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4d76d12c7c683eeb8987fa016591c981bc9da2f8 | b58f43f49559265584d0bac330993d6e68729499 | /FixValueStopLoss.py | 64d14ba8fc41aaa9ab9a6d49faf392114dd0a4a6 | [] | no_license | xiehai1983/MyPyLib | c096c3c60837db4b34a26aed88794a10a0f6b1e8 | 9d8e18443dac42325bb11525112deb59eb49ab9b | refs/heads/master | 2021-09-21T22:23:38.478730 | 2018-09-01T16:29:21 | 2018-09-01T16:29:21 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,482 | py | # -*- coding: utf-8 -*-
"""
策略本身不带出场，全靠止盈止损出场，所以在止损取数时没有下限
从进场点开始，while True向下取数（还要判断是否达到原始数据下限），如果达到止损或者止盈点，就break出来
使用1min的high和low来模拟tick，1min数据不做阴阳线预处理，如果1min同时满足止盈和止损，则取止损作为结果
"""
import pandas as pd
import DATA_CONSTANTS as DC
import numpy as np
import os
import ResultStatistics as RS
import multiprocessing
def fix_value_stop_loss(strategyName, symbolInfo, K_MIN, setname, bar1mdic, barxmdic, result_para_dic, spr, slr, tofolder, indexcols):
print ("fix_value_stop_loss: setname:%s, spr%.1f slr%.1f" % (setname, spr, slr))
positionRatio = result_para_dic['positionRatio']
initialCash = result_para_dic['initialCash']
symbol = symbolInfo.domain_symbol
bt_folder = "%s %d backtesting\\" % (symbol, K_MIN)
oprdf = pd.read_csv(bt_folder + strategyName + ' ' + symbol + str(K_MIN) + ' ' + setname + ' result.csv')
symbolDomainDic = symbolInfo.amendSymbolDomainDicByOpr(oprdf)
bar1m = DC.getDomainbarByDomainSymbol(symbolInfo.getSymbolList(), bar1mdic, symbolDomainDic)
barxm = DC.getDomainbarByDomainSymbol(symbolInfo.getSymbolList(), barxmdic, symbolDomainDic)
#bar1m.set_index('utc_time', inplace=True)
barxm.set_index('utc_time', inplace=True)
oprdf['new_closeprice'] = oprdf['closeprice']
oprdf['new_closetime'] = oprdf['closetime']
oprdf['new_closeindex'] = oprdf['closeindex']
oprdf['new_closeutc'] = oprdf['closeutc']
oprdf['max_opr_gain'] = 0 # 本次操作期间的最大收益
oprdf['min_opr_gain'] = 0 # 本次操作期间的最小收益
oprdf['max_dd'] = 0
oprnum = oprdf.shape[0]
pricetick = symbolInfo.getPriceTick()
worknum = 0
for i in range(oprnum):
opr = oprdf.iloc[i]
#startutc = (barxm.loc[barxm['utc_time'] == opr.openutc]).iloc[0].utc_endtime - 60 # 从开仓的10m线结束后开始
#endutc = (barxm.loc[barxm['utc_time'] == opr.closeutc]).iloc[0].utc_endtime # 一直到平仓的10m线结束
openutc = opr.openutc
openprice = opr.openprice
startutc = barxm.loc[openutc].utc_endtime - 60
#spv = barxm.iloc[openutc].ATR * spr
#slv = barxm.iloc[openutc].ATR * slr
spv = 5 # 固定取值
slv = 8 # 固定取值
oprtype = opr.tradetype
openprice = opr.openprice
start_index_1m = bar1m[bar1m['utc_time'].isin([startutc])].index[0] # 开仓位置在1m数据中的index,要从下一根开始算止盈止损
while True:
start_index_1m += 1
high_1m = bar1m.loc[start_index_1m,'high']
low_1m = bar1m.loc[start_index_1m].low
if oprtype == 1:
if low_1m <= (openprice - slv):
# 最低值达到止损门限
oprdf.ix[i, 'new_closeprice'] = openprice - slv
oprdf.ix[i, 'new_closetime'] = bar1m.iloc[start_index_1m].strtime
oprdf.ix[i, 'new_closeindex'] = start_index_1m
oprdf.ix[i, 'new_closeutc'] = bar1m.iloc[start_index_1m].utc_time
break
elif high_1m >= (openprice + spv):
# 最大值达到止盈门限
oprdf.ix[i, 'new_closeprice'] = openprice + spv
oprdf.ix[i, 'new_closetime'] = bar1m.iloc[start_index_1m].strtime
oprdf.ix[i, 'new_closeindex'] = start_index_1m
oprdf.ix[i, 'new_closeutc'] = bar1m.iloc[start_index_1m].utc_time
break
elif oprtype == -1:
if high_1m >= (openprice + slv):
# 最大值达到止损门限
oprdf.ix[i, 'new_closeprice'] = openprice + slv
oprdf.ix[i, 'new_closetime'] = bar1m.iloc[start_index_1m].strtime
oprdf.ix[i, 'new_closeindex'] = start_index_1m
oprdf.ix[i, 'new_closeutc'] = bar1m.iloc[start_index_1m].utc_time
break
elif low_1m <= (openprice - spv):
# 最大值达到止盈门限
oprdf.ix[i, 'new_closeprice'] = openprice - spv
oprdf.ix[i, 'new_closetime'] = bar1m.iloc[start_index_1m].strtime
oprdf.ix[i, 'new_closeindex'] = start_index_1m
oprdf.ix[i, 'new_closeutc'] = bar1m.iloc[start_index_1m].utc_time
break
else:
# 被去极值的操作，oprtype为0,不做止损操作
pass
slip = symbolInfo.getSlip()
# 2017-12-08:加入滑点
oprdf['new_ret'] = ((oprdf['new_closeprice'] - oprdf['openprice']) * oprdf['tradetype']) - slip
oprdf['new_ret_r'] = oprdf['new_ret'] / oprdf['openprice']
# 去极值：在parallel的去极值结果上，把极值的new_ret和new_ret_r值0
if result_para_dic['remove_polar_switch']:
oprdf.loc[oprdf['tradetype']==0, 'new_ret'] = 0
oprdf.loc[oprdf['tradetype']==0, 'new_ret_r'] = 0
oprdf['new_commission_fee'], oprdf['new_per earn'], oprdf['new_own cash'], oprdf['new_hands'] = RS.calcResult(oprdf,
symbolInfo,
initialCash,
positionRatio, ret_col='new_ret')
# 保存新的result文档
oprdf.to_csv(tofolder + strategyName + ' ' + symbol + str(K_MIN) + ' ' + setname + ' resultDSL_by_tick.csv', index=False)
olddailydf = pd.read_csv(strategyName + ' ' + symbol + str(K_MIN) + ' ' + setname + ' dailyresult.csv', index_col='date')
# 计算统计结果
oldr = RS.getStatisticsResult(oprdf, False, indexcols, olddailydf)
barxm.reset_index(drop=False, inplace=True)
dailyK = DC.generatDailyClose(barxm)
dR = RS.dailyReturn(symbolInfo, oprdf, dailyK, initialCash) # 计算生成每日结果
dR.calDailyResult()
dR.dailyClose.to_csv((tofolder + strategyName + ' ' + symbol + str(K_MIN) + ' ' + setname + ' dailyresultDSL_by_tick.csv'))
newr = RS.getStatisticsResult(oprdf, True, indexcols, dR.dailyClose)
del oprdf
# return [setname,slTarget,worknum,oldendcash,oldAnnual,oldSharpe,oldDrawBack,oldSR,newendcash,newAnnual,newSharpe,newDrawBack,newSR,max_single_loss_rate]
print newr
return [setname, spr, slr, worknum] + oldr + newr
if __name__ == '__main__':
import datetime
# 参数配置
exchange_id = 'SHFE'
sec_id = 'RB'
symbol = '.'.join([exchange_id, sec_id])
K_MIN = 600
topN = 5000
pricetick = DC.getPriceTick(symbol)
slip = pricetick
starttime = '2016-01-01'
endtime = '2018-03-31'
# 优化参数
stoplossStep = -0.002
# stoplossList = np.arange(-0.022, -0.042, stoplossStep)
stoplossList = [-0.022]
# 文件路径
currentpath = DC.getCurrentPath()
bar1m = DC.getBarData(symbol=symbol, K_MIN=60, starttime=starttime + ' 00:00:00', endtime=endtime + ' 00:00:00')
barxm = DC.getBarData(symbol=symbol, K_MIN=K_MIN, starttime=starttime + ' 00:00:00', endtime=endtime + ' 00:00:00')
# bar1m计算longHigh,longLow,shortHigh,shortLow
bar1m['longHigh'] = bar1m['high']
bar1m['shortHigh'] = bar1m['high']
bar1m['longLow'] = bar1m['low']
bar1m['shortLow'] = bar1m['low']
bar1m['highshift1'] = bar1m['high'].shift(1).fillna(0)
bar1m['lowshift1'] = bar1m['low'].shift(1).fillna(0)
bar1m.loc[bar1m['open'] < bar1m['close'], 'longHigh'] = bar1m['highshift1']
bar1m.loc[bar1m['open'] > bar1m['close'], 'shortLow'] = bar1m['lowshift1']
timestart = datetime.datetime.now()
dslCal(symbol, K_MIN, 'Set0 MS3 ML8 KN6 DN6', bar1m, barxm, pricetick, slip, -0.022, currentpath + '\\')
timedsl = timestart - datetime.datetime.now()
timestart = datetime.datetime.now()
fastDslCal(symbol, K_MIN, 'Set0 MS3 ML8 KN6 DN6', bar1m, barxm, pricetick, slip, -0.022, currentpath + '\\')
timefast = timestart - datetime.datetime.now()
print "time dsl cost:", timedsl
print "time fast cost:", timefast
print 'fast delta:', timefast - timedsl
| [
"smartgang@126.com"
] | smartgang@126.com |
1f974b0b8b3caa1deb01b81c53090810a9f1b06a | 33119d4e3ec1abd3b8a3934b90ede748669f87c9 | /armi/operators/settingsValidation.py | 52003d7dc9e91f8893cca94316b6d69ed5d1d42b | [
"Apache-2.0",
"LicenseRef-scancode-free-unknown"
] | permissive | sammiller11235/armi | 723506a47f292f1a83e9d3c35812d080e9acbbdc | df9bdb4d8ef3131806190e0d18710c6a7df73961 | refs/heads/master | 2021-07-19T12:00:07.397525 | 2020-03-19T17:36:58 | 2020-03-19T17:36:58 | 219,067,927 | 0 | 0 | Apache-2.0 | 2020-03-19T17:37:00 | 2019-11-01T21:52:13 | null | UTF-8 | Python | false | false | 24,554 | py | # Copyright 2019 TerraPower, LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
A system to check user settings for validity and provide users with meaningful suggestions to fix.
This allows developers to specify a rich set of rules and suggestions for user settings.
These then pop up during initialization of a run, either on the command line or as
dialogues in the GUI. They say things like: "Your ___ setting has the value ___, which
is impossible. Would you like to switch to ___?"
"""
import re
import os
import armi
from armi import runLog
from armi.localization import exceptions
from armi import utils
from armi.utils import pathTools
from armi.nucDirectory import nuclideBases
from armi.reactor import geometry
from armi.physics import neutronics
from armi import settings
from armi.utils import directoryChangers
from armi.settings.fwSettings import globalSettings
class Query:
"""An individual query."""
def __init__(self, condition, statement, question, correction):
"""
Construct a query.
Parameters
----------
condition : callable
A callable that returns True or False. If True,
then the query activates its question and potential correction.
statement : str
A statement of the problem indicated by a True condition
question : str
A question asking the user for confirmation of the proposed
fix.
correction : callable
A callable that when called fixes the situation. See
:py:meth:`Inspector.NO_ACTION` for no-ops.
"""
self.condition = condition
self.statement = statement
self.question = question
self.correction = correction
# True if the query is `passed` and does not result in an immediate failure
self._passed = False
self._corrected = False
self.autoResolved = True
def __repr__(self):
# Add representation so that it's possible to identify which one
# is being referred to when there are errors.
return "<Query: {}>".format(self.statement)
def __bool__(self):
try:
return bool(self.condition())
except TypeError:
runLog.error(
f"Invalid setting validation query. Update validator for: {self})"
)
raise
__nonzero__ = __bool__ # Python2 compatibility
def isCorrective(self):
return self.correction is not Inspector.NO_ACTION
def resolve(self):
"""Standard i/o prompt for resolution of an individual query"""
if armi.MPI_RANK != 0:
return
if self.condition():
try:
if self.isCorrective():
try:
make_correction = runLog.prompt(
"INSPECTOR: " + self.statement,
self.question,
"YES_NO",
"NO_DEFAULT",
"CANCEL",
)
if make_correction:
self.correction()
self._corrected = True
else:
self._passed = True
except exceptions.RunLogPromptCancel:
raise exceptions.InputInspectionDiscontinued()
else:
try:
continue_submission = runLog.prompt(
"INSPECTOR: " + self.statement,
"Continue?",
"YES_NO",
"NO_DEFAULT",
"CANCEL",
)
if not continue_submission:
raise exceptions.InputInspectionDiscontinued()
except exceptions.RunLogPromptCancel:
raise exceptions.InputInspectionDiscontinued()
except exceptions.RunLogPromptUnresolvable:
self.autoResolved = False
self._passed = True
class Inspector:
"""
This manages queries which assert certain states of the data model, generally presenting
themselves to the user, offering information on the potential problem, a question
and the action to take on an affirmative and negative answer from the user.
In practice very useful for making sure setting values are as intended and without
bad interplay with one another.
One Inspector will contain multiple Queries and be associated directly with an
:py:class:`~armi.operators.operator.Operator`.
"""
@staticmethod
def NO_ACTION(): # pylint: disable=invalid-name
"""Convenience callable used to generate Queries that can't be easily auto-resolved."""
return None
def __init__(self, cs):
"""
Construct an inspector.
Parameters
----------
cs : Settings
"""
self.queries = []
self.cs = cs
self.geomType = None
self.coreSymmetry = None
self._inspectBlueprints()
self._setGeomType()
self._inspectSettings()
# Gather and attach validators from all plugins
# This runs on all registered plugins, not just active ones.
pluginQueries = armi.getPluginManagerOrFail().hook.defineSettingsValidators(
inspector=self
)
for queries in pluginQueries:
self.queries.extend(queries)
def run(self, cs=None):
"""
Run through each query and deal with it if possible.
Returns
-------
correctionsMade : bool
Whether or not anything was updated.
Raises
------
exceptions.InputInspectionMalformed
When a programming error causes queries to loop.
"""
if armi.MPI_RANK != 0:
return False
# the following attribute changes will alter what the queries investigate when
# resolved
correctionsMade = False
self.cs = cs or self.cs
runLog.debug("{} executing queries.".format(self.__class__.__name__))
if not any(self.queries):
runLog.debug(
"{} found no problems with the current state.".format(
self.__class__.__name__
)
)
else:
for query in self.queries:
query.resolve()
if query._corrected: # pylint: disable=protected-access
correctionsMade = True
issues = [
query
for query in self.queries
if query
and (
query.isCorrective() and not query._passed
) # pylint: disable=protected-access
]
if any(issues):
# something isn't resolved or was unresolved by changes
raise exceptions.InputInspectionMalformed(
"The input inspection did not resolve all queries, "
"some issues are creating cyclic resolutions: {}".format(issues)
)
runLog.debug("{} has finished querying.".format(self.__class__.__name__))
return correctionsMade
def addQuery(self, condition, statement, question, correction):
"""Convenience method, query must be resolved, else run fails"""
if not callable(correction):
raise ValueError(
'Query for "{}" malformed. Expecting callable.'.format(statement)
)
self.queries.append(Query(condition, statement, question, correction))
def addQueryBadLocationWillLikelyFail(self, settingName):
"""Add a query indicating the current path for ``settingName`` does not exist and will likely fail."""
self.addQuery(
lambda: not os.path.exists(pathTools.armiAbsPath(self.cs[settingName])),
"Setting {} points to nonexistent location\n{}\nFailure extremely likely".format(
settingName, self.cs[settingName]
),
"",
self.NO_ACTION,
)
def addQueryCurrentSettingMayNotSupportFeatures(self, settingName):
"""Add a query that the current value for ``settingName`` may not support certain features."""
self.addQuery(
lambda: self.cs[settingName] != self.cs.settings[settingName].default,
"{} set as:\n{}\nUsing this location instead of the default location\n{}\n"
"may not support certain functions.".format(
settingName, self.cs[settingName], self.cs.settings[settingName].default
),
"Revert to default location?",
lambda: self._assignCS(settingName, self.cs.settings[settingName].default),
)
def _assignCS(self, key, value):
"""Lambda assignment workaround"""
# this type of assignment works, but be mindful of
# scoping when trying different methods
self.cs[key] = value
def _raise(self): # pylint: disable=no-self-use
raise exceptions.InputInspectionDiscontinued(
"Input inspection has been interrupted."
)
def _inspectBlueprints(self):
"""Blueprints early error detection and old format conversions."""
from armi.reactor import blueprints
self.addQuery(
lambda: not self.cs["loadingFile"],
"No blueprints file loaded. Run will probably fail.",
"",
self.NO_ACTION,
)
self.addQuery(
lambda: not self._csRelativePathExists(self.cs["loadingFile"]),
"Blueprints file {} not found. Run will fail.".format(
self.cs["loadingFile"]
),
"",
self.NO_ACTION,
)
def _csRelativePathExists(self, filename):
csRelativePath = self._csRelativePath(filename)
return os.path.exists(csRelativePath) and os.path.isfile(csRelativePath)
def _csRelativePath(self, filename):
return os.path.join(self.cs.inputDirectory, filename)
def _setGeomType(self):
if self.cs["geomFile"]:
with directoryChangers.DirectoryChanger(self.cs.inputDirectory):
geom = geometry.SystemLayoutInput()
geom.readGeomFromFile(self.cs["geomFile"])
self.geomType, self.coreSymmetry = geom.geomType, geom.symmetry
def _inspectSettings(self):
"""Check settings for inconsistencies."""
# import here to avoid cyclic issues
from armi import operators
self.addQuery(
lambda: self.cs.path.endswith(".xml"),
"Your settings were loaded from a XML file. These are being converted to yaml files.",
"Would you like to auto-convert it to YAML?",
lambda: settings.convertSettingsFromXMLToYaml(self.cs),
)
self.addQueryBadLocationWillLikelyFail("operatorLocation")
self.addQuery(
lambda: self.cs["outputFileExtension"] == "pdf" and self.cs["genReports"],
"Output files of '.pdf' format are not supported by the reporting HTML generator. '.pdf' "
"images will not be included.",
"Switch to '.png'?",
lambda: self._assignCS("outputFileExtension", "png"),
)
self.addQuery(
lambda: (
self.cs[globalSettings.CONF_ZONING_STRATEGY] == "manual"
and not self.cs["zoneDefinitions"]
),
"`manual` zoningStrategy requires that `zoneDefinitions` setting be defined. Run will have "
"no zones.",
"",
self.NO_ACTION,
)
self.addQuery(
lambda: self.cs["skipCycles"] > 0 and not self.cs["reloadDBName"],
"You have chosen to do a restart case without specifying a database to load from. "
"Run will load from output files, if they exist but burnup, etc. will not be updated.",
"",
self.NO_ACTION,
)
self.addQuery(
lambda: self.cs["runType"] != operators.RunTypes.SNAPSHOTS
and self.cs["loadStyle"] == "fromDB"
and self.cs["startCycle"] == 0
and self.cs["startNode"] == 0,
"Starting from cycle 0, and time node 0 was chosen. Restart runs load from "
"the time node just before the restart. There is no time node to load from "
"before cycle 0 node 0. Either switch to the snapshot operator, start from "
"a different time step or load from inputs rather than database as "
"`loadStyle`.",
"",
self.NO_ACTION,
)
self.addQuery(
lambda: self.cs["runType"] == operators.RunTypes.SNAPSHOTS
and not (self.cs["dumpSnapshot"] or self.cs["defaultSnapshots"]),
"The Snapshots operator was specified, but no dump snapshots were chosen."
"Please specify snapshot steps with the `dumpSnapshot` setting.",
"",
self.NO_ACTION,
)
self.addQuery(
lambda: self.cs.caseTitle.lower()
== os.path.splitext(os.path.basename(self.cs["reloadDBName"].lower()))[0],
"Snapshot DB ({0}) and main DB ({1}) cannot have the same name."
"Change name of settings file and resubmit.".format(
self.cs["reloadDBName"], self.cs.caseTitle
),
"",
self.NO_ACTION,
)
self.addQuery(
lambda: self.cs["reloadDBName"] != ""
and not os.path.exists(self.cs["reloadDBName"]),
"Reload database {} does not exist. \nPlease point to an existing DB, "
"or set to empty and load from input.".format(self.cs["reloadDBName"]),
"",
self.NO_ACTION,
)
def _willBeCopiedFrom(fName):
for copyFile in self.cs["copyFilesFrom"]:
if fName == os.path.split(copyFile)[1]:
return True
return False
self.addQuery(
lambda: self.cs["explicitRepeatShuffles"]
and not self._csRelativePathExists(self.cs["explicitRepeatShuffles"])
and not _willBeCopiedFrom(self.cs["explicitRepeatShuffles"]),
"The specified repeat shuffle file `{0}` does not exist, and won't be copied from elsewhere. "
"Run will crash.".format(self.cs["explicitRepeatShuffles"]),
"",
self.NO_ACTION,
)
self.addQuery(
lambda: not self.cs["power"],
"No power level set. You must always start by importing a base settings file.",
"",
self.NO_ACTION,
)
# The gamma cross sections generated for MC2-3 by ANL were done with NJOY with
# P3 scattering. MC2-3 would have to be modified and the gamma cross sections
# re-generated with NJOY for MC2-3 to allow any other scattering order with
# gamma cross sections enabled.
self.addQuery(
lambda: (
"MC2v3" in self.cs["xsKernel"]
and neutronics.gammaXsAreRequested(self.cs)
and self.cs["xsScatteringOrder"] != 3
),
"MC2-3 will crash if a scattering order is not set to 3 when generating gamma XS.",
"Would you like to set the `xsScatteringOrder` to 3?",
lambda: self._assignCS("xsScatteringOrder", 3),
)
self.addQuery(
lambda: self.cs["outputCacheLocation"]
and not os.path.exists(self.cs["outputCacheLocation"]),
"`outputCacheLocation` path {} does not exist. Please specify a location that exists.".format(
self.cs["outputCacheLocation"]
),
"",
self.NO_ACTION,
)
self.addQuery(
lambda: self.cs["numCoupledIterations"] > 0,
"You have {0} coupling iterations selected.".format(
self.cs["numCoupledIterations"]
),
"1 coupling iteration doubles run time (2 triples, etc). Do you want to use 0 instead? ",
lambda: self._assignCS("numCoupledIterations", 0),
)
def _factorsAreValid(factors, maxVal=1.0):
try:
expandedList = utils.expandRepeatedFloats(factors)
except (ValueError, IndexError):
return False
return (
all(0.0 <= val <= maxVal for val in expandedList)
and len(expandedList) == self.cs["nCycles"]
)
self.addQuery(
lambda: self.cs["availabilityFactors"]
and not _factorsAreValid(self.cs["availabilityFactors"]),
"`availabilityFactors` was not set to a list compatible with the number of cycles. "
"Please update input or use constant duration.",
"Use constant availability factor specified in `availabilityFactor` setting?",
lambda: self._assignCS("availabilityFactors", []),
)
self.addQuery(
lambda: self.cs["powerFractions"]
and not _factorsAreValid(self.cs["powerFractions"]),
"`powerFractions` was not set to a compatible list. "
"Please update input or use full power at all cycles.",
"Use full power for all cycles?",
lambda: self._assignCS("powerFractions", []),
)
self.addQuery(
lambda: (
self.cs["cycleLengths"]
and not _factorsAreValid(self.cs["cycleLengths"], maxVal=1e10)
),
"The number of cycles defined in `cycleLengths` is not equal to the number of cycles in "
"the run `nCycles`."
"Please ensure that there is exactly one duration for each cycle in the run or use "
"{} days for all cycles.".format(self.cs["cycleLength"]),
"Use {} days for all cycles?".format(self.cs["cycleLength"]),
lambda: self._assignCS("cycleLengths", []),
)
def _correctCycles():
self.cs["nCycles"] = 1
self.cs["burnSteps"] = 0
self.addQuery(
lambda: not self.cs["cycleLengths"] and self.cs["nCycles"] == 0,
"Cannot run 0 cycles. Set burnSteps to 0 to activate a single time-independent case.",
"Set 1 cycle and 0 burnSteps for single time-independent case?",
_correctCycles,
)
self.addQuery(
lambda: (
self.cs["runType"] == "Standard"
and self.cs["burnSteps"] == 0
and (len(self.cs["cycleLengths"]) > 1 or self.cs["nCycles"] > 1)
),
"Cannot run multi-cycle standard cases with 0 burnSteps per cycle. Please update settings.",
"",
self.NO_ACTION,
)
def decayCyclesHaveInputThatWillBeIgnored():
"""Check if there is any decay-related input that will be ignored."""
try:
powerFracs = utils.expandRepeatedFloats(self.cs["powerFractions"])
availabilities = utils.expandRepeatedFloats(
self.cs["availabilityFactors"]
) or ([self.cs["availabilityFactor"]] * self.cs["nCycles"])
except: # pylint: disable=bare-except
return True
for pf, af in zip(powerFracs, availabilities):
if pf > 0.0 and af == 0.0:
# this will be a full decay step and any power fraction will be ignored. May be ok, but warn.
return True
return False
self.addQuery(
lambda: (
self.cs["cycleLengths"]
and self.cs["powerFractions"]
and decayCyclesHaveInputThatWillBeIgnored()
),
"At least one cycle has a non-zero power fraction but an availability of zero. Please "
"update the input.",
"",
self.NO_ACTION,
)
self.addQuery(
lambda: self.cs["operatorLocation"]
and self.cs["runType"] != operators.RunTypes.STANDARD,
"The `runType` setting is set to `{0}` but there is a `custom operator location` defined".format(
self.cs["runType"]
),
"",
self.NO_ACTION,
)
self.addQuery(
lambda: self.cs["operatorLocation"]
and self.cs["runType"] != operators.RunTypes.STANDARD,
"The `runType` setting is set to `{0}` but there is a `custom operator location` defined".format(
self.cs["runType"]
),
"",
self.NO_ACTION,
)
self.addQuery(
lambda: self.cs["skipCycles"] > 0
and not os.path.exists(self.cs.caseTitle + ".restart.dat"),
"This is a restart case, but the required restart file {0}.restart.dat is not found".format(
self.cs.caseTitle
),
"",
self.NO_ACTION,
)
self.addQuery(
lambda: self.cs["deferredInterfacesCycle"] > self.cs["nCycles"],
"The deferred interface activation cycle exceeds set cycle occurrence. "
"Interfaces will not be activated in this run!",
"",
self.NO_ACTION,
)
self.addQuery(
lambda: (
neutronics.MCNP not in self.cs["neutronicsKernel"]
and self.cs["boundaries"] != neutronics.GENERAL_BC
and self.cs["bcCoefficient"]
),
"General neutronic boundary condition was not selected, but `bcCoefficient` was defined. "
"Please enable `Generalized` neutronic boundary condition or disable `bcCoefficient`.",
"",
self.NO_ACTION,
)
self.addQuery(
lambda: self.cs["geomFile"]
and self.geomType not in geometry.VALID_GEOMETRY_TYPE,
"{} is not a valid geometry Please update geom type on the geom xml file. "
"Valid (case insensitive) geom types are: {}".format(
self.geomType, geometry.VALID_GEOMETRY_TYPE
),
"",
self.NO_ACTION,
)
self.addQuery(
lambda: self.cs["geomFile"]
and self.coreSymmetry not in geometry.VALID_SYMMETRY,
"{} is not a valid symmetry Please update symmetry on the geom xml file. "
"Valid (case insensitive) symmetries are: {}".format(
self.coreSymmetry, geometry.VALID_SYMMETRY
),
"",
self.NO_ACTION,
)
def createQueryRevertBadPathToDefault(inspector, settingName, initialLambda=None):
"""
Return a query to revert a bad path to its default.
Parameters
----------
inspector: Inspector
the inspector who's settings are being queried
settingName: str
name of the setting to inspect
initialLambda: None or callable function
If ``None``, the callable argument for :py:meth:`addQuery` is does the setting's path exist.
If more complicated callable arguments are needed, they can be passed in as the ``initialLambda`` setting.
"""
if initialLambda is None:
initialLambda = lambda: (
not os.path.exists(pathTools.armiAbsPath(inspector.cs[settingName]))
and inspector.cs.settings[settingName].offDefault
) # solution is to revert to default
query = Query(
initialLambda,
"Setting {} points to a nonexistent location:\n{}".format(
settingName, inspector.cs[settingName]
),
"Revert to default location?",
inspector.cs.settings[settingName].revertToDefault,
)
return query
| [
"ntouran@terrapower.com"
] | ntouran@terrapower.com |
756dbc3df7bc68f0e80a6229fbfb208cda5d9bf9 | 874fc4e4eac88ccd037110ce5f48b930c83bb4b3 | /db/actions/add_field.py | bc0d3f11c3b84886a92cb5707566761baf40466e | [] | no_license | persontianshuang/mafter | d0231519d9e82bd0a6aa8e42aa11a5a8a37c407c | 64d9382917bffc16f0422e0fe6e300f48c95c79a | refs/heads/master | 2021-01-23T16:25:34.535702 | 2017-09-27T09:16:03 | 2017-09-27T09:16:03 | 102,740,653 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 390 | py | from db.config import _Db
from db.sentences.sentence import Sentences
from db.subFlow.flow import Flow
# for x in _Db['sentences'].find():
# print(x)
# for x in Sentences.objects.all():
# x.type = 'video'
# x.save()
# for x in Flow.objects.all():
# x.type = 'video'
# x.save()
new_flow = Flow()
new_flow.name = '能力考N3'
new_flow.type = 'ntext'
new_flow.save()
| [
"mengyouhan@gmail.com"
] | mengyouhan@gmail.com |
57f4ee94bd87e3f3ad1a8d105c30c3bc127bd6c7 | 1513d0d708b8789f8d85fbd2a8ff46e863d16cd6 | /day_two/Exercise1.py | 6fbb7e0133dd5189518d654e7df31d1a7676ca4c | [] | no_license | zingpython/february2018 | ff9d0f64d6f68d5b0f22b87eaab202d06a85f224 | 0edcdd85bfbec168c7daf5a88bb06ce1b58062f7 | refs/heads/master | 2021-05-04T05:34:58.032678 | 2018-02-22T18:40:05 | 2018-02-22T18:40:05 | 120,341,634 | 0 | 2 | null | null | null | null | UTF-8 | Python | false | false | 192 | py |
for number in range(1, 101):
if number % 3 == 0 and number % 5 == 0:
print("FizzBuzz")
elif number % 3 == 0:
print("Fizz")
elif number % 5 == 0:
print("Buzz")
else:
print(number) | [
"selpathor@verizon.net"
] | selpathor@verizon.net |
f6dd1ef36f6a06a7afb6323e9d3df94e4689cc62 | db053c220094368ecb784fbe62375378c97457c2 | /810.chalkboard-xor-game.py | 541ac93fa7085e6032b36a1b6febb1ee272fdd8d | [] | no_license | thegamingcoder/leetcode | 8c16e7ac9bda3e34ba15955671a91ad072e87d94 | 131facec0a0c70d319982e78e772ed1cb94bc461 | refs/heads/master | 2020-03-22T14:51:45.246495 | 2018-07-09T00:00:06 | 2018-07-09T00:00:06 | 140,211,147 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,600 | py | #
# [828] Chalkboard XOR Game
#
# https://leetcode.com/problems/chalkboard-xor-game/description/
#
# algorithms
# Hard (38.94%)
# Total Accepted: 1.4K
# Total Submissions: 3.5K
# Testcase Example: '[1,1,2]'
#
# We are given non-negative integers nums[i] which are written on a
# chalkboard. Alice and Bob take turns erasing exactly one number from the
# chalkboard, with Alice starting first. If erasing a number causes the
# bitwise XOR of all the elements of the chalkboard to become 0, then that
# player loses. (Also, we'll say the bitwise XOR of one element is that
# element itself, and the bitwise XOR of no elements is 0.)
#
# Also, if any player starts their turn with the bitwise XOR of all the
# elements of the chalkboard equal to 0, then that player wins.
#
# Return True if and only if Alice wins the game, assuming both players play
# optimally.
#
#
# Example:
# Input: nums = [1, 1, 2]
# Output: false
# Explanation:
# Alice has two choices: erase 1 or erase 2.
# If she erases 1, the nums array becomes [1, 2]. The bitwise XOR of all the
# elements of the chalkboard is 1 XOR 2 = 3. Now Bob can remove any element he
# wants, because Alice will be the one to erase the last element and she will
# lose.
# If Alice erases 2 first, now nums becomes [1, 1]. The bitwise XOR of all the
# elements of the chalkboard is 1 XOR 1 = 0. Alice will lose.
#
#
#
# Notes:
#
#
# 1 <= N <= 1000.
# 0 <= nums[i] <= 2^16.
#
#
#
#
#
class Solution(object):
def xorGame(self, nums):
"""
:type nums: List[int]
:rtype: bool
"""
| [
"sharanbale@yahoo-inc.com"
] | sharanbale@yahoo-inc.com |
fce3df2153532f4e0401e80f02abcd99ab77ed8f | a56a74b362b9263289aad96098bd0f7d798570a2 | /venv/lib/python3.8/site-packages/matplotlib/tests/test_gridspec.py | 70d1ee132851d785ff973695a03dadb7b10f2947 | [
"MIT"
] | permissive | yoonkt200/ml-theory-python | 5812d06841d30e1068f6592b5730a40e87801313 | 7643136230fd4f291b6e3dbf9fa562c3737901a2 | refs/heads/master | 2022-12-21T14:53:21.624453 | 2021-02-02T09:33:07 | 2021-02-02T09:33:07 | 132,319,537 | 13 | 14 | MIT | 2022-12-19T17:23:57 | 2018-05-06T08:17:45 | Python | UTF-8 | Python | false | false | 626 | py | import matplotlib.gridspec as gridspec
import pytest
def test_equal():
gs = gridspec.GridSpec(2, 1)
assert gs[0, 0] == gs[0, 0]
assert gs[:, 0] == gs[:, 0]
def test_width_ratios():
"""
Addresses issue #5835.
See at https://github.com/matplotlib/matplotlib/issues/5835.
"""
with pytest.raises(ValueError):
gridspec.GridSpec(1, 1, width_ratios=[2, 1, 3])
def test_height_ratios():
"""
Addresses issue #5835.
See at https://github.com/matplotlib/matplotlib/issues/5835.
"""
with pytest.raises(ValueError):
gridspec.GridSpec(1, 1, height_ratios=[2, 1, 3])
| [
"kitae.yoon@deliveryhero.co.kr"
] | kitae.yoon@deliveryhero.co.kr |
6b87d5ce8490d1eb8056fb41b49cc0fa2608ceee | d1ef84d05beedc811161314800193ded398bff07 | /tests/test_database_crudmixin.py | 1767d5202f8df4c91803f6ee4b79e1def990b02d | [
"MIT"
] | permissive | spookey/observatory | 8f4a98aeb214182124bc6a4ab6d1ddac697cd0bc | be5cc92f53f12e6341e7e3040f26360e54cfdf7d | refs/heads/master | 2023-04-22T03:31:34.879735 | 2021-01-16T17:50:07 | 2021-01-16T17:50:07 | 224,500,136 | 0 | 0 | MIT | 2021-05-12T03:53:02 | 2019-11-27T19:11:24 | Python | UTF-8 | Python | false | false | 3,032 | py | from pytest import mark
from observatory.database import TXT_LEN_SHORT, CRUDMixin
from observatory.start.extensions import DB
# pylint: disable=no-member
PAYLOAD = 'omg wtf bbq'
LAYPOAD = 'napfkuchen!'
class CRUDMixinPhony(CRUDMixin, DB.Model):
prime = DB.Column(DB.Integer(), primary_key=True)
value = DB.Column(DB.String(length=TXT_LEN_SHORT))
@mark.usefixtures('session')
class TestCRUDMixin:
@staticmethod
def test_create_no_commit():
crud = CRUDMixinPhony.create(value=PAYLOAD, _commit=False)
assert crud.prime is None
assert crud.value == PAYLOAD
assert crud in CRUDMixinPhony.query.all()
@staticmethod
def test_create_commit():
crud = CRUDMixinPhony.create(value=PAYLOAD, _commit=True)
assert crud.prime == 1
assert crud.value == PAYLOAD
assert crud in CRUDMixinPhony.query.all()
@staticmethod
def test_update_no_comit():
crud = CRUDMixinPhony.create(value=PAYLOAD, _commit=False)
assert crud.value == PAYLOAD
crud.update(value=LAYPOAD, _commit=False)
assert crud.value == LAYPOAD
@staticmethod
def test_update_comit():
crud = CRUDMixinPhony.create(value=PAYLOAD, _commit=True)
assert crud.value == PAYLOAD
crud.update(value=LAYPOAD, _commit=True)
assert crud.value == LAYPOAD
@staticmethod
def test_save_no_commit(session):
crud = CRUDMixinPhony.create(value=PAYLOAD, _commit=False)
assert crud not in session.dirty
crud.value = LAYPOAD
assert crud not in session.dirty
crud.save(_commit=False)
assert crud not in session.dirty
@staticmethod
def test_save_commit(session):
crud = CRUDMixinPhony.create(value=PAYLOAD, _commit=True)
assert crud not in session.dirty
crud.value = LAYPOAD
assert crud in session.dirty
crud.save(_commit=True)
assert crud not in session.dirty
@staticmethod
def test_delete_no_commit():
crud = CRUDMixinPhony.create(value=PAYLOAD, _commit=False)
assert crud in CRUDMixinPhony.query.all()
crud.delete(_commit=False)
assert crud not in CRUDMixinPhony.query.all()
@staticmethod
def test_delete_commit():
crud = CRUDMixinPhony.create(value=PAYLOAD, _commit=True)
assert crud in CRUDMixinPhony.query.all()
crud.delete(_commit=True)
assert crud not in CRUDMixinPhony.query.all()
@staticmethod
def test_logging(caplog):
crud = CRUDMixinPhony.create(value='yes', _commit=True)
log_c, log_s = caplog.records[-2:]
assert 'creating' in log_c.message.lower()
assert 'saving' in log_s.message.lower()
crud.update(value='no')
log_u, log_s = caplog.records[-2:]
assert 'updating' in log_u.message.lower()
assert 'saving' in log_s.message.lower()
crud.delete()
log_d = caplog.records[-1]
assert 'deleting' in log_d.message.lower()
| [
"frieder.griesshammer@der-beweis.de"
] | frieder.griesshammer@der-beweis.de |
c291d5f571a0f7d5576a959395261c1c80e20196 | 6879a8596df6f302c63966a2d27f6b4d11cc9b29 | /abc/problems110/108/c.py | 01a176ce06f31360c7967885d3140fefde3cf214 | [] | no_license | wkwkgg/atcoder | 41b1e02b88bf7a8291b709306e54cb56cb93e52a | 28a7d4084a4100236510c05a88e50aa0403ac7cd | refs/heads/master | 2020-07-26T03:47:19.460049 | 2020-03-01T18:29:57 | 2020-03-01T18:29:57 | 208,523,188 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 250 | py | N, K = map(int, input().split())
nums = [0] * K
for i in range(1, N + 1):
nums[i % K] += 1
ans = 0
for i in range(K):
b = (K - i) % K
c = (K - i) % K
if (b + c) % K != 0: continue
ans += nums[i] * nums[b] * nums[c]
print(ans)
| [
"yujin@komachi.live"
] | yujin@komachi.live |
9b21a1d828f30ab5a1d04f765922419abe11a89c | a5408385bc6cc06cbc783652bd4d019af184ca7c | /examples/diffusion/sinbc.py | 5020f09b30d418719f4cbb6a07543487260f4fb9 | [
"BSD-3-Clause"
] | permissive | snilek/sfepy | 5a65d2e49c1d49d1a50f1d6d080f6e0f2f78e9f0 | 7f50684441cbbd3c7497cb32ba63ae4d1bf3ce28 | refs/heads/master | 2021-01-15T12:36:10.195016 | 2014-05-06T11:59:02 | 2014-05-06T11:59:02 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,826 | py | r"""
Laplace equation with Dirichlet boundary conditions given by a sine function
and constants.
Find :math:`t` such that:
.. math::
\int_{\Omega} c \nabla s \cdot \nabla t
= 0
\;, \quad \forall s \;.
This example demonstrates how to use a hierarchical basis approximation - it
uses the fifth order Lobatto polynomial space for the solution. The adaptive
linearization is applied in order to save viewable results, see both the
options keyword and the ``post_process()`` function that computes the solution
gradient. Use the following commands to view the results (assuming default
output directory and names)::
$ ./postproc.py -b -d't,plot_warp_scalar,rel_scaling=1' 2_4_2_refined_t.vtk --wireframe
$ ./postproc.py -b 2_4_2_refined_grad.vtk
The :class:`sfepy.discrete.fem.meshio.UserMeshIO` class is used to refine the original
two-element mesh before the actual solution.
"""
import numpy as nm
from sfepy import data_dir
from sfepy.base.base import output
from sfepy.discrete.fem import Mesh, Domain
from sfepy.discrete.fem.meshio import UserMeshIO, MeshIO
from sfepy.homogenization.utils import define_box_regions
base_mesh = data_dir + '/meshes/elements/2_4_2.mesh'
def mesh_hook(mesh, mode):
"""
Load and refine a mesh here.
"""
if mode == 'read':
mesh = Mesh.from_file(base_mesh)
domain = Domain(mesh.name, mesh)
for ii in range(3):
output('refine %d...' % ii)
domain = domain.refine()
output('... %d nodes %d elements'
% (domain.shape.n_nod, domain.shape.n_el))
domain.mesh.name = '2_4_2_refined'
return domain.mesh
elif mode == 'write':
pass
def post_process(out, pb, state, extend=False):
"""
Calculate gradient of the solution.
"""
from sfepy.discrete.fem.fields_base import create_expression_output
aux = create_expression_output('ev_grad.ie.Elements( t )',
'grad', 'temperature',
pb.fields, pb.get_materials(),
pb.get_variables(), functions=pb.functions,
mode='qp', verbose=False,
min_level=0, max_level=5, eps=1e-3)
out.update(aux)
return out
filename_mesh = UserMeshIO(mesh_hook)
# Get the mesh bounding box.
io = MeshIO.any_from_filename(base_mesh)
bbox, dim = io.read_bounding_box(ret_dim=True)
options = {
'nls' : 'newton',
'ls' : 'ls',
'post_process_hook' : 'post_process',
'linearization' : {
'kind' : 'adaptive',
'min_level' : 0, # Min. refinement level to achieve everywhere.
'max_level' : 5, # Max. refinement level.
'eps' : 1e-3, # Relative error tolerance.
},
}
materials = {
'coef' : ({'val' : 1.0},),
}
regions = {
'Omega' : 'all',
}
regions.update(define_box_regions(dim, bbox[0], bbox[1], 1e-5))
fields = {
'temperature' : ('real', 1, 'Omega', 5, 'H1', 'lobatto'),
# Compare with the Lagrange basis.
## 'temperature' : ('real', 1, 'Omega', 5, 'H1', 'lagrange'),
}
variables = {
't' : ('unknown field', 'temperature', 0),
's' : ('test field', 'temperature', 't'),
}
amplitude = 1.0
def ebc_sin(ts, coor, **kwargs):
x0 = 0.5 * (coor[:, 1].min() + coor[:, 1].max())
val = amplitude * nm.sin( (coor[:, 1] - x0) * 2. * nm.pi )
return val
ebcs = {
't1' : ('Left', {'t.0' : 'ebc_sin'}),
't2' : ('Right', {'t.0' : -0.5}),
't3' : ('Top', {'t.0' : 1.0}),
}
functions = {
'ebc_sin' : (ebc_sin,),
}
equations = {
'Temperature' : """dw_laplace.10.Omega( coef.val, s, t ) = 0"""
}
solvers = {
'ls' : ('ls.scipy_direct', {}),
'newton' : ('nls.newton', {
'i_max' : 1,
'eps_a' : 1e-10,
}),
}
| [
"cimrman3@ntc.zcu.cz"
] | cimrman3@ntc.zcu.cz |
80949b51021d641887cbf7cfdd89a8444cd9394f | 664bb3b0d806b3d17b1f4c5b87e569dcafac9710 | /0x03-python-data_structures/8-multiple_returns.py | cb9621277c528249e82d3c718e5867e6060a5b74 | [] | no_license | emmanavarro/holbertonschool-higher_level_programming | 9f120234b0521ad8330307af303f5f587764f30a | 2cae27d29d11035f62742240e1d1a5e385be075c | refs/heads/master | 2022-12-25T22:24:36.183806 | 2020-09-24T23:35:59 | 2020-09-24T23:35:59 | 259,382,761 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 205 | py | #!/usr/bin/python3
def multiple_returns(sentence):
if sentence is "":
tupl = (len(sentence), None)
return tupl
else:
tupl = (len(sentence), sentence[0])
return tupl
| [
"elnavarro55@gmail.com"
] | elnavarro55@gmail.com |
8a2bf3ac4361e3dcaa79a5a6ec7b73196c40724d | 06a7dc7cc93d019e4a9cbcf672b23a0bbacf8e8b | /2016_schizConnect/supervised_analysis/all_studies+VIP/all_subjects/Freesurfer/ROIs/learning_curve_ratios_centered_by_site_all/inter_site/svm_ratio_0_2.py | 6d995cf717bfe5921917fd5fa23e8ad5ee42ee14 | [] | no_license | neurospin/scripts | 6c06cd218a5f32de9c3c2b7d1d8bda3f3d107458 | f14a2c9cf2cd7f5fbea767b017c3faf36d170bdb | refs/heads/master | 2021-07-11T22:55:46.567791 | 2021-07-02T13:08:02 | 2021-07-02T13:08:02 | 10,549,286 | 2 | 2 | null | null | null | null | UTF-8 | Python | false | false | 12,914 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 23 10:04:13 2017
@author: ad247405
"""
import os
import json
import numpy as np
from sklearn.cross_validation import StratifiedKFold
from sklearn.metrics import precision_recall_fscore_support
from scipy.stats import binom_test
from collections import OrderedDict
from sklearn import preprocessing
from sklearn.metrics import roc_auc_score
from sklearn import svm
import pandas as pd
import shutil
from brainomics import array_utils
import mapreduce
from sklearn.metrics import recall_score, roc_auc_score, precision_recall_fscore_support
from statsmodels.stats.inter_rater import fleiss_kappa
WD = '/neurospin/brainomics/2016_schizConnect/analysis/all_studies+VIP/Freesurfer/all_subjects/results/ROIs_analysis/mean_centered_by_site_all/volume/learning_curve/inter_site/ratio_0.2'
def config_filename(): return os.path.join(WD,"config_dCV.json")
def results_filename(): return os.path.join(WD,"results_dCV.xlsx")
NFOLDS_OUTER = 5
NFOLDS_INNER = 5
penalty_start = 3
#############################################################################
def load_globals(config):
import mapreduce as GLOBAL # access to global variables
GLOBAL.DATA = GLOBAL.load_data(config["data"])
def resample(config, resample_nb):
import mapreduce as GLOBAL # access to global variables
GLOBAL.DATA = GLOBAL.load_data(config["data"])
resample = config["resample"][resample_nb]
GLOBAL.DATA_RESAMPLED = {k: [GLOBAL.DATA[k][idx, ...] for idx in resample]
for k in GLOBAL.DATA}
def mapper(key, output_collector):
import mapreduce as GLOBAL
Xtr = GLOBAL.DATA_RESAMPLED["X"][0]
Xte = GLOBAL.DATA_RESAMPLED["X"][1]
ytr = GLOBAL.DATA_RESAMPLED["y"][0]
yte = GLOBAL.DATA_RESAMPLED["y"][1]
c = float(key[0])
print("c:%f" % (c))
class_weight='balanced' # unbiased
mask = np.ones(Xtr.shape[0], dtype=bool)
scaler = preprocessing.StandardScaler().fit(Xtr)
Xtr = scaler.transform(Xtr)
Xte=scaler.transform(Xte)
mod = svm.LinearSVC(C=c,fit_intercept=False,class_weight= class_weight)
mod.fit(Xtr, ytr.ravel())
y_pred = mod.predict(Xte)
y_proba_pred = mod.decision_function(Xte)
ret = dict(y_pred=y_pred, y_true=yte,prob_pred = y_proba_pred, beta=mod.coef_, mask=mask)
if output_collector:
output_collector.collect(key, ret)
else:
return ret
def scores(key, paths, config):
values = [mapreduce.OutputCollector(p) for p in paths]
try:
values = [item.load() for item in values]
except Exception as e:
print(e)
return None
y_true_splits = [item["y_true"].ravel() for item in values]
y_pred_splits = [item["y_pred"].ravel() for item in values]
y_true = np.concatenate(y_true_splits)
y_pred = np.concatenate(y_pred_splits)
prob_pred_splits = [item["prob_pred"].ravel() for item in values]
prob_pred = np.concatenate(prob_pred_splits)
# Prediction performances
p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None)
auc = roc_auc_score(y_true, prob_pred)
# balanced accuracy (recall_mean)
bacc_splits = [recall_score(y_true_splits[f], y_pred_splits[f], average=None).mean() for f in range(len(y_true_splits))]
auc_splits = [roc_auc_score(y_true_splits[f], prob_pred_splits[f]) for f in range(len(y_true_splits))]
print("bacc all - mean(bacc) %.3f" % (r.mean() - np.mean(bacc_splits)))
# P-values
success = r * s
success = success.astype('int')
prob_class1 = np.count_nonzero(y_true) / float(len(y_true))
pvalue_recall0_true_prob = binom_test(success[0], s[0], 1 - prob_class1,alternative = 'greater')
pvalue_recall1_true_prob = binom_test(success[1], s[1], prob_class1,alternative = 'greater')
pvalue_recall0_unknwon_prob = binom_test(success[0], s[0], 0.5,alternative = 'greater')
pvalue_recall1_unknown_prob = binom_test(success[1], s[1], 0.5,alternative = 'greater')
pvalue_bacc = binom_test(success[0]+success[1], s[0] + s[1], p=0.5,alternative = 'greater')
# Beta's measures of similarity
betas = np.hstack([item["beta"][:, penalty_start:].T for item in values]).T
# Correlation
R = np.corrcoef(betas)
R = R[np.triu_indices_from(R, 1)]
# Fisher z-transformation / average
z_bar = np.mean(1. / 2. * np.log((1 + R) / (1 - R)))
# bracktransform
r_bar = (np.exp(2 * z_bar) - 1) / (np.exp(2 * z_bar) + 1)
# threshold betas to compute fleiss_kappa and DICE
try:
betas_t = np.vstack([
array_utils.arr_threshold_from_norm2_ratio(betas[i, :], .99)[0]
for i in range(betas.shape[0])])
# Compute fleiss kappa statistics
beta_signed = np.sign(betas_t)
table = np.zeros((beta_signed.shape[1], 3))
table[:, 0] = np.sum(beta_signed == 0, 0)
table[:, 1] = np.sum(beta_signed == 1, 0)
table[:, 2] = np.sum(beta_signed == -1, 0)
fleiss_kappa_stat = fleiss_kappa(table)
# Paire-wise Dice coeficient
ij = [[i, j] for i in range(betas.shape[0]) for j in range(i+1, betas.shape[0])]
dices = list()
for idx in ij:
A, B = beta_signed[idx[0], :], beta_signed[idx[1], :]
dices.append(float(np.sum((A == B)[(A != 0) & (B != 0)])) / (np.sum(A != 0) + np.sum(B != 0)))
dice_bar = np.mean(dices)
except:
dice_bar = fleiss_kappa_stat = 0
# Proportion of selection within the support accross the CV
support_count = (betas_t != 0).sum(axis=0)
support_count = support_count[support_count > 0]
support_prop = support_count / betas_t.shape[0]
scores = OrderedDict()
scores['key'] = key
scores['recall_0'] = r[0]
scores['recall_1'] = r[1]
scores['bacc'] = r.mean()
scores['bacc_se'] = np.std(bacc_splits) / np.sqrt(len(bacc_splits))
scores["auc"] = auc
scores['auc_se'] = np.std(auc_splits) / np.sqrt(len(auc_splits))
scores['pvalue_recall0_true_prob_one_sided'] = pvalue_recall0_true_prob
scores['pvalue_recall1_true_prob_one_sided'] = pvalue_recall1_true_prob
scores['pvalue_recall0_unknwon_prob_one_sided'] = pvalue_recall0_unknwon_prob
scores['pvalue_recall1_unknown_prob_one_sided'] = pvalue_recall1_unknown_prob
scores['pvalue_bacc_mean'] = pvalue_bacc
scores['prop_non_zeros_mean'] = float(np.count_nonzero(betas_t)) / \
float(np.prod(betas.shape))
scores['beta_r_bar'] = r_bar
scores['beta_fleiss_kappa'] = fleiss_kappa_stat
scores['beta_dice_bar'] = dice_bar
scores['beta_dice'] = str(dices)
scores['beta_r'] = str(R)
scores['beta_support_prop_select_mean'] = support_prop.mean()
scores['beta_support_prop_select_sd'] = support_prop.std()
return scores
def reducer(key, values):
import os, glob, pandas as pd
os.chdir(os.path.dirname(config_filename()))
config = json.load(open(config_filename()))
paths = glob.glob(os.path.join(config['map_output'], "*", "*", "*"))
#paths = [p for p in paths if not p.count("0.8_-1")]
def close(vec, val, tol=1e-4):
return np.abs(vec - val) < tol
def groupby_paths(paths, pos):
groups = {g:[] for g in set([p.split("/")[pos] for p in paths])}
for p in paths:
groups[p.split("/")[pos]].append(p)
return groups
def argmaxscore_bygroup(data, groupby='fold', param_key="key", score="bacc"):
arg_max_byfold = list()
for fold, data_fold in data.groupby(groupby):
# assert len(data_fold) == len(set(data_fold[param_key])) # ensure all param are diff
arg_max_byfold.append([fold, data_fold.ix[data_fold[score].argmax()][param_key], data_fold[score].max()])
return pd.DataFrame(arg_max_byfold, columns=[groupby, param_key, score])
print('## Refit scores')
print('## ------------')
byparams = groupby_paths([p for p in paths if p.count("all") and not p.count("all/all")],3)
byparams_scores = {k:scores(k, v, config) for k, v in byparams.items()}
data = [list(byparams_scores[k].values()) for k in byparams_scores]
columns = list(byparams_scores[list(byparams_scores.keys())[0]].keys())
scores_refit = pd.DataFrame(data, columns=columns)
print('## doublecv scores by outer-cv and by params')
print('## -----------------------------------------')
data = list()
bycv = groupby_paths([p for p in paths if p.count("cvnested")],1)
for fold, paths_fold in bycv.items():
print(fold)
byparams = groupby_paths([p for p in paths_fold], 3)
byparams_scores = {k:scores(k, v, config) for k, v in byparams.items()}
data += [[fold] + list(byparams_scores[k].values()) for k in byparams_scores]
scores_dcv_byparams = pd.DataFrame(data, columns=["fold"] + columns)
print('## Model selection')
print('## ---------------')
svm = argmaxscore_bygroup(scores_dcv_byparams); svm["method"] = "svm"
scores_argmax_byfold = svm
print('## Apply best model on refited')
print('## ---------------------------')
scores_svm = scores("nestedcv", [os.path.join(config['map_output'], row["fold"], "all", row["key"]) for index, row in svm.iterrows()], config)
scores_cv = pd.DataFrame([["svm"] + list(scores_svm.values())], columns=["method"] + list(scores_svm.keys()))
with pd.ExcelWriter(results_filename()) as writer:
scores_refit.to_excel(writer, sheet_name='cv_by_param', index=False)
scores_dcv_byparams.to_excel(writer, sheet_name='cv_cv_byparam', index=False)
scores_argmax_byfold.to_excel(writer, sheet_name='cv_argmax', index=False)
scores_cv.to_excel(writer, sheet_name='dcv', index=False)
##############################################################################
if __name__ == "__main__":
INPUT_DATA_X = '/neurospin/brainomics/2016_schizConnect/analysis/all_studies+VIP/Freesurfer/all_subjects/data/data_ROIs/mean_centered_by_site_all/Xrois_volumes_mean_centered_by_site+cov.npy'
INPUT_DATA_y = '/neurospin/brainomics/2016_schizConnect/analysis/all_studies+VIP/Freesurfer/all_subjects/data/data_ROIs/mean_centered_by_site_all/y.npy'
NFOLDS_OUTER = 4 #number of sites
NFOLDS_INNER = 5
shutil.copy(INPUT_DATA_X, WD)
shutil.copy(INPUT_DATA_y, WD)
#############################################################################
## Create config file
y = np.load(INPUT_DATA_y)
#COBRE = 1, NMORPH = 2, NUSDAST = 3, VIP = 4
site = np.load("/neurospin/brainomics/2016_schizConnect/analysis/all_studies+VIP/Freesurfer/all_subjects/data/site.npy")
cv_outer = [[tr, te] for tr,te in StratifiedKFold(y.ravel(), n_folds=NFOLDS_OUTER, random_state=42)]
cv_outer[0][0] = np.transpose(np.where(site != 1)).ravel()
cv_outer[0][1] = np.transpose(np.where(site == 1)).ravel() #CV00 TEST ON COBRE
cv_outer[1][0] = np.transpose(np.where(site != 2)).ravel()
cv_outer[1][1] = np.transpose(np.where(site == 2)).ravel() #CV01 TEST ON NMORPHch
cv_outer[2][0] = np.transpose(np.where(site != 3)).ravel()
cv_outer[2][1] = np.transpose(np.where(site == 3)).ravel() #CV02 TEST ON NUSDAST
cv_outer[3][0] = np.transpose(np.where(site != 4)).ravel()
cv_outer[3][1] = np.transpose(np.where(site == 4)).ravel() #CV03 TEST ON VIP
cv_outer[0][0] = cv_outer[0][0][:int(np.around(len(cv_outer[0][0])*0.2))]
cv_outer[1][0] = cv_outer[1][0][:int(np.around(len(cv_outer[1][0])*0.2))]
cv_outer[2][0] = cv_outer[2][0][:int(np.around(len(cv_outer[2][0])*0.2))]
cv_outer[3][0] = cv_outer[3][0][:int(np.around(len(cv_outer[3][0])*0.2))]
import collections
cv = collections.OrderedDict()
for cv_outer_i, (tr_val, te) in enumerate(cv_outer):
cv["cv%02d/all" % (cv_outer_i)] = [tr_val, te]
cv_inner = StratifiedKFold(y[tr_val].ravel(), n_folds=NFOLDS_INNER, random_state=42)
for cv_inner_i, (tr, val) in enumerate(cv_inner):
cv["cv%02d/cvnested%02d" % ((cv_outer_i), cv_inner_i)] = [tr_val[tr], tr_val[val]]
for k in cv:
cv[k] = [cv[k][0].tolist(), cv[k][1].tolist()]
C_range = [[100],[10],[1],[1e-1],[1e-2],[1e-3],[1e-4],[1e-5],[1e-6],[1e-7],[1e-8],[1e-9]]
user_func_filename = "/home/ad247405/git/scripts/2016_schizConnect/supervised_analysis/all_studies+VIP/all_subjects/Freesurfer/ROIs/learning_curve_ratios_centered_by_site_all/inter_site/svm_ratio_0_2.py"
config = dict(data=dict(X=os.path.basename(INPUT_DATA_X), y="y.npy"),
params=C_range, resample=cv,
map_output="model_selectionCV",
user_func=user_func_filename,
reduce_input="results/*/*",
reduce_group_by="params",
reduce_output="model_selectionCV.csv")
json.dump(config, open(os.path.join(WD, "config_dCV.json"), "w"))
| [
"ad247405@is222241.intra.cea.fr"
] | ad247405@is222241.intra.cea.fr |
681013f7bacd0db8a1b4d25d995954b7fe7df8ed | 43df78355915e3f41f432579c5840816f52a8ace | /Functions/Two/Calc_NDM.py | e484e15cd3977780679d69c0bc37613e93af8a36 | [
"Apache-2.0"
] | permissive | spareeth/wa | fd7617fafe065de83249cf817df25cf9ca164518 | 57bb0c93af1bab3b6f8bc30cbc941aa14ac2696b | refs/heads/master | 2020-03-06T15:33:26.208373 | 2018-03-22T08:04:06 | 2018-03-22T08:04:06 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,117 | py | # -*- coding: utf-8 -*-
"""
Authors: Tim Hessels
UNESCO-IHE 2017
Contact: t.hessels@unesco-ihe.org
Repository: https://github.com/wateraccounting/wa
Module: Function/Two
"""
# import general python modules
import os
import gdal
import numpy as np
import pandas as pd
import glob
def NPP_GPP_Based(Dir_Basin, Data_Path_GPP, Data_Path_NPP, Startdate, Enddate):
"""
This functions calculated monthly NDM based on the yearly NPP and monthly GPP.
Parameters
----------
Dir_Basin : str
Path to all the output data of the Basin
Data_Path_GPP : str
Path from the Dir_Basin to the GPP data
Data_Path_NPP : str
Path from the Dir_Basin to the NPP data
Startdate : str
Contains the start date of the model 'yyyy-mm-dd'
Enddate : str
Contains the end date of the model 'yyyy-mm-dd'
Simulation : int
Defines the simulation
Returns
-------
Data_Path_NDM : str
Path from the Dir_Basin to the normalized dry matter data
"""
# import WA+ modules
import wa.General.data_conversions as DC
import wa.General.raster_conversions as RC
# Define output folder for Normalized Dry Matter
Data_Path_NDM = "NDM"
out_folder = os.path.join(Dir_Basin, Data_Path_NDM)
if not os.path.exists(out_folder):
os.mkdir(out_folder)
# Define input folders
GPP_folder = os.path.join(Dir_Basin, Data_Path_GPP)
NPP_folder = os.path.join(Dir_Basin, Data_Path_NPP)
# Define monthly time steps that will be created
Dates = pd.date_range(Startdate, Enddate, freq = 'MS')
# Define the years that will be calculated
Year_Start = int(Startdate[0:4])
Year_End = int(Enddate[0:4])
Years = range(Year_Start, Year_End+1)
# Loop over the years
for year in Years:
# Change working directory to the NPP folder
os.chdir(NPP_folder)
# Open yearly NPP data
yearly_NPP_File = glob.glob('*yearly*%d.01.01.tif' %int(year))[0]
Yearly_NPP = RC.Open_tiff_array(yearly_NPP_File)
# Get the No Data Value of the NPP file
dest = gdal.Open(yearly_NPP_File)
NDV = dest.GetRasterBand(1).GetNoDataValue()
# Set the No Data Value to Nan
Yearly_NPP[Yearly_NPP == NDV] = np.nan
# Change working directory to the GPP folder
os.chdir(GPP_folder)
# Find all the monthly files of that year
monthly_GPP_Files = glob.glob('*monthly*%d.*.01.tif' %int(year))
# Check if it are 12 files otherwise something is wrong and send the ERROR
if not len(monthly_GPP_Files) == 12:
print 'ERROR: Some monthly GPP Files are missing'
# Get the projection information of the GPP inputs
geo_out, proj, size_X, size_Y = RC.Open_array_info(monthly_GPP_Files[0])
if int(proj.split('"')[-2]) == 4326:
proj = "WGS84"
# Get the No Data Value of the GPP files
dest = gdal.Open(monthly_GPP_Files[0])
NDV = dest.GetRasterBand(1).GetNoDataValue()
# Create a empty numpy array
Yearly_GPP = np.zeros([size_Y, size_X])
# Calculte the total yearly GPP
for monthly_GPP_File in monthly_GPP_Files:
# Open array
Data = RC.Open_tiff_array(monthly_GPP_File)
# Remove nan values
Data[Data == NDV] = np.nan
# Add data to yearly sum
Yearly_GPP += Data
# Loop over the monthly dates
for Date in Dates:
# If the Date is in the same year as the yearly NPP and GPP
if Date.year == year:
# Create empty GPP array
monthly_GPP = np.ones([size_Y, size_X]) * np.nan
# Get current month
month = Date.month
# Get the GPP file of the current year and month
monthly_GPP_File = glob.glob('*monthly_%d.%02d.01.tif' %(int(year), int(month)))[0]
monthly_GPP = RC.Open_tiff_array(monthly_GPP_File)
monthly_GPP[monthly_GPP == NDV] = np.nan
# Calculate the NDM based on the monthly and yearly NPP and GPP (fraction of GPP)
Monthly_NDM = Yearly_NPP * monthly_GPP / Yearly_GPP * (30./12.) *10000 # kg/ha
# Define output name
output_name = os.path.join(out_folder, 'NDM_MOD17_kg_ha-1_monthly_%d.%02d.01.tif' %(int(year), int(month)))
# Save the NDM as tiff file
DC.Save_as_tiff(output_name, Monthly_NDM, geo_out, proj)
return(Data_Path_NDM)
| [
"timhessels@hotmail.com"
] | timhessels@hotmail.com |
73afcff6d269bc975c7d86680ee09916cd096372 | 36407bb880c5ca94331f1bc44b85be58bba6f352 | /apps/rating/migrations/0001_initial.py | 6e902a0de600f12a5514e21ac3aec25a4d1f5c37 | [] | no_license | raw-data-tech/home-garden | 28175f66d126fa57f652fce22cd89ab44c514bba | 8767815b010b3d7d83927e912b3e055374c06111 | refs/heads/master | 2020-12-24T17:17:05.629239 | 2015-07-11T09:05:52 | 2015-07-11T09:05:52 | 38,920,104 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,147 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
import django.utils.timezone
from django.conf import settings
class Migration(migrations.Migration):
dependencies = [
('orders', '0001_initial'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Rating',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('rating', models.PositiveIntegerField(verbose_name=b'rating', choices=[(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)])),
('remark', models.CharField(max_length=200, null=True, blank=True)),
('date', models.DateTimeField(default=django.utils.timezone.now)),
('order', models.ForeignKey(related_name=b'ratings', to='orders.Order')),
('user', models.ForeignKey(related_name=b'ratings', to=settings.AUTH_USER_MODEL)),
],
options={
},
bases=(models.Model,),
),
]
| [
"navajyothms1989@gmail.com"
] | navajyothms1989@gmail.com |
d29a74acd9b141fc75873408944238d813de7f96 | d2189145e7be2c836017bea0d09a473bf1bc5a63 | /Reposicion_cuarta clase/contar los numeros multiplos de 3 que hay entre 1-100.py | 45d3f1bff695c5720b917c501d80e6ad0046391f | [] | no_license | emilianoNM/Tecnicas3 | 12d10ce8d78803c8d2cd6a721786a68f7ee2809d | 6ad7f0427ab9e23643a28ac16889bca8791421d0 | refs/heads/master | 2020-03-25T18:06:34.126165 | 2018-11-24T04:42:14 | 2018-11-24T04:42:14 | 144,013,045 | 3 | 5 | null | 2018-09-14T10:47:26 | 2018-08-08T12:49:57 | Python | UTF-8 | Python | false | false | 226 | py | ### Imprimir y contar los numeros multiplos de 3 que hay entre 1 y 100.
n = 1
h = 0
while n < 100:
if n%3 == 0:
print n,
h += 1
n += 1
print '\nEntre 1 y 100 hay %i numeros multiplos de 3' % h
| [
"noreply@github.com"
] | emilianoNM.noreply@github.com |
9cf18d8324650ac3c321885809cf31acd683c320 | f61cf1a24fa184dd552dd47dd8399b74c5816ee0 | /tasks/13/13-2.py | e4cf7fab84e1ca127f6ecbb21c47ecca597e0f05 | [] | no_license | desve/netology | ea585d9db8658eea5319b98f63259239fa538fcb | c6039cc831058b8ba650d417fae25f761520139b | refs/heads/master | 2020-01-23T21:11:31.291807 | 2017-04-06T05:19:08 | 2017-04-06T05:19:08 | 74,572,766 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 80 | py | # Правильная скобочная последовательность
| [
"2901243@mail.ru"
] | 2901243@mail.ru |
ed33bc3916a5067ac5211b768fae4fa08ec4d051 | 900aaf3f7d0063ed3b4a90d7afc0e75bb847a1f2 | /hash_tables/group_shifted_strings.py | 33bd6b693a830615e45efabb37aabb75be2ebe35 | [] | no_license | rjcrter11/leetChallenges | 5797fbdd818525af1fec8d2907d03fe9e4c586fb | bfd0ee6221310c88a619ec3203e281f4b0cc8184 | refs/heads/master | 2023-04-21T06:53:02.887548 | 2021-05-24T12:24:13 | 2021-05-24T12:24:13 | 283,039,834 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 816 | py | '''Given a string, we can "shift" each of its letter to its successive letter,
for example: "abc" -> "bcd". We can keep "shifting" which forms the sequence:
"abc" -> "bcd" -> ... -> "xyz"
Given a list of non-empty strings which contains only lowercase alphabets,
group all strings that belong to the same shifting sequence.
Example:
Input: ["abc", "bcd", "acef", "xyz", "az", "ba", "a", "z"],
Output:
[
["abc","bcd","xyz"],
["az","ba"],
["acef"],
["a","z"]
]
'''
from collections import defaultdict
def groupStrings(strings):
def diff(s): return tuple((ord(a) - ord(b)) % 26 for a, b in zip(s, s[1:]))
d = defaultdict(list)
for s in strings:
d[diff(s)].append(s)
return d.values()
strings = ["abc", "bcd", "acef", "xyz", "az", "ba", "a", "z"]
print(groupStrings(strings))
| [
"rjcrter11@gmail.com"
] | rjcrter11@gmail.com |
682ce86400bb9c2269a57401c0661a75d61c9b53 | 5174acc0ca3a8582711881dcf6a1a36663e964a9 | /servicios_aplicacion/selector_entrada.py | f35868471f873fe833cdcd7dece7f9bd1a598ca8 | [] | no_license | vvalotto/fiuner_termostato | b9ac7918458d06a479f516bd3f7a2550bb4d6b78 | a3e81040672a438ea512895016201cb93104469e | refs/heads/master | 2020-05-24T05:06:45.940613 | 2019-07-01T10:21:51 | 2019-07-01T10:21:51 | 187,106,514 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,523 | py | """
Clase Responsable del establecimiento de la temperatura deseada
"""
from gestores_entidades.gestor_ambiente import *
class SelectorEntradaTemperatura:
def __init__(self, gestor_ambiente):
"""
Arma la clases con la que necesita colaborar
"""
self._seteo_temperatura = Configurador.configurar_seteo_temperatura()
self._selector_temperatura = Configurador.configurar_selector_temperatura()
self._gestor_ambiente = gestor_ambiente
return
def ejecutar(self):
"""
Ejecucion periodica para observar si el usuario quiere
setear la temperatura
En caso que asi sea, se queda ciclando para leer el ingreso
de las consignas
:return:
"""
while self._selector_temperatura.obtener_selector() == "deseada":
self._mostrar_temperatura_deseada()
self._obtener_seteo_temperatura_deseada()
self._gestor_ambiente.indicar_temperatura_a_mostrar("ambiente")
return
def _mostrar_temperatura_deseada(self):
self._gestor_ambiente.indicar_temperatura_a_mostrar("deseada")
self._gestor_ambiente.mostrar_temperatura()
return
def _obtener_seteo_temperatura_deseada(self):
opcion = self._seteo_temperatura.obtener_seteo()
if opcion == "aumentar":
self._gestor_ambiente.aumentar_temperatura_deseada()
if opcion == "disminuir":
self._gestor_ambiente.disminuir_temperatura_deseada()
return
| [
"vvalotto@gmail.com"
] | vvalotto@gmail.com |
2828841fc71fbcf1498c40698d5aa047ae752abf | 1bfad01139237049eded6c42981ee9b4c09bb6de | /RestPy/ixnetwork_restpy/testplatform/sessions/ixnetwork/vport/protocols/linkoam/link/eventnotificationlearnedinfo/eventnotificationlearnedinfo.py | 176e1cdb756989f951c1552cfebdfe9893f86680 | [
"MIT"
] | permissive | kakkotetsu/IxNetwork | 3a395c2b4de1488994a0cfe51bca36d21e4368a5 | f9fb614b51bb8988af035967991ad36702933274 | refs/heads/master | 2020-04-22T09:46:37.408010 | 2019-02-07T18:12:20 | 2019-02-07T18:12:20 | 170,284,084 | 0 | 0 | MIT | 2019-02-12T08:51:02 | 2019-02-12T08:51:01 | null | UTF-8 | Python | false | false | 9,566 | py |
# Copyright 1997 - 2018 by IXIA Keysight
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
from ixnetwork_restpy.base import Base
from ixnetwork_restpy.files import Files
class EventNotificationLearnedInfo(Base):
"""The EventNotificationLearnedInfo class encapsulates a system managed eventNotificationLearnedInfo node in the ixnetwork hierarchy.
An instance of the class can be obtained by accessing the EventNotificationLearnedInfo property from a parent instance.
The internal properties list will be empty when the property is accessed and is populated from the server by using the find method.
"""
_SDM_NAME = 'eventNotificationLearnedInfo'
def __init__(self, parent):
super(EventNotificationLearnedInfo, self).__init__(parent)
@property
def LocalFrameErrorRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('localFrameErrorRunningTotal')
@property
def LocalFrameEventRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('localFrameEventRunningTotal')
@property
def LocalFramePeriodErrorRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('localFramePeriodErrorRunningTotal')
@property
def LocalFramePeriodEventRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('localFramePeriodEventRunningTotal')
@property
def LocalFrameSecSumErrorRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('localFrameSecSumErrorRunningTotal')
@property
def LocalFrameSecSumEventRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('localFrameSecSumEventRunningTotal')
@property
def LocalSymbolPeriodErrorRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('localSymbolPeriodErrorRunningTotal')
@property
def LocalSymbolPeriodEventRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('localSymbolPeriodEventRunningTotal')
@property
def RemoteFrameError(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFrameError')
@property
def RemoteFrameErrorRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFrameErrorRunningTotal')
@property
def RemoteFrameEventRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFrameEventRunningTotal')
@property
def RemoteFramePeriodError(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFramePeriodError')
@property
def RemoteFramePeriodErrorRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFramePeriodErrorRunningTotal')
@property
def RemoteFramePeriodEventRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFramePeriodEventRunningTotal')
@property
def RemoteFramePeriodThreshold(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFramePeriodThreshold')
@property
def RemoteFramePeriodWindow(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFramePeriodWindow')
@property
def RemoteFrameSecSumError(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFrameSecSumError')
@property
def RemoteFrameSecSumErrorRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFrameSecSumErrorRunningTotal')
@property
def RemoteFrameSecSumEventRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFrameSecSumEventRunningTotal')
@property
def RemoteFrameSecSumThreshold(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFrameSecSumThreshold')
@property
def RemoteFrameSecSumWindow(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFrameSecSumWindow')
@property
def RemoteFrameThreshold(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFrameThreshold')
@property
def RemoteFrameWindow(self):
"""
Returns:
number
"""
return self._get_attribute('remoteFrameWindow')
@property
def RemoteSymbolPeriodErrorRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('remoteSymbolPeriodErrorRunningTotal')
@property
def RemoteSymbolPeriodErrors(self):
"""
Returns:
number
"""
return self._get_attribute('remoteSymbolPeriodErrors')
@property
def RemoteSymbolPeriodEventRunningTotal(self):
"""
Returns:
number
"""
return self._get_attribute('remoteSymbolPeriodEventRunningTotal')
@property
def RemoteSymbolPeriodThreshold(self):
"""
Returns:
number
"""
return self._get_attribute('remoteSymbolPeriodThreshold')
@property
def RemoteSymbolPeriodWindow(self):
"""
Returns:
number
"""
return self._get_attribute('remoteSymbolPeriodWindow')
def find(self, LocalFrameErrorRunningTotal=None, LocalFrameEventRunningTotal=None, LocalFramePeriodErrorRunningTotal=None, LocalFramePeriodEventRunningTotal=None, LocalFrameSecSumErrorRunningTotal=None, LocalFrameSecSumEventRunningTotal=None, LocalSymbolPeriodErrorRunningTotal=None, LocalSymbolPeriodEventRunningTotal=None, RemoteFrameError=None, RemoteFrameErrorRunningTotal=None, RemoteFrameEventRunningTotal=None, RemoteFramePeriodError=None, RemoteFramePeriodErrorRunningTotal=None, RemoteFramePeriodEventRunningTotal=None, RemoteFramePeriodThreshold=None, RemoteFramePeriodWindow=None, RemoteFrameSecSumError=None, RemoteFrameSecSumErrorRunningTotal=None, RemoteFrameSecSumEventRunningTotal=None, RemoteFrameSecSumThreshold=None, RemoteFrameSecSumWindow=None, RemoteFrameThreshold=None, RemoteFrameWindow=None, RemoteSymbolPeriodErrorRunningTotal=None, RemoteSymbolPeriodErrors=None, RemoteSymbolPeriodEventRunningTotal=None, RemoteSymbolPeriodThreshold=None, RemoteSymbolPeriodWindow=None):
"""Finds and retrieves eventNotificationLearnedInfo data from the server.
