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__author__ = 'Irina.Chegodaeva'
| python |
from django.shortcuts import render, get_object_or_404
from .models import BlogPost
def blogIndex(request):
blogposts = BlogPost.objects.order_by('-pub_date')
context = {
'heading':'The Blog',
'subheading':'',
'title':'Blog',
'copyright':'Pending',
'blogposts':blogposts,
}
return render(request,'blog-home-2.html',context)
def blogDetail(request,postid):
post = get_object_or_404(BlogPost, pk=postid)
context = {
'post' : post,
'copyright':'Pending',
}
return render(request,'blog-post.html',context) | python |
"""Module test_listwrapper.
The MIT License
Copyright 2022 Thomas Lehmann.
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.
"""
# pylint: disable=compare-to-zero,no-self-use
from unittest import TestCase
from responsive.data import make_responsive
from responsive.wrapper import ListWrapper
class ListWrapperTest(TestCase):
"""Testing class ListWrapper."""
def test_len(self):
"""Testing length of list."""
wrapper = ListWrapper([1, 2, 3, 4], make_responsive)
self.assertEqual(len(wrapper), 4)
def test_set_and_get_by_index(self):
"""Testing __setitem__ and __getitem__."""
data = [1, 2, 3, 4]
wrapper = ListWrapper([1, 2, 3, 4], make_responsive)
wrapper[2] = 9
self.assertEqual(wrapper[2], 9)
self.assertEqual(data, [1, 2, 3, 4])
def test_eq(self):
"""Testing __eq__."""
data = [1, 2, 3, 4]
wrapper = ListWrapper(data, make_responsive)
self.assertEqual(wrapper, data)
self.assertNotEqual(wrapper, 1234)
def test_iter(self):
"""Testing in and not in."""
data = [1, 2, 3, 4]
wrapper = ListWrapper(data, make_responsive)
self.assertTrue(2 in wrapper)
self.assertTrue(5 not in wrapper)
self.assertEqual(list(wrapper), data)
| python |
import cv2
import dlib
import imutils
from imutils import face_utils
import winsound
from scipy.spatial import distance
detector=dlib.get_frontal_face_detector()
predict=dlib.shape_predictor("C:/Users/kushal asn/Downloads/shape_predictor_68_face_landmarks.dat")
def eye_aspect_ratio(Eye):
A=distance.euclidean(Eye[1],Eye[5])
B=distance.euclidean(Eye[2],Eye[4])
C=distance.euclidean(Eye[0],Eye[3])
ear=(A+B)/(2*C)
return ear
thresh=0.30
frame_rate=30
duration=1000
frequency=2500
(lstart,lend)=face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
(rstart,rend)=face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
cap=cv2.VideoCapture(0)
flag=0
while(True):
ret,frame=cap.read()
frame=imutils.resize(frame,width=500)
if ret:
gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
subjects=detector(gray,0)
for subject in subjects:
shape=predict(gray,subject)
shape=face_utils.shape_to_np(shape)
leye=shape[lstart:lend]
reye=shape[rstart:rend]
lear=eye_aspect_ratio(leye)
rear=eye_aspect_ratio(reye)
lhull=cv2.convexHull(leye)
rhull=cv2.convexHull(reye)
ear=(lear+rear)/2
if(ear<thresh):
flag+=1
print(flag)
if(flag>frame_rate):
winsound.Beep(frequency,duration)
print("drowsy alert")
else:
flag=0
cv2.imshow("Frame",frame)
if(cv2.waitKey(1)==ord("q")):
break
cv2.destroyAllWindows()
cap.release()
| python |
import re
import cltk.corpus.persian.alphabet as alphabet
from cltk.corpus.arabic.alphabet import *
to_reform = [
{
"characters": [
HAMZA,
HAMZA_BELOW,
HAMZA_ABOVE,
HAMZA_ISOLATED,
MINI_ALEF,
SMALL_ALEF,
SMALL_WAW,
SMALL_YEH,
KASHEEDA,
FATHATAN,
DAMMATAN,
KASRATAN,
FATHA,
DAMMA,
KASRA,
SHADDA,
SUKUN,
alphabet.THOUSANDS,
alphabet.DECIMAL
],
"to_be": ""
},
{
"characters": [
ALEF_MADDA,
ALEF_WASLA,
HAMZA_BELOW_ALEF,
HAMZA_ABOVE_ALEF,
],
"to_be": alphabet.ALEF
},
{
"characters": [
ALEF_MAKSURA,
YEH,
],
"to_be": alphabet.YE
},
{
"characters": [KAF],
"to_be": alphabet.KAF
},
{
"characters": [
LAM_ALEF,
LAM_ALEF_HAMZA_ABOVE,
LAM_ALEF_HAMZA_BELOW,
LAM_ALEF_MADDA_ABOVE,
],
"to_be": alphabet.LAM + alphabet.ALEF
},
{
"characters": [TEH_MARBUTA],
"to_be": alphabet.HE2
},
]
replacementDict = {}
for rule in toReform:
for character in rule["characters"]:
replacementDict[character] = rule["to_be"]
for originalForm, shapedForms in SHAPED_FORMS.items():
for form in shapedForms:
replacementDict[form] = replacementDict.get(originalForm, originalForm)
for i in range(10):
replacementDict[EASTERN_ARABIC_NUMERALS[i]] = alphabet.NUMERALS[i]
replacementDict[WESTERN_ARABIC_NUMERALS[i]] = alphabet.NUMERALS[i]
# Use the commented parts for Word2Vec embeddings
# replacementDict[alphabet.NUMERALS[i]] = " %s " % alphabet.NUMERALS_WRITINGS[i]
# for char in '[!"#%\'()*+,-./:;<=>?@\[\]^_`{|}~’”“′‘\\\]؟؛«»،٪':
# replacementDict[char] = " "
#
# replacementDict[" +"] = " "
replacementRegex = re.compile("(%s)" % "|".join(map(re.escape, replacementDict.keys())))
def standardize(text):
return replacementRegex.sub(lambda mo: replacementDict[mo.string[mo.start():mo.end()]], text)
| python |
from __future__ import absolute_import, division, print_function
VERSION = '1.4.0'
def get_version():
return VERSION
__version__ = get_version()
def get_changelist():
# Legacy from the perforce era, but keeping this. It's not worth breaking
return "UnknownChangelist"
| python |
"""
* Copyright 2019 TIBCO Software Inc. 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.
* A copy of the License is included in the distribution package with this file.
* You also 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.
*
* File name :connectionimpl.py
* Created on: 5/15/2019
* Created by: suresh
*
* SVN Id: $Id: connectionimpl.py 3256 2019-06-10 03:31:30Z ssubrama $
*
* This file encapsulates connection interfaces
"""
from tgdb.log import *
import tgdb.log as tglog
from tgdb.utils import *
from tgdb.impl.atomics import *
import typing
import tgdb.channel as tgchannel
import tgdb.impl.channelimpl as tgchannelimpl
import tgdb.pdu as tgpdu
import tgdb.impl.pduimpl as tgpduimpl
import tgdb.connection as tgconn
import tgdb.model as tgmodel
import tgdb.impl.entityimpl as tgentimpl
import tgdb.impl.gmdimpl as tggmdimpl
import tgdb.query as tgquery
import tgdb.impl.queryimpl as tgqueryimpl
import tgdb.exception as tgexception
import tgdb.bulkio as tgbulk
import tgdb.admin as tgadm
def findCommandForLang(lang: str) -> tgquery.TGQueryCommand:
retCommand: tgquery.TGQueryCommand
if lang == "tgql":
retCommand = tgquery.TGQueryCommand.Execute
elif lang == "gremlin":
retCommand = tgquery.TGQueryCommand.ExecuteGremlinStr
elif lang == "gbc":
retCommand = tgquery.TGQueryCommand.ExecuteGremlin
else:
raise tgexception.TGException("Unknown property for ConnectionDefaultQueryLanguage: %s", lang)
return retCommand
def findCommandAndQueryString(query: str, props: tgchannel.TGProperties) -> typing.Tuple[tgquery.TGQueryCommand, str]:
lang: str = props.get(ConfigName.ConnectionDefaultQueryLanguage,
ConfigName.ConnectionDefaultQueryLanguage.defaultvalue)
retCommand: tgquery.TGQueryCommand
retStr = query
try:
idx: int = query.index("://")
prefix = query[:idx].lower()
retCommand = findCommandForLang(prefix)
retStr = query[idx + 3:]
except ValueError:
lang = lang.lower()
retCommand = findCommandForLang(lang)
return retCommand, retStr
class ConnectionImpl(tgconn.TGConnection):
def __init__(self, url, username, password, dbName: typing.Optional[str], env):
self.__url__ = url
self.__username__ = username
self.__password__ = password
self.__props__: TGProperties = TGProperties(env)
self._dbName = dbName
self.__channel__: tgchannel.TGChannel = tgchannel.TGChannel.createChannel(url, username, password, dbName,
self.__props__)
self.__props__.update(tgchannelimpl.LinkUrl.parse(url).properties)
self.__gof__: tggmdimpl.GraphObjectFactoryImpl = tggmdimpl.GraphObjectFactoryImpl(self)
self.__addEntities__: typing.Dict[int, tgentimpl.AbstractEntity] = {}
self.__updateEntities__: typing.Dict[int, tgentimpl.AbstractEntity] = {}
self.__removeEntities__: typing.Dict[int, tgentimpl.AbstractEntity] = {}
self.__requestIds__ = AtomicReference('i', 0)
def _genBCRWaiter(self) -> tgchannelimpl.BlockingChannelResponseWaiter:
timeout = self.__props__.get(ConfigName.ConnectionOperationTimeoutSeconds, None)
if timeout is not None and isinstance(timeout, str):
timeout = float(timeout)
requestId = self.__requestIds__.increment()
return tgchannelimpl.BlockingChannelResponseWaiter(requestId, timeout)
def connect(self):
tglog.gLogger.log(tglog.TGLevel.Debug, "Attempting to connect")
self.__channel__.connect()
tglog.gLogger.log(tglog.TGLevel.Debug, "Connected, now logging in.")
self.__channel__.start()
tglog.gLogger.log(tglog.TGLevel.Debug, "Logged in, now acquiring metadata.")
self.__initMetadata__()
tglog.gLogger.log(tglog.TGLevel.Debug, "Acquired metadata, now sending connection properties.")
self.__sendConnectionProperties()
tglog.gLogger.log(tglog.TGLevel.Debug, 'Connected successfully')
def __initMetadata__(self):
waiter = self._genBCRWaiter()
request = tgpduimpl.TGMessageFactory.createMessage(tgpdu.VerbId.MetadataRequest,
authtoken=self.__channel__.authtoken, sessionid=self.__channel__.sessionid)
response = self.__channel__.send(request, waiter)
if response.verbid != tgpdu.VerbId.MetadataResponse:
raise tgexception.TGException('Invalid response object received')
self.__gof__.graphmetadata.registry = response.typeregistry
def disconnect(self):
self.__channel__.disconnect()
self.__channel__.stop()
def commit(self):
channelResponse = self._genBCRWaiter()
try:
if gLogger.level is TGLevel.Debug:
def echoAttributes(ent: tgmodel.TGEntity):
gLogger.log(TGLevel, "Entity ID: %d", ent.virtualId)
attr: tgmodel.TGAttribute
for attr in ent.attributes:
gLogger.log(TGLevel, " Attribute: %s", attr._value)
[echoAttributes(ent) for ent in self.__addEntities__.values()]
[echoAttributes(ent) for ent in self.__updateEntities__.values()]
[echoAttributes(ent) for ent in self.__removeEntities__.values()]
request: tgpduimpl.CommitTransactionRequestMessage = tgpduimpl.TGMessageFactory.createMessage(
tgpdu.VerbId.CommitTransactionRequest, authtoken=self.__channel__.authtoken,
sessionid=self.__channel__.sessionid)
attrDescSet = self.graphObjectFactory.graphmetadata.attritubeDescriptors
request.addCommitList(self.__addEntities__, self.__updateEntities__, self.__removeEntities__, attrDescSet)
response: tgpduimpl.CommitTransactionResponseMessage = self.__channel__.send(request, channelResponse)
if response.exception is not None:
raise response.exception
response.finishReadWith(self.__addEntities__, self.__updateEntities__, self.__removeEntities__,
self.__gof__.graphmetadata.registry)
for id in self.__removeEntities__:
self.__removeEntities__[id].markDeleted()
if gLogger.isEnabled(TGLevel.Debug):
gLogger.log(TGLevel.Debug, "Transaction commit succeeded")
except IOError as e:
raise tgexception.TGException.buildException("IO Error", cause=e)
finally:
for id in self.__addEntities__:
self.__addEntities__[id].resetModifiedAttributes()
for id in self.__updateEntities__:
self.__updateEntities__[id].resetModifiedAttributes()
self.__addEntities__.clear()
self.__updateEntities__.clear()
self.__removeEntities__.clear()
def refreshMetadata(self):
self.__initMetadata__()
def rollback(self):
self.__addEntities__.clear()
self.__updateEntities__.clear()
self.__removeEntities__.clear()
def __sendConnectionProperties(self):
request: tgpduimpl.ConnectionPropertiesMessage = tgpduimpl.TGMessageFactory.createMessage(
tgpdu.VerbId.ConnectionPropertiesMessage, authtoken=self.__channel__.authtoken,
sessionid=self.__channel__.sessionid)
request.props = self.__channel__.properties
self.__channel__.send(request)
"""
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Begin Bulk Import Stuff //
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
"""
def startImport(self, loadopt: typing.Union[str, tgbulk.TGLoadOptions] = tgbulk.TGLoadOptions.Insert,
erroropt: typing.Union[str, tgbulk.TGErrorOptions] = tgbulk.TGErrorOptions.Stop,
dateformat: typing.Union[str, tgbulk.TGDateFormat] = tgbulk.TGDateFormat.YMD,
props: typing.Optional[TGProperties] = None):
import tgdb.impl.bulkioimpl as tgbulkimpl
ret: tgbulkimpl.BulkImportImpl
channelResponseWaiter = self._genBCRWaiter()
request: tgpduimpl.BeginImportSessionRequest
request = tgpduimpl.TGMessageFactory.createMessage(tgpdu.VerbId.BeginImportRequest,
authtoken=self.__channel__.authtoken, sessionid=self.__channel__.sessionid)
if isinstance(loadopt, str):
loadopt = tgbulk.TGErrorOptions.findVal(loadopt)
if loadopt == tgbulk.TGLoadOptions.Invalid:
raise tgexception.TGException("Bad argument: cannot have an invalid load option!")
if isinstance(erroropt, str):
erroropt = tgbulk.TGErrorOptions.findVal(erroropt)
if erroropt == tgbulk.TGErrorOptions.Invalid:
raise tgexception.TGException("Bad argument: cannot have an invalid error option!")
if isinstance(dateformat, str):
dateformat = tgbulk.TGDateFormat.findVal(dateformat)
if dateformat == tgbulk.TGDateFormat.Invalid:
raise tgexception.TGException("Bad argument: cannot have an invalid Date-Time Format!")
request.loadopt = loadopt
request.erroropt = erroropt
request.dtformat = dateformat
response: tgpduimpl.BeginImportSessionResponse = self.__channel__.send(request, channelResponseWaiter)
if response.error is not None:
raise response.error
ret = tgbulkimpl.BulkImportImpl(self, props)
return ret
def partialImportEntity(self, entType: tgmodel.TGEntityType, reqIdx: int, totReqs: int, data: str,
attrList: typing.List[str]) -> typing.List[tgadm.TGImportDescriptor]:
channelResponseWaiter = self._genBCRWaiter()
request: tgpduimpl.PartialImportRequest = tgpduimpl.TGMessageFactory.createMessage(
tgpdu.VerbId.PartialImportRequest,
authtoken=self.__channel__.authtoken, sessionid=self.__channel__.sessionid)
request.type = entType
request.reqIdx = reqIdx
request.totalRequestsForType = totReqs
request.data = data
request.attrList = attrList
response: tgpduimpl.PartialImportResponse = self.__channel__.send(request, channelResponseWaiter)
if response.error is not None:
raise response.error
return response.resultList
def endBulkImport(self):
channelResponseWaiter = self._genBCRWaiter()
request: tgpduimpl.EndBulkImportSessionRequest = tgpduimpl.TGMessageFactory.createMessage(
tgpdu.VerbId.EndImportRequest,
authtoken=self.__channel__.authtoken, sessionid=self.__channel__.sessionid)
response: tgpduimpl.PartialImportResponse = self.__channel__.send(request, channelResponseWaiter)
return response.resultList
"""
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// End Bulk Import Stuff //
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
"""
"""
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Begin Bulk Export Stuff //
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
"""
def startExport(self, props: typing.Optional[TGProperties] = None, zip: typing.Optional[str] = None,
isBatch: bool = True):
import tgdb.impl.bulkioimpl as tgbulkimpl
channelResponseWaiter = self._genBCRWaiter()
request: tgpduimpl.BeginExportRequest = tgpduimpl.TGMessageFactory.createMessage(
tgpdu.VerbId.BeginExportRequest, authtoken=self.__channel__.authtoken, sessionid=self.__channel__.sessionid)
request.zipName = zip
request.isBatch = isBatch
request.maxBatchEntities = int(ConfigName.BulkIOEntityBatchSize.defaultvalue)\
if props is None or props[ConfigName.BulkIOEntityBatchSize] is None else\
int(props[ConfigName.BulkIOEntityBatchSize])
response: tgpduimpl.BeginExportResponse = self.__channel__.send(request, channelResponseWaiter)
if response.error is not None:
raise response.error
return tgbulkimpl.BulkExportImpl(self, props, response.typeList, response.numRequests)
def partialExport(self, reqNum: int) -> typing.Tuple[str, bytes, bool, int,
typing.Optional[typing.Tuple[str, typing.List[str]]]]:
channelResponseWaiter = self._genBCRWaiter()
request: tgpduimpl.PartialExportRequest = tgpduimpl.TGMessageFactory.createMessage(
tgpdu.VerbId.PartialExportRequest, authtoken=self.__channel__.authtoken,
sessionid=self.__channel__.sessionid)
request.requestNum = reqNum
response: tgpduimpl.PartialExportResponse = self.__channel__.send(request, channelResponseWaiter)
return response.fileName, response.data, response.hasMore, response.numEntities,\
(response.typeName, response.attrList) if response.newType else None
"""
def startExport(self, props: Optional[TGProperties] = None) -> tgbulk.TGBulkExport:
channelResponseWaiter = self.__genBCRWaiter()
request = tgpduimpl.TGMessageFactory.createMessage(tgpdu.VerbId.BeginBulkExportSessionRequest,
authtoken=self.__channel__.authtoken,
sessionid=self.__channel__.sessionid)
_ = self.__channel__.send(request, channelResponseWaiter)
return tgbulkimpl.BulkExportImpl(self, props)
def beginBatchExportEntity(self, entkind: tgmodel.TGEntityKind, enttype: tgmodel.TGEntityType, batchSize: int) \
-> Tuple[int, List[str]]:
channelResponseWaiter = self.__genBCRWaiter()
request: tgpduimpl.BeginBatchExportEntityRequest = tgpduimpl.TGMessageFactory.createMessage(
tgpdu.VerbId.BeginBatchExportEntityRequest, authtoken=self.__channel__.authtoken,
sessionid=self.__channel__.sessionid)
request.entKind = entkind
request.entType = enttype
request.batchSize = batchSize
response: tgpduimpl.BeginBatchExportEntityResponse = self.__channel__.send(request, channelResponseWaiter)
return response.descriptor, response.columnLabels
def singleBatchExportEntity(self, desc: int) -> Tuple[int, str, bool]:
channelResponseWaiter = self.__genBCRWaiter()
request: tgpduimpl.SingleBatchExportEntityRequest = tgpduimpl.TGMessageFactory.createMessage(
tgpdu.VerbId.SingleBatchExportEntityRequest, authtoken=self.__channel__.authtoken,
sessionid=self.__channel__.sessionid)
request.descriptor = desc
response: tgpduimpl.SingleBatchExportEntityResponse = self.__channel__.send(request, channelResponseWaiter)
return response.numEnts, response.data, response.hasMore
def endBulkExportSession(self):
channelResponseWaiter = self.__genBCRWaiter()
request: tgpduimpl.EndBulkExportSessionRequest = tgpduimpl.TGMessageFactory.createMessage(
tgpdu.VerbId.EndBulkExportSessionRequest, authtoken=self.__channel__.authtoken,
sessionid=self.__channel__.sessionid)
_ = self.__channel__.send(request, channelResponseWaiter)
"""
"""
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// End Bulk Export Stuff //
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
"""
def getEntity(self, key: tgmodel.TGKey, option: tgquery.TGQueryOption = tgquery.DefaultQueryOption) ->\
tgmodel.TGEntity:
channelResponseWaiter = self._genBCRWaiter()
requestMessage: tgpduimpl.GetEntityRequestMessage
retV: tgmodel.TGEntity = None
try:
requestMessage = tgpduimpl.TGMessageFactory.createMessage(tgpdu.VerbId.GetEntityRequest,
authtoken=self.__channel__.authtoken,
sessionid=self.__channel__.sessionid)
requestMessage.command = tgpduimpl.GetEntityCommand.GetEntity
requestMessage.key = key
response: tgpduimpl.GetEntityResponseMessage = self.__channel__.send(requestMessage, channelResponseWaiter)
if response.hasResult:
response.finishReadWith(self.graphObjectFactory)
fetchedEntities = response.fetchedEntities
for id in fetchedEntities:
fetchedEnt: tgmodel.TGEntity = fetchedEntities[id]
if key.matches(fetchedEnt):
retV = fetchedEnt
break
finally:
pass
return retV
def insertEntity(self, entity: tgmodel.TGEntity):
if not entity.isNew:
raise tgexception.TGException("Should only be calling insertEntity on a new entity!")
if entity.virtualId not in self.__removeEntities__:
self.__addEntities__[entity.virtualId] = entity
self.__updateEdge__(entity)
if gLogger.isEnabled(TGLevel.Debug):
gLogger.log(TGLevel.Debug, 'Insert entity called')
def updateEntity(self, entity: tgmodel.TGEntity):
if entity.isNew:
raise tgexception.TGException('Should not be calling update on a new entity!')
if entity.isDeleted:
raise tgexception.TGException('Should not be calling update on an already deleted entity!')
if entity.virtualId not in self.__removeEntities__:
self.__updateEntities__[entity.virtualId] = entity
self.__updateEdge__(entity)
def __updateEdge__(self, entity: tgmodel.TGEntity):
if isinstance(entity, tgentimpl.EdgeImpl):
edge: tgmodel.TGEdge = entity
fr, to = edge.vertices
if not fr.isNew and fr.virtualId not in self.__removeEntities__:
self.__updateEntities__[fr.virtualId] = fr
if not to.isNew and to.virtualId not in self.__removeEntities__:
self.__updateEntities__[to.virtualId] = to
def deleteEntity(self, entity: tgentimpl.AbstractEntity):
if entity.isDeleted:
raise tgexception.TGException('Should not be calling delete on an already deleted entity!')
# Remove any entities added to the add changelist
if entity.virtualId in self.__addEntities__:
del self.__addEntities__[entity.virtualId]
# Remove any entities added to the update changelist
if entity.virtualId in self.__updateEntities__:
del self.__updateEntities__[entity.virtualId]
if entity.isNew:
entity.markDeleted()
else:
self.__removeEntities__[entity.virtualId] = entity
self.__updateEdge__(entity)
def createQuery(self, query: str) -> tgquery.TGQuery:
channelResponseWaiter: tgchannel.TGChannelResponseWaiter
result: int
ret: tgquery.TGQuery = None
channelResponseWaiter = self._genBCRWaiter()
try:
request: tgpduimpl.QueryRequestMessage = tgpduimpl.TGMessageFactory.createMessage(tgpdu.VerbId.QueryRequest,
authtoken=self.__channel__.authtoken,
sessionid=self.__channel__.sessionid)
request.command = tgquery.TGQueryCommand.Create
request.query = query
response: tgpduimpl.QueryResponseMessage = self.__channel__.send(request, channelResponseWaiter)
gLogger.log(TGLevel.Debug, "Send query completed")
result: int = response.result
queryHashId: int = response.queryHashId
if result == 0 and queryHashId > 0: #TODO Create error reporting for query result.
ret = tgqueryimpl.QueryImpl(self, queryHashId)
finally:
pass
return ret
def executeQuery(self, query: typing.Optional[str] = None,
option: tgquery.TGQueryOption = tgquery.DefaultQueryOption) -> tgquery.TGResultSet:
if query is None:
try:
query = option.queryExpr
except KeyError as e:
raise tgexception.TGException("Need to specify a query string!", cause=e)
channelResponseWaiter: tgchannel.TGChannelResponseWaiter = self._genBCRWaiter()
result: int
try:
request: tgpduimpl.QueryRequestMessage = tgpduimpl.TGMessageFactory.createMessage(tgpdu.VerbId.QueryRequest,
authtoken=self.__channel__.authtoken,
sessionid=self.__channel__.sessionid)
request.option = option
request.command, request.query = findCommandAndQueryString(query, self.__props__)
response: tgpduimpl.QueryResponseMessage = self.__channel__.send(request, channelResponseWaiter)
if response.error is not None:
raise response.error
return response.finishReadWith(request.command, self.__gof__)
except (Exception, tgexception.TGException):
raise
# TODO implement some form of compiled queries
def executeQueryWithId(self, queryId: int, option: tgquery.TGQueryOption = tgquery.DefaultQueryOption) -> \
tgquery.TGResultSet:
result: int
channelResponseWaiter: tgchannel.TGChannelResponseWaiter = self._genBCRWaiter()
try:
request: tgpduimpl.QueryRequestMessage = tgpduimpl.TGMessageFactory.createMessage(tgpdu.VerbId.QueryRequest,
authtoken=self.__channel__.authtoken,
sessionid=self.__channel__.sessionid)
request.command = tgquery.TGQueryCommand.ExecuteID
request.queryHashId = queryId
request.option = option
response: tgpduimpl.QueryResponseMessage = self.__channel__.send(request, channelResponseWaiter)
return response.finishReadWith(tgquery.TGQueryCommand.ExecuteID, self.__gof__)
except Exception as e:
raise tgexception.TGException("Exception in executeQueryWithId", cause=e)
def closeQuery(self, queryId: int):
channelResponseWaiter: tgchannel.TGChannelResponseWaiter = self._genBCRWaiter()
try:
request: tgpduimpl.QueryRequestMessage = tgpduimpl.TGMessageFactory.createMessage(tgpdu.VerbId.QueryRequest,
authtoken=self.__channel__.authtoken,
sessionid=self.__channel__.sessionid)
request.command = tgquery.TGQueryCommand.Close
request.queryHashId = queryId
_: tgpduimpl.QueryResponseMessage = self.__channel__.send(request, channelResponseWaiter)
# TODO check response state
gLogger.log(TGLevel.Debug, "Send close query completed")
except Exception as e:
raise tgexception.TGException("Exception in closeQuery", cause=e)
def getLargeObjectAsBytes(self, entityId: int, encrypted: bool = False) -> bytes:
channelResponseWaiter = self._genBCRWaiter()
if encrypted: # TODO Decrypt encrypted entities
raise tgexception.TGProtocolNotSupported("Blob/Clob encryption/decryption not implemented.")
request: tgpduimpl.GetLargeObjectRequestMessage = tgpduimpl.TGMessageFactory.createMessage(
tgpdu.VerbId.GetLargeObjectRequest, authtoken=self.__channel__.authtoken, sessionid=self.__channel__.sessionid)
request.entityId = entityId
request.decrypt = encrypted
response: tgpduimpl.GetLargeObjectResponseMessage = self.__channel__.send(request, channelResponseWaiter)
if entityId != response.entityId:
raise tgexception.TGException("Server responded with different entityId than expected!")
data = bytes() if response.data is None else response.data
return data
@property
def linkState(self) -> tgchannel.LinkState:
return self.__channel__.linkstate
@property
def outboxaddr(self) -> str:
return self.__channel__.outboxaddr
@property
def connectedUsername(self) -> str:
return self.__username__
@property
def graphMetadata(self) -> tgmodel.TGGraphMetadata:
return self.__gof__.graphmetadata
@property
def graphObjectFactory(self) -> tgmodel.TGGraphObjectFactory:
return self.__gof__
| python |
import base64
import requests
import uuid
import time
class MGTV:
def __init__(self, url):
self.url = url
def get_video_id(self):
return self.url.split("/", 5)[-1].split(".")[0]
def get_pm2(self):
did = "e6e13014-393b-43e7-b6be-2323e4960939"
suuid = uuid.uuid4()
pno = "1030"
# tk2 = self.encode_tk2(did, pno)
params = {
"did": did,
"suuid": suuid,
"cxid": "",
"tk2": self.encode_tk2(did, pno),
"type": "pch5",
"video_id": self.get_video_id(),
"_support": "10000000",
"auth_mode": "1",
"src": "",
"abroad": "",
}
res = requests.get("https://pcweb.api.mgtv.com/player/video", params=params).json()
return res['data']['atc']['pm2']
def encode_tk2(self, did="e6e13014-393b-43e7-b6be-2323e4960939", pno="1030"):
tk2 = bytes(f"did={did}|pno={pno}|ver=0.3.0301|clit={int(time.time())}".encode())
tk2 = base64.b64encode(tk2).decode().replace("/\+/g", "_").replace("/\//g", "~").replace("/=/g", "-")
tk2 = list(' '.join(tk2).split())
tk2.reverse()
return "".join(tk2)
def start(self):
params = {
"_support": "10000000",
"tk2": self.encode_tk2(),
"pm2": self.get_pm2(),
"video_id": self.get_video_id(),
"type": "pch5",
"auth_mode": "1",
"src": "",
"abroad": "",
}
res = requests.get("https://pcweb.api.mgtv.com/player/getSource", params=params).json()
print(res)
return res
if __name__ == '__main__':
MGTV().start()
| python |
import copy
import pickle
import torch
import types
from . import layers
from . import rules
Rules = rules.Rules
def flatten_model(module):
'''
flatten modul to base operation like Conv2, Linear, ...
'''
modules_list = []
for m_1 in module.children():
if len(list(m_1.children())) == 0:
modules_list.append(m_1)
else:
modules_list = modules_list + flatten_model(m_1)
return modules_list
def copy_module(module):
'''
sometimes copy.deepcopy() does not work
'''
module = copy.deepcopy(pickle.loads(pickle.dumps(module)))
module._forward_hooks.popitem() # remove hooks from module copy
module._backward_hooks.popitem() # remove hooks from module copy
return module
def redefine_nn(model, rule, input_lowest, input_highest):
'''
go over model layers and overload chosen instance methods (e.g. forward()).
New methods come from classes of layers module
'''
rule_func = Rules(rule)
list_of_layers = dir(layers) #list of redefined layers in layers module
for num, module in enumerate(flatten_model(model)):
if module.__class__.__name__ in list_of_layers:
local_class = module.__class__ #current layer class
layer_module_class = layers.__getattr__(local_class.__name__) # get same redefined layer class
list_of_methods = [attr for attr in dir(layer_module_class) if attr[:2] != '__'] #methods which was redefined
for l in list_of_methods:
#overload object method from https://stackoverflow.com/questions/394770/override-a-method-at-instance-level
setattr(module, l, types.MethodType(getattr(layer_module_class, l), module)) #set redefined methods to object
if num == 0:
setattr(module, 'rule_func', Rules('z_box_no_bias', lowest=input_lowest, highest=input_highest)) #first layer always z_box
else:
setattr(module, 'rule_func', rule_func)
return model
| python |
import pytest
import os
from tddc import common
def test_get_base_filename():
assert common.get_base_filename('/Users/foo/bar.txt') == 'bar'
assert common.get_base_filename('bar.txt') == 'bar'
assert common.get_base_filename('bar') == 'bar'
assert common.get_base_filename('bar.txt.gz') == 'bar.txt'
def test_write_summary(tmpdir):
summary_data = {'a': 1, 'b': {'c': 2, 'd': 3}, 'e': [1, 2, 3]}
filename = common.write_summary(summary_data, tmpdir.strpath, 'foo', 'bar')
assert os.path.basename(filename) == 'foo_barsummary.json'
summary_data_from_file = common.read_json_file(filename)
assert summary_data_from_file == summary_data
def test_file_exists_or_exit():
with pytest.raises(SystemExit) as exception_info:
common.file_exists_or_exit('foo.bar.baz')
assert exception_info.value.code == 1
assert common.file_exists_or_exit(__file__) is None
| python |
# compare gene numbers in different samples
import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
from scipy.stats import ttest_ind
import scipy.stats as stats
import scikit_posthocs as sp
#-------------------variable--------------------------------
fmt='png'
fd_in='./out/a00_preprocess_00_pp'
fd_out='./out/a01_plot-pp_00_compare'
l_sample=['Ctrl', 'MethFix', 'RNAlater']
dic_cmap={'Ctrl': '#4287f5', 'MethFix': '#f5a142', 'RNAlater': '#4bf542'}
#--------------------setup---------------------------------
Path(fd_out).mkdir(exist_ok=True, parents=True)
#--------------------function----------------------------
def get_gene_cnt(prefix, l_sample=l_sample, fd_in=fd_in):
'''count genes in each adata, and concat count dfs
'''
#1. add gene count df to list
l_df=[]
for sample in l_sample:
adata=sc.read(f'{fd_in}/{prefix}_{sample}.h5ad')
sc.pp.filter_cells(adata, min_genes=0) #this will count genes in each cell
l_df.append(adata.obs)
#2. concat df
df=pd.concat(l_df)
return df
def plot_gene(df, f_out, title, dic_cmap=dic_cmap, ylim=None):
#1. plot
sns.set()
fig, ax=plt.subplots(figsize=(8, 5))
ax=sns.violinplot(x='sample', y='n_genes', data=df, hue='sample', linewidth=0.5, width=1.5, palette=dic_cmap)
#2. adjust
ax.set_title(title, fontsize=20, pad=15, weight='medium')
plt.xlabel('')
plt.ylabel('Gene Number', fontsize=22, labelpad=15, weight='medium')
plt.xticks([-0.5, 1, 2.5], fontsize=22, rotation=0, va='center')
ax.tick_params(axis='x', which='major', pad=15)
plt.xlim([-1, 3])
plt.ylim(ylim)
ax.get_legend().remove()
#3. save
plt.tight_layout()
plt.savefig(f_out, dpi=300)
plt.close()
return
############################################################################
#----------------------raw data------------------------------
prefix='raw'
#1. count df
df=get_gene_cnt(prefix)
##2. plot
#f_out=f'{fd_out}/{prefix}_gene.{fmt}'
#title=f'Gene Numbers ({prefix.capitalize()})'
#plot_gene(df, f_out, title, ylim=[-1000, 16000])
##3. calculate p value
#ctrl=df.loc[df['sample']=='Ctrl']['n_genes']
#meth=df.loc[df['sample']=='MethFix']['n_genes']
#later=df.loc[df['sample']=='RNAlater']['n_genes']
#t1, p1=ttest_ind(ctrl, meth)
#print(p1) #0
#t2, p2=ttest_ind(ctrl, later)
#print(p2) #0
#t3, p3=ttest_ind(meth, later)
#print(p3) #0.03836
#---------------------anova------------------------------------
##1. get data
#l_ctrl=df.loc[df['sample']=='Ctrl', ['n_genes']]['n_genes'].tolist()
#l_meth=df.loc[df['sample']=='MethFix', ['n_genes']]['n_genes'].tolist()
#l_later=df.loc[df['sample']=='RNAlater', ['n_genes']]['n_genes'].tolist()
#l_all=[l_ctrl, l_meth, l_later]
##2. avova
#fvalue, pvalue=stats.f_oneway(l_ctrl, l_meth, l_later)
#print(fvalue, pvalue) #4148.3173795985 0.0
##3. post hoc ttest
#p=sp.posthoc_conover(l_all, p_adjust='holm')
#print(p)
'''
1 2 3
1 -1.0 0.000000 0.000000
2 0.0 -1.000000 0.880754
3 0.0 0.880754 -1.000000
'''
#########################################################################
##----------------------cleaned data------------------------------
#prefix='clean'
##1. count df
#df=get_gene_cnt(prefix)
##2. plot
#f_out=f'{fd_out}/{prefix}_gene.{fmt}'
#title=f'Gene Numbers ({prefix.capitalize()})'
#plot_gene(df, f_out, title, ylim=[0, 4500])
##3. calculate p value
#ctrl=df.loc[df['sample']=='Ctrl']['n_genes']
#meth=df.loc[df['sample']=='MethFix']['n_genes']
#later=df.loc[df['sample']=='RNAlater']['n_genes']
#t1, p1=ttest_ind(ctrl, meth)
#print(p1) #0
#t2, p2=ttest_ind(ctrl, later)
#print(p2) #0
#t3, p3=ttest_ind(meth, later)
