Package: bats Version: 0.4.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 Depends: neurodebian-popularity-contest Homepage: https://github.com/sstephenson/bats Priority: optional Section: shells Filename: pool/main/b/bats/bats_0.4.0-1~nd14.04+1_all.deb Size: 14380 SHA256: fddb023e52a6515b50557af46c086354d4096c16874c9cd9f375586299d472de SHA1: 618cbfadea7eb8069c32c0b3075a3033adb283be MD5sum: f13ced8df8376670cb14d9be8d8e7798 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: 0.23.2-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 295 Depends: neurodebian-popularity-contest, perl, btrfs-progs (>= 3.18.2) | btrfs-tools (>= 3.18.2) Recommends: openssh-client, pv Homepage: http://digint.ch/btrbk/ Priority: optional Section: utils Filename: pool/main/b/btrbk/btrbk_0.23.2-1~nd14.04+1_all.deb Size: 67496 SHA256: ed91d74bdb196eaadba2f91eaabc12295d88a189d5518f0b4b74be07b9b6e370 SHA1: 0b2dfcf634cb795aa39f20b1c6cb97e23c6ee664 MD5sum: 679e519b169e6c4bac49b3c1a148a3c8 Description: backup tool for btrfs subvolumes Backup tool for btrfs subvolumes, using a configuration file, allows creation of backups from multiple sources to multiple destinations, with ssh and flexible retention policy support (hourly, daily, weekly, monthly). Package: condor Version: 8.4.2~dfsg.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 46 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~nd14.04+1_all.deb Size: 15720 SHA256: d7733566674c51836cd7ea782fa4ca1bc31f2c8df9d5718e0832d712ed12ce70 SHA1: 53efcf6b6b6f3986a2d16921648f5a03c715375b MD5sum: 355bab86f15980da222a09ecdfb9383a 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 46 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~nd14.04+1_all.deb Size: 15740 SHA256: 6211739c90133636c75c560b06c9aeccd64f6dbe3524d9700790b2085777a343 SHA1: 874bf64b4a01b97369a7cc520d2cd6f8e63f806a MD5sum: 3717e70c257abdde6f9e16a207f3d9f4 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 46 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~nd14.04+1_all.deb Size: 15750 SHA256: 4dd0ffafbe5cbd99976cfbb706b28ddb220fdbaa06dd238426addd1ceeb54711 SHA1: 85669aae0740478750b4396f493153f83dae633a MD5sum: 77063b8041e125c458683c9b85f1bf57 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 46 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~nd14.04+1_all.deb Size: 15744 SHA256: 46720ddc9795c239c6c8130dbaea3ed0b82e8b07b75eed827d5e6306a963bf8a SHA1: 5f3b5cd5d64fc1cb5c859c49b0206b7a126a1e75 MD5sum: 0f52c3c43be3249c687f7d0c86587f28 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: datalad Version: 0.2.3-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 82 Depends: neurodebian-popularity-contest, python-argcomplete, python-datalad (= 0.2.3-1~nd14.04+1), python Homepage: http://datalad.org Priority: optional Section: science Filename: pool/main/d/datalad/datalad_0.2.3-1~nd14.04+1_all.deb Size: 27936 SHA256: 8b254d2c0827ab0b2c66681428b82dd74a7418db7967a940da8c6c72b49415dc SHA1: 69738ba5593b795f00157dc7f9c110b767708026 MD5sum: 2dd55f215db940a94a4e9803e7226c71 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. Install without Recommends if you need only core functionality. . DISCLAIMER: DataLad is Work-In-Progress and this package is not yet for regular user consumption. Primarily intended for demonstrations and development. Package: dh-systemd Source: init-system-helpers Version: 1.18~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, perl, debhelper Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/dh-systemd_1.18~nd13.10+1+nd14.04+1_all.deb Size: 13814 SHA256: d174181f267afbaf3c6c7d6108b65eca78861aa6d3c71288a03db9b5cafd5a13 SHA1: 15700758d679f3bda80f55b5f435edad45d1b39e MD5sum: 4ed20ea08d8c497a1a3e9b7ce46fe4c8 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~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8109 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 7062228 SHA256: aa1e0c88dbb25feff7d4a79637ce14e2bd7fccf5b2e73f675ba5b88baebdcb3b SHA1: e5d9d261fdfa5d96e1fe0b8e6ce4b67948bb54c4 MD5sum: 0bf506b1eed312f76a0e48cb663cb40a 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: 0.9.5-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1212 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python:any (<< 2.8), python, lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: mailx, system-log-daemon, monit, python-systemd Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.9.5-1~nd14.04+1_all.deb Size: 254112 SHA256: 798684a41fb1e5741f1c66eaa6bd7b18078ddbc6d857220c948f75a111c7b59b SHA1: 1fcc5938df9a473095bb2735082deb2a073973ed MD5sum: 45b9946091042c375994295d9e9615db 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd14.04+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~nd14.04+1_all.deb Size: 1172 SHA256: ddbc7d7bbb61097f230807cfc6c0a77d391bf1d4a6e31410718041006b827f32 SHA1: d2a247bce06f052b9d54033f81738a20cb1b9077 MD5sum: 2951d4e9077337872d5db574dfbd131f 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 305 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~nd14.04+1_all.deb Size: 189106 SHA256: 705f1b5454ee66c6fb40ef496ffe68656c0b7524dd0e51b1ce2b3dc2a8e7762e SHA1: af7f15103d39b2d03ef557295a8d218997ef76a2 MD5sum: 223c7b78bf96ba503d34c6129041813f 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 108 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python-nibabel, python, python-numpy, python:any (<< 2.8), python-pkg-resources, python-scipy, python-matplotlib, 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~nd14.04+1_all.deb Size: 13908 SHA256: bd401b3672c86e560a1927b583637a89617ab68c7e7785458566f6aac708be7b SHA1: 3b05c5b556d57b3764ca8a2c8d52363aeacb801f MD5sum: b8a48a2a910611bd834f22e8f1974fac 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: 4.0.1-7~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 2898 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-7~nd14.04+1_all.deb Size: 2228538 SHA256: 090d2faad68a2d0bd98956f35958dc9aeb5163f534d3ba4368817f656c06d70f SHA1: cbc2784174659eb5743bb2c7c8f5ab1d658004d7 MD5sum: 5d36ffd8a0146a965379b234953844e1 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gcalcli Version: 3.3.2-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1739 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~nd14.04+1_all.deb Size: 1669830 SHA256: fd31d1e902264566bf7e32785bbd20cebbbd92513ced28e1e233c02bf12f29e6 SHA1: e86d7094c16c125f0b1e01c73b63c6d6d5606d00 MD5sum: 24dd2e76ba03e082e4dedd8a99e9fc61 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 78 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.5-1~nd14.04+1_all.deb Size: 13800 SHA256: 4127230a0b3a6b132f2e98087b496cddbabf8efd64fb0573ac384d4ec292ddab SHA1: 16ab5cc30564be2024ea5ea282213fc38a320743 MD5sum: 75f0db3af8b2efad55c4794e50b84412 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: heudiconv Version: 0.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 79 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~nd14.04+1_all.deb Size: 10218 SHA256: 43684321833fd0cc620b87b9edf3e20f1071e00fbfb1c9d9115a5938f4df236e SHA1: a5a75c8ce1c59b56fc4a82892e034ef636ee2532 MD5sum: 3155ae84167a24442e1810d0d49f75dd 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 5917 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~nd14.04+1_all.deb Size: 1067822 SHA256: 6d774f6adf7fa696f2bf430fc2c7bd2f547a1e905e32390df9d17611ba626b37 SHA1: 6f58cf5718e706683eef5bfe3f7480522f9ba71c MD5sum: 015a3b775dff163b996768b66d213b33 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 466 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~nd14.04+1_all.deb Size: 175686 SHA256: 93a2cfe442cded96df94ade891d2c9892a0b09d43b4fd86073a479f8ca4ba0b8 SHA1: 18a29af861ececa34319234d1929b3aeb2ff77b4 MD5sum: 34273046bb09d48c5331dd6c982e85f3 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~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 9150 SHA256: 6221480f9dac530be0388cb543cb7222a71f2eeb5a05e3b7684189951be779a9 SHA1: d6e2bc39ee2ea2858d5aa50a8b825dcc1a9766ef MD5sum: 42c1f57576c0b1537c531816653e0f04 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~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29 Depends: neurodebian-popularity-contest, perl Breaks: systemd (<< 44-12) Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/init-system-helpers_1.18~nd13.10+1+nd14.04+1_all.deb Size: 13450 SHA256: 9b738273e06fa645d7746ddcfc18257e82b1aa81991b60f4940c8336ca7c276b SHA1: a69ef0da8cacfe37a1898934c6feb74737e63597 MD5sum: c519d25c91c535528c645290c7201987 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2836 Depends: neurodebian-popularity-contest Suggests: libinsighttoolkit4-dev Conflicts: insighttoolkit-examples Replaces: insighttoolkit-examples Homepage: http://www.itk.org/ Priority: optional Section: devel Filename: pool/main/i/insighttoolkit4/insighttoolkit4-examples_4.7.0-1~nd14.04+1_all.deb Size: 2498446 SHA256: ce5b7ee80d764522163321fea16c74d1159642fcb4a74a28f5c51fc9438552ab SHA1: a8a9342033fbc34ed40d182ecfbe1991bc1c9c31 MD5sum: 0abb2152be3c1e64724f0cabcb7be373 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest Homepage: http://ismrmrd.github.io/ Priority: optional Section: science Filename: pool/main/i/ismrmrd/ismrmrd-schema_1.3.2-1~nd14.04+1_all.deb Size: 5018 SHA256: 30af8e92d10d414bf87e8c4699a93b2a65aede0538a750d23b741b6c31b1d0f9 SHA1: 026b10c125ea9fb70fd9f19f71d809cf33d4c274 MD5sum: 5fc45130f6fa18505f9de5ab7ee5b221 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: 1:0.5.3-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 677 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.5.3-1~nd14.04+1_all.deb Size: 92014 SHA256: 064146257b1c0cf6dfa96e77798278903cfa4733476bfd174ba941afb1b8c52d SHA1: 024619574042575fc101f79d8b57e876a7b4fb65 MD5sum: e429a46c5c5ecd339ff069594169ba2c Description: library for accessing Kinect device -- documentation libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package contains the documentation of the API of libfreenect. Package: libismrmrd-doc Source: ismrmrd Version: 1.3.2-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2003 Depends: neurodebian-popularity-contest Homepage: http://ismrmrd.github.io/ Priority: optional Section: doc Filename: pool/main/i/ismrmrd/libismrmrd-doc_1.3.2-1~nd14.04+1_all.deb Size: 148632 SHA256: da7fb8cdacb033da65e02cb52138088d8feb3a83d26e0ebd9525d7c554661c1f