def generateDataFramesRangeSaving(StartDate, EndDate, DateStep, Gender='None', SaveEvery=1, File='OBOextract', Path='Data/Raw/', Overwrite = 1):
"""Generate the dataframes from a gender and datelist"""
import os
import pandas as pd
import urllib3
retry = urllib3.util.Retry(total=100, read=100, connect=100, backoff_factor=1)
timeout = urllib3.util.Timeout(connect=4.0, read=8.0)
http=urllib3.PoolManager(retry=retry, timeout=timeout, maxsize=5)
_Iteration = 0
DummyFrame = pd.DataFrame(columns=c2n.generateCategories())
if Overwrite is 0:
# Does the file exist
if os.path.isfile(Path + File+'.csv'):
try:
DummyFrame = pd.DataFrame.from_csv(Path + File+'.csv')
_TempStartDate = int(DummyFrame.tail(1).values[0,0] + DateStep)
if _TempStartDate >= StartDate:
StartDate = _TempStartDate + DateStep
except:
DummyFrame = pd.DataFrame(columns=c2n.generateCategories())
for _startDate in range(StartDate,EndDate+1,DateStep):
_Iteration += 1
_endDate= _startDate+DateStep-1
print('Generating row for {} to {}, Gender: {}'.format(_startDate,_endDate,Gender))
DummyFrame.loc[len(DummyFrame)] = generateRowRange(_startDate, _endDate, Gender)
if _Iteration >= SaveEvery:
_Iteration = 0
DummyFrame.to_csv(Path + File+'.csv')
return DummyFrame
def generateDummyDataFrame(Delta=5):
""" Generate a DataFrame of dummy data to mimic an Emperical Categories frame.
We want to produce dummy data to test our model selection. We have two trials in one:
Detect the optimal delta, and detect the optimal partitionings.
We do this with one data set where we have some small number of slightly different
distributions, repeated the number of times for our.
We only need distinct distributions for offences, repeated for all 7 punishments
Arbitrarily we will choose deltat = 5 and three distinct partitions.
One of size one,
One of size two,
One of size seve
"""
offenceDistributions = [
[0.05,0.2,0.05,0.05,0.25,0.05,0.05,0.25,0.05], # C,A,C,C,B,C,C,B,C
[0.25,0.05,0.05,0.05,0.25,0.05,0.05,0.2,0.05], # B,C,C,C,B,C,C,A,C
[0.05,0.25,0.25,0.05,0.05,0.05,0.05,0.05,0.2], # C,B,B,C,C,C,C,C,A
[0.05,0.2,0.05,0.05,0.25,0.05,0.05,0.25,0.05] # C,A,C,C,B,C,C,B,C
]
probabilities = []
for offDis in offenceDistributions:
probabilities.append([x for x in [z/7 for z in offDis] for y in [1,2,3,4,5,6,7]])
dummyEmp = pd.DataFrame(index=list(range(1674,(1674+4*Delta))), columns=c2n.generateCategories()[1:64])
for offenDist in range(4):
for delta in range(Delta):
dummyEmp.iloc[offenDist*Delta+delta] = discreteRandomSamples(probabilities[offenDist])
return dummyEmp
def generateDataFramesParallel(Date_List, Gender):
"""Generate the dataframes from a gender and datelist"""
import workerpool
import json
import pandas as pd
Gender = 'None'
DummyFrame = pd.DataFrame(columns=c2n.generateCategories())
NUM_SOCKETS = 3
NUM_WORKERS = 5
# We want a few more workers than sockets so that they have extra
# time to parse things and such.
workers = workerpool.WorkerPool(size=NUM_WORKERS)
class MyJob(workerpool.Job):
def __init__(self, dAte, Gender):
self.dAte = dAte
self.Gender = Gender
def run(self):
print('Generating row for {}'.format(self.dAte))
DummyFrame.loc[len(DummyFrame)] = generateRow(self.dAte,self.Gender,NUM_SOCKETS)
for daTe in Date_List:
workers.put(MyJob(daTe,Gender))
# Send shutdown jobs to all threads, and wait until all the jobs have been completed
# (If you don't do this, the script might hang due to a rogue undead thread.)
workers.shutdown()
workers.wait()
return DummyFrame
def generateDataFrames(Date_List, Gender):
"""Generate the dataframes from a gender and datelist"""
import pandas as pd
DummyFrame = pd.DataFrame(columns=c2n.generateCategories())
for dAte in Date_List:
print('Generating row for {}'.format(dAte))
DummyFrame.loc[len(DummyFrame)] = generateRow(dAte,Gender)
return DummyFrame
def generateDataFramesRollingRangeSaving(StartDate, EndDate, DateStep, Delta=1000, Gender='None', SaveEvery=1, File='OBOextract', Path='Data/Raw/', Overwrite = 1):
"""Generate the dataframes from a gender and datelist
For dates from StartDate to EndDate stepped through by DateStep,
data is extracted for the date to date+Delta-1.
The data is saved in a Pandas DataFrame and cumulativly updated, and saved every SaveEvery retrievals to the file File.
