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"""
Performs a two-sided T-test that two independent samples
come from (normal) distributions with the same mean.
"""
from __future__ import print_function
import numpy
import pyferret
import scipy.stats
def ferret_init(id):
"""
Initialization for the stats_ttest2ind PyEF
"""
axes_values = [ pyferret.AXIS_DOES_NOT_EXIST ] * pyferret.MAX_FERRET_NDIM
axes_values[0] = pyferret.AXIS_CUSTOM
false_influences = [ False ] * pyferret.MAX_FERRET_NDIM
retdict = { "numargs": 2,
"descript": "Returns two-sided T-test stat. and prob. that two independent " \
"samples comes from (normal) distribs. with the same mean",
"axes": axes_values,
"argnames": ( "SAMPLEA", "SAMPLEB", ),
"argdescripts": ( "First sample data array",
"Second sample data array", ),
"argtypes": ( pyferret.FLOAT_ARRAY, pyferret.FLOAT_ARRAY, ),
"influences": ( false_influences, false_influences, ),
}
return retdict
def ferret_custom_axes(id):
"""
Define custom axis of the stats_ttest2ind Ferret PyEF
"""
axis_defs = [ None ] * pyferret.MAX_FERRET_NDIM
axis_defs[0] = ( 1, 2, 1, "T,P", False )
return axis_defs
def ferret_compute(id, result, resbdf, inputs, inpbdfs):
"""
Performs a two-sided T-test that two independent samples come from
(normal) distributions with the same mean. The samples are given
in inputs[0] and inputs[1]. The test statistic value and two-tailed
probability are returned in result. Undefined data given in each
sample are removed (independently from each other) before performing
the test. Note that the samples do not need to be the same size;
thus there are no restrictions on the relative dimensions of sample
arrays.
"""
badmask = ( numpy.fabs(inputs[0] - inpbdfs[0]) < 1.0E-5 )
badmask = numpy.logical_or(badmask, numpy.isnan(inputs[0]))
goodmask = numpy.logical_not(badmask)
sampa = inputs[0][goodmask]
badmask = ( numpy.fabs(inputs[1] - inpbdfs[1]) < 1.0E-5 )
badmask = numpy.logical_or(badmask, numpy.isnan(inputs[1]))
goodmask = numpy.logical_not(badmask)
sampb = inputs[1][goodmask]
fitparams = scipy.stats.ttest_ind(sampa, sampb)
result[:] = resbdf
# T-test statistic
result[0] = fitparams[0]
# probability
result[1] = fitparams[1]
#
# The rest of this is just for testing this module at the command line
#
if __name__ == "__main__":
# make sure ferret_init and ferret_custom_axes do not have problems
info = ferret_init(0)
info = ferret_custom_axes(0)
# Get random samples from normal distribution
# with the same mean and with a different mean
ydima = 250
zdima = 150
sampa = scipy.stats.norm(5.0,2.0).rvs(ydima * zdima)
ydimb = 175
zdimb = 75
sampb = scipy.stats.norm(5.0,0.5).rvs(ydimb * zdimb)
sampu = scipy.stats.norm(5.5,0.5).rvs(ydimb * zdimb)
# setup for the call to ferret_compute
inpbdfs = numpy.array([-9999.0, -8888.0], dtype=numpy.float64)
resbdf = numpy.array([-7777.0], dtype=numpy.float64)
arraya = numpy.empty((1, ydima, zdima, 1, 1, 1), dtype=numpy.float64, order='F')
arrayb = numpy.empty((ydimb, 1, 1, zdimb, 1, 1), dtype=numpy.float64, order='F')
arrayu = numpy.empty((ydimb, 1, 1, zdimb, 1, 1), dtype=numpy.float64, order='F')
index = 0
for j in range(ydima):
for k in range(zdima):
if (index % 23) == 3:
arraya[0, j, k, 0, 0, 0] = inpbdfs[0]
else:
arraya[0, j, k, 0, 0, 0] = sampa[index]
index += 1
index = 0
for j in range(ydimb):
for k in range(zdimb):
if (index % 53) == 3:
arrayb[j, 0, 0, k, 0, 0] = inpbdfs[1]
arrayu[j, 0, 0, k, 0, 0] = inpbdfs[1]
else:
arrayb[j, 0, 0, k, 0, 0] = sampb[index]
arrayu[j, 0, 0, k, 0, 0] = sampu[index]
index += 1
resultb = -6666.0 * numpy.ones((2, 1, 1, 1, 1, 1), dtype=numpy.float64, order='F')
resultu = -6666.0 * numpy.ones((2, 1, 1, 1, 1, 1), dtype=numpy.float64, order='F')
# call ferret_compute with the samples from distribs with the same mean and check
ferret_compute(0, resultb, resbdf, (arraya, arrayb), inpbdfs)
resultb = resultb.reshape(-1)
print("result from same mean: %s" % str(resultb))
if (abs(resultb[0]) > 2.0) or \
(resultb[1] < 0.1) or (resultb[1] > 1.0):
raise ValueError("Unexpected result")
# call ferret_compute with the samples from distribs with different means and check
ferret_compute(0, resultu, resbdf, (arraya, arrayu), inpbdfs)
resultu = resultu.reshape(-1)
print("result from diff mean: %s" % str(resultu))
if (resultu[0] > -20.0) or \
(resultu[1] < 0.0) or (resultu[1] > 1.0E-5):
raise ValueError("Unexpected result")
# All successful
print("Success")
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