uqmodels.visualization package

Submodules

uqmodels.visualization.visualization module

Visualization module.

uqmodels.visualization.visualization.aux_fill_area(context, **kwargs)

uqmodels.visualization.visualization.aux_plot_PIs(ax, x, list_PIs, list_alpha_PIs, list_colors_PIs=None, list_alpha_fig_PIs=None, list_label_PIs=None)

Plot PIs enveloppe on ax suplot

Parameters:

• ax (_type_) – ax suplot

• x (_type_) – x_scale

• list_PIs (_type_) – list of PIs ordered from minumum to maximum: ex [PI_low_1,PI_low_2,PI_high_2,PI_high_1]

• list_alpha (_type_) – List of alpha of PIs

• list_colors_PIs (list, optional) – List of color by pair. Defaults to None -> use grey.

• list_alpha_PIs (_type_, optional) – List of color by pair. Defaults to None -> 0.2 as transparancy

uqmodels.visualization.visualization.aux_plot_anom(ax, x, y)

uqmodels.visualization.visualization.aux_plot_conf_score(ax, x, pred, confidence_lvl, label, mode_res=False)

uqmodels.visualization.visualization.aux_plot_confiance(ax, y, pred, var_A, var_E, born=None, born_bis=None, ylim=None, split_values=-1, x=None, mode_res=False, min_A=0.08, min_E=0.02, env=[0.95, 0.68], **kwarg)

uqmodels.visualization.visualization.aux_plot_pred(ax, x, y, pred)

uqmodels.visualization.visualization.dim_1d_check(y)

Reshape (n,1) 2D array to (n) 1D array else do nothing

uqmodels.visualization.visualization.plot_anom_matrice(score, score2=None, f_obs=None, true_label=None, data=None, x=None, vmin=-3, vmax=3, cmap=None, list_anom_ind=None, figsize=(15, 6), grid_spec=None, x_date=False, show_plot=True, setup=None)

Plot score_anomalie matrice and true label if there is. :param score: Anomaly score matrice or list of Anomaly matrix :type score: _type_ :param f_obs: mask_focus :type f_obs: _type_, optional :param true_label: True label or None :type true_label: _type_, optional :param vmin: _description_. Defaults to -3. :type vmin: int, optional :param vmax: _description_. Defaults to 3. :type vmax: int, optional :param cmap: _description_. Defaults to None. :type cmap: _type_, optional :param figsize: _description_. Defaults to (15, 6). :type figsize: tuple, optional

uqmodels.visualization.visualization.plot_pi(y, y_pred, y_pred_lower, y_pred_upper, mode_res=False, f_obs=None, X=None, size=(12, 2), name=None, show_plot=True)

uqmodels.visualization.visualization.plot_prediction_interval(y: array, y_pred_lower: array, y_pred_upper: array, X: array | None = None, y_pred: array | None = None, save_path: str | None = None, sort_X: bool = False, **kwargs) → None

Plot prediction intervals whose bounds are given by y_pred_lower and y_pred_upper. True values and point estimates are also plotted if given as argument.

Parameters:

• y – label true values.

• y_pred_lower – lower bounds of the prediction interval.

• y_pred_upper – upper bounds of the prediction interval.

• <optionnal> (y_pred) – abscisse vector.

• <optionnal> – predicted values.

• kwargs – plot parameters.

uqmodels.visualization.visualization.plot_sorted_pi(y: array, y_pred_lower: array, y_pred_upper: array, X: array | None = None, y_pred: array | None = None, **kwargs) → None

Plot prediction intervals in an ordered fashion (lowest to largest width), showing the upper and lower bounds for each prediction. :param y: label true values. :param y_pred_lower: lower bounds of the prediction interval. :param y_pred_upper: upper bounds of the prediction interval. :param X <optionnal>: abscisse vector. :param y_pred <optionnal>: predicted values. :param kwargs: plot parameters.

uqmodels.visualization.visualization.plot_var(Y, data_full, variance, impact_anom=None, anom=None, f_obs=None, dim=(400, 20, 3), g=0, res_flag=False, fig_s=(20, 3), title=None, ylim=None)

uqmodels.visualization.visualization.provide_cmap(mode='bluetored')

Generate a bluetored or a cyantopurple cutsom cmap

Parameters:

mode (str, optional) – Values: bluetored’ or ‘cyantopurple ‘

Returns:

Colormap matplotlib

uqmodels.visualization.visualization.show_dUQ_refinement(UQ, y=None, d=0, f_obs=None, max_cut_A=0.99, q_Eratio=2, E_cut_in_var_nominal=False, A_res_in_var_atypic=False)

uqmodels.visualization.visualization.uncertainty_plot(y, output, context=None, size=(15, 5), f_obs=None, name='UQplot', mode_res=False, born=None, born_bis=None, dim=0, confidence_lvl=None, list_percent=[0.8, 0.9, 0.99, 0.999, 1], env=[0.95, 0.65], type_UQ='old', show_plot=True, with_colorbar=False, **kwarg)

uqmodels.visualization.visualization.visu_latent_space(grid_dim, embedding, f_obs, context_grid, context_grid_name=None)

uqmodels.visualization.visualization_mutisource module

Visualization_multisource module.

uqmodels.visualization.visualization_mutisource.apply_cmap(val, vmin, vmax, cmap)

Transform valyes array into color values array using cmap and considering bound [vmin,vmax]

Parameters:

• val (array) – Values to turn to color

• vmin (float) – min_val

• vmax (float) – max_val

• cmap (cmap) – matplotlib cmap

Returns:

Array of color

Return type:

_type_

uqmodels.visualization.visualization_mutisource.compute_dev_score(val, y, vmin, vmax)

Compute signed relative errors

Parameters:

• val (array) – Prediction

• y (array) – Target

• vmin (float) – negative minimum sensitivité

• vmax (float) – positive minimun sensitivity

Returns:

signed relative errors.

Return type:

r

uqmodels.visualization.visualization_mutisource.load_and_select(storing, keys, x_min, x_max)

uqmodels.visualization.visualization_mutisource.load_from_metadata(storing, str_keys_metadata)

uqmodels.visualization.visualization_mutisource.plot_analysis(storing_data, storing_res, sensors_mask, x_min, x_max, figsize=(20, 4), matplot=True)

uqmodels.visualization.visualization_mutisource.plot_anom_mat(storing_data, storing_res, sensors_mask, x_min, x_max, figsize=(20, 12), metadata=None)

uqmodels.visualization.visualization_mutisource.plot_channel(storing_data, storing_res, sensors_mask, x_min, x_max, mode=None, figsize=(20, 4), canal_nature=['Mean', 'Std', 'ExT'])

uqmodels.visualization.visualization_mutisource.plot_row_series(storing, sensors_mask, x_min, x_max, figsize=(12, 10), date_init=None, anom=None)

uqmodels.visualization.visualization_mutisource.plot_row_series_statistics(storing, sensors_mask, x_min, x_max, figsize=(12, 10), interpolate_data=False, date_init=None)

Module contents