def by_time_points_amount(_simulations, _time_points, _exactly=False):
if _exactly:
return [
_simulation for _simulation in _simulations
if len(load.properties(_simulation)['time_points']) == _time_points
]
else:
return [
_simulation for _simulation in _simulations
if len(load.properties(_simulation)['time_points']) >= _time_points
]
def window_fiber_density_time_point(_arguments):
if 'properties' not in _arguments:
_arguments['properties'] = properties(_arguments['simulation'])
_time_point_properties = _arguments['properties']['time_points'][
_arguments['time_point']]
if _arguments['direction'] == 'inside':
_new_direction = 'right' if _arguments[
'cell_id'] == 'left_cell' else 'left'
elif _arguments['direction'] == 'outside':
_new_direction = 'left' if _arguments[
'cell_id'] == 'left_cell' else 'right'
else:
_new_direction = _arguments['direction']
_time_point_window = window(
_length_x=_arguments['length_x'] * CELL_DIAMETER,
_length_y=_arguments['length_y'] * CELL_DIAMETER,
_offset_x=_arguments['offset_x'] * CELL_DIAMETER,
_offset_y=_arguments['offset_y'] * CELL_DIAMETER,
_cell_coordinates=_time_point_properties[
_arguments['cell_id']]['coordinates'],
_cell_diameter=_time_point_properties[
_arguments['cell_id']]['diameter'],
_direction=_new_direction)
if 'print' in _arguments and _arguments['print']:
print('Computing:', _arguments['simulation'], _arguments['cell_id'],
'window', _time_point_window, 'direction',
_arguments['direction'], 'tp', _arguments['time_point'])
_window_fiber_density = \
quantification_window_fiber_density(_arguments['simulation'], _arguments['time_point'], _time_point_window)
return _arguments, _window_fiber_density
def window_fiber_density_by_time(_arguments):
_simulation_properties = properties(_arguments['simulation'])
_windows = []
for _time_point in range(
min(_arguments['time_points'],
len(_simulation_properties['time_points']))):
_arguments['time_point'] = _time_point
_windows.append(window_fiber_density_time_point(_arguments)[1])
return _arguments, _windows
def by_pair_distance(_simulations, _reverse=False):
_organized_simulations = {}
for _simulation in _simulations:
_simulation_properties = load.properties(_simulation)
_pair_distance = compute.pair_distance(_simulation_properties)
if _pair_distance in _organized_simulations:
_organized_simulations[_pair_distance].append(_simulation)
else:
_organized_simulations[_pair_distance] = [_simulation]
return {
_distance: _organized_simulations[_distance]
for _distance in sorted(_organized_simulations.keys(),
reverse=_reverse)
}
def main():
_simulations = load.structured()
_simulations = filtering.by_categories(
_simulations,
_is_single_cell=False,
_is_heterogeneity=None,
_is_low_connectivity=False,
_is_causality=CAUSALITY,
_is_dominant_passive=DOMINANT_PASSIVE,
_is_fibrin=False)
_simulations = filtering.by_pair_distances(_simulations,
_distances=PAIR_DISTANCE)
print('Total simulations:', len(_simulations))
_arguments = []
for _simulation in _simulations:
for _cell_id in ['left_cell', 'right_cell']:
_arguments.append({
'simulation': _simulation,
'length_x':
config.QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'length_y':
config.QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'offset_x': 0,
'offset_y': 0,
'cell_id': _cell_id,
'direction': 'inside',
'time_points': TIME_POINTS
})
_fiber_densities = {}
with Pool(CPUS_TO_USE) as _p:
for _keys, _value in tqdm(_p.imap_unordered(
compute.window_fiber_density_by_time, _arguments),
total=len(_arguments),
desc='Computing windows & fiber densities'):
_fiber_densities[(_keys['simulation'], _keys['cell_id'])] = _value
_p.close()
_p.join()
