Free scales software for linux

Music::Scales can supply necessary notes / offsets for musical scales.

SYNOPSIS

use Music::Scales;

my @maj = get_scale_notes(Eb); # defaults to major
print join(" ",@maj); # "Eb F G Ab Bb C D"
my @blues = get_scale_nums(bl); # bl,blu,blue,blues
print join(" ",@blues); # "0 3 5 6 7 10"
my %min = get_scale_offsets (G,mm,1); # descending melodic minor
print map {"$_=$min{$_} "} sort keys %min; # "A=0 B=-1 C=0 D=0 E=-1 F=0 G=0"

Given a keynote A-G(#/b) and a scale-name, will return the scale, either as an array of notenames or as a hash of semitone-offsets for each note.

METHODS

get_scale_nums($scale[,$descending])

returns an array of semitone offsets for the requested scale, ascending/descending the given scale for one octave. The descending flag determines the direction of the scale, and also affects those scales (such as melodic minor) where the notes vary depending upon the direction. Scaletypes and valid values for $scale are listed below.

get_scale_notes($notename[,$scale,$descending,$keypref])

returns an array of notenames, starting from the given keynote. Enharmonic equivalencies (whether to use F# or Gb, for instance) are calculated based on the keynote and the scale. Basically, it attempts to do the Right Thing if the scale is an 8-note one, (the 7th in G harmonic minor being F# rather than Gb, although G minor is a flat key), but for any other scales, (Chromatic, blues etc.) it picks equivalencies based upon the keynote. This can be overidden with $keypref, setting to be either # or b for sharps and flats respectively. Cruftiness abounds here.

get_scale_offsets($notename[,$scale,$descending,$keypref])

as get_scale_notes(), except it returns a hash of notenames with the values being a semitone offset (-1, 0 or 1) as shown in the synopsis.

get_scale_MIDI($notename,$octave[,$scale,$descending])

as get_scale_notes(), but returns an array of MIDI note-numbers, given an octave number (-1..9).

get_scale_PDL($notename,$octave[,$scale,$descending])

as get_scale_MIDI(), but returns an array of PDL-format notes.

is_scale($scalename)

returns true if $scalename is a valid scale name used in this module.

Celestia is a free real-time space simulation that lets you experience our universe in three dimensions.
Unlike most planetarium software, Celestia doesnt confine you to the surface of the Earth. You can travel throughout the solar system, to any of over 100,000 stars, or even beyond the galaxy.
All movement in Celestia is seamless; the exponential zoom feature lets you explore space across a huge range of scales, from galaxy clusters down to spacecraft only a few meters across. A point-and-goto interface makes it simple to navigate through the universe to the object you want to visit.
Celestia is expandable. Celestia comes with a large catalog of stars, planets, moons, asteroids, comets, and spacecraft. If thats not enough, you can download dozens of easy to install add-ons with more objects.
Enhancements:
- This version is a mostly a bugfix release for the Celestia Engine.
- The GTK/GNOME front-ends feature a new slash screen.
- The install was cleaned up and packages should now be several megabytes smaller.

dxgallery is a static HTML image gallery generator which uses dynamic columns and fixed pixel image scaling. It also tries to look pretty.
I created dxgallery because it is the only one I know of that:
- Uses a dynamic number of columns.
- Uses consistent pixel count scaling.
- Is the prettiest.
It also uses entirely static html which can be generated on a different machine from the one which serves it.
And it conforms to XHTML 1.0 Transitional.
Enhancements:
- Fixed unsightly wrapping.

