CaTransDecodingCode/SlidingWindowAnalysis.py at master · rweaton/CaTransDecodingCode · GitHub

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 19 09:33:08 2019

@author: thugwithyoyo
"""
importnumpyasnp
fromPeriEventTraceFuncLibimport*

# Paths to data in JSON formatted files
PathToBehavFile='/home/thugwithyoyo/CaTransDecoding/CalciumImagingData/2018-12-10-11-37-56_unique_B.json'
PathToFluorFile='/home/thugwithyoyo/CaTransDecoding/CalciumImagingData/2018-12-10-11-37-56_unique_C.json'

# Peripheral target entry events
RefEventsList= ['M6T0_Entry_ts', 'M6T1_Entry_ts']

# Scalar values assigned to event types listed above.
AssignedEventVals= [-1, 1]

# Pack into a dict the two lists above that specify info for reference
# events
RefEventsDict= {'RefEventsList' : RefEventsList,
'AssignedEventVals' : AssignedEventVals}

# Specify outcome measures to be plotted
PerformancePlotSpecDict= {'measure': 'performance',
'measure_median': 'performance_median',
'measure_CLs': 'performance_CLs'}

ShuffledPerformancePlotSpecDict= {'measure': 'performance_median',
'measure_median': 'performance_median',
'measure_CLs': 'performance_CLs'}

MutInfoPlotSpecDict= {'measure': 'mutual_info',
'measure_median': 'mutual_info_median',
'measure_CLs': 'mutual_info_CLs'}

ShuffledMutInfoPlotSpecDict= {'measure': 'mutual_info_median',
'measure_median': 'mutual_info_median',
'measure_CLs': 'mutual_info_CLs'}

# Set parameters for peri-event extraction
BoundaryWindow= [-1., 2.]
StepWidth=0.1
WindowWidth=0.4

ArrayOfSlidWindows=SlidingWindowGen(BoundaryWindow, StepWidth, WindowWidth)

# Set parameters for PLS
NumLatents=5

# Set parameters for Monte Carlo estimation of confidence intervals
NumRepetitions=30
ConfLevel=0.95

# Specified anti-tolerance window, relative to target entry, for detecting and
# removing repeat entries that followed shortly after the initial entry.
RelativeTolWindow= (0.0001, 2.5)

# Generate the unfiltered behavior dictionary.
BehavDict=BehavDictGen(PathToBehavFile)

# Detect rapid repeats within each event list.
#EventFilters = RemoveRepeatTargetEntries(BehavDict, RefEventsList,
# RelativeTolWindow)

# Remove repeat events
#for ef in EventFilters:

# BehavDict[ef] = BehavDict[ef][EventFilters[ef]]

# Generate the data frame of calcium transients.
CellFluorTraces_Frame=CellFluorTraces_FrameGen(PathToFluorFile)

# Grow window forwards from floor
(NumDomains, _) =ArrayOfSlidWindows.shape

# Initialize an empty array to contain output dictionaries from the
# decoder cross-validation perfomance and monte carlo bootstrap routines
Performance=np.empty((NumDomains,), dtype=dict)
ConfInts=np.empty((NumDomains,), dtype=dict)
EventsShuffled=np.empty((NumDomains,), dtype=dict)

foriinnp.arange(0, NumDomains):

PeriEventExtractorDict=PeriEventExtractor_Trace(BehavDict,
CellFluorTraces_Frame, RefEventsDict,
ArrayOfSlidWindows[i])

# Generate a set of indices to test the inclusion portion of the performance code.
PEA_Array=PeriEventExtractorDict['PEA_Array']
 (NumTotalTrials, NumTotalFeatures) =PEA_Array.shape
#InclusionSet = np.random.randint(0, high=NumTotalTrials, size=(NumTotalTrials,))

Performance[i] =PLS_DecoderPerformance(PeriEventExtractorDict, NumLatents)
Performance[i].update({'PeriEventDomain': ArrayOfSlidWindows[i]})

ConfInts[i] =PLS_MonteCarlo(PeriEventExtractorDict,
NumLatents, NumRepetitions,
ConfLevel)
ConfInts[i].update({'PeriEventDomain': ArrayOfSlidWindows[i]})

EventsShuffled[i] =PLS_Shuffle(PeriEventExtractorDict,
NumLatents, NumRepetitions,
ConfLevel)
EventsShuffled[i].update({'PeriEventDomain': ArrayOfSlidWindows[i]})


#### Plot outcome measures #####

# Plot performance dependence on increasing peri-event window span
fig1, axs1=plt.subplots()
#fig1.suptitle(PerformancePlotSpecDict['measure'])
GenerateConfIntsPlot(ConfInts, Performance, PerformancePlotSpecDict,
axs1, 'fw_sliding')

GenerateConfIntsPlot(EventsShuffled, EventsShuffled, ShuffledPerformancePlotSpecDict,
axs1, 'fw_sliding')
axs1.set_xbound(lower=BoundaryWindow[0], upper=BoundaryWindow[1])
axs1.set_ybound(lower=0.4, upper=1.)

# Plot mutual information dependence on increasing peri-event window span
fig2, axs2=plt.subplots()
#fig2.suptitle(MutInfoPlotSpecDict['measure'])
GenerateConfIntsPlot(ConfInts, Performance, MutInfoPlotSpecDict,
axs2, 'fw_sliding')
GenerateConfIntsPlot(EventsShuffled, EventsShuffled, ShuffledMutInfoPlotSpecDict,
axs2, 'fw_sliding')
axs2.set_xbound(lower=BoundaryWindow[0], upper=BoundaryWindow[1])
axs2.set_ybound(lower=0., upper=1.)