Free positive predictive value software for linux

Resistor Color Code Calculator project calculates the value of a resistor (an electronic component).

The colors on the resistor are input into the program, and the result displays the resistance value of the resistor.

This is much easier than memorizing rhymes to remember colors.

The Analysis & Reconstruction Sound Engine also known as ARSE, is a program that analyses a sound file into a spectrogram and is able to synthetise this spectrogram, or any other user-created image, back into a sound.
The ARSE consists in two main parts, a spectrographer with a base-2 logarithmic frequency scale, and a spectrogram synthetiser.
Unlike most spectrographers which are based on STFTs and perform the analysis by cutting the signal into small time slices to analyse these slices in the frequency domain, the ARSE is based on a filter bank followed by envelope detection, which means that the signal is cut into small frequency-domain slices, and then analysed in the time domain.
The filter bank is, as of now, made up with overlapping bandpass FIR filters defined logarithmically. Once the original signal is filtered with the filter bank, each resulting signal is sent to envelope detection.
Envelope detection in the ARSE isnt based on a Hilbert transform and peak detection, as its usually done. To achieve envelope detection, we first perform a FFT on the signal, zero-pad the beginning of the signal in the frequency domain according to a user-defined setting, then we perform an IFFT, and, now in the time domain, we turn every negative sample into a positive one, and we low-pass filter (and eventually decimate) the signal according to the same user-defined setting as we previously used.
For instance, lets say we have a signal with a sampling frequency of 44,100 Hz, and that we want to obtain an envelope for it which sampling frequency would be 100 Hz. Once we perform the FFT, we add enough zeroes in the frequency domain at the beginning of our signal so that every frequency component shifts by 50 Hz (100 Hz divided by two, it will later appear obvious why), and we perform an IFFT. Our signal now has a sampling frequency of 44,200 Hz (44,100 + 100 Hz), and the original signal which previously spanned from 0 Hz to 22,050 Hz now spans from 50 Hz to 22,100 Hz.
Now we turn every time-domain sample into its absolute value by turning every negative sample into a positive one. To perform this on a signal means that, for example, a sine wave of a certain frequency would become a signal which periodicity would be twice that frequency. Once we low-pass filter that signal to twice that frequency we obtain that signals envelope. In our case, now that we have obtained the absolute values for our signal, since the periodicity of a sine at the lowest frequency - 50 Hz - would now be 100 Hz, we only low-pass filter our signal at 100 Hz to obtain the original signals envelope. We can now decimate the signal to a sample rate of 100 Hz.
The resulting envelope for each frequency band makes the horizontal lines of the image representing the spectrogram. The amplitude of the envelopes translate linearly into intensity in the image.
The spectrogram synthetiser is based on modulation using horizontal lines of the image as envelopes. Each horizontal line is upsampled to the sampling rate of the desired final signals sampling rate, and is then modulated with, depending on the synthetisation mode chosen by the user, sines matching to the central frequency each horizontal line represents, or noise filtered through the filter bank.
Enhancements:
- Replaced fixed phase sine generation with random phase sine generation
- Changed the PRNG
- Removed the unused code
- Removed every call of nearbyint() due to compatibility issues
- Included the necessary files in order to make using ./configure && make && make install

PDL::LinearAlgebra Perl module contains linear algebra utils for PDL.

SYNOPSIS

use PDL::LinearAlgebra;

$a = random (100,100);
($U, $s, $V) = mdsvd($a);

This module provides a convenient interface to PDL::LinearAlgebra::Real and PDL::LinearAlgebra::Complex.

FUNCTIONS

setlaerror

Set action type when error is encountered, returns previous type. Available values are NO, WARN and BARF (predefined constants). If, for example, in computation of the inverse, singularity is detected, the routine can silently return values from computation (see manuals), warn about singularity or barf. BARF is the default value.

$a = sequence(5,5);
$err = setlaerror(NO);
($inv, $info)= minv($a);
if ($info){
# Change the diagonal (the inverse doesnt exist but its an example)
$a->diagonal(0,1)+=1e-8;
($inv, $info)= minv($a);
}
if ($info){
print "Cant compute the inversen";
}
else{
print "Inverse of $a is $inv";
}
setlaerror($err);

getlaerror

Get error type.

0 => NO,
1 => WARN,
2 => BARF
t
PDL = t(PDL, SCALAR(conj))
conj : Conjugate Transpose = 1 | Transpose = 0, default = 1;

Convenient function for transposing real or complex 2D array(s). For PDL::Complex, if conj is true returns conjugate transpose array(s) and doesnt support dataflow. Supports threading.

issym

PDL = issym(PDL, SCALAR|PDL(tol),SCALAR(hermitian))
tol : tolerance value, default: 1e-8 for double else 1e-5
hermitian : Hermitian = 1 | Symmetric = 0, default = 1;

Check symmetricity/Hermitianicity of matrix. Supports threading.

diag

Return i-th diagonal if matrix in entry or matrix with i-th diagonal with entry. I-th diagonal returned flows data back&forth. Can be used as lvalue subs if your perl supports it. Supports threading.

