rail.plotting.plotting_functions module
- rail.plotting.plotting_functions.get_subplot_nrow_ncol(nfig)[source]
Get the number of rows and columns of sub-plots for a particular number of plots
- Return type:
tuple[int,int]- Parameters:
nfig (int) – Number of figures
- Return type:
Number of rows and columns as (nrow, ncol)
- rail.plotting.plotting_functions.plot_colors_v_colors_with_templates(color_data, zmax=4.0, templates=None, labels=None)[source]
- Return type:
Figure- Parameters:
color_data (ndarray)
zmax (float)
templates (dict | None)
labels (list[str] | None)
- rail.plotting.plotting_functions.plot_colors_v_redshifts_with_templates(redshifts, color_data, zmax=4.0, templates=None, labels=None)[source]
- Return type:
Figure- Parameters:
redshifts (ndarray)
color_data (ndarray)
zmax (float)
templates (dict | None)
labels (list[str] | None)
- rail.plotting.plotting_functions.plot_feature_histograms(data, labels=None, bins=100)[source]
Plot Histograms of the features being used to train a ML algorithm on a single, busy figure
- Return type:
Figure- Parameters:
data (ndarray) – Input data
labels (list[str] | None) – Labels for the various features
bins (int | ndarray) – Bins for the histogram
- Return type:
Figure with requested plots
- rail.plotting.plotting_functions.plot_feature_target_hist2d(data, targets, labels=None, bins=(100, 100))[source]
Plot input data v. target redshift value as 2D histogram
- Return type:
Figure- Parameters:
data (ndarray) – Input data [N_objects, N_features]
targets (ndarray) – Target redshifts [N_objects]
labels (list[str] | None) – Labels for the data columns [N_features]
bins (tuple[int | ndarray, int | ndarray]) – Bins for the histogram
- Return type:
Figure with requested plots
Notes
This will create N_features sub-plots