Package: condor-doc
Source: condor
Version: 7.8.1~dfsg.1-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 6100
Depends: neurodebian-popularity-contest
Homepage: http://research.cs.wisc.edu/condor
Priority: extra
Section: doc
Filename: pool/main/c/condor/condor-doc_7.8.1~dfsg.1-1~nd+1_all.deb
Size: 1332776
SHA256: 76cc1ec019982dd093fecf00aa0206fa9b9c17a6b40e6e547dd8a5ac272d23a7
SHA1: 78bcb3647dfcaacbafa28a2f27b30d95b9a108b1
MD5sum: 0a9fb0c69dfa011cbca94d6318714347
Description: distributed workload management system - documentation
 Like other full-featured batch systems, Condor provides a job queueing
 mechanism, scheduling policy, priority scheme, resource monitoring, and
 resource management. Users submit their serial or parallel jobs to Condor;
 Condor places them into a queue. It chooses when and where to run the jobs
 based upon a policy, carefully monitors their progress, and ultimately
 informs the user upon completion.
 .
 Unlike more traditional batch queueing systems, Condor can also effectively
 harness wasted CPU power from otherwise idle desktop workstations. Condor
 does not require a shared file system across machines - if no shared file
 system is available, Condor can transfer the job's data files on behalf of
 the user.
 .
 This package provides Condor's documentation in HTML and PDF format, as well
 as configuration and other examples.

Package: connectomeviewer
Version: 2.1.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 1578
Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-cfflib (>= 2.0.5), python-networkx (>= 1.4), python-nibabel, python-numpy (>= 1.3.0), python-scipy, python-chaco, mayavi2 (>= 4.0.0), ipython
Recommends: python-nipype, python-dipy, python-matplotlib, python-qscintilla2
Suggests: nipy-suite
Homepage: http://www.connectomeviewer.org
Priority: extra
Section: python
Filename: pool/main/c/connectomeviewer/connectomeviewer_2.1.0-1~nd+1_all.deb
Size: 1356156
SHA256: 434aff9b028c4333df4aff71cc45e6b82a98574f6297ddab70d0ebc260ff5e6a
SHA1: 5dc49f902c6d89fd0fea7758ce53c9462ec73db4
MD5sum: a9b946a201ad29742748d1c152b6fd57
Description: Interactive Analysis and Visualization for MR Connectomics
 The Connectome Viewer is a extensible, scriptable, pythonic research
 environment for visualization and (network) analysis in neuroimaging
 and connectomics.
 .
 Employing the Connectome File Format, diverse data types such as
 networks, surfaces, volumes, tracks and metadata are handled and
 integrated. The Connectome Viewer is part of the MR Connectome Toolkit.

Package: debian-handbook
Version: 6.0+20120509~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 23215
Depends: neurodebian-popularity-contest
Homepage: http://debian-handbook.info
Priority: optional
Section: doc
Filename: pool/main/d/debian-handbook/debian-handbook_6.0+20120509~nd+1_all.deb
Size: 21998670
SHA256: b33f038d8363175473cc056a5f98fc7af52386a466b45d4b2e42d2f25233a3ed
SHA1: 7a0b369b4548a3f4fb61aa1ef9efa2ddf2b319e2
MD5sum: 3e3d2cf990fcc5ed1ed6bdbfb5c1c3dd
Description: reference book for Debian users and system administrators
 Accessible to all, the Debian Administrator's Handbook teaches the essentials
 to anyone who wants to become an effective and independent Debian GNU/Linux
 administrator.
 .
 It covers all the topics that a competent Linux administrator should master,
 from the installation and the update of the system, up to the creation of
 packages and the compilation of the kernel, but also monitoring, backup and
 migration, without forgetting advanced topics like SELinux setup to secure
 services, automated installations, or virtualization with Xen, KVM or LXC.
 .
 The Debian Administrator's Handbook has been written by two Debian
 developers — Raphaël Hertzog and Roland Mas.
 .
 This package contains the English book covering Debian 6.0 “Squeeze”.

Package: fail2ban
Version: 0.8.6-3~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 339
Depends: neurodebian-popularity-contest, python (>= 2.4), python-central (>= 0.6.11), lsb-base (>= 2.0-7)
Recommends: iptables, whois, python-gamin
Suggests: mailx
Homepage: http://www.fail2ban.org
Priority: optional
Section: net
Filename: pool/main/f/fail2ban/fail2ban_0.8.6-3~nd+1_all.deb
Size: 103466
SHA256: 29d8077c99805470e6a7d0196cd44707905e159e564f70a5f83300364c85b4a0
SHA1: 9ae59643b846517bc94a88724a18c7f12b9ac5b6
MD5sum: 37b3ab7b9c3a6ffea223927df59411a8
Description: ban hosts that cause multiple authentication errors
 Fail2ban monitors log files (e.g. /var/log/auth.log,
 /var/log/apache/access.log) and temporarily or persistently bans
 failure-prone addresses by updating existing firewall rules.  Fail2ban allows
 easy specification of different actions to be taken such as to ban an
 IP using iptables or hostsdeny rules, or simply to send a
 notification email.
 .
 By default, it comes with filter expressions for various services
 (sshd, apache, qmail, proftpd, sasl etc.) but configuration can be
 easily extended for monitoring any other text file.  All filters and
 actions are given in the config files, thus fail2ban can be adopted
 to be used with a variety of files and firewalls.
Python-Version: current, >= 2.4

Package: freeipmi
Version: 1.1.5-3~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 1
Depends: neurodebian-popularity-contest, freeipmi-common, freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog
Homepage: http://www.gnu.org/software/freeipmi/
Priority: extra
Section: admin
Filename: pool/main/f/freeipmi/freeipmi_1.1.5-3~nd+1_all.deb
Size: 928
SHA256: 05f29073e2746666e0fbc4c111150ef67a4ea5a91974f3d43fb41e2bb16e60ff
SHA1: 54dbd1897168ea82b4c803ac9cdf3185b1e1c24f
MD5sum: 7a7f53cc7987a2216d7d16bf3b5f0ca9
Description: GNU implementation of the IPMI protocol
 FreeIPMI is a collection of Intelligent Platform Management IPMI
 system software. It provides in-band and out-of-band software and a
 development library conforming to the Intelligent Platform Management
 Interface (IPMI v1.5 and v2.0) standards.
 .
 This meta-package depends on all separate modules of freeipmi.

Package: freeipmi-common
Source: freeipmi
Version: 1.1.5-3~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 380
Pre-Depends: dpkg (>= 1.15.7.2~)
Depends: neurodebian-popularity-contest
Suggests: freeipmi-tools
Homepage: http://www.gnu.org/software/freeipmi/
Priority: extra
Section: admin
Filename: pool/main/f/freeipmi/freeipmi-common_1.1.5-3~nd+1_all.deb
Size: 296942
SHA256: f1e328eeb5e8aa8cde083ce4c3b1e115f911921a7165f6719cc109685e5fa76f
SHA1: 368c7818e51c28f1d98d426a4e687df0b70a0d32
MD5sum: 1b586f2ac96616824bb815a6b8addf7f
Description: GNU implementation of the IPMI protocol - common files
 FreeIPMI is a collection of Intelligent Platform Management IPMI
 system software. It provides in-band and out-of-band software and a
 development library conforming to the Intelligent Platform Management
 Interface (IPMI v1.5 and v2.0) standards.
 .
 This package provides configuration used by the rest of FreeIPMI
 framework and generic documentation to orient the user.

