estats.stats package

Submodules

estats.stats.CLs module

estats.stats.CLs.CLs(map, weights, ctx)[source]

Calculates the Angular Power spectrum. :param map: A Healpix convergence map :param weights: A Healpix map with pixel weights :param ctx: Context instance :return: Cross CLs

estats.stats.CLs.context()[source]

Defines the paramters used by the plugin

estats.stats.CLs.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CLs.filter(ctx)[source]
estats.stats.CLs.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CLs.slice(ctx)[source]

estats.stats.CrossCLs module

estats.stats.CrossCLs.CrossCLs(map1, map2, weights1, weights2, ctx)[source]

Calculates the Cross Angular Power spectrum of two convergence maps. :param map1: A Healpix convergence map :param map2: A second Healpix convergence map :param weights1: A Healpix map with pixel weights :param weights2: A second Healpix map with pixel weights :param ctx: Context instance :return: Cross CLs

estats.stats.CrossCLs.context()[source]

Defines the paramters used by the plugin

estats.stats.CrossCLs.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CrossCLs.filter(ctx)[source]
estats.stats.CrossCLs.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CrossCLs.slice(ctx)[source]

estats.stats.CrossMinkowski module

estats.stats.CrossMinkowski.CrossMinkowski(kappa_w, weights, ctx)[source]

Calculates Minkowski functionals on a convergence map. This is a very crude approximation that will not match with theory! Preferable for forward modelling due to speed. :param kappa_w: A Healpix convergence map :param weights: A Healpix map with pixel weights :param ctx: Context instance :return: Minkowski functionals as V0,V1,V2.

estats.stats.CrossMinkowski.CrossMinkowski_proper(kappa, weights, ctx)[source]

Proper calculation of Minkowski functionals. nvolves a lot of alm decompositions and is therfore quite slow. For forward modelling use CrossMinkowski function instead. :param kappa: A Healpix convergence map :param weights: A Healpix map with pixel weights :param ctx: Context instance :return: Minkowski functionals as V0,V1,V2.

estats.stats.CrossMinkowski.context()[source]

Defines the paramters used by the plugin

estats.stats.CrossMinkowski.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CrossMinkowski.filter(ctx)[source]
estats.stats.CrossMinkowski.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CrossMinkowski.slice(ctx)[source]

estats.stats.CrossPeaks module

estats.stats.CrossPeaks.CrossPeaks(map_w, weights, ctx)[source]

Calculates Peaks on a convergence map. :param map_w: A Healpix convergence map :param weights: A Healpix map with pixel weights :param ctx: Context instance :return: Peak abundance function

estats.stats.CrossPeaks.context()[source]

Defines the paramters used by the plugin

estats.stats.CrossPeaks.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CrossPeaks.filter(ctx)[source]
estats.stats.CrossPeaks.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CrossPeaks.slice(ctx)[source]

estats.stats.CrossShearCLs module

estats.stats.CrossShearCLs.CrossShearCLs(map_1, map_2, map_1_sec, map_2_sec, weight_map_1, weight_map_2, ctx)[source]

Calculates cross angular power spectrum of two sets of shear maps. :param map_1: A Healpix shear map, first shear component. :param map_2: A Healpix shear map, second shear component. :param map_1_sec: A second Healpix shear map, first shear component. :param map_2_sec: A second Healpix shear map, second shear component. :param weight_map_1: A Healpix map with pixel weights for the first shear maps :param weight_map_2: A Healpix map with pixel weights for the second shear maps

estats.stats.CrossShearCLs.context()[source]

Defines the paramters used by the plugin

estats.stats.CrossShearCLs.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CrossShearCLs.filter(ctx)[source]
estats.stats.CrossShearCLs.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CrossShearCLs.slice(ctx)[source]

estats.stats.CrossStarletL1Norm module

estats.stats.CrossStarletL1Norm.CrossStarletL1Norm(map_w, weights, ctx)[source]

Performs Starlet decompostion of map and calculates the L1 norm of each filter band. :param map: A Healpix convergence map :param weights: A Healpix map with pixel weights :param ctx: Context instance :return: Starlet L1 norm (num filter bands * Starlet_steps)

estats.stats.CrossStarletL1Norm.context()[source]

Defines the paramters used by the plugin

estats.stats.CrossStarletL1Norm.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CrossStarletL1Norm.filter(ctx)[source]
estats.stats.CrossStarletL1Norm.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CrossStarletL1Norm.slice(ctx)[source]

estats.stats.CrossStarletL1NormDi module

estats.stats.CrossStarletL1NormDi.CrossStarletL1NormDi(map_w, weights, ctx)[source]

