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.\" ========================================================================
.\"
.IX Title "Ufunc 3pm"
.TH Ufunc 3pm "2022-12-02" "perl v5.36.0" "User Contributed Perl Documentation"
.\" For nroff, turn off justification.  Always turn off hyphenation; it makes
.\" way too many mistakes in technical documents.
.if n .ad l
.nh
.SH "NAME"
PDL::CCS::Ufunc \- Ufuncs for compressed storage sparse PDLs
.SH "SYNOPSIS"
.IX Header "SYNOPSIS"
.Vb 2
\& use PDL;
\& use PDL::CCS::Ufunc;
\&
\& ##\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-
\& ## ... stuff happens
.Ve
.SH "FUNCTIONS"
.IX Header "FUNCTIONS"
.SS "ccs_accum_prod"
.IX Subsection "ccs_accum_prod"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&       [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated product over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
If $N() is specified and greater than zero, then the quantity:
.PP
.Vb 1
\& $missing ** ($N \- (rlevec($ixIn))[0])
.Ve
.PP
is multiplied into \f(CW$nzvalsOut:\fR this is probably What You Want if you are computing the product over a virtual
dimension in a sparse index-encoded \s-1PDL\s0 (see PDL::CCS::Nd for a wrapper class).
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_prod processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_dprod"
.IX Subsection "ccs_accum_dprod"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&    double [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated double-precision product over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
If $N() is specified and greater than zero, then the quantity:
.PP
.Vb 1
\& $missing ** ($N \- (rlevec($ixIn))[0])
.Ve
.PP
is multiplied into \f(CW$nzvalsOut:\fR this is probably What You Want if you are computing the product over a virtual
dimension in a sparse index-encoded \s-1PDL\s0 (see PDL::CCS::Nd for a wrapper class).
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_dprod processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_sum"
.IX Subsection "ccs_accum_sum"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&       [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated sum over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
If $N() is specified and greater than zero, then the quantity:
.PP
.Vb 1
\& $missing * ($N \- (rlevec($ixIn))[0])
.Ve
.PP
is added to \f(CW$nzvalsOut:\fR this is probably What You Want if you are summing over a virtual
dimension in a sparse index-encoded \s-1PDL\s0 (see PDL::CCS::Nd for a wrapper class).
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_sum processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_dsum"
.IX Subsection "ccs_accum_dsum"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&    double [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated double-precision sum over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
If $N() is specified and greater than zero, then the quantity:
.PP
.Vb 1
\& $missing * ($N \- (rlevec($ixIn))[0])
.Ve
.PP
is added to \f(CW$nzvalsOut:\fR this is probably What You Want if you are summing over a virtual
dimension in a sparse index-encoded \s-1PDL\s0 (see PDL::CCS::Nd for a wrapper class).
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_dsum processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_or"
.IX Subsection "ccs_accum_or"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&       [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated logical \*(L"or\*(R" over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
If $N() is specified and greater than zero, $\fBmissing()\fR is logically (or)ed
into each result value at each output index with a run length of less than $N() in $\fBixIn()\fR.
This is probably What You Want.
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_or processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_and"
.IX Subsection "ccs_accum_and"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&       [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated logical \*(L"and\*(R" over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
If $N() is specified and greater than zero, $\fBmissing()\fR is logically (and)ed
into each result value at each output index with a run length of less than $N() in $\fBixIn()\fR.
This is probably What You Want.
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_and processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_bor"
.IX Subsection "ccs_accum_bor"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&       [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated bitwise \*(L"or\*(R" over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
If $N() is specified and greater than zero, $\fBmissing()\fR is bitwise (or)ed
into each result value at each output index with a run length of less than $N() in $\fBixIn()\fR.
This is probably What You Want.
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_bor processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_band"
.IX Subsection "ccs_accum_band"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&       [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated bitwise \*(L"and\*(R" over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
If $N() is specified and greater than zero, $\fBmissing()\fR is bitwise (and)ed
into each result value at each output index with a run length of less than $N() in $\fBixIn()\fR.
This is probably What You Want.
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_band processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_maximum"
.IX Subsection "ccs_accum_maximum"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&       [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated maximum over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
If $N() is specified and greater than zero,
and if $\fBmissing()\fR is greater than any listed value for a vector key with a run-length
of less than $N(), then $\fBmissing()\fR is used as the output value for that key.
This is probably What You Want.