Package: guacamole
Version: 0.6.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 302
Depends: neurodebian-popularity-contest, guacd (>= 0.6), guacd (<< 0.7)
Recommends: libguac-client-vnc0
Suggests: tomcat6 | jetty
Homepage: http://guacamole.sourceforge.net/
Priority: extra
Section: net
Filename: pool/main/g/guacamole/guacamole_0.6.0-1~nd+1_all.deb
Size: 277608
SHA256: ada9b621c7f57e36ff8722e8b187fb30cbf293fe6fd7ecf8715baeac88239e81
SHA1: 6359e73297b027d6210692e05b9c69eabca20070
MD5sum: 3d9919419c971cf623b5f0ec1832115a
Description: HTML5 web application for accessing remote desktops
 Guacamole is an HTML5 web application that provides access to a desktop
 environment using remote desktop protocols. A centralized server acts as a
 tunnel and proxy, allowing access to multiple desktops through a web browser.
 No plugins are needed: the client requires nothing more than a web browser
 supporting HTML5 and AJAX.

Package: guacamole-tomcat
Source: guacamole
Version: 0.6.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 7
Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0
Homepage: http://guacamole.sourceforge.net/
Priority: extra
Section: net
Filename: pool/main/g/guacamole/guacamole-tomcat_0.6.0-1~nd+1_all.deb
Size: 5164
SHA256: b13a9f5de4bb0b00a2711a3d003a09d9595f5c9ab3734d3f01ce9dbd00732c25
SHA1: aa4e0a55a61260617777ccb2b5c96e072d300524
MD5sum: 13cb30f66b360e09322236cb119d69f6
Description: Tomcat-based Guacamole install with VNC support
 Guacamole is an HTML5 web application that provides access to a desktop
 environment using remote desktop protocols. A centralized server acts as a
 tunnel and proxy, allowing access to multiple desktops through a web browser.
 No plugins are needed: the client requires nothing more than a web browser
 supporting HTML5 and AJAX.
 .
 This metapackage depends on Tomcat, Guacamole, and the VNC support plugin for
 guacamole. Guacamole is automatically installed and configured under
 Tomcat.

Package: libfreenect-doc
Source: libfreenect
Version: 1:0.1.2+dfsg-6~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 482
Depends: neurodebian-popularity-contest
Multi-Arch: foreign
Homepage: http://openkinect.org/
Priority: extra
Section: doc
Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-6~nd+1_all.deb
Size: 90816
SHA256: 6f15ee9429e9a77208075aa20ac716d28777bb6ae56b6d928d2394f8eba532f5
SHA1: f983624268cf3366f85733d527b08deb8581e0be
MD5sum: 812834b439f4a80838af58f2d5ecacc4
Description: library for accessing Kinect device -- documentation
 libfreenect is a cross-platform library that provides the necessary interfaces
 to activate, initialize, and communicate data with the Kinect hardware.
 Currently, the library supports access to RGB and depth video streams, motors,
 accelerometer and LED and provide binding in different languages (C++,
 Python...)
 .
 This library is the low level component of the OpenKinect project which is an
 open community of people interested in making use of the Xbox Kinect hardware
 with PCs and other devices.
 .
 This package contains the documentation of the API of libfreenect.

Package: neurodebian-desktop
Source: neurodebian
Version: 0.28~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 141
Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, gnome-icon-theme, neurodebian-popularity-contest
Homepage: http://neuro.debian.net
Priority: optional
Section: science
Filename: pool/main/n/neurodebian/neurodebian-desktop_0.28~nd+1_all.deb
Size: 114152
SHA256: d241379aaf2e467644b58be3f2e32a7565c7129847fd54926cd5a82c89c72669
SHA1: 3d6c186d77fa4f28a360c1e2744ee826c21cbd5b
MD5sum: a4ef8fa8662b749bdcd5861521d9003f
Description: neuroscience research environment
 This package contains NeuroDebian artwork (icons, background image)
 and a NeuroDebian menu featuring most popular neuroscience tools
 automatically installed upon initial invocation.

Package: neurodebian-dev
Source: neurodebian
Version: 0.28~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 5746
Depends: devscripts, cowbuilder, neurodebian-keyring
Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring
Suggests: virtualbox-ose, virtualbox-ose-fuse
Homepage: http://neuro.debian.net
Priority: optional
Section: science
Filename: pool/main/n/neurodebian/neurodebian-dev_0.28~nd+1_all.deb
Size: 5345858
SHA256: 0109bc95a3277f1d22ab26238f6ba2f5a00624cf5bcc447cd73a108c7c956c35
SHA1: b30c711d4dcd2d8d056ae70e13915987bed35e41
MD5sum: a45ea5b9fb8d985290f51241572da28e
Description: NeuroDebian development tools
 neuro.debian.net sphinx website sources and development tools used by
 NeuroDebian to provide backports for a range of Debian/Ubuntu
 releases.

Package: neurodebian-guest-additions
Source: neurodebian
Version: 0.28~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 105
Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms
Depends: sudo, neurodebian-desktop, gdm | gdm3, update-manager-gnome, update-notifier
Recommends: chromium-browser
Homepage: http://neuro.debian.net
Priority: optional
Section: science
Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.28~nd+1_all.deb
Size: 13622
SHA256: 111f1435c2a5e29a61a92dea16f0a576c1af5edfab90a19cd7543c34381d5716
SHA1: 328aec452c31259b2dffa67b96d05ed1589ff1e6
MD5sum: f8aef06136d78051cf876801c269dcf5
Description: NeuroDebian guest additions (DO NOT INSTALL OUTSIDE VIRTUALBOX)
 This package configures a Debian installation as a guest operating system
 in a VirtualBox-based virtual machine for NeuroDebian.
 .
 DO NOT install this package unless you know what you are doing! For example,
 installation of this package relaxes several security mechanisms.

Package: neurodebian-keyring
Source: neurodebian
Version: 0.28~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 7
Homepage: http://neuro.debian.net
Priority: optional
Section: science
Filename: pool/main/n/neurodebian/neurodebian-keyring_0.28~nd+1_all.deb
Size: 6556
SHA256: 53b718ea4fcd7a40d75eaab25576abf3a7475b97a8f88d6d4e18df39e6230fec
SHA1: 50648a2dd49540d62bfa4af070ff491351e78ba5
MD5sum: 472df23430fe17692e8da061a08aa6ff
Description: GnuPG archive keys of the NeuroDebian archive
 The NeuroDebian project digitally signs its Release files. This package
 contains the archive keys used for that.

Package: neurodebian-popularity-contest
Source: neurodebian
Version: 0.28~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 6
Depends: popularity-contest
Homepage: http://neuro.debian.net
Priority: optional
Section: science
Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.28~nd+1_all.deb
Size: 5720
SHA256: 742fbad04b42d4403bd747c39ee494d3874feddb4490253fa7beedc76c7c0f7e
SHA1: 6140882188957334b7b1bee3343580e51830cc51
MD5sum: 6d340546440561c6dc415371b7482b73
Description: Helper for NeuroDebian popularity contest submissions
 This package is a complement to the generic popularity-contest
 package to enable anonymous submission of usage statistics to
 NeuroDebian in addition to the popcon submissions to the underlying
 distribution (e.g. Debian or Ubuntu) popcon server.
 .
 Your participation in popcon is important for following reasons:
  - Popular packages receive more attention from developers, bugs are
    fixed faster and updates are provided quicker.
  - Assure that we do not drop support for a previous release of Debian
    or Ubuntu while are active users.
  - User statistics could be used by upstream research software
    developers to acquire funding for continued development.
 .
 It has an effect only if you have decided to participate in the
 Popularity Contest of your distribution, i.e. Debian or Ubuntu.
 You can always enable or disable your participation in popcon by
 running 'dpkg-reconfigure popularity-contest' as root.

