def get_trial_outcomes(dfns):
trialdata = {} # initiate output dict
for dfn in dfns:
df = None
df = pymworks.open(dfn) # open the datafile
devs = df.get_events('#stimDisplayUpdate') # get all display update events
# stims = pymworks.events.display.to_stims(devs)
resptypes = ['session_correct_lick','session_correct_ignore',\
'session_bad_lick','session_bad_ignore'] #,\
# 'aborted_counter'] # list of response variables to track...
outevs = df.get_events(resptypes) # get all events with these vars
responses = []
for r in resptypes: # get list of incrementing variables
revs = df.get_events(r)
incr_resp = session_incremented(revs)
responses.append(incr_resp)
responses = list(itertools.chain.from_iterable(responses))
real_responses = [r for r in responses if r.value != 0]
trials = pymworks.events.display.to_trials(devs, responses,\
remove_unknown=True) # get rid of orphan events
#synctrials = syncdicts(stims,real_responses,direction=-1)
#trash = [t for t in synctrials if t is None]
#trials = [t for t in synctrials if t not in trash]
trialdata[dfn] = trials
return trialdata
def process_datafiles(dfns, stats):
data = {}
for dfn in dfns:
logging.debug("Datafile: %s" % dfn)
df = None
df = pymworks.open(dfn)
r = {}
for s in stats:
r[s] = stats[s][0](df)
logging.debug("\t%s: %s" % (s, r[s]))
df.close()
data[dfn] = r
return data
def process_datafiles(dfns, stats):
data = {}
try:
for dfn in dfns:
logging.debug("Datafile: %s" % dfn)
df = None
df = pymworks.open(dfn)
r = {}
for s in stats:
r[s] = stats[s][0](df)
logging.debug("\t%s: %s" % (s, r[s]))
df.close()
data[dfn] = r
return data
except KeyError:
print "NO KEY FOUND: %s", dfn
def process_datafiles(dfns, stats):
data = {}
for dfn in dfns:
logging.debug("Datafile: %s" % dfn)
df = None
df = pymworks.open(dfn)
r = {}
for s in stats:
try:
r[s] = stats[s][0](df)
logging.debug("\t%s: %s" % (s, r[s]))
except TypeError:
print "stat: ", s, "file: ", dfn
df.close()
data[dfn] = r
return data
def process_outcomes(dfns, stats):
data = {}
for dfn in dfns:
df = None
df = pymworks.open(fn)
r = {}
evs = df.get_events('#stimDisplayUpdate')
outcomes = df.get_events(('session_correct_lick', 'session_correct_ignore',\
'session_bad_lick', 'session_bad_ignore'))
ts = pymworks.events.display.to_trials(evs, outcomes)
gts = datautils.grouping.groupn(ts, ('name', 'outcome'))
nOutcomes = datautils.grouping.ops.stat(gts, len)
respkeys = [k for k in nOutcomes.keys()]
corrlicks = []
badignores = []
corrignores = []
badlicks = []
for k in respkeys:
resptypes = nOutcomes[k].keys()
for t in resptypes:
if 'correct_lick' in t:
corrlicks.append(nOutcomes[k][t])
if 'bad_ignore' in t:
badignores.append(nOutcomes[k][t])
if 'correct_ignore' in t:
corrignores.append(nOutcomes[k][t])
if 'bad_lick' in t:
badlicks.append(nOutcomes[k][t])
correct_licks = sum(corrlicks)
bad_ignores = sum(badignores)
correct_ignores = sum(corrignores)
bad_licks = sum(badlicks)
df.close()
data[dfn] = r
return data
def parse_trials(dfns, remove_orphans=True):
trialdata = {} # initiate output dict
for dfn in dfns:
df = None
df = pymworks.open(dfn) # open the datafile
sname = os.path.split(dfn)[1]
trialdata[sname] = []
modes = df.get_events('#state_system_mode')
run_idxs = np.where(np.diff([i['time'] for i in modes])<20)
bounds = []
for r in run_idxs[0]:
stop_ev = next(i for i in modes[r:] if i['value']==0)
bounds.append([modes[r]['time'], stop_ev['time']])
print "................................................................"
