Free extract data files software for linux

Data::ICal is a Perl module that generates iCalendar (RFC 2445) calendar files.

SYNOPSIS

use Data::ICal;

my $calendar = Data::ICal->new();

my $vtodo = Data::ICal::Entry::Todo->new();
$vtodo->add_properties(
# ... see Data::ICal::Entry::Todo documentation
);

# ... or

$calendar = Data::ICal->new(filename => foo.ics); # parse existing file
$calendar = Data::ICal->new(data => BEGIN:VCALENDAR...); # parse existing file


$calendar->add_entry($vtodo);

print $calendar->as_string;

# Or, if youre printing to something you want google to read:
print $calendar->as_string(fold => 0);

A Data::ICal object represents a VCALENDAR object as defined in the iCalendar protocol (RFC 2445, MIME type "text/calendar"), as implemented in many popular calendaring programs such as Apples iCal.

Each Data::ICal object is a collection of "entries", which are objects of a subclass of Data::ICal::Entry. The types of entries defined by iCalendar (which refers to them as "components") include events, to-do items, journal entries, free/busy time indicators, and time zone descriptors; in addition, events and to-do items can contain alarm entries. (Currently, Data::ICal only implements to-do items and events.)
Data::ICal is a subclass of Data::ICal::Entry; see its manpage for more methods applicable to Data::ICal.

Data::CTable is a Perl module that helps you read, write, manipulate tabular data.

SYNOPSIS

## Read some data files in various tabular formats
use Data::CTable;
my $People = Data::CTable->new("people.merge.mac.txt");
my $Stats = Data::CTable->new("stats.tabs.unix.txt");

## Clean stray whitespace in fields
$People->clean_ws();
$Stats ->clean_ws();

## Retrieve columns
my $First = $People->col(FirstName);
my $Last = $People->col(LastName );

## Calculate a new column based on two others
my $Full = [map {"$First->[$_] $Last->[$_]"} @{$People->all()}];

## Add new column to the table
$People->col(FullName => $Full);

## Another way to calculate a new column
$People->col(Key);
$People->calc(sub {no strict vars; $Key = "$Last,$First";});

## "Left join" records matching Stats:PersonID to People:Key
$Stats->join($People, PersonID => Key);

## Find certain records
$Stats->select_all();
$Stats->select(Department => sub {/Sale/i }); ## Sales depts
$Stats->omit (Department => sub {/Resale/i}); ## not Resales
$Stats->select(UsageIndex => sub {$_ > 20.0}); ## high usage

## Sort the found records
$Stats->sortspec(DeptNum , {SortType => Integer});
$Stats->sortspec(UsageIndex, {SortType => Number });
$Stats->sort([qw(DeptNum UsageIndex Last First)]);

## Make copy of table with only found/sorted data, in order
my $Report = $Stats->snapshot();

## Write an output file
$Report->write(_FileName => "Rept.txt", _LineEnding => "mac");

## Print a final progress message.
$Stats->progress("Done!");

## Dozens more methods and parameters available...

OVERVIEW

Data::CTable is a comprehensive utility for reading, writing, manipulating, cleaning and otherwise transforming tabular data. The distribution includes several illustrative subclasses and utility scripts.

A Columnar Table represents a table as a hash of data columns, making it easy to do data cleanup, formatting, searching, calculations, joins, or other complex operations.

The objects hash keys are the field names and the hash values hold the data columns (as array references).

Tables also store a "selection" -- a list of selected / sorted record numbers, and a "field list" -- an ordered list of all or some fields to be operated on. Select() and sort() methods manipulate the selection list. Later, you can optionally rewrite the table in memory or on disk to reflect changes in the selection list or field list.
Data::CTable reads and writes any tabular text file format including Merge, CSV, Tab-delimited, and variants. It transparently detects, reads, and preserves Unix, Mac, and/or DOS line endings and tab or comma field delimiters -- regardless of the runtime platform.

In addition to reading data files, CTable is a good way to gather, store, and operate on tabular data in memory, and to export data to delimited text files to be read by other programs or interactive productivity applications.

To achieve extremely fast data loading, CTable caches data file contents using the Storable module. This can be helpful in CGI environments or when operating on very large data files. CTable can read an entire cached table of about 120 megabytes into memory in about 10 seconds on an average mid-range computer.

For simple data-driven applications needing to store and quickly retrieve simple tabular data sets, CTable provides a credible alternative to DBM files or SQL.
For data hygiene applications, CTable forms the foundation for writing utility scripts or compilers to transfer data from external sources, such as FileMaker, Excel, Access, personal organizers, etc. into compiled or validated formats -- or even as a gateway to loading data into SQL databases or other destinations. You can easily write short, repeatable scripts in Perl to do reporting, error checking, analysis, or validation that would be hard to duplicate in less-flexible application environments.

