Package: afni-atlases Source: afni-data Version: 0.20180120-1.1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 109419 Homepage: http://afni.nimh.nih.gov Priority: extra Section: science Filename: pool/main/a/afni-data/afni-atlases_0.20180120-1.1_all.deb Size: 98215048 SHA256: b7b30ce4345671d92cb08f939b76de42f81a6839abe3d47dba1db0620fe64e0c SHA1: 792d6506cc866acfa54fc71475f823e686f169f7 MD5sum: deaddf5e6992face9b5edeb62644187c Description: standard space brain atlases for AFNI AFNI is an environment for processing and displaying functional MRI data. It provides a complete analysis toolchain, including 3D cortical surface models, and mapping of volumetric data (SUMA). . This package provide AFNI's standard space brain templates in HEAD/BRIK format. Package: condor-doc Source: condor Version: 7.8.7~dfsg.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6964 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.7~dfsg.1-1~nd11.10+1_all.deb Size: 1333684 SHA256: 9e3e08eba374f37490e9e93cc63a0409026fe882bb5f463b5fd409396b0dee47 SHA1: 4643ddd08a3b01dd2543a108d7c0b200a9a806ce MD5sum: 5bb946af2bc61808b5e0d0f1554fe17d 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1888 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~nd11.10+1_all.deb Size: 1355528 SHA256: e9b4a0a11015223cc11a4584628a4311ab652d5bdab4fc48fcf41be90bbbbd4d SHA1: e4af53fb990202ac3fc96db4e6945862682dc33d MD5sum: 54d65738ed41265fa85a9e51dfdd60e1 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: datalad Version: 0.17.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 224 Depends: neurodebian-popularity-contest, python3-datalad (= 0.17.5-1~nd+1), python3-argcomplete (>= 1.12.3), python3:any Suggests: datalad-container, datalad-crawler, datalad-neuroimaging Homepage: https://datalad.org Priority: optional Section: science Filename: pool/main/d/datalad/datalad_0.17.5-1~nd+1_all.deb Size: 187092 SHA256: dcfab5ab31ab85c685b4439648c3095efb236b34c92eb2f870fc1376dd0dbab1 SHA1: e8a088bc96e73f10444588eede93689410943c07 MD5sum: a4020bc221d05979fe1738d432660717 Description: data files management and distribution platform DataLad is a data management and distribution platform providing access to a wide range of data resources already available online. Using git-annex as its backend for data logistics it provides following facilities built-in or available through additional extensions . - command line and Python interfaces for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get) - extract, aggregate, and search through various sources of metadata (xmp, EXIF, etc; install datalad-neuroimaging for DICOM, BIDS, NIfTI support) - crawl web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc (install datalad-crawler) Package: debian-handbook Version: 6.0+20120509~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23215 Depends: neurodebian-popularity-contest Homepage: http://debian-handbook.info Priority: optional Section: doc Filename: pool/main/d/debian-handbook/debian-handbook_6.0+20120509~nd+1_all.deb Size: 21998670 SHA256: b33f038d8363175473cc056a5f98fc7af52386a466b45d4b2e42d2f25233a3ed SHA1: 7a0b369b4548a3f4fb61aa1ef9efa2ddf2b319e2 MD5sum: 3e3d2cf990fcc5ed1ed6bdbfb5c1c3dd Description: reference book for Debian users and system administrators Accessible to all, the Debian Administrator's Handbook teaches the essentials to anyone who wants to become an effective and independent Debian GNU/Linux administrator. . It covers all the topics that a competent Linux administrator should master, from the installation and the update of the system, up to the creation of packages and the compilation of the kernel, but also monitoring, backup and migration, without forgetting advanced topics like SELinux setup to secure services, automated installations, or virtualization with Xen, KVM or LXC. . The Debian Administrator's Handbook has been written by two Debian developers — Raphaël Hertzog and Roland Mas. . This package contains the English book covering Debian 6.0 “Squeeze”. Package: fail2ban Version: 0.8.10-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 728 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~nd11.10+1_all.deb Size: 134658 SHA256: 2c968d636427537562e00e665c865c56b7c32b54c026398a1b57bf65f288eaf7 SHA1: f70f58934f9c239497005aa492574477b51d5610 MD5sum: 8a1d1242eb0e6f1d14ff8dc1c9d06012 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 0 Depends: neurodebian-popularity-contest, freeipmi-common, freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi_1.1.5-3~nd11.10+1_all.deb Size: 934 SHA256: 9a74a802658e54c5939bbd5ebc21b4fc7d70900d0ad520046109457aae980399 SHA1: 940b77fe6cb6191a17460422dd771cb7d2af0ada MD5sum: f28f330b4b4051ab5a1944ba813890a3 Description: GNU implementation of the IPMI protocol FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This meta-package depends on all separate modules of freeipmi. Package: freeipmi-common Source: freeipmi Version: 1.1.5-3~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 472 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest Suggests: freeipmi-tools Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-common_1.1.5-3~nd11.10+1_all.deb Size: 296938 SHA256: 887bb67cfa7d82ac20dfa9d4a4e6f377aba44a39bd05632950f7b9a8f4ac4f85 SHA1: 2bb34ca7dbd18d927c228b6a482aab1db19c330a MD5sum: eecf6cdbc8bf6f9e07b545953301009e 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3112 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-2~nd11.10+1_all.deb Size: 2346546 SHA256: edb34fea5ce95118037adad645e679e7629871b068daef935b8934a55b35c33a SHA1: 793c7ac82c7ba85dd78844c4eecb1ef574e87b63 MD5sum: d8747cba87c48952ab3f152626fb296e Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: git-annex-remote-rclone Version: 0.5-1~ndall+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23 Depends: neurodebian-popularity-contest, git-annex | git-annex-standalone, rclone Homepage: https://github.com/DanielDent/git-annex-remote-rclone Priority: optional Section: utils Filename: pool/main/g/git-annex-remote-rclone/git-annex-remote-rclone_0.5-1~ndall+1_all.deb Size: 7842 SHA256: 0b1d65c740ce1073ecdae6db121d304fe02c4bb95df552326894118a65b38319 SHA1: 34a2323c4387e61c4a69617150c463f9a7b772c5 MD5sum: 00c5a0407a998eba72d4f5eb0ad71189 Description: rclone-based git annex special remote This is a wrapper around rclone to make any destination supported by rclone usable with git-annex. . Cloud storage providers supported by rclone currently include: * Google Drive * Amazon S3 * Openstack Swift / Rackspace cloud files / Memset Memstore * Dropbox * Google Cloud Storage * Microsoft One Drive * Hubic * Backblaze B2 * Yandex Disk . Note: although Amazon Cloud Drive support is implemented, it is broken ATM see https://github.com/DanielDent/git-annex-remote-rclone/issues/22 . Package: gmsl Version: 