def post_lesion_silent_comparison2():
fig = plt.figure()
for subjID in subjIDs:
d = pd.HDFStore(os.path.join(studydir, 'Combined', '%s_silent.h5' % subjID))
df = d['df']
d.close()
df = df[df.good]
epochgp = df.groupby(['epoch', 'hemi'])
df['FFTabs'] = np.abs(df.FFT)
epochmean = epochgp.FFTabs.apply(np.mean)
epocherr = epochgp.FFTabs.apply(pd_std)
colors = dict(Pre = 'b', Post = 'r')
axs = dict(l = 1, r = 2)
for (k, v) in epochmean.iterkv():
ax = fig.add_subplot(1, 2, axs[k[1]])
ax.set_title('%s hemisphere' % k[1])
misc.errorfill(fft_freq, np.log10(v), epocherr[k], color = colors[k[0]], ax = ax)
fig.suptitle('%s (%s ear lesion)' % (subjID, lesionear[subjID]))
fig.savefig(os.path.join(studydir, 'Analysis', '%s_silent_pre_post.png' % subjID))
fig.suptitle('')
fig.clf()
def plot_group_results(df):
df = df[df.cued==1]
# group by session to get session mean gap detection
gp_sess = df.groupby(['sess', 'freq'])
gapratio_sess = gp_sess.agg(dict(gapratio = np.mean, freq = get_first, gen = get_first, exp = get_first, animalID = get_first))
# combine sessions for each animal to give animal performance by condition
gp_animal = gapratio_sess.groupby(['animalID', 'exp', 'freq'])
gapratio_animal = gp_animal.agg(dict(gapratio = np.mean, freq = get_first, gen = get_first, exp = get_first))
gp_exp = gapratio_animal.groupby(['freq', 'exp'])
gapratio_mean = gp_exp.gapratio.apply(np.mean).unstack()
gapratio_sem = gp_exp.gapratio.apply(apply_sem).unstack()
fig = plt.figure();
ax = fig.add_subplot(111);
for (k, v), (kerr, verr) in zip(gapratio_mean.iteritems(), gapratio_sem.iteritems()):
misc.errorfill(np.arange(freqs.size), v, verr, ax = ax, color = colors[k], marker = '.', ls = '-', label = k)
ax.legend()
maxy = ax.get_ylim()[1]
ax.set_ylim([0, maxy])
ax.set_ylabel('Gap ratio')
ax.set_xlabel('Frequency (kHz)')
ax.set_xticks(range(freqs.size))
ax.set_xticklabels(freqs)
return ax
def single_subject_compare_conditions(self):
'''
For each subject, plot the condition means for each condition
on the same plot for comparison.
'''
fig, ax = plt.subplots(figsize = (10, 8))
for animalID in self.animalIDs:
animaldf = self.df[self.df.animalID==animalID]
condgp = animaldf.groupby(('condition', 'freq'))
ufreqs = np.unique(animaldf.freq)
x = np.arange(ufreqs.size)
y = condgp.agg(dict(gapratio = np.mean))
yerr = condgp.agg(dict(gapratio = st.sem))
for ((i, y_), (i, yerr_)) in zip(y.unstack().iterrows(), yerr.unstack().iterrows()):
# ax.errorbar(x, y_, yerr_, label = i, marker = 'o', color = colors[i], ls = lss[i])
misc.errorfill(x, y_, yerr_, label = i, ax = ax, marker = 'o', color = colors[i], ls = lss[i])
ax.set_xticks(x)
ax.set_xticklabels(np.int32(ufreqs/1000))
self._format_axis(ax)
ax.set_title('%s %s' % (self.studyID, os.path.split(self.studydir)[1]))
ax.legend()
figpath = os.path.join(self.figdir, 'single_subject_compareconditions_%s.%s' % (animalID, self.figformat))
fig.savefig(figpath)
ax.cla();
plt.close(fig)
def single_subject_compare_conditions_by_day(self):
'''
For each subject, plot the mean gap ratio for each condition
binned by date. (same as single_subject_compare_conditions, but
uses binned date as an additional condition)
'''
fig, ax = plt.subplots(figsize = (10, 8))
df = self.df
for animalID in self.animalIDs:
animaldf = df[df.animalID==animalID]
condgp = animaldf.groupby(('condition_postdate', 'freq'))
ufreqs = np.unique(animaldf.freq)
x = np.arange(ufreqs.size)
y = condgp.agg(dict(gapratio = np.mean))
yerr = condgp.agg(dict(gapratio = st.sem))
for ((i, y_), (i, yerr_)) in zip(y.unstack().iterrows(), yerr.unstack().iterrows()):
misc.errorfill(x, y_, yerr_, label = i, ax = ax, marker = 'o')
ax.set_xticks(x)
ax.set_xticklabels(np.int32(ufreqs/1000))
self._format_axis(ax)
