# -*- coding: utf-8 -*-
# mcsas_test.py
from __future__ import print_function # python3 print()
import os.path
import pickle
import logging
import numpy
from mcsas.mcsas import McSAS
TOL = 1e-16 # usual eps value for 64bit float
FN_TEST_DATA = "test_data.pydat"
FN_EXPECTED_DATA = "test_data_result.pydat"
[docs]def isEqualFloat(a, b, tol = TOL):
"""Return TRUE if both float arrays can be considered as equal.
*tol* Tolerance in relative mean difference
Supposed to be symmetrical."""
# equal = numpy.allclose(a, b, rtol = 0.0, atol = TOL)
# one could also think about using the relative mean difference:
# http://stackoverflow.com/a/4029392
diff = (abs(a-b) / (abs(a)+abs(b)) / 2.)
equal = numpy.all(diff < tol)
print("max difference: {0} ({1})".format(numpy.max(diff), tol), end = "")
return equal
[docs]def getTestData(filename):
# read test data file
with open(filename) as fd:
qie = pickle.load(fd)
# separate input data vectors
return (qie[0,:], qie[1,:], qie[2,:])
[docs]def getExpectedData(filename):
exp = None
if os.path.exists(filename):
print("Reading expected test results from file ... ")
with open(filename) as fd:
exp = pickle.load(fd)
print("done.")
return exp
[docs]def storeResultData(filename, result):
print("Storing test results to file ... ")
assert not os.path.exists(filename),\
"Target file for test results exists already! "\
"Not overwriting, remove first."
with open(filename, 'w') as fd:
pickle.dump(result, fd)
print("done.")
[docs]def getSettings(testfn, expectedfn):
"""Test settings for mcsas routine.
Using number of repetitions and contributions
from expected test data to improve comparability."""
q, i, e = getTestData(testfn)
expected = getExpectedData(expectedfn)
settings = dict(Q = q, I = i, IError = numpy.maximum(0.01*i, e),
numContribs = 200, numReps = 20,
convergenceCriterion = 1, maxIterations = 1e5,
deltaRhoSquared = 1e30,
doPlot = False)
# get number of repetitions+contributions from expected test data
if isinstance(expected, dict):
rrep = expected.get("Rrep", None)
try:
settings["numContribs"], dummy, settings["numReps"] = tuple(rrep.shape)
except:
pass
return settings, expected
[docs]def test():
"""Testing the algorithm in 1D. Atm, we just test as much as possible.
Testing post-processing routines should be separated later
as it's deterministic and though easier to test."""
settings, expected = getSettings(FN_TEST_DATA, FN_EXPECTED_DATA)
# run the monte carlo routine
mcsas = McSAS.factory()
mcsas.calc(**settings)
result = mcsas.result[0]
if False:
# print result keys and shape for debugging
for key, item in result.iteritems():
print(key, end="")
try:
print(item.shape)
except:
pass
if expected is None:
storeResultData(FN_EXPECTED_DATA, result)
return # done here
# testing against data from file
# test only items which are averaged over all repetitions
# individual tolerances because of large deviations for few repetitions(4)
success = True
for oldKey, key, tol in ( ("Hx", "histogramXLowerEdge", TOL),
("Hmid", "histogramXMean", TOL),
("Hwidth", "histogramXWidth", TOL),
("Hmean", "volumeHistogramYMean", 0.2),
("Hnmean", "numberHistogramYMean", 0.2),
("Hstd", "volumeHistogramYStd", 0.2),
("Hnstd", "numberHistogramYStd", 0.2),
("vfminbins", "volumeHistogramMinimumRequired", 0.01),
("nfminbins", "numberHistogramMinimumRequired", 0.1),
("Qfit", "fitQ", TOL),
("Imean", "fitIntensityMean", 0.005),
("Istd", "fitIntensityStd", 0.25)):
print("testing {0:10} ".format(key), end = "")
if not isEqualFloat(result[key], expected[oldKey], tol):
success = False
print(" !")
else:
print()
if not success:
logging.error("Test for {0} failed!".format(key))
#if __name__ == "__main__":
# test() # FIXME? -> outdated
# vim: set ts=4 sts=4 sw=4 tw=0: