an array (e.g., sparsity, difference, etc).master
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# $Id: __init__.py,v 1.1 2010-10-07 15:57:29 wirawan Exp $ |
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# |
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# wpylib.math.stats.array_stats module |
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# Created: 20140404 |
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# Wirawan Purwanto |
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# |
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|
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""" |
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wpylib.math.stats.array_stats module |
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Tools for studying the statistics of an array, the difference of |
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two or more arrays, etc. |
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""" |
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import os |
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import sys |
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import numpy |
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|
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# Functions below were imported from V2b_inspect.py, 20140404 |
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""" |
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CONVENTIONS |
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- Usually, M1 is the matrix under examination, M2 is the reference matrix. |
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""" |
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def report_diff_stat(M1, M2, out=sys.stdout): |
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# Original function name: statdiff |
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"""Studies the difference of two arrays (or matrices). |
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Usually, M1 is the matrix under examination, M2 is the reference |
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matrix. |
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Prints out standard report to a given text file, or returns |
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the report as a string. |
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""" |
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dM = M1 - M2 |
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if len(M1.shape) == 2: |
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dM_diag = numpy.diagonal(dM) |
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stats = ( |
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dM.max(), |
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dM.min(), |
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dM.mean(), |
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numpy.abs(dM).mean(), |
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rms(dM), |
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dM_diag.max(), |
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dM_diag.min(), |
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dM_diag.mean(), |
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numpy.abs(dM_diag).mean(), |
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rms(dM_diag), |
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) |
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if out == tuple: |
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return stats |
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|
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rslt = """\ |
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- max difference = %13.6e |
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- min difference = %13.6e |
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- mean difference = %13.6e |
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- mean abs diff = %13.6e |
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- rms difference = %13.6e |
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- max difference = %13.6e on diagonal |
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- min difference = %13.6e on diagonal |
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- mean difference = %13.6e on diagonal |
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- mean abs diff = %13.6e on diagonal |
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- rms difference = %13.6e on diagonal |
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""" % stats |
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else: |
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stats = ( |
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dM.max(), |
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dM.min(), |
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dM.mean(), |
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numpy.abs(dM).mean(), |
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rms(dM), |
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) |
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if out == tuple: |
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return stats |
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|
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rslt = """\ |
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- max difference = %13.6e |
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- min difference = %13.6e |
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- mean difference = %13.6e |
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- mean abs diff = %13.6e |
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- rms difference = %13.6e |
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""" % stats |
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if out == str: |
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return rslt |
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else: |
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out.write(rslt) |
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out.flush() |
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|
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def maxadiff(M1, M2): |
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# Original function name: maxdiff |
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"""Returns the maximum absolute difference between two matrices. |
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Used for checking whether two matrices are identical.""" |
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return numpy.abs(M1 - M2).max() |
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def maxadiff_by_col(M1, M2): |
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# Original function name: maxdiff_by_col |
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"""Assuming two-dimensional array (where first index is the row |
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index), computes the maximum absolute difference. |
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""" |
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return numpy.abs(M1 - M2).max(axis=0) |
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def maxradiff(M1, M2, ztol=1e-15): |
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# Original function name: maxrdiff |
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"""Prints the maximum *relative* absolute difference between two matrices. |
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Used for checking whether two matrices are identical. |
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CAVEATS: |
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- Zero elements in M2 (reference matrix) are converted to unity for |
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calculating the relative difference. |
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""" |
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(mapprox, mref) = (M1, M2) |
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diff = numpy.asarray(numpy.abs(mapprox - mref)) |
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mref0 = numpy.asarray(numpy.abs(mref)) |
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numpy.putmask(mref0, mref0 < ztol, [1.0]) |
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return (diff / mref0).max() |
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class ArrayStat(object): |
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"""A class to compute the statistics of an array. |
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""" |
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def __init__(self, mat, save=False): |
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mat = numpy.asarray(mat) |
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amat = numpy.abs(mat) |
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if save: |
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self.mat = mat |
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self.min = mat.min() |
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self.max = mat.max() |
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self.mean = mat.mean() |
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self.amin = amat.min() |
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self.amax = amat.max() |
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self.amean = amat.mean() |
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self.rms = numpy.sqrt(numpy.sum(amat**2) / amat.size) |
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def report(self, out=sys.stdout): |
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"""Prints out a standard report. |
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""" |
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#print_histogram(study_sparsity(self.diff, (log_delta-5.5,log_delta+4.5+0.1)), xaxis='left_edge') |
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rslt = """\ |
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. min = %12.6g |
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. max = %12.6g |
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. mean = %12.6g |
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. absmin = %12.6g |
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. absmax = %12.6g |
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. absmean = %12.6g |
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. rms = %12.6g |
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""" % (self.min, self.max, self.mean, |
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self.amin, self.amax, self.amean, |
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self.rms) |
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if out == str: |
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return rslt |
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else: |
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out.write(rslt) |
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out.flush() |
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