Package: nifti2dicom-data
Source: nifti2dicom
Version: 0.4.5-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 606
Depends: neurodebian-popularity-contest
Homepage: https://github.com/biolab-unige/nifti2dicom
Priority: optional
Section: science
Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.5-1~nd+1_all.deb
Size: 614932
SHA256: 470880703592fab1d6b6635ef65e3b79f4701022018537e65fba4e2fd33adb01
SHA1: 454735537a9b01e0bf39d1ad7b00a842d3768139
MD5sum: c444b61f29e753aae382e9560ae2ab64
Description: data files for nifti2dicom
 This package contains architecture-independent supporting data files
 required for use with nifti2dicom, such as such as documentation, icons,
 and translations.

Package: nuitka
Version: 0.3.23.1+ds-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 1325
Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5, scons (>= 2.0.0), python-dev (>= 2.6.6-2), python (>= 2.6.6-7~)
Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6)
Suggests: ccache
Homepage: http://nuitka.net
Priority: optional
Section: python
Filename: pool/main/n/nuitka/nuitka_0.3.23.1+ds-1~nd+1_all.deb
Size: 331654
SHA256: eba10a4455c20b4c77d6d6f3d32b39dd5850bceea570524524f40b7da29b0f04
SHA1: 725ec8e8346c9e40dd5d8aa772a2845716c80c75
MD5sum: 6620bd72cc8ab081874eea6140e1f572
Description: Python compiler with full language support and CPython compatibility
 This Python compiler achieves full language compatibility and compiles Python
 code into compiled objects that are not second class to pure Python objects
 at all.

Package: packaging-tutorial
Version: 0.5~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 1155
Depends: neurodebian-popularity-contest
Priority: extra
Section: doc
Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.5~nd+1_all.deb
Size: 1111034
SHA256: 3410f99232ee6a8cff11c2d97b4cd50f56d4ae5d71f5dadaa077d92457842996
SHA1: 8756d44b1a608c8c0e29fde5813d6146e67c5026
MD5sum: 7d653f7b7bc96d627e73720627567851
Description: introduction to Debian packaging
 This tutorial is an introduction to Debian packaging. It teaches prospective
 developers how to modify existing packages, how to create their own packages,
 and how to interact with the Debian community. In addition to the main
 tutorial, it includes three practical sessions on modifying the 'grep'
 package, and packaging the 'gnujump' game and a Java library.

Package: psychopy
Version: 1.73.06.dfsg-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 4463
Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-scipy, python-matplotlib, python-lxml, python-configobj
Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, libavbin0, ipython
Suggests: python-iolabs, python-pyxid
Homepage: http://www.psychopy.org
Priority: optional
Section: science
Filename: pool/main/p/psychopy/psychopy_1.73.06.dfsg-1~nd+1_all.deb
Size: 2688728
SHA256: 50180e06888296862a93e58fa7a813575f673b11459b99fad415bba6328c56bf
SHA1: 40986f97e19748ed2fe43af0e57c2bcc1006fa6e
MD5sum: 9bbf64094767294e0f1d26ebd83ce196
Description: environment for creating psychology stimuli in Python
 PsychoPy provides an environment for creating psychology stimuli
 using Python scripting language.  It combines the graphical
 strengths of OpenGL with easy Python syntax to give psychophysics
 a free and simple stimulus presentation and control package.
 .
 The goal is to provide, for the busy scientist, tools to control
 timing and windowing and a simple set of pre-packaged stimuli and
 methods.  PsychoPy features
 .
  - IDE GUI for coding in a powerful scripting language (Python)
  - Builder GUI for rapid development of stimulation sequences
  - Use of hardware-accelerated graphics (OpenGL)
  - Integration with Spectrascan PR650 for easy monitor calibration
  - Simple routines for staircase and constant stimuli experimental
    methods as well as curve-fitting and bootstrapping
  - Simple (or complex) GUIs via wxPython
  - Easy interfaces to joysticks, mice, sound cards etc. via PyGame
  - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli
Python-Version: 2.6, 2.7

Package: psychtoolbox-3-common
Source: psychtoolbox-3
Version: 3.0.9+svn2579.dfsg1-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 47050
Depends: neurodebian-popularity-contest
Recommends: subversion
Homepage: http://psychtoolbox.org
Priority: extra
Section: science
Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.9+svn2579.dfsg1-1~nd+1_all.deb
Size: 19434030
SHA256: b9a74e5ec9e31d2346e55b156072e21531b62373e4723d6c2fca201a5c186da3
SHA1: 1269e6c7d0b877a16ad376eace4b4b87a29bf7fa
MD5sum: 68d9a493aee2dd28da0e8fc48783c47b
Description: toolbox for vision research -- arch/interpreter independent part
 Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and
 GNU/Octave functions for vision research. It makes it easy to
 synthesize and show accurately controlled visual and auditory stimuli
 and interact with the observer.
 .
 The Psychophysics Toolbox interfaces between Matlab or Octave and the
 computer hardware. The Psychtoolbox's core routines provide access to
 the display frame buffer and color lookup table, allow
 synchronization with the vertical retrace, support millisecond
 timing, allow access to OpenGL commands, and facilitate the
 collection of observer responses. Ancillary routines support common
 needs like color space transformations and the QUEST threshold
 seeking algorithm.
 .
 This package contains architecture independent files (such as .m scripts)

Package: python-joblib
Source: joblib
Version: 0.6.4-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 178
Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0)
Recommends: python-numpy, python-nose, python-simplejson
Homepage: http://packages.python.org/joblib/
Priority: optional
Section: python
Filename: pool/main/j/joblib/python-joblib_0.6.4-1~nd+1_all.deb
Size: 51836
SHA256: 41c863ccd9bc38593a9579395123e9cc6141abaad298aab08cb19b5db994add4
SHA1: 7de5cfabcc304803ac2060ff6d4c04e39d3b5cbc
MD5sum: eff58c8425d78c1d0b08588151359061
Description: tools to provide lightweight pipelining in Python
 Joblib is a set of tools to provide lightweight pipelining in
 Python. In particular, joblib offers:
  - transparent disk-caching of the output values and lazy
    re-evaluation (memoize pattern)
  - easy simple parallel computing
  - logging and tracing of the execution
 .
 Joblib is optimized to be fast and robust in particular on large,
 long-running functions and has specific optimizations for numpy arrays.

Package: python-lazyarray
Source: lazyarray
Version: 0.1.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 19
Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy
Homepage: http://bitbucket.org/apdavison/lazyarray/
Priority: optional
Section: python
Filename: pool/main/l/lazyarray/python-lazyarray_0.1.0-1~nd+1_all.deb
Size: 7328
SHA256: dbb35c5dc374c7bc62e95a56d3a14314105025852a66ba61f2472e4ea5b8be65
SHA1: a7bdc4dd42a3963a810fe0c0e73e4ad7ed6a7995
MD5sum: 1d204a47646dc6ed4152895171c87bc9
Description: Python module providing a NumPy-compatible lazily-evaluated array
 The 'larray' class is a NumPy-compatible numerical array where operations on
 the array (potentially including array construction) are not performed
 immediately, but are delayed until evaluation is specifically requested.
 Evaluation of only parts of the array is also possible. Consequently,
 use of an 'larray' can potentially save considerable computation time
 and memory in cases where arrays are used conditionally, or only parts of an
 array are used (for example in distributed computation, in which each MPI node
 operates on a subset of the elements of the array).