#print(p3) #6.629720921642305e-78
| python |
#
# Este arquivo é parte do programa multi_agenda
#
# Esta obra está licenciada com uma
# Licença Creative Commons Atribuição 4.0 Internacional.
# (CC BY 4.0 Internacional)
#
# Para ver uma cópia da licença, visite
# https://creativecommons.org/licenses/by/4.0/legalcode
#
# WELLINGTON SAMPAIO - wsampaio@yahoo.com
# https://www.linkedin.com/in/wellsampaio/
#
import objetos.financeiro.TipoConta as TipoConta
import objetos.dbConn.CRUD as CRUD
class TipoContaDAO(CRUD.CRUD):
__sqlInsert = ""
__sqlUpdate = ""
def __init__(self):
schema = "financeiro"
tabela = "tiposContas"
pk = "codTipoConta"
super().__init__(schema, tabela, pk)
self.__sqlInsert = super().strINSERT()
self.__sqlUpdate = super().strUPDATE()
# ==================================== CRUD ====================================
# ==============================================================================
def insert(self, tipoConta):
self.setStatement(tipoConta, self.__sqlInsert)
def select(self, pk):
obj = TipoConta.TipoConta()
super().setSelect(pk, obj)
return obj
def update(self, tipoConta):
self.setStatement(tipoConta, self.__sqlUpdate)
def setStatement2(self, obj):
getPk = getattr(obj,
"get" +
self.__pk[:1].upper() +
self.__pk[1:]
)
return getPk()
# ==================================== CRUD ====================================
# ==============================================================================
def getLista(self):
sql = \
"""
SELECT
*
FROM
tiposContas
ORDER BY
codTipoConta
;
"""
return super().getList(sql)
def listaPrincipais(self):
sql = \
"""
SELECT
*
FROM
tiposContas
WHERE
tipoContaAtivo = 1
ORDER BY
TipoConta
;
"""
return super().getList(sql)
def naoListadasNoPeriodo(self, dtRef):
sql = \
"""
SELECT
*
FROM
tiposContas
WHERE
codTipoConta NOT IN(
SELECT
codTipoConta
FROM
contas
WHERE
strftime('%Y-%m',dtVencimento) = '{}'
) AND
tipoContaAtivo = 1
ORDER BY
tipoConta
;
""".format(dtRef)
return super().getList(sql)
def contaOcorrenciasPelaReferencia(self, codTipoConta, dtRef, tipoRef):
if tipoRef == "pgto":
tipoRef = "receita.mesReferencia"
#elif tipoRef = "venc":
else:
tipoRef = "contas.dtVencimento"
sql = \
"""
SELECT COUNT(codConta) FROM contas LEFT JOIN receita ON contas.codReceitaPagadora = receita.codReceita
WHERE strftime("%Y-%m", {}) = '{}' AND codTipoConta = {};
""".format(tipoRef, dtRef, codTipoConta)
return "{0:.0f}".format(super().getValue(sql, 0.0))
def listaCmb(self):
sql = \
"""
SELECT
*
FROM
tiposContas
ORDER BY
tipoConta
;
"""
return super().getList(sql)
| python |
# Generated by Django 2.2.16 on 2020-09-21 16:18
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('contest', '0001_initial'),
('lecture', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='signup_class',
name='contest',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='contest.Contest'),
),
]
| python |
import torch.nn as nn
import torch.nn.functional as F
from torch import cat, stack, sqrt
class MLPNetwork(nn.Module):
"""
MLP network (can be used as value or policy)
"""
def __init__(self, input_dim, out_dim, hidden_dim=64, nonlin=F.relu,
constrain_out=False, norm_in=True, discrete_action=True):
"""
Inputs:
input_dim (int): Number of dimensions in input
out_dim (int): Number of dimensions in output
hidden_dim (int): Number of hidden dimensions
nonlin (PyTorch function): Nonlinearity to apply to hidden layers
"""
super(MLPNetwork, self).__init__()
if norm_in: # normalize inputs
self.in_fn = nn.BatchNorm1d(input_dim)
self.in_fn.weight.data.fill_(1)
self.in_fn.bias.data.fill_(0)
else:
self.in_fn = lambda x: x
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, out_dim)
self.nonlin = nonlin
if constrain_out and not discrete_action:
# initialize small to prevent saturation
self.fc3.weight.data.uniform_(-3e-3, 3e-3)
self.out_fn = F.tanh
else: # logits for discrete action (will softmax later)
self.out_fn = lambda x: x
def forward(self, X):
"""
Inputs:
X (PyTorch Matrix): Batch of observations
Outputs:
out (PyTorch Matrix): Output of network (actions, values, etc)
"""
h1 = self.nonlin(self.fc1(self.in_fn(X)))
h2 = self.nonlin(self.fc2(h1))
out = self.out_fn(self.fc3(h2))
return out
class ConvMLPNetwork(nn.Module):
"""
Conv + MLP network (can be used as value or policy)
"""
def __init__(self, input_dim, out_dim):
"""
Inputs:
input_dim (int): Number of dimensions in input
out_dim (int): Number of dimensions in output
hidden_dim (int): Number of hidden dimensions
nonlin (PyTorch function): Nonlinearity to apply to hidden layers
"""
super(ConvMLPNetwork, self).__init__()
self.in_fn = nn.BatchNorm2d(3)
self.in_fn.weight.data.fill_(1)
self.in_fn.bias.data.fill_(0)
# Define image embedding
self.image_conv = nn.Sequential(
nn.Conv2d(3, 16, (2, 2)),
nn.ReLU(),
nn.MaxPool2d((2, 2)),
nn.Conv2d(16, 32, (2, 2)),
nn.ReLU(),
nn.Conv2d(32, 64, (2, 2)),
nn.ReLU()
)
self.mlpnet = nn.Sequential(
nn.Linear(input_dim, 64),
nn.Tanh(),
nn.Linear(64, out_dim)
)
self.apply(init_params)
def forward(self, obss, actions=None, critic=False, debug=False):
"""
Inputs:
X (PyTorch Matrix): Batch of observations
Outputs:
out (PyTorch Matrix): Output of network (actions, values, etc)
"""
if not critic:
x = obss
if debug:
print('----------')
print('obss:')
print(x)
if len(x.shape) < 4:
x = x.unsqueeze(0).transpose(1, 3).transpose(2, 3)
else:
x = x.transpose(1, 3).transpose(2, 3)
x = self.in_fn(x)
x = self.image_conv(x)
x = x.reshape(x.shape[0], -1)
if debug:
print('----------')
print('conv out:')
print(x)
out = self.mlpnet(x)
if debug:
print('----------')
print('mlp out:')
print(out)
return out
else:
x = stack(obss)
num_agents = x.shape[0]
num_batches = x.shape[1]
x = x.reshape(-1, *x.shape[-3:])
x = x.transpose(1, 3).transpose(2, 3)
x = self.in_fn(x)
x = self.image_conv(x)
x = x.reshape(x.shape[0], -1)
x = x.reshape(num_agents, num_batches, x.shape[1])
act = stack(actions)
concat = cat((*x, *act), dim=1)
out = self.mlpnet(concat)
return out
# Function from https://github.com/ikostrikov/pytorch-a2c-ppo-acktr/blob/master/model.py
def init_params(m):
classname = m.__class__.__name__
if classname.find("Linear") != -1:
m.weight.data.normal_(0, 1)
m.weight.data *= 1 / sqrt(m.weight.data.pow(2).sum(1, keepdim=True))
if m.bias is not None:
m.bias.data.fill_(0)
| python |
import re
from datetime import date, datetime, timezone
from urllib.parse import urljoin, urlparse
import pytest
from swpt_debtors import procedures as p
from swpt_debtors import models as m
@pytest.fixture(scope='function')
def client(app, db_session):
return app.test_client()
@pytest.fixture(scope='function')
def debtor(db_session):
debtor = m.Debtor(debtor_id=123, status_flags=0)
debtor.activate()
db_session.add(debtor)
db_session.commit()
return p.get_debtor(123)
def _get_all_pages(client, url, page_type, streaming=False):
r = client.get(url)
assert r.status_code == 200
data = r.get_json()
assert data['type'] == page_type
assert urlparse(data['uri']) == urlparse(url)
if streaming:
assert 'next' in data or 'forthcoming' in data
assert 'next' not in data or 'forthcoming' not in data
else:
assert 'forthcoming' not in data
items = data['items']
assert isinstance(items, list)
if 'next' in data:
items.extend(_get_all_pages(client, urljoin(url, data['next']), page_type, streaming))
return items
def test_auto_genereate_debtor_id(client):
r = client.post('/debtors/.debtor-reserve', json={})
assert r.status_code == 200
data = r.get_json()
assert data['type'] == 'DebtorReservation'
assert isinstance(data['debtorId'], str)
assert isinstance(data['reservationId'], int)
assert datetime.fromisoformat(data['validUntil'])
assert datetime.fromisoformat(data['createdAt'])
def test_create_debtor(client):
r = client.get('/debtors/4294967296/')
assert r.status_code == 403
r = client.post('/debtors/4294967296/reserve', headers={'X-Swpt-User-Id': 'INVALID_USER_ID'}, json={})
assert r.status_code == 403
r = client.post('/debtors/2/reserve', headers={'X-Swpt-User-Id': 'debtors:4294967296'}, json={})
assert r.status_code == 403
r = client.post('/debtors/4294967296/reserve', json={})
assert r.status_code == 200
data = r.get_json()
assert data['type'] == 'DebtorReservation'
assert data['debtorId'] == '4294967296'
assert isinstance(data['reservationId'], int)
assert datetime.fromisoformat(data['validUntil'])
assert datetime.fromisoformat(data['createdAt'])
reservation_id = data['reservationId']
r = client.post('/debtors/4294967296/reserve', json={})
assert r.status_code == 409
r = client.get('/debtors/4294967296/')
assert r.status_code == 403
r = client.post('/debtors/4294967296/activate', json={
'reservationId': 123,
})
assert r.status_code == 422
assert 'reservationId' in r.get_json()['errors']['json']
r = client.post('/debtors/4294967296/activate', json={
'reservationId': reservation_id,
})
assert r.status_code == 200
data = r.get_json()
assert data['type'] == 'Debtor'
assert data['uri'] == '/debtors/4294967296/'
assert data['identity'] == {'type': 'DebtorIdentity', 'uri': 'swpt:4294967296'}
assert data['transfersList'] == {'uri': '/debtors/4294967296/transfers/'}
assert data['createTransfer'] == {'uri': '/debtors/4294967296/transfers/'}
assert datetime.fromisoformat(data['createdAt'])
r = client.post('/debtors/4294967296/activate', json={
'reservationId': reservation_id,
})
assert r.status_code == 200
r = client.post('/debtors/8589934591/activate', json={
'reservationId': 123,
})
assert r.status_code == 422
assert 'reservationId' in r.get_json()['errors']['json']
r = client.post('/debtors/8589934591/activate', json={})
assert r.status_code == 200
data = r.get_json()
assert data['type'] == 'Debtor'
assert data['uri'] == '/debtors/8589934591/'
assert data['balance'] == 0
assert datetime.fromisoformat(data['createdAt'])
assert 'info' not in data
r = client.post('/debtors/8589934591/activate', json={})
assert r.status_code == 409
r = client.get('/debtors/4294967296/')
assert r.status_code == 200
data = r.get_json()
assert data['type'] == 'Debtor'
assert data['uri'] == '/debtors/4294967296/'
assert data['balance'] == 0
assert datetime.fromisoformat(data['createdAt'])
r = client.get('/debtors/8589934591/')
assert r.status_code == 200
r = client.post('/debtors/8589934591/deactivate', headers={'X-Swpt-User-Id': 'debtors:8589934591'}, json={})
assert r.status_code == 403
r = client.post('/debtors/8589934591/deactivate', headers={'X-Swpt-User-Id': 'debtors-supervisor'}, json={})
assert r.status_code == 403
r = client.post('/debtors/8589934591/deactivate', headers={'X-Swpt-User-Id': 'debtors-superuser'}, json={})
assert r.status_code == 204
r = client.post('/debtors/8589934591/deactivate', json={})
assert r.status_code == 204
r = client.get('/debtors/8589934591/')
assert r.status_code == 403
r = client.post('/debtors/8589934591/deactivate', json={})
assert r.status_code == 204
def test_get_debtors_list(client):
r = client.post('/debtors/4294967296/reserve', json={})
assert r.status_code == 200
r = client.post('/debtors/4294967297/activate', json={})
assert r.status_code == 200
r = client.post('/debtors/4294967298/activate', json={})
assert r.status_code == 200
r = client.post('/debtors/8589934591/activate', json={})
assert r.status_code == 200
r = client.get('/debtors/.list')
assert r.status_code == 200
data = r.get_json()
assert data['type'] == 'DebtorsList'
assert data['uri'] == '/debtors/.list'
assert data['itemsType'] == 'ObjectReference'
assert data['first'] == '/debtors/9223372036854775808/enumerate'
entries = _get_all_pages(client, data['first'], page_type='ObjectReferencesPage')
assert entries == [
{'uri': '/debtors/4294967297/'},
{'uri': '/debtors/4294967298/'},
{'uri': '/debtors/8589934591/'},
]
def test_get_debtor(client, debtor):
r = client.get('/debtors/123/')
assert r.status_code == 200
data = r.get_json()
assert data['type'] == 'Debtor'
assert data['uri'] == '/debtors/123/'
assert data['config'] == {
'type': 'DebtorConfig',
'uri': '/debtors/123/config',
'configData': '',
'latestUpdateId': 1,
'latestUpdateAt': '1970-01-01T00:00:00+00:00',
'debtor': {'uri': '/debtors/123/'},
}
assert data['transfersList'] == {'uri': '/debtors/123/transfers/'}
assert data['createTransfer'] == {'uri': '/debtors/123/transfers/'}
assert data['balance'] == 0
assert datetime.fromisoformat(data['createdAt'])
assert data['identity'] == {'type': 'DebtorIdentity', 'uri': 'swpt:123'}
assert data['noteMaxBytes'] == 0
assert 'configError' not in data
assert 'account' not in data
def test_change_debtor_config(client, debtor):
r = client.get('/debtors/123/config')
assert r.status_code == 200
data = r.get_json()
assert data['type'] == 'DebtorConfig'
assert data['uri'] == '/debtors/123/config'
assert data['configData'] == ''
assert data['latestUpdateId'] == 1
latest_update_at = data['latestUpdateAt']
assert datetime.fromisoformat(latest_update_at)
assert data['debtor'] == {'uri': '/debtors/123/'}
request = {
'configData': 'TEST',
'latestUpdateId': 2
}
r = client.patch('/debtors/123/config', json=request)
assert r.status_code == 200
r = client.get('/debtors/123/config')
assert r.status_code == 200
data = r.get_json()
assert data['type'] == 'DebtorConfig'
assert data['uri'] == '/debtors/123/config'
assert data['configData'] == 'TEST'
assert data['latestUpdateId'] == 2
assert datetime.fromisoformat(data['latestUpdateAt'])
assert latest_update_at != data['latestUpdateAt']
assert data['debtor'] == {'uri': '/debtors/123/'}
empty_request = {
'configData': '',
'latestUpdateId': 2,
}
r = client.patch('/debtors/666/config', json=empty_request)
assert r.status_code == 404
r = client.patch('/debtors/123/config', json=empty_request)
assert r.status_code == 409
data = r.get_json()
for _ in range(9):
r = client.patch('/debtors/123/config', json=request)
assert r.status_code == 200
r = client.patch('/debtors/123/config', json=request)
assert r.status_code == 403
def test_initiate_running_transfer(client, debtor):
r = client.get('/debtors/666/transfers/')
assert r.status_code == 404
r = client.get('/debtors/123/transfers/')
assert r.status_code == 200
data = r.get_json()
assert data['debtor'] == {'uri': '/debtors/123/'}
assert data['type'] == 'TransfersList'
assert data['uri'] == '/debtors/123/transfers/'
assert data['items'] == []
json_request_body = {
'amount': 1000,
'noteFormat': 'fmt',
'note': 'test',
'recipient': {'uri': 'swpt:123/1111'},
'transferUuid': '123e4567-e89b-12d3-a456-426655440000',
}
r = client.post('/debtors/123/transfers/', json=json_request_body)
assert r.status_code == 201
data = r.get_json()
assert data['amount'] == 1000
assert datetime.fromisoformat(data['initiatedAt'])
assert 'result' not in data
assert data['recipient'] == {'type': 'AccountIdentity', 'uri': 'swpt:123/1111'}
assert data['type'] == 'Transfer'
assert data['uri'] == '/debtors/123/transfers/123e4567-e89b-12d3-a456-426655440000'
assert data['noteFormat'] == 'fmt'
assert data['note'] == 'test'
assert data['transfersList'] == {'uri': '/debtors/123/transfers/'}
assert r.headers['Location'] == 'http://example.com/debtors/123/transfers/123e4567-e89b-12d3-a456-426655440000'
r = client.get('/debtors/123/transfers/123e4567-e89b-12d3-a456-426655440000')
assert r.status_code == 200
data = r.get_json()
assert data['type'] == 'Transfer'
assert data['uri'] == '/debtors/123/transfers/123e4567-e89b-12d3-a456-426655440000'
assert data['amount'] == 1000
r = client.post('/debtors/123/transfers/', json=json_request_body)
assert r.status_code == 303
assert r.headers['Location'] == 'http://example.com/debtors/123/transfers/123e4567-e89b-12d3-a456-426655440000'
json_request_body['amount'] += 1
r = client.post('/debtors/123/transfers/', json=json_request_body)
assert r.status_code == 409
r = client.post('/debtors/123/transfers/', json={**json_request_body, **{'recipient': {'uri': 'INVALID'}}})
assert r.status_code == 422
r = client.post('/debtors/123/transfers/', json={**json_request_body, **{'recipient': {'uri': 'swpt:555/1111'}}})
assert r.status_code == 422
r = client.post('/debtors/555/transfers/', json={**json_request_body, **{'recipient': {'uri': 'swpt:555/1111'}}})
assert r.status_code == 404
r = client.get('/debtors/123/transfers/')
assert r.status_code == 200
data = r.get_json()
assert sorted(data['items']) == [
{'uri': '123e4567-e89b-12d3-a456-426655440000'},
]
r = client.delete('/debtors/123/transfers/123e4567-e89b-12d3-a456-426655440001')
assert r.status_code == 204
r = client.get('/debtors/123/transfers/')
assert r.status_code == 200
data = r.get_json()
assert sorted(data['items']) == [
{'uri': '123e4567-e89b-12d3-a456-426655440000'},
]
for i in range(2, 12):
suffix = '{:0>4}'.format(i)
json_request_body = {
'amount': 1,
'recipient': {'uri': 'swpt:123/1111'},
'transferUuid': f'123e4567-e89b-12d3-a456-42665544{suffix}',
}
r = client.post('/debtors/123/transfers/', json=json_request_body)
if i == 11:
assert r.status_code == 403
else:
assert r.status_code == 201
def test_cancel_running_transfer(client, debtor):
json_request_body = {
'amount': 1000,
'note': 'test',
'recipient': {'uri': 'swpt:123/1111'},
'transferUuid': '123e4567-e89b-12d3-a456-426655440000',
}
r = client.post('/debtors/123/transfers/', json=json_request_body)
assert r.status_code == 201
data = r.get_json()
assert 'result' not in data
r = client.post('/debtors/123/transfers/123e4567-e89b-12d3-a456-426655440001', json={})
assert r.status_code == 404
r = client.post('/debtors/123/transfers/123e4567-e89b-12d3-a456-426655440000', json={})
assert r.status_code == 200
r = client.get('/debtors/123/transfers/123e4567-e89b-12d3-a456-426655440000')
assert r.status_code == 200
data = r.get_json()
result = data['result']
error = result['error']
assert error['errorCode'] == 'CANCELED_BY_THE_SENDER'
r = client.post('/debtors/123/transfers/123e4567-e89b-12d3-a456-426655440000', json={})
assert r.status_code == 200
def test_unauthorized_debtor_id(debtor, client):
json_request_body = {
'type': 'DebtorConfig',
'configData': '',
'latestUpdateId': 2,
}
r = client.get('/debtors/123/')
assert r.status_code == 200
r = client.get('/debtors/123/', headers={'X-Swpt-User-Id': 'INVALID_USER_ID'})
assert r.status_code == 403
r = client.patch('/debtors/123/config', json=json_request_body, headers={'X-Swpt-User-Id': 'debtors-supervisor'})
assert r.status_code == 403
r = client.patch('/debtors/123/config', json=json_request_body, headers={'X-Swpt-User-Id': 'debtors:666'})
assert r.status_code == 403
r = client.patch('/debtors/123/config', json=json_request_body, headers={'X-Swpt-User-Id': 'debtors:123'})
assert r.status_code == 200
with pytest.raises(ValueError):
r = client.get(
'/debtors/18446744073709551615/',
json=json_request_body,
headers={'X-Swpt-User-Id': 'debtors:18446744073709551616'},
)
def test_redirect_to_debtor(client, debtor):
r = client.get('/debtors/.debtor')
assert r.status_code == 204
r = client.get('/debtors/.debtor', headers={'X-Swpt-User-Id': 'debtors:2'})
assert r.status_code == 303
assert r.headers['Location'] == 'http://example.com/debtors/2/'
r = client.get('/debtors/.debtor', headers={'X-Swpt-User-Id': 'debtors:18446744073709551615'})
assert r.status_code == 303
assert r.headers['Location'] == 'http://example.com/debtors/18446744073709551615/'
def test_redirect_to_latest_info(client, debtor):
r = client.get('/debtors/123/public')
assert r.status_code == 404
request = {
'configData': '{"info": {"iri": "https://example.com/"}}',
'latestUpdateId': 2
}
r = client.patch('/debtors/123/config', json=request)
assert r.status_code == 200
debtor = p.get_debtor(123)
current_ts = datetime.now(tz=timezone.utc)
p.process_account_update_signal(
debtor_id=debtor.debtor_id,
creditor_id=p.ROOT_CREDITOR_ID,
creation_date=date(2020, 1, 1),
last_change_ts=current_ts,
last_change_seqnum=1,
principal=0,
interest_rate=0.0,
last_config_ts=debtor.last_config_ts,
last_config_seqnum=debtor.last_config_seqnum,
negligible_amount=p.HUGE_NEGLIGIBLE_AMOUNT,
config_data='INCORRECT CONFIG DATA',
config_flags=debtor.config_flags,
account_id='',
transfer_note_max_bytes=0,
ts=current_ts,
ttl=10000000,
)
r = client.get('/debtors/123/public')
assert r.status_code == 404
p.process_account_update_signal(
debtor_id=debtor.debtor_id,
creditor_id=p.ROOT_CREDITOR_ID,
creation_date=date(2020, 1, 1),
last_change_ts=current_ts,
last_change_seqnum=2,
principal=0,
interest_rate=0.0,
last_config_ts=debtor.last_config_ts,
last_config_seqnum=debtor.last_config_seqnum,
negligible_amount=p.HUGE_NEGLIGIBLE_AMOUNT,
config_data=debtor.config_data,
config_flags=debtor.config_flags,
account_id='',
transfer_note_max_bytes=0,
ts=current_ts,
ttl=10000000,
)
r = client.get('/debtors/123/public')
assert r.status_code == 302
assert r.headers['Location'] == 'https://example.com/'
assert r.headers['Cache-Control'] == 'max-age=86400'
def test_save_document(client, debtor):
r = client.get('/debtors/123/documents/0/public')
assert r.status_code == 404
r = client.post(
'/debtors/123/documents/',
content_type='application/octet-stream',
data=101 * b'1',
)
assert r.status_code == 413
content = 100 * b'1'
r = client.post(
'/debtors/123/documents/',
content_type='application/octet-stream',
data=content,
)
assert r.status_code == 201
assert r.content_type == 'application/octet-stream'
assert r.get_data() == content
location = r.headers['Location']
m = re.match(r'http://example.com/debtors/123/documents/(\d)+/public', location)
assert m is not None
document_id = int(m.group(1))
assert document_id >= 0
r = client.get(location)
assert r.status_code == 200
assert r.content_type == 'application/octet-stream'
assert r.get_data() == content
r = client.post(
'/debtors/123/documents/',
content_type='application/octet-stream',
data=content,
)
assert r.status_code == 201
assert r.content_type == 'application/octet-stream'
assert r.get_data() == content
assert location != r.headers['Location']
r = client.post(
'/debtors/123/documents/',
content_type='application/octet-stream',
data=content,
)
assert r.status_code == 403
r = client.post(
'/debtors/666/documents/',
content_type='application/octet-stream',
data=content,
)
assert r.status_code == 404
| python |
from lxml import etree
from defusedxml.lxml import fromstring
import uuid
from django.db import models
from django.http import HttpResponse
from django.urls import reverse
from django.core.exceptions import ObjectDoesNotExist
from acs.response import get_soap_envelope
from acs.models import AcsHttpBaseModel
from acs.utils import create_xml_document
class AcsHttpRequest(AcsHttpBaseModel):
""" Every HTTP request received on the ACS server URL is saved as an instance
of this model. """
acs_session = models.ForeignKey('acs.AcsSession', related_name='acs_http_requests', on_delete=models.PROTECT)
rpc_response_to = models.ForeignKey('acs.AcsHttpResponse', related_name='rpc_responses', null=True, blank=True, on_delete=models.PROTECT) # a foreignkey to the http response containing the acs rpc request which triggered the current http request (where relevant)
request_headers = models.TextField(blank=True)
request_xml_valid = models.BooleanField(default=False)
request_soap_valid = models.BooleanField(default=False)
class Meta:
ordering = ['-created_date']
def __str__(self):
return str(self.tag)
def get_absolute_url(self):
return reverse('acshttprequest_detail', kwargs={'pk': self.pk})
@property
def is_request(self):
return True
@property
def is_response(self):
return False
def get_response(self, empty_response=False):
'''
get_response() is called when the CPE is waiting for the ACS
to do something. This happens after the CPE does an empty POST, or after
the CPE has responded to an RPC call initiated by the ACS. It simply pops
a job from the queue and returns it in a http response.
'''
job = False
if not empty_response:
### get the first job from the queue (if any)
#jobs = AcsQueueJob.objects.filter(acs_device=self.acs_session.acs_device, processed=False).order_by('created_date')
jobs = self.acs_session.acs_device.acs_queue_jobs.filter(processed=False).order_by('created_date')
self.acs_session.acs_log("Found %s unprocessed acs queue jobs for the device %s" % (jobs.count(), self.acs_session.acs_device))
if jobs:
job = jobs.first()
self.acs_session.acs_log("Picked job %s" % job)
if not empty_response and job:
### get blank SOAP response envelope
response_cwmp_id = uuid.uuid4().hex
root, body = get_soap_envelope(response_cwmp_id, self.acs_session)
### add the cwmp soap object to the soap body
cwmpobj = fromstring(job.cwmp_rpc_object_xml.encode('utf-8'))
body.append(cwmpobj)
### get the rpc method
response_cwmp_rpc_method = job.cwmp_rpc_method
### put HTTP response together
output = etree.tostring(root, encoding='utf-8', xml_declaration=True)
response = HttpResponse(output, content_type='text/xml; charset=utf-8')
else:
### no jobs in queue for this acs device (or an empty response was requested), so return empty body to end this cwmp session
response = HttpResponse(status=204)
response_cwmp_rpc_method = '(empty response body)'
response_cwmp_id = ''
### save the http response
from acs.models import AcsHttpResponse
acs_http_response = AcsHttpResponse.objects.create(
http_request=self,
fk_body=create_xml_document(xml=response.content),
cwmp_id=response_cwmp_id,
soap_element="{%s}%s" % (self.acs_session.soap_namespaces['cwmp'], response_cwmp_rpc_method),
)
self.acs_session.acs_log("Created ACS HTTP response %s" % acs_http_response)
if job:
self.acs_session.acs_log("Saving AcsQueueJob %s" % job)
### save job
job.handled_in = acs_http_response
job.processed = True
job.save()
### all good, return response
self.acs_session.acs_log("Responding to CPE %s with %s" % (self.acs_session.acs_device, response_cwmp_rpc_method))
return response
| python |
from typing import List
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Product(BaseModel):
id: str
class Review(BaseModel):
body: str
product: Product
class User(BaseModel):
reviews: List[Review]
USER_DATA = {
"1": User(reviews=[Review(body="Great!", product=Product(id="1"))]),
"2": User(reviews=[Review(body="Great!", product=Product(id="2"))]),
"3": User(reviews=[Review(body="Great!", product=Product(id="3"))]),
}
@app.get("/users/{id}", response_model=User)
async def get_user_review(id: str) -> User:
return USER_DATA[id]
| python |
import bpy
from bpy.props import *
PROP_TYPE_ICONS = {
"String": "SORTALPHA",
"Int": "CHECKBOX_DEHLT",
"Float": "RADIOBUT_OFF",
"Bool": "CHECKMARK",
"Vec2": "ORIENTATION_VIEW",
"Vec3": "ORIENTATION_GLOBAL",
"Vec4": "MESH_ICOSPHERE",
"Object": "OBJECT_DATA",
"CameraObject": "CAMERA_DATA",
"LightObject": "LIGHT_DATA",
"MeshObject": "MESH_DATA",
"SpeakerObject": "OUTLINER_DATA_SPEAKER"
}
def filter_objects(item, b_object):
if item.type == "CameraObject":
return b_object.type == "CAMERA"
if item.type == "LightObject":
return b_object.type == "LIGHT"
if item.type == "MeshObject":
return b_object.type == "MESH"
if item.type == "SpeakerObject":
return b_object.type == "SPEAKER"
if item.type == "Object":
return True
class ArmTraitPropWarning(bpy.types.PropertyGroup):
warning: StringProperty(name="Warning")
class ArmTraitPropListItem(bpy.types.PropertyGroup):
"""Group of properties representing an item in the list."""
name: StringProperty(
name="Name",
description="The name of this property",
default="Untitled")
type: EnumProperty(
items=(
# (Haxe Type, Display Name, Description)
("String", "String", "String Type"),
("Int", "Integer", "Integer Type"),
("Float", "Float", "Float Type"),
("Bool", "Boolean", "Boolean Type"),
("Vec2", "Vec2", "2D Vector Type"),
("Vec3", "Vec3", "3D Vector Type"),
("Vec4", "Vec4", "4D Vector Type"),
("Object", "Object", "Object Type"),
("CameraObject", "Camera Object", "Camera Object Type"),
("LightObject", "Light Object", "Light Object Type"),
("MeshObject", "Mesh Object", "Mesh Object Type"),
("SpeakerObject", "Speaker Object", "Speaker Object Type")),
name="Type",
description="The type of this property",
default="String")
# === VALUES ===
value_string: StringProperty(name="Value", default="")
value_int: IntProperty(name="Value", default=0)
value_float: FloatProperty(name="Value", default=0.0)
value_bool: BoolProperty(name="Value", default=False)
value_vec2: FloatVectorProperty(name="Value", size=2)
value_vec3: FloatVectorProperty(name="Value", size=3)
value_vec4: FloatVectorProperty(name="Value", size=4)
value_object: PointerProperty(
name="Value", type=bpy.types.Object, poll=filter_objects)
def set_value(self, val):
# Would require way too much effort, so it's out of scope here.
if self.type.endswith("Object"):
return
if self.type == "Int":
self.value_int = int(val)
elif self.type == "Float":
self.value_float = float(val)
elif self.type == "Bool":
self.value_bool = val == "true"
elif self.type in ("Vec2", "Vec3", "Vec4"):
if isinstance(val, str):
dimensions = int(self.type[-1])
# Parse "new VecX(...)"
val = val.split("(")[1].split(")")[0].split(",")
val = [value.strip() for value in val]
# new VecX() without parameters
if len(val) == 1 and val[0] == "":
# Use default value
return
# new VecX() with less parameters than its dimensions
while len(val) < dimensions:
val.append(0.0)
val = [float(value) for value in val]
setattr(self, "value_" + self.type.lower(), val)
else:
self.value_string = str(val)
def get_value(self):
if self.type == "Int":
return self.value_int
if self.type == "Float":
return self.value_float
if self.type == "Bool":
return self.value_bool
if self.type in ("Vec2", "Vec3", "Vec4"):
return list(getattr(self, "value_" + self.type.lower()))
if self.type.endswith("Object"):
if self.value_object is not None:
return self.value_object.name
return ""
return self.value_string
class ARM_UL_PropList(bpy.types.UIList):
def draw_item(self, context, layout, data, item, icon, active_data, active_propname, index):
item_value_ref = "value_" + item.type.lower()
custom_icon = PROP_TYPE_ICONS[item.type]
sp = layout.split(factor=0.3)
sp.label(text=item.type, icon=custom_icon)
sp = sp.split(factor=0.6)
sp.label(text=item.name)
# Make sure your code supports all 3 layout types
if self.layout_type in {'DEFAULT', 'COMPACT'}:
if item.type.endswith("Object"):
sp.prop_search(item, "value_object", context.scene, "objects", text="", icon=custom_icon)
else:
use_emboss = item.type in ("Bool", "String")
sp.prop(item, item_value_ref, text="", emboss=use_emboss)
elif self.layout_type in {'GRID'}:
layout.alignment = 'CENTER'
def register():
bpy.utils.register_class(ArmTraitPropWarning)
bpy.utils.register_class(ArmTraitPropListItem)
bpy.utils.register_class(ARM_UL_PropList)
def unregister():
bpy.utils.unregister_class(ARM_UL_PropList)
bpy.utils.unregister_class(ArmTraitPropListItem)
bpy.utils.unregister_class(ArmTraitPropWarning)
| python |
import numpy as np
import pandas as pd
import sklearn
from typing import Dict, Tuple
from sklearn.base import BaseEstimator
class RuleAugmentedEstimator(BaseEstimator):
"""Augments sklearn estimators with rule-based logic.
This class is a wrapper class for sklearn estimators with the additional
possibility of adding rule-based logic to the underlying estimator.
The provided rules are hard-coded and take precedence over the underlying
estimator's predictions.
"""
def __init__(self, base_model: BaseEstimator, rules: Dict, **base_params):
"""Initializes the RuleAugmentedEstimator instance.
Initializes the rule-augmented estimator by supplying the underlying
sklearn estimator as well as the hard-coded rules.
Args:
base_model: The underlying sklearn estimator.
Must implement a fit and predict method.
rules: The hard-coded rules in the format of a dictionary,
with keys being the pandas dataframe column name, and the values
being a tuple in the following form:
(comparison operator, value, return value)
Acceptable comparison operators are:
"=", "<", ">", "<=", ">="
Example:
{"House Type": [
("=", "Penthouse", 1.0),
("=", "Shack", 0.0)
],
"House Price": [
("<", 1000.0, 0.0),
(">=", 500000.0, 1.0)
]}
**base_params: Optional keyword arguments which will be passed on
to the ``base_model``.
Examples:
The below example illustrates how an instance of the
RuleAugmentedEstimator class can be initialized with a trained
sklearn GradientBoostingRegressor instance.
>>> gbr = GradientBoostingRegressor()
>>> rules = {"House Type": [
("=", "Penthouse", 1.0),
("=", "Shack", 0.0)
],
"House Price": [
("<", 1000.0, 0.0),
(">=", 500000.0, 1.0)
]}
>>> ra_estimator = RuleAugmentedEstimator(gbr, rules)
"""
self.rules = rules
self.base_model = base_model
self.base_model.set_params(**base_params)
def __repr__(self):
return "Rule Augmented Estimator:\n\n\t Base Model: {}\n\t Rules: {}".format(self.base_model, self.rules)
def __str__(self):
return self.__str__
def _get_base_model_data(self, X: pd.DataFrame, y: pd.Series) -> Tuple[pd.DataFrame, pd.Series]:
"""Filters the trainig data for data points not affected by the rules."""
train_x = X
for category, rules in self.rules.items():
if category not in train_x.columns.values: continue
for rule in rules:
if rule[0] == "=":
train_x = train_x.loc[train_x[category] != rule[1]]
elif rule[0] == "<":
train_x = train_x.loc[train_x[category] >= rule[1]]
elif rule[0] == ">":
train_x = train_x.loc[train_x[category] <= rule[1]]
elif rule[0] == "<=":
train_x = train_x.loc[train_x[category] > rule[1]]
elif rule[0] == ">=":
train_x = train_x.loc[train_x[category] < rule[1]]
else:
print("Invalid rule detected: {}".format(rule))
indices = train_x.index.values
train_y = y.iloc[indices]
train_x = train_x.reset_index(drop=True)
train_y = train_y.reset_index(drop=True)
return train_x, train_y
def fit(self, X: pd.DataFrame, y: pd.Series, **kwargs):
"""Fits the estimator to the data.
Fits the estimator to the data, only training the underlying estimator
on data which isn't affected by the hard-coded rules.
Args:
X: The training feature data.
y: The training label data.
**kwargs: Optional keyword arguments passed to the underlying
estimator's fit function.
"""
train_x, train_y = self._get_base_model_data(X, y)
self.base_model.fit(train_x, train_y, **kwargs)
def predict(self, X: pd.DataFrame) -> np.array:
"""Gets predictions for the provided feature data.
The predicitons are evaluated using the provided rules wherever possible
otherwise the underlying estimator is used.
Args:
X: The feature data to evaluate predictions for.
Returns:
np.array: Evaluated predictions.
"""
p_X = X.copy()
p_X['prediction'] = np.nan
for category, rules in self.rules.items():
if category not in p_X.columns.values: continue
for rule in rules:
if rule[0] == "=":
p_X.loc[p_X[category] == rule[1], 'prediction'] = rule[2]
elif rule[0] == "<":
p_X.loc[p_X[category] < rule[1], 'prediction'] = rule[2]
elif rule[0] == ">":
p_X.loc[p_X[category] > rule[1], 'prediction'] = rule[2]
elif rule[0] == "<=":
p_X.loc[p_X[category] <= rule[1], 'prediction'] = rule[2]
elif rule[0] == ">=":
p_X.loc[p_X[category] >= rule[1], 'prediction'] = rule[2]
else:
print("Invalid rule detected: {}".format(rule))
if len(p_X.loc[p_X['prediction'].isna()].index != 0):
base_X = p_X.loc[p_X['prediction'].isna()].copy()
base_X.drop('prediction', axis=1, inplace=True)
p_X.loc[p_X['prediction'].isna(), 'prediction'] = self.base_model.predict(base_X)
return p_X['prediction'].values
def get_params(self, deep: bool = True) -> Dict:
"""Return the model's and base model's parameters.
Args:
deep: Whether to recursively return the base model's parameters.
Returns
Dict: The model's parameters.
"""
params = {'base_model': self.base_model,
'outcome_range': self.outcome_range,
'rules': self.rules
}
params.update(self.base_model.get_params(deep=deep))
return params
def set_params(self, **params):
"""Sets parameters for the model and base model.
Args:
**params: Optional keyword arguments.
"""
parameters = params
param_keys = parameters.keys()
if 'base_model' in param_keys:
value = parameters.pop('base_model')
self.base_model = value
if 'rules' in param_keys:
value = parameters.pop('rules')
self.rules = value
self.base_model.set_params(**parameters)
| python |
# jay mahakal
import Resources.Work_By_Raj.Google_Calender_api.Resources.Setup
import Resources.Work_By_Raj.Google_Calender_api.Resources.Return_events_info
# below function [Setup.setup_calendar_credentials_return_service()] should run only once
# service = Setup.setup_calendar_credentials_return_service()
# print(Return_events_info.return_events_info("Give details about calendar events for today", service=service))
| python |
print "Ejercicio de ciclos -Granizada-"
def par(n):
n=n/2
def impar(n):
n=n*3+1
n=int(raw_input("digite numero "))
while n>=1:
if n%2==0:
par(n)
print n
else:
impar(n)
print n
| python |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 1 15:39:06 2020
@author: jireh.park
"""
import pandas as pd
import os
from tqdm import tqdm
from google.cloud import storage
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = os.path.dirname(os.path.abspath(os.path.dirname(__file__)))+ '/key/level-district.json'
def list_blob(bucket_name):
global credentials
"""Uploads a file to the bucket."""
# bucket_name = "your-bucket-name"
# source_file_name = "local/path/to/file"
# destination_blob_name = "storage-object-name"
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blobs = list(bucket.list_blobs())
#blob = bucket.blob(destination_blob_name)
#blob.upload_from_filename("tmp", content_type='text/csv')
# blob.upload_from_filename(source_file_name)
print(blobs)
)
bucket_name = 'j-first-bucket'
save_path = 'route/'
list_blob('bucket_name')
os.chdir("/Users/jireh.park/jireh_module/svc_data/route")
# 데이터 불러오기
df = pd.DataFrame()
for fl in os.listdir():
if 'txt' in fl:
data = pd.read_csv(fl,
engine = 'python', encoding = 'cp949', sep = '|', dtype = str)
df = df.append(data)
df = df.reset_index(drop = True)
# start, destination 뒤집어서 저
size = len(df)
col = ['time', 'num_station', 'transfer']
for ii in tqdm(df.index):
aa = df.loc[ii, 'route'][2:-2].split("', '")
aa.reverse()
df.loc[size + ii, 'start'] = df.loc[ii, 'destination']
df.loc[size + ii, 'destination'] = df.loc[ii, 'start']
for cl in col:
df.loc[size + ii, cl] = df.loc[ii, cl]
df.loc[size + ii, 'route'] = aa
df = df.reset_index(drop = True)
df.to_csv("route.csv", encoding = 'cp949', index = False)
#df.to_json("route.json")
| python |
#!/usr/bin/env python2.7
import os
import codecs
import json
import random
with codecs.open(os.path.join(os.path.dirname(os.path.realpath(__file__)), "apps.txt"), encoding="utf-8") as f:
apps = f.read().splitlines()
with codecs.open(os.path.join(os.path.dirname(os.path.realpath(__file__)), "networks.txt"), encoding="utf-8") as f:
networks = f.read().splitlines()
dim_groups = [
["install_date", "install_country", "ad_network", "campaign"],
["install_date", "ad_network", "campaign"],
["install_date", "install_country", "ad_network"],
["install_date", "ad_network", "campaign", "event_name"],
["install_date", "install_country", "ad_network", "campaign", "event_name"]
]
metric_groups = [
["installs_count", "clicks_count", "launches_count"],
["installs_count", "install_cost", "install_cost_alt", "revenue", "revenue_alt"],
["revenue", "revenue_alt", "inapps_count"],
["clicks_count", "impressions_count", "installs_count", "launches_count"],
["clicks_count", "impressions_count", "installs_count", "uninstalls_count"]
]
date_ranges = [
["2015-01-01", "2015-01-14"],
["2015-01-01", "2015-01-30"],
["2013-05-01", "2013-05-14"],
["2014-01-01", "2015-03-01"],
["2014-12-01", "2015-01-01"],
["2015-02-01", "2015-02-08"],
["2013-01-01", "2013-03-01"]
]
country_groups = [
["US", "IR", "UK", "MX"],
["US", "IL", "KZ"],
["RU", "BE"],
["TG", "TH", "TJ", "TL", "TM", "TN", "TO", "TR", "TT", "TV", "TZ", "UA", "UG", "US", "UY", "UZ", "VA", "VC", "VE", "VN", "VU", "WS", "YE", "ZA", "ZM", "ZW"]
]
for i in range(1000):
dates = random.choice(date_ranges)
query = {
"type": "aggregate",
"table": "activity",
"select": [],
"filter": {
"op": "and",
"filters": [
{"op": "eq", "column": "app_id", "value": random.choice(apps)},
{"op": "ge", "column": "install_date", "value": dates[0]},
{"op": "lt", "column": "install_date", "value": dates[1]}
]
}
}
query["select"] = [{"column": c} for c in random.choice(dim_groups) + random.choice(metric_groups)]
if random.random() < 0.2:
query["filter"]["filters"].append({"op": "in", "column": "install_country", "values": random.choice(country_groups)})
if random.random() < 0.5:
query["filter"]["filters"].append({"op": "eq", "column": "ad_network", "value": random.choice(networks)})
print "http://localhost:5000/query POST %s" % json.dumps(query)
| python |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""user表测试"""
from executor.database.models.user import Users
from executor.tests.database.base import DatabaseTestCase
from executor.exceptions import UserAlreadyExistException, \
IncorrectPasswordException
class TestOperatorUser(DatabaseTestCase):
data_file_path = "database_user_data.yaml"
def test_create_user(self):
user = Users.from_json(self.get_test_date("test_create_user"))
new_user = self.db.create_user(self.context, user)
self.assertIsInstance(new_user, Users)
self.db.delete_user(self.context, new_user.id, new_user.password)
def test_create_same_name_user(self):
user1 = Users.from_json(
self.get_test_date(
"test_create_same_name_user", "test_create_same_name_user1"))
user2 = Users.from_json(
self.get_test_date(
"test_create_same_name_user", "test_create_same_name_user2"))
self.db.create_user(self.context, user1)
self.assertRaises(UserAlreadyExistException,
self.db.create_user, self.context, user2)
self.db.delete_user(self.context, user1.phone, user1.password)
def test_create_same_phone_user(self):
user1 = Users.from_json(
self.get_test_date(
"test_create_same_phone_user", "test_create_same_phone_user1"
))
user2 = Users.from_json(
self.get_test_date(
"test_create_same_phone_user", "test_create_same_phone_user2"
))
self.db.create_user(self.context, user1)
self.assertRaises(UserAlreadyExistException,
self.db.create_user, self.context, user2)
self.db.delete_user(self.context, user1.phone, user1.password)
def test_get_user_by_id(self):
user = Users.from_json(
self.get_test_date("test_get_user_by_id")
)
n_user = self.db.create_user(self.context, user)
self.assertEqual(
n_user,
self.db.get_user(self.context, user.id, user.password))
self.db.delete_user(self.context, n_user.id, n_user.password)
def test_get_user_by_user_id(self):
user = Users.from_json(
self.get_test_date("test_get_user_by_user_id")
)
n_user = self.db.create_user(self.context, user)
self.assertEqual(
n_user,
self.db.get_user(self.context, user.user_id, user.password))
self.db.delete_user(self.context, n_user.user_id, n_user.password)
def test_get_user_by_name(self):
user = Users.from_json(
self.get_test_date("test_get_user_by_name")
)
n_user = self.db.create_user(self.context, user)
self.assertEqual(
n_user,
self.db.get_user(self.context, user.username, user.password))
self.db.delete_user(self.context, n_user.username, n_user.password)
def test_get_user_by_phone(self):
user = Users.from_json(
self.get_test_date("test_get_user_by_phone")
)
n_user = self.db.create_user(self.context, user)
self.assertEqual(
n_user,
self.db.get_user(self.context, user.phone, user.password))
self.db.delete_user(self.context, n_user.phone, n_user.password)
def test_get_user_with_incorrect_password(self):
user = Users.from_json(
self.get_test_date("test_get_user_with_incorrect_password")
)
n_user = self.db.create_user(self.context, user)
self.assertRaises(
IncorrectPasswordException,
self.db.get_user, self.context, n_user.phone,
n_user.password + "_"
)
self.db.delete_user(self.context, n_user.id, n_user.password)
| python |
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Defines endpoints for the landing page.
TODO(shifucun): once this is well tested, can deprecate corresponding code
in chart.py and place.py
"""
import collections
import copy
import json
import logging
import urllib
from flask import Blueprint, current_app, Response, url_for, g
from flask_babel import gettext
from collections import defaultdict
from cache import cache
import services.datacommons as dc_service
import routes.api.place as place_api
import lib.range as lib_range
# Define blueprint
bp = Blueprint("api.landing_page", __name__, url_prefix='/api/landingpage')
BAR_CHART_TYPES = ['parent', 'similar', 'nearby', 'child']
MAX_DENOMINATOR_BACK_YEAR = 3
MIN_CHART_TO_KEEP_TOPICS = 30
OVERVIEW = 'Overview'
def get_landing_page_data(dcid, new_stat_vars):
response = dc_service.fetch_data('/landing-page', {
'place': dcid,
'newStatVars': new_stat_vars,
},
compress=False,
post=True,
has_payload=False)
return response
def build_url(dcids, statvar_to_denom, is_scaled=False):
anchor = '&place=' + ','.join(dcids)
parts = []
for statvar, denom in statvar_to_denom.items():
part = statvar
if denom:
part += '|' + denom
parts.append(part)
anchor += ('&statsVar=' + '__'.join(parts))
if is_scaled:
anchor = anchor + '&pc'
return urllib.parse.unquote(url_for('tools.timeline', _anchor=anchor))
def fill_translation(chart):
chart['title'] = gettext(chart['titleId'])
del chart['titleId']
if 'description' in chart:
del chart['description']
return chart
# TODO: add test for chart_config for assumption that each combination of stat vars will only have one config in chart_config.
def build_spec(chart_config):
"""Builds hierachical spec based on chart config."""
spec = defaultdict(lambda: defaultdict(list))
# Map: category -> topic -> [config]
for conf in chart_config:
config = copy.deepcopy(conf)
config = fill_translation(config)
if 'relatedChart' in config and config['relatedChart']['scale']:
config['relatedChart'] = fill_translation(config['relatedChart'])
is_overview = ('isOverview' in config and config['isOverview'])
category = config['category']
if 'isOverview' in config:
del config['isOverview']
del config['category']
if is_overview:
spec[OVERVIEW][category].append(copy.deepcopy(config))
spec[category][config['title']].append(config)
return spec
def get_denom(cc, related_chart=False):
"""Get the numerator and denominator map."""
# If chart requires denominator, use it for both primary and related charts.
if 'denominator' in cc:
result = {}
if len(cc['denominator']) != len(cc['statsVars']):
raise ValueError('Denominator number not matching: %s', cc)
for num, denom in zip(cc['statsVars'], cc['denominator']):
result[num] = denom
return result
# For related chart, use the denominator that is specified in the
# 'relatedChart' field if present.
if related_chart and cc.get('relatedChart', {}).get('scale', False):
return cc['relatedChart'].get('denominator', 'Count_Person')
return None
def get_series(data, place, stat_vars):
"""Get time series from the landing page data.
Aggregate for all the stat vars and return empty series if any stat var data
is missing
Returns:
series and sources.
"""
all_series = []
sources = set()
num_sv = len(stat_vars)
for sv in stat_vars:
if 'data' not in data[place] or sv not in data[place]['data']:
return {}, []
series = data[place]['data'][sv]
all_series.append(series['val'])
sources.add(series['metadata']['provenanceUrl'])
# One series, no need to aggregate
if num_sv == 1:
return all_series[0], sources
merged_series = defaultdict(list)
for series in all_series:
for date, value in series.items():
merged_series[date].append(value)
# Aggregate
agg_series = {}
for date, values in merged_series.items():
if len(values) == num_sv:
agg_series[date] = sum(values)
return agg_series, sources
def get_stat_var_group(cc, data, places):
"""Get the stat var grouping for aggregation."""
if 'aggregate' in cc:
agg_type = lib_range.get_aggregate_config(cc['aggregate'])
place_stat_vars = defaultdict(list)
for place in places:
if place not in data or 'data' not in data[place]:
continue
for sv in cc['statsVars']:
if sv in data[place]['data']:
place_stat_vars[place].append(sv)
result = lib_range.aggregate_stat_var(place_stat_vars, agg_type)
for place in places:
if place not in result:
result[place] = {}
else:
result = {}
for place in places:
result[place] = {sv: [sv] for sv in cc['statsVars']}
return result
def get_snapshot_across_places(cc, data, places):
"""Get the snapshot used for bar data across a few places.
This will scale the value if required and pick the latest date that has the
most <place, stat_var> entries.