ax.set_title('%s %s' % (self.studyID, os.path.split(self.studydir)[1]))
ax.legend()
figpath = os.path.join(self.figdir, 'single_subject_compare_conditions_by_day_%s.%s' % (animalID, self.figformat))
fig.savefig(figpath)
ax.cla();
plt.close(fig)
def make_noise_rr_sheets():
fpaths = glob.glob(os.path.join(studydir, 'Sessions', 'Pre', 'both', '*.h5'))
for fpath in fpaths:
absol, relat = os.path.split(fpath)
fname, _ = os.path.splitext(relat)
f = pd.HDFStore(fpath, 'r')
df = f['df']
f.close()
df = df[df.rr>0] # subset only non-silent trials
df = df[df.good] # use only good trials
urrs = np.unique(df.rr)
nrrs = urrs.size
gp = df.groupby(('rr', 'hemi'))
nsamp = df.lfp.values[0].size
Fs = nsamp / trial_duration
lfp_mean = gp.lfp.apply(np.mean)
lfp_err = gp.lfp.apply(np.std)
fft_mean = gp.fft.apply(np.mean)
fft_err = gp.fft.apply(np.std)
t = np.linspace(0, trial_duration, nsamp)
f = np.linspace(0, Fs/2., df.fft.values[0].size)
fig = plt.figure(figsize = (14, 8.8))
ax_lfp = []; ax_fft = []
for i, ((k, lfp_mean_), (k, lfp_err_), (k, fft_mean_), (k, fft_err_)) in enumerate(zip(lfp_mean.iterkv(), lfp_err.iterkv(),\
fft_mean.iterkv(), fft_err.iterkv())):
rr, hemi = k
if hemi=='l':
j=1
else: j=2
ax_lfp.append(fig.add_subplot(nrrs, 4, ((i/2)*4)+j))
misc.errorfill(t, lfp_mean_, lfp_err_, ax = ax_lfp[-1])
ax_lfp[-1].set_title(k)
ax_fft.append(fig.add_subplot(nrrs, 4, ((i/2)*4)+2+j))
misc.errorfill(f, np.abs(fft_mean_), np.abs(fft_err_), ax = ax_fft[-1])
misc.sameyaxis(ax_lfp); misc.sameyaxis(ax_fft)
misc.samexaxis(ax_lfp, [0, stim_duration]); misc.samexaxis(ax_fft, [0, 100.])
[a.set_yticklabels('') for a in ax_lfp[1::2]]
fig.savefig(os.path.join(studydir, 'Sheets', '%s_noise.png'%fname))
plt.close(fig)
def pairwise_compare_conditions_by_freq(self, control = 'prenihl', conditions = ['tnfa', 'vehicle']):
df = self.df
ucond = np.unique(df.condition)
ncond = len(ucond)
# make animal/condition/freq groups
animalgp = df.groupby(('animalID', 'condition', 'freq'))
# take means of gap performance for those groups
animalmeans = animalgp.agg(dict(gapratio=np.mean))
# put the control and manipulation columns side-by-side
condstack = animalmeans.unstack('condition')
# add a column indicating which condition this animal is
animalcondition = []
for key, value in condstack.iterrows():
if not pd.isnull(value['gapratio']['tnfa']):
animalcondition.append('tnfa')
else:
animalcondition.append('vehicle')
condstack['animalcondition'] = animalcondition
# make a column that is the result after manipulation
# (combines multiple columns for each manipulation)
# subtract the control from the manipulation values (for each frequency)
manip = condstack.gapratio.vehicle.copy()
manip.update(condstack.gapratio.tnfa)
condstack['manip'] = manip
condstack['manipdiff'] = condstack.manip - condstack.gapratio[control]
# add a frequency column (turns frequency index into column)
condstack['freq'] = condstack.index.get_level_values('freq')
# group changes in gap detection by condition and frequency
condgp = condstack.groupby(('animalcondition', 'freq'))
condmeans = condgp.manipdiff.apply(np.mean).unstack('animalcondition')
condsems = condgp.manipdiff.apply(st.sem).unstack('animalcondition')
# line plot (change in gap detection VS frequency)
ufreqs = np.unique(condstack.freq)
x = range(len(ufreqs))
fig, ax = plt.subplots()
styles = dict(tnfa=dict(color='r', hatch='///'), vehicle=dict(color='b', hatch=None))
for (key, y), (_, yerr) in zip(condmeans.iteritems(), condsems.iteritems()):
misc.errorfill(x, y, yerr, ax=ax, label=key, color=styles[key]['color'])
ax.set_xticks(x)
ax.set_xticklabels((ufreqs/1000.).astype(int))
ax.legend()
fig.savefig(os.path.join(self.figdir, 'pairwise_compare_conditions_by_freq.%s' % self.figformat))
plt.close(fig)
def make_silent_sheets(epoch = 'Pre'):
