Package: bats Version: 0.4.0-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41 Depends: neurodebian-popularity-contest Homepage: https://github.com/sstephenson/bats Priority: optional Section: shells Filename: pool/main/b/bats/bats_0.4.0-1~nd80+1+nd90+1_all.deb Size: 14426 SHA256: 70825f0e6845181b88902047cd7191b4fb222bb5fce4aed35e1cdb68823cf664 SHA1: 773936e0d736d5819cd688087d93f4e110652c10 MD5sum: 023d79d882ecaed97b5121b86834d8b3 Description: bash automated testing system Bats is a TAP-compliant testing framework for Bash. It provides a simple way to verify that the UNIX programs you write behave as expected. Bats is most useful when testing software written in Bash, but you can use it to test any UNIX program. Package: btrbk Version: 0.19.3-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 124 Depends: neurodebian-popularity-contest, perl, libdate-calc-perl, btrfs-tools (>= 3.14) Suggests: openssh-client Homepage: http://www.digint.ch/btrbk/ Priority: optional Section: utils Filename: pool/main/b/btrbk/btrbk_0.19.3-1~nd90+1_all.deb Size: 33226 SHA256: 4034b7118aea765a1fd1fe8e3e18edb262bd0dd42a9530d8dd602a91d9b4f0dd SHA1: 77ac1d58842997351e63f930723ebaa99690b890 MD5sum: ed8a326e6d38325cb263ee4fe38a0d93 Description: backup tool for btrfs volumes Backup tool for btrfs volumes, using a configuration file, allows creation of backups from multiple sources to multiple destinations at once, with ssh and configurable retention support (daily/weekly/monthly). Package: condor Version: 8.2.8~dfsg.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 28 Depends: neurodebian-popularity-contest, htcondor Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor_8.2.8~dfsg.1-1~nd80+1+nd90+1_all.deb Size: 14830 SHA256: 6e041756b5c9efe1ff89b71e8c6e6032f9bf7c35f6afce534e2f0300ecc65b07 SHA1: 7e30cebb268de64b987015fe432915c0cbefc3b2 MD5sum: 445f74118ee23d433e3dcb6de8ee31f5 Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: condor-dbg Source: condor Version: 8.2.8~dfsg.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 28 Depends: neurodebian-popularity-contest, htcondor-dbg Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dbg_8.2.8~dfsg.1-1~nd80+1+nd90+1_all.deb Size: 14846 SHA256: 57fb43b8d82ceee9d333d7c189a94e234bbeac351cd3a134ff59820225245d73 SHA1: ded3ab4a70da9beaf29f35b2ebd221bc72fad762 MD5sum: ff9b3c49b7b533642d773429b92f373f Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: condor-dev Source: condor Version: 8.2.8~dfsg.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 28 Depends: neurodebian-popularity-contest, htcondor-dev Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dev_8.2.8~dfsg.1-1~nd80+1+nd90+1_all.deb Size: 14838 SHA256: 20092edb7e596f2a7617a021a9994201fcac586284e5ede3b7f2b2718396b535 SHA1: 16f161e7fe498784abb9e574eecdc7355672e778 MD5sum: 8a5e05afcda406148ad420671a156427 Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: condor-doc Source: condor Version: 8.2.8~dfsg.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 28 Depends: neurodebian-popularity-contest, htcondor-doc Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-doc_8.2.8~dfsg.1-1~nd80+1+nd90+1_all.deb Size: 14838 SHA256: 74ea37a6be3881588f92fcc21b7f0c2a24fb82a76e1effe75264890e5abfec0b SHA1: d204f84d3f2891c210414ca8896eda1fe57cd798 MD5sum: fdfbd9dbd6fa09ae5f7898512d79563d Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: connectomeviewer Version: 2.1.0-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1652 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~nd70+1+nd90+1_all.deb Size: 1150994 SHA256: 1010e605b6a3e8827a5299a1ecf248cb610c8c3bb77d922740cfcba689bc981c SHA1: 32ccf0c290df8fb04b3b660d507e3a0f0125b9c9 MD5sum: acbc76e7ac065581a48f09e5bdfb4968 Description: Interactive Analysis and Visualization for MR Connectomics The Connectome Viewer is a extensible, scriptable, pythonic research environment for visualization and (network) analysis in neuroimaging and connectomics. . Employing the Connectome File Format, diverse data types such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The Connectome Viewer is part of the MR Connectome Toolkit. Package: coop-computing-tools-doc Source: cctools Version: 3.4.2-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2821 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: doc Filename: pool/main/c/cctools/coop-computing-tools-doc_3.4.2-1~nd70+1+nd90+1_all.deb Size: 211826 SHA256: da05214a18baf5e069af50d860dbcb3efcf3bf210dca18f9e4e96bb1aeca249d SHA1: 6cc266c35d33a6090c5c33c9b8f308971a24cb83 MD5sum: adc3cb13d9be11595ff7978d1fea0eed Description: documentation for coop-computing-tools These tools are a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. . This package provides the documentation (manual and API reference) in HTML format. Package: debian-handbook Version: 6.0+20120509~nd+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23450 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+nd90+1_all.deb Size: 21615222 SHA256: a85ad362072ccad3a1034ac7ae2073b117a7a9001e79b131a6929272734e7f46 SHA1: 065382f6d0be6a5cbe2372ba15a0509f85b73894 MD5sum: 201becf6284759af070b8ee950fab0fb 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: dh-systemd Source: init-system-helpers Version: 1.18~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49 Depends: neurodebian-popularity-contest, perl, debhelper Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/dh-systemd_1.18~nd80+1+nd90+1_all.deb Size: 13810 SHA256: e3c6d4ea450b9b4462716a4379e06303dca077c6e37664293366eb1645700d16 SHA1: 0fe321df5ef5262d0395dcd70106aa69091d7822 MD5sum: 768da456c2645982f0a93d8ace2c098d Description: debhelper add-on to handle systemd unit files dh-systemd provides a debhelper sequence addon named 'systemd' and the dh_systemd_enable/dh_systemd_start commands. . The dh_systemd_enable command adds the appropriate code to the postinst, prerm and postrm maint scripts to properly enable/disable systemd service files. The dh_systemd_start command deals with start/stop/restart on upgrades for systemd-only service files. Package: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8117 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd70+1+nd90+1_all.deb Size: 7075168 SHA256: 5d83d4720ebbec466e44e83c6e6a8a5b67bc8486f93bc571cde2403982dfaf0c SHA1: 062335d0be9ced61269e7ab4ae3bb9034993d9b6 MD5sum: 62a51e7e7f38cd6cdc21ef2931432201 Description: sample EEG data for EEGLAB tutorials EEGLAB is sofwware for processing continuous or event-related EEG or other physiological data. . This package provide some tutorial data files shipped with the EEGLAB distribution. Package: fail2ban Version: 0.8.13-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 654 Depends: neurodebian-popularity-contest, python:any (>= 2.6.6-7~), 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_0.8.13-1~nd80+1+nd90+1_all.deb Size: 165002 SHA256: 8891f13b27c416ffae35e3f221c9e515895727db7582851ff7bfbdcdbf3ffbb6 SHA1: cfe5df32ace15f32a7a080eadaa7a84d0efb49c1 MD5sum: 54131b95bc2301368bf604dcbb8f3a4c 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: 1.4.9-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd90+1), freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi_1.4.9-1~nd90+1_all.deb Size: 1176 SHA256: 0e4e167e592b924f79472395fbee7033689ee077ee3574f35ef74577fdeffec3 SHA1: 3365f20281a15110bd4bd43778b9a980ccc99f72 MD5sum: 46c90a6d59e5a2a2d9f9c3c2a1c01232 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 metapackage depends on all separate modules of freeipmi. Package: freeipmi-common Source: freeipmi Version: 1.4.9-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 491 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_1.4.9-1~nd90+1_all.deb Size: 339408 SHA256: 2ef4d1b9c56554a9d5f40d860ae0da8bf8469166b892ccd06ab53c8d7e847f0c SHA1: d7c76e4e962bac7828af674c926009546d927d99 MD5sum: 302ae30afdce18d894d054859912176e 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: fsl-melview Source: melview Version: 1.0.1+git9-ge661e05~dfsg.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 74 Depends: neurodebian-popularity-contest, python, python-matplotlib, python-numpy, python-pkg-resources, python-scipy, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-nibabel, python-pyface, python-traits, python-traitsui, python-enthoughtbase Suggests: fsl-core Homepage: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Melview Priority: optional Section: science Filename: pool/main/m/melview/fsl-melview_1.0.1+git9-ge661e05~dfsg.1-1~nd90+1_all.deb Size: 13912 SHA256: 645e077ce9ef18f5ed060f06cfe1db6182307f8bd85ed6c3700847be65513549 SHA1: 27fffc42b2ab3cd055e60861ee89b66c41ba53c6 MD5sum: 8f155d7108a9e153239de07847443ded Description: viewer for the output of FSL's MELODIC This viewer can be used to facilitate manual inspection and classification of ICA components computed by MELODIC. As such, it is suited to generate hand-curated labels for FSL's ICA-based denoising tool FIX. Python-Version: 2.7 Package: fslview-doc Source: fslview Version: 4.0.1-2~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2929 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~nd80+1+nd90+1_all.deb Size: 2226908 SHA256: 8b844c2c11167f41b2d20536095b36c3404514cb21c283097fc0cc876990d949 SHA1: a1a17b9b680e41e9757918f98c497c5ef1b253ec MD5sum: 2554c2d2bf8c93da5554a559ca7aaa58 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gcalcli Version: 3.3-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1759 Depends: neurodebian-popularity-contest, python, python-dateutil, python-gflags, python-googleapi Recommends: gxmessage, python-parsedatetime, python-simplejson, python-vobject Homepage: https://github.com/insanum/gcalcli Priority: extra Section: utils Filename: pool/main/g/gcalcli/gcalcli_3.3-1~nd90+1_all.deb Size: 1666496 SHA256: 5044daba25fb879ef7cf21f2724730a7e850d8efb7bd187944bf7a0e26673adc SHA1: 4e94fd11bcd60e6ea9bf790da67d8cb1a1d7791e MD5sum: b9f9c04f257be618fab434c9cb520079 Description: Google Calendar Command Line Interface gcalcli is a Python application that allows you to access your Google Calendar from a command line. It's easy to get your agenda, search for events, and quickly add new events. Additionally gcalcli can be used as a reminder service to execute any application you want. Package: gmsl Version: 1.1.5-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 89 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.5-1~nd80+1+nd90+1_all.deb Size: 13846 SHA256: f245e355fff5b0333d20373a3733b34a6d5b89ecbb07e4a9f6dfe46266429ee1 SHA1: 96f7917de1151cc9ac199889d3346600c5beb585 MD5sum: 036fe63e248473476a6832cdce6324e0 Description: extra functions to extend functionality of GNU Makefiles The GNU Make Standard Library (GMSL) is a collection of functions implemented using native GNU Make functionality that provide list and string manipulation, integer arithmetic, associative arrays, stacks, and debugging facilities. . Note that despite the name of this project, this library is NOT standard and is NOT written or distributed by the GNU project. Package: guacamole Source: guacamole-client Version: 0.8.3-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 516 Depends: neurodebian-popularity-contest, guacd Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole_0.8.3-1~nd80+1+nd90+1_all.deb Size: 431706 SHA256: 5baa52ac170db52217a2be61aa88e322b8b07d6547ff413c7c07a7ac5bf553fc SHA1: 38c24c6ff1aff4765fa37cdb6da96279569d4152 MD5sum: 523c9f22b9b0eba895a126a68c17b946 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-client Version: 0.8.3-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole-tomcat_0.8.3-1~nd80+1+nd90+1_all.deb Size: 6780 SHA256: 6c7669e2313d4005be8fcd92836431a6acb0a8f6ff4b8c47108098e729bb4de1 SHA1: 8a20da50241cc0df8ca6b943c6896fca09230629 MD5sum: e152fd7e06d9454ac0c22f08b3dd4b9f 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: htcondor-doc Source: condor Version: 8.2.8~dfsg.