Performs Starlet decompostion of map and calculates the L1 norm of each filter band. Uses the dyadic scheme. :param map: A Healpix convergence map :param weights: A Healpix map with pixel weights :param ctx: Context instance :return: Starlet L1 norm (num filter bands * Starlet_steps)

estats.stats.CrossStarletL1NormDi.context()[source]

Defines the paramters used by the plugin

estats.stats.CrossStarletL1NormDi.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CrossStarletL1NormDi.filter(ctx)[source]
estats.stats.CrossStarletL1NormDi.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CrossStarletL1NormDi.slice(ctx)[source]

estats.stats.CrossStarletMinkowski module

estats.stats.CrossStarletMinkowski.CrossStarletMinkowski(map_w, weights, ctx)[source]

Performs the starlet-wavelet decomposition of map and counts the local maxima in each filter band. :param map: A Healpix convergence map :param weights: A Healpix map with pixel weights (integer >=0) :param ctx: Context instance :return: Starlet counts (num filter bands, Starlet_steps + 1)

estats.stats.CrossStarletMinkowski.context()[source]

Defines the paramters used by the plugin

estats.stats.CrossStarletMinkowski.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CrossStarletMinkowski.filter(ctx)[source]
estats.stats.CrossStarletMinkowski.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CrossStarletMinkowski.slice(ctx)[source]

estats.stats.CrossStarletMinkowskiDi module

estats.stats.CrossStarletMinkowskiDi.CrossStarletMinkowskiDi(map_w, weights, ctx)[source]

Performs the starlet-wavelet decomposition of map and counts the local maxima in each filter band. :param map: A Healpix convergence map :param weights: A Healpix map with pixel weights (integer >=0) :param ctx: Context instance :return: Starlet counts (num filter bands, Starlet_steps + 1)

estats.stats.CrossStarletMinkowskiDi.context()[source]

Defines the paramters used by the plugin

estats.stats.CrossStarletMinkowskiDi.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CrossStarletMinkowskiDi.filter(ctx)[source]
estats.stats.CrossStarletMinkowskiDi.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CrossStarletMinkowskiDi.slice(ctx)[source]

estats.stats.CrossStarletPeaks module

estats.stats.CrossStarletPeaks.CrossStarletPeaks(map_w, weights, ctx)[source]

Performs the starlet-wavelet decomposition of map and counts the local maxima in each filter band. :param map: A Healpix convergence map :param weights: A Healpix map with pixel weights (integer >=0) :param ctx: Context instance :return: Starlet counts (num filter bands, Starlet_steps + 1)

estats.stats.CrossStarletPeaks.context()[source]

Defines the paramters used by the plugin

estats.stats.CrossStarletPeaks.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CrossStarletPeaks.filter(ctx)[source]
estats.stats.CrossStarletPeaks.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CrossStarletPeaks.slice(ctx)[source]

estats.stats.CrossStarletPeaksDi module

estats.stats.CrossStarletPeaksDi.CrossStarletPeaksDi(map_w, weights, ctx)[source]

Performs the starlet-wavelet decomposition of map and counts the local maxima in each filter band. :param map: A Healpix convergence map :param weights: A Healpix map with pixel weights (integer >=0) :param ctx: Context instance :return: Starlet counts (num filter bands, Starlet_steps + 1)

estats.stats.CrossStarletPeaksDi.context()[source]

Defines the paramters used by the plugin

estats.stats.CrossStarletPeaksDi.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CrossStarletPeaksDi.filter(ctx)[source]
estats.stats.CrossStarletPeaksDi.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CrossStarletPeaksDi.slice(ctx)[source]

estats.stats.CrossStarletVoids module

estats.stats.CrossStarletVoids.CrossStarletVoids(map_w, weights, ctx)[source]

Performs the starlet-wavelet decomposition of map and counts the local maxima in each filter band. :param map: A Healpix convergence map :param weights: A Healpix map with pixel weights (integer >=0) :param ctx: Context instance :return: Starlet counts (num filter bands, Starlet_steps + 1)

estats.stats.CrossStarletVoids.context()[source]

Defines the paramters used by the plugin

estats.stats.CrossStarletVoids.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CrossStarletVoids.filter(ctx)[source]
estats.stats.CrossStarletVoids.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CrossStarletVoids.slice(ctx)[source]

estats.stats.CrossStarletVoidsDi module

estats.stats.CrossStarletVoidsDi.CrossStarletVoidsDi(map_w, weights, ctx)[source]