1.1.3-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 96 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.3-2~nd11.10+1_all.deb Size: 16300 SHA256: a65ededdd615be4706a4f9b997a4fcffe07db5912def78632af2254c381b4c4c SHA1: 27e1588752e29afc9d38d08d6758fc42470b59fe MD5sum: c1f172d20efa1e5f13b5f030f657231a 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: golang-github-ncw-rclone-dev Source: rclone Version: 1.41-1~ndall0 Architecture: all Maintainer: Debian Go Packaging Team Installed-Size: 2492 Depends: golang-bazil-fuse-dev, golang-github-aws-aws-sdk-go-dev, golang-github-mreiferson-go-httpclient-dev, golang-github-ncw-go-acd-dev, golang-github-ncw-swift-dev, golang-github-pkg-errors-dev, golang-github-pkg-sftp-dev, golang-github-rfjakob-eme-dev, golang-github-skratchdot-open-golang-dev, golang-github-spf13-cobra-dev, golang-github-spf13-pflag-dev, golang-github-stacktic-dropbox-dev, golang-github-stretchr-testify-dev, golang-github-tsenart-tb-dev, golang-github-unknwon-goconfig-dev, golang-github-vividcortex-ewma-dev, golang-golang-x-crypto-dev, golang-golang-x-net-dev, golang-golang-x-oauth2-google-dev, golang-golang-x-sys-dev, golang-golang-x-text-dev, golang-google-api-dev Homepage: https://github.com/ncw/rclone Priority: optional Section: devel Filename: pool/main/r/rclone/golang-github-ncw-rclone-dev_1.41-1~ndall0_all.deb Size: 399416 SHA256: 528b53f3312375d31d5cebb95472a57272cf242e14a92cfdf99c45be2ff5511d SHA1: 75f8871fd668e815023267a857b37ad60b9d1c2f MD5sum: a87865eafe10185420838e2e4ffd7b55 Description: go source code of rclone Rclone is a program to sync files and directories between the local file system and a variety of commercial cloud storage providers. . This package contains rclone's source code. Package: guacamole Version: 0.6.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 348 Depends: neurodebian-popularity-contest, guacd (>= 0.6), guacd (<< 0.7) Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole_0.6.0-1~nd11.10+1_all.deb Size: 277290 SHA256: 3c5f20224ce82075dfb4b8fb6aa54ab2f880fb7090f21364aae21558c15e4369 SHA1: 7e28d55421f0b391410a7740e79d53ea63402b3c MD5sum: c7e929aeb3b79b891f62b82758d24d04 Description: HTML5 web application for accessing remote desktops Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. Package: guacamole-tomcat Source: guacamole Version: 0.6.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole-tomcat_0.6.0-1~nd11.10+1_all.deb Size: 5176 SHA256: e0d25a3269c7106d2195c7143a2d8766b29c4f7b2d297cc6a54fad52025dd12c SHA1: e83b6aed27cf78415d9c62a2c3961017902697ee MD5sum: 7f06977609c49671a7890dc2af525b3e 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 356 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~nd11.10+1_all.deb Size: 156004 SHA256: 51a3963e1be000bf45d85f1044ca3261068b14dc749341a7882cb214c94d49d1 SHA1: 3c9a04629e98e1dab2afcb7f1b9bef4d9ae29a03 MD5sum: 72fa3b863e6eb174224e26a214814b15 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd11.10+1_all.deb Size: 9670 SHA256: 38fd834ee0212bbda9b6e23163c1f144f36bd94db407ac4f88e841b39f900e99 SHA1: cea79dee3c33ef42b2d65ec64e1cab7f2dc10db3 MD5sum: 7ca3af87fced0a8134eb92caf3e0281b 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: ipython01x Version: 0.13.2-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6204 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python2.7 | python2.6, 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~nd11.10+1_all.deb Size: 1306432 SHA256: e2c0c0868fce41b63bf1a86e48f108fa67879592e5f395b1627ea80d71ed7c7a SHA1: 567f8e9540d98f00f98da31849079a8e6fe0fb04 MD5sum: be694ac7cf590373c5eae93a48b4a661 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17776 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~nd11.10+1_all.deb Size: 7035216 SHA256: e265a9e211d269316cef4ff6d813b8cba17e788fa9d7dd25d0e396e1c7488548 SHA1: 094ba1e5cd4f3b30a8f750d178574696925d16fb MD5sum: f70606351aaf257b7a62d3d8876bb231 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 0 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~nd11.10+1_all.deb Size: 902 SHA256: fc43f212687a471099834e7793e0082962990af1557b413f78998db220b7e1e4 SHA1: 0f8dd6e35ae88c41cae7a3aa80aee6ca36cce6a9 MD5sum: 9738e501270759e776b1aeeeee82a2f1 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 0 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~nd11.10+1_all.deb Size: 826 SHA256: 78909dfb6744135a5ab9fb45f864f20c8139b6e1ec8e1e72087a75713393ab26 SHA1: 69c281a0aaa438a555cad4bbe3134c74530bc089 MD5sum: a93161d02e04878c2019a1c4f23a9379 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 0 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~nd11.10+1_all.deb Size: 912 SHA256: fdfa7705491c9c98f3588a4c3254ba2650e814bbeb894203c3bbce81a7686d92 SHA1: 10a51ab8ee7f0d3feae18d65be6ed59882572b6d MD5sum: 7185e682b89656502c3a1fe8351c02d7 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14796 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python2.7 | python2.6, 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~nd11.10+1_all.deb Size: 4530502 SHA256: 1f1d68d0b5681cccb845a0a0a88e932f07cc46bd2f7c0e530efc80fd043e9ea7 SHA1: c64d1f8bc75d537438feed471d7d386bee53be0c MD5sum: 6bccd75cae124912454964963b13775f 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11908 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~nd11.10+1_all.deb Size: 4120430 SHA256: 09d9590bd3ca27e3215ef0888fe4d88daac43e4f7f59699d2616e7d3932287d9 SHA1: c3a710c31f1c57f34833880e91671bd29115d95a MD5sum: f149fffa25255c03f8dc4f5df9b7019f 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 0 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~nd11.10+1_all.deb Size: 896 SHA256: 7d4fd510b2a8e989af346bf2920746429bcd2f68f036a6f88671d18981c839b7 SHA1: b28d29af88a66adc1e54042534a3accb03437431 MD5sum: f9adfcfbb324dc3544971b5cbfe0b6e4 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 0 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~nd11.10+1_all.deb Size: 826 SHA256: 27879e6a5caa288b4b150ed82065529c8aa433668a14a76373b90af094ff2967 SHA1: 0a0c18ae1b442d5bf293de3101615b919a578345 MD5sum: 12f6ea31ffeeb266de7aa306b42c9051 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 0 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~nd11.10+1_all.deb Size: 908 SHA256: b8e2203989a5f07d4a751d7ac9db3971b334549541564fabdad3632e39938448 SHA1: 08796366b0f8c9115fce561ec85171a2e7a8b74d MD5sum: 85e7a6bacfea1a9ecbc32469a424ae2c 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 596 Depends: neurodebian-popularity-contest Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-6~nd11.10+1_all.deb Size: 89476 SHA256: 8e365d03a4f610d00bfa85305eebd9ed2868992b74385789ed861527557baa09 SHA1: 8f1cfb43a9d6f9dabcfaf55ee397bcd0d267dd43 MD5sum: bf516848c37dc6abd53603b7a5806d9c 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: libopenwalnut1-doc Source: openwalnut Version: 1.3.1+hg5849-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36500 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~nd11.10+1_all.deb Size: 3904888 SHA256: 20bce21f498dd895e06b333129a4979e8b68348ce3046d38548d536534289949 SHA1: 148a47abc7cc80e9f87da3345a538e92caa8a0d1 MD5sum: c43597be89b069d69d2e02a6c80e1cf4 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: matlab-support-dev Source: matlab-support Version: 0.0.19~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 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~nd11.10+1_all.deb Size: 7222 SHA256: 9c8a8decac151557548479bd7e1c9c23267d92aeacefcc89a4d2a83750d0565f SHA1: 9796919d5a9567ab984656934e3485f60b697caf MD5sum: 924358bb64d2a143d55abb0c823cb8d2 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: 0.20120505.1~dfsg.