In [1]:
%load_ext autoreload
%autoreload 2
%matplotlib inline
In [2]:
%run nb_init.py
import IPython.core.display as disp
import hdfgraph
import pandas as pd
In [3]:
!ls -lth /media/data/Simulations/
In [4]:
graph_dir = "/media/data/Simulations/"
def get_lists(graph_dir, cond):
dir_list = os.listdir(graph_dir)
dir_list.sort()
dir_list = [d for d in dir_list if cond(d)]
png_dir_list = [os.path.join(graph_dir, d, 'png') for d in dir_list]
svg_list = [os.path.join(graph_dir, d, 'svg', 'avg_rho_%s.svg' % d) for d in dir_list]
json_list = [os.path.join(graph_dir, d, 'params_%s.json' %d) for d in dir_list]
xml_list = [os.path.join(graph_dir, d, 'xml', 'after_apopto.xml') for d in dir_list]
return (dir_list, png_dir_list, svg_list, json_list, xml_list)
cond = lambda d: d.startswith('100') or d.startswith('000')
dir_list, png_dir_list, svg_list, json_list, xml_list = get_lists(graph_dir, cond)
conditions = pd.DataFrame(index=np.arange(len(json_list)),
columns=['max_ci', 'radial_tension', 'num_cells', 'width_apopto', 'json'])
for i, json_fname in enumerate(json_list):
#continue
with open(json_fname) as json_file:
kwargs = json.load(json_file)
print('index: {} => \n'.format(i))
conditions.loc[i, 'json'] = json_fname
print('\t max_ci: {}'.format(
kwargs['post_kwargs']['max_ci']))
conditions.loc[i, 'max_ci'] = kwargs['post_kwargs']['max_ci']
print('\t radial_tension : {}'.format(
kwargs['apopto_kwargs']['radial_tension']))
conditions.loc[i, 'radial_tension'] = kwargs['apopto_kwargs']['radial_tension']
print('\t num cells : {}'.format(
kwargs['seq_kwargs']['num_cells']))
conditions.loc[i, 'num_cells'] = kwargs['seq_kwargs']['num_cells']
print('\t width : {}'.format(
kwargs['seq_kwargs']['width_apopto']))
conditions.loc[i, 'width_apopto'] = kwargs['seq_kwargs']['width_apopto']
print('\n')
In [5]:
conditions
Out[5]:
In [6]:
xml_paths = {'before': '../leg_joint/data/graphs/before_apoptosis.xml',
'5_random': xml_list[0],
'10_random': xml_list[1],
'20_random': xml_list[2],
'rt_1_ct2': xml_list[3],
'rt_0_ct2': xml_list[4],
'rt_05_ct2': xml_list[5],
'rt_2_ct2': '/media/data/Simulations/0006_2014-05-24T09_04_38/xml/after_apopto.xml', ### backup run
'ectopic': '/media/data/Simulations/joint_2014-06-06T08_48_11/xml/after_apopto.xml',
'rt_1_ct1': xml_list[8],
'rt_1_ct15': xml_list[13],
'rt_1_ct3': xml_list[15]
}
In [11]:
xml_paths2 = {'before': '../saved_graphs/xml/before_apoptosis.xml',
'intermediate': '../saved_graphs/xml/single_cell_before_elimination.xml',
'single_after': '../saved_graphs/xml/single_cell_after_elimination.xml',
'intermediate_nf': '../saved_graphs/xml/single_cell_before_elimination_noforce.xml',
'single_after_nf': '../saved_graphs/xml/single_cell_after_elimination_noforce.xml'}
epithelia = {name: lj.Epithelium(xmlfile, copy=False)
for name, xmlfile in [('before', xml_paths['before']),]}
In [12]:
eptm_b = epithelia['before']
lj.local_slice(eptm_b, theta_amp=np.pi/8,
