use strict;
use warnings;
use PDL::Types qw(ppdefs_all types);
my $F = [map $_->ppsym, grep $_->real && !$_->integer, types()];
my $AF = [map $_->ppsym, grep !$_->integer, types];
pp_addpm({At=>'Top'},<<'EOD');
use strict;
use warnings;
use PDL::Slices;
use Carp;
{ package PDL;
use overload (
'x' => sub {
PDL::Primitive::matmult(@_[0,1], my $foo=$_[0]->null());
$foo;
},
);
}
=head1 NAME
PDL::Primitive - primitive operations for pdl
=head1 DESCRIPTION
This module provides some primitive and useful functions defined
using PDL::PP and able to use the new indexing tricks.
See L<PDL::Indexing> for how to use indices creatively.
For explanation of the signature format, see L<PDL::PP>.
=head1 SYNOPSIS
# Pulls in PDL::Primitive, among other modules.
use PDL;
# Only pull in PDL::Primitive:
use PDL::Primitive;
=cut
EOD
################################################################
# a whole bunch of quite basic functions for inner, outer
# and matrix products (operations that are not normally
# available via operator overloading)
################################################################
pp_def('inner',
HandleBad => 1,
Pars => 'a(n); b(n); [o]c();',
GenericTypes => [ppdefs_all],
Code =>
'complex long double tmp = 0;
PDL_IF_BAD(int badflag = 0;,)
loop(n) %{
PDL_IF_BAD(if (!($ISGOOD(a()) && $ISGOOD(b()))) { badflag = 1; break; }
else,) { tmp += $a() * $b(); }
%}
PDL_IF_BAD(if (badflag) { $SETBAD(c()); $PDLSTATESETBAD(c); }
else,) { $c() = tmp; }',
Doc => '
=for ref
Inner product over one dimension
c = sum_i a_i * b_i
',
BadDoc => '
=for bad
If C<a() * b()> contains only bad data,
C<c()> is set bad. Otherwise C<c()> will have its bad flag cleared,
as it will not contain any bad values.
',
); # pp_def( inner )
pp_def('outer',
HandleBad => 1,
Pars => 'a(n); b(m); [o]c(n,m);',
GenericTypes => [ppdefs_all],
Code =>
'loop(n,m) %{
PDL_IF_BAD(if ( $ISBAD(a()) || $ISBAD(b()) ) {
$SETBAD(c());
} else,) {
$c() = $a() * $b();
}
%}',
Doc => '
=for ref
outer product over one dimension
Naturally, it is possible to achieve the effects of outer
product simply by broadcasting over the "C<*>"
operator but this function is provided for convenience.
'); # pp_def( outer )
pp_addpm(<<'EOD');
=head2 x
=for sig
Signature: (a(i,z), b(x,i),[o]c(x,z))
=for ref
Matrix multiplication
PDL overloads the C<x> operator (normally the repeat operator) for
matrix multiplication. The number of columns (size of the 0
dimension) in the left-hand argument must normally equal the number of
rows (size of the 1 dimension) in the right-hand argument.
Row vectors are represented as (N x 1) two-dimensional PDLs, or you
may be sloppy and use a one-dimensional PDL. Column vectors are
represented as (1 x N) two-dimensional PDLs.
Broadcasting occurs in the usual way, but as both the 0 and 1 dimension
(if present) are included in the operation, you must be sure that
you don't try to broadcast over either of those dims.
Of note, due to how Perl v5.14.0 and above implement operator overloading of
the C<x> operator, the use of parentheses for the left operand creates a list
context, that is
pdl> ( $x * $y ) x $z
ERROR: Argument "..." isn't numeric in repeat (x) ...
treats C<$z> as a numeric count for the list repeat operation and does not call
the scalar form of the overloaded operator. To use the operator in this case,
use a scalar context:
pdl> scalar( $x * $y ) x $z
or by calling L</matmult> directly:
pdl> ( $x * $y )->matmult( $z )
EXAMPLES
Here are some simple ways to define vectors and matrices:
pdl> $r = pdl(1,2); # A row vector
pdl> $c = pdl([[3],[4]]); # A column vector
pdl> $c = pdl(3,4)->(*1); # A column vector, using NiceSlice
pdl> $m = pdl([[1,2],[3,4]]); # A 2x2 matrix
Now that we have a few objects prepared, here is how to
matrix-multiply them:
pdl> print $r x $m # row x matrix = row
[
[ 7 10]
]
pdl> print $m x $r # matrix x row = ERROR
PDL: Dim mismatch in matmult of [2x2] x [2x1]: 2 != 1
pdl> print $m x $c # matrix x column = column
[
[ 5]
[11]
]
pdl> print $m x 2 # Trivial case: scalar mult.
[
[2 4]
[6 8]
]
pdl> print $r x $c # row x column = scalar
[
[11]
]
pdl> print $c x $r # column x row = matrix
[
[3 6]
[4 8]
]
INTERNALS
The mechanics of the multiplication are carried out by the
L</matmult> method.
=cut
EOD
pp_def('matmult',
HandleBad=>0,
Pars => 'a(t,h); b(w,t); [o]c(w,h);',
GenericTypes => [ppdefs_all],
PMCode => pp_line_numbers(__LINE__, <<'EOPM'),
sub PDL::matmult {
my ($x,$y,$c) = @_;
$y = PDL->topdl($y);
$c = PDL->null unless do { local $@; eval { $c->isa('PDL') } };
while($x->getndims < 2) {$x = $x->dummy(-1)}
while($y->getndims < 2) {$y = $y->dummy(-1)}
return ($c .= $x * $y) if( ($x->dim(0)==1 && $x->dim(1)==1) ||
($y->dim(0)==1 && $y->dim(1)==1) );
barf sprintf 'Dim mismatch in matmult of [%1$dx%2$d] x [%3$dx%4$d]: %1$d != %4$d',$x->dim(0),$x->dim(1),$y->dim(0),$y->dim(1)
if $y->dim(1) != $x->dim(0);
PDL::_matmult_int($x,$y,$c);
$c;
}
EOPM
Code => <<'EOC',
PDL_Indx tsiz = 8 * sizeof(double) / sizeof($GENERIC());
// Cache the dimincs to avoid constant lookups
PDL_Indx atdi = PDL_REPRINCS($PDL(a))[0];
PDL_Indx btdi = PDL_REPRINCS($PDL(b))[1];
broadcastloop %{
// Loop over tiles
loop (h=::tsiz,w=::tsiz) %{
PDL_Indx h_outer = h, w_outer = w;
// Zero the output for this tile
loop (h=h_outer:h_outer+tsiz,w=w_outer:w_outer+tsiz) %{ $c() = 0; %}
loop (t=::tsiz,h=h_outer:h_outer+tsiz,w=w_outer:w_outer+tsiz) %{
// Cache the accumulated value for the output
$GENERIC() cc = $c();
// Cache data pointers before 't' run through tile
$GENERIC() *ad = &($a());
$GENERIC() *bd = &($b());
// Hotspot - run the 't' summation
PDL_Indx t_outer = t;
loop (t=t_outer:t_outer+tsiz) %{
cc += *ad * *bd;
ad += atdi;
bd += btdi;
%}
// put the output back to be further accumulated later
$c() = cc;
%}
%}
%}
EOC
Doc => <<'EOD'
=for ref
Matrix multiplication
Notionally, matrix multiplication $x x $y is equivalent to the
broadcasting expression
$x->dummy(1)->inner($y->xchg(0,1)->dummy(2),$c);
but for large matrices that breaks CPU cache and is slow. Instead,
matmult calculates its result in 32x32x32 tiles, to keep the memory
footprint within cache as long as possible on most modern CPUs.
For usage, see L</x>, a description of the overloaded 'x' operator
EOD
);
pp_def('innerwt',
HandleBad => 1,
Pars => 'a(n); b(n); c(n); [o]d();',
GenericTypes => [ppdefs_all],
Code =>
'complex long double tmp = 0;
PDL_IF_BAD(int flag = 0;,)
loop(n) %{
PDL_IF_BAD(if (!( $ISGOOD(a()) && $ISGOOD(b()) && $ISGOOD(c()) )) continue;,)
tmp += $a() * $b() * $c();
PDL_IF_BAD(flag = 1;,)
%}
PDL_IF_BAD(if (!flag) { $SETBAD(d()); }
else,) { $d() = tmp; }',
Doc => '
=for ref
Weighted (i.e. triple) inner product
d = sum_i a(i) b(i) c(i)
=cut
'
);
pp_def('inner2',
HandleBad => 1,
Pars => 'a(n); b(n,m); c(m); [o]d();',
GenericTypes => [ppdefs_all],
Code =>
'complex long double tmp = 0;
PDL_IF_BAD(int flag = 0;,)
loop(n,m) %{
PDL_IF_BAD(if (!( $ISGOOD(a()) && $ISGOOD(b()) && $ISGOOD(c()) )) continue;,)
tmp += $a() * $b() * $c();
PDL_IF_BAD(flag = 1;,)
%}
PDL_IF_BAD(if (!flag) { $SETBAD(d()); }
else,) { $d() = tmp; }',
Doc => '
=for ref
Inner product of two vectors and a matrix
d = sum_ij a(i) b(i,j) c(j)
Note that you should probably not broadcast over C<a> and C<c> since that would be
very wasteful. Instead, you should use a temporary for C<b*c>.
'
);
pp_def('inner2d',
HandleBad => 1,
Pars => 'a(n,m); b(n,m); [o]c();',
GenericTypes => [ppdefs_all],
Code =>
'complex long double tmp = 0;
PDL_IF_BAD(int flag = 0;,)
loop(n,m) %{
PDL_IF_BAD(if (!( $ISGOOD(a()) && $ISGOOD(b()) )) continue;,)
tmp += $a() * $b();
PDL_IF_BAD(flag = 1;,)
%}
PDL_IF_BAD(if (!flag) { $SETBAD(c()); }
else,) { $c() = tmp; }',
Doc => '
=for ref
Inner product over 2 dimensions.
Equivalent to
$c = inner($x->clump(2), $y->clump(2))
=cut
'
);
pp_def('inner2t',
HandleBad => 1,
Pars => 'a(j,n); b(n,m); c(m,k); [t]tmp(n,k); [o]d(j,k);',
GenericTypes => [ppdefs_all],
Code =>
'loop(n,k) %{
complex long double tmp0 = 0;
PDL_IF_BAD(int flag = 0;,)
loop(m) %{
PDL_IF_BAD(if (!( $ISGOOD(b()) && $ISGOOD(c()) )) continue;,)
tmp0 += $b() * $c();
PDL_IF_BAD(flag = 1;,)
%}
PDL_IF_BAD(if (!flag) { $SETBAD(tmp()); }
else,) { $tmp() = tmp0; }
%}
loop(j,k) %{
complex long double tmp1 = 0;
PDL_IF_BAD(int flag = 0;,)
loop(n) %{
PDL_IF_BAD(if (!( $ISGOOD(a()) && $ISGOOD(tmp()) )) continue;,)
tmp1 += $a() * $tmp();
PDL_IF_BAD(flag = 1;,)
%}
PDL_IF_BAD(if (!flag) { $SETBAD(d()); }
else,) { $d() = tmp1; }
%}',
Doc => '
=for ref
Efficient Triple matrix product C<a*b*c>
Efficiency comes from by using the temporary C<tmp>. This operation only
scales as C<N**3> whereas broadcasting using L</inner2> would scale
as C<N**4>.
The reason for having this routine is that you do not need to
have the same broadcast-dimensions for C<tmp> as for the other arguments,
which in case of large numbers of matrices makes this much more
memory-efficient.
It is hoped that things like this could be taken care of as a kind of
closures at some point.
=cut
'
); # pp_def inner2t()
# a helper function for the cross product definition
sub crassgn {
"\$c(tri => $_[0]) = \$a(tri => $_[1])*\$b(tri => $_[2]) -
\$a(tri => $_[2])*\$b(tri => $_[1]);"
}
pp_def('crossp',
Doc => <<'EOD',
=for ref
Cross product of two 3D vectors
After
=for example
$c = crossp $x, $y
the inner product C<$c*$x> and C<$c*$y> will be zero, i.e. C<$c> is
orthogonal to C<$x> and C<$y>
=cut
EOD
Pars => 'a(tri=3); b(tri); [o] c(tri)',
GenericTypes => [ppdefs_all],
Code =>
crassgn(0,1,2)."\n".
crassgn(1,2,0)."\n".
crassgn(2,0,1),
);
pp_def('norm',
HandleBad => 1,
Pars => 'vec(n); [o] norm(n)',
GenericTypes => [ppdefs_all],
Doc => 'Normalises a vector to unit Euclidean length',
Code =>
'long double sum=0;
PDL_IF_BAD(int flag = 0;,)
loop(n) %{
PDL_IF_BAD(if (!( $ISGOOD(vec()) )) continue;,)
sum +=
PDL_IF_GENTYPE_REAL(
$vec()*$vec(),
creall($vec())*creall($vec()) + cimagl($vec())*cimagl($vec())
);
PDL_IF_BAD(flag = 1;,)
%}
PDL_IF_BAD(if ( !flag ) {
loop(n) %{ $SETBAD(norm()); %}
continue;
},)
if (sum > 0) {
sum = sqrt(sum);
loop(n) %{
PDL_IF_BAD(if ( $ISBAD(vec()) ) { $SETBAD(norm()); }
else ,) {
$norm() = $vec()/sum;
}
%}
} else {
loop(n) %{
PDL_IF_BAD(if ( $ISBAD(vec()) ) { $SETBAD(norm()); }
else ,) { $norm() = $vec(); }
%}
}
',
);
# this one was motivated by the need to compute
# the circular mean efficiently
# without it could not be done efficiently or without
# creating large intermediates (check pdl-porters for
# discussion)
# see PDL::ImageND for info about the circ_mean function
pp_def(
'indadd',
HandleBad => 1,
Pars => 'input(n); indx ind(n); [io] sum(m)',
GenericTypes => [ppdefs_all],
Code =>
'loop(n) %{
register PDL_Indx this_ind = $ind();
if ( PDL_IF_BAD($ISBADVAR(this_ind,ind) ||,) this_ind<0 || this_ind>=$SIZE(m) )
$CROAK("invalid index %"IND_FLAG"; range 0..%"IND_FLAG, this_ind, $SIZE(m));
PDL_IF_BAD(
if ( $ISBAD(input()) ) { $SETBAD(sum(m => this_ind)); }
else,) { $sum(m => this_ind) += $input(); }
%}',
BadDoc => '
=for bad
The routine barfs on bad indices, and bad inputs set target outputs bad.
=cut
',
Doc=>'
=for ref
Broadcasting index add: add C<input> to the C<ind> element of C<sum>, i.e:
sum(ind) += input
=for example
Simple example:
$x = 2;
$ind = 3;
$sum = zeroes(10);
indadd($x,$ind, $sum);
print $sum
#Result: ( 2 added to element 3 of $sum)
# [0 0 0 2 0 0 0 0 0 0]
Broadcasting example:
$x = pdl( 1,2,3);
$ind = pdl( 1,4,6);
$sum = zeroes(10);
indadd($x,$ind, $sum);
print $sum."\n";
#Result: ( 1, 2, and 3 added to elements 1,4,6 $sum)
# [0 1 0 0 2 0 3 0 0 0]
=cut
');
# 1D convolution
# useful for broadcasted 1D filters
pp_def('conv1d',
Doc => << 'EOD',
=for ref
1D convolution along first dimension
The m-th element of the discrete convolution of an input ndarray
C<$a> of size C<$M>, and a kernel ndarray C<$kern> of size C<$P>, is
calculated as
n = ($P-1)/2
====
\
($a conv1d $kern)[m] = > $a_ext[m - n] * $kern[n]
/
====
n = -($P-1)/2
where C<$a_ext> is either the periodic (or reflected) extension of
C<$a> so it is equal to C<$a> on C< 0..$M-1 > and equal to the
corresponding periodic/reflected image of C<$a> outside that range.
