Package: caret Version: 5.6.2~dfsg.1-1~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 18712 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libgomp1 (>= 4.2.1), libminc2-1, libqt4-network (>= 4:4.5.3), libqt4-opengl (>= 4:4.5.3), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.5.3), libqwt5-qt4, libstdc++6 (>= 4.4.0), libvtk5.4, zlib1g (>= 1:1.2.3.3.dfsg) Recommends: qt-assistant-compat Suggests: caret-data (>= 5.6.2~dfsg.1~) Homepage: http://brainvis.wustl.edu/wiki/index.php/Caret:About Priority: optional Section: science Filename: pool/main/c/caret/caret_5.6.2~dfsg.1-1~nd70+1_i386.deb Size: 7292744 SHA256: 02d9cf5f9f8394217b1d25f0662a925803ba5bf0bbceda00c76e5ebf78b00b31 SHA1: 776fda9404617a1f04eaaa87d4057010488011b4 MD5sum: 3f5abc7695c1291587543dd685fd99fd Description: Computerized Anatomical Reconstruction and Editing Toolkit This software allows for creating, viewing and manipulating surface reconstructions of the cerebral and cerebellar cortex, viewing volumes and for displaying experimental data on the surfaces and volumes. While Caret is primarily a GUI application with 'caret_command' there is also a versatile command line tool, that allows access to a substantial proportion of Caret's functionality. . Caret can download and use stereotaxic atlases (human, monkey, mouse and rat) from an open online database. . Some functionality of Caret is only available when additional data files, provided by the caret-data package, are available. This includes: . - Map volumes to surface via PALS atlas - Multi-resolution morphing - Projection of foci via PALS atlas - Surface-based registration - Surface flattening . Currently the caret-data package is only available from the NeuroDebian repository. Please see http://neuro.debian.net for more imformation. Package: classads Version: 1.0.9-2~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 84 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), libclassad0 (= 1.0.9-2~nd70+1), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cs.wisc.edu/condor/classad Priority: extra Section: misc Filename: pool/main/c/classads/classads_1.0.9-2~nd70+1_i386.deb Size: 36374 SHA256: 36b5cd148d1a458a13307142e43e45286f6c6b8fcead79780a82c0aa7ec03d16 SHA1: 5f5e4a01495eae5f3e44859e670b845a4e7df712 MD5sum: eaac9b6f0a047b4e5785aed98dd09587 Description: Condor's classad utilities A classad (classified ad) is a mapping from attribute names to expressions. In the simplest cases, the expressions are simple constants (integer, floating point, or string), thus a form of property list. Attribute expressions can also be more complicated. There is a protocol for evaluating an attribute expression of a classad vis a vis another ad. Two classads match if each ad has attribute requirements that evaluate to true in the context of the other ad. Classad matching is used by the Condor central manager to determine the compatibility of jobs and workstations where they may be run. . This package provides command line tools to manipulate, test and evaluate classads. Package: libclassad-dev Source: classads Version: 1.0.9-2~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1656 Depends: neurodebian-popularity-contest, libclassad0 (= 1.0.9-2~nd70+1) Conflicts: libclassad0-dev Replaces: libclassad0-dev Homepage: http://www.cs.wisc.edu/condor/classad Priority: extra Section: libdevel Filename: pool/main/c/classads/libclassad-dev_1.0.9-2~nd70+1_i386.deb Size: 528626 SHA256: f6498c41254a6cff2e647f0ffa345566927630a500e89432d1414c617ad19bfd SHA1: 6c9f45bab0a7ff8982c0735a9e3786bd0cb72401 MD5sum: 49dc8c1525fde07c18cff7d7942d94fe Description: library for Condor's classads expression language (development) A classad (classified ad) is a mapping from attribute names to expressions. In the simplest cases, the expressions are simple constants (integer, floating point, or string), thus a form of property list. Attribute expressions can also be more complicated. There is a protocol for evaluating an attribute expression of a classad vis a vis another ad. Two classads match if each ad has attribute requirements that evaluate to true in the context of the other ad. Classad matching is used by the Condor central manager to determine the compatibility of jobs and workstations where they may be run. . This package provides the static library and header files. Package: libclassad0 Source: classads Version: 1.0.9-2~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1016 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cs.wisc.edu/condor/classad