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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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 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. The state of the bad-value flag of the output ndarrays is unknown.

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

This package is free software, and entirely without warranty. You may redistribute it and/or modify it under the same terms as Perl itself.

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NAME

SYNOPSIS

FUNCTIONS

ccs_accum_prod

ccs_accum_dprod

ccs_accum_sum

ccs_accum_dsum

ccs_accum_or

ccs_accum_and

ccs_accum_bor

ccs_accum_band

ccs_accum_maximum

ccs_accum_minimum

ccs_accum_maximum_nz_ind

ccs_accum_minimum_nz_ind

ccs_accum_nbad

ccs_accum_ngood

ccs_accum_nnz

ccs_accum_average

TODO / NOT YET IMPLEMENTED

ACKNOWLEDGEMENTS

KNOWN BUGS

AUTHOR

Copyright Policy

SEE ALSO

Module Install Instructions