Package: condor-doc Source: condor Version: 7.8.8~dfsg.1-2~nd12.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 6118 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.8~dfsg.1-2~nd12.04+1_all.deb Size: 1459556 SHA256: bd31098c20f5b12d1c002f687bf50cc3e898ff251768c800f4192d5784f2f348 SHA1: 5f4af10ddfff69d5fcb6aa83d3c0ad6684e338cb MD5sum: 32e51b88f28280995d43804293f68e8e Description: distributed workload management system - documentation Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor; Condor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing systems, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . This package provides Condor's documentation in HTML and PDF format, as well as configuration and other examples. Package: connectomeviewer Version: 2.1.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1576 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-cfflib (>= 2.0.5), python-networkx (>= 1.4), python-nibabel, python-numpy (>= 1.3.0), python-scipy, python-chaco, mayavi2 (>= 4.0.0), ipython Recommends: python-nipype, python-dipy, python-matplotlib, python-qscintilla2 Suggests: nipy-suite Homepage: http://www.connectomeviewer.org Priority: extra Section: python Filename: pool/main/c/connectomeviewer/connectomeviewer_2.1.0-1~nd12.04+1_all.deb Size: 1355528 SHA256: 8255392e769af2ca1b745f6707a441213b408db7ae6a12c299698c495f618f0b SHA1: 1834b0270b864861f5c97b2a7ecdfabb7cc1ae14 MD5sum: 09125f910fb0ecec7fd33d65efb0c75f Description: Interactive Analysis and Visualization for MR Connectomics The Connectome Viewer is a extensible, scriptable, pythonic research environment for visualization and (network) analysis in neuroimaging and connectomics. . Employing the Connectome File Format, diverse data types such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The Connectome Viewer is part of the MR Connectome Toolkit. Package: coop-computing-tools-doc Source: cctools Version: 3.4.2-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2319 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: doc Filename: pool/main/c/cctools/coop-computing-tools-doc_3.4.2-1~nd11.10+1+nd12.04+1_all.deb Size: 310964 SHA256: a39fa9bc2251d5a045a6126278b1e772f41883e8ef1dd939b2b2c903df0fe40a SHA1: 79255768567dff8808dd954c9e64dc6c99dfd89c MD5sum: 3b540998ed6b8eda89421dd11cd61d6a Description: documentation for coop-computing-tools These tools are a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. . This package provides the documentation (manual and API reference) in HTML format. 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: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8109 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+1_all.deb Size: 7224748 SHA256: 896e3a64a84c1ecfa3f8aeb72849dbad8afb923046a6efb8f85cef680dd88880 SHA1: 4d6ca1909de2b2ec07ad124633c1ea2a0e41806c MD5sum: 66c6e6dde6068a39cf8a541bd1b66848 Description: sample EEG data for EEGLAB tutorials EEGLAB is sofwware for processing continuous or event-related EEG or other physiological data. . This package provide some tutorial data files shipped with the EEGLAB distribution. Package: fail2ban Version: 0.8.10-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 402 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx, system-log-daemon Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.10-1~nd12.04+1_all.deb Size: 134676 SHA256: bc2c80c7c5ee07244cd5dacc32d51ec551395db7baced5489e9342523a922fb8 SHA1: 21b24a4ea4093104acb714ba9a47569b73fe2b54 MD5sum: 222b0c9b4a283cba99f84668bcc06f00 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. Package: freeipmi Version: 1.1.5-3~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, freeipmi-common, freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi_1.1.5-3~nd12.04+1_all.deb Size: 934 SHA256: 5a9ad975eb80c528882a5bf930cacf35016d96dbcd9acd3b76c4beee5083ca3d SHA1: 87ed2db8c7b24d9c42b88e17cd3796a7ea41bb47 MD5sum: b231a9985cf7fdd0379a36951f434615 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~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 380 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest Suggests: freeipmi-tools Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-common_1.1.5-3~nd12.04+1_all.deb Size: 296948 SHA256: ded72c10b600960c8e28eb582a2da41e8d1b3c4db3f5c202c825a96bb4b93dbf SHA1: b79e420de532b04ba33d7f3baa0a0c32478c9c17 MD5sum: 6e1a9030c40445d77ccd328c63f1b995 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: 4.0.1-2~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2874 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-2~nd12.04+1_all.deb Size: 2346538 SHA256: 084fea9846b183ef6f945d06eef68ac4cc672bdd7670bf3d84230fc34b800569 SHA1: ff8ec632c035df587d42e0c4bb8502d497d29066 MD5sum: 4fd07de3ffd5bcc72995b174045f6234 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gmsl Version: 1.1.3-2~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 78 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.3-2~nd12.04+1_all.deb Size: 16308 SHA256: 2afc80dea7b9eccba40d28c5c2da10c3c04e028d52aea4bed56a1e940e8b9cca SHA1: 40d8ddaf956a29ab0229eb0e29cc657b50a1c395 MD5sum: 20bd30bb41ebcaf1e670ebbf4945e44e Description: extra functions to extend functionality of GNU Makefiles The GNU Make Standard Library (GMSL) is a collection of functions implemented using native GNU Make functionality that provide list and string manipulation, integer arithmetic, associative arrays, stacks, and debugging facilities. . Note that despite the name of this project, this library is NOT standard and is NOT written or distributed by the GNU project. Package: guacamole Version: 0.6.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 302 Depends: neurodebian-popularity-contest, guacd (>= 0.6), guacd (<< 0.7) Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole_0.6.0-1~nd12.04+1_all.deb Size: 277626 SHA256: 031ed40a75c0f5ab6f1fd0494f9776baf8b8100a76424d54ec0766d9f90ec40a SHA1: 5c6e2a9d1697f9ac0b4fadd3446853f6b986bf72 MD5sum: cb834423bc0f2462911800a2ee6c9a59 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~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole-tomcat_0.6.0-1~nd12.04+1_all.deb Size: 5172 SHA256: 8a9423cc80eecd320a8902746df9692514c78b60d25eb847001b184820ce8174 SHA1: 615d0b822e7ff888796cc40ac5ffb44a317b23af MD5sum: f7c8aeaa54bb5a3b1bc56c49bdd781bf 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: impressive Version: 0.10.3+svn61-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 326 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-opengl, python-pygame, python-imaging, poppler-utils | xpdf-utils (>= 3.02-2), perl Recommends: pdftk Suggests: ghostscript, latex-beamer Conflicts: keyjnote (<< 0.10.2r-0) Replaces: keyjnote (<< 0.10.2r-0) Provides: keyjnote Homepage: http://impressive.sourceforge.net/ Priority: optional Section: x11 Filename: pool/main/i/impressive/impressive_0.10.3+svn61-1~nd12.04+1_all.deb Size: 155966 SHA256: 3178048db990e6799e4c9c1a92ae8f772af744a9141e34eb1bc8ed4ecca1d85c SHA1: 3ffa09d23c68f7081a9c95732c367ff1122ef284 MD5sum: 2c10e75e0c97c33b87dea7fd7b6fe07e Description: PDF presentation tool with eye candies Impressive is a program that displays presentation slides using OpenGL. Smooth alpha-blended slide transitions are provided for the sake of eye candy, but in addition to this, Impressive offers some