def main(_high_temporal_resolution=True):
_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=MOVING_WINDOW_LENGTH[_high_temporal_resolution])
_tuples = filtering.by_pair_distance_range(
_tuples, _distance_range=PAIR_DISTANCE_RANGE)
_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
_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
}
for _tuple in _tuples:
_correlations = []
_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)
for _start_time_frame in \
range(0, END_TIME_FRAME[_high_temporal_resolution], TIME_FRAME_STEP[_high_temporal_resolution]):
_left_cell_fiber_densities_window = _left_cell_fiber_densities[
_start_time_frame:_start_time_frame +
MOVING_WINDOW_LENGTH[_high_temporal_resolution]]
_right_cell_fiber_densities_window = _right_cell_fiber_densities[
_start_time_frame:_start_time_frame +
MOVING_WINDOW_LENGTH[_high_temporal_resolution]]
_left_cell_fiber_densities_filtered, _right_cell_fiber_densities_filtered = \
compute.longest_same_indices_shared_in_borders_sub_array(
_left_cell_fiber_densities_window, _right_cell_fiber_densities_window)
# ignore small arrays
if len(_left_cell_fiber_densities_filtered
) < MOVING_WINDOW_LENGTH[_high_temporal_resolution]:
_correlations.append(None)
continue
_correlations.append(
compute_lib.correlation(
compute_lib.derivative(_left_cell_fiber_densities_filtered,
_n=DERIVATIVE),
compute_lib.derivative(
_right_cell_fiber_densities_filtered, _n=DERIVATIVE)))
# plot
_temporal_resolution = compute.temporal_resolution_in_minutes(
_experiment)
_fig = go.Figure(data=go.Scatter(
x=np.arange(start=0, stop=len(_correlations), step=1) *
_temporal_resolution * TIME_FRAME_STEP[_high_temporal_resolution],
y=_correlations,
mode='lines+markers',
line={'dash': 'solid'}),
layout={
'xaxis': {
'title': 'Window start time (minutes)',
'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_' + str(_experiment) + '_' +
str(_series_id) + '_' + str(_group))
def main(_real_cells=True,
_static=False,
_band=True,
_high_temporal_resolution=False,
_pair_distance_range=None,
_offset_y=0.5):
if _pair_distance_range is None:
_pair_distance_range = PAIR_DISTANCE_RANGE
_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_pair_distance_range(_tuples, _pair_distance_range)
_tuples = filtering.by_real_pairs(_tuples, _real_pairs=_real_cells)
_tuples = filtering.by_fake_static_pairs(_tuples,
_fake_static_pairs=_static)
_tuples = filtering.by_band(_tuples, _band=_band)
print('Total tuples:', len(_tuples))
_arguments = []
_longest_time_frame = 0
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_latest_time_frame = compute.latest_time_frame_before_overlapping(
_experiment, _series_id, _group, OFFSET_X)
# save for later
if _latest_time_frame > _longest_time_frame:
_longest_time_frame = _latest_time_frame
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)
_experiments_fiber_densities = {
_key:
[_fiber_densities[_tuple] for _tuple in _windows_dictionary[_key]]
for _key in _windows_dictionary
}
_valid_tuples = []
_valid_cells = []
_densities = [[] for _ in range(_longest_time_frame)]
for _tuple in tqdm(_tuples, desc='Experiments loop'):
_experiment, _series_id, _group = _tuple
_normalization = load.normalization_series_file_data(
_experiment, _series_id)
_properties = load.group_properties(_experiment, _series_id, _group)
for _cell_id in ['left_cell', 'right_cell']:
_cell_fiber_densities = \
_experiments_fiber_densities[(_experiment, _series_id, _group, _cell_id)]
_cell_fiber_densities = compute.remove_blacklist(
_experiment, _series_id, _properties['cells_ids'][_cell_id],
_cell_fiber_densities)
_previous_cell_fiber_density_normalized = None
for _time_frame, _cell_fiber_density in enumerate(
_cell_fiber_densities):
# not out of border
if _cell_fiber_density[1]:
_previous_cell_fiber_density_normalized = None
continue
# normalize
_cell_fiber_density_normalized = compute_lib.z_score(
_x=_cell_fiber_density[0],
_average=_normalization['average'],
_std=_normalization['std'])
# no previous
if _previous_cell_fiber_density_normalized is None:
_previous_cell_fiber_density_normalized = _cell_fiber_density_normalized
continue
# change
_cell_fiber_density_normalized_change = _cell_fiber_density_normalized - _previous_cell_fiber_density_normalized
_previous_cell_fiber_density_normalized = _cell_fiber_density_normalized
# save
_densities[_time_frame].append(
_cell_fiber_density_normalized_change)
if _tuple not in _valid_tuples:
_valid_tuples.append(_tuple)
_cell_tuple = (_experiment, _series_id, _group, _cell_id)
