def compute_data():
_simulations = load.structured()
_simulations = filtering.by_time_points_amount(_simulations,
_time_points=TIME_POINTS)
_simulations = filtering.by_categories(_simulations,
_is_single_cell=False,
_is_heterogeneity=False,
_is_low_connectivity=False,
_is_causality=False,
_is_dominant_passive=False,
_is_fibrin=False)
_simulations = filtering.by_pair_distance(_simulations,
_distance=PAIR_DISTANCE)
print('Total simulations:', len(_simulations))
_fiber_densities = compute_fiber_densities(_simulations)
_simulations_fiber_densities = [[] for _i in range(TIME_POINTS)]
for _simulation in tqdm(_simulations, desc='Main loop'):
_normalization = load.normalization(_simulation)
for _time_point in range(TIME_POINTS):
for _cell_id in ['left_cell', 'right_cell']:
_fiber_density = _fiber_densities[(_simulation,
_cell_id)][_time_point]
_normalized_fiber_density = compute_lib.z_score(
_fiber_density, _normalization['average'],
_normalization['std'])
_simulations_fiber_densities[_time_point].append(
_normalized_fiber_density)
print('Total pairs:', len(_simulations_fiber_densities[0]))
return _simulations_fiber_densities
def compute_cell_pairs(_low_connectivity):
_x_array = []
_y_array = []
_names_array = []
_max_offsets_x = []
for _distance in PAIR_DISTANCE:
print('Pair distance ' + str(_distance))
_simulations = load.structured()
_simulations = filtering.by_categories(
_simulations,
_is_single_cell=False,
_is_heterogeneity=False,
_is_low_connectivity=_low_connectivity,
_is_causality=False,
_is_dominant_passive=False,
_is_fibrin=False)
_simulations = filtering.by_pair_distance(_simulations,
_distance=_distance)
_simulations = filtering.by_time_points_amount(
_simulations, _time_points=TIME_POINT[_low_connectivity])
_offsets_x = np.arange(start=0,
stop=_distance / 2 - 0.25 -
QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
step=OFFSET_X_STEP)
if len(_offsets_x) > len(_max_offsets_x):
_max_offsets_x = _offsets_x
_fiber_densities = compute_fiber_densities(_simulations, _offsets_x,
_low_connectivity)
_pair_distance_fiber_densities = [[] for _i in range(len(_offsets_x))]
for _simulation in _simulations:
_offset_index = 0
_normalization = load.normalization(_simulation)
for _offset_x in _offsets_x:
for _cell_id in ['left_cell', 'right_cell']:
_fiber_density = _fiber_densities[(_simulation, _offset_x,
_cell_id)]
_normalized_fiber_density = compute_lib.z_score(
_fiber_density, _normalization['average'],
_normalization['std'])
_pair_distance_fiber_densities[_offset_index].append(
_normalized_fiber_density)
_offset_index += 1
_x_array.append(_offsets_x)
_y_array.append(_pair_distance_fiber_densities)
_names_array.append('Pair distance ' + str(_distance))
return _names_array, _x_array, _y_array
def compute_data(_arguments):
_offset_y_index, _offset_y = _arguments
_fiber_densities_array = []
for _simulation in _simulations:
_simulation_normalization = load.normalization(_simulation)
for _cell_id in ['left_cell', 'right_cell']:
_fiber_density = _fiber_densities[(_simulation, _offset_y, _cell_id)]
_normalized_fiber_density = compute_lib.z_score(
_x=_fiber_density,
_average=_simulation_normalization['average'],
_std=_simulation_normalization['std']
)
_fiber_densities_array.append(_normalized_fiber_density)
if len(_fiber_densities_array) > 0:
return _offset_y_index, np.mean(_fiber_densities_array)
else:
return _offset_y_index, None
def compute_simulations_data(_low_connectivity):
_simulations = simulations_load.structured()
_simulations = simulations_filtering.by_time_points_amount(
_simulations, SIMULATIONS_TIME_POINT[_low_connectivity])
_simulations = simulations_filtering.by_categories(
_simulations,
_is_single_cell=False,
_is_heterogeneity=False,