Package: python-mdp
Source: mdp
Version: 3.2+git78-g7db3c50-3~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 1523
Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy
Recommends: python-scipy, python-libsvm, python-joblib, python-scikits-learn | python-sklearn, python-pp
Suggests: python-py, shogun-python-modular
Enhances: python-mvpa
Homepage: http://mdp-toolkit.sourceforge.net/
Priority: optional
Section: python
Filename: pool/main/m/mdp/python-mdp_3.2+git78-g7db3c50-3~nd+1_all.deb
Size: 482118
SHA256: 24e6dd391ec24d010fe4be95667fe752c98de13783aa92e9689a311fcf293ee5
SHA1: 0031ca284ce3d7b32591f4313fee4426eec343fa
MD5sum: 38347ef098f945cb819eb6f7cd2faea0
Description: Modular toolkit for Data Processing
 Python data processing framework for building complex data processing software
 by combining widely used machine learning algorithms into pipelines and
 networks. Implemented algorithms include: Principal Component Analysis (PCA),
 Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent
 Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis,
 Fisher Discriminant Analysis (FDA), and Gaussian Classifiers.
 .
 This package contains MDP for Python 2.

Package: python-mpi4py-doc
Source: mpi4py
Version: 1.3-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 284
Depends: neurodebian-popularity-contest, libjs-jquery
Suggests: python-mpi4py
Homepage: http://code.google.com/p/mpi4py/
Priority: extra
Section: doc
Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3-1~nd+1_all.deb
Size: 79200
SHA256: 37d54a31f1699cbc1d666506cf987751fb1fcdce617ce6207ba6db0866bce976
SHA1: 5b9f6f6359b83eee1e9f6485af70b7e72cc4e816
MD5sum: ba6a2ae4a4d36eb373b06b425acf436e
Description: bindings of the MPI standard -- documentation
 MPI for Python (mpi4py) provides bindings of the Message Passing
 Interface (MPI) standard for the Python programming language,
 allowing any Python program to exploit multiple processors.
 .
 mpi4py is constructed on top of the MPI-1/MPI-2 specification
 and provides an object oriented interface which closely follows MPI-2
 C++ bindings.  It supports point-to-point (sends, receives) and
 collective (broadcasts, scatters, gathers) communications of any
 picklable Python object as well as optimized communications of Python
 object exposing the single-segment buffer interface (NumPy arrays,
 builtin bytes/string/array objects).
 .
 This package provides HTML rendering of the user's manual.

Package: python-mvpa
Source: pymvpa
Version: 0.4.8-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 3547
Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy, python-support (>= 0.90.0), python2.7, python-mvpa-lib (>= 0.4.8-1~nd+1)
Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab
Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc
Provides: python2.6-mvpa, python2.7-mvpa
Homepage: http://www.pymvpa.org
Priority: optional
Section: python
Filename: pool/main/p/pymvpa/python-mvpa_0.4.8-1~nd+1_all.deb
Size: 2205002
SHA256: 41465c88b5c5d855bb5cfb183ef31b621031eb691ba5a8f3ac481bec2fe61bd8
SHA1: 40e31da97e30b6c2af3f28dfcd4b255560f765e2
MD5sum: b36ff1ec87893ae209624c75e8934b87
Description: multivariate pattern analysis with Python
 PyMVPA eases pattern classification analyses of large datasets, with an
 accent on neuroimaging. It provides high-level abstraction of typical
 processing steps (e.g. data preparation, classification, feature selection,
 generalization testing), a number of implementations of some popular
 algorithms (e.g. kNN, GNB, Ridge Regressions, Sparse Multinomial Logistic
 Regression), and bindings to external machine learning libraries (libsvm,
 shogun).
 .
 While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it
 is eminently suited for such datasets.
Python-Version: 2.6, 2.7

Package: python-mvpa-doc
Source: pymvpa
Version: 0.4.8-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 37565
Depends: neurodebian-popularity-contest, libjs-jquery
Suggests: python-mvpa
Homepage: http://www.pymvpa.org
Priority: optional
Section: doc
Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.8-1~nd+1_all.deb
Size: 8454400
SHA256: 9f730cbbc6fdcfce45ecca5ef036d74ea074eaedf2b4105fde7baf0028f11350
SHA1: 4510a24072100ffb1d4220f2d66d21abde733b9d
MD5sum: 32c7629e7f9e01d9f7ca4d2c621b85be
Description: documentation and examples for PyMVPA
 PyMVPA documentation in various formats (HTML, TXT) including
  * User manual
  * Developer guidelines
  * API documentation
  * BibTeX references file
 .
 Additionally, all example scripts shipped with the PyMVPA sources are
 included.

Package: python-mvpa2
Source: pymvpa2
Version: 2.1.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 4105
Depends: neurodebian-popularity-contest, python (>= 2.4), python-numpy, python-support (>= 0.90.0), python-mvpa2-lib (>= 2.1.0-1~nd+1)
Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, shogun-python-modular, liblapack-dev
Suggests: fslview, fsl, python-mvpa2-doc, python-nose, python-openopt, python-rpy2
Provides: python2.6-mvpa2, python2.7-mvpa2
Homepage: http://www.pymvpa.org
Priority: optional
Section: python
Filename: pool/main/p/pymvpa2/python-mvpa2_2.1.0-1~nd+1_all.deb
Size: 2354480
SHA256: eed61d48e090d43d5f91d375ceb57fc20b218a3837c75a204d1903f1b59505d6
SHA1: f941a688cb6196154993402215d2ff81f6a004a3
MD5sum: db7d563eb8edce016baecf289a2ed3ec
Description: multivariate pattern analysis with Python v. 2
 PyMVPA eases pattern classification analyses of large datasets, with an
 accent on neuroimaging. It provides high-level abstraction of typical
 processing steps (e.g. data preparation, classification, feature selection,
 generalization testing), a number of implementations of some popular
 algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic
 Regression), and bindings to external machine learning libraries (libsvm,
 shogun).
 .
 While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it
 is eminently suited for such datasets.
 .
 This is a package of PyMVPA v.2.  Previously released stable version
 is provided by the python-mvpa package.
Python-Version: 2.6, 2.7

Package: python-mvpa2-doc
Source: pymvpa2
Version: 2.1.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 16043
Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore
Suggests: python-mvpa2
Homepage: http://www.pymvpa.org
Priority: optional
Section: doc
Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.1.0-1~nd+1_all.deb
Size: 4911588
SHA256: c304d190aef38ea7b3c52996d1cef59fc7e2f137cba4f02cbd7630c3caa16dc3
SHA1: f921bfef0448c65c3c330ab4531c424a54e6034c
MD5sum: 17e98b145e0fb36964397d80706037e1
Description: documentation and examples for PyMVPA v. 2
 This is an add-on package for the PyMVPA framework. It provides a
 HTML documentation (tutorial, FAQ etc.) as well as example scripts.

