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
plt.figure(70)
plt.clf()
fplt.plot_image_array([dat_bin[s][order[s]] for s in strains],
names=strains,
vmax=1.0,
cmap='jet')
#%% Calculate distributions
max_feat = 1.0
#distributions
dist_t = {}
dist_s = {}
dist_g = {}
dist_a = np.zeros((dbins, 1))
for strain in strains:
#time resolved distribution
dist_t[strain] = np.zeros((dbins, tbins))
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
plt.figure(70)
plt.clf()
fplt.plot_image_array([dat_bin[s][order[s]] for s in strains],
names=strains,
vmax=1.0,
cmap='jet')
#%% Calculate distributions
max_feat = 1.0
#distributions
dist_t = {}
dist_s = {}
dist_g = {}
dist_a = np.zeros((dbins, 1))
for strain in strains:
#time resolved distribution
dist_t[strain] = np.zeros((dbins, tbins))
ax.scatter(Y_full[(i*nbins):((i+1)*nbins), 0], Y_full[(i*nbins):((i+1)*nbins), 1], Y_full[(i*nbins):((i+1)*nbins),2], c = color, cmap=cmap)
plt.title("MDS %s" % s1);
plt.tight_layout();
fplt.plot_pca()
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)
plt.plot(dat_var, 'b');
plt.title('%s_%s var' % (strain, feat))
# entropies /cross entropies
ent = np.zeros((nbins, nbins));
for b in range(nbins):
for b2 in range(nbins):
if b==b2:
ent[b,b2] = stats.entropy(dist[:,b]);
else:
dur_dw_max[w,b] = 1;
dur_dw_min[w,b] = 0;
dur_dw_mean[w,b] = 0;
dord = exp.load('%s_%s_order.npy' % (strain, feat));
mm = dur_up_mean.max()
dur_up_mean[dur_up_mean == mm] = 0;
plt.figure(2); plt.clf();
dur_names = ['dur_up_max', 'dur_up_min', 'dur_dw_max', 'dur_dw_min', 'dur_up_mean', 'dur_dw_mean', 'ndurs_up'];
fplt.plot_image_array((dur_up_max, dur_up_min, dur_dw_max, dur_dw_min, dur_up_mean, dur_dw_mean, ndurs_up), order = dord, names = dur_names)
plt.tight_layout()
# make histograms for each worm
dbins = 10;
dist_dur_up = np.zeros((nworms, nbins, dbins));
dist_dur_dw = np.zeros((nworms, nbins, dbins));
for w in range(nworms):
for b in range(nbins):
dist_dur_up[w,b] = np.histogram(durs_up[w][b], range = (0, dur_up_max[w,b]), bins = dbins)[0];
dist_dur_dw[w,b] = np.histogram(durs_dw[w][b], range = (0, dur_dw_max[w,b]), bins = dbins)[0];
cutoff = 5;