=for example
$con = conv1d sequence(10), pdl(-1,0,1);
$con = conv1d sequence(10), pdl(-1,0,1), {Boundary => 'reflect'};
By default, periodic boundary conditions are assumed (i.e. wrap around).
Alternatively, you can request reflective boundary conditions using
the C<Boundary> option:
{Boundary => 'reflect'} # case in 'reflect' doesn't matter
The convolution is performed along the first dimension. To apply it across
another dimension use the slicing routines, e.g.
$y = $x->mv(2,0)->conv1d($kernel)->mv(0,2); # along third dim
This function is useful for broadcasted filtering of 1D signals.
Compare also L<conv2d|PDL::Image2D/conv2d>, L<convolve|PDL::ImageND/convolve>,
L<fftconvolve|PDL::FFT/fftconvolve()>
=for bad
WARNING: C<conv1d> processes bad values in its inputs as
the numeric value of C<< $pdl->badvalue >> so it is not
recommended for processing pdls with bad values in them
unless special care is taken.
=cut
EOD
Pars => 'a(m); kern(p); [o]b(m);',
GenericTypes => [ppdefs_all],
OtherPars => 'int reflect;',
HandleBad => 0,
PMCode => pp_line_numbers(__LINE__, <<'EOPM'),
sub PDL::conv1d {
my $opt = pop @_ if ref($_[-1]) eq 'HASH';
die 'Usage: conv1d( a(m), kern(p), [o]b(m), {Options} )'
if @_<2 || @_>3;
my($x,$kern) = @_;
my $c = @_ == 3 ? $_[2] : PDL->null;
PDL::_conv1d_int($x,$kern,$c,
!(defined $opt && exists $$opt{Boundary}) ? 0 :
lc $$opt{Boundary} eq "reflect");
return $c;
}
EOPM
CHeader => '
/* Fast Modulus with proper negative behaviour */
#define REALMOD(a,b) while ((a)>=(b)) (a) -= (b); while ((a)<0) (a) += (b);
',
Code => '
int reflect = $COMP(reflect);
PDL_Indx m_size = $SIZE(m), p_size = $SIZE(p);
PDL_Indx poff = (p_size-1)/2;
loop(m) %{
complex long double tmp = 0;
loop(p) %{
PDL_Indx pflip = p_size - 1 - p, i2 = m+p - poff;
if (reflect && i2<0)
i2 = -i2;
if (reflect && i2>=m_size)
i2 = m_size-(i2-m_size+1);
REALMOD(i2,m_size);
tmp += $a(m=>i2) * $kern(p=>pflip);
%}
$b() = tmp;
%}
');
# this can be achieved by
# ($x->dummy(0) == $y)->orover
# but this one avoids a larger intermediate and potentially shortcuts
pp_def('in',
Pars => 'a(); b(n); [o] c()',
GenericTypes => [ppdefs_all],
Code => '$c() = 0;
loop(n) %{ if ($a() == $b()) {$c() = 1; break;} %}',
Doc => <<'EOD',
=for ref
test if a is in the set of values b
=for example
$goodmsk = $labels->in($goodlabels);
print pdl(3,1,4,6,2)->in(pdl(2,3,3));
[1 0 0 0 1]
C<in> is akin to the I<is an element of> of set theory. In principle,
PDL broadcasting could be used to achieve its functionality by using a
construct like
$msk = ($labels->dummy(0) == $goodlabels)->orover;
However, C<in> doesn't create a (potentially large) intermediate
and is generally faster.
=cut
EOD
);
pp_add_exported ('', 'uniq');
pp_addpm (<< 'EOPM');
=head2 uniq
=for ref
return all unique elements of an ndarray
The unique elements are returned in ascending order.
=for example
PDL> p pdl(2,2,2,4,0,-1,6,6)->uniq
[-1 0 2 4 6] # 0 is returned 2nd (sorted order)
PDL> p pdl(2,2,2,4,nan,-1,6,6)->uniq
[-1 2 4 6 nan] # NaN value is returned at end
Note: The returned pdl is 1D; any structure of the input
ndarray is lost. C<NaN> values are never compare equal to
any other values, even themselves. As a result, they are
always unique. C<uniq> returns the NaN values at the end
of the result ndarray. This follows the Matlab usage.
See L</uniqind> if you need the indices of the unique
elements rather than the values.
=for bad
Bad values are not considered unique by uniq and are ignored.
$x=sequence(10);
$x=$x->setbadif($x%3);
print $x->uniq;
[0 3 6 9]
=cut
*uniq = \&PDL::uniq;
# return unique elements of array
# find as jumps in the sorted array
# flattens in the process
sub PDL::uniq {
my ($arr) = @_;
return $arr if($arr->nelem == 0); # The null list is unique (CED)
return $arr->flat if($arr->nelem == 1); # singleton list is unique
my $aflat = $arr->flat;
my $srt = $aflat->where($aflat==$aflat)->qsort; # no NaNs or BADs for qsort
my $nans = $aflat->where($aflat!=$aflat);
my $uniq = ($srt->nelem > 1) ? $srt->where($srt != $srt->rotate(-1)) : $srt;
# make sure we return something if there is only one value
(
$uniq->nelem > 0 ? $uniq :
$srt->nelem == 0 ? $srt :
PDL::pdl( ref($srt), [$srt->index(0)] )
)->append($nans);
}
EOPM
pp_add_exported ('', 'uniqind');
pp_addpm (<< 'EOPM');
=head2 uniqind
=for ref
Return the indices of all unique elements of an ndarray
The order is in the order of the values to be consistent
with uniq. C<NaN> values never compare equal with any
other value and so are always unique. This follows the
Matlab usage.
=for example
PDL> p pdl(2,2,2,4,0,-1,6,6)->uniqind
[5 4 1 3 6] # the 0 at index 4 is returned 2nd, but...
PDL> p pdl(2,2,2,4,nan,-1,6,6)->uniqind
[5 1 3 6 4] # ...the NaN at index 4 is returned at end
Note: The returned pdl is 1D; any structure of the input
ndarray is lost.
See L</uniq> if you want the unique values instead of the
indices.
=for bad
Bad values are not considered unique by uniqind and are ignored.
=cut
*uniqind = \&PDL::uniqind;
# return unique elements of array
# find as jumps in the sorted array
# flattens in the process
sub PDL::uniqind {
use PDL::Core 'barf';
my ($arr) = @_;
return $arr if($arr->nelem == 0); # The null list is unique (CED)
# Different from uniq we sort and store the result in an intermediary
my $aflat = $arr->flat;
my $nanind = which($aflat!=$aflat); # NaN indexes
my $good = PDL->sequence(indx, $aflat->dims)->where($aflat==$aflat); # good indexes
my $i_srt = $aflat->where($aflat==$aflat)->qsorti; # no BAD or NaN values for qsorti
my $srt = $aflat->where($aflat==$aflat)->index($i_srt);
my $uniqind;
if ($srt->nelem > 0) {
$uniqind = which($srt != $srt->rotate(-1));
$uniqind = $i_srt->slice('0') if $uniqind->isempty;
} else {
$uniqind = which($srt);
}
# Now map back to the original space
my $ansind = $nanind;
if ( $uniqind->nelem > 0 ) {
$ansind = ($good->index($i_srt->index($uniqind)))->append($ansind);
} else {
$ansind = $uniqind->append($ansind);
}
return $ansind;
}
EOPM
pp_add_exported ('', 'uniqvec');
pp_addpm (<< 'EOPM');
=head2 uniqvec
=for ref
Return all unique vectors out of a collection
NOTE: If any vectors in the input ndarray have NaN values
they are returned at the end of the non-NaN ones. This is
because, by definition, NaN values never compare equal with
any other value.
NOTE: The current implementation does not sort the vectors
containing NaN values.
The unique vectors are returned in lexicographically sorted
ascending order. The 0th dimension of the input PDL is treated
as a dimensional index within each vector, and the 1st and any
higher dimensions are taken to run across vectors. The return
value is always 2D; any structure of the input PDL (beyond using
the 0th dimension for vector index) is lost.
See also L</uniq> for a unique list of scalars; and
L<qsortvec|PDL::Ufunc/qsortvec> for sorting a list of vectors
lexicographcally.
=for bad
If a vector contains all bad values, it is ignored as in L</uniq>.
If some of the values are good, it is treated as a normal vector. For
example, [1 2 BAD] and [BAD 2 3] could be returned, but [BAD BAD BAD]
could not. Vectors containing BAD values will be returned after any
non-NaN and non-BAD containing vectors, followed by the NaN vectors.
=cut
sub PDL::uniqvec {
my($pdl) = shift;
return $pdl if ( $pdl->nelem == 0 || $pdl->ndims < 2 );
return $pdl if ( $pdl->slice("(0)")->nelem < 2 ); # slice isn't cheap but uniqvec isn't either
my $pdl2d = $pdl->clump(1..$pdl->ndims-1);
my $ngood = $pdl2d->ngoodover;
$pdl2d = $pdl2d->mv(0,-1)->dice($ngood->which)->mv(-1,0); # remove all-BAD vectors
my $numnan = ($pdl2d!=$pdl2d)->sumover; # works since no all-BADs to confuse
my $presrt = $pdl2d->mv(0,-1)->dice($numnan->not->which)->mv(0,-1); # remove vectors with any NaN values
my $nanvec = $pdl2d->mv(0,-1)->dice($numnan->which)->mv(0,-1); # the vectors with any NaN values
my $srt = $presrt->qsortvec->mv(0,-1); # BADs are sorted by qsortvec
my $srtdice = $srt;
my $somebad = null;
if ($srt->badflag) {
$srtdice = $srt->dice($srt->mv(0,-1)->nbadover->not->which);
$somebad = $srt->dice($srt->mv(0,-1)->nbadover->which);
}
my $uniq = $srtdice->nelem > 0
? ($srtdice != $srtdice->rotate(-1))->mv(0,-1)->orover->which
: $srtdice->orover->which;
my $ans = $uniq->nelem > 0 ? $srtdice->dice($uniq) :
($srtdice->nelem > 0) ? $srtdice->slice("0,:") :
$srtdice;
return $ans->append($somebad)->append($nanvec->mv(0,-1))->mv(0,-1);
}
EOPM
#####################################################################
# clipping routines
#####################################################################
# clipping
for my $opt (
['hclip','PDLMIN'],
['lclip','PDLMAX']
) {
my $name = $opt->[0];
my $op = $opt->[1];
my $code = '$c() = '.$op.'($b(), $a());';
pp_def(
$name,
HandleBad => 1,
Pars => 'a(); b(); [o] c()',
Code =>
'PDL_IF_BAD(if ( $ISBAD(a()) || $ISBAD(b()) ) {
$SETBAD(c());
} else,) { '.$code.' }',
Doc => 'clip (threshold) C<$a> by C<$b> (C<$b> is '.
($name eq 'hclip' ? 'upper' : 'lower').' bound)',
PMCode=>pp_line_numbers(__LINE__, <<"EOD"),
sub PDL::$name {
my (\$x,\$y) = \@_;
my \$c;
if (\$x->is_inplace) {
\$x->set_inplace(0); \$c = \$x;
} elsif (\@_ > 2) {\$c=\$_[2]} else {\$c=PDL->nullcreate(\$x)}
PDL::_${name}_int(\$x,\$y,\$c);
return \$c;
}
EOD
); # pp_def $name
} # for: my $opt
pp_add_exported('', 'clip');
pp_addpm(<<'EOD');
=head2 clip
=for ref
Clip (threshold) an ndarray by (optional) upper or lower bounds.
=for usage
$y = $x->clip(0,3);
$c = $x->clip(undef, $x);
=for bad
clip handles bad values since it is just a
wrapper around L</hclip> and
L</lclip>.
=cut
EOD
pp_def(
'clip',
HandleBad => 1,
Pars => 'a(); l(); h(); [o] c()',
Code => <<'EOBC',
PDL_IF_BAD(
if( $ISBAD(a()) || $ISBAD(l()) || $ISBAD(h()) ) {
$SETBAD(c());
} else,) {
$c() = PDLMIN($h(), PDLMAX($l(), $a()));
}
EOBC
PMCode=>pp_line_numbers(__LINE__, <<'EOPM'),
*clip = \&PDL::clip;
sub PDL::clip {
my($x, $l, $h) = @_;
my $d;
unless(defined($l) || defined($h)) {
# Deal with pathological case
if($x->is_inplace) {
$x->set_inplace(0);
return $x;
} else {
return $x->copy;
}
}
if($x->is_inplace) {
$x->set_inplace(0); $d = $x
} elsif (@_ > 3) {
$d=$_[3]
} else {
$d = PDL->nullcreate($x);
}
if(defined($l) && defined($h)) {
PDL::_clip_int($x,$l,$h,$d);
} elsif( defined($l) ) {
PDL::_lclip_int($x,$l,$d);
} elsif( defined($h) ) {
PDL::_hclip_int($x,$h,$d);
} else {
die "This can't happen (clip contingency) - file a bug";
}
return $d;
}
EOPM
); # end of clip pp_def call
############################################################
# elementary statistics and histograms
############################################################
pp_def('wtstat',
HandleBad => 1,
Pars => 'a(n); wt(n); avg(); [o]b();',
GenericTypes => [ppdefs_all],
OtherPars => 'int deg',
Code =>
'complex long double wtsum = 0;
complex long double statsum = 0;
PDL_IF_BAD(int flag = 0;,)
loop(n) %{
PDL_IF_BAD(if (!($ISGOOD(wt()) && $ISGOOD(a()) && $ISGOOD(avg()))) continue;,)
PDL_Indx i;
wtsum += $wt();
complex long double tmp=1;
for(i=0; i<$COMP(deg); i++)
tmp *= $a();
statsum += $wt() * (tmp - $avg());
PDL_IF_BAD(flag = 1;,)
%}
PDL_IF_BAD(if (!flag) { $SETBAD(b()); $PDLSTATESETBAD(b); }
else,) { $b() = statsum / wtsum; }',
Doc => '
=for ref
Weighted statistical moment of given degree
This calculates a weighted statistic over the vector C<a>.
The formula is
b() = (sum_i wt_i * (a_i ** degree - avg)) / (sum_i wt_i)
',
BadDoc => '
=for bad
Bad values are ignored in any calculation; C<$b> will only
have its bad flag set if the output contains any bad data.