Package: python-neo
Source: neo
Version: 0.2.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 2204
Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy (>= 1:1.3~), python-quantities (>= 0.9.0~)
Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore
Homepage: http://neuralensemble.org/trac/neo
Priority: extra
Section: python
Filename: pool/main/n/neo/python-neo_0.2.0-1~nd+1_all.deb
Size: 1382874
SHA256: 59ff26940fc53d1cdbd7dc9319c5247a36ebdba7308a66051d52a6cfd8f39a4a
SHA1: fb983cdf4ee7efca0b9951e4026efa07939a3f50
MD5sum: e7d065a1d9177eb62eebf8a66bfb31ea
Description: Python IO library for electrophysiological data formats
 NEO stands for Neural Ensemble Objects and is a project to provide common
 classes and concepts for dealing with electro-physiological (in vivo
 and/or simulated) data to facilitate collaborative software/algorithm
 development. In particular Neo provides: a set a classes for data
 representation with precise definitions, an IO module with a simple API,
 documentation, and a set of examples.
 .
 NEO offers support for reading data from numerous proprietary file formats
 (e.g. Spike2, Plexon, AlphaOmega, BlackRock, Axon), read/write support for
 various open formats (e.g. KlustaKwik, Elan, WinEdr, WinWcp, PyNN), as well
 as support common file formats, such as HDF5 with Neo-structured content
 (NeoHDF5, NeoMatlab).
 .
 Neo's IO facilities can be seen as a pure-Python and open-source Neuroshare
 replacement.

Package: python-nibabel
Source: nibabel
Version: 1.2.2-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 4139
Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy
Recommends: python-dicom, python-fuse
Suggests: python-nibabel-doc
Provides: python2.6-nibabel, python2.7-nibabel
Homepage: http://nipy.sourceforge.net/nibabel
Priority: extra
Section: python
Filename: pool/main/n/nibabel/python-nibabel_1.2.2-1~nd+1_all.deb
Size: 1822314
SHA256: 7429c239c5ad69d875788bd7679ea195fcb9d69ce1aae27b8b22ac380f648040
SHA1: 6db1f0c58ad393232a075d93d14c7b302d9b116e
MD5sum: 67a22b8ae05f5c9583edc15252ba42f7
Description: Python bindings to various neuroimaging data formats
 NiBabel provides read and write access to some common medical and
 neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI,
 NIfTI1, MINC, as well as PAR/REC. The various image format classes give full
 or selective access to header (meta) information and access to the image data
 is made available via NumPy arrays.  NiBabel is the successor of PyNIfTI.
 .
 This package also provides a commandline tools:
 .
  - dicomfs - FUSE filesystem on top of a directory with DICOMs
  - nib-ls - 'ls' for neuroimaging files
  - parrec2nii - for conversion of PAR/REC to NIfTI images
Python-Version: 2.6, 2.7

Package: python-nibabel-doc
Source: nibabel
Version: 1.2.2-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 2443
Depends: neurodebian-popularity-contest, libjs-jquery
Homepage: http://nipy.sourceforge.net/nibabel
Priority: extra
Section: doc
Filename: pool/main/n/nibabel/python-nibabel-doc_1.2.2-1~nd+1_all.deb
Size: 447570
SHA256: 9a2bb045175699219f681c4a77c3b55c2f3c01584003ea8788e976c2deaca291
SHA1: c7aeef952b23786d15a8865593858465ef579975
MD5sum: 9140de67d4c6517d83efa54fe675d4e1
Description: documentation for NiBabel
 NiBabel provides read and write access to some common medical and
 neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI,
 NIfTI1, MINC, as well as PAR/REC. The various image format classes give full
 or selective access to header (meta) information and access to the image data
 is made available via NumPy arrays.  NiBabel is the successor of PyNIfTI.
 .
 This package provides the documentation in HTML format.

Package: python-nipy
Source: nipy
Version: 0.2.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 2777
Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.2), python-support (>= 0.90.0), python-scipy, python-nibabel, python-nipy-lib (>= 0.2.0-1~nd+1)
Recommends: python-matplotlib, mayavi2, python-sympy
Suggests: python-mvpa
Provides: python2.6-nipy, python2.7-nipy
Homepage: http://neuroimaging.scipy.org
Priority: extra
Section: python
Filename: pool/main/n/nipy/python-nipy_0.2.0-1~nd+1_all.deb
Size: 763512
SHA256: cb0b55cba5547edc01d2586a5c565f35cf542e3b920ae2093b2e0faa6c8061e3
SHA1: 7b65acab3d1851cc30cfa4e6dcba13387de77a53
MD5sum: e2ad09e52d01ca7ad5bcfb0bf34bb890
Description: Analysis of structural and functional neuroimaging data
 NiPy is a Python-based framework for the analysis of structural and
 functional neuroimaging data.  It provides functionality for
  - General linear model (GLM) statistical analysis
  - Combined slice time correction and motion correction
  - General image registration routines with flexible cost functions,
    optimizers and re-sampling schemes
  - Image segmentation
  - Basic visualization of results in 2D and 3D
  - Basic time series diagnostics
  - Clustering and activation pattern analysis across subjects
  - Reproducibility analysis for group studies
Python-Version: 2.6, 2.7

Package: python-nipy-doc
Source: nipy
Version: 0.2.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 9981
Depends: neurodebian-popularity-contest, libjs-jquery
Recommends: python-nipy
Homepage: http://neuroimaging.scipy.org
Priority: extra
Section: doc
Filename: pool/main/n/nipy/python-nipy-doc_0.2.0-1~nd+1_all.deb
Size: 3763510
SHA256: f2a881db5dd689b601a0bfee42dd008954c0811523daa6405acd4a4c9f298617
SHA1: 9eb1a09efe4c7c9466271af66f563b18db2f1d5e
MD5sum: 0ad5192e1871b9548107407313ea4a10
Description: documentation and examples for NiPy
 This package contains NiPy documentation in various formats (HTML,
 TXT) including
  * User manual
  * Developer guidelines
  * API documentation

Package: python-nipype
Source: nipype
Version: 0.6.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 2320
Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib
Recommends: ipython, python-nose, graphviz
Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat
Provides: python2.6-nipype, python2.7-nipype
Homepage: http://nipy.sourceforge.net/nipype/
Priority: optional
Section: python
Filename: pool/main/n/nipype/python-nipype_0.6.0-1~nd+1_all.deb
Size: 521724
SHA256: 31032d8f53e4dae48dccd26061cdebeaebe277814a3fbd0808df5d7e80167794
SHA1: 86058e978a39a6a6405e2b386659caca87f85612
MD5sum: aea999a55c8f894e412958312a4fb857
Description: Neuroimaging data analysis pipelines in Python
 Nipype interfaces Python to other neuroimaging packages and creates
 an API for specifying a full analysis pipeline in Python.  Currently,
 it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be
 extended for other packages (such as lipsia).

Package: python-nipype-doc
Source: nipype
Version: 0.6.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 12433
Depends: neurodebian-popularity-contest, libjs-jquery
Suggests: python-nipype
Homepage: http://nipy.sourceforge.net/nipype/
Priority: optional
Section: doc
Filename: pool/main/n/nipype/python-nipype-doc_0.6.0-1~nd+1_all.deb
Size: 5727448
SHA256: 522902b32cb88a54b81966bb4dd59a92cb0468a5ea17daf0275f4bc9fc4cad25
SHA1: a8d0691ab4c82d59b0b4732a84f6b8bc047b8ed8
MD5sum: ffdb91454a5d00614b85994bb3b4fabe
Description: Neuroimaging data analysis pipelines in Python -- documentation
 Nipype interfaces Python to other neuroimaging packages and creates
 an API for specifying a full analysis pipeline in Python.  Currently,
 it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be
 extended for other packages (such as lipsia).
 .
 This package contains Nipype examples and documentation in various formats.

