estats.stats package
estats.stats.CLs module
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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
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estats.stats.CLs.context()[source]
Defines the paramters used by the plugin
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estats.stats.CLs.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CLs.filter(ctx)[source]
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estats.stats.CLs.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CLs.slice(ctx)[source]
estats.stats.CrossCLs module
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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
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estats.stats.CrossCLs.context()[source]
Defines the paramters used by the plugin
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estats.stats.CrossCLs.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CrossCLs.filter(ctx)[source]
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estats.stats.CrossCLs.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CrossCLs.slice(ctx)[source]
estats.stats.CrossMinkowski module
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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.
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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.
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estats.stats.CrossMinkowski.context()[source]
Defines the paramters used by the plugin
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estats.stats.CrossMinkowski.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CrossMinkowski.filter(ctx)[source]
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estats.stats.CrossMinkowski.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CrossMinkowski.slice(ctx)[source]
estats.stats.CrossPeaks module
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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
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estats.stats.CrossPeaks.context()[source]
Defines the paramters used by the plugin
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estats.stats.CrossPeaks.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CrossPeaks.filter(ctx)[source]
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estats.stats.CrossPeaks.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CrossPeaks.slice(ctx)[source]
estats.stats.CrossShearCLs module
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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
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estats.stats.CrossShearCLs.context()[source]
Defines the paramters used by the plugin
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estats.stats.CrossShearCLs.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CrossShearCLs.filter(ctx)[source]
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estats.stats.CrossShearCLs.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CrossShearCLs.slice(ctx)[source]
estats.stats.CrossStarletL1Norm module
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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)
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estats.stats.CrossStarletL1Norm.context()[source]
Defines the paramters used by the plugin
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estats.stats.CrossStarletL1Norm.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CrossStarletL1Norm.filter(ctx)[source]
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estats.stats.CrossStarletL1Norm.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CrossStarletL1Norm.slice(ctx)[source]
estats.stats.CrossStarletL1NormDi module
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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)
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estats.stats.CrossStarletL1NormDi.context()[source]
Defines the paramters used by the plugin
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estats.stats.CrossStarletL1NormDi.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CrossStarletL1NormDi.filter(ctx)[source]
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estats.stats.CrossStarletL1NormDi.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CrossStarletL1NormDi.slice(ctx)[source]
estats.stats.CrossStarletMinkowski module
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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)
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estats.stats.CrossStarletMinkowski.context()[source]
Defines the paramters used by the plugin
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estats.stats.CrossStarletMinkowski.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CrossStarletMinkowski.filter(ctx)[source]
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estats.stats.CrossStarletMinkowski.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CrossStarletMinkowski.slice(ctx)[source]
estats.stats.CrossStarletMinkowskiDi module
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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)
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estats.stats.CrossStarletMinkowskiDi.context()[source]
Defines the paramters used by the plugin
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estats.stats.CrossStarletMinkowskiDi.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CrossStarletMinkowskiDi.filter(ctx)[source]
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estats.stats.CrossStarletMinkowskiDi.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CrossStarletMinkowskiDi.slice(ctx)[source]
estats.stats.CrossStarletPeaks module
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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)
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estats.stats.CrossStarletPeaks.context()[source]
Defines the paramters used by the plugin
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estats.stats.CrossStarletPeaks.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CrossStarletPeaks.filter(ctx)[source]
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estats.stats.CrossStarletPeaks.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CrossStarletPeaks.slice(ctx)[source]
estats.stats.CrossStarletPeaksDi module
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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)
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estats.stats.CrossStarletPeaksDi.context()[source]
Defines the paramters used by the plugin
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estats.stats.CrossStarletPeaksDi.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CrossStarletPeaksDi.filter(ctx)[source]
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estats.stats.CrossStarletPeaksDi.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CrossStarletPeaksDi.slice(ctx)[source]
estats.stats.CrossStarletVoids module
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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)
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estats.stats.CrossStarletVoids.context()[source]
Defines the paramters used by the plugin
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estats.stats.CrossStarletVoids.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CrossStarletVoids.filter(ctx)[source]
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estats.stats.CrossStarletVoids.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CrossStarletVoids.slice(ctx)[source]
estats.stats.CrossStarletVoidsDi module
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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)
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estats.stats.CrossStarletVoidsDi.context()[source]
Defines the paramters used by the plugin
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estats.stats.CrossStarletVoidsDi.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CrossStarletVoidsDi.filter(ctx)[source]
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estats.stats.CrossStarletVoidsDi.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CrossStarletVoidsDi.slice(ctx)[source]
estats.stats.CrossVoids module
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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
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estats.stats.CrossVoids.context()[source]
Defines the paramters used by the plugin
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estats.stats.CrossVoids.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.CrossVoids.filter(ctx)[source]
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estats.stats.CrossVoids.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.CrossVoids.slice(ctx)[source]
estats.stats.Fulltest module
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estats.stats.Fulltest.Fulltest(maps, weights, ctx)[source]
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estats.stats.Fulltest.context()[source]
Defines the parameters used by the plugin
estats.stats.Minkowski module
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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.
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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.
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estats.stats.Minkowski.context()[source]
Defines the paramters used by the plugin
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estats.stats.Minkowski.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.Minkowski.filter(ctx)[source]
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estats.stats.Minkowski.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.Minkowski.slice(ctx)[source]
estats.stats.Peaks module
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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
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estats.stats.Peaks.context()[source]
Defines the paramters used by the plugin
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estats.stats.Peaks.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.Peaks.filter(ctx)[source]
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estats.stats.Peaks.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.Peaks.slice(ctx)[source]
estats.stats.ShearCLs module
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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
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estats.stats.ShearCLs.context()[source]
Defines the paramters used by the plugin
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estats.stats.ShearCLs.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.ShearCLs.filter(ctx)[source]
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estats.stats.ShearCLs.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.ShearCLs.slice(ctx)[source]
estats.stats.Voids module
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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
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estats.stats.Voids.context()[source]
Defines the paramters used by the plugin
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estats.stats.Voids.decide_binning_scheme(data, meta, bin, ctx)[source]
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estats.stats.Voids.filter(ctx)[source]
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estats.stats.Voids.process(data, ctx, scale_to_unity=False)[source]
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estats.stats.Voids.slice(ctx)[source]