',
);
pp_def('statsover',
HandleBad => 1,
Pars => 'a(n); w(n); float+ [o]avg(); float+ [o]prms(); int+ [o]median(); int+ [o]min(); int+ [o]max(); float+ [o]adev(); float+ [o]rms()',
Code =>
'$GENERIC(avg) tmp = 0;
$GENERIC(avg) tmp1 = 0;
$GENERIC(avg) diff = 0;
$GENERIC(min) curmin = 0, curmax = 0;
$GENERIC(w) norm = 0;
int flag = 0;
loop(n) %{ /* Accumulate sum and summed weight. */
/* perhaps should check w() for bad values too ? */
PDL_IF_BAD(if (!( $ISGOOD(a()) )) continue;,)
tmp += $a()*$w();
norm += ($GENERIC(avg)) $w();
if (!flag) { curmin = $a(); curmax = $a(); flag=1; }
if ($a() < curmin) {
curmin = $a();
} else if ($a() > curmax) {
curmax = $a();
}
%}
/* have at least one valid point if flag true */
PDL_IF_BAD(if ( !flag ) {
$SETBAD(avg()); $PDLSTATESETBAD(avg);
$SETBAD(rms()); $PDLSTATESETBAD(rms);
$SETBAD(adev()); $PDLSTATESETBAD(adev);
$SETBAD(min()); $PDLSTATESETBAD(min);
$SETBAD(max()); $PDLSTATESETBAD(max);
$SETBAD(prms()); $PDLSTATESETBAD(prms);
} else,) {
$avg() = tmp / norm; /* Find mean */
$min() = curmin;
$max() = curmax;
/* Calculate the RMS and standard deviation. */
tmp = 0;
loop(n) %{
PDL_IF_BAD(if (!$ISGOOD(a())) continue;,)
diff = $a()-$avg();
tmp += diff * diff * $w();
tmp1 += fabsl(diff) * $w();
%}
$rms() = sqrt( tmp/norm );
if(norm>1)
$prms() = sqrt( tmp/(norm-1) );
else
PDL_IF_BAD($SETBAD(prms()),$prms() = 0);
$adev() = tmp1 / norm ;
}',
PMCode=>pp_line_numbers(__LINE__, <<'EOPM'),
sub PDL::statsover {
barf('Usage: ($mean,[$prms, $median, $min, $max, $adev, $rms]) = statsover($data,[$weights])') if @_>2;
my ($data, $weights) = @_;
$weights //= $data->ones();
my $median = $data->medover;
my $mean = PDL->nullcreate($data);
my $rms = PDL->nullcreate($data);
my $min = PDL->nullcreate($data);
my $max = PDL->nullcreate($data);
my $adev = PDL->nullcreate($data);
my $prms = PDL->nullcreate($data);
PDL::_statsover_int($data, $weights, $mean, $prms, $median, $min, $max, $adev, $rms);
wantarray ? ($mean, $prms, $median, $min, $max, $adev, $rms) : $mean;
}
EOPM
Doc => '
=for ref
Calculate useful statistics over a dimension of an ndarray
=for usage
($mean,$prms,$median,$min,$max,$adev,$rms) = statsover($ndarray, $weights);
This utility function calculates various useful
quantities of an ndarray. These are:
=over 3
=item * the mean:
MEAN = sum (x)/ N
with C<N> being the number of elements in x
=item * the population RMS deviation from the mean:
PRMS = sqrt( sum( (x-mean(x))^2 )/(N-1) )
The population deviation is the best-estimate of the deviation
of the population from which a sample is drawn.
=item * the median
The median is the 50th percentile data value. Median is found by
L<medover|PDL::Ufunc/medover>, so WEIGHTING IS IGNORED FOR THE MEDIAN CALCULATION.
=item * the minimum
=item * the maximum
=item * the average absolute deviation:
AADEV = sum( abs(x-mean(x)) )/N
=item * RMS deviation from the mean:
RMS = sqrt(sum( (x-mean(x))^2 )/N)
(also known as the root-mean-square deviation, or the square root of the
variance)
=back
This operator is a projection operator so the calculation
will take place over the final dimension. Thus if the input
is N-dimensional each returned value will be N-1 dimensional,
to calculate the statistics for the entire ndarray either
use C<clump(-1)> directly on the ndarray or call C<stats>.
=cut
',
BadDoc =>'
=for bad
Bad values are simply ignored in the calculation, effectively reducing
the sample size. If all data are bad then the output data are marked bad.
=cut
',
);
pp_add_exported('','stats');
pp_addpm(<<'EOD');
=head2 stats
=for ref
Calculates useful statistics on an ndarray
=for usage
($mean,$prms,$median,$min,$max,$adev,$rms) = stats($ndarray,[$weights]);
This utility calculates all the most useful quantities in one call.
It works the same way as L</statsover>, except that the quantities are
calculated considering the entire input PDL as a single sample, rather
than as a collection of rows. See L</statsover> for definitions of the
returned quantities.
=for bad
Bad values are handled; if all input values are bad, then all of the output
values are flagged bad.
=cut
*stats = \&PDL::stats;
sub PDL::stats {
barf('Usage: ($mean,[$rms]) = stats($data,[$weights])') if @_>2;
my ($data,$weights) = @_;
# Ensure that $weights is properly broadcasted over; this could be
# done rather more efficiently...
if(defined $weights) {
$weights = pdl($weights) unless UNIVERSAL::isa($weights,'PDL');
if( ($weights->ndims != $data->ndims) or
(pdl($weights->dims) != pdl($data->dims))->or
) {
$weights = $weights + zeroes($data)
}
$weights = $weights->flat;
}
return PDL::statsover($data->flat,$weights);
}
EOD
my $histogram_doc = <<'EOD';
=for ref
Calculates a histogram for given stepsize and minimum.
=for usage
$h = histogram($data, $step, $min, $numbins);
$hist = zeroes $numbins; # Put histogram in existing ndarray.
histogram($data, $hist, $step, $min, $numbins);
The histogram will contain C<$numbins> bins starting from C<$min>, each
C<$step> wide. The value in each bin is the number of
values in C<$data> that lie within the bin limits.
Data below the lower limit is put in the first bin, and data above the
upper limit is put in the last bin.
The output is reset in a different broadcastloop so that you
can take a histogram of C<$a(10,12)> into C<$b(15)> and get the result
you want.
For a higher-level interface, see L<hist|PDL::Basic/hist>.
=for example
pdl> p histogram(pdl(1,1,2),1,0,3)
[0 2 1]
=cut
EOD
my $whistogram_doc = <<'EOD';
=for ref
Calculates a histogram from weighted data for given stepsize and minimum.
=for usage
$h = whistogram($data, $weights, $step, $min, $numbins);
$hist = zeroes $numbins; # Put histogram in existing ndarray.
whistogram($data, $weights, $hist, $step, $min, $numbins);
The histogram will contain C<$numbins> bins starting from C<$min>, each
C<$step> wide. The value in each bin is the sum of the values in C<$weights>
that correspond to values in C<$data> that lie within the bin limits.
Data below the lower limit is put in the first bin, and data above the
upper limit is put in the last bin.
The output is reset in a different broadcastloop so that you
can take a histogram of C<$a(10,12)> into C<$b(15)> and get the result
you want.
=for example
pdl> p whistogram(pdl(1,1,2), pdl(0.1,0.1,0.5), 1, 0, 4)
[0 0.2 0.5 0]
=cut
EOD
for(
{Name => 'histogram',
WeightPar => '',
HistType => 'int+',
HistOp => '++',
Doc => $histogram_doc,
},
{Name => 'whistogram',
WeightPar => 'float+ wt(n);',
HistType => 'float+',
HistOp => '+= $wt()',
Doc => $whistogram_doc,
}
)
{
pp_def($_->{Name},
Pars => 'in(n); '.$_->{WeightPar}.$_->{HistType}. '[o] hist(m)',
GenericTypes => [ppdefs_all],
# set outdim by Par!
OtherPars => 'double step; double min; int msize => m',
HandleBad => 1,
RedoDimsCode => 'if ($SIZE(m) == 0) $CROAK("called with m dim of 0");',
Code => pp_line_numbers(__LINE__-1, '
register double min = $COMP(min), step = $COMP(step);
broadcastloop %{
loop(m) %{ $hist() = 0; %}
loop(n) %{
PDL_IF_BAD(if ( !$ISGOOD(in()) ) continue;,)
PDL_Indx j = round((($in()-min)/step)-0.5);
j = PDLMIN(PDLMAX(j, 0), $SIZE(m)-1);
($hist(m => j))'.$_->{HistOp}.';
%}
%}'),
Doc=>$_->{Doc});
}
my $histogram2d_doc = <<'EOD';
=for ref
Calculates a 2d histogram.
=for usage
$h = histogram2d($datax, $datay, $stepx, $minx,
$nbinx, $stepy, $miny, $nbiny);
$hist = zeroes $nbinx, $nbiny; # Put histogram in existing ndarray.
histogram2d($datax, $datay, $hist, $stepx, $minx,
$nbinx, $stepy, $miny, $nbiny);
The histogram will contain C<$nbinx> x C<$nbiny> bins, with the lower
limits of the first one at C<($minx, $miny)>, and with bin size
C<($stepx, $stepy)>.
The value in each bin is the number of
values in C<$datax> and C<$datay> that lie within the bin limits.
Data below the lower limit is put in the first bin, and data above the
upper limit is put in the last bin.
=for example
pdl> p histogram2d(pdl(1,1,1,2,2),pdl(2,1,1,1,1),1,0,3,1,0,3)
[
[0 0 0]
[0 2 2]
[0 1 0]
]
=cut
EOD
my $whistogram2d_doc = <<'EOD';
=for ref
Calculates a 2d histogram from weighted data.
=for usage
$h = whistogram2d($datax, $datay, $weights,
$stepx, $minx, $nbinx, $stepy, $miny, $nbiny);
$hist = zeroes $nbinx, $nbiny; # Put histogram in existing ndarray.
whistogram2d($datax, $datay, $weights, $hist,
$stepx, $minx, $nbinx, $stepy, $miny, $nbiny);
The histogram will contain C<$nbinx> x C<$nbiny> bins, with the lower
limits of the first one at C<($minx, $miny)>, and with bin size
C<($stepx, $stepy)>.
The value in each bin is the sum of the values in
C<$weights> that correspond to values in C<$datax> and C<$datay> that lie within the bin limits.
Data below the lower limit is put in the first bin, and data above the
upper limit is put in the last bin.
=for example
pdl> p whistogram2d(pdl(1,1,1,2,2),pdl(2,1,1,1,1),pdl(0.1,0.2,0.3,0.4,0.5),1,0,3,1,0,3)
[
[ 0 0 0]
[ 0 0.5 0.9]
[ 0 0.1 0]
]
=cut
EOD
for(
{Name => 'histogram2d',
WeightPar => '',
HistType => 'int+',
HistOp => '++',
Doc => $histogram2d_doc,
},
{Name => 'whistogram2d',
WeightPar => 'float+ wt(n);',
HistType => 'float+',
HistOp => '+= $wt()',
Doc => $whistogram2d_doc,
}
)
{
pp_def($_->{Name},
Pars => 'ina(n); inb(n); '.$_->{WeightPar}.$_->{HistType}. '[o] hist(ma,mb)',
GenericTypes => [ppdefs_all],
# set outdim by Par!
OtherPars => 'double stepa; double mina; int masize => ma;
double stepb; double minb; int mbsize => mb;',
HandleBad => 1,
RedoDimsCode => '
if ($SIZE(ma) == 0) $CROAK("called with ma dim of 0");
if ($SIZE(mb) == 0) $CROAK("called with mb dim of 0");
',
Code => pp_line_numbers(__LINE__-1, '
register double mina = $COMP(mina), minb = $COMP(minb), stepa = $COMP(stepa), stepb = $COMP(stepb);
broadcastloop %{
loop(ma,mb) %{ $hist() = 0; %}
loop(n) %{
PDL_IF_BAD(if (!( $ISGOOD(ina()) && $ISGOOD(inb()) )) continue;,)
PDL_Indx ja = round((($ina()-mina)/stepa)-0.5);
PDL_Indx jb = round((($inb()-minb)/stepb)-0.5);
ja = PDLMIN(PDLMAX(ja, 0), $SIZE(ma)-1);
jb = PDLMIN(PDLMAX(jb, 0), $SIZE(mb)-1);
($hist(ma => ja,mb => jb))'.$_->{HistOp}.';
%}
%}'),
Doc=> $_->{Doc});
}
###########################################################
# a number of constructors: fibonacci, append, axisvalues &
# random numbers
###########################################################
pp_def('fibonacci',
Pars => 'i(n); indx [o]x(n)',
Inplace => 1,
GenericTypes => [ppdefs_all],
Doc=>'Constructor - a vector with Fibonacci\'s sequence',
PMFunc=>'',
PMCode=>pp_line_numbers(__LINE__, <<'EOD'),
sub fibonacci { ref($_[0]) && ref($_[0]) ne 'PDL::Type' ? $_[0]->fibonacci : PDL->fibonacci(@_) }
sub PDL::fibonacci{
my $x = &PDL::Core::_construct;
my $is_inplace = $x->is_inplace;
my ($in, $out) = $x->flat;
$out = $is_inplace ? $in->inplace : PDL->null;
PDL::_fibonacci_int($in, $out);
$out;
}
EOD
Code => '
PDL_Indx i=0;
$GENERIC() x1, x2;
x1 = 1; x2 = 0;
loop(n) %{
$x() = x1 + x2;
if (i++>0) {
x2 = x1;
x1 = $x();
}
%}
');
pp_def('append',
Pars => 'a(n); b(m); [o] c(mn=CALC($SIZE(n)+$SIZE(m)))',
GenericTypes => [ppdefs_all],
PMCode => pp_line_numbers(__LINE__-1, '
sub PDL::append {
my ($i1, $i2, $o) = map PDL->topdl($_), @_;
$_ = empty() for grep $_->isnull, $i1, $i2;
my $nempty = grep $_->isempty, $i1, $i2;
if ($nempty == 2) {
my @dims = $i1->dims;
$dims[0] += $i2->dim(0);
return PDL->zeroes($i1->type, @dims);
}
$o //= PDL->null;
$o .= $i1->isempty ? $i2 : $i1, return $o if $nempty == 1;
PDL::_append_int($i1, $i2->convert($i1->type), $o);
$o;
}
'),
Code => 'PDL_Indx ns = $SIZE(n);
broadcastloop %{
loop(n) %{ $c(mn => n) = $a(); %}
loop(mn=ns) %{ $c() = $b(m=>mn-ns); %}
%}',
Doc => '
=for ref
append two ndarrays by concatenating along their first dimensions
=for example
$x = ones(2,4,7);
$y = sequence 5;
$c = $x->append($y); # size of $c is now (7,4,7) (a jumbo-ndarray ;)
C<append> appends two ndarrays along their first dimensions. The rest of the
dimensions must be compatible in the broadcasting sense. The resulting
size of the first dimension is the sum of the sizes of the first dimensions
of the two argument ndarrays - i.e. C<n + m>.
Similar functions include L</glue> (below), which can append more
than two ndarrays along an arbitrary dimension, and
L<cat|PDL::Core/cat>, which can append more than two ndarrays that all
have the same sized dimensions.
'
);
pp_addpm(<<'EOD');
=head2 glue
=for usage
$c = $x->glue(<dim>,$y,...)
=for ref
Glue two or more PDLs together along an arbitrary dimension
(N-D L</append>).
Sticks $x, $y, and all following arguments together along the
specified dimension. All other dimensions must be compatible in the
broadcasting sense.
Glue is permissive, in the sense that every PDL is treated as having an
infinite number of trivial dimensions of order 1 -- so C<< $x->glue(3,$y) >>
works, even if $x and $y are only one dimensional.
