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From 8e62ac7ee97e6046745ba086545ac9c2511279f8 Mon Sep 17 00:00:00 2001
From: Hood Chatham <roberthoodchatham@gmail.com>
Date: Sat, 25 Dec 2021 16:02:23 -0800
Subject: [PATCH] remove mvnun -- not fortran 77 compliant
These two functions use features from newer fortran standards which won't work
with f2c. This deletes them.
---
scipy/stats/mvn.pyf | 29 ----------
scipy/stats/mvndst.f | 132 -------------------------------------------
2 files changed, 161 deletions(-)
diff --git a/scipy/stats/mvn.pyf b/scipy/stats/mvn.pyf
index c9022b8de..cb0862a76 100644
--- a/scipy/stats/mvn.pyf
+++ b/scipy/stats/mvn.pyf
@@ -3,35 +3,6 @@
python module mvn ! in
interface ! in :mvn
- subroutine mvnun(d,n,lower,upper,means,covar,maxpts,abseps,releps,value,inform) ! in :mvn:mvndst.f
- integer intent(hide) :: d=shape(means,0)
- integer intent(hide) :: n=shape(means,1)
- double precision dimension(d) :: lower
- double precision dimension(d) :: upper
- double precision dimension(d,n) :: means
- double precision dimension(d,d) :: covar
- integer intent(optional) :: maxpts=d*1000
- double precision intent(optional) :: abseps=1e-6
- double precision intent(optional) :: releps=1e-6
- double precision intent(out) :: value
- integer intent(out) :: inform
- end subroutine mvnun
-
- subroutine mvnun_weighted(d,n,lower,upper,means,weights,covar,maxpts,abseps,releps,value,inform) ! in :mvn:mvndst.f
- integer intent(hide) :: d=shape(means,0)
- integer intent(hide) :: n=shape(means,1)
- double precision dimension(d) :: lower
- double precision dimension(d) :: upper
- double precision dimension(d,n) :: means
- double precision dimension(n) :: weights
- double precision dimension(d,d) :: covar
- integer intent(optional) :: maxpts=d*1000
- double precision intent(optional) :: abseps=1e-6
- double precision intent(optional) :: releps=1e-6
- double precision intent(out) :: value
- integer intent(out) :: inform
- end subroutine mvnun_weighted
-
subroutine mvndst(n,lower,upper,infin,correl,maxpts,abseps,releps,error,value,inform) ! in :mvn:mvndst.f
integer intent(hide) :: n=len(lower)
double precision dimension(n) :: lower
diff --git a/scipy/stats/mvndst.f b/scipy/stats/mvndst.f
index 41afa7e74..76d9690e3 100644
--- a/scipy/stats/mvndst.f
+++ b/scipy/stats/mvndst.f
@@ -21,138 +21,6 @@
* Pullman, WA 99164-3113
* Email : alangenz@wsu.edu
*
- SUBROUTINE mvnun(d, n, lower, upper, means, covar, maxpts,
- & abseps, releps, value, inform)
-* Parameters
-*
-* d integer, dimensionality of the data
-* n integer, the number of data points
-* lower double(2), the lower integration limits
-* upper double(2), the upper integration limits
-* means double(n), the mean of each kernel
-* covar double(2,2), the covariance matrix
-* maxpts integer, the maximum number of points to evaluate at
-* abseps double, absolute error tolerance
-* releps double, relative error tolerance
-* value double intent(out), integral value
-* inform integer intent(out),
-* if inform == 0: error < eps
-* elif inform == 1: error > eps, all maxpts used
- integer n, d, infin(d), maxpts, inform, tmpinf
- double precision lower(d), upper(d), releps, abseps,
- & error, value, stdev(d), rho(d*(d-1)/2),
- & covar(d,d),
- & nlower(d), nupper(d), means(d,n), tmpval,
- & inf
- integer i, j
-
- inf = 0d0
- inf = 1d0 / inf
-
- do i=1,d
- stdev(i) = dsqrt(covar(i,i))
- if (upper(i).eq.inf.and.lower(i).eq.-inf) then
- infin(i) = -1
- else if (lower(i).eq.-inf) then
- infin(i) = 0
- else if (upper(i).eq.inf) then
- infin(i) = 1
- else
- infin(i) = 2
- end if
- end do
- do i=1,d
- do j=1,i-1
- rho(j+(i-2)*(i-1)/2) = covar(i,j)/stdev(i)/stdev(j)
- end do
- end do
- value = 0d0
-
- inform = 0
-
- do i=1,n
- do j=1,d
- nlower(j) = (lower(j) - means(j,i))/stdev(j)
- nupper(j) = (upper(j) - means(j,i))/stdev(j)
- end do
- call mvndst(d,nlower,nupper,infin,rho,maxpts,abseps,releps,
- & error,tmpval,tmpinf)
- value = value + tmpval
- if (tmpinf .eq. 1) then
- inform = 1
- end if
- end do
-
- value = value / n
-
- END
-
-
- SUBROUTINE mvnun_weighted(d, n, lower, upper, means, weights,
- & covar, maxpts, abseps, releps,
- & value, inform)
-* Parameters
-*
-* d integer, dimensionality of the data
-* n integer, the number of data points
-* lower double(2), the lower integration limits
-* upper double(2), the upper integration limits
-* means double(n), the mean of each kernel
-* weights double(n), the weight of each kernel
-* covar double(2,2), the covariance matrix
-* maxpts integer, the maximum number of points to evaluate at
-* abseps double, absolute error tolerance
-* releps double, relative error tolerance
-* value double intent(out), integral value
-* inform integer intent(out),
-* if inform == 0: error < eps
-* elif inform == 1: error > eps, all maxpts used
- integer n, d, infin(d), maxpts, inform, tmpinf
- double precision lower(d), upper(d), releps, abseps,
- & error, value, stdev(d), rho(d*(d-1)/2),
- & covar(d,d),
- & nlower(d), nupper(d), means(d,n), tmpval,
- & inf, weights(n)
- integer i, j
-
- inf = 0d0
- inf = 1d0 / inf
-
- do i=1,d
- stdev(i) = dsqrt(covar(i,i))
- if (upper(i).eq.inf.and.lower(i).eq.-inf) then
- infin(i) = -1
- else if (lower(i).eq.-inf) then
- infin(i) = 0
- else if (upper(i).eq.inf) then
- infin(i) = 1
- else
- infin(i) = 2
- end if
- end do
- do i=1,d
- do j=1,i-1
- rho(j+(i-2)*(i-1)/2) = covar(i,j)/stdev(i)/stdev(j)
- end do
- end do
- value = 0d0
-
- inform = 0
-
- do i=1,n
- do j=1,d
- nlower(j) = (lower(j) - means(j,i))/stdev(j)
- nupper(j) = (upper(j) - means(j,i))/stdev(j)
- end do
- call mvndst(d,nlower,nupper,infin,rho,maxpts,abseps,releps,
- & error,tmpval,tmpinf)
- value = value + tmpval * weights(i)
- if (tmpinf .eq. 1) then
- inform = 1
- end if
- end do
-
- END
SUBROUTINE MVNDST( N, LOWER, UPPER, INFIN, CORREL, MAXPTS,
& ABSEPS, RELEPS, ERROR, VALUE, INFORM )
--
2.25.1
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