Package: debian-handbook Version: 6.0+20120509~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 612 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~nd11.04+1_all.deb Size: 103468 SHA256: d3327c84e7ab3f07be88f6a4e6942b129518be2cb722866360b24df0e4d01ba3 SHA1: 5050c018f615ff82fbda3d048fffbfd56cd1dd33 MD5sum: 771a9dcf792f6851068d12e917cf49e8 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 0 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~nd11.04+1_all.deb Size: 934 SHA256: d1579ba48aed1f9bd263073af0308463c0568250dd60955c4918b60e2e4c6e73 SHA1: 4214237b971851c04c89c19b6aaee5f9f37894fc MD5sum: 00068ce69e03414c73889aa851f20478 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 472 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~nd11.04+1_all.deb Size: 296938 SHA256: 3c4fd8502ed44592672b80185d19472e926f68a30e258a80247992230d111560 SHA1: e740f624c5f925720c5cc5ee36b8cb692f54b082 MD5sum: ce62945e56c568f1a9b0698fbe2720b7 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: fslview-doc Source: fslview Version: 4.0.0~beta1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3112 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.0~beta1-1~nd11.04+1_all.deb Size: 2346152 SHA256: df74d59bdfa3991f2e817aef11d3aa9cc099498568c4c884d9386ad10878f6fa SHA1: eb8259ab226ff1cf0fd94d55fea7b11f636258bb MD5sum: ecddb337b71f7fb582d31a8715e28b02 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: guacamole Version: 0.6.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 344 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~nd11.04+1_all.deb Size: 275498 SHA256: a2f6796935e555594f6aff7d2cb3d88e4782f45fa075c115204d71987008c6f9 SHA1: 68455f3f2434a553a66f04213099df969696ee63 MD5sum: 44096bb567a8b09fe04b075190e885f2 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 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~nd11.04+1_all.deb Size: 3938 SHA256: fa0bc017ff0d776e72ff367b9ceda72a7c809915734ae36abaf3c0eb33ffe9d6 SHA1: bba32bcf12f2e6f8ba73a759fa88e9e8d5174d4c MD5sum: a820ae140599e8d4b1fd5ea896630b0b 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: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd11.04+1_all.deb Size: 9664 SHA256: 476c535d8943eb938b35a8e65e47a4b2646f1e5443d6d7237f665a752ba2603d SHA1: 3e3f62712d1730074a587cf8af38329d5992ce48 MD5sum: 184c37775c1c83319cef97cd9b860ff5 Description: utility for pushing DICOM data to the INCF datasharing server A command line utility for anonymizing and sending DICOM data to the XNAT image database at the International Neuroinformatics Coordinating Facility (INCF). This tool is maintained by the INCF NeuroImaging DataSharing (NIDASH) task force. Package: libfreenect-doc Source: libfreenect Version: 1:0.1.2+dfsg-6~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 592 Depends: neurodebian-popularity-contest Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-6~nd11.04+1_all.deb Size: 89398 SHA256: 094a6343ffe26c301daa99d2c043c6341b32a671ff06b5db95aa09f3c8a8738b SHA1: 53eb0feb7dbee47587b62871c61f0590c23301df MD5sum: 73e0df6d0713669e506c2649edcb3d0c 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 268 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~nd11.04+1_all.deb Size: 114142 SHA256: 753f61829869cde8e9e4ad150ea4d5f59598399130eff73b90c7c8ea6d69a87d SHA1: 2153f036aaf80b26c5748a9a41913af92efc3af3 MD5sum: 2704bf1e6371c206875b6ef391f8d12d 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6184 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~nd11.04+1_all.deb Size: 5345870 SHA256: c28885378b9101910112b6481faa66887cac4f390b7304d2ac7bb22fa7967bb3 SHA1: 984621c6c62354c36f2598bc014cbc6e13edbd93 MD5sum: 7eeaa2d2b8be282f62618e9f1490929b 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 148 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~nd11.04+1_all.deb Size: 13632 SHA256: c8d97ad11f91d634df40c73987fb737b0d7e03f8931664592bec4943c00963db SHA1: caf712e886bedd13c2999e54b318f8444f9d65f3 MD5sum: 845ec0859f245031db82613bbb329663 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.28~nd11.04+1_all.deb Size: 6560 SHA256: 4b93cae074eb2370690a1b2f8f0b128af442b737b78f764b0f7e5ba6e6d8dac0 SHA1: 69ad8e48c008cc427f9399bed153e79cda85c745 MD5sum: 7c7c9349206bf73c8198a4f6a3fefb75 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.28~nd11.04+1_all.deb Size: 5734 SHA256: 1c35fa7be34e06f472b46a1f8b7b09b3914e749ff0bab1333aa55e5799add684 SHA1: 5a86fad4299019abc8a994c2dd4ae0930e7c9608 MD5sum: bc90af064bdff3c77c1acc0fadba0685 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 652 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~nd11.04+1_all.deb Size: 614942 SHA256: c8ad96e34099488d6a35ac6a68afc0c203ca4b990a538531b9763ec25636f9ea SHA1: 517cc41491f6f300fa192cbe538a05da8de60993 MD5sum: 01b860c8bcef0ceb8bcc0b23a6369b3d 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: 0.3.24+ds-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1716 Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python (>= 2.7.1-0ubuntu2) 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_0.3.24+ds-1~nd11.04+1_all.deb Size: 343538 SHA256: 820a1187c32ca0f9b1ed50bde37b387ab62608bc03e273fae4dcecf1cb646608 SHA1: ad92db07aedb7f824f91f9e3bfb54c21787271b8 MD5sum: 135ccfddeebb778564fbaff327e81abe 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: 0.7~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1545 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.7~nd+1_all.deb Size: 1482008 SHA256: adc5cfa1161cb2c81de6dfe8ef28337496f4482dbd4e81529fdca5bb7f99d234 SHA1: 9326aef75840496b2113097a67ab254223056afe MD5sum: 08e90e8b604b39dea04f6eb7b4359b21 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: 1.74.03.dfsg-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6040 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_1.74.03.dfsg-1~nd11.04+1_all.deb Size: 3102248 SHA256: e8b7746268331b7b1351e180bc5e2e32c06fd8e69ed2b1a4cdac76eeb1b2ab61 SHA1: f8aa6959505ff0141105d2abd5daab2b89125e3d MD5sum: c8582bd4d9a1d4dcb84079bb12ea8895 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 53608 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~nd11.04+1_all.deb Size: 19433950 SHA256: 8462e06a500378d16daa1e2c4bf74a0da563ae536abef821d12247d696f93cb7 SHA1: 5b7958f820521a713dc65821cee1455c551bc451 MD5sum: 0270a5ed08257f707e86e309021808f8 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-brian Source: brian Version: 1.4.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2728 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.0-1~nd11.04+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.4.0-1~nd11.04+1_all.deb Size: 503146 SHA256: 5eee9be4b4e4f3495c765363190d2506df3adc5e8d4ed0cbd3107db2b096af34 SHA1: f557e091811866f5a518ca9d55ea6f6743013ec8 MD5sum: 291513f4ebf91949eee0ad36dfab6118 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 Package: python-brian-doc Source: brian Version: 1.4.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7196 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-brian Homepage: http://www.briansimulator.org/ Priority: extra Section: doc Filename: pool/main/b/brian/python-brian-doc_1.4.0-1~nd11.04+1_all.deb Size: 2149582 SHA256: 124dfaae7e445fdfa87ff1c1838e96d56eada6cac8ca44b941722a1905a9b1a6 SHA1: 7abb15f2e1b5cbff0b3b51a7d9cfd5f15d64a3ca MD5sum: 9380029b3d372c84303816b2665d3676 Description: simulator for spiking neural networks - documentation Brian is a clock-driven simulator for spiking neural networks. . This package provides user's manual (in HTML format), examples and demos. Package: python-joblib Source: joblib Version: 0.6.4-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 252 Depends: neurodebian-popularity-contest, python (>= 2.5), 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~nd11.04+1_all.deb Size: 51616 SHA256: 