Package: arno-iptables-firewall Version: 1.9.2.k-3~sid.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 844 Depends: iptables (>= 1.2.11), gawk, debconf (>= 1.3.22) | cdebconf (>= 0.43), debconf (>= 0.5) | debconf-2.0, iproute Recommends: lynx, dnsutils Homepage: http://rocky.eld.leidenuniv.nl/ Priority: optional Section: net Filename: pool/main/a/arno-iptables-firewall/arno-iptables-firewall_1.9.2.k-3~sid.nd1_all.deb Size: 132466 SHA256: f27127b8c1dc917c0286a9387f8fa457376ded10b07a5908485636c27a2a14ff SHA1: 696de58c79bec6fd3efa3cf7dbbeecaa18d1ea8e MD5sum: da7a5641d17921fad83cbb534f2ebb22 Description: single- and multi-homed firewall script with DSL/ADSL support Unlike other lean iptables frontends in Debian, arno-iptables-firewall will setup and load a secure, restrictive firewall by just asking a few question. This includes configuring internal networks for internet access via NAT and potential network services (e.g. http or ssh). . However, it is in no way restricted to this simple setup. Some catch words of additional features, that can be enabled in the well documented configuration file are: DSL/ADSL, Port forwarding, DMZ's, portscan detection, MAC address filtering. Package: bats Version: 0.4.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 Depends: neurodebian-popularity-contest Homepage: https://github.com/sstephenson/bats Priority: optional Section: shells Filename: pool/main/b/bats/bats_0.4.0-1~nd+1_all.deb Size: 14428 SHA256: 0f03a145105517af6b77b17a29c29136a50d1addd60bd676402ce7c9dab1ec2c SHA1: c946bfca85b5ec6ecc6be6c1ddcb960908fcbaea MD5sum: e14bddbd95bc1a10d6fd2f8fd5cfab64 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: 0.25.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 270 Depends: neurodebian-popularity-contest, perl, btrfs-progs (>= 3.18.2) | btrfs-tools (>= 3.18.2) Recommends: openssh-client, pv Homepage: http://digint.ch/btrbk/ Priority: optional Section: utils Filename: pool/main/b/btrbk/btrbk_0.25.0-1~nd+1_all.deb Size: 75872 SHA256: 0a451851cd36604fbb90944b3e2318c14cbe8080f1131dda9ad8522db0c078a3 SHA1: 829bf5382389a8867f6d00b25f8a8bc479363495 MD5sum: 97e282559e6a47335adc809c3ed14422 Description: backup tool for btrfs subvolumes Backup tool for btrfs subvolumes, using a configuration file, allows creation of backups from multiple sources to multiple destinations, with ssh and flexible retention policy support (hourly, daily, weekly, monthly). Package: condor Version: 8.4.9~dfsg.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 29 Depends: neurodebian-popularity-contest, htcondor Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor_8.4.9~dfsg.1-2~nd+1_all.deb Size: 16196 SHA256: 0df170fb5cfeab10ad871bc3238b09a8d519b8f6d1cbcde8c62cf10f2b2b67ef SHA1: 1894f7b63cde9fcbdbecccde5d330c73d67e6dcb MD5sum: a83f38ecd6537a2e586cef6b133d5ea6 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: 8.4.9~dfsg.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 29 Depends: neurodebian-popularity-contest, htcondor-dbg Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dbg_8.4.9~dfsg.1-2~nd+1_all.deb Size: 16212 SHA256: a6391c2c69fb0af4410309ad51f785110449edb5e1eecd2d80c22c0322e7b732 SHA1: e0f67336ccee360a1a17dd05b5ad9b88726a75cf MD5sum: b11d51f702abc0ea608d7f682d239f96 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: 8.4.9~dfsg.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 29 Depends: neurodebian-popularity-contest, htcondor-dev Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dev_8.4.9~dfsg.1-2~nd+1_all.deb Size: 16212 SHA256: 12adaa9950c7f6043021e2e279c364f26fcb4e04f2aa198b4af87f7b26b5b05d SHA1: 6d88fcd75d8d043b2b874f079b9bd4efc4c67a19 MD5sum: 9d60ff99fcf13800d62d98bf8bcb8732 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: 8.4.9~dfsg.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 29 Depends: neurodebian-popularity-contest, htcondor-doc Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-doc_8.4.9~dfsg.1-2~nd+1_all.deb Size: 16212 SHA256: 605c57b870e82b5345cffdb05d4a61ebc3abc4442b6b9b7450ebe3afbd9f83f1 SHA1: 60b965d995a858ceecc889b81ad4536d715151c4 MD5sum: c0f780575f44d50a6a79a557fedd89c8 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1578 Depends: neurodebian-popularity-contest, python (<< 2.8), 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~nd+1_all.deb Size: 1356156 SHA256: 434aff9b028c4333df4aff71cc45e6b82a98574f6297ddab70d0ebc260ff5e6a SHA1: 5dc49f902c6d89fd0fea7758ce53c9462ec73db4 MD5sum: a9b946a201ad29742748d1c152b6fd57 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2319 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: doc Filename: pool/main/c/cctools/coop-computing-tools-doc_3.4.2-1~nd+1_all.deb Size: 310822 SHA256: 0642839d664eb82995250674899981ef923b580886bfee044c3d7fb0423d5c59 SHA1: ac36fb2e56a520976ccafeb3149cbb74e53a5783 MD5sum: 24357820bf176e53a7aafe81b73d5de2 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: datalad Version: 0.5.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 84 Depends: neurodebian-popularity-contest, python-argcomplete, python-datalad (= 0.5.1-1~nd+1), python:any Homepage: http://datalad.org Priority: optional Section: science Filename: pool/main/d/datalad/datalad_0.5.1-1~nd+1_all.deb Size: 53950 SHA256: ea2c91f60cf8b8c310cf51788fda7a9e18c0831391d0c9ad486cb0a769a2e34d SHA1: 1230b0e187b79305ad9b6116d2d80cb7431416bf MD5sum: 6ac62333f89f434f9e818ba1d30aa208 Description: data files crawler and data distribution DataLad is a data distribution providing access to a wide range of data resources already available online (initially aiming at neuroscience domain). Using git-annex as its backend for data logistics it provides following facilities . - crawling of web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc - command line interface for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get), as well as search within aggregated meta-data . This package provides the command line tool. Install without Recommends if you need only core functionality. Package: debian-handbook Version: 6.0+20120509~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23215 Depends: neurodebian-popularity-contest Homepage: http://debian-handbook.info Priority: optional Section: doc Filename: pool/main/d/debian-handbook/debian-handbook_6.0+20120509~nd+1_all.deb Size: 21998670 SHA256: b33f038d8363175473cc056a5f98fc7af52386a466b45d4b2e42d2f25233a3ed SHA1: 7a0b369b4548a3f4fb61aa1ef9efa2ddf2b319e2 MD5sum: 3e3d2cf990fcc5ed1ed6bdbfb5c1c3dd Description: reference book for Debian users and system administrators Accessible to all, the Debian Administrator's Handbook teaches the essentials to anyone who wants to become an effective and independent Debian GNU/Linux administrator. . It covers all the topics that a competent Linux administrator should master, from the installation and the update of the system, up to the creation of packages and the compilation of the kernel, but also monitoring, backup and migration, without forgetting advanced topics like SELinux setup to secure services, automated installations, or virtualization with Xen, KVM or LXC. . The Debian Administrator's Handbook has been written by two Debian developers — Raphaël Hertzog and Roland Mas. . This package contains the English book covering Debian 6.0 “Squeeze”. Package: dh-systemd Source: init-system-helpers Version: 1.18~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, perl, debhelper Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/dh-systemd_1.18~nd+1_all.deb Size: 13820 SHA256: 5c6c86d4863c6322a9e6512918f65d3621106a79bb50d159df73068dc4f82efb SHA1: 99fb38f6bfb0c38a01dd2e304f1c32ffbe0c0b2b MD5sum: 7a19fa0768f1c1d627df931054cb945b 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: docker-compose Version: 1.5.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 278 Depends: neurodebian-popularity-contest, python-docker (>= 1.3.0), python-dockerpty (>= 0.3.4), python-docopt, python-enum34 (>= 1.0.4), python-jsonschema, python-requests (>= 2.6.1), python-six (>= 1.3.0), python-texttable, python-websocket, python-yaml, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: docker.io (>= 1.6.0) Homepage: http://docs.docker.com/compose/ Priority: optional Section: admin Filename: pool/main/d/docker-compose/docker-compose_1.5.2-1~nd+1_all.deb Size: 87420 SHA256: 8e1c046f74ca70d5335a5700e0349e295a7d29c4f9519ecff6f3503bec8ce2d8 SHA1: 60b4209fd79c7a44d8fc80b65b80d6465e9d763f MD5sum: 746a3e0890d396e0a4f5cb21b58c1c2e Description: Punctual, lightweight development environments using Docker docker-compose is a service management software built on top of docker. Define your services and their relationships in a simple YAML file, and let compose handle the rest. Package: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8109 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd+1_all.deb Size: 7224686 SHA256: 54ff518513962d0d5f50f6194edc9210fd1280ccb3dd2d277aee94711522513f SHA1: 0f8e5ec99bfea7724ce386136c4733c175063e78 MD5sum: 489d2beaf9e6e7e581abbfb6cebbef44 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: 0.9.6-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1290 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), init-system-helpers (>= 1.18~), lsb-base (>= 2.0-7) Recommends: python, iptables, whois, python3-pyinotify, python3-systemd Suggests: mailx, system-log-daemon, monit Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.9.6-1~nd+1_all.deb Size: 287494 SHA256: f104e13fbfa3b1d8e49e13621af856348cfee7a4a97bcf997e081730de705035 SHA1: 8847df6ae06253701955925306cb77eaae44f307 MD5sum: bcafc7f5e8f822ad4f242c26d14bc48b 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. Following recommends are listed: . - iptables -- default installation uses iptables for banning. You most probably need it - whois -- used by a number of *mail-whois* actions to send notification emails with whois information about attacker hosts. Unless you will use those you don't need whois - python3-pyinotify -- unless you monitor services logs via systemd, you need pyinotify for efficient monitoring for log files changes Package: freeipmi Version: 1.4.9-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd+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~nd+1_all.deb Size: 1166 SHA256: 7dda9d1542d1494e9a7fee60f42db2b304fd486139b040580e1719fcc4f3a72e SHA1: 4992038df8faf051041e20e9509db82a264606c5 MD5sum: 0627a974c392edde19e7e09ff3454195 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 490 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~nd+1_all.deb Size: 339368 SHA256: 630feacdcc5462e2cb14ec9cc1ab0df88ed40258cec8fc465d00ca2b35195650 SHA1: 37a7e8b5ad9ebb187cfa538fde4bc36977682a8b MD5sum: 461e09f72e0e80921f61d0d42aaae393 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~nd+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~nd+1_all.deb Size: 13894 SHA256: 9756e4fb5df49c082874797043964c50688944e09807b3c2e3ae5f8ef57a29fc SHA1: c8188453f7a785172e154a3ed522bd45758a23b6 MD5sum: 810ba8a6c4ee3c0548200411b906c2a2 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: fsleyes Version: 0.10.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 119988 Depends: neurodebian-popularity-contest, python-fsl, python-props, python-wxgtk3.0, python-six, python-scipy, python-matplotlib, python-numpy, python-opengl (>= 3.1~), python:any (<< 2.8), python:any (>= 2.7.5-5~) Priority: optional Section: science Filename: pool/main/f/fsleyes/fsleyes_0.10.1-1~nd+1_all.deb Size: 10266178 SHA256: 8f96669eb44cd8c2b166f30c294544f9ca22ea2881a3b686a6ae46624cf7542b SHA1: cf7886f2d36407c13ba941ba5bbc3c2d7386191d MD5sum: a8840db64a1e9630e24a7ecc1b5e87dc Description: FSL image viewer Feature-rich viewer for volumetric (medical) images. Package: fslview-doc Source: fslview Version: 4.0.1-6~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2930 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-6~nd+1_all.deb Size: 2227654 SHA256: 76b56902c93037a6b1047c3e407923921380d785c518c9fb3578b298e9bf571e SHA1: 230f1be25b2d41fb8a1b741677ba1af77a3fcfe2 MD5sum: fdd711020a889d9831643710482d3d31 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gcalcli Version: 3.4.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1766 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_3.4.0-1~nd+1_all.deb Size: 1669934 SHA256: a942c1135cb5ea2389e82adeeeddd0c6d52dbb92a9d5ec5b6fbd38dc344a85e8 SHA1: 5e465d9f3df150aa6315f64305a6fc8066123b0d MD5sum: f3d53c3e565d66af9033c8eb536f27b0 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: git-hub Version: 0.10.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 Depends: neurodebian-popularity-contest, python, git (>= 1:1.7.7) Homepage: https://github.com/sociomantic/git-hub Priority: optional Section: vcs Filename: pool/main/g/git-hub/git-hub_0.10.3-1~nd+1_all.deb Size: 34098 SHA256: 8040a3bcf2b0edb26f64479ad263802c54abaa84c5c634916bac6cc38d0f0b86 SHA1: fa08d17ce7635fb46ae42ae7a828a421253be7b3 MD5sum: a5f8f25a39285cdb58716bbb34332c85 Description: Git command line interface to GitHub git hub is a simple command line interface to GitHub, enabling most useful GitHub tasks (like creating and listing pull request or issues) to be accessed directly through the Git command line. . Although probably the most outstanding feature (and the one that motivated the creation of this tool) is the pull rebase command, which is the rebasing version of the GitHub Merge (TM) button. This enables an easy workflow that doesn't involve thousands of merges which makes the repository history unreadable. . Another unique feature is the ability to transform an issue into a pull request by attaching commits to it (this is something offered by the GitHub API but not by the web interface). Package: gmsl Version: 1.1.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 78 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.5-1~nd+1_all.deb Size: 13860 SHA256: d5e113a0261e40edce8fb498d2bb7852c648002247fffdff0452381a4b2ecdef SHA1: 11595f3dcf608a93147968af5da25409ce67f80e MD5sum: 1f3da7118ebb92d891e0eca8f578fe12 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 476 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~nd+1_all.deb Size: 426802 SHA256: 30425f953711295f9fd13c922f1f6cdfb967eae6d2b35805872681de6cab0984 SHA1: 10dc2e2a857d30f9a48b314928809fa24c950efb MD5sum: 146eaa1ce187ce8df480c722af1b3657 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11 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~nd+1_all.deb Size: 6738 SHA256: 35e27bb61847fd126702dfd459303228bb781ce180fa78b663d7263479f5b2a6 SHA1: 97eec3f03694256ce8d38f1ae5db05b2e6de793a MD5sum: abb7483920ef5ea1c5619fd31786a892 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: heudiconv Version: 0.1+git94-g85a2afb-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 91 Depends: neurodebian-popularity-contest, python, python-dcmstack, python-dicom, python-nibabel, python-numpy, python-nipype Recommends: mricron, dcm2niix Homepage: https://github.com/nipy/heudiconv Priority: optional Section: science Filename: pool/main/h/heudiconv/heudiconv_0.1+git94-g85a2afb-1~nd+1_all.deb Size: 19472 SHA256: b782c7096ef81442fe1d14d5e12743b9e89010b3b050a202eced06b59e924e39 SHA1: e65296413d638158ecb88fc8fcab28576009fc01 MD5sum: 93a4f97a099d04135eba8c9266def3d1 Description: DICOM converter with support for structure heuristics This is a flexible dicom converter for organizing brain imaging data into structured directory layouts. It allows for flexible directory layouts and naming schemes through customizable heuristics implementations. It only converts the necessary dicoms, not everything in a directory. It tracks the provenance of the conversion from dicom to nifti in w3c prov format. Package: htcondor-doc Source: condor Version: 8.4.9~dfsg.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 6120 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_8.4.9~dfsg.1-2~nd+1_all.deb Size: 1066620 SHA256: 55dc5961bd5648362a13d2431f210b729c50bf943db110e3101a66321d44fe1f SHA1: 65fabc7bfa4fa14faa3ae8f09d7d26ef01b28b3e MD5sum: a96409da9a66d6f3490983ac7333db74 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: 0.11.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 436 Depends: neurodebian-popularity-contest, python, python-pygame, python-pil | python-imaging, poppler-utils | mupdf-tools | xpdf-utils (>= 3.02-2) Recommends: mplayer, pdftk, perl, xdg-utils 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_0.11.1-1~nd+1_all.deb Size: 175634 SHA256: aa6b3094a51db5f60c086659775a5dd4f1bdb1b3efc3e6cfb7b2a8a8eaf55505 SHA1: 80326f67f2bf666891aeafd556ee40cbfb5f0d1f MD5sum: 2e373cb2275ad290ace5b30382a3c4bb 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 * Support of movies presentation * Active hyperlinks within PDFs Package: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd+1_all.deb Size: 9644 SHA256: 0d13ef08a008124bb9da089c6b0ee0b6786334ccc1f455d0fbf23dc513dd40df SHA1: bc8cb6cadf98cc14994f98441238f49353e3a04c MD5sum: 4ea2b3f0bbadd9c29c191cb08ac94709 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29 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~nd+1_all.deb Size: 13476 SHA256: d19f7f7a8cc30eebf191c3e5ad052e7bed02fdc1193cd7d909b96fcb70fb0a92 SHA1: 030b394a1bb44ed637be9f28365d434d5b7b76b9 MD5sum: 1082547d50241a858f76eaea6a830965 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2836 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~nd+1_all.deb Size: 2500548 SHA256: 0531b48d93ec829a52e19ef69ade463eb0d13b9b1d738db0d3b56ba6773293a6 SHA1: 1827fac02b3ef405aff4374a4cb2563a9c0dca06 MD5sum: ed36ba29f41f8508c077ae627f0bcfa9 Description: Image processing toolkit for registration and segmentation - examples ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the source for example programs. Package: ipython01x Version: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4808 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.6.6-7~), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython01x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x_0.13.2-1~nd+1_all.deb Size: 1306542 SHA256: 33fc418d5aa20d8ed5764ba27113cf8b7dfd6e161f925ce1b3bf179bf11fb31c SHA1: 39a91cecc912b7b453902b3746d62849a55e0b52 MD5sum: 9f74e872ca8b460a4350a340d804f98b Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16672 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.2-1~nd+1_all.deb Size: 7241256 SHA256: a0d2235483d3300b6213c473d783db7e62487249a9bed0d418dbad6d44693be8 SHA1: 947c3033758d7974461bd0758e0d765af1750dc6 MD5sum: c52d4bb47c6d7375400b74e24c2f9fd8 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-notebook Source: ipython01x Version: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-notebook_0.13.2-1~nd+1_all.deb Size: 900 SHA256: 610ebc3a7bcc05bd450b367852ab26dad5c2f8b668e000fc73880a07cdffda2f SHA1: c722a2a679e2931d56dc828366f160165f16d9d0 MD5sum: 9e7976ee869362eaa11884d2e1ff00c0 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython01x-parallel Source: ipython01x Version: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython01x/ipython01x-parallel_0.13.2-1~nd+1_all.deb Size: 828 SHA256: 1f0ce5d07095b2529a2f935f8715d834215d6c4f016179fc33ba6b11dd5855f7 SHA1: e2194703b6e2426cbb732cc34070157910c6c0ca MD5sum: fe3fa702999665bd8751b4daf6afb0d2 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython01x-qtconsole Source: ipython01x Version: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.13.2-1~nd+1_all.deb Size: 914 SHA256: ed093f1f0751c34cedd9910b094deecb52e3a7d8865119c5de7d47996acdea7b SHA1: 4aebbc9ff1ce980725e419f8754294d3ea472dc6 MD5sum: 96a49d95cc8ce5bff8422fb5d3f378ba Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11748 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.7), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython1x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython1x/ipython1x_1.1.0+git7-gf5891e9-1~nd+1_all.deb Size: 3842882 SHA256: 7c1441bc77ba7c7fd3bdbe815f7c7f7f6463fa9ef0313a9e93609cdfe179dd8c SHA1: 4caf2b8e43102cc073fa5d48befbdea5f0bd2f59 MD5sum: 47491a39015e2f209b730ad0cb5ada5d Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-doc Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10389 Depends: neurodebian-popularity-contest, libjs-jquery, ipython1x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython1x/ipython1x-doc_1.1.0+git7-gf5891e9-1~nd+1_all.deb Size: 3694410 SHA256: 791e5bf6963a169984fc99ea7876edcf8ee4d9d395d324c12dda2aef4f0a602a SHA1: 94c9c4fed79527354a553fd0e0a794eb18c92ae6 MD5sum: 4a3eea4d61a721ca8acfda4d8c57ef61 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-notebook Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-notebook_1.1.0+git7-gf5891e9-1~nd+1_all.deb Size: 962 SHA256: fe4b4c9a3c3c0f4a0a6fea5b8988cfdf1116d52d69e2c1a0fee034193a621b73 SHA1: 7fff71aa3955c95a06d37dc68325299f9d5304ef MD5sum: cd9e1c312a5b0daaa52b9744d53c19d0 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython1x-parallel Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython1x/ipython1x-parallel_1.1.0+git7-gf5891e9-1~nd+1_all.deb Size: 888 SHA256: b92af7fb89a64885bac32001ff8af087e44e8b5b7d2ff631255d786fe8a80d01 SHA1: aaa3c95e540ffc04b95c335b8059fa1d82c1da4f MD5sum: 8ffb02a51001d8c55e7f68aad8820104 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython1x-qtconsole Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-qtconsole_1.1.0+git7-gf5891e9-1~nd+1_all.deb Size: 972 SHA256: 899ea9da63a3f655e738b2853dc87c805ce75a92303b2f1baf93e31c9aa5d743 SHA1: b8d030862023fbf54da8e753ce0b86d306982254 MD5sum: 6db341ac45a0e710a04f62bd052e45c6 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: ipython2x Version: 2.0.0+git8-gee204ae-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12337 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.7), python (<< 2.8) Recommends: python-tornado (>= 3.