_is_low_connectivity=_low_connectivity,
_is_causality=False,
_is_dominant_passive=False,
_is_fibrin=False)
_simulations = simulations_filtering.by_pair_distance(
_simulations, _distance=PAIR_DISTANCE)
print('Total simulations:', len(_simulations))
_fiber_densities = compute_simulations_fiber_densities(
_simulations, _low_connectivity)
_simulations_fiber_densities = [[]
for _i in range(len(SIMULATIONS_OFFSETS_X))
]
for _simulation in tqdm(_simulations, desc='Simulations Loop'):
_normalization = simulations_load.normalization(_simulation)
for _offset_x_index, _offset_x in enumerate(SIMULATIONS_OFFSETS_X):
for _cell_id in ['left_cell', 'right_cell']:
_fiber_density = _fiber_densities[(_simulation, _offset_x,
_cell_id)]
_normalized_fiber_density = compute_lib.z_score(
_fiber_density, _normalization['average'],
_normalization['std'])
_simulations_fiber_densities[_offset_x_index].append(
_normalized_fiber_density)
return _simulations_fiber_densities
def compute_simulations_data(_low_connectivity):
_simulations = simulations_load.structured()
_simulations = simulations_filtering.by_time_points_amount(
_simulations, _time_points=SIMULATIONS_TIME_POINT[_low_connectivity]
)
_simulations = simulations_filtering.by_categories(
_simulations,
_is_single_cell=True,
_is_heterogeneity=False,
_is_low_connectivity=_low_connectivity,
_is_causality=False,
_is_dominant_passive=False,
_is_fibrin=False
)
_fiber_densities = compute_simulations_fiber_densities(_simulations, _low_connectivity)
_simulations_fiber_densities = [[] for _i in range(len(OFFSETS_X))]
for _simulation in tqdm(_simulations, desc='Simulations Loop'):
_normalization = simulations_load.normalization(_simulation)
for _offset_x_index, _offset_x in enumerate(OFFSETS_X):
_direction_fiber_densities = []
for _direction in ['left', 'right', 'up', 'down']:
_fiber_density = _fiber_densities[(_simulation, _offset_x, _direction)]
_normalized_fiber_density = compute_lib.z_score(
_fiber_density,
_normalization['average'],
_normalization['std']
)
_direction_fiber_densities.append(_normalized_fiber_density)
_simulations_fiber_densities[_offset_x_index].append(np.mean(_direction_fiber_densities))
return _simulations_fiber_densities
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 main():
_simulations = load.structured()
_simulations = filtering.by_categories(_simulations,
_is_single_cell=False,
_is_heterogeneity=HETEROGENEITY,
_is_low_connectivity=False,
_is_causality=False,
_is_dominant_passive=False,
_is_fibrin=False)
_simulations = filtering.by_pair_distances(_simulations, PAIR_DISTANCE)
print('Total simulations:', len(_simulations))
_simulations_by_distances = organize.by_pair_distance(_simulations)
for _distance in _simulations_by_distances:
print('Distance ', _distance, ', total simulations:',
len(_simulations_by_distances[_distance]))
_fiber_densities = compute_fiber_densities(_simulations)
_heatmap_fiber = []
_heatmap_fiber_change = []
for _simulation in _simulations:
_simulation_normalization = load.normalization(_simulation)
for _cell_id in ['left_cell', 'right_cell']:
_cell_fiber_densities = _fiber_densities[(_simulation, _cell_id)]
# not enough data
if len(_cell_fiber_densities) < DERIVATIVE + 1:
continue
_z_score_fiber_density = compute_lib.z_score_array(
_array=_cell_fiber_densities,
_average=_simulation_normalization['average'],
_std=_simulation_normalization['std'])
_heatmap_fiber += _z_score_fiber_density[DERIVATIVE:]
_heatmap_fiber_change += compute_lib.derivative(
_z_score_fiber_density, _n=DERIVATIVE)
print(
compute_lib.correlation(_heatmap_fiber,
_heatmap_fiber_change,
_with_p_value=True))
if PLOT:
_y_shape = int(round((Y_LABELS_END - Y_LABELS_START) * Y_BINS))