# print "----------------------------------------------------------------"
print "Parsing file\n%s... " % dfn
print "Found %i start events in session." % len(bounds)
print "................................................................"
for bidx,boundary in enumerate(bounds):
print "----------------------------------------------------------------"
print "SECTION %i" % bidx
print "----------------------------------------------------------------"
tmp_devs = df.get_events('#stimDisplayUpdate') # get *all* display update events
tmp_devs = [i for i in tmp_devs if i['time']<= boundary[1] and i['time']>=boundary[0]]
dimmed = df.get_events('flag_dim_target')
dimmed = np.mean([i.value for i in dimmed]) # set dim flag to separate trials based on initial stim presentation
resptypes = ['session_correct_lick','session_correct_ignore','session_bad_lick','session_bad_ignore'] #,\ # 'aborted_counter'] # list of response variables to track...
outevs = df.get_events(resptypes) # get all events with these vars
outevs = [i for i in outevs if i['time']<= boundary[1] and i['time']>=boundary[0]]
# responses = []
# for r in resptypes: # get list of incrementing variables
# revs = df.get_events(r)
# incr_resp = [r for r in revs if r['time']>=boundary[0] and r['time']<=boundary[1]]
# incr_resp = session_incremented(revs)
# responses.append(incr_resp)
# responses = list(itertools.chain.from_iterable(responses))
# real_responses = [r for r in responses if r.value != 0] # remove initial 0 value set at session start
# R = sorted(real_responses, key=lambda e: e.time) # sort by time
R = sorted([r for r in outevs if r.value > 0], key=lambda e: e.time)
T = pymworks.events.display.to_trials(tmp_devs, R, remove_unknown=False)# match stim events to response events
if dimmed: # don't double count stimulus for "faded" target stimuli
trials = [i for i in T if i['alpha_multiplier']==1.0]
else:
trials = T
print "N total response events: ", len(trials)
if remove_orphans: # this should always be true, generally...
orphans = [(i,x) for i,x in enumerate(trials) if x['outcome']=='unknown']
trials = [t for t in trials if not t['outcome']=='unknown'] # get rid of display events without known outcome within 'duration_multiplier' time
print "Found and removed %i orphan stimulus events in file %s" % (len(orphans), dfn)
print "N valid trials: %i" % len(trials)
trialdata[sname].append(trials)
return trialdata
# print 'Distractor count off by %d, in %s' % (tmp_nDistractors-(tmp_correct_ignores+tmp_bad_licks), fn)
difference = tmp_nDistractors-(tmp_correct_ignores+tmp_bad_licks)
distractor_count_check.append([fn, difference])
if ((tmp_correct_licks+tmp_bad_ignores) == tmp_nTargets) \
and ((tmp_correct_ignores+tmp_bad_licks) == tmp_nDistractors):
good_counts.append([fn])
if target_count_check != [] or distractor_count_check != []:
print "Targets: ", target_count_check
print "Distractors: ", distractor_count_check
else:
print "Session trial counts good."
# get stimulus display update events, and parse them into trials:
df = pymworks.open(fn)
evs = df.get_events('#stimDisplayUpdate')
outcome_events = df.get_events(('correct_lick', 'bad_lick', 'correct_ignore', 'bad_ignore'))
stims = pymworks.events.display.to_stims(evs)
trials = pymworks.events.display.to_trials(evs, outcome_events)
if len(stims) != len(trials):
print "Stims vs Trials: %s, %d" % (fn, len(stims)-len(trials))
elif len(stims) == 0 or len(trials) == 0:
print len(stims), len(trials), fn
else:
print "Events by stim and trial are matched."