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib, environment-modules Suggests: ipython2x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython2x/ipython2x_2.0.0+git8-gee204ae-1~nd+1_all.deb Size: 5617136 SHA256: 8bd356916406ac6cd32400a0f84f7c961c575e597f1cb27359d8fc3557311155 SHA1: f8ff90471d165b6b6f5364285f5943c8c56b8112 MD5sum: d3ea479e159c72a987a2fcb370b51985 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython 2.x seres with all fresh goodness from the IPython team. It provides IPython2X module thus not conflicting with system-wide installed IPython Package: ipython2x-doc Source: ipython2x Version: 2.0.0+git8-gee204ae-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12949 Depends: neurodebian-popularity-contest, libjs-jquery, ipython2x (= 2.0.0+git8-gee204ae-1~nd+1) Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython2x/ipython2x-doc_2.0.0+git8-gee204ae-1~nd+1_all.deb Size: 4678124 SHA256: b5c017712653a30fa8a62aaa8601c3b9146f3c63184419ef17121da49c38e864 SHA1: 571824bad0b488d73fdc42eac1a4fe70bb10d9f6 MD5sum: 855a4ce18a4ed06cef96e0796756cdd6 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython 2.x. It provides IPython2X module thus not conflicting with system-wide installed IPython Package: ismrmrd-schema Source: ismrmrd Version: 1.3.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25 Depends: neurodebian-popularity-contest Homepage: http://ismrmrd.github.io/ Priority: optional Section: science Filename: pool/main/i/ismrmrd/ismrmrd-schema_1.3.2-1~nd+1_all.deb Size: 5014 SHA256: 912f3638ef54d138a31d05609f4e92f791abe30ad5df28d5e39c45c8d55fb258 SHA1: 9868caa57e8404a539336933627057b221713c81 MD5sum: 10f20c9b72aefdbd2b0e54ab0cfd049c Description: ISMRM Raw Data format (ISMRMRD) - XML schema The ISMRMRD format combines a mix of flexible data structures (XML header) and fixed structures (equivalent to C-structs) to represent MRI data. . In addition, the ISMRMRD format also specifies an image header for storing reconstructed images and the accompanying C++ library provides a convenient way of writing such images into HDF5 files along with generic arrays for storing less well defined data structures, e.g. coil sensitivity maps or other calibration data. . This package provides the XML schema. Package: libeigen3-doc Source: eigen3 Version: 3.0.1-1.1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10344 Depends: neurodebian-popularity-contest, ttf-freefont, libjs-jquery Suggests: libeigen3-dev Homepage: http://eigen.tuxfamily.org Priority: extra Section: doc Filename: pool/main/e/eigen3/libeigen3-doc_3.0.1-1.1~nd+1_all.deb Size: 2377404 SHA256: c6e5364649cf5f6b1db3f3ae2ba998964c4fe8f2eb419b325c156d12f699ebba SHA1: 43b49a1ec0f5cb0655454679d1d1655729ff6721 MD5sum: bd7d512332aa9a1e81f26066dcd42be1 Description: eigen3 API docmentation Eigen 3 is a lightweight C++ template library for vector and matrix math, a.k.a. linear algebra. . This package provides the complete eigen3 API documentation in HTML format. Package: libfreenect-doc Source: libfreenect Version: 1:0.5.2+git6-g5455843+dfsg-1~nd+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~nd+1_all.deb Size: 90086 SHA256: 68f42b33ec077a3faf58c19fe5cf2c27447d8fee3138c77f150b768b7e9f82dc SHA1: 528662d27cbab6b251d1a9ed0ef62a9e4ff297d9 MD5sum: e6f8806207d425ef8660693e40b6c021 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: libismrmrd-doc Source: ismrmrd Version: 1.3.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1987 Depends: neurodebian-popularity-contest Homepage: http://ismrmrd.github.io/ Priority: optional Section: doc Filename: pool/main/i/ismrmrd/libismrmrd-doc_1.3.2-1~nd+1_all.deb Size: 158244 SHA256: 731a984503486f267ec68521dd5d3465dce1ba96a6808b77fc7e2345b729de6c SHA1: e3aa32efb2aaec1cdf7169e6a9afcc00afe36e95 MD5sum: 7a48add23e72992c4367c723dd4642f9 Description: ISMRM Raw Data format (ISMRMRD) - documentation The ISMRMRD format combines a mix of flexible data structures (XML header) and fixed structures (equivalent to C-structs) to represent MRI data. . In addition, the ISMRMRD format also specifies an image header for storing reconstructed images and the accompanying C++ library provides a convenient way of writing such images into HDF5 files along with generic arrays for storing less well defined data structures, e.g. coil sensitivity maps or other calibration data. . This package provides the documentation. Package: libmia-2.0-doc Source: mia Version: 2.0.13-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14011 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~nd+1_all.deb Size: 837904 SHA256: 6efc28d4222b4fb91ba00745e720dcd6ae7588b802cf7b12051065e2f784bcd0 SHA1: e36b5e877dfe57819cadb4ca52228fa321a74390 MD5sum: de045c0e2212f343fde74be4c895c334 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 233 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~nd+1_all.deb Size: 21290 SHA256: b0dd34278ad9a52597e66191c62a74baff467c13c2c870179302634477b9c8fa SHA1: 838394dd07ce9586d20f35968c02f61bac4c766f MD5sum: 7eefb55e9ae26db08d0750f3da3ec2a7 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~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1692 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~nd+1_all.deb Size: 140120 SHA256: 686d57e91867e23c55f747d863502416f84742afb987fd4ebb04c9c92d37d998 SHA1: 082412016af6c5d134f591ec409101525ab5916d MD5sum: 2e3ad2d99bfd2cebade7b1bcf6aaff07 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48075 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~nd+1_all.deb Size: 2681636 SHA256: d042340fe7dcca76e8158aa438e6b3e15ff18b784521652317a66f70e98c397e SHA1: 3b9b8845f6836b1497663e58556a9c2ed9497b4c MD5sum: d3f9e8e06482d3b6b8bca3c09e3737cd 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: libsvm-java Source: libsvm Version: 3.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest, libsvm3-java Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: java Filename: pool/main/libs/libsvm/libsvm-java_3.0-1~nd+1_all.deb Size: 13482 SHA256: 1492b2687984b9a78136924b2b39e6e61aa7cce3db838336fee65922035b3ee0 SHA1: 13a26757ee98ac492d11734e3a474b294266c9f2 MD5sum: 98d98d2c95a2b57a1d0f0fd2ccbf5e27 Description: Java API to support vector machine library The functionality of the libsvm are offered in a single jar file. It includes one-class, two-class, multiclass, regression-mode, and probablistic output functionality. . This package solely provides a symbolic link from svm.jar svm3.jar. Package: libsvm3-java Source: libsvm Version: 3.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 104 Depends: neurodebian-popularity-contest, gij | java-gcj-compat | java1-runtime | java2-runtime Suggests: java-virtual-machine Conflicts: libsvm2-java (<= 2.91-2) Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: java Filename: pool/main/libs/libsvm/libsvm3-java_3.0-1~nd+1_all.deb Size: 60478 SHA256: 801debe37d8eb71cf96cc8acb1f192c1ff8b0a237ff08c17575a3294f5f65b68 SHA1: e237f6806ac39e2fccaca7e875b4a02449cb425a MD5sum: 5f50baac283e77ea67d18c68132f91b8 Description: Java API to support vector machine library The functionality of the libsvm are offered in a single jar file. It includes one-class, two-class, multiclass, regression-mode, and probablistic output functionality. Package: libvia-doc Source: via Version: 2.0.4-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 903 Depends: neurodebian-popularity-contest Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/v/via/libvia-doc_2.0.4-2~nd+1_all.deb Size: 118466 SHA256: b016bb696ef74d79bfb463aa989ab3d6b853f6dde7786e5fca7158c2cdc241bc SHA1: a75f6603432044f4e7a27b88453e558b9d97dd8d MD5sum: d816c6e69ff20beb455556cf9a20fcd0 Description: VIA library API documentation VIA is a volumetric image analysis suite. The included libraries provide about 70 image analysis functions. . This package provides the library API reference documentation. Package: matlab-support-dev Source: matlab-support Version: 0.0.21~nd+1 Architecture: all Maintainer: NeuroDebian Team 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_0.0.21~nd+1_all.deb Size: 7512 SHA256: 42b003748752925c2ffbeacc36fadfbaa2db1073c5e6db92ccbca5f4e359be07 SHA1: 16e884448f647ec24616bfb561e976f3a01830bf MD5sum: eb6d5800a7effbe0135889275e6d7d53 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1145 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~nd+1_all.deb Size: 78990 SHA256: 51558554d8205e9835e53d3e5a38f2f9da5222d20da8798e81788580b9265918 SHA1: 356c818a63c03035944b985e1419040aaa7a2278 MD5sum: c9a4f6051dec225ca8244a32e5feb682 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~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1710 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20140804.1~dfsg.1-1~nd+1_all.deb Size: 1661584 SHA256: a8efc9af5e1b70f033b6614058ec4922357638a5a358d3150ef484c10bbf345c SHA1: 647ab896ea9b9c2d8c1a02661725b8aaea9c1390 MD5sum: 73a4aef17ff9025d2365cb04480512ec 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~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1022 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~nd+1_all.deb Size: 580068 SHA256: a8ca01a92a6803851700c880726c6e0eeaed4ada4184718c44c07e41c600816e SHA1: 3ca8963aadc1f5609e9c52a9287c012fa659b3e6 MD5sum: 0d1df88b9a4fd32bf31c1c184c0cbc17 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: mridefacer Version: 0.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 647 Depends: neurodebian-popularity-contest, num-utils, fsl-5.0-core | fsl-core Homepage: https://github.com/hanke/mridefacer Priority: optional Section: science Filename: pool/main/m/mridefacer/mridefacer_0.2-1~nd+1_all.deb Size: 637206 SHA256: 30a53d376792e4c3a86b72073f5d992bf7785e4adeac0d734b16a3f3445d571e SHA1: 937cfe6d76f82623163780047690a52977e5b7e2 MD5sum: 24f9bd4c5329daf7c6c167998750a077 Description: de-identification of MRI data This tool creates a de-face mask for volumetric images by aligning a template mask to the input. Such a mask can be used to remove image data from the vicinity of the facial surface, the auricles, and teeth in order to prevent a possible identification of a person based on these features. mrideface can process individual or series of images. In the latter case, the computed transformation between template image and input image will be updated incrementally for the next image in the series. This feature is most suitable for processing images that have been recorded in temporal succession. Package: mrtrix-doc Source: mrtrix Version: 0.2.12-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3528 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~nd+1_all.deb Size: 3199776 SHA256: 4abb82cb1f240754d3bead18288426bdd8862b186cbca4ace358b42c97a1dfe7 SHA1: b0e995bc80a61c78c328a8a3d226cb35155ebd31 MD5sum: 6e421c1e6eb30afffc6a98884e70112e 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26 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~nd+1_all.deb Size: 16790 SHA256: 97280acc29ad04928a2171d14f076325e227c153af82af6002a68cc2f04cdc97 SHA1: d5262a5b9d1465c562d40e782a920ff0b19c6c0e MD5sum: 22f92cf44ff49488ceeed27c77ee0d4e 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: 0.37.5~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 91 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_0.37.5~nd+1_all.deb Size: 34540 SHA256: 9cdc50fbfd82c854d6405d91450f466fbc870815d1dfee8ff883cdb67504cfa2 SHA1: b7d1177788da779db6d063c3cbe5e09c985fcb2b MD5sum: 803c44fb6ec128023307cb68bed1b81a 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: 0.37.5~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23 Depends: gnupg2 | gnupg, dirmngr 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_0.37.5~nd+1_all.deb Size: 10336 SHA256: 4a91ea7e4b52196a2d3f8dc5c0364eef3d212a1c73da2e7e3edbd1e3a7cec4f3 SHA1: bd51fee3598ad553499e3aa6b792b738c22a2486 MD5sum: 7a1b106f528e632a441d6bc1b68d48a5 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: 0.37.5~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 189 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, adwaita-icon-theme | gnome-icon-theme, neurodebian-popularity-contest Recommends: reportbug-ng Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.37.5~nd+1_all.deb Size: 116328 SHA256: 668ad8dfceacac066cc00cafb1bcc71ea387a32894f90d43737502ae2dee95ad SHA1: d7e25d3efc1abf2ed20404727e215e539eb12178 MD5sum: 374bbd87451999747ed09fc2e71b0507 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: 0.37.5~nd+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_0.37.5~nd+1_all.deb Size: 32764 SHA256: c21052322b7bcdcc789d14c5b6f2769a1dd12b7058ce5e583fd4e9ed86498681 SHA1: f890ebe869538064ae046c0d3b7591e00c9cbd31 MD5sum: 130b23ca9a2d228c73abcb13991e0636 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-guest-additions Source: neurodebian Version: 0.32~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm | lightdm, zenity Recommends: chromium-browser, update-manager-gnome, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.32~nd+1_all.deb Size: 14030 SHA256: a7b1c22382ef38bcb02197d3f88ef51f8e1f2f5152ad7c18e6d040b6803d3c9f SHA1: 0b8bca805ed756dadec92b4e76d3701ed9b400c6 MD5sum: 6bd47ac8ce904b7e445aca38e8d806a8 Description: NeuroDebian guest additions (DO NOT INSTALL OUTSIDE VIRTUALBOX) This package configures a Debian installation as a guest operating system in a VirtualBox-based virtual machine for NeuroDebian. . DO NOT install this package unless you know what you are doing! For example, installation of this package relaxes several security mechanisms. Package: neurodebian-keyring Source: neurodebian Version: 0.32~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.32~nd+1_all.deb Size: 7418 SHA256: 6a6686a0d7186992aeeb0520359ca34c15210792010af928b382b5f4caa888a7 SHA1: 89cec44d84d971babd0be50534f2560073246957 MD5sum: 778a5b4f7b7cd1933cfb8808beedb823 Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.37.5~nd+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_0.37.5~nd+1_all.deb Size: 12366 SHA256: 41ad7493a475c34c4bc292607d74a8fcc646e8b034efdc360fee7636429f16e3 SHA1: 4c9d5b969448484ec651951d2019b4a1780c8d86 MD5sum: f25cf019eb2f9781fee16fc3fab7a1e8 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: 0.4.11-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 647 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.11-1~nd+1_all.deb Size: 616676 SHA256: b4e0d532aed4c6f71600c96de5ce5c284f2dcff6142c17c104b285fb5c608ce0 SHA1: 2dd827d7ffea474a3b83920ce6cebe36e28d55c9 MD5sum: 8df5623f6d0fe2926cf1c6c32ebe7a80 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: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 36 Depends: python-nibabel (>= 1.0.0), python-nipy (>= 0.1.2+20110114), python-dipy (>= 0.5.0), python-nipype (>= 0.3.3), python-nitime (>= 0.2) Suggests: python-mvpa, psychopy Homepage: http://www.nipy.org Priority: extra Section: python Filename: pool/main/n/nipy-suite/nipy-suite_0.1.0-2_all.deb Size: 3898 SHA256: 882c8580ebd2d458a92f8d851d1ec9291fecf05f6ed98a8b754eb831c95368c8 SHA1: 6501d1d201160520f5aad29d0f9007c17b7d9778 MD5sum: eb090e568264d2f439892bcb98485b8c Description: Neuroimaging in Python NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. nipy-suite is a metapackage depending on the projects developed under NiPy project umbrella, such as - nibabel: bindings to various neuroimaging data formats - nipy: analysis of structural and functional neuroimaging data - nitime: timeseries analysis - dipy: analysis of MR diffusion imaging data - nipype: pipelines and worfklows Package: nipy-suite-doc Source: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 32 Depends: python-nibabel-doc (>= 1.0.0), python-nipy-doc (>= 0.1.2+20110114), python-dipy-doc (>= 0.5.0), python-nipype-doc (>= 0.3.3), python-nitime-doc (>= 0.2) Suggests: python-mvpa-doc Homepage: http://www.nipy.org Priority: extra Section: doc Filename: pool/main/n/nipy-suite/nipy-suite-doc_0.1.0-2_all.deb Size: 2250 SHA256: 54985bd9d6eaa352608b357f2deeb066bd2ac12d3c2e463082f5d9178701bbad SHA1: 5d2f5e94ff6b7ff737fe966f4a2e5ff67df93cca MD5sum: 37d2f8b6b6d203edf208afb0cdb56fa3 Description: Neuroimaging in Python -- documentation NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. . nipy-suite-doc is a metapackage depending on the documentation packages for NiPy projects. Package: nuitka Version: 0.5.25+ds-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3209 Depends: neurodebian-popularity-contest, gcc (>= 5.0) | g++ (>= 4.4) | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python:any (>= 2.7.5-5~) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6), strace, chrpath Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.5.25+ds-1~nd+1_all.deb Size: 655354 SHA256: 9f9b41d95d84fbc99d40ed5d88e19df81eec207f8586f776ac67b969d2825525 SHA1: fb744e627643b90c7865352a68d09bfb395c274f MD5sum: ff2457a12c78f1373548a2a9f898e691 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: openelectrophy Version: 0.0.svn143-1~sid.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 92 Depends: python, python-pyssdh (= 0.0.svn143-1~sid.nd1) Homepage: http://neuralensemble.org/trac/OpenElectrophy Priority: extra Section: science Filename: pool/main/o/openelectrophy/openelectrophy_0.0.svn143-1~sid.nd1_all.deb Size: 34360 SHA256: 15e2e7aefc8b1af85c120f648897950db56fb71fe5999c5a3ca51b1c70bc0fb4 SHA1: 84e8c88b4d56f44c987808ba5c54b1799a0403ee MD5sum: 0eaf72ffeedd568782315315e95b4dfe Description: data analysis framework for intra- and extra-cellular recordings This software aims to simplify data and analysis sharing for intra- and extra-cellular recordings. It supports time frequency plots, spike detection, spike rate calculation, and analysis of phase locked signals. . Data handling and storage utilizes a MySQL database, allowing to handle large amounts of data easily and efficiently. Therefore, a MySQL server running locally or on a remote machine is required. . This package provides the OpenElectrophy GUI. Package: opensesame Version: 0.27.4-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26639 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~), python-qscintilla2, gnome-icon-theme Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1), expyriment (>= 0.5.2), ipython-qtconsole (>= 0.12), python-markdown Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27.4-2~nd+1_all.deb Size: 24612138 SHA256: b223ca6a17d69da2863cd4ddba9b499d0a3b8b554862fe6623ec17c63eab74a4 SHA1: bf7885235942fade9f1256a1968979918212a7bf MD5sum: daca1a779d091bd428ceb9f90818b04d Description: graphical experiment builder for the social sciences This graphical environment provides an easy to use, point-and-click interface for creating psychological experiments. In addition to a powerful sketchpad for creating visual stimuli, OpenSesame features a sampler and synthesizer for sound playback. For more complex tasks, OpenSesame supports Python scripting using the built-in editor with syntax highlighting. Package: openstack-pkg-tools Version: 52~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 191 Depends: neurodebian-popularity-contest, autopkgtest, libxml-xpath-perl, madison-lite, pristine-tar Priority: extra Section: devel Filename: pool/main/o/openstack-pkg-tools/openstack-pkg-tools_52~nd+1_all.deb Size: 52240 SHA256: ba46048f89647d249f8ddfb0e4ce6f3adcb9c6979658840004a8722b471638af SHA1: c636a2b5d00d5966d376afd02bd6f1faace38cc4 MD5sum: ecacd02d3a6cf711d4620b0a5f111440 Description: Tools and scripts for building Openstack packages in Debian This package contains some useful shell scripts and helpers for building the Openstack packages in Debian, including: . * shared code for maintainer scripts (.config, .postinst, ...). * init script templates to automatically generate init scripts for sysv-rc, systemd and upstart. * tools to build backports using sbuild and/or Jenkins based on gbp workflow. * utility to maintain git packaging (to be included in a debian/rules). . Even if this package is maintained in order to build OpenStack packages, it is of a general purpose, and it can be used for building any package. Package: openvibe-data Source: openvibe Version: 0.14.3+dfsg2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9328 Depends: neurodebian-popularity-contest Homepage: http://openvibe.inria.fr Priority: extra Section: science Filename: pool/main/o/openvibe/openvibe-data_0.14.3+dfsg2-1~nd+1_all.deb Size: 2024448 SHA256: 577686111aba7c2eafbe4c25ab1052d26958bd64471319884a324b9825c07d16 SHA1: 2ab042fb217b2ffec6f55b478cf1e8405786832a MD5sum: 8d7802d259052ef28a0dd2febd7fa4e9 Description: Software platform for BCI (Data files) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains the data files. Package: packaging-tutorial Version: 0.8~nd0 Architecture: all Maintainer: Lucas Nussbaum Installed-Size: 1550 Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.8~nd0_all.deb Size: 1488332 SHA256: 491bc5917f698fee06888998e8a295a6caac2950148bb160b457aff72437eadb SHA1: c5d75d04b01f681ead660ce8d8fe068ab887fba0 MD5sum: 8fbf7c362fd4091a78c50404eb694402 Description: introduction to Debian packaging This tutorial is an introduction to Debian packaging. It teaches prospective developers how to modify existing packages, how to create their own packages, and how to interact with the Debian community. In addition to the main tutorial, it includes three practical sessions on modifying the 'grep' package, and packaging the 'gnujump' game and a Java library. Package: prov-tools Source: python-prov Version: 1.4.