Package: bats Version: 0.4.0-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41 Depends: neurodebian-popularity-contest Homepage: https://github.com/sstephenson/bats Priority: optional Section: shells Filename: pool/main/b/bats/bats_0.4.0-1~nd80+1+nd90+1_all.deb Size: 14426 SHA256: 70825f0e6845181b88902047cd7191b4fb222bb5fce4aed35e1cdb68823cf664 SHA1: 773936e0d736d5819cd688087d93f4e110652c10 MD5sum: 023d79d882ecaed97b5121b86834d8b3 Description: bash automated testing system Bats is a TAP-compliant testing framework for Bash. It provides a simple way to verify that the UNIX programs you write behave as expected. Bats is most useful when testing software written in Bash, but you can use it to test any UNIX program. Package: btrbk Version: 0.22.2-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 199 Depends: neurodebian-popularity-contest, perl, libdate-calc-perl, btrfs-tools (>= 3.14) Suggests: openssh-client Homepage: http://www.digint.ch/btrbk/ Priority: optional Section: utils Filename: pool/main/b/btrbk/btrbk_0.22.2-1~nd90+1_all.deb Size: 54926 SHA256: 8bfe04102fa1261c7c948d2dd536d4138949958202e4295f6cc10b1068edf946 SHA1: cfe11d7602ecf6cdbe8816b91af923dc0ac6bae9 MD5sum: 90260d8146b7039274b8ccc17464a579 Description: backup tool for btrfs volumes Backup tool for btrfs volumes, using a configuration file, allows creation of backups from multiple sources to multiple destinations at once, with ssh and configurable retention support (daily/weekly/monthly). Package: condor Version: 8.4.2~dfsg.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 28 Depends: neurodebian-popularity-contest, htcondor Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor_8.4.2~dfsg.1-1~nd90+1_all.deb Size: 15706 SHA256: 9c215ac3b0e3385aadbb9169dfd4ea5ddb61df636fa15cabead3a29d34665c77 SHA1: 0e7082fd454b225c9bfe58f41155c15946c4d5b9 MD5sum: 063f58417f90f99ecb600bc2f0fcdc9c 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: 8.4.2~dfsg.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 28 Depends: neurodebian-popularity-contest, htcondor-dbg Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dbg_8.4.2~dfsg.1-1~nd90+1_all.deb Size: 15724 SHA256: 5930b1f4adb24c923e13e5a27ae5b048a941f187ec2989fc19253442e205a71f SHA1: b1779dca2c4f13e1657f7bc0fb7c01636a1d62bf MD5sum: fcd86d768a1d3ba8d8ef0a03ac1458dc 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: 8.4.2~dfsg.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 28 Depends: neurodebian-popularity-contest, htcondor-dev Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dev_8.4.2~dfsg.1-1~nd90+1_all.deb Size: 15732 SHA256: d6270236ee14f6b1cafd99722927d1921c77c7723efe9e561a125e31edd7e96d SHA1: 6c3ebb9e70e59027ef67cbb816542a8b75de6851 MD5sum: 43c4ac37193f38922f18f931b5cdec8a 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: 8.4.2~dfsg.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 28 Depends: neurodebian-popularity-contest, htcondor-doc Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-doc_8.4.2~dfsg.1-1~nd90+1_all.deb Size: 15726 SHA256: 143d8336a3a86fde5dfc5a4b382db45e90355a571f966c0e215c8f417505e8da SHA1: 7cbcd7046986ed0499f35ec821a662745ccb13bb MD5sum: aa1ea880e8b652ae4910f2df37a83b96 Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: connectomeviewer Version: 2.1.0-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1652 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-cfflib (>= 2.0.5), python-networkx (>= 1.4), python-nibabel, python-numpy (>= 1.3.0), python-scipy, python-chaco, mayavi2 (>= 4.0.0), ipython Recommends: python-nipype, python-dipy, python-matplotlib, python-qscintilla2 Suggests: nipy-suite Homepage: http://www.connectomeviewer.org Priority: extra Section: python Filename: pool/main/c/connectomeviewer/connectomeviewer_2.1.0-1~nd70+1+nd90+1_all.deb Size: 1150994 SHA256: 1010e605b6a3e8827a5299a1ecf248cb610c8c3bb77d922740cfcba689bc981c SHA1: 32ccf0c290df8fb04b3b660d507e3a0f0125b9c9 MD5sum: acbc76e7ac065581a48f09e5bdfb4968 Description: Interactive Analysis and Visualization for MR Connectomics The Connectome Viewer is a extensible, scriptable, pythonic research environment for visualization and (network) analysis in neuroimaging and connectomics. . Employing the Connectome File Format, diverse data types such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The Connectome Viewer is part of the MR Connectome Toolkit. Package: coop-computing-tools-doc Source: cctools Version: 3.4.2-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2821 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: doc Filename: pool/main/c/cctools/coop-computing-tools-doc_3.4.2-1~nd70+1+nd90+1_all.deb Size: 211826 SHA256: da05214a18baf5e069af50d860dbcb3efcf3bf210dca18f9e4e96bb1aeca249d SHA1: 6cc266c35d33a6090c5c33c9b8f308971a24cb83 MD5sum: adc3cb13d9be11595ff7978d1fea0eed Description: documentation for coop-computing-tools These tools are a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. . This package provides the documentation (manual and API reference) in HTML format. Package: datalad Version: 0.1+git589-g4f196dc-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 91 Depends: neurodebian-popularity-contest, python-argcomplete, python-datalad (= 0.1+git589-g4f196dc-1~nd90+1), python:any Homepage: http://datalad.org Priority: optional Section: science Filename: pool/main/d/datalad/datalad_0.1+git589-g4f196dc-1~nd90+1_all.deb Size: 50460 SHA256: dbdd19ae957e2ea9132e354081db713ff40b3dd778c2983b0ad2e1ba742df45c SHA1: 0b0e1cc802c94984e82ad9c158b06ac3c8874522 MD5sum: 5b46de6db13cd3d3af16098708042252 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 for neuroscience domain). Using git-annex as its backend for data logistics it provides following facilities . - crawling of web sites to automatically prepare or update git-annex repositories with content from online websites, S3, etc - command line interface for manipulation of datasets (search, install, uninstall, upgrade etc) . This package provides the command line tool. . DISCLAIMER: DataLad is Work-In-Progress and this package is not yet for regular user consumption. Primarily intended for demonstrations and development. Package: debian-handbook Version: 6.0+20120509~nd+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23450 Depends: neurodebian-popularity-contest Homepage: http://debian-handbook.info Priority: optional Section: doc Filename: pool/main/d/debian-handbook/debian-handbook_6.0+20120509~nd+1+nd90+1_all.deb Size: 21615222 SHA256: a85ad362072ccad3a1034ac7ae2073b117a7a9001e79b131a6929272734e7f46 SHA1: 065382f6d0be6a5cbe2372ba15a0509f85b73894 MD5sum: 201becf6284759af070b8ee950fab0fb Description: reference book for Debian users and system administrators Accessible to all, the Debian Administrator's Handbook teaches the essentials to anyone who wants to become an effective and independent Debian GNU/Linux administrator. . It covers all the topics that a competent Linux administrator should master, from the installation and the update of the system, up to the creation of packages and the compilation of the kernel, but also monitoring, backup and migration, without forgetting advanced topics like SELinux setup to secure services, automated installations, or virtualization with Xen, KVM or LXC. . The Debian Administrator's Handbook has been written by two Debian developers — Raphaël Hertzog and Roland Mas. . This package contains the English book covering Debian 6.0 “Squeeze”. Package: dh-systemd Source: init-system-helpers Version: 1.18~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49 Depends: neurodebian-popularity-contest, perl, debhelper Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/dh-systemd_1.18~nd80+1+nd90+1_all.deb Size: 13810 SHA256: e3c6d4ea450b9b4462716a4379e06303dca077c6e37664293366eb1645700d16 SHA1: 0fe321df5ef5262d0395dcd70106aa69091d7822 MD5sum: 768da456c2645982f0a93d8ace2c098d Description: debhelper add-on to handle systemd unit files dh-systemd provides a debhelper sequence addon named 'systemd' and the dh_systemd_enable/dh_systemd_start commands. . The dh_systemd_enable command adds the appropriate code to the postinst, prerm and postrm maint scripts to properly enable/disable systemd service files. The dh_systemd_start command deals with start/stop/restart on upgrades for systemd-only service files. Package: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8117 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd70+1+nd90+1_all.deb Size: 7075168 SHA256: 5d83d4720ebbec466e44e83c6e6a8a5b67bc8486f93bc571cde2403982dfaf0c SHA1: 062335d0be9ced61269e7ab4ae3bb9034993d9b6 MD5sum: 62a51e7e7f38cd6cdc21ef2931432201 Description: sample EEG data for EEGLAB tutorials EEGLAB is sofwware for processing continuous or event-related EEG or other physiological data. . This package provide some tutorial data files shipped with the EEGLAB distribution. Package: fail2ban Version: 0.9.4-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1224 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_0.9.4-1~nd90+1_all.deb Size: 275412 SHA256: f36d12b73218e843b28c3a4afa46bd2bbcb8798f4eaec3d99b4d7f5ce47513d0 SHA1: 666669ab9655f76ead459dd6f6d8f5d4123e4682 MD5sum: f5b0690ef324bc9d9c246a5bcfb5925b 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~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd90+1), freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi_1.4.9-1~nd90+1_all.deb Size: 1176 SHA256: 0e4e167e592b924f79472395fbee7033689ee077ee3574f35ef74577fdeffec3 SHA1: 3365f20281a15110bd4bd43778b9a980ccc99f72 MD5sum: 46c90a6d59e5a2a2d9f9c3c2a1c01232 Description: GNU implementation of the IPMI protocol FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This metapackage depends on all separate modules of freeipmi. Package: freeipmi-common Source: freeipmi Version: 1.4.9-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 491 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest Suggests: freeipmi-tools Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-common_1.4.9-1~nd90+1_all.deb Size: 339408 SHA256: 2ef4d1b9c56554a9d5f40d860ae0da8bf8469166b892ccd06ab53c8d7e847f0c SHA1: d7c76e4e962bac7828af674c926009546d927d99 MD5sum: 302ae30afdce18d894d054859912176e Description: GNU implementation of the IPMI protocol - common files FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package provides configuration used by the rest of FreeIPMI framework and generic documentation to orient the user. Package: fsl-melview Source: melview Version: 1.0.1+git9-ge661e05~dfsg.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 74 Depends: neurodebian-popularity-contest, python, python-matplotlib, python-numpy, python-pkg-resources, python-scipy, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-nibabel, python-pyface, python-traits, python-traitsui, python-enthoughtbase Suggests: fsl-core Homepage: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Melview Priority: optional Section: science Filename: pool/main/m/melview/fsl-melview_1.0.1+git9-ge661e05~dfsg.1-1~nd90+1_all.deb Size: 13912 SHA256: 645e077ce9ef18f5ed060f06cfe1db6182307f8bd85ed6c3700847be65513549 SHA1: 27fffc42b2ab3cd055e60861ee89b66c41ba53c6 MD5sum: 8f155d7108a9e153239de07847443ded Description: viewer for the output of FSL's MELODIC This viewer can be used to facilitate manual inspection and classification of ICA components computed by MELODIC. As such, it is suited to generate hand-curated labels for FSL's ICA-based denoising tool FIX. Python-Version: 2.7 Package: fslview-doc Source: fslview Version: 4.0.1-2~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2929 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-2~nd80+1+nd90+1_all.deb Size: 2226908 SHA256: 8b844c2c11167f41b2d20536095b36c3404514cb21c283097fc0cc876990d949 SHA1: a1a17b9b680e41e9757918f98c497c5ef1b253ec MD5sum: 2554c2d2bf8c93da5554a559ca7aaa58 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gcalcli Version: 3.3.2-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1763 Depends: neurodebian-popularity-contest, python, python-dateutil, python-gflags, python-googleapi Recommends: gxmessage, python-parsedatetime, python-simplejson, python-vobject Homepage: https://github.com/insanum/gcalcli Priority: extra Section: utils Filename: pool/main/g/gcalcli/gcalcli_3.3.2-1~nd90+1_all.deb Size: 1669858 SHA256: e1da96411512bbdce753fb1610f6d15245a50547cd891d4aba6c55b343fee1ce SHA1: 7d321b4e2ddecb10c9192f919c8376ea1f635d17 MD5sum: 81cad400ed04d87e55a7f51fc23fb05f Description: Google Calendar Command Line Interface gcalcli is a Python application that allows you to access your Google Calendar from a command line. It's easy to get your agenda, search for events, and quickly add new events. Additionally gcalcli can be used as a reminder service to execute any application you want. Package: gmsl Version: 1.1.5-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 89 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.5-1~nd80+1+nd90+1_all.deb Size: 13846 SHA256: f245e355fff5b0333d20373a3733b34a6d5b89ecbb07e4a9f6dfe46266429ee1 SHA1: 96f7917de1151cc9ac199889d3346600c5beb585 MD5sum: 036fe63e248473476a6832cdce6324e0 Description: extra functions to extend functionality of GNU Makefiles The GNU Make Standard Library (GMSL) is a collection of functions implemented using native GNU Make functionality that provide list and string manipulation, integer arithmetic, associative arrays, stacks, and debugging facilities. . Note that despite the name of this project, this library is NOT standard and is NOT written or distributed by the GNU project. Package: guacamole Source: guacamole-client Version: 0.8.3-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 516 Depends: neurodebian-popularity-contest, guacd Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole_0.8.3-1~nd80+1+nd90+1_all.deb Size: 431706 SHA256: 5baa52ac170db52217a2be61aa88e322b8b07d6547ff413c7c07a7ac5bf553fc SHA1: 38c24c6ff1aff4765fa37cdb6da96279569d4152 MD5sum: 523c9f22b9b0eba895a126a68c17b946 Description: HTML5 web application for accessing remote desktops Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. Package: guacamole-tomcat Source: guacamole-client Version: 0.8.3-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole-tomcat_0.8.3-1~nd80+1+nd90+1_all.deb Size: 6780 SHA256: 6c7669e2313d4005be8fcd92836431a6acb0a8f6ff4b8c47108098e729bb4de1 SHA1: 8a20da50241cc0df8ca6b943c6896fca09230629 MD5sum: e152fd7e06d9454ac0c22f08b3dd4b9f Description: Tomcat-based Guacamole install with VNC support Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. . This metapackage depends on Tomcat, Guacamole, and the VNC support plugin for guacamole. Guacamole is automatically installed and configured under Tomcat. Package: heudiconv Version: 0.