# get targetprob events within the time-range of trials:
def get_object_rot(datadir):
'''
Object rotations are made by rotating blob in POVray.
All stimulus-rotation values will be 0.
Get true rotation relative to default using image path name.
'''
processed_dir = os.path.join(os.path.split(datadir)[0], 'processed')
animals = os.listdir(processed_dir)
animals = [i for i in animals if i.endswith('.pkl')]
animals = [os.path.splitext(a)[0] for a in animals]
for animal in animals:
fn = os.path.join(processed_dir, animal+'.pkl')
with open(fn, 'rb') as f:
curr_trials = pkl.load(f)
session_dates = []
for t in curr_trials.keys():
subject, date, epoch = parse_filename(t)
session_dates.append(date)
for t in curr_trials.keys():
print t
if len(curr_trials[t])==0:
continue
else:
if len(curr_trials[t])==1:
ctrials = curr_trials[t][0]
else:
ctrials = curr_trials[t]
df = pymworks.open(os.path.join(datadir, subject, t))
# if no morphs, move on...
# mtrials = [i for i in ctrials if 'morph' in i['name']]
# if len(mtrials)>0:
# continue
# Get TRUE SIZES AND ROTATIONS:
AspRat_name = 'StimAspectRatio'
AspRat = df.get_events(AspRat_name)[-1].value
sizs = [round(i['size_x']/AspRat) for i in ctrials]
print set(sizs)
stims = [i['name'] for i in ctrials]
rots = []
for s in stims:
if 'Blob' in s:
rots.append(s.rsplit('y', 1)[1].rsplit('.',1)[0])
else:
rots.append(0)
for sz,rot,ctrial in zip(sizs, rots, ctrials):
try:
ctrial['actual_rotation'] = rot
ctrial['actual_size'] = str(sz)
except KeyError:
print ctrial
curr_trials[t] = ctrials
save_dict(curr_trials, os.path.split(fn)[0], os.path.split(fn)[1])
print "Saved to %s" % fn
# devs = [e for e in evs if e.value]
# im_names = [i['name'] for d in devs for i in d.value if '.png' in i['name']]
# morphs = [i for i in im_names if 'morph' in i]
# print len(morphs)
# from collections import Counter
# from pandas import Series
# fn = [i for i in fns if '_SD' in i] #[0]
# print fn
# # f = fn
for f in fns:
# f = fn[4]
print f
df = pymworks.open(os.path.join(file_dir, f))
# Check stimDisplayUpdate events vs announceStimulus:
stim_evs = df.get_events('#stimDisplayUpdate')
devs = [e for e in stim_evs if not e.value==[None]]
idevs = [i for i in devs for v in i.value if 'png' in v['name']]
ann_evs = df.get_events('#announceStimulus')
aevs = [e for e in ann_evs if type(e.value) is dict]
sevs = [i for i in aevs if 'png' in i.value['name']]
if not len(idevs)==len(sevs):
print "MISMATCH in event counts in DF: %s" % f
print "-------------------------"
print "#stimDisplayUpdate %i and #announceStimulus %i." % (len(idevs), len(sevs))
def parse_trials(dfns, remove_orphans=True):
"""
Parse session .mwk files.
Key is session name values are lists of dicts for each trial in session.
Looks for all response and display events that occur within session.
dfns : list of strings
contains paths to each .mwk file to be parsed
remove_orphans : boolean
for each response event, best matching display update event
set this to 'True' to remove display events with unknown outcome events
"""
trialdata = {} # initiate output dict
for dfn in dfns:
df = None
df = pymworks.open(dfn) # open the datafile
sname = os.path.split(dfn)[1]
trialdata[sname] = []
modes = df.get_events('#state_system_mode') # find timestamps for run-time start and end (2=run)
run_idxs = np.where(np.diff([i['time'] for i in modes])<20) # 20 is kind of arbitray, but mode is updated twice for "run"
bounds = []
for r in run_idxs[0]:
try:
stop_ev = next(i for i in modes[r:] if i['value']==0 or i['value']==1)
except StopIteration:
end_event_name = 'trial_end'
print "NO STOP DETECTED IN STATE MODES. Using alternative timestamp: %s." % end_event_name
stop_ev = df.get_events(end_event_name)[-1]
print stop_ev
bounds.append([modes[r]['time'], stop_ev['time']])
