Package: condor-doc Source: condor Version: 7.8.7~dfsg.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6155 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.7~dfsg.1-1~nd12.04+1_all.deb Size: 1334256 SHA256: 894b6a7bf1698aa1a4659aa0da42fea2f0f922dcc88c4de8105a9785026589a7 SHA1: f525acd1a355ef6bab5f00bc07f058a0fba3a0f9 MD5sum: 5d393a9bef114e99c52fd3c2269a83f7 Description: distributed workload management system - documentation Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor; Condor 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, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . This package provides Condor's documentation in HTML and PDF format, as well as configuration and other examples. Package: connectomeviewer Version: 2.1.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1576 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-cfflib (>= 2.0.5), python-networkx (>= 1.4), python-nibabel, python-numpy (>= 1.3.0), python-scipy, python-chaco, mayavi2 (>= 4.0.0), ipython Recommends: python-nipype, python-dipy, python-matplotlib, python-qscintilla2 Suggests: nipy-suite Homepage: http://www.connectomeviewer.org Priority: extra Section: python Filename: pool/main/c/connectomeviewer/connectomeviewer_2.1.0-1~nd12.04+1_all.deb Size: 1355528 SHA256: 8255392e769af2ca1b745f6707a441213b408db7ae6a12c299698c495f618f0b SHA1: 1834b0270b864861f5c97b2a7ecdfabb7cc1ae14 MD5sum: 09125f910fb0ecec7fd33d65efb0c75f Description: Interactive Analysis and Visualization for MR Connectomics The Connectome Viewer is a extensible, scriptable, pythonic research environment for visualization and (network) analysis in neuroimaging and connectomics. . Employing the Connectome File Format, diverse data types such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The Connectome Viewer is part of the MR Connectome Toolkit. Package: coop-computing-tools-doc Source: cctools Version: 3.4.2-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2319 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: doc Filename: pool/main/c/cctools/coop-computing-tools-doc_3.4.2-1~nd11.10+1+nd12.04+1_all.deb Size: 310964 SHA256: a39fa9bc2251d5a045a6126278b1e772f41883e8ef1dd939b2b2c903df0fe40a SHA1: 79255768567dff8808dd954c9e64dc6c99dfd89c MD5sum: 3b540998ed6b8eda89421dd11cd61d6a Description: documentation for coop-computing-tools These tools are a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. . This package provides the documentation (manual and API reference) in HTML format. Package: debian-handbook Version: 6.0+20120509~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23215 Depends: neurodebian-popularity-contest Homepage: http://debian-handbook.info Priority: optional Section: doc Filename: pool/main/d/debian-handbook/debian-handbook_6.0+20120509~nd+1_all.deb Size: 21998670 SHA256: b33f038d8363175473cc056a5f98fc7af52386a466b45d4b2e42d2f25233a3ed SHA1: 7a0b369b4548a3f4fb61aa1ef9efa2ddf2b319e2 MD5sum: 3e3d2cf990fcc5ed1ed6bdbfb5c1c3dd Description: reference book for Debian users and system administrators Accessible to all, the Debian Administrator's Handbook teaches the essentials to anyone who wants to become an effective and independent Debian GNU/Linux administrator. . It covers all the topics that a competent Linux administrator should master, from the installation and the update of the system, up to the creation of packages and the compilation of the kernel, but also monitoring, backup and migration, without forgetting advanced topics like SELinux setup to secure services, automated installations, or virtualization with Xen, KVM or LXC. . The Debian Administrator's Handbook has been written by two Debian developers — Raphaël Hertzog and Roland Mas. . This package contains the English book covering Debian 6.0 “Squeeze”. Package: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+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_all.deb Size: 7224748 SHA256: 896e3a64a84c1ecfa3f8aeb72849dbad8afb923046a6efb8f85cef680dd88880 SHA1: 4d6ca1909de2b2ec07ad124633c1ea2a0e41806c MD5sum: 66c6e6dde6068a39cf8a541bd1b66848 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: 0.8.8-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 359 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.8-1~nd12.04+1_all.deb Size: 112566 SHA256: c7eefe01284bdd6fd7305b7af754b1cbdf4be79d3802e49fe1cdd31ab2e36efe SHA1: 74663dde071336c241b3573f5a89beba0e4d45b1 MD5sum: e40b813f733a7445d15891239a37dc75 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: 1.1.5-3~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, freeipmi-common, freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi_1.1.5-3~nd12.04+1_all.deb Size: 934 SHA256: 5a9ad975eb80c528882a5bf930cacf35016d96dbcd9acd3b76c4beee5083ca3d SHA1: 87ed2db8c7b24d9c42b88e17cd3796a7ea41bb47 MD5sum: b231a9985cf7fdd0379a36951f434615 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 meta-package depends on all separate modules of freeipmi. Package: freeipmi-common Source: freeipmi Version: 1.1.5-3~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 380 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_1.1.5-3~nd12.04+1_all.deb Size: 296948 SHA256: ded72c10b600960c8e28eb582a2da41e8d1b3c4db3f5c202c825a96bb4b93dbf SHA1: b79e420de532b04ba33d7f3baa0a0c32478c9c17 MD5sum: 6e1a9030c40445d77ccd328c63f1b995 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: 4.0.0~beta1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2873 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.0~beta1-1~nd12.04+1_all.deb Size: 2346150 SHA256: f2189b5846f99bab04478e8d9b64a022d57d658f5703eeee0778a27a77540c7d SHA1: 42d493739a248e3252289daf223ce2958da3d94b MD5sum: 65e95f7907069429dd363ddeae59b8f7 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: guacamole Version: 0.6.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 302 Depends: neurodebian-popularity-contest, guacd (>= 0.6), guacd (<< 0.7) Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole_0.6.0-1~nd12.04+1_all.deb Size: 277626 SHA256: 031ed40a75c0f5ab6f1fd0494f9776baf8b8100a76424d54ec0766d9f90ec40a SHA1: 5c6e2a9d1697f9ac0b4fadd3446853f6b986bf72 MD5sum: cb834423bc0f2462911800a2ee6c9a59 Description: HTML5 web application for accessing remote desktops Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. Package: guacamole-tomcat Source: guacamole Version: 0.6.