unique tools that are really useful for presentations. Some of them are: * Overview screen * Highlight boxes * Spotlight effect * Presentation scripting and customization Package: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 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~nd12.04+1_all.deb Size: 9652 SHA256: 2e8333994fe771da783ce3abdf490b2250b2643c8e879a9ed656a123425f949f SHA1: fcd79a61add1454528aa8aaaff1f438ae66859d5 MD5sum: 66bc6f30071a526052024744e99d9448 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: insighttoolkit4-examples Source: insighttoolkit4 Version: 4.2.1-2~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2677 Depends: neurodebian-popularity-contest Suggests: libinsighttoolkit4-dev Conflicts: insighttoolkit-examples Replaces: insighttoolkit-examples Homepage: http://www.itk.org/ Priority: optional Section: devel Filename: pool/main/i/insighttoolkit4/insighttoolkit4-examples_4.2.1-2~nd12.04+1_all.deb Size: 2408058 SHA256: c42b24bedd5b66b7523faa969f6bd7c6c4e22c43101db56aa451a2d5292dcfbd SHA1: 6b775c06008483ca78a6f588cb314bbf5febdcc9 MD5sum: 7f80fd84d705080ba7f957dca4b8555f Description: Image processing toolkit for registration and segmentation - examples ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the source for example programs. Package: ipython01x Version: 0.13.2-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4665 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython01x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x_0.13.2-1~nd12.04+1_all.deb Size: 1286038 SHA256: 16d6d5b7b507f59549666293a141e328687021ff78b1abb9e8216b17f9640cb6 SHA1: 4a45a68818a3ae7d6d32cb44808385e8b76dc2d2 MD5sum: 3d3ef76055db419ab9679c87e685f2ac Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.13.2-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16655 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.2-1~nd12.04+1_all.deb Size: 7232394 SHA256: 9d592a832ec275b572fcc8064278e5dce94ee48ec1560e06e4c56adabed20d1f SHA1: a2cb4113a1570bead3fde2303d3c714215981896 MD5sum: 9877028a6b3d79cd2dc1ec867f1d43d4 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-notebook Source: ipython01x Version: 0.13.2-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-notebook_0.13.2-1~nd12.04+1_all.deb Size: 900 SHA256: 8fa7dc1ca1b1ae2ba6c52f9bb16f784271a2b01f97137b0d56f0b4b50205bf11 SHA1: e1b13e384ad986e718a0c4e62b8af76b32d1d0da MD5sum: a9a320cbb8c1fe1e4d90e147508ebcdf Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython01x-parallel Source: ipython01x Version: 0.13.2-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython01x/ipython01x-parallel_0.13.2-1~nd12.04+1_all.deb Size: 826 SHA256: 4b5a4d23881ba8686922fef8146a9af51cebecdded01b3e6701c0472bd4e8046 SHA1: e2cafae23f7dd67754b7cf75700947670d5f5c95 MD5sum: 76473935bcaad0b0076d2d8c944b68c4 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython01x-qtconsole Source: ipython01x Version: 0.13.2-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.13.2-1~nd12.04+1_all.deb Size: 910 SHA256: 9972b51729ec7cdc7540414552fd435785581244d4970fafb194a86602eee9df SHA1: 224dcbb7104d6e1cda8ceb604fc84d2cf199aa28 MD5sum: 9cde899e5d8670759c6a496658cc5139 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: ipython1x Version: 1.0.0+1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11705 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython1x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython1x/ipython1x_1.0.0+1-1~nd12.04+1_all.deb Size: 4479524 SHA256: 1beee5db2521b6602472c1fdea40a39171263fabd0e5e0b07ad56c8d2006919b SHA1: ca581dc0fb957cfbceda5687a7d71294ea9fc85b MD5sum: becb0a1a1a21cec7d24fb59aad021c02 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-doc Source: ipython1x Version: 1.0.0+1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10314 Depends: neurodebian-popularity-contest, libjs-jquery, ipython1x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython1x/ipython1x-doc_1.0.0+1-1~nd12.04+1_all.deb Size: 4190366 SHA256: 1863ebb51cb3b87bc7ea6f91120b8022666ea33a2fc3df6d601aa93afdd24c02 SHA1: 12446a314cdfccbeff3c3b84dcc4318b36be4e56 MD5sum: e390e806367c5d523d6d1d4c76c016c4 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-notebook Source: ipython1x Version: 1.0.0+1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-notebook_1.0.0+1-1~nd12.04+1_all.deb Size: 902 SHA256: b94d42c83f5a5b3c4ce9d6e353b017200254c70e4b013306c831709bdf2dfa82 SHA1: db21e332d8390882a829fad47d16b10c167e5462 MD5sum: 2ea07e076035d97e2784561f5eabf1dc Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython1x-parallel Source: ipython1x Version: 1.0.0+1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython1x/ipython1x-parallel_1.0.0+1-1~nd12.04+1_all.deb Size: 826 SHA256: f4c911fd52531101980883771846807d3581430a637905ea3d24446406951f3e SHA1: e1835c2fc6af047ebb0ebc15a72ad92f0fd485a5 MD5sum: fa51bffa1b3265288cf5387d2c7a0129 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython1x-qtconsole Source: ipython1x Version: 1.0.0+1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-qtconsole_1.0.0+1-1~nd12.04+1_all.deb Size: 910 SHA256: b179af74121d9f40957f1bca0e11dae6243887be4fdc74aee040c78c6a55a70c SHA1: a1f7667270822562e7217866ab4e878032b8f5f6 MD5sum: 844ace52eda1316abedce088c02282ba Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: libfreenect-doc Source: libfreenect Version: 1:0.1.2+dfsg-6~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 482 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-6~nd12.04+1_all.deb Size: 90826 SHA256: ce3aa05ed1adb1052c95f64d8d6bca0c9b68ca36b62d16a84153a932f7a95edd SHA1: 953a5d482244b055130a34ea227d553dc89b9e6e MD5sum: 77f83c1d42d041e8642df0f5227f05fe 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: libisis-core-dev Source: isis Version: 0.4.7-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 260 Depends: neurodebian-popularity-contest, libisis-core0 (>= 0.4.7-1~nd11.10+1+nd12.04+1), libisis-core0 (<< 0.4.7-1~nd11.10+1+nd12.04+1.1~) Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-core-dev_0.4.7-1~nd11.10+1+nd12.04+1_all.deb Size: 69044 SHA256: 676aa659129766e70ed2a833d4341b47e95766cffe931f88ed304d34e700696e SHA1: d8c229966b0716f28ff127aa29d3909149fc1aa9 MD5sum: 8b63e70f78d6b4906a79298a3cb3d11a Description: I/O framework for neuroimaging data This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This package provides headers and library to develop applications with ISIS. Package: libisis-qt4-dev Source: isis Version: 0.4.7-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Depends: neurodebian-popularity-contest, libisis-qt4-0 (>= 0.4.7-1~nd11.10+1+nd12.04+1), libisis-qt4-0 (<< 0.4.7-1~nd11.10+1+nd12.04+1.1~), libqt4-dev Conflicts: isis-qt4-dev Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-qt4-dev_0.4.7-1~nd11.10+1+nd12.04+1_all.deb Size: 6062 SHA256: d421f9a584c148a1d0506dc3fe98ef26ded298ba20cdd7852f4800209078f8da SHA1: 4b1fa88ac55f52e9b6bb1531467fb40c6c6ee386 MD5sum: ff41be8c4a67e54cd9b0c8427491a549 Description: Qt4 bindings for ISIS data I/O framework (development headers) This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. Package: libopenwalnut1-doc Source: openwalnut Version: 1.3.1+hg5849-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29959 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.3.1+hg5849-1~nd12.04+1_all.deb