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 5843 Depends: neurodebian-popularity-contest Breaks: condor-doc (<< 8.0.5~) Replaces: condor-doc (<< 8.0.5~) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: doc Filename: pool/main/c/condor/htcondor-doc_8.2.8~dfsg.1-1~nd80+1+nd90+1_all.deb Size: 1028976 SHA256: f6dabba692154c5db62a57c1a45b48550f44d4edd5f3d0a8be27333b41af50de SHA1: c6f2ace9862bd0bc11f269c9948b22aea551c9cd MD5sum: db4d996f09078a58b256a7a36f8b436f Description: distributed workload management system - documentation Like other full-featured batch systems, HTCondor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to HTCondor; HTCondor 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, HTCondor can also effectively harness wasted CPU power from otherwise idle desktop workstations. HTCondor does not require a shared file system across machines - if no shared file system is available, HTCondor can transfer the job's data files on behalf of the user. . This package provides HTCondor's documentation in HTML and PDF format, as well as configuration and other examples. Package: impressive Version: 0.10.5-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 347 Depends: neurodebian-popularity-contest, python-opengl, python-pygame, python-imaging, poppler-utils | xpdf-utils (>= 3.02-2) Recommends: pdftk, perl 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_0.10.5-1~nd80+1+nd90+1_all.deb Size: 151610 SHA256: 8c5ea64991279a13c427ff3d812e99bf86ee623bf641279f51bf2808c6d4e39c SHA1: c1f7f89435edc36583a33aa5e60d23126865299d MD5sum: 34bfc7b2579bc4eaa976794a289fc235 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~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 39 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~nd70+1+nd90+1_all.deb Size: 9058 SHA256: dcb57631757a7635c3ee68bb989b2f3f77856c1f88d842cd64848f9776236e69 SHA1: e1caeb23b63a7b84829314c2a60b5130edb4d200 MD5sum: c220623e4e8976077d0c3de0d45bec30 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: init-system-helpers Version: 1.18~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49 Depends: neurodebian-popularity-contest, perl Breaks: systemd (<< 44-12) Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/init-system-helpers_1.18~nd80+1+nd90+1_all.deb Size: 13434 SHA256: 62d797ebebb6d054e9bfd1e70bfbc4eb760d8e06fa249d32af9f365f170f9f26 SHA1: 0d36a24804a540495ffcbba060615184e3708bbf MD5sum: 4d9b782a3c2f9164950b1f12bc035513 Description: helper tools for all init systems This package contains helper tools that are necessary for switching between the various init systems that Debian contains (e.g. sysvinit, upstart, systemd). An example is deb-systemd-helper, a script that enables systemd unit files without depending on a running systemd. . While this package is maintained by pkg-systemd-maintainers, it is NOT specific to systemd at all. Maintainers of other init systems are welcome to include their helpers in this package. Package: insighttoolkit4-examples Source: insighttoolkit4 Version: 4.7.0-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3081 Depends: neurodebian-popularity-contest Suggests: libinsighttoolkit4-dev Conflicts: insighttoolkit-examples Replaces: insighttoolkit-examples Homepage: http://www.itk.org/ Priority: optional Section: devel Filename: pool/main/i/insighttoolkit4/insighttoolkit4-examples_4.7.0-1~nd80+1+nd90+1_all.deb Size: 2498516 SHA256: d34d9328e9e8bd7bfb745a1cd0b915f0f61485c07ec1e0f65fc4c0f14eb68aed SHA1: 90d42a16d1a1b428e004c26671f83663ca9b35d0 MD5sum: 276b12088d4b89a13556404f5b95b99c Description: Image processing toolkit for registration and segmentation - examples ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the source for example programs. Package: libfreenect-doc Source: libfreenect Version: 1:0.5.2+git6-g5455843+dfsg-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 647 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.5.2+git6-g5455843+dfsg-1~nd90+1_all.deb Size: 90116 SHA256: 7b1f3f8718bae55e7d612ff46474321e316a7b6c3995e61c2d1f9bd3206f34f8 SHA1: 2dabe10f4ec42810d36c3f92740c078eae537399 MD5sum: fb51b5583055b3c4a2f2eb4facd36aab 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: libmia-2.0-doc Source: mia Version: 2.0.13-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16445 Depends: neurodebian-popularity-contest, libjs-jquery Enhances: libmia-2.0-dev Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/libmia-2.0-doc_2.0.13-1~nd80+1+nd90+1_all.deb Size: 1145474 SHA256: 22353c91f476fe6f63f1a34963bd303a1bd030f369a334dc9ad85e91cd14fcb4 SHA1: 207ee6bcc687e9307274cf8def76f6d865690575 MD5sum: 27dbf2673981e05d840fd884459a2f2b Description: library for 2D and 3D gray scale image processing, documentation libmia comprises a set of libraries and plug-ins for general purpose 2D and 3D gray scale image processing and basic handling of triangular meshes. The libraries provide a basic infrastructure and generic algorithms, that can be specialized by specifying the apropriate plug-ins. This package provides the Doxygen generated API reference. Package: libmialm-doc Source: libmialm Version: 1.0.7-2~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 249 Depends: neurodebian-popularity-contest Suggests: devhelp Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/libm/libmialm/libmialm-doc_1.0.7-2~nd80+1+nd90+1_all.deb Size: 21262 SHA256: b818b86b8b03b2de2d9028afb4a6fcefc7efac47e9922d583ca24642e136f7ba SHA1: 4068ababf8509c40454974d12b1f613f93cc5a6f MD5sum: 20aa66e7a83a9b83d8626eabb1a0ca36 Description: Documentation for the MIA landmark library This library implements handling for landmarks and 3D view positioning for optimal landmark visibility, and in-and output of these landmarks. This library is part of the MIA tool chain for medical image analysis. This package contains the library documentation. Package: libnifti-doc Source: nifticlib Version: 2.0.0-2~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1705 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://niftilib.sourceforge.net Priority: optional Section: doc Filename: pool/main/n/nifticlib/libnifti-doc_2.0.0-2~nd80+1+nd90+1_all.deb Size: 137744 SHA256: f5f624f2a3073c1676ff234d94abc713ce4c73e4a17989868035bc67d1e4d9e9 SHA1: defc10d95d0f843756f9402e05ba12d41e1546c0 MD5sum: 4dbe6f75c01b260f4534930c082b2774 Description: NIfTI library API documentation Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the library API reference documentation. Package: libopenwalnut1-doc Source: openwalnut Version: 1.4.0~rc1+hg3a3147463ee2-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49240 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.4.0~rc1+hg3a3147463ee2-1~nd80+1+nd90+1_all.deb Size: 2681858 SHA256: c83b9176f3ff3cfa9e83ee97caabd28ecede43bf5ffa8dbfe9ce9b72977bcf9e SHA1: 2bc64d4f70f52d587d898216e09ae1457a527dc1 MD5sum: b2239dfc83b9656f9ba915d4fcf8284e 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~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17 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~nd70+1+nd90+1_all.deb Size: 7078 SHA256: 04fecd43be8cde2fcfc36c631587495cde2c09170103c7d8af87f9dba73f62fe SHA1: 1d8036b3970776a3c9157e073e0cda32535f74da MD5sum: 9a1a35a8f774cfa7ae6813157471f994 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: mia-tools-doc Source: mia Version: 2.0.13-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1227 Depends: neurodebian-popularity-contest Enhances: mia-tools Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/mia-tools-doc_2.0.13-1~nd80+1+nd90+1_all.deb Size: 78588 SHA256: b2e28ac06616de16419dca9ffe55d4cd31ce25627201cb084f64f13bdbfd9ccd SHA1: eabc90ca30982d35ac6e45697cd8f2670cdcbfd9 MD5sum: 39002ffe1546edf4a27c57fa26bf9c66 Description: Cross-referenced documentation of the MIA command line tools Cross referenced documentation of the command line tools and plug-ins that are provided by the MIA gray scale image processing tool chain. These lines tools to provide the means to run general purpose image processing tasks on 2D and 3D gray scale images, and basic operations on triangular meshes interactively from the command line. Supported image processing algorithms are image filtering, combining, image registration, motion compensation for image series, and the estimation of various statistics over images. Package: mricron-data Source: mricron Version: 0.20140804.1~dfsg.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1696 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20140804.1~dfsg.1-1~nd80+1+nd90+1_all.deb Size: 1661008 SHA256: c72c5a52aa36f95b969d40b6696b3fd6825e56f2b4c747c8c7583fee75f818a3 SHA1: f7adfccd1977f34cb146ba91f62f47348c91d8ce MD5sum: ac38c72fa2c712209849a6c07d7e7997 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: 0.20140804.1~dfsg.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1024 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20140804.1~dfsg.1-1~nd80+1+nd90+1_all.deb Size: 579888 SHA256: eed56b4447c212858640a3e24d776b3404f9d3b81d732faa0dcbeab494a9e1f7 SHA1: ad06900073ad2fff7af240ab1c812298c638efc1 MD5sum: 4b028808fdef22505256fe03fbb307ac Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: mrtrix-doc Source: mrtrix Version: 0.2.12-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3542 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.12-1~nd80+1+nd90+1_all.deb Size: 3198612 SHA256: c4759ba0583f737edb2e578bfce4f4243f46325d84ae08ac123fa2cb32858f1c SHA1: 5418da876c4b3fbd5f96f5fe3a670828a46683b8 MD5sum: 53aca395bed6599b118a7115d696cd3b Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: netselect-apt Source: netselect Version: 0.3.ds1-25~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 38 Depends: neurodebian-popularity-contest, wget, netselect (>= 0.3.ds1-17) Recommends: curl Suggests: dpkg-dev Enhances: apt Homepage: http://github.com/apenwarr/netselect Priority: optional Section: net Filename: pool/main/n/netselect/netselect-apt_0.3.ds1-25~nd80+1+nd90+1_all.deb Size: 16768 SHA256: 0855800d052bec8451592fc24b243e211b528f2f09f4638271b33344b4f75fd6 SHA1: 009fe8913a3e0df14c68d36bc8cbfbc3bdb718a2 MD5sum: 364fd6a7cc867d537b4c2d9bce6860ed Description: speed tester for choosing a fast Debian mirror This package provides a utility that can choose the best Debian mirror by downloading the full mirror list and using netselect to find the fastest/closest one. . It can output a sources.list(5) file that can be used with package management tools such as apt or aptitude. Package: neurodebian Version: 0.37.1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 58 Depends: python, wget, neurodebian-archive-keyring, debconf (>= 0.5) | debconf-2.0 Recommends: netselect Suggests: neurodebian-desktop, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian_0.37.1~nd90+1_all.deb Size: 22366 SHA256: 4e4bc712736587ad4f8fe4c20f0ca110322757d75d9411f36662825d929ee3c5 SHA1: 29a926ade79c6dcbf66b6f136a53d35655ec2b07 MD5sum: a775bd9ad6f401df86fbe28aa7db3c93 Description: neuroscience-oriented distribution - repository configuration The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package enables the NeuroDebian repository on top of a standard Debian or Ubuntu system. Package: neurodebian-archive-keyring Source: neurodebian Version: 0.37.1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23 Breaks: neurodebian-keyring (<< 0.34~) Replaces: neurodebian-keyring (<< 0.34~) Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-archive-keyring_0.37.1~nd90+1_all.deb Size: 9748 SHA256: 8215aaddc9e6b3a0b07e69f9f7edadd860a928e05b8312608200a1c67a095b29 SHA1: fb8fb55b4380e9c3a268e5c0701109493ce5f2ea MD5sum: fd80e13dc0111f4fb4e041a403dded09 Description: neuroscience-oriented distribution - GnuPG archive keys