Priority: extra Section: libs Filename: pool/main/c/classads/libclassad0_1.0.9-2~nd70+1_i386.deb Size: 423762 SHA256: 48424de024d51e9518f099b9ac0e4ae446845277641bb0454127575254486c2e SHA1: 99b51bbeacaa51dd4f70a158a34277e03fe258b4 MD5sum: b675799e2db483a31053aeee88b50fea Description: library for Condor's classads expression language A classad (classified ad) is a mapping from attribute names to expressions. In the simplest cases, the expressions are simple constants (integer, floating point, or string), thus a form of property list. Attribute expressions can also be more complicated. There is a protocol for evaluating an attribute expression of a classad vis a vis another ad. Two classads match if each ad has attribute requirements that evaluate to true in the context of the other ad. Classad matching is used by the Condor central manager to determine the compatibility of jobs and workstations where they may be run. . This package provides the runtime library. Package: mriconvert Version: 2.0-2~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2112 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.10.1), libwxgtk2.8-0 (>= 2.8.10.1) Homepage: http://lcni.uoregon.edu/~jolinda/MRIConvert/ Priority: optional Section: science Filename: pool/main/m/mriconvert/mriconvert_2.0-2~nd70+1_i386.deb Size: 756344 SHA256: 2d2d64182f54ae5e554550c984b1431ad656093a645b3cf1297d9594a9b6220c SHA1: 90c0883a1d52cf7f17db4e4369790062a323eef1 MD5sum: 3639bc47f8a8e0b02e7a1ea980c2d9bd Description: medical image file conversion utility MRIConvert is a medical image file conversion utility that converts DICOM files to NIfTI 1.1, Analyze 7.5 , SPM99/Analyze, BrainVoyager, and MetaImage volume formats. Package: neurodebian-desktop Source: neurodebian Version: 0.24~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 272 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_0.24~nd70+1_all.deb Size: 113100 SHA256: 572dbf8124aa9849efeaf5165b0162b1acdd5ca76e2326110398c750c591acd6 SHA1: b4d6279cd7f631880ca58938656aed8ac25957c1 MD5sum: fe01a963273510aaca63e79cae163eb8 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: 0.24~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4216 Depends: devscripts, cowbuilder, python, neurodebian-keyring Recommends: virtualbox-ose, virtualbox-ose-fuse, zerofree Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.24~nd70+1_all.deb Size: 3797954 SHA256: bb759b9573967142a65d3aa0bf4b5ca90ce47ea6caad46d9bca998fb10cf703b SHA1: 1128309d5bb1cae9fef9ede511841b8e2de6ffd8 MD5sum: f5619f5d1b518fb04d6a3991f90f755b 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: 0.24~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 148 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm, update-manager-gnome, update-notifier Recommends: chromium-browser Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.24~nd70+1_all.deb Size: 12736 SHA256: cfa30411f28993acb5384c7acadf2ea6584e6851cb21033ad9450e94950ad0a9 SHA1: 0b42f1f7eccc6a9a31ec721a2ad8d05216ea2b2b MD5sum: 5df498181b8d9344c2fcdd925a9f2f40 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: 0.24~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.24~nd70+1_all.deb Size: 5792 SHA256: a562a6990a8647bd6746828db46fd1514bb57534feea43ae04cab7983788cc9b SHA1: d471fb10af39e970522598102a1601e692cb44ce MD5sum: 445a59f8074c518efd107e170ce88151 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: 0.24~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.24~nd70+1_all.deb Size: 4956 SHA256: 94e508e2a7013df483fe40657c19a758e6f2133aa878f359cab2b14b44f6183d SHA1: 0c0c77f7b33fa531b427bfcb19fa9c61ae9dc04f MD5sum: 763739730919af3ae470de2b8d2be7f2 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: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 36 Depends: python-nibabel (>= 1.0.0), python-nipy (>= 0.1.2+20110114), python-dipy (>= 0.5.0), python-nipype (>= 0.3.3), python-nitime (>= 0.2) Suggests: python-mvpa, psychopy Homepage: http://www.nipy.org Priority: extra Section: python Filename: pool/main/n/nipy-suite/nipy-suite_0.1.0-2_all.deb Size: 3898 SHA256: 882c8580ebd2d458a92f8d851d1ec9291fecf05f6ed98a8b754eb831c95368c8 SHA1: 6501d1d201160520f5aad29d0f9007c17b7d9778 MD5sum: eb090e568264d2f439892bcb98485b8c Description: Neuroimaging in Python NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. nipy-suite is a metapackage depending on the projects developed under NiPy project umbrella, such as - nibabel: bindings to various neuroimaging data formats - nipy: analysis of structural and