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole-tomcat_0.6.0-1~nd12.04+1_all.deb Size: 5172 SHA256: 8a9423cc80eecd320a8902746df9692514c78b60d25eb847001b184820ce8174 SHA1: 615d0b822e7ff888796cc40ac5ffb44a317b23af MD5sum: f7c8aeaa54bb5a3b1bc56c49bdd781bf Description: Tomcat-based Guacamole install with VNC support Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. . This metapackage depends on Tomcat, Guacamole, and the VNC support plugin for guacamole. Guacamole is automatically installed and configured under Tomcat. Package: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd12.04+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_all.deb Size: 9652 SHA256: 2e8333994fe771da783ce3abdf490b2250b2643c8e879a9ed656a123425f949f SHA1: fcd79a61add1454528aa8aaaff1f438ae66859d5 MD5sum: 66bc6f30071a526052024744e99d9448 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: ipython01x Version: 0.13.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4661 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython01x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x_0.13.1-1~nd12.04+1_all.deb Size: 1285094 SHA256: cc56db8f75a2b65d27ef76ee95aad1f8dcbf6663ec050b5b7c15f97e97a37ed9 SHA1: cdae090acca28a0d80bc1c5422bc5c92efa55994 MD5sum: d0915ce231800504abbdaa8c259b56ba Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.13.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16634 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.1-1~nd12.04+1_all.deb Size: 7228126 SHA256: 52674694474ce571434d7a8b43198455f37a7095279d8df7172400eb85f93284 SHA1: f771de810775c2ee16c2d967ae56c90523029123 MD5sum: 336e8588c76d58a82324cd5ea877f300 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-notebook Source: ipython01x Version: 0.13.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-notebook_0.13.1-1~nd12.04+1_all.deb Size: 900 SHA256: 664f168f031b836eeaa95420078cff1ee7a38465d22f69f24c2196ddf68d11d6 SHA1: 5c6203c7e76345dcd1709b674fd2674bd33a9610 MD5sum: 297db5ba5af9f793a750b5ee549fa687 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython01x-parallel Source: ipython01x Version: 0.13.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython01x/ipython01x-parallel_0.13.1-1~nd12.04+1_all.deb Size: 828 SHA256: 5811094192792eadf06ea11549bb9ce509ad234099d689fa62a01a1c6e963242 SHA1: 13f366bd7052c1c4982b4923968dff9262fd195e MD5sum: a202fdbc6899ff94206e280f2e6ec7e0 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython01x-qtconsole Source: ipython01x Version: 0.13.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.13.1-1~nd12.04+1_all.deb Size: 912 SHA256: 93b370bad61225ed3411b11fe62641a1d76bff09f1ac768e4d0f0a37bfa17dca SHA1: 7b2cd643e2bacabac9396c65386a9c40b89bd6b9 MD5sum: 6b1d69b213ee425acea7689defc0eae0 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: libfreenect-doc Source: libfreenect Version: 1:0.1.2+dfsg-6~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 482 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~nd12.04+1_all.deb Size: 90826 SHA256: ce3aa05ed1adb1052c95f64d8d6bca0c9b68ca36b62d16a84153a932f7a95edd SHA1: 953a5d482244b055130a34ea227d553dc89b9e6e MD5sum: 77f83c1d42d041e8642df0f5227f05fe 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: libisis-core-dev Source: isis Version: 0.4.7-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 260 Depends: neurodebian-popularity-contest, libisis-core0 (>= 0.4.7-1~nd11.10+1+nd12.04+1), libisis-core0 (<< 0.4.7-1~nd11.10+1+nd12.04+1.1~) Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-core-dev_0.4.7-1~nd11.10+1+nd12.04+1_all.deb Size: 69044 SHA256: 676aa659129766e70ed2a833d4341b47e95766cffe931f88ed304d34e700696e SHA1: d8c229966b0716f28ff127aa29d3909149fc1aa9 MD5sum: 8b63e70f78d6b4906a79298a3cb3d11a Description: I/O framework for neuroimaging data This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This package provides headers and library to develop applications with ISIS. Package: libisis-qt4-dev Source: isis Version: 0.4.7-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Depends: neurodebian-popularity-contest, libisis-qt4-0 (>= 0.4.7-1~nd11.10+1+nd12.04+1), libisis-qt4-0 (<< 0.4.7-1~nd11.10+1+nd12.04+1.1~), libqt4-dev Conflicts: isis-qt4-dev Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-qt4-dev_0.4.7-1~nd11.10+1+nd12.04+1_all.deb Size: 6062 SHA256: d421f9a584c148a1d0506dc3fe98ef26ded298ba20cdd7852f4800209078f8da SHA1: 4b1fa88ac55f52e9b6bb1531467fb40c6c6ee386 MD5sum: ff41be8c4a67e54cd9b0c8427491a549 Description: Qt4 bindings for ISIS data I/O framework (development headers) This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. Package: libvia-doc Source: via Version: 2.0.4-2~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 903 Depends: neurodebian-popularity-contest Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/v/via/libvia-doc_2.0.4-2~nd11.10+1+nd12.04+1_all.deb Size: 118526 SHA256: 70f7d7d0530cd4a253c866ab3a50af8055d66a646e80aeb59d04c02a6c8f3766 SHA1: 80e49482b9207ed42f89c6b98dc5ea1ba7ff8cae MD5sum: c5bb11d4da40e97b8af9c9577f74be1b Description: VIA library API documentation VIA is a volumetric image analysis suite. The included libraries provide about 70 image analysis functions. . This package provides the library API reference documentation. Package: matlab-support-dev Source: matlab-support Version: 0.0.17~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: neurodebian-popularity-contest Conflicts: matlab-dev (<= 0.0.14~) Replaces: matlab-dev (<= 0.0.14~) Priority: optional Section: devel Filename: pool/main/m/matlab-support/matlab-support-dev_0.0.17~nd11.10+1+nd12.04+1_all.deb Size: 6772 SHA256: 0d01b3b6c9ed47786ef3d930392b0639a3b59336c4671ec6944d220c90eb7993 SHA1: d4736843c30bb93f510d5d8ec3eae3bc188ae040 MD5sum: 558db64d457ea46f05be4326b59049fd Description: helpers for packages building MATLAB toolboxes This package provides a Makefile snippet (analogous to the one used for Octave) that configures the locations for architecture independent M-files, binary MEX-extensions, and their corresponding sources. This package can be used as a build-dependency by other packages shipping MATLAB toolboxes. Package: mricron-data Source: mricron Version: 0.20120505.1~dfsg.