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1804 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20120505.1~dfsg.1-1~nd11.10+1_all.deb Size: 1663990 SHA256: 57a67faa032a7cbb1a1d8d9295e971f4e10662b6c85e7730ae330927f2418aa2 SHA1: ef1ce70993c8aa4b1936b67351b4c86bc02c1683 MD5sum: bf04d2e9b077d9d4a172661f23de9516 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: 0.20120505.1~dfsg.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1176 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20120505.1~dfsg.1-1~nd11.10+1_all.deb Size: 735712 SHA256: d81d7ef15eb924b87d587143c266c148d2a809949aee74e7fd57c1a1eaec4820 SHA1: 55d141c48923b332cc98a230cf6ca324e063b7e8 MD5sum: aafbd101ef1573edbe13722f3dcb96ba 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: neurodebian-desktop Source: neurodebian Version: 0.31~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 272 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~nd11.10+1_all.deb Size: 115306 SHA256: a2c4dbbb87de31d712d16d7e3dbf9d85701db7f832108c2427bf5181989bdc42 SHA1: 79015b214a2291bb24d32c8cef0be3a06ba12d12 MD5sum: 7f09eb954ae8dfaf383b13bef224373f 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6200 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~nd11.10+1_all.deb Size: 5351204 SHA256: ae9f3a30e85933b0157a60b9d1e23efb32642678eb99fa5ad9ec72da7a676eee SHA1: 474f3584cc672a4b2ecdd30c1e775fc7719196ec MD5sum: fb00ab2405407fce039a24856f8ab902 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 148 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm | 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~nd11.10+1_all.deb Size: 15202 SHA256: a48fd3918441e8773dad35eac5013f9eb376b70beedd2ca9223b8450f6993c4f SHA1: 0fae334750e69b8b3553a63923969aee5e1e34c9 MD5sum: 0a812926012e26bc78eb6eda74d2fa26 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.31~nd11.10+1_all.deb Size: 7498 SHA256: b911a53c05a2e0890449c36e9e013b073b91999bd0b97e41a46c58cbc3b6f5db SHA1: affa6ddb8f266de6a37472f8d0d81273df7eeed6 MD5sum: adc2d0da731cc121fab85b5d845871f3 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.31~nd11.10+1_all.deb Size: 6722 SHA256: 603277bc6315f72bfff44b353627d55e1624325f300f842ee05495b35c363696 SHA1: c9476ccf97ad250ea273ce2bf4b165e8ea96a6ff MD5sum: fa960c77acba5b3f51a87d0288c3ca9f 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: 0.4.6-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 652 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.6-1~nd11.10+1_all.deb Size: 615132 SHA256: 8d4e4b19869b1b5e6916ecb2af813eb80dd56d8802d95c95f0060572bfa1a2bf SHA1: 1080c530d325f90d81152d32fb67d7effdb270ef MD5sum: faf5e00e0904f37b42be0bba44890f11 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+ds-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2180 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+ds-1~nd11.10+1_all.deb Size: 440924 SHA256: efaf782db10e8571ce778d7aa5b1c2a97b615053a6c1ae5aeef257b49f37382d SHA1: 15e3ca8eec0c4515ce0470a42ab6c899609804e0 MD5sum: 27793f9a383fda52356ceef5886228cb 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: 0.27.2-4~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26676 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_0.27.2-4~nd11.10+1_all.deb Size: 24409906 SHA256: 063be134ef66e2b2e0424fa55c93827aadaa605c2c6ca538f71d5bd7a4c91881 SHA1: f3f10f5913fe84b21126c0f43b4767b5e06f64b3 MD5sum: 20e93c05b83c062d62b5e87bb9ab06fa 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9860 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~nd11.10+1_all.deb Size: 2024480 SHA256: f5dcf6a8305554b34071c5c91226794d926a710640271a5931a4178462a1c004 SHA1: ded3fb81f200739236836663ebf5beb074f7db2e MD5sum: 05989c0100e43807b223fb490f46ce52 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10420 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~nd11.10+1_all.deb Size: 5822200 SHA256: e46d17e7750cc02923d91eb85d635b011a00f2d194446a6937003995fc75deba SHA1: c3c9e8d33eff46ad138a177249f3a10fbe99b853 MD5sum: 909321a15c8b0358e66b4441989d442f Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features . - IDE GUI for coding in a powerful scripting language (Python) - Builder GUI for rapid development of stimulation sequences - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.6, 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.11.20130711.dfsg1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 57212 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~nd11.10+1_all.deb Size: 19883548 SHA256: 1e476e68edfb81e145b9208c80b3dff7853561a0ab7d40fb38615ea2e493ca8e SHA1: 98c0042852d117b5f29cedab541cb5522b3b8c71 MD5sum: cd1ed027a60ba562af5d58cfd58cad2e 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2928 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.1-1~nd11.10+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~nd11.10+1_all.deb Size: 549136 SHA256: 24543fb1e73eae4057881c610aa6cb1388a29c68e65e5c5ed2672fa95f45fc43 SHA1: 460b7431bdd4ceab03b003f41322670bb0236f7d MD5sum: 0ffa36ded655aaa19c06221378e2e328 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7952 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~nd11.10+1_all.deb Size: 2223538 SHA256: a6875121f295f909e41a36d8c7c97e241cead6d095946a731d92351477f7ae5d SHA1: 50cf55bf72489f8e419ffa238d4d2b3ec029e293 MD5sum: 9d7f9ba1a40f518b8cb74171f7316e27 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2032 Depends: neurodebian-popularity-contest, python2.7 | python2.6, 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~nd11.10+1_all.deb Size: 425406 SHA256: 1431b078744d8cf21fce7a9f73e09ab42e840a375b878811665d78af83cdd681 SHA1: b47da33e7d6efe0e27d3d05184e7a27a5355c74f MD5sum: 5367f033ea76911e18cd97b673848176 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2664 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.6.0-1~nd11.10+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.6-dipy, python2.7-dipy Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.6.0-1~nd11.10+1_all.deb Size: 1586430 SHA256: bfb0e2c7bb63a44ad174bb427bdacf2f429c8ee6832c9117b199317af29ed9f7 SHA1: f5f2087d61109cdd53f414521170f7f1f6c2dad2 MD5sum: 13c4a681b8a73f1c5998eaffe1c53d45 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: 2.6, 2.7 Package: python-dipy-doc Source: dipy Version: 0.6.