Package: condor Version: 8.2.3~dfsg.1-3~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 45 Depends: neurodebian-popularity-contest, htcondor Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor_8.2.3~dfsg.1-3~nd14.04+1_all.deb Size: 14080 SHA256: fcef221fba7ce8ecb66ef7aecb4187d9f16eb5f8bf6a01473d9a92ac9a946805 SHA1: 59acd255f6697a8878a3ade5394ecc833343a332 MD5sum: e0f15cb2e15d3ca0c84830abd588d021 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. 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Package: condor-dev Source: condor Version: 8.2.3~dfsg.1-3~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 45 Depends: neurodebian-popularity-contest, htcondor-dev Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dev_8.2.3~dfsg.1-3~nd14.04+1_all.deb Size: 14100 SHA256: 6884798177763779a276813924d07957356dcaaad12836237b0aa5046299ceeb SHA1: af1c76cf4eced08fc93c4dd795c14e2089d86921 MD5sum: b986ec83c8fc416bb81af205d087d206 Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: condor-doc Source: condor Version: 8.2.3~dfsg.1-3~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 45 Depends: neurodebian-popularity-contest, htcondor-doc Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-doc_8.2.3~dfsg.1-3~nd14.04+1_all.deb Size: 14090 SHA256: 5293852e5282beb9020fa260b84fa750cc987fcf5e5813f3827b4d14f4556c19 SHA1: 57eaa25c1726ebf73b7411482df3cf290dc61d88 MD5sum: dc1021f456027208dc207818891b92be 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: dh-systemd Source: init-system-helpers Version: 1.18~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, perl, debhelper Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/dh-systemd_1.18~nd13.10+1+nd14.04+1_all.deb Size: 13814 SHA256: d174181f267afbaf3c6c7d6108b65eca78861aa6d3c71288a03db9b5cafd5a13 SHA1: 15700758d679f3bda80f55b5f435edad45d1b39e MD5sum: 4ed20ea08d8c497a1a3e9b7ce46fe4c8 Description: debhelper add-on to handle systemd unit files dh-systemd provides a debhelper sequence addon named 'systemd' and the dh_systemd_enable/dh_systemd_start commands. . The dh_systemd_enable command adds the appropriate code to the postinst, prerm and postrm maint scripts to properly enable/disable systemd service files. The dh_systemd_start command deals with start/stop/restart on upgrades for systemd-only service files. Package: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8109 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 7062228 SHA256: aa1e0c88dbb25feff7d4a79637ce14e2bd7fccf5b2e73f675ba5b88baebdcb3b SHA1: e5d9d261fdfa5d96e1fe0b8e6ce4b67948bb54c4 MD5sum: 0bf506b1eed312f76a0e48cb663cb40a Description: sample EEG data for EEGLAB tutorials EEGLAB is sofwware for processing continuous or event-related EEG or other physiological data. . This package provide some tutorial data files shipped with the EEGLAB distribution. Package: fail2ban Version: 0.8.13-1~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 563 Depends: neurodebian-popularity-contest, python:any (>= 2.7.1-0ubuntu2), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx, system-log-daemon Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.13-1~nd13.10+1+nd14.04+1_all.deb Size: 165042 SHA256: e127f8ed110707b842f8965f0995ff6a4177040a785b17a4d0ccb39be90dad9a SHA1: c1603990e18d3f45b3dc14b2e66ef38fa8fc29ba MD5sum: dbbcec95193e5e863c3e18aa21f8af6e Description: ban hosts that cause multiple authentication errors Fail2ban monitors log files (e.g. /var/log/auth.log, /var/log/apache/access.log) and temporarily or persistently bans failure-prone addresses by updating existing firewall rules. Fail2ban allows easy specification of different actions to be taken such as to ban an IP using iptables or hostsdeny rules, or simply to send a notification email. . By default, it comes with filter expressions for various services (sshd, apache, qmail, proftpd, sasl etc.) but configuration can be easily extended for monitoring any other text file. All filters and actions are given in the config files, thus fail2ban can be adopted to be used with a variety of files and firewalls. Package: freeipmi Version: 1.4.5-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.5-2~nd14.04+1), freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi_1.4.5-2~nd14.04+1_all.deb Size: 1176 SHA256: 351ab497edf77e27057224f06e672245168ca0085478caf33e3a7f01364ac2f7 SHA1: cd509e11e871bc75c9395149003f56ef280ea88a MD5sum: 93127239881403163abd01b5aaa00625 Description: GNU implementation of the IPMI protocol FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This metapackage depends on all separate modules of freeipmi. Package: freeipmi-common Source: freeipmi Version: 1.4.5-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 304 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest Suggests: freeipmi-tools Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-common_1.4.5-2~nd14.04+1_all.deb Size: 187850 SHA256: d4dddb1d121a4517d005cf1be28bbc8e0f1ca21ecc51f74c3772f02ce3bf1343 SHA1: 7e102038afd8da8c55626ff7c8cbb9faa064ddf5 MD5sum: 18fb02f84cb18329f0e76eb58480c1c0 Description: GNU implementation of the IPMI protocol - common files FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package provides configuration used by the rest of FreeIPMI framework and generic documentation to orient the user. Package: fslview-doc Source: fslview Version: 4.0.1-2~nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2874 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-2~nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 2227008 SHA256: 9b2fd16b794a16978563ce66f865f124613b7bfd5e3dafa7fef33fe08fc00799 SHA1: 8dcabe069cb78ea29e21fc607cdaa60ff6b73bb0 MD5sum: be7ec9467211319b53022709eb7d3126 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gmsl Version: 1.1.5-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 78 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.5-1~nd14.04+1_all.deb Size: 13800 SHA256: 4127230a0b3a6b132f2e98087b496cddbabf8efd64fb0573ac384d4ec292ddab SHA1: 16ab5cc30564be2024ea5ea282213fc38a320743 MD5sum: 75f0db3af8b2efad55c4794e50b84412 Description: extra functions to extend functionality of GNU Makefiles The GNU Make Standard Library (GMSL) is a collection of functions implemented using native GNU Make functionality that provide list and string manipulation, integer arithmetic, associative arrays, stacks, and debugging facilities. . Note that despite the name of this project, this library is NOT standard and is NOT written or distributed by the GNU project. Package: htcondor-doc Source: condor Version: 8.2.3~dfsg.1-3~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 5607 Depends: neurodebian-popularity-contest Breaks: condor-doc (<< 8.0.5~) Replaces: condor-doc (<< 