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
2014-11-14 14:49:45,649 -leg_joint -INFO -successfully imported leg_joint
In [3]:
!ls -lth /media/data/Simulations/
total 108K
drwxr-xr-x 6 guillaume guillaume 4,0K juin   6 10:54 joint_2014-06-06T08_48_11
drwxr-xr-x 6 guillaume guillaume 4,0K juin   2 09:35 1000_2014-06-02T07_34_15
drwxr-xr-x 6 guillaume guillaume 4,0K juin   2 09:35 1001_2014-06-02T07_34_15
drwxr-xr-x 6 guillaume guillaume 4,0K juin   2 09:35 1002_2014-06-02T07_34_15
-rw-r--r-- 1 guillaume guillaume 1,1K mai   30 08:59 conditions.py
drwxr-xr-x 6 guillaume guillaume 4,0K mai   26 00:21 1009_2014-05-25T22_18_54
drwxr-xr-x 6 guillaume guillaume 4,0K mai   26 00:20 1002_2014-05-25T22_18_54
drwxr-xr-x 6 guillaume guillaume 4,0K mai   26 00:20 1006_2014-05-25T22_18_54
drwxr-xr-x 6 guillaume guillaume 4,0K mai   26 00:20 1008_2014-05-25T22_18_54
drwxr-xr-x 6 guillaume guillaume 4,0K mai   26 00:20 1000_2014-05-25T22_18_54
drwxr-xr-x 6 guillaume guillaume 4,0K mai   26 00:20 1003_2014-05-25T22_18_54
drwxr-xr-x 6 guillaume guillaume 4,0K mai   26 00:20 1004_2014-05-25T22_18_54
drwxr-xr-x 6 guillaume guillaume 4,0K mai   26 00:20 1007_2014-05-25T22_18_54
drwxr-xr-x 6 guillaume guillaume 4,0K mai   26 00:20 1001_2014-05-25T22_18_54
drwxr-xr-x 6 guillaume guillaume 4,0K mai   26 00:20 1005_2014-05-25T22_18_54
drwxr-xr-x 6 guillaume guillaume 4,0K mai   24 11:07 0009_2014-05-24T09_04_38
drwxr-xr-x 6 guillaume guillaume 4,0K mai   24 11:06 0004_2014-05-24T09_04_38
drwxr-xr-x 6 guillaume guillaume 4,0K mai   24 11:06 0000_2014-05-24T09_04_38
drwxr-xr-x 6 guillaume guillaume 4,0K mai   24 11:06 0002_2014-05-24T09_04_38
drwxr-xr-x 6 guillaume guillaume 4,0K mai   24 11:06 0001_2014-05-24T09_04_38
drwxr-xr-x 6 guillaume guillaume 4,0K mai   24 11:06 0003_2014-05-24T09_04_38
drwxr-xr-x 6 guillaume guillaume 4,0K mai   24 11:06 0005_2014-05-24T09_04_38
drwxr-xr-x 6 guillaume guillaume 4,0K mai   24 11:06 0006_2014-05-24T09_04_38
drwxr-xr-x 6 guillaume guillaume 4,0K mai   24 11:06 0007_2014-05-24T09_04_38
drwxr-xr-x 6 guillaume guillaume 4,0K mai   24 11:06 0008_2014-05-24T09_04_38
drwxr-xr-x 2 guillaume guillaume 4,0K mars  23  2014 xml
drwxr-xr-x 2 guillaume guillaume 4,0K mars  23  2014 blender
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')
index: 0 => 

