GUIBRUSHR.core.functions.plotting module

Plotting utility functions for GUIBRUSHR atmospheric retrieval.

This module contains visualization functions extracted from general_functions.py:

  • make_smooth_mono_cmap: Create a smooth monochromatic colormap.

  • plot_tp_profile: Plot the pressure-temperature (PT) profile for an atmosphere.

  • plot_corner_metallicity: Generate a corner plot for molecular abundance posteriors.

  • opacities_contribution_plot: Plot the contribution of different opacity sources.

  • plot_vmr: Plot volume mixing ratio (VMR) profiles for different chemical species groups.

GUIBRUSHR.core.functions.plotting.make_smooth_mono_cmap(color: str, name: str = 'custom') → LinearSegmentedColormap[source]

Create a smooth monochromatic colormap with 5 control points.

The colormap transitions from black through dark shade, saturated color, light shade, to white, creating a smooth gradient suitable for visualization.

Parameters:

  • color (str) – Hex color code or named color for the saturated color point

  • name (str, optional) – Name for the colormap. Defaults to ‘custom’.

Returns:

Matplotlib colormap object with 256 color levels

Return type:

LinearSegmentedColormap

GUIBRUSHR.core.functions.plotting.plot_tp_profile(range_min_pressures: float, range_max_pressures: float, n_layers: int, parameters_tp_profile: Any, gravity: float, format_temperature: str, path_imm: str | Path, err: Any, rng: Any, mcmc_chain: dict | None = None, plot_median_sample: bool = False) → None[source]

Plot the pressure-temperature (PT) profile for an atmosphere and save the figure.

Parameters:

  • range_min_pressures (float) – Log10 of the minimum pressure (bar).

  • range_max_pressures (float) – Log10 of the maximum pressure (bar).

  • n_layers (int) – Number of pressure layers.

  • parameters_tp_profile (Any) – Parameters for the temperature profile.

  • gravity (float) – Surface gravity.

  • format_temperature (str) – Name of the temperature profile method to use.

  • path_imm (Union[str, Path]) – Path to save the output image.

  • err (Any) – Error parameter for the temperature profile.

  • rng (Any) – Random number generator or seed for the temperature profile.

  • mcmc_chain (dict, optional) – Dictionary mapping parameter names to 1D arrays of MCMC posterior samples for percentile-based error bands.

  • plot_median_sample (bool, optional) – If True and an MCMC sample is available, also save tp_profile_median_sample.jpg — identical to the base figure (same 1σ/2σ bands) but with the central curve set to the per-pressure median of the posterior temperature profiles instead of the best-fit profile. Defaults to False.

Returns:

None

GUIBRUSHR.core.functions.plotting.plot_corner_metallicity(array_molecules, names_molecules_corner, folder_retrieval, filename, title_figure)[source]

Generate and save a corner plot for molecular abundance posteriors.

Parameters:

  • array_molecules (np.ndarray) – 2D array of posterior samples (n_samples x n_params).

  • names_molecules_corner (list) – Labels for each parameter axis.

  • folder_retrieval (str) – Path to the retrieval output folder.

  • filename (str) – Base filename for the saved plot.

  • title_figure (str) – Title displayed on the plot window.

GUIBRUSHR.core.functions.plotting.opacities_contribution_plot(path_results: str | Path, opacities_contribution: Dict[str, Any], resolution: str, function: str = 'plot', wl_scale: str = 'log', invert_y_axis: int = 0, instruments_LR=None, offsetLR_arr=None, rp=None, rs=None, rad_mode: str = 'Transmission') → None[source]

Plot and save the contribution of different opacity sources to the atmospheric model.

This function creates a visualization showing how different opacity sources (line species, gas continua, clouds, etc.) contribute to the total atmospheric opacity. The plot is saved as a high-resolution image in the specified output directory.

Parameters:

  • path_results (Union[str, Path]) – Directory path where the output plot will be saved.

  • opacities_contribution (Dict[str, Any]) – Dictionary containing opacity contributions from different sources. Keys are source types (e.g., ‘line_species’, ‘cloud_species’), values are dictionaries mapping species names to their contribution data.

  • resolution (str) – Resolution identifier used in the plot title and filename.

  • function (str, optional) – Plotting function to use (‘plot’, ‘scatter’, etc.). Defaults to “plot”.

  • wl_scale (str, optional) – Scale for the wavelength axis (‘log’ or ‘linear’). Defaults to “log”.

  • invert_y_axis (int, optional) – Whether to invert the y-axis. Defaults to False.

  • instruments_LR – Dictionary containing the instruments and their LR values.

  • offsetLR_arr – Array containing the offset LR values.

  • rp – Radius of the planet.

  • rs – Radius of the star.

  • rad_mode – Radiative mode.

Returns:

None

GUIBRUSHR.core.functions.plotting.plot_vmr(pressures: ndarray, vmr_dict: Dict[str, ndarray], folder_plot: str | Path) → None[source]

Plot volume mixing ratio (VMR) profiles for different chemical species groups.

This function creates a 2x3 grid of plots showing VMR profiles for: - All species - HCNO neutrals - Ions - Alkali metals - Metals - Metal oxides

Parameters:

  • pressures (np.ndarray) – Pressure values (in bar)

  • vmr_dict (Dict[str, np.ndarray]) – Dictionary mapping species names to their VMR profiles

  • folder_plot (Union[str, Path]) – Directory where the plot will be saved