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 49 Depends: neurodebian-popularity-contest, python, python-dcmstack, python-dicom, python-nibabel, python-numpy, python-nipype Recommends: mricron Homepage: https://github.com/nipy/heudiconv Priority: optional Section: science Filename: pool/main/h/heudiconv/heudiconv_0.1-1~nd90+1_all.deb Size: 10212 SHA256: fd410489a7bda60b3ba01724da25044d079abdce7929d292a0c87de88635bb33 SHA1: b1d0e9f116b86a94db4d8d730ccee1bca0146182 MD5sum: 1204725a882221fd3a4826868a75be10 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: 8.4.2~dfsg.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 6058 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_8.4.2~dfsg.1-1~nd90+1_all.deb Size: 1066946 SHA256: e53815f57a0c3d54515bd133f2a18623c8180f6b2773435bcf90fa0098006594 SHA1: e2a8babac474de938eed74c77552be5c5ec9f45a MD5sum: 5da4843c50b44739c7d57e479ab3711a 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~nd90+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~nd90+1_all.deb Size: 175628 SHA256: bbf5e949aa516536c8f677bbbca1faeb65c0f9599881d9a7273fdd024606a0b0 SHA1: 7c8c50564c63dad435161d236fbd33c85f85435e MD5sum: 63edc042ded61939ba1d7a9f6cbdb7f2 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~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 39 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd70+1+nd90+1_all.deb Size: 9058 SHA256: dcb57631757a7635c3ee68bb989b2f3f77856c1f88d842cd64848f9776236e69 SHA1: e1caeb23b63a7b84829314c2a60b5130edb4d200 MD5sum: c220623e4e8976077d0c3de0d45bec30 Description: utility for pushing DICOM data to the INCF datasharing server A command line utility for anonymizing and sending DICOM data to the XNAT image database at the International Neuroinformatics Coordinating Facility (INCF). This tool is maintained by the INCF NeuroImaging DataSharing (NIDASH) task force. Package: init-system-helpers Version: 1.18~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49 Depends: neurodebian-popularity-contest, perl Breaks: systemd (<< 44-12) Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/init-system-helpers_1.18~nd80+1+nd90+1_all.deb Size: 13434 SHA256: 62d797ebebb6d054e9bfd1e70bfbc4eb760d8e06fa249d32af9f365f170f9f26 SHA1: 0d36a24804a540495ffcbba060615184e3708bbf MD5sum: 4d9b782a3c2f9164950b1f12bc035513 Description: helper tools for all init systems This package contains helper tools that are necessary for switching between the various init systems that Debian contains (e.g. sysvinit, upstart, systemd). An example is deb-systemd-helper, a script that enables systemd unit files without depending on a running systemd. . While this package is maintained by pkg-systemd-maintainers, it is NOT specific to systemd at all. Maintainers of other init systems are welcome to include their helpers in this package. Package: insighttoolkit4-examples Source: insighttoolkit4 Version: 4.7.0-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3081 Depends: neurodebian-popularity-contest Suggests: libinsighttoolkit4-dev Conflicts: insighttoolkit-examples Replaces: insighttoolkit-examples Homepage: http://www.itk.org/ Priority: optional Section: devel Filename: pool/main/i/insighttoolkit4/insighttoolkit4-examples_4.7.0-1~nd80+1+nd90+1_all.deb Size: 2498516 SHA256: d34d9328e9e8bd7bfb745a1cd0b915f0f61485c07ec1e0f65fc4c0f14eb68aed SHA1: 90d42a16d1a1b428e004c26671f83663ca9b35d0 MD5sum: 276b12088d4b89a13556404f5b95b99c Description: Image processing toolkit for registration and segmentation - examples ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the source for example programs. Package: ismrmrd-schema Source: ismrmrd Version: 1.3.2-1~nd90+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~nd90+1_all.deb Size: 5024 SHA256: 47783565e8cf25043a7b556a2b246d873b3c1ceb958483c976a42656adc4829f SHA1: ab4854bd21f47ff471d488fdb44faa6bae9982bb MD5sum: 216c6a39349f3df9045fab732fcef2ef 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: libfreenect-doc Source: libfreenect Version: 1:0.5.2+git6-g5455843+dfsg-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 647 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.5.2+git6-g5455843+dfsg-1~nd90+1_all.deb Size: 90116 SHA256: 7b1f3f8718bae55e7d612ff46474321e316a7b6c3995e61c2d1f9bd3206f34f8 SHA1: 2dabe10f4ec42810d36c3f92740c078eae537399 MD5sum: fb51b5583055b3c4a2f2eb4facd36aab Description: library for accessing Kinect device -- documentation libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package contains the documentation of the API of libfreenect. Package: libghc-network-protocol-xmpp-doc Source: haskell-network-protocol-xmpp Version: 0.4.8-2+ndb1~nd90 Architecture: all Maintainer: Debian Haskell Group Installed-Size: 475 Depends: haddock-interface-27 Recommends: ghc-doc, libghc-monads-tf-doc, libghc-network-doc, libghc-text-doc, libghc-xml-types-doc Homepage: https://john-millikin.com/software/haskell-xmpp/ Priority: extra Section: doc Filename: pool/main/h/haskell-network-protocol-xmpp/libghc-network-protocol-xmpp-doc_0.4.8-2+ndb1~nd90_all.deb Size: 55164 SHA256: 81833a4491c651759b4cfeec459bee321e860d56681dd588e47c69380475202d SHA1: 6d97683ea5cf2cbfd37ef064dbe786e970e97202 MD5sum: fac4a8b7f49ccde1df6012b14266b0f3 Description: Haskell XMPP (Jabber) library; documentation; documentation This library defines an XMPP data type and functions. . This package provides the documentation for a library for the Haskell programming language. See http://www.haskell.org/ for more information on Haskell. Package: libismrmrd-doc Source: ismrmrd Version: 1.3.2-1~nd90+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~nd90+1_all.deb Size: 158250 SHA256: cd29ecf75ed8a2f2c6d14b83f63fe38b4d15ad1220bd2153bdfdf266921d1736 SHA1: 7e43ee7fc12a4f0468d6cd6cb359bd39131f829c MD5sum: 882d568635f9cab550288c0009160547 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~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16445 Depends: neurodebian-popularity-contest, libjs-jquery Enhances: libmia-2.0-dev Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/libmia-2.0-doc_2.0.13-1~nd80+1+nd90+1_all.deb Size: 1145474 SHA256: 22353c91f476fe6f63f1a34963bd303a1bd030f369a334dc9ad85e91cd14fcb4 SHA1: 207ee6bcc687e9307274cf8def76f6d865690575 MD5sum: 27dbf2673981e05d840fd884459a2f2b Description: library for 2D and 3D gray scale image processing, documentation libmia comprises a set of libraries and plug-ins for general purpose 2D and 3D gray scale image processing and basic handling of triangular meshes. The libraries provide a basic infrastructure and generic algorithms, that can be specialized by specifying the apropriate plug-ins. This package provides the Doxygen generated API reference. Package: libmialm-doc Source: libmialm Version: 1.0.7-2~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 249 Depends: neurodebian-popularity-contest Suggests: devhelp Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/libm/libmialm/libmialm-doc_1.0.7-2~nd80+1+nd90+1_all.deb Size: 21262 SHA256: b818b86b8b03b2de2d9028afb4a6fcefc7efac47e9922d583ca24642e136f7ba SHA1: 4068ababf8509c40454974d12b1f613f93cc5a6f MD5sum: 20aa66e7a83a9b83d8626eabb1a0ca36 Description: Documentation for the MIA landmark library This library implements handling for landmarks and 3D view positioning for optimal landmark visibility, and in-and output of these landmarks. This library is part of the MIA tool chain for medical image analysis. This package contains the library documentation. Package: libnifti-doc Source: nifticlib Version: 2.0.0-2~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1705 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://niftilib.sourceforge.net Priority: optional Section: doc Filename: pool/main/n/nifticlib/libnifti-doc_2.0.0-2~nd80+1+nd90+1_all.deb Size: 137744 SHA256: f5f624f2a3073c1676ff234d94abc713ce4c73e4a17989868035bc67d1e4d9e9 SHA1: defc10d95d0f843756f9402e05ba12d41e1546c0 MD5sum: 4dbe6f75c01b260f4534930c082b2774 Description: NIfTI library API documentation Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the library API reference documentation. Package: libopenwalnut1-doc Source: openwalnut Version: 1.4.0~rc1+hg3a3147463ee2-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49240 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.4.0~rc1+hg3a3147463ee2-1~nd80+1+nd90+1_all.deb Size: 2681858 SHA256: c83b9176f3ff3cfa9e83ee97caabd28ecede43bf5ffa8dbfe9ce9b72977bcf9e SHA1: 2bc64d4f70f52d587d898216e09ae1457a527dc1 MD5sum: b2239dfc83b9656f9ba915d4fcf8284e Description: Developer documentation for the OpenWalnut visualization framework OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the core API documentation of OpenWalnut. Package: matlab-support-dev Source: matlab-support Version: 0.0.21~nd90+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~nd90+1_all.deb Size: 7514 SHA256: 70e460df62d8e62f429592dd9223cbc009adf3f0409340eb0345a1a3c730b037 SHA1: 1d4018d1abd28a97c88b67824194116e817c516b MD5sum: fd6d59f8278c996b8a4f2f245113ea76 Description: helpers for packages building MATLAB toolboxes This package provides a Makefile snippet (analogous to the one used for Octave) that configures the locations for architecture independent M-files, binary MEX-extensions, and their corresponding sources. This package can be used as a build-dependency by other packages shipping MATLAB toolboxes. Package: mia-tools-doc Source: mia Version: 2.0.13-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1227 Depends: neurodebian-popularity-contest Enhances: mia-tools Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/mia-tools-doc_2.0.13-1~nd80+1+nd90+1_all.deb Size: 78588 SHA256: b2e28ac06616de16419dca9ffe55d4cd31ce25627201cb084f64f13bdbfd9ccd SHA1: eabc90ca30982d35ac6e45697cd8f2670cdcbfd9 MD5sum: 39002ffe1546edf4a27c57fa26bf9c66 Description: Cross-referenced documentation of the MIA command line tools Cross referenced documentation of the command line tools and plug-ins that are provided by the MIA gray scale image processing tool chain. These lines tools to provide the means to run general purpose image processing tasks on 2D and 3D gray scale images, and basic operations on triangular meshes interactively from the command line. Supported image processing algorithms are image filtering, combining, image registration, motion compensation for image series, and the estimation of various statistics over images. Package: mricron-data Source: mricron Version: 0.20140804.1~dfsg.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1696 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20140804.1~dfsg.1-1~nd80+1+nd90+1_all.deb Size: 1661008 SHA256: c72c5a52aa36f95b969d40b6696b3fd6825e56f2b4c747c8c7583fee75f818a3 SHA1: f7adfccd1977f34cb146ba91f62f47348c91d8ce MD5sum: ac38c72fa2c712209849a6c07d7e7997 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20140804.1~dfsg.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1024 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20140804.1~dfsg.1-1~nd80+1+nd90+1_all.deb Size: 579888 SHA256: eed56b4447c212858640a3e24d776b3404f9d3b81d732faa0dcbeab494a9e1f7 SHA1: ad06900073ad2fff7af240ab1c812298c638efc1 MD5sum: 4b028808fdef22505256fe03fbb307ac Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: mridefacer Version: 0.