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doc/astronomical-notes.pod  view on Meta::CPAN

=item E

The Earth runs along an orbit around the Sun, with noticeable perturbations
caused by the Moon, Jupiter, Saturn, etc.

Which is a formulation equivalent to assertion D.

=item F

The movement of the Earth with the Solar System is a I<n>-body problem,
with I<n> ≥ 3. Therefore, there is no analytical solution.

doc/astronomical-notes.pod  view on Meta::CPAN

but all other planets have a perihelion precession, including
the Earth.

=head3 Other Drifts And Fluctuations

The formulas computing the positions of celestial bodies
use some constants. But these values are constant only
on a short timespan (astronomically speaking; or, with the
metaphor above, on a "meteorological" timespan). But they are
variable on a longer timespan (or a "climatic" timespan) For example,
everybody knows that the day lasts 24 hours (the mean

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lib/Astro/Coord/ECI.pm  view on Meta::CPAN

Unfortunately, as of early 2021 the National Council of Teachers of
Mathematics restricted the Dr. Math content to their members, but an
annotated and expanded version of the article on haversines is available
at
L<https://www.themathdoctors.org/distances-on-earth-2-the-haversine-formula/>.
If you want the original article, you can feed the URL
C<http://mathforum.org/library/drmath/view/51879.html> to the Wayback
Machine.

Prior to version 0.011_03 the law of cosines was used, but this produced

lib/Astro/Coord/ECI.pm  view on Meta::CPAN

    if ( $elevation > $horizon - TWO_DEGREES ) {

#	Thorsteinn Saemundsson's algorithm for refraction, as reported
#	in Meeus, page 106, equation 16.4, and adjusted per the
#	suggestion in Meeus' following paragraph. Thorsteinn's
#	formula is in terms of angles in degrees and produces
#	a correction in minutes of arc. Meeus reports the original
#	publication as Sky and Telescope, volume 72 page 70, July 1986.

#	In deference to Thorsteinn I will point out:
#	* The Icelanders do not use family names. The "Saemundsson"

lib/Astro/Coord/ECI.pm  view on Meta::CPAN

    my $E = $y * sin( 2 * $L0 ) - 2 * $e * sin( $M ) +
	4 * $e * $y * sin( $M ) * cos( 2 * $L0 ) -
	$y * $y * .5 * sin( 4 * $L0 ) -
	1.25 * $e * $e * sin( 2 * $M );				# Meeus (28.3)

    return $E * SECSPERDAY / TWOPI;	# The formula gives radians.
}

=item $coord->equatorial ($rightasc, $declin, $range, $time);

This method sets the L</Equatorial> coordinates represented by the

lib/Astro/Coord/ECI.pm  view on Meta::CPAN

Jean Meeus does in "Astronomical Algorithms".

For practical purposes, TT = TAI + 32.184 seconds. If I ever get the
gumption to do a re-implementation (or alternate implementation) of time
in terms of the DateTime object, this will be the definition of
dynamical time. Until then, though, formula 10.2 on page 78 of Jean
Meeus' "Astronomical Algorithms" second edition, Chapter 10 (Dynamical
Time and Universal Time) is used.

Compare and contrast this to L</Universal time>. This explanation leans
heavily on C<http://star-www.rl.ac.uk/star/docs/sun67.htx/node226.html>,

lib/Astro/Coord/ECI.pm  view on Meta::CPAN

technical definition differs in detail from GMT (Greenwich Mean Time).
The former is a clock-based time, whose second is the SI second (defined
in terms of atomic clocks), but which is kept within 0.9 seconds of UT1
by the introduction of leap seconds. These are introduced (typically at
midyear or year end) by prior agreement among the various timekeeping
bodies based on observation; there is no formula for computing when a
leap second will be needed, because of irregularities in the Earth's
rotation.