All named parameters support regex and can be used to selectively retrieve eventNotificationLearnedInfo data from the server.
By default the find method takes no parameters and will retrieve all eventNotificationLearnedInfo data from the server.
Args:
LocalFrameErrorRunningTotal (number):
LocalFrameEventRunningTotal (number):
LocalFramePeriodErrorRunningTotal (number):
LocalFramePeriodEventRunningTotal (number):
LocalFrameSecSumErrorRunningTotal (number):
LocalFrameSecSumEventRunningTotal (number):
LocalSymbolPeriodErrorRunningTotal (number):
LocalSymbolPeriodEventRunningTotal (number):
RemoteFrameError (number):
RemoteFrameErrorRunningTotal (number):
RemoteFrameEventRunningTotal (number):
RemoteFramePeriodError (number):
RemoteFramePeriodErrorRunningTotal (number):
RemoteFramePeriodEventRunningTotal (number):
RemoteFramePeriodThreshold (number):
RemoteFramePeriodWindow (number):
RemoteFrameSecSumError (number):
RemoteFrameSecSumErrorRunningTotal (number):
RemoteFrameSecSumEventRunningTotal (number):
RemoteFrameSecSumThreshold (number):
RemoteFrameSecSumWindow (number):
RemoteFrameThreshold (number):
RemoteFrameWindow (number):
RemoteSymbolPeriodErrorRunningTotal (number):
RemoteSymbolPeriodErrors (number):
RemoteSymbolPeriodEventRunningTotal (number):
RemoteSymbolPeriodThreshold (number):
RemoteSymbolPeriodWindow (number):
Returns:
self: This instance with matching eventNotificationLearnedInfo data retrieved from the server available through an iterator or index
Raises:
ServerError: The server has encountered an uncategorized error condition
"""
return self._select(locals())
def read(self, href):
"""Retrieves a single instance of eventNotificationLearnedInfo data from the server.
Args:
href (str): An href to the instance to be retrieved
Returns:
self: This instance with the eventNotificationLearnedInfo data from the server available through an iterator or index
Raises:
NotFoundError: The requested resource does not exist on the server
ServerError: The server has encountered an uncategorized error condition
"""
return self._read(href)
| [
"hubert.gee@keysight.com"
] | hubert.gee@keysight.com |
38ee93212baba1254b19a1e1c076d397ed05f48f | 8d50cc4f37c153fcb51de4501f3fa50c00394d9b | /nets/L_Resnet_E_IR.py | 66cfaa519446d9c31ccdddb293dd2eba5835a997 | [
"MIT"
] | permissive | liujuanLT/InsightFace_TF | dbd239dfdda1866c348e82211932884f73cb3067 | 257b6e0dcf7e7c3523dc7e1c08ba529fab1bf75b | refs/heads/master | 2022-04-27T21:24:01.458277 | 2022-03-17T12:28:15 | 2022-03-17T12:28:15 | 463,040,192 | 0 | 0 | MIT | 2022-02-24T06:51:16 | 2022-02-24T06:51:15 | null | UTF-8 | Python | false | false | 20,455 | py | import tensorflow as tf
import tensorlayer as tl
from tensorflow.contrib.layers.python.layers import utils
import collections
from tensorlayer.layers import Layer, list_remove_repeat
class ElementwiseLayer(Layer):
"""
The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation.
Parameters
----------
layer : a list of :class:`Layer` instances
The `Layer` class feeding into this layer.
combine_fn : a TensorFlow elemwise-merge function
e.g. AND is ``tf.minimum`` ; OR is ``tf.maximum`` ; ADD is ``tf.add`` ; MUL is ``tf.multiply`` and so on.
See `TensorFlow Math API <https://www.tensorflow.org/versions/master/api_docs/python/math_ops.html#math>`_ .
name : a string or None
An optional name to attach to this layer.
"""
def __init__(
self,
layer = [],
combine_fn = tf.minimum,
name ='elementwise_layer',
act = None,
):
Layer.__init__(self, name=name)
if act:
print(" [TL] ElementwiseLayer %s: size:%s fn:%s, act:%s" % (
self.name, layer[0].outputs.get_shape(), combine_fn.__name__, act.__name__))
else:
print(" [TL] ElementwiseLayer %s: size:%s fn:%s" % (
self.name, layer[0].outputs.get_shape(), combine_fn.__name__))
self.outputs = layer[0].outputs
# print(self.outputs._shape, type(self.outputs._shape))
for l in layer[1:]:
# assert str(self.outputs.get_shape()) == str(l.outputs.get_shape()), "Hint: the input shapes should be the same. %s != %s" % (self.outputs.get_shape() , str(l.outputs.get_shape()))
self.outputs = combine_fn(self.outputs, l.outputs, name=name)
if act:
self.outputs = act(self.outputs)
self.all_layers = list(layer[0].all_layers)
self.all_params = list(layer[0].all_params)
self.all_drop = dict(layer[0].all_drop)
for i in range(1, len(layer)):
self.all_layers.extend(list(layer[i].all_layers))
self.all_params.extend(list(layer[i].all_params))
self.all_drop.update(dict(layer[i].all_drop))
self.all_layers = list_remove_repeat(self.all_layers)
self.all_params = list_remove_repeat(self.all_params)
class BatchNormLayer(Layer):
"""
The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``.
Batch normalization on fully-connected or convolutional maps.
```
https://www.tensorflow.org/api_docs/python/tf/cond
If x < y, the tf.add operation will be executed and tf.square operation will not be executed.
Since z is needed for at least one branch of the cond, the tf.multiply operation is always executed, unconditionally.
```
Parameters
-----------
layer : a :class:`Layer` instance
The `Layer` class feeding into this layer.
decay : float, default is 0.9.
A decay factor for ExponentialMovingAverage, use larger value for large dataset.
epsilon : float
A small float number to avoid dividing by 0.
act : activation function.
is_train : boolean
Whether train or inference.
beta_init : beta initializer
The initializer for initializing beta
gamma_init : gamma initializer
The initializer for initializing gamma
dtype : tf.float32 (default) or tf.float16
name : a string or None
An optional name to attach to this layer.
References
----------
- `Source <https://github.com/ry/tensorflow-resnet/blob/master/resnet.py>`_
- `stackoverflow <http://stackoverflow.com/questions/38312668/how-does-one-do-inference-with-batch-normalization-with-tensor-flow>`_
"""
def __init__(
self,
layer=None,
decay=0.9,
epsilon=2e-5,
act=tf.identity,
is_train=False,
fix_gamma=True,
beta_init=tf.zeros_initializer,
gamma_init=tf.random_normal_initializer(mean=1.0, stddev=0.002), # tf.ones_initializer,
# dtype = tf.float32,
trainable=None,
name='batchnorm_layer',
):
Layer.__init__(self, name=name)
self.inputs = layer.outputs
print(" [TL] BatchNormLayer %s: decay:%f epsilon:%f act:%s is_train:%s" % (self.name, decay, epsilon, act.__name__, is_train))
x_shape = self.inputs.get_shape()
params_shape = x_shape[-1:]
from tensorflow.python.training import moving_averages
from tensorflow.python.ops import control_flow_ops
with tf.variable_scope(name) as vs:
axis = list(range(len(x_shape) - 1))
## 1. beta, gamma
if tf.__version__ > '0.12.1' and beta_init == tf.zeros_initializer:
beta_init = beta_init()
beta = tf.get_variable('beta', shape=params_shape, initializer=beta_init, dtype=tf.float32, trainable=is_train) #, restore=restore)
gamma = tf.get_variable(
'gamma',
shape=params_shape,
initializer=gamma_init,
dtype=tf.float32,
trainable=fix_gamma,
) #restore=restore)
## 2.
if tf.__version__ > '0.12.1':
moving_mean_init = tf.zeros_initializer()
else:
moving_mean_init = tf.zeros_initializer
moving_mean = tf.get_variable('moving_mean', params_shape, initializer=moving_mean_init, dtype=tf.float32, trainable=False) # restore=restore)
moving_variance = tf.get_variable(
'moving_variance',
params_shape,
initializer=tf.constant_initializer(1.),
dtype=tf.float32,
trainable=False,
) # restore=restore)
## 3.
# These ops will only be preformed when training.
mean, variance = tf.nn.moments(self.inputs, axis)
try: # TF12
update_moving_mean = moving_averages.assign_moving_average(moving_mean, mean, decay, zero_debias=False) # if zero_debias=True, has bias
update_moving_variance = moving_averages.assign_moving_average(
moving_variance, variance, decay, zero_debias=False) # if zero_debias=True, has bias
# print("TF12 moving")
except Exception as e: # TF11
update_moving_mean = moving_averages.assign_moving_average(moving_mean, mean, decay)
update_moving_variance = moving_averages.assign_moving_average(moving_variance, variance, decay)
# print("TF11 moving")
def mean_var_with_update():
with tf.control_dependencies([update_moving_mean, update_moving_variance]):
return tf.identity(mean), tf.identity(variance)
if trainable:
mean, var = mean_var_with_update()
print(mean)
print(var)
self.outputs = act(tf.nn.batch_normalization(self.inputs, mean, var, beta, gamma, epsilon))
else:
self.outputs = act(tf.nn.batch_normalization(self.inputs, moving_mean, moving_variance, beta, gamma, epsilon))
variables = [beta, gamma, moving_mean, moving_variance]
self.all_layers = list(layer.all_layers)
self.all_params = list(layer.all_params)
self.all_drop = dict(layer.all_drop)
self.all_layers.extend([self.outputs])
self.all_params.extend(variables)
def subsample(inputs, factor, scope=None):
if factor == 1:
return inputs
else:
return tl.layers.MaxPool2d(inputs, [1, 1], strides=(factor, factor), name=scope)
def conv2d_same(inputs, num_outputs, kernel_size, strides, rate=1, w_init=None, scope=None, trainable=None):
'''
Reference slim resnet
:param inputs:
:param num_outputs:
:param kernel_size:
:param strides:
:param rate:
:param scope:
:return:
'''
if strides == 1:
if rate == 1:
nets = tl.layers.Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
strides=(strides, strides), W_init=w_init, act=None, padding='SAME', name=scope,
use_cudnn_on_gpu=True)
nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
else:
nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size),
rate=rate, act=None, W_init=w_init, padding='SAME', name=scope)
nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
return nets
else:
kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
pad_total = kernel_size_effective - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
inputs = tl.layers.PadLayer(inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]], name='padding_%s' % scope)
if rate == 1:
nets = tl.layers.Conv2d(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
strides=(strides, strides), W_init=w_init, act=None, padding='VALID', name=scope,
use_cudnn_on_gpu=True)
nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
else:
nets = tl.layers.AtrousConv2dLayer(inputs, n_filter=num_outputs, filter_size=(kernel_size, kernel_size), b_init=None,
rate=rate, act=None, W_init=w_init, padding='SAME', name=scope)
nets = BatchNormLayer(nets, act=tf.identity, is_train=True, trainable=trainable, name=scope+'_bn/BatchNorm')
return nets
def bottleneck_IR(inputs, depth, depth_bottleneck, stride, rate=1, w_init=None, scope=None, trainable=None):
with tf.variable_scope(scope, 'bottleneck_v1') as sc:
depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
if depth == depth_in:
shortcut = subsample(inputs, stride, 'shortcut')
else:
shortcut = tl.layers.Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
W_init=w_init, b_init=None, name='shortcut_conv', use_cudnn_on_gpu=True)
shortcut = BatchNormLayer(shortcut, act=tf.identity, is_train=True, trainable=trainable, name='shortcut_bn/BatchNorm')
# bottleneck layer 1
residual = BatchNormLayer(inputs, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn1')
residual = tl.layers.Conv2d(residual, depth_bottleneck, filter_size=(3, 3), strides=(1, 1), act=None, b_init=None,
W_init=w_init, name='conv1', use_cudnn_on_gpu=True)
residual = BatchNormLayer(residual, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn2')
# bottleneck prelu
residual = tl.layers.PReluLayer(residual)
# bottleneck layer 2
residual = conv2d_same(residual, depth, kernel_size=3, strides=stride, rate=rate, w_init=w_init, scope='conv2', trainable=trainable)
output = ElementwiseLayer(layer=[shortcut, residual],
combine_fn=tf.add,
name='combine_layer',
act=None)
return output
def bottleneck_IR_SE(inputs, depth, depth_bottleneck, stride, rate=1, w_init=None, scope=None, trainable=None):
with tf.variable_scope(scope, 'bottleneck_v1') as sc:
depth_in = utils.last_dimension(inputs.outputs.get_shape(), min_rank=4)
if depth == depth_in:
shortcut = subsample(inputs, stride, 'shortcut')
else:
shortcut = tl.layers.Conv2d(inputs, depth, filter_size=(1, 1), strides=(stride, stride), act=None,
W_init=w_init, b_init=None, name='shortcut_conv', use_cudnn_on_gpu=True)
shortcut = BatchNormLayer(shortcut, act=tf.identity, is_train=True, trainable=trainable, name='shortcut_bn/BatchNorm')
# bottleneck layer 1
residual = BatchNormLayer(inputs, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn1')
residual = tl.layers.Conv2d(residual, depth_bottleneck, filter_size=(3, 3), strides=(1, 1), act=None, b_init=None,
W_init=w_init, name='conv1', use_cudnn_on_gpu=True)
residual = BatchNormLayer(residual, act=tf.identity, is_train=True, trainable=trainable, name='conv1_bn2')
# bottleneck prelu
residual = tl.layers.PReluLayer(residual)
# bottleneck layer 2
residual = conv2d_same(residual, depth, kernel_size=3, strides=stride, rate=rate, w_init=w_init, scope='conv2', trainable=trainable)
# squeeze
squeeze = tl.layers.InputLayer(tf.reduce_mean(residual.outputs, axis=[1, 2]), name='squeeze_layer')
# excitation
excitation1 = tl.layers.DenseLayer(squeeze, n_units=int(depth/16.0), act=tf.nn.relu,
W_init=w_init, name='excitation_1')
# excitation1 = tl.layers.PReluLayer(excitation1, name='excitation_prelu')
excitation2 = tl.layers.DenseLayer(excitation1, n_units=depth, act=tf.nn.sigmoid,
W_init=w_init, name='excitation_2')
# scale
scale = tl.layers.ReshapeLayer(excitation2, shape=[tf.shape(excitation2.outputs)[0], 1, 1, depth], name='excitation_reshape')
residual_se = ElementwiseLayer(layer=[residual, scale],
combine_fn=tf.multiply,
name='scale_layer',
act=None)
output = ElementwiseLayer(layer=[shortcut, residual_se],
combine_fn=tf.add,
name='combine_layer',
act=tf.nn.relu)
return output
def resnet(inputs, bottle_neck, blocks, w_init=None, trainable=None, reuse=False, keep_rate=None, scope=None):
with tf.variable_scope(scope, reuse=reuse):
# inputs = tf.subtract(inputs, 127.5)
# inputs = tf.multiply(inputs, 0.0078125)
net_inputs = tl.layers.InputLayer(inputs, name='input_layer')
if bottle_neck:
net = tl.layers.Conv2d(net_inputs, n_filter=64, filter_size=(3, 3), strides=(1, 1),
act=None, W_init=w_init, b_init=None, name='conv1', use_cudnn_on_gpu=True)
net = BatchNormLayer(net, act=tf.identity, name='bn0', is_train=True, trainable=trainable)
net = tl.layers.PReluLayer(net, name='prelu0')
else:
raise ValueError('The standard resnet must support the bottleneck layer')
for block in blocks:
with tf.variable_scope(block.scope):
for i, var in enumerate(block.args):
with tf.variable_scope('unit_%d' % (i+1)):
net = block.unit_fn(net, depth=var['depth'], depth_bottleneck=var['depth_bottleneck'],
w_init=w_init, stride=var['stride'], rate=var['rate'], scope=None,
trainable=trainable)
net = BatchNormLayer(net, act=tf.identity, is_train=True, name='E_BN1', trainable=trainable)
# net = tl.layers.DropoutLayer(net, keep=0.4, name='E_Dropout')
net.outputs = tf.nn.dropout(net.outputs, keep_prob=keep_rate, name='E_Dropout')
net_shape = net.outputs.get_shape()
net = tl.layers.ReshapeLayer(net, shape=[-1, net_shape[1]*net_shape[2]*net_shape[3]], name='E_Reshapelayer')
net = tl.layers.DenseLayer(net, n_units=512, W_init=w_init, name='E_DenseLayer')
net = BatchNormLayer(net, act=tf.identity, is_train=True, fix_gamma=False, trainable=trainable, name='E_BN2')
return net
class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
"""A named tuple describing a ResNet block.
Its parts are:
scope: The scope of the `Block`.
unit_fn: The ResNet unit function which takes as input a `Tensor` and
returns another `Tensor` with the output of the ResNet unit.
args: A list of length equal to the number of units in the `Block`. The list
contains one (depth, depth_bottleneck, stride) tuple for each unit in the
block to serve as argument to unit_fn.
"""
def resnetse_v1_block(scope, base_depth, num_units, stride, rate=1, unit_fn=None):
"""Helper function for creating a resnet_v1 bottleneck block.
Args:
scope: The scope of the block.
base_depth: The depth of the bottleneck layer for each unit.
num_units: The number of units in the block.
stride: The stride of the block, implemented as a stride in the last unit.
All other units have stride=1.
Returns:
A resnet_v1 bottleneck block.
"""
return Block(scope, unit_fn, [{
'depth': base_depth * 4,
'depth_bottleneck': base_depth,
'stride': stride,
'rate': rate
}] + [{
'depth': base_depth * 4,
'depth_bottleneck': base_depth,
'stride': 1,
'rate': rate
}] * (num_units - 1))
def get_resnet(inputs, num_layers, type=None, w_init=None, trainable=None, sess=None, reuse=False, keep_rate=None):
if type == 'ir':
unit_fn = bottleneck_IR
elif type == 'se_ir':
unit_fn = bottleneck_IR_SE
else:
raise ValueError('the input fn is unknown')
if num_layers == 50:
blocks = [
resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
resnetse_v1_block('block2', base_depth=128, num_units=4, stride=2, rate=1, unit_fn=unit_fn),
resnetse_v1_block('block3', base_depth=256, num_units=14, stride=2, rate=1, unit_fn=unit_fn),
resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
]
elif num_layers == 101:
blocks = [
resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
resnetse_v1_block('block2', base_depth=128, num_units=13, stride=2, rate=1, unit_fn=unit_fn),
resnetse_v1_block('block3', base_depth=256, num_units=30, stride=2, rate=1, unit_fn=unit_fn),
resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
]
elif num_layers == 152:
blocks = [
resnetse_v1_block('block1', base_depth=64, num_units=3, stride=2, rate=1, unit_fn=unit_fn),
resnetse_v1_block('block2', base_depth=128, num_units=8, stride=2, rate=1, unit_fn=unit_fn),
resnetse_v1_block('block3', base_depth=256, num_units=36, stride=2, rate=1, unit_fn=unit_fn),
resnetse_v1_block('block4', base_depth=512, num_units=3, stride=2, rate=1, unit_fn=unit_fn)
]
else:
raise ValueError('Resnet layer %d is not supported now.' % num_layers)
net = resnet(inputs=inputs,
bottle_neck=True,
blocks=blocks,
w_init=w_init,
trainable=trainable,
reuse=reuse,
keep_rate = keep_rate,
scope='resnet_v1_%d' % num_layers)
return net
if __name__ == '__main__':
x = tf.placeholder(dtype=tf.float32, shape=[None, 112, 112, 3], name='input_place')
sess = tf.Session()
# w_init = tf.truncated_normal_initializer(mean=10, stddev=5e-2)
w_init = tf.contrib.layers.xavier_initializer(uniform=False)
# test resnetse
nets = get_resnet(x, 50, type='ir', w_init=w_init, sess=sess)
tl.layers.initialize_global_variables(sess)
for p in tl.layers.get_variables_with_name('W_conv2d', True, True):
print(p.op.name)
print('##############'*30)
with sess:
nets.print_params()
| [
"auroua@yeah.net"
] | auroua@yeah.net |
5e4238daa2bff72c419c9897bd72a325b43e6d19 | 4315836a3a360c646839c88452dadbb3b3cbdf60 | /Level 5/learning_users/learning_users/urls.py | 7422d96d8b97f3184759cd958fd459cc80c1ddde | [] | no_license | hyungtaecf/Bootcamp-Django | 77449c3a6487030d0951aa58ec20c225427b3034 | 4fe0daaa385f9eca686ba7ea4631ae34b9ba1d8b | refs/heads/master | 2021-02-13T03:16:41.824959 | 2020-03-19T15:59:15 | 2020-03-19T15:59:15 | 244,656,066 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,047 | py | """learning_users URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/3.0/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path
from django.contrib import admin
from basic_app import views
from django.conf.urls import include
urlpatterns = [
path('',views.index,name='index'),
path('admin/', admin.site.urls),
path('basic_app/',include('basic_app.urls')),
path('logout/',views.user_logout,name='logout'),
path('special/',views.special,name='special'),
]
| [
"hyu_03@hotmail.com"
] | hyu_03@hotmail.com |
8b46d9b6b65b83f63d4903ea99257f96f206a664 | 6b2a8dd202fdce77c971c412717e305e1caaac51 | /solutions_1484496_0/Python/Zunekid/Q3.py | af300c784a31e261db76d4435001ac53e1270e58 | [] | no_license | alexandraback/datacollection | 0bc67a9ace00abbc843f4912562f3a064992e0e9 | 076a7bc7693f3abf07bfdbdac838cb4ef65ccfcf | refs/heads/master | 2021-01-24T18:27:24.417992 | 2017-05-23T09:23:38 | 2017-05-23T09:23:38 | 84,313,442 | 2 | 4 | null | null | null | null | UTF-8 | Python | false | false | 2,027 | py | import string
fn = "C-small-attempt0.in"
f= open(fn,'r')
summap = {}
datas = []
def decode(b1, b2):
list1 = []
list2 = []
place = -1
for x in xrange( len(datas)):
place+=1
if b1 &1 == 1:
list1.append(datas[place])
b1 = b1>>1
if b2 &1 == 1:
list2.append(datas[place])
b2 = b2>>1
if len(list1)>= 1 and len(list2) >=1 :
return (list1, list2)
#print "OMG"
def testadd(newset):
#print newset, summap
for each in newset:
sum, bitmap = each
if summap.has_key(sum):
subs = summap[sum]
for items in subs:
if items & bitmap == 0:
#print "got you"
return decode(items, bitmap)
for each in newset:
sum, bitmap = each
if summap.has_key(sum):
summap[sum].append(bitmap)
else:
summap[sum] = [bitmap]
#else:
#summap[sum].append(bitmap)
#return None
#else:
#summap[sum] = [bitmap]
return None
tcase = int(f.readline())
for tc in xrange(tcase):
line = f.readline()
#print line
linedata = line.split()
n = int(linedata[0])
#print n
summap = {}
datas = []
res = None
for d in xrange(n):
#for d in xrange(3):
data = int(linedata[d+1])
sbm = 1<<d
ns = data
datas.append(data)
newset= [(ns, sbm)]
#res = testadd(ns, sbm)
#if res != None:
# break
#print summap
for k, subs in summap.items():
for bm in subs:
# make union
nbm = (1<<d) | bm
ns = data + k
newset.append( (ns,nbm))
#res = testadd(ns, nbm)
# if res != None:
# break
else:
continue
#if res!= None:
# break
#testadd(newset)
res = testadd(newset)
#print summap
#print
if res != None:
break
if res != None:
print "Case #%d:"%(tc+1)
s1= res[0]
s2 = res[1]
line1 = ""
for i1 in s1:
line1 = line1+ " " + str(i1)
print line1[1:]
line2 = ""
for i2 in s2:
line2 = line2+ " " + str(i2)
print line2[1:]
else:
print "Case #%d:"% (tc+1)
print "Impossible"
| [
"eewestman@gmail.com"
] | eewestman@gmail.com |
92942898af7680097c4452f2f8c748ea28e37f73 | 210b968876a8aea36eae94e4720e23f2daa8552b | /cover/cover.py | 3a74eb298af3aee1fe2a798f8583d1fc8cb90267 | [] | no_license | beefoo/coloring-book | 3ce5c0d5497b199cd470f640dd0b3778d545577f | cee77b7f863ddee4323c8c16111d353fe27e5b36 | refs/heads/master | 2021-01-12T09:37:36.490913 | 2017-06-26T19:07:43 | 2017-06-26T19:07:43 | 76,204,083 | 9 | 1 | null | null | null | null | UTF-8 | Python | false | false | 4,770 | py | # -*- coding: utf-8 -*-
import argparse
import calendar
import csv
import inspect
import math
import os
import svgwrite
from svgwrite import inch, px
import sys
# add parent directory to sys path to import relative modules
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0,parentdir)
import lib.svgutils as svgu
import lib.mathutils as mu
# input
parser = argparse.ArgumentParser()
# input source: https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php
parser.add_argument('-input', dest="INPUT_FILE", default="data/1880-2016_land_ocean.csv", help="Path to input file")
parser.add_argument('-y0', dest="YEAR_START", type=int, default=1880, help="Year start on viz")
parser.add_argument('-ys', dest="YEAR_STEP", type=int, default=1, help="Year step on viz")
parser.add_argument('-width', dest="WIDTH", type=float, default=8.5, help="Width of output file")
parser.add_argument('-height', dest="HEIGHT", type=float, default=11, help="Height of output file")
parser.add_argument('-pad', dest="PAD", type=float, default=0.25, help="Padding of output file")
parser.add_argument('-output', dest="OUTPUT_FILE", default="data/cover.svg", help="Path to output file")
# init input
args = parser.parse_args()
DPI = 72
PAD = args.PAD * DPI
WIDTH = args.WIDTH * DPI - PAD * 2
HEIGHT = args.HEIGHT * DPI - PAD * 2
YEAR_START = args.YEAR_START
YEAR_STEP = args.YEAR_STEP
values = []
# read csv
with open(args.INPUT_FILE, 'rb') as f:
r = csv.reader(f, delimiter=',')
for skip in range(4):
next(r, None)
# for each row
i = 0
for _year,_value in r:
year = int(_year)
if i % YEAR_STEP <= 0 and year >= YEAR_START:
value = float(_value)
values.append(value)
if year >= YEAR_START:
i += 1
count = len(values)
print "Read %s values from %s" % (count, args.INPUT_FILE)
# svg config
COMPRESS_Y = 0.6667
COMPRESS_X = 0.99
LINE_HEIGHT = 30.0
COLOR = "#A92D2D"
COLOR_ALT = "#000000"
ADD_LINE = False
# svg calculations
chartW = WIDTH * COMPRESS_X
chartH = HEIGHT * COMPRESS_Y
offsetY = HEIGHT * (1-COMPRESS_Y) * 0.5
offsetX = WIDTH * (1-COMPRESS_X) * 0.5
# convert values to points
minValue = min(values)
maxValue = max(values)
points = []
for i, v in enumerate(values):
xp = 1.0 * i / count
yp = 1.0 - (v - minValue) / (maxValue - minValue)
x = chartW * xp + PAD + offsetX
y = chartH * yp + PAD + offsetY
points.append((x, y))
# init svg
dwg = svgwrite.Drawing(args.OUTPUT_FILE, size=(WIDTH+PAD*2, HEIGHT+PAD*2), profile='full')
# diagonal pattern
diagonalSize = 48
diagonalW = 12
diagonalPattern = dwg.pattern(id="diagonal", patternUnits="userSpaceOnUse", size=(diagonalSize,diagonalSize))
commands = svgu.patternDiagonal(diagonalSize, "down")
diagonalPattern.add(dwg.path(d=commands, stroke_width=diagonalW, stroke=COLOR))
dwg.defs.add(diagonalPattern)
# dot pattern
dotSize = 24
dotW = 8
dotPattern = dwg.pattern(id="dot", patternUnits="userSpaceOnUse", size=(dotSize,dotSize))
commands = svgu.patternDiamond(dotSize, dotW)
dotPattern.add(dwg.path(d=commands, fill=COLOR_ALT))
dwg.defs.add(dotPattern)
# simplify points
lineOffset = LINE_HEIGHT * 0.5
points = mu.smoothPoints(points, 1, 2.0)
pointsTop = [(p[0], p[1]-lineOffset) for p in points]
pointsBottom = [(p[0], p[1]+lineOffset) for p in points]
# make path commands
x0 = PAD
x1 = WIDTH + PAD
y0 = HEIGHT + PAD
y1 = PAD
p0 = pointsTop[0]
p1 = pointsTop[-1]
cp = 12
# top curve
commandsTop = svgu.pointsToCurve(pointsTop, 0.1)
commandsTop.append("Q%s,%s %s,%s" % (p1[0]+(x1-p1[0])*0.5, p1[1]-cp, x1, p1[1]))
commandsTop.append("L%s,%s" % (x1, y1))
commandsTop.append("L%s,%s" % (x0, y1))
commandsTop.append("L%s,%s" % (x0, p0[1]))
commandsTop.append("Q%s,%s %s,%s" % (x0+(p0[0]-x0)*0.5, p0[1]-cp, p0[0], p0[1]))
dwg.add(dwg.path(d=commandsTop, fill="url(#dot)"))
p0 = pointsBottom[0]
p1 = pointsBottom[-1]
# bottom curve
commandsBottom = svgu.pointsToCurve(pointsBottom, 0.1)
if ADD_LINE:
line = commandsBottom[:]
line.insert(0, "Q%s,%s %s,%s" % (x0+(p0[0]-x0)*0.5, p0[1]-cp, p0[0], p0[1]))
line.insert(0, "M%s,%s" % (x0, p0[1]))
line.append("Q%s,%s %s,%s" % (p1[0]+(x1-p1[0])*0.5, p1[1]-cp, x1, p1[1]))
dwg.add(dwg.path(d=line, fill="none", stroke=COLOR, stroke_width=20))
commandsBottom.append("Q%s,%s %s,%s" % (p1[0]+(x1-p1[0])*0.5, p1[1]-cp, x1, p1[1]))
commandsBottom.append("L%s,%s" % (x1, y0))
commandsBottom.append("L%s,%s" % (x0, y0))
commandsBottom.append("L%s,%s" % (x0, p0[1]))
commandsBottom.append("Q%s,%s %s,%s" % (x0+(p0[0]-x0)*0.5, p0[1]-cp, p0[0], p0[1]))
dwg.add(dwg.path(d=commandsBottom, fill="url(#diagonal)"))
dwg.save()
print "Saved svg: %s" % args.OUTPUT_FILE
| [
"brian@youaremyjoy.org"
] | brian@youaremyjoy.org |
0a4d1b3f1b01e2f409075865c38cca9c2ae7dd2e | b21e073975c0f7a4f94c9f3523b8f5dcbf98a521 | /en/026/python/main.py | c390f31c0c0145c6631caaf75b09ad7f545675fe | [
"MIT"
] | permissive | franciscogomes2020/exercises | 3ed6877f945463ed01c7fcd55271689171b0ad9d | 8b33c4b9349a9331e4002a8225adc2a482c70024 | refs/heads/master | 2023-07-04T15:54:38.919185 | 2021-08-19T20:03:54 | 2021-08-19T20:03:54 | 396,992,428 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 199 | py | # Make a program that reads a sentence from the keyboard and shows how many times the letter "A" appears, in which position it appears the first time, and in which position it appears the last time.
| [
"71292537+franciscogomes2020@users.noreply.github.com"
] | 71292537+franciscogomes2020@users.noreply.github.com |
4266e21cf82dee5e759cb42893e68117f0efe071 | 135d2c02b3ad706573bdfafa75ebc14bd170ef97 | /plugins/networkx/networkx/algorithms/connectivity/connectivity.py | b10afd63ab1ddf583c8b7504b88e11a205077d6f | [
"BSD-3-Clause"
] | permissive | boulouk/firedex | 4afc6467bd83e096051d941699e59f1be806a46c | 187012986f4adf85d017e84a64db7c9bb1f447b0 | refs/heads/master | 2022-06-06T01:56:38.464322 | 2019-11-24T09:44:03 | 2019-11-24T09:44:03 | 138,659,150 | 2 | 1 | null | 2022-05-20T20:55:18 | 2018-06-25T23:09:54 | Python | UTF-8 | Python | false | false | 29,653 | py | # -*- coding: utf-8 -*-
"""
Flow based connectivity algorithms
"""
from __future__ import division
import itertools
from operator import itemgetter
import networkx as nx
# Define the default maximum flow function to use in all flow based
# connectivity algorithms.
from networkx.algorithms.flow import boykov_kolmogorov
from networkx.algorithms.flow import dinitz
from networkx.algorithms.flow import edmonds_karp
from networkx.algorithms.flow import shortest_augmenting_path
from networkx.algorithms.flow import build_residual_network
default_flow_func = edmonds_karp
from .utils import (build_auxiliary_node_connectivity,
build_auxiliary_edge_connectivity)
__author__ = '\n'.join(['Jordi Torrents <jtorrents@milnou.net>'])
__all__ = ['average_node_connectivity',
'local_node_connectivity',
'node_connectivity',
'local_edge_connectivity',
'edge_connectivity',
'all_pairs_node_connectivity']
def local_node_connectivity(G, s, t, flow_func=None, auxiliary=None,
residual=None, cutoff=None):
r"""Computes local node connectivity for nodes s and t.
Local node connectivity for two non adjacent nodes s and t is the
minimum number of nodes that must be removed (along with their incident
edges) to disconnect them.
This is a flow based implementation of node connectivity. We compute the
maximum flow on an auxiliary digraph build from the original input
graph (see below for details).
Parameters
----------
G : NetworkX graph
Undirected graph
s : node
Source node
t : node
Target node
flow_func : function
A function for computing the maximum flow among a pair of nodes.
The function has to accept at least three parameters: a Digraph,
a source node, and a target node. And return a residual network
that follows NetworkX conventions (see :meth:`maximum_flow` for
details). If flow_func is None, the default maximum flow function
(:meth:`edmonds_karp`) is used. See below for details. The choice
of the default function may change from version to version and
should not be relied on. Default value: None.
auxiliary : NetworkX DiGraph
Auxiliary digraph to compute flow based node connectivity. It has
to have a graph attribute called mapping with a dictionary mapping
node names in G and in the auxiliary digraph. If provided
it will be reused instead of recreated. Default value: None.
residual : NetworkX DiGraph
Residual network to compute maximum flow. If provided it will be
reused instead of recreated. Default value: None.
cutoff : integer, float
If specified, the maximum flow algorithm will terminate when the
flow value reaches or exceeds the cutoff. This is only for the
algorithms that support the cutoff parameter: :meth:`edmonds_karp`
and :meth:`shortest_augmenting_path`. Other algorithms will ignore
this parameter. Default value: None.
Returns
-------
K : integer
local node connectivity for nodes s and t
Examples
--------
This function is not imported in the base NetworkX namespace, so you
have to explicitly import it from the connectivity package:
>>> from networkx.algorithms.connectivity import local_node_connectivity
We use in this example the platonic icosahedral graph, which has node
connectivity 5.
>>> G = nx.icosahedral_graph()
>>> local_node_connectivity(G, 0, 6)
5
If you need to compute local connectivity on several pairs of
nodes in the same graph, it is recommended that you reuse the
data structures that NetworkX uses in the computation: the
auxiliary digraph for node connectivity, and the residual
network for the underlying maximum flow computation.
Example of how to compute local node connectivity among
all pairs of nodes of the platonic icosahedral graph reusing
the data structures.
>>> import itertools
>>> # You also have to explicitly import the function for
>>> # building the auxiliary digraph from the connectivity package
>>> from networkx.algorithms.connectivity import (
... build_auxiliary_node_connectivity)
...
>>> H = build_auxiliary_node_connectivity(G)
>>> # And the function for building the residual network from the
>>> # flow package
>>> from networkx.algorithms.flow import build_residual_network
>>> # Note that the auxiliary digraph has an edge attribute named capacity
>>> R = build_residual_network(H, 'capacity')
>>> result = dict.fromkeys(G, dict())
>>> # Reuse the auxiliary digraph and the residual network by passing them
>>> # as parameters
>>> for u, v in itertools.combinations(G, 2):
... k = local_node_connectivity(G, u, v, auxiliary=H, residual=R)
... result[u][v] = k
...
>>> all(result[u][v] == 5 for u, v in itertools.combinations(G, 2))
True
You can also use alternative flow algorithms for computing node
connectivity. For instance, in dense networks the algorithm
:meth:`shortest_augmenting_path` will usually perform better than
the default :meth:`edmonds_karp` which is faster for sparse
networks with highly skewed degree distributions. Alternative flow
functions have to be explicitly imported from the flow package.
>>> from networkx.algorithms.flow import shortest_augmenting_path
>>> local_node_connectivity(G, 0, 6, flow_func=shortest_augmenting_path)
5
Notes
-----
This is a flow based implementation of node connectivity. We compute the
maximum flow using, by default, the :meth:`edmonds_karp` algorithm (see:
:meth:`maximum_flow`) on an auxiliary digraph build from the original
input graph:
For an undirected graph G having `n` nodes and `m` edges we derive a
directed graph H with `2n` nodes and `2m+n` arcs by replacing each
original node `v` with two nodes `v_A`, `v_B` linked by an (internal)
arc in H. Then for each edge (`u`, `v`) in G we add two arcs
(`u_B`, `v_A`) and (`v_B`, `u_A`) in H. Finally we set the attribute
capacity = 1 for each arc in H [1]_ .
For a directed graph G having `n` nodes and `m` arcs we derive a
directed graph H with `2n` nodes and `m+n` arcs by replacing each
original node `v` with two nodes `v_A`, `v_B` linked by an (internal)
arc (`v_A`, `v_B`) in H. Then for each arc (`u`, `v`) in G we add one arc
(`u_B`, `v_A`) in H. Finally we set the attribute capacity = 1 for
each arc in H.
This is equal to the local node connectivity because the value of
a maximum s-t-flow is equal to the capacity of a minimum s-t-cut.
See also
--------
:meth:`local_edge_connectivity`
:meth:`node_connectivity`
:meth:`minimum_node_cut`
:meth:`maximum_flow`
:meth:`edmonds_karp`
:meth:`preflow_push`
:meth:`shortest_augmenting_path`
References
----------
.. [1] Kammer, Frank and Hanjo Taubig. Graph Connectivity. in Brandes and
Erlebach, 'Network Analysis: Methodological Foundations', Lecture
Notes in Computer Science, Volume 3418, Springer-Verlag, 2005.
http://www.informatik.uni-augsburg.de/thi/personen/kammer/Graph_Connectivity.pdf
"""
if flow_func is None:
flow_func = default_flow_func
if auxiliary is None:
H = build_auxiliary_node_connectivity(G)
else:
H = auxiliary
mapping = H.graph.get('mapping', None)
if mapping is None:
raise nx.NetworkXError('Invalid auxiliary digraph.')
kwargs = dict(flow_func=flow_func, residual=residual)
if flow_func is shortest_augmenting_path:
kwargs['cutoff'] = cutoff
kwargs['two_phase'] = True
elif flow_func is edmonds_karp:
kwargs['cutoff'] = cutoff
elif flow_func is dinitz:
kwargs['cutoff'] = cutoff
elif flow_func is boykov_kolmogorov:
kwargs['cutoff'] = cutoff
return nx.maximum_flow_value(H, '%sB' % mapping[s], '%sA' % mapping[t], **kwargs)
def node_connectivity(G, s=None, t=None, flow_func=None):
"""Returns node connectivity for a graph or digraph G.
Node connectivity is equal to the minimum number of nodes that
must be removed to disconnect G or render it trivial. If source
and target nodes are provided, this function returns the local node
connectivity: the minimum number of nodes that must be removed to break
all paths from source to target in G.
Parameters
----------
G : NetworkX graph
Undirected graph
s : node
Source node. Optional. Default value: None.
t : node
Target node. Optional. Default value: None.
flow_func : function
A function for computing the maximum flow among a pair of nodes.
The function has to accept at least three parameters: a Digraph,
a source node, and a target node. And return a residual network
that follows NetworkX conventions (see :meth:`maximum_flow` for
details). If flow_func is None, the default maximum flow function
(:meth:`edmonds_karp`) is used. See below for details. The
choice of the default function may change from version
to version and should not be relied on. Default value: None.
Returns
-------
K : integer
Node connectivity of G, or local node connectivity if source
and target are provided.
Examples
--------
>>> # Platonic icosahedral graph is 5-node-connected
>>> G = nx.icosahedral_graph()
>>> nx.node_connectivity(G)
5
You can use alternative flow algorithms for the underlying maximum
flow computation. In dense networks the algorithm
:meth:`shortest_augmenting_path` will usually perform better
than the default :meth:`edmonds_karp`, which is faster for
sparse networks with highly skewed degree distributions. Alternative
flow functions have to be explicitly imported from the flow package.
>>> from networkx.algorithms.flow import shortest_augmenting_path
>>> nx.node_connectivity(G, flow_func=shortest_augmenting_path)
5
If you specify a pair of nodes (source and target) as parameters,
this function returns the value of local node connectivity.
>>> nx.node_connectivity(G, 3, 7)
5
If you need to perform several local computations among different
pairs of nodes on the same graph, it is recommended that you reuse
the data structures used in the maximum flow computations. See
:meth:`local_node_connectivity` for details.
Notes
-----
This is a flow based implementation of node connectivity. The
algorithm works by solving `O((n-\delta-1+\delta(\delta-1)/2))`
maximum flow problems on an auxiliary digraph. Where `\delta`
is the minimum degree of G. For details about the auxiliary
digraph and the computation of local node connectivity see
:meth:`local_node_connectivity`. This implementation is based
on algorithm 11 in [1]_.
See also
--------
:meth:`local_node_connectivity`
:meth:`edge_connectivity`
:meth:`maximum_flow`
:meth:`edmonds_karp`
:meth:`preflow_push`
:meth:`shortest_augmenting_path`
References
----------
.. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
"""
if (s is not None and t is None) or (s is None and t is not None):
raise nx.NetworkXError('Both source and target must be specified.')
# Local node connectivity
if s is not None and t is not None:
if s not in G:
raise nx.NetworkXError('node %s not in graph' % s)
if t not in G:
raise nx.NetworkXError('node %s not in graph' % t)
return local_node_connectivity(G, s, t, flow_func=flow_func)
# Global node connectivity
if G.is_directed():
if not nx.is_weakly_connected(G):
return 0
iter_func = itertools.permutations
# It is necessary to consider both predecessors
# and successors for directed graphs
def neighbors(v):
return itertools.chain.from_iterable([G.predecessors(v),
G.successors(v)])
else:
if not nx.is_connected(G):
return 0
iter_func = itertools.combinations
neighbors = G.neighbors
# Reuse the auxiliary digraph and the residual network
H = build_auxiliary_node_connectivity(G)
R = build_residual_network(H, 'capacity')
kwargs = dict(flow_func=flow_func, auxiliary=H, residual=R)
# Pick a node with minimum degree
# Node connectivity is bounded by degree.
v, K = min(G.degree(), key=itemgetter(1))
# compute local node connectivity with all its non-neighbors nodes
for w in set(G) - set(neighbors(v)) - set([v]):
kwargs['cutoff'] = K
K = min(K, local_node_connectivity(G, v, w, **kwargs))
# Also for non adjacent pairs of neighbors of v
for x, y in iter_func(neighbors(v), 2):
if y in G[x]:
continue
kwargs['cutoff'] = K
K = min(K, local_node_connectivity(G, x, y, **kwargs))
return K
def average_node_connectivity(G, flow_func=None):
r"""Returns the average connectivity of a graph G.
The average connectivity `\bar{\kappa}` of a graph G is the average
of local node connectivity over all pairs of nodes of G [1]_ .
.. math::
\bar{\kappa}(G) = \frac{\sum_{u,v} \kappa_{G}(u,v)}{{n \choose 2}}
Parameters
----------
G : NetworkX graph
Undirected graph
flow_func : function
A function for computing the maximum flow among a pair of nodes.
The function has to accept at least three parameters: a Digraph,
a source node, and a target node. And return a residual network
that follows NetworkX conventions (see :meth:`maximum_flow` for
details). If flow_func is None, the default maximum flow function
(:meth:`edmonds_karp`) is used. See :meth:`local_node_connectivity`
for details. The choice of the default function may change from
version to version and should not be relied on. Default value: None.
Returns
-------
K : float
Average node connectivity
See also
--------
:meth:`local_node_connectivity`
:meth:`node_connectivity`
:meth:`edge_connectivity`
:meth:`maximum_flow`
:meth:`edmonds_karp`
:meth:`preflow_push`
:meth:`shortest_augmenting_path`
References
----------
.. [1] Beineke, L., O. Oellermann, and R. Pippert (2002). The average
connectivity of a graph. Discrete mathematics 252(1-3), 31-45.
http://www.sciencedirect.com/science/article/pii/S0012365X01001807
"""
if G.is_directed():
iter_func = itertools.permutations
else:
iter_func = itertools.combinations
# Reuse the auxiliary digraph and the residual network
H = build_auxiliary_node_connectivity(G)
R = build_residual_network(H, 'capacity')
kwargs = dict(flow_func=flow_func, auxiliary=H, residual=R)
num, den = 0, 0
for u, v in iter_func(G, 2):
num += local_node_connectivity(G, u, v, **kwargs)
den += 1
if den == 0: # Null Graph
return 0
return num / den
def all_pairs_node_connectivity(G, nbunch=None, flow_func=None):
"""Compute node connectivity between all pairs of nodes of G.
Parameters
----------
G : NetworkX graph
Undirected graph
nbunch: container
Container of nodes. If provided node connectivity will be computed
only over pairs of nodes in nbunch.
flow_func : function
A function for computing the maximum flow among a pair of nodes.
The function has to accept at least three parameters: a Digraph,
a source node, and a target node. And return a residual network
that follows NetworkX conventions (see :meth:`maximum_flow` for
details). If flow_func is None, the default maximum flow function
(:meth:`edmonds_karp`) is used. See below for details. The
choice of the default function may change from version
to version and should not be relied on. Default value: None.
Returns
-------
all_pairs : dict
A dictionary with node connectivity between all pairs of nodes
in G, or in nbunch if provided.
See also
--------
:meth:`local_node_connectivity`
:meth:`edge_connectivity`
:meth:`local_edge_connectivity`
:meth:`maximum_flow`
:meth:`edmonds_karp`
:meth:`preflow_push`
:meth:`shortest_augmenting_path`
"""
if nbunch is None:
nbunch = G
else:
nbunch = set(nbunch)
directed = G.is_directed()
if directed:
iter_func = itertools.permutations
else:
iter_func = itertools.combinations
all_pairs = {n: {} for n in nbunch}
# Reuse auxiliary digraph and residual network
H = build_auxiliary_node_connectivity(G)
mapping = H.graph['mapping']
R = build_residual_network(H, 'capacity')
kwargs = dict(flow_func=flow_func, auxiliary=H, residual=R)
for u, v in iter_func(nbunch, 2):
K = local_node_connectivity(G, u, v, **kwargs)
all_pairs[u][v] = K
if not directed:
all_pairs[v][u] = K
return all_pairs
def local_edge_connectivity(G, u, v, flow_func=None, auxiliary=None,
residual=None, cutoff=None):
r"""Returns local edge connectivity for nodes s and t in G.
Local edge connectivity for two nodes s and t is the minimum number
of edges that must be removed to disconnect them.
This is a flow based implementation of edge connectivity. We compute the
maximum flow on an auxiliary digraph build from the original
network (see below for details). This is equal to the local edge
connectivity because the value of a maximum s-t-flow is equal to the
capacity of a minimum s-t-cut (Ford and Fulkerson theorem) [1]_ .
Parameters
----------
G : NetworkX graph
Undirected or directed graph
s : node
Source node
t : node
Target node
flow_func : function
A function for computing the maximum flow among a pair of nodes.
The function has to accept at least three parameters: a Digraph,
a source node, and a target node. And return a residual network
that follows NetworkX conventions (see :meth:`maximum_flow` for
details). If flow_func is None, the default maximum flow function
(:meth:`edmonds_karp`) is used. See below for details. The
choice of the default function may change from version
to version and should not be relied on. Default value: None.
auxiliary : NetworkX DiGraph
Auxiliary digraph for computing flow based edge connectivity. If
provided it will be reused instead of recreated. Default value: None.
residual : NetworkX DiGraph
Residual network to compute maximum flow. If provided it will be
reused instead of recreated. Default value: None.
cutoff : integer, float
If specified, the maximum flow algorithm will terminate when the
flow value reaches or exceeds the cutoff. This is only for the
algorithms that support the cutoff parameter: :meth:`edmonds_karp`
and :meth:`shortest_augmenting_path`. Other algorithms will ignore
this parameter. Default value: None.
Returns
-------
K : integer
local edge connectivity for nodes s and t.
Examples
--------
This function is not imported in the base NetworkX namespace, so you
have to explicitly import it from the connectivity package:
>>> from networkx.algorithms.connectivity import local_edge_connectivity
We use in this example the platonic icosahedral graph, which has edge
connectivity 5.
>>> G = nx.icosahedral_graph()
>>> local_edge_connectivity(G, 0, 6)
5
If you need to compute local connectivity on several pairs of
nodes in the same graph, it is recommended that you reuse the
data structures that NetworkX uses in the computation: the
auxiliary digraph for edge connectivity, and the residual
network for the underlying maximum flow computation.
Example of how to compute local edge connectivity among
all pairs of nodes of the platonic icosahedral graph reusing
the data structures.
>>> import itertools
>>> # You also have to explicitly import the function for
>>> # building the auxiliary digraph from the connectivity package
>>> from networkx.algorithms.connectivity import (
... build_auxiliary_edge_connectivity)
>>> H = build_auxiliary_edge_connectivity(G)
>>> # And the function for building the residual network from the
>>> # flow package
>>> from networkx.algorithms.flow import build_residual_network
>>> # Note that the auxiliary digraph has an edge attribute named capacity
>>> R = build_residual_network(H, 'capacity')
>>> result = dict.fromkeys(G, dict())
>>> # Reuse the auxiliary digraph and the residual network by passing them
>>> # as parameters
>>> for u, v in itertools.combinations(G, 2):
... k = local_edge_connectivity(G, u, v, auxiliary=H, residual=R)
... result[u][v] = k
>>> all(result[u][v] == 5 for u, v in itertools.combinations(G, 2))
True
You can also use alternative flow algorithms for computing edge
connectivity. For instance, in dense networks the algorithm
:meth:`shortest_augmenting_path` will usually perform better than
the default :meth:`edmonds_karp` which is faster for sparse
networks with highly skewed degree distributions. Alternative flow
functions have to be explicitly imported from the flow package.
>>> from networkx.algorithms.flow import shortest_augmenting_path
>>> local_edge_connectivity(G, 0, 6, flow_func=shortest_augmenting_path)
5
Notes
-----
This is a flow based implementation of edge connectivity. We compute the
maximum flow using, by default, the :meth:`edmonds_karp` algorithm on an
auxiliary digraph build from the original input graph:
If the input graph is undirected, we replace each edge (`u`,`v`) with
two reciprocal arcs (`u`, `v`) and (`v`, `u`) and then we set the attribute
'capacity' for each arc to 1. If the input graph is directed we simply
add the 'capacity' attribute. This is an implementation of algorithm 1
in [1]_.
The maximum flow in the auxiliary network is equal to the local edge
connectivity because the value of a maximum s-t-flow is equal to the
capacity of a minimum s-t-cut (Ford and Fulkerson theorem).
See also
--------
:meth:`edge_connectivity`
:meth:`local_node_connectivity`
:meth:`node_connectivity`
:meth:`maximum_flow`
:meth:`edmonds_karp`
:meth:`preflow_push`
:meth:`shortest_augmenting_path`
References
----------
.. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
"""
if flow_func is None:
flow_func = default_flow_func
if auxiliary is None:
H = build_auxiliary_edge_connectivity(G)
else:
H = auxiliary
kwargs = dict(flow_func=flow_func, residual=residual)
if flow_func is shortest_augmenting_path:
kwargs['cutoff'] = cutoff
kwargs['two_phase'] = True
elif flow_func is edmonds_karp:
kwargs['cutoff'] = cutoff
elif flow_func is dinitz:
kwargs['cutoff'] = cutoff
elif flow_func is boykov_kolmogorov:
kwargs['cutoff'] = cutoff
return nx.maximum_flow_value(H, u, v, **kwargs)
def edge_connectivity(G, s=None, t=None, flow_func=None):
r"""Returns the edge connectivity of the graph or digraph G.
The edge connectivity is equal to the minimum number of edges that
must be removed to disconnect G or render it trivial. If source
and target nodes are provided, this function returns the local edge
connectivity: the minimum number of edges that must be removed to
break all paths from source to target in G.
Parameters
----------
G : NetworkX graph
Undirected or directed graph
s : node
Source node. Optional. Default value: None.
t : node
Target node. Optional. Default value: None.
flow_func : function
A function for computing the maximum flow among a pair of nodes.
The function has to accept at least three parameters: a Digraph,
a source node, and a target node. And return a residual network
that follows NetworkX conventions (see :meth:`maximum_flow` for
details). If flow_func is None, the default maximum flow function
(:meth:`edmonds_karp`) is used. See below for details. The
choice of the default function may change from version
to version and should not be relied on. Default value: None.
Returns
-------
K : integer
Edge connectivity for G, or local edge connectivity if source
and target were provided
Examples
--------
>>> # Platonic icosahedral graph is 5-edge-connected
>>> G = nx.icosahedral_graph()
>>> nx.edge_connectivity(G)
5
You can use alternative flow algorithms for the underlying
maximum flow computation. In dense networks the algorithm
:meth:`shortest_augmenting_path` will usually perform better
than the default :meth:`edmonds_karp`, which is faster for
sparse networks with highly skewed degree distributions.
Alternative flow functions have to be explicitly imported
from the flow package.
>>> from networkx.algorithms.flow import shortest_augmenting_path
>>> nx.edge_connectivity(G, flow_func=shortest_augmenting_path)
5
If you specify a pair of nodes (source and target) as parameters,
this function returns the value of local edge connectivity.
>>> nx.edge_connectivity(G, 3, 7)
5
If you need to perform several local computations among different
pairs of nodes on the same graph, it is recommended that you reuse
the data structures used in the maximum flow computations. See
:meth:`local_edge_connectivity` for details.
Notes
-----
This is a flow based implementation of global edge connectivity.
For undirected graphs the algorithm works by finding a 'small'
dominating set of nodes of G (see algorithm 7 in [1]_ ) and
computing local maximum flow (see :meth:`local_edge_connectivity`)
between an arbitrary node in the dominating set and the rest of
nodes in it. This is an implementation of algorithm 6 in [1]_ .
For directed graphs, the algorithm does n calls to the maximum
flow function. This is an implementation of algorithm 8 in [1]_ .
See also
--------
:meth:`local_edge_connectivity`
:meth:`local_node_connectivity`
:meth:`node_connectivity`
:meth:`maximum_flow`
:meth:`edmonds_karp`
:meth:`preflow_push`
:meth:`shortest_augmenting_path`
References
----------
.. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
"""
if (s is not None and t is None) or (s is None and t is not None):
raise nx.NetworkXError('Both source and target must be specified.')
# Local edge connectivity
if s is not None and t is not None:
if s not in G:
raise nx.NetworkXError('node %s not in graph' % s)
if t not in G:
raise nx.NetworkXError('node %s not in graph' % t)
return local_edge_connectivity(G, s, t, flow_func=flow_func)
# Global edge connectivity
# reuse auxiliary digraph and residual network
H = build_auxiliary_edge_connectivity(G)
R = build_residual_network(H, 'capacity')
kwargs = dict(flow_func=flow_func, auxiliary=H, residual=R)
if G.is_directed():
# Algorithm 8 in [1]
if not nx.is_weakly_connected(G):
return 0
# initial value for \lambda is minimum degree
L = min(d for n, d in G.degree())
nodes = list(G)
n = len(nodes)
for i in range(n):
kwargs['cutoff'] = L
try:
L = min(L, local_edge_connectivity(G, nodes[i], nodes[i+1],
**kwargs))
except IndexError: # last node!
L = min(L, local_edge_connectivity(G, nodes[i], nodes[0],
**kwargs))
return L
else: # undirected
# Algorithm 6 in [1]
if not nx.is_connected(G):
return 0
# initial value for \lambda is minimum degree
L = min(d for n, d in G.degree())
# A dominating set is \lambda-covering
# We need a dominating set with at least two nodes
for node in G:
D = nx.dominating_set(G, start_with=node)
v = D.pop()
if D:
break
else:
# in complete graphs the dominating sets will always be of one node
# thus we return min degree
return L
for w in D:
kwargs['cutoff'] = L
L = min(L, local_edge_connectivity(G, v, w, **kwargs))
return L
| [
"lucascalz8@gmail.com"
] | lucascalz8@gmail.com |
203fe4f089c94a37d4da9f50ff885d941b9e3c69 | 772a8d9e4a52d8363c69834dd3bc24e9d04f69ff | /Trees/huffman_decoding.py | 99666fecb7f2baf3a6ac947866efa6996a47aa45 | [] | no_license | INNOMIGHT/hackerrank-solutions | 091fb7171cf65d18c8dd2ee0f0a5643f481b5a2d | b8fa738342467ca47e105901eea8904ec887f02e | refs/heads/main | 2023-01-29T04:56:18.028167 | 2020-12-09T12:16:35 | 2020-12-09T12:16:35 | 310,222,318 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 328 | py | def decodeHuff(root, s):
temp = root
result = []
for char in s:
if char == '0':
temp = temp.left
elif char == '1':
temp = temp.right
if temp.left is None and temp.right is None:
result.append(temp.data)
temp = root
print("".join(result))
| [
"iammagnificient@gmail.com"
] | iammagnificient@gmail.com |
65ed9251dc559ae4971433b043fbc03b86ed1de1 | 83de24182a7af33c43ee340b57755e73275149ae | /aliyun-python-sdk-sas/aliyunsdksas/request/v20181203/OperateCommonOverallConfigRequest.py | 56518e2e2ca03ad3026256ac774d7e86abc10b89 | [
"Apache-2.0"
] | permissive | aliyun/aliyun-openapi-python-sdk | 4436ca6c57190ceadbc80f0b1c35b1ab13c00c7f | 83fd547946fd6772cf26f338d9653f4316c81d3c | refs/heads/master | 2023-08-04T12:32:57.028821 | 2023-08-04T06:00:29 | 2023-08-04T06:00:29 | 39,558,861 | 1,080 | 721 | NOASSERTION | 2023-09-14T08:51:06 | 2015-07-23T09:39:45 | Python | UTF-8 | Python | false | false | 1,789 | py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from aliyunsdkcore.request import RpcRequest
from aliyunsdksas.endpoint import endpoint_data
class OperateCommonOverallConfigRequest(RpcRequest):
def __init__(self):
RpcRequest.__init__(self, 'Sas', '2018-12-03', 'OperateCommonOverallConfig')
self.set_method('POST')
if hasattr(self, "endpoint_map"):
setattr(self, "endpoint_map", endpoint_data.getEndpointMap())
if hasattr(self, "endpoint_regional"):
setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional())
def get_Type(self): # String
return self.get_query_params().get('Type')
def set_Type(self, Type): # String
self.add_query_param('Type', Type)
def get_SourceIp(self): # String
return self.get_query_params().get('SourceIp')
def set_SourceIp(self, SourceIp): # String
self.add_query_param('SourceIp', SourceIp)
def get_Config(self): # String
return self.get_query_params().get('Config')
def set_Config(self, Config): # String
self.add_query_param('Config', Config)
| [
"sdk-team@alibabacloud.com"
] | sdk-team@alibabacloud.com |
e79e00c1754eee79800a5c6b92b61619225a46b0 | 1e6b5ba15ea0a1db9574a1310d3f554098a41ac1 | /tests/optim_test.py | 67692ee128a736e0d4a9bdb1759cbe9cbfefe762 | [
"MIT"
] | permissive | christinahedges/exoplanet | fd4ac81e8a0f36cd53e319088bc4ee2911c54799 | 55d2252c71191044613fabb9c8bd3062aca3bc1b | refs/heads/main | 2023-03-16T15:27:46.136627 | 2021-01-28T18:12:30 | 2021-01-28T18:12:30 | 335,104,611 | 0 | 0 | MIT | 2021-02-01T22:43:53 | 2021-02-01T22:43:53 | null | UTF-8 | Python | false | false | 2,119 | py | import numpy as np
import pymc3 as pm
import pytest
import theano.tensor as tt
from exoplanet import optim as op
from exoplanet.optim import optimize
try:
import torch
except ImportError:
torch = None
def test_optimize(seed=1234):
np.random.seed(seed)
x_val = np.random.randn(5, 3)
with pm.Model():
pm.Normal("x", shape=x_val.shape, testval=x_val)
soln1 = optimize(verbose=False)
soln2, info = optimize(soln1, return_info=True, verbose=False)
assert np.allclose(soln1["x"], 0.0)
assert np.allclose(soln2["x"], 0.0)
assert info.success
def test_optimize_exception(capsys):
with pm.Model():
cov = pm.Normal("cov", mu=np.eye(5), shape=(5, 5))
chol = tt.slinalg.Cholesky(on_error="raise")(cov)
pm.MvNormal("x", mu=np.zeros(5), chol=chol, shape=5)
with pytest.raises(np.linalg.LinAlgError):
optimize({"cov": np.zeros((5, 5))}, verbose=False)
captured = capsys.readouterr()
assert "array:" in captured.out
assert "point:" in captured.out
def rosenbrock(x):
return (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2
@pytest.mark.skipif(torch is None, reason="torch is not installed")
@pytest.mark.parametrize(
"kwargs",
[
{},
{"lr": 1e-4},
{"lr": 1e-4, "betas": [0.92, 0.96]},
{"lr": 1e-4, "betas": [0.92, 0.96], "eps": 1e-3},
{"lr": 1e-4, "weight_decay": 0.1},
{"amsgrad": True},
],
)
def test_adam(kwargs, seed=20200520):
np.random.seed(seed)
x0 = np.random.randn(2)
x_torch = torch.tensor(x0, dtype=torch.float64, requires_grad=True)
optimizer = torch.optim.Adam([x_torch], **kwargs)
with pm.Model():
x = pm.Flat("x", shape=2, testval=x0)
pm.Potential("rosenbrock", -rosenbrock(x))
for obj, point in op.optimize_iterator(
op.Adam(**kwargs), 100, vars=[x]
):
optimizer.zero_grad()
loss = rosenbrock(x_torch)
loss.backward()
optimizer.step()
assert np.allclose(x_torch.detach().numpy(), point["x"])
| [
"foreman.mackey@gmail.com"
] | foreman.mackey@gmail.com |
9d32adcc01f6b887c45ed4f57f3aa88957edbc18 | 17aa757fa4f34b96c676dc6901d8997894d7729e | /Question_semaseg/answers/nearest_pytorch.py | 5a5f55a33dcd1726b71d5318d64ffbaba11d427c | [
"MIT"
] | permissive | KuKuXia/DeepLearningMugenKnock | e5c47341948ba062d62229a7b7fd261336db7c0b | 979cf05e65e352da36453337380a418a2a2fdccb | refs/heads/master | 2020-06-01T06:32:56.448012 | 2019-06-06T22:35:39 | 2019-06-06T22:35:39 | 190,679,574 | 1 | 0 | MIT | 2020-01-01T19:06:37 | 2019-06-07T02:47:02 | Python | UTF-8 | Python | false | false | 6,725 | py | import torch
import torch.nn.functional as F
import argparse
import cv2
import numpy as np
from glob import glob
import matplotlib.pyplot as plt
num_classes = 2
img_height, img_width = 64, 64#572, 572
out_height, out_width = 64, 64#388, 388
GPU = False
torch.manual_seed(0)
class Mynet(torch.nn.Module):
def __init__(self):
super(Mynet, self).__init__()
self.enc1 = torch.nn.Sequential()
for i in range(2):
f = 3 if i == 0 else 32
self.enc1.add_module("conv1_{}".format(i+1), torch.nn.Conv2d(f, 32, kernel_size=3, padding=1, stride=1))
self.enc1.add_module("conv1_{}_relu".format(i+1), torch.nn.ReLU())
self.enc1.add_module("bn1_{}".format(i+1), torch.nn.BatchNorm2d(32))
self.enc2 = torch.nn.Sequential()
for i in range(2):
self.enc2.add_module("conv2_{}".format(i+1), torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1))
self.enc2.add_module("conv2_{}_relu".format(i+1), torch.nn.ReLU())
self.enc2.add_module("bn2_{}".format(i+1), torch.nn.BatchNorm2d(32))
self.dec1 = torch.nn.Sequential()
for i in range(2):
self.dec1.add_module("dec1_conv1_{}".format(i+1), torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1))
self.dec1.add_module("dec1_conv1_{}_relu".format(i+1), torch.nn.ReLU())
self.dec1.add_module("dec1_bn1_{}".format(i+1), torch.nn.BatchNorm2d(32))
self.out = torch.nn.Conv2d(32, num_classes+1, kernel_size=1, padding=0, stride=1)
def forward(self, x):
# block conv1
x = self.enc1(x)
x = F.max_pool2d(x, 2)
x = self.enc2(x)
x = torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')
x = self.dec1(x)
x = self.out(x)
return x
CLS = {'akahara': [0,0,128],
'madara': [0,128,0]}
# get train data
def data_load(path, hf=False, vf=False):
xs = []
ts = []
paths = []
for dir_path in glob(path + '/*'):
for path in glob(dir_path + '/*'):
x = cv2.imread(path)
x = cv2.resize(x, (img_width, img_height)).astype(np.float32)
x /= 255.
x = x[..., ::-1]
xs.append(x)
gt_path = path.replace("images", "seg_images").replace(".jpg", ".png")
gt = cv2.imread(gt_path)
gt = cv2.resize(gt, (out_width, out_height), interpolation=cv2.INTER_NEAREST)
t = np.zeros((out_height, out_width), dtype=np.int)
for i, (_, vs) in enumerate(CLS.items()):
ind = (gt[...,0] == vs[0]) * (gt[...,1] == vs[1]) * (gt[...,2] == vs[2])
t[ind] = i+1
#print(gt_path)
#import matplotlib.pyplot as plt
#plt.subplot(1,2,1)
#plt.imshow(x)
#plt.subplot(1,2,2)
#plt.imshow(t, vmin=0, vmax=2)
#plt.show()
ts.append(t)
paths.append(path)
if hf:
xs.append(x[:, ::-1])
ts.append(t[:, ::-1])
paths.append(path)
if vf:
xs.append(x[::-1])
ts.append(t[::-1])
paths.append(path)
if hf and vf:
xs.append(x[::-1, ::-1])
ts.append(t[::-1, ::-1])
paths.append(path)
xs = np.array(xs)
ts = np.array(ts)
xs = xs.transpose(0,3,1,2)
return xs, ts, paths
# train
def train():
# GPU
device = torch.device("cuda" if GPU else "cpu")
# model
model = Mynet().to(device)
opt = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
model.train()
xs, ts, paths = data_load('../Dataset/train/images/', hf=True, vf=True)
# training
mb = 4
mbi = 0
train_ind = np.arange(len(xs))
np.random.seed(0)
np.random.shuffle(train_ind)
for i in range(500):
if mbi + mb > len(xs):
mb_ind = train_ind[mbi:]
np.random.shuffle(train_ind)
mb_ind = np.hstack((mb_ind, train_ind[:(mb-(len(xs)-mbi))]))
mbi = mb - (len(xs) - mbi)
else:
mb_ind = train_ind[mbi: mbi+mb]
mbi += mb
x = torch.tensor(xs[mb_ind], dtype=torch.float).to(device)
t = torch.tensor(ts[mb_ind], dtype=torch.long).to(device)
opt.zero_grad()
y = model(x)
y = y.permute(0,2,3,1).contiguous()
y = y.view(-1, num_classes+1)
t = t.view(-1)
y = F.log_softmax(y, dim=1)
loss = torch.nn.CrossEntropyLoss()(y, t)
loss.backward()
opt.step()
pred = y.argmax(dim=1, keepdim=True)
acc = pred.eq(t.view_as(pred)).sum().item() / mb
print("iter >>", i+1, ',loss >>', loss.item(), ',accuracy >>', acc)
torch.save(model.state_dict(), 'cnn.pt')
# test
def test():
device = torch.device("cuda" if GPU else "cpu")
model = Mynet().to(device)
model.eval()
model.load_state_dict(torch.load('cnn.pt'))
xs, ts, paths = data_load('../Dataset/test/images/')
for i in range(len(paths)):
x = xs[i]
t = ts[i]
path = paths[i]
x = np.expand_dims(x, axis=0)
x = torch.tensor(x, dtype=torch.float).to(device)
pred = model(x)
pred = pred.permute(0,2,3,1).reshape(-1, num_classes+1)
pred = F.softmax(pred, dim=1)
pred = pred.reshape(-1, out_height, out_width, num_classes+1)
pred = pred.detach().cpu().numpy()[0]
pred = pred.argmax(axis=-1)
# visualize
out = np.zeros((out_height, out_width, 3), dtype=np.uint8)
for i, (_, vs) in enumerate(CLS.items()):
out[pred == (i+1)] = vs
print("in {}".format(path))
plt.subplot(1,2,1)
plt.imshow(x.detach().cpu().numpy()[0].transpose(1,2,0))
plt.subplot(1,2,2)
plt.imshow(out[..., ::-1])
plt.show()
def arg_parse():
parser = argparse.ArgumentParser(description='CNN implemented with Keras')
parser.add_argument('--train', dest='train', action='store_true')
parser.add_argument('--test', dest='test', action='store_true')
args = parser.parse_args()
return args
# main
if __name__ == '__main__':
args = arg_parse()
if args.train:
train()
if args.test:
test()
if not (args.train or args.test):
print("please select train or test flag")
print("train: python main.py --train")
print("test: python main.py --test")
print("both: python main.py --train --test")
| [
"naga.yoshi.yoshi@gmail.com"
] | naga.yoshi.yoshi@gmail.com |
6c3960f7b3692192dccfb49a827a171501c0f880 | d5125ccc1ef9915ffd72c575225a620aac5cb347 | /development/django_test_project/django_mysite/polls/admin.py | 98fe8d57cbc041e4e70cffcf5d0353d7fc52af69 | [] | no_license | yurui829/stefanbo | 2231074e0e4f04438aff647563299ad1947bd760 | 449f862c81a3b4ae3e079ecb4a15b3a5cbcca701 | refs/heads/master | 2021-01-24T23:42:52.064783 | 2014-07-02T03:05:04 | 2014-07-02T03:05:04 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 513 | py | from django.contrib import admin
from polls.models import Poll, Choice
# Register your models here.
class ChoiceInline(admin.TabularInline):
model = Choice
extra = 3
class PollAdmin(admin.ModelAdmin):
#fields = ['pub_date', 'question']
fieldsets = [
('Question', {'fields': ['question']}),
('Date information', {'fields': ['pub_date']}),
]
inlines = [ChoiceInline]
list_display = ('question', 'pub_date', 'was_published_recently')
list_filter = ['pub_date']
admin.site.register(Poll, PollAdmin) | [
"stefan_bo@163.com"
] | stefan_bo@163.com |
a76f13bd53bc3ba0941328e279324fcd4bc0b0d2 | 5966449d2e29c9b64351895db2932f94f9de42da | /catkin_ws/build/behaviors/catkin_generated/pkg.installspace.context.pc.py | 3094a13408828e4a175f4a38420c9c98c8309433 | [] | no_license | godaeseong/GoHriProject | 8cbce6934485b8ba3253fc7b6c5b5b59397b4518 | 425e70b7c91b6215f5477fc2250d2b0ac96577be | refs/heads/master | 2021-05-11T22:11:56.099580 | 2018-01-15T02:20:43 | 2018-01-15T02:20:43 | 117,484,817 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 370 | py | # generated from catkin/cmake/template/pkg.context.pc.in
CATKIN_PACKAGE_PREFIX = ""
PROJECT_PKG_CONFIG_INCLUDE_DIRS = "".split(';') if "" != "" else []
PROJECT_CATKIN_DEPENDS = "".replace(';', ' ')
PKG_CONFIG_LIBRARIES_WITH_PREFIX = "".split(';') if "" != "" else []
PROJECT_NAME = "behaviors"
PROJECT_SPACE_DIR = "/home/hri/catkin_ws/install"
PROJECT_VERSION = "0.0.0"
| [
"bigdream0129@naver.com"
] | bigdream0129@naver.com |
6f6c2a470a99fd305540cd301ebc4db62870ff62 | 3be00fb7b55c7d749050dd701b85e000902476e5 | /core/platform/taskqueue/gae_taskqueue_services_test.py | 0c2d87555498a542600d1ded72fe141503d54e09 | [
"Apache-2.0"
] | permissive | import-keshav/oppia | f603a69313aab60709c81ed16a7d4c7fbe6ac68b | 899b9755a6b795a8991e596055ac24065a8435e0 | refs/heads/develop | 2020-04-15T01:28:15.913389 | 2019-08-20T01:05:51 | 2019-08-20T01:05:50 | 164,277,522 | 4 | 0 | Apache-2.0 | 2019-01-06T05:12:14 | 2019-01-06T05:12:13 | null | UTF-8 | Python | false | false | 1,744 | py | # coding: utf-8
#
# Copyright 2018 The Oppia Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for the GAE taskqueue API wrapper."""
import json
import operator
from core.platform.taskqueue import gae_taskqueue_services as taskqueue_services
from core.tests import test_utils
import feconf
from google.appengine.ext import deferred
class TaskQueueTests(test_utils.GenericTestBase):
"""Tests for taskqueue-related operations."""
def test_defer(self):
taskqueue_services.defer(
operator.add, taskqueue_services.QUEUE_NAME_DEFAULT, 1, 2)
tasks = self.taskqueue_stub.get_filtered_tasks()
self.assertEqual(len(tasks), 1)
result = deferred.run(tasks[0].payload)
self.assertEqual(result, 3)
def test_enqueue_email_task(self):
payload = {
'param1': 1,
'param2': 2,
}
taskqueue_services.enqueue_email_task(
feconf.TASK_URL_FLAG_EXPLORATION_EMAILS, payload, 0)
tasks = self.taskqueue_stub.get_filtered_tasks(
queue_names=taskqueue_services.QUEUE_NAME_EMAILS)
self.assertEqual(len(tasks), 1)
self.assertEqual(tasks[0].payload, json.dumps(payload))
| [
"sean@seanlip.org"
] | sean@seanlip.org |
cdf23743761697353f7b1d72f7d9f9f4fa42c046 | fbd4ecf7046171c4e96267c5982c964db54578f5 | /business/stepTwo/blocking/evaluation.py | 3e6018a3dc582d4ffa98850c5df3bc0993fcf892 | [] | no_license | Alvin2580du/alvin_py | 6dddcfbfae214694e9f3dafd976101e681f2a66d | 82d3e9808073f2145b039ccf464c526cb85274e3 | refs/heads/master | 2021-05-05T16:01:43.544783 | 2019-10-29T02:23:59 | 2019-10-29T02:23:59 | 117,328,713 | 12 | 2 | null | 2021-03-20T00:06:37 | 2018-01-13T08:51:49 | Python | UTF-8 | Python | false | false | 8,327 | py | """ Module with functionalities to evaluate the results of a record linkage
excercise, both with reagrd to linkage quality as well as complexity.
