ramjet.analysis.lightcurve_visualizer¶
Code for visualizing lightcurves.
Module Contents¶
Functions¶
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 |
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 |
create_dual_lightcurve_figure(fluxes0, times0, name0, fluxes1, times1, name1, title, x_axis_label=’Time (days)’, y_axis_label=’Relative flux’) → Figure |
Plots two lightcurves together. Mostly for comparing a lightcurve cleaned by two different methods. |
calculate_inlier_range(points: np.ndarray) → (float, float) |
Calculates the inlier range for a set of points. |
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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)[source]¶ 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).
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is_outlier(points: np.ndarray, threshold: float = 5)[source]¶ 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.
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create_dual_lightcurve_figure(fluxes0, times0, name0, fluxes1, times1, name1, title, x_axis_label='Time (days)', y_axis_label='Relative flux') → Figure[source]¶ Plots two lightcurves together. Mostly for comparing a lightcurve cleaned by two different methods.
Parameters: - fluxes0 – The fluxes of the first plot.
- times0 – The times of the first plot.
- name0 – The name of the first plot.
- fluxes1 – The fluxes of the second plot.
- times1 – The times of the second plot.
- name1 – The name of the second plot.
- title – The title of the figure.
- x_axis_label – The label of the x axis.
- y_axis_label – The label of the y axis.
Returns: The resulting figure.