_x_shape = int(round((X_LABELS_END - X_LABELS_START) * X_BINS))
_total_points = 0
_z_array = np.zeros(shape=(_y_shape, _x_shape))
for _y, _x in zip(_heatmap_fiber_change, _heatmap_fiber):
_y_rounded, _x_rounded = int(round(_y * Y_BINS)), int(
round(_x * X_BINS))
_y_index, _x_index = int(_y_rounded - Y_LABELS_START *
Y_BINS), int(_x_rounded -
X_LABELS_START * X_BINS)
if 0 <= _y_index < _z_array.shape[
0] and 0 <= _x_index < _z_array.shape[1]:
_z_array[_y_index][_x_index] += 1
_total_points += 1
_z_array = _z_array / _total_points
if not CONDITIONAL_NORMALIZATION:
_z_array[_z_array == 0] = None
else:
_z_array_plot = np.zeros(shape=np.array(_z_array).shape)
for _fiber_index, _fiber_density_z_score in enumerate(_z_array):
_sum = np.sum(_fiber_density_z_score)
for _change_index, _change_z_score in enumerate(
_fiber_density_z_score):
_z_array_plot[_fiber_index][_change_index] = (
_change_z_score / _sum) if _sum != 0 else 0
_z_array_plot[_z_array_plot == 0] = None
_fig = go.Figure(
data=go.Heatmap(x=np.arange(start=X_LABELS_START,
stop=X_LABELS_END,
step=1 / X_BINS),
y=np.arange(start=Y_LABELS_START,
stop=Y_LABELS_END,
step=1 / Y_BINS),
z=_z_array,
colorscale='Viridis',
colorbar={
'tickmode': 'array',
'tickvals': [0, 0.025, 0.05],
'ticktext': ['0', 'Fraction', '0.05'],
'tickangle': -90
},
zmin=Z_MIN,
zmax=Z_MAX[CONDITIONAL_NORMALIZATION]),
layout={
'xaxis': {
'title': 'fiber densities z-score',
'zeroline': False
},
'yaxis': {
'title': 'Change in fiber<br>density (z-score)',
'zeroline': False
},
'shapes': [{
'type': 'line',
'x0': X_LABELS_START,
'y0': Y_LABELS_START,
'x1': X_LABELS_END,
'y1': Y_LABELS_START,
'line': {
'color': 'black',
'width': 2
}
}, {
'type': 'line',
'x0': X_LABELS_START,
'y0': Y_LABELS_START,
'x1': X_LABELS_START,
'y1': Y_LABELS_END,
'line': {
'color': 'black',
'width': 2
}
}]
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_direction_' + DIRECTION)
def main():
_simulations = load.structured()
_simulations = filtering.by_time_points_amount(_simulations,
_time_points=TIME_POINTS)
_simulations = filtering.by_categories(_simulations,
_is_single_cell=False,
_is_heterogeneity=False,
_is_low_connectivity=False,
_is_causality=False,
_is_dominant_passive=False,
_is_fibrin=False)
_simulations = filtering.by_pair_distance(_simulations,
_distance=PAIR_DISTANCE)
print('Total simulations:', len(_simulations))
_fiber_densities = compute_fiber_densities(_simulations)
_y_arrays = [[] for _i in DERIVATIVES]
for _simulation in tqdm(_simulations, desc='Simulations loop'):
_normalization = load.normalization(_simulation)
for _cell_id in ['left_cell', 'right_cell']:
_cell_fiber_densities = _fiber_densities[(_simulation, _cell_id)]
_cell_fiber_densities_normalized = compute_lib.z_score_array(
_array=_cell_fiber_densities,
_average=_normalization['average'],
_std=_normalization['std'])
for _derivative_index, _derivative in enumerate(DERIVATIVES):
_y_arrays[_derivative_index].append(
compute_lib.derivative(_cell_fiber_densities_normalized,
_n=_derivative))
print('Total cells:', len(_y_arrays[0]))
# plot
for _derivative, _y_array in zip(DERIVATIVES, _y_arrays):
_fig = go.Figure(
data=go.Scatter(x=list(range(TIME_POINTS))[::TIME_POINTS_STEP],
y=np.mean(_y_array, axis=0)[::TIME_POINTS_STEP],
name='Fiber density (z-score)',
error_y={
'type':
'data',
'array':
np.std(_y_array, axis=0)[::TIME_POINTS_STEP],
'thickness':
1,
'color':
DERIVATIVES_COLORS[_derivative]
},
mode='markers',
marker={
'size': 15,
'color': DERIVATIVES_COLORS[_derivative]
},
opacity=0.7),
layout={
'xaxis': {
'title': 'Cell contraction (%)',
'zeroline': False
},
'yaxis': {
'title': 'Fiber density (z-score)',
'zeroline': False,
'tickmode': 'array',
'tickvals': DERIVATIVES_Y_TICKVALS[_derivative]
},
