Package: btrbk Version: 0.25.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 270 Depends: neurodebian-popularity-contest, perl, btrfs-progs (>= 3.18.2) | btrfs-tools (>= 3.18.2) Recommends: openssh-client, pv Homepage: http://digint.ch/btrbk/ Priority: optional Section: utils Filename: pool/main/b/btrbk/btrbk_0.25.0-1~nd16.04+1_all.deb Size: 75928 SHA256: ff50dc68379eac24bf35721c821f0eb37276c6248cea6a6b17cc9da9724eab75 SHA1: 190beeeb3bfdb9f42f17cb21d3a165403eea81e6 MD5sum: a81b2914b467169576d94b2e79c2aa1a Description: backup tool for btrfs subvolumes Backup tool for btrfs subvolumes, using a configuration file, allows creation of backups from multiple sources to multiple destinations, with ssh and flexible retention policy support (hourly, daily, weekly, monthly). Package: condor Version: 8.4.9~dfsg.1-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 29 Depends: neurodebian-popularity-contest, htcondor Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor_8.4.9~dfsg.1-2~nd16.04+1_all.deb Size: 16238 SHA256: 924a250e1bd7ec5712ba4e4d1b2a205ad154b72c7d38a5ecbd2fc0fccea19429 SHA1: ba5cbaaa726fc483256e9f5290e0149d81075b95 MD5sum: fea7b065cc3515413551b14dca22889d Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: condor-dbg Source: condor Version: 8.4.9~dfsg.1-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 29 Depends: neurodebian-popularity-contest, htcondor-dbg Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dbg_8.4.9~dfsg.1-2~nd16.04+1_all.deb Size: 16248 SHA256: 4bead7f7bbc458cb62193c47d509c5542ccd6a78587e7b31b1e8044f9dfb0fa6 SHA1: 6e4eafa61b7512a8c44323d03cc98552d85e02a4 MD5sum: b44e2b9c8a82520233d2300f279d7ef5 Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: condor-dev Source: condor Version: 8.4.9~dfsg.1-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 29 Depends: neurodebian-popularity-contest, htcondor-dev Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dev_8.4.9~dfsg.1-2~nd16.04+1_all.deb Size: 16250 SHA256: e3ba0b70729b2cf5a31e7520a8139429bee9ef72e77e8d7e5b8d91be52d14b40 SHA1: 1be8c0ca974ff69b8119b248d6c21fa48ce2dbec MD5sum: a6b3362321345b33de7f9c94f313ee55 Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: condor-doc Source: condor Version: 8.4.9~dfsg.1-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 29 Depends: neurodebian-popularity-contest, htcondor-doc Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-doc_8.4.9~dfsg.1-2~nd16.04+1_all.deb Size: 16242 SHA256: 1dd6eca6bc2317250e89af6c59c499f3b0bdd2caf7fde1edc3511dba3cc94046 SHA1: 6ab39f994bfa2d1e7f16f12a6f8d22e1b9900a9e MD5sum: 0628b2da9ac86ab37554bb2ea191b358 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: 0.5.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 84 Depends: neurodebian-popularity-contest, python-argcomplete, python-datalad (= 0.5.1-1~nd16.04+1), python:any Homepage: http://datalad.org Priority: optional Section: science Filename: pool/main/d/datalad/datalad_0.5.1-1~nd16.04+1_all.deb Size: 53916 SHA256: db1d1fd996f343a10a32be3fdaa85eaf33ae22043ca30309296e60542c2355e6 SHA1: 9f389382b2da958ad8eda281b5c25273fcb87dd1 MD5sum: b9e7a489160488600ff127df0a075f35 Description: data files crawler and data distribution DataLad is a data distribution providing access to a wide range of data resources already available online (initially aiming at neuroscience domain). Using git-annex as its backend for data logistics it provides following facilities . - crawling of web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc - command line interface for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get), as well as search within aggregated meta-data . This package provides the command line tool. Install without Recommends if you need only core functionality. Package: docker-compose Version: 1.5.2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 267 Depends: neurodebian-popularity-contest, python-docker (>= 1.3.0), python-dockerpty (>= 0.3.4), python-docopt, python-enum34, python-jsonschema, python-requests (>= 2.6.1), python-six (>= 1.3.0), python-texttable, python-websocket, python-yaml, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: docker.io (>= 1.6.0) Homepage: http://docs.docker.com/compose/ Priority: optional Section: admin Filename: pool/main/d/docker-compose/docker-compose_1.5.2-1~nd16.04+1_all.deb Size: 76930 SHA256: 316cea0fe5c368a1842c83370416a42eb29e3932fefa0d1465a86bd5dd6d8e49 SHA1: daabface947f49a630be5939805f0aa9706a4ce4 MD5sum: caa54173f115f40f815c49117eee989c Description: Punctual, lightweight development environments using Docker docker-compose is a service management software built on top of docker. Define your services and their relationships in a simple YAML file, and let compose handle the rest. Package: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8117 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd+1+nd16.04+1_all.deb Size: 7059424 SHA256: 3f090fdf3072e4e5b6ac524be1fff62f6d940c0e49f39e0843ba76d43e5b7d2b SHA1: 3f7081f142023ddee661a2980ff92df8a18bf7fe MD5sum: b9da13b2959435f2ec53d8cfda008794 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: 0.9.7-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1274 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), init-system-helpers (>= 1.18~), lsb-base (>= 2.0-7) Recommends: python, iptables, whois, python3-pyinotify, python3-systemd Suggests: mailx, system-log-daemon, monit Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.9.7-1~nd16.04+1_all.deb Size: 263620 SHA256: b03d502cda5aa71922761c1df64c50cd71e07b59c6aca7556718d7ec6bc1cd80 SHA1: 8d69c55f37571cb94facc41de4d5d7385c5c6729 MD5sum: 8e61ce74c577ebf7d0af2a971ca61d7c 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: fsl-melview Source: melview Version: 1.0.1+git9-ge661e05~dfsg.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 73 Depends: neurodebian-popularity-contest, python-matplotlib, python-nibabel, python-numpy, python-pkg-resources, python-scipy, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-pyface, python-traits, python-traitsui, python-enthoughtbase Suggests: fsl-core Homepage: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Melview Priority: optional Section: science Filename: pool/main/m/melview/fsl-melview_1.0.1+git9-ge661e05~dfsg.1-1~nd+1+nd16.04+1_all.deb Size: 13946 SHA256: 98fec451744471ae6b47ee84ac52821b45ffd63401f9f29586a9aa6be46a8702 SHA1: 0fb8488befc01e715be4378c0affa5f4d8bce34c MD5sum: 7b7b1bf6546af8b8bcfa435171ebfc89 Description: viewer for the output of FSL's MELODIC This viewer can be used to facilitate manual inspection and classification of ICA components computed by MELODIC. As such, it is suited to generate hand-curated labels for FSL's ICA-based denoising tool FIX. Python-Version: 2.7 Package: fsleyes Version: 0.10.1-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 119988 Depends: neurodebian-popularity-contest, python-fsl, python-props, python-wxgtk3.0, python-six, python-jinja2, python-scipy, python-matplotlib, python-numpy, python-opengl (>= 3.1~), python:any (<< 2.8), python:any (>= 2.7.5-5~) Priority: optional Section: science Filename: pool/main/f/fsleyes/fsleyes_0.10.1-2~nd16.04+1_all.deb Size: 13080378 SHA256: 9a0a8443b72288773127a42de703b41fd54e55da118c455ef74fdf35e99efe3c SHA1: 20c58a18264ddb07773e0a2e0161a304a1c2cca2 MD5sum: f8c4bc29a4acbedb874ec27eb258706b Description: FSL image viewer Feature-rich viewer for volumetric (medical) images. Package: fslview-doc Source: fslview Version: 4.0.1-6~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2930 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-6~nd+1+nd16.04+1_all.deb Size: 2227648 SHA256: d85f59cee9b040e9680c9817ee0e831d88dca517d2880029a7e9674aead08469 SHA1: 326b580203ce06c723591e460854d73845119293 MD5sum: 9b50fc95856220ca1a5876e4772eff2f Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gcalcli Version: 3.4.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1766 Depends: neurodebian-popularity-contest, python, python-dateutil, python-gflags, python-googleapi Recommends: gxmessage, python-parsedatetime, python-simplejson, python-vobject Homepage: https://github.com/insanum/gcalcli Priority: extra Section: utils Filename: pool/main/g/gcalcli/gcalcli_3.4.0-1~nd16.04+1_all.deb Size: 1669680 SHA256: 4a58773ebb4576609bd4161572178d94854997e74ff3145fb33e74ab97a987ff SHA1: e62edfdb9467c933ae868eb3630a0b9229c77b7e MD5sum: 2446d0cab9c54b99c87e2fee70e5cd85 Description: Google Calendar Command Line Interface gcalcli is a Python application that allows you to access your Google Calendar from a command line. It's easy to get your agenda, search for events, and quickly add new events. Additionally gcalcli can be used as a reminder service to execute any application you want. Package: git-hub Version: 0.10.3-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 Depends: neurodebian-popularity-contest, python, git (>= 1:1.7.7) Homepage: https://github.com/sociomantic/git-hub Priority: optional Section: vcs Filename: pool/main/g/git-hub/git-hub_0.10.3-1~nd16.04+1_all.deb Size: 34122 SHA256: f648dabac3d22d9594b73d0981f146638cd0fb011a163b5640dce71b1c2fddff SHA1: 92b5c34727ed27f277544db7dc276f31f1ddfd54 MD5sum: fd17abf7b6323d8b898c55b3a1af3c75 Description: Git command line interface to GitHub git hub is a simple command line interface to GitHub, enabling most useful GitHub tasks (like creating and listing pull request or issues) to be accessed directly through the Git command line. . Although probably the most outstanding feature (and the one that motivated the creation of this tool) is the pull rebase command, which is the rebasing version of the GitHub Merge (TM) button. This enables an easy workflow that doesn't involve thousands of merges which makes the repository history unreadable. . Another unique feature is the ability to transform an issue into a pull request by attaching commits to it (this is something offered by the GitHub API but not by the web interface). Package: heudiconv Version: 0.1+git94-g85a2afb-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 91 Depends: neurodebian-popularity-contest, python, python-dcmstack, python-dicom, python-nibabel, python-numpy, python-nipype Recommends: mricron, dcm2niix Homepage: https://github.com/nipy/heudiconv Priority: optional Section: science Filename: pool/main/h/heudiconv/heudiconv_0.1+git94-g85a2afb-1~nd16.04+1_all.deb Size: 19482 SHA256: 9b80115f5bfc054e8bca6757b9aa6bc87037a5b09925657342d1ea8990d40795 SHA1: ef3b88e3339c47789c9809cad2818b2c72219304 MD5sum: 5a292432de6b1d070ce7b11ecf5e3004 Description: DICOM converter with support for structure heuristics This is a flexible dicom converter for organizing brain imaging data into structured directory layouts. It allows for flexible directory layouts and naming schemes through customizable heuristics implementations. It only converts the necessary dicoms, not everything in a directory. It tracks the provenance of the conversion from dicom to nifti in w3c prov format. Package: htcondor-doc Source: condor Version: 8.4.9~dfsg.1-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 6108 Depends: neurodebian-popularity-contest Breaks: condor-doc (<< 8.0.5~) Replaces: condor-doc (<< 8.0.5~) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: doc Filename: pool/main/c/condor/htcondor-doc_8.4.9~dfsg.1-2~nd16.04+1_all.deb Size: 1065712 SHA256: e19399294a01d36957e0481ff5a7548b187099d290ec5f968f110a127abb8230 SHA1: 94b6487128920243d3dc94a1943da0a3a3aabe2e MD5sum: 2bca3f07fbb7d1bfcf4f285102560861 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: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 39 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd+1+nd16.04+1_all.deb Size: 9092 SHA256: 3768f288d0fb7988e227131b3e41a31c00436992b06d2b18e8113e0db08835c0 SHA1: 65c2eeeeb2ec075ec99aecec567a613b9ed2343c MD5sum: 4ff78d0c6fe0960cbf435512258152c0 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: ismrmrd-schema Source: ismrmrd Version: 1.3.2-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25 Depends: neurodebian-popularity-contest Homepage: http://ismrmrd.github.io/ Priority: optional Section: science Filename: pool/main/i/ismrmrd/ismrmrd-schema_1.3.2-1~nd+1+nd16.04+1_all.deb Size: 5088 SHA256: 5336188e50c28a623d14ce0305ec77a4efef641cdef4c53c5e52157daf080def SHA1: 3ec074921fdde47d6c380e07226bacd940068f41 MD5sum: 349af0ebe53b5ed02810a3874940a35c 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: libismrmrd-doc Source: ismrmrd Version: 1.3.2-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1961 Depends: neurodebian-popularity-contest Homepage: http://ismrmrd.github.io/ Priority: optional Section: doc Filename: pool/main/i/ismrmrd/libismrmrd-doc_1.3.2-1~nd+1+nd16.04+1_all.deb Size: 157932 SHA256: 2f24afbb9b3936557b75c9f69827bad728cd057e3a34aac31466f47d948cb216 SHA1: accfe2ca334b0b97c931424db47de6e435fae5f7 MD5sum: f81b7692ba25e05413fe88a248cc60a4 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: mridefacer Version: 0.2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 647 Depends: neurodebian-popularity-contest, num-utils, fsl-5.0-core | fsl-core Homepage: https://github.com/hanke/mridefacer Priority: optional Section: science Filename: pool/main/m/mridefacer/mridefacer_0.2-1~nd16.04+1_all.deb