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 647 Depends: neurodebian-popularity-contest, fsl-5.0-core | fsl-core Homepage: https://github.com/hanke/mridefacer Priority: optional Section: science Filename: pool/main/m/mridefacer/mridefacer_0.1-1~nd90+1_all.deb Size: 636972 SHA256: 9813c8335d1487a8e918a599444815c91d13f6e54c60ed4f8008468bd795670d SHA1: 87a0e6b40f52b7dbc9ea6be68c3a14869c932873 MD5sum: 452a003a446b82260057a35271bd0596 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~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3542 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.12-1~nd80+1+nd90+1_all.deb Size: 3198612 SHA256: c4759ba0583f737edb2e578bfce4f4243f46325d84ae08ac123fa2cb32858f1c SHA1: 5418da876c4b3fbd5f96f5fe3a670828a46683b8 MD5sum: 53aca395bed6599b118a7115d696cd3b Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: netselect-apt Source: netselect Version: 0.3.ds1-25~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 38 Depends: neurodebian-popularity-contest, wget, netselect (>= 0.3.ds1-17) Recommends: curl Suggests: dpkg-dev Enhances: apt Homepage: http://github.com/apenwarr/netselect Priority: optional Section: net Filename: pool/main/n/netselect/netselect-apt_0.3.ds1-25~nd80+1+nd90+1_all.deb Size: 16768 SHA256: 0855800d052bec8451592fc24b243e211b528f2f09f4638271b33344b4f75fd6 SHA1: 009fe8913a3e0df14c68d36bc8cbfbc3bdb718a2 MD5sum: 364fd6a7cc867d537b4c2d9bce6860ed Description: speed tester for choosing a fast Debian mirror This package provides a utility that can choose the best Debian mirror by downloading the full mirror list and using netselect to find the fastest/closest one. . It can output a sources.list(5) file that can be used with package management tools such as apt or aptitude. Package: neurodebian Version: 0.37.4~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 88 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_0.37.4~nd90+1_all.deb Size: 33180 SHA256: d59ffcf6256d49cb6cd8d9ae64b59b640a17395b618b202e0f54d91358e1bccc SHA1: 4a78f23931971ed59b2ba73b0444bb14abbdda85 MD5sum: dc59e687ee5f0f4d938aaff97980e267 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: 0.37.4~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23 Breaks: neurodebian-keyring (<< 0.34~) Replaces: neurodebian-keyring (<< 0.34~) Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-archive-keyring_0.37.4~nd90+1_all.deb Size: 10184 SHA256: 9bfb2dc068d7528b4b6843a42e52b52cd246c0bde198137bedfd5b0418297306 SHA1: aad4b3854802adb45f8d36b727e708c84300a106 MD5sum: cb80a2c2e58a5a64c9e4cd1d7d9cb7b2 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: 0.37.4~nd90+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 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.37.4~nd90+1_all.deb Size: 116186 SHA256: e333f91b50d9f93596ad8d79f8bb18d695725ef0dee6c0b309420ad2959dd957 SHA1: 644311ef768c66386807a14642d99845e2587e72 MD5sum: 6d2faacb1cbdc8c4fae18c68ee6fb5b6 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: 0.37.4~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 109 Depends: devscripts, neurodebian-archive-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring, apt-utils, cowbuilder Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.37.4~nd90+1_all.deb Size: 32536 SHA256: ad984f8fee4c7bd2eebba3498a1b8704b221621f8fdf15cdc22a421699f40716 SHA1: 7608f198df6aca9c537f00613d3c266bac07780f MD5sum: eb00173980d41eb813160c046ae7331c 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: 0.37.4~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 141 Depends: virtualbox-guest-utils, virtualbox-guest-x11, virtualbox-guest-dkms, sudo, neurodebian-desktop, lightdm | x-display-manager, zenity Recommends: chromium, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.37.4~nd90+1_all.deb Size: 16772 SHA256: b77a047dd618d191239d84a1141e1276085d1358846c56904f81a417b80391ad SHA1: 94e0469f5a9cd9ebdfee86e60a5dfcc54830dac1 MD5sum: c194435630c148c7a93db90903f96a5b 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-popularity-contest Source: neurodebian Version: 0.37.4~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 27 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.37.4~nd90+1_all.deb Size: 12204 SHA256: 70b76cf09892afed4acac260250206eb551e9355d68bc49f6109cba06cfb3f47 SHA1: 4056b6e59f3feeb58defd3331742faa132594f87 MD5sum: cff4bdd0de954e8e68074e1e3c134de6 Description: neuroscience-oriented distribution - popcon integration The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package is a complement to the generic popularity-contest package to enable anonymous submission of usage statistics to NeuroDebian in addition to the popcon submissions to the underlying distribution (either Debian or Ubuntu) popcon server. . Participating in popcon is important for the following reasons: * Popular packages receive more attention from developers; bugs are fixed faster and updates are provided quicker. * It ensures that support is not dropped for a previous release of Debian or Ubuntu while there are active users. * User statistics may be useful for upstream research software developers seeking funding for continued development. . This requires that popcon is activated for the underlying distribution (Debian or Ubuntu), which can be achieved by running "dpkg-reconfigure popularity-contest" as root. Package: nifti2dicom-data Source: nifti2dicom Version: 0.4.9-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 643 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.9-1~nd80+1+nd90+1_all.deb Size: 615920 SHA256: 25fdd19e2a1d27cd7e03c17dc874ec0bf5bfe53862d0edd400da96b9649362e1 SHA1: b63957274fc4d210c1a0b12766144d56a78752d5 MD5sum: 56bd9fd876a7a3458bccdcac9a1bc456 Description: data files for nifti2dicom This package contains architecture-independent supporting data files required for use with nifti2dicom, such as such as documentation, icons, and translations. Package: nuitka Version: 0.5.21+ds-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2918 Depends: neurodebian-popularity-contest, g++-4.9 | g++-4.8 | g++-4.7 | g++-4.6 (>= 4.6.1) | g++-4.5 | g++-4.4 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python:any (>= 2.7.5-5~) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6), strace Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.5.21+ds-1~nd90+1_all.deb Size: 612068 SHA256: f06aa5a6d3b7dfe3c51e3d2782f29a1cacacd1a593f9450246aee5c579facf53 SHA1: b31794eae89dc7ff1bdcb4a7c5b5a70d407ba251 MD5sum: b1165d434350f8c519c0ab4c3c08c136 Description: Python compiler with full language support and CPython compatibility This Python compiler achieves full language compatibility and compiles Python code into compiled objects that are not second class at all. Instead they can be used in the same way as pure Python objects. Package: psychopy Version: 1.83.04.dfsg-2~nd90+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~nd90+1_all.deb Size: 6134548 SHA256: 0bd2955e77566512ccc5b15f7273924031b02b022300039b606ca55948a82c70 SHA1: bfc14d007712702cfdec7cb2256204154986bf68 MD5sum: 77e2b854c9ac34c57c2261b0bc24b3a0 Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features . - IDE GUI for coding in a powerful scripting language (Python) - Builder GUI for rapid development of stimulation sequences - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.12.20160126.dfsg1-2~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 253576 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_3.0.12.20160126.dfsg1-2~nd90+1_all.deb Size: 24290826 SHA256: bd69dcef4ef9f89c9292f9dc3e18cf7a37bb9155707392439ec164da8c2121b3 SHA1: ffce2e5975f1bceb85e2b194fc05ed4ea90bc03b MD5sum: 886e61d8da9ecbc458380f9cf1ba7ca3 Description: toolbox for vision research -- arch/interpreter independent part Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains architecture independent files (such as .m scripts) Package: python-argcomplete Version: 1.0.0-1~nd90+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~nd90+1_all.deb Size: 25860 SHA256: 9770b9ab43c4c2d9278efb849353440e401bd6597a145741a41ddb0a7b28ee2b SHA1: 7902f84d52f282728cea4a6593ce5677265a01d9 MD5sum: 669cb251550f489e0a3ddaf00626b287 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-brian Source: brian Version: 1.4.3-1~nd90+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~nd90+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~nd90+1_all.deb Size: 401758 SHA256: 26b36aeba8693591db3604ded9e5852ae651a5766193e0bfea7d5aab171d0f31 SHA1: c2f7d445d3c681da9cce12435170627f94cee2db MD5sum: 452096fa5825d8ab802bf5a6f501e378 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~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7329 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~nd90+1_all.deb Size: 1985698 SHA256: 835c5afdbb4fb11f14d66598ecc38cb5a843a79b3f1bb0dac172168999441e61 SHA1: 8f96312d74a074629b85cd1958684525730613ad MD5sum: 5fbe363a3b8a2cd72948371cd7b9d00b Description: simulator for spiking neural networks - documentation Brian is a clock-driven simulator for spiking neural networks. . This package provides user's manual (in HTML format), examples and demos. Package: python-cfflib Source: cfflib Version: 2.0.5-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 736 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-lxml, python-numpy, python-networkx (>= 1.4), python-nibabel (>= 1.1.0) Recommends: python-nose, python-sphinx, python-tables, python-h5py Provides: python2.7-cfflib Homepage: http://cmtk.org/cfflib Priority: extra Section: python Filename: pool/main/c/cfflib/python-cfflib_2.0.5-1~nd70+1+nd90+1_all.deb Size: 202534 SHA256: 76cb03a4761b10f0ba07dc6eb4cf60e126c2d362aeaa2f981d1a79003a265982 SHA1: 9129804539bb9895714a6229ae4d56a544bf47ed MD5sum: fd58e3bf366a779d34c534a279c590d3 Description: Multi-modal connectome and metadata management and integration The Connectome File Format Library (cfflib) is a Python module for multi-modal neuroimaging connectome data and metadata management and integration. . It enables single subject and multi-subject data integration for a variety of modalities, such as networks, surfaces, volumes, fiber tracks, timeseries, scripts, arbitrary data objects such as homogeneous arrays or CSV/JSON files. It relies on existing Python modules and the standard library for basic data I/O, and adds a layer of metadata annotation as tags or with structured properties to individual data objects. Package: python-citeproc Source: citeproc-py Version: 0.3.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 769 Depends: neurodebian-popularity-contest, python-lxml, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/brechtm/citeproc-py Priority: optional Section: python Filename: pool/main/c/citeproc-py/python-citeproc_0.3.0-1~nd90+1_all.deb Size: 80364 SHA256: d1545645e4616ee893390ac616db5525b8b6540f8962c978df9489762081a8c4 SHA1: 0a2c8756cc2aca7f3c4e34da3dec7f9e24da23dd MD5sum: a3d3a59ef819bfaae15b840ea2e0d164 Description: Citation Style Language (CSL) processor for Python Citeproc-py is a library that produces formatted bibliographies and citations from bibliographic databases following formatting instructions provided by XML style files written in the Citation Style Language (CSL). . Currently, BibTeX and JSON are supported as input database formats, and plain text, reStructuredText and HTML as output format. . This package contains the Python modules. Package: python-contextlib2 Source: contextlib2 Version: 0.4.0-3~nd90+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~nd90+1_all.deb Size: 8662 SHA256: 30037e9a438387a47f50b5d1d066cc01c56d74a9058caa4692d76ccb7fff9ac7 SHA1: 844fcf185faf677382fcb2e8b9632fe858c07873 MD5sum: 2143c9cc6e11f28cf85623860e3524e3 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: 0.1+git589-g4f196dc-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1372 Depends: neurodebian-popularity-contest, git-annex (>= 6.20160219) | git-annex-standalone (>= 6.20160219), python-appdirs, python-git (>= 1.0.1+git137~), python-humanize, python-keyring, python-mock, python-progressbar, python-rdflib (>= 4.1.2~), python-six (>= 1.8.0~), python-sparqlwrapper, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: patool, python-html5lib, python-httpretty, python-msgpack, python-nose, python-numpy, python-scrapy, python-testtools Suggests: python-bs4, python-vcr Provides: python2.7-datalad Homepage: http://datalad.org Priority: optional Section: python Filename: pool/main/d/datalad/python-datalad_0.1+git589-g4f196dc-1~nd90+1_all.deb Size: 238548 SHA256: bb093050f29d85d7e99df20449e82fbe08f8e4f5140998dbd9a9f288b580a323 SHA1: 946188c72b0da015e3cb753b452e826fe63a59c2 MD5sum: 3684d2b587702282229018ff8a5761bb 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 for neuroscience domain). Using git-annex as its backend for data logistics it provides following facilities . - crawling of web sites to automatically prepare or update git-annex repositories with content from online websites, S3, etc - command line interface for manipulation of datasets (search, install, uninstall, upgrade etc) . This package installs the module for Python 2. . DISCLAIMER: DataLad is Work-In-Progress and this package is not yet for regular user consumption. Primarily intended for demonstrations and development. Package: python-dcmstack Source: dcmstack Version: 0.6.2+git33-gb43919a.1-1~nd90+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~nd90+1_all.deb Size: 77446 SHA256: 4ae24436da15707dc9c69956c6f774fad589a8d849689a2dbfb65e8f07147dfa SHA1: 21c67443d0d1770214c5126d99314cc99924cc4e MD5sum: 3fb6313f751296b45eae6a83179ae9f7 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~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1577 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://pydicom.org/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.9-1~nd80+1+nd90+1_all.deb Size: 357482 SHA256: 333cc88a2a89e9e497b59c70b4972f54879fc829709837408617f2cba6c05900 SHA1: 7a178766cae90f6a7a7f024eab6064433eb66c59 MD5sum: 5104770e0d0083777c17d5a64d13c8a3 Description: DICOM medical file reading and writing pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. Package: python-dipy Source: dipy Version: 0.10.1-1~nd90+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~nd90+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.7-dipy Homepage: http://nipy.org/dipy Priority: optional Section: python Filename: pool/main/d/dipy/python-dipy_0.10.1-1~nd90+1_all.deb Size: 2434010 SHA256: 34ac70f357cf1a99c5c00d3be428738024fc46a461bc8f2bc428d05ae47c1057 SHA1: b1d213b49ad781ffde642fa70b88cb1848b4957e MD5sum: 7f7b7deb57415276ddbf74efed4d558b 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~nd90+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~nd90+1_all.deb Size: 11464036 SHA256: 6ae301881087cfc7a59e693dafd2dba49d64b96189faa8a7ad8e0985045f46f5 SHA1: bff7e54e3ff6cf34d5c3d943764c8001697d31ee MD5sum: 92cb9e9e93f57ea25ea9a0521579efe3 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-duecredit Source: duecredit Version: 0.4.5.3-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 224 Depends: neurodebian-popularity-contest, python-citeproc, python-requests, 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_0.4.5.3-1~nd90+1_all.deb Size: 46738 SHA256: 1f686c337fb876a8de5ba22dc085fd2e6d4a0235889879f53015372f3f9095ab SHA1: b7baeb74c4a79535fc6f04b9aaf2e2958e643709 MD5sum: 5a6ec56c10caf57975c336ea4c90ac00 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~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2571 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-support (>= 0.90.0), python-pygame (>= 1.9.1~), python-opengl (>= 3.0.0), ttf-freefont, libjs-jquery, libjs-underscore Recommends: python-serial (>= 2.5~), python-numpy (>= 1.3.0~) Suggests: python-parallel (>= 0.2), python-pyxid Homepage: http://www.expyriment.org Priority: optional Section: science Filename: pool/main/p/python-expyriment/python-expyriment_0.7.0+git34-g55a4e7e-3~nd80+1+nd90+1_all.deb Size: 695880 SHA256: 0cd86f3708c27c1840b144158946a886c4c9f95be05b1e066841e2c1c8bb1a14 SHA1: bb870ded3d8999b7897d0952949260de3e7a4f0a MD5sum: 9c3064599b09b61824b028c8da226384 Description: Python library for cognitive and neuroscientific experiments Expyriment is a light-weight Python library for designing and conducting timing-critical behavioural and neuroimaging experiments. The major goal is to provide a well-structured Python library for a script-based experiment development with a high priority on the readability of the resulting programme code. Due to the availability of an Android runtime environment, Expyriment is also suitable for the development of experiments running on tablet PCs or smart-phones. Package: python-future Version: 0.15.2-1~nd90+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~nd90+1_all.deb Size: 348400 SHA256: 07482bfed3626a9dddd770d8db61ff5c1ef148d1af33a27c79bcada7aaad1d30 SHA1: a9283aa0f1dcab1137b0ec8904daabbe6560d4c3 MD5sum: 3a9f05bf57a91be288cc8bd42e2ef4a3 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~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1887 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~nd90+1_all.deb Size: 319712 SHA256: 31effa5d59fca4cd0960b401bf1e5a88366656a98488afcdc43291fcccd7b108 SHA1: 01d0c23dca8a7b18893260980075932666105867 MD5sum: 4d646f0bc2c071a010f34c798cbed74e 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: 2.0.2-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1571 Depends: neurodebian-popularity-contest, git (>= 1:1.7) | git-core (>= 1:1.5.3.7), python-gitdb (>= 0.6.4), 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_2.0.2-1~nd90+1_all.deb Size: 289138 SHA256: b35d2f8853e9b444cfc7f7eeecae5e64bd7ce66b56bea46f18df39a19668bf3f SHA1: 59fab7ab9c380d5e44c504a9f2b2717e599d0598 MD5sum: 2c97b857cbf5f39c0f474f8616728276 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: 2.0.2-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 930 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_2.0.2-1~nd90+1_all.deb Size: 118704 SHA256: 1da230323f4e6ef29deaccdad1282fa103a6914764ae10924b9547d80c286c88 SHA1: be16c53f3c95de029b695dacd48166aad73c90e1 MD5sum: 3c3a77ef738155f2e07b1ec248fa6aa0 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~nd90+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~nd90+1_all.deb Size: 12952 SHA256: 258fbf845777c01e6ddb4bcba4e5983c82248711fdd9c67970c1b69215bd9b85 SHA1: d9d3b3067bbb56e31873d92a313462d862e016d3 MD5sum: 1ece0e916f32fe2b842370b94869f266 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-jdcal Source: jdcal Version: 1.0-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 39 Depends: neurodebian-popularity-contest Homepage: https://github.com/phn/jdcal Priority: optional Section: python Filename: pool/main/j/jdcal/python-jdcal_1.0-1~nd80+1+nd90+1_all.deb Size: 7716 SHA256: fa7278a583fb30f2c0f244e46d4176c125e058cfb9440bf00988b098091dfb52 SHA1: c46cec73f8ed98b465cc051e97390f4996bce87b MD5sum: e67938a52dc086acbbea4f392b75b962 Description: Julian dates from proleptic Gregorian and Julian calendars This module contains functions for converting between Julian dates and calendar dates. . Different regions of the world switched to Gregorian calendar from Julian calendar on different dates. Having separate functions for Julian and Gregorian calendars allow maximum flexibility in choosing the relevant calendar. Package: python-joblib Source: joblib Version: 0.9.4-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 363 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.9.4-1~nd90+1_all.deb Size: 81358 SHA256: 6632faebedf23fc619d248b1cfe72b7e23a047792e612d5c79838e8bc0e775c8 SHA1: aad60ea32df6a68b2002161f21cef651a7635047 MD5sum: e3cbb722c3865b235819cf55f074184e Description: tools to provide lightweight pipelining in Python Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers: . - transparent disk-caching of the output values and lazy re-evaluation (memoize pattern) - easy simple parallel computing - logging and tracing of the execution . Joblib is optimized to be fast and robust in particular on large, long-running functions and has specific optimizations for numpy arrays. . This package contains the Python 2 version. Package: python-lazyarray Source: lazyarray Version: 0.1.0-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy Homepage: http://bitbucket.org/apdavison/lazyarray/ Priority: optional Section: python Filename: pool/main/l/lazyarray/python-lazyarray_0.1.0-1~nd70+1+nd90+1_all.deb Size: 6898 SHA256: 64177357eee6f7ee78eb01dda91c6727c789370851c697165f8153dc784b557c SHA1: 91e0a31cda5c95c4bf1c97a8c20c47db3f00e5af MD5sum: 7441821b3bd04e06759e8a26db5429a7 Description: Python module providing a NumPy-compatible lazily-evaluated array The 'larray' class is a NumPy-compatible numerical array where operations on the array (potentially including array construction) are not performed immediately, but are delayed until evaluation is specifically requested. Evaluation of only parts of the array is also possible. Consequently, use of an 'larray' can potentially save considerable computation time and memory in cases where arrays are used conditionally, or only parts of an array are used (for example in distributed computation, in which each MPI node operates on a subset of the elements of the array). Package: python-mdp Source: mdp Version: 3.5-1~nd90+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~nd90+1_all.deb Size: 277566 SHA256: 4e2e0e0e69030e414855c5c81f7fffb899f3739e7fbdb858a75a8501ba1d225f SHA1: f9967df922702377f0089c8df9af016cb9a330cb MD5sum: 8a3c5afcf54c122b08c7d4b41cca5130 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: 0.8.6+dfsg-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7380 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-sklearn, python-matplotlib, python-joblib (>= 0.4.5), xvfb, xauth, libgl1-mesa-dri, help2man, libjs-jquery, libjs-jquery-ui Recommends: python-nose, mayavi2 Suggests: python-dap, ipython Provides: python2.7-mne Homepage: http://martinos.org/mne Priority: optional Section: python Filename: pool/main/p/python-mne/python-mne_0.8.6+dfsg-1~nd80+1+nd90+1_all.deb Size: 4021650 SHA256: d0756158b44e6ab6c2d77d8b227ce7b0d221b8b7f90487150d5136e089e0fd7f SHA1: 3a606009686cd734193202abe37c9c9884b231df MD5sum: 438d89ab3fe0fe7488916f463a7b1b52 Description: Python modules for MEG and EEG data analysis This package is designed for sensor- and source-space analysis of MEG and EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. Package: python-mpi4py-doc Source: mpi4py Version: 2.0.0-1~nd90+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~nd90+1_all.deb Size: 55420 SHA256: 90022b1d2b6f29244d82401a44b75ed326d56104d17f91c091a9479974497879 SHA1: 828119647eba12d56b281921fc2dbe95624b6f95 MD5sum: 8b30e6af5ec64d0be639dd8bbda8ed39 Description: bindings of the MPI standard -- documentation MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides HTML rendering of the user's manual. Package: python-mvpa2 Source: pymvpa2 Version: 2.4.3-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8375 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy, python-mvpa2-lib (>= 2.4.3-1~nd90+1) Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-nipy, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, python-shogun, liblapack-dev, python-pprocess, python-statsmodels, python-joblib, python-duecredit Suggests: fslview, fsl, python-mvpa2-doc, python-nose, python-openopt, python-rpy2 Provides: python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.4.3-1~nd90+1_all.deb Size: 5064946 SHA256: 96e6816a5d2fa7eef8a63ff86e8e8d13e2d18bbf143bd8745a8cedf073a4bd9e SHA1: a1980298f67000d01cd35fa4179305e79bc91134 MD5sum: 4d64dbd8432da19ac5a489a25ebc1f3a Description: multivariate pattern analysis with Python v. 2 PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. . This is a package of PyMVPA v.2. Previously released stable version is provided by the python-mvpa package. Python-Version: 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.4.3-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 34361 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2, python-mvpa2-tutorialdata, ipython-notebook Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.4.3-1~nd90+1_all.deb Size: 4537450 SHA256: 5ef5b93464e3fe0fc3cd0ebcd250c26bdee88d202a8d712f39bd58c3b791338a SHA1: 27f32aa6cd9fd57d30c81afdef74d197e08003c4 MD5sum: 9e369cd0a792981d082fff6f4204cff3 Description: documentation and examples for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a HTML documentation (tutorial, FAQ etc.), and example scripts. In addition the PyMVPA tutorial is also provided as IPython notebooks. Package: python-neo Source: neo Version: 0.3.3-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2974 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy (>= 1:1.3~), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Suggests: python-nose Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.3.3-1~nd80+1+nd90+1_all.deb Size: 1383640 SHA256: 7e3b8dcf1a6e7c971437d744c5bb781a34c6272677f9760fd74d262f0f7e12b1 SHA1: 8c96c831525d26230be96771de115c7f2cb574d8 MD5sum: a1c098adc1493d4cd4f12a7c564a5508 Description: Python IO library for electrophysiological data formats NEO stands for Neural Ensemble Objects and is a project to provide common classes and concepts for dealing with electro-physiological (in vivo and/or simulated) data to facilitate collaborative software/algorithm development. In particular Neo provides: a set a classes for data representation with precise definitions, an IO module with a simple API, documentation, and a set of examples. . NEO offers support for reading data from numerous proprietary file formats (e.g. Spike2, Plexon, AlphaOmega, BlackRock, Axon), read/write support for various open formats (e.g. KlustaKwik, Elan, WinEdr, WinWcp, PyNN), as well as support common file formats, such as HDF5 with Neo-structured content (NeoHDF5, NeoMatlab). . Neo's IO facilities can be seen as a pure-Python and open-source Neuroshare replacement. Package: python-neuroshare-doc Source: python-neuroshare Version: 0.9.2-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 271 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: http://www.g-node.org/neuroshare-tools Priority: extra Section: doc Filename: pool/main/p/python-neuroshare/python-neuroshare-doc_0.9.2-1~nd80+1+nd90+1_all.deb Size: 95554 SHA256: 2ce47764501482655bdadd0c5e71fb26ef3652e141cc6f493fa897afaec80e93 SHA1: ec00e3c7a84c81e3a4a3eae42b249b0aeb16078e MD5sum: 02d7853bc814b8e6df159b51b2685b81 Description: Python interface and tools for Neuroshare The Neuroshare API is a standardized interface to access electrophysiology data stored in various different file formats. To do so, it uses format- specific shared libraries. . This package provides a high-level Python interface to the Neuroshare API that focuses on convenience for the user and enables access to all available metadata and data. The data is returned in NumPy arrays, which provides a quick route to further examination and analysis. . In addition, this package contains the ns2hdf converter tool that converts neuroshare-compatible files into the HDF5 (Hierarchical Data Format, ver. 5) file format. . This package contains HTML documentation files for python-neuroshare. Package: python-nibabel Source: nibabel Version: 2.0.2-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63351 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_2.0.2-1~nd90+1_all.deb Size: 1971946 SHA256: 657d68582289a09cfc31966a0efc03496ba5f77618a2770331870bf35ba2f7d3 SHA1: d19d4cc238af1e65e202e4a87e5304f229c670cb MD5sum: 805bbd4ea53b5ff3c82f955163670465 Description: Python bindings to various neuroimaging data formats NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package also provides a commandline tools: . - dicomfs - FUSE filesystem on top of a directory with DICOMs - nib-ls - 'ls' for neuroimaging files - parrec2nii - for conversion of PAR/REC to NIfTI images Package: python-nibabel-doc Source: nibabel Version: 2.0.2-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5512 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-mathjax Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_2.0.2-1~nd90+1_all.deb Size: 2506278 SHA256: e8a1d6f1d60ee5576c951389c96cf006e6d647a0077a8978efa7f6a9bd21f8e9 SHA1: 92045a7ff644db1185db4610cf479c44ef19d8d5 MD5sum: a93c8e2e0878eb00078b72a5941ec678 Description: documentation for NiBabel NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package provides the documentation in HTML format. Package: python-nilearn Source: nilearn Version: 0.2.3~dfsg.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2322 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_0.2.3~dfsg.1-1~nd90+1_all.deb Size: 713926 SHA256: 6f696093add42e6971fdda1985718d486faa649a4c36a515e7faa9973ce418f2 SHA1: 169b77a9c08b8b21646d4ff227f7f656171c7afe MD5sum: c9c40dd6d13b3e647455f6fd3a3a3ea5 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: 0.3.0+git262-gbb838d7-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3185 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.2), python (>= 2.7), python (<< 2.8), python-scipy, python-nibabel, python-nipy-lib (>= 0.3.0+git262-gbb838d7-1~nd80+1+nd90+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.3.0+git262-gbb838d7-1~nd80+1+nd90+1_all.deb Size: 724228 SHA256: 6b7a48095dcfd24367acee0a5fee6ff54c39b914fff4cd4b9f184138ebb1e0b0 SHA1: 07defa82143344ef33689b077ad6b70e538a00b4 MD5sum: c736b2c0484f4c9ffe32ac49380239ef Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It provides functionality for - General linear model (GLM) statistical analysis - Combined slice time correction and motion correction - General image registration routines with flexible cost functions, optimizers and re-sampling schemes - Image segmentation - Basic visualization of results in 2D and 3D - Basic time series diagnostics - Clustering and activation pattern analysis across subjects - Reproducibility analysis for group studies Python-Version: 2.7 Package: python-nipy-doc Source: nipy Version: 0.3.0+git262-gbb838d7-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10844 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.3.0+git262-gbb838d7-1~nd80+1+nd90+1_all.deb Size: 3215602 SHA256: bb81791880acbbb409bb1d8148f9e8cc93e4dc9479741116ae7eabad945a266d SHA1: c9b0ba4f6e2ec1345510755f7042432dbe986539 MD5sum: f685e5c756759976203d2e838480f81c Description: documentation and examples for NiPy This package contains NiPy documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation Package: python-nipype Source: nipype Version: 0.11.