leaspy.io.logs.visualization.plotter

Classes

Plotter

Class defining some plotting tools.

Module Contents

class Plotter(output_path=None)

Class defining some plotting tools.

Parameters:

output_pathstr, (optional)

Folder where plots will be saved. If None, default to current working directory.

Parameters:

output_path (Optional[str])

output_path = None

plt_show()

Display the current matplotlib figure if self._show is True.

This method wraps matplotlib.pyplot.show using the internal flags _show and _block to control interactive plotting.

plot_mean_trajectory(model, *, n_pts=100, n_std_left=3, n_std_right=6, **kwargs)

Plot the mean model trajectory.

Parameters:

modelMcmcSaemCompatibleModel or iterable of such

Model(s) to compute the mean trajectory for.

n_ptsint, optional

Number of timepoints to evaluate between the start and end. Default=100.

n_std_leftint, optional

How many standard deviations before the mean to plot. Default=3.

n_std_rightint, optional

How many standard deviations after the mean to plot. Default=6.

**kwargs

• color : iterable of colors

• title : str, plot title

• save_as : str, filename to save the plot

Raises:

LeaspyInputError

If the model(s) is/are not initialized.

Parameters:

• model (McmcSaemCompatibleModel)

• n_pts (int)

• n_std_left (int)

• n_std_right (int)

plot_mean_validity(model, results, **kwargs)

Plot histogram of reparametrized times for all individuals.

Parameters:

modelMcmcSaemCompatibleModel

Fitted model providing tau_mean and tau_std.

resultsobject

Results containing data and individual_parameters with keys xi and tau.

**kwargs

• save_as : str, filename to save the plot.

Parameters:

model (McmcSaemCompatibleModel)

Return type:

None

plot_patient_trajectory(model, results, indices, **kwargs)

Plot observed data and reconstructed trajectory for one or more patients.

Parameters:

modelMcmcSaemCompatibleModel

Model to use for computing individual trajectories.

resultsobject

Results containing data and individual parameters.

indicesstr or list of int

Patient index/indices to plot.

**kwargs

• ax : matplotlib axis to plot on.

• color : iterable of colors.

• title : str, plot title.

• save_as : str, filename to save the plot.

Parameters:

model (McmcSaemCompatibleModel)

Return type:

None

plot_from_individual_parameters(model, indiv_parameters, timepoints, **kwargs)

Plot a trajectory computed from given individual parameters.

Parameters:

modelMcmcSaemCompatibleModel

Model used to compute the trajectory.

indiv_parametersDictParamsTorch

Individual parameter dictionary.

timepointstorch.Tensor

Timepoints at which to compute the trajectory.

**kwargs

• color : iterable of colors.

• save_as : str, filename to save the plot.

Parameters:

• model (McmcSaemCompatibleModel)

• indiv_parameters (DictParamsTorch)

• timepoints (Tensor)

Return type:

None

plot_distribution(results, parameter, cofactor=None, **kwargs)

Plot histogram(s) of an estimated parameter distribution.

Parameters:

resultsobject

Results containing a get_parameter_distribution method.

parameterstr

Parameter name to plot.

cofactoroptional

Grouping variable; if given, plot separate histograms per group.

**kwargs

• save_as : str, filename to save the plot.

Parameters:

parameter (str)

plot_correlation(results, parameter_1, parameter_2, cofactor=None, **kwargs)

Plot scatter correlation between two parameters.

Parameters:

resultsobject

Results containing a get_parameter_distribution method.

parameter_1str

First parameter name.

parameter_2str

Second parameter name.

cofactoroptional

Grouping variable; if given, scatter different colors per group.

**kwargs

• save_as : str, filename to save the plot.

plot_patients_mapped_on_mean_trajectory(model, results, *, n_std_left=2, n_std_right=4, n_pts=100)

Plot observed patient values mapped onto the mean trajectory.

Parameters:

modelMcmcSaemCompatibleModel

Model used for computing mean and individual trajectories.

resultsobject

Results containing data and individual parameters.

n_std_leftint, optional

How many standard deviations before the mean to plot. Default=2.

n_std_rightint, optional

How many standard deviations after the mean to plot. Default=4.

n_ptsint, optional

Number of timepoints to evaluate. Default=100.

Parameters:

• model (McmcSaemCompatibleModel)

• n_std_left (int)

• n_std_right (int)

• n_pts (int)

Return type:

None

classmethod plot_error(path, dataset, model, param_ind, colors=None, labels=None)

Parameters:

model (McmcSaemCompatibleModel)

classmethod plot_patient_reconstructions(path, dataset, model, param_ind, *, max_patient_number=5, attribute_type=None)

Parameters:

• path (str)

• dataset (Dataset)

• model (McmcSaemCompatibleModel)

• param_ind (DictParamsTorch)

• max_patient_number (int)

static plot_param_ind(path, param_ind)

static plot_convergence_model_parameters(path, path_saveplot_1, path_saveplot_2, model)