85 lines
3.3 KiB
Python
85 lines
3.3 KiB
Python
import numpy as np
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import unittest
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class CanteraTest(unittest.TestCase):
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def assertNear(self, a, b, rtol=1e-8, atol=1e-12, msg=None):
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cmp = 2 * abs(a - b)/(abs(a) + abs(b) + 2 * atol / rtol)
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if cmp > rtol:
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message = ('AssertNear: %.14g - %.14g = %.14g\n' % (a, b, a-b) +
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'Relative error of %10e exceeds rtol = %10e' % (cmp, rtol))
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if msg:
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message = msg + '\n' + message
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self.fail(message)
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def assertArrayNear(self, A, B, rtol=1e-8, atol=1e-12, msg=None):
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if len(A) != len(B):
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self.fail("Arrays are of different lengths ({0}, {1})".format(len(A), len(B)))
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A = np.asarray(A)
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B = np.asarray(B)
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for a,b in zip(A.flat, B.flat):
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self.assertNear(a,b, rtol, atol, msg)
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def compareProfiles(reference, sample, rtol=1e-5, atol=1e-12, xtol=1e-5):
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"""
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Compare two 2D arrays of spatial or time profiles. Each data set should
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contain the time or space coordinate in the first column and data series
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to be compared in successive columns.
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The coordinates in each data set do not need to be the same: The data from
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the second data set will be interpolated onto the coordinates in the first
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data set before being compared. This means that the range of the "sample"
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data set should be at least as long as the "reference" data set.
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After interpolation, each data point must satisfy a combined relative and absolute
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error criterion specified by `rtol` and `atol`.
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If the comparison succeeds, this function returns `None`. If the comparison
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fails, a formatted report of the differing elements is returned.
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"""
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if isinstance(reference, str):
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reference = np.genfromtxt(reference, delimiter=',').T
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else:
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reference = np.asarray(reference).T
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if isinstance(sample, str):
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sample = np.genfromtxt(sample, delimiter=',').T
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else:
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sample = np.asarray(sample).T
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assert reference.shape[0] == sample.shape[0]
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nVars = reference.shape[0]
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nTimes = reference.shape[1]
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bad = []
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template = '{0:9.4e} {1: 3d} {2:14.7e} {3:14.7e} {4:9.3e} {5:9.3e} {6:9.3e}'
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for i in range(1, nVars):
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scale = max(max(abs(reference[i])), reference[i].ptp(),
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max(abs(sample[i])), sample[i].ptp())
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slope = np.zeros(nTimes)
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slope[1:] = np.diff(reference[i]) / np.diff(reference[0]) * reference[0].ptp()
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comp = np.interp(reference[0], sample[0], sample[i])
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for j in range(nTimes):
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a = reference[i,j]
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b = comp[j]
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abserr = abs(a-b)
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relerr = abs(a-b) / (scale + atol)
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# error that can be accounted for by shifting the profile along
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# the time / spatial coordinate
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xerr = abserr / (abs(slope[j]) + atol)
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if abserr > atol and relerr > rtol and xerr > xtol:
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bad.append(template.format(reference[0][j], i, a, b, abserr, relerr, xerr))
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if bad:
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header = ['Failed series comparisons:',
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'coordinate comp. reference val test value abs. err rel. err pos. err',
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'---------- --- -------------- -------------- --------- --------- ---------']
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return '\n'.join(header + bad)
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else:
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return None
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