ConvertAll allows you to combine the units any way you want. If you want to convert from inches per decade, thats fine.
Or from meter-pounds. Or from cubic nautical miles. The units dont have to make sense to anyone else.
ConvertAll is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either Version 2 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY. See the LICENSE file provided with this program for more information.
As a special exception, the author gives permission to link and distribute this program with the Qt Non-Commercial Edition without including the source code for Qt.
Main features:
- The base units for conversion may be either typed (with auto-completion) or selected from a list.
- Units may be selected using either an abbreviation or a full name.
- Units may be combined with the "*" and "/" operators.
- Units may be raised to powers with the "^" operator.
- Units with non-linear scales, such as temperature, can also be converted.
- A unit list may be filtered and searched
- Numbers may be entered on either the "From" or the "To" units side, for conversions in both directions.
- Basic mathematical expressions may be entered in place of numbers.
- Options control the formatting of numerical results.
- The unit data includes over 400 units.
- The format of the unit data file makes it easy to add additional units.

GNU Archimedes is the GNU package for the design and simulation of submicron semiconductor devices. Archimedes is a 2D Fast Monte Carlo simulator which can take into account all the relevant quantum effects, thank to the implementation of the Bohm effective potential method.

The physics and geometry of a general device is introduced by typing a simple script, which makes, in this sense, GNU Archimedes a powerfull tool for the simulation of quite general semiconductor devices.

In the present release, GNU Archimedes is able to simulate electrons and heavy holes in Silicon and GaAs (Gamma and L-valleys) devices (holes are simulated by means of a simplified MEP model), and in the next release, which is in preparation, it will be able to make simulations in 1D, 2D and 3D (this release will be delivered as soon as possible).

The Scientifical and Industrial Motivations
In today semiconductor technology, the miniaturization of devices is more and more progressing. In this context, it is easy to see that numerical simulations play an important role at every level of device manufacture. In fact, the cost of designing and physically constructing prototypes for VLSI semiconductor devices is very high and without the availability of advanced simulators the efforts for devices miniaturization would, likely, be brought to a halt. From assessing the performance of individual transistors, to circuits and systems, and, consequently, with the promise of improved device performance, industries are encouraged to keep on miniaturizing with lower manufacture costs.

But, unfortunately, such simulations are not whithout their challenges... A first consequence of device miniaturization is that simulations of submicron semicondutor devices requires advanced transport models. Because of the presence of very high and rapidly varying electric field, phenomena occur which cannot be described by means of the well-known drift-diffusion models, which do not incorporate energy as a dynamical variable.

That is why some generalization has been sought in order to obtain more physically accurate models, like energy-transport and hydrodynamical models. The energy-transport models which are implemented in commercial simulators are based on phenomenological constitutive equations for the particle flux and energy flux depending on a set of parameters which are fitted to homogeneous bulk material Monte Carlo simulations. So, this is not, certainly, a satisfactory physical description of the internal electronic dynamics in a semiconductor device.

As current device technologies quickly approach the scales whereby quantum effects due to strong confinement of carriers and direct source-drain tunneling will begin to dominate, new simulation techniques are required in order to fully understand and acurately simulate the physics behind the technology operation.

Of all the simulation methods currently employed, ensemble Monte Carlo has always been, both in the accademic and industrial community, the most vigorous and trusted method for device simulation, as it is proven to be reliable and predictive, as one can easily see from the vast bibliography on this subject.

However, as Monte Carlo relies on the particle nature of the electron (in fact we consider an electron like a biliard ball), quantum effects associated with the wave-like nature of electrons cannot fully incorporated into the actual simulators, i.e. the ensemble Monte Carlo have to be lightly (or strongly, it depends on the point of view and on the methods implemented...) modified to take into account the quantum effects, at least at a first order of approximation, which is certainly enough to take into account correctly all the relevant quantum effects present in the present-day semiconductor devices (till 2015 probably...). In order to take into account the wave-like nature of electrons we use a recently introduced quantum theory, the so-called Bohm effective potential theory.

So it is challenging and very interesting to develop such a code for 2D quantum submicron semiconductor devices. This is why I have decided to implement this code, but these are not the only motivations...

The Ethical Motivations
The very sad situation you quickly observe working in a semiconductor industry, but also in all places in which researches about semiconductor devices are made, the only codes for simulation you can find are not free and are proprietary codes.