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5304 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~nd11.10+1_all.deb Size: 3592976 SHA256: 3e139ba08b29259256c4a0acd9cbf69f6d975968adc9878e88ccd72eeedb8179 SHA1: f2138d2ae83c1b35ca9e026b45aa904e068aef5e MD5sum: 5bae21c2903a78da4664274104464d33 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 264 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~nd11.10+1_all.deb Size: 54828 SHA256: 3bd5d0216a5df099925a678c5d1b2e4ef60443b02e54075f257103b38ec6976c SHA1: 25cbd17544a6b6e622cd855878f91506b4c5cc71 MD5sum: af75c12163eb129d437f07a7912ef30c 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 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy Homepage: http://bitbucket.org/apdavison/lazyarray/ Priority: optional Section: python Filename: pool/main/l/lazyarray/python-lazyarray_0.1.0-1~nd11.10+1_all.deb Size: 7320 SHA256: a6173047b4834e63ae2a25dd4d1df3d705b14a56a878e9e6ada4828e22938a38 SHA1: 8727ceef57f94c0c47c1394ac652d0860eebdfb5 MD5sum: f45dad983f038670b80cf5904d49f7f6 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1916 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy Recommends: python-scipy, python-libsvm, python-joblib, python-scikits-learn | python-sklearn, python-pp Suggests: python-py, shogun-python-modular Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.3+git6-g7bbd889-1~nd11.10+1_all.deb Size: 484220 SHA256: 5f9662803c96ac327d480ac52ba2a91337194666d291134e9d8c760a6879375c SHA1: cdacfe30cefade1651537d62bc23f0f0574eeee7 MD5sum: d0e0d6dbf5a1838805da508d1b55ba08 Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 2. Package: python-mvpa Source: pymvpa Version: 0.4.8-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4104 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0), python2.7, python-mvpa-lib (>= 0.4.8-1~nd11.10+1) Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc Provides: python2.6-mvpa, python2.7-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.8-1~nd11.10+1_all.deb Size: 2205048 SHA256: 80c6f60748519b38a980781555bb3aa9e4aacb37b559e181395cc0cc592fb2ff SHA1: 3c565e370c198a6c4ec10822d4bc45eee914ba0a MD5sum: 79b54669d7f087b1fb11b010cb33d349 Description: multivariate pattern analysis with Python PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, GNB, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. Python-Version: 2.6, 2.7 Package: python-mvpa-doc Source: pymvpa Version: 0.4.8-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40796 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_all.deb Size: 8486982 SHA256: 1c6b0fedbfa215a04e43cf8cee1152348a62b66c69effb13ee846261db73adf7 SHA1: 0889baeb9497ecccc2a534d93e8bf9520051d14f MD5sum: 22620f53bb3d0bd9a208aaaefc240687 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4956 Depends: neurodebian-popularity-contest, python, python-numpy (>= 1:1.5.1), python-numpy (<< 1:1.6), python-support (>= 0.90.0), python-mvpa2-lib (>= 2.2.0-3~nd11.10+1) Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, shogun-python-modular, liblapack-dev Suggests: fslview, fsl, python-mvpa2-doc, python-nose, python-openopt, python-rpy2 Provides: python2.6-mvpa2, python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.2.0-3~nd11.10+1_all.deb Size: 2400422 SHA256: 601e3084ab11efe7e694e24818217793caa10b7337d1a4a5f0a096a31567451d SHA1: 85b740256e42623768f506ef64ac8f0a6e4a4e62 MD5sum: 8ce9f5dc44ea56c5a3a3f2a5ee226ceb Description: multivariate pattern analysis with Python v. 2 PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. . This is a package of PyMVPA v.2. Previously released stable version is provided by the python-mvpa package. Python-Version: 2.6, 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.2.0-3~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26784 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~nd11.10+1_all.deb Size: 5178780 SHA256: c2cc5ec6b6485100ee884bd8a65e1d20a45fa9cf316f3b737f05dac4f134d23b SHA1: 373ad5942f8cc6caa7a8cd9fdae02685a4eca9a8 MD5sum: 5554befe69d3e985b768c5d9635c6fe2 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: 0.2.0-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2488 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy (>= 1:1.5.1), python-numpy (<< 1:1.6), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.2.0-2~nd11.10+1_all.deb Size: 1372760 SHA256: 29d53f881d5c2cfebd97379fc75ea8dabf14c3dd015ead1aeba4e366f3939a78 SHA1: 09fb69fa97a26d7592eceb0560e1296d57a4e989 MD5sum: 693ecd3d8fcfc6b59178e69a5c988a4e 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 172 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~nd11.10+1_all.deb Size: 32520 SHA256: c563c869fb3f1af06efd6649a2756d23a7337beec40a07fc62550a1c77e2e67a SHA1: 66a8a4f932e4b212e0444b2ae4330018189dba74 MD5sum: de8c033a38023796344277a6b19c57f3 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4468 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Provides: python2.6-nibabel, python2.7-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.3.0-1~nd11.10+1_all.deb Size: 1816568 SHA256: 31703c73d3a7529c9661d58a06525b245eb23cc89237879e795f22da3d50cd65 SHA1: ee28d5b9538d4552b4750b305fb6d1df82d22b9d MD5sum: 4f8eeea16e17cd29eb2c6fe4183bc7f7 Description: Python bindings to various neuroimaging data formats NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package also provides a commandline tools: . - dicomfs - FUSE filesystem on top of a directory with DICOMs - nib-ls - 'ls' for neuroimaging files - parrec2nii - for conversion of PAR/REC to NIfTI images Python-Version: 2.6, 2.7 Package: python-nibabel-doc Source: nibabel Version: 1.3.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2856 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.3.0-1~nd11.10+1_all.deb Size: 418580 SHA256: 81aa0e70e635ef4a48b15ed7173552e6adf3e72209caf5fdd87b13a8a9b8ae87 SHA1: 7b93d5f20f371ae26cff4fe9aa9d9c83ea447e2a MD5sum: 0a9e5c7875e7554a5d4eec37b28b7755 Description: documentation for NiBabel NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package provides the documentation in HTML format. Package: python-nipy Source: nipy Version: 0.2.