def test_measure_pipeline():
def f(x):
return True
d = Distribution([0.1, 0.2])
output = wrappers.measure_pipeline(f, d)
assert output['arg0'] == True
def min(dist, **kwargs):
"""Measures the minimum value of a distribution.
Args:
dist (Distribution): Distribution to be analyzed.
Returns:
Dictionary holding the measure's outputs.
"""
logger.info('Finding minimum value ...')
output = w.measure_pipeline(np.min, dist, **kwargs)
logger.info('Minimum value found.')
logger.debug(output)
return output
def median(dist, **kwargs):
"""Measures the median of a distribution.
Args:
dist (Distribution): Distribution to be analyzed.
Returns:
Dictionary holding the measure's outputs.
"""
logger.info('Calculating median ...')
output = w.measure_pipeline(np.median, dist, **kwargs)
logger.info('Median calculated.')
logger.debug(output)
return output
def var(dist, **kwargs):
"""Measures the variance of a distribution.
Args:
dist (Distribution): Distribution to be analyzed.
Returns:
Dictionary holding the measure's outputs.
"""
logger.info('Calculating variance ...')
output = w.measure_pipeline(np.var, dist, **kwargs)
logger.info('Variance calculated.')
logger.debug(output)
return output
def std(dist, **kwargs):
"""Measures the standard deviation of a distribution.
Args:
dist (Distribution): Distribution to be analyzed.
Returns:
Dictionary holding the measure's outputs.
"""
logger.info('Calculating standard deviation ...')
output = w.measure_pipeline(np.std, dist, **kwargs)
logger.info('Standard deviation calculated.')
logger.debug(output)
return output
def skewness(dist, **kwargs):
"""Measures the skewness of a distribution.
Args:
dist (Distribution): Distribution to be analyzed.
Returns:
Dictionary holding the measure's outputs.
"""
logger.info('Calculating skewness ...')
output = w.measure_pipeline(s.skew, dist, **kwargs)
logger.info('Skewness calculated.')
logger.debug(output)
return output
def kurtosis(dist, **kwargs):
"""Measures the kurtosis of a distribution.
Args:
dist (Distribution): Distribution to be analyzed.
Returns:
Dictionary holding the measure's outputs.
"""
logger.info('Calculating kurtosis ...')
output = w.measure_pipeline(s.kurtosis, dist, **kwargs)
logger.info('Kurtosis calculated.')
logger.debug(output)
return output
def rank(dist, **kwargs):
"""Ranks the values of a distribution.
Args:
dist (Distribution): Distribution to be analyzed.
Returns:
Dictionary holding the measure's outputs.
"""
logger.info('Ranking distribution ...')
output = w.measure_pipeline(s.rankdata, dist, **kwargs)
logger.info('Distribution ranked.')
logger.debug(output)
return output