_simulations_by_heterogeneity = organize.by_heterogeneity(_simulations)
_headers = [
'time_point',
'simulation',
'pair_distance_in_cell_diameter',
'heterogeneity_std',
'left_cell_fiber_density',
'left_cell_fiber_density_z_score',
'right_cell_fiber_density',
'right_cell_fiber_density_z_score',
]
_csv_path = os.path.join(paths.OUTPUTS,
'simulations_density_cell_pairs.csv')
with open(_csv_path, 'w', newline='') as _csv_file:
_csv_writer = csv.writer(_csv_file)
_csv_writer.writerow(_headers)
for _simulation_std in _simulations_by_heterogeneity:
print('STD:', _simulation_std)
_std_simulations = _simulations_by_heterogeneity[_simulation_std]
for _simulation in tqdm(_std_simulations, desc='Simulations loop'):
_properties = load.properties(_simulation)
_pair_distance = compute.pair_distance(_properties)
_average, _std = load.normalization(_simulation).values()
_left_cell_fiber_densities = _fiber_densities[(_simulation,
'left_cell')]
_right_cell_fiber_densities = _fiber_densities[(_simulation,
'right_cell')]
for _time_point, (_left_cell_fiber_density, _right_cell_fiber_density) in \
enumerate(zip(_left_cell_fiber_densities, _right_cell_fiber_densities)):
_left_cell_fiber_density_z_score = compute_lib.z_score(
_left_cell_fiber_density, _average, _std)
_right_cell_fiber_density_z_score = compute_lib.z_score(
_right_cell_fiber_density, _average, _std)
_csv_writer.writerow([
_time_point, _simulation, _pair_distance,
_simulation_std, _left_cell_fiber_density,
_left_cell_fiber_density_z_score,
_right_cell_fiber_density,
_right_cell_fiber_density_z_score
])
def by_pair_distances(_simulations, _distances):
return [
_simulation for _simulation in _simulations
if pair_distance(load.properties(_simulation)) in _distances
]
def main():
_simulations = load.structured()
_simulations = filtering.by_time_points_amount(_simulations, TIME_POINT)
_simulations = filtering.by_categories(_simulations,
_is_single_cell=False,
_is_heterogeneity=True,
_is_low_connectivity=False,
_is_causality=False,
_is_dominant_passive=False,
_is_fibrin=False)
_simulations = filtering.by_heterogeneity(_simulations, _std=STD)
print('Total simulations:', len(_simulations))
_fiber_densities = compute_fiber_densities(_simulations)
_correlations = []
_pair_distances = []
for _simulation in tqdm(_simulations, desc='Simulations loop'):
_properties = load.properties(_simulation)
_left_cell_fiber_densities = _fiber_densities[(_simulation,
'left_cell')]
_right_cell_fiber_densities = _fiber_densities[(_simulation,
'right_cell')]
_correlations.append(
compute_lib.correlation(
compute_lib.derivative(_left_cell_fiber_densities,
_n=DERIVATIVE),
compute_lib.derivative(_right_cell_fiber_densities,
_n=DERIVATIVE)))
_pair_distances.append(compute.pair_distance(_properties))
print('Total pairs:', len(_correlations))
print(
'Pearson:',
compute_lib.correlation(_correlations,
_pair_distances,
_with_p_value=True))
# plot
_fig = go.Figure(
data=go.Scatter(x=_pair_distances,
y=_correlations,
mode='markers',
marker={
'size': 10,
'color': 'black'
},
showlegend=False),
layout={
'xaxis': {
'title': 'Pair distance (cell diameter)',
'zeroline': False,
# 'tickmode': 'array',
# 'tickvals': [4, 6, 8, 10]
},
'yaxis': {
'title': 'Correlation',
'zeroline': False,
'range': [-1.1, 1.2],
'tickmode': 'array',
'tickvals': [-1, -0.5, 0, 0.5, 1]
},
# 'shapes': [
# {
# 'type': 'line',
# 'x0': -1,
# 'y0': -1,
# 'x1': -1,
# 'y1': 1,
# 'line': {
# 'color': 'black',
# 'width': 2
# }
# },
# {
# 'type': 'line',
# 'x0': -1,
# 'y0': -1,
# 'x1': 1,
# 'y1': -1,
# 'line': {
# 'color': 'black',
# 'width': 2
# }
# }
# ]
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot')