def compute_single_cell_mean(_experiment, _series_id, _cell_tuples,
_windows_dictionary, _fiber_densities):
_cell_fiber_densities = []
for _time_frame in range(compute.density_time_frame(_experiment)):
_time_frame_fiber_densities = []
for _cell_tuple in _cell_tuples:
_, _, _group = _cell_tuple
for _direction in ['left', 'right']:
_window_tuple = _windows_dictionary[(_experiment, _series_id,
_group,
_direction)][_time_frame]
_fiber_density = _fiber_densities[_window_tuple]
if not OUT_OF_BOUNDARIES and _fiber_density[1]:
continue
_time_frame_fiber_densities.append(_fiber_density)
_cell_fiber_densities.append(np.mean(_time_frame_fiber_densities))
return _cell_fiber_densities
def compute_matched_fiber(_tuples):
_tuples = filtering.by_time_frames_amount(
_tuples, compute.density_time_frame(_tuples[0]))
_tuples = filtering.by_pair_distance_range(_tuples, PAIR_DISTANCE_RANGE)
_experiments_matched = organize.by_matched_real_and_fake(_tuples)
print('Total matched pairs:', len(_experiments_matched))
_max_offsets_x = []
_arguments = []
for _matched_tuple in _experiments_matched:
for _tuple in _matched_tuple:
_experiment, _series_id, _group = _tuple
_time_frame = compute.density_time_frame(_experiment)
_pair_distance = compute.pair_distance_in_cell_size_time_frame(
_experiment, _series_id, _group, _time_frame - 1)
_offsets_x = \
np.arange(start=0, stop=_pair_distance / 2 - 0.5 - QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
step=OFFSET_X_STEP)
if len(_offsets_x) > len(_max_offsets_x):
_max_offsets_x = _offsets_x
for _offset_x in _offsets_x:
for _cell_id in ['left_cell', 'right_cell']:
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x':
QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y':
QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z':
QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': _offset_x,
'offset_y': OFFSET_Y,
'offset_z': OFFSET_Z,
'cell_id': _cell_id,
'direction': 'inside',
'time_point': _time_frame - 1
})
_windows_dictionary, _windows_to_compute = \
compute.windows(_arguments, _keys=['experiment', 'series_id', 'group', 'offset_x', 'cell_id'])
_fiber_densities = compute.fiber_densities(_windows_to_compute)
_experiments_fiber_densities_real = [[]
for _i in range(len(_max_offsets_x))]
_experiments_fiber_densities_fake = [[]
for _i in range(len(_max_offsets_x))]
for _tuple in tqdm(_experiments_matched, desc='Experiments loop'):
_tuple_real, _tuple_fake = _tuple
_experiment_real, _series_id_real, _group_real = _tuple_real
_experiment_fake, _series_id_fake, _group_fake = _tuple_fake
for _offset_x_index, _offset_x in enumerate(_max_offsets_x):
for _cell_id in ['left_cell', 'right_cell']:
if (_experiment_real, _series_id_real, _group_real, _offset_x, _cell_id) and \
(_experiment_fake, _series_id_fake, _group_fake, _offset_x, _cell_id) in _windows_dictionary:
_normalization = load.normalization_series_file_data(
_experiment_real, _series_id_real)
_window_tuple_real = \
_windows_dictionary[(_experiment_real, _series_id_real, _group_real, _offset_x, _cell_id)][0]
_fiber_density_real = _fiber_densities[_window_tuple_real]
_window_tuple_fake = \
_windows_dictionary[(_experiment_fake, _series_id_fake, _group_fake, _offset_x, _cell_id)][0]
_fiber_density_fake = _fiber_densities[_window_tuple_fake]
if not OUT_OF_BOUNDARIES and (_fiber_density_real[1]
or _fiber_density_fake[1]):
continue
_normalized_fiber_density_real = compute_lib.z_score(
_x=_fiber_density_real[0],
_average=_normalization['average'],
_std=_normalization['std'])
_normalized_fiber_density_fake = compute_lib.z_score(
_x=_fiber_density_fake[0],
_average=_normalization['average'],
_std=_normalization['std'])
if not np.isnan(
_normalized_fiber_density_real) and not np.isnan(
_normalized_fiber_density_fake):
_experiments_fiber_densities_real[
_offset_x_index].append(
_normalized_fiber_density_real)
_experiments_fiber_densities_fake[
_offset_x_index].append(
_normalized_fiber_density_fake)
return _experiments_fiber_densities_real, _experiments_fiber_densities_fake, _max_offsets_x
def compute_experiments_data():
_experiments = all_experiments()
_experiments = experiments_filtering.by_categories(
_experiments=_experiments,
_is_single_cell=False,
_is_high_temporal_resolution=False,
_is_bleb=False,
_is_dead_dead=False,
_is_live_dead=False,
_is_bead=False,
_is_metastasis=False
)
_tuples = experiments_load.experiments_groups_as_tuples(_experiments)
_tuples = experiments_filtering.by_time_frames_amount(_tuples, EXPERIMENTS_TIME_FRAMES)
_tuples = experiments_filtering.by_real_pairs(_tuples)
_tuples = experiments_filtering.by_pair_distance_range(_tuples, PAIR_DISTANCE_RANGE)
_tuples = experiments_filtering.by_band(_tuples)
print('Total tuples:', len(_tuples))
_arguments = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_time_frame = experiments_compute.density_time_frame(_experiment)
for _cell_id in ['left_cell', 'right_cell']:
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x': experiments_config.QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y': experiments_config.QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z': experiments_config.QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': OFFSET_X,
'offset_y': OFFSET_Y,
'offset_z': OFFSET_Z,
'cell_id': _cell_id,
'direction': 'inside',
'time_points': _time_frame
})
_windows_dictionary, _windows_to_compute = \
experiments_compute.windows(_arguments, _keys=['experiment', 'series_id', 'group', 'cell_id'])
_fiber_densities = experiments_compute.fiber_densities(_windows_to_compute)
_experiments_fiber_densities = [[] for _i in range(EXPERIMENTS_TIME_FRAMES)]
for _tuple in tqdm(_tuples, desc='Experiments loop'):
_experiment, _series_id, _group = _tuple
_normalization = experiments_load.normalization_series_file_data(_experiment, _series_id)
for _time_frame in range(EXPERIMENTS_TIME_FRAMES):
for _cell_id in ['left_cell', 'right_cell']:
_window_tuple = _windows_dictionary[(_experiment, _series_id, _group, _cell_id)][_time_frame]
_fiber_density = _fiber_densities[_window_tuple]
if not OUT_OF_BOUNDARIES and _fiber_density[1]:
continue
_normalized_fiber_density = compute_lib.z_score(
_x=_fiber_density[0],
_average=_normalization['average'],
_std=_normalization['std']
)
if not np.isnan(_normalized_fiber_density):
_experiments_fiber_densities[_time_frame].append(_normalized_fiber_density)
print('Total experiments pairs:', len(_experiments_fiber_densities[0]))
return _experiments_fiber_densities
def main():
_experiments = all_experiments()
_experiments = filtering.by_categories(_experiments=_experiments,
_is_single_cell=False,
_is_high_temporal_resolution=False,
_is_bleb=False,
_is_dead_dead=False,
_is_live_dead=False,
_is_bead=False,
_is_metastasis=False)
_tuples = load.experiments_groups_as_tuples(_experiments)
_tuples = filtering.by_time_frames_amount(
_tuples, compute.density_time_frame(_experiments[0]))
_tuples = filtering.by_real_pairs(_tuples)
_tuples = filtering.by_band(_tuples)
_tuples = filtering.by_pair_distance_range(_tuples, PAIR_DISTANCE_RANGE)
print('Total tuples:', len(_tuples))
_arguments = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_latest_time_frame = compute.latest_time_frame_before_overlapping(
_experiment, _series_id, _group, OFFSET_X)
for _cell_id in ['left_cell', 'right_cell']:
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x': QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y': QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z': QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': OFFSET_X,
'offset_y': OFFSET_Y,
'offset_z': OFFSET_Z,
'cell_id': _cell_id,
'direction': 'inside',
'time_points': _latest_time_frame
})
_windows_dictionary, _windows_to_compute = \
compute.windows(_arguments, _keys=['experiment', 'series_id', 'group', 'cell_id'])
_fiber_densities = compute.fiber_densities(_windows_to_compute,
_subtract_border=True)
_experiments_fiber_densities = {
_key:
[_fiber_densities[_tuple] for _tuple in _windows_dictionary[_key]]
for _key in _windows_dictionary
}
_kpss_y_arrays = [[] for _i in DERIVATIVES]
_adf_y_arrays = [[] for _i in DERIVATIVES]
for _tuple in tqdm(_tuples, desc='Experiments loop'):
_experiment, _series_id, _group = _tuple
_properties = load.group_properties(_experiment, _series_id, _group)
_left_cell_fiber_densities = _experiments_fiber_densities[(
_experiment, _series_id, _group, 'left_cell')]
_left_cell_fiber_densities = compute.remove_blacklist(
_experiment, _series_id, _properties['cells_ids']['left_cell'],
_left_cell_fiber_densities)
_right_cell_fiber_densities = _experiments_fiber_densities[(
_experiment, _series_id, _group, 'right_cell')]
_right_cell_fiber_densities = compute.remove_blacklist(
_experiment, _series_id, _properties['cells_ids']['right_cell'],
_right_cell_fiber_densities)
if not OUT_OF_BOUNDARIES:
_left_cell_fiber_densities = \
compute.longest_fiber_densities_ascending_sequence(_left_cell_fiber_densities)
_right_cell_fiber_densities = \
compute.longest_fiber_densities_ascending_sequence(_right_cell_fiber_densities)
else:
_left_cell_fiber_densities = [
_fiber_density[0]
for _fiber_density in _left_cell_fiber_densities
]
_right_cell_fiber_densities = [
_fiber_density[0]
for _fiber_density in _right_cell_fiber_densities
]
# ignore small arrays
_minimum_time_frames_for_correlation = compute.minimum_time_frames_for_correlation(
_experiment)
if len(_left_cell_fiber_densities) < _minimum_time_frames_for_correlation or \
len(_right_cell_fiber_densities) < _minimum_time_frames_for_correlation:
continue
for _derivative_index, _derivative in enumerate(DERIVATIVES):
for _cell_fiber_densities in [
_left_cell_fiber_densities, _right_cell_fiber_densities
]:
_cell_fiber_densities_derivative = compute_lib.derivative(
_cell_fiber_densities, _n=_derivative)
_, _kpss_p_value, _, _ = kpss(_cell_fiber_densities_derivative,
nlags='legacy')
_kpss_y_arrays[_derivative_index].append(_kpss_p_value)
_, _adf_p_value, _, _, _, _ = adfuller(
_cell_fiber_densities_derivative)
_adf_y_arrays[_derivative_index].append(_adf_p_value)
print('Total pairs:', len(_kpss_y_arrays[0]) / 2)
# print results
print('KPSS:')
for _derivative_index, _derivative in enumerate(DERIVATIVES):
_stationary_count = len([
_value for _value in _kpss_y_arrays[_derivative_index]
if _value > 0.05
])
print(
'Derivative:', _derivative, 'Stationary:',
str(_stationary_count / len(_kpss_y_arrays[_derivative_index]) *
100) + '%')
print('ADF:')
for _derivative_index, _derivative in enumerate(DERIVATIVES):
_stationary_count = len([
_value for _value in _adf_y_arrays[_derivative_index]
if _value < 0.05
])
print(
'Derivative:', _derivative, 'Stationary:',