Size: 637352 SHA256: 77d8497006f219516a5d682f4dcbc432cafef37779349576a55adc80daea7509 SHA1: f5e58b7c8b273455ab745b500c367c0747692c56 MD5sum: 11a7610ad8e894687feae0d8b5a6c618 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: netselect-apt Source: netselect Version: 0.3.ds1-25~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 38 Depends: neurodebian-popularity-contest, wget, netselect (>= 0.3.ds1-17) Recommends: curl Suggests: dpkg-dev Enhances: apt Homepage: http://github.com/apenwarr/netselect Priority: optional Section: net Filename: pool/main/n/netselect/netselect-apt_0.3.ds1-25~nd+1+nd16.04+1_all.deb Size: 16814 SHA256: 22e5cc29f87c08ceb667084f2af3810aaefd1b5fc0828b2f0b0e29cb2a4dc46e SHA1: 90467c75aaba4e464a72ac39884410acc60cfc49 MD5sum: 60f499833563a70a8dc586c029092380 Description: speed tester for choosing a fast Debian mirror This package provides a utility that can choose the best Debian mirror by downloading the full mirror list and using netselect to find the fastest/closest one. . It can output a sources.list(5) file that can be used with package management tools such as apt or aptitude. Package: neurodebian Version: 0.37.5~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 91 Depends: python, wget, neurodebian-archive-keyring, debconf (>= 0.5) | debconf-2.0 Recommends: netselect Suggests: neurodebian-desktop, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian_0.37.5~nd16.04+1_all.deb Size: 34692 SHA256: a782eeb34e4e6c636517778c4ff979c12e9a230fbcedb23c122e7002655b6451 SHA1: 9315d5405e9604f9b7d20f83240b5d9b4097c9c1 MD5sum: 918d375cf39b03b83676ed89631ea9dd Description: neuroscience-oriented distribution - repository configuration The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package enables the NeuroDebian repository on top of a standard Debian or Ubuntu system. Package: neurodebian-archive-keyring Source: neurodebian Version: 0.37.5~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23 Depends: gnupg2 | gnupg, dirmngr Breaks: neurodebian-keyring (<< 0.34~) Replaces: neurodebian-keyring (<< 0.34~) Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-archive-keyring_0.37.5~nd16.04+1_all.deb Size: 10378 SHA256: 43d94888c0bbb446b9f2a2d796441b71067126e5152efa50ae52bf6f0d70b829 SHA1: d8c7b1b10beaf2c54d021faddc31b0db56812875 MD5sum: e0c8f57ee231061ca4f5cbe445371af8 Description: neuroscience-oriented distribution - GnuPG archive keys The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-desktop Source: neurodebian Version: 0.37.5~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 189 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, adwaita-icon-theme | gnome-icon-theme, neurodebian-popularity-contest Recommends: reportbug-ng Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.37.5~nd16.04+1_all.deb Size: 116402 SHA256: 338f1476f9040ead6f5c01c581e3a3b8ed847592f4fdd441d358f7a9188bcc79 SHA1: 3f92954e7fcb8dd6435b6a99da191290c314b689 MD5sum: c732398e4e41daae5a5a16883ef5c9d0 Description: neuroscience-oriented distribution - desktop integration The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package provides NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring the most popular neuroscience tools, which will be automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.37.5~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 109 Depends: devscripts, neurodebian-archive-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring, apt-utils, cowbuilder Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.37.5~nd16.04+1_all.deb Size: 32768 SHA256: b607a59ddd213529a6b7074dcb368280f9f0f04a19e42dab4cdb5cbc919f0d58 SHA1: a04beb5d390f0d731f625291fec9831e24fe4931 MD5sum: 7de749bf47072b733c9696f9e5110c3e Description: neuroscience-oriented distribution - development tools The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package provides sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.37.5~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 27 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.37.5~nd16.04+1_all.deb Size: 12380 SHA256: 9206f3a429df0b39a73d49bea5cb2979721e3a00ab38ebe53e0cc08ffe5a0fef SHA1: da69b2b40f9dccd606151011bdf9680ac1fa9a8e MD5sum: c7ff04f1195fe0fe3d15bc072dce04f8 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: nuitka Version: 0.5.25+ds-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3209 Depends: neurodebian-popularity-contest, gcc (>= 5.0) | g++ (>= 4.4) | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python:any (>= 2.7.5-5~) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6), strace, chrpath Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.5.25+ds-1~nd16.04+1_all.deb Size: 656590 SHA256: 2ea6f2c1189058ecb138f1669f4bc9ed39ec29fcb4f38c42d52e2c32f338743b SHA1: c0ff19a0d363298d1307a3f36966f4546875b503 MD5sum: 8cafbd514d3422fc8f8eda9c8fbb5479 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~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 191 Depends: neurodebian-popularity-contest, autopkgtest, libxml-xpath-perl, madison-lite, pristine-tar Priority: extra Section: devel Filename: pool/main/o/openstack-pkg-tools/openstack-pkg-tools_52~nd16.04+1_all.deb Size: 52268 SHA256: ce9a5f309792b3a87d6f8c00259b993da4eae392c3cd59c2d63acb6640964cc5 SHA1: 9b2978d36e9fe7878444813081916d17fc5178d1 MD5sum: 421e6c2753576c0ad60b3b768f92362c 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: prov-tools Source: python-prov Version: 1.4.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 Depends: neurodebian-popularity-contest, python3:any (>= 3.3~), python3-prov (= 1.4.0-1~nd16.04+1) Homepage: https://github.com/trungdong/prov Priority: optional Section: python Filename: pool/main/p/python-prov/prov-tools_1.4.0-1~nd16.04+1_all.deb Size: 6446 SHA256: bdbee846a60a80a04461221ce243e1d122e6487696020fee2bc8a9eb4daa20bf SHA1: d4ca2cf1ddc0c1064c702378baec913c7a0be031 MD5sum: 6d7dbfa568d26547c324f3bcff31e0d9 Description: tools for prov A library for W3C Provenance Data Model supporting PROV-JSON and PROV- XML import/export. . Features: - An implementation of the W3C PROV Data Model in Python. - In-memory classes for PROV assertions, which can then be output as PROV-N. - Serialization and deserializtion support: PROV-JSON and PROV-XML. - Exporting PROV documents into various graphical formats (e.g. PDF, PNG, SVG). . This package provides the command-line tools for the prov library. Package: psychopy Version: 1.83.04.dfsg-2~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15088 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-pyglet | python-pygame, python-opengl, python-numpy, python-scipy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk3.0, python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-opencv, python-imaging, python-serial, python-pyo, python-psutil, python-gevent, python-msgpack, python-yaml, python-xlib, python-pandas, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid, libavbin0 Conflicts: libavbin0 (= 7-4+b1) Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.83.04.dfsg-2~nd+1+nd16.04+1_all.deb Size: 6133828 SHA256: 7bc5b24a1849eaf939d573157788037c0f1f6a6a54bc84c45b025780d09b207f SHA1: 60bb27e236f204f1f43c10b98e20170e50ac4f60 MD5sum: 269a0abc067dfa3a1d79234abf54295b Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features . - IDE GUI for coding in a powerful scripting language (Python) - Builder GUI for rapid development of stimulation sequences - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.14.20170103+git6-g605ff5c.dfsg1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 253898 Depends: neurodebian-popularity-contest Recommends: alsa-utils Suggests: gnuplot Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.14.20170103+git6-g605ff5c.dfsg1-1~nd16.04+1_all.deb Size: 24285916 SHA256: c4dce0dcafdb0049aa7a5529382e4c93aca11d894b5dc0f82f656ecabac6ff05 SHA1: b304fd795258e4c5a4091d89dd110b12e8dd38b6 MD5sum: 626d651482a1819a821ed573fab0b98b Description: toolbox for vision research -- arch/interpreter independent part Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains architecture independent files (such as .m scripts) Package: pypy-hypothesis Source: python-hypothesis Version: 3.6.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 386 Depends: neurodebian-popularity-contest, pypy-enum34, pypy Suggests: python-hypothesis-doc Homepage: https://github.com/DRMacIver/hypothesis Priority: optional Section: python Filename: pool/main/p/python-hypothesis/pypy-hypothesis_3.6.0-1~nd16.04+1_all.deb Size: 70086 SHA256: 107d15e7462f80624f5eec39a65f3db0ac8f1b3d6f9dc18d0f4bab69441dffd6 SHA1: 5fad1e5f0f66c35bf6865eee9b89d8f1e2e32003 MD5sum: 7b841c7aa1ddc645501e9b63427b89ea Description: advanced Quickcheck style testing library for PyPy Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It's based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow. . Hypothesis is both extremely practical and also advances the state of the art of unit testing by some way. It's easy to use, stable, and extremely powerful. If you're not using Hypothesis to test your project then you're missing out. . This package contains the PyPy module. Package: pypy-pkg-resources Source: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 412 Depends: neurodebian-popularity-contest, pypy Suggests: pypy-setuptools Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: python Filename: pool/main/p/python-setuptools/pypy-pkg-resources_20.10.1-1.1~bpo8+1~nd16.04+1_all.deb Size: 111986 SHA256: a46960af40580a044791897da065b2134da92f6d08d8ec79d1f33deeb625ecc0 SHA1: dac82101358b768239ad58198ddb5026edd9d48a MD5sum: a0c65b88155a87713bb05df6d5d6d571 Description: Package Discovery and Resource Access using pkg_resources The pkg_resources module provides an API for Python libraries to access their resource files, and for extensible applications and frameworks to automatically discover plugins. It also provides runtime support for using C extensions that are inside zipfile-format eggs, support for merging packages that have separately-distributed modules or subpackages, and APIs for managing Python's current "working set" of active packages. Package: pypy-py Source: python-py Version: 1.4.31-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 320 Depends: neurodebian-popularity-contest, pypy, pypy-pkg-resources Suggests: subversion, pypy-pytest Homepage: https://bitbucket.org/pytest-dev/py Priority: optional Section: python Filename: pool/main/p/python-py/pypy-py_1.4.31-2~nd16.04+1_all.deb Size: 82320 SHA256: 8f9a86a093a652dd90c1f5c9c5e0a5ea8fbfc982fab835a965029fc36e85f123 SHA1: 58774cbf15de9d824528961f26c28db4c7e3b004 MD5sum: 09b9d8ba0ae2193fc758c6b3aed02782 Description: Advanced Python development support library (PyPy) The Codespeak py lib aims at supporting a decent Python development process addressing deployment, versioning and documentation perspectives. It includes: . * py.path: path abstractions over local and Subversion files * py.code: dynamic code compile and traceback printing support . This package provides the PyPy 2 modules. Package: pypy-pytest Source: pytest Version: 3.0.4-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 600 Depends: neurodebian-popularity-contest, pypy-pkg-resources, pypy-py (>= 1.4.29), pypy Homepage: http://pytest.org/ Priority: optional Section: python Filename: pool/main/p/pytest/pypy-pytest_3.0.4-1~nd16.04+1_all.deb Size: 136092 SHA256: d33dfeeb58239ad6c8688e0dc8a20c61f85d52f44ec2c09cb50aaa37e7d4ddfa SHA1: aa54b5c6064c48a84fe956b64531abb42494f9be MD5sum: 6a035f11b30459b792627e5d5b6cd712 Description: Simple, powerful testing in PyPy This testing tool has for objective to allow the developers to limit the boilerplate code around the tests, promoting the use of built-in mechanisms such as the `assert` keyword. . This package provides the PyPy module and the py.test-pypy script. Package: pypy-setuptools Source: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 431 Depends: neurodebian-popularity-contest, pypy-pkg-resources (= 20.10.1-1.1~bpo8+1~nd16.04+1), pypy Suggests: python-setuptools-doc Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: python Filename: pool/main/p/python-setuptools/pypy-setuptools_20.10.1-1.1~bpo8+1~nd16.04+1_all.deb Size: 121934 SHA256: 1bb6cba9bde6ce0c1d41ecaa37e8ed9ef9f752fc1c3b129e8ba537b8c951deea SHA1: cfdd22d42984b1473f6dd9b5626cb0d36e93b036 MD5sum: aa12a9636bd95f3d3331dc4481c41e7c Description: PyPy Distutils Enhancements Extensions to the python-distutils for large or complex distributions. Package: python-argcomplete Version: 1.0.0-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 111 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Priority: optional Section: python Filename: pool/main/p/python-argcomplete/python-argcomplete_1.0.0-1~nd+1+nd16.04+1_all.deb Size: 24608 SHA256: 156b7dbfd8a99d398fe740c822ba51762781af630c9ce000cdebb5622fe4cd2a SHA1: 937a26dd82a75e4b14bb613ae8f6dd187ee9e10b MD5sum: 3f721e38ee6f5930587547ae87875163 Description: bash tab completion for argparse Argcomplete provides easy, extensible command line tab completion of arguments for your Python script. . It makes two assumptions: . * You're using bash as your shell * You're using argparse to manage your command line arguments/options . Argcomplete is particularly useful if your program has lots of options or subparsers, and if your program can dynamically suggest completions for your argument/option values (for example, if the user is browsing resources over the network). . This package provides the module for Python 2.x. Package: python-boto3 Version: 1.2.2-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 720 Depends: neurodebian-popularity-contest, python-botocore, python-concurrent.futures, python-jmespath, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-requests, python-six Homepage: https://github.com/boto/boto3 Priority: optional Section: python Filename: pool/main/p/python-boto3/python-boto3_1.2.2-2~nd16.04+1_all.deb Size: 58388 SHA256: 7b073c2e5bedfe1fb511389140c06e6bc7d763b8506c840f4bfeff826468834f SHA1: d557ed14ac3dcfc1d3e3185437b8a28c852a6442 MD5sum: 07ba3320ddb3a4001a9efaf1405a73f5 Description: Python interface to Amazon's Web Services - Python 2.x Boto is the Amazon Web Services interface for Python. It allows developers to write software that makes use of Amazon services like S3 and EC2. Boto provides an easy to use, object-oriented API as well as low-level direct service access. Package: python-datalad Source: datalad Version: 0.5.