Package: python-nitime
Source: nitime
Version: 0.4-2~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 9294
Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy
Recommends: python-matplotlib, python-nose, python-nibabel, python-networkx
Homepage: http://nipy.org/nitime
Priority: extra
Section: python
Filename: pool/main/n/nitime/python-nitime_0.4-2~nd+1_all.deb
Size: 3908880
SHA256: 9b84b1e4c4790ccc493da1e3bf4821527daeeea31373bc9ad826ca06310b069d
SHA1: 6cd41f955262beada04f56a20e76bec57566a89d
MD5sum: 39ba98d1a26fb31da6f01dd7e2158aa9
Description: timeseries analysis for neuroscience data (nitime)
 Nitime is a Python module for time-series analysis of data from
 neuroscience experiments.  It contains a core of numerical algorithms
 for time-series analysis both in the time and spectral domains, a set
 of container objects to represent time-series, and auxiliary objects
 that expose a high level interface to the numerical machinery and
 make common analysis tasks easy to express with compact and
 semantically clear code.

Package: python-nitime-doc
Source: nitime
Version: 0.4-2~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 6842
Depends: neurodebian-popularity-contest, libjs-jquery
Suggests: python-nitime
Homepage: http://nipy.org/nitime
Priority: extra
Section: doc
Filename: pool/main/n/nitime/python-nitime-doc_0.4-2~nd+1_all.deb
Size: 5338022
SHA256: 0898b98c18494ae1229649b0999f9c4d7a3d5f4879ca9ba759b5f003559b79f3
SHA1: 96d08bb174a9d70283889f9958b7a3d843036720
MD5sum: e3a376c097907d751641e5f0a77e5400
Description: timeseries analysis for neuroscience data (nitime) -- documentation
 Nitime is a Python module for time-series analysis of data from
 neuroscience experiments.
 .
 This package provides the documentation in HTML format.

Package: python-openopt
Source: openopt
Version: 0.38+svn1589-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 954
Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy
Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle
Suggests: lp-solve
Conflicts: python-scikits-openopt
Replaces: python-scikits-openopt
Provides: python2.6-openopt, python2.7-openopt
Homepage: http://www.openopt.org
Priority: extra
Section: python
Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd+1_all.deb
Size: 245088
SHA256: 720267e7fc1297916d72081d7bffedfc4e911f4cba267f9e83f65ee6cf7eac3b
SHA1: 2a31c5c6bad612fa5d880b23d6c2c2628c1aef20
MD5sum: 5ffcdd148bf0a2e648d7c3960953fc20
Description: Python module for numerical optimization
 Numerical optimization framework developed in Python which provides
 connections to lots of solvers with easy and unified OpenOpt
 syntax. Problems which can be tackled with OpenOpt
  * Linear Problem (LP)
  * Mixed-Integer Linear Problem (MILP)
  * Quadratic Problem (QP)
  * Non-Linear Problem (NLP)
  * Non-Smooth Problem (NSP)
  * Non-Linear Solve Problem (NLSP)
  * Least Squares Problem (LSP)
  * Linear Least Squares Problem (LLSP)
  * Mini-Max Problem (MMP)
  * Global Problem (GLP)
 .
 A variety of solvers is available (e.g. IPOPT, ALGENCAN).
Python-Version: 2.6, 2.7

Package: python-openpyxl
Source: openpyxl
Version: 1.5.8-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 357
Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0)
Recommends: python-nose
Homepage: http://bitbucket.org/ericgazoni/openpyxl/
Priority: optional
Section: python
Filename: pool/main/o/openpyxl/python-openpyxl_1.5.8-1~nd+1_all.deb
Size: 71790
SHA256: 573946bde70e1d9f92b4c1b16b44cc3c380dd40a8c45b32c849d3388dc1e2bb9
SHA1: b1f2724d2f023befbbd64e3a8857c5b25ed7d058
MD5sum: b8c5557b275a4ddf7e271bc9f9df17f5
Description: module to read/write OpenXML xlsx/xlsm files
 Openpyxl is a pure Python module to read/write Excel 2007 (OpenXML)
 xlsx/xlsm files.

Package: python-pandas
Source: pandas
Version: 0.8.1-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 2793
Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy (>= 1:1.6~), python-dateutil, python-pandas-lib (>= 0.8.1-1~nd+1)
Recommends: python-scipy, python-matplotlib, python-tables, python-tz, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt
Suggests: python-pandas-doc
Provides: python2.6-pandas, python2.7-pandas
Homepage: http://pandas.sourceforge.net
Priority: optional
Section: python
Filename: pool/main/p/pandas/python-pandas_0.8.1-1~nd+1_all.deb
Size: 628614
SHA256: dac80069c373b34cd8353445b866a75d0ee6978d07acc4c57ab4481608e9a514
SHA1: f0312eba6a26a00d7dbffc2fff2a54324b62958a
MD5sum: 0e5f6af21919a0722a4172bb978a9d51
Description: data structures for "relational" or "labeled" data
 pandas is a Python package providing fast, flexible, and expressive
 data structures designed to make working with "relational" or
 "labeled" data both easy and intuitive. It aims to be the fundamental
 high-level building block for doing practical, real world data
 analysis in Python. pandas is well suited for many different kinds of
 data:
 .
  - Tabular data with heterogeneously-typed columns, as in an SQL
    table or Excel spreadsheet
  - Ordered and unordered (not necessarily fixed-frequency) time
    series data.
  - Arbitrary matrix data (homogeneously typed or heterogeneous) with
    row and column labels
  - Any other form of observational / statistical data sets. The data
    actually need not be labeled at all to be placed into a pandas
    data structure

Package: python-pyentropy
Source: pyentropy
Version: 0.4.1-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 73
Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy (>= 1.3)
Recommends: python-scipy
Suggests: python-nose
Provides: python2.6-pyentropy, python2.7-pyentropy
Homepage: http://code.google.com/p/pyentropy
Priority: extra
Section: python
Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd+1_all.deb
Size: 21334
SHA256: 3ab479e9d42286158d724eb219d6205e3c8071a2a8fd6436afc501b57ecf086b
SHA1: 19b81597aeb2806a30580c43b1fcf5f5ad3d586d
MD5sum: 662336ec73a1d4c272a6d2763ef118df
Description: Python module for estimation information theoretic quantities
 A Python module for estimation of entropy and information theoretic
 quantities using cutting edge bias correction methods, such as
  * Panzeri-Treves (PT)
  * Quadratic Extrapolation (QE)
  * Nemenman-Shafee-Bialek (NSB)
Python-Version: 2.6, 2.7

Package: python-pynn
Source: pynn
Version: 0.7.4-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 778
Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0)
Recommends: python-jinja2, python-cheetah
Suggests: python-neuron, python-brian, python-csa
Homepage: http://neuralensemble.org/trac/PyNN
Priority: extra
Section: python
Filename: pool/main/p/pynn/python-pynn_0.7.4-1~nd+1_all.deb
Size: 192252
SHA256: 3ff4c5c883a1c5bb02a2be535fa1dcd948c85255fdcb77c96f0409962c746ec5
SHA1: ae69cf3effd0755cd0ce1aa67082b7f8263e0c34
MD5sum: 942acc8914d772f0cb8350f54ad5173a
Description: simulator-independent specification of neuronal network models
 PyNN allows for coding a model once and run it without modification
 on any simulator that PyNN supports (currently NEURON, NEST, PCSIM
 and Brian).  PyNN translates standard cell-model names and parameter
 names into simulator-specific names.

Package: python-quantities
Version: 0.10.1-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 323
Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy (>= 1.4)
Homepage: http://packages.python.org/quantities/
Priority: extra
Section: python
Filename: pool/main/p/python-quantities/python-quantities_0.10.1-1~nd+1_all.deb
Size: 62610
SHA256: 24764ab44e8e2357cdb8d4882acce352d96b34ed6b3af8be217617eb51848f83
SHA1: 9367905e8af4cb696831c327b20e43a3e2d52616
MD5sum: d08b442a214c35f1e1f9fa595d311cf6
Description: Library for computation of physical quantities with units, based on numpy
 Quantities is designed to handle arithmetic and
 conversions of physical quantities, which have a magnitude, dimensionality
 specified by various units, and possibly an uncertainty.
 Quantities builds on the popular numpy library and is
 designed to work with numpy ufuncs, many of which are already
 supported.