SHA1: d996863712189c004d651b50fa974c1f852401a1 MD5sum: b4dac15a86155424c1c2cbcf61d975f2 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~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14003 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~nd13.10+1+nd14.04+1_all.deb Size: 828262 SHA256: b80877b4eb7ac26a8d128219be2df273b0d1115bdc039118aa39f0928a03a878 SHA1: 4f2d66594f12e0670fb739d4c7333bd5ffce4b44 MD5sum: 4880b2c099431c4b8afaa3e78dee6e67 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: libnifti-doc Source: nifticlib Version: 2.0.0-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1675 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~nd14.04+1_all.deb Size: 137676 SHA256: 3bab4349c0f35948663f799794b88328b12952dbb9eadb0e8a4085c0f270a5e6 SHA1: 8efa4c33a93f08cd665a505ac7aa01ce2a943db6 MD5sum: b29b153538f23cfdeddd32fc9dab6436 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~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48075 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.4.0~rc1+hg3a3147463ee2-1~nd13.10+1+nd14.04+1_all.deb Size: 2673508 SHA256: 6bfe8da2878784c3df24ef11993ad9d5b82019204eb362235bd094ac6865c0f8 SHA1: 714ad73ca23e5c34f65c7632c395534c4baf7898 MD5sum: 5f8cbf8fba45be0c2da0d39481c9c931 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 39 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~nd14.04+1_all.deb Size: 7520 SHA256: 306f62724d477630ccc2dfec6de9087b8453f9f7aa47f3fbc994fc4c43b72ac0 SHA1: ebcd6a3887968b987548d284ef4398e4b7403402 MD5sum: 4ebc4831c36822a6b9606d0c7b735787 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~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1138 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~nd13.10+1+nd14.04+1_all.deb Size: 71894 SHA256: be1b730b60e4e46c09f731c458418f51468a54bd0bcb30c1b3ae62895cf5195c SHA1: 13b72c29850c963b4b7163d5728101f262982fad MD5sum: 68aa342ecdbdd76b549b62f3a8a0cefb 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1708 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~nd14.04+1_all.deb Size: 1658672 SHA256: 8c6fbf4d4201736009058951a8b0a0649b14eb2b31222086e4c40337b7b701fb SHA1: d390758dd46655f60156a87578c4e8bde2f62f7f MD5sum: 5c2f269adc054ae2960074a3cfec33ba 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1019 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20140804.1~dfsg.1-1~nd14.04+1_all.deb Size: 577154 SHA256: 99c69da1658d3ad0d38a2e617b99c1e97b6dd659de73b4d17de6abe2c836bee6 SHA1: 40d30c9da283129d64c2490423bc06de65605f3d MD5sum: 1e4972d196978a16b51c6ab0b5170a0b Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: mridefacer Version: 0.2-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 674 Depends: neurodebian-popularity-contest, num-utils, fsl-5.0-core | fsl-core Homepage: https://github.com/hanke/mridefacer Priority: optional Section: science Filename: pool/main/m/mridefacer/mridefacer_0.2-1~nd14.04+1_all.deb Size: 637348 SHA256: 4886e1aec5e9f3f839fdd2784eef4113eb90cd82ecdd9a4966d4d2eecf48e091 SHA1: 1feb4d5652dbc9bea40667c98ba830b8e31fa69b MD5sum: e7e127d2deea7a141e45d8ebee81107d 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~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3490 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~nd13.10+1+nd14.04+1_all.deb Size: 3191882 SHA256: f32e1267d094094ab3cc0c0dea48e1ccf69fe473877e2e60136e4e6a27db354b SHA1: 31f466b044fd9b37817cda76e708ae9f65413a1e MD5sum: 391268dd332daa65f1524ad5d28fd893 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26 Depends: neurodebian-popularity-contest, wget, netselect (>= 0.3.ds1-17) Recommends: curl Suggests: dpkg-dev Enhances: apt Homepage: http://github.com/apenwarr/netselect Priority: optional Section: net Filename: pool/main/n/netselect/netselect-apt_0.3.ds1-25~nd14.04+1_all.deb Size: 16732 SHA256: 6736e45053839e6ccff6ae6acee08c1b6946082a3551db89f5b75cf011f56942 SHA1: 5459e0e22973aeac24902aeb831afaca776152d2 MD5sum: 27d1f7525d5b1676fab50f43523897b3 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 112 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~nd14.04+1_all.deb Size: 33158 SHA256: 7dbab3849074592a24d14505a34706635835eb275297011d7f759ff9cc16c7d0 SHA1: ba952d94fb9da1e08189966d23c01c5c637577f3 MD5sum: b571a53854b09e20c256e9d070958556 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 47 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~nd14.04+1_all.deb Size: 10158 SHA256: 37103a8438fbb474612330926c9c5a4b0f4c76b0c184b9356c9ce46c1fb5af78 SHA1: 596ca175299596ae1e5a2fb6f338fc6c1fc50435 MD5sum: 653b913b548c36903cc83a1d491e42fe 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 223 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~nd14.04+1_all.deb Size: 116182 SHA256: a600f37341160010abee96acaa86917c1cc7b9c35ed8b4caa5d50c4af016b14b SHA1: 868f918b560fd3bb8fa6eee4d58f12d857ea8192 MD5sum: 2d36e3d641ac89859d417cbcf20e34a7 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 139 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~nd14.04+1_all.deb Size: 32506 SHA256: ffbc60ace270312eba3cd46be3af5f82fb118b5b1ae86a053eabc068ca6ce2bb SHA1: f70cda066b99c7c2aab3757ea8d1fb841898cc66 MD5sum: fea9398f3d3003e1208f492aa5b71776 Description: neuroscience-oriented distribution - development tools The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package provides sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-guest-additions Source: neurodebian Version: 0.32~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 108 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm | lightdm, zenity Recommends: chromium-browser, update-manager-gnome, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.32~nd13.10+1+nd14.04+1_all.deb Size: 14088 SHA256: 97679301db4c313bf776a5d18ff76e0b1af04b77da1156d1b500a56e308379b9 SHA1: 0b00e3321e0d1bc70c40437abc74430adcf4db07 MD5sum: f3f984c91e04f7b9ab57e22d1bb1af9b Description: NeuroDebian guest additions (DO NOT INSTALL OUTSIDE VIRTUALBOX) This package configures a Debian installation as a guest operating system in a VirtualBox-based virtual machine for NeuroDebian. . DO NOT install this package unless you know what you are doing! For example, installation of this package relaxes several security mechanisms. Package: neurodebian-keyring Source: neurodebian Version: 0.32~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.32~nd13.10+1+nd14.04+1_all.deb Size: 7470 SHA256: 8da1af69542f153184f6d344861f1557e1a7a783b6c0b6d90b67e8dee8a855e6 SHA1: fc17ac754d0a08a79a0b1615c6ae10dcd89f36ea MD5sum: 341bf775ee30c2071e1c49a1acf6f88e Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.37.4~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 50 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.37.4~nd14.04+1_all.deb Size: 12190 SHA256: f6e03c9e34d591312c6234848faa98db9c115a919b4aa53db52c89f1e1826e13 SHA1: d5dcbba2f28b7e551c91743b3f8108a78ce0af20 MD5sum: 859bda3cf2b37d755b1e213b10e769e7 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: 0.4.8-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 606 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.8-1~nd14.04+1_all.deb Size: 615402 SHA256: 5c0c4de741d502aed671afa90ffd5f9d83bbb8d9b8fe3fb61e87a83207c51387 SHA1: 1e2cfa4676c02305b4cf2b7c081125917e51667b MD5sum: 35e250c61104ee3c777aa9d7ef48b08f 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: 0.5.21.2+ds-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2911 Depends: neurodebian-popularity-contest, g++-5 | 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_0.5.21.2+ds-1~nd14.04+1_all.deb Size: 612330 SHA256: c8ebb7ab24c0fae47ea782d135120f1428e3389858849ff588f2b8bd457a139b SHA1: 014a9a010488864341c6289b1414d8603779941a MD5sum: 9b0dcf878e94cc59eeef96b5c7ba58b2 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: openstack-pkg-tools Version: 52~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 208 Depends: neurodebian-popularity-contest, autopkgtest, libxml-xpath-perl, madison-lite, pristine-tar Priority: extra Section: devel Filename: pool/main/o/openstack-pkg-tools/openstack-pkg-tools_52~nd14.04+1_all.deb Size: 52286 SHA256: a4df7374f259be2c071c76914b8360bb558b4312d78d890aec9c6118324a87cc SHA1: 19ba62f52e16fd00f449f4049759a8873f5a2b81 MD5sum: 9d4b190d850abd2a8956ca7a14f57edb Description: Tools and scripts for building Openstack packages in Debian This package contains some useful shell scripts and helpers for building the Openstack packages in Debian, including: . * shared code for maintainer scripts (.config, .postinst, ...). * init script templates to automatically generate init scripts for sysv-rc, systemd and upstart. * tools to build backports using sbuild and/or Jenkins based on gbp workflow. * utility to maintain git packaging (to be included in a debian/rules). . Even if this package is maintained in order to build OpenStack packages, it is of a general purpose, and it can be used for building any package. Package: patool Version: 1.7-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 154 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2) Recommends: file Suggests: arj, bzip2 | lbzip2 | pbzip2, cabextract | lcab, ncompress, cpio | bsdcpio, lzop, p7zip-full, rar | unrar | unrar-nonfree, zip | unzip, rpm2cpio, binutils, lha, unace | unace-nonfree, arc | nomarch, unalz, lrzip (>= 0.551), tar (>= 1.26) | star | bsdtar, rzip, zoo, xdms, orange, lzip | plzip | clzip | pdlzip, sharutils, flac, shorten, unadf, archmage, genisoimage, python-argcomplete Homepage: http://wummel.github.io/patool/ Priority: optional Section: utils Filename: pool/main/p/patool/patool_1.7-1~nd14.04+1_all.deb Size: 32710 SHA256: b55a4dd7d4b6fcae897c79098ea6c7fc3f44db099ae976bfbc8c2fe0e90e4023 SHA1: 9d8e823445d7bd29dfe4977ec96c5443660117d7 MD5sum: 7c8650415068489375b6a4744b344781 Description: command line archive file manager Various archive formats can be created, extracted, tested, listed, compared, searched and repacked by patool. The archive format is determined with file(1) and as a fallback by the archive file extension. . patool supports 7z (.7z), ACE (.ace), ADF (.adf), ALZIP (.alz), AR (.a), ARC (.arc), ARJ (.arj), BZIP2 (.bz2), CAB (.cab), compress (.Z), CPIO (.cpio), DEB (.deb), DMS (.dms), FLAC (.flac), GZIP (.gz), ISO (.iso), LZH (.lha, .lzh), LZIP (.lz), LZMA (.lzma), LZOP (.lzo), RAR (.rar), RPM (.rpm), RZIP (.rz), SHAR (.shar), SHN (.shn), TAR (.tar), XZ (.xz), ZIP (.zip, .jar) and ZOO (.zoo) formats. . It relies on helper applications to handle those archive formats (for example bzip2 for BZIP2 archives). . The archive formats TAR, ZIP, BZIP2 and GZIP are supported natively and do not require helper applications to be installed. Package: psychopy Version: 1.83.04.dfsg-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15239 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), 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~nd14.04+1_all.deb Size: 6139662 SHA256: 3d8762b44b5c77b4400a1c50764d575806e7889b8a3ec091aa1e7f89d392c649 SHA1: ff4b55dc30099dba80b0d0a269957e0e3058be39 MD5sum: 48c612f217f0d00154e7bf9cb8d57bf6 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: 3.0.12.20160514.dfsg1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 220631 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_3.0.12.20160514.dfsg1-1~nd14.04+1_all.deb Size: 24198142 SHA256: 0144d9a6b3ca329a09b78e4533770e8b8a61a93399be68c8aaa0488a485fb9e1 SHA1: 7ac8a4f3e6d51668b47052ddd982cd135bcd0456 MD5sum: 9e5c29289569e230e34f55b89bd3cd24 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 144 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python:any (<< 2.8), python Priority: optional Section: python Filename: pool/main/p/python-argcomplete/python-argcomplete_1.0.0-1~nd14.04+1_all.deb Size: 24554 SHA256: d5cabbc1905eb2d7ea5f55e77e97716ba9c00b77df63e9898b994ac7d61ca0f2 SHA1: cf9bae80c9fd4f1a7b8e350fbd2aa88d1a58a236 MD5sum: 7324a2d86401f52c399054fb6e46effc 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2449 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-brian-lib (>= 1.4.3-1~nd14.04+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.4.3-1~nd14.04+1_all.deb Size: 401994 SHA256: f5134e5254775f86b341da337f269211f4f57b6a6d000f2c1379a46f34a3d69f SHA1: 3cb36dfc2d17dfadd13dae4498e5d6e81ea9c35a MD5sum: 1c64dd142a5972a930f1f894b6feb127 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7021 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~nd14.04+1_all.deb Size: 1985046 SHA256: 16712a925e735cb2152f8f06887d85f19cd2f596a8b257ab0f3d8946c7e1b47f SHA1: 3cd8e0dc358b894b0e8c9af157a4d40f303577a1 MD5sum: d37f6f42d788d3674072ab728bb407da 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-citeproc Source: citeproc-py Version: 0.3.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 727 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python-lxml, python:any (<< 2.8) Homepage: https://github.com/brechtm/citeproc-py Priority: optional Section: python Filename: pool/main/c/citeproc-py/python-citeproc_0.3.0-1~nd14.04+1_all.deb Size: 80330 SHA256: 5b5f6be622f54003050fb0f8a663cf3aa8a7f97366b5ea77b57533b8c8f07446 SHA1: 8decfdd25deee813d966d6406d9dd709e9995c5b MD5sum: 454929edf434ef2e5af0a46e869c0dd7 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 55 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python:any (<< 2.8) Homepage: http://contextlib2.readthedocs.org/ Priority: optional Section: python Filename: pool/main/c/contextlib2/python-contextlib2_0.4.0-3~nd14.04+1_all.deb Size: 8664 SHA256: 0b7d2e2de082b5a6fd9cc18b1a1141ce13073a3012289ad768da93e3a9ae0b70 SHA1: 28c6d174d1c27f1e81bdbfa47a91232246ece17d MD5sum: 1a277de9fb763099abadab3313179e1c 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: 0.2.3-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1375 Depends: neurodebian-popularity-contest, git-annex (>= 6.20160425) | git-annex-standalone (>= 6.20160425), patool, python-appdirs, python-git (>= 2.0.2~), python-humanize, python-keyrings.alt | python-keyring (<= 8), python-keyring, python-mock, python-msgpack, python-tqdm, python-six (>= 1.8.0~), python:any (>= 2.7.5-5~), python:any (<< 2.8), python-boto, python-requests Recommends: python-html5lib, python-httpretty, python-nose, python-numpy, python-scrapy, python-testtools, python-vcr Suggests: python-bs4 Provides: python2.7-datalad Homepage: http://datalad.org Priority: optional Section: python Filename: pool/main/d/datalad/python-datalad_0.2.3-1~nd14.04+1_all.deb Size: 254974 SHA256: e2d637d97a9f2ca4549901bf4c757e17fa67efa2dcd0e7cf52875a292ac0a0e8 SHA1: 6ee8671f388a1d4d1820b1c261fa6a975ebde966 MD5sum: bf8b3604f439a920585bada66ccfa814 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, and Recommends install all dependencies necessary for crawling and testing. If you need base functionality, install without Recommends. . 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 515 Depends: neurodebian-popularity-contest, python-dicom (>= 0.9.7~), python-nibabel (>= 2.0~), python-numpy, python:any (>= 2.7.5-5~), python, python:any (<< 2.8), 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~nd14.04+1_all.deb Size: 78266 SHA256: 7a23acdf2448ad1c837600309ef4412059656d6bf9cad8e40fa5a25a48f8b9cc SHA1: ce40c5fa0c77453a2031a77c09afd13115749d80 MD5sum: e7c62e3fb270103f855d7c836bc61486 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1522 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2) 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~nd14.04+1_all.deb Size: 357476 SHA256: 945b26004df0bd99c707955bbd4c2f62ca2354a9634b35eac851deb93f8b4072 SHA1: 4068d98bbb0c39f2cb4de12dca51e71fa35a0733 MD5sum: dbe2aff786b7ce4469b7bf6b7773f623 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5778 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-numpy (>= 1:1.7.1~), python-scipy, python-dipy-lib (>= 0.10.1-1~nd14.04+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.7-dipy Homepage: http://nipy.org/dipy Priority: optional Section: python Filename: pool/main/d/dipy/python-dipy_0.10.1-1~nd14.04+1_all.deb Size: 2441610 SHA256: f167bcd77c6b5384bf0c7f67950735ba1a3efb2020f2456ab4777bf47f1d3d32 SHA1: 973c68b2f91da696229df112ae0695630deb01ec MD5sum: bd4aa46a15b82c7cb5a937e4dc4e4691 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14345 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~nd14.04+1_all.deb Size: 11464772 SHA256: 7e1e4e10d931dfe1b6a82e9a4338973bb09869411486e308a1b7b9df69ac9e8e SHA1: a9c4969eb66647b387444314baed86e6e37ba549 MD5sum: f324973ae8f011dad397eaf266bfe307 Description: toolbox for analysis of MR diffusion imaging data -- documentation Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. Package: python-expyriment Version: 0.7.0+git34-g55a4e7e-3~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2388 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), 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~nd13.10+1+nd14.04+1_all.deb Size: 695948 SHA256: 37604b739e17ae561b68e1ffa8fd89495abab699acaa75ce4a4160ab0e9f1dc9 SHA1: ede08d0df1746f31ccb9eb6fbcdc49722e3b1b5b MD5sum: 95df9057ee0432389482bebb2bebc420 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-funcsigs Version: 0.4-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 89 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-funcsigs-doc Homepage: http://funcsigs.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-funcsigs/python-funcsigs_0.4-2~nd14.04+1_all.deb Size: 12750 SHA256: a6c50a81866eb65c9c537e5aeaf0759bc7462e40907c9fe0fe3ef74207e805fe SHA1: 7d3a78f5d60304466ea78ec3322afdbe3402ed80 MD5sum: 73c673b7466d986891e893635cc99ebc Description: function signatures from PEP362 - Python 2.7 funcsigs is a backport of the PEP 362 function signature features from Python 3.3's inspect module. The backport is compatible with Python 2.6, 2.7 as well as 3.2 and up. . This package contains the Python 2.7 module. Package: python-funcsigs-doc Source: python-funcsigs Version: 0.4-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 130 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: http://funcsigs.readthedocs.org Priority: optional Section: doc Filename: pool/main/p/python-funcsigs/python-funcsigs-doc_0.4-2~nd14.04+1_all.deb Size: 24170 SHA256: 91020210e46dbf1ba30776a52fb45b5a2ebb0efd4e5a6b0d2812bf9fc40b2fde SHA1: e8a88a1101c2daefd42fdf0db9dcd120937fbe90 MD5sum: f68afc7bff6e189b89513589e9f83594 Description: function signatures from PEP362 - doc funcsigs is a backport of the PEP 362 function signature features from Python 3.3's inspect module. The backport is compatible with Python 2.6, 2.7 as well as 3.2 and up. . This package contains the documentation. Package: python-future Version: 0.15.2-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1732 Pre-Depends: dpkg (>= 1.15.6~) Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python:any (<< 2.8), python2.7 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~nd14.04+1_all.deb Size: 336038 SHA256: dc7401310968162b4ebed3645055285c874be325278a45db71dd1f7843632105 SHA1: 9d81fea409dba56a49279ccec79875de6f81ee04 MD5sum: 52e5592c4017dc73747fd1376c878a94 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1577 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~nd14.04+1_all.deb Size: 293316 SHA256: 35ae3d991bf118c268c62b0e11b65d3ee55f5383d4bd83214045e9c6d98c03cb SHA1: 0e113bbe784a0b1b4d9519a81dd347b85a8a8a86 MD5sum: f915855e035e4f2650021f7b348888a0 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: 2.0.5-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1563 Depends: neurodebian-popularity-contest, git (>= 1:1.7) | git-core (>= 1:1.5.3.7), python-gitdb (>= 0.6.4), python:any (>= 2.7.5-5~), python:any (<< 2.8) 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_2.0.5-1~nd14.04+1_all.deb Size: 290196 SHA256: 658aeed45c4bc69a4661d6bed91ec541be898f4b319f727ea7ed12a7f0159f9f SHA1: a373372680da7905a94ba7c37a213a11b8a133f6 MD5sum: 57c4029905e7cc2b0a6f5fd876f889f2 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: 2.0.5-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 925 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_2.0.5-1~nd14.04+1_all.deb Size: 122246 SHA256: 8ff16b1fce1a89f747c1867c304bdb19f3e7c8ac029982c62ca7e9d601133dae SHA1: c72d7e0771a8d9cb12d96bbde550539294632f7c MD5sum: 4e3583623c1666da0e3f9ba14484670b 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 117 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~nd14.04+1_all.deb Size: 12952 SHA256: b9524acfb16d327e1a07b147b15f7904766d20a3ad66ca41dc5364025c5e5429 SHA1: 9f57df6560f1c54888b2f4054166e0d04459aad1 MD5sum: 681f7ac6228c80c5a7cabb101baf0983 