str(_stationary_count / len(_adf_y_arrays[_derivative_index]) *
100) + '%')
# plot
_colors_array = config.colors(3)
for _test_name, _y_title, _y_tickvals, _p_value_line, _y_arrays in \
zip(
['kpss', 'adf'],
['KPSS test p-value', 'ADF test p-value'],
[[0.05, 0.1], [0.05, 1]],
[0.05, 0.05],
[_kpss_y_arrays, _adf_y_arrays]
):
_fig = go.Figure(data=[
go.Box(y=_y,
name=_derivative,
boxpoints='all',
jitter=1,
pointpos=0,
line={'width': 1},
fillcolor='white',
marker={
'size': 10,
'color': _color
},
opacity=0.7,
showlegend=False) for _y, _derivative, _color in zip(
_y_arrays, DERIVATIVES_TEXT, _colors_array)
],
layout={
'xaxis': {
'title': 'Fiber density derivative',
'zeroline': False
},
'yaxis': {
'title': _y_title,
'zeroline': False,
'tickmode': 'array',
'tickvals': _y_tickvals
},
'shapes': [{
'type': 'line',
'x0': DERIVATIVES[0] - 0.75,
'y0': _p_value_line,
'x1': DERIVATIVES[-1] + 0.75,
'y1': _p_value_line,
'line': {
'color': 'red',
'width': 2,
'dash': 'dash'
}
}]
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_' + _test_name)
def main():
_experiments = all_experiments()
_experiments = filtering.by_categories(_experiments=_experiments,
_is_single_cell=False,
_is_high_temporal_resolution=False,
_is_bleb=False,
_is_dead_dead=False,
_is_live_dead=False,
_is_bead=False,
_is_metastasis=False)
_tuples = load.experiments_groups_as_tuples(_experiments)
_tuples = filtering.by_time_frames_amount(
_tuples, compute.density_time_frame(_experiments[0]))
_tuples = filtering.by_real_pairs(_tuples)
_tuples = filtering.by_band(_tuples)
_max_offsets_x = []
_arguments = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_time_frame = compute.minimum_time_frames_for_correlation(_experiment)
_pair_distance = \
compute.pair_distance_in_cell_size_time_frame(_experiment, _series_id, _group, _time_frame=_time_frame - 1)
_offsets_x = \
np.arange(start=0, stop=_pair_distance / 2 - 0.5 - QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
step=OFFSET_X_STEP)
if len(_offsets_x) > len(_max_offsets_x):
_max_offsets_x = _offsets_x
for _offset_x in _offsets_x:
for _cell_id in ['left_cell', 'right_cell']:
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x': QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y': QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z': QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': _offset_x,
'offset_y': OFFSET_Y,
'offset_z': OFFSET_Z,
'cell_id': _cell_id,
'direction': 'inside',
'time_point': _time_frame - 1
})
_windows_dictionary, _windows_to_compute = \
compute.windows(_arguments, _keys=['experiment', 'series_id', 'group', 'offset_x', 'cell_id'])
_fiber_densities = compute.fiber_densities(_windows_to_compute)
_x_array = []
_y_array = []
_n_array = []
_p_value_array = []
for _offset_x in _max_offsets_x:
_pair_distances = []
_z_scores = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
for _cell_id in ['left_cell', 'right_cell']:
if (_experiment, _series_id, _group, _offset_x,
_cell_id) in _windows_dictionary:
_normalization = load.normalization_series_file_data(
_experiment, _series_id)
_window_tuple = _windows_dictionary[(_experiment,
_series_id, _group,
_offset_x,
_cell_id)][0]
_fiber_density = _fiber_densities[_window_tuple]
if not OUT_OF_BOUNDARIES and _fiber_density[1]:
continue
_normalized_fiber_density = compute_lib.z_score(
_x=_fiber_density[0],
_average=_normalization['average'],
_std=_normalization['std'])
if not np.isnan(_normalized_fiber_density):
_pair_distances.append(
compute.pair_distance_in_cell_size_time_frame(
_experiment, _series_id, _group,
_time_frame=0))
_z_scores.append(_normalized_fiber_density)
if len(_pair_distances) > 2:
_x_array.append(round(_offset_x, 1))
_correlation = compute_lib.correlation(_pair_distances,
_z_scores,
_with_p_value=True)
_y_array.append(round(_correlation[0], 2))
_n_array.append(len(_pair_distances))
_p_value_array.append(round(_correlation[1], 2))
print('Pearson:')
print(compute_lib.correlation(_x_array, _y_array, _with_p_value=True))
# plot
_significant_x_array = [
_x for _x, _p_value in zip(_x_array, _p_value_array) if _p_value < 0.05
]
_fig = go.Figure(data=[
go.Scatter(x=_x_array,
y=_y_array,
mode='markers',
marker={
'size': 15,
'color': 'black'
},
showlegend=False),
go.Scatter(x=_significant_x_array,
y=[-0.79] * len(_significant_x_array),
mode='text',
text='*',
textfont={'color': 'red'},
showlegend=False)
],
layout={
'xaxis': {
'title': 'Window distance (cell diameter)',
'zeroline': False
},
'yaxis': {
'title':
'Correlation:<br>pair distance vs. fiber density',
'zeroline': False,
'range': [-0.82, 0.3],
'tickmode': 'array',
'tickvals': [-0.75, -0.25, 0.25]
},
'shapes': [{
'type': 'line',
'x0': -0.2,
'y0': -0.8,
'x1': 3.2,
'y1': -0.8,
'line': {
'color': 'black',
'width': 2
}
}, {
'type': 'line',
'x0': -0.2,
'y0': -0.8,
'x1': -0.2,
'y1': 0.3,
'line': {
'color': 'black',
'width': 2
}
}]
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot')
# table
print('Window distance (cell diameter)',
'Correlation: pair distance vs. fiber density',
'N',
'P-value',
sep='\t')
for _x, _y, _n, _p_value in zip(_x_array, _y_array, _n_array,
_p_value_array):
print(_x, _y, _n, _p_value, sep='\t')
def main():
_experiments = all_experiments()
_experiments = filtering.by_categories(_experiments=_experiments,
_is_single_cell=True,
