selfisys.utils package

Submodules

selfisys.utils.examples_utils module

selfisys.utils.examples_utils.clear_large_plot(fig)

Clear a figure to free up memory.

Parameters:

fig (matplotlib.figure.Figure) – The figure to clear.

selfisys.utils.logger module

class selfisys.utils.logger.CustomLogger(name, level=0)

Bases: Logger

Custom logger class supporting custom verbosity levels in diagnostic messages.

diagnostic(msg, *args, verbosity=3, **kwargs) → None

Log a message with DIAGNOSTIC level.

Parameters:

• msg (str) – The message to log.

• verbosity (int, optional) – The verbosity level required to log this message.

class selfisys.utils.logger.CustomLoggerHandler(level=0)

Bases: Handler

Custom logging handler to redirect Python logger messages to custom print functions, with support for verbosity levels in debug messages.

emit(record)

Emit a log record.

selfisys.utils.logger.INDENT()

Indents the current level of outputs.

selfisys.utils.logger.PrintDiagnostic(verbosity, message)

Prints a diagnostic to screen.

Parameters:

• (int) (verbosity)

• (string) (message)

selfisys.utils.logger.PrintError(message)

Prints an error to screen.

Parameters:

(string) (message)

selfisys.utils.logger.PrintInfo(message)

Prints an information to screen.

Parameters:

(string) (message)

selfisys.utils.logger.PrintLeftType(message_type, FONT_COLOR)

Prints the type of output to screen.

Parameters:

• (string) (FONT_COLOR)

• (string)

selfisys.utils.logger.PrintWarning(message)

Prints a warning to screen.

Parameters:

(string) (message)

selfisys.utils.logger.UNINDENT()

Unindents the current level of outputs.

selfisys.utils.logger.getCustomLogger(name: str) → CustomLogger

Get as CustomLogger instance to use the custom printing routines.

Parameters:

name (str) – The name of the logger.

Returns:

logger – The custom logger instance.

Return type:

logging.Logger

selfisys.utils.low_level module

selfisys.utils.low_level.stderr_redirector(stream)

A context manager that redirects stderr to the given stream. For instance, this can be used to redirect C code stderr to None (to avoid cluttering the log, e.g., when using tqdm).

Use with caution.

Parameters:

stream (file-like object) – The stream to which stdout should be redirected.

selfisys.utils.low_level.stdout_redirector(stream)

A context manager that redirects stdout to the given stream. For instance, this can be used to redirect C code stdout to None (to avoid cluttering the log, e.g., when using tqdm).

Parameters:

stream (file-like object) – The stream to which stdout should be redirected.

Example

>>> with stdout_redirector(stream):
>>>     print("Hello world!") # Will be printed to stream
>>>                           # instead of stdout.

selfisys.utils.parser module

selfisys.utils.parser.bool_sh(value)

Convert a string to a boolean.

Parameters:

value (str) – String to convert.

Returns:

Converted value.

Return type:

bool

selfisys.utils.parser.check_files_exist(files)

Check if all files in the list exist.

Parameters:

files (list of str) – List of file paths to check.

Returns:

True if all files exist, False otherwise.

Return type:

bool

selfisys.utils.parser.intNone(value)

Convert a string to None or int.

Parameters:

value (str) – String to convert.

Returns:

Converted value.

Return type:

None or int

selfisys.utils.parser.joinstrs(list_of_strs)

Join a list of strings into a single string.

Parameters:

list_of_strs (list of str) – List of strings to join.

Returns:

Concatenated string.

Return type:

str

selfisys.utils.parser.joinstrs_only(list_of_strs)

Join a list of strings into a single string, ignoring all non-string elements such as None values.

Parameters:

list_of_strs (list of str) – List of strings to join.

Returns:

Concatenated string.

Return type:

str

selfisys.utils.parser.none_or_bool_or_str(value)

Convert a string to None, bool, or str.

Parameters:

value (str) – String to convert.

Returns:

Converted value.