# print "................................................................"
print "****************************************************************"
print "Parsing file\n%s... " % dfn
print "Found %i start events in session." % len(bounds)
print "****************************************************************"
for bidx,boundary in enumerate(bounds):
# print "................................................................"
print "SECTION %i" % bidx
print "................................................................"
tmp_devs = df.get_events('#stimDisplayUpdate') # get *all* display update events
tmp_devs = [i for i in tmp_devs if i['time']<= boundary[1] and\
i['time']>=boundary[0]] # only grab events within run-time bounds (see above)
dimmed = df.get_events('flag_dim_target')
dimmed = np.mean([i.value for i in dimmed]) # set dim flag to separate trials based on initial stim presentation
resptypes = ['session_correct_lick','session_correct_ignore',\
'session_bad_lick','session_bad_ignore']
#,\ # 'aborted_counter'] # list of response variables to track...
outevs = df.get_events(resptypes) # get all events with these vars
outevs = [i for i in outevs if i['time']<= boundary[1] and\
i['time']>=boundary[0]]
R = sorted([r for r in outevs if r.value > 0], key=lambda e: e.time)
T = pymworks.events.display.to_trials(tmp_devs, R,\
remove_unknown=False) # match stim events to response events
if dimmed: # don't double count stimulus for "faded" target stimuli
trials = [i for i in T if i['alpha_multiplier']==1.0]
else:
trials = T
print "N total response events: ", len(trials)
if remove_orphans: # this should always be true, generally...
orphans = [(i,x) for i,x in enumerate(trials) if\
x['outcome']=='unknown']
trials = [t for t in trials if not t['outcome']=='unknown'] # get rid of display events without known outcome within 'duration_multiplier' time
print "Found and removed %i orphan stimulus events in file %s"\
% (len(orphans), dfn)
print "N valid trials: %i" % len(trials)
trialdata[sname].append(trials)
return trialdata
def get_morph_stats(datadir, baseline, show_curve=True):
if baseline is True:
processed_dir = os.path.join(os.path.split(datadir)[0], 'processed_premorphs')
else:
processed_dir = os.path.join(os.path.split(datadir)[0], 'processed')
animals = os.listdir(processed_dir)
animals = [i for i in animals if i.endswith('_trials.pkl')]
animals = [os.path.splitext(a)[0] for a in animals]
P = dict()
for animal in animals:
P[animal] = dict()
fn = os.path.join(processed_dir, animal+'.pkl')
with open(fn, 'rb') as f:
curr_trials = pkl.load(f)
session_dates = []
for t in curr_trials.keys():
print t
subject, date, epoch = parse_filename(t)
session_dates.append(date)
true_middle_idx = False # Bad indexing, incorrect assignment of halway point bw morphs in MW
M = dict()
for t in sorted(curr_trials.keys(), key=natural_keys):
print t
if len(curr_trials[t])==0:
continue
elif len(curr_trials[t])==1 or len(curr_trials[t])==2:
ctrials = []
check_idx = 0
while ctrials==[]:
ctrials = curr_trials[t][check_idx]
check_idx += 1
else:
print "LEN: ", len(curr_trials[t])
ctrials = curr_trials[t]
df = pymworks.open(os.path.join(datadir, subject, t))
# if no morphs, move on...
mtrials = [i for i in ctrials if 'morph' in i['name']]
if len(mtrials)==0:
continue
# Assign appropriate 1/2-way point to calculate percent RIGHT choice (or LEFT):
# BEFORE 08/18/2016 -- incorrect indexing, so "halway" should be 11.
# Otherwise, halfway should be 10.
if '160818' in date:
true_middle_idx = True # Keep this flag ON for all dates after this.