Jean Meeus' "Astronomical Algorithms", second edition, deals with the
relationship between Universal time and L</Dynamical time> in Chapter 10

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t/07-shm_lock.t  view on Meta::CPAN

    cmp_ok $child_lock, '>',  0;
    cmp_ok $child_lock, '<=', 32767;
}

# A second process started fresh (not via fork) generates its own lock
# value based on its own $$. We can verify the formula by simulating
# what such a process would compute.

{
    my $other_pid       = ($$ + 1) % (1 << 16);  # arbitrary different PID
    my $other_lock      = 1 + ($other_pid % 32767);

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lib/Audio/Analyzer/ToneDetect.pm  view on Meta::CPAN

=item min_tone_length 0.5

Minimum durration of a tone, in seconds, before we consider it detected.  Due to
sample rate, chunk size, and integer math, with defaults this ends up being
0.448 seconds.  The formula for actual seconds is int( min_length * sample_rate
/ chunk_size ) * chunk_size / sample_rate.  Default to 0.5

=item valid_tones undef, 'builtin', or ARRAYREF

A list of valid (expected) tones.  If supplied, the closest expected tone for

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lib/Audio/NoiseGen.pm  view on Meta::CPAN

  split
  sequence
  note
  rest
  segment
  formula
  hardlimit
  amp
  oneshot
  lowpass
  highpass

lib/Audio/NoiseGen.pm  view on Meta::CPAN

    $last_sample = $cur_gen->();
    return $last_sample || 0;
  }
}

=head2 formula( formula => sub { $_*(42&$_>>10) } )

Plays a formula. Takes 'formula', 'bits', and 'sample_rate'. 'bits' defaults to 8, 'sample_rate' defaults to 8000.

Formula uses C<< $_ >> instead of 't', but is otherwise similar to what is described at L<http://countercomplex.blogspot.com/2011/10/algorithmic-symphonies-from-one-line-of.html>.

=cut

sub formula {
  my %params = generalize(
    bits        => 8,
    sample_rate => 8000,
    @_
  );
  my $formula = $params{formula};
  my $formula_increment = $params{sample_rate}->() / $sample_rate;
  my $max = 2 ** $params{bits}->();
  my $t = 0;
  return sub {
    $t += $formula_increment;
    local $_ = int $t;
    return (((
      $formula->(int $t)
    ) % $max - ($max/2))/($max/2))
  }
}

 # Return RC low-pass filter output samples, given input samples,

lib/Audio/NoiseGen.pm  view on Meta::CPAN

  my $gen = shift;
  if(!ref $gen) {
    print STDERR "segement '$gen'\n";
    $gen = segment($gen);
  # } elsif(ref $gen eq 'CODE') {
    # $gen = formula($gen);
  }
  my $self = {
    gen => $gen,
  };
  bless $self, $class;

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mpg123/mpg123.c  view on Meta::CPAN

	    if(param.verbose)
		print_stat(rd,fr,frameNum,xfermem_get_usedspace(buffermem),&ai); 

	    if (!param.quiet) {
		/* 
		 * This formula seems to work at least for
		 * MPEG 1.0/2.0 layer 3 streams.
		 */
		int secs = get_songlen(rd,fr,frameNum);
		fprintf(stderr,"\n[%d:%02d] Decoding of %s finished.\n", secs / 60,
			secs % 60, filename);

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lib/Authen/PluggableCaptcha/Tutorial.pm  view on Meta::CPAN

For example:

  key= md5( $site_secret , $time , $page_name , $session_id ) + ':' + $session
  key= 'xxxxxxxxxxxxxxxxxx:10000001'

If we know the site_secret under that formula, we always have every components of the item at our disposal -- and can validate the key for integrity

The default KeyManager class uses a site_secret to create the key.

=head3 Also, from the example in the Tutorial, it isn't quite clear if you first have to generate a new CAPTCHA, just to get its key, and then use that key to construct an existing CAPTCHA to create the JPEG. This isn't the case, is it? I could call ...