If Overwrite=1 then the process will attempt to restart from interruption based on the xistingesaeved DataFdramed.dd"""
import os
import pandas as pd
import urllib3
retry = urllib3.util.Retry(total=100, read=100, connect=100, backoff_factor=1)
timeout = urllib3.util.Timeout(connect=4.0, read=8.0)
http=urllib3.PoolManager(retry=retry, timeout=timeout, maxsize=5)
_Iteration = 0
DummyFrame = pd.DataFrame(columns=c2n.generateCategories())
if Overwrite is 0:
# Does the file exist
if os.path.isfile(Path + File+'.csv'):
try:
DummyFrame = pd.DataFrame.from_csv(Path + File+'.csv')
_TempStartDate = int(DummyFrame.tail(1).values[0,0] + DateStep)
if _TempStartDate >= StartDate:
StartDate = _TempStartDate + DateStep
except:
DummyFrame = pd.DataFrame(columns=c2n.generateCategories())
for _startDate in range(StartDate,EndDate+1,DateStep):
_Iteration += 1
_endDate= _startDate+Delta-1
print('Generating row for {} to {}, Gender: {}'.format(_startDate,_endDate,Gender))
DummyFrame.loc[len(DummyFrame)] = generateRowRange(_startDate, _endDate, Gender)
if _Iteration >= SaveEvery:
_Iteration = 0
DummyFrame.to_csv(Path + File+'.csv')
return DummyFrame
def generateDataFrameInChunks(Date_List,Gender,ChunkSize=10, File='OBOextract', Path='Data/Raw/', Overwrite = 1, Start=0, Stop=0):
""" Generate DataFrames, but save to csv every so often"""
import pandas as pd
if Stop <= Start:
Stop=len(Date_List)
DummyFrame = pd.DataFrame(columns=c2n.generateCategories())
for i in range(Start,Stop,ChunkSize):
for dAte in Date_List[i:i+ChunkSize-1]:
print('Generating row for date {}, and gender {}'.format(dAte,Gender))
DummyFrame.loc[len(DummyFrame)] = generateRow(dAte,Gender)
DummyFrame.to_csv(File+'.csv')
return DummyFrame
def generateDataFramesRange(StartDate, EndDate, DateStep, Gender='None'):
"""Generate the dataframes from a gender and datelist"""
import pandas as pd
import urllib3
retry = urllib3.util.Retry(total=1000, read=200, connect=200, backoff_factor=0.5)
timeout = urllib3.util.Timeout(connect=2.0, read=4.0)
http=urllib3.PoolManager(retry=retry, timeout=timeout, maxsize=10)
DummyFrame = pd.DataFrame(columns=c2n.generateCategories())
for _startDate in range(StartDate,EndDate+1,DateStep):
_endDate= _startDate+DateStep-1
print('Generating row for {} to {}:'.format(_startDate,_endDate))
DummyFrame.loc[len(DummyFrame)] = generateRowRange(_startDate, _endDate, Gender)
return DummyFrame
def initialiseEmptyArray():
"""Initialise an empty array the same length as the Categories"""
return [0]*len(c2n.generateCategories())
def initialiseDataFrame():
"""Create an empty dataframe with nothing but categories"""
import pandas as pd
return pd.DataFrame(columns=c2n.generateCategories())
def generateRowRange(StartDate, EndDate, Gender='None', http='None',Sockets=2):
"""Generate one row of data for a given date and defendent gender
We are not atomising the code to generalise URL gets such that
the socket opened by urllib3 can stay open. This may be messy
looking...
"""
import CategoryToNumberAssignment as c2n
import urllib3
import json
if http is 'None':
http=urllib3.PoolManager(maxsize=Sockets)
sStartDate = str(StartDate)
sEndDate = str(EndDate)
_Columns = c2n.generateCategories()
#Generate empty columns
Row = [sStartDate]
"""Get not breakdown of punishments by category and subcategory for the period"""
# Categories
for Category in c2n.offcat:
_TempCategories = initialiseEmptyCatArray()
#Get the Json data
#print("Generating URL: {}".format(generateURLCategoryRange(sStartDate,sEndDate,Category, Gender)))
_Json = URLtoJSON(generateURLCategoryRange(sStartDate,sEndDate,Category, Gender),http)
#Find the punishment totals and place in the correct position in the array
# adding 1 place for the Not guilty
for Totals in _Json['breakdown']:
_TempCategories[c2n.puncat.index(Totals['term'])+1]=Totals['total']
# Append _Temps to the Rows
Row = Row + _TempCategories
#Associate Offence Subcategories with Punishment Subategories
for Category in c2n.offsubcat:
_TempCategories = initialiseEmptySubCatArray()
#Get the Json data
_Json = URLtoJSON(generateURLSubCategoryRange(sStartDate,sEndDate,Category, Gender),http)
#Find the punishment totals and place in the correct position in the array
# adding 1 place for the Not guilty
for Totals in _Json['breakdown']:
_TempCategories[c2n.punsubcat.index(Totals['term'])+1]=Totals['total']
# Append _Temps to the Rows
Row = Row + _TempCategories
"""Get not guilties by category and subcategory for the period"""
#Get not guilties:
_JsonNotGuiltyCat = URLtoJSON(generateURLCategoryNotGuiltyRange(sStartDate,sEndDate,Gender),http)
_JsonNotGuiltySubCat = URLtoJSON(generateURLSubCategoryNotGuiltyRange(sStartDate,sEndDate,Gender),http)
# Place values associated with locations in Row:
for Totals in _JsonNotGuiltyCat['breakdown']:
Row[_Columns.index(c2n.upcaseFirstLetter(Totals['term'])+'NotGuilty')] = Totals['total']
for Totals in _JsonNotGuiltySubCat['breakdown']:
Row[_Columns.index(c2n.upcaseFirstLetter(Totals['term'])+'NotGuilty')] = Totals['total']
return Row