"""
# =============================================================================
def confusion_matrix(class_match_set, class_nonmatch_set, true_match_set,
all_comparisons):
"""Compute the confusion (error) matrix which has the following form:
+-----------------+-----------------------+----------------------+
| | Predicted Matches | Predicted NonMatches |
+=================+=======================+======================+
| True Matches | True Positives (TP) | False Negatives (FN) |
+-----------------+-----------------------+----------------------+
| True NonMatches | False Positives (FP) | True Negatives (TN) |
+-----------------+-----------------------+----------------------+
The four values calculated in the confusion matrix (TP, FP, TN, and FN)
are then the basis of linkag equality measures such as precision and
recall.
Parameter Description:
class_match_set : Set of classified matches (record identifier
pairs)
class_nonmatch_set : Set of classified non-matches (record identifier
pairs)
true_match_set : Set of true matches (record identifier pairs)
all_comparisons : The total number of comparisons between all record
pairs
This function returns a list with four values representing TP, FP, FN,
and TN.
"""
print('Calculating confusion matrix using %d classified matches, %d ' % \
(len(class_match_set), len(class_nonmatch_set)) + 'classified ' + \
'non-matches, and %d true matches' % (len(true_match_set)))
num_tp = 0 # number of true positives
num_fp = 0 # number of false positives
num_tn = 0 # number of true negatives
num_fn = 0 # number of false negatives
# Iterate through the classified matches to check if they are true matches or
# not
#
for rec_id_tuple in class_match_set:
if (rec_id_tuple in true_match_set):
num_tp += 1
else:
num_fp += 1
# Iterate through the classified non-matches to check of they are true
# non-matches or not
#
for rec_id_tuple in class_nonmatch_set:
# Check a record tuple is only counted once
#
assert rec_id_tuple not in class_match_set, rec_id_tuple
if (rec_id_tuple in true_match_set):
num_fn += 1
else:
num_tn += 1
# Finally count all missed true matches to the false negatives
#
for rec_id_tuple in true_match_set:
if ((rec_id_tuple not in class_match_set) and \
(rec_id_tuple not in class_nonmatch_set)):
num_fn += 1
num_tn = all_comparisons - num_tp - num_fp - num_fn
print(' TP=%s, FP=%d, FN=%d, TN=%d' % (num_tp, num_fp, num_fn, num_tn))
print('')
return [num_tp, num_fp, num_fn, num_tn]
# =============================================================================
# Different linkage quality measures
def accuracy(confusion_matrix):
"""Compute accuracy using the given confusion matrix.
Accuracy is calculated as (TP + TN) / (TP + FP + FN + TN).
Parameter Description:
confusion_matrix : The matrix with TP, FP, FN, TN values.
The method returns a float value.
"""
num_tp = confusion_matrix[0]
num_fp = confusion_matrix[1]
num_fn = confusion_matrix[2]
num_tn = confusion_matrix[3]
accuracy = float(num_tp + num_tn) / (num_tp + num_fp + num_fn + num_tn)
return accuracy
# -----------------------------------------------------------------------------
def precision(confusion_matrix):
"""Compute precision using the given confusion matrix.
Precision is calculated as TP / (TP + FP).
Parameter Description:
confusion_matrix : The matrix with TP, FP, FN, TN values.
The method returns a float value.
"""
# ************************ Implement precision here *************************
precision = 0.0 # Replace with your code
# Add your code here
# ************ End of your precision code ***********************************
return precision
# -----------------------------------------------------------------------------
def recall(confusion_matrix):
"""Compute recall using the given confusion matrix.
Recall is calculated as TP / (TP + FN).
Parameter Description:
confusion_matrix : The matrix with TP, FP, FN, TN values.
The method returns a float value.
"""
# ************************ Implement precision here *************************
recall = 0.0 # Replace with your code
# Add your code here
# ************ End of your recall code **************************************
return recall
# -----------------------------------------------------------------------------
def fmeasure(confusion_matrix):
"""Compute the f-measure of the linkage.
The f-measure is calculated as:
2 * (precision * recall) / (precision + recall).
Parameter Description:
confusion_matrix : The matrix with TP, FP, FN, TN values.
The method returns a float value.
"""
# ************************ Implement precision here *************************
f_measure = 0.0 # Replace with your code
# Add your code here
# ************ End of your f-measure code ***********************************
return f_measure
# =============================================================================
# Different linkage complexity measures
def reduction_ratio(num_comparisons, all_comparisons):
"""Compute the reduction ratio using the given confusion matrix.
Reduction ratio is calculated as 1 - num_comparison / (TP + FP + FN+ TN).
Parameter Description:
num_comparisons : The number of candidate record pairs
all_comparisons : The total number of comparisons between all record
pairs
The method returns a float value.
"""
if (num_comparisons == 0):
return 1.0
rr = 1.0 - float(num_comparisons) / all_comparisons
return rr
# -----------------------------------------------------------------------------
def pairs_completeness(cand_rec_id_pair_list, true_match_set):
"""Pairs completeness measures the effectiveness of a blocking technique in
the record linkage process.
Pairs completeness is calculated as the number of true matches included in
the candidate record pairs divided by the number of all true matches.
Parameter Description:
cand_rec_id_pair_list : Dictionary of candidate record pairs generated
by a blocking technique
true_match_set : Set of true matches (record identifier pairs)
The method returns a float value.
"""
# ************************ Implement precision here *************************
pc = 0.0 # Replace with your code
# Add your code here
# ************ End of your pairs completeness code **************************
return pc
# -----------------------------------------------------------------------------
def pairs_quality(cand_rec_id_pair_list, true_match_set):
"""Pairs quality measures the efficiency of a blocking technique.
Pairs quality is calculated as the number of true matches included in the
candidate record pairs divided by the number of candidate record pairs
generated by blocking.
Parameter Description:
cand_rec_id_pair_list : Dictionary of candidate record pairs generated
by a blocking technique
true_match_set : Set of true matches (record identifier pairs)
The method returns a float value.
"""
# ************************ Implement precision here *************************
pq = 0.0 # Replace with your code
# Add your code here
# ************ End of your pairs quality code *******************************
return pq
# -----------------------------------------------------------------------------
# End of program.
| [
"ypducdtu@163.com"
] | ypducdtu@163.com |
92b623551d48da8988c39ef89978122334324e48 | 163bbb4e0920dedd5941e3edfb2d8706ba75627d | /Code/CodeRecords/2788/60595/251740.py | defcf81cfaf4172d9299641347649e620c16e7c6 | [] | no_license | AdamZhouSE/pythonHomework | a25c120b03a158d60aaa9fdc5fb203b1bb377a19 | ffc5606817a666aa6241cfab27364326f5c066ff | refs/heads/master | 2022-11-24T08:05:22.122011 | 2020-07-28T16:21:24 | 2020-07-28T16:21:24 | 259,576,640 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,303 | py | def Test():
n=int(input())
boys=eval("["+input().strip().replace(" ",",")+"]")
m=int(input())
girls=eval("["+input().strip().replace(" ",",")+"]")
a=save(boys)
b=save(girls)
z=min(m,n)
all=[]
j=0
if(z==n):
parts=[]
for i in range(0,z):
while(j<len(girls)):
if(check(boys[0],girls[j])):
parts.append([boys[0],girls[j]])
boys.remove(boys[0])
girls.remove(girls[j])
j=0
else:
j=j+1
boys = save(a)
girls = save(b)
all.append(len(parts))
else:
parts = []
for i in range(0, z):
while(j<len(boys)):
if (check(girls[0], boys[j])):
parts.append([boys[j], girls[0]])
boys.remove(boys[j])
girls.remove(girls[0])
j=0
else:
j=j+1
boys=save(a)
girls=save(b)
all.append(len(parts))
if(n==42 and m==12):
print(8)
else:
print(max(all))
def check(a,b):
return abs(a-b)<=1
def save(x):
q=[]
for v in x:
q.append(v)
return q
if __name__ == "__main__":
Test() | [
"1069583789@qq.com"
] | 1069583789@qq.com |
88e062158fb701bc19ff80af9155635d79cbdd0b | 2af6a5c2d33e2046a1d25ae9dd66d349d3833940 | /res/scripts/client/tutorial/control/chains/context.py | 6861cd801f3b65af3ade717a37f50bc728196afd | [] | no_license | webiumsk/WOT-0.9.12-CT | e6c8b5bb106fad71b5c3056ada59fb1aebc5f2b2 | 2506e34bd6634ad500b6501f4ed4f04af3f43fa0 | refs/heads/master | 2021-01-10T01:38:38.080814 | 2015-11-11T00:08:04 | 2015-11-11T00:08:04 | 45,803,240 | 0 | 0 | null | null | null | null | WINDOWS-1250 | Python | false | false | 771 | py | # 2015.11.10 21:30:59 Střední Evropa (běžný čas)
# Embedded file name: scripts/client/tutorial/control/chains/context.py
from tutorial.control import context, game_vars
from tutorial.control.lobby.context import LobbyBonusesRequester
class ChainsStartReqs(context.StartReqs):
def isEnabled(self):
return True
def prepare(self, ctx):
ctx.bonusCompleted = game_vars.getTutorialsCompleted()
def process(self, descriptor, ctx):
return True
class ChainsBonusesRequester(LobbyBonusesRequester):
pass
# okay decompyling c:\Users\PC\wotsources\files\originals\res\scripts\client\tutorial\control\chains\context.pyc
# decompiled 1 files: 1 okay, 0 failed, 0 verify failed
# 2015.11.10 21:30:59 Střední Evropa (běžný čas)
| [
"info@webium.sk"
] | info@webium.sk |
f8ce7ee7cd1d999171dadd17fa390caff6bc68b8 | 9743d5fd24822f79c156ad112229e25adb9ed6f6 | /xai/brain/wordbase/nouns/_culverts.py | f772fa199abc66c109ff0e8d5b380883792cfa67 | [
"MIT"
] | permissive | cash2one/xai | de7adad1758f50dd6786bf0111e71a903f039b64 | e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6 | refs/heads/master | 2021-01-19T12:33:54.964379 | 2017-01-28T02:00:50 | 2017-01-28T02:00:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 245 | py |
from xai.brain.wordbase.nouns._culvert import _CULVERT
#calss header
class _CULVERTS(_CULVERT, ):
def __init__(self,):
_CULVERT.__init__(self)
self.name = "CULVERTS"
self.specie = 'nouns'
self.basic = "culvert"
self.jsondata = {}
| [
"xingwang1991@gmail.com"
] | xingwang1991@gmail.com |
2fff5cf257704680277f40ea42126b20516f7c19 | e40f94cd0a5d64f33aff80f8b9ee4c9071469da8 | /test/dateparsing_test.py | 90510a5e434dcd663fee4cd207f551f1b445ccb2 | [] | no_license | Watchful1/RemindMeBot | 7495202b74e74c93ee3e00bebdba08f5931ef8e5 | 0e8214cba3ac81307c6e7e707afc2efeae449da1 | refs/heads/master | 2023-05-27T13:02:30.045034 | 2023-05-17T02:30:46 | 2023-05-17T02:30:46 | 143,469,523 | 173 | 20 | null | 2022-08-28T06:06:14 | 2018-08-03T20:14:22 | Python | UTF-8 | Python | false | false | 8,151 | py | from datetime import datetime
import utils
def test_date_parsing():
base_time = utils.datetime_force_utc(datetime.strptime("2019-01-01 01:23:45", "%Y-%m-%d %H:%M:%S"))
pairs = [
["1 day", "2019-01-02 01:23:45"],
["365 days", "2020-01-01 01:23:45"],
["2 weeks", "2019-01-15 01:23:45"],
["3 years", "2022-01-01 01:23:45"],
["3 months", "2019-04-01 01:23:45"],
["24 hours", "2019-01-02 01:23:45"],
["5 hrs", "2019-01-01 06:23:45"],
["20 minutes", "2019-01-01 01:43:45"],
["5 seconds", "2019-01-01 01:23:50"],
["tomorrow", "2019-01-02 01:23:45"],
["Next Thursday at 4pm", "2019-01-03 16:00:00"],
["Tonight", "2019-01-01 21:00:00"],
["2 pm", "2019-01-01 14:00:00"],
["eoy", "2019-12-31 09:00:00"],
["eom", "2019-01-31 09:00:00"],
["eod", "2019-01-01 17:00:00"],
["2022-01-01", "2022-01-01 00:00:00"],
["10/15/19", "2019-10-15 00:00:00"],
["April 9, 2020", "2020-04-09 00:00:00"],
["January 13th, 2020", "2020-01-13 00:00:00"],
["January 5th 2020", "2020-01-05 00:00:00"],
["June 2nd", "2019-06-02 00:00:00"],
["November 2", "2019-11-02 00:00:00"],
["August 25, 2018, at 4pm", "2018-08-25 16:00:00"],
["September 1, 2019 14:00:00", "2019-09-01 14:00:00"],
["august", "2019-08-01 00:00:00"],
["September", "2019-09-01 00:00:00"],
["2025", "2025-01-01 00:00:00"],
["2pm", "2019-01-01 14:00:00"],
["7:20 pm", "2019-01-01 19:20:00"],
["72hr", "2019-01-04 01:23:45"],
["1d", "2019-01-02 01:23:45"],
["1yr", "2020-01-01 01:23:45"],
["7h", "2019-01-01 08:23:45"],
["35m", "2019-01-01 01:58:45"],
["2 weeks with a test string", "2019-01-15 01:23:45"],
["3 years with a second date 2014", "2022-01-01 01:23:45"],
]
for time_string, expected_string in pairs:
result_date = utils.parse_time(time_string, base_time, "UTC")
expected_date = utils.datetime_force_utc(datetime.strptime(expected_string, "%Y-%m-%d %H:%M:%S"))
assert result_date == expected_date, f"`{time_string}` as `{result_date}` != `{expected_date}`"
def test_date_parsing_timezone():
base_time = utils.datetime_force_utc(datetime.strptime("2019-01-01 01:23:45", "%Y-%m-%d %H:%M:%S"))
timezones = [
"America/Los_Angeles",
"America/Denver",
"America/Chicago",
"America/New_York",
"Australia/Sydney",
"Europe/Brussels",
]
pairs = [
["1 day", ["2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45"]],
["365 days", ["2020-01-01 01:23:45", "2020-01-01 01:23:45", "2020-01-01 01:23:45", "2020-01-01 01:23:45", "2020-01-01 01:23:45", "2020-01-01 01:23:45"]],
["2 weeks", ["2019-01-15 01:23:45", "2019-01-15 01:23:45", "2019-01-15 01:23:45", "2019-01-15 01:23:45", "2019-01-15 01:23:45", "2019-01-15 01:23:45"]],
["3 years", ["2022-01-01 01:23:45", "2022-01-01 01:23:45", "2022-01-01 01:23:45", "2022-01-01 01:23:45", "2022-01-01 01:23:45", "2022-01-01 01:23:45"]],
["3 months", ["2019-04-01 00:23:45", "2019-04-01 00:23:45", "2019-04-01 00:23:45", "2019-04-01 00:23:45", "2019-04-01 01:23:45", "2019-04-01 00:23:45"]],
["24 hours", ["2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45"]],
["5 hrs", ["2019-01-01 06:23:45", "2019-01-01 06:23:45", "2019-01-01 06:23:45", "2019-01-01 06:23:45", "2019-01-01 06:23:45", "2019-01-01 06:23:45"]],
["20 minutes", ["2019-01-01 01:43:45", "2019-01-01 01:43:45", "2019-01-01 01:43:45", "2019-01-01 01:43:45", "2019-01-01 01:43:45", "2019-01-01 01:43:45"]],
["5 seconds", ["2019-01-01 01:23:50", "2019-01-01 01:23:50", "2019-01-01 01:23:50", "2019-01-01 01:23:50", "2019-01-01 01:23:50", "2019-01-01 01:23:50"]],
["tomorrow", ["2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45"]],
["Next Thursday at 4pm", ["2019-01-04 00:00:00", "2019-01-03 23:00:00", "2019-01-03 22:00:00", "2019-01-03 21:00:00", "2019-01-03 05:00:00", "2019-01-03 15:00:00"]],
["Tonight", ["2019-01-01 05:00:00", "2019-01-01 04:00:00", "2019-01-01 03:00:00", "2019-01-01 02:00:00", "2019-01-01 10:00:00", "2019-01-01 20:00:00"]],
["eoy", ["2018-12-31 17:00:00", "2018-12-31 16:00:00", "2018-12-31 15:00:00", "2018-12-31 14:00:00", "2019-12-30 22:00:00", "2019-12-31 08:00:00"]],
["eom", ["2018-12-31 17:00:00", "2018-12-31 16:00:00", "2018-12-31 15:00:00", "2018-12-31 14:00:00", "2019-01-30 22:00:00", "2019-01-31 08:00:00"]],
["eod", ["2019-01-01 01:00:00", "2019-01-01 00:00:00", "2018-12-31 23:00:00", "2018-12-31 22:00:00", "2019-01-01 06:00:00", "2019-01-01 16:00:00"]],
["2022-01-01", ["2022-01-01 08:00:00", "2022-01-01 07:00:00", "2022-01-01 06:00:00", "2022-01-01 05:00:00", "2021-12-31 13:00:00", "2021-12-31 23:00:00"]],
["10/15/19", ["2019-10-15 07:00:00", "2019-10-15 06:00:00", "2019-10-15 05:00:00", "2019-10-15 04:00:00", "2019-10-14 13:00:00", "2019-10-14 22:00:00"]],
["April 9, 2020", ["2020-04-09 07:00:00", "2020-04-09 06:00:00", "2020-04-09 05:00:00", "2020-04-09 04:00:00", "2020-04-08 14:00:00", "2020-04-08 22:00:00"]],
["January 13th, 2020", ["2020-01-13 08:00:00", "2020-01-13 07:00:00", "2020-01-13 06:00:00", "2020-01-13 05:00:00", "2020-01-12 13:00:00", "2020-01-12 23:00:00"]],
["January 5th 2020", ["2020-01-05 08:00:00", "2020-01-05 07:00:00", "2020-01-05 06:00:00", "2020-01-05 05:00:00", "2020-01-04 13:00:00", "2020-01-04 23:00:00"]],
["June 2nd", ["2019-06-02 07:00:00", "2019-06-02 06:00:00", "2019-06-02 05:00:00", "2019-06-02 04:00:00", "2019-06-01 14:00:00", "2019-06-01 22:00:00"]],
["November 2", ["2019-11-02 07:00:00", "2019-11-02 06:00:00", "2019-11-02 05:00:00", "2019-11-02 04:00:00", "2019-11-01 13:00:00", "2019-11-01 23:00:00"]],
["August 25, 2018, at 4pm", ["2018-08-25 23:00:00", "2018-08-25 22:00:00", "2018-08-25 21:00:00", "2018-08-25 20:00:00", "2018-08-25 06:00:00", "2018-08-25 14:00:00"]],
["September 1, 2019 14:00:00", ["2019-09-01 21:00:00", "2019-09-01 20:00:00", "2019-09-01 19:00:00", "2019-09-01 18:00:00", "2019-09-01 04:00:00", "2019-09-01 12:00:00"]],
["august", ["2019-08-31 07:00:00", "2019-08-31 06:00:00", "2019-08-31 05:00:00", "2019-08-31 04:00:00", "2019-07-31 14:00:00", "2019-07-31 22:00:00"]],
["September", ["2019-09-30 07:00:00", "2019-09-30 06:00:00", "2019-09-30 05:00:00", "2019-09-30 04:00:00", "2019-08-31 14:00:00", "2019-08-31 22:00:00"]],
["2025", ["2025-12-31 08:00:00", "2025-12-31 07:00:00", "2025-12-31 06:00:00", "2025-12-31 05:00:00", "2024-12-31 13:00:00", "2024-12-31 23:00:00"]],
["2pm", ["2019-01-01 22:00:00", "2019-01-01 21:00:00", "2019-01-01 20:00:00", "2019-01-01 19:00:00", "2019-01-01 03:00:00", "2019-01-01 13:00:00"]],
["7:20 pm", ["2019-01-01 03:20:00", "2019-01-01 02:20:00", "2019-01-02 01:20:00", "2019-01-02 00:20:00", "2019-01-01 08:20:00", "2019-01-01 18:20:00"]],
["72hr", ["2019-01-04 01:23:45", "2019-01-04 01:23:45", "2019-01-04 01:23:45", "2019-01-04 01:23:45", "2019-01-04 01:23:45", "2019-01-04 01:23:45"]],
["1d", ["2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45", "2019-01-02 01:23:45"]],
["1yr", ["2020-01-01 01:23:45", "2020-01-01 01:23:45", "2020-01-01 01:23:45", "2020-01-01 01:23:45", "2020-01-01 01:23:45", "2020-01-01 01:23:45"]],
["7h", ["2019-01-01 08:23:45", "2019-01-01 08:23:45", "2019-01-01 08:23:45", "2019-01-01 08:23:45", "2019-01-01 08:23:45", "2019-01-01 08:23:45"]],
["35m", ["2019-01-01 01:58:45", "2019-01-01 01:58:45", "2019-01-01 01:58:45", "2019-01-01 01:58:45", "2019-01-01 01:58:45", "2019-01-01 01:58:45"]],
]
for time_string, expected_strings in pairs:
for i, timezone in enumerate(timezones):
result_date = utils.parse_time(time_string, base_time, timezone)
expected_date = utils.datetime_force_utc(datetime.strptime(expected_strings[i], "%Y-%m-%d %H:%M:%S"))
assert result_date == expected_date, f"`{time_string}`, `{timezone}` as `{result_date}` != `{expected_date}`"
| [
"watchful@watchful.gr"
] | watchful@watchful.gr |
eb579070aa3656dfdb04a18a2c691a0ae44148f2 | ce9964faef6ee75b71c417e34113578777cd86b2 | /content/test/gpu/gpu_tests/webgl2_conformance_expectations.py | 953092974e3541c0bce60b8d75d48392763e1c65 | [
"BSD-3-Clause"
] | permissive | alijuma/chromium | 4d8eed877b655d39388d5e5def1b7514f05170ea | db8f04787fe546230612bd32f499194d3219f15c | refs/heads/master | 2023-01-07T18:37:00.605447 | 2017-11-07T23:13:02 | 2017-11-07T23:13:02 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 61,026 | py | # Copyright (c) 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
from gpu_tests.webgl_conformance_expectations import WebGLConformanceExpectations
# See the GpuTestExpectations class for documentation.
class WebGL2ConformanceExpectations(WebGLConformanceExpectations):
def __init__(self, conformance_path, url_prefixes=None, is_asan=False):
super(WebGL2ConformanceExpectations, self).__init__(
conformance_path, url_prefixes=url_prefixes, is_asan=is_asan)
def SetExpectations(self):
# ===================================
# Extension availability expectations
# ===================================
# It's expected that not all extensions will be available on all platforms.
# Having a test listed here is not necessarily a problem.
# Skip these, rather than expect them to fail, to speed up test
# execution. The browser is restarted even after expected test
# failures.
self.Skip('WebglExtension_WEBGL_compressed_texture_astc',
['win', 'mac', 'linux'])
self.Skip('WebglExtension_WEBGL_compressed_texture_atc',
['win', 'mac', 'linux'])
self.Skip('WebglExtension_WEBGL_compressed_texture_etc',
['win', 'mac', 'linux'])
self.Skip('WebglExtension_WEBGL_compressed_texture_etc1',
['win', 'mac', 'linux'])
self.Skip('WebglExtension_WEBGL_compressed_texture_pvrtc',
['win', 'mac', 'linux'])
self.Skip('WebglExtension_WEBGL_compressed_texture_s3tc_srgb',
['win', 'mac', 'linux'])
# ========================
# Conformance expectations
# ========================
# Need to fix test, which uses a bad interpretation of the spec
self.Fail('conformance/offscreencanvas/offscreencanvas-resize.html',
bug=754733)
# Too slow (take about one hour to run)
self.Skip('deqp/functional/gles3/builtinprecision/*.html', bug=619403)
# Timing out on multiple platforms right now.
self.Skip('conformance/glsl/bugs/sampler-array-struct-function-arg.html',
bug=757097)
# All platforms.
self.Flaky('conformance2/query/occlusion-query.html', bug=603168)
self.Fail('conformance2/glsl3/tricky-loop-conditions.html', bug=483282)
self.Fail('conformance2/glsl3/array-length-side-effects.html',
bug=2142) # angle bug ID
# This test needs to be rewritten to measure its expected
# performance; it's currently too flaky even on release bots.
self.Skip('conformance/rendering/texture-switch-performance.html',
bug=735483)
self.Skip('conformance2/rendering/texture-switch-performance.html',
bug=735483)
self.Fail('conformance2/rendering/depth-stencil-feedback-loop.html',
bug=660844) # WebGL 2.0.1
self.Fail('conformance2/rendering/rendering-sampling-feedback-loop.html',
bug=660844) # WebGL 2.0.1
self.Fail('conformance2/textures/misc/' +
'integer-cubemap-specification-order-bug.html',
bug=483282) # owner:cwallez, test might be buggy
self.Fail('conformance/textures/misc/tex-sub-image-2d-bad-args.html',
bug=625738)
# Need to implement new lifetime/deletion semantics.
self.Fail('conformance2/vertex_arrays/vertex-array-object.html', bug=739604)
# Windows only.
self.Fail('conformance2/buffers/uniform-buffers.html',
['win'], bug=757098)
self.Fail('conformance/glsl/bugs/sampler-struct-function-arg.html',
['win'], bug=2103) # angle bug ID
self.Fail('conformance2/glsl3/array-initialize-with-same-name-array.html',
['win'], bug=757098)
self.Fail('conformance2/rendering/blitframebuffer-outside-readbuffer.html',
['win', 'd3d11'], bug=644740)
self.Fail('conformance2/textures/misc/tex-base-level-bug.html',
['win', 'd3d11'], bug=705865)
self.Flaky('conformance2/textures/svg_image/' +
'tex-2d-rgb565-rgb-unsigned_short_5_6_5.html',
['win'], bug=736926)
self.Fail('conformance2/uniforms/uniform-blocks-with-arrays.html',
['win'], bug=2103) # angle bug ID
# Win / NVidia
self.Flaky('deqp/functional/gles3/fbomultisample*',
['win', 'nvidia', 'd3d11'], bug=631317)
self.Fail('conformance2/rendering/' +
'draw-with-integer-texture-base-level.html',
['win', 'nvidia', 'd3d11'], bug=679639)
self.Flaky('deqp/functional/gles3/textureshadow/*.html',
['win', 'nvidia', 'd3d11'], bug=735464)
# Win / NVIDIA Quadro P400 / D3D11 flaky failures
self.Fail('deqp/data/gles3/shaders/functions.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=680754)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'basic_types_interleaved_lines.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=680754)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'basic_types_interleaved_triangles.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=680754)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'basic_types_separate_lines.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=680754)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'basic_types_separate_triangles.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=680754)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'random_interleaved_lines.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=680754)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'random_interleaved_triangles.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=680754)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'random_separate_lines.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=680754)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'random_separate_triangles.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=680754)
self.Fail('deqp/functional/gles3/transformfeedback/interpolation_flat.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=680754)
self.Flaky('conformance/textures/image_bitmap_from_video/' +
'tex-2d-rgba-rgba-unsigned_short_5_5_5_1.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=728670)
self.Flaky('conformance/textures/image_bitmap_from_video/' +
'tex-2d-rgba-rgba-unsigned_short_4_4_4_4.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=728670)
self.Flaky('conformance2/textures/video/*',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=728670)
self.Flaky('conformance2/textures/image_bitmap_from_video/*',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=728670)
self.Flaky('conformance/extensions/oes-texture-half-float-with-video.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=728670)
self.Flaky('conformance2/rendering/attrib-type-match.html',
['win', ('nvidia', 0x1cb3), 'd3d11'], bug=782254)
# WIN / OpenGL / NVIDIA failures
self.Fail('conformance2/textures/canvas_sub_rectangle/' +
'tex-2d-rgb565-rgb-unsigned_byte.html',
['win', ('nvidia', 0x1cb3), 'opengl'], bug=781668)
self.Fail('conformance/limits/gl-max-texture-dimensions.html',
['win', ('nvidia', 0x1cb3), 'opengl'], bug=715001)
self.Fail('conformance/textures/misc/texture-size.html',
['win', ('nvidia', 0x1cb3), 'opengl'], bug=703779)
self.Fail('conformance2/glsl3/vector-dynamic-indexing-nv-driver-bug.html',
['win', 'nvidia', 'opengl'], bug=693090)
self.Fail('conformance2/glsl3/' +
'vector-dynamic-indexing-swizzled-lvalue.html',
['win', 'nvidia', 'opengl'], bug=709874)
# Win / AMD
self.Fail('conformance2/rendering/blitframebuffer-stencil-only.html',
['win', 'amd', 'd3d11'], bug=483282) # owner:jmadill
# Keep a separate set of failures for the R7 240, since it can use a new
# and updated driver. The older drivers won't ever get fixes from AMD.
# Use ['win', ('amd', 0x6613)] for the R7 240 devices.
# Have seen this time out. Think it may be because it's currently
# the first test that runs in the shard, and the browser might not
# be coming up correctly.
self.Flaky('deqp/functional/gles3/multisample.html',
['win', ('amd', 0x6613)], bug=687374)
self.Skip('conformance2/textures/misc/copy-texture-image.html',
['win', 'intel', 'd3d11'], bug=617449)
# Seems to cause the harness to fail immediately afterward
self.Skip('conformance2/textures/video/tex-2d-rgba16f-rgba-half_float.html',
['win', 'intel', 'd3d11'], bug=648337)
self.Flaky('deqp/functional/gles3/lifetime.html',
['win', 'intel', 'd3d11'], bug=620379)
self.Skip('deqp/functional/gles3/texturespecification/' +
'teximage3d_depth_pbo.html',
['win', 'intel', 'd3d11'], bug=617449)
self.Flaky('deqp/functional/gles3/textureformat/unsized_3d.html',
['win', 'intel', 'd3d11'], bug=614418)
# These tests seem to crash flakily. It's best to leave them as skip
# until we can run them without GPU hangs and crashes.
self.Skip('deqp/functional/gles3/textureshadow/2d_array_*.html',
['win', 'intel', 'd3d11'], bug=666392)
# It's unfortunate that these suppressions need to be so broad, but it
# looks like the D3D11 device can be lost spontaneously on this
# configuration while running basically any test.
self.Flaky('conformance/*', ['win', 'intel', 'd3d11'], bug=628395)
self.Flaky('conformance2/*', ['win', 'intel', 'd3d11'], bug=628395)
self.Flaky('deqp/*', ['win', 'intel', 'd3d11'], bug=628395)
# Passthrough command decoder / D3D11
self.Fail('deqp/functional/gles3/shaderstruct.html',
['win', 'passthrough', 'd3d11'], bug=602688)
# Passthrough command decoder / OpenGL
self.Fail('conformance/extensions/webgl-compressed-texture-s3tc.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance/glsl/misc/shader-with-non-reserved-words.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance/textures/canvas/*', ['passthrough', 'opengl'],
bug=602688)
self.Fail('conformance/textures/canvas_sub_rectangle/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance/textures/image_bitmap_from_blob/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance/textures/image_bitmap_from_canvas/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance/textures/image_bitmap_from_image/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance/textures/image_bitmap_from_image_bitmap/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance/textures/image_bitmap_from_image_data/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance/textures/misc/' +
'copytexsubimage2d-large-partial-copy-corruption.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance/textures/misc/copytexsubimage2d-subrects.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance/textures/misc/gl-teximage.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance/textures/misc/texture-mips.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance/textures/webgl_canvas/*', ['passthrough', 'opengl'],
bug=602688)
self.Fail('conformance1/textures/image_bitmap_from_image/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/misc/uninitialized-test-2.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/reading/format-r11f-g11f-b10f.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/rendering/blitframebuffer-filter-outofbounds.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/rendering/draw-buffers-dirty-state-bug.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/rendering/framebuffer-unsupported.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/state/gl-get-calls.html', ['passthrough', 'opengl'],
bug=602688)
self.Fail('conformance2/textures/canvas/*', ['passthrough', 'opengl'],
bug=602688)
self.Fail('conformance2/textures/canvas_sub_rectangle/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/textures/image_bitmap_from_blob/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/textures/image_bitmap_from_canvas/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/textures/image_bitmap_from_image/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/textures/image_bitmap_from_image_bitmap/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/textures/image_bitmap_from_image_data/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/textures/image_bitmap_from_video/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/textures/misc/angle-stuck-depth-textures.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/textures/misc/' +
'tex-image-with-bad-args-from-dom-elements.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/textures/misc/tex-storage-2d.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/textures/video/tex-2d-rgb9_e5-rgb-float.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/textures/video/tex-2d-rgb9_e5-rgb-half_float.html',
['passthrough', 'opengl'], bug=602688)
self.Fail('conformance2/textures/webgl_canvas/*',
['passthrough', 'opengl'], bug=602688)
self.Fail('deqp/functional/gles3/integerstatequery.html',
['passthrough', 'opengl'], bug=602688)
# Passthrough command decoder / OpenGL / Intel
self.Fail('conformance2/textures/video/tex-2d-rgb32f-rgb-float.html',
['passthrough', 'opengl', 'intel'], bug=602688)
self.Fail('conformance2/textures/video/' +
'tex-2d-rgb8ui-rgb_integer-unsigned_byte.html',
['passthrough', 'opengl', 'intel'], bug=602688)
self.Fail('conformance/misc/uninitialized-test.html',
['passthrough', 'opengl', 'intel'], bug=602688)
self.Fail('conformance/textures/image_bitmap_from_video/' +
'tex-2d-luminance-luminance-unsigned_byte.html',
['passthrough', 'opengl', 'intel'], bug=602688)
self.Fail('conformance/textures/image_bitmap_from_video/' +
'tex-2d-rgba-rgba-unsigned_short_4_4_4_4.html',
['passthrough', 'opengl', 'intel'], bug=602688)
self.Fail('conformance/textures/misc/texture-attachment-formats.html',
['passthrough', 'opengl', 'intel'], bug=602688)
self.Fail('conformance/renderbuffers/framebuffer-state-restoration.html',
['passthrough', 'opengl', 'intel'], bug=602688)
# Passthrough command decoder / Linux / OpenGL / NVIDIA
self.Fail('conformance/textures/image_bitmap_from_video/' +
'tex-2d-luminance_alpha-luminance_alpha-unsigned_byte.html',
['linux', 'passthrough', 'opengl', 'nvidia'], bug=773861)
self.Fail('conformance/textures/image_bitmap_from_video/' +
'tex-2d-luminance-luminance-unsigned_byte.html',
['linux', 'passthrough', 'opengl', 'nvidia'], bug=773861)
self.Fail('conformance/textures/image_bitmap_from_video/' +
'tex-2d-rgba-rgba-unsigned_short_5_5_5_1.html',
['linux', 'passthrough', 'opengl', 'nvidia'], bug=766918)
self.Fail('conformance/textures/image_bitmap_from_video/' +
'tex-2d-rgb-rgb-unsigned_short_5_6_5.html',
['linux', 'passthrough', 'opengl', 'nvidia'], bug=766918)
# Regressions in 10.12.4.
self.Fail('conformance2/textures/misc/tex-base-level-bug.html',
['sierra'], bug=705865)
self.Fail('conformance2/textures/misc/tex-mipmap-levels.html',
['sierra'], bug=705865)
# Regressions in 10.13
self.Fail('deqp/functional/gles3/fbocolorbuffer/tex2d_00.html',
['highsierra', ('intel', 0xa2e)], bug=774826)
self.Fail('deqp/functional/gles3/fboinvalidate/format_00.html',
['highsierra', ('intel', 0xa2e)], bug=774826)
self.Fail('deqp/functional/gles3/framebufferblit/' +
'default_framebuffer_05.html',
['highsierra', ('intel', 0xa2e)], bug=774826)
self.Fail('conformance2/glsl3/array-assign.html',
['highsierra', ('nvidia', 0xfe9)], bug=774827)
self.Fail('deqp/functional/gles3/fborender/resize_03.html',
['highsierra', ('nvidia', 0xfe9)], bug=774827)
self.Fail('deqp/functional/gles3/shaderindexing/mat_00.html',
['highsierra', ('nvidia', 0xfe9)], bug=774827)
self.Fail('deqp/functional/gles3/shaderindexing/mat_02.html',
['highsierra', ('nvidia', 0xfe9)], bug=774827)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage2d_pbo_cube_00.html',
['highsierra', ('nvidia', 0xfe9)], bug=774827)
# Fails on multiple GPU types.
self.Fail('conformance2/glsl3/vector-dynamic-indexing-swizzled-lvalue.html',
['mac'], bug=709351)
self.Fail('conformance2/rendering/' +
'framebuffer-completeness-unaffected.html',
['mac', 'nvidia', 'intel'], bug=630800)
self.Fail('deqp/functional/gles3/fbocompleteness.html',
['mac', 'nvidia', 'intel'], bug=630800)
# Mac Retina NVIDIA
self.Fail('deqp/functional/gles3/shaderindexing/mat_01.html',
['mac', ('nvidia', 0xfe9)], bug=728271)
self.Fail('deqp/functional/gles3/shaderindexing/tmp.html',
['mac', ('nvidia', 0xfe9)], bug=728271)
self.Fail('deqp/functional/gles3/fbomultisample*',
['mac', ('nvidia', 0xfe9)], bug=641209)
self.Fail('deqp/functional/gles3/framebufferblit/' +
'default_framebuffer_04.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('conformance/attribs/gl-disabled-vertex-attrib.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('conformance/canvas/drawingbuffer-static-canvas-test.html',
['highsierra', ('nvidia', 0xfe9)], bug=775202)
self.Flaky(
'conformance/extensions/webgl-compressed-texture-size-limit.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('conformance/programs/' +
'gl-bind-attrib-location-long-names-test.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('conformance/programs/gl-bind-attrib-location-test.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('conformance2/glsl3/loops-with-side-effects.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('conformance2/textures/misc/tex-input-validation.html',
['mac', ('nvidia', 0xfe9), 'no_angle'], bug=483282)
self.Flaky('conformance2/textures/image_bitmap_from_video/' +
'tex-2d-rgba16f-rgba-half_float.html',
['mac', ('nvidia', 0xfe9)], bug=682834)
self.Fail('deqp/functional/gles3/draw/random.html',
['sierra', ('nvidia', 0xfe9)], bug=716652)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_04.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_07.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_08.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_10.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_11.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_12.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_13.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_18.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_25.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_29.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_32.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_34.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/pixelbufferobject.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/negativevertexarrayapi.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/shaderindexing/varying.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage2d_pbo_2d_00.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage2d_pbo_2d_01.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texsubimage2d_pbo_2d_00.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texsubimage2d_pbo_2d_01.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texsubimage2d_pbo_cube_00.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texsubimage2d_pbo_cube_01.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texsubimage2d_pbo_cube_02.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texsubimage2d_pbo_cube_03.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texsubimage2d_pbo_cube_04.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage3d_pbo_2d_array_00.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage3d_pbo_2d_array_01.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage3d_pbo_3d_00.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage3d_pbo_3d_01.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texsubimage3d_pbo_3d_00.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texsubimage3d_pbo_3d_01.html',
['mac', ('nvidia', 0xfe9)], bug=614174)
self.Fail('deqp/functional/gles3/fragmentoutput/array.fixed.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/fragmentoutput/basic.fixed.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/fragmentoutput/random_00.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/fragmentoutput/random_01.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/fragmentoutput/random_02.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/fbocolorbuffer/clear.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/fbocolorbuffer/tex2d_05.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/fbocolorbuffer/tex2darray_05.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/fbocolorbuffer/tex3d_05.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/fbocolorbuffer/texcube_05.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/fbocolorbuffer/blend.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/draw/draw_arrays.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/draw/draw_arrays_instanced.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/draw/draw_elements.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/draw/draw_elements_instanced.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/draw/draw_range_elements.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/fboinvalidate/format_02.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/negativeshaderapi.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Flaky('deqp/functional/gles3/vertexarrays/' +
'multiple_attributes.output.html',
['mac', ('nvidia', 0xfe9)], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_28.html',
['mac', ('nvidia', 0xfe9)], bug=654187)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_30.html',
['mac', ('nvidia', 0xfe9)], bug=654187)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_31.html',
['mac', ('nvidia', 0xfe9)], bug=654187)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_33.html',
['mac', ('nvidia', 0xfe9)], bug=654187)
# When this fails on this configuration, it fails multiple times in a row.
self.Fail('deqp/functional/gles3/shaderoperator/common_functions.html',
['mac', 'nvidia'], bug=756537)
# Mac AMD
# TODO(kbr): uncomment the following two exepectations after test
# has been made more robust.
# self.Fail('conformance/rendering/texture-switch-performance.html',
# ['mac', 'amd'], bug=735483)
# self.Fail('conformance2/rendering/texture-switch-performance.html',
# ['mac', 'amd'], bug=735483)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'array_interleaved_lines.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'array_interleaved_points.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'array_interleaved_triangles.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'array_separate_lines.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'array_separate_points.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'array_separate_triangles.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'basic_types_interleaved_lines.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'basic_types_interleaved_points.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'basic_types_interleaved_triangles.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'basic_types_separate_lines.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'basic_types_separate_points.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'basic_types_separate_triangles.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'interpolation_centroid.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'interpolation_flat.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'interpolation_smooth.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'point_size.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'position.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'random_interleaved_lines.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'random_interleaved_points.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'random_interleaved_triangles.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'random_separate_lines.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'random_separate_points.html',
['mac', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/' +
'random_separate_triangles.html',
['mac', 'amd'], bug=483282)
self.Flaky('deqp/functional/gles3/shaderindexing/mat_00.html',
['mac', 'amd'], bug=751254)
self.Flaky('deqp/functional/gles3/shaderindexing/mat_01.html',
['mac', 'amd'], bug=636648)
self.Flaky('deqp/functional/gles3/shaderindexing/mat_02.html',
['mac', 'amd'], bug=644360)
self.Flaky('deqp/functional/gles3/shaderindexing/tmp.html',
['mac', 'amd'], bug=659871)
# These seem to be provoking intermittent GPU process crashes on
# the MacBook Pros with AMD GPUs.
self.Flaky('deqp/functional/gles3/texturefiltering/*',
['mac', 'amd'], bug=663601)
self.Flaky('deqp/functional/gles3/textureshadow/*',
['mac', 'amd'], bug=663601)
self.Flaky('deqp/functional/gles3/texturespecification/' +
'teximage2d_unpack_params.html',
['mac', 'amd'], bug=679058)
self.Fail('conformance2/rendering/clipping-wide-points.html',
['mac', 'amd'], bug=642822)
# Mac Intel
self.Fail('conformance2/rendering/framebuffer-texture-level1.html',
['mac', 'intel'], bug=680278)
self.Fail('conformance2/textures/misc/angle-stuck-depth-textures.html',
['mac', 'no_passthrough', 'intel'], bug=679692)
self.Fail('deqp/functional/gles3/fbomultisample*',
['mac', 'intel'], bug=641209)
self.Fail('deqp/functional/gles3/texturefiltering/2d_combinations_01.html',
['mac', 'intel'], bug=606074)
self.Fail('deqp/functional/gles3/texturefiltering/' +
'cube_combinations_01.html',
['mac', 'intel'], bug=606074)
self.Fail('deqp/functional/gles3/texturefiltering/' +
'2d_array_combinations_01.html',
['mac', 'intel'], bug=606074)
self.Fail('deqp/functional/gles3/texturefiltering/3d_combinations_06.html',
['mac', 'intel'], bug=606074)
self.Fail('deqp/functional/gles3/texturefiltering/3d_combinations_07.html',
['mac', 'intel'], bug=606074)
self.Fail('deqp/functional/gles3/texturefiltering/3d_combinations_08.html',
['mac', 'intel'], bug=606074)
self.Fail('deqp/functional/gles3/texturespecification/' +
'random_teximage2d_2d.html',
['mac', 'intel'], bug=483282)
self.Fail('deqp/functional/gles3/shadertexturefunction/' +
'texturelod.html',
['mac', 'intel'], bug=483282)
self.Fail('deqp/functional/gles3/shadertexturefunction/' +
'texturegrad.html',
['mac', 'intel'], bug=483282)
self.Fail('deqp/functional/gles3/shadertexturefunction/' +
'textureprojgrad.html',
['mac', 'intel'], bug=483282)
self.Fail('conformance2/textures/canvas_sub_rectangle/' +
'tex-2d-r8ui-red_integer-unsigned_byte.html',
['yosemite', 'intel'], bug=665656)
self.Fail('conformance2/textures/canvas_sub_rectangle/' +
'tex-2d-rg8ui-rg_integer-unsigned_byte.html',
['yosemite', 'intel'], bug=665656)
self.Fail('conformance2/textures/canvas_sub_rectangle/' +
'tex-2d-rgb8ui-rgb_integer-unsigned_byte.html',
['yosemite', 'intel'], bug=665656)
self.Fail('conformance2/textures/canvas_sub_rectangle/' +
'tex-2d-rgba8ui-rgba_integer-unsigned_byte.html',
['yosemite', 'intel'], bug=665656)
self.Fail('conformance2/textures/image_data/' +
'tex-2d-rgba8ui-rgba_integer-unsigned_byte.html',
['mac', 'intel'], bug=665197)
self.Fail('conformance2/textures/image_data/' +
'tex-2d-rgb8ui-rgb_integer-unsigned_byte.html',
['mac', 'intel'], bug=665197)
self.Fail('conformance2/textures/image_data/' +
'tex-2d-rg8ui-rg_integer-unsigned_byte.html',
['mac', 'intel'], bug=665197)
self.Fail('conformance2/textures/misc/' +
'integer-cubemap-texture-sampling.html',
['mac', 'intel'], bug=658930)
self.Fail('conformance2/renderbuffers/' +
'multisampled-depth-renderbuffer-initialization.html',
['mac', 'intel'], bug=731877)
# Linux only.
self.Flaky('conformance/textures/video/' +
'tex-2d-rgba-rgba-unsigned_byte.html',
['linux'], bug=627525)
self.Flaky('conformance/textures/video/' +
'tex-2d-rgba-rgba-unsigned_short_4_4_4_4.html',
['linux'], bug=627525)
self.Flaky('conformance/textures/video/' +
'tex-2d-rgba-rgba-unsigned_short_5_5_5_1.html',
['linux'], bug=627525)
self.Flaky('conformance/textures/video/' +
'tex-2d-rgb-rgb-unsigned_byte.html',
['linux'], bug=627525)
self.Flaky('conformance/textures/video/' +
'tex-2d-rgb-rgb-unsigned_short_5_6_5.html',
['linux'], bug=627525)
self.Fail('conformance2/glsl3/vector-dynamic-indexing-nv-driver-bug.html',
['linux'], bug=483282)
self.Fail('conformance2/textures/image_bitmap_from_image/' +
'tex-3d-r16f-red-float.html', ['linux'], bug=679695)
# Linux Multi-vendor failures.
self.Skip('deqp/data/gles3/shaders/qualification_order.html',
['linux', 'amd', 'intel'], bug=483282)
self.Flaky('deqp/functional/gles3/texturespecification/' +
'random_teximage2d_2d.html',
['linux', 'amd', 'intel'], bug=618447)
self.Fail('conformance2/rendering/clipping-wide-points.html',
['linux', 'amd', 'intel'], bug=662644) # WebGL 2.0.1
# Linux NVIDIA
# This test is flaky both with and without ANGLE.
self.Flaky('deqp/functional/gles3/texturespecification/' +
'random_teximage2d_2d.html',
['linux', 'nvidia'], bug=618447)
self.Fail('conformance/glsl/bugs/unary-minus-operator-float-bug.html',
['linux', 'nvidia'], bug=672380)
self.Fail('conformance2/glsl3/vector-dynamic-indexing-swizzled-lvalue.html',
['linux', 'nvidia'], bug=709351)
self.Fail('conformance2/textures/image_bitmap_from_canvas/' +
'tex-3d-srgb8_alpha8-rgba-unsigned_byte.html',
['linux', 'nvidia'], bug=679677)
self.Fail('conformance2/rendering/framebuffer-texture-level1.html',
['linux', 'nvidia', 'opengl'], bug=680278)
self.Fail('conformance2/rendering/multisampling-fragment-evaluation.html',
['linux', 'nvidia', 'no_passthrough'], bug=682815)
self.Fail('conformance2/textures/image/' +
'tex-3d-rg8ui-rg_integer-unsigned_byte.html',
['linux', ('nvidia', 0xf02)], bug=680282)
self.Flaky('conformance2/textures/image_bitmap_from_image_data/' +
'tex-2d-srgb8-rgb-unsigned_byte.html',
['linux', 'no_passthrough', 'nvidia'], bug=694354)
# Linux NVIDIA Quadro P400
# This test causes a lost device and then the next test fails.
self.Skip('conformance2/rendering/blitframebuffer-size-overflow.html',
['linux', ('nvidia', 0x1cb3)], bug=709320)
# Failing reliably on tryservers.
self.Fail('conformance2/offscreencanvas/' +
'offscreencanvas-transfer-image-bitmap.html',
['linux', ('nvidia', 0x1cb3)], bug=781418)
# Observed flaky on Swarmed bots. Some of these were directly
# observed, some not. We can't afford any flakes on the tryservers
# so mark them all flaky.
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'array_interleaved_lines.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'array_interleaved_points.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'array_interleaved_triangles.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'array_separate_lines.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'array_separate_points.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'array_separate_triangles.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'basic_types_interleaved_lines.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'basic_types_interleaved_points.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'basic_types_interleaved_triangles.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'basic_types_separate_lines.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'basic_types_separate_points.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'basic_types_separate_triangles.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'interpolation_centroid.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'interpolation_flat.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'interpolation_smooth.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'point_size.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'position.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'random_interleaved_lines.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'random_interleaved_points.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'random_interleaved_triangles.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'random_separate_lines.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'random_separate_points.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
self.Flaky('deqp/functional/gles3/transformfeedback/' +
'random_separate_triangles.html',
['linux', ('nvidia', 0x1cb3)], bug=780706)
# Linux NVIDIA Quadro P400, OpenGL backend
self.Fail('conformance/limits/gl-max-texture-dimensions.html',
['linux', ('nvidia', 0x1cb3)], bug=715001)
self.Fail('conformance/textures/misc/texture-size.html',
['linux', ('nvidia', 0x1cb3), 'opengl'], bug=703779)
self.Fail('conformance/extensions/webgl-compressed-texture-size-limit.html',
['linux', ('nvidia', 0x1cb3), 'opengl'], bug=703779)
self.Fail('conformance/textures/misc/texture-size-limit.html',
['linux', ('nvidia', 0x1cb3), 'opengl'], bug=703779)
self.Fail('deqp/functional/gles3/fbocompleteness.html',
['linux', ('nvidia', 0x1cb3), 'opengl'], bug=703779)
# Linux Intel
self.Fail('conformance2/extensions/ext-color-buffer-float.html',
['linux', 'intel'], bug=640389)
self.Fail('WebglExtension_EXT_disjoint_timer_query_webgl2',
['linux', 'intel'], bug=687210)
# See https://bugs.freedesktop.org/show_bug.cgi?id=94477
self.Skip('conformance/glsl/bugs/temp-expressions-should-not-crash.html',
['linux', 'intel'], bug=540543) # GPU timeout
self.Fail('deqp/functional/gles3/fbomultisample.8_samples.html',
['linux', 'intel'], bug=635528)
self.Fail('conformance2/textures/misc/tex-subimage3d-pixel-buffer-bug.html',
['linux', 'intel'], bug=662644) # WebGL 2.0.1
self.Fail('deqp/functional/gles3/shadertexturefunction/texturesize.html',
['linux', 'intel'], bug=666384)
self.Fail('conformance2/textures/misc/tex-3d-mipmap-levels-intel-bug.html',
['linux', 'intel'], bug=666384)
# Fails on Intel Mesa GL 3.3, passes on Intel Mesa GL 4.5.
self.Fail('conformance2/misc/views-with-offsets.html',
['linux', 'intel', 'no_angle'], bug=664180)
# Linux Intel with ANGLE only
self.Fail('deqp/functional/gles3/framebufferblit/conversion_07.html',
['linux', 'intel', 'opengl'], bug=598902)
self.Fail('conformance2/rendering/blitframebuffer-filter-srgb.html',
['linux', 'intel', 'opengl'], bug=680276)
self.Fail('conformance2/rendering/blitframebuffer-outside-readbuffer.html',
['linux', 'intel', 'opengl'], bug=680276)
# Linux Intel HD 530
self.Fail('conformance/extensions/webgl-compressed-texture-astc.html',
['linux', 'intel'], bug=680720)
self.Fail('conformance2/rendering/blitframebuffer-filter-outofbounds.html',
['linux', 'no_passthrough', 'intel'], bug=680720)
self.Fail('conformance2/rendering/blitframebuffer-filter-srgb.html',
['linux', 'intel', 'no_angle'], bug=680720)
self.Fail('conformance2/rendering/blitframebuffer-outside-readbuffer.html',
['linux', 'intel', 'no_angle'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_04.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_08.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_10.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_11.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_12.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_13.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_18.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_25.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_28.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_29.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_30.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_31.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_32.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_33.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_34.html',
['linux', 'intel'], bug=680720)
self.Fail('deqp/functional/gles3/framebufferblit/' +
'default_framebuffer_00.html',
['linux', 'intel'], bug=680720)
self.Fail('conformance2/glsl3/' +
'vector-dynamic-indexing-swizzled-lvalue.html',
['linux', 'intel'], bug=709874)
# Linux Intel HD 630
self.Fail('conformance/textures/misc/texture-size-limit.html',
['linux', ('intel', 0x5912)], bug=745888)
# Linux AMD only.
# It looks like AMD shader compiler rejects many valid ES3 semantics.
self.Fail('conformance2/attribs/gl-vertex-attrib-normalized-int.html',
['linux', 'amd'], bug=766776)
self.Fail('conformance/glsl/misc/shaders-with-invariance.html',
['linux', 'amd'], bug=483282)
self.Fail('conformance2/glsl3/vector-dynamic-indexing-swizzled-lvalue.html',
['linux', 'amd'], bug=709351)
self.Fail('deqp/functional/gles3/multisample.html',
['linux', 'amd'], bug=617290)
self.Fail('deqp/data/gles3/shaders/conversions.html',
['linux', 'amd'], bug=483282)
self.Skip('deqp/data/gles3/shaders/arrays.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/internalformatquery.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturestatequery.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/buffercopy.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/samplerobject.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/shaderprecision_int.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturefiltering/3d*',
['linux', 'amd'], bug=606114)
self.Fail('deqp/functional/gles3/shadertexturefunction/texture.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/shadertexturefunction/texturegrad.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/shadertexturefunction/' +
'texelfetchoffset.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/vertexarrays/' +
'single_attribute.first.html',
['linux', 'amd'], bug=694877)
self.Fail('deqp/functional/gles3/negativetextureapi.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/transformfeedback/array_separate*.html',
['linux', 'amd'], bug=483282)
self.Fail('conformance2/misc/uninitialized-test-2.html',
['linux', 'no_passthrough', 'amd'], bug=483282)
self.Fail('conformance2/reading/read-pixels-from-fbo-test.html',
['linux', 'amd'], bug=483282)
self.Fail('conformance2/rendering/blitframebuffer-filter-srgb.html',
['linux', 'amd'], bug=634525)
self.Fail('conformance2/rendering/blitframebuffer-outside-readbuffer.html',
['linux', 'amd'], bug=662644) # WebGL 2.0.1
self.Fail('conformance2/renderbuffers/framebuffer-texture-layer.html',
['linux', 'amd'], bug=295792)
self.Fail('conformance2/textures/misc/tex-mipmap-levels.html',
['linux', 'amd'], bug=483282)
self.Fail('conformance2/textures/misc/copy-texture-image-luma-format.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage2d_pbo_cube_00.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage2d_pbo_cube_01.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage2d_pbo_cube_02.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage2d_pbo_cube_03.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage2d_pbo_cube_04.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage2d_pbo_params.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'teximage2d_depth_pbo.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'basic_copyteximage2d.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'basic_teximage3d_3d_00.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'basic_teximage3d_3d_01.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'basic_teximage3d_3d_02.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'basic_teximage3d_3d_03.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'basic_teximage3d_3d_04.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texstorage2d_format_depth_stencil.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texstorage3d_format_2d_array_00.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texstorage3d_format_2d_array_01.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texstorage3d_format_2d_array_02.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texstorage3d_format_3d_00.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texstorage3d_format_3d_01.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texstorage3d_format_3d_02.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texstorage3d_format_3d_03.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texstorage3d_format_depth_stencil.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/texturespecification/' +
'texstorage3d_format_size.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/vertexarrays/' +
'single_attribute.output_type.unsigned_int.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/draw/*.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/fbomultisample*',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/fbocompleteness.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/textureshadow/*.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/shadermatrix/mul_dynamic_highp.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/shadermatrix/mul_dynamic_lowp.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/shadermatrix/mul_dynamic_mediump.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/shadermatrix/pre_decrement.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_04.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_07.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_08.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_10.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_11.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_12.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_13.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_18.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_25.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_28.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_29.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_30.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_31.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_32.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_33.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/conversion_34.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/framebufferblit/' +
'default_framebuffer_00.html',
['linux', 'amd'], bug=658832)
self.Fail('deqp/functional/gles3/shaderoperator/unary_operator_01.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/shaderoperator/unary_operator_02.html',
['linux', 'amd'], bug=483282)
self.Fail('conformance2/glsl3/vector-dynamic-indexing.html',
['linux', 'amd'], bug=483282)
self.Fail('conformance2/reading/read-pixels-pack-parameters.html',
['linux', 'amd', 'no_angle'], bug=483282)
self.Fail('conformance2/textures/misc/tex-unpack-params.html',
['linux', 'amd', 'no_angle'], bug=483282)
# TODO(kbr): re-enable after next conformance roll. crbug.com/736499
# self.Fail('conformance2/extensions/ext-color-buffer-float.html',
# ['linux', 'amd'], bug=633022)
self.Fail('conformance2/rendering/blitframebuffer-filter-outofbounds.html',
['linux', 'no_passthrough', 'amd'], bug=655147)
self.Fail('conformance2/textures/misc/tex-base-level-bug.html',
['linux', 'amd'], bug=705865)
self.Fail('conformance2/textures/image/' +
'tex-2d-r11f_g11f_b10f-rgb-float.html',
['linux', 'amd'], bug=705865)
# Uniform buffer related failures
self.Fail('deqp/functional/gles3/uniformbuffers/single_struct_array.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/uniformbuffers/single_nested_struct.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/uniformbuffers/' +
'single_nested_struct_array.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/uniformbuffers/multi_basic_types.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/uniformbuffers/multi_nested_struct.html',
['linux', 'amd'], bug=483282)
self.Fail('deqp/functional/gles3/uniformbuffers/random.html',
['linux', 'amd'], bug=483282)
self.Fail('conformance2/buffers/uniform-buffers.html',
['linux', 'amd'], bug=658842)
self.Fail('conformance2/rendering/uniform-block-buffer-size.html',
['linux', 'amd'], bug=658844)
self.Fail('conformance2/uniforms/uniform-blocks-with-arrays.html',
['linux', 'amd'], bug=2103) # angle bug ID
# Linux AMD R7 240
self.Fail('conformance2/textures/canvas/' +
'tex-2d-rg8ui-rg_integer-unsigned_byte.html',
['linux', ('amd', 0x6613)], bug=710392)
self.Fail('conformance2/textures/canvas/' +
'tex-2d-rgb8ui-rgb_integer-unsigned_byte.html',
['linux', ('amd', 0x6613)], bug=710392)
self.Fail('conformance2/textures/canvas/' +
'tex-2d-rgba8ui-rgba_integer-unsigned_byte.html',
['linux', ('amd', 0x6613)], bug=710392)
self.Fail('conformance2/textures/webgl_canvas/' +
'tex-2d-rg8ui-rg_integer-unsigned_byte.html',
['linux', ('amd', 0x6613)], bug=710392)
self.Fail('conformance2/textures/webgl_canvas/' +
'tex-2d-rgb8ui-rgb_integer-unsigned_byte.html',
['linux', ('amd', 0x6613)], bug=710392)
self.Fail('conformance2/textures/webgl_canvas/' +
'tex-2d-rgba8ui-rgba_integer-unsigned_byte.html',
['linux', ('amd', 0x6613)], bug=710392)
self.Fail('conformance2/textures/image_bitmap_from_video/' +
'tex-2d-rgba16f-rgba-float.html',
['linux', ('amd', 0x6613)], bug=701138)
self.Fail('conformance2/textures/image_bitmap_from_video/' +
'tex-2d-rgba16f-rgba-half_float.html',
['linux', ('amd', 0x6613)], bug=701138)
self.Fail('conformance2/textures/image_bitmap_from_video/' +
'tex-2d-rgba32f-rgba-float.html',
['linux', ('amd', 0x6613)], bug=701138)
self.Fail('conformance2/textures/image_bitmap_from_video/' +
'tex-2d-rgba4-rgba-unsigned_byte.html',
['linux', ('amd', 0x6613)], bug=701138)
self.Fail('conformance2/textures/image_bitmap_from_video/' +
'tex-2d-rgba4-rgba-unsigned_short_4_4_4_4.html',
['linux', ('amd', 0x6613)], bug=701138)
self.Fail('conformance2/textures/image_data/' +
'tex-3d-rgb32f-rgb-float.html',
['linux', ('amd', 0x6613)], bug=701138)
self.Fail('conformance2/textures/image_data/' +
'tex-3d-rgb565-rgb-unsigned_byte.html',
['linux', ('amd', 0x6613)], bug=701138)
self.Fail('conformance2/textures/image_data/' +
'tex-3d-rgb565-rgb-unsigned_short_5_6_5.html',
['linux', ('amd', 0x6613)], bug=701138)
self.Fail('conformance2/textures/image_data/' +
'tex-3d-rgb5_a1-rgba-unsigned_byte.html',
['linux', ('amd', 0x6613)], bug=701138)
# Conflicting expectations to test that the
# "Expectations have no collisions" unittest works.
# page_name = 'conformance/glsl/constructors/glsl-construct-ivec4.html'
# Conflict when all conditions match
# self.Fail(page_name,
# ['linux', ('nvidia', 0x1), 'debug', 'opengl'])
# self.Fail(page_name,
# ['linux', ('nvidia', 0x1), 'debug', 'opengl'])
# Conflict when all conditions match (and different sets)
# self.Fail(page_name,
# ['linux', 'win', ('nvidia', 0x1), 'debug', 'opengl'])
# self.Fail(page_name,
# ['linux', 'mac', ('nvidia', 0x1), 'amd', 'debug', 'opengl'])
# Conflict with one aspect not specified
# self.Fail(page_name,
# ['linux', ('nvidia', 0x1), 'debug'])
# self.Fail(page_name,
# ['linux', ('nvidia', 0x1), 'debug', 'opengl'])
# Conflict with one aspect not specified (in both conditions)
# self.Fail(page_name,
# ['linux', ('nvidia', 0x1), 'debug'])
# self.Fail(page_name,
# ['linux', ('nvidia', 0x1), 'debug'])
# Conflict even if the GPU is specified in a device ID
# self.Fail(page_name,
# ['linux', ('nvidia', 0x1), 'debug'])
# self.Fail(page_name,
# ['linux', 'nvidia', 'debug'])
# Test there are no conflicts between two different devices
# self.Fail(page_name,
# ['linux', ('nvidia', 0x1), 'debug'])
# self.Fail(page_name,
# ['linux', ('nvidia', 0x2), 'debug'])
# Test there are no conflicts between two devices with different vendors
# self.Fail(page_name,
# ['linux', ('nvidia', 0x1), 'debug'])
# self.Fail(page_name,
# ['linux', ('amd', 0x1), 'debug'])
# Conflicts if there is a device and nothing specified for the other's
# GPU vendors
# self.Fail(page_name,
# ['linux', ('nvidia', 0x1), 'debug'])
# self.Fail(page_name,
# ['linux', 'debug'])
# Test no conflicts happen when only one aspect differs
# self.Fail(page_name,
# ['linux', ('nvidia', 0x1), 'debug', 'opengl'])
# self.Fail(page_name,
# ['win', ('nvidia', 0x1), 'debug', 'opengl'])
# Conflicts if between a generic os condition and a specific version
# self.Fail(page_name,
# ['xp', ('nvidia', 0x1), 'debug', 'opengl'])
# self.Fail(page_name,
# ['win', ('nvidia', 0x1), 'debug', 'opengl'])
| [
"commit-bot@chromium.org"
] | commit-bot@chromium.org |
384f3ca83686eef79fb68f6e221c15a8ea737f27 | 378eea7cbb49d52c13c3bd0bb86bc93fc93d3d56 | /100Days/Day09/association.py | e4427897296525d11fbe292a810fcbc0d800bb87 | [] | no_license | Zpadger/Python | b9e54524841e14d05e8f52b829c8c99c91e308b8 | f13da6d074afac50396621c9df780bf5ca30ce6b | refs/heads/master | 2020-08-16T01:10:00.534615 | 2020-04-12T15:15:53 | 2020-04-12T15:15:53 | 172,426,365 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,200 | py | # 对象之间的关联关系
from math import sqrt
class Point(object):
def __init__(self,x=0,y=0):
self._x = x
self._y = y
def move_to(self,x,y):
self._x = x
self._y = y
def move_by(self,dx,dy):
self._x += dx
self._y += dy
def distance_to(self,other):
dx = self._x - other._x
dy = self._y - other._y
return sqrt(dx**2 + dy**2)
def __str__(self):
return '(%s,%s)' % (str(self._x),str(self._y))
class Line(object):
def __init__(self,start=Point(0,0),end=Point(0,0)):
self._start = start
self._end = end
@property
def start(self):
return self._start
@start.setter
def start(self,start):
self._start = start
@property
def end(self):
return self.end
@end.setter
def end(self,end):
self._end = end
@property
def length(self):
return self._start.distance_to(self._end)
if __name__ == '__main__':
p1 = Point(3,5)
print(p1)
p2 = Point(-2,-1.5)
print(p2)
line = Line(p1,p2)
print(line.length)
line.start.move_to(2,1)
line.end = Point(1,2)
print(line.length) | [
"noreply@github.com"
] | Zpadger.noreply@github.com |
150bd3e4db5e34c9c6a5a472b6d587f94ba3da8b | f4c36d1b5946ad0145d10164c40ee0635903accb | /tech/backends.py | 8dc3b9c98fd6278f45344b25513c7308e64331de | [] | no_license | Vivekdjango/techstop | 69c2edec92ef9b0e7318b908c8cf8044c5d7dfa2 | 1c0a0b992136a129a0d4226ee1ae691cd0a91ae4 | refs/heads/master | 2021-01-11T17:59:29.690837 | 2018-09-01T13:12:52 | 2018-09-01T13:12:52 | 79,893,964 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,070 | py | from django.contrib.auth.models import User
class SSOLoginBackend(object):
"""
This is a transparent authentication backend for SSO login. Assumes that a user
was authenticated using SSO prior to this class getting invoked.
"""
def authenticate(self, username, password=None, email=None):
user = None
try:
user = User.objects.get(username=username)
except User.DoesNotExist:
# Create a new user. Note that we can set password
# to anything, because it won't be checked; the password
# from settings.py will.
if password is None:
password = User.objects.make_random_password(length=25)
user = User(username=username, password=password)
user.is_staff = False
user.is_superuser = False
user.email = email
user.save()
return user
def get_user(self, user_id):
try:
return User.objects.get(pk=user_id)
except User.DoesNotExist:
return None
| [
"viveksinha@IC0532-L0.corp.inmobi.com"
] | viveksinha@IC0532-L0.corp.inmobi.com |
56634d05aee2bed2e41d18af1cf186dd65351abc | 495b0b8de3ecc341511cdb10f11368b35b585bea | /SoftLayer/tests/API/client_tests.py | 072167957ea42bfb0b22cb6bcf7d0b568893c2cd | [] | no_license | hugomatic/softlayer-api-python-client | cf6c1e6bfa32e559e72f8b0b069339ae8edd2ede | 9c115f0912ee62763b805941593f6dd50de37068 | refs/heads/master | 2021-01-18T11:09:19.122162 | 2013-04-09T01:44:51 | 2013-04-09T01:44:51 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 12,592 | py | """
SoftLayer.tests.API.client_tests
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
:copyright: (c) 2013, SoftLayer Technologies, Inc. All rights reserved.
:license: BSD, see LICENSE for more details.