if true_middle_idx is True:
middle_idx = 10
else:
middle_idx = 11
# Do this funky stuff to get default size:
sizes = [i['size_x'] for i in ctrials]
sizes = list(set(sizes))
tmp_default_size = 40*df.get_events('StimAspectRatio')[0].value
diffs = np.array(sizes) - tmp_default_size
find_match = np.where(diffs==min(diffs, key=lambda x: abs(float(x) - tmp_default_size)))
default_size = sizes[find_match[0][0]]
anchor_trials = [i for i in ctrials if '_y0' in i['name'] and i['size_x']==default_size]
anchor_names = list(set([i['name'] for i in anchor_trials]))
morph_names = sorted(list(set([i['name'] for i in mtrials])), key=natural_keys)
morphs = dict()
for m in morph_names:
morphs[m] = dict()
morphs[m]['success'] = sum([1 for i in mtrials if i['name']==m and i['outcome']=='success'])
morphs[m]['failure'] = sum([1 for i in mtrials if i['name']==m and i['outcome']=='failure'])
morphs[m]['ignore'] = sum([1 for i in mtrials if i['name']==m and i['outcome']=='ignore'])
morphs[m]['total'] = sum([morphs[m]['success'] + morphs[m]['failure'] + morphs[m]['ignore']])
for m in anchor_names:
if '_N1' in m: # THIS IS SPACESHIP -- AG group, this is RIGHT
aname = 'morph0.png'
elif '_N2' in m: # THIS IS BUNNY -- this is LEFT
aname = 'morph21.png'
if aname not in morphs.keys():
morphs[aname] = dict()
morphs[aname]['success'] = sum([1 for i in anchor_trials if i['name']==m and i['outcome']=='success'])
morphs[aname]['failure'] = sum([1 for i in anchor_trials if i['name']==m and i['outcome']=='failure'])
morphs[aname]['ignore'] = sum([1 for i in anchor_trials if i['name']==m and i['outcome']=='ignore'])
morphs[aname]['total'] = sum([morphs[aname]['success'] + morphs[aname]['failure'] + morphs[aname]['ignore']])
M[t] = morphs
# all_morph_names = ['morph%i.png' % int(i) for i in range(22)]
all_morph_names = ['morph%i.png' % int(i) for i in range(22)]
P[animal]['session'] = dict()
session_keys = ['percent', 'total', 'success', 'failure', 'ignore']
# initiate dict() for each session/morph:
for session_key in session_keys:
P[animal]['session'][session_key] = dict()
for msession in M.keys():
P[animal]['session'][session_key][msession] = dict()
for mkey in M.keys():
for morph in all_morph_names:
morph_num = int(re.findall("[-+]?\d+?\d*", morph)[0])
if morph not in M[mkey].keys() or M[mkey][morph]['total']==0:
for skey in P[animal]['session'].keys():
P[animal]['session'][skey][mkey][str(morph_num)] = 0.
else:
for skey in P[animal]['session'].keys():
# if just need totals for a given morph in a given session:
if skey is not 'percent':
P[animal]['session'][skey][mkey][str(morph_num)] = float(M[mkey][morph][skey])
