def generate(plotInfo):
'''
FIXME: finish this docstring
plotList should be a list of dictionaries, each dictionary with the following parameters:
'type': (string) Type of plot. Accepted values are 'psychometric', 'summary', or 'dynamics'
'filename': (string) Output file name.
THIS IS NOT NECESSARY, THIS METHOD SHOULD NOT CARE: using format "mousename_YYYYMMDD_plotType.svg"
'data': raw behavior data created by jaratoolbox.loadbehavior.FlexCategBehaviorData()
Files are saved to settings.IMAGE_PATH
: '''
plotData = plotInfo['data']
if plotInfo['type'] == 'psychometric':
behavioranalysis.plot_frequency_psycurve(plotData, fontsize=FONTSIZE)
elif plotInfo['type'] == 'summary':
freqsToUse = [plotData['lowFreq'][-1], plotData['highFreq'][-1]]
behavioranalysis.plot_summary(plotData,
fontsize=FONTSIZE,
soundfreq=freqsToUse)
elif plotInfo['type'] == 'dynamics':
freqsToUse = [plotData['lowFreq'][-1], plotData['highFreq'][-1]]
behavioranalysis.plot_dynamics(plotData,
winsize=40,
fontsize=FONTSIZE,
soundfreq=freqsToUse)
else:
raise ValueError('Plot type {0} is not valid'.format(plotInfo['Type']))
outputFile = os.path.join(settings.IMAGE_PATH, plotInfo['filename'])
print 'Saving plot to {0}'.format(outputFile)
plt.savefig(outputFile)
def Generate(plotList):
#plotList is the list of plot dictionaries as specified above
#print plotList
for plot in plotList:
#get the data from the dictionary
plotData = plot['data']
#print plot['type']
#check what the plot type is and run the coorisponding method
#basis for method calls taken from "test032_example_read_bdata.py" by Santiago Jaramillo
type = str(plot['type'])
if type == "psychometric":
print "psychometricpsychometricpsychometricpsychometric"
#As mentioned by Santiago: parameters returned from psychometric plot are not used. Were put in place for improvments that were never finished
(pline, pcaps, pbars, pdots) = behavioranalysis.plot_frequency_psycurve(plotData,fontsize=14)
elif type == "summary":
freqsToUse = [plotData['lowFreq'][-1],plotData['highFreq'][-1]]
behavioranalysis.plot_summary(plotData,fontsize=14,soundfreq=freqsToUse)
elif type == "dynamics":
freqsToUse = [plotData['lowFreq'][-1],plotData['highFreq'][-1]]
behavioranalysis.plot_dynamics(plotData,winsize=40,fontsize=14,soundfreq=freqsToUse)
else:
#ERROR case: plot type invalid
print "error"
return False
#save the plot under the given filepath
patha = os.path.join(path, plot['filename'])
#print patha,"patha"
plt.savefig(patha)
return True
def Generate(plotList):
#plotList is the list of plot dictionaries as specified above
for plot in plotList:
#get the data from the dictionary
plotData = plot['data']
#check what the plot type is and run the coorisponding method
#basis for method calls taken from "test032_example_read_bdata.py" by Santiago Jaramillo
if plot['type'] is "psychometric":
#As mentioned by Santiago: parameters returned from psychometric plot are not used. Were put in place for improvments that were never finished
(pline, pcaps, pbars,
pdots) = behavioranalysis.plot_frequency_psycurve(plotData,
fontsize=14)
elif plot['type'] is "summary":
freqsToUse = [plotData['lowFreq'][-1], plotData['highFreq'][-1]]
behavioranalysis.plot_summary(plotData,
fontsize=14,
soundfreq=freqsToUse)
elif plot['type'] is "dynamics":
freqsToUse = [plotData['lowFreq'][-1], plotData['highFreq'][-1]]
behavioranalysis.plot_dynamics(plotData,
winsize=40,
fontsize=14,
soundfreq=freqsToUse)
else:
#ERROR case: plot type invalid
return False
#save the plot under the given filepath
plt.savefig(os.path.join(path, plot['filename']))
return True
def psycurveLog(bdata):
behavioranalysis.plot_frequency_psycurve(bdata,fontsize=FONTSIZE)
plt.gcf().set_size_inches([8,6])
if (tight or (len(subplot.images)>0 and
len(subplot.lines)==0 and
len(subplot.patches)==0)):
if scalex:
subplot.set_xbound(x0, x1)
if scaley:
subplot.set_ybound(y0, y1)
return
if scalex:
XL = subplot.xaxis.get_major_locator().view_limits(x0, x1)
subplot.set_xbound(XL)
if scaley:
YL = subplot.yaxis.get_major_locator().view_limits(y0, y1)
subplot.set_ybound(YL)
(bdataObjs, bdataSoundTypes) = soundtypes.load_behavior_sessions_sound_type(subject, [session])
#Not because we are plotting small values...
