From abd3c88209ac8107882de7f8a61b5d06bdb3cefa Mon Sep 17 00:00:00 2001
From: Aaron LI <aly@aaronly.me>
Date: Wed, 30 Jan 2019 10:42:50 +0800
Subject: utils/analyze: Add countdist() function
Modified from the original countdist_integrated().
---
fg21sim/utils/analyze.py | 24 ++++++++++++++++--------
1 file changed, 16 insertions(+), 8 deletions(-)
(limited to 'fg21sim/utils')
diff --git a/fg21sim/utils/analyze.py b/fg21sim/utils/analyze.py
index f33f65c..6c7d89a 100644
--- a/fg21sim/utils/analyze.py
+++ b/fg21sim/utils/analyze.py
@@ -23,11 +23,9 @@ def inverse_cumsum(x):
return x[::-1].cumsum()[::-1]
-def countdist_integrated(x, nbin, log=True, xmin=None, xmax=None):
+def countdist(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.
+ Calculate the counts distribution, i.e., a histogram.
Parameters
----------
@@ -47,7 +45,7 @@ def countdist_integrated(x, nbin, log=True, xmin=None, xmax=None):
Returns
-------
counts : 1D `~numpy.ndarray`
- The integrated counts for each bin, of length ``nbin``.
+ The counts in each bin, of length ``nbin``.
bins : 1D `~numpy.ndarray`
The central positions of every bin, of length ``nbin``.
binedges : 1D `~numpy.ndarray`
@@ -69,14 +67,24 @@ def countdist_integrated(x, nbin, log=True, xmin=None, xmax=None):
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)
+ return counts, bins, binedges
+
+
+def countdist_integrated(x, nbin, log=True, xmin=None, xmax=None):
+ """
+ Calculate the integrated counts distribution (e.g., luminosity
+ function, mass function), representing the counts with a greater
+ value, e.g., N(>flux), N(>mass).
+ """
+ counts, bins, binedges = countdist(x=x, nbin=nbin, log=log,
+ xmin=xmin, xmax=xmax)
+ counts = inverse_cumsum(counts)
+ return counts, bins, binedges
def logfit(x, y):
--
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