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3325 Depends: neurodebian-popularity-contest, git-annex (>= 6.20160808~) | git-annex-standalone (>= 6.20160808~), patool, python-appdirs, python-git (>= 2.1~), python-github, python-humanize, python-iso8601, python-keyrings.alt | python-keyring (<= 8), python-secretstorage | python-keyring (<< 9.2), python-keyring, python-mock, python-msgpack, python-pyld, python-requests, python-tqdm, python-simplejson, python-six (>= 1.8.0), python-boto, python-jsmin, python-pygithub, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-html5lib, python-httpretty, python-nose, python-numpy, python-requests-ftp, python-scrapy, python-vcr, python-yaml Suggests: python-bs4 Provides: python2.7-datalad Homepage: http://datalad.org Priority: optional Section: python Filename: pool/main/d/datalad/python-datalad_0.5.1-1~nd16.04+1_all.deb Size: 652006 SHA256: 713cdaa005e2e0206342f874f1dddc3b7c3ed2319c8bdfd2abc04f8d3d5a32c2 SHA1: e96f4e22858106d85f73e4174db91b6357e08dbc MD5sum: 42dc5f040d8b99baae32b3ea5a2078e1 Description: data files crawler and data distribution (Python 2) DataLad is a data distribution providing access to a wide range of data resources already available online (initially aiming at neuroscience domain). Using git-annex as its backend for data logistics it provides following facilities . - crawling of web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc - command line interface for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get), as well as search within aggregated meta-data . This package installs the module for Python 2, and Recommends install all dependencies necessary for crawling, publishing, and testing. If you need base functionality, install without Recommends. Package: python-dcmstack Source: dcmstack Version: 0.6.2+git33-gb43919a.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 505 Depends: neurodebian-popularity-contest, python-dicom (>= 0.9.7~), python-nibabel (>= 2.0~), python-numpy, python:any (<< 2.8), python:any (>= 2.7.5-5~), libjs-sphinxdoc (>= 1.0) Provides: python2.7-dcmstack Homepage: https://github.com/moloney/dcmstack Priority: optional Section: python Filename: pool/main/d/dcmstack/python-dcmstack_0.6.2+git33-gb43919a.1-1~nd+1+nd16.04+1_all.deb Size: 77560 SHA256: fb442364f1761b5203c259ef22654f0f4bb883a8eac5dba645ca88b0ec327125 SHA1: 809e0e779921d515682e8f3b543df9321b89650c MD5sum: 307b067807c7a48930ebcc46f0121e7a 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-docker Version: 1.7.2-1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 150 Depends: neurodebian-popularity-contest, python-requests, python-six (>= 1.4.0), python-websocket, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/dotcloud/docker-py/ Priority: optional Section: python Filename: pool/main/p/python-docker/python-docker_1.7.2-1~bpo8+1~nd16.04+1_all.deb Size: 27114 SHA256: cf672dc82599d06380f63ec2b29efa7c1439ed54bcebdf7f810eed51edd5f197 SHA1: 2e97a63835aeb38fae5504be8830b9618a62525d MD5sum: e615eb319e74907ebd074ad677599dbe Description: Python wrapper to access docker.io's control socket This package contains oodles of routines that aid in controling docker.io over it's socket control, the same way the docker.io client controls the daemon. . This package provides Python 2 module bindings only. Package: python-dockerpty Source: dockerpty Version: 0.4.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-docker (>= 0.7.1) Homepage: https://github.com/d11wtq/dockerpty Priority: optional Section: python Filename: pool/main/d/dockerpty/python-dockerpty_0.4.1-1~nd16.04+1_all.deb Size: 10952 SHA256: 3cba67f7413ae8865a000aba13ac5515a9978f1775d39020dd68f13010549fbb SHA1: 9eac24e87250203de569c904eeec7b2ea1e659b6 MD5sum: 97d1d3d43c73659e98560e6612ea3d6d Description: Pseudo-tty handler for docker Python client (Python 2.x) Provides the functionality needed to operate the pseudo-tty (PTY) allocated to a docker container, using the Python client. . This package provides Python 2.x version of dockerpty. Package: python-duecredit Source: duecredit Version: 0.5.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 234 Depends: neurodebian-popularity-contest, python-citeproc, python-requests, python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/duecredit/duecredit Priority: optional Section: python Filename: pool/main/d/duecredit/python-duecredit_0.5.0-1~nd16.04+1_all.deb Size: 49836 SHA256: 8f6b727151ec14685e5ec685c741f6a80c6e392914e444d2c8c625be85b00468 SHA1: 7f3ad805ef2aa096fd0348549186326edd60215b MD5sum: af07180f2cd5a642d3a14d34cc7fc61f Description: Publications (and donations) tracer - Python 2.X duecredit is being conceived to address the problem of inadequate citation of scientific software and methods, and limited visibility of donation requests for open-source software. . It provides a simple framework (at the moment for Python only) to embed publication or other references in the original code so they are automatically collected and reported to the user at the necessary level of reference detail, i.e. only references for actually used functionality will be presented back if software provides multiple citeable implementations. . To get a sense of what duecredit is about, simply run or your analysis script with `-m duecredit`, e.g. . python -m duecredit examples/example_scipy.py Python-Egg-Name: duecredit Package: python-fsl Source: fslpy Version: 0.11.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 467 Depends: neurodebian-popularity-contest, python-lxml, python-nibabel, python-indexed-gzip, python-matplotlib, python-numpy, python-six (>= 1.10.0), python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-wxgtk3.0 Conflicts: fsl-melview (<= 1.0.1+git9-ge661e05~dfsg.1-1) Provides: python2.7-fsl Priority: optional Section: python Filename: pool/main/f/fslpy/python-fsl_0.11.0-1~nd16.04+1_all.deb Size: 96802 SHA256: 9d0dc46c855c2ab828feca334bd12560c9092622e4f5491779a4c54323bde32e SHA1: f14a921c9f71a5b8f61ca3608c34d34c9be6f48c MD5sum: 4b1169619f1f77565002c1f642c8344a Description: FSL Python library Support library for FSL. . This package provides the Python 2 module. Package: python-funcsigs Version: 0.4-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63 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~nd16.04+1_all.deb Size: 12746 SHA256: aa1594275088c19766b1ff28be4990783ecc4aad4b170a9a15fdab129d82765e SHA1: 7e6d04894fa13b3be7c6498bcc832877611b799b MD5sum: 2c202fea13d1545151b20b6b94dbd917 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~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 121 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~nd16.04+1_all.deb Size: 23504 SHA256: 0faa043d8d03c5cec5db82b53c9aa38070bf5468f05d153f2052a4a25698ca67 SHA1: 964ef9081f948d5efd2bffb9ad20f032b7ea69ba MD5sum: 49d0850dc20522e3d5f6fa56aa05c6ae 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-git Version: 2.1.1-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1625 Depends: neurodebian-popularity-contest, git (>= 1:1.7) | git-core (>= 1:1.5.3.7), python-gitdb (>= 2), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-smmap, python-git-doc Homepage: https://github.com/gitpython-developers/GitPython Priority: optional Section: python Filename: pool/main/p/python-git/python-git_2.1.1-2~nd16.04+1_all.deb Size: 299866 SHA256: d6cc40fd7663cbe8a5f681a99737f0e82ed18dad488877f56701cca660a06680 SHA1: d0d292154ec8bf3d367e62b754d140a021292f5e MD5sum: d44d689edbef7083dd30a4365f2fe370 Description: Python library to interact with Git repositories - Python 2.7 python-git provides object model access to a Git repository, so Python can be used to manipulate it. Repository objects can be opened or created, which can then be traversed to find parent commit(s), trees, blobs, etc. . This package provides the Python 2.7 module. Python-Version: 2.7 Package: python-git-doc Source: python-git Version: 2.1.1-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 978 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: https://github.com/gitpython-developers/GitPython Priority: optional Section: doc Filename: pool/main/p/python-git/python-git-doc_2.1.1-2~nd16.04+1_all.deb Size: 125954 SHA256: ed1c133dc03d3e88a748dc88dc7b8b92d1fd5f5f805c04d886e217c1aeff0d0b SHA1: 35bba5d60fcbc8fbe05fc9318ed3246b3f55f806 MD5sum: 05c925c228112a10aeb0123b1c3aae61 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-github Source: pygithub Version: 1.26.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 632 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Conflicts: python-pygithub Replaces: python-pygithub Provides: python-pygithub Homepage: https://pypi.python.org/pypi/PyGithub Priority: optional Section: python Filename: pool/main/p/pygithub/python-github_1.26.0-1~nd16.04+1_all.deb Size: 44830 SHA256: 066f7113c45087342009432eda00640d0a7fe268e6ae8d8df31df62f44adc80f SHA1: f70b5c50fa136dd5e6513f3c37712b8c33aa70eb MD5sum: 1d9d0c0285b697913307a360b531db43 Description: Access to full Github API v3 from Python2 This is a Python2 library to access the Github API v3. With it, you can manage Github resources (repositories, user profiles, organizations, etc.) from Python scripts. . It covers almost the full API and all methods are tested against the real Github site. Package: python-hypothesis Version: 3.6.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 386 Depends: neurodebian-popularity-contest, python-enum34, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-hypothesis-doc Homepage: https://github.com/DRMacIver/hypothesis Priority: optional Section: python Filename: pool/main/p/python-hypothesis/python-hypothesis_3.6.0-1~nd16.04+1_all.deb Size: 69974 SHA256: 531b352f7c6a7f5a3ffb31fe21909e9ef7ba1cd6192882bcff852fc8b464fcd3 SHA1: 0b67184aefc58fb6675817dce569897b9d9c02a0 MD5sum: 90247384b9834c164567214150c30898 Description: advanced Quickcheck style testing library for Python 2 Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It's based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow. . Hypothesis is both extremely practical and also advances the state of the art of unit testing by some way. It's easy to use, stable, and extremely powerful. If you're not using Hypothesis to test your project then you're missing out. . This package contains the Python 2 module. Package: python-hypothesis-doc Source: python-hypothesis Version: 3.6.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1115 Depends: neurodebian-popularity-contest Homepage: https://github.com/DRMacIver/hypothesis Priority: extra Section: doc Filename: pool/main/p/python-hypothesis/python-hypothesis-doc_3.6.0-1~nd16.04+1_all.deb Size: 137930 SHA256: 3a633af68ee56811cfe94370a3f17b492d450593d76af8ea56c571b10185349b SHA1: 4819283511ddcc4509d79a970d37202d027a103b MD5sum: 26ca85398902b3a7785166aaf5aeeef0 Description: advanced Quickcheck style testing library (documentation) Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It's based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow. . Hypothesis is both extremely practical and also advances the state of the art of unit testing by some way. It's easy to use, stable, and extremely powerful. If you're not using Hypothesis to test your project then you're missing out. . This package contains the documentation for Hypothesis. Package: python-joblib Source: joblib Version: 0.10.3+git55-g660fe5d-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 488 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Recommends: python-numpy, python-pytest, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.10.3+git55-g660fe5d-1~nd16.04+1_all.deb Size: 116086 SHA256: f2afce84d554e1814e05e4df962dae90175aae9a02cac1816b078392afc9bdd7 SHA1: 964611d58a727d1177c0f427db00e6faea0a77e4 MD5sum: 52e4c90d4a25874dd9a1756dc1f0a67d Description: tools to provide lightweight pipelining in Python Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers: . - transparent disk-caching of the output values and lazy re-evaluation (memoize pattern) - easy simple parallel computing - logging and tracing of the execution . Joblib is optimized to be fast and robust in particular on large, long-running functions and has specific optimizations for numpy arrays. . This package contains the Python 2 version. Package: python-jsmin Version: 2.2.