8.0.5~) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: doc Filename: pool/main/c/condor/htcondor-doc_8.2.3~dfsg.1-3~nd14.04+1_all.deb Size: 1027048 SHA256: f925016c4befc268f99bb3eb7854d76f800e009ceee84a0c297f8f340b69c388 SHA1: 5a50185436dbf7a138b45ff678faa748ab325d9e MD5sum: 85cfc0447b2fcc2e5232f569d019bbc4 Description: distributed workload management system - documentation Like other full-featured batch systems, HTCondor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to HTCondor; HTCondor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing systems, HTCondor can also effectively harness wasted CPU power from otherwise idle desktop workstations. HTCondor does not require a shared file system across machines - if no shared file system is available, HTCondor can transfer the job's data files on behalf of the user. . This package provides HTCondor's documentation in HTML and PDF format, as well as configuration and other examples. Package: impressive Version: 0.10.5-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 331 Depends: neurodebian-popularity-contest, python-opengl, python-pygame, python-imaging, poppler-utils | xpdf-utils (>= 3.02-2) Recommends: pdftk, perl Suggests: ghostscript, latex-beamer Conflicts: keyjnote (<< 0.10.2r-0) Replaces: keyjnote (<< 0.10.2r-0) Provides: keyjnote Homepage: http://impressive.sourceforge.net/ Priority: optional Section: x11 Filename: pool/main/i/impressive/impressive_0.10.5-1~nd14.04+1_all.deb Size: 151652 SHA256: 20cc65f855d2a8efe0c6b964f7a534902caed0d7bf4a14d25e53e26b7ce27ba0 SHA1: 975160d58edcc2b16da666817fbcf1508144bdbe MD5sum: 8a21546ca0990ed7753c52a9038ddd69 Description: PDF presentation tool with eye candies Impressive is a program that displays presentation slides using OpenGL. Smooth alpha-blended slide transitions are provided for the sake of eye candy, but in addition to this, Impressive offers some unique tools that are really useful for presentations. Some of them are: * Overview screen * Highlight boxes * Spotlight effect * Presentation scripting and customization Package: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 9150 SHA256: 6221480f9dac530be0388cb543cb7222a71f2eeb5a05e3b7684189951be779a9 SHA1: d6e2bc39ee2ea2858d5aa50a8b825dcc1a9766ef MD5sum: 42c1f57576c0b1537c531816653e0f04 Description: utility for pushing DICOM data to the INCF datasharing server A command line utility for anonymizing and sending DICOM data to the XNAT image database at the International Neuroinformatics Coordinating Facility (INCF). This tool is maintained by the INCF NeuroImaging DataSharing (NIDASH) task force. Package: init-system-helpers Version: 1.18~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29 Depends: neurodebian-popularity-contest, perl Breaks: systemd (<< 44-12) Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/init-system-helpers_1.18~nd13.10+1+nd14.04+1_all.deb Size: 13450 SHA256: 9b738273e06fa645d7746ddcfc18257e82b1aa81991b60f4940c8336ca7c276b SHA1: a69ef0da8cacfe37a1898934c6feb74737e63597 MD5sum: c519d25c91c535528c645290c7201987 Description: helper tools for all init systems This package contains helper tools that are necessary for switching between the various init systems that Debian contains (e.g. sysvinit, upstart, systemd). An example is deb-systemd-helper, a script that enables systemd unit files without depending on a running systemd. . While this package is maintained by pkg-systemd-maintainers, it is NOT specific to systemd at all. Maintainers of other init systems are welcome to include their helpers in this package. Package: libfreenect-doc Source: libfreenect Version: 1:0.1.2+dfsg-6~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 592 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-6~nd14.04+1_all.deb Size: 81624 SHA256: df19e1dd46b09ddc473c299b3683291b50a49cfffbcfffcd9eaad334a024985e SHA1: b8e22475326b83f36d4ca0dbcf1dea85c0109a03 MD5sum: aa0821d2cd61753792fbbb8f8f11d243 Description: library for accessing Kinect device -- documentation libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package contains the documentation of the API of libfreenect. Package: libmia-2.0-doc Source: mia Version: 2.0.13-1~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14003 Depends: neurodebian-popularity-contest, libjs-jquery Enhances: libmia-2.0-dev Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/libmia-2.0-doc_2.0.13-1~nd13.10+1+nd14.04+1_all.deb Size: 828262 SHA256: b80877b4eb7ac26a8d128219be2df273b0d1115bdc039118aa39f0928a03a878 SHA1: 4f2d66594f12e0670fb739d4c7333bd5ffce4b44 MD5sum: 4880b2c099431c4b8afaa3e78dee6e67 Description: library for 2D and 3D gray scale image processing, documentation libmia comprises a set of libraries and plug-ins for general purpose 2D and 3D gray scale image processing and basic handling of triangular meshes. The libraries provide a basic infrastructure and generic algorithms, that can be specialized by specifying the apropriate plug-ins. This package provides the Doxygen generated API reference. Package: libnifti-doc Source: nifticlib Version: 2.0.0-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1675 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://niftilib.sourceforge.net Priority: optional Section: doc Filename: pool/main/n/nifticlib/libnifti-doc_2.0.0-2~nd14.04+1_all.deb Size: 137676 SHA256: 3bab4349c0f35948663f799794b88328b12952dbb9eadb0e8a4085c0f270a5e6 SHA1: 8efa4c33a93f08cd665a505ac7aa01ce2a943db6 MD5sum: b29b153538f23cfdeddd32fc9dab6436 Description: NIfTI library API documentation Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the library API reference documentation. Package: libopenwalnut1-doc Source: openwalnut Version: 1.4.0~rc1+hg3a3147463ee2-1~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48075 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.4.0~rc1+hg3a3147463ee2-1~nd13.10+1+nd14.04+1_all.deb Size: 2673508 SHA256: 6bfe8da2878784c3df24ef11993ad9d5b82019204eb362235bd094ac6865c0f8 SHA1: 714ad73ca23e5c34f65c7632c395534c4baf7898 MD5sum: 5f8cbf8fba45be0c2da0d39481c9c931 Description: Developer documentation for the OpenWalnut visualization framework OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the core API documentation of OpenWalnut. Package: mia-tools-doc Source: mia Version: 2.0.13-1~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1138 Depends: neurodebian-popularity-contest Enhances: mia-tools Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/mia-tools-doc_2.0.13-1~nd13.10+1+nd14.04+1_all.deb Size: 71894 SHA256: be1b730b60e4e46c09f731c458418f51468a54bd0bcb30c1b3ae62895cf5195c SHA1: 13b72c29850c963b4b7163d5728101f262982fad MD5sum: 68aa342ecdbdd76b549b62f3a8a0cefb Description: Cross-referenced documentation of the MIA command line tools Cross referenced documentation of the command line tools and plug-ins that are provided by the MIA gray scale image processing tool chain. These lines tools to provide the means to run general purpose image processing tasks on 2D and 3D gray scale images, and basic operations on triangular meshes interactively from the command line. Supported image processing algorithms are image filtering, combining, image registration, motion compensation for image series, and the estimation of various statistics over images. Package: mricron-data Source: mricron Version: 0.20140804.1~dfsg.