If one of the PDLs has no elements, it is ignored. Likewise, if one
of them is actually the undefined value, it is treated as if it had no
elements.
If the first parameter is a defined perl scalar rather than a pdl,
then it is taken as a dimension along which to glue everything else,
so you can say C<$cube = PDL::glue(3,@image_list);> if you like.
C<glue> is implemented in pdl, using a combination of L<xchg|PDL::Slices/xchg> and
L</append>. It should probably be updated (one day) to a pure PP
function.
Similar functions include L</append> (above), which appends
only two ndarrays along their first dimension, and
L<cat|PDL::Core/cat>, which can append more than two ndarrays that all
have the same sized dimensions.
=cut
sub PDL::glue{
my($x) = shift;
my($dim) = shift;
($dim, $x) = ($x, $dim) if defined $x && !ref $x;
confess 'dimension must be Perl scalar' if ref $dim;
if(!defined $x || $x->nelem==0) {
return $x unless(@_);
return shift() if(@_<=1);
$x=shift;
return PDL::glue($x,$dim,@_);
}
if($dim - $x->dim(0) > 100) {
print STDERR "warning:: PDL::glue allocating >100 dimensions!\n";
}
while($dim >= $x->ndims) {
$x = $x->dummy(-1,1);
}
$x = $x->xchg(0,$dim) if 0 != $dim;
while(scalar(@_)){
my $y = shift;
next unless(defined $y && $y->nelem);
while($dim >= $y->ndims) {
$y = $y->dummy(-1,1);
}
$y = $y->xchg(0,$dim) if 0 != $dim;
$x = $x->append($y);
}
0 == $dim ? $x : $x->xchg(0,$dim);
}
EOD
pp_def( 'axisvalues',
Pars => 'i(n); [o]a(n)',
Inplace => 1,
Code => 'loop(n) %{ $a() = n; %}',
GenericTypes => [ppdefs_all],
Doc => undef,
); # pp_def: axisvalues
pp_add_macros(
CMPVEC => sub {
my ($a, $b, $dim, $ret, $anybad) = @_;
my $badbit = !defined $anybad ? '' : <<EOF;
PDL_IF_BAD(if (\$ISBAD($a) || \$ISBAD($b)) { $anybad = 1; break; } else,)
EOF
<<EOF;
$ret = 0;
loop($dim) %{ $badbit if ($a != $b) { $ret = $a < $b ? -1 : 1; break; } %}
EOF
},
);
pp_def(
'cmpvec',
HandleBad => 1,
Pars => 'a(n); b(n); sbyte [o]c();',
Code => '
PDL_IF_BAD(char anybad = 0;,)
broadcastloop %{
$CMPVEC($a(), $b(), n, $c(), anybad);
PDL_IF_BAD(if (anybad) $SETBAD(c());,)
%}
PDL_IF_BAD(if (anybad) $PDLSTATESETBAD(c);,)
',
Doc => '
=for ref
Compare two vectors lexicographically.
Returns -1 if a is less, 1 if greater, 0 if equal.
',
BadDoc => '
The output is bad if any input values up to the point of inequality are
bad - any after are ignored.
',
);
pp_def(
'eqvec',
HandleBad => 1,
Pars => 'a(n); b(n); sbyte [o]c();',
Code => '
PDL_IF_BAD(char anybad = 0;,)
broadcastloop %{
$c() = 1;
loop(n) %{
PDL_IF_BAD(if ($ISBAD(a()) || $ISBAD(b())) { $SETBAD(c()); anybad = 1; break; }
else,) if ($a() != $b()) { $c() = 0; PDL_IF_BAD(,break;) }
%}
%}
PDL_IF_BAD(if (anybad) $PDLSTATESETBAD(c);,)
',
Doc => '
=for ref
Compare two vectors, returning 1 if equal, 0 if not equal.
',
BadDoc => 'The output is bad if any input values are bad.',
);
pp_def('enumvec',
Pars => 'v(M,N); indx [o]k(N)',
Code => pp_line_numbers(__LINE__, <<'EOC'),
loop (N) %{
PDL_Indx vn = N; /* preserve value of N into inner N loop */
loop (N=vn) %{
$k() = N-vn;
loop (M) %{
if ($v(N=>vn) == $v(N=>N+1)) continue;
vn = N; /* and back out */
goto NOMATCH_$GENERIC();
%}
%}
NOMATCH_$GENERIC(): N = vn; /* skip forward */
%}
EOC
Doc =><<'EOD',
=for ref
Enumerate a list of vectors with locally unique keys.
Given a sorted list of vectors $v, generate a vector $k containing locally unique keys for the elements of $v
(where an "element" is a vector of length $M occurring in $v).
Note that the keys returned in $k are only unique over a run of a single vector in $v,
so that each unique vector in $v has at least one 0 (zero) index in $k associated with it.
If you need global keys, see enumvecg().
Contributed by Bryan Jurish E<lt>moocow@cpan.orgE<gt>.
EOD
);
##------------------------------------------------------
## enumvecg()
pp_def('enumvecg',
Pars => 'v(M,N); indx [o]k(N)',
Code =><<'EOC',
if (!$SIZE(N)) return PDL_err;
PDL_Indx Nprev = 0, ki = $k(N=>0) = 0;
loop(N=1) %{
loop (M) %{
if ($v(N=>Nprev) == $v()) continue;
++ki;
break;
%}
$k() = ki;
Nprev = N;
%}
EOC
Doc =><<'EOD',
=for ref
Enumerate a list of vectors with globally unique keys.
Given a sorted list of vectors $v, generate a vector $k containing globally unique keys for the elements of $v
(where an "element" is a vector of length $M occurring in $v).
Basically does the same thing as:
$k = $v->vsearchvec($v->uniqvec);
... but somewhat more efficiently.
Contributed by Bryan Jurish E<lt>moocow@cpan.orgE<gt>.
EOD
);
pp_def('vsearchvec',
Pars => 'find(M); which(M,N); indx [o]found();',
Code => q(
int carp=0;
broadcastloop %{
PDL_Indx n1=$SIZE(N)-1, nlo=-1, nhi=n1, nn;
int cmpval, is_asc_sorted;
//
//-- get sort direction
$CMPVEC($which(N=>n1),$which(N=>0),M,cmpval);
is_asc_sorted = (cmpval > 0);
//
//-- binary search
while (nhi-nlo > 1) {
nn = (nhi+nlo) >> 1;
$CMPVEC($find(),$which(N=>nn),M,cmpval);
if ((cmpval > 0) == is_asc_sorted)
nlo=nn;
else
nhi=nn;
}
if (nlo==-1) {
nhi=0;
} else if (nlo==n1) {
$CMPVEC($find(),$which(N=>n1),M,cmpval);
if (cmpval != 0) carp = 1;
nhi = n1;
} else {
nhi = nlo+1;
}
$found() = nhi;
%}
if (carp) warn("some values had to be extrapolated");
),
Doc=><<'EOD'
=for ref
Routine for searching N-dimensional values - akin to vsearch() for vectors.
=for usage
$found = vsearchvec($find, $which);
$nearest = $which->dice_axis(1,$found);
Returns for each row-vector in C<$find> the index along dimension N
of the least row vector of C<$which>
greater or equal to it.
C<$which> should be sorted in increasing order.
If the value of C<$find> is larger
than any member of C<$which>, the index to the last element of C<$which> is
returned.
See also: L</vsearch>.
Contributed by Bryan Jurish E<lt>moocow@cpan.orgE<gt>.
EOD
);
pp_def('unionvec',
Pars => 'a(M,NA); b(M,NB); [o]c(M,NC=CALC($SIZE(NA) + $SIZE(NB))); indx [o]nc()',
PMCode=>pp_line_numbers(__LINE__, <<'EOD'),
sub PDL::unionvec {
my ($a,$b,$c,$nc) = @_;
$c = PDL->null if (!defined($nc));
$nc = PDL->null if (!defined($nc));
PDL::_unionvec_int($a,$b,$c,$nc);
return ($c,$nc) if (wantarray);
return $c->slice(",0:".($nc->max-1));
}
EOD
Code => q(
PDL_Indx nai=0, nbi=0, nci=$SIZE(NC), sizeNA=$SIZE(NA), sizeNB=$SIZE(NB);
int cmpval;
loop (NC) %{
if (nai < sizeNA && nbi < sizeNB) {
$CMPVEC($a(NA=>nai),$b(NB=>nbi),M,cmpval);
}
else if (nai < sizeNA) { cmpval = -1; }
else if (nbi < sizeNB) { cmpval = 1; }
else { nci=NC; break; }
//
if (cmpval < 0) {
//-- CASE: a < b
loop (M) %{ $c() = $a(NA=>nai); %}
nai++;
}
else if (cmpval > 0) {
//-- CASE: a > b
loop (M) %{ $c() = $b(NB=>nbi); %}
nbi++;
}
else {
//-- CASE: a == b
loop (M) %{ $c() = $a(NA=>nai); %}
nai++;
nbi++;
}
%}
$nc() = nci;
//-- zero unpopulated outputs
loop(NC=nci,M) %{ $c() = 0; %}
),
Doc=><<'EOD'
=for ref
Union of two vector-valued PDLs.
Input PDLs $a() and $b() B<MUST> be sorted in lexicographic order.
On return, $nc() holds the actual number of vector-values in the union.
In scalar context, slices $c() to the actual number of elements in the union
and returns the sliced PDL.
Contributed by Bryan Jurish E<lt>moocow@cpan.orgE<gt>.
EOD
);
pp_def('intersectvec',
Pars => 'a(M,NA); b(M,NB); [o]c(M,NC=CALC(PDLMIN($SIZE(NA),$SIZE(NB)))); indx [o]nc()',
PMCode=>pp_line_numbers(__LINE__, <<'EOD'),
sub PDL::intersectvec {
my ($a,$b,$c,$nc) = @_;
$c = PDL->null if (!defined($c));
$nc = PDL->null if (!defined($nc));
PDL::_intersectvec_int($a,$b,$c,$nc);
return ($c,$nc) if (wantarray);
my $nc_max = $nc->max;
return ($nc_max > 0
? $c->slice(",0:".($nc_max-1))
: $c->reshape($c->dim(0), 0, ($c->dims)[2..($c->ndims-1)]));
}
EOD
Code => q(
PDL_Indx nai=0, nbi=0, nci=0, sizeNA=$SIZE(NA), sizeNB=$SIZE(NB), sizeNC=$SIZE(NC);
int cmpval;
for ( ; nci < sizeNC && nai < sizeNA && nbi < sizeNB; ) {
$CMPVEC($a(NA=>nai),$b(NB=>nbi),M,cmpval);
//
if (cmpval < 0) {
//-- CASE: a < b
nai++;
}
else if (cmpval > 0) {
//-- CASE: a > b
nbi++;
}
else {
//-- CASE: a == b
loop (M) %{ $c(NC=>nci) = $a(NA=>nai); %}
nai++;
nbi++;
nci++;
}
}
$nc() = nci;
//-- zero unpopulated outputs
loop(NC=nci,M) %{ $c() = 0; %}
),
Doc=><<'EOD'
=for ref
Intersection of two vector-valued PDLs.
Input PDLs $a() and $b() B<MUST> be sorted in lexicographic order.
On return, $nc() holds the actual number of vector-values in the intersection.
In scalar context, slices $c() to the actual number of elements in the intersection
and returns the sliced PDL.
Contributed by Bryan Jurish E<lt>moocow@cpan.orgE<gt>.
EOD
);
pp_def('setdiffvec',
Pars => 'a(M,NA); b(M,NB); [o]c(M,NC=CALC($SIZE(NA))); indx [o]nc()',
PMCode=>pp_line_numbers(__LINE__, <<'EOD'),
sub PDL::setdiffvec {
my ($a,$b,$c,$nc) = @_;
$c = PDL->null if (!defined($c));
$nc = PDL->null if (!defined($nc));
PDL::_setdiffvec_int($a,$b,$c,$nc);
return ($c,$nc) if (wantarray);
my $nc_max = $nc->max;
return ($nc_max > 0
? $c->slice(",0:".($nc_max-1))
: $c->reshape($c->dim(0), 0, ($c->dims)[2..($c->ndims-1)]));
}
EOD
Code => q(
PDL_Indx nai=0, nbi=0, nci=0, sizeNA=$SIZE(NA), sizeNB=$SIZE(NB), sizeNC=$SIZE(NC);
int cmpval;
for ( ; nci < sizeNC && nai < sizeNA && nbi < sizeNB ; ) {
$CMPVEC($a(NA=>nai),$b(NB=>nbi),M,cmpval);
//
if (cmpval < 0) {
//-- CASE: a < b
loop (M) %{ $c(NC=>nci) = $a(NA=>nai); %}
nai++;
nci++;
}
else if (cmpval > 0) {
//-- CASE: a > b
nbi++;
}
else {
//-- CASE: a == b
nai++;
nbi++;
}
}
for ( ; nci < sizeNC && nai < sizeNA ; nai++,nci++ ) {
loop (M) %{ $c(NC=>nci) = $a(NA=>nai); %}
}
$nc() = nci;
//-- zero unpopulated outputs
loop (NC=nci,M) %{ $c() = 0; %}
),
Doc=><<'EOD'
=for ref
Set-difference ($a() \ $b()) of two vector-valued PDLs.
Input PDLs $a() and $b() B<MUST> be sorted in lexicographic order.
On return, $nc() holds the actual number of vector-values in the computed vector set.
In scalar context, slices $c() to the actual number of elements in the output vector set
and returns the sliced PDL.
Contributed by Bryan Jurish E<lt>moocow@cpan.orgE<gt>.
EOD
);
pp_add_macros(
CMPVAL => sub {
my ($val1, $val2) = @_;
"(($val1) < ($val2) ? -1 : ($val1) > ($val2) ? 1 : 0)";
},
);
pp_def('union_sorted',
Pars => 'a(NA); b(NB); [o]c(NC=CALC($SIZE(NA) + $SIZE(NB))); indx [o]nc()',
PMCode=>pp_line_numbers(__LINE__, <<'EOD'),
sub PDL::union_sorted {
my ($a,$b,$c,$nc) = @_;
$c = PDL->null if (!defined($c));
$nc = PDL->null if (!defined($nc));
PDL::_union_sorted_int($a,$b,$c,$nc);
return ($c,$nc) if (wantarray);
return $c->slice("0:".($nc->max-1));
}
EOD
Code => q(
PDL_Indx nai=0, nbi=0, nci=$SIZE(NC), sizeNA=$SIZE(NA), sizeNB=$SIZE(NB);
int cmpval;
loop (NC) %{
if (nai < sizeNA && nbi < sizeNB) {
cmpval = $CMPVAL($a(NA=>nai), $b(NB=>nbi));
}
else if (nai < sizeNA) { cmpval = -1; }
else if (nbi < sizeNB) { cmpval = 1; }
else { nci = NC; break; }
//
if (cmpval < 0) {
//-- CASE: a < b
$c() = $a(NA=>nai);
nai++;
}
else if (cmpval > 0) {
//-- CASE: a > b
$c() = $b(NB=>nbi);
nbi++;
}
else {
//-- CASE: a == b
$c() = $a(NA=>nai);
nai++;
nbi++;
}
%}
$nc() = nci;
loop (NC=nci) %{
//-- zero unpopulated outputs
$c() = 0;
%}
),
Doc=><<'EOD'
=for ref
Union of two flat sorted unique-valued PDLs.