theta_c=0, zed_amp=2, zed_c=0)
local_cells = eptm_b.is_cell_vert.copy()
local_cells.a = local_cells.a * eptm_b.is_local_vert.a
eptm_b.graph.set_vertex_filter(local_cells)
avg_area = eptm_b.cells.areas.fa.mean()
avg_rho = eptm_b.rhos.fa.mean()
# fig, ax = plt.subplots()
# ax.plot(eptm_b.zeds.fa, eptm_b.cells.areas.fa, 'o')
eptm_b.graph.set_vertex_filter(None)
In [13]:
eptm_b.is_local_vert.a.sum()
Out[13]:
In [14]:
avg_area, avg_rho
Out[14]:
In [15]:
def new_2pannels(eptm, rot=45):
eptm.update_rhotheta()
rot = -(rot + 90) * np.pi / 180
eptm.rotate(rot)
lj.local_slice(eptm, theta_amp=np.pi/8,
theta_c=0, zed_amp=4, zed_c=0)
local_patch = eptm.is_local_vert.copy()
lj.local_slice(eptm, theta_amp=np.pi/5, theta_c=0, zed_amp=10, zed_c=0)
colors = eptm.rhos.copy()
eptm.graph.set_vertex_filter(eptm.is_cell_vert)
colors.fa = colors.fa - 5
colors.fa = colors.fa / 25#colors.fa.max()
colors.a *= local_patch.a
eptm.graph.set_vertex_filter(None)
fig, (ax_zs, ax_3d) = plt.subplots(1, 2, figsize=(12, 4), sharey=True)
ax_zs = lj.plot_eptm_generic(eptm, eptm.zeds, eptm.proj_sigma(), ax=ax_zs,
cell_kwargs={'c_text':False,
'cell_colors':colors,
#'c':'k',
'alpha':0.4, 'normalize':False},
edge_kwargs={'j_text':False,
'c':'k',
'lw':0.4,
'alpha':0.5})
eptm.rotate(- rot)
rhos, thetas, zeds = eptm.rhos, eptm.thetas, eptm.zeds
z_angle=np.pi/24
d_theta = - 6 * np.pi / 7
pseudo_x = eptm.graph.new_vertex_property('float')
pseudo_y = eptm.graph.new_vertex_property('float')
pseudo_x.a = zeds.a * np.cos(z_angle) - rhos.a * np.cos(
thetas.a + d_theta) * np.sin(z_angle)
pseudo_y.a = rhos.a * np.sin(thetas.a + d_theta)
edge_alpha = eptm.dzeds.copy()
v_depth = eptm.zeds.copy()
v_depth.a = rhos.a * (1 - np.cos(thetas.a + d_theta))
v_prop = v_depth
alpha0 = 0.
edge_alpha.a = np.array([(v_prop[s] + v_prop[t])
for s, t in eptm.graph.edges()])
edge_alpha.a -= edge_alpha.a.min()
edge_alpha.a += alpha0 * edge_alpha.a.max()
edge_alpha.a /= edge_alpha.a.max()
ax_3d = lj.plot_edges_generic(eptm, pseudo_x, pseudo_y, ax=ax_3d, efilt=None,
edge_alpha=edge_alpha,
edge_color=edge_alpha,
j_text=False,
c='g',
lw=0.6)
ax_3d.plot([-40, -40], [-15, -20], '-k', lw=2)
ax_zs.plot([-20, -20], [-15, -20], '-k', lw=2)
for ax in (ax_zs, ax_3d):
ax.set_xticks([])
ax.set_yticks([])
ax.set_frame_on(False)
ax.set_ylim(-35, 35)
plt.savefig(os.path.join(eptm.paths['svg'], 'two_pannels.svg'))
In [16]:
import pandas as pd
dfs = {}
def new_aniso(eptm, name, dfs, rot=45):
rot = -(rot + 90) * np.pi / 180
eptm.rotate(rot)
sigmas = eptm.wys #ixs #eptm.sigmas #eptm.proj_sigma()
coords = [eptm.zeds, sigmas]
anisotropies, orientations = eptm.cells.get_anisotropies([eptm.zeds, eptm.sigmas])
areas = eptm.cells.areas
rhos = eptm.rhos
eptm.graph.set_vertex_filter(eptm.is_cell_vert)
n_cells = eptm.is_cell_vert.a.sum()
df = pd.DataFrame(np.zeros((n_cells, 5)),
columns=['zeds', 'anisotropies',
'orientations', 'areas', 'rhos'])
df['zeds'] = eptm.zeds.fa
