Package: condor-doc Source: condor Version: 7.7.5~dfsg.1-2~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5972 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.7.5~dfsg.1-2~nd10.04+1_all.deb Size: 1276632 SHA256: 2f663efdf3a6030d172ea826daea57a4c90692ff86d9e8967197d0ce7fcb672f SHA1: cf5fddcf714ce449e947f2b2f313948123994a3d MD5sum: 991a45eaa0eab99dda5c7eb32c04a05b Description: documentation for Condor 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 system, 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: 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~nd10.04+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~nd10.04+1_all.deb Size: 103498 SHA256: a6cbd680d17b38454e3879b3522595363274e259ef8ae384b74d96a59ca06b64 SHA1: 0576ec90baa2aaa0a359cf970d0c053ff5cad722 MD5sum: 9323fb420bc9e8eea74a61a8c69ef6e5 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~nd10.04+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~nd10.04+1_all.deb Size: 934 SHA256: 3769cc9cc9bf0203401ad60daa0dc9078cd44b1321447232a60a7eed7611bbe3 SHA1: 41ae04f4b899d090a94761d388e16a26dd980eac MD5sum: 7f6d6e9e0cc4f5164d81767e45a16d1b 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~nd10.04+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~nd10.04+1_all.deb Size: 296958 SHA256: ec43459f9eccaae6c872189adaa36713cc3c00ba13fe223049639ad58f875eb5 SHA1: 3dec137930af1b7b223d0a3c9c981a0fd853835b MD5sum: 72a459678ee1318d96957b9b1089594d 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~nd10.04+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~nd10.04+1_all.deb Size: 274886 SHA256: 1337d0da35ba8e576cb208bc912b42171d2c6da0dbeae513c2e094ec71c0f321 SHA1: 5b7c432c1ad5c30e843a1e1e93d3b020986f178e MD5sum: e088cc8eb882f6c9262bf9b2ba0dacd3 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~nd10.04+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~nd10.04+1_all.deb Size: 3934 SHA256: 39859176349ffb1ea3f2e9492fda61e49e86eb8efb32d3ef436d203564aef909 SHA1: 06266d05d9c0c3c1601ea4af53734ba2de503815 MD5sum: 3c6de2e27be48cd990c9a62caf3f0210 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~nd10.04+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~nd10.04+1_all.deb Size: 9668 SHA256: 11171c867a557dccf1005f009ed4a6af97fb1d06146002c6c34cfe21894eec7c SHA1: 62f5fdd078c2f939b1bb20ca95b3669296a5a9b6 MD5sum: 9777e4ba6307e448ad7788652363f120 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~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 548 Depends: neurodebian-popularity-contest Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-6~nd10.04+1_all.deb Size: 85888 SHA256: eb7e328d32a3c95d598f79eeb7d2d7220060e1ff6d64cdc10006103ff31f05ff SHA1: 9d08af477dc490067cb014fa48899f7f817d8c29 MD5sum: 8d6e307534fdb0341a2b3d722b92aba5 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: 0.29~nd10.04+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_0.29~nd10.04+1_all.deb Size: 114128 SHA256: 0b19172065df943f731f4b9b252eefefbc7c814ebcb559cf7bf23742662b811a SHA1: 0b8c1cbf50b76b90965987402832f17dff598e7a MD5sum: 2ac5f83708923c3b52948714f5dd7d6b 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: 0.29~nd10.04+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_0.29~nd10.04+1_all.deb Size: 5346694 SHA256: 0b909066fc53d0cc088303231a26d2d2a0afb5c4214011d653a3403d726aa2fc SHA1: f7210a3778fbf03af2cfdee2a7febfd8b79222cf MD5sum: 9e30b80b4bf7f1779f59e31ca5a731f4 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: 0.29~nd10.04+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_0.29~nd10.04+1_all.deb Size: 14228 SHA256: 535bfb026a45c57dcd4454bdfc0ba75e98237780dbd20c05a6899adc7e55176f SHA1: 78e4e69672a30a8028539b9d828536541cf4af5e MD5sum: a586a04bed4c31bde7749194a388bc20 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: 0.29~nd10.04+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_0.29~nd10.04+1_all.deb Size: 6932 SHA256: dac0b15a396d4bd4b676eba961c528769c06a2de54ba4ce7d4f6acc81cc2a8fa SHA1: a6590c748fba0cdb90ceef1f6a1eb5f3562e3cbf MD5sum: 89e7b6894d299ceb66f6770b667afb6e 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: 0.29~nd10.04+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_0.29~nd10.04+1_all.deb Size: 6094 SHA256: baa0d206f67d95ba368daac72489ebad173c2b04dbc30a745249fdf7fb575ec1 SHA1: 0667c9be7bca4ee361f8264470ae7384f66cfca1 MD5sum: 0790eb3537f4cd691a903bb0c07e36f3 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: 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~nd10.04+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~nd10.04+1_all.deb