	 max_ci: 2.0
	 radial_tension : 0.1
	 num cells : 5
	 width : 2.0


index: 1 => 

	 max_ci: 2.0
	 radial_tension : 0.1
	 num cells : 10
	 width : 2.0


index: 2 => 

	 max_ci: 2.0
	 radial_tension : 0.1
	 num cells : 20
	 width : 2.0


index: 3 => 

	 max_ci: 2.0
	 radial_tension : 0.1
	 num cells : 30
	 width : 2.0


index: 4 => 

	 max_ci: 2.0
	 radial_tension : 0.0
	 num cells : 30
	 width : 2.0


index: 5 => 

	 max_ci: 2.0
	 radial_tension : 0.05
	 num cells : 30
	 width : 2.0


index: 6 => 

	 max_ci: 2.0
	 radial_tension : 0.2
	 num cells : 30
	 width : 2.0


index: 7 => 

	 max_ci: 0.0
	 radial_tension : 0.1
	 num cells : 30
	 width : 2.0


index: 8 => 

	 max_ci: 1.0
	 radial_tension : 0.1
	 num cells : 30
	 width : 2.0


index: 9 => 

	 max_ci: 2.0
	 radial_tension : 0.1
	 num cells : 30
	 width : 30.0


index: 10 => 

	 max_ci: 2.0
	 radial_tension : 0.1
	 num cells : 5
	 width : 2.0


index: 11 => 

	 max_ci: 1.0
	 radial_tension : 0.1
	 num cells : 30
	 width : 2.0


index: 12 => 

	 max_ci: 2.0
	 radial_tension : 0.1
	 num cells : 10
	 width : 2.0


index: 13 => 

	 max_ci: 1.5
	 radial_tension : 0.1
	 num cells : 30
	 width : 2.0


index: 14 => 

	 max_ci: 2.0
	 radial_tension : 0.1
	 num cells : 20
	 width : 2.0


index: 15 => 

	 max_ci: 3.0
	 radial_tension : 0.1
	 num cells : 30
	 width : 2.0


index: 16 => 

	 max_ci: 2.0
	 radial_tension : 0.1
	 num cells : 30
	 width : 2.0


index: 17 => 

	 max_ci: 2.0
	 radial_tension : 0.0
	 num cells : 30
	 width : 2.0


index: 18 => 

	 max_ci: 2.0
	 radial_tension : 0.05
	 num cells : 30
	 width : 2.0


index: 19 => 

	 max_ci: 2.0
	 radial_tension : 0.2
	 num cells : 30
	 width : 2.0


index: 20 => 

	 max_ci: 0.0
	 radial_tension : 0.1
	 num cells : 30
	 width : 2.0


index: 21 => 

	 max_ci: 1.0
	 radial_tension : 0.1
	 num cells : 30
	 width : 2.0


index: 22 => 

	 max_ci: 2.0
	 radial_tension : 0.1
	 num cells : 30
	 width : 30.0


In [5]:
conditions
Out[5]:
max_ci radial_tension num_cells width_apopto json
0 2 0.1 5 2 /media/data/Simulations/0000_2014-05-24T09_04_...
1 2 0.1 10 2 /media/data/Simulations/0001_2014-05-24T09_04_...
2 2 0.1 20 2 /media/data/Simulations/0002_2014-05-24T09_04_...
3 2 0.1 30 2 /media/data/Simulations/0003_2014-05-24T09_04_...
4 2 0 30 2 /media/data/Simulations/0004_2014-05-24T09_04_...
5 2 0.05 30 2 /media/data/Simulations/0005_2014-05-24T09_04_...
6 2 0.2 30 2 /media/data/Simulations/0006_2014-05-24T09_04_...
7 0 0.1 30 2 /media/data/Simulations/0007_2014-05-24T09_04_...
8 1 0.1 30 2 /media/data/Simulations/0008_2014-05-24T09_04_...
9 2 0.1 30 30 /media/data/Simulations/0009_2014-05-24T09_04_...
10 2 0.1 5 2 /media/data/Simulations/1000_2014-05-25T22_18_...
11 1 0.1 30 2 /media/data/Simulations/1000_2014-06-02T07_34_...
12 2 0.1 10 2 /media/data/Simulations/1001_2014-05-25T22_18_...
13 1.5 0.1 30 2 /media/data/Simulations/1001_2014-06-02T07_34_...
14 2 0.1 20 2 /media/data/Simulations/1002_2014-05-25T22_18_...
15 3 0.1 30 2 /media/data/Simulations/1002_2014-06-02T07_34_...
16 2 0.1 30 2 /media/data/Simulations/1003_2014-05-25T22_18_...
17 2 0 30 2 /media/data/Simulations/1004_2014-05-25T22_18_...
18 2 0.05 30 2 /media/data/Simulations/1005_2014-05-25T22_18_...
19 2 0.2 30 2 /media/data/Simulations/1006_2014-05-25T22_18_...
20 0 0.1 30 2 /media/data/Simulations/1007_2014-05-25T22_18_...
21 1 0.1 30 2 /media/data/Simulations/1008_2014-05-25T22_18_...
22 2 0.1 30 30 /media/data/Simulations/1009_2014-05-25T22_18_...
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']),]}
    
