# # GENERATED WITH PDL::PP! Don't modify! # package PDL::Ufunc; our @EXPORT_OK = qw(PDL::PP prodover PDL::PP cprodover PDL::PP dprodover PDL::PP cumuprodover PDL::PP dcumuprodover PDL::PP sumover PDL::PP csumover PDL::PP dsumover PDL::PP cumusumover PDL::PP dcumusumover PDL::PP andover PDL::PP bandover PDL::PP borover PDL::PP orover PDL::PP zcover PDL::PP intover PDL::PP average PDL::PP avgover PDL::PP caverage PDL::PP daverage PDL::PP davgover PDL::PP medover PDL::PP oddmedover PDL::PP modeover PDL::PP pctover PDL::PP oddpctover pct oddpct avg sum prod davg dsum dprod zcheck and band or bor min max median mode oddmedian any all minmax PDL::PP qsort PDL::PP qsorti PDL::PP qsortvec PDL::PP qsortveci PDL::PP minimum PDL::PP minimum_ind PDL::PP minimum_n_ind PDL::PP maximum PDL::PP maximum_ind PDL::PP maximum_n_ind PDL::PP maxover PDL::PP maxover_ind PDL::PP maxover_n_ind PDL::PP minover PDL::PP minover_ind PDL::PP minover_n_ind PDL::PP minmaximum PDL::PP minmaxover ); our %EXPORT_TAGS = (Func=>[@EXPORT_OK]); use PDL::Core; use PDL::Exporter; use DynaLoader; our @ISA = ( 'PDL::Exporter','DynaLoader' ); push @PDL::Core::PP, __PACKAGE__; bootstrap PDL::Ufunc ; =head1 NAME PDL::Ufunc - primitive ufunc operations for pdl =head1 DESCRIPTION This module provides some primitive and useful functions defined using PDL::PP based on functionality of what are sometimes called I<ufuncs> (for example NumPY and Mathematica talk about these). It collects all the functions generally used to C<reduce> or C<accumulate> along a dimension. These all do their job across the first dimension but by using the slicing functions you can do it on any dimension. The L<PDL::Reduce> module provides an alternative interface to many of the functions in this module. =head1 SYNOPSIS use PDL::Ufunc; =cut use PDL::Slices; use Carp; =head1 FUNCTIONS =cut =head2 prodover =for sig Signature: (a(n); int+ [o]b()) =for ref Project via product to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the product along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = prodover($x); =for example $spectrum = prodover $image->xchg(0,1) =for bad prodover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *prodover = \&PDL::prodover; =head2 cprodover =for sig Signature: (a(n); cdouble [o]b()) =for ref Project via product to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the product along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = dprodover($x); =for example $spectrum = dprodover $image->xchg(0,1) Unlike L<prodover|/prodover>, the calculations are performed in complex double precision. =for bad cprodover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *cprodover = \&PDL::cprodover; =head2 dprodover =for sig Signature: (a(n); double [o]b()) =for ref Project via product to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the product along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = dprodover($x); =for example $spectrum = dprodover $image->xchg(0,1) Unlike L</prodover>, the calculations are performed in double precision. =for bad dprodover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *dprodover = \&PDL::dprodover; =head2 cumuprodover =for sig Signature: (a(n); int+ [o]b(n)) =for ref Cumulative product This function calculates the cumulative product along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. The sum is started so that the first element in the cumulative product is the first element of the parameter. =for usage $y = cumuprodover($x); =for example $spectrum = cumuprodover $image->xchg(0,1) =for bad cumuprodover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *cumuprodover = \&PDL::cumuprodover; =head2 dcumuprodover =for sig Signature: (a(n); double [o]b(n)) =for ref Cumulative product This function calculates the cumulative product along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. The sum is started so that the first element in the cumulative product is the first element of the parameter. =for usage $y = cumuprodover($x); =for example $spectrum = cumuprodover $image->xchg(0,1) Unlike L</cumuprodover>, the calculations are performed in double precision. =for bad dcumuprodover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *dcumuprodover = \&PDL::dcumuprodover; =head2 sumover =for sig Signature: (a(n); int+ [o]b()) =for ref Project via sum to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the sum along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = sumover($x); =for example $spectrum = sumover $image->xchg(0,1) =for bad sumover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *sumover = \&PDL::sumover; =head2 csumover =for sig Signature: (a(n); cdouble [o]b()) =for ref Project via sum to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the