sesspaths = glob.glob(os.path.join(studydir, 'Sessions', epoch, '*'))
for sesspath in sesspaths:
fpaths = glob.glob(os.path.join(sesspath, 'fileconversion', '*.h5'))
for fpath in fpaths:
absol, relat = os.path.split(fpath)
fname, _ = os.path.splitext(relat)
f = pd.HDFStore(fpath, 'r')
df = f['df']
f.close()
df = df[df.rr==-1]
df = df[df.good]
gp = df.groupby('hemi')
nsamp = df.lfp.values[0].size
Fs = nsamp / trial_duration
lfp_mean = gp.lfp.apply(np.mean)
lfp_err = gp.lfp.apply(np.std)
fft_mean = gp.fft.apply(np.mean)
fft_err = gp.fft.apply(np.std)
t = np.linspace(0, trial_duration, nsamp)
f = np.linspace(0, Fs/2., df.fft.values[0].size)
fig = plt.figure()
ax_lfp = []; ax_fft = []
for i, ((k, lfp_mean_), (k, lfp_err_), (k, fft_mean_), (k, fft_err_)) in enumerate(zip(lfp_mean.iterkv(), lfp_err.iterkv(),\
fft_mean.iterkv(), fft_err.iterkv())):
# lfp_err_ = lfp_err[k]
ax_lfp.append(fig.add_subplot(2, 2, i+1));
misc.errorfill(t, lfp_mean_, lfp_err_, ax = ax_lfp[-1])
ax_lfp[-1].set_title(k)
ax_fft.append(fig.add_subplot(2, 2, i+3));
misc.errorfill(f, np.log(fft_mean_), np.log(fft_err_), ax = ax_fft[-1])
misc.sameyaxis(ax_lfp); misc.sameyaxis(ax_fft)
misc.samexaxis(ax_lfp, [0, stim_duration]); misc.samexaxis(ax_fft, [f.min(), f.max()])
figpath = os.path.join(studydir, 'Sheets', '%s_silent.png' % fname)
fig.savefig(figpath)
plt.close(fig)
def make_noise_singleburst_sheets():
fpaths = glob.glob(os.path.join(studydir, 'Sessions', 'Pre', 'both', '*.h5'))
fig = plt.figure(figsize = (14, 8.8))
ax_lfp = []
for j, fpath in enumerate(fpaths):
absol, relat = os.path.split(fpath)
fname, _ = os.path.splitext(relat)
f = pd.HDFStore(fpath, 'r')
df = f['df']
f.close()
df = df[np.logical_and(df.rr>0, df.rr<16, df.good)] # subset only non-silent trials
gp = df.groupby('hemi')
nsamp = df.lfp.values[0].size
Fs = nsamp / trial_duration
lfp_mean = gp.lfp.apply(np.mean)
lfp_err = gp.lfp.apply(np.std)
fft_mean = gp.fft.apply(np.mean)
fft_err = gp.fft.apply(np.std)
t = np.linspace(0, trial_duration, nsamp)
f = np.linspace(0, Fs/2., df.fft.values[0].size)
for i, ((k, lfp_mean_), (k, lfp_err_), (k, fft_mean_), (k, fft_err_)) in enumerate(zip(lfp_mean.iterkv(), lfp_err.iterkv(),\
fft_mean.iterkv(), fft_err.iterkv())):
ax_lfp.append(fig.add_subplot(len(fpaths), 2, (j*2)+i+1))
misc.errorfill(t, lfp_mean_, lfp_err_, ax = ax_lfp[-1])
if i==0:
ax_lfp[-1].set_title('%s / %s' % (fname, k))
else:
ax_lfp[-1].set_title(k)
misc.sameyaxis(ax_lfp, [-0.0004, 0.0004])
misc.samexaxis(ax_lfp, [0.05, 0.175])
[a.set_yticklabels('') for a in ax_lfp[1::2]]
fig.savefig(os.path.join(studydir, 'Sheets', 'noise_singleburst.png'))
plt.close(fig)
def make_contactsheets():
subjIDs = ['Red102', 'Green102', 'Blue102', 'Red101']
epochs = ['Pre', 'Post']
for subjID in subjIDs:
for epoch in epochs:
sesspaths = glob.glob(os.path.join(studydir, 'Sessions', epoch, '*'))
for sesspath in sesspaths:
# fpaths =
for fpath in fpaths:
absol, relat = os.path.split(fpath)
fname, _ = os.path.splitext(relat)
print fname
fin = pd.HDFStore(fpath, 'r')
df = fin['df']
fin.close()
ntrials = 100
duration = 0.333
nattens = np.unique(df.atten).size
gp = df.groupby(('atten', 'hemi', 'relhemi'))
means = gp.lfp.apply(np.mean)
errs = gp.lfp.apply(np.std) / float(np.sqrt(ntrials))
t = np.linspace(0, duration, df.lfp[0].size)
fig = plt.figure()
ax = []
for i, ((k, m), (k, er)) in enumerate(zip(means.iterkv(), errs.iterkv())):
ax.append(fig.add_subplot(nattens, 4, i+1))
misc.errorfill(t[:-1], m[:-1], er[:-1], ax = ax[-1])
ax[-1].set_title(k)
ax[-1].set_xlim([0, duration])
misc.sameyaxis(ax)
[a.set_xticklabels('') for a in ax[:-1]]
[a.set_yticklabels('') for a in ax[1:]]
fig.tight_layout()
fig.savefig(os.path.join(studydir, 'Sheets', '%s_sheet.png' % fname))
plt.close(fig)
