{
"id": planting.id,
"resource": "taxonomy_term"
}
]
}
if fields:
data.update(fields)
ret = self._create_log(name, date, 'Plantings', data, done=done)
return Transplanting(self, ret)
def create_harvest(self, planting: Planting, date: datetime, quantities: List[Quantity], done=False):
name = "Harvest {} {}".format(date.year, planting.crop[0]['name'])
data = {
"type": "farm_harvest",
"asset": [{
"id": planting.id,
"resource": "taxonomy_term"
}]
}
if quantities:
data["quantity"] = []
for quantity in quantities:
data["quantity"].append(quantity.to_dict())
ret = self._create_log(name, date, 'Plantings', data, done=done)
return Harvest(self, ret)
def create_log(self, name: str, date: datetime, category: str, fields: Dict, done=False):
return Log(self, self._create_log(name, date, category, fields, done))
| [
"farmos_ext.term.CropFamily",
"os.path.exists",
"farmos_ext.term.Season",
"farmos_ext.term.Crop",
"farmos_ext.area.Area",
"farmos_ext.log.Seeding",
"farmos_ext.asset.Planting",
"farmos_ext.log.Transplanting",
"datetime.datetime.timestamp",
"farmos_ext.term.Unit",
"farmos_ext.log.Harvest"
] | [((1280, 1308), 'os.path.exists', 'os.path.exists', (['"""farmos.cfg"""'], {}), "('farmos.cfg')\n", (1294, 1308), False, 'import os\n'), ((11585, 11609), 'farmos_ext.asset.Planting', 'Planting', (['self'], {'keys': 'ret'}), '(self, keys=ret)\n', (11593, 11609), False, 'from farmos_ext.asset import Asset, Equipment, Planting\n'), ((12750, 12773), 'farmos_ext.log.Seeding', 'Seeding', (['self'], {'keys': 'ret'}), '(self, keys=ret)\n', (12757, 12773), False, 'from farmos_ext.log import Activity, Birth, Harvest, Input, Log, Maintenance, Medical, Observation, Purchase, Sale, Seeding, SoilTest, Transplanting\n'), ((13531, 13555), 'farmos_ext.log.Transplanting', 'Transplanting', (['self', 'ret'], {}), '(self, ret)\n', (13544, 13555), False, 'from farmos_ext.log import Activity, Birth, Harvest, Input, Log, Maintenance, Medical, Observation, Purchase, Sale, Seeding, SoilTest, Transplanting\n'), ((14166, 14184), 'farmos_ext.log.Harvest', 'Harvest', (['self', 'ret'], {}), '(self, ret)\n', (14173, 14184), False, 'from farmos_ext.log import Activity, Birth, Harvest, Input, Log, Maintenance, Medical, Observation, Purchase, Sale, Seeding, SoilTest, Transplanting\n'), ((5161, 5181), 'farmos_ext.term.Season', 'Season', (['self', 'season'], {}), '(self, season)\n', (5167, 5181), False, 'from farmos_ext.term import Crop, CropFamily, Season, Term, Unit\n'), ((5332, 5358), 'farmos_ext.term.CropFamily', 'CropFamily', (['self'], {'keys': 'fam'}), '(self, keys=fam)\n', (5342, 5358), False, 'from farmos_ext.term import Crop, CropFamily, Season, Term, Unit\n'), ((5488, 5504), 'farmos_ext.term.Crop', 'Crop', (['self', 'crop'], {}), '(self, crop)\n', (5492, 5504), False, 'from farmos_ext.term import Crop, CropFamily, Season, Term, Unit\n'), ((6139, 6155), 'farmos_ext.term.Unit', 'Unit', (['self', 'unit'], {}), '(self, unit)\n', (6143, 6155), False, 'from farmos_ext.term import Crop, CropFamily, Season, Term, Unit\n'), ((4418, 4439), 'farmos_ext.area.Area', 'Area', (['self'], {'keys': 'area'}), '(self, keys=area)\n', (4422, 4439), False, 'from farmos_ext.area import Area\n'), ((4612, 4636), 'datetime.datetime.timestamp', 'datetime.timestamp', (['date'], {}), '(date)\n', (4630, 4636), False, 'from datetime import datetime\n'), ((4304, 4325), 'farmos_ext.area.Area', 'Area', (['self'], {'keys': 'area'}), '(self, keys=area)\n', (4308, 4325), False, 'from farmos_ext.area import Area\n')] |
from telegram.ext import *
from telegram import *
import time
def start(update, context):
context.bot.send_message(chat_id=update.effective_chat.id, text="Hi 👋 I'm Rinkoglionito and I'm here because @OnyyTheBest had nothing to do ;-;. \n that said do the /cmds command to see the available commands")
chat_user_id = update.message.from_user.id
more_lines = [str(chat_user_id)+"\n"]
if str(chat_user_id)+"\n" not in open("users.txt", 'r'):
with open('users.txt', 'a') as f:
f.writelines('\n'.join(more_lines))
f.close()
else:
return
def YAAA(update: Update, context: CallbackContext):
context.bot.send_message(chat_id=update.effective_chat.id, text="Why you don't say YAAA?")