sum along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = dsumover($x); =for example $spectrum = dsumover $image->xchg(0,1) Unlike L<sumover|/sumover>, the calculations are performed in complex double precision. =for bad csumover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *csumover = \&PDL::csumover; =head2 dsumover =for sig Signature: (a(n); double [o]b()) =for ref Project via sum to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the sum along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = dsumover($x); =for example $spectrum = dsumover $image->xchg(0,1) Unlike L</sumover>, the calculations are performed in double precision. =for bad dsumover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *dsumover = \&PDL::dsumover; =head2 cumusumover =for sig Signature: (a(n); int+ [o]b(n)) =for ref Cumulative sum This function calculates the cumulative sum along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. The sum is started so that the first element in the cumulative sum is the first element of the parameter. =for usage $y = cumusumover($x); =for example $spectrum = cumusumover $image->xchg(0,1) =for bad cumusumover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *cumusumover = \&PDL::cumusumover; =head2 dcumusumover =for sig Signature: (a(n); double [o]b(n)) =for ref Cumulative sum This function calculates the cumulative sum along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. The sum is started so that the first element in the cumulative sum is the first element of the parameter. =for usage $y = cumusumover($x); =for example $spectrum = cumusumover $image->xchg(0,1) Unlike L</cumusumover>, the calculations are performed in double precision. =for bad dcumusumover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *dcumusumover = \&PDL::dcumusumover; =head2 andover =for sig Signature: (a(n); int+ [o]b()) =for ref Project via and to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the and along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = andover($x); =for example $spectrum = andover $image->xchg(0,1) =for bad If C<a()> contains only bad data (and its bad flag is set), C<b()> is set bad. Otherwise C<b()> will have its bad flag cleared, as it will not contain any bad values. =cut *andover = \&PDL::andover; =head2 bandover =for sig Signature: (a(n); [o]b()) =for ref Project via bitwise and to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the bitwise and along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = bandover($x); =for example $spectrum = bandover $image->xchg(0,1) =for bad If C<a()> contains only bad data (and its bad flag is set), C<b()> is set bad. Otherwise C<b()> will have its bad flag cleared, as it will not contain any bad values. =cut *bandover = \&PDL::bandover; =head2 borover =for sig Signature: (a(n); [o]b()) =for ref Project via bitwise or to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the bitwise or along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = borover($x); =for example $spectrum = borover $image->xchg(0,1) =for bad If C<a()> contains only bad data (and its bad flag is set), C<b()> is set bad. Otherwise C<b()> will have its bad flag cleared, as it will not contain any bad values. =cut *borover = \&PDL::borover; =head2 orover =for sig Signature: (a(n); int+ [o]b()) =for ref Project via or to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the or along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = orover($x); =for example $spectrum = orover $image->xchg(0,1) =for bad If C<a()> contains only bad data (and its bad flag is set), C<b()> is set bad. Otherwise C<b()> will have its bad flag cleared, as it will not contain any bad values. =cut *orover = \&PDL::orover; =head2 zcover =for sig Signature: (a(n); int+ [o]b()) =for ref Project via == 0 to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the == 0 along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = zcover($x); =for example $spectrum = zcover $image->xchg(0,1) =for bad If C<a()> contains only bad data (and its bad flag is set), C<b()> is set bad. Otherwise C<b()> will have its bad flag cleared, as it will not contain any bad values. =cut *zcover = \&PDL::zcover; =head2 intover =for sig Signature: (a(n); float+ [o]b()) =for ref Project via integral to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the integral along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = intover($x); =for example $spectrum = intover $image->xchg(0,1) Notes: C<intover> uses a point spacing of one (i.e., delta-h==1). You will need to scale the result to correct for the true point delta). For C<n E<gt> 3>, these are all C<O(h^4)> (like Simpson's rule), but are integrals between the end points assuming the pdl gives values just at these centres: for such `functions', sumover is correct to C<O(h)>, but is the natural (and correct) choice for binned data, of course. =for bad intover ignores the bad-value flag of the input ndarrays. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *intover = \&PDL::intover; =head2 average =for sig Signature: (a(n); int+ [o]b()) =for ref Project via average to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the average along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = average($x); =for example $spectrum = average $image->xchg(0,1) =for bad average processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *average = \&PDL::average; *PDL::avgover = \&PDL::average; *avgover = \&PDL::average; =head2 avgover =for ref Synonym for average. =cut =head2 caverage =for sig Signature: (a(n); cdouble [o]b()) =for ref Project via average to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the average along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = daverage($x); =for example $spectrum = daverage $image->xchg(0,1) Unlike L<average|/average>, the calculation is performed in complex double precision. =for bad caverage processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *caverage = \&PDL::caverage; =head2 daverage =for sig Signature: (a(n); double [o]b()) =for ref Project via average to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the average along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = daverage($x); =for example $spectrum = daverage $image->xchg(0,1) Unlike L</average>, the calculation is performed in double precision. =for bad daverage processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *daverage = \&PDL::daverage; *PDL::davgover = \&PDL::daverage; *davgover = \&PDL::daverage; =head2 davgover =for ref Synonym for daverage. =cut =head2 medover =for sig Signature: (a(n); [o]b(); [t]tmp(n)) =for ref Project via median to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the median along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = medover($x); =for example $spectrum = medover $image->xchg(0,1) =for bad medover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *medover = \&PDL::medover; =head2 oddmedover =for sig Signature: (a(n); [o]b(); [t]tmp(n)) =for ref Project via oddmedian to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the oddmedian along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = oddmedover($x); =for example $spectrum = oddmedover $image->xchg(0,1) The median is sometimes not a good choice as if the array has an even number of elements it lies half-way between the two middle values - thus it does not always correspond to a data value. The lower-odd median is just the lower of these two values and so it ALWAYS sits on an actual data value which is useful in some circumstances. =for bad oddmedover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *oddmedover = \&PDL::oddmedover; =head2 modeover =for sig Signature: (data(n); [o]out(); [t]sorted(n)) =for ref Project via mode to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the mode along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = modeover($x); =for example $spectrum = modeover $image->xchg(0,1) The mode is the single element most frequently found in a discrete data set. It I<only> makes sense for integer data types, since floating-point types are demoted to integer before the mode is calculated. C<modeover> treats BAD the same as any other value: if BAD is the most common element, the returned value is also BAD. =for bad modeover does not process bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *modeover = \&PDL::modeover; =head2 pctover =for sig Signature: (a(n); p(); [o]b(); [t]tmp(n)) =for ref Project via percentile to N-1 dimensions This function reduces the dimensionality of an ndarray by one by finding the specified percentile (p) along the 1st dimension. The specified percentile must be between 0.0 and 1.0. When the specified percentile falls between data points, the result is interpolated. Values outside the allowed range are clipped to 0.0 or 1.0 respectively. The algorithm implemented here is based on the interpolation variant described at L<http://en.wikipedia.org/wiki/Percentile> as used by Microsoft Excel and recommended by NIST. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = pctover($x, $p); =for example $spectrum = pctover $image->xchg(0,1), $p =for bad pctover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *pctover = \&PDL::pctover; =head2 oddpctover =for sig Signature: (a(n); p(); [o]b(); [t]tmp(n)) Project via percentile to N-1 dimensions This function reduces the dimensionality of an ndarray by one by finding the specified percentile along the 1st dimension. The specified percentile must be between 0.0 and 1.0. When the specified percentile falls between two values, the nearest data value is the result. The algorithm implemented is from the textbook version described first at L<http://en.wikipedia.org/wiki/Percentile>. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = oddpctover($x, $p); =for example $spectrum = oddpctover $image->xchg(0,1), $p =for bad oddpctover processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays. =cut *oddpctover = \&PDL::oddpctover; =head2 pct =for ref Return the specified percentile of all elements in an ndarray. The specified percentile (p) must be between 0.0 and 1.0. When the specified percentile falls between data points, the result is interpolated. =for usage $x = pct($data, $pct); =cut *pct = \&PDL::pct; sub PDL::pct { my($x, $p) = @_; my $tmp; $x->clump(-1)->pctover($p, $tmp=PDL->nullcreate($x)); return $tmp->at(); } =head2 oddpct =for ref Return the specified percentile of all elements in an ndarray. The specified percentile must be between 0.0 and 1.0. When the specified percentile falls between two values, the nearest data value is the result. =for usage $x = oddpct($data, $pct); =cut *oddpct = \&PDL::oddpct; sub PDL::oddpct { my($x, $p) = @_; my $tmp; $x->clump(-1)->oddpctover($p, $tmp=PDL->nullcreate($x)); return $tmp->at(); } =head2 avg =for ref Return the average of all elements in an ndarray. See the documentation for L</average> for more information. =for usage $x = avg($data); =for bad This routine handles bad values. =cut *avg = \&PDL::avg; sub PDL::avg { my($x) = @_; my $tmp; $x->clump(-1)->average( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 sum =for ref Return the sum of all elements in an ndarray. See the documentation for L</sumover> for more information. =for usage $x = sum($data); =for bad This routine handles bad values. =cut *sum = \&PDL::sum; sub PDL::sum { my($x) = @_; my $tmp; $x->clump(-1)->sumover( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 prod =for ref Return the product of all elements in an ndarray. See the documentation for L</prodover> for more information. =for usage $x = prod($data); =for bad This routine handles bad values. =cut *prod = \&PDL::prod; sub PDL::prod { my($x) = @_; my $tmp; $x->clump(-1)->prodover( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 davg =for ref Return the average (in double precision) of all elements in an ndarray. See the documentation for L</daverage> for more information. =for usage $x = davg($data); =for bad This routine handles bad values. =cut *davg = \&PDL::davg; sub PDL::davg { my($x) = @_; my $tmp; $x->clump(-1)->daverage( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 dsum =for ref Return the sum (in double precision) of all elements in an ndarray. See the documentation for L</dsumover> for more information. =for usage $x = dsum($data); =for bad This routine handles bad values. =cut *dsum = \&PDL::dsum; sub PDL::dsum { my($x) = @_; my $tmp; $x->clump(-1)->dsumover( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 dprod =for ref Return the product (in double precision) of all elements in an ndarray. See the documentation for L</dprodover> for more information. =for usage $x = dprod($data); =for bad This routine handles bad values. =cut *dprod = \&PDL::dprod; sub PDL::dprod { my($x) = @_; my $tmp; $x->clump(-1)->dprodover( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 zcheck =for ref Return the check for zero of all elements in an ndarray. See the documentation for L</zcover> for more information. =for usage $x = zcheck($data); =for bad This routine handles bad values. =cut *zcheck = \&PDL::zcheck; sub PDL::zcheck { my($x) = @_; my $tmp; $x->clump(-1)->zcover( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 and =for ref Return the logical and of all elements in an ndarray. See the documentation for L</andover> for more information. =for usage $x = and($data); =for bad This routine handles bad values. =cut *and = \&PDL::and; sub PDL::and { my($x) = @_; my $tmp; $x->clump(-1)->andover( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 band =for ref Return the bitwise and of all elements in an ndarray. See the documentation for L</bandover> for more information. =for usage $x = band($data); =for bad This routine handles bad values. =cut *band = \&PDL::band; sub PDL::band { my($x) = @_; my $tmp; $x->clump(-1)->bandover( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 or =for ref Return the logical or of all elements in an ndarray. See the documentation for L</orover> for more information. =for usage $x = or($data); =for bad This routine handles bad values. =cut *or = \&PDL::or; sub PDL::or { my($x) = @_; my $tmp; $x->clump(-1)->orover( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 bor =for ref Return the bitwise or of all elements in an ndarray. See the documentation for L</borover> for more information. =for usage $x = bor($data); =for bad This routine handles bad values. =cut *bor = \&PDL::bor; sub PDL::bor { my($x) = @_; my $tmp; $x->clump(-1)->borover( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 min =for ref Return the minimum of all elements in an ndarray. See the documentation for L</minimum> for more information. =for usage $x = min($data); =for bad This routine handles bad values. =cut *min = \&PDL::min; sub PDL::min { my($x) = @_; my $tmp; $x->clump(-1)->minimum( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 max =for ref Return the maximum of all elements in an ndarray. See the documentation for L</maximum> for more information. =for usage $x = max($data); =for bad This routine handles bad values. =cut *max = \&PDL::max; sub PDL::max { my($x) = @_; my $tmp; $x->clump(-1)->maximum( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 median =for ref Return the median of all elements in an ndarray. See the documentation for L</medover> for more information. =for usage $x = median($data); =for bad This routine handles bad values. =cut *median = \&PDL::median; sub PDL::median { my($x) = @_; my $tmp; $x->clump(-1)->medover( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 mode =for ref Return the mode of all elements in an ndarray. See the documentation for L</modeover> for more information. =for usage $x = mode($data); =for bad This routine handles bad values. =cut *mode = \&PDL::mode; sub PDL::mode { my($x) = @_; my $tmp; $x->clump(-1)->modeover( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 oddmedian =for ref Return the oddmedian of all elements in an ndarray. See the documentation for L</oddmedover> for more information. =for usage $x = oddmedian($data); =for bad This routine handles bad values. =cut *oddmedian = \&PDL::oddmedian; sub PDL::oddmedian { my($x) = @_; my $tmp; $x->clump(-1)->oddmedover( $tmp=PDL->nullcreate($x) ); return $tmp->at(); } =head2 any =for ref Return true if any element in ndarray set Useful in conditional expressions: =for example if (any $x>15) { print "some values are greater than 15\n" } =for bad See L</or> for comments on what happens when all elements in the check are bad. =cut *any = \∨ *PDL::any = \&PDL::or; =head2 all =for ref Return true if all elements in ndarray set Useful in conditional expressions: =for example if (all $x>15) { print "all values are greater than 15\n" } =for bad See L</and> for comments on what happens when all elements in the check are bad. =cut *all = \∧ *PDL::all = \&PDL::and; =head2 minmax =for ref Returns a list with minimum and maximum values of an ndarray. =for usage ($mn, $mx) = minmax($pdl); This routine does I<not> thread over the dimensions of C<$pdl>; it returns the minimum and maximum values of the whole ndarray. See L</minmaximum> if this is not what is required. The two values are returned as Perl scalars similar to min/max, and therefore ignore whether the values are bad. =for example pdl> $x = pdl [1,-2,3,5,0] pdl> ($min, $max) = minmax($x); pdl> p "$min $max\n"; -2 5 =cut *minmax = \&PDL::minmax; sub PDL::minmax { my ($x)=@_; my $tmp; my @arr = $x->clump(-1)->minmaximum; return map {$_->sclr} @arr[0,1]; # return as scalars ! } =head2 qsort =for sig Signature: (a(n); [o]b(n)) =for ref Quicksort a vector into ascending order. =for example print qsort random(10); =for bad Bad values are moved to the end of the array: pdl> p $y [42 47 98 BAD 22 96 74 41 79 76 96 BAD 32 76 25 59 BAD 96 32 BAD] pdl> p qsort($y) [22 25 32 32 41 42 47 59 74 76 76 79 96 96 96 98 BAD BAD BAD BAD] =cut *qsort = \&PDL::qsort; =head2 qsorti =for sig Signature: (a(n); indx [o]indx(n)) =for ref Quicksort a vector and return index of elements in ascending order. =for example $ix = qsorti $x; print $x->index($ix); # Sorted list =for bad Bad elements are moved to the end of the array: pdl> p $y [42 47 98 BAD 22 96 74 41 79 76 96 BAD 32 76 25 59 BAD 96 32 BAD] pdl> p $y->index( qsorti($y) ) [22 25 32 32 41 42 47 59 74 76 76 79 96 96 96 98 BAD BAD BAD BAD] =cut *qsorti = \&PDL::qsorti; =head2 qsortvec =for sig Signature: (a(n,m); [o]b(n,m)) =for ref Sort a list of vectors lexicographically. The 0th dimension of the source ndarray is dimension in the vector; the 1st dimension is list order. Higher dimensions are threaded over. =for example print qsortvec pdl([[1,2],[0,500],[2,3],[4,2],[3,4],[3,5]]); [ [ 0 500] [ 1 2] [ 2 3] [ 3 4] [ 3 5] [ 4 2] ] =for bad Vectors with bad components should be moved to the end of the array: =cut *qsortvec = \&PDL::qsortvec; =head2 qsortveci =for sig Signature: (a(n,m); indx [o]indx(m)) =for ref Sort a list of vectors lexicographically, returning the indices of the sorted vectors rather than the sorted list itself. As with C<qsortvec>, the input PDL should be an NxM array containing M separate N-dimensional vectors. The return value is an integer M-PDL containing the M-indices of original array rows, in sorted order. As with C<qsortvec>, the zeroth element of the vectors runs slowest in the sorted list. Additional dimensions are threaded over: each plane is sorted separately, so qsortveci may be thought of as a collapse operator of sorts (groan). =for bad Vectors with bad components should be moved to the end of the array: =cut *qsortveci = \&PDL::qsortveci; =head2 minimum =for sig Signature: (a(n); [o]c()) =for ref Project via minimum to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the minimum along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = minimum($x); =for example $spectrum = minimum $image->xchg(0,1) =for bad Output is set bad if all elements of the input are bad, otherwise the bad flag is cleared for the output ndarray. Note that C<NaNs> are considered to be valid values; see L<isfinite|PDL::Math/isfinite> and L<badmask|PDL::Math/badmask> for ways of masking NaNs. =cut *minimum = \&PDL::minimum; =head2 minimum_ind =for sig Signature: (a(n); indx [o] c()) =for ref Like minimum but returns the index rather than the value =for bad Output is set bad if all elements of the input are bad, otherwise the bad flag is cleared for the output ndarray. =cut *minimum_ind = \&PDL::minimum_ind; =head2 minimum_n_ind =for sig Signature: (a(n); indx [o]c(m)) =for ref Returns the index of C<m> minimum elements =for bad Not yet been converted to ignore bad values =cut *minimum_n_ind = \&PDL::minimum_n_ind; =head2 maximum =for sig Signature: (a(n); [o]c()) =for ref Project via maximum to N-1 dimensions This function reduces the dimensionality of an ndarray by one by taking the maximum along the 1st dimension. By using L<xchg|PDL::Slices/xchg> etc. it is possible to use I<any> dimension. =for usage $y = maximum($x); =for example $spectrum = maximum $image->xchg(0,1) =for bad Output is set bad if all elements of the input are bad, otherwise the bad flag is cleared for the output ndarray. Note that C<NaNs> are considered to be valid values; see L<isfinite|PDL::Math/isfinite> and L<badmask|PDL::Math/badmask> for ways of masking NaNs. =cut *maximum = \&PDL::maximum; =head2 maximum_ind =for sig Signature: (a(n); indx [o] c()) =for ref Like maximum but returns the index rather than the value =for bad Output is set bad if all elements of the input are bad, otherwise the bad flag is cleared for the output ndarray. =cut *maximum_ind = \&PDL::maximum_ind; =head2 maximum_n_ind =for sig Signature: (a(n); indx [o]c(m)) =for ref Returns the index of C<m> maximum elements =for bad Not yet been converted to ignore bad values =cut *maximum_n_ind = \&PDL::maximum_n_ind; *PDL::maxover = \&PDL::maximum; *maxover = \&PDL::maximum; =head2 maxover =for ref Synonym for maximum. =cut *PDL::maxover_ind = \&PDL::maximum_ind; *maxover_ind = \&PDL::maximum_ind; =head2 maxover_ind =for ref Synonym for maximum_ind. =cut *PDL::maxover_n_ind = \&PDL::maximum_n_ind; *maxover_n_ind = \&PDL::maximum_n_ind; =head2 maxover_n_ind =for ref Synonym for maximum_n_ind. =cut *PDL::minover = \&PDL::minimum; *minover = \&PDL::minimum; =head2 minover =for ref Synonym for minimum. =cut *PDL::minover_ind = \&PDL::minimum_ind; *minover_ind = \&PDL::minimum_ind; =head2 minover_ind =for ref Synonym for minimum_ind. =cut *PDL::minover_n_ind = \&PDL::minimum_n_ind; *minover_n_ind = \&PDL::minimum_n_ind; =head2 minover_n_ind =for ref Synonym for minimum_n_ind =cut =head2 minmaximum =for sig Signature: (a(n); [o]cmin(); [o] cmax(); indx [o]cmin_ind(); indx [o]cmax_ind()) =for ref Find minimum and maximum and their indices for a given ndarray; =for usage pdl> $x=pdl [[-2,3,4],[1,0,3]] pdl> ($min, $max, $min_ind, $max_ind)=minmaximum($x) pdl> p $min, $max, $min_ind, $max_ind [-2 0] [4 3] [0 1] [2 2] See also L</minmax>, which clumps the ndarray together. =for bad If C<a()> contains only bad data, then the output ndarrays will be set bad, along with their bad flag. Otherwise they will have their bad flags cleared, since they will not contain any bad values. =cut *minmaximum = \&PDL::minmaximum; *PDL::minmaxover = \&PDL::minmaximum; *minmaxover = \&PDL::minmaximum; =head2 minmaxover =for ref Synonym for minmaximum. =cut ; =head1 AUTHOR Copyright (C) Tuomas J. Lukka 1997 (lukka@husc.harvard.edu). Contributions by Christian Soeller (c.soeller@auckland.ac.nz) and Karl Glazebrook (kgb@aaoepp.aao.gov.au). 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. =cut # Exit with OK status 1;