Size: 3790214 SHA256: 2d783c85b8a4227905b82bb8e5c9034971759b13a06e58ff845edc367d5c0f8f SHA1: 0e440af05376b6212139162671d710d75a7e3210 MD5sum: 1c1ac44f81ca94bfe29d3171600c37a4 Description: Developer documentation for the OpenWalnut visualization framework OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the core API documentation of OpenWalnut. Package: libvia-doc Source: via Version: 2.0.4-2~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 903 Depends: neurodebian-popularity-contest Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/v/via/libvia-doc_2.0.4-2~nd11.10+1+nd12.04+1_all.deb Size: 118526 SHA256: 70f7d7d0530cd4a253c866ab3a50af8055d66a646e80aeb59d04c02a6c8f3766 SHA1: 80e49482b9207ed42f89c6b98dc5ea1ba7ff8cae MD5sum: c5bb11d4da40e97b8af9c9577f74be1b Description: VIA library API documentation VIA is a volumetric image analysis suite. The included libraries provide about 70 image analysis functions. . This package provides the library API reference documentation. Package: matlab-support-dev Source: matlab-support Version: 0.0.19~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: neurodebian-popularity-contest Conflicts: matlab-dev (<= 0.0.14~) Replaces: matlab-dev (<= 0.0.14~) Priority: optional Section: devel Filename: pool/main/m/matlab-support/matlab-support-dev_0.0.19~nd12.04+1_all.deb Size: 7228 SHA256: 31c19cdfae1f85a684e2ede445c7b66ef344d28cbe7136e3b9e91da1f3c57027 SHA1: 1e6447a869052d377c5060cadb5b34fa90b6bcb0 MD5sum: 35aba9fa1b8918525557800def8e4abe Description: helpers for packages building MATLAB toolboxes This package provides a Makefile snippet (analogous to the one used for Octave) that configures the locations for architecture independent M-files, binary MEX-extensions, and their corresponding sources. This package can be used as a build-dependency by other packages shipping MATLAB toolboxes. Package: mricron-data Source: mricron Version: 0.20130828.1~dfsg.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1710 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20130828.1~dfsg.1-1~nd12.04+1_all.deb Size: 1664248 SHA256: aeb9d4fa24bd09bdc8b1740c501cc00d23dbff40f0e9db77911d077537a8cb10 SHA1: 8ad7c5f4f1328334d1e6d02c80d02580097f244b MD5sum: e73944c2763475b6158e9916a48b6486 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20130828.1~dfsg.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1019 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20130828.1~dfsg.1-1~nd12.04+1_all.deb Size: 736318 SHA256: dc7a9b181a5b22f039eafc7c5d6cd0675bc59ab6e7033edb007769468ae5f55c SHA1: ddc71aeb0570188c95d86dc7ce87b456bfdf0c23 MD5sum: 933d4dcfc91b9a06913771fd8caf8dfd Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: mrtrix-doc Source: mrtrix Version: 0.2.10-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3485 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.10-1~nd11.10+1+nd12.04+1_all.deb Size: 3315494 SHA256: 94dbee8905712de4c824ee39eb7a83e3cd44eeebc0e39c601a94222acae7d6ef SHA1: 43dd82edbe0141bbe64ba8911db7d23760243a3e MD5sum: 8829e0008103ae5a0eb17ad53039ac26 Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: neurodebian-desktop Source: neurodebian Version: 0.31~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 142 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_0.31~nd12.04+1_all.deb Size: 115296 SHA256: dfb554346520a94f686400485a2509d5e9a503147feb20a9af652b7fde6c7e25 SHA1: f51ea8c9d6304b9fed5cc988e1f26b4873c440bf MD5sum: cc2af443a2671cc57c0e0195997fcf46 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: 0.31~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5762 Depends: devscripts, cowbuilder, neurodebian-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring, apt-utils Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.31~nd12.04+1_all.deb Size: 5351192 SHA256: 0110fcb2575c2f4d0eaadf0eed170cd65484a31a918bdd9cbe2f5cb4c7b20186 SHA1: 70d0934926ba15d8be127065ffa46c7bed4ce24c MD5sum: 8a9d89d6cb190f7f29ad4e4592799591 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: 0.31~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm | lightdm, zenity Recommends: chromium-browser, update-manager-gnome, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.31~nd12.04+1_all.deb Size: 15186 SHA256: 74d6c347860177d5d12d38cde97b4a886a2bdf4b15a2ab064575145874a05945 SHA1: 3c0eb00d844f97a09770c9583603e61e08da4089 MD5sum: 3095b579923726566b015fa299a7bba1 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: 0.31~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.31~nd12.04+1_all.deb Size: 7494 SHA256: 243aa54fcab2192beb39d0107f0c9e6911e4a590498af6290f853f8b30183d01 SHA1: af3a740917db3a2bd99194e35d4428a63bf30745 MD5sum: 9ea73b9071203ef9996d71a7b8c91eb0 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: 0.31~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.31~nd12.04+1_all.deb Size: 6718 SHA256: 23a22c58c5b39506b148e5e536482ef83419f615d9206eacea525cd4b9e53b58 SHA1: 0af990eb52b8b6f9d9d3ed5b636a657a9ef7f358 MD5sum: ab61c70940108868be02e5c6df65be75 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: 0.4.6-2~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 606 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.6-2~nd12.04+1_all.deb Size: 615174 SHA256: 0e9204cc71d4ebd4fa0f9103a330f34024c14b4636450702e6aac9fb75306070 SHA1: adb56007ffe650b1b43c3f366bb4c2da09fd8b03 MD5sum: 98c8b0e2728a50cc5cac49f4a42330c1 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: 0.4.5.1+ds-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1785 Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5 | g++-4.4 | 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_0.4.5.1+ds-1~nd12.04+1_all.deb Size: 441360 SHA256: 6af9c611c2581ab8fdf9bf86a5f7e2add345153a1f8fab5a0c90d3e25ea57d8b SHA1: 855299c47a75266c4750c3e4b84dc966dfc084c3 MD5sum: 54053a4980577213b182588ca31bfd40 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 at all. Instead they can be used in the same way as pure Python objects. Package: opensesame Version: 0.27.4-2~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26639 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~), python-qscintilla2, gnome-icon-theme Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1), expyriment (>= 0.5.2), ipython-qtconsole (>= 0.12), python-markdown Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27.4-2~nd12.04+1_all.deb Size: 25359348 SHA256: f1470d55899db30eb48bb5a9de4dca539f7b11796aa735c50ab406aa86c0fbec SHA1: e6e5bf7af41ad35096476278ac7cf91ad71014a5 MD5sum: 0e776cb5535a5b24713de386c14646c3 Description: graphical experiment builder for the social sciences This graphical environment provides an easy to use, point-and-click interface for creating psychological experiments. In addition to a powerful sketchpad for creating visual stimuli, OpenSesame features a sampler and synthesizer for sound playback. For more complex tasks, OpenSesame supports Python scripting using the built-in editor with syntax highlighting. Package: openvibe-data Source: openvibe Version: 0.14.3+dfsg2-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9328 Depends: neurodebian-popularity-contest Homepage: http://openvibe.inria.fr Priority: extra Section: science Filename: pool/main/o/openvibe/openvibe-data_0.14.3+dfsg2-1~nd12.04+1_all.deb Size: 2024428 SHA256: e7be5d76d8705af09254195c6a5322019829604d6384e803e696e2d34dcf2fb6 