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 Depends: neurodebian-popularity-contest, python3:any (>= 3.3~), python3-prov (= 1.4.0-1~nd+1) Homepage: https://github.com/trungdong/prov Priority: optional Section: python Filename: pool/main/p/python-prov/prov-tools_1.4.0-1~nd+1_all.deb Size: 6464 SHA256: 6ffeb72e63c20f41fc6cb210bf9894c26114ab31bf3492f72a094b0d0385dc1b SHA1: b9f38a334042f3c2bce65f745929341ea19e3d2f MD5sum: 531be97829c3ba53d6be585b9963f7dd Description: tools for prov A library for W3C Provenance Data Model supporting PROV-JSON and PROV- XML import/export. . Features: - An implementation of the W3C PROV Data Model in Python. - In-memory classes for PROV assertions, which can then be output as PROV-N. - Serialization and deserializtion support: PROV-JSON and PROV-XML. - Exporting PROV documents into various graphical formats (e.g. PDF, PNG, SVG). . This package provides the command-line tools for the prov library. Package: psychopy Version: 1.83.04.dfsg-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15088 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-opencv, python-imaging, python-serial, python-pyo, python-psutil, python-gevent, python-msgpack, python-yaml, python-xlib, python-pandas, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid, libavbin0 Conflicts: libavbin0 (= 7-4+b1) Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.83.04.dfsg-2~nd+1_all.deb Size: 6133504 SHA256: 5a51428a188f230695b67857a223d408641d3a5a349bf3f0737dfdabe13e8d8b SHA1: 89e33dba2565b3a998863ab68559d263a0b99491 MD5sum: 9aa8755ac3045cc18cfeb071ba269bcd 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: 3.0.14.20170103+git6-g605ff5c.dfsg1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 253898 Depends: neurodebian-popularity-contest Recommends: alsa-utils Suggests: gnuplot Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.14.20170103+git6-g605ff5c.dfsg1-1~nd+1_all.deb Size: 24153226 SHA256: bb28c91d9e75bfac98426b6d5cb626435437b3567fe47f76d71b4a58cc73c7cf SHA1: 65f346510259f8fa74bf516416a8c3bc1f21ad03 MD5sum: 7cba0bf5ab43e1a9ae106e788cf28a9d 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: pypy-hypothesis Source: python-hypothesis Version: 3.6.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 411 Depends: neurodebian-popularity-contest, pypy-enum34, pypy Suggests: python-hypothesis-doc Homepage: https://github.com/DRMacIver/hypothesis Priority: optional Section: python Filename: pool/main/p/python-hypothesis/pypy-hypothesis_3.6.0-1~nd+1_all.deb Size: 95378 SHA256: e01c780069e5b2e3514b2da1b13ff19bcdc4734e83086af84a66ae7d4840d3b3 SHA1: bbddd3788c3c13c8ce80bfa98a2b3af3a8a02595 MD5sum: 2237ede9a3edd4b7a4db4608a689b0d6 Description: advanced Quickcheck style testing library for PyPy Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It's based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow. . Hypothesis is both extremely practical and also advances the state of the art of unit testing by some way. It's easy to use, stable, and extremely powerful. If you're not using Hypothesis to test your project then you're missing out. . This package contains the PyPy module. Package: pypy-pkg-resources Source: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 412 Depends: neurodebian-popularity-contest, pypy Suggests: pypy-setuptools Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: python Filename: pool/main/p/python-setuptools/pypy-pkg-resources_20.10.1-1.1~bpo8+1~nd+1_all.deb Size: 111974 SHA256: b78188cea2681341ce323fbd36e9d12817a54e509566b931106d6954205b46ff SHA1: af3ee3c64c7baef47af10db015369be678bbd30d MD5sum: 8e3e882391ac969e4cc4e2fe5f3afd08 Description: Package Discovery and Resource Access using pkg_resources The pkg_resources module provides an API for Python libraries to access their resource files, and for extensible applications and frameworks to automatically discover plugins. It also provides runtime support for using C extensions that are inside zipfile-format eggs, support for merging packages that have separately-distributed modules or subpackages, and APIs for managing Python's current "working set" of active packages. Package: pypy-py Source: python-py Version: 1.4.31-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 320 Depends: neurodebian-popularity-contest, pypy, pypy-pkg-resources Suggests: subversion, pypy-pytest Homepage: https://bitbucket.org/pytest-dev/py Priority: optional Section: python Filename: pool/main/p/python-py/pypy-py_1.4.31-2~nd+1_all.deb Size: 82242 SHA256: fb104d8144ba49ce0749d43b82315c43a3ba8d91cc40f7cd2b3f6390ab87abfb SHA1: 83c9a768d5b8e4c60277d3613096311974a8bb88 MD5sum: a94959cf70b61f84d403ce89e57057c3 Description: Advanced Python development support library (PyPy) 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 PyPy 2 modules. Package: pypy-pytest Source: pytest Version: 3.0.4-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 644 Depends: neurodebian-popularity-contest, pypy-pkg-resources, pypy-py (>= 1.4.29), pypy Homepage: http://pytest.org/ Priority: optional Section: python Filename: pool/main/p/pytest/pypy-pytest_3.0.4-1~nd+1_all.deb Size: 180974 SHA256: ffa719a5cfa6c4a0dc367f85adc059771ac0594dc9107b6767d7a91915480578 SHA1: 371068c9e2a074171a040842ba6c39e00b3f8deb MD5sum: eba9e00726fb95fbbbe9f769155368e9 Description: Simple, powerful testing in PyPy 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 PyPy module and the py.test-pypy script. Package: pypy-setuptools Source: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 431 Depends: neurodebian-popularity-contest, pypy-pkg-resources (= 20.10.1-1.1~bpo8+1~nd+1), pypy Suggests: python-setuptools-doc Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: python Filename: pool/main/p/python-setuptools/pypy-setuptools_20.10.1-1.1~bpo8+1~nd+1_all.deb Size: 121856 SHA256: 35889dc13125b06712f81173a5ac6d0fa62544a0d9ad787c35746c3808762ab6 SHA1: 73a775f25eae45257fcefffed988ef04fecf07db MD5sum: 0efb61e2b9546a7383e2bf03545af2dc Description: PyPy Distutils Enhancements Extensions to the python-distutils for large or complex distributions. Package: python-argcomplete Version: 1.0.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 113 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Priority: optional Section: python Filename: pool/main/p/python-argcomplete/python-argcomplete_1.0.0-1~nd+1_all.deb Size: 25852 SHA256: e961c7c94be985b71d0b9c6496f2342ca95aed4bec75d0a92d8c28837deb3390 SHA1: f14e66ab17ffd8ee30482d8a7d43d51871e8845c MD5sum: ac0f6a6a42aabd4109473218a09e9e21 Description: bash tab completion for argparse Argcomplete provides easy, extensible command line tab completion of arguments for your Python script. . It makes two assumptions: . * You're using bash as your shell * You're using argparse to manage your command line arguments/options . Argcomplete is particularly useful if your program has lots of options or subparsers, and if your program can dynamically suggest completions for your argument/option values (for example, if the user is browsing resources over the network). . This package provides the module for Python 2.x. Package: python-boto3 Version: 1.2.2-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 725 Depends: neurodebian-popularity-contest, python-botocore, python-concurrent.futures, python-jmespath, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-requests, python-six Homepage: https://github.com/boto/boto3 Priority: optional Section: python Filename: pool/main/p/python-boto3/python-boto3_1.2.2-2~nd+1_all.deb Size: 63240 SHA256: 09591687fa5a2f6e26bd6c1c8d469e4fb159d63b28f1fc9e75d30af54183f6b7 SHA1: 373f10155429cfc95b9b13a555642e1a4d7fa274 MD5sum: 8c4def388d440191cc6a54f5203c2609 Description: Python interface to Amazon's Web Services - Python 2.x Boto is the Amazon Web Services interface for Python. It allows developers to write software that makes use of Amazon services like S3 and EC2. Boto provides an easy to use, object-oriented API as well as low-level direct service access. Package: python-brian Source: brian Version: 1.4.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2456 Depends: neurodebian-popularity-contest, python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0), python:any (<< 2.8), python:any (>= 2.7.5-5~), python-brian-lib (>= 1.4.3-1~nd+1) 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_1.4.3-1~nd+1_all.deb Size: 402054 SHA256: 4d8b568ca6ce53db9403438830dc7a17577f3e0ad5c79979ec2a2366959f0c4f SHA1: 5109b5627ea5c22fbed8c9259ede95dc14b63a97 MD5sum: f8ddc31d598c27195a3055a212932cad 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: 1.4.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7316 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_1.4.3-1~nd+1_all.deb Size: 1976078 SHA256: 9d045a63f64430c967de2d6d0f706773a1bd2d110136445345305435f9cc4cf2 SHA1: fdb9dcc1f2bdcd0ba7fee379170094eeaeb1d237 MD5sum: 75abb4ae9227a4a793743e2b5e09da95 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 768 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: python2.6-cfflib, python2.7-cfflib Homepage: http://cmtk.org/cfflib Priority: extra Section: python Filename: pool/main/c/cfflib/python-cfflib_2.0.5-1~nd+1_all.deb Size: 217692 SHA256: 4da2bdfd6e65beb307156093efe58c8242305e225741677f043c95133dd02928 SHA1: f5afff0937ac80443da91ebd51b53ce9c454f296 MD5sum: e6da3383a7a7a89da2d0d5b33f4910b5 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~nd+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~nd+1_all.deb Size: 80320 SHA256: 697b8521e8930432817b1503eb837c18fcc58f31d87fc3f958bfb110ca13378a SHA1: d4971cc97575a0c8c08f44c2cfb1de51bb281917 MD5sum: 634926f49184a46c2a5d8b35eec480f4 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-contextlib2 Source: contextlib2 Version: 0.4.0-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 32 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: http://contextlib2.readthedocs.org/ Priority: optional Section: python Filename: pool/main/c/contextlib2/python-contextlib2_0.4.0-3~nd+1_all.deb Size: 8654 SHA256: d3a0ccdd81271c47039bb787b192809749186f00bbadd53643e2299ac3dbf0cb SHA1: 8315929753d4260508bca756c44798061e425b97 MD5sum: 6cbc70f15c714d3bcdc17fbca8d88318 Description: Backport and enhancements for the contextlib module - Python 2.7 contextlib2 is a backport of the standard library's contextlib module to earlier Python versions. . It also serves as a real world proving ground for possible future enhancements to the standard library version. . This package contains the Python 2.7 module. Package: python-datalad Source: datalad Version: 0.5.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3325 Depends: neurodebian-popularity-contest, git-annex (>= 6.20160808~) | git-annex-standalone (>= 6.20160808~), patool, python-appdirs, python-git (>= 2.1~), python-github, python-humanize, python-iso8601, python-keyrings.alt | python-keyring (<= 8), python-secretstorage | python-keyring (<< 9.2), python-keyring, python-mock, python-msgpack, python-pyld, python-requests, python-tqdm, python-simplejson, python-six (>= 1.8.0), python-boto, python-jsmin, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-html5lib, python-httpretty, python-nose, python-numpy, python-requests-ftp, python-scrapy, python-vcr, python-yaml Suggests: python-bs4 Provides: python2.7-datalad Homepage: http://datalad.org Priority: optional Section: python Filename: pool/main/d/datalad/python-datalad_0.5.1-1~nd+1_all.deb Size: 650646 SHA256: 3ecae48ab4714e9bb446ce98d401d809e41fec1b930b793e022fe9c42ff7a02d SHA1: 6d0b946a63d29c83483a55915ea46abf3042fe56 MD5sum: 51bf3afd78fd57cfb85d63bed7469988 Description: data files crawler and data distribution (Python 2) DataLad is a data distribution providing access to a wide range of data resources already available online (initially aiming at neuroscience domain). Using git-annex as its backend for data logistics it provides following facilities . - crawling of web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc - command line interface for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get), as well as search within aggregated meta-data . This package installs the module for Python 2, and Recommends install all dependencies necessary for crawling, publishing, and testing. If you need base functionality, install without Recommends. Package: python-dcmstack Source: dcmstack Version: 0.6.2+git33-gb43919a.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 509 Depends: neurodebian-popularity-contest, python-dicom (>= 0.9.7~), python-nibabel (>= 2.0~), python-numpy, python, python:any (<< 2.8), python:any (>= 2.7.5-5~), libjs-sphinxdoc (>= 1.0) Provides: python2.7-dcmstack Homepage: https://github.com/moloney/dcmstack Priority: optional Section: python Filename: pool/main/d/dcmstack/python-dcmstack_0.6.2+git33-gb43919a.1-1~nd+1_all.deb Size: 77460 SHA256: 1bde8fbdc4666fd59d20bd49513099826a1624dd961f4aee5c4fba6dcf7b223f SHA1: 8b4862f49a899b0e500b9a748eadd91f2dbf4e0a MD5sum: 6bfb2dd5c3a8f1394ec264c8309b9adc Description: DICOM to Nifti conversion DICOM to Nifti conversion with the added ability to extract and summarize meta data from the source DICOMs. The meta data can be injected into a Nifti header extension or written out as a JSON formatted text file. . This package provides the Python package, command line tools (dcmstack, and nitool), as well as the documentation in HTML format. Package: python-dicom Source: pydicom Version: 0.9.9-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1522 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~nd+1_all.deb Size: 357756 SHA256: 438973cbca9e0829b3f9a70b5cb6c86f5ed945e4140d798288ba8576655d447a SHA1: 018cbb7f235a0fe220323e858cd241db2b40ea80 MD5sum: 734af5fe26acb1f95dfbfacfb16d6d46 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: 0.10.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5799 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy (>= 1:1.7.1~), python-scipy, python-dipy-lib (>= 0.10.1-1~nd+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_0.10.1-1~nd+1_all.deb Size: 2435144 SHA256: 2e8075e916ee5d45fe0e5488a5788246415d6856dac3a81b5b091a7c962998ee SHA1: cb28bf853dc8023afd1053fe08de5681532359d7 MD5sum: 68618b3cad69fbde37ce5a51c414dbca 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: 0.10.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14485 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_0.10.1-1~nd+1_all.deb Size: 11463990 SHA256: fd636c451cff35647e431fa18dbdfc24f5bd6122a251dc273c68f76c9b7db671 SHA1: 3442bd8cd769d598df4f309c2af19970298cb441 MD5sum: 776a7425812024681f21963fe9b7861c 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-docker Version: 1.7.2-1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 150 Depends: neurodebian-popularity-contest, python-requests, python-six (>= 1.4.0), python-websocket, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/dotcloud/docker-py/ Priority: optional Section: python Filename: pool/main/p/python-docker/python-docker_1.7.2-1~bpo8+1~nd+1_all.deb Size: 27060 SHA256: 5cd7d553edec43fd4ece03e57ba315cd8cd992c035022158aee56ce171f8f03b SHA1: de68d545918b913f6baa5fa7453567314afa226e MD5sum: e770ce1dc614a1606b6ea23b8b88ab30 Description: Python wrapper to access docker.io's control socket This package contains oodles of routines that aid in controling docker.io over it's socket control, the same way the docker.io client controls the daemon. . This package provides Python 2 module bindings only. Package: python-dockerpty Source: dockerpty Version: 0.4.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-docker (>= 0.7.1) Homepage: https://github.com/d11wtq/dockerpty Priority: optional Section: python Filename: pool/main/d/dockerpty/python-dockerpty_0.4.1-1~nd+1_all.deb Size: 10882 SHA256: b5d6cc788c5184e41a059687050b2d8bf2e898abaff971f0ae22b8f538e652b9 SHA1: 6c3ee553947a51ba3b54fd1e561bc7f9062b202f MD5sum: 25b8f8456e493fef48261ed6b9514060 Description: Pseudo-tty handler for docker Python client (Python 2.x) Provides the functionality needed to operate the pseudo-tty (PTY) allocated to a docker container, using the Python client. . This package provides Python 2.x version of dockerpty. Package: python-duecredit Source: duecredit Version: 0.5.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 237 Depends: neurodebian-popularity-contest, python-citeproc, python-requests, python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/duecredit/duecredit Priority: optional Section: python Filename: pool/main/d/duecredit/python-duecredit_0.5.0-1~nd+1_all.deb Size: 52502 SHA256: 5a397c52164cecbb671000e71ccd7cb8cfce9553c1f0859e834e49bcd7994b2e SHA1: 79df197750262cbb6c19b9bfd5907556f6c8bdeb MD5sum: b5a778b5ae0b41215abbd3e0260c0bf2 Description: Publications (and donations) tracer - Python 2.X duecredit is being conceived to address the problem of inadequate citation of scientific software and methods, and limited visibility of donation requests for open-source software. . It provides a simple framework (at the moment for Python only) to embed publication or other references in the original code so they are automatically collected and reported to the user at the necessary level of reference detail, i.e. only references for actually used functionality will be presented back if software provides multiple citeable implementations. . To get a sense of what duecredit is about, simply run or your analysis script with `-m duecredit`, e.g. . python -m duecredit examples/example_scipy.py Python-Egg-Name: duecredit Package: python-expyriment Version: 0.7.0+git34-g55a4e7e-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2419 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~nd+1_all.deb Size: 698616 SHA256: 0b51538d13146d68e561a4e41f6434c5d2da0f595efe67c50f01a4e534ef8aaf SHA1: dac42ac93d31516c2d67b397da1446658d3238ab MD5sum: baf8ddfb4a408c0e43865522241da0e7 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-fsl Source: fslpy Version: 0.11.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 467 Depends: neurodebian-popularity-contest, python-lxml, python-nibabel, python-indexed-gzip, python-matplotlib, python-numpy, python-six (>= 1.10.0), python-six (<< 2.0), python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-wxgtk3.0 Conflicts: fsl-melview (<= 1.0.1+git9-ge661e05~dfsg.1-1) Provides: python2.7-fsl Priority: optional Section: python Filename: pool/main/f/fslpy/python-fsl_0.11.0-1~nd+1_all.deb Size: 96724 SHA256: 3a516fbc29836058f40bff5cfd4db28737116492a52767247bf1b79fefe25714 SHA1: f512ebb301e1819e31985e09f62f0784763ee158 MD5sum: a22ddac58fca07d17efc1bc1c830a4a9 Description: FSL Python library Support library for FSL. . This package provides the Python 2 module. Package: python-funcsigs Version: 0.4-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 64 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-funcsigs-doc Homepage: http://funcsigs.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-funcsigs/python-funcsigs_0.4-2~nd+1_all.deb Size: 12992 SHA256: ffa5a791589955ce10c1f51009145f0258412f756260a8e041e10c5768a731d5 SHA1: 576edf525d446f2e0b1614396380b718ba1189e4 MD5sum: b37731fa0021f337711055db08ea669d Description: function signatures from PEP362 - Python 2.7 funcsigs is a backport of the PEP 362 function signature features from Python 3.3's inspect module. The backport is compatible with Python 2.6, 2.7 as well as 3.2 and up. . This package contains the Python 2.7 module. Package: python-funcsigs-doc Source: python-funcsigs Version: 0.4-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 122 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: http://funcsigs.readthedocs.org Priority: optional Section: doc Filename: pool/main/p/python-funcsigs/python-funcsigs-doc_0.4-2~nd+1_all.deb Size: 23708 SHA256: 4b7ab90b7981cb7d54ec60ce531d3118fc451153e75c7ab913505376437efc9d SHA1: 58ca05578ee852376c61fba2ef8b49e3b87c1551 MD5sum: 29990bf500d495d67ac4a88c369b69e6 Description: function signatures from PEP362 - doc funcsigs is a backport of the PEP 362 function signature features from Python 3.3's inspect module. The backport is compatible with Python 2.6, 2.7 as well as 3.2 and up. . This package contains the documentation. Package: python-future Version: 0.15.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1712 Pre-Depends: dpkg (>= 1.15.6~) Depends: neurodebian-popularity-contest, python2.7:any, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-future-doc Homepage: https://python-future.org Priority: optional Section: python Filename: pool/main/p/python-future/python-future_0.15.2-1~nd+1_all.deb Size: 348338 SHA256: cfa260c22586caec87848fde7b08104a1a6d613245185f72d22d5f2c631f45af SHA1: a300c53c8048687b2ca7575f1958869c720209f1 MD5sum: 0f64bb428c1fd4d3066f1969a59fc594 Description: single-source support for Python 3 and 2 - Python 2.x Future is the missing compatibility layer between Python 2 and Python 3. It allows one to use a single, clean Python 3.x-compatible codebase to support both Python 2 and Python 3 with minimal overhead. . The imports have no effect on Python 3. On Python 2, they shadow the corresponding builtins, which normally have different semantics on Python 3 versus 2, to provide their Python 3 semantics. . This package contains the Python 2.x module. Package: python-future-doc Source: python-future Version: 0.15.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1889 Pre-Depends: dpkg (>= 1.15.6~) Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: https://python-future.org Priority: optional Section: doc Filename: pool/main/p/python-future/python-future-doc_0.15.2-1~nd+1_all.deb Size: 319714 SHA256: 849dbdad405ea50ea53f40b2b3767e4619b518eb5bba2d6d074d9a175e0082b6 SHA1: a7e9e6457db2dd27ea31b0dbb3d4867eb18c2872 MD5sum: 651fc20d4acb400cf43ee6d43c9e41d8 Description: Clean single-source support for Python 3 and 2 - doc Future is the missing compatibility layer between Python 2 and Python 3. It allows one to use a single, clean Python 3.x-compatible codebase to support both Python 2 and Python 3 with minimal overhead. . The imports have no effect on Python 3. On Python 2, they shadow the corresponding builtins, which normally have different semantics on Python 3 versus 2, to provide their Python 3 semantics. . This package contains the documentation. Package: python-git Version: 2.1.