The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-desktop Source: neurodebian Version: 0.37.1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 188 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, adwaita-icon-theme | gnome-icon-theme, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.37.1~nd90+1_all.deb Size: 115472 SHA256: 11b850f9ff942e28235c0bf5de41ca70d20d2de1c20561c49b4db4435482aaa8 SHA1: 5e9f44a4bfccdd9835230a158f23fba2aca4ccf3 MD5sum: 720fb7a86c4c4122e9ae29bd47ad3119 Description: neuroscience-oriented distribution - desktop integration The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package provides NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring the most popular neuroscience tools, which will be automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.37.1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 109 Depends: devscripts, neurodebian-archive-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring, apt-utils, cowbuilder Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.37.1~nd90+1_all.deb Size: 31846 SHA256: 5dd3b2aab51f877830d2ca7835f3d078c9f76bc299aa3afb2f2830b0b92b3bed SHA1: 6709bdd4931cacbff4fd45d5a680ac6bfcc91809 MD5sum: 96a5883c0e2ee89b87222aef394037ed Description: neuroscience-oriented distribution - development tools The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package provides sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.37.1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 27 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.37.1~nd90+1_all.deb Size: 11780 SHA256: 569dd9d7e3e1c29d40539da473b12826991041d3571cfd57cac6a6ed698ff6a3 SHA1: dbd947e83093ce1fcdafee4098d43f48b3da9e1a MD5sum: 6e8cd4d213411dc951bfdb9e8ee33bf9 Description: neuroscience-oriented distribution - popcon integration The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . 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 (either Debian or Ubuntu) popcon server. . Participating in popcon is important for the following reasons: * Popular packages receive more attention from developers; bugs are fixed faster and updates are provided quicker. * It ensures that support is not dropped for a previous release of Debian or Ubuntu while there are active users. * User statistics may be useful for upstream research software developers seeking funding for continued development. . This requires that popcon is activated for the underlying distribution (Debian or Ubuntu), which can be achieved by running "dpkg-reconfigure popularity-contest" as root. Package: nifti2dicom-data Source: nifti2dicom Version: 0.4.9-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 643 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.9-1~nd80+1+nd90+1_all.deb Size: 615920 SHA256: 25fdd19e2a1d27cd7e03c17dc874ec0bf5bfe53862d0edd400da96b9649362e1 SHA1: b63957274fc4d210c1a0b12766144d56a78752d5 MD5sum: 56bd9fd876a7a3458bccdcac9a1bc456 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: 0.5.13+ds-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2402 Depends: neurodebian-popularity-contest, g++-4.9 | g++-4.8 | g++-4.7 | 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:any (>= 2.6.6-7~) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6), strace Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.5.13+ds-1~nd90+1_all.deb Size: 557740 SHA256: e5121e1f576339f39aec5bb9a54971a867f26bf355b4966f7f1c5b007b8bc9c3 SHA1: 1a502117824ccc5044998f7c0d0bee23ccf913f7 MD5sum: e80fc78faa529a78dccae904957ac980 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: psychopy Version: 1.82.01.dfsg-2~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14414 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-pyglet | python-pygame, python-opengl, python-numpy, python-scipy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk3.0, python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, python-pyo, python-psutil, python-gevent, python-msgpack, python-yaml, python-xlib, python-pandas, libavbin0, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid Conflicts: libavbin0 (= 7-4+b1) Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.82.01.dfsg-2~nd90+1_all.deb Size: 6058792 SHA256: 139621d2fecb29ada4099761f66b2a9e779ad7a2f8377bf9faca9dfa687c88ad SHA1: b883b55eaa13b5cd3aae4d0fcd97118b069a57ae MD5sum: 2f1813473039cb4ccd367e4bd9f71872 Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features . - IDE GUI for coding in a powerful scripting language (Python) - Builder GUI for rapid development of stimulation sequences - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.12.20150419.dfgs1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 233231 Depends: neurodebian-popularity-contest Recommends: subversion Suggests: gnuplot Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.12.20150419.dfgs1-1~nd90+1_all.deb Size: 23890344 SHA256: 73d6c80bc4d73e55c0c229b9ab1f0803e5cc313d6653a6e64e2f40f41743acde SHA1: 55cc4f1241d931c1982ce59467049897bb1a0b77 MD5sum: cd7a3f7f0b8d75a73b8788ba9cc7f4d5 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~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2453 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.1-1~nd70+1+nd90+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~nd70+1+nd90+1_all.deb Size: 400126 SHA256: 5034cb0a6acb433164b5967ba3ca575638d146424e48f471b17f5a76fbf813a0 SHA1: 50850e4166ec42d035e2d3ff1e97c1487bf1ba82 MD5sum: d8760f6408bd1b679709be326702ed44 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~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7060 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~nd70+1+nd90+1_all.deb Size: 1974528 SHA256: 50db30375655ee0a42762932fb31d4f29cacf8ca4100da03298e2995dd2c63e8 SHA1: 1f0e0622e44d236d59808871d85d4dd69630e719 MD5sum: c085e71d20e796a9bb79a03612ddacfa 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-cfflib Source: cfflib Version: 2.0.5-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 736 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-lxml, python-numpy, python-networkx (>= 1.4), python-nibabel (>= 1.1.0) Recommends: python-nose, python-sphinx, python-tables, python-h5py Provides: python2.7-cfflib Homepage: http://cmtk.org/cfflib Priority: extra Section: python Filename: pool/main/c/cfflib/python-cfflib_2.0.5-1~nd70+1+nd90+1_all.deb Size: 202534 SHA256: 76cb03a4761b10f0ba07dc6eb4cf60e126c2d362aeaa2f981d1a79003a265982 SHA1: 9129804539bb9895714a6229ae4d56a544bf47ed MD5sum: fd58e3bf366a779d34c534a279c590d3 Description: Multi-modal connectome and metadata management and integration The Connectome File Format Library (cfflib) is a Python module for multi-modal neuroimaging connectome data and metadata management and integration. . It enables single subject and multi-subject data integration for a variety of modalities, such as networks, surfaces, volumes, fiber tracks, timeseries, scripts, arbitrary data objects such as homogeneous arrays or CSV/JSON files. It relies on existing Python modules and the standard library for basic data I/O, and adds a layer of metadata annotation as tags or with structured properties to individual data objects. Package: python-citeproc Source: citeproc-py Version: 0.3.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 769 Depends: neurodebian-popularity-contest, python-lxml, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/brechtm/citeproc-py Priority: optional Section: python Filename: pool/main/c/citeproc-py/python-citeproc_0.3.0-1~nd90+1_all.deb Size: 80364 SHA256: d1545645e4616ee893390ac616db5525b8b6540f8962c978df9489762081a8c4 SHA1: 0a2c8756cc2aca7f3c4e34da3dec7f9e24da23dd MD5sum: a3d3a59ef819bfaae15b840ea2e0d164 Description: Citation Style Language (CSL) processor for Python Citeproc-py is a library that produces formatted bibliographies and citations from bibliographic databases following formatting instructions provided by XML style files written in the Citation Style Language (CSL). . Currently, BibTeX and JSON are supported as input database formats, and plain text, reStructuredText and HTML as output format. . This package contains the Python modules. Package: python-dicom Source: pydicom Version: 0.9.9-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1577 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://pydicom.org/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.9-1~nd80+1+nd90+1_all.deb Size: 357482 SHA256: 333cc88a2a89e9e497b59c70b4972f54879fc829709837408617f2cba6c05900 SHA1: 7a178766cae90f6a7a7f024eab6064433eb66c59 MD5sum: 5104770e0d0083777c17d5a64d13c8a3 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: 0.9.2-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4765 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-dipy-lib (>= 0.9.2-1~nd80+1+nd90+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.7-dipy Homepage: http://nipy.org/dipy Priority: optional Section: python Filename: pool/main/d/dipy/python-dipy_0.9.2-1~nd80+1+nd90+1_all.deb Size: 2349806 SHA256: aa8c79acc024ffdffcdb8b6b42b3ecbe59b949d161a38239445a3151e2b7d82b SHA1: cf00a29a80b70e4fb98a86883f0ab47c84fd3bd0 MD5sum: ba6336de815ba541ed4356ba9fe91a75 Description: toolbox for analysis of MR diffusion imaging data Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. It features: - Reconstruction algorithms, e.g. GQI, DTI - Tractography generation algorithms, e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space, e.g. MNI space - Reading many different file formats, e.g. Trackvis or NIfTI - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing Python-Version: 2.7 Package: python-dipy-doc Source: dipy Version: 0.9.2-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12614 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: optional Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.9.2-1~nd80+1+nd90+1_all.deb Size: 10230982 SHA256: 471026b160a5bf637a22cde83fbf090947ceb79233c62345f3b446d1afe4a8bc SHA1: 34c5db181ca87d2bef37f2fc938ec98690219ce2 MD5sum: 577d1aa8554252276248d01fdc11fcee 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-expyriment Version: 0.7.0+git34-g55a4e7e-3~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2571 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-support (>= 0.90.0), python-pygame (>= 1.9.1~), python-opengl (>= 3.0.0), ttf-freefont, libjs-jquery, libjs-underscore Recommends: python-serial (>= 2.5~), python-numpy (>= 1.3.0~) Suggests: python-parallel (>= 0.2), python-pyxid Homepage: http://www.expyriment.org Priority: optional Section: science Filename: pool/main/p/python-expyriment/python-expyriment_0.7.0+git34-g55a4e7e-3~nd80+1+nd90+1_all.deb Size: 695880 SHA256: 0cd86f3708c27c1840b144158946a886c4c9f95be05b1e066841e2c1c8bb1a14 SHA1: bb870ded3d8999b7897d0952949260de3e7a4f0a MD5sum: 9c3064599b09b61824b028c8da226384 Description: Python library for cognitive and neuroscientific experiments Expyriment is a light-weight Python library for designing and conducting timing-critical behavioural and neuroimaging experiments. The major goal is to provide a well-structured Python library for a script-based experiment development with a high priority on the readability of the resulting programme code. Due to the availability of an Android runtime environment, Expyriment is also suitable for the development of experiments running on tablet PCs or smart-phones. Package: python-jdcal Source: jdcal Version: 1.0-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 39 Depends: neurodebian-popularity-contest Homepage: https://github.com/phn/jdcal Priority: optional Section: python Filename: pool/main/j/jdcal/python-jdcal_1.0-1~nd80+1+nd90+1_all.deb Size: 7716 SHA256: fa7278a583fb30f2c0f244e46d4176c125e058cfb9440bf00988b098091dfb52 SHA1: c46cec73f8ed98b465cc051e97390f4996bce87b MD5sum: e67938a52dc086acbbea4f392b75b962 Description: Julian dates from