Survey Setup Module¶

MGLensing.specs¶

class MGLensing.specs.CLOESetUp(config: dict)¶: Bases: object

class MGLensing.specs.EuclidSetUp(config: dict)¶

Bases: object

A class to set up the Euclid survey specifications and configurations.

Attributes:

survey_namestr: Name of the survey, default is ‘Euclid’.
observablestr: Observable configuration from the input config.
zminfloat: Minimum redshift value.
zmaxfloat: Maximum redshift value.
fskyfloat: Fraction of the sky covered by the survey.
gal_per_sqarcmnfloat: Number of galaxies per square arcminute.
gal_per_sqarcmn_sfloat: Number of source galaxies per square arcminute.
gal_per_sqarcmn_lfloat: Number of lens galaxies per square arcminute.
rms_shearfloat: Root mean square of the shear.
nbinint: Number of redshift bins.
z_bin_center_snp.ndarray: Centers of the source redshift bins.
z_bin_center_lnp.ndarray: Centers of the lens redshift bins.
eta_z_snp.ndarray: Normalized source galaxy distribution as a function of redshift.
eta_z_lnp.ndarray: Normalized lens galaxy distribution as a function of redshift.
zz_integrnp.ndarray: Array of redshift values used for integration.
zbin_integrint: Number of redshift bins used for integration.
noisedict: Dictionary containing noise values for different combinations of lensing and galaxy-galaxy correlations.
aa_integrnp.ndarray: Array of scale factors corresponding to the redshift values used for integration.
lminint: Minimum multipole value.
lbinint: Number of multipole bins.
lmaxfloat: Maximum multipole value.
ellnp.ndarray: Array of multipole values.
ell_bin_edgesnp.ndarray: Edges of the multipole bins.
d_ell_binnp.ndarray: Differences between consecutive multipole bin edges.
l_wl_bin_centersnp.ndarray: Centers of the multipole bins.
lum_funcnp.ndarray: Luminosity function data.
likelihoodstr: Likelihood configuration from the input config.
lmax_wl_vals, lmax_gc_valsnp.ndarray or float: Maximum scale-cuts.
l_wl, l_xc, l_gcnp.ndarray: Binned ell-values for different probes, for now l_xc=l_gc.
nell_wl, nell_xc, nell_gcint: Number of ell-bins per probe.
ells_wl, ells_gcnp.ndarray: All integer ell-values from lmin to lmax; required for likelihood with determinants.
ell_jump: int: Equals to lmax_gc, as we assume that lmax_gc<lmax_wl; required for likelihood with determinants.
mask_data_vector_wl, mask_data_vector_gc, mask_data_vector_3x2ptnp.ndarray: Boolean arrays to mask out data vectors for different lmax cuts in each redshift-bin.
mask_cov_wl, mask_cov_gc, mask_cov_3x2ptnp.ndarray: Boolean arrays to mask out covariances for different lmax cuts in each redshift-bin.
d_ell_bin_cut_wl, d_ell_bin_cut_xc, d_ell_bin_cut_gcnp.ndarray: Delta ell or bin width required for covariance computation.
ells_wl_max, ells_gc_maxnp.ndarray: Scale-cuts in the form appropriate for our plotting scripts.

galaxy_distribution(z)¶

Calculate the unnormalized galaxy distribution at a given redshift.

Parameters:¶

zfloat: The redshift at which to calculate the galaxy distribution.

Returns:¶

float: The unnormalized galaxy distribution at the given redshift.

get_norm_galaxy_distrib()¶

Returns the normalized source and lens galaxy distributions for each redshift value in each redshift bin. This function calculates the normalized galaxy distribution n(z) and the photo-z error for each redshift bin. The galaxy distribution is normalized using the trapezoidal rule for numerical integration.

Returns:¶

tuple

A tuple containing two numpy arrays:

norm_nz (numpy.ndarray): The normalized galaxy distribution for each redshift bin. Shape: (zbin_integr, nbin).
norm_nz (numpy.ndarray): The same normalized galaxy distribution, returned twice as the source distribution is equal to the lens distribution in Euclid. Shape: (zbin_integr, nbin).

photo_z_distribution(z, bin, tracer='s')¶

Calculate the photo-z error distribution for a given redshift and bin. It follows Eqs. (112, 113, 115) in https://arxiv.org/pdf/1910.09273.

Parameters:¶

zfloat: The redshift at which to calculate the photo-z error.
binint: The redshift bin index.

Returns:¶

float: The photo-z error distribution at the given redshift and bin.

class MGLensing.specs.LSSTSetUp(config: dict)¶

Bases: object

A class to set up the LSST survey specifications and configurations.

Attributes:

survey_namestr: Name of the survey, default is ‘LSST’.
observablestr: Observable configuration from the input config.
zminfloat: Minimum redshift value.
zmaxfloat: Maximum redshift value.
fskyfloat: Fraction of the sky covered by the survey.
gal_per_sqarcmnfloat: Number of galaxies per square arcminute.
gal_per_sqarcmn_sfloat: Number of source galaxies per square arcminute.
gal_per_sqarcmn_lfloat: Number of lens galaxies per square arcminute.
rms_shearfloat: Root mean square of the shear.
nbinint: Number of redshift bins.
z_bin_center_snp.ndarray: Centers of the source redshift bins.
z_bin_center_lnp.ndarray: Centers of the lens redshift bins.
eta_z_snp.ndarray: Normalized source galaxy distribution as a function of redshift.
eta_z_lnp.ndarray: Normalized lens galaxy distribution as a function of redshift.
zz_integrnp.ndarray: Array of redshift values used for integration.
zbin_integrint: Number of redshift bins used for integration.
noisedict: Dictionary containing noise values for different combinations of lensing and galaxy-galaxy correlations.
aa_integrnp.ndarray: Array of scale factors corresponding to the redshift values used for integration.
lminint: Minimum multipole value.
lbinint: Number of multipole bins.
lmaxfloat: Maximum multipole value.
ellnp.ndarray: Array of multipole values.
ell_bin_edgesnp.ndarray: Edges of the multipole bins.
d_ell_binnp.ndarray: Differences between consecutive multipole bin edges.
l_wl_bin_centersnp.ndarray: Centers of the multipole bins.
lum_funcnp.ndarray: Luminosity function data.
likelihoodstr: Likelihood configuration from the input config.
lmax_wl_valsnp.ndarray or float: Maximum scale-cuts for weak lensing.
lmax_gc_valsnp.ndarray or float: Maximum scale-cuts for galaxy clustering.
l_wlnp.ndarray: Binned ell-values for weak lensing.
l_xcnp.ndarray: Binned ell-values for cross-correlation.
l_gcnp.ndarray: Binned ell-values for galaxy clustering.
nell_wlint: Number of ell-bins for weak lensing.
nell_xcint: Number of ell-bins for cross-correlation.
nell_gcint: Number of ell-bins for galaxy clustering.
ells_wlnp.ndarray: All integer ell-values from lmin to lmax for weak lensing.
ells_gcnp.ndarray: All integer ell-values from lmin to lmax for galaxy clustering.
ell_jumpint: Equals to lmax_gc, as we assume that lmax_gc < lmax_wl; required for likelihood with determinants.
mask_data_vector_wlnp.ndarray: Boolean array to mask out data vectors for weak lensing.
mask_data_vector_gcnp.ndarray: Boolean array to mask out data vectors for galaxy clustering.
mask_data_vector_3x2ptnp.ndarray: Boolean array to mask out data vectors for 3x2pt.
mask_cov_wlnp.ndarray: Boolean array to mask out covariances for weak lensing.
mask_cov_gcnp.ndarray: Boolean array to mask out covariances for galaxy clustering.
mask_cov_3x2ptnp.ndarray: Boolean array to mask out covariances for 3x2pt.
d_ell_bin_cut_wlnp.ndarray: Delta ell or bin width required for covariance computation for weak lensing.
d_ell_bin_cut_xcnp.ndarray: Delta ell or bin width required for covariance computation for cross-correlation.
d_ell_bin_cut_gcnp.ndarray: Delta ell or bin width required for covariance computation for galaxy clustering.
ells_wl_maxnp.ndarray: Scale-cuts for weak lensing in the form appropriate for plotting scripts.
ells_gc_maxnp.ndarray: Scale-cuts for galaxy clustering in the form appropriate for plotting scripts.

get_norm_galaxy_distrib(file_name_l, file_name_s)¶: Reads the source and lens galaxy normalized distributions from a npy file and compresses them to the required number of redshifts.

get_norm_galaxy_distrib_sacc(file_name)¶: Reads the source and lens galaxy normalized distributions from a sacc file and compresses them to the required number of redshifts.

MGLensing.specs.check_length(scale_cuts_info, nbin_s, nbin_l)¶

Check the length of scale cuts information based on the type and number of bins.

Parameters:¶

scale_cuts_infodict: Dictionary containing scale cuts information with keys ‘type’, ‘max_WL’, and ‘max_GC’.
nbinint: Number of bins.

Raises:¶

ValueError: If the length of ‘max_WL’ or ‘max_GC’ does not match the expected number of bins.

MGLensing.specs.from_keff_to_lmax(n, k_eff, z_mod, params_dic)¶: Calculate lmax for given effective wavenumbers and redshift. Always for a lower photo-z: e.g., bin 2-1 or bin 4-1 will take k_eff for bin 1 to compute ell_max.

Parameters:¶

n (int): The number of bins. k_eff (list of float): The effective wavenumbers for each bin in h/Mpc. z_mod (list of float): The redshift, peak of the kernel, for each bin. params_dic (dict): Dictionary containing cosmological parameters.

Returns:¶

list of float: The calculated lmax values for each bin combination.

MGLensing.specs.get_luminosity_func(file_name)¶

Reads the luminosity function data from a file and returns an interpolation function.

Parameters:¶

file_namestr: The path to the file containing the luminosity function data.

Returns:¶

function: An interpolation function that takes redshift values and returns the corresponding luminosity values.

MGLensing.specs.get_noise(nbins, gal_per_sqarcmn)¶

Calculate the noise for a given number of redshift bins and galaxy density.

Parameters:¶

nbinsint: The number of redshift bins.
gal_per_sqarcmnfloat: The number of galaxies per square arcminute.

Returns:¶

float: The noise value for the given number of redshift bins and galaxy density.

MGLensing.specs.get_rcom(params_dic, z_max)¶

Calculate the comoving distance to a given redshift z_max.

Parameters:¶

params_dic (dict): Dictionary containing cosmological parameters.: Expected keys are ‘Omega_m’, ‘w0’, and ‘wa’. Defaults are used if keys are not present: ‘Omega_m’ defaults to 0.31, ‘w0’ defaults to -1.0, ‘wa’ defaults to 0.0.

z_max (float): The maximum redshift value to calculate the comoving distance for.

Returns:¶

float: The comoving distance to the given redshift z_max in units of Mpc/h.

MGLensing.specs.max_ells_for_plots(n1, n2, flat_array)¶

MGLensing.specs.reduce_len_by_averaging(arr_, target_len=400)¶

Reduces the length of an array by averaging its elements to match the target length.

Parameters:¶

arrlist or numpy.ndarray: The input array to be reduced.
target_lenint, optional: The desired length of the output array. Default is z_bins_for_integration.

Returns:¶

numpy.ndarray: The reduced array with the specified target length.

MGLensing.specs.setup_const_lmax(obj, likelihood)¶

Sets up the maximum multipole moments (lmax) for weak lensing (wl) and galaxy clustering (gc) based on the provided likelihood type. It also configures the ell bins, masks, and delta ells for covariance computation.

Parameters:¶

objobject: An object containing various attributes related to ell bins and lmax values.
likelihoodstr: The type of likelihood (‘determinants’ or other) to determine the setup process.

Returns:¶

None

MGLensing.specs.setup_lmax(obj)¶

Sets up the maximum multipole moments (lmax) for different probes in the given object. The function computes masks for data vectors and covariance matrices for weak lensing (wl) and galaxy clustering (gc) probes.

Parameters:¶

objobject: The object containing the necessary attributes for setting up lmax.

Attributes used from obj:¶

l_wl_bin_centers
d_ell_bin
ell_bin_edges
lmax_wl_vals
lmax_gc_vals
nbin

Returns:¶

None

MGLensing.specs.validate_and_setup_lmax(obj, scale_cuts_info, likelihood, lmin, lmax, zz_mod_wl, zz_mod_gg)¶

Validates and sets up the lmax values for weak lensing (WL) and galaxy clustering (GC) based on the provided scale cuts information.

Parameters:¶

objobject: The object containing the necessary attributes for setting up lmax values.
scale_cuts_infodict: Dictionary containing scale cuts information with keys: - ‘type’ (str): Type of scale cut (‘const_lmax’, ‘lmax’, or ‘kmax’). - ‘max_WL’ (list): Maximum l values for weak lensing. - ‘max_GC’ (list): Maximum l values for galaxy clustering. - ‘cosmo’ (dict, optional): Cosmological parameters required for ‘kmax’ type.
likelihoodstr: The likelihood approach (‘binned’ or other).
lminint: Minimum l value.
lmaxint: Maximum l value.
zz_mod_wlarray: Redshift modification array for weak lensing.
zz_mod_ggarray: Redshift modification array for galaxy clustering.

Raises:¶

ValueError: If any validation checks fail, such as: - lmax exceeding the provided maximum value. - Invalid scale cut type. - Incompatible likelihood approach for varied lmax. - lmax_gc being greater than lmax_wl in ‘const_lmax’ type.