2014-11-14 15:31:19,318 -leg_joint.epithelium -INFO -Instanciating epithelium 0
2014-11-14 15:31:20,963 -leg_joint.epithelium -INFO -Initial cells
2014-11-14 15:31:20,995 -leg_joint.epithelium -INFO -Initial junctions
2014-11-14 15:31:32,423 -leg_joint.epithelium -INFO -Update geometry
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]:
array(100, dtype=uint64)
In [14]:
avg_area, avg_rho
Out[14]:
(11.075637301194847, 25.215618796766609)
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)
    
2014-11-14 15:43:22,017 -leg_joint.epithelium -INFO -Instanciating epithelium 0
2014-11-14 15:43:23,649 -leg_joint.epithelium -INFO -Initial cells
2014-11-14 15:43:23,650 -leg_joint.epithelium -INFO -Initial junctions
2014-11-14 15:43:37,842 -leg_joint.epithelium -INFO -Update geometry
2014-11-14 15:44:03,420 -leg_joint.epithelium -INFO -Instanciating epithelium 0
2014-11-14 15:44:05,041 -leg_joint.epithelium -INFO -Initial cells
2014-11-14 15:44:05,077 -leg_joint.epithelium -INFO -Initial junctions
2014-11-14 15:44:18,405 -leg_joint.epithelium -INFO -Update geometry
saved to /home/guillaume/Projets/Droso/Simulations/10_random_two_pannels.svg
saved to /home/guillaume/Projets/Droso/Simulations/20_random_two_pannels.svg
2014-11-14 15:44:44,778 -leg_joint.epithelium -INFO -Instanciating epithelium 0
2014-11-14 15:44:46,368 -leg_joint.epithelium -INFO -Initial cells
2014-11-14 15:44:46,383 -leg_joint.epithelium -INFO -Initial junctions
2014-11-14 15:44:58,047 -leg_joint.epithelium -INFO -Update geometry
saved to /home/guillaume/Projets/Droso/Simulations/ectopic_two_pannels.svg
2014-11-14 15:45:23,619 -leg_joint.epithelium -INFO -Instanciating epithelium 0
2014-11-14 15:45:25,161 -leg_joint.epithelium -INFO -Initial cells
2014-11-14 15:45:25,173 -leg_joint.epithelium -INFO -Initial junctions
2014-11-14 15:45:37,059 -leg_joint.epithelium -INFO -Update geometry
saved to /home/guillaume/Projets/Droso/Simulations/rt_1_ct15_two_pannels.svg
2014-11-14 15:46:05,816 -leg_joint.epithelium -INFO -Instanciating epithelium 0
2014-11-14 15:46:07,450 -leg_joint.epithelium -INFO -Initial cells
2014-11-14 15:46:07,465 -leg_joint.epithelium -INFO -Initial junctions
2014-11-14 15:46:21,616 -leg_joint.epithelium -INFO -Update geometry
saved to /home/guillaume/Projets/Droso/Simulations/rt_1_ct3_two_pannels.svg
2014-11-14 15:46:55,062 -leg_joint.epithelium -INFO -Instanciating epithelium 0
2014-11-14 15:46:56,713 -leg_joint.epithelium -INFO -Initial cells
2014-11-14 15:46:56,738 -leg_joint.epithelium -INFO -Initial junctions
2014-11-14 15:47:09,633 -leg_joint.epithelium -INFO -Update geometry
saved to /home/guillaume/Projets/Droso/Simulations/5_random_two_pannels.svg