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 68 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/tikitu/jsmin Priority: optional Section: python Filename: pool/main/p/python-jsmin/python-jsmin_2.2.1-1~nd16.04+1_all.deb Size: 21544 SHA256: 0246adb0afaf4dbbe0ce01017668a5d702a13ffcd2b07f05cdfb3613ceef5fa2 SHA1: 9e01dde8a81ad77de521112cb737bc8962975904 MD5sum: a855dba74b7e95f5a8f60b6e1ebaf677 Description: JavaScript minifier written in Python - Python 2.x Python-jsmin is a JavaScript minifier, it is written in pure Python and actively maintained. . This package provides the Python 2.x module. Package: python-mne Version: 0.13.1+dfsg-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9796 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-sklearn, python-matplotlib, python-joblib (>= 0.4.5), xvfb, xauth, libgl1-mesa-dri, help2man, libjs-jquery, libjs-jquery-ui Recommends: python-nose, mayavi2 Suggests: python-dap, python-pycuda, ipython Provides: python2.7-mne Homepage: http://martinos.org/mne Priority: optional Section: python Filename: pool/main/p/python-mne/python-mne_0.13.1+dfsg-1~nd16.04+1_all.deb Size: 4506736 SHA256: 6a4586ccfa9cc3da1b92d02d335216fce957d0143ac8e6a5562436e94d7d304b SHA1: dafcfca4ccf723f376190679e8eee382e191b180 MD5sum: 08e3076cbe4a05e0d993e144ecf5f2e5 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-mvpa2 Source: pymvpa2 Version: 2.6.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8560 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy, python-mvpa2-lib (>= 2.6.1-1~nd16.04+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, python-mock Suggests: fslview, fsl, python-mvpa2-doc, python-nose, python-openopt, python-rpy2 Provides: python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.6.1-1~nd16.04+1_all.deb Size: 5101034 SHA256: 18927a3db579dcc461e507705b152cc183d57e595a4b405560a784a1baa28378 SHA1: a7b1abb6a0a907f8e8510ec1a72a6ef210fc0892 MD5sum: 205f79a00fd5c7898f4001e9f65ad675 Description: multivariate pattern analysis with Python v. 2 PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. . This is a package of PyMVPA v.2. Previously released stable version is provided by the python-mvpa package. Python-Version: 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.6.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36253 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2, python-mvpa2-tutorialdata, ipython-notebook Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.6.1-1~nd16.04+1_all.deb Size: 4648838 SHA256: 70e1af36e47816135b87bb22080a204ff1399f6557b4ee0fdcbe2ebc64f7b2d0 SHA1: e1794ad10ee26b1dc1544e9a7875818016b6917c MD5sum: 324b15487e159732a0a8d623356e4eee 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~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 115 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-nibabel, python-ply Recommends: python-nose, fsl-mni152-templates Suggests: python-testkraut Homepage: http://neurosynth.org Priority: extra Section: python Filename: pool/main/n/neurosynth/python-neurosynth_0.3-1~nd+1+nd16.04+1_all.deb Size: 28850 SHA256: 35e58bca96796988f7c3097c71ba0e078b6063215ce782dfb2461f30da939f7a SHA1: 37df2d9b9330ef7948d4bb86173b30a9eccaa0ca MD5sum: 6fbc209e0a5e48cd60f07cdbfc7aba56 Description: large-scale synthesis of functional neuroimaging data NeuroSynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. This Python module at the moment provides functionality for processing the database of collected terms and spatial coordinates to generate associated spatial statistical maps. Package: python-nibabel Source: nibabel Version: 2.1.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 64211 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy Recommends: python-dicom, python-fuse, python-mock Suggests: python-nibabel-doc Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_2.1.0-1~nd16.04+1_all.deb Size: 2161982 SHA256: e1c42772ae8c2c60f721d9002f9be42cc734ffdd2a0af7982ff72286cb0ccef3 SHA1: b3b4f34c4995b562befdf801cede8ca5e39cd4ac MD5sum: d7ec21b39d1014be8930de0cbdd8ae9f Description: Python bindings to various neuroimaging data formats NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package also provides a commandline tools: . - dicomfs - FUSE filesystem on top of a directory with DICOMs - nib-ls - 'ls' for neuroimaging files - parrec2nii - for conversion of PAR/REC to NIfTI images Package: python-nibabel-doc Source: nibabel Version: 2.1.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20346 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-mathjax Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_2.1.0-1~nd16.04+1_all.deb Size: 2686026 SHA256: 6f0f3b598a2f8d45f9ef7266c1644d5fc094b7f6f3222bbc6810d6ceb90b6209 SHA1: bd2001cf891ed8b707ea486ef3f1ecb1756a8e04 MD5sum: 3943c2901476d4803c7916457420dd2d 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~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2422 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.6), python-nibabel (>= 1.1.0), python:any (<< 2.8), python:any (>= 2.7.5-5~), python-scipy (>= 0.9), python-sklearn (>= 0.12.1) Recommends: python-matplotlib Provides: python2.7-nilearn Homepage: https://nilearn.github.io Priority: extra Section: python Filename: pool/main/n/nilearn/python-nilearn_0.2.5~dfsg.1-1~nd16.04+1_all.deb Size: 731264 SHA256: 556fa551b0a378975a60af21259067980996ec5baef83b02c1c10d8ad461f800 SHA1: e6db7f5f06afd7c6986c6b9d7756cfb63ac3fae0 MD5sum: 604a86c38f7b85a5c13f8f103b4a8d63 Description: fast and easy statistical learning on neuroimaging data (Python 2) This Python module leverages the scikit-learn toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. . This package provides the Python 2 version. Python-Version: 2.7 Package: python-nipy Source: nipy Version: 0.4.0+git26-gf8d3149-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3277 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.2), python (>= 2.7), python (<< 2.8), python-scipy, python-nibabel, python-nipy-lib (>= 0.4.0+git26-gf8d3149-2~nd16.04+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-gf8d3149-2~nd16.04+1_all.deb Size: 738188 SHA256: 5055e9ccf0463f9edc78aec5246bd68ef7bf8395b02f31df1726997a3df0a320 SHA1: 91ed2b61cb376ac857927a25816ebe01e92b37a6 MD5sum: b9c693770043bde500570a9e4ea5f280 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It provides functionality for - General linear model (GLM) statistical analysis - Combined slice time correction and motion correction - General image registration routines with flexible cost functions, optimizers and re-sampling schemes - Image segmentation - Basic visualization of results in 2D and 3D - Basic time series diagnostics - Clustering and activation pattern analysis across subjects - Reproducibility analysis for group studies Python-Version: 2.7 Package: python-nipy-doc Source: nipy Version: 0.4.0+git26-gf8d3149-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10710 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.4.0+git26-gf8d3149-2~nd16.04+1_all.deb Size: 2649996 SHA256: 7996ac0e44000d3b24df81a48a9daf9e69197da02d5086d15501641f3b6c6965 SHA1: cb1976b8dd55b0763b9decb0cd7f2f3237b6a9ef MD5sum: d5e5ce94783fd8a0cda52318f12019bc Description: documentation and examples for NiPy This package contains NiPy documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation Package: python-nipype Source: nipype Version: 0.12.1+git4-gbc3a0b5-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9364 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib, python-funcsigs, python-future, python-prov, python-psutil Recommends: ipython, python-nose, graphviz, python-xvfbwrapper, mayavi2, python-mock Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat, mne-python, elastix, ants Provides: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.12.1+git4-gbc3a0b5-1~nd16.04+1_all.deb Size: 1534790 SHA256: d6d24f37e41d190609c5ab1d72c262ae412c6a41ec3e85160a0fec6e1705709d SHA1: 7648c0b0acf90c6e78db46d33f8690733d1d809d MD5sum: 2ae984321bf33797a772dc1c962b9d5d Description: Neuroimaging data analysis pipelines in Python Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). Package: python-nipype-doc Source: nipype Version: 0.12.1+git4-gbc3a0b5-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 30429 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.12.1+git4-gbc3a0b5-1~nd16.04+1_all.deb Size: 12830732 SHA256: 3829a209775209162000cb503489a73e85eb6c8a9170db3dde0e6e933856da9d SHA1: cbeb8ee31ae76ee8ca5b1784c4e150a336ae35ae MD5sum: fa1d56284d197454a4567cfdb52bbcb4 Description: Neuroimaging data analysis pipelines in Python -- documentation Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). . This package contains Nipype examples and documentation in various formats. Package: python-nitime Source: nitime Version: 0.7-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9377 Depends: neurodebian-popularity-contest, python-matplotlib, python-numpy, python-scipy, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.7-1~nd16.04+1_all.deb Size: 2553146 SHA256: 54569482245e3bf5eae0a48f6b2d893b640f450693a70911a96a1aaf5a7811ee SHA1: b0ca6ab172e04d8206d9b3e5df35bf3b41cf7f21 MD5sum: 3892f505c6932c69b3de3e752f5cfa1e Description: timeseries analysis for neuroscience data (nitime) Nitime is a Python module for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code. Package: python-nitime-doc Source: nitime Version: 0.7-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7826 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-nitime Homepage: http://nipy.org/nitime Priority: extra Section: doc Filename: pool/main/n/nitime/python-nitime-doc_0.7-1~nd16.04+1_all.deb Size: 5699092 SHA256: cbe0e629d8af9f87d1c565e4590c2a2b888e576a92b2999bc51cd9521f98e1a5 SHA1: 29be357c1931a18aee0abed914a6d0592432c9fd MD5sum: 347cf7b23ed7dea21929ac201045e264 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-opengl Source: pyopengl Version: 3.1.0+dfsg-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 5289 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-ctypes, libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, freeglut3 Suggests: python-tk, python-numpy, libgle3 Homepage: http://pyopengl.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pyopengl/python-opengl_3.1.0+dfsg-1~nd16.04+1_all.deb Size: 502206 SHA256: c6f1c589672c7853ad0315795a69f847ff136d7f62bf0d193fd5a15700fa613c SHA1: fdb313a3f7f8856deb626df2e2a8138a19616ca2 MD5sum: 6d3d2268519ba7f557ba1fa1584dd9a0 Description: Python bindings to OpenGL (Python 2) PyOpenGL is a cross-platform open source Python binding to the standard OpenGL API providing 2D and 3D graphic drawing. PyOpenGL supports the GL, GLU, GLE, and GLUT libraries. The library can be used with the Tkinter, wxPython, FxPy, and Win32GUI windowing libraries (or almost any Python windowing library which can provide an OpenGL context). . This is the Python 2 version of the package. Package: python-openpyxl Source: openpyxl Version: 2.3.0-3~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1325 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-jdcal, python-lxml (>= 3.3.4) | python-et-xmlfile Recommends: python-pytest, python-pil, python-imaging Homepage: http://bitbucket.org/openpyxl/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_2.3.0-3~nd16.04+1_all.deb Size: 199536 SHA256: 11635479c85c62cfe1190e1672fd06e44a9ce11126b29058b70cbb577a318fda SHA1: 3fdf7da3c8cfed9c62a980a227d27f2420d6807e MD5sum: dd7806981040a7e113d034693acd997b 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: 0.19.2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25229 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.7~), python-pandas-lib (>= 0.19.2-1~nd16.04+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_0.19.2-1~nd16.04+1_all.deb Size: 2601224 SHA256: 4978eb7a1162131513496cc527a75abccfaa6a78134cd36953851332d0f5d1d4 SHA1: 3dddfafe1a3ed0e51a58b99db89de40c947f1770 MD5sum: 1251275238a0c73645433537108e17e9 Description: data structures for "relational" or "labeled" data pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 2 version. Package: python-pandas-doc Source: pandas Version: 0.19.2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 58841 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: doc Filename: pool/main/p/pandas/python-pandas-doc_0.19.2-1~nd16.04+1_all.deb Size: 10193446 SHA256: 2e91ca3328337bba23af7932f10392add466bf3e8af30ce3caee88ce04f92531 SHA1: 2e966fb7962fc6a07395fd99b66df827a5af6fb7 MD5sum: 663270f93696187938afede7b3ce6294 Description: documentation and examples for pandas This package contains documentation and example scripts for python-pandas. Package: python-patsy Source: patsy Version: 0.4.1+git34-ga5b54c2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 779 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-six, python-numpy Recommends: python-pandas, python-openpyxl Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python-patsy_0.4.1+git34-ga5b54c2-1~nd16.04+1_all.deb Size: 169084 SHA256: 67873a5e82cf0ed24e7af34d09203a7babb95f840b596f567764d80ceeee4566 SHA1: 97fde9a8651212cc78294fa626feec237a1b7bd0 MD5sum: d73aee7388dd52e26dfab42afe9643b2 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 2 version. Package: python-patsy-doc Source: patsy Version: 0.4.1+git34-ga5b54c2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1408 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-patsy Homepage: http://github.com/pydata/patsy Priority: optional Section: doc Filename: pool/main/p/patsy/python-patsy-doc_0.4.1+git34-ga5b54c2-1~nd16.04+1_all.deb Size: 362406 SHA256: b16c06da21efe26063c34e3f330be8b18cbe0bca209f57812cb44fad621ef785 SHA1: 6b4cdff1e078b2ea9be829c4d92ee1d48d88e3a6 MD5sum: 1c7ecc644ad0a486f3ba5add4590b825 Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pkg-resources Source: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 441 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-setuptools Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: python Filename: pool/main/p/python-setuptools/python-pkg-resources_20.10.1-1.1~bpo8+1~nd16.04+1_all.deb Size: 141288 SHA256: b037bfbbddbe376e51050e582f7c81917a5466891d6d3629c27fb1217741fed6 SHA1: 2d6583f201e404099c1126acbd8bf172fe84a8d6 MD5sum: 23873c2cfca07961c552409c3a596e31 Description: Package Discovery and Resource Access using pkg_resources The pkg_resources module provides an API for Python libraries to access their resource files, and for extensible applications and frameworks to automatically discover plugins. It also provides runtime support for using C extensions that are inside zipfile-format eggs, support for merging packages that have separately-distributed modules or subpackages, and APIs for managing Python's current "working set" of active packages. Package: python-props Source: props Version: 0.10.