proleptic Gregorian and Julian calendars This module contains functions for converting between Julian dates and calendar dates. . Different regions of the world switched to Gregorian calendar from Julian calendar on different dates. Having separate functions for Julian and Gregorian calendars allow maximum flexibility in choosing the relevant calendar. Package: python-joblib Source: joblib Version: 0.8.4-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 279 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.8.4-1~nd80+1+nd90+1_all.deb Size: 64328 SHA256: f5d2ef3a6993eaea2f35805d0d31370fb8b45840be19579aa629d237bdd30633 SHA1: 5a35dca74aa1e784f7fa828785d880317abbae7e MD5sum: 7296401f7a59c7c124e832aaa4f9f0e3 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. . This package contains the Python 2 version. Package: python-lazyarray Source: lazyarray Version: 0.1.0-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy Homepage: http://bitbucket.org/apdavison/lazyarray/ Priority: optional Section: python Filename: pool/main/l/lazyarray/python-lazyarray_0.1.0-1~nd70+1+nd90+1_all.deb Size: 6898 SHA256: 64177357eee6f7ee78eb01dda91c6727c789370851c697165f8153dc784b557c SHA1: 91e0a31cda5c95c4bf1c97a8c20c47db3f00e5af MD5sum: 7441821b3bd04e06759e8a26db5429a7 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-mne Version: 0.8.6+dfsg-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7380 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-sklearn, python-matplotlib, python-joblib (>= 0.4.5), xvfb, xauth, libgl1-mesa-dri, help2man, libjs-jquery, libjs-jquery-ui Recommends: python-nose, mayavi2 Suggests: python-dap, ipython Provides: python2.7-mne Homepage: http://martinos.org/mne Priority: optional Section: python Filename: pool/main/p/python-mne/python-mne_0.8.6+dfsg-1~nd80+1+nd90+1_all.deb Size: 4021650 SHA256: d0756158b44e6ab6c2d77d8b227ce7b0d221b8b7f90487150d5136e089e0fd7f SHA1: 3a606009686cd734193202abe37c9c9884b231df MD5sum: 438d89ab3fe0fe7488916f463a7b1b52 Description: Python modules for MEG and EEG data analysis This package is designed for sensor- and source-space analysis of MEG and EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. Package: python-mpi4py-doc Source: mpi4py Version: 1.3.1+hg20131106-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 283 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3.1+hg20131106-1~nd80+1+nd90+1_all.deb Size: 52626 SHA256: 3c641d61d815c02c5a59e9ec1a09bd1cd8b533676ce1a5680d6b271a57b11530 SHA1: 50db5f262d34c1b6eb93de57157972ef888355fe MD5sum: 138682e271a7ee24ff44b778bc40cefb Description: bindings of the MPI standard -- documentation MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides HTML rendering of the user's manual. Package: python-mvpa2 Source: pymvpa2 Version: 2.4.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7903 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy, python-mvpa2-lib (>= 2.4.0-1~nd90+1) Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-nipy, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, python-shogun, liblapack-dev, python-pprocess, python-statsmodels, python-joblib Suggests: fslview, fsl, python-mvpa2-doc, python-nose, python-openopt, python-rpy2 Provides: python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.4.0-1~nd90+1_all.deb Size: 5034172 SHA256: ae37b38f52f0b48c6bf68e1eede58f59e3554a9f4351dd35f92efb92527d5d99 SHA1: 7181390303989dc418b7459e4738f2542119b937 MD5sum: ca72c9f8d500dd4cae5468eedb29d1be Description: multivariate pattern analysis with Python v. 2 PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. . This is a package of PyMVPA v.2. Previously released stable version is provided by the python-mvpa package. Python-Version: 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.4.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29062 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_2.4.0-1~nd90+1_all.deb Size: 4654180 SHA256: c95dfad2ccc38d2fb6a1fede3ea94ae229bd55189df14838066794e02cade753 SHA1: c5f00ff4b3e2eec654369ccadb228a3629b2ccb8 MD5sum: 27b35f45a7748d7fb164b73d661bea4d Description: documentation and examples for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a HTML documentation (tutorial, FAQ etc.), and example scripts. In addition the PyMVPA tutorial is also provided as IPython notebooks. Package: python-neo Source: neo Version: 0.3.3-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2974 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy (>= 1:1.3~), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Suggests: python-nose Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.3.3-1~nd80+1+nd90+1_all.deb Size: 1383640 SHA256: 7e3b8dcf1a6e7c971437d744c5bb781a34c6272677f9760fd74d262f0f7e12b1 SHA1: 8c96c831525d26230be96771de115c7f2cb574d8 MD5sum: a1c098adc1493d4cd4f12a7c564a5508 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-neuroshare-doc Source: python-neuroshare Version: 0.9.2-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 271 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: http://www.g-node.org/neuroshare-tools Priority: extra Section: doc Filename: pool/main/p/python-neuroshare/python-neuroshare-doc_0.9.2-1~nd80+1+nd90+1_all.deb Size: 95554 SHA256: 2ce47764501482655bdadd0c5e71fb26ef3652e141cc6f493fa897afaec80e93 SHA1: ec00e3c7a84c81e3a4a3eae42b249b0aeb16078e MD5sum: 02d7853bc814b8e6df159b51b2685b81 Description: Python interface and tools for Neuroshare The Neuroshare API is a standardized interface to access electrophysiology data stored in various different file formats. To do so, it uses format- specific shared libraries. . This package provides a high-level Python interface to the Neuroshare API that focuses on convenience for the user and enables access to all available metadata and data. The data is returned in NumPy arrays, which provides a quick route to further examination and analysis. . In addition, this package contains the ns2hdf converter tool that converts neuroshare-compatible files into the HDF5 (Hierarchical Data Format, ver. 5) file format. . This package contains HTML documentation files for python-neuroshare. Package: python-nibabel Source: nibabel Version: 2.0.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63384 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_2.0.1-1~nd90+1_all.deb Size: 1979544 SHA256: 94ad81f9fafd8128476bf2a82ac0af3de0b77f1b231c9a16a6b3bbf1319dd61b SHA1: b04c0cf2fe9a19b1826b325ec1e3d955e392d2d5 MD5sum: 7a18f0d4b0bc45152503f0717cde9588 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 Package: python-nibabel-doc Source: nibabel Version: 2.0.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5611 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-mathjax Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_2.0.1-1~nd90+1_all.deb Size: 2683508 SHA256: 442f366c9680fbe543d2f52805a7666b29fe5315a1a071823f87efdd4f60890d SHA1: 24e2d7f4730f52a7ffd052c210496ff9c3b57d26 MD5sum: 7f122ee5ab44d73d23f06196c271a428 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-nilearn Source: nilearn Version: 0.1.4+git3-g60d2a1b~dfsg.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1842 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.6), python-nibabel (>= 1.1.0), python:any (<< 2.8), python:any (>= 2.7.5-5~), python-scipy (>= 0.9), python-sklearn (>= 0.12.1) Recommends: python-matplotlib Provides: python2.7-nilearn Homepage: https://nilearn.github.io Priority: extra Section: python Filename: pool/main/n/nilearn/python-nilearn_0.1.4+git3-g60d2a1b~dfsg.1-1~nd90+1_all.deb Size: 634934 SHA256: b46d3630b7b3f66fe3802a1073443003205de209356522fe65ca3734a479552c SHA1: 7f653728d53498fd92d5c02fc5fb4326706a0e57 MD5sum: f7184560418464e027a92ea7132f3507 Description: fast and easy statistical learning on neuroimaging data This Python module leverages the scikit-learn toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. Python-Version: 2.7 Package: python-nipy Source: nipy Version: 0.3.0+git262-gbb838d7-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3185 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.2), python (>= 2.7), python (<< 2.8), python-scipy, python-nibabel, python-nipy-lib (>= 0.3.0+git262-gbb838d7-1~nd80+1+nd90+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.3.0+git262-gbb838d7-1~nd80+1+nd90+1_all.deb Size: 724228 SHA256: 6b7a48095dcfd24367acee0a5fee6ff54c39b914fff4cd4b9f184138ebb1e0b0 SHA1: 07defa82143344ef33689b077ad6b70e538a00b4 MD5sum: c736b2c0484f4c9ffe32ac49380239ef Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It provides functionality for - General linear model (GLM) statistical analysis - Combined slice time correction and motion correction - General image registration routines with flexible cost functions, optimizers and re-sampling schemes - Image segmentation - Basic visualization of results in 2D and 3D - Basic time series diagnostics - Clustering and activation pattern analysis across subjects - Reproducibility analysis for group studies Python-Version: 2.7 Package: python-nipy-doc Source: nipy Version: 0.3.0+git262-gbb838d7-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10844 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.3.0+git262-gbb838d7-1~nd80+1+nd90+1_all.deb Size: 3215602 SHA256: bb81791880acbbb409bb1d8148f9e8cc93e4dc9479741116ae7eabad945a266d SHA1: c9b0ba4f6e2ec1345510755f7042432dbe986539 MD5sum: f685e5c756759976203d2e838480f81c 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: 0.10.0-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4924 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), 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, mne-python, elastix, ants Provides: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.10.0-1~nd80+1+nd90+1_all.deb Size: 1159012 SHA256: 47389f2c3e1917f0e256c94ce8b078e811ad64575b73adef04ec13d84e836bb9 SHA1: 33040ea7b98de6606bfc0820d6a881d4dc23e4cb MD5sum: f8c83a093eb027eb11f2d042d50cf3a5 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: 0.10.0-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 21382 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.10.0-1~nd80+1+nd90+1_all.deb Size: 8892266 SHA256: a789ed865f17a32439be5dbca7334f67afba4249cb8bada74764b8df60ae1409 SHA1: abbf7205295295f2e302b190865527a8b5b47767 MD5sum: 424e485e6df2f01bceb92b2a2614abab 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: 0.5-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9381 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_0.5-1~nd80+1+nd90+1_all.deb Size: 2542980 SHA256: 4178606e8f6b3be286007ee192936a4b9d4d4d89b2751e7634cc11e6d157ebf8 SHA1: 0386f85a68e4043afa5533efeb5d15c8b5afba35 MD5sum: 8dccd4e9f388dbbeaf1ea6348a1172eb 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: 0.5-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7830 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_0.5-1~nd80+1+nd90+1_all.deb Size: 5764054 SHA256: 9877e2db6bbfe2a130f1b6cabc59af1fd7e577f3140e95b1fb38f3af6a9806fd SHA1: 57527c7c6cceb9b658d9292817a5633dd675fa14 MD5sum: 9349ac0462dc08453091dca5cf806b2c 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~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1065 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.7-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd70+1+nd90+1_all.deb Size: 179826 SHA256: e289031fd70e3234dd9bf775fda6ce9332235b63580fc3f21d95f5bf7d2ae59d SHA1: 0b6e1f2a56ea52c4ec110effc58bdb067c8a9887 MD5sum: bdffb8b3128e6731bf3a3af978b2667b Description: Python module for numerical optimization Numerical optimization framework developed in Python which provides connections to lots of solvers with easy and unified OpenOpt syntax. Problems which can be tackled with OpenOpt * Linear Problem (LP) * Mixed-Integer Linear Problem (MILP) * Quadratic Problem (QP) * Non-Linear Problem (NLP) * Non-Smooth Problem (NSP) * Non-Linear Solve Problem (NLSP) * Least Squares Problem (LSP) * Linear Least Squares Problem (LLSP) * Mini-Max Problem (MMP) * Global Problem (GLP) . A variety of solvers is available (e.g. IPOPT, ALGENCAN). Python-Version: 2.7 Package: python-openpyxl Source: openpyxl Version: 2.3.0~b1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1257 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-jdcal Recommends: python-pytest, python-pil, python-imaging, python-lxml Homepage: http://bitbucket.org/openpyxl/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_2.3.0~b1-1~nd90+1_all.deb Size: 190320 SHA256: c5e4a89721b7d8c7784051857caf08786c0e6c8f4b602dba09fdcb76eda822b1 SHA1: c07d605f648784fd8bc07d040e680e92b381fa7a MD5sum: 7560532dcd19316950631a405a0ef8bd 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: 0.16.2+git65-g054821d-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10737 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.7~), python-pandas-lib (>= 0.16.2+git65-g054821d-1~nd90+1), python-six Recommends: python-scipy, python-matplotlib, python-tables, python-numexpr, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt, python-bs4, python-html5lib, python-lxml Suggests: python-pandas-doc Provides: python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.16.2+git65-g054821d-1~nd90+1_all.deb Size: 1501314 SHA256: 19bab76ecb03403d94b39eb100d25b70d4cab8c91e6750a2dfcfcc7b38161bfd SHA1: a3f230c9145adc307a6068d666e7698554df505d MD5sum: 25455bde8165bedec5e57ff77857231a Description: data structures for "relational" or "labeled" data pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 2 version. Package: python-pandas-doc Source: pandas Version: 0.16.2+git65-g054821d-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 39323 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: doc Filename: pool/main/p/pandas/python-pandas-doc_0.16.2+git65-g054821d-1~nd90+1_all.deb Size: 6940600 SHA256: 23e12afc4dbb84ae1d93f1b534fddf75937450174d7589de32020ad1fed1f5aa SHA1: 1a6585a8ec330cb8a940e25c31d9e5c4e06ceec3 MD5sum: dfa5a18117553ee5deb34654ad96314c Description: documentation and examples for pandas This package contains documentation and example scripts for python-pandas. Package: python-patsy Source: patsy Version: 0.4.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 784 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-six, python-numpy Recommends: python-pandas, python-openpyxl Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python-patsy_0.4.0-1~nd90+1_all.deb Size: 171686 SHA256: 0099503d4e31fc7b3f23aa298a8bd17316fc784e6a6c0b237772ad3c39d86302 SHA1: 82a7ff1b15f713571666378900764211bdcc1e2c MD5sum: 2f5414259b0ed690bc9609fab96d0511 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 2 version. Package: python-patsy-doc Source: patsy Version: 0.4.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1382 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-patsy Homepage: http://github.com/pydata/patsy Priority: optional Section: doc Filename: pool/main/p/patsy/python-patsy-doc_0.4.0-1~nd90+1_all.deb Size: 361540 SHA256: 8c8c80752048c2351288c4eb8194580240388b6a63b6f0832ab38390b0f4eb29 SHA1: 052cd6df5eec534660ab5acc5511729f2250e95c MD5sum: fe096c941e8ea1a7e6209c935b2b7893 Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pp Source: parallelpython Version: 1.6.2-2~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 143 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~nd70+1+nd90+1_all.deb Size: 28994 SHA256: 9d2b9bc5a9f1f2f7ab2c1f65d14edab85d349c71f7c32d5b03a8a84752927571 SHA1: 9a77e4793d44f572146a67eaa8af06f5e2be9bc4 MD5sum: 7b25f8fbdf69d07695113ddb51235f8d Description: parallel and distributed programming toolkit for Python Parallel Python module (pp) provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. pp module features cross-platform portability and dynamic load balancing. Thus application written with PP will parallelize efficiently even on heterogeneous and multi-platform clusters (including clusters running other application with variable CPU loads). Python-Version: 2.7 Package: python-pprocess Source: pprocess Version: 0.5-1+nd0~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 758 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.boddie.org.uk/python/pprocess.html Priority: optional Section: python Filename: pool/main/p/pprocess/python-pprocess_0.5-1+nd0~nd80+1+nd90+1_all.deb Size: 81664 SHA256: 5327d46e6e8873ff262f9d40181b5169ac4a2173b18c195b572732169a7dcce2 SHA1: 4e7bc21bf1dadeb4014e694348397dd906b8cff6 MD5sum: cf0bced2928e4ed9c897e2a3c2d0afac Description: elementary parallel programming for Python The pprocess module provides elementary support for parallel programming in Python using a fork-based process creation model in conjunction with a channel-based communications model implemented using socketpair and poll. On systems with multiple CPUs or multicore CPUs, processes should take advantage of as many CPUs or cores as the operating system permits. Python-Version: 2.7 Package: python-py Version: 1.4.30-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 306 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-pkg-resources Suggests: subversion, python-pytest, python-pytest-xdist Homepage: https://bitbucket.org/pytest-dev/py Priority: optional Section: python Filename: pool/main/p/python-py/python-py_1.4.30-1~nd90+1_all.deb Size: 66758 SHA256: ec883a7d749a52c460f40bd5d98807df52000a97f138f047bc2dacbc790060cf SHA1: 26148d6657c97312e4b59fdbd1ff62235d49f052 MD5sum: bb113a6f468da35a8aa8339a767709fb Description: Advanced Python development support library (Python 2) The Codespeak py lib aims at supporting a decent Python development process addressing deployment, versioning and documentation perspectives. It includes: . * py.path: path abstractions over local and Subversion files * py.code: dynamic code compile and traceback printing support . This package provides the Python 2 modules. Package: python-pyentropy Source: pyentropy Version: 0.4.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 91 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy (>= 1.3) Recommends: python-scipy Suggests: python-nose Provides: python2.7-pyentropy Homepage: http://code.google.com/p/pyentropy Priority: extra Section: python Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd70+1+nd90+1_all.deb Size: 19484 SHA256: 90a65872dff84dfdd9080faae4df963e355d03c4f5c818b6dee37ae7265c8dd5 SHA1: 41cee054cc47be20e73203718874a5aae00fc35e MD5sum: e90627411895dd8862ef69d2670d0968 Description: Python module for estimation information theoretic quantities A Python module for estimation of entropy and information theoretic quantities using cutting edge bias correction methods, such as * Panzeri-Treves (PT) * Quadratic Extrapolation (QE) * Nemenman-Shafee-Bialek (NSB) Python-Version: 2.7 Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-2~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 822 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-g365f8e3-2~nd80+1+nd90+1_all.deb Size: 819326 SHA256: 3bb7c4a8afd71f527affe1dc9fc35cc8767aebfb4a22ca35c975346202ccfd15 SHA1: b65162864b6ef1f6b6f21f3a81b7221a8d9f5e6f MD5sum: ae5bd1ef9c1dcee42bc9e64308126c62 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-pymc-doc Source: pymc Version: 2.3.4+ds-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1894 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Homepage: http://pymc-devs.github.com/pymc/ Priority: extra Section: doc Filename: pool/main/p/pymc/python-pymc-doc_2.3.4+ds-1~nd80+1+nd90+1_all.deb Size: 839788 SHA256: af2ce4bafd9fe4585d82802881bc4a1ceb28e9076d529d7cc57ac90478281dd4 SHA1: 9f2d1b73a0c55bd49be8229926cc64a9e63a88e3 MD5sum: ad1e1963e2b23c06beb3c6fa20c513e1 Description: Bayesian statistical models and fitting algorithms PyMC is a Python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics. . This package provides the documentation in HTML format. Package: python-pynn Source: pynn Version: 0.7.5-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 832 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~nd70+1+nd90+1_all.deb Size: 137374 SHA256: 370ece10f2dd605fc721d1d7d7efb38db0d4c150a969f9a7788b0a18ac0d2eaf SHA1: a9207324cee6efc6f2830f3af2faa6a2a930d723 MD5sum: fe3284f568042b1fe19972884b23c76a 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-pytest Source: pytest Version: 2.7.2-2~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 493 Depends: neurodebian-popularity-contest, python-pkg-resources, python-py (>= 1.4.29), python, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-mock (>= 1.0.1) Homepage: http://pytest.org/ Priority: optional Section: python Filename: pool/main/p/pytest/python-pytest_2.7.2-2~nd90+1_all.deb Size: 132284 SHA256: 6ff620a9324dfeb8189fd224f82cfb8087002fe8dec53cd02815d6ad52e52077 SHA1: e469a272602f058e45ba3c0612c1fa38d05df811 MD5sum: f47c8d1731b85f0e39dac9574910fccc Description: Simple, powerful testing in Python This testing tool has for objective to allow the developers to limit the boilerplate code around the tests, promoting the use of built-in mechanisms such as the `assert` keyword. Package: python-pytest-doc Source: pytest Version: 2.7.2-2~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3002 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Recommends: python-pytest | python3-pytest Homepage: http://pytest.org/ Priority: optional Section: doc Filename: pool/main/p/pytest/python-pytest-doc_2.7.2-2~nd90+1_all.deb Size: 431304 SHA256: 652b8e09afc3715c2883b10e7e1ce2cc46f236542c0ec4228d4d65b7533f0905 SHA1: 26ceb27b3c48e7c051a73a90d54b98f8c5f11912 MD5sum: b947df3994d1953e2a0755781e9b0e21 Description: Simple, powerful testing in Python - Documentation This testing tool has for objective to allow the developers to limit the boilerplate code around the tests, promoting the use of built-in mechanisms such as the `assert` keyword. . This package contains the documentation for pytest. Package: python-pytest-localserver Source: pytest-localserver Version: 0.3.4-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 59 Depends: neurodebian-popularity-contest, python-pytest, python-werkzeug (>= 0.10), python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://pypi.python.org/pypi/pytest-localserver Priority: optional Section: python Filename: pool/main/p/pytest-localserver/python-pytest-localserver_0.3.4-1~nd90+1_all.deb Size: 18152 SHA256: 09ff67eba0f36d2f5bb0a405300bd4aa4315eb9a75a9fac48fd57a187c9d64f0 SHA1: 71b04f6861c66ac85684d5c7f0b0d233a60ff8df MD5sum: db59b4614f943159b2537882aabd2474 Description: py.test plugin to test server connections locally (Python 2) pytest-localserver is a plugin for the Pytest testing framework which enables to test server connections locally. . This package contains the modules for Python 2. Package: python-pytest-tornado Source: pytest-tornado Version: 0.4.4-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 32 Depends: neurodebian-popularity-contest, python-pytest, python-tornado, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/eugeniy/pytest-tornado Priority: optional Section: python Filename: pool/main/p/pytest-tornado/python-pytest-tornado_0.4.4-1~nd90+1_all.deb Size: 5658 SHA256: 2baad3999ed0fc99b2329ebc1285fb06d5a94d4dfec4d619613979b24eb68a26 SHA1: fbbac0c1dd798a073e540e8f99e02174beb1450b MD5sum: 374e5ad6bb5047154499315f47d46017 Description: py.test plugin to test Tornado applications pytest-tornado is a plugin for the Pytest testing framework which provides fixtures and markers to simplify testing of Tornado applications (Python web framework and ansynchronous networking library). . This package contains the plugin for Python 2 code. Package: python-pyxid Source: pyxid Version: 1.0-1~nd+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Homepage: https://github.com/cedrus-opensource/pyxid Priority: optional Section: python Filename: pool/main/p/pyxid/python-pyxid_1.0-1~nd+1+nd90+1_all.deb Size: 10358 SHA256: e2db09bf0e002d89bb3690e59e5bc46747809822675915da6e28646cedddf5f2 SHA1: 431b7f4e71d4a33d6687832ccbef84848c4fd2bc MD5sum: 93daf5487203df271ed363dc65e968d0 Description: interface for Cedrus XID and StimTracker devices pyxid is a Python library for interfacing with Cedrus XID (eXperiment Interface Device) and StimTracker devices. XID devices are used in software such as SuperLab, Presentation, and ePrime for receiving input as part of stimulus/response testing experiments. . pyxid handles all of the low level device handling for XID devices in Python projects. Package: python-quantities Version: 0.10.