%run nb_init.py
import IPython.core.display as disp
%matplotlib inline

disp.SVG(lj.data.get_image('one_cell.svg'))

disp.SVG(lj.data.get_image('geometrie.svg'))

eptm = lj.Epithelium(graphXMLfile=lj.data.before_apoptosis_xml(),
                     save_dir='saved_graphs',
                     identifier='tuto',
                     copy=True)
print(eptm)

eptm.graph.list_properties()

eptm.paths

output3d = os.path.join(eptm.paths['png'], 'tissue_3d.png')
output2d = os.path.join(eptm.paths['png'], 'tissue_2d.png')


lj.draw(eptm, 
        output3d=output3d, 
        output2d=output2d)

disp.Image(filename=output3d)


disp.Image(filename='../doc/imgs/tissue_2d.png')

eptm.graph.set_vertex_filter(None)

## The number of side is equal to the number of junction vertices
## thus to the number of out neighbors of each cell
num_sides = eptm.graph.degree_property_map('out')

## Filter living cells
live_cells = eptm.is_alive.copy()
live_cells.a *= eptm.is_cell_vert.a
eptm.graph.set_vertex_filter(live_cells)

## Computing nomrmalized area
normed_areas = eptm.cells.areas.fa / eptm.cells.areas.fa.mean()

## Getting the numbr of sides only for living cells
num_sides = np.asarray(num_sides.fa)
u_num_sides = np.unique(num_sides)

## Plotting it all
avg_areas = np.zeros(u_num_sides.size)
stm_areas = np.zeros(u_num_sides.size)
bins, n_cells = np.histogram(num_sides, bins=11, range=(0, 11))

for n, n_sides in enumerate(u_num_sides):
    avg_areas[n] = normed_areas[num_sides == n_sides].mean()
    stm_areas[n] = normed_areas[num_sides == n_sides].std() / np.sqrt(n_cells[np.int(n_sides)])
        
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(6, 6))
ax1.plot(num_sides, normed_areas, 'o', alpha=0.05)    
ax1.errorbar(u_num_sides, avg_areas, yerr=stm_areas, lw=2, elinewidth=2)
ax1.set_xlim(0, 12)


ax2.hist(num_sides, bins=11, range=(0.5, 11.5),
         normed=True, color='g', alpha=0.7)
ax2.set_xlim(0, 12)
eptm.graph.set_vertex_filter(None)
ax2.set_xlabel('Number of cell neighbors')
ax2.set_ylabel('Frequency')
ax1.set_ylabel('Normalized cell area')
plt.draw()

fig.savefig(lj.data.get_image('tissue_geometry.svg'))