`rtCommon.validationUtils`

ValidationUtils - utils to help validate that arrays and data structures match. For example in testing and comparing to a known-good run from matlab.

Module Contents

`compareArrays`(→ dict)	Compute element-wise percent difference between A and B
`areArraysClose`(→ bool)	Compare to arrays element-wise and compute the percent difference.
`compareMatStructs`(→ dict)	For each field, not like __*__, walk the fields and compare the values.
`isMeanWithinThreshold`(→ bool)	Examine all ßmean stats in dictionary and compare to threshold value
`compareMatFiles`(→ dict)	Load both matlab files and call compareMatStructs.
`pearsons_mean_corr`(A, B)

rtCommon.validationUtils.compareArrays(A: numpy.ndarray, B: numpy.ndarray) → dict: Compute element-wise percent difference between A and B Return the mean, max, stddev, histocounts, histobins in a Dict

rtCommon.validationUtils.areArraysClose(A: numpy.ndarray, B: numpy.ndarray, mean_limit=0.01, stddev_limit=1.0) → bool: Compare to arrays element-wise and compute the percent difference. Return True if the mean and stddev are withing the supplied limits. Default limits:{mean: .01, stddev: 1.0}, i.e. no stddev limit by default

exception rtCommon.validationUtils.StructureMismatchError

Bases: ValueError

Inappropriate argument value (of correct type).

rtCommon.validationUtils.compareMatStructs(A: rtCommon.structDict.MatlabStructDict, B: rtCommon.structDict.MatlabStructDict, field_list=None) → dict: For each field, not like __*__, walk the fields and compare the values. If a field is missing from one of the structs raise an exception. If field_list is supplied, then only compare those fields. Return a dict with {fieldname: stat_results}.

rtCommon.validationUtils.isMeanWithinThreshold(cmpStats: dict, threshold: float) → bool: Examine all ßmean stats in dictionary and compare to threshold value

rtCommon.validationUtils.compareMatFiles(filename1: str, filename2: str) → dict: Load both matlab files and call compareMatStructs. Inspect the resulting stats_result to see if any mean is beyond some threshold. Also print out the stats results. Return the result stats.

rtCommon.validationUtils.pearsons_mean_corr(A: numpy.ndarray, B: numpy.ndarray)