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 321 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy (>= 1.4) Homepage: http://packages.python.org/quantities/ Priority: extra Section: python Filename: pool/main/p/python-quantities/python-quantities_0.10.1-1~nd70+1+nd90+1_all.deb Size: 51442 SHA256: bfacf89e8ba78fd59315adc47dd03899f4b4594354eafbb38bbe186128958b1a SHA1: 53773551206da3c1249b9d38cf0ee65a40b9d6c4 MD5sum: 5e0cd9e39a08b2233b4ede2ab7e5eb72 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: 0.16.1-2~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 58 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_0.16.1-2~nd90+1_all.deb Size: 47806 SHA256: 3f80bee4269be55aeffa50f0aab45ffcdc2573685b3c1b283bd9695af709a924 SHA1: 742866ad430b7b2a7bc0d2019e31d9f353e62195 MD5sum: b606910614dadd819bf9f7ed408d683d 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: 0.6.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17 Depends: neurodebian-popularity-contest, python-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.6.1-1~nd80+1+nd90+1_all.deb Size: 6130 SHA256: 14c152e3d33f957bbfb131feb4b7f3b7635e8c951deaaa0bc14f4f61e201dd5f SHA1: aa7c9553756ceae24e09c14cb0896f3c2f47c7f0 MD5sum: ea75505ebffde41bf933adf8b994a3a9 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-scrapy Version: 1.0.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 978 Depends: neurodebian-popularity-contest, python-boto, python-cssselect, python-libxml2, python-queuelib, python-twisted-conch, python-twisted-core, python-twisted-mail, python-twisted-web, python-w3lib (>= 1.8), python (>= 2.7), python (<< 2.8) Recommends: ipython, python-django, python-guppy, python-imaging, python-lxml, python-mysqldb, python-pygments Suggests: python-openssl Provides: python2.7-scrapy Homepage: http://scrapy.org/ Priority: optional Section: python Filename: pool/main/p/python-scrapy/python-scrapy_1.0.0-1~nd90+1_all.deb Size: 174236 SHA256: 7ec60aa70592d4df6ebd15244889d07c06a2b091f8ca31586f20a5424c7e9423 SHA1: cb6eaf960a003969b4a731c09797665b98bd89aa MD5sum: a3b5e7741043e3e4820bef2201cbd2e9 Description: Python web scraping and crawling framework Scrapy is a fast high-level screen scraping and web crawling framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing. . This package provides the python-scrapy script and modules. Package: python-scrapy-doc Source: python-scrapy Version: 1.0.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7068 Depends: neurodebian-popularity-contest Recommends: libjs-jquery, libjs-underscore Homepage: http://scrapy.org/ Priority: optional Section: doc Filename: pool/main/p/python-scrapy/python-scrapy-doc_1.0.0-1~nd90+1_all.deb Size: 1521014 SHA256: ba2d75e9e95374fd122fe20707688a53a48b32f900d7bba1f125a7e81ea7aabf SHA1: 49868f4270f26288a915221b95bbff3c7d4a31cb MD5sum: a66b3b692688309550edf0a45de5f933 Description: Python web scraping and crawling framework documentation Scrapy is a fast high-level screen scraping and web crawling framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing. . This package provides the python-scrapy documentation in HTML format. Package: python-simplegeneric Source: simplegeneric Version: 0.7-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 34 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Provides: python2.7-simplegeneric Homepage: http://pypi.python.org/pypi/simplegeneric Priority: extra Section: python Filename: pool/main/s/simplegeneric/python-simplegeneric_0.7-1~nd70+1+nd90+1_all.deb Size: 9264 SHA256: 15a747617db233795675c1aa67ca9de17cd9e13af6912664e0b88eca1ddc6ddf SHA1: 94c949ff1fa89b6d155fa03308f301354c498766 MD5sum: 8594dcbe5f67eccd178894087d1d54e8 Description: Simple generic functions for Python The simplegeneric module lets you define simple single-dispatch generic functions, akin to Python's built-in generic functions like len(), iter() and so on. However, instead of using specially-named methods, these generic functions use simple lookup tables, akin to those used by e.g. pickle.dump() and other generic functions found in the Python standard library. Package: python-sklearn Source: scikit-learn Version: 0.16.1-2~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4719 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-sklearn-lib (>= 0.16.1-2~nd90+1), python-joblib (>= 0.4.5) Recommends: python-nose, python-matplotlib Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Enhances: python-mdp, python-mvpa2 Breaks: python-scikits-learn (<< 0.9~) Replaces: python-scikits-learn (<< 0.9~) Provides: python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.16.1-2~nd90+1_all.deb Size: 1111668 SHA256: 6de677d549b0f8693a1b482a295a4aeed0ffe1bf3178792711bfd9b797af9ebe SHA1: e54a0e186255af7260949b74c5bb90cf00686164 MD5sum: 35acc2110e78e0a0ac96508bd4db9788 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: 0.16.1-2~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24740 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_0.16.1-2~nd90+1_all.deb Size: 3961628 SHA256: 5358c1ede3ed22d808c9b69329aa297236e1c30967de68bbc217023eb45126da SHA1: 985797eaa1541ec45bdccf82785c72ad8b9f5165 MD5sum: 6f1d6db82c9f18adb433dd72a7ad8977 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-smmap Version: 0.8.3-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 91 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Suggests: python-nose Provides: python2.7-smmap Homepage: https://github.com/Byron/smmap Priority: extra Section: python Filename: pool/main/p/python-smmap/python-smmap_0.8.3-1~nd80+1+nd90+1_all.deb Size: 19908 SHA256: 4f3a938e3cd5d842366c6e5a2f57a5d493444be2b8867c65446516c2a9c68ce7 SHA1: a0c276a4b42578636b352bcd7ff10100d321f4e3 MD5sum: dc9c19db53e54d317da33ca8bde8197d Description: pure Python implementation of a sliding window memory map manager Smmap wraps an interface around mmap and tracks the mapped files as well as the amount of clients who use it. If the system runs out of resources, or if a memory limit is reached, it will automatically unload unused maps to allow continued operation. Package: python-sphinx Source: sphinx Version: 1.0.7-2~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3602 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-docutils (>= 0.5), python-pygments (>= 0.8), python-jinja2 (>= 2.2), libjs-jquery Recommends: python (>= 2.6) | python-simplejson, python-imaging Suggests: jsmath Homepage: http://sphinx.pocoo.org/ Priority: optional Section: python Filename: pool/main/s/sphinx/python-sphinx_1.0.7-2~nd70+1+nd90+1_all.deb Size: 1039184 SHA256: c0343e0061b8ee91e77c7ae4929f848936635d296d62555c27663d36bef902c1 SHA1: b0ebef92fc597a739bc948e7cee12df496a8aefe MD5sum: d1a38c525e04f208e5c87ceb4cbb58a8 Description: tool for producing documentation for Python projects Sphinx is a tool for producing documentation for Python projects, using reStructuredText as markup language. . Sphinx features: * HTML, CHM, LaTeX output, * Cross-referencing source code, * Automatic indices, * Code highlighting, using Pygments, * Extensibility. Existing extensions: - automatic testing of code snippets, - including doctrings from Python modules. Package: python-sphinx-rtd-theme Source: sphinx-rtd-theme Version: 0.1.8-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 359 Depends: neurodebian-popularity-contest, fonts-font-awesome, fonts-lato, libjs-modernizr, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-sphinx Homepage: https://github.com/snide/sphinx_rtd_theme Priority: optional Section: python Filename: pool/main/s/sphinx-rtd-theme/python-sphinx-rtd-theme_0.1.8-1~nd90+1_all.deb Size: 117144 SHA256: eab67c48e24b93cff0b649e5a427eb10c52aa359d3a4fd74ea6cd4b069a9e95c SHA1: 34abec450f80ae0621d2647f38fddacebc0e09ef MD5sum: cc42c408e9505fba4fe618b6df197db5 Description: sphinx theme from readthedocs.org (Python 2) This mobile-friendly sphinx theme was initially created for readthedocs.org, but can be incorporated in any project. . Among other things, it features a left panel with a browseable table of contents, and a search bar. . This is the Python 2 version of the package. Package: python-spyderlib Source: spyder Version: 2.2.5+dfsg-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4197 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), libjs-sphinxdoc (>= 1.0), libjs-jquery, libjs-mathjax, python-qt4 Recommends: ipython-qtconsole, pep8, pyflakes (>= 0.5.0), pylint, python-matplotlib, python-numpy, python-psutil (>= 0.3.0), python-rope, python-scipy, python-sphinx Suggests: tortoisehg, gitk Breaks: spyder (<< 2.0.12-1) Replaces: spyder (<< 2.0.12-1) Provides: python2.7-spyderlib Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: python Filename: pool/main/s/spyder/python-spyderlib_2.2.5+dfsg-1~nd80+1+nd90+1_all.deb Size: 1647356 SHA256: b4a2d401e100aa223d1cd764287958b07aae66471c4bc8b0f81cb56b4592f175 SHA1: 29ea7e0b238e8c437fb77ce4c5a77a910856a7b2 MD5sum: 46e38b5131ab14d5811e5b6e8ecfe690 Description: python IDE for scientists Originally written to design Spyder (the Scientific PYthon Development EnviRonment), the spyderlib Python library provides ready-to-use pure-Python widgets: source code editor with syntax highlighting and code introspection/analysis features, NumPy array editor, dictionary editor, Python console, etc. It's based on the Qt Python binding module PyQt4 (and is compatible with PySide since v2.2). Package: python-spykeutils Source: spykeutils Version: 0.4.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2063 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-scipy, python-quantities, python-neo (>= 0.2.1), python-nose, python-sphinx Recommends: python-guidata, python-guiqwt, python-tables, libjs-jquery, libjs-underscore, python-sklearn (>= 0.11), python-joblib (>= 0.4.5) Provides: python2.7-spykeutils Homepage: https://github.com/rproepp/spykeutils Priority: extra Section: python Filename: pool/main/s/spykeutils/python-spykeutils_0.4.1-1~nd80+1+nd90+1_all.deb Size: 306366 SHA256: 54f16aa9aae0e04213ef62a6da64edf887e6ce04ea85897dc1029687034a2b5b SHA1: 5d399820c3954d54a2b0ab16509b0a2aeff7d167 MD5sum: d141c2690d36ce705abbd284ba226bc1 Description: utilities for analyzing electrophysiological data spykeutils is a Python library for analyzing and plotting data from neurophysiological recordings. It can be used by itself or in conjunction with Spyke Viewer, a multi-platform GUI application for navigating electrophysiological datasets. Package: python-statsmodels Source: statsmodels Version: 0.6.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13052 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-statsmodels-lib (>= 0.6.1-1~nd80+1+nd90+1), python-patsy Recommends: python-pandas, python-matplotlib, python-nose, python-joblib, python-cvxopt Suggests: python-statsmodels-doc Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Provides: python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.6.1-1~nd80+1+nd90+1_all.deb Size: 2568752 SHA256: 693c35635920eaa1e3b294a7f878c2101e94d4dd4c03cafb8b64d5ee5d555896 SHA1: 3126fcf7e2081751d0f193f47b003860065e5fd7 MD5sum: 09c02024043dcdf9d3a40e127736ae65 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: 0.6.