rpt = RNNPreprocessTransform.load(preproc_file)
rpt.write_pred_file(rnn_file, pred_file, lfp_enc_file=linear_file)
visualize_pred_file(pred_file, 3, 0, 2.4, electrode_order=ROSTRAL_CAUDAL_ELECTRODES_LEFT[::-1], dbnoise=4.0)
leg = custom_legend(['k', 'r', 'b'], ['Real', 'Linear', 'RNN'])
plt.legend(handles=leg)
fname = os.path.join(get_this_dir(), 'rnn_preds.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
plt.show()
def draw_figures():
agg = RNNAggregator.load('/auto/tdrive/mschachter/data/aggregate/rnn.h5')
# stats(agg)
draw_preds(agg)
if __name__ == '__main__':
set_font()
draw_figures()
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()