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1626 Depends: neurodebian-popularity-contest, git (>= 1:1.7) | git-core (>= 1:1.5.3.7), python-gitdb (>= 2), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-smmap, python-git-doc Homepage: https://github.com/gitpython-developers/GitPython Priority: optional Section: python Filename: pool/main/p/python-git/python-git_2.1.1-2~nd+1_all.deb Size: 298708 SHA256: 0762cc966e165779d865e650b937c2bbe96fcc818470c7d914fb4e1118aa0fb4 SHA1: 84e19dcbb8d5ceb3b3a3a730562444a620dbf2b9 MD5sum: 493a22219c8a074ee99f3b3f14ca28ad Description: Python library to interact with Git repositories - Python 2.7 python-git provides object model access to a Git repository, so Python can be used to manipulate it. Repository objects can be opened or created, which can then be traversed to find parent commit(s), trees, blobs, etc. . This package provides the Python 2.7 module. Python-Version: 2.7 Package: python-git-doc Source: python-git Version: 2.1.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 979 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: https://github.com/gitpython-developers/GitPython Priority: optional Section: doc Filename: pool/main/p/python-git/python-git-doc_2.1.1-2~nd+1_all.deb Size: 127512 SHA256: 94607b959281966ac0a61fd6bf247eebbb789e083178de112e57a743bb80c94c SHA1: 5b96a5db8e38b7240f0652fb035a342e6fc60595 MD5sum: 00618f9a836e12c414b8eec26cc18e81 Description: Python library to interact with Git repositories - docs python-git provides object model access to a Git repository, so Python can be used to manipulate it. Repository objects can be opened or created, which can then be traversed to find parent commit(s), trees, blobs, etc. . This package provides the documentation. Package: python-humanize Version: 0.5.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 77 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: http://github.com/jmoiron/humanize Priority: optional Section: python Filename: pool/main/p/python-humanize/python-humanize_0.5.1-2~nd+1_all.deb Size: 12948 SHA256: 4c4af0de554075bad41bfcb0e3cb8e192e87c5f97642ef0346d1f53df52a3b5f SHA1: f426f555e49355e10cb0cb480840235c56275883 MD5sum: 926ebef411d9a842e0385128133118d7 Description: Python Humanize library (Python 2) This library proposes various common humanization utilities, like turning a number into a fuzzy human readable duration ('3 minutes ago') or into a human readable size or throughput. . This is the Python 2 version of the package. Package: python-hypothesis Version: 3.6.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 411 Depends: neurodebian-popularity-contest, python-enum34, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-hypothesis-doc Homepage: https://github.com/DRMacIver/hypothesis Priority: optional Section: python Filename: pool/main/p/python-hypothesis/python-hypothesis_3.6.0-1~nd+1_all.deb Size: 95266 SHA256: 8b552f81ac1acafa3c610a571016f3e1c2a6da093d179eff1f52bbb66432c5f9 SHA1: e10b1d3b885ad05e7edd2d5a40489384dffefd61 MD5sum: bf4213814cd87f0a43d2f402aa04858b Description: advanced Quickcheck style testing library for Python 2 Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It's based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow. . Hypothesis is both extremely practical and also advances the state of the art of unit testing by some way. It's easy to use, stable, and extremely powerful. If you're not using Hypothesis to test your project then you're missing out. . This package contains the Python 2 module. Package: python-hypothesis-doc Source: python-hypothesis Version: 3.6.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1159 Depends: neurodebian-popularity-contest Homepage: https://github.com/DRMacIver/hypothesis Priority: extra Section: doc Filename: pool/main/p/python-hypothesis/python-hypothesis-doc_3.6.0-1~nd+1_all.deb Size: 163988 SHA256: ab3a23b2b5c894288b31fc4527e5e8bb364992467c7712db9de8092122d365dd SHA1: 5cc06ef2fe7dc7fefde1e7a1da9b078b4cb0909f MD5sum: 32d0ead2e160a2273644aae49ca7271a Description: advanced Quickcheck style testing library (documentation) Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It's based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow. . Hypothesis is both extremely practical and also advances the state of the art of unit testing by some way. It's easy to use, stable, and extremely powerful. If you're not using Hypothesis to test your project then you're missing out. . This package contains the documentation for Hypothesis. Package: python-jdcal Source: jdcal Version: 1.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 27 Depends: neurodebian-popularity-contest Homepage: https://github.com/phn/jdcal Priority: optional Section: python Filename: pool/main/j/jdcal/python-jdcal_1.0-1~nd+1_all.deb Size: 7758 SHA256: 94412136ef6ac30b47b2fbde317ab011af097fef09813125147eaab6eefc8478 SHA1: 1e15930886d85192e3364b7efb6450730911da89 MD5sum: 75204676241ba5bb009d2f05a88634b5 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: 0.10.3+git55-g660fe5d-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 488 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.6.6-7~) Recommends: python-numpy, python-pytest, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.10.3+git55-g660fe5d-1~nd+1_all.deb Size: 115512 SHA256: 26a6c7ab72cda03611eddf6d8af6bcf8075d822a64c1fbf488671094a5fdbe8c SHA1: 6a41ed42c4d3b1e93c64e83825279b3918054edd MD5sum: e3a66cca28e4446046734142e97dbe95 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-jsmin Version: 2.2.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 68 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/tikitu/jsmin Priority: optional Section: python Filename: pool/main/p/python-jsmin/python-jsmin_2.2.1-1~nd+1_all.deb Size: 21496 SHA256: ed21143372ccce7789de21af21b3a18fe46c9228a6d256bb5005603d4e7f96ec SHA1: 52e446ef90ca8dfef7d594e004b55c9dee3791ad MD5sum: 93563a3f269126625636d8b03d9cd7e3 Description: JavaScript minifier written in Python - Python 2.x Python-jsmin is a JavaScript minifier, it is written in pure Python and actively maintained. . This package provides the Python 2.x module. Package: python-lazyarray Source: lazyarray Version: 0.1.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 19 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), 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~nd+1_all.deb Size: 7328 SHA256: dbb35c5dc374c7bc62e95a56d3a14314105025852a66ba61f2472e4ea5b8be65 SHA1: a7bdc4dd42a3963a810fe0c0e73e4ad7ed6a7995 MD5sum: 1d204a47646dc6ed4152895171c87bc9 Description: Python module providing a NumPy-compatible lazily-evaluated array The 'larray' class is a NumPy-compatible numerical array where operations on the array (potentially including array construction) are not performed immediately, but are delayed until evaluation is specifically requested. Evaluation of only parts of the array is also possible. Consequently, use of an 'larray' can potentially save considerable computation time and memory in cases where arrays are used conditionally, or only parts of an array are used (for example in distributed computation, in which each MPI node operates on a subset of the elements of the array). Package: python-mdp Source: mdp Version: 3.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1391 Depends: neurodebian-popularity-contest, python-future, python-numpy, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-pytest, python-scipy, python-libsvm, python-joblib, python-sklearn, python-pp Enhances: python-mvpa2 Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.5-1~nd+1_all.deb Size: 277554 SHA256: f4e162fc6a74c97cafc113d1f6fa3ab2c71c02e13efe9b73316d6570a3ae8905 SHA1: 4f1d93c2e31cc3157fb0716b0d236d214f85f671 MD5sum: 56e7c89d5d1c7b5317f610324edf6425 Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 2. Package: python-mne Version: 0.13.1+dfsg-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9796 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.6.6-7~), 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, python-pycuda, ipython Provides: python2.7-mne Homepage: http://martinos.org/mne Priority: optional Section: python Filename: pool/main/p/python-mne/python-mne_0.13.1+dfsg-1~nd+1_all.deb Size: 4506444 SHA256: df01427391c700ffc39e1c1683426b22c1f77a130603dc932fe28cde1efdbb42 SHA1: 7c4245e3744d565df9cc34809a8724bce251e600 MD5sum: e934c1bd0902ebfef9d93ac5bb6910ea 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: 2.0.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 318 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_2.0.0-1~nd+1_all.deb Size: 55418 SHA256: 7b55e630cb0ef72ec388d187b78381f643b9f0542f6f3e50443a79109f9a903b SHA1: 8deea62d2295fd769d5dc228382176f790d8a9af MD5sum: 807a332c99480abebbc8150caab3f00f Description: bindings of the MPI standard -- documentation MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides HTML rendering of the user's manual. Package: python-mvpa Source: pymvpa Version: 0.4.8-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3547 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy, python-support (>= 0.90.0), python2.7, python-mvpa-lib (>= 0.4.8-1~nd+1) Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc Provides: python2.6-mvpa, python2.7-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.8-1~nd+1_all.deb Size: 2205002 SHA256: 41465c88b5c5d855bb5cfb183ef31b621031eb691ba5a8f3ac481bec2fe61bd8 SHA1: 40e31da97e30b6c2af3f28dfcd4b255560f765e2 MD5sum: b36ff1ec87893ae209624c75e8934b87 Description: multivariate pattern analysis with Python PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, GNB, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. Python-Version: 2.6, 2.7 Package: python-mvpa-doc Source: pymvpa Version: 0.4.8-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 37565 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.8-1~nd+1_all.deb Size: 8454400 SHA256: 9f730cbbc6fdcfce45ecca5ef036d74ea074eaedf2b4105fde7baf0028f11350 SHA1: 4510a24072100ffb1d4220f2d66d21abde733b9d MD5sum: 32c7629e7f9e01d9f7ca4d2c621b85be Description: documentation and examples for PyMVPA PyMVPA documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation * BibTeX references file . Additionally, all example scripts shipped with the PyMVPA sources are included. Package: python-mvpa2 Source: pymvpa2 Version: 2.6.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8560 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy, python:any (>= 2.6.6-7~), python-mvpa2-lib (>= 2.6.1-1~nd+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, python-duecredit, python-mock 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_2.6.1-1~nd+1_all.deb Size: 5098834 SHA256: 77c4de8d810cb3e3030bd4da6e6e8c296f5b06b3b190f6d21fa7dccc06b1e541 SHA1: 2359892ed5010837e0552e69c944408c91db0d42 MD5sum: 676c230d231e3ad7b4085d93d18d717f 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: 2.6.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36355 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_2.6.1-1~nd+1_all.deb Size: 4689900 SHA256: a909cc877d177010abb1e03ab022c84737fda350ac3d59fbc85a3722a5172927 SHA1: 75ecf0e0482f31d60466edfb3698a4f0e06421a0 MD5sum: fb13916c121ac0947199a49406fe494b 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2909 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~nd+1_all.deb Size: 1378610 SHA256: 268bcbe349cdd59dcd6c81b69f509e39abc526f812bd5f07208932a109266e6b SHA1: be5f458f69e1f2108a65756ef29c1a542be0bbe1 MD5sum: 5da5190a50f6ef3282f67c895329fb02 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-networkx Version: 1.4-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2672 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-numpy, python-scipy, python-pygraphviz | python-pydot, python-pkg-resources, python-matplotlib, python-yaml Homepage: http://networkx.lanl.gov/ Priority: optional Section: python Filename: pool/main/p/python-networkx/python-networkx_1.4-2~nd+1_all.deb Size: 647276 SHA256: 8686482d59b2830b57bbb4c95a26e38598c5d2b46b8bfc3c41ef5079c7de3f85 SHA1: 8e2dcdffdac74665f7c48190e2594d95466e3c37 MD5sum: e610b0ef6d98a399e29c3b2be7968038 Description: tool to create, manipulate and study complex networks NetworkX is a Python-based package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. . The structure of a graph or network is encoded in the edges (connections, links, ties, arcs, bonds) between nodes (vertices, sites, actors). If unqualified, by graph it's meant a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network it's usually meant a graph with weights (fields, properties) on nodes and/or edges. . The potential audience for NetworkX includes: mathematicians, physicists, biologists, computer scientists, social scientists. Package: python-networkx-doc Source: python-networkx Version: 1.4-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15804 Depends: neurodebian-popularity-contest Homepage: http://networkx.lanl.gov/ Priority: optional Section: doc Filename: pool/main/p/python-networkx/python-networkx-doc_1.4-2~nd+1_all.deb Size: 6175086 SHA256: 7576cef9c963357c491c8ed98372493e4567e864c9bf9b30c1da2cbc2dd0cf80 SHA1: 861dba2262ad5310d0b4065953ee4838e7575ad7 MD5sum: ba0a43a93a019711296e4657536ef25b Description: tool to create, manipulate and study complex networks - documentation NetworkX is a Python-based package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. . The structure of a graph or network is encoded in the edges (connections, links, ties, arcs, bonds) between nodes (vertices, sites, actors). If unqualified, by graph it's meant a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network it's usually meant a graph with weights (fields, properties) on nodes and/or edges. . The potential audience for NetworkX includes: mathematicians, physicists, biologists, computer scientists, social scientists. . This package contains documentation for NetworkX. Package: python-neuroshare-doc Source: python-neuroshare Version: 0.9.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 284 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~nd+1_all.deb Size: 95620 SHA256: 4b008b4e512ecc425d792b05a5f237701cff8f0b10847f9ae4cefbe836b52f12 SHA1: 0c55225790940875eff3b0dc7ac1454c7dea4230 MD5sum: acccb1a0fb534f3233a73ff94d58d8a9 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-neurosynth Source: neurosynth Version: 0.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 83 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-nibabel, python-ply Recommends: python-nose, fsl-mni152-templates Suggests: python-testkraut Homepage: http://neurosynth.org Priority: extra Section: python Filename: pool/main/n/neurosynth/python-neurosynth_0.3-1~nd+1_all.deb Size: 32506 SHA256: 1b7a6109b4cd73ca4ed17d0f33010df1d73c8bfcdd469311c48d58714fd99755 SHA1: 101a6b891d69881db9e5bd6bfc176aaa2de3ca28 MD5sum: 191a7178983f856cb47820d478a3a791 Description: large-scale synthesis of functional neuroimaging data NeuroSynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. This Python module at the moment provides functionality for processing the database of collected terms and spatial coordinates to generate associated spatial statistical maps. Package: python-nibabel Source: nibabel Version: 2.1.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 64222 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.6.6-7~), python-numpy, python-scipy Recommends: python-dicom, python-fuse, python-mock Suggests: python-nibabel-doc Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_2.1.0-1~nd+1_all.deb Size: 2172068 SHA256: 95905905bcf2e54e2dc3497eecf19eeee2c7d0755ad6a72aa60a6e3423a2c924 SHA1: ea4597a93edf101b632124d18764f7552d1c415a MD5sum: a2c35e7733d2a19d1e4257f5ab589e44 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: 2.1.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20478 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_2.1.0-1~nd+1_all.deb Size: 2910884 SHA256: 335d9ba0d9e3986fa3729268afbe5bae8f8418d00137b4798245a118c335ede3 SHA1: 261458207c8c78f0edcb6bf2d6d043a0ac9cdafe MD5sum: a00c7657795573bc3a6347ca6b49d968 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-nibabel-snapshot Source: nibabel-snapshot Version: 1.0.0.dev+137+gf1c6-1~sid.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 964 Depends: python (>= 2.5), python-support (>= 0.90.0), python-numpy, libjs-jquery, python-scipy Conflicts: python-nibabel Provides: python2.5-nibabel-snapshot, python2.6-nibabel-snapshot Homepage: http://nipy.sourceforge.net/nibabel Priority: optional Section: python Filename: pool/main/n/nibabel-snapshot/python-nibabel-snapshot_1.0.0.dev+137+gf1c6-1~sid.nd1_all.deb Size: 469776 SHA256: 674d6faa8c47cc5d2abded6bf10d56d3c7b2041b70390b254d6bed4fe0b89f92 SHA1: 1e06be036a09d6114c43bcccf080aa256f7c7a69 MD5sum: 26e58a8ca88e85dfba68eae891bdcdeb Description: Python bindings to various neuroimaging data formats Currently supported formats are: . * ANALYZE (including SPM2 and SPM99 variants) * MINC * NIfTI * PAR/REC . This package also provides a commandline tool for conversion of PAR/REC to NIfTI images. Python-Version: 2.5, 2.6 Package: python-nilearn Source: nilearn Version: 0.2.5~dfsg.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2454 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_0.2.5~dfsg.1-1~nd+1_all.deb Size: 730826 SHA256: 86ba4f45f523aaddd13c318fcdf9583326386b119f3ee0ec294912f9c1a7c3c7 SHA1: 7ce2818339748b007d6c28c6b5efa1f5a8961603 MD5sum: cc139e26c6e7e3ddc6bff44e47515d36 Description: fast and easy statistical learning on neuroimaging data (Python 2) This Python module leverages the scikit-learn toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. . This package provides the Python 2 version. Python-Version: 2.7 Package: python-nipy Source: nipy Version: 0.4.0+git26-gf8d3149-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3380 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.2), python (>= 2.7), python (<< 2.8), python:any (>= 2.6.6-7~), python-scipy, python-nibabel, python-nipy-lib (>= 0.4.0+git26-gf8d3149-2~nd+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_0.4.0+git26-gf8d3149-2~nd+1_all.deb Size: 738120 SHA256: 013fc6cb302f1636d51d15ffbd29bcdd28d5d0a71253f1086ca849d5e01b8674 SHA1: f1cc922333dd586b0a7c601f1380064f16359b7b MD5sum: ed29367709c67964397e837561579eb7 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: 0.4.0+git26-gf8d3149-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10714 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_0.4.0+git26-gf8d3149-2~nd+1_all.deb Size: 2880428 SHA256: 2d698f2d8b12e329565aef286500383f1b7392e04e988bb75ffa3319a2440c89 SHA1: 7f843ba36b34f678ec61f12ce5dda31404cd4165 MD5sum: ca271237b077a62a700907d9358ffd65 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: 0.12.1+git4-gbc3a0b5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9364 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.6.6-7~), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib, python-funcsigs, python-future, python-prov, python-psutil Recommends: ipython, python-nose, graphviz, python-xvfbwrapper, mayavi2, python-mock 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_0.12.1+git4-gbc3a0b5-1~nd+1_all.deb Size: 1536338 SHA256: b6afcf9bb0629127754f500f612c719b3579fcbb0158f15355423c8e79c95f8b SHA1: b7ec5d512341994917e77cfc7caa0eb443aae86b MD5sum: 1141ae8fcb801f38418565919dbfcd73 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: 0.12.1+git4-gbc3a0b5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 31924 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_0.12.1+git4-gbc3a0b5-1~nd+1_all.deb Size: 13293580 SHA256: 6aaa26264411b98d8bcae47c15e29a6e9f2b3d3fd94f695d2e26b00ebddabe71 SHA1: 97d64cd3cf6404bba2f0d03c46102ff2e11db166 MD5sum: 60267213f89d85bb67eea0fb402526b8 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: 0.7-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9377 Depends: neurodebian-popularity-contest, python-matplotlib, python-numpy, python-scipy, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.7-1~nd+1_all.deb Size: 2549110 SHA256: 1f1979920ff2f9b1c9fa05c97dbfbc473df07b69028d955805897a7ab43d73bc SHA1: a8296a50a9c5721f421db29628c2728e6a00d4dd MD5sum: 8a5a855a038d84a38be179231de34b0a 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: 0.7-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6905 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-nitime Homepage: http://nipy.org/nitime Priority: extra Section: doc Filename: pool/main/n/nitime/python-nitime-doc_0.7-1~nd+1_all.deb Size: 4813592 SHA256: 2006c0a2e9687480f7d9ccfd3d2fabcc184de3fc4cbd62bcc4ef0f10f7e75ea6 SHA1: 8f7e17e74e44aa70838faf9e6c62f2b6721428c2 MD5sum: b85157562421c2d8240c267925be8f67 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-nosexcover Source: nosexcover Version: 1.0.10-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-nose, python-coverage (>= 3.4) Homepage: http://pypi.python.org/pypi/nosexcover Priority: extra Section: python Filename: pool/main/n/nosexcover/python-nosexcover_1.0.10-2~nd+1_all.deb Size: 5128 SHA256: cb2e2c1da583fc4dd2c737eb6e748c26717ff623d08d0d28b22deacc09eafd51 SHA1: 06cd26e47b00d11c56c9f57261f5f51dcd3e5c34 MD5sum: e40a78d353eefee1076e93c269f9e889 Description: Add Cobertura-style XML coverage report to nose A companion to the built-in nose.plugins.cover, this plugin will write out an XML coverage report to a file named coverage.xml. . It will honor all the options you pass to the Nose coverage plugin, especially --cover-package. Package: python-openopt Source: openopt Version: 0.38+svn1589-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 954 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.6-openopt, python2.7-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd+1_all.deb Size: 245088 SHA256: 720267e7fc1297916d72081d7bffedfc4e911f4cba267f9e83f65ee6cf7eac3b SHA1: 2a31c5c6bad612fa5d880b23d6c2c2628c1aef20 MD5sum: 5ffcdd148bf0a2e648d7c3960953fc20 Description: Python module for numerical optimization Numerical optimization framework developed in Python which provides connections to lots of solvers with easy and unified OpenOpt syntax. Problems which can be tackled with OpenOpt * Linear Problem (LP) * Mixed-Integer Linear Problem (MILP) * Quadratic Problem (QP) * Non-Linear Problem (NLP) * Non-Smooth Problem (NSP) * Non-Linear Solve Problem (NLSP) * Least Squares Problem (LSP) * Linear Least Squares Problem (LLSP) * Mini-Max Problem (MMP) * Global Problem (GLP) . A variety of solvers is available (e.g. IPOPT, ALGENCAN). Python-Version: 2.6, 2.7 Package: python-openpyxl Source: openpyxl Version: 2.3.0-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1325 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-jdcal, python:any (>= 2.6.6-7~), python-lxml (>= 3.3.4) | python-et-xmlfile Recommends: python-pytest, python-pil, python-imaging Homepage: http://bitbucket.org/openpyxl/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_2.3.0-3~nd+1_all.deb Size: 199140 SHA256: 9ea2ad0e1a177ccd382ee005722214c999046f12bdd719a5e57940ffb5560a87 SHA1: 980366d84540731f6d8416944af4c4b9852c375f MD5sum: b2e77ff7962457433aa451ea05f0ecac 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-packaging Version: 16.2-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 85 Depends: neurodebian-popularity-contest, python-pyparsing, python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://pypi.python.org/pypi/packaging Priority: optional Section: python Filename: pool/main/p/python-packaging/python-packaging_16.2-2~nd+1_all.deb Size: 17192 SHA256: 9d0ec2e40865cd2c2a838f179534b6a56f48aad4e4916473f74d2eac663a9837 SHA1: f93b19dec144a8424524e50a3699ac63765dfe04 MD5sum: 44b021089123e22cde0541630c5b36f6 Description: core utilities for python packages These core utilities currently consist of: - Version Handling (PEP 440) - Dependency Specification (PEP 440) Package: python-pandas Source: pandas Version: 0.19.