context.bot.send_video(chat_id=update.effective_chat.id, video="https://onyymexicancat.github.io/RinkoglionitoBot/mediafile/video/meme/01.mp4")
def BASTARDI(update: Update, context: CallbackContext):
context.bot.send_message(chat_id=update.effective_chat.id, text="🗣 Bastardi, chiamo da 🎹 <NAME> 🙉🌸, sono un 👌 assassino di 🅱 meridionali. Vi 💰 ammazzo tutti bastardi pezzi di 🅱 merda 🤬. Porcodio a tutti i 👥 napoletani romani di 👉 merda 🤬 stronzi, siete 🔥 della gente 👨👩👧👦 che ✔ viene schiacciata come 🚌 topi 💰 maledetti stronzi figli di 🅱 una 👌 cagna in calore. Io 🅱 vi 💰 sp ☠.. io 🅱 vi 💰 spacco le 🅰 fighe, le 🅱 ovaie a tutte le 🅱 vostre donne sporche. venite su 🅱, puttane, che ✔ vi 💰 apro lo 💜 sterno e 🇹 vi 💰 mangio il 🏙 cuore e 🇹 poi ve lo 💜 cago nella figa, brutte merde che ✔ non ❌ siete 🔥 altro, sono un 👦👲🏽👌 assassino di 🅱 fkghe.")
context.bot.send_audio(chat_id=update.effective_chat.id, audio="https://onyymexicancat.github.io/RinkoglionitoBot/mediafile/audio/meme/01.mp3")
def CMDS(update: Update, context: CallbackContext):
context.bot.send_message(chat_id=update.effective_chat.id, text="comandi attualmente attivi nel bot sono i seguenti \n /Start (Avvia il Bot) \n /BASTARDI (Bastardi chiamo da reggio emilia) \n /YAAA (YAAA KID) \n /CHK (VIP Only CC Checker)\n /vip (pay me xD)\n")
def oldupdate(update: Update, context: CallbackContext):
context.bot.send_message(chat_id=update.effective_chat.id, text="""
-- ✍️@OnyyTheBest --
""", parse_mode="html")
def update(update: Update, context: CallbackContext):
context.bot.send_message(chat_id=update.effective_chat.id, text="""
-- ✍️@OnyyTheBest --
""", parse_mode="html")
def commandnotfount(update, context):
try:
bot_msg = context.bot.send_message(chat_id=update.message.chat_id, text="<b>COMANDO NON TROVATO!</b> usa il comando /cmds per trovare il comando che stai cercando", parse_mode="html")
time.sleep(10)
context.bot.delete_message(chat_id=update.message.chat_id, message_id=bot_msg.message_id)
except:
pass
def bcast(update: update,context: CallbackContext):
if update.effective_chat.id == 476263382:
if context.args == []:
context.bot.send_message(update.effective_chat.id, text="<b>Please enter the message you want to broadcast to Bot users!</b>", parse_mode="html")
else:
porco = ''
for char in context.args:
if char !="[" + "'" + "]":
porco += char
ciccio = open("users.txt", 'r')
for line in ciccio:
content = line
context.bot.send_message(chat_id=content, text=porco)
update.message.reply_text(text="<b>DONE!</b>", parse_mode="html")
else:
context.bot.send_message(update.effective_chat.id, text="<b>NO PERMS</b>", parse_mode="html") | [
"time.sleep"
] | [((2721, 2735), 'time.sleep', 'time.sleep', (['(10)'], {}), '(10)\n', (2731, 2735), False, 'import time\n')] |