gs_e = gs_i + middle_height + 1
f = d['psd_freq']
ax = plt.subplot(gs[gs_i:gs_e, (left_width+5):])
plt.imshow(sprops['lfp_psd'], interpolation='nearest', aspect='auto', origin='upper',
extent=(f.min(), f.max(), nelectrodes, 0), cmap=viridis, vmin=-2., vmax=2.)
plt.colorbar(label='Z-scored Log Power')
plt.xlabel('Frequency (Hz)')
plt.yticks(np.arange(nelectrodes)+0.5, d['electrode_order'])
plt.ylabel('Electrode')
fname = os.path.join(get_this_dir(), 'figure.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
plt.show()
def draw_figures():
d = get_full_data('GreBlu9508M', 'Site4', 'Call1', 'L', 268)
# d = get_full_data('GreBlu9508M', 'Site4', 'Call1', 'L', 277)
plot_full_data(d, 1)
plt.show()
if __name__ == '__main__':
spec_colormap()
set_font()
draw_figures()
def plot_syllable_comps(agg, stim_id=43, syllable_order=1, data_dir='/auto/tdrive/mschachter/data'):
sprops = get_syllable_props(agg, stim_id, syllable_order, data_dir)
wave = sprops['wave']
wave_t = sprops['wave_t']
wave_si = sprops['wave_si']
wave_ei = sprops['wave_ei']
ps_freq = sprops['ps_freq']
ps = sprops['ps']
amp_env = sprops['amp_env']
aprops = sprops['aprops']
start_time = aprops['start_time']
spec = sprops['spec']
spec_t = sprops['spec_t']
spec_freq = sprops['spec_freq']
spec_si = sprops['spec_si']
spec_ei = sprops['spec_ei']
aprop_specs = dict()
aprop_spec_props = ['maxAmp', 'meanspect', 'sal']
for aprop in aprop_spec_props:
aprop_specs[aprop] = get_syllable_examples(agg, aprop, data_dir, bird='GreBlu9508M')
figsize = (23, 13)
fig = plt.figure(figsize=figsize, facecolor='w')
fig.subplots_adjust(top=0.95, bottom=0.08, right=0.97, left=0.06, hspace=0.30, wspace=0.30)
gs = plt.GridSpec(3, 100)
sp_width = 20
ax = plt.subplot(gs[0, :sp_width])
plt.plot((wave_t[wave_si:wave_ei] - start_time)*1e3, wave[wave_si:wave_ei], 'k-', linewidth=2.)
plt.plot((wave_t[wave_si:wave_ei] - start_time)*1e3, amp_env[wave_si:wave_ei], 'r-', linewidth=4., alpha=0.7)
plt.xlabel('Time (ms)')
plt.ylabel('Waveform')
plt.axis('tight')
aprops_to_get = ['skewtime', 'kurtosistime', 'entropytime', 'maxAmp']
units = ['s', '', '', 'bits', '']
ax = plt.subplot(gs[0, sp_width:(sp_width+18)])
txt_space = 0.1
for k,aprop in enumerate(aprops_to_get):
aval = aprops[aprop]
if aval > 10:
txt = '%s: %d' % (ACOUSTIC_PROP_NAMES[aprop], aval)
else:
txt = '%s: %0.2f' % (ACOUSTIC_PROP_NAMES[aprop], aval)
txt += ' %s' % units[k]
plt.text(0.1, 1-((k+1)*txt_space), txt, fontsize=18)
ax.set_axis_off()
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.xticks([])
plt.yticks([])
ax = plt.subplot(gs[1, :sp_width])
fi = (ps_freq > 0) & (ps_freq <= 8000.)
plt.plot(ps_freq[fi]*1e-3, ps[fi], 'k-', linewidth=3., alpha=1.)
