%run nb_init.py
num_cells = ['05_cells', '10_cells', '20_cells', '30_cells']
save_dirs = []
for num in num_cells:
save_dirs.append('random_{0}_apical_tension'.format(num))
save_dirs.append('random_{0}'.format(num))
for n_cells in [5, 10, 20, 30]:
save_dirs.append('regular_{}_cells_tension_increase'.format(n_cells))
import os
from itertools import repeat
vr = 0.7
ctr = 1.0
rts = [0, 0.1, 0.2]
tags = ['apopto_vr%.2f_ctr%.2f_rt%.2f' % (vol_reduction,
contractility,
radial_tension)
for (vol_reduction,contractility, radial_tension)
in zip(repeat(vr), repeat(ctr), rts)]
png_dir = '../saved_graphs/png'
xml_dir = '../saved_graphs/xml'
all_pngs = [os.path.join(png_dir, save_dir, tag)
for save_dir in save_dirs for tag in tags]
all_xmls = [os.path.join(xml_dir, save_dir, tag)
for save_dir in save_dirs for tag in tags]
# all_xmls.append('../saved_graphs/xml/before_apoptosis.xml')
for path in all_xmls:
if not os.path.isdir(path):
print(path)
../saved_graphs/xml/random_05_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.20 ../saved_graphs/xml/regular_5_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.00 ../saved_graphs/xml/regular_10_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.00 ../saved_graphs/xml/regular_20_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.00 ../saved_graphs/xml/regular_30_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.00 ../saved_graphs/xml/regular_30_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.10
epithelia = {}
lasts = {}
for xml_dir in all_xmls:
try:
xml_list = os.listdir(xml_dir)
xml_list.sort()
last = os.path.join(xml_dir, xml_list[-1])
print('{} : {}'.format(xml_dir, last))
epithelia[xml_dir] = lj.Epithelium(graphXMLfile=last,
paramfile='../default/params.xml')
lasts[xml_dir] = last
except:
print('Failed for {}'.format(xml_dir))
#raise
continue
../saved_graphs/xml/random_05_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.00 : ../saved_graphs/xml/random_05_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.00/apopto_0050.xml ../saved_graphs/xml/random_05_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.10 : ../saved_graphs/xml/random_05_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.10/apopto_0050.xml Failed for ../saved_graphs/xml/random_05_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.20 ../saved_graphs/xml/random_05_cells/apopto_vr0.70_ctr1.00_rt0.00 : ../saved_graphs/xml/random_05_cells/apopto_vr0.70_ctr1.00_rt0.00/apopto_0050.xml ../saved_graphs/xml/random_05_cells/apopto_vr0.70_ctr1.00_rt0.10 : ../saved_graphs/xml/random_05_cells/apopto_vr0.70_ctr1.00_rt0.10/apopto_0050.xml ../saved_graphs/xml/random_05_cells/apopto_vr0.70_ctr1.00_rt0.20 : ../saved_graphs/xml/random_05_cells/apopto_vr0.70_ctr1.00_rt0.20/apopto_0050.xml ../saved_graphs/xml/random_10_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.00 : ../saved_graphs/xml/random_10_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.00/apopto_0100.xml ../saved_graphs/xml/random_10_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.10 : ../saved_graphs/xml/random_10_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.10/apopto_0100.xml ../saved_graphs/xml/random_10_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.20 : ../saved_graphs/xml/random_10_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.20/apopto_0100.xml ../saved_graphs/xml/random_10_cells/apopto_vr0.70_ctr1.00_rt0.00 : ../saved_graphs/xml/random_10_cells/apopto_vr0.70_ctr1.00_rt0.00/apopto_0100.xml ../saved_graphs/xml/random_10_cells/apopto_vr0.70_ctr1.00_rt0.10 : ../saved_graphs/xml/random_10_cells/apopto_vr0.70_ctr1.00_rt0.10/apopto_0100.xml ../saved_graphs/xml/random_10_cells/apopto_vr0.70_ctr1.00_rt0.20 : ../saved_graphs/xml/random_10_cells/apopto_vr0.70_ctr1.00_rt0.20/apopto_0100.xml ../saved_graphs/xml/random_20_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.00 : ../saved_graphs/xml/random_20_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.00/apopto_0200.xml ../saved_graphs/xml/random_20_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.10 : ../saved_graphs/xml/random_20_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.10/apopto_0200.xml ../saved_graphs/xml/random_20_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.20 : ../saved_graphs/xml/random_20_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.20/apopto_0200.xml ../saved_graphs/xml/random_20_cells/apopto_vr0.70_ctr1.00_rt0.00 : ../saved_graphs/xml/random_20_cells/apopto_vr0.70_ctr1.00_rt0.00/apopto_0200.xml ../saved_graphs/xml/random_20_cells/apopto_vr0.70_ctr1.00_rt0.10 : ../saved_graphs/xml/random_20_cells/apopto_vr0.70_ctr1.00_rt0.10/apopto_0200.xml ../saved_graphs/xml/random_20_cells/apopto_vr0.70_ctr1.00_rt0.20 : ../saved_graphs/xml/random_20_cells/apopto_vr0.70_ctr1.00_rt0.20/apopto_0200.xml ../saved_graphs/xml/random_30_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.00 : ../saved_graphs/xml/random_30_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.00/apopto_0196.xml ../saved_graphs/xml/random_30_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.10 : ../saved_graphs/xml/random_30_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.10/apopto_0300.xml ../saved_graphs/xml/random_30_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.20 : ../saved_graphs/xml/random_30_cells_apical_tension/apopto_vr0.70_ctr1.00_rt0.20/apopto_0300.xml ../saved_graphs/xml/random_30_cells/apopto_vr0.70_ctr1.00_rt0.00 : ../saved_graphs/xml/random_30_cells/apopto_vr0.70_ctr1.00_rt0.00/apopto_0196.xml ../saved_graphs/xml/random_30_cells/apopto_vr0.70_ctr1.00_rt0.10 : ../saved_graphs/xml/random_30_cells/apopto_vr0.70_ctr1.00_rt0.10/apopto_0300.xml ../saved_graphs/xml/random_30_cells/apopto_vr0.70_ctr1.00_rt0.20 : ../saved_graphs/xml/random_30_cells/apopto_vr0.70_ctr1.00_rt0.20/apopto_0300.xml Failed for ../saved_graphs/xml/regular_5_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.00 ../saved_graphs/xml/regular_5_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.10 : ../saved_graphs/xml/regular_5_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.10/apopto_0050.xml ../saved_graphs/xml/regular_5_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.20 : ../saved_graphs/xml/regular_5_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.20/apopto_0050.xml Failed for ../saved_graphs/xml/regular_10_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.00 ../saved_graphs/xml/regular_10_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.10 : ../saved_graphs/xml/regular_10_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.10/apopto_0100.xml ../saved_graphs/xml/regular_10_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.20 : ../saved_graphs/xml/regular_10_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.20/apopto_0100.xml Failed for ../saved_graphs/xml/regular_20_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.00