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 651 Depends: neurodebian-popularity-contest, python-wxgtk3.0, python-matplotlib, python-numpy, python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Provides: python2.7-props Priority: optional Section: python Filename: pool/main/p/props/python-props_0.10.1-1~nd16.04+1_all.deb Size: 117640 SHA256: cb58c06763b1e763bfbdd1b04229f28b1e95757eb762ba26fd58c5b32805592e SHA1: 635cd8386e5cc0ab283ae447bedfe816ee30e0ad MD5sum: 0496927a902b3f920142edcd38033201 Description: Python descriptor framework Event programming framework with the ability for automatic CLI generation, and automatic GUI generation (with wxPython). . This package provides the Python 2 module. Package: python-prov Version: 1.4.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1235 Depends: neurodebian-popularity-contest, python-dateutil, python-lxml, python-networkx, python-six (>= 1.9.0), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-prov-doc, python-pydotplus Homepage: https://github.com/trungdong/prov Priority: optional Section: python Filename: pool/main/p/python-prov/python-prov_1.4.0-1~nd16.04+1_all.deb Size: 72412 SHA256: 3e144e0a0760856a48b24d284fd4d6108a499f63cd99d83bcc09ea17613c93d8 SHA1: 6340eb932f0fdfa50fabb48723db8f775d781cbf MD5sum: 8d6d9dfb535595c3d219c87e24bc4bd3 Description: W3C Provenance Data Model (Python 2) A library for W3C Provenance Data Model supporting PROV-JSON and PROV- XML import/export. . Features: - An implementation of the W3C PROV Data Model in Python. - In-memory classes for PROV assertions, which can then be output as PROV-N. - Serialization and deserializtion support: PROV-JSON and PROV-XML. - Exporting PROV documents into various graphical formats (e.g. PDF, PNG, SVG). . This package provides the prov library for Python 2. Package: python-prov-doc Source: python-prov Version: 1.4.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 816 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: https://github.com/trungdong/prov Priority: optional Section: doc Filename: pool/main/p/python-prov/python-prov-doc_1.4.0-1~nd16.04+1_all.deb Size: 69138 SHA256: 046640dd0ce708f60592fe7049baaac44b3f4e9a5de273733d29e6359cceccfb SHA1: 44692dd8ccb838fda977b247aeb22965be531459 MD5sum: 0b63a3dbece70949384995f2644cf462 Description: documentation for prov A library for W3C Provenance Data Model supporting PROV-JSON and PROV- XML import/export. . Features: - An implementation of the W3C PROV Data Model in Python. - In-memory classes for PROV assertions, which can then be output as PROV-N. - Serialization and deserializtion support: PROV-JSON and PROV-XML. - Exporting PROV documents into various graphical formats (e.g. PDF, PNG, SVG). . This package provides the documentation for the prov library. Package: python-py Version: 1.4.31-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 320 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-pkg-resources Suggests: subversion, python-pytest, python-pytest-xdist Homepage: https://bitbucket.org/pytest-dev/py Priority: optional Section: python Filename: pool/main/p/python-py/python-py_1.4.31-2~nd16.04+1_all.deb Size: 82250 SHA256: dd06fc78eb9eb64409c7b6de85b869c609e88300b4cea3c263bea0f7d5653d27 SHA1: b885dde7ca83a94a38105e7deadb39b61d208aba MD5sum: 508eae290a26634907683e8e0f11750d 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~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 104 Depends: neurodebian-popularity-contest, graphviz, python-pyparsing (>= 2.0.1), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-pydotplus-doc Homepage: http://pydotplus.readthedocs.org/ Priority: optional Section: python Filename: pool/main/p/python-pydotplus/python-pydotplus_2.0.2-1~nd16.04+1_all.deb Size: 20240 SHA256: 2f2cbd16ebdf9303b3f63c0dabc1d0cb2eae3d15bb106c9cf7909b260c9560b4 SHA1: fd723ed2ffdf2a961a70954a6dbdf77c3cdea09c MD5sum: 7d94224fbe37b88a456bc0034ce397b2 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~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 525 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Homepage: http://pydotplus.readthedocs.org/ Priority: optional Section: doc Filename: pool/main/p/python-pydotplus/python-pydotplus-doc_2.0.2-1~nd16.04+1_all.deb Size: 46688 SHA256: 084d0a9e4882149dbbef0d18600ebc885f8cc725aa2aa72bca43a8d3ce414f76 SHA1: 1eed1e9afc09ba215f373b5a58ca248d95693529 MD5sum: 0b6ff044fc8b77c4f2fb1e7b09f0bb6f 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~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 822 Depends: neurodebian-popularity-contest, python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0+git12-g365f8e3-2~nd+1+nd16.04+1_all.deb Size: 819350 SHA256: 2e955547503f670039815376e4ac8860679cb376009788bc07c133ad3cf0dc02 SHA1: 349b67ae265dd0cbb576d6f2cc991cb1b9582a46 MD5sum: d7b4b656eb6f9dacf1c258cfa9df35bb 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~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 312 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~nd16.04+1_all.deb Size: 67408 SHA256: c1224a2179507867f56f20c63434a263faa02bc206dc14e3aed3e1f7acd7076e SHA1: 2d1f19d174498015d80ca43a7a1a2b805a6532e7 MD5sum: 44c786b37a5b2e55cd781dd78c953f93 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-pytest Source: pytest Version: 3.0.4-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 605 Depends: neurodebian-popularity-contest, python-pkg-resources, python-py (>= 1.4.29), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-mock (>= 1.0.1) Homepage: http://pytest.org/ Priority: optional Section: python Filename: pool/main/p/pytest/python-pytest_3.0.4-1~nd16.04+1_all.deb Size: 136446 SHA256: 4eac544d5793e2f59c640678d7c65261955ced7802e86863cfbc4a3c106ad781 SHA1: b3805eb51656f116e39fd66752ec1f39b7e13e4d MD5sum: 29c2a057ae9e61da6a2f763e592ae548 Description: Simple, powerful testing in Python This testing tool has for objective to allow the developers to limit the boilerplate code around the tests, promoting the use of built-in mechanisms such as the `assert` keyword. . This package provides the Python 2 modules and the py.test script. Package: python-pytest-doc Source: pytest Version: 3.0.4-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3939 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Recommends: python-pytest | python3-pytest Homepage: http://pytest.org/ Priority: optional Section: doc Filename: pool/main/p/pytest/python-pytest-doc_3.0.4-1~nd16.04+1_all.deb Size: 620420 SHA256: 5203020975510c490a97695c394b3ab272331e6a93e780b9e3202fd52e666caa SHA1: f6ff3f77f5ea42a8644ee30fcf1a5138dafe496d MD5sum: 3b598d5ab9a0dd8716ca8a91c870dab3 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-runner Source: pytest-runner Version: 2.7.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 31 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/pytest-dev/pytest-runner Priority: optional Section: python Filename: pool/main/p/pytest-runner/python-pytest-runner_2.7.1-1~nd16.04+1_all.deb Size: 6122 SHA256: e32cbbf3bfb304bbc9c6a901571198457ff968fe74650c9d97a25b9b969dc6a7 SHA1: 60c152dafcd3f7e359e2e038625702c9a6d370c4 MD5sum: 1e5ed45580cfbf12d31130fb71e84b67 Description: Invoke py.test as distutils command with dependency resolution Setup scripts can use pytest-runner to add setup.py test support for pytest runner. . This package contains the Python 2 module. Package: python-requests Source: requests Version: 2.8.1-1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 243 Depends: neurodebian-popularity-contest, python-urllib3 (>= 1.12), python:any (<< 2.8), python:any (>= 2.7.5-5~), ca-certificates, python-chardet Suggests: python-ndg-httpsclient, python-openssl, python-pyasn1 Breaks: httpie (<< 0.9.2) Homepage: http://python-requests.org Priority: optional Section: python Filename: pool/main/r/requests/python-requests_2.8.1-1~bpo8+1~nd16.04+1_all.deb Size: 67986 SHA256: 306c896e980cfc8919b8c16b0405d9532318d831452b92c9836ed0251abb15ed SHA1: 2cd529883b66acdaebe41ca8c93a2542f695eaf8 MD5sum: e44028d143ddd17295f7789105974770 Description: elegant and simple HTTP library for Python2, built for human beings Requests allow you to send HTTP/1.1 requests. You can add headers, form data, multipart files, and parameters with simple Python dictionaries, and access the response data in the same way. It's powered by httplib and urllib3, but it does all the hard work and crazy hacks for you. . Features . - International Domains and URLs - Keep-Alive & Connection Pooling - Sessions with Cookie Persistence - Browser-style SSL Verification - Basic/Digest Authentication - Elegant Key/Value Cookies - Automatic Decompression - Unicode Response Bodies - Multipart File Uploads - Connection Timeouts Package: python-requests-whl Source: requests Version: 2.8.1-1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 353 Depends: neurodebian-popularity-contest, ca-certificates, python-urllib3-whl Homepage: http://python-requests.org Priority: optional Section: python Filename: pool/main/r/requests/python-requests-whl_2.8.1-1~bpo8+1~nd16.04+1_all.deb Size: 319056 SHA256: 7ea89f759a4f88b54031523c58c568fcd12e099fb8142b6cf131d4a355f7675b SHA1: 76999e7ed545950bd1fe8232547c9e0994d1c83f MD5sum: 4398bb2a447ca10983b49d0e5a077ec3 Description: elegant and simple HTTP library for Python, built for human beings Requests allow you to send HTTP/1.1 requests. You can add headers, form data, multipart files, and parameters with simple Python dictionaries, and access the response data in the same way. It's powered by httplib and urllib3, but it does all the hard work and crazy hacks for you. . Features . - International Domains and URLs - Keep-Alive & Connection Pooling - Sessions with Cookie Persistence - Browser-style SSL Verification - Basic/Digest Authentication - Elegant Key/Value Cookies - Automatic Decompression - Unicode Response Bodies - Multipart File Uploads - Connection Timeouts . This package provides the universal wheel. Package: python-scikits-learn Source: scikit-learn Version: 0.18.