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 27 Depends: neurodebian-popularity-contest Homepage: https://github.com/phn/jdcal Priority: optional Section: python Filename: pool/main/j/jdcal/python-jdcal_1.0-1~nd14.04+1_all.deb Size: 7670 SHA256: 1f7d63bfde1855c23e02ebfce05181789c84b273daab7ea32c66f78b1cca884e SHA1: 9d0226fb987ab11a64f01766cf92eacb9543bd28 MD5sum: 35bcdfe5fe51152dddaaaffafe9b8576 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 363 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2) 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~nd14.04+1_all.deb Size: 81814 SHA256: 80fea9079f6cb37e8d9a822ca4d806781c1549d7085f2354f7c0a0543eed86b4 SHA1: 629ef5bba84de5bd4e4667a2bd62c1fd82958244 MD5sum: 72280420e364781aa4460a4fccae1326 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-mdp Source: mdp Version: 3.5-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1375 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python-numpy, python:any (<< 2.8), python-future 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~nd14.04+1_all.deb Size: 277596 SHA256: b341f34d09056803b902d8ba6074b485f8fd96027352d147298090555a2ba731 SHA1: 95e7b548f0a5e1854f7f6b40be03e11fd602f13c MD5sum: 1dc1ac7dca838571b91743cf7b31369f 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: 0.12+dfsg-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9463 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-numpy, python-scipy, python-sklearn, python-matplotlib, python-joblib (>= 0.4.5), xvfb, xauth, libgl1-mesa-dri, help2man, libjs-jquery, libjs-jquery-ui Recommends: python-nose, mayavi2 Suggests: python-dap, python-pycuda, ipython Provides: python2.7-mne Homepage: http://martinos.org/mne Priority: optional Section: python Filename: pool/main/p/python-mne/python-mne_0.12+dfsg-1~nd14.04+1_all.deb Size: 4432150 SHA256: 3f961a9d4529324d06c298ff2fed7ca34ab6180ef30d774f29413b018982dc76 SHA1: 1a153c12ed8b24fb7037e3b5406e5a53cdf4a658 MD5sum: a69af4bbc8bf059903cc8c8adeb2d2bd Description: Python modules for MEG and EEG data analysis This package is designed for sensor- and source-space analysis of MEG and EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. Package: python-mpi4py-doc Source: mpi4py Version: 1.3.1+hg20131106-1~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 257 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3.1+hg20131106-1~nd13.10+1+nd14.04+1_all.deb Size: 52650 SHA256: 89c6b2097aa4c46452fc4c94f25c9552c6bdbb11de0b74d3499a9fc731fcb138 SHA1: 180f3b068696bf9372da2ab0ebdeab29e18a44c1 MD5sum: ccd5f97d5b5360920b157174f29dcace 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: 2.5.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8370 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy, python:any (>= 2.7.1-0ubuntu2), python-mvpa2-lib (>= 2.5.0-1~nd14.04+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_2.5.0-1~nd14.04+1_all.deb Size: 5076572 SHA256: 0686f2027ee6b2a23713b1d34c4d5f7366a042d83e59fa95066137415f7c636a SHA1: 9e61130c9a8230efd2977286ab4da2ff2983ebcb MD5sum: 93382048f9ab7a54bd54f92bad6e118c 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: 2.5.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 30074 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_2.5.0-1~nd14.04+1_all.deb Size: 4633538 SHA256: 244204357d3c038822484d84fe5d6a92b8a762121e183e9ce21b654e81ffafdb SHA1: be865c868ab76e2b969f4ca3369584b17e4bae67 MD5sum: 80fb042a940b552eebae4791e2324df3 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-neurosynth Source: neurosynth Version: 0.3-1~nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 81 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-numpy, python-scipy, python-nibabel, python-ply Recommends: python-nose, fsl-mni152-templates Suggests: python-testkraut Homepage: http://neurosynth.org Priority: extra Section: python Filename: pool/main/n/neurosynth/python-neurosynth_0.3-1~nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 28812 SHA256: 15d6ad200903f48f7d0ac38e08d3aea9a417b73085929fcdacce541b5ecb0f05 SHA1: ab997820ecbef62ee9805767c8810a4a4663c6a4 MD5sum: 6ca9dceaed50e4921f7759c6fe0b948f Description: large-scale synthesis of functional neuroimaging data NeuroSynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. This Python module at the moment provides functionality for processing the database of collected terms and spatial coordinates to generate associated spatial statistical maps. Package: python-nibabel Source: nibabel Version: 2.0.2-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63313 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), 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~nd14.04+1_all.deb Size: 1962668 SHA256: 073f965071e19de979c9fbe85522edac0c26c9087c6b3c80323620d9599e1548 SHA1: 1fa5e5e2da1081c28ca5800c8b5bd09439123dff MD5sum: 27ff137eedffd09b7cfd86e0fba98d10 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5565 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~nd14.04+1_all.deb Size: 2682562 SHA256: 0f7a7a9658f5a0ef2b7762b8cd4eb2960227d776515e803c7d5ecddb3093c28c SHA1: 4b3721ce86e330b591716a044042a24bc3e26cfd MD5sum: b333e32895ab59e3dd2e61a11e03eb73 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: 0.2.5~dfsg.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2437 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.6), python:any (>= 2.7.5-5~), python-nibabel (>= 1.1.0), python:any (<< 2.8), python-scipy (>= 0.9), python-sklearn (>= 0.12.1) Recommends: python-matplotlib Provides: python2.7-nilearn Homepage: https://nilearn.github.io Priority: extra Section: python Filename: pool/main/n/nilearn/python-nilearn_0.2.5~dfsg.1-1~nd14.04+1_all.deb Size: 731138 SHA256: 27e64f205d899c6c771515afe37216ea3580025aa23cdb54c4e3215d4446fb78 SHA1: 0366f70e98fa1b0cbc268f6a28bfa1e096bf1880 MD5sum: aeae2c4d9b9365339daff1c25b57d453 Description: fast and easy statistical learning on neuroimaging data (Python 2) This Python module leverages the scikit-learn toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. . This package provides the Python 2 version. Python-Version: 2.7 Package: python-nipy Source: nipy Version: 0.4.0+git26-gf8d3149-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3338 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.2), python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-scipy, python-nibabel, python-nipy-lib (>= 0.4.0+git26-gf8d3149-1~nd14.04+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.4.0+git26-gf8d3149-1~nd14.04+1_all.deb Size: 738094 SHA256: b78f66f8fc6ddb33f8e1555ccacdb550e2dfa7eacecf60b7f7a3ad8d0a344328 SHA1: 6f1a79e1ef39570ec54e63921bf0c461543c69d7 MD5sum: 19c74717eb684916121ebd736a63aa3d Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It provides functionality for - General linear model (GLM) statistical analysis - Combined slice time correction and motion correction - General image registration routines with flexible cost functions, optimizers and re-sampling schemes - Image segmentation - Basic visualization of results in 2D and 3D - Basic time series diagnostics - Clustering and activation pattern analysis across subjects - Reproducibility analysis for group studies Python-Version: 2.7 Package: python-nipy-doc Source: nipy Version: 0.4.0+git26-gf8d3149-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8213 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.4.0+git26-gf8d3149-1~nd14.04+1_all.deb Size: 1151748 SHA256: fb6decbab2e2f8d9c4e2825e7bd359b612b5941acc6fe2269eb6c1a8301345d8 SHA1: cb8fe719d0d1a5e3b1198b218923e0c020a99f46 MD5sum: 42b54ba5ebb7b67405a65faba18aaa5e Description: documentation and examples for NiPy This package contains NiPy documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation Package: python-nipype Source: nipype Version: 0.10.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4573 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib Recommends: ipython, python-nose, graphviz Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat, mne-python, elastix, ants Provides: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.10.0-1~nd14.04+1_all.deb Size: 1158946 SHA256: 39836fcb648f64546c684c1831a50292c701b204b127ee8ea9a3ac3d162dce69 SHA1: fc0b5f3620bc163b435ce94c3537f601bd89ce0c MD5sum: f56846a6794f64b052f434dac5e5e4ca Description: Neuroimaging data analysis pipelines in Python Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). Package: python-nipype-doc Source: nipype Version: 0.10.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20779 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.10.0-1~nd14.04+1_all.deb Size: 8759220 SHA256: 4b2c7602f68541b83fefb69f9e2b3277109f40fb53ce0ab93fffdd461303f5e7 SHA1: b5dd929f7a8df02f697c2ca7e98fc28cf8f1e95a MD5sum: 91069fca772934ae6d749a309252bb74 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+git15-g4951606-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9397 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python:any (<< 2.8), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.6+git15-g4951606-1~nd14.04+1_all.deb Size: 2562110 SHA256: c314436d74f5c82ea236f12ac2e32fe961e479072c1f321160cbdb7847c857ad SHA1: 8bd382687d2550bb9035bf5f1bdd826202686f38 MD5sum: b964a8493b60fbaebc44ab84de4e6066 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+git15-g4951606-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7690 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+git15-g4951606-1~nd14.04+1_all.deb Size: 5698292 SHA256: 40c86e26dc49cb4db962fdcd1ed062879a8c68e0d16b4e4af5c7a931e0fbd5e4 SHA1: f7593b5774bd1ec5b259eb5a389e7ce5602feb53 MD5sum: ddc6bc0a8bf740b8f7f8c483feb07bfe 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), 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~nd14.04+1_all.deb Size: 5250 SHA256: b2ae93571b0725e740f89e8b03cb902089365182868a9a8c8545c5037a2ccbf0 SHA1: 256b21a4c6b869033aa366e3cd70e4557846983c MD5sum: 04cf2021c398ef47e5fcf18a72394497 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-openpyxl Source: openpyxl Version: 2.3.0~b2-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1121 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-jdcal Recommends: python-pytest, python-pil, python-imaging, python-lxml Homepage: http://bitbucket.org/openpyxl/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_2.3.0~b2-1~nd14.04+1_all.deb Size: 191632 SHA256: df1c6182bb909af72b09ea8851da8e2c35717fdff7d298c5f8df9e1c01e96a6e SHA1: ce80135d40a9fc5e629b1ea13983beae49942ef9 MD5sum: 0631060ddd63fb027edeca77f0d8a195 Description: module to read/write OpenXML xlsx/xlsm files Openpyxl is a pure Python module to read/write Excel 2007 (OpenXML) xlsx/xlsm files. Package: python-pandas Source: pandas Version: 0.18.