Return type:

None, bool, or str

selfisys.utils.parser.safe_npload(path)

Load a numpy array from a file.

Parameters:

path (str) – Path to the file to load.

Returns:

Loaded array or None if the file does not exist.

Return type:

None or np.ndarray

selfisys.utils.path_utils module

selfisys.utils.path_utils.file_names_evaluate(simdir: str, sd: str, d: int, i: int, sim_params: str, TimeSteps: list[int], prefix_mocks: str, abc: bool = False, gravity_on: bool = True) → dict

Generate file paths for the given simulation id and parameters.

Parameters:

• simdir (str) – Path to the simulation directory.

• sd (str) – Path to the simulation directory for the given direction.

• d (int) – Direction index (-1 for mock data, 0 for the expansion point, or 1 to S).

• i (int) – Simulation index.

• sim_params (str) – Simulation parameters.

• TimeSteps (list of int) – List of time steps.

• prefix_mocks (str | None) – Prefix for mock data files. If None, defaults to “mocks”.

• abc (bool, optional) – If True, appends the ABC index to the white noise path.

• gravity_on (bool, optional) – Whether gravity is active. Default is True.

Returns:

Dictionary containing simulation inputs / outputs file paths.

Return type:

dict

selfisys.utils.path_utils.get_file_names(fsimdir: str, sim_id: int, sim_params: str, TimeSteps: list[int], prefix_mocks: str, gravity_on: bool = True, return_g: bool = False) → dict

Generate file paths for a given simulation ID and parameters.

Parameters:

• fsimdir (str) – Path to the simulation directory.

• sim_id (int) – Simulation ID.

• sim_params (str) – Simulation parameters.

• TimeSteps (list of int) – List of time steps.

• prefix_mocks (str | None) – Prefix for mock data files. If None, defaults to “mocks”.

• gravity_on (bool, optional) – Whether gravity is active. Default is True.

• return_g (bool, optional) – If True, return the file name for the observed galaxy field. Default is False.

Returns:

Dictionary containing simulation inputs / outputs file paths.

Return type:

dict

selfisys.utils.plot_examples module

selfisys.utils.plot_examples.plot_comoving_distance_redshift(zz, cosmo, means_com, L, Lcorner, wd, colours_list=['C4', 'C5', 'C6', 'C7'], display=True)

Plot comoving distance as a function of redshift, highlighting key scales.

Parameters:

• zz (array-like) – Redshift range for the plot.

• cosmo (astropy.cosmology object) – Cosmology instance for calculating comoving distances.

• means_com (array-like) – Mean comoving distances of selection functions.

• L (float) – Box side length in Gpc/h.

• Lcorner (float) – Diagonal of the box (sqrt(3) * L) in Gpc/h.

• wd (str) – Working directory for saving figures.

• colours_list (list) – List of colours for selection function annotations.

• display (bool, optional) – Whether to display the figure. Default is True.

selfisys.utils.plot_examples.plot_galaxy_field_slice(g, size, L, wd, id_obs, limits='minmax', display=True)

Plot a 2D slice of the observed field.

Parameters:

• g (ndarray) – 2D array representing the observed field slice.

• size (int) – Number of grid points along each axis.

• L (float) – Size of the simulation box (in Mpc/h).

• wd (str) – Working directory for saving output files.

• id_obs (int or str) – Identifier for the observation, used in file naming.

• limits (str, optional) – Colormap scaling method. Options: ‘minmax’, ‘truncate’, ‘max’.

• display (bool, optional) – Whether to display the figure. Default is True.

selfisys.utils.plot_examples.plot_power_spectrum(G_sim, true_P, k_s, planck_Pk, Pbins, Pbins_bnd, size, L, wd, title=None, display=True)

Plot a power spectrum over Fourier modes, its linear interpolation over specified support points, and a given binning for comparison.

Parameters:

• G_sim (pysbmy.power.FourierGrid) – Fourier grid object containing the k_modes attribute.

• true_P (pysbmy.power.PowerSpectrum) – Power spectrum object containing the powerspectrum attribute.

• k_s (array-like) – Support points in k-space.

• planck_Pk (array-like) – Power spectrum values at the support points.

• Pbins (array-like) – Centres of the Φ bins in k-space.

• Pbins_bnd (array-like) – Boundaries of the Φ bins in k-space.

• size (float) – Box size in number of grid cells.

• L (float) – Box length in Mpc/h.

• wd (str) – Working directory path for saving the figure.

• title (str, optional) – Title for the figure. Default is None.

• display (bool, optional) – Whether to display the figure. Default is True.

Return type:

None

selfisys.utils.plot_examples.plot_selection_functions_def_in_z(xx_of_zs, res, res_mis, z_means, cosmo, L, stds_z, wd, display=True)

Plot radial lognormal (in redshift) selection functions against comoving distances.

Parameters:

• xx_of_zs (array-like) – Comoving distances mapped from redshift.

• res (list of array-like) – Selection functions for the well-specified model.

• res_mis (list of array-like) – Selection functions for the mis-specified model.

• z_means (array-like) – Mean redshifts of every galaxy population.

• cosmo (object) – Cosmology object.

• L (float) – Box side length in comoving distance units.

• stds_z (array-like) – Standard deviations of redshift distributions.

• wd (str) – Working directory for saving figures.

• display (bool, optional) – Whether to display the figure. Default is True.

Return type:

None

selfisys.utils.plot_examples.redshift_distance_conversion(zz, cosmo, means_com, L, Lcorner, xx, wd, colours_list=['C4', 'C5', 'C6', 'C7'], display=True)

Plot the conversion between comoving distance and redshift; return the redshifts corresponding to the selection functions’ means.

Parameters:

• zz (array-like) – Redshift range for the plot.

• cosmo (astropy.cosmology object) – Cosmology instance for calculating comoving distances.

• means_com (array-like) – Mean comoving distances of selection functions.

• L (float) – Box side length in Gpc/h.

• Lcorner (float) – Diagonal of the box (sqrt(3) * L) in Gpc/h.

• xx (array-like) – Comoving distances at which to compute redshift.

• wd (str) – Working directory for saving figures.

• colours_list (list) – List of colours for selection function annotations.

• display (bool, optional) – Whether to display the figure. Default is True.

Returns:

spline – Linear interpolator to convert comoving distances to redshifts.

Return type:

scipy.interpolate.UnivariateSpline

selfisys.utils.plot_examples.relative_error_analysis(G_sim, true_P, k_s, planck_Pk, Pbins, Pbins_bnd, size, L, wd, display=True)

Compute and plot the relative error between the interpolated and true power spectra.

Parameters:

• G_sim (pysbmy.power.FourierGrid) – Fourier grid object containing the k_modes attribute.

• true_P (pysbmy.power.PowerSpectrum) – Power spectrum object containing the powerspectrum attribute.

• k_s (array-like) – Support points in k-space.

• planck_Pk (array-like) – Power spectrum values at the support points.

• Pbins (array-like) – Centres of the Φ bins in k-space.

• Pbins_bnd (array-like) – Boundaries of the Φ bins in k-space.

• size (float) – Box size in number of grid cells.

• L (float) – Box length in Mpc/h.

• wd (str) – Working directory path for saving the figure.

• display (bool, optional) – Whether to display the figure. Default is True.

Return type:

None

selfisys.utils.plot_params module

class selfisys.utils.plot_params.ScalarFormatterForceFormat_11(useOffset=True, useMathText=True, useLocale=None)

Bases: object

Custom scalar formatter to enforce a specific number format with an offset.

This formatter displays tick labels with one decimal place and includes the offset notation for powers of ten.

get_offset()

set_scientific(b)

set_useOffset(b)

selfisys.utils.plot_params.create_all_colormaps()

Create all custom colormaps.

Returns:

colormaps – Dictionary containing all custom colormaps.

Return type:

dict

selfisys.utils.plot_params.create_colormap(name)

Create a custom colormap based on the specified name.

Parameters:

name (str) – The name of the colormap to create.

