peak_performance.plots#
The plots module offer convenience functions for visualizing results and enabling quality control.
Functions for preparing diagnostic and QC plots.
- peak_performance.plots.plot_density(*, ax, x: ndarray, samples, percentiles=(5, 95), percentile_kwargs=None, **kwargs)#
Method to plot the original data points alongside the posterior predictive plot (percentiles marked with a black, dashed line).
- Parameters:
- ax
Axes of a matplotlib figure.
- x
Values of the x dimension of the plot (here: time).
- samples
Posterior predictive samples taken from an inference data obejct.
- percentiles
Lower and upper percentiles to be plotted.
- **kwargs
The keyword arguments are used for plotting with ax.plot() and ax.stairs(), e.g. the following:
- linestyle
Style of the line marking the border of the chosen percentiles (default = “–”, i.e. a dashed line).
- color
Color of the line marking the border of the chosen percentiles (default = “black”).
- peak_performance.plots.plot_model_comparison(df_comp: DataFrame, identifier: str, path: str | PathLike | None, save_formats: Sequence[str] = ('png', 'svg'))#
Function to plot the results of a model comparison.
- Parameters:
- df_comp
DataFrame containing the ranking of the given models.
- identifier
Unique identifier of this particular signal (e.g. filename).
- path
Path to the folder containing the results of the current run.
- save_formats
Which file formats to save as. Must be supported by plt.savefig(), e.g.
("png", "svg", "pdf").
- peak_performance.plots.plot_posterior(identifier: str, time: ndarray, intensity: ndarray, path: str | PathLike | None, idata: InferenceData, discarded: bool, save_formats: Sequence[str] = ('png', 'svg'))#
Saves plot of posterior, estimated baseline, and original data points.
- Parameters:
- identifier
Unique identifier of this particular signal (e.g. filename).
- time
NumPy array with the time values of the relevant timeframe.
- intensity
NumPy array with the intensity values of the relevant timeframe.
- path
Path to the folder containing the results of the current run.
- idata
Infernce data object.
- discarded
Alters the name of the saved plot. If True, a “_NoPeak” is added to the name.
- save_formats
Which file formats to save as. Must be supported by plt.savefig(), e.g.
("png", "svg", "pdf").
- peak_performance.plots.plot_posterior_predictive(identifier: str, time: ndarray, intensity: ndarray, path: str | PathLike | None, idata: InferenceData, discarded: bool, save_formats: Sequence[str] = ('png', 'svg'))#
Save plot of posterior_predictive with 95 % HDI and original data points.
- Parameters:
- identifier
Unique identifier of this particular signal (e.g. filename).
- time
NumPy array with the time values of the relevant timeframe.
- intensity
NumPy array with the intensity values of the relevant timeframe.
- path
Path to the folder containing the results of the current run.
- idata
Infernce data object.
- discarded
Alters the name of the saved plot. If True, a “_NoPeak” is added to the name.
- save_formats
Which file formats to save as. Must be supported by plt.savefig(), e.g.
("png", "svg", "pdf").
- peak_performance.plots.plot_raw_data(identifier: str, time: ndarray, intensity: ndarray, path: str | PathLike | None, save_formats: Sequence[str] = ('png', 'svg'))#
Plot just the raw data in case no peak was found.
- Parameters:
- identifier
Unique identifier of this particular signal (e.g. filename).
- time
NumPy array with the time values of the relevant timeframe.
- intensity
NumPy array with the intensity values of the relevant timeframe.
- path
Path to the folder containing the results of the current run.
- save_formats
Which file formats to save as. Must be supported by plt.savefig(), e.g.
("png", "svg", "pdf").