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1708 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20140804.1~dfsg.1-1~nd14.04+1_all.deb Size: 1658672 SHA256: 8c6fbf4d4201736009058951a8b0a0649b14eb2b31222086e4c40337b7b701fb SHA1: d390758dd46655f60156a87578c4e8bde2f62f7f MD5sum: 5c2f269adc054ae2960074a3cfec33ba Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20140804.1~dfsg.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1019 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20140804.1~dfsg.1-1~nd14.04+1_all.deb Size: 577154 SHA256: 99c69da1658d3ad0d38a2e617b99c1e97b6dd659de73b4d17de6abe2c836bee6 SHA1: 40d30c9da283129d64c2490423bc06de65605f3d MD5sum: 1e4972d196978a16b51c6ab0b5170a0b Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: mrtrix-doc Source: mrtrix Version: 0.2.12-1~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3490 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.12-1~nd13.10+1+nd14.04+1_all.deb Size: 3191882 SHA256: f32e1267d094094ab3cc0c0dea48e1ccf69fe473877e2e60136e4e6a27db354b SHA1: 31f466b044fd9b37817cda76e708ae9f65413a1e MD5sum: 391268dd332daa65f1524ad5d28fd893 Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: netselect-apt Source: netselect Version: 0.3.ds1-25~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26 Depends: neurodebian-popularity-contest, wget, netselect (>= 0.3.ds1-17) Recommends: curl Suggests: dpkg-dev Enhances: apt Homepage: http://github.com/apenwarr/netselect Priority: optional Section: net Filename: pool/main/n/netselect/netselect-apt_0.3.ds1-25~nd14.04+1_all.deb Size: 16732 SHA256: 6736e45053839e6ccff6ae6acee08c1b6946082a3551db89f5b75cf011f56942 SHA1: 5459e0e22973aeac24902aeb831afaca776152d2 MD5sum: 27d1f7525d5b1676fab50f43523897b3 Description: speed tester for choosing a fast Debian mirror This package provides a utility that can choose the best Debian mirror by downloading the full mirror list and using netselect to find the fastest/closest one. . It can output a sources.list(5) file that can be used with package management tools such as apt or aptitude. Package: neurodebian Version: 0.34~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41 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_0.34~nd14.04+1_all.deb Size: 20130 SHA256: 26acd7fb0b1babef2f0926043a2828c864cda9c888a5c1c5b633c8fdd1a91c55 SHA1: d4b26ffa52859ca3129c943d66d95a4eba398d76 MD5sum: a79e9a3de0c34cdac9189badc4377c0e Description: turnkey platform for the neuroscience The NeuroDebian project integrates and maintain a variety of neuroscience-oriented (such as AFNI, FSL, PsychoPy, etc.) and many generic computational (such as condor, pandas, etc.) software projects within Debian. . This package enables NeuroDebian repository on top of the stock Debian or Ubuntu system. Package: neurodebian-archive-keyring Source: neurodebian Version: 0.34~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11 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_0.34~nd14.04+1_all.deb Size: 9066 SHA256: 58d41866e436036d07debe2404c8cf3cac8571876c99c740297e89ec894ad485 SHA1: 927ec9b54334b655d3f47336b963ad2e97a17c83 MD5sum: 2a09c396a0fa22ed7acdab13350bebdc Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-desktop Source: neurodebian Version: 0.34~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 146 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, gnome-icon-theme, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.34~nd14.04+1_all.deb Size: 114594 SHA256: 1903d3884c878f619467d29b60b9c8048b6dfa532cfd6b87f110b9b3b16b2150 SHA1: f027fc293da08ddedf4be6bc6e4ba2ee4c739ebc MD5sum: 540d72c3bca34b37301845ad0ac83d26 Description: neuroscience research environment This package contains NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring most popular neuroscience tools automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.34~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 81 Depends: devscripts, cowbuilder, neurodebian-archive-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring, apt-utils Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.34~nd14.04+1_all.deb Size: 30882 SHA256: 299a7cea710d5483461c6d53dd92a3e3df6f918e0587f1a428cf5e88bfe12328 SHA1: 3994580f5974e44ab3356a462f9fb1e8a91a5e3b MD5sum: d34af390ef9dcd01ec4c6b5724324486 Description: NeuroDebian development tools neuro.debian.net sphinx website sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-guest-additions Source: neurodebian Version: 0.32~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 108 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm | lightdm, zenity Recommends: chromium-browser, update-manager-gnome, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.32~nd13.10+1+nd14.04+1_all.deb Size: 14088 SHA256: 97679301db4c313bf776a5d18ff76e0b1af04b77da1156d1b500a56e308379b9 SHA1: 0b00e3321e0d1bc70c40437abc74430adcf4db07 MD5sum: f3f984c91e04f7b9ab57e22d1bb1af9b Description: NeuroDebian guest additions (DO NOT INSTALL OUTSIDE VIRTUALBOX) This package configures a Debian installation as a guest operating system in a VirtualBox-based virtual machine for NeuroDebian. . DO NOT install this package unless you know what you are doing! For example, installation of this package relaxes several security mechanisms. Package: neurodebian-keyring Source: neurodebian Version: 0.32~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.32~nd13.10+1+nd14.04+1_all.deb Size: 7470 SHA256: 8da1af69542f153184f6d344861f1557e1a7a783b6c0b6d90b67e8dee8a855e6 SHA1: fc17ac754d0a08a79a0b1615c6ae10dcd89f36ea MD5sum: 341bf775ee30c2071e1c49a1acf6f88e Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.34~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.34~nd14.04+1_all.deb Size: 11150 SHA256: fa537d32580e550ef36f5bdf62390199d521faf97b070bc4908f721e284fc7c5 SHA1: 9e1f8d887d4dd299f4f818056471bef2e7771356 MD5sum: d51e943f2ac21c2fd3779370fee2ac91 Description: Helper for NeuroDebian popularity contest submissions 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 (e.g. Debian or Ubuntu) popcon server. . Your participation in popcon is important for following reasons: - Popular packages receive more attention