"""
if not places:
return {}, {}
# date_to_data is a dictionary from date to place and a tuple of
# (stat_var, value) pair.
# Example:
# {
# "2018": {
# "geoId/06":[("Count_Person", 200), ("Count_Person_Female", 100)],
# "geoId/08":[("Count_Person", 300), ("Count_Person_Female", 150)],
# },
# "2017": {
# "geoId/06":[("Count_Person", 300), ("Count_Person_Female", 150)],
# "geoId/08":[("Count_Person", 400), ("Count_Person_Female", 200)],
# },
# }
date_to_data = collections.defaultdict(
lambda: collections.defaultdict(list))
# TODO(shifucun/beets): add a unittest to ensure denominator is set
# explicitly when scale==True
num_denom = get_denom(cc, related_chart=True)
sources = set()
place_stat_var_group = get_stat_var_group(cc, data, places)
statvar_to_denom = {}
for place in places:
if place not in data:
continue
stat_var_group = place_stat_var_group[place]
for num_sv, sv_list in stat_var_group.items():
num_series, num_sources = get_series(data, place, sv_list)
if not num_series:
continue
sources.update(num_sources)
if num_denom:
if isinstance(num_denom, dict):
denom_sv = num_denom[num_sv]
else:
denom_sv = num_denom
statvar_to_denom[num_sv] = denom_sv
denom_series, denom_sources = get_series(
data, place, [denom_sv])
if not denom_series:
continue
sources.update(denom_sources)
result_series = scale_series(num_series, denom_series)
else:
result_series = num_series
statvar_to_denom[num_sv] = None
# Turn the value to be keyed by date.
for date, value in result_series.items():
date_to_data[date][place].append((num_sv, value))
# Pick a date that has the most series across places.
dates = sorted(date_to_data.keys(), reverse=True)
if not dates:
return {}, {}
count = 0
chosen_date = None
for date in dates:
if len(date_to_data[date]) > count:
count = len(date_to_data[date])
chosen_date = date
result = {'date': chosen_date, 'data': [], 'sources': list(sources)}
for place in places:
points = {}
for stat_var, value in date_to_data[chosen_date][place]:
points[stat_var] = value
if points:
result['data'].append({'dcid': place, 'data': points})
return result, statvar_to_denom
# TODO(shifucun): Add unittest for these helper functions
def get_bar(cc, data, places):
"""Get the bar data across a few places.
This will scale the value if required and pick the latest date that has the
most <place, stat_var> entries.
"""
result, statvar_denom = get_snapshot_across_places(cc, data, places)
if not result:
return {}
# Should have data other than the primary place. Return empty struct to
# so client won't draw chart.
if len(result['data']) <= 1:
return {}
is_scaled = (('relatedChart' in cc and
cc['relatedChart'].get('scale', False)) or
('denominator' in cc))
result['exploreUrl'] = build_url(places, statvar_denom, is_scaled)
return result
def get_trend(cc, data, place):
"""Get the time series data for a place."""
if place not in data:
return {}
result_series = {}
sources = set()
num_denom = get_denom(cc)
stat_var_group = get_stat_var_group(cc, data, [place])[place]
statvar_denom = {}
for num_sv, sv_list in stat_var_group.items():
num_series, num_sources = get_series(data, place, sv_list)
if not num_series:
continue
sources.update(num_sources)
if num_denom:
if isinstance(num_denom, dict):
denom_sv = num_denom[num_sv]
else:
denom_sv = num_denom
denom_sv = num_denom[num_sv]
statvar_denom[num_sv] = denom_sv
denom_series, denom_sources = get_series(data, place, [denom_sv])
if not denom_series:
continue
sources.update(denom_sources)
result_series[num_sv] = scale_series(num_series, denom_series)
else:
result_series[num_sv] = num_series
statvar_denom[num_sv] = None
# filter out time series with single data point.
for sv in list(result_series.keys()):
if len(result_series[sv]) <= 1:
del result_series[sv]
if not result_series:
return {}
is_scaled = ('denominator' in cc)
return {
'series': result_series,
'sources': list(sources),
'exploreUrl': build_url([place], statvar_denom, is_scaled)
}
def get_year(date):
try:
return int(date.split('-')[0])
except IndexError:
raise ValueError('no valid date format found %s', date)
# TODO(shifucun): Add unittest.
def scale_series(numerator, denominator):
"""Scale two time series.
The date of the two time series may not be exactly aligned. Here we use
year alignment to match two date. If no denominator is found for a
numerator, then the data is removed.
"""
data = {}
for date, value in numerator.items():
if date in denominator:
if denominator[date] > 0:
data[date] = value / denominator[date]
else:
data[date] = 0
else:
try:
numerator_year = get_year(date)
for i in range(0, MAX_DENOMINATOR_BACK_YEAR + 1):
year = str(numerator_year - i)
if year in denominator:
if denominator[year] > 0:
data[date] = value / denominator[year]
else:
data[date] = 0
break
except ValueError:
return {}
return data
def get_i18n_all_child_places(raw_page_data):
all_child_places = raw_page_data.get('allChildPlaces', {})
all_dcids = []
for place_type in list(all_child_places.keys()):
for place in all_child_places[place_type]['places']:
all_dcids.append(place.get('dcid', ''))
i18n_names = place_api.get_i18n_name(all_dcids,
False) # Don't resolve en-only names
for place_type in list(all_child_places.keys()):
for place in all_child_places[place_type]['places']:
dcid = place.get('dcid')
i18n_name = i18n_names.get(dcid, '')
if i18n_name:
place['name'] = i18n_name
for place_type in list(all_child_places.keys()):
all_child_places[place_type] = all_child_places[place_type]['places']
return all_child_places
@bp.route('/data/<path:dcid>')
@cache.cached(timeout=3600 * 24, query_string=True) # Cache for one day.
def data(dcid):
"""
Get chart spec and stats data of the landing page for a given place.
"""
logging.info("Landing Page: cache miss for %s, fetch and process data ...",
dcid)
spec_and_stat = build_spec(current_app.config['CHART_CONFIG'])
new_stat_vars = current_app.config['NEW_STAT_VARS']
raw_page_data = get_landing_page_data(dcid, new_stat_vars)
if not 'statVarSeries' in raw_page_data:
logging.info("Landing Page: No data for %s", dcid)
return Response(json.dumps({}), 200, mimetype='application/json')
# Filter out Metropolitan France parent place.
parent_places = [
el for el in raw_page_data.get('parentPlaces', [])
if el != 'country/FXX'
]
raw_page_data['parentPlaces'] = parent_places
# Only US places have comparison charts.
is_usa_place = False
for place in [dcid] + raw_page_data.get('parentPlaces', []):
if place == 'country/USA':
is_usa_place = True
break
# Populate the data for each chart
all_stat = raw_page_data['statVarSeries']
for category in spec_and_stat:
if category == OVERVIEW:
if is_usa_place:
chart_types = ['nearby', 'child']
else:
chart_types = ['similar']
else:
chart_types = BAR_CHART_TYPES
for topic in spec_and_stat[category]:
for chart in spec_and_stat[category][topic]:
# Trend data
chart['trend'] = get_trend(chart, all_stat, dcid)
if 'aggregate' in chart:
aggregated_stat_vars = list(chart['trend'].get(
'series', {}).keys())
if aggregated_stat_vars:
chart['trend']['statsVars'] = aggregated_stat_vars
else:
chart['trend'] = {}
# Bar data
for t in chart_types:
chart[t] = get_bar(chart, all_stat, [dcid] +
raw_page_data.get(t + 'Places', []))
if t == 'similar' and 'data' in chart[t]:
# If no data for current place, do not serve similar
# place data.
keep_chart = False
for d in chart[t]['data']:
if d['dcid'] == dcid:
keep_chart = True
break
if not keep_chart:
chart[t] = {}
# Update stat vars for aggregated stats
if 'aggregate' in chart and chart[t]:
chart[t]['statsVars'] = []
for place_data in chart[t].get('data', []):
stat_vars = list(place_data['data'].keys())
if len(stat_vars) > len(chart[t]['statsVars']):
chart[t]['statsVars'] = stat_vars
elif len(stat_vars) == 0:
chart[t] = {}
if 'aggregate' in chart:
chart['statsVars'] = []
# Remove empty category and topics
for category in list(spec_and_stat.keys()):
for topic in list(spec_and_stat[category].keys()):
filtered_charts = []
for chart in spec_and_stat[category][topic]:
keep_chart = False
for t in ['trend'] + BAR_CHART_TYPES:
if chart.get(t, None):
keep_chart = True
break
if keep_chart:
filtered_charts.append(chart)
if not filtered_charts:
del spec_and_stat[category][topic]
else:
spec_and_stat[category][topic] = filtered_charts
if not spec_and_stat[category]:
del spec_and_stat[category]
# Only keep the "Overview" category if the number of total chart is less
# than certain threshold.
overview_set = set()
non_overview_set = set()
chart_count = 0
# Get the overview charts
for topic, charts in spec_and_stat[OVERVIEW].items():
for chart in charts:
overview_set.add((topic, chart['title']))
chart_count += 1
# Get the non overview charts
for category, topic_data in spec_and_stat.items():
if category == OVERVIEW:
continue
for topic in topic_data:
if (category, topic) not in overview_set:
non_overview_set.add((category, topic))
chart_count += 1
# If the total number of chart is too small, then merge all charts to
# the overview category and remove other categories
if chart_count < MIN_CHART_TO_KEEP_TOPICS:
for category, topic in non_overview_set:
spec_and_stat[OVERVIEW][category].extend(
spec_and_stat[category][topic])
for category in list(spec_and_stat.keys()):
if category != OVERVIEW:
del spec_and_stat[category]
# Get chart category name translations
categories = {}
for category in list(spec_and_stat.keys()) + list(spec_and_stat[OVERVIEW]):
categories[category] = gettext(f'CHART_TITLE-CHART_CATEGORY-{category}')
# Get display name for all places
all_places = [dcid]
for t in BAR_CHART_TYPES:
all_places.extend(raw_page_data.get(t + 'Places', []))
names = place_api.get_display_name('^'.join(sorted(all_places)), g.locale)
# Pick data to highlight - only population for now
population, statvar_denom = get_snapshot_across_places(
{'statsVars': ['Count_Person']}, all_stat, [dcid])
highlight = {gettext('CHART_TITLE-Population'): population}
response = {
'pageChart': spec_and_stat,
'allChildPlaces': get_i18n_all_child_places(raw_page_data),
'childPlacesType': raw_page_data.get('childPlacesType', ""),
'childPlaces': raw_page_data.get('childPlaces', []),
'parentPlaces': raw_page_data.get('parentPlaces', []),
'similarPlaces': raw_page_data.get('similarPlaces', []),
'nearbyPlaces': raw_page_data.get('nearbyPlaces', []),
'categories': categories,
'names': names,
'highlight': highlight,
}
return Response(json.dumps(response), 200, mimetype='application/json')
| python |
# OBS
# Imagem celular original do vírus recentemente descoberto SARS-CoV-2,
# popularmente chamado de COVID-19 ou Coronavirus.
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
!wget "https://raw.githubusercontent.com/PedroHaupenthal/Image-Processing/master/watershed/covid_19.jpg" -O "covid_19.jpg"
img1 = cv.imread("covid_19.jpg")
img1 = cv.cvtColor(img1, cv.COLOR_BGR2RGB)
img2 = cv.cvtColor(img1, cv.COLOR_RGB2GRAY)
img2 = cv.bitwise_not(img2)
ret, img2 = cv.threshold(img2,
0,
255,
cv.THRESH_BINARY_INV + cv.THRESH_OTSU)
kernel = np.ones((3,3), np.uint8)
img3 = cv.morphologyEx(img2,
cv.MORPH_CLOSE,
kernel,
iterations = 2)
img4 = cv.dilate(img3,
kernel,
iterations = 5)
img5 = cv.distanceTransform(img3,
cv.DIST_L2,
5)
ret,img6 = cv.threshold(img5,
0.65 * img5.max(),
255,
0)
img6 = np.uint8(img6)
img7 = cv.subtract(img4, img6)
ret, count = cv.connectedComponents(img6)
count = count + 1
count[img7 == 255] = 0
img8 = cv.watershed(img1, count)
img1[count == -1] = [255, 0, 0]
plt.figure(figsize=(30,30))
plt.subplot(121), plt.imshow(img1), plt.title("ORIGINAL"), plt.axis("off")
plt.subplot(122), plt.imshow(img8, cmap='jet'), plt.title("RESULTADO"), plt.axis("off")
plt.show() | python |
import math
import threading
from django.core.cache import caches
from .settings import CACHE_HELPERS_ALIAS
CACHE_HELPERS_KEY = 'cache_helpers_key'
def set_cache_bust_status(bust_key=None):
cache = caches[CACHE_HELPERS_ALIAS]
cache.set(CACHE_HELPERS_KEY, bust_key)
def get_bust_key():
cache = caches[CACHE_HELPERS_ALIAS]
return cache.get(CACHE_HELPERS_KEY, None)
def mark_response_as_processed(response):
setattr(response, '_already_cahed', True)
def check_response_has_been_processed(response):
return getattr(response, '_already_cahed', False)
def check_bust_header(request):
bust_key = request.META.get('HTTP_BUST', '')
return False if (not bust_key or bust_key != get_bust_key()) else True
# TODO avoid list user generator
def threaded_cue(cue, callback, threads):
def process_chunk(begining, end, worker_num):
for index, item in enumerate(cue[begining:end]):
real_index = (begining + index) if begining > 0 else index
result = callback(item)
if result:
cue[real_index] = result
CHUNK_SIZE = math.ceil(len(cue) / threads)
end = 0
threads_refs = []
for i in range(threads):
begining = end
end = begining + CHUNK_SIZE
t = threading.Thread(target=process_chunk, args=(begining, end if end < len(cue) else len(cue), i))
t.start()
threads_refs.append(t)
for i in threads_refs:
t.join()
return cue
def get_ref_from_func(func):
if hasattr(func, '__self__'):
return func.__self__.__class__
return func
def get_func_from_func(func):
if hasattr(func, '__wrapped__'):
return func.__wrapped__
return func
def func_to_string(func):
func = func.func if hasattr(func, 'func') else func
ref = get_ref_from_func(func)
chunks = [
ref.__module__,
ref.__name__,
]
func = get_func_from_func(func)
if func.__name__ != chunks[-1]:
chunks.append(func.__name__)
return '.'.join(chunks)
def invalidate_cache(cache_key, cache=None):
cache = caches[cache if cache is not None else CACHE_HELPERS_ALIAS]
cache.delete(cache_key)
| python |
"""
Provides the functionality to feed TF templates with Jerakia lookups
"""
import sys
import os
from jerakia import Jerakia
from terraform_external_data import terraform_external_data
def retrieveLookupInfo(query,item):
lookitem = query[item]
lookuppath =lookitem.split('/')
key = lookuppath.pop()
namespace = lookuppath
if not namespace:
raise Exception("No namespace given %s" % item )
return namespace,key
@terraform_external_data
def lookupJerakia(query,variables=None):
jerakia = Jerakia(configfile=os.path.abspath('utils/jerakia.yaml'))
resdict = {}
for item in query:
namespace,key = retrieveLookupInfo(query,item)
ret = []
response = jerakia.lookup(key=key, namespace=namespace, variables=variables)
ret.append(response['payload'])
resdict.update({item: str(ret)})
return resdict
if __name__ == '__main__':
lookupJerakia() | python |
"""Internal helpers for dataset validation."""
from pathlib import Path
from typing import Any, Iterable, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
from biopsykit.utils._types import _Hashable, path_t
from biopsykit.utils.exceptions import FileExtensionError, ValidationError, ValueRangeError
def _assert_is_dir(path: path_t, raise_exception: Optional[bool] = True) -> Optional[bool]:
"""Check if a path is a directory.
Parameters
----------
path : path or str
path to check if it's a directory
raise_exception : bool, optional
whether to raise an exception or return a bool value
Returns
-------
``True`` if ``path`` is a directory, ``False`` otherwise (if ``raise_exception`` is ``False``)
Raises
------
ValueError
if ``raise_exception`` is ``True`` and ``path`` is not a directory
"""
# ensure pathlib
file_name = Path(path)
if not file_name.is_dir():
if raise_exception:
raise ValueError("The path '{}' is expected to be a directory, but it's not!".format(path))
return False
return True
def _assert_file_extension(
file_name: path_t, expected_extension: Union[str, Sequence[str]], raise_exception: Optional[bool] = True
) -> Optional[bool]:
"""Check if a file has the correct file extension.
Parameters
----------
file_name : path or str
file name to check for correct extension
expected_extension : str or list of str
file extension (or a list of file extensions) to check for
raise_exception : bool, optional
whether to raise an exception or return a bool value
Returns
-------
``True`` if ``file_name`` ends with one of the specified file extensions, ``False`` otherwise
(if ``raise_exception`` is ``False``)
Raises
------
:exc:`~biopsykit.exceptions.FileExtensionError`
if ``raise_exception`` is ``True`` and ``file_name`` does not end with any of the specified
``expected_extension``
"""
# ensure pathlib
file_name = Path(file_name)
if isinstance(expected_extension, str):
expected_extension = [expected_extension]
if file_name.suffix not in expected_extension:
if raise_exception:
raise FileExtensionError(
"The file name extension is expected to be one of {}. "
"Instead it has the following extension: {}".format(expected_extension, file_name.suffix)
)
return False
return True
def _assert_is_dtype(
obj, dtype: Union[type, Tuple[type, ...]], raise_exception: Optional[bool] = True
) -> Optional[bool]:
"""Check if an object has a specific data type.
Parameters
----------
obj : any object
object to check
dtype : type or list of type
data type of tuple of data types to check
raise_exception : bool, optional
whether to raise an exception or return a bool value
Returns
-------
``True`` if ``obj`` is one of the expected data types, ``False`` otherwise (if ``raise_exception`` is ``False``)
Raises
------
:exc:`~biopsykit.exceptions.ValidationError`
if ``raise_exception`` is ``True`` and ``obj`` is none of the expected data types
"""
if not isinstance(obj, dtype):
if raise_exception:
raise ValidationError(
"The data object is expected to be one of ({},). But it is a {}".format(dtype, type(obj))
)
return False
return True
def _assert_has_multiindex(
df: pd.DataFrame,
expected: Optional[bool] = True,
nlevels: Optional[int] = 2,
nlevels_atleast: Optional[int] = False,
raise_exception: Optional[bool] = True,
) -> Optional[bool]:
"""Check if a :any:`pandas.DataFrame` has a :any:`pandas.MultiIndex` as index.
Parameters
----------
df : :class:`~pandas.DataFrame`
The dataframe to check
expected : bool, optional
Whether the df is expected to have a MultiIndex index or not
nlevels : int, optional
If MultiIndex is expected, how many levels the MultiIndex index should have
nlevels_atleast : bool, optional
Whether the MultiIndex has to have at least ``nlevels`` (``True``)
or exactly match the number of levels (``False``)
raise_exception : bool, optional
whether to raise an exception or return a bool value
Returns
-------
``True`` if ``df`` meets the expected index format, ``False`` otherwise (if ``raise_exception`` is ``False``)
Raises
------
:exc:`~biopsykit.exceptions.ValidationError`
if ``raise_exception`` is ``True`` and ``df`` does not meet the expected index format
"""
return _multiindex_check_helper(
df=df,
idx_or_col="index",
expected=expected,
nlevels=nlevels,
nlevels_atleast=nlevels_atleast,
raise_exception=raise_exception,
)
def _assert_has_index_levels(
df: pd.DataFrame,
index_levels: Iterable[_Hashable],
match_atleast: Optional[bool] = False,
match_order: Optional[bool] = False,
raise_exception: Optional[bool] = True,
) -> Optional[bool]:
"""Check if the dataframe has all index level names.
Parameters
----------
df : :class:`~pandas.DataFrame`
The dataframe to check
index_levels : list
Set of index level names to check
match_atleast : bool, optional
Whether the MultiIndex columns have to have at least the specified column levels (``True``)
or exactly match the column levels (``False``)
match_order : bool, optional
Whether to also match the level order
raise_exception : bool, optional
whether to raise an exception or return a bool value
Returns
-------
``True`` if ``df`` has the expected index level names, ``False`` otherwise (if ``raise_exception`` is ``False``)
Raises
------
:exc:`~biopsykit.exceptions.ValidationError`
if ``raise_exception`` is ``True`` and ``df`` does not have the expected index level names
"""
return _multiindex_level_names_helper(
df,
level_names=index_levels,
idx_or_col="index",
match_atleast=match_atleast,
match_order=match_order,
raise_exception=raise_exception,
)
def _assert_has_columns(
df: pd.DataFrame,
columns_sets: Sequence[Union[List[_Hashable], List[str], pd.Index]],
raise_exception: Optional[bool] = True,
) -> Optional[bool]:
"""Check if the dataframe has at least all columns sets.
Parameters
----------
df : :class:`~pandas.DataFrame`
The dataframe to check
columns_sets : list
Column set or list of column sets to check
raise_exception : bool, optional
whether to raise an exception or return a bool value
Returns
-------
``True`` if ``df`` has the expected column names, ``False`` otherwise (if ``raise_exception`` is ``False``)
Raises
------
:exc:`~biopsykit.exceptions.ValidationError`
if ``raise_exception`` is ``True`` and ``df`` does not have the expected index level names
Examples
--------
>>> df = pd.DataFrame()
>>> df.columns = ["col1", "col2"]
>>> _assert_has_columns(df, [["other_col1", "other_col2"], ["col1", "col2"]])
>>> # This raises no error, as df contains all columns of the second set
"""
columns = df.columns
result = False
for col_set in columns_sets:
result = result or all(v in columns for v in col_set)
if result is False:
if len(columns_sets) == 1:
helper_str = "the following columns: {}".format(columns_sets[0])
else:
helper_str = "one of the following sets of columns: {}".format(columns_sets)
if raise_exception:
raise ValidationError(
"The dataframe is expected to have {}. Instead it has the following columns: {}".format(
helper_str, list(df.columns)
)
)
return result
def _assert_has_column_multiindex(
df: pd.DataFrame,
expected: Optional[bool] = True,
nlevels: Optional[int] = 2,
nlevels_atleast: Optional[int] = False,
raise_exception: Optional[bool] = True,
) -> Optional[bool]:
"""Check if a :any:`pandas.DataFrame` has a :any:`pandas.MultiIndex` as columns.
Parameters
----------
df : :class:`~pandas.DataFrame`
The dataframe to check
expected : bool, optional
Whether the df is expected to have MultiIndex column or not
nlevels : int, optional
If MultiIndex is expected, how many levels the MultiIndex columns should have
nlevels_atleast : bool, optional
Whether the MultiIndex has to have at least ``nlevels`` (``True``)
or exactly match the number of levels (``False``)
raise_exception : bool, optional
Whether to raise an exception or return a bool value
Returns
-------
``True`` if ``df`` meets the expected column index format, ``False`` otherwise (if ``raise_exception`` is ``False``)
Raises
------
:exc:`~biopsykit.exceptions.ValidationError`
if ``raise_exception` is ``True`` and ``df`` does not meet the expected column index format
"""
return _multiindex_check_helper(
df=df,
idx_or_col="column",
expected=expected,
nlevels=nlevels,
nlevels_atleast=nlevels_atleast,
raise_exception=raise_exception,
)
def _assert_has_columns_any_level(
df: pd.DataFrame,
columns_sets: Sequence[Union[List[_Hashable], List[str], pd.Index]],
raise_exception: Optional[bool] = True,
) -> Optional[bool]:
"""Check if the dataframe has the expected set of column names at any level of a :any:`pandas.MultiIndex`.
Parameters
----------
df : :class:`~pandas.DataFrame`
The dataframe to check
columns_sets : list
Column set of list of column sets to check
raise_exception : bool, optional
whether to raise an exception or return a bool value
Returns
-------
``True`` if ``df`` has the expected column names at any :any:`pandas.MultiIndex` level,
``False`` otherwise (if ``raise_exception`` is ``False``)
Raises
------
:exc:`~biopsykit.exceptions.ValidationError`
if ``raise_exception`` is ``True`` and ``df`` does not have the expected column names
Examples
--------
>>> df = pd.DataFrame()
>>> df.columns = pd.MultiIndex.from_tuples([("Name", "col1"), ("Name", "col2")])
>>> _assert_has_columns_any_level(df, [["col1", "col2"]])
>>> # This raises no error, as df contains all columns in the seconds level
"""
_assert_has_column_multiindex(df, expected=True, nlevels_atleast=True)
column_levels = [np.array(df.columns.get_level_values(i)) for i in range(df.columns.nlevels)]
result = False
for columns in column_levels:
for col_set in columns_sets:
result = result or all(v in columns for v in col_set)
if result is False:
if len(columns_sets) == 1:
helper_str = "the following columns: {}".format(columns_sets[0])
else:
helper_str = "one of the following sets of columns: {}".format(columns_sets)
if raise_exception:
raise ValidationError(
"The dataframe is expected to have {} at any level of the MultiIndex. Instead it has the "
"following MultiIndex columns: {}".format(helper_str, column_levels)
)
return result
def _assert_has_column_levels(
df: pd.DataFrame,
column_levels: Iterable[_Hashable],
match_atleast: Optional[bool] = False,
match_order: Optional[bool] = False,
raise_exception: Optional[bool] = True,
) -> Optional[bool]:
"""Check if the dataframe has all column level names of a MultiIndex column.
Parameters
----------
df : :class:`~pandas.DataFrame`
The dataframe to check
column_levels : list
Set of column level names to check
match_atleast : bool, optional
Whether the MultiIndex columns have to have at least the specified column levels (``True``)
or exactly match the column levels (``False``)
match_order : bool, optional
Whether to also match the level order
raise_exception : bool, optional
Whether to raise an exception or return a bool value
Returns
-------
``True`` if ``df`` has the expected column level names, ``False`` otherwise (if ``raise_exception`` is ``False``)
Raises
------
:exc:`~biopsykit.exceptions.ValidationError`
if ``raise_exception`` is ``True`` and ``df`` does not have the expected index level names
"""
return _multiindex_level_names_helper(
df,
level_names=column_levels,
idx_or_col="column",
match_atleast=match_atleast,
match_order=match_order,
raise_exception=raise_exception,
)
def _assert_value_range(
data: Union[pd.DataFrame, pd.Series],
value_range: Sequence[Union[int, float]],
raise_exception: Optional[bool] = True,
) -> Optional[bool]:
"""Check if all values are within the specified range.
Parameters
----------
data : :class:`~pandas.DataFrame`
data to check values
value_range : tuple of numbers
value range in the format [min_val, max_val]
raise_exception : bool, optional
Whether to raise an exception or return a bool value
Returns
-------
``True`` if all values in ``data`` are within ``value_range``, ``False`` otherwise
(if ``raise_exception`` is ``False``)
Raises
------
:exc:`~biopsykit.exceptions.ValueRangeError`
if ``raise_exception`` is ``True`` and any value of ``data`` is not within ``value_range``
"""
max_val = np.nanmax(data)
min_val = np.nanmin(data)
if not (min_val >= value_range[0] and max_val <= value_range[1]):
if raise_exception:
raise ValueRangeError(
"Some of the values are out of the expected range. "
"Expected were values in the range {}, got values in the range {}. "
"If values are part of questionnaire scores, "
"you can convert questionnaire items into the correct range by calling "
"`biopsykit.questionnaire.utils.convert_scale()`.".format(value_range, [min_val, max_val])
)
return False
return True
def _assert_num_columns(
data: pd.DataFrame, num_cols: Union[int, Sequence[int]], raise_exception: Optional[bool] = True
) -> Optional[bool]:
"""Check if dataframe has (any of) the required number of columns.
Parameters
----------
data : :class:`~pandas.DataFrame`
data to check
num_cols : int or list of int
the required number of columns (or any of the required number of columns in case ``num_cols`` is a list)
raise_exception : bool, optional
Whether to raise an exception or return a bool value
Returns
-------
``True`` if ``data`` has the required number of columns, ``False`` otherwise (if ``raise_exception`` is ``False``)
Raises
------
:exc:`~biopsykit.exceptions.ValidationError`
if ``raise_exception`` is ``True`` and ``data`` does not have the required number of columns
"""
if isinstance(num_cols, int):
num_cols = [num_cols]
if not any(len(data.columns) == num for num in num_cols):
if raise_exception:
raise ValidationError(
"The dataframe does not have the required number of columns. "
"Expected were any of {} columns, but has {} columns.".format(num_cols, len(data.columns))
)
return False
return True
def _assert_len_list(data: Sequence, length: int, raise_exception: Optional[bool] = True) -> Optional[bool]:
"""Check if a list has the required length.
Parameters
----------
data : list
list to check
length : int
the required length or the list
raise_exception : bool, optional
Whether to raise an exception or return a bool value
Returns
-------
``True`` if ``data`` has the required length, ``False`` otherwise (if ``raise_exception`` is ``False``)
Raises
------
:exc:`~biopsykit.exceptions.ValidationError`
if ``raise_exception`` is ``True`` and ``data`` does not have the required length
"""
_assert_is_dtype(data, (list, tuple, np.ndarray))
if len(data) != length:
if raise_exception:
raise ValidationError(
"The list does not have the required length. "
"Expected was length {}, but it has length {}.".format(length, len(data))
)
return False
return True
def _assert_dataframes_same_length(
df_list: Sequence[pd.DataFrame], raise_exception: Optional[bool] = True
) -> Optional[bool]:
"""Check if all dataframes have same length.
Parameters
----------
df_list : list
list of dataframes to check
raise_exception : bool, optional
Whether to raise an exception or return a bool value
Returns
-------
``True`` if all dataframes in ``df_list`` have same length, ``False`` otherwise
(if ``raise_exception`` is ``False``)
Raises
------
:exc:`~biopsykit.exceptions.ValidationError`
if ``raise_exception`` is ``True`` and ``data`` does not have the required length
"""
if len(set(len(df) for df in df_list)) != 1:
if raise_exception:
raise ValidationError("Not all dataframes have the same length!")
return False
return True
def _multiindex_level_names_helper_get_expected_levels(
ac_levels: Sequence[str],
ex_levels: Sequence[str],
match_atleast: Optional[bool] = False,
match_order: Optional[bool] = False,
) -> bool:
if match_order:
if match_atleast:
ac_levels_slice = ac_levels[: len(ex_levels)]
expected = ex_levels == ac_levels_slice
else:
expected = ex_levels == ac_levels
else:
if match_atleast:
expected = all(level in ac_levels for level in ex_levels)
else:
expected = sorted(ex_levels) == sorted(ac_levels)
return expected
def _multiindex_level_names_helper(
df: pd.DataFrame,
level_names: Iterable[_Hashable],
idx_or_col: str,
match_atleast: Optional[bool] = False,
match_order: Optional[bool] = False,
raise_exception: Optional[bool] = True,
) -> Optional[bool]:
if isinstance(level_names, str):
level_names = [level_names]
ex_levels = list(level_names)
if idx_or_col == "index":
ac_levels = list(df.index.names)
else:
ac_levels = list(df.columns.names)
expected = _multiindex_level_names_helper_get_expected_levels(ac_levels, ex_levels, match_atleast, match_order)
if not expected:
if raise_exception:
raise ValidationError(
"The dataframe is expected to have exactly the following {} level names {}, "
"but it has {}".format(idx_or_col, level_names, ac_levels)
)
return False
return True
def _multiindex_check_helper(
df: pd.DataFrame,
idx_or_col: str,
expected: Optional[bool] = True,
nlevels: Optional[int] = 2,
nlevels_atleast: Optional[int] = False,
raise_exception: Optional[bool] = True,
) -> Optional[bool]:
has_multiindex, nlevels_act = _multiindex_check_helper_get_levels(df, idx_or_col)
if has_multiindex is not expected:
return _multiindex_check_helper_not_expected(idx_or_col, nlevels, nlevels_act, expected, raise_exception)
if has_multiindex is True:
if nlevels_atleast:
expected = nlevels_act >= nlevels
else:
expected = nlevels_act == nlevels
if not expected:
if raise_exception:
raise ValidationError(
"The dataframe is expected to have a MultiIndex with {0} {1} levels. "
"But it has a MultiIndex with {2} {1} levels.".format(nlevels, idx_or_col, nlevels_act)
)
return False
return True
def _multiindex_check_helper_get_levels(df: pd.DataFrame, idx_or_col: str) -> Tuple[bool, int]:
if idx_or_col == "index":
has_multiindex = isinstance(df.index, pd.MultiIndex)
nlevels_act = df.index.nlevels
else:
has_multiindex = isinstance(df.columns, pd.MultiIndex)
nlevels_act = df.columns.nlevels
return has_multiindex, nlevels_act
def _multiindex_check_helper_not_expected(
idx_or_col: str, nlevels: int, nlevels_act: int, expected: bool, raise_exception: bool
) -> Optional[bool]:
if not expected:
if raise_exception:
raise ValidationError(
"The dataframe is expected to have a single level as {0}. "
"But it has a MultiIndex with {1} {0} levels.".format(idx_or_col, nlevels_act)
)
return False
if raise_exception:
raise ValidationError(
"The dataframe is expected to have a MultiIndex with {0} {1} levels. "
"It has just a single normal {1} level.".format(nlevels, idx_or_col)
)
return False
def _assert_has_column_prefix(
columns: Sequence[str], prefix: str, raise_exception: Optional[bool] = True
) -> Optional[bool]:
"""Check whether all columns start with the same prefix.
Parameters
----------
columns : list of str
list of column names
prefix : str
expected prefix of all columns
raise_exception : bool, optional
Whether to raise an exception or return a bool value
Returns
-------
``True`` if ``columns`` all start with ``prefix``, ``False`` otherwise (if ``raise_exception`` is ``False``)
Raises
------
ValidationError
if ``raise_exception`` is ``True`` and one of ``columns`` is not a string or does not start with ``prefix``
"""
if prefix is None or len(prefix) == 0:
if raise_exception:
raise ValidationError("'prefix' is None or empty!")
return False
for col in columns:
return _check_has_column_prefix_single_col(columns, col, prefix, raise_exception)
return True
def _check_has_column_prefix_single_col(
columns: Sequence[str], col: Any, prefix: str, raise_exception: bool
) -> Optional[bool]:
if not _assert_is_dtype(col, str, raise_exception=False):
if raise_exception:
raise ValidationError("Column '{}' from {} is not a string!".format(col, columns))
return False
if not col.startswith(prefix):
if raise_exception:
raise ValidationError(
"Column '{}' from {} are starting with the required prefix '{}'!".format(col, columns, prefix)
)
return False
return True
| python |
from terminaltables import SingleTable
import requests
import os
from dotenv import load_dotenv
def predict_salary(min_salary, max_salary):
if min_salary == None or min_salary == 0:
average_salary = max_salary*0.8
elif max_salary == None or max_salary == 0:
average_salary = min_salary*1.2
else:
average_salary = ((max_salary+min_salary)/2)
return average_salary
def get_vacancies_hh(profession):
hh_vacancies = []
page = 0
pages = 1
while page < pages:
url = 'https://api.hh.ru/vacancies'
user_request = {'text': profession, 'area': '4', 'period': '30',
'per_page': '10', 'page': page}
page_response = requests.get(url, params=user_request)
pages = page_response.json()['pages']
page += 1
page_answer_hh = page_response.json()
hh_vacancies.append(page_answer_hh)
return hh_vacancies
def predict_rub_salary_hh(hh_vacancies, profession):
total_vacancies = hh_vacancies[0]['found']
total_salary = 0
total_number = 0
for vacancy in hh_vacancies:
prepare_vacancies = vacancy['items']
number = 0
sum_salary = 0
total_average_salary = 0
for prepare_vacancy in prepare_vacancies:
if prepare_vacancy['salary'] is not None:
salary = prepare_vacancy['salary']
if salary['currency'] == 'RUR':
number += 1
min_salary = salary['from']
max_salary = salary['to']
average_salary = predict_salary(min_salary, max_salary)
sum_salary += average_salary
total_salary += sum_salary
total_number += number
try:
total_average_salary = int(total_salary/total_number)
except ZeroDivisionError:
pass
hh_response = [profession, total_vacancies, total_number, total_average_salary]
return hh_response
def get_vacancies_sj(profession, secret_key_sj):
sj_vacancies = []
page = 0
pages = 1
while page < pages:
url = 'https://api.superjob.ru/2.0/vacancies/'
headers = {'X-Api-App-Id': secret_key_sj}
user_request = {'keyword': profession,
'town': 4,
'period': 30,
'count': 10,
'page': page}
page_response = requests.get(url, headers=headers, params=user_request)
page_response.raise_for_status()
more_vacancies = page_response.json()['more']
if more_vacancies:
page += 1
pages += 1
if not more_vacancies:
break
page_answer_sj = page_response.json()
sj_vacancies.append(page_answer_sj)
return sj_vacancies
def predict_rub_salary_sj(sj_vacancies, profession):
total_vacancies = sj_vacancies[0]['total']
total_salary = 0
total_number = 0
for vacancy in sj_vacancies:
prepare_vacancies = vacancy['objects']
number = 0
sum_salary = 0
total_average_salary = 0
for prepare_vacancy in prepare_vacancies:
if prepare_vacancy['currency'] == 'rub':
min_salary = prepare_vacancy['payment_from']
max_salary = prepare_vacancy['payment_to']
if min_salary or max_salary != 0:
number += 1
average_salary = predict_salary(min_salary, max_salary)
sum_salary += average_salary
total_salary += sum_salary
total_number += number
try:
total_average_salary = int(total_salary/total_number)
except ZeroDivisionError:
pass
sj_response = [profession, total_vacancies, total_number, total_average_salary]
return sj_response
def get_table(table, title):
table_template = [['Язык программирования', 'Вакансий найдено',
'Вакансий обработано', 'Средняя зарплата'], ]
for line in table:
table_template.append(line)
table_instance = SingleTable(table_template, title)
table_instance.justify_columns[2] = 'right'
table_result = table_instance.table
return table_result
def main():
load_dotenv()
secret_key_sj = os.getenv('SECRET_KEY')
table_hh = []
table_sj = []
professions = ("C#", "Objective-C",
"Ruby", "Java", "C",
"Typescript", "Scala",
"Go", "Swift",
"C++", "PHP",
"JavaScript", "Python")
for profession in professions:
hh_vacancies = get_vacancies_hh(profession)
hh_response = predict_rub_salary_hh(hh_vacancies, profession)
table_hh.append(hh_response)
title_hh = 'HEADHUNTER_MOSCOW'
sj_vacancies = get_vacancies_sj(profession, secret_key_sj)
try:
sj_response = predict_rub_salary_sj(sj_vacancies, profession)
table_sj.append(sj_response)
except (IndexError, ValueError):
pass
title_sj = 'SUPERJOB_MOSCOW'
print (get_table(table_hh, title_hh))
print()
print (get_table(table_sj, title_sj))
print()
if __name__ == '__main__':
main()
| python |
import numpy as np
import matplotlib.pyplot as plt
teilnehmer = int(input("Teilnehmer: "))
a = list()
x = np.arange(1, teilnehmer+1)
y = np.zeros(teilnehmer)
a.append(float(input("Geheimnis: ")))
for i in range(teilnehmer-1):
a.append(float(input(f"Koeffizient a{i+1}: ")))
for i in range(teilnehmer):
for j in range(len(x)):
y[i]+=a[j]*x[i]**j
for i in range(teilnehmer):
print(f"Punkt für Teilnehmer {i+1}: x{i+1} = {x[i]}, y{i+1} = {y[i]}")
berechnetes_geheimnis = 0
for i in range(teilnehmer):
lagrange = 1
for j in range(teilnehmer):
if i != j:
print(f"((0 - {x[j]})/({x[i]} - {x[j]})) * ", end = '')
lagrange *= (0 - x[j])/(x[i] - x[j])
print(f"{y[i]} = {lagrange * y[i]}")
berechnetes_geheimnis += lagrange * y[i]
print(f"Berechnetes Geheimnis: {berechnetes_geheimnis}")
p = np.poly1d(np.polyfit(x, y, teilnehmer-1))
x_plot = np.linspace(-2, 6, 100)
_ = plt.plot(x, y, '.', x_plot, p(x_plot), '-')
plt.ylim(0, 20)
plt.show()
| python |
#/usr/bin/env python
from __future__ import absolute_import
# Charge transfer efficiency by EPER, now as a pipe task!
import lsst.pex.config as pexConfig
import lsst.pipe.base as pipeBase
import sys
import numpy as np
import argparse
from .MaskedCCD import MaskedCCD
import lsst.geom as lsstGeom
import lsst.afw.math as afwMath
from lsst.eotest.Estimator import Estimator
class SubImage(object):
"""Functor to produce sub-images depending on scan direction."""
def __init__(self, ccd, amp, overscans, task):
geom = ccd.amp_geom
self.ccd = ccd
self.imaging = geom.imaging
self.image = ccd[amp] # This is the masked image for the desired amp.
if task.config.direction == 'p':
self._bbox = self._parallel_box
llc = lsstGeom.Point2I(geom.parallel_overscan.getMinX(),
geom.parallel_overscan.getMinY() + overscans)
urc = geom.parallel_overscan.getCorners()[2]
self._bias_reg = lsstGeom.Box2I(llc, urc)
self.lastpix = self.imaging.getMaxY()
elif task.config.direction == 's':
self._bbox = self._serial_box
llc = lsstGeom.Point2I(geom.serial_overscan.getMinX() + overscans,
geom.serial_overscan.getMinY())
urc = geom.serial_overscan.getCorners()[2]
#
# Omit the last 4 columns to avoid the bright column in the
# last overscan column in the e2v vendor data.
#
urc[0] -= 4
self._bias_reg = lsstGeom.Box2I(llc, urc)
self.lastpix = self.imaging.getMaxX()
else:
task.log.error("Unknown scan direction: " + str(direction))
sys.exit(1)
def bias_est(self, statistic=afwMath.MEAN, gain=1):
subim = self.image.Factory(self.image, self._bias_reg)
bias_estimate = Estimator()
bias_estimate.value = \
gain*afwMath.makeStatistics(subim, statistic).getValue()
num_pix = len(subim.getImage().getArray().flatten())
bias_estimate.error = \
gain*afwMath.makeStatistics(subim, afwMath.STDEV).getValue()/np.sqrt(float(num_pix))
return bias_estimate
def __call__(self, start, end=None):
if end is None:
end = start
my_exp = self.image.Factory(self.image, self._bbox(start, end))
return my_exp
def _parallel_box(self, start, end):
llc = lsstGeom.PointI(self.imaging.getMinX(), start)
urc = lsstGeom.PointI(self.imaging.getMaxX(), end)
return lsstGeom.BoxI(llc, urc)
def _serial_box(self, start, end):
llc = lsstGeom.PointI(start, self.imaging.getMinY())
urc = lsstGeom.PointI(end, self.imaging.getMaxY())
return lsstGeom.BoxI(llc, urc)
class EPERConfig(pexConfig.Config):
"""Configuration for the EPERTask."""
direction = pexConfig.Field("Select either parallel or serial direction",
str, default="p")
verbose = pexConfig.Field("Turn verbosity on", bool, default=True)
cti = pexConfig.Field('Return CTI instead of CTE', bool, default=False)
class EPERTask(pipeBase.Task):
"""Task to calculate either parallel or serial charge transfer
efficiency via EPER."""