SHA1: 408f561a02bfdbb352758df3ff54fd65283bf5fa MD5sum: 4f3c8f65f2b3324b27321c5115acd5c1 Description: Software platform for BCI (Data files) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains the data files. Package: packaging-tutorial Version: 0.8~nd0 Architecture: all Maintainer: Lucas Nussbaum Installed-Size: 1550 Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.8~nd0_all.deb Size: 1488332 SHA256: 491bc5917f698fee06888998e8a295a6caac2950148bb160b457aff72437eadb SHA1: c5d75d04b01f681ead660ce8d8fe068ab887fba0 MD5sum: 8fbf7c362fd4091a78c50404eb694402 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: 1.77.02.dfsg-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9277 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, python-pyo, libavbin0, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.77.02.dfsg-1~nd12.04+1_all.deb Size: 5822196 SHA256: a2fa6db3af44f641097903239b83aebe087d20b59eed43d61ba9b127a44a88fc SHA1: d865f582d38f1c11b35156f3f41b6ecec22933d9 MD5sum: 8cd3467fd7011ba23807de9fceaf69a2 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: 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.11.20130711.dfsg1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49324 Depends: neurodebian-popularity-contest Recommends: subversion Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.11.20130711.dfsg1-1~nd12.04+1_all.deb Size: 19883634 SHA256: 2ba81e8532d4f3202d3a8ac456f67f3005925ae4ee07d0925381db14e7af4162 SHA1: 03da01726f0af9558241b49150432bea24f28d17 MD5sum: ffc507499edff0c53f4f25099ea704d4 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: 1.4.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2336 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.1-1~nd12.04+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_1.4.1-1~nd12.04+1_all.deb Size: 549166 SHA256: 541e11301a9398f8d77338a51a61ad3ef7f6a40327e22ce9c56bbf766edd3215 SHA1: fb52fa438a689e0cc10560357dfbf34551a705b7 MD5sum: 5db98c4eeb1c8d00fe730309b10daad3 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: 1.4.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6799 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_1.4.1-1~nd12.04+1_all.deb Size: 2245652 SHA256: 2fbc7a93c51dfc29fc312bbbca9a70d0c9aca014f7af0a07c826e6781bc8564f SHA1: d297cb4e18f5c07737ba4c3e04697f280cda222a MD5sum: 2ac50e95d6831462dbfe81fd85cb0d33 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-dicom Source: pydicom Version: 0.9.7-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1790 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://code.google.com/p/pydicom/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.7-1~nd12.04+1_all.deb Size: 419144 SHA256: ff2d47b401f993dc306b9e975ec4f391c5b0ec785404dcfbb6947b31e2747260 SHA1: 15ac595edc152a5586adc589d65fb5f9d6b68a7e MD5sum: 356c677bbfecb4505448c0217b0e0b43 Description: DICOM medical file reading and writing pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. Package: python-dipy Source: dipy Version: 0.6.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2285 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.6.0-1~nd12.04+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.7-dipy Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.6.0-1~nd12.04+1_all.deb Size: 1586232 SHA256: a01f8b532023b07e12f59ad4c87db9ab1aa2ff7b9756accc058e68b630e59199 SHA1: 642069e62209610a8d99aad3d405dcb061416d08 MD5sum: 4f2372c250f4ff61635216b599ec55ff Description: toolbox for analysis of MR diffusion imaging data Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. It features: - Reconstruction algorithms, e.g. GQI, DTI - Tractography generation algorithms, e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space, e.g. MNI space - Reading many different file formats, e.g. Trackvis or NIfTI - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing Python-Version: 2.7 Package: python-dipy-doc Source: dipy Version: 0.6.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5067 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: extra Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.6.0-1~nd12.04+1_all.deb Size: 3611556 SHA256: 125fda03168bc1e8e4d1ed92ca6e4b6abc20691b425a7a76c9aa945ef3870e28 SHA1: 9b24b76f53fd5aec2d512e17cf8eba6bd610a825 MD5sum: 98f7e0d9488cb5469df459525f107f14 Description: toolbox for analysis of MR diffusion imaging data -- documentation Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. Package: python-joblib Source: joblib Version: 0.7.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 182 Depends: neurodebian-popularity-contest, python, 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_0.7.1-1~nd12.04+1_all.deb Size: 54832 SHA256: 6ce24a39df583f4aab758bc94557d9d1ebde4b9ca4dfd7b97b648b1acc5e1d34 SHA1: 373540cffdd3a0de2ad2a8774f7ed9155b10f956 MD5sum: 9a9b51812d16e39fd7edaff0908441f1 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~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17 Depends: neurodebian-popularity-contest, python2.7, 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~nd11.10+1+nd12.04+1_all.deb Size: 7346 SHA256: 119a709e7c8d3e6452027994781665214ff7df777b409d2ecfbbbf805c1c6240 SHA1: ca46afca4f6d4e692a37427d5a85d3eb2f6f2c86 MD5sum: 69478e3c00645627a84e5b965942f006 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: 3.3+git6-g7bbd889-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1495 Depends: neurodebian-popularity-contest, python2.7, 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_3.3+git6-g7bbd889-1~nd12.04+1_all.deb Size: 478618 SHA256: d515003bbfa0d0dc1208aae5e2bda578fa80925cf08dccd1da3e152097d9e916 SHA1: 1e9e9e6faeef010409ee186e9a4031b4d790cc3e MD5sum: ffed437993bb7b44c052aaa1deac99ff Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 2. Package: python-mpi4py-doc Source: mpi4py Version: 1.3+hg20120611-2~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 275 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3+hg20120611-2~nd12.04+1_all.deb Size: 76606 SHA256: 3707b5fef00c097c3b5c0a49250d73374c29f36a93d1033acfd1118115a5b1e1 SHA1: 50a93fe441f840a56622b062161ea4ea211d9b7b MD5sum: 356fa1f7b509bf0e06d6a13a1a183499 Description: bindings of the MPI standard -- documentation MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides HTML rendering of the user's manual. Package: python-mvpa Source: pymvpa Version: 0.4.8-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3547 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy, python-support (>= 0.90.0), python2.7, python-mvpa-lib (>= 0.4.8-1~nd11.10+1+nd12.04+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: python2.7-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.8-1~nd11.10+1+nd12.04+1_all.deb Size: 2205054 SHA256: 157565eb22e6a64cca8e7b9369dccf884a44fd2b483184b2f9740d4818cc3f3d SHA1: 522691bdde24041e16a7feadd234cc1866586da4 MD5sum: 2f14582aa4fdd1736736b78e7026ef43 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: 2.7 Package: python-mvpa-doc Source: pymvpa Version: 0.4.8-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 37578 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~nd11.10+1+nd12.04+1_all.deb Size: 8480396 SHA256: 0b362f8c219e02d176900b865bc51b26b54f6350eacf1d66bcae93a48b3415ff SHA1: eaafef89dd957e059ad348d1038e565c8ecf0db8 MD5sum: 0bee5fc34f30a40bb7a1e3b88f187d5b 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: 2.2.0-3~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4241 Depends: neurodebian-popularity-contest, python, python-numpy, python-support (>= 0.90.0), python-mvpa2-lib (>= 2.2.0-3~nd12.04+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: python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.2.0-3~nd12.04+1_all.deb Size: 2400322 SHA256: 01568ea516b95d014aec18cd74991af69b88e355d65ba7f312c75ad3a499e243 SHA1: ae9eb373ffc81263f5a29c2e34c8335d134dfccc MD5sum: 2f27cdfc6d34ed63fcc3680c436010b0 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: 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.2.0-3~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17221 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_2.2.0-3~nd12.04+1_all.deb Size: 5141836 SHA256: 650527004c9dbc0947be00aed3704363c4b28ea10be0afac88a510bc577cdb8b SHA1: 18e27d2d3df5469f1c3b99b52edb6dce71de6838 MD5sum: 6d851bcfb4f2e78c24ac64cbc7e4d13c 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-neo Source: neo Version: 0.3.