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1678 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20120505.1~dfsg.1-1~nd12.04+1_all.deb Size: 1663990 SHA256: 50cb35643a0344a976f1d7cc3640acba63d8bf6455a68d41d7ce72002f8d69bc SHA1: 13d30293c271a1834ef3476abc2a406d950f5f87 MD5sum: ceacdbdbee629ec1c239a75e60bcc177 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: 0.20120505.1~dfsg.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 979 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20120505.1~dfsg.1-1~nd12.04+1_all.deb Size: 735726 SHA256: 42637c601f13e774c8e06b84df3094acd471bfdf7b48728488c849d97c8a0498 SHA1: 349ecc6f7aa7d1ce9e1401dfaf2dd2475cbb2cd7 MD5sum: 3ce51fe9f23ba621df0d8d6c65e8833c 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: 0.2.10-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3485 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.10-1~nd11.10+1+nd12.04+1_all.deb Size: 3315494 SHA256: 94dbee8905712de4c824ee39eb7a83e3cd44eeebc0e39c601a94222acae7d6ef SHA1: 43dd82edbe0141bbe64ba8911db7d23760243a3e MD5sum: 8829e0008103ae5a0eb17ad53039ac26 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: neurodebian-desktop Source: neurodebian Version: 0.30~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 142 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_0.30~nd12.04+1_all.deb Size: 115064 SHA256: b8199a197a2e4ba1b2d63a3b903cb24cac24cd29b027771d589ac04d00954916 SHA1: 980f2aead26f62445648a79d5163ed363969c238 MD5sum: 7c98e68e5f143a27f524d29296bdcfdd 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: 0.30~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5751 Depends: devscripts, cowbuilder, neurodebian-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_0.30~nd12.04+1_all.deb Size: 5348068 SHA256: 8f929f02e7c3eac0ddceae133ecf93e1b60ec46cb277a7fce99135b1e4817930 SHA1: b251dd75ee77e5d6bb9688a689953140687a8123 MD5sum: a7ddcf4020b61d9dc044d4bfd168ef2b 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: 0.30~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm | lightdm, zenity Recommends: chromium-browser, update-manager-gnome, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.30~nd12.04+1_all.deb Size: 14930 SHA256: 3fca67e718d06feebc15e20bc9ddfa0ff8eb67d4e6473d3e2c93b0b03f4e7f86 SHA1: 13a9e92018dcc66462cabdcdbbacd7a27f4cafe3 MD5sum: e4ed7943a90e36345ce6d1569a42232e 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: 0.30~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.30~nd12.04+1_all.deb Size: 7256 SHA256: 2026703ccd27fb47a1f2d2c580557dc6325ce4ad89c274f8667a2baaca136038 SHA1: c773e68c6ecbbd81a7bd5050dc200c9f88aa85cb MD5sum: a0899cbc0af9944192e830d218467af9 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: 0.30~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.30~nd12.04+1_all.deb Size: 6418 SHA256: 2dd77c3c49ed86aa0fd66595297aff2ceb385f370a6e756370afe92fa9b4b33d SHA1: b547bef3aa799dc0bffe67a7096b91059cf4569c MD5sum: e3cb3b29299413bfa2c0b7634c45b6f1 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: 0.4.5-1~nd12.04+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_0.4.5-1~nd12.04+1_all.deb Size: 614948 SHA256: 5eb6f74513acc4a66431b3cb9f5131a54513f2d9b4bc8848645d1aa29ff4aa4d SHA1: 87633545ec8b02aba87ce4d215ee12d5b07f8aa1 MD5sum: 283327b9bcae97331518bcdeb56604ca 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: 0.3.25+ds-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1377 Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python (>= 2.7.1-0ubuntu2) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6) Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.3.25+ds-1~nd12.04+1_all.deb Size: 349506 SHA256: d35e119d1768c31d699a3156901904a3334d76dfd86d3f427171f4fc8fd43c18 SHA1: b109518aee26a68342024095b21287dc962c19b2 MD5sum: bc4ceda946013cf40cf0bd6564da823e 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 to pure Python objects at all. Package: opensesame Version: 0.27-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25055 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~), python-qscintilla2, gnome-icon-theme Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1), expyriment (>= 0.5.2), ipython-qtconsole (>= 0.12) Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27-1~nd12.04+1_all.deb Size: 24027302 SHA256: 4e9e7dfe4881380976ffdaa88be25983b3415c0fee970e2e97d9d5f0131c0f14 SHA1: f6b77a591b9be30f07dce4af8fb71b82353adc53 MD5sum: 32ffae2a0802e724538e2cc7dada0851 Description: graphical experiment builder for the social sciences This graphical environment provides an easy to use, point-and-click interface for creating psychological experiments. In addition to a powerful sketchpad for creating visual stimuli, OpenSesame features a sampler and synthesizer for sound playback. For more complex tasks, OpenSesame supports Python scripting using the built-in editor with syntax highlighting. Package: packaging-tutorial Version: 0.7~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1545 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.7~nd+1_all.deb Size: 1482008 SHA256: adc5cfa1161cb2c81de6dfe8ef28337496f4482dbd4e81529fdca5bb7f99d234 SHA1: 9326aef75840496b2113097a67ab254223056afe MD5sum: 08e90e8b604b39dea04f6eb7b4359b21 Description: introduction to Debian packaging This tutorial is an introduction to Debian packaging. It teaches prospective developers how to modify existing packages, how to create their own packages, and how to interact with the Debian community. In addition to the main tutorial, it includes three practical sessions on modifying the 'grep' package, and packaging the 'gnujump' game and a Java library. Package: psychopy Version: 1.74.03.dfsg-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5203 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, libavbin0, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.74.03.dfsg-1~nd12.04+1_all.deb Size: 3102242 SHA256: 977bfcfda6079a214a99ae12c666a7937ec0d63070aa6b8c5dfa3dbb07f77632 SHA1: c414509c0f59f29c6bd0d7313ded1a93a6af0a17 MD5sum: 9678040b3578edddc84cdf547d3adce1 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: 3.0.10.20130114.dfsg1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48860 Depends: neurodebian-popularity-contest Recommends: subversion Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.10.20130114.dfsg1-1~nd12.04+1_all.deb Size: 19678522 SHA256: 8d27dba1df1971775b6237864bc9cf5f90e028e8e2ac35865528e86ac6d72985 SHA1: f00542db6f21faa51c8b0a5a3247b4c32d0f60f6 MD5sum: b864112894fce59508b31570e7721ddc 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: 1.4.