diff options
| -rw-r--r-- | fg21sim/utils/analyze.py | 17 |
1 files changed, 15 insertions, 2 deletions
diff --git a/fg21sim/utils/analyze.py b/fg21sim/utils/analyze.py index f8137e9..ad9f1b4 100644 --- a/fg21sim/utils/analyze.py +++ b/fg21sim/utils/analyze.py @@ -23,7 +23,7 @@ def inverse_cumsum(x): return x[::-1].cumsum()[::-1] -def countdist_integrated(x, nbin, log=True): +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 @@ -39,6 +39,10 @@ def countdist_integrated(x, nbin, log=True): 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 ------- @@ -50,10 +54,19 @@ def countdist_integrated(x, nbin, log=True): 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(x.min(), x.max(), num=nbin+1) + 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 |