ConfigClass = EPERConfig
_DefaultName = "eper"
@pipeBase.timeMethod
def run(self, infilename, nframes, amps, overscans, gains=None,
mask_files=(), linearity_correction=None):
if not infilename:
self.log.error("Please specify an input file path.")
sys.exit(1)
if gains is None:
gains = dict([(amp, 1) for amp in amps])
ccd = MaskedCCD(infilename, mask_files=mask_files,
linearity_correction=linearity_correction)
# iterate through amps
cte = {}
bias_estimates = {}
for amp in amps:
subimage = SubImage(ccd, amp, overscans, self)
lastpix = subimage.lastpix
# find signal in last image vector (i.e., row or column)
last_im = Estimator(subimage(lastpix), ccd.stat_ctrl,
gain=gains[amp], var_wt=nframes)
if self.config.verbose:
self.log.info("Last imaging row/column = " + str(last_im))
# find signal in each overscan vector
overscan_ests = []
for i in range(1, overscans+1):
overscan_ests.append(Estimator(subimage(lastpix+i),
ccd.stat_ctrl, gain=gains[amp],
var_wt=nframes))
if self.config.verbose:
self.log.info("Overscan values = " + str(overscan_ests))
# sum medians of first n overscan rows
summed = sum(overscan_ests)
if self.config.verbose:
self.log.info("summed overscans = " + str(summed))
# Find bias level.
bias_est = subimage.bias_est(gain=gains[amp],
statistic=afwMath.MEAN)
bias_estimates[amp] = bias_est
if self.config.verbose:
self.log.info("bias value = " + str(bias_est))
# signal = last - bias
sig = last_im - bias_est
# trailed = sum(last2) - bias
trailed = summed - overscans*bias_est
# charge loss per transfer = (trailed/signal)/N
chargelosspt = (trailed/sig)/(lastpix + 1.)
if self.config.cti:
cte[amp] = chargelosspt
cte[amp].set_format_str("{0:.5e}")
else:
cte[amp] = 1. - chargelosspt
cte[amp].set_format_str("{0:.16f}")
if self.config.verbose:
if self.config.cti:
self.log.info('cti, amp ' + str(amp) + " = "
+ str(cte[amp]) + '\n')
else:
self.log.info('cte, amp ' + str(amp) + " = "
+ str(cte[amp]) + '\n')
return cte, bias_estimates
if __name__ == '__main__':
#import pdb; pdb.set_trace()
parser = argparse.ArgumentParser(description='Calculate either parallel or serial CTE via EPER.')
parser.add_argument('infilename', help="image file to be used for analysis")
parser.add_argument('-o', '--overscans',
help="number of overscan rows/columns to use", type=int, default=3)
parser.add_argument('-d', '--direction',
help="specify either parallel ('p') or serial ('s') direction", default='p')
parser.add_argument('-a', '--amps', help="amps to be analyzed, separated by a space",
type=int, nargs='+', default=list(range(1, 17)))
parser.add_argument('-v', '--verbose', help="turn verbosity on", action='store_true', default=False)
parser.add_argument('-i', '--cti', help='return CTI (not CTE)',
action='store_true', default=False)
args = parser.parse_args()
task = EPERTask()
task.config.direction = args.direction
task.config.verbose = args.verbose
task.config.cti = args.cti
task.run(args.infilename, args.amps, args.overscans)
| python |
from datetime import datetime
from sqlalchemy import create_engine, Column, Integer, DateTime
from sqlalchemy.ext.declarative import as_declarative, declared_attr
from sqlalchemy.orm import sessionmaker, scoped_session
from config.config import SQLALCHEMY_DATABASE_URI
engine = create_engine(SQLALCHEMY_DATABASE_URI)
Session = scoped_session(sessionmaker(bind=engine))
@as_declarative()
class Base:
@declared_attr
def __tablename__(cls):
return cls.__name__.lower()
id = Column(Integer, primary_key=True)
created_at = Column(DateTime, default=datetime.now)
updated_at = Column(DateTime, default=datetime.now)
@classmethod
def count(cls):
session = Session()
return session.query(cls).count()
| python |
from typing import List
class Solution:
def plusOne(self, digits: List[int]) -> List[int]:
N = len(digits)
for i in reversed(range(N)):
digit = digits[i]
if digit == 9:
digits[i] = 0
else:
digits[i] += 1
return digits
digits[0] = 1
digits.append(0)
return digits
| python |
import timeit
import CoolProp.CoolProp as CP
def time_check(N, h, p, TTSE = False, mode = 'TTSE'):
if TTSE:
if mode =='TTSE':
setup = "import CoolProp; import CoolProp.CoolProp as CP; CP.enable_TTSE_LUT('Water'); CP.set_TTSE_mode('Water','TTSE'); CP.Props('T','H',500,'P',10000,'Water'); IWater = CP.get_Fluid_index('Water'); from CoolProp.param_constants import iT,iH,iP,iD"
elif mode =='BICUBIC':
setup = "import CoolProp; import CoolProp.CoolProp as CP; CP.enable_TTSE_LUT('Water'); CP.set_TTSE_mode('Water','BICUBIC'); CP.Props('T','H',500,'P',10000,'Water'); IWater = CP.get_Fluid_index('Water'); from CoolProp.param_constants import iT,iH,iP,iD"
else:
raise ValueError()
else:
setup = "import CoolProp.CoolProp as CP; IWater = CP.get_Fluid_index('Water'); CP.disable_TTSE_LUT('Water'); from CoolProp.param_constants import iT,iH,iP,iD"
time = timeit.Timer("CP.IProps(iD,iH,"+str(h)+",iP,"+str(p)+",IWater)",setup).timeit(N)/N*1e6
value = CP.Props('D','H',h,'P',p,'Water')
return time, value
values = dict(subcooled = (500,10000), twophase = (2000,10000), superheated = (3000,10000), supercritical = (2000,30000))
N = 10000
for k in ['subcooled','twophase','superheated','supercritical']:
h, p = values[k]
time_EOS, value_EOS = time_check(N, h, p, TTSE = False)
time_TTSE, value_TTSE = time_check(N, h, p, TTSE = True)
time_BICUBIC, value_BICUBIC = time_check(N, h, p, TTSE = True, mode='BICUBIC')
print("%s %s %s %s %s %s %s" % (k, h, p, (value_TTSE/value_EOS-1.0)*100, (value_BICUBIC/value_EOS-1.0)*100, time_EOS/time_TTSE, time_EOS/time_BICUBIC))
| python |
import numpy as np
import pandas as pd
import logging
logger = logging.getLogger(__name__)
def approximate_curve(data, bin_number):
binned = pd.cut(data.capacity_factor, bin_number)
# bins = np.arange(1, len(data.datetime) / bin_number + 1)
# logger.debug("bins: {}".format(bins))
# digitized = np.digitize(data, bins)
# bin_means = [data[digitized == i].mean()
# for i in range(1, len(bin_number))]
return binned
| python |
"""There is a vehicle obscuring a pedestrian that conflicts with your path."""
from flow.envs.multiagent import Bayesian0NoGridEnv
from flow.networks import Bayesian1Network
from flow.core.params import SumoParams, EnvParams, InitialConfig, NetParams
from flow.core.params import SumoCarFollowingParams, VehicleParams
from flow.core.params import PedestrianParams
from flow.controllers import SimCarFollowingController, GridRouter, RLController
from flow.utils.registry import make_create_env
from flow.utils.rllib import FlowParamsEncoder
# Experiment parameters
N_ROLLOUTS = 20 # number of rollouts per training iteration
N_CPUS = 8 # number of parallel workers
# Environment parameters
# TODO(@klin) make sure these parameters match what you've set up in the SUMO version here
V_ENTER = 30 # enter speed for departing vehicles
INNER_LENGTH = 50 # length of inner edges in the traffic light grid network
# number of vehicles originating in the left, right, top, and bottom edges
N_LEFT, N_RIGHT, N_TOP, N_BOTTOM = 0, 1, 1, 1
def make_flow_params():
"""
Generate the flow params for the experiment.
Parameters
----------
Returns
-------
dict
flow_params object
"""
pedestrian_params = PedestrianParams()
pedestrian_params.add(
ped_id='ped_0',
depart_time='0.00',
start='(1.0)--(1.1)',
end='(1.1)--(1.2)',
depart_pos='40')
# we place a sufficient number of vehicles to ensure they confirm with the
# total number specified above. We also use a "right_of_way" speed mode to
# support traffic light compliance
vehicles = VehicleParams()
vehicles.add(
veh_id="human",
acceleration_controller=(SimCarFollowingController, {}),
car_following_params=SumoCarFollowingParams(
min_gap=2.5,
max_speed=V_ENTER,
decel=7.5, # avoid collisions at emergency stops
speed_mode="right_of_way",
),
routing_controller=(GridRouter, {}),
num_vehicles=2)
vehicles.add(
veh_id='rl',
acceleration_controller=(RLController, {}),
car_following_params=SumoCarFollowingParams(
speed_mode="aggressive",
),
routing_controller=(GridRouter, {}),
num_vehicles=1)
'''
vehicles.add(
veh_id="human_1",
acceleration_controller=(SimCarFollowingController, {}),
car_following_params=SumoCarFollowingParams(
min_gap=2.5,
max_speed=V_ENTER,
decel=7.5, # avoid collisions at emergency stops
speed_mode="right_of_way",
),
routing_controller=(GridRouter, {}),
num_vehicles=1)
'''
n_rows = 1
n_columns = 1
# define initial configs to pass into dict
initial_config = InitialConfig(
spacing='custom',
shuffle=False,
sidewalks=True,
lanes_distribution=float('inf'))
flow_params = dict(
# name of the experiment
exp_tag="bayesian_1_env",
# name of the flow environment the experiment is running on
env_name=Bayesian0NoGridEnv,
# name of the network class the experiment is running on
network=Bayesian1Network,
# simulator that is used by the experiment
simulator='traci',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=SumoParams(
restart_instance=False,
sim_step=0.1,
render=False,
),
env=EnvParams(
horizon=500,
# environment related parameters (see flow.core.params.EnvParams)
additional_params={
# maximum acceleration of autonomous vehicles
'max_accel': 2.6,
# maximum deceleration of autonomous vehicles
'max_decel': 4.5,
# desired velocity for all vehicles in the network, in m/s
"target_velocity": 25,
# how many objects in our local radius we want to return
"max_num_objects": 3,
# how large of a radius to search in for a given vehicle in meters
"search_veh_radius": 50,
# how large of a radius to search for pedestrians in for a given vehicle in meters (create effect of only seeing pedestrian only when relevant)
"search_ped_radius": 22,
# whether or not we have a discrete action space,
"discrete": False,
# whether to randomize which edge the vehicles are coming from
"randomize_vehicles": False,
# whether to append the prior into the state
"inference_in_state": False,
# whether to grid the cone "search_veh_radius" in front of us into 6 grid cells
"use_grid": False
},
),
# network-related parameters (see flow.core.params.NetParams and the
# network's documentation or ADDITIONAL_NET_PARAMS component)
net=NetParams(
additional_params={
"speed_limit": V_ENTER + 5, # inherited from grid0 benchmark
"grid_array": {
"inner_length": INNER_LENGTH,
"row_num": n_rows,
"col_num": n_columns,
"cars_left": N_LEFT,
"cars_right": N_RIGHT,
"cars_top": N_TOP,
"cars_bot": N_BOTTOM,
},
"horizontal_lanes": 1,
"vertical_lanes": 1,
"randomize_routes": True,
},
),
# vehicles to be placed in the network at the start of a rollout (see
# flow.core.params.VehicleParams)
veh=vehicles,
ped=pedestrian_params,
# parameters specifying the positioning of vehicles upon initialization
# or reset (see flow.core.params.InitialConfig)
initial = initial_config
)
return flow_params
# define callbacks for tensorboard
| python |
from datetime import datetime, timedelta
from typing import Optional
from utils.utils import format_date
class Event:
"""Event object to store data about a Google Calendar event"""
def __init__(
self,
event_id: str,
link: str,
title: str,
location: Optional[str],
description: Optional[str],
all_day: bool,
start: datetime,
end: datetime,
):
self.__id = event_id
self.__link = link
self.__title = title
self.__location = location
self.__description = description
self.__all_day = all_day
self.__start = start.replace(tzinfo=None)
self.__end = end.replace(tzinfo=None)
@property
def id(self) -> str:
"""Returns the event id"""
return self.__id
@property
def link(self) -> str:
"""Returns the link to the event in Google Calendar"""
return self.__link
@property
def title(self) -> str:
"""Returns the title of the event"""
return self.__title
@property
def location(self) -> Optional[str]:
"""Returns the location of the event"""
return self.__location
@property
def description(self) -> Optional[str]:
"""Returns the description of the event"""
return self.__description
@property
def all_day(self) -> bool:
"""Returns whether or not the event is an all day event"""
return self.__all_day
@property
def start(self) -> datetime:
"""Returns the start date as a datetime object"""
return self.__start
@property
def end(self) -> datetime:
"""Returns the end date as a datetime object"""
return self.__end
@property
def __one_day(self) -> bool:
"""Returns whether or not the event is a one day event"""
return self.all_day and self.end - self.start <= timedelta(days=1)
def relative_date_range_str(self, base=datetime.now()) -> str:
"""Returns a formatted string of the start to end date range"""
start_str = self.__relative_start_str(base=base)
end_str = self.__relative_end_str(base=self.start)
# all day event
if self.__one_day:
return f"{start_str} - All day"
# include end time if it is not the same as the start time
return f"{start_str} - {end_str}" if end_str else start_str
def __relative_start_str(self, base=datetime.now()) -> str:
"""Returns a formatted string of the start date"""
return format_date(self.start, all_day=self.all_day, base=base) or "Today"
def __relative_end_str(self, base=datetime.now()) -> str:
"""Returns a formatted string of the end date"""
end_date = self.end
# use previous day if end of multi-day, all-day event
if self.all_day and not self.__one_day:
end_date -= timedelta(days=1)
return format_date(end_date, all_day=self.all_day, base=base)
| python |
from __future__ import absolute_import, division, print_function
from cctbx.array_family.flex import ( # noqa: F401; lgtm
abs,
acos,
arg,
asin,
atan,
atan2,
bool,
ceil,
compare_derivatives,
complex_double,
condense_as_ranges,
conj,
cos,
cosh,
cost_of_m_handle_in_af_shared,
double,
double_from_byte_str,
double_range,
empty_container_sizes_double,
empty_container_sizes_int,
exercise_versa_packed_u_to_flex,
exp,
extract_double_attributes,
fabs,
first_index,
flex_argument_passing,
float,
float_range,
floor,
fmod,
fmod_positive,
get_random_seed,
grid,
hendrickson_lattman,
histogram,
imag,
int,
int_from_byte_str,
int_range,
integer_offsets_vs_pointers,
intersection,
last_index,
linear_correlation,
linear_interpolation,
linear_regression,
linear_regression_core,
log,
log10,
long,
long_range,
mat3_double,
max,
max_absolute,
max_default,
max_index,
mean,
mean_and_variance,
mean_default,
mean_sq,
mean_sq_weighted,
mean_weighted,
median,
median_functor,
median_statistics,
mersenne_twister,
miller_index,
min,
min_default,
min_index,
min_max_mean_double,
nested_loop,
norm,
order,
permutation_generator,
polar,
pow,
pow2,
product,
py_object,
random_bool,
random_double,
random_double_point_on_sphere,
random_double_r3_rotation_matrix,
random_double_r3_rotation_matrix_arvo_1992,
random_double_unit_quaternion,
random_generator,
random_int_gaussian_distribution,
random_permutation,
random_selection,
random_size_t,
reindexing_array,
rows,
select,
set_random_seed,
show,
show_count_stats,
sin,
sinh,
size_t,
size_t_from_byte_str,
size_t_range,
slice_indices,
smart_selection,
sort_permutation,
sorted,
split_lines,
sqrt,
std_string,
sum,
sum_sq,
sym_mat3_double,
tan,
tanh,
tiny_size_t_2,
to_list,
union,
vec2_double,
vec3_double,
vec3_int,
weighted_histogram,
xray_scatterer,
)
from dials.array_family.flex_ext import ( # noqa: F401; lgtm
real,
reflection_table_selector,
)
from dials_array_family_flex_ext import ( # noqa: F401; lgtm
Binner,
PixelListShoeboxCreator,
int6,
observation,
reflection_table,
reflection_table_to_list_of_reflections,
shoebox,
)
| python |
# @Author: BingWu Yang <detailyang>
# @Date: 2016-03-29T17:47:44+08:00
# @Email: detailyang@gmail.com
# @Last modified by: detailyang
# @Last modified time: 2016-04-10T16:54:56+08:00
# @License: The MIT License (MIT)
import ply.yacc as yacc
import eslast as ast
from esllexer import ESLLexer
tokens = ESLLexer.tokens
def p_request(p):
'''request : URL
| URL METHOD
| URL METHOD OPTIONS'''
if len(p) == 2:
p[0] = ast.RequestNode(ast.MethodNode('GET'), ast.URLNode(p[1]), None)
elif len(p) == 3:
p[0] = ast.RequestNode(ast.MethodNode(p[2]), ast.URLNode(p[1]), None)
else:
p[0] = ast.RequestNode(ast.MethodNode(p[2]), ast.URLNode(p[1]), p[3])
def p_options(p):
'''OPTIONS :
| OPTION
| OPTIONS OPTION'''
if len(p) == 2:
p[0] = ast.OptionListNode([p[1]])
else:
p[0] = p[1].append(p[2])
def p_option_empty(p):
' OPTION : empty '
p[0] = p[1]
def p_option_header(p):
' OPTION : HEADERVALUE '
p[0] = p[1]
def p_option_querystring(p):
' OPTION : QUERYSTRINGVALUE '
p[0] = p[1]
def p_option_body(p):
' OPTION : BODYVALUE '
p[0] = p[1]
def p_empty(p):
'empty :'
p[0] = []
def p_querystring_value(p):
'''QUERYSTRINGVALUE : QUERYSTRING VALUE '''
p[0] = ast.OptionNode(ast.QueryStringNode(p[1]), ast.ValueNode(p[2]))
def p_querystring_shell(p):
'''QUERYSTRINGVALUE : QUERYSTRING SHELL '''
p[0] = ast.OptionNode(ast.QueryStringNode(p[1]), ast.ShellNode(p[2]))
def p_header_value(p):
'''HEADERVALUE : HEADER VALUE '''
p[0] = ast.OptionNode(ast.HeaderNode(p[1]), ast.ValueNode(p[2]))
def p_header_shell(p):
'''HEADERVALUE : HEADER SHELL '''
p[0] = ast.OptionNode(ast.HeaderNode(p[1]), ast.ShellNode(p[2]))
def p_body_value(p):
'''BODYVALUE : BODY VALUE '''
p[0] = ast.OptionNode(ast.BodyNode(p[1]), ast.ValueNode(p[2]))
def p_body_shell(p):
'''BODYVALUE : BODY SHELL '''
p[0] = ast.OptionNode(ast.BodyNode(p[1]), ast.ShellNode(p[2]))
def p_error(p):
print("Syntax Error")
print("ESL format: {URL} {METHOD} {OPTIONS}")
print("{URL}: https://example.com|examples.com|/api/endpoints")
print("{METHOD}: GET|get|POST|post|DELETE|delete|PUT|put")
print("{OPTIONS}: --hContent-Type=application/json")
print("{OPTIONS}: --qper_page=1")
print("{OPTIONS}: --busername=xxxx")
def parse(text):
parser = yacc.yacc(debug=True)
ast = parser.parse(text, ESLLexer().build())
return ast
if __name__ == '__main__':
ast = parse("/api/cmdb/peoples/ get --qhost_ip=!(ifconfig eth0) --qhost_name=bj-sdf --hContent-Type=abcd --bslkjsdf=123") # Test it
print(ast.left)
print(ast.method)
for option in ast.right.options:
key = option.key
value = option.value
| python |
from discord.ext import commands
import config
class Bot(commands.Bot):
async def invoke(self, ctx):
if self.user.mentioned_in(ctx.message):
# Mention was processed in on_message.
return
if ctx.invoked_with:
await ctx.send(config.response)
async def on_message(self, message):
# bot?
if message.author.bot:
return
# mention?
if self.user.mentioned_in(message):
await message.channel.send(config.response)
return
# invoke command
await self.process_commands(message)
| python |
#!/usr/bin/env python3
import altair as alt
import pandas
import selenium
def vegaGraphics(
cmdTag,
id1,
id2,
parameters,
sql,
transformedData,
verbose,):
"""Create interactive charts for specified data"""
# making function more explicit
cmdTag = cmdTag
id1 = id1
id2 = id2
parameters = parameters
sql = sql
transformedData = transformedData
verbose = verbose
if verbose >= 1:
print(
"Creating Vega Graphics"
)
transformedData = transformedData.rename(
columns = {
id1 : "id1",
id2 : "id2",
sql : "sql",
cmdTag : "cmdTag",
parameters : "parameters"})
dataInfo = transformedData.copy()
data = transformedData[["total_duration",
"cmdTag",
"id1",
"id2",
"sql",
"parameters"]].copy()
data = data.sort_values(by = ["total_duration"],
ascending = True,
inplace = False).dropna().reset_index(drop = True)
data["length"] = data["sql"].str.len() + data["parameters"].str.len()
alt.data_transformers.disable_max_rows()
brush = alt.selection_interval()
# -----> create the scatter plot graph
line = alt.Chart(data.reset_index()).mark_point().encode(
x = alt.X(
"length:Q",
axis = alt.Axis(title = "Query Length")),
y=alt.Y(
"total_duration:Q",
axis = alt.Axis(title = "Latency (ms)")),
color = alt.condition(
brush,
"cmdTag:N",
alt.value("lightgray")),
shape = "cmdTag:N",
tooltip = ["index:O",
"total_duration:Q",
"length:Q",
"log_time_with_tz:N",
"sql:N",
"parameters:N",
"cmdTag:N",
"id1:N",
"id2:N"]
).properties(
width = 500,
height = 500,
title = "Einherjar Queries"
).add_selection(
brush
).interactive()
# -----> display the mean via a line across our chart
rule = alt.Chart(data).mark_rule(color = "red").encode(
y = "median(total_duration):Q",
size = alt.value(2)
)
alt.Chart(data).configure_title(
fontSize = 30
)
# -----> display number of interations per table insert
dog = dataInfo[["inserted_data", "cmdTag"]].dropna()
bars1 = alt.Chart(dog).mark_bar().encode(
y = "inserted_into:N",
color = "cmdTag:N",
x = "count(inserted_into):Q"
).transform_filter(
brush
)
# -----> display number of interations per table select
cat = dataInfo[["selected_from", "cmdTag"]].dropna()
bars2 = alt.Chart(cat).mark_bar().encode(
y = "selected_from:N",
color = "cmdTag:N",
x = "count(selected_from):Q"
).transform_filter(
brush
)
# -----> add the line and rule charts to the base chart
chart = line + rule
chart = chart & bars1 & bars2
chart.save("results/data.json")
chart.save("results/data.html")
if verbose >= 1:
print(
"Vega Graphics have been completed"
) | python |
from ark.thread_handler import ThreadHandler
from factory import Factory
import time
class GuiTasks(object):
@classmethod
def loop(cls):
time.sleep(1)
GuiTasks.get_active_threads()
@classmethod
def get_active_threads(cls):
GUI = Factory.get('GUI')
max_threads = len(ThreadHandler.activethreads)
active_threads = 0
for key,timestamp in ThreadHandler.activethreads.items():
if timestamp > (time.time()-30):
active_threads += 1
GUI.active_threads['text'] = "{} / {}".format(active_threads,max_threads)
| python |
import inject
from flask import Flask, Response, send_from_directory, send_file
class StaticRoute:
@staticmethod
@inject.autoparams()
def init(flask: Flask) -> None:
@flask.route("/static/<path:path>")
def send_static(path: str) -> Response:
return send_from_directory(f"static", path)
@flask.route("/")
def index() -> Response:
return send_file("static/html/index.html")
| python |
# Exercise 31: Making Decisions
print "You enter a dark room with two doors. Do you go through door #1 or door #2?"
door = raw_input("> ")
if door == "1":
print "There's a giant bear here eating a cheese cake. What do you do?"
print "1. Take the cake."
print "2. Scream at the bear."
print "3. Turn back quietly"
print "4. Look around"
bear = raw_input("> ")
if bear == "1":
print "The bear eats your face off. Good job!"
elif bear == "2":
print "The bear eats your legs off. Good job!"
elif bear == "3":
print "One plank creaked and bear eats you. Good job!"
elif bear == "4":
print "There have rifle. Will you get it?"
print "1. Yes!"
print "2. No!"
rifle = raw_input("> ")
if rifle == "1":
print "Did you want to shoot the bear?"
print "1. Yes, of course!"
print "2. No!"
choice = raw_input("> ")
if choice == "1":
print """
The rifle isn't loaded!
You look around and see bullets on the table.
You are going to get them,
but the bear see you and eat you!!!
Good job! :D
"""
elif choice == "2":
print "While you thinking what to do the bear see you and eat you! Good job!"
else:
print "You can't choice other, for that you die! Good Job!"
elif rifle == "2":
print """
This is stupid decision
and what will do now?
Okey, just die. Good job!
"""
else:
print "You can't choce other, for that you die! Good Job!"
else:
print "Well, doing %s is probably better. Bear runs away." % bear
elif door == "2":
print "You stare into the endless abyss at Cthulhu's retina."
print "1. Blueberries."
print "2. Yellow jacket clothespins."
print "3. Understanding revolvers yelling melodies."
insanity = raw_input("> ")
if insanity == "1" or insanity == "2":
print "Your body survives powered by a mind of jello. Good job!"
else:
print "The insanity rots your eyes into a pool of muck. Good job!"
else:
print "You stumble around and fall on a knife and die. Good job!"
# Study Drills:
# 1. Make new parts of the game and change what decisions people
# can make. Expand the game out as much as you can before it get
# ridiculous.
# 2. Write a copletely new game. Maybe you don't like this one, so
# make your own. This is your computer, do what you want. | python |
# Generated by Django 3.1.1 on 2020-10-08 02:15
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('rameniaapp', '0009_auto_20201002_0243'),
]
operations = [
migrations.CreateModel(
name='Edit',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('image', models.ImageField(blank=True, upload_to='')),
('change', models.JSONField(blank=True, null=True)),
('editor', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
('noodle', models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='rameniaapp.noodle')),
],
),
]
| python |
#!/usr/bin/env python
# -*- coding: utf-8; py-indent-offset:4 -*-
###############################################################################
#
# Copyright (C) 2015, 2016, 2017 Daniel Rodriguez
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# 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, see <http://www.gnu.org/licenses/>.
#
###############################################################################
from __future__ import (absolute_import, division, print_function,
unicode_literals)
tableau20 = [
'steelblue', # 0
'lightsteelblue', # 1
'darkorange', # 2
'peachpuff', # 3
'green', # 4
'lightgreen', # 5
'crimson', # 6
'lightcoral', # 7
'mediumpurple', # 8
'thistle', # 9
'saddlebrown', # 10
'rosybrown', # 11
'orchid', # 12
'lightpink', # 13
'gray', # 14
'lightgray', # 15
'olive', # 16
'palegoldenrod', # 17
'mediumturquoise', # 18
'paleturquoise', # 19
]
tableau10 = [
'blue', # 'steelblue', # 0
'darkorange', # 1
'green', # 2
'crimson', # 3
'mediumpurple', # 4
'saddlebrown', # 5
'orchid', # 6
'gray', # 7
'olive', # 8
'mediumturquoise', # 9
]
tableau10_light = [
'lightsteelblue', # 0
'peachpuff', # 1
'lightgreen', # 2
'lightcoral', # 3
'thistle', # 4
'rosybrown', # 5
'lightpink', # 6
'lightgray', # 7
'palegoldenrod', # 8
'paleturquoise', # 9
]
tab10_index = [3, 0, 2, 1, 2, 4, 5, 6, 7, 8, 9]
class PlotScheme(object):
def __init__(self):
# to have a tight packing on the chart wether only the x axis or also
# the y axis have (see matplotlib)
self.ytight = False
# y-margin (top/bottom) for the subcharts. This will not overrule the
# option plotinfo.plotymargin
self.yadjust = 0.0
# Each new line is in z-order below the previous one. change it False
# to have lines paint above the previous line
self.zdown = True
# Rotation of the date labes on the x axis
self.tickrotation = 15
# How many "subparts" takes a major chart (datas) in the overall chart
# This is proportional to the total number of subcharts
self.rowsmajor = 5
# How many "subparts" takes a minor chart (indicators/observers) in the
# overall chart. This is proportional to the total number of subcharts
# Together with rowsmajor, this defines a proportion ratio betwen data
# charts and indicators/observers charts
self.rowsminor = 1
# Distance in between subcharts
self.plotdist = 0.0
# Have a grid in the background of all charts
self.grid = True
# Default plotstyle for the OHLC bars which (line -> line on close)
# Other options: 'bar' and 'candle'
self.style = 'line'
# Default color for the 'line on close' plot
self.loc = 'black'
# Default color for a bullish bar/candle (0.75 -> intensity of gray)
self.barup = '0.75'
# Default color for a bearish bar/candle
self.bardown = 'red'
# Level of transparency to apply to bars/cancles (NOT USED)
self.bartrans = 1.0
# Wether the candlesticks have to be filled or be transparent
self.barupfill = True
self.bardownfill = True
# Wether the candlesticks have to be filled or be transparent
self.fillalpha = 0.20
# Wether to plot volume or not. Note: if the data in question has no
# volume values, volume plotting will be skipped even if this is True
self.volume = True
# Wether to overlay the volume on the data or use a separate subchart
self.voloverlay = True
# Scaling of the volume to the data when plotting as overlay
self.volscaling = 0.33
# Pushing overlay volume up for better visibiliy. Experimentation
# needed if the volume and data overlap too much
self.volpushup = 0.00
# Default colour for the volume of a bullish day
self.volup = '#aaaaaa' # 0.66 of gray
# Default colour for the volume of a bearish day
self.voldown = '#cc6073' # (204, 96, 115)
# Transparency to apply to the volume when overlaying
self.voltrans = 0.50
# Transparency for text labels (NOT USED CURRENTLY)
self.subtxttrans = 0.66
# Default font text size for labels on the chart
self.subtxtsize = 9
# Transparency for the legend (NOT USED CURRENTLY)
self.legendtrans = 0.25
# Wether indicators have a leged displaey in their charts
self.legendind = True
# Location of the legend for indicators (see matplotlib)
self.legendindloc = 'upper left'
# Location of the legend for datafeeds (see matplotlib)
self.legenddataloc = 'upper left'
# Plot the last value of a line after the Object name
self.linevalues = True
# Plot a tag at the end of each line with the last value
self.valuetags = True
# Default color for horizontal lines (see plotinfo.plothlines)
self.hlinescolor = '0.66' # shade of gray
# Default style for horizontal lines
self.hlinesstyle = '--'
# Default width for horizontal lines
self.hlineswidth = 1.0
# Default color scheme: Tableau 10
self.lcolors = tableau10
# strftime Format string for the display of ticks on the x axis
self.fmt_x_ticks = None
# strftime Format string for the display of data points values
self.fmt_x_data = None
def color(self, idx):
colidx = tab10_index[idx % len(tab10_index)]
return self.lcolors[colidx]
| python |
#!/usr/bin/env python
#Creates an instance in /home/pi/.config/lxsession/LXDE-pi/autostart which will autolaunch the server on the pi user account.
import time
print "Copy the path of the shortcut file by right clicking it and clicking 'copy path(s)'."
print "Paste the path when prompted by right clicking in the terminal and clicking 'paste'."
dspath = raw_input("Paste the full path to the server shortcut: ")
atspath = "/home/pi/.config/lxsession/LXDE-pi/autostart"
desktopentry = open(dspath, "r")
desktopcnt = desktopentry.readlines()
desktopentry.close()
workingline = "failsafe"
for line in desktopcnt:
if line[0:4] == "Exec":
workingline = line
if workingline == "failsafe":
print "no Exec line was found in the file you specified."
print "The program will terminate"
time.sleep(5)
exit()
workingline = workingline.strip()
workingline = workingline[6:len(workingline)]
autostartline = "@"+workingline+"\n"
readcurrent = open(atspath, "r")
readcnt = readcurrent.readlines()
readcurrent.close()
memory = []
for line in readcnt:
if len(line) > 2:
memory.append(line)
memory.insert(0, autostartline)
print memory
overwritecurrent = open(atspath, "w")
lenmem = len(memory)
for x in range(lenmem):
overwritecurrent.write("%s" %(memory[x]))
overwritecurrent.close()
print "Autostart entry created."
print "Program will terminate"
time.sleep(5)
exit()
| python |
class Book():
'''
Creates a book object that can be used to populate a web page
Inputs:
- title: the title of the book [str]
- author: the author of the book [str]
- series: the series the book belongs to or None [str]
- review_text: a short blurb about the book [str]
- image_url: a place to find the cover image of the book [str]
'''
def __init__(self, title, author, series, review_text, image_url):
self.title = title
self.author = author
self.series = series
self.review_text = review_text
self.image_url = image_url
def create_book_info(self):
if self.series == None:
self.series = 'This is a stand alone book.'
else:
self.series = 'This book is part of the series {}'.format(self.series)
return {
'title': self.title,
'author': self.author,
'series': self.series,
'review_text': self.review_text,
'image_url': self.image_url
}
class Movie():
'''
Creates a book object that can be used to populate a web page
Inputs:
- title: the title of the book [str]
- author: the author of the book [str]
- series: the series the book belongs to or None [str]
- review_text: a short blurb about the book [str]
- image_url: a place to find the cover image of the book [str]
'''
def __init__(self, title, image_url, trailer_url):
self.title = title
self.poster_image_url = image_url
self.trailer_youtube_url = trailer_url
def create_movie_info(self):
return {
'title': self.title,
'image_url': self.image_url,
'trailer_url': self.trailer_url
}
| python |
# -*- coding: utf-8 -*-
from .base import Smoother
__all__ = ['Smoother']
| python |
from os.path import join, dirname
from textx import metamodel_for_language
def test_example():
mm = metamodel_for_language('questionnaire')
questionnaire = mm.model_from_file(join(dirname(__file__), 'example.que'))
assert len(questionnaire.questions) == 6
assert questionnaire.questions[3].text == 'Author name'
assert questionnaire.questions[2].type.__class__.__name__ == 'Free'
assert questionnaire.questions[0].type.__class__.__name__ == 'Choice'
assert questionnaire.questions[5].text == \
'This question is to test multiline feature and indenting.'
opt = questionnaire.questions[5].type.options
assert len(opt) == 2
assert opt[0].num == 1
assert opt[0].text == 'Working'
# Multiline
assert opt[1].text == \
'Not working. This is also to test multiline in choices.'
| python |
# -*- coding: UTF-8 -*-
import threading
import json
import re
from datetime import datetime
from flask import current_app
from flask_jwt import current_identity, jwt_required
from flask_restful import Resource, request
from marshmallow import EXCLUDE, ValidationError
from sqlalchemy.exc import SQLAlchemyError
from common.utils import paginate_parse, pretty_response
from common.tasks import analysis_dataset, analysis_dataset_block, fetch_collection, delete_collection
from models.dataset import DatasetModel, DatasetSchema
from models.blockset import BlocksetModel, BlocksetSchema
class DatasetList(Resource):
@jwt_required()
def get(self):
""" Query all instances """
if current_identity.roles not in ['super']:
return pretty_response(403)
title = request.args.get('title', '')
page = request.args.get('page', 1, type=int)
per_page = request.args.get('per_page', 10, type=int)
paginate = DatasetModel.query.filter(DatasetModel.title.like('%' + title + '%')).paginate(
page, per_page, max_per_page=100)
data = paginate_parse(paginate)
data['items'] = DatasetSchema(many=True).dump(paginate.items)
return pretty_response(200, data)
@jwt_required()
def post(self):
""" Insert multi-instances """
if current_identity.roles not in ['super']:
return pretty_response(403)
jsondata = request.get_json()
if DatasetModel.query.filter_by(title=jsondata['title']).first():
return pretty_response(40002)
headers = jsondata.get('header', [])
catalog = jsondata.get('catalog', None)
if '数值' not in headers \
or (catalog == 'block' and '板块' not in headers) \
or (catalog == 'point' and '标题' not in headers):
return pretty_response(40001)
try:
dataset_instance = DatasetSchema().load(jsondata, unknown=EXCLUDE)
dataset_instance.add(dataset_instance)
blocksets = BlocksetModel.query.all()
data = json.loads(
re.sub(r'[\s+]', '', json.dumps(jsondata.get('data', []))))
if catalog == 'block':
# 导入板块数据
t = threading.Thread(target=analysis_dataset_block, args=(
'T' + dataset_instance.uuid, data, blocksets, headers))
t.start()
else:
# 导入集合数据
t = threading.Thread(target=analysis_dataset, args=(
'T' + dataset_instance.uuid, data, blocksets, headers))
t.start()
dataset_dump = DatasetSchema().dump(dataset_instance)
return pretty_response(200, dataset_dump)
except ValidationError as e:
current_app.logger.error(e.messages)
return pretty_response(40003)
except SQLAlchemyError as e:
current_app.logger.error(e)
return pretty_response(50001)
def put(self):
""" Update multi-instances """
return pretty_response(405)
def delete(self):
""" Batch-delete instances """
return pretty_response(405)
class Dataset(Resource):
@jwt_required()
def get(self, uuid):
""" Query specific instance """
if current_identity.roles not in ['super']:
return pretty_response(403)
dataset_instance = DatasetModel.query.get_or_404(uuid)
dataset_dump = DatasetSchema().dump(dataset_instance)
result = fetch_collection('T' + dataset_instance.uuid, [])
dataset_dump['data'] = result
return pretty_response(200, dataset_dump)
@jwt_required()
def post(self, uuid):
""" Update specific instance """
if current_identity.roles not in ['super']:
return pretty_response(403)
dataset_instance = DatasetModel.query.get_or_404(uuid)
jsondata = request.get_json()
if not jsondata:
return pretty_response(40001)
catalog = jsondata.get('catalog', '')
match = jsondata.get('match', {})
pipeline = []
aggregate_items = []
aggregate_max = 0
if catalog == 'block':
pipeline = [{
'$match': match
}, {
'$group': {'_id': "$板块", 'value': {dataset_instance.mode: '$数值'}}
}]
result = fetch_collection('T' + dataset_instance.uuid, pipeline)
blockset_list = BlocksetModel.query.all()
for blockset in blockset_list:
temp = {
'title': blockset.title,
'area': blockset.area,
'centroid': json.loads(blockset.centroid),
'coordinates': json.loads(blockset.coordinates),
'org_value': 0,
'value': 0,
}
for item in result:
if blockset.title == item.get('_id', ''):
if not item.get('value', None):
break
item_value = item.get('value', 0)
temp['org_value'] = item_value
temp['value'] = round(
item_value / float(blockset.area), 4) if dataset_instance.inc_area else item_value
if temp['value'] > aggregate_max:
aggregate_max = temp['value']
break
aggregate_items.append(temp)
else:
pipeline = [{
'$match': match
}]
result = fetch_collection('T' + dataset_instance.uuid, pipeline)
for item in result:
aggregate_item = {
'title': item.get('标题', ''),
'address': item.get('地址', ''),
'lng': item.get('经度', ''),
'lat': item.get('纬度', ''),
'value': item.get('数值', 0),
}
if aggregate_item['value'] > aggregate_max:
aggregate_max = aggregate_item['value']
aggregate_items.append(aggregate_item)
return pretty_response(200, {'max': aggregate_max, 'items': aggregate_items})
@jwt_required()
def put(self, uuid):
""" Update specific instance """
if current_identity.roles not in ['super']:
return pretty_response(403)
dataset_instance = DatasetModel.query.get_or_404(uuid)
try:
jsondata = request.get_json()
DatasetSchema().load(jsondata, unknown=EXCLUDE)
for key, val in jsondata.items():
setattr(dataset_instance, key, val)
dataset_instance.updatetime = datetime.now()
dataset_instance.update()
dataset_dump = DatasetSchema().dump(dataset_instance)
return pretty_response(200, dataset_dump)
except ValidationError as e:
current_app.logger.error(e.messages)
return pretty_response(40003)
except SQLAlchemyError as e:
current_app.logger.error(e)
return pretty_response(50001)
@jwt_required()
def delete(self, uuid):
""" Delete specific instance """
if current_identity.roles not in ['super']:
return pretty_response(403)
dataset_instance = DatasetModel.query.get_or_404(uuid)
delete_collection('T' + dataset_instance.uuid)
try:
dataset_instance.delete(dataset_instance)
return pretty_response(20003)
except SQLAlchemyError as e:
current_app.logger.error(e)
pretty_response(50001)
class DatasetFree(Resource):
def put(self, uuid):
""" Update specific instance """
dataset_instance = DatasetModel.query.get_or_404(uuid)
try:
jsondata = request.get_json()
DatasetSchema().load(jsondata, unknown=EXCLUDE)
for key, val in jsondata.items():
setattr(dataset_instance, key, val)
dataset_instance.updatetime = datetime.now()
dataset_instance.update()
dataset_dump = DatasetSchema().dump(dataset_instance)
return pretty_response(200, dataset_dump)
except ValidationError as e:
current_app.logger.error(e.messages)
return pretty_response(40003)
except SQLAlchemyError as e:
current_app.logger.error(e)
return pretty_response(50001)
| python |
"""
Discovering structure in heatmap data
=====================================
_thumb: .4, .2
"""
import pandas as pd
import seaborn as sns
sns.set(font="monospace")
# Load the brain networks example dataset
df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)
# Select a subset of the networks
used_networks = [1, 5, 6, 7, 8, 11, 12, 13, 16, 17]
used_columns = (df.columns.get_level_values("network")
.astype(int)
.isin(used_networks))
df = df.loc[:, used_columns]
# Create a custom palette to identify the networks
network_pal = sns.cubehelix_palette(len(used_networks),
light=.9, dark=.1, reverse=True,
start=1, rot=-2)
network_lut = dict(zip(map(str, used_networks), network_pal))
# Convert the palette to vectors that will be drawn on the side of the matrix
networks = df.columns.get_level_values("network")
network_colors = pd.Series(networks, index=df.columns).map(network_lut)
# Create a custom colormap for the heatmap values
cmap = sns.diverging_palette(h_neg=210, h_pos=350, s=90, l=30, as_cmap=True)
# Draw the full plot
sns.clustermap(df.corr(), row_colors=network_colors, linewidths=.5,
col_colors=network_colors, figsize=(13, 13), cmap=cmap)
| python |
from django.db import models
class Position(models.Model):
w = models.CharField(max_length=128, null=True, blank=True)
x = models.CharField(max_length=128, null=True, blank=True)
y = models.CharField(max_length=128, null=True, blank=True)
z = models.CharField(max_length=128, null=True, blank=True)
time_received = models.DateField()
| python |
# -*- coding: UTF-8 -*-
# Copyright 2016-2018 Rumma & Ko Ltd
# License: BSD, see LICENSE for more details.
"""
A library of `invoke
<http://docs.pyinvoke.org/en/latest/index.html>`__ tasks. See
:doc:`/invlib`.
.. autosummary::
:toctree:
tasks
utils
"""
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import os
import six
from importlib import import_module
from invoke import Collection
from unipath import Path
import atelier
def setup_from_tasks(
globals_dict, main_package=None,
settings_module_name=None, **kwargs):
"""
This is the function you must call from your :xfile:`tasks.py` file
in order to activate the tasks defined by atelier.
"""
if '__file__' not in globals_dict:
raise Exception(
"No '__file__' in %r. "
"First parameter to must be `globals()`" % globals_dict)
tasks_file = Path(globals_dict['__file__'])
if not tasks_file.exists():
raise Exception("No such file: %s" % tasks_file)
# print("20180428 setup_from_tasks() : {}".format(root_dir))
from atelier.invlib import tasks
from atelier.projects import get_project_from_tasks
prj = get_project_from_tasks(tasks_file.parent)
atelier.current_project = prj
if kwargs:
prj.config.update(kwargs)
if settings_module_name is not None:
os.environ['DJANGO_SETTINGS_MODULE'] = settings_module_name
from django.conf import settings
prj.config.update(
languages=[lng.name for lng in settings.SITE.languages])
if isinstance(main_package, six.string_types):
main_package = import_module(main_package)
if main_package:
prj.set_main_package(main_package)
self = Collection.from_module(tasks)
prj.set_namespace(self)
return self
| python |
from flask import request, jsonify, current_app, make_response, session
import random
from info.libs.yuntongxun import sms
from . import passport_blue
from info.utils.response_code import RET
from info.utils.captcha.captcha import captcha
from info import redis_store,constants,db
# 导入模型类
from info.models import User
import re
from datetime import datetime
"""
json.loads:把json字符串转成字典
json.dumps: 把字典转成json字符串
json.load/json.dump(操作的是文件对象)
var data={
"mobile":mobile,
"image_code":imagecode,
...
}
a='123';
json的概念:本质字符串,基于键值对的字符串;轻量级的数据交互格式;
json的作用:实现跨语言,跨平台的数据交互;
xml 格式: 作用是用来传输数据;都是闭合标签
XML: xmltodic模块,xmltodict.parse()/unparse() 微信,
html用来展示数据;
<xml>
<mobile>12223234</mobile>
<image_code>12223234</image_code>
</xml>
JSON
{
"mobile":mobile,
"image_code":imagecode,
}
JSON.Stringify(data) 前端把对象转成json字符串;
"""
@passport_blue.route('/image_code')
def generate_image_code():
"""
1.获取前端生成的uuid,/image_code?image_code_id=uuid
request.args.get('image_code_id')
2. 判断参数是否存在,如果不存在uuid,直接return
3.调用工具生成图片验证码,
4.存储redis图片验证码的text文本,构造redis数据实例,用来存储业务相关的数据比如 :图片验证码
5. 返回图片给浏览器,
状态码:
return jsonify(errno=666,errmsg='uuid未获取到')
1. 自定义的状态码: 用来实现前后端的数据交互.