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8454 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib Recommends: ipython, python-nose, graphviz, python-xvfbwrapper, mayavi2 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_0.11.0-1~nd90+1_all.deb Size: 1413634 SHA256: 210a0face4cd262eeab2e93c8657734ecbe41621c40dfd4f14ebb2bcc01ddabf SHA1: 37c8c88b9ed0024e97160ecef48fc292fecb3059 MD5sum: 33f40de5ed0bfd81653325d494139ab7 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: 0.11.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23170 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_0.11.0-1~nd90+1_all.deb Size: 8949952 SHA256: 4800ae867394c013414572e47c4c79556283e002dcb22b0314b6d200d1501d56 SHA1: fd48a5d73fbad59fa8e8265ec914a1745bb749cd MD5sum: eb2b9a3c61cefc67a93036a3d4026c89 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: 0.6-1~nd90+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_0.6-1~nd90+1_all.deb Size: 2560576 SHA256: 9f848877e503e81245d8fd23e41f7fce1fdd596bae792e0d9572dac313aca6b1 SHA1: a71fbdafb6079dbb8dfd75c2b0efce0eebdd750a MD5sum: 179ea715f968a8107d2a5f56f132da50 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: 0.6-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7857 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_0.6-1~nd90+1_all.deb Size: 5756788 SHA256: 3297fca8ae9de3a78ba95cbbe3334f9aff547410e13ca3115285ea5473ab8dd6 SHA1: f9907c999d7e4aa455a4726e68866e7f4cee0a5d MD5sum: db9c06ed6214a526b1bf91f0d0ff5980 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~nd90+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~nd90+1_all.deb Size: 5124 SHA256: 0690e2f53e965d973b5427bf4a5dfb5199bb9868e33fb2ceefff034936546db0 SHA1: fd93ee50ff6d54caef0722f9db35460dc06d145a MD5sum: 56644cbe0eef91d6c280ff1ffe1479b4 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~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1065 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.7-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd70+1+nd90+1_all.deb Size: 179826 SHA256: e289031fd70e3234dd9bf775fda6ce9332235b63580fc3f21d95f5bf7d2ae59d SHA1: 0b6e1f2a56ea52c4ec110effc58bdb067c8a9887 MD5sum: bdffb8b3128e6731bf3a3af978b2667b Description: Python module for numerical optimization Numerical optimization framework developed in Python which provides connections to lots of solvers with easy and unified OpenOpt syntax. Problems which can be tackled with OpenOpt * Linear Problem (LP) * Mixed-Integer Linear Problem (MILP) * Quadratic Problem (QP) * Non-Linear Problem (NLP) * Non-Smooth Problem (NSP) * Non-Linear Solve Problem (NLSP) * Least Squares Problem (LSP) * Linear Least Squares Problem (LLSP) * Mini-Max Problem (MMP) * Global Problem (GLP) . A variety of solvers is available (e.g. IPOPT, ALGENCAN). Python-Version: 2.7 Package: python-openpyxl Source: openpyxl Version: 2.3.0-2~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1323 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-jdcal, 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_2.3.0-2~nd90+1_all.deb Size: 199214 SHA256: 4640b1dd8229171bd0ece2ce2e6e90a42867a5a7358f44e1c066a6ece8c0f956 SHA1: ed42cb278608f5dc0fd198c4c723818d894335e5 MD5sum: 9e0e163f143ebd4f50cfec2ea8cb61a1 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~nd90+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~nd90+1_all.deb Size: 17204 SHA256: d00371df4d5a73963138386d93de0bafa8cb2722c0851f7694c249f7fbaa76df SHA1: efb06ae7d0a596e847ce8e9930a285b61a68efcc MD5sum: 95b08012df65ef3a2f60e3502382bd7b 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: 0.18.0+git114-g6c692ae-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13062 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.7~), python-pandas-lib (>= 0.18.0+git114-g6c692ae-1~nd90+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_0.18.0+git114-g6c692ae-1~nd90+1_all.deb Size: 1761342 SHA256: c16aabc0931f15c13106dc16a692731131e7fc55e1a99a91d296f2a3173ed828 SHA1: 120ebbca2bdd4690a9d13fcfbc146322d5c40b67 MD5sum: 3a277a4e3ff036d7a7d0b920a00d4538 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: 0.18.0+git114-g6c692ae-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 56955 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_0.18.0+git114-g6c692ae-1~nd90+1_all.deb Size: 10945864 SHA256: 3a239015f01b21345a63ab0c51801b952902b0181c8795f56ecd9c2efb410b2a SHA1: 9e96f011c5232bd18bbfa444b64c9d2944498e4d MD5sum: 6dec79c35202c197a99453d76582e91f Description: documentation and examples for pandas This package contains documentation and example scripts for python-pandas. Package: python-patsy Source: patsy Version: 0.4.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 784 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-six, python-numpy Recommends: python-pandas, python-openpyxl Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python-patsy_0.4.1-1~nd90+1_all.deb Size: 174438 SHA256: 0d38eaf3c31968d7cc0e67ca37b8758a5a1d3b2d11624b24b43cdc5e4af67b84 SHA1: 67f9621b19d45da36963bdfbd9131948c371209d MD5sum: 6601d79dcd3c4d704cebdfc7ee5f960c 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: 0.4.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1407 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_0.4.1-1~nd90+1_all.deb Size: 364182 SHA256: b6be653138e89cc385bccf696833b1687134ee05a8b193f88497344724e4899c SHA1: 24be6c80a22e57d9630183535659fd9a24cf6500 MD5sum: 72f04d6461107777dcc1975ade4f07f4 Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pp Source: parallelpython Version: 1.6.2-2~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 143 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.parallelpython.com/ Priority: optional Section: python Filename: pool/main/p/parallelpython/python-pp_1.6.2-2~nd70+1+nd90+1_all.deb Size: 28994 SHA256: 9d2b9bc5a9f1f2f7ab2c1f65d14edab85d349c71f7c32d5b03a8a84752927571 SHA1: 9a77e4793d44f572146a67eaa8af06f5e2be9bc4 MD5sum: 7b25f8fbdf69d07695113ddb51235f8d Description: parallel and distributed programming toolkit for Python Parallel Python module (pp) provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. pp module features cross-platform portability and dynamic load balancing. Thus application written with PP will parallelize efficiently even on heterogeneous and multi-platform clusters (including clusters running other application with variable CPU loads). Python-Version: 2.7 Package: python-pprocess Source: pprocess Version: 0.5-1+nd0~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 758 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.boddie.org.uk/python/pprocess.html Priority: optional Section: python Filename: pool/main/p/pprocess/python-pprocess_0.5-1+nd0~nd80+1+nd90+1_all.deb Size: 81664 SHA256: 5327d46e6e8873ff262f9d40181b5169ac4a2173b18c195b572732169a7dcce2 SHA1: 4e7bc21bf1dadeb4014e694348397dd906b8cff6 MD5sum: cf0bced2928e4ed9c897e2a3c2d0afac Description: elementary parallel programming for Python The pprocess module provides elementary support for parallel programming in Python using a fork-based process creation model in conjunction with a channel-based communications model implemented using socketpair and poll. On systems with multiple CPUs or multicore CPUs, processes should take advantage of as many CPUs or cores as the operating system permits. Python-Version: 2.7 Package: python-py Version: 1.4.30-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 306 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-pkg-resources Suggests: subversion, python-pytest, python-pytest-xdist Homepage: https://bitbucket.org/pytest-dev/py Priority: optional Section: python Filename: pool/main/p/python-py/python-py_1.4.30-1~nd90+1_all.deb Size: 66758 SHA256: ec883a7d749a52c460f40bd5d98807df52000a97f138f047bc2dacbc790060cf SHA1: 26148d6657c97312e4b59fdbd1ff62235d49f052 MD5sum: bb113a6f468da35a8aa8339a767709fb Description: Advanced Python development support library (Python 2) The Codespeak py lib aims at supporting a decent Python development process addressing deployment, versioning and documentation perspectives. It includes: . * py.path: path abstractions over local and Subversion files * py.code: dynamic code compile and traceback printing support . This package provides the Python 2 modules. Package: python-pyentropy Source: pyentropy Version: 0.4.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 91 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy (>= 1.3) Recommends: python-scipy Suggests: python-nose Provides: python2.7-pyentropy Homepage: http://code.google.com/p/pyentropy Priority: extra Section: python Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd70+1+nd90+1_all.deb Size: 19484 SHA256: 90a65872dff84dfdd9080faae4df963e355d03c4f5c818b6dee37ae7265c8dd5 SHA1: 41cee054cc47be20e73203718874a5aae00fc35e MD5sum: e90627411895dd8862ef69d2670d0968 Description: Python module for estimation information theoretic quantities A Python module for estimation of entropy and information theoretic quantities using cutting edge bias correction methods, such as * Panzeri-Treves (PT) * Quadratic Extrapolation (QE) * Nemenman-Shafee-Bialek (NSB) Python-Version: 2.7 Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-2~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 822 Depends: neurodebian-popularity-contest, python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0+git12-g365f8e3-2~nd80+1+nd90+1_all.deb Size: 819326 SHA256: 3bb7c4a8afd71f527affe1dc9fc35cc8767aebfb4a22ca35c975346202ccfd15 SHA1: b65162864b6ef1f6b6f21f3a81b7221a8d9f5e6f MD5sum: ae5bd1ef9c1dcee42bc9e64308126c62 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides common files such as images. Package: python-pymc-doc Source: pymc Version: 2.3.4+ds-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1894 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Homepage: http://pymc-devs.github.com/pymc/ Priority: extra Section: doc Filename: pool/main/p/pymc/python-pymc-doc_2.3.4+ds-1~nd80+1+nd90+1_all.deb Size: 839788 SHA256: af2ce4bafd9fe4585d82802881bc4a1ceb28e9076d529d7cc57ac90478281dd4 SHA1: 9f2d1b73a0c55bd49be8229926cc64a9e63a88e3 MD5sum: ad1e1963e2b23c06beb3c6fa20c513e1 Description: Bayesian statistical models and fitting algorithms PyMC is a Python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics. . This package provides the documentation in HTML format. Package: python-pynn Source: pynn Version: 0.7.5-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 832 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-jinja2, python-cheetah Suggests: python-neuron, python-brian, python-csa Homepage: http://neuralensemble.org/trac/PyNN Priority: extra Section: python Filename: pool/main/p/pynn/python-pynn_0.7.5-1~nd70+1+nd90+1_all.deb Size: 137374 SHA256: 370ece10f2dd605fc721d1d7d7efb38db0d4c150a969f9a7788b0a18ac0d2eaf SHA1: a9207324cee6efc6f2830f3af2faa6a2a930d723 MD5sum: fe3284f568042b1fe19972884b23c76a Description: simulator-independent specification of neuronal network models PyNN allows for coding a model once and run it without modification on any simulator that PyNN supports (currently NEURON, NEST, PCSIM and Brian). PyNN translates standard cell-model names and parameter names into simulator-specific names. Package: python-pytest Source: pytest Version: 2.7.2-2~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 493 Depends: neurodebian-popularity-contest, python-pkg-resources, python-py (>= 1.4.29), python, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-mock (>= 1.0.1) Homepage: http://pytest.org/ Priority: optional Section: python Filename: pool/main/p/pytest/python-pytest_2.7.2-2~nd90+1_all.deb Size: 132284 SHA256: 6ff620a9324dfeb8189fd224f82cfb8087002fe8dec53cd02815d6ad52e52077 SHA1: e469a272602f058e45ba3c0612c1fa38d05df811 MD5sum: f47c8d1731b85f0e39dac9574910fccc Description: Simple, powerful testing in Python This testing tool has for objective to allow the developers to limit the boilerplate code around the tests, promoting the use of built-in mechanisms such as the `assert` keyword. Package: python-pytest-doc Source: pytest Version: 2.7.2-2~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3002 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Recommends: python-pytest | python3-pytest Homepage: http://pytest.org/ Priority: optional Section: doc Filename: pool/main/p/pytest/python-pytest-doc_2.7.2-2~nd90+1_all.deb Size: 431304 SHA256: 652b8e09afc3715c2883b10e7e1ce2cc46f236542c0ec4228d4d65b7533f0905 SHA1: 26ceb27b3c48e7c051a73a90d54b98f8c5f11912 MD5sum: b947df3994d1953e2a0755781e9b0e21 Description: Simple, powerful testing in Python - Documentation This testing tool has for objective to allow the developers to limit the boilerplate code around the tests, promoting the use of built-in mechanisms such as the `assert` keyword. . This package contains the documentation for pytest. Package: python-pytest-localserver Source: pytest-localserver Version: 0.3.4-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 59 Depends: neurodebian-popularity-contest, python-pytest, python-werkzeug (>= 0.10), python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://pypi.python.org/pypi/pytest-localserver Priority: optional Section: python Filename: pool/main/p/pytest-localserver/python-pytest-localserver_0.3.4-1~nd90+1_all.deb Size: 18152 SHA256: 09ff67eba0f36d2f5bb0a405300bd4aa4315eb9a75a9fac48fd57a187c9d64f0 SHA1: 71b04f6861c66ac85684d5c7f0b0d233a60ff8df MD5sum: db59b4614f943159b2537882aabd2474 Description: py.test plugin to test server connections locally (Python 2) pytest-localserver is a plugin for the Pytest testing framework which enables to test server connections locally. . This package contains the modules for Python 2. Package: python-pytest-tornado Source: pytest-tornado Version: 0.4.4-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 32 Depends: neurodebian-popularity-contest, python-pytest, python-tornado, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/eugeniy/pytest-tornado Priority: optional Section: python Filename: pool/main/p/pytest-tornado/python-pytest-tornado_0.4.4-1~nd90+1_all.deb Size: 5658 SHA256: 2baad3999ed0fc99b2329ebc1285fb06d5a94d4dfec4d619613979b24eb68a26 SHA1: fbbac0c1dd798a073e540e8f99e02174beb1450b MD5sum: 374e5ad6bb5047154499315f47d46017 Description: py.test plugin to test Tornado applications pytest-tornado is a plugin for the Pytest testing framework which provides fixtures and markers to simplify testing of Tornado applications (Python web framework and ansynchronous networking library). . This package contains the plugin for Python 2 code. Package: python-pyxid Source: pyxid Version: 1.0-1~nd+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Homepage: https://github.com/cedrus-opensource/pyxid Priority: optional Section: python Filename: pool/main/p/pyxid/python-pyxid_1.0-1~nd+1+nd90+1_all.deb Size: 10358 SHA256: e2db09bf0e002d89bb3690e59e5bc46747809822675915da6e28646cedddf5f2 SHA1: 431b7f4e71d4a33d6687832ccbef84848c4fd2bc MD5sum: 93daf5487203df271ed363dc65e968d0 Description: interface for Cedrus XID and StimTracker devices pyxid is a Python library for interfacing with Cedrus XID (eXperiment Interface Device) and StimTracker devices. XID devices are used in software such as SuperLab, Presentation, and ePrime for receiving input as part of stimulus/response testing experiments. . pyxid handles all of the low level device handling for XID devices in Python projects. Package: python-quantities Version: 0.10.