petitRADTRANS.plotlib ===================== .. py:module:: petitRADTRANS.plotlib Submodules ---------- .. toctree:: :maxdepth: 1 /autoapi/petitRADTRANS/plotlib/plotlib/index /autoapi/petitRADTRANS/plotlib/style/index Attributes ---------- .. autoapisummary:: petitRADTRANS.plotlib.wavelength_units petitRADTRANS.plotlib.wavenumber_units petitRADTRANS.plotlib.flux_units Functions --------- .. autoapisummary:: petitRADTRANS.plotlib.contour_corner petitRADTRANS.plotlib.plot_cloud_condensation_curves petitRADTRANS.plotlib.plot_data petitRADTRANS.plotlib.plot_multiple_posteriors petitRADTRANS.plotlib.plot_opacity_contributions petitRADTRANS.plotlib.plot_planet_context petitRADTRANS.plotlib.plot_posterior petitRADTRANS.plotlib.plot_radtrans_opacities petitRADTRANS.plotlib.plot_result_corner petitRADTRANS.plotlib.get_species_color petitRADTRANS.plotlib.set_petitradtrans_plot_style petitRADTRANS.plotlib.update_figure_font_size Package Contents ---------------- .. py:function:: contour_corner(sampledict: dict[str, Any], parameter_names: dict[str, list[str]], output_file: str = None, parameter_ranges: dict[str, numpy.typing.NDArray[numpy.floating]] = None, parameter_plot_indices: dict[str, numpy.typing.NDArray[numpy.integer]] = None, true_values: dict[str, numpy.typing.NDArray[numpy.floating]] = None, short_name: dict[str, str] = None, quantiles: list[float] = None, hist2d_levels: list[float] = None, legend: bool = False, prt_plot_style: bool = True, plot_best_fit: bool = False, color_list: list[str] = None, bins: int = 20, axes_scale: str = 'linear', weights: numpy.typing.NDArray[numpy.floating] = None, hist_bin_factor: numpy.typing.NDArray[numpy.floating] = 1.0, smooth: float = None, smooth1d: float = None, titles: list = None, show_titles: bool = True, title_quantiles: list[float] = None, title_fmt: str = '.2f', truth_color: str = 'r', scale_hist: bool = False, fig: matplotlib.axes.Axes = None, max_n_ticks: int = 5, top_ticks: bool = False, use_math_text: bool = False, reverse: bool = False, labelpad: float = 0.0, plot_contours: bool = True, label_kwargs: dict = None, title_kwargs: dict = None, hist_kwargs: dict = None, hist2d_kwargs: dict = None, contour_kwargs: dict = None, **_) Use the corner package to plot the posterior distributions produced by pymultinest. Args: sampledict : dict A dictionary of samples, each sample has shape (N_Samples,N_params). The keys of the dictionary correspond to the names of each retrieval, and are the prefixes to the post_equal_weights.dat files. These are passed as arguments to retrieve.py. By default, this is only the current retrieval, and plots the posteriors for a single retrieval. If multiple names are passed, they are overplotted on the same figure. parameter_names : dict A dictionary with keys for each retrieval name, as in sampledict. Each value of the dictionary is the names of the parameters to be plotted, as set in the run_definition file. output_file : str Output file name parameter_ranges : dict A dictionary with keys for each retrieval name as in sampledict. Each value contains the ranges of parameters that have a range set with corner_range in the parameter class. Otherwise, the range is +/- 4 sigma parameter_plot_indices : dict A dictionary with keys for each retrieval name as in sampledict. Each value contains the indices of the sample to plotlib, as set by the plot_in_corner parameter of the parameter class true_values : dict A dictionary with keys for each retrieval name as in sampledict. Each value contains the known values of the parameters. short_name : dict A dictionary with keys for each retrieval name as in sampledict. Each value contains the names to be plotted in the corner plotlib legend. If non, uses the retrieval names used as keys for sampledict quantiles : list A list with the quantiles to plotlib over the 1D histograms. Note: the conversion from sigma to quantile is: quantile_m = (1 - erf(sigma / sqrt(2))) / 2 quantile_p = 1 - quantile_m hist2d_levels : list A list with the sigmas-level to plotlib over the 