Input PDLs $a() and $b() B<MUST> be sorted in lexicographic order and contain no duplicates.
On return, $nc() holds the actual number of values in the union.
In scalar context, reshapes $c() to the actual number of elements in the union and returns it.
Contributed by Bryan Jurish E<lt>moocow@cpan.orgE<gt>.
EOD
);
pp_def('intersect_sorted',
Pars => 'a(NA); b(NB); [o]c(NC=CALC(PDLMIN($SIZE(NA),$SIZE(NB)))); indx [o]nc()',
PMCode=>pp_line_numbers(__LINE__, <<'EOD'),
sub PDL::intersect_sorted {
my ($a,$b,$c,$nc) = @_;
$c = PDL->null if (!defined($c));
$nc = PDL->null if (!defined($nc));
PDL::_intersect_sorted_int($a,$b,$c,$nc);
return ($c,$nc) if (wantarray);
my $nc_max = $nc->max;
return ($nc_max > 0
? $c->slice("0:".($nc_max-1))
: $c->reshape(0, ($c->dims)[1..($c->ndims-1)]));
}
EOD
Code => q(
PDL_Indx nai=0, nbi=0, nci=0, sizeNA=$SIZE(NA), sizeNB=$SIZE(NB), sizeNC=$SIZE(NC);
int cmpval;
for ( ; nci < sizeNC && nai < sizeNA && nbi < sizeNB; ) {
cmpval = $CMPVAL($a(NA=>nai),$b(NB=>nbi));
//
if (cmpval < 0) {
//-- CASE: a < b
nai++;
}
else if (cmpval > 0) {
//-- CASE: a > b
nbi++;
}
else {
//-- CASE: a == b
$c(NC=>nci) = $a(NA=>nai);
nai++;
nbi++;
nci++;
}
}
$nc() = nci;
for ( ; nci < sizeNC; nci++) {
//-- zero unpopulated outputs
$c(NC=>nci) = 0;
}
),
Doc=><<'EOD'
=for ref
Intersection of two flat sorted unique-valued PDLs.
Input PDLs $a() and $b() B<MUST> be sorted in lexicographic order and contain no duplicates.
On return, $nc() holds the actual number of values in the intersection.
In scalar context, reshapes $c() to the actual number of elements in the intersection and returns it.
Contributed by Bryan Jurish E<lt>moocow@cpan.orgE<gt>.
EOD
);
pp_def('setdiff_sorted',
Pars => 'a(NA); b(NB); [o]c(NC=CALC($SIZE(NA))); indx [o]nc()',
PMCode=>pp_line_numbers(__LINE__, <<'EOD'),
sub PDL::setdiff_sorted {
my ($a,$b,$c,$nc) = @_;
$c = PDL->null if (!defined($c));
$nc = PDL->null if (!defined($nc));
PDL::_setdiff_sorted_int($a,$b,$c,$nc);
return ($c,$nc) if (wantarray);
my $nc_max = $nc->max;
return ($nc_max > 0
? $c->slice("0:".($nc_max-1))
: $c->reshape(0, ($c->dims)[1..($c->ndims-1)]));
}
EOD
Code => q(
PDL_Indx nai=0, nbi=0, nci=0, sizeNA=$SIZE(NA), sizeNB=$SIZE(NB), sizeNC=$SIZE(NC);
int cmpval;
for ( ; nci < sizeNC && nai < sizeNA && nbi < sizeNB ; ) {
cmpval = $CMPVAL($a(NA=>nai),$b(NB=>nbi));
//
if (cmpval < 0) {
//-- CASE: a < b
$c(NC=>nci) = $a(NA=>nai);
nai++;
nci++;
}
else if (cmpval > 0) {
//-- CASE: a > b
nbi++;
}
else {
//-- CASE: a == b
nai++;
nbi++;
}
}
for ( ; nci < sizeNC && nai < sizeNA ; nai++,nci++ ) {
$c(NC=>nci) = $a(NA=>nai);
}
$nc() = nci;
for ( ; nci < sizeNC; nci++) {
//-- zero unpopulated outputs
$c(NC=>nci) = 0;
}
),
Doc=><<'EOD'
=for ref
Set-difference ($a() \ $b()) of two flat sorted unique-valued PDLs.
Input PDLs $a() and $b() B<MUST> be sorted in lexicographic order and contain no duplicate values.
On return, $nc() holds the actual number of values in the computed vector set.
In scalar context, reshapes $c() to the actual number of elements in the difference set and returns it.
Contributed by Bryan Jurish E<lt>moocow@cpan.orgE<gt>.
EOD
);
pp_addhdr(<<'EOH');
extern int pdl_srand_threads;
extern uint64_t *pdl_rand_state;
void pdl_srand(uint64_t **s, uint64_t seed, int n);
double pdl_drand(uint64_t *s);
#define PDL_MAYBE_SRAND \
if (pdl_srand_threads < 0) \
pdl_srand(&pdl_rand_state, PDL->pdl_seed(), PDL->online_cpus());
#define PDL_RAND_SET_OFFSET(v, thr, pdl) \
if (v < 0) { \
if (thr.mag_nthr >= 0) { \
int thr_no = PDL->magic_get_thread(pdl); \
if (thr_no < 0) return PDL->make_error_simple(PDL_EFATAL, "Invalid pdl_magic_get_thread!"); \
v = thr_no == 0 ? thr_no : thr_no % PDL->online_cpus(); \
} else { \
v = 0; \
} \
}
EOH
pp_def(
'srand',
Pars=>'a();',
GenericTypes => ['Q'],
Code => <<'EOF',
pdl_srand(&pdl_rand_state, (uint64_t)$a(), PDL->online_cpus());
EOF
NoPthread => 1,
HaveBroadcasting => 0,
Doc=> <<'EOF',
=for ref
Seed random-number generator with a 64-bit int. Will generate seed data
for a number of threads equal to the return-value of
L<PDL::Core/online_cpus>.
As of 2.062, the generator changed from Perl's generator to xoshiro256++
(see L<https://prng.di.unimi.it/>).
=for usage
srand(); # uses current time
srand(5); # fixed number e.g. for testing
EOF
PMCode=>pp_line_numbers(__LINE__, <<'EOD'),
*srand = \&PDL::srand;
sub PDL::srand { PDL::_srand_int($_[0] // PDL::Core::seed()) }
EOD
);
pp_def(
'random',
Pars=>'[o] a();',
PMFunc => '',
Code => <<'EOF',
PDL_MAYBE_SRAND
int rand_offset = -1;
broadcastloop %{
PDL_RAND_SET_OFFSET(rand_offset, $PRIV(broadcast), $PDL(a));
$a() = pdl_drand(pdl_rand_state + 4*rand_offset);
%}
EOF
Doc=> <<'EOF',
=for ref
Constructor which returns ndarray of random numbers, real data-types only.
=for usage
$x = random([type], $nx, $ny, $nz,...);
$x = random $y;
etc (see L<zeroes|PDL::Core/zeroes>).
This is the uniform distribution between 0 and 1 (assumedly
excluding 1 itself). The arguments are the same as C<zeroes>
(q.v.) - i.e. one can specify dimensions, types or give
a template.
You can use the PDL function L</srand> to seed the random generator.
If it has not been called yet, it will be with the current time.
As of 2.062, the generator changed from Perl's generator to xoshiro256++
(see L<https://prng.di.unimi.it/>).
EOF
PMCode=>pp_line_numbers(__LINE__, <<'EOD'),
sub random { ref($_[0]) && ref($_[0]) ne 'PDL::Type' ? $_[0]->random : PDL->random(@_) }
sub PDL::random {
splice @_, 1, 0, double() if !ref($_[0]) and @_<=1;
my $x = &PDL::Core::_construct;
PDL::_random_int($x);
return $x;
}
EOD
);
pp_def(
'randsym',
Pars=>'[o] a();',
PMFunc => '',
Code => <<'EOF',
PDL_MAYBE_SRAND
int rand_offset = -1;
broadcastloop %{
PDL_RAND_SET_OFFSET(rand_offset, $PRIV(broadcast), $PDL(a));
long double tmp;
do tmp = pdl_drand(pdl_rand_state + 4*rand_offset); while (tmp == 0.0); /* 0 < tmp < 1 */
$a() = tmp;
%}
EOF
Doc=> <<'EOF',
=for ref
Constructor which returns ndarray of random numbers, real data-types only.
=for usage
$x = randsym([type], $nx, $ny, $nz,...);
$x = randsym $y;
etc (see L<zeroes|PDL::Core/zeroes>).
This is the uniform distribution between 0 and 1 (excluding both 0 and
1, cf L</random>). The arguments are the same as C<zeroes> (q.v.) -
i.e. one can specify dimensions, types or give a template.
You can use the PDL function L</srand> to seed the random generator.
If it has not been called yet, it will be with the current time.
EOF
PMCode=>pp_line_numbers(__LINE__, <<'EOD'),
sub randsym { ref($_[0]) && ref($_[0]) ne 'PDL::Type' ? $_[0]->randsym : PDL->randsym(@_) }
sub PDL::randsym {
splice @_, 1, 0, double() if !ref($_[0]) and @_<=1;
my $x = &PDL::Core::_construct;
PDL::_randsym_int($x);
return $x;
}
EOD
);
pp_add_exported('','grandom');
pp_addpm(<<'EOD');
=head2 grandom
=for ref
Constructor which returns ndarray of Gaussian random numbers
=for usage
$x = grandom([type], $nx, $ny, $nz,...);
$x = grandom $y;
etc (see L<zeroes|PDL::Core/zeroes>).
This is generated using the math library routine C<ndtri>.
Mean = 0, Stddev = 1
You can use the PDL function L</srand> to seed the random generator.
If it has not been called yet, it will be with the current time.
=cut
sub grandom { ref($_[0]) && ref($_[0]) ne 'PDL::Type' ? $_[0]->grandom : PDL->grandom(@_) }
sub PDL::grandom {
my $x = &PDL::Core::_construct;
use PDL::Math 'ndtri';
$x .= ndtri(randsym($x));
return $x;
}
EOD
###############################################################
# binary searches in an ndarray; various forms
###############################################################
# generic front end; defaults to vsearch_sample for backwards compatibility
pp_add_exported('','vsearch');
pp_addpm(<<'EOD');
=head2 vsearch
=for sig
Signature: ( vals(); xs(n); [o] indx(); [\%options] )
=for ref
Efficiently search for values in a sorted ndarray, returning indices.
=for usage
$idx = vsearch( $vals, $x, [\%options] );
vsearch( $vals, $x, $idx, [\%options ] );
B<vsearch> performs a binary search in the ordered ndarray C<$x>,
for the values from C<$vals> ndarray, returning indices into C<$x>.
What is a "match", and the meaning of the returned indices, are determined
by the options.
The C<mode> option indicates which method of searching to use, and may
be one of:
=over
=item C<sample>
invoke L<B<vsearch_sample>|/vsearch_sample>, returning indices appropriate for sampling
within a distribution.
=item C<insert_leftmost>
invoke L<B<vsearch_insert_leftmost>|/vsearch_insert_leftmost>, returning the left-most possible
insertion point which still leaves the ndarray sorted.
=item C<insert_rightmost>
invoke L<B<vsearch_insert_rightmost>|/vsearch_insert_rightmost>, returning the right-most possible
insertion point which still leaves the ndarray sorted.
=item C<match>
invoke L<B<vsearch_match>|/vsearch_match>, returning the index of a matching element,
else -(insertion point + 1)
=item C<bin_inclusive>
invoke L<B<vsearch_bin_inclusive>|/vsearch_bin_inclusive>, returning an index appropriate for binning
on a grid where the left bin edges are I<inclusive> of the bin. See
below for further explanation of the bin.
=item C<bin_exclusive>
invoke L<B<vsearch_bin_exclusive>|/vsearch_bin_exclusive>, returning an index appropriate for binning
on a grid where the left bin edges are I<exclusive> of the bin. See
below for further explanation of the bin.
=back
The default value of C<mode> is C<sample>.
=for example
use PDL;
my @modes = qw( sample insert_leftmost insert_rightmost match
bin_inclusive bin_exclusive );
# Generate a sequence of 3 zeros, 3 ones, ..., 3 fours.
my $x = zeroes(3,5)->yvals->flat;
for my $mode ( @modes ) {
# if the value is in $x
my $contained = 2;
my $idx_contained = vsearch( $contained, $x, { mode => $mode } );
my $x_contained = $x->copy;
$x_contained->slice( $idx_contained ) .= 9;
# if the value is not in $x
my $not_contained = 1.5;
my $idx_not_contained = vsearch( $not_contained, $x, { mode => $mode } );
my $x_not_contained = $x->copy;
$x_not_contained->slice( $idx_not_contained ) .= 9;
print sprintf("%-23s%30s\n", '$x', $x);
print sprintf("%-23s%30s\n", "$mode ($contained)", $x_contained);
print sprintf("%-23s%30s\n\n", "$mode ($not_contained)", $x_not_contained);
}
# $x [0 0 0 1 1 1 2 2 2 3 3 3 4 4 4]
# sample (2) [0 0 0 1 1 1 9 2 2 3 3 3 4 4 4]
# sample (1.5) [0 0 0 1 1 1 9 2 2 3 3 3 4 4 4]
#
# $x [0 0 0 1 1 1 2 2 2 3 3 3 4 4 4]
# insert_leftmost (2) [0 0 0 1 1 1 9 2 2 3 3 3 4 4 4]
# insert_leftmost (1.5) [0 0 0 1 1 1 9 2 2 3 3 3 4 4 4]
#
# $x [0 0 0 1 1 1 2 2 2 3 3 3 4 4 4]
# insert_rightmost (2) [0 0 0 1 1 1 2 2 2 9 3 3 4 4 4]
# insert_rightmost (1.5) [0 0 0 1 1 1 9 2 2 3 3 3 4 4 4]
#
# $x [0 0 0 1 1 1 2 2 2 3 3 3 4 4 4]
# match (2) [0 0 0 1 1 1 2 9 2 3 3 3 4 4 4]
# match (1.5) [0 0 0 1 1 1 2 2 9 3 3 3 4 4 4]
#
# $x [0 0 0 1 1 1 2 2 2 3 3 3 4 4 4]
# bin_inclusive (2) [0 0 0 1 1 1 2 2 9 3 3 3 4 4 4]
# bin_inclusive (1.5) [0 0 0 1 1 9 2 2 2 3 3 3 4 4 4]
#
# $x [0 0 0 1 1 1 2 2 2 3 3 3 4 4 4]
# bin_exclusive (2) [0 0 0 1 1 9 2 2 2 3 3 3 4 4 4]
# bin_exclusive (1.5) [0 0 0 1 1 9 2 2 2 3 3 3 4 4 4]
Also see
L<B<vsearch_sample>|/vsearch_sample>,
L<B<vsearch_insert_leftmost>|/vsearch_insert_leftmost>,
L<B<vsearch_insert_rightmost>|/vsearch_insert_rightmost>,
L<B<vsearch_match>|/vsearch_match>,
L<B<vsearch_bin_inclusive>|/vsearch_bin_inclusive>, and
L<B<vsearch_bin_exclusive>|/vsearch_bin_exclusive>
=cut
sub vsearch {
my $opt = 'HASH' eq ref $_[-1]
? pop
: { mode => 'sample' };
croak( "unknown options to vsearch\n" )
if ( ! defined $opt->{mode} && keys %$opt )
|| keys %$opt > 1;
my $mode = $opt->{mode};
goto
$mode eq 'sample' ? \&vsearch_sample
: $mode eq 'insert_leftmost' ? \&vsearch_insert_leftmost
: $mode eq 'insert_rightmost' ? \&vsearch_insert_rightmost
: $mode eq 'match' ? \&vsearch_match
: $mode eq 'bin_inclusive' ? \&vsearch_bin_inclusive
: $mode eq 'bin_exclusive' ? \&vsearch_bin_exclusive
: croak( "unknown vsearch mode: $mode\n" );
}
*PDL::vsearch = \&vsearch;
EOD
use Text::Tabs qw[ expand ];
sub undent {
my $txt = expand( shift );
$txt =~ s/^([ \t]+)-{4}.*$//m;
$txt =~ s/^$1//mg
if defined $1;
$txt;
}
for my $func ( [
vsearch_sample => {
low => -1,
high => '$SIZE(n)',
up => '($x(n => n1) > $x(n => 0))',
code => q[
while ( high - low > 1 ) {
mid = %MID%;
if ( ( value > $x(n => mid ) ) == up ) low = mid;
else high = mid;
}
$idx() = low >= n1 ? n1
: up ? low + 1
: PDLMAX(low, 0);
----
],
ref =>
'Search for values in a sorted array, return index appropriate for sampling from a distribution',
doc_pre => q[
B<%FUNC%> returns an index I<I> for each value I<V> of C<$vals> appropriate
for sampling C<$vals>
----
],
doc_incr => q[
V <= x[0] : I = 0
x[0] < V <= x[-1] : I s.t. x[I-1] < V <= x[I]
x[-1] < V : I = $x->nelem -1
----
],
doc_decr => q[
V > x[0] : I = 0
x[0] >= V > x[-1] : I s.t. x[I] >= V > x[I+1]
x[-1] >= V : I = $x->nelem - 1
----
],
doc_post => q[
If all elements of C<$x> are equal, I<< I = $x->nelem - 1 >>.
