Analysis

class nnero.analysis.AxesGrid(n: int, *, scale: float = 2.0, wspace: float = 0.05, hspace: float = 0.05, labels: list[str] | None = None, names: list[str] | None = None, **kwargs)

Bases: object

add_title(axis: int, new_titles: str, color: str | None = None)

get(i: int | str, j: int | str)

index_1D(i: int, j: int)

index_from_name(name: str | list[str])

indices_2D(k)

set_label(i: int, name: str)

show()

update_edges(axis: int | str, min: float, max: float)

update_labels(**kwargs)

update_titles(height=1.05, spacing=1.9, fontsize=None)

class nnero.analysis.EMCEESamples(path: str, add_tau: bool = False)

Bases: Samples

Daughter class of Samples for emcee chains.

convergence()

flat(discard: ndarray | None = None, thin: None | int = None, **kwargs) → ndarray

load_chains()

class nnero.analysis.GaussianInfo(mean: numpy.ndarray | None = None, cov: numpy.ndarray | None = None, param_names: numpy.ndarray | list[str] | None = None)

Bases: object

compatible_with(other: Self) → bool

cov: ndarray | None = None

mean: ndarray | None = None

param_names: ndarray | list[str] | None = None

class nnero.analysis.GaussianSamples(gaussians: list[GaussianInfo | str] | GaussianInfo | str, add_tau: bool = False, params: list[str] | None = None, *, n: int = 200000)

Bases: Samples

Daughter class of Samples for gaussian generated chains.

flat(discard: ndarray | None = None, thin: None | int = None, **kwargs) → ndarray

load_data(filename) → GaussianInfo

class nnero.analysis.MPChain(filename: str)

Bases: object

Class MPChain reading chains from MontePython output files

Parameters:

filename (str) – Path to the the file where the chain is stored.

compute_stats() → None

Compute the mean and standard deviation within the chain. Should be called after remove_burnin().

load() → None

Load the chain and automatically remove the non-markovian points.

remove_burnin(global_max_lnlkl: float) → None

Remove the burnin points according to the value of the maximum log likelihood over all chains. Only points of the chain that are after its overcrossing of global_max_lnlkl - 3 are kept.

Parameters:

global_max_lnlkl (float) – Global maximum log likelihood over all chains

values(discard: int = 0, thin: int = 1) → ndarray

Get the values of the chain.

Parameters:

• discard (int, optional) – Number of initial points to discard, by default 0.

• thin (int, optional) – Thining factor (taking only one value every value of thin), by default 1.

Return type:

np.ndarray with dimension (number of parameters, length of chain)

class nnero.analysis.MPSamples(path: str, ids: list[int] | ndarray | None = None)

Bases: Samples

Daughter class of Samples for MontePython chains.

convergence() → ndarray

Gelman-Rubin criterion weighted by the length of the chain as implemented in MontePython.

Returns:

R-1 for all parameters

Return type:

np.ndarray

covmat(discard: ndarray | None = None, params_in_cov: list[str] | None = None) → ndarray

Covariance matrix.

Parameters:

• discard (np.ndarray | None, optional) – Number of points to discard at begining of the chain, by default None (0).

• data_to_cov (list[str] | None, optional) – List of parameters to put in the covariance matrix (in the order of that list). If None consider all parameters available.

Returns:

Covariance matric (n, n) array

Return type:

np.ndarray

flat(discard: ndarray | None = None, thin: None | int = None, **kwargs) → ndarray

Flatten samples of all chains.

Parameters:

• discard (np.ndarray | None, optional) – Number of points to discard at begining of the chain, by default None (0).

• thin (None | int, optional) – Reduce the size of the sample by taking one point every thin, by default None. If Nont compute the reduced size such that the total length of the sample is 10000.

Returns:

Sample in a 2 dimensional array of shape (# of parameters, # of points)

Return type:

np.ndarray

load_chains() → None

load_paramnames()

load_scaling_factor() → None

print_best_fit(discard: ndarray | None = None, **kwargs)

class nnero.analysis.ProcessedData(hists_1D: numpy.ndarray | None = None, hists_2D: numpy.ndarray | None = None, edges: numpy.ndarray | None = None, centers: numpy.ndarray | None = None, levels: numpy.ndarray | None = None, q: numpy.ndarray | None = None, mean: numpy.ndarray | None = None, median: numpy.ndarray | None = None, bestfit: numpy.ndarray | None = None, quantiles: numpy.ndarray | None = None, samples: numpy.ndarray | None = None, size: int | None = None)

Bases: object

bestfit: ndarray | None = None

centers: ndarray | None = None

edges: ndarray | None = None

hists_1D: ndarray | None = None

hists_2D: ndarray | None = None

levels: ndarray | None = None

mean: ndarray | None = None

median: ndarray | None = None

q: ndarray | None = None

quantiles: ndarray | None = None

samples: ndarray | None = None

size: int | None = None

class nnero.analysis.Samples(path: str, ids: list[int] | ndarray | None = None)

Bases: ABC

Class containing all chains of a MCMC analysis

Parameters:

• path (str) – Path to the chains.

• ids (list | np.ndarray | None, optional) – List of chains to take into accoung. If none all possible found chains are added. By default None.

abstract flat(discard: ndarray | None = None, thin: None | int = None, **kwargs) → ndarray

nnero.analysis.compute_parameter(flat_chain: ndarray, param_names: list[str] | ndarray, classifier: Classifier | None = None, interpolator: Regressor | None = None, parameter: str | None = None) → ndarray

nnero.analysis.compute_quantiles(sample: ndarray, q: float, bins: int | ndarray = 30) → tuple[ndarray, ndarray]

Give the q-th quantile of the input sample.

Parameters:

• sample (np.ndarray) – 1D array of data points.

• q (float) – Quantile value.

• bins (int | np.ndarray, optional) – Binning of the histogram that is used for a first approximation of the quantile edges, by default 30.

Returns:

• tuple[np.ndarray, np.ndarray]

• min,max bounds

nnero.analysis.compute_tau(flat_chain: ndarray, param_names: list[str] | ndarray, classifier: Classifier | None = None, regressor: Regressor | None = None) → ndarray

nnero.analysis.generate_contours(samples: ndarray, bins: int = 20, q=[0.68, 0.95], smooth_2D: bool = False, smooth_1D: bool = False, sigma_smooth: float = 1.5) → ProcessedData

nnero.analysis.get_Xe_stats(samples: Samples, data_to_plot: list[str] | ndarray, nbins: int = 100, discard: int = 0, thin: int = 100, *, classifier: Classifier | None = None, regressor: Regressor | None = None, smooth: bool = False, sigma_smooth: float = 1.5, **kwargs)

nnero.analysis.get_Xe_tanh_stats(samples: Samples, nbins: int = 100, discard: int = 0, thin: int = 100, x_inf: float = 0.0002, *, smooth: bool = False, sigma_smooth: float = 1.5, **kwargs)

nnero.analysis.neutrino_masses(mnu_1, mnu_2=0.0, mnu_3=0.0, hierarchy='NORMAL')

nnero.analysis.plot_2D_marginal(ax: Axes, data: ProcessedData, i: int, j: int, show_hist: bool = False, show_surface: bool = True, show_contour: bool = False, show_points: bool = False, colors: list[str] = 'orange', alphas: list[float] = 1.0)

nnero.analysis.plot_data(grid: AxesGrid, data: ProcessedData, show_hist: bool = False, show_surface: bool = True, show_contour: bool = False, show_quantiles: list[bool] = [False, False], show_mean: bool = False, show_title: bool = True, show_points: bool = False, redefine_edges: bool = True, q_in_title: int = 0.68, colors: list[str] = 'orange', axes: list[int] | ndarray | None = None, exclude_quantiles: int | str | list[int] | list[str] = [], exclude_mean: int | str | list[int] | list[str] = [], exclude_title: int | str | list[int] | list[str] = [], alphas: list[float] = 1.0)

nnero.analysis.prepare_data_Xe(samples: Samples, data_to_plot: list[str] | ndarray, discard: int = 0, thin: int = 100, *, classifier: Classifier | None = None, regressor: Regressor | None = None)

nnero.analysis.prepare_data_plot(samples: Samples, data_to_plot, discard=0, thin=1, **kwargs)

nnero.analysis.save_sampling_parameters(filename: str, parameters_theta: list[str], parameters_xi: list[str], xi: ndarray)

nnero.analysis.to_21cmFAST_names(array: list[str] | ndarray)

nnero.analysis.to_CLASS_names(array: list[str] | ndarray)