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3764 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0), python-scipy, python-nibabel, python-nipy-lib (>= 0.2.0-1~nd11.10+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.6-nipy, python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.2.0-1~nd11.10+1_all.deb Size: 763128 SHA256: b04eea5a4e729927d7f9a934e1b4446a086e5e1c56fee8e81cecff392e8ad817 SHA1: e9e48f1a4a34e0d93bce0369fe1fe0b04251db12 MD5sum: fdb9f9a1917c81a01c5c8e95caebe1c4 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It provides functionality for - General linear model (GLM) statistical analysis - Combined slice time correction and motion correction - General image registration routines with flexible cost functions, optimizers and re-sampling schemes - Image segmentation - Basic visualization of results in 2D and 3D - Basic time series diagnostics - Clustering and activation pattern analysis across subjects - Reproducibility analysis for group studies Python-Version: 2.6, 2.7 Package: python-nipy-doc Source: nipy Version: 0.2.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9692 Depends: neurodebian-popularity-contest, libjs-jquery Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.2.0-1~nd11.10+1_all.deb Size: 2495506 SHA256: 93a0ecd6bf0936a6f611c459040b4dbba1df8bc300860d3520224700549f7556 SHA1: fe1e2c0a2ca54d2c1aa12171165e755df8807948 MD5sum: 913dd9beafed84a2881334226f353359 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3476 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib Recommends: ipython, python-nose, graphviz Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat Provides: python2.6-nipype, python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.8-1~nd11.10+1_all.deb Size: 591966 SHA256: 0c3a375571d01e7a1b656d0c942c95543ea4b04c10e763e264ba8211cf5193f4 SHA1: b81c857cf47b228b44b0d01cfb24c3e13b894c78 MD5sum: 337a4700cf57c84d4b262fa7679d7c76 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16068 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~nd11.10+1_all.deb Size: 7146360 SHA256: 1ae8b8df8f75bfd9c95a384b004baa9b1349937ba5a2c90160cca0ff27840d6f SHA1: 01b27d94fcf19ec833b0a781d480b41a6cdc4477 MD5sum: b1c0ab9e9ceca5303b2a4ae66623f24f Description: Neuroimaging data analysis pipelines in Python -- documentation Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). . This package contains Nipype examples and documentation in various formats. Package: python-nitime Source: nitime Version: 0.4-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9444 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.4-2~nd11.10+1_all.deb Size: 3908920 SHA256: eb82dbfb873469b52d7d46fa57b1a8e806b4580f95847d2156b77ac9fa451a92 SHA1: cf437a6279966daf3ff7ab27957cbccad7378a40 MD5sum: dd1a12936e95c4c6f7b7ee462db5ef5f Description: timeseries analysis for neuroscience data (nitime) Nitime is a Python module for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code. Package: python-nitime-doc Source: nitime Version: 0.4-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7104 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nitime Homepage: http://nipy.org/nitime Priority: extra Section: doc Filename: pool/main/n/nitime/python-nitime-doc_0.4-2~nd11.10+1_all.deb Size: 5271362 SHA256: edd183edaea4d04fb9c8312fd11bf16c6a6aa946d746a020bf53ddbcac1d0429 SHA1: 676be025c8a5320c90b99359fabcab2e3b023e3d MD5sum: 92f78c40fee0405ddefe5ae6dabac0c7 Description: timeseries analysis for neuroscience data (nitime) -- documentation Nitime is a Python module for time-series analysis of data from neuroscience experiments. . This package provides the documentation in HTML format. Package: python-openopt Source: openopt Version: 0.38+svn1589-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1612 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.6-openopt, python2.7-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd11.10+1_all.deb Size: 245078 SHA256: dbd0618beef263545059368cce47ecc8ca5219cf428081e9e239c61a7b4da37f SHA1: 78ed3d3d491d1025833ecc5c1f891da94783c243 MD5sum: 220377b2ffc32c02689afb73de092625 Description: Python module for numerical optimization Numerical optimization framework developed in Python which provides connections to lots of solvers with easy and unified OpenOpt syntax. Problems which can be tackled with OpenOpt * Linear Problem (LP) * Mixed-Integer Linear Problem (MILP) * Quadratic Problem (QP) * Non-Linear Problem (NLP) * Non-Smooth Problem (NSP) * Non-Linear Solve Problem (NLSP) * Least Squares Problem (LSP) * Linear Least Squares Problem (LLSP) * Mini-Max Problem (MMP) * Global Problem (GLP) . A variety of solvers is available (e.g. IPOPT, ALGENCAN). Python-Version: 2.6, 2.7 Package: python-openpyxl Source: openpyxl Version: 1.6.1+hg2-g4bff8e3-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 404 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~nd11.10+1_all.deb Size: 62046 SHA256: 2ed93a9990e1a6d79b01d1590b02913b892502a34a1bcb7e2e9ee05e1e679ff2 SHA1: c0df2c2659777e8a7186e62f8aeac3c0eb6f291f MD5sum: 611cb634dd61a28f8b84191d39df77d2 Description: module to read/write OpenXML xlsx/xlsm files Openpyxl is a pure Python module to read/write Excel 2007 (OpenXML) xlsx/xlsm files. Package: python-pandas Source: pandas Version: 0.7.3-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2220 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-dateutil, python-pandas-lib (>= 0.7.3-1~nd11.10+1) Recommends: python-scipy, python-matplotlib, python-tables, python-tz, python-xlrd, python-scikits.statsmodels, python-openpyxl, python-xlwt Suggests: python-pandas-doc Provides: python2.6-pandas, python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.7.3-1~nd11.10+1_all.deb Size: 460900 SHA256: 1f007666eb9baeb1ce834bf32c88dbea86de368ff864c1b1db2e0426d697e9f0 SHA1: 37366ddb925a3eb1abd60334eb4e6bc96583b1f8 MD5sum: 0be0bce12ac8e0683de58b3b0950ccf3 Description: data structures for "relational" or "labeled" data pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure Package: python-pp Source: parallelpython Version: 1.6.2-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 176 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~nd11.10+1_all.deb Size: 34280 SHA256: cf10c101240f57f6490dcfe517641ba4c4cdf1be8e848a82cf0bc8a96eb15022 SHA1: 0269d308912dd34a97527c29c1624ea50e8727dc MD5sum: db335dbdd2fafdc1666c09bd415002b9 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: 2.6, 2.7 Package: python-pyentropy Source: pyentropy Version: 0.4.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 108 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy (>= 1.3) Recommends: python-scipy Suggests: python-nose Provides: python2.6-pyentropy, python2.7-pyentropy Homepage: http://code.google.com/p/pyentropy Priority: extra Section: python Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd11.10+1_all.deb Size: 21332 SHA256: e0d255f8e67cbb1157e49c1486b4f1130df8560959a0ae7f72f844f8ead02fd2 SHA1: 253d2f3413a555abad753170ee53f53fea933416 MD5sum: 6f065ec917691608e170ab909493600b Description: Python module for estimation information theoretic quantities A Python module for estimation of entropy and information theoretic quantities using cutting edge bias correction methods, such as * Panzeri-Treves (PT) * Quadratic Extrapolation (QE) * Nemenman-Shafee-Bialek (NSB) Python-Version: 2.6, 2.7 Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 820 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~nd11.10+1_all.deb Size: 818166 SHA256: 8e31d1185e6ff80f74a5ea0960844e11a764c044b1adcfe01bd4e64f64ca119b SHA1: 09ad2a795dfd7ec3b5beb667408d770e5d4be848 MD5sum: 9e40a7386eb5b4840b20a8fb1082f19b 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1008 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-jinja2, python-cheetah Suggests: python-neuron, python-brian, python-csa Homepage: http://neuralensemble.org/trac/PyNN Priority: extra Section: python Filename: pool/main/p/pynn/python-pynn_0.7.5-1~nd11.10+1_all.deb Size: 175774 SHA256: 92d0dccfd9f848ad55ea3e05d2a75a1b640d34864d499c71be15c2d405d3ee3c SHA1: 18fdbee3bac1bfb57a519a118b4b282b2a7ead24 MD5sum: cee1cc7543c34cf2eb42ead51d0945a9 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 672 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-lxml, python-simplejson, python-httplib2 (>= 0.7.0) Recommends: python-networkx, python-matplotlib Provides: python2.6-pyxnat, python2.7-pyxnat Homepage: http://packages.python.org/pyxnat/ Priority: extra Section: python Filename: pool/main/p/pyxnat/python-pyxnat_0.9.1+git39-g96bf069-1~nd11.10+1_all.deb Size: 107400 SHA256: 74c780f87f767ec3bba44385db4e9feb09de073e8c161621641968dd4b92671b SHA1: 9fb7d09c9afd8d4182c488db8b80971d29c4a7f9 MD5sum: 7ba228b3bdbce2027ba52e86a1f5c1ff 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 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 504 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy (>= 1.4) Homepage: http://packages.python.org/quantities/ Priority: extra Section: python Filename: pool/main/p/python-quantities/python-quantities_0.10.1-1~nd11.10+1_all.deb Size: 60204 SHA256: db3834ba60ab0a28385895e01e87ee77196d468252a2196c4396322aba9b8032 SHA1: 3debe5c10b56978500cc395f0a1ea825f08ab3ff MD5sum: 31cd2ec5a63a5d73d902c83d66008e7c 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40 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~nd11.10+1_all.deb Size: 33354 SHA256: e719ca72dfeb559929774e3d0b0acc6b293e5f3d068a178c02f54977138a8c5e SHA1: b142980cf353a45d4c6a1310a67c0892b7d624f4 MD5sum: 9a7bab50f332cb8d2f9347a9b8864dfd Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-scikits.statsmodels Source: statsmodels Version: 0.4.0-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 116 Depends: neurodebian-popularity-contest, python-statsmodels, python (>= 2.5), python-support (>= 0.90.0) Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.4.0-2~nd11.10+1_all.deb Size: 7176 SHA256: 53d5ae442110a0cc00eb3ac0e84c1af9e15cad473ab399410799ea048246348f SHA1: 87e91cb60dab714fb4072e44bda3e188b84b197a MD5sum: b41b5cbecc293433aa4c631ff12fd462 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: 0.5.0+git100-gfeb3e92-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4060 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0), python2.6, python-scipy (>= 0.9), python-skimage-lib (>= 0.5.0+git100-gfeb3e92-1~nd11.10+1), libfreeimage3 Recommends: python-nose, python-matplotlib (>= 1.0), python-imaging Suggests: python-skimage-doc, python-opencv Provides: python2.6-skimage, python2.7-skimage Homepage: http://scikits-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.5.0+git100-gfeb3e92-1~nd11.10+1_all.deb Size: 2526508 SHA256: cbcaa961ba15535bce93d0976654457ecf886641541d1b930cdf8d90d1f8638d SHA1: 41280a0b4a1d5a476257885f4e87d657d019efd1 MD5sum: e06604621eb16416e7aad48d3b91b832 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: 0.5.0+git100-gfeb3e92-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5148 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_0.5.0+git100-gfeb3e92-1~nd11.10+1_all.deb Size: 3652802 SHA256: e68f8ee76d235413e761fa8b6b2562e8cd216b5f5d6c055d7e0acc5c0059c81f SHA1: d665de630b1640eeb3ae371e8430e4a4e19cda01 MD5sum: 288568578622862041ac2a7dba438d33 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4244 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-sklearn-lib (>= 0.14.1-1~nd11.10+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: python2.6-sklearn, 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~nd11.10+1_all.deb Size: 1110366 SHA256: 40e278d606db488dee7e8eb52aabeefa48b79c1225b89cfab785004cb5e7108b SHA1: 2e34631228aea66402e8250efe39a29fc11a53ca MD5sum: b6c22332f817f0ca6071de306b00445c 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 988 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~nd11.10+1_all.deb Size: 190022 SHA256: 847228a956663995fddfc30ddca5219195e2256aa5b212ffbb90ea05aa632268 SHA1: 95266642f44e26604b179576061f986fba3f9d5b MD5sum: 87350849bb1f9252f555a1a5b4139882 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-statsmodels Source: statsmodels Version: 0.4.0-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13488 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-statsmodels-lib (>= 0.4.0-2~nd11.10+1) Recommends: python-pandas, python-matplotlib, python-nose, python-joblib Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Provides: python2.6-statsmodels, python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.4.0-2~nd11.10+1_all.deb Size: 3079214 SHA256: 489e3c6232aafa2cfda5ae0911c3259e99b04eda30306fa5a09fc4f7aaf06b13 SHA1: 0675cf34ab10e32eac2bc54074c3ae79ac60d068 MD5sum: b02001cd8541fbd71e3643c1d7fc7c71 Description: Python module for the estimation of statistical models statsmodels Python module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Package: python-statsmodels-doc Source: statsmodels Version: 0.4.0-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24300 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-statsmodels Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.4.0-2~nd11.10+1_all.deb Size: 3964892 SHA256: 48f3e09c3c0661bedb56510eff105a31091c97c5b08497817c762a5be51db0a8 SHA1: cb9838c35006ff747146fcbd72e6d8cfafef7ce1 MD5sum: de0fb28f7e4b3c74246ae1dc345531c1 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 152 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-nibabel, python-imaging, mayavi2, python-argparse, ipython Recommends: mencoder Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.3+git15-gae6cbb1-1~nd11.10+1_all.deb Size: 28006 SHA256: 2b8658acba6ed0dba471f47cb6ca6ca3dc81757daf42c1b72924d263ccfc605f SHA1: 35ae235b413674a69a5baadad6e09753cfa1ab7a MD5sum: f3c063ce47419d636db1c3caa446e656 Description: visualize Freesurfer's data in Python This is a Python package for visualization and interaction with cortical surface representations of neuroimaging data from Freesurfer. It extends Mayavi’s powerful visualization engine with a high-level interface for working with MRI and MEG data. . PySurfer offers both a command-line interface designed to broadly replicate Freesurfer’s Tksurfer program as well as a Python library for writing scripts to efficiently explore complex datasets. Python-Version: 2.6, 2.7 Package: python-tz Version: 2012c-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 160 Depends: neurodebian-popularity-contest, tzdata, python2.7 | python2.6, 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~nd11.10+1_all.deb Size: 38210 SHA256: 39ea39455d410f8ea22430afc9dd96e3c13e18477d8de40882a047f38cae80be SHA1: 420e210ed2362c5864d5382d4ce86fc395fb3b39 MD5sum: 9cda3729eb71f938f04307cd9fedd4e5 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-datalad Source: datalad Version: 0.17.