from developers, bugs are fixed faster and updates are provided quicker. - Assure that we do not drop support for a previous release of Debian or Ubuntu while are active users. - User statistics could be used by upstream research software developers to acquire funding for continued development. . It has an effect only if you have decided to participate in the Popularity Contest of your distribution, i.e. Debian or Ubuntu. You can always enable or disable your participation in popcon by running 'dpkg-reconfigure popularity-contest' as root. Package: nifti2dicom-data Source: nifti2dicom Version: 0.4.8-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 606 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.8-1~nd14.04+1_all.deb Size: 615402 SHA256: 5c0c4de741d502aed671afa90ffd5f9d83bbb8d9b8fe3fb61e87a83207c51387 SHA1: 1e2cfa4676c02305b4cf2b7c081125917e51667b MD5sum: 35e250c61104ee3c777aa9d7ef48b08f Description: data files for nifti2dicom This package contains architecture-independent supporting data files required for use with nifti2dicom, such as such as documentation, icons, and translations. Package: nuitka Version: 0.5.5.3+ds-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2211 Depends: neurodebian-popularity-contest, g++-4.9 | g++-4.8 | g++-4.7 | g++-4.6 (>= 4.6.1) | g++-4.5 | g++-4.4 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python:any (>= 2.7.1-0ubuntu2) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6), strace Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.5.5.3+ds-1~nd14.04+1_all.deb Size: 512286 SHA256: 176f856920e7359e797981030df88f18090fcc16005063a342ef3f44bf44e21c SHA1: 0e9be06245a55f54db697b585f766624581e40c5 MD5sum: 1265a79066dc4eab0fbc8281afa7b170 Description: Python compiler with full language support and CPython compatibility This Python compiler achieves full language compatibility and compiles Python code into compiled objects that are not second class at all. Instead they can be used in the same way as pure Python objects. Package: psychopy Version: 1.79.00+git16-g30c9343.dfsg-1~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12186 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-scipy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, python-pyo, libavbin0, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.79.00+git16-g30c9343.dfsg-1~nd13.10+1+nd14.04+1_all.deb Size: 5533598 SHA256: 398a30f43a27dd95fa793912c348e0e60e180548dfa0ce2683c0e5610a14b599 SHA1: d128f327e6b1f302252bca4289a020367c739364 MD5sum: a53a915619303ddd06fd2393cd53bf1b 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: 3.0.11.20140816.dfsg1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 58459 Depends: neurodebian-popularity-contest Recommends: subversion Suggests: gnuplot Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.11.20140816.dfsg1-1~nd14.04+1_all.deb Size: 19222842 SHA256: ec457f15991824c506ca305147b53ef1be6befb0b5f94d50e0e97a40f2133175 SHA1: 5fe1e0eef2380fefc0439050c945580e590694b9 MD5sum: e39a1a2bb16605ad54a9fdbd28f68a03 Description: toolbox for vision research -- arch/interpreter independent part Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains architecture independent files (such as .m scripts) Package: python-brian Source: brian Version: 1.4.1-1~nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2336 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.1-1~nd13.04+1+nd13.10+1+nd14.04+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.4.1-1~nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 399980 SHA256: 9b102fb44ba8ef99f962b24b26d8728f156056718f3eb9623913fc7b7caba662 SHA1: d86f3a0654ed4295d05de2f18f0eeb913556a87b MD5sum: 09ddbc80109b6a58ee6b674f29e7951a Description: simulator for spiking neural networks Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include: - a system for specifying quantities with physical dimensions - exact numerical integration for linear differential equations - Euler, Runge-Kutta and exponential Euler integration for nonlinear differential equations - synaptic connections with delays - short-term and long-term plasticity (spike-timing dependent plasticity) - a library of standard model components, including integrate-and-fire equations, synapses and ionic currents - a toolbox for automatically fitting spiking neuron models to electrophysiological recordings Package: python-brian-doc Source: brian Version: 1.4.1-1~nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6821 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-brian Homepage: http://www.briansimulator.org/ Priority: extra Section: doc Filename: pool/main/b/brian/python-brian-doc_1.4.1-1~nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 1974362 SHA256: d9c426b885976a7b29dde32cf747a24871a7a3635002c278e935eb11c57af91d SHA1: e20e051b8a911939382513cd791a53912f7cb300 MD5sum: 8fcfe3c4554b2af0690ca333000ca2ac Description: simulator for spiking neural networks - documentation Brian is a clock-driven simulator for spiking neural networks. . This package provides user's manual (in HTML format), examples and demos. Package: python-dicom Source: pydicom Version: 0.9.8-1~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1784 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://code.google.com/p/pydicom/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.8-1~nd13.10+1+nd14.04+1_all.deb Size: 357388 SHA256: 91219b2707d65b9c90115e228f0b7436b504a73b77240f30a1c9b83d28e8306f SHA1: 9a0c1416a19680e3f11e6a20f05a6fd4a8715ed6 MD5sum: 8c08ac8bc362936c87c2027a4b407bdd Description: DICOM medical file reading and writing pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. Package: python-dipy Source: dipy Version: 0.7.1-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2952 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.7.1-2~nd14.04+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.7-dipy Homepage: http://nipy.org/dipy Priority: optional Section: python Filename: pool/main/d/dipy/python-dipy_0.7.1-2~nd14.04+1_all.deb Size: 1769938 SHA256: c76d91ebda1a3ea310ae5fe6ae7dcd7035b6b20f2fc828e866f3cb8b31d370e5 SHA1: ea87b35937fcb4c2d080ab1be4b96500f0de0b45 MD5sum: 4fff0a58f604f417ad83c67035b4e546 Description: toolbox for analysis of MR diffusion imaging data Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. It features: - Reconstruction algorithms, e.g. GQI, DTI - Tractography generation algorithms, e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space, e.g. MNI space - Reading many different file formats, e.g. Trackvis or NIfTI - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing Python-Version: 2.7 Package: python-dipy-doc Source: dipy Version: 0.7.1-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9457 