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 46850 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_0.6.1-1~nd80+1+nd90+1_all.deb Size: 11620476 SHA256: 68283825798fd3cf12421873e7c4d7a8066d37454f05c0f7b46d58d689eb62a5 SHA1: 4bf6b88bab1a09f8c347e1e9f272066602e2b026 MD5sum: 062bb026c8bdaee126a4da8a07ace314 Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-surfer Source: pysurfer Version: 0.5-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 180 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-nibabel, python-pil | python-imaging, mayavi2, python-argparse Recommends: mencoder Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.5-1~nd80+1+nd90+1_all.deb Size: 39598 SHA256: 7a5b2cecd134b49b18a9bbbad2feae578e679dc906a3932548d8236b0680a56c SHA1: 70f9f52a803873f9e6e46e23e4aeee052d786ab2 MD5sum: b54cd1c369e677a6939753a01de476ea Description: visualize Freesurfer's data in Python This is a Python package for visualization and interaction with cortical surface representations of neuroimaging data from Freesurfer. It extends Mayavi’s powerful visualization engine with a high-level interface for working with MRI and MEG data. . PySurfer offers both a command-line interface designed to broadly replicate Freesurfer’s Tksurfer program as well as a Python library for writing scripts to efficiently explore complex datasets. Python-Version: 2.7 Package: python-tz Version: 2012c-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 138 Depends: neurodebian-popularity-contest, tzdata, python (>= 2.7), python (<< 2.8) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2012c-1~nd70+1+nd90+1_all.deb Size: 34052 SHA256: 183fa2c4d83c7b02405c5f9101694d4d85961edf4bcee6dba215e9e20b67d8cb SHA1: 8c91f1d29e858933f13fea6bdaf815c0890e1850 MD5sum: ceac8a71c4038ba6e4f4c78a07232199 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: python-w3lib Version: 1.11.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 59 Depends: neurodebian-popularity-contest, python-six (>= 1.6.1), python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: http://pypi.python.org/pypi/w3lib Priority: optional Section: python Filename: pool/main/p/python-w3lib/python-w3lib_1.11.0-1~nd90+1_all.deb Size: 14104 SHA256: 85ac8e1aa7d2bed41025a2c9b343b8002bc057f78290f03c709e6fc940d1a633 SHA1: c22e5a569842cc69a1b33afea2a6936cba76ba60 MD5sum: e355ae8758f67732c9dafe97e3f34885 Description: Collection of web-related functions for Python (Python 2) Python module with simple, reusable functions to work with URLs, HTML, forms, and HTTP, that aren’t found in the Python standard library. . This module is used to, for example: - remove comments, or tags from HTML snippets - extract base url from HTML snippets - translate entites on HTML strings - encoding mulitpart/form-data - convert raw HTTP headers to dicts and vice-versa - construct HTTP auth header - RFC-compliant url joining - sanitize urls (like browsers do) - extract arguments from urls . The code of w3lib was originally part of the Scrapy framework but was later stripped out of Scrapy, with the aim of make it more reusable and to provide a useful library of web functions without depending on Scrapy. . This is the Python 2 version of the package. Package: python-werkzeug Version: 0.10.4+dfsg1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 765 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), libjs-jquery Recommends: python-simplejson | python (>= 2.6), python-openssl, python-pyinotify Suggests: ipython, python-genshi, python-pkg-resources, python-lxml, python-greenlet, python-redis, python-pylibmc | python-memcache, python-werkzeug-doc Homepage: http://werkzeug.pocoo.org/ Priority: optional Section: python Filename: pool/main/p/python-werkzeug/python-werkzeug_0.10.4+dfsg1-1~nd90+1_all.deb Size: 165572 SHA256: 7be17184bec25c4a421825b50b8d03493cdbdb30e57f485188f06e479b1bc755 SHA1: 44d678dad8ad3b38ee2e09a7f122d9c5863a998f MD5sum: aee38b21de85d50eabf69ff03ce3e31d Description: collection of utilities for WSGI applications The Web Server Gateway Interface (WSGI) is a standard interface between web server software and web applications written in Python. . Werkzeug is a lightweight library for interfacing with WSGI. It features request and response objects, an interactive debugging system and a powerful URI dispatcher. Combine with your choice of third party libraries and middleware to easily create a custom application framework. Package: python-werkzeug-doc Source: python-werkzeug Version: 0.10.4+dfsg1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2709 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Conflicts: python-werkzeug (<< 0.9.3+dfsg-2) Replaces: python-werkzeug (<< 0.9.3+dfsg-2) Homepage: http://werkzeug.pocoo.org/ Priority: extra Section: doc Filename: pool/main/p/python-werkzeug/python-werkzeug-doc_0.10.4+dfsg1-1~nd90+1_all.deb Size: 881876 SHA256: 4a559109102aee2f3bb9267ac2200775e2afd8328e79fa27e3591e6773438a15 SHA1: 389736200858b2336fafbd741e83ead639acb312 MD5sum: 0c95fbcbaed8ab4ef2cc312bfaa821f5 Description: documentation for the werkzeug Python library Werkzeug is a lightweight library for interfacing with WSGI. It features request and response objects, an interactive debugging system and a powerful URI dispatcher. Combine with your choice of third party libraries and middleware to easily create a custom application framework. Package: python3-citeproc Source: citeproc-py Version: 0.3.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 777 Depends: neurodebian-popularity-contest, python3, python3-lxml, python3:any (>= 3.3.2-2~) Homepage: https://github.com/brechtm/citeproc-py Priority: optional Section: python Filename: pool/main/c/citeproc-py/python3-citeproc_0.3.0-1~nd90+1_all.deb Size: 81802 SHA256: 9771600798fda289f3bf331af6e87ce9b39eac0c38520bdf30f8c509457b82aa SHA1: 39ad5faaf9069dda3e4691ad8faa2e38b490e6bb MD5sum: eda798e4e44857e62a279e19dd0b3223 Description: Citation Style Language (CSL) processor for Python3 Citeproc-py is a library that produces formatted bibliographies and citations from bibliographic databases following formatting instructions provided by XML style files written in the Citation Style Language (CSL). . Currently, BibTeX and JSON are supported as input database formats, and plain text, reStructuredText and HTML as output format. . This package contains the Python 3 modules and the CLI tool csl_unsorted. Package: python3-jdcal Source: jdcal Version: 1.0-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 35 Depends: neurodebian-popularity-contest Homepage: https://github.com/phn/jdcal Priority: optional Section: python Filename: pool/main/j/jdcal/python3-jdcal_1.0-1~nd80+1+nd90+1_all.deb Size: 7518 SHA256: 509687fb7218b6ba604b6c6224a4274f12f43d040c9d85ee47157e8ebb9b8c9c SHA1: b0c638b1dff6c85861e5f9fe52c8418bb82de3a5 MD5sum: a9021e0701397e533368a236de8df6b3 Description: Julian dates from proleptic Gregorian and Julian calendars This module contains functions for converting between Julian dates and calendar dates. . Different regions of the world switched to Gregorian calendar from Julian calendar on different dates. Having separate functions for Julian and Gregorian calendars allow maximum flexibility in choosing the relevant calendar. Package: python3-joblib Source: joblib Version: 0.8.4-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 273 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Recommends: python3-numpy, python3-nose, python3-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python3-joblib_0.8.4-1~nd80+1+nd90+1_all.deb Size: 61490 SHA256: 937f60c65dfd74f27413ce80845a22f41e73dbfa1c089c45ab9e71ec66c87c49 SHA1: fd5d049bb08156e50033559fe0e6655647e662c1 MD5sum: 8b6d5434f02011df66d875380e22f57d 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. . This package contains the Python 3 version. Package: python3-nibabel Source: nibabel Version: 2.0.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63347 Depends: neurodebian-popularity-contest, python3-numpy, python3-scipy Suggests: python-nibabel-doc, python3-dicom, python3-fuse Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python3-nibabel_2.0.1-1~nd90+1_all.deb Size: 1970746 SHA256: de7932d796b4e88878f195d9e6828f5a1a2c888a23867ce88c0c6826ccc1163c SHA1: f4f8c77852166109d7225f746ef84ddfac2442cc MD5sum: eebcd5a5b3a4faada7497f9c3a25dd07 Description: Python3 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. Package: python3-openpyxl Source: openpyxl Version: 2.3.0~b1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1249 Depends: neurodebian-popularity-contest, python3-jdcal, python3:any (>= 3.3.2-2~) Recommends: python3-pytest, python3-pil, python3-lxml Homepage: http://bitbucket.org/openpyxl/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python3-openpyxl_2.3.0~b1-1~nd90+1_all.deb Size: 188874 SHA256: 24b3c374aa52c010928f2a1c9490446abf9494c1acf67e84f8e64704b77fcbe1 SHA1: d97f86306f4f37b6b122546c1b7abac602039e35 MD5sum: 6381cf74d10221afccc9b0e9bdb44458 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: python3-pandas Source: pandas Version: 0.16.2+git65-g054821d-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10718 Depends: neurodebian-popularity-contest, python3-dateutil, python3-numpy (>= 1:1.7~), python3-tz, python3:any (>= 3.3.2-2~), python3-pandas-lib (>= 0.16.2+git65-g054821d-1~nd90+1) Recommends: python3-scipy, python3-matplotlib, python3-numexpr, python3-tables, python3-bs4, python3-html5lib, python3-six, python3-lxml Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.16.2+git65-g054821d-1~nd90+1_all.deb Size: 1498584 SHA256: bac54f57bbaaab58b155da25f5f914487053f8ff5782d17c4789706fbdfb7edf SHA1: a1826ccd0f92eecf39c7ccca48b56f0e30c50a11 MD5sum: 15b482266a448c7a6fe9432b7683b8bd Description: data structures for "relational" or "labeled" data - Python 3 pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 3 version. Package: python3-patsy Source: patsy Version: 0.4.