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24201 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-dateutil, python-tz, python-numpy (>= 1:1.7~), python-pandas-lib (>= 0.18.1-1~nd14.04+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_0.18.1-1~nd14.04+1_all.deb Size: 2527632 SHA256: 7d7f696b3f35a86f86bffad275cba3cd9c50ffc019d6a6bf7528274c8c4d897c SHA1: 1537850796be6784bdc63d047e40873d657a777c MD5sum: da8eb9f836bef8fce71cd7faff5ce5ed 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: 0.18.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 56875 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_0.18.1-1~nd14.04+1_all.deb Size: 11547442 SHA256: 109386dee6d9887c77eeb31c9c3845e2c4fa7e78b50976892a20dc233d4921f3 SHA1: 775dd6018dad37dc79deb26aa7550e7fd1a674a6 MD5sum: 2a804c6a815ba54c61200c664345b4b9 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 795 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), 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~nd14.04+1_all.deb Size: 171752 SHA256: 51be9da2ab4bf0707c425f8c975291f4f123ce78d586d59daf9da6607061e4dc SHA1: 6f27fe7cb2cc5436eae94d90d9b533039c3fe47b MD5sum: 878edc3d6090b111444cd1b04f692f97 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1303 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~nd14.04+1_all.deb Size: 357686 SHA256: 38dec6587534ab4a253473578e2387eaf0e57c119206e9c9954b763e071d23c0 SHA1: 5969531a0aa16eb62f889ed7f8cb8db6bd1e4aa4 MD5sum: f8d189c4a9b883d42ff82ec0a0f1347c Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pprocess Source: pprocess Version: 0.5-1+nd0~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 716 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.boddie.org.uk/python/pprocess.html Priority: optional Section: python Filename: pool/main/p/pprocess/python-pprocess_0.5-1+nd0~nd13.10+1+nd14.04+1_all.deb Size: 81638 SHA256: a3672edffea33c0135dc765fe3dbe3524115cf8cd1ae636f2bf7cbc09cfc47be SHA1: c317f00152e89dbf84b5a85ea883b44920eef65a MD5sum: 421e4d9f4c03a34b12fbffb0d0f92b25 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 269 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~nd14.04+1_all.deb Size: 66656 SHA256: b247e9257aa8067c8d3d4798716dd149ffcdeee0f161f7ddc0ea0ca6575e932e SHA1: 0a2216498793395a85739146301a250231177798 MD5sum: 62f120862eaaf8fd92e04296bda302e6 Description: Advanced Python development support library (Python 2) The Codespeak py lib aims at supporting a decent Python development process addressing deployment, versioning and documentation perspectives. It includes: . * py.path: path abstractions over local and Subversion files * py.code: dynamic code compile and traceback printing support . This package provides the Python 2 modules. Package: python-pydotplus Version: 2.0.2-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 130 Depends: neurodebian-popularity-contest, graphviz, python-pyparsing (>= 2.0.1), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-pydotplus-doc Homepage: http://pydotplus.readthedocs.org/ Priority: optional Section: python Filename: pool/main/p/python-pydotplus/python-pydotplus_2.0.2-1~nd14.04+1_all.deb Size: 20246 SHA256: 2680420223b68b4e90a0e9fa995a31a5cc60f9f01d3f66d9ad667efbb7d42d60 SHA1: dfe94fade1ce954bdc6d6d2d91cd8022638af42c MD5sum: 406e4724c36431b6b0044f1d30ee4f51 Description: interface to Graphviz's Dot language - Python 2.7 PyDotPlus is an improved version of the old pydot project that provides a Python Interface to Graphviz's Dot language. . Differences with pydot: * Compatible with PyParsing 2.0+. * Python 2.7 - Python 3 compatible. * Well documented. * CI Tested. . This package contains the Python 2.7 module. Package: python-pydotplus-doc Source: python-pydotplus Version: 2.0.2-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 755 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: http://pydotplus.readthedocs.org/ Priority: optional Section: doc Filename: pool/main/p/python-pydotplus/python-pydotplus-doc_2.0.2-1~nd14.04+1_all.deb Size: 178508 SHA256: 6b083d0f654230dd8213720aa6cb97bdfac7e23bd8b4ebf0df14d83345b74e54 SHA1: 12eb8384d62b1467bf227659d49ac60786613235 MD5sum: 313af7833740ba616df312eca87c064e Description: interface to Graphviz's Dot language - doc PyDotPlus is an improved version of the old pydot project that provides a Python Interface to Graphviz's Dot language. . Differences with pydot: * Compatible with PyParsing 2.0+. * Python 2.7 - Python 3 compatible. * Well documented. * CI Tested. . This package contains the documentation. Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-2~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 813 Depends: neurodebian-popularity-contest, python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0+git12-g365f8e3-2~nd13.10+1+nd14.04+1_all.deb Size: 819336 SHA256: 4fd57971c92c6cd4cefaf9f32063e2c926a4cb901726c02263d9b4ea8cc24bb8 SHA1: 13d0d3aa4656070b80d4bed6e11ef0228e43b195 MD5sum: 335b1dfa97a9d8678444c11354131088 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-pygraphviz-doc Source: python-pygraphviz Version: 1.3.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 323 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: https://pygraphviz.github.io/ Priority: optional Section: doc Filename: pool/main/p/python-pygraphviz/python-pygraphviz-doc_1.3.1-1~nd14.04+1_all.deb Size: 67900 SHA256: 7c34df50d8f08bc1e04ea61edf29492ec557263a8f5c990a7c3bb22142d0f6e2 SHA1: c3a73b8871316b164182e70e862941e6db5847c1 MD5sum: 7e0f8aaa9f527c56ca61e3bad03d97ba Description: Python interface to the Graphviz graph layout and visualization package (doc) Pygraphviz is a Python interface to the Graphviz graph layout and visualization package. . With Pygraphviz you can create, edit, read, write, and draw graphs using Python to access the Graphviz graph data structure and layout algorithms. . This package contains documentation for python-pygraphviz. Package: python-pymc-doc Source: pymc Version: 2.3.4+ds-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1860 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Homepage: http://pymc-devs.github.com/pymc/ Priority: extra Section: doc Filename: pool/main/p/pymc/python-pymc-doc_2.3.4+ds-1~nd14.04+1_all.deb Size: 839822 SHA256: ca5b4a1c229d97f73a5efabb5bce300e4461516be6d6ca3fb09a71accd13d6dc SHA1: b7faad248279ffb84dc5464d0d050883c20afe1c MD5sum: c2f9774d355e63c5a38e9d90065b3aed 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~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 763 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-jinja2, python-cheetah Suggests: python-neuron, python-brian, python-csa Homepage: http://neuralensemble.org/trac/PyNN Priority: extra Section: python Filename: pool/main/p/pynn/python-pynn_0.7.5-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 122882 SHA256: 62e294043371c55fc47adddcd8c00ee9b823bfc2885a7fe6a17545f5a9ba2cea SHA1: ac1a6014356d9f0d27fe921f17abf9a006bc6dfd MD5sum: d08fcaa0e9cdb30bfb91d7d5082d4941 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 424 Depends: neurodebian-popularity-contest, python-pkg-resources, python-py (>= 1.4.29), python:any (<< 2.8), python:any (>= 2.7.5-5~), python 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~nd14.04+1_all.deb Size: 102268 SHA256: a5bc67d196abb4d30d1d59f9ddb6cc31ba957acd0cd6a1238661d5f1d9b64eac SHA1: 4dcfe73451f8b239c438acb5319988f1f6f42608 MD5sum: c0a87dff9798b7418e011f2d0d38428e 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2879 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~nd14.04+1_all.deb Size: 401394 SHA256: 9454eed49ee66eae353e2980c3d4eb49cda6377b0a129038702726d6e993a988 SHA1: f4c4aa66f6106f3715640dd31bc4d987df510199 MD5sum: 6daf8937fe0654a85e2290e98d04119a Description: Simple, powerful testing in Python - Documentation This testing tool has for objective to allow the developers to limit the boilerplate code around the tests, promoting the use of built-in mechanisms such as the `assert` keyword. . This package contains the documentation for pytest. Package: python-pytest-localserver Source: pytest-localserver Version: 0.3.4-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 82 Depends: neurodebian-popularity-contest, python-pytest, python-werkzeug (>= 0.10), python:any (>= 2.7.5-5~), python:any (<< 2.8) Homepage: https://bitbucket.org/pytest-dev/pytest-localserver/ Priority: optional Section: python Filename: pool/main/p/pytest-localserver/python-pytest-localserver_0.3.4-2~nd14.04+1_all.deb Size: 19208 SHA256: 6dc0826378989aa452e64dd8197b2daf87e5421da9dcd9780c7cfcdce32d90d0 SHA1: 918c1eed4cda552d75b0fb8d2cb4f9c59558c0ee MD5sum: 77c5995e27fabad5d0e0dacd2d6e9f85 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python:any (<< 2.8), python-pytest, python-tornado Homepage: https://github.com/eugeniy/pytest-tornado Priority: optional Section: python Filename: pool/main/p/pytest-tornado/python-pytest-tornado_0.4.4-1~nd14.04+1_all.deb Size: 5674 SHA256: 3df1aabe30adc5bcdb999720babc481798f61c8bd2af76cc81f4cb84a5fb8b8c SHA1: 0cc076b5af86d42d85122d8b771de2a4dd734835 MD5sum: 1d7b0e30f16aa344c6dbfb70f9900944 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-scikits-learn Source: scikit-learn Version: 0.17.