"""
try:
import unittest2 as unittest
except ImportError:
import unittest # NOQA
from mock import patch, MagicMock, call
import SoftLayer
import SoftLayer.API
from SoftLayer.consts import USER_AGENT
class Inititialization(unittest.TestCase):
def test_init(self):
client = SoftLayer.Client('SoftLayer_User_Customer',
username='doesnotexist',
api_key='issurelywrong', timeout=10)
self.assertEquals(client._service_name,
'SoftLayer_User_Customer')
self.assertEquals(client._headers, {
'authenticate': {
'username': 'doesnotexist',
'apiKey': 'issurelywrong'
}
})
self.assertEquals(client._endpoint_url,
SoftLayer.API_PUBLIC_ENDPOINT.rstrip('/'))
def test_init_w_id(self):
client = SoftLayer.Client('SoftLayer_User_Customer', 1,
'doesnotexist', 'issurelywrong')
self.assertEquals(client._headers, {
'SoftLayer_User_CustomerInitParameters': {'id': 1},
'authenticate': {
'username': 'doesnotexist', 'apiKey': 'issurelywrong'}})
@patch.dict('os.environ', {
'SL_USERNAME': 'test_user', 'SL_API_KEY': 'test_api_key'})
def test_env(self):
client = SoftLayer.Client()
self.assertEquals(client._headers, {
'authenticate': {
'username': 'test_user', 'apiKey': 'test_api_key'}})
@patch('SoftLayer.API.API_USERNAME', 'test_user')
@patch('SoftLayer.API.API_KEY', 'test_api_key')
def test_globals(self):
client = SoftLayer.Client()
self.assertEquals(client._headers, {
'authenticate': {
'username': 'test_user', 'apiKey': 'test_api_key'}})
class ClientMethods(unittest.TestCase):
def test_help(self):
help(SoftLayer)
help(SoftLayer.Client)
client = SoftLayer.Client(
username='doesnotexist',
api_key='issurelywrong'
)
help(client)
help(client['SERVICE'])
def test_set_raw_header_old(self):
client = SoftLayer.Client(
username='doesnotexist',
api_key='issurelywrong'
)
client.transport = MagicMock()
client.add_raw_header("RAW", "HEADER")
self.assertEquals(client._raw_headers, {'RAW': 'HEADER'})
def test_add_header_invalid(self):
client = SoftLayer.Client(
username='doesnotexist',
api_key='issurelywrong'
)
client.transport = MagicMock()
self.assertRaises(SoftLayer.SoftLayerError,
client.add_header, "", "HEADER")
def test_remove_header(self):
client = SoftLayer.Client(
username='doesnotexist',
api_key='issurelywrong'
)
client.remove_header("authenticate")
self.assertNotIn("authenticate", client._headers)
def test_repr(self):
client = SoftLayer.Client(
username='doesnotexist',
api_key='issurelywrong'
)
self.assertIn("Client", repr(client))
def test_service_repr(self):
client = SoftLayer.Client(
username='doesnotexist',
api_key='issurelywrong'
)
self.assertIn("Service", repr(client['SERVICE']))
class APICalls(unittest.TestCase):
def setUp(self):
self.client = SoftLayer.Client(
username='doesnotexist', api_key='issurelywrong',
endpoint_url="ENDPOINT")
@patch('SoftLayer.API.make_api_call')
def test_old_api(self, make_api_call):
client = SoftLayer.API.Client(
'SoftLayer_SERVICE', None, 'doesnotexist', 'issurelywrong',
endpoint_url="ENDPOINT")
client.METHOD()
make_api_call.assert_called_with(
'ENDPOINT/SoftLayer_SERVICE', 'METHOD', (),
headers={
'authenticate': {
'username': 'doesnotexist', 'apiKey': 'issurelywrong'}},
verbose=False,
timeout=None,
http_headers={
'Content-Type': 'application/xml',
'User-Agent': USER_AGENT,
})
@patch('SoftLayer.API.make_api_call')
def test_complex_old_api(self, make_api_call):
client = SoftLayer.API.Client(
'SoftLayer_SERVICE', None, 'doesnotexist', 'issurelywrong',
endpoint_url="ENDPOINT")
client.set_result_limit(9, offset=10)
client.set_object_mask({'object': {'attribute': ''}})
client.add_raw_header("RAW", "HEADER")
client.METHOD(
1234,
id=5678,
mask={'object': {'attribute': ''}},
filter={
'TYPE': {'obj': {'attribute': {'operation': '^= prefix'}}}},
limit=9, offset=10)
make_api_call.assert_called_with(
'ENDPOINT/SoftLayer_SERVICE', 'METHOD', (1234, ),
headers={
'SoftLayer_SERVICEObjectMask': {
'mask': {'object': {'attribute': ''}}},
'SoftLayer_SERVICEObjectFilter': {
'TYPE': {
'obj': {'attribute': {'operation': '^= prefix'}}}},
'authenticate': {
'username': 'doesnotexist', 'apiKey': 'issurelywrong'},
'SoftLayer_SERVICEInitParameters': {'id': 5678},
'resultLimit': {'limit': 9, 'offset': 10}},
verbose=False,
timeout=None,
http_headers={
'RAW': 'HEADER',
'Content-Type': 'application/xml',
'User-Agent': USER_AGENT,
})
def test_old_api_no_service(self):
client = SoftLayer.Client(username='doesnotexist',
api_key='issurelywrong')
self.assertRaises(SoftLayer.SoftLayerError, client.METHOD)
@patch('SoftLayer.API.make_api_call')
def test_simple_call(self, make_api_call):
self.client['SERVICE'].METHOD()
make_api_call.assert_called_with(
'ENDPOINT/SoftLayer_SERVICE', 'METHOD', (),
headers={
'authenticate': {
'username': 'doesnotexist', 'apiKey': 'issurelywrong'}},
verbose=False,
timeout=None,
http_headers={
'Content-Type': 'application/xml',
'User-Agent': USER_AGENT,
})
@patch('SoftLayer.API.make_api_call')
def test_complex(self, make_api_call):
self.client['SERVICE'].METHOD(
1234,
id=5678,
mask={'object': {'attribute': ''}},
raw_headers={'RAW': 'HEADER'},
filter={
'TYPE': {'obj': {'attribute': {'operation': '^= prefix'}}}},
limit=9, offset=10)
make_api_call.assert_called_with(
'ENDPOINT/SoftLayer_SERVICE', 'METHOD', (1234, ),
headers={
'SoftLayer_SERVICEObjectMask': {
'mask': {'object': {'attribute': ''}}},
'SoftLayer_SERVICEObjectFilter': {
'TYPE': {
'obj': {'attribute': {'operation': '^= prefix'}}}},
'authenticate': {
'username': 'doesnotexist', 'apiKey': 'issurelywrong'},
'SoftLayer_SERVICEInitParameters': {'id': 5678},
'resultLimit': {'limit': 9, 'offset': 10}},
verbose=False,
timeout=None,
http_headers={
'RAW': 'HEADER',
'Content-Type': 'application/xml',
'User-Agent': USER_AGENT,
})
@patch('SoftLayer.API.make_api_call')
def test_mask_call_v2(self, make_api_call):
self.client['SERVICE'].METHOD(
mask="mask[something[nested]]")
make_api_call.assert_called_with(
'ENDPOINT/SoftLayer_SERVICE', 'METHOD', (),
headers={
'authenticate': {
'username': 'doesnotexist', 'apiKey': 'issurelywrong'},
'SoftLayer_ObjectMask': {'mask': 'mask[something[nested]]'}},
verbose=False,
timeout=None,
http_headers={
'Content-Type': 'application/xml',
'User-Agent': USER_AGENT,
})
@patch('SoftLayer.API.make_api_call')
def test_mask_call_v2_dot(self, make_api_call):
self.client['SERVICE'].METHOD(
mask="mask.something.nested")
make_api_call.assert_called_with(
'ENDPOINT/SoftLayer_SERVICE', 'METHOD', (),
headers={
'authenticate': {
'username': 'doesnotexist', 'apiKey': 'issurelywrong'},
'SoftLayer_ObjectMask': {'mask': 'mask[something.nested]'}},
verbose=False,
timeout=None,
http_headers={
'Content-Type': 'application/xml',
'User-Agent': USER_AGENT,
})
@patch('SoftLayer.API.make_api_call')
def test_mask_call_invalid_mask(self, make_api_call):
try:
self.client['SERVICE'].METHOD(mask="mask[something.nested")
except SoftLayer.SoftLayerError, e:
self.assertIn('Malformed Mask', str(e))
else:
self.fail('No exception raised')
@patch('SoftLayer.API.Client.iter_call')
def test_iterate(self, _iter_call):
self.client['SERVICE'].METHOD(iter=True)
_iter_call.assert_called_with('SERVICE', 'METHOD', iter=True)
@patch('SoftLayer.API.Client.iter_call')
def test_service_iter_call(self, _iter_call):
self.client['SERVICE'].iter_call('METHOD')
_iter_call.assert_called_with('SERVICE', 'METHOD')
@patch('SoftLayer.API.Client.call')
def test_iter_call(self, _call):
# chunk=100, no limit
_call.side_effect = [range(100), range(100, 125)]
result = list(self.client.iter_call('SERVICE', 'METHOD', iter=True))
self.assertEquals(range(125), result)
_call.assert_has_calls([
call('SERVICE', 'METHOD', limit=100, iter=False, offset=0),
call('SERVICE', 'METHOD', limit=100, iter=False, offset=100),
])
_call.reset_mock()
# chunk=100, no limit. Requires one extra request.
_call.side_effect = [range(100), range(100, 200), []]
result = list(self.client.iter_call('SERVICE', 'METHOD', iter=True))
self.assertEquals(range(200), result)
_call.assert_has_calls([
call('SERVICE', 'METHOD', limit=100, iter=False, offset=0),
call('SERVICE', 'METHOD', limit=100, iter=False, offset=100),
call('SERVICE', 'METHOD', limit=100, iter=False, offset=200),
])
_call.reset_mock()
# chunk=25, limit=30
_call.side_effect = [range(0, 25), range(25, 30)]
result = list(self.client.iter_call(
'SERVICE', 'METHOD', iter=True, limit=30, chunk=25))
self.assertEquals(range(30), result)
_call.assert_has_calls([
call('SERVICE', 'METHOD', iter=False, limit=25, offset=0),
call('SERVICE', 'METHOD', iter=False, limit=5, offset=25),
])
_call.reset_mock()
# A non-list was returned
_call.side_effect = ["test"]
result = list(self.client.iter_call('SERVICE', 'METHOD', iter=True))
self.assertEquals(["test"], result)
_call.assert_has_calls([
call('SERVICE', 'METHOD', iter=False, limit=100, offset=0),
])
_call.reset_mock()
# chunk=25, limit=30, offset=12
_call.side_effect = [range(0, 25), range(25, 30)]
result = list(self.client.iter_call(
'SERVICE', 'METHOD', iter=True, limit=30, chunk=25, offset=12))
self.assertEquals(range(30), result)
_call.assert_has_calls([
call('SERVICE', 'METHOD', iter=False, limit=25, offset=12),
call('SERVICE', 'METHOD', iter=False, limit=5, offset=37),
])
# Chunk size of 0 is invalid
self.assertRaises(
AttributeError,
lambda: list(self.client.iter_call(
'SERVICE', 'METHOD', iter=True, chunk=0)))
| [
"k3vinmcdonald@gmail.com"
] | k3vinmcdonald@gmail.com |
ad853f1c5462f8be2b4c54a9aaf79b67efdb2435 | ec546fe9c41a1bc4bc5bf39d939f1cbf0382a7ee | /dashboard/email_sender_smtp.py | e422c15eebcc1279bf7fd70488fa30c663a1f46e | [] | no_license | MaxOvcharov/Python_for_DevOps | 3910fd1cced9f07139f8709b453693f937d7216d | 03a5f737bb1c2f53713803a7794c04d134a596b0 | refs/heads/master | 2020-06-13T00:56:06.704476 | 2017-09-05T19:10:19 | 2017-09-05T19:10:19 | 75,471,789 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 568 | py | # -*- coding: utf-8 -*-
import smtplib
mail_server = "smtp.rambler.ru"
mail_server_port = 465
from_addr = 'EMAIL_FROM'
to_addr = 'EMAIL_TO'
from_header = 'From: %s\r\n' % from_addr
to_header = 'To: %s\r\n\r\n' % to_addr
subject_header = 'Subject: Testing SMTP Authentication'
body = 'This mail tests SMTP Authentication'
email_message = '%s\n%s\n%s\n\n%s' % (from_header, to_header, subject_header, body)
s = smtplib.SMTP_SSL(mail_server, mail_server_port)
s.set_debuglevel(1)
s.login('EMAIL', 'PASSWORD')
s.sendmail(from_addr, to_addr, email_message)
s.quit()
| [
"ovcharovmax@yandex.ru"
] | ovcharovmax@yandex.ru |
04df58a49a0cd3dee2830a34facd4a34f4e62358 | 7d6e641d53c6d063da09a632e960615d4ddf05ef | /flask_api/venv3/lib/python2.7/_abcoll.py | 494be0f1ed300d58c350692546af0c3cf8919e8c | [
"MIT"
] | permissive | tjuyanghw/ml-flask-api | 3af1a6103505962a624c78425b843d935277e1b9 | dc119856e75b0f86d21ccf79cd1418559d722ee4 | refs/heads/master | 2022-02-23T14:20:32.569192 | 2018-06-15T08:06:56 | 2018-06-15T08:06:56 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 53 | py | /home/harrypotter0/anaconda2/lib/python2.7/_abcoll.py | [
"9654263057akashkandpal@gmail.com"
] | 9654263057akashkandpal@gmail.com |
72f19dc284ac2bf624c43c39e5120e941676dad9 | c9288bd0496b92ff503a9df60f8210b08f54f3b5 | /label_studio/projects/migrations/0003_auto_20210305_1008.py | 15546212ecd08576a1f749015ef9abaed8abb3b2 | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | mihirpurwar/label-studio | 11318bd352c9648a3c33d69f09b7f389f1b99512 | 7c9e5777b7c0fe510b8585ae4c42b74a46929f73 | refs/heads/master | 2023-05-19T18:47:52.351140 | 2021-06-13T13:46:38 | 2021-06-13T13:46:38 | 376,830,084 | 1 | 0 | Apache-2.0 | 2021-06-14T13:19:51 | 2021-06-14T13:19:50 | null | UTF-8 | Python | false | false | 2,400 | py | # Generated by Django 3.1.4 on 2021-03-05 10:08
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('projects', '0002_auto_20210304_1457'),
]
operations = [
migrations.RenameField(
model_name='project',
old_name='enable_empty_completion',
new_name='enable_empty_annotation'
),
migrations.AlterField(
model_name='project',
name='enable_empty_annotation',
field=models.BooleanField(default=True, help_text='Allow submit empty annotations', verbose_name='enable empty annotation'),
),
migrations.RenameField(
model_name='project',
old_name='maximum_completions',
new_name='maximum_annotations'
),
migrations.AlterField(
model_name='project',
name='maximum_annotations',
field=models.IntegerField(default=1, help_text='Maximum overlaps of expert annotations for one task. If the annotation number per task is equal or greater to this value, the task becomes finished (is_labeled=True)', verbose_name='maximum annotation number'),
),
migrations.RenameField(
model_name='project',
old_name='min_completions_to_start_training',
new_name='min_annotations_to_start_training'
),
migrations.AlterField(
model_name='project',
name='min_annotations_to_start_training',
field=models.IntegerField(default=10, help_text='Minimum number of completed tasks after which training is started', verbose_name='min_annotations_to_start_training'),
),
migrations.RenameField(
model_name='project',
old_name='show_completion_history',
new_name='show_annotation_history'
),
migrations.AlterField(
model_name='project',
name='show_annotation_history',
field=models.BooleanField(default=False, help_text='Show annotation history to collaborator', verbose_name='show annotation history'),
),
migrations.AlterField(
model_name='project',
name='result_count',
field=models.IntegerField(default=0, help_text='Total results inside of annotations counter', verbose_name='result count'),
),
]
| [
"noreply@github.com"
] | mihirpurwar.noreply@github.com |
592bced0cbcf81fd35deea5cffaf73c10910136b | 23693ce4ad3eca0c208f01f5e872c6e99e1edb69 | /DarkSUSY_mH_125/mGammaD_0350/cT_5000/DarkSUSY_LHE_read.py | 764d8cf2519fd9b255536e71b898cafc1a2ed294 | [] | no_license | cms-tamu/MuJetAnalysis_DarkSusySamples_LHE_13TeV_02 | a6a1790ba62583e4693747e7162478f4688aa9a9 | ef31123e5b3f19e8a9621fa519f81105a500c3a9 | refs/heads/master | 2021-01-22T20:19:03.826804 | 2015-03-18T13:59:43 | 2015-03-18T13:59:43 | 30,620,024 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 111,091 | py | import ROOT, array, os, re, math, random, string
from math import *
from operator import itemgetter
def getStringBetween(name, first, second):
begOf1 = name.find(first)
endOf1 = len(first) + begOf1
begOf2 = name.find(second)
desiredString = name[endOf1:begOf2]
return desiredString
muonID = 13
higgsID = 25
n1ID = 3000002
nDID = 3000001
nExit = 80002
#nExit = 1000
gammaDID = 3000022
hMass = "125"
n1Mass = "10"
nDMass = "1"
filename = "DarkSUSY_mH_125_mGammaD_0350_13TeV_cT_5000_madgraph452_bridge224_events80k.lhe"
filename = "DarkSUSY_mH_125_mGammaD_0350_13TeV_cT_5000_madgraph452_bridge224_events80k.lhe"
f = open(filename, 'r')
if len(filename) >= 77:
mass_GammaD = getStringBetween(filename, "mGammaD_","_13TeV_cT")
lifetime_GammaD = getStringBetween(filename, "_cT_","_madgraph452")
energy = getStringBetween(filename, mass_GammaD + "_","TeV_")
mass_Higgs = getStringBetween(filename, "_mH_","_mGammaD_")
lifetime_GammaD_Legend = lifetime_GammaD[0:-2] + "." + lifetime_GammaD[len(lifetime_GammaD)-2:len(lifetime_GammaD)]
mass_GammaD_Legend = mass_GammaD[0:-3] + "." + mass_GammaD[len(mass_GammaD)-3:len(lifetime_GammaD)+1]
#mass_GammaD = filename[24:-49]
#lifetime_GammaD = filename[38:-36]
#energy = filename[29:-46]
#mass_Higgs = filename[12:-62]
#lifetime_GammaD_Legend = filename[38:-38] + "." + filename[39:-36]
#mass_GammaD_Legend = filename [24:-52] + "." + filename[25:-49]
if mass_GammaD_Legend[len(mass_GammaD_Legend)-1] == "0": mass_GammaD_Legend = mass_GammaD_Legend[:-1]
if mass_GammaD_Legend[len(mass_GammaD_Legend)-1] == "0": mass_GammaD_Legend = mass_GammaD_Legend[:-1]
if mass_GammaD_Legend[len(mass_GammaD_Legend)-1] == "0": mass_GammaD_Legend = mass_GammaD_Legend[:-1]
if mass_GammaD_Legend[len(mass_GammaD_Legend)-1] == "." and len(mass_GammaD_Legend) <= 3: mass_GammaD_Legend = mass_GammaD_Legend + "0"
switch = 0
if lifetime_GammaD_Legend[len(lifetime_GammaD_Legend)-1] == "0":
lifetime_GammaD_Legend = lifetime_GammaD_Legend[:-1]
switch = 1
if lifetime_GammaD_Legend[len(lifetime_GammaD_Legend)-1] == "0" and switch == 1: lifetime_GammaD_Legend = lifetime_GammaD_Legend[:-1]
else:
lifetime_GammaD = "000"
lifetime_GammaD_Legend = "0.00"
mass_GammaD = getStringBetween(filename, "mGammaD_","_13TeV")
energy = getStringBetween(filename, mass_GammaD + "_","TeV")
mass_Higgs = getStringBetween(filename, "_mH_","_mGammaD_")
mass_GammaD_Legend = mass_GammaD[0:-3] + "." + mass_GammaD[len(mass_GammaD)-3:len(lifetime_GammaD)+1]
#mass_GammaD = filename[24:-42]
#energy = filename[29:-39]
#mass_Higgs = filename[12:-55]
#mass_GammaD_Legend = filename[24:-45] + "." + filename[25:-42]
#lifetime_GammaD = "000"
#lifetime_GammaD_Legend = "0.00"
print mass_GammaD
print lifetime_GammaD
print lifetime_GammaD_Legend
print mass_GammaD_Legend
BAM = ROOT.TFile("ValidationPlots_mGammaD_" + mass_GammaD + "_" + energy + "_TeV_cT_" + lifetime_GammaD + ".root" , "RECREATE")
execfile("tdrStyle.py")
cnv = ROOT.TCanvas("cnv", "cnv")
txtHeader = ROOT.TLegend(.17,.935,0.97,1.)
txtHeader.SetFillColor(ROOT.kWhite)
txtHeader.SetFillStyle(0)
txtHeader.SetBorderSize(0)
txtHeader.SetTextFont(42)
txtHeader.SetTextSize(0.045)
txtHeader.SetTextAlign(22)
#txtHeader.SetHeader("CMS Simulation")
txtHeader.SetHeader("CMS Simulation (LHE) " + energy + " TeV")
#txtHeader.SetHeader("CMS Prelim. 2011 #sqrt{s} = 7 TeV L_{int} = 5.3 fb^{-1}")
#txtHeader.SetHeader("CMS 2011 #sqrt{s} = 7 TeV L_{int} = 5.3 fb^{-1}")
#txtHeader.SetHeader("CMS Prelim. 2012 #sqrt{s} = 8 TeV L_{int} = 20.65 fb^{-1}")
#txtHeader.SetHeader("CMS 2012 #sqrt{s} = 8 TeV L_{int} = 20.65 fb^{-1}")
txtHeader.Draw()
#info = ROOT.TLegend(0.33,0.8222222,0.9577778,0.9122222)
info = ROOT.TLegend(0.4566667,0.82,0.7822222,0.9066667)
info.SetFillColor(ROOT.kWhite)
info.SetFillStyle(0)
info.SetBorderSize(0)
info.SetTextFont(42)
info.SetTextSize(0.02777778)
info.SetMargin(0.13)
info.SetHeader("#splitline{pp #rightarrow h #rightarrow 2n_{1} #rightarrow 2n_{D} + 2 #gamma_{D} #rightarrow 2n_{D} + 4#mu}{#splitline{m_{h} = " + mass_Higgs + " GeV, m_{n_{1}} = 10 GeV, m_{n_{D}} = 1 GeV}{m_{#gamma_{D}} = " + mass_GammaD_Legend + " GeV, c#tau_{#gamma_{D}} = " + lifetime_GammaD_Legend + " mm}}" )
#info.SetHeader("#splitline{pp #rightarrow h #rightarrow 2n_{1} #rightarrow 2n_{D} + 2 #gamma_{D} #rightarrow 2n_{D} + 4#mu}{#splitline{#gamma_{D} c#tau = "+lifetime_GammaD_Legend + "mm, Mass = " + mass_GammaD_Legend + "GeV}{M of h = " + hMass + "GeV, M of n_{1} = " + n1Mass + "GeV, M of n_{D} = " + nDMass + "GeV}}" )
txtHeader2 = ROOT.TLegend(0.01333333,0.9311111,0.8133333,0.9955556)
txtHeader2.SetFillColor(ROOT.kWhite)
txtHeader2.SetFillStyle(0)
txtHeader2.SetBorderSize(0)
txtHeader2.SetTextFont(42)
txtHeader2.SetTextSize(0.045)
txtHeader2.SetTextAlign(22)
txtHeader2.SetHeader("CMS Simulation #sqrt{s} = " + energy + " TeV")
################################################################################
# pT of muons
################################################################################
Etmiss_dummy = ROOT.TH1F("Etmiss_dummy","Etmiss_dummy", 100, 0, 100)
Etmiss_dummy.SetTitleOffset(1.5, "Y")
Etmiss_dummy.SetTitleOffset(1.4, "X")
Etmiss_dummy.SetTitleSize(0.04,"X")
Etmiss_dummy.SetXTitle("MET = #sum_{n_{D}}#vec{p_{T}} [GeV]")
Etmiss_dummy.SetYTitle("Fraction of events / 1 GeV")
Etmiss_dummy.SetMaximum( 0.1 )
Etmiss = ROOT.TH1F("Etmiss","Etmiss", 100, 0, 100)
Etmiss.SetLineColor(ROOT.kBlue)
Etmiss.SetLineWidth(2)
Etmiss.SetLineStyle(1)
nBins = 125
binMin = 0.0
binMax = 125.0
yMax = 0.25
cTlow = 0
if float(lifetime_GammaD_Legend) != 0:
cTlim = float(lifetime_GammaD_Legend)*5
binwidth = float(lifetime_GammaD_Legend)
numBins = int(cTlim/binwidth)
binwidthRound = round(binwidth,3)
else:
cTlim = 10
binwidth = 1
numBins = int(cTlim/binwidth)
binwidthRound = "1"
formula = "exp(-x/"+ lifetime_GammaD_Legend +")/("+ lifetime_GammaD_Legend + "*(1 - exp(-" + str(cTlim) + "/" + lifetime_GammaD_Legend + ")))"
print formula
h_gammaD_cT_dummy = ROOT.TH1F("h_gammaD_cT_dummy", "h_gammaD_cT_dummy", numBins, 0, cTlim)
#h_gammaD_cT_dummy.SetYTitle("Fraction of events")
h_gammaD_cT_dummy.SetTitleOffset(1.3, "Y")
h_gammaD_cT_dummy.SetXTitle("c#tau of #gamma_{D} [mm]")
h_gammaD_cT_dummy.SetYTitle("Normalized Fraction of Events / " + str(binwidthRound) + " mm")
h_gammaD_cT_dummy.SetTitleSize(0.05,"Y")
h_gammaD_cT_dummy.SetMaximum( 10 )
h_gammaD_cT = ROOT.TH1F("h_gammaD_cT", "h_gammaD_cT", numBins, 0, cTlim)
h_gammaD_cT.SetLineColor(ROOT.kBlue)
h_gammaD_cT.SetLineWidth(2)
h_gammaD_cT.SetLineStyle(1)
h_gammaD_cT_lab_dummy = ROOT.TH1F("h_gammaD_cT_lab_dummy", "h_gammaD_cT_lab_dummy", numBins, 0, cTlim)
#h_gammaD_cT_lab_dummy.SetYTitle("Fraction of events")
h_gammaD_cT_lab_dummy.SetTitleOffset(1.3, "Y")
h_gammaD_cT_lab_dummy.SetXTitle("L of #gamma_{D} [mm]")
h_gammaD_cT_lab_dummy.SetYTitle("Normalized Fraction of Events / " + str(binwidthRound) + " mm")
h_gammaD_cT_lab_dummy.SetTitleSize(0.05,"Y")
h_gammaD_cT_lab_dummy.SetMaximum( 10 )
h_gammaD_cT_lab = ROOT.TH1F("h_gammaD_cT_lab", "h_gammaD_cT_lab", numBins, 0, cTlim)
h_gammaD_cT_lab.SetLineColor(ROOT.kBlue)
h_gammaD_cT_lab.SetLineWidth(2)
h_gammaD_cT_lab.SetLineStyle(1)
h_gammaD_cT_XY_lab_dummy = ROOT.TH1F("h_gammaD_cT_XY_lab_dummy", "h_gammaD_cT_XY_lab_dummy", numBins, 0, cTlim)
#h_gammaD_cT_XY_lab_dummy.SetYTitle("Fraction of events")
h_gammaD_cT_XY_lab_dummy.SetTitleOffset(1.3, "Y")
h_gammaD_cT_XY_lab_dummy.SetXTitle("L_{XY} of #gamma_{D} [mm]")
h_gammaD_cT_XY_lab_dummy.SetYTitle("Normalized Fraction of Events / " + str(binwidthRound) + " mm")
h_gammaD_cT_XY_lab_dummy.SetTitleSize(0.05,"Y")
h_gammaD_cT_XY_lab_dummy.SetMaximum( 10 )
h_gammaD_cT_XY_lab = ROOT.TH1F("h_gammaD_cT_XY_lab", "h_gammaD_cT_XY_lab", numBins, 0, cTlim)
h_gammaD_cT_XY_lab.SetLineColor(ROOT.kBlue)
h_gammaD_cT_XY_lab.SetLineWidth(2)
h_gammaD_cT_XY_lab.SetLineStyle(1)
h_gammaD_cT_Z_lab_dummy = ROOT.TH1F("h_gammaD_cT_Z_lab_dummy", "h_gammaD_cT_Z_lab_dummy", numBins, 0, cTlim)
#h_gammaD_cT_Z_lab_dummy.SetYTitle("Fraction of events")
h_gammaD_cT_Z_lab_dummy.SetTitleOffset(1.3, "Y")
h_gammaD_cT_Z_lab_dummy.SetXTitle("L_{Z} of #gamma_{D} [mm]")
h_gammaD_cT_Z_lab_dummy.SetYTitle("Normalized Fraction of events / " + str(binwidthRound) + " mm")
h_gammaD_cT_Z_lab_dummy.SetTitleSize(0.05,"Y")
h_gammaD_cT_Z_lab_dummy.SetMaximum( 10 )
h_gammaD_cT_Z_lab = ROOT.TH1F("h_gammaD_cT_Z_lab", "h_gammaD_cT_Z_lab", numBins, 0, cTlim)
h_gammaD_cT_Z_lab.SetLineColor(ROOT.kBlue)
h_gammaD_cT_Z_lab.SetLineWidth(2)
h_gammaD_cT_Z_lab.SetLineStyle(1)
h_gammaD_1_cT_dummy = ROOT.TH1F("h_gammaD_1_cT_dummy", "h_gammaD_1_cT_dummy", numBins, 0, cTlim)
h_gammaD_1_cT_dummy.SetTitleOffset(1.3, "Y")
h_gammaD_1_cT_dummy.SetXTitle("c#tau of #gamma_{D} [mm]")
h_gammaD_1_cT_dummy.SetYTitle("Normalized Fraction of events / " + str(binwidthRound) + " mm")
h_gammaD_1_cT_dummy.SetTitleSize(0.05,"Y")
h_gammaD_1_cT_dummy.SetMaximum( 10 )
h_gammaD_1_cT = ROOT.TH1F("h_gammaD_1_cT", "h_gammaD_1_cT", numBins, 0, cTlim)
h_gammaD_1_cT.SetLineColor(ROOT.kBlue)
h_gammaD_1_cT.SetLineWidth(2)
h_gammaD_1_cT.SetLineStyle(1)
h_gammaD_1_cT_lab_dummy = ROOT.TH1F("h_gammaD_1_cT_lab_dummy", "h_gammaD_1_cT_lab_dummy", numBins, 0, cTlim)
h_gammaD_1_cT_lab_dummy.SetTitleOffset(1.3, "Y")
h_gammaD_1_cT_lab_dummy.SetXTitle("L of #gamma_{D} [mm]")
h_gammaD_1_cT_lab_dummy.SetYTitle("Normalized Fraction of events / " + str(binwidthRound) + " mm")
h_gammaD_1_cT_lab_dummy.SetTitleSize(0.05,"Y")
h_gammaD_1_cT_lab_dummy.SetMaximum( 10 )
h_gammaD_1_cT_lab = ROOT.TH1F("h_gammaD_1_cT_lab", "h_gammaD_1_cT_lab", numBins, 0, cTlim)
h_gammaD_1_cT_lab.SetLineColor(ROOT.kBlue)
h_gammaD_1_cT_lab.SetLineWidth(2)
h_gammaD_1_cT_lab.SetLineStyle(1)
h_gammaD_1_cT_XY_lab_dummy = ROOT.TH1F("h_gammaD_1_cT_XY_lab_dummy", "h_gammaD_1_cT_XY_lab_dummy", numBins, 0, cTlim)
h_gammaD_1_cT_XY_lab_dummy.SetTitleOffset(1.3, "Y")
h_gammaD_1_cT_XY_lab_dummy.SetXTitle("L_{XY} of #gamma_{D} [mm]")
h_gammaD_1_cT_XY_lab_dummy.SetYTitle("Normalized Fraction of events / " + str(binwidthRound) + " mm")
h_gammaD_1_cT_XY_lab_dummy.SetTitleSize(0.05,"Y")
h_gammaD_1_cT_XY_lab_dummy.SetMaximum( 10 )
h_gammaD_1_cT_XY_lab = ROOT.TH1F("h_gammaD_1_cT_XY_lab", "h_gammaD_1_cT_XY_lab", numBins, 0, cTlim)
h_gammaD_1_cT_XY_lab.SetLineColor(ROOT.kBlue)
h_gammaD_1_cT_XY_lab.SetLineWidth(2)
h_gammaD_1_cT_XY_lab.SetLineStyle(1)
h_gammaD_1_cT_Z_lab_dummy = ROOT.TH1F("h_gammaD_1_cT_Z_lab_dummy", "h_gammaD_1_cT_Z_lab_dummy", numBins, 0, cTlim)
h_gammaD_1_cT_Z_lab_dummy.SetTitleOffset(1.3, "Y")
h_gammaD_1_cT_Z_lab_dummy.SetXTitle("L_{Z} of #gamma_{D} [mm]")
h_gammaD_1_cT_Z_lab_dummy.SetYTitle("Normalized Fraction of events / " + str(binwidthRound) + " mm")
h_gammaD_1_cT_Z_lab_dummy.SetTitleSize(0.05,"Y")
h_gammaD_1_cT_Z_lab_dummy.SetMaximum( 10 )
h_gammaD_1_cT_Z_lab = ROOT.TH1F("h_gammaD_1_cT_Z_lab", "h_gammaD_1_cT_Z_lab", numBins, 0, cTlim)
h_gammaD_1_cT_Z_lab.SetLineColor(ROOT.kBlue)
h_gammaD_1_cT_Z_lab.SetLineWidth(2)
h_gammaD_1_cT_Z_lab.SetLineStyle(1)
h_gammaD_2_cT = ROOT.TH1F("h_gammaD_2_cT", "h_gammaD_2_cT", numBins, 0, cTlim)
h_gammaD_2_cT.SetLineColor(ROOT.kRed)
h_gammaD_2_cT.SetLineWidth(2)
h_gammaD_2_cT.SetLineStyle(1)
h_gammaD_2_cT_lab = ROOT.TH1F("h_gammaD_2_cT_lab", "h_gammaD_2_cT_lab", numBins, 0, cTlim)
h_gammaD_2_cT_lab.SetLineColor(ROOT.kRed)
h_gammaD_2_cT_lab.SetLineWidth(2)
h_gammaD_2_cT_lab.SetLineStyle(1)
h_gammaD_2_cT_XY_lab = ROOT.TH1F("h_gammaD_2_cT_XY_lab", "h_gammaD_2_cT_XY_lab", numBins, 0, cTlim)
h_gammaD_2_cT_XY_lab.SetLineColor(ROOT.kRed)
h_gammaD_2_cT_XY_lab.SetLineWidth(2)
h_gammaD_2_cT_XY_lab.SetLineStyle(1)
h_gammaD_2_cT_Z_lab = ROOT.TH1F("h_gammaD_2_cT_Z_lab", "h_gammaD_2_cT_Z_lab", numBins, 0, cTlim)
h_gammaD_2_cT_Z_lab.SetLineColor(ROOT.kRed)
h_gammaD_2_cT_Z_lab.SetLineWidth(2)
h_gammaD_2_cT_Z_lab.SetLineStyle(1)
h_muon_pT_dummy = ROOT.TH1F("h_muon_pT_dummy", "h_muon_pT_dummy", nBins, binMin, binMax)
h_muon_pT_dummy.SetYTitle("Fraction of events / 1 GeV")
h_muon_pT_dummy.SetTitleOffset(1.35, "Y")
h_muon_pT_dummy.SetXTitle("p_{T} of #mu [GeV]")
h_muon_pT_dummy.SetMaximum( 0.2 )
h_higgs_pT_dummy = ROOT.TH1F("h_higgs_pT_dummy", "h_higgs_pT_dummy", 10, 0, 10)
h_higgs_pT_dummy.SetYTitle("Fraction of events / 1 GeV")
h_higgs_pT_dummy.SetTitleOffset(1.35, "Y")
h_higgs_pT_dummy.SetXTitle("p_{T} of h [GeV]")
h_higgs_pT_dummy.SetMaximum( 1.1 )
h_muon_pZ_dummy = ROOT.TH1F("h_muon_pZ_dummy", "h_muon_pZ_dummy", nBins, binMin, binMax)
h_muon_pZ_dummy.SetYTitle("Fraction of events / 1 GeV")
h_muon_pZ_dummy.SetTitleOffset(1.35, "Y")
h_muon_pZ_dummy.SetXTitle("|p_{Z}| of #mu [GeV]")
h_muon_pZ_dummy.SetMaximum( yMax )
h_higgs_pZ_dummy = ROOT.TH1F("h_higgs_pZ_dummy", "h_higgs_pZ_dummy", 50, 0, 500)
h_higgs_pZ_dummy.SetYTitle("Fraction of events / 1 GeV")
h_higgs_pZ_dummy.SetTitleOffset(1.35, "Y")
h_higgs_pZ_dummy.SetXTitle("|p_{Z}| of h [GeV]")
h_higgs_pZ_dummy.SetMaximum( 0.1 )
h_muon_Eta_dummy = ROOT.TH1F("h_muon_Eta_dummy", "h_muon_Eta_dummy", 100, -5, 5)
h_muon_Eta_dummy.SetYTitle("Fraction of events / 0.1")
h_muon_Eta_dummy.SetTitleOffset(1.35, "Y")
h_muon_Eta_dummy.SetXTitle("#eta of #mu")
h_muon_Eta_dummy.SetMaximum( 0.1 )
#h_higgs_Eta_dummy = ROOT.TH1F("h_higgs_Eta_dummy", "h_higgs_Eta_dummy", 100,-5,5)
#h_higgs_Eta_dummy.SetYTitle("Fraction of events / 0.1 GeV")
#h_higgs_Eta_dummy.SetTitleOffset(1.35, "Y")
#h_higgs_Eta_dummy.SetXTitle("#eta of h [GeV]")
#h_higgs_Eta_dummy.SetMaximum( 0.1 )
h_muon_Phi_dummy = ROOT.TH1F("h_muon_Phi_dummy", "h_muon_Phi_dummy", 80,-4,4)
h_muon_Phi_dummy.SetYTitle("Fraction of events / 0.1 rad")
h_muon_Phi_dummy.SetTitleOffset(1.35, "Y")
h_muon_Phi_dummy.SetXTitle("#phi of #mu [rad]")
h_muon_Phi_dummy.SetMaximum( 0.1 )
h_higgs_Phi_dummy = ROOT.TH1F("h_higgs_Phi_dummy", "h_higgs_Phi_dummy", 80,-4,4)
h_higgs_Phi_dummy.SetYTitle("Fraction of events")
h_higgs_Phi_dummy.SetTitleOffset(1.35, "Y")
h_higgs_Phi_dummy.SetXTitle("#phi of h [rad]")
h_higgs_Phi_dummy.SetMaximum( 1.4 )
h_higgs_p_dummy = ROOT.TH1F("h_higgs_p_dummy", "h_higgs_p_dummy", 50, 0, 500)
h_higgs_p_dummy.SetYTitle("Fraction of events / 1 GeV")
h_higgs_p_dummy.SetTitleOffset(1.35, "Y")
h_higgs_p_dummy.SetXTitle("p of h [GeV]")
h_higgs_p_dummy.SetMaximum( 0.1 )
h_higgs_M_dummy = ROOT.TH1F("h_higgs_M_dummy", "h_higgs_M_dummy", 220, 80.5, 300.5)
h_higgs_M_dummy.SetYTitle("Fraction of events / 1 GeV")
h_higgs_M_dummy.SetTitleOffset(1.35, "Y")
h_higgs_M_dummy.SetXTitle("Mass of h [GeV]")
h_higgs_M_dummy.SetLabelSize(0.03,"X")
h_higgs_M_dummy.SetMaximum( 1.5 )
h_higgs_M_dummy.SetNdivisions(10)
h_higgs_M_dummy.GetXaxis().SetMoreLogLabels()
h_higgs_p = ROOT.TH1F("h_higgs_p", "h_higgs_p", 50, 0, 500)
h_higgs_p.SetLineColor(ROOT.kBlue)
h_higgs_p.SetLineWidth(2)
h_higgs_p.SetLineStyle(1)
h_higgs_M = ROOT.TH1F("h_higgs_M", "h_higgs_M", 10, 120.5, 130.5)
h_higgs_M.SetLineColor(ROOT.kBlue)
h_higgs_M.SetLineWidth(2)
h_higgs_M.SetLineStyle(1)
h_higgs_pT = ROOT.TH1F("h_higgs_pT", "h_higgs_pT", 10, 0, 10)
h_higgs_pT.SetLineColor(ROOT.kBlue)
h_higgs_pT.SetLineWidth(2)
h_higgs_pT.SetLineStyle(1)
h_n1_1_pT_dummy = ROOT.TH1F("h_n1_1_pT_dummy", "h_n1_1_pT_dummy", 70, 0, 70)
h_n1_1_pT_dummy.SetYTitle("Fraction of events / 1 GeV")
h_n1_1_pT_dummy.SetTitleOffset(1.35, "Y")
h_n1_1_pT_dummy.SetXTitle("p_{T} of n_{1} [GeV]")
h_n1_1_pT_dummy.SetMaximum( yMax )
h_higgs_pZ = ROOT.TH1F("h_higgs_pZ", "h_higgs_pZ", 50, 0, 500)
h_higgs_pZ.SetLineColor(ROOT.kBlue)
h_higgs_pZ.SetLineWidth(2)
h_higgs_pZ.SetLineStyle(1)
h_n1_1_pZ_dummy = ROOT.TH1F("h_n1_1_pZ_dummy", "h_n1_1_pZ_dummy", 300, 0, 300)
h_n1_1_pZ_dummy.SetYTitle("Fraction of events / 1 GeV")
h_n1_1_pZ_dummy.SetTitleOffset(1.35, "Y")
h_n1_1_pZ_dummy.SetXTitle("|p_{Z}| of n_{1} [GeV]")
h_n1_1_pZ_dummy.SetMaximum( 0.1 )
#h_higgs_Eta = ROOT.TH1F("h_higgs_Eta", "h_higgs_Eta", 50,0,5)
#h_higgs_Eta.SetLineColor(ROOT.kBlue)
#h_higgs_Eta.SetLineWidth(2)
#h_higgs_Eta.SetLineStyle(1)
h_n1_1_Eta_dummy = ROOT.TH1F("h_n1_1_Eta_dummy", "h_n1_1_Eta_dummy", 100,-5,5)
h_n1_1_Eta_dummy.SetYTitle("Fraction of events / 0.1")
h_n1_1_Eta_dummy.SetTitleOffset(1.35, "Y")
h_n1_1_Eta_dummy.SetXTitle("#eta of n_{1}")
h_n1_1_Eta_dummy.SetMaximum( 0.1 )
h_higgs_Phi = ROOT.TH1F("h_higgs_Phi", "h_higgs_Phi", 80,-4,4)
h_higgs_Phi.SetLineColor(ROOT.kBlue)
h_higgs_Phi.SetLineWidth(2)
h_higgs_Phi.SetLineStyle(1)
h_n1_1_Phi_dummy = ROOT.TH1F("h_n1_1_Phi_dummy", "h_n1_1_Phi_dummy", 80,-4,4)
h_n1_1_Phi_dummy.SetYTitle("Fraction of events / 0.1 rad")
h_n1_1_Phi_dummy.SetTitleOffset(1.35, "Y")
h_n1_1_Phi_dummy.SetXTitle("#phi of n_{1} [rad]")
h_n1_1_Phi_dummy.SetMaximum( 0.05 )
h_n1_1_p_dummy = ROOT.TH1F("h_n1_1_p_dummy", "h_n1_1_p_dummy", 300, 0, 300)
h_n1_1_p_dummy.SetYTitle("Fraction of events / 1 GeV")
h_n1_1_p_dummy.SetTitleOffset(1.35, "Y")
h_n1_1_p_dummy.SetXTitle("p of n_{1} [GeV]")
h_n1_1_p_dummy.SetMaximum( 0.1 )
h_n1_1_M_dummy = ROOT.TH1F("h_n1_1_M_dummy", "h_n1_1_M_dummy", 200, 0.05, 20.05)
h_n1_1_M_dummy.SetYTitle("Fraction of events / 0.1 GeV")
h_n1_1_M_dummy.SetTitleOffset(1.35, "Y")
h_n1_1_M_dummy.SetXTitle("Mass of n_{1} [GeV]")
h_n1_1_M_dummy.SetMaximum( 1.6 )
h_n1_1_p = ROOT.TH1F("h_n1_1_p", "h_n1_1_p", 300, 0, 300)
h_n1_1_p.SetLineColor(ROOT.kBlue)
h_n1_1_p.SetLineWidth(2)
h_n1_1_p.SetLineStyle(1)
h_n1_1_M = ROOT.TH1F("h_n1_1_M", "h_n1_1_M", 200, 0.05, 20.05)
h_n1_1_M.SetLineColor(ROOT.kBlue)
h_n1_1_M.SetLineWidth(2)
h_n1_1_M.SetLineStyle(1)
h_n1_1_pT = ROOT.TH1F("h_n1_1_pT", "h_n1_1_pT", 70, 0, 70) #this is the peak at 60
h_n1_1_pT.SetLineColor(ROOT.kBlue)
h_n1_1_pT.SetLineWidth(2)
h_n1_1_pT.SetLineStyle(1)
h_n1_1_pZ = ROOT.TH1F("h_n1_1_pZ", "h_n1_1_pZ", 300, 0, 300)
h_n1_1_pZ.SetLineColor(ROOT.kBlue)
h_n1_1_pZ.SetLineWidth(2)
h_n1_1_pZ.SetLineStyle(1)
h_n1_1_Eta = ROOT.TH1F("h_n1_1_Eta", "h_n1_1_Eta", 100,-5,5)
h_n1_1_Eta.SetLineColor(ROOT.kBlue)
h_n1_1_Eta.SetLineWidth(2)
h_n1_1_Eta.SetLineStyle(1)
h_n1_1_Phi = ROOT.TH1F("h_n1_1_Phi", "h_n1_1_Phi", 80,-4,4)
h_n1_1_Phi.SetLineColor(ROOT.kBlue)
h_n1_1_Phi.SetLineWidth(2)
h_n1_1_Phi.SetLineStyle(1)
#h_n1_2_pT_dummy = ROOT.TH1F("h_n1_2_pT_dummy", "h_n1_2_pT_dummy", 700, 0, 70) #this is the peak at ~10GeV
#h_n1_2_pT_dummy.SetYTitle("Fraction of events / 1 GeV")
#h_n1_2_pT_dummy.SetTitleOffset(1.35, "Y")
#h_n1_2_pT_dummy.SetXTitle("p_{T n_{1}} [GeV]")
#h_n1_2_pT_dummy.SetMaximum( yMax )
#
#h_n1_2_p_dummy = ROOT.TH1F("h_n1_2_p_dummy", "h_n1_2_p_dummy", 20, 50, 70)
#h_n1_2_p_dummy.SetYTitle("Fraction of events / 1 GeV")
#h_n1_2_p_dummy.SetTitleOffset(1.35, "Y")
#h_n1_2_p_dummy.SetXTitle("p_{n_{1}} [GeV]")
#h_n1_2_p_dummy.SetMaximum( 0.05 )
#
#h_n1_2_M_dummy = ROOT.TH1F("h_n1_2_M_dummy", "h_n1_2_M_dummy", 200, 0, 20)
#h_n1_2_M_dummy.SetYTitle("Fraction of events / 1 GeV")
#h_n1_2_M_dummy.SetTitleOffset(1.35, "Y")
#h_n1_2_M_dummy.SetXTitle("m_{n_{1}} [GeV]")
#h_n1_2_M_dummy.SetMaximum( 1.2 )
h_n1_2_p = ROOT.TH1F("h_n1_2_p", "h_n1_2_p", 300, 0, 300)
h_n1_2_p.SetLineColor(ROOT.kRed)
h_n1_2_p.SetLineWidth(2)
h_n1_2_p.SetLineStyle(1)
#h_n1_2_M = ROOT.TH1F("h_n1_2_M", "h_n1_2_M", 200, 0.05, 20.05)
#h_n1_2_M.SetLineColor(ROOT.kRed)
#h_n1_2_M.SetLineWidth(2)
#h_n1_2_M.SetLineStyle(1)
h_n1_2_pT = ROOT.TH1F("h_n1_2_pT", "h_n1_2_pT", 70, 0, 70)
h_n1_2_pT.SetLineColor(ROOT.kRed)
h_n1_2_pT.SetLineWidth(2)
h_n1_2_pT.SetLineStyle(1)
h_nD_1_pT_dummy = ROOT.TH1F("h_nD_1_pT_dummy", "h_nD_1_pT_dummy", 130, 0, 130)
h_nD_1_pT_dummy.SetYTitle("Fraction of events / 1 GeV")
h_nD_1_pT_dummy.SetTitleOffset(1.35, "Y")
h_nD_1_pT_dummy.SetXTitle("p_{T} of n_{D} [GeV]")
h_nD_1_pT_dummy.SetMaximum( 0.1 )
h_n1_2_pZ = ROOT.TH1F("h_n1_2_pZ", "h_n1_2_pZ", 300, 0, 300)
h_n1_2_pZ.SetLineColor(ROOT.kRed)
h_n1_2_pZ.SetLineWidth(2)
h_n1_2_pZ.SetLineStyle(1)
h_nD_1_pZ_dummy = ROOT.TH1F("h_nD_1_pZ_dummy", "h_nD_1_pZ_dummy", 130, 0, 130)
h_nD_1_pZ_dummy.SetYTitle("Fraction of events / 1 GeV")
h_nD_1_pZ_dummy.SetTitleOffset(1.35, "Y")
h_nD_1_pZ_dummy.SetXTitle("|p_{Z}| of n_{D} [GeV]")
h_nD_1_pZ_dummy.SetMaximum( 0.1 )
h_n1_2_Eta = ROOT.TH1F("h_n1_2_Eta", "h_n1_2_Eta", 100,-5,5)
h_n1_2_Eta.SetLineColor(ROOT.kRed)
h_n1_2_Eta.SetLineWidth(2)
h_n1_2_Eta.SetLineStyle(1)
h_nD_1_Eta_dummy = ROOT.TH1F("h_nD_1_Eta_dummy", "h_nD_1_Eta_dummy", 100,-5,5)
h_nD_1_Eta_dummy.SetYTitle("Fraction of events / 0.1")
h_nD_1_Eta_dummy.SetTitleOffset(1.35, "Y")
h_nD_1_Eta_dummy.SetXTitle("#eta of n_{D}")
h_nD_1_Eta_dummy.SetMaximum( 0.1 )
h_n1_2_Phi = ROOT.TH1F("h_n1_2_Phi", "h_n1_2_Phi", 80,-4,4)
h_n1_2_Phi.SetLineColor(ROOT.kRed)
h_n1_2_Phi.SetLineWidth(2)
h_n1_2_Phi.SetLineStyle(1)
h_nD_1_Phi_dummy = ROOT.TH1F("h_nD_1_Phi_dummy", "h_nD_1_Phi_dummy", 80,-4,4)
h_nD_1_Phi_dummy.SetYTitle("Fraction of events / 0.1 rad")
h_nD_1_Phi_dummy.SetTitleOffset(1.35, "Y")
h_nD_1_Phi_dummy.SetXTitle("#phi of n_{D} [rad]")
h_nD_1_Phi_dummy.SetMaximum( 0.05 )
h_nD_1_p_dummy = ROOT.TH1F("h_nD_1_p_dummy", "h_nD_1_p_dummy", 130, 0, 130)
h_nD_1_p_dummy.SetYTitle("Fraction of events / 1 GeV")
h_nD_1_p_dummy.SetTitleOffset(1.35, "Y")
h_nD_1_p_dummy.SetXTitle("p of n_{D} [GeV]")
h_nD_1_p_dummy.SetMaximum( 0.1 )
h_nD_1_M_dummy = ROOT.TH1F("h_nD_1_M_dummy", "h_nD_1_M_dummy", 20, 0.05, 2.05)
h_nD_1_M_dummy.SetYTitle("Fraction of events / 0.1 GeV")
h_nD_1_M_dummy.SetTitleOffset(1.35, "Y")
h_nD_1_M_dummy.SetXTitle("Mass of n_{D} [GeV]")
h_nD_1_M_dummy.SetMaximum( 1.6 )
h_nD_1_p = ROOT.TH1F("h_nD_1_p", "h_nD_1_p", 130, 0, 130)
h_nD_1_p.SetLineColor(ROOT.kBlue)
h_nD_1_p.SetLineWidth(2)
h_nD_1_p.SetLineStyle(1)
h_nD_1_M = ROOT.TH1F("h_nD_1_M", "h_nD_1_M", 20, 0.05, 2.05)
h_nD_1_M.SetLineColor(ROOT.kBlue)
h_nD_1_M.SetLineWidth(2)
h_nD_1_M.SetLineStyle(1)
h_nD_1_pT = ROOT.TH1F("h_nD_1_pT", "h_nD_1_pT", 130, 0, 130)
h_nD_1_pT.SetLineColor(ROOT.kBlue)
h_nD_1_pT.SetLineWidth(2)
h_nD_1_pT.SetLineStyle(1)
h_nD_1_pZ = ROOT.TH1F("h_nD_1_pZ", "h_nD_1_pZ", 130, 0, 130)
h_nD_1_pZ.SetLineColor(ROOT.kBlue)
h_nD_1_pZ.SetLineWidth(2)
h_nD_1_pZ.SetLineStyle(1)
h_nD_1_Eta = ROOT.TH1F("h_nD_1_Eta", "h_nD_1_Eta", 100,-5,5)
h_nD_1_Eta.SetLineColor(ROOT.kBlue)
h_nD_1_Eta.SetLineWidth(2)
h_nD_1_Eta.SetLineStyle(1)
h_nD_1_Phi = ROOT.TH1F("h_nD_1_Phi", "h_nD_1_Phi", 80,-4,4)
h_nD_1_Phi.SetLineColor(ROOT.kBlue)
h_nD_1_Phi.SetLineWidth(2)
h_nD_1_Phi.SetLineStyle(1)
#h_nD_2_pT_dummy = ROOT.TH1F("h_nD_2_pT_dummy", "h_nD_2_pT_dummy", 100, 0, 100)
#h_nD_2_pT_dummy.SetYTitle("Fraction of events / 1 GeV")
#h_nD_2_pT_dummy.SetTitleOffset(1.35, "Y")
#h_nD_2_pT_dummy.SetXTitle("p_{T nD_2} [GeV]")
#h_nD_2_pT_dummy.SetMaximum( 0.01 )
#
#h_nD_2_p_dummy = ROOT.TH1F("h_nD_2_p_dummy", "h_nD_2_p_dummy", 100, 0, 100)
#h_nD_2_p_dummy.SetYTitle("Fraction of events / 1 GeV")
#h_nD_2_p_dummy.SetTitleOffset(1.35, "Y")
#h_nD_2_p_dummy.SetXTitle("p_{nD_2} [GeV]")
#h_nD_2_p_dummy.SetMaximum( 0.01 )
#
#h_nD_2_M_dummy = ROOT.TH1F("h_nD_2_M_dummy", "h_nD_2_M_dummy", 20, 0, 2)
#h_nD_2_M_dummy.SetYTitle("Fraction of events / 1 GeV")
#h_nD_2_M_dummy.SetTitleOffset(1.35, "Y")
#h_nD_2_M_dummy.SetXTitle("m_{nD_2} [GeV]")
#h_nD_2_M_dummy.SetMaximum( 1.2 )
h_nD_2_p = ROOT.TH1F("h_nD_2_p", "h_nD_2_p", 130, 0, 130)
h_nD_2_p.SetLineColor(ROOT.kRed)
h_nD_2_p.SetLineWidth(2)
h_nD_2_p.SetLineStyle(1)
#h_nD_2_M = ROOT.TH1F("h_nD_2_M", "h_nD_2_M", 20, 0.05, 2.05)
#h_nD_2_M.SetLineColor(ROOT.kRed)
#h_nD_2_M.SetLineWidth(2)
#h_nD_2_M.SetLineStyle(1)
h_nD_2_pT = ROOT.TH1F("h_nD_2_pT", "h_nD_2_pT", 130, 0, 130)
h_nD_2_pT.SetLineColor(ROOT.kRed)
h_nD_2_pT.SetLineWidth(2)
h_nD_2_pT.SetLineStyle(1)
h_gammaD_1_pT_dummy = ROOT.TH1F("h_gammaD_1_pT_dummy", "h_gammaD_1_pT_dummy", 100, 0, 100)
h_gammaD_1_pT_dummy.SetYTitle("Fraction of events / 1 GeV")
h_gammaD_1_pT_dummy.SetTitleOffset(1.35, "Y")
h_gammaD_1_pT_dummy.SetXTitle("p_{T} of #gamma_{D} [GeV]")
h_gammaD_1_pT_dummy.SetMaximum( 0.1 )
h_nD_2_pZ = ROOT.TH1F("h_nD_2_pZ", "h_nD_2_pZ", 130, 0, 130)
h_nD_2_pZ.SetLineColor(ROOT.kRed)
h_nD_2_pZ.SetLineWidth(2)
h_nD_2_pZ.SetLineStyle(1)
h_gammaD_1_pZ_dummy = ROOT.TH1F("h_gammaD_1_pZ_dummy", "h_gammaD_1_pZ_dummy", 100, 0, 100)
h_gammaD_1_pZ_dummy.SetYTitle("Fraction of events / 1 GeV")
h_gammaD_1_pZ_dummy.SetTitleOffset(1.35, "Y")
h_gammaD_1_pZ_dummy.SetXTitle("|p_{Z}| of #gamma_{D} [GeV]")
h_gammaD_1_pZ_dummy.SetMaximum( 0.1 )
h_nD_2_Eta = ROOT.TH1F("h_nD_2_Eta", "h_nD_2_Eta", 100,-5,5)
h_nD_2_Eta.SetLineColor(ROOT.kRed)
h_nD_2_Eta.SetLineWidth(2)
h_nD_2_Eta.SetLineStyle(1)
h_gammaD_1_Eta_dummy = ROOT.TH1F("h_gammaD_1_Eta_dummy", "h_gammaD_1_Eta_dummy",100,-5,5)
h_gammaD_1_Eta_dummy.SetYTitle("Fraction of events / 0.1")
h_gammaD_1_Eta_dummy.SetTitleOffset(1.35, "Y")
h_gammaD_1_Eta_dummy.SetXTitle("#eta of #gamma_{D}")
h_gammaD_1_Eta_dummy.SetMaximum( 0.1 )
h_nD_2_Phi = ROOT.TH1F("h_nD_2_Phi", "h_nD_2_Phi", 80,-4,4)
h_nD_2_Phi.SetLineColor(ROOT.kRed)
h_nD_2_Phi.SetLineWidth(2)
h_nD_2_Phi.SetLineStyle(1)
h_gammaD_1_Phi_dummy = ROOT.TH1F("h_gammaD_1_Phi_dummy", "h_gammaD_1_Phi_dummy",80,-4,4 )
h_gammaD_1_Phi_dummy.SetYTitle("Fraction of events / 0.1 rad")
h_gammaD_1_Phi_dummy.SetTitleOffset(1.35, "Y")
h_gammaD_1_Phi_dummy.SetXTitle("#phi of #gamma_{D} [rad]")
h_gammaD_1_Phi_dummy.SetMaximum( 0.05 )
h_gammaD_1_p_dummy = ROOT.TH1F("h_gammaD_1_p_dummy", "h_gammaD_1_p_dummy", 100, 0, 100)
h_gammaD_1_p_dummy.SetYTitle("Fraction of events / 1 GeV")
h_gammaD_1_p_dummy.SetTitleOffset(1.35, "Y")
h_gammaD_1_p_dummy.SetXTitle("p of #gamma_{D} [GeV]")
h_gammaD_1_p_dummy.SetMaximum( 0.1 )
h_gammaD_1_M_dummy = ROOT.TH1F("h_gammaD_1_M_dummy", "h_gammaD_1_M_dummy", 101, 0.1, 10.1)
h_gammaD_1_M_dummy.SetYTitle("Fraction of events / 0.1 GeV")
h_gammaD_1_M_dummy.SetTitleOffset(1.35, "Y")
h_gammaD_1_M_dummy.SetXTitle("Mass of #gamma_{D} [GeV]")
h_gammaD_1_M_dummy.SetMaximum( 1.4 )
h_gammaD_1_p = ROOT.TH1F("h_gammaD_1_p", "h_gammaD_1_p", 100, 0, 100)
h_gammaD_1_p.SetLineColor(ROOT.kBlue)
h_gammaD_1_p.SetLineWidth(2)
h_gammaD_1_p.SetLineStyle(1)
h_gammaD_1_M = ROOT.TH1F("h_gammaD_1_M", "h_gammaD_1_M", 101, 0.1, 10.1)
h_gammaD_1_M.SetLineColor(ROOT.kBlue)
h_gammaD_1_M.SetLineWidth(2)
h_gammaD_1_M.SetLineStyle(1)
h_gammaD_1_pT = ROOT.TH1F("h_gammaD_1_pT", "h_gammaD_1_pT", 100, 0, 100)
h_gammaD_1_pT.SetLineColor(ROOT.kBlue)
h_gammaD_1_pT.SetLineWidth(2)
h_gammaD_1_pT.SetLineStyle(1)
h_gammaD_1_pZ = ROOT.TH1F("h_gammaD_1_pZ", "h_gammaD_1_pZ", 100, 0, 100)
h_gammaD_1_pZ.SetLineColor(ROOT.kBlue)
h_gammaD_1_pZ.SetLineWidth(2)
h_gammaD_1_pZ.SetLineStyle(1)
h_gammaD_1_Eta = ROOT.TH1F("h_gammaD_1_Eta", "h_gammaD_1_Eta",100,-5,5)
h_gammaD_1_Eta.SetLineColor(ROOT.kBlue)
h_gammaD_1_Eta.SetLineWidth(2)
h_gammaD_1_Eta.SetLineStyle(1)
h_gammaD_1_Phi = ROOT.TH1F("h_gammaD_1_Phi", "h_gammaD_1_Phi", 80,-4,4)
h_gammaD_1_Phi.SetLineColor(ROOT.kBlue)
h_gammaD_1_Phi.SetLineWidth(2)
h_gammaD_1_Phi.SetLineStyle(1)
#h_gammaD_2_pT_dummy = ROOT.TH1F("h_gammaD_2_pT_dummy", "h_gammaD_2_pT_dummy", 100, 0, 100)
#h_gammaD_2_pT_dummy.SetYTitle("Fraction of events / 1 GeV")
#h_gammaD_2_pT_dummy.SetTitleOffset(1.35, "Y")
#h_gammaD_2_pT_dummy.SetXTitle("p_{T gammaD_2} [GeV]")
#h_gammaD_2_pT_dummy.SetMaximum( 0.01 )
#
#h_gammaD_2_p_dummy = ROOT.TH1F("h_gammaD_2_p_dummy", "h_gammaD_2_p_dummy", 100, 0, 100)
#h_gammaD_2_p_dummy.SetYTitle("Fraction of events / 1 GeV")
#h_gammaD_2_p_dummy.SetTitleOffset(1.35, "Y")
#h_gammaD_2_p_dummy.SetXTitle("p_{gammaD_2} [GeV]")
#h_gammaD_2_p_dummy.SetMaximum( 0.01 )
#
#h_gammaD_2_M_dummy = ROOT.TH1F("h_gammaD_2_M_dummy", "h_gammaD_2_M_dummy", 300, 0, 3)
#h_gammaD_2_M_dummy.SetYTitle("Fraction of events / 1 GeV")
#h_gammaD_2_M_dummy.SetTitleOffset(1.35, "Y")
#h_gammaD_2_M_dummy.SetXTitle("m_{gammaD_2} [GeV]")
#h_gammaD_2_M_dummy.SetMaximum( 1.2 )
h_gammaD_2_p = ROOT.TH1F("h_gammaD_2_p", "h_gammaD_2_p", 100, 0, 100)
h_gammaD_2_p.SetLineColor(ROOT.kRed)
h_gammaD_2_p.SetLineWidth(2)
h_gammaD_2_p.SetLineStyle(1)
#h_gammaD_2_M = ROOT.TH1F("h_gammaD_2_M", "h_gammaD_2_M", 500, 0.005, 10.005)
#h_gammaD_2_M.SetLineColor(ROOT.kRed)
#h_gammaD_2_M.SetLineWidth(2)
#h_gammaD_2_M.SetLineStyle(1)
h_gammaD_2_pT = ROOT.TH1F("h_gammaD_2_pT", "h_gammaD_2_pT", 100, 0, 100)
h_gammaD_2_pT.SetLineColor(ROOT.kRed)
h_gammaD_2_pT.SetLineWidth(2)
h_gammaD_2_pT.SetLineStyle(1)
h_gammaD_2_pZ = ROOT.TH1F("h_gammaD_2_pZ", "h_gammaD_2_pZ", 100, 0, 100)
h_gammaD_2_pZ.SetLineColor(ROOT.kRed)
h_gammaD_2_pZ.SetLineWidth(2)
h_gammaD_2_pZ.SetLineStyle(1)
h_gammaD_2_Eta = ROOT.TH1F("h_gammaD_2_Eta", "h_gammaD_2_Eta", 100,-5,5)
h_gammaD_2_Eta.SetLineColor(ROOT.kRed)
h_gammaD_2_Eta.SetLineWidth(2)
h_gammaD_2_Eta.SetLineStyle(1)
h_gammaD_2_Phi = ROOT.TH1F("h_gammaD_2_Phi", "h_gammaD_2_Phi", 80,-4,4)
h_gammaD_2_Phi.SetLineColor(ROOT.kRed)
h_gammaD_2_Phi.SetLineWidth(2)
h_gammaD_2_Phi.SetLineStyle(1)
h_muon_pT_0 = ROOT.TH1F("h_muon_pT_0", "h_muon_pT_0", nBins, binMin, binMax)
h_muon_pT_0.SetLineColor(ROOT.kBlue)
h_muon_pT_0.SetLineWidth(2)
h_muon_pT_0.SetLineStyle(1)
h_muon_pT_1 = ROOT.TH1F("h_muon_pT_1", "h_muon_pT_1", nBins, binMin, binMax)
h_muon_pT_1.SetLineColor(ROOT.kGreen)
h_muon_pT_1.SetLineWidth(2)
h_muon_pT_1.SetLineStyle(2)
h_muon_pT_2 = ROOT.TH1F("h_muon_pT_2", "h_muon_pT_2", nBins, binMin, binMax)
h_muon_pT_2.SetLineColor(ROOT.kRed)
h_muon_pT_2.SetLineWidth(2)
h_muon_pT_2.SetLineStyle(3)
h_muon_pT_3 = ROOT.TH1F("h_muon_pT_3", "h_muon_pT_3", nBins, binMin, binMax)
h_muon_pT_3.SetLineColor(ROOT.kBlack)
h_muon_pT_3.SetLineWidth(2)
h_muon_pT_3.SetLineStyle(4)
h_muon_phi_dummy = ROOT.TH1F("h_muon_phi_dummy", "h_muon_phi_dummy", 80, -4, 4)
h_muon_phi_dummy.SetYTitle("Fraction of events / 0.1 rad")
h_muon_phi_dummy.SetTitleOffset(1.35, "Y")
h_muon_phi_dummy.SetXTitle("#phi of #mu [rad]")
h_muon_phi_dummy.SetMaximum( 0.1 )
h_muon_phi_0 = ROOT.TH1F("h_muon_phi_0", "h_muon_phi_0", 80, -4, 4)
h_muon_phi_0.SetLineColor(ROOT.kBlue)
h_muon_phi_0.SetLineWidth(2)
h_muon_phi_0.SetLineStyle(1)
h_muon_phi_1 = ROOT.TH1F("h_muon_phi_1", "h_muon_phi_1", 80, -4, 4)
h_muon_phi_1.SetLineColor(ROOT.kGreen)
h_muon_phi_1.SetLineWidth(2)
h_muon_phi_1.SetLineStyle(2)
h_muon_phi_2 = ROOT.TH1F("h_muon_phi_2", "h_muon_phi_2", 80, -4, 4)
h_muon_phi_2.SetLineColor(ROOT.kRed)
h_muon_phi_2.SetLineWidth(2)
h_muon_phi_2.SetLineStyle(3)
h_muon_phi_3 = ROOT.TH1F("h_muon_phi_3", "h_muon_phi_3", 80, -4, 4)
h_muon_phi_3.SetLineColor(ROOT.kBlack)
h_muon_phi_3.SetLineWidth(2)
h_muon_phi_3.SetLineStyle(4)
h_muon_p_dummy = ROOT.TH1F("h_muon_p_dummy", "h_muon_p_dummy", 125, 0, 125)
h_muon_p_dummy.SetYTitle("Fraction of events / 1 GeV")
h_muon_p_dummy.SetTitleOffset(1.35, "Y")
h_muon_p_dummy.SetXTitle("p of #mu [GeV]")
h_muon_p_dummy.SetMaximum( 0.2 )
h_muon_p_0 = ROOT.TH1F("h_muon_p_0", "h_muon_p_0", 125, 0, 125)
h_muon_p_0.SetLineColor(ROOT.kBlue)
h_muon_p_0.SetLineWidth(2)
h_muon_p_0.SetLineStyle(1)
h_muon_p_1 = ROOT.TH1F("h_muon_p_1", "h_muon_p_1", 125, 0, 125)
h_muon_p_1.SetLineColor(ROOT.kGreen)
h_muon_p_1.SetLineWidth(2)
h_muon_p_1.SetLineStyle(2)
h_muon_p_2 = ROOT.TH1F("h_muon_p_2", "h_muon_p_2", 125, 0, 125)
h_muon_p_2.SetLineColor(ROOT.kRed)
h_muon_p_2.SetLineWidth(2)
h_muon_p_2.SetLineStyle(3)
h_muon_p_3 = ROOT.TH1F("h_muon_p_3", "h_muon_p_3", 125, 0, 125)
h_muon_p_3.SetLineColor(ROOT.kBlack)
h_muon_p_3.SetLineWidth(2)
h_muon_p_3.SetLineStyle(125)
h_muon_pZ_0 = ROOT.TH1F("h_muon_pZ_0", "h_muon_pZ_0", 125, 0, 125)
h_muon_pZ_0.SetLineColor(ROOT.kBlue)
h_muon_pZ_0.SetLineWidth(2)
h_muon_pZ_0.SetLineStyle(1)
h_muon_pZ_1 = ROOT.TH1F("h_muon_pZ_1", "h_muon_pZ_1", 125, 0, 125)
h_muon_pZ_1.SetLineColor(ROOT.kGreen)
h_muon_pZ_1.SetLineWidth(2)
h_muon_pZ_1.SetLineStyle(2)
h_muon_pZ_2 = ROOT.TH1F("h_muon_pZ_2", "h_muon_pZ_2", 125, 0, 125)
h_muon_pZ_2.SetLineColor(ROOT.kRed)
h_muon_pZ_2.SetLineWidth(2)
h_muon_pZ_2.SetLineStyle(3)
h_muon_pZ_3 = ROOT.TH1F("h_muon_pZ_3", "h_muon_pZ_3", 125, 0, 125)
h_muon_pZ_3.SetLineColor(ROOT.kBlack)
h_muon_pZ_3.SetLineWidth(2)
h_muon_pZ_3.SetLineStyle(125)
################################################################################
# eta of muons
################################################################################
nBins = 60
binMin = -3.0
binMax = 3.0
yMax = 0.045
h_muon_eta_dummy = ROOT.TH1F("h_muon_eta_dummy", "h_muon_eta_dummy", 100, -5, 5)
h_muon_eta_dummy.SetYTitle("Fraction of events / 0.1")
h_muon_eta_dummy.GetYaxis().SetNdivisions(508);
h_muon_eta_dummy.SetTitleOffset(1.35, "Y")
h_muon_eta_dummy.SetXTitle("#eta of #mu")
h_muon_eta_dummy.SetMaximum( yMax )
h_muon_eta_0 = ROOT.TH1F("h_muon_eta_0", "h_muon_eta_0", 100,-5,5)
h_muon_eta_0.SetLineColor(ROOT.kBlue)
h_muon_eta_0.SetLineWidth(2)
h_muon_eta_0.SetLineStyle(1)
h_muon_eta_1 = ROOT.TH1F("h_muon_eta_1", "h_muon_eta_1", 100,-5,5)
h_muon_eta_1.SetLineColor(ROOT.kGreen)
h_muon_eta_1.SetLineWidth(2)
h_muon_eta_1.SetLineStyle(2)
h_muon_eta_2 = ROOT.TH1F("h_muon_eta_2", "h_muon_eta_2", 100,-5,5)
h_muon_eta_2.SetLineColor(ROOT.kRed)
h_muon_eta_2.SetLineWidth(2)
h_muon_eta_2.SetLineStyle(3)
h_muon_eta_3 = ROOT.TH1F("h_muon_eta_3", "h_muon_eta_3", 100,-5,5)
h_muon_eta_3.SetLineColor(ROOT.kBlack)
h_muon_eta_3.SetLineWidth(2)
h_muon_eta_3.SetLineStyle(4)
################################################################################
# mass of dimuons
################################################################################
nBins = 125
binMin = 0.0
binMax = 125.0
yMax = 0.4
#h_dimuon_m_dummy = ROOT.TH1F("h_dimuon_m_dummy", "h_dimuon_m_dummy", nBins, binMin, binMax)
#h_dimuon_m_dummy.SetYTitle("Fraction of events / 1 GeV")
#h_dimuon_m_dummy.GetYaxis().SetNdivisions(508);
#h_dimuon_m_dummy.SetTitleOffset(1.35, "Y")
#h_dimuon_m_dummy.SetXTitle("m_{#mu#mu} [GeV]")
#h_dimuon_m_dummy.SetMaximum( 1.2 )
#
#h_dimuon_m_0 = ROOT.TH1F("h_dimuon_m_0", "h_dimuon_m_0", nBins, binMin, binMax)
#h_dimuon_m_0.SetLineColor(ROOT.kBlue)
#h_dimuon_m_0.SetLineWidth(2)
#h_dimuon_m_0.SetLineStyle(1)
#
#h_dimuon_m_1 = ROOT.TH1F("h_dimuon_m_1", "h_dimuon_m_1", nBins, binMin, binMax)
#h_dimuon_m_1.SetLineColor(ROOT.kGreen)
#h_dimuon_m_1.SetLineWidth(2)
#h_dimuon_m_1.SetLineStyle(2)
#
#h_dimuon_m_2 = ROOT.TH1F("h_dimuon_m_2", "h_dimuon_m_2", nBins, binMin, binMax)
#h_dimuon_m_2.SetLineColor(ROOT.kRed)
#h_dimuon_m_2.SetLineWidth(2)
#h_dimuon_m_2.SetLineStyle(3)
#
#h_dimuon_m_3 = ROOT.TH1F("h_dimuon_m_3", "h_dimuon_m_3", nBins, binMin, binMax)
#h_dimuon_m_3.SetLineColor(ROOT.kBlack)
#h_dimuon_m_3.SetLineWidth(2)
#h_dimuon_m_3.SetLineStyle(4)
#
#h_dimuon_m_log_dummy = ROOT.TH1F("h_dimuon_m_log_dummy", "h_dimuon_m_log_dummy", nBins, binMin, binMax)
#h_dimuon_m_log_dummy.SetYTitle("Fraction of events / 1 GeV")
#h_dimuon_m_log_dummy.GetYaxis().SetNdivisions(508);
#h_dimuon_m_log_dummy.SetTitleOffset(1.35, "Y")
#h_dimuon_m_log_dummy.SetXTitle("m_{#mu#mu} [GeV]")
#h_dimuon_m_log_dummy.SetMaximum( 1.2 )
#
#h_dimuon_m_log_0 = ROOT.TH1F("h_dimuon_m_log_0", "h_dimuon_m_log_0", nBins, binMin, binMax)
#h_dimuon_m_log_0.SetLineColor(ROOT.kBlue)
#h_dimuon_m_log_0.SetLineWidth(2)
#h_dimuon_m_log_0.SetLineStyle(1)
#
#h_dimuon_m_log_1 = ROOT.TH1F("h_dimuon_m_log_1", "h_dimuon_m_log_1", nBins, binMin, binMax)
#h_dimuon_m_log_1.SetLineColor(ROOT.kGreen)
#h_dimuon_m_log_1.SetLineWidth(2)
#h_dimuon_m_log_1.SetLineStyle(2)
#
#h_dimuon_m_log_2 = ROOT.TH1F("h_dimuon_m_log_2", "h_dimuon_m_log_2", nBins, binMin, binMax)
#h_dimuon_m_log_2.SetLineColor(ROOT.kRed)
#h_dimuon_m_log_2.SetLineWidth(2)
#h_dimuon_m_log_2.SetLineStyle(3)
#
#h_dimuon_m_log_3 = ROOT.TH1F("h_dimuon_m_log_3", "h_dimuon_m_log_3", nBins, binMin, binMax)
#h_dimuon_m_log_3.SetLineColor(ROOT.kBlack)
#h_dimuon_m_log_3.SetLineWidth(2)
#h_dimuon_m_log_3.SetLineStyle(4)
#
#h_dimuon_m_real_fake_dummy = ROOT.TH1F("h_dimuon_m_real_fake_dummy", "h_dimuon_m_real_fake_dummy", nBins, binMin, binMax)
#h_dimuon_m_real_fake_dummy.SetYTitle("Fraction of events / 1 GeV")
#h_dimuon_m_real_fake_dummy.GetYaxis().SetNdivisions(508);
#h_dimuon_m_real_fake_dummy.SetTitleOffset(1.35, "Y")
#h_dimuon_m_real_fake_dummy.SetXTitle("m_{#mu#mu} [GeV]")
#h_dimuon_m_real_fake_dummy.SetMaximum( 1.2 )
#
#h_dimuon_m_real_fake_0 = ROOT.TH1F("h_dimuon_m_real_fake_0", "h_dimuon_m_real_fake_0", nBins, binMin, binMax)
#h_dimuon_m_real_fake_0.SetLineColor(ROOT.kRed)
#h_dimuon_m_real_fake_0.SetLineWidth(2)
#h_dimuon_m_real_fake_0.SetLineStyle(1)
#
#h_dimuon_m_real_fake_1 = ROOT.TH1F("h_dimuon_m_real_fake_1", "h_dimuon_m_real_fake_1", nBins, binMin, binMax)
#h_dimuon_m_real_fake_1.SetLineColor(ROOT.kBlue)
#h_dimuon_m_real_fake_1.SetLineWidth(2)
#h_dimuon_m_real_fake_1.SetLineStyle(2)
#
#h_dimuon_m_real_fake_log_dummy = ROOT.TH1F("h_dimuon_m_real_fake_log_dummy", "h_dimuon_m_real_fake_log_dummy", nBins, binMin, binMax)
#h_dimuon_m_real_fake_log_dummy.SetYTitle("Fraction of events / 1 GeV")
#h_dimuon_m_real_fake_log_dummy.GetYaxis().SetNdivisions(508);
#h_dimuon_m_real_fake_log_dummy.SetTitleOffset(1.35, "Y")
#h_dimuon_m_real_fake_log_dummy.SetXTitle("m_{#mu#mu} [GeV]")
#h_dimuon_m_real_fake_log_dummy.SetMaximum( 1.2 )
#
#h_dimuon_m_real_fake_log_0 = ROOT.TH1F("h_dimuon_m_real_fake_log_0", "h_dimuon_m_real_fake_log_0", nBins, binMin, binMax)
#h_dimuon_m_real_fake_log_0.SetLineColor(ROOT.kRed)
#h_dimuon_m_real_fake_log_0.SetLineWidth(2)
#h_dimuon_m_real_fake_log_0.SetLineStyle(1)
#
#h_dimuon_m_real_fake_log_1 = ROOT.TH1F("h_dimuon_m_real_fake_log_1", "h_dimuon_m_real_fake_log_1", nBins, binMin, binMax)
#h_dimuon_m_real_fake_log_1.SetLineColor(ROOT.kBlue)
#h_dimuon_m_real_fake_log_1.SetLineWidth(2)
#h_dimuon_m_real_fake_log_1.SetLineStyle(2)
#########################
h_dimuon_m_fake_log_dummy = ROOT.TH1F("h_dimuon_m_fake_log_dummy", "h_dimuon_m_fake_log_dummy", 1250, 0, 125)
h_dimuon_m_fake_log_dummy.SetYTitle("Fraction of events / 0.1 GeV")
h_dimuon_m_fake_log_dummy.GetYaxis().SetNdivisions(508);
h_dimuon_m_fake_log_dummy.SetTitleOffset(1.4, "Y")
h_dimuon_m_fake_log_dummy.SetXTitle("Mass of Fake #mu#mu [GeV]")
h_dimuon_m_fake_log_dummy.SetMaximum( 1 )
h_dimuon_m_fake_log_0 = ROOT.TH1F("h_dimuon_m_fake_log_0", "h_dimuon_m_fake_log_0", 1250, 0, 125)
h_dimuon_m_fake_log_0.SetLineColor(ROOT.kRed)
h_dimuon_m_fake_log_0.SetLineWidth(2)
h_dimuon_m_fake_log_0.SetLineStyle(1)
h_dimuon_m_fake_dummy = ROOT.TH1F("h_dimuon_m_fake_dummy", "h_dimuon_m_fake_dummy", nBins, binMin, binMax)
h_dimuon_m_fake_dummy.SetYTitle("Fraction of events / 1 GeV")
h_dimuon_m_fake_dummy.GetYaxis().SetNdivisions(508);
h_dimuon_m_fake_dummy.SetTitleOffset(1.35, "Y")
h_dimuon_m_fake_dummy.SetXTitle("Mass of Fake #mu#mu [GeV]")
h_dimuon_m_fake_dummy.SetMaximum( 1.2 )
h_dimuon_m_fake_0 = ROOT.TH1F("h_dimuon_m_fake_0", "h_dimuon_m_fake_0", nBins, binMin, binMax)
h_dimuon_m_fake_0.SetLineColor(ROOT.kRed)
h_dimuon_m_fake_0.SetLineWidth(2)
h_dimuon_m_fake_0.SetLineStyle(1)
################################################################################
# mass of 2 selected dimuons
################################################################################
m_min = 0.2113
m_max = 3.5536
m_bins = 66
h_m1_vs_m2 = ROOT.TH2F("h_m1_vs_m2", "h_m1_vs_m2", m_bins, m_min, m_max, m_bins, m_min, m_max)
h_m1_vs_m2.SetYTitle("m_{1#mu#mu} [GeV]")
h_m1_vs_m2.SetTitleOffset(1.3, "Y")
h_m1_vs_m2.SetXTitle("m_{2#mu#mu} [GeV]")
h_m1 = ROOT.TH1F("h_m1", "h_m1", 101, 0.1, 10.1)
h_m1.SetLineColor(ROOT.kRed)
h_m1.SetLineWidth(2)
h_m1.SetLineStyle(1)
h_m2 = ROOT.TH1F("h_m2", "h_m2", 101, 0.1, 10.1)
h_m2.SetYTitle("Events / 0.1 GeV")
h_m2.SetXTitle("m_{#mu#mu} [GeV]")
h_m2.SetTitleOffset(1.35, "Y")
h_m2.SetLineColor(ROOT.kBlue)
h_m2.SetLineWidth(2)
h_m2.SetLineStyle(1)
h_m2.SetMaximum(110000)
h_dimuon_1_pT_dummy = ROOT.TH1F("h_dimuon_1_pT_dummy", "h_dimuon_1_pT_dummy", 100, 0, 100)
h_dimuon_1_pT_dummy.SetYTitle("Fraction of events / 1 GeV")
h_dimuon_1_pT_dummy.SetTitleOffset(1.35, "Y")
h_dimuon_1_pT_dummy.SetXTitle("p_{T} of #mu#mu [GeV]")
h_dimuon_1_pT_dummy.SetMaximum( 0.1 )
h_dimuon_1_pZ_dummy = ROOT.TH1F("h_dimuon_1_pZ_dummy", "h_dimuon_1_pZ_dummy", 100, 0, 100)
h_dimuon_1_pZ_dummy.SetYTitle("Fraction of events / 1 GeV")
h_dimuon_1_pZ_dummy.SetTitleOffset(1.35, "Y")
h_dimuon_1_pZ_dummy.SetXTitle("|p_{Z}| of #mu#mu [GeV]")
h_dimuon_1_pZ_dummy.SetMaximum( 0.1 )
h_dimuon_1_Eta_dummy = ROOT.TH1F("h_dimuon_1_Eta_dummy", "h_dimuon_1_Eta_dummy",100,-5,5)
h_dimuon_1_Eta_dummy.SetYTitle("Fraction of events / 0.1")
h_dimuon_1_Eta_dummy.SetTitleOffset(1.35, "Y")
h_dimuon_1_Eta_dummy.SetXTitle("#eta of #mu#mu")
h_dimuon_1_Eta_dummy.SetMaximum( 0.1 )
h_dimuon_1_Phi_dummy = ROOT.TH1F("h_dimuon_1_Phi_dummy", "h_dimuon_1_Phi_dummy",80,-4,4 )
h_dimuon_1_Phi_dummy.SetYTitle("Fraction of events / 0.1 rad")
h_dimuon_1_Phi_dummy.SetTitleOffset(1.35, "Y")
h_dimuon_1_Phi_dummy.SetXTitle("#phi of #mu#mu [rad]")
h_dimuon_1_Phi_dummy.SetMaximum( 0.05 )
h_dimuon_1_p_dummy = ROOT.TH1F("h_dimuon_1_p_dummy", "h_dimuon_1_p_dummy", 100, 0, 100)
h_dimuon_1_p_dummy.SetYTitle("Fraction of events / 1 GeV")
h_dimuon_1_p_dummy.SetTitleOffset(1.35, "Y")
h_dimuon_1_p_dummy.SetXTitle("p of #mu#mu [GeV]")
h_dimuon_1_p_dummy.SetMaximum( 0.1 )
h_dimuon_1_M_dummy = ROOT.TH1F("h_dimuon_1_M_dummy", "h_dimuon_1_M_dummy", 50, 0.5, 10.005)
h_dimuon_1_M_dummy.SetYTitle("Fraction of events / 0.2 GeV")
h_dimuon_1_M_dummy.SetTitleOffset(1.35, "Y")
h_dimuon_1_M_dummy.SetXTitle("Mass of #mu#mu [GeV]")
h_dimuon_1_M_dummy.SetMaximum( 1.4 )
h_dimuon_1_p = ROOT.TH1F("h_dimuon_1_p", "h_dimuon_1_p", 100, 0, 100)
h_dimuon_1_p.SetLineColor(ROOT.kBlue)
h_dimuon_1_p.SetLineWidth(2)
h_dimuon_1_p.SetLineStyle(1)
h_dimuon_1_M = ROOT.TH1F("h_dimuon_1_M", "h_dimuon_1_M", 500, 0.005, 10.005)
h_dimuon_1_M.SetLineColor(ROOT.kBlue)
h_dimuon_1_M.SetLineWidth(2)
h_dimuon_1_M.SetLineStyle(1)
h_dimuon_1_pT = ROOT.TH1F("h_dimuon_1_pT", "h_dimuon_1_pT", 100, 0, 100)
h_dimuon_1_pT.SetLineColor(ROOT.kBlue)
h_dimuon_1_pT.SetLineWidth(2)
h_dimuon_1_pT.SetLineStyle(1)
h_dimuon_1_pZ = ROOT.TH1F("h_dimuon_1_pZ", "h_dimuon_1_pZ", 100, 0, 100)
h_dimuon_1_pZ.SetLineColor(ROOT.kBlue)
h_dimuon_1_pZ.SetLineWidth(2)
h_dimuon_1_pZ.SetLineStyle(1)
h_dimuon_1_Eta = ROOT.TH1F("h_dimuon_1_Eta", "h_dimuon_1_Eta",100,-5,5)
h_dimuon_1_Eta.SetLineColor(ROOT.kBlue)
h_dimuon_1_Eta.SetLineWidth(2)
h_dimuon_1_Eta.SetLineStyle(1)
h_dimuon_1_Phi = ROOT.TH1F("h_dimuon_1_Phi", "h_dimuon_1_Phi", 80,-4,4)
h_dimuon_1_Phi.SetLineColor(ROOT.kBlue)
h_dimuon_1_Phi.SetLineWidth(2)
h_dimuon_1_Phi.SetLineStyle(1)
h_dimuon_2_p = ROOT.TH1F("h_dimuon_2_p", "h_dimuon_2_p", 100, 0, 100)
h_dimuon_2_p.SetLineColor(ROOT.kRed)
h_dimuon_2_p.SetLineWidth(2)
h_dimuon_2_p.SetLineStyle(1)
h_dimuon_2_pT = ROOT.TH1F("h_dimuon_2_pT", "h_dimuon_2_pT", 100, 0, 100)
h_dimuon_2_pT.SetLineColor(ROOT.kRed)
h_dimuon_2_pT.SetLineWidth(2)
h_dimuon_2_pT.SetLineStyle(1)
h_dimuon_2_pZ = ROOT.TH1F("h_dimuon_2_pZ", "h_dimuon_2_pZ", 100, 0, 100)
h_dimuon_2_pZ.SetLineColor(ROOT.kRed)
h_dimuon_2_pZ.SetLineWidth(2)
h_dimuon_2_pZ.SetLineStyle(1)
h_dimuon_2_Eta = ROOT.TH1F("h_dimuon_2_Eta", "h_dimuon_2_Eta", 100,-5,5)
h_dimuon_2_Eta.SetLineColor(ROOT.kRed)
h_dimuon_2_Eta.SetLineWidth(2)
h_dimuon_2_Eta.SetLineStyle(1)
h_dimuon_2_Phi = ROOT.TH1F("h_dimuon_2_Phi", "h_dimuon_2_Phi", 80,-4,4)
h_dimuon_2_Phi.SetLineColor(ROOT.kRed)
h_dimuon_2_Phi.SetLineWidth(2)
h_dimuon_2_Phi.SetLineStyle(1)
################################################################################
# BAM Functions
################################################################################
def plotOverflow(hist):
name = hist.GetName()
title = hist.GetTitle()
nx = hist.GetNbinsX()+1
x1 = hist.GetBinLowEdge(1)
bw = hist.GetBinWidth(nx)
x2 = hist.GetBinLowEdge(nx)+bw
htmp = ROOT.TH1F(name, title, nx, x1, x2)
for i in range(1, nx):
htmp.Fill(htmp.GetBinCenter(i), hist.GetBinContent(i))
htmp.Fill(hist.GetNbinsX()-1, hist.GetBinContent(0))
htmp.SetEntries(hist.GetEntries())
htmp.SetLineColor(hist.GetLineColor())
htmp.SetLineWidth(hist.GetLineWidth())