if _cell_tuple not in _valid_cells:
_valid_cells.append(_cell_tuple)
print('Total pairs:', len(_valid_tuples))
print('Total cells:', len(_valid_cells))
# plot
_temporal_resolution = compute.temporal_resolution_in_minutes(
_experiments[0])
_fig = go.Figure(
data=go.Scatter(x=np.array(range(_longest_time_frame)) *
_temporal_resolution,
y=[np.mean(_array) for _array in _densities],
name='Fiber density change (z-score)',
error_y={
'type': 'data',
'array': [np.std(_array) for _array in _densities],
'thickness': 1
},
mode='lines+markers',
marker={
'size': 5,
'color': '#ea8500'
},
line={'dash': 'solid'},
showlegend=False),
layout={
'xaxis': {
'title': 'Time (minutes)',
# 'zeroline': False
},
'yaxis': {
'title': 'Fiber density change (z-score)',
# 'zeroline': False
},
# 'shapes': [
# {
# 'type': 'line',
# 'x0': -_temporal_resolution,
# 'y0': -0.5,
# 'x1': -_temporal_resolution,
# 'y1': 2,
# 'line': {
# 'color': 'black',
# 'width': 2
# }
# },
# {
# 'type': 'line',
# 'x0': -_temporal_resolution,
# 'y0': -0.5,
# 'x1': 350,
# 'y1': -0.5,
# 'line': {
# 'color': 'black',
# 'width': 2
# }
# }
# ]
})
save.to_html(
_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_real_' + str(_real_cells) + '_static_' + str(_static) +
'_band_' + str(_band) + '_high_time_' +
str(_high_temporal_resolution) + '_range_' +
'_'.join([str(_distance) for _distance in _pair_distance_range]) +
'_y_' + str(_offset_y))
def is_high_temporal_resolution(_experiment):
return compute.temporal_resolution_in_minutes(
_experiment) == HIGH_TEMPORAL_RESOLUTION_IN_MINUTES
def main(_band=True,
_high_temporal_resolution=True,
_plots=None,
_plot_types=None):
if _plots is None:
_plots = ['same', 'different']
if _plot_types is None:
_plot_types = ['scatter', 'box', 'bar']
_same_correlation_vs_time_lag, _same_time_lags_arrays, _different_time_lags_arrays, _same_time_lags_highest, \
_different_time_lags_highest = compute_fiber_densities(_band, _high_temporal_resolution)
if _plots is not None:
# individual plots
if 'scatter' in _plot_types:
for _same_tuple in _same_correlation_vs_time_lag:
_experiment, _series_id, _group = _same_tuple
_temporal_resolution = compute.temporal_resolution_in_minutes(
_experiment)
_fig = go.Figure(
data=go.Scatter(
x=np.array(TIME_LAGS[_high_temporal_resolution]) *
_temporal_resolution,
y=_same_correlation_vs_time_lag[_same_tuple],
mode='markers',
marker={
'size': 25,
'color': '#ea8500'
}),
layout={
'xaxis': {
'title':
'Time lag (minutes)',
'zeroline':
False,
'tickmode':
'array',
'tickvals':
np.array(TIME_LAGS[_high_temporal_resolution]) *
_temporal_resolution
},
'yaxis': {
'title': 'Correlation',
'range': [-1, 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_' + _experiment + '_' +
str(_series_id) + '_' + _group)
# box plots
if 'box' in _plot_types:
for _name, _arrays in zip(
['same', 'different'],
[_same_time_lags_arrays, _different_time_lags_arrays]):
if _name in _plots:
_fig = go.Figure(
data=[
go.Box(y=_y,
name=_time_lag *
5 if _high_temporal_resolution else 15,
boxpoints=False,
line={'width': 1},
marker={
'size': 10,
'color': '#ea8500'
},
showlegend=False)
for _y, _time_lag in zip(
_arrays, TIME_LAGS[_high_temporal_resolution])
],
layout={
'xaxis': {
'title':
'Time lag (minutes)',
'zeroline':
False,
'tickmode':
'array',
'tickvals':
np.array(TIME_LAGS[_high_temporal_resolution])
* 5 if _high_temporal_resolution else 15
},
'yaxis': {
'title':
'Inner correlation' if _name == 'same' else
'Different network correlation',
'range': [-1, 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_box_high_temporal_res_' +
str(_high_temporal_resolution) + '_' + _name)
# bar plot
if 'bar' in _plot_types:
for _name, _sums in zip(
['same', 'different'],
[_same_time_lags_highest, _different_time_lags_highest]):
if _name in _plots:
_fig = go.Figure(
data=go.Bar(
x=np.array(TIME_LAGS[_high_temporal_resolution]) *
5 if _high_temporal_resolution else 15,
y=np.array(_sums) / sum(_sums),
marker={'color': '#ea8500'}),
layout={
'xaxis': {
'title':
'Time lag (minutes)',
'zeroline':
False,
'tickmode':
'array',
'tickvals':
np.array(TIME_LAGS[_high_temporal_resolution])
* 5 if _high_temporal_resolution else 15
},
'yaxis': {
'title': 'Highest correlations fraction',
'range': [0, 1.1],
'zeroline': False,
'tickmode': 'array',
'tickvals': [0, 0.5, 1]
}
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS,
save.get_module_name()),
_filename='plot_bar_high_temporal_res_' +
str(_high_temporal_resolution) + '_' + _name)
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')