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2451 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy (>= 1:1.3~), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Suggests: python-nose Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.3.0-1~nd12.04+1_all.deb Size: 1434218 SHA256: 25974f62c1d0fafe37e77560f3f150fab873600deb5c890dd86336405d7562c4 SHA1: 62ec6921f1e5e5a79f48bd0e04a7ac524ee168d2 MD5sum: 5b149ea5488d40b76d089bdf20ea6494 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-neurosynth Source: neurosynth Version: 0.3-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 83 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-nibabel, python-ply Recommends: python-nose, fsl-mni152-templates Suggests: python-testkraut Homepage: http://neurosynth.org Priority: extra Section: python Filename: pool/main/n/neurosynth/python-neurosynth_0.3-1~nd12.04+1_all.deb Size: 32524 SHA256: 27b0be2cf8a587add547129ac1dda06a3b91425c3424fe158a65e1f9d4b2ed65 SHA1: 3a483f8f8559a01e10bd69466f6cb3a5c424ad57 MD5sum: 0f1cd4f35fbab87210af56d863cb5631 Description: large-scale synthesis of functional neuroimaging data NeuroSynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. This Python module at the moment provides functionality for processing the database of collected terms and spatial coordinates to generate associated spatial statistical maps. Package: python-nibabel Source: nibabel Version: 1.3.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4152 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: python2.7-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.3.0-1~nd12.04+1_all.deb Size: 1816162 SHA256: 88819d664d277fbcc5e26832b67fcab70ad6ffd4d5e6973b607cd008dd514ab8 SHA1: 2819a59270ae2436d44aaa5b6a3347d3d2a3f2fe MD5sum: 825ab55abef608f9c8dfd6fdb8f234e2 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: 2.7 Package: python-nibabel-doc Source: nibabel Version: 1.3.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2431 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~nd12.04+1_all.deb Size: 437630 SHA256: a5d1d7d9218c6754de56bd0cdd756022594a71b565d56c3a1a1ca9299c282079 SHA1: aa02540d99de86531ca794d2029c7d71a75e306b MD5sum: 1da3073ae4c852e35e465bdc3279db3f 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: 0.3.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2863 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy (>= 1:1.2), python-support (>= 0.90.0), python-scipy, python-nibabel, python-nipy-lib (>= 0.3.0-1~nd12.04+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.3.0-1~nd12.04+1_all.deb Size: 784402 SHA256: 73b2c0475c69675e669a4e995e07bc3d94d24e7b2234311509760b7fe1cff75f SHA1: f7f02bcf31fe4bc7c5135ddd44160fe422e55242 MD5sum: 3bfc464329d59fbfdfe9f1b61b3312b0 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: 2.7 Package: python-nipy-doc Source: nipy Version: 0.3.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10210 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_0.3.0-1~nd12.04+1_all.deb Size: 3836640 SHA256: 980dc8bd38195c3893f6be77835ab0a832d05ae11ca2caa2a07ff90cbb1f54bb SHA1: 9a4cc3019c8209c5385648d15ea1e3fcebec058b MD5sum: e2ce5718b1a3279635a0c43f1d376805 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: 0.8-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2657 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: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.8-1~nd12.04+1_all.deb Size: 591968 SHA256: 379cf11578c24ac249098a9a0b30a7234eb700b39562873a286b1503e17eb10f SHA1: 51074a67de422c4a7502587180933342e553b2ea MD5sum: 0e446230a480bc6f7347066c36621c31 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: 0.8-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15033 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_0.8-1~nd12.04+1_all.deb Size: 7161770 SHA256: 1ffcc3129dec315afb2510f22005a43a1799feabe95c2fecbfa0563df859d4b6 SHA1: 15794c5f36f7a56b2485ae212532095fdeda1f0d MD5sum: b03108e25f8f85139b86aa4f3db44a5f 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~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9294 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.4-2~nd12.04+1_all.deb Size: 3908918 SHA256: de2749874abdd7d0bfd2a5c7c3347a3ae0eb16ea268c181298e412232f995363 SHA1: 7f090b879ce7198ff6dd8a5a329b325aa27e50c6 MD5sum: a8ef238a666742742803586226b3161e 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~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6795 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~nd12.04+1_all.deb Size: 5296608 SHA256: e9ab9bb447b6cc8771152c44ebe90c1ffd3977555fc3db8da22956bd8102410e SHA1: 5336c6cd6243a1c58c9845a1dea1e2408033c67a MD5sum: a72da32d9822a50975c57f8e1be46130 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-numpydoc Source: numpydoc Version: 0.4-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 118 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-sphinx (>= 1.0.1) Suggests: python-matplotlib Homepage: https://github.com/numpy/numpy/tree/master/doc/sphinxext Priority: optional Section: python Filename: pool/main/n/numpydoc/python-numpydoc_0.4-1~nd12.04+1_all.deb Size: 30416 SHA256: f0ec6c6bb8bbf961ad97650bcdd1fe22fd339aae379cf0734062e2ad4e6db3e3 SHA1: e8213cba00b6930e0a7711398e1e1d69efe5837f MD5sum: c04cb18fa2d37487d90476988c1b5c66 Description: Sphinx extension to support docstrings in Numpy format This package defines several extensions for the Sphinx documentation system, shipped in the numpydoc Python package. In particular, these provide support for the Numpy docstring format in Sphinx. Package: python-openopt Source: openopt Version: 0.38+svn1589-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 954 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.7-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd12.04+1_all.deb Size: 245070 SHA256: c6d2c1cf48ce88af0d065731ab604db6b11738340fa4bdcce47f2bc8ee4f255e SHA1: be29b8639736fec932e1186ecba123ac9885261b MD5sum: 8e53da6514861d3666b0b66f6db764c7 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: 2.7 Package: python-openpyxl Source: openpyxl Version: 1.6.1+hg2-g4bff8e3-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 291 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_1.6.1+hg2-g4bff8e3-1~nd12.04+1_all.deb Size: 62046 SHA256: 2a9f5beb01e770e9b06ee571c6351c96cca06395d73008cd7bb2c054a77f0ea3 SHA1: d9bd8cad4d7e37b27651bcb187db50ecf2c4f4dc MD5sum: ec8d6ab1acc835b448117f19b978aaca 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: 0.12.