Size: 3102352 SHA256: 7cab70d35ecfd48dcb87dc1f419d27a96dff87b16aee87ac254993ab807f2abc SHA1: 77b9e44746ddfb7ad6f722dcc47d1dc5e3ee19bc MD5sum: 3ac5f300b2dc090853f96a790b9b4311 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 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.9+svn2579.dfsg1-1~nd10.04+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~nd10.04+1_all.deb Size: 19434404 SHA256: 1e0a6939d26a5debd944bf1acaece3dff5cbdbd14591a07ec90f6e5c3c1918ed SHA1: 8e3ba262934eff7f7568d77be4558b6156996bb4 MD5sum: afac568d1e1f06470c7b17554c57ec0c 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: 0.6.5-1~nd10.04+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_0.6.5-1~nd10.04+1_all.deb Size: 52638 SHA256: 38fe5692d67b33d5982ba6bf616803e163035282071504992f78b7f6b2c4b808 SHA1: a278fe9cbec8ff336d807d8b3602433d39da53a2 MD5sum: 63b6b3908752f260e73c964e15745cc7 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-mvpa Source: pymvpa Version: 0.4.8-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4092 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-lib (>= 0.4.8-1~nd10.04+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 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.8-1~nd10.04+1_all.deb Size: 2195398 SHA256: 7429e8b63f838a1821b8f444ff85f95162caf21916382f9439b45b2ae1d0cb44 SHA1: c9795cc37ac3b277a8687a7b8e099f0ba776529c MD5sum: 3e21707251da18a094a5a7e40f37fc47 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 Package: python-mvpa-doc Source: pymvpa Version: 0.4.8-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41240 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~nd10.04+1_all.deb Size: 8772578 SHA256: 9d3dff72aee22b019c206e41988f805c39b2b972580a868baf56b828ca835aea SHA1: 11ea76c7b25370bd570dff4433e2eb9205f2b720 MD5sum: 16f6ef2ef23880e96db96360f11835cf 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: 2.2.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4956 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-mvpa2-lib (>= 2.2.0-1~nd10.04+1), python-numpy 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 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.2.0-1~nd10.04+1_all.deb Size: 2399934 SHA256: 2c9c232aca5c92f26790f3073d69021b08cc519c9b8f9cbd15ff12feb38f9897 SHA1: 4bb2fe242a2da7461a66cf3a3f47a2f966ee18c5 MD5sum: b0cbad7ce5d19953f4d3fae758827ba0 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 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.2.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26980 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2, python-mvpa2-tutorialdata, ipython-notebook Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.2.0-1~nd10.04+1_all.deb Size: 5258892 SHA256: 4c3299ebea05fbe4def32b528ea364ad1120b000b7c94e83828bcb6e08967b01 SHA1: 7ef6d1b27c91a7e06fbf93ce113a1487ac3e9858 MD5sum: 6aabe170fb71118e3d0b50709e52ada2 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.), and example scripts. In addition the PyMVPA tutorial is also provided as IPython notebooks. Package: python-nibabel Source: nibabel Version: 1.3.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4472 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Provides: python2.6-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.3.0-1~nd10.04+1_all.deb Size: 1826272 SHA256: 6ce4559d731f23439c69646a9d807292fca810914ba1c6292f3cc21591c16e7d SHA1: 265bd0e5f0ed0494bbd73a864e282c488c966142 MD5sum: 634f7acdafeec977f061f19f4ee3451f 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 Package: python-nibabel-doc Source: nibabel Version: 1.3.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2848 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~nd10.04+1_all.deb Size: 421144 SHA256: cb99401a3ad44dd5d370f1e970511b3c82897997fd77bae769f35395ed7b2cc4 SHA1: 24926e49e4b87844448b28d578476116a601797b MD5sum: d7b77c1b09ba6bbd77af9cd14ac2be93 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-nitime Source: nitime Version: 0.4-2~nd10.04+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~nd10.04+1_all.deb Size: 3908878 SHA256: 5c8519c0b1fa888422578bcb17075de7c13a38f65625ce48602074f677b2f3bb SHA1: c894d1f792483ac1ea260e1aa5112f7a701a8e9f MD5sum: 4459d4a88c22652701e1b1f87ac59c42 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~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7100 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~nd10.04+1_all.deb Size: 5284628 SHA256: 2896180e643badfa156225f11b0c16728e36ae11c18779e73b3ed684732f53d9 