454e4e4305b9230ee30eadbab007099c86f2684321f82fc2444a617eec198315 SHA1: 478946b50da5435350ba55c0569f62e29ecdd80a MD5sum: 0e2d2dd3cda9281ec5830df555420a8d 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), 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~nd11.04+1_all.deb Size: 7434 SHA256: 406466499e0bc085da82ed8c52f44c0d3923019fcbd6cb8d6488e4353828019d SHA1: 9981902e387971ec03dbd4213cae64843ae940a1 MD5sum: 7dfc493b2e0805c3d3a6e140ecf43e68 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1912 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), 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~nd11.04+1_all.deb Size: 482294 SHA256: 771806dadb9ad024c311981dee450efe4656f4dac51247810c959f438f0e777f SHA1: e4d58e174a40bb1a07c0f31b7a7a7ce8c32867cf MD5sum: 76a9b4acffab5a72b197a2ba1202938b 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-mvpa Source: pymvpa Version: 0.4.8-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4092 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0), python2.7, python-mvpa-lib (>= 0.4.8-1~nd11.04+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~nd11.04+1_all.deb Size: 2193322 SHA256: bb14ed8aafad29298d799b356ef1bf683e76243fd25eed3e56642c3eda7271e8 SHA1: c8244e845665a986bba5ba278533e01efbd0ce0d MD5sum: 63d524d59c8ad7d9b7184cb40ce58f97 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40768 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~nd11.04+1_all.deb Size: 8474824 SHA256: 7810175fa14490cd1307eb000af584a1a105b619952516f6569c9b2b43636cd7 SHA1: 1c90b3a310d08ba85badfba9bad86a58e850665b MD5sum: 1beb40b5ec3c0b23af75be364d5914b5 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4800 Depends: neurodebian-popularity-contest, python (>= 2.4), python-numpy (>= 1:1.5.1), python-numpy (<< 1:1.6), python-support (>= 0.90.0), python-mvpa2-lib (>= 2.1.0-1~nd11.04+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~nd11.04+1_all.deb Size: 2354506 SHA256: 03df71a492623d1c09e727ce1f9f4bd9b3638ff7aa607d23a172bae2787e1266 SHA1: 7602ddb72a30eb29f611450859639ed534fc3a8d MD5sum: e683816705b1046a49577e8bbe9f30ac 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25204 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~nd11.04+1_all.deb Size: 4909390 SHA256: bbc45384823756db5f7e6729c293720c9799a7ce3ece01e88e2daa856a06827b SHA1: fc926ae98c2fdbe0f003974ca7df5e2dc4c28675 MD5sum: d5d5ed616f7e146aedd134a38fc5a2d2 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2492 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy (>= 1:1.5.1), python-numpy (<< 1:1.6), 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~nd11.04+1_all.deb Size: 1372780 SHA256: 766354875c026c79a2470a546aae4ea79c6a287bf91a636b846abd84a352745b SHA1: f17942409e9b082acf591e5ef10c537508e7c6d1 MD5sum: 0d12a5e4cddee2c85fe72faffc061698 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: 1.3.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4468 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_1.3.0-1~nd11.04+1_all.deb Size: 1816566 SHA256: 56a549ccde33688997b96895b931a08db33446feeb376e5f9f1c1060093f5492 SHA1: c6f01d928311fddd47b81e71013d82322f6b38a3 MD5sum: 3e4aba078c4ad97f2f7fe18b158c99cd 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: 1.3.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2840 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.3.0-1~nd11.04+1_all.deb Size: 413538 SHA256: c60e713abb0fd104d65f9801a6633df4d75962cfaffe9b46e70c8943f9fbf9a2 SHA1: 4909de2452fb456b41cececa383ffc813edc656b MD5sum: d479e4ed9735281f322322711a31c630 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3764 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0), python-scipy, python-nibabel, python-nipy-lib (>= 0.2.0-1~nd11.04+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~nd11.04+1_all.deb Size: 763078 SHA256: 0c4ee8aa6a2cdfdc438473bb4b175307bf2f72db21311ac8f78253593e94bf44 SHA1: 016789e2d381fe75cfbd519854f65dcf41ae3f1c MD5sum: b8e91cecb6dc161375a040894adeb01a 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9496 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~nd11.04+1_all.deb Size: 2371180 SHA256: 8a56f4c9604394812d1039eedfc79f9334e3436839d14c626f15cbfaeb4145c9 SHA1: 1b30671b998fb4ef694f99c57a31670b344782b8 MD5sum: 469b251f291e72e3096b04a2a94cadeb 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3056 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~nd11.04+1_all.deb Size: 517700 SHA256: 3096853878e519710f03f7ac44ce3723b4993145ca4a9dae9051b5eb0ce5d0d9 SHA1: a1fc099b6674bc8c4fc42f8fbd577ac89793a881 MD5sum: 6ac40613ba2258017404e0c241098149 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13464 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~nd11.04+1_all.deb Size: 5788332 SHA256: a3e0fe2283e04031f035136a447f5c3c92c63182132e0c2ee30e813f03ff1555 SHA1: f5a2c68dd4746aceba3cec450ab77d9fc1015436 MD5sum: 2dbe57a7b506c761f64aacb059af8b42 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9444 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~nd11.04+1_all.deb Size: 3908914 SHA256: f3c86735ec1c07de8a3ae548643d79d7419711aab9eaa4f536c6800c40727273 SHA1: ae724375a62f57786f419ea34db719d26a180c6c MD5sum: 96af915baae7b203433db3214fc3a49d 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7128 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~nd11.04+1_all.deb Size: 5294220 SHA256: e3b2bcd163c3cc783fa6d22297c69ff7dc7ea15098c3cb6190a1715fd1dbd97e SHA1: 4615c07f881bf16ab6cef945e07e6cd3275f862d MD5sum: 4838c1f168c9557f36a13148e7b0eb67 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1612 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~nd11.04+1_all.deb Size: 245060 SHA256: 25adc0e03ebcd4df7c4b42a215e69b0b54cd4fa32d32be48fb092879bf959c96 SHA1: 0abb57edb20fa7abe6e33d69a1a88f861afc0e1a MD5sum: 1bda3d05f1868d93ae0d5c09a01c8bbb 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 504 Depends: neurodebian-popularity-contest, 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~nd11.04+1_all.deb Size: 71602 SHA256: 8535bb65d52179942e3bff84c89eb7e77ea74ed2d00a5b3f4aacaaaef66caa2e SHA1: 5079d085996610fa3adc507374b37a21bdbedb3d MD5sum: bdf8ab57b76bc68bb57a54ec565a4ae5 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: 0.7.3-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2220 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-dateutil, python-pandas-lib (>= 0.7.3-1~nd11.04+1) Recommends: python-scipy, python-matplotlib, python-tables, python-tz, python-xlrd, python-scikits.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_0.7.3-1~nd11.04+1_all.deb Size: 460914 SHA256: 4db83900523de2a81465ee1de273c90efe39ace7dd6e731444c7c37d71addfba SHA1: 3dc4ee1157163fa22e13b1dbccdada1f68b301ae MD5sum: 3e068f630295bbb058f3192b7e06849b 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 108 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~nd11.04+1_all.deb Size: 21340 SHA256: d3e1aebdb6d350a4091b2864bc1cfbfd4650b5e42b909f5b1834935fa4a2b502 SHA1: 79e7f896b1e98a39cb2653e7c28e0115780e2d3a MD5sum: 883ef75c66bb64ec02d2b11e09795932 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1008 Depends: neurodebian-popularity-contest, python (>= 2.5), 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~nd11.04+1_all.deb Size: 175636 SHA256: 2a6428ed5654d11968781e6a1e304e42e6f8c5346b3a453bec246ed7766dd365 SHA1: 69fe2023dcd0b31a5de449bb139a2b973342f76a MD5sum: d3644b9ba5edf08db164845015cec7dd 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 508 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), 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~nd11.04+1_all.deb Size: 60344 SHA256: 7198da79d70c3b046900ef5c9934b2b64d8d005f9135f485eb2176a5d43ae0b6 SHA1: 55272357a667712976a080c6dbee2dce5817a06e MD5sum: 186aa6d0a5d0d575d368ac1961e72874 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: 0.12.