Provided by: libpdl-ccs-perl_1.24.1-1_amd64 
NAME
PDL::CCS::Ufunc - Ufuncs for compressed storage sparse PDLs
SYNOPSIS
use PDL;
use PDL::CCS::Ufunc;
##---------------------------------------------------------------------
## ... stuff happens
FUNCTIONS
ccs_accum_prod
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
[o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated product over values $nzvalsIn() associated with non-missing vector-valued keys $ixIn(). On
return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the associated
values, and $nOut() stores the number of unique non-missing values computed.
If $N() is specified and greater than zero, then the quantity:
$missing ** ($N - (rlevec($ixIn))[0])
is multiplied into $nzvalsOut: this is probably What You Want if you are computing the product over a
virtual dimension in a sparse index-encoded PDL (see PDL::CCS::Nd for a wrapper class).
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_prod processes bad values. It will set the bad-value flag of all output ndarrays if the flag
is set for any of the input ndarrays.
ccs_accum_dprod
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
double [o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated double-precision product over values $nzvalsIn() associated with non-missing vector-valued
keys $ixIn(). On return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds
the associated values, and $nOut() stores the number of unique non-missing values computed.
If $N() is specified and greater than zero, then the quantity:
$missing ** ($N - (rlevec($ixIn))[0])
is multiplied into $nzvalsOut: this is probably What You Want if you are computing the product over a
virtual dimension in a sparse index-encoded PDL (see PDL::CCS::Nd for a wrapper class).
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_dprod processes bad values. It will set the bad-value flag of all output ndarrays if the flag
is set for any of the input ndarrays.
ccs_accum_sum
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
[o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated sum over values $nzvalsIn() associated with non-missing vector-valued keys $ixIn(). On
return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the associated
values, and $nOut() stores the number of unique non-missing values computed.
If $N() is specified and greater than zero, then the quantity:
$missing * ($N - (rlevec($ixIn))[0])
is added to $nzvalsOut: this is probably What You Want if you are summing over a virtual dimension in a
sparse index-encoded PDL (see PDL::CCS::Nd for a wrapper class).
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_sum processes bad values. It will set the bad-value flag of all output ndarrays if the flag is
set for any of the input ndarrays.
ccs_accum_dsum
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
double [o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated double-precision sum over values $nzvalsIn() associated with non-missing vector-valued keys
$ixIn(). On return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the
associated values, and $nOut() stores the number of unique non-missing values computed.
If $N() is specified and greater than zero, then the quantity:
$missing * ($N - (rlevec($ixIn))[0])
is added to $nzvalsOut: this is probably What You Want if you are summing over a virtual dimension in a
sparse index-encoded PDL (see PDL::CCS::Nd for a wrapper class).
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_dsum processes bad values. It will set the bad-value flag of all output ndarrays if the flag
is set for any of the input ndarrays.
ccs_accum_or
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
[o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated logical "or" over values $nzvalsIn() associated with non-missing vector-valued keys $ixIn().
On return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the associated
values, and $nOut() stores the number of unique non-missing values computed.
If $N() is specified and greater than zero, $missing() is logically (or)ed into each result value at each
output index with a run length of less than $N() in $ixIn(). This is probably What You Want.
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_or processes bad values. It will set the bad-value flag of all output ndarrays if the flag is
set for any of the input ndarrays.
ccs_accum_and
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
[o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated logical "and" over values $nzvalsIn() associated with non-missing vector-valued keys $ixIn().
On return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the associated
values, and $nOut() stores the number of unique non-missing values computed.
If $N() is specified and greater than zero, $missing() is logically (and)ed into each result value at
each output index with a run length of less than $N() in $ixIn(). This is probably What You Want.
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_and processes bad values. It will set the bad-value flag of all output ndarrays if the flag is
set for any of the input ndarrays.
ccs_accum_bor
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
[o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated bitwise "or" over values $nzvalsIn() associated with non-missing vector-valued keys $ixIn().
On return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the associated
values, and $nOut() stores the number of unique non-missing values computed.
If $N() is specified and greater than zero, $missing() is bitwise (or)ed into each result value at each
output index with a run length of less than $N() in $ixIn(). This is probably What You Want.
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_bor processes bad values. It will set the bad-value flag of all output ndarrays if the flag is
set for any of the input ndarrays.
ccs_accum_band
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
[o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated bitwise "and" over values $nzvalsIn() associated with non-missing vector-valued keys $ixIn().
On return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the associated
values, and $nOut() stores the number of unique non-missing values computed.
If $N() is specified and greater than zero, $missing() is bitwise (and)ed into each result value at each
output index with a run length of less than $N() in $ixIn(). This is probably What You Want.
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_band processes bad values. It will set the bad-value flag of all output ndarrays if the flag
is set for any of the input ndarrays.
ccs_accum_maximum
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
[o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated maximum over values $nzvalsIn() associated with non-missing vector-valued keys $ixIn(). On
return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the associated
values, and $nOut() stores the number of unique non-missing values computed.
