1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
|
"""
Returns Spearman's rank correlation coefficient between two samples of data.
"""
from __future__ import print_function
import math
import numpy
import pyferret
import scipy.stats
def ferret_init(id):
"""
Initialization for the stats_spearmanr 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 Spearman's rank correlation coeff, " \
"and num good points, between two samples of data",
"axes": axes_values,
"argnames": ( "SAMPLEA", "SAMPLEB", ),
"argdescripts": ( "First array of sample data",
"Second array of sample data", ),
"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_spearmanr Ferret PyEF
"""
axis_defs = [ None ] * pyferret.MAX_FERRET_NDIM
axis_defs[0] = ( 1, 2, 1, "R,N", False )
return axis_defs
def ferret_compute(id, result, resbdf, inputs, inpbdfs):
"""
Assigns result with Spearman's rank correlation coefficient,
and the number of good point, between the two samples of
data given in inputs[0] and inputs[1]. Values compared are
only from positions that are defined in both arrays.
"""
if inputs[0].shape != inputs[1].shape :
shp0 = inputs[0].squeeze().shape
shp1 = inputs[1].squeeze().shape
if (len(shp0) > 1) or (len(shp1) > 1) or (shp0 != shp1):
raise ValueError("SAMPLEA and SAMPLEB must either have identical dimensions or "\
"both have only one defined non-singular axis of the same length")
sampa = inputs[0].reshape(-1)
sampb = inputs[1].reshape(-1)
bada = ( numpy.fabs(sampa - inpbdfs[0]) < 1.0E-5 )
bada = numpy.logical_or(bada, numpy.isnan(sampa))
badb = ( numpy.fabs(sampb - inpbdfs[1]) < 1.0E-5 )
badb = numpy.logical_or(badb, numpy.isnan(sampb))
goodmask = numpy.logical_not(numpy.logical_or(bada, badb))
valsa = numpy.array(sampa[goodmask], dtype=numpy.float64)
numpts = len(valsa)
if numpts < 2:
raise ValueError("Not enough defined points in common in SAMPLEA and SAMPLEB")
valsb = numpy.array(sampb[goodmask], dtype=numpy.float64)
fitparams = scipy.stats.spearmanr(valsa, valsb)
result[:] = resbdf
# correlation coefficient
result[0] = fitparams[0]
# ignore the probability of uncorrelated
# number of good pts
result[1] = numpts
#
# 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 a random sample from a normal distribution
ydim = 83
zdim = 17
samplesize = ydim * zdim
sampa = scipy.stats.norm(15.0, 2.0).rvs(samplesize)
# Create a correlated distribution
sampc = -numpy.log(sampa)
# Create an uncorrelated distribution
sampu = scipy.stats.norm(15.0, 2.0).rvs(samplesize)
# 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)
inputa = numpy.empty((1, ydim, zdim, 1, 1, 1), dtype=numpy.float64, order='F')
inputc = numpy.empty((1, ydim, zdim, 1, 1, 1), dtype=numpy.float64, order='F')
inputu = numpy.empty((1, ydim, zdim, 1, 1, 1), dtype=numpy.float64, order='F')
index = 0
numgood = 0
numpos = 0
for j in range(ydim):
for k in range(zdim):
if (index % 23) == 3:
inputa[0, j, k, 0, 0, 0] = inpbdfs[0]
else:
inputa[0, j, k, 0, 0, 0] = sampa[index]
if (index % 31) == 3:
inputc[0, j, k, 0, 0, 0] = inpbdfs[1]
inputu[0, j, k, 0, 0, 0] = inpbdfs[1]
else:
inputc[0, j, k, 0, 0, 0] = sampc[index]
inputu[0, j, k, 0, 0, 0] = sampu[index]
if ((index % 23) != 3) and ((index % 31) != 3):
numgood += 1
if sampa[index] > 0.0:
numpos += 1
index += 1
resultc = -6666.0 * numpy.ones((2, 1, 1, 1, 1, 1), dtype=numpy.float64, order='F')
expectc = numpy.empty((2, 1, 1, 1, 1, 1), dtype=numpy.float64, order='F')
expectc[0,0,0,0,0,0] = -1.0
expectc[1,0,0,0,0,0] = numpos
resultu = -6666.0 * numpy.ones((2, 1, 1, 1, 1, 1), dtype=numpy.float64, order='F')
expectu = numpy.empty((2, 1, 1, 1, 1, 1), dtype=numpy.float64, order='F')
# rough expected correlation coefficient for uncorrelated
expectu[0,0,0,0,0,0] = 0.0
expectu[1,0,0,0,0,0] = numgood
# call ferret_compute with correlated data and check the results
ferret_compute(0, resultc, resbdf, (inputa, inputc), inpbdfs)
if not numpy.allclose(resultc, expectc):
raise ValueError("Unexpected result; expected: %s; found %s" % \
(str(expectc.reshape(-1)), str(resultc.reshape(-1))))
# call ferret_compute with uncorrelated data and check the results
ferret_compute(0, resultu, resbdf, (inputa, inputu), inpbdfs)
if not numpy.allclose(resultu, expectu, atol=0.08):
raise ValueError("Unexpected result; expected: %s; found %s" % \
(str(expectu.reshape(-1)), str(resultu.reshape(-1))))
# All successful
print("Success")
|