If C<$x> contains duplicated elements, I<I> is the index of the
leftmost (by position in array) duplicate if I<V> matches.
=for example
This function is useful e.g. when you have a list of probabilities
for events and want to generate indices to events:
$x = pdl(.01,.86,.93,1); # Barnsley IFS probabilities cumulatively
$y = random 20;
$c = %FUNC%($y, $x); # Now, $c will have the appropriate distr.
It is possible to use the L<cumusumover|PDL::Ufunc/cumusumover>
function to obtain cumulative probabilities from absolute probabilities.
----
],
},
],
[
# return left-most possible insertion point.
# lowest index where x[i] >= value
vsearch_insert_leftmost => {
low => 0,
high => 'n1',
code => q[
while (low <= high ) {
mid = %MID%;
if ( ( $x(n => mid) >= value ) == up ) high = mid - 1;
else low = mid + 1;
}
$idx() = up ? low : high;
],
ref =>
'Determine the insertion point for values in a sorted array, inserting before duplicates.',
doc_pre => q[
B<%FUNC%> returns an index I<I> for each value I<V> of
C<$vals> equal to the leftmost position (by index in array) within
C<$x> that I<V> may be inserted and still maintain the order in
C<$x>.
Insertion at index I<I> involves shifting elements I<I> and higher of
C<$x> to the right by one and setting the now empty element at index
I<I> to I<V>.
----
],
doc_incr => q[
V <= x[0] : I = 0
x[0] < V <= x[-1] : I s.t. x[I-1] < V <= x[I]
x[-1] < V : I = $x->nelem
----
],
doc_decr => q[
V > x[0] : I = -1
x[0] >= V >= x[-1] : I s.t. x[I] >= V > x[I+1]
x[-1] >= V : I = $x->nelem -1
----
],
doc_post => q[
If all elements of C<$x> are equal,
i = 0
If C<$x> contains duplicated elements, I<I> is the index of the
leftmost (by index in array) duplicate if I<V> matches.
----
],
},
],
[
# return right-most possible insertion point.
# lowest index where x[i] > value
vsearch_insert_rightmost => {
low => 0,
high => 'n1',
code => q[
while (low <= high ) {
mid = %MID%;
if ( ( $x(n => mid) > value ) == up ) high = mid - 1;
else low = mid + 1;
}
$idx() = up ? low : high;
],
ref =>
'Determine the insertion point for values in a sorted array, inserting after duplicates.',
doc_pre => q[
B<%FUNC%> returns an index I<I> for each value I<V> of
C<$vals> equal to the rightmost position (by index in array) within
C<$x> that I<V> may be inserted and still maintain the order in
C<$x>.
Insertion at index I<I> involves shifting elements I<I> and higher of
C<$x> to the right by one and setting the now empty element at index
I<I> to I<V>.
----
],
doc_incr => q[
V < x[0] : I = 0
x[0] <= V < x[-1] : I s.t. x[I-1] <= V < x[I]
x[-1] <= V : I = $x->nelem
----
],
doc_decr => q[
V >= x[0] : I = -1
x[0] > V >= x[-1] : I s.t. x[I] >= V > x[I+1]
x[-1] > V : I = $x->nelem -1
----
],
doc_post => q[
If all elements of C<$x> are equal,
i = $x->nelem - 1
If C<$x> contains duplicated elements, I<I> is the index of the
leftmost (by index in array) duplicate if I<V> matches.
----
],
},
],
[
# return index of matching element, else -( insertion point + 1 )
# patterned after the Java binarySearch interface; see
# http://docs.oracle.com/javase/7/docs/api/java/util/Arrays.html
vsearch_match => {
low => 0,
high => 'n1',
code => q[
int done = 0;
while (low <= high ) {
$GENERIC() mid_value;
mid = %MID%;
mid_value = $x(n=>mid);
if ( up ) {
if ( mid_value > value ) { high = mid - 1; }
else if ( mid_value < value ) { low = mid + 1; }
else { done = 1; break; }
}
else {
if ( mid_value < value ) { high = mid - 1; }
else if ( mid_value > value ) { low = mid + 1; }
else { done = 1; break; }
}
}
$idx() = done ? mid
: up ? - ( low + 1 )
: - ( high + 1 );
],
ref => 'Match values against a sorted array.',
doc_pre => q[
B<%FUNC%> returns an index I<I> for each value I<V> of
C<$vals>. If I<V> matches an element in C<$x>, I<I> is the
index of that element, otherwise it is I<-( insertion_point + 1 )>,
where I<insertion_point> is an index in C<$x> where I<V> may be
inserted while maintaining the order in C<$x>. If C<$x> has
duplicated values, I<I> may refer to any of them.
----
],
},
],
[
# x[i] is the INclusive left edge of the bin
# return i, s.t. x[i] <= value < x[i+1].
# returns -1 if x[0] > value
# returns N-1 if x[-1] <= value
vsearch_bin_inclusive => {
low => 0,
high => 'n1',
code => q[
while (low <= high ) {
mid = %MID%;
if ( ( $x(n => mid) <= value ) == up ) low = mid + 1;
else high = mid - 1;
}
$idx() = up ? high: low;
],
ref =>
'Determine the index for values in a sorted array of bins, lower bound inclusive.',
doc_pre => q[
C<$x> represents the edges of contiguous bins, with the first and
last elements representing the outer edges of the outer bins, and the
inner elements the shared bin edges.
The lower bound of a bin is inclusive to the bin, its outer bound is exclusive to it.
B<%FUNC%> returns an index I<I> for each value I<V> of C<$vals>
----
],
doc_incr => q[
V < x[0] : I = -1
x[0] <= V < x[-1] : I s.t. x[I] <= V < x[I+1]
x[-1] <= V : I = $x->nelem - 1
----
],
doc_decr => q[
V >= x[0] : I = 0
x[0] > V >= x[-1] : I s.t. x[I+1] > V >= x[I]
x[-1] > V : I = $x->nelem
----
],
doc_post => q[
If all elements of C<$x> are equal,
i = $x->nelem - 1
If C<$x> contains duplicated elements, I<I> is the index of the
righmost (by index in array) duplicate if I<V> matches.
----
],
},
],
[
# x[i] is the EXclusive left edge of the bin
# return i, s.t. x[i] < value <= x[i+1].
# returns -1 if x[0] >= value
# returns N-1 if x[-1] < value
vsearch_bin_exclusive => {
low => 0,
high => 'n1',
code => q[
while (low <= high ) {
mid = %MID%;
if ( ( $x(n => mid) < value ) == up ) low = mid + 1;
else high = mid - 1;
}
$idx() = up ? high: low;
],
ref =>
'Determine the index for values in a sorted array of bins, lower bound exclusive.',
doc_pre => q[
C<$x> represents the edges of contiguous bins, with the first and
last elements representing the outer edges of the outer bins, and the
inner elements the shared bin edges.
The lower bound of a bin is exclusive to the bin, its upper bound is inclusive to it.
B<%FUNC%> returns an index I<I> for each value I<V> of C<$vals>.
----
],
doc_incr => q[
V <= x[0] : I = -1
x[0] < V <= x[-1] : I s.t. x[I] < V <= x[I+1]
x[-1] < V : I = $x->nelem - 1
----
],
doc_decr => q[
V > x[0] : I = 0
x[0] >= V > x[-1] : I s.t. x[I-1] >= V > x[I]
x[-1] >= V : I = $x->nelem
----
],
doc_post => q[
If all elements of C<$x> are equal,
i = $x->nelem - 1
If C<$x> contains duplicated elements, I<I> is the index of the
righmost (by index in array) duplicate if I<V> matches.
----
],
}
],
)
{
my ( $func, $algo ) = @$func;
my %replace = (
# calculate midpoint of range; ensure we don't overflow
# (low+high)>>1 for large values of low + high
# see sf.net bug #360
'%MID%' => 'low + (( high - low )>> 1);',
# determine which way the data are sorted. vsearch_sample
# overrides this.
'%UP%' => '$x(n => n1) >= $x(n => 0)',
'%FUNC%' => $func,
'%PRE%' => undent(
q[
%DOC_PRE%
----
]
),
'%BODY%' => undent(
q[
I<I> has the following properties:
=over
=item *
if C<$x> is sorted in increasing order
%DOC_INCR%
=item *
if C<$x> is sorted in decreasing order
%DOC_DECR%
=back
----
]
),
'%POST%' => undent(
q[
%DOC_POST%
----
]
),
map { ( "%\U$_%" => undent( $algo->{$_} ) ) } keys %$algo,
);
$replace{'%PRE%'} = '' unless defined $replace{'%DOC_PRE%'};
$replace{'%BODY%'} = ''
unless defined $replace{'%DOC_INCR%'} || defined $replace{'%DOC_DECR%'};
$replace{'%POST%'} = '' unless defined $replace{'%DOC_POST%'};
my $code = undent q[
loop(n) %{
if ( $ISGOOD(vals()) ) {
PDL_Indx n1 = $SIZE(n)-1;
PDL_Indx low = %LOW%;
PDL_Indx high = %HIGH%;
PDL_Indx mid;
$GENERIC() value = $vals();
/* determine sort order of data */
int up = %UP%;
%CODE%
}
else {
$SETBAD(idx());
}
%}
----
];
my $doc = undent q[
=for ref
%REF%
=for usage
$idx = %FUNC%($vals, $x);
C<$x> must be sorted, but may be in decreasing or increasing
order. if C<$x> is empty, then all values in C<$idx> will be
set to the bad value.
%PRE%
%BODY%
%POST%
----
];
# redo until nothing changes
for my $tref ( \$code, \$doc ) {
1 while $$tref =~ s/(%[\w_]+%)/$replace{$1}/ge;
}
pp_def(
$func,
HandleBad => 1,
BadDoc => 'bad values in vals() result in bad values in idx()',
Pars => 'vals(); x(n); indx [o]idx()',
GenericTypes => $F, # too restrictive ?
Code => $code,
Doc => $doc,
);
}
###############################################################
# routines somehow related to interpolation
###############################################################
pp_def('interpolate',
HandleBad => 0,
BadDoc => 'needs major (?) work to handles bad values',
Pars => 'real xi(); real x(n); y(n); [o] yi(); int [o] err()',
GenericTypes => $AF,
PMCode=>pp_line_numbers(__LINE__, <<'EOD'),
sub PDL::interpolate {
my ($xi, $x, $y, $yi, $err) = @_;
croak "x must be real" if (ref($x) && ! $x->type->real);
croak "xi must be real" if (ref($xi) && ! $xi->type->real);
$yi //= PDL->null;
$err //= PDL->null;
PDL::_interpolate_int($xi, $x, $y, $yi, $err);
($yi, $err);
}
EOD
Code => '
PDL_Indx n = $SIZE(n), n1 = n-1;
broadcastloop %{
PDL_Indx jl = -1, jh = n;
int carp = 0, up = ($x(n => n1) > $x(n => 0));
while (jh-jl > 1) { /* binary search */
PDL_Indx m = (jh+jl) >> 1;
if (($xi() > $x(n => m)) == up)
jl = m;
else
jh = m;
}
if (jl == -1) {
if ($xi() != $x(n => 0)) carp = 1;
jl = 0;
} else if (jh == n) {
if ($xi() != $x(n => n1)) carp = 1;
jl = n1-1;
}
jh = jl+1;
$GENERIC() d = $x(n=>jh) - $x(n=>jl);
if (d == 0) $CROAK("identical abscissas");
d = ($x(n=>jh) - $xi())/d;
$yi() = d*$y(n => jl) + (1-d)*$y(n => jh);
$err() = carp;
%}
', Doc=><<'EOD');
=for ref
routine for 1D linear interpolation
=for usage
( $yi, $err ) = interpolate($xi, $x, $y)
Given a set of points C<($x,$y)>, use linear interpolation
to find the values C<$yi> at a set of points C<$xi>.
C<interpolate> uses a binary search to find the suspects, er...,
interpolation indices and therefore abscissas (ie C<$x>)
have to be I<strictly> ordered (increasing or decreasing).
For interpolation at lots of
closely spaced abscissas an approach that uses the last index found as
a start for the next search can be faster (compare Numerical Recipes
C<hunt> routine). Feel free to implement that on top of the binary
search if you like. For out of bounds values it just does a linear
extrapolation and sets the corresponding element of C<$err> to 1,
which is otherwise 0.
See also L</interpol>, which uses the same routine,
differing only in the handling of extrapolation - an error message
is printed rather than returning an error ndarray.
Note that C<interpolate> can use complex values for C<$y> and C<$yi> but
C<$x> and C<$xi> must be real.
=cut
EOD
pp_add_exported('', 'interpol');
pp_addpm(<<'EOD');
=head2 interpol
=for sig
Signature: (xi(); x(n); y(n); [o] yi())
=for ref
routine for 1D linear interpolation
=for usage
$yi = interpol($xi, $x, $y)
C<interpol> uses the same search method as L</interpolate>,
hence C<$x> must be I<strictly> ordered (either increasing or decreasing).
The difference occurs in the handling of out-of-bounds values; here
an error message is printed.