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2139 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.0-1~nd12.04+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_1.4.0-1~nd12.04+1_all.deb Size: 503182 SHA256: 86898730bb5dfe509ff20ecae6ad6185d5b995b515b551774841ada3dded9b4c SHA1: 77a6a08af98f556dca0a9d43df5314964487ef2c MD5sum: 333fb44dbb45efb8e8c8bcbfb55e1b1e 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: 1.4.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6126 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_1.4.0-1~nd12.04+1_all.deb Size: 2173382 SHA256: bfb869867ac04be0f5f9535d75b07f964917b9e8b90423d6f64fc26657cbe129 SHA1: 2c70ef46253fdce2c5953c497f3d4ce9088ef2fe MD5sum: d1fcfa2674ed2ac83d0bac27044317e5 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: 0.9.7-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1790 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) 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_0.9.7-1~nd12.04+1_all.deb Size: 419144 SHA256: ff2d47b401f993dc306b9e975ec4f391c5b0ec785404dcfbb6947b31e2747260 SHA1: 15ac595edc152a5586adc589d65fb5f9d6b68a7e MD5sum: 356c677bbfecb4505448c0217b0e0b43 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-joblib Source: joblib Version: 0.6.5-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 175 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.6.5-1~nd12.04+1_all.deb Size: 52654 SHA256: ee9cdc485f4913479e6a9e418886c48ee8576b2d3c1554272090a22a2d2f4425 SHA1: d93ac433b7e957a49f672bf8cd3a5039445191b2 MD5sum: e6e5bbc049d4ac4415cb483ceb09bba8 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. Package: python-lazyarray Source: lazyarray Version: 0.1.0-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy Homepage: http://bitbucket.org/apdavison/lazyarray/ Priority: optional Section: python Filename: pool/main/l/lazyarray/python-lazyarray_0.1.0-1~nd11.10+1+nd12.04+1_all.deb Size: 7346 SHA256: 119a709e7c8d3e6452027994781665214ff7df777b409d2ecfbbbf805c1c6240 SHA1: ca46afca4f6d4e692a37427d5a85d3eb2f6f2c86 MD5sum: 69478e3c00645627a84e5b965942f006 Description: Python module providing a NumPy-compatible lazily-evaluated array The 'larray' class is a NumPy-compatible numerical array where operations on the array (potentially including array construction) are not performed immediately, but are delayed until evaluation is specifically requested. Evaluation of only parts of the array is also possible. Consequently, use of an 'larray' can potentially save considerable computation time and memory in cases where arrays are used conditionally, or only parts of an array are used (for example in distributed computation, in which each MPI node operates on a subset of the elements of the array). Package: python-mdp Source: mdp Version: 3.3+git6-g7bbd889-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1495 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy Recommends: python-scipy, python-libsvm, python-joblib, python-scikits-learn | python-sklearn, python-pp Suggests: python-py, shogun-python-modular Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.3+git6-g7bbd889-1~nd12.04+1_all.deb Size: 478618 SHA256: d515003bbfa0d0dc1208aae5e2bda578fa80925cf08dccd1da3e152097d9e916 SHA1: 1e9e9e6faeef010409ee186e9a4031b4d790cc3e MD5sum: ffed437993bb7b44c052aaa1deac99ff Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 2. Package: python-mpi4py-doc Source: mpi4py Version: 1.3+hg20120611-2~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 275 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3+hg20120611-2~nd12.04+1_all.deb Size: 76606 SHA256: 3707b5fef00c097c3b5c0a49250d73374c29f36a93d1033acfd1118115a5b1e1 SHA1: 50a93fe441f840a56622b062161ea4ea211d9b7b MD5sum: 356fa1f7b509bf0e06d6a13a1a183499 Description: bindings of the MPI standard -- documentation MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides HTML rendering of the user's manual. Package: python-mvpa Source: pymvpa Version: 0.4.8-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3547 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy, python-support (>= 0.90.0), python2.7, python-mvpa-lib (>= 0.4.8-1~nd11.10+1+nd12.04+1) Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc Provides: python2.7-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.8-1~nd11.10+1+nd12.04+1_all.deb Size: 2205054 SHA256: 157565eb22e6a64cca8e7b9369dccf884a44fd2b483184b2f9740d4818cc3f3d SHA1: 522691bdde24041e16a7feadd234cc1866586da4 MD5sum: 2f14582aa4fdd1736736b78e7026ef43 Description: multivariate pattern analysis with Python PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, GNB, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. Python-Version: 2.7 Package: python-mvpa-doc Source: pymvpa Version: 0.4.8-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 37578 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.8-1~nd11.10+1+nd12.04+1_all.deb Size: 8480396 SHA256: 0b362f8c219e02d176900b865bc51b26b54f6350eacf1d66bcae93a48b3415ff SHA1: eaafef89dd957e059ad348d1038e565c8ecf0db8 MD5sum: 0bee5fc34f30a40bb7a1e3b88f187d5b Description: documentation and examples for PyMVPA PyMVPA documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation * BibTeX references file . Additionally, all example scripts shipped with the PyMVPA sources are included. Package: python-mvpa2 Source: pymvpa2 Version: 2.2.