for aprop in ['q1', 'q2', 'q3']:
plt.axvline(aprops[aprop]*1e-3, color='#606060', linestyle='--', linewidth=3.0, alpha=0.9)
# plt.text(aprops[aprop]*1e-3 - 0.6, 3000., aprop, fontsize=14)
plt.ylabel("Power")
plt.yticks([])
plt.xlabel('Frequency (kHz)')
plt.axis('tight')
aprops_to_get = ['meanspect', 'stdspect', 'skewspect', 'kurtosisspect', 'entropyspect', 'q1', 'q2', 'q3']
units = ['Hz', 'Hz', '', '', 'bits', 'Hz', 'Hz', 'Hz']
ax = plt.subplot(gs[1, sp_width:(sp_width+18)])
for k,aprop in enumerate(aprops_to_get):
aval = aprops[aprop]
if aval > 10:
txt = '%s: %d' % (ACOUSTIC_PROP_NAMES[aprop], aval)
else:
txt = '%s: %0.2f' % (ACOUSTIC_PROP_NAMES[aprop], aval)
txt += ' %s' % units[k]
plt.text(0.1, 1-((k+1)*txt_space), txt, fontsize=18)
ax.set_axis_off()
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.xticks([])
plt.yticks([])
spec_colormap()
ax = plt.subplot(gs[2, :sp_width])
plot_spectrogram((spec_t[spec_si:spec_ei]-start_time)*1e3, spec_freq*1e-3, spec[:, spec_si:spec_ei], ax, colormap='SpectroColorMap', colorbar=False)
plt.axhline(aprops['fund']*1e-3, color='k', linestyle='--', linewidth=3.0)
plt.ylabel("Frequency (kHz)")
plt.xlabel('Time (ms)')
for k,aprop in enumerate(aprop_spec_props):
full_spec_freq, full_spec, centers, propvals, stypes = aprop_specs[aprop]
ax = plt.subplot(gs[k, (sp_width+20):])
full_spec_t = np.arange(full_spec.shape[1]) / sprops['spec_sample_rate']
plot_spectrogram(full_spec_t, full_spec_freq*1e-3, full_spec, ax, colormap='SpectroColorMap', colorbar=False)
for c,st in zip(centers,stypes):
plt.text(c/sprops['spec_sample_rate'], 7., CALL_TYPE_NAMES[st], fontsize=20)
pstrs = list()
for p in propvals:
if p > 10:
if aprop == 'meanspect':
pstrs.append('%d Hz' % p)
else:
pstrs.append('%d' % p)
else:
pstrs.append('%0.3f' % p)
for c,p in zip(centers,pstrs):
plt.text(c, 6, p, fontsize=20)
# plt.xticks(centers, pstrs)
plt.xticks([])
plt.ylabel('Frequency (kHz)')
scale_t = (full_spec_t.max()*0.95) - np.linspace(0, 0.050, 20)
scale_y = np.zeros_like(scale_t) + 3
plt.plot(scale_t, scale_y, 'k-', linewidth=4.0)
plt.text(scale_t.min(), 2.0, '50ms', fontsize=20, fontweight='bold')
upper_right_x = np.percentile(full_spec_t, 80)
upper_right_y = 7
plt.title(ACOUSTIC_PROP_FULLNAMES[aprop], fontsize=22)
aprops_to_get = ['fund', 'fund2', 'sal', 'voice2percent', 'maxfund', 'minfund', 'cvfund']
units = ['Hz', 'Hz', '', '', 'Hz', 'Hz', 'Hz']
ax = plt.subplot(gs[2, sp_width:(sp_width + 18)])
for k, aprop in enumerate(aprops_to_get):
aval = aprops[aprop]
if aval > 10:
txt = '%s: %d' % (ACOUSTIC_PROP_NAMES[aprop], aval)
else:
txt = '%s: %0.2f' % (ACOUSTIC_PROP_NAMES[aprop], aval)
txt += ' %s' % units[k]
plt.text(0.1, 1 - ((k + 1) * txt_space), txt, fontsize=18)
ax.set_axis_off()
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.xticks([])
plt.yticks([])
fname = os.path.join(get_this_dir(), 'figure.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
plt.show()
def plot_nofund(agg, data_dir='/auto/tdrive/mschachter/data'):
spec_colormap()
aprops = ['fund', 'maxfund', 'minfund', 'cvfund', 'fund2', 'voice2percent',
'stdspect', 'skewspect', 'kurtosisspect', 'entropyspect', 'sal',
'meanspect', 'q1', 'q2', 'q3', 'skewtime', 'kurtosistime', 'entropytime', 'maxAmp']
Xz, good_indices = agg.remove_duplicates(acoustic_props=aprops)
stim_durs = agg.df.end_time.values - agg.df.start_time.values
good_indices = [gi for gi in good_indices if stim_durs[gi] > 0.040 and stim_durs[gi] < 0.450]
stims = [(stim_id, order, stim_type) for stim_id, order, stim_type in zip(agg.df.stim_id[good_indices],
agg.df.syllable_order[good_indices],
agg.df.stim_type[good_indices])]
plt.figure()
plt.hist(stim_durs[good_indices], bins=25)
plt.show()
fund_i = agg.acoustic_props.index('fund')