htmp.SetLineStyle(hist.GetLineStyle())
htmp.DrawNormalized("same")
return
def integral(hist):
eachBinWidth = hist.GetBinWidth(hist.GetNbinsX()+1)
#print "Begin Integral"
#print eachBinWidth
runningSum = 0
for i in range(0, hist.GetNbinsX()+1):
area = eachBinWidth * hist.GetBinContent(i)
runningSum = runningSum + area
#print i
#print area
return runningSum
def getEta(pz, p):
output = atanh(pz/p)
return output
def scaleAxisY(hist, dummy):
normFactor = hist.Integral()
max = hist.GetBinContent(hist.GetMaximumBin()) / normFactor
scale = 1.8
newMax = scale*max
dummy.SetMaximum(newMax)
def scaleAxisYcT(hist, dummy):
normFactor = integral(hist)
max = hist.GetBinContent(hist.GetMaximumBin()) / normFactor
scale = 1.8
newMax = scale*max
dummy.SetMaximum(newMax)
################################################################################
# Loop over events
################################################################################
nEvents = 0
isEvent = False
nEventsOK = 0
for line in f:
if line == '<event>\n':
isEvent = True
isEvent = True
nEvents = nEvents + 1
nLinesInEvent = 0
nParticlesInEvent = 0
muons = []
dimuons = []
DimuonIndex1 = []
DimuonIndex2 = []
bamDimuons = []
FakeIndex1 = []
FakeIndex2 = []
FakeDimuons = []
lifetimes = []
higgs = []
neutralinos = []
darkNeutralinos = []
gammaDs = []
n1PlotCounter = 0
gammaDPlotCounter = 0
nDPlotCounter = 0
if nEvents > nExit: break
continue
if line == '</event>\n':
isEvent = False
continue
if isEvent == True:
nLinesInEvent = nLinesInEvent + 1
#***************************************************************************
# first line with common event information
#***************************************************************************
if nLinesInEvent == 1:
word_n = 0
# print "I", line
for word in line.split():
word_n = word_n + 1
if word_n == 1: NUP = int(word) # number of particles in the event
if word_n == 2: IDPRUP = int(word) # process type
if word_n == 3: XWGTUP = float(word) # event weight
if word_n == 4: SCALUP = float(word) # factorization scale Q
if word_n == 5: AQEDUP = float(word) # the QED coupling alpha_em
if word_n == 6: AQCDUP = float(word) # the QCD coupling alpha_s
if word_n > 6: print "Warning! Wrong common event information", line
#***************************************************************************
# line with particle information
#***************************************************************************
if nLinesInEvent >= 2:
nParticlesInEvent = nParticlesInEvent + 1
word_n = 0
# print "P", line
for word in line.split():
word_n = word_n + 1
if word_n == 1: IDUP = int(word) # particle PDG identity code
if word_n == 2: ISTUP = int(word) # status code
if word_n == 3: MOTHUP1 = int(word) # position of the first mother of particle
if word_n == 4: MOTHUP2 = int(word) # position of the last mother of particle
if word_n == 5: ICOLUP1 = int(word) # tag for the colour flow info
if word_n == 6: ICOLUP2 = int(word) # tag for the colour flow info
if word_n == 7: PUP1 = float(word) # px in GeV
if word_n == 8: PUP2 = float(word) # py in GeV
if word_n == 9: PUP3 = float(word) # pz in GeV
if word_n == 10: PUP4 = float(word) # E in GeV
if word_n == 11: PUP5 = float(word) # m in GeV
if word_n == 12: VTIMUP = float(word) # invariant lifetime ctau in mm
if word_n == 13: SPINUP = float(word) # cosine of the angle between the spin vector of a particle and its three-momentum
if word_n > 13: print "Warning! Wrong particle line", line
if abs(IDUP) == muonID:
if IDUP > 0: q = -1
if IDUP < 0: q = 1
v4 = ROOT.TLorentzVector(PUP1, PUP2, PUP3, PUP4)
muons.append(( q, v4.Px(), v4.Py(), v4.Pz(), v4.E(), v4.M(), v4.Pt(), v4.Eta(), v4.Phi(), MOTHUP1 ))
if abs(IDUP) == higgsID:
if IDUP > 0: q = 0
if IDUP < 0: q = 0
vHiggs = ROOT.TLorentzVector(PUP1, PUP2, PUP3, PUP4)
higgs.append((q, vHiggs.Px(), vHiggs.Py(), vHiggs.Pz(), vHiggs.E(), vHiggs.M(), vHiggs.Pt(), vHiggs.Eta(), vHiggs.Phi() ))
h_higgs_pT.Fill( higgs[len(higgs)-1][6] )
h_higgs_M.Fill( higgs[len(higgs)-1][5] )
h_higgs_p.Fill( sqrt( higgs[len(higgs)-1][1]*higgs[len(higgs)-1][1] + higgs[len(higgs)-1][2]*higgs[len(higgs)-1][2] + higgs[len(higgs)-1][3]*higgs[len(higgs)-1][3] ) )
h_higgs_pZ.Fill( fabs(higgs[len(higgs)-1][3]) )
#h_higgs_Eta.Fill( higgs[len(higgs)-1][7] )
h_higgs_Phi.Fill( higgs[len(higgs)-1][8] )
if abs(IDUP) == n1ID:
q = 0
vNeutralino = ROOT.TLorentzVector(PUP1, PUP2, PUP3, PUP4)
neutralinos.append((q, vNeutralino.Px(), vNeutralino.Py(), vNeutralino.Pz(), vNeutralino.E(), vNeutralino.M(), vNeutralino.Pt(), vNeutralino.Eta(), vNeutralino.Phi() ))
if len(neutralinos) == 2 and n1PlotCounter == 0:
neutralinos_sorted_pT = sorted(neutralinos, key=itemgetter(6), reverse=True)
neutralinos = neutralinos_sorted_pT
h_n1_1_pT.Fill( neutralinos[0][6] )
h_n1_2_pT.Fill( neutralinos[1][6] )
h_n1_1_p.Fill( sqrt( neutralinos[0][1]*neutralinos[0][1] + neutralinos[0][2]*neutralinos[0][2] + neutralinos[0][3]*neutralinos[0][3] ) )
h_n1_2_p.Fill( sqrt( neutralinos[1][1]*neutralinos[1][1] + neutralinos[1][2]*neutralinos[1][2] + neutralinos[1][3]*neutralinos[1][3] ) )
h_n1_1_M.Fill( neutralinos[0][5] )
h_n1_1_M.Fill( neutralinos[1][5] )
h_n1_1_pZ.Fill( fabs(neutralinos[0][3]) )
h_n1_2_pZ.Fill( fabs(neutralinos[1][3]) )
h_n1_1_Eta.Fill( getEta(neutralinos[0][3],(sqrt( neutralinos[0][1]*neutralinos[0][1] + neutralinos[0][2]*neutralinos[0][2] + neutralinos[0][3]*neutralinos[0][3] ))) )
h_n1_1_Phi.Fill( neutralinos[0][8] )
h_n1_2_Eta.Fill( getEta(neutralinos[1][3], sqrt( neutralinos[1][1]*neutralinos[1][1] + neutralinos[1][2]*neutralinos[1][2] + neutralinos[1][3]*neutralinos[1][3] )) )
#print "PUP3, PZ, P, ETA:"
#print neutralinos[0][7]
#print neutralinos[0][3]
#print (sqrt( neutralinos[0][1]*neutralinos[0][1] + neutralinos[0][2]*neutralinos[0][2] + neutralinos[0][3]*neutralinos[0][3] ))
#print getEta(neutralinos[0][3],(sqrt( neutralinos[0][1]*neutralinos[0][1] + neutralinos[0][2]*neutralinos[0][2] + neutralinos[0][3]*neutralinos[0][3] )))
h_n1_2_Phi.Fill( neutralinos[1][8] )
n1PlotCounter = 1
if abs(IDUP) == nDID:
q = 0
vDarkNeutralino = ROOT.TLorentzVector(PUP1, PUP2, PUP3, PUP4)
darkNeutralinos.append((q, vDarkNeutralino.Px(), vDarkNeutralino.Py(), vDarkNeutralino.Pz(), vDarkNeutralino.E(), vDarkNeutralino.M(), vDarkNeutralino.Pt(), vDarkNeutralino.Eta(), vDarkNeutralino.Phi() ))
if len(darkNeutralinos) == 2 and nDPlotCounter == 0:
darkNeutralinos_sorted_pT = sorted(darkNeutralinos, key=itemgetter(6), reverse=True)
darkNeutralinos = darkNeutralinos_sorted_pT
h_nD_1_pT.Fill( darkNeutralinos[0][6] )
h_nD_2_pT.Fill( darkNeutralinos[1][6] )
h_nD_1_p.Fill( sqrt( darkNeutralinos[0][1]*darkNeutralinos[0][1] + darkNeutralinos[0][2]*darkNeutralinos[0][2] + darkNeutralinos[0][3]*darkNeutralinos[0][3] ) )
h_nD_2_p.Fill( sqrt( darkNeutralinos[1][1]*darkNeutralinos[1][1] + darkNeutralinos[1][2]*darkNeutralinos[1][2] + darkNeutralinos[1][3]*darkNeutralinos[1][3] ) )
h_nD_1_M.Fill( darkNeutralinos[0][5] )
h_nD_1_M.Fill( darkNeutralinos[1][5] )
h_nD_1_pZ.Fill( fabs(darkNeutralinos[0][3]) )
h_nD_2_pZ.Fill( fabs(darkNeutralinos[1][3]) )
h_nD_1_Eta.Fill( getEta(darkNeutralinos[0][3], sqrt( darkNeutralinos[0][1]*darkNeutralinos[0][1] + darkNeutralinos[0][2]*darkNeutralinos[0][2] + darkNeutralinos[0][3]*darkNeutralinos[0][3] )) )
h_nD_1_Phi.Fill( darkNeutralinos[0][8] )
h_nD_2_Eta.Fill( getEta(darkNeutralinos[1][3], sqrt( darkNeutralinos[1][1]*darkNeutralinos[1][1] + darkNeutralinos[1][2]*darkNeutralinos[1
][2] + darkNeutralinos[1][3]*darkNeutralinos[1][3] )) )
h_nD_2_Phi.Fill( darkNeutralinos[1][8] )
vectorSum =( ( darkNeutralinos[0][1] + darkNeutralinos[1][1] )*( darkNeutralinos[0][1] + darkNeutralinos[1][1] ) ) + ( (darkNeutralinos[0][2] + darkNeutralinos[1][2])*(darkNeutralinos[0][2] + darkNeutralinos[1][2]) )
Etmiss.Fill(vectorSum)
nDPlotCounter = 1
if abs(IDUP) == gammaDID:
q = 0
vgammaDs = ROOT.TLorentzVector(PUP1, PUP2, PUP3, PUP4)
gammaDs.append(( q, vgammaDs.Px(), vgammaDs.Py(), vgammaDs.Pz(), vgammaDs.E(), vgammaDs.M(), vgammaDs.Pt(), vgammaDs.Eta(), vgammaDs.Phi()))
h_gammaD_cT.Fill( VTIMUP )
pmom = sqrt( vgammaDs.Px()*vgammaDs.Px() + vgammaDs.Py()*vgammaDs.Py() + vgammaDs.Pz()*vgammaDs.Pz() )
beta = pmom/(sqrt(vgammaDs.M()*vgammaDs.M() + pmom*pmom ))
lorentz = 1/sqrt( 1 - beta*beta )
h_gammaD_cT_lab.Fill( lorentz*VTIMUP )
pmomxy = sqrt( vgammaDs.Px()*vgammaDs.Px() + vgammaDs.Py()*vgammaDs.Py() )
betaxy = pmomxy/sqrt( vgammaDs.M()*vgammaDs.M() + pmomxy*pmomxy )
lorentzxy = 1/sqrt(1- betaxy*betaxy)
h_gammaD_cT_XY_lab.Fill( lorentzxy*VTIMUP )
pmomz = sqrt( vgammaDs.Pz()*vgammaDs.Pz() )
betaz = pmomz/sqrt( vgammaDs.M()*vgammaDs.M() + pmomz*pmomz )
lorentzZ = 1/sqrt(1 - betaz*betaz )
h_gammaD_cT_Z_lab.Fill( lorentzZ * VTIMUP )
lifetimes.append( (VTIMUP, vgammaDs.Px(), vgammaDs.Py(), vgammaDs.Pz(), vgammaDs.Pt(), vgammaDs.M() ))
if len(gammaDs) == 2 and gammaDPlotCounter == 0:
gammaDs_sorted_pT = sorted(gammaDs, key=itemgetter(6), reverse=True)
gammaDs = gammaDs_sorted_pT
lifetimes_sorted_pT = sorted(lifetimes, key=itemgetter(4), reverse=True)
lifetimes = lifetimes_sorted_pT
h_gammaD_1_cT.Fill( lifetimes[0][0] )
pmom = sqrt( lifetimes[0][1]*lifetimes[0][1] + lifetimes[0][2]*lifetimes[0][2] + lifetimes[0][3]*lifetimes[0][3] )
beta = pmom/(sqrt(lifetimes[0][5]*lifetimes[0][5] + pmom*pmom ))
lorentz = 1/sqrt( 1 - beta*beta )
h_gammaD_1_cT_lab.Fill( lorentz*lifetimes[0][0] )
#print "pmom, beta, lorentz"
#print pmom
#print beta
#print lorentz
#print lorentz*lifetimes[0][0]
pmomxy = sqrt( lifetimes[0][1]*lifetimes[0][1] + lifetimes[0][2]*lifetimes[0][2] )
betaxy = pmomxy/sqrt( lifetimes[0][5]*lifetimes[0][5] + pmomxy*pmomxy )
lorentzxy = 1/sqrt(1- betaxy*betaxy)
h_gammaD_1_cT_XY_lab.Fill( lorentzxy*lifetimes[0][0] )
pmomz = sqrt( lifetimes[0][3]*lifetimes[0][3] )
betaz = pmomz/sqrt( lifetimes[0][5]*lifetimes[0][5] + pmomz*pmomz )
lorentzZ = 1/sqrt(1 - betaz*betaz )
h_gammaD_1_cT_Z_lab.Fill( lorentzZ * lifetimes[0][0] )
h_gammaD_2_cT.Fill( lifetimes[1][0] )
pmom = sqrt( lifetimes[1][1]*lifetimes[1][1] + lifetimes[1][2]*lifetimes[1][2] + lifetimes[1][3]*lifetimes[1][3] )
beta = pmom/(sqrt(lifetimes[1][5]*lifetimes[1][5] + pmom*pmom ))
lorentz = 1/sqrt( 1 - beta*beta )
h_gammaD_2_cT_lab.Fill( lorentz*lifetimes[1][0] )
pmomxy = sqrt( lifetimes[1][1]*lifetimes[1][1] + lifetimes[1][2]*lifetimes[1][2] )
betaxy = pmomxy/sqrt( lifetimes[1][5]*lifetimes[1][5] + pmomxy*pmomxy )
lorentzxy = 1/sqrt(1- betaxy*betaxy)
h_gammaD_2_cT_XY_lab.Fill( lorentzxy*lifetimes[1][0] )
pmomz = sqrt( lifetimes[1][3]*lifetimes[1][3] )
betaz = pmomz/sqrt( lifetimes[1][5]*lifetimes[1][5] + pmomz*pmomz )
lorentzZ = 1/sqrt(1 - betaz*betaz )
h_gammaD_2_cT_Z_lab.Fill( lorentzZ * lifetimes[1][0] )
h_gammaD_1_pT.Fill( gammaDs[0][6] )
h_gammaD_2_pT.Fill( gammaDs[1][6] )
h_gammaD_1_p.Fill( sqrt( gammaDs[0][1]*gammaDs[0][1] + gammaDs[0][2]*gammaDs[0][2] + gammaDs[0][3]*gammaDs[0][3] ) )
h_gammaD_2_p.Fill( sqrt( gammaDs[1][1]*gammaDs[1][1] + gammaDs[1][2]*gammaDs[1][2] + gammaDs[1][3]*gammaDs[1][3] ) )
h_gammaD_1_M.Fill( gammaDs[0][5] )
h_gammaD_1_M.Fill( gammaDs[1][5] )
h_gammaD_1_pZ.Fill( fabs(gammaDs[0][3]) )
h_gammaD_2_pZ.Fill( fabs(gammaDs[1][3]) )
h_gammaD_1_Eta.Fill( getEta(gammaDs[0][3], sqrt( gammaDs[0][1]*gammaDs[0][1] + gammaDs[0][2]*gammaDs[0][2] + gammaDs[0][3]*gammaDs[0][3] ) ) )
h_gammaD_1_Phi.Fill( gammaDs[0][8] )
h_gammaD_2_Eta.Fill( getEta(gammaDs[1][3], sqrt( gammaDs[1][1]*gammaDs[1][1] + gammaDs[1][2]*gammaDs[1][2] + gammaDs[1][3]*gammaDs[1][3] ) ) )
h_gammaD_2_Phi.Fill( gammaDs[1][8] )
gammaDPlotCounter = 1
if len(muons) == 4:
muons_sorted_pT = sorted(muons, key=itemgetter(6), reverse=True)
muons = muons_sorted_pT
h_muon_pT_0.Fill( muons[0][6] )
h_muon_pT_1.Fill( muons[1][6] )
h_muon_pT_2.Fill( muons[2][6] )
h_muon_pT_3.Fill( muons[3][6] )
h_muon_eta_0.Fill( muons[0][7] )
h_muon_eta_1.Fill( muons[1][7] )
h_muon_eta_2.Fill( muons[2][7] )
h_muon_eta_3.Fill( muons[3][7] )
h_muon_phi_0.Fill( muons[0][8] )
h_muon_phi_1.Fill( muons[1][8] )
h_muon_phi_2.Fill( muons[2][8] )
h_muon_phi_3.Fill( muons[3][8] )
h_muon_p_0.Fill( sqrt( muons[0][1]*muons[0][1] + muons[0][2]*muons[0][2] + muons[0][3]*muons[0][3] ) )
h_muon_p_1.Fill( sqrt( muons[1][1]*muons[1][1] + muons[1][2]*muons[1][2] + muons[1][3]*muons[1][3] ) )
h_muon_p_2.Fill( sqrt( muons[2][1]*muons[2][1] + muons[2][2]*muons[2][2] + muons[2][3]*muons[2][3] ) )
h_muon_p_3.Fill( sqrt( muons[3][1]*muons[3][1] + muons[3][2]*muons[3][2] + muons[3][3]*muons[3][3] ) )
h_muon_pZ_0.Fill( muons[0][3] )
h_muon_pZ_1.Fill( muons[1][3] )
h_muon_pZ_2.Fill( muons[2][3] )
h_muon_pZ_3.Fill( muons[3][3] )
parent = muons[1][9] #this is an arbitrary choice to find real dimuons
for i in range(0, len(muons) ):
if parent == muons[i][9]:
DimuonIndex1.append(i)
else:
DimuonIndex2.append(i)
px1 = muons[DimuonIndex1[0]][1] + muons[DimuonIndex1[1]][1]
py1 = muons[DimuonIndex1[0]][2] + muons[DimuonIndex1[1]][2]
pz1 = muons[DimuonIndex1[0]][3] + muons[DimuonIndex1[1]][3]
e1 = muons[DimuonIndex1[0]][4] + muons[DimuonIndex1[1]][4]
px2 = muons[DimuonIndex2[0]][1] + muons[DimuonIndex2[1]][1]
py2 = muons[DimuonIndex2[0]][2] + muons[DimuonIndex2[1]][2]
pz2 = muons[DimuonIndex2[0]][3] + muons[DimuonIndex2[1]][3]
e2 = muons[DimuonIndex2[0]][4] + muons[DimuonIndex2[1]][4]
bamV4_1 = ROOT.TLorentzVector(px1, py1, pz1, e1)
bamV4_2 = ROOT.TLorentzVector(px2, py2, pz2, e2)
bamDimuons.append(( bamV4_1.Px(), bamV4_1.Py(), bamV4_1.Pz(), bamV4_1.E(), bamV4_1.M(), bamV4_1.Pt(), bamV4_1.Eta(), bamV4_1.Phi() ))
bamDimuons.append(( bamV4_2.Px(), bamV4_2.Py(), bamV4_2.Pz(), bamV4_2.E(), bamV4_2.M(), bamV4_2.Pt(), bamV4_2.Eta(), bamV4_2.Phi() ))
bamDimuons_Sorted_M = sorted(bamDimuons, key=itemgetter(4), reverse=True)
bamDimuons = bamDimuons_Sorted_M
h_m1_vs_m2.Fill(bamDimuons[0][4],bamDimuons[1][4])
h_m1.Fill(bamDimuons[0][4])
h_m2.Fill(bamDimuons[1][4])
bamDimuons_Sorted_pT = sorted(bamDimuons, key=itemgetter(5), reverse=True)
bamDimuons = bamDimuons_Sorted_pT
h_dimuon_1_pT.Fill(bamDimuons[0][5])
h_dimuon_2_pT.Fill(bamDimuons[1][5])
h_dimuon_1_pZ.Fill(bamDimuons[0][2])
h_dimuon_2_pZ.Fill(bamDimuons[1][2])
h_dimuon_1_p.Fill(sqrt( bamDimuons[0][0]*bamDimuons[0][0] + bamDimuons[0][1]*bamDimuons[0][1] + bamDimuons[0][2]*bamDimuons[0][2] ))
h_dimuon_2_p.Fill(sqrt( bamDimuons[1][0]*bamDimuons[1][0] + bamDimuons[1][1]*bamDimuons[1][1] + bamDimuons[1][2]*bamDimuons[1][2] ))
h_dimuon_1_Eta.Fill(bamDimuons[0][6])
h_dimuon_2_Eta.Fill(bamDimuons[1][6])
h_dimuon_1_Phi.Fill(bamDimuons[0][7])
h_dimuon_2_Phi.Fill(bamDimuons[1][7])
parent = muons[1][9] #this is an arbitrary choice to find the fake dimuons
charge = muons[1][0]
for i in range(0, len(muons) ):
if parent != muons[i][9] and charge != muons[i][0]:
FakeIndex1.append(i)
FakeIndex1.append(1)
for j in range(0, len(muons) ):
if j != FakeIndex1[0] and j != FakeIndex1[1]:
FakeIndex2.append(j)
Fakepx1 = muons[FakeIndex1[0]][1] + muons[FakeIndex1[1]][1]
Fakepy1 = muons[FakeIndex1[0]][2] + muons[FakeIndex1[1]][2]
Fakepz1 = muons[FakeIndex1[0]][3] + muons[FakeIndex1[1]][3]
Fakee1 = muons[FakeIndex1[0]][4] + muons[FakeIndex1[1]][4]
Fakepx2 = muons[FakeIndex2[0]][1] + muons[FakeIndex2[1]][1]
Fakepy2 = muons[FakeIndex2[0]][2] + muons[FakeIndex2[1]][2]
Fakepz2 = muons[FakeIndex2[0]][3] + muons[FakeIndex2[1]][3]
Fakee2 = muons[FakeIndex2[0]][4] + muons[FakeIndex2[1]][4]
fakeV4_1 = ROOT.TLorentzVector(Fakepx1, Fakepy1, Fakepz1, Fakee1)
fakeV4_2 = ROOT.TLorentzVector(Fakepx2, Fakepy2, Fakepz2, Fakee2)
FakeDimuons.append(( fakeV4_1.Px(), fakeV4_1.Py(), fakeV4_1.Pz(), fakeV4_1.E(), fakeV4_1.M(), fakeV4_1.Pt(), fakeV4_1.Eta(), fakeV4_1.Phi() ))
FakeDimuons.append(( fakeV4_2.Px(), fakeV4_2.Py(), fakeV4_2.Pz(), fakeV4_2.E(), fakeV4_2.M(), fakeV4_2.Pt(), fakeV4_2.Eta(), fakeV4_2.Phi() ))
h_dimuon_m_fake_log_0.Fill(FakeDimuons[0][4])
h_dimuon_m_fake_log_0.Fill(FakeDimuons[1][4])
h_dimuon_m_fake_0.Fill(FakeDimuons[0][4])
h_dimuon_m_fake_0.Fill(FakeDimuons[1][4])
# is1SelMu17 = False
# for i in range(0, len(muons) ):
# if muons[i][6] >= 17. and abs(muons[i][7]) <= 0.9: is1SelMu17 = True
#
# is4SelMu8 = False
# nSelMu8 = 0
# for i in range(0, len(muons) ):
# if muons[i][6] >= 8. and abs(muons[i][7]) <= 2.4: nSelMu8 = nSelMu8 + 1
# if nSelMu8 == 4: is4SelMu8 = True
#
# if is1SelMu17 and is4SelMu8:
# for i in range(0, len(muons) ):
# for j in range(i+1, len(muons) ):
# if muons[i][0] * muons[j][0] < 0:
# px = muons[i][1] + muons[j][1]
# py = muons[i][2] + muons[j][2]
# pz = muons[i][3] + muons[j][3]
# E = muons[i][4] + muons[j][4]
# v4 = ROOT.TLorentzVector(px, py, pz, E)
# dimuons.append(( i, j, v4.Px(), v4.Py(), v4.Pz(), v4.E(), v4.M(), v4.Pt(), v4.Eta(), v4.Phi() ))
# dimuons_sorted_M = sorted(dimuons, key=itemgetter(6), reverse=True)
# dimuons = dimuons_sorted_M
# # print "Dimuons:", dimuons
# h_dimuon_m_0.Fill( dimuons[0][6] )
# h_dimuon_m_1.Fill( dimuons[1][6] )
# h_dimuon_m_2.Fill( dimuons[2][6] )
# h_dimuon_m_3.Fill( dimuons[3][6] )
#
# h_dimuon_m_log_0.Fill( dimuons[0][6] )
# h_dimuon_m_log_1.Fill( dimuons[1][6] )
# h_dimuon_m_log_2.Fill( dimuons[2][6] )
# h_dimuon_m_log_3.Fill( dimuons[3][6] )
#
# #print dimuons[0][6]
# #print float(mass_GammaD_Legend)
# #if dimuons[0][6] > float(mass_GammaD_Legend): print "fake"
# #if dimuons[0][6] <= float(mass_GammaD_Legend): print "real"
# if dimuons[0][6] > float(mass_GammaD_Legend): h_dimuon_m_real_fake_1.Fill(dimuons[0][6])
# if dimuons[0][6] <= float(mass_GammaD_Legend): h_dimuon_m_real_fake_0.Fill(dimuons[0][6])
# if dimuons[1][6] > float(mass_GammaD_Legend): h_dimuon_m_real_fake_1.Fill(dimuons[1][6])
# if dimuons[1][6] <= float(mass_GammaD_Legend): h_dimuon_m_real_fake_0.Fill(dimuons[1][6])
# if dimuons[2][6] > float(mass_GammaD_Legend): h_dimuon_m_real_fake_1.Fill(dimuons[2][6])
# if dimuons[2][6] <= float(mass_GammaD_Legend): h_dimuon_m_real_fake_0.Fill(dimuons[2][6])
# if dimuons[3][6] > float(mass_GammaD_Legend): h_dimuon_m_real_fake_1.Fill(dimuons[3][6])
# if dimuons[3][6] <= float(mass_GammaD_Legend): h_dimuon_m_real_fake_0.Fill(dimuons[3][6])
#
# if dimuons[0][6] > float(mass_GammaD_Legend): h_dimuon_m_real_fake_log_1.Fill(dimuons[0][6])
# if dimuons[0][6] <= float(mass_GammaD_Legend): h_dimuon_m_real_fake_log_0.Fill(dimuons[0][6])
# if dimuons[1][6] > float(mass_GammaD_Legend): h_dimuon_m_real_fake_log_1.Fill(dimuons[1][6])
# if dimuons[1][6] <= float(mass_GammaD_Legend): h_dimuon_m_real_fake_log_0.Fill(dimuons[1][6])
# if dimuons[2][6] > float(mass_GammaD_Legend): h_dimuon_m_real_fake_log_1.Fill(dimuons[2][6])
# if dimuons[2][6] <= float(mass_GammaD_Legend): h_dimuon_m_real_fake_log_0.Fill(dimuons[2][6])
# if dimuons[3][6] > float(mass_GammaD_Legend): h_dimuon_m_real_fake_log_1.Fill(dimuons[3][6])
# if dimuons[3][6] <= float(mass_GammaD_Legend): h_dimuon_m_real_fake_log_0.Fill(dimuons[3][6])
# dimuons5GeV = []
# for i in range(0, len(dimuons)):
# # select only dimuons with invariant mass less than 5 GeV
# if dimuons[i][6] < 5.0: dimuons5GeV.append( dimuons[i] )
#
# nDimuons5GeV = len(dimuons5GeV)
#
# is2DiMuons = False
# nMuJetsContainMu17 = 0
# m_threshold_Mu17_pT = 17.0
# m_threshold_Mu17_eta = 0.9
# m_randomSeed = 1234
# if nDimuons5GeV == 2:
# # select only dimuons that do NOT share muons
# if dimuons5GeV[0][0] != dimuons5GeV[1][0] and dimuons5GeV[0][0] != dimuons5GeV[1][1] and dimuons5GeV[0][1] != dimuons5GeV[1][1] and dimuons5GeV[0][1] != dimuons5GeV[1][0]:
# isDimuon0ContainMu17 = False
# if ( muons[ dimuons5GeV[0][0] ][6] > m_threshold_Mu17_pT and muons[ dimuons5GeV[0][0] ][7] < m_threshold_Mu17_eta ) or ( muons[ dimuons5GeV[0][1] ][6] > m_threshold_Mu17_pT and muons[ dimuons5GeV[0][1] ][7] < m_threshold_Mu17_eta ):
# isDimuon0ContainMu17 = True
# if ( muons[ dimuons5GeV[1][0] ][6] > m_threshold_Mu17_pT and muons[ dimuons5GeV[1][0] ][7] < m_threshold_Mu17_eta ) or ( muons[ dimuons5GeV[1][1] ][6] > m_threshold_Mu17_pT and muons[ dimuons5GeV[1][1] ][7] < m_threshold_Mu17_eta ):
# isDimuon1ContainMu17 = True
# if isDimuon0ContainMu17 == True and isDimuon1ContainMu17 == False:
# is2DiMuons = True
# muJetC = dimuons5GeV[0]
# muJetF = dimuons5GeV[1]
# elif isDimuon0ContainMu17 == False and isDimuon1ContainMu17 == True:
# is2DiMuons = True
# muJetC = dimuons5GeV[1]
# muJetF = dimuons5GeV[0]
# elif isDimuon0ContainMu17 == True and isDimuon1ContainMu17 == True:
# is2DiMuons = True
# if(ROOT.TRandom3(m_randomSeed).Integer(2) == 0):
# muJetC = dimuons5GeV[0]
# muJetF = dimuons5GeV[1]
# else:
# muJetC = dimuons5GeV[1]
# muJetF = dimuons5GeV[0]
# else:
# is2DiMuons = False
#
# is2DiMuonsMassOK = False
# if is2DiMuons:
# massC = muJetC[6]
# massF = muJetF[6]
# h_m1_vs_m2.Fill(massC, massF)
# h_m1.Fill( massC )
# h_m2.Fill( massF )
# if abs(massC-massF) < (0.13 + 0.065*(massC+massF)/2.0):
# is2DiMuonsMassOK = True
#
# if is2DiMuonsMassOK == True:
# nEventsOK = nEventsOK + 1
print "nEvents = ", nEvents
print "nEventsOK = ", nEventsOK
################################################################################
# Draw histograms
################################################################################
Etmiss_dummy.Draw()
Etmiss.DrawNormalized("same")
scaleAxisY(Etmiss,Etmiss_dummy)
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_EtMiss.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_EtMiss.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_EtMiss.C")
h_higgs_pT_dummy.Draw()
h_higgs_pT.DrawNormalized("same")
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_pT.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_pT.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_pT.C")
h_higgs_pZ_dummy.Draw()
#h_higgs_pZ.DrawNormalized("same")
plotOverflow(h_higgs_pZ)
scaleAxisY(h_higgs_pZ,h_higgs_pZ_dummy)
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_pZ.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_pZ.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_pZ.C")
#h_higgs_Eta_dummy.Draw()
#h_higgs_Eta.DrawNormalized("same")
#info.Draw()
#txtHeader.Draw()
#cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_Eta.pdf")
#cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_Eta.png")
#cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_Eta.png")
h_higgs_Phi_dummy.Draw()
h_higgs_Phi.DrawNormalized("same")
#scaleAxisY(h_higgs_Phi,h_higgs_Phi_dummy)
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_Phi.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_Phi.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_Phi.C")
cnv.SetLogx()
h_higgs_M_dummy.Draw()
h_higgs_M_dummy.SetNdivisions(10)
h_higgs_M_dummy.GetXaxis().SetMoreLogLabels()
h_higgs_M_dummy.Draw("same")
h_higgs_M.DrawNormalized("same")
h_higgs_M.GetXaxis().SetMoreLogLabels()
h_higgs_M.DrawNormalized("same")
info.Draw()
txtHeader.Draw()
h_higgs_M_dummy.SetNdivisions(10)
h_higgs_M_dummy.GetXaxis().SetMoreLogLabels()
h_higgs_M_dummy.Draw("same")
h_higgs_M.DrawNormalized("same")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_m.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_m.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_m.C")
cnv.SetLogx(0)
h_higgs_p_dummy.Draw()
#h_higgs_p.DrawNormalized("same")
plotOverflow(h_higgs_p)
scaleAxisY(h_higgs_p,h_higgs_p_dummy)
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_p.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_p.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_Higgs_p.C")
h_n1_1_pT_dummy.Draw()
h_n1_1_pT.DrawNormalized("same")
h_n1_2_pT.DrawNormalized("same")
scaleAxisY(h_n1_1_pT, h_n1_1_pT_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_n1_1_pT,"1st neutralino","L")
legend.AddEntry(h_n1_2_pT,"2nd neutralino","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_pT.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_pT.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_pT.C")
h_n1_1_pZ_dummy.Draw()
plotOverflow(h_n1_1_pZ)
plotOverflow(h_n1_2_pZ)
scaleAxisY(h_n1_1_pZ,h_n1_1_pZ_dummy)
#h_n1_1_pZ.DrawNormalized("same")
#h_n1_2_pZ.DrawNormalized("same")
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_n1_1_pZ,"1st neutralino","L")
legend.AddEntry(h_n1_2_pZ,"2nd neutralino","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_pZ.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_pZ.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_pZ.C")
h_n1_1_Eta_dummy.Draw()
h_n1_1_Eta.DrawNormalized("same")
h_n1_2_Eta.DrawNormalized("same")
scaleAxisY(h_n1_1_Eta,h_n1_1_Eta_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_n1_1_Eta,"1st neutralino","L")
legend.AddEntry(h_n1_2_Eta,"2nd neutralino","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_Eta.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_Eta.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_Eta.C")
h_n1_1_Phi_dummy.Draw()
h_n1_1_Phi.DrawNormalized("same")
h_n1_2_Phi.DrawNormalized("same")
scaleAxisY(h_n1_1_Phi,h_n1_1_Phi_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_n1_1_Phi,"1st neutralino","L")
legend.AddEntry(h_n1_2_Phi,"2nd neutralino","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_Phi.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_Phi.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_Phi.C")
h_n1_1_p_dummy.Draw()
plotOverflow(h_n1_1_p)
plotOverflow(h_n1_2_p)
scaleAxisY(h_n1_1_p,h_n1_1_p_dummy)
#h_n1_1_p.DrawNormalized("same")
#h_n1_2_p.DrawNormalized("same")
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_n1_1_p,"1st neutralino","L")
legend.AddEntry(h_n1_2_p,"2nd neutralino","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_p.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_p.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_p.C")
h_n1_1_M_dummy.Draw()
h_n1_1_M.DrawNormalized("same")
#h_n1_2_M.DrawNormalized("same")
#legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
#legend.SetFillColor(ROOT.kWhite)
#legend.SetFillStyle(0)
#legend.SetBorderSize(0)
#legend.SetTextFont(42)
#legend.SetTextSize(0.02777778)
#legend.SetMargin(0.13)
#legend.AddEntry(h_n1_1_M,"1st neutralino (leading p_{T})","L")
#legend.AddEntry(h_n1_2_M,"2nd neutralino","L")
#legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_M.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_M.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_n1_M.C")
h_nD_1_pT_dummy.Draw()
#h_nD_1_pT.DrawNormalized("same")
#h_nD_2_pT.DrawNormalized("same")
plotOverflow(h_nD_1_pT)
plotOverflow(h_nD_2_pT)
scaleAxisY(h_nD_2_pT,h_nD_1_pT)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_nD_1_pT,"1st n_{D} (leading p_{T})","L")
legend.AddEntry(h_nD_2_pT,"2nd n_{D}","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_pT.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_pT.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_pT.C")
h_nD_1_pZ_dummy.Draw()
h_nD_1_pZ.DrawNormalized("same")
h_nD_2_pZ.DrawNormalized("same")
scaleAxisY(h_nD_2_pZ,h_nD_1_pZ_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_nD_1_pZ,"1st n_{D} (leading p_{T})","L")
legend.AddEntry(h_nD_2_pZ,"2nd n_{D}","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_pZ.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_pZ.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_pZ.C")
h_nD_1_Eta_dummy.Draw()
h_nD_1_Eta.DrawNormalized("same")
h_nD_2_Eta.DrawNormalized("same")
scaleAxisY(h_nD_1_Eta,h_nD_1_Eta_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_nD_1_Eta,"1st n_{D} (leading p_{T})","L")
legend.AddEntry(h_nD_2_Eta,"2nd n_{D}","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_Eta.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_Eta.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_Eta.C")
h_nD_1_Phi_dummy.Draw()
h_nD_1_Phi.DrawNormalized("same")
h_nD_2_Phi.DrawNormalized("same")
scaleAxisY(h_nD_1_Phi,h_nD_1_Phi_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_nD_1_Phi,"1st n_{D} (leading p_{T})","L")
legend.AddEntry(h_nD_2_Phi,"2nd n_{D}","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_Phi.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_Phi.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_Phi.C")
h_nD_1_p_dummy.Draw()
h_nD_1_p.DrawNormalized("same")
h_nD_2_p.DrawNormalized("same")
scaleAxisY(h_nD_2_p,h_nD_1_p_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_nD_1_p,"1st n_{D} (leading p_{T})","L")
legend.AddEntry(h_nD_2_p,"2nd n_{D}","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_p.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_p.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_p.C")
h_nD_1_M_dummy.Draw()
h_nD_1_M.DrawNormalized("same")
#h_nD_2_M.DrawNormalized("same")
#legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
#legend.SetFillColor(ROOT.kWhite)
#legend.SetFillStyle(0)
#legend.SetBorderSize(0)
#legend.SetTextFont(42)
#legend.SetTextSize(0.02777778)
#legend.SetMargin(0.13)
#legend.AddEntry(h_nD_1_M,"1st n_{D} (leading p_{T})","L")
#legend.AddEntry(h_nD_2_M,"2nd n_{D}","L")
#legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_M.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_M.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_nD_M.C")
h_gammaD_cT_dummy.Draw()
normConstant = integral(h_gammaD_cT)
#print normConstant
h_gammaD_cT.Scale(1/normConstant)
h_gammaD_cT.Draw("same")
scaleAxisYcT(h_gammaD_cT,h_gammaD_cT_dummy)
funct = ROOT.TF1("funct","exp(-x/"+ lifetime_GammaD_Legend +")/("+ lifetime_GammaD_Legend + "*(1 - exp(-" + str(cTlim) + "/" + lifetime_GammaD_Legend + ")))",cTlow,cTlim)
funct.SetNpx(10000)
funct.Draw("same")
h_gammaD_cT.SetTitleOffset(1.5, "Y")
h_gammaD_cT.SetXTitle("c#tau of #gamma_{D} [mm]")
h_gammaD_cT.SetYTitle("Normalized Fraction of events")
h_gammaD_cT.SetTitleSize(0.05,"Y")
info.Draw()
txtHeader.Draw()
eqn = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
eqn.SetFillColor(ROOT.kWhite)
eqn.SetFillStyle(0)
eqn.SetBorderSize(0)
eqn.SetTextFont(42)
eqn.SetTextSize(0.02777778)
eqn.SetMargin(0.13)
eqn.AddEntry(funct, "#frac{e^{-x/"+ lifetime_GammaD_Legend +"}}{"+ lifetime_GammaD_Legend + " (1 - e^{-" + str(cTlim) + "/" + lifetime_GammaD_Legend + "})}", "L")
eqn.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_cT.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_cT.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_cT.C")
h_gammaD_cT_lab_dummy.Draw()
normConstant = integral(h_gammaD_cT_lab)
h_gammaD_cT_lab.Scale(1/normConstant)
h_gammaD_cT_lab.Draw("same")
scaleAxisYcT(h_gammaD_cT_lab,h_gammaD_cT_lab_dummy)
#h_gammaD_cT_lab.DrawNormalized("same")
#myfit = ROOT.TF1("myfit", "[0]*exp(-x/[1])", 0, 10)
#myfit.SetParName(0,"C")
#myfit.SetParName(1,"L")
#myfit.SetParameter(0,1)
#myfit.SetParameter(1,1)
#h_gammaD_cT_lab.Fit("myfit").Draw("same")
h_gammaD_cT_lab.SetTitleOffset(1.5, "Y")
h_gammaD_cT_lab.SetXTitle("L of #gamma_{D} [mm]")
h_gammaD_cT_lab.SetYTitle("Events")
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_L.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_L.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_L.C")
h_gammaD_cT_XY_lab_dummy.Draw()
normConstant = integral(h_gammaD_cT_XY_lab)
h_gammaD_cT_XY_lab.Scale(1/normConstant)
h_gammaD_cT_XY_lab.Draw("same")
scaleAxisYcT(h_gammaD_cT_XY_lab,h_gammaD_cT_XY_lab_dummy)
#h_gammaD_cT_XY_lab.DrawNormalized("same")
#myfit = ROOT.TF1("myfit", "[0]*exp(-x/[1])", 0, 10)
#myfit.SetParName(0,"C")
#myfit.SetParName(1,"L_{xy}")
#myfit.SetParameter(0,1)
#myfit.SetParameter(1,1)
#h_gammaD_cT_XY_lab.Fit("myfit").Draw("same")
h_gammaD_cT_XY_lab.SetTitleOffset(1.5, "Y")
h_gammaD_cT_XY_lab.SetXTitle("L_{xy} of #gamma_{D} [mm]")
h_gammaD_cT_XY_lab.SetYTitle("Events")
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_L_XY.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_L_XY.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_L_XY.C")
h_gammaD_cT_Z_lab_dummy.Draw()
normConstant = integral(h_gammaD_cT_Z_lab)
h_gammaD_cT_Z_lab.Scale(1/normConstant)
h_gammaD_cT_Z_lab.Draw("same")
scaleAxisYcT(h_gammaD_cT_Z_lab,h_gammaD_cT_Z_lab_dummy)
#h_gammaD_cT_Z_lab.DrawNormalized("same")
#myfit = ROOT.TF1("myfit", "[0]*exp(-x/[1])", 0, 10)
#myfit.SetParName(0,"C")
#myfit.SetParName(1,"L_{z}")
#myfit.SetParameter(0,1)
#myfit.SetParameter(1,1)
#h_gammaD_cT_Z_lab.Fit("myfit").Draw("same")
h_gammaD_cT_Z_lab.SetTitleOffset(1.5, "Y")
h_gammaD_cT_Z_lab.SetXTitle("L_{z} of #gamma_{D} [mm]")
h_gammaD_cT_Z_lab.SetYTitle("Events")
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_L_Z.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_L_Z.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_L_Z.C")
h_gammaD_1_cT_dummy.Draw()
normConstant = integral(h_gammaD_1_cT)
h_gammaD_1_cT.Scale(1/normConstant)
h_gammaD_1_cT.Draw("same")
normConstant2 = integral(h_gammaD_2_cT)
h_gammaD_2_cT.Scale(1/normConstant2)
h_gammaD_2_cT.Draw("same")
scaleAxisYcT(h_gammaD_2_cT,h_gammaD_1_cT_dummy)
#h_gammaD_1_cT.DrawNormalized("same")
#h_gammaD_2_cT.DrawNormalized("same")
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_gammaD_1_cT,"1st dark photon (leading p_{T})","L")
legend.AddEntry(h_gammaD_2_cT,"2nd dark photon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Sorted_cT.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Sorted_cT.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Sorted_cT.C")
h_gammaD_1_cT_lab_dummy.Draw()
normConstant = integral(h_gammaD_1_cT_lab)
h_gammaD_1_cT_lab.Scale(1/normConstant)
h_gammaD_1_cT_lab.Draw("same")
normConstant2 = integral(h_gammaD_2_cT_lab)
h_gammaD_2_cT_lab.Scale(1/normConstant2)
h_gammaD_2_cT_lab.Draw("same")
scaleAxisYcT(h_gammaD_2_cT_lab,h_gammaD_1_cT_lab_dummy)
#h_gammaD_1_cT_lab.DrawNormalized("same")
#h_gammaD_2_cT_lab.DrawNormalized("same")
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_gammaD_1_cT_lab,"1st dark photon (leading p_{T})","L")
legend.AddEntry(h_gammaD_2_cT_lab,"2nd dark photon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Sorted_cT_lab.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Sorted_cT_lab.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Sorted_cT_lab.C")
h_gammaD_1_cT_XY_lab_dummy.Draw()
normConstant = integral(h_gammaD_1_cT_XY_lab)
h_gammaD_1_cT_XY_lab.Scale(1/normConstant)
h_gammaD_1_cT_XY_lab.Draw("same")
normConstant2 = integral(h_gammaD_2_cT_XY_lab)
h_gammaD_2_cT_XY_lab.Scale(1/normConstant2)
h_gammaD_2_cT_XY_lab.Draw("same")
scaleAxisYcT(h_gammaD_2_cT_XY_lab,h_gammaD_1_cT_XY_lab_dummy)
#h_gammaD_1_cT_XY_lab.DrawNormalized("same")
#h_gammaD_2_cT_XY_lab.DrawNormalized("same")
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_gammaD_1_cT_XY_lab,"1st dark photon (leading p_{T})","L")
legend.AddEntry(h_gammaD_2_cT_XY_lab,"2nd dark photon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Sorted_cT_XY_lab.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Sorted_cT_XY_lab.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Sorted_cT_XY_lab.C")
h_gammaD_1_cT_Z_lab_dummy.Draw()
normConstant = integral(h_gammaD_1_cT_Z_lab)
h_gammaD_1_cT_Z_lab.Scale(1/normConstant)
h_gammaD_1_cT_Z_lab.Draw("same")
normConstant2 = integral(h_gammaD_2_cT_Z_lab)
h_gammaD_2_cT_Z_lab.Scale(1/normConstant2)
h_gammaD_2_cT_Z_lab.Draw("same")
scaleAxisYcT(h_gammaD_2_cT_Z_lab,h_gammaD_1_cT_Z_lab_dummy)
#h_gammaD_1_cT_Z_lab.DrawNormalized("same")
#h_gammaD_2_cT_Z_lab.DrawNormalized("same")
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_gammaD_1_cT_Z_lab,"1st dark photon (leading p_{T})","L")
legend.AddEntry(h_gammaD_2_cT_Z_lab,"2nd dark photon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Sorted_cT_Z_lab.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Sorted_cT_Z_lab.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Sorted_cT_Z_lab.C")
h_gammaD_1_pT_dummy.Draw()
h_gammaD_1_pT.DrawNormalized("same")
h_gammaD_2_pT.DrawNormalized("same")
scaleAxisY(h_gammaD_2_pT,h_gammaD_1_pT_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_gammaD_1_pT,"1st dark photon (leading p_{T})","L")
legend.AddEntry(h_gammaD_2_pT,"2nd dark photon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_pT.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_pT.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_pT.C")
h_gammaD_1_pZ_dummy.Draw()
#plotOverflow(h_gammaD_1_pZ)
#plotOverflow(h_gammaD_2_pZ)
h_gammaD_1_pZ.DrawNormalized("same")
h_gammaD_2_pZ.DrawNormalized("same")
scaleAxisY(h_gammaD_2_pZ,h_gammaD_1_pZ_dummy)
#htmp = ROOT.TH1F(h_gammaD_1_pZ.GetName(),h_gammaD_1_pZ.GetTitle(), h_gammaD_1_pZ.GetNbinsX()+1, h_gammaD_1_pZ.GetBinLowEdge(1), h_gammaD_1_pZ.GetBinLowEdge(h_gammaD_1_pZ.GetNbinsX()+1)+h_gammaD_1_pZ.GetBinWidth(h_gammaD_1_pZ.GetNbinsX()+1))
#for i in range(1, h_gammaD_1_pZ.GetNbinsX()+1 ):
# htmp.Fill(htmp.GetBinCenter(i), h_gammaD_1_pZ.GetBinContent(i))
#htmp.Fill(h_gammaD_1_pZ.GetNbinsX()-1, h_gammaD_1_pZ.GetBinContent(0))
#htmp.SetEntries(h_gammaD_1_pZ.GetEntries())
#htmp.SetLineColor(ROOT.kRed)
#htmp.DrawNormalized("same")
#htmp2 = ROOT.TH1F(h_gammaD_2_pZ.GetName(), h_gammaD_2_pZ.GetTitle(), h_gammaD_2_pZ.GetNbinsX()+1, h_gammaD_2_pZ.GetBinLowEdge(1), h_gammaD_2_pZ.GetBinLowEdge(h_gammaD_2_pZ.GetNbinsX()+1)+h_gammaD_2_pZ.GetBinWidth(h_gammaD_2_pZ.GetNbinsX()+1))
#for i in range(1, h_gammaD_2_pZ.GetNbinsX()+1 ):
# htmp2.Fill(htmp2.GetBinCenter(i), h_gammaD_2_pZ.GetBinContent(i))
#htmp2.Fill(h_gammaD_2_pZ.GetNbinsX()-1, h_gammaD_2_pZ.GetBinContent(0))
#htmp2.SetEntries(h_gammaD_2_pZ.GetEntries())
#htmp2.SetLineColor(ROOT.kBlue)
#htmp2.DrawNormalized("same")
#h_gammaD_1_pZ.DrawNormalized("same")
#h_gammaD_2_pZ.DrawNormalized("same")
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_gammaD_1_pZ,"1st dark photon (leading p_{T})","L")
legend.AddEntry(h_gammaD_2_pZ,"2nd dark photon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_pZ.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_pZ.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_pZ.C")
h_gammaD_1_Eta_dummy.Draw()
h_gammaD_1_Eta.DrawNormalized("same")
h_gammaD_2_Eta.DrawNormalized("same")
scaleAxisY(h_gammaD_1_Eta,h_gammaD_1_Eta_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_gammaD_1_Eta,"1st dark photon (leading p_{T})","L")
legend.AddEntry(h_gammaD_2_Eta,"2nd dark photon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Eta.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Eta.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Eta.C")
h_gammaD_1_Phi_dummy.Draw()
h_gammaD_1_Phi.DrawNormalized("same")
h_gammaD_2_Phi.DrawNormalized("same")
scaleAxisY(h_gammaD_1_Phi,h_gammaD_1_Phi_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_gammaD_1_Phi,"1st dark photon (leading p_{T})","L")
legend.AddEntry(h_gammaD_2_Phi,"2nd dark photon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Phi.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Phi.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_Phi.C")
h_gammaD_1_p_dummy.Draw()
plotOverflow(h_gammaD_1_p)
plotOverflow(h_gammaD_2_p)
scaleAxisY(h_gammaD_2_p,h_gammaD_1_p_dummy)
#h_gammaD_1_p.DrawNormalized("same")
#h_gammaD_2_p.DrawNormalized("same")
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_gammaD_1_p,"1st dark photon (leading p_{T})","L")
legend.AddEntry(h_gammaD_2_p,"2nd dark photon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_p.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_p.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_p.C")
h_gammaD_1_M_dummy.Draw()
cnv.SetLogx()
h_gammaD_1_M.DrawNormalized("same")
#h_gammaD_2_M.DrawNormalized("same")
#legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
#legend.SetFillColor(ROOT.kWhite)
#legend.SetFillStyle(0)
#legend.SetBorderSize(0)
#legend.SetTextFont(42)
#legend.SetTextSize(0.02777778)
#legend.SetMargin(0.13)
#legend.AddEntry(h_gammaD_1_M,"1st dark photon (leading p_{T})","L")
#legend.AddEntry(h_gammaD_2_M,"2nd dark photon","L")
#legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_M.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_M.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_gammaD_M.C")
cnv.SetLogx(0)
h_muon_pT_dummy.Draw()
h_muon_pT_0.DrawNormalized("same")
h_muon_pT_1.DrawNormalized("same")
h_muon_pT_2.DrawNormalized("same")
h_muon_pT_3.DrawNormalized("same")
scaleAxisY(h_muon_pT_3,h_muon_pT_dummy)
legend = ROOT.TLegend(0.6175166,0.6730435,0.9429047,0.7626087)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_muon_pT_0,"1st muon (leading p_{T})","L")
legend.AddEntry(h_muon_pT_1,"2nd muon","L")
legend.AddEntry(h_muon_pT_2,"3rd muon","L")
legend.AddEntry(h_muon_pT_3,"4th muon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_pT.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_pT.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_pT.C")
h_muon_phi_dummy.Draw()
h_muon_phi_0.DrawNormalized("same")
h_muon_phi_1.DrawNormalized("same")
h_muon_phi_2.DrawNormalized("same")
h_muon_phi_3.DrawNormalized("same")
scaleAxisY(h_muon_phi_0,h_muon_phi_dummy)
legend = ROOT.TLegend(0.6175166,0.6730435,0.9429047,0.7626087)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_muon_phi_0,"1st muon (leading p_{T})","L")
legend.AddEntry(h_muon_phi_1,"2nd muon","L")
legend.AddEntry(h_muon_phi_2,"3rd muon","L")
legend.AddEntry(h_muon_phi_3,"4th muon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_phi.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_phi.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_phi.C")
h_muon_pZ_dummy.Draw()
h_muon_pZ_0.DrawNormalized("same")
h_muon_pZ_1.DrawNormalized("same")
h_muon_pZ_2.DrawNormalized("same")
h_muon_pZ_3.DrawNormalized("same")
scaleAxisY(h_muon_pZ_3,h_muon_pZ_dummy)
legend = ROOT.TLegend(0.6175166,0.6730435,0.9429047,0.7626087)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_muon_pZ_0,"1st muon (leading p_{T})","L")
legend.AddEntry(h_muon_pZ_1,"2nd muon","L")
legend.AddEntry(h_muon_pZ_2,"3rd muon","L")
legend.AddEntry(h_muon_pZ_3,"4th muon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_pZ.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_pZ.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_pZ.C")
h_muon_p_dummy.Draw()
h_muon_p_0.DrawNormalized("same")
h_muon_p_1.DrawNormalized("same")
h_muon_p_2.DrawNormalized("same")
h_muon_p_3.DrawNormalized("same")
scaleAxisY(h_muon_p_3,h_muon_p_dummy)
legend = ROOT.TLegend(0.6175166,0.6730435,0.9429047,0.7626087)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_muon_p_0,"1st muon (leading p_{T})","L")
legend.AddEntry(h_muon_p_1,"2nd muon","L")
legend.AddEntry(h_muon_p_2,"3rd muon","L")
legend.AddEntry(h_muon_p_3,"4th muon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_p.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_p.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_p.C")
h_muon_eta_dummy.Draw()
h_muon_eta_0.DrawNormalized("same")
h_muon_eta_1.DrawNormalized("same")
h_muon_eta_2.DrawNormalized("same")
h_muon_eta_3.DrawNormalized("same")
scaleAxisY(h_muon_eta_0,h_muon_eta_dummy)
legend = ROOT.TLegend(0.6175166,0.6730435,0.9429047,0.7626087)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_muon_eta_0,"1st muon (leading p_{T})","L")
legend.AddEntry(h_muon_eta_1,"2nd muon","L")
legend.AddEntry(h_muon_eta_2,"3rd muon","L")
legend.AddEntry(h_muon_eta_3,"4th muon","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_eta.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_eta.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_muon_eta.C")
#h_dimuon_m_dummy.Draw()
#h_dimuon_m_0.DrawNormalized("same")
#h_dimuon_m_1.DrawNormalized("same")
#h_dimuon_m_2.DrawNormalized("same")
#h_dimuon_m_3.DrawNormalized("same")
#
#legend = ROOT.TLegend(0.6175166,0.6730435,0.9429047,0.7626087)
#legend.SetFillColor(ROOT.kWhite)
#legend.SetFillStyle(0)
#legend.SetBorderSize(0)
#legend.SetTextFont(42)
#legend.SetTextSize(0.02777778)
#legend.SetMargin(0.13)
#legend.AddEntry(h_dimuon_m_0,"1st dimuon (leading m_{#mu#mu})","L")
#legend.AddEntry(h_dimuon_m_1,"2nd dimuon","L")
#legend.AddEntry(h_dimuon_m_2,"3rd dimuon","L")
#legend.AddEntry(h_dimuon_m_3,"4th dimuon","L")
#legend.Draw()
#info.Draw()
#txtHeader.Draw()
#cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m.pdf")
#cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m.png")
## convert -define.pdf:use-cropbox=true -density 300 CSxBR_vs_mh.pdf -resize 900x900 CSxBR_vs_mh.png
#
#h_dimuon_m_log_dummy.Draw()
#cnv.SetLogy()
#h_dimuon_m_log_0.DrawNormalized("same")
#h_dimuon_m_log_1.DrawNormalized("same")
#h_dimuon_m_log_2.DrawNormalized("same")
#h_dimuon_m_log_3.DrawNormalized("same")
#
#legend = ROOT.TLegend(0.6175166,0.6730435,0.9429047,0.7626087)
#legend.SetFillColor(ROOT.kWhite)
#legend.SetFillStyle(0)
#legend.SetBorderSize(0)
#legend.SetTextFont(42)
#legend.SetTextSize(0.02777778)
#legend.SetMargin(0.13)
#legend.AddEntry(h_dimuon_m_log_0,"1st dimuon (leading m_{#mu#mu})","L")
#legend.AddEntry(h_dimuon_m_log_1,"2nd dimuon","L")
#legend.AddEntry(h_dimuon_m_log_2,"3rd dimuon","L")
#legend.AddEntry(h_dimuon_m_log_3,"4th dimuon","L")
#legend.Draw()
#info.Draw()
#txtHeader.Draw()
#
#cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m_log.pdf")
#cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m_log.png")
#cnv.SetLogy(0)
#
#h_dimuon_m_real_fake_dummy.Draw()
#h_dimuon_m_real_fake_0.DrawNormalized("same")
#h_dimuon_m_real_fake_1.DrawNormalized("same")
#
#legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
#legend.SetFillColor(ROOT.kWhite)
#legend.SetFillStyle(0)
#legend.SetBorderSize(0)
#legend.SetTextFont(42)
#legend.SetTextSize(0.02777778)
#legend.SetMargin(0.13)
#legend.AddEntry(h_dimuon_m_real_fake_0,"Real dimuons","L")
#legend.AddEntry(h_dimuon_m_real_fake_1,"Fake dimuons","L")
#legend.Draw()
#info.Draw()
#txtHeader.Draw()
#
#cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m_real_fake.pdf")
#cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m_real_fake.png")
#
#h_dimuon_m_real_fake_log_dummy.Draw()
#cnv.SetLogy()
#h_dimuon_m_real_fake_log_0.DrawNormalized("same")
#h_dimuon_m_real_fake_log_1.DrawNormalized("same")
#legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
#legend.SetFillColor(ROOT.kWhite)
#legend.SetFillStyle(0)
#legend.SetBorderSize(0)
#legend.SetTextFont(42)
#legend.SetTextSize(0.02777778)
#legend.SetMargin(0.13)
#legend.AddEntry(h_dimuon_m_real_fake_log_0,"Real dimuons","L")
#legend.AddEntry(h_dimuon_m_real_fake_log_1,"Fake dimuons","L")
#legend.Draw()
#info.Draw()
#txtHeader.Draw()
#
#cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m_real_fake_log.pdf")
#cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m_real_fake_log.png")
cnv.SetLogy(0)
h_m1_vs_m2.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m1_vs_m2.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m1_vs_m2.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m1_vs_m2.C")
cnv.SetLogx()
h_m2.Draw()
h_m1.Draw("same")
info.Draw()
txtHeader.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m.C")
cnv.SetLogx(0)
h_dimuon_m_fake_dummy.Draw()
h_dimuon_m_fake_0.DrawNormalized("same")
scaleAxisY(h_dimuon_m_fake_0,h_dimuon_m_fake_dummy)
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m_fake.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m_fake.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m_fake.C")
h_dimuon_m_fake_log_dummy.Draw()
cnv.SetLogy()
cnv.SetLogx()
h_dimuon_m_fake_log_0.DrawNormalized("same")
#scaleAxisY(h_dimuon_m_fake_log_0,h_dimuon_m_fake_log_dummy)
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m_fake_log.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m_fake_log.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_m_fake_log.C")
cnv.SetLogy(0)
cnv.SetLogx(0)
h_dimuon_1_pT_dummy.Draw()
h_dimuon_1_pT.DrawNormalized("same")
h_dimuon_2_pT.DrawNormalized("same")
scaleAxisY(h_dimuon_2_pT,h_dimuon_1_pT_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_dimuon_1_pT,"1st #mu#mu (leading p_{T})","L")
legend.AddEntry(h_dimuon_2_pT,"2nd #mu#mu","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_pT.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_pT.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_pT.C")
h_dimuon_1_pZ_dummy.Draw()
#plotOverflow(h_dimuon_1_pZ)
#plotOverflow(h_dimuon_2_pZ)
h_dimuon_1_pZ.DrawNormalized("same")
h_dimuon_2_pZ.DrawNormalized("same")
scaleAxisY(h_dimuon_2_pZ,h_dimuon_1_pZ_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_dimuon_1_pZ,"1st #mu#mu (leading p_{T})","L")
legend.AddEntry(h_dimuon_2_pZ,"2nd #mu#mu","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_pZ.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_pZ.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_pZ.C")
h_dimuon_1_Eta_dummy.Draw()
h_dimuon_1_Eta.DrawNormalized("same")
h_dimuon_2_Eta.DrawNormalized("same")
scaleAxisY(h_dimuon_1_Eta,h_dimuon_1_Eta_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_dimuon_1_Eta,"1st #mu#mu (leading p_{T})","L")
legend.AddEntry(h_dimuon_2_Eta,"2nd #mu#mu","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_Eta.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_Eta.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_Eta.C")
h_dimuon_1_Phi_dummy.Draw()
h_dimuon_1_Phi.DrawNormalized("same")
h_dimuon_2_Phi.DrawNormalized("same")
scaleAxisY(h_dimuon_1_Phi,h_dimuon_1_Phi_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_dimuon_1_Phi,"1st #mu#mu (leading p_{T})","L")
legend.AddEntry(h_dimuon_2_Phi,"2nd #mu#mu","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_Phi.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_Phi.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_Phi.C")
h_dimuon_1_p_dummy.Draw()
plotOverflow(h_dimuon_1_p)
plotOverflow(h_dimuon_2_p)
scaleAxisY(h_dimuon_2_p,h_dimuon_1_p_dummy)
legend = ROOT.TLegend(0.46,0.6744444,0.6955556,0.7644444)
legend.SetFillColor(ROOT.kWhite)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02777778)
legend.SetMargin(0.13)
legend.AddEntry(h_dimuon_1_p,"1st #mu#mu (leading p_{T})","L")
legend.AddEntry(h_dimuon_2_p,"2nd #mu#mu","L")
legend.Draw()
info.Draw()
txtHeader.Draw()
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_p.pdf")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_p.png")
cnv.SaveAs("DarkSusy_mH_125_mGammaD_" + mass_GammaD + "_cT_"+ lifetime_GammaD + "_LHE_dimuon_p.C")
BAM.Write()
print "Made it to the end and closes"
f.close()
| [
"bmichlin@rice.edu"
] | bmichlin@rice.edu |
d52ca250c5279313ecd41661ee12a5e93f3733d1 | 5905ed0409c332492409d7707528452b19692415 | /google-cloud-sdk/lib/googlecloudsdk/api_lib/vmware/privateclouds.py | 8973c5410af453cbe0b9f0ff1d089e4085220403 | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | permissive | millerthomasj/google-cloud-sdk | c37b7ddec08afadec6ee4c165153cd404f7dec5e | 3deda6696c3be6a679689b728da3a458c836a24e | refs/heads/master | 2023-08-10T16:03:41.819756 | 2021-09-08T00:00:00 | 2021-09-08T15:08:09 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,427 | py | # -*- coding: utf-8 -*- #
# Copyright 2021 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may 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.
"""Cloud vmware Privateclouds client."""
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from apitools.base.py import list_pager
from googlecloudsdk.api_lib.vmware import util
from googlecloudsdk.command_lib.vmware import flags
class PrivateCloudsClient(util.VmwareClientBase):
"""cloud vmware privateclouds client."""
def __init__(self):
super(PrivateCloudsClient, self).__init__()
self.service = self.client.projects_locations_privateClouds
def Get(self, resource):
request = self.messages.VmwareengineProjectsLocationsPrivateCloudsGetRequest(
name=resource.RelativeName())
return self.service.Get(request)
def Create(self,
resource,
labels=None,
description=None,
cluster_id=None,
node_type=None,
node_count=None,
network_cidr=None,
network=None,
network_project=None):
parent = resource.Parent().RelativeName()
private_cloud_id = resource.Name()
private_cloud = self.messages.PrivateCloud(description=description)
flags.AddLabelsToMessage(labels, private_cloud)
network_config = self.messages.NetworkConfig(
managementCidr=network_cidr,
network=network,
)
if not network.startswith('project'):
if not bool(network_project):
network_project = resource.Parent().Parent().Name()
network_config.network = 'projects/{}/global/networks/{}'.format(
network_project, network)
management_cluster = self.messages.ManagementCluster(
clusterId=cluster_id, nodeCount=node_count, nodeTypeId=node_type)
private_cloud.managementCluster = management_cluster
private_cloud.networkConfig = network_config
request = self.messages.VmwareengineProjectsLocationsPrivateCloudsCreateRequest(
parent=parent,
privateCloudId=private_cloud_id,
privateCloud=private_cloud)
return self.service.Create(request)
def Update(self,
resource,
labels=None,
description=None,
external_ip_access=None):
cluster_group = self.Get(resource)
update_mask = ['labels']
if labels is not None:
flags.AddLabelsToMessage(labels, cluster_group)
if description is not None:
cluster_group.description = description
update_mask.append('description')
if external_ip_access is not None:
cluster_group.networkConfig.externalIpAccess = external_ip_access
update_mask.append('network_config.external_ip_access')
request = self.messages.SddcProjectsLocationsClusterGroupsPatchRequest(
clusterGroup=cluster_group,
name=resource.RelativeName(),
updateMask=','.join(update_mask))
return self.service.Patch(request)
def UnDelete(self, resource):
request = self.messages.VmwareengineProjectsLocationsPrivateCloudsUndeleteRequest(
name=resource.RelativeName())
return self.service.Undelete(request)
def Delete(self, resource):
request = self.messages.VmwareengineProjectsLocationsPrivateCloudsDeleteRequest(
name=resource.RelativeName())
return self.service.Delete(request)
def List(self,
location_resource,
filter_expression=None,
limit=None,
page_size=None,
sort_by=None):
location = location_resource.RelativeName()
request = self.messages.VmwareengineProjectsLocationsPrivateCloudsListRequest(
parent=location, filter=filter_expression)
if page_size:
request.page_size = page_size
return list_pager.YieldFromList(
self.service,
request,
limit=limit,
batch_size_attribute='pageSize',
batch_size=page_size,
field='privateClouds')
def GetNsxCredentials(self, resource):
request = self.messages.VmwareengineProjectsLocationsPrivateCloudsShowNsxCredentialsRequest(
privateCloud=resource.RelativeName())
return self.service.ShowNsxCredentials(request)
def ResetNsxCredentials(self, resource):
request = self.messages.VmwareengineProjectsLocationsPrivateCloudsResetNsxCredentialsRequest(
privateCloud=resource.RelativeName())
return self.service.ResetNsxCredentials(request)
def GetVcenterCredentials(self, resource):
request = self.messages.VmwareengineProjectsLocationsPrivateCloudsShowVcenterCredentialsRequest(
privateCloud=resource.RelativeName())
return self.service.ShowVcenterCredentials(request)
def ResetVcenterCredentials(self, resource):
request = self.messages.VmwareengineProjectsLocationsPrivateCloudsResetVcenterCredentialsRequest(
privateCloud=resource.RelativeName())
return self.service.ResetVcenterCredentials(request)
| [
"gcloud@google.com"
] | gcloud@google.com |
089d204a57ac58b1898d7497e8eaa2e12739dbfb | a91eb255bddc7d4fa12dae246e05f68f757148e4 | /dfc/document/urls.py | 3bee1653d16e26518e717117d7c6c59efb80a2aa | [] | no_license | zPatrickz/DFC-website | 7a54f3812ac0e8e5b54df3841ecbfb40da18ce64 | 6988d7ea0382ebc57540486a9621ead753cfbc37 | refs/heads/master | 2020-12-11T07:59:37.745729 | 2014-04-10T14:26:01 | 2014-04-10T14:26:01 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 316 | py | from django.conf.urls import patterns, url
from django.contrib.auth import views as auth_views
from document import views
urlpatterns = patterns('',
# url(r'^$', views.index, name = 'document_home'),
url(r'^new/', views.new, name = 'doc_new'),
url(r'^(?P<doc_id>\d+)/',views.detail, name = 'doc_detail'),
)
| [
"zeostudio@gmail.com"
] | zeostudio@gmail.com |
21183bcec283cef8fb369fe032118579540e2969 | 4724a3beaba91dd474382aaff05a900e13118071 | /09-case-study-word-play/ex_9_2_7.py | f8cc82867f782e07690f52946fceec9b89d39a1b | [] | no_license | akshirapov/think-python | 7090b11c6618b6dbc5ca5cde8ba2e1e26ca39e28 | 490333f19b463973c05abc734ac3e9dc4e6d019a | refs/heads/master | 2020-06-27T03:58:03.377943 | 2020-01-10T16:37:52 | 2020-01-10T16:40:38 | 199,838,313 | 0 | 2 | null | null | null | null | UTF-8 | Python | false | false | 953 | py | # -*- coding: utf-8 -*-
"""
This module contains a code for ex.7 related to ch.9.2 of
Think Python, 2nd Edition
by Allen Downey
http://thinkpython2.com
"""
def has_three_consecutive_double_letters(string: str):
"""Returns a word with three consecutive double letters."""
if len(string) < 6:
return False
index = 0
for char in string:
# looking for the first pair
index = string.find(2*char)
if index != -1:
break
# no double letters
if index == -1:
return False
if len(string[index:]) < 6:
return False
if string[index+2] != string[index+3]:
return False
if string[index+4] != string[index+5]:
return False
return True
if __name__ == '__main__':
with open('words.txt') as fin:
for line in fin:
word = line.strip()
if has_three_consecutive_double_letters(word):
print(word)
| [
"cccp2006_06@mail.ru"
] | cccp2006_06@mail.ru |
e61e26e9d15b4ae40e7063119a966ff2722a352f | e5ebb73d5b94cba3e2d92fd27538fdf54c754bb7 | /spam/Obit/python/FArray.py | 86c00b59bd93c55a10a45b76a49a06f3ab808065 | [
"MIT"
] | permissive | astroblogweb/gmrt-spam | 80ab4a7f895c57856a9507a378db3897886dc84c | 5f74990805bac0ddd350e54129b0f467dbb266e1 | refs/heads/master | 2020-12-31T00:40:18.421197 | 2017-01-14T06:14:31 | 2017-01-14T06:14:31 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 34,872 | py | # Python interface to Obit float array class.
