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

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 10 01:00:07 2019

@author: thugwithyoyo
"""

importnumpyasnp
importmatplotlib.pyplotasplt
importmatplotlibasmpl
importos

importtkinterastk
fromtkinter.filedialogimportaskopenfilename

defGetSessionDecodingMaximum():

# Acquire path of workspace to load.
root=tk.Tk()
RestoreFilePath=askopenfilename()
root.withdraw()

# Open workspace.
#exec(open('./RestoreShelvedWorkspaceScript.py').read())
try:

exec(open('./RestoreShelvedWorkspaceScript.py').read())

except:

print('Unshelving error. Will attempt to continue...')

# Determine parent directory and filename from complete path.
drive, path_and_file=os.path.splitdrive(RestoreFilePath)
path, file=os.path.split(path_and_file)

PerfIndices=np.arange(1, Performance.shape[0])
MaxPerfVal=Performance[0]['performance']
MaxPerfIndex=0

SessionName=file[0:19]

foriinPerfIndices:

ifPerformance[i]['performance'] >MaxPerfVal:

MaxPerfIndex=i
MaxPerfVal=Performance[i]['performance']

return {
'MaxPerfIndex': np.array([MaxPerfIndex]),
'MaxPerfVal': np.array([MaxPerfVal]),
'MaxPerfErrorBar': np.array([ConfInts[MaxPerfIndex]['performance_SE']]),
'ShuffledPerfMedian': np.array([EventsShuffled[MaxPerfIndex]['performance_median']]),
'ShuffledPerfErrorBar': np.array([EventsShuffled[MaxPerfIndex]['performance_SE']]),
'SessionName': np.array([SessionName])
 }

# Integer width to position clusters.
ClusterIncrement=1.

# The proportion of space to use to separate bar groups.
GroupSepWidthProportion=0.25

# Calculate barwidth
BarWidth=ClusterIncrement*(1.-GroupSepWidthProportion)/2.

# Set colorscheme. Is this necessary?
mpl.rc('image', cmap='nipy_spectral')

SessionPerfMeans=np.empty((0,))
SessionPerfSEs=np.empty((0,))
SessionShuffledPerfMeans=np.empty((0,))
SessionShuffledPerfSEs=np.empty((0,))

SessionNames=np.array([])

#EntriesFilt = np.array([1, 3, 5, 7, 9, -1])
#EntriesFilt = EntriesFilt[::-1]

GetAnotherSession=True

while (GetAnotherSession==True):

SessionDict=GetSessionDecodingMaximum()

# SessionPerfMeans = np.hstack([SessionPerfMeans, np.array([MaxPerfVal])])
# SessionPerfSEs = np.hstack([SessionPerfSEs, np.array([ConfInts[i]['performance_SE']])])
# SessionShuffledPerfMeans = np.hstack([SessionShuffledPerfMeans, np.array([EventsShuffled[i]['performance_median']])])
# SessionShuffledPerfSEs = np.hstack([SessionShuffledPerfSEs, np.array([EventsShuffled[i]['performance_SE']])])
#
# SessionNames = np.hstack([SessionNames, SessionName])

SessionPerfMeans=np.hstack([SessionPerfMeans, SessionDict['MaxPerfVal']])
SessionPerfSEs=np.hstack([SessionPerfSEs, SessionDict['MaxPerfErrorBar']])
SessionShuffledPerfMeans=np.hstack([SessionShuffledPerfMeans, SessionDict['ShuffledPerfMedian']])
SessionShuffledPerfSEs=np.hstack([SessionShuffledPerfSEs, SessionDict['ShuffledPerfErrorBar']])

SessionNames=np.hstack([SessionNames, SessionDict['SessionName']])

root=tk.Tk()

GetAnotherSession=tk.messagebox.askyesno(message='Include another session?')

root.withdraw()

#
# Extract recording IDs from filenames
NiceSessionNames=SessionNames

foriinnp.arange(0, SessionNames.shape[0]):

NiceSessionNames[i] =SessionNames[i][0:10]

SortMap=np.argsort(NiceSessionNames)

# Begin plot generation
(NumSessions, ) =SessionPerfMeans.shape
fig1, ax1=plt.subplots(nrows=1, ncols=1)

fig1.suptitle('Peak trace decoding accuracy across sessions')

xLocs=np.arange(ClusterIncrement, (NumSessions+1)*ClusterIncrement,
ClusterIncrement)

# Plot bars and errorbars of OBSERVED peak decoder performance across sessions.
ax1.bar(xLocs-BarWidth/2., SessionPerfMeans[SortMap],
color='orange', ecolor='orange', label='Observed outcomes',
width=BarWidth)

ax1.errorbar(xLocs-BarWidth/2., SessionPerfMeans[SortMap],
yerr=SessionPerfSEs[SortMap],
color='black', ecolor='black', fmt=',')

# Plot bars and errorbars of SHUFFLED peak decoder performance across sessions.
ax1.bar(xLocs+BarWidth/2., SessionShuffledPerfMeans[SortMap],
color='gray', ecolor='gray', label='Shuffled outcomes',
width=BarWidth)

ax1.errorbar(xLocs+BarWidth/2., SessionShuffledPerfMeans[SortMap],
yerr=SessionShuffledPerfSEs[SortMap],
color='black', ecolor='black', fmt=',')

ax1.set_xlabel('Session')
ax1.set_ylabel('Peak Decoding Accuracy (%)')
ax1.set_ylim([0., 1.])
ax1.set_yticks([0., 0.5, 1.0])
ax1.set_yticklabels(['0', '50', '100'])


#ax1.set_xticks(range(1, NumSessions+1, 1))
ax1.set_xticks(xLocs)
#ax1.set_xticklabels(NiceSessionNames[SortMap], rotation=45, ha='center')
ax1.set_xticklabels(np.arange(1,12,1), rotation=0)
ax1.legend(loc='lower right')
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.set_title('PLS-DA: 400ms window, slid over 100ms increments')