"""
================================================================================
Logscaled Histogram
================================================================================
| Calculates a logarithmically spaced histogram for a data map.
| Written By: Matthew Stadelman
| Date Written: 2016/03/07
| Last Modifed: 2016/10/20
"""
import scipy as sp
from .histogram import Histogram
[docs]class HistogramLogscale(Histogram):
r"""
Performs a histogram where the bin limits are logarithmically spaced
based on the supplied scale factor. If there are negative values then
the first bin contains everything below 0, the next bin will contain
everything between 0 and 1.
kwargs include:
scale_fact - numeric value to generate axis scale for bins. A
scale fact of 10 creates bins: 0-1, 1-10, 10-100, etc.
"""
def __init__(self, field, **kwargs):
super().__init__(field)
self.args.update(kwargs)
self.output_key = 'hist_logscale'
self.action = 'histogram_logscale'
@classmethod
[docs] def _add_subparser(cls, subparsers, parent):
r"""
Adds a specific action based sub-parser to the supplied arg_parser
instance.
"""
parser = subparsers.add_parser(cls.__name__,
aliases=['histlog'],
parents=[parent],
help=cls.__doc__)
#
parser.add_argument('scale_fact', type=float, nargs='?', default=10.0,
help='base to generate logscale from')
parser.set_defaults(func=cls)
[docs] def define_bins(self, **kwargs):
r"""
This defines the bins for a logscaled histogram
"""
self.data_vector.sort()
sf = self.args['scale_fact']
num_bins = int(sp.logn(sf, self.data_vector[-1]) + 1)
#
# generating initial bins from 1 - sf**num_bins
low = list(sp.logspace(0, num_bins, num_bins + 1, base=sf))[:-1]
high = list(sp.logspace(0, num_bins, num_bins + 1, base=sf))[1:]
#
# Adding "catch all" bins for anything between 0 - 1 and less than 0
if self.data_vector[0] < 1.0:
low.insert(0, 0.0)
high.insert(0, 1.0)
if self.data_vector[0] < 0.0:
low.insert(0, self.data_vector[0])
high.insert(0, 0.0)
#
self.bins = [bin_ for bin_ in zip(low, high)]