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ramblings/remark.js  view on Meta::CPAN

require=function(e,t,n){function i(n,s){if(!t[n]){if(!e[n]){var o=typeof require=="function"&&require;if(!s&&o)return o(n,!0);if(r)return r(n,!0);throw new Error("Cannot find module '"+n+"'")}var u=t[n]={exports:{}};e[n][0].call(u.exports,function(t)...
this.QUOTE_STRING_MODE={className:"string",begin:'"',end:'"',illegal:"\\n",contains:[this.BACKSLASH_ESCAPE]};this.PHRASAL_WORDS_MODE={begin:/\b(a|an|the|are|I|I'm|isn't|don't|doesn't|won't|but|just|should|pretty|simply|enough|gonna|going|wtf|so|such)...
SUBST.contains=EXPRESSIONS;return{aliases:["coffee","cson","iced"],keywords:KEYWORDS,contains:EXPRESSIONS.concat([{className:"comment",begin:"###",end:"###"},hljs.HASH_COMMENT_MODE,{className:"function",begin:"("+JS_IDENT_RE+"\\s*=\\s*)?(\\(.*\\))?\\...
}()},{}],8:[function(require,module,exports){exports.addClass=function(element,className){element.className=exports.getClasses(element).concat([className]).join(" ")};exports.removeClass=function(element,className){element.className=exports.getClasse...
events.on("slideChanged",updateHash);navigateByHash()}function navigateByHash(){var slideNoOrName=(dom.getLocationHash()||"").substr(1);events.emit("gotoSlide",slideNoOrName)}function updateHash(slideNoOrName){dom.setLocationHash("#"+slideNoOrName)}}...

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zlib/adler32.c  view on Meta::CPAN

{
    unsigned long sum1;
    unsigned long sum2;
    unsigned rem;

    /* the derivation of this formula is left as an exercise for the reader */
    rem = (unsigned)(len2 % BASE);
    sum1 = adler1 & 0xffff;
    sum2 = rem * sum1;
    MOD(sum2);
    sum1 += (adler2 & 0xffff) + BASE - 1;

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lib/BalanceOfPower/Role/Shopper.pm  view on Meta::CPAN

    my $self = shift;
    my $y = shift;
    my $type = shift;
    my $nation = shift;

    #Price formula is MaxPrice - (( MaxPrice - MinPrice) / MaxValue) * Value
    #MaxPrice and MinPrice are constant

    my $min_price = PRICE_RANGES->{$type}->[0];
    my $max_price = PRICE_RANGES->{$type}->[1];

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lib/Baseball/Sabermetrics.pm  view on Meta::CPAN

  $league->define(
      rc => 'ab * obp',
      babip => '(h_allowed - hr_allowed) / (p_pa - h_allowed - p_so - p_bb - hr_allowed',
      # what started with '$' will be reserved.
      # Players have team and league predefined, and team has league.
      formula1 => 'hr / $_->team->hr';
      formula2 => 'hr / $_->league->hr';
      complex => sub {
	    print "You can write a sub directly\n";
	    $_->slg - $_->ba;
      },
      ...
  );

  # Some formulas can be applied to players, teams, and league, depend on what
  # columns are used in the formula.  For example, ab and obp are defined for
  # players, teams, and league, so that rc is available for all of them.

  # top 5 obp of teams
  $_->print qw/ team name ba obp slg isop / for $league->top('teams', 5, 'obp');

lib/Baseball/Sabermetrics.pm  view on Meta::CPAN

  $league->{Yankees}->report_pitchers qw/ name ip p_so p_bb whip go_ab /;
  $league->{Yankees}->report_batters  qw/ name ba obp slg isop /;

  $league->report_teams qw/ name win lose era obp /;

  # show all available formula
  print join ' ', $league->formula_list;

=head1 Data Structure

Baseball::Sabermetrics is aimed for providing a base class of your interested teams (a league, for example).  You'll need to provide a data retriever to pull data out.  The following example shows how you have to fill data into this structure.

