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problem.py
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problem.py
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import sys
import importlib
import os
import pickle
import numpy as np
import random
class Problem(object):
settings = {}
def __init__(self, default_settings_filename = None):
# set defaults
if default_settings_filename:
self.load_from_file(default_settings_filename, only_settings=True)
def __getitem__(self, index):
return self.settings[index]
def set_settings_from_dictionary(self, settings=None, check_for_None=True):
self.settings.update(settings)
def load_from_file(self, filename, only_settings=False):
sys.path.append(os.path.dirname(filename))
modulename = os.path.splitext(os.path.basename(filename))[0]
imported = importlib.import_module(modulename)
sys.path.pop()
settings = imported.settings
self.set_settings_from_dictionary(settings=settings,
check_for_None=False)
if not only_settings:
materials = imported.materials
boundaries = imported.boundaries
moving_cells = imported.moving_cells
return materials, boundaries, moving_cells
def load_model(self, filename):
materials, boundaries, moving_cells = self.load_from_file(filename)
mxx, myy, values, moving_cells_index_list, markers_index_list = self.load_image(filename, moving_cells)
rho_key = np.asarray([material['rho'] for material in materials])
eta_key = np.asarray([material['eta'] for material in materials])
mu_key = np.asarray([material['mu'] for material in materials])
C_key = np.asarray([material['C'] for material in materials])
sinphi_key = np.asarray([material['sinphi'] for material in materials])
m_cat = np.copy(values)
m_rho = rho_key[values]
m_eta = eta_key[values]
m_mu = mu_key[values]
m_C = C_key[values]
m_sinphi = sinphi_key[values]
self.settings["mxx"] = mxx
self.settings["myy"] = myy
self.settings["m_cat"] = m_cat
self.settings["m_rho"] = m_rho
self.settings["m_eta"] = m_eta
self.settings["m_mu"] = m_mu
self.settings["m_C"] = m_C
self.settings["m_sinphi"] = m_sinphi
self.settings["top_bound"] = boundaries['top_bound']
self.settings["bottom_bound"] = boundaries['bottom_bound']
self.settings["left_bound"] = boundaries['left_bound']
self.settings["right_bound"] = boundaries['right_bound']
self.settings["m_s_xx"] = np.zeros(np.shape(mxx))
self.settings["m_s_xy"] = np.zeros(np.shape(mxx))
self.settings["m_e_xx"] = np.zeros(np.shape(mxx))
self.settings["m_e_xy"] = np.zeros(np.shape(mxx))
self.settings["m_P"] = np.zeros(np.shape(mxx))
self.settings['moving_points_index_list'] = moving_cells_index_list
self.settings['markers_index_list'] = markers_index_list
def create_grid_of_points(self, mxx, myy, res):
x_res, y_res = res
x = np.linspace(0,self['j_res']-2,x_res)
y = np.linspace(0,self['i_res']-2,y_res)
x_ = np.linspace(0,self['j_res']-2,x_res*5)
y_ = np.linspace(0,self['i_res']-2,y_res*5)
xx, yy = np.meshgrid(x,y)
list_of_indexes = []
for x1 in x[:-1]:
# for y1 in np.delete(y_, np.s_[:-1:5]):
for y1 in y_:
mxx.append(np.asarray([x1]))
myy.append(np.asarray([y1]))
list_of_indexes.append(len(mxx)-1)
for y1 in y[:-1]:
# for x1 in np.delete(x_, np.s_[:-1:5]):
for x1 in x_:
mxx.append(np.asarray([x1]))
myy.append(np.asarray([y1]))
list_of_indexes.append(len(mxx)-1)
# for x in xx.flatten():
# mxx.append(np.asarray([x]))
# list_of_indexes.append(len(mxx)-1)
# for y in yy.flatten():
# myy.append(np.asarray([y]))
return list_of_indexes
def load_image(self, fname, moving_cells):
image = np.load(f'{fname[:-3]}.npy')
image_i, image_j = image.shape
j_res = self['j_res']
i_res = self['i_res']
marker_density = self['pdensity']
if not self['seed'] is None:
print('seed')
np.random.seed(self['seed'])
# markers
mxx = []
myy = []
for x in range(j_res-1):
for y in range(i_res-1):
for _ in range(marker_density):
mxx.append(x+np.random.uniform(0,.5,1))
myy.append(y+np.random.uniform(0,.5,1))
mxx.append(x+np.random.uniform(0,.5,1))
myy.append(y+np.random.uniform(.5,1,1))
mxx.append(x+np.random.uniform(.5,1,1))
myy.append(y+np.random.uniform(.5,1,1))
mxx.append(x+np.random.uniform(.5,1,1))
myy.append(y+np.random.uniform(0,.5,1))
moving_cells_index_list = []
moving_cells_coordinates_list = [(xy) for xy, VxVy in moving_cells]
print(moving_cells_coordinates_list)
moving_x = np.asarray([x for (x,y), VxVy in moving_cells])
moving_y = np.asarray([y for (x,y), VxVy in moving_cells])
moving_j = (moving_x*(j_res-1)/image_j).astype(int)
moving_i = (moving_y*(i_res-1)/image_i).astype(int)
moving_points = []
for ind, (j,i) in enumerate(zip(moving_j, moving_i)):
_, (VxVy) = moving_cells[ind]
# j,i = int(j), int(i)
mxx.append(np.asarray([j]))
myy.append(np.asarray([i]))
moving_points.append((len(mxx)-1,VxVy))
markers_index_list = []
if self['markers_grid'] != (0,0):
markers_index_list = self.create_grid_of_points(mxx, myy, self['markers_grid'])
mxx = np.asarray(mxx)
myy = np.asarray(myy)
# TODO: Refactor following block to be inside previous cascade of for loops
mj = (mxx*image_j/(j_res-1)).astype(int)
mi = (myy*image_i/(i_res-1)).astype(int)
values = np.zeros(np.shape(mxx))
for idx in range(len(mxx)):
j,i = mj[idx], mi[idx]
values[idx] = image[i,j]
if (j,i) in moving_cells_coordinates_list:
idx_ = moving_cells_coordinates_list.index((j,i))
_, (Vx, Vy) = moving_cells[idx_]
moving_cells_index_list.append((idx, Vx, Vy))
if moving_cells_index_list:
moving_cells_index_list = [random.choice(moving_cells_index_list)
for _ in range(5)]
values = values.astype(int)
return mxx, myy, values, moving_points, markers_index_list