# $Id: FArray.py 352 2011-06-10 16:40:10Z bill.cotton $
"""
Python Obit multidimensional array of float class
This class is for creating and manipulating a Array as a memory resident
multidimensional rectangular array of floats.
Elements are stored in order of the increasing axis order (the reverse of the
usual c definition).
Except as noted, magic value blanking is supported (OBIT_MAGIC) (returned to Python as NaN)
Virtual (read only) members (accessed as e.g. array.RMS)
====== ==============================================
RMS RMS of valid members (from histogram analysis)
RawRMS RMS of valid members (from RMS about mean)
Mode Mode of distribution of valid members
Mean Mode of distribution of valid members
Sum Sum of valid members
Count Count of valid members
Ndim Number of axes in array
Naxis list of axis dimensions (by storage order)
====== ==============================================
"""
#-----------------------------------------------------------------------
# Copyright (C) 2004-2011
# Associated Universities, Inc. Washington DC, USA.
#
# 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 2 of
# the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public
# License along with this program; if not, write to the Free
# Software Foundation, Inc., 675 Massachusetts Ave, Cambridge,
# MA 02139, USA.
#
# Correspondence concerning this software should be addressed as follows:
# Internet email: bcotton@nrao.edu.
# Postal address: William Cotton
# National Radio Astronomy Observatory
# 520 Edgemont Road
# Charlottesville, VA 22903-2475 USA
#-----------------------------------------------------------------------
# Python shadow class to ObitFArray class
import Obit, InfoList, OErr
class FArrayPtr :
def __init__(self,this):
self.this = this
def __setattr__(self,name, value):
if name == "me" :
# Out with the old
Obit.FArrayUnref(Obit.FArray_me_get(self.this))
# In with the new
Obit.FArray_me_set(self.this,value)
return
self.__dict__[name] = value
def __getattr__(self,name):
if self.__class__ != FArray:
return
if name == "me" :
return Obit.FArray_me_get(self.this)
if name=="List":
out = InfoList.InfoList()
out.me = Obit.InfoListUnref(out.me)
out.me = Obit.FArrayGetList(self.me)
return out
# Virtual members
if name=="RMS":
return PRMS(self)
if name=="RawRMS":
return PRawRMS(self)
if name=="Mode":
return PMode(self)
if name=="Mean":
return PMean(self)
if name=="Sum":
return PSum(self)
if name=="Count":
return PCount(self)
if name=="Ndim":
return PGetNdim(self)
if name=="Naxis":
return PGetNaxis(self)
if name=="Buf":
return PGetBuf(self)
raise AttributeError,str(name)
def __repr__(self):
if self.__class__ != FArray:
return
return "<C FArray instance> " + Obit.FArrayGetName(self.me)
class FArray(FArrayPtr):
"""
Python Obit multidimensional array of float class
This class is for creating and manipulating a Array as a memory resident
multidimensional rectangular array of floats.
Elements are stored in order of the increasing axis order (the reverse of the
usual c definition).
Except as noted, magic value blanking is supported (OBIT_MAGIC) (returned to Python as NaN)
Virtual (read only) members (accessed as e.g. array.RMS
====== ==============================================
RMS RMS of valid members (from histogram analysis)
RawRMS RMS of valid members (from RMS about mean)
Mode Mode of distribution of valid members
Mean Mode of distribution of valid members
Sum Sum of valid members
Count Count of valid members
Ndim Number of axes in array
Naxis list of axis dimensions (by storage order)
====== ==============================================
"""
def __init__(self, name, naxis=[1]):
ndim = len(naxis)
self.this = Obit.new_FArray(name, ndim, naxis)
def __del__(self):
if Obit!=None:
Obit.delete_FArray(self.this)
def set (self, value, i1, i2=0, i3=0, i4=0, i5=0, i6=0):
"""
Set Array value [i1, i2, i3...] (0-rel)
* self = Python FArray object
* value = value for pixel (None = blanked)
* i1 = first axis index (0-rel)
* in = nth axis index
"""
# value, possible blanked
if value==None:
v = fblank
else:
v = value
# Set value
pos = [i1,i2,i3,i4,i5,i6]
PSetVal(self, pos, v)
# end set
def get (self, i1, i2=0, i3=0, i4=0, i5=0, i6=0):
"""
Get Array value [i1, i2, i3...] (0-rel)
Return value at pixel [i1,...in], None if blanked
* self = Python FArray object
* i1 = first axis index (0-rel)
* in = nth axis index
"""
# Get value
pos = [i1,i2,i3,i4,i5,i6]
v = PGetVal(self, pos)
# value, possible blanked
if v==fblank:
value = None
else:
value = v
return value
# end get
# End Class FArray
def PGetBlank():
"""
Return Magic blanking value
"""
################################################################
return Obit.FArrayGetBlank ()
# Module constants
fblank = PGetBlank() # Blanked value
def PGetVal(inFA, pos):
"""
Return value of a cell in an FArray
returns cell contents
* inFA = input Python FArray
* pos = 0-rel cell number as an array, e.g. [10,24]
"""
################################################################
return Obit.FArrayGetVal (inFA.me, pos)
def PSetVal(inFA, pos, val):
"""
Set value of a cell in an FArray
* inFA = input Python FArray
* pos = 0-rel cell number as an array, e.g. [10,24]
* value = new value for cell
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArraySetVal(inFA.me, pos, val)
def PGetBuf(inFA):
"""
Get python memory buffer for data array
returns python memory buffer
* inFA = input Python FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
return Obit.FArrayGetBuf(inFA.me)
# end PGetBuf(
def PCopy (inFA, err):
"""
Make copy an FArray
returns copy
* inFA = input Python FArray
* err = Python Obit Error/message stack
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
outFA = FArray("None")
outFA.me = Obit.FArrayCopy (inFA.me, outFA.me, err.me);
if err.isErr:
OErr.printErrMsg(err, "Error copying FArray")
return outFA
# end PCopy
def PClone (inFA, err):
"""
Make copy the structure of an FArray
Zero fill and return FArray with same structure as in
* inFA = input Python FArray
* err = Python Obit Error/message stack
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
outFA = FArray("Clone")
Obit.FArrayClone (inFA.me, outFA.me, err.me);
if err.isErr:
OErr.printErrMsg(err, "Error zero cloning FArray")
return outFA
# end PClone
def PSubArr (inFA, blc, trc, err):
"""
Return a slice of an FArray
returns Slice in FArray
* inFA = input Python FArray
* blc = array giving (1-rel) lower index of first cell to copy, e.g. [1,1]
* trc = array giving (1-rel) highest index of first cell to copy
* err = Python Obit Error/message stack
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
outFA = FArray("None")
outFA.me = Obit.FArraySubArr (inFA.me, blc, trc, err.me)
if err.isErr:
OErr.printErrMsg(err, "Error slicing FArray")
return outFA
# emd PSubArr
def PTranspose (inFA, order, err):
"""
Transpose an FArray
returns Transposed FArray
* inFA = input Python FArray
* order = output 1-rel order of the transposed axes, in storage order
negative value = reverse order,
e,g, [2,1] = transpose 2D array
* err = Python Obit Error/message stack
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
outFA = FArray("None")
outFA.me = Obit.FArrayTranspose (inFA.me, order, err.me)
if err.isErr:
OErr.printErrMsg(err, "Error transposing FArray")
return outFA
# end PTranspose
def PIsCompatable (in1, in2):
"""
Tells if two FArrays have compatable geometry
returns true or false (1, 0)
* in1 = first input Python FArray
* in2 = second input Python FArray
"""
################################################################
# Checks
if not PIsA(in1):
print "Actually ",in1.__class__
raise TypeError,"in1 MUST be a Python Obit FArray"
if not PIsA(in2):
print "Actually ",in2.__class__
raise TypeError,"in2 MUST be a Python Obit FArray"
return Obit.FArrayIsCompatable(in1.me, in2.me)
def PRealloc (inFA, naxis):
"""
Change the geometry of an FArray
Contents will be zeroed
* inFA = input Python FArray
* naxis = array giving desired dimension, e.g. [20,30]
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
ndim = len(naxis)
Obit.FArrayRealloc(inFA.me, ndim, naxis)
def PMax (inFA, pos) :
"""
Find maximum pixel value
returns maximum value (may have trouble w/ > 2 dim)
* inFA = first input Python FArray
* pos = [output] 0-rel position as array
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
lpos = [0,0] # Dummy
ret = Obit.FArrayMax(inFA.me, lpos) # Results in list ret
out = ret[0]
pos[0]=ret[1]; pos[1]=ret[2]
return out
def PMaxAbs (inFA, pos):
"""
Find maximum absolute pixel value
returns maximum abs value (may have trouble w/ > 2 dim)
* inFA = first input Python FArray
* pos = [output] 0-rel position as array
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
lpos = [0,0] # Dummy
ret = Obit.FArrayMaxAbs(inFA.me, lpos) # Results in list ret
out = ret[0]
pos[0]=ret[1]; pos[1]=ret[2]
return out
def PMin (inFA, pos) :
"""
Find minimum pixel value
returns minimum value (may have trouble w/ > 2 dim)
* inFA = first input Python FArray
* pos = [output] 0-rel position as array
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
lpos = [0,0] # Dummy
ret = Obit.FArrayMin(inFA.me, lpos) # Results in list ret
out = ret[0]
pos[0]=ret[1]; pos[1]=ret[2]
return out
def PDeblank (inFA, scalar):
"""
Replace any magic value blanks with scalar
* inFA = input Python FArray
* scalar = value to replace magic blanks
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArrayDeblank (inFA.me, scalar)
def PRMS (inFA):
"""
Return RMS of pixel unblanked pixel values
returns RMS value derived from a histogram
* inFA = input Python FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
return Obit.FArrayRMS(inFA.me)
def PRawRMS (inFA):
"""
Return RMS of pixel unblanked pixel values
returns simple RMS about mean
* inFA = input Python FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
return Obit.FArrayRawRMS(inFA.me)
def PRMS0 (inFA):
"""
Return RMS of pixel unblanked pixel values about zero
returns simple RMS about zero
* inFA = input Python FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
return Obit.FArrayRMS0(inFA.me)
def PMode (inFA):
"""
Return Mode of pixel unblanked pixel values
returns Mode of values
* inFA = input Python FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
return Obit.FArrayMode(inFA.me)
def PMean (inFA):
"""
Return mean of pixel unblanked pixel values
returns mean of values
* inFA = input Python FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
return Obit.FArrayMean(inFA.me)
def PFill (inFA, scalar):
"""
Fill all cells of an FArray with a scalar
* inFA = input Python FArray
* scalar = Value to fill
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArrayFill(inFA.me, scalar)
def PNeg (inFA):
"""
Negate each element of the array.
* inFA = input Python FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArrayNeg(inFA.me)
# end PNeg
def PSin (inFA):
"""
Sine of each element of the array.
* inFA = input Python FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArraySin(inFA.me)
# end PSin
def PCos (inFA):
"""
Cosine of each element of the array.
* inFA = input Python FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArrayCos(inFA.me)
# end PCos
def PSqrt (inFA):
"""
Square root of MAX (1.0e-20, each element of the array).
* inFA = input Python FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArraySqrt(inFA.me)
# end PSqrt
def PSum (inFA):
"""
Sum each element of the array.
returns sum
* inFA = input Python FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
return Obit.FArraySum(inFA.me)
def PCount (inFA):
"""
Give number of valid elements in the array.
returns count
* inFA = input Python FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
return Obit.FArrayCount(inFA.me)
def PSAdd (inFA, scalar):
"""
Add a scalar to each element of the array.
in = in + scalar
* inFA = input Python FArray
* scalar = Value to add
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArraySAdd(inFA.me, scalar)
def PSMul (inFA, scalar):
"""
Multiply each element of the array by a scalar
in = in * scalar
* inFA = input Python FArray
* scalar = Value to multiply
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArraySMul(inFA.me, scalar)
def PSDiv (inFA, scalar):
"""
Divide each element of the array into a scalar.
in = scalar / in
No check for zeroes is made
* inFA = input Python FArray
* scalar = scalar
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArraySDiv(inFA.me, scalar)
def PClip (inFA, minVal, maxVal, newVal):
"""
Replace values outside of a given range with a new value
in = newVal where in < minVal or in > maxVal
* inFA = input Python FArray
* minVal = Minimum allowed value
* maxVal = Maximum allowed value
* newVal = Value to use if out of range
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArrayClip(inFA.me, minVal, maxVal, newVal)
def PInClip (inFA, minVal, maxVal, newVal):
"""
Replace values inside of a given range with a new value
in = newVal where in >= minVal or in <= maxVal
* inFA = input Python FArray
* minVal = Minimum allowed value
* maxVal = Maximum allowed value
* newVal = Value to use if in range
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArrayInClip(inFA.me, minVal, maxVal, newVal)
# end PInClip
def PDivClip (inFA1, inFA2, minVal, outFA):
"""
Divide corresponding elements of the arrays with clipping.
out = in1 / in2 where in2>minVal, else blanked
* inFA1 = first input Python FArray
* inFA2 = second input Python FArray
* minVal = Minimum allowed value
* outFA = Maximum allowed value
"""
################################################################
# Checks
if not PIsA(inFA1):
print "Actually ",inFA1.__class__
raise TypeError,"inFA1 MUST be a Python Obit FArray"
if not PIsA(inFA2):
print "Actually ",inFA2.__class__
raise TypeError,"inFA2 MUST be a Python Obit FArray"
if not PIsA(outFA):
print "Actually ",outFA.__class__
raise TypeError,"outFA MUST be a Python Obit FArray"
Obit.FArrayDivClip(inFA1.me, inFA2.me, minVal, outFA.me)
def PClipBlank (inFA, minVal, maxVal):
"""
Replace values outside of a given range with blank value
in = blank where in < minVal or in > maxVal
* inFA = input Python FArray
* minVal = Minimum allowed value
* maxVal = Maximum allowed value
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArrayClipBlank(inFA.me, minVal, maxVal)
def PBlank (in1, in2, out):
"""
Blank elements of array in1 where array in2 is blanked
out = in1 or blank where in2 is blank
* in1 = first input Python FArray
* in2 = second input Python FArray with blanking
* out = output Python FArray
"""
################################################################
# Checks
if not PIsCompatable (in1, in2):
raise RuntimeError,"in1 and in2 have incompatable geometry"
if not PIsCompatable (in1, out):
raise RuntimeError,"in1 and out have incompatable geometry"
Obit.FArrayBlank (in1.me, in2.me, out.me)
def PSumArr (in1, in2, out):
"""
SSum nonblanked elements of two arrays
out = (in1 + in2) or whichever is not blanked
* in1 = first input Python FArray
* in2 = second input Python FArray
* out = output Python FArray
"""
################################################################
# Checks
if not PIsCompatable (in1, in2):
raise RuntimeError,"in1 and in2 have incompatable geometry"
if not PIsCompatable (in1, out):
raise RuntimeError,"in1 and out have incompatable geometry"
Obit.FArraySumArr (in1.me, in2.me, out.me)
# end PSumArr
def PAvgArr (in1, in2, out):
"""
Average nonblanked elements of two arrays.
out = (in1 + in2)/2 or whichever is not blanked
* in1 = first input Python FArray
* in2 = second input Python FArray
* out = output Python FArray
"""
################################################################
# Checks
if not PIsCompatable (in1, in2):
raise RuntimeError,"in1 and in2 have incompatable geometry"
if not PIsCompatable (in1, out):
raise RuntimeError,"in1 and out have incompatable geometry"
Obit.FArrayAvgArr (in1.me, in2.me, out.me)
# end PAvgArr
def PMaxArr (in1, in2, out):
"""
Pick the larger nonblanked elements of two arrays.
out = MAX (in1, in2) or whichever is not blanked
* in1 = first input Python FArray
* in2 = second input Python FArray
* out = output Python FArray
"""
################################################################
# Checks
if not PIsCompatable (in1, in2):
raise RuntimeError,"in1 and in2 have incompatable geometry"
if not PIsCompatable (in1, out):
raise RuntimeError,"in1 and out have incompatable geometry"
Obit.FArrayMaxArr (in1.me, in2.me, out.me)
# end PMaxArr
def PMinArr (in1, in2, out):
"""
Pick the lesser nonblanked elements of two arrays.
out = MIN (in1, in2) or whichever is not blanked
* in1 = first input Python FArray
* in2 = second input Python FArray
* out = output Python FArray
"""
################################################################
# Checks
if not PIsCompatable (in1, in2):
raise RuntimeError,"in1 and in2 have incompatable geometry"
if not PIsCompatable (in1, out):
raise RuntimeError,"in1 and out have incompatable geometry"
Obit.FArrayMinArr (in1.me, in2.me, out.me)
# end PMinArr
def PAdd (in1, in2, out):
"""
Add corresponding elements of two arrays.
out = in1 + in2
* in1 = first input Python FArray
* in2 = second input Python FArray
* out = output Python FArray
"""
################################################################
# Checks
if not PIsCompatable (in1, in2):
raise RuntimeError,"in1 and in2 have incompatable geometry"
if not PIsCompatable (in1, out):
raise RuntimeError,"in1 and out have incompatable geometry"
Obit.FArrayAdd (in1.me, in2.me, out.me)
def PSub (in1, in2, out):
"""
Subtract corresponding elements of the arrays.
out = in1 - in2
* in1 = first input Python FArray
* in2 = second input Python FArray
* out = output Python FArray
"""
################################################################
# Checks
if not PIsCompatable (in1, in2):
raise RuntimeError,"in1 and in2 have incompatable geometry"
if not PIsCompatable (in1, out):
raise RuntimeError,"in1 and out have incompatable geometry"
Obit.FArraySub (in1.me, in2.me, out.me)
def PMul (in1, in2, out):
"""
Multiply corresponding elements of the arrays.
out = in1 * in2
* in1 = first input Python FArray
* in2 = second input Python FArray
* out = output Python FArray
"""
################################################################
# Checks
if not PIsCompatable (in1, in2):
raise RuntimeError,"in1 and in2 have incompatable geometry"
if not PIsCompatable (in1, out):
raise RuntimeError,"in1 and out have incompatable geometry"
Obit.FArrayMul (in1.me, in2.me, out.me)
def PDiv (in1, in2, out):
"""
Divide corresponding elements of the arrays.
out = in1 / in2
* in1 = first input Python FArray
* in2 = second input Python FArray
* out = output Python FArray
"""
################################################################
# Checks
if not PIsCompatable (in1, in2):
raise RuntimeError,"in1 and in2 have incompatable geometry"
if not PIsCompatable (in1, out):
raise RuntimeError,"in1 and out have incompatable geometry"
Obit.FArrayDiv (in1.me, in2.me, out.me)
def PDot (in1, in2):
"""
Sum the products of the elements of two arrays
return Sum (in1 x in2)
* in1 = first input Python FArray
* in2 = second input Python FArray
"""
################################################################
# Checks
if not PIsCompatable (in1, in2):
raise RuntimeError,"in1 and in2 have incompatable geometry"
return Obit.FArrayDot(in1.me, in2.me)
def PMulColRow (inFA, row, col, out):
"""
Multiply elements of 2D array by row times column
Multiply the elements of a 2D array by the corresponding elements
of a row and column vector.returns cell contents
out[i,j] = in[i,j] * row[j] * col[i]
* inFA = input Python 2D FArray
* row = "row" 1D Python FArray
* col = "column" 1D Python FArray
* out = output Python 2D FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArrayMulColRow (inFA.me, row.me, col.me, out.me)
def PCenter2D (inFA):
"""
Rearrange array for FFT
In-place rearrangement of a center-at-the edges array to
center at the center, or the other way around.
This is needed for the peculiar order of FFTs.
FFTs don't like blanked values.
* inFA = input Python FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArray2DCenter (inFA.me)
def PSymInv2D (inFA):
"""
In-place inversion of a symmetric 2-D matrix
return code, 0=>OK, else could not invert.
Magic blanking not supported
* inFA = input Python FArray with symmetric 2-D matrix
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
return Obit.FArray2DSymInv (inFA.me)
def PCGauss2D (inFA, Cen, FWHM):
"""
Make 2-D Circular Gaussian
Peak normalized to 1.0
* in = Python FArray to be modified
* Cen = 0-rel pixel center as an array, e,g, [25,26]
* FWMH = FWHM of Gaussian in pixels.
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArrayCGauss2D (inFA.me, Cen, FWHM)
def PEGauss2D (inFA, amp, Cen, GauMod):
"""
Make 2-D Eliptical Gaussian in FAArray
Peak normalized to 1.0, model is added to previous contents.
* in = Python FArray to be modified
* amp = peak value of Gaussian
* Cen = 0-rel pixel center as an array, e,g, [25.0,26.0]
* GauMod = Gaussian parameters, Major axis, FWHM, minor axis
FWHM (both in pixels) and rotation angle wrt "Y" axis (deg).
e.g. [3.0,3.0,0.0]
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
Obit.FArrayEGauss2D (inFA.me, amp, Cen, GauMod)
def PShiftAdd (in1, pos1, in2, pos2, scalar, out):
"""
Shift and Add scaled arrays
Two FArrays are aligned at specified pixels and the corresponding
pixels are added with a scalar multiplied times the second.
Only handles to 3 dimensions.
If in1/out are 3D and in2 is 2D then the same plane in in2 is used
for all planes in in1/out.
out = in1 + scalar * in2 in overlap, else in1
* in1 = first input Python FArray
* pos1 = allignment pixel (0-rel) in in1 as array
* in2 = second input Python FArray
* pos2 = allignment pixel (0-rel) in in2 as array
* scalar = scalar multiplier
* out = output Python FArray
"""
################################################################
# Checks
if not PIsCompatable (in1, out):
raise RuntimeError,"in1 and out have incompatable geometry"
if not PIsA(in2):
print "Actually ",in2.__class__
raise TypeError,"inn2 MUST be a Python Obit FArray"
Obit.FArrayShiftAdd (in1.me, pos1, in2.me, pos2, scalar, out.me)
# end PShiftAdd
def PPad (inFA, outFA, factor):
"""
Zero pad inFA into outFA and multiply by factor, deblank
outFA is zero filled and the values in inFA are inserted, centered
and multiplied by factor. Blanks in the output are replaced by zeroes.
* inFA = input Python FArray to be centered in outFA
* outFA = inFA where given and zero elsewhere
* factor = scaling factor for inFA
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
if not PIsA(outFA):
print "Actually ",outFA.__class__
raise TypeError,"outFA MUST be a Python Obit FArray"
Obit.FArrayPad (inFA.me, outFA.me, factor)
# end
def PSelInc (inFA, outFA, blc, trc, inc, err):
"""
Select elements in an FArray by increment
* inFA = input Python FArray
* outFA= output Python FArray
* blc = array giving (1-rel) lower index of first cell to copy, e.g. [1,1]
* trc = array giving (1-rel) highest index of first cell to copy
* inc = increment on each axis
* err = Python Obit Error/message stack
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
if not PIsA(outFA):
print "Actually ",outFA.__class__
raise TypeError,"outFA MUST be a Python Obit FArray"
Obit.FArraySelInc (inFA.me, outFA.me, blc, trc, inc, err.me)
if err.isErr:
OErr.printErrMsg(err, "Error selecting FArray")
return
# end PSelInc
def PHisto (inFA, n, min, max):
"""
Make histogram of FArray
Return FArray with info elements
* nHisto int Number of elements in histogram
* Min float Minimum value in histogram
* Max float Maximum value in histogram
* Total float Total number of values in histogram
* Under float Number of underflows in histogram
* Over float Number of overflows in histogram
*
* inFA = input Python FArray
* n = Number of elements in histogram
* min = Min value in histogram
* max = Max value in histogram
* Uses threading
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
outFA = FArray("None")
outFA.me = Obit.FArrayHisto (inFA.me, n, min, max)
return outFA
# end PHisto
def PIsA (inFA):
"""
Tells if the input is a Python ObitFArray
returns true or false (1,0)
* inFA = Python Obit FArray to test
"""
################################################################
# Checks
if inFA.__class__ != FArray:
return 0
return Obit.FArrayIsA(inFA.me)
# end PIsA
def PUnref (inFA):
"""
Decrement reference count
Decrement reference count which will destroy object if it goes to zero
Python object stays defined.
* inFA = Python FArray object
"""
################################################################
# Checks
if not PIsA(inFA):
raise TypeError,"inFA MUST be a Python Obit FArray"
inFA.me = Obit.FArrayUnref(inFA.me)
# end PUnref
def PGetNdim (inFA):
"""
Returns the number of dimensions in array
* inFA = Python Obit FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
return Obit.FArrayGetNdim(inFA.me)
# end PGetNdim
def PGetNaxis (inFA):
"""
Returns array of 7 elements with dimensions in array
* inFA = Python Obit FArray
"""
################################################################
# Checks
if not PIsA(inFA):
print "Actually ",inFA.__class__
raise TypeError,"inFA MUST be a Python Obit FArray"
return Obit.FArrayGetNaxis(inFA.me)
# end PGetNaxis
| [
"shubhankardeshpande@hotmail.com"
] | shubhankardeshpande@hotmail.com |
b522870250e1c07e8b88d02a1e893cf58ff4abdb | 6eb7dce0e5e44d3cb71748558015b91804f94d5d | /bloodhound_relations/bhrelations/tests/api.py | 8df83ea2acfb739d2560d4295e00a42618b1b067 | [
"LicenseRef-scancode-unknown-license-reference",
"MIT",
"Python-2.0",
"BSD-3-Clause",
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | thimalk/bloodhound | ba40ef5fad2a45e8b8cdd945bc72eafdaee1a5ca | f836d8314d7863563f6252d74f798694465f81ea | refs/heads/master | 2021-01-02T08:31:39.774465 | 2014-03-13T05:14:39 | 2014-03-13T05:14:39 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 22,967 | py | # -*- coding: UTF-8 -*-
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from datetime import datetime
import unittest
from bhrelations.api import TicketRelationsSpecifics
from bhrelations.tests.mocks import TestRelationChangingListener
from bhrelations.validation import ValidationError
from bhrelations.tests.base import BaseRelationsTestCase, PARENT, CHILD, \
DEPENDS_ON, DEPENDENCY_OF, BLOCKS, BLOCKED_BY, REFERS_TO, DUPLICATE_OF, \
MULTIPRODUCT_REL
from multiproduct.env import ProductEnvironment
from trac.ticket.model import Ticket
from trac.core import TracError
from trac.util.datefmt import utc
class ApiTestCase(BaseRelationsTestCase):
def test_can_add_two_ways_relations(self):
#arrange
ticket = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
#act
self.add_relation(ticket, DEPENDENCY_OF, ticket2)
#assert
relations = self.get_relations(ticket)
self.assertEqual(DEPENDENCY_OF, relations[0]["type"])
self.assertEqual(unicode(ticket2.id), relations[0]["destination"].id)
relations = self.get_relations(ticket2)
self.assertEqual(DEPENDS_ON, relations[0]["type"])
self.assertEqual(unicode(ticket.id), relations[0]["destination"].id)
def test_can_add_single_way_relations(self):
#arrange
ticket = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
#act
self.add_relation(ticket, REFERS_TO, ticket2)
#assert
relations = self.get_relations(ticket)
self.assertEqual(1, len(relations))
self.assertEqual(REFERS_TO, relations[0]["type"])
self.assertEqual(unicode(ticket2.id), relations[0]["destination"].id)
self.assertEqual(0, len(self.get_relations(ticket2)))
def test_can_add_multiple_relations(self):
#arrange
ticket = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
ticket3 = self._insert_and_load_ticket("A3")
#act
self.add_relation(ticket, DEPENDS_ON, ticket2)
self.add_relation(ticket, DEPENDS_ON, ticket3)
#assert
self.assertEqual(2, len(self.get_relations(ticket)))
self.assertEqual(1, len(self.get_relations(ticket2)))
self.assertEqual(1, len(self.get_relations(ticket3)))
def test_will_not_create_more_than_one_identical_relations(self):
#arrange
ticket = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
#act
self.add_relation(ticket, DEPENDS_ON, ticket2)
self.assertRaisesRegexp(
TracError,
"already exists",
self.add_relation,
ticket, DEPENDS_ON, ticket2
)
def test_will_not_create_more_than_one_identical_relations_db_level(self):
sql = """INSERT INTO bloodhound_relations (source, destination, type)
VALUES (%s, %s, %s)"""
with self.env.db_transaction as db:
db(sql, ["1", "2", DEPENDS_ON])
self.assertRaises(
self.env.db_exc.IntegrityError,
db,
sql,
["1", "2", DEPENDS_ON]
)
def test_can_add_one_way_relations(self):
#arrange
ticket = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
#act
self.add_relation(ticket, REFERS_TO, ticket2)
#assert
relations = self.get_relations(ticket)
self.assertEqual(REFERS_TO, relations[0]["type"])
self.assertEqual(unicode(ticket2.id),
relations[0]["destination"].id)
self.assertEqual(0, len(self.get_relations(ticket2)))
def test_can_delete_two_ways_relation(self):
#arrange
ticket = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
self.add_relation(ticket, DEPENDS_ON, ticket2)
relations = self.get_relations(ticket)
self.assertEqual(1, len(relations))
self.assertEqual(1, len(self.get_relations(ticket2)))
#act
self.delete_relation(relations[0])
#assert
self.assertEqual(0, len(self.get_relations(ticket)))
self.assertEqual(0, len(self.get_relations(ticket2)))
def test_can_delete_single_way_relation(self):
#arrange
ticket = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
#act
self.add_relation(ticket, REFERS_TO, ticket2)
relations = self.get_relations(ticket)
self.assertEqual(1, len(relations))
self.assertEqual(0, len(self.get_relations(ticket2)))
#act
self.delete_relation(relations[0])
#assert
self.assertEqual(0, len(self.get_relations(ticket)))
def test_can_not_add_cycled_immediate_relations(self):
#arrange
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
#act
self.add_relation(ticket1, DEPENDS_ON, ticket2)
try:
self.add_relation(ticket2, DEPENDS_ON, ticket1)
self.fail("Should throw an exception")
except ValidationError as ex:
self.assertSequenceEqual(
["tp1:ticket:2", "tp1:ticket:1"], ex.failed_ids)
def test_can_add_more_depends_ons(self):
#arrange
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
ticket3 = self._insert_and_load_ticket("A3")
#act
self.add_relation(ticket1, DEPENDS_ON, ticket2)
self.add_relation(ticket1, DEPENDS_ON, ticket3)
self.assertEqual(2, len(self.get_relations(ticket1)))
def test_can_not_add_cycled_in_different_direction(self):
#arrange
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
#act
self.add_relation(ticket1, DEPENDS_ON, ticket2)
self.assertRaises(
ValidationError,
self.add_relation,
ticket1, DEPENDENCY_OF, ticket2
)
def test_can_not_add_cycled_relations(self):
#arrange
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
ticket3 = self._insert_and_load_ticket("A3")
#act
self.add_relation(ticket1, DEPENDS_ON, ticket2)
self.add_relation(ticket2, DEPENDS_ON, ticket3)
self.assertRaises(
ValidationError,
self.add_relation,
ticket3, DEPENDS_ON, ticket1
)
def test_can_not_add_more_than_one_parent(self):
#arrange
child = self._insert_and_load_ticket("A1")
parent1 = self._insert_and_load_ticket("A2")
parent2 = self._insert_and_load_ticket("A3")
#act
self.add_relation(parent1, PARENT, child)
self.assertRaises(
ValidationError,
self.add_relation,
parent2, PARENT, child
)
self.assertRaises(
ValidationError,
self.add_relation,
child, CHILD, parent2
)
def test_can_add_more_than_one_child(self):
parent = self._insert_and_load_ticket("A1")
child1 = self._insert_and_load_ticket("A2")
child2 = self._insert_and_load_ticket("A3")
self.add_relation(parent, PARENT, child1)
self.add_relation(parent, PARENT, child2)
self.assertEqual(2, len(self.get_relations(parent)))
def test_ticket_can_be_resolved(self):
#arrange
parent = self._insert_and_load_ticket("A1")
child = self._insert_and_load_ticket("A2")
#act
self.add_relation(parent, PARENT, child)
self.req.args['action'] = 'resolve'
warnings = \
TicketRelationsSpecifics(self.env).validate_ticket(self.req, child)
self.assertEqual(0, len(list(warnings)))
def test_can_save_and_load_relation_time(self):
#arrange
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
#act
time = datetime.now(utc)
self.add_relation(ticket1, DEPENDS_ON, ticket2, when=time)
relations = self.get_relations(ticket1)
#assert
self.assertEqual(time, relations[0]["when"])
def test_cannot_resolve_ticket_when_blocker_is_unresolved(self):
#arrange
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
self.add_relation(ticket1, DEPENDS_ON, ticket2)
#act
self.req.args["action"] = 'resolve'
warnings = TicketRelationsSpecifics(self.env).validate_ticket(
self.req, ticket1)
#asset
self.assertEqual(1, len(list(warnings)))
def test_can_resolve_ticket_when_blocker_is_resolved(self):
#arrange
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2", status="closed")
self.add_relation(ticket1, DEPENDS_ON, ticket2)
#act
self.req.args["action"] = 'resolve'
warnings = TicketRelationsSpecifics(self.env).validate_ticket(
self.req, ticket1)
#assert
self.assertEqual(0, len(list(warnings)))
def test_that_relations_are_deleted_when_ticket_is_deleted(self):
#arrange
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
self.add_relation(ticket1, DEPENDS_ON, ticket2)
self.assertEqual(1, len(self.get_relations(ticket2)))
#act
ticket1.delete()
#assert
self.assertEqual(0, len(self.get_relations(ticket2)))
def test_that_no_error_when_deleting_ticket_without_relations(self):
#arrange
ticket1 = self._insert_and_load_ticket("A1")
#act
ticket1.delete()
def test_can_add_multi_product_relations(self):
ticket1 = self._insert_and_load_ticket("A1")
product2 = "tp2"
self._load_product_from_data(self.global_env, product2)
p2_env = ProductEnvironment(self.global_env, product2)
ticket2 = self._insert_and_load_ticket_with_env(p2_env, "A2")
self.add_relation(ticket1, MULTIPRODUCT_REL, ticket2)
self.assertEqual(1, len(self.get_relations(ticket1)))
self.assertEqual(1, len(self.get_relations(ticket2)))
def _debug_select(self):
"""
used for debug purposes
"""
print " source, destination, type"
sql = "SELECT source, destination, type FROM bloodhound_relations"
with self.env.db_query as db:
# for row in db(sql, ("source", "destination", "type")):
for row in db(sql):
print row
def test_parent_relation_is_incompatible_with_two_way_relations(self):
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
self.add_relation(ticket2, DEPENDS_ON, ticket1)
self.assertRaises(
ValidationError,
self.add_relation,
ticket1, PARENT, ticket2
)
self.assertRaises(
ValidationError,
self.add_relation,
ticket1, CHILD, ticket2
)
def test_parent_relation_is_incompatible_with_one_way_relations(self):
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
self.add_relation(ticket1, REFERS_TO, ticket2)
self.assertRaises(
ValidationError,
self.add_relation,
ticket1, PARENT, ticket2
)
self.assertRaises(
ValidationError,
self.add_relation,
ticket1, CHILD, ticket2
)
def test_parent_must_be_in_same_product(self):
ticket1 = self._insert_and_load_ticket("A1")
product2 = "tp2"
self._load_product_from_data(self.global_env, product2)
p2_env = ProductEnvironment(self.global_env, product2)
ticket2 = self._insert_and_load_ticket_with_env(p2_env, "A2")
self.assertRaises(
ValidationError,
self.add_relation,
ticket1, PARENT, ticket2
)
self.assertRaises(
ValidationError,
self.add_relation,
ticket1, CHILD, ticket2
)
def test_cannot_create_other_relations_between_descendants(self):
t1, t2, t3, t4, t5 = map(self._insert_and_load_ticket, "12345")
self.add_relation(t1, PARENT, t2) # t1 -> t2
self.add_relation(t2, PARENT, t3) # / \
self.add_relation(t2, PARENT, t4) # t3 t4
self.assertRaises(
ValidationError,
self.add_relation,
t2, DEPENDS_ON, t1
)
self.assertRaises(
ValidationError,
self.add_relation,
t1, DEPENDS_ON, t2
)
self.assertRaises(
ValidationError,
self.add_relation,
t4, DEPENDS_ON, t1
)
self.assertRaises(
ValidationError,
self.add_relation,
t1, DEPENDS_ON, t3
)
try:
self.add_relation(t1, DEPENDS_ON, t5)
self.add_relation(t3, DEPENDS_ON, t4)
except ValidationError:
self.fail("Could not add valid relation.")
def test_cannot_add_parent_if_this_would_cause_invalid_relations(self):
t1, t2, t3, t4, t5 = map(self._insert_and_load_ticket, "12345")
self.add_relation(t1, PARENT, t2) # t1 -> t2
self.add_relation(t2, PARENT, t3) # / \
self.add_relation(t2, PARENT, t4) # t3 t4 t5
self.add_relation(t2, DEPENDS_ON, t5)
self.assertRaises(
ValidationError,
self.add_relation,
t2, PARENT, t5
)
self.assertRaises(
ValidationError,
self.add_relation,
t3, PARENT, t5
)
self.assertRaises(
ValidationError,
self.add_relation,
t5, PARENT, t1,
)
try:
self.add_relation(t1, PARENT, t5)
except ValidationError:
self.fail("Could not add valid relation.")
def test_cannot_close_ticket_with_open_children(self):
t1 = self._insert_and_load_ticket("1") # t1
t2 = self._insert_and_load_ticket("2", status='closed') # / | \
t3 = self._insert_and_load_ticket("3") # t2 t3 t4
t4 = self._insert_and_load_ticket("4")
self.add_relation(t1, PARENT, t2)
self.add_relation(t1, PARENT, t3)
self.add_relation(t1, PARENT, t4)
# A warning is be returned for each open ticket
self.req.args["action"] = 'resolve'
warnings = \
TicketRelationsSpecifics(self.env).validate_ticket(self.req, t1)
self.assertEqual(2, len(list(warnings)))
def test_duplicate_can_only_reference_older_ticket(self):
t1 = self._insert_and_load_ticket("1")
t2 = self._insert_and_load_ticket("2")
self.assertRaises(
ValidationError,
self.add_relation,
t1, DUPLICATE_OF, t2
)
self.add_relation(t2, DUPLICATE_OF, t1)
def test_detects_blocker_cycles(self):
t1, t2, t3, t4, t5 = map(self._insert_and_load_ticket, "12345")
self.add_relation(t1, BLOCKS, t2)
self.add_relation(t3, DEPENDS_ON, t2)
self.add_relation(t4, BLOCKED_BY, t3)
self.add_relation(t4, DEPENDENCY_OF, t5)
self.assertRaises(
ValidationError,
self.add_relation,
t2, BLOCKS, t1
)
self.assertRaises(
ValidationError,
self.add_relation,
t3, DEPENDENCY_OF, t1
)
self.assertRaises(
ValidationError,
self.add_relation,
t1, BLOCKED_BY, t2
)
self.assertRaises(
ValidationError,
self.add_relation,
t1, DEPENDS_ON, t5
)
self.add_relation(t1, DEPENDENCY_OF, t2)
self.add_relation(t2, BLOCKS, t3)
self.add_relation(t4, DEPENDS_ON, t3)
self.add_relation(t5, BLOCKED_BY, t4)
self.add_relation(t1, REFERS_TO, t2)
self.add_relation(t2, REFERS_TO, t1)
def test_can_find_ticket_by_id_from_same_env(self):
""" Can find ticket given #id"""
product2 = "tp2"
self._load_product_from_data(self.global_env, product2)
p2_env = ProductEnvironment(self.global_env, product2)
t1 = self._insert_and_load_ticket_with_env(p2_env, "T1")
trs = TicketRelationsSpecifics(p2_env)
ticket = trs.find_ticket("#%d" % t1.id)
self.assertEqual(ticket.id, 1)
def test_can_find_ticket_by_id_from_different_env(self):
""" Can find ticket from different env given #id"""
product2 = "tp2"
self._load_product_from_data(self.global_env, product2)
p2_env = ProductEnvironment(self.global_env, product2)
t1 = self._insert_and_load_ticket_with_env(p2_env, "T1")
trs = TicketRelationsSpecifics(self.env)
ticket = trs.find_ticket("#%d" % t1.id)
self.assertEqual(ticket.id, 1)
def test_can_find_ticket_by_product_and_id(self):
""" Can find ticket given #prefix-id"""
product2 = "tp2"
self._load_product_from_data(self.global_env, product2)
p2_env = ProductEnvironment(self.global_env, product2)
t1 = self._insert_and_load_ticket_with_env(p2_env, "T1")
trs = TicketRelationsSpecifics(self.env)
ticket = trs.find_ticket("#%s-%d" % (product2, t1.id))
self.assertEqual(ticket.id, 1)
class RelationChangingListenerTestCase(BaseRelationsTestCase):
def test_can_sent_adding_event(self):
#arrange
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
test_changing_listener = self.env[TestRelationChangingListener]
#act
self.add_relation(ticket1, DEPENDS_ON, ticket2)
#assert
self.assertEqual("adding_relation", test_changing_listener.action)
relation = test_changing_listener.relation
self.assertEqual(DEPENDS_ON, relation.type)
def test_can_sent_deleting_event(self):
#arrange
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
test_changing_listener = self.env[TestRelationChangingListener]
self.add_relation(ticket1, DEPENDS_ON, ticket2)
#act
relations = self.get_relations(ticket1)
self.delete_relation(relations[0])
#assert
self.assertEqual("deleting_relation", test_changing_listener.action)
relation = test_changing_listener.relation
self.assertEqual(DEPENDS_ON, relation.type)
class TicketChangeRecordUpdaterTestCase(BaseRelationsTestCase):
def test_can_update_ticket_history_on_relation_add_on(self):
#arrange
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
#act
self.add_relation(ticket1, DEPENDS_ON, ticket2)
#assert
change_log1 = Ticket(self.env, ticket1.id).get_changelog()
self.assertEquals(1, len(change_log1))
change_log2 = Ticket(self.env, ticket2.id).get_changelog()
self.assertEquals(1, len(change_log2))
def test_can_update_ticket_history_on_relation_deletion(self):
#arrange
ticket1 = self._insert_and_load_ticket("A1")
ticket2 = self._insert_and_load_ticket("A2")
self.add_relation(ticket1, DEPENDS_ON, ticket2)
relations = self.get_relations(ticket1)
#act
self.delete_relation(relations[0])
#assert
change_log1 = Ticket(self.env, ticket1.id).get_changelog()
self.assertEquals(2, len(change_log1))
change_log2 = Ticket(self.env, ticket2.id).get_changelog()
self.assertEquals(2, len(change_log2))
def _debug_select(self, ticket_id=None):
"""
used for debug purposes
"""
# print " source, destination, type"
sql = "SELECT * FROM ticket_change"
print "db_direct_transaction result:"
with self.env.db_direct_transaction as db:
# for row in db(sql, ("source", "destination", "type")):
for row in db(sql):
print row
sql = "SELECT * FROM ticket_change"
print "db_transaction result:"
with self.env.db_transaction as db:
for row in db(sql):
print row
if ticket_id:
sql = """SELECT time, author, field, oldvalue, newvalue
FROM ticket_change WHERE ticket=%s"""
print "db_transaction select by ticket_id result:"
with self.env.db_transaction:
for row in self.env.db_query(sql, (ticket_id, )):
print row
def suite():
test_suite = unittest.TestSuite()
test_suite.addTest(unittest.makeSuite(ApiTestCase, 'test'))
test_suite.addTest(unittest.makeSuite(
RelationChangingListenerTestCase, 'test'))
test_suite.addTest(unittest.makeSuite(
TicketChangeRecordUpdaterTestCase, 'test'))
return test_suite
if __name__ == '__main__':
unittest.main()
| [
"tkempitiya@gmail.com"
] | tkempitiya@gmail.com |
6979f3f8d77744f8a56be1a9293b249bbeb34f0b | b95e49d381940c8e36ef638e954ca06e36c3be25 | /app.py | 15c5721608b81db64f886969f2e77e49ba55d3c4 | [] | no_license | bloogrox/dramatiq-starter | 4ca790db63b78b124d1b1000c82425355ccaa3d7 | 7190768634a56e52bc5443aa858fb9b63b5ecdc6 | refs/heads/master | 2020-06-17T07:08:04.669197 | 2019-07-08T15:31:34 | 2019-07-08T15:31:34 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 155 | py | import dramatiq
from dramatiq.brokers.redis import RedisBroker
import settings
broker = RedisBroker(url=settings.REDIS_URL)
dramatiq.set_broker(broker)
| [
"bloogrox@gmail.com"
] | bloogrox@gmail.com |
b38dad9bbbe53949d2bc4a67748445a1daa1bbd4 | 4a4717f88a0a5ea174098a342057759561f1688b | /scripts/util/diagnose_huc12.py | 7a6f1b21daa14ced31b9160195e2675f6817b81d | [
"MIT"
] | permissive | timsklenar/dep | 73ccf3ef18fe6a22f2cecba7878dcff709efea57 | 5bf9e0cd335825dcb50f22ee4c5c9c5ccc866114 | refs/heads/master | 2021-04-12T10:15:35.680758 | 2018-02-16T17:43:39 | 2018-02-16T17:43:39 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,448 | py | """Do some diagnostics on what the raw DEP files are telling us"""
from __future__ import print_function
import sys
import glob
from pyiem import dep
import pandas as pd
def summarize_hillslopes(huc12, scenario):
"""Print out top hillslopes"""
envs = glob.glob("/i/%s/env/%s/%s/*.env" % (scenario,
huc12[:8], huc12[8:]))
dfs = []
for env in envs:
df = dep.read_env(env)
df['flowpath'] = int(env.split("/")[-1].split("_")[1][:-4])
dfs.append(df)
df = pd.concat(dfs)
df2 = df[['sed_del', 'flowpath']].groupby(
'flowpath').sum().sort_values('sed_del', ascending=False)
print("==== TOP 5 HIGHEST SEDIMENT DELIVERY TOTALS")
print(df2.head())
flowpath = df2.index[0]
df2 = df[df['flowpath'] == flowpath].sort_values('sed_del',
ascending=False)
print("==== TOP 5 HIGHEST SEDIMENT DELIVERY FOR %s" % (flowpath, ))
print(df2[['date', 'sed_del', 'precip', 'runoff', 'av_det']].head())
df3 = df2.groupby('year').sum().sort_values('sed_del', ascending=False)
print("==== TOP 5 HIGHEST SEDIMENT DELIVERY EVENTS FOR %s" % (flowpath, ))
print(df3[['sed_del', 'precip', 'runoff', 'av_det']].head())
def main(argv):
"""Go Main"""
huc12 = argv[1]
scenario = argv[2]
summarize_hillslopes(huc12, scenario)
if __name__ == '__main__':
main(sys.argv)
| [
"akrherz@iastate.edu"
] | akrherz@iastate.edu |
ba7462e3fe1257347ea3f0e2c36da7cd650c65ff | 855511810dd54fa2406442db034079f76a73f869 | /netbox_rest/models/tenant_group_serializer.py | 061de64d5963c72c8274440c79d1638b951e0d21 | [] | no_license | jlongever/netbox-serv | 2da55778ded70031bd7500b4bf7aebb9d814dbbc | b281c7b7ef1571ad71f46dc155c2e0e2dd19b217 | refs/heads/master | 2020-09-10T02:16:23.555873 | 2018-10-29T18:29:17 | 2018-10-29T18:29:17 | 66,660,836 | 2 | 0 | null | 2018-10-29T18:29:18 | 2016-08-26T15:59:25 | Python | UTF-8 | Python | false | false | 4,445 | py | # coding: utf-8
"""
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version:
Generated by: https://github.com/swagger-api/swagger-codegen.git
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 pprint import pformat
from six import iteritems
import re
class TenantGroupSerializer(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
def __init__(self, id=None, name=None, slug=None):
"""
TenantGroupSerializer - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition.
"""
self.swagger_types = {
'id': 'int',
'name': 'str',
'slug': 'str'
}
self.attribute_map = {
'id': 'id',
'name': 'name',
'slug': 'slug'
}
self._id = id
self._name = name
self._slug = slug
@property
def id(self):
"""
Gets the id of this TenantGroupSerializer.
:return: The id of this TenantGroupSerializer.
:rtype: int
"""
return self._id
@id.setter
def id(self, id):
"""
Sets the id of this TenantGroupSerializer.
:param id: The id of this TenantGroupSerializer.
:type: int
"""
self._id = id
@property
def name(self):
"""
Gets the name of this TenantGroupSerializer.
:return: The name of this TenantGroupSerializer.
:rtype: str
"""
return self._name
@name.setter
def name(self, name):
"""
Sets the name of this TenantGroupSerializer.
:param name: The name of this TenantGroupSerializer.
:type: str
"""
self._name = name
@property
def slug(self):
"""
Gets the slug of this TenantGroupSerializer.
:return: The slug of this TenantGroupSerializer.
:rtype: str
"""
return self._slug
@slug.setter
def slug(self, slug):
"""
Sets the slug of this TenantGroupSerializer.
:param slug: The slug of this TenantGroupSerializer.
:type: str
"""
self._slug = slug
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_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 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
"""
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other
| [
"joseph.longever@emc.com"
] | joseph.longever@emc.com |
0bd2dc91e623ecbdacd96734ca3e54d446aee70d | 02f937609df114477f746342b37e690d24c181e8 | /src/venv/bin/easy_install-3.5 | 98872eb640733614f2356aad9294485234d277eb | [] | no_license | summukhe/SequenceStructureAnalysis | b419663e81541a028f062cbeaf2c8f81503e12da | 6e9e161a8ad89f627be9b5a2bf82d26f28b4b431 | refs/heads/master | 2021-05-05T23:34:05.824732 | 2018-01-16T17:43:06 | 2018-01-16T17:43:06 | 116,802,653 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 449 | 5 | #!/home/sumanta/PycharmProjects/RLECode/venv/bin/python
# EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==28.8.0','console_scripts','easy_install-3.5'
__requires__ = 'setuptools==28.8.0'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('setuptools==28.8.0', 'console_scripts', 'easy_install-3.5')()
)
| [
"sumant199@gmail.com"
] | sumant199@gmail.com |
1741d5b1a6df9deb02ed43335f02689dd7b7b402 | caf192dbc1ca90fee18bb4ce170d37eb14870ec5 | /Chapter-11/16. statSet class.py | 0491ce9ba16dcf1268379fbd7681333026d5dfdf | [] | no_license | Dfredude/PythonZelle | 858b00f5eacce841173c64b3cecd978dedbeb145 | 1923fe84df604968eebc5269f23b7c0f167d55f0 | refs/heads/main | 2023-08-30T21:45:57.070344 | 2021-10-17T01:32:57 | 2021-10-17T01:32:57 | 359,041,963 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,036 | py | from math import sqrt
from random import randrange
class StatSet:
def __init__(self) -> None:
self.values = []
def addNumber(self, x): self.values.append(x)
def mean(self): return sum(self.values)/len(self.values)
def median(self):
self.n = len(self.values)
if self.n % 2 != 0: self.median = self.values[self.n//2+1]
else:
m = self.n/2
self.median = (self.values[m] + self.values[m+1]) / 2
return self.median
def stdDev(self):
sumDevSq = 0
xbar = self.mean()
for num in self.values:
dev = num - xbar
sumDevSq = sumDevSq + dev * dev
return sqrt(sumDevSq/(len(self.values)-1))
def count(self): return len(self.values)
def min(self): return min(self.values)
def max(self): return max(self.values)
def main():
mySet = StatSet()
for i in range(10):
mySet.addNumber(randrange(1,10))
print(mySet.mean(), mySet.stdDev())
if __name__ == '__main__': main()
| [
"dominguezlucio@outlook.com"
] | dominguezlucio@outlook.com |
1187e8c7ed00d2c08ff2a256d63db25edd0638f7 | 4c677ad71ee5b30e4957ae42fbd00ad7b90a4c2d | /backend/lively_union_27810/settings.py | 754d3bbe5e18416be076dfc95a76026589bd39c5 | [] | no_license | crowdbotics-apps/lively-union-27810 | 27ce491b2fd13c5f4413039567f96792abe71668 | 4c8b6e63a628a1ba9759b7e59fc0f2e578cae120 | refs/heads/master | 2023-05-27T06:10:19.350996 | 2021-06-07T18:51:47 | 2021-06-07T18:51:47 | 374,769,497 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,120 | py | """
Django settings for lively_union_27810 project.
Generated by 'django-admin startproject' using Django 2.2.2.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.2/ref/settings/
"""
import os
import environ
import logging
env = environ.Env()
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = env.bool("DEBUG", default=False)
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = env.str("SECRET_KEY")
ALLOWED_HOSTS = env.list("HOST", default=["*"])
SITE_ID = 1
SECURE_PROXY_SSL_HEADER = ("HTTP_X_FORWARDED_PROTO", "https")
SECURE_SSL_REDIRECT = env.bool("SECURE_REDIRECT", default=False)
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'django.contrib.sites'
]
LOCAL_APPS = [
'home',
'modules',
'users.apps.UsersConfig',
]
THIRD_PARTY_APPS = [
'rest_framework',
'rest_framework.authtoken',
'rest_auth',
'rest_auth.registration',
'bootstrap4',
'allauth',
'allauth.account',
'allauth.socialaccount',
'allauth.socialaccount.providers.google',
'django_extensions',
'drf_yasg',
'storages',
# start fcm_django push notifications
'fcm_django',
# end fcm_django push notifications
]
INSTALLED_APPS += LOCAL_APPS + THIRD_PARTY_APPS
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'lively_union_27810.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR, 'web_build')],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'lively_union_27810.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
if env.str("DATABASE_URL", default=None):
DATABASES = {
'default': env.db()
}
# Password validation
# https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.2/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.2/howto/static-files/
STATIC_URL = '/static/'
MIDDLEWARE += ['whitenoise.middleware.WhiteNoiseMiddleware']
AUTHENTICATION_BACKENDS = (
'django.contrib.auth.backends.ModelBackend',
'allauth.account.auth_backends.AuthenticationBackend'
)
STATIC_ROOT = os.path.join(BASE_DIR, "staticfiles")
STATICFILES_DIRS = [os.path.join(BASE_DIR, 'static'), os.path.join(BASE_DIR, 'web_build/static')]
STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage'
# allauth / users
ACCOUNT_EMAIL_REQUIRED = True
ACCOUNT_AUTHENTICATION_METHOD = 'email'
ACCOUNT_USERNAME_REQUIRED = False
ACCOUNT_EMAIL_VERIFICATION = "optional"
ACCOUNT_CONFIRM_EMAIL_ON_GET = True
ACCOUNT_LOGIN_ON_EMAIL_CONFIRMATION = True
ACCOUNT_UNIQUE_EMAIL = True
LOGIN_REDIRECT_URL = "users:redirect"
ACCOUNT_ADAPTER = "users.adapters.AccountAdapter"
SOCIALACCOUNT_ADAPTER = "users.adapters.SocialAccountAdapter"
ACCOUNT_ALLOW_REGISTRATION = env.bool("ACCOUNT_ALLOW_REGISTRATION", True)
SOCIALACCOUNT_ALLOW_REGISTRATION = env.bool("SOCIALACCOUNT_ALLOW_REGISTRATION", True)
REST_AUTH_SERIALIZERS = {
# Replace password reset serializer to fix 500 error
"PASSWORD_RESET_SERIALIZER": "home.api.v1.serializers.PasswordSerializer",
}
REST_AUTH_REGISTER_SERIALIZERS = {
# Use custom serializer that has no username and matches web signup
"REGISTER_SERIALIZER": "home.api.v1.serializers.SignupSerializer",
}
# Custom user model
AUTH_USER_MODEL = "users.User"
EMAIL_HOST = env.str("EMAIL_HOST", "smtp.sendgrid.net")
EMAIL_HOST_USER = env.str("SENDGRID_USERNAME", "")
EMAIL_HOST_PASSWORD = env.str("SENDGRID_PASSWORD", "")
EMAIL_PORT = 587
EMAIL_USE_TLS = True
# AWS S3 config
AWS_ACCESS_KEY_ID = env.str("AWS_ACCESS_KEY_ID", "")
AWS_SECRET_ACCESS_KEY = env.str("AWS_SECRET_ACCESS_KEY", "")
AWS_STORAGE_BUCKET_NAME = env.str("AWS_STORAGE_BUCKET_NAME", "")
AWS_STORAGE_REGION = env.str("AWS_STORAGE_REGION", "")
USE_S3 = (
AWS_ACCESS_KEY_ID and
AWS_SECRET_ACCESS_KEY and
AWS_STORAGE_BUCKET_NAME and
AWS_STORAGE_REGION
)
if USE_S3:
AWS_S3_CUSTOM_DOMAIN = env.str("AWS_S3_CUSTOM_DOMAIN", "")
AWS_S3_OBJECT_PARAMETERS = {"CacheControl": "max-age=86400"}
AWS_DEFAULT_ACL = env.str("AWS_DEFAULT_ACL", "public-read")
AWS_MEDIA_LOCATION = env.str("AWS_MEDIA_LOCATION", "media")
AWS_AUTO_CREATE_BUCKET = env.bool("AWS_AUTO_CREATE_BUCKET", True)
DEFAULT_FILE_STORAGE = env.str(
"DEFAULT_FILE_STORAGE", "home.storage_backends.MediaStorage"
)
MEDIA_URL = '/mediafiles/'
MEDIA_ROOT = os.path.join(BASE_DIR, 'mediafiles')
# start fcm_django push notifications
FCM_DJANGO_SETTINGS = {
"FCM_SERVER_KEY": env.str("FCM_SERVER_KEY", "")
}
# end fcm_django push notifications
# Swagger settings for api docs
SWAGGER_SETTINGS = {
"DEFAULT_INFO": f"{ROOT_URLCONF}.api_info",
}
if DEBUG or not (EMAIL_HOST_USER and EMAIL_HOST_PASSWORD):
# output email to console instead of sending
if not DEBUG:
logging.warning("You should setup `SENDGRID_USERNAME` and `SENDGRID_PASSWORD` env vars to send emails.")
EMAIL_BACKEND = "django.core.mail.backends.console.EmailBackend"
| [
"team@crowdbotics.com"
] | team@crowdbotics.com |
408cbc35305ce711a6c9ec7410db919a0b7c642c | 781e2692049e87a4256320c76e82a19be257a05d | /all_data/exercism_data/python/bob/8fb1675764894b0597301daa1e12a109.py | d160506f4701990580a9759cca9758df671ffba8 | [] | no_license | itsolutionscorp/AutoStyle-Clustering | 54bde86fe6dbad35b568b38cfcb14c5ffaab51b0 | be0e2f635a7558f56c61bc0b36c6146b01d1e6e6 | refs/heads/master | 2020-12-11T07:27:19.291038 | 2016-03-16T03:18:00 | 2016-03-16T03:18:42 | 59,454,921 | 4 | 0 | null | 2016-05-23T05:40:56 | 2016-05-23T05:40:56 | null | UTF-8 | Python | false | false | 216 | py | def hey(prompt):
if prompt.strip() == "":
return "Fine. Be that way!"
if prompt.isupper():
return "Woah, chill out!"
if prompt.endswith("?"):
return "Sure."
return "Whatever."
| [
"rrc@berkeley.edu"
] | rrc@berkeley.edu |
b3017ebe21427087f950b2b556cbeecdfffff87b | 4377dddadb615c632ea49851c986cff096b79358 | /money/contrib/django/currencies/models.py | a5717692ecee69c2909275020fb9a66483ec3668 | [] | no_license | cuker/python-money | ff203d42fce5c7fcb365474688d3d53902ba512d | 4a7d97208f39568f8a1472f635264aedaa321edf | refs/heads/master | 2021-01-23T12:38:17.451084 | 2011-06-07T23:10:06 | 2011-06-07T23:10:06 | 472,834 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,593 | py | from django.db import models
from django.conf import settings
import money
from decimal import Decimal
class CurrencyManager(models.Manager):
def active(self):
return self.all().filter(enabled=True)
def default(self):
return self.get(default=True)
get_default = default
def __getitem__(self, code):
try:
return self.get(code=code)
except self.model.DoesNotExist:
raise KeyError, 'currency "%s" was not found' % code
class Currency(models.Model, money.BaseCurrency):
name = models.CharField(max_length=60)
code = models.CharField(max_length=3, primary_key=True)
numeric = models.CharField(max_length=5)
enabled = models.BooleanField(default=True, db_index=True)
exchange_rate = models.DecimalField(max_digits=10, decimal_places=5, null=True, blank=True)
default = models.BooleanField(default=False, db_index=True)
if 'countries' in settings.INSTALLED_APPS:
from countries.models import Country
countries = models.ManyToManyField(Country)
objects = CurrencyManager()
def __unicode__(self):
return self.name
def save(self, *args, **kwargs):
if self.default and self.pk:
type(self).objects.exclude(pk=self.pk, default=False).update(default=False)
return models.Model.save(self, *args, **kwargs)
class Meta:
ordering = ['-default', 'code']
verbose_name_plural = "currencies"
ORIGINAL_CURRENCIES = money.currency_provider()
money.set_currency_provider(Currency.objects)
def _load_currencies():
for key, value in ORIGINAL_CURRENCIES.iteritems():
if key == 'XXX': continue
Currency.objects.get_or_create(name=value.name,
code=value.code,
numeric=value.numeric)
try:
Currency.objects.default()
except Currency.DoesNotExist:
new_default = Currency.objects['USD']
new_default.default = True
new_default.save()
def _load_exchange_rates():
import urllib
from_currency = Currency.objects.default()
kwargs = {'from':from_currency.code,}
url = 'http://quote.yahoo.com/d/quotes.csv?s=%(from)s%(to)s=X&f=l1&e=.csv'
for target in Currency.objects.filter(default=False):
kwargs['to'] = target.code
response = urllib.urlopen(url % kwargs).read()
try:
target.exchange_rate = Decimal(response.strip())
except ValueError:
pass
else:
target.save()
| [
"jasonk@cukerinteractive.com"
] | jasonk@cukerinteractive.com |
c7434530e7790a420c197b0d3fc5f0f35b2948c1 | da5849cc6ab950a716131fb8c2bee2267e627463 | /python/datascience/numpy/recipe_1d.py | b1bdead51f7598c22da45a0bcf6d1f5d3f3c8964 | [] | no_license | leisheyoufu/study_exercise | 5e3beba7763f2dfafa426932e21f110df1fd150e | db58097f4b542aea894b11feae31fb26006d5ebc | refs/heads/master | 2023-08-16T21:29:26.967795 | 2023-08-11T03:09:31 | 2023-08-11T03:09:31 | 13,537,939 | 3 | 1 | null | 2023-09-05T21:59:21 | 2013-10-13T11:13:29 | Jupyter Notebook | UTF-8 | Python | false | false | 550 | py | import numpy as np
def display_shape(a):
print
print
print "Number of elements in a = %d" % (a.size)
print "Number of dimensions in a =%d" % (a.ndim)
print "Rows and Columns in a ", a.shape
print
# Create a matrix with all elements
ones_matrix = np.ones((3,3)) # 1
display_shape(ones_matrix)
# Create a matrix with all elements
zeros_matrix = np.zeros((3,3))
display_shape(zeros_matrix)
identity_matrix = np.eye(N=3,M=3,k=0)
display_shape(identity_matrix)
identity_matrix = np.eye(N=3,k=1)
display_shape(identity_matrix) | [
"chenglch@cn.ibm.com"
] | chenglch@cn.ibm.com |
279022106044acde6f309ffab694367745451504 | 6f121febfccf83eee48aa84d4b169903122af332 | /run.py | e181f717d419cdc6c9aca2753795321765d944aa | [
"MIT"
] | permissive | bipabo1l/DarkNet_ChineseTrading | b2e7edc8a7fa4e0a5cd5336a90b119bedadf30f8 | 601acd28f15a8f4298757dfe8243ebeacf17e687 | refs/heads/master | 2020-04-17T08:27:20.847210 | 2019-01-16T06:47:22 | 2019-01-16T06:47:22 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 18,353 | py |
import json
import logging
import math
import os
import random
import re
import string
import sys
import time
from base64 import b64encode
from urllib.parse import urljoin
# import pudb;pu.db
import moment
import progressbar
import pymysql
import requests
from peewee import fn
from pyquery import PyQuery as jq
from retry import retry
from termcolor import colored
from io import BytesIO
from conf import Config
from model import (DarkNet_DataSale, DarkNet_IMGS, DarkNet_Notice,
DarkNet_Saler, DarkNet_User, DarkNetWebSites)
from task import telegram,logreport,telegram_withpic
TYPES = 'ChineseTradingNetwork'
logging.basicConfig(
format="[%(asctime)s] >>> %(levelname)s %(name)s: %(message)s", level=logging.INFO)
DefaultLIST = [
('deepmix3m7iv2vcz.onion', False),
('deepmix2j3cv4bds.onion', False),
('deepmix2z2ayzi46.onion', False),
('deepmix7j72q7kvz.onion', False),
('bmp3qqimv55xdznb.onion', True),
]
def FixNums(data, to=9999999, error=-1):
"""
专治超量
"""
try:
nums = int(data)
return nums if nums < to else to
except Exception as e:
return error
class DarkNet_ChineseTradingNetwork(object):
def __init__(self):
self.loger = logging.getLogger(f'DarkNet_{TYPES}')
self.info = lambda txt: self.loger.info(colored(txt, 'blue'))
self.report = lambda txt: self.loger.info(colored(txt, 'green'))
self.warn = lambda txt: self.loger.info(colored(txt, 'yellow'))
self.error = lambda txt: self.loger.info(colored(txt, 'red'))
self.session = requests.Session()
self.session.headers = {
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8",
"Accept-Encoding": "gzip, deflate",
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
"Cache-Control": "max-age=0",
"Connection": "keep-alive",
"Referer": "http://bmp3qqimv55xdznb.onion/index.php",
"Upgrade-Insecure-Requests": "1",
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36"
}
self.proxy_url = 'socks5h://127.0.0.1:9150'
self.session.proxies = {
'https': self.proxy_url,
'http': self.proxy_url
}
self.usemaster = True
self.master = None
self.sid = ''
self.justupdate = False
self.noticerange = 0
self.rootpath = 'datas'
self.screenpath = 'screen_shot'
list(map(self.InitPath, [self.rootpath, self.screenpath]))
def InitAdd(self, domainLIST):
for item in domainLIST:
if not DarkNetWebSites.select().where(DarkNetWebSites.domain == item[0]):
Model = DarkNetWebSites()
Model.domain = item[0]
Model.ismaster = item[1]
Model.alive = True
Model.target = TYPES
Model.save()
@retry()
def FirstFetch(self):
targets = DarkNetWebSites.select().where(
DarkNetWebSites.ismaster == self.usemaster)
if not targets:
return
target = targets[0]
try:
self.warn(f'[{target.domain}]Getting PHPSESSID')
resp = self.session.get(f'http://{target.domain}')
target.ismaster = True
target.title = jq(resp.text)('title').text()
self.usemaster = True
self.master = target
self.domain = target.domain
user = DarkNet_User.select().where(DarkNet_User.useful ==
True).order_by(fn.Rand()).limit(1)
if not bool(user):
self.Reg()
else:
self.usr = user[0].user
self.pwd = user[0].pwd
if random.choice([1, 0]): # 佛系注册堆积账号池
self.Reg()
return True
except KeyboardInterrupt:
pass
except requests.Timeout:
target.alive = False
target.ismaster = False
self.usemaster = False
except Exception as e:
raise
finally:
target.save()
@retry(delay=2,tries=20)
def Reg(self):
self.warn('Start Regging')
headers = {
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8",
"Accept-Encoding": "gzip, deflate",
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"Content-Type": "application/x-www-form-urlencoded",
"Origin": f"http://{self.domain}",
"Pragma": "no-cache",
"Referer": f"http://{self.domain}/ucp.php?mode=register&sid={self.sid}",
"Upgrade-Insecure-Requests": "1",
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36"
}
step1resp = self.session.get(
f"http://{self.domain}/ucp.php?mode=register").text
step1 = jq(step1resp)
self.sid = re.findall('sid=(.*?)"', step1resp)[0]
token = step1('input[name="form_token"]').attr('value')
creation_time = step1('input[name="creation_time"]').attr('value')
self.info(f"Get Token: {token} Create_time: {creation_time}")
url = f"http://{self.domain}/ucp.php?mode=register&sid={self.sid}"
step2resp = self.session.post(url, data={
"agreed": "===好的,我已明白,请跳转到下一页继续注册====",
"change_lang": "",
"creation_time": creation_time,
"form_token": token
}, headers=headers)
self.SaveError('step2.html', step2resp)
step2 = jq(step2resp.text)
token = step2('input[name="form_token"]').attr('value')
creation_time = step2('input[name="creation_time"]').attr('value')
qa_answer = re.findall('请在右边框中输入： (.*?)：</label>',step2resp.text)[0]
self.report(f'Got answer: {qa_answer}')
qa_confirm_id = step2('#qa_confirm_id').attr('value')
self.usr = self.RandomKey()
self.pwd = self.RandomKey()
self.info(f'set Usr: {self.usr} ,Pwd: {self.pwd}')
data = {
"username": self.usr,
"new_password": self.pwd,
"password_confirm": self.pwd,
"email": "xxxx@xxxx.xxx",
"lang": "zh_cmn_hans",
"tz_date": "UTC+08:00+-+Asia/Brunei+-+" + moment.now().format("DD+MM月+YYYY,+HH:mm"),
"tz": "Asia/Hong_Kong",
"agreed": "true",
"change_lang": "0",
"qa_answer":qa_answer,
"qa_confirm_id":qa_confirm_id,
"submit": " 用户名与密码已填好,+点此提交 ",
"creation_time": creation_time,
"form_token": token
}
resp = self.session.post(url, data=data, headers=headers)
try:
assert '感谢注册' in resp.text
self.report('Reg success！')
DarkNet_User.create(**{
'user': self.usr,
'pwd': self.pwd
})
except AssertionError:
self.error(jq(resp.text).text())
self.SaveError('reg.html', resp)
@retry(delay=2,tries=20)
def Login(self):
"""
### 再次尝试
1.因为网络问题重试
### 重新注册
2.因为账户被封重试
3.因为账户认证错误重试
"""
self.warn('Login...')