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 321 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy (>= 1.4) Homepage: http://packages.python.org/quantities/ Priority: extra Section: python Filename: pool/main/p/python-quantities/python-quantities_0.10.1-1~nd70+1+nd90+1_all.deb Size: 51442 SHA256: bfacf89e8ba78fd59315adc47dd03899f4b4594354eafbb38bbe186128958b1a SHA1: 53773551206da3c1249b9d38cf0ee65a40b9d6c4 MD5sum: 5e0cd9e39a08b2233b4ede2ab7e5eb72 Description: Library for computation of physical quantities with units, based on numpy Quantities is designed to handle arithmetic and conversions of physical quantities, which have a magnitude, dimensionality specified by various units, and possibly an uncertainty. Quantities builds on the popular numpy library and is designed to work with numpy ufuncs, many of which are already supported. Package: python-scikits-learn Source: scikit-learn Version: 0.17.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 65 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_0.17.1-1~nd90+1_all.deb Size: 55986 SHA256: fa3def160a745a8fb0a1d8dcf41de234916bed7e6d2f5581483dcb7b08cf2748 SHA1: 213799f54e1c65aaf9b418359f454600dcf47a48 MD5sum: cbd31d58e7308b358a4790e251c81ae5 Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-scikits.statsmodels Source: statsmodels Version: 0.6.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17 Depends: neurodebian-popularity-contest, python-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.6.1-1~nd80+1+nd90+1_all.deb Size: 6130 SHA256: 14c152e3d33f957bbfb131feb4b7f3b7635e8c951deaaa0bc14f4f61e201dd5f SHA1: aa7c9553756ceae24e09c14cb0896f3c2f47c7f0 MD5sum: ea75505ebffde41bf933adf8b994a3a9 Description: transitional compatibility package for statsmodels migration Provides old namespace (scikits.statsmodels) and could be removed if dependent code migrated to use statsmodels for clarity of the namespace. Package: python-scrapy Version: 1.0.3-1~nd90+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~nd90+1_all.deb Size: 175836 SHA256: 4260fa520a995cdba1e0ef7a93e2012685b6ba641c452aca4a16c62c13cf23a9 SHA1: c669cf541b6215f69a418a8c24b8fcc1a2b1535b MD5sum: 1e24fc66d2270804340c78ef55b335c4 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~nd90+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~nd90+1_all.deb Size: 1540304 SHA256: 3680a439513ce144108222c9ef83853e240c9e58b9e635527309a1ff6079ebda SHA1: ace8165525e67c38faac44f433dc390ed3d6b231 MD5sum: 2c46c761285144e992af8ec8b0aeba62 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-setuptools-scm Source: setuptools-scm Version: 1.8.0-1~bpo8+1~nd90+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~nd90+1_all.deb Size: 11450 SHA256: f58c242f4adf132cd1dfa7bdcb70cb88bec6b5af319d005d4398abfc1104952b SHA1: 704b1c46a6f49ee40904d9f0137bb013f88e6983 MD5sum: 91e7cb0fbfb78d34485684539fca493a 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~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 34 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Provides: python2.7-simplegeneric Homepage: http://pypi.python.org/pypi/simplegeneric Priority: extra Section: python Filename: pool/main/s/simplegeneric/python-simplegeneric_0.7-1~nd70+1+nd90+1_all.deb Size: 9264 SHA256: 15a747617db233795675c1aa67ca9de17cd9e13af6912664e0b88eca1ddc6ddf SHA1: 94c949ff1fa89b6d155fa03308f301354c498766 MD5sum: 8594dcbe5f67eccd178894087d1d54e8 Description: Simple generic functions for Python The simplegeneric module lets you define simple single-dispatch generic functions, akin to Python's built-in generic functions like len(), iter() and so on. However, instead of using specially-named methods, these generic functions use simple lookup tables, akin to those used by e.g. pickle.dump() and other generic functions found in the Python standard library. Package: python-six Source: six Version: 1.9.0-3~bpo8+1~nd90+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~nd90+1_all.deb Size: 13800 SHA256: 8530c3e58e97e2c76ff67f6400b3e335c89f48f69533718c7127fe73146b7f8b SHA1: 93f6ecba506a59e335b1eb73d0b651a6ec98bcf8 MD5sum: 1cd28c124dcad7f3f2e8808357337703 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~nd90+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~nd90+1_all.deb Size: 14070 SHA256: 923b4ed69614fd0ade8375e3d07d912ac516bedf8001c82935a25addfb890e3b SHA1: 75ddd3467b2e6bf4808f11965d7629707ddd55b1 MD5sum: 3bf5284c215ab22af0423fff495f2a38 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-sklearn Source: scikit-learn Version: 0.17.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5283 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-sklearn-lib (>= 0.17.1-1~nd90+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_0.17.1-1~nd90+1_all.deb Size: 1223510 SHA256: 414b079b046f07a735858828cb6b3fe6abd2d43ade30be738b8fc58c1f0c5fdc SHA1: 2364130e86df43bf3a90cf1d28e4b3743eb96860 MD5sum: 3e1c3da616c9f0d25b3f77b92eb4bbb2 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: 0.17.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24871 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_0.17.1-1~nd90+1_all.deb Size: 4018540 SHA256: 606f074a6a5bd109df5f22191260f21a2cf5d67f658299becb468cc74028ed97 SHA1: 68cd610cad8a59f1a71449f219219e584eb9e50f MD5sum: 89e939d2ced0aeb96bc563982a54fa19 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-smmap Version: 0.9.0-3~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 90 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Suggests: python-nose Provides: python2.7-smmap Homepage: https://github.com/Byron/smmap Priority: extra Section: python Filename: pool/main/p/python-smmap/python-smmap_0.9.0-3~nd90+1_all.deb Size: 20142 SHA256: 8a42f359b34272d3ae6b5f44802ea9e79f2da1847979acab4edbc5b0f616fb46 SHA1: e186d0af0c8c08af10aaed43b5002f48d10c7696 MD5sum: f742a6bed2473ecc2a207b5e5ecc3cc1 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~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3602 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-docutils (>= 0.5), python-pygments (>= 0.8), python-jinja2 (>= 2.2), libjs-jquery Recommends: python (>= 2.6) | python-simplejson, python-imaging Suggests: jsmath Homepage: http://sphinx.pocoo.org/ Priority: optional Section: python Filename: pool/main/s/sphinx/python-sphinx_1.0.7-2~nd70+1+nd90+1_all.deb Size: 1039184 SHA256: c0343e0061b8ee91e77c7ae4929f848936635d296d62555c27663d36bef902c1 SHA1: b0ebef92fc597a739bc948e7cee12df496a8aefe MD5sum: d1a38c525e04f208e5c87ceb4cbb58a8 Description: tool for producing documentation for Python projects Sphinx is a tool for producing documentation for Python projects, using reStructuredText as markup language. . Sphinx features: * HTML, CHM, LaTeX output, * Cross-referencing source code, * Automatic indices, * Code highlighting, using Pygments, * Extensibility. Existing extensions: - automatic testing of code snippets, - including doctrings from Python modules. Package: python-sphinx-rtd-theme Source: sphinx-rtd-theme Version: 0.1.8-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 359 Depends: neurodebian-popularity-contest, fonts-font-awesome, fonts-lato, libjs-modernizr, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-sphinx Homepage: https://github.com/snide/sphinx_rtd_theme Priority: optional Section: python Filename: pool/main/s/sphinx-rtd-theme/python-sphinx-rtd-theme_0.1.8-1~nd90+1_all.deb Size: 117144 SHA256: eab67c48e24b93cff0b649e5a427eb10c52aa359d3a4fd74ea6cd4b069a9e95c SHA1: 34abec450f80ae0621d2647f38fddacebc0e09ef MD5sum: cc42c408e9505fba4fe618b6df197db5 Description: sphinx theme from readthedocs.org (Python 2) This mobile-friendly sphinx theme was initially created for readthedocs.org, but can be incorporated in any project. . Among other things, it features a left panel with a browseable table of contents, and a search bar. . This is the Python 2 version of the package. Package: python-spyderlib Source: spyder Version: 2.2.5+dfsg-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4197 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), libjs-sphinxdoc (>= 1.0), libjs-jquery, libjs-mathjax, python-qt4 Recommends: ipython-qtconsole, pep8, pyflakes (>= 0.5.0), pylint, python-matplotlib, python-numpy, python-psutil (>= 0.3.0), python-rope, python-scipy, python-sphinx Suggests: tortoisehg, gitk Breaks: spyder (<< 2.0.12-1) Replaces: spyder (<< 2.0.12-1) Provides: python2.7-spyderlib Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: python Filename: pool/main/s/spyder/python-spyderlib_2.2.5+dfsg-1~nd80+1+nd90+1_all.deb Size: 1647356 SHA256: b4a2d401e100aa223d1cd764287958b07aae66471c4bc8b0f81cb56b4592f175 SHA1: 29ea7e0b238e8c437fb77ce4c5a77a910856a7b2 MD5sum: 46e38b5131ab14d5811e5b6e8ecfe690 Description: python IDE for scientists Originally written to design Spyder (the Scientific PYthon Development EnviRonment), the spyderlib Python library provides ready-to-use pure-Python widgets: source code editor with syntax highlighting and code introspection/analysis features, NumPy array editor, dictionary editor, Python console, etc. It's based on the Qt Python binding module PyQt4 (and is compatible with PySide since v2.2). Package: python-spykeutils Source: spykeutils Version: 0.4.2-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2094 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_0.4.2-1~nd90+1_all.deb Size: 308630 SHA256: c56f8e2d1a3184c8bda97a917bbad50ab1fb0735e6847c750162788c553ce69a SHA1: 61fc54b1b17852300f5e9191ba72f8b9fe314361 MD5sum: 451f10b232b5ac99eb1add45a414c099 Description: utilities for analyzing electrophysiological data spykeutils is a Python library for analyzing and plotting data from neurophysiological recordings. It can be used by itself or in conjunction with Spyke Viewer, a multi-platform GUI application for navigating electrophysiological datasets. Package: python-statsmodels Source: statsmodels Version: 0.6.