Returns:

The requested custom colormap.

Return type:

ListedColormap

Raises:

ValueError – If the specified colormap name is not recognised.

selfisys.utils.plot_params.dynamic_text_scaling(fig_height)

Dynamically scale text sizes based on the vertical height of the figure.

Parameters:

fig_height (float) – Height of the figure in inches.

Returns:

Dictionary of scaled font sizes for consistent appearance.

Return type:

dict

selfisys.utils.plot_params.get_contours(Z, nBins, confLevels=(0.3173, 0.0455, 0.0027))

Compute contour levels for given confidence levels.

Parameters:

• Z (ndarray) – 2D histogram or density estimate.

• nBins (int) – Number of bins along one axis.

• confLevels (tuple of float) – Confidence levels for which to compute contour levels.

Returns:

chainLevels – Contour levels corresponding to the provided confidence levels.

Return type:

ndarray

selfisys.utils.plot_params.reset_plotting()

selfisys.utils.plot_params.setup_plotting()

Configure Matplotlib plotting settings for consistent appearance.

selfisys.utils.plot_utils module

selfisys.utils.plot_utils.plot_C(C_0, X, Y, Pbins, CMap, binning=True, suptitle=None, savepath=None, force=False)

Plot covariance matrix.

Parameters:

• C_0 (ndarray) – Covariance matrix.

• X (ndarray) – X-axis grid for plotting.

• Y (ndarray) – Y-axis grid for plotting.

• Pbins (ndarray) – Vector of bin boundaries for the summary statistics.

• CMap (str) – Colormap for the plot.

• binning (bool, optional) – Whether to overlay bin lines on the plot. Default is True.

• suptitle (str, optional) – Title for the plot. If None, a default title is used.

• savepath (str or Path, optional) – Path to save the plot. If None, the plot is displayed.

• force (bool, optional) – If True, displays the plot even if savepath is specified.

Raises:

RuntimeError – If unexpected errors occur during plotting.

selfisys.utils.plot_utils.plot_fisher(F0, params_names_fisher, title=None, path=None)

Plot the Fisher matrix as a heatmap.

Parameters:

• F0 (ndarray) – Fisher matrix.

• params_names_fisher (list of str) – Names for the axes.

• title (str, optional) – Title of the plot. Default is “Fisher matrix”.

• path (str or Path, optional) – Path to save the plot. If None, the plot is displayed.

Raises:

RuntimeError – If unexpected errors occur during plotting.

selfisys.utils.plot_utils.plot_gradients(Pbins, P, df_16_full, df_32_full, df_48_full, df_full, k_s, X, Y, fixscale=False, force=False, suptitle='', savepath=None)

Plot gradients.

Parameters:

• Pbins (ndarray) – Vector of bin boundaries for the summary statistics.

• P (int) – Number of bins.

• df_16_full (ndarray) – Derivative with respect to the 16th input.

• df_32_full (ndarray) – Derivative with respect to the 32nd input.

• df_48_full (ndarray) – Derivative with respect to the 48th input.

• df_full (ndarray) – Full derivative.

• k_s (ndarray) – Wavenumbers.

• X (ndarray) – X-axis grid.

• Y (ndarray) – Y-axis grid.

• fixscale (bool, optional) – Fix the y-axis scale. Default is False.

• force (bool, optional) – Display plot even if savepath is set.

• suptitle (str, optional) – Title for the plot.

• savepath (str or Path, optional) – Path to save the plot. If None, the plot is displayed.

Raises:

RuntimeError – If unexpected errors occur during plotting.

selfisys.utils.plot_utils.plot_histogram(data, recmahal, suptitle, savepath, bins=30, alpha=0.5, color='blue')

Plot a Mahalanobis distance histogram with key reference lines.

Parameters:

• data (array-like) – Collection of Mahalanobis distances.

• recmahal (float) – Single Mahalanobis distance for the reconstruction (e.g. posterior vs. prior mean).

• suptitle (str) – Figure title.