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: optional Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.7.1-2~nd14.04+1_all.deb Size: 6747422 SHA256: cb908710b381a52263bf5fa045044baeb44087cce583aef4a05127a85433d2db SHA1: a2effaf1fe3262065f838c40cd7f8caf0cc2bb31 MD5sum: 1ef17522216fa8c959a882e7f568949f Description: toolbox for analysis of MR diffusion imaging data -- documentation Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. Package: python-expyriment Version: 0.7.0+git34-g55a4e7e-3~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2388 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-support (>= 0.90.0), python-pygame (>= 1.9.1~), python-opengl (>= 3.0.0), ttf-freefont, libjs-jquery, libjs-underscore Recommends: python-serial (>= 2.5~), python-numpy (>= 1.3.0~) Suggests: python-parallel (>= 0.2), python-pyxid Homepage: http://www.expyriment.org Priority: optional Section: science Filename: pool/main/p/python-expyriment/python-expyriment_0.7.0+git34-g55a4e7e-3~nd13.10+1+nd14.04+1_all.deb Size: 695948 SHA256: 37604b739e17ae561b68e1ffa8fd89495abab699acaa75ce4a4160ab0e9f1dc9 SHA1: ede08d0df1746f31ccb9eb6fbcdc49722e3b1b5b MD5sum: 95df9057ee0432389482bebb2bebc420 Description: Python library for cognitive and neuroscientific experiments Expyriment is a light-weight Python library for designing and conducting timing-critical behavioural and neuroimaging experiments. The major goal is to provide a well-structured Python library for a script-based experiment development with a high priority on the readability of the resulting programme code. Due to the availability of an Android runtime environment, Expyriment is also suitable for the development of experiments running on tablet PCs or smart-phones. Package: python-joblib Source: joblib Version: 0.8.3-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 255 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.8.3-1~nd14.04+1_all.deb Size: 64082 SHA256: 55d339fc04586adb2cbdda1a833ebb6a1c78fa8593ddf2c28e25dd053e98d3ca SHA1: 5a58179815f6c1a1f102f997e90e13a3d0be8ff0 MD5sum: 3fd5090fe4fe7c248325d5d155082eab 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-mne Version: 0.8.6+dfsg-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7181 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-numpy, python-scipy, python-sklearn, python-matplotlib, python-joblib (>= 0.4.5), xvfb, xauth, libgl1-mesa-dri, help2man, libjs-jquery, libjs-jquery-ui Recommends: python-nose, mayavi2 Suggests: python-dap, ipython Provides: python2.7-mne Homepage: http://martinos.org/mne Priority: optional Section: python Filename: pool/main/p/python-mne/python-mne_0.8.6+dfsg-1~nd14.04+1_all.deb Size: 4021862 SHA256: 671246db49db07372b109782df562aaae4fcf1f64ed9d2c7d1ec631ccfb74b96 SHA1: 4706eb6d66ece935d20bbdf9cff1bf1c06636379 MD5sum: 54123e86d60e9e3b0355eae25a35ca58 Description: Python modules for MEG and EEG data analysis This package is designed for sensor- and source-space analysis of MEG and EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. Package: python-mpi4py-doc Source: mpi4py Version: 1.3.1+hg20131106-1~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 257 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3.1+hg20131106-1~nd13.10+1+nd14.04+1_all.deb Size: 52650 SHA256: 89c6b2097aa4c46452fc4c94f25c9552c6bdbb11de0b74d3499a9fc731fcb138 SHA1: 180f3b068696bf9372da2ab0ebdeab29e18a44c1 MD5sum: ccd5f97d5b5360920b157174f29dcace Description: bindings of the MPI standard -- documentation MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides HTML rendering of the user's manual. Package: python-mvpa2 Source: pymvpa2 Version: 2.3.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6458 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy, python:any (>= 2.7.1-0ubuntu2), python-mvpa2-lib (>= 2.3.1-1~nd14.04+1) Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-nipy, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, shogun-python-modular, liblapack-dev, python-pprocess 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_2.3.1-1~nd14.04+1_all.deb Size: 3684314 SHA256: 89058980b2241508a5c38ecca7b6ce5d83b4f69cfb305a8cdd250ab235f2f55c SHA1: d820614dee9226565deb95dee9243197798988a4 MD5sum: 0f99a985742a17cb761b88a2ed3587b6 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: 2.3.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 27622 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_2.3.1-1~nd14.04+1_all.deb Size: 4511476 SHA256: 780a40e6d583db7a6199380b73e1cb6690c9c0b1955aa1b7cea3a52bd661fe4a SHA1: ee152b7296927d11ace4831a2e1abfd1ad75da17 MD5sum: eb9c3ff87c38f99d1c01fcf3bb949cd7 Description: documentation and examples for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a HTML documentation (tutorial, FAQ etc.), and example scripts. In addition the PyMVPA tutorial is also provided as IPython notebooks. Package: python-neurosynth Source: neurosynth Version: 0.3-1~nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 81 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-numpy, python-scipy, python-nibabel, python-ply Recommends: python-nose, fsl-mni152-templates Suggests: python-testkraut Homepage: http://neurosynth.org Priority: extra Section: python Filename: pool/main/n/neurosynth/python-neurosynth_0.3-1~nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 28812 SHA256: 15d6ad200903f48f7d0ac38e08d3aea9a417b73085929fcdacce541b5ecb0f05 SHA1: ab997820ecbef62ee9805767c8810a4a4663c6a4 MD5sum: 6ca9dceaed50e4921f7759c6fe0b948f Description: large-scale synthesis of functional neuroimaging data NeuroSynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. This Python module at the moment provides functionality for processing the database of collected terms and spatial coordinates to generate associated spatial statistical maps. Package: python-nibabel Source: nibabel Version: 1.3.0-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4152 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Provides: python2.7-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.3.0-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 1719980 SHA256: ee744c5748b3d2cc5a8c7241d76198227c788d51f817108e01533f8ad9c91f72 SHA1: efbe4d81861db1ff40dd49ac92757b7c40944e30 MD5sum: 143b3deb6c33f6af87d90167c6039312 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 Python-Version: 2.7 Package: python-nibabel-doc Source: nibabel Version: 1.3.0-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2452 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.3.0-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 361828 SHA256: 327f4c0f8751aebc623aac350b7298edccad690fb84848ce299e9f759e860673 SHA1: 4af73218d9a6aec7440962a8bd511f6ea0e7ca92 MD5sum: dc8908929e75c6f5a7d48f2868187e68 