$.ajax({
url:'/image_code,
type:'get'
data:data,
contentType:'application/json'
success:function(resp){
if (resp == 666){
alert(成功)
}else{
alert(失败)
}
}
})
:return:
"""
# 获取参数
image_code_id=request.args.get('image_code_id')
# 校验参数是否存在,如果UUID不存在,返回错误信息
if not image_code_id:
return jsonify(errno=RET.PARAMERR,errmsg='参数缺失')
# 调用工具captcha生成图片验证码
name,text,image=captcha.generate_captcha()
# 保存图片验证码的文本
try:
redis_store.setex('ImageCode_'+image_code_id,constants.IMAGE_CODE_REDIS_EXPIRES,text)
except Exception as e:
current_app.logger.error(e)
return jsonify(errno=RET.DBERR,errmsg='保存图片验证码失败')
else:
response=make_response(image)
# 默认的响应报文Content-Type:text/html,应该修改默认的响应报文
response.headers['Content-Type']='image/jpg'
return response
@passport_blue.route('/sms_code',methods=['POST'])
def send_sms_code():
"""
发送短信验证码
获取参数---校验参数---业务处理(查询数据)---返回结果
1、获取post请求的三个参数;前端使用ajax传入的参数,前端如何传入json?
mobile/image_code/image_code_id
request.json.get()
2、检查参数的完整性
3、检查手机号的格式是否符合要求,使用正则
4、比较图片验证码,从redis数据库中获取真实的图片验证码
get()
5、判断图片验证码是否过期
6、需要先删除Redis中真实存在的图片验证码,因为图片验证码只能获取一次,比较一次.
7、比较图片验证码,如果图片验证码正确
**检查手机号是否注册过???
8、生成短信的随机数,六位数的随机数 random
9、保存短信随机数到Redis数据库中,
10、调用云通讯接口,发送短信,保存发送结果
11、返回发送结果
:return:
"""
mobile=request.json.get('mobile')
image_code=request.json.get('image_code')
image_code_id=request.json.get('image_code_id')
# 检查参数的完整性
if not all([mobile,image_code,image_code_id]):
return jsonify(errno=RET.PARAMERR,errmsg='参数不完整')
# 检查手机号的格式,13012345678
if not re.match(r'1[3456789]\d{9}$',mobile):
return jsonify(errno=RET.PARAMERR,errmsg='手机号格式错误')
# 尝试从redis数据库中获取真实的图片验证码
try:
real_image_code=redis_store.get('ImageCode_'+image_code_id)
except Exception as e:
current_app.logger.error(e)
return jsonify(errno=RET.DBERR,errmsg='获取图片验证码数据失败')
# 判断图片验证码是否过期
if not real_image_code:
return jsonify(errno=RET.NODATA,errmsg='图片验证码已过期')
# 删除Redis数据库中的图片验证码
try:
redis_store.delete('ImageCode_'+image_code_id)
except Exception as e:
current_app.logger.error(e)
# 比较图片验证码是否一致,忽略大小写
if real_image_code.lower() != image_code.lower():
return jsonify(errno=RET.DATAERR,errmsg='图片验证码错误')
# 确认用户是否注册过?
try:
# User.query.filter_by(mobile=mobile).first()
user=User.query.filter(User.mobile==mobile).first()
except Exception as e:
current_app.logger.error(e)
return jsonify(errno=RET.DBERR,errmsg='查询用户数据失败')
else:
# 判断查询结果是否存在
if user is not None:
return jsonify(errno=RET.DATAEXIST,errmsg='用户已存在')
#生成6位数短信随机数,使用随机数模块
sms_code='%06d' % random.randint (0, 999999)
print(sms_code)
try:
redis_store.setex('SMSCode_'+mobile,constants.SMS_CODE_REDIS_EXPIRES,sms_code)
except Exception as e:
current_app.logger.error(e)
return jsonify(errno=RET.DBERR,errmsg='保存短信数据失败')
# 调用云通讯扩展,发送短信
try:
ccp=sms.CCP()
result=ccp.send_template_sms(mobile,[sms_code,constants.SMS_CODE_REDIS_EXPIRES/60],1)
except Exception as e:
current_app.logger.error(e)
return jsonify(errno=RET.THIRDERR,errmsg='发送短信异常')
# 判断发送是否成功
if result==0:
return jsonify(errno=RET.OK,errmsg='发送成功')
else:
return jsonify(errno=RET.THIRDERR,errmsg='发送失败')
@passport_blue.route('/register',methods=['POST'])
def register():
"""
用户注册
1、获取参数,mobile,sms_code,password
2、检查参数的完整性
3、检查手机号的格式
4、检查短信验证码,尝试从Redis数据库中获取真实的短信验证码
5、判断获取结果是否过期
6、先比较短信验证码是否一致
7、删除Redis数据库中的短信验证码
8、构造模型类对象
user=User()
user.password=password
9、提交数据到数据库中,mysql
10、把用户基本信息缓存到Redis数据库中
session['user_id']=user.id
session['mobile']=mobile
session['nick_name']=mobile
11、返回结果
:return:
"""
mobile=request.json.get('mobile')
sms_code=request.json.get('sms_code')
password=request.json.get('password')
# 检查参数完整性
if not all([mobile,sms_code,password]):
return jsonify(errno=RET.PARAMERR,errmsg='参数缺失')
# 检查手机号格式
if not re.match(r'1[3456789]\d{9}$',mobile):
return jsonify(errno=RET.PARAMERR,errmsg='手机号格式错误')
# 尝试从Redis中获取真实的短信验证码
try:
real_sms_code=redis_store.get('SMSCode_'+mobile)
except Exception as e:
current_app.logger.error(e)
return jsonify(errno=RET.DBERR,errmsg='查询短信验证码失败')
# 判断查询结果
if not real_sms_code:
return jsonify(errno=RET.NODATA,errmsg='短信验证码已过期')
# 比较短信验证码是否正确
if real_sms_code !=str(sms_code):
return jsonify(errno=RET.DATAERR,errmsg='短信验证码不一致')
# 删除redis数据库中存储的短信验证码
try:
redis_store.delete('SMSCode_'+mobile)
except Exception as e:
current_app.logger.error(e)
# 构造模型类对象
user=User()
user.mobile=mobile
user.nick_name=mobile
# 调用了模型类中的generate_password_hash实现了密码 加密储存,sha256
user.password=password
# 提交用户注册信息数据到mysql数据库中
try:
db.session.add(user)
db.session.commit()
except Exception as e:
current_app.logger.error(e)
# 存储数据如果发生异常,需要进行回滚
db.session.rollback()
return jsonify(errno=RET.DBERR,errmsg='保存用户数据失败')
# 返回用户信息到Redis数据库中
session['user_id']=user.id
session['mobile']=mobile
session['nick_name']=mobile
# 返回结果
return jsonify(errno=RET.OK,errmsg='注册成功')
@passport_blue.route("/login",methods=['POST'])
def login():
"""
用户登录
1、获取参数:mobile,password
2、检查参数完整性
3、检查手机号的格式
4、根据手机号查询数据库,确认用户user存在
5、调用模型类检查密码是否正确的方法
6、记录用户的登录时间
user.last_login=datetime.now()
7、提交数据库,如果发生异常需要回滚
8、缓存用户信息session,昵称要换成user.nick_name
8、返回结果
:return:
"""
# 获取参数
mobile=request.json.get('mobile')
password=request.json.get('password')
# 检查参数的完整性
if not all([mobile,password]):
return jsonify(errno=RET.PARAMERR,errmsg='参数缺失')
# 检查手机号格式
if not re.match(r'1[3456789]\d{9}$',mobile):
return jsonify(errno=RET.PARAMERR,errmsg='手机号格式错误')
# 根据手机号查询数据库,确认用户已注册.
try:
user=User.query.filter_by(mobile=mobile).first()
except Exception as e:
current_app.logger.error(e)
return jsonify(errno=RET.DBERR,errmsg='查询用户数据失败')
# 判断用户是否注册,以及密码是否正确.
if user is None or not user.check_password(password):
return jsonify(errno=RET.DATAERR,errmsg='用户名或密码错误')
# 记录用户的登录时间
user.last_login=datetime.now()
# 提交数据到数据库中
try:
db.session.add(user)
db.session.commit()
except Exception as e:
current_app.logger.error(e)
db.session.rollback()
return jsonify(errno=RET.DBERR,errmsg='保存数据失败')
# 缓存用户信息到redis数据库中
session['user_id']=user.id
session['mobile']=mobile
# 缓存的用户昵称和注册时要有区别,因为登录可以登录多次,昵称有可能会修改
session['nick_name']=user.nick_name
# 返回结果
return jsonify(errno=RET.OK,errmsg='ok')
@passport_blue.route("/logout")
def logout():
"""
如果是前后端分离,以及符合RESTful风格,(表现层状态转换),退出的请求方法为delete
get/post/put/delete 获取/新建/修改/删除
退出登录
1、本质是清除服务器缓存的用户信息
:return:
"""
session.pop('user_id',None)
session.pop('mobile',None)
session.pop('nick_name',None)
return jsonify(errno=RET.OK,errmsg='OK')
pass
| python |
from mpf.tests.MpfGameTestCase import MpfGameTestCase
from mpf.core.rgb_color import RGBColor
class TestBlinkenlight(MpfGameTestCase):
def get_config_file(self):
return 'config.yaml'
def get_platform(self):
return 'smart_virtual'
def get_machine_path(self):
return 'tests/machine_files/blinkenlight/'
def test_add_color_to_one_blinkenlight(self):
self.post_event('start_mode1')
self.assertPlaceholderEvaluates(0, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertPlaceholderEvaluates(0, 'device.blinkenlights.my_blinkenlight2.num_colors')
self.post_event('add_color_to_first_blinkenlight')
self.assertPlaceholderEvaluates(1, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertPlaceholderEvaluates(0, 'device.blinkenlights.my_blinkenlight2.num_colors')
def test_add_color_to_two_blinkenlights(self):
self.post_event('start_mode1')
self.assertPlaceholderEvaluates(0, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertPlaceholderEvaluates(0, 'device.blinkenlights.my_blinkenlight2.num_colors')
self.post_event('add_color_to_all_blinkenlights')
self.assertPlaceholderEvaluates(1, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertPlaceholderEvaluates(1, 'device.blinkenlights.my_blinkenlight2.num_colors')
def test_remove_color_from_one_blinkenlight(self):
self.post_event('start_mode1')
self.post_event('add_color_to_second_blinkenlight')
self.assertPlaceholderEvaluates(0, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertPlaceholderEvaluates(1, 'device.blinkenlights.my_blinkenlight2.num_colors')
self.post_event('remove_color_from_first_blinkenlight')
self.assertPlaceholderEvaluates(0, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertPlaceholderEvaluates(1, 'device.blinkenlights.my_blinkenlight2.num_colors')
self.post_event('remove_color_from_second_blinkenlight')
self.assertPlaceholderEvaluates(0, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertPlaceholderEvaluates(0, 'device.blinkenlights.my_blinkenlight2.num_colors')
def test_remove_all_colors_from_all_blinkenlights(self):
self.post_event('start_mode1')
self.post_event('start_mode2')
self.post_event('add_color_to_first_blinkenlight')
self.post_event('add_color_to_second_blinkenlight')
self.post_event('add_color_to_third_blinkenlight')
self.post_event('add_color_to_all_blinkenlights')
self.post_event('mode2_add_color_to_first_blinkenlight')
self.assertPlaceholderEvaluates(3, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertPlaceholderEvaluates(2, 'device.blinkenlights.my_blinkenlight2.num_colors')
self.assertPlaceholderEvaluates(1, 'device.blinkenlights.my_blinkenlight3.num_colors')
self.post_event('remove_all_colors_from_all_blinkenlights')
self.assertPlaceholderEvaluates(0, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertPlaceholderEvaluates(0, 'device.blinkenlights.my_blinkenlight2.num_colors')
self.assertPlaceholderEvaluates(0, 'device.blinkenlights.my_blinkenlight3.num_colors')
def test_remove_mode_colors_from_one_blinkenlight(self):
self.post_event('start_mode1')
self.post_event('start_mode2')
self.post_event('add_color_to_first_blinkenlight')
self.post_event('mode2_add_color_to_first_blinkenlight')
self.post_event('mode2_add_color2_to_first_blinkenlight')
self.assertPlaceholderEvaluates(3, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.post_event('mode2_remove_mode_colors_from_first_blinkenlight')
self.assertPlaceholderEvaluates(1, 'device.blinkenlights.my_blinkenlight1.num_colors')
def test_remove_mode_colors_when_mode_ends(self):
self.post_event('start_mode1')
self.post_event('start_mode2')
self.post_event('add_color_to_first_blinkenlight')
self.post_event('add_color_to_second_blinkenlight')
self.post_event('mode2_add_color_to_first_blinkenlight')
self.post_event('mode2_add_color2_to_first_blinkenlight')
self.post_event('mode2_add_color_to_second_blinkenlight')
self.assertPlaceholderEvaluates(3, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertPlaceholderEvaluates(2, 'device.blinkenlights.my_blinkenlight2.num_colors')
self.post_event('stop_mode2')
self.assertPlaceholderEvaluates(1, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertPlaceholderEvaluates(1, 'device.blinkenlights.my_blinkenlight2.num_colors')
def test_flashing_cycle(self):
self.post_event('start_mode1')
self.post_event('add_color_to_all_blinkenlights')
self.post_event('add_color_to_first_blinkenlight')
self.post_event('add_color_to_second_blinkenlight')
self.post_event('add_color_to_third_blinkenlight')
self.assertPlaceholderEvaluates(2, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertPlaceholderEvaluates(2, 'device.blinkenlights.my_blinkenlight2.num_colors')
self.assertPlaceholderEvaluates(1, 'device.blinkenlights.my_blinkenlight3.num_colors')
blinkenlight1 = self.machine.blinkenlights['my_blinkenlight1']
blinkenlight2 = self.machine.blinkenlights['my_blinkenlight2']
blinkenlight3 = self.machine.blinkenlights['my_blinkenlight3']
blue = RGBColor('blue')
green = RGBColor('green')
red = RGBColor('red')
yellow = RGBColor('yellow')
purple = RGBColor('purple')
cyan = RGBColor('cyan')
off = RGBColor('off')
self.assertEqual(blue, blinkenlight1.light._color)
self.assertEqual(green, blinkenlight2.light._color)
self.assertEqual(purple, blinkenlight3.light._color)
self.advance_time_and_run(1)
self.assertEqual(red, blinkenlight1.light._color)
self.assertEqual(green, blinkenlight2.light._color)
self.assertEqual(off, blinkenlight3.light._color)
self.advance_time_and_run(1)
self.assertEqual(off, blinkenlight1.light._color)
self.assertEqual(yellow, blinkenlight2.light._color)
self.assertEqual(purple, blinkenlight3.light._color)
self.advance_time_and_run(1)
self.assertEqual(blue, blinkenlight1.light._color)
self.assertEqual(yellow, blinkenlight2.light._color)
self.assertEqual(off, blinkenlight3.light._color)
self.advance_time_and_run(1)
self.assertEqual(red, blinkenlight1.light._color)
self.assertEqual(green, blinkenlight2.light._color)
self.assertEqual(purple, blinkenlight3.light._color)
self.advance_time_and_run(1)
self.assertEqual(off, blinkenlight1.light._color)
self.assertEqual(green, blinkenlight2.light._color)
self.assertEqual(off, blinkenlight3.light._color)
self.advance_time_and_run(1)
self.assertEqual(blue, blinkenlight1.light._color)
self.assertEqual(yellow, blinkenlight2.light._color)
self.assertEqual(purple, blinkenlight3.light._color)
self.post_event("remove_color_from_third_blinkenlight")
self.advance_time_and_run(1)
self.assertEqual(red, blinkenlight1.light._color)
self.assertEqual(yellow, blinkenlight2.light._color)
self.assertEqual(off, blinkenlight3.light._color)
self.advance_time_and_run(1)
self.assertEqual(off, blinkenlight1.light._color)
self.assertEqual(green, blinkenlight2.light._color)
self.assertEqual(off, blinkenlight3.light._color)
self.advance_time_and_run(1)
self.assertEqual(blue, blinkenlight1.light._color)
self.assertEqual(green, blinkenlight2.light._color)
self.assertEqual(off, blinkenlight3.light._color)
def test_priority_order(self):
self.post_event('start_mode1')
self.post_event('start_mode2')
blinkenlight1 = self.machine.blinkenlights['my_blinkenlight1']
red = RGBColor('red')
orange = RGBColor('orange')
off = RGBColor('off')
self.post_event('add_color_to_first_blinkenlight')
self.post_event('mode2_add_color_to_first_blinkenlight')
self.assertEqual(orange, blinkenlight1.light._color)
self.advance_time_and_run(1)
self.assertEqual(red, blinkenlight1.light._color)
self.advance_time_and_run(1)
self.assertEqual(off, blinkenlight1.light._color)
self.post_event('remove_all_colors_from_all_blinkenlights')
self.advance_time_and_run(1)
self.post_event('mode2_add_color_to_first_blinkenlight')
self.post_event('add_color_to_first_blinkenlight')
self.assertEqual(orange, blinkenlight1.light._color)
self.advance_time_and_run(1)
self.assertEqual(red, blinkenlight1.light._color)
self.advance_time_and_run(1)
self.assertEqual(off, blinkenlight1.light._color)
def test_replace_existing_color(self):
self.post_event('start_mode1')
blinkenlight1 = self.machine.blinkenlights['my_blinkenlight1']
blue = RGBColor('blue')
darkred = RGBColor('darkred')
off = RGBColor('off')
self.post_event('add_color_to_all_blinkenlights')
self.assertPlaceholderEvaluates(1, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertEqual(blue, blinkenlight1.light._color)
self.advance_time_and_run(1.5)
self.assertEqual(off, blinkenlight1.light._color)
self.advance_time_and_run(1.5)
self.assertEqual(blue, blinkenlight1.light._color)
self.advance_time_and_run(1.5)
self.assertEqual(off, blinkenlight1.light._color)
self.advance_time_and_run(1.5)
self.post_event('add_color_to_first_blinkenlight_with_duplicate_key')
self.assertPlaceholderEvaluates(1, 'device.blinkenlights.my_blinkenlight1.num_colors')
self.assertEqual(darkred, blinkenlight1.light._color)
self.advance_time_and_run(1.5)
self.assertEqual(off, blinkenlight1.light._color)
self.advance_time_and_run(1.5)
self.assertEqual(darkred, blinkenlight1.light._color)
self.advance_time_and_run(1.5)
self.assertEqual(off, blinkenlight1.light._color)
def test_show_with_tokens(self):
self.post_event('start_mode2')
blinkenlight = self.machine.blinkenlights['my_blinkenlight2']
gray = RGBColor('gray')
off = RGBColor('off')
self.assertPlaceholderEvaluates(0, 'device.blinkenlights.my_blinkenlight2.num_colors')
self.post_event('play_blinkenlight_token_show')
self.assertPlaceholderEvaluates(1, 'device.blinkenlights.my_blinkenlight2.num_colors')
self.assertEqual(gray, blinkenlight.light._color)
self.advance_time_and_run(2)
self.assertEqual(off, blinkenlight.light._color)
self.advance_time_and_run(2)
self.assertEqual(gray, blinkenlight.light._color)
self.advance_time_and_run(2)
self.assertEqual(off, blinkenlight.light._color)
| python |
import mysql.connector
mydb = mysql.connector.connect(
host = 'localhost',
user = "root",
#passwd = "ant904",
database = "spl"
#auth_plugin='mysql_native_password'
)
myCursor = mydb.cursor()
qusTimeList=list()
qusTimeList.append("0:00:03")
qusTimeList.append("0:00:02")
qusTimeList.append("0:00:05")
qusTimeList.append("0:00:06")
qusTimeList.append("0:00:08")
qusTimeList.append("0:00:02")
qusTimeList.append("0:00:03")
qusTimeList.append("0:00:03")
qusTimeList.append("0:00:04")
qusTimeList.append("0:00:05")
qusTimeList.append("0:00:08")
gameTimeList=list()
gameTimeList.append("0:00:13")
gameTimeList.append("0:00:19")
gameTimeList.append("0:00:24")
gameTimeList.append("0:00:08")
gameTimeList.append("0:00:09")
gameTimeList.append("0:00:13")
gameTimeList.append("0:00:08")
gameTimeList.append("0:00:09")
gameTimeList.append("0:00:13")
gameTimeList.append("0:00:14")
gameTimeList.append("0:00:12")
#myCursor.execute("CREATE database test222")
sql="INSERT into controlGroup(questionTime , gameTime) VALUES (%s, %s)"
val=(qusTimeList[9],gameTimeList[9])
myCursor.execute(sql,val)
mydb.commit()
myCursor.close()
mydb.close()
| python |
from toee import *
def OnBeginSpellCast( spell ):
print "Vampiric Touch OnBeginSpellCast"
print "spell.target_list=", spell.target_list
print "spell.caster=", spell.caster, " caster.level= ", spell.caster_level
game.particles( "sp-necromancy-conjure", spell.caster )
def OnSpellEffect( spell ):
print "Vampiric Touch OnSpellEffect"
dice = dice_new("1d6")
dice.number = min(10, (spell.caster_level) / 2)
spell.duration = 600
target = spell.target_list[0]
if not (target.obj == spell.caster):
attack_successful = spell.caster.perform_touch_attack( target.obj , 1)
if attack_successful & D20CAF_HIT:
old_hp = target.obj.stat_level_get( stat_hp_current )
target.obj.spell_damage_weaponlike( spell.caster, D20DT_NEGATIVE_ENERGY, dice, D20DAP_UNSPECIFIED, 100, D20A_CAST_SPELL, spell.id, attack_successful, 0 )
new_hp = target.obj.stat_level_get( stat_hp_current )
damage = old_hp - new_hp
if damage > (old_hp + 10):
damage = old_hp + 10
#spell.caster.condition_add_with_args( 'Temporary_Hit_Points', spell.id, spell.duration, damage )
spell.caster.condition_add_with_args( 'sp-Vampiric Touch', spell.id, spell.duration, damage )
spell.caster.float_mesfile_line( 'mes\\spell.mes', 20005, 0 )
else:
#target.obj.float_mesfile_line( 'mes\\spell.mes', 30021 )
game.particles( 'Fizzle', target.obj )
spell.target_list.remove_target( target.obj )
game.particles( 'sp-Vampiric Touch', spell.caster )
def OnBeginRound( spell ):
print "Vampiric Touch OnBeginRound"
def OnEndSpellCast( spell ):
print "Vampiric Touch OnEndSpellCast" | python |
from django.contrib import admin
from .models import District, Quarter, Community
admin.site.register(District)
admin.site.register(Quarter)
admin.site.register(Community) | python |
# Copyright (C) 2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions
# and limitations under the License.
# pylint: disable=W1203,C0411,C0413,no-value-for-parameter
import argparse
import json
import logging
import os
import subprocess
import sys
import tempfile
from mmcv.utils import Config
import yaml
from eval import main as evaluate
sys.path.append(f'{os.path.abspath(os.path.dirname(__file__))}/../../')
from tools.misc import train, get_work_dir
def parse_args():
""" Parses input args. """
parser = argparse.ArgumentParser()
parser.add_argument('config',
help='A path to model training configuration file (.py).')
parser.add_argument('gpu_num', type=int,
help='A number of GPUs to use in training.')
parser.add_argument('out',
help='A path to output file where models metrics will be saved (.yml).')
parser.add_argument('--update_config',
help='Update configuration file by parameters specified here.'
'Use quotes if you are going to change several params.',
default='')
parser.add_argument('--show-dir', '--show_dir', dest='show_dir',
help='A directory where images with drawn detected objects will be saved.')
return parser.parse_args()
def is_clustering_needed(cfg):
if cfg.total_epochs > 0:
return False
if not hasattr(cfg.model, 'bbox_head') or not cfg.model.bbox_head.type == 'SSDHead':
return False
if not cfg.model.bbox_head.anchor_generator.type == 'SSDAnchorGeneratorClustered':
return False
return True
def cluster(cfg, config_path, update_config):
mmdetection_tools = f'{os.path.dirname(__file__)}/../../../../external/mmdetection/tools'
logging.info('Clustering started...')
widths = cfg.model.bbox_head.anchor_generator.widths
n_clust = 0
for w in widths:
n_clust += len(w) if isinstance(w, (list, tuple)) else 1
n_clust = ' --n_clust ' + str(n_clust)
group_as = ''
if isinstance(widths[0], (list, tuple)):
group_as = ' --group_as ' + ' '.join([str(len(w)) for w in widths])
config = ' --config ' + config_path
tmp_file = tempfile.NamedTemporaryFile(delete=False)
out = f' --out {tmp_file.name}'
if 'pipeline' in cfg.data.train:
img_shape = [t for t in cfg.data.train.pipeline if t['type'] == 'Resize'][0][
'img_scale']
else:
img_shape = [t for t in cfg.data.train.dataset.pipeline if t['type'] == 'Resize'][0][
'img_scale']
img_shape = f' --image_size_wh {img_shape[0]} {img_shape[1]}'
subprocess.run(f'python {mmdetection_tools}/cluster_boxes.py'
f'{config}'
f'{n_clust}'
f'{group_as}'
f'{update_config}'
f'{img_shape}'
f'{out}'.split(' '), check=True)
with open(tmp_file.name) as src_file:
content = json.load(src_file)
widths, heights = content['widths'], content['heights']
if not update_config:
update_config = ' --update_config'
update_config += f' model.bbox_head.anchor_generator.widths={str(widths).replace(" ", "")}'
update_config += f' model.bbox_head.anchor_generator.heights={str(heights).replace(" ", "")}'
logging.info('... clustering completed.')
return update_config
def main():
""" Main function. """
logging.basicConfig(level=logging.INFO)
args = parse_args()
logging.info(f'Commandline:\n{" ".join(sys.argv)}')
cfg = Config.fromfile(args.config)
update_config = f' --update_config {args.update_config}' if args.update_config else ''
if is_clustering_needed(cfg):
update_config = cluster(cfg, args.config, update_config)
logging.info('Training started ...')
training_info = train(args.config, args.gpu_num, update_config)
logging.info('... training completed.')
work_dir = get_work_dir(cfg, args.update_config)
logging.info('Evaluation started ...')
evaluate(os.path.join(work_dir, "config.py"), os.path.join(work_dir, "latest.pth"), args.out, '', args.show_dir)
logging.info('... evaluation completed.')
with open(args.out, 'a+') as dst_file:
yaml.dump(training_info, dst_file)
if __name__ == '__main__':
main()
| python |
import os
import codecs
from io import StringIO
from pytest import fixture
from rave import filesystem
class DummyProvider:
def __init__(self, files):
self.files = files;
def list(self):
return self.files
def has(self, filename):
return filename in self.list()
def open(self, filename, *args, **kwargs):
if not self.has(filename):
raise filesystem.FileNotFound(filename)
if not self.isfile(filename):
raise filesystem.NotAFile(filename)
return DummyFile(self, filename)
def isfile(self, filename):
return self.has(filename) and '.' in filename
def isdir(self, filename):
return self.has(filename) and not self.isfile(filename)
class FaultyProvider(DummyProvider):
def __init__(self, files, faulty_files, err=filesystem.FileNotFound):
super().__init__(files)
self.faulty_files = faulty_files
self.error_class = err
def open(self, filename, *args, **kwargs):
if filename in self.faulty_files:
raise self.error_class(filename)
return super().open(filename, *args, **kwargs)
class DummyFile(filesystem.File):
def __init__(self, parent, filename, content='merry saltmas'):
self.parent = parent
self.filename = filename
self._buffer = StringIO(content)
self._closed = False
def close(self):
if self._closed:
raise filesystem.FileClosed(self.filename)
self._closed = True
def opened(self):
return not self._closed
def readable(self):
return True
def writable(self):
return True
def seekable(self):
return True
def read(self, amount=None):
if self.closed:
raise filesystem.FileClosed(self.filename)
return self._buffer.read(amount)
def write(self, buffer):
if self.closed:
raise filesystem.FileClosed(self.filename)
return self._buffer.write(buffer)
def seek(self, offset, mode=os.SEEK_CUR):
return self._buffer.seek(offset, mode)
def tell(self):
return self._buffer.tell()
class DummyTransformer:
CONSUME = False
RELATIVE = False
def __init__(self, filename, handle):
self.filename = filename
self.handle = handle
self.files = [ self.filename + '.rot13' ]
def list(self):
return self.files
def has(self, filename):
return filename in self.list()
def open(self, filename, *args, **kwargs):
if not self.has(filename):
raise filesystem.FileNotFound(filename)
return ROT13File(self, filename, self.handle)
def isfile(self, filename):
return self.has(filename)
def isdir(self, filename):
return False
def relative(self):
return self.RELATIVE
def consumes(self):
return self.CONSUME
def valid(self):
return True
class FaultyTransformer:
def __init__(self, filename, handle):
raise FileNotFound(filename)
class InvalidTransformer(DummyTransformer):
def valid(self):
return False
class ROT13File(filesystem.File):
def __init__(self, parent, filename, handle):
self.parent = parent
self.filename = filename
self.handle = handle
def close(self):
return self.handle.close()
def opened(self):
return self.handle.opened()
def readable(self):
return self.handle.readable()
def writable(self):
return self.handle.writable()
def seekable(self):
return self.handle.seekable()
def read(self, amount=None):
return codecs.encode(self.handle.read(amount), 'rot13')
def write(self, buffer):
return self.handle.write(codecs.encode(buffer, 'rot13'))
def seek(self, offset, mode=os.SEEK_CUR):
return self.handle.seek(offset, mode)
def tell(self):
return self.handle.tell()
@fixture
def fs():
return filesystem.FileSystem()
@fixture
def dummyfs():
fs = filesystem.FileSystem()
fs.mount('/x', DummyProvider({ '/a.txt', '/b.png' }))
return fs
@fixture
def nestedfs():
fs = filesystem.FileSystem()
fs.mount('/x', DummyProvider({ '/y', '/y/c.txt', '/y/p.png', '/y/z' }))
return fs
@fixture
def parentlessfs():
fs = filesystem.FileSystem()
fs.mount('/x', DummyProvider({ '/z/k.txt' }))
return fs
@fixture
def doublefs():
fs = filesystem.FileSystem()
fs.mount('/x', DummyProvider({ '/a.txt', '/b.png' }))
fs.mount('/y', DummyProvider({ '/c.exe', '/d.jpg' }))
return fs
@fixture
def mergedfs():
fs = filesystem.FileSystem()
fs.mount('/x', DummyProvider({ '/a.txt', '/b.png' }))
fs.mount('/x', DummyProvider({ '/c.exe', '/d.jpg' }))
return fs
@fixture
def transfs():
fs = dummyfs()
fs.transform('\.txt$', DummyTransformer)
return fs
| python |
"""Forward measurements from Xiaomi Mi plant sensor via MQTT.
See https://github.com/ChristianKuehnel/plantgateway for more details.
"""
##############################################
#
# This is open source software licensed under the Apache License 2.0
# http://www.apache.org/licenses/LICENSE-2.0
#
##############################################
from enum import Enum
import os
import logging
import json
import time
from datetime import datetime
from typing import List, Optional
import yaml
import paho.mqtt.client as mqtt
from miflora.miflora_poller import MiFloraPoller, MI_BATTERY, MI_LIGHT, MI_CONDUCTIVITY, MI_MOISTURE, MI_TEMPERATURE, MI_FWVERSION
from btlewrap.bluepy import BluepyBackend
from plantgw import __version__
class MQTTAttributes(Enum):
"""Attributes sent in the json dict."""
BATTERY = 'battery'
TEMPERATURE = 'temperature'
BRIGHTNESS = 'brightness'
MOISTURE = 'moisture'
CONDUCTIVITY = 'conductivity'
TIMESTAMP = 'timestamp'
FIRMWARE = 'firmware'
# unit of measurement for the different attributes
UNIT_OF_MEASUREMENT = {
MQTTAttributes.BATTERY: '%',
MQTTAttributes.TEMPERATURE: '°C',
MQTTAttributes.BRIGHTNESS: 'lux',
MQTTAttributes.MOISTURE: '%',
MQTTAttributes.CONDUCTIVITY: 'µS/cm',
MQTTAttributes.TIMESTAMP: 's',
MQTTAttributes.FIRMWARE: '',
}
# home assistant device classes for the different attributes
DEVICE_CLASS = {
MQTTAttributes.BATTERY: 'battery',
MQTTAttributes.TEMPERATURE: 'temperature',
MQTTAttributes.BRIGHTNESS: 'illuminance',
MQTTAttributes.MOISTURE: None,
MQTTAttributes.CONDUCTIVITY: None,
MQTTAttributes.TIMESTAMP: 'timestamp',
MQTTAttributes.FIRMWARE: None,
}
# pylint: disable-msg=too-many-instance-attributes
class Configuration:
"""Stores the program configuration."""
def __init__(self, config_file_path):
with open(config_file_path, 'r') as config_file:
config = yaml.load(config_file, Loader=yaml.FullLoader)
self._configure_logging(config)
self.interface = 0
if 'interface' in config:
self.interface = config['interface']
self.mqtt_port = 8883 # type: int
self.mqtt_user = None # type: Optional[str]
self.mqtt_password = None # type: Optional[str]
self.mqtt_ca_cert = None # type: Optional[str]
self.mqtt_client_id = None # type: Optional[str]
self.mqtt_trailing_slash = True # type:bool
self.mqtt_timestamp_format = None # type: Optional[str]
self.mqtt_discovery_prefix = None # type: Optional[str]
self.sensors = [] # type: List[SensorConfig]
if 'port' in config['mqtt']:
self.mqtt_port = config['mqtt']['port']
if 'user' in config['mqtt']:
self.mqtt_user = config['mqtt']['user']
if 'password' in config['mqtt']:
self.mqtt_password = config['mqtt']['password']
if 'ca_cert' in config['mqtt']:
self.mqtt_ca_cert = config['mqtt']['ca_cert']
if 'client_id' in config['mqtt']:
self.mqtt_client_id = config['mqtt']['client_id']
if 'trailing_slash' in config['mqtt'] and not config['mqtt']['trailing_slash']:
self.mqtt_trailing_slash = False
if 'timestamp_format' in config['mqtt']:
self.mqtt_timestamp_format = config['mqtt']['timestamp_format']
self.mqtt_server = config['mqtt']['server']
self.mqtt_prefix = config['mqtt']['prefix']
for sensor_config in config['sensors']:
fail_silent = 'fail_silent' in sensor_config
self.sensors.append(SensorConfig(sensor_config['mac'], sensor_config.get('alias', None), fail_silent, sensor_config.get('cache_timeout', 600), sensor_config.get('cache_retries', 3)))
if 'discovery_prefix' in config['mqtt']:
self.mqtt_discovery_prefix = config['mqtt']['discovery_prefix']
@staticmethod
def _configure_logging(config):
timeform = '%a, %d %b %Y %H:%M:%S'
logform = '%(asctime)s %(levelname)-8s %(message)s'
loglevel = logging.INFO
if 'debug' in config:
loglevel = logging.DEBUG
if 'logfile' in config:
logfile = os.path.abspath(os.path.expanduser(config['logfile']))
logging.basicConfig(filename=logfile, level=loglevel, datefmt=timeform, format=logform)
else:
logging.basicConfig(level=loglevel, datefmt=timeform, format=logform)
class SensorConfig:
"""Stores the configuration of a sensor."""
def __init__(self, mac: str, alias: str = None, fail_silent: bool = False, cache_timeout: int = 600, cache_retries: int = 3):
if mac is None:
msg = 'mac of sensor must not be None'
logging.error(msg)
raise Exception('mac of sensor must not be None')
self.mac = mac
self.alias = alias
self.fail_silent = fail_silent
self.cache_timeout = cache_timeout
self.cache_retries = cache_retries
def get_topic(self) -> str:
"""Get the topic name for the sensor."""
if self.alias is not None:
return self.alias
return '0x' + self.short_mac
def __str__(self) -> str:
if self.alias:
result = self.alias
else:
result = self.mac
if self.fail_silent:
result += ' (fail silent)'
return result
@property
def short_mac(self):
"""Get the sensor mac without ':' in it."""
return self.mac.replace(':', '')
@staticmethod
def get_name_string(sensor_list) -> str:
"""Convert a list of sensor objects to a nice string."""
return ', '.join([str(sensor) for sensor in sensor_list])
class PlantGateway:
"""Main class of the module."""
def __init__(self, config_file_path: str = '~/.plantgw.yaml'):
config_file_path = os.path.abspath(os.path.expanduser(config_file_path))
self.config = Configuration(config_file_path) # type: Configuration
logging.info('PlantGateway version %s', __version__)
logging.info('loaded config file from %s', config_file_path)
self.mqtt_client = None
self.connected = False # type: bool
def start_client(self):
"""Start the mqtt client."""
if not self.connected:
self._start_client()
def stop_client(self):
"""Stop the mqtt client."""
if self.connected:
self.mqtt_client.disconnect()
self.connected = False
self.mqtt_client.loop_stop()
logging.info('Disconnected MQTT connection')
def _start_client(self):
self.mqtt_client = mqtt.Client(self.config.mqtt_client_id)
if self.config.mqtt_user is not None:
self.mqtt_client.username_pw_set(self.config.mqtt_user, self.config.mqtt_password)
if self.config.mqtt_ca_cert is not None:
self.mqtt_client.tls_set(self.config.mqtt_ca_cert, cert_reqs=mqtt.ssl.CERT_REQUIRED)
def _on_connect(client, _, flags, return_code):
self.connected = True
logging.info("MQTT connection returned result: %s", mqtt.connack_string(return_code))
self.mqtt_client.on_connect = _on_connect
self.mqtt_client.connect(self.config.mqtt_server, self.config.mqtt_port, 60)
self.mqtt_client.loop_start()
def _publish(self, sensor_config: SensorConfig, poller: MiFloraPoller):
self.start_client()
state_topic = self._get_state_topic(sensor_config)
data = {
MQTTAttributes.BATTERY.value: poller.parameter_value(MI_BATTERY),
MQTTAttributes.TEMPERATURE.value: '{0:.1f}'.format(poller.parameter_value(MI_TEMPERATURE)),
MQTTAttributes.BRIGHTNESS.value: poller.parameter_value(MI_LIGHT),
MQTTAttributes.MOISTURE.value: poller.parameter_value(MI_MOISTURE),
MQTTAttributes.CONDUCTIVITY.value: poller.parameter_value(MI_CONDUCTIVITY),
MQTTAttributes.FIRMWARE.value: poller.parameter_value(MI_FWVERSION),
MQTTAttributes.TIMESTAMP.value: datetime.now().isoformat(),
}
for key, value in data.items():
logging.debug("%s: %s", key, value)
if self.config.mqtt_timestamp_format is not None:
data['timestamp'] = datetime.now().strftime(self.config.mqtt_timestamp_format)
json_payload = json.dumps(data)
self.mqtt_client.publish(state_topic, json_payload, qos=1, retain=True)
logging.info('sent data to topic %s', state_topic)
logging.info('payload: %s', data)
def _get_state_topic(self, sensor_config: SensorConfig) -> str:
prefix_fmt = '{}/{}'
if self.config.mqtt_trailing_slash:
prefix_fmt += '/'
prefix = prefix_fmt.format(self.config.mqtt_prefix,
sensor_config.get_topic())
return prefix
def process_mac(self, sensor_config: SensorConfig):
"""Get data from one Sensor."""
logging.info('Getting data from sensor %s', sensor_config.get_topic())
poller = MiFloraPoller(sensor_config.mac, BluepyBackend, sensor_config.cache_timeout, sensor_config.cache_retries)
self.announce_sensor(sensor_config)
self._publish(sensor_config, poller)
def process_all(self):
"""Get data from all sensors."""
next_list = self.config.sensors
timeout = 1 # initial timeout in seconds
max_retry = 6 # number of retries
retry_count = 0
while retry_count < max_retry and next_list:
# if this is not the first try: wait some time before trying again
if retry_count > 0:
logging.info('try %d of %d: could not process sensor(s) %s. Waiting %d sec for next try',
retry_count, max_retry, SensorConfig.get_name_string(next_list), timeout)
time.sleep(timeout)
timeout *= 2 # exponential backoff-time
current_list = next_list
retry_count += 1
next_list = []
for sensor in current_list:
try:
self.process_mac(sensor)
# pylint: disable=bare-except, broad-except
except Exception as exception:
next_list.append(sensor) # if it failed, we'll try again in the next round
msg = "could not read data from {} ({}) with reason: {}".format(
sensor.mac, sensor.alias, str(exception))
if sensor.fail_silent:
logging.error(msg)
logging.warning('fail_silent is set for sensor %s, so not raising an exception.', sensor.alias)
else:
logging.exception(msg)
# print(msg)
# return sensors that could not be processed after max_retry
return next_list
def announce_sensor(self, sensor_config: SensorConfig):
"""Announce the sensor via Home Assistant MQTT Discovery.
see https://www.home-assistant.io/docs/mqtt/discovery/
"""
if self.config.mqtt_discovery_prefix is None:
return
self.start_client()
self_name = 'plantgateway'
device_name = '0x{}'.format(sensor_config.short_mac)
for attribute in MQTTAttributes:
unique_id = '{}_{}_{}'.format(self_name, device_name, attribute.value)
topic = '{}/sensor/{}_{}/{}/config'.format(self.config.mqtt_discovery_prefix, self_name, device_name, attribute.value)
payload = {
'state_topic': self._get_state_topic(sensor_config),
'json_attributes_topic': self._get_state_topic(sensor_config),
'unit_of_measurement': UNIT_OF_MEASUREMENT[attribute],
'value_template': '{{value_json.'+attribute.value+'}}',
'unique_id': unique_id,
'device': {
'identifiers': [ '{}_{}'.format(self_name, device_name), ],
'name': device_name,
'sw_version': 'plantgw dev',
'model': "MiFlora compatible plant humidity, brightness, conductivity, temperature sensor",
'manufacturer': 'to be deternmined',
}
}
if sensor_config.alias is not None:
payload['name'] = '{}_{}'.format(sensor_config.alias, attribute.value)
else:
payload['name'] = '{}_{}'.format(device_name, attribute.value)
if DEVICE_CLASS[attribute] is not None:
payload['device_class'] = DEVICE_CLASS[attribute]
json_payload = json.dumps(payload)
self.mqtt_client.publish(topic, json_payload, qos=1, retain=False)
logging.info('sent sensor config to topic %s', topic)
logging.info('payload: %s', payload)
| python |
#
# This file contains the Python code from Program 6.2 of
# "Data Structures and Algorithms
# with Object-Oriented Design Patterns in Python"
# by Bruno R. Preiss.
#
# Copyright (c) 2003 by Bruno R. Preiss, P.Eng. All rights reserved.
#
# http://www.brpreiss.com/books/opus7/programs/pgm06_02.txt
#
class StackAsArray(Stack):
def __init__(self, size = 0):
super(StackAsArray, self).__init__()
self._array = Array(size)
def purge(self):
while self._count > 0:
self._array[self._count] = None
self._count -= 1
#...
| python |
print('----->DESAFIO 48<-----')
print('Vou te mostrar a soma de todos os números impares múltiplos de 3 que estão no intervalo de 1 a 500!')