# otherwise, need to calculate %-choose-RIGHT (or LEFT) port:
# SEE NOTE BELOW on halway cutoff point...
elif skey == 'percent':
if morph_num <= middle_idx: # 11: #morph_num <= 11:
P[animal]['session'][skey][mkey][str(morph_num)] = float(M[mkey][morph]['success']/float(M[mkey][morph]['total']))
elif morph_num > middle_idx: #>= 11: #> 11:
P[animal]['session'][skey][mkey][str(morph_num)] = float(M[mkey][morph]['failure']/float(M[mkey][morph]['total']))
ALL = dict()
for morph in sorted(all_morph_names, key=natural_keys):
ALL[morph] = dict()
ALL[morph]['success'] = []
ALL[morph]['failure'] = []
ALL[morph]['ignore'] = []
ALL[morph]['total'] = []
for i in M.keys():
if morph in M[i].keys():
ALL[morph]['success'].append(M[i][morph]['success'])
ALL[morph]['failure'].append(M[i][morph]['failure'])
ALL[morph]['ignore'].append(M[i][morph]['ignore'])
ALL[morph]['total'].append(M[i][morph]['total'])
else:
ALL[morph]['success'].append(0)
ALL[morph]['failure'].append(0)
ALL[morph]['ignore'].append(0)
ALL[morph]['total'].append(0)
percents = dict()
totals = dict()
successes = dict()
failures = dict()
ignores = dict()
for m in sorted(all_morph_names, key=natural_keys):
morph_num = int(re.findall("[-+]?\d+?\d*", m)[0])
print morph_num
totals[m] = float(sum(ALL[m]['total']))
successes[m] = float(sum(ALL[m]['success']))
failures[m] = float(sum(ALL[m]['failure']))
ignores[m] = float(sum(ALL[m]['ignore']))
if m not in ALL.keys() or sum(ALL[m]['total'])==0:
percents[m] = 0
else:
# With nmorphs = 20, morphs 1-10.png are CHOOSE-RIGHT.
# For now, plot %-choose-right (i.e., morphs1-10)
# This means, "success" for morphs1-10, and "failure" for morphs11-20 (i.e,. animal went RIGHT, when should have gone LEFT)
# ALL STUFF BEFORE 08/17/2016 was incorrectly assignign the halfway point in MW protocol:
# i.e,. was assigning indices <=10 to "left" and >10 to "right"
# This should really be: <=9 to left, >=10 to right... fixed on 08/17
# SEE ABOVE, for session-parsed %s...
if morph_num <= middle_idx: #< 11:
percents[m] = float(sum(ALL[m]['success'])) / float(sum(ALL[m]['total']))
elif morph_num > middle_idx: #>= 11:
percents[m] = float(sum(ALL[m]['failure'])) / float(sum(ALL[m]['total']))
P[animal]['percent_right'] = percents
print percents
P[animal]['totals'] = totals
P[animal]['success'] = successes
P[animal]['failure'] = failures
P[animal]['ignore'] = ignores
x = []
xlabel = []
for p in sorted(percents.keys(), key=natural_keys):
x.append(percents[p])
xlabel.append(p)
x = np.flipud(x)
xlabel = np.flipud(x)
fig = plt.figure()
plt.plot(x, 'r*')
plt.ylabel('percent choose RIGHT port')
plt.xlabel('morph #')
subject = animal.split('_')[0]
plt.title('%s' % subject)
imname = '%s.png' % subject
outdir = os.path.join(os.path.split(datadir)[0], 'figures')
if not os.path.exists(outdir):
os.makedirs(outdir)
fig.savefig(outdir + '/' + imname)
print outdir + '/' + imname
if show_curve is True:
plt.show()
return P
def parse_trials(dfns, remove_orphans=True):
"""
Parse session .mwk files.
Key is session name values are lists of dicts for each trial in session.