• savepath (str) – Full path (including filename) to save the resulting plot.

• bins (int, optional) – Number of bins for the histogram. Default is 30.

• alpha (float, optional) – Transparency level for the histogram bars. Default is 0.5.

• color (str, optional) – Colour of the histogram bars. Default is “blue”.

Raises:

• OSError – If the file cannot be saved to the specified path.

• RuntimeError – For unexpected plotting failures.

selfisys.utils.plot_utils.plot_mocks(NORM, N, P, Pbins, phi_obs, Phi_0, f_0, C_0, X, Y, CMap, suptitle=None, force_plot=False, savepath=None)

Plot mocks.

Parameters:

• NORM (float) – Normalisation factor.

• N (int) – Number of mocks.

• P (int) – Number of bins.

• Pbins (ndarray) – Vector of bin boundaries for the summary statistics.

• phi_obs (ndarray) – Observed power spectrum.

• Phi_0 (ndarray) – Mock power spectra.

• f_0 (ndarray) – Averaged power spectrum.

• C_0 (ndarray) – Covariance matrix.

• X (ndarray) – X-axis grid.

• Y (ndarray) – Y-axis grid.

• CMap (str) – Colormap.

• suptitle (str, optional) – Title for the plot.

• force_plot (bool, optional) – Display plot even if savepath is set.

• savepath (str or Path, optional) – Path to save the plot. If None, the plot is displayed.

Raises:

RuntimeError – If unexpected errors occur during plotting.

selfisys.utils.plot_utils.plot_mocks_compact(NORM, N, P, Pbins, phi_obs, Phi_0, f_0, C_0, suptitle=None, force_plot=False, savepath=None)

Plot and compare observed and simulated power spectra.

Parameters:

• NORM (float) – Normalisation factor for the observed spectra.

• N (int) – Number of mock data realisations.

• P (int) – Number of bins for the summaries.

• Pbins (ndarray) – Vector of bin boundaries for the summary statistics.

• phi_obs (ndarray) – Observed power spectrum.

• Phi_0 (ndarray) – Mock realisations of the power spectrum.

• f_0 (ndarray) – Mean power spectrum.

• C_0 (ndarray) – Covariance matrix of the mock summaries.

• suptitle (str, optional) – Title for the plot.

• force_plot (bool, optional) – If True, displays the plot even if a path is specified.

• savepath (str or Path, optional) – Path to save the plot.

Raises:

RuntimeError – If an unexpected error occurs during plotting.

selfisys.utils.plot_utils.plot_observations(k_s, theta_gt, planck_Pk_EH, P_0, Pbins, phi_obs, Npop, path=None, force_plot=False)

Plot the observed power spectra and related quantities.

Parameters:

• k_s (ndarray) – Array of wavenumbers.

• theta_gt (ndarray) – Ground truth theta values.

• planck_Pk_EH (ndarray) – Planck power spectrum values.

• P_0 (float) – Normalisation constant for power spectra.

• Pbins (ndarray) – Vector of bin boundaries for the summary statistics.

• phi_obs (ndarray) – Observed summaries.

• Npop (int) – Number of populations.

• path (str, optional) – Path to save the output plot.

• force_plot (bool, optional) – If True, displays the plot even if a path is specified.

Raises:

RuntimeError – If unexpected errors occur during plotting.

selfisys.utils.plot_utils.plot_prior_and_posterior_covariances(X, Y, k_s, prior_theta_covariance, prior_covariance, posterior_theta_covariance, P_0, force_plot=False, suptitle='', savepath=None)

Plot prior and posterior covariance matrices.

Parameters:

• X (ndarray) – X-axis grid.

• Y (ndarray) – Y-axis grid.

• k_s (ndarray) – Wavenumbers.

• prior_theta_covariance (ndarray) – Prior covariance matrix for normalised spectra.

• prior_covariance (ndarray) – Prior covariance matrix for unnormalised spectra.

• posterior_theta_covariance (ndarray) – Posterior covariance matrix for normalised spectra.

• P_0 (ndarray) – Fiducial power spectrum used for normalisation.

• force_plot (bool, optional) – Display plot even if savepath is set.

• suptitle (str, optional) – Title for the plot.

• savepath (str or Path, optional) – Path to save the plot. If None, the plot is displayed.

• verbose (int, optional) – Verbosity level (0=silent, 1=default, 2=detailed).

Raises:

RuntimeError – If unexpected errors occur during plotting.

selfisys.utils.plot_utils.plot_reconstruction(k_s, Pbins, prior_theta_mean, prior_theta_covariance, posterior_theta_mean, posterior_theta_covariance, theta_gt, P_0, phi_obs=None, suptitle=None, theta_fid=None, savepath=None, force_plot=False, legend_loc='upper right', enforce_ylims=True)

Plot the prior, posterior and ground truth power spectra.

Parameters:

• k_s (ndarray) – Array of wavenumbers.

• Pbins (ndarray) – Vector of bin boundaries for the summary statistics.

• prior_theta_mean (ndarray) – Mean of the prior distribution.

• prior_theta_covariance (ndarray) – Covariance of the prior distribution.

• posterior_theta_mean (ndarray) – Mean of the posterior distribution.

• posterior_theta_covariance (ndarray) – Covariance of the posterior distribution.

• theta_gt (ndarray) – Ground truth power spectrum.

• P_0 (float) – Normalisation constant for the power spectrum.

• phi_obs (ndarray, optional) – Observed summaries.

• suptitle (str, optional) – Plot title. Leave empty for no title.

• theta_fid (ndarray, optional) – Fiducial theta for hyperparameter tuning (for some priors).

• savepath (str or Path, optional) – Path to save the plot.

• force_plot (bool, optional) – If True, displays the plot even if a path is specified.

• legend_loc (str, optional) – Location of the plot legend.

• enforce_ylims (bool, optional) – Enforce y-axis limits.

Raises:

RuntimeError – If unexpected errors occur during plotting.

selfisys.utils.plot_utils.plot_selection_functions(x, res, res_mis, params, L, corner, axis='com', zz=None, zcorner=None, z_L=None, path=None, force_plot=False, labsAB=True)

Plot selection functions.

Parameters:

• x (array-like) – x-axis values (e.g., comoving distances or redshifts).

• res (list of array-like) – Selection functions for Model A.

• res_mis (list of array-like, optional) – Selection functions for Model B (optional).

• params (tuple of (array-like, array-like, array-like), optional) – Standard deviations, means, and normalisation factors for the multiple galaxy populations.

• L (float) – Box size.

• corner (float) – Diagonal box size.

• axis (str, optional) – x-axis type (‘com’ for comoving distance, ‘redshift’ for redshift).

• zz (array-like, optional) – Mapping between comoving distances and redshifts.

• zcorner (float, optional) – Redshift corresponding to the diagonal box size.

• z_L (float, optional) – Redshift corresponding to the box side length.

• path (str, optional) – Path to save the output plot.

• force_plot (bool, optional) – If True, displays the plot even if a path is specified.

• labsAB (bool, optional) – If True, labels models as ‘Model A’ and ‘Model B’.

Raises:

RuntimeError – If unexpected errors occur during plotting.

selfisys.utils.plot_utils.plotly_3d(field, size=128, L=None, colormap='RdYlBu', limits='max')

Create an interactive 3D plot of volume slices using Plotly.

Parameters:

• field (array-like) – 3D data field to visualise.

• size (int, optional) – Size of the field along one dimension. Default is 128.

• L (float, optional) – Physical size of the field in Mpc/h. Used for axis labels only.

• colormap (str, optional) – Colour map for visualisation. Default is ‘RdYlBu’.

• limits (str, optional) – Colour scale limits (‘max’, ‘truncate’, or ‘default’). Default is ‘max’.

Returns:

Plotly figure object.

Return type:

go.Figure

selfisys.utils.timestepping module

selfisys.utils.timestepping.merge_nTS(ts_path_list, merged_path)

Merge multiple time-stepping objects into a single file.

