def plot_targ_perc_correct(hdf, plot_seq=True, nested_te_list=None, indicator_labels=None):
cnt = -1
flat_te_list = list(np.hstack((nested_te_list)))
if nested_te_list is not None:
cm = []
for te_day in nested_te_list:
cnt += 1
for d in te_day:
cm.append(cnt)
flat_te_list = list(np.hstack((nested_te_list)))
else:
flat_te_list = np.unique(hdf.root.meta_metrics[:]['task_entry'])
print cm
te_dict = {}
for row in hdf.root.meta_metrics[:]:
for tg in range(8):
if (row['task_entry'], tg) in te_dict.keys() and row['task_entry'] in flat_te_list:
te_dict[row['task_entry'], tg].append(row['targ_percent_success'][tg])
else:
te_dict[row['task_entry'], tg] = [row['targ_percent_success'][tg]]
input_dict = dict()
for k in te_dict.keys():
#Find entry in other table with same number:
ix = np.nonzero(hdf.root.trial_metrics[:]['task_entry']==k[0])[0]
input_type = hdf.root.trial_metrics[ix[0]]['input_type']
input_dict[k] = input_type
f, ax = plt.subplots(nrows=3, ncols=3)
off = 0
for i, k in enumerate(flat_te_list):
# if input_dict[k] == 'all':
# color='k'
# elif input_dict[k] == 'shared_sc':
# color = 'r'
color = cmap[cm[i]]
for tg in range(8):
ax_ix = bha.targ_ix_to_3x3_subplot(tg)
axi = ax[ax_ix[0], ax_ix[1]]
if (k, tg) in te_dict.keys():
axi.plot((np.arange(len(te_dict[k, tg]))*2.5)+off, te_dict[k, tg], '-', color=color)
else:
print ' no: ', k, tg
try:
off += len(te_dict[k, tg])*2.5
except:
print 'no need to increment off'
#ax.set_ylabel('Perc Success Rate')
#ax.set_xlabel('Time (min) over Session')
for axi in ax:
for axii in axi:
axii.set_ylim([0, 1.])
plt.tight_layout()
def plot_by_targ_and_input_type(metric_wanted, met_hdf, ylabel=None, ylim=None, stats=True,
input_type=None, collapse_targ=False, subset_tes=None, subset_cond=None):
#Init figure
if collapse_targ:
f, ax = plt.subplots()
else:
f, ax = plt.subplots(nrows=3, ncols=3)
if subset_cond is None:
add_subset = ''
else:
add_subset = ' & '+subset_cond
targs = np.unique(met_hdf.root.trial_metrics[:]['target_index'])
if subset_tes is None:
subset_tes = np.unique(met_hdf.root.trial_metrics[:]['task_entry'])
ix = np.arange(len(met_hdf.root.trial_metrics))
else:
ix = np.array([i for i, j in enumerate(met_hdf.root.trial_metrics[:]['task_entry']) if j in subset_tes])
if input_type is None:
inputs = np.unique(met_hdf.root.trial_metrics[ix]['input_type'])
else:
tmp_inp = np.array([input_type[te] for te in subset_tes])
inputs = np.unique(tmp_inp)
tr_table = met_hdf.root.trial_metrics
master_times = []
#Iterate through target num (target_index in hdf file)
for it in targs:
#Init empty arryays and names:
times = []
nms = []
#Iterate through 'fa_inputs':
for inp in inputs:
#Set pytable query
if input_type is None:
cond = '(target_index == it) & (input_type == inp)'+add_subset
else:
inp_times = []
#Get TEs w/ correct input:
te_arr = np.array([te for te in input_type.keys() if input_type[te] == inp])
cond = []
for te in te_arr:
cond.append('(target_index == it) & (task_entry == te)'+add_subset)
#Add desired metric of trials that are of correct input type and target index
if type(cond) == str:
times.append([x[metric_wanted] for x in tr_table.where(cond)])
else:
for c in cond:
inp_times.extend([x[metric_wanted] for x in tr_table.where(c)])
times.append(inp_times)
nms.append(inp)
if collapse_targ:
if len(master_times) == 0:
master_times = times
else:
for i, t in enumerate(times):
master_times[i].extend(times[i])
else:
#Map target index to correct subplot location
axi = ax[bha.targ_ix_to_3x3_subplot(it)]
#Boxplot
z = seaborn.boxplot(times, names=nms, ax=axi, fliersize=0, whis=0.)
plt.setp(z.get_xticklabels(), rotation=45)
#Plot stats:
if stats:
print 'stats for targ ix: ', it, len(times), len(nms)
axi, dy = add_stats(axi, times, nms, ylim)
if ylim is not None:
axi.set_ylim([ylim[0], ylim[1]+6*dy])
if collapse_targ:
z = seaborn.boxplot(master_times, names=nms, ax=ax, fliersize=0, whis=0.)
plt.setp(z.get_xticklabels(), rotation=45)
if stats:
print 'stats for master'
axi, dy = add_stats(ax, master_times, nms, ylim)
if ylim is not None:
ax.set_ylim([ylim[0], ylim[1]+6*dy])
ax.set_title(metric_wanted)
else:
ax[0, 1].set_title(metric_wanted)
if ylabel is not None:
for i in range(3):
ax[i, 0].set_ylabel(ylabel)
#Magic
plt.tight_layout()