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4665 Depends: neurodebian-popularity-contest, git-annex (>= 8.20200309~) | git-annex-standalone (>= 8.20200309~), patool, p7zip-full, python3 (>= 3.7), python3-annexremote, python3-distro, python3-distutils | libpython3-stdlib (<= 3.6.4~rc1-2), python3-fasteners (>= 0.14~), python3-gitlab, python3-humanize, python3-importlib-metadata | python3 (>> 3.10), python3-iso8601, python3-keyring, python3-keyrings.alt | python3-keyring (<= 8), python3-mock, python3-msgpack, python3-pil, python3-platformdirs, python3-requests (>= 1.2), python3-secretstorage, python3-simplejson, python3-six, python3-tqdm, python3-chardet, python3-packaging, python3:any Recommends: python3-boto, python3-exif, python3-html5lib, python3-httpretty, python3-jsmin, python3-libxmp, python3-lzma, python3-mutagen, python3-pytest, python3-pyperclip, python3-requests-ftp, python3-vcr, python3-whoosh Suggests: python3-duecredit, datalad-container, datalad-crawler, datalad-neuroimaging, python3-bs4, python3-numpy Breaks: datalad-container (<< 1.1.2) Homepage: https://datalad.org Priority: optional Section: python Filename: pool/main/d/datalad/python3-datalad_0.17.5-1~nd+1_all.deb Size: 958872 SHA256: 1f3e16c16863bab40ba92405109ab26c78f19e3e86e2b38733a035221c4e7744 SHA1: 873da190eb5ee83576ff519c2d564e1f841abe5b MD5sum: 7a97a6f55929cc103dd60d7783a9565e Description: data files management and distribution platform DataLad is a data management and distribution platform providing access to a wide range of data resources already available online. Using git-annex as its backend for data logistics it provides following facilities built-in or available through additional extensions . - command line and Python interfaces for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get) - extract, aggregate, and search through various sources of metadata (xmp, EXIF, etc; install datalad-neuroimaging for DICOM, BIDS, NIfTI support) - crawl web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc (install datalad-crawler) . This package installs the module for Python 3, and Recommends install all dependencies necessary for searching and managing datasets, publishing, and testing. If you need base functionality, install without Recommends. Package: python3-dateutil Version: 2.0+dfsg1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 208 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~nd11.10+1_all.deb Size: 49692 SHA256: 2cea0e3f06dc717e5e11a088c4b0626e8ac4e7bcb7dc3c111065f9779cb9f4a7 SHA1: 48adb415809dc18fcbe0895606d6354e18ed0dc5 MD5sum: 9802ffb6f93ca6c9f08187f560f3fa1c 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-tz Source: python-tz Version: 2012c-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 144 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~nd11.10+1_all.deb Size: 31104 SHA256: 3f3bf78b94eeab48a0a1b1265b8ad975a28730adea169278b6a2538fa2b33d84 SHA1: 7a9bc9136a6b3f2563077f4cedc0d1e76ed542b9 MD5sum: 3f265f316cfb72b39827f91dfca95fb3 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: 8.4667~dfsg.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 22352 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.4667~dfsg.1-1~nd11.10+1_all.deb Size: 10573720 SHA256: f0321ca7b21561e4d36761c8b822a658935beed84da70603e6c85c49c8df54a7 SHA1: a90f50f9eaf6dc3c4dafb24896e014b1f46ad17e MD5sum: 60163a673bd38fc493e44f6ff60f68e9 Description: analysis of brain imaging data sequences Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the platform-independent M-files. Package: spm8-data Source: spm8 Version: 8.4667~dfsg.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73084 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.4667~dfsg.1-1~nd11.10+1_all.deb Size: 52167722 SHA256: e19c67624dae801e90d3d2ac4513fd291a52c679dda6a17539f9b81281400e8b SHA1: a4516b85aed0b0507ffb9fb655f77fee607896b2 MD5sum: 6f8b8c766b3a7a13083842d70d61f325 Description: data files for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provide the data files shipped with the SPM distribution, such as various stereotaxic brain space templates and EEG channel setups. Package: spm8-doc Source: spm8 Version: 8.4667~dfsg.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9380 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.4667~dfsg.1-1~nd11.10+1_all.deb Size: 8648920 SHA256: de82f4ec4f61b92d16f055186e425295077205616998b56a507c773648c00eb2 SHA1: 4f226546eddd4adcc049cf993c2dd1bebefb696c MD5sum: f78a3cda6f161c0975499c70718050ee 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: testkraut Version: 0.0.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 484 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~nd11.10+1_all.deb Size: 83450 SHA256: ba07d8a04ddb92fd46e907381bf1da22a1be781d5786cf792b447335f275a2ce SHA1: 19907aab2aea4a01e04627b87775fb6b8cc7c5f6 MD5sum: f32ee8d927adb263b85540ce29b1ac64 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: 2.6, 2.7 Package: youtube-dl Version: 2021.12.17-1~nd110+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5937 Depends: neurodebian-popularity-contest, python3-pkg-resources, python3:any Recommends: aria2 | wget | curl, ca-certificates, ffmpeg, mpv | mplayer, python3-pyxattr, rtmpdump, python3-pycryptodome Suggests: libfribidi-bin | bidiv, phantomjs Homepage: https://ytdl-org.github.io/youtube-dl/ Priority: optional Section: web Filename: pool/main/y/youtube-dl/youtube-dl_2021.12.17-1~nd110+1_all.deb Size: 1128692 SHA256: 75859d2f34a475fc0f199cd6d2b73e18c29cda44406530964890dcb790008eca SHA1: 09f85f2abc32eb5e9c2ccd6bfd1354ea332b6489 MD5sum: 6d04814be91bd9f85a7d3793ff2a2fb3 Description: downloader of videos from YouTube and other sites youtube-dl is a small command-line program to download videos from YouTube.com and other sites that don't provide direct links to the videos served. . youtube-dl allows the user, among other things, to choose a specific video quality to download (if available) or let the program automatically determine the best (or worst) quality video to grab. It supports downloading entire playlists and all videos from a given user. . Currently supported sites (or features of sites) are: . 