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-nipy Source: nipy Version: 0.3.0+git262-gbb838d7-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2953 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.2), python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-scipy, python-nibabel, python-nipy-lib (>= 0.3.0+git262-gbb838d7-1~nd14.04+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.3.0+git262-gbb838d7-1~nd14.04+1_all.deb Size: 723360 SHA256: 9e3c2ae899faf3920f66003f1311689b96e17352ac55a9684611d80dfd63e878 SHA1: ebaee3a20531d25fe59e8903b2999c9e889fabc6 MD5sum: f8ae83557d9bb61ee81fc7d1e20d6085 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It provides functionality for - General linear model (GLM) statistical analysis - Combined slice time correction and motion correction - General image registration routines with flexible cost functions, optimizers and re-sampling schemes - Image segmentation - Basic visualization of results in 2D and 3D - Basic time series diagnostics - Clustering and activation pattern analysis across subjects - Reproducibility analysis for group studies Python-Version: 2.7 Package: python-nipy-doc Source: nipy Version: 0.3.0+git262-gbb838d7-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7995 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.3.0+git262-gbb838d7-1~nd14.04+1_all.deb Size: 1142090 SHA256: 3437ed7d6491ff3082554ce73985c500339cde50cb407c51ba3fc774f5d2691e SHA1: 0e211e07439d4e3593742f8d3727ce2d2569b762 MD5sum: 9309cdf86776c79bee31e67e1ba1ad93 Description: documentation and examples for NiPy This package contains NiPy documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation Package: python-nipype Source: nipype Version: 0.10.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4573 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib Recommends: ipython, python-nose, graphviz Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat, mne-python, elastix, ants Provides: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.10.0-1~nd14.04+1_all.deb Size: 1158946 SHA256: 39836fcb648f64546c684c1831a50292c701b204b127ee8ea9a3ac3d162dce69 SHA1: fc0b5f3620bc163b435ce94c3537f601bd89ce0c MD5sum: f56846a6794f64b052f434dac5e5e4ca Description: Neuroimaging data analysis pipelines in Python Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). Package: python-nipype-doc Source: nipype Version: 0.10.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20779 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.10.0-1~nd14.04+1_all.deb Size: 8759220 SHA256: 4b2c7602f68541b83fefb69f9e2b3277109f40fb53ce0ab93fffdd461303f5e7 SHA1: b5dd929f7a8df02f697c2ca7e98fc28cf8f1e95a MD5sum: 91069fca772934ae6d749a309252bb74 Description: Neuroimaging data analysis pipelines in Python -- documentation Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). . This package contains Nipype examples and documentation in various formats. Package: python-nitime Source: nitime Version: 0.5-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9348 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.5-1~nd14.04+1_all.deb Size: 2543180 SHA256: 4e7fb89d19eb0ea03cc9f7f5250620dff4f2d761927f03cfabb3cc68ad11a3c3 SHA1: e1fff7796a91f419a4a8333e365ccfa3bd6dcc14 MD5sum: 30f8838e072e488df104ebce556dbd3a Description: timeseries analysis for neuroscience data (nitime) Nitime is a Python module for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code. Package: python-nitime-doc Source: nitime Version: 0.5-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7695 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nitime Homepage: http://nipy.org/nitime Priority: extra Section: doc Filename: pool/main/n/nitime/python-nitime-doc_0.5-1~nd14.04+1_all.deb Size: 5725954 SHA256: a99fe69605554c7fcecd941e29ad9906e9ce7e566a590d862ce4526048878c49 SHA1: 16bdfb5d90abff3b774016371686669a3df32fad MD5sum: de116c3ccfe8fb5cc2cb69458c80cc2a 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-pandas Source: pandas Version: 0.14.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8918 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-dateutil, python-tz, python-numpy (>= 1:1.6~), python-pandas-lib (>= 0.14.1-1~nd14.04+1), python-six Recommends: python-scipy, python-matplotlib, python-tables, python-numexpr, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt, python-bs4, python-html5lib Suggests: python-pandas-doc Provides: python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.14.1-1~nd14.04+1_all.deb Size: 1250804 SHA256: 2e7821948f7b793f7309b04041fb1dbb00eda43529acce534c2ac6c79315d0f6 SHA1: 10a08107031ac2dc0e2158a2c33ff584d8ee98a8 MD5sum: 367712ebb4fc8f082bf9193e48eaf2be 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-patsy Source: patsy Version: 0.3.0-3~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 720 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), 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_0.3.0-3~nd14.04+1_all.deb Size: 162554 SHA256: 5dedc1fc454a1f938cbe4c075cda04dca81a02d99c6285d1522050af3f49827a SHA1: 82531c7115a139e1f0f76f1d33b8e9434eac7033 MD5sum: 38ee87afa0d292810409b31136bfb0bf 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: 0.3.0-3~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1244 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_0.3.0-3~nd14.04+1_all.deb Size: 352540 SHA256: a25375fcb24f4db0442e6e66e4692ef1f13c143b30ecfe4375c62ac0c679d7f4 SHA1: 7cf26c4e5b3b277a941384ac1a687e44bf75e26c MD5sum: 1547c7e45c8298fe81a121c9b97bb6e1 Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pprocess Source: pprocess Version: 0.5-1+nd0~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 716 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.boddie.org.uk/python/pprocess.html Priority: optional Section: python Filename: pool/main/p/pprocess/python-pprocess_0.5-1+nd0~nd13.10+1+nd14.04+1_all.deb Size: 81638 SHA256: a3672edffea33c0135dc765fe3dbe3524115cf8cd1ae636f2bf7cbc09cfc47be SHA1: c317f00152e89dbf84b5a85ea883b44920eef65a MD5sum: 421e4d9f4c03a34b12fbffb0d0f92b25 Description: elementary parallel programming for Python The pprocess module provides elementary support for parallel programming in Python using a fork-based process creation model in conjunction with a channel-based communications model implemented using socketpair and poll. On systems with multiple CPUs or multicore CPUs, processes should take advantage of as many CPUs or cores as the operating system permits. Python-Version: 2.7 Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-2~nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 813 Depends: neurodebian-popularity-contest, python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0+git12-g365f8e3-2~nd13.10+1+nd14.04+1_all.deb