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 81 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~nd14.04+1_all.deb Size: 55990 SHA256: f0a6f294e3314f775f3ea42fd4b2ae4da1d08f0f376aac017f6ef11f08640d00 SHA1: 17235989aac1916f6ffe19efad0250b791f2cb09 MD5sum: 9b6898ed16828f80823a0dd746861d98 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-scrapy Version: 1.0.3-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 812 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:any (>= 2.7.5-5~), python:any (<< 2.8), python-twisted, python-six, python-pyopenssl, python, python-service-identity Recommends: ipython, python-django, python-guppy, python-imaging, python-mysqldb, python-pygments, python-simplejson | python (>= 2.6) Suggests: python-openssl Provides: python2.7-scrapy Homepage: http://scrapy.org/ Priority: optional Section: python Filename: pool/main/p/python-scrapy/python-scrapy_1.0.3-1~nd14.04+1_all.deb Size: 175830 SHA256: 43ae160995859d66d0969c6597daa3610ac3a38b35ac822e0185b15c57c9bc11 SHA1: 5a96e9daa65d1cd722efd64527f2f4d7089e38f4 MD5sum: 05bd415ef1af3f5e412b2e611376f0e8 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5944 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~nd14.04+1_all.deb Size: 651116 SHA256: 3c566a34a50e23f25f2202b780c695376e27978f404620c6bc4d389616af90ae SHA1: 9790445a5217b150a3649ac65456f9a1305cce43 MD5sum: 1e260cbd681a4cbf11874aa000a97b81 Description: Python web scraping and crawling framework documentation Scrapy is a fast high-level screen scraping and web crawling framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing. . This package provides the python-scrapy documentation in HTML format. Package: python-seaborn Source: seaborn Version: 0.6.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 676 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python:any (<< 2.8), python-numpy, python-scipy, python-pandas, python-matplotlib Recommends: python-statsmodels, python-patsy Homepage: https://github.com/mwaskom/seaborn Priority: optional Section: python Filename: pool/main/s/seaborn/python-seaborn_0.6.0-1~nd14.04+1_all.deb Size: 117710 SHA256: e47fbc84aea13872455b4266a9a8834d61341ee207b83a1d0fbde3e2fa20b799 SHA1: 81de27b81b120ac6e276a41cecb6dd862dde2442 MD5sum: 644fb5dad9a465cec2748c4c8131977f Description: statistical visualization library Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. . Some of the features that seaborn offers are . - Several built-in themes that improve on the default matplotlib aesthetics - Tools for choosing color palettes to make beautiful plots that reveal patterns in your data - Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data - Tools that fit and visualize linear regression models for different kinds of independent and dependent variables - A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate - High-level abstractions for structuring grids of plots that let you easily build complex visualizations . This is the Python 2 version of the package. Package: python-setuptools-scm Source: setuptools-scm Version: 1.8.0-1~bpo8+1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 69 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python:any (<< 2.8) 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~nd14.04+1_all.deb Size: 10144 SHA256: 81fa741285e7fcba5088c45e5b0438f16fb94ecb2ce15d54362a66bdc635b62a SHA1: 0337593c9317611d4e8437b4a08a1767d868925f MD5sum: 4bdc1a720e93fecf17c65b8e1690ce09 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-six Source: six Version: 1.9.0-3~bpo8+1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 34 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python:any (<< 2.8) 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~nd14.04+1_all.deb Size: 11018 SHA256: e541fbf52c8d157e98be2e3d3d54fb033d5e30880458d9b143aab22f9fcbe65e SHA1: dd2095a9d2b56400abeecd526982b047084f0bdf MD5sum: 0bf7c71f9bb8cc5137d0e12156ad46d5 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 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~nd14.04+1_all.deb Size: 13266 SHA256: 3a2a5d68772088d86ddca061fa261ce001e746c4c5b567216bf9f8aa641569bd SHA1: a86caffffa3a87341f18e5d13fa820e2e764bf1c MD5sum: 770748590bd24a026041c4970f925111 Description: Python 2 and 3 compatibility library (universal wheel) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six as a universal wheel. Package: python-skimage Source: skimage Version: 0.10.1-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15134 Depends: neurodebian-popularity-contest, libfreeimage3, python-numpy, python-scipy (>= 0.10), python-six (>= 1.3.0), python-skimage-lib (>= 0.10.1-2~nd14.04+1), python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2) Recommends: python-imaging, python-matplotlib (>= 1.0), python-nose, python-pil, python-qt4 Suggests: python-opencv, python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.10.1-2~nd14.04+1_all.deb Size: 11927494 SHA256: 82c528be9e874b39de21a4bba62421c1d6bd7589c9596093dcf97becccbfa3ae SHA1: 172a2e19e17e3b5e2b165127f92fe4ef51f60003 MD5sum: 5c1c7e773ca81f8494f6b4b97e17b06d Description: Python modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 2 module. Package: python-skimage-doc Source: skimage Version: 0.10.1-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 21865 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-skimage Homepage: http://scikit-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.10.1-2~nd14.04+1_all.deb Size: 17205200 SHA256: 60b9c823532f9aad362ddf844c783ae8afc19152e3a8633699c1b906979ba876 SHA1: ffda4a2dd76fb4d7df9deefa82715439553fa6fd MD5sum: 34188666367a224580d2a0306199c599 Description: Documentation and examples for scikit-image This package contains documentation and example scripts for python-skimage. Package: python-sklearn Source: scikit-learn Version: 0.17.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5281 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~nd14.04+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~nd14.04+1_all.deb Size: 1223580 SHA256: 2d118f52164ca688511e479bd62286496b99cdde3d73a3ba24e4700557950bc4 SHA1: 576003c44a3dc80b51792a10bbacf8417d6bfbc4 MD5sum: 890ba71b39aebeac176ee887320d5eac 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23790 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~nd14.04+1_all.deb Size: 4063478 SHA256: 5dec4e0df4d0d8ca6cbda979a4f418d997fe6f8842689a7b878ae4c72e461c1c SHA1: c4ad9be521ca1fc044d1256951f26faa7a6aa2fb MD5sum: d8e683e99f46957afe49c0eced832100 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 116 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2) 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~nd14.04+1_all.deb Size: 20296 SHA256: c3f503b544b506d56122a20792630e01f6f96796d047b791382c973d15159aea SHA1: 15bc5137e3e36e7a60019e5118f6901ba140af1d MD5sum: e7e6c67383d824cef31be886c5c9dbdf 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-rtd-theme Source: sphinx-rtd-theme Version: 0.1.8-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 332 Depends: neurodebian-popularity-contest, fonts-font-awesome, fonts-lato, libjs-modernizr, python:any (>= 2.7.5-5~), python:any (<< 2.8) 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~nd14.04+1_all.deb Size: 117170 SHA256: 8d17f4cf1b75a6ea2e35dcc4ca9eb270006eeed87c54cd5eb0899e710fef410b SHA1: 7d682ebcb2df6a4681a2347d1d6f2931718ef0c4 MD5sum: 74729ef3e6968eb8ae00592b5ba23e8f 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-spykeutils Source: spykeutils Version: 0.4.3-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2090 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-scipy, python-quantities, python-neo (>= 0.2.1), python-nose, python-sphinx Recommends: python-guidata, python-guiqwt, python-tables, libjs-jquery, libjs-underscore, python-sklearn (>= 0.11), python-joblib (>= 0.4.5) Provides: python2.7-spykeutils Homepage: https://github.com/rproepp/spykeutils Priority: extra Section: python Filename: pool/main/s/spykeutils/python-spykeutils_0.4.3-1~nd14.04+1_all.deb Size: 309540 SHA256: 820c3ca420f026ec91cc0fa2018226bacd069ca010fdbf3a7a612a12be31ee15 SHA1: 11d04fd42bf9a14850534d5bf4a7b33347902451 MD5sum: 0722e50cf3dff91d8fab11c5a79c3d1b 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-surfer Source: pysurfer Version: 0.6-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 226 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), 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~nd14.04+1_all.deb Size: 41646 SHA256: 80e873c3d00ac287b75dbf915977f7e49557aa514d7d3abcc2827b79e3629e5b SHA1: b372b5e75e7c68857b885f461ec60fcef1722073 MD5sum: ed47bdcff3940d9b0bcbdb732db98535 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-tqdm Source: tqdm Version: 4.4.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 160 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python:any (<< 2.8) Homepage: https://github.com/tqdm/tqdm Priority: optional Section: python Filename: pool/main/t/tqdm/python-tqdm_4.4.1-1~nd14.04+1_all.deb Size: 35334 SHA256: c14b06ee30bcdfd69cedb08eba44ca00ab87fd50d52fc395180d3989a6a46987 SHA1: 1742f84dc9001263c9b1d060838d60e26ed9fcd3 MD5sum: 8e9fd211de40853dd1287bdad3bbd529 Description: fast, extensible progress bar for Python 2 tqdm (read taqadum, تقدّم) means “progress” in Arabic. tqdm instantly makes your loops show a smart progress meter, just by wrapping any iterable with "tqdm(iterable)". . This package contains the Python 2 version of tqdm . Package: python-w3lib Version: 1.11.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40 Depends: neurodebian-popularity-contest, python-six (>= 1.6.1), python:any (>= 2.7.5-5~), python:any (<< 2.8) Homepage: http://pypi.python.org/pypi/w3lib Priority: optional Section: python Filename: pool/main/p/python-w3lib/python-w3lib_1.11.0-1~nd14.04+1_all.deb Size: 14094 SHA256: a143ab050c507692654ddeae18ca7ac254b7d86ac239c5046c0fe27d9f426366 SHA1: 8eb4554d5350ada80f23c95f42aea045a4554427 MD5sum: bbc1d292a81d284a45060953fac74aa8 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 727 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python:any (<< 2.8), 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~nd14.04+1_all.deb Size: 163266 SHA256: 30d72ddc76c40209588e9aca6110a9e7ccad863cb1e2e56d94652993743512f4 SHA1: 49839c379ce6906a8c7d6673650a6530ab1db7e7 MD5sum: a5499bc02b89cf7549e9e8cfd8e65ad9 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2559 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~nd14.04+1_all.deb Size: 879504 SHA256: 587cfad920e8ede1479ad00bd06417bd6839666b44fa595e8a6fcaec11d135a1 SHA1: 0901b6f1e3dca6bbc5800ae98324e4972a848276 MD5sum: 7cef970aad6fc4f6ce0fd38f8b68c13a 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 123 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~nd14.04+1_all.deb Size: 20990 SHA256: fcfb82d58ed9db706b5fb445a5d1a7b92c8af3c6e50e9ab6824a4b044ffa06c9 SHA1: 56062ca9770fd9bb9dbb58b7ba697a1395277aaa MD5sum: 62290f7da03fbe39f1ca8ffb65d81006 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 731 Depends: neurodebian-popularity-contest, python3, python3:any (>= 3.3.2-2~), python3-lxml Homepage: https://github.com/brechtm/citeproc-py Priority: optional Section: python Filename: pool/main/c/citeproc-py/python3-citeproc_0.3.0-1~nd14.04+1_all.deb Size: 81778 SHA256: 5ca21fbd35228899a0a548d3b2fbd7b70c3d3a90d59ee7598948acf90656e426 SHA1: 9a1da1d78f649a29d9957a000aa9d96323c7812a MD5sum: 6b26c48e4aaa89ffb0cc338355c500ea 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 55 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~nd14.04+1_all.deb Size: 8736 SHA256: 72841c88eabc50f3a870806d07a94f2e4c8b8c17f132af58a14eaac4099af14a SHA1: 1668fe8f673231165b07873c808a929702e3c90b MD5sum: 87622dd630ad0afc1284146b2ead624f 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-funcsigs Source: python-funcsigs Version: 0.4-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 89 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Suggests: python-funcsigs-doc Homepage: http://funcsigs.