Looks for all response and display events that occur within session.
dfns : list of strings
contains paths to each .mwk file to be parsed
remove_orphans : boolean
for each response event, best matching display update event
set this to 'True' to remove display events with unknown outcome events
"""
trialdata = {} # initiate output dict
for dfn in dfns:
df = None
df = pymworks.open(dfn) # open the datafile
sname = os.path.split(dfn)[1]
trialdata[sname] = []
modes = df.get_events('#state_system_mode') # find timestamps for run-time start and end (2=run)
# run_idxs = np.where(np.diff([i['time'] for i in modes])<20) # 20 is kind of arbitray, but mode is updated twice for "run"
start_ev = [i for i in modes if i['value']==2][0]
# stop_ev_ev = [i for i in modes if i['time']>start_ev['time'] and (i['value']==0 or i['value']==1)]
run_idxs = [i for i,e in enumerate(modes) if e['time']>start_ev['time']]
bounds = []
for r in run_idxs: #[0]:
try:
# stop_ev = next(i for i in modes[r:] if i['value']==0 or i['value']==1)
stop_ev = next(i for i in modes[r:] if i['value']==0 or i['value']==1)
except StopIteration:
end_event_name = 'trial_end'
print "NO STOP DETECTED IN STATE MODES. Using alternative timestamp: %s." % end_event_name
stop_ev = df.get_events(end_event_name)[-1]
print stop_ev
bounds.append([modes[r]['time'], stop_ev['time']])
bounds[:] = [x for x in bounds if not x[1]-x[0]<1]
# print "................................................................"
print "****************************************************************"
print "Parsing file\n%s... " % dfn
print "Found %i start events in session." % len(bounds)
print "****************************************************************"
for bidx,boundary in enumerate(bounds):
if (boundary[1]-boundary[0])<1:
del bounds[bidx]
# print "................................................................"
print "SECTION %i" % bidx
print "................................................................"
#M1:#tmp_devs = df.get_events('#stimDisplayUpdate') # get *all* display update events
# tmp_devs = [i for i in tmp_devs if i['time']<= boundary[1] and\
# i['time']>=boundary[0]] # only grab events within run-time bounds (see above)
#M1:#devs = [e for e in tmp_devs if not e.value[0]==[None]]
# Check stimDisplayUpdate events vs announceStimulus:
stim_evs = df.get_events('#stimDisplayUpdate')
devs = [e for e in stim_evs if not e.value[0]==None]
idevs = [i for i in devs for v in i.value if 'png' in v['name']]
ann_evs = df.get_events('#announceStimulus')
# SEE: AG8_160705.mwk -- issue with sevs...
try:
sevs = [i for i in ann_evs if 'png' in i.value['name']]
except TypeError as e:
print dfn
print e
if not len(idevs)==len(sevs):
print "MISMATCH in event counts in DF: %s" % dfn
print "-------------------------"
print "#stimDisplayUpdate %i and #announceStimulus %i." % (len(idevs), len(sevs))
#M1:#devs = [e for e in stim_evs if e.value[0] is not None]
im_names = sorted([i['name'] for d in idevs for i in d.value if '.png' in i['name']], key=natural_keys)
#im_names = sorted([i['name'] for d in devs for i in d.value if '.png' in i['name']], key=natural_keys)
resptypes = ['success','ignore','failure']
#,\ # 'aborted_counter'] # list of response variables to track...
outevs = df.get_events(resptypes) # get all events with these vars
outevs = [i for i in outevs if i['time']<= boundary[1] and\
i['time']>=boundary[0]]
R = sorted([r for r in outevs if r.value > 0], key=lambda e: e.time)
T = pymworks.events.display.to_trials(stim_evs, R,\
remove_unknown=False) # match stim events to response events
trials = T
print "N total response events: ", len(trials)
if remove_orphans: # this should always be true, generally...
orphans = [(i,x) for i,x in enumerate(trials) if\
x['outcome']=='unknown']
trials = [t for t in trials if not t['outcome']=='unknown'] # get rid of display events without known outcome within 'duration_multiplier' time
print "Found and removed %i orphan stimulus events in file %s"\
% (len(orphans), dfn)
print "N valid trials: %i" % len(trials)
trialdata[sname].append(trials)
return trialdata
def get_non_floats(fn, name=''):
df = pymworks.open(fn)
evs = df.get_events(name)
for ev in evs:
if type(ev.value) != float:
print fn