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beamer-reveal-example_files/libs/revealjs/plugin/highlight/highlight.esm.js  view on Meta::CPAN

function e(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}function t(e){return e instanceof Map?e.clear=e.delete=e.set=function(){throw new Error("map is read-only")}:e instanceof Set&&(e.add=e.clear=e.delete...
/*!
 * reveal.js plugin that adds syntax highlight support.
 */
const Rs={id:"highlight",HIGHLIGHT_STEP_DELIMITER:"|",HIGHLIGHT_LINE_DELIMITER:",",HIGHLIGHT_LINE_RANGE_DELIMITER:"-",hljs:fs,init:function(e){let t=e.getConfig().highlight||{};t.highlightOnLoad="boolean"!=typeof t.highlightOnLoad||t.highlightOnLoad,...

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share/SpamAssassin/easy_ham/00271.b67b5b37ce874d5ccea3391922f14506  view on Meta::CPAN

to another. This is why, under international 
contracts, it is necessary to specify to which 
laws one is referring (French law, American &c.).

The authors only found three public licences 
which were correctly formulated on this point: 
QPL, IBM Public Licence and the Mozilla Public
Licence).

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lib/BigIP/iControl.pm  view on Meta::CPAN

=head3 get_pool_statistics_stringified ($pool)

	my %stats = $ic->get_pool_statistics_stringified($pool);
	print "Pool $pool bytes in: $stats{stat}{STATISTIC_SERVER_SIDE_BYTES_OUT}";

Returns a hash containing all pool statistics for the specified pool in a delicious, easily digestable and improved formula.

=cut

sub get_pool_statistics_stringified {
	my ($self, $pool)= @_;

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lib/Bio/DB/BigBed.pm  view on Meta::CPAN

In addition, the bin objects add the following convenience methods:

 $bin->count()    Same as $bin->score->{validCount}
 $bin->minVal()   Same as $bin->score->{minVal}
 $bin->maxVal()   Same as $bin->score->{maxVal}
 $bin->mean()     The mean of values in the bin (from the formula above)
 $bin->variance() The variance of values in the bin (ditto)
 $bin->stdev()    The standard deviation of values in the bin (ditto)

From these values one can determine the mean, variance and standard
deviation across one or more genomic intervals. The formulas are as
follows:

 sub mean {
    my ($sumData,$validCount) = @_;
    return $sumData/$validCount;

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lib/Bio/CUA/CUB/Calculator.pm  view on Meta::CPAN

{
	my ($self, $seq) = @_;
	$self->_xai($seq, 'CAI');
}

# the real calculator of tAI or CAI as both have the same formula
sub _xai
{
	my ($self, $seq, $type) = @_;

	my $name;

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lib/Bio/Cellucidate.pm  view on Meta::CPAN

A Model has one or many simulation runs and a simulation run belongs to
a model.

=item L<Bio::Cellucidate::OdeResult>

Represents the set of ODE formulas generated when running a simulation in ODE-mode.
The results can be used directly in MATLAB.

=item L<Bio::Cellucidate::Plot>

A plot contains a single time series and a number of data series representing the

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lib/Bio/GeneDesign.pm  view on Meta::CPAN

arguments; they are 50mm (.05) and 100 pm (.0000001) respectively.

You can pass either a string variable, a Bio::Seq object, or a Bio::SeqFeatureI
object to be analyzed with the -sequence flag.

There are four different formulae to choose from. If you wish to use the nearest
neighbor method, use the -nearest_neighbor flag. Otherwise the appropriate
formula will be determined by the length of your -sequence argument.

For sequences under 14 base pairs:
  Tm = (4 * #GC) + (2 * #AT).