'shapes': [{
'type':
'line',
'x0':
-2,
'y0': (np.mean(_y_array, axis=0)[0] -
np.std(_y_array, axis=0)[0]) * 1.5,
'x1':
53,
'y1': (np.mean(_y_array, axis=0)[0] -
np.std(_y_array, axis=0)[0]) * 1.5,
'line': {
'color': 'black',
'width': 2
}
}, {
'type':
'line',
'x0':
-2,
'y0': (np.mean(_y_array, axis=0)[0] -
np.std(_y_array, axis=0)[0]) * 1.5,
'x1':
-2,
'y1': (np.mean(_y_array, axis=0)[-1] +
np.std(_y_array, axis=0)[-1]),
'line': {
'color': 'black',
'width': 2
}
}]
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_derivative_' + str(_derivative))
def main():
# simulations
print('Simulations')
_simulations = simulations_load.structured()
_simulations = simulations_filtering.by_time_points_amount(
_simulations, _time_points=SIMULATIONS_TIME_POINTS)
_simulations = simulations_filtering.by_categories(
_simulations,
_is_single_cell=True,
_is_heterogeneity=False,
_is_low_connectivity=False,
_is_causality=False,
_is_dominant_passive=False,
_is_fibrin=False)
print('Total simulations:', len(_simulations))
_fiber_densities = compute_simulations_fiber_densities(_simulations)
_simulations_fiber_densities = [[]
for _i in range(SIMULATIONS_TIME_POINTS)]
for _simulation in tqdm(_simulations, desc='Simulations Loop'):
_normalization = simulations_load.normalization(_simulation)
for _time_frame in range(SIMULATIONS_TIME_POINTS):
_direction_fiber_densities = []
for _direction in ['left', 'right', 'up', 'down']:
_fiber_density = _fiber_densities[(_simulation,
_direction)][_time_frame]
_normalized_fiber_density = compute_lib.z_score(
_fiber_density, _normalization['average'],
_normalization['std'])
_direction_fiber_densities.append(_normalized_fiber_density)
_simulations_fiber_densities[_time_frame].append(
np.mean(_direction_fiber_densities))
print('Total simulations cells:', len(_simulations_fiber_densities[0]))
# experiments
print('Experiments')
_experiments_fiber_densities = compute_experiments_fiber_densities()
# plot
_fig = go.Figure(
data=[
go.Scatter(
x=list(range(SIMULATIONS_TIME_POINTS))[::SIMULATIONS_STEP],
y=[np.mean(_array) for _array in _simulations_fiber_densities
][::SIMULATIONS_STEP],
name='Simulations',
error_y={
'type':
'data',
'array': [
np.std(_array)
for _array in _simulations_fiber_densities
][::SIMULATIONS_STEP],
'thickness':
1,
'color':
'#005b96'
},
mode='markers',
marker={
'size': 15,
'color': '#005b96'
},
opacity=0.7),
go.Scatter(
x=np.array(range(EXPERIMENTS_TIME_FRAMES)) *
EXPERIMENTS_TEMPORAL_RESOLUTION,
xaxis='x2',
y=[np.mean(_array) for _array in _experiments_fiber_densities],
name='Experiments',
error_y={
'type':
'data',
'array': [
np.std(_array)
for _array in _experiments_fiber_densities
],
'thickness':
1,
'color':
'#ea8500'
},
mode='markers',
marker={
'size': 15,
'color': '#ea8500'
},
opacity=0.7)
],
layout={
'xaxis': {
'title': 'Cell contraction (%)',
'titlefont': {
'color': '#005b96'
},
'tickfont': {
'color': '#005b96'
},
'zeroline': False
},
'xaxis2': {
'title': 'Time (minutes)',
'titlefont': {
'color': '#ea8500'
},
'tickfont': {
'color': '#ea8500'
},
# 'anchor': 'free',
'overlaying': 'x',
'side': 'bottom',
'showgrid': False,
'zeroline': False
},
'yaxis': {
'title': 'Fiber density (z-score)',
# 'domain': [0.3, 1],
'range': [-1.7, 14],
'zeroline': False,
'tickmode': 'array',
'tickvals': [0, 4, 8, 12]
},
'legend': {
'xanchor': 'left',
'x': 0.1,
'yanchor': 'top',
'bordercolor': 'black',
'borderwidth': 2
},
'shapes': [{
'type': 'line',
'x0': -2,
'y0': -1.5,
'x1': 53,
'y1': -1.5,
'line': {
'color': 'black',
'width': 2
}
}, {
'type': 'line',
'x0': -2,
'y0': -1.5,
'x1': -2,
'y1': 14,
'line': {
'color': 'black',
'width': 2
}
}]
})
save.to_html(_fig=_fig,
_path=os.path.join(paths_lib.PLOTS, save.get_module_name()),
_filename='plot')