Performs the starlet-wavelet decomposition of map and counts the local maxima in each filter band. :param map: A Healpix convergence map :param weights: A Healpix map with pixel weights (integer >=0) :param ctx: Context instance :return: Starlet counts (num filter bands, Starlet_steps + 1)

estats.stats.CrossStarletVoidsDi.context()[source]

Defines the paramters used by the plugin

estats.stats.CrossStarletVoidsDi.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CrossStarletVoidsDi.filter(ctx)[source]
estats.stats.CrossStarletVoidsDi.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CrossStarletVoidsDi.slice(ctx)[source]

estats.stats.CrossVoids module

estats.stats.CrossVoids.CrossVoids(map_w, weights, ctx)[source]

Calculates Voids on a convergence map. :param map_w: A Healpix convergence map :param weights: A Healpix map with pixel weights :param ctx: Context instance :return: Void abundance function

estats.stats.CrossVoids.context()[source]

Defines the paramters used by the plugin

estats.stats.CrossVoids.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.CrossVoids.filter(ctx)[source]
estats.stats.CrossVoids.process(data, ctx, scale_to_unity=False)[source]
estats.stats.CrossVoids.slice(ctx)[source]

estats.stats.Fulltest module

estats.stats.Fulltest.Fulltest(maps, weights, ctx)[source]
estats.stats.Fulltest.context()[source]

Defines the parameters used by the plugin

estats.stats.Minkowski module

estats.stats.Minkowski.Minkowski(kappa_w, weights, ctx)[source]

Calculates Minkowski functionals on a convergence map. This is a very crude approximation that will not match with theory! Preferable for forward modelling due to speed. :param kappa_w: A Healpix convergence map :param weights: A Healpix map with pixel weights :param ctx: Context instance :return: Minkowski functionals as V0,V1,V2.

estats.stats.Minkowski.Minkowski_proper(kappa, weights, ctx)[source]

Proper calculation of Minkowski functionals. nvolves a lot of alm decompositions and is therfore quite slow. For forward modelling use Minkowski function instead. :param kappa: A Healpix convergence map :param weights: A Healpix map with pixel weights :param ctx: Context instance :return: Minkowski functionals as V0,V1,V2.

estats.stats.Minkowski.context()[source]

Defines the paramters used by the plugin

estats.stats.Minkowski.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.Minkowski.filter(ctx)[source]
estats.stats.Minkowski.process(data, ctx, scale_to_unity=False)[source]
estats.stats.Minkowski.slice(ctx)[source]

estats.stats.Peaks module

estats.stats.Peaks.Peaks(map_w, weights, ctx)[source]

Calculates Peaks on a convergence map. :param map_w: A Healpix convergence map :param weights: A Healpix map with pixel weights :param ctx: Context instance :return: Peak abundance function

estats.stats.Peaks.context()[source]

Defines the paramters used by the plugin

estats.stats.Peaks.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.Peaks.filter(ctx)[source]
estats.stats.Peaks.process(data, ctx, scale_to_unity=False)[source]
estats.stats.Peaks.slice(ctx)[source]

estats.stats.ShearCLs module

estats.stats.ShearCLs.ShearCLs(map_1, map_2, weight_map, ctx)[source]

Calculates cross angular power spectrum of two sets of shear maps. :param map_1: A Healpix shear map, first shear component. :param map_2: A Healpix shear map, second shear component. :param weight_map: A Healpix map with pixel weights for the first shear maps

estats.stats.ShearCLs.context()[source]

Defines the paramters used by the plugin

estats.stats.ShearCLs.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.ShearCLs.filter(ctx)[source]
estats.stats.ShearCLs.process(data, ctx, scale_to_unity=False)[source]
estats.stats.ShearCLs.slice(ctx)[source]

estats.stats.Voids module

estats.stats.Voids.Voids(map_w, weights, ctx)[source]

Calculates Voids on a convergence map. :param map_w: A Healpix convergence map :param weights: A Healpix map with pixel weights :param ctx: Context instance :return: Void abundance function

estats.stats.Voids.context()[source]

Defines the paramters used by the plugin

estats.stats.Voids.decide_binning_scheme(data, meta, bin, ctx)[source]
estats.stats.Voids.filter(ctx)[source]
estats.stats.Voids.process(data, ctx, scale_to_unity=False)[source]
estats.stats.Voids.slice(ctx)[source]

Module contents