funds = np.array([agg.Xraw[xindex, fund_i] for xindex in agg.df.xindex[good_indices]])
print 'funds.min=%f, q1=%f, q2=%f' % (funds.min(), np.percentile(funds, 1), np.percentile(funds, 2))
no_fund = np.where(funds <= 0)[0]
yes_fund = np.where(funds > 0)[0]
print 'no_fund=', no_fund
plt.figure()
plt.hist(funds, bins=20)
plt.title('funds')
# plt.show()
# np.random.seed(1234567)
# np.random.shuffle(no_fund)
# np.random.shuffle(yes_fund)
# get syllable properties for some examples of syllables with and without fundamental frequencies
set_font(10)
figsize = (23, 10)
fig = plt.figure(figsize=figsize)
fig.subplots_adjust(wspace=0.35, hspace=0.35, left=0.05, right=0.95)
num_syllables = 5
no_fund_i = no_fund[:num_syllables]
yes_fund_i = yes_fund[:num_syllables]
gs = plt.GridSpec(2, num_syllables)
for k in range(num_syllables):
fi = no_fund_i[k]
f = funds[fi]
stim_id,stim_order,stim_type = stims[fi]
sprops = get_syllable_props(agg, stim_id, stim_order, data_dir)
ax = plt.subplot(gs[0, k])
si = sprops['spec_si']
ei = sprops['spec_ei']
plot_spectrogram(sprops['spec_t'][si:ei], sprops['spec_freq'], sprops['spec'][:, si:ei], ax=ax,
colormap='SpectroColorMap', colorbar=False)
plt.title('%d_%d (%s), fund=%0.2f' % (stim_id, stim_order, stim_type, f))
fi = yes_fund_i[k]
f = funds[fi]
stim_id, stim_order, stim_type = stims[fi]
sprops = get_syllable_props(agg, stim_id, stim_order, data_dir)
ax = plt.subplot(gs[1, k])
si = sprops['spec_si']
ei = sprops['spec_ei']
plot_spectrogram(sprops['spec_t'][si:ei], sprops['spec_freq'], sprops['spec'][:, si:ei], ax=ax,
colormap='SpectroColorMap', colorbar=False)
plt.title('%d_%d (%s), fund=%0.2f' % (stim_id, stim_order, stim_type, f))
plt.show()
def draw_figures(data_dir='/auto/tdrive/mschachter/data', bird='GreBlu9508M', output_dir='/auto/tdrive/mschachter/data/sounds'):
spec_colormap()
exp_dir = os.path.join(data_dir, bird)
exp_file = os.path.join(exp_dir, '%s.h5' % bird)
stim_file = os.path.join(exp_dir, 'stims.h5')
exp = Experiment.load(exp_file, stim_file)
bird = exp.bird_name
all_stim_ids = list()
# iterate through the segments and get the stim ids from each epoch table
for seg in exp.get_all_segments():
etable = exp.get_epoch_table(seg)
stim_ids = etable['id'].unique()
all_stim_ids.extend(stim_ids)
stim_ids = np.unique(all_stim_ids)
stim_info = list()
for stim_id in stim_ids:
si = exp.stim_table['id'] == stim_id
assert si.sum() == 1, "More than one stimulus defined for id=%d" % stim_id
stype = exp.stim_table['type'][si].values[0]
if stype == 'call':
stype = exp.stim_table['callid'][si].values[0]
# get sound pressure waveform
sound = exp.sound_manager.reconstruct(stim_id)
waveform = np.array(sound.squeeze())
sample_rate = float(sound.samplerate)
stim_dur = len(waveform) / sample_rate
stim_info.append( (stim_id, stype, sample_rate, waveform, stim_dur))
durations = np.array([x[-1] for x in stim_info])
max_dur = durations.max()
min_dur = durations.min()
max_fig_size = 15.
min_fig_size = 5.
for stim_id,stype,sample_rate,waveform,stim_dur in stim_info:
fname = os.path.join(output_dir, '%s_stim_%d.wav' % (stype, stim_id))
print 'Writing %s...' % fname
wavfile.write(fname, sample_rate, waveform)
dfrac = (stim_dur - min_dur) / (max_dur - min_dur)
fig_width = dfrac*(max_fig_size - min_fig_size) + min_fig_size
spec_t,spec_freq,spec,rms = spectrogram(waveform, sample_rate, sample_rate, 136., min_freq=300, max_freq=8000,
log=True, noise_level_db=80, rectify=True, cmplx=False)
figsize = (fig_width, 5)
fig = plt.figure(figsize=figsize)
plot_spectrogram(spec_t, spec_freq, spec, colormap='SpectroColorMap', colorbar=False)
plt.title('Stim %d: %s' % (stim_id, stype))
fname = os.path.join(output_dir, '%s_stim_%d.png' % (stype, stim_id))
plt.savefig(fname, facecolor='w')
plt.close('all')
