Free data ctable software for linux

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.

Data::Walker is a tool for navigating through Perl data structures.

SYNOPSIS

Without any explicit objects:

use Data::Walker;
Data::Walker->cli( $data_structure );

Object-style invocation:

use Data::Walker;
my $w = new Data::Walker;
$w->walk( $data_structure );
$w->ls("-al");
$w->pwd;
$w->cli;

Importing methods into the current package:

use Data::Walker qw(:direct);
walk $data_structure;
ls "-al";
pwd;
cli;

This module allows you to "walk" an arbitrary Perl data structure in the same way that you can walk a directory tree from a UNIX command line. It reuses familiar unix commands (such as "ls", "cd", "pwd") and applies these to data structures.

It has a command-line interface which behaves like a UNIX shell. You can also use object-style sytax to invoke the CLI commands from outside the CLI. Data::Walker objects are encapsulated, so that you can hop into and out of a CLI without losing state, and you can have several Data::Walker objects pointing at different structures.

The main functions can also be imported and used directly from within the Perl debuggers CLI.

Data::Page is a Perl module that helps when paging through sets of results.

SYNOPSIS

use Data::Page;

my $page = Data::Page->new();
$page->total_entries($total_entries);
$page->entries_per_page($entries_per_page);
$page->current_page($current_page);

print " First page: ", $page->first_page, "n";
print " Last page: ", $page->last_page, "n";
print "First entry on page: ", $page->first, "n";
print " Last entry on page: ", $page->last, "n";

When searching through large amounts of data, it is often the case that a result set is returned that is larger than we want to display on one page. This results in wanting to page through various pages of data. The maths behind this is unfortunately fiddly, hence this module.

The main concept is that you pass in the number of total entries, the number of entries per page, and the current page number. You can then call methods to find out how many pages of information there are, and what number the first and last entries on the current page really are.

For example, say we wished to page through the integers from 1 to 100 with 20 entries per page. The first page would consist of 1-20, the second page from 21-40, the third page from 41-60, the fourth page from 61-80 and the fifth page from 81-100. This module would help you work this out.

MIDI Data Miner uses a neural network to learn correlations between notes and control changes in a MIDI file.

After training MDM can augment a live MIDI stream, adding control changes based on notes received.

Briefly: use MDM by connecting a MIDI device, open preferences and set MIDI input and output ports. Open a MIDI file, create a new neural net and train. Now play notes on your MIDI device, MDM will automatically add control changes.

MDM creates a collection of note and controller pairs from the file and uses them to train a net. The notes are used as inputs and the controls as outputs. The window is the number of previous notes considered when determining the output for the current note. Larger windows give less predictable more interesting responses. Enter a number in the window box and press enter to set the window size.

This is an early alpha release, buggy and incomplete. Please send feedback! Note that this alpha version only reads from the first track in a MIDI file, and only uses the first controller. Look at the included MIDI file in the working/midi folder for details.

Data::Stag::HashDB is a perl used for building indexes over Stag files or objects.

SYNOPSIS

# parsing a file into a hash
my $hdb = Data::Stag::HashDB->new;
$hdb->unique_key("ss_details/social_security_no");
$hdb->record_type("person");
my $obj = {};
$hdb->index_hash($obj);
Data::Stag->parse(-file=>$fn, -handler=>$hdb);
my $person = $obj->{999-9999-9999};
print $person->xml;

# indexing an existing stag tree into a hash
my $personset = Data::Stag->parse($fn);
my $hdb = Data::Stag::HashDB->new;
$hdb->unique_key("ss_details/social_security_no");
$hdb->record_type("person");
my $obj = {};
$hdb->index_hash($obj);
$personset->sax($hdb);
my $person = $obj->{999-9999-9999};
print $person->xml;

You need to provide a record_type - this is the type of element that will be indexed

You need to provide a unique_key - this is a single value used to index the record_types

For example, if we have data in the stag structure below, and if ss_no is unique (we assume it is) then we can index all the people in the database using the code above

publicinfo:
persondata:
person:
ss_details:
social_security_no:
name:
address:

There is a subclass of this method callsed Data::Stag::StagDB, which makes the hash persistent

Sunrise Data Dictionary is a library for hashtable storage of arbitrary data objects with built-in reference counting and guaranteed order iteration for the C programming language.
Sunrise Data Dictionary library can participate in external reference counting systems or use its own built-in reference counting. It comes with a variety of hash functions and allows the use of runtime supplied hash functions via callback mechanism. The source code is well documented.
The Sunrise Data Dictionary was specifically designed for use within the Afelio and Callweaver telephony servers, the implementation focuses on performance and scalability.
Enhancements:
- This is the initial release of the full API (all header files) and a developer snapshot of the implementation.

Local Data Manager (LDM) is a collection of cooperating programs that select, capture, manage, and distribute arbitrary data products.
The system is designed for event-driven data distribution, and is currently used in the Unidata Internet Data Distribution (IDD) project. The LDM system includes network client and server programs and their shared protocols.
An important characteristic of the LDM is its support for flexible, site-specific configuration.
Enhancements:
- Fixes for timestamp bugs.