df['anisotropies'] = anisotropies.fa
df['rhos'] = rhos.fa / avg_rho
df['areas'] = areas.fa / avg_area
df['orientations'] = orientations.fa
eptm.graph.set_vertex_filter(None)
dfs[name] = df
lj.local_slice(eptm, theta_amp=np.pi/12,
theta_c=0, zed_amp=2, zed_c=0)
local_patch = eptm.is_local_vert.copy()
lj.local_slice(eptm, theta_amp=np.pi/2,
theta_c=0, zed_amp=10, zed_c=0)
fig, axes = plt.subplots(1, 4, figsize=(10, 4))
ax_depth, ax_aniso, ax_area, ax_orient = axes
### Depth
colors = eptm.rhos.copy()
eptm.graph.set_vertex_filter(eptm.is_cell_vert)
colors.fa = colors.fa - 5
colors.fa = colors.fa / 25
colors.a = colors.a * local_patch.a
eptm.graph.set_vertex_filter(None)
ax_depth = lj.plot_eptm_generic(eptm, eptm.zeds, sigmas,
ax=ax_depth,
cell_kwargs={'c_text': False,
'cell_colors': colors,
'cmap': 'afmhot_r',
'alpha': 0.5,},# 'c':'b',
edge_kwargs={'j_text':False,
'c':'k',
'lw':0.4,
'alpha':0.5})
ax_depth.set_title('{}: Depth'.format(name))
## Anisotropy
colors = anisotropies.copy()
eptm.graph.set_vertex_filter(eptm.is_cell_vert)
colors.fa = colors.fa.clip(0, 4)
colors.fa = colors.fa / 4
eptm.graph.set_vertex_filter(None)
colors.a = colors.a * local_patch.a
ax_aniso = lj.plot_eptm_generic(eptm, eptm.zeds, sigmas, ax=ax_aniso,
cell_kwargs={'c_text':False,
'cell_colors':colors,
'cmap':'afmhot_r',
'alpha':1.},
edge_kwargs={'j_text':False,
'c':'k',
'lw':0.4,
'alpha':0.5})
ax_aniso.set_title('Anisotropy')
### Areas
colors = eptm.cells.areas.copy()
eptm.graph.set_vertex_filter(eptm.is_cell_vert)
colors.fa = colors.fa / avg_area
colors.fa = colors.fa.clip(0, 1.5)
colors.fa = colors.fa / 1.5
eptm.graph.set_vertex_filter(None)
colors.a *= local_patch.a
ax_area = lj.plot_eptm_generic(eptm, eptm.zeds, sigmas, ax=ax_area,
cell_kwargs={'c_text':False,
'cell_colors':colors,
'cmap':'afmhot_r',
'alpha':1.},
edge_kwargs={'j_text':False,
'c':'k',
'lw':0.4,
'alpha':0.5})
ax_area.set_title('Area (µm²)')
### Orientation
colors = orientations.copy()
eptm.graph.set_vertex_filter(eptm.is_cell_vert)
colors.fa = (colors.fa.clip(30, 90) - 30) / 60
eptm.graph.set_vertex_filter(None)
colors.a = colors.a * local_patch.a
ax_orient = lj.plot_eptm_generic(eptm, eptm.zeds, sigmas, ax=ax_orient,
cell_kwargs={'c_text':False,
'cell_colors':colors,
'cmap':'afmhot_r',
'alpha':0.8},
edge_kwargs={'j_text':False,
'c':'k',
'lw':0.4,
'alpha':0.5})
eptm.rotate(- rot)
for ax in axes:
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlim(-7, 7)
ax.set_ylim(-12, 12)
ax.set_aspect('equal')
ax_orient.set_title('Orientation (°)')
saved_name = os.path.join('/home/guillaume/Projets/Droso/Simulations/', name+'_two_pannels.svg')
plt.savefig(saved_name)
print('saved to {}'.format(saved_name))
In [17]:
for name, path in xml_paths.items():
try:
eptm = lj.Epithelium(path, copy=False)
except OSError:
print('******* Problem with {} *********\n'
'File {} not found \n'
'************************\n'.format(name, path))
continue
new_aniso(eptm, name, dfs)
