def plot_lfp_grouped_by_sound(ssl, plot_difference=True, p_adj_meth=None,
mark_significance=True, t_start=None, t_stop=None, **kwargs):
# First get grouped averages
res_d = ssl.average_grouped_by_sound(**kwargs)
# set time limits
if t_start is None:
t_start = ssl.t[0]
if t_stop is None:
t_stop = ssl.t[-1]
t1bin = np.argmin(np.abs(ssl.t - t_start))
t2bin = np.argmin(np.abs(ssl.t - t_stop)) + 1
f = plt.figure()
for n, sound in enumerate(['lehi', 'rihi', 'lelo', 'rilo']):
# Create an axis for this sound and plot both blocks
ax = f.add_subplot(2, 2, n+1)
for block in ['LB', 'PB']:
myutils.plot_mean_trace(ax=ax, x=ssl.t[t1bin:t2bin],
data=res_d[sound][block][:, t1bin:t2bin], label=block)
# Optionally plot difference
if plot_difference:
di = res_d[sound]['LB'] - res_d[sound]['PB']
myutils.plot_mean_trace(ax=ax, x=ssl.t[t1bin:t2bin],
data=di[:, t1bin:t2bin], label='diff', color='m')
# Optionally mark significance
if mark_significance:
p_vals = scipy.stats.ttest_rel(res_d[sound]['LB'][:, t1bin:t2bin],
res_d[sound]['PB'][:, t1bin:t2bin])[1]
if p_adj_meth is not None:
p_vals = myutils.r_adj_pval(p_vals, meth=p_adj_meth)
pp = np.where(p_vals < .05)[0]
plt.plot(ssl.t[t1bin:t2bin][pp], np.zeros_like(pp), 'k*')
pp = np.where(p_vals < .01)[0]
plt.plot(ssl.t[t1bin:t2bin][pp], np.zeros_like(pp), 'ko',
markerfacecolor='w')
plt.legend(loc='best')
ax.set_title(sound)
ax.set_xlim((t_start, t_stop))
plt.show()
def plot_psths_by_sound(df, plot_difference=True, split_on=None,
mark_significance=False, plot_errorbars=True, p_adj_meth=None,
plot_all=False):
"""Plots PSTHs for each sound, for a single unit or average across multiple.
df : DataFrame containing binned data with following columns
:split_on: - Tell me how to group the data
eg 'nid', or ['session', 'unit'], or None (single trace)
time - time in sec
counts - number of spikes in that bin
trials - number of trials included in that bin
sound, block - what the stimulus was
Run bin_flat_spike_data and replace_stim_numbers_with_names to produce
these columns.
"""
# Create figure
f = plt.figure(figsize=(9,9))
# Pivot data to allow sound/block access.
if split_on is None:
# Aggregate all data into a single trace
pdata = df.pivot_table(rows=['time'], cols=['sound', 'block'],
values=['counts', 'trials'], aggfunc=np.sum)
else:
# Split according to request
pdata = df.pivot_table(rows=['time'], cols=(['sound', 'block'] + split_on),
values=['counts', 'trials'], aggfunc=np.sum)
# Double-check we didn't lose data
#if len(df) != pdata['counts'].shape[0] * pdata['counts'].shape[1]:
if len(df) != np.sum(~np.isnan(pdata['counts'].values)):
print "warning: refolded data doesn't match shape"
print "avoid this warning by passing a single trace, or specifying merge column"
# Iterate over sounds (one axis per sound)
for n, sound_name in enumerate(['lehi', 'rihi', 'lelo', 'rilo']):
# extract data
LB_counts = pdata['counts'][sound_name]['LB'].dropna(axis=1).values.astype(np.int)
LB_trials = pdata['trials'][sound_name]['LB'].dropna(axis=1).values.astype(np.int)
PB_counts = pdata['counts'][sound_name]['PB'].dropna(axis=1).values.astype(np.int)
PB_trials = pdata['trials'][sound_name]['PB'].dropna(axis=1).values.astype(np.int)
assert(LB_counts.shape == LB_trials.shape)
assert(PB_counts.shape == PB_trials.shape)
assert(PB_counts.shape == LB_trials.shape)
# get time vector and check for consistency
# technically could be in arbitrary order, in which case will error
times = pdata['counts'][sound_name]['LB'].index.values.astype(np.float)
assert (times == pdata['trials'][sound_name]['LB'].index.values).all()
assert (times == pdata['counts'][sound_name]['PB'].index.values).all()
assert (times == pdata['trials'][sound_name]['PB'].index.values).all()
# remove columns with no trials
bad_cols1 = np.array([np.all(t == 0) for t in LB_trials.transpose()])
bad_cols2 = np.array([np.all(t == 0) for t in PB_trials.transpose()])
bad_cols = bad_cols1 | bad_cols2
print "dropping %d sessions with insufficient trials" % np.sum(bad_cols)
LB_trials = LB_trials[:, ~bad_cols]
LB_counts = LB_counts[:, ~bad_cols]
PB_trials = PB_trials[:, ~bad_cols]
PB_counts = PB_counts[:, ~bad_cols]
assert LB_counts.shape[1] != 0
assert PB_counts.shape[1] != 0
# Create axis for this plot and plot means with errorbars
ax = f.add_subplot(2, 2, n + 1)
if plot_all:
ax.plot(times, LB_counts / LB_trials.astype(np.float),
label='LB', color='b')
ax.plot(times, PB_counts / PB_trials.astype(np.float),
label='PB', color='r')
else:
myutils.plot_mean_trace(ax=ax, x=times,
data=LB_counts / LB_trials.astype(np.float),
label='LB', color='b', axis=1, errorbar=True)
myutils.plot_mean_trace(ax=ax, x=times,
data=PB_counts / PB_trials.astype(np.float),
label='PB', color='r', axis=1, errorbar=True)
assert(LB_counts.shape == LB_trials.shape)
assert(PB_counts.shape == PB_trials.shape)
assert(PB_counts.shape == LB_trials.shape)
if plot_difference:
myutils.plot_mean_trace(ax=ax, x=times,
data=(
LB_counts / LB_trials.astype(np.float) -
PB_counts / PB_trials.astype(np.float)),
label='diff', color='m', axis=1, errorbar=True)
ax.plot(times, np.zeros_like(times), 'k')
if mark_significance:
LB_rate = LB_counts / LB_trials.astype(np.float)
PB_rate = PB_counts / PB_trials.astype(np.float)
p_vals = scipy.stats.ttest_rel(LB_rate.transpose(),
PB_rate.transpose())[1]
if p_adj_meth is not None:
p_vals = myutils.r_adj_pval(p_vals, meth=p_adj_meth)
pp = np.where(p_vals < .05)[0]
plt.plot(times[pp], np.zeros_like(pp), 'k*')
pp = np.where(p_vals < .01)[0]
plt.plot(times[pp], np.zeros_like(pp), 'ko',
markerfacecolor='w')
ax.set_title(myutils.longname[sound_name])
plt.legend(loc='best')
plt.show()
return f