[Blends-commit] r2802 - in /projects/science/trunk/debian-science/tasks: electrophysiology machine-learning mathematics neuroscience-cognitive neuroscience-modeling

tille at users.alioth.debian.org tille at users.alioth.debian.org
Fri Apr 29 14:19:28 UTC 2011 

Author: tille
Date: Fri Apr 29 14:19:27 2011
New Revision: 2802

URL: http://svn.debian.org/wsvn/blends/?sc=1&rev=2802
Log:
More closed ITPs

Modified:
    projects/science/trunk/debian-science/tasks/electrophysiology
    projects/science/trunk/debian-science/tasks/machine-learning
    projects/science/trunk/debian-science/tasks/mathematics
    projects/science/trunk/debian-science/tasks/neuroscience-cognitive
    projects/science/trunk/debian-science/tasks/neuroscience-modeling

Modified: projects/science/trunk/debian-science/tasks/electrophysiology
URL: http://svn.debian.org/wsvn/blends/projects/science/trunk/debian-science/tasks/electrophysiology?rev=2802&op=diff
==============================================================================
--- projects/science/trunk/debian-science/tasks/electrophysiology (original)
+++ projects/science/trunk/debian-science/tasks/electrophysiology Fri Apr 29 14:19:27 2011
@@ -94,25 +94,8 @@
  RTAI project. It can be used as virtual oscilloscope and monitoring
  application for interacting with the real-time external.
 
- ; Added by blends-inject 0.0.7. [Please note here if modified manually]
+ ; Added by blends-inject 0.0.7. [now official package]
 Suggests: stimfit
-Homepage: http://code.google.com/p/stimfit/
-Language: C, Python
-WNPP: 612375
-License: GPL-2
-Pkg-Description: view and analyze electrophysiological data
- Stimfit is a free, fast and simple program for viewing and analyzing
- electrophysiological data.  It features an embedded Python shell that
- allows you to extend the program functionality by using numerical
- libraries such as NumPy and SciPy.
- .
- It includes stfio module which supports some electrophysiological
- file formats:
-  - Read/write: CFS binary data, HDF5 files, Axon text files,
-    ASCII files
-  - Read-only: Axon binary files (*.abf), Axograph files (*.axgd,
-    *.axgx), HEKA files (*.dat, from version 0.10)
-    * Write-only: Igor binary waves (*.ibw)
 
  ; Added by blends-inject 0.0.3. [Please note here if modified manually]
 Suggests: trellis-neuro

Modified: projects/science/trunk/debian-science/tasks/machine-learning
URL: http://svn.debian.org/wsvn/blends/projects/science/trunk/debian-science/tasks/machine-learning?rev=2802&op=diff
==============================================================================
--- projects/science/trunk/debian-science/tasks/machine-learning (original)
+++ projects/science/trunk/debian-science/tasks/machine-learning Fri Apr 29 14:19:27 2011
@@ -139,22 +139,5 @@
 Suggests: science-typesetting
 Meta-Suggests: svn://svn.debian.org/blends/projects/science/trunk/debian-science/tasks/typesetting
 
- ; Added by blends-inject 0.0.7. [Please note here if modified manually]
+ ; Added by blends-inject 0.0.7. [now official package]
 Depends: python-pebl
-Homepage: https://code.google.com/p/pebl-project/
-WNPP: 612761
-Responsible: Debian Python Modules Team <python-modules-team at lists.alioth.debian.org>
-Vcs-Browser: http://svn.debian.org/viewsvn/python-apps/packages/pebl/trunk/
-Vcs-Svn: svn://svn.debian.org/svn/python-apps/packages/pebl/trunk
-Pkg-Description: Python Environment for Bayesian Learning
- Pebl is a Python library and command line application for learning
- the structure of a Bayesian network given prior knowledge and
- observations. Pebl includes the following features:
-   * Can learn with observational and interventional data
-   * Handles missing values and hidden variables using exact and heuristic
-     methods
-   * Provides several learning algorithms; makes creating new ones simple
-   * Has facilities for transparent parallel execution using several
-     cluster/grid resources
-   * Calculates edge marginals and consensus networks
-   * Presents results in a variety of formats

Modified: projects/science/trunk/debian-science/tasks/mathematics
URL: http://svn.debian.org/wsvn/blends/projects/science/trunk/debian-science/tasks/mathematics?rev=2802&op=diff
==============================================================================
--- projects/science/trunk/debian-science/tasks/mathematics (original)
+++ projects/science/trunk/debian-science/tasks/mathematics Fri Apr 29 14:19:27 2011
@@ -99,22 +99,6 @@
  prototyping of deductive systems.
 
