def computeStatsBlocks(subjID):
with open('BehV3_subjID_' + subjID + '.pkl', "rb") as fin:
sub = (pickle.load(fin))[0]
statsDay = np.zeros((5, 8))
statsBlock = np.zeros((4, 16, 5))
statsBlock_day2 = np.zeros((48, 5))
idx_c = 0
for idx, expDay in enumerate(['1', '2', '3', '4', '5']):
# Load catFile
catFile = 'P:\\closed_loop_data\\' + str(
subjID) + '\\createIndices_' + subjID + '_day_' + expDay + '.csv'
if subjID == '11' and expDay == '2':
responseTimes = [np.nan]
nKeypress = np.nan
else:
responseTimes = sub['responseTimes_day' + expDay]
nKeypress = sub['TOTAL_keypress_day' + expDay]
if subjID == '11' and expDay == '2':
CI_lure, NI_lure, I_nlure, NI_nlure, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = [
np.nan
] * 8
else:
CI_lure, NI_lure, I_nlure, NI_nlure, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = findRTsBlocks(
catFile, responseTimes, block=False)
# Compute stats (for visibility)
TP = NI_nlure
FP = NI_lure
TN = CI_lure
FN = I_nlure
sensitivity = TP / (TP + FN)
specificity = TN / (TN + FP)
accuracy = (TP + TN) / (TP + TN + FP + FN)
# accuracy_all = (TP+TN)/nKeypress # Out of all the keypresses that day
statsDay[
idx, :] = sensitivity, specificity, accuracy, lure_RT_mean, nonlure_RT_mean, RT_mean, nKeypress, nNaN
# Block-wise
for block in range(1, int(len(responseTimes) / 50) + 1):
print(block)
if subjID == '11' and expDay == '2':
TN, FP, FN, TP, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = [
np.nan
] * 8
else:
TN, FP, FN, TP, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = findRTsBlocks(
catFile, responseTimes, block=block)
sensitivity = TP / (TP + FN)
specificity = TN / (TN + FP)
accuracy = (TP + TN) / (TP + TN + FP + FN)
if expDay == '2':
statsBlock_day2[
block -
1, :] = sensitivity, specificity, accuracy, RT_mean, nNaN
if expDay != '2':
statsBlock[
idx_c, block -
1, :] = sensitivity, specificity, accuracy, RT_mean, nNaN
if expDay != '2':
idx_c += 1
return statsDay, statsBlock, statsBlock_day2
def computeHitandFA(subjID):
'''
Computes hit and FA based on days (statsDay)
'''
with open(saveDir + 'BehV3_subjID_' + subjID + '.pkl', "rb") as fin:
sub = (pickle.load(fin))[0]
statsDay = np.zeros((5,2))
for idx, expDay in enumerate(['1','2','3','4','5']):
# Load catFile
catFile = 'P:\\closed_loop_data\\' + str(subjID) + '\\createIndices_'+subjID+'_day_'+expDay+'.csv'
if subjID == '11' and expDay == '2':
responseTimes = [np.nan]
else:
responseTimes = sub['responseTimes_day'+expDay]
if subjID == '11' and expDay == '2':
CI_lure, NI_lure, I_nlure, NI_nlure, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = [np.nan]*8
else:
CI_lure, NI_lure, I_nlure, NI_nlure, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = findRTsBlocks(catFile,responseTimes,block=False)
# Compute stats (for visibility)
TP = NI_nlure # Hit rate
FP = NI_lure # False alarm
TN = CI_lure
FN = I_nlure
if expDay == '2':
H = TP/2160
FA = FP/240
else:
H = TP/720
FA = FP/80
statsDay[idx,:] = H, FA
return statsDay
def computeStats(subjID):
'''
Adds A'
Computes stats based on days (statsDay) and blocks for each day (both statsBlock: day 1, 3, 4, 5 and statsBlock_day2)
'''
with open(saveDir + 'BehV3_subjID_' + subjID + '.pkl', "rb") as fin:
sub = (pickle.load(fin))[0]
statsDay = np.zeros((5,8))
statsBlock = np.zeros((4,16,7))
statsBlock_day2 = np.zeros((48,7))
idx_c = 0
for idx, expDay in enumerate(['1','2','3','4','5']):
# Load catFile
catFile = 'P:\\closed_loop_data\\' + str(subjID) + '\\createIndices_'+subjID+'_day_'+expDay+'.csv'
if subjID == '11' and expDay == '2':
responseTimes = [np.nan]
else:
responseTimes = sub['responseTimes_day'+expDay]
if subjID == '11' and expDay == '2':
CI_lure, NI_lure, I_nlure, NI_nlure, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = [np.nan]*8
else:
CI_lure, NI_lure, I_nlure, NI_nlure, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = findRTsBlocks(catFile,responseTimes,block=False)