=cut
# kept in for backwards compatibility
sub interpol ($$$;$) {
my $xi = shift;
my $x = shift;
my $y = shift;
my $yi = @_ == 1 ? $_[0] : PDL->null;
interpolate( $xi, $x, $y, $yi, my $err = PDL->null );
print "some values had to be extrapolated\n"
if any $err;
return $yi if @_ == 0;
} # sub: interpol()
*PDL::interpol = \&interpol;
EOD
pp_add_exported('','interpND');
pp_addpm(<<'EOD');
=head2 interpND
=for ref
Interpolate values from an N-D ndarray, with switchable method
=for example
$source = 10*xvals(10,10) + yvals(10,10);
$index = pdl([[2.2,3.5],[4.1,5.0]],[[6.0,7.4],[8,9]]);
print $source->interpND( $index );
InterpND acts like L<indexND|PDL::Slices/indexND>,
collapsing C<$index> by lookup
into C<$source>; but it does interpolation rather than direct sampling.
The interpolation method and boundary condition are switchable via
an options hash.
By default, linear or sample interpolation is used, with constant
value outside the boundaries of the source pdl. No dataflow occurs,
because in general the output is computed rather than indexed.
All the interpolation methods treat the pixels as value-centered, so
the C<sample> method will return C<< $a->(0) >> for coordinate values on
the set [-0.5,0.5], and all methods will return C<< $a->(1) >> for
a coordinate value of exactly 1.
Recognized options:
=over 3
=item method
Values can be:
=over 3
=item * 0, s, sample, Sample (default for integer source types)
The nearest value is taken. Pixels are regarded as centered on their
respective integer coordinates (no offset from the linear case).
=item * 1, l, linear, Linear (default for floating point source types)
The values are N-linearly interpolated from an N-dimensional cube of size 2.
=item * 3, c, cube, cubic, Cubic
The values are interpolated using a local cubic fit to the data. The
fit is constrained to match the original data and its derivative at the
data points. The second derivative of the fit is not continuous at the
data points. Multidimensional datasets are interpolated by the
successive-collapse method.
(Note that the constraint on the first derivative causes a small amount
of ringing around sudden features such as step functions).
=item * f, fft, fourier, Fourier
The source is Fourier transformed, and the interpolated values are
explicitly calculated from the coefficients. The boundary condition
option is ignored -- periodic boundaries are imposed.
If you pass in the option "fft", and it is a list (ARRAY) ref, then it
is a stash for the magnitude and phase of the source FFT. If the list
has two elements then they are taken as already computed; otherwise
they are calculated and put in the stash.
=back
=item b, bound, boundary, Boundary
This option is passed unmodified into L<indexND|PDL::Slices/indexND>,
which is used as the indexing engine for the interpolation.
Some current allowed values are 'extend', 'periodic', 'truncate', and 'mirror'
(default is 'truncate').
=item bad
contains the fill value used for 'truncate' boundary. (default 0)
=item fft
An array ref whose associated list is used to stash the FFT of the source
data, for the FFT method.
=back
=cut
*interpND = *PDL::interpND;
sub PDL::interpND {
my $source = shift;
my $index = shift;
my $options = shift;
barf 'Usage: interp_nd($source,$index,[{%options}])\n'
if(defined $options and ref $options ne 'HASH');
my($opt) = (defined $options) ? $options : {};
my($method) = $opt->{m} || $opt->{meth} || $opt->{method} || $opt->{Method};
$method //= $source->type->integer ? 'sample' : 'linear';
my($boundary) = $opt->{b} || $opt->{boundary} || $opt->{Boundary} || $opt->{bound} || $opt->{Bound} || 'extend';
my($bad) = $opt->{bad} || $opt->{Bad} || 0.0;
if($method =~ m/^s(am(p(le)?)?)?/i) {
return $source->range(PDL::Math::floor($index+0.5),0,$boundary);
}
elsif (($method eq 1) || $method =~ m/^l(in(ear)?)?/i) {
## key: (ith = index broadcast; cth = cube broadcast; sth = source broadcast)
my $d = $index->dim(0);
my $di = $index->ndims - 1;
# Grab a 2-on-a-side n-cube around each desired pixel
my $samp = $source->range($index->floor,2,$boundary); # (ith, cth, sth)
# Reorder to put the cube dimensions in front and convert to a list
$samp = $samp->reorder( $di .. $di+$d-1,
0 .. $di-1,
$di+$d .. $samp->ndims-1) # (cth, ith, sth)
->clump($d); # (clst, ith, sth)
# Enumerate the corners of an n-cube and convert to a list
# (the 'x' is the normal perl repeat operator)
my $crnr = PDL::Basic::ndcoords( (2) x $index->dim(0) ) # (index,cth)
->mv(0,-1)->clump($index->dim(0))->mv(-1,0); # (index, clst)
# a & b are the weighting coefficients.
my($x,$y);
my($indexwhere);
($indexwhere = $index->where( 0 * $index )) .= -10; # Change NaN to invalid
{
my $bb = PDL::Math::floor($index);
$x = ($index - $bb) -> dummy(1,$crnr->dim(1)); # index, clst, ith
$y = ($bb + 1 - $index) -> dummy(1,$crnr->dim(1)); # index, clst, ith
}
# Use 1/0 corners to select which multiplier happens, multiply
# 'em all together to get sample weights, and sum to get the answer.
my $out0 = ( ($x * ($crnr==1) + $y * ($crnr==0)) #index, clst, ith
-> prodover #clst, ith
);
my $out = ($out0 * $samp)->sumover; # ith, sth
# Work around BAD-not-being-contagious bug in PDL <= 2.6 bad handling code --CED 3-April-2013
if ($source->badflag) {
my $baddies = $samp->isbad->orover;
$out = $out->setbadif($baddies);
}
return $out;
} elsif(($method eq 3) || $method =~ m/^c(u(b(e|ic)?)?)?/i) {
my ($d,@di) = $index->dims;
my $di = $index->ndims - 1;
# Grab a 4-on-a-side n-cube around each desired pixel
my $samp = $source->range($index->floor - 1,4,$boundary) #ith, cth, sth
->reorder( $di .. $di+$d-1, 0..$di-1, $di+$d .. $source->ndims-1 );
# (cth, ith, sth)
# Make a cube of the subpixel offsets, and expand its dims to
# a 4-on-a-side N-1 cube, to match the slices of $samp (used below).
my $y = $index - $index->floor;
for my $i(1..$d-1) {
$y = $y->dummy($i,4);
}
# Collapse by interpolation, one dimension at a time...
for my $i(0..$d-1) {
my $a0 = $samp->slice("(1)"); # Just-under-sample
my $a1 = $samp->slice("(2)"); # Just-over-sample
my $a1a0 = $a1 - $a0;
my $gradient = 0.5 * ($samp->slice("2:3")-$samp->slice("0:1"));
my $s0 = $gradient->slice("(0)"); # Just-under-gradient
my $s1 = $gradient->slice("(1)"); # Just-over-gradient
my $bb = $y->slice("($i)");
# Collapse the sample...
$samp = ( $a0 +
$bb * (
$s0 +
$bb * ( (3 * $a1a0 - 2*$s0 - $s1) +
$bb * ( $s1 + $s0 - 2*$a1a0 )
)
)
);
# "Collapse" the subpixel offset...
$y = $y->slice(":,($i)");
}
return $samp;
} elsif($method =~ m/^f(ft|ourier)?/i) {
local $@;
eval "use PDL::FFT;";
my $fftref = $opt->{fft};
$fftref = [] unless(ref $fftref eq 'ARRAY');
if(@$fftref != 2) {
my $x = $source->copy;
my $y = zeroes($source);
fftnd($x,$y);
$fftref->[0] = sqrt($x*$x+$y*$y) / $x->nelem;
$fftref->[1] = - atan2($y,$x);
}
my $i;
my $c = PDL::Basic::ndcoords($source); # (dim, source-dims)
for $i(1..$index->ndims-1) {
$c = $c->dummy($i,$index->dim($i))
}
my $id = $index->ndims-1;
my $phase = (($c * $index * 3.14159 * 2 / pdl($source->dims))
->sumover) # (index-dims, source-dims)
->reorder($id..$id+$source->ndims-1,0..$id-1); # (src, index)
my $phref = $fftref->[1]->copy; # (source-dims)
my $mag = $fftref->[0]->copy; # (source-dims)
for $i(1..$index->ndims-1) {
$phref = $phref->dummy(-1,$index->dim($i));
$mag = $mag->dummy(-1,$index->dim($i));
}
my $out = cos($phase + $phref ) * $mag;
$out = $out->clump($source->ndims)->sumover;
return $out;
} else {
barf("interpND: unknown method '$method'; valid ones are 'linear' and 'sample'.\n");
}
}
EOD
##############################################################
# things related to indexing: one2nd, which, where
##############################################################
pp_add_exported("", 'one2nd');
pp_addpm(<<'EOD');
=head2 one2nd
=for ref
Converts a one dimensional index ndarray to a set of ND coordinates
=for usage
@coords=one2nd($x, $indices)
returns an array of ndarrays containing the ND indexes corresponding to
the one dimensional list indices. The indices are assumed to
correspond to array C<$x> clumped using C<clump(-1)>. This routine is
used in the old vector form of L</whichND>, but is useful on
its own occasionally.
Returned ndarrays have the L<indx|PDL::Core/indx> datatype. C<$indices> can have
values larger than C<< $x->nelem >> but negative values in C<$indices>
will not give the answer you expect.
=for example
pdl> $x=pdl [[[1,2],[-1,1]], [[0,-3],[3,2]]]; $c=$x->clump(-1)
pdl> $maxind=maximum_ind($c); p $maxind;
6
pdl> print one2nd($x, maximum_ind($c))
0 1 1
pdl> p $x->at(0,1,1)
3
=cut
*one2nd = \&PDL::one2nd;
sub PDL::one2nd {
barf "Usage: one2nd \$array \$indices\n" if @_ != 2;
my ($x, $ind)=@_;
my @dimension=$x->dims;
$ind = indx($ind);
my(@index);
my $count=0;
foreach (@dimension) {
$index[$count++]=$ind % $_;
$ind /= $_;
}
return @index;
}
EOD
my $doc_which = <<'EOD';
=for ref
Returns indices of non-zero values from a 1-D PDL
=for usage
$i = which($mask);
returns a pdl with indices for all those elements that are nonzero in
the mask. Note that the returned indices will be 1D. If you feed in a
multidimensional mask, it will be flattened before the indices are
calculated. See also L</whichND> for multidimensional masks.
If you want to index into the original mask or a similar ndarray
with output from C<which>, remember to flatten it before calling index:
$data = random 5, 5;
$idx = which $data > 0.5; # $idx is now 1D
$bigsum = $data->flat->index($idx)->sum; # flatten before indexing
Compare also L</where> for similar functionality.
SEE ALSO:
L</which_both> returns separately the indices of both nonzero and zero
values in the mask.
L</where_both> returns separately slices of both nonzero and zero
values in the mask.
L</where> returns associated values from a data PDL, rather than
indices into the mask PDL.
L</whichND> returns N-D indices into a multidimensional PDL.
=for example
pdl> $x = sequence(10); p $x
[0 1 2 3 4 5 6 7 8 9]
pdl> $indx = which($x>6); p $indx
[7 8 9]
=cut
EOD
my $doc_which_both = <<'EOD';
=for ref
Returns indices of nonzero and zero values in a mask PDL
=for usage
($i, $c_i) = which_both($mask);
This works just as L</which>, but the complement of C<$i> will be in
C<$c_i>.
=for example
pdl> p $x = sequence(10)
[0 1 2 3 4 5 6 7 8 9]
pdl> ($big, $small) = which_both($x >= 5); p "$big\n$small"
[5 6 7 8 9]
[0 1 2 3 4]
=cut
EOD
for (
{Name=>'which',
Pars => 'mask(n); indx [o] inds(n); indx [o]lastout()',
Variables => 'PDL_Indx dm=0;',
Elseclause => "",
Outclause => '$lastout() = dm; loop(n=dm) %{ $inds() = -1; %}',
Doc => $doc_which,
PMCode=>pp_line_numbers(__LINE__, <<'EOD'),
sub which { my ($this,$out) = @_;
$this = $this->flat;
$out //= $this->nullcreate;
PDL::_which_int($this,$out,my $lastout = $this->nullcreate);
my $lastoutmax = $lastout->max->sclr;
$lastoutmax ? $out->slice('0:'.($lastoutmax-1))->sever : empty(indx);
}
*PDL::which = \&which;
EOD
},
{Name => 'which_both',
Pars => 'mask(n); indx [o] inds(n); indx [o]notinds(n); indx [o]lastout(); indx [o]lastoutn()',
Variables => 'PDL_Indx dm=0; int dm2=0;',
Elseclause => "else { \n \$notinds(n => dm2)=n; \n dm2++;\n }",
Outclause => '$lastout() = dm; $lastoutn() = dm2; loop(n=dm) %{ $inds() = -1; %} loop(n=dm2) %{ $notinds() = -1; %}',
Doc => $doc_which_both,
PMCode=>pp_line_numbers(__LINE__, <<'EOD'),
sub which_both { my ($this,$outi,$outni) = @_;
$this = $this->flat;
$outi //= $this->nullcreate;
$outni //= $this->nullcreate;
PDL::_which_both_int($this,$outi,$outni,my $lastout = $this->nullcreate,my $lastoutn = $this->nullcreate);
my $lastoutmax = $lastout->max->sclr;
$outi = $lastoutmax ? $outi->slice('0:'.($lastoutmax-1))->sever : empty(indx);
return $outi if !wantarray;
my $lastoutnmax = $lastoutn->max->sclr;
($outi, $lastoutnmax ? $outni->slice('0:'.($lastoutnmax-1))->sever : empty(indx));
}
*PDL::which_both = \&which_both;
EOD
}
)
{
pp_def($_->{Name},
HandleBad => 1,
Doc => $_->{Doc},
Pars => $_->{Pars},
GenericTypes => [ppdefs_all],
PMCode => $_->{PMCode},
Code => $_->{Variables} .'
loop(n) %{
if ( $mask() PDL_IF_BAD(&& $ISGOOD($mask()),) ) {
$inds(n => dm) = n;
dm++;
}'.$_->{Elseclause}.'
%}'.$_->{Outclause},
);
}
pp_def('whichover',
HandleBad => 1,
Inplace => 1,
Pars => 'a(n); [o]o(n)',
GenericTypes => [ppdefs_all],
Code => <<'EOF',
PDL_Indx last = 0;
loop(n) %{
if ($a() PDL_IF_BAD(&& $ISGOOD($a()),)) $o(n=>last++) = n;
%}
loop(n=last) %{ $o() = -1; %}
EOF
Doc => <<'EOF',
=for ref
Collects the coordinates of non-zero, non-bad values in each 1D mask in
ndarray at the start of the output, and fills the rest with -1. Works
inplace.
By using L<PDL::Slices/xchg> etc. it is possible to use I<any> dimension.
=for usage
my $a = pdl q[3 4 2 3 2 3 1];
my $b = $a->uniq;
my $c = +($a->dummy(0) == $b)->transpose;
print $c, $c->whichover;
# [
# [0 0 0 0 0 0 1]
# [0 0 1 0 1 0 0]
# [1 0 0 1 0 1 0]
# [0 1 0 0 0 0 0]
# ]
# [
# [ 6 -1 -1 -1 -1 -1 -1]
# [ 2 4 -1 -1 -1 -1 -1]
# [ 0 3 5 -1 -1 -1 -1]
# [ 1 -1 -1 -1 -1 -1 -1]
# ]
EOF
);
pp_add_exported("", 'where');
pp_addpm(<<'EOD');
=head2 where
=for ref
Use a mask to select values from one or more data PDLs
C<where> accepts one or more data ndarrays and a mask ndarray. It
returns a list of output ndarrays, corresponding to the input data
ndarrays. Each output ndarray is a 1-dimensional list of values in its
corresponding data ndarray. The values are drawn from locations where
the mask is nonzero.
