axhline(50, color = '0.7')
axhline(100, color = '0.7')
margins(0.2)
plt.subplots_adjust(bottom=0.2)
title(animal)
ylabel('Average Percent Correct')
show()
#### Need to start moving towards loading the data already split by sound type and then using generic fxns from behavioranalysis and extraplots
animal = 'amod002'
dataObjs, dataSoundTypes = behavioranalysis.load_behavior_sessions_sound_type(animal, ['20160421a'])
for bdata in dataObjs
bdata = dataObjs[0]
hitTrials = bdata['choice']==bdata.labels['choice']['right']
paramValueEachTrial = bdata['targetFrequency']
valid = bdata['valid']
(possibleValues,fractionHitsEachValue,ciHitsEachValue,nTrialsEachValue,nHitsEachValue) = behavioranalysis.calculate_psychometric(hitTrials, paramValueEachTrial, valid)
clf()
extraplots.plot_psychometric(possibleValues,fractionHitsEachValue,ciHitsEachValue)
salineSessions = ['20160412a', '20160414a', '20160416a', '20160418a', '20160420a']
allnCorr = []
allnVal = []
allfracCorr = []
sessionInds = []
animalInds = []
soundType = [] #0 = chords, 1 = mod
muscimol = [] #0 = no muscimol, 1 = muscimol
for indAnimal, animal in enumerate(animals):
for indSession, session in enumerate(muscimolSessions):
muscimolDataObjs, muscimolSoundTypes = behavioranalysis.load_behavior_sessions_sound_type(animal, [session])
mdataChords = muscimolDataObjs[muscimolSoundTypes['chords']]
mdataMod = muscimolDataObjs[muscimolSoundTypes['amp_mod']]
#Process muscimol data for chords
bdata = mdataChords
#Boilerplate
nCorr = sum(bdata['outcome']==bdata.labels['outcome']['correct'])
nVal = sum(bdata['valid'])
allfracCorr.append(nCorr.astype(float)/nVal)
allnCorr.append(nCorr)
allnVal.append(nVal)
sessionInds.append(indSession)
animalInds.append(indAnimal)
from jaratoolbox.test.nick import behavioranalysis_vnick as ba
animals = ['amod002', 'amod003']
sessions = ['20160426a', '20160427a', '20160428a', '20160429a']
side = [0, 1, 0, 1] #0 - Right, 1 - Left
allnCorr = []
allnVal = []
allfracCorr = []
sessionInds = []
animalInds = []
sides = []
for indAnimal, animal in enumerate(animals):
for indSession, session in enumerate(sessions):
(bdataObjs, bdataSoundTypes) = ba.load_behavior_sessions_sound_type(
animal, [session])
bdata = bdataObjs[0] #analyze chord discrim
nCorr = sum(bdata['outcome'] == bdata.labels['outcome']['correct'])
nVal = sum(bdata['valid'])
allfracCorr.append(nCorr.astype(float) / nVal)
allnCorr.append(nCorr)
allnVal.append(nVal)
sessionInds.append(indSession)
animalInds.append(indAnimal)
sides.append(side[indSession])
from jaratoolbox.test.nick import behavioranalysis_vnick as behavioranalysis
from jaratoolbox import extraplots
from jaratoolbox import colorpalette
animal = 'amod004'
session = '20160502a'
dataObjs, soundTypes = behavioranalysis.load_behavior_sessions_sound_type(animal, [session])
figure()
clf()
bdata = dataObjs[soundTypes['tones']]
ax = subplot(121)
est = plot_psycurve_fit_and_data(bdata, 'k')
extraplots.boxoff(ax)
extraplots.set_ticks_fontsize(ax, 20)
fitline = ax.lines[3]
setp(fitline, lw=3)
setp(fitline, color=colorpalette.TangoPalette['Chameleon2'])
# pcaps= ax.lines[0]
# pbars = ax.lines[2]
# setp(pcaps, lw=2)
# setp(pbars, lw=2)
xticklabels = [item.get_text() for item in ax.get_xticklabels()]
xticks = ax.get_xticks()
newXtickLabels = logspace(xticks[0], xticks[-1], 3, base=2)
plt.ylim(-0.03, 1.03)
ax.set_xticks(np.log2(np.array(newXtickLabels)))
ax.set_xticklabels(['{:.3}'.format(x/1000.0) for x in newXtickLabels])
from jaratoolbox.test.nick import behavioranalysis_vnick as behavioranalysis
from jaratoolbox import extraplots
from jaratoolbox import colorpalette
animal = 'amod004'
session = '20160502a'
dataObjs, soundTypes = behavioranalysis.load_behavior_sessions_sound_type(
animal, [session])
figure()
clf()
bdata = dataObjs[soundTypes['tones']]
ax = subplot(121)
est = plot_psycurve_fit_and_data(bdata, 'k')
extraplots.boxoff(ax)
extraplots.set_ticks_fontsize(ax, 20)
fitline = ax.lines[3]
setp(fitline, lw=3)
setp(fitline, color=colorpalette.TangoPalette['Chameleon2'])
# pcaps= ax.lines[0]
# pbars = ax.lines[2]
# setp(pcaps, lw=2)
# setp(pbars, lw=2)
xticklabels = [item.get_text() for item in ax.get_xticklabels()]
xticks = ax.get_xticks()
newXtickLabels = logspace(xticks[0], xticks[-1], 3, base=2)
plt.ylim(-0.03, 1.03)
ax.set_xticks(np.log2(np.array(newXtickLabels)))
ax.set_xticklabels(['{:.3}'.format(x / 1000.0) for x in newXtickLabels])