soma = 0
for c in range(0, 501):
if c > 0 and c % 2 != 0 and c % 3 == 0:
soma += c
print(soma)
| python |
#################################################################
# Name: randDLA.py #
# Authors: Michael Battaglia #
# Function: Program simulates diffusion limited aggregation #
# using Monte Carlo Methods. #
#################################################################
#essential modules
import numpy as np
import matplotlib.pyplot as plt
#function: 2D diffusion limited random walk
def randDLA(lims, sink=False, source=False, periodic=True, N=False):
"""
sink: position vector for aggregation sink
if False, then boundary is sink
source: position vector for particle source
if False, then particles randomly appear on Free spaces
lims: vector of dimension lengths
if False, then periodic boundaries
N: number of participating particles
if False, then spawn particles until source or boundary is taken
"""
if sink is False:
if periodic:
#there will be no aggregate
print("No aggregate can form")
return float("NaN")
if N is False:
if source is False:
if not periodic:
#aggregate will never end
print("No end condition for aggregate")
return float("NaN")
#initialize list of occupied positions
occupied_pos = []
anchored=np.zeros(lims,dtype=int)
#generate particles
generate = True
while generate:
if source:
#specified source
pos = pos_0
else:
#random source particle
pos = np.array([np.random.randint(0,lim) for lim in lims],dtype=int)
if not anchored[pos[0]][pos[1]]:
#take each step if position is not in a stuck position
while not isStuck(pos, lims, sink, periodic, anchored):
#take a random step in a random direction with a random orientation
step = np.zeros(len(pos))
step[np.random.randint(0,len(pos))] = 1-2*np.random.randint(0,2)
pos = pos + step
#impose periodic boundary
pos = np.mod(pos,lims).astype(int)
if len(occupied_pos)==0:
print("Position:", pos)
occupied_pos.append(pos)
anchored[pos[0],pos[1]] = 1
print("Anchored:",len(occupied_pos))
print("Anchor pos:",pos)
if N:
#generate until N particles
if len(occupied_pos) == N:
#generated N particles
generate = False
else:
#generate until
if source:
#source covered triggers end
if all(pos == source):
#occupied source
generate = False
if periodic:
#boundary covered triggers end
if any(pos==lims-1) or any(pos==0):
#occupied boundary
generate = False
#return list of occupied positions
return anchored, np.array(occupied_pos)
#function: check if particle is stuck (to edge, or other particle)
def isStuck(pos, lims, sink, periodic, anchored):
xp = pos[0]
yp = pos[1]
if not periodic:
#not periodic, gets stuck on wall
if any(pos==lims-1) or any(pos==0):
#if the particle has reached a wall
return True
if all(pos==sink):
#if particle hits sink
return True
if anchored[xp-1:xp+2,yp-1:yp+2].any():
#if particle is adjacent to an anchored particle
return True
else:
#particle is free
return False
#function: animated plot of 2D random walk
def D2plot(pos, animate=0.01):
if animate:
for i in range(len(pos)):
plt.cla()
plt.title('diffusion limited aggregation')
plt.scatter(pos.T[0][:i+1],pos.T[1][:i+1])
plt.xlabel('x position')
plt.ylabel('y position')
plt.draw()
plt.pause(animate)
else:
plt.title('diffusion limited aggregation')
plt.scatter(pos.T[0],pos.T[1])
plt.xlabel('x position')
plt.ylabel('y position')
plt.show()
#function: evaluate fractal dimension
def fracDim(image):
print(image.shape)
cen = (np.array([image.shape[0],image.shape[1]])-1)/2
r = range(1, min(cen)+1)
m = np.zeros(len(r))
for i in range(len(r)):
subimage = image[cen[0]-r[i]:cen[0]+r[i]+1,cen[1]-r[i]:cen[1]+r[i]+1]
m[i] = subimage.sum()
plt.title("fractal dimension")
plt.plot(r,m)
plt.ylabel('mass')
plt.xlabel('radius')
plt.yscale('log')
plt.xscale('log')
plt.show()
#function: main
if __name__ == '__main__':
#size of box
L = np.array([201,201])
#central position
central = (L-1)/2
#take a random walk until aggregation reaches edge
image, pos = randDLA(L, central)
#plot path
D2plot(pos, animate=True)
#plot fractal dimension
fracDim(image)
| python |
#import
import random
import os
import numpy
dic = {}
with open("points3D.txt","r") as n:
for line in n:
a = line.split(" ")
temp = []
temp.append(float(a[1]))
temp.append(float(a[2]))
temp.append(float(a[3]))
dic[a[0]] = temp[:]
print(dic["1"])
#end
| python |
def getone(coll, key, default=None):
try:
value = coll[key]
except [IndexError, KeyError, TypeError]:
return default;
else:
return value;
| python |
#!/usr/bin/env python2.7
# Copyright 2019 The Fuchsia 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 __future__ import print_function
import argparse
import itertools
import json
import os
import sys
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
SCRIPTS_DIR = os.path.dirname(SCRIPT_DIR)
FUCHSIA_ROOT = os.path.dirname(SCRIPTS_DIR)
# The maximum number of size percentage points a binary is allowed to drop.
# A greater amount will raise a flag.
MAX_SIZE_DECREASE = 10
# The maximum number of size percentage points a binary is allowed to gain.
# A greater amount will raise a flag.
MAX_SIZE_INCREASE = 1
class Type(object):
AUX = 'aux'
IMAGE = 'image'
TESTS = 'tests'
@classmethod
def all(cls): return [cls.AUX, cls.IMAGE, cls.TESTS]
class Origin(object):
LEGACY = 'legacy'
MIGRATED = 'migrated'
@classmethod
def all(cls): return [cls.LEGACY, cls.MIGRATED]
class Manifest(object):
'''Lists the contents of a manifest file'''
def __init__(self, origin, type, contents):
self.origin = origin
self.type = type
self.contents = contents
def __repr__(self):
return 'M[%s-%s]' % (self.origin, self.type)
class CustomJSONEncoder(json.JSONEncoder):
'''A JSON encoder that handles sets and sorts lists.'''
def default(self, object):
if isinstance(object, FileDataSet) or isinstance(object, FileData):
return object.to_json()
return json.JSONEncoder.default(self, object)
class FileData(object):
'''Represents a file referred to in a manifest.'''
def __init__(self, path, size=None):
self.path = path
self.size = size if size else os.path.getsize(path)
def __eq__(self, other):
return self.path == other.path
def __ne__(self, other):
return not self.__eq__(other)
def __hash__(self):
return len(hash(self.path))
def __repr__(self):
return 'F[' + self.path + ']'
def to_json(self):
return {
'path': self.path,
'size': self.size,
}
@classmethod
def from_json(cls, input):
return FileData(input['path'], input['size'])
class FileDataSet(object):
'''Represents a set of files.'''
def __init__(self):
# map { name --> FileData }
self.files = {}
def add(self, name, file):
if name == 'lib/libdriver.so':
# libdriver is a complicated hydra whose many heads we don't need to
# worry about here.
return
if name in self.files and file != self.files[name]:
print('Error: different file under path ' + name + ':')
print(' - ' + str(file))
print(' - ' + str(self.files[name]))
return
self.files[name] = file
def filenames(self):
return set(self.files.keys())
def get_file(self, name):
return self.files[name]
def __len__(self):
return len(self.files)
def to_json(self):
return self.files
@classmethod
def from_json(cls, input):
result = FileDataSet()
for name, data in input.iteritems():
result.add(name, FileData.from_json(data))
return result
class Summary(object):
'''Data for a particular state of the build.'''
def __init__(self):
# map { type --> FileDataSet }
self.objects = {}
def add_objects(self, type, objects):
dataset = self.objects.setdefault(type, FileDataSet())
for name, path in objects.iteritems():
dataset.add(name, FileData(path))
def get_objects(self, type):
return self.objects[type]
def __repr__(self):
items = ['%s=%s' % (t, len(o)) for (t, o) in self.objects.iteritems()]
return 'S[' + ', '.join(items) + ']'
def to_json(self, output):
json.dump(self.objects, output, cls=CustomJSONEncoder, indent=2,
sort_keys=True, separators=(',', ': '))
@classmethod
def from_json(cls, input):
result = Summary()
data = json.load(input)
for type in Type.all():
result.objects[type] = FileDataSet.from_json(data[type])
return result
def generate_summary(manifests, base_dir):
'''Generates a summary based on the manifests found in the build.'''
result = Summary()
for type in Type.all():
for manifest in filter(lambda m: m.type == type, manifests):
contents = manifest.contents.copy()
contents = dict([(n, os.path.join(base_dir, p))
for (n, p) in contents.iteritems()])
result.add_objects(type, contents)
return result
def report(manifest, is_error, message):
type = 'Error' if is_error else 'Warning'
print('%s%s%s' % (type.ljust(10), manifest.ljust(8), message))
def compare_summaries(reference, current):
'''Compares summaries for two states of the build.'''
match = True
for type in Type.all():
reference_objects = reference.get_objects(type)
current_objects = current.get_objects(type)
reference_names = reference_objects.filenames()
current_names = current_objects.filenames()
# Missing and new files.
if reference_names != current_names:
match = False
removed = reference_names - current_names
if removed:
for element in removed:
report(type, True, 'element removed: ' + element)
added = current_names - reference_names
if added:
for element in added:
report(type, True, 'element removed: ' + element)
# Size changes.
for name in reference_names & current_names:
reference_size = reference_objects.get_file(name).size
current_size = current_objects.get_file(name).size
if current_size == reference_size:
continue
is_diff_positive = current_size > reference_size
diff_percentage = 100 * (current_size - reference_size) / reference_size
is_error = False
if (diff_percentage < -MAX_SIZE_DECREASE or
diff_percentage > MAX_SIZE_INCREASE):
match = False
is_error = True
report(type, is_error, 'size change for ' + name + ': ' +
('+' if is_diff_positive else '-') +
str(abs(diff_percentage)) + '%')
return match
def main():
parser = argparse.ArgumentParser(
description='Performs verifications after moving an element from '
'ZN to GN.')
parser.add_argument('--build-dir',
help='path to the GN build dir',
default=os.path.join(FUCHSIA_ROOT, 'out', 'default'))
parser.add_argument('--summary',
help='path to the summary file to generate')
parser.add_argument('--reference',
help='path to the summary file to compare against')
args = parser.parse_args()
if not args.summary and not args.reference:
print('At least one of --summary or --reference needs to be set.')
parser.print_help()
return 1
# Load up manifests from the current build.
manifests = []
for origin in Origin.all():
for type in Type.all():
path = os.path.join(args.build_dir, 'obj', 'build', 'unification',
'images',
'%s-%s.unification.manifest' % (origin, type))
with open(path, 'r') as manifest_file:
contents = dict(map(lambda line: line.strip().split('=', 1),
manifest_file.readlines()))
manifests.append(Manifest(origin, type, contents))
# Generate a summary for the current build.
summary = generate_summary(manifests, args.build_dir)
# If applicable, save the current build's summary.
if args.summary:
dirname = os.path.dirname(args.summary)
if not os.path.exists(dirname):
os.makedirs(dirname)
with open(args.summary, 'w') as output_file:
summary.to_json(output_file)
# If applicable, compare the current summary to a previously-saved one.
if args.reference:
with open(args.reference, 'r') as input_file:
reference = Summary.from_json(input_file)
if not compare_summaries(reference, summary):
print('Error: summaries do not match!')
return 1
return 0
if __name__ == '__main__':
sys.exit(main())
| python |
#!/usr/bin/env python
import vtk
from vtk.test import Testing
from vtk.util.misc import vtkGetDataRoot
VTK_DATA_ROOT = vtkGetDataRoot()
# Create renderer stuff
#
ren1 = vtk.vtkRenderer()
renWin = vtk.vtkRenderWindow()
renWin.AddRenderer(ren1)
iren = vtk.vtkRenderWindowInteractor()
iren.SetRenderWindow(renWin)
# create pipeline
#
cow = vtk.vtkBYUReader()
cow.SetGeometryFileName("" + str(VTK_DATA_ROOT) + "/Data/Viewpoint/cow.g")
cowMapper = vtk.vtkPolyDataMapper()
cowMapper.SetInputConnection(cow.GetOutputPort())
cowActor = vtk.vtkActor()
cowActor.SetMapper(cowMapper)
cowActor.GetProperty().SetDiffuseColor(0.9608,0.8706,0.7020)
cowAxesSource = vtk.vtkAxes()
cowAxesSource.SetScaleFactor(10)
cowAxesSource.SetOrigin(0,0,0)
cowAxesMapper = vtk.vtkPolyDataMapper()
cowAxesMapper.SetInputConnection(cowAxesSource.GetOutputPort())
cowAxes = vtk.vtkActor()
cowAxes.SetMapper(cowAxesMapper)
ren1.AddActor(cowAxes)
cowAxes.VisibilityOff()
# Add the actors to the renderer, set the background and size
#
ren1.AddActor(cowActor)
ren1.SetBackground(0.1,0.2,0.4)
renWin.SetSize(320,240)
ren1.ResetCamera()
ren1.GetActiveCamera().Azimuth(0)
ren1.GetActiveCamera().Dolly(1.4)
ren1.ResetCameraClippingRange()
cowAxes.VisibilityOn()
renWin.Render()
# render the image
#
# prevent the tk window from showing up then start the event loop
#
def RotateX (__vtk__temp0=0,__vtk__temp1=0):
cowActor.SetOrientation(0,0,0)
ren1.ResetCameraClippingRange()
renWin.Render()
renWin.Render()
renWin.EraseOff()
i = 1
while i <= 6:
cowActor.RotateX(60)
renWin.Render()
renWin.Render()
i = i + 1
renWin.EraseOn()
def RotateY (__vtk__temp0=0,__vtk__temp1=0):
cowActor.SetOrientation(0,0,0)
ren1.ResetCameraClippingRange()
renWin.Render()
renWin.Render()
renWin.EraseOff()
i = 1
while i <= 6:
cowActor.RotateY(60)
renWin.Render()
renWin.Render()
i = i + 1
renWin.EraseOn()
def RotateZ (__vtk__temp0=0,__vtk__temp1=0):
cowActor.SetOrientation(0,0,0)
ren1.ResetCameraClippingRange()
renWin.Render()
renWin.Render()
renWin.EraseOff()
i = 1
while i <= 6:
cowActor.RotateZ(60)
renWin.Render()
renWin.Render()
i = i + 1
renWin.EraseOn()
def RotateXY (__vtk__temp0=0,__vtk__temp1=0):
cowActor.SetOrientation(0,0,0)
cowActor.RotateX(60)
ren1.ResetCameraClippingRange()
renWin.Render()
renWin.Render()
renWin.EraseOff()
i = 1
while i <= 6:
cowActor.RotateY(60)
renWin.Render()
renWin.Render()
i = i + 1
renWin.EraseOn()
RotateX()
RotateY()
RotateZ()
RotateXY()
renWin.EraseOff()
# --- end of script --
| python |
from collections import defaultdict
with open('day10/input.txt', 'r') as file:
data = sorted([int(x.strip()) for x in file.readlines()])
data = [0] + data
data.append(data[-1] + 3)
jolt_1, jolt_3 = 0, 0
for i in range(len(data)):
current = data[i - 1]
if (data[i] - current) == 1:
jolt_1 += 1
elif (data[i] - current) == 3:
jolt_3 += 1
jumps = [1, 2, 3]
routes = defaultdict(int) # default value is 0
routes[0] = 1
for i in data[1:]:
routes[i] = sum([routes[i - j] for j in jumps])
print(f"Result 1: {jolt_1 * jolt_3}\nResult 2: {routes[data[-1]]}") | python |
from django.contrib.auth.hashers import make_password
from rest_framework import serializers
from .models import User
from rest_framework_simplejwt.serializers import TokenObtainPairSerializer
from rest_framework import response, status
class RegisterSerializer(serializers.ModelSerializer):
class Meta:
model = User
fields = ('username', 'email', 'password')
def validate_password(self, value: str) -> str:
return make_password(value)
class MyTokenObtainPairSerializer(TokenObtainPairSerializer):
@classmethod
def get_token(cls, user):
token = super(MyTokenObtainPairSerializer, cls).get_token(user)
return token
class UsersSerializers(serializers.ModelSerializer):
class Meta:
model = User
fields = ('id', 'username', 'email', 'phone_number')
class UserUpdateSerializer(serializers.ModelSerializer):
class Meta:
model = User
fields = ('username', 'email', 'phone_number')
| python |
import tensorflow as tf
# GPU版Tensor Flowを、特定のGPUで実行する
GPU_INDEX = 2
tf.config.set_soft_device_placement(True)
tf.debugging.set_log_device_placement(True)
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
print(gpus)
print(logical_gpus)
except RuntimeError as e:
print(e)
try:
with tf.device('/device:GPU:{}'.format(GPU_INDEX)): # GPUの番号を指定する
# MNIST
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
except RuntimeError as e:
print(e)
| python |
import pytest
from package_one.module_one import IntegerAdder
@pytest.fixture
def adder():
print("Test set-up!")
yield IntegerAdder()
print("Test tear-down")
def test_integer_adder(adder):
assert adder.add(1, 2) == 3
"""
In case you'd like to declare a fixture that executes only once per module, then declare a fixture like this:
@pytest.fixture(scope="module")
"""
@pytest.mark.parametrize(
"operand_one, operand_two, expected_result",
[
(1, 2, 3),
(10, 20, 30),
(-5, -10, -15)
]
)
def test_integer_adder_complex(
adder, operand_one, operand_two, expected_result
):
assert adder.add(operand_one, operand_two) == expected_result
| python |
def snail(array):
results = []
while len(array) > 0:
results += array[0]
del array[0]
if len(array) > 0:
for i in array:
results += [i[-1]]
del i[-1]
if array[-1]:
results += array[-1][::-1]
del array[-1]
for i in reversed(array):
results += [i[0]]
del i[0]
return results
| python |
import os
from google.appengine.ext.webapp import template
from base_controller import CacheableHandler
from models.event import Event
class EventWizardHandler(CacheableHandler):
CACHE_VERSION = 1
CACHE_KEY_FORMAT = "event_wizard"
def __init__(self, *args, **kw):
super(EventWizardHandler, self).__init__(*args, **kw)
self.cache_expiration = 60 * 60
def _render(self, *args, **kw):
path = os.path.join(os.path.dirname(__file__), "../templates/eventwizard.html")
selected_event_key = self.request.get('event', '')
if selected_event_key and Event.validate_key_name(selected_event_key):
selected_event = Event.get_by_id(selected_event_key)
if selected_event:
self.template_values['selected_event'] = selected_event
return template.render(path, self.template_values)
class ReactEventWizardHandler(CacheableHandler):
CACHE_VERSION = 1
CACHE_KEY_FORMAT = "event_wizard_react"
def __init__(self, *args, **kw):
super(ReactEventWizardHandler, self).__init__(*args, **kw)
self.cache_expiration = 60 * 60
def _render(self, *args, **kw):
path = os.path.join(os.path.dirname(__file__), "../templates/react-eventwizard.html")
return template.render(path, self.template_values)
| python |
"""
MIT License
Copyright (c) 2021 martinpflaum
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 torchvision
import math
import torch
import torch.nn as nn
import numpy as np
import lightly
import pandas as pd
from data.data_utils import get_train_val_test_split,open_image,\
post_load_default,post_load_scene_depth,post_load_normal,\
post_load_lightning,load_edge_tensor,load_pickle,save_pickle
from perceptual_loss import PerceptualLoss
from torch.utils.data import Dataset
class GaussianNoise:
"""
this is from lightly https://docs.lightly.ai
Applies random Gaussian noise to a tensor.
The intensity of the noise is dependent on the mean of the pixel values.
See https://arxiv.org/pdf/2101.04909.pdf for more information.
"""
def __call__(self, sample: torch.Tensor) -> torch.Tensor:
mu = sample.mean()
snr = np.random.randint(low=4, high=8)
sigma = mu / snr
noise = torch.normal(torch.zeros(sample.shape), sigma)
return sample + noise
def depth_calc_std_mean(img_data_set_root):
train,val,test = get_train_val_test_split("./data_splits/train_test_split.csv")
out = []
for name in train:
scene_depth = post_load_scene_depth(open_image(name,"scene_depth",img_data_set_root)).reshape(-1)
out += [scene_depth]
out = torch.cat(out).reshape(-1)
return torch.std_mean(out, unbiased=False)
def get_all(file_name):
df = pd.read_csv(file_name)
df = df[["train_val_test"]]
split = np.array(df)
return split
class BrainDatasetSceneDepth(Dataset):
def __init__(self,img_data_set_root,indicies) :
super().__init__()
self.img_data_set_root = img_data_set_root
self.indicies = indicies
self.size = len(indicies)
def __len__(self):
#print("get_len")
return self.size
def __getitem__(self, index):
name = self.indicies[index]
return post_load_scene_depth(open_image(name,"scene_depth",self.img_data_set_root))
img_data_set_root="D:/ImageDatasetBig"
indicies = get_all("./data_splits/train_test_split.csv")
indicies
#%%
dset = BrainDatasetSceneDepth(img_data_set_root,indicies)
#%%
num_workers = 0
batch_size = 128
seed = 1
epochs = 50
input_size = 64
# dimension of the embeddings
num_ftrs = 512
# dimension of the output of the prediction and projection heads
out_dim = proj_hidden_dim = 512
# the prediction head uses a bottleneck architecture
pred_hidden_dim = 128
# use 2 layers in the projection head
num_mlp_layers = 2
mean,std = torch.tensor(0),torch.tensor(1)
mean,std = depth_calc_std_mean(img_data_set_root)
mean,std = mean.item(),std.item()
mean,std = (mean,mean,mean),(std,std,std)
transform = torchvision.transforms.Compose([
torchvision.transforms.Grayscale(num_output_channels=3),
torchvision.transforms.RandomResizedCrop(size=(64,64), scale=(0.2, 1.0)),
torchvision.transforms.RandomHorizontalFlip(p=0.5),
torchvision.transforms.RandomVerticalFlip(p=0.5),
torchvision.transforms.GaussianBlur(21),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean,std),
GaussianNoise(),
])
collate_fn = lightly.data.BaseCollateFunction(transform)
torch.manual_seed(0)
np.random.seed(0)
# set the path to the dataset
path_to_data = 'C:/Users/Martin/Downloads/test'
dataset_train_simsiam = lightly.data.LightlyDataset(
input_dir=path_to_data
)
dataloader_train_simsiam = torch.utils.data.DataLoader(
dataset_train_simsiam,
batch_size=batch_size,
shuffle=True,
collate_fn=collate_fn,
drop_last=True,
num_workers=num_workers
)
resnet = torchvision.models.resnet18()
backbone = nn.Sequential(*list(resnet.children())[:-1])
# create the SimSiam model using the backbone from above
model = lightly.models.SimSiam(
backbone,
num_ftrs=num_ftrs,
proj_hidden_dim=pred_hidden_dim,
pred_hidden_dim=pred_hidden_dim,
out_dim=out_dim,
num_mlp_layers=num_mlp_layers
)
# SimSiam uses a symmetric negative cosine similarity loss
criterion = lightly.loss.SymNegCosineSimilarityLoss()
# scale the learning rate
lr = 0.05 * batch_size / 256
# use SGD with momentum and weight decay
optimizer = torch.optim.SGD(
model.parameters(),
lr=lr,
momentum=0.9,
weight_decay=5e-4
)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(device)
avg_loss = 0.
avg_output_std = 0.
for e in range(epochs):
for (x0, x1), _, _ in dataloader_train_simsiam:
# move images to the gpu
x0 = x0.to(device)
x1 = x1.to(device)
# run the model on both transforms of the images
# the output of the simsiam model is a y containing the predictions
# and projections for each input x
y0, y1 = model(x0, x1)
# backpropagation
loss = criterion(y0, y1)
loss.backward()
optimizer.step()
optimizer.zero_grad()
# calculate the per-dimension standard deviation of the outputs
# we can use this later to check whether the embeddings are collapsing
output, _ = y0
output = output.detach()
output = torch.nn.functional.normalize(output, dim=1)
output_std = torch.std(output, 0)
output_std = output_std.mean()
# use moving averages to track the loss and standard deviation
w = 0.9
avg_loss = w * avg_loss + (1 - w) * loss.item()
avg_output_std = w * avg_output_std + (1 - w) * output_std.item()
# the level of collapse is large if the standard deviation of the l2
# normalized output is much smaller than 1 / sqrt(dim)
collapse_level = max(0., 1 - math.sqrt(out_dim) * avg_output_std)
# print intermediate results
print(f'[Epoch {e:3d}] '
f'Loss = {avg_loss:.2f} | '
f'Collapse Level: {collapse_level:.2f} / 1.00')
model = PerceptualLoss(model.backbone.cpu(),mean,std).cpu()
save_pickle(model,"perceptual_loss.pth")
# %%
| python |
__author__ = "Jeremy Nelson"
import csv
import datetime
import json
import urllib2
from json_ld.utilities.creator import JSONLinkedDataCreator
class JohnPeabodyHarringtonJSONLinkedDataCreator(JSONLinkedDataCreator):
CC_URI = 'http://id.loc.gov/authorities/names/n84168445'
LOC_URI = 'http://id.loc.gov/authorities/names/no2008011986'
def __init__(self,
creator_id=None,
csv_filename=None):
"""Initializes instance of John Peabody Harrington JSON-LD creator
Parameters:
creator_id -- LOC ID of creator, defaults to Colorado College
csv_filename -- Filename of CSV file, defaults to None
"""
if creator_id is None:
creator_id = self.CC_URI
super(JohnPeabodyHarringtonJSONLinkedDataCreator, self).__init__(
**{'creator_id': creator_id})
self.title_prefix = 'John P. Harrington Papers 1907-1959 (some earlier)'
jph_csv_reader = csv.DictReader(open(csv_filename, 'rb'))
for row in jph_csv_reader:
self.records.append(row)
def __generate_topics__(self,
lcsh_subjects,
work_dict):
"""Internal function generates a list of topics from
a list of LCSH uri
Parameters:
lcsh_subjects -- list of http://id.loc.gov subject uri
work_dict -- Dictionary of properties for the Creative Work
"""
if len(lcsh_subjects) > 0:
work_dict['bf:subject'] = []
for subject_uri in lcsh_subjects:
uri = subject_uri.replace('"','').strip()
if not self.topics.has_key(uri):
loc_uri = json.load(
urllib2.urlopen('{0}.json'.format(uri)))
loc_key = u"<{0}>".format(uri)
self.topics[uri] = {
'@type': 'bf:Topic',
'prov:Generation': self.__generate_provenance__(),
'bf:label': loc_uri[loc_key].get(
u'<http://www.w3.org/2004/02/skos/core#prefLabel>',
[{'value':uri},])[0].get('value'),
'bf:identifier': uri,
'bf:hasAuthority': self.LOC_URI}
lcc_classification = loc_uri[loc_key].get(
u'<http://www.loc.gov/mads/rdf/v1#classification>',
None)
if lcc_classification is not None:
class_value = lcc_classification[0].get('value')
if not work_dict.has_key('bf:class-lcc'):
work_dict['bf:class-lcc'] = [class_value, ]
else:
work_dict['bf:class-lcc'].append(class_value)
work_dict['bf:subject'].append(self.topics[uri])
return work_dict
def generate(self):
"Linked Data Cataloging for John Peabody Harrington Collection"
for row in self.records:
work_dict = self.__generate_work__(
creative_work_class='bf:Manuscript')
instance_dict = self.__generate_instance__('online resource')
instance_dict['bf:publication'] = {
'providerName': 'National Anthropological Archives',
'identifier': 'http://id.loc.gov/authorities/names/n50065490'}
if len(row.get('Part')) > 0:
title_prefix = '{0} {1}'.format(self.title_prefix,
row.get('Part'))
else:
title_prefix = self.title_prefix
title_str = '{0} Microfilm {1}, Reel {2}'.format(
title_prefix,
row.get('Microfilm #'),
row.get('Reel #'))
title_parts = row.get('Title').replace('"','').split(",")
if len(title_parts) > 1:
sub_titles = []
for sub in title_parts:
sub = sub.strip()
sub_titles.append(sub)
title_str = '{0} "{1}'.format(title_str,
'", "'.join(sub_titles))
title_str += '"'
elif len(title_parts) == 1:
title_str = "{0} {1}".format(title_str,
title_parts[0])
work_dict['bf:title'] = {'@type': 'bf:TitleEntity',
'bf:titleValue': title_str,
'bf:label': title_str}
instance_dict['schema:contentUrl'] = '/pdf/{0}'.format(
row.get('Filename'))
work_dict['bf:hasInstance'] = [instance_dict,]
work_dict['rda:dateOfPublicationManifestation'] = row.get('Publication Date')
subjects = row.get('LCSH').split(",")
work_dict = self.__generate_topics__(subjects, work_dict)
self.works.append(work_dict)
| python |
from .algo.algo_endpoints import AlgoEndpoints
from .graph.graph_endpoints import GraphEndpoints
from .query_runner.query_runner import QueryRunner
class IndirectEndpoints(AlgoEndpoints, GraphEndpoints):
def __init__(self, query_runner: QueryRunner, namespace: str):
super().__init__(query_runner, namespace)
| python |
#-*- coding:utf-8 -*-
import generate_chat
import seq2seq_model
import tensorflow as tf
import numpy as np
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
if __name__ == '__main__':
_, _, source_vocab_size = generate_chat.get_vocabs(generate_chat.vocab_encode_file)
_, _, target_vocab_size = generate_chat.get_vocabs(generate_chat.vocab_decode_file)
train_set = generate_chat.read_data(generate_chat.train_encode_vec_file, generate_chat.train_decode_vec_file)
test_set = generate_chat.read_data(generate_chat.test_encode_vec_file, generate_chat.test_decode_vec_file)
train_bucket_sizes = [len(train_set[i]) for i in range(len(generate_chat._buckets))]
train_total_size = float(sum(train_bucket_sizes))
train_buckets_scale = [sum(train_bucket_sizes[:i + 1]) / train_total_size for i in range(len(train_bucket_sizes))]
with tf.Session() as sess:
model = seq2seq_model.Seq2SeqModel(source_vocab_size,
target_vocab_size,
generate_chat._buckets,
generate_chat.units_num,
generate_chat.num_layers,
generate_chat.max_gradient_norm,
generate_chat.batch_size,
generate_chat.learning_rate,
generate_chat.learning_rate_decay_factor,
use_lstm=True)
ckpt = tf.train.get_checkpoint_state('.')
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(sess, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
sess.run(tf.global_variables_initializer())
loss = 0.0
step = 0
previous_losses = []
run = True
while run:
random_number_01 = np.random.random_sample()
bucket_id = min([i for i in range(len(train_buckets_scale)) if train_buckets_scale[i] > random_number_01])
encoder_inputs, decoder_inputs, target_weights = model.get_batch(train_set, bucket_id)
_, step_loss, _ = model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id, False)
print("step:%d,loss:%f" % (step, step_loss))
loss += step_loss / 2000
step += 1
if step % 1000 == 0:
print("step:%d,per_loss:%f" % (step, loss))
if len(previous_losses) > 2 and loss > max(previous_losses[-3:]):
sess.run(model.learning_rate_decay_op)
previous_losses.append(loss)
model.saver.save(sess, "./../../datas/model/share/rebot/chatbot.ckpt", global_step=model.global_step)
loss = 0.0
if step % 5000 == 0:
for bucket_id in range(len(generate_chat._buckets)):
if len(test_set[bucket_id]) == 0:
continue
encoder_inputs, decoder_inputs, target_weights = model.get_batch(test_set, bucket_id)
_, eval_loss, _ = model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id,
True)
print("bucket_id:%d,eval_loss:%f" % (bucket_id, eval_loss))
| python |
import tkinter as tk
from src.ui.core import SortableTable
from src.library.model import PlaylistModel
class Table(SortableTable):
def __init__(self, parent, logger, library):
SortableTable.__init__(self, parent, logger)
self.library = library
self.add_column('Playlist Name', sortable=True)
self.init_treeview()
self.on_playback_event = None
self.context_view_switcher = None
def get_unsorted_item_list(self):
return self.library.session.query(PlaylistModel).all()
def create_column_values_for(self, item):
return (item.name,)
def compare_items(self, a, b):
from src.utility import compare_strings
multiplier = -1 if self.sort_in_reverse else 1
return compare_strings(a.name, b.name) * multiplier
def set_on_playback_event(self, on_playback_event):
self.on_playback_event = on_playback_event
def dispatch_playback_event(self, event):
if self.on_playback_event is not None:
self.on_playback_event(event)
def display_context_menu(self, event):
context_menu = tk.Menu(master=self.frame, tearoff=0)
context_menu.add_command(label='Set as Queue', command=self.play_playlist)
context_menu.add_command(label='Set as Queue (Shuffled)', command=self.play_playlist_shuffled)
context_menu.add_command(label='More Info', command=lambda: self.view_playlist_info(event.x_root, event.y_root))
context_menu.add_command(label='Delete', command=self.delete_playlist)
context_menu.post(event.x_root, event.y_root)
def play_playlist(self):
from src.backend.event import PlayPlaylist
self.dispatch_playback_event(PlayPlaylist(self.get_selected_item(), shuffled=False))
def play_playlist_shuffled(self):
from src.backend.event import PlayPlaylist
self.dispatch_playback_event(PlayPlaylist(self.get_selected_item(), shuffled=True))
def view_playlist_info(self, x, y):
from ...info.playlist_info import PlaylistInfo
if self.context_view_switcher is not None:
info = PlaylistInfo(self.context_view_switcher, self.logger, self.library)
info.set_item(self.get_selected_item())
self.context_view_switcher.open_page(info)
def delete_playlist(self):
item = self.get_selected_item()
self.library.session.delete(item)
self.library.session.commit()
self.refresh() | python |
from slicegan import preprocessing, util
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import time
import matplotlib
import wandb
# 1. Start a new run
wandb.init(project='SuperRes', name='SliceGAN train', entity='tldr-group')
def train(pth, imtype, datatype, real_data, Disc, Gen, nc, l, nz, sf):
"""
train the generator
:param pth: path to save all files, imgs and data
:param imtype: image type e.g nphase, colour or gray
:param datatype: training data format e.g. tif, jpg ect
:param real_data: path to training data
:param Disc:
:param Gen:
:param nc: channels
:param l: image size
:param nz: latent vector size
:param sf: scale factor for training data
:return:
"""
if len(real_data) == 1:
real_data *= 3
isotropic = True
else:
isotropic = False
print('Loading Dataset...')
dataset_xyz = preprocessing.batch(real_data, datatype, l, sf)
## Constants for NNs
matplotlib.use('Agg')
ngpu = 1
num_epochs = 30
# batch sizes
batch_size = 32
D_batch_size = 8
# optimiser params for G and D
lrg = 0.0001
lrd = 0.0001
beta1 = 0
beta2 = 0.9
Lambda = 10
critic_iters = 5
cudnn.benchmark = True
workers = 0
lz = 4
##Dataloaders for each orientation
device = torch.device("cuda:0" if(torch.cuda.is_available() and ngpu > 0) else "cpu")
print(device, " will be used.\n")
# D trained using different data for x, y and z directions
dataloaderx = torch.utils.data.DataLoader(dataset_xyz[0], batch_size=batch_size,
shuffle=True, num_workers=workers)
dataloadery = torch.utils.data.DataLoader(dataset_xyz[1], batch_size=batch_size,
shuffle=True, num_workers=workers)
dataloaderz = torch.utils.data.DataLoader(dataset_xyz[2], batch_size=batch_size,
shuffle=True, num_workers=workers)
# Create the Genetator network
netG = Gen().to(device)
if ('cuda' in str(device)) and (ngpu > 1):
netG = nn.DataParallel(netG, list(range(ngpu)))
optG = optim.Adam(netG.parameters(), lr=lrg, betas=(beta1, beta2))
# Define 1 Discriminator and optimizer for each plane in each dimension
netDs = []
optDs = []
for i in range(3):
netD = Disc()
netD = (nn.DataParallel(netD, list(range(ngpu)))).to(device)
netDs.append(netD)
optDs.append(optim.Adam(netDs[i].parameters(), lr=lrd, betas=(beta1, beta2)))
disc_real_log = []
disc_fake_log = []
gp_log = []
Wass_log = []
print("Starting Training Loop...")
# For each epoch
start = time.time()
for epoch in range(num_epochs):
# sample data for each direction
for i, (datax, datay, dataz) in enumerate(zip(dataloaderx, dataloadery, dataloaderz), 1):
dataset = [datax, datay, dataz]
### Initialise
### Discriminator
## Generate fake image batch with G
noise = torch.randn(D_batch_size, nz, lz,lz,lz, device=device)
fake_data = netG(noise).detach()
# for each dim (d1, d2 and d3 are used as permutations to make 3D volume into a batch of 2D images)
for dim, (netD, optimizer, data, d1, d2, d3) in enumerate(
zip(netDs, optDs, dataset, [2, 3, 4], [3, 2, 2], [4, 4, 3])):
if isotropic:
netD = netDs[0]
optimizer = optDs[0]
netD.zero_grad()
##train on real images
real_data = data[0].to(device)
out_real = netD(real_data).view(-1).mean()
## train on fake images
# perform permutation + reshape to turn volume into batch of 2D images to pass to D
fake_data_perm = fake_data.permute(0, d1, 1, d2, d3).reshape(l * D_batch_size, nc, l, l)
out_fake = netD(fake_data_perm).mean()
gradient_penalty = util.calc_gradient_penalty(netD, real_data, fake_data_perm[:batch_size],
batch_size, l,
device, Lambda, nc)
disc_cost = out_fake - out_real + gradient_penalty
disc_cost.backward()
optimizer.step()
#logs for plotting
wandb.log({'out real': out_real.item()})
wandb.log({'out fake': out_fake.item()})
wandb.log({'wass': out_real.item() - out_fake.item()})
### Generator Training
if i % int(critic_iters) == 0:
netG.zero_grad()
errG = 0
noise = torch.randn(batch_size, nz, lz,lz,lz, device=device)
fake = netG(noise)
for dim, (netD, d1, d2, d3) in enumerate(
zip(netDs, [2, 3, 4], [3, 2, 2], [4, 4, 3])):
if isotropic:
#only need one D
netD = netDs[0]
# permute and reshape to feed to disc
fake_data_perm = fake.permute(0, d1, 1, d2, d3).reshape(l * batch_size, nc, l, l)
output = netD(fake_data_perm)
errG -= output.mean()
# Calculate gradients for G
errG.backward()
optG.step()
# Output training stats & show imgs
if i % 25 == 0:
netG.eval()
with torch.no_grad():
torch.save(netG.state_dict(), pth + '_Gen.pt')
wandb.save(pth + '_Gen.pt')
torch.save(netD.state_dict(), pth + '_Disc.pt')
noise = torch.randn(1, nz,lz,lz,lz, device=device)
img = netG(noise)
###Print progress
## calc ETA
steps = len(dataloaderx)
util.calc_eta(steps, time.time(), start, i, epoch, num_epochs)
###save example slices
util.test_plotter(img, 5, imtype, pth)
# plotting graphs
# util.graph_plot([disc_real_log, disc_fake_log], ['real', 'perp'], pth, 'LossGraph')
# util.graph_plot([Wass_log], ['Wass Distance'], pth, 'WassGraph')
# util.graph_plot([gp_log], ['Gradient Penalty'], pth, 'GpGraph')
netG.train()
| python |
from typing import List
from ..error import GraphQLError
from ..language import DocumentNode
from ..type import GraphQLSchema
__all__ = ["find_deprecated_usages"]
def find_deprecated_usages(
schema: GraphQLSchema, ast: DocumentNode
) -> List[GraphQLError]: # pragma: no cover
"""Get a list of GraphQLError instances describing each deprecated use.
.. deprecated:: 3.1.3
Please use ``validate`` with ``NoDeprecatedCustomRule`` instead::
from graphql import validate, NoDeprecatedCustomRule
errors = validate(schema, document, [NoDeprecatedCustomRule])
"""
from ..validation import validate, NoDeprecatedCustomRule
return validate(schema, ast, [NoDeprecatedCustomRule])
| python |
from .target_generators import HeatmapGenerator
from .target_generators import ScaleAwareHeatmapGenerator
from .target_generators import JointsGenerator
__all__ = ['HeatmapGenerator', 'ScaleAwareHeatmapGenerator', 'JointsGenerator']
| python |
import re
from typing import Annotated, Any, Optional
import pytest
from arti import (
Annotation,
Artifact,
Fingerprint,
PartitionDependencies,
Producer,
StoragePartitions,
)
from arti import producer as producer_decorator # Avoid shadowing
from arti.internal.models import Model
from arti.internal.utils import frozendict
from arti.producers import ValidateSig
from arti.types import Collection, Int64, Struct
from arti.versions import String as StringVersion
from arti.views import python as python_views
from tests.arti.dummies import A1, A2, A3, A4, P1, P2, DummyStorage
Int64Artifact = Artifact.from_type(Int64())
class DummyProducer(Producer):
a1: A1
@staticmethod
def build(a1: dict) -> tuple[Annotated[dict, A2], Annotated[dict, A3]]: # type: ignore
pass
@staticmethod
def map(a1: StoragePartitions) -> PartitionDependencies:
pass
def check_model_matches(a: Model, b: Model, *, exclude: set[str]) -> None:
assert a.dict(exclude=exclude) == b.dict(exclude=exclude)
def test_Producer() -> None:
a1 = A1()
producer = DummyProducer(a1=a1)
assert producer.a1 == a1
assert len(list(producer)) == 2
expected_output_classes = [A2, A3]
for i, output in enumerate(producer):
assert isinstance(output, expected_output_classes[i])
def test_producer_decorator() -> None:
@producer_decorator()
def dummy_producer(a1: Annotated[dict, A1]) -> Annotated[dict, A2]: # type: ignore
return {}
assert dummy_producer.__name__ == "dummy_producer"
assert dummy_producer._input_artifact_types_ == frozendict(a1=A1)
assert len(dummy_producer._output_metadata_) == 1
assert dummy_producer._output_metadata_[0][0] == A2
assert dummy_producer(a1=A1()).annotations == Producer.__fields__["annotations"].default
assert dummy_producer(a1=A1()).version == Producer.__fields__["version"].default
class MyAnnotation(Annotation):
pass
def mapper() -> PartitionDependencies:
return PartitionDependencies()
@producer_decorator(
annotations=(MyAnnotation(),), map=mapper, name="test", version=StringVersion(value="test")
)
def dummy_producer2(a1: Annotated[dict, A1]) -> Annotated[dict, A2]: # type: ignore
return {}
assert dummy_producer2.__name__ == "test"
assert dummy_producer2.map == mapper
assert dummy_producer2(a1=A1()).annotations == (MyAnnotation(),)
assert dummy_producer2(a1=A1()).version == StringVersion(value="test")
def test_producer_input_metadata() -> None:
@producer_decorator()
def dummy_producer(
a1: Annotated[dict, A1], *, a: int, b: Annotated[int, "non-Artifact"] # type: ignore
) -> Annotated[dict, A2]: # type: ignore
return {}
assert dummy_producer._input_artifact_types_ == frozendict(
a1=A1, a=Int64Artifact, b=Int64Artifact
)
def test_Producer_partitioned_input_validation() -> None:
class A(Artifact):
type = Collection(element=Struct(fields={"x": Int64()}), partition_by=("x",))
class P(Producer):
a: A
@staticmethod
def build(a: list[dict]) -> Annotated[dict, A2]: # type: ignore
pass
assert P._input_artifact_types_ == frozendict(a=A)
assert P._build_input_views_ == frozendict(a=python_views.List)
with pytest.raises(ValueError, match="dict.* cannot be used to represent Collection"):
class SingularInput(Producer):
a: A
@staticmethod
def build(a: dict) -> Annotated[dict, A2]: # type: ignore
pass
with pytest.raises(
ValueError, match=re.escape("list[int] cannot be used to represent Collection")
):
class IncompatibleInput(Producer):
a: A
@staticmethod
def build(a: list[int]) -> Annotated[dict, A]: # type: ignore
pass
def test_Producer_output_metadata() -> None:
assert DummyProducer._output_metadata_ == ((A2, python_views.Dict), (A3, python_views.Dict))
class ImplicitArtifact(Producer):
a1: A1
@classmethod
def build(cls, a1: dict) -> tuple[int, Annotated[dict, A2]]: # type: ignore
pass
assert ImplicitArtifact._output_metadata_ == (
(Artifact.from_type(Int64()), python_views.Int),
(A2, python_views.Dict),
)
class ExplicitView(Producer):
a1: A1
@staticmethod
def build(a1: dict) -> Annotated[dict, A2, python_views.Dict]: # type: ignore
pass
assert ExplicitView._output_metadata_ == ((A2, python_views.Dict),)
with pytest.raises(
ValueError, match=re.escape("DupView.build 1st return (A2) - multiple Views set")
):
class DupView(Producer):
a1: A1
@staticmethod
def build(a1: dict) -> Annotated[dict, A2, python_views.Dict, python_views.Int]: # type: ignore
pass
with pytest.raises(ValueError, match="DupArtifact.build 1st return - multiple Artifacts set"):
class DupArtifact(Producer):
a1: A1
@staticmethod
def build(a1: dict) -> Annotated[dict, A1, A2]: # type: ignore
pass
def test_Producer_string_annotation() -> None:
# This may be from `x: "Type"` or `from __future__ import annotations`.
class StrAnnotation(Producer):
a1: "A1"
@staticmethod
def build(a1: "dict") -> "Annotated[dict, A2]": # type: ignore
pass
assert isinstance(StrAnnotation(a1=A1()).out(), A2)
def test_Producer_fingerprint() -> None:
p1 = P1(a1=A1())
assert p1.fingerprint == Fingerprint.from_string(
f'P1:{{"a1": {p1.a1.fingerprint.key}, "version": {p1.version.fingerprint.key}}}'
)
def test_Producer_compute_input_fingerprint() -> None:
p1 = P1(a1=A1(storage=DummyStorage(key="test")))
assert p1.compute_input_fingerprint(
frozendict(a1=StoragePartitions())
) == Fingerprint.from_string(p1._class_key_).combine(p1.version.fingerprint)
storage_partition = p1.a1.storage.generate_partition().copy(
update={"content_fingerprint": Fingerprint.from_int(10)}
)
assert p1.compute_input_fingerprint(
frozendict(a1=StoragePartitions([storage_partition]))
) == Fingerprint.from_string(p1._class_key_).combine(
p1.version.fingerprint, storage_partition.content_fingerprint
)
with pytest.raises(
ValueError, match=re.escape("Mismatched dependency inputs; expected {'a1'}, got {'junk'}")
):
p1.compute_input_fingerprint(frozendict(junk=StoragePartitions()))
def test_Producer_out() -> None:
a1, a2, a3, a4 = A1(), A2(), A3(), A4()
# single return Producer
p1 = P1(a1=a1)
a2_ = p1.out(a2)
# multi return Producer
p2 = P2(a2=a2)
a3_, a4_ = p2.out(a3, a4)
for (producer, inp, out, type_, position) in (
(p1, a2, a2_, A2, 0),
(p2, a3, a3_, A3, 0),
(p2, a4, a4_, A4, 1),
):
assert inp is not out
assert isinstance(out, type_)
assert out.producer_output is not None
assert out.producer_output.producer == producer
assert out.producer_output.position == position
check_model_matches(inp, out, exclude={"producer_output"})
assert list(p1) == [a2_]
assert list(p2) == [a3_, a4_]
def test_Producer_map_artifacts() -> None:
class P(Producer):
a1: A1
@staticmethod
def build(a1: dict) -> Annotated[dict, A2]: # type: ignore
pass
@staticmethod
def map(a1: StoragePartitions) -> PartitionDependencies:
pass
assert P._map_input_metadata_ == frozendict(a1=A1)
with pytest.raises(
ValueError,
match="BadMapParam.map a1 param - type hint must be `StoragePartitions`",
):
class BadMapParam(P):
@staticmethod
def map(a1: list) -> PartitionDependencies: # type: ignore
pass
def test_Producer_validate_output() -> None:
positive, negative = (True, "Positive"), (False, "Negative")
def is_positive(i: int) -> tuple[bool, str]:
return positive if i >= 0 else negative
@producer_decorator(validate_outputs=is_positive)
def p(x: int) -> int:
return x
assert p.validate_outputs(p.build(1)) == positive
assert p.validate_outputs(p.build(-1)) == negative
def test_Producer_validate_output_hint_validation() -> None:
def validate_any(i: Any) -> tuple[bool, str]:
return bool(i), ""
def validate_vargs_any(*vals: Any) -> tuple[bool, str]:
return bool(vals), ""
def validate_int(i: int) -> tuple[bool, str]:
return bool(i), ""
for validate_outputs in list[ValidateSig](
[
lambda x: (True, ""),
validate_any,
validate_vargs_any,
validate_int,
]
):
@producer_decorator(validate_outputs=validate_outputs)
def single_return_build(x: int) -> int:
return x
assert single_return_build.validate_outputs(5)
with pytest.raises(ValueError, match="i param - type hint must be `Any` or "):
def accepts_vargs_float(*i: float) -> tuple[bool, str]:
return bool(i), ""
@producer_decorator(validate_outputs=accepts_vargs_float)
def bad_vargs(x: int) -> int:
return x
with pytest.raises(ValueError, match="validate_output - must match the `.build` return"):
@producer_decorator(validate_outputs=validate_int)
def too_few_arg(x: int) -> tuple[int, int]:
return x, x + 1
with pytest.raises(ValueError, match="validate_output i param - must not have a default."):
@producer_decorator(validate_outputs=lambda i=5: (True, ""))
def bad_default(x: int) -> int:
return x
with pytest.raises(
ValueError, match="validate_output i param - must be usable as a positional argument."