# bdataObjs[0]['targetFrequency'] = bdataObjs[0]['targetFrequency'] * 1e3
plt.clf()
for indbdata, bdata in enumerate(bdataObjs):
ax = plt.subplot(1, 2, indbdata+1)
(pline, pcaps, pbars, pdots) = behavioranalysis.plot_frequency_psycurve(bdata)
loose_autoscale_view(ax, 0.05, scaley=False)
# plt.margins(x=0.1, y=0)
# ax.autoscale(axis='x', tight=True)
plt.show()
def sound_type_behavior_summary(subjects,
sessions,
output_dir,
trialslim=None):
'''
Nick 2016-05-20
Plots a report for behavior data that has multiple sound types. Will plot a psycurve for each sound type,
and then a dynamics plot with the different sound types as different colored lines
Args:
subjects (list of str): The animals to plot (example: ['amod002', 'amod003'])
sessions (list of str): The sessions to plot for each animal (example: ['20160529a'])
output_dir (str): FIXME currently unused, for saving the report
trialslim (list): upper and lower trial number to plot dynamics
'''
if isinstance(subjects, str):
subjects = [subjects]
if isinstance(sessions, str):
sessions = [sessions]
nSessions = len(sessions)
nAnimals = len(subjects)
for indAnimal, animalName in enumerate(subjects):
for indSession, session in enumerate(sessions):
#The sound types separated into different bdata objects
try:
(bdataObjs,
bdataSoundTypes) = load_behavior_sessions_sound_type(
animalName, [session])
except KeyError: #Will happen if the bdata does not have 'soundType' key'
print "ERROR: Data for {} - {} may not be in the correct format".format(
animalName, session)
break
#All the bdata together for plotting the dynamics
allBehavData = behavioranalysis.load_many_sessions(
animalName, [session])
nSoundTypes = len(
bdataSoundTypes
) #This should hopefully be the same for all the animals being plotted
#Keeps track of the min and max for each sound type for plotting dynamics
allFreqsToUse = []
#The y inds for plotting
yInd = indAnimal + indSession + indAnimal * (nSessions - 1)
for indSoundType, soundType in enumerate(bdataSoundTypes):
plt.subplot2grid((nAnimals * nSessions, nSoundTypes + 2),
(yInd, indSoundType))
thisBehavData = bdataObjs[indSoundType]
#Find min and max freqs for plotting
possibleFreq = np.unique(thisBehavData['targetFrequency'])
freqsToUse = [min(possibleFreq), max(possibleFreq)]
allFreqsToUse.extend(freqsToUse)
#Plot psycurves or summaries
if any(thisBehavData['psycurveMode']):
(pline, pcaps, pbars,
pdots) = behavioranalysis.plot_frequency_psycurve(
thisBehavData)
thisColor = FREQCOLORS[indSoundType * 2]
plt.setp(pline, color=thisColor)
plt.setp(pcaps, color=thisColor)
plt.setp(pbars, color=thisColor)
plt.setp(pdots, markerfacecolor=thisColor)
plt.title(soundType)
if not indSoundType == 0:
plt.ylabel('')
else:
thisBehavData.find_trials_each_block()
behavioranalysis.plot_summary(thisBehavData,
fontsize=8,
soundfreq=freqsToUse)
plt.title(soundType)
#Plot the dynamics for all the sound types
plt.subplot2grid((nAnimals * nSessions, nSoundTypes + 2),
(yInd, nSoundTypes),
colspan=2)
behavioranalysis.plot_dynamics(allBehavData,
soundfreq=allFreqsToUse)
plt.ylabel('')
if trialslim:
plt.xlim(trialslim)
plt.title('{}, {}'.format(animalName, session))
plt.subplots_adjust(hspace=0.7)
subject = 'adap021'
session = '20160310a' # This is the date formatted as YYYYMMDD and one more character (usually 'a')
# -- Find the data filename and load the data into a data object (similar to a Python dict) --
behavFile = loadbehavior.path_to_behavior_data(subject, EXPERIMENTER, paradigm,
session)
behavData = loadbehavior.FlexCategBehaviorData(behavFile, readmode='full')
# -- Calculate average performance --
nValidTrials = behavData['nValid'][-1]
nRewardedTrials = behavData['nRewarded'][-1]
print 'Average performance: {:0.1%}'.format(
float(nRewardedTrials) / nValidTrials)
# -- Plot psychometric curve --
plt.clf()
(pline, pcaps, pbars,
pdots) = behavioranalysis.plot_frequency_psycurve(behavData, fontsize=14)
plt.show()
'''
plt.clf()
freqsToUse = [behavData['lowFreq'][-1],behavData['highFreq'][-1]]
behavioranalysis.plot_summary(behavData,fontsize=14,soundfreq=freqsToUse)