For sequences between 14 and 50 base pairs:

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lib/Bio/Graphics/Panel.pm  view on Meta::CPAN

responsible for allocating sufficient -pad_left or -pad_right room for
the labels to appear.  The necessary width is the number of characters
in the longest key times the font width (gdMediumBoldFont by default)
plus 3 pixels of internal padding.  The simplest way to calculate this
is to iterate over the possible track labels, find the largest one,
and then to compute its width using the formula:

  $width = gdMediumBoldFont->width * length($longest_key) +3;

In order to obtain scalable vector graphics (SVG) output, you should
pass new() the -image_class=E<gt>'GD::SVG' parameter. This will cause

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scripts/Data/psi-mi.obo  view on Meta::CPAN

subset: Drugable
is_a: MI:0300 ! alias type

[Term]
id: MI:2008
name: chemical formula
def: "Chemical formula describing atomic or elemental composition" [PMID:14755292]
subset: Drugable
is_a: MI:2086 ! physicochemical attribute name

[Term]
id: MI:2009

scripts/Data/psi-mi.obo  view on Meta::CPAN

is_a: MI:0353 ! cross-reference type

[Term]
id: MI:2025
name: molecular weight
def: "Molecular weight in g/mol, determined from molecular formula or sequence." [PMID:14755292]
subset: Drugable
is_a: MI:0640 ! parameter type

[Term]
id: MI:2026

scripts/Data/psi-mi.obo  view on Meta::CPAN

is_a: MI:2054 ! bioactive entity reference

[Term]
id: MI:2155
name: average molecular weight
def: "Molecular weight in g/mol, determined from molecular formula or sequence." [PMID:14755292]
subset: Drugable
synonym: "avrg mol weight" EXACT PSI-MI-short []
is_a: MI:2025 ! molecular weight

[Term]
id: MI:2156
name: monoisotopic molecular weight
def: "Molecular weight in g/mol, determined from molecular formula or sequence." [PMID:14755292]
subset: Drugable
synonym: "monoisotopic mol wgt" EXACT PSI-MI-short []
is_a: MI:2025 ! molecular weight

[Term]

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lib/Bio/KBase/CDMI/CDMI_APIImpl.pm  view on Meta::CPAN

=item Description

Since we accumulate data relating to the co-occurrence (i.e., chromosomal
clustering) of genes in prokaryotic genomes,  we can note which pairs of genes tend to co-occur.
From this data, one can compute the protein families that tend to co-occur (i.e., tend to
cluster on the chromosome).  This allows one to formulate conjectures for unclustered pairs, based
on clustered pairs from the same protein_families.

=back

=cut

lib/Bio/KBase/CDMI/CDMI_APIImpl.pm  view on Meta::CPAN

=item Description

A substem is composed of two components: a set of roles that are gathered to be annotated
simultaneously and a spreadsheet depicting the proteins within each genome that implement
the roles.  The set of roles may correspond to a pathway, a complex, an inventory (say, "transporters")
or whatever other principle an annotator used to formulate the subsystem.

The subsystem spreadsheet is a list of "rows", each representing the subsytem in a specific genome.
Each row includes a variant code (indicating what version of the molecular machine exists in the
genome) and cells.  Each cell is a 2-tuple:

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t/cd_hit.t  view on Meta::CPAN

https://github.com/weizhongli/cdhit
If you --force installation, I will eventually try to install CD-HIT with brew:
https://brew.sh/
EOT
}
# TODO: fix this as CD-HIT formula currently fails on OS X Mojave
#       This can be done with a --build-from-source option of brew

# expected members for
my $exp_clstr_file = file('test', 'cdHit.out.groups');
open my $in, '<', $exp_clstr_file;

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lib/Bio/Phylo/EvolutionaryModels.pm  view on Meta::CPAN

    #Loop for sampling each tree
    while ( scalar @sample < $options{sample_size} ) {
        my @nodes;

       #Compute the random tree age from the inverse CDF (different formulas for
       #birth rate == death rate and otherwise)
        my $tree_age;

        #The uniform random variable
        my $r = rand;

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CHANGELOG.md  view on Meta::CPAN

## [v3.6.9](https://github.com/sanger-pathogens/Roary/tree/v3.6.9) (2016-09-22)
[Full Changelog](https://github.com/sanger-pathogens/Roary/compare/v3.6.8...v3.6.9)