):
def validate_kwarg(*, i: int) -> tuple[bool, str]:
return bool(i), ""
@producer_decorator(validate_outputs=validate_kwarg)
def kwarg_only(x: int) -> int:
return x
with pytest.raises(
ValueError, match="validate_output i param - type hint must match the 1st `.build` return"
):
def accepts_float(i: float) -> tuple[bool, str]:
return bool(i), ""
@producer_decorator(validate_outputs=accepts_float)
def mismatched_hint(x: int) -> int:
return x
def test_Producer_build_outputs_check() -> None:
class A(Artifact):
type = Int64()
class B(Artifact):
type = Int64()
class C(Artifact):
type = Collection(element=Struct(fields={"a": Int64()}), partition_by=("a",))
class D(Artifact):
type = Collection(element=Struct(fields={"a": Int64(), "b": Int64()}), partition_by=("b",))
class NoPartitioning(Producer):
@staticmethod
def build() -> tuple[Annotated[int, A], Annotated[int, B]]:
pass
class MatchingPartitioning(Producer):
@staticmethod
def build() -> tuple[Annotated[list[dict], C], Annotated[list[dict], C]]: # type: ignore
pass
@staticmethod
def map() -> PartitionDependencies:
return PartitionDependencies()
for first_output in [Annotated[int, A], Annotated[list[dict], C]]: # type: ignore
with pytest.raises(
ValueError, match="all output Artifacts must have the same partitioning scheme"
):
class MixedPartitioning(Producer):
@staticmethod
def build() -> tuple[first_output, Annotated[list[dict], D]]: # type: ignore
pass
with pytest.raises(
ValueError,
match=r"BadProducer.map - must be implemented when the `build` outputs are partitioned",
):
class BadProducer(Producer): # noqa: F811
@staticmethod
def build() -> Annotated[list[dict], C]: # type: ignore
pass
def test_Producer_bad_signature() -> None: # noqa: C901
# pylint: disable=function-redefined
# Ensure no error if _abstract_
class OkProducer(Producer):
_abstract_ = True
with pytest.raises(ValueError, match="BadProducer.build - must be implemented"):
class BadProducer(Producer):
pass
with pytest.raises(
ValueError,
match=r"BadProducer.build - the following parameter\(s\) must be defined as a field: {'a1'}",
):
class BadProducer(Producer): # type: ignore # noqa: F811
@classmethod
def build(cls, a1: dict) -> Annotated[dict, A2]: # type: ignore
pass
with pytest.raises(
ValueError,
match=r"BadProducer.map - the following parameter\(s\) must be defined as a field: {'a1'}",
):
class BadProducer(Producer): # type: ignore # noqa: F811
@classmethod
def build(cls) -> Annotated[dict, A2]: # type: ignore
pass
@classmethod
def map(cls, a1: StoragePartitions) -> PartitionDependencies:
pass
with pytest.raises(
ValueError,
match=r"BadProducer - the following fields aren't used in `.build` or `.map`: {'a2'}",
):
class BadProducer(Producer): # type: ignore # noqa: F811
a1: A1
a2: A2
@classmethod
def build(cls, a1: dict) -> Annotated[dict, A3]: # type: ignore
pass
with pytest.raises(ValueError, match="must have a type hint"):
class BadProducer(Producer): # type: ignore # noqa: F811
a1: A1
@classmethod
def build(cls, a1): # type: ignore
pass
with pytest.raises(ValueError, match="type hint must be an Artifact subclass"):
class BadProducer(Producer): # type: ignore # noqa: F811
a1: str
@classmethod
def build(cls, a1: str) -> tuple[A2, A3]:
pass
with pytest.raises(ValueError, match="must not have a default"):
class BadProducer(Producer): # type: ignore # noqa: F811
a1: A1
@classmethod
def build(cls, a1: dict = A1()): # type: ignore
pass
with pytest.raises(ValueError, match="must be usable as a keyword argument"):
class BadProducer(Producer): # type: ignore # noqa: F811
a1: A1
@classmethod
def build(cls, a1: dict, /): # type: ignore
pass
with pytest.raises(ValueError, match="must be usable as a keyword argument"):
class BadProducer(Producer): # type: ignore # noqa: F811
a1: A1
@classmethod
def build(cls, *a1: dict): # type: ignore
pass
with pytest.raises(ValueError, match="must be usable as a keyword argument"):
class BadProducer(Producer): # type: ignore # noqa: F811
a1: A1
@classmethod
def build(cls, **a1: dict): # type: ignore
pass
with pytest.raises(ValueError, match="a return value must be set"):
class BadProducer(Producer): # type: ignore # noqa: F811
a1: A1
@classmethod
def build(cls, a1: dict): # type: ignore
pass
with pytest.raises(ValueError, match="missing return signature"):
class BadProducer(Producer): # type: ignore # noqa: F811
a1: A1
@classmethod
def build(cls, a1: dict) -> None: # type: ignore
pass
with pytest.raises(
ValueError, match="BadProducer.a1 - field must not have a default nor be Optional."
):
class BadProducer(Producer): # type: ignore # noqa: F811
a1: A1 = None # type: ignore
@classmethod
def build(cls, a1: dict): # type: ignore
pass
with pytest.raises(
ValueError, match="BadProducer.a1 - field must not have a default nor be Optional."
):
class BadProducer(Producer): # type: ignore # noqa: F811
a1: Optional[A1]
@classmethod
def build(cls, a1: dict): # type: ignore
pass
with pytest.raises(
ValueError,
match=r"BadProducer.a1 - field must not have a default nor be Optional.",
):
class BadProducer(Producer): # type: ignore # noqa: F811
a1: A1 = A1()
@classmethod
def build(cls, a1: dict) -> A2: # type: ignore
pass
with pytest.raises(ValueError, match=r"str.* cannot be used to represent Struct"):
class BadProducer(Producer): # type: ignore # noqa: F811
@classmethod
def build(cls) -> Annotated[str, A2]:
pass
with pytest.raises(
ValueError,
match=r"BadProducer.build - must be a @classmethod or @staticmethod",
):
class BadProducer(Producer): # type: ignore # noqa: F811
def build(cls) -> Annotated[dict, A2]: # type: ignore
pass
with pytest.raises(
ValueError,
match=r"BadProducer.map - must be a @classmethod or @staticmethod",
):
class BadProducer(Producer): # type: ignore # noqa: F811
@classmethod
def build(cls) -> Annotated[dict, A2]: # type: ignore
pass
def map(cls) -> PartitionDependencies:
pass
def test_Producer_bad_init() -> None:
with pytest.raises(ValueError, match="cannot be instantiated directly"):
Producer()
with pytest.raises(ValueError, match="extra fields not permitted"):
DummyProducer(junk=5)
with pytest.raises(ValueError, match="field required"):
DummyProducer()
with pytest.raises(ValueError, match="expected an instance of"):
DummyProducer(a1=5)
with pytest.raises(ValueError, match="expected an instance of"):
DummyProducer(a1=A2())
def test_Producer_bad_out() -> None:
producer = DummyProducer(a1=A1())
with pytest.raises(ValueError, match="expected 2 arguments of"):
producer.out(1) # type: ignore
with pytest.raises(
ValueError, match=r"DummyProducer.out\(\) 1st argument - expected instance of"
):
producer.out(1, 2) # type: ignore
with pytest.raises(
ValueError, match=r"DummyProducer.out\(\) 2nd argument - expected instance of"
):
producer.out(A2(), A2())
output = producer.out(A2(), A3())
with pytest.raises(ValueError, match="is produced by"):
producer.out(*output)
| python |
from pathlib import Path
from fhir.resources.valueset import ValueSet as _ValueSet
from oops_fhir.utils import ValueSet
from oops_fhir.r4.code_system.feeding_device_codes import (
FeedingDeviceCodes as FeedingDeviceCodes_,
)
from oops_fhir.r4.code_system.snomed_ct import SNOMEDCT
__all__ = ["FeedingDeviceCodes"]
_resource = _ValueSet.parse_file(Path(__file__).with_suffix(".json"))
class FeedingDeviceCodes(ValueSet):
"""
Feeding Device Codes
Materials used or needed to feed the patient.
Status: draft - Version: 4.0.1
http://hl7.org/fhir/ValueSet/feeding-device
"""
# TODO: fix this template issue1
pass
class Meta:
resource = _resource
| python |
import time
import os
import numpy as np
from perform.constants import REAL_TYPE
class RomSpaceMapping:
"""Base class for mapping to/from the state/latent space."""
def __init__(self, sol_domain, rom_domain, rom_model):
rom_dict = rom_domain.rom_dict
model_idx = rom_model.model_idx
self.latent_dim = rom_model.latent_dim
self.sol_shape = rom_model.sol_shape
# all mappings require scaling by default, specific methods may include additional scalings
model_dir = rom_dict["model_dir"]
self.cent_prof = self.load_feature_scaling(
os.path.join(model_dir, rom_dict["cent_profs"][model_idx]), default="zeros"
)
self.norm_fac_prof = self.load_feature_scaling(
os.path.join(model_dir, rom_dict["norm_fac_profs"][model_idx]), default="ones"
)
self.norm_sub_prof = self.load_feature_scaling(
os.path.join(model_dir, rom_dict["norm_sub_profs"][model_idx]), default="zeros"
)
if callable(getattr(rom_domain.rom_method, "load_extra_scalings", None)):
rom_domain.rom_method.load_extra_scalings(model_idx, sol_domain, rom_domain)
# specific mapping loading functions implemented by child classes
self.load_mapping()
# TODO: initialize decoder Jacobian memory once
def load_feature_scaling(self, scaling_input, default="zeros"):
"""Load a normalization or centering profile from NumPy binary.
Args:
scaling_input: String path to scaling profile NumPy binary.
default: String indicating default profile if loading fails due to size mismatch or load failure.
Returns:
scaling_prof: NumPy array of scaling profile loaded (or default, if load failed).
"""
try:
# Load single complete standardization profile from file
scaling_prof = np.load(scaling_input)
assert scaling_prof.shape == self.sol_shape
return scaling_prof
except AssertionError:
print("Standardization profile at " + scaling_input + " did not match solution shape")
if default == "zeros":
print("WARNING: standardization load failed or not specified, defaulting to zeros")
time.sleep(1.0)
scaling_prof = np.zeros(self.sol_shape, dtype=REAL_TYPE)
elif default == "ones":
print("WARNING: standardization load failed or not specified, defaulting to ones")
time.sleep(1.0)
scaling_prof = np.zeros(self.sol_shape, dtype=REAL_TYPE)
else:
raise ValueError("Invalid default: " + str(default))
return scaling_prof
def scale_profile(
self, arr_in, normalize=True, norm_fac_prof=None, norm_sub_prof=None, center=True, cent_prof=None, inverse=False
):
"""(De-)centers and/or (de-)normalizes solution profile.
Depending on argument flags, centers and/or normalizes solution profile, or de-normalizes
and/or de-centers solution profile.
If inverse is False:
arr = (arr_in - cent_prof - norm_sub_prof) / norm_fac_prof
If inverse is True:
arr = arr_in * norm_fac_prof + norm_sub_prof + cent_prof
Args:
arr_in: NumPy array of solution profile to be scaled.
normalize: Boolean flag indicating whether arr_in should be (de-)normalized.
norm_fac_prof: NumPy array of divisive normalization profile.
norm_sub_prof: NumPy array of subtractive normalization profile.
center: Boolean flag indicating whether arr_in should be (de-)centered.
cent_prof: NumPy array of centering profile.
inverse: If True, de-normalize and de-center. If False, center and normalize.
Returns:
(De)-centered and/or (de)-normalized copy of arr_in.
"""
arr = arr_in.copy()
assert normalize or center, "Must either (de-)center or (de-)normalize."
if normalize:
assert norm_fac_prof is not None, "Must provide normalization division factor to normalize"
assert norm_sub_prof is not None, "Must provide normalization subtractive factor to normalize"
if center:
assert cent_prof is not None, "Must provide centering profile to center"
# de-normalize and de-center
if inverse:
if normalize:
arr = self.normalize(arr, norm_fac_prof, norm_sub_prof, denormalize=True)
if center:
arr = self.center(arr, cent_prof, decenter=True)
# center and normalize
else:
if center:
arr = self.center(arr, cent_prof, decenter=False)
if normalize:
arr = self.normalize(arr, norm_fac_prof, norm_sub_prof, denormalize=False)
return arr
def center(self, arr_in, cent_prof, decenter=False):
"""(De)center input vector according to provided centering profile.
Args:
arr_in: NumPy array to be (de-)centered.
cent_prof: NumPy array of centering profile.
decenter: If True, decenter profile. If False, center profile.
Returns:
(De-)centered copy of arr_in.
"""
if decenter:
arr = arr_in + cent_prof
else:
arr = arr_in - cent_prof
return arr
def normalize(self, arr_in, norm_fac_prof, norm_sub_prof, denormalize=False):
"""(De)normalize input vector according to subtractive and divisive normalization profiles.
Args:
arr_in: NumPy array to be (de-)normalized.
norm_fac_prof: NumPy array of divisive normalization profile.
norm_sub_prof: NumPy array of subtractive normalization profile.
denormalize: If True, denormalize profile. If False, normalize profile.
Returns:
(De-)normalized copy of arr_in.
"""
if denormalize:
arr = arr_in * norm_fac_prof + norm_sub_prof
else:
arr = (arr_in - norm_sub_prof) / norm_fac_prof
return arr
def encode_decode_series(self, sol_series_in):
"""Compute encoding and decoding of a list of solution arrays"""
if isinstance(sol_series_in, np.ndarray):
sol_series_in = [sol_series_in]
code_series_out = []
sol_series_out = []
for sol in sol_series_in:
code_series_out.append(self.encode_sol(sol))
sol_series_out.append(self.decode_sol(code_series_out[-1]))
return code_series_out, sol_series_out
def encode_sol(self, sol_in):
sol = self.scale_profile(
sol_in,
normalize=True,
norm_fac_prof=self.norm_fac_prof,
norm_sub_prof=self.norm_sub_prof,
center=True,
cent_prof=self.cent_prof,
inverse=False,
)
code = self.apply_encoder(sol)
return code
def decode_sol(self, code_in):
"""Compute full decoding of solution, including de-centering and de-normalization.
Maps low-dimensional code to full-dimensional state, and de-centers and de-normalizes.
Note that the apply_decoder is implemented within child classes, as these are specific to a given mapping.
Args:
code_in: low-dimensional code to be decoded.
Returns:
Full-dimensional solution NumPy array resulting from decoding and de-scaling.
"""
sol = self.apply_decoder(code_in)
sol = self.scale_profile(
sol,
normalize=True,
norm_fac_prof=self.norm_fac_prof,
norm_sub_prof=self.norm_sub_prof,
center=True,
cent_prof=self.cent_prof,
inverse=True,
)
return sol
| python |
from dl.nn.Module import Module
import dl.graph.op as OP
from dl.graph import variable
class DropoutLayer(Module):
"""
Dropout layer object.
"""
def __init__(self, rate: float):
"""
Dropout layer object.
Parameters
----------
rate:
Dropout rate.
"""
super().__init__()
self.op = OP.Dropout(rate)
def forward(self, x) -> variable.Variable:
"""
Process the dropout operation.
See details at dl.graph.op.Dropout
Parameters
----------
x:
Input
Returns
-------
out:
output
"""
return self.op(x)
def eval(self):
"""
Set the layer to evaluation mode. in this mode, dropout will not be performed.
Returns
-------
out:
None
"""
self.op.eval = True
def train(self):
"""
Set the layer to evaluation mode. in this mode, dropout will be performed.
Returns
-------
out:
None
"""
self.op.eval = False
| python |
import torch.distributed as dist
from .trainer import Trainer
from ..util import DDP
def average_gradients(model):
""" Gradient averaging. """
size = float(dist.get_world_size())
for param in model.parameters():
if param.grad is not None:
dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
param.grad.data /= size
class DistTrainer(Trainer):
"""
Distributed trainer for multi-gpu training. (not finish yet)
"""
def run_step(self, model, batch, mode='train'):
output, loss, loss_stats = model.module.forward_train(batch)
loss = loss.mean()
if mode == 'train':
self.optimizer.zero_grad()
loss.backward()
average_gradients(model)
self.optimizer.step()
return output, loss, loss_stats
def set_device(self, batch_per_gpu, rank, device):
"""
Set model device for Distributed-Data-Parallel
:param batch_per_gpu: batch size of each gpu
:param rank: distributed training process rank
:param device: cuda
"""
self.rank = rank
self.model = DDP(batch_per_gpu, module=self.model.cuda(), device_ids=[rank], output_device=rank)
| python |
from .answer import Answer, CalculatedAnswer, DragText, NumericalAnswer
from .enums import *
from .questions import (QCalculated, QCalculatedMultichoice, QCalculatedSimple,
QCloze, QDescription, QDragAndDropImage,
QDragAndDropMarker, QDragAndDropText, QEssay,
QMatching, QMissingWord, QMultichoice, QNumerical,
QRandomMatching, QShortAnswer, QTrueFalse)
__author__ = "Lucas Wolfgang"
__version__ = "0.0.1"
__all__ = ["GUI", "main", "Answer", "DragText", "NumericalAnswer", "CalculatedAnswer",
"QDescription", "QCalculated", "QCalculatedSimple",
"QCalculatedMultichoice", "QCloze", "QDragAndDropText",
"QDragAndDropImage", "QDragAndDropMarker", "QEssay",
"QMatching", "QRandomMatching", "QMissingWord", "QMultichoice",
"QNumerical", "QShortAnswer", "QTrueFalse"]
| python |
import warnings
from collections import OrderedDict
import pandas as pd
from . import dtypes, utils
from .alignment import align
from .variable import IndexVariable, Variable, as_variable
from .variable import concat as concat_vars
def concat(
objs,
dim=None,
data_vars="all",
coords="different",
compat="equals",
positions=None,
indexers=None,
mode=None,
concat_over=None,
fill_value=dtypes.NA,
join="outer",
):
"""Concatenate xarray objects along a new or existing dimension.
Parameters
----------
objs : sequence of Dataset and DataArray objects
xarray objects to concatenate together. Each object is expected to
consist of variables and coordinates with matching shapes except for
along the concatenated dimension.
dim : str or DataArray or pandas.Index
Name of the dimension to concatenate along. This can either be a new
dimension name, in which case it is added along axis=0, or an existing
dimension name, in which case the location of the dimension is
unchanged. If dimension is provided as a DataArray or Index, its name
is used as the dimension to concatenate along and the values are added
as a coordinate.
data_vars : {'minimal', 'different', 'all' or list of str}, optional
These data variables will be concatenated together:
* 'minimal': Only data variables in which the dimension already
appears are included.
* 'different': Data variables which are not equal (ignoring
attributes) across all datasets are also concatenated (as well as
all for which dimension already appears). Beware: this option may
load the data payload of data variables into memory if they are not
already loaded.
* 'all': All data variables will be concatenated.
* list of str: The listed data variables will be concatenated, in
addition to the 'minimal' data variables.
If objects are DataArrays, data_vars must be 'all'.
coords : {'minimal', 'different', 'all' or list of str}, optional
These coordinate variables will be concatenated together:
* 'minimal': Only coordinates in which the dimension already appears
are included.
* 'different': Coordinates which are not equal (ignoring attributes)
across all datasets are also concatenated (as well as all for which
dimension already appears). Beware: this option may load the data
payload of coordinate variables into memory if they are not already
loaded.
* 'all': All coordinate variables will be concatenated, except
those corresponding to other dimensions.
* list of str: The listed coordinate variables will be concatenated,
in addition to the 'minimal' coordinates.
compat : {'equals', 'identical'}, optional
String indicating how to compare non-concatenated variables and
dataset global attributes for potential conflicts. 'equals' means
that all variable values and dimensions must be the same;
'identical' means that variable attributes and global attributes
must also be equal.
positions : None or list of integer arrays, optional
List of integer arrays which specifies the integer positions to which
to assign each dataset along the concatenated dimension. If not
supplied, objects are concatenated in the provided order.
fill_value : scalar, optional
Value to use for newly missing values
join : {'outer', 'inner', 'left', 'right', 'exact'}, optional
String indicating how to combine differing indexes
(excluding dim) in objects
- 'outer': use the union of object indexes
- 'inner': use the intersection of object indexes
- 'left': use indexes from the first object with each dimension
- 'right': use indexes from the last object with each dimension
- 'exact': instead of aligning, raise `ValueError` when indexes to be
aligned are not equal
- 'override': if indexes are of same size, rewrite indexes to be
those of the first object with that dimension. Indexes for the same
dimension must have the same size in all objects.
indexers, mode, concat_over : deprecated
Returns
-------
concatenated : type of objs
See also
--------
merge
auto_combine
"""
# TODO: add ignore_index arguments copied from pandas.concat
# TODO: support concatenating scalar coordinates even if the concatenated
# dimension already exists
from .dataset import Dataset
from .dataarray import DataArray
try:
first_obj, objs = utils.peek_at(objs)
except StopIteration:
raise ValueError("must supply at least one object to concatenate")
if dim is None:
warnings.warn(
"the `dim` argument to `concat` will be required "
"in a future version of xarray; for now, setting it to "
"the old default of 'concat_dim'",
FutureWarning,
stacklevel=2,
)
dim = "concat_dims"
if indexers is not None: # pragma: no cover
warnings.warn(
"indexers has been renamed to positions; the alias "
"will be removed in a future version of xarray",
FutureWarning,
stacklevel=2,
)
positions = indexers
if mode is not None:
raise ValueError(
"`mode` is no longer a valid argument to "
"xarray.concat; it has been split into the "
"`data_vars` and `coords` arguments"
)
if concat_over is not None:
raise ValueError(
"`concat_over` is no longer a valid argument to "
"xarray.concat; it has been split into the "
"`data_vars` and `coords` arguments"
)
if isinstance(first_obj, DataArray):
f = _dataarray_concat
elif isinstance(first_obj, Dataset):
f = _dataset_concat
else:
raise TypeError(
"can only concatenate xarray Dataset and DataArray "
"objects, got %s" % type(first_obj)
)
return f(objs, dim, data_vars, coords, compat, positions, fill_value, join)
def _calc_concat_dim_coord(dim):
"""
Infer the dimension name and 1d coordinate variable (if appropriate)
for concatenating along the new dimension.
"""
from .dataarray import DataArray
if isinstance(dim, str):
coord = None
elif not isinstance(dim, (DataArray, Variable)):
dim_name = getattr(dim, "name", None)
if dim_name is None:
dim_name = "concat_dim"
coord = IndexVariable(dim_name, dim)
dim = dim_name
elif not isinstance(dim, DataArray):
coord = as_variable(dim).to_index_variable()
dim, = coord.dims
else:
coord = dim
dim, = coord.dims
return dim, coord
def _calc_concat_over(datasets, dim, data_vars, coords):
"""
Determine which dataset variables need to be concatenated in the result,
and which can simply be taken from the first dataset.
"""
# Return values
concat_over = set()
equals = {}
if dim in datasets[0]:
concat_over.add(dim)
for ds in datasets:
concat_over.update(k for k, v in ds.variables.items() if dim in v.dims)
def process_subset_opt(opt, subset):
if isinstance(opt, str):
if opt == "different":
# all nonindexes that are not the same in each dataset
for k in getattr(datasets[0], subset):
if k not in concat_over:
# Compare the variable of all datasets vs. the one
# of the first dataset. Perform the minimum amount of
# loads in order to avoid multiple loads from disk
# while keeping the RAM footprint low.
v_lhs = datasets[0].variables[k].load()
# We'll need to know later on if variables are equal.
computed = []
for ds_rhs in datasets[1:]:
v_rhs = ds_rhs.variables[k].compute()
computed.append(v_rhs)
if not v_lhs.equals(v_rhs):
concat_over.add(k)
equals[k] = False
# computed variables are not to be re-computed
# again in the future
for ds, v in zip(datasets[1:], computed):
ds.variables[k].data = v.data
break
else:
equals[k] = True
elif opt == "all":
concat_over.update(
set(getattr(datasets[0], subset)) - set(datasets[0].dims)
)
elif opt == "minimal":
pass
else:
raise ValueError("unexpected value for %s: %s" % (subset, opt))
else:
invalid_vars = [k for k in opt if k not in getattr(datasets[0], subset)]
if invalid_vars:
if subset == "coords":
raise ValueError(
"some variables in coords are not coordinates on "
"the first dataset: %s" % (invalid_vars,)
)
else:
raise ValueError(
"some variables in data_vars are not data variables "
"on the first dataset: %s" % (invalid_vars,)
)
concat_over.update(opt)
process_subset_opt(data_vars, "data_vars")
process_subset_opt(coords, "coords")
return concat_over, equals
def _dataset_concat(
datasets,
dim,
data_vars,
coords,
compat,
positions,
fill_value=dtypes.NA,
join="outer",
):
"""
Concatenate a sequence of datasets along a new or existing dimension
"""
from .dataset import Dataset
if compat not in ["equals", "identical"]:
raise ValueError(
"compat=%r invalid: must be 'equals' " "or 'identical'" % compat
)
dim, coord = _calc_concat_dim_coord(dim)
# Make sure we're working on a copy (we'll be loading variables)
datasets = [ds.copy() for ds in datasets]
datasets = align(
*datasets, join=join, copy=False, exclude=[dim], fill_value=fill_value
)
concat_over, equals = _calc_concat_over(datasets, dim, data_vars, coords)
def insert_result_variable(k, v):
assert isinstance(v, Variable)
if k in datasets[0].coords:
result_coord_names.add(k)
result_vars[k] = v
# create the new dataset and add constant variables
result_vars = OrderedDict()
result_coord_names = set(datasets[0].coords)
result_attrs = datasets[0].attrs
result_encoding = datasets[0].encoding
for k, v in datasets[0].variables.items():
if k not in concat_over:
insert_result_variable(k, v)
# check that global attributes and non-concatenated variables are fixed
# across all datasets
for ds in datasets[1:]:
if compat == "identical" and not utils.dict_equiv(ds.attrs, result_attrs):
raise ValueError("dataset global attributes not equal")
for k, v in ds.variables.items():
if k not in result_vars and k not in concat_over:
raise ValueError("encountered unexpected variable %r" % k)
elif (k in result_coord_names) != (k in ds.coords):
raise ValueError(
"%r is a coordinate in some datasets but not " "others" % k
)
elif k in result_vars and k != dim:
# Don't use Variable.identical as it internally invokes
# Variable.equals, and we may already know the answer
if compat == "identical" and not utils.dict_equiv(
v.attrs, result_vars[k].attrs
):
raise ValueError("variable %s not identical across datasets" % k)
# Proceed with equals()
try:
# May be populated when using the "different" method
is_equal = equals[k]
except KeyError:
result_vars[k].load()
is_equal = v.equals(result_vars[k])
if not is_equal:
raise ValueError("variable %s not equal across datasets" % k)
# we've already verified everything is consistent; now, calculate
# shared dimension sizes so we can expand the necessary variables
dim_lengths = [ds.dims.get(dim, 1) for ds in datasets]
non_concat_dims = {}
for ds in datasets:
non_concat_dims.update(ds.dims)
non_concat_dims.pop(dim, None)
def ensure_common_dims(vars):
# ensure each variable with the given name shares the same
# dimensions and the same shape for all of them except along the
# concat dimension
common_dims = tuple(pd.unique([d for v in vars for d in v.dims]))
if dim not in common_dims:
common_dims = (dim,) + common_dims
for var, dim_len in zip(vars, dim_lengths):
if var.dims != common_dims:
common_shape = tuple(
non_concat_dims.get(d, dim_len) for d in common_dims
)
var = var.set_dims(common_dims, common_shape)
yield var
# stack up each variable to fill-out the dataset (in order)
for k in datasets[0].variables:
if k in concat_over:
vars = ensure_common_dims([ds.variables[k] for ds in datasets])
combined = concat_vars(vars, dim, positions)
insert_result_variable(k, combined)
result = Dataset(result_vars, attrs=result_attrs)
result = result.set_coords(result_coord_names)
result.encoding = result_encoding
if coord is not None:
# add concat dimension last to ensure that its in the final Dataset
result[coord.name] = coord
return result
def _dataarray_concat(
arrays,
dim,
data_vars,
coords,
compat,
positions,
fill_value=dtypes.NA,
join="outer",
):
arrays = list(arrays)
if data_vars != "all":
raise ValueError(
"data_vars is not a valid argument when " "concatenating DataArray objects"
)
datasets = []
for n, arr in enumerate(arrays):
if n == 0:
name = arr.name
elif name != arr.name:
if compat == "identical":
raise ValueError("array names not identical")
else:
arr = arr.rename(name)
datasets.append(arr._to_temp_dataset())
ds = _dataset_concat(
datasets,
dim,
data_vars,
coords,
compat,
positions,
fill_value=fill_value,
join=join,
)
return arrays[0]._from_temp_dataset(ds, name)
| python |
import re
import os
try:
from urlparse import urlparse
except:
from urllib.parse import urlparse
from .exceptions import FieldValidationException
from .universal_forwarder_compatiblity import UF_MODE, make_splunkhome_path
from .contrib.ipaddress import ip_network
try:
from .server_info import ServerInfo
except ImportError:
ServerInfo = None
class Field(object):
"""
This is the base class that should be used to for field validators. Sub-class this and
override to_python if you need custom validation.
"""
DATA_TYPE_STRING = 'string'
DATA_TYPE_NUMBER = 'number'
DATA_TYPE_BOOLEAN = 'boolean'
def get_data_type(self):
"""
Get the type of the field.
"""
return Field.DATA_TYPE_STRING
def __init__(self, name, title, description, none_allowed=False, empty_allowed=True,
required_on_create=None, required_on_edit=None):
"""
Create the field.
Arguments:
name -- Set the name of the field (e.g. "database_server")
title -- Set the human readable title (e.g. "Database server")
description -- Set the human readable description of the field (e.g. "The IP or domain name
of the database server")
none_allowed -- Is a value of none allowed?
empty_allowed -- Is an empty string allowed?
required_on_create -- Is this field required when creating?
required_on_edit -- Is this field required when editing?
"""
# Try to set required_on_create and required_on_edit to sane defaults if not defined
if required_on_create is None and none_allowed:
required_on_create = False
elif required_on_create is None and not none_allowed:
required_on_create = True
if required_on_edit is None and required_on_create is not None:
required_on_edit = required_on_create
if name is None:
raise ValueError("The name parameter cannot be none")
if len(name.strip()) == 0:
raise ValueError("The name parameter cannot be empty")
if title is None:
raise ValueError("The title parameter cannot be none")
if len(title.strip()) == 0:
raise ValueError("The title parameter cannot be empty")
if description is None:
raise ValueError("The description parameter cannot be none")
if len(description.strip()) == 0:
raise ValueError("The description parameter cannot be empty")
self.name = name
self.title = title
self.description = description
self.none_allowed = none_allowed
self.empty_allowed = empty_allowed
self.required_on_create = required_on_create
self.required_on_edit = required_on_edit
def to_python(self, value, session_key=None):
"""
Convert the field to a Python object. Should throw a FieldValidationException if the data
is invalid.
Arguments:
value -- The value to convert
session_key- The session key to access Splunk (if needed)
"""
if not self.none_allowed and value is None:
raise FieldValidationException("The value for the '%s' parameter cannot be empty" % (self.name))
if not self.empty_allowed and len(str(value).strip()) == 0:
raise FieldValidationException("The value for the '%s' parameter cannot be empty" % (self.name))
return value
def to_string(self, value):
"""
Convert the field to a string value that can be returned. Should throw a
FieldValidationException if the data is invalid.
Arguments:
value -- The value to convert
"""
return str(value)
class BooleanField(Field):
"""
A validator that converts string versions of boolean to a real boolean.
"""
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
if value in [True, False]:
return value
elif str(value).strip().lower() in ["true", "1"]:
return True
elif str(value).strip().lower() in ["false", "0"]:
return False
raise FieldValidationException("The value of '%s' for the '%s' parameter is not a valid boolean" % (str(value), self.name))
def to_string(self, value):
if value == True:
return "1"
elif value == False:
return "0"
return str(value)
def get_data_type(self):
return Field.DATA_TYPE_BOOLEAN
class ListField(Field):
"""
A validator that converts a comma seperated string to an array.
You can use the instance_class argument to convert individual items in the array to particular
type. That way, you can have a list of Python objects that are already converted to the values
you want. Consider this example that will include a list of parsed IP network ranges:
list_field = ListField('name', 'title', 'description', instance_class=IPNetworkField)
parsed_ip_ranges = list_field.to_python(u'10.0.0.0/28,1.2.3.4,10.0.1.0/28')
"""
def __init__(self, name, title, description, none_allowed=False, empty_allowed=True,
required_on_create=None, required_on_edit=None, instance_class=None,
trim_values=False):
"""
Create the field.
Arguments:
name -- Set the name of the field (e.g. "database_server")
title -- Set the human readable title (e.g. "Database server")
description -- Set the human readable description of the field (e.g. "The IP or domain name
of the database server")
none_allowed -- Is a value of none allowed?
empty_allowed -- Is an empty string allowed?
required_on_create -- Is this field required when creating?
required_on_edit -- Is this field required when editing?
instance_class -- The name of the class to use for constructing individual objects
trim_values -- Trim whitespace off of the ends of the values in case that spaces between
the list are not included
"""
super(ListField, self).__init__(name, title, description, none_allowed, empty_allowed, required_on_create, required_on_edit)
self.instance_class = instance_class
self.trim_values = trim_values
# Create an instance for converting the values
if self.instance_class is not None:
self.instance = self.instance_class(self.name, self.title, self.description)
else:
self.instance = None
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
# Convert the value into an array
values_list = None
if value is not None:
values_list = value.split(",")
else:
values_list = []
# Trim the values if requested
if self.trim_values:
values_list = [value.strip() for value in values_list]
# If we have no instances class, then just return the plain list
if self.instance_class is None:
return values_list
# Otherwise, convert the instances accordingly
else:
# Convert the value
instances_list = []
for instance_value in values_list:
instances_list.append(self.instance.to_python(instance_value))
return instances_list
def to_string(self, value):
if value is not None:
# Use the instance to_string if we have an instance
if self.instance is not None:
values_list = []
for individual_value in value:
values_list.append(self.instance.to_string(individual_value))
return ",".join(values_list)
# Otherwise, process it as a string
else:
return ",".join(value)
return ""
class StaticListField(Field):
"""
This allows you to specify a list of field values that are allowed.
All other values will be rejected.
"""
_valid_values = None
def __init__(self, name, title, description, none_allowed=False, empty_allowed=True, required_on_create=None, required_on_edit=None, valid_values=None):
super(StaticListField, self).__init__(name, title, description, none_allowed, empty_allowed, required_on_create, required_on_edit)
self.valid_values = valid_values
@property
def valid_values(self):
return self._valid_values
@valid_values.setter
def valid_values(self, values):
self._valid_values = values
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
if value is None:
return None
elif value not in self.valid_values:
raise FieldValidationException('The value of the "' + self.name + '" field is invalid, it must be one of:' + ','.join(self.valid_values))
else:
return value
class RegexField(Field):
"""
A validator that validates input matches a regular expression.
"""
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
if value is not None:
try:
return re.compile(value)
except Exception as exception:
raise FieldValidationException(str(exception))
else:
return None
def to_string(self, value):
if value is not None:
return value.pattern
return ""
class WildcardField(Field):
"""
Much like a regular expression field but takes wildcards. This will return a regular expression.
"""
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
if value is not None:
try:
regex_escaped = re.escape(value)
regex_escaped = regex_escaped.replace('\*', ".*")
return re.compile(regex_escaped)
except Exception as exception:
raise FieldValidationException(str(exception))
else:
return None
def to_string(self, value):
if value is not None:
return value.pattern
return ""
class IntegerField(Field):
"""
A validator that converts string input to an integer.
"""
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
if value is not None:
try:
return int(value)
except ValueError as exception:
raise FieldValidationException(str(exception))
else:
return None
def to_string(self, value):
if value is not None:
return str(value)
return ""
def get_data_type(self):
return Field.DATA_TYPE_NUMBER
class FloatField(Field):
"""
A validator that converts string input to a float.
"""
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
if value is not None:
try:
return float(value)
except ValueError as exception:
raise FieldValidationException(str(exception))
else:
return None
def to_string(self, value):
if value is not None:
return str(value)
return ""
def get_data_type(self):
return Field.DATA_TYPE_NUMBER
class RangeField(Field):
"""
A validator that converts string input to a pair of integers indicating a range.
"""
def __init__(self, name, title, description, low, high, none_allowed=False, empty_allowed=True, required_on_create=None, required_on_edit=None):
super(RangeField, self).__init__(name, title, description, none_allowed,
empty_allowed, required_on_create, required_on_edit)
self.low = low
self.high = high
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
if value is not None:
try:
tmp = int(value)
if tmp < self.low:
raise FieldValidationException("The value of '%s' for the '%s' parameter must be greater than or equal to '%r'" % (str(value), self.name, self.low))
if tmp > self.high:
raise FieldValidationException("The value of '%s' for the '%s' parameter must be less than or equal to '%r'" % (str(value), self.name, self.high))
return tmp
except ValueError as exception:
raise FieldValidationException(str(exception))
else:
return None
def to_string(self, value):
if value is not None:
return str(value)
return ""
def get_data_type(self):
return Field.DATA_TYPE_NUMBER
class URLField(Field):
"""
Represents a URL. The URL is converted to a Python object that was created via urlparse.
"""
require_https_on_cloud = False
def __init__(self, name, title, description, none_allowed=False, empty_allowed=True,
required_on_create=None, required_on_edit=None, require_https_on_cloud=False):
super(URLField, self).__init__(name, title, description, none_allowed,
empty_allowed, required_on_create, required_on_edit)
self.require_https_on_cloud = require_https_on_cloud
@classmethod
def parse_url(cls, value, name):
"""
Parse a URL and generation an exception if it is invalid.BaseException
Otherwise, return a parsed URL (via urlparse).
"""
parsed_value = urlparse(value)
if parsed_value.hostname is None or len(parsed_value.hostname) <= 0:
raise FieldValidationException("The value of '%s' for the '%s' parameter does not contain a host name" % (str(value), name))
if parsed_value.scheme not in ["http", "https"]:
raise FieldValidationException("The value of '%s' for the '%s' parameter does not contain a valid protocol (only http and https are supported)" % (str(value), name))
return parsed_value
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
parsed_value = URLField.parse_url(value.strip(), self.name)
if self.require_https_on_cloud and parsed_value.scheme == "http" and session_key is not None and ServerInfo.is_on_cloud(session_key):
raise FieldValidationException("The value of '%s' for the '%s' parameter must use encryption (be HTTPS not HTTP)" % (str(value), self.name))
return parsed_value
def to_string(self, value):
return value.geturl()
class DurationField(Field):
"""
The duration field represents a duration as represented by a string such as 1d for a 24 hour
period.
The string is converted to an integer indicating the number of seconds.
"""
DURATION_RE = re.compile("(?P<duration>[0-9]+)\s*(?P<units>[a-z]*)", re.IGNORECASE)
MINUTE = 60
HOUR = 60 * MINUTE
DAY = 24 * HOUR
WEEK = 7 * DAY
UNITS = {
'w' : WEEK,
'week' : WEEK,
'd' : DAY,
'day' : DAY,
'h' : HOUR,
'hour' : HOUR,
'm' : MINUTE,
'min' : MINUTE,
'minute' : MINUTE,
's' : 1
}
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
# Parse the duration
duration_match = DurationField.DURATION_RE.match(value)
# Make sure the duration could be parsed
if duration_match is None:
raise FieldValidationException("The value of '%s' for the '%s' parameter is not a valid duration" % (str(value), self.name))
# Get the units and duration
match_dict = duration_match.groupdict()
units = match_dict['units']
# Parse the value provided
try:
duration = int(match_dict['duration'])
except ValueError:
raise FieldValidationException("The duration '%s' for the '%s' parameter is not a valid number" % (match_dict['duration'], self.name))
# Make sure the units are valid
if len(units) > 0 and units not in DurationField.UNITS:
raise FieldValidationException("The unit '%s' for the '%s' parameter is not a valid unit of duration" % (units, self.name))
# Convert the units to seconds
if len(units) > 0:
return duration * DurationField.UNITS[units]
else:
return duration
def to_string(self, value):
return str(value)
class DeprecatedField(Field):
"""
Represents a field that is no longer used. This should be used when you want the input to pass
validation with arguments that are no longer used.
"""
def __init__(self, name, title, description, none_allowed=True, empty_allowed=True,
required_on_create=False, required_on_edit=False):
"""
Create the field.
Arguments:
name -- Set the name of the field (e.g. "database_server")
title -- Set the human readable title (e.g. "Database server")
description -- Set the human readable description of the field (e.g. "The IP or domain name of the database server")
none_allowed -- Is a value of none allowed?
empty_allowed -- Is an empty string allowed?
required_on_create -- Is this field required when creating?
required_on_edit -- Is this field required when editing?
"""
super(DeprecatedField, self).__init__(name, title, description,
none_allowed=none_allowed,
empty_allowed=empty_allowed,
required_on_create=required_on_create,
required_on_edit=required_on_edit)
def to_python(self, value, session_key=None):
return None
def to_string(self, value):
return ""
class FilePathField(Field):
'''
Represents a path to file.
'''
def __init__(self, name, title, description, none_allowed=False, empty_allowed=True,
required_on_create=None, required_on_edit=None, validate_file_existence=True):
"""
Create the field.