PDL = diag(PDL, SCALAR(i), SCALAR(vector)))
i : i-th diagonal, default = 0
vector : create diagonal matrices by threading over row vectors, default = 0
my $a = random(5,5);
my $diag = diag($a,2);
# If your perl support lvaluable subroutines.
$a->diag(-2) .= pdl(1,2,3);
# Construct a (5,5,5) PDL (5 matrices) with
# diagonals from row vectors of $a
$a->diag(0,1)

tritosym

Return symmetric or Hermitian matrix from lower or upper triangular matrix. Supports inplace and threading. Uses tricpy or ctricpy from Lapack.

PDL = tritosym(PDL, SCALAR(uplo), SCALAR(conj))
uplo : UPPER = 0 | LOWER = 1, default = 0
conj : Hermitian = 1 | Symmetric = 0, default = 1;
# Assume $a is symmetric triangular
my $a = random(10,10);
my $b = tritosym($a);

positivise

Return entry pdl with changed sign by row so that average of positive sign > 0. In other words thread among dimension 1 and row = -row if Sum(sign(row)) < 0. Works inplace.

my $a = random(10,10);
$a -= 0.5;
$a->xchg(0,1)->inplace->positivise;

mcrossprod

Compute the cross-product of two matrix: A x B. If only one matrix is given, take B to be the same as A. Supports threading. Uses crossprod or ccrossprod.

PDL = mcrossprod(PDL(A), (PDL(B))
my $a = random(10,10);
my $crossproduct = mcrossprod($a);

mrank

Compute the rank of a matrix, using a singular value decomposition. from Lapack.

SCALAR = mrank(PDL, SCALAR(TOL))
TOL: tolerance value, default : mnorm(dims(PDL),inf) * mnorm(PDL) * EPS
my $a = random(10,10);
my $b = mrank($a, 1e-5);

mnorm

Compute norm of real or complex matrix Supports threading.
PDL(norm) = mnorm(PDL, SCALAR(ord));

ord :
0|inf : Infinity norm
1|one : One norm
2|two : norm 2 (default)
3|fro : frobenius norm
my $a = random(10,10);
my $norm = mnrom($a);

mdet

Compute determinant of a general square matrix using LU factorization. Supports threading. Uses getrf or cgetrf from Lapack.

PDL(determinant) = mdet(PDL);
my $a = random(10,10);
my $det = mdet($a);

mposdet

Compute determinant of a symmetric or Hermitian positive definite square matrix using Cholesky factorization. Supports threading. Uses potrf or cpotrf from Lapack.

(PDL, PDL) = mposdet(PDL, SCALAR)
SCALAR : UPPER = 0 | LOWER = 1, default = 0
my $a = random(10,10);
my $det = mposdet($a);

mcond

Compute the condition number (two-norm) of a general matrix.
The condition number (two-norm) is defined:

norm (a) * norm (inv (a)).

Uses a singular value decomposition. Supports threading.

PDL = mcond(PDL)
my $a = random(10,10);
my $cond = mcond($a);

mrcond

Estimate the reciprocal condition number of a general square matrix using LU factorization in either the 1-norm or the infinity-norm.
The reciprocal condition number is defined:

1/(norm (a) * norm (inv (a)))

Supports threading.

PDL = mrcond(PDL, SCALAR(ord))
ord :
0 : Infinity norm (default)
1 : One norm
my $a = random(10,10);
my $rcond = mrcond($a,1);

morth

Return an orthonormal basis of the range space of matrix A.

PDL = morth(PDL(A), SCALAR(tol))
tol : tolerance for determining rank, default: 1e-8 for double else 1e-5
my $a = random(10,10);
my $ortho = morth($a, 1e-8);

mnull

Return an orthonormal basis of the null space of matrix A.

PDL = mnull(PDL(A), SCALAR(tol))
tol : tolerance for determining rank, default: 1e-8 for double else 1e-5
my $a = random(10,10);
my $null = mnull($a, 1e-8);

minv

Compute inverse of a general square matrix using LU factorization. Supports inplace and threading. Uses getrf and getri or cgetrf and cgetri from Lapack and return inverse, info in array context.

PDL(inv) = minv(PDL)
my $a = random(10,10);
my $inv = minv($a);

mtriinv

Compute inverse of a triangular matrix. Supports inplace and threading. Uses trtri or ctrtri from Lapack. Returns inverse, info in array context.

(PDL, PDL(info))) = mtriinv(PDL, SCALAR(uplo), SCALAR|PDL(diag))
uplo : UPPER = 0 | LOWER = 1, default = 0
diag : UNITARY DIAGONAL = 1, default = 0
# Assume $a is upper triangular
my $a = random(10,10);
my $inv = mtriinv($a);

Data::Diver is a simple, ad-hoc access to elements of deeply nested structures.

SUMMARY

Data::Diver provides the Dive() and DiveVal() functions for ad-hoc access to elements of deeply nested data structures, and the DiveRef(), DiveError(), DiveClear(), and DiveDie() support functions.