**Implemented enhancements:**

- I have published a Roary homebrew formula [\#208](https://github.com/sanger-pathogens/Roary/issues/208)
- Getting Roary into Homebrew [\#152](https://github.com/sanger-pathogens/Roary/issues/152)

**Closed issues:**

- roary\_plots.py missing  [\#277](https://github.com/sanger-pathogens/Roary/issues/277)

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lib/Bio/Tools/CodonOptTable.pm  view on Meta::CPAN

    return 1;
}

#    Function : Calculate the RSCU(Relative Synonymous Codons Uses).
#    Note     : The formula is used in the following references.
#	 http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=3547335

sub _calculate_rscu {
    my $self = shift;

lib/Bio/Tools/CodonOptTable.pm  view on Meta::CPAN

    }
    return ( \@myCodons, \%rscu_max_table );
}

#    Function : Calculate the RAC (Relative Adaptiveness of a Codon).
#    Note     : The formula is used in the following references.
#	 http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=3547335

sub _calculate_rac {
    my ( $self, $codons, $max_rscu ) = @_;
    my ( $rac, @myCodons );

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DNAGen.pm  view on Meta::CPAN

Default is 'undef', which means gc-ratio is not related to sequence selection.

=head2 set_mt

Setting for the melting temperature. For now, Wallace formula is adopted for calculation.

You can give it a specific value, like

    $gen->set_mt(30);

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lib/Bio/Tools/ProteinogenicAA.pm  view on Meta::CPAN

		$info[7] eq 'X' ? $aa->is_hydrophobic(1) : $aa->is_hydrophobic(0);
		$info[8] eq 'X' ? $aa->is_polar(1) : $aa->is_polar(0);
		$aa->pH($info[9]);
		$aa->van_der_waals_volume($info[10]);
		$aa->codons($info[11]);
		$aa->formula($info[12]);
		$aa->monoisotopic_mass($info[13]);
		$aa->avg_mass($info[14]);
		
		push(@list, $aa);

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lib/Bio/ViennaNGS/Expression.pm  view on Meta::CPAN

  my ($self,$sample,$rl) = @_;
  my ($TPM,$T,$totalTPM) = (0)x3;
  my ($i,$meanTPM);

  # iterate through $self->data[$i] twice:
  # 1. for computing T (denominator in TPM formula)
  foreach $i (keys %{${$self->data}[$sample]}){
    my $count  = ${${$self->data}[$sample]}{$i}{count};
    my $length =  ${${$self->data}[$sample]}{$i}{length};
    #print "count: $count\nlength: $length\n";
    $T += $count * $rl / $length;

lib/Bio/ViennaNGS/Expression.pm  view on Meta::CPAN

  my ($self,$sample,$rl) = @_;
  my ($R,$T,$length,$count,$totalRPKM) = (0)x5;
  my ($i,$meanRPKM);

  # iterate through $self->data[$i] twice:
  # 1. compute T (denominator in TPM formula) and total number of reads
  foreach $i (keys %{${$self->data}[$sample]}){
    $count  = ${${$self->data}[$sample]}{$i}{count};
    $length =  ${${$self->data}[$sample]}{$i}{length};
    #print "count: $count\nlength: $length\n";
    $T += $count * $rl / $length;

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Bio/SeqFeature/Primer.pm  view on Meta::CPAN

         : -oligo : set the oligo concentration on which to base the
                    calculation (default=0.00000025 molar).
 Notes   : Calculation of Tm as per Allawi et. al Biochemistry 1997
           36:10581-10594. Also see documentation at
           http://www.idtdna.com/Scitools/Scitools.aspx as they use this
           formula and have a couple nice help pages. These Tm values will be
           about are about 0.5-3 degrees off from those of the idtdna web tool.
           I don't know why.

           This was suggested by Barry Moore (thanks!). See the discussion on
           the bioperl-l with the subject "Bio::SeqFeature::Primer Calculating