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 79 Depends: neurodebian-popularity-contest, python-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.18.1-1~nd16.04+1_all.deb Size: 70642 SHA256: 8c5fe804659ee8d226716764621cdc23c0356e3f47acba70f059d9c55a58455b SHA1: cc5ab968328a4b90b836ff72901f5df87b3ffb23 MD5sum: 668c24b8dc0e62f1835ff2bfcdecca14 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-seaborn Source: seaborn Version: 0.7.1-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 766 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-pandas, python-matplotlib Recommends: python-statsmodels, python-patsy Homepage: https://github.com/mwaskom/seaborn Priority: optional Section: python Filename: pool/main/s/seaborn/python-seaborn_0.7.1-2~nd16.04+1_all.deb Size: 128300 SHA256: 86a2a9f8c05f612d9894e9eb0a762e46af1ff746b26c0d50e6a6bcad4c0ebce4 SHA1: c3fa00cdc23d36db72e059d45d99d4149f1b8313 MD5sum: af65ebef49f9b4c1aca6f40e72a3838f Description: statistical visualization library Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. . Some of the features that seaborn offers are . - Several built-in themes that improve on the default matplotlib aesthetics - Tools for choosing color palettes to make beautiful plots that reveal patterns in your data - Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data - Tools that fit and visualize linear regression models for different kinds of independent and dependent variables - A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate - High-level abstractions for structuring grids of plots that let you easily build complex visualizations . This is the Python 2 version of the package. Package: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 520 Depends: neurodebian-popularity-contest, python-pkg-resources (= 20.10.1-1.1~bpo8+1~nd16.04+1), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-setuptools-doc Provides: python-distribute Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: python Filename: pool/main/p/python-setuptools/python-setuptools_20.10.1-1.1~bpo8+1~nd16.04+1_all.deb Size: 202950 SHA256: 9db9a54136a49d35839778199b490639438e43271c08ce0743fb0760dfaf7469 SHA1: 9e93db246bb5d01d7acf9290dd8e12346b59fdfc MD5sum: 1e7b9797bfce6b0083a6c2d1615c71bf Description: Python Distutils Enhancements Extensions to the python-distutils for large or complex distributions. Package: python-setuptools-doc Source: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1129 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: doc Filename: pool/main/p/python-setuptools/python-setuptools-doc_20.10.1-1.1~bpo8+1~nd16.04+1_all.deb Size: 199256 SHA256: 6ebf63ca9734f4049aa848f2794d7d27a20a5a2d82899a13497124cb8fede833 SHA1: fa350942229b8c6cee34ea9a4675a0776a9c3b4e MD5sum: c413017bb2dd35fde176e997d4343f33 Description: Python Distutils Enhancements (documentation) Extensions to the Python distutils for large or complex distributions. The package contains the documentation in html format. Package: python-six Source: six Version: 1.9.0-3~bpo8+1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Multi-Arch: foreign Homepage: http://pythonhosted.org/six/ Priority: optional Section: python Filename: pool/main/s/six/python-six_1.9.0-3~bpo8+1~nd+1+nd16.04+1_all.deb Size: 11102 SHA256: e53e9566f3e3305d5ec1a8b3ed6a12f61a5f0d3279112666903e47ce68c4498c SHA1: e2e8f56e31d6f67994297cf8ef87bd10077f81b0 MD5sum: 19367b5b0032f4ee89cd3f543ed27e5d Description: Python 2 and 3 compatibility library (Python 2 interface) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six on the Python 2 module path. It is complemented by python3-six. Package: python-six-whl Source: six Version: 1.9.0-3~bpo8+1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 21 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://pythonhosted.org/six/ Priority: optional Section: python Filename: pool/main/s/six/python-six-whl_1.9.0-3~bpo8+1~nd+1+nd16.04+1_all.deb Size: 13318 SHA256: c8c2f26a64c9b1993f231546b6d5d3f4d0f8a4e96f0653c53060c6dde6b36d59 SHA1: 3abcec44fa903592ff069a138e1f57a4ddc7a93a MD5sum: ce48e44e76d54ab53124a9eb02b58690 Description: Python 2 and 3 compatibility library (universal wheel) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six as a universal wheel. Package: python-sklearn Source: scikit-learn Version: 0.18.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6609 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-sklearn-lib (>= 0.18.1-1~nd16.04+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_0.18.1-1~nd16.04+1_all.deb Size: 1391834 SHA256: fa253e81475d08caf5d3f81a39f341f8f6f608747553f4e63b5694172593be77 SHA1: 89b4a53bb27cd06708c72f279a3a0dd496519b65 MD5sum: 93ffde56abae9122367c7060598890a8 Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Package: python-sklearn-doc Source: scikit-learn Version: 0.18.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29461 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.18.1-1~nd16.04+1_all.deb Size: 4757876 SHA256: 9c46fd81c1e02e462db9e1613156dd59dc78d7948dc29c0c9a10e1ba49b9f3e7 SHA1: 4247f8eac4fb113c5cef59cae08fb45eb6d4f46f MD5sum: 44db0d8eaed0e39c514917728b7373f5 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-smmap Version: 2.0.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 93 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Suggests: python-nose Provides: python2.7-smmap Homepage: https://github.com/Byron/smmap Priority: extra Section: python Filename: pool/main/p/python-smmap/python-smmap_2.0.1-1~nd16.04+1_all.deb Size: 20090 SHA256: 699c22f53c18a5c057f8fc5a8a95b9b5f45377d61c0aafd9d7aa37e9f0144851 SHA1: 42f1ffcb2dea935c2a89c044c1eee529ec64ff09 MD5sum: 7431762d66785a834a247a47df04e086 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-statsmodels Source: statsmodels Version: 0.8.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15922 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-statsmodels-lib (>= 0.8.0-1~nd16.04+1), python-patsy Recommends: python-pandas, python-matplotlib, python-nose, python-joblib, python-cvxopt Suggests: python-statsmodels-doc Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Provides: python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.8.0-1~nd16.04+1_all.deb Size: 3340100 SHA256: 133d42cbfbb1c1651f9b7913a39660a6c9cd0af10a18ab87c5398980e3d79174 SHA1: 3a7d1e327ff574cfd2906f74a18f82c24bf76b8f MD5sum: 8b330e6d8a3854e09fe5a61bed4f3a3a Description: Python module for the estimation of statistical models statsmodels Python module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Package: python-statsmodels-doc Source: statsmodels Version: 0.8.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 55368 Depends: neurodebian-popularity-contest, libjs-jquery Recommends: libjs-mathjax Suggests: python-statsmodels Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.8.0-1~nd16.04+1_all.deb Size: 9810956 SHA256: 7663604107ba5269e57dc3c40759d0587466e771c27f977d463edee8261de208 SHA1: ce0165cf5425d34d981e95497725bca17a4c9a01 MD5sum: 9edbe8604cf20441a8907018b2dd535e Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-surfer Source: pysurfer Version: 0.7-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 197 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-nibabel, python-numpy, python-scipy, python-pil | python-imaging, mayavi2, python-argparse Recommends: mencoder Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.7-1~nd16.04+1_all.deb Size: 42524 SHA256: 167ae695f1ade8edcad4c6bc0056578813ffa33199fa3b6264f03db670b9b7ee SHA1: ae13b9b963c87bf893e68854855cdfc42fe958a5 MD5sum: ccfb36d4a0a69845263bf2dfc3565700 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: 4.11.2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 179 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/tqdm/tqdm Priority: optional Section: python Filename: pool/main/t/tqdm/python-tqdm_4.11.2-1~nd16.04+1_all.deb Size: 49804 SHA256: ab5898263779a7cccbfa96e88d16e55225e7dc003c7b9f3f8b71709b00d371ff SHA1: 82cee46a654514928793380200cd0a810d72ae11 MD5sum: d46b0fb89d9409486b5b0c0772fbd164 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-urllib3 Version: 1.12-1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 254 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-six Recommends: ca-certificates, python-ndg-httpsclient, python-openssl, python-pyasn1 Suggests: python-ntlm Homepage: http://urllib3.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-urllib3/python-urllib3_1.12-1~bpo8+1~nd16.04+1_all.deb Size: 65338 SHA256: d15178f468f84a7b89caaf81b71c96897d5ae8242e664001531739624267e905 SHA1: 5d061603c38174218587941f515e58bd0bf49425 MD5sum: 5fb1f4092ef4fb6654af2107cd9b7db1 Description: HTTP library with thread-safe connection pooling for Python urllib3 supports features left out of urllib and urllib2 libraries. . - Re-use the same socket connection for multiple requests (HTTPConnectionPool and HTTPSConnectionPool) (with optional client-side certificate verification). - File posting (encode_multipart_formdata). - Built-in redirection and retries (optional). - Supports gzip and deflate decoding. - Thread-safe and sanity-safe. - Small and easy to understand codebase perfect for extending and building upon. Package: python-urllib3-whl Source: python-urllib3 Version: 1.12-1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 102 Depends: neurodebian-popularity-contest, python-six-whl Recommends: ca-certificates Homepage: http://urllib3.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-urllib3/python-urllib3-whl_1.12-1~bpo8+1~nd16.04+1_all.deb Size: 92846 SHA256: 9719b066fa4881282495c799c2b8471ea1353e3c75437987272164e2b133d64a SHA1: 0caa3703bdc22530c3cb78bf74dc532f30e9cb7f MD5sum: fdcf56c01fcf742292840cc0c544b432 Description: HTTP library with thread-safe connection pooling urllib3 supports features left out of urllib and urllib2 libraries. . - Re-use the same socket connection for multiple requests (HTTPConnectionPool and HTTPSConnectionPool) (with optional client-side certificate verification). - File posting (encode_multipart_formdata). - Built-in redirection and retries (optional). - Supports gzip and deflate decoding. - Thread-safe and sanity-safe. - Small and easy to understand codebase perfect for extending and building upon. . This package contains the universal wheel. Package: python-vcr Source: vcr.py Version: 1.7.3-1.0.1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 157 Depends: neurodebian-popularity-contest, python-contextlib2, python-mock, python-six (>= 1.5), python-wrapt, python-yaml, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/kevin1024/vcrpy/ Priority: optional Section: python Filename: pool/main/v/vcr.py/python-vcr_1.7.3-1.0.1~nd+1+nd16.04+1_all.deb Size: 43580 SHA256: d13dfc1adc96024f35330d4c72825e3f09b9767177330c804cc3575079e11498 SHA1: 38af2662eef11399c694c53bdca1e4159e830bc8 MD5sum: 212bc7388c199867c3f9d80be6d829c2 Description: record and replay HTML interactions (Python library) vcr.py records all interactions that take place through the HTML libraries it supports and writes them to flat files, called cassettes (YAML format by default). These cassettes could be replayed then for fast, deterministic and accurate HTML testing. . vcr.py supports the following Python HTTP libraries: - urllib2 (stdlib) - urllib3 - http.client (Python3 stdlib) - Requests - httplib2 - Boto (interface to Amazon Web Services) - Tornado's HTTP client . This package contains the modules for Python 2. Package: python3-argcomplete Source: python-argcomplete Version: 1.0.0-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 96 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Priority: optional Section: python Filename: pool/main/p/python-argcomplete/python3-argcomplete_1.0.0-1~nd+1+nd16.04+1_all.deb Size: 21058 SHA256: c93bdaf1b23b65d220a8c08b7043df874b281287d7145de0427dd1c2234f151d SHA1: 16ffed2622ee3e42555797e3f02591e8097e9419 MD5sum: 9fb059ae75b85aec11d96ed5c6c686ed Description: bash tab completion for argparse (for Python 3) Argcomplete provides easy, extensible command line tab completion of arguments for your Python script. . It makes two assumptions: . * You're using bash as your shell * You're using argparse to manage your command line arguments/options . Argcomplete is particularly useful if your program has lots of options or subparsers, and if your program can dynamically suggest completions for your argument/option values (for example, if the user is browsing resources over the network). . This package provides the module for Python 3.x. Package: python3-boto3 Source: python-boto3 Version: 1.2.2-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 716 Depends: neurodebian-popularity-contest, python3-botocore, python3-jmespath, python3:any (>= 3.3.2-2~), python3-requests, python3-six Homepage: https://github.com/boto/boto3 Priority: optional Section: python Filename: pool/main/p/python-boto3/python3-boto3_1.2.2-2~nd16.04+1_all.deb Size: 58080 SHA256: 413d967eb1e92a29223c47517db82b3c216e2ccee47c0df766800113ea6bb482 SHA1: d1c429e14196e1d8fdac8a053e714cd6431a8cd3 MD5sum: 8fc1b3425641b60db58bf864bea336c9 Description: Python interface to Amazon's Web Services - Python 3.x Boto is the Amazon Web Services interface for Python. It allows developers to write software that makes use of Amazon services like S3 and EC2. Boto provides an easy to use, object-oriented API as well as low-level direct service access. Package: python3-docker Source: python-docker Version: 1.7.2-1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 150 Depends: neurodebian-popularity-contest, python3-requests, python3-six (>= 1.4.0), python3-websocket, python3:any (>= 3.3.2-2~) Homepage: https://github.com/dotcloud/docker-py/ Priority: optional Section: python Filename: pool/main/p/python-docker/python3-docker_1.7.2-1~bpo8+1~nd16.04+1_all.deb Size: 27204 SHA256: 60788007aa403bdae6fb6b392cb8d61f891d4c1a7681fbb961b95bc4eb0e7845 SHA1: 9f340bb539e19d740fa08ddfdd0946cf4359552f MD5sum: b4bf94c8feaffb305d6c34999f113c27 Description: Python 3 wrapper to access docker.io's control socket This package contains oodles of routines that aid in controling docker.io over it's socket control, the same way the docker.io client controls the daemon. . This package provides Python 3 module bindings only. Package: python3-dockerpty Source: dockerpty Version: 0.4.