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4241 Depends: neurodebian-popularity-contest, python (>= 2.4), python-numpy, python-support (>= 0.90.0), python-mvpa2-lib (>= 2.2.0-1~nd12.04+1) Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, shogun-python-modular, liblapack-dev 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_2.2.0-1~nd12.04+1_all.deb Size: 2399816 SHA256: bda1a7ff4f9d43195e87e6f0a32f4d544bb6bbd8b8c0c5bccb6b6f76fcc0dc70 SHA1: 8d3cbd0a94da6bc4e230a94aced5a724395a1225 MD5sum: 46668ec252c77f5bb49b5fcea2e1be3c 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: 2.2.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17220 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_2.2.0-1~nd12.04+1_all.deb Size: 5141184 SHA256: 8c480efd0e0eed96d60c07db9e779e605f392f0b1b15cec575a71738ede96524 SHA1: 2ccaecc320fbd4934945dadf62bdffdc5c4edf61 MD5sum: 25a618084be2c7f3c9f4eafc9fdc4479 Description: documentation and examples for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a HTML documentation (tutorial, FAQ etc.), and example scripts. In addition the PyMVPA tutorial is also provided as IPython notebooks. Package: python-neo Source: neo Version: 0.2.1.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2414 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy (>= 1:1.3~), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Suggests: python-nose Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.2.1.1-1~nd12.04+1_all.deb Size: 1428598 SHA256: 9675db07c583d454cb47edcadb743d91cd9214284a05819f7e00c45ba450c637 SHA1: 21e71edda70a82b2a3096009b4cadb970df5d1a7 MD5sum: 9d67169ce6766d741f04ffd6eb053e41 Description: Python IO library for electrophysiological data formats NEO stands for Neural Ensemble Objects and is a project to provide common classes and concepts for dealing with electro-physiological (in vivo and/or simulated) data to facilitate collaborative software/algorithm development. In particular Neo provides: a set a classes for data representation with precise definitions, an IO module with a simple API, documentation, and a set of examples. . NEO offers support for reading data from numerous proprietary file formats (e.g. Spike2, Plexon, AlphaOmega, BlackRock, Axon), read/write support for various open formats (e.g. KlustaKwik, Elan, WinEdr, WinWcp, PyNN), as well as support common file formats, such as HDF5 with Neo-structured content (NeoHDF5, NeoMatlab). . Neo's IO facilities can be seen as a pure-Python and open-source Neuroshare replacement. Package: python-nibabel Source: nibabel Version: 1.3.0-1~nd12.04+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_all.deb Size: 1816162 SHA256: 88819d664d277fbcc5e26832b67fcab70ad6ffd4d5e6973b607cd008dd514ab8 SHA1: 2819a59270ae2436d44aaa5b6a3347d3d2a3f2fe MD5sum: 825ab55abef608f9c8dfd6fdb8f234e2 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 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2431 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_all.deb Size: 437630 SHA256: a5d1d7d9218c6754de56bd0cdd756022594a71b565d56c3a1a1ca9299c282079 SHA1: aa02540d99de86531ca794d2029c7d71a75e306b MD5sum: 1da3073ae4c852e35e465bdc3279db3f 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: 0.2.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2776 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy (>= 1:1.2), python-support (>= 0.90.0), python-scipy, python-nibabel, python-nipy-lib (>= 0.2.0-1~nd12.04+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_0.2.0-1~nd12.04+1_all.deb Size: 762948 SHA256: 805ebb60411bff53076ec31329becf45bd24e732b86aca63107027c46ed521b6 SHA1: 3763b7e4757a26baf49024f13256b777e65cd220 MD5sum: 3004172c069c71dbd12c64adce9e6ca8 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: 0.2.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10061 Depends: neurodebian-popularity-contest, libjs-jquery Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.2.0-1~nd12.04+1_all.deb Size: 3849264 SHA256: cdfb5d3cc5078527bbeda224f931d7bfe438634f9bf12cce8e6baef24971c239 SHA1: 84af7f625c493e23abb9055e369aa82a9afa6fa8 MD5sum: 5eeffabc0ffe7af2b9d622dcfac21953 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: 0.7-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2544 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), 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 Provides: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.7-1~nd12.04+1_all.deb Size: 567658 SHA256: bf3d03a70e8af8be7ee0dc57d638702f2c2329a544437ffbab17ceeeffce5ce1 SHA1: c477b163df47122a0197f690b4d44f1a42039705 MD5sum: 18891c2a40dc2b80a1a7e546874ebaf4 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: 0.7-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14124 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.7-1~nd12.04+1_all.deb Size: 6747032 SHA256: 3b25e6c83adb673c296019a3adfa3dcc5c81c862b7501732f9a1fcf9c7c61c3d SHA1: 63f42e3060114566f5f8f027f5f0df1458bf51c3 MD5sum: b560c028512f999d846ddfe5cbe5a1c1 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: 0.4-2~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9294 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_0.4-2~nd12.04+1_all.deb Size: 3908918 SHA256: de2749874abdd7d0bfd2a5c7c3347a3ae0eb16ea268c181298e412232f995363 SHA1: 7f090b879ce7198ff6dd8a5a329b325aa27e50c6 MD5sum: a8ef238a666742742803586226b3161e 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: 0.4-2~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6795 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_0.4-2~nd12.04+1_all.deb Size: 5296608 SHA256: e9ab9bb447b6cc8771152c44ebe90c1ffd3977555fc3db8da22956bd8102410e SHA1: 5336c6cd6243a1c58c9845a1dea1e2408033c67a MD5sum: a72da32d9822a50975c57f8e1be46130 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-openopt Source: openopt Version: 0.38+svn1589-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 954 