# Compute stats (for visibility)
TP = NI_nlure # Hit rate
FP = NI_lure # False alarm
TN = CI_lure
FN = I_nlure
if expDay == '2':
H = TP/2160
FA = FP/240
else:
H = TP/720
FA = FP/80
# plt.figure(8)
# plt.scatter(FA,0.85,color='black',label='(FA,H)')
# plt.ylim(0,1)
# plt.xlim(0,1)
# plt.xticks(np.arange(0,1.1,0.1))
# plt.yticks(np.arange(0,1.1,0.1))
# plt.xlabel('False alarm rate (FA)')
# plt.ylabel('Hit rate (H)')
# plt.plot([0,FA],[0,0.85],'k-',color='black')
# plt.plot([1,FA],[1,0.85],'k-',color='black')
# plt.text(0.05,0.6,s='$A_C$')
# plt.text(0.28,0.9,s='$A_L$')
# plt.text(0.5,0.5,s='$I$')
# plt.text(0.25,0.8,s='(FA,H)')
# plt.title('A\' behavioral measure')
# plt.grid(color='gainsboro',linewidth=0.5)
# plt.savefig('aprime.png',dpi=300)
sensitivity = TP/(TP+FN)
specificity = TN/(TN+FP)
FPR = FP/(FP+TN)
accuracy = (TP+TN)/(TP+TN+FP+FN)
errorrate = 1-accuracy
RER = accuracy/(1-accuracy)
A = 1/2 + (((H-FA)*(1+H-FA))/((4*H)*(1-FA)))
ARER = A/(1-A)
statsDay[idx,:] = sensitivity, FPR, accuracy, errorrate, RER, A, ARER, RT_mean
# Block-wise
for block in range(1,int(len(responseTimes)/50)+1):
print(block)
if subjID == '11' and expDay == '2':
TN, FP, FN, TP, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = [np.nan]*8
else:
TN, FP, FN, TP, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = findRTsBlocks(catFile,responseTimes,block=block)
sensitivity = TP/(TP+FN)
specificity = TN/(TN+FP)
FPR = FP/(FP+TN)
accuracy = (TP+TN)/(TP+TN+FP+FN)
H = TP/50
FA = FP/50
sensitivity = TP/(TP+FN)
specificity = TN/(TN+FP)
FPR = FP/(FP+TN)
accuracy = (TP+TN)/(TP+TN+FP+FN)
errorrate = 1-accuracy
A = 1/2 + (((H-FA)*(1+H-FA))/((4*H)*(1-FA)))
ARER = A/(1-A)
if expDay == '2':
statsBlock_day2[block-1,:] = sensitivity, FPR, accuracy, errorrate, A, ARER, RT_mean
if expDay != '2':
statsBlock[idx_c,block-1,:] = sensitivity, FPR, accuracy, errorrate, A, ARER, RT_mean
if expDay != '2':
idx_c += 1
return statsDay, statsBlock, statsBlock_day2
def computeStats(subjID):
'''
Adds A'
Computes stats based on days (statsDay) and blocks for each day (both statsBlock: day 1, 3, 4, 5 and statsBlock_day2)
'''
with open(saveDir + 'BehV3_subjID_' + subjID + '.pkl', "rb") as fin:
sub = (pickle.load(fin))[0]
statsDay = np.zeros((5, 9))
statsBlock = np.zeros((4, 16, 6))
statsBlock_day2 = np.zeros((48, 6))
idx_c = 0
for idx, expDay in enumerate(['1', '2', '3', '4', '5']):
# Load catFile
catFile = 'P:\\closed_loop_data\\' + str(
subjID) + '\\createIndices_' + subjID + '_day_' + expDay + '.csv'
if subjID == '11' and expDay == '2':
responseTimes = [np.nan]
else:
responseTimes = sub['responseTimes_day' + expDay]
if subjID == '11' and expDay == '2':
CI_lure, NI_lure, I_nlure, NI_nlure, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = [
np.nan
] * 8
else:
CI_lure, NI_lure, I_nlure, NI_nlure, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = findRTsBlocks(
catFile, responseTimes, block=False)
# Compute stats (for visibility)
TP = NI_nlure
FP = NI_lure
TN = CI_lure
FN = I_nlure
sensitivity = TP / (TP + FN)
specificity = TN / (TN + FP)
FPR = FP / (FP + TN)
accuracy = (TP + TN) / (TP + TN + FP + FN)
A
statsDay[
idx, :] = sensitivity, specificity, FPR, accuracy, errorrate, A, RT_mean
# Block-wise
for block in range(1, int(len(responseTimes) / 50) + 1):
print(block)
if subjID == '11' and expDay == '2':
TN, FP, FN, TP, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = [
np.nan
] * 8
else:
TN, FP, FN, TP, lure_RT_mean, nonlure_RT_mean, RT_mean, nNaN = findRTsBlocks(
catFile, responseTimes, block=block)
sensitivity = TP / (TP + FN)
specificity = TN / (TN + FP)
FPR = FP / (FP + TN)
accuracy = (TP + TN) / (TP + TN + FP + FN)
if expDay == '2':
statsBlock_day2[
block -
1, :] = sensitivity, specificity, FPR, accuracy, RT_mean, nNaN
if expDay != '2':
statsBlock[
idx_c, block -
1, :] = sensitivity, specificity, FPR, accuracy, RT_mean, nNaN
if expDay != '2':
idx_c += 1
return statsDay, statsBlock, statsBlock_day2