The data representation is simple and open so you can directly access the data in the object if you feel like it -- or you can use accessors to request "clean" structures containing only the data or copies of it. Or you can build your own columns in memory and then when youre ready, turn them into a table object using the very flexible new() method.

The highly factored interface and implementation allow fine-grained subclassing so you can easily create useful lightweight subclasses. Several subclasses are included with the distribution.

Most defaults and parameters can be customized by subclassing, overridden at the instance level (avoiding the need to subclass too often), and further overridden via optional named-parameter arguments to most major method calls.

The Audio File Library provides a uniform and elegant API for accessing a variety of audio file formats, such as AIFF/AIFF-C, WAVE, NeXT/Sun .snd/.au, Berkeley/IRCAM/CARL Sound File, Audio Visual Research, Amiga IFF/8SVX, and NIST SPHERE. Supported compression formats are currently G.711 mu-law and A-law and IMA and MS ADPCM.

Key goals of the Audio File Library are file format transparency and data format transparency. The same calls for opening a file, accessing and manipulating audio metadata (e.g. sample rate, sample format, textual information, MIDI parameters), and reading/writing sample data will work with any supported audio file format. Likewise, the format of the audio data presented to the application need not be tied to the format of the data contained in the file.

The Audio File Library distributed under the GNU Library General Public License.

Data::Locations - magic insertion points in your data
Did you already encounter the problem that you had to produce some data in a particular order, but that some piece of the data was still unavailable at the point in the sequence where it belonged and where it should have been produced?
Did you also have to resort to cumbersome and tedious measures such as storing the first and the last part of your data separately, then producing the missing middle part, and finally putting it all together?
In this simple case, involving only one deferred insertion, you might still put up with this solution.
But if there is more than one deferred insertion, requiring the handling of many fragments of data, you will probably get annoyed and frustrated.
You might even have to struggle with limitations of the file system of your operating system, or handling so many files might considerably slow down your application due to excessive file input/output.
And if you dont know exactly beforehand how many deferred insertions there will be (if this depends dynamically on the data being processed), and/or if the pieces of data you need to insert need additional (nested) insertions themselves, things will get really tricky, messy and troublesome.
In such a case you might wonder if there wasnt an elegant solution to this problem.
This is where the "Data::Locations" module comes in: It handles such insertion points automatically for you, no matter how many and how deeply nested, purely in memory, requiring no (inherently slower) file input/output operations.
(The underlying operating system will automatically take care if the amount of data becomes too large to be handled fully in memory, though, by swapping out unneeded parts.)
Moreover, it also allows you to insert the same fragment of data into SEVERAL different places.
This increases space efficiency because the same data is stored in memory only once, but used multiple times.
Potential infinite recursion loops are detected automatically and refused.
In order to better understand the underlying concept, think of "Data::Locations" as virtual files with almost random access: You can write data to them, you can say "reserve some space here which I will fill in later", and continue writing data.
And you can of course also read from these virtual files, at any time, in order to see the data that a given virtual file currently contains.
When you are finished filling in all the different parts of your virtual file, you can write out its contents in flattened form to a physical, real file this time, or process it otherwise (purely in memory, if you wish).
You can also think of "Data::Locations" as bubbles and bubbles inside of other bubbles. You can inflate these bubbles in any arbitrary order you like through a straw (i.e., the bubbles object reference).
Note that this module handles your data completely transparently, which means that you can use it equally well for text AND binary data.
You might also be interested in knowing that this module and its concept have already been heavily used in the automatic code generation of large software projects.
Enhancements:
- Fixed test file "02___refcount.t" to auto-adjust the offset dealing with self-ties not incrementing
- the refcount in some Perl versions.
- Added a warning to "Makefile.PL" that Perl versions including and between v5.6.1 and v5.7.0 are not fully supported (test file "11_______dump.t" dumps core).

Bio::NEXUS::DataBlock is a Perl module that represents the deprecated DATA Block in NEXUS file.

SYNOPSIS

$block_object = new Bio::NEXUS::DataBlock($type, $block, $verbose, $taxlabels_ref);

The DataBlock class represents the deprecated Data Block in a NEXUS file. Data Blocks are still used by some prominent programs, unfortunately, although they are essentially the same as a Characters Block and a Taxa Block combined. Data Blocks may be used as input, but are not output by the NEXPL library. For more information on Data Blocks, see the Characters Block documentation.

METHODS

new

Title : new
Usage : block_object = new Bio::NEXUS::CharactersBlock($block_type, $block, $verbose, $taxa);
Function: Creates a new Bio::NEXUS::CharactersBlock object
Returns : Bio::NEXUS::CharactersBlock object
Args : verbose flag (0 or 1), type (string) and the block to parse (string)