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5629 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.6~), python-pandas-lib (>= 0.12.0-1~nd12.04+1), python-six Recommends: python-scipy, python-matplotlib, python-tables, python-numexpr, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt, python-bs4, python-html5lib Suggests: python-pandas-doc Provides: python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.12.0-1~nd12.04+1_all.deb Size: 1080662 SHA256: fd7b38e38dbd83099425b44d34245d92a946d22d0aaa4e944c3246b4893ac787 SHA1: b008d9984dd142fc4fc151729dd771936da0056d MD5sum: 86eb8ed163d109e548c29de0f39ec47b 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 . This package contains the Python 2 version. Package: python-patsy Source: patsy Version: 0.2.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 541 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy Recommends: python-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python-patsy_0.2.1-1~nd12.04+1_all.deb Size: 141302 SHA256: 5f3f0181acc5eb9887bd6b1238e87a34dc8f5688ddc46b9a3387a6c2fd2043a4 SHA1: 751b6c8a325320690c05c0e4ff4433351d4fe5c4 MD5sum: b65cdcb55dceba0f9bd872a0fa19957a Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 2 version. Package: python-patsy-doc Source: patsy Version: 0.2.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 825 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-patsy Homepage: http://github.com/pydata/patsy Priority: optional Section: doc Filename: pool/main/p/patsy/python-patsy-doc_0.2.1-1~nd12.04+1_all.deb Size: 271608 SHA256: 8342aa0c9560095c26052383eaa303e5f8705cecbd7fbe4769ca9e94d64e5067 SHA1: a7f4238988e32de7e33511793f660fad9eb5d72d MD5sum: c07356c688af502a6c808c08b9e63b6f Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pp Source: parallelpython Version: 1.6.2-2~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.parallelpython.com/ Priority: optional Section: python Filename: pool/main/p/parallelpython/python-pp_1.6.2-2~nd12.04+1_all.deb Size: 34276 SHA256: ab62cec05fc9f0a9305943c5ff846339715c8c7dfe90329c84dcb579c387f522 SHA1: 34efa2eef021e6ee1a8ba277579f2a8b263b4d54 MD5sum: ff73261f718ab0f59e7f09c2c9513f39 Description: parallel and distributed programming toolkit for Python Parallel Python module (pp) provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. pp module features cross-platform portability and dynamic load balancing. Thus application written with PP will parallelize efficiently even on heterogeneous and multi-platform clusters (including clusters running other application with variable CPU loads). Python-Version: 2.7 Package: python-pyentropy Source: pyentropy Version: 0.4.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy (>= 1.3) Recommends: python-scipy Suggests: python-nose Provides: python2.7-pyentropy Homepage: http://code.google.com/p/pyentropy Priority: extra Section: python Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd12.04+1_all.deb Size: 21328 SHA256: ddd9ba108b54448eea94d0ae6ea3ee5d64d53ddf5ce1a495b6d2cf3fcc3fb990 SHA1: 54e328922f69edc526dd9e4fee36c0a579751237 MD5sum: a54388c4f18d1e1d251afce2e209e3f1 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: 2.7 Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 813 Depends: neurodebian-popularity-contest, python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0+git12-g365f8e3-1~nd12.04+1_all.deb Size: 818174 SHA256: 7ce88510e05fe54c3f9bc7f327f1b525977bfc4fe3076555200d02d0c68a94b5 SHA1: 97d066e23c47e923a858c4dd626868e5b443bd32 MD5sum: a84013e6a8601fcbd3681717892c40ee Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides common files such as images. Package: python-pynn Source: pynn Version: 0.7.5-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 763 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_0.7.5-1~nd12.04+1_all.deb Size: 175770 SHA256: fe6e3032350714f860c4c2b7720692ef7ef01d69cb7b075d00f1b164f7ffe727 SHA1: 5a5347319df65e9930dae7e44ff885eeb610503f MD5sum: fa0cbb1bd44a13d0fe2bc52cac2ca0de 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-pyxnat Source: pyxnat Version: 0.9.1+git39-g96bf069-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 862 Depends: neurodebian-popularity-contest, python-lxml, python-simplejson, python-httplib2 (>= 0.7.0) Recommends: python-networkx, python-matplotlib Homepage: http://packages.python.org/pyxnat/ Priority: extra Section: python Filename: pool/main/p/pyxnat/python-pyxnat_0.9.1+git39-g96bf069-1~nd12.04+1_all.deb Size: 190346 SHA256: 251e5eee918d64f1c8bf4cd4c1e155433e42d04c034ff9c9abda5633ff611f6c SHA1: 5e173439a42b6c0d59cb373bb402c6e1868df3f1 MD5sum: c4c2961165b4dc02a1c076d63045e85b Description: Interface to access neuroimaging data on XNAT servers pyxnat is a simple Python library that relies on the REST API provided by the XNAT platform since its 1.4 version. XNAT is an extensible database for neuroimaging data. The main objective is to ease communications with an XNAT server to plug-in external tools or Python scripts to process the data. It features: . - resources browsing capabilities - read and write access to resources - complex searches - disk-caching of requested files and resources Package: python-quantities Version: 0.10.1-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 301 Depends: neurodebian-popularity-contest, python2.7, 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~nd11.10+1+nd12.04+1_all.deb Size: 58804 SHA256: e8cc2d0a4d86512648fb8593ef8dbc22e198d5a23dba4290c2fad574a1705185 SHA1: db73d2cfddb1e9b6e19e5f8d674d94cb8b5f10b3 MD5sum: 34ca36fdfe957727bfb6967fddc589f5 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: 0.14.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 35 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_0.14.1-1~nd12.04+1_all.deb Size: 33362 SHA256: 31c13e1f5c69f6b3fe73a5b549f0dfad0389b1627872c7e4deef086fa3a7c0f2 SHA1: 96d688afd858bd8689f13c15e7d218c56a30f72a MD5sum: 8fb165ef8c3826a372ae84f0f8ff8636 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: 0.5.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9 Depends: neurodebian-popularity-contest, python-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.5.0-1~nd12.04+1_all.deb Size: 5644 SHA256: 81b2847e6dd356b960096d8852026279a697f0891afdabd75324771d35da4219 SHA1: 6595d21ccb1350359ca19e6f36cc653a4a917dec MD5sum: de7aad820573dd302128f9a240bf7245 Description: transitional compatibility package for statsmodels migration Provides old namespace (scikits.statsmodels) and could be removed if dependent code migrated to use statsmodels for clarity of the namespace. Package: python-skimage Source: skimage Version: 0.6.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3641 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python2.7, python-scipy (>= 0.9), python-skimage-lib (>= 0.6.1-1~nd12.04+1), libfreeimage3 Recommends: python-nose, python-matplotlib (>= 1.0), python-imaging Suggests: python-skimage-doc, python-opencv Provides: python2.7-skimage Homepage: http://scikits-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.6.1-1~nd12.04+1_all.deb Size: 2539146 SHA256: b2d5c79f9c1219dcc1e7946d05c7c58a028485e0bcb1b00d73fd82a2fc8ce936 SHA1: c43d51b7b5ebc4d015d5a423853efacc091432fc MD5sum: 526222710563493309ed1208f5edc660 Description: Python modules for image processing scikits-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. Package: python-skimage-doc Source: skimage Version: 0.6.