Arguments:
name -- Set the name of the field (e.g. "database_server")
title -- Set the human readable title (e.g. "Database server")
description -- Set the human readable description of the field (e.g. "The IP or domain name
of the database server")
none_allowed -- Is a value of none allowed?
empty_allowed -- Is an empty string allowed?
required_on_create -- Is this field required when creating?
required_on_edit -- Is this field required when editing?
validate_file_existence -- If true, this field will generate an error if the file doesn't exist
"""
super(FilePathField, self).__init__(name, title, description, none_allowed, empty_allowed, required_on_create, required_on_edit)
self.validate_file_existence = validate_file_existence
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
# Don't bother validating if the parameter wasn't provided
if value is None or len(value.strip()) == 0:
return value
# Resolve the file path as necessary
resolved_path = None
if value is not None:
if os.path.isabs(value) or UF_MODE:
resolved_path = value
else:
path = os.path.join(make_splunkhome_path([value]))
resolved_path = path
# Validate the file existence if requested
if self.validate_file_existence and not os.path.isfile(resolved_path):
raise FieldValidationException("The parameter '%s' is not a valid path; '%s' does not exist" % (self.name, resolved_path))
return resolved_path
def to_string(self, value):
return value
class DomainNameField(Field):
"""
A validator that accepts domain names.
"""
def is_valid_hostname(self, dn):
"""
Determine if the given hostname is valid.
See https://stackoverflow.com/questions/2532053/validate-a-hostname-string
"""
if dn.endswith('.'):
dn = dn[:-1]
if len(dn) < 1 or len(dn) > 253:
return False
ldh_re = re.compile('^[a-z0-9]([a-z0-9-]{0,61}[a-z0-9])?$',
re.IGNORECASE)
return all(ldh_re.match(x) for x in dn.split('.'))
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
if value is not None:
if not self.is_valid_hostname(value):
raise FieldValidationException("The value of '%s' for the '%s' parameter is not a valid domain name" % (value, self.name))
return value
else:
return None
class MultiValidatorField(Field):
def __init__(self, name, title, description, none_allowed=False, empty_allowed=True,
required_on_create=None, required_on_edit=None, validators=None, default_message=None):
"""
Create the field.
Arguments:
name -- Set the name of the field (e.g. "database_server")
title -- Set the human readable title (e.g. "Database server")
description -- Set the human readable description of the field (e.g. "The IP or domain name
of the database server")
none_allowed -- Is a value of none allowed?
empty_allowed -- Is an empty string allowed?
required_on_create -- Is this field required when creating?
required_on_edit -- Is this field required when editing?
validate_file_existence -- If true, this field will generate an error if the file doesn't exist
"""
super(MultiValidatorField, self).__init__(name, title, description, none_allowed, empty_allowed, required_on_create, required_on_edit)
# Stop if no validators were supplied
if validators is None or len(validators) == 0:
raise Exception("A list of the validators is required for the MultiValidatorField to test against")
# Here is where all of the instances of the validators will be stored
self.validators = []
# Construct the validator instances
for validator in validators:
self.validators.append(validator(self.name, self.title, self.description, self.none_allowed, self.empty_allowed, self.required_on_create, self.required_on_edit))
# This will point to the last validator instance that accepted the last value
self.last_used_validator = None
# Persist the error message
self.default_message = default_message
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
if value is not None:
messages =[]
for validator in self.validators:
try:
python_value = validator.to_python(value, session_key)
self.last_used_validator = validator
return python_value
except FieldValidationException as e:
messages.append(str(e))
# Generate an exception since the field could not be validated
if self.default_message is None:
raise FieldValidationException(";".join(messages))
else:
raise FieldValidationException(self.default_message)
else:
return None
def to_string(self, value):
if value is not None:
return self.last_used_validator.to_string(value)
return ""
class IPNetworkField(Field):
"""
A validator that accepts IP addresses.
"""
def to_python(self, value, session_key=None):
Field.to_python(self, value, session_key)
if value is not None:
# Convert the incoming string to bytes
# For Python 2, str works fine since it is just bytes. Python 3 defaults to unicode which needs to be converted.
try:
unicode
if not isinstance(value, unicode):
value = unicode(value)
# The interpreter is Python 2
except NameError:
# The interpreter is Python 3, it is unicode already
pass
try:
return ip_network(value, strict=False)
except ValueError as exception:
raise FieldValidationException(str(exception))
else:
return None
def to_string(self, value):
if value is not None:
# Get the main address if this is a single address
if value.num_addresses == 1:
return str(value.network_address)
else:
return str(value)
return ""
| python |
import os
import pandas as pd
import pytest
from probatus.feature_elimination import EarlyStoppingShapRFECV, ShapRFECV
from probatus.utils import preprocess_labels
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import get_scorer
from sklearn.model_selection import RandomizedSearchCV, StratifiedGroupKFold, StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
@pytest.fixture(scope="function")
def X():
"""
Fixture for X.
"""
return pd.DataFrame(
{
"col_1": [1, 1, 1, 1, 1, 1, 1, 0],
"col_2": [0, 0, 0, 0, 0, 0, 0, 1],
"col_3": [1, 0, 1, 0, 1, 0, 1, 0],
},
index=[1, 2, 3, 4, 5, 6, 7, 8],
)
@pytest.fixture(scope="session")
def catboost_classifier_class():
"""This fixture allows to reuse the import of the CatboostClassifier class across different tests.
It is equivalent to importing the package at the beginning of the file.
Importing catboost multiple times results in a ValueError: I/O operation on closed file.
"""
from catboost import CatBoostClassifier
return CatBoostClassifier
@pytest.fixture(scope="function")
def y():
"""
Fixture for y.
"""
return pd.Series([1, 0, 1, 0, 1, 0, 1, 0], index=[1, 2, 3, 4, 5, 6, 7, 8])
@pytest.fixture(scope="function")
def sample_weight():
"""
Fixture for sample_weight.
"""
return pd.Series([1, 1, 1, 1, 1, 1, 1, 1], index=[1, 2, 3, 4, 5, 6, 7, 8])
@pytest.fixture(scope="function")
def groups():
"""
Fixture for groups.
"""
return pd.Series(["grp1", "grp1", "grp1", "grp1", "grp2", "grp2", "grp2", "grp2"], index=[1, 2, 3, 4, 5, 6, 7, 8])
def test_shap_rfe_randomized_search(X, y, capsys):
"""
Test with RandomizedSearchCV.
"""
clf = DecisionTreeClassifier(max_depth=1)
param_grid = {"criterion": ["gini"], "min_samples_split": [1, 2]}
search = RandomizedSearchCV(clf, param_grid, cv=2, n_iter=2)
with pytest.warns(None) as record:
shap_elimination = ShapRFECV(search, step=0.8, cv=2, scoring="roc_auc", n_jobs=4, random_state=1)
report = shap_elimination.fit_compute(X, y)
assert shap_elimination.fitted
shap_elimination._check_if_fitted()
assert report.shape[0] == 2
assert shap_elimination.get_reduced_features_set(1) == ["col_3"]
_ = shap_elimination.plot(show=False)
# Ensure that number of warnings was at least 2 for the verbose (2 generated by probatus + possibly more by SHAP)
assert len(record) >= 2
# Check if there is any prints
out, _ = capsys.readouterr()
assert len(out) == 0
def test_shap_rfe(X, y, sample_weight, capsys):
"""
Test with ShapRFECV.
"""
clf = DecisionTreeClassifier(max_depth=1, random_state=1)
with pytest.warns(None) as record:
shap_elimination = ShapRFECV(
clf,
random_state=1,
step=1,
cv=2,
scoring="roc_auc",
n_jobs=4,
)
shap_elimination = shap_elimination.fit(
X, y, sample_weight=sample_weight, approximate=True, check_additivity=False
)
assert shap_elimination.fitted
shap_elimination._check_if_fitted()
report = shap_elimination.compute()
assert report.shape[0] == 3
assert shap_elimination.get_reduced_features_set(1) == ["col_3"]
_ = shap_elimination.plot(show=False)
# Ensure that number of warnings was 0
assert len(record) == 0
# Check if there is any prints
out, _ = capsys.readouterr()
assert len(out) == 0
def test_shap_rfe_group_cv(X, y, groups, sample_weight, capsys):
"""
Test ShapRFECV with StratifiedGroupKFold.
"""
clf = DecisionTreeClassifier(max_depth=1, random_state=1)
cv = StratifiedGroupKFold(n_splits=2, shuffle=True, random_state=1)
with pytest.warns(None) as record:
shap_elimination = ShapRFECV(
clf,
random_state=1,
step=1,
cv=cv,
scoring="roc_auc",
n_jobs=4,
)
shap_elimination = shap_elimination.fit(
X, y, groups=groups, sample_weight=sample_weight, approximate=True, check_additivity=False
)
assert shap_elimination.fitted
shap_elimination._check_if_fitted()
report = shap_elimination.compute()
assert report.shape[0] == 3
assert shap_elimination.get_reduced_features_set(1) == ["col_3"]
_ = shap_elimination.plot(show=False)
# Ensure that number of warnings was 0
assert len(record) == 0
# Check if there is any prints
out, _ = capsys.readouterr()
assert len(out) == 0
def test_shap_pipeline_error(X, y, capsys):
"""
Test with ShapRFECV for pipelines.
"""
clf = Pipeline(
[
("scaler", StandardScaler()),
("dt", DecisionTreeClassifier(max_depth=1, random_state=1)),
]
)
with pytest.raises(TypeError):
shap_elimination = ShapRFECV(
clf,
random_state=1,
step=1,
cv=2,
scoring="roc_auc",
n_jobs=4,
)
shap_elimination = shap_elimination.fit(X, y, approximate=True, check_additivity=False)
def test_shap_rfe_linear_model(X, y, capsys):
"""
Test ShapRFECV with linear model.
"""
clf = LogisticRegression(C=1, random_state=1)
with pytest.warns(None) as record:
shap_elimination = ShapRFECV(clf, random_state=1, step=1, cv=2, scoring="roc_auc", n_jobs=4)
shap_elimination = shap_elimination.fit(X, y)
assert shap_elimination.fitted
shap_elimination._check_if_fitted()
report = shap_elimination.compute()
assert report.shape[0] == 3
assert shap_elimination.get_reduced_features_set(1) == ["col_3"]
_ = shap_elimination.plot(show=False)
# Ensure that number of warnings was 0
assert len(record) == 0
# Check if there is any prints
out, _ = capsys.readouterr()
assert len(out) == 0
def test_shap_rfe_svm(X, y, capsys):
"""
Test with ShapRFECV with SVM.
"""
clf = SVC(C=1, kernel="linear", probability=True)
with pytest.warns(None) as record:
shap_elimination = ShapRFECV(clf, random_state=1, step=1, cv=2, scoring="roc_auc", n_jobs=4)
shap_elimination = shap_elimination.fit(X, y)
assert shap_elimination.fitted
shap_elimination._check_if_fitted()
report = shap_elimination.compute()
assert report.shape[0] == 3
assert shap_elimination.get_reduced_features_set(1) == ["col_3"]
_ = shap_elimination.plot(show=False)
# Ensure that number of warnings was 0
assert len(record) == 0
# Check if there is any prints
out, _ = capsys.readouterr()
assert len(out) == 0
def test_shap_rfe_cols_to_keep(X, y, capsys):
"""
Test for shap_rfe_cv with feautures to keep parameter.
"""
clf = DecisionTreeClassifier(max_depth=1, random_state=1)
with pytest.warns(None) as record:
shap_elimination = ShapRFECV(
clf,
random_state=1,
step=2,
cv=2,
scoring="roc_auc",
n_jobs=4,
min_features_to_select=1,
)
shap_elimination = shap_elimination.fit(X, y, columns_to_keep=["col_2", "col_3"])
assert shap_elimination.fitted
shap_elimination._check_if_fitted()
report = shap_elimination.compute()
assert report.shape[0] == 2
reduced_feature_set = set(shap_elimination.get_reduced_features_set(num_features=2))
assert reduced_feature_set == set(["col_2", "col_3"])
# Ensure that number of warnings was 0
assert len(record) == 0
# Check if there is any prints
out, _ = capsys.readouterr()
assert len(out) == 0
def test_shap_rfe_randomized_search_cols_to_keep(X, y, capsys):
"""
Test with ShapRFECV with column to keep param.
"""
clf = DecisionTreeClassifier(max_depth=1)
param_grid = {"criterion": ["gini"], "min_samples_split": [1, 2]}
search = RandomizedSearchCV(clf, param_grid, cv=2, n_iter=2)
with pytest.warns(None) as record:
shap_elimination = ShapRFECV(search, step=0.8, cv=2, scoring="roc_auc", n_jobs=4, random_state=1)
report = shap_elimination.fit_compute(X, y, columns_to_keep=["col_2", "col_3"])
assert shap_elimination.fitted
shap_elimination._check_if_fitted()
assert report.shape[0] == 2
reduced_feature_set = set(shap_elimination.get_reduced_features_set(num_features=2))
assert reduced_feature_set == set(["col_2", "col_3"])
_ = shap_elimination.plot(show=False)
# Ensure that number of warnings was at least 2 for the verbose (2 generated by probatus + possibly more by SHAP)
assert len(record) >= 2
# Check if there is any prints
out, _ = capsys.readouterr()
assert len(out) == 0
def test_calculate_number_of_features_to_remove():
"""
Test with ShapRFECV with n features to remove.
"""
assert 3 == ShapRFECV._calculate_number_of_features_to_remove(
current_num_of_features=10, num_features_to_remove=3, min_num_features_to_keep=5
)
assert 3 == ShapRFECV._calculate_number_of_features_to_remove(
current_num_of_features=8, num_features_to_remove=5, min_num_features_to_keep=5
)
assert 0 == ShapRFECV._calculate_number_of_features_to_remove(
current_num_of_features=5, num_features_to_remove=1, min_num_features_to_keep=5
)
assert 4 == ShapRFECV._calculate_number_of_features_to_remove(
current_num_of_features=5, num_features_to_remove=7, min_num_features_to_keep=1
)
def test_get_feature_shap_values_per_fold(X, y):
"""
Test with ShapRFECV with features per fold.
"""
clf = DecisionTreeClassifier(max_depth=1)
shap_elimination = ShapRFECV(clf)
(shap_values, train_score, test_score,) = shap_elimination._get_feature_shap_values_per_fold(
X,
y,
clf,
train_index=[2, 3, 4, 5, 6, 7],
val_index=[0, 1],
scorer=get_scorer("roc_auc"),
)
assert test_score == 1
assert train_score > 0.9
assert shap_values.shape == (2, 3)
@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled")
def test_complex_dataset(complex_data, complex_lightgbm):
"""
Test on complex dataset.
"""
X, y = complex_data
param_grid = {
"n_estimators": [5, 7, 10],
"num_leaves": [3, 5, 7, 10],
}
search = RandomizedSearchCV(complex_lightgbm, param_grid, n_iter=1)
shap_elimination = ShapRFECV(clf=search, step=1, cv=10, scoring="roc_auc", n_jobs=3, verbose=50)
with pytest.warns(None) as record:
report = shap_elimination.fit_compute(X, y)
assert report.shape[0] == X.shape[1]
assert len(record) >= 2
@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled")
def test_shap_rfe_early_stopping_lightGBM(complex_data, capsys):
"""
Test EarlyStoppingShapRFECV with a LGBMClassifier.
"""
from lightgbm import LGBMClassifier
clf = LGBMClassifier(n_estimators=200, max_depth=3)
X, y = complex_data
with pytest.warns(None) as record:
shap_elimination = EarlyStoppingShapRFECV(
clf,
random_state=1,
step=1,
cv=10,
scoring="roc_auc",
n_jobs=4,
early_stopping_rounds=5,
eval_metric="auc",
)
shap_elimination = shap_elimination.fit(X, y, approximate=False, check_additivity=False)
assert shap_elimination.fitted
shap_elimination._check_if_fitted()
report = shap_elimination.compute()
assert report.shape[0] == 5
assert shap_elimination.get_reduced_features_set(1) == ["f5"]
_ = shap_elimination.plot(show=False)
# Ensure that number of warnings was 0
assert len(record) == 0
# Check if there is any prints
out, _ = capsys.readouterr()
assert len(out) == 0
@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled")
def test_shap_rfe_early_stopping_XGBoost(complex_data, capsys):
"""
Test EarlyStoppingShapRFECV with a LGBMClassifier.
"""
from xgboost import XGBClassifier
clf = XGBClassifier(n_estimators=200, max_depth=3, use_label_encoder=False, random_state=42)
X, y = complex_data
X["f1_categorical"] = X["f1_categorical"].astype(float)
with pytest.warns(None) as record:
shap_elimination = EarlyStoppingShapRFECV(
clf,
random_state=1,
step=1,
cv=10,
scoring="roc_auc",
n_jobs=4,
early_stopping_rounds=5,
eval_metric="auc",
)
shap_elimination = shap_elimination.fit(X, y, approximate=False, check_additivity=False)
assert shap_elimination.fitted
shap_elimination._check_if_fitted()
report = shap_elimination.compute()
assert report.shape[0] == 5
assert shap_elimination.get_reduced_features_set(1) == ["f4"]
_ = shap_elimination.plot(show=False)
# Ensure that number of warnings was 0
assert len(record) == 0
# Check if there is any prints
out, _ = capsys.readouterr()
assert len(out) == 0
# For now this test fails, catboost has issues with categorical variables and
@pytest.mark.xfail
@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled")
def test_shap_rfe_early_stopping_CatBoost(complex_data, capsys, catboost_classifier_class):
"""
Test EarlyStoppingShapRFECV with a CatBoostClassifier.
"""
clf = catboost_classifier_class(random_seed=42)
X, y = complex_data
with pytest.warns(None) as record:
shap_elimination = EarlyStoppingShapRFECV(
clf,
random_state=1,
step=1,
cv=10,
scoring="roc_auc",
n_jobs=4,
early_stopping_rounds=5,
eval_metric="auc",
)
shap_elimination = shap_elimination.fit(X, y, approximate=False, check_additivity=False)
assert shap_elimination.fitted
shap_elimination._check_if_fitted()
report = shap_elimination.compute()
assert report.shape[0] == 5
assert shap_elimination.get_reduced_features_set(1)[0] in ["f4", "f5"]
_ = shap_elimination.plot(show=False)
# Ensure that number of warnings was 0
assert len(record) == 0
# Check if there is any prints
out, _ = capsys.readouterr()
assert len(out) == 0
@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled")
def test_shap_rfe_randomized_search_early_stopping_lightGBM(complex_data):
"""
Test EarlyStoppingShapRFECV with RandomizedSearchCV and a LGBMClassifier on complex dataset.
"""
from lightgbm import LGBMClassifier
clf = LGBMClassifier(n_estimators=200)
X, y = complex_data
param_grid = {
"max_depth": [3, 4, 5],
}
search = RandomizedSearchCV(clf, param_grid, cv=2, n_iter=2)
with pytest.warns(None) as record:
shap_elimination = EarlyStoppingShapRFECV(
search,
step=1,
cv=10,
scoring="roc_auc",
early_stopping_rounds=5,
eval_metric="auc",
n_jobs=4,
verbose=50,
random_state=1,
)
report = shap_elimination.fit_compute(X, y)
assert shap_elimination.fitted
shap_elimination._check_if_fitted()
assert report.shape[0] == X.shape[1]
assert shap_elimination.get_reduced_features_set(1) == ["f5"]
_ = shap_elimination.plot(show=False)
# Ensure that number of warnings was at least 3 for the verbose (2 generated by probatus + possibly more by SHAP)
assert len(record) >= 3
@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled")
def test_get_feature_shap_values_per_fold_early_stopping_lightGBM(complex_data):
"""
Test with ShapRFECV with features per fold.
"""
from lightgbm import LGBMClassifier
clf = LGBMClassifier(n_estimators=200, max_depth=3)
X, y = complex_data
y = preprocess_labels(y, y_name="y", index=X.index)
shap_elimination = EarlyStoppingShapRFECV(clf, early_stopping_rounds=5)
(shap_values, train_score, test_score,) = shap_elimination._get_feature_shap_values_per_fold(
X,
y,
clf,
train_index=list(range(5, 50)),
val_index=[0, 1, 2, 3, 4],
scorer=get_scorer("roc_auc"),
)
assert test_score > 0.6
assert train_score > 0.6
assert shap_values.shape == (5, 5)
@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled")
def test_get_feature_shap_values_per_fold_early_stopping_CatBoost(complex_data, catboost_classifier_class):
"""
Test with ShapRFECV with features per fold.
"""
clf = catboost_classifier_class(random_seed=42)
X, y = complex_data
X["f1_categorical"] = X["f1_categorical"].astype(str).astype("category")
y = preprocess_labels(y, y_name="y", index=X.index)
shap_elimination = EarlyStoppingShapRFECV(clf, early_stopping_rounds=5)
(shap_values, train_score, test_score,) = shap_elimination._get_feature_shap_values_per_fold(
X,
y,
clf,
train_index=list(range(5, 50)),
val_index=[0, 1, 2, 3, 4],
scorer=get_scorer("roc_auc"),
)
assert test_score > 0
assert train_score > 0.6
assert shap_values.shape == (5, 5)
@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled")
def test_get_feature_shap_values_per_fold_early_stopping_XGBoost(complex_data):
"""
Test with ShapRFECV with features per fold.
"""
from xgboost import XGBClassifier
clf = XGBClassifier(n_estimators=200, max_depth=3, use_label_encoder=False, random_state=42)
X, y = complex_data
X["f1_categorical"] = X["f1_categorical"].astype(float)
y = preprocess_labels(y, y_name="y", index=X.index)
shap_elimination = EarlyStoppingShapRFECV(clf, early_stopping_rounds=5)
(shap_values, train_score, test_score,) = shap_elimination._get_feature_shap_values_per_fold(
X,
y,
clf,
train_index=list(range(5, 50)),
val_index=[0, 1, 2, 3, 4],
scorer=get_scorer("roc_auc"),
)
assert test_score > 0
assert train_score > 0.6
assert shap_values.shape == (5, 5)
@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled")
def test_EarlyStoppingShapRFECV_no_categorical(complex_data):
"""Test EarlyStoppingShapRFECV when no categorical features are present."""
from lightgbm import LGBMClassifier
model = LGBMClassifier(n_estimators=50, max_depth=3, num_leaves=3)
shap_elimination = EarlyStoppingShapRFECV(
clf=model,
step=0.33,
cv=5,
scoring="accuracy",
eval_metric="logloss",
early_stopping_rounds=5,
)
X, y = complex_data
X = X.drop(columns=["f1_categorical"])
report = shap_elimination.fit_compute(X, y, feature_perturbation="tree_path_dependent")
assert shap_elimination.fitted
shap_elimination._check_if_fitted()
assert report.shape[0] == X.shape[1]
assert shap_elimination.get_reduced_features_set(1) == ["f5"]
_ = shap_elimination.plot(show=False)
@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled")
def test_LightGBM_stratified_kfold():
"""
Test added to check for https://github.com/ing-bank/probatus/issues/170.
"""
from lightgbm import LGBMClassifier
X = pd.DataFrame(
[
[1, 2, 3, 4, 5, 101, 102, 103, 104, 105],
[-1, -2, 2, -5, -7, 1, 2, 5, -1, 3],
["a", "b"] * 5, # noisy categorical will dropped first
]
).transpose()
X[2] = X[2].astype("category")
X[1] = X[1].astype("float")
X[0] = X[0].astype("float")
y = [0] * 5 + [1] * 5
model = LGBMClassifier()
n_iter = 2
n_folds = 3
for _ in range(n_iter):
skf = StratifiedKFold(n_folds, shuffle=True, random_state=42)
shap_elimination = EarlyStoppingShapRFECV(
clf=model,
step=1 / (n_iter + 1),
cv=skf,
scoring="accuracy",
eval_metric="logloss",
early_stopping_rounds=5,
)
report = shap_elimination.fit_compute(X, y, feature_perturbation="tree_path_dependent")
assert shap_elimination.fitted
shap_elimination._check_if_fitted()
assert report.shape[0] == X.shape[1]
shap_elimination.plot(show=False)
| python |
# -*- coding: utf8 -*-
from base import Stock
class Uzmanpara(Stock):
stockURL = "http://uzmanpara.milliyet.com.tr/borsa/hisse-senetleri/{0}/"
priceQuery = '.realTime > .price-arrow-down, .realTime > .price-arrow-up'
volumeQuery = '.realTime table tr td'
timezone = "Europe/Istanbul"
@classmethod
def extractVolume(cls, d):
return d(cls.volumeQuery)[7].text[1:].replace(".", "")
| python |
# -*- coding: utf-8 -*-
from __future__ import absolute_import, unicode_literals
import sys
from pkg_resources import load_entry_point
from subprocess import check_call
def main():
check_call([sys.executable, 'setup.py', 'build_ext', '--inplace'])
if '--with-coverage' not in sys.argv:
sys.argv.extend(('--with-coverage', '--cover-package=cg'))
sys.exit(
load_entry_point('nose', 'console_scripts', 'nosetests')()
)
if __name__ == '__main__':
main()
| python |
"""Tests for ht.events.manager module."""
# =============================================================================
# IMPORTS
# =============================================================================
# Third Party
import pytest
# Houdini Toolbox
import ht.events.manager
from ht.events.event import HoudiniEvent
from ht.events.group import HoudiniEventGroup
from ht.events.item import HoudiniEventItem
# =============================================================================
# FIXTURES
# =============================================================================
@pytest.fixture
def init_manager(mocker):
"""Fixture to initialize a manager."""
mocker.patch.object(
ht.events.manager.HoudiniEventManager, "__init__", lambda x: None
)
def _create():
return ht.events.manager.HoudiniEventManager()
return _create
# =============================================================================
# TESTS
# =============================================================================
class Test_HoudiniEventManager:
"""Test ht.events.manager.HoudiniEventManager class."""
def test___init__(self):
"""Test object initialization."""
manager = ht.events.manager.HoudiniEventManager()
assert manager._data == {}
assert manager._events == {}
assert manager._event_states == {}
# Properties
def test_data(self, init_manager, mocker):
"""Test the 'data' property"""
mock_value = mocker.MagicMock(spec=dict)
manager = init_manager()
manager._data = mock_value
assert manager.data == mock_value
def test_events(self, init_manager, mocker):
"""Test the 'events' property"""
mock_event = mocker.MagicMock(spec=HoudiniEvent)
events = {mocker.MagicMock(spec=str): mock_event}
manager = init_manager()
manager._events = events
assert manager.events == events
# Methods
# _disable_events
def test__disable_events__all(self, init_manager, mocker):
"""Test disabling all events."""
mock_events = mocker.patch.object(
ht.events.manager.HoudiniEventManager,
"events",
new_callable=mocker.PropertyMock,
)
mock_event1 = mocker.MagicMock(spec=HoudiniEvent)
mock_enabled1 = mocker.PropertyMock(return_value=False)
type(mock_event1).enabled = mock_enabled1
mock_event2 = mocker.MagicMock(spec=HoudiniEvent)
mock_enabled2 = mocker.PropertyMock(return_value=True)
type(mock_event2).enabled = mock_enabled2
mock_events.return_value = {
mock_event1.name: mock_event1,
mock_event2.name: mock_event2,
}
manager = init_manager()
manager._event_states = {}
manager._disable_events()
# Each event should have it's enabled property accessed twice:
# once to store the current value and then to set the value to False
mock_enabled1.assert_has_calls([mocker.call(), mocker.call(False)])
mock_enabled2.assert_has_calls([mocker.call(), mocker.call(False)])
assert not manager._event_states[mock_event1.name]
assert manager._event_states[mock_event2.name]
def test__disable_events__specific_names(self, init_manager, mocker):
"""Test disabling specific events."""
mock_events = mocker.patch.object(
ht.events.manager.HoudiniEventManager,
"events",
new_callable=mocker.PropertyMock,
)
mock_event1 = mocker.MagicMock(spec=HoudiniEvent)
mock_enabled1 = mocker.PropertyMock(return_value=True)
type(mock_event1).enabled = mock_enabled1
mock_event2 = mocker.MagicMock(spec=HoudiniEvent)
mock_enabled2 = mocker.PropertyMock(return_value=True)
type(mock_event2).enabled = mock_enabled2
mock_events.return_value = {
mock_event1.name: mock_event1,
mock_event2.name: mock_event2,
}
manager = init_manager()
manager._event_states = {}
manager._disable_events(names=[mock_event2.name])
# Event 1's enabled property should not have been accessed.
mock_enabled1.assert_not_called()
# Event 2's should have been accessed to get the current value
# and once to disable it.
mock_enabled2.assert_has_calls([mocker.call(), mocker.call(False)])
assert manager._event_states[mock_event2.name]
assert len(manager._event_states) == 1
def test__restore_events(self, init_manager, mocker):
"""Test restoring disabled events."""
mock_events = mocker.patch.object(
ht.events.manager.HoudiniEventManager,
"events",
new_callable=mocker.PropertyMock,
)
mock_event1 = mocker.MagicMock(spec=HoudiniEvent)
mock_enabled1 = mocker.PropertyMock(return_value=False)
type(mock_event1).enabled = mock_enabled1
mock_event2 = mocker.MagicMock(spec=HoudiniEvent)
mock_enabled2 = mocker.PropertyMock(return_value=False)
type(mock_event2).enabled = mock_enabled2
mock_events.return_value = {
mock_event1.name: mock_event1,
mock_event2.name: mock_event2,
}
mock_states = mocker.MagicMock(spec=dict)
states = {mock_event1.name: False, mock_event2.name: True}
mock_states.items.return_value = list(states.items())
manager = init_manager()
manager._event_states = mock_states
manager._restore_events()
# Event 1's enable should have been set to False, 2's True
mock_enabled1.assert_has_calls([mocker.call(False)])
mock_enabled2.assert_has_calls([mocker.call(True)])
mock_states.clear.assert_called_once()
def test_create_event(self, init_manager, mocker):
"""Test creating an event."""
mock_events = mocker.patch.object(
ht.events.manager.HoudiniEventManager,
"events",
new_callable=mocker.PropertyMock,
)
mock_factory = mocker.patch("ht.events.manager.HoudiniEventFactory")
mock_event = mocker.MagicMock(spec=HoudiniEvent)
mock_factory.get_event_type.return_value = mock_event
events = {}
mock_events.return_value = events
manager = init_manager()
mock_name = mocker.MagicMock(spec=str)
result = manager.create_event(mock_name)
assert result == mock_event
assert mock_event in list(events.values())
mock_factory.get_event_type.assert_called_with(mock_name)
def test_event_disabler(self, init_manager, mocker):
"""Test the event_disabler context manager."""
mock_disable = mocker.patch.object(
ht.events.manager.HoudiniEventManager, "_disable_events"
)
mock_restore = mocker.patch.object(
ht.events.manager.HoudiniEventManager, "_restore_events"
)
manager = init_manager()
mock_names = mocker.MagicMock(spec=tuple)
with manager.event_disabler(names=mock_names):
pass
mock_disable.assert_called_with(mock_names)
mock_restore.assert_called_once()
# register_event_group
def test_register_event_group__invalid_type(self, init_manager, mocker):
"""Test registering an event group with an invalid object type."""
# Don't spec so it will fail isinstance(EventGroup)
mock_group = mocker.MagicMock()
manager = init_manager()
with pytest.raises(TypeError):
manager.register_event_group(mock_group)
def test_register_event_group__single_items(self, init_manager, mocker):
"""Test registering a group where no event of that name has been created."""
mock_events = mocker.patch.object(
ht.events.manager.HoudiniEventManager,
"events",
new_callable=mocker.PropertyMock,
)
mock_create = mocker.patch.object(
ht.events.manager.HoudiniEventManager, "create_event"
)
mock_item1 = mocker.MagicMock(spec=HoudiniEventItem)
mock_item2 = mocker.MagicMock(spec=HoudiniEventItem)
mock_event_name1 = mocker.MagicMock(spec=str)
mock_event_name2 = mocker.MagicMock(spec=str)
event_map = {mock_event_name1: mock_item1, mock_event_name2: mock_item2}
mock_group = mocker.MagicMock(spec=HoudiniEventGroup)
type(mock_group).event_map = mocker.PropertyMock(return_value=event_map)
mock_event1 = mocker.MagicMock(spec=HoudiniEvent)
mock_event2 = mocker.MagicMock(spec=HoudiniEvent)
events = {mock_event_name2: mock_event2}
mock_events.return_value = events
mock_create.side_effect = lambda name: events.setdefault(name, mock_event1)
manager = init_manager()
manager.register_event_group(mock_group)
mock_create.assert_called_with(mock_event_name1)
mock_event1.register_item.assert_called_with(mock_item1)
mock_event2.register_item.assert_called_with(mock_item2)
def test_register_event_group__item_lists(self, init_manager, mocker):
"""Test registering a group where no event of that name has been created."""
mock_events = mocker.patch.object(
ht.events.manager.HoudiniEventManager,
"events",
new_callable=mocker.PropertyMock,
)
mock_create = mocker.patch.object(
ht.events.manager.HoudiniEventManager, "create_event"
)
mock_item1 = mocker.MagicMock(spec=HoudiniEventItem)
mock_item2 = mocker.MagicMock(spec=HoudiniEventItem)
mock_event_name1 = mocker.MagicMock(spec=str)
mock_event_name2 = mocker.MagicMock(spec=str)
event_map = {mock_event_name1: [mock_item1], mock_event_name2: [mock_item2]}
mock_group = mocker.MagicMock(spec=HoudiniEventGroup)
type(mock_group).event_map = mocker.PropertyMock(return_value=event_map)
event_name1 = mock_event_name1
mock_event1 = mocker.MagicMock(spec=HoudiniEvent)
mock_event2 = mocker.MagicMock(spec=HoudiniEvent)
events = {mock_event_name2: mock_event2}
mock_events.return_value = events
mock_create.side_effect = lambda name: events.setdefault(name, mock_event1)
manager = init_manager()
manager.register_event_group(mock_group)
mock_create.assert_called_with(event_name1)
mock_event1.register_item.assert_called_with(mock_item1)
mock_event2.register_item.assert_called_with(mock_item2)
# register_item
def test_register_item__invalid_type(self, init_manager, mocker):
"""Test registering an invalid type."""
# Don't spec so it will fail isinstance(HoudiniEventItem)
manager = init_manager()
with pytest.raises(TypeError):
manager.register_item(None, mocker.MagicMock(spec=str))
def test_register_item__new_event(self, init_manager, mocker):
"""Test registering an item whose event does not exist yet."""
mock_events = mocker.patch.object(
ht.events.manager.HoudiniEventManager,
"events",
new_callable=mocker.PropertyMock,
)
mock_create = mocker.patch.object(
ht.events.manager.HoudiniEventManager, "create_event"
)
mock_event_name = mocker.MagicMock(spec=str)
mock_event = mocker.MagicMock(spec=HoudiniEvent)
events = {}
mock_events.return_value = events
mock_create.side_effect = lambda name: events.setdefault(name, mock_event)
mock_item = mocker.MagicMock(spec=HoudiniEventItem)
manager = init_manager()
manager.register_item(mock_item, mock_event_name)
mock_create.assert_called_with(mock_event_name)
mock_event.register_item.assert_called_with(mock_item)
def test_register_item__existing_event(self, init_manager, mocker):
"""Test registering an item to an existing event."""
mock_events = mocker.patch.object(
ht.events.manager.HoudiniEventManager,
"events",
new_callable=mocker.PropertyMock,
)
mock_create = mocker.patch.object(
ht.events.manager.HoudiniEventManager, "create_event"
)
mock_event_name = mocker.MagicMock(spec=str)
mock_event = mocker.MagicMock(spec=HoudiniEvent)
mock_events.return_value = {mock_event_name: mock_event}
mock_item = mocker.MagicMock(spec=HoudiniEventItem)
manager = init_manager()
manager.register_item(mock_item, mock_event_name)
mock_create.assert_not_called()
mock_event.register_item.assert_called_with(mock_item)
# run_event
def test_run_event__no_event(self, init_manager, mocker):
"""Test running an event where there are no matching events."""
mock_events = mocker.patch.object(
ht.events.manager.HoudiniEventManager,
"events",
new_callable=mocker.PropertyMock,
)
mock_event_name = mocker.MagicMock(spec=str)
mock_events.return_value = {}
scriptargs = {}
manager = init_manager()
manager.run_event(mock_event_name, scriptargs)
assert scriptargs == {}
def test_run_event__no_scriptargs(self, init_manager, mocker):
"""Test running an event with no particular args."""
mock_events = mocker.patch.object(
ht.events.manager.HoudiniEventManager,
"events",
new_callable=mocker.PropertyMock,
)
mock_event_name = mocker.MagicMock(spec=str)
mock_event = mocker.MagicMock(spec=HoudiniEvent)
mock_events.return_value = {mock_event_name: mock_event}
manager = init_manager()
manager.run_event(mock_event_name)
scriptargs = {"_manager_": manager}
mock_event.run.assert_called_with(scriptargs)
def test_run_event__scriptargs(self, init_manager, mocker):
"""Test running an event while passing in args."""
mock_events = mocker.patch.object(
ht.events.manager.HoudiniEventManager,
"events",
new_callable=mocker.PropertyMock,
)
mock_event_name = mocker.MagicMock(spec=str)
mock_event = mocker.MagicMock(spec=HoudiniEvent)
mock_events.return_value = {mock_event_name: mock_event}
manager = init_manager()
scriptargs = {"key": "value"}
manager.run_event(mock_event_name, scriptargs)
expected_scriptargs = {"key": "value", "_manager_": manager}
mock_event.run.assert_called_with(expected_scriptargs)
assert scriptargs == expected_scriptargs
def test_register_event_group(mocker):
"""Test ht.events.manager.register_event_group."""
mock_manager = mocker.patch("ht.events.manager.EVENT_MANAGER")
mock_group = mocker.MagicMock(spec=HoudiniEventGroup)
ht.events.manager.register_event_group(mock_group)
mock_manager.register_event_group.assert_called_with(mock_group)
class Test_register_function:
"""Test ht.events.manager.register_function."""
def test_not_callable(self, mocker):
"""Test registering a non-callable object."""
mock_event_name = mocker.MagicMock(spec=str)
mock_item_name = mocker.MagicMock(spec=str)
mock_priority = mocker.MagicMock(spec=int)
mock_tags = mocker.MagicMock(spec=list)
with pytest.raises(TypeError):
ht.events.manager.register_function(
None, mock_event_name, mock_item_name, mock_priority, mock_tags
)
def test(self, mocker):
"""Test registering a callable object."""
mock_cls = mocker.patch("ht.events.manager.HoudiniEventItem", autospec=True)
mock_register_item = mocker.patch("ht.events.manager.register_item")
mock_func = mocker.MagicMock()
mock_event_name = mocker.MagicMock(spec=str)
mock_item_name = mocker.MagicMock(spec=str)
mock_priority = mocker.MagicMock(spec=int)
mock_tags = mocker.MagicMock(spec=list)
ht.events.manager.register_function(
mock_func, mock_event_name, mock_item_name, mock_priority, mock_tags
)
mock_cls.assert_called_with(
(mock_func,), mock_item_name, mock_priority, stat_tags=mock_tags
)
mock_register_item.assert_called_with(mock_cls.return_value, mock_event_name)
class Test_register_item:
"""Test ht.events.manager.register_item."""
def test_not_item(self, mocker):
"""Test registering an invalid type."""
mock_event_name = mocker.MagicMock(spec=str)
with pytest.raises(TypeError):
ht.events.manager.register_item(None, mock_event_name)
def test(self, mocker):
"""Test registering a valid item."""
mock_manager = mocker.patch("ht.events.manager.EVENT_MANAGER")
mock_event_name = mocker.MagicMock(spec=str)
mock_item = mocker.MagicMock(spec=HoudiniEventItem)
ht.events.manager.register_item(mock_item, mock_event_name)
mock_manager.register_item.assert_called_with(mock_item, mock_event_name)
def test_run_event(mocker):
"""Test ht.events.manager.run_event."""
mock_manager = mocker.patch("ht.events.manager.EVENT_MANAGER")
mock_event_name = mocker.MagicMock(spec=str)
mock_scriptargs = mocker.MagicMock(spec=dict)
ht.events.manager.run_event(mock_event_name, mock_scriptargs)
mock_manager.run_event.assert_called_with(mock_event_name, mock_scriptargs)
| python |
from .. cupy_utils import to_numpy, trapz, xp
from ..utils import powerlaw
import numpy as np
from astropy.cosmology import Planck15
class PowerLawRedshift(object):
"""
Redshift model from Fishbach+ https://arxiv.org/abs/1805.10270
Note that this is deliberately off by a factor of dVc/dz
"""
def __init__(self):
self.zs_ = np.linspace(1e-3, 1, 1000)
self.zs = xp.asarray(self.zs_)
self.dvc_dz_ = (
Planck15.differential_comoving_volume(self.zs_).value * 4 * np.pi)
self.dvc_dz = xp.asarray(self.dvc_dz_)
self.cached_dvc_dz = None
def __call__(self, dataset, lamb):
p_z = powerlaw(1 + dataset['redshift'], alpha=(lamb - 1),
high=(1 + self.zs_[-1]), low=1)
try:
p_z *= self.cached_dvc_dz
except (TypeError, ValueError):
self._cache_dvc_dz(dataset['redshift'])
p_z *= self.cached_dvc_dz
p_z /= self.normalisation(lamb)
return p_z
def normalisation(self, lamb):
p_z_ = powerlaw(1 + self.zs, alpha=(lamb - 1),
high=(1 + self.zs_[-1]), low=1)
norm = trapz(p_z_ * self.dvc_dz, self.zs)
return norm
def _cache_dvc_dz(self, redshifts):
self.cached_dvc_dz = xp.asarray(np.interp(
to_numpy(redshifts), self.zs_, self.dvc_dz_))
power_law_redshift = PowerLawRedshift()
| python |
from flask import Flask
from flask_bootstrap import Bootstrap
app = Flask(__name__)
Bootstrap(app)
with app.app_context():
import routes
import stats
if __name__ == "__main__":
app.config['DEBUG'] = True
app.run()
| python |
from receptor_affinity.mesh import Mesh
from wait_for import TimedOutError
import time
import pytest
@pytest.yield_fixture(
scope="function",
params=[
"test/perf/flat-mesh.yaml",
"test/perf/tree-mesh.yaml",
"test/perf/random-mesh.yaml",
],
ids=["flat", "tree", "random"],
)
def mesh(request):
mesh = Mesh.load_mesh_from_file(request.param, use_diag_node=True)
try:
mesh.start(wait=True)
yield mesh
except TimedOutError:
raise
finally:
print(f"{time.time()} - Stopping current mesh")
print(mesh.nodes['controller'])
mesh.stop()
def test_pings_perf(mesh):
results = mesh.ping()
mesh.validate_ping_results(results)
| python |
# Copyright 2021 Gakuto Furuya
#
# 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 sudachipy import tokenizer
from sudachipy import dictionary
def main():
tokenizer_obj = dictionary.Dictionary().create()
mode = tokenizer.Tokenizer.SplitMode.C
while True:
sentence = input()
tokens = tokenizer_obj.tokenize(sentence, mode)
pekofied_sentence = ''
noun_flag = False
final_form_flag = False
for t in tokens:
if noun_flag:
if t.part_of_speech()[1] == '句点':
pekofied_sentence += 'ぺこ' + t.surface()
elif t.part_of_speech()[1] == '終助詞':
pekofied_sentence += 'ぺこ' + t.surface()
elif t.part_of_speech()[0] == '助動詞' and t.part_of_speech()[5] == '終止形-一般':
pekofied_sentence += 'ぺこ' + t.surface()
else:
pekofied_sentence += t.surface()
noun_flag = False
elif final_form_flag:
if t.part_of_speech()[0] == '助動詞':
pekofied_sentence += t.surface()
elif t.part_of_speech()[1] == '終助詞':
if t.dictionary_form() == 'じゃん':
pekofied_sentence += 'ぺこ' + t.surface()
else:
pekofied_sentence += t.surface()
elif t.part_of_speech()[1] == '接続助詞':
if t.dictionary_form() == 'と' or t.dictionary_form() == 'けれど':
pekofied_sentence += t.surface()
else:
pekofied_sentence += 'ぺこだ' + t.surface()
else:
pekofied_sentence += 'ぺこ' + t.surface()
final_form_flag = False
elif t.part_of_speech()[0] == '名詞':
pekofied_sentence += t.surface()
noun_flag = True
elif t.part_of_speech()[5] == '終止形-一般':
pekofied_sentence += t.surface()
final_form_flag = True
else:
pekofied_sentence += t.surface()
if noun_flag:
pekofied_sentence += 'ぺこ'
if final_form_flag:
pekofied_sentence += 'ぺこ'
print(pekofied_sentence)
if __name__ == '__main__':
main() | python |