def gen_fig_1(var):
labels = [
r'$\sigma_s/\langle \tilde{\Delta}\rangle$', '$\sigma_{t}/t_{db}$'
]
titles = ['$f=0.5$, $(C+D)/t_{db}=0.7$', '$f=0.5$, $(C+D)/t_{db}=0.9$']
tit_num = ['(A)', '(B)', '(C)', '(D)']
cmap_lims = [0.0, 2.0]
ticks = np.linspace(0.01, 0.3, num=30)
figs = []
for i0 in range(len(var)):
print var[i0]
temp = np.load(var[i0] + '.npy')
fig = plt.figure(figsize=[8, 8])
ax = plt.subplot(1, 1, 1)
a = np.absolute(temp - 1.0) < 0.1
ax = plt.imshow(scipy.ndimage.morphology.binary_fill_holes(a))
fig.savefig(var[i0] + '_bin.png', bbox_inches='tight', dpi=fig.dpi)
del fig
fig = plt.figure(figsize=[8, 8])
ax = plt.subplot(1, 1, 1)
ax = g.heat_map_pd(temp,
ticks,
ticks,
ax,
xlabel=labels[0],
ylabel=labels[1],
title=tit_num[i0] + titles[np.mod(i0, 2)],
cmap_lims=cmap_lims)
plt.show()
fig.savefig(var[i0] + '.eps', bbox_inches='tight', dpi=fig.dpi)
del temp, fig
return var
labels = [r'$\sigma_s/\langle \tilde{\Delta}\rangle$', '$\sigma_{t}/t_{db}$']
titles = ['$f=0.5$, $(C+D)/t_{db}=0.7$', '$f=0.5$, $(C+D)/t_{db}=0.9$']
tit_num = ['(A)', '(B)', '(C)', '(D)']
cmap_lims = [0.0, 2.0]
ticks = np.linspace(0.01, 0.3, num=30)
figs = []
i0 = 0
# for i0 in range(len(var)):
print var[i0]
temp = np.load(var[i0] + '.npy')
fig = plt.figure(figsize=[8, 8])
ax = plt.subplot(1, 1, 1)
a = np.absolute(temp - 1.0) < 0.1
ax = plt.imshow(scipy.ndimage.morphology.binary_fill_holes(a))
fig.savefig(var[i0] + '_bin.png', bbox_inches='tight', dpi=fig.dpi)
del fig
fig = plt.figure(figsize=[8, 8])
ax = plt.subplot(1, 1, 1)
ax = g.heat_map_pd(temp,
ticks,
ticks,
ax,
xlabel=labels[0],
ylabel=labels[1],
title=tit_num[i0] + titles[np.mod(i0, 2)],
cmap_lims=cmap_lims)
fig.savefig(var[i0] + '.eps', bbox_inches='tight', dpi=fig.dpi)
del temp, fig
for i0 in range(len(g1_thresh_std)):
for i1 in range(len(r_nums)):
for i2 in range(num_meas):
i3 = 1
i4=0 # Mothers
plotlab = ['(A) ', '(B) ', '(C) ', '(D) ']
fig = plt.figure(figsize=[8, 8])
# plots for the paper. Daughters only. Individual plots so can be mixed and matched.
# Discretized time only.
ax = plt.subplot(1, 1, 1)
obs = np.mean(data[r_nums[i1], :, i0, :, :, i4, i3, i2], axis=-1)
temp = plotlab[2*np.mod(i0,2)+i1]+labels[3] + str(g1_thresh_std[i0]) + r', $\langle x\rangle=$' + str(r[r_nums[i1]])
if i2 == 0:
ax = g.heat_map_pd(obs, r_std, d_std, ax, xlabel=labels[0], ylabel=labels[1], title=temp,
color='black', cmap_lims=cmap_lims)
fig.savefig(
'./1705_inh_yeast_asymm_fig_{0}_meas_{1}_si_{2}_r_{3}_sim_{4}_celltype_{5}_v2.eps'.format(
str(par1['modeltype']), str(i2), str(i0), str(r_nums[i1]), str(i3), str(i4)),
bbox_inches='tight', dpi=fig.dpi)
del fig, obs, ax
i4=1 #Daughters
plotlab = ['(C) ', '(D) ', '(E) ', '(F) ']
fig = plt.figure(figsize=[8, 8])
# plots for the paper. Daughters only. Individual plots so can be mixed and matched.
# Discretized time only.
ax = plt.subplot(1, 1, 1)
obs = np.mean(data[r_nums[i1], :, i0, :, :, i4, i3, i2], axis=-1)
temp = plotlab[2*np.mod(i0,2)+i1]+labels[3] + str(g1_thresh_std[i0]) + r', $\langle x\rangle=$' + str(r[r_nums[i1]])
if i2 == 0:
ax = g.heat_map_pd(obs, r_std, d_std, ax, xlabel=labels[0], ylabel=labels[1], title=temp,
label = ['(A) ', '(B) ', '(C) ', '(D) ']
cmap_lims = [0.7, 1.3]
for i1 in range(num_celltype):
# for i2 in range(num_sims):
i2 = 1 # discretized time. num_sims
i3 = 0 # slope only. num_meas
# for i3 in range(num_meas):
for i4 in range(len(r_vals)):
obs = np.mean(data[r_vals[i4], :, :, :, i1, i2, i3], axis=-1)
fig = plt.figure(figsize=[8, 8])
ax = plt.subplot(1, 1, 1)
ax = plt.imshow(np.absolute(obs - 1.0) > 0.1)
fig.savefig('./temp_{0}_{1}_{2}_{3}'.format(str(i1), str(i3),
str(i2), str(r_vals[i4])))
del fig
fig = plt.figure(figsize=[8, 8])
ax = plt.subplot(1, 1, 1)
temp = label[2*(1-i1)+i4] + celltype[i1]+', '+r' $\langle x\rangle=${0}'.format(np.round(r[r_vals[i4]], 2))
print i1, i3, i2, i4
ax = g.heat_map_pd(obs, r_std, g1_thresh_std, ax, xlabel=lab[0], ylabel=lab[1], title=temp,
cmap_lims=cmap_lims)
#ax = g.heat_map(obs, r_std, g1_thresh_std, ax, xlabel=lab[0], ylabel=lab[1], title=temp)
fig.savefig('./1705_init_yeast_asymm_celltype_{0}_meas_{1}_sim_{2}_r_{3}_v1.eps'.format(str(i1), str(i3),
str(i2), str(r_vals[i4])), bbox_inches='tight', dpi=fig.dpi)
del fig
# April17_paper_scripts_4_model_{0}_celltype_{1}_meas_{2}_sim_{3}_r_{4}.eps allows negative growth through noise, but not
# in G1. Comes from March17_accumulator1_V0.npy
# April17_paper_scripts_4_model_{0}_celltype_{1}_meas_{2}_sim_{3}_r_{4}.eps has no negative growth whatsoever in models
# 2 and 4. comes from April17_paper_scripts_5_v0.