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4867 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-skimage Homepage: http://scikits-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.6.1-1~nd12.04+1_all.deb Size: 3591424 SHA256: 0e4f5f1b5ceb2c4a2eb1bd63d6f8c0a3729b53ca04d8a80d1a8e63e23dd5bd32 SHA1: 8e6083120069f5be3820340df0ceb062cfdf028e MD5sum: a1f61e601d45c9f39bd4ba98c54b6847 Description: Documentation and examples for scikits-image This package contains documentation and example scripts for python-skimage. Package: python-sklearn Source: scikit-learn Version: 0.14.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3549 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-sklearn-lib (>= 0.14.1-1~nd12.04+1), python-joblib (>= 0.4.5) Recommends: python-nose, python-matplotlib 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: python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.14.1-1~nd12.04+1_all.deb Size: 1103698 SHA256: 3fdeb6f83236eea97239fa8754795841a4d681684a669fecd85bb324a56c51b8 SHA1: c98a313238747adf136ae530f5a8767c0e2a7d65 MD5sum: 1727a0bae4724bbb05cd0f3b1cd88328 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) Package: python-sklearn-doc Source: scikit-learn Version: 0.14.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 579 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_0.14.1-1~nd12.04+1_all.deb Size: 190052 SHA256: 94fab5bf875e775b42d918e35328b2879c54e9ef48a8ad7ce123504e0fbf4d9b SHA1: 19e79dd551dd5424a152974f86285dace9111f53 MD5sum: 8aec872b9a6896364c724fa71f31a5ac Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-spykeutils Source: spykeutils Version: 0.4.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2015 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-scipy, python-quantities, python-neo (>= 0.2.1), python-nose, python-sphinx Recommends: python-guidata, python-guiqwt, python-tables, libjs-jquery, libjs-underscore, python-sklearn (>= 0.11), python-joblib (>= 0.4.5) Provides: python2.7-spykeutils Homepage: https://github.com/rproepp/spykeutils Priority: extra Section: python Filename: pool/main/s/spykeutils/python-spykeutils_0.4.0-1~nd12.04+1_all.deb Size: 400872 SHA256: 0e9cdc8416bdb389c66353c18977196945631ce88f72d56ed71042aca373dc56 SHA1: 0928823167fdeb9ea7705b5d40aabbe8148e6333 MD5sum: 9408a0eb155fc6aacbd0b7d801f84555 Description: utilities for analyzing electrophysiological data spykeutils is a Python library for analyzing and plotting data from neurophysiological recordings. It can be used by itself or in conjunction with Spyke Viewer, a multi-platform GUI application for navigating electrophysiological datasets. Package: python-statsmodels Source: statsmodels Version: 0.5.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20309 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-statsmodels-lib (>= 0.5.0-1~nd12.04+1), python-patsy Recommends: python-pandas, python-matplotlib, python-nose, python-joblib Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Provides: python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.5.0-1~nd12.04+1_all.deb Size: 4668824 SHA256: 0539430689364d488d46c8809c6b8c357e9dffdde7bd7c25473a39b585951192 SHA1: 4396bb38104c53d0e49fdde445a4bdc273481b75 MD5sum: 2fb758c0c8250d94f061224035432d36 Description: Python module for the estimation of statistical models statsmodels Python module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Package: python-statsmodels-doc Source: statsmodels Version: 0.5.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29874 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-statsmodels Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.5.0-1~nd12.04+1_all.deb Size: 7060402 SHA256: b797b9a6e9e601d739fb0b54a95681826916d2221ddf8fdff5e057af43a1a9a9 SHA1: 51df3b3e188e055010777e8d200f20c3f940c88c MD5sum: 5590937444cd73845f88b4f8a530a433 Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-surfer Source: pysurfer Version: 0.3+git15-gae6cbb1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 93 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~nd12.04+1_all.deb Size: 28016 SHA256: fd4a0787b83bdc6cc7dc4a09768b63fed71f0c8edd1b79f546c6099764d32235 SHA1: fa2a6482c1b0fad09935a62e2abe30f83aa2bfa7 MD5sum: 5e852946add3d76dde9d292d9def3d10 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: 2.7 Package: python-tz Version: 2012c-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 114 Depends: neurodebian-popularity-contest, tzdata, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2012c-1~nd12.04+1_all.deb Size: 39076 SHA256: 5e5a0c4143db73704376f78d5393a8d2562d1090e1ce0971aa074845eb6c7365 SHA1: d587a5c518fc1e8cc2b100cbcfdfa5ef3130e89e MD5sum: 1f27dc834f8849b16c7551116a435410 Description: Python version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). Package: python3-dateutil Version: 2.0+dfsg1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 153 Depends: neurodebian-popularity-contest, python3 (>= 3.1.3-13~), tzdata Homepage: http://labix.org/python-dateutil Priority: optional Section: python Filename: pool/main/p/python3-dateutil/python3-dateutil_2.0+dfsg1-1~nd12.04+1_all.deb Size: 49686 SHA256: 44eb8a99c2b13b8bd30809e636ea82734f3632c0e30dfe46e767aba4aaac530c SHA1: d7b1ee6c7d91825d3ae29daf32b386a697764c0d MD5sum: 62681c6efef26912ccad3d3e1c74dc26 Description: powerful extensions to the standard datetime module in Python 3 The dateutil package extends the standard datetime module with: . * computing of relative deltas (next month, next year, next Monday, last week of month, etc); * computing of relative deltas between two given date and/or datetime objects * computing of dates based on very flexible recurrence rules, using a superset of the iCalendar specification. Parsing of RFC strings is supported as well. * generic parsing of dates in almost any string format * timezone (tzinfo) implementations for tzfile(5) format files (/etc/localtime, /usr/share/zoneinfo, etc), TZ environment string (in all known formats), iCalendar format files, given ranges (with help from relative deltas), local machine timezone, fixed offset timezone, UTC timezone * computing of Easter Sunday dates for any given year, using Western, Orthodox or Julian algorithms Package: python3-mdp Source: mdp Version: 3.3+git6-g7bbd889-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1457 Depends: neurodebian-popularity-contest, python3 (>= 3.1.3-13~), python3-numpy Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python3-mdp_3.3+git6-g7bbd889-1~nd12.04+1_all.deb Size: 472476 SHA256: 2bc4470eac01996dfa8237bde53d42f433ac6367220e8cccf791b9d294ebe191 SHA1: ceee8aec82712893687f3de5bcd1dfd03dea5c4d MD5sum: c46ba24a20b25f1f2557623154156144 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 3. Package: python3-pandas Source: pandas Version: 0.12.