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python3-six, python3:any (>= 3.3.2-2~) Recommends: python3-docker (>= 0.7.1) Homepage: https://github.com/d11wtq/dockerpty Priority: optional Section: python Filename: pool/main/d/dockerpty/python3-dockerpty_0.4.1-1~nd16.04+1_all.deb Size: 11020 SHA256: 573d709625a95da1a1e3574ea8da9da25fb0bb10fbf06253e29e48d1e7cb066e SHA1: cba110c5fa5c368521fd06764d24cf7383f2eb51 MD5sum: d6d8248ba19824c5e4ebaf1385e61ae1 Description: Pseudo-tty handler for docker Python client (Python 3.x) Provides the functionality needed to operate the pseudo-tty (PTY) allocated to a docker container, using the Python client. . This package provides Python 3.x version of dockerpty. Package: python3-duecredit Source: duecredit Version: 0.5.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 236 Depends: neurodebian-popularity-contest, python3-citeproc, python3-requests, python3-six, python3:any (>= 3.3.2-2~) Homepage: https://github.com/duecredit/duecredit Priority: optional Section: python Filename: pool/main/d/duecredit/python3-duecredit_0.5.0-1~nd16.04+1_all.deb Size: 50062 SHA256: 234ba4405e3239bd4e409a9a04f55bdd571e7eebad9e5961f87c86795b09aa71 SHA1: f040a55ba952a28624501c3a51e4c2d57dd88832 MD5sum: abaa1e044235cb9607cd608816e8e41c Description: Publications (and donations) tracer duecredit is being conceived to address the problem of inadequate citation of scientific software and methods, and limited visibility of donation requests for open-source software. . It provides a simple framework (at the moment for Python only) to embed publication or other references in the original code so they are automatically collected and reported to the user at the necessary level of reference detail, i.e. only references for actually used functionality will be presented back if software provides multiple citeable implementations. . To get a sense of what duecredit is about, simply run or your analysis script with `-m duecredit`, e.g. . python3 -m duecredit examples/example_scipy.py Python-Egg-Name: duecredit Package: python3-fsl Source: fslpy Version: 0.11.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 464 Depends: neurodebian-popularity-contest, python3-lxml, python3-nibabel, python3-indexed-gzip, python3-matplotlib, python3-numpy, python3-six (>= 1.10.0), python3:any (>= 3.3.2-2~) Priority: optional Section: python Filename: pool/main/f/fslpy/python3-fsl_0.11.0-1~nd16.04+1_all.deb Size: 96574 SHA256: 0a2884dd6399c971f41221c7e4228afa837e4c7b55e26bc36ff2b3bd12da4a50 SHA1: 7e9ebe9160177ee123816ff16ac86f4030b9e027 MD5sum: 353347bee1274811d87479cf200ad655 Description: FSL Python library Support library for FSL. . This package provides the Python 3 module. Package: python3-funcsigs Source: python-funcsigs Version: 0.4-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63 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~nd16.04+1_all.deb Size: 12840 SHA256: 30ac14fecd0e1891912e0b3e81201fc8ed42978cfe3262adc77afb8698bc1997 SHA1: c10eb7d912d55099dbfe4d9652a1d23faf3226b3 MD5sum: cbd2d39553eb4ca7a3b1295af66d310a 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-git Source: python-git Version: 2.1.1-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1622 Depends: neurodebian-popularity-contest, git (>= 1:1.7) | git-core (>= 1:1.5.3.7), python3-gitdb (>= 2), python3:any (>= 3.3.2-2~) Suggests: python-git-doc Homepage: https://github.com/gitpython-developers/GitPython Priority: optional Section: python Filename: pool/main/p/python-git/python3-git_2.1.1-2~nd16.04+1_all.deb Size: 299646 SHA256: 04e71e8d6183d73bd6a21c9816d97b028ca0018f0c705d08e8c1592178408519 SHA1: deccca6499bfbac23eb51f96565439f6657e63bb MD5sum: ef2938c4bf9f213a938cb0e625d1172c 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-github Source: pygithub Version: 1.26.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 629 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Conflicts: python3-pygithub Replaces: python3-pygithub Homepage: https://pypi.python.org/pypi/PyGithub Priority: optional Section: python Filename: pool/main/p/pygithub/python3-github_1.26.0-1~nd16.04+1_all.deb Size: 44896 SHA256: 9b0cb31263e2e0a368feb76d999e38d3cd3c4d12ab130cba07fac8c0b1357c24 SHA1: fadd6f4a75021a1d05dd975b06694c04750a2350 MD5sum: 430f2aca1c40484a5d7b20b85aa09726 Description: Access the full Github API v3 from Python3 This is a Python3 library to access the Github API v3. With it, you can manage Github resources (repositories, user profiles, organizations, etc.) from Python scripts. . It covers almost the full API and all methods are tested against the real Github site. Package: python3-hypothesis Source: python-hypothesis Version: 3.6.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 386 Depends: neurodebian-popularity-contest Suggests: python-hypothesis-doc Homepage: https://github.com/DRMacIver/hypothesis Priority: optional Section: python Filename: pool/main/p/python-hypothesis/python3-hypothesis_3.6.0-1~nd16.04+1_all.deb Size: 70076 SHA256: 90847bb415568c3fdc9393162a4f13f5024f9bc78ea38900c0a791071b6f5ea5 SHA1: d2b3be72d531d2ed33413b37af651b7595a02869 MD5sum: ce51599e1281a64d1a9a356a149e864a Description: advanced Quickcheck style testing library for Python 3 Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It's based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow. . Hypothesis is both extremely practical and also advances the state of the art of unit testing by some way. It's easy to use, stable, and extremely powerful. If you're not using Hypothesis to test your project then you're missing out. . This package contains the Python 3 module. Package: python3-joblib Source: joblib Version: 0.10.3+git55-g660fe5d-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 483 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Recommends: python3-numpy, python3-pytest, python3-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python3-joblib_0.10.3+git55-g660fe5d-1~nd16.04+1_all.deb Size: 113338 SHA256: 03169b9c6a9216b6175927bd15a8e74d916423a0660784f3a31487cfe99da2f7 SHA1: 0fe2a1b3e1a9bee5f90370b4d9f2446921ef42fb MD5sum: 684986f976bb5e4671f007b0ab8f381d Description: tools to provide lightweight pipelining in Python Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers: . - transparent disk-caching of the output values and lazy re-evaluation (memoize pattern) - easy simple parallel computing - logging and tracing of the execution . Joblib is optimized to be fast and robust in particular on large, long-running functions and has specific optimizations for numpy arrays. . This package contains the Python 3 version. Package: python3-jsmin Source: python-jsmin Version: 2.2.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 68 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Homepage: https://github.com/tikitu/jsmin Priority: optional Section: python Filename: pool/main/p/python-jsmin/python3-jsmin_2.2.1-1~nd16.04+1_all.deb Size: 21612 SHA256: 3344d1c8edcf36faf774fdeafdbb2669d3c6df114603d9352a397bf7ed2d160d SHA1: db767406e3300c3f557df9c3feb51516e06c482e MD5sum: 8afeb5e7e7b9db0b819a30de472482a6 Description: JavaScript minifier written in Python - Python 3.x Python-jsmin is a JavaScript minifier, it is written in pure Python and actively maintained. . This package provides the Python 3.x module. Package: python3-nibabel Source: nibabel Version: 2.1.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 64183 Depends: neurodebian-popularity-contest, python3-numpy, python3-scipy Suggests: python-nibabel-doc, python3-dicom, python3-fuse, python3-mock Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python3-nibabel_2.1.0-1~nd16.04+1_all.deb Size: 2154656 SHA256: 9c760991a0a887cdd65153791a3722534203cdf66dc16029f99c2ef9fccec0cd SHA1: 5df7ed77d30ce807373999d8294e95f46bfa053c MD5sum: 2fa45514b87aa0c0e6e6c3252b76f3a3 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~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2158 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~nd16.04+1_all.deb Size: 685224 SHA256: 50d5820df0e96c898a62291baf032c83862b57bdcd5b144618143bd9ef79a719 SHA1: 11e003ff9cbe8bab038c64bbb46277793027021d MD5sum: e477063ef5c537c9b2cf5e48ce3e8981 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-opengl Source: pyopengl Version: 3.1.0+dfsg-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 5289 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, freeglut3 Suggests: python3-tk, python3-numpy, libgle3 Homepage: http://pyopengl.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pyopengl/python3-opengl_3.1.0+dfsg-1~nd16.04+1_all.deb Size: 502442 SHA256: 70d36f1d4930e6b529dc5e338a93b80c397b5c2e18702b7aaeb652473865c9c3 SHA1: 04fcb1dfc2af7e36e299d3367512cea231f9ab32 MD5sum: 152c0535f97dee9f6fac13ab6ff1487d Description: Python bindings to OpenGL (Python 3) PyOpenGL is a cross-platform open source Python binding to the standard OpenGL API providing 2D and 3D graphic drawing. PyOpenGL supports the GL, GLU, GLE, and GLUT libraries. The library can be used with the Tkinter, wxPython, FxPy, and Win32GUI windowing libraries (or almost any Python windowing library which can provide an OpenGL context). . This is the Python 3 version of the package. Package: python3-openpyxl Source: openpyxl Version: 2.3.0-3~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1321 Depends: neurodebian-popularity-contest, python3-jdcal, python3:any (>= 3.3.2-2~), python3-lxml (>= 3.3.4) | python3-et-xmlfile Recommends: python3-pytest, python3-pil Homepage: http://bitbucket.org/openpyxl/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python3-openpyxl_2.3.0-3~nd16.04+1_all.deb Size: 198654 SHA256: da4c1478271e65cf9a3d6a2f0db24751a0609c052ca2bf73f5315dd895a613ce SHA1: 1c9f69acbcd5b9eb1d3f68b4c0d92029359c05be MD5sum: 7bc7b08384af486e80c08f9b879c63f3 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: 0.19.2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25227 Depends: neurodebian-popularity-contest, python3-dateutil, python3-numpy (>= 1:1.7~), python3-tz, python3:any (>= 3.3.2-2~), python3-pandas-lib (>= 0.19.2-1~nd16.04+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_0.19.2-1~nd16.04+1_all.deb Size: 2601416 SHA256: c84fc9fef43a496a971e61bd46680659228d9e9fd4ee29bc617455a24a407160 SHA1: f3b03bfd5354423bd3fb5be38eb35eb2bf79beb8 MD5sum: b0ae30aef1d47b5d32f5502c8b1eda78 Description: data structures for "relational" or "labeled" data - Python 3 pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 3 version. Package: python3-patsy Source: patsy Version: 0.4.1+git34-ga5b54c2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 778 Depends: neurodebian-popularity-contest, python3-numpy, python3-six, python3:any (>= 3.3.2-2~) Recommends: python3-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python3-patsy_0.4.1+git34-ga5b54c2-1~nd16.04+1_all.deb Size: 169194 SHA256: f1918e6deeb48daa4b33e7556fe6e78a2d6557e5a72bfd12d9e45bc30a43adc0 SHA1: 411f03d31699f24bc3c71ea7f775c489837352fe MD5sum: 0866f30d9d2d1d10fcbb82945158218e Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 3 version. Package: python3-pkg-resources Source: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 412 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Suggests: python3-setuptools Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: python Filename: pool/main/p/python-setuptools/python3-pkg-resources_20.10.1-1.1~bpo8+1~nd16.04+1_all.deb Size: 111924 SHA256: 37436362884361cbe450a55d71d3b5e0c137945715f545e7b7be7c9b84e64d51 SHA1: 3a081bf0d880a0241694983fee33109f2107d602 MD5sum: 8f25c359bdd869855af0c2693dfd8e1c Description: Package Discovery and Resource Access using pkg_resources The pkg_resources module provides an API for Python libraries to access their resource files, and for extensible applications and frameworks to automatically discover plugins. It also provides runtime support for using C extensions that are inside zipfile-format eggs, support for merging packages that have separately-distributed modules or subpackages, and APIs for managing Python's current "working set" of active packages. Package: python3-prov Source: python-prov Version: 1.4.