def _calculate_media(self, check_start=40, check_interval_growth=5):
check_interval = check_interval_growth
limiting = max(self.spec.uptake_rate, self.spec.diffusion_constant)
dt = 1.0/(4*limiting)
cells = self._narrow_cells()
boundary = self._make_boundary_layer(cells)
media = np.ones_like(cells, np.float32)*self.spec.media_concentration
# guess a linear solution for faster convergence
heights = utils.compute_heights(np.logical_not(boundary))
for col, height in enumerate(heights):
if height == 0: continue
media[:height, col] = np.linspace(0, self.spec.media_concentration,
height)
media_next = np.empty_like(media)
# def error_handler(type, flag):
# if type == "underflow": return
# np.seterr(all='warn')
# print "Floating point error (%s), with flag %s" % (type, flag)
# print dt, self.spec.diffusion_constant, self.spec.uptake_rate
# print self.spec.media_concentration
# from matplotlib import pyplot as plt
# plt.figure(); plt.imshow(media); plt.colorbar()
# plt.figure(); plt.imshow(media_next); plt.colorbar()
# plt.figure(); plt.imshow(boundary); plt.colorbar()
# plt.figure(); plt.imshow(cells); plt.colorbar()
# plt.show()
# print step
# raise Exception()
# np.seterrcall(error_handler)
# np.seterr(all='call')
for step in range(self.spec.max_diffusion_iterations):
laplace(media, output=media_next)
media_next *= self.spec.diffusion_constant*dt
media_next += media
media_next[cells] *= 1 - self.spec.uptake_rate*dt
media_next[boundary] = self.spec.media_concentration
media, media_next = media_next, media
if step >= check_start and step%check_interval == 0:
media_next -= media
np.abs(media_next, out=media_next)
error = media_next.sum()/(dt*media_next.size)
if error <= self.spec.diffusion_tol: break
check_interval += check_interval_growth
self.media = media
def _compute_heights(result):
return utils.compute_heights(result.image)