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5575 Depends: neurodebian-popularity-contest, python3 (>= 3.2), python3-dateutil, python3-tz, python3-numpy (>= 1:1.6~), python3-pandas-lib (>= 0.12.0-1~nd12.04+1) Recommends: python3-scipy, python3-matplotlib, python3-numexpr, python3-tables, python3-bs4, python3-html5lib, python3-six Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.12.0-1~nd12.04+1_all.deb Size: 1076246 SHA256: 24629e73739f8be39f137093dd25a3ab441152eb7d6792e617aeac3da82c0b22 SHA1: 2400b825286cbf1aad602a6be48006dbdfc04b16 MD5sum: 22c7330b674e3b8e3e80710499949009 Description: data structures for "relational" or "labeled" data - Python 3 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 . This package contains the Python 3 version. Package: python3-patsy Source: patsy Version: 0.2.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 535 Depends: neurodebian-popularity-contest, python3 (>= 3.2), python3-numpy Recommends: python3-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python3-patsy_0.2.1-1~nd12.04+1_all.deb Size: 140704 SHA256: 507ba077dc0d303b183d7cb2fd4784a78bf2f8eb845dc56a2ea76e26f5c98035 SHA1: 81527f27e27ab59cf3ea3b7eae4e4f2af1f0abc1 MD5sum: 33501fab12821ef85b95b823d61526c0 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 3 version. Package: python3-tz Source: python-tz Version: 2012c-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 Depends: neurodebian-popularity-contest, tzdata, python3 (>= 3.1.3-13~) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python3-tz_2012c-1~nd12.04+1_all.deb Size: 31110 SHA256: 27f424360b0610de71d2281248500e85f830a8e931e4c4eeb3d8d218513f0b23 SHA1: bf1c3ee7248c84b5f1dbd09a2a789bc0c243d1ca MD5sum: 81bd1a01652926741eea5c70cd2072a4 Description: Python3 version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). . This package contains the Python 3 version of the library. Package: spm8-common Source: spm8 Version: 8.5236~dfsg.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 18626 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.5236~dfsg.1-1~nd12.04+1_all.deb Size: 10747760 SHA256: f2995cdaf4676933a4158d79a1eada6dc9321714c2f14fbdbdf0d246fd07381f SHA1: 31c5b4cbfba815812413a92d50e30610bd5b3033 MD5sum: 372feeb5e1535ecfd97b0f48839093d3 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: 8.5236~dfsg.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 73046 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd12.04+1_all.deb Size: 52176876 SHA256: d3a5268ccf35f741f9cf59605f78345ac61ecb07bb8c01bfb316ceafe81269fe SHA1: 7a6973208307ff01cf0717ebbda5df2c82abf005 MD5sum: f21ec46fddd30a2ae0fe355ee4c01bba 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: 8.5236~dfsg.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9401 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd12.04+1_all.deb Size: 8648994 SHA256: 322ebbb54b520746a660d41d6b88cd85033741a0f6d1322c1e9f17b868e371fd SHA1: b12832b37ef5c2dc669ec210c2fda133a813f40d MD5sum: 3bdf0a02f35291934fb90f7e12e436fb Description: manual for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the SPM manual in PDF format. Package: spykeviewer Version: 0.4.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1119 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.4.0), python-neo (>= 0.2.1), python-matplotlib, python-scipy, python-nose, python-sphinx, python-tables Recommends: libjs-jquery, libjs-underscore, ipython-qtconsole (>= 0.12) Homepage: http://www.ni.tu-berlin.de/software/spykeviewer Priority: extra Section: python Filename: pool/main/s/spykeviewer/spykeviewer_0.4.0-1~nd12.04+1_all.deb Size: 574738 SHA256: 14cf4b5c5555d9fb3dff9615637ccc3ee45150d40c1bd2eef823eb2d152ef41f SHA1: 68ebe18caab186edf959ef48e15462729007a4e7 MD5sum: eece40053188043facb5f15e700818a2 Description: graphical utility for analyzing electrophysiological data Spyke Viewer is a multi-platform GUI application for navigating, analyzing and visualizing electrophysiological datasets. Based on the Neo framework, it works with a wide variety of data formats. Spyke Viewer includes an integrated Python console and a plugin system for custom analyses and plots. Package: stabilitycalc Version: 0.1-1~nd11.04+1+nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy, python-nifti Recommends: python-dicom Homepage: https://github.com/bbfrederick/stabilitycalc Priority: extra Section: science Filename: pool/main/s/stabilitycalc/stabilitycalc_0.1-1~nd11.04+1+nd11.10+1+nd12.04+1_all.deb Size: 28730 SHA256: add473af6d9eb0497a244d721862483deeb0fd562cabf84305eefa9e9c522897 SHA1: f6952357804556ee3b33d3242d205b4aa3cc49c7 MD5sum: 472471b057239fd1029854d1f42c735e Description: evaluate fMRI scanner stability Command-line tools to calculate numerous fMRI scanner stability metrics, based on the FBIRN quality assurance test protocal. Any 4D volumetric timeseries image in NIfTI format is support input. Output is a rich HTML report. Python-Version: 2.7 Package: testkraut Version: 0.0.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 358 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, libjs-underscore, libjs-jquery, python-argparse Recommends: strace, python-scipy, python-colorama, python-apt Homepage: https://github.com/neurodebian/testkraut Priority: extra Section: python Filename: pool/main/t/testkraut/testkraut_0.0.1-1~nd12.04+1_all.deb Size: 99594 SHA256: 67cab9b7c779f963fb4cdd501878d78e45f462601868990f829fe9bfdc7fd325 SHA1: 8d6344c0c06c876467678560b03d0e185c6c97e5 MD5sum: 74b55de26e189ab9a6b7c97f1d733816 Description: test and evaluate heterogeneous data processing pipelines This is a framework for software testing. That being said, testkraut tries to minimize the overlap with the scopes of unit testing, regression testing, and continuous integration testing. Instead, it aims to complement these kinds of testing, and is able to re-use them, or can be integrated with them. . In a nutshell testkraut helps to facilitate statistical analysis of test results. In particular, it focuses on two main scenarios: . * Comparing results of a single (test) implementation across different or changing computational environments (think: different operating systems, different hardware, or the same machine before an after a software upgrade). * Comparing results of different (test) implementations generating similar output from identical input (think: performance of various signal detection algorithms). . While such things can be done using other available tools as well, testkraut aims to provide a lightweight, yet comprehensive description of a test run. Such a description allows for decoupling test result generation and analysis – opening up the opportunity to “crowd-source” software testing efforts, and aggregate results beyond the scope of a single project, lab, company, or site. Python-Version: 2.7 Package: vowpal-wabbit-doc Source: vowpal-wabbit Version: 7.3-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 70918 Depends: neurodebian-popularity-contest Recommends: vowpal-wabbit Homepage: http://hunch.net/~vw/ Priority: optional Section: doc Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-doc_7.3-1~nd12.04+1_all.deb Size: 50202392 SHA256: 8fe71c3ad76acac1d58d7b100005c3fd7533ec52a969d22b7ee311a3a59d8b9c SHA1: 19d0a8afda5d7cc5c002aa69041fb0f4ced976d1 MD5sum: f944be3d128553cc5761e11c385af764 Description: fast and scalable online machine learning algorithm - documentation Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains examples (tests) for vowpal-wabbit.