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# Copyright (c) 2017 Weitian LI <weitian@aaronly.me>
# MIT license
"""
Utilities to help analyze the simulation results.
"""
import logging
import numpy as np
logger = logging.getLogger(__name__)
def inverse_cumsum(x):
"""
Do cumulative sum reversely.
Credit: https://stackoverflow.com/a/28617608/4856091
"""
x = np.asarray(x)
return x[::-1].cumsum()[::-1]
def countdist_integrated(x, nbin, log=True, xmin=None, xmax=None):
"""
Calculate the integrated counts distribution (i.e., luminosity
function), representing the counts (number of objects) with a
greater value.
Parameters
----------
x : list[float]
Array of quantities of every object/source.
nbin : int
Number of bins to calculate the counts distribution.
log : bool, optional
Whether to take logarithm on the ``x`` quantities to determine
the bin edges?
Default: True
xmin, xmax : float, optional
The lower and upper boundaries within which to calculate the
counts distribution. They are default to the minimum and
maximum of the given ``x``.
Returns
-------
counts : 1D `~numpy.ndarray`
The integrated counts for each bin, of length ``nbin``.
bins : 1D `~numpy.ndarray`
The central positions of every bin, of length ``nbin``.
binedges : 1D `~numpy.ndarray`
The edge positions of every bin, of length ``nbin+1``.
"""
x = np.asarray(x)
if xmin is None:
xmin = x.min()
if xmax is None:
xmax = x.max()
x = x[(x >= xmin) & (x <= xmax)]
if log is True:
if xmin <= 0:
raise ValueError("log=True but x have elements <= 0")
x = np.log(x)
xmin, xmax = np.log([xmin, xmax])
binedges = np.linspace(xmin, xmax, num=nbin+1)
bins = (binedges[1:] + binedges[:-1]) / 2
counts, __ = np.histogram(x, bins=binedges)
# Convert to the integrated counts distribution
counts = inverse_cumsum(counts)
if log is True:
bins = np.exp(bins)
binedges = np.exp(binedges)
return (counts, bins, binedges)
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