Package: python-scikits-learn
Source: scikit-learn
Version: 0.11.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 30
Depends: neurodebian-popularity-contest, python-sklearn, python (>= 2.6), python-support (>= 0.90.0)
Homepage: http://scikit-learn.sourceforge.net
Priority: optional
Section: oldlibs
Filename: pool/main/s/scikit-learn/python-scikits-learn_0.11.0-1~nd+1_all.deb
Size: 22866
SHA256: 01234353863d8c5e32a3721fcc18218c309721199aaef89d6612a94610a06bd1
SHA1: c785594f203c192167bd228651a7937d7cb02300
MD5sum: f401fba13f899c3521e4b9e30c9e81c9
Description: transitional compatibility package for scikits.learn -> sklearn migration
 Provides old namespace (scikits.learn) and could be removed if
 dependent code migrated to use sklearn for clarity of the namespace.

Package: python-scikits.statsmodels
Source: statsmodels
Version: 0.4.2-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 22
Depends: neurodebian-popularity-contest, python-statsmodels, python (>= 2.5), python-support (>= 0.90.0)
Homepage: http://statsmodels.sourceforge.net/
Priority: extra
Section: oldlibs
Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.4.2-1~nd+1_all.deb
Size: 10262
SHA256: 5a1ec9605a89c3f19ea93e03977c5696e0788c3d14aae128d08349b344d8096b
SHA1: 03cd323aca7b3aaa6267e90a2a913b9a952d40e3
MD5sum: 35729332919cbc14d7622507553402bd
Description: transitional compatibility package for statsmodels migration
 Provides old namespace (scikits.statsmodels) and could be removed if
 dependent code migrated to use statsmodels for clarity of the namespace.

Package: python-scikits.statsmodels-doc
Source: statsmodels
Version: 0.3.1-4~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 15096
Depends: neurodebian-popularity-contest, libjs-jquery
Suggests: python-scikits.statsmodels
Conflicts: python-scikits-statsmodels-doc
Replaces: python-scikits-statsmodels-doc
Homepage: http://statsmodels.sourceforge.net/
Priority: extra
Section: doc
Filename: pool/main/s/statsmodels/python-scikits.statsmodels-doc_0.3.1-4~nd+1_all.deb
Size: 1894226
SHA256: cd2772a5572779e19faeaeed78f056d1c1c2cdb363af2b61a62bf5869991b627
SHA1: 3fbe0791b5753270777ad088300273615771b19a
MD5sum: db4a944c73e6085af650252f7beb3b85
Description: documentation and examples for python-scikits.statsmodels
 This package contains HTML documentation and example scripts for
 python-scikits.statsmodels.

Package: python-skimage
Source: skimage
Version: 0.6.1-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 3642
Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy, python-support (>= 0.90.0), python2.6, python-scipy (>= 0.9), python-skimage-lib (>= 0.6.1-1~nd+1), libfreeimage3
Recommends: python-nose, python-matplotlib (>= 1.0), python-imaging
Suggests: python-skimage-doc, python-opencv
Provides: python2.6-skimage, python2.7-skimage
Homepage: http://scikits-image.org
Priority: optional
Section: python
Filename: pool/main/s/skimage/python-skimage_0.6.1-1~nd+1_all.deb
Size: 2539226
SHA256: a0a9f2bbce4b28ded7277a251d0ba2fed2ebc3e8a0a56a846bea745ea0cfb968
SHA1: cd5947f22982a94026dc30c3aa21b354cee77f05
MD5sum: 5b7dca9d533b708a2a0d5a7f674da02f
Description: Python modules for image processing
 scikits-image is a collection of image processing algorithms for
 Python.  It performs tasks such as image loading, filtering,
 morphology, segmentation, color conversions, and transformations.

Package: python-skimage-doc
Source: skimage
Version: 0.6.1-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 4893
Depends: neurodebian-popularity-contest, libjs-jquery
Suggests: python-skimage
Homepage: http://scikits-image.org
Priority: optional
Section: doc
Filename: pool/main/s/skimage/python-skimage-doc_0.6.1-1~nd+1_all.deb
Size: 3608462
SHA256: 780fd10f98b0e07d7177e2043f9038f50e0ae28f5604477b490148f357af70a0
SHA1: 75b9529baa59de3e31c374a0f25dd9a4352ff1cd
MD5sum: 4d806fe7a5168b1558284da7d4670de9
Description: Documentation and examples for scikits-image
 This package contains documentation and example scripts for
 python-skimage.

Package: python-sklearn
Source: scikit-learn
Version: 0.11.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 2500
Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.11.0-1~nd+1)
Recommends: python-nose, python-psyco, python-matplotlib, python-joblib (>= 0.4.5)
Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython
Enhances: python-mdp, python-mvpa2
Breaks: python-scikits-learn (<< 0.9~)
Replaces: python-scikits-learn (<< 0.9~)
Provides: python2.6-sklearn, python2.7-sklearn
Homepage: http://scikit-learn.sourceforge.net
Priority: optional
Section: python
Filename: pool/main/s/scikit-learn/python-sklearn_0.11.0-1~nd+1_all.deb
Size: 890724
SHA256: 9056bd4f54d98a315c78286b21268624f146591982e71f62ff69ac5a3bde97a1
SHA1: 91ab02ccb0a4aed5a2703f52156cf2c5182f0d3c
MD5sum: 88601fb490686312f56711d6685dffa8
Description: Python modules for machine learning and data mining
 scikit-learn is a collection of Python modules relevant to
 machine/statistical learning and data mining.  Non-exhaustive list of
 included functionality:
  - Gaussian Mixture Models
  - Manifold learning
  - kNN
  - SVM (via LIBSVM)
Python-Version: 2.6, 2.7

Package: python-sklearn-doc
Source: scikit-learn
Version: 0.11.0-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 35019
Depends: neurodebian-popularity-contest, libjs-jquery
Suggests: python-sklearn
Conflicts: python-scikits-learn-doc
Replaces: python-scikits-learn-doc
Homepage: http://scikit-learn.sourceforge.net
Priority: optional
Section: doc
Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.11.0-1~nd+1_all.deb
Size: 25521112
SHA256: f98c34ec4fb603346675665dd5b78bc8b0e8e0ed410dd94d35aefec98cec8224
SHA1: 30c38dc8dd85a7ae3003fb0c5447663f2659bc11
MD5sum: e7b4232d991e2e197b146fc59059aeae
Description: documentation and examples for scikit-learn
 This package contains documentation and example scripts for
 python-sklearn.

Package: python-statsmodels
Source: statsmodels
Version: 0.4.2-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 12433
Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-statsmodels-lib (>= 0.4.2-1~nd+1)
Recommends: python-pandas, python-matplotlib, python-nose, python-joblib
Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4)
Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4)
Provides: python2.6-statsmodels, python2.7-statsmodels
Homepage: http://statsmodels.sourceforge.net/
Priority: extra
Section: python
Filename: pool/main/s/statsmodels/python-statsmodels_0.4.2-1~nd+1_all.deb
Size: 3104792
SHA256: a81091e1b48cd71e93c566a120872cd491c9fa0a8ca9ed9421a28c779f05f8f4
SHA1: 7ce0061bc0757b101051605b015f9a4754944c38
MD5sum: e2fe656c2074d522e7cecde0d4b5fda3
Description: Python module for the estimation of statistical models
 statsmodels Python module provides classes and functions for the
 estimation of several categories of statistical models. These
 currently include linear regression models, OLS, GLS, WLS and GLS
 with AR(p) errors, generalized linear models for six distribution
 families and M-estimators for robust linear models. An extensive list
 of result statistics are available for each estimation problem.