2D histograms. The sigmas are converted into their corresponding level-value following the formula for 2D normal distribution (see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4358977/pdf/pone.0118537.pdf). legend : bool Turn the legend on or off prt_plot_style : bool Use the prt plotlib style, changes the colour scheme and fonts to match the rest of the prt plots. plot_best_fit : bool Plot the maximum likelihood values as vertical lines for each parameter. color_list : list List of colours for plotting multiple retrievals. bins : int or array_like[ndim,] The number of bins to use in histograms, either as a fixed value for all dimensions, or as a list of integers for each dimension. axes_scale : str or iterable (ndim,) Scale (``"linear"``, ``"log"``) to use for each data dimension. If only one scale is specified, use that for all dimensions. weights : array_like[nsamples,] The weight of each sample. If `None` (default), samples are given equal weight. hist_bin_factor : float or array_like[ndim,] This is a factor (or list of factors, one for each dimension) that will multiply the bin specifications when making the 1-D histograms. This is generally used to increase the number of bins in the 1-D plots to provide more resolution. smooth : float The standard deviation for Gaussian kernel passed to scipy.ndimage.gaussian_filter` to smooth the 2-D histogram. If `None` (default), no smoothing is applied. smooth1d : float The standard deviation for Gaussian kernel passed to `scipy.ndimage.gaussian_filter` to smooth the 1-D histogram. If `None` (default), no smoothing is applied. titles : iterable (ndim,) A list of titles for the dimensions. If `None` (default), uses labels as titles. show_titles : bool Displays a title above each 1-D histogram showing the 0.5 quantile with the upper and lower errors supplied by the quantiles argument. title_quantiles : iterable A list of 3 fractional quantiles to show as the upper and lower errors. If `None` (default), inherit the values from quantiles, unless quantiles is `None`, in which case it defaults to [0.16, 0.5, 0.84]. title_fmt : string The format string for the quantiles given in titles. If you explicitly set ``show_titles=True`` and ``title_fmt=None``, the labels will be shown as the titles. (default: ``.2f``). truth_color : str A ``matplotlib`` style colour for the ``truths`` makers. scale_hist : bool Should the 1-D histograms be scaled in such a way that the zero line is visible? fig : `~matplotlib.figure.Figure` Overplot onto the provided figure object, which must either have no axes yet, or ``ndim * ndim`` axes already present. If not set, the plot will be drawn on a newly created figure. max_n_ticks: int Maximum number of ticks to try to use. top_ticks : bool If true, label the top ticks of each axis. use_math_text : bool If true, then axis tick labels for very large or small exponents will be displayed as powers of 10 rather than using `e`. reverse : bool If true, plot the corner plot starting in the upper-right corner instead of the usual bottom-left corner. labelpad : float Padding between the axis and the x- and y-labels in units of the fraction of the axis from the lower left. plot_contours : bool Draw contours for dense regions of the plot. kwargs : dict Each kwarg can be one of the kwargs used in corner.corner. These can be used to adjust the title_kwargs,label_kwargs,hist_kwargs, hist2d_kwargs or the contour kwargs. Each kwarg must be a dictionary with the arguments as keys and values as the values. .. py:function:: plot_cloud_condensation_curves(metallicities, co_ratios, pressures=None, temperatures=None) .. py:function:: plot_data(fig, ax, data, resolution=None, scaling=1.0) .. py:function:: plot_multiple_posteriors(result_directory, retrieved_parameters, log_evidences=None, true_values=None, add_rectangle=None, parameter_names_ref=None, bins=15, color='C0', figure_font_size=11, use_titles=True, save=False, figure_directory='./', figure_name='fig', image_format='png', fig_size=19.2) .. py:function:: plot_opacity_contributions(radtrans_object: petitRADTRANS.radtrans.Radtrans, mode: str, include: list = 'all', exclude: list = None, opacity_contributions: dict = None, colors: dict = None, line_styles: dict = None, fill_below: bool = False, fill_alpha: float = 0.5, x_axis_scale: str = 'linear', y_axis_scale: str = 'linear', fig_size: tuple = (12.8, 4.8), opaque_cloud_top_pressure: float = None, power_law_opacity_350nm: float = None, power_law_opacity_coefficient: float = None, gray_opacity: float = None, cloud_photosphere_median_optical_depth: float = None, additional_absorption_opacities_function: Callable = None, additional_scattering_opacities_function: Callable = None, stellar_intensities: numpy.typing.NDArray[numpy.floating] = None, star_radius: float = None, **kwargs) -> dict[str, numpy.typing.NDArray[numpy.floating]] .. py:function:: plot_planet_context(planet_name: str, mass_radius_uncertainty_tolerance: float = 0.15, fig_size: tuple[float, float] = (6.4, 5.6), figure_font_size: float = 12, plot_annotations: bool = True, plot_planet_references: bool = True, tight_layout: bool = True, save: bool = False, figure_directory: str = './', figure_name: str = 'result_corner', image_format: str = 'png') -> None Plot an exoplanet mass-radius scatter plot highlighting a planet using the NASA Exoplanet Archive. Args: planet_name: Name of the planet to highlight. mass_radius_uncertainty_tolerance: Plot only planets with a maximum relative mass and radius uncertainty lower to this value. fig_size: Size of the figure. figure_font_size: Base size of the figure font. plot_annotations: If True, plot the mass limits between rocky planets, sub-neptunes, ice giants and gas giants. plot_planet_references: If True, place Earth, Neptune and Jupiter on the scatter plot. tight_layout: If True, apply tight layout on the figure. save: If True, save the figure. figure_directory: Directory in which to save the figure. figure_name: Name of the figure. image_format: Image format of the saved figure. .. py:function:: plot_posterior(data, label=None, true_value=None, cmp=None, bins=15, color='C0', axe=None, y_max=None, y_label=None, tight_layout=True, fmt='.2f') .. py:function:: plot_radtrans_opacities(radtrans, species, temperature, pressure_bar, mass_fractions=None, co_ratio=0.55, log10_metallicity=0.0, return_opacities=False, **kwargs) .. py:function:: plot_result_corner(retrieval_directory: str, retrieved_parameters: dict, retrieval_name=None, true_values: dict[str, float] = None, spectral_model: petitRADTRANS.spectral_model.SpectralModel = None, smooth: float = 1.0, figure_font_size: float = 8, save: bool = True, figure_directory: str = './', figure_name: str = 'result_corner', image_format: str = 'png', **kwargs) Plot the posteriors of one or multiple retrievals in the same corner plot. Args: retrieval_directory: String or list of string containing the retrieval directories. retrieved_parameters: Dictionary containing all the retrieved parameters and their prior. retrieval_name: Name of the retrieval. If None, the name is extracted from the retrieval directory. true_values: True values for the parameters. spectral_model: SpectralModel used to make the retrievals. smooth : float The standard deviation for Gaussian kernel passed to scipy.ndimage.gaussian_filter` to smooth the 2-D histogram. If `None` (default), no smoothing is applied. figure_font_size: Size of the font in the figure. save: If True, save the figure. figure_directory: Directory to save the figure. figure_name: Name of the figure. image_format: Format (extension) of the figure. **kwargs: contour_corner keyword arguments. .. py:function:: get_species_color(species, implemented_only=False) .. py:data:: wavelength_units :value: 'cm' .. py:data:: wavenumber_units :value: 'cm$^{-1}$' .. py:data:: flux_units :value: 'erg$\\cdot$s$^{-1}\\cdot$cm${-2}$/cm$^{-1}$' .. py:function:: set_petitradtrans_plot_style() .. py:function:: update_figure_font_size(font_size) Update the figure font size in a nice way. :param font_size: new font size