Size: 819336 SHA256: 4fd57971c92c6cd4cefaf9f32063e2c926a4cb901726c02263d9b4ea8cc24bb8 SHA1: 13d0d3aa4656070b80d4bed6e11ef0228e43b195 MD5sum: 335b1dfa97a9d8678444c11354131088 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides common files such as images. Package: python-pynn Source: pynn Version: 0.7.5-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 763 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-jinja2, python-cheetah Suggests: python-neuron, python-brian, python-csa Homepage: http://neuralensemble.org/trac/PyNN Priority: extra Section: python Filename: pool/main/p/pynn/python-pynn_0.7.5-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 122882 SHA256: 62e294043371c55fc47adddcd8c00ee9b823bfc2885a7fe6a17545f5a9ba2cea SHA1: ac1a6014356d9f0d27fe921f17abf9a006bc6dfd MD5sum: d08fcaa0e9cdb30bfb91d7d5082d4941 Description: simulator-independent specification of neuronal network models PyNN allows for coding a model once and run it without modification on any simulator that PyNN supports (currently NEURON, NEST, PCSIM and Brian). PyNN translates standard cell-model names and parameter names into simulator-specific names. Package: python-scikits-learn Source: scikit-learn Version: 0.15.2-3~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 44 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_0.15.2-3~nd14.04+1_all.deb Size: 40880 SHA256: 967ee0b8ffcf0e1f0f5b741e680bab5cd631b770ba237ac1e5699139a272bdea SHA1: 5dfd7f4b844e9257766af9e7f7d384e73f0045a4 MD5sum: 085c1b7acc00de249f1805c75e08939a 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: 0.4.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 383 Depends: neurodebian-popularity-contest, python:any (>= 2.7.5-5~), python:any (<< 2.8), python-numpy, python-scipy, python-pandas, python-matplotlib Recommends: python-statsmodels, python-patsy Homepage: https://github.com/mwaskom/seaborn Priority: optional Section: python Filename: pool/main/s/seaborn/python-seaborn_0.4.0-1~nd14.04+1_all.deb Size: 74778 SHA256: e88ddb28118ea63bd4e588060420e0ff98ca8c526c60eb1cbc45f29ed14a5305 SHA1: 5dbca29c9f71778d63679a66bdfa690835c34a3d MD5sum: 640b8c07ec9ddef95d1f3369822c3820 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-skimage Source: skimage Version: 0.10.1-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15134 Depends: neurodebian-popularity-contest, libfreeimage3, python-numpy, python-scipy (>= 0.10), python-six (>= 1.3.0), python-skimage-lib (>= 0.10.1-2~nd14.04+1), python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2) Recommends: python-imaging, python-matplotlib (>= 1.0), python-nose, python-pil, python-qt4 Suggests: python-opencv, python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.10.1-2~nd14.04+1_all.deb Size: 11927494 SHA256: 82c528be9e874b39de21a4bba62421c1d6bd7589c9596093dcf97becccbfa3ae SHA1: 172a2e19e17e3b5e2b165127f92fe4ef51f60003 MD5sum: 5c1c7e773ca81f8494f6b4b97e17b06d Description: Python modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 2 module. Package: python-skimage-doc Source: skimage Version: 0.10.1-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 21865 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-skimage Homepage: http://scikit-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.10.1-2~nd14.04+1_all.deb Size: 17205200 SHA256: 60b9c823532f9aad362ddf844c783ae8afc19152e3a8633699c1b906979ba876 SHA1: ffda4a2dd76fb4d7df9deefa82715439553fa6fd MD5sum: 34188666367a224580d2a0306199c599 Description: Documentation and examples for scikit-image This package contains documentation and example scripts for python-skimage. Package: python-sklearn Source: scikit-learn Version: 0.15.2-3~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3969 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-numpy, python-scipy, python-sklearn-lib (>= 0.15.2-3~nd14.04+1), python-joblib (>= 0.4.5) Recommends: python-nose, python-matplotlib Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Enhances: python-mdp, python-mvpa2 Breaks: python-scikits-learn (<< 0.9~) Replaces: python-scikits-learn (<< 0.9~) Provides: python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.15.2-3~nd14.04+1_all.deb Size: 1011128 SHA256: f28bed4332637dc4098ab64059fc10ae881dac510617c5e5112f27ead12716f8 SHA1: cb9a39bd815220aae314c3b6afddd9d0f996654e MD5sum: 70a17fb4fdfcd51bfe48f09d61865bc6 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: 0.15.2-3~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 66567 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.15.2-3~nd14.04+1_all.deb Size: 42379350 SHA256: 3b5e5eaca5b63e9f368d67052e1c1da96b645bd374a97fcd39d3356a9de85538 SHA1: a85191aaa2d61314853c20f700cd0c1cb21c4c46 MD5sum: 56b585dc16dde146edf4a66b0f43049f Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-surfer Source: pysurfer Version: 0.5-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 213 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-numpy, python-scipy, python-nibabel, python-pil | python-imaging, mayavi2, python-argparse Recommends: mencoder Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.5-1~nd14.04+1_all.deb Size: 38530 SHA256: e91ef2ae3278790c4c6249653e455c3b3d92162f3230d1f0d49c9a520a5486af SHA1: fd99fa261b9afb9d40da4bb54fee42a27bfb5875 MD5sum: 891bbab8bcfc7e859ad5ecfc953ef4e8 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: python3-joblib Source: joblib Version: 0.8.3-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 250 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Recommends: python3-numpy, python3-nose, python3-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python3-joblib_0.8.3-1~nd14.04+1_all.deb Size: 61200 SHA256: 482a0434b02a97662549e703ee1560516307e196b209d9b2d9c47a56624604d3 SHA1: cf572583e859813d0028801cffda7adb0073b919 MD5sum: cdda8e52a79a47c429ef0596c9a48618 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-pandas Source: pandas Version: 0.14.