Parameters:

• ts_path_list (list of str) – Paths to the individual time-stepping files to be merged.

• merged_path (str) – Path to save the merged time-stepping file.

Return type:

None

selfisys.utils.tools module

selfisys.utils.tools.cosmo_vector_to_Simbelmyne_dict(x, kmax=1.4)

Convert a vector of cosmological parameters into a dictionary compatible with pysbmy.

Parameters:

• x (array-like) – Vector of cosmological parameters.

• kmax (float, optional) – Maximum wavenumber for the power spectrum computation.

Returns:

Dictionary of cosmological parameters compatible with pysbmy.

Return type:

dict

selfisys.utils.tools.cosmo_vector_to_class_dict(x, lmax=2500, kmax=1.4)

Convert a vector of cosmological parameters into a dictionary compatible with classy.

Parameters:

• x (array-like) – Vector of cosmological parameters.

• lmax (int, optional) – Maximum multipole for the power spectrum computation.

• kmax (float, optional) – Maximum wavenumber for the power spectrum computation.

Returns:

Dictionary of cosmological parameters compatible with classy.

Return type:

dict

selfisys.utils.tools.custom_stat(vec)

Compute a custom statistic for use with scipy.stats.binned_statistic.

Assumes the data power spectrum is inverse-Gamma distributed (as in [jasche2010bayesian] and [leclercq2019primordial]). Returns “NaN” for vectors with insufficient elements, as expected by scipy.stats.binned_statistic.

Parameters:

vec (array-like) – Input vector for computation.

Returns:

Custom statistic or NaN if input is invalid.

Return type:

float or str

selfisys.utils.tools.fisher_rao(Com, Com_obs, F0)

Compute the Fisher-Rao distance between two summaries.

Parameters:

• Com (array-like) – Computed summary.

• Com_obs (array-like) – Observed summary.

• F0 (array-like) – Fisher information matrix.

Returns:

Fisher-Rao distance.

Return type:

float

selfisys.utils.tools.get_k_max(L, size)

Compute the maximum wavenumber for a given box size.

Parameters:

• L (float) – Size of the box in Mpc/h.

• size (int) – Number of grid cells along each dimension.

Returns:

Maximum wavenumber in h/Mpc.

Return type:

float

selfisys.utils.tools.get_summary(params_vals, params_ids, Omegas_fixed, bins, normalisation=None, kmax=1.4)

Compute the normalised power spectrum summary for a given parameter set.

Parameters:

• params_vals (array-like) – Parameter values to update.

• params_ids (array-like) – Indices of the parameters to update.

• Omegas_fixed (array-like) – Fixed base values of parameters.

• bins (array-like) – Power spectrum bins.

• normalisation (float, optional) – Normalisation factor for the summary.

• kmax (float, optional) – Maximum wavenumber for power spectrum computation.

Returns:

Normalised power spectrum summary.

Return type:

array

selfisys.utils.tools.none_or_bool_or_str(value)

Convert string representations of None, True, and False to their respective Python objects; otherwise, return the input value.

selfisys.utils.tools.params_ids_to_Simbelmyne_dict(params_vals, params_ids, fixed, kmax)

Convert a list of cosmological parameters into a dictionary compatible with pysbmy.

Fixed parameters remain unchanged unless overridden by params_vals.

Parameters:

• params_vals (array-like) – Values of the parameters to be modified.

• params_ids (array-like) – Indices of the parameters to be modified.

• fixed (array-like) – Base values of the parameters.

• kmax (float) – Maximum wavenumber for the power spectrum computation.

Returns:

Dictionary of cosmological parameters compatible with pysbmy.

Return type:

dict

selfisys.utils.tools.sample_omega_from_prior(nsample, omega_mean, omega_cov, params_ids, seed=None)

Sample cosmological parameters from a prior distribution.

Ensures physical validity by clipping values to [eps, 1-eps].

Parameters:

• nsample (int) – Number of samples to draw.

• omega_mean (array-like) – Prior mean vector.

