leaspy.io.logs.visualization.plotting

Classes

Plotting

Module Contents

class Plotting(model, output_path='.', palette='tab10', max_colors=10)

Deprecated since version 1.2.

Class defining some plotting tools.

Parameters:

modelBaseModel

The used model.

output_pathstr, (optional)

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

palettestr (palette name) or matplotlib.colors.Colormap (ListedColormap or LinearSegmentedColormap)

The palette to use.

max_colorsint > 0, optional (default, corresponding to model nb of features)

Only used if palette is a string

model

color_palette = None

standard_size = (8, 4)

linestyle

linewidth

alpha

output_path = '.'

set_palette(palette, max_colors=None)

Set palette of plots

Parameters:

palettestr (palette name) or matplotlib.colors.Colormap (ListedColormap or LinearSegmentedColormap)

The palette to use.

max_colorsint > 0, optional (default, corresponding to model nb of features)

Only used if palette is a string

colors(at=None)

Wrapper over color_palette iterator to get colors

Parameters:

atany legit color_palette arg (int, float or iterable of any of these) or None (default)

if None returns all colors of palette upto model dimension

Returns:

colorssingle color tuple (RGBA) or np.array of RGBA colors (number of colors x 4)

average_trajectory(**kwargs)

Plot the population average trajectories. They are parametrized by the population parameters derived during the calibration.

Parameters:

**kwargs

• alpha: float, default 0.6

  Matplotlib’s transparency option. Must be in [0, 1].

• linestyle: {‘-’, ‘–’, ‘-.’, ‘:’, ‘’, (offset, on-off-seq), …}

  Matplotlib’s linestyle option.

• linewidth: float

  Matplotlib’s linewidth option.

• features: list[str]

  Name of features (if set it must be a subset of model features) Default: all model features.

• colors: list[str]

  Contains matplotlib compatible colors. At least as many as number of features.

• labels: list[str]

  Used to rename features in the plot. Exactly as many as number of features. Default: raw variable name of each feature

• ax: matplotlib.axes.Axes

  Axes object to modify, instead of creating a new one.

• figsize: tuple of int

  The figure’s size.

• save_as: str, default None

  Path to save the figure.

• title: str

• n_tpts: int

  Number of timepoints in plot (default: 100)

• n_std_left, n_std_right: float (default: 3 and 6 resp.)

  Time window around tau_mean, expressed as times of max(tau_std, 4)

Returns:

matplotlib.axes.Axes

patient_observations(data, patients_idx='all', individual_parameters=None, **kwargs)

Plot patient observations

Parameters:

dataData

patients_idx‘all’ (default), str or list[str]

Patients to display (by their ID).

individual_parametersIndividualParameters or pandas.DataFrame (as may be output by ip.to_dataframe()) or dict (Pytorch ip format), optional

If not None, observations are plotted with respect to reparametrized ages.

patient_observations_reparametrized(data, individual_parameters, patients_idx='all', **kwargs)

Plot patient observations (reparametrized ages)

patient_trajectories(data, individual_parameters, patients_idx='all', reparametrized_ages=False, **kwargs)

Plot patient observations together with model individual reconstruction

Parameters:

dataData

individual_parametersIndividualParameters or pandas.DataFrame (as may be output by ip.to_dataframe()) or dict (Pytorch ip format)

patients_idx‘all’ (default), str or list[str]

Patients to display (by their ID).

reparametrized_agesbool (default False)

Should we plot trajectories in reparam age or not? to study source impact essentially

**kwargs

cf. _plot_model_trajectories() In particular, pass marker=None if you don’t want observations besides model