Free technical analysis software for linux

Sequence Analysis project is a collage of coding projects which I have written over the past several years for various clients in my work as a bioinformatics consultant.

These clients have graciously allowed me to release these works into the public domain as freeware for Macintosh OS X in order to promote the platform and to encourage migration from Classic.

The upper window panel can hold several sequences, which are both editable and selectable. The tabs in the lower analysis panel try to keep up with the current sequence selection to provide immediate feedback. The selection is used in some modules as only the portion being analyzed for other modules i.e. Digest is used to determine if enzymes cut in the in or outside of the selection.

Most commonly available sequence formats have been reverse engineered. You can also access a sequences from the NCBI via its GID or UID. This currently cannot be done from behind a firewall.

Most of the analyses are simple enough that they are obvious to use, Composition, pI. Others could stand some documenation i.e. Pairwise and Primer Design. The Publish tab uses a string to control the layout. Click on the Legend button for some help.

Net::Analysis are modules for analysing network traffic.

SYNOPSIS

Using an existing analyser:

$ perl -MNet::Analysis -e main help
$ perl -MNet::Analysis -e main TCP,v=1 dump.tcp - basic TCP info
$ perl -MNet::Analysis -e main HTTP,v=1 dump.tcp - HTTP stuff
$ perl -MNet::Analysis -e main Example2,regex=img dump.tcp - run an example

Writing your own analyser:

package MyExample;
use base qw(Net::Analysis::Listener::Base);
# Listen to events from other modules
sub tcp_monologue {
my ($self, $args) = @_;
my ($mono) = $args->{monologue};
my $t = $mono->t_elapsed()->as_number();
my $l = $mono->length();
# Emit your own event
$self->emit(name => example_event,
args => { kb_sec => ($t) ? $l/($t*1024) : N/A }
);
}
# Process your own event
sub example_event {
my ($self, $args) = @_;
printf "Bandwidth: %10.2f KB/secn", $args->{kb_sec};
}
1;
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ABSTRACT

Net::Analysis is a suite of modules that parse tcpdump files, reconstruct TCP sessions from the packets, and provide a very lightweight framework for writing protocol anaylsers.
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I wanted a batch version of Ethereal in Perl, so I could:

- sift through parsed protocols with structured filters
- write custom reports that mixed events from multiple protocols

So here it is. Net::Analysis is a stack of protocol handlers that emit, and listen for, events.

Directory Analysis Tool is used to analyze LDAP directories and report on their contents.

Useful if you want to find inactive accounts, people who havent changed passwords, or who has administrator privileges.

Market Analysis System (MAS) is an open-source software application that provides tools for analysis of financial markets using technical analysis.
Market Analysis System provides facilities for stock charting and futures charting, including price, volume, and a wide range of technical analysis indicators. Market Analysis System also allows automated processing of market data - applying technical analysis indicators with user-selected criteria to market data to automatically generate trading signals - and can be used as the main component of a sophisticated trading system.
Main features:
- Includes basic technical analysis indicators, such as Simple Moving Average, Exponential Moving Average, Stochastic, MACD, RSI, On Balance Volume, and Momentum.
- Includes more advanced indicators, such as Standard Deviation, Slope of EMA of Volume, Slope of MACD Signal Line, Bollinger Bands, and Parabolic SAR.
- User can create new technical analysis indicators, including complex indicators based on existing indicators.
- User can configure criteria for automated trading-signal generation.
- Creation of weekly, monthly, quarterly, and yearly data from daily data.
- Handles intraday data.
- Handles stock and futures data.
- Accepts input data from files, from a database, or from the web. (Includes a configuration for obtaining end-of-day data from yahoo.com.)
- Can be configured and run as a server that provides services for several clients at a time running on remote machines.

Picalo Data Analysis Software is an free and open-source application that helps data analysts.
Picalo Data Analysis Software is an open source application that helps data analysts, fraud investigators, and auditors search through data sets for anomalies, trends, and other information.
Picalo is a scripting-oriented, data analysis toolkit that is focused on forensic data analysis. It is based in the Python language, and it includes a number of modules to crunch and massage data. I use it in my fraud research, and I publish it as open source in hopes that others will find it useful, contribute additions, and better the suite.
Main features:
- Picalo is an open framework. Users can either use the built-in routines or write their own. Those who write their own can share their routines with others in the Picalo community. The goal is to create a large set of analysis routines that meet many different needs--on a scale that a single company could never do.
- The philosophy of Picalo is to bridge the gap between technically-oriented analysts and non-technical analysts. Data analysts who know basic scripting routines (for loops, for example), are more efficient and effective than those who do not--usually by several orders of magnitude. Picalo allows those who are technical to quickly write wizard-based analyses that others in an organization can use. See the user manual for more information about the plugin Detectlet architecture.
- Picalo includes advanced analysis routines not found in competing products. For example, it supports grouping by a number of days for analysis of labor and time card data. Picalo can also automatically group records to achieve a specified degree of smoothness in data.
- Picalos language is based in Python, a powerful and easy-to-learn language. Rather than creating its own language (like competing packages do), Picalo rises on the shoulders of an extremely well-done language. You can download any of thousands of Python libraries from the Internet to use in your analyses.
- Picalo runs on Windows, Mac OS X, Linux, and many other systems. Most competing data analysis applications run only on Windows.
The Picalo community believes that data analysis is best done through scripting. However, we realize that not all analysts will learn and enjoy scripting. Picalos Deteclet architecture allows those who know how to script support those who do not through customizable wizards.
Picalo is also based in open source principles. This doesnt mean the designers cant make money with Picalo, it just means that the software code is open for others to fix bugs, code review, and improve upon. Profits should be made in using the software (on the job or in consulting practice) rather than in selling the software.

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

RTL-check is a framework for static analysis of programs from a safety and security perspective.
RTL-check project performs analysis on RTL, which is the low-level intermediate representation generated by GCC.
Enhancements:
- The performance of the analysis was improved, and a minor bug was fixed.