If $N() is specified and greater than zero, and if $missing() is greater than any listed value for a
vector key with a run-length of less than $N(), then $missing() is used as the output value for that key.
This is probably What You Want.
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_maximum processes bad values. It will set the bad-value flag of all output ndarrays if the
flag is set for any of the input ndarrays.
ccs_accum_minimum
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
[o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated minimum over values $nzvalsIn() associated with non-missing vector-valued keys $ixIn(). On
return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the associated
values, and $nOut() stores the number of unique non-missing values computed.
If $N() is specified and greater than zero, and if $missing() is less than any listed value for a vector
key with a run-length of less than $N(), then $missing() is used as the output value for that key. This
is probably What You Want.
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_minimum processes bad values. It will set the bad-value flag of all output ndarrays if the
flag is set for any of the input ndarrays.
ccs_accum_maximum_nz_ind
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
indx [o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated maximum_nz_ind over values $nzvalsIn() associated with non-missing vector-valued keys
$ixIn(). On return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the
associated values, and $nOut() stores the number of unique non-missing values computed.
Output indices index $nzvalsIn, -1 indicates that the missing value is maximal.
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_maximum_nz_ind processes bad values. It will set the bad-value flag of all output ndarrays if
the flag is set for any of the input ndarrays.
ccs_accum_minimum_nz_ind
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
indx [o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated minimum_nz_ind over values $nzvalsIn() associated with non-missing vector-valued keys
$ixIn(). On return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the
associated values, and $nOut() stores the number of unique non-missing values computed.
Output indices index $nzvalsIn, -1 indicates that the missing value is minimal.
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_minimum_nz_ind processes bad values. It will set the bad-value flag of all output ndarrays if
the flag is set for any of the input ndarrays.
ccs_accum_nbad
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
indx [o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated number of bad values over values $nzvalsIn() associated with non-missing vector-valued keys
$ixIn(). On return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the
associated values, and $nOut() stores the number of unique non-missing values computed.
Should handle missing values appropriately.
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_nbad processes bad values. It will set the bad-value flag of all output ndarrays if the flag
is set for any of the input ndarrays.
ccs_accum_ngood
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
indx [o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated number of good values over values $nzvalsIn() associated with non-missing vector-valued keys
$ixIn(). On return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the
associated values, and $nOut() stores the number of unique non-missing values computed.
Should handle missing values appropriately.
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_ngood processes bad values. It will set the bad-value flag of all output ndarrays if the flag
is set for any of the input ndarrays.
ccs_accum_nnz
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
indx [o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated number of non-zero values over values $nzvalsIn() associated with non-missing vector-valued
keys $ixIn(). On return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds
the associated values, and $nOut() stores the number of unique non-missing values computed.
Should handle missing values appropriately.
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_nnz processes bad values. It will set the bad-value flag of all output ndarrays if the flag is
set for any of the input ndarrays.
ccs_accum_average
Signature: (
indx ixIn(Ndims,NnzIn);
nzvalsIn(NnzIn);
missing();
indx N();
indx [o]ixOut(Ndims,NnzOut);
float+ [o]nzvalsOut(NnzOut);
indx [o]nOut();
)
Accumulated average over values $nzvalsIn() associated with non-missing vector-valued keys $ixIn(). On
return, $ixOut() holds the unique non-"missing" values of $ixIn(), $nzvalsOut() holds the associated
values, and $nOut() stores the number of unique non-missing values computed.
If $N() is specified and greater than zero, then the quantity:
$missing * ($N - (rlevec($ixIn))[0]) / $N
is added to $nzvalsOut: this is probably What You Want if you are averaging over a virtual dimension in a
sparse index-encoded PDL (see PDL::CCS::Nd for a wrapper class).
Returned PDLs are implicitly sliced such that NnzOut==$nOut().
In scalar context, returns only $nzvalsOut().
ccs_accum_average processes bad values. It will set the bad-value flag of all output ndarrays if the
flag is set for any of the input ndarrays.
TODO / NOT YET IMPLEMENTED
extrema indices
maximum_ind, minimum_ind: not quite compatible...
statistical aggregates
daverage, medover, oddmedover, pctover, ...
cumulative functions
cumusumover, cumuprodover, ...
other stuff
zcover, intover, minmaximum
ACKNOWLEDGEMENTS
Perl by Larry Wall.
PDL by Karl Glazebrook, Tuomas J. Lukka, Christian Soeller, and others.
KNOWN BUGS
Probably many.
AUTHOR
Bryan Jurish <moocow@cpan.org>
Copyright Policy
Copyright (C) 2007-2024, Bryan Jurish. All rights reserved.
This package is free software, and entirely without warranty. You may redistribute it and/or modify it
under the same terms as Perl itself.
SEE ALSO
perl(1), PDL(3perl)
perl v5.40.0 2025-01-04 Ufunc(3pm)