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25229 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-dateutil, python-numpy (>= 1:1.7~), python-tz, python:any (>= 2.6.6-7~), python-pandas-lib (>= 0.19.2-1~nd+1), python-pkg-resources, 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_0.19.2-1~nd+1_all.deb Size: 2614296 SHA256: 5d9b5aabc7445477a12a7d0de2c96d2426e01836e965c3a10facc6a2a58e93a4 SHA1: c3b577c1be09ff0ddd7ae92ccdc6e8e028e078b2 MD5sum: aedd114817cc3d8df00e9a1807ea3975 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: 0.19.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 56882 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_0.19.2-1~nd+1_all.deb Size: 5061500 SHA256: d1bb28999e625615449523ed369c4b4db568cd5052b680a3d9ecf895fb84b67d SHA1: 6b6c5b23dd1f554e5b087e015343235fa80a8d73 MD5sum: 385d5b7dab06953634fd990a66a666c6 Description: documentation and examples for pandas This package contains documentation and example scripts for python-pandas. Package: python-patsy Source: patsy Version: 0.4.1+git34-ga5b54c2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 783 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.6.6-7~), 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_0.4.1+git34-ga5b54c2-1~nd+1_all.deb Size: 171488 SHA256: 5a16740bcdfe521a93a3d595251a662b967be847823d702c8aee6b088cfa4f89 SHA1: ec77b76fa166628a05dff1a5307722de2dcb537d MD5sum: 7a421430e7b1b9e30df92c2752c9b63b 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: 0.4.1+git34-ga5b54c2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1066 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_0.4.1+git34-ga5b54c2-1~nd+1_all.deb Size: 236658 SHA256: dcbf0fe0f0e8c68027f80161be5a7ded3e833f803f2a45b0f21938ca5b6df1a3 SHA1: 1dc05739d69b36e6b2c6277599ab6fcfd10f12e4 MD5sum: c71dc8df1f2621d729e7cdd37af68bc0 Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pkg-resources Source: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 441 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-setuptools Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: python Filename: pool/main/p/python-setuptools/python-pkg-resources_20.10.1-1.1~bpo8+1~nd+1_all.deb Size: 141372 SHA256: 4f8e5de7035f1e6394fd7be0f0423350f6e4d98126a8f23fd63aef3ae0ce25f3 SHA1: 5e502b07a69b4933aad7bf1e99c056b2d1dd91e0 MD5sum: 1511960fe4a84a84b6194f9462a88ce7 Description: Package Discovery and Resource Access using pkg_resources The pkg_resources module provides an API for Python libraries to access their resource files, and for extensible applications and frameworks to automatically discover plugins. It also provides runtime support for using C extensions that are inside zipfile-format eggs, support for merging packages that have separately-distributed modules or subpackages, and APIs for managing Python's current "working set" of active packages. Package: python-pp Source: parallelpython Version: 1.6.2-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.parallelpython.com/ Priority: optional Section: python Filename: pool/main/p/parallelpython/python-pp_1.6.2-2~nd+1_all.deb Size: 34266 SHA256: 6ef3aa699e927edfc8ec788d98beac10bec4de47387f92d79d44b0183b3c3c3d SHA1: d8942b2e7dddc5e29cc52b2a4cca69a392348a09 MD5sum: 36530320f9038b882b0e8b9d5be61505 Description: parallel and distributed programming toolkit for Python Parallel Python module (pp) provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. pp module features cross-platform portability and dynamic load balancing. Thus application written with PP will parallelize efficiently even on heterogeneous and multi-platform clusters (including clusters running other application with variable CPU loads). Python-Version: 2.6, 2.7 Package: python-pprocess Source: pprocess Version: 0.5-1+nd0~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 716 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~nd+1_all.deb Size: 81598 SHA256: 3842060cc266ddff9a61fb88f4c4e34004ad6e6aba6ba60d74ce046dc8c1b126 SHA1: 36cc022ca9cf83a947a9c1a3c7eabffa69dea1f2 MD5sum: 23b94adc9313ef79112c781728008a69 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-props Source: props Version: 0.10.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 651 Depends: neurodebian-popularity-contest, python-wxgtk3.0, python-matplotlib, python-numpy, python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Provides: python2.7-props Priority: optional Section: python Filename: pool/main/p/props/python-props_0.10.1-1~nd+1_all.deb Size: 117330 SHA256: d3f6f6c5cb13da28c91e71de41ec38dfcdb31261f6ddb80cbfc773ecd2e7786e SHA1: 90a3bd01e6a61094554a3d241d80b06e19e6f811 MD5sum: 075bbd0b2d044a5c277277986a1f98aa Description: Python descriptor framework Event programming framework with the ability for automatic CLI generation, and automatic GUI generation (with wxPython). . This package provides the Python 2 module. Package: python-prov Version: 1.4.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1235 Depends: neurodebian-popularity-contest, python-dateutil, python-lxml, python-networkx, python-six (>= 1.9.0), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-prov-doc, python-pydotplus Homepage: https://github.com/trungdong/prov Priority: optional Section: python Filename: pool/main/p/python-prov/python-prov_1.4.0-1~nd+1_all.deb Size: 69980 SHA256: 0dd3c2efbf3b45e9400ad88faa9b3011b54c5b6c3f234023a737918d82109f0b SHA1: f3edc6ecdc850abb3eb38ba54f86016622f424ea MD5sum: b2e51315211190021ccaebcc070e7fa8 Description: W3C Provenance Data Model (Python 2) A library for W3C Provenance Data Model supporting PROV-JSON and PROV- XML import/export. . Features: - An implementation of the W3C PROV Data Model in Python. - In-memory classes for PROV assertions, which can then be output as PROV-N. - Serialization and deserializtion support: PROV-JSON and PROV-XML. - Exporting PROV documents into various graphical formats (e.g. PDF, PNG, SVG). . This package provides the prov library for Python 2. Package: python-prov-doc Source: python-prov Version: 1.4.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 814 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: https://github.com/trungdong/prov Priority: optional Section: doc Filename: pool/main/p/python-prov/python-prov-doc_1.4.0-1~nd+1_all.deb Size: 69190 SHA256: 6405fa03081e5f117dbf6e85cfb472b28002ac787f9bd4fc27dcc332f0a27f7e SHA1: 73a48d939942467ccad77c4f292295d81eeea920 MD5sum: 2b40eb8842cde2ece66d4f20c9f57318 Description: documentation for prov A library for W3C Provenance Data Model supporting PROV-JSON and PROV- XML import/export. . Features: - An implementation of the W3C PROV Data Model in Python. - In-memory classes for PROV assertions, which can then be output as PROV-N. - Serialization and deserializtion support: PROV-JSON and PROV-XML. - Exporting PROV documents into various graphical formats (e.g. PDF, PNG, SVG). . This package provides the documentation for the prov library. Package: python-py Version: 1.4.31-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 320 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_1.4.31-2~nd+1_all.deb Size: 82202 SHA256: 2a7ab07684889a19419f62000f613277009e21b2947d793dbe67a0e3408240a0 SHA1: 0545a23cc3b644673cf663a2670aa5708613da6b MD5sum: 910e023f99ba83a1cd988cd00ca8d7ff 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-pydotplus Version: 2.0.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 104 Depends: neurodebian-popularity-contest, graphviz, python-pyparsing (>= 2.0.1), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-pydotplus-doc Homepage: http://pydotplus.readthedocs.org/ Priority: optional Section: python Filename: pool/main/p/python-pydotplus/python-pydotplus_2.0.2-1~nd+1_all.deb Size: 20174 SHA256: 69c4d5e3b836af825c47f4a7eddb17adb6b76a639e45514db3d2832027a4698a SHA1: 2ea8d4e2da2f282573fb8d3f42b1e79eb8e7bf20 MD5sum: f8fdbf53270a8715a4d11059d00f1f04 Description: interface to Graphviz's Dot language - Python 2.7 PyDotPlus is an improved version of the old pydot project that provides a Python Interface to Graphviz's Dot language. . Differences with pydot: * Compatible with PyParsing 2.0+. * Python 2.7 - Python 3 compatible. * Well documented. * CI Tested. . This package contains the Python 2.7 module. Package: python-pydotplus-doc Source: python-pydotplus Version: 2.0.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 531 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Homepage: http://pydotplus.readthedocs.org/ Priority: optional Section: doc Filename: pool/main/p/python-pydotplus/python-pydotplus-doc_2.0.2-1~nd+1_all.deb Size: 47424 SHA256: 029e1fe6d5d0649d4c411f8cdb222e8d68ee917be8dca3dd40ac3af11af73cd0 SHA1: b67474f69655dfb35ae47aac35fed0077177447d MD5sum: bbee946696aa2a9cd3bb5a18a4e7e7a5 Description: interface to Graphviz's Dot language - doc PyDotPlus is an improved version of the old pydot project that provides a Python Interface to Graphviz's Dot language. . Differences with pydot: * Compatible with PyParsing 2.0+. * Python 2.7 - Python 3 compatible. * Well documented. * CI Tested. . This package contains the documentation. Package: python-pyentropy Source: pyentropy Version: 0.4.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy (>= 1.3) Recommends: python-scipy Suggests: python-nose Provides: python2.6-pyentropy, python2.7-pyentropy Homepage: http://code.google.com/p/pyentropy Priority: extra Section: python Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd+1_all.deb Size: 21334 SHA256: 3ab479e9d42286158d724eb219d6205e3c8071a2a8fd6436afc501b57ecf086b SHA1: 19b81597aeb2806a30580c43b1fcf5f5ad3d586d MD5sum: 662336ec73a1d4c272a6d2763ef118df Description: Python module for estimation information theoretic quantities A Python module for estimation of entropy and information theoretic quantities using cutting edge bias correction methods, such as * Panzeri-Treves (PT) * Quadratic Extrapolation (QE) * Nemenman-Shafee-Bialek (NSB) Python-Version: 2.6, 2.7 Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 813 Depends: neurodebian-popularity-contest, python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0+git12-g365f8e3-2~nd+1_all.deb Size: 819288 SHA256: 7eb7da90f6e629e4f1eb828beac597cd0d9950fd05a06f9bb135d2e58d6a2d13 SHA1: 6fd9ebae91ca87f9a3efa61b64ccb01de6b033f7 MD5sum: b608bf8ddef20a65f3bef3f99781ed82 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-pyglet Source: pyglet Version: 1.1.4.dfsg-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4356 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-ctypes | python (>= 2.5), libgtk2.0-0, libgl1 | libgl1-mesa-swx11, libglu1 | libglu1-mesa Recommends: libasound2 | libopenal1 Provides: python2.5-pyglet, python2.6-pyglet Homepage: http://www.pyglet.org Priority: optional Section: python Filename: pool/main/p/pyglet/python-pyglet_1.1.4.dfsg-1~nd+1_all.deb Size: 972190 SHA256: d192998b5a0ad23a8014afd611a21ad4300c71dbd5d14b3f64e3f0fd669b6210 SHA1: 5e072bcd364c59d478f4006386eb7487a8ba4dbd MD5sum: 920b7e086aa6042bef058e20b3c5b057 Description: cross-platform windowing and multimedia library This library provides an object-oriented programming interface for developing games and other visually-rich applications with Python. pyglet has virtually no external dependencies. For most applications and game requirements, pyglet needs nothing else besides Python, simplifying distribution and installation. It also handles multiple windows and fully aware of multi-monitor setups. . pyglet might be seen as an alternative to PyGame. Package: python-pygraphviz-doc Source: python-pygraphviz Version: 1.3.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 316 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: https://pygraphviz.github.io/ Priority: optional Section: doc Filename: pool/main/p/python-pygraphviz/python-pygraphviz-doc_1.3.1-1~nd+1_all.deb Size: 68268 SHA256: d5e07fcdd236fd0c4eaf53f0006d03964386d7862fd6118efafcf3ef0438b75f SHA1: fb4510a060f5da5eddeb7f796e53a444484b820b MD5sum: 1574b28a58f374f740581b375c1a2ebc Description: Python interface to the Graphviz graph layout and visualization package (doc) Pygraphviz is a Python interface to the Graphviz graph layout and visualization package. . With Pygraphviz you can create, edit, read, write, and draw graphs using Python to access the Graphviz graph data structure and layout algorithms. . This package contains documentation for python-pygraphviz. Package: python-pymc-doc Source: pymc Version: 2.3.4+ds-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1860 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~nd+1_all.deb Size: 839812 SHA256: 7b1065ed4bec2186333d05434ac2369fc6d0f67bc6b97416ea71a067b35c1674 SHA1: cf73784906e058cec17d8d2ba831d09b877bd10d MD5sum: 253948e97486b725a69427e9806b090c 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 777 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~nd+1_all.deb Size: 192126 SHA256: 4c25bbb4a6efbe9c9614c69977d255840ceb5469e917e9e42631963e11fa73b5 SHA1: 4d68ad72d900c06b6d482eaabab6847de44296f2 MD5sum: bb6a423667c3716a6baee943a4d73d50 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-pyoptical Source: pyoptical Version: 0.2-1~sid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 72 Depends: python-serial Enhances: psychopy, python-pyepl Homepage: http://github.com/esc/pyoptical Priority: extra Section: python Filename: pool/main/p/pyoptical/python-pyoptical_0.2-1~sid.nd1_all.deb Size: 6946 SHA256: 61b96afae4d2c43351ad598253b8b38fff6b0c2d99669f49f431b8d8678f89be SHA1: 8442b14c93a7d2c3718d78655dc85fef951bcfaf MD5sum: 1eaea3d3d51bcd440299d8aa65220111 Description: python interface to the CRS 'OptiCAL' photometer The 'OptiCAL' is a photometer that is produced by Cambridge Research Systems (CRS). This device is a standard tool for gamma-calibration of display devices in vision research. This package provides a free-software replacement for the Windows-software distributed by the manufacturer that allows querying an OptiCAL via a serial connection. pyoptical can be used as a library for third-party applications or as a standalone command line tool. Python-Version: 2.5, 2.6 Package: python-pyssdh Source: openelectrophy Version: 0.0.svn143-1~sid.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 792 Depends: python-support (>= 0.90.0), python-numpy, python-scipy, python-qt4, python-mysqldb, python-matplotlib Recommends: g++ | c++-compiler, python-mdp Suggests: mysql-server Provides: python2.4-pyssdh, python2.5-pyssdh Homepage: http://neuralensemble.org/trac/OpenElectrophy Priority: extra Section: python Filename: pool/main/o/openelectrophy/python-pyssdh_0.0.svn143-1~sid.nd1_all.deb Size: 119482 SHA256: 047337422d8c671d1ca38e938384c985fc1fac566d178123b6cb5ee4d1fccc51 SHA1: 2fb56ca17ad07ee58955caf8de17a4cd24d3d85a MD5sum: 1790628c9012a2ae40aff02998bd9c41 Description: data analysis framework for intra- and extra-cellular recordings This software aims to simplify data and analysis sharing for intra- and extra-cellular recordings. It supports time frequency plots, spike detection, spike rate calculation, and analysis of phase locked signals. . Data handling and storage utilizes a MySQL database, allowing to handle large amounts of data easily and efficiently. Therefore, a MySQL server running locally or on a remote machine is required. . This package provides the OpenElectrophy Python module. Python-Version: 2.4, 2.5 Package: python-pytest Source: pytest Version: 3.0.4-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 649 Depends: neurodebian-popularity-contest, python-pkg-resources, python-py (>= 1.4.29), 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_3.0.4-1~nd+1_all.deb Size: 181322 SHA256: cdfd679ecb7bfc48ae1c7898e0e78cea0d4662296e0dc5ad69b187bc3be4a268 SHA1: 4130c6041564a80cf302e4c742d9ec171349f6bf MD5sum: 51aa55e3d75f545ab5f4705bbf7ac372 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. . This package provides the Python 2 modules and the py.test script. Package: python-pytest-doc Source: pytest Version: 3.0.4-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4029 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_3.0.4-1~nd+1_all.deb Size: 668172 SHA256: b12cc635cde03357d0246ddec2b894f21cc5a72f4a921ea2e9b18b0da502e0c3 SHA1: b0e9f47b222acd11ede6c7a982cb9daa93d67bcb MD5sum: 654469dfeecdaa21d6cff6de89919829 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: 0.3.4-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 61 Depends: neurodebian-popularity-contest, python-pytest, python-werkzeug (>= 0.10), python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://bitbucket.org/pytest-dev/pytest-localserver/ Priority: optional Section: python Filename: pool/main/p/pytest-localserver/python-pytest-localserver_0.3.4-2~nd+1_all.deb Size: 19198 SHA256: 2f64f1b2a625a270c68948df4a43096ab186027c6bd069c70269ecbcfdaf3624 SHA1: ab00c431d40fa8348c18a58c0caf96b243fbed45 MD5sum: a98bc5d4e1452d03901d3255e66ffa5f 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~nd+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~nd+1_all.deb Size: 5648 SHA256: e5920e43e010f58bc1e1330d50856b4bd7835295c5493ed9bc5de4dcdbee6700 SHA1: 5346fa774430224e768520a0c1d2a9ef999e9c4f MD5sum: 5b573689c8eec7d51ce53b18bcf26728 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 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 80 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_all.deb Size: 11020 SHA256: 1031c0d69dd73cb38f3e0b826193211706a94bfd04da4287288418b257e54249 SHA1: 0f0d0524354e5d07eb89efcb11779d9acd9d57e2 MD5sum: 1f2a9bc07952b1f5c6b65fc5c092f75c 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-pyxnat Source: pyxnat Version: 0.9.1+git39-g96bf069-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1722 Depends: neurodebian-popularity-contest, python-lxml, python-simplejson, python-httplib2 (>= 0.7.0) Recommends: python-networkx, python-matplotlib Homepage: http://packages.python.org/pyxnat/ Priority: extra Section: python Filename: pool/main/p/pyxnat/python-pyxnat_0.9.1+git39-g96bf069-1~nd+1_all.deb Size: 376516 SHA256: c44e63035749ea2429ed9f2aab12e3ce41aa533de51f0cf3ce9836f882e3a477 SHA1: 2baec2a6b0c311f22f1a6e685cea20ae7019dac7 MD5sum: 603bfa4501f120f7ca01ed5ed95293b0 Description: Interface to access neuroimaging data on XNAT servers pyxnat is a simple Python library that relies on the REST API provided by the XNAT platform since its 1.4 version. XNAT is an extensible database for neuroimaging data. The main objective is to ease communications with an XNAT server to plug-in external tools or Python scripts to process the data. It features: . - resources browsing capabilities - read and write access to resources - complex searches - disk-caching of requested files and resources Package: python-quantities Version: 0.10.