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 781 Depends: neurodebian-popularity-contest, python3-numpy, python3-six, python3:any (>= 3.3.2-2~) Recommends: python3-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python3-patsy_0.4.0-1~nd90+1_all.deb Size: 171166 SHA256: 0a1f3b49084dc15c6ad46d2785d9f2f9b4a606ce27bf674853cb69fc275a414e SHA1: 35a1ef9576961e2f98bebe4bba037c0998772491 MD5sum: e6f0ba39c8e807a4e41aed017439e83d Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 3 version. Package: python3-py Source: python-py Version: 1.4.30-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 306 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-pkg-resources Suggests: subversion, python3-pytest Homepage: https://bitbucket.org/pytest-dev/py Priority: optional Section: python Filename: pool/main/p/python-py/python3-py_1.4.30-1~nd90+1_all.deb Size: 66824 SHA256: 0a6bf648d072410a214f4bd473a38ca3c056a330af4956a0a6b934fb0e0821dc SHA1: f90e5ce29005050a9187daeb98f57f3f4354dc43 MD5sum: 0847005900d103c72566871f0d601b17 Description: Advanced Python development support library (Python 3) The Codespeak py lib aims at supporting a decent Python development process addressing deployment, versioning and documentation perspectives. It includes: . * py.path: path abstractions over local and Subversion files * py.code: dynamic code compile and traceback printing support . This package provides the Python 3 modules. Package: python3-pytest Source: pytest Version: 2.7.2-2~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 494 Depends: neurodebian-popularity-contest, python3-pkg-resources, python3-py (>= 1.4.29), python3, python3:any (>= 3.3.2-2~) Homepage: http://pytest.org/ Priority: optional Section: python Filename: pool/main/p/pytest/python3-pytest_2.7.2-2~nd90+1_all.deb Size: 132468 SHA256: 48cbe9c4d4e7e631e765cda5872bb6bf51f82ba04470cb556b0d2c06041fda6f SHA1: dff11186bbd9e23bdbfa2fe9b60faee516890c2f MD5sum: 0e782127d1040f8b9af7be441cb8e640 Description: Simple, powerful testing in Python3 This testing tool has for objective to allow the developers to limit the boilerplate code around the tests, promoting the use of built-in mechanisms such as the `assert` keyword. . This package provides the Python 3 module and the py3.test script. Package: python3-pytest-localserver Source: pytest-localserver Version: 0.3.4-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 59 Depends: neurodebian-popularity-contest, python3-pytest, python3-werkzeug (>= 0.10), python3:any (>= 3.3.2-2~) Homepage: https://pypi.python.org/pypi/pytest-localserver Priority: optional Section: python Filename: pool/main/p/pytest-localserver/python3-pytest-localserver_0.3.4-1~nd90+1_all.deb Size: 18238 SHA256: ae2dc72d9a97c9d8d71bbc222a18030530c065e0bf309bbbda1844271195fabb SHA1: 4d9501a6f30c0c709c453e1d98464bd1f8131b54 MD5sum: 8c29f136aad81be7440f9ab8fcdfc5dc Description: py.test plugin to test server connections locally (Python 3) pytest-localserver is a plugin for the Pytest testing framework which enables to test server connections locally. . This package contains the modules for Python 3. Package: python3-pytest-tornado Source: pytest-tornado Version: 0.4.4-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 32 Depends: neurodebian-popularity-contest, python3-pytest, python3-tornado, python3:any (>= 3.3.2-2~) Homepage: https://github.com/eugeniy/pytest-tornado Priority: optional Section: python Filename: pool/main/p/pytest-tornado/python3-pytest-tornado_0.4.4-1~nd90+1_all.deb Size: 5734 SHA256: 8ab0046915b5cf9640e19a16d43ce44f498fd01fb0a365be49c8aaa1c30ef57a SHA1: c81514d37073e109155141a339b73c41e2b1e7fa MD5sum: 6955e8fad9eb8dcc0b1a8e6690a1f171 Description: py.test plugin to test Tornado applications (Python 3) pytest-tornado is a plugin for the Pytest testing framework which provides fixtures and markers to simplify testing of Tornado applications (Python web framework and ansynchronous networking library). . This package contains the plugin for Python 3 code. Package: python3-sphinx-rtd-theme Source: sphinx-rtd-theme Version: 0.1.8-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 359 Depends: neurodebian-popularity-contest, fonts-font-awesome, fonts-lato, libjs-modernizr, python3:any (>= 3.3.2-2~) Recommends: python3-sphinx Homepage: https://github.com/snide/sphinx_rtd_theme Priority: optional Section: python Filename: pool/main/s/sphinx-rtd-theme/python3-sphinx-rtd-theme_0.1.8-1~nd90+1_all.deb Size: 117208 SHA256: 16750e72490dfd83a69334d631b79056b547727b706529f6eb8eb2d1fbca5443 SHA1: 0f30cc73b8863ed74a3e4aa8ac6e2ba911bfff3b MD5sum: bd47026d57bfab7cdccbd9a6f3211976 Description: sphinx theme from readthedocs.org (Python 3) This mobile-friendly sphinx theme was initially created for readthedocs.org, but can be incorporated in any project. . Among other things, it features a left panel with a browseable table of contents, and a search bar. . This is the Python 3 version of the package. Package: python3-tz Source: python-tz Version: 2012c-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 109 Depends: neurodebian-popularity-contest, tzdata, python3:any (>= 3.3.2-2~) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python3-tz_2012c-1~nd70+1+nd90+1_all.deb Size: 25318 SHA256: f3b641a1dccf7188209fbbd4b68c9462e4cfc35e8c371603ac02a451676c132e SHA1: 04ab5dc0076b526b09c4b1ae563c20f70636fba2 MD5sum: 34437b0794a75cccfcbca371eb5db224 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: python3-w3lib Source: python-w3lib Version: 1.11.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 59 Depends: neurodebian-popularity-contest, python3-six (>= 1.6.1), python3:any (>= 3.3.2-2~) Homepage: http://pypi.python.org/pypi/w3lib Priority: optional Section: python Filename: pool/main/p/python-w3lib/python3-w3lib_1.11.0-1~nd90+1_all.deb Size: 14190 SHA256: 936b5cf9912553d27c7f1f2995ad57113ae0d0e50748a86b8b34d94a95210adb SHA1: c323083c3fd7909615fd05c74cf34df27c849b99 MD5sum: eabd0e00c9049ae6861823d2a2f35e24 Description: Collection of web-related functions for Python (Python 3) Python module with simple, reusable functions to work with URLs, HTML, forms, and HTTP, that aren’t found in the Python standard library. . This module is used to, for example: - remove comments, or tags from HTML snippets - extract base url from HTML snippets - translate entites on HTML strings - encoding mulitpart/form-data - convert raw HTTP headers to dicts and vice-versa - construct HTTP auth header - RFC-compliant url joining - sanitize urls (like browsers do) - extract arguments from urls . The code of w3lib was originally part of the Scrapy framework but was later stripped out of Scrapy, with the aim of make it more reusable and to provide a useful library of web functions without depending on Scrapy. . This is the Python 3 version of the package. Package: python3-werkzeug Source: python-werkzeug Version: 0.10.4+dfsg1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 765 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), libjs-jquery Recommends: python3-simplejson | python3, python3-openssl, python3-pyinotify Suggests: ipython3, python3-pkg-resources, python3-lxml, python-werkzeug-doc Homepage: http://werkzeug.pocoo.org/ Priority: optional Section: python Filename: pool/main/p/python-werkzeug/python3-werkzeug_0.10.4+dfsg1-1~nd90+1_all.deb Size: 165550 SHA256: baff81afd62f70d40a88d0730c28c741d896df6817923fc5c1c750b479ad7f7f SHA1: e64561b10bba46f959268074a0f6591208c7c5b9 MD5sum: 4831b3c44099af4fb3c32d574576b5a4 Description: collection of utilities for WSGI applications The Web Server Gateway Interface (WSGI) is a standard interface between web server software and web applications written in Python. . Werkzeug is a lightweight library for interfacing with WSGI. It features request and response objects, an interactive debugging system and a powerful URI dispatcher. Combine with your choice of third party libraries and middleware to easily create a custom application framework. Package: spm8-common Source: spm8 Version: 8.5236~dfsg.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 19187 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.5236~dfsg.1-1~nd70+1+nd90+1_all.deb Size: 9748000 SHA256: 680f43e45495a677b3c38f5e3709ec28b4ad877e4315eeff99fe2582ea1fcb7c SHA1: ceac6e0f3811c55e8125edbe21b3efdaf1a16e12 MD5sum: 244ac5f32442def5cfc872ea3f873f88 Description: analysis of brain imaging data sequences Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the platform-independent M-files. Package: spm8-data Source: spm8 Version: 8.5236~dfsg.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 73020 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd70+1+nd90+1_all.deb Size: 45492378 SHA256: aa9b3d42abaacaa7d476fd31e6d9af9abf9aadfca4f0a603b0369bb26a3c8dc5 SHA1: 0725897b8a1f9fa64abe092992fb35dbcfcd5270 MD5sum: 746ebce5f1c0180331a6be5e080b959c Description: data files for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provide the data files shipped with the SPM distribution, such as various stereotaxic brain space templates and EEG channel setups. Package: spm8-doc Source: spm8 Version: 8.5236~dfsg.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9252 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd70+1+nd90+1_all.deb Size: 8934900 SHA256: 4a07f5603644623f064ef741e279bc7c152ea20cc5a134ecd4734b7958e37836 SHA1: a37985ea601c14ce314860c55a42f45fd00405de MD5sum: 40142910687b4dfbbac3805fe593d0d6 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: spyder Version: 2.2.5+dfsg-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 136 Depends: neurodebian-popularity-contest, python:any, python-spyderlib (= 2.2.5+dfsg-1~nd80+1+nd90+1) Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: devel Filename: pool/main/s/spyder/spyder_2.2.5+dfsg-1~nd80+1+nd90+1_all.deb Size: 53012 SHA256: b2dc7e51ef78e68819125e10a50cd4ea706a77ba409e0bb27fca9b63c8a34c5f SHA1: c6e5131a5eaafdf8960050d01e2812ba76c94730 MD5sum: 74994062b3b51941db390ea57c13b8e8 Description: python IDE for scientists Spyder (previously known as Pydee) is a free open-source Python development environment providing MATLAB-like features in a simple and light-weighted software Package: spykeviewer Version: 0.4.2-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1189 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.4.0), python-neo (>= 0.2.1), python-matplotlib, python-scipy, python-nose, python-sphinx, python-tables Recommends: libjs-jquery, libjs-underscore, ipython-qtconsole (>= 0.12) Homepage: http://www.ni.tu-berlin.de/software/spykeviewer Priority: extra Section: python Filename: pool/main/s/spykeviewer/spykeviewer_0.4.2-1~nd80+1+nd90+1_all.deb Size: 536744 SHA256: cb4cd4ca11a3653360964d74b290962dae62889d58c862e881de4d74064017fc SHA1: 18afd67d6f70a23b422e0d67f4cd9c3870633c10 MD5sum: 8dc65293cc871f2d48bde9f0f4be6e34 Description: graphical utility for analyzing electrophysiological data Spyke Viewer is a multi-platform GUI application for navigating, analyzing and visualizing electrophysiological datasets. Based on the Neo framework, it works with a wide variety of data formats. Spyke Viewer includes an integrated Python console and a plugin system for custom analyses and plots. Package: stabilitycalc Version: 0.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 134 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy, python-nifti Recommends: python-dicom Homepage: https://github.com/bbfrederick/stabilitycalc Priority: extra Section: science Filename: pool/main/s/stabilitycalc/stabilitycalc_0.1-1~nd70+1+nd90+1_all.deb Size: 22486 SHA256: 8766352aac1f4ad1638c04104058b2872d436a43f15fb3a43f84ea9c99ce5787 SHA1: ab2985bd761da2904cfdddb227bf81a1b91534d0 MD5sum: 8d9b482369a29f4801e6ef1119852fcd Description: evaluate fMRI scanner stability Command-line tools to calculate numerous fMRI scanner stability metrics, based on the FBIRN quality assurance test protocal. Any 4D volumetric timeseries image in NIfTI format is support input. Output is a rich HTML report. Python-Version: 2.7 Package: svgtune Version: 0.1.0-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25 Depends: neurodebian-popularity-contest, python, python-lxml Suggests: inkscape Homepage: http://github.com/yarikoptic/svgtune Priority: optional Section: graphics Filename: pool/main/s/svgtune/svgtune_0.1.0-1~nd70+1+nd90+1_all.deb Size: 6544 SHA256: 932580cf411cba683c923965479d65b533fe87f46590d4e18612bb7906fd639c SHA1: 9077b3e2d8dc1f892ae80bd15112d369c68aa96f MD5sum: b023b0ccfe544567ead1371438900c92 Description: tool to generate a set of .svg files out of a single .svg file svgtune is just a little helper to generate a set of .svg files out of a single .svg file, by tuning respective groups/layers visibility, transparency or anything else. . It might come very handy for generation of incremental figures to be embedded into the presentation in any format which inkscape could render using original .svg file (e.g. pdf, png). Package: ubuntu-keyring Version: 2010.+09.30~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 Recommends: gpgv Priority: important Section: misc Filename: pool/main/u/ubuntu-keyring/ubuntu-keyring_2010.+09.30~nd70+1+nd90+1_all.deb Size: 11666 SHA256: 9261b6c61e5f4c66cc1bc803a2fafddd4b76383058127496b5d6c2af04ea1b9e SHA1: 705794dc4d5af0849731d8a6ebc9d90b24b7bf19 MD5sum: efa94037736492ce55a9d611841f8bdc Description: GnuPG keys of the Ubuntu archive The Ubuntu project digitally signs its Release files. This package contains the archive keys used for that.