• omega_cov (array-like) – Prior covariance matrix.

• params_ids (array-like) – Indices of the parameters to sample.

• seed (int, optional) – Seed for the random number generator.

Returns:

Sampled cosmological parameters.

Return type:

array

selfisys.utils.tools.summary_to_score(params_ids, omega0, F0, F0_inv, f0, dw_f0, C0_inv, phi)

Compute the Fisher score.

Parameters:

• params_ids (array-like) – Indices of the parameters.

• omega0 (array-like) – Cosmological parameters at the expansion point.

• F0 (array-like) – Fisher information matrix.

• F0_inv (array-like) – Inverse Fisher information matrix.

• f0 (array-like) – Mean model at the expansion point.

• dw_f0 (array-like) – Derivative of the mean model.

• C0_inv (array-like) – Inverse covariance matrix.

• phi (array-like) – Observed summary.

Returns:

Fisher score.

Return type:

array

selfisys.utils.workers module

selfisys.utils.workers.Simbelmyne_worker(args) → Tuple[float, Any]

Worker function used for implicit likelihood inference of cosmological parameters.

Parameters:

args (tuple) – A tuple of arguments to be unpacked for the worker routine: (index, param_val, param_id, fsimdir, k_s, Pbins_bnd, selection_params, norm_csts, P_ss_obj_path, obs_density, lin_bias, noise, survey_mask_path, G_sim_path, G_ss_path, Np0, Npm0, seedphase_init, seednoise_init, size, L, radial_selection, sim_params, wd, batch_idx, dbg, modeldir, local_mask_prefix, TimeStepDistribution, indices_steps_cumul, eff_redshifts, poolname_abc, setup_only, prefix_mocks).

Returns:

(param_val, Phi) where param_val is the parameter value used, and Phi is the resulting summary from evaluating the model.

Return type:

tuple

Raises:

• OSError – If file I/O (reading or writing mock data) fails.

• RuntimeError – For unexpected errors in the worker routine.

selfisys.utils.workers.evaluate_gradient_of_Symbelmyne(omega, param_index: int, k_s, coeffs: List[float] = [0.6666666666666666, -0.08333333333333333], deltas_x: List[float] = [0.01, 0.02], delta: float = 0.01, kmax: float = 1.4)

Estimate the gradient of CLASS with respect to the cosmological parameters using central finite differences of arbitrary order.

Parameters:

• omega (ndarray) – Base cosmological parameter vector.

• param_index (int) – Index of the parameter to differentiate against.

• k_s (ndarray) – Wavenumbers for the power spectrum.

• coeffs (list of float, optional) – Coefficients for the finite-difference scheme, typically [2/3, -1/12] etc. Default is [2/3.0, -1/12.0].

• deltas_x (list of float, optional) – Step sizes. The corresponding negative steps are generated automatically. Default is [0.01, 0.02].

• delta (float, optional) – Scale for the finite difference in the denominator. Default is 1e-2.

• kmax (float, optional) – Maximum wavenumber for the power spectrum. Default is 1.4.

Returns:

The gradient of the power spectrum wrt the specified parameter.

Return type:

ndarray

Raises:

RuntimeError – If the gradient evaluation fails.

selfisys.utils.workers.worker_gradient_Symbelmyne(coeff: float, delta_x: float, omega, param_index: int, k_s, delta: float, kmax: float)

Worker function for evaluating the gradient of the power spectrum using finite differences.

Parameters:

• coeff (float) – Coefficient for the finite difference.

• delta_x (float) – Step size in the parameter space.

• omega (ndarray) – Base cosmological parameter vector.

• param_index (int) – Index of the parameter being varied.

• k_s (ndarray) – Array of wavenumbers.

• delta (float) – Denominator for finite differences (scaled).

• kmax (float) – Maximum wavenumber for power spectrum.

Returns:

The gradient of the power spectrum wrt the specified parameter.

Return type:

ndarray

Raises:

RuntimeError – If the gradient evaluation fails.

Module contents