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-funcsigs/python3-funcsigs_0.4-2~nd14.04+1_all.deb Size: 12832 SHA256: 37684478429b457dee602d6aa1519743da4149c9be0a9b9631853f5072a5be68 SHA1: 5186c91038a589d1f00ffb17022c1ed706403718 MD5sum: e225fa83d1cdfeec6ca881c05924e9a0 Description: function signatures from PEP362 - Python 3.x funcsigs is a backport of the PEP 362 function signature features from Python 3.3's inspect module. The backport is compatible with Python 2.6, 2.7 as well as 3.2 and up. . This package contains the Python 3.x module. Package: python3-future Source: python-future Version: 0.15.2-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1663 Pre-Depends: dpkg (>= 1.15.6~) Depends: neurodebian-popularity-contest, python3.4, 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~nd14.04+1_all.deb Size: 333824 SHA256: d80df414502fa8896b82cd934dfdb63b1e8322696c947f84d8254fed9145b758 SHA1: fa57c7e6361593c539b26afbd413a9a9b3cf34de MD5sum: 062922c9fd88c6a3d8a84ab08dd474fa 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: 2.0.5-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1559 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_2.0.5-1~nd14.04+1_all.deb Size: 289978 SHA256: 9d5c78c46d91fa8936535b0364f165a05b8a9fd40aa5249923b00dc639d7c3dc SHA1: 45d7dcb21d7d9de7580879e54c1f1d1cc9b4fbe4 MD5sum: e651de95a2263b58d08b121e8ac5d4b0 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 114 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~nd14.04+1_all.deb Size: 12682 SHA256: 392c4a2e07c72ad76f98a7bee9625ed7631477c377a61275e855c48a5ebafbd9 SHA1: d6f38ce9bbf537c685316b39f486acf21e96f11f MD5sum: 83735535f9e1e28f2251831c80e2e640 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23 Depends: neurodebian-popularity-contest Homepage: https://github.com/phn/jdcal Priority: optional Section: python Filename: pool/main/j/jdcal/python3-jdcal_1.0-1~nd14.04+1_all.deb Size: 7468 SHA256: 94b7e4cf3470fc765314561c161e497952f1597f87ab641b702080c2dc2c4249 SHA1: 64f8dbeedd11a34c60e16344e5943e5977723c4a MD5sum: 3f05614962ed5a2ba097afe9a6646e99 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 357 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~nd14.04+1_all.deb Size: 78572 SHA256: 94ad8727e392dd6fce9cbb3e58085b74b06f2f393d806ff07173ac2b043ad7d0 SHA1: c8e4d9151cfacf5251722c58cfe2096bb6069354 MD5sum: 4d58f34d3b7f49f4edf99dd48e2fb6fd 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1373 Depends: neurodebian-popularity-contest, python3-future, python3:any (>= 3.3.2-2~), python3-numpy, 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~nd14.04+1_all.deb Size: 275288 SHA256: 933a89a5ed29ea4fac21629f5c29ea097b0677a531e0ce61b501c5f7dc10fb9c SHA1: 4cf2240a0cebaacccb73390a1d3eba3f61a2df27 MD5sum: 67945f3d1e3c5dd87c3c3549ce0d29ea 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63272 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~nd14.04+1_all.deb Size: 1953050 SHA256: 0fa3cf6d49f9d175568cf4169598f7c38b56378af64613cf7689c9fd4889d1b3 SHA1: eeee5b416997d7e0160d501571f90db2fff92ee7 MD5sum: 959eb490c9b7870cd9f8cca1abecf7bf Description: Python3 bindings to various neuroimaging data formats NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. Package: python3-nilearn Source: nilearn Version: 0.2.5~dfsg.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2189 Depends: neurodebian-popularity-contest, python3-nibabel (>= 1.1.0), python3:any (>= 3.3.2-2~), python3-numpy (>= 1:1.6), python3-scipy (>= 0.9), python3-sklearn (>= 0.12.1) Recommends: python-matplotlib Homepage: https://nilearn.github.io Priority: extra Section: python Filename: pool/main/n/nilearn/python3-nilearn_0.2.5~dfsg.1-1~nd14.04+1_all.deb Size: 685238 SHA256: c44dbee085adfab56fcbfbf6c1f47665af6fd05aceb20aebfaa0d7465eea65eb SHA1: a6d967748befc9a05af6fbd3a9838afac2dc4fb0 MD5sum: e897612bf3bb824aaeb0289859073b7c 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 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~nd14.04+1_all.deb Size: 5190 SHA256: ec5797bfbbb38a2fd876dba8ae84283f67241da2c3e556e304c23a77f77845df SHA1: af0de88db9af2a743a7c6fac7099ad5eb0da11f7 MD5sum: cd2819a0290d34d8d1e4db6474e2d9fb 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~b2-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1115 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-jdcal Recommends: python3-pytest, python3-pil, python3-lxml Homepage: http://bitbucket.org/openpyxl/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python3-openpyxl_2.3.0~b2-1~nd14.04+1_all.deb Size: 190148 SHA256: fde43ee0bc57973ed26742d30d0cf5b1afb96c75dfc77e8b310ef1a639f97ba3 SHA1: 94781407d5fbe3630d81ae0779d03b080210ea3a MD5sum: 162cda742a0df264ecb31c12a2cc20e0 Description: module to read/write OpenXML xlsx/xlsm files Openpyxl is a pure Python module to read/write Excel 2007 (OpenXML) xlsx/xlsm files. Package: python3-pandas Source: pandas Version: 0.18.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24174 Depends: neurodebian-popularity-contest, python3-tz, python3:any (>= 3.3.2-2~), python3-dateutil, python3-numpy (>= 1:1.7~), python3-pandas-lib (>= 0.18.1-1~nd14.04+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_0.18.1-1~nd14.04+1_all.deb Size: 2524926 SHA256: 987b81ef49a5b74e7f8dbbf4b1d7f4994bfae5b333ed3ffcbf482f92f0675892 SHA1: e3b55eb16705c59357df7f869a87f3f65f3e7715 MD5sum: 0a5dc35ad1dccdbb250aacb3c33cb3b4 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 793 Depends: neurodebian-popularity-contest, python3-numpy, python3:any (>= 3.3.2-2~), python3-six 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~nd14.04+1_all.deb Size: 170658 SHA256: 2b1c4715c9dbd5a7dd3b7b24b6b78bc132b3d6c14fa377389fbfde0a175104ce SHA1: 5567b2335a55f4bdcea62906b76a2c9e30d7c402 MD5sum: ee4642ab47735474651deb6bcdec1b31 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 270 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~nd14.04+1_all.deb Size: 66732 SHA256: e4dcfe9e8309d67391f594dda9d7269daafc070875fe12b73024f8ed01380fbd SHA1: 094aa133c0e1a436c886640ebb23027d7156c562 MD5sum: 64d7b6a21473e75644ae095e83d922e9 Description: Advanced Python development support library (Python 3) The Codespeak py lib aims at supporting a decent Python development process addressing deployment, versioning and documentation perspectives. It includes: . * py.path: path abstractions over local and Subversion files * py.code: dynamic code compile and traceback printing support . This package provides the Python 3 modules. Package: python3-pydotplus Source: python-pydotplus Version: 2.0.2-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 130 Depends: neurodebian-popularity-contest, graphviz, python3-pyparsing (>= 2.0.1), python3:any (>= 3.3.2-2~) Suggests: python-pydotplus-doc Homepage: http://pydotplus.readthedocs.org/ Priority: optional Section: python Filename: pool/main/p/python-pydotplus/python3-pydotplus_2.0.2-1~nd14.04+1_all.deb Size: 20328 SHA256: f878b5238169859cdfca8f65be40ae18381ba955417d26ee2f9817d559ce616b SHA1: ab8151f6d6dc0ba917068caf83dc90953f3db4b7 MD5sum: a3b03af3de91f24f3694bf839962a4d5 Description: interface to Graphviz's Dot language - Python 3.x PyDotPlus is an improved version of the old pydot project that provides a Python Interface to Graphviz's Dot language. . Differences with pydot: * Compatible with PyParsing 2.0+. * Python 2.7 - Python 3 compatible. * Well documented. * CI Tested. . This package contains the Python 3.x module. Package: python3-pytest Source: pytest Version: 2.7.2-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 424 Depends: neurodebian-popularity-contest, python3-pkg-resources, python3-py (>= 1.4.29), python3:any (>= 3.3.2-2~), python3 Homepage: http://pytest.org/ Priority: optional Section: python Filename: pool/main/p/pytest/python3-pytest_2.7.2-2~nd14.04+1_all.deb Size: 102354 SHA256: 7d9fb489c914e606248a0294617b538a5193215ecf854ad29f08f28a65b97b68 SHA1: b3c50c08769a54606316afd8ec30a8c563c5a82a MD5sum: 26b41695fda6c1ec4c738f37d4570110 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: 0.3.4-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 82 Depends: neurodebian-popularity-contest, python3-pytest, python3-werkzeug (>= 0.10), python3:any (>= 3.3.2-2~) Homepage: https://bitbucket.org/pytest-dev/pytest-localserver/ Priority: optional Section: python Filename: pool/main/p/pytest-localserver/python3-pytest-localserver_0.3.4-2~nd14.04+1_all.deb Size: 19282 SHA256: f04709d1c2db63f580e469b1f85733085237f906a06df554696474abd2df6f35 SHA1: 956eca0fd74095f7bf25e43f4dad4004e1bdd606 MD5sum: bb5531e1156ea9242f6270db14158e07 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-pytest, python3-tornado Homepage: https://github.com/eugeniy/pytest-tornado Priority: optional Section: python Filename: pool/main/p/pytest-tornado/python3-pytest-tornado_0.4.4-1~nd14.04+1_all.deb Size: 5738 SHA256: ed4ef503575a9ddc8f97b3d71b5b187de160ed49dd9c0a191fc2816a3ea31ef3 SHA1: 4dd119a69a7f70acfa2924f149ac973a10c9b35a MD5sum: 085c9055f034291d1ce05eec7de6655e 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-seaborn Source: seaborn Version: 0.6.