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 323 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), 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~nd+1_all.deb Size: 62610 SHA256: 24764ab44e8e2357cdb8d4882acce352d96b34ed6b3af8be217617eb51848f83 SHA1: 9367905e8af4cb696831c327b20e43a3e2d52616 MD5sum: d08b442a214c35f1e1f9fa595d311cf6 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-requests Source: requests Version: 2.8.1-1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 243 Depends: neurodebian-popularity-contest, python-urllib3 (>= 1.12), python:any (<< 2.8), python:any (>= 2.7.5-5~), ca-certificates, python-chardet Suggests: python-ndg-httpsclient, python-openssl, python-pyasn1 Breaks: httpie (<< 0.9.2) Homepage: http://python-requests.org Priority: optional Section: python Filename: pool/main/r/requests/python-requests_2.8.1-1~bpo8+1~nd+1_all.deb Size: 68094 SHA256: eb91a7265a33845b9a7d111f5328560c3b3131e9799ff3e8a85118509163969c SHA1: 2230a4dbff6c7ddfdbf027b44be12c8bb9247408 MD5sum: 6a4d3e4142dc715bbb19a2f066163bfa Description: elegant and simple HTTP library for Python2, built for human beings Requests allow you to send HTTP/1.1 requests. You can add headers, form data, multipart files, and parameters with simple Python dictionaries, and access the response data in the same way. It's powered by httplib and urllib3, but it does all the hard work and crazy hacks for you. . Features . - International Domains and URLs - Keep-Alive & Connection Pooling - Sessions with Cookie Persistence - Browser-style SSL Verification - Basic/Digest Authentication - Elegant Key/Value Cookies - Automatic Decompression - Unicode Response Bodies - Multipart File Uploads - Connection Timeouts Package: python-requests-whl Source: requests Version: 2.8.1-1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 353 Depends: neurodebian-popularity-contest, ca-certificates, python-urllib3-whl Homepage: http://python-requests.org Priority: optional Section: python Filename: pool/main/r/requests/python-requests-whl_2.8.1-1~bpo8+1~nd+1_all.deb Size: 318990 SHA256: e7bdf804a4a112774d95c37d3dec8c74cc89e1dd097622ddc66fd3a4383a4056 SHA1: f6c61d789ccd724c608531946dcea388ea8345cb MD5sum: e7ba98f7af12e72068c7d563bd2059c5 Description: elegant and simple HTTP library for Python, built for human beings Requests allow you to send HTTP/1.1 requests. You can add headers, form data, multipart files, and parameters with simple Python dictionaries, and access the response data in the same way. It's powered by httplib and urllib3, but it does all the hard work and crazy hacks for you. . Features . - International Domains and URLs - Keep-Alive & Connection Pooling - Sessions with Cookie Persistence - Browser-style SSL Verification - Basic/Digest Authentication - Elegant Key/Value Cookies - Automatic Decompression - Unicode Response Bodies - Multipart File Uploads - Connection Timeouts . This package provides the universal wheel. Package: python-scikits-learn Source: scikit-learn Version: 0.18-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 78 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_0.18-3~nd+1_all.deb Size: 69554 SHA256: 7c4daec80806fe90ab14eac0f4c19581288c10f5515c3a938fc84822d1ab2eb3 SHA1: 2b18ad62eefdb1f83702f1068f6a589f7df21698 MD5sum: a5bd0a60ec0c4be283f9fdf82ffe5038 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9 Depends: neurodebian-popularity-contest, python-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.6.1-1~nd+1_all.deb Size: 5916 SHA256: 9bc5375614e5a8fe5d0971b590cc7418ed54ba3c13f74c19608f9f0bd942bcda SHA1: cf13e973da4b7e8bdc21f82a02a155afe465ee97 MD5sum: eb4bf6a7aa273d56602a55d601134c11 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: 1.0.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 986 Depends: neurodebian-popularity-contest, python-boto, python-cssselect, python-libxml2, python-lxml, python-queuelib, python-twisted-conch, python-twisted-core, python-twisted-mail, python-twisted-web, python-w3lib (>= 1.8), python, python-openssl, python-service-identity, python-six, python-twisted, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: ipython, python-django, python-guppy, python-imaging, python-mysqldb, python-pygments, python-simplejson | python (>= 2.6) Provides: python2.7-scrapy Homepage: http://scrapy.org/ Priority: optional Section: python Filename: pool/main/p/python-scrapy/python-scrapy_1.0.3-1~nd+1_all.deb Size: 175798 SHA256: 6dc7f6e606591df2b833589c36efc783d5ced94cbb56de524e6b49eb8ce1106e SHA1: a6c48350ffdbdd0f2b364c5499c0a8bf4c6b767a MD5sum: 2b4bd67679a9e89b6695bedb4ebdd9cf 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: 1.0.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7133 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_1.0.3-1~nd+1_all.deb Size: 1540270 SHA256: 7df7413c2bf7b2719ad9909232e3cce4c32527fae7a1d3abf1d27028161977d3 SHA1: 58bf858628c0aa49dd7603e8b170de65105c1e69 MD5sum: 83e38127fe1d883c462951c0a8600788 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-seaborn Source: seaborn Version: 0.7.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 766 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-pandas, python-matplotlib Recommends: python-statsmodels, python-patsy Homepage: https://github.com/mwaskom/seaborn Priority: optional Section: python Filename: pool/main/s/seaborn/python-seaborn_0.7.1-2~nd+1_all.deb Size: 128136 SHA256: 9b7286a9c5b491dd0e9187c25cfa30633be18731a0c359124088cfcd94e1ba3d SHA1: e1eddb400592de29535e0b7a87dc60030c4f5d57 MD5sum: 84bcead78cea6d9900aa75d6cd83f959 Description: statistical visualization library Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. . Some of the features that seaborn offers are . - Several built-in themes that improve on the default matplotlib aesthetics - Tools for choosing color palettes to make beautiful plots that reveal patterns in your data - Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data - Tools that fit and visualize linear regression models for different kinds of independent and dependent variables - A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate - High-level abstractions for structuring grids of plots that let you easily build complex visualizations . This is the Python 2 version of the package. Package: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 520 Depends: neurodebian-popularity-contest, python-pkg-resources (= 20.10.1-1.1~bpo8+1~nd+1), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-setuptools-doc Provides: python-distribute Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: python Filename: pool/main/p/python-setuptools/python-setuptools_20.10.1-1.1~bpo8+1~nd+1_all.deb Size: 202886 SHA256: f75d02695f9b9a8ce273133bd2cd224cdcbd7defe2ac02a1d21a80462ea53057 SHA1: 1ee8a1b68aabe5d8ce93e32de45460af1c09ffbc MD5sum: f4322845a4f428a447adebe9a8c7e2e0 Description: Python Distutils Enhancements Extensions to the python-distutils for large or complex distributions. Package: python-setuptools-doc Source: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1151 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: doc Filename: pool/main/p/python-setuptools/python-setuptools-doc_20.10.1-1.1~bpo8+1~nd+1_all.deb Size: 200410 SHA256: ce141310ed5a27a7c77a54302af242b1ad6b796013ce3b5d83ba4d0f49f822e1 SHA1: 35af707aa000d7a3cb2dc27d80c17a1116acbf4f MD5sum: 87c7cdf68fd72ec860414792cef121f0 Description: Python Distutils Enhancements (documentation) Extensions to the Python distutils for large or complex distributions. The package contains the documentation in html format. Package: python-setuptools-scm Source: setuptools-scm Version: 1.8.0-1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/pypa/setuptools_scm Priority: optional Section: python Filename: pool/main/s/setuptools-scm/python-setuptools-scm_1.8.0-1~bpo8+1~nd+1_all.deb Size: 11454 SHA256: 2471d7fee8d771b29d6767532798cfa448170efbd1b52b756804f09bc238c6da SHA1: a00adfcd002ea4b8c101ba54564e67e573e85809 MD5sum: 139fa01be309df44565764efe6cf6ef9 Description: blessed package to manage your versions by scm tags for Python 2 setuptools_scm handles managing your Python package versions in scm metadata. It also handles file finders for the suppertes scm's. . This package installs the library for Python 2. Package: python-simplegeneric Source: simplegeneric Version: 0.7-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Provides: python2.6-simplegeneric, python2.7-simplegeneric Homepage: http://pypi.python.org/pypi/simplegeneric Priority: extra Section: python Filename: pool/main/s/simplegeneric/python-simplegeneric_0.7-1~nd+1_all.deb Size: 9802 SHA256: ccfadfca5d3a2796000fcc3d0ada9731e0f45258b365c0ee84591f3770427913 SHA1: b4bd46ca82b53ea624e1ffba0de0728089a89140 MD5sum: 0482bb6d6be4162e4e3e57a5a181d55c 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-six Source: six Version: 1.9.0-3~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Multi-Arch: foreign Homepage: http://pythonhosted.org/six/ Priority: optional Section: python Filename: pool/main/s/six/python-six_1.9.0-3~bpo8+1~nd+1_all.deb Size: 13788 SHA256: 953a2b0df284d7c3cee751384adc2920f8b2f89f963574ea7563780df9619af7 SHA1: 74034947c69b6b0151e894fa8573fbdc6ec5e451 MD5sum: e23399a91b6f44f46044ab7dec6970a2 Description: Python 2 and 3 compatibility library (Python 2 interface) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six on the Python 2 module path. It is complemented by python3-six. Package: python-six-whl Source: six Version: 1.9.0-3~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://pythonhosted.org/six/ Priority: optional Section: python Filename: pool/main/s/six/python-six-whl_1.9.0-3~bpo8+1~nd+1_all.deb Size: 14068 SHA256: 2170b7f8588c9c50ffde947fe70ba7ee23e4a899dd7bcd4fdd8e4acf018b7aa0 SHA1: c9da3c5c5bc83e15932ce61841bee6534a6a3cb5 MD5sum: 11d3292a4ddb176dedd09264dc4d1420 Description: Python 2 and 3 compatibility library (universal wheel) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six as a universal wheel. Package: python-skimage Source: skimage Version: 0.10.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15134 Depends: neurodebian-popularity-contest, libfreeimage3, python-numpy, python-scipy (>= 0.10), python-six (>= 1.3.0), python-skimage-lib (>= 0.10.1-1~nd+1), python (>= 2.7), python (<< 2.8) Recommends: python-imaging, python-matplotlib (>= 1.0), python-nose, python-pil, python-qt4 Suggests: python-opencv, python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.10.1-1~nd+1_all.deb Size: 11929000 SHA256: 26a15e982952e9b25c3a01429758b7c907961ec356cca9752891db15ad8908aa SHA1: 08847ca24011d9e0f6628a01f29f0133981f7482 MD5sum: f526a0e7d0fb0fbd9aaf45f64a046ca5 Description: Python modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 2 module. Package: python-skimage-doc Source: skimage Version: 0.10.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 21906 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-skimage Homepage: http://scikit-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.10.1-1~nd+1_all.deb Size: 17242376 SHA256: d2a4a015f0485af34b6b6f65092b9f09dc0cfa7176ba05d8679bbb0f91ed2ce4 SHA1: c47549b8d38b9b2e79035324d81121e63b6c344c MD5sum: 2abdd9cf6de3cb46714cb1a422bc3221 Description: Documentation and examples for scikit-image This package contains documentation and example scripts for python-skimage. Package: python-sklearn Source: scikit-learn Version: 0.18-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6545 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-sklearn-lib (>= 0.18-3~nd+1), python-joblib (>= 0.9.2) 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_0.18-3~nd+1_all.deb Size: 1383310 SHA256: af80d604a8f0af9e58c3a8f36a6c0c7f615d10d777a865fb7c3d333f495897a8 SHA1: 4102c5fdcce369cd95e3ce3c70b99df5ab289b6c MD5sum: a2021b2a602ede208d13e01671aad25c 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: 0.18-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28671 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore 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_0.18-3~nd+1_all.deb Size: 4682414 SHA256: 61cb82a30a804632df47d3d874ace272a161dbddc73333025861afd56fdbdc5e SHA1: 8769c3585c182a16867951dbc9c4610d99f424de MD5sum: 6d64ffb79f99a0a84cfbfa1557b18cf5 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-smmap Version: 2.0.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 93 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.6.6-7~) 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_2.0.1-1~nd+1_all.deb Size: 20052 SHA256: 2fd0d0433f9948631bc65d6971bdb5581c174cde85ed6d4cedaf7f14e61f10ab SHA1: 6585b2538d477256a26618a973a17b67680c28fc MD5sum: d8ce5f18942e0fc88876f8d54d93d458 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. . This package for Python 2. Package: python-sphinx Source: sphinx Version: 1.0.7-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4188 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~nd+1_all.deb Size: 1260238 SHA256: 6b0bdebb3903a4eb0a75440f121dde7c531d0b7b060d33223f1185d9e0a27ce9 SHA1: 555f959d260ce486fb4395838b0918eaf71fab5e MD5sum: c52dab199b31675ed95d4298586b8ed2 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~nd+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~nd+1_all.deb Size: 117140 SHA256: 6c9a6db7304b5e89b7234974e577b9c57f42d4f363a8e84d2e29ec9f8a64616d SHA1: 9ce250a1f9b8fc1728058c2808026b47da96ebcc MD5sum: 185c9304fc5463f7488db9b1fd36e992 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4028 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~nd+1_all.deb Size: 1651710 SHA256: a848e076d4c5edb65fb283f745212616fcf9a34e60d9b1a8856567a394ba1107 SHA1: 50297650b3e49c95185c7857e668e4e21c720064 MD5sum: eabe7544210191b0f23c9a80a9065e86 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: 0.4.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2096 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_0.4.3-1~nd+1_all.deb Size: 308836 SHA256: 0246c36a593a643572eac1c12920230c02b37799bd332843ac51d360b967051b SHA1: f8731b78bfc5462a11c7af4da709cae146e41e9d MD5sum: 4ac90d2bbf1755bd864efa79c691103a 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: 0.8.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15923 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python:any (>= 2.6.6-7~), python-numpy, python-scipy, python-statsmodels-lib (>= 0.8.0-1~nd+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_0.8.0-1~nd+1_all.deb Size: 3320972 SHA256: 169707892445240457fd1082caf00de439d1495fd223b9d4ee8d07288f6459c4 SHA1: 61a036f23218f41fd11865f348d7b6ef7a704a70 MD5sum: 20505541f72a15419d602bb97979cba5 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: 0.8.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 54543 Depends: neurodebian-popularity-contest, libjs-jquery Recommends: libjs-mathjax 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_0.8.0-1~nd+1_all.deb Size: 8984612 SHA256: 736cbe366ee7e18105c6e63c5c2cc59887f49184dce9f4dc9a77648aca7cb8a8 SHA1: 5f0095505de6fd55a1d5c356a94b221d4b41b525 MD5sum: 4972cede41b5cac702e6a374602b99ac Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-surfer Source: pysurfer Version: 0.6-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 193 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-nibabel, python-numpy, python-scipy, 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_0.6-1~nd+1_all.deb Size: 43190 SHA256: acc7fbfc89d8c5200633705b40325ad457971313204c3f737aa4a49e645657ed SHA1: 0f70b1794d04389e6680c11ee2a98d4d98fc2227 MD5sum: 706f549c94471dfd7ac06207255436e8 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-sympy Source: sympy Version: 0.6.7-1.1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9268 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Recommends: python-imaging, python-ctypes, ipython Homepage: http://code.google.com/p/sympy/ Priority: optional Section: python Filename: pool/main/s/sympy/python-sympy_0.6.7-1.1~nd+1_all.deb Size: 1696300 SHA256: 65cc1db7a2ef35ab86aef147e04c6b2d8b7c2ca10e689a3e9767e3bce6291484 SHA1: b6182a46ed673beb7b3568356ac27cb73ca33585 MD5sum: 16047fa2cf0d90581ff5df51e1de35b1 Description: Computer Algebra System (CAS) in Python SymPy is a Python library for symbolic mathematics (manipulation). It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries, except optionally for plotting support. Package: python-tables Source: pytables Version: 3.2.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2815 Depends: neurodebian-popularity-contest, python-numpy, python, python-numexpr, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-tables-lib (>= 3.2.1-1~nd+1), python-tables-lib (<< 3.2.1-1~nd+1.1~), python-tables-data (= 3.2.1-1~nd+1) Suggests: python-tables-doc, python-netcdf, vitables Homepage: http://www.pytables.org Priority: optional Section: python Filename: pool/main/p/pytables/python-tables_3.2.1-1~nd+1_all.deb Size: 344840 SHA256: 3493c792072ab913aef70030022c75ea4e63783024c7347848bec635dba5456c SHA1: 9a3a846299057d4f75db3e005ef2486fc1c34c67 MD5sum: 1d45c911dac8c853a81718c16e8eaed6 Description: hierarchical database for Python based on HDF5 PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . - Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them). - The tables are both enlargeable and compressible. - I/O is buffered, so you can get very fast I/O, specially with large tables. - Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well. - It supports the complete set of NumPy, Numeric and numarray objects. . This is the Python 2 version of the package. Package: python-tables-data Source: pytables Version: 3.2.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 957 Depends: neurodebian-popularity-contest Homepage: http://www.pytables.org Priority: optional Section: python Filename: pool/main/p/pytables/python-tables-data_3.2.1-1~nd+1_all.deb Size: 51248 SHA256: 37549f40bb3af4758ae9d9a456fec6902b07b257a0076264727c4398f3b5f393 SHA1: f0f3c1a62e58c989da869240aafc32f3b2f8a528 MD5sum: 9292011acdf9edc4d7988c5d9060f467 Description: hierarchical database for Python based on HDF5 - test data PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . This package includes daya fils used for unit testing. Package: python-tables-doc Source: pytables Version: 3.2.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8931 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0), libjs-jquery-cookie Suggests: xpdf | pdf-viewer, www-browser Homepage: http://www.pytables.org Priority: optional Section: doc Filename: pool/main/p/pytables/python-tables-doc_3.2.1-1~nd+1_all.deb Size: 4249302 SHA256: 0261a6906b3f11e19de72907f72181e28b0ceaa39ee09cc4f986a0aa37109618 SHA1: 4b89ae7eb63a55068fb800ce79157454b8067e4d MD5sum: 1110d75f3980e0431b8739c7d08c485a Description: hierarchical database for Python based on HDF5 - documentation PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . This package includes the manual in PDF and HTML formats. Package: python-tqdm Source: tqdm Version: 4.11.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 179 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/tqdm/tqdm Priority: optional Section: python Filename: pool/main/t/tqdm/python-tqdm_4.11.2-1~nd+1_all.deb Size: 49624 SHA256: 7604eaf9c616c43a5471f33580c8bc84e3998d84c27f3218ea57f0505079ecb2 SHA1: 1a59d105cbbcd130eacbf60c7db11e608f59e66c MD5sum: d21f4200e1240e8ba7959068c299f4fb Description: fast, extensible progress bar for Python 2 tqdm (read taqadum, تقدّم) means “progress” in Arabic. tqdm instantly makes your loops show a smart progress meter, just by wrapping any iterable with "tqdm(iterable)". . This package contains the Python 2 version of tqdm . Package: python-tz Version: 2011h-0.1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 180 Depends: neurodebian-popularity-contest, tzdata, python, python-central (>= 0.6.11) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2011h-0.1~nd+1_all.deb Size: 46912 SHA256: 0e8fdcb7a39493961a0d36f489eb13080b7f1e52a6d8324fedeb2d9772af249a SHA1: b1818fcc06cc8630f2af4eb25229e441028a0d81 MD5sum: cdd882a56d74682e4229bc00421ee82b 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). Python-Version: all Package: python-urllib3 Version: 1.12-1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 254 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-six Recommends: ca-certificates, python-ndg-httpsclient, python-openssl, python-pyasn1 Suggests: python-ntlm Homepage: http://urllib3.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-urllib3/python-urllib3_1.12-1~bpo8+1~nd+1_all.deb Size: 65356 SHA256: dd3c3e31a0d8bb5471cb1330d9bbe68a9a58e28e1ff5e4898405e1603b0c8474 SHA1: 800bd6128fc8a035a7afdcd5fe8a5c349017ff7f MD5sum: 2edc4725cb1341b7f9488733bd593694 Description: HTTP library with thread-safe connection pooling for Python urllib3 supports features left out of urllib and urllib2 libraries. . - Re-use the same socket connection for multiple requests (HTTPConnectionPool and HTTPSConnectionPool) (with optional client-side certificate verification). - File posting (encode_multipart_formdata). - Built-in redirection and retries (optional). - Supports gzip and deflate decoding. - Thread-safe and sanity-safe. - Small and easy to understand codebase perfect for extending and building upon. Package: python-urllib3-whl Source: python-urllib3 Version: 1.12-1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 102 Depends: neurodebian-popularity-contest, python-six-whl Recommends: ca-certificates Homepage: http://urllib3.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-urllib3/python-urllib3-whl_1.12-1~bpo8+1~nd+1_all.deb Size: 92820 SHA256: 21412c99d8b2a8b234f00ccd587610414c5bddb9eae3bd159e987d2730c44e2c SHA1: faf759e621b5fa423b40773db728fd823a5ea27a MD5sum: 08adec2012fd6895cfc8ce53ff7d6907 Description: HTTP library with thread-safe connection pooling urllib3 supports features left out of urllib and urllib2 libraries. . - Re-use the same socket connection for multiple requests (HTTPConnectionPool and HTTPSConnectionPool) (with optional client-side certificate verification). - File posting (encode_multipart_formdata). - Built-in redirection and retries (optional). - Supports gzip and deflate decoding. - Thread-safe and sanity-safe. - Small and easy to understand codebase perfect for extending and building upon. . This package contains the universal wheel. Package: python-vcr Source: vcr.py Version: 1.7.3-1.0.1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 157 Depends: neurodebian-popularity-contest, python-contextlib2, python-mock, python-six (>= 1.5), python-wrapt, python-yaml, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/kevin1024/vcrpy/ Priority: optional Section: python Filename: pool/main/v/vcr.py/python-vcr_1.7.3-1.0.1~nd+1_all.deb Size: 43522 SHA256: 77ab97cd48b87aa68889d39a235da7456510032690244b58d17debfd9668f851 SHA1: 21b51ade6789d7d502a864ce2d8b8720a19ca094 MD5sum: 64b0712a730827639201fdfc39e8f372 Description: record and replay HTML interactions (Python library) vcr.py records all interactions that take place through the HTML libraries it supports and writes them to flat files, called cassettes (YAML format by default). These cassettes could be replayed then for fast, deterministic and accurate HTML testing. . vcr.py supports the following Python HTTP libraries: - urllib2 (stdlib) - urllib3 - http.client (Python3 stdlib) - Requests - httplib2 - Boto (interface to Amazon Web Services) - Tornado's HTTP client . This package contains the modules for Python 2. Package: python-w3lib Version: 1.11.0-1~nd+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~nd+1_all.deb Size: 14094 SHA256: b7160742d1d6655d949e6a11303b3d861d8a9ffe4cd2b5dc02b90e4690c25bcc SHA1: 619b171f50cf2fc3f8a3d0ae40681464f2bd2ab7 MD5sum: abfa0ccef999dae58e5894ffa4af82f9 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~nd+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~nd+1_all.deb Size: 165572 SHA256: 7be4f3e227cb2aa792e79c5b7ccc6c3b878436e040471324db1dd284bc7ccdb6 SHA1: c0910fc03848e4825f9e1d276edd2a728981ce3e MD5sum: 35744ae66a39ca40f586d3da5dc66a57 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~nd+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~nd+1_all.deb Size: 881836 SHA256: 23b0fda78028dc71c9645cce224cb3c01269827fc90c6429f94c3cb7ebc533dc SHA1: a9cbe941509ee644d02f93eff5076afb4dfcaf9d MD5sum: 6b2655fad599fafea5535c47d130fcc1 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-argcomplete Source: python-argcomplete Version: 1.0.