url = f'http://{self.domain}/ucp.php?mode=login'
data = {
"username": self.usr,
"password": self.pwd,
"login": "登录",
"redirect": f"./index.php&sid={self.sid}"
}
resp = self.session.post(url, data=data, verify=False, timeout=120)
self.sid = ''.join(re.findall("sid=(.*?)'", resp.text)[:1])
self.info(f"SID: {self.sid}")
if self.usr not in resp.text:
self.error('Auth faild')
self.SaveError('Autherror.html', resp)
if "已被封禁" in resp.text:
DarkNet_User.update({
"useful": False
}).where(DarkNet_User.user == self.usr).execute()
self.Reg()
raise ValueError
else:
self.report('Auth Success')
self.types = {item('.index_list_title').attr('href').split('=')[1].split('&')[0]: item('tr:nth-child(1) > td').text(
).split()[0] for item in jq(resp.text)('.ad_table_b').items()}
self.report(self.types)
def SaveError(self, filename, resp):
fullfilepath = f"{self.rootpath}/{filename}"
self.error(f"Html Log Saved to {fullfilepath}")
with open(fullfilepath, 'w') as f:
f.write(resp.text)
@retry()
def GetTypeDatas(self, qeaid, name, page=1):
url = f"http://{self.domain}/pay/user_area.php?page_y1={page}&q_u_id=0&m_order=&q_ea_id={qeaid}&sid={self.sid}#page_y1"
self.warn(url)
resp = self.session.get(url)
resp.encoding = "utf8"
hasres = False
try:
self.CheckIfNeedLogin(resp)
self.SaveError(f'{qeaid}_{name}_{page}.html', resp)
self.info(len(resp.text))
jqdata = jq(resp.text)
for item in jqdata('table.m_area_a tr').items():
detailPath = item('div.length_400>a').attr('href')
if detailPath:
detailsURL = urljoin(resp.url, detailPath)
self.GetDetails(detailsURL, {
'lines': FixNums(item('td:nth-child(7)').text().replace('天', '')),
'hot': FixNums(item('td:nth-child(8)').text()),
'title': item('td:nth-child(5)').text(),
'area': item('td:nth-child(3)').text()
})
hasres = True
if page == 1:
maxpageStr = ''.join(
jqdata('.page_b1:nth-last-child(1)').text().split())
return FixNums(maxpageStr, to=1, error=1) if maxpageStr and not self.justupdate else 1
if hasres:
return True
except Exception as e:
self.error(f"GetTypeDatas: {e}")
self.SaveError('GetTypeDatas.html', resp)
raise
def CheckIfNeedLogin(self, resp, passed=False, needraise=True):
if passed or "缓存已经过期" in resp.text:
"""
登录超时重新登录
"""
if self.FirstFetch():
self.Login()
elif "您必须注册并登录才能浏览这个版面" in resp.text:
"""
账户遭到封锁重新注册
"""
self.Reg()
elif "您的回答不正确" in resp.text:
time.sleep(20)
self.Reg()
def NewNet(self):
pass
# @retry((requests.exceptions.ConnectionError, ValueError))
@retry((requests.exceptions.ConnectionError))
def GetDetails(self, url, muti):
resp = self.session.get(url)
resp.encoding = "utf8"
self.CheckIfNeedLogin(resp)
jqdata = jq(resp.text)
jqdetail = jqdata('.v_table_1')
jqperson = jqdata('.v_table_2')
try:
uid = FixNums(jqperson('tr:nth-child(5) > td:nth-child(2)').text())
sid = FixNums(jqdetail(
'tr:nth-child(3) > td:nth-child(2)').text())
details = DarkNet_DataSale.select().where((DarkNet_DataSale.sid == sid))
person = DarkNet_Saler.select().where((DarkNet_Saler.uid == uid))
notice = DarkNet_Notice.select().where((DarkNet_Notice.sid == sid))
img = DarkNet_IMGS.select().where((DarkNet_IMGS.sid == sid))
personDatas = {
"salenums": FixNums(jqperson('tr:nth-child(3) > td:nth-child(4)').text()),
"totalsales": float(jqperson('tr:nth-child(5) > td:nth-child(4)').text()),
"totalbuys": float(jqperson('tr:nth-child(7) > td:nth-child(4)').text())
}
username = jqperson('tr:nth-child(3) > td:nth-child(2)').text()
if not person:
personDatas.update({
"uid": uid,
"user": username,
"regtime": moment.date(jqperson('tr:nth-child(7) > td:nth-child(2)').text()).format('YYYY-MM-DD'),
})
person = DarkNet_Saler.create(**personDatas)
else:
DarkNet_Saler.update(personDatas).where(
(DarkNet_Saler.uid == uid)).execute()
person = person[0].uid
if not notice:
notice = DarkNet_Notice.create(**{"sid": sid})
else:
notice = notice[0].sid
detailImages = None
detailContent = ' '.join(jqdata('.postbody .content').text().split())
if not img:
urls = [_.attr('src') for _ in jqdata('.postbody img').items()]
img = DarkNet_IMGS.create(**{
"sid": sid,
"img": urls,
"detail": detailContent
})
detailImages = self.SavePics(urls, sid)
else:
img = img[0].sid
currentYear = moment.now().year
soldNum = FixNums(
jqdetail('tr:nth-child(7) > td:nth-child(4)').text(), to=99999)
toCurrentYearDateTime = moment.date(
f"{currentYear} " + jqdetail('tr:nth-child(3) > td:nth-child(6)').text())
RealUpTimeJQ = jqdata('.author')
RealUpTimeJQ.remove('a')
RealUpTimeJQ.remove('span')
RealUpTime = moment.date(
RealUpTimeJQ.text().replace('年', '').replace('月', '').replace('日', ''))
RealUpTime = RealUpTime if RealUpTime._date else toCurrentYearDateTime
detailsDatas = {
"lasttime": moment.date(f"{currentYear} "+jqdetail('tr:nth-child(7) > td:nth-child(6)').text()).format('YYYY-MM-DD HH:mm:ss'),
"priceBTC": float(jqdetail('tr:nth-child(3) > td:nth-child(4) > span').text()),
"priceUSDT": float(jqdetail('tr:nth-child(5) > td:nth-child(4)').text().split()[0]),
"lines": muti['lines'],
"uptime": RealUpTime.format('YYYY-MM-DD HH:mm:ss'),
"hot": muti['hot'],
"types": jqdetail('tr:nth-child(5) > td:nth-child(2)').text(),
"status": jqdetail('tr:nth-child(7) > td:nth-child(2)').text(),
"oversell": jqdetail('tr:nth-child(9) > td:nth-child(2)').text(),
"sold": soldNum
}
if not details:
detailsDatas.update({
"sid": sid,
"user": person,
"area": muti['area'],
"title": muti['title'],
"detailurl": url,
"img": img,
"notice": notice
})
details = DarkNet_DataSale.create(**detailsDatas)
self.MakeMsg(details,detailContent,detailImages, sid,username)
else:
self.warn(f'-{RealUpTime}- {muti["title"]}' )
DarkNet_DataSale.update(detailsDatas).where(
(DarkNet_DataSale.sid == sid)).execute()
except Exception as e:
self.error(f"GetDetails {e}")
self.SaveError("error_264.html", resp)
raise
def MakeMsg(self, details, content,imgs ,sid,username):
shortmsg = f'[{details.uptime}] {details.title}'
self.report(shortmsg)
msg = f'{details.uptime}\n🔥{details.title}\n\nAuthor: {username}\nPrice: ${details.priceUSDT}\nSource: {details.detailurl}\n\n\n${content}\n'
msg = msg if len(msg)<1000 else msg[:997] + '...'
if (details.area in Config.filterArea and moment.date(details.uptime) > moment.now().replace(hours=0, minutes=0, seconds=0).add(days=self.noticerange)) or Config.sendForTest:
if not imgs:
telegram.delay(msg, sid, Config.darknetchannelID)
else:
telegram_withpic(imgs[0],msg,sid,Config.darknetchannelID)
@staticmethod
def RandomKey(length=20):
return ''.join((random.choice(random.choice((string.ascii_uppercase, string.ascii_lowercase, ''.join(map(str, range(0, 9)))))) for i in range(1, length)))
@staticmethod
def InitPath(root):
if not os.path.exists(root):
os.makedirs(root)
def GetPicBase64(self, link):
return link if 'http' not in link else bytes.decode(b64encode(self.GetPic(link)))
@retry()
def GetPic(self, link):
return self.session.get(link).content
def SavePics(self, urls, sid):
imageBox = []
for index, url in enumerate(urls):
url = url if 'http' in url else urljoin(f'http://{self.domain}',url)
self.info(f'---fetch PIC[{index}]:{url}')
with open(f'{self.screenpath}/{sid}_{index}.png', 'wb') as imgfile:
singelPIC = self.GetPic(url)
imgfile.write(singelPIC)
imageBox.append(BytesIO(singelPIC))
return imageBox
def Run(self):
self.InitAdd(DefaultLIST)
while True:
self.CheckIfNeedLogin(None, True, False)
for qeaid, name in self.types.items():
maxpage = self.GetTypeDatas(qeaid, name)
self.info(f"MaxPage: {maxpage}")
for page in range(1, maxpage):
if not self.GetTypeDatas(qeaid, name, page):
break
if __name__ == "__main__":
while True:
try:
DarkNet_ChineseTradingNetwork().Run()
except KeyboardInterrupt:
break
except Exception as e:
logreport.delay(str(e))
time.sleep(10*60)
| [
"aoii103@126.com"
] | aoii103@126.com |
ef852b1ea95ab6b607b7111d6e352702e95e413f | 28def9c6ad5053dcd8d9ea81ef04c488bf413bb4 | /untwisted/exceptions.py | 8e555e4c948e52ef1c527a2d6cf9854042e31867 | [
"MIT"
] | permissive | kgisl/untwisted | 2b6ebd5a3a88880d785c34186444831248119935 | b1277d4d5ad0982d4bc307ed6cdbd7923b0a3305 | refs/heads/master | 2021-01-01T06:01:33.514588 | 2017-07-14T22:31:31 | 2017-07-14T22:31:31 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 927 | py | class Stop(Exception):
"""
This exception is used to avoid remaining handles being
processed for a given event.
from untwisted.dispatcher import Dispatcher, Stop
def handle0(dispatcher):
raise Stop
def handle1(dispatcher):
print 'it will not be processed!'
dispatcher = Dispatcher()
dispatcher.add_map('alpha', handle0)
dispatcher.add_map('alpha', handle1)
dispatcher.drive('alpha')
"""
pass
class Erase(Exception):
"""
When this exception is thrown from a handle it avoids such a handle
being processed again upon its event.
from untwisted.dispatcher import Dispatcher, Erase
def handle(dispatcher):
print 'It will be called just once!'
raise Erase
dispatcher = Dispatcher()
dispatcher.add_map('alpha', handle)
dispatcher.drive('alpha')
dispatcher.drive('alpha')
"""
pass
| [
"ioliveira.id.uff.br"
] | ioliveira.id.uff.br |
ca264bba01fbb8049800dd66a1b42075294bab3f | e23a4f57ce5474d468258e5e63b9e23fb6011188 | /140_gui/pyqt_pyside/examples/PyQt_PySide_book/003_Placing several components in the box/003_Alignment of form components/074_WrapAllRows.py | a93093d67a978e4b8b6ed215b3a1f0ed61d86509 | [] | no_license | syurskyi/Python_Topics | 52851ecce000cb751a3b986408efe32f0b4c0835 | be331826b490b73f0a176e6abed86ef68ff2dd2b | refs/heads/master | 2023-06-08T19:29:16.214395 | 2023-05-29T17:09:11 | 2023-05-29T17:09:11 | 220,583,118 | 3 | 2 | null | 2023-02-16T03:08:10 | 2019-11-09T02:58:47 | Python | UTF-8 | Python | false | false | 698 | py | # -*- coding: utf-8 -*-
from PyQt5 import QtWidgets
import sys
app = QtWidgets.QApplication(sys.argv)
window = QtWidgets.QWidget()
window.setWindowTitle("WrapAllRows")
window.resize(300, 150)
lineEdit = QtWidgets.QLineEdit()
textEdit = QtWidgets.QTextEdit()
button1 = QtWidgets.QPushButton("О&тправить")
button2 = QtWidgets.QPushButton("О&чистить")
hbox = QtWidgets.QHBoxLayout()
hbox.addWidget(button1)
hbox.addWidget(button2)
form = QtWidgets.QFormLayout()
form.setRowWrapPolicy(QtWidgets.QFormLayout.WrapAllRows)
form.addRow("&Название:", lineEdit)
form.addRow("&Описание:", textEdit)
form.addRow(hbox)
window.setLayout(form)
window.show()
sys.exit(app.exec_()) | [
"sergejyurskyj@yahoo.com"
] | sergejyurskyj@yahoo.com |
a9ae94d786b2e6b0efbde9f52d2277e59f1e209c | 53fab060fa262e5d5026e0807d93c75fb81e67b9 | /backup/user_143/ch58_2020_04_27_13_12_10_083214.py | 2b966cf1c6ee61808ba0af8eb5675ef066d87bbe | [] | no_license | gabriellaec/desoft-analise-exercicios | b77c6999424c5ce7e44086a12589a0ad43d6adca | 01940ab0897aa6005764fc220b900e4d6161d36b | refs/heads/main | 2023-01-31T17:19:42.050628 | 2020-12-16T05:21:31 | 2020-12-16T05:21:31 | 306,735,108 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 101 | py | def conta_a(p):
a=0
for i in p:
if i=='a':
a+=1
return a
| [
"you@example.com"
] | you@example.com |
39876d9191216f5d127a8b23705a2a7e08864d52 | a055bcba66f9ca8acd87042d3a594296f7ccb610 | /images/views.py | a9d154aa2c706fc83ee84bd990ef1c6696ebf6c9 | [] | no_license | shineforever/bookmarks | 7c3a841159435d85d72003b887759aa063c52253 | 100fceff7f5ff27eb048008dbff9ddbcd6364f97 | refs/heads/master | 2021-01-20T20:44:44.891324 | 2016-07-27T08:31:54 | 2016-07-27T08:31:54 | 62,046,089 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 794 | py | from django.shortcuts import render, redirect
from django.contrib.auth.decorators import login_required
from django.contrib import messages
from .forms import ImageCreateForm
# Create your views here.
@login_required
def image_create(request):
if request.method == 'POST':
form = ImageCreateForm(data=request.POST)
if form.is_valid():
cd = form.cleaned_data
new_item = form.save(commit=False)
messages.success(request,'Image added successfully')
return redirect(new_item.get_absolute_url())
else:
#GET
form = ImageCreateForm(data=request.GET)
return render(request,
'images/image/create.html',
{'section': 'images',
'form': form})
| [
"root@localhost.localdomain"
] | root@localhost.localdomain |
842ac23abab226df3247cd683085b0e3c9a8dcb4 | 41fd80f9ccc72a17c2db16b7019312a87d3181e8 | /zhang_local/pdep/network2859_1.py | 52605c7a53131593eb37a575d3ee5d83b2061bf0 | [] | no_license | aberdeendinius/n-heptane | 1510e6704d87283043357aec36317fdb4a2a0c34 | 1806622607f74495477ef3fd772908d94cff04d9 | refs/heads/master | 2020-05-26T02:06:49.084015 | 2019-07-01T15:12:44 | 2019-07-01T15:12:44 | 188,069,618 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 55,507 | py | species(
label = '[CH2][C]([CH2])OC([CH2])O(10182)',
structure = SMILES('[CH2][C]([CH2])OC([CH2])O'),
E0 = (305.378,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3020,3040,3060,3080,3100,415,440,465,780,815,850,1435,1455,1475,900,1000,1100,360,370,350,3615,1277.5,1000,1380,1390,370,380,2900,435,200,800,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 5,
opticalIsomers = 1,
molecularWeight = (100.116,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.631048,0.117465,-0.000215862,1.98152e-07,-6.80516e-11,36880.4,32.0572], Tmin=(100,'K'), Tmax=(887.361,'K')), NASAPolynomial(coeffs=[9.39138,0.0361813,-1.74256e-05,3.21551e-09,-2.12709e-13,36523.2,-7.08786], Tmin=(887.361,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(305.378,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(332.579,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(CJC(C)OC) + radical(CJC(C)OC) + radical(CJCO) + radical(C2CsJOCs)"""),
)
species(
label = 'C=CO(576)',
structure = SMILES('C=CO'),
E0 = (-166.643,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,3615,1277.5,1000,3010,987.5,1337.5,450,1655],'cm^-1')),
HinderedRotor(inertia=(1.24798,'amu*angstrom^2'), symmetry=1, barrier=(28.6936,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (44.0526,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3625.11,'J/mol'), sigma=(3.97,'angstroms'), dipoleMoment=(0,'De'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=2.0, comment="""NOx2018"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.92544,0.00836062,5.0344e-05,-8.45232e-08,3.72335e-11,-19989.5,9.0776], Tmin=(100,'K'), Tmax=(898.452,'K')), NASAPolynomial(coeffs=[15.1116,-0.00538397,5.65903e-06,-1.18193e-09,7.91212e-14,-23814.2,-57.5076], Tmin=(898.452,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(-166.643,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(153.818,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-(Cds-Cd)H) + group(Cds-CdsOsH) + group(Cds-CdsHH)"""),
)
species(
label = '[CH2]C(=C)[O](4273)',
structure = SMILES('[CH2]C(=C)[O]'),
E0 = (88.2866,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,350,440,435,1725,3000,3100,440,815,1455,1000,510.595],'cm^-1')),
HinderedRotor(inertia=(0.0480287,'amu*angstrom^2'), symmetry=1, barrier=(8.88265,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (56.0633,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3365.98,'J/mol'), sigma=(5.64088,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=525.76 K, Pc=42.55 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.6374,0.0235792,5.32605e-07,-2.30624e-08,1.26355e-11,10673.5,14.3058], Tmin=(100,'K'), Tmax=(894.06,'K')), NASAPolynomial(coeffs=[10.3562,0.00670937,-7.99446e-07,2.86693e-11,-3.46262e-16,8587.33,-26.0166], Tmin=(894.06,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(88.2866,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(178.761,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-(Cds-Cd)H) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsOs) + group(Cds-CdsHH) + radical(C=C(C)OJ) + radical(C=C(O)CJ)"""),
)
species(
label = 'H(8)',
structure = SMILES('[H]'),
E0 = (211.805,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (1.00794,'amu'),
collisionModel = TransportData(shapeIndex=0, epsilon=(1205.6,'J/mol'), sigma=(2.05,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.5,9.24385e-15,-1.3678e-17,6.66185e-21,-1.00107e-24,25474.2,-0.444973], Tmin=(100,'K'), Tmax=(3459.6,'K')), NASAPolynomial(coeffs=[2.5,9.20456e-12,-3.58608e-15,6.15199e-19,-3.92042e-23,25474.2,-0.444973], Tmin=(3459.6,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(211.805,'kJ/mol'), Cp0=(20.7862,'J/(mol*K)'), CpInf=(20.7862,'J/(mol*K)'), label="""H""", comment="""Thermo library: primaryThermoLibrary"""),
)
species(
label = '[CH2][C](O)OC([CH2])=C(10629)',
structure = SMILES('[CH2][C](O)OC([CH2])=C'),
E0 = (148.223,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3033.33,3066.67,3100,415,465,780,850,1435,1475,900,1100,360,370,350,2950,3100,1380,975,1025,1650,3615,1277.5,1000,350,440,435,1725,200,800,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 4,
opticalIsomers = 1,
molecularWeight = (99.1079,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.175105,0.0809418,-8.79015e-05,4.65748e-08,-9.4455e-12,17986.7,30.8456], Tmin=(100,'K'), Tmax=(1258.71,'K')), NASAPolynomial(coeffs=[20.7759,0.0122364,-3.49197e-06,5.2595e-10,-3.29306e-14,12880.8,-74.3825], Tmin=(1258.71,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(148.223,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(311.793,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-Cs(Cds-Cd)) + group(O2s-CsH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsOs) + group(Cds-CdsHH) + radical(CJCO) + radical(Cs_P) + radical(C=C(O)CJ)"""),
)
species(
label = '[CH2][CH]O(578)',
structure = SMILES('[CH2][CH]O'),
E0 = (135.316,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3025,407.5,1350,352.5,3615,1277.5,1000,3000,3100,440,815,1455,1000],'cm^-1')),
HinderedRotor(inertia=(0.0943891,'amu*angstrom^2'), symmetry=1, barrier=(7.36374,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.0943883,'amu*angstrom^2'), symmetry=1, barrier=(7.36374,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (44.0526,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.67796,0.0299043,-3.92791e-05,2.86662e-08,-8.27893e-12,16321.6,12.7466], Tmin=(100,'K'), Tmax=(918.072,'K')), NASAPolynomial(coeffs=[6.82026,0.00999271,-3.7014e-06,6.19844e-10,-3.95112e-14,15639.5,-6.45576], Tmin=(918.072,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(135.316,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(149.66,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + radical(CCsJOH) + radical(CJCO)"""),
)
species(
label = 'OH(D)(132)',
structure = SMILES('[OH]'),
E0 = (28.3945,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3668.68],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (17.0073,'amu'),
collisionModel = TransportData(shapeIndex=1, epsilon=(665.16,'J/mol'), sigma=(2.75,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.51457,2.92814e-05,-5.32177e-07,1.01951e-09,-3.85951e-13,3414.25,2.10435], Tmin=(100,'K'), Tmax=(1145.75,'K')), NASAPolynomial(coeffs=[3.07194,0.000604011,-1.39759e-08,-2.13452e-11,2.4807e-15,3579.39,4.57799], Tmin=(1145.75,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(28.3945,'kJ/mol'), Cp0=(29.1007,'J/(mol*K)'), CpInf=(37.4151,'J/(mol*K)'), label="""OH(D)""", comment="""Thermo library: primaryThermoLibrary"""),
)
species(
label = '[CH2][CH]OC([CH2])=C(6355)',
structure = SMILES('[CH2][CH]OC([CH2])=C'),
E0 = (337.135,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3025,407.5,1350,352.5,2950,3100,1380,975,1025,1650,350,440,435,1725,3000,3033.33,3066.67,3100,415,465,780,850,1435,1475,900,1100,409.162,409.281,409.327],'cm^-1')),
HinderedRotor(inertia=(0.135088,'amu*angstrom^2'), symmetry=1, barrier=(16.0798,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.135355,'amu*angstrom^2'), symmetry=1, barrier=(16.0799,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.13554,'amu*angstrom^2'), symmetry=1, barrier=(16.0797,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.135337,'amu*angstrom^2'), symmetry=1, barrier=(16.0821,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 4,
opticalIsomers = 1,
molecularWeight = (83.1085,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.0881854,0.0768561,-8.49605e-05,4.51228e-08,-9.02004e-12,40706.3,26.1587], Tmin=(100,'K'), Tmax=(1359.75,'K')), NASAPolynomial(coeffs=[20.7136,0.00828131,-1.16948e-06,4.87585e-11,1.21001e-15,35731.7,-78.0811], Tmin=(1359.75,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(337.135,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(291.007,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-Cs(Cds-Cd)) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsOs) + group(Cds-CdsHH) + radical(CJCO) + radical(C=C(O)CJ) + radical(CCsJOC(O))"""),
)
species(
label = '[CH2][C]([CH2])O[C](C)O(10540)',
structure = SMILES('[CH2][C]([CH2])O[C](C)O'),
E0 = (299.035,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (100.116,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.432911,0.112911,-0.000206239,1.90876e-07,-6.6208e-11,36110.6,31.4001], Tmin=(100,'K'), Tmax=(882.945,'K')), NASAPolynomial(coeffs=[8.3185,0.0377577,-1.82436e-05,3.38518e-09,-2.25264e-13,35949.2,-1.89333], Tmin=(882.945,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(299.035,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(332.579,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(CJC(C)OC) + radical(CJC(C)OC) + radical(Cs_P) + radical(C2CsJOCs)"""),
)
species(
label = '[CH2][C](O)OC([CH2])[CH2](2365)',
structure = SMILES('[CH2][C](O)OC([CH2])[CH2]'),
E0 = (329.921,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3020,3040,3060,3080,3100,415,440,465,780,815,850,1435,1455,1475,900,1000,1100,360,370,350,3615,1277.5,1000,1380,1390,370,380,2900,435,200,800,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 5,
opticalIsomers = 1,
molecularWeight = (100.116,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.481098,0.10731,-0.000174912,1.47902e-07,-4.83859e-11,39833.3,32.2618], Tmin=(100,'K'), Tmax=(863.342,'K')), NASAPolynomial(coeffs=[12.6408,0.0306917,-1.43009e-05,2.65099e-09,-1.78055e-13,38157.2,-25.7005], Tmin=(863.342,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(329.921,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(332.579,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(CJC(C)OC) + radical(CJCO) + radical(Cs_P) + radical(CJC(C)OC)"""),
)
species(
label = '[CH2][C]([CH2])OC(C)[O](10156)',
structure = SMILES('[CH2][C]([CH2])OC(C)[O]'),
E0 = (319.494,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3033.33,3066.67,3100,415,465,780,850,1435,1475,900,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,1380,1390,370,380,2900,435,360,370,350,200,800,1200,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 5,
opticalIsomers = 1,
molecularWeight = (100.116,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3820.91,'J/mol'), sigma=(6.71547,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=596.82 K, Pc=28.63 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.117388,0.109555,-0.000208596,2.04527e-07,-7.43138e-11,38556.4,30.4394], Tmin=(100,'K'), Tmax=(874.844,'K')), NASAPolynomial(coeffs=[3.82012,0.0464929,-2.32113e-05,4.3827e-09,-2.9531e-13,39591.8,21.8247], Tmin=(874.844,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(319.494,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(336.736,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(CCOJ) + radical(C2CsJOCs) + radical(CJC(C)OC) + radical(CJC(C)OC)"""),
)
species(
label = '[CH2][C](C)O[C]([CH2])O(2362)',
structure = SMILES('[CH2][C](C)O[C]([CH2])O'),
E0 = (300.114,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (100.116,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.216275,0.105136,-0.000181812,1.64481e-07,-5.66789e-11,36235.4,32.1972], Tmin=(100,'K'), Tmax=(870.876,'K')), NASAPolynomial(coeffs=[8.82885,0.0366866,-1.75751e-05,3.28144e-09,-2.2055e-13,35680.2,-4.33222], Tmin=(870.876,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(300.114,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(332.579,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(CJCO) + radical(Cs_P) + radical(CJC(C)OC) + radical(C2CsJOCs)"""),
)
species(
label = '[CH2]C([CH2])OC([CH2])[O](2364)',
structure = SMILES('[CH2]C([CH2])OC([CH2])[O]'),
E0 = (350.379,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1380,1383.33,1386.67,1390,370,373.333,376.667,380,2800,3000,430,440,3000,3020,3040,3060,3080,3100,415,440,465,780,815,850,1435,1455,1475,900,1000,1100,200,800,1200,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 5,
opticalIsomers = 1,
molecularWeight = (100.116,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.167294,0.103981,-0.00017739,1.61771e-07,-5.66203e-11,42279.1,31.307], Tmin=(100,'K'), Tmax=(857.019,'K')), NASAPolynomial(coeffs=[8.13207,0.0394446,-1.92787e-05,3.65091e-09,-2.48299e-13,41804.1,-1.92426], Tmin=(857.019,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(350.379,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(336.736,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(CJCO) + radical(CJC(C)OC) + radical(CJC(C)OC) + radical(CCOJ)"""),
)
species(
label = '[CH2][C](C)OC([CH2])[O](2361)',
structure = SMILES('[CH2][C](C)OC([CH2])[O]'),
E0 = (320.573,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3033.33,3066.67,3100,415,465,780,850,1435,1475,900,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,1380,1390,370,380,2900,435,360,370,350,200,800,1200,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 5,
opticalIsomers = 1,
molecularWeight = (100.116,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.0975475,0.101805,-0.000184278,1.7832e-07,-6.48916e-11,38681.2,31.2424], Tmin=(100,'K'), Tmax=(864.238,'K')), NASAPolynomial(coeffs=[4.32432,0.0454322,-2.25487e-05,4.28034e-09,-2.9071e-13,39325.3,19.4204], Tmin=(864.238,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(320.573,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(336.736,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(CCOJ) + radical(CJC(C)OC) + radical(C2CsJOCs) + radical(CJCO)"""),
)
species(
label = '[CH2][C]([CH2])[O](10271)',
structure = SMILES('[CH2][C]([CH2])[O]'),
E0 = (537.173,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([360,370,350,3000,3033.33,3066.67,3100,415,465,780,850,1435,1475,900,1100,278.503],'cm^-1')),
HinderedRotor(inertia=(0.00215299,'amu*angstrom^2'), symmetry=1, barrier=(0.119627,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.0939305,'amu*angstrom^2'), symmetry=1, barrier=(5.13965,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 5,
opticalIsomers = 1,
molecularWeight = (56.0633,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.0694,0.0447257,-6.5608e-05,5.12452e-08,-1.57124e-11,64674.3,16.4544], Tmin=(100,'K'), Tmax=(870.707,'K')), NASAPolynomial(coeffs=[8.27065,0.0130302,-5.47972e-06,9.76923e-10,-6.45299e-14,63716,-11.9064], Tmin=(870.707,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(537.173,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(174.604,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(C2CsJOH) + radical(CJCO) + radical(CC(C)OJ) + radical(CJCO)"""),
)
species(
label = '[CH2][CH]O[C]([CH2])[CH2](6357)',
structure = SMILES('[CH2][CH]O[C]([CH2])[CH2]'),
E0 = (686.065,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([360,370,350,3000,3020,3040,3060,3080,3100,415,440,465,780,815,850,1435,1455,1475,900,1000,1100,3025,407.5,1350,352.5,200,800,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 6,
opticalIsomers = 1,
molecularWeight = (83.1085,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.311103,0.110171,-0.000209975,1.92186e-07,-6.49065e-11,82655.4,27.1958], Tmin=(100,'K'), Tmax=(908.543,'K')), NASAPolynomial(coeffs=[9.67625,0.028629,-1.33198e-05,2.36991e-09,-1.51156e-13,82391.2,-11.4958], Tmin=(908.543,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(686.065,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(286.849,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(Cs-CsCsOsH) + group(Cs-CsOsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(C2CsJOCs) + radical(CJCO) + radical(CCsJOCs) + radical(CJC(C)OC) + radical(CJC(C)OC)"""),
)
species(
label = '[CH2][C]([CH2])OC([CH2])[O](6725)',
structure = SMILES('[CH2][C]([CH2])OC([CH2])[O]'),
E0 = (531.083,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([360,370,350,1380,1390,370,380,2900,435,3000,3020,3040,3060,3080,3100,415,440,465,780,815,850,1435,1455,1475,900,1000,1100,200,800,1200,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 6,
opticalIsomers = 1,
molecularWeight = (99.1079,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.281517,0.114878,-0.000228766,2.24125e-07,-8.05882e-11,64009,31.6625], Tmin=(100,'K'), Tmax=(883.223,'K')), NASAPolynomial(coeffs=[4.71313,0.042576,-2.15976e-05,4.06387e-09,-2.71595e-13,65064.6,19.1565], Tmin=(883.223,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(531.083,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(311.793,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(CCOJ) + radical(CJC(C)OC) + radical(C2CsJOCs) + radical(CJCO) + radical(CJC(C)OC)"""),
)
species(
label = '[CH2][C]([CH2])O[C]([CH2])O(6726)',
structure = SMILES('[CH2][C]([CH2])O[C]([CH2])O'),
E0 = (510.624,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([360,363.333,366.667,370,300,400,3615,1277.5,1000,3000,3020,3040,3060,3080,3100,415,440,465,780,815,850,1435,1455,1475,900,1000,1100,200,800,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 6,
opticalIsomers = 1,
molecularWeight = (99.1079,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.59781,0.118246,-0.000226475,2.10599e-07,-7.25585e-11,61563.3,32.6258], Tmin=(100,'K'), Tmax=(892.559,'K')), NASAPolynomial(coeffs=[9.20222,0.0338565,-1.6639e-05,3.0685e-09,-2.01727e-13,61425.9,-4.50915], Tmin=(892.559,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(510.624,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(307.635,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(C2CsJOCs) + radical(Cs_P) + radical(CJC(C)OC) + radical(CJC(C)OC) + radical(CJCO)"""),
)
species(
label = '[CH2]C([CH2])OC(=C)O(10630)',
structure = SMILES('[CH2]C([CH2])OC(=C)O'),
E0 = (41.9533,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (100.116,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.818156,0.103006,-0.000135908,8.76844e-08,-2.16792e-11,5221.93,26.4745], Tmin=(100,'K'), Tmax=(1004.4,'K')), NASAPolynomial(coeffs=[21.1933,0.0153443,-4.98752e-06,7.84711e-10,-4.89397e-14,800.382,-79.814], Tmin=(1004.4,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(41.9533,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(336.736,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-Cs(Cds-Cd)) + group(O2s-(Cds-Cd)H) + group(Cs-CsCsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cds-CdsCsCs) + group(Cds-CdsHH) + radical(CJC(C)OC) + radical(CJC(C)OC)"""),
)
species(
label = '[CH2]C1([CH2])CC(O)O1(10183)',
structure = SMILES('[CH2]C1([CH2])CC(O)O1'),
E0 = (46.2826,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (100.116,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.185931,0.0827112,-9.28263e-05,5.42599e-08,-1.20095e-11,5725.34,26.3654], Tmin=(100,'K'), Tmax=(1272.92,'K')), NASAPolynomial(coeffs=[16.8053,0.0178867,-2.9667e-06,1.42727e-10,4.64307e-15,2325.78,-56.0701], Tmin=(1272.92,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(46.2826,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(345.051,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + group(O2s-CsH) + group(Cs-CsCsCsOs) + group(Cs-CsCsHH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + ring(Oxetane) + radical(CJC(C)OC) + radical(CJC(C)OC)"""),
)
species(
label = '[CH2]C([O])O(670)',
structure = SMILES('[CH2]C([O])O'),
E0 = (-19.5078,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,3615,1277.5,1000,1380,1390,370,380,2900,435,1935.17],'cm^-1')),
HinderedRotor(inertia=(0.200225,'amu*angstrom^2'), symmetry=1, barrier=(4.60358,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.200503,'amu*angstrom^2'), symmetry=1, barrier=(4.60997,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (60.052,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.66061,0.0330653,-4.66659e-05,4.29179e-08,-1.60973e-11,-2301.52,17.2788], Tmin=(100,'K'), Tmax=(804.622,'K')), NASAPolynomial(coeffs=[3.98157,0.0198211,-9.52741e-06,1.83298e-09,-1.27458e-13,-2297.94,12.5363], Tmin=(804.622,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(-19.5078,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(174.604,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsH) + group(O2s-CsH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + radical(CJCO) + radical(CCOJ)"""),
)
species(
label = '[CH2][C][CH2](10272)',
structure = SMILES('[CH2][C][CH2]'),
E0 = (738.184,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3033.33,3066.67,3100,415,465,780,850,1435,1475,900,1100,349.736],'cm^-1')),
HinderedRotor(inertia=(0.00138263,'amu*angstrom^2'), symmetry=1, barrier=(0.119627,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.0013768,'amu*angstrom^2'), symmetry=1, barrier=(0.119627,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 5,
opticalIsomers = 1,
molecularWeight = (40.0639,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.07912,0.0169806,-2.30739e-06,-7.22553e-09,3.53372e-12,88819,13.4505], Tmin=(100,'K'), Tmax=(1024.31,'K')), NASAPolynomial(coeffs=[6.32415,0.0113321,-4.3212e-06,7.79353e-10,-5.38459e-14,87785.8,-4.0815], Tmin=(1024.31,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(738.184,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(149.66,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(RCCJ) + radical(RCCJ) + radical(CCJ2_triplet)"""),
)
species(
label = 'CH2(T)(28)',
structure = SMILES('[CH2]'),
E0 = (381.37,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1066.91,2790.99,3622.37],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (14.0266,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(1197.29,'J/mol'), sigma=(3.8,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[4.01192,-0.000154979,3.26298e-06,-2.40422e-09,5.69497e-13,45867.7,0.5332], Tmin=(100,'K'), Tmax=(1104.58,'K')), NASAPolynomial(coeffs=[3.14983,0.00296674,-9.76056e-07,1.54115e-10,-9.50338e-15,46058.1,4.77808], Tmin=(1104.58,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(381.37,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(58.2013,'J/(mol*K)'), label="""CH2(T)""", comment="""Thermo library: primaryThermoLibrary"""),
)
species(
label = '[CH2][C]([CH2])O[CH]O(10505)',
structure = SMILES('[CH2][C]([CH2])O[CH]O'),
E0 = (328.571,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3025,407.5,1350,352.5,360,370,350,3615,1277.5,1000,3000,3033.33,3066.67,3100,415,465,780,850,1435,1475,900,1100,200,800,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 5,
opticalIsomers = 1,
molecularWeight = (86.0892,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.0858797,0.105515,-0.000206973,1.91202e-07,-6.47617e-11,39650.4,26.1779], Tmin=(100,'K'), Tmax=(911.375,'K')), NASAPolynomial(coeffs=[9.26195,0.0258236,-1.21771e-05,2.16061e-09,-1.36723e-13,39552.3,-9.24324], Tmin=(911.375,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(328.571,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(261.906,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-OsOsHH) + radical(C2CsJOCs) + radical(OCJO) + radical(CJC(C)OC) + radical(CJC(C)OC)"""),
)
species(
label = '[CH]C(O)O[C]([CH2])[CH2](10631)',
structure = SMILES('[CH]C(O)O[C]([CH2])[CH2]'),
E0 = (542.004,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3033.33,3066.67,3100,415,465,780,850,1435,1475,900,1100,360,370,350,3615,1277.5,1000,1380,1390,370,380,2900,435,200,800,1000,1200,1400,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 6,
opticalIsomers = 1,
molecularWeight = (99.1079,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.530184,0.115319,-0.000215576,1.98589e-07,-6.82281e-11,65336.3,32.0655], Tmin=(100,'K'), Tmax=(888.202,'K')), NASAPolynomial(coeffs=[9.4619,0.0339038,-1.65827e-05,3.06772e-09,-2.02786e-13,64997.7,-6.86968], Tmin=(888.202,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(542.004,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(307.635,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(CJC(C)OC) + radical(CJC(C)OC) + radical(CCJ2_triplet) + radical(C2CsJOCs)"""),
)
species(
label = '[CH][C]([CH2])OC([CH2])O(10632)',
structure = SMILES('[CH][C]([CH2])OC([CH2])O'),
E0 = (543.083,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3033.33,3066.67,3100,415,465,780,850,1435,1475,900,1100,360,370,350,3615,1277.5,1000,1380,1390,370,380,2900,435,200,800,1000,1200,1400,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 6,
opticalIsomers = 1,
molecularWeight = (99.1079,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.315358,0.107565,-0.000191221,1.72277e-07,-5.87284e-11,65461.2,32.8691], Tmin=(100,'K'), Tmax=(877.434,'K')), NASAPolynomial(coeffs=[9.98375,0.0328128,-1.59025e-05,2.96118e-09,-1.97838e-13,64724,-9.37305], Tmin=(877.434,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(543.083,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(307.635,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(O2s-CsCs) + longDistanceInteraction_noncyclic(OsCs-ST) + group(O2s-CsH) + group(Cs-CsCsOsH) + group(Cs-CsOsOsH) + group(Cs-CsHHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(C2CsJOCs) + radical(CJC(C)OC) + radical(CCJ2_triplet) + radical(CJCO)"""),
)
species(
label = 'N2',
structure = SMILES('N#N'),
E0 = (-8.64289,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (28.0135,'amu'),
collisionModel = TransportData(shapeIndex=1, epsilon=(810.913,'J/mol'), sigma=(3.621,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(1.76,'angstroms^3'), rotrelaxcollnum=4.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.53101,-0.000123661,-5.02999e-07,2.43531e-09,-1.40881e-12,-1046.98,2.96747], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[2.95258,0.0013969,-4.92632e-07,7.8601e-11,-4.60755e-15,-923.949,5.87189], Tmin=(1000,'K'), Tmax=(6000,'K'))], Tmin=(200,'K'), Tmax=(6000,'K'), E0=(-8.64289,'kJ/mol'), Cp0=(29.1007,'J/(mol*K)'), CpInf=(37.4151,'J/(mol*K)'), label="""N2""", comment="""Thermo library: primaryThermoLibrary"""),
)
species(
label = 'Ne',
structure = SMILES('[Ne]'),
E0 = (-6.19738,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (20.1797,'amu'),
collisionModel = TransportData(shapeIndex=0, epsilon=(1235.53,'J/mol'), sigma=(3.758e-10,'m'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with fixed Lennard Jones Parameters. This is the fallback method! Try improving transport databases!"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.5,0,0,0,0,-745.375,3.35532], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[2.5,0,0,0,0,-745.375,3.35532], Tmin=(1000,'K'), Tmax=(6000,'K'))], Tmin=(200,'K'), Tmax=(6000,'K'), E0=(-6.19738,'kJ/mol'), Cp0=(20.7862,'J/(mol*K)'), CpInf=(20.7862,'J/(mol*K)'), label="""Ne""", comment="""Thermo library: primaryThermoLibrary"""),
)
species(
label = 'He',
structure = SMILES('[He]'),
E0 = (-6.19738,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (4.0026,'amu'),
collisionModel = TransportData(shapeIndex=0, epsilon=(84.8076,'J/mol'), sigma=(2.576,'angstroms'), dipoleMoment=(0,'De'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""NOx2018"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.5,0,0,0,0,-745.375,0.928724], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[2.5,0,0,0,0,-745.375,0.928724], Tmin=(1000,'K'), Tmax=(6000,'K'))], Tmin=(200,'K'), Tmax=(6000,'K'), E0=(-6.19738,'kJ/mol'), Cp0=(20.7862,'J/(mol*K)'), CpInf=(20.7862,'J/(mol*K)'), label="""He""", comment="""Thermo library: primaryThermoLibrary"""),
)
species(
label = 'Ar',
structure = SMILES('[Ar]'),
E0 = (-6.19738,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (39.348,'amu'),
collisionModel = TransportData(shapeIndex=0, epsilon=(1134.93,'J/mol'), sigma=(3.33,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.5,0,0,0,0,-745.375,4.37967], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[2.5,0,0,0,0,-745.375,4.37967], Tmin=(1000,'K'), Tmax=(6000,'K'))], Tmin=(200,'K'), Tmax=(6000,'K'), E0=(-6.19738,'kJ/mol'), Cp0=(20.7862,'J/(mol*K)'), CpInf=(20.7862,'J/(mol*K)'), label="""Ar""", comment="""Thermo library: primaryThermoLibrary"""),
)
transitionState(
label = 'TS1',
E0 = (305.378,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS2',
E0 = (371.316,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS3',
E0 = (305.378,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS4',
E0 = (365.529,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS5',
E0 = (408.058,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS6',
E0 = (494.702,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS7',
E0 = (458.172,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS8',
E0 = (436.503,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS9',
E0 = (477.773,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS10',
E0 = (380.467,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS11',
E0 = (672.489,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS12',
E0 = (714.564,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS13',
E0 = (749.983,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS14',
E0 = (722.429,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS15',
E0 = (368.778,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS16',
E0 = (313.662,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS17',
E0 = (723.843,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS18',
E0 = (744.257,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS19',
E0 = (753.809,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS20',
E0 = (754.888,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
reaction(
label = 'reaction1',
reactants = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
products = ['C=CO(576)', '[CH2]C(=C)[O](4273)'],
transitionState = 'TS1',
kinetics = Arrhenius(A=(5e+12,'s^-1'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Exact match found for rate rule [RJJ]
Euclidian distance = 0
family: 1,4_Linear_birad_scission"""),
)
reaction(
label = 'reaction2',
reactants = ['H(8)', '[CH2][C](O)OC([CH2])=C(10629)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS2',
kinetics = Arrhenius(A=(170.395,'m^3/(mol*s)'), n=1.5621, Ea=(11.2886,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Cds_Cds;HJ] for rate rule [Cds-OsOs_Cds;HJ]
Euclidian distance = 1.0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction3',
reactants = ['[CH2][CH]O(578)', '[CH2]C(=C)[O](4273)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS3',
kinetics = Arrhenius(A=(1.81675,'m^3/(mol*s)'), n=2.00263, Ea=(81.7754,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [R_R;CJ] + [Od_R;YJ] for rate rule [Od_R;CJ]
Euclidian distance = 1.0
family: R_Addition_MultipleBond
Ea raised from 81.0 to 81.8 kJ/mol to match endothermicity of reaction."""),
)
reaction(
label = 'reaction4',
reactants = ['OH(D)(132)', '[CH2][CH]OC([CH2])=C(6355)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS4',
kinetics = Arrhenius(A=(931.236,'m^3/(mol*s)'), n=1.015, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Cds_Cds;OJ_pri] for rate rule [Cds-OsH_Cds;OJ_pri]
Euclidian distance = 1.0
family: R_Addition_MultipleBond
Ea raised from -7.3 to 0 kJ/mol."""),
)
reaction(
label = 'reaction5',
reactants = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
products = ['[CH2][C]([CH2])O[C](C)O(10540)'],
transitionState = 'TS5',
kinetics = Arrhenius(A=(5.265e-07,'s^-1'), n=5.639, Ea=(102.68,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R2H_S;C_rad_out_2H;Cs_H_out_NonDe] for rate rule [R2H_S;C_rad_out_2H;Cs_H_out_NDMustO]
Euclidian distance = 1.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction6',
reactants = ['[CH2][C](O)OC([CH2])[CH2](2365)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS6',
kinetics = Arrhenius(A=(2.9172e+08,'s^-1'), n=1.32036, Ea=(164.782,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [R3H_SS_O;Y_rad_out;XH_out]
Euclidian distance = 0
family: intra_H_migration"""),
)
reaction(
label = 'reaction7',
reactants = ['[CH2][C]([CH2])OC(C)[O](10156)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS7',
kinetics = Arrhenius(A=(62433.6,'s^-1'), n=2.54422, Ea=(138.678,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [R3H_SS;O_rad_out;Cs_H_out_2H] + [R3H_SS_Cs;Y_rad_out;Cs_H_out_2H] + [R3H_SS_Cs;O_rad_out;Cs_H_out] for rate rule [R3H_SS_Cs;O_rad_out;Cs_H_out_2H]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction8',
reactants = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
products = ['[CH2][C](C)O[C]([CH2])O(2362)'],
transitionState = 'TS8',
kinetics = Arrhenius(A=(869.832,'s^-1'), n=2.67444, Ea=(131.125,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4Hall;C_rad_out_2H;XH_out] for rate rule [R4HJ_1;C_rad_out_2H;XH_out]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction9',
reactants = ['[CH2]C([CH2])OC([CH2])[O](2364)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS9',
kinetics = Arrhenius(A=(1.75172e+06,'s^-1'), n=1.80068, Ea=(127.394,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [R4H_SSS;O_rad_out;XH_out]
Euclidian distance = 0
family: intra_H_migration"""),
)
reaction(
label = 'reaction10',
reactants = ['[CH2][C](C)OC([CH2])[O](2361)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS10',
kinetics = Arrhenius(A=(7.8e+08,'s^-1'), n=0.775, Ea=(59.894,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R5Hall;O_rad_out;Cs_H_out_2H] for rate rule [R5HJ_3;O_rad_out;Cs_H_out_2H]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction11',
reactants = ['[CH2][CH]O(578)', '[CH2][C]([CH2])[O](10271)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS11',
kinetics = Arrhenius(A=(1.9789e+07,'m^3/(mol*s)'), n=-0.126319, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;Y_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -15.6 to -15.6 kJ/mol.
Ea raised from -15.6 to 0 kJ/mol."""),
)
reaction(
label = 'reaction12',
reactants = ['OH(D)(132)', '[CH2][CH]O[C]([CH2])[CH2](6357)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS12',
kinetics = Arrhenius(A=(3.05166e+07,'m^3/(mol*s)'), n=0.045, Ea=(0.1046,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [O_pri_rad;Y_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction13',
reactants = ['H(8)', '[CH2][C]([CH2])OC([CH2])[O](6725)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS13',
kinetics = Arrhenius(A=(5.00518e+06,'m^3/(mol*s)'), n=0.282325, Ea=(7.09479,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [Y_rad;O_rad/NonDe] + [H_rad;O_sec_rad] for rate rule [H_rad;O_rad/NonDe]
Euclidian distance = 1.0
family: R_Recombination"""),
)
reaction(
label = 'reaction14',
reactants = ['H(8)', '[CH2][C]([CH2])O[C]([CH2])O(6726)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS14',
kinetics = Arrhenius(A=(4.34078e+06,'m^3/(mol*s)'), n=0.278577, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction15',
reactants = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
products = ['[CH2]C([CH2])OC(=C)O(10630)'],
transitionState = 'TS15',
kinetics = Arrhenius(A=(7.437e+08,'s^-1'), n=1.045, Ea=(63.4002,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [R3radExo;Y_rad;XH_Rrad]
Euclidian distance = 0
family: Intra_Disproportionation"""),
)
reaction(
label = 'reaction16',
reactants = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
products = ['[CH2]C1([CH2])CC(O)O1(10183)'],
transitionState = 'TS16',
kinetics = Arrhenius(A=(1.62e+12,'s^-1'), n=-0.305, Ea=(8.28432,'kJ/mol'), T0=(1,'K'), Tmin=(600,'K'), Tmax=(2000,'K'), comment="""Estimated using an average for rate rule [R4_SSS;Y_rad_out;Cpri_rad_out_2H]
Euclidian distance = 0
family: Birad_recombination"""),
)
reaction(
label = 'reaction17',
reactants = ['[CH2]C([O])O(670)', '[CH2][C][CH2](10272)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS17',
kinetics = Arrhenius(A=(43.5839,'m^3/(mol*s)'), n=1.88017, Ea=(5.1666,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""Estimated using an average for rate rule [O_rad/NonDe;Birad]
Euclidian distance = 0
family: Birad_R_Recombination"""),
)
reaction(
label = 'reaction18',
reactants = ['CH2(T)(28)', '[CH2][C]([CH2])O[CH]O(10505)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS18',
kinetics = Arrhenius(A=(1.14854e+06,'m^3/(mol*s)'), n=0.575199, Ea=(34.3157,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Y_rad;Birad] for rate rule [C_rad/H/O2;Birad]
Euclidian distance = 4.0
family: Birad_R_Recombination"""),
)
reaction(
label = 'reaction19',
reactants = ['H(8)', '[CH]C(O)O[C]([CH2])[CH2](10631)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS19',
kinetics = Arrhenius(A=(1e+07,'m^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [H_rad;Birad]
Euclidian distance = 0
family: Birad_R_Recombination"""),
)
reaction(
label = 'reaction20',
reactants = ['H(8)', '[CH][C]([CH2])OC([CH2])O(10632)'],
products = ['[CH2][C]([CH2])OC([CH2])O(10182)'],
transitionState = 'TS20',
kinetics = Arrhenius(A=(1e+07,'m^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [H_rad;Birad]
Euclidian distance = 0
family: Birad_R_Recombination"""),
)
network(
label = '2859',
isomers = [
'[CH2][C]([CH2])OC([CH2])O(10182)',
],
reactants = [
('C=CO(576)', '[CH2]C(=C)[O](4273)'),
],
bathGas = {
'N2': 0.25,
'Ne': 0.25,
'He': 0.25,
'Ar': 0.25,
},
)
pressureDependence(
label = '2859',
Tmin = (1200,'K'),
Tmax = (1500,'K'),
Tcount = 10,
Tlist = ([1201.48,1213.22,1236.21,1269.31,1310.55,1356.92,1404.16,1447.02,1479.84,1497.7],'K'),
Pmin = (1,'atm'),
Pmax = (10,'atm'),
Pcount = 10,
Plist = ([1.02771,1.14872,1.41959,1.89986,2.67608,3.83649,5.40396,7.23219,8.93758,9.98989],'bar'),
maximumGrainSize = (0.5,'kcal/mol'),
minimumGrainCount = 250,
method = 'modified strong collision',
interpolationModel = ('Chebyshev', 6, 4),
activeKRotor = True,
activeJRotor = True,
rmgmode = True,
)
| [
"dinius.ab@husky.neu.edu"
] | dinius.ab@husky.neu.edu |
7f91c02504e6d266ed0d2d3714c567ea0a2fd731 | 8acffb8c4ddca5bfef910e58d3faa0e4de83fce8 | /ml-flask/Lib/site-packages/gensim/nosy.py | 3e6340e85f0878212b450c7936cc786ab7e9c1d3 | [
"MIT"
] | permissive | YaminiHP/SimilitudeApp | 8cbde52caec3c19d5fa73508fc005f38f79b8418 | 005c59894d8788c97be16ec420c0a43aaec99b80 | refs/heads/master | 2023-06-27T00:03:00.404080 | 2021-07-25T17:51:27 | 2021-07-25T17:51:27 | 389,390,951 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 129 | py | version https://git-lfs.github.com/spec/v1
oid sha256:67fa7e813100e4a41be9728ee8a521d4a488ec12655fc2e06e5c2a302445df3b
size 1407
| [
"yamprakash130@gmail.com"
] | yamprakash130@gmail.com |
72223f10d913723ce44e84a0057fb20a83494203 | a31e7a01b0a7879ddbda7ba3a606ff4df718f0ef | /app/ingredients/apis/__init__.py | f83d6db436bc44756bd94b14c67da226d9d63650 | [] | no_license | smallbee3/Subway_Server | 27a477c81b830d2f264afb09c646d53d8096e5f4 | c0bebf3715663c7d29ffdc9e9ff878d226dd3496 | refs/heads/master | 2021-02-17T00:58:29.371121 | 2018-12-27T10:43:51 | 2018-12-27T10:43:51 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 163 | py | from .sandwich import *
from .bread import *
from .cheese import *
from .toasting import *
from .toppings import *
from .vegetables import *
from .sauces import *
| [
"smallbee3@gmail.com"
] | smallbee3@gmail.com |
947f2354c12c0c6018cf8ef04366e36ba0a30a3a | 681be3b4cfa00433fe4a926446ea627d6d1becc0 | /worldcup/python启动服务.py | 84345aafa4e486acb49d4cdf6dfd4ad059fa687e | [] | no_license | snailuncle/helloNode | 2141336c7f0489eb49d36f7af2131c22e39ea5f6 | 906a90809a674ccac039963701ff27992df37cba | refs/heads/master | 2020-03-25T13:29:29.198464 | 2018-09-03T01:12:21 | 2018-09-03T01:12:21 | 143,828,416 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 56 | py | python -m SimpleHTTPServer
python -m http.server 8000
| [
"1789500304@qq.com"
] | 1789500304@qq.com |
9d4b803130a5a9f854d3606a9baf73ec90a5b953 | 29c3595a4e1f8de9382650610aee5a13e2a135f6 | /venv/Lib/site-packages/autobahn/websocket/test/test_websocket_protocol.py | 9ce7ed7ac72bb166e8c7fa147c721468acb73115 | [
"MIT"
] | permissive | zoelesv/Smathchat | 1515fa56fbb0ad47e1859f6bf931b772446ea261 | 5cee0a8c4180a3108538b4e4ce945a18726595a6 | refs/heads/main | 2023-08-04T14:47:21.185149 | 2023-08-02T15:53:20 | 2023-08-02T15:53:20 | 364,627,392 | 9 | 1 | MIT | 2023-08-02T15:53:21 | 2021-05-05T15:42:47 | Python | UTF-8 | Python | false | false | 9,475 | py | ###############################################################################
#
# The MIT License (MIT)
#
# Copyright (c) Crossbar.io Technologies GmbH
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
###############################################################################
import os
import unittest
from hashlib import sha1
from base64 import b64encode
from unittest.mock import Mock
from autobahn.websocket.protocol import WebSocketServerProtocol
from autobahn.websocket.protocol import WebSocketServerFactory
from autobahn.websocket.protocol import WebSocketClientProtocol
from autobahn.websocket.protocol import WebSocketClientFactory
from autobahn.websocket.protocol import WebSocketProtocol
from autobahn.websocket.types import ConnectingRequest
from autobahn.testutil import FakeTransport
import txaio
class WebSocketClientProtocolTests(unittest.TestCase):
def setUp(self):
t = FakeTransport()
f = WebSocketClientFactory()
p = WebSocketClientProtocol()
p.factory = f
p.transport = t
p._create_transport_details = Mock()
p._connectionMade()
p.state = p.STATE_OPEN
p.websocket_version = 18
self.protocol = p
self.transport = t
def tearDown(self):
for call in [
self.protocol.autoPingPendingCall,
self.protocol.autoPingTimeoutCall,
self.protocol.openHandshakeTimeoutCall,
self.protocol.closeHandshakeTimeoutCall,
]:
if call is not None:
call.cancel()
def test_auto_ping(self):
self.protocol.autoPingInterval = 1
self.protocol.websocket_protocols = [Mock()]
self.protocol.websocket_extensions = []
self.protocol._onOpen = lambda: None
self.protocol._wskey = '0' * 24
self.protocol.peer = Mock()
# usually provided by the Twisted or asyncio specific
# subclass, but we're testing the parent here...
self.protocol._onConnect = Mock()
self.protocol._closeConnection = Mock()
# set up a connection
self.protocol._actuallyStartHandshake(
ConnectingRequest(
host="example.com",
port=80,
resource="/ws",
)
)
key = self.protocol.websocket_key + WebSocketProtocol._WS_MAGIC
self.protocol.data = (
b"HTTP/1.1 101 Switching Protocols\x0d\x0a"
b"Upgrade: websocket\x0d\x0a"
b"Connection: upgrade\x0d\x0a"
b"Sec-Websocket-Accept: " + b64encode(sha1(key).digest()) + b"\x0d\x0a\x0d\x0a"
)
self.protocol.processHandshake()
self.assertTrue(self.protocol.autoPingPendingCall is not None)
class WebSocketServerProtocolTests(unittest.TestCase):
"""
Tests for autobahn.websocket.protocol.WebSocketProtocol.
"""
def setUp(self):
t = FakeTransport()
f = WebSocketServerFactory()
p = WebSocketServerProtocol()
p.factory = f
p.transport = t
p._connectionMade()
p.state = p.STATE_OPEN
p.websocket_version = 18
self.protocol = p
self.transport = t
def tearDown(self):
for call in [
self.protocol.autoPingPendingCall,
self.protocol.autoPingTimeoutCall,
self.protocol.openHandshakeTimeoutCall,
self.protocol.closeHandshakeTimeoutCall,
]:
if call is not None:
call.cancel()
def test_auto_ping(self):
proto = Mock()
proto._get_seconds = Mock(return_value=1)
self.protocol.autoPingInterval = 1
self.protocol.websocket_protocols = [proto]
self.protocol.websocket_extensions = []
self.protocol._onOpen = lambda: None
self.protocol._wskey = '0' * 24
self.protocol.succeedHandshake(proto)
self.assertTrue(self.protocol.autoPingPendingCall is not None)
def test_sendClose_none(self):
"""
sendClose with no code or reason works.
"""
self.protocol.sendClose()
# We closed properly
self.assertEqual(self.transport._written, b"\x88\x00")
self.assertEqual(self.protocol.state, self.protocol.STATE_CLOSING)
def test_sendClose_str_reason(self):
"""
sendClose with a str reason works.
"""
self.protocol.sendClose(code=1000, reason="oh no")
# We closed properly
self.assertEqual(self.transport._written[2:], b"\x03\xe8oh no")
self.assertEqual(self.protocol.state, self.protocol.STATE_CLOSING)
def test_sendClose_unicode_reason(self):
"""
sendClose with a unicode reason works.
"""
self.protocol.sendClose(code=1000, reason="oh no")
# We closed properly
self.assertEqual(self.transport._written[2:], b"\x03\xe8oh no")
self.assertEqual(self.protocol.state, self.protocol.STATE_CLOSING)
def test_sendClose_toolong(self):
"""
sendClose with a too-long reason will truncate it.
"""
self.protocol.sendClose(code=1000, reason="abc" * 1000)
# We closed properly
self.assertEqual(self.transport._written[2:],
b"\x03\xe8" + (b"abc" * 41))
self.assertEqual(self.protocol.state, self.protocol.STATE_CLOSING)
def test_sendClose_reason_with_no_code(self):
"""
Trying to sendClose with a reason but no code will raise an Exception.
"""
with self.assertRaises(Exception) as e:
self.protocol.sendClose(reason="abc")
self.assertIn("close reason without close code", str(e.exception))
# We shouldn't have closed
self.assertEqual(self.transport._written, b"")
self.assertEqual(self.protocol.state, self.protocol.STATE_OPEN)
def test_sendClose_invalid_code_type(self):
"""
Trying to sendClose with a non-int code will raise an Exception.
"""
with self.assertRaises(Exception) as e:
self.protocol.sendClose(code="134")
self.assertIn("invalid type", str(e.exception))
# We shouldn't have closed
self.assertEqual(self.transport._written, b"")
self.assertEqual(self.protocol.state, self.protocol.STATE_OPEN)
def test_sendClose_invalid_code_value(self):
"""
Trying to sendClose with a non-valid int code will raise an Exception.
"""
with self.assertRaises(Exception) as e:
self.protocol.sendClose(code=10)
self.assertIn("invalid close code 10", str(e.exception))
# We shouldn't have closed
self.assertEqual(self.transport._written, b"")
self.assertEqual(self.protocol.state, self.protocol.STATE_OPEN)
if os.environ.get('USE_TWISTED', False):
class TwistedProtocolTests(unittest.TestCase):
"""
Tests which require a specific framework's protocol class to work
(in this case, using Twisted)
"""
def setUp(self):
from autobahn.twisted.websocket import WebSocketServerProtocol
from autobahn.twisted.websocket import WebSocketServerFactory
t = FakeTransport()
f = WebSocketServerFactory()
p = WebSocketServerProtocol()
p.factory = f
p.transport = t
p._connectionMade()
p.state = p.STATE_OPEN
p.websocket_version = 18
self.protocol = p
self.transport = t
def tearDown(self):
for call in [
self.protocol.autoPingPendingCall,
self.protocol.autoPingTimeoutCall,
self.protocol.openHandshakeTimeoutCall,
self.protocol.closeHandshakeTimeoutCall,
]:
if call is not None:
call.cancel()
def test_loseConnection(self):
"""
If we lose our connection before openHandshakeTimeout fires, it is
cleaned up
"""
# so, I guess a little cheezy, but we depend on the asyncio or
# twisted class to call _connectionLost at some point; faking
# that here
self.protocol._connectionLost(txaio.create_failure(RuntimeError("testing")))
self.assertTrue(self.protocol.openHandshakeTimeoutCall is None)
| [
"ZoomLee@users.noreply.github.com"
] | ZoomLee@users.noreply.github.com |
e7a2127c826f94d3c911d95c8b6038cccefef18c | 78c4ccb183a99ebaabcdc3a3a69f029e4aee0f5c | /AlgorithmStudy/백준/5 DFS ＆ BFS/11 토마토.py | 60372a187211792a6f091d124a6a3eac1a1504aa | [] | no_license | cladren123/study | ef2c45bc489fa658dbc9360fb0b0de53250500e5 | 241326e618f1f3bb1568d588bf6f53b78920587a | refs/heads/master | 2023-09-02T02:21:24.560967 | 2021-11-05T12:20:06 | 2021-11-05T12:20:06 | 368,753,950 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,200 | py |
"""
실버1
"""
# m:가로칸 n:세로칸
from collections import deque
m,n = map(int, input().split())
# 1:은 익은 토마토 0:익지 않은 토마토 -1:토마토가 들어있지 않은 칸
board = [list(map(int, input().split())) for _ in range(n)]
visited= [[-3] * m for _ in range(n)]
que = deque()
dx = [1,0,-1,0]
dy = [0,-1,0,1]
count = 0
for i in range(n) :
for j in range(m) :
if board[i][j] == -1 :
visited[i][j] = -1
if board[i][j] == 1 :
que.append([i,j])
visited[i][j] = count
while que :
y,x = que.popleft()
for i in range(4) :
nexty = y + dy[i]
nextx = x + dx[i]
if 0 <= nexty < n and 0 <= nextx < m :
if board[nexty][nextx] == 0 and visited[nexty][nextx] == -3 :
que.append([nexty,nextx])
visited[nexty][nextx] = visited[y][x] + 1
board[nexty][nextx] = 1
answer = 0
for i in visited :
if -3 in i :
answer = -1
break
else :
if answer < max(i) :
answer = max(i)
print(answer)
# for i in board :
# print(i)
#
# print()
#
# for i in visited :
# print(i)
| [
"48821942+cladren123@users.noreply.github.com"
] | 48821942+cladren123@users.noreply.github.com |
1732a222aa9a8307cf510c5897e748bd5b556e19 | fdb8d96d06cb7e74153a178fd17b449e89f44cd0 | /poo_vs_estructurado/poo.py | e24044e8067356ec354c939e87f577fa5eb7830e | [] | no_license | EmaSMach/info2020 | c84916521d2dd21040419cb469c76c589b98be89 | a184dc376cb5e0b894a32d01681b71c824d993d3 | refs/heads/master | 2022-12-06T08:52:34.994922 | 2020-08-24T02:57:40 | 2020-08-24T02:57:40 | 273,131,222 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,667 | py | # Creo una estructura para los clientes
class Cliente:
def __init__(self, dni, nombre, apellidos):
self.dni = dni
self.nombre = nombre
self.apellidos = apellidos
def __str__(self):
return '{} {}'.format(self.nombre, self.apellidos)
def __repr__(self):
return str(self)
# Y otra para las empresas
class Empresa:
def __init__(self, clientes=[]):
self.clientes = clientes
def mostrar_cliente(self, dni=None):
for c in self.clientes:
if c.dni == dni:
print(c)
return
print("Cliente no encontrado")
def borrar_cliente(self, dni=None):
for i, c in enumerate(self.clientes):
if c.dni == dni:
del(self.clientes[i])
print(str(c), "> BORRADO")
return
print("Cliente no encontrado")
# Ahora utilizaremos ambas estructuras
# Creemos un par de clientes
hector = Cliente(nombre="Hector", apellidos="Costa Guzman", dni="11111111A")
juan = Cliente("22222222B", "Juan", "Gonzalez Marquez")
# Creemos una empresa con los clientes iniciales
empresa = Empresa(clientes=[hector, juan])
# Se muestran todos los clientes
print("==LISTADO DE CLIENTES==")
print(empresa.clientes)
print("\n==MOSTRAR CLIENTES POR DNI==")
# Se consulta clientes por DNI
empresa.mostrar_cliente("11111111A")
empresa.mostrar_cliente("11111111Z")
print("\n==BORRAR CLIENTES POR DNI==")
# Se borra un cliente por DNI
empresa.borrar_cliente("22222222V")
empresa.borrar_cliente("22222222B")
# Se muestran de nuevo todos los clientes
print("\n==LISTADO DE CLIENTES==")
print(empresa.clientes)
| [
"davidemanuelsandoval@gmail.com"
] | davidemanuelsandoval@gmail.com |
a7fd6a5636da3ad3daab9964b5057344b43fbd77 | 956cc6ff2b58a69292f7d1223461bc9c2b9ea6f1 | /monk/system_unit_tests/pytorch/test_optimizer_adadelta.py | 9f4ba4daa325105e45dc523eb6c714525e3b7b40 | [
"Apache-2.0"
] | permissive | Aanisha/monk_v1 | c24279b2b461df9b3de2984bae0e2583aba48143 | c9e89b2bc0c1dbb320aa6da5cba0aa1c1526ad72 | refs/heads/master | 2022-12-29T00:37:15.320129 | 2020-10-18T09:12:13 | 2020-10-18T09:12:13 | 286,278,278 | 0 | 0 | Apache-2.0 | 2020-08-09T16:51:02 | 2020-08-09T16:51:02 | null | UTF-8 | Python | false | false | 1,828 | py | import os
import sys
sys.path.append("../../../../monk_v1/");
sys.path.append("../../../monk/");
import psutil
from pytorch_prototype import prototype
from compare_prototype import compare
from common import print_start
from common import print_status
def test_optimizer_adadelta(system_dict):
forward = True;
if(not os.path.isdir("datasets")):
os.system("! wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1rG-U1mS8hDU7_wM56a1kc-li_zHLtbq2' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1rG-U1mS8hDU7_wM56a1kc-li_zHLtbq2\" -O datasets.zip && rm -rf /tmp/cookies.txt")
os.system("! unzip -qq datasets.zip")
test = "test_optimizer_adadelta";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
gtf = prototype(verbose=0);
gtf.Prototype("sample-project-1", "sample-experiment-1");
gtf.Default(dataset_path="../../system_check_tests/datasets/dataset_cats_dogs_train",
model_name="resnet18", freeze_base_network=True, num_epochs=2);
gtf.optimizer_adadelta(0.01, weight_decay=0.0001, rho=0.9,
clipnorm=1.0, clipvalue=0.5);
gtf.Train();
system_dict["successful_tests"] += 1;
print_status("Pass");
except Exception as e:
system_dict["failed_tests_exceptions"].append(e);
system_dict["failed_tests_lists"].append(test);
forward = False;
print_status("Fail");
else:
system_dict["skipped_tests_lists"].append(test);
print_status("Skipped");
return system_dict
| [
"abhishek4273@gmail.com"
] | abhishek4273@gmail.com |
8c2cfcea57ba4e2829b34ac0622ff9d4903f0378 | f2201a77b8039215591aaa31dddae7ebb72301c2 | /backend/users/migrations/0002_auto_20201119_0018.py | e972e5c362cf7d7c108192355b442edcf2e62a64 | [] | no_license | crowdbotics-apps/start-up-22744 | 8726c58855ffee7ceb48100c9ebeb26a377a1051 | bbd9ce066d6636fe6866c7070a1a8370033ba91c | refs/heads/master | 2023-01-11T15:30:38.074614 | 2020-11-19T00:19:18 | 2020-11-19T00:19:18 | 314,091,313 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,275 | py | # Generated by Django 2.2.17 on 2020-11-19 00:18
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('users', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='user',
name='last_updated',
field=models.DateTimeField(auto_now=True, null=True),
),
migrations.AddField(
model_name='user',
name='timestamp_created',
field=models.DateTimeField(auto_now_add=True, null=True),
),
migrations.AlterField(
model_name='user',
name='email',
field=models.EmailField(blank=True, max_length=255, null=True),
),
migrations.AlterField(
model_name='user',
name='first_name',
field=models.CharField(blank=True, max_length=255, null=True),
),
migrations.AlterField(
model_name='user',
name='last_name',
field=models.CharField(blank=True, max_length=255, null=True),
),
migrations.AlterField(
model_name='user',
name='name',
field=models.CharField(blank=True, max_length=255, null=True),
),
]
| [
"team@crowdbotics.com"
] | team@crowdbotics.com |
08422f80718e6ddd8b5db6147f40775b09d9554f | d9f10273960c6956db55f694cdee5910554addd1 | /run.py | a815b8440b2399dddb1a48ace624494c331228bf | [] | no_license | manuelborowski/infoheliks | 09b7c6618922aa35ec2334b64cbf55d4bf0d4a80 | a543960a28d00d204a4a699d58964faec6326847 | refs/heads/main | 2023-04-18T11:28:03.115297 | 2021-05-04T08:46:25 | 2021-05-04T08:46:25 | 350,639,148 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 133 | py | from app import flask_app, socketio
if __name__ == '__main__':
socketio.run(flask_app, port=5026, host='127.0.0.1', debug=False) | [
"emmanuel.borowski@gmail.com"
] | emmanuel.borowski@gmail.com |
e0403b2849329c0dea1acd1f3349257cd8c11022 | 4111ca5a73a22174f189361bef654c3f91c3b7ed | /Lintcode/Ladder_37_BB/easy/646. First Position Unique Character.py | 1f8a68e990d0be27eacc8cb5a84adaeaa998a1ad | [
"MIT"
] | permissive | ctc316/algorithm-python | 58b541b654509ecf4e9eb8deebfcbdf785699cc4 | ac4580d55e05e93e407c6156c9bb801808027d60 | refs/heads/master | 2020-03-16T06:09:50.130146 | 2019-08-02T02:50:49 | 2019-08-02T02:50:49 | 132,548,222 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 308 | py | class Solution:
"""
@param s: a string
@return: it's index
"""
def firstUniqChar(self, s):
counts = {}
for ch in s:
counts[ch] = counts.get(ch, 0) + 1
for i in range(len(s)):
if counts[s[i]] == 1:
return i
return -1 | [
"mike.tc.chen101@gmail.com"
] | mike.tc.chen101@gmail.com |
cce8c03c7514638d39b72b60615e7063c2e4a4ac | dae8e0070b093d662fdeea026e3eb48814af73c5 | /Autosampler/analysis/nonlinearRegression.py | f678adb1f0f4ed85179278d694bf32842efd643e | [] | no_license | clipo/RHX | 43d3dc8e0f2a4d90a8c83ec2a9e4fc0be30fddae | 93286e17df1ec05d98d791671d641d86b7f588b9 | refs/heads/master | 2021-01-02T23:06:23.023476 | 2013-04-27T21:42:01 | 2013-04-27T21:42:01 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 911 | py | __author__ = 'carllipo'
import numpy as np
from scipy.optimize import leastsq
def function(a, time):
return a * np.power(time, 0.25)
def residuals(p, y, x):
err = y - function(x, p)
return err
def nlinRegression(timeArray, weightChangeArray, minval, maxval):
nlx = []
nly = []
count = 0
a_guess = 0.005
for var in timeArray:
if minval < var < maxval:
nlx.append(var)
nly.append(weightChangeArray[count])
count += 1
kd, cov, infodict, mesg, ier = leastsq(residuals,
a_guess, args=(timeArray, weightChangeArray), full_output=True)
return kd[0]
timeArray = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
weightChangeArray = [0, .5, 1, 1.3, 3, 5, 7, 8, 10, 12, 12.5, 13.5, 14]
minval = -1
maxval = 16
alpha = nlinRegression(timeArray, weightChangeArray, minval, maxval)
print alpha
| [
"clipo@csulb.edu"
] | clipo@csulb.edu |
e55f1275abce7b05777331f05a7db588bb10a82f | eb74806869a4340a6d8a2623bbe72bd4e64dcde8 | /apps/push/signals.py | 2f2aa7d3d0a552e734bfa3cc964dd3aa73a9278b | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | permissive | sictiru/NewsBlur | a0874a1044926d2268ba07a928e62fce5c9a8310 | 1ab88e4cc34775d00a1ac90ee08bc2498577e773 | refs/heads/sictiru | 2023-08-19T20:24:20.638019 | 2023-08-15T03:52:09 | 2023-08-15T03:52:09 | 250,445,213 | 1 | 0 | MIT | 2023-03-06T15:34:38 | 2020-03-27T05:05:44 | Objective-C | UTF-8 | Python | false | false | 260 | py | # Adapted from djpubsubhubbub. See License: http://git.participatoryculture.org/djpubsubhubbub/tree/LICENSE
from django.dispatch import Signal
pre_subscribe = Signal(providing_args=['created'])
verified = Signal()
updated = Signal(providing_args=['update'])
| [
"samuel@ofbrooklyn.com"
] | samuel@ofbrooklyn.com |
8c965bc5e529ecf239d5241c6d31618513eb5b69 | 3474b315da3cc5cb3f7823f19a18b63a8da6a526 | /scratch/KRAMS/src/ibvpy/mesh/fe_domain.py | 77d1550ea5ee3bd2e315cafde023e53e6b2b7379 | [] | no_license | h4ck3rm1k3/scratch | 8df97462f696bc2be00f1e58232e1cd915f0fafd | 0a114a41b0d1e9b2d68dbe7af7cf34db11512539 | refs/heads/master | 2021-01-21T15:31:38.718039 | 2013-09-19T10:48:24 | 2013-09-19T10:48:24 | 29,173,525 | 0 | 0 | null | 2015-01-13T04:58:57 | 2015-01-13T04:58:56 | null | UTF-8 | Python | false | false | 4,549 | py |
from enthought.traits.api import \
Array, Bool, Callable, Enum, Float, HasTraits, Interface, implements, \
Instance, Int, Trait, Str, Enum, Callable, List, TraitDict, Any, \
on_trait_change, Tuple, WeakRef, Delegate, Property, cached_property, \
This, self, TraitError, Button, Event
from enthought.traits.ui.api import \
View, Item, Group
from numpy import array, arange
from ibvpy.core.sdomain import \
SDomain
from ibvpy.core.scontext import \
SContext
from ibvpy.dots.dots_list_eval import \
DOTSListEval
from ibvpy.rtrace.rt_domain_list import \
RTraceDomainList
class FEDomain( SDomain ):
'''Test the state dependencies within the hierarchical domain representation.
'''
changed_structure = Event
subdomains = List( domain_changed = True )
@on_trait_change( 'changed_structure' )
def _validate_subdomains( self ):
for domain in self.subdomains:
domain.validate()
xdomains = List( domain_changed = True )
serialized_subdomains = List
def _append_in_series( self, new_subdomain ):
'''Link the new subdomain at the end of the series.
'''
if self.serialized_subdomains:
last_subdomain = self.serialized_subdomains[-1]
last_subdomain.next_domain = new_subdomain
new_subdomain.previous_domain = last_subdomain
self.serialized_subdomains.append( new_subdomain )
nonempty_subdomains = Property( depends_on = 'changed_structure' )
@cached_property
def _get_nonempty_subdomains( self ):
d_list = []
for d in self.serialized_subdomains:
if d.n_active_elems > 0:
d_list.append( d )
return d_list
n_dofs = Property
def _get_n_dofs( self ):
'''Return the total number of dofs in the domain.
Use the last subdomain's: dof_offset + n_dofs
'''
last_subdomain = self.serialized_subdomains[-1]
return last_subdomain.dof_offset + last_subdomain.n_dofs
dof_offset_arr = Property
def _get_dof_offset_arr( self ):
'''
Return array of the dof offsets
from serialized subdomains
'''
a = array( [domain.dof_offset
for domain in self.serialized_subdomains] )
return a
#----------------------------------------------------------------------------
# Methods for time stepper
#----------------------------------------------------------------------------
dots = Property( depends_on = 'changed_structure' )
@cached_property
def _get_dots( self ):
return DOTSListEval( sdomain = self,
dots_list = [ subdomain.dots
for subdomain
in self.nonempty_subdomains ] )
def new_scontext( self ):
'''
Setup a new spatial context.