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_maximum processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_minimum"
.IX Subsection "ccs_accum_minimum"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&       [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated minimum over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
If $N() is specified and greater than zero,
and if $\fBmissing()\fR is less than any listed value for a vector key with a run-length
of less than $N(), then $\fBmissing()\fR is used as the output value for that key.
This is probably What You Want.
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_minimum processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_maximum_nz_ind"
.IX Subsection "ccs_accum_maximum_nz_ind"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&    indx [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated maximum_nz_ind over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
Output indices index \f(CW$nzvalsIn\fR, \-1 indicates that the missing value is maximal.
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_maximum_nz_ind processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_minimum_nz_ind"
.IX Subsection "ccs_accum_minimum_nz_ind"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&    indx [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated minimum_nz_ind over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
Output indices index \f(CW$nzvalsIn\fR, \-1 indicates that the missing value is minimal.
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_minimum_nz_ind processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_nbad"
.IX Subsection "ccs_accum_nbad"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&    int+ [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated number of bad values over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
Should handle missing values appropriately.
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_nbad processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_ngood"
.IX Subsection "ccs_accum_ngood"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&    indx [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated number of good values over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
Should handle missing values appropriately.
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_ngood processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_nnz"
.IX Subsection "ccs_accum_nnz"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&    indx [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated number of non-zero values over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
Should handle missing values appropriately.
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_nnz processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SS "ccs_accum_average"
.IX Subsection "ccs_accum_average"
.Vb 9
\&  Signature: (
\&    indx ixIn(Ndims,NnzIn);
\&    nzvalsIn(NnzIn);
\&    missing();
\&    indx N();
\&    indx [o]ixOut(Ndims,NnzOut);
\&    float+ [o]nzvalsOut(NnzOut);
\&    indx [o]nOut();
\&    )
.Ve
.PP
Accumulated average over values $\fBnzvalsIn()\fR associated with vector-valued keys $\fBixIn()\fR.
On return,
$\fBixOut()\fR holds the unique non\-\*(L"missing\*(R" values of $\fBixIn()\fR,
$\fBnzvalsOut()\fR holds the associated values,
and
$\fBnOut()\fR stores the number of unique non-missing values computed.
.PP
If $N() is specified and greater than zero, then the quantity:
.PP
.Vb 1
\& $missing * ($N \- (rlevec($ixIn))[0]) / $N
.Ve
.PP
is added to \f(CW$nzvalsOut:\fR this is probably What You Want if you are averaging over a virtual
dimension in a sparse index-encoded \s-1PDL\s0 (see PDL::CCS::Nd for a wrapper class).
.PP
Returned PDLs are implicitly sliced such that NnzOut==$\fBnOut()\fR.
.PP
In scalar context, returns only $\fBnzvalsOut()\fR.
.PP
ccs_accum_average processes bad values.
The state of the bad-value flag of the output ndarrays is unknown.
.SH "TODO / NOT YET IMPLEMENTED"
.IX Header "TODO / NOT YET IMPLEMENTED"
.IP "extrema indices" 4
.IX Item "extrema indices"
maximum_ind, minimum_ind: not quite compatible...
.IP "statistical aggregates" 4
.IX Item "statistical aggregates"
daverage, medover, oddmedover, pctover, ...
.IP "cumulative functions" 4
.IX Item "cumulative functions"
cumusumover, cumuprodover, ...
.IP "other stuff" 4
.IX Item "other stuff"
zcover, intover, minmaximum
.SH "ACKNOWLEDGEMENTS"
.IX Header "ACKNOWLEDGEMENTS"
Perl by Larry Wall.
.PP
\&\s-1PDL\s0 by Karl Glazebrook, Tuomas J. Lukka, Christian Soeller, and others.
.SH "KNOWN BUGS"
.IX Header "KNOWN BUGS"
Probably many.
.SH "AUTHOR"
.IX Header "AUTHOR"
Bryan Jurish <moocow@cpan.org>
.SS "Copyright Policy"
.IX Subsection "Copyright Policy"
Copyright (C) 2007\-2022, Bryan Jurish. All rights reserved.
.PP
This package is free software, and entirely without warranty.
You may redistribute it and/or modify it under the same terms
as Perl itself.
.SH "SEE ALSO"
.IX Header "SEE ALSO"
\&\fBperl\fR\|(1), \s-1\fBPDL\s0\fR\|(3perl)