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#! /usr/bin/env python
import openturns as ot
ot.TESTPREAMBLE()
# pStudent
nuMin = 0.2
nuMax = 5.0
n1 = 5
xMin = 0.1
xMax = 0.9
nX = 10
grid = [0.0] * nX
for i1 in range(n1):
nu = nuMin + (nuMax - nuMin) * i1 / (n1 - 1)
for iX in range(nX):
x = xMin + (xMax - xMin) * iX / (nX - 1)
grid[iX] = x
print(
"pStudent(",
nu,
", %.12g" % x,
")=%.6g" % ot.DistFunc.pStudent(nu, x),
", complementary=%.6g" % ot.DistFunc.pStudent(nu, x, True),
)
print("pStudent(", grid, ")=", ot.DistFunc.pStudent(nu, grid))
# check for nans
for x in [
-1e300,
-1e200,
-1e100,
1e10,
-10.0,
-0.1,
0.0,
0.1,
10.0,
1e10,
1e100,
1e200,
1e300,
]:
for nu in [2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5]:
for tail in [False, True]:
p = ot.DistFunc.pStudent(nu, x, tail)
assert ot.SpecFunc.IsNormal(p), "pStudent returns nan"
# qStudent
nuMin = 0.2
nuMax = 5.0
n1 = 5
qMin = 0.1
qMax = 0.9
nQ = 10
grid = [0.0] * nQ
for i1 in range(n1):
nu = nuMin + (nuMax - nuMin) * i1 / (n1 - 1)
for iQ in range(nQ):
q = qMin + (qMax - qMin) * iQ / (nQ - 1)
grid[iQ] = q
print(
"qStudent(",
nu,
", %.12g" % q,
")=%.6g" % ot.DistFunc.qStudent(nu, q),
", complementary=%.6g" % ot.DistFunc.qStudent(nu, q, True),
)
print("qStudent(", grid, ")=", ot.DistFunc.qStudent(nu, grid))
# rStudent
nuMin = 0.2
nuMax = 5.0
n1 = 5
nR = 10
for i1 in range(n1):
nu = nuMin + (nuMax - nuMin) * i1 / (n1 - 1)
for iR in range(nR):
print("rStudent(", nu, ")=%.6g" % ot.DistFunc.rStudent(nu))
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