/home/guillaume/Python/leg_joint/leg_joint/objects.py:470: RuntimeWarning: invalid value encountered in true_divide self.u_cross = self.cross / (2. * self.area) /home/guillaume/Python/leg_joint/leg_joint/objects.py:352: RuntimeWarning: invalid value encountered in true_divide self.u_dixs.a = self.dixs.a / edge_lengths
../saved_graphs/xml/regular_20_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.10 : ../saved_graphs/xml/regular_20_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.10/apopto_0165.xml ../saved_graphs/xml/regular_20_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.20 : ../saved_graphs/xml/regular_20_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.20/apopto_0200.xml Failed for ../saved_graphs/xml/regular_30_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.00 Failed for ../saved_graphs/xml/regular_30_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.10 ../saved_graphs/xml/regular_30_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.20 : ../saved_graphs/xml/regular_30_cells_tension_increase/apopto_vr0.70_ctr1.00_rt0.20/apopto_0300.xml
/home/guillaume/Python/leg_joint/leg_joint/objects.py:353: RuntimeWarning: invalid value encountered in true_divide self.u_dwys.a = self.dwys.a / edge_lengths /home/guillaume/Python/leg_joint/leg_joint/objects.py:354: RuntimeWarning: invalid value encountered in true_divide self.u_dzeds.a = self.dzeds.a / edge_lengths
epithelia['before'] = lj.Epithelium(graphXMLfile='../saved_graphs/xml/before_apoptosis.xml',
paramfile='../default/params.xml')
for keys in epithelia.keys():
print('_'.join(keys.split('/')[-2:]))
random_05_cells_apopto_vr0.70_ctr1.00_rt0.10 random_30_cells_apopto_vr0.70_ctr1.00_rt0.10 regular_5_cells_tension_increase_apopto_vr0.70_ctr1.00_rt0.10 regular_30_cells_tension_increase_apopto_vr0.70_ctr1.00_rt0.20 regular_10_cells_tension_increase_apopto_vr0.70_ctr1.00_rt0.10 random_10_cells_apopto_vr0.70_ctr1.00_rt0.00 random_10_cells_apopto_vr0.70_ctr1.00_rt0.10 before regular_20_cells_tension_increase_apopto_vr0.70_ctr1.00_rt0.20 random_20_cells_apopto_vr0.70_ctr1.00_rt0.20 random_30_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.20 random_30_cells_apopto_vr0.70_ctr1.00_rt0.20 random_05_cells_apopto_vr0.70_ctr1.00_rt0.20 regular_20_cells_tension_increase_apopto_vr0.70_ctr1.00_rt0.10 regular_10_cells_tension_increase_apopto_vr0.70_ctr1.00_rt0.20 random_30_cells_apopto_vr0.70_ctr1.00_rt0.00 random_20_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.00 random_20_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.10 random_05_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.10 random_05_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.00 random_20_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.20 random_30_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.10 random_20_cells_apopto_vr0.70_ctr1.00_rt0.10 random_20_cells_apopto_vr0.70_ctr1.00_rt0.00 random_30_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.00 random_10_cells_apopto_vr0.70_ctr1.00_rt0.20 random_10_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.20 random_10_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.10 random_10_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.00 random_05_cells_apopto_vr0.70_ctr1.00_rt0.00 regular_5_cells_tension_increase_apopto_vr0.70_ctr1.00_rt0.20
def average_rho(eptm, bin_width=10):
eptm.update_rhotheta()
zeds = eptm.zeds.a
rhos = eptm.rhos.a
rhos = rhos[np.argsort(zeds)]
zeds = np.sort(zeds)
rhos_cliped = rhos[: -(rhos.size % bin_width)]
rhos_cliped = rhos_cliped.reshape((rhos_cliped.size // bin_width, bin_width))
rhos_avg = rhos_cliped.mean(axis=1)
rhos_max = rhos_cliped.max(axis=1)
rhos_min = rhos_cliped.min(axis=1)
zeds_cliped = zeds[: -(zeds.size % bin_width)]
zeds_cliped = zeds_cliped.reshape((zeds_cliped.size // bin_width, bin_width))
zeds_avg = zeds_cliped.mean(axis=1)
return zeds_avg, rhos_avg, rhos_max, rhos_min
def plot_avg_rho(eptm, bin_width, ax=None, retall=False, cut=50):
if ax is None:
fig, ax = plt.subplots(figsize=(12,4))
else:
fig = ax.get_figure()
zeds_avg, rhos_avg, rhos_max, rhos_min = average_rho(eptm, bin_width)
ax.fill_between(zeds_avg[cut:-(cut+1)],
rhos_max[cut:-(cut+1)],
rhos_min[cut:-(cut+1)],
facecolor='0.5', edgecolor='0.9')
ax.plot(zeds_avg[cut:-(cut+1)], rhos_avg[cut:-(cut+1)], 'r-', lw=2, alpha=0.7)
ax.set_aspect('equal')
#max_zed = ax.get_ylim()[1]
ax.set_ylim(0, 30)
if not retall:
return ax
return ax, (zeds_avg, rhos_avg, rhos_max, rhos_min)
ax, (zeds_avg, rhos_avg, rhos_max, rhos_min) = plot_avg_rho(epithelia['before'],
bin_width=200, ax=None,
retall=True, cut=0)
!ls ../doc/imgs/radial_profiles/
old
profiles = {}
for keys, eptm in epithelia.items():
name = '_'.join(keys.split('/')[-2:])
ax, data = plot_avg_rho(eptm, bin_width=20, ax=None, retall=True, cut=50)
(zeds_avg, rhos_avg, rhos_max, rhos_min) = data
profiles[name] = {'zeds':zeds_avg,
'rhos':rhos_avg,
'rhos_min':rhos_min,
'rhos_max':rhos_max}
fig = ax.get_figure()
fig.savefig('../doc/imgs/radial_profiles/'+name+'.svg')
plt.close(fig)
srt_keys = [k for k in profiles.keys()]
srt_keys.sort()
print('\n'.join([k for k in srt_keys if 'random_' in k and 'rt0.20' in k]))
print('\n'.join([k for k in srt_keys if 'regular_' in k and 'rt0.20' in k]))
random_05_cells_apopto_vr0.70_ctr1.00_rt0.20 random_10_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.20 random_10_cells_apopto_vr0.70_ctr1.00_rt0.20 random_20_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.20 random_20_cells_apopto_vr0.70_ctr1.00_rt0.20 random_30_cells_apical_tension_apopto_vr0.70_ctr1.00_rt0.20 random_30_cells_apopto_vr0.70_ctr1.00_rt0.20 regular_10_cells_tension_increase_apopto_vr0.70_ctr1.00_rt0.20 regular_20_cells_tension_increase_apopto_vr0.70_ctr1.00_rt0.20 regular_30_cells_tension_increase_apopto_vr0.70_ctr1.00_rt0.20 regular_5_cells_tension_increase_apopto_vr0.70_ctr1.00_rt0.20
rdms = [k for k in srt_keys if 'random_' in k and 'rt0.20' in k]
regs = [k for k in srt_keys if 'regular_' in k and 'rt0.20' in k]
rdms_ncells = np.array([0, 5, 10, 15, 20, 25, 30])
regs_ncells = np.array([0, 10, 15, 30, 5])
rdms_avg_ptp = np.zeros(rdms_ncells.size, dtype=np.float)
rdms_max_ptp = np.zeros(rdms_ncells.size, dtype=np.float)
rdms_min_ptp = np.zeros(rdms_ncells.size, dtype=np.float)
regs_avg_ptp = np.zeros(rdms_ncells.size, dtype=np.float)
regs_max_ptp = np.zeros(rdms_ncells.size, dtype=np.float)
regs_min_ptp = np.zeros(rdms_ncells.size, dtype=np.float)
for n, key in enumerate(rdms):
data = profiles[key]
fold_center = np.where((data['zeds'] < 20) & (data['zeds'] > -20))
rdms_avg_ptp[n+1] = data['rhos'][fold_center].ptp()
rdms_max_ptp[n+1] = data['rhos_min'][fold_center].ptp()
rdms_min_ptp[n+1] = data['rhos_max'][fold_center].ptp()
for n, key in enumerate(regs):
data = profiles[key]
fold_center = np.where((data['zeds'] < 20) & (data['zeds'] > -20))
regs_avg_ptp[n+1] = data['rhos'][fold_center].ptp()
regs_max_ptp[n+1] = data['rhos_min'][fold_center].ptp()
regs_min_ptp[n+1] = data['rhos_max'][fold_center].ptp()
idxs = np.argsort(regs_ncells)
regs_ncells = regs_ncells[idxs]
regs_avg_ptp = regs_avg_ptp[idxs]
regs_max_ptp = regs_max_ptp[idxs]
regs_min_ptp = regs_min_ptp[idxs]
regs_avg_ptp