 Depends: bliss
-Homepage: http://www.tcs.hut.fi/Software/bliss/index.shtml
-License: GPL2
-WNPP: 528925
-Responsible: David Bremner <bremner at unb.ca>
-Pkg-Description: tool for computing automorphism groups and canonical labelings of graphs
- Bliss is a backtracking algorithm based on individualization and
- refinement for labeling a graph.  Data structures, subroutines, and
- pruning heuristics especially for fast handling of large and sparse
- graphs are provided. This package provides the command line tool
- bliss; a C++ and C API is also available.
- .
- There is also a libbliss-dev which changes the last line of the long
- description.  At the moment I propose not to create a shared library
- package since upstream doesn't make one, and in the short term there
- won't be any rdepends in debian. I could be convinced otherwise of
- course.
 
 Depends: life-apps
 Why: Partial differential equation library, FEA, CFD

Modified: projects/science/trunk/debian-science/tasks/neuroscience-cognitive
URL: http://svn.debian.org/wsvn/blends/projects/science/trunk/debian-science/tasks/neuroscience-cognitive?rev=2802&op=diff
==============================================================================
--- projects/science/trunk/debian-science/tasks/neuroscience-cognitive (original)
+++ projects/science/trunk/debian-science/tasks/neuroscience-cognitive Fri Apr 29 14:19:27 2011
@@ -222,19 +222,8 @@
 Published-URL: http://www.medicalimagingandgraphics.com/article/S0895-6111(01)00008-8
 Published-DOI: 10.1016/S0895-6111(01)00008-8
 
- ; Added by blends-inject 0.0.5. [Please note here if modified manually]
+ ; Added by blends-inject 0.0.5. [now official package]
 Depends: openmeeg-tools
-Pkg-Description: library for solving EEG and MEG forward and inverse problems
- OpenMEEG consists of state-of-the art solvers for forward problems in
- the field of MEG and EEG.
- .
- Solvers are based on the symmetric Boundary Element method [Kybic et
- al, 2005], providing excellent accuracy, particularly for superficial
- cortical sources. OpenMEEG can compute four types of lead fields
- (EEG, MEG, Internal Potential and Electrical Impedence Tomography).
- .
- It can be used from the command line, from Python and from Matlab via
- Fieldtrip.
 Published-Authors: Alexandre Gramfort, Théodore Papadopoulo, Emmanuel Olivi, Maureen Clerc
 Published-In: BioMedical Engineering OnLine 45:9
 Published-Title: OpenMEEG: opensource software for quasistatic bioelectromagnetics

Modified: projects/science/trunk/debian-science/tasks/neuroscience-modeling
URL: http://svn.debian.org/wsvn/blends/projects/science/trunk/debian-science/tasks/neuroscience-modeling?rev=2802&op=diff
==============================================================================
--- projects/science/trunk/debian-science/tasks/neuroscience-modeling (original)
+++ projects/science/trunk/debian-science/tasks/neuroscience-modeling Fri Apr 29 14:19:27 2011
@@ -13,34 +13,8 @@
 
 
 
- ; Added by blends-inject 0.0.2. [Please note here if modified manually]
+ ; Added by blends-inject 0.0.2. [now official package]
 Depends: python-brian
-Homepage: http://www.briansimulator.org/
-Language: Python, C
-WNPP: 602246
-Responsible: NeuroDebian Team <team at neuro.debian.net>
-License: CeCILL-2
-Vcs-Browser: http://git.debian.org/?p=pkg-exppsy/brian.git
-Vcs-Git: git://git.debian.org/git/pkg-exppsy/brian.git
-Pkg-URL: http://neuro.debian.net/pkgs/python-brian.html
-Pkg-Description: simulator for spiking neural networks
- Brian is a clock-driven simulator for spiking neural networks. It is
- designed with an emphasis on flexibility and extensibility, for rapid
- development and refinement of neural models. Neuron models are
- specified by sets of user-specified differential equations, threshold
- conditions and reset conditions (given as strings). The focus is
- primarily on networks of single compartment neuron models (e.g. leaky
- integrate-and-fire or Hodgkin-Huxley type neurons). Features include:
-  - a system for specifying quantities with physical dimensions
-  - exact numerical integration for linear differential equations
-  - Euler, Runge-Kutta and exponential Euler integration for nonlinear
-    differential equations
-  - synaptic connections with delays
-  - short-term and long-term plasticity (spike-timing dependent plasticity)
-  - a library of standard model components, including integrate-and-fire
-    equations, synapses and ionic currents
-  - a toolbox for automatically fitting spiking neuron models to
-    electrophysiological recordings
 Published-Authors: Goodman D.F. and Brette R.
 Published-DOI: 10.3389/neuro.11.005.2008
 Published-In: Front. Neuroinform

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