SHA1: d5aa42a09080577ed15ef1da6def38119e6e2a2b MD5sum: f164b9a242a718a9071d3e8cf2c56d13 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~nd10.04+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 Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd10.04+1_all.deb Size: 245206 SHA256: fa59841cc93f8aca5496a7459a5901fd178766472cb795a9d0d29f42ba9ba8c4 SHA1: 872744e09191a35ef0b5c3a59c8cd2cf14a0908a MD5sum: 925ae988aa5c48249f2b1b47dc0d5066 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 Package: python-openpyxl Source: openpyxl Version: 1.5.8-1~nd10.04+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~nd10.04+1_all.deb Size: 71622 SHA256: 1d1a28113078057de46cacad522ad075face1bc8aabbf9f69ca825e482697ab9 SHA1: 9a92ed3547c55b8f588b188bf1589811069a57ba MD5sum: d0c8d3a553f4a1c00cf2019f4398a51d 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-pyentropy Source: pyentropy Version: 0.4.1-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 108 Depends: neurodebian-popularity-contest, python-support (>= 0.90.0), python-numpy (>= 1.3) Recommends: python-scipy Suggests: python-nose Provides: python2.6-pyentropy Homepage: http://code.google.com/p/pyentropy Priority: extra Section: python Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd10.04+1_all.deb Size: 21328 SHA256: 518de5852d1a914ab56f7a20216d864d668c6f4a2130c4f70e591b6320da7a05 SHA1: cf065ff5510f94d8f139751429cfafac6f72b00c MD5sum: 89b8c06967692f897b588b1d7c33e0ce 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 Package: python-pynn Source: pynn Version: 0.7.4-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1024 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~nd10.04+1_all.deb Size: 191952 SHA256: 493be42cce4377a226afb5bbb822432426a2488a79d0dda559cf47c25e77692c SHA1: fe6df512b0bb1ca009c4c0b85afbf60669bec3d3 MD5sum: aa58b0315529f43eef5258feb54ec2c5 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-scikits-learn Source: scikit-learn Version: 0.12.0-1~nd10.04+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~nd10.04+1_all.deb Size: 23928 SHA256: 87aff11c1a3222f936823180097e753011851449af8830d33520f742f734dc12 SHA1: 0a77d6857029b634d74d826644bdf76e48ccf17d MD5sum: 9eaab0fbb3a244b45193fd46b03e6403 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-sklearn Source: scikit-learn Version: 0.12.0-1~nd10.04+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~nd10.04+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 Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.12.0-1~nd10.04+1_all.deb Size: 924322 SHA256: dde141c61066d3a702a4eacee66c2ce825b97cedfb78f94d52ea891e12e67329 SHA1: 75de07f64d58892fae8ecae1fc9e4b2381104a3f MD5sum: 541dd15b1b1de6b1ae90ebf236db753b 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 Package: python-sklearn-doc Source: scikit-learn Version: 0.12.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 30432 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~nd10.04+1_all.deb Size: 16967684 SHA256: 3877409c0364ba06290c54a081318ccc2f5666dbeb9310ffb746a8b61db9e164 SHA1: a1da4de9c6a2c63760b3eea389ce09701cada1a9 MD5sum: 0f5c78d3a39becb5d791622fff1faa64 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~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 156 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~nd10.04+1_all.deb Size: 28756 SHA256: 8f17d3b7caf6efc0dfa7f0622bdcf5b399256f2ada512960b391853484c6f6d8 SHA1: 9f00ab60dbcf3ee47da88bfdb20c9b253864e4a6 MD5sum: cb96e746ed2a7b8a09e52f137579457e 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 Package: spm8-common Source: spm8 Version: 8.4667~dfsg.1-1~nd10.04+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~nd10.04+1_all.deb Size: 10573676 SHA256: 885839fee58c5da6818fb8d61615c3a3b8a2c6cdf17455d12b325ebfc6335dbb SHA1: 44051a62ef000e395e598372db51c0c97d013f67 MD5sum: 1403e1d413153dbfc75a2fc87c537a6a 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~nd10.04+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~nd10.04+1_all.deb Size: 52167700 SHA256: 892fd5132f9f07c9c18ba513240f89b078807ca90a5709153732acd61ae75ca0 SHA1: bf466428a78d725441b20d810f923df0110a5906 MD5sum: f8d0da1cd8deccb95f4f3dc0700b9c7b 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~nd10.04+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~nd10.04+1_all.deb Size: 8648932 SHA256: 0f720d86c9e975b3033db96e9c37f54f9349d5371f577788b5214cfcd25a50b4 SHA1: d47adec8d0a15e91be7ec799f3a84ac20099301d MD5sum: 0a60d682d6cb6b05f84a5916092e813d 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.