valska_hera_beam.plotting

Plotting helpers for ValSKA-HERA-beam-FWHM analysis outputs.

Functions

plot_gleam_analysis([base_chains_dir])	Example function that reproduces the GLEAM analysis plot.
plot_gsm_comparison([base_chains_dir])	Example function comparing different GSM perturbation levels.

Classes

BeamAnalysisPlotter([base_chains_dir, ...])

Class for plotting beam analysis results for HERA FWHM validation studies.

class valska_hera_beam.plotting.BeamAnalysisPlotter(base_chains_dir: str | Path | None = None, paths_file: str | Path | None = None, paths: dict[str, str] | None = None, eor_ps: float = 214777.66068216303, noise_ratio: float = 0.5, default_expected_ps: float | None = None)

Class for plotting beam analysis results for HERA FWHM validation studies.

Initialize the plotter with a base directory for chains.

Parameters

base_chains_dirstr or Path, optional

Base directory for chains. If None, uses the default BayesEoR path

paths_filestr or Path, optional

YAML file containing analysis paths. If None, uses the default paths file.

pathsDict[str, str], optional

Dictionary of analysis paths relative to base_chains_dir. If None, uses default paths.

eor_psfloat, optional

EoR power spectrum value in mK^2 Mpc^3. Default is 214777.66068216303.

noise_ratiofloat, optional

Ratio of noise PS to EoR PS. Default is 0.5 (noise_ps = eor_ps / 2).

default_expected_psfloat, optional

Default expected power spectrum to use in plots. If None, uses noise_ps.

add_analysis_path(key: str, path: str) → None

Add a new analysis path to the plotter.

Parameters

keystr

Key for the analysis path

pathstr

Path to the analysis directory, relative to base_chains_dir

create_comparison_plot(groups: dict[str, list[str]], group_labels: dict[str, str] | None = None, suptitle: str = 'HERA FWHM Sensitivity Analysis', **kwargs) → matplotlib.figure.Figure

Create a comparison plot for different analysis groups.

Parameters

groupsdict of str: list of str

Dictionary mapping group names to lists of analysis keys

group_labelsdict of str: str, optional

Dictionary mapping group names to display labels

suptitlestr, optional

Super title for the plot

**kwargsdict

Additional arguments to pass to plot_analysis_results

Returns

figmatplotlib.figure.Figure

The resulting figure

get_data_container(analysis_keys: list[str], labels: list[str] | None = None, expected_ps: float | None = None, **kwargs) → bayeseor.analyze.analyze.DataContainer

Create a DataContainer for the specified analyses.

Parameters

analysis_keyslist of str

Keys for analyses to plot from self.paths

labelslist of str, optional

Labels for each analysis. If None, uses analysis_keys

expected_psfloat, optional

Expected power spectrum value. If None, uses self.default_expected_ps

**kwargsdict

Additional arguments to pass to DataContainer

Returns

dataDataContainer

DataContainer for the specified analyses

plot_analysis_results(analysis_keys: list[str], labels: list[str] | None = None, suptitle: str = 'UKSRC validation: Burba et al. 2023', expected_ps: float | None = None, expected_label: str = 'Noise level', upper_limit_indices: list[int] | None = None, detection_indices: dict[str, list[int]] | None = None, ignore_uplims: bool = False, plot_fracdiff: bool = True, plot_priors: bool = True, ls_expected: str = ':', figsize: tuple[float, float] | None = None, data_container_kwargs: dict[str, Any] | None = None, plot_kwargs: dict[str, Any] | None = None) → matplotlib.figure.Figure

Plot power spectra and posteriors for selected analyses.

Parameters

analysis_keyslist of str

Keys for analyses to plot from self.paths

labelslist of str, optional

Labels for each analysis. If None, uses analysis_keys

suptitlestr, optional

Super title for the plot

expected_psfloat, optional

Expected power spectrum value. If None, uses self.default_expected_ps

labelsstr, optional

Label for the expected PS line

upper_limit_indiceslist of int, optional

Indices of k-modes to treat as upper limits for ALL analyses. If None, assume no upper limits.

detection_indicesdict, optional

Dictionary mapping analysis keys to lists of k-mode indices that should be treated as detections rather than upper limits. This overrides upper_limit_indices for specific analyses.

ignore_uplimsbool, optional

Whether to ignore upper limits entirely

plot_fracdiffbool, optional

Whether to plot fractional differences

plot_priorsbool, optional

Whether to plot priors

ls_expectedstr, optional

Line style for expected PS

figsizetuple of float, optional

Figure size in inches (width, height)

data_container_kwargsdict, optional

Additional arguments to pass to DataContainer

plot_kwargsdict, optional

Additional arguments to pass to plot_power_spectra_and_posteriors

Returns

figmatplotlib.figure.Figure

The resulting figure

valska_hera_beam.plotting.plot_gleam_analysis(base_chains_dir: str | Path | None = None) → matplotlib.figure.Figure

Example function that reproduces the GLEAM analysis plot.

Parameters

base_chains_dirstr or Path, optional

Base directory for chains. If None, uses the default BayesEoR path.

Returns

figmatplotlib.figure.Figure

The resulting figure

valska_hera_beam.plotting.plot_gsm_comparison(base_chains_dir: str | Path | None = None) → matplotlib.figure.Figure

Example function comparing different GSM perturbation levels.

Parameters

base_chains_dirstr or Path, optional

Base directory for chains. If None, uses the default BayesEoR path.

Returns

figmatplotlib.figure.Figure

The resulting figure