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 676 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-pandas, python3-matplotlib Recommends: python3-patsy Homepage: https://github.com/mwaskom/seaborn Priority: optional Section: python Filename: pool/main/s/seaborn/python3-seaborn_0.6.0-1~nd14.04+1_all.deb Size: 117808 SHA256: 6a23a5022fcb745801cc9224189e0789a03f8e182b949890012fd76c1cc92b72 SHA1: 16d74485aabf829249323d555da245fec8fec2cf MD5sum: f23b413abf7ffa1927df6b54c6da4a86 Description: statistical visualization library Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. . Some of the features that seaborn offers are . - Several built-in themes that improve on the default matplotlib aesthetics - Tools for choosing color palettes to make beautiful plots that reveal patterns in your data - Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data - Tools that fit and visualize linear regression models for different kinds of independent and dependent variables - A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate - High-level abstractions for structuring grids of plots that let you easily build complex visualizations . This is the Python 3 version of the package. Package: python3-setuptools-scm Source: setuptools-scm Version: 1.8.0-1~bpo8+1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 70 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~nd14.04+1_all.deb Size: 10198 SHA256: ff11bd9f5b7c1d7d2e2eed7f9219d3ded1d3d8d73d01e08637362b6c7e35a84f SHA1: 27fcdd8880354be354775a704569bcc0f80a5d17 MD5sum: e63ae3aa2d57a735ac152d987a263b4e 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 35 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~nd14.04+1_all.deb Size: 11092 SHA256: 79a6de23973f24df722d22b027db5db5b132861197a2ca85de60db65a4045cb9 SHA1: a8ae88a5d47c0725e8e7db7f9485915ab5594566 MD5sum: 54c44c9fe9707b2f852bda2e5fed9373 Description: Python 2 and 3 compatibility library (Python 3 interface) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six on the Python 3 module path. It is complemented by python-six. Package: python3-skimage Source: skimage Version: 0.10.1-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15115 Depends: neurodebian-popularity-contest, libfreeimage3, python3-numpy, python3-scipy (>= 0.10), python3-six (>= 1.3.0), python3-skimage-lib (>= 0.10.1-2~nd14.04+1), python3:any (>= 3.3.2-2~) Recommends: python3-imaging, python3-matplotlib (>= 1.0), python3-nose, python3-pil Suggests: python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python3-skimage_0.10.1-2~nd14.04+1_all.deb Size: 11919718 SHA256: 1d1b5c2fbf5fef5eb25d8d619bac87cfcb6362a1c97dbd10fae204f3acbeb3f4 SHA1: ca840ff46b54b7653bfbc18b965e09f2cfe9dd97 MD5sum: 42a0247cf46cf44a61b843f8c6c13542 Description: Python 3 modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 3 module. Package: python3-sklearn Source: scikit-learn Version: 0.17.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5281 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-sklearn-lib (>= 0.17.1-1~nd14.04+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~nd14.04+1_all.deb Size: 1223446 SHA256: ab6211ebbc737d34504299e8509826fd95f1a17c789a817803661f99ebd6dc6c SHA1: 383caec81131296f77620f551c2608789d245444 MD5sum: d0ef996d48f5c5eaa5ed5e971db194f1 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 116 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~nd14.04+1_all.deb Size: 20214 SHA256: cda44a9bd7fd2d600821890244e1efb781e16a47c3304851e94261a4b46a179e SHA1: f7befc9c26523273862deb8eae4d8d2667ec2cc0 MD5sum: 65c3b4e6a7390cfcd750393173ec8121 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 332 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~nd14.04+1_all.deb Size: 117200 SHA256: 16e1aaa6f2ea3e37894d69d01a1b30aa08b677a195343faf5f359d3c7adbea0f SHA1: e9a95bac6f5e7264d3035deb962fe65dfe02d031 MD5sum: abb571938ace7e00ea171c003e50a9ad 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-tqdm Source: tqdm Version: 4.4.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 164 Depends: neurodebian-popularity-contest, python3, python3:any (>= 3.3.2-2~) Homepage: https://github.com/tqdm/tqdm Priority: optional Section: python Filename: pool/main/t/tqdm/python3-tqdm_4.4.1-1~nd14.04+1_all.deb Size: 35610 SHA256: d069307c26a64719686d40835ad5bd3af381fe12033abcc7dc9352834f413c8e SHA1: 8b3170ee1f2931f35b5813c18533e3e3f1ed72d4 MD5sum: 8c0c54aaae44b01164cfe57386e0b2e9 Description: fast, extensible progress bar for Python 3 and CLI tool tqdm (read taqadum, تقدّم) means “progress” in Arabic. tqdm instantly makes your loops show a smart progress meter, just by wrapping any iterable with "tqdm(iterable)". . This package contains the Python 3 version of tqdm and its command-line tool. Package: python3-w3lib Source: python-w3lib Version: 1.11.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-six (>= 1.6.1) Homepage: http://pypi.python.org/pypi/w3lib Priority: optional Section: python Filename: pool/main/p/python-w3lib/python3-w3lib_1.11.0-1~nd14.04+1_all.deb Size: 14190 SHA256: 008f9ec2dd25186b977ab66c67873429efcf26bbc5ffaa02650ab0ed4c103a63 SHA1: 775d95cf272476a5deaa8eef7c1941e362c784a5 MD5sum: 0562a201189207e45c218e5a5465deb2 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~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 727 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~nd14.04+1_all.deb Size: 163216 SHA256: b685e053e4c5536d78e641e5b26441a9938a61038832b9d616215ddd4082b56b SHA1: 4787e0c8be6967ba696d4b011edd2562cbde87cb MD5sum: c02d0f4c7f7e88cc1acde12249702472 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~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 18499 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 9749152 SHA256: c83baf314478407e2f1b908e55554b5645b4a1d52f9ef5be18864a6ec74c454b SHA1: 993dd179e97b25766a9dd6b1d5884041448089a3 MD5sum: d92e890135a7c0c8eb5f4102b380b07c Description: analysis of brain imaging data sequences Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the platform-independent M-files. Package: spm8-data Source: spm8 Version: 8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 72987 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 45484386 SHA256: 182e2818ac165f6a04ef610a17226e4019e76b6403242ce5106dc8084088f456 SHA1: 4ede6932c3e3b32e11bd0e1360522b3cda6e69e2 MD5sum: dd2edf6746682da9d77ea73d1ee36418 Description: data files for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provide the data files shipped with the SPM distribution, such as various stereotaxic brain space templates and EEG channel setups. Package: spm8-doc Source: spm8 Version: 8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9242 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 8935290 SHA256: 5742ed7248b597e91212ca03e53541e520bbeccc2d59f865b278b7c94362661e SHA1: 32542240cefbb5d936731db01ffc3175348bc80e MD5sum: 511c71c1ed452c9f1c5dd547eaade5c8 Description: manual for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the SPM manual in PDF format. Package: spykeviewer Version: 0.4.4-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1975 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), 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~nd14.04+1_all.deb Size: 1291946 SHA256: 60a2857f96c3cd0e6f58d20a188950adf7574b8d5289daf8ba2d7dabe63f58dc SHA1: 69a899c9566acd0b4a3babe34e37a5173d046249 MD5sum: 777bf20e134ee592f75d0ef5c1d63a1f Description: graphical utility for analyzing electrophysiological data Spyke Viewer is a multi-platform GUI application for navigating, analyzing and visualizing electrophysiological datasets. Based on the Neo framework, it works with a wide variety of data formats. Spyke Viewer includes an integrated Python console and a plugin system for custom analyses and plots. Package: stabilitycalc Version: 0.1-1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy, python-nifti Recommends: python-dicom Homepage: https://github.com/bbfrederick/stabilitycalc Priority: extra Section: science Filename: pool/main/s/stabilitycalc/stabilitycalc_0.1-1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 22678 SHA256: ea800adb74820759f1c8041031b4b396c15b127a50f03a44c9e7e374649c351e SHA1: 0b16001f8fc76a1fadef374d9f61187cc11edfcf MD5sum: 3ece230b8a5225d2691618b7b10e78ba Description: evaluate fMRI scanner stability Command-line tools to calculate numerous fMRI scanner stability metrics, based on the FBIRN quality assurance test protocal. Any 4D volumetric timeseries image in NIfTI format is support input. Output is a rich HTML report. Python-Version: 2.7 Package: testkraut Version: 0.0.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 358 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, libjs-underscore, libjs-jquery, python-argparse Recommends: strace, python-scipy, python-colorama, python-apt Homepage: https://github.com/neurodebian/testkraut Priority: extra Section: python Filename: pool/main/t/testkraut/testkraut_0.0.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 85950 SHA256: 9a51b163a417b1a421415111ce4ddedea08a840e943a7a144f782b37944d8699 SHA1: 77a25da70008038d7ccaa99042ef1b0fe2a04229 MD5sum: f54d2b8e28fcffe650211599731dab19 Description: test and evaluate heterogeneous data processing pipelines This is a framework for software testing. That being said, testkraut tries to minimize the overlap with the scopes of unit testing, regression testing, and continuous integration testing. Instead, it aims to complement these kinds of testing, and is able to re-use them, or can be integrated with them. . In a nutshell testkraut helps to facilitate statistical analysis of test results. In particular, it focuses on two main scenarios: . * Comparing results of a single (test) implementation across different or changing computational environments (think: different operating systems, different hardware, or the same machine before an after a software upgrade). * Comparing results of different (test) implementations generating similar output from identical input (think: performance of various signal detection algorithms). . While such things can be done using other available tools as well, testkraut aims to provide a lightweight, yet comprehensive description of a test run. Such a description allows for decoupling test result generation and analysis – opening up the opportunity to “crowd-source” software testing efforts, and aggregate results beyond the scope of a single project, lab, company, or site. Python-Version: 2.7