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.7-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd12.04+1_all.deb Size: 245070 SHA256: c6d2c1cf48ce88af0d065731ab604db6b11738340fa4bdcce47f2bc8ee4f255e SHA1: be29b8639736fec932e1186ecba123ac9885261b MD5sum: 8e53da6514861d3666b0b66f6db764c7 Description: Python module for numerical optimization Numerical optimization framework developed in Python which provides connections to lots of solvers with easy and unified OpenOpt syntax. Problems which can be tackled with OpenOpt * Linear Problem (LP) * Mixed-Integer Linear Problem (MILP) * Quadratic Problem (QP) * Non-Linear Problem (NLP) * Non-Smooth Problem (NSP) * Non-Linear Solve Problem (NLSP) * Least Squares Problem (LSP) * Linear Least Squares Problem (LLSP) * Mini-Max Problem (MMP) * Global Problem (GLP) . A variety of solvers is available (e.g. IPOPT, ALGENCAN). Python-Version: 2.7 Package: python-openpyxl Source: openpyxl Version: 1.5.8-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 356 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-nose Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.5.8-1~nd11.10+1+nd12.04+1_all.deb Size: 71670 SHA256: 7d0fe39de7b9a4f5b0f452b1cc659b330611eb2cab4dbc53c228c6672dc53266 SHA1: b0ebf399607650c01aaea0edd98913478500eda1 MD5sum: 9fb7775a21b0a29f6c58a3bd02787427 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: 0.10.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4160 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.6~), python-pandas-lib (>= 0.10.1-1~nd12.04+1) Recommends: python-scipy, python-matplotlib, python-tables, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt Suggests: python-pandas-doc Provides: python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.10.1-1~nd12.04+1_all.deb Size: 849810 SHA256: 402503bb5fa3288b077ee15a8ae5973d6ed59bb05be95ef666d876c683fc31f6 SHA1: af4eab07f8e72abd20ebaece160cca3bdbcc3c3f MD5sum: 4eb0ecd26b446fdd2f7db7032bfeafcb 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-pyentropy Source: pyentropy Version: 0.4.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy (>= 1.3) Recommends: python-scipy Suggests: python-nose Provides: python2.7-pyentropy Homepage: http://code.google.com/p/pyentropy Priority: extra Section: python Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd12.04+1_all.deb Size: 21328 SHA256: ddd9ba108b54448eea94d0ae6ea3ee5d64d53ddf5ce1a495b6d2cf3fcc3fb990 SHA1: 54e328922f69edc526dd9e4fee36c0a579751237 MD5sum: a54388c4f18d1e1d251afce2e209e3f1 Description: Python module for estimation information theoretic quantities A Python module for estimation of entropy and information theoretic quantities using cutting edge bias correction methods, such as * Panzeri-Treves (PT) * Quadratic Extrapolation (QE) * Nemenman-Shafee-Bialek (NSB) Python-Version: 2.7 Package: python-pynn Source: pynn Version: 0.7.4-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 762 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_0.7.4-1~nd12.04+1_all.deb Size: 175618 SHA256: 7485aec60f581aa9630a0e2d1ac91d7bd03b09dca445b05f95b564120b5e8790 SHA1: 0b5b29d71cd0218aae387dd560752f3df3b8f308 MD5sum: 7f5c149059732b963a476b5c0baf9444 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-pyxnat Source: pyxnat Version: 0.9.1+git39-g96bf069-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 862 Depends: neurodebian-popularity-contest, python-lxml, python-simplejson, python-httplib2 (>= 0.7.0) Recommends: python-networkx, python-matplotlib Homepage: http://packages.python.org/pyxnat/ Priority: extra Section: python Filename: pool/main/p/pyxnat/python-pyxnat_0.9.1+git39-g96bf069-1~nd12.04+1_all.deb Size: 190346 SHA256: 251e5eee918d64f1c8bf4cd4c1e155433e42d04c034ff9c9abda5633ff611f6c SHA1: 5e173439a42b6c0d59cb373bb402c6e1868df3f1 MD5sum: c4c2961165b4dc02a1c076d63045e85b Description: Interface to access neuroimaging data on XNAT servers pyxnat is a simple Python library that relies on the REST API provided by the XNAT platform since its 1.4 version. XNAT is an extensible database for neuroimaging data. The main objective is to ease communications with an XNAT server to plug-in external tools or Python scripts to process the data. It features: . - resources browsing capabilities - read and write access to resources - complex searches - disk-caching of requested files and resources Package: python-quantities Version: 0.10.1-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 301 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy (>= 1.4) Homepage: http://packages.python.org/quantities/ Priority: extra Section: python Filename: pool/main/p/python-quantities/python-quantities_0.10.1-1~nd11.10+1+nd12.04+1_all.deb Size: 58804 SHA256: e8cc2d0a4d86512648fb8593ef8dbc22e198d5a23dba4290c2fad574a1705185 SHA1: db73d2cfddb1e9b6e19e5f8d674d94cb8b5f10b3 MD5sum: 34ca36fdfe957727bfb6967fddc589f5 Description: Library for computation of physical quantities with units, based on numpy Quantities is designed to handle arithmetic and conversions of physical quantities, which have a magnitude, dimensionality specified by various units, and possibly an uncertainty. Quantities builds on the popular numpy library and is designed to work with numpy ufuncs, many of which are already supported. Package: python-scikits-learn Source: scikit-learn Version: 0.12.