comparison asciihist.py @ 8:032e930ef6a1 draft default tip

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/bigwig_outlier_bed commit 7eac2f224d3126002edd2c02d1133b23ac1a4881

8:032e930ef6a1

from __future__ import print_function
import numpy as np


def asciihist(
    it,
    bins=10,
    minmax=None,
    str_tag="",
    scale_output=30,
    generate_only=False,
    print_function=print,
    ):
    """Create an ASCII histogram from an interable of numbers.
    Author: Boris Gorelik [e-mail]. based on  http://econpy.googlecode.com/svn/trunk/pytrix/pytrix.py
    License: MIT
    """
    ret = []
    itarray = np.asanyarray(it)
    if minmax == "auto":
        minmax = np.percentile(it, [5, 95])
        if minmax[0] == minmax[1]:
            # for very ugly distributions
            minmax = None
    if minmax is not None:
        # discard values that are outside minmax range
        mn = minmax[0]
        mx = minmax[1]
        itarray = itarray[itarray >= mn]
        itarray = itarray[itarray <= mx]
    if itarray.size:
        total = len(itarray)
        counts, cutoffs = np.histogram(itarray, bins=bins)
        cutoffs = cutoffs[1:]
        if str_tag:
            str_tag = "%s " % str_tag
        else:
            str_tag = ""
        if scale_output is not None:
            scaled_counts = counts.astype(float) / counts.sum() * scale_output
        else:
            scaled_counts = counts

        if minmax is not None:
            ret.append("Trimmed to range (%s - %s)" % (str(minmax[0]), str(minmax[1])))
        for cutoff, original_count, scaled_count in zip(cutoffs, counts, scaled_counts):
            ret.append(
                "{:s}{:>8.2f} |{:<7,d} | {:s}".format(
                    str_tag, cutoff, original_count, "*" * int(scaled_count)
                )
            )
        ret.append("{:s}{:s} |{:s} | {:s}".format(str_tag, "-" * 8, "-" * 7, "-" * 7))
        ret.append("{:s}{:>8s} |{:<7,d}".format(str_tag, "N=", total))
    else:
        ret = []
    if not generate_only:
        for line in ret:
            print_function(line)
    ret = "\n".join(ret)
    return ret


if __name__ == "__main__":
    np.random.seed(11)
    asciihist(np.random.randn(10000), minmax="auto", str_tag="Normal")