1tv, 20min, 220.ro, 23video, 24video, 3qsdn, 3sat, 4tube, 56.com, 5min, 6play, 7plus, 8tracks, 91porn, 9c9media, 9gag, 9now.com.au, abc.net.au, abc.net.au:iview, abcnews, abcnews:video, abcotvs, abcotvs:clips, AcademicEarth:Course, acast, acast:channel, ADN, AdobeConnect, adobetv, adobetv:channel, adobetv:embed, adobetv:show, adobetv:video, AdultSwim, aenetworks, aenetworks:collection, aenetworks:show, afreecatv, AirMozilla, AliExpressLive, AlJazeera, Allocine, AlphaPorno, Amara, AMCNetworks, AmericasTestKitchen, AmericasTestKitchenSeason, anderetijden, AnimeOnDemand, Anvato, aol.com, APA, Aparat, AppleConnect, AppleDaily, ApplePodcasts, appletrailers, appletrailers:section, archive.org, ArcPublishing, ARD, ARD:mediathek, ARDBetaMediathek, Arkena, arte.sky.it, ArteTV, ArteTVEmbed, ArteTVPlaylist, AsianCrush, AsianCrushPlaylist, AtresPlayer, ATTTechChannel, ATVAt, AudiMedia, AudioBoom, audiomack, audiomack:album, AWAAN, awaan:live, awaan:season, awaan:video, AZMedien, BaiduVideo, Bandcamp, Bandcamp:album, Bandcamp:weekly, bangumi.bilibili.com, bbc, bbc.co.uk, bbc.co.uk:article, bbc.co.uk:iplayer:playlist, bbc.co.uk:playlist, BBVTV, Beatport, Beeg, BehindKink, Bellator, BellMedia, Bet, bfi:player, bfmtv, bfmtv:article, bfmtv:live, BibelTV, Bigflix, Bild, BiliBili, BilibiliAudio, BilibiliAudioAlbum, BiliBiliPlayer, BioBioChileTV, Biography, BIQLE, BitChute, BitChuteChannel, BleacherReport, BleacherReportCMS, blinkx, Bloomberg, BokeCC, BongaCams, BostonGlobe, Box, Bpb, BR, BravoTV, Break, brightcove:legacy, brightcove:new, BRMediathek, bt:article, bt:vestlendingen, BusinessInsider, BuzzFeed, BYUtv, Camdemy, CamdemyFolder, CamModels, CamTube, CamWithHer, canalc2.tv, Canalplus, Canvas, CanvasEen, CarambaTV, CarambaTVPage, CartoonNetwork, cbc.ca, cbc.ca:olympics, cbc.ca:player, cbc.ca:watch, cbc.ca:watch:video, CBS, CBSInteractive, CBSLocal, CBSLocalArticle, cbsnews, cbsnews:embed, cbsnews:livevideo, CBSSports, CCMA, CCTV, CDA, CeskaTelevize, CeskaTelevizePorady, channel9, CharlieRose, Chaturbate, Chilloutzone, chirbit, chirbit:profile, cielotv.it, Cinchcast, Cinemax, CiscoLiveSearch, CiscoLiveSession, CJSW, cliphunter, Clippit, ClipRs, Clipsyndicate, CloserToTruth, CloudflareStream, Cloudy, Clubic, Clyp, cmt.com, CNBC, CNBCVideo, CNN, CNNArticle, CNNBlogs, ComedyCentral, ComedyCentralTV, CommonMistakes, CondeNast, CONtv, Corus, Coub, Cracked, Crackle, CrooksAndLiars, crunchyroll, crunchyroll:playlist, CSpan, CtsNews, CTV, CTVNews, cu.ntv.co.jp, Culturebox, CultureUnplugged, curiositystream, curiositystream:collection, CWTV, DailyMail, dailymotion, dailymotion:playlist, dailymotion:user, daum.net, daum.net:clip, daum.net:playlist, daum.net:user, DBTV, DctpTv, DeezerPlaylist, defense.gouv.fr, democracynow, DHM, Digg, DigitallySpeaking, Digiteka, Discovery, DiscoveryGo, DiscoveryGoPlaylist, DiscoveryNetworksDe, DiscoveryVR, Disney, dlive:stream, dlive:vod, Dotsub, DouyuShow, DouyuTV, DPlay, DRBonanza, Dropbox, DrTuber, drtv, drtv:live, DTube, Dumpert, dvtv, dw, dw:article, EaglePlatform, EbaumsWorld, EchoMsk, egghead:course, egghead:lesson, ehftv, eHow, EinsUndEinsTV, Einthusan, eitb.tv, EllenTube, EllenTubePlaylist, EllenTubeVideo, ElPais, Embedly, EMPFlix, Engadget, Eporner, EroProfile, Escapist, ESPN, ESPNArticle, EsriVideo, Europa, EWETV, ExpoTV, Expressen, ExtremeTube, EyedoTV, facebook, FacebookPluginsVideo, faz.net, fc2, fc2:embed, Fczenit, filmon, filmon:channel, Filmweb, FiveThirtyEight, FiveTV, Flickr, Folketinget, FootyRoom, Formula1, FOX, FOX9, FOX9News, Foxgay, foxnews, foxnews:article, FoxSports, france2.fr:generation-what, FranceCulture, FranceInter, FranceTV, FranceTVEmbed, francetvinfo.fr, FranceTVJeunesse, FranceTVSite, Freesound, freespeech.org, FreshLive, FrontendMasters, FrontendMastersCourse, FrontendMastersLesson, FujiTVFODPlus7, Funimation, Funk, Fusion, Fux, Gaia, GameInformer, GameSpot, GameStar, Gaskrank, Gazeta, GDCVault, generic, Gfycat, GiantBomb, Giga, GlattvisionTV, Glide, Globo, GloboArticle, Go, GodTube, Golem, google:podcasts, google:podcasts:feed, GoogleDrive, Goshgay, GPUTechConf, Groupon, hbo, HearThisAt, Heise, HellPorno, Helsinki, HentaiStigma, hetklokhuis, hgtv.com:show, HiDive, HistoricFilms, history:player, history:topic, hitbox, hitbox:live, HitRecord, hketv, HornBunny, HotNewHipHop, hotstar, hotstar:playlist, Howcast, HowStuffWorks, HRTi, HRTiPlaylist, Huajiao, HuffPost, Hungama, HungamaSong, Hypem, ign.com, IGNArticle, IGNVideo, IHeartRadio, iheartradio:podcast, imdb, imdb:list, Imgur, imgur:album, imgur:gallery, Ina, Inc, IndavideoEmbed, InfoQ, Instagram, instagram:tag, instagram:user, Internazionale, InternetVideoArchive, IPrima, iqiyi, Ir90Tv, ITTF, ITV, ITVBTCC, ivi, ivi:compilation, ivideon, Iwara, Izlesene, Jamendo, JamendoAlbum, JeuxVideo, Joj, Jove, JWPlatform, Kakao, Kaltura, Kankan, Karaoketv, KarriereVideos, Katsomo, KeezMovies, Ketnet, khanacademy, khanacademy:unit, KickStarter, KinjaEmbed, KinoPoisk, KonserthusetPlay, KrasView, Ku6, KUSI, kuwo:album, kuwo:category, kuwo:chart, kuwo:mv, kuwo:singer, kuwo:song, la7.it, laola1tv, laola1tv:embed, lbry, lbry:channel, LCI, Lcp, LcpPlay, Le, Lecture2Go, Lecturio, LecturioCourse, LecturioDeCourse, LEGO, Lemonde, Lenta, LePlaylist, LetvCloud, Libsyn, life, life:embed, limelight, limelight:channel, limelight:channel_list, LineTV, linkedin:learning, linkedin:learning:course, LinuxAcademy, LiTV, LiveJournal, LiveLeak, LiveLeakEmbed, livestream, livestream:original, livestream:shortener, LnkGo, loc, LocalNews8, LoveHomePorn, lrt.lt, lynda, lynda:course, m6, mailru, mailru:music, mailru:music:search, MallTV, mangomolo:live, mangomolo:video, ManyVids, Markiza, MarkizaPage, massengeschmack.tv, MatchTV, MDR, MedalTV, media.ccc.de, media.ccc.de:lists, Medialaan, Mediaset, Mediasite, MediasiteCatalog, MediasiteNamedCatalog, Medici, megaphone.fm, Meipai, MelonVOD, META, metacafe, Metacritic, mewatch, Mgoon, MGTV, MiaoPai, minds, minds:channel, minds:group, MinistryGrid, Minoto, miomio.tv, MiTele, mixcloud, mixcloud:playlist, mixcloud:user, MLB, Mms, Mnet, MNetTV, MoeVideo, Mofosex, MofosexEmbed, Mojvideo, Morningstar, Motherless, MotherlessGroup, Motorsport, MovieClips, MovieFap, Moviezine, MovingImage, MSN, mtg, mtv, mtv.de, mtv:video, mtvjapan, mtvservices:embedded, MTVUutisetArticle, MuenchenTV, mva, mva:course, Mwave, MwaveMeetGreet, MyChannels, MySpace, MySpace:album, MySpass, Myvi, MyVidster, MyviEmbed, MyVisionTV, n-tv.de, natgeo:video, NationalGeographicTV, Naver, NBA, nba:watch, nba:watch:collection, NBAChannel, NBAEmbed, NBAWatchEmbed, NBC, NBCNews, nbcolympics, nbcolympics:stream, NBCSports, NBCSportsStream, NBCSportsVPlayer, ndr, ndr:embed, ndr:embed:base, NDTV, NerdCubedFeed, netease:album, netease:djradio, netease:mv, netease:playlist, netease:program, netease:singer, netease:song, NetPlus, Netzkino, Newgrounds, NewgroundsPlaylist, Newstube, NextMedia, NextMediaActionNews, NextTV, Nexx, 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