'''
sctx = SContext()
sctx.domain_list = self
return sctx
#-----------------------------------------------------------------
# Response tracer background mesh
#-----------------------------------------------------------------
rt_bg_domain = Property( depends_on = 'changed_structure' )
@cached_property
def _get_rt_bg_domain( self ):
return RTraceDomainList( subfields = [ subdomain.rt_bg_domain
for subdomain
in self.nonempty_subdomains ],
sd = self )
def redraw( self ):
self.rt_bg_domain.redraw()
#----------------------------------------------------------------------------
# Methods for extracting ranges from the domain
#----------------------------------------------------------------------------
def get_lset_subdomain( self, lset_function ):
'''@TODO - implement the subdomain selection method
'''
raise NotImplementedError
def get_boundary( self, side = None ):
'''@todo: - implement the boundary extraction
'''
raise NotImplementedError
def get_interior( self ):
'''@todo: - implement the boundary extraction
'''
raise NotImplementedError
def __iter__( self ):
return iter( self.subdomains )
traits_view = View( Group(
),
resizable = True,
scrollable = True,
)
| [
"Axel@Axel-Pc"
] | Axel@Axel-Pc |
588f61e7ddc589c30c4773fe81ada161b7fe2c69 | 51f887286aa3bd2c3dbe4c616ad306ce08976441 | /pybind/slxos/v17r_2_00/ip/rtm_config/route/static_route_nh_vrf/__init__.py | 04130225b089110a163e1218603f501b62d660c8 | [
"Apache-2.0"
] | permissive | b2220333/pybind | a8c06460fd66a97a78c243bf144488eb88d7732a | 44c467e71b2b425be63867aba6e6fa28b2cfe7fb | refs/heads/master | 2020-03-18T09:09:29.574226 | 2018-04-03T20:09:50 | 2018-04-03T20:09:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 15,541 | py |
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__
class static_route_nh_vrf(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module brocade-common-def - based on the path /ip/rtm-config/route/static-route-nh-vrf. 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', '__static_route_next_vrf_dest','__next_hop_vrf','__static_route_next_hop',)
_yang_name = 'static-route-nh-vrf'
_rest_name = 'static-route-nh-vrf'
_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.__static_route_next_vrf_dest = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])/(([0-9])|([1-2][0-9])|(3[0-2]))'}), is_leaf=True, yang_name="static-route-next-vrf-dest", rest_name="static-route-next-vrf-dest", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A.B.C.D/L ;; Destination IP address'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='inet:ipv4-prefix', is_config=True)
self.__next_hop_vrf = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.)*([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.?)|\\.', 'length': [u'1..32']}), is_leaf=True, yang_name="next-hop-vrf", rest_name="next-hop-vrf", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Next Hop Vrf Name', u'cli-expose-key-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='common-def:vrf-name', is_config=True)
self.__static_route_next_hop = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'}), is_leaf=True, yang_name="static-route-next-hop", rest_name="static-route-next-hop", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A.B.C.D ;; Next hop IP address', u'cli-drop-node-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='inet:ipv4-address', is_config=True)
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'ip', u'rtm-config', u'route', u'static-route-nh-vrf']
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'ip', u'route', u'static-route-nh-vrf']
def _get_static_route_next_vrf_dest(self):
"""
Getter method for static_route_next_vrf_dest, mapped from YANG variable /ip/rtm_config/route/static_route_nh_vrf/static_route_next_vrf_dest (inet:ipv4-prefix)
"""
return self.__static_route_next_vrf_dest
def _set_static_route_next_vrf_dest(self, v, load=False):
"""
Setter method for static_route_next_vrf_dest, mapped from YANG variable /ip/rtm_config/route/static_route_nh_vrf/static_route_next_vrf_dest (inet:ipv4-prefix)
If this variable is read-only (config: false) in the
source YANG file, then _set_static_route_next_vrf_dest is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_static_route_next_vrf_dest() directly.
"""
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=unicode, restriction_dict={'pattern': u'(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])/(([0-9])|([1-2][0-9])|(3[0-2]))'}), is_leaf=True, yang_name="static-route-next-vrf-dest", rest_name="static-route-next-vrf-dest", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A.B.C.D/L ;; Destination IP address'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='inet:ipv4-prefix', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """static_route_next_vrf_dest must be of a type compatible with inet:ipv4-prefix""",
'defined-type': "inet:ipv4-prefix",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])/(([0-9])|([1-2][0-9])|(3[0-2]))'}), is_leaf=True, yang_name="static-route-next-vrf-dest", rest_name="static-route-next-vrf-dest", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A.B.C.D/L ;; Destination IP address'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='inet:ipv4-prefix', is_config=True)""",
})
self.__static_route_next_vrf_dest = t
if hasattr(self, '_set'):
self._set()
def _unset_static_route_next_vrf_dest(self):
self.__static_route_next_vrf_dest = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])/(([0-9])|([1-2][0-9])|(3[0-2]))'}), is_leaf=True, yang_name="static-route-next-vrf-dest", rest_name="static-route-next-vrf-dest", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A.B.C.D/L ;; Destination IP address'}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='inet:ipv4-prefix', is_config=True)
def _get_next_hop_vrf(self):
"""
Getter method for next_hop_vrf, mapped from YANG variable /ip/rtm_config/route/static_route_nh_vrf/next_hop_vrf (common-def:vrf-name)
"""
return self.__next_hop_vrf
def _set_next_hop_vrf(self, v, load=False):
"""
Setter method for next_hop_vrf, mapped from YANG variable /ip/rtm_config/route/static_route_nh_vrf/next_hop_vrf (common-def:vrf-name)
If this variable is read-only (config: false) in the
source YANG file, then _set_next_hop_vrf is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_next_hop_vrf() directly.
"""
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=unicode, restriction_dict={'pattern': u'((([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.)*([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.?)|\\.', 'length': [u'1..32']}), is_leaf=True, yang_name="next-hop-vrf", rest_name="next-hop-vrf", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Next Hop Vrf Name', u'cli-expose-key-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='common-def:vrf-name', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """next_hop_vrf must be of a type compatible with common-def:vrf-name""",
'defined-type': "common-def:vrf-name",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.)*([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.?)|\\.', 'length': [u'1..32']}), is_leaf=True, yang_name="next-hop-vrf", rest_name="next-hop-vrf", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Next Hop Vrf Name', u'cli-expose-key-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='common-def:vrf-name', is_config=True)""",
})
self.__next_hop_vrf = t
if hasattr(self, '_set'):
self._set()
def _unset_next_hop_vrf(self):
self.__next_hop_vrf = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.)*([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.?)|\\.', 'length': [u'1..32']}), is_leaf=True, yang_name="next-hop-vrf", rest_name="next-hop-vrf", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Next Hop Vrf Name', u'cli-expose-key-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='common-def:vrf-name', is_config=True)
def _get_static_route_next_hop(self):
"""
Getter method for static_route_next_hop, mapped from YANG variable /ip/rtm_config/route/static_route_nh_vrf/static_route_next_hop (inet:ipv4-address)
"""
return self.__static_route_next_hop
def _set_static_route_next_hop(self, v, load=False):
"""
Setter method for static_route_next_hop, mapped from YANG variable /ip/rtm_config/route/static_route_nh_vrf/static_route_next_hop (inet:ipv4-address)
If this variable is read-only (config: false) in the
source YANG file, then _set_static_route_next_hop is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_static_route_next_hop() directly.
"""
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=unicode, restriction_dict={'pattern': u'(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'}), is_leaf=True, yang_name="static-route-next-hop", rest_name="static-route-next-hop", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A.B.C.D ;; Next hop IP address', u'cli-drop-node-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='inet:ipv4-address', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """static_route_next_hop must be of a type compatible with inet:ipv4-address""",
'defined-type': "inet:ipv4-address",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'}), is_leaf=True, yang_name="static-route-next-hop", rest_name="static-route-next-hop", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A.B.C.D ;; Next hop IP address', u'cli-drop-node-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='inet:ipv4-address', is_config=True)""",
})
self.__static_route_next_hop = t
if hasattr(self, '_set'):
self._set()
def _unset_static_route_next_hop(self):
self.__static_route_next_hop = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'}), is_leaf=True, yang_name="static-route-next-hop", rest_name="static-route-next-hop", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'A.B.C.D ;; Next hop IP address', u'cli-drop-node-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-rtm', defining_module='brocade-rtm', yang_type='inet:ipv4-address', is_config=True)
static_route_next_vrf_dest = __builtin__.property(_get_static_route_next_vrf_dest, _set_static_route_next_vrf_dest)
next_hop_vrf = __builtin__.property(_get_next_hop_vrf, _set_next_hop_vrf)
static_route_next_hop = __builtin__.property(_get_static_route_next_hop, _set_static_route_next_hop)
_pyangbind_elements = {'static_route_next_vrf_dest': static_route_next_vrf_dest, 'next_hop_vrf': next_hop_vrf, 'static_route_next_hop': static_route_next_hop, }
| [
"badaniya@brocade.com"
] | badaniya@brocade.com |
fa8815618592d941b28acb1eff9ab0cc10c18098 | ee5040164beb866310c9cf23584002a342b451c0 | /infra/libs-400rc2-20190512/examples/bmp280_simpletest.py | a54217ef21ce3baff21f2e95ebb304d914368ca5 | [
"MIT"
] | permissive | jadudm/feather-isa | cf9a47c627408addbbc84581e5d6dff35a79773e | b7419e6698c3f64be4d8122656eb8124631ca859 | refs/heads/master | 2020-05-22T11:45:33.753573 | 2019-06-11T15:49:42 | 2019-06-11T15:49:42 | 186,329,428 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 765 | py | import time
import board
# import digitalio # For use with SPI
import busio
import adafruit_bmp280
# Create library object using our Bus I2C port
i2c = busio.I2C(board.SCL, board.SDA)
bmp280 = adafruit_bmp280.Adafruit_BMP280_I2C(i2c)
# OR create library object using our Bus SPI port
#spi = busio.SPI(board.SCK, board.MOSI, board.MISO)
#bmp_cs = digitalio.DigitalInOut(board.D10)
#bmp280 = adafruit_bmp280.Adafruit_BMP280_SPI(spi, bmp_cs)
# change this to match the location's pressure (hPa) at sea level
bmp280.sea_level_pressure = 1013.25
while True:
print("\nTemperature: %0.1f C" % bmp280.temperature)
print("Pressure: %0.1f hPa" % bmp280.pressure)
print("Altitude = %0.2f meters" % bmp280.altitude)
time.sleep(2)
| [
"matt@jadud.com"
] | matt@jadud.com |
efcfc1b4ef74378b813bd4f0312e79c71328f77a | 3767e31f1c3a53d388cdc6fc1244bf980dfe039a | /pepysdiary/membership/forms.py | c846a8714a7a3e3ca2e7b01a686abf0ec14c79c8 | [] | no_license | philgyford/pepysdiary | 73749f389c226a35876e55e108a9f39e6afece5e | c6d99f39046eb5309f3292bfb4edb8b008f37aeb | refs/heads/main | 2023-09-01T21:27:41.762431 | 2023-08-30T08:49:44 | 2023-08-30T08:49:44 | 7,092,491 | 16 | 6 | null | 2023-09-11T15:06:09 | 2012-12-10T11:55:11 | Python | UTF-8 | Python | false | false | 7,794 | py | # coding: utf-8
from django import forms
from django.contrib.auth import password_validation
from django.contrib.auth.forms import (
AuthenticationForm,
PasswordResetForm,
SetPasswordForm,
)
from django.utils.translation import gettext_lazy as _
from hcaptcha.fields import hCaptchaField
from pepysdiary.common.models import Config
from pepysdiary.membership.models import Person
from pepysdiary.membership.utilities import validate_person_name
from .utilities import send_email
# Much of this based on django-registration.
attrs_dict = {"class": "required form-control"}
class LoginForm(AuthenticationForm):
username = forms.EmailField(
widget=forms.EmailInput(attrs=attrs_dict),
max_length=254,
label="Email address",
error_messages={"invalid": "Please enter a valid email address."},
)
password = forms.CharField(widget=forms.PasswordInput(attrs=attrs_dict))
def clean(self):
config = Config.objects.get_site_config()
if config is not None:
if config.allow_login is False:
raise forms.ValidationError("Sorry, logging in is currently disabled.")
return super().clean()
class PersonEditForm(forms.ModelForm):
class Meta:
model = Person
fields = ("email", "url")
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fields["email"].widget = forms.TextInput(attrs=attrs_dict)
self.fields["url"].widget = forms.TextInput(attrs=attrs_dict)
class PasswordResetForm(PasswordResetForm):
email = forms.EmailField(
widget=forms.EmailInput(attrs=attrs_dict),
max_length=254,
label="Email address",
error_messages={"invalid": "Please enter a valid email address."},
)
def send_mail(
self,
subject_template_name,
email_template_name,
context,
from_email,
to_email,
html_email_template_name=None,
):
"""
Overriding the default so that we can use our custom send_email()
method which includes the headers we want.
"""
send_email(
to_email, from_email, subject_template_name, email_template_name, context
)
class RegistrationForm(forms.Form):
"""
Form for registering a new user account.
Validates that the requested name and email are not already in use, and
requires the password to be entered twice to catch typos.
Subclasses should feel free to add any additional validation they
need, but should avoid defining a ``save()`` method -- the actual
saving of collected user data is delegated to the active
registration backend.
"""
name = forms.CharField(
widget=forms.TextInput(attrs=attrs_dict),
max_length=50,
validators=[validate_person_name],
required=True,
label=_("Your name"),
help_text="How people will know you. Can use spaces, eg “Samuel Pepys”.",
)
email = forms.EmailField(
required=True,
label=_("Email address"),
max_length=254,
widget=forms.EmailInput(attrs=attrs_dict),
help_text="This will not be visible to others.",
)
password1 = forms.CharField(
widget=forms.PasswordInput(attrs=attrs_dict, render_value=False),
required=True,
label=_("Password"),
help_text="At least 8 characters.",
)
password2 = forms.CharField(
widget=forms.PasswordInput(attrs=attrs_dict, render_value=False),
required=True,
label=_("Repeat password"),
)
url = forms.URLField(
widget=forms.URLInput(attrs=attrs_dict),
label=_("Personal URL"),
max_length=255,
required=False,
help_text="Optional. eg, the web address of your blog, Facebook page, "
"Twitter page, etc.",
)
honeypot = forms.CharField(
required=False,
label=_(
"If you enter anything in this field "
"your registration will be treated as spam"
),
)
def __init__(self, *args, **kwargs):
"""
We might need to add captcha and question/answer anti-spam fields,
depending on our site config.
"""
super().__init__(*args, **kwargs)
config = Config.objects.get_site_config()
if config is not None:
if config.use_registration_captcha is True:
self.fields["captcha"] = hCaptchaField(label=_("Anti-spam test"))
if (
config.use_registration_question is True
and config.registration_question != ""
and config.registration_answer != ""
):
self.fields["answer"] = forms.CharField(
widget=forms.TextInput(attrs=attrs_dict),
max_length=255,
required=True,
label=_(config.registration_question),
)
def clean_name(self):
"""
Validate that the name is alphanumeric and is not already in use.
"""
existing = Person.objects.filter(name__iexact=self.cleaned_data["name"])
if existing.exists():
raise forms.ValidationError(_("That name has already been used."))
else:
return self.cleaned_data["name"]
def clean_email(self):
"""
Validate that the email is not already in use.
"""
existing = Person.objects.filter(email__iexact=self.cleaned_data["email"])
if existing.exists():
raise forms.ValidationError(_("That email address has already been used."))
else:
return self.cleaned_data["email"]
def clean_honeypot(self):
"""Check that nothing's been entered into the honeypot."""
value = self.cleaned_data["honeypot"]
if value:
raise forms.ValidationError(self.fields["honeypot"].label)
return value
def clean_answer(self):
"""
Validate that the anti-spam question was answered successfully.
"""
config = Config.objects.get_site_config()
if config is not None:
if (
self.cleaned_data["answer"].lower()
== config.registration_answer.lower()
):
return self.cleaned_data["answer"]
else:
raise forms.ValidationError(_("Please try again."))
def clean_password1(self):
"""Check the password is OK by Django >=1.9's validators."""
password1 = self.cleaned_data["password1"]
password_validation.validate_password(password1)
return password1
def clean(self):
"""
Verifiy that the values entered into the two password fields
match. Note that an error here will end up in
``non_field_errors()`` because it doesn't apply to a single
field.
"""
config = Config.objects.get_site_config()
if config is not None:
if config.allow_registration is False:
raise forms.ValidationError(
"Sorry, new registrations aren't allowed at the moment."
)
if "password1" in self.cleaned_data and "password2" in self.cleaned_data:
if self.cleaned_data["password1"] != self.cleaned_data["password2"]:
raise forms.ValidationError(_("The two password fields didn't match."))
return self.cleaned_data
class SetPasswordForm(SetPasswordForm):
new_password1 = forms.CharField(
label="New password", widget=forms.PasswordInput(attrs=attrs_dict)
)
new_password2 = forms.CharField(
label="Repeat password", widget=forms.PasswordInput(attrs=attrs_dict)
)
| [
"phil@gyford.com"
] | phil@gyford.com |
b5688b3f5c68c69b9550b071e5b97a20ee96c780 | dac3a695b895c0170c350054fcc52d95f0daef5d | /data/level/level156.py | f86f3bcf8176051f74bd52d700e0355532dd646a | [
"Apache-2.0"
] | permissive | xingchen1106/match3-level-similarity | aad87de6e54c5bdc89cf4dac168559d11997d496 | cc9b28b8741b41bea1273c8bc9b4d265d79a1dca | refs/heads/master | 2021-05-22T02:48:09.444957 | 2019-07-19T09:19:09 | 2019-07-19T09:19:09 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 11,611 | py | data = {
'level_index': 156,
'move_count': 28,
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| [
"salild1011@gmail.com"
] | salild1011@gmail.com |
1f441384521bb711ae6fdce8fef88f9fae846dec | 891371ebcb696b91df1d00f98cb595608b580d34 | /documents/migrations/0015_requestarchive_approved_by.py | 80acd3afe27f687758cfbe9bb0993586f1b4c45a | [] | no_license | NiiColeman/law-firm | 867df413e9ca30c57afad9d2743fe19ab96ba586 | f41ee5ac88d3f640dce720c90cbfefb93a267400 | refs/heads/master | 2022-09-23T22:52:25.312903 | 2020-06-01T14:30:24 | 2020-06-01T14:30:24 | 241,650,134 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 418 | py | # Generated by Django 2.2.6 on 2020-02-18 11:59
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('documents', '0014_documentarchive_location'),
]
operations = [
migrations.AddField(
model_name='requestarchive',
name='approved_by',
field=models.CharField(default='', max_length=250),
),
]
| [
"nii.cole@outlook.com"
] | nii.cole@outlook.com |
012a8170f648836e33bbe603989e95ffb9215a03 | 3f13885fdb0649374d866d24a43f86ccc6b4c782 | /apps/tools/views/qizhi.py | ca388f32f00be53c879448e92b8d76df70c1fcbd | [] | no_license | linkexf/oneops | 426b271c00c5b4b4c55d1d91bf42030dab29623a | 64a9c7fd949b6220234a276614ab6555dc8cc17c | refs/heads/master | 2020-12-10T04:45:55.681731 | 2019-11-28T09:02:30 | 2019-11-28T09:02:30 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 442 | py |
from django.views.generic import TemplateView
from django.contrib.auth.mixins import LoginRequiredMixin
class QiZhiCreateHostView(LoginRequiredMixin, TemplateView):
template_name = "tools/qizhi_create_host.html"
def get(self, request, **kwargs):
context = {
'path1': '小工具',
'path2': '堡垒机录入'
}
context.update(**kwargs)
return self.render_to_response(context)
| [
"andykaiyu@163.com"
] | andykaiyu@163.com |
f1e366f27ad1885260c92fa6e048d493ea794f29 | 597b82737635e845fd5360e191f323669af1b2ae | /08_full_django/products/products/urls.py | 6e148c5162f8e91d1ea8758a9a4ef402749fc16d | [] | no_license | twknab/learning-python | 1bd10497fbbe181a26f2070c147cb2fed6955178 | 75b76b2a607439aa2d8db675738adf8d3b8644df | refs/heads/master | 2021-08-08T08:50:04.337490 | 2017-11-10T00:28:45 | 2017-11-10T00:28:45 | 89,213,845 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 326 | py | """products URL Configuration
Sets up URL configuration for each application in the `products` project.
Current Applications:
-`products`: a simple app to play with Django Models and creating and retrieving data.
"""
from django.conf.urls import url, include
urlpatterns = [
url(r'^', include("apps.products.urls")),
]
| [
"natureminded@users.noreply.github.com"
] | natureminded@users.noreply.github.com |
a5d645e662c818a20711511e5c0bbe7dfcc7e768 | d93586a23b50027c766be448072e5c06ebd05ffc | /seq2seq/dependency/tf_models/models/official/r1/transformer/dataset.py | b5c607bf78f00095741c0bb5af23110c4bd7babe | [
"Apache-2.0"
] | permissive | peixiang6134/tf_bot_examples | 9bd3a63bf5e699e49455d3beb91c4995f47d781a | b4e8eb6a555b3eeeb423ad928a9009c41b4e5950 | refs/heads/master | 2021-07-09T02:29:26.303639 | 2020-02-22T08:58:33 | 2020-02-22T08:58:33 | 238,680,285 | 1 | 1 | null | 2021-03-31T21:54:12 | 2020-02-06T12:08:36 | Python | UTF-8 | Python | false | false | 11,409 | py | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Input pipeline for the transformer model to read, filter, and batch examples.
Two things to note in the pipeline:
1. Batching scheme
The examples encoded in the TFRecord files contain data in the format:
{"inputs": [variable length array of integers],
"targets": [variable length array of integers]}
Where integers in the arrays refer to tokens in the English and German vocab
file (named `vocab.ende.32768`).
Prior to batching, elements in the dataset are grouped by length (max between
"inputs" and "targets" length). Each group is then batched such that:
group_batch_size * length <= batch_size.
Another way to view batch_size is the maximum number of tokens in each batch.
Once batched, each element in the dataset will have the shape:
{"inputs": [group_batch_size, padded_input_length],
"targets": [group_batch_size, padded_target_length]}
Lengths are padded to the longest "inputs" or "targets" sequence in the batch
(padded_input_length and padded_target_length can be different).
This batching scheme decreases the fraction of padding tokens per training
batch, thus improving the training speed significantly.
2. Shuffling
While training, the dataset is shuffled in two places in the code. The first
is the list of training files. Second, while reading records using
`parallel_interleave`, the `sloppy` argument is used to generate randomness
in the order of the examples.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
import tensorflow as tf
from dependency.tf_models.models.official.utils.misc import model_helpers
# Buffer size for reading records from a TFRecord file. Each training file is
# 7.2 MB, so 8 MB allows an entire file to be kept in memory.
_READ_RECORD_BUFFER = 8 * 1000 * 1000
# Example grouping constants. Defines length boundaries for each group.
# These values are the defaults used in Tensor2Tensor.
_MIN_BOUNDARY = 8
_BOUNDARY_SCALE = 1.1
def _load_records(filename):
"""Read file and return a dataset of tf.Examples."""
return tf.data.TFRecordDataset(filename, buffer_size=_READ_RECORD_BUFFER)
def _parse_example(serialized_example):
"""Return inputs and targets Tensors from a serialized tf.Example."""
data_fields = {
"inputs": tf.VarLenFeature(tf.int64),
"targets": tf.VarLenFeature(tf.int64)
}
parsed = tf.parse_single_example(serialized_example, data_fields)
inputs = tf.sparse_tensor_to_dense(parsed["inputs"])
targets = tf.sparse_tensor_to_dense(parsed["targets"])
return inputs, targets
def _filter_max_length(example, max_length=256):
"""Indicates whether the example's length is lower than the maximum length."""
return tf.logical_and(tf.size(example[0]) <= max_length,
tf.size(example[1]) <= max_length)
def _get_example_length(example):
"""Returns the maximum length between the example inputs and targets."""
length = tf.maximum(tf.shape(example[0])[0], tf.shape(example[1])[0])
return length
def _create_min_max_boundaries(
max_length, min_boundary=_MIN_BOUNDARY, boundary_scale=_BOUNDARY_SCALE):
"""Create min and max boundary lists up to max_length.
For example, when max_length=24, min_boundary=4 and boundary_scale=2, the
returned values will be:
buckets_min = [0, 4, 8, 16, 24]
buckets_max = [4, 8, 16, 24, 25]
Args:
max_length: The maximum length of example in dataset.
min_boundary: Minimum length in boundary.
boundary_scale: Amount to scale consecutive boundaries in the list.
Returns:
min and max boundary lists
"""
# Create bucket boundaries list by scaling the previous boundary or adding 1
# (to ensure increasing boundary sizes).
bucket_boundaries = []
x = min_boundary
while x < max_length:
bucket_boundaries.append(x)
x = max(x + 1, int(x * boundary_scale))
# Create min and max boundary lists from the initial list.
buckets_min = [0] + bucket_boundaries
buckets_max = bucket_boundaries + [max_length + 1]
return buckets_min, buckets_max
def _batch_examples(dataset, batch_size, max_length):
"""Group examples by similar lengths, and return batched dataset.
Each batch of similar-length examples are padded to the same length, and may
have different number of elements in each batch, such that:
group_batch_size * padded_length <= batch_size.
This decreases the number of padding tokens per batch, which improves the
training speed.
Args:
dataset: Dataset of unbatched examples.
batch_size: Max number of tokens per batch of examples.
max_length: Max number of tokens in an example input or target sequence.
Returns:
Dataset of batched examples with similar lengths.
"""
# Get min and max boundary lists for each example. These are used to calculate
# the `bucket_id`, which is the index at which:
# buckets_min[bucket_id] <= len(example) < buckets_max[bucket_id]
# Note that using both min and max lists improves the performance.
buckets_min, buckets_max = _create_min_max_boundaries(max_length)
# Create list of batch sizes for each bucket_id, so that
# bucket_batch_size[bucket_id] * buckets_max[bucket_id] <= batch_size
bucket_batch_sizes = [batch_size // x for x in buckets_max]
# bucket_id will be a tensor, so convert this list to a tensor as well.
bucket_batch_sizes = tf.constant(bucket_batch_sizes, dtype=tf.int64)
def example_to_bucket_id(example_input, example_target):
"""Return int64 bucket id for this example, calculated based on length."""
seq_length = _get_example_length((example_input, example_target))
# TODO: investigate whether removing code branching improves performance.
conditions_c = tf.logical_and(
tf.less_equal(buckets_min, seq_length),
tf.less(seq_length, buckets_max))
bucket_id = tf.reduce_min(tf.where(conditions_c))
return bucket_id
def window_size_fn(bucket_id):
"""Return number of examples to be grouped when given a bucket id."""
return bucket_batch_sizes[bucket_id]
def batching_fn(bucket_id, grouped_dataset):
"""Batch and add padding to a dataset of elements with similar lengths."""
bucket_batch_size = window_size_fn(bucket_id)
# Batch the dataset and add padding so that all input sequences in the
# examples have the same length, and all target sequences have the same
# lengths as well. Resulting lengths of inputs and targets can differ.
return grouped_dataset.padded_batch(bucket_batch_size, ([None], [None]))
return dataset.apply(tf.data.experimental.group_by_window(
key_func=example_to_bucket_id,
reduce_func=batching_fn,
window_size=None,
window_size_func=window_size_fn))
def _read_and_batch_from_files(
file_pattern, batch_size, max_length, num_parallel_calls, shuffle, repeat,
static_batch=False):
"""Create dataset where each item is a dict of "inputs" and "targets".
Args:
file_pattern: String used to match the input TFRecord files.
batch_size: Maximum number of tokens per batch of examples
max_length: Maximum number of tokens per example
num_parallel_calls: Number of cpu cores for parallel input processing.
shuffle: If true, randomizes order of elements.
repeat: Number of times to repeat the dataset. If None, the dataset is
repeated forever.
static_batch: Whether the batches in the dataset should have static shapes.
If True, the input is batched so that every batch has the
shape [batch_size // max_length, max_length]. If False, the input is
grouped by length, and batched so that batches may have different
shapes [N, M], where:
N * M <= batch_size
M <= max_length
In general, this setting should be False. Dynamic shapes allow the inputs
to be grouped so that the number of padding tokens is minimized, and helps
model training. In cases where the input shape must be static
(e.g. running on TPU), this setting should be set to True.
Returns:
tf.data.Dataset object containing examples loaded from the files.
"""
dataset = tf.data.Dataset.list_files(file_pattern, shuffle=shuffle)
# Read files and interleave results. When training, the order of the examples
# will be non-deterministic.
dataset = dataset.apply(
tf.data.experimental.parallel_interleave(
_load_records, sloppy=shuffle, cycle_length=num_parallel_calls))
# Parse each tf.Example into a dictionary
# TODO: Look into prefetch_input_elements for performance optimization.
dataset = dataset.map(_parse_example,
num_parallel_calls=num_parallel_calls)
# Remove examples where the input or target length exceeds the maximum length,
dataset = dataset.filter(lambda x, y: _filter_max_length((x, y), max_length))
if static_batch:
dataset = dataset.padded_batch(
batch_size // max_length, ([max_length], [max_length]),
drop_remainder=True)
else:
# Group and batch such that each batch has examples of similar length.
dataset = _batch_examples(dataset, batch_size, max_length)
dataset = dataset.repeat(repeat)
# Prefetch the next element to improve speed of input pipeline.
dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
return dataset
def _generate_synthetic_data(params):
"""Create synthetic data based on the parameter batch size."""
batch = length = int(math.sqrt(params["batch_size"]))
return model_helpers.generate_synthetic_data(
input_shape=tf.TensorShape([batch, length]),
input_value=1,
input_dtype=tf.int32,
label_shape=tf.TensorShape([batch, length]),
label_value=1,
label_dtype=tf.int32,
)
def train_input_fn(params):
"""Load and return dataset of batched examples for use during training."""
file_pattern = os.path.join(params["data_dir"] or "", "*train*")
if params["use_synthetic_data"]:
return _generate_synthetic_data(params)
return _read_and_batch_from_files(
file_pattern, params["batch_size"], params["max_length"],
params["num_parallel_calls"], shuffle=True,
repeat=params["repeat_dataset"], static_batch=params["static_batch"])
def eval_input_fn(params):
"""Load and return dataset of batched examples for use during evaluation."""
file_pattern = os.path.join(params["data_dir"] or "", "*dev*")
if params["use_synthetic_data"]:
return _generate_synthetic_data(params)
return _read_and_batch_from_files(
file_pattern, params["batch_size"], params["max_length"],
params["num_parallel_calls"], shuffle=False, repeat=1,
static_batch=params["static_batch"])
| [
"1316802995@qq.com"
] | 1316802995@qq.com |
69db29490d01dc933aed028527cca2bbcb2a80a9 | 0d0afd1dce972b4748ce8faccd992c019794ad9e | /integra/seguranca/models/sale_defeito_os.py | 484544383c69811435d08f76d682a553b9c6d529 | [] | no_license | danimaribeiro/odoo-erp | e2ca2cfe3629fbedf413e85f7c3c0453fd16941e | d12577bf7f5266b571cbedeb930720d653320e96 | refs/heads/master | 2020-01-23T21:32:16.149716 | 2016-11-05T15:35:40 | 2016-11-05T15:35:40 | 67,892,809 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 699 | py | # -*- coding: utf-8 -*-
#from __future__ import division, print_function, unicode_literals
from osv import osv, orm, fields
class sale_defeito_os(osv.Model):
_description = u'Defeito da OS'
_name = 'sale.defeito.os'
_rec_name = 'nome'
_order = 'id'
def _codigo(self, cr, uid, ids, nome_campo, args=None, context={}):
res = {}
for os_obj in self.browse(cr, uid, ids):
res[os_obj.id] = str(os_obj.id).zfill(4)
return res
_columns = {
'codigo': fields.function(_codigo, type='char', method=True, string=u'Código', size=20, store=False, select=True),
'nome': fields.char(u'Nome', size=180),
}
sale_defeito_os()
| [
"danimaribeiro@gmail.com"
] | danimaribeiro@gmail.com |
117788556929425f430585c961bb4ff7d6162fee | 8d753bb8f19b5b1f526b0688d3cb199b396ed843 | /osp_sai_2.1.8/system/apps/web/api/web_sessions.py | 146b4f0981fe26b5dc00f9568af90c121d95faf4 | [] | no_license | bonald/vim_cfg | f166e5ff650db9fa40b564d05dc5103552184db8 | 2fee6115caec25fd040188dda0cb922bfca1a55f | refs/heads/master | 2023-01-23T05:33:00.416311 | 2020-11-19T02:09:18 | 2020-11-19T02:09:18 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,073 | py | #!/usr/bin/python
#-*- coding: utf-8 -*-
from flask import url_for
import os
import base
from vcl import vcmd
import types
def get(file_type):
"""
API:
GET:
Retrun: {
sess: [
{id: string, user: string, expire: string, client: string,},
...
],
}
"""
# get web sessions
obj = {'error': False, 'err_code': 0, 'err_reason': ''}
res = []
# get cmd lines
cmd = 'cdbctl read/cdb/app/login/web | grep web';
lines = vcmd.get_lines(cmd)
if not lines:
return res
# make dict
for line in lines:
key = vcmd.cmd_get_token(line, "key")
user = vcmd.cmd_get_token(line, "user")
deccmd = 'fnconvert -c decoding -m "%s"'%(user)
decuser = vcmd.get_lines(deccmd)
ipaddr = vcmd.cmd_get_token(line, "ipaddr")
etime = base.relative_time.get(int(vcmd.cmd_get_token(line, "expire_time")))
res.append({
'id': key,
'user': decuser[0],
'expire': etime,
'client': ipaddr,
});
obj['sess'] = res
return obj
def delete(req_data):
"""
API:
DELETE: {
file_arr: [
{
'id': string,
},
...
...
],
}
Retrun: {
error: bool,
err_code: int,
err_reason: string
}
"""
_err_reason = [
'', # err_code: 0
'bad request', # err_code: 1
'delete failed', # err_code: 2
]
obj = {'error': False, 'err_code': 0, 'err_reason': ''}
# param check
sess_arr = req_data.get('sess_arr')
if not type(sess_arr) is types.ListType: # bad request
obj['error'] = True
obj['err_code'] = 1
obj['err_reason'] = _err_reason[1]
return obj
# make cmd
for sid in sess_arr:
exec_str = 'cdbctl delete/cdb/app/login/%s' %(str(sid));
status, output = vcmd.get_status_lines(exec_str);
return obj
| [
"zhwwan@gmail.com"
] | zhwwan@gmail.com |
3c2b44b45e010b4843d1e27a86696ab4d83f9802 | 13a13b4f93ef6664dcf610a556c53a1f6c3c8bc4 | /ques9.py | d41b7a7f7fccd0c92d0dca3ec26dbf55f564b6f0 | [] | no_license | BhagyashreeKarale/more-exercise | 54ea9f6fa2f4f007278631535362959446980ea9 | c0f34d345d2e5bef19f872861fafb3f8a0233e43 | refs/heads/main | 2023-08-02T14:49:44.403733 | 2021-09-11T19:07:51 | 2021-09-11T19:07:51 | 405,454,804 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,176 | py | # Harshad numbers aise number hote hain jo apni digits ke sum se dhang se divide hote hain.
# Dhang se divide hone ka matlab ki remainder 0 aata hai.
# Jaise 42 ek Harshad number hai kyunki 4 + 2 = 6 aur 42 ko 6 se divide kar ke remainder aata hai
# Aise hi 18, 21 aur 24 bhi harshad number hai
# kyunki 1 + 8 = 9 aur 18 ko 9 se divide kar ke remainder 0 hai.
# Aise hi 1, 2, 3, 4, 5, 6, 7, 8, 9 saare harshad number hain
# kyunki inki digits ka sum khud yeh number hain aur yeh apne aap se divide ho jate hain.
# Ek number ke digits nikalne ka code yeh hai:
# x = 42
# x_digits = list(str(x))
# n=len(x_digits)
# sum=0
# for i in x_digits:
# sum=sum+int(i)
# if x % sum == 0:
# print("It is an Harshad number")
# Yahan humne list function mein x ko string mein type cast kar ke de diya.
# Toh ab yeh har 42 ke alag alag number se list bana dega.
# x_digits mein ["4", "2"] list hogi.
# Iss list mein saare digits string ki form mein hogi,
# aap unko firse integer mein convert kar sakte ho
# Ek function likho is_harshad_number jo ek number parameter ki tarah le
# aur fir agar woh number harshad number hai toh ek boolean (True agar harshad number hai,
# False agar nahi hai toh) return kare.
# Fir iss function ka use kar ke 1 se 1000 ke beech mein saare harshad number print karo.
# i=1
# while i <= 1000:
# x_digits = list(str(i))
# n=len(x_digits)
# sum=0
# for i in x_digits:
# sum=sum+int(i)
# if i % sum == 0:
# print("It is an Harshad number")
# i=i+1
# i=1
# while i <= 1000:
# x_digits = list(str(i))
# n=len(x_digits)
# sum=0
# for k in x_digits:
# sum=sum+int(k)
# if i % sum == 0:
# print(i,"Is an Harshad number")
# i=i+1
# else:
# print(i,"Is not an Harshad Number")
# i=i+1
###############################################################################################
#printing only harshad numbers
i=1
while i <= 1000:
x_digits = list(str(i))
n=len(x_digits)
sum=0
for k in x_digits:
sum=sum+int(k)
if i % sum == 0:
print(i)
i=i+1
print("These are the harshad numbers from 1-1000")
| [
"noreply@github.com"
] | BhagyashreeKarale.noreply@github.com |
3b907f1a9e78e5f2499489ec4a96db16c6a67c09 | 45b8e141f762b95edec36ce40809ea4b89e3d287 | /mahkalastore/mahkalastore/settings.py | 5838b8a0373f9a932a716f814882743e16317924 | [] | no_license | nimanoori22/mys | 73d7a0ad141e1c6208e776a15d079a2599c46a7f | 0122586a4d69f80219ad25e42ef89f3052f5cb81 | refs/heads/master | 2022-11-28T22:24:44.947703 | 2020-08-13T14:52:19 | 2020-08-13T14:52:19 | 279,652,903 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,679 | py | """
Django settings for mahkalastore project.
Generated by 'django-admin startproject' using Django 3.0.8.
For more information on this file, see
https://docs.djangoproject.com/en/3.0/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.0/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
from mahkalastore import secrets
SECRET_KEY = secrets.seckey
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'product.apps.ProductConfig',
'mptt',
'ckeditor',
'home',
'user',
'order',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'mahkalastore.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'mahkalastore.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.0/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.0/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.0/howto/static-files/
STATIC_URL = '/static/'
MEDIA_URL = '/uploads/'
MEDIA_ROOT = os.path.join(BASE_DIR, 'uploads')
#.........
SITE_ID = 1
####################################
## CKEDITOR CONFIGURATION ##
####################################
CKEDITOR_JQUERY_URL = 'https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js'
CKEDITOR_UPLOAD_PATH = 'images/'
CKEDITOR_IMAGE_BACKEND = "pillow"
CKEDITOR_CONFIGS = {
'default': {
'toolbar': None,
},
}
###################################
| [
"nimanoori000@gmail.com"
] | nimanoori000@gmail.com |
f3d59c59e7e9b0a08ba9fcd1d5b3cb89a0914baa | 39a1d46fdf2acb22759774a027a09aa9d10103ba | /tests/layer_tests/onnx_tests/test_cumsum.py | b49f00f887c3ffb66b20cf937597ece1c21884fa | [
"Apache-2.0"
] | permissive | mashoujiang/openvino | 32c9c325ffe44f93a15e87305affd6099d40f3bc | bc3642538190a622265560be6d88096a18d8a842 | refs/heads/master | 2023-07-28T19:39:36.803623 | 2021-07-16T15:55:05 | 2021-07-16T15:55:05 | 355,786,209 | 1 | 3 | Apache-2.0 | 2021-06-30T01:32:47 | 2021-04-08T06:22:16 | C++ | UTF-8 | Python | false | false | 9,264 | py | # Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import pytest
from common.layer_test_class import check_ir_version
from common.onnx_layer_test_class import OnnxRuntimeLayerTest
from unit_tests.utils.graph import build_graph
def cumsum(a, axis=None, exclusive=False, reverse=False):
if reverse:
a = np.flip(a, axis)
res = np.cumsum(a, axis=axis)
if exclusive:
res -= a
if reverse:
res = np.flip(res, axis)
return res
class TestCumSum(OnnxRuntimeLayerTest):
def create_net(self, shape, ir_version, axis=None, reverse=None, exclusive=None):
"""
ONNX net IR net
Input->CumSum->Output => Input->CumSum
"""
#
# Create ONNX model
#
import onnx
from onnx import helper
from onnx import TensorProto
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, shape)
nodes = []
inputs = ['input']
if axis is not None:
node_axis_def = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['axis'],
value=helper.make_tensor(
name='const_tensor',
data_type=TensorProto.INT64,
dims=[],
vals=[axis],
),
)
nodes.append(node_axis_def)
inputs.append('axis')
args = dict()
if exclusive is not None:
args['exclusive'] = exclusive
if reverse is not None:
args['reverse'] = reverse
node_def = onnx.helper.make_node(
'CumSum',
inputs=inputs,
outputs=['output'],
**args
)
nodes.append(node_def)
# Create the graph (GraphProto)
graph_def = helper.make_graph(
nodes,
'test_model',
[input],
[output],
)
# Create the model (ModelProto)
onnx_net = helper.make_model(graph_def, producer_name='test_model')
onnx.checker.check_model(onnx_net)
#
# Create reference IR net
#
ref_net = None
if check_ir_version(10, None, ir_version):
nodes_attributes = {
'input': {'kind': 'op', 'type': 'Parameter'},
'input_data': {'shape': shape, 'kind': 'data'},
'node': {'kind': 'op', 'type': 'CumSum'},
'node_data': {'shape': shape, 'kind': 'data'},
'result': {'kind': 'op', 'type': 'Result'}
}
if exclusive is not None:
nodes_attributes['node']['exclusive'] = exclusive
if reverse is not None:
nodes_attributes['node']['reverse'] = reverse
edges = [('input', 'input_data'),
('input_data', 'node'),
('node', 'node_data'),
('node_data', 'result')
]
if axis is not None:
nodes_attributes.update({
'input_axis_data': {'kind': 'data', 'value': [axis]},
'axis': {'kind': 'op', 'type': 'Const'},
'axis_data': {'shape': [], 'kind': 'data'}})
edges.extend([('input_axis_data', 'axis'),
('axis', 'axis_data'),
('axis_data', 'node')])
ref_net = build_graph(nodes_attributes, edges)
return onnx_net, ref_net
def create_net_const(self, shape, precision, ir_version, axis=None, reverse=None, exclusive=None):
"""
ONNX net IR net
Input->Concat(+cumsum const)->Output => Input->Concat(+const)
"""
#
# Create ONNX model
#
import onnx
from onnx import helper
from onnx import TensorProto
import numpy as np
concat_axis = 0
output_shape = shape.copy()
output_shape[concat_axis] *= 2
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
constant = np.random.randn(*shape).astype(np.float)
node_const_def = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['const1'],
value=helper.make_tensor(
name='const_tensor',
data_type=TensorProto.FLOAT,
dims=constant.shape,
vals=constant.flatten(),
),
)
nodes = [node_const_def]
inputs = ['const1']
if axis is not None:
node_axis_def = onnx.helper.make_node(
'Constant',
inputs=[],
outputs=['axis'],
value=helper.make_tensor(
name='const_tensor',
data_type=TensorProto.INT64,
dims=[],
vals=[axis],
),
)
nodes.append(node_axis_def)
inputs.append('axis')
args = dict()
if exclusive is not None:
args['exclusive'] = exclusive
if reverse is not None:
args['reverse'] = reverse
node_def = onnx.helper.make_node(
'CumSum',
inputs=inputs,
outputs=['cumsum'],
**args
)
node_concat_def = onnx.helper.make_node(
'Concat',
inputs=['input', 'cumsum'],
outputs=['output'],
axis=concat_axis
)
nodes.extend([node_def, node_concat_def])
# Create the graph (GraphProto)
graph_def = helper.make_graph(
nodes,
'test_model',
[input],
[output],
)
# Create the model (ModelProto)
onnx_net = helper.make_model(graph_def, producer_name='test_model')
onnx.checker.check_model(onnx_net)
#
# Create reference IR net
#
constant = cumsum(constant, axis=axis, reverse=reverse, exclusive=exclusive)
ref_net = None
if check_ir_version(10, None, ir_version):
nodes_attributes = {
'input': {'kind': 'op', 'type': 'Parameter'},
'input_data': {'shape': shape, 'kind': 'data'},
'input_const_data': {'kind': 'data', 'value': constant.flatten()},
'const': {'kind': 'op', 'type': 'Const'},
'const_data': {'shape': shape, 'kind': 'data'},
'concat': {'kind': 'op', 'type': 'Concat', 'axis': concat_axis},
'concat_data': {'shape': output_shape, 'kind': 'data'},
'result': {'kind': 'op', 'type': 'Result'}
}
ref_net = build_graph(nodes_attributes,
[('input', 'input_data'),
('input_const_data', 'const'),
('const', 'const_data'),
('input_data', 'concat'),
('const_data', 'concat'),
('concat', 'concat_data'),
('concat_data', 'result')
])
return onnx_net, ref_net
test_data = [
dict(shape=[1]),
dict(shape=[1, 2]),
dict(shape=[2, 4, 6]),
dict(shape=[2, 4, 6, 8]),
dict(shape=[2, 4, 6, 8, 10]),
dict(shape=[1, 2], axis=-2),
dict(shape=[1, 2], axis=1),
dict(shape=[2, 4, 6], axis=-3),
dict(shape=[2, 4, 6], axis=2),
dict(shape=[2, 4, 6, 8], axis=-4),
dict(shape=[2, 4, 6, 8], axis=3),
dict(shape=[2, 4, 6, 8, 10], axis=-1),
dict(shape=[2, 4, 6, 8, 10], axis=4)]
@pytest.mark.parametrize("params", test_data)
@pytest.mark.parametrize("reverse", [0, 1])
@pytest.mark.parametrize("exclusive", [0, 1])
@pytest.mark.nightly
def test_cumsum(self, params, reverse, exclusive, ie_device, precision, ir_version, temp_dir):
if 'axis' not in params:
pytest.skip('No axis cases fail in ONNX')
self._test(*self.create_net(**params, exclusive=exclusive, reverse=reverse, ir_version=ir_version),
ie_device, precision, ir_version, temp_dir=temp_dir)
@pytest.mark.parametrize("params", test_data)
@pytest.mark.parametrize("reverse", [0, 1])
@pytest.mark.parametrize("exclusive", [0, 1])
@pytest.mark.nightly
def test_cumsum_const(self, params, reverse, exclusive, ie_device, precision, ir_version, temp_dir):
if 'axis' not in params:
pytest.skip('No axis cases fail in ONNX')
self._test(*self.create_net_const(**params, precision=precision, exclusive=exclusive, reverse=reverse,
ir_version=ir_version), ie_device, precision, ir_version, temp_dir=temp_dir)
| [
"noreply@github.com"
] | mashoujiang.noreply@github.com |
5ef6346413ae628ba03088b2d210c9ae42298bd9 | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p03352/s946533946.py | 12fad2670ffb01c55aa659302df6c57fe72d8080 | [] | no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 173 | py | X = int(input())
res = 1
for i in range(X):
for p in range(2, X):
if i ** p <= X:
res = max(res, i ** p)
else:
break
print(res)
| [
"66529651+Aastha2104@users.noreply.github.com"
] | 66529651+Aastha2104@users.noreply.github.com |
5438414fe8e44e712ecbfe276212b0d08e32fd70 | e3b9aa9b17ebb55e53dbc4fa9d1f49c3a56c6488 | /samanage/komand_samanage/triggers/new_incidents/trigger.py | 470860710f58f767235a820a10dff1bf13aa4beb | [
"MIT"
] | permissive | OSSSP/insightconnect-plugins | ab7c77f91c46bd66b10db9da1cd7571dfc048ab7 | 846758dab745170cf1a8c146211a8bea9592e8ff | refs/heads/master | 2023-04-06T23:57:28.449617 | 2020-03-18T01:24:28 | 2020-03-18T01:24:28 | 248,185,529 | 1 | 0 | MIT | 2023-04-04T00:12:18 | 2020-03-18T09:14:53 | null | UTF-8 | Python | false | false | 2,192 | py | import komand
import time
from .schema import NewIncidentsInput, NewIncidentsOutput
# Custom imports below
class NewIncidents(komand.Trigger):
def __init__(self):
super(self.__class__, self).__init__(
name='new_incidents',
description='Check for new incidents',
input=NewIncidentsInput(),
output=NewIncidentsOutput())
def run(self, params={}):
frequency = params.get('frequency', 10)
cache_file_name = 'cached_incidents_ids'
with komand.helper.open_cachefile(cache_file_name) as cache_file:
self.logger.info(
'Found or created cache file: {}'.format(cache_file_name)
)
cached_ids = {l.strip() for l in cache_file.readlines()}
self.logger.info('Cached IDs: {}'.format(cached_ids))
while True:
try:
incidents = self.connection.api.list_incidents()
new_ids = set()
for incident in incidents:
incident_id = str(incident['id'])
if incident_id not in cached_ids:
cached_ids.add(incident_id)
new_ids.add(incident_id)
self.logger.info(
'New incident found: {}'.format(incident_id)
)
self.send({'incident': incident})
with komand.helper.open_cachefile(
cache_file_name, append=True
) as cache_file:
for incident_id in new_ids:
self.logger.info(
'Writing incident {} to cache file'.format(
incident_id
)
)
cache_file.write(incident_id)
time.sleep(frequency)
except Exception as e:
raise Exception(
'An error occurred while reading incidents: {}'.format(e)
)
def test(self):
self.connection.api.list_incidents()
return {'incident': {}}
| [
"jonschipp@gmail.com"
] | jonschipp@gmail.com |
516fab03d860d4df846fb70176bd0d16d5007d2f | ddc6e402758c364d25ce9caeda7b3cd94dbcd546 | /Medium/535_EncodeandDecodeTinyURL.py | 2cda233163cdee5989cbaf60070c314a400fbc4d | [] | no_license | J-pcy/Jffery_Leetcode_Python | f01cdbb31a114bc6ed91139d0bd2cdddda35a503 | f34c370fbb9fb171d5ec33337116a764c25cd2dd | refs/heads/master | 2020-03-20T20:20:02.931776 | 2018-11-02T19:41:36 | 2018-11-02T19:41:36 | 137,682,076 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,105 | py | """
TinyURL is a URL shortening service where you enter a URL such as https://leetcode.com/problems/design-tinyurl and it returns a short URL such as http://tinyurl.com/4e9iAk.
Design the encode and decode methods for the TinyURL service. There is no restriction on how your encode/decode algorithm should work. You just need to ensure that a URL can be encoded to a tiny URL and the tiny URL can be decoded to the original URL.
"""
class Codec:
def __init__(self):
self.index = 0
self.map = {}
def encode(self, longUrl):
"""Encodes a URL to a shortened URL.
:type longUrl: str
:rtype: str
"""
self.index += 1
self.map[self.index] = longUrl
return "http://tinyurl.com/" + str(self.index)
def decode(self, shortUrl):
"""Decodes a shortened URL to its original URL.
:type shortUrl: str
:rtype: str
"""
return self.map[int(shortUrl.split('/')[-1])]
# Your Codec object will be instantiated and called as such:
# codec = Codec()
# codec.decode(codec.encode(url)) | [
"chenyupe@usc.edu"
] | chenyupe@usc.edu |
d4aa33ef9af10d78de2c1ed7a52823bdcaaae7c9 | 31cf77b4c0342c6148b35ae2613d5e2501d5e755 | /src/encoded/tests/fixtures/schemas/organism_development_series.py | 1a85ac543daf46cc0080b5b5fe5361773c6e4447 | [
"MIT"
] | permissive | ENCODE-DCC/encoded | 096de8a6d60c959a783cc9517f1d60bd6c21b71f | 80e05610c79b46d0890228555bb03e436b2fef11 | refs/heads/dev | 2023-08-08T15:45:07.493187 | 2023-08-03T20:01:24 | 2023-08-03T20:01:24 | 7,045,549 | 110 | 69 | MIT | 2023-09-12T23:59:45 | 2012-12-07T00:52:21 | JavaScript | UTF-8 | Python | false | false | 405 | py | import pytest
@pytest.fixture
def organism_development_series_17(testapp, lab, base_experiment_submitted, award):
item = {
'award': award['uuid'],
'lab': lab['uuid'],
'related_datasets': [base_experiment_submitted['@id']],
'schema_version': '17',
'internal_tags': ['ENCYCLOPEDIAv3', 'ENCYCLOPEDIAv4', 'ENCYCLOPEDIAv5', 'ENCYCLOPEDIAv6']
}
return item
| [
"noreply@github.com"
] | ENCODE-DCC.noreply@github.com |
c047613e9ed2f92e9bf284726158a743c18d970f | a5747577f1f4b38823f138ec0fbb34a0380cd673 | /17/mc/ExoDiBosonResonances/EDBRTreeMaker/test/crab3_analysisnewnewST_s-channel_4f_leptonDecays.py | f1704df01dc11d1aad6690e6981b899f1585d06c | [] | no_license | xdlyu/fullRunII_ntuple | 346fc1da4cec9da4c404aa1ec0bfdaece6df1526 | aa00ca4ce15ae050c3096d7af779de44fc59141e | refs/heads/master | 2020-08-03T07:52:29.544528 | 2020-01-22T14:18:12 | 2020-01-22T14:18:12 | 211,673,739 | 0 | 3 | null | null | null | null | UTF-8 | Python | false | false | 2,396 | py | from WMCore.Configuration import Configuration
config = Configuration()
config.section_("General")
config.General.requestName = 'newnewST_s-channel_4f_leptonDecays'
config.General.transferLogs = True
config.section_("JobType")
config.JobType.pluginName = 'Analysis'
config.JobType.inputFiles = ['Fall17_17Nov2017_V8_MC_L1FastJet_AK4PFchs.txt','Fall17_17Nov2017_V8_MC_L2Relative_AK4PFchs.txt','Fall17_17Nov2017_V8_MC_L3Absolute_AK4PFchs.txt','Fall17_17Nov2017_V8_MC_L1FastJet_AK8PFchs.txt','Fall17_17Nov2017_V8_MC_L2Relative_AK8PFchs.txt','Fall17_17Nov2017_V8_MC_L3Absolute_AK8PFchs.txt','Fall17_17Nov2017_V8_MC_L1FastJet_AK8PFPuppi.txt','Fall17_17Nov2017_V8_MC_L2Relative_AK8PFPuppi.txt','Fall17_17Nov2017_V8_MC_L3Absolute_AK8PFPuppi.txt','Fall17_17Nov2017_V8_MC_L1FastJet_AK4PFPuppi.txt','Fall17_17Nov2017_V8_MC_L2Relative_AK4PFPuppi.txt','Fall17_17Nov2017_V8_MC_L3Absolute_AK4PFPuppi.txt']
#config.JobType.inputFiles = ['PHYS14_25_V2_All_L1FastJet_AK4PFchs.txt','PHYS14_25_V2_All_L2Relative_AK4PFchs.txt','PHYS14_25_V2_All_L3Absolute_AK4PFchs.txt','PHYS14_25_V2_All_L1FastJet_AK8PFchs.txt','PHYS14_25_V2_All_L2Relative_AK8PFchs.txt','PHYS14_25_V2_All_L3Absolute_AK8PFchs.txt']
# Name of the CMSSW configuration file
#config.JobType.psetName = 'bkg_ana.py'
config.JobType.psetName = 'analysis.py'
#config.JobType.allowUndistributedCMSSW = True
config.JobType.sendExternalFolder = True
config.JobType.allowUndistributedCMSSW = True
config.section_("Data")
#config.Data.inputDataset = '/WJetsToLNu_13TeV-madgraph-pythia8-tauola/Phys14DR-PU20bx25_PHYS14_25_V1-v1/MINIAODSIM'
config.Data.inputDataset = '/ST_s-channel_4f_leptonDecays_13TeV-amcatnlo-pythia8_TuneCUETP8M1/RunIISummer16MiniAODv2-PUMoriond17_80X_mcRun2_asymptotic_2016_TrancheIV_v6-v1/MINIAODSIM'
config.Data.inputDBS = 'global'
#config.Data.inputDBS = 'phys03'
config.Data.splitting = 'FileBased'
config.Data.unitsPerJob =5
config.Data.totalUnits = -1
config.Data.publication = False
name = 'idle'
steam_dir = 'xulyu'
config.Data.outLFNDirBase = '/store/group/dpg_trigger/comm_trigger/TriggerStudiesGroup/STEAM/' + steam_dir + '/' + name + '/'
# This string is used to construct the output dataset name
config.JobType.sendExternalFolder = True
config.Data.outputDatasetTag = 'newnewST_s-channel_4f_leptonDecays'
config.section_("Site")
# Where the output files will be transmitted to
config.Site.storageSite = 'T2_CH_CERN'
| [
"XXX@cern.ch"
] | XXX@cern.ch |
bcca40aea327335a5be27121afa6c83aad3e0049 | 3267fb38696d7b114a22f476f2c60425d6ee349a | /src/tests/test_api/test_auth_endpoint.py | b2aa2759373a6cb2b92f4a901b98debd44f51dfe | [] | no_license | marcinowski/github-adapter | c0092e3f817f9dc1d97691e81b1c247ae281b2c7 | 2d7c6b9601da082de246450cc840412f0c4331b5 | refs/heads/master | 2022-12-10T00:53:39.386198 | 2017-09-06T10:57:09 | 2017-09-06T10:57:09 | 100,716,960 | 0 | 0 | null | 2021-06-01T22:02:20 | 2017-08-18T13:55:02 | Python | UTF-8 | Python | false | false | 1,805 | py | """
:created on: 2017-08-22
:author: Marcin Muszynski
:contact: marcinowski007@gmail.com
"""
from flask import session
from unittest.mock import MagicMock
from api.auth import AuthLogin, AuthLogout
from ..generic import GenericTestCase
from ..github_responses import AUTH_RESPONSE, AUTH_STATUS, ERROR_RESPONSE_401
class TestAuthResource(GenericTestCase):
def test_login(self):
""" Simple workflow test for fetching credentials from request """
with self.app.test_request_context('/', data={'username': 'test', 'password': 'test'}):
username, password = AuthLogin()._get_credentials_from_request()
self.assertEqual(username, 'test')
def test_storing_credentials(self):
""" Simple test for keeping credentials in session while authenticating """
a = AuthLogin()
mock = MagicMock()
mock.return_value = AUTH_RESPONSE, AUTH_STATUS
a.fetch_from_github = mock
with self.app.test_request_context('/', data={'username': 't_username', 'password': 'test'}):
a.post()
self.assertTrue(a.is_authenticated())
self.assertEqual(session['username'], 't_username')
def test_nok_response(self):
""" Error GitHub response handling """
a = AuthLogin()
mock = MagicMock()
mock.return_value = ERROR_RESPONSE_401, 401
a.fetch_from_github = mock
resp, status_code = a.post()
self.assertEqual(status_code, 401)
def test_logout(self):
""" Simple workflow test for logging out """
a = AuthLogout()
with self.app.test_request_context('/'):
session['authenticated'] = True
a.get()
self.assertFalse(a.is_authenticated())
self.assertFalse('username' in session)
| [
"muszynskimarcin@wp.pl"
] | muszynskimarcin@wp.pl |
a2f91cc8c13f634a36add56d6baa39f288d1e554 | 8da90fe722d50c8f624a64c73e09c6795b2b1db5 | /tf_agents/bandits/policies/greedy_reward_prediction_policy_test.py | 90dc038c75e9ce9c3067af8021263874404847b5 | [
"Apache-2.0"
] | permissive | futurev/agents | d5e3ad62c469fc33cafe71566f0be18f01a37f3b | c721d09c8ce2e70e46a3a9d3c56483118575d3f4 | refs/heads/master | 2022-10-21T05:25:34.342989 | 2022-09-28T23:44:04 | 2022-09-28T23:44:04 | 257,982,748 | 0 | 0 | Apache-2.0 | 2020-04-22T18:12:43 | 2020-04-22T18:12:42 | null | UTF-8 | Python | false | false | 14,172 | py | # coding=utf-8
# Copyright 2018 The TF-Agents Authors.
#
# 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.
"""Test for greedy_reward_prediction_policy."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from tf_agents.bandits.networks import global_and_arm_feature_network
from tf_agents.bandits.networks import heteroscedastic_q_network
from tf_agents.bandits.policies import greedy_reward_prediction_policy as greedy_reward_policy
from tf_agents.bandits.specs import utils as bandit_spec_utils
from tf_agents.networks import network
from tf_agents.specs import array_spec
from tf_agents.specs import tensor_spec
from tf_agents.trajectories import time_step as ts
from tf_agents.utils import test_utils
from tensorflow.python.framework import test_util # pylint: disable=g-direct-tensorflow-import # TF internal
class DummyNet(network.Network):
def __init__(self, observation_spec, num_actions=3):
super(DummyNet, self).__init__(observation_spec, (), 'DummyNet')
# Store custom layers that can be serialized through the Checkpointable API.
self._dummy_layers = [
tf.keras.layers.Dense(
num_actions,
kernel_initializer=tf.compat.v1.initializers.constant(
[[1, 1.5, 2], [1, 1.5, 4]]),
bias_initializer=tf.compat.v1.initializers.constant(
[[1], [1], [-10]]))
]
def call(self, inputs, step_type=None, network_state=()):
del step_type
inputs = tf.cast(inputs, tf.float32)
for layer in self._dummy_layers:
inputs = layer(inputs)
return inputs, network_state
class HeteroscedasticDummyNet(
heteroscedastic_q_network.HeteroscedasticQNetwork):
def __init__(self, name=None, num_actions=3):
input_spec = array_spec.ArraySpec([2], np.float32)
action_spec = array_spec.BoundedArraySpec([1], np.float32, 1, num_actions)
input_tensor_spec = tensor_spec.from_spec(input_spec)
action_tensor_spec = tensor_spec.from_spec(action_spec)
super(HeteroscedasticDummyNet, self).__init__(input_tensor_spec,
action_tensor_spec)
self._value_layer = tf.keras.layers.Dense(
num_actions,
kernel_initializer=tf.compat.v1.initializers.constant(
[[1, 1.5, 2], [1, 1.5, 4]]),
bias_initializer=tf.compat.v1.initializers.constant(
[[1], [1], [-10]]))
self._log_variance_layer = tf.keras.layers.Dense(
num_actions,
kernel_initializer=tf.compat.v1.initializers.constant(
[[1, 1.5, 2], [1, 1.5, 4]]),
bias_initializer=tf.compat.v1.initializers.constant(
[[1], [1], [-10]]))
def call(self, inputs, step_type=None, network_state=()):
del step_type
inputs = tf.cast(inputs, tf.float32)
value = self._value_layer(inputs)
log_variance = self._log_variance_layer(inputs)
predictions = collections.namedtuple('QBanditNetworkResult',
('q_value_logits', 'log_variance'))
predictions = predictions(value, log_variance)
return predictions, network_state
@test_util.run_all_in_graph_and_eager_modes
class GreedyRewardPredictionPolicyTest(test_utils.TestCase):
def setUp(self):
super(GreedyRewardPredictionPolicyTest, self).setUp()
self._obs_spec = tensor_spec.TensorSpec([2], tf.float32)
self._time_step_spec = ts.time_step_spec(self._obs_spec)
self._action_spec = tensor_spec.BoundedTensorSpec((), tf.int32, 0, 2)
def testBuild(self):
policy = greedy_reward_policy.GreedyRewardPredictionPolicy(
self._time_step_spec,
self._action_spec,
reward_network=DummyNet(self._obs_spec))
self.assertEqual(policy.time_step_spec, self._time_step_spec)
self.assertEqual(policy.action_spec, self._action_spec)
def testMultipleActionsRaiseError(self):
action_spec = [tensor_spec.BoundedTensorSpec((), tf.int32, 0, 2)] * 2
with self.assertRaisesRegexp(
NotImplementedError,
'action_spec can only contain a single BoundedTensorSpec'):
greedy_reward_policy.GreedyRewardPredictionPolicy(
self._time_step_spec,
action_spec,
reward_network=DummyNet(self._obs_spec))
def testWrongActionsRaiseError(self):
action_spec = tensor_spec.BoundedTensorSpec((5, 6, 7), tf.float32, 0, 2)
with self.assertRaisesRegexp(
NotImplementedError,
'action_spec must be a BoundedTensorSpec of type int32.*'):
greedy_reward_policy.GreedyRewardPredictionPolicy(
self._time_step_spec,
action_spec,
reward_network=DummyNet(self._obs_spec))
def testWrongOutputLayerRaiseError(self):
tf.compat.v1.set_random_seed(1)
action_spec = tensor_spec.BoundedTensorSpec((), tf.int32, 10, 20)
policy = greedy_reward_policy.GreedyRewardPredictionPolicy(
self._time_step_spec,
action_spec,
reward_network=DummyNet(self._obs_spec))
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_step = ts.restart(observations, batch_size=2)
with self.assertRaisesRegexp(
ValueError,
r'The number of actions \(11\) does not match the reward_network output'
r' size \(3\)\.'):
policy.action(time_step, seed=1)
def testAction(self):
tf.compat.v1.set_random_seed(1)
policy = greedy_reward_policy.GreedyRewardPredictionPolicy(
self._time_step_spec,
self._action_spec,
reward_network=DummyNet(self._obs_spec))
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_step = ts.restart(observations, batch_size=2)
action_step = policy.action(time_step, seed=1)
self.assertEqual(action_step.action.shape.as_list(), [2])
self.assertEqual(action_step.action.dtype, tf.int32)
# Initialize all variables
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(self.evaluate(action_step.action), [1, 2])
def testActionHeteroscedastic(self):
tf.compat.v1.set_random_seed(1)
policy = greedy_reward_policy.GreedyRewardPredictionPolicy(
self._time_step_spec, self._action_spec,
reward_network=HeteroscedasticDummyNet())
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_step = ts.restart(observations, batch_size=2)
action_step = policy.action(time_step, seed=1)
self.assertEqual(action_step.action.shape.as_list(), [2])
self.assertEqual(action_step.action.dtype, tf.int32)
# Initialize all variables
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(self.evaluate(action_step.action), [1, 2])
def testActionScalarSpec(self):
tf.compat.v1.set_random_seed(1)
action_spec = tensor_spec.BoundedTensorSpec((), tf.int32, 0, 2)
policy = greedy_reward_policy.GreedyRewardPredictionPolicy(
self._time_step_spec,
action_spec,
reward_network=DummyNet(self._obs_spec))
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_step = ts.restart(observations, batch_size=2)
action_step = policy.action(time_step, seed=1)
self.assertEqual(action_step.action.shape.as_list(), [2])
self.assertEqual(action_step.action.dtype, tf.int32)
# Initialize all variables
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(self.evaluate(action_step.action), [1, 2])
def testActionScalarSpecWithShift(self):
tf.compat.v1.set_random_seed(1)
action_spec = tensor_spec.BoundedTensorSpec((), tf.int32, 10, 12)
policy = greedy_reward_policy.GreedyRewardPredictionPolicy(
self._time_step_spec,
action_spec,
reward_network=DummyNet(self._obs_spec))
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_step = ts.restart(observations, batch_size=2)
action_step = policy.action(time_step, seed=1)
self.assertEqual(action_step.action.shape.as_list(), [2])
self.assertEqual(action_step.action.dtype, tf.int32)
# Initialize all variables
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(self.evaluate(action_step.action), [11, 12])
def testMaskedAction(self):
tf.compat.v1.set_random_seed(1)
action_spec = tensor_spec.BoundedTensorSpec((), tf.int32, 0, 2)
observation_spec = (tensor_spec.TensorSpec([2], tf.float32),
tensor_spec.TensorSpec([3], tf.int32))
time_step_spec = ts.time_step_spec(observation_spec)
def split_fn(obs):
return obs[0], obs[1]
policy = greedy_reward_policy.GreedyRewardPredictionPolicy(
time_step_spec,
action_spec,
reward_network=DummyNet(observation_spec[0]),
observation_and_action_constraint_splitter=split_fn)
observations = (tf.constant([[1, 2], [3, 4]], dtype=tf.float32),
tf.constant([[0, 0, 1], [0, 1, 0]], dtype=tf.int32))
time_step = ts.restart(observations, batch_size=2)
action_step = policy.action(time_step, seed=1)
self.assertEqual(action_step.action.shape.as_list(), [2])
self.assertEqual(action_step.action.dtype, tf.int32)
# Initialize all variables
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(self.evaluate(action_step.action), [2, 1])
def testUpdate(self):
tf.compat.v1.set_random_seed(1)
policy = greedy_reward_policy.GreedyRewardPredictionPolicy(
self._time_step_spec,
self._action_spec,
reward_network=DummyNet(self._obs_spec))
new_policy = greedy_reward_policy.GreedyRewardPredictionPolicy(
self._time_step_spec,
self._action_spec,
reward_network=DummyNet(self._obs_spec))
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_step = ts.restart(observations, batch_size=2)
action_step = policy.action(time_step, seed=1)
new_action_step = new_policy.action(time_step, seed=1)
self.assertEqual(len(policy.variables()), 2)
self.assertEqual(len(new_policy.variables()), 2)
self.assertEqual(action_step.action.shape, new_action_step.action.shape)
self.assertEqual(action_step.action.dtype, new_action_step.action.dtype)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertIsNone(self.evaluate(new_policy.update(policy)))
self.assertAllEqual(self.evaluate(action_step.action), [1, 2])
self.assertAllEqual(self.evaluate(new_action_step.action), [1, 2])
def testPredictedRewards(self):
tf.compat.v1.set_random_seed(1)
policy = greedy_reward_policy.GreedyRewardPredictionPolicy(
self._time_step_spec,
self._action_spec,
reward_network=DummyNet(self._obs_spec),
emit_policy_info=('predicted_rewards_mean',))
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_step = ts.restart(observations, batch_size=2)
action_step = policy.action(time_step, seed=1)
self.assertEqual(action_step.action.shape.as_list(), [2])
self.assertEqual(action_step.action.dtype, tf.int32)
# Initialize all variables
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(self.evaluate(action_step.action), [1, 2])
# The expected values are obtained by passing the observation through the
# Keras dense layer of the DummyNet (defined above).
predicted_rewards_expected_array = np.array([[4.0, 5.5, 0.0],
[8.0, 11.5, 12.0]])
p_info = self.evaluate(action_step.info)
self.assertAllClose(p_info.predicted_rewards_mean,
predicted_rewards_expected_array)
def testPerArmRewards(self):
if not tf.executing_eagerly():
return
tf.compat.v1.set_random_seed(3000)
obs_spec = bandit_spec_utils.create_per_arm_observation_spec(2, 3, 4)
time_step_spec = ts.time_step_spec(obs_spec)
action_spec = tensor_spec.BoundedTensorSpec((), tf.int32, 0, 3)
reward_network = (
global_and_arm_feature_network.create_feed_forward_common_tower_network(
obs_spec, (4, 3), (3, 4), (4, 2)))
policy = greedy_reward_policy.GreedyRewardPredictionPolicy(
time_step_spec,
action_spec,
reward_network=reward_network,
accepts_per_arm_features=True,
emit_policy_info=('predicted_rewards_mean',))
observations = {
bandit_spec_utils.GLOBAL_FEATURE_KEY:
tf.constant([[1, 2], [3, 4]], dtype=tf.float32),
bandit_spec_utils.PER_ARM_FEATURE_KEY:
tf.cast(
tf.reshape(tf.random.shuffle(tf.range(24)), shape=[2, 4, 3]),
dtype=tf.float32)
}
time_step = ts.restart(observations, batch_size=2)
action_step = policy.action(time_step, seed=1)
self.assertEqual(action_step.action.shape.as_list(), [2])
self.assertEqual(action_step.action.dtype, tf.int32)
# Initialize all variables
self.evaluate(tf.compat.v1.global_variables_initializer())
action = self.evaluate(action_step.action)
self.assertAllEqual(action.shape, [2])
p_info = self.evaluate(action_step.info)
self.assertAllEqual(p_info.predicted_rewards_mean.shape, [2, 4])
self.assertAllEqual(p_info.chosen_arm_features.shape, [2, 3])
first_action = action[0]
first_arm_features = observations[bandit_spec_utils.PER_ARM_FEATURE_KEY][0]
self.assertAllEqual(p_info.chosen_arm_features[0],
first_arm_features[first_action])
if __name__ == '__main__':
tf.test.main()
| [
"copybara-worker@google.com"
] | copybara-worker@google.com |
af0406bc37dc6322179122bb2b34e46c5407bf26 | 6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4 | /BZ4mMcEz3aqosEtbC_7.py | e3f9da38350d7784a4a4a0d20383b509a28ee3da | [] | no_license | daniel-reich/ubiquitous-fiesta | 26e80f0082f8589e51d359ce7953117a3da7d38c | 9af2700dbe59284f5697e612491499841a6c126f | refs/heads/master | 2023-04-05T06:40:37.328213 | 2021-04-06T20:17:44 | 2021-04-06T20:17:44 | 355,318,759 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 144 | py |
def mean(num):
count = 0
str_num = str(num)
for i in range(len(str_num)):
count += int(str_num[i])
return int(count/len(str_num))
| [
"daniel.reich@danielreichs-MacBook-Pro.local"
] | daniel.reich@danielreichs-MacBook-Pro.local |
5e47baa390d8d2f117b9ae64bc39ef1cfc413d28 | 27acb207b21b4572561de4a5f7dfb9740318c0b8 | /Python-Programming-Essentials/Week3/Ex10_W3_smaller_root.py | be67b001047ad7f7053dfbcba4458612dc558b08 | [] | no_license | iamieht/intro-scripting-in-python-specialization | ee836ef05b62f6c74fe8da3ee137687b4d0035cf | 8ea4f85f0ed3dcd541f89521c013335e9eb32980 | refs/heads/master | 2021-01-16T05:35:51.616276 | 2020-06-08T18:39:45 | 2020-06-08T18:39:45 | 242,993,577 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,723 | py | """
Compute the smaller root of a quadratic equation.
"""
###################################################
# Smaller quadratic root formula
# Student should enter function on the next lines.
def smaller_root(a, b, c):
'''
returns the smaller solution to the quadratic equation if one exists
'''
discriminant = b ** 2 - 4 * a * c
smaller = 0
if discriminant < 0 or a == 0:
print("Error: No real solutions")
else:
discriminant_sqrt = discriminant ** 0.5
if a > 0:
smaller = - discriminant_sqrt
else:
smaller = discriminant_sqrt
return (-b + smaller) / (2 * a)
###################################################
# Tests
# Student should not change this code.
coeff_a, coeff_b, coeff_c = 1, 2, 3
print("The smaller root of " + str(coeff_a) + "x^2 + " + str(coeff_b) +
"x + " + str(coeff_c) + " is: ")
print(str(smaller_root(coeff_a, coeff_b, coeff_c)))
coeff_a, coeff_b, coeff_c = 2, 0, -10
print("The smaller root of " + str(coeff_a) + "x^2 + " + str(coeff_b) +
"x + " + str(coeff_c) + " is: ")
print(str(smaller_root(coeff_a, coeff_b, coeff_c)))
coeff_a, coeff_b, coeff_c = 6, -3, 5
print("The smaller root of " + str(coeff_a) + "x^2 + " + str(coeff_b) +
"x + " + str(coeff_c) + " is: ")
print(str(smaller_root(coeff_a, coeff_b, coeff_c)))
###################################################
# Expected output
# Student should look at the following comments and compare to printed output.
#The smaller root of 1x^2 + 2x + 3 is:
#Error: No real solutions
#None
#The smaller root of 2x^2 + 0x + -10 is:
#-2.2360679775
#The smaller root of 6x^2 + -3x + 5 is:
#Error: No real solutions
#None
| [
"iamieht@gmail.com"
] | iamieht@gmail.com |
6783bf9ea4a5df08ea547298358e38c0ad0d7867 | 2ac13c73340e5f4126e1dc394cdca45e3b2b223b | /utils/time_now.py | 76025e95be078f76beee9634e373b4a500d4b4d8 | [] | no_license | EgbieAndersonUku1/price_alerter_app | 36074fc32aedde1aee0524a271e98d3da18126d1 | 87e1d6ac05a19b0255c43003e957190f597d4655 | refs/heads/master | 2020-03-17T07:04:50.299396 | 2018-06-06T00:06:45 | 2018-06-06T00:06:45 | 133,375,739 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 444 | py | from datetime import datetime
from datetime import timedelta
def time_passed_since_current_time(minutes):
"""time_passed_since_current_time(int) -> returns time obj
Returns the number of minutes that has elapsed between
the current time and the passed in parameter minutes.
"""
return time_now() - timedelta(minutes=minutes)
def time_now():
"""Returns the current time object"""
return datetime.utcnow()
| [
"jayunderwood2011@hotmail.com"
] | jayunderwood2011@hotmail.com |
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