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 27 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_0.12.1-1~nd12.04+1_all.deb Size: 24318 SHA256: f09a3295ff82949701097230f43ea8ebfede23dfa175f679531c5f0414da961f SHA1: df23bedb7ae09bad2d4afa8cdc348e54698cca7c MD5sum: 949e35caaad15e46e27a17fe99224ccf Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-scikits.statsmodels Source: statsmodels Version: 0.4.2-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 19 Depends: neurodebian-popularity-contest, python-statsmodels, python (>= 2.5), python-support (>= 0.90.0) Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.4.2-1~nd12.04+1_all.deb Size: 7324 SHA256: e054ec54143674b855e621d4dc86855d34c8cc28743986632f86f9994709166a SHA1: fd195d89d08e798de6a81767466a73127b696cdc MD5sum: 56ab4047feacc06cb8d020fdf59581fa Description: transitional compatibility package for statsmodels migration Provides old namespace (scikits.statsmodels) and could be removed if dependent code migrated to use statsmodels for clarity of the namespace. Package: python-scikits.statsmodels-doc Source: statsmodels Version: 0.3.1-4~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15099 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-scikits.statsmodels Conflicts: python-scikits-statsmodels-doc Replaces: python-scikits-statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-scikits.statsmodels-doc_0.3.1-4~nd11.10+1+nd12.04+1_all.deb Size: 1902080 SHA256: 87ee6a09bddb246fd587b66220a660f0cd95a6d71b67c81df6df82614ca63c18 SHA1: 2eb8360dc1fbd3155eaf05be5fe08624036ba0dc MD5sum: ac6dbb4753f548add0bb878e62d6d087 Description: documentation and examples for python-scikits.statsmodels This package contains HTML documentation and example scripts for python-scikits.statsmodels. Package: python-skimage Source: skimage Version: 0.6.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3641 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python2.7, python-scipy (>= 0.9), python-skimage-lib (>= 0.6.1-1~nd12.04+1), libfreeimage3 Recommends: python-nose, python-matplotlib (>= 1.0), python-imaging Suggests: python-skimage-doc, python-opencv Provides: python2.7-skimage Homepage: http://scikits-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.6.1-1~nd12.04+1_all.deb Size: 2539146 SHA256: b2d5c79f9c1219dcc1e7946d05c7c58a028485e0bcb1b00d73fd82a2fc8ce936 SHA1: c43d51b7b5ebc4d015d5a423853efacc091432fc MD5sum: 526222710563493309ed1208f5edc660 Description: Python modules for image processing scikits-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. Package: python-skimage-doc Source: skimage Version: 0.6.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4867 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-skimage Homepage: http://scikits-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.6.1-1~nd12.04+1_all.deb Size: 3591424 SHA256: 0e4f5f1b5ceb2c4a2eb1bd63d6f8c0a3729b53ca04d8a80d1a8e63e23dd5bd32 SHA1: 8e6083120069f5be3820340df0ceb062cfdf028e MD5sum: a1f61e601d45c9f39bd4ba98c54b6847 Description: Documentation and examples for scikits-image This package contains documentation and example scripts for python-skimage. Package: python-sklearn Source: scikit-learn Version: 0.12.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2658 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.12.1-1~nd12.04+1) Recommends: python-nose, python-matplotlib, python-joblib (>= 0.4.5) 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_0.12.1-1~nd12.04+1_all.deb Size: 927452 SHA256: 08385499746cfc539af235643b20226c79f1de702c32854f20d2181d16ff2655 SHA1: bffc51d5a8701ec17cf092ec73123281b88d1b22 MD5sum: 02f2b57dc272b73c01c7e635ed7a92e0 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) Python-Version: 2.7 Package: python-sklearn-doc Source: scikit-learn Version: 0.12.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36623 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_0.12.1-1~nd12.04+1_all.deb Size: 26697384 SHA256: 743a9f9e8771830799dd6ec9b4dd8e99ae6ed5ede8933e00ed5241975ff026fd SHA1: 33b454abb43d9edc81f96d022fe08078eb21b5a9 MD5sum: 58700781191ec83f03b73a86453f4f13 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-spykeutils Source: spykeutils Version: 0.2.0-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1254 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-scipy, python-quantities, python-neo (>= 0.2.1), python-nose, python-sphinx Recommends: python-guidata, python-guiqwt, python-tables, libjs-jquery, libjs-underscore Provides: python2.7-spykeutils Homepage: https://github.com/rproepp/spykeutils Priority: extra Section: python Filename: pool/main/s/spykeutils/python-spykeutils_0.2.0-1~nd12.04+1_all.deb Size: 302130 SHA256: 1c4784f897a227890f622fbd8ffe4d650d6dcf24f80419e4ff9c86af4b5aabd4 SHA1: 2b4c72b54e73ebd9f2294d40c18494ced12d0b04 MD5sum: 6e3000ee6aa6c583e1006dfe3ce81d44 Description: utilities for analyzing electrophysiological data spykeutils is a Python library for analyzing and plotting data from neurophysiological recordings. It can be used by itself or in conjunction with Spyke Viewer, a multi-platform GUI application for navigating electrophysiological datasets. Package: python-statsmodels Source: statsmodels Version: 0.4.2-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12282 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy, python-statsmodels-lib (>= 0.4.2-1~nd12.04+1) Recommends: python-pandas, python-matplotlib, python-nose, python-joblib 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_0.4.2-1~nd12.04+1_all.deb Size: 3086880 SHA256: c2d3f3cae6c73c908c514903ff22bf873f123ae3c71a0a07a7bb979a513ed9ee SHA1: 5456c916e7d6cb68336e11e8c1ca674efebefeb9 MD5sum: 472c17f8cff1940955befff4c4ae566a 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: 0.4.2-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23671 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-statsmodels Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.4.2-1~nd12.04+1_all.deb Size: 7376282 SHA256: 3bde6c1934a180e07a80011af3a883f976a9b29bbf2ea5a4db5ed02041bea0f8 SHA1: fa796898186c9c6aacf3935c78ad94ea9149828f MD5sum: 69aac046ed475861b232f5cd510415cf Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-surfer Source: pysurfer Version: 0.3+git15-gae6cbb1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 93 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-nibabel, python-imaging, mayavi2, python-argparse, ipython Recommends: mencoder Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.3+git15-gae6cbb1-1~nd12.04+1_all.deb Size: 28016 SHA256: fd4a0787b83bdc6cc7dc4a09768b63fed71f0c8edd1b79f546c6099764d32235 SHA1: fa2a6482c1b0fad09935a62e2abe30f83aa2bfa7 MD5sum: 5e852946add3d76dde9d292d9def3d10 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-tz Version: 2012c-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 114 Depends: neurodebian-popularity-contest, tzdata, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2012c-1~nd12.04+1_all.deb Size: 39076 SHA256: 5e5a0c4143db73704376f78d5393a8d2562d1090e1ce0971aa074845eb6c7365 SHA1: d587a5c518fc1e8cc2b100cbcfdfa5ef3130e89e MD5sum: 1f27dc834f8849b16c7551116a435410 Description: Python version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). Package: python3-dateutil Version: 2.0+dfsg1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 153 Depends: neurodebian-popularity-contest, python3 (>= 3.1.3-13~), tzdata Homepage: http://labix.org/python-dateutil Priority: optional Section: python Filename: pool/main/p/python3-dateutil/python3-dateutil_2.0+dfsg1-1~nd12.04+1_all.deb Size: 49686 SHA256: 44eb8a99c2b13b8bd30809e636ea82734f3632c0e30dfe46e767aba4aaac530c SHA1: d7b1ee6c7d91825d3ae29daf32b386a697764c0d MD5sum: 62681c6efef26912ccad3d3e1c74dc26 Description: powerful extensions to the standard datetime module in Python 3 The dateutil package extends the standard datetime module with: . * computing of relative deltas (next month, next year, next Monday, last week of month, etc); * computing of relative deltas between two given date and/or datetime objects * computing of dates based on very flexible recurrence rules, using a superset of the iCalendar specification. Parsing of RFC strings is supported as well. * generic parsing of dates in almost any string format * timezone (tzinfo) implementations for tzfile(5) format files (/etc/localtime, /usr/share/zoneinfo, etc), TZ environment string (in all known formats), iCalendar format files, given ranges (with help from relative deltas), local machine timezone, fixed offset timezone, UTC timezone * computing of Easter Sunday dates for any given year, using Western, Orthodox or Julian algorithms Package: python3-mdp Source: mdp Version: 3.3+git6-g7bbd889-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1457 Depends: neurodebian-popularity-contest, python3 (>= 3.1.3-13~), python3-numpy Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python3-mdp_3.3+git6-g7bbd889-1~nd12.04+1_all.deb Size: 472476 SHA256: 2bc4470eac01996dfa8237bde53d42f433ac6367220e8cccf791b9d294ebe191 SHA1: ceee8aec82712893687f3de5bcd1dfd03dea5c4d MD5sum: c46ba24a20b25f1f2557623154156144 Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 3. Package: python3-pandas Source: pandas Version: 0.10.1-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4113 Depends: neurodebian-popularity-contest, python3 (>= 3.2), python3-dateutil, python3-tz, python3-numpy (>= 1:1.6~), python3-pandas-lib (>= 0.10.1-1~nd12.04+1) Recommends: python3-scipy, python3-matplotlib, python3-tables Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.10.1-1~nd12.04+1_all.deb Size: 843284 SHA256: 89eccb9caa6458513763c6534e2c998daf5175ef6b628089d7fb16844137ed34 SHA1: bfde377f14986dc9ce22ffd5fe099fd2da97ce6d MD5sum: d80add54946b5b0226f06cf72598951b 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-tz Source: python-tz Version: 2012c-1~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 Depends: neurodebian-popularity-contest, tzdata, python3 (>= 3.1.3-13~) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python3-tz_2012c-1~nd12.04+1_all.deb Size: 31110 SHA256: 27f424360b0610de71d2281248500e85f830a8e931e4c4eeb3d8d218513f0b23 SHA1: bf1c3ee7248c84b5f1dbd09a2a789bc0c243d1ca MD5sum: 81bd1a01652926741eea5c70cd2072a4 Description: Python3 version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). . This package contains the Python 3 version of the library. Package: spm8-common Source: spm8 Version: 8.4667~dfsg.1-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 18467 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.4667~dfsg.1-1~nd11.10+1+nd12.04+1_all.deb Size: 10573752 SHA256: eedaff94855047442b4e87a150ee5790c4f85cee4944a31f374fddaf09c5d135 SHA1: 38cd5b25a48b8f7e59ffae64c289e240efdcd675 MD5sum: 9ff1b4420be237904ddbcdc6baa39b12 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: 8.4667~dfsg.1-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 72987 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.4667~dfsg.1-1~nd11.10+1+nd12.04+1_all.deb Size: 52167750 SHA256: d35056af43e554c2fb498839cdc2879b41b3e9cb3a17ebb7cee908b41003f47a SHA1: 01a7758933f7769692dd266201019b0c99fbf68d MD5sum: c904d656f804c259bca71a6a7b351375 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: 8.4667~dfsg.1-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9370 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.4667~dfsg.1-1~nd11.10+1+nd12.04+1_all.deb Size: 8649042 SHA256: e0b1b632ef8dd21c9f400996da1042d8d3af9cf58b04c9f9ffaeb8e43b232e51 SHA1: abd1d4462abbf95b4e2691c7e44584b0ebcb8a55 MD5sum: 422eb0c57e44691ac1af685fcaecf77f 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: 0.2.0-2~nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 789 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-guidata, python-guiqwt, python-spyderlib, python-spykeutils (>= 0.2.0), python-neo (>= 0.2.1), python-nose, python-scipy, python-sphinx, python-tables Recommends: libjs-jquery, libjs-underscore Homepage: http://www.ni.tu-berlin.de/software/spykeviewer Priority: extra Section: python Filename: pool/main/s/spykeviewer/spykeviewer_0.2.0-2~nd12.04+1_all.deb Size: 431802 SHA256: 05b5ca78f59e4df491642af857a42de9fc5b5ed1b5997d699a7a92af91419a9c SHA1: 5313ad290c8c2c5096f8cbd29d8906764d74cb2e MD5sum: 012f9ed537d83e2d3562b84a123e788c 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 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_all.deb Size: 28730 SHA256: add473af6d9eb0497a244d721862483deeb0fd562cabf84305eefa9e9c522897 SHA1: f6952357804556ee3b33d3242d205b4aa3cc49c7 MD5sum: 472471b057239fd1029854d1f42c735e 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~nd12.04+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~nd12.04+1_all.deb Size: 99594 SHA256: 67cab9b7c779f963fb4cdd501878d78e45f462601868990f829fe9bfdc7fd325 SHA1: 8d6344c0c06c876467678560b03d0e185c6c97e5 MD5sum: 74b55de26e189ab9a6b7c97f1d733816 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