_is_high_temporal_resolution=False,
_is_bleb=False,
_is_dead_dead=False,
_is_live_dead=False,
_is_bead=False,
_is_metastasis=False)
_tuples = load.experiments_groups_as_tuples(_experiments)
_tuples = filtering.by_time_frames_amount(
_tuples, compute.density_time_frame(_experiments[0]))
_tuples = filtering.by_main_cell(_tuples)
_arguments = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_time_frame = compute.density_time_frame(_experiment)
for _direction in ['left', 'right']:
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x': QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y': QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z': QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': OFFSET_X,
'offset_y': OFFSET_Y,
'offset_z': OFFSET_Z,
'cell_id': 'cell',
'direction': _direction,
'time_points': _time_frame
})
_windows_dictionary, _windows_to_compute = \
compute.windows(_arguments, _keys=['experiment', 'series_id', 'group', 'direction'])
_fiber_densities = compute.fiber_densities(_windows_to_compute,
_subtract_border=True)
_tuples = organize.by_single_cell_id(_tuples)
print('Total experiments:', len(_tuples))
_kpss_y_arrays = [[] for _i in DERIVATIVES]
_adf_y_arrays = [[] for _i in DERIVATIVES]
for _tuple in tqdm(_tuples, desc='Experiments loop'):
_experiment, _series_id, _cell_id = _tuple
_cell_fiber_densities = compute_single_cell_mean(
_experiment=_experiment,
_series_id=_series_id,
_cell_tuples=_tuples[_tuple],
_windows_dictionary=_windows_dictionary,
_fiber_densities=_fiber_densities)
for _derivative_index, _derivative in enumerate(DERIVATIVES):
_cell_fiber_densities_derivative = compute_lib.derivative(
_cell_fiber_densities, _n=_derivative)
_, _kpss_p_value, _, _ = kpss(_cell_fiber_densities_derivative,
nlags='legacy')
_kpss_y_arrays[_derivative_index].append(_kpss_p_value)
_, _adf_p_value, _, _, _, _ = adfuller(
_cell_fiber_densities_derivative)
_adf_y_arrays[_derivative_index].append(_adf_p_value)
print('Total cells:', len(_kpss_y_arrays[0]))
# print results
print('KPSS:')
for _derivative_index, _derivative in enumerate(DERIVATIVES):
_stationary_count = len([
_value for _value in _kpss_y_arrays[_derivative_index]
if _value > 0.05
])
print(
'Derivative:', _derivative, 'Stationary:',
str(_stationary_count / len(_kpss_y_arrays[_derivative_index]) *
100) + '%')
print('ADF:')
for _derivative_index, _derivative in enumerate(DERIVATIVES):
_stationary_count = len([
_value for _value in _adf_y_arrays[_derivative_index]
if _value < 0.05
])
print(
'Derivative:', _derivative, 'Stationary:',
str(_stationary_count / len(_adf_y_arrays[_derivative_index]) *
100) + '%')
# plot
_colors_array = config.colors(3)
for _test_name, _y_title, _y_tickvals, _p_value_line, _y_arrays in \
zip(
['kpss', 'adf'],
['KPSS test p-value', 'ADF test p-value'],
[[0.05, 0.1], [0.05, 1]],
[0.05, 0.05],
[_kpss_y_arrays, _adf_y_arrays]
):
_fig = go.Figure(data=[
go.Box(y=_y,
name=_derivative,
boxpoints='all',
jitter=1,
pointpos=0,
line={'width': 1},
fillcolor='white',
marker={
'size': 10,
'color': _color
},
opacity=0.7,
showlegend=False) for _y, _derivative, _color in zip(
_y_arrays, DERIVATIVES_TEXT, _colors_array)
],
layout={
'xaxis': {
'title': 'Fiber density derivative',
'zeroline': False
},
'yaxis': {
'title': _y_title,
'zeroline': False,
'tickmode': 'array',
'tickvals': _y_tickvals
},
'shapes': [{
'type': 'line',
'x0': DERIVATIVES[0] - 0.75,
'y0': _p_value_line,
'x1': DERIVATIVES[-1] + 0.75,
'y1': _p_value_line,
'line': {
'color': 'red',
'width': 2,
'dash': 'dash'
}
}]
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_' + _test_name)
def main():
_experiments = all_experiments()
_experiments = filtering.by_categories(_experiments=_experiments,
_is_single_cell=True,
_is_high_temporal_resolution=False,
_is_bleb=False,
_is_dead_dead=False,
_is_live_dead=False,
_is_bead=False,
_is_metastasis=False)
_tuples = load.experiments_groups_as_tuples(_experiments)
_tuples = filtering.by_time_frames_amount(
_tuples, compute.density_time_frame(_experiments[0]))
_tuples = filtering.by_main_cell(_tuples)
_arguments = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_time_frame = compute.density_time_frame(_experiment)
for _direction in ['left', 'right']:
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x': QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y': QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z': QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': OFFSET_X,
'offset_y': OFFSET_Y,
'offset_z': OFFSET_Z,
'cell_id': 'cell',
'direction': _direction,
'time_points': _time_frame
})
_windows_dictionary, _windows_to_compute = \
compute.windows(_arguments, _keys=['experiment', 'series_id', 'group', 'direction'])
_fiber_densities = compute.fiber_densities(_windows_to_compute,
_subtract_border=True)
_tuples = organize.by_single_cell_id(_tuples)
print('Total tuples:', len(_tuples))
_experiments_ids = list(_tuples.keys())
_y_arrays = [[] for _i in DERIVATIVES]
for _index_1 in tqdm(range(len(_experiments_ids)), desc='Main loop'):
_tuple_1 = _experiments_ids[_index_1]
_experiment_1, _series_id_1, _cell_id_1 = _tuple_1
_fiber_densities_1 = compute_single_cell_mean(
_experiment=_experiment_1,
_series_id=_series_id_1,
_cell_tuples=_tuples[_tuple_1],
_windows_dictionary=_windows_dictionary,