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1235 Depends: neurodebian-popularity-contest, python3-dateutil, python3-lxml, python3-networkx, python3-six (>= 1.9.0), python3:any (>= 3.3.2-2~) Suggests: python-prov-doc, python3-pydotplus Homepage: https://github.com/trungdong/prov Priority: optional Section: python Filename: pool/main/p/python-prov/python3-prov_1.4.0-1~nd16.04+1_all.deb Size: 72570 SHA256: 6e2836dd7da34d0a68513ac450fb3cd6a13be3aa79e9f284b3ccd16596d97ed4 SHA1: 77cd01fd8a21b7f58be5e975f7f32f36742d4dd9 MD5sum: ca78f5a4c2b8d6093898c99c5a2e2043 Description: W3C Provenance Data Model (Python 3) A library for W3C Provenance Data Model supporting PROV-JSON and PROV- XML import/export. . Features: - An implementation of the W3C PROV Data Model in Python. - In-memory classes for PROV assertions, which can then be output as PROV-N. - Serialization and deserializtion support: PROV-JSON and PROV-XML. - Exporting PROV documents into various graphical formats (e.g. PDF, PNG, SVG). . This package provides the prov library for Python 3. Package: python3-py Source: python-py Version: 1.4.31-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 320 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-pkg-resources Suggests: subversion, python3-pytest Homepage: https://bitbucket.org/pytest-dev/py Priority: optional Section: python Filename: pool/main/p/python-py/python3-py_1.4.31-2~nd16.04+1_all.deb Size: 82310 SHA256: 744838b493212a784bb0d94d5dd5c54347475dd987b8d3f0d32f4c029881bf40 SHA1: bf95bda1b6fa38d0262fba6fab890efb332d22a0 MD5sum: 010f582c231efd751bfc1261da1aae6f 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~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 104 Depends: neurodebian-popularity-contest, graphviz, python3-pyparsing (>= 2.0.1), python3:any (>= 3.3.2-2~) Suggests: python-pydotplus-doc Homepage: http://pydotplus.readthedocs.org/ Priority: optional Section: python Filename: pool/main/p/python-pydotplus/python3-pydotplus_2.0.2-1~nd16.04+1_all.deb Size: 20322 SHA256: b19df8771f49c2b313d2a1acb1ecd2771576485c6ad9156596b50714764bdfef SHA1: 6410b7286d956011e3f522ce1db8d460294c4743 MD5sum: bacc9756268bd576896ea738f9e31359 Description: interface to Graphviz's Dot language - Python 3.x PyDotPlus is an improved version of the old pydot project that provides a Python Interface to Graphviz's Dot language. . Differences with pydot: * Compatible with PyParsing 2.0+. * Python 2.7 - Python 3 compatible. * Well documented. * CI Tested. . This package contains the Python 3.x module. Package: python3-pytest Source: pytest Version: 3.0.4-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 604 Depends: neurodebian-popularity-contest, python3-pkg-resources, python3-py (>= 1.4.29), python3:any (>= 3.3.2-2~) Homepage: http://pytest.org/ Priority: optional Section: python Filename: pool/main/p/pytest/python3-pytest_3.0.4-1~nd16.04+1_all.deb Size: 136296 SHA256: 7351bf0f466638febe71c34923db203a23b2f20f7777ec28352378f8eb82f76e SHA1: 3c82f55dff3306fbdaec22eea08d2fa78c1b754b MD5sum: f05f902283edf2562c655a36187a643a Description: Simple, powerful testing in Python3 This testing tool has for objective to allow the developers to limit the boilerplate code around the tests, promoting the use of built-in mechanisms such as the `assert` keyword. . This package provides the Python 3 module and the py.test-3 script. Package: python3-pytest-runner Source: pytest-runner Version: 2.7.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 28 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Homepage: https://github.com/pytest-dev/pytest-runner Priority: optional Section: python Filename: pool/main/p/pytest-runner/python3-pytest-runner_2.7.1-1~nd16.04+1_all.deb Size: 6138 SHA256: c0d765fdbc263503b35ddaf7b5542aa5d96478d9454452d5562da8153342fc98 SHA1: f336e89023459de8693b4a12cfb7203559adc7c0 MD5sum: 761f7237281476ff3a8c53174cec36dd Description: Invoke py.test as distutils command with dependency resolution Setup scripts can use pytest-runner to add setup.py test support for pytest runner. . This package contains the Python 3 module. Package: python3-requests Source: requests Version: 2.8.1-1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 240 Depends: neurodebian-popularity-contest, python3-urllib3 (>= 1.12), python3:any (>= 3.3.2-2~), ca-certificates, python3-chardet Suggests: python3-ndg-httpsclient, python3-openssl, python3-pyasn1 Homepage: http://python-requests.org Priority: optional Section: python Filename: pool/main/r/requests/python3-requests_2.8.1-1~bpo8+1~nd16.04+1_all.deb Size: 67746 SHA256: ac6b753621a743a0bc80f56fcc7e031f1b9a981da60ee933cc5c249d68d88465 SHA1: 157fbd7d0c97a904fd4c4d7ad6acdc4dea9fc319 MD5sum: 4a17ba8733800086ea871f737b00566b Description: elegant and simple HTTP library for Python3, built for human beings Requests allow you to send HTTP/1.1 requests. You can add headers, form data, multipart files, and parameters with simple Python dictionaries, and access the response data in the same way. It's powered by httplib and urllib3, but it does all the hard work and crazy hacks for you. . Features . - International Domains and URLs - Keep-Alive & Connection Pooling - Sessions with Cookie Persistence - Browser-style SSL Verification - Basic/Digest Authentication - Elegant Key/Value Cookies - Automatic Decompression - Unicode Response Bodies - Multipart File Uploads - Connection Timeouts . This package contains the Python 3 version of the library. Package: python3-seaborn Source: seaborn Version: 0.7.1-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 766 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-pandas, python3-matplotlib Recommends: python3-patsy Homepage: https://github.com/mwaskom/seaborn Priority: optional Section: python Filename: pool/main/s/seaborn/python3-seaborn_0.7.1-2~nd16.04+1_all.deb Size: 128352 SHA256: 66bf9c26034f0925ba87e88ec57da2182501b5e6122faf5ffaf7f3dc37aed194 SHA1: f178a36166ba2524b7b5d4d373264263bf6a1d01 MD5sum: ea11b84936961fce0ee1045b416c1038 Description: statistical visualization library Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. . Some of the features that seaborn offers are . - Several built-in themes that improve on the default matplotlib aesthetics - Tools for choosing color palettes to make beautiful plots that reveal patterns in your data - Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data - Tools that fit and visualize linear regression models for different kinds of independent and dependent variables - A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate - High-level abstractions for structuring grids of plots that let you easily build complex visualizations . This is the Python 3 version of the package. Package: python3-setuptools Source: python-setuptools Version: 20.10.1-1.1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 431 Depends: neurodebian-popularity-contest, python3-pkg-resources (= 20.10.1-1.1~bpo8+1~nd16.04+1), python3:any (>= 3.3.2-2~) Suggests: python-setuptools-doc Homepage: https://pypi.python.org/pypi/setuptools Priority: optional Section: python Filename: pool/main/p/python-setuptools/python3-setuptools_20.10.1-1.1~bpo8+1~nd16.04+1_all.deb Size: 121894 SHA256: 9c7fe7ae89e029a3c69597b27157f54739cfd6407f8b9e46396eb69e07c00943 SHA1: de63fb4b7eb655da6330a12b36514cc99301ef8f MD5sum: 58a2cbd8fd310c0a198285f421fe9a0e Description: Python3 Distutils Enhancements Extensions to the python-distutils for large or complex distributions. Package: python3-six Source: six Version: 1.9.0-3~bpo8+1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49 Depends: neurodebian-popularity-contest, python3:any (>= 3.4~) Multi-Arch: foreign Homepage: http://pythonhosted.org/six/ Priority: optional Section: python Filename: pool/main/s/six/python3-six_1.9.0-3~bpo8+1~nd+1+nd16.04+1_all.deb Size: 11162 SHA256: de89c30fb266478195674d595d12a1513db7e2956c8af953ce1f2720e45d4ad9 SHA1: 4c90cc7b7fc37bedacd44c403965b35de5a1751b MD5sum: ab68168bc1f5b17416eb4c2f602f33a3 Description: Python 2 and 3 compatibility library (Python 3 interface) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six on the Python 3 module path. It is complemented by python-six. Package: python3-sklearn Source: scikit-learn Version: 0.18.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6608 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-sklearn-lib (>= 0.18.1-1~nd16.04+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_0.18.1-1~nd16.04+1_all.deb Size: 1391702 SHA256: f88e4aada943699c0d117b4d29e27612ca0c9de16896e5081d17abd69964191a SHA1: 6672ab0c871d0f3f45748c1027a5e80c2f5540f6 MD5sum: 8cc01abefc61543e9a8542ffe9399d44 Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) . This package contains the Python 3 version. Package: python3-smmap Source: python-smmap Version: 2.0.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 93 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Suggests: python3-nose Homepage: https://github.com/Byron/smmap Priority: extra Section: python Filename: pool/main/p/python-smmap/python3-smmap_2.0.1-1~nd16.04+1_all.deb Size: 20176 SHA256: 1cb1b9b738cfb35ca19ff27fd377111c1eac7aa7e41303d477b7e16ce546875f SHA1: 353febe116a610d81ed286782fea5b7c930cef2e MD5sum: 7bd6b73d32e59b25816632eaab44ba76 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-tqdm Source: tqdm Version: 4.11.2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 181 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Homepage: https://github.com/tqdm/tqdm Priority: optional Section: python Filename: pool/main/t/tqdm/python3-tqdm_4.11.2-1~nd16.04+1_all.deb Size: 50092 SHA256: ef6ef312041d0af9d825d0c6e600ee946e19f1bbfd2ca633bd20c5165dea9ed3 SHA1: 7dcd58cbe44933ed9c3cac9ddbb07855739e661f MD5sum: 9c3ab503bab47c52297b45f2fe8803f6 Description: fast, extensible progress bar for Python 3 and CLI tool tqdm (read taqadum, تقدّم) means “progress” in Arabic. tqdm instantly makes your loops show a smart progress meter, just by wrapping any iterable with "tqdm(iterable)". . This package contains the Python 3 version of tqdm and its command-line tool. Package: python3-urllib3 Source: python-urllib3 Version: 1.12-1~bpo8+1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 254 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-six Recommends: ca-certificates Suggests: python3-ndg-httpsclient, python3-openssl, python3-pyasn1 Homepage: http://urllib3.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-urllib3/python3-urllib3_1.12-1~bpo8+1~nd16.04+1_all.deb Size: 65460 SHA256: f64733ffe86e9e4fbec330998afddd392db1a86b1c64059687b989d8f99af98f SHA1: a737acfc24ced71f1de94337feff3698b46593b7 MD5sum: 2120aceab8d183efda1e1033d8d10398 Description: HTTP library with thread-safe connection pooling for Python3 urllib3 supports features left out of urllib and urllib2 libraries. . - Re-use the same socket connection for multiple requests (HTTPConnectionPool and HTTPSConnectionPool) (with optional client-side certificate verification). - File posting (encode_multipart_formdata). - Built-in redirection and retries (optional). - Supports gzip and deflate decoding. - Thread-safe and sanity-safe. - Small and easy to understand codebase perfect for extending and building upon. . This package contains the Python 3 version of the library. Package: python3-vcr Source: vcr.py Version: 1.7.3-1.0.1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 157 Depends: neurodebian-popularity-contest, python3-six (>= 1.5), python3-wrapt, python3-yaml, python3:any (>= 3.3.2-2~) Homepage: https://github.com/kevin1024/vcrpy/ Priority: optional Section: python Filename: pool/main/v/vcr.py/python3-vcr_1.7.3-1.0.1~nd+1+nd16.04+1_all.deb Size: 43654 SHA256: 18be5074cff6cd48dd691e1921cc15a96ad10de55944bad110958f0842ca8628 SHA1: 4de90dcf566f64fceaaa00a00af8fe588e105295 MD5sum: 4bb489a1b4d0759306109fcd31ec7e58 Description: record and replay HTML interactions (Python3 library) vcr.py records all interactions that take place through the HTML libraries it supports and writes them to flat files, called cassettes (YAML format by default). These cassettes could be replayed then for fast, deterministic and accurate HTML testing. . vcr.py supports the following Python HTTP libraries: - urllib2 (stdlib) - urllib3 - http.client (Python3 stdlib) - Requests - httplib2 - Boto (interface to Amazon Web Services) - Tornado's HTTP client . This package contains the modules for Python 3. Package: spm8-common Source: spm8 Version: 8.5236~dfsg.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 19186 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.5236~dfsg.1-1~nd+1+nd16.04+1_all.deb Size: 9781970 SHA256: 30cac74b9ad9db32f093eece5bae13d28ce4e1222878358f3afc6e6a67b55b67 SHA1: 4899f3ba6a1ff156b67ed1b324ae4a83ffbbab9e MD5sum: 093fd447d8b43ebafc21585625545b37 Description: analysis of brain imaging data sequences Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the platform-independent M-files. Package: spm8-data Source: spm8 Version: 8.5236~dfsg.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 73019 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd+1+nd16.04+1_all.deb Size: 45497774 SHA256: bde3c93b9c1168ff6fa5b3269782006cf5613ba3f2b7b49abd5249d07600335b SHA1: af6dce85c4ee9079c720d544dfacfe2fc2cffa67 MD5sum: 494ca73671489f3feaf525e4295caff3 Description: data files for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provide the data files shipped with the SPM distribution, such as various stereotaxic brain space templates and EEG channel setups. Package: spm8-doc Source: spm8 Version: 8.5236~dfsg.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9251 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd+1+nd16.04+1_all.deb Size: 8934936 SHA256: 7f6f08608f31115b8e51d149f8560a2cfe399bd44562ff34bf2aeb24a9632bc9 SHA1: 31e802efc7a2237d47eb5a13d1d6f6a6f9ed7324 MD5sum: c96ec4b1e942cf1f8f8eb55b327d7c40 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: ubuntu-keyring Version: 2010.+09.30~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 Recommends: gpgv Priority: important Section: misc Filename: pool/main/u/ubuntu-keyring/ubuntu-keyring_2010.+09.30~nd+1+nd16.04+1_all.deb Size: 11702 SHA256: e6052ad683b7b3eac5152f3790fcf69f3340f2526d81e9e394a6c4b11fbb26c0 SHA1: 288fe25a2be199481113954438cd17bc01060293 MD5sum: f432078db30b3298f5dbac78eaad7c06 Description: GnuPG keys of the Ubuntu archive The Ubuntu project digitally signs its Release files. This package contains the archive keys used for that.