%load_ext autoreload
%autoreload 2

%matplotlib inline

%run nb_init.py

eptm = lj.Epithelium(lj.data.small_xml(),
                     save_dir='saved_graphs',
                     identifier='division',
                     copy=True)
eptm.isotropic_relax()

eptm.set_local_mask(None)
lj.local_slice(eptm, theta_c=0, theta_amp=np.pi/9, zed_c=0, zed_amp=6.)
fig, axes=plt.subplots(1, 2, figsize=(12, 8), sharey=True)


lj.plot_2pannels(eptm,
                 cell_kwargs={'c_text':True},
                 edge_kwargs={'c':'g', 'lw':1, 'alpha':0.4},
                 axes=axes)

eptm.set_local_mask(None)


mother_cell = eptm.graph.vertex(3)

## Clear local mask and set it to be centered on 
## the mother cell
eptm.set_local_mask(None)
eptm.set_local_mask(mother_cell, wider=True)

## Create a figure
fig, axes=plt.subplots(1, 2, figsize=(10, 4))#, sharey=True)
axes = lj.plot_2pannels(eptm, axes=axes)

## Show the forces exerted on the vertices of our cell
axes = lj.plot_2pannels_gradients(eptm, axes=axes, scale=10., approx=1)
axes = lj.plot_2pannels_gradients(eptm, axes=axes, scale=10., approx=0)
plt.tight_layout()
plt.draw()

area0 = eptm.cells.areas[mother_cell]
growth_rate = 1.5
eptm.cells.prefered_vol[mother_cell] *= growth_rate

eptm.update_gradient()

fig, axes=plt.subplots(1, 2, figsize=(10, 4))

axes = lj.plot_2pannels(eptm, axes=axes)
axes = lj.plot_2pannels_gradients(eptm, axes=axes, scale=20., approx=0)
axes = lj.plot_2pannels_gradients(eptm, axes=axes, scale=20., approx=1)
plt.tight_layout()


jv0, jv1 = eptm.cells.junctions[mother_cell][2]

def grad_norm(jv):
    return (eptm.grad_ix[jv]**2 + eptm.grad_zed[jv]**2 + eptm.grad_wy[jv]**2)**0.5


grad_norm(jv0), grad_norm(jv1)

for jv0, jv1 in eptm.cells.junctions[mother_cell]:
    print(jv0, jv1, grad_norm(jv0), grad_norm(jv1))
    

pos0, pos1 = lj.find_energy_min(eptm, method='fmin_ncg')
print('Current relative area: %.3f' 
      % (eptm.cells.areas[mother_cell] / area0))


pos0, pos1 = lj.find_energy_min(eptm, method='fmin_ncg')

%pdb

@before_after
def show_division(eptm, mother_cell):
    septum = lj.cell_division(eptm, mother_cell, verbose=True)
    return septum
eptm.graph.set_edge_filter(None)
eptm.graph.set_vertex_filter(None)
septum = show_division(eptm, mother_cell)
if septum is not None:
    jv0, jv1, mother_cell, daughter_cell = septum


import scipy.optimize as opt

opt.fmin_ncg?

eptm.isotropic_relax()

pos0, pos1 = show_optimisation(eptm)
eptm.isotropic_relax()


eptm.set_local_mask(None)
cell1, cell3 = eptm.graph.vertex(912), eptm.graph.vertex(4745)
eptm.set_local_mask(cell1)
eptm.set_local_mask(cell3)

%pdb

@before_after
def show_type1(eptm, cells):
    cell1, cell3 = cells
    modified_cells, modified_jverts = lj.type1_transition(eptm, (cell1, cell3))
    
show_type1(eptm, (cell1, cell3))

po0, pos1 = show_optimisation(eptm, tol=1e-5, approx_grad=0)