Package: python-statsmodels-doc
Source: statsmodels
Version: 0.4.2-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 23647
Depends: neurodebian-popularity-contest, libjs-jquery
Suggests: python-statsmodels
Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc
Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc
Homepage: http://statsmodels.sourceforge.net/
Priority: extra
Section: doc
Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.4.2-1~nd+1_all.deb
Size: 7342598
SHA256: dacbb7d4e0cbcac0de0fb1d541ab55c2c4e3c21cc65aaf3b999527f0ace6efb8
SHA1: b313e92edeeeadd3992c44c76d4e4ea37c26792d
MD5sum: 43176138c31ec98297cd792d03604bde
Description: documentation and examples for statsmodels
 This package contains HTML documentation and example scripts for
 python-statsmodels.

Package: python-surfer
Source: pysurfer
Version: 0.3+git15-gae6cbb1-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 95
Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-nibabel, python-imaging, mayavi2, python-argparse, ipython
Recommends: mencoder
Homepage: http://pysurfer.github.com
Priority: extra
Section: python
Filename: pool/main/p/pysurfer/python-surfer_0.3+git15-gae6cbb1-1~nd+1_all.deb
Size: 28904
SHA256: bf545c8b3b5ece2227156e364694d07e7df8b5c2a98cc662c61077e70539e87d
SHA1: 096d69ae640c7a9dbdbd958e260da4745308ac9e
MD5sum: e49aa156e4c8f1ab8f34630c44d82bbb
Description: visualize Freesurfer's data in Python
 This is a Python package for visualization and interaction with cortical
 surface representations of neuroimaging data from Freesurfer. It
 extends Mayavi’s powerful visualization engine with a high-level interface for
 working with MRI and MEG data.
 .
 PySurfer offers both a command-line interface designed to broadly replicate
 Freesurfer’s Tksurfer program as well as a Python library for writing scripts
 to efficiently explore complex datasets.
Python-Version: 2.6, 2.7

Package: spm8-common
Source: spm8
Version: 8.4667~dfsg.1-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 18467
Depends: neurodebian-popularity-contest
Recommends: spm8-data, spm8-doc
Priority: extra
Section: science
Filename: pool/main/s/spm8/spm8-common_8.4667~dfsg.1-1~nd+1_all.deb
Size: 10573658
SHA256: 226ac256f894993c96fc6a40908bf6e47ceab8d4ff9f3af30652c1775edf1213
SHA1: 339aaf8fe8966497fff28cafe3021364675aa6de
MD5sum: aa23e361a358e383cb29207af47bf5bf
Description: analysis of brain imaging data sequences
 Statistical Parametric Mapping (SPM) refers to the construction and assessment
 of spatially extended statistical processes used to test hypotheses about
 functional brain imaging data. These ideas have been instantiated in software
 that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG
 and MEG data.
 .
 This package provides the platform-independent M-files.

Package: spm8-data
Source: spm8
Version: 8.4667~dfsg.1-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 72987
Depends: neurodebian-popularity-contest
Priority: extra
Section: science
Filename: pool/main/s/spm8/spm8-data_8.4667~dfsg.1-1~nd+1_all.deb
Size: 52167698
SHA256: 4a4eeb861f45cf0858446512080c0b2f56b5db493d28826ba5180fd349214910
SHA1: d1c4922bc6fcfdae670687602dd11d3663126406
MD5sum: b31d5e36ca9c135ddcc036ca808b3470
Description: data files for SPM8
 Statistical Parametric Mapping (SPM) refers to the construction and assessment
 of spatially extended statistical processes used to test hypotheses about
 functional brain imaging data. These ideas have been instantiated in software
 that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG
 and MEG data.
 .
 This package provide the data files shipped with the SPM distribution, such as
 various stereotaxic brain space templates and EEG channel setups.

Package: spm8-doc
Source: spm8
Version: 8.4667~dfsg.1-1~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 9370
Depends: neurodebian-popularity-contest
Priority: extra
Section: doc
Filename: pool/main/s/spm8/spm8-doc_8.4667~dfsg.1-1~nd+1_all.deb
Size: 8648902
SHA256: b4ee2536e640c2f0a282f3860135bee537c99e28a1d82faf241f6709de0ec628
SHA1: 72570a0e4f49ee16527d7e4f0c8f5d40d925aef2
MD5sum: 7d5c029e5f5aa10a0c0d79bdaeede8d6
Description: manual for SPM8
 Statistical Parametric Mapping (SPM) refers to the construction and assessment
 of spatially extended statistical processes used to test hypotheses about
 functional brain imaging data. These ideas have been instantiated in software
 that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG
 and MEG data.
 .
 This package provides the SPM manual in PDF format.

Package: vtk-doc
Source: vtk
Version: 5.8.0-7+b0~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 342007
Depends: neurodebian-popularity-contest, doc-base
Suggests: libvtk5-dev, vtk-examples, vtkdata
Homepage: http://www.vtk.org/
Priority: optional
Section: doc
Filename: pool/main/v/vtk/vtk-doc_5.8.0-7+b0~nd+1_all.deb
Size: 66710216
SHA256: ef2921e37681f7364119b79457483cd3ca7da8cd063a96438cffe23aeba52938
SHA1: abc4b1ccf35fd6c0cc20f67836fb7ffcbfc69161
MD5sum: b7ef2d7972fe60ad7ce2f891faac4205
Description: VTK class reference documentation
 The Visualization Toolkit (VTK) is an object oriented, high level
 library that allows one to easily write C++ programs, Tcl, Python and
 Java scripts that do 3D visualization.
 .
 This package contains exhaustive HTML documentation for the all the
 documented VTK C++ classes.  The documentation was generated using
 doxygen and some excellent perl scripts from Sebastien Barre et. al.
 Please read the README.docs in /usr/share/doc/vtk-doc/ for details.
 The documentation is available under /usr/share/doc/vtk/html.

Package: vtk-examples
Source: vtk
Version: 5.8.0-7+b0~nd+1
Architecture: all
Maintainer: NeuroDebian Maintainers <team@neuro.debian.net>
Installed-Size: 2521
Depends: neurodebian-popularity-contest
Suggests: libvtk5-dev, tcl-vtk, python-vtk, vtk-doc, python, tclsh, libqt4-dev
Homepage: http://www.vtk.org/
Priority: optional
Section: graphics
Filename: pool/main/v/vtk/vtk-examples_5.8.0-7+b0~nd+1_all.deb
Size: 578892
SHA256: fab181213376a1077411077e48a5640af76ceb2868302e2e03b18e4e6a0859fd
SHA1: e0087beef829cbfd4d09abfd52a4e526b2b11963
MD5sum: efe0f5b35bccb7b5f9d251a25970a0ac
Description: C++, Tcl and Python example programs/scripts for VTK
 The Visualization Toolkit (VTK) is an object oriented, high level
 library that allows one to easily write C++ programs, Tcl, Python and
 Java scripts that do 3D visualization.
 .
 This package contains examples from the VTK source. To compile
 the C++ examples you will need to install the vtk-dev package as well.
 Some of them require the libqt4-dev package.
 .
 The Python and Tcl examples can be run with the corresponding packages
 (python-vtk, tcl-vtk).