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13052 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-statsmodels-lib (>= 0.6.1-1~nd80+1+nd90+1), python-patsy Recommends: python-pandas, python-matplotlib, python-nose, python-joblib, python-cvxopt Suggests: python-statsmodels-doc Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Provides: python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.6.1-1~nd80+1+nd90+1_all.deb Size: 2568752 SHA256: 693c35635920eaa1e3b294a7f878c2101e94d4dd4c03cafb8b64d5ee5d555896 SHA1: 3126fcf7e2081751d0f193f47b003860065e5fd7 MD5sum: 09c02024043dcdf9d3a40e127736ae65 Description: Python module for the estimation of statistical models statsmodels Python module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Package: python-statsmodels-doc Source: statsmodels Version: 0.6.1-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 46850 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-statsmodels Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.6.1-1~nd80+1+nd90+1_all.deb Size: 11620476 SHA256: 68283825798fd3cf12421873e7c4d7a8066d37454f05c0f7b46d58d689eb62a5 SHA1: 4bf6b88bab1a09f8c347e1e9f272066602e2b026 MD5sum: 062bb026c8bdaee126a4da8a07ace314 Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-surfer Source: pysurfer Version: 0.6-1~nd90+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~nd90+1_all.deb Size: 43192 SHA256: 48e6c25bed2827cbd7cdba09df7772aa3e28c554328fb665ebd90e166cd04966 SHA1: 7b0394249aca4ae1167b160cab20d70b40ee0e13 MD5sum: b8e11c84250d4a7071cbb1c714d0dee7 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-tables Source: pytables Version: 3.2.1-1~nd90+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~nd90+1), python-tables-lib (<< 3.2.1-1~nd90+1.1~), python-tables-data (= 3.2.1-1~nd90+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~nd90+1_all.deb Size: 344806 SHA256: e66e257a076d39aa14d4204d5cf7b49114e518c8bec02735b5a3e8bac0c02da9 SHA1: a148f32ac501ace9855b063ab8390d8e9022261d MD5sum: 8df7e8284168c245806d6cae620f649f 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~nd90+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~nd90+1_all.deb Size: 51306 SHA256: 938dad82d3f85942804284edfcde696fad4eb67d713e1fb962bf5f4639156fda SHA1: a0de2ee3c0e22768d4558eb90271e3cebdcb8ff8 MD5sum: 266358c542dd429090e1ca9595fd3887 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~nd90+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~nd90+1_all.deb Size: 4249128 SHA256: a40038c806f2e49ca56bf8f36e962ce1505bb1de33b75dc1115c6a3b3c41c50e SHA1: d2f16e4f4dcbc57265ef62e3c5be8a9a0f632735 MD5sum: 2ef20d66ba9810f5bf35e4bd789ef2d2 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-tz Version: 2012c-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 138 Depends: neurodebian-popularity-contest, tzdata, python (>= 2.7), python (<< 2.8) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2012c-1~nd70+1+nd90+1_all.deb Size: 34052 SHA256: 183fa2c4d83c7b02405c5f9101694d4d85961edf4bcee6dba215e9e20b67d8cb SHA1: 8c91f1d29e858933f13fea6bdaf815c0890e1850 MD5sum: ceac8a71c4038ba6e4f4c78a07232199 Description: Python version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). Package: python-vcr Source: vcr.py Version: 1.7.3-1.0.1~nd90+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~nd90+1_all.deb Size: 43540 SHA256: 5ffd32e41d6b09622a23f4d3abfe55c387b39645decd096cc75fd6db807b7d46 SHA1: b30f74457f548b02effee54982003cdd445bdca5 MD5sum: b3fbd72ceee13814f0d16b9072be2a6f 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~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 59 Depends: neurodebian-popularity-contest, python-six (>= 1.6.1), python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: http://pypi.python.org/pypi/w3lib Priority: optional Section: python Filename: pool/main/p/python-w3lib/python-w3lib_1.11.0-1~nd90+1_all.deb Size: 14104 SHA256: 85ac8e1aa7d2bed41025a2c9b343b8002bc057f78290f03c709e6fc940d1a633 SHA1: c22e5a569842cc69a1b33afea2a6936cba76ba60 MD5sum: e355ae8758f67732c9dafe97e3f34885 Description: Collection of web-related functions for Python (Python 2) Python module with simple, reusable functions to work with URLs, HTML, forms, and HTTP, that aren’t found in the Python standard library. . This module is used to, for example: - remove comments, or tags from HTML snippets - extract base url from HTML snippets - translate entites on HTML strings - encoding mulitpart/form-data - convert raw HTTP headers to dicts and vice-versa - construct HTTP auth header - RFC-compliant url joining - sanitize urls (like browsers do) - extract arguments from urls . The code of w3lib was originally part of the Scrapy framework but was later stripped out of Scrapy, with the aim of make it more reusable and to provide a useful library of web functions without depending on Scrapy. . This is the Python 2 version of the package. Package: python-werkzeug Version: 0.10.4+dfsg1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 765 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), libjs-jquery Recommends: python-simplejson | python (>= 2.6), python-openssl, python-pyinotify Suggests: ipython, python-genshi, python-pkg-resources, python-lxml, python-greenlet, python-redis, python-pylibmc | python-memcache, python-werkzeug-doc Homepage: http://werkzeug.pocoo.org/ Priority: optional Section: python Filename: pool/main/p/python-werkzeug/python-werkzeug_0.10.4+dfsg1-1~nd90+1_all.deb Size: 165572 SHA256: 7be17184bec25c4a421825b50b8d03493cdbdb30e57f485188f06e479b1bc755 SHA1: 44d678dad8ad3b38ee2e09a7f122d9c5863a998f MD5sum: aee38b21de85d50eabf69ff03ce3e31d Description: collection of utilities for WSGI applications The Web Server Gateway Interface (WSGI) is a standard interface between web server software and web applications written in Python. . Werkzeug is a lightweight library for interfacing with WSGI. It features request and response objects, an interactive debugging system and a powerful URI dispatcher. Combine with your choice of third party libraries and middleware to easily create a custom application framework. Package: python-werkzeug-doc Source: python-werkzeug Version: 0.10.4+dfsg1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2709 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Conflicts: python-werkzeug (<< 0.9.3+dfsg-2) Replaces: python-werkzeug (<< 0.9.3+dfsg-2) Homepage: http://werkzeug.pocoo.org/ Priority: extra Section: doc Filename: pool/main/p/python-werkzeug/python-werkzeug-doc_0.10.4+dfsg1-1~nd90+1_all.deb Size: 881876 SHA256: 4a559109102aee2f3bb9267ac2200775e2afd8328e79fa27e3591e6773438a15 SHA1: 389736200858b2336fafbd741e83ead639acb312 MD5sum: 0c95fbcbaed8ab4ef2cc312bfaa821f5 Description: documentation for the werkzeug Python library Werkzeug is a lightweight library for interfacing with WSGI. It features request and response objects, an interactive debugging system and a powerful URI dispatcher. Combine with your choice of third party libraries and middleware to easily create a custom application framework. Package: python3-argcomplete Source: python-argcomplete Version: 1.0.0-1~nd90+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~nd90+1_all.deb Size: 22308 SHA256: 896b8c5b419eed7ceb8bea3107bc7127880960b1fa7d095b440e162d3599b2f2 SHA1: 52fa718876f85f5e4343f1609d4adb564528bfc3 MD5sum: 0b2f1bfe1efee40720a8985e945144af 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-citeproc Source: citeproc-py Version: 0.3.0-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 777 Depends: neurodebian-popularity-contest, python3, python3-lxml, python3:any (>= 3.3.2-2~) Homepage: https://github.com/brechtm/citeproc-py Priority: optional Section: python Filename: pool/main/c/citeproc-py/python3-citeproc_0.3.0-1~nd90+1_all.deb Size: 81802 SHA256: 9771600798fda289f3bf331af6e87ce9b39eac0c38520bdf30f8c509457b82aa SHA1: 39ad5faaf9069dda3e4691ad8faa2e38b490e6bb MD5sum: eda798e4e44857e62a279e19dd0b3223 Description: Citation Style Language (CSL) processor for Python3 Citeproc-py is a library that produces formatted bibliographies and citations from bibliographic databases following formatting instructions provided by XML style files written in the Citation Style Language (CSL). . Currently, BibTeX and JSON are supported as input database formats, and plain text, reStructuredText and HTML as output format. . This package contains the Python 3 modules and the CLI tool csl_unsorted. Package: python3-contextlib2 Source: contextlib2 Version: 0.4.0-3~nd90+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~nd90+1_all.deb Size: 8734 SHA256: 8208ef7d303412211e9f184a05a3ea1ab1b9b86e7a253cc9726d1baa98722aba SHA1: c1a2992ecf62af90c8a66f0725dee7c137bc924a MD5sum: 91c06b98f55387e4a1dbaa5948271c49 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-duecredit Source: duecredit Version: 0.4.5.3-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 226 Depends: neurodebian-popularity-contest, python3-citeproc, python3-requests, python3:any (>= 3.3.2-2~) Homepage: https://github.com/duecredit/duecredit Priority: optional Section: python Filename: pool/main/d/duecredit/python3-duecredit_0.4.5.3-1~nd90+1_all.deb Size: 46986 SHA256: 5c209334a2d344a85b819d15032cad083f827e049c293d9717506f15119ed58a SHA1: 159fbe1d87613ae4ebdc2770f8a367c6ab21485d MD5sum: f4fb8ce185662f3d527646e445ff68f0 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-future Source: python-future Version: 0.15.2-1~nd90+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~nd90+1_all.deb Size: 346132 SHA256: 0b86fec70d0f5de4794a05779c5d7017892df211db0b07bcbfffdf898cfa36d2 SHA1: ecd64620aadfed37312d2fd64a242d66af3dc4d7 MD5sum: bc2756c27fbf3c290513779740efa42b 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: 2.0.2-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1568 Depends: neurodebian-popularity-contest, git (>= 1:1.7) | git-core (>= 1:1.5.3.7), python3-gitdb (>= 0.6.4), 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_2.0.2-1~nd90+1_all.deb Size: 289172 SHA256: 40182693d299c6f014a92d99a41b4cf7ef44b0fca8a36c508941f3666700450f SHA1: 98357d499c99b07f14ec361c157debc7e0e5e760 MD5sum: dd511e59a4e5f8d9e5e955594bdfb585 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~nd90+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~nd90+1_all.deb Size: 12668 SHA256: 09e180429c6ce4e29725b5eba27eadb80b41aee505906744cb3402a9ac6e8fca SHA1: 426390f0559742af2e08fc87e1f4aaec1e5c8f59 MD5sum: c4749e78654afa0b8c5def4772b32583 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-jdcal Source: jdcal Version: 1.0-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 35 Depends: neurodebian-popularity-contest Homepage: https://github.com/phn/jdcal Priority: optional Section: python Filename: pool/main/j/jdcal/python3-jdcal_1.0-1~nd80+1+nd90+1_all.deb Size: 7518 SHA256: 509687fb7218b6ba604b6c6224a4274f12f43d040c9d85ee47157e8ebb9b8c9c SHA1: b0c638b1dff6c85861e5f9fe52c8418bb82de3a5 MD5sum: a9021e0701397e533368a236de8df6b3 Description: Julian dates from proleptic Gregorian and Julian calendars This module contains functions for converting between Julian dates and calendar dates. . Different regions of the world switched to Gregorian calendar from Julian calendar on different dates. Having separate functions for Julian and Gregorian calendars allow maximum flexibility in choosing the relevant calendar. Package: python3-joblib Source: joblib Version: 0.9.4-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 358 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Recommends: python3-numpy, python3-nose, python3-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python3-joblib_0.9.4-1~nd90+1_all.deb Size: 78604 SHA256: 97e15f221ef315a24fb24a60f0ba045720571f5dcaa6a444193cc85286087aae SHA1: 8879c7b07ceaefd9c6578bd1255a4f4f2a92a23f MD5sum: d024996eccca59afa722af48d494ec76 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-mdp Source: mdp Version: 3.5-1~nd90+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~nd90+1_all.deb Size: 275298 SHA256: c7ebf3dacb49fd9c33db382c47b6755564873fe4c73a86e5d3fece2171790014 SHA1: 820f14dd16401efa4797608ab93556fa5427c51d MD5sum: 3d9b7923dfecefc7392e1ad046309a1f 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: 2.0.2-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63314 Depends: neurodebian-popularity-contest, python3-numpy, python3-scipy Suggests: python-nibabel-doc, python3-dicom, python3-fuse Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python3-nibabel_2.0.2-1~nd90+1_all.deb Size: 1962326 SHA256: a7b8e8022eb2bdf83cd5b6cc539f2ac5d60c8ae380194e6f5760a33a13e72bf3 SHA1: 92c8b67d79b666f7072e3f35f03d932a2b4e8075 MD5sum: 4cf6de1fca257cbcb4dbc070d82cb13a 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: 0.2.3~dfsg.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2165 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_0.2.3~dfsg.1-1~nd90+1_all.deb Size: 681124 SHA256: 8d90c5b43bb93c08e1576ac67e36f70615f2b502f93a8ed8b65cf607617b9f27 SHA1: 8c0a3671774d5efb3b40036026ae7ee88ab99247 MD5sum: d948ccfb290067d4313916ca493373c5 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~nd90+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~nd90+1_all.deb Size: 5190 SHA256: c647378e4ac68d6753801dde93798cd63acb59c1bb2d7edaddf8753a9883dc08 SHA1: 106e055cabc299003c29cc626e1c8c7f41c98c79 MD5sum: 75d7fb5666e53c4dd70cbb41a35601d5 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: 2.3.0-2~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1319 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_2.3.0-2~nd90+1_all.deb Size: 198372 SHA256: e7f5bc944890147a3b16d5bd7b786229a1137706c0f5c82098838459a9f60264 SHA1: d35e163bf73c7d047c437ca94236a505f18af94b MD5sum: 425e49ba04d3fc74de7890f207e3a5f4 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~nd90+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~nd90+1_all.deb Size: 17270 SHA256: 1d3e30f82be2e97addff8b78bcc6505de87ba5a1b9d724259e7cf406739901d1 SHA1: adaba2f1a49b83f8af262406647ffa424ab72487 MD5sum: 6225df2547e50f3d13e80996b697ac7b 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: 0.18.0+git114-g6c692ae-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13060 Depends: neurodebian-popularity-contest, python3-dateutil, python3-numpy (>= 1:1.7~), python3-tz, python3:any (>= 3.3.2-2~), python3-pandas-lib (>= 0.18.0+git114-g6c692ae-1~nd90+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_0.18.0+git114-g6c692ae-1~nd90+1_all.deb Size: 1761568 SHA256: e0d93e748dbed8b37c1a51bbc2f7108e0ce7196bd5123953bc61b6edfba4a481 SHA1: a599a8f415c19f3383f9918ccdfbd2ad28ef830a MD5sum: a7e2efdcd0bc46b6d0f8f729b6e843e8 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: 0.4.