_fiber_densities=_fiber_densities)
for _index_2 in range(_index_1 + 1, len(_experiments_ids)):
_tuple_2 = _experiments_ids[_index_2]
_experiment_2, _series_id_2, _cell_id_2 = _tuple_2
_fiber_densities_2 = compute_single_cell_mean(
_experiment=_experiment_2,
_series_id=_series_id_2,
_cell_tuples=_tuples[_tuple_2],
_windows_dictionary=_windows_dictionary,
_fiber_densities=_fiber_densities)
for _derivative_index, _derivative in enumerate(DERIVATIVES):
_y_arrays[_derivative_index].append(
compute_lib.correlation(
compute_lib.derivative(_fiber_densities_1,
_n=_derivative),
compute_lib.derivative(_fiber_densities_2,
_n=_derivative)))
print('Total points:', len(_y_arrays[0]))
print('Wilcoxon around the zero')
for _y_array, _derivative in zip(_y_arrays, DERIVATIVES):
print('Derivative:', _derivative, wilcoxon(_y_array))
# plot
_colors_array = config.colors(3)
_fig = go.Figure(data=[
go.Box(y=_y,
name=_derivative,
boxpoints='all',
jitter=1,
pointpos=0,
line={'width': 1},
fillcolor='white',
marker={
'size': 10,
'color': _color
},
opacity=0.7,
showlegend=False) for _y, _derivative, _color in zip(
_y_arrays, DERIVATIVES_TEXT, _colors_array)
],
layout={
'xaxis': {
'title': 'Fiber density derivative',
'zeroline': False
},
'yaxis': {
'title': 'Correlation',
'range': [-1, 1],
'zeroline': False,
'tickmode': 'array',
'tickvals': [-1, -0.5, 0, 0.5, 1]
}
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot')
def main():
_experiments = all_experiments()
_experiments = filtering.by_categories(_experiments=_experiments,
_is_single_cell=False,
_is_high_temporal_resolution=False,
_is_bleb=False,
_is_dead_dead=False,
_is_live_dead=False,
_is_bead=False,
_is_metastasis=False)
_tuples = load.experiments_groups_as_tuples(_experiments)
_tuples = filtering.by_time_frames_amount(
_tuples, compute.density_time_frame(_experiments[0]))
_tuples = filtering.by_real_pairs(_tuples)
_tuples = filtering.by_band(_tuples)
print('Total tuples:', len(_tuples))
_arguments = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_time_frame = compute.density_time_frame(_experiment)
_pair_distance = \
compute.pair_distance_in_cell_size_time_frame(_experiment, _series_id, _group, _time_frame=_time_frame - 1)
for _offset_x in OFFSETS_X:
if _pair_distance / 2 - 0.5 - QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER >= _offset_x:
for _cell_id in ['left_cell', 'right_cell']:
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x':
QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y':
QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z':
QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': _offset_x,
'offset_y': OFFSET_Y,
'offset_z': OFFSET_Z,
'cell_id': _cell_id,
'direction': 'inside',
'time_point': _time_frame - 1
})
_windows_dictionary, _windows_to_compute = \
compute.windows(_arguments, _keys=['experiment', 'series_id', 'group', 'cell_id', 'offset_x'])
_fiber_densities = compute.fiber_densities(_windows_to_compute)
for _offset_x in OFFSETS_X:
_x_array = []
_y_array = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
for _cell_id in ['left_cell', 'right_cell']:
if (_experiment, _series_id, _group, _cell_id,
_offset_x) in _windows_dictionary:
_pair_distance = \
compute.pair_distance_in_cell_size_time_frame(_experiment, _series_id, _group, _time_frame=0)
_normalization = load.normalization_series_file_data(
_experiment, _series_id)
_window_tuple = _windows_dictionary[(_experiment,
_series_id, _group,
_cell_id,
_offset_x)][0]
_fiber_density = _fiber_densities[_window_tuple]
if not OUT_OF_BOUNDARIES and _fiber_density[1]:
continue
_normalized_fiber_density = compute_lib.z_score(
_x=_fiber_density[0],
_average=_normalization['average'],
_std=_normalization['std'])
if not np.isnan(_normalized_fiber_density):
_x_array.append(_pair_distance)
_y_array.append(_normalized_fiber_density)
print('Offset x (cell diameter):', _offset_x)
print('Total pairs:', len(_x_array))
print(compute_lib.correlation(_x_array, _y_array, _with_p_value=True))
# plot
_fig = go.Figure(data=go.Scatter(x=_x_array,
y=_y_array,
mode='markers',
marker={
'size': 15,
'color': 'black'
}),
layout={
'xaxis': {
'title': 'Pair distance (cell diameter)',
'zeroline': False
},
'yaxis': {
'title': 'Fiber density (z-score)',
'zeroline': False,
'range': [-2.2, 13],
'tickmode': 'array',
'tickvals': [0, 4, 8, 12]
},
'shapes': [{
'type': 'line',
'x0': 4.5,
'y0': -2,
'x1': 9.5,
'y1': -2,
'line': {
'color': 'black',
'width': 2
}
}, {
'type': 'line',
'x0': 4.5,
'y0': -2,
'x1': 4.5,
'y1': 13,
'line': {
'color': 'black',
'width': 2
}
}]
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_offset_x_' + str(_offset_x))
def compute_cell_pairs():
_x_array = []
_y_array = []
_names_array = []
for _distances_range in PAIR_DISTANCE_RANGES:
print('Pair distance range:', str(_distances_range))
_experiments = all_experiments()
_experiments = filtering.by_categories(
_experiments=_experiments,
_is_single_cell=False,
_is_high_temporal_resolution=False,
_is_bleb=False,
_is_dead_dead=False,
_is_live_dead=False,
_is_bead=False,
_is_metastasis=False)
_tuples = load.experiments_groups_as_tuples(_experiments)
_tuples = filtering.by_time_frames_amount(
_tuples, compute.density_time_frame(_experiments[0]))
_tuples = filtering.by_real_pairs(_tuples)
_tuples = filtering.by_pair_distance_range(_tuples, _distances_range)