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 98 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Priority: optional Section: python Filename: pool/main/p/python-argcomplete/python3-argcomplete_1.0.0-1~nd+1_all.deb Size: 22284 SHA256: 7ce1a8bc33d9665bdfca2e9f62fdba6e45b3cb152a185dc2d42e99b3fdc46bd3 SHA1: bb77b998936973e2f1724c1ee467037fb9582154 MD5sum: 709af60d648e61d95834e2b45a63c6f2 Description: bash tab completion for argparse (for Python 3) Argcomplete provides easy, extensible command line tab completion of arguments for your Python script. . It makes two assumptions: . * You're using bash as your shell * You're using argparse to manage your command line arguments/options . Argcomplete is particularly useful if your program has lots of options or subparsers, and if your program can dynamically suggest completions for your argument/option values (for example, if the user is browsing resources over the network). . This package provides the module for Python 3.x. Package: python3-boto3 Source: python-boto3 Version: 1.2.2-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 721 Depends: neurodebian-popularity-contest, python3-botocore, python3-jmespath, python3:any (>= 3.3.2-2~), python3-requests, python3-six Homepage: https://github.com/boto/boto3 Priority: optional Section: python Filename: pool/main/p/python-boto3/python3-boto3_1.2.2-2~nd+1_all.deb Size: 63014 SHA256: 2b7f52391e596a78d603d278f48a14f1ddfad83c0541a1f52b9e92b8b8fdd57b SHA1: 9061d3b81293a1d88d33665d93cc4fe71ed3a7ad MD5sum: 43184c4f35a4b326d7e941de518ebab9 Description: Python interface to Amazon's Web Services - Python 3.x Boto is the Amazon Web Services interface for Python. It allows developers to write software that makes use of Amazon services like S3 and EC2. Boto provides an easy to use, object-oriented API as well as low-level direct service access. Package: python3-citeproc Source: citeproc-py Version: 0.3.0-1~nd+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~nd+1_all.deb Size: 81822 SHA256: 397553ba2c393746e303667d70871e29928088f030709f71ddc1e9cf050b8ea8 SHA1: fa10008219f71e5bc589696cb6cde597b1111080 MD5sum: 2bedbc4c2743ffb36b7e764446621051 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-contextlib2 Source: contextlib2 Version: 0.4.0-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 32 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Homepage: http://contextlib2.readthedocs.org/ Priority: optional Section: python Filename: pool/main/c/contextlib2/python3-contextlib2_0.4.0-3~nd+1_all.deb Size: 8722 SHA256: 0a491f030c8c742f313aaea5f9b006b37c1870c6e4005220ebf2afe9be397d1e SHA1: c244a752ad56c9b2156ffd09d8eb9724291b6161 MD5sum: 9ed808ffc27d17ac4c668f60131205d9 Description: Backport and enhancements for the contextlib module - Python 3.x contextlib2 is a backport of the standard library's contextlib module to earlier Python versions. . It also serves as a real world proving ground for possible future enhancements to the standard library version. . This package contains the Python 3.x module. Package: python3-docker Source: python-docker Version: 1.7.2-1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 150 Depends: neurodebian-popularity-contest, python3-requests (>= 2.5.3~), python3-six (>= 1.4.0), python3-websocket (>= 0.18.0~), python3:any (>= 3.3.2-2~) Homepage: https://github.com/dotcloud/docker-py/ Priority: optional Section: python Filename: pool/main/p/python-docker/python3-docker_1.7.2-1~bpo8+1~nd+1_all.deb Size: 27174 SHA256: 2bba5abed5c3488d76d40d11ffe6092063cbd6206eda1a27e44569976c49295d SHA1: 7ea0cc04691204a219624ca14828df341c783cf2 MD5sum: 3fa6848799867d77d3318a0e3a14bcfe Description: Python 3 wrapper to access docker.io's control socket This package contains oodles of routines that aid in controling docker.io over it's socket control, the same way the docker.io client controls the daemon. . This package provides Python 3 module bindings only. Package: python3-dockerpty Source: dockerpty Version: 0.4.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python3-six, python3:any (>= 3.3.2-2~) Recommends: python3-docker (>= 0.7.1) Homepage: https://github.com/d11wtq/dockerpty Priority: optional Section: python Filename: pool/main/d/dockerpty/python3-dockerpty_0.4.1-1~nd+1_all.deb Size: 10966 SHA256: 6ef0c8fffb1650b53b6293e7a53ced450146d254a6e759ca6045c1c60b3c20ac SHA1: c47ba7ad5fdb1c05e405b09e4080c845cc3d71c5 MD5sum: c2afbb903ec7e0ac611037936de6edf5 Description: Pseudo-tty handler for docker Python client (Python 3.x) Provides the functionality needed to operate the pseudo-tty (PTY) allocated to a docker container, using the Python client. . This package provides Python 3.x version of dockerpty. Package: python3-duecredit Source: duecredit Version: 0.5.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 239 Depends: neurodebian-popularity-contest, python3-citeproc, python3-requests, python3-six, python3:any (>= 3.3.2-2~) Homepage: https://github.com/duecredit/duecredit Priority: optional Section: python Filename: pool/main/d/duecredit/python3-duecredit_0.5.0-1~nd+1_all.deb Size: 52728 SHA256: 87f31dbf36c1fd52d00284cc46f6854ea5b66b8e2e17f6d13c2ae1f96c2e8f8f SHA1: e1bec41490051d24a2667f6f22fc90260e9928dc MD5sum: 723e8714947229ea08354dbcbd8051c8 Description: Publications (and donations) tracer duecredit is being conceived to address the problem of inadequate citation of scientific software and methods, and limited visibility of donation requests for open-source software. . It provides a simple framework (at the moment for Python only) to embed publication or other references in the original code so they are automatically collected and reported to the user at the necessary level of reference detail, i.e. only references for actually used functionality will be presented back if software provides multiple citeable implementations. . To get a sense of what duecredit is about, simply run or your analysis script with `-m duecredit`, e.g. . python3 -m duecredit examples/example_scipy.py Python-Egg-Name: duecredit Package: python3-fsl Source: fslpy Version: 0.11.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 464 Depends: neurodebian-popularity-contest, python3-lxml, python3-nibabel, python3-indexed-gzip, python3-matplotlib, python3-numpy, python3-six (>= 1.10.0), python3-six (<< 2.0), python3:any (>= 3.3.2-2~) Priority: optional Section: python Filename: pool/main/f/fslpy/python3-fsl_0.11.0-1~nd+1_all.deb Size: 96490 SHA256: 2899e3c03a0f6c1213b98776f50daeb3a25e13ad86308e940a5f8af71e784a53 SHA1: a5c12c1140210506b217f4edea4417755d9166bd MD5sum: fa647e2d2ce4198ad19c5cd612865842 Description: FSL Python library Support library for FSL. . This package provides the Python 3 module. Package: python3-funcsigs Source: python-funcsigs Version: 0.4-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 64 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Suggests: python-funcsigs-doc Homepage: http://funcsigs.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-funcsigs/python3-funcsigs_0.4-2~nd+1_all.deb Size: 13072 SHA256: 15fa59a1800b32094bf5f09458e09a7e14966957b6680c167e2e6fd75b2927e2 SHA1: d984c9873b79c7bc61b8ee3082591fdcc6e0283b MD5sum: 304a096119e3599f3c528b649e5e8caf Description: function signatures from PEP362 - Python 3.x funcsigs is a backport of the PEP 362 function signature features from Python 3.3's inspect module. The backport is compatible with Python 2.6, 2.7 as well as 3.2 and up. . This package contains the Python 3.x module. Package: python3-future Source: python-future Version: 0.15.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1662 Pre-Depends: dpkg (>= 1.15.6~) Depends: neurodebian-popularity-contest, python3.5:any, python3:any (>= 3.3.2-2~) Suggests: python-future-doc Homepage: https://python-future.org Priority: optional Section: python Filename: pool/main/p/python-future/python3-future_0.15.2-1~nd+1_all.deb Size: 346170 SHA256: bbc88a3bfe1da8b189ae590ddd8c9c5299b48a80a620427eb3098d39e48df52f SHA1: 6012b63a4e8c5ffba035de00a29c11492bade492 MD5sum: b618e2950a1d2b3acde7e2283e507420 Description: Clean single-source support for Python 3 and 2 - Python 3.x Future is the missing compatibility layer between Python 2 and Python 3. It allows one to use a single, clean Python 3.x-compatible codebase to support both Python 2 and Python 3 with minimal overhead. . The imports have no effect on Python 3. On Python 2, they shadow the corresponding builtins, which normally have different semantics on Python 3 versus 2, to provide their Python 3 semantics. . This package contains the Python 3.x module. Package: python3-git Source: python-git Version: 2.1.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1623 Depends: neurodebian-popularity-contest, git (>= 1:1.7) | git-core (>= 1:1.5.3.7), python3-gitdb (>= 2), python3:any (>= 3.3.2-2~) Suggests: python-git-doc Homepage: https://github.com/gitpython-developers/GitPython Priority: optional Section: python Filename: pool/main/p/python-git/python3-git_2.1.1-2~nd+1_all.deb Size: 299340 SHA256: b50df28d141c38d93341e92f170584c6c48f55bd6e8bef48fc7eeb9dadb87953 SHA1: 56d944939aa2ebcd9b906ea7adf9072923f2fa10 MD5sum: 2213e18149ecac709b86670647b34ab7 Description: Python library to interact with Git repositories - Python 3.x python-git provides object model access to a Git repository, so Python can be used to manipulate it. Repository objects can be opened or created, which can then be traversed to find parent commit(s), trees, blobs, etc. . This package provides the Python 3.x module. Package: python3-humanize Source: python-humanize Version: 0.5.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Homepage: http://github.com/jmoiron/humanize Priority: optional Section: python Filename: pool/main/p/python-humanize/python3-humanize_0.5.1-2~nd+1_all.deb Size: 12670 SHA256: eb53b7253b33997c80ef27e33d37519cf929e44eb64153d987a6b19d2833e7fd SHA1: 957dac3e198cfec8d7edb319555011c665550a83 MD5sum: a6f39e5b551bf6671a35dc023ffff692 Description: Python Humanize library (Python 3) This library proposes various common humanization utilities, like turning a number into a fuzzy human readable duration ('3 minutes ago') or into a human readable size or throughput. . This is the Python 3 version of the package. Package: python3-hypothesis Source: python-hypothesis Version: 3.6.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 411 Depends: neurodebian-popularity-contest Suggests: python-hypothesis-doc Homepage: https://github.com/DRMacIver/hypothesis Priority: optional Section: python Filename: pool/main/p/python-hypothesis/python3-hypothesis_3.6.0-1~nd+1_all.deb Size: 95334 SHA256: 2f943202622ba5112466988710b0317cbd9ab1987e0235918de6661e73e0b860 SHA1: 102c967f811e58126e257521deb404fde51e0a99 MD5sum: be41a236ff21346949742e1188a8d7c8 Description: advanced Quickcheck style testing library for Python 3 Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It's based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow. . Hypothesis is both extremely practical and also advances the state of the art of unit testing by some way. It's easy to use, stable, and extremely powerful. If you're not using Hypothesis to test your project then you're missing out. . This package contains the Python 3 module. Package: python3-jdcal Source: jdcal Version: 1.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23 Depends: neurodebian-popularity-contest Homepage: https://github.com/phn/jdcal Priority: optional Section: python Filename: pool/main/j/jdcal/python3-jdcal_1.0-1~nd+1_all.deb Size: 7562 SHA256: 9f25374a1984cc55029085f0842ebcc556cb604d77ed5a3d9f2413d74d8d18db SHA1: 0b803e0c91b3084849b81ef4edebf8c82ba5a9b8 MD5sum: 9addeaab0579f0e325d2cc0be513923f 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: 0.10.3+git55-g660fe5d-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 483 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Recommends: python3-numpy, python3-pytest, python3-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python3-joblib_0.10.3+git55-g660fe5d-1~nd+1_all.deb Size: 112788 SHA256: 7c0e3c043078b0c36fe283c7d6545e5b3a174fe920d7719dcf5511d4f328f304 SHA1: 57f81002f441f8c0cbcac4fc916a0c258973787c MD5sum: ffa40097ebd42bf2e0d7e87fe45edece 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-jsmin Source: python-jsmin Version: 2.2.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 68 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Homepage: https://github.com/tikitu/jsmin Priority: optional Section: python Filename: pool/main/p/python-jsmin/python3-jsmin_2.2.1-1~nd+1_all.deb Size: 21580 SHA256: a9c914e0e7d279672a487222e15f91575cc406f6276ed013cbd894c46b9bc435 SHA1: 522f5c43a921c02244542fca2fe4bd2ea43e7c06 MD5sum: 7785af8648130c52963347d743f25cc4 Description: JavaScript minifier written in Python - Python 3.x Python-jsmin is a JavaScript minifier, it is written in pure Python and actively maintained. . This package provides the Python 3.x module. Package: python3-mdp Source: mdp Version: 3.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1387 Depends: neurodebian-popularity-contest, python3-future, python3-numpy, python3:any (>= 3.3.2-2~), python-numpy, python-future Recommends: python3-pytest, python3-scipy, python3-joblib, python3-sklearn Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python3-mdp_3.5-1~nd+1_all.deb Size: 275288 SHA256: daea6afa80bd07e998e6619f4723b5e1e2481829971d83057bb823f82688cdad SHA1: 83c40cc3e9af5c666412136996bd5d5467086036 MD5sum: b7a08f083ad842fe1764419227f14c1d Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 3. Package: python3-nibabel Source: nibabel Version: 2.1.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 64194 Depends: neurodebian-popularity-contest, python3-numpy, python3-scipy Suggests: python-nibabel-doc, python3-dicom, python3-fuse, python3-mock Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python3-nibabel_2.1.0-1~nd+1_all.deb Size: 2164702 SHA256: 6adfaa4b0f6be9196bcb1c7148e50a3df8e235e73c75e756a30eeba3a4a714aa SHA1: ba21150ee825b7932595fe49ff673c6110663a49 MD5sum: 28280bb7035801957d40ed3ca8eb26df 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-nilearn Source: nilearn Version: 0.2.5~dfsg.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2190 Depends: neurodebian-popularity-contest, python3-nibabel (>= 1.1.0), python3:any (>= 3.3.2-2~), python3-numpy (>= 1:1.6), python3-scipy (>= 0.9), python3-sklearn (>= 0.12.1) Recommends: python-matplotlib Homepage: https://nilearn.github.io Priority: extra Section: python Filename: pool/main/n/nilearn/python3-nilearn_0.2.5~dfsg.1-1~nd+1_all.deb Size: 686336 SHA256: 359ecabd6ca6401141f095cac886ce63b0832feb791aecaba8d0a7764f95bd9d SHA1: 590eb8e987ec3e6d57c0fac7f96404d619663340 MD5sum: 57bfa80b7b9dd2829cacc28d4aa4782f Description: fast and easy statistical learning on neuroimaging data (Python 3) This Python module leverages the scikit-learn toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. . This package provides the Python 3 version. Package: python3-nosexcover Source: nosexcover Version: 1.0.10-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, python3-coverage, python3-nose, python3:any (>= 3.3.2-2~), python-nose, python-coverage (>= 3.4) Homepage: http://pypi.python.org/pypi/nosexcover Priority: extra Section: python Filename: pool/main/n/nosexcover/python3-nosexcover_1.0.10-2~nd+1_all.deb Size: 5188 SHA256: 3bcb86b62cdd25a1ec6517f1ede04a23e9e3c6377da80877180ba8f6678f743b SHA1: 75aa68d8e79156378313cccd6c08dcf0cf211a03 MD5sum: 76f88c19868c3489c6271b1a7a4be9bb Description: Add Cobertura-style XML coverage report to nose A companion to the built-in nose.plugins.cover, this plugin will write out an XML coverage report to a file named coverage.xml. . It will honor all the options you pass to the Nose coverage plugin, especially --cover-package. Package: python3-openpyxl Source: openpyxl Version: 2.3.0-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1321 Depends: neurodebian-popularity-contest, python3-jdcal, python3:any (>= 3.3.2-2~), python3-lxml (>= 3.3.4) | python3-et-xmlfile Recommends: python3-pytest, python3-pil Homepage: http://bitbucket.org/openpyxl/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python3-openpyxl_2.3.0-3~nd+1_all.deb Size: 198350 SHA256: 2ce962c4e9e8d0ddda66123f407806a5561500ce014136a756cfd12edc6a29f4 SHA1: b85e3bc56ccb805dfbcd1071edea4c5e59e4cfbe MD5sum: d0c2d07c727f289d7608f7b61dc3e48d 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-packaging Source: python-packaging Version: 16.2-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 85 Depends: neurodebian-popularity-contest, python3-pyparsing, python3-six, python3:any (>= 3.3.2-2~) Homepage: https://pypi.python.org/pypi/packaging Priority: optional Section: python Filename: pool/main/p/python-packaging/python3-packaging_16.2-2~nd+1_all.deb Size: 17286 SHA256: a147fcff55f750bc7b8b992bf795e9cf38490231fa5276c97036dd340b62ad8c SHA1: 7c08b0dc58f1c9f02768d848848efbcafc80a4d8 MD5sum: 6568daac84251afcef7f916f40d4e2d7 Description: core utilities for python packages These core utilities currently consist of: - Version Handling (PEP 440) - Dependency Specification (PEP 440) Package: python3-pandas Source: pandas Version: 0.19.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25227 Depends: neurodebian-popularity-contest, python3-dateutil, python3-numpy (>= 1:1.7~), python3-tz, python3:any (>= 3.3.2-2~), python3-pandas-lib (>= 0.19.2-1~nd+1), python3-pkg-resources, python3-six Recommends: python3-scipy, python3-matplotlib, python3-numexpr, python3-tables, python3-bs4, python3-html5lib, python3-lxml Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.19.2-1~nd+1_all.deb Size: 2614206 SHA256: ce29c0d0e222cb92b80183474a12794aa89b590ca0ace2a86dcc4e57a2bb5de5 SHA1: 2da7542563c0c01102e329581e8ca0b1d5de51cd MD5sum: 6c600d758b1e3eb7f4c9b3c479b73c6a 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: 0.4.1+git34-ga5b54c2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 782 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_0.4.1+git34-ga5b54c2-1~nd+1_all.deb Size: 171540 SHA256: fd0bf2f3ad1e6adf5b9b2cdcda74f3624d10f55420ae6303fc6f9ad90fe838a3 SHA1: e0abe996099891aefddd1dbfa60bc72cbfccb974 MD5sum: d9543939758554f197f0397a58af7885 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-pkg-resources Source: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 412 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Suggests: python3-setuptools Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: python Filename: pool/main/p/python-setuptools/python3-pkg-resources_20.10.1-1.1~bpo8+1~nd+1_all.deb Size: 111978 SHA256: 36a64437ff69d22a329972aefeb1317472c003cf880e151757932245370e5972 SHA1: bfb67674ced9d86162dcb2899fbcc613be113a2f MD5sum: ce4fa06bc2f539f762ef488dfb9fb14e Description: Package Discovery and Resource Access using pkg_resources The pkg_resources module provides an API for Python libraries to access their resource files, and for extensible applications and frameworks to automatically discover plugins. It also provides runtime support for using C extensions that are inside zipfile-format eggs, support for merging packages that have separately-distributed modules or subpackages, and APIs for managing Python's current "working set" of active packages. Package: python3-prov Source: python-prov Version: 1.4.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1235 Depends: neurodebian-popularity-contest, python3-dateutil, python3-lxml, python3-networkx (>= 1.10), python3-six (>= 1.9.0), python3:any (>= 3.3.2-2~) Suggests: python-prov-doc, python3-pydotplus Homepage: https://github.com/trungdong/prov Priority: optional Section: python Filename: pool/main/p/python-prov/python3-prov_1.4.0-1~nd+1_all.deb Size: 70164 SHA256: eb3b5a75434c818231aa350501a0c794046e1da39f8bfcff6b7ba206cf522c7f SHA1: d450e242d7e966eb82ac27bce407520dfacf0197 MD5sum: bccacf307043a31ed94436083233acf5 Description: W3C Provenance Data Model (Python 3) A library for W3C Provenance Data Model supporting PROV-JSON and PROV- XML import/export. . Features: - An implementation of the W3C PROV Data Model in Python. - In-memory classes for PROV assertions, which can then be output as PROV-N. - Serialization and deserializtion support: PROV-JSON and PROV-XML. - Exporting PROV documents into various graphical formats (e.g. PDF, PNG, SVG). . This package provides the prov library for Python 3. Package: python3-py Source: python-py Version: 1.4.31-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 320 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_1.4.31-2~nd+1_all.deb Size: 82268 SHA256: 65bc42d600c55da39ffd1381f8cdc4a732996142758881f068c6fe9a14221a13 SHA1: 38d97058b9accd5b06929df289ccc3ac6450dc9a MD5sum: d30561ac4dc95895b7d705e28fb85820 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-pydotplus Source: python-pydotplus Version: 2.0.