def norm(i):
print 'scale %d' % i
fres = exp.load('%s_%s_scales_mean.npy' % (strain, feat), memmap='r')
fres_tn = exp.load('%s_%s_time_normalized_scales_mean.npy' %
(strain, feat),
memmap='r+')
fres_tn[:, i, :] = exp.bin_data(fres[:, i, :], sbins)
fig.savefig(
os.path.join(save_fig, '%s_%s.png' % (strain, 'stage_durations')))
### Mean feature accross worms and stages
d = getattr(data, feat)
#average+order
dm = np.sum(d, axis=1) * 1.0 / data.total_time
dorder = np.argsort(dm)
exp.save(dorder, '%s_%s_order.npy' % (strain, feat))
#stage resolved averages
sbins = exp.stage_bins(data, nbins=1)
dms = exp.bin_data(d, sbins)
dms_shuffle = dms.copy()
for i in range(dms.shape[1]):
dms_shuffle[:, i] = np.random.permutation(dms_shuffle[:, i])
dms_data = [dms, dms_shuffle]
fig = plt.figure(20 + i)
plt.clf()
plt.subplot(3, 2, 1)
plt.plot(dm[dorder])
for s in range(data.nstages):
plt.plot(dm[np.argsort(dms[:, s])])
plt.title('%s %s - total mean' % (strain, feat))
for s in range(data.nstages):
plt.subplot(3, 2, s + 2)
dat_bin = {}
dat_mean = {}
dat_var = {}
dat = {}
stage_bins = {}
dat_bin_s = {}
for strain in strains:
print 'processing %s...' % strain
data[strain] = exp.load_data(strain)
dat[strain] = getattr(data[strain], feat)
sbinsb = exp.stage_bins(data[strain], nbins=sbins)
dat_bin[strain] = exp.bin_data(dat[strain], sbinsb)
dat_mean[strain] = np.mean(dat_bin[strain], axis=0)
dat_var[strain] = np.var(dat_bin[strain], axis=0)
stage_bins[strain] = exp.stage_bins(data[strain], nbins=1)
dat_bin_s[strain] = exp.bin_data(dat[strain], stage_bins[strain])
#%% Order by activity
order = {}
for strain in strains:
order[strain] = np.argsort(np.sum(dat[strain], axis=1))
#%% Plot Data
# plot roaming fraction
# calculate roaming a and dwelling intervalls -> ideally after smoothing with HHM ?
data = exp.load_data(strain);
dat = getattr(data, feat);
#dat_scales = exp.load('%s_%s_time_normalized_scales_mean.npy' % (strain , feat));
dbins = 7;
sbins = 50;
nbins = 5 * sbins;
sbins = exp.stage_bins(data, nbins= sbins);
dat_bin = exp.bin_data(dat, sbins);
dat_mean = np.mean(dat_bin, axis = 0);
dat_var = np.var(dat_bin, axis = 0);
dist = np.zeros((dbins, nbins));
for b in range(nbins):
dist[:,b] += np.histogram(dat_bin[:,b], range = (0, 0.9), bins = dbins)[0];
dist[:,b] /= np.sum(dist[:,b]);
plt.figure(6); plt.clf();
fplt.plot_image_array((dist,), names = ['dist_%s' % feat], invert_y = True, nplots = 3);
plt.subplot(3,1,2);
plt.plot(dat_mean, 'r');
plt.title('%s_%s mean' % (strain, feat))
plt.subplot(3,1,3)
import plot as fplt
strain = 'N2'
feat = 'roam'
nbins = 2**13
save_fig = True
data = exp.load_data(strain)
### Bin data
sbins = exp.stage_bins(data, nbins=nbins)
d = getattr(data, feat)
tn = exp.bin_data(d, sbins)
exp.save(tn, '%s_%s_time_normalized.npy' % (strain, feat))
#fig = plt.figure(1); plt.clf();
#plt.imshow(tn, aspect = 'auto');
#plt.tight_layout();
#plt.title('%s %s time normalized' % (strain, feat));
#if save_fig:
# fig.savefig(exp.figname('%s_%s_time_normalized.png'% (strain, feat)));
fig = fplt.plot_array(tn, title='%s %s time normalized' % (strain, feat))
fplt.savefig(fig,
exp.figname('%s_%s_time_normalized.png' % (strain, feat)),
width=2500)
sbins_start, sbins_end = exp.stage_bins(data, nbins=2**5)
nbins_total = sbins_start.shape[1]
#nparam = nstates * (nstates-1) + nstates; #transition parameter + emission prob for 1
nparam = 6
nworms = d.shape[0]
probs = np.zeros((nparam, nbins_total))
for i in range(nbins_total):
print '%d/%d' % (i, nbins_total)
model = hmm(nstates=2)
dd = [d[:, sbins_start[j][i]:sbins_end[j][i]] for j in range(nworms)]
model.fit(dd, max_iterations=500, stop_threshold=10e-5, n_jobs=12)
probs[:, i] = model_to_param(model)
probs[3, :] = 1 - probs[3, :]
droam = exp.bin_data(d, (sbins_start, sbins_end))
plt.figure(2)
plt.clf()
plt.subplot(4, 1, 1)
plt.imshow(probs[:2, :], aspect='auto', interpolation='none', vmin=0.8)
plt.subplot(4, 1, 2)
plt.imshow(probs[2:4, :], aspect='auto', interpolation='none', vmin=0.85)
plt.subplot(4, 1, 3)
plt.imshow(probs[4:, :], aspect='auto', interpolation='none')
plt.subplot(4, 1, 4)
plt.plot(np.mean(droam, axis=0))
plt.xlim(0, droam.shape[1])
plt.tight_layout()
np.save('%s_hmm.npy' % strain, probs)