_tuples = filtering.by_band(_tuples)
print('Total tuples:', len(_tuples))
_max_offsets_x = []
_arguments = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_time_frame = compute.minimum_time_frames_for_correlation(
_experiment)
_pair_distance = \
compute.pair_distance_in_cell_size_time_frame(_experiment, _series_id, _group, _time_frame - 1)
_offsets_x = np.arange(
start=0,
stop=_pair_distance / 2 - 0.5 -
QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
step=OFFSET_X_STEP)
if len(_offsets_x) > len(_max_offsets_x):
_max_offsets_x = _offsets_x
for _offset_x in _offsets_x:
for _cell_id in ['left_cell', 'right_cell']:
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x':
QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y':
QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z':
QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': _offset_x,
'offset_y': OFFSET_Y,
'offset_z': OFFSET_Z,
'cell_id': _cell_id,
'direction': 'inside',
'time_point': _time_frame - 1
})
_windows_dictionary, _windows_to_compute = \
compute.windows(_arguments, _keys=['experiment', 'series_id', 'group', 'offset_x', 'cell_id'])
_fiber_densities = compute.fiber_densities(_windows_to_compute)
_pair_distance_fiber_densities = [[]
for _i in range(len(_max_offsets_x))]
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
for _offset_x_index, _offset_x in enumerate(_max_offsets_x):
for _cell_id in ['left_cell', 'right_cell']:
if (_experiment, _series_id, _group, _offset_x,
_cell_id) in _windows_dictionary:
_normalization = load.normalization_series_file_data(
_experiment, _series_id)
_window_tuple = _windows_dictionary[(_experiment,
_series_id,
_group, _offset_x,
_cell_id)][0]
_fiber_density = _fiber_densities[_window_tuple]
if not OUT_OF_BOUNDARIES and _fiber_density[1]:
continue
_normalized_fiber_density = compute_lib.z_score(
_x=_fiber_density[0],
_average=_normalization['average'],
_std=_normalization['std'])
if not np.isnan(_normalized_fiber_density):
_pair_distance_fiber_densities[
_offset_x_index].append(
_normalized_fiber_density)
_x_array.append(_max_offsets_x)
_y_array.append(_pair_distance_fiber_densities)
_names_array.append('Pair distance ' + str(_distances_range[0]) + '-' +
str(_distances_range[1]))
return _names_array, _x_array, _y_array
def main(_band=None,
_high_temporal_resolution=True,
_tuples_to_mark=None,
_tuples_to_plot=None,
_plots=None):
if _plots is None:
_plots = ['whiteness', 'granger']
_experiments = all_experiments()
_experiments = filtering.by_categories(
_experiments=_experiments,
_is_single_cell=False,
_is_high_temporal_resolution=_high_temporal_resolution,
_is_bleb=False,
_is_dead_dead=False,
_is_live_dead=False,
_is_bead=False,
_is_metastasis=False)
_tuples = load.experiments_groups_as_tuples(_experiments)
_tuples = filtering.by_time_frames_amount(_tuples,
_time_frames=MINIMUM_TIME_FRAMES)
_tuples = filtering.by_pair_distance_range(
_tuples, _distance_range=PAIR_DISTANCE_RANGE)
_tuples = filtering.by_real_pairs(_tuples)
_tuples = filtering.by_band(_tuples, _band=_band)
print('Total tuples:', len(_tuples))
_arguments = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_latest_time_frame = compute.latest_time_frame_before_overlapping(
_experiment, _series_id, _group, OFFSET_X)
for _cell_id in ['left_cell', 'right_cell']:
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x': QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y': QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z': QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': OFFSET_X,
'offset_y': OFFSET_Y,
'offset_z': OFFSET_Z,
'cell_id': _cell_id,
'direction': 'inside',
'time_points': _latest_time_frame
})
_windows_dictionary, _windows_to_compute = compute.windows(
_arguments, _keys=['experiment', 'series_id', 'group', 'cell_id'])
_fiber_densities = compute.fiber_densities(_windows_to_compute,
_subtract_border=True)
_experiments_fiber_densities = {
_key:
[_fiber_densities[_tuple] for _tuple in _windows_dictionary[_key]]
for _key in _windows_dictionary
}
_n_pairs = 0
_n_pairs_with_band = 0
_whiteness_p_values = []
_n_passed_whiteness_with_band = 0
_granger_causality_p_values = []
_n_passed_granger_causality_with_band = 0
_correlations = []
_time_lag_correlations = []
_end_fiber_densities = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_left_cell_fiber_densities = \
_experiments_fiber_densities[(_experiment, _series_id, _group, 'left_cell')]
_right_cell_fiber_densities = \
_experiments_fiber_densities[(_experiment, _series_id, _group, 'right_cell')]
_properties = load.group_properties(_experiment, _series_id, _group)
_left_cell_fiber_densities = compute.remove_blacklist(
_experiment, _series_id, _properties['cells_ids']['left_cell'],
_left_cell_fiber_densities)
_right_cell_fiber_densities = compute.remove_blacklist(
_experiment, _series_id, _properties['cells_ids']['right_cell'],
_right_cell_fiber_densities)
_left_cell_fiber_densities_filtered, _right_cell_fiber_densities_filtered = \
compute.longest_same_indices_shared_in_borders_sub_array(
_left_cell_fiber_densities, _right_cell_fiber_densities)
# ignore small arrays
if len(_left_cell_fiber_densities_filtered) < MINIMUM_TIME_FRAMES:
continue
_n_pairs += 1
if _properties['band']:
_n_pairs_with_band += 1
_start_time_frame = 0
