diff options
| -rw-r--r-- | fg21sim/utils/analyze.py | 24 |
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): |