2014-11-14 15:47:33,120 -leg_joint.epithelium -INFO -Instanciating epithelium 0
2014-11-14 15:47:34,744 -leg_joint.epithelium -INFO -Initial cells
2014-11-14 15:47:34,767 -leg_joint.epithelium -INFO -Initial junctions
2014-11-14 15:47:47,559 -leg_joint.epithelium -INFO -Update geometry
saved to /home/guillaume/Projets/Droso/Simulations/rt_1_ct2_two_pannels.svg
2014-11-14 15:48:21,584 -leg_joint.epithelium -INFO -Instanciating epithelium 0
2014-11-14 15:48:23,246 -leg_joint.epithelium -INFO -Initial cells
2014-11-14 15:48:23,258 -leg_joint.epithelium -INFO -Initial junctions
2014-11-14 15:48:36,052 -leg_joint.epithelium -INFO -Update geometry
saved to /home/guillaume/Projets/Droso/Simulations/rt_1_ct1_two_pannels.svg
2014-11-14 15:48:59,476 -leg_joint.epithelium -INFO -Instanciating epithelium 0
2014-11-14 15:49:01,109 -leg_joint.epithelium -INFO -Initial cells
2014-11-14 15:49:01,135 -leg_joint.epithelium -INFO -Initial junctions
2014-11-14 15:49:16,920 -leg_joint.epithelium -INFO -Update geometry
saved to /home/guillaume/Projets/Droso/Simulations/rt_2_ct2_two_pannels.svg
/home/guillaume/anaconda/envs/python3/lib/python3.4/site-packages/scipy/optimize/minpack.py:419: RuntimeWarning: Number of calls to function has reached maxfev = 800.
  warnings.warn(errors[info][0], RuntimeWarning)
2014-11-14 15:49:52,688 -leg_joint.epithelium -INFO -Instanciating epithelium 0
2014-11-14 15:49:54,294 -leg_joint.epithelium -INFO -Initial cells
2014-11-14 15:49:54,310 -leg_joint.epithelium -INFO -Initial junctions
2014-11-14 15:50:06,994 -leg_joint.epithelium -INFO -Update geometry
saved to /home/guillaume/Projets/Droso/Simulations/rt_05_ct2_two_pannels.svg
2014-11-14 15:50:38,511 -leg_joint.epithelium -INFO -Instanciating epithelium 0
2014-11-14 15:50:40,076 -leg_joint.epithelium -INFO -Initial cells
2014-11-14 15:50:40,077 -leg_joint.epithelium -INFO -Initial junctions
2014-11-14 15:50:53,002 -leg_joint.epithelium -INFO -Update geometry
saved to /home/guillaume/Projets/Droso/Simulations/before_two_pannels.svg
2014-11-14 15:51:13,614 -leg_joint.epithelium -INFO -Instanciating epithelium 0
2014-11-14 15:51:15,253 -leg_joint.epithelium -INFO -Initial cells
2014-11-14 15:51:15,271 -leg_joint.epithelium -INFO -Initial junctions
2014-11-14 15:51:27,890 -leg_joint.epithelium -INFO -Update geometry
saved to /home/guillaume/Projets/Droso/Simulations/rt_0_ct2_two_pannels.svg
/home/guillaume/anaconda/envs/python3/lib/python3.4/site-packages/matplotlib-1.4.x-py3.4-linux-x86_64.egg/matplotlib/figure.py:1644: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.
  warnings.warn("This figure includes Axes that are not "