That is a very bad situation because, at the present time, if you need to develop your own code for the purpose of simulating a device it is IMPOSSIBLE to obtain an advanced one in a short time, and, trust me, this is EXTREMELY BAD for scientific research... (Immagine if you had to re-discover the Newtonian laws every time you need them...) So, you can find a huge amount of papers describing a lot of numerical methods for simulating, in a very advanced way, semiconductor devices (even in the quantum case), but nobody will give you a code on which you can construct your own method (with the unlikely exception that at least one of the programmers is a friend of yours :) ).

Even worst, if you are a semiconductor device designer and you want to simulate "realistically" a new device, you have to pay (trust me, at very high costs!) a BINARY (just a binary and not the code!) from some well-known software industry. This binary will certainly have some bugs (because it is coded by humans which are not perfect...) and you will never have the possibility of fix them on your own. Of course, you can write to the software house and tell them that there is a bug, but, how many time do you will wait for a new release without those bugs? I dont think it will be a short time...

My impression is that, after a long research on the Web for a Free Software dealing with advanced 2D semiconductor device simulation, there was not a free code for the purpose of semiconductor devices simulation (i mean under GPL license). To be sure about it, I asked to the great Richard Stallman (by mail) if it will be worth to do a code like this and he encouraged me to code it, because there wasnt a code like this as free. So I decided to write this code..

Apache2::ASP was developed in an attempt to address some of the problems associated with web application development in Perl.
Main features:
- Easy to install and get going.
- Requires only a small number of dependencies, each of which install automatically.
- Offers some structure without enforcing undue rigidity.
- Scales out instantly across multiple servers without requiring front-end proxies.

mcl-algorithm is a scalable cluster algorithm for graphs based on stochastic flow.
The flow process employed by the algorithm is mathematically sound and intrinsically tied to cluster structure in graphs, which is revealed as the imprint left by the process.
The threaded implementation has handled graphs of up to one million nodes within hours, and is widely used in the field of protein family analysis. It comes with a wide range of sibling utilities for handling and analyzing graphs, matrices, and clusterings.
The MCL algorithm simulates flow using (alternating) two simple algebraic operations on matrices. Its formulation is simple and elegant. There are no high-level procedural instructions for assembling, joining, or splitting of groups - cluster structure is bootstrapped via a flow process that is inherently affected by any cluster structure present.
The first operation used by MCL is expansion, which coincides with normal matrix multiplication. Expansion models the spreading out of flow, it becoming more homogeneous. The second is inflation, which is mathematically speaking a Hadamard power followed by a diagonal scaling. Inflation models the contraction of flow, it becoming thicker in regions of higher current and thinner in regions of lower current. The MCL process causes flow to spread out within natural clusters and evaporate inbetween different clusters.
- By varying parameters, clusterings on different scales of granularity can be found. The number of clusters can not and need not be specified in advance, but the algorithm can be adapted to different contexts.
- The issue how many clusters? is not dealt with in an arbitrary manner, but rather by strong internal logic. Cluster structure leaves its marks on the flow process simulated by the algorithm, and the flow parameters control the granularity of the cluster imprint.
- The limit of the MCL process (the process simulated by the algorithm) is in general extremely sparse, and the iterands are sparse in a weighted sense. This gives the means to scale the algorithm drastically, leading to a worst-case complexity of order Nk^2, where N is the number of nodes of the input graph, and where k is a threshold for the number of resources allocated per node.
- The rate of convergence of the MCL process, and projection of the iterands afterwards onto the resulting clustering, give hooks for unsupervised parameter adjustment.
- The iterands of the MCL process have structural properties which allow a cluster interpretation, and which generalize the mapping of MCL limits onto clusterings. The mathematics associated with the MCL process shows that there is an intrinsic relationship between the MCL process and cluster structure in graphs. This is very valuable given the many heuristic approaches in cluster analysis.
Enhancements:
- Numerous cleanups in much of the code.
- Improvements in caching intermediate results.