plt.show()
plt.clf()
freqsToUse = [behavData['lowFreq'][-1],behavData['highFreq'][-1]]
behavioranalysis.plot_dynamics(behavData,winsize=40,fontsize=14,soundfreq=freqsToUse)
plt.show()
'''
paradigm = '2afc'
subject = 'adap021'
session = '20160310a' # This is the date formatted as YYYYMMDD and one more character (usually 'a')
# -- Find the data filename and load the data into a data object (similar to a Python dict) --
behavFile = loadbehavior.path_to_behavior_data(subject,EXPERIMENTER,paradigm,session)
behavData = loadbehavior.FlexCategBehaviorData(behavFile,readmode='full')
# -- Calculate average performance --
nValidTrials = behavData['nValid'][-1]
nRewardedTrials = behavData['nRewarded'][-1]
print 'Average performance: {:0.1%}'.format(float(nRewardedTrials)/nValidTrials)
# -- Plot psychometric curve --
plt.clf()
(pline, pcaps, pbars, pdots) = behavioranalysis.plot_frequency_psycurve(behavData,fontsize=14)
plt.show()
'''
plt.clf()
freqsToUse = [behavData['lowFreq'][-1],behavData['highFreq'][-1]]
behavioranalysis.plot_summary(behavData,fontsize=14,soundfreq=freqsToUse)
plt.show()
plt.clf()
freqsToUse = [behavData['lowFreq'][-1],behavData['highFreq'][-1]]
behavioranalysis.plot_dynamics(behavData,winsize=40,fontsize=14,soundfreq=freqsToUse)
plt.show()
'''
def sound_type_behavior_summary(subjects, sessions, output_dir, trialslim=None):
'''
Nick 2016-05-20
Plots a report for behavior data that has multiple sound types. Will plot a psycurve for each sound type,
and then a dynamics plot with the different sound types as different colored lines
Args:
subjects (list of str): The animals to plot (example: ['amod002', 'amod003'])
sessions (list of str): The sessions to plot for each animal (example: ['20160529a'])
output_dir (str): FIXME currently unused, for saving the report
trialslim (list): upper and lower trial number to plot dynamics
'''
if isinstance(subjects,str):
subjects = [subjects]
if isinstance(sessions,str):
sessions = [sessions]
nSessions = len(sessions)
nAnimals = len(subjects)
for indAnimal, animalName in enumerate(subjects):
for indSession, session in enumerate(sessions):
#The sound types separated into different bdata objects
try:
(bdataObjs, bdataSoundTypes) = load_behavior_sessions_sound_type(animalName,
[session])
except KeyError: #Will happen if the bdata does not have 'soundType' key'
print "ERROR: Data for {} - {} may not be in the correct format".format(animalName, session)
break
#All the bdata together for plotting the dynamics
allBehavData = behavioranalysis.load_many_sessions(animalName, [session])
nSoundTypes = len(bdataSoundTypes) #This should hopefully be the same for all the animals being plotted
#Keeps track of the min and max for each sound type for plotting dynamics
allFreqsToUse = []
#The y inds for plotting
yInd = indAnimal + indSession + indAnimal*(nSessions-1)
for indSoundType, soundType in enumerate(bdataSoundTypes):
plt.subplot2grid((nAnimals*nSessions, nSoundTypes+2), (yInd, indSoundType))
thisBehavData = bdataObjs[indSoundType]
#Find min and max freqs for plotting
possibleFreq = np.unique(thisBehavData['targetFrequency'])
freqsToUse = [min(possibleFreq), max(possibleFreq)]
allFreqsToUse.extend(freqsToUse)
#Plot psycurves or summaries
if any(thisBehavData['psycurveMode']):
(pline, pcaps, pbars, pdots) = behavioranalysis.plot_frequency_psycurve(thisBehavData)
thisColor = FREQCOLORS[indSoundType*2]
plt.setp(pline, color=thisColor)
plt.setp(pcaps, color=thisColor)
plt.setp(pbars, color=thisColor)
plt.setp(pdots, markerfacecolor=thisColor)
plt.title(soundType)
if not indSoundType==0:
plt.ylabel('')
else:
thisBehavData.find_trials_each_block()
behavioranalysis.plot_summary(thisBehavData,fontsize=8,soundfreq=freqsToUse)
plt.title(soundType)
#Plot the dynamics for all the sound types
plt.subplot2grid((nAnimals*nSessions, nSoundTypes+2), (yInd, nSoundTypes), colspan=2)
behavioranalysis.plot_dynamics(allBehavData, soundfreq = allFreqsToUse)
plt.ylabel('')
if trialslim:
plt.xlim(trialslim)
plt.title('{}, {}'.format(animalName, session))
plt.subplots_adjust(hspace = 0.7)