for _left in _left_cell_fiber_densities:
if _left[0] == _left_cell_fiber_densities_filtered[0]:
break
_start_time_frame += 1
# stationary test
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=InterpolationWarning)
# find derivative for stationary
for _derivative in range(10):
_left_cell_fiber_densities_derivative = \
compute_lib.derivative(_left_cell_fiber_densities_filtered, _n=_derivative)
_right_cell_fiber_densities_derivative = \
compute_lib.derivative(_right_cell_fiber_densities_filtered, _n=_derivative)
if ADF_TEST:
_, _left_cell_adf_p_value, _, _, _, _ = adfuller(
_left_cell_fiber_densities_derivative)
_, _right_cell_adf_p_value, _, _, _, _ = adfuller(
_right_cell_fiber_densities_derivative)
if _left_cell_adf_p_value > 0.05 or _right_cell_adf_p_value > 0.05:
continue
if KPSS_TEST:
_, _left_cell_kpss_p_value, _, _ = kpss(
_left_cell_fiber_densities_derivative, nlags='legacy')
_, _right_cell_kpss_p_value, _, _ = kpss(
_right_cell_fiber_densities_derivative, nlags='legacy')
if _left_cell_kpss_p_value < 0.05 or _right_cell_kpss_p_value < 0.05:
continue
# stationary
break
# causality
try:
_x = pd.DataFrame(data=[[_left_value, _right_value]
for _left_value, _right_value in zip(
_left_cell_fiber_densities_derivative,
_right_cell_fiber_densities_derivative)
],
columns=['left', 'right'])
# var model to retrieve lag
_var_model = VAR(_x)
_lag_order_results = _var_model.select_order()
_estimators_lags = [
_lag_order_results.aic, _lag_order_results.bic,
_lag_order_results.fpe, _lag_order_results.hqic
]
_min_estimator_lag = min(_estimators_lags)
# found a lag
if 0 < _min_estimator_lag <= MAXIMUM_LAG:
_var_model_results = _var_model.fit(maxlags=_min_estimator_lag,
ic=None)
_whiteness = _var_model_results.test_whiteness(
nlags=_min_estimator_lag + 1)
_whiteness_p_values.append(_whiteness.pvalue)
if _tuples_to_mark is not None and _tuple in _tuples_to_mark and _whiteness.pvalue > 0.05:
print(_tuple, 'marked whiteness p-value:',
_whiteness.pvalue)
# no autocorrelation in the residuals
if _whiteness.pvalue > 0.05:
if _properties['band']:
_n_passed_whiteness_with_band += 1
# time lag = 0
_correlation = compute_lib.correlation(
_left_cell_fiber_densities_derivative,
_right_cell_fiber_densities_derivative)
# if _correlation < 0.5:
# continue
# granger causality
for _caused, _causing in zip(['left', 'right'],
['right', 'left']):
_granger = _var_model_results.test_causality(
caused=_caused, causing=_causing)
_granger_causality_p_values.append(_granger.pvalue)
# time lag = 0
_correlations.append(_correlation)
# time lag = min estimator
if _causing == 'left':
_left_fiber_densities_time_lag = \
_left_cell_fiber_densities_derivative[:-_min_estimator_lag]
_right_fiber_densities_time_lag = \
_right_cell_fiber_densities_derivative[_min_estimator_lag:]
else:
_left_fiber_densities_time_lag = \
_left_cell_fiber_densities_derivative[_min_estimator_lag:]
_right_fiber_densities_time_lag = \
_right_cell_fiber_densities_derivative[:-_min_estimator_lag]
_time_lag_correlation = compute_lib.correlation(
_left_fiber_densities_time_lag,
_right_fiber_densities_time_lag)
_time_lag_correlations.append(_time_lag_correlation)
# end fiber density
_time_frame = compute.density_time_frame(_experiment)
if len(_left_cell_fiber_densities_filtered
) > _time_frame:
_end_fiber_density = \
(_left_cell_fiber_densities_filtered[_time_frame] +
_right_cell_fiber_densities_filtered[_time_frame]) / 2
else:
_end_fiber_density = \
(_left_cell_fiber_densities_filtered[-1] +
_right_cell_fiber_densities_filtered[-1]) / 2
_normalization = load.normalization_series_file_data(
_experiment, _series_id)
_normalized_fiber_density = compute_lib.z_score(
_end_fiber_density, _normalization['average'],
_normalization['std'])
_end_fiber_densities.append(_normalized_fiber_density)
# marking
if _tuples_to_mark is not None and _tuple in _tuples_to_mark and _granger.pvalue < 0.05:
print(_tuple, 'causing:', _causing,
'marked granger p-value:', _granger.pvalue)
if _granger.pvalue < 0.05:
if _properties['band']:
_n_passed_granger_causality_with_band += 1
_normality = _var_model_results.test_normality()
_inst_granger = _var_model_results.test_inst_causality(
causing=_causing)
print(
_tuple,
_causing.capitalize() + ' causes ' + _caused +
'!',
'time-points: ' +
str(len(
_left_cell_fiber_densities_derivative)),
'stationary derivative: ' + str(_derivative),
'band:' + str(_properties['band']),
'p-value: ' + str(round(_granger.pvalue, 4)),
'lag: ' + str(_min_estimator_lag),
'normality p-value: ' +
str(round(_normality.pvalue, 4)),
'inst p-value: ' +
str(round(_inst_granger.pvalue, 4)),
sep='\t')
# lag = 0
print('Time lag = 0 correlation:', _correlation)
# rest of lags
for _lag in range(1, _min_estimator_lag + 1):
if _causing == 'left':
_left_fiber_densities_time_lag = _left_cell_fiber_densities_derivative[:
-_lag]
_right_fiber_densities_time_lag = _right_cell_fiber_densities_derivative[
_lag:]
else:
_left_fiber_densities_time_lag = _left_cell_fiber_densities_derivative[
_lag:]
_right_fiber_densities_time_lag = _right_cell_fiber_densities_derivative[:
-_lag]
_correlation = compute_lib.correlation(
_left_fiber_densities_time_lag,
_right_fiber_densities_time_lag)
print(
'Time lag = ' + str(_lag) +
' correlation:', _correlation)
# plots
if _tuples_to_plot is not None and _tuple in _tuples_to_plot:
_y_arrays = [
_left_cell_fiber_densities_derivative,
_right_cell_fiber_densities_derivative
]
_names_array = ['Left cell', 'Right cell']
_colors_array = config.colors(2)
_temporal_resolution = compute.temporal_resolution_in_minutes(
_experiment)
_fig = go.Figure(data=[
go.Scatter(x=np.arange(
start=_start_time_frame,
stop=_start_time_frame +
len(_left_cell_fiber_densities_derivative
),
step=1) * _temporal_resolution,
y=_y,
name=_name,
mode='lines',
line={
'color': _color,
'width': 1
})
for _y, _name, _color in zip(
_y_arrays, _names_array, _colors_array)
],
layout={
'xaxis': {
'title':
'Time (minutes)',
'zeroline': False
},
'yaxis': {
'title':
'Fiber density (z-score)'
+ '\'' * _derivative,
'zeroline':
False
},
'legend': {
'xanchor': 'left',
'x': 0.1,
'yanchor': 'top',
'bordercolor':
'black',
'borderwidth': 2,
'bgcolor': 'white'
},
})
_experiment, _series_id, _group = _tuple
save.to_html(
_fig=_fig,
_path=os.path.join(paths.PLOTS,
save.get_module_name()),
_filename='plot_' + _experiment + '_' +
str(_series_id) + '_' + _group)
# residuals
_y_arrays = \
[_var_model_results.resid.values[:, 0], _var_model_results.resid.values[:, 1]]
_fig = go.Figure(data=[
go.Scatter(x=np.arange(
start=_start_time_frame,
stop=_start_time_frame + len(_y),
step=1) * _temporal_resolution,
y=_y,
name=_name,
mode='lines',
line={
'color': _color,
'width': 1
})
for _y, _name, _color in zip(
_y_arrays, _names_array, _colors_array)
],
layout={
'xaxis': {
'title':
'Time (minutes)',
'zeroline': False
},
'yaxis': {
'title': 'Residual',
'zeroline': False
},
'legend': {
'xanchor': 'left',
'x': 0.1,
'yanchor': 'top',
'bordercolor':
'black',
'borderwidth': 2,
'bgcolor': 'white'
},
})
_experiment, _series_id, _group = _tuple
save.to_html(
_fig=_fig,
_path=os.path.join(paths.PLOTS,
save.get_module_name()),
_filename='plot_residuals_' + _experiment +
'_' + str(_series_id) + '_' + _group)
# not enough time points
except ValueError:
continue
print('Total pairs:', _n_pairs)
print('Total pairs with band:', _n_pairs_with_band)
print('Total pairs passed whiteness:',
(np.array(_whiteness_p_values) > 0.05).sum())
print('Total pairs passed whiteness with band:',
_n_passed_whiteness_with_band)
print('Total cells passed granger causality:',
(np.array(_granger_causality_p_values) < 0.05).sum())
print('Total cells passed granger causality with band:',
_n_passed_granger_causality_with_band)
# p-value correction
print('Corrections of GC p-value < 0.05:')
_granger_causality_p_values_corrected = multipletests(
pvals=_granger_causality_p_values, method='fdr_bh')
for _p_value, _p_value_corrected in zip(
_granger_causality_p_values,
_granger_causality_p_values_corrected[1]):
if _p_value < 0.05:
print('Original GC p-value:', _p_value, 'corrected:',
_p_value_corrected)
# plots
for _test_name, _y_title, _y_array in \
zip(
['whiteness', 'granger'],
['Whiteness p-value', 'Granger causality p-value'],
[_whiteness_p_values, _granger_causality_p_values]
):
if _test_name in _plots:
_fig = go.Figure(data=go.Box(y=_y_array,
boxpoints='all',
jitter=1,
pointpos=0,
line={'width': 1},
fillcolor='white',
marker={
'size': 10,
'color': '#ea8500'
},
opacity=0.7,
showlegend=False),
layout={
'xaxis': {
'zeroline': False
},
'yaxis': {
'title': _y_title,
'zeroline': False,
'range': [-0.1, 1.1],
'tickmode': 'array',
'tickvals': [0.05, 1]
},
'shapes': [{
'type': 'line',
'x0': -0.75,
'y0': 0.05,
'x1': 0.75,
'y1': 0.05,
'line': {
'color': 'red',
'width': 2,
'dash': 'dash'
}
}]
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS,
save.get_module_name()),
_filename='plot_' + _test_name)
# granger versus correlation
print(
'GC vs. correlation pearson correlation:',
compute_lib.correlation(_granger_causality_p_values,
_correlations,
_with_p_value=True))
_fig = go.Figure(data=go.Scatter(x=_granger_causality_p_values,
y=_correlations,
mode='markers',
marker={
'size': 10,
'color': '#ea8500'
},
showlegend=False),
layout={
'xaxis': {
'title': 'Granger causality p-value',
'zeroline': False,
},
'yaxis': {
'title': 'Inner correlation',
'zeroline': False,
}
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_gc_vs_correlation')
# granger versus time lag correlation
print(
'GC vs. time lag correlation pearson correlation:',
compute_lib.correlation(_granger_causality_p_values,
_time_lag_correlations,
_with_p_value=True))
_fig = go.Figure(data=go.Scatter(x=_granger_causality_p_values,
y=_time_lag_correlations,
mode='markers',
marker={
'size': 10,
'color': '#ea8500'
},
showlegend=False),
layout={
'xaxis': {
'title': 'Granger causality p-value',
'zeroline': False,
},
'yaxis': {
'title': 'GC lag inner correlation',
'zeroline': False,
}
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_gc_vs_time_lag_correlation')
# granger versus end fiber density
print(
'GC vs. end fiber density pearson correlation:',
compute_lib.correlation(_granger_causality_p_values,
_end_fiber_densities,
_with_p_value=True))
_fig = go.Figure(data=go.Scatter(x=_granger_causality_p_values,
y=_end_fiber_densities,
mode='markers',
marker={
'size': 10,
'color': '#ea8500'
},
showlegend=False),
layout={
'xaxis': {
'title': 'Granger causality p-value',
'zeroline': False,
},
'yaxis': {
'title': 'End fiber density (z-score)',
'zeroline': False,
}
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_gc_vs_end_density')