ramjet.analysis.lightcurve_visualizer

Code for visualizing lightcurves.

Module Contents

plot_lightcurve(times: np.ndarray, fluxes: np.ndarray, labels: np.ndarray = None, predictions: np.ndarray = None, title: str = None, x_label: str = 'Days', y_label: str = 'Flux', x_limits: (float, float) = (None, None), y_limits: (float, float) = (None, None), save_path: Union[Path, str] = None, exclude_flux_outliers: bool = False, base_data_point_size: float = 3)

Plots a lightcurve with a consistent styling. If true labels and/or predictions are included, these will additionally be plotted.

Parameters:

• times – The times of the measurements.
• fluxes – The fluxes of the measurements.
• labels – The binary labels for each time step.
• predictions – The probability prediction for each time step.
• title – The title of the plot.
• x_label – The label for the x axis.
• y_label – The label for the y axis.
• x_limits – Optional axis limiting for the x axis.
• y_limits – Optional axis limiting for the y axis.
• save_path – The path to save the plot to. If None, the plot will be shown instead.
• exclude_flux_outliers – Whether or not to exclude flux outlier data points when plotting.
• base_data_point_size – The size of the data points to use when plotting (and related sizes).

is_outlier(points: np.ndarray, threshold: float = 5)

Uses the median absolute deviation to determine if the input data points are “outliers” for the purpose of plotting.

Parameters:

• points – The observations to search for outliers in.
• threshold – The modified z-score to use as a threshold. Observations with a modified z-score based on the median absolute deviation greater than this value will be classified as outliers.