array([0, 2, 3, 4, 8])
fig, ax = plt.subplots()
ax.plot(regs_ncells, regs_avg_ptp, 'ko-', lw=2, alpha=0.8)
#ax.fill_between(regs_ncells, regs_min_ptp, regs_max_ptp,
# facecolor='0.7', edgecolor='None')
ax.plot(rdms_ncells, rdms_avg_ptp, 'go-', lw=2, alpha=0.8)
#ax.fill_between(rdms_ncells, rdms_min_ptp, rdms_max_ptp,
# facecolor='g', alpha=0.7, edgecolor='None')
ax.set_ylim(0, 12)
ax.set_xlim(-0.5, 32)
ax.set_ylabel(u'Fold depth (µm)')
ax.set_xlabel(u'Number of apoptotic cells')
fig.savefig('../doc/imgs/fold_depth_vs_ncells.svg')
eptm = epithelia['regular_30cells_gamma_1_apopto_vr0.70_ctr1.00_rt0.20']
ax = plot_avg_rho(eptm, bin_width=20, ax=None, cut=50)
ax.set_xlabel('Proximal - distal')
ax.set_ylabel('Basal - apical')
<matplotlib.text.Text at 0x8f96e62c>
!mkdir ../saved_graphs/png/after_apopto_nt_3d/
thetas = np.linspace(0, np.pi, 180)
eptm_t = epithelia['random_theta_bias_apopto_vr0.70_ctr1.00_rt0.20']
eptm_nt = epithelia['random_theta_bias_apopto_vr0.70_ctr1.00_rt0.00']
for n, theta in enumerate(thetas):
output_nt= '../saved_graphs/png/after_apopto_nt_3d/angle_%03i.png' %n
output_t= '../saved_graphs/png/after_apopto_t_3d/angle_%03i.png' % n
lj.draw(eptm_nt, d_theta=theta, output3d=output_nt)
lj.draw(eptm_t, d_theta=theta, output3d=output_t)
n_cells = [5, 10, 15, 20]
avg_ptp = np.zeros_like(ab_forces)
max_ptp = np.zeros_like(ab_forces)
min_ptp = np.zeros_like(ab_forces)
for n, radial in enumerate(ab_forces):
(zeds_avg, rhos_avg, rhos_max, rhos_min) = rho_data[radial]
fold_center = np.where((zeds_avg < 20) & (zeds_avg > -20))
avg_ptp[n] = rhos_avg[fold_center].ptp()
max_ptp[n] = rhos_max[fold_center].ptp()
min_ptp[n] = rhos_min[fold_center].ptp()
fig, axes = plt.subplots(3, 3, figsize=(21, 9), sharex=True, sharey=True)
sorted_conditions = ['theta_bias', 'no_theta_bias', 'residual_tension',
'ectopic', '05_cells', '10_cells',
'15_cells', '20_cells', '25_cells']
for cond, ax in zip(sorted_conditions, axes.ravel()):
eptm = epithelia[cond]
ax = plot_avg_rho(eptm, bin_width=20, ax=ax)
ax.set_title(conditions[cond]['descr'])
fig.savefig('../doc/imgs/radial_profiles_comparisons.svg')
!cat ../var_tension.sh
python joint_var_radial_tension.py 10 0.7 1.0 0.01 & python joint_var_radial_tension.py 10 0.7 1.0 0.025 & python joint_var_radial_tension.py 10 0.7 1.0 0.05 & python joint_var_radial_tension.py 10 0.7 1.0 0.1 & python joint_var_radial_tension.py 10 0.7 1.0 0.25 & python joint_var_radial_tension.py 10 0.7 1.0 0.5 & python joint_var_radial_tension.py 10 0.7 1.0 1. &
!ls ../saved_graphs/xml/variable_tension
apopto_vr0.70_ctr1.00_rt0.01 apopto_vr0.70_ctr1.00_rt0.25 apopto_vr0.70_ctr1.00_rt0.03 apopto_vr0.70_ctr1.00_rt0.50 apopto_vr0.70_ctr1.00_rt0.05 apopto_vr0.70_ctr1.00_rt1.00 apopto_vr0.70_ctr1.00_rt0.10
fig, axes = plt.subplots(4, 2, figsize=(21, 9))
fig.delaxes(axes[-1,-1])
rho_data = {}
for radial, ax in zip(ab_forces, axes.ravel()):
eptm = epithelia_vr[radial]
ax, rho_data[radial] = plot_avg_rho(eptm, bin_width=10, ax=ax, retall=True)
ax.set_title('Radial tension: %.3f' % radial)
ax.set_ylim(0, 35)
fig.tight_layout()
plt.draw()
fig, ax = plt.subplots()
ax.plot(, avg_ptp, 'r-', lw=2, alpha=0.8)
ax.fill_between(ab_forces * 10, min_ptp, max_ptp, facecolor='g', edgecolor='None', alpha=0.7)
ax.set_ylim(0, 32)
ax.set_xlim(-0.5, 10.5)
ax.set_ylabel(u'Fold depth (µm)')
ax.set_xlabel(r'a-b force magnitude (units of $\Lambda$)')
ax.plot([0, 10], [26, 26], 'k-')
fig.savefig('../doc/imgs/fold_depth_vs_ab_forces.svg')
num_steps = 10
vol_red = 0.7
contract = 1.