SYNOPSIS

use Data::Diver qw( Dive DiveRef DiveError );

my $root= {
top => [
{ first => 1 },
{ second => {
key => [
0, 1, 2, {
three => {
exists => yes,
},
},
],
},
},
],
};

# Sets $value to yes
# ( $root->{top}[1]{second}{key}[3]{three}{exists} ):
my $value= Dive( $root, qw( top 1 second key 3 three exists ) );

# Sets $value to undef() because "missing" doesnt exist:
$value= Dive( $root, qw( top 1 second key 3 three missing ) );

# Sets $value to undef() because
# $root->{top}[1]{second}{key}[4] is off the end of the array:
$value= Dive( $root, qw( top 1 second key 4 ... ) );

# Sets $value to undef() because
# $root->{top}[1]{second}{key}[-5] would be a fatal error:
$value= Dive( $root, qw( top 1 second key -5 ... ) );

# Sets $ref to $root->{top}[9]{new}{sub} (which grows
# @{ $root->{top} } and autovifies two anonymous hashes):
my $ref= DiveRef( $root, qw( top 9 new sub ) );

# die()s because "other" isnt a valid number:
$ref= DiveRef( $root, qw( top other ... ) );

# Does: $root->{num}{1}{2}= 3;
# (Autovivifies hashes despite the numeric keys.)
DiveVal( $root, ( qw( num 1 2 ) ) ) = 3;
# Same thing:
${ DiveRef( $root, num, 1, 2 ) } = 3;

# Retrieves above value, $value= 3:
$value= DiveVal( $root, num, 1, 2 );
# Same thing:
$value= ${ DiveRef( $root, ( qw( num 1 2 ) ) ) };

# Tries to do $root->{top}{1} and dies
# because $root->{top} is an array reference:
DiveRef( $root, top, 1 );

# To only autovivify at the last step:
$ref= DiveRef(
Dive( $root, qw( top 1 second key 3 three ) ),
missing );
if( $ref ) {
$$ref= me too
} else {
my( $nestedRef, $svKey, $errDesc )= DiveError();
die "Couldnt dereference $nestedRef via $$svKey: $errDescn";
}

ipfmeta is used to simplify the maintenance of an IPfilter ruleset. It does this through the use of objects. A matching object gets replaced by its values at runtime. ipfmeta is specifically geared towards IPfilter. It is line oriented: if an object has multiple values, the line with the object is duplicated and substituted for each value. It is also recursive: an object may have another object as a value.

ipfmeta is a program written in Perl.

Config::Model::ValueComputer is a Perl module that provides configuration value computation.

SYNOPSIS

my $model = Config::Model->new() ;

$model ->create_config_class
(
name => "Master",
element
=> [
[qw/av bv/] => {type => leaf,
value_type => integer,
},
compute_int
=> { type => leaf,
value_type => integer,
compute => [ $a + $b, a => - av, b => - bv ],
min => -4,
max => 4,
},
[qw/sav sbv/] => {type => leaf,
value_type => string,
},
compute_string =>
=> { type => leaf,
value_type => string,
compute => [ meet $a and $b, a => - sav, b => - sbv ],
},
]
) ;

Statistics::Descriptive is a Perl module of basic descriptive statistical functions.

SYNOPSIS

use Statistics::Descriptive;
$stat = Statistics::Descriptive::Full->new();
$stat->add_data(1,2,3,4); $mean = $stat->mean();
$var = $stat->variance();
$tm = $stat->trimmed_mean(.25);
$Statistics::Descriptive::Tolerance = 1e-10;

This module provides basic functions used in descriptive statistics. It has an object oriented design and supports two different types of data storage and calculation objects: sparse and full. With the sparse method, none of the data is stored and only a few statistical measures are available. Using the full method, the entire data set is retained and additional functions are available.

Whenever a division by zero may occur, the denominator is checked to be greater than the value $Statistics::Descriptive::Tolerance, which defaults to 0.0. You may want to change this value to some small positive value such as 1e-24 in order to obtain error messages in case of very small denominators.
Many of the methods (both Sparse and Full) cache values so that subsequent calls with the same arguments are faster.

METHODS

Sparse Methods

$stat = Statistics::Descriptive::Sparse->new();

Create a new sparse statistics object.

$stat->add_data(1,2,3);

Adds data to the statistics variable. The cached statistical values are updated automatically.

$stat->count();

Returns the number of data items.

$stat->mean();

Returns the mean of the data.

$stat->sum();

Returns the sum of the data.

$stat->variance();

Returns the variance of the data. Division by n-1 is used.

$stat->standard_deviation();

Returns the standard deviation of the data. Division by n-1 is used.

$stat->min();

Returns the minimum value of the data set.

$stat->mindex();

Returns the index of the minimum value of the data set.

$stat->max();

Returns the maximum value of the data set.

$stat->maxdex();

Returns the index of the maximum value of the data set.

$stat->sample_range();

Returns the sample range (max - min) of the data set.