Output Analysis Distribution Normalizer

Class providing normalizers for binned distributions in on-the-fly output analysis—operations that convert a raw histogram (e.g.galaxy counts per bin) into the desired normalized quantity (e.g.a number density per unit dex or a probability distribution). The normalize method accepts the distribution array and its covariance matrix and modifies them in place, applying bin widths, volume normalizations, or other scale factors. Implementations include identity (no normalization), bin-width division, and survey-volume normalization for luminosity and mass functions.

Default implementation: outputAnalysisDistributionNormalizerIdentity

Methods

normalize → void

Normalize the supplied binned distribution array and its covariance matrix in place, applying the bin-width or volume factors appropriate for this normalizer class.

  • double precision, intent(inout), dimension(: ), optional :: distribution

  • double precision, intent(inout), dimension(:,:), optional :: covariance

  • double precision, intent(in ), dimension(: ) :: propertyValueMinimum, propertyValueMaximum

outputAnalysisDistributionNormalizerBinWidth

An output analysis distribution normalizer class that divides each bin’s count by the width of that bin, converting raw counts into a number density per unit property interval (e.g.\(\mathrm{d}N/\mathrm{d}\log_{10}M\)).

outputAnalysisDistributionNormalizerIdentity

An identity (no-op) output analysis distribution normalizer class that leaves the distribution and its covariance unchanged, used as the default when no normalization is required.

(Default implementation)

Parameters

  • [redshift] (default 0.0d0) — The redshift at which the transfer function is defined.

outputAnalysisDistributionNormalizerLog10ToLog

Converts distribution normalizations from \(\log_{10}\) scaling to natural \(\log\) scaling, enabling comparison of model predictions to observed distributions that use different logarithmic bases.

outputAnalysisDistributionNormalizerSequence

An output analysis distribution normalizer class that applies a linked list of child outputAnalysisDistributionNormalizerClass objects in sequence, enabling composite normalization pipelines (e.g.bin-width division followed by volume normalization).

Methods

  • prepend — Prepend an operator to a sequence of weight operators.

outputAnalysisDistributionNormalizerUnitarity

An output analysis distribution normalizer class that rescales the distribution so that its total (sum over all bins) equals unity, converting raw counts into a probability distribution.