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 783 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_0.4.1-1~nd90+1_all.deb Size: 174422 SHA256: 7ca8d2cca95554bb6d9100040ee19421dc0552644e3978fce7785fa418443c52 SHA1: 308366e88a3c072dce0c1f9d8f5a0809b11672f8 MD5sum: e3318eb3e6b19f4cdb58ce51ab29de76 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 3 version. Package: python3-py Source: python-py Version: 1.4.30-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 306 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-pkg-resources Suggests: subversion, python3-pytest Homepage: https://bitbucket.org/pytest-dev/py Priority: optional Section: python Filename: pool/main/p/python-py/python3-py_1.4.30-1~nd90+1_all.deb Size: 66824 SHA256: 0a6bf648d072410a214f4bd473a38ca3c056a330af4956a0a6b934fb0e0821dc SHA1: f90e5ce29005050a9187daeb98f57f3f4354dc43 MD5sum: 0847005900d103c72566871f0d601b17 Description: Advanced Python development support library (Python 3) The Codespeak py lib aims at supporting a decent Python development process addressing deployment, versioning and documentation perspectives. It includes: . * py.path: path abstractions over local and Subversion files * py.code: dynamic code compile and traceback printing support . This package provides the Python 3 modules. Package: python3-pytest Source: pytest Version: 2.7.2-2~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 494 Depends: neurodebian-popularity-contest, python3-pkg-resources, python3-py (>= 1.4.29), python3, python3:any (>= 3.3.2-2~) Homepage: http://pytest.org/ Priority: optional Section: python Filename: pool/main/p/pytest/python3-pytest_2.7.2-2~nd90+1_all.deb Size: 132468 SHA256: 48cbe9c4d4e7e631e765cda5872bb6bf51f82ba04470cb556b0d2c06041fda6f SHA1: dff11186bbd9e23bdbfa2fe9b60faee516890c2f MD5sum: 0e782127d1040f8b9af7be441cb8e640 Description: Simple, powerful testing in Python3 This testing tool has for objective to allow the developers to limit the boilerplate code around the tests, promoting the use of built-in mechanisms such as the `assert` keyword. . This package provides the Python 3 module and the py3.test script. Package: python3-pytest-localserver Source: pytest-localserver Version: 0.3.4-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 59 Depends: neurodebian-popularity-contest, python3-pytest, python3-werkzeug (>= 0.10), python3:any (>= 3.3.2-2~) Homepage: https://pypi.python.org/pypi/pytest-localserver Priority: optional Section: python Filename: pool/main/p/pytest-localserver/python3-pytest-localserver_0.3.4-1~nd90+1_all.deb Size: 18238 SHA256: ae2dc72d9a97c9d8d71bbc222a18030530c065e0bf309bbbda1844271195fabb SHA1: 4d9501a6f30c0c709c453e1d98464bd1f8131b54 MD5sum: 8c29f136aad81be7440f9ab8fcdfc5dc Description: py.test plugin to test server connections locally (Python 3) pytest-localserver is a plugin for the Pytest testing framework which enables to test server connections locally. . This package contains the modules for Python 3. Package: python3-pytest-tornado Source: pytest-tornado Version: 0.4.4-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 32 Depends: neurodebian-popularity-contest, python3-pytest, python3-tornado, python3:any (>= 3.3.2-2~) Homepage: https://github.com/eugeniy/pytest-tornado Priority: optional Section: python Filename: pool/main/p/pytest-tornado/python3-pytest-tornado_0.4.4-1~nd90+1_all.deb Size: 5734 SHA256: 8ab0046915b5cf9640e19a16d43ce44f498fd01fb0a365be49c8aaa1c30ef57a SHA1: c81514d37073e109155141a339b73c41e2b1e7fa MD5sum: 6955e8fad9eb8dcc0b1a8e6690a1f171 Description: py.test plugin to test Tornado applications (Python 3) pytest-tornado is a plugin for the Pytest testing framework which provides fixtures and markers to simplify testing of Tornado applications (Python web framework and ansynchronous networking library). . This package contains the plugin for Python 3 code. Package: python3-setuptools-scm Source: setuptools-scm Version: 1.8.0-1~bpo8+1~nd90+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~nd90+1_all.deb Size: 11508 SHA256: 2730e344dba8d1a2aba862116e1a7f04401a3d60f55c7b3711a0f38eab6d9e9c SHA1: a3d1c80be76a5d5707c55d8dfcbb9151c5d1bbc1 MD5sum: 2ebfe54cfd23e1c418056e26342aff1e 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~nd90+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~nd90+1_all.deb Size: 13872 SHA256: 9cb9ab855f3ba597af73fe0e05ea2be6c467c151335c301d5799875b9f642ee9 SHA1: 41193de28375c29b8c5471613d329b2a05b6f244 MD5sum: 1335922a59966e3db6c70a9007218d03 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-sklearn Source: scikit-learn Version: 0.17.1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5282 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-sklearn-lib (>= 0.17.1-1~nd90+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_0.17.1-1~nd90+1_all.deb Size: 1223228 SHA256: f61b73b19ae799341895b0bd1b8a51c38bcd20891bb4e542af0773534afdd2f0 SHA1: f067dc608a99e412bdb7b1b811a1397401476040 MD5sum: 6bf275bbb73b7f6cbb22f3fb51bebbd0 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: 0.9.0-3~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 90 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_0.9.0-3~nd90+1_all.deb Size: 20210 SHA256: 771e8d55dade7b4b78e2e5cf0d22d8758ed1ce5caca0c7a274cb064b46754e28 SHA1: 51f853f40b79d6ad6af08c80b0899f9ef335e472 MD5sum: e58479267d75bff9f0a3360f017ff7b0 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~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 359 Depends: neurodebian-popularity-contest, fonts-font-awesome, fonts-lato, libjs-modernizr, python3:any (>= 3.3.2-2~) Recommends: python3-sphinx Homepage: https://github.com/snide/sphinx_rtd_theme Priority: optional Section: python Filename: pool/main/s/sphinx-rtd-theme/python3-sphinx-rtd-theme_0.1.8-1~nd90+1_all.deb Size: 117208 SHA256: 16750e72490dfd83a69334d631b79056b547727b706529f6eb8eb2d1fbca5443 SHA1: 0f30cc73b8863ed74a3e4aa8ac6e2ba911bfff3b MD5sum: bd47026d57bfab7cdccbd9a6f3211976 Description: sphinx theme from readthedocs.org (Python 3) This mobile-friendly sphinx theme was initially created for readthedocs.org, but can be incorporated in any project. . Among other things, it features a left panel with a browseable table of contents, and a search bar. . This is the Python 3 version of the package. Package: python3-tables Source: pytables Version: 3.2.1-1~nd90+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~nd90+1), python3-tables-lib (<< 3.2.1-1~nd90+1.1~), python-tables-data (= 3.2.1-1~nd90+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~nd90+1_all.deb Size: 334588 SHA256: e7ed085c96cfd227c9cc95eebc50aeffe52a4c1ed7c7b58f5bff63e6621b8625 SHA1: 02e6c841145e979f6f500de3a160740e6ce03577 MD5sum: 4d9654daf36f879161599612f3df2460 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-tz Source: python-tz Version: 2012c-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 109 Depends: neurodebian-popularity-contest, tzdata, python3:any (>= 3.3.2-2~) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python3-tz_2012c-1~nd70+1+nd90+1_all.deb Size: 25318 SHA256: f3b641a1dccf7188209fbbd4b68c9462e4cfc35e8c371603ac02a451676c132e SHA1: 04ab5dc0076b526b09c4b1ae563c20f70636fba2 MD5sum: 34437b0794a75cccfcbca371eb5db224 Description: Python3 version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). . This package contains the Python 3 version of the library. Package: python3-vcr Source: vcr.py Version: 1.7.3-1.0.1~nd90+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~nd90+1_all.deb Size: 43596 SHA256: d4eb47e29c624f66b0e2cf6d0c3d30a7ab13e0fb50d30735693c46e38bc7f5b7 SHA1: ef87422db4cbcb229e68dce4c080ee274210421e MD5sum: 786cfa64706c4543ce3dcaf15b5e1a4b 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~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 59 Depends: neurodebian-popularity-contest, python3-six (>= 1.6.1), python3:any (>= 3.3.2-2~) Homepage: http://pypi.python.org/pypi/w3lib Priority: optional Section: python Filename: pool/main/p/python-w3lib/python3-w3lib_1.11.0-1~nd90+1_all.deb Size: 14190 SHA256: 936b5cf9912553d27c7f1f2995ad57113ae0d0e50748a86b8b34d94a95210adb SHA1: c323083c3fd7909615fd05c74cf34df27c849b99 MD5sum: eabd0e00c9049ae6861823d2a2f35e24 Description: Collection of web-related functions for Python (Python 3) Python module with simple, reusable functions to work with URLs, HTML, forms, and HTTP, that aren’t found in the Python standard library. . This module is used to, for example: - remove comments, or tags from HTML snippets - extract base url from HTML snippets - translate entites on HTML strings - encoding mulitpart/form-data - convert raw HTTP headers to dicts and vice-versa - construct HTTP auth header - RFC-compliant url joining - sanitize urls (like browsers do) - extract arguments from urls . The code of w3lib was originally part of the Scrapy framework but was later stripped out of Scrapy, with the aim of make it more reusable and to provide a useful library of web functions without depending on Scrapy. . This is the Python 3 version of the package. Package: python3-werkzeug Source: python-werkzeug Version: 0.10.4+dfsg1-1~nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 765 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), libjs-jquery Recommends: python3-simplejson | python3, python3-openssl, python3-pyinotify Suggests: ipython3, python3-pkg-resources, python3-lxml, python-werkzeug-doc Homepage: http://werkzeug.pocoo.org/ Priority: optional Section: python Filename: pool/main/p/python-werkzeug/python3-werkzeug_0.10.4+dfsg1-1~nd90+1_all.deb Size: 165550 SHA256: baff81afd62f70d40a88d0730c28c741d896df6817923fc5c1c750b479ad7f7f SHA1: e64561b10bba46f959268074a0f6591208c7c5b9 MD5sum: 4831b3c44099af4fb3c32d574576b5a4 Description: collection of utilities for WSGI applications The Web Server Gateway Interface (WSGI) is a standard interface between web server software and web applications written in Python. . Werkzeug is a lightweight library for interfacing with WSGI. It features request and response objects, an interactive debugging system and a powerful URI dispatcher. Combine with your choice of third party libraries and middleware to easily create a custom application framework. Package: spm8-common Source: spm8 Version: 8.5236~dfsg.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 19187 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.5236~dfsg.1-1~nd70+1+nd90+1_all.deb Size: 9748000 SHA256: 680f43e45495a677b3c38f5e3709ec28b4ad877e4315eeff99fe2582ea1fcb7c SHA1: ceac6e0f3811c55e8125edbe21b3efdaf1a16e12 MD5sum: 244ac5f32442def5cfc872ea3f873f88 Description: analysis of brain imaging data sequences Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the platform-independent M-files. Package: spm8-data Source: spm8 Version: 8.5236~dfsg.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 73020 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd70+1+nd90+1_all.deb Size: 45492378 SHA256: aa9b3d42abaacaa7d476fd31e6d9af9abf9aadfca4f0a603b0369bb26a3c8dc5 SHA1: 0725897b8a1f9fa64abe092992fb35dbcfcd5270 MD5sum: 746ebce5f1c0180331a6be5e080b959c Description: data files for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provide the data files shipped with the SPM distribution, such as various stereotaxic brain space templates and EEG channel setups. Package: spm8-doc Source: spm8 Version: 8.5236~dfsg.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9252 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd70+1+nd90+1_all.deb Size: 8934900 SHA256: 4a07f5603644623f064ef741e279bc7c152ea20cc5a134ecd4734b7958e37836 SHA1: a37985ea601c14ce314860c55a42f45fd00405de MD5sum: 40142910687b4dfbbac3805fe593d0d6 Description: manual for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the SPM manual in PDF format. Package: spyder Version: 2.2.5+dfsg-1~nd80+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 136 Depends: neurodebian-popularity-contest, python:any, python-spyderlib (= 2.2.5+dfsg-1~nd80+1+nd90+1) Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: devel Filename: pool/main/s/spyder/spyder_2.2.5+dfsg-1~nd80+1+nd90+1_all.deb Size: 53012 SHA256: b2dc7e51ef78e68819125e10a50cd4ea706a77ba409e0bb27fca9b63c8a34c5f SHA1: c6e5131a5eaafdf8960050d01e2812ba76c94730 MD5sum: 74994062b3b51941db390ea57c13b8e8 Description: python IDE for scientists Spyder (previously known as Pydee) is a free open-source Python development environment providing MATLAB-like features in a simple and light-weighted software Package: spykeviewer Version: 0.4.4-1~nd90+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~nd90+1_all.deb Size: 1291112 SHA256: 2e83375531323c142b40cfbbcf0ecd5dc636a6d7a9e2529ad8e420d5946829ce SHA1: d83259f4f990fe9a0786aa922d110fe735d98ac9 MD5sum: fb561409c7b2eb55831da4fc16996663 Description: graphical utility for analyzing electrophysiological data Spyke Viewer is a multi-platform GUI application for navigating, analyzing and visualizing electrophysiological datasets. Based on the Neo framework, it works with a wide variety of data formats. Spyke Viewer includes an integrated Python console and a plugin system for custom analyses and plots. Package: stabilitycalc Version: 0.1-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 134 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy, python-nifti Recommends: python-dicom Homepage: https://github.com/bbfrederick/stabilitycalc Priority: extra Section: science Filename: pool/main/s/stabilitycalc/stabilitycalc_0.1-1~nd70+1+nd90+1_all.deb Size: 22486 SHA256: 8766352aac1f4ad1638c04104058b2872d436a43f15fb3a43f84ea9c99ce5787 SHA1: ab2985bd761da2904cfdddb227bf81a1b91534d0 MD5sum: 8d9b482369a29f4801e6ef1119852fcd Description: evaluate fMRI scanner stability Command-line tools to calculate numerous fMRI scanner stability metrics, based on the FBIRN quality assurance test protocal. Any 4D volumetric timeseries image in NIfTI format is support input. Output is a rich HTML report. Python-Version: 2.7 Package: svgtune Version: 0.1.0-1~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25 Depends: neurodebian-popularity-contest, python, python-lxml Suggests: inkscape Homepage: http://github.com/yarikoptic/svgtune Priority: optional Section: graphics Filename: pool/main/s/svgtune/svgtune_0.1.0-1~nd70+1+nd90+1_all.deb Size: 6544 SHA256: 932580cf411cba683c923965479d65b533fe87f46590d4e18612bb7906fd639c SHA1: 9077b3e2d8dc1f892ae80bd15112d369c68aa96f MD5sum: b023b0ccfe544567ead1371438900c92 Description: tool to generate a set of .svg files out of a single .svg file svgtune is just a little helper to generate a set of .svg files out of a single .svg file, by tuning respective groups/layers visibility, transparency or anything else. . It might come very handy for generation of incremental figures to be embedded into the presentation in any format which inkscape could render using original .svg file (e.g. pdf, png). Package: ubuntu-keyring Version: 2010.+09.30~nd70+1+nd90+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 Recommends: gpgv Priority: important Section: misc Filename: pool/main/u/ubuntu-keyring/ubuntu-keyring_2010.+09.30~nd70+1+nd90+1_all.deb Size: 11666 SHA256: 9261b6c61e5f4c66cc1bc803a2fafddd4b76383058127496b5d6c2af04ea1b9e SHA1: 705794dc4d5af0849731d8a6ebc9d90b24b7bf19 MD5sum: efa94037736492ce55a9d611841f8bdc Description: GnuPG keys of the Ubuntu archive The Ubuntu project digitally signs its Release files. This package contains the archive keys used for that.