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-rw-r--r--fg21sim/utils/analyze.py24
1 files changed, 16 insertions, 8 deletions
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):