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 104 Depends: neurodebian-popularity-contest, graphviz, python3-pyparsing (>= 2.0.1), python3:any (>= 3.3.2-2~) Suggests: python-pydotplus-doc Homepage: http://pydotplus.readthedocs.org/ Priority: optional Section: python Filename: pool/main/p/python-pydotplus/python3-pydotplus_2.0.2-1~nd+1_all.deb Size: 20262 SHA256: 6fa7cc65f60e53673a95ac0b7710158035358804412728addc9c01ce7eee4fbd SHA1: 0e8c34d5118c421ba3d472f8225ba7cbb62a0e86 MD5sum: 0d7c20a842addf30fd582fdf688cf0eb Description: interface to Graphviz's Dot language - Python 3.x PyDotPlus is an improved version of the old pydot project that provides a Python Interface to Graphviz's Dot language. . Differences with pydot: * Compatible with PyParsing 2.0+. * Python 2.7 - Python 3 compatible. * Well documented. * CI Tested. . This package contains the Python 3.x module. Package: python3-pytest Source: pytest Version: 3.0.4-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 648 Depends: neurodebian-popularity-contest, python3-pkg-resources, python3-py (>= 1.4.29), python3:any (>= 3.3.2-2~) Homepage: http://pytest.org/ Priority: optional Section: python Filename: pool/main/p/pytest/python3-pytest_3.0.4-1~nd+1_all.deb Size: 181276 SHA256: fe3f6152bfc2cd968b3589c896fb3df5276213b8cda7405a9d67b6a0ed25ebc8 SHA1: 10539b671cab14e268dbf1ad05d15d69ffefda4e MD5sum: 51e85dd49f028a65ddb9657707ea3b5a 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 py.test-3 script. Package: python3-pytest-localserver Source: pytest-localserver Version: 0.3.4-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 61 Depends: neurodebian-popularity-contest, python3-pytest, python3-werkzeug (>= 0.10), python3:any (>= 3.3.2-2~) Homepage: https://bitbucket.org/pytest-dev/pytest-localserver/ Priority: optional Section: python Filename: pool/main/p/pytest-localserver/python3-pytest-localserver_0.3.4-2~nd+1_all.deb Size: 19274 SHA256: 97a7b78b2e02d5065ec6804cb7d890befc54aeeeccf4eb28e6cf3b60287ac3de SHA1: 39e1d74dd953692835179b37f5b8f1cf1455813c MD5sum: 4e985d26f70005cc1d1f4cddded2fe4e 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~nd+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~nd+1_all.deb Size: 5724 SHA256: d1d4de5834aedf07c5048c0240c07b6b48ccad80957e7d656bf0773326b9cdc0 SHA1: a878173779a4f9651dd416792ef1c446d0aad9ee MD5sum: 30ba6a3d4ecfa3acc9d387e1fb338a6d 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-requests Source: requests Version: 2.8.1-1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 240 Depends: neurodebian-popularity-contest, python3-urllib3 (>= 1.12), python3:any (>= 3.3.2-2~), ca-certificates, python3-chardet Suggests: python3-ndg-httpsclient, python3-openssl, python3-pyasn1 Homepage: http://python-requests.org Priority: optional Section: python Filename: pool/main/r/requests/python3-requests_2.8.1-1~bpo8+1~nd+1_all.deb Size: 67920 SHA256: 85db1db645bfe5f6d02bde57c8c8df004f026b94e237011d2b44ad3ce52db677 SHA1: 345e61d12fa61e9a0cc4e3274652ee7d3f94d8dc MD5sum: bd062c8c6c8e61e5fd5435079bcfab9d Description: elegant and simple HTTP library for Python3, built for human beings Requests allow you to send HTTP/1.1 requests. You can add headers, form data, multipart files, and parameters with simple Python dictionaries, and access the response data in the same way. It's powered by httplib and urllib3, but it does all the hard work and crazy hacks for you. . Features . - International Domains and URLs - Keep-Alive & Connection Pooling - Sessions with Cookie Persistence - Browser-style SSL Verification - Basic/Digest Authentication - Elegant Key/Value Cookies - Automatic Decompression - Unicode Response Bodies - Multipart File Uploads - Connection Timeouts . This package contains the Python 3 version of the library. Package: python3-seaborn Source: seaborn Version: 0.7.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 766 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-pandas, python3-matplotlib Recommends: python3-patsy Homepage: https://github.com/mwaskom/seaborn Priority: optional Section: python Filename: pool/main/s/seaborn/python3-seaborn_0.7.1-2~nd+1_all.deb Size: 128254 SHA256: 88da7abdee1e8ed2c7cd24662994acfafe422547ac306dc4b9dcb82c9eaaf8f7 SHA1: 8429767031471ceeca21eeca0044f53294f93286 MD5sum: ecf7b4b1c636f94376c9e1beca400565 Description: statistical visualization library Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. . Some of the features that seaborn offers are . - Several built-in themes that improve on the default matplotlib aesthetics - Tools for choosing color palettes to make beautiful plots that reveal patterns in your data - Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data - Tools that fit and visualize linear regression models for different kinds of independent and dependent variables - A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate - High-level abstractions for structuring grids of plots that let you easily build complex visualizations . This is the Python 3 version of the package. Package: python3-setuptools Source: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 431 Depends: neurodebian-popularity-contest, python3-pkg-resources (= 20.10.1-1.1~bpo8+1~nd+1), python3:any (>= 3.3.2-2~) Suggests: python-setuptools-doc Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: python Filename: pool/main/p/python-setuptools/python3-setuptools_20.10.1-1.1~bpo8+1~nd+1_all.deb Size: 121934 SHA256: d36543b92692f75fd41013a92eb7b83804f31efa3e2fa2e8dba0286be5e67f66 SHA1: 89da5ed3ef4930101f70601d1027b86e8b638eb2 MD5sum: 18756da7c9bda50df0d48df6bf417b55 Description: Python3 Distutils Enhancements Extensions to the python-distutils for large or complex distributions. Package: python3-setuptools-scm Source: setuptools-scm Version: 1.8.0-1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49 Depends: neurodebian-popularity-contest Homepage: https://github.com/pypa/setuptools_scm Priority: optional Section: python Filename: pool/main/s/setuptools-scm/python3-setuptools-scm_1.8.0-1~bpo8+1~nd+1_all.deb Size: 11504 SHA256: 18ecb5022d4c925922743c9be93a67d14d46299c444fc16cd44886a062f714b9 SHA1: f3863998d2b04d2a9c86e558aedd803eb211f799 MD5sum: b02bc4c26bc7a1acddbc97da381c5cf9 Description: blessed package to manage your versions by scm tags for Python 3 setuptools_scm handles managing your Python package versions in scm metadata. It also handles file finders for the suppertes scm's. . This package installs the library for Python 3. Package: python3-six Source: six Version: 1.9.0-3~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python3:any (>= 3.4~) Multi-Arch: foreign Homepage: http://pythonhosted.org/six/ Priority: optional Section: python Filename: pool/main/s/six/python3-six_1.9.0-3~bpo8+1~nd+1_all.deb Size: 13864 SHA256: 04e34cf5aa7ccee58a9ad967bdba4aea42566f635f61cdf6210cefd6a835d984 SHA1: ba8c5074dad26145c72e90ded5311486da064efb MD5sum: bfaaee600a2763c6130dfbde60a41e0f Description: Python 2 and 3 compatibility library (Python 3 interface) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six on the Python 3 module path. It is complemented by python-six. Package: python3-skimage Source: skimage Version: 0.10.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15115 Depends: neurodebian-popularity-contest, libfreeimage3, python3-numpy, python3-scipy (>= 0.10), python3-six (>= 1.3.0), python3-skimage-lib (>= 0.10.1-1~nd+1), python3:any (>= 3.3.2-2~) Recommends: python3-imaging, python3-matplotlib (>= 1.0), python3-nose, python3-pil Suggests: python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python3-skimage_0.10.1-1~nd+1_all.deb Size: 11915866 SHA256: cd53b6ec9524fc904a752a41f025002dff4be06130bd005bef04287cfcd8cf84 SHA1: 158f4ec9919b03f9c936cfa6f281c86e97091041 MD5sum: 3ad25fc688fff35b82afb80ef9667383 Description: Python 3 modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 3 module. Package: python3-sklearn Source: scikit-learn Version: 0.18-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6544 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-sklearn-lib (>= 0.18-3~nd+1), python3-joblib (>= 0.9.2) Recommends: python3-nose, python3-matplotlib Suggests: python3-dap, python-sklearn-doc, ipython3 Enhances: python3-mdp, python3-mvpa2 Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python3-sklearn_0.18-3~nd+1_all.deb Size: 1383136 SHA256: b3650281172485ff0df9a86ca0f930360b09ba4e26cfd208a8404f337dcd4309 SHA1: ace5ea9fc8bc2c50288a4eb36e2febfaec9d18dd MD5sum: d88dcf1339a706f2270a1d5a6d6ba395 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) . This package contains the Python 3 version. Package: python3-smmap Source: python-smmap Version: 2.0.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 93 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Suggests: python3-nose Homepage: https://github.com/Byron/smmap Priority: extra Section: python Filename: pool/main/p/python-smmap/python3-smmap_2.0.1-1~nd+1_all.deb Size: 20132 SHA256: f0f59f24b9277b123ffe71a3dcb868376278e911ac405607d5b982ab93716b68 SHA1: 5ccbbbb6dd7df149254420ba3a507434646e6fed MD5sum: 3ab5b2131263549f011c1af0b491aacd 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. . This package for Python 3. Package: python3-sphinx-rtd-theme Source: sphinx-rtd-theme Version: 0.1.8-1~nd+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~nd+1_all.deb Size: 117236 SHA256: c18c9383781a8fc99ff2f1cd1b24c00c37c27553b34cf97bc8386a80e1063a8a SHA1: e5bb14868d4e0ccad143c02bc7f8f791491c74ea MD5sum: da5e186dfba00386b91466258fee5c75 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-tables Source: pytables Version: 3.2.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2791 Depends: neurodebian-popularity-contest, python3-numpy, python3-numexpr, python3:any (>= 3.3.2-2~), python3-tables-lib (>= 3.2.1-1~nd+1), python3-tables-lib (<< 3.2.1-1~nd+1.1~), python-tables-data (= 3.2.1-1~nd+1) Suggests: python-tables-doc, python-netcdf, vitables Homepage: http://www.pytables.org Priority: optional Section: python Filename: pool/main/p/pytables/python3-tables_3.2.1-1~nd+1_all.deb Size: 334588 SHA256: 852f700742c25b047d53d4e8b6808ef62ed049dab623c996ac54cbcb53ae0a74 SHA1: b45f903684c952b6e6a622786be10b1291903da6 MD5sum: 5a897064e93c5b23d1edabb8f7aea48b Description: hierarchical database for Python3 based on HDF5 PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . - Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them). - The tables are both enlargeable and compressible. - I/O is buffered, so you can get very fast I/O, specially with large tables. - Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well. - It supports the complete set of NumPy, Numeric and numarray objects. . This is the Python 3 version of the package. Package: python3-tqdm Source: tqdm Version: 4.11.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 181 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Homepage: https://github.com/tqdm/tqdm Priority: optional Section: python Filename: pool/main/t/tqdm/python3-tqdm_4.11.2-1~nd+1_all.deb Size: 49940 SHA256: d34171f6f59aa28768f82b75b6743a9a5aeeb9a44667e853ac612ed82b4336f5 SHA1: b90c04ae49af968b2b7d09414c242ce859bf89a8 MD5sum: c1c971c0fb1cf8de6d7678eace54b613 Description: fast, extensible progress bar for Python 3 and CLI tool tqdm (read taqadum, تقدّم) means “progress” in Arabic. tqdm instantly makes your loops show a smart progress meter, just by wrapping any iterable with "tqdm(iterable)". . This package contains the Python 3 version of tqdm and its command-line tool. Package: python3-urllib3 Source: python-urllib3 Version: 1.12-1~bpo8+1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 254 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-six Recommends: ca-certificates Suggests: python3-ndg-httpsclient, python3-openssl, python3-pyasn1 Homepage: http://urllib3.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-urllib3/python3-urllib3_1.12-1~bpo8+1~nd+1_all.deb Size: 65488 SHA256: 1ada7f90b1ab3d41825cb89accbc80c06e6b6566653e8a0c8b95dd9226fac74f SHA1: f6d02f35691c66648412cb7fbd9048ce55c92864 MD5sum: 3279cd2b1b5ec0b3f754a3dc9da02a6d Description: HTTP library with thread-safe connection pooling for Python3 urllib3 supports features left out of urllib and urllib2 libraries. . - Re-use the same socket connection for multiple requests (HTTPConnectionPool and HTTPSConnectionPool) (with optional client-side certificate verification). - File posting (encode_multipart_formdata). - Built-in redirection and retries (optional). - Supports gzip and deflate decoding. - Thread-safe and sanity-safe. - Small and easy to understand codebase perfect for extending and building upon. . This package contains the Python 3 version of the library. Package: python3-vcr Source: vcr.py Version: 1.7.3-1.0.1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 157 Depends: neurodebian-popularity-contest, python3-six (>= 1.5), python3-wrapt, python3-yaml, python3:any (>= 3.3.2-2~) Homepage: https://github.com/kevin1024/vcrpy/ Priority: optional Section: python Filename: pool/main/v/vcr.py/python3-vcr_1.7.3-1.0.1~nd+1_all.deb Size: 43584 SHA256: 03bf642db456d3b20d1126ed18400c9e268ae3e23f4016f0e007864528343114 SHA1: 8a45187f301eb74e3b892703c43e0216b9cd2581 MD5sum: aa1eb1d97032c236712fa2c291c3e7ef Description: record and replay HTML interactions (Python3 library) vcr.py records all interactions that take place through the HTML libraries it supports and writes them to flat files, called cassettes (YAML format by default). These cassettes could be replayed then for fast, deterministic and accurate HTML testing. . vcr.py supports the following Python HTTP libraries: - urllib2 (stdlib) - urllib3 - http.client (Python3 stdlib) - Requests - httplib2 - Boto (interface to Amazon Web Services) - Tornado's HTTP client . This package contains the modules for Python 3. Package: python3-w3lib Source: python-w3lib Version: 1.11.0-1~nd+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~nd+1_all.deb Size: 14200 SHA256: 3a79d12f832d860e8feada19fd1443aec07c08d35e6df6b83e0d5a194d085789 SHA1: f6171c53aab3128a336c59d832f146ea1e10cf65 MD5sum: 6cebff033f0e6809d210f716e0b5bf83 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~nd+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~nd+1_all.deb Size: 165542 SHA256: 36285a90f4bee14c4e98680c74ebdaa3bee13755c8f55fbbb161933926a38a20 SHA1: a3c8437f41a567a54d24841aa7111b15141547f8 MD5sum: 2e29700478a2d7c595cff6aa001cfd7a 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~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 18626 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.5236~dfsg.1-1~nd+1_all.deb Size: 10737524 SHA256: b6a893c9b80b40421f5d12d9a135bdc12fb17f3fab59e0106ef1fc24ad3e77af SHA1: 235814d62d21157760fbae3cf4401cc6d48cf555 MD5sum: 33cd04a3593f6f216ac68a3fc0ea82a4 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~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 73046 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd+1_all.deb Size: 52180218 SHA256: 92b31d00b8ee13b7bcdf249cff509ec988cc2fc0703e301eed662571b89135f3 SHA1: 6540ca3feacd1d7efd53733b78fde41c7defb2c2 MD5sum: 973ce7224b20331a4ccfde26eba8acbe 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~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9273 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd+1_all.deb Size: 8991072 SHA256: a10f23addd5b16acad8feabb200ab8fda604f9ea177e7c32053ab0c60a768d9b SHA1: 65a715c185007eaccc71513dcad03da43d23cbdc MD5sum: 890e9a307f742f1573165ce80a18032f 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 113 Depends: neurodebian-popularity-contest, python:any, python-spyderlib (= 2.2.5+dfsg-1~nd+1) Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: devel Filename: pool/main/s/spyder/spyder_2.2.5+dfsg-1~nd+1_all.deb Size: 52982 SHA256: eddafac432df7d273ddf59837b6ff169aa3ec1776348d59dec5add709bdab935 SHA1: fb37bcc12d9ed07c06c79368f8da3b195323ecaf MD5sum: 96c4abda1f6c31f93c1b3b88f4771106 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: 0.4.4-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1962 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_0.4.4-1~nd+1_all.deb Size: 1291144 SHA256: 18e37d83ec7009a425bd2e555c51862c1be29603896bade502678c236dba81e8 SHA1: 379613ed9f79ab4816a97724522fd8e3d446518e MD5sum: 14c2106d870bad90b4c86bc5971f9eff 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 148 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~nd+1_all.deb Size: 28590 SHA256: c50f821d6eb4b0afe501c575de1b66aa196ac7745f412ca7c3064eed3f4d3c43 SHA1: 8002f08bea6021c1fa12d9e2de8b2e2482fcd799 MD5sum: aa37bec3c54a92373aeb2871266f1e77 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: 2.6, 2.7 Package: svgtune Version: 0.1.0-2 Architecture: all Maintainer: Yaroslav Halchenko Installed-Size: 64 Depends: python, python-lxml Suggests: inkscape Homepage: http://github.com/yarikoptic/svgtune Priority: optional Section: graphics Filename: pool/main/s/svgtune/svgtune_0.1.0-2_all.deb Size: 6680 SHA256: 69b4df1e0b4c247673265c7f5bb2b2ffe2209d783617bf7f6eadce86633f80e1 SHA1: bec339e4453c35a05a616deef6769a6f2ad2d00d MD5sum: ef6797498477a73f930ad9bc0db3ba73 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: testkraut Version: 0.0.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 358 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, libjs-underscore, libjs-jquery, python-argparse Recommends: strace, python-scipy, python-colorama, python-apt Homepage: https://github.com/neurodebian/testkraut Priority: extra Section: python Filename: pool/main/t/testkraut/testkraut_0.0.1-1~nd+1_all.deb Size: 100016 SHA256: 5ea3d436c473902040c138cb6100770fc3d0969e891ce84eabf2f644ee367a5a SHA1: b29570908455d37b7397bd9eed303b13e59b090b MD5sum: eafa9781d2a05681ca03188015f96302 Description: test and evaluate heterogeneous data processing pipelines This is a framework for software testing. That being said, testkraut tries to minimize the overlap with the scopes of unit testing, regression testing, and continuous integration testing. Instead, it aims to complement these kinds of testing, and is able to re-use them, or can be integrated with them. . In a nutshell testkraut helps to facilitate statistical analysis of test results. In particular, it focuses on two main scenarios: . * Comparing results of a single (test) implementation across different or changing computational environments (think: different operating systems, different hardware, or the same machine before an after a software upgrade). * Comparing results of different (test) implementations generating similar output from identical input (think: performance of various signal detection algorithms). . While such things can be done using other available tools as well, testkraut aims to provide a lightweight, yet comprehensive description of a test run. Such a description allows for decoupling test result generation and analysis – opening up the opportunity to “crowd-source” software testing efforts, and aggregate results beyond the scope of a single project, lab, company, or site. Python-Version: 2.6, 2.7 Package: ubuntu-keyring Version: 2010.+09.30~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 Recommends: gpgv Priority: important Section: misc Filename: pool/main/u/ubuntu-keyring/ubuntu-keyring_2010.+09.30~nd+1_all.deb Size: 11788 SHA256: 87608e20a998b8cb9799d0613e97f0b4f592a26c1433b383ddd813aa69365155 SHA1: e943e925a1106701b6e0c160fd9c06a04490355d MD5sum: 24b363866e0f915e14ecd5ed541ee822 Description: GnuPG keys of the Ubuntu archive The Ubuntu project digitally signs its Release files. This package contains the archive keys used for that. Package: vowpal-wabbit-doc Source: vowpal-wabbit Version: 7.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 70918 Depends: neurodebian-popularity-contest Recommends: vowpal-wabbit Homepage: http://hunch.net/~vw/ Priority: optional Section: doc Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-doc_7.3-1~nd+1_all.deb Size: 46989872 SHA256: 451a2f7f540a9cf2f8c276fbb575156845d0cd996733c38a7e11c5da17ac8ce8 SHA1: 18bac3730e0836a77d5f83cb380856e65a1e873b MD5sum: 87ceda4a59e22e74b68c7758551cb4b2 Description: fast and scalable online machine learning algorithm - documentation Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains examples (tests) for vowpal-wabbit. Package: vtk-doc Source: vtk Version: 5.8.0-7+b0~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 342007 Depends: neurodebian-popularity-contest, doc-base Suggests: libvtk5-dev, vtk-examples, vtkdata Homepage: http://www.vtk.org/ Priority: optional Section: doc Filename: pool/main/v/vtk/vtk-doc_5.8.0-7+b0~nd+1_all.deb Size: 66710216 SHA256: ef2921e37681f7364119b79457483cd3ca7da8cd063a96438cffe23aeba52938 SHA1: abc4b1ccf35fd6c0cc20f67836fb7ffcbfc69161 MD5sum: b7ef2d7972fe60ad7ce2f891faac4205 Description: VTK class reference documentation The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package contains exhaustive HTML documentation for the all the documented VTK C++ classes. The documentation was generated using doxygen and some excellent perl scripts from Sebastien Barre et. al. Please read the README.docs in /usr/share/doc/vtk-doc/ for details. The documentation is available under /usr/share/doc/vtk/html. Package: vtk-examples Source: vtk Version: 5.8.0-7+b0~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2521 Depends: neurodebian-popularity-contest Suggests: libvtk5-dev, tcl-vtk, python-vtk, vtk-doc, python, tclsh, libqt4-dev Homepage: http://www.vtk.org/ Priority: optional Section: graphics Filename: pool/main/v/vtk/vtk-examples_5.8.0-7+b0~nd+1_all.deb Size: 578892 SHA256: fab181213376a1077411077e48a5640af76ceb2868302e2e03b18e4e6a0859fd SHA1: e0087beef829cbfd4d09abfd52a4e526b2b11963 MD5sum: efe0f5b35bccb7b5f9d251a25970a0ac Description: C++, Tcl and Python example programs/scripts for VTK The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package contains examples from the VTK source. To compile the C++ examples you will need to install the vtk-dev package as well. Some of them require the libqt4-dev package. . The Python and Tcl examples can be run with the corresponding packages (python-vtk, tcl-vtk).