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, python-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.12.0-1~nd11.04+1_all.deb Size: 23930 SHA256: e55fc7657a53a36b448a866d08f5ede0e0aa670b0fa736776c3d2de6c722bdfd SHA1: 8bab26098eefde2f7ce9c13481727cd8c953e1f6 MD5sum: 82f0af4c9ba79538caf3d256b2a193df 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: 0.3.1-4~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13276 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-rpy Conflicts: python-scikits-statsmodels Replaces: python-scikits-statsmodels Provides: python2.6-scikits.statsmodels, python2.7-scikits.statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.3.1-4~nd11.04+1_all.deb Size: 3099052 SHA256: a3783f4a0225f1740a8b525e78faa5543cc8834fc5d5c90eabda9613e7a7c5b3 SHA1: fb89ac3a90e29472587037cb3d04afc00ce0c92c MD5sum: d23ba58c04184484f6a96ae209b6da25 Description: classes and functions for the estimation of statistical models scikits.statsmodels is a pure Python package that 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. Python-Version: 2.6, 2.7 Package: python-scikits.statsmodels-doc Source: statsmodels Version: 0.3.1-4~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 18740 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~nd11.04+1_all.deb Size: 1877710 SHA256: 8daa6c45ed85370df37e7b64df1c588ffef9755206bc21404601db42fa69f946 SHA1: 44e41d473a07c8acd1d5f6ef4306c66c018107c4 MD5sum: 73b386a9ee0240feab087607b1750fb7 Description: documentation and examples for python-scikits.statsmodels This package contains HTML documentation and example scripts for python-scikits.statsmodels. Package: python-sklearn Source: scikit-learn Version: 0.12.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3200 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.12.0-1~nd11.04+1) Recommends: python-nose, 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_0.12.0-1~nd11.04+1_all.deb Size: 924332 SHA256: dc5e0513be4d6780806bc4c558f13ee766a5b2c19318d476770869e7d39c1ef7 SHA1: 395442037be235aec7a7b0b268bd4b122797fb44 MD5sum: 26f5e28a7010fc3035f8b7f869379b3a 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: 0.12.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 30392 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_0.12.0-1~nd11.04+1_all.deb Size: 16921346 SHA256: e827975c0e2614790fdcd4de203bdcc1404c6e6c95ea8aa1878349f35f0e2d52 SHA1: 585efa93d383b5ca35ae4595ff5bdc49ae8599da MD5sum: 23a0f584f3e64070cf5deeabfc0271a3 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-surfer Source: pysurfer Version: 0.3+git15-gae6cbb1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 152 Depends: neurodebian-popularity-contest, 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~nd11.04+1_all.deb Size: 28010 SHA256: 58e38d33f0ecb23e6d5419e3690b6f8a4fd889eae7b24a1dc0c60f30b9d3a627 SHA1: f79f29c444f4eccd81b81122225fffdb543cfaee MD5sum: f81ddb8fb0acf7b4c263295015061b6f 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 22352 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~nd11.04+1_all.deb Size: 10573730 SHA256: 1ed89a6bdc612f421a81d873339a371d525db78787353ef924ff9c84959ba3c0 SHA1: 3f9ca30b40af16c9789bce60d5f248a7b3dfe446 MD5sum: d640a888ad262e3cdf83dcf52c835f16 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73084 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.4667~dfsg.1-1~nd11.04+1_all.deb Size: 52167688 SHA256: ce55009bec2f76b96ce90263f625630697db37418b8a3252f4c26ad1dbccca70 SHA1: 483782bd568d9f95d9062bf168dcfb1a766742f2 MD5sum: 99c20008937ad48f7e8163d887be2af4 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9380 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.4667~dfsg.1-1~nd11.04+1_all.deb Size: 8648920 SHA256: 735926447772fa98fadc948f4b83de1a4be1cb628df1d508c178185e575bc57f SHA1: 8b47dfda11247dc24e77c835e83129ccf8c379f5 MD5sum: baba74d55f63322228a92cd8ecb4901d 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.