radial = 0.1
ab_forces = np.array([0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.])
tags = ['vr%.2f_ctr%.2f_rt%.2f' %
(vol_red, contract, radial) for radial in ab_forces]
graph_xml_dir = '../saved_graphs/xml/variable_tension'
sample_xml_dirs = {radial: os.path.join(graph_xml_dir, 'apopto_'+tag)
for radial, tag in zip(ab_forces, tags)}
graph_png_dir = '../saved_graphs/png/variable_tension'
sample_png_dirs = {radial: os.path.join(graph_png_dir, 'apopto_'+tag)
for radial, tag in zip(ab_forces, tags)}
epithelia_vr = {}
for radial in ab_forces:
xml_list = os.listdir(sample_xml_dirs[radial])
xml_list.sort()
last = os.path.join(sample_xml_dirs[radial], xml_list[-1])
print(last)
epithelia_vr[radial] = lj.Epithelium(graphXMLfile=last,
paramfile='../default/params.xml')
../saved_graphs/xml/variable_tension/apopto_vr0.70_ctr1.00_rt0.01/apopto_0340.xml ../saved_graphs/xml/variable_tension/apopto_vr0.70_ctr1.00_rt0.03/apopto_0340.xml ../saved_graphs/xml/variable_tension/apopto_vr0.70_ctr1.00_rt0.05/apopto_0340.xml ../saved_graphs/xml/variable_tension/apopto_vr0.70_ctr1.00_rt0.10/apopto_0340.xml ../saved_graphs/xml/variable_tension/apopto_vr0.70_ctr1.00_rt0.25/apopto_0340.xml ../saved_graphs/xml/variable_tension/apopto_vr0.70_ctr1.00_rt0.50/apopto_0340.xml ../saved_graphs/xml/variable_tension/apopto_vr0.70_ctr1.00_rt1.00/apopto_0340.xml
fig.savefig('../doc/imgs/radial_profiles_vs_ab_forces.svg')
(zeds_avg, rhos_avg, rhos_max, rhos_min) = rho_data[0.01]
fold_center = np.where((zeds_avg < 20) & (zeds_avg > -20))
print(rhos_avg[fold_center][0])
25.9245020462
avg_ptp = np.zeros_like(ab_forces)
max_ptp = np.zeros_like(ab_forces)
min_ptp = np.zeros_like(ab_forces)
for n, radial in enumerate(ab_forces):
(zeds_avg, rhos_avg, rhos_max, rhos_min) = rho_data[radial]
fold_center = np.where((zeds_avg < 20) & (zeds_avg > -20))
avg_ptp[n] = rhos_avg[fold_center].ptp()
max_ptp[n] = rhos_max[fold_center].ptp()
min_ptp[n] = rhos_min[fold_center].ptp()
fig, ax = plt.subplots()
ax.plot(ab_forces * 10, avg_ptp, 'r-', lw=2, alpha=0.8)
ax.plot(ab_forces * 10, avg_ptp, 'k+', lw=2, alpha=0.8)
ax.fill_between(ab_forces * 10, min_ptp, max_ptp, facecolor='0.7', edgecolor='0.9')
ax.set_ylim(0, 32)
ax.set_xlim(-0.5, 10.5)
ax.set_ylabel(u'Fold depth (µm)')
ax.set_xlabel(r'a-b force magnitude (units of $\Lambda$)')
ax.plot([0, 10], [26, 26], 'k-')
fig.savefig('../doc/imgs/fold_depth_vs_ab_forces.svg')