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8903 Depends: neurodebian-popularity-contest, python3-dateutil, python3-tz, python3:any (>= 3.3.2-2~), python3-numpy (>= 1:1.6~), python3-pandas-lib (>= 0.14.1-1~nd14.04+1) Recommends: python3-scipy, python3-matplotlib, python3-numexpr, python3-tables, python3-bs4, python3-html5lib, python3-six Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.14.1-1~nd14.04+1_all.deb Size: 1247938 SHA256: d016a5929006b1adcc15ccf6066d2797e88a5ce4640c35959a6ac18e406b3897 SHA1: 0d590c6d22d66179223c33775ba1a0a851b947b3 MD5sum: 2250055c14cf83b0631aee5ec0f93e5a 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: 0.3.0-3~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 717 Depends: neurodebian-popularity-contest, python3-numpy, 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_0.3.0-3~nd14.04+1_all.deb Size: 162128 SHA256: 5301c49cfd30c0d88f549c2752d7dde4f890dc39633ea1cf0b87fbf61700e9cc SHA1: 948eb86c0ce9f3037e6a4d5cbbf97268e7dc78a4 MD5sum: f1d71d0c6f644bf1299fe7e64fc7af50 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-seaborn Source: seaborn Version: 0.4.0-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 383 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_0.4.0-1~nd14.04+1_all.deb Size: 74854 SHA256: 498877ef7135130a56695b8fac5a64f844ed79cd00735abd3dbb473d36846f4a SHA1: f48dbe10ede07da838e89a55d029c96f1870ce22 MD5sum: 895c6d97ad00ee3c2c59b62075ed160b 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-skimage Source: skimage Version: 0.10.1-2~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15115 Depends: neurodebian-popularity-contest, libfreeimage3, python3-numpy, python3-scipy (>= 0.10), python3-six (>= 1.3.0), python3-skimage-lib (>= 0.10.1-2~nd14.04+1), python3:any (>= 3.3.2-2~) Recommends: python3-imaging, python3-matplotlib (>= 1.0), python3-nose, python3-pil Suggests: python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python3-skimage_0.10.1-2~nd14.04+1_all.deb Size: 11919718 SHA256: 1d1b5c2fbf5fef5eb25d8d619bac87cfcb6362a1c97dbd10fae204f3acbeb3f4 SHA1: ca840ff46b54b7653bfbc18b965e09f2cfe9dd97 MD5sum: 42a0247cf46cf44a61b843f8c6c13542 Description: Python 3 modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 3 module. Package: spm8-common Source: spm8 Version: 8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 18499 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 9749152 SHA256: c83baf314478407e2f1b908e55554b5645b4a1d52f9ef5be18864a6ec74c454b SHA1: 993dd179e97b25766a9dd6b1d5884041448089a3 MD5sum: d92e890135a7c0c8eb5f4102b380b07c Description: analysis of brain imaging data sequences Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the platform-independent M-files. Package: spm8-data Source: spm8 Version: 8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 72987 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 45484386 SHA256: 182e2818ac165f6a04ef610a17226e4019e76b6403242ce5106dc8084088f456 SHA1: 4ede6932c3e3b32e11bd0e1360522b3cda6e69e2 MD5sum: dd2edf6746682da9d77ea73d1ee36418 Description: data files for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provide the data files shipped with the SPM distribution, such as various stereotaxic brain space templates and EEG channel setups. Package: spm8-doc Source: spm8 Version: 8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9242 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 8935290 SHA256: 5742ed7248b597e91212ca03e53541e520bbeccc2d59f865b278b7c94362661e SHA1: 32542240cefbb5d936731db01ffc3175348bc80e MD5sum: 511c71c1ed452c9f1c5dd547eaade5c8 Description: manual for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the SPM manual in PDF format. Package: spykeviewer Version: 0.4.2-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1122 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python:any (>= 2.7.1-0ubuntu2), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.4.0), python-neo (>= 0.2.1), python-matplotlib, python-scipy, python-nose, python-sphinx, python-tables Recommends: libjs-jquery, libjs-underscore, ipython-qtconsole (>= 0.12) Homepage: http://www.ni.tu-berlin.de/software/spykeviewer Priority: extra Section: python Filename: pool/main/s/spykeviewer/spykeviewer_0.4.2-1~nd14.04+1_all.deb Size: 537176 SHA256: d70fdb4a5c3c12495f55ccb2e61d774899e728ca550e2c024031f996aad3a5b3 SHA1: 45ba1480cf0f37f86f1bea60d94c6a6d197ce1fe MD5sum: 6b1e59da19077161f62e0fd0ec72d4d7 Description: graphical utility for analyzing electrophysiological data Spyke Viewer is a multi-platform GUI application for navigating, analyzing and visualizing electrophysiological datasets. Based on the Neo framework, it works with a wide variety of data formats. Spyke Viewer includes an integrated Python console and a plugin system for custom analyses and plots. Package: stabilitycalc Version: 0.1-1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy, python-nifti Recommends: python-dicom Homepage: https://github.com/bbfrederick/stabilitycalc Priority: extra Section: science Filename: pool/main/s/stabilitycalc/stabilitycalc_0.1-1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 22678 SHA256: ea800adb74820759f1c8041031b4b396c15b127a50f03a44c9e7e374649c351e SHA1: 0b16001f8fc76a1fadef374d9f61187cc11edfcf MD5sum: 3ece230b8a5225d2691618b7b10e78ba Description: evaluate fMRI scanner stability Command-line tools to calculate numerous fMRI scanner stability metrics, based on the FBIRN quality assurance test protocal. Any 4D volumetric timeseries image in NIfTI format is support input. Output is a rich HTML report. Python-Version: 2.7 Package: testkraut Version: 0.0.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 358 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, libjs-underscore, libjs-jquery, python-argparse Recommends: strace, python-scipy, python-colorama, python-apt Homepage: https://github.com/neurodebian/testkraut Priority: extra Section: python Filename: pool/main/t/testkraut/testkraut_0.0.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1_all.deb Size: 85950 SHA256: 9a51b163a417b1a421415111ce4ddedea08a840e943a7a144f782b37944d8699 SHA1: 77a25da70008038d7ccaa99042ef1b0fe2a04229 MD5sum: f54d2b8e28fcffe650211599731dab19 Description: test and evaluate heterogeneous data processing pipelines This is a framework for software testing. That being said, testkraut tries to minimize the overlap with the scopes of unit testing, regression testing, and continuous integration testing. Instead, it aims to complement these kinds of testing, and is able to re-use them, or can be integrated with them. . In a nutshell testkraut helps to facilitate statistical analysis of test results. In particular, it focuses on two main scenarios: . * Comparing results of a single (test) implementation across different or changing computational environments (think: different operating systems, different hardware, or the same machine before an after a software upgrade). * Comparing results of different (test) implementations generating similar output from identical input (think: performance of various signal detection algorithms). . While such things can be done using other available tools as well, testkraut aims to provide a lightweight, yet comprehensive description of a test run. Such a description allows for decoupling test result generation and analysis – opening up the opportunity to “crowd-source” software testing efforts, and aggregate results beyond the scope of a single project, lab, company, or site. Python-Version: 2.7