The output PDLs are still connected to the original data PDLs, for the
purpose of dataflow.
C<where> combines the functionality of L</which> and L<index|PDL::Slices/index>
into a single operation.
BUGS:
While C<where> works OK for most N-dimensional cases, it does not
broadcast properly over (for example) the (N+1)th dimension in data
that is compared to an N-dimensional mask. Use C<whereND> for that.
=for usage
$i = $x->where($x+5 > 0); # $i contains those elements of $x
# where mask ($x+5 > 0) is 1
$i .= -5; # Set those elements (of $x) to -5. Together, these
# commands clamp $x to a maximum of -5.
It is also possible to use the same mask for several ndarrays with
the same call:
($i,$j,$k) = where($x,$y,$z, $x+5>0);
Note: C<$i> is always 1-D, even if C<$x> is E<gt>1-D.
WARNING: The first argument
(the values) and the second argument (the mask) currently have to have
the exact same dimensions (or horrible things happen). You *cannot*
broadcast over a smaller mask, for example.
=cut
sub PDL::where {
barf "Usage: where( \$pdl1, ..., \$pdlN, \$mask )\n" if @_ == 1;
my $mask = pop->flat->which;
@_ == 1 ? $_[0]->flat->index($mask) : map $_->flat->index($mask), @_;
}
*where = \&PDL::where;
EOD
pp_add_exported("", 'where_both');
pp_addpm(<<'EOD');
=head2 where_both
=for ref
Returns slices (non-zero in mask, zero) of an ndarray according to a mask
=for usage
($match_vals, $non_match_vals) = where_both($pdl, $mask);
This works like L</which_both>, but (flattened) data-flowing slices
rather than index-sets are returned.
=for example
pdl> p $x = sequence(10) + 2
[2 3 4 5 6 7 8 9 10 11]
pdl> ($big, $small) = where_both($x, $x > 5); p "$big\n$small"
[6 7 8 9 10 11]
[2 3 4 5]
pdl> p $big += 2, $small -= 1
[8 9 10 11 12 13] [1 2 3 4]
pdl> p $x
[1 2 3 4 8 9 10 11 12 13]
=cut
sub PDL::where_both {
barf "Usage: where_both(\$pdl, \$mask)\n" if @_ != 2;
my ($arr, $mask) = @_; # $mask has 0==false, 1==true
my $arr_flat = $arr->flat;
map $arr_flat->index1d($_), PDL::which_both($mask);
}
*where_both = \&PDL::where_both;
EOD
pp_add_exported("", 'whereND');
pp_addpm(<<'EOD');
=head2 whereND
=for ref
C<where> with support for ND masks and broadcasting
C<whereND> accepts one or more data ndarrays and a
mask ndarray. It returns a list of output ndarrays,
corresponding to the input data ndarrays. The values
are drawn from locations where the mask is nonzero.
C<whereND> differs from C<where> in that the mask
dimensionality is preserved which allows for
proper broadcasting of the selection operation over
higher dimensions.
As with C<where> the output PDLs are still connected
to the original data PDLs, for the purpose of dataflow.
=for usage
$sdata = whereND $data, $mask
($s1, $s2, ..., $sn) = whereND $d1, $d2, ..., $dn, $mask
where
$data is M dimensional
$mask is N < M dimensional
dims($data) 1..N == dims($mask) 1..N
with broadcasting over N+1 to M dimensions
=for example
$data = sequence(4,3,2); # example data array
$mask4 = (random(4)>0.5); # example 1-D mask array, has $n4 true values
$mask43 = (random(4,3)>0.5); # example 2-D mask array, has $n43 true values
$sdat4 = whereND $data, $mask4; # $sdat4 is a [$n4,3,2] pdl
$sdat43 = whereND $data, $mask43; # $sdat43 is a [$n43,2] pdl
Just as with C<where>, you can use the returned value in an
assignment. That means that both of these examples are valid:
# Used to create a new slice stored in $sdat4:
$sdat4 = $data->whereND($mask4);
$sdat4 .= 0;
# Used in lvalue context:
$data->whereND($mask4) .= 0;
SEE ALSO:
L</whichND> returns N-D indices into a multidimensional PDL, from a mask.
=cut
sub PDL::whereND :lvalue {
barf "Usage: whereND( \$pdl1, ..., \$pdlN, \$mask )\n" if @_ == 1;
my $mask = pop @_; # $mask has 0==false, 1==true
my @to_return;
my $n = PDL::sum($mask);
my $maskndims = $mask->ndims;
foreach my $arr (@_) {
# count the number of dims in $mask and $arr
# $mask = a b c d e f.....
my @idims = dims($arr);
splice @idims, 0, $maskndims; # pop off the number of dims in $mask
if (!$n or $arr->isempty) {
push @to_return, PDL->zeroes($arr->type, $n, @idims);
next;
}
my $sub_i = $mask * ones($arr);
my $where_sub_i = PDL::where($arr, $sub_i);
my $ndim = 0;
foreach my $id ($n, @idims[0..($#idims-1)]) {
$where_sub_i = $where_sub_i->splitdim($ndim++,$id) if $n>0;
}
push @to_return, $where_sub_i;
}
return (@to_return == 1) ? $to_return[0] : @to_return;
}
*whereND = \&PDL::whereND;
EOD
pp_add_exported("", 'whichND');
pp_addpm(<<'EOD');
=head2 whichND
=for ref
Return the coordinates of non-zero values in a mask.
=for usage
WhichND returns the N-dimensional coordinates of each nonzero value in
a mask PDL with any number of dimensions. The returned values arrive
as an array-of-vectors suitable for use in
L<indexND|PDL::Slices/indexND> or L<range|PDL::Slices/range>.
$coords = whichND($mask);
returns a PDL containing the coordinates of the elements that are non-zero
in C<$mask>, suitable for use in L<PDL::Slices/indexND>. The 0th dimension contains the
full coordinate listing of each point; the 1st dimension lists all the points.
For example, if $mask has rank 4 and 100 matching elements, then $coords has
dimension 4x100.
If no such elements exist, then whichND returns a structured empty PDL:
an Nx0 PDL that contains no values (but matches, broadcasting-wise, with
the vectors that would be produced if such elements existed).
DEPRECATED BEHAVIOR IN LIST CONTEXT:
whichND once delivered different values in list context than in scalar
context, for historical reasons. In list context, it returned the
coordinates transposed, as a collection of 1-PDLs (one per dimension)
in a list. This usage is deprecated in PDL 2.4.10, and will cause a
warning to be issued every time it is encountered. To avoid the
warning, you can set the global variable "$PDL::whichND" to 's' to
get scalar behavior in all contexts, or to 'l' to get list behavior in
list context.
In later versions of PDL, the deprecated behavior will disappear. Deprecated
list context whichND expressions can be replaced with:
@list = $x->whichND->mv(0,-1)->dog;
SEE ALSO:
L</which> finds coordinates of nonzero values in a 1-D mask.
L</where> extracts values from a data PDL that are associated
with nonzero values in a mask PDL.
L<PDL::Slices/indexND> can be fed the coordinates to return the values.
=for example
pdl> $s=sequence(10,10,3,4)
pdl> ($x, $y, $z, $w)=whichND($s == 203); p $x, $y, $z, $w
[3] [0] [2] [0]
pdl> print $s->at(list(cat($x,$y,$z,$w)))
203
=cut
*whichND = \&PDL::whichND;
sub PDL::whichND {
my $mask = PDL->topdl(shift);
# List context: generate a perl list by dimension
if(wantarray) {
if(!defined($PDL::whichND)) {
printf STDERR "whichND: WARNING - list context deprecated. Set \$PDL::whichND. Details in pod.";
} elsif($PDL::whichND =~ m/l/i) {
# old list context enabled by setting $PDL::whichND to 'l'
return $mask->one2nd($mask->flat->which);
}
# if $PDL::whichND does not contain 'l' or 'L', fall through to scalar context
}
# Scalar context: generate an N-D index ndarray
return PDL->new_from_specification(indx,$mask->ndims,0) if !$mask->nelem;
return $mask ? pdl(indx,0) : PDL->new_from_specification(indx,0)
if !$mask->getndims;
my $ind = $mask->flat->which->dummy(0,$mask->getndims)->make_physical;
# In the empty case, explicitly return the correct type of structured empty
return PDL->new_from_specification(indx,$mask->ndims, 0) if !$ind->nelem;
my $mult = ones(indx, $mask->getndims);
my @mdims = $mask->dims;
for my $i (0..$#mdims-1) {
# use $tmp for 5.005_03 compatibility
(my $tmp = $mult->index($i+1)) .= $mult->index($i)*$mdims[$i];
}
for my $i (0..$#mdims) {
my($s) = $ind->index($i);
$s /= $mult->index($i);
$s %= $mdims[$i];
}
return $ind;
}
EOD
#
# Set operations suited for manipulation of the operations above.
#
pp_add_exported("", 'setops');
pp_addpm(<<'EOD');
=head2 setops
=for ref
Implements simple set operations like union and intersection
=for usage
Usage: $set = setops($x, <OPERATOR>, $y);
The operator can be C<OR>, C<XOR> or C<AND>. This is then applied
to C<$x> viewed as a set and C<$y> viewed as a set. Set theory says
that a set may not have two or more identical elements, but setops
takes care of this for you, so C<$x=pdl(1,1,2)> is OK. The functioning
is as follows:
=over
=item C<OR>
The resulting vector will contain the elements that are either in C<$x>
I<or> in C<$y> or both. This is the union in set operation terms
=item C<XOR>
The resulting vector will contain the elements that are either in C<$x>
or C<$y>, but not in both. This is
Union($x, $y) - Intersection($x, $y)
in set operation terms.
=item C<AND>
The resulting vector will contain the intersection of C<$x> and C<$y>, so
the elements that are in both C<$x> and C<$y>. Note that for convenience
this operation is also aliased to L</intersect>.
=back
It should be emphasized that these routines are used when one or both of
the sets C<$x>, C<$y> are hard to calculate or that you get from a separate
subroutine.
Finally IDL users might be familiar with Craig Markwardt's C<cmset_op.pro>
routine which has inspired this routine although it was written independently
However the present routine has a few less options (but see the examples)
=for example
You will very often use these functions on an index vector, so that is
what we will show here. We will in fact something slightly silly. First
we will find all squares that are also cubes below 10000.
Create a sequence vector:
pdl> $x = sequence(10000)
Find all odd and even elements:
pdl> ($even, $odd) = which_both( ($x % 2) == 0)
Find all squares
pdl> $squares= which(ceil(sqrt($x)) == floor(sqrt($x)))
Find all cubes (being careful with roundoff error!)
pdl> $cubes= which(ceil($x**(1.0/3.0)) == floor($x**(1.0/3.0)+1e-6))
Then find all squares that are cubes:
pdl> $both = setops($squares, 'AND', $cubes)
And print these (assumes that C<PDL::NiceSlice> is loaded!)
pdl> p $x($both)
[0 1 64 729 4096]
Then find all numbers that are either cubes or squares, but not both:
pdl> $cube_xor_square = setops($squares, 'XOR', $cubes)
pdl> p $cube_xor_square->nelem()
112
So there are a total of 112 of these!
Finally find all odd squares:
pdl> $odd_squares = setops($squares, 'AND', $odd)
Another common occurrence is to want to get all objects that are
in C<$x> and in the complement of C<$y>. But it is almost always best
to create the complement explicitly since the universe that both are
taken from is not known. Thus use L</which_both> if possible
to keep track of complements.
If this is impossible the best approach is to make a temporary:
This creates an index vector the size of the universe of the sets and
set all elements in C<$y> to 0
pdl> $tmp = ones($n_universe); $tmp($y) .= 0;
This then finds the complement of C<$y>
pdl> $C_b = which($tmp == 1);
and this does the final selection:
pdl> $set = setops($x, 'AND', $C_b)
=cut
*setops = \&PDL::setops;
sub PDL::setops {
my ($x, $op, $y)=@_;
# Check that $x and $y are 1D.
if ($x->ndims() > 1 || $y->ndims() > 1) {
warn 'setops: $x and $y must be 1D - flattening them!'."\n";
$x = $x->flat;
$y = $y->flat;
}
#Make sure there are no duplicate elements.
$x=$x->uniq;
$y=$y->uniq;
my $result;
if ($op eq 'OR') {
# Easy...
$result = uniq(append($x, $y));
} elsif ($op eq 'XOR') {
# Make ordered list of set union.
my $union = append($x, $y)->qsort;
# Index lists.
my $s1=zeroes(byte, $union->nelem());
my $s2=zeroes(byte, $union->nelem());
# Find indices which are duplicated - these are to be excluded
#
# We do this by comparing x with x shifted each way.
my $i1 = which($union != rotate($union, 1));
my $i2 = which($union != rotate($union, -1));
#
# We then mark/mask these in the s1 and s2 arrays to indicate which ones
# are not equal to their neighbours.
#
my $ts;
($ts = $s1->index($i1)) .= 1 if $i1->nelem() > 0;
($ts = $s2->index($i2)) .= 1 if $i2->nelem() > 0;
my $inds=which($s1 == $s2);
if ($inds->nelem() > 0) {
return $union->index($inds);
} else {
return $inds;
}
} elsif ($op eq 'AND') {
# The intersection of the arrays.
return $x if $x->isempty;
return $y if $y->isempty;
# Make ordered list of set union.
my $union = append($x, $y)->qsort;
return $union->where($union == rotate($union, -1))->uniq;
} else {
print "The operation $op is not known!";
return -1;
}
}
EOD
pp_add_exported("", 'intersect');
pp_addpm(<<'EOD');
=head2 intersect
=for ref
Calculate the intersection of two ndarrays
=for usage
Usage: $set = intersect($x, $y);
This routine is merely a simple interface to L</setops>. See
that for more information
=for example
Find all numbers less that 100 that are of the form 2*y and 3*x
pdl> $x=sequence(100)
pdl> $factor2 = which( ($x % 2) == 0)
pdl> $factor3 = which( ($x % 3) == 0)
pdl> $ii=intersect($factor2, $factor3)
pdl> p $x($ii)
[0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96]
=cut
*intersect = \&PDL::intersect;
sub PDL::intersect {
return setops($_[0], 'AND', $_[1]);
}
EOD
pp_addpm({At=>'Bot'},<<'EOD');
=head1 AUTHOR
Copyright (C) Tuomas J. Lukka 1997 (lukka@husc.harvard.edu). Contributions
by Christian Soeller (c.soeller@auckland.ac.nz), Karl Glazebrook
(kgb@aaoepp.aao.gov.au), Craig DeForest (deforest@boulder.swri.edu)
and Jarle Brinchmann (jarle@astro.up.pt)
All rights reserved. There is no warranty. You are allowed
to redistribute this software / documentation under certain
conditions. For details, see the file COPYING in the PDL
distribution. If this file is separated from the PDL distribution,
the copyright notice should be included in the file.
Updated for CPAN viewing compatibility by David Mertens.
=cut
EOD
pp_done();