functional neuroimaging data - nitime: timeseries analysis - dipy: analysis of MR diffusion imaging data - nipype: pipelines and worfklows Package: nipy-suite-doc Source: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 32 Depends: python-nibabel-doc (>= 1.0.0), python-nipy-doc (>= 0.1.2+20110114), python-dipy-doc (>= 0.5.0), python-nipype-doc (>= 0.3.3), python-nitime-doc (>= 0.2) Suggests: python-mvpa-doc Homepage: http://www.nipy.org Priority: extra Section: doc Filename: pool/main/n/nipy-suite/nipy-suite-doc_0.1.0-2_all.deb Size: 2250 SHA256: 54985bd9d6eaa352608b357f2deeb066bd2ac12d3c2e463082f5d9178701bbad SHA1: 5d2f5e94ff6b7ff737fe966f4a2e5ff67df93cca MD5sum: 37d2f8b6b6d203edf208afb0cdb56fa3 Description: Neuroimaging in Python -- documentation NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. . nipy-suite-doc is a metapackage depending on the documentation packages for NiPy projects. Package: opensesame Version: 0.22+git9-g8633c14-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2504 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~) Recommends: python-serial (>= 2.3~) Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.22+git9-g8633c14-1~nd70+1_all.deb Size: 711664 SHA256: 4ee04ceb418a8eda6b40c9d41ed4a319ab16a6bf7e662e32a9c3eb5764be9523 SHA1: 1ab0cb53119744f582b83f3a4071acf4123192d3 MD5sum: e1f53763343255e08c36f4ced4123221 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. Python-Version: 2.6 Package: psychopy Version: 1.63.04.dfsg-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4480 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, python-scipy, libavbin0, ipython Suggests: python-iolabs Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.63.04.dfsg-1~nd70+1_all.deb Size: 2370828 SHA256: a1e1bf33c2f85c956f0b63cd2f0fb7ee79c3aaa3199c0fe20e3d1c0b4156cb12 SHA1: 881b21f5b5105ef11abd43dfd13d68a1eb04635a MD5sum: 5ab64be23b1ae1d50af752d4de5439b0 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: 2.5, 2.6 Package: python-mvpa Source: pymvpa Version: 0.4.7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4076 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-lib (>= 0.4.7-1~nd70+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: python2.5-mvpa, python2.6-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.7-1~nd70+1_all.deb Size: 2196208 SHA256: 84eee2b4c7b9466cce94e2e2300a3289783e0c82d07cac668820fb6bb1640280 SHA1: e581201630af4be049391ddb15052073dc433fcc MD5sum: e0b779cd8e741e1e1a1124838ab89cd2 Description: multivariate pattern analysis with Python Python module to ease pattern classification analyses of large datasets. 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: 2.5, 2.6 Package: python-mvpa-doc Source: pymvpa Version: 0.4.7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40732 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_0.4.7-1~nd70+1_all.deb Size: 8729194 SHA256: 58d2f364eeae0db284d65d68798f93a09fc6100baea6cd6a3449aa7beaced7c8 SHA1: a105bdffef91a04ed11f0fc6e533ad0718d444c7 MD5sum: 9db9c31638b2d75ea2897828a91dd902 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-mvpa-lib Source: pymvpa Version: 0.4.7-1~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 204 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy Provides: python2.5-mvpa-lib, python2.6-mvpa-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa-lib_0.4.7-1~nd70+1_i386.deb Size: 68972 SHA256: bd814756cac2eb030e33be8eca94a010d3d8286a6737866531d00874e7529234 SHA1: 45722575c63695fad65d3bb79ad4d8f884464fa9 MD5sum: 3e1c027f1e371ba957099a364feffb3b Description: low-level implementations and bindings for PyMVPA This is an add-on package for the PyMVPA framework. It provides a low-level implementation of an SMLR classifier and custom Python bindings for the LIBSVM library. Python-Version: 2.5, 2.6 Package: python-mvpa-snapshot Source: pymvpa-snapshot Version: 0.6.0~rc2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4644 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-numpy, python-mvpa-snapshot-lib (>= 0.6.0~rc2-1~nd70+1) Recommends: python-nibabel, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab, python-h5py Suggests: fslview, fsl, python-nose, python-lxml, python-scikits-openopt, python-rpy, python-mvpa-doc Conflicts: python-mvpa Provides: python2.5-mvpa-snapshot, python2.6-mvpa-snapshot Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot_0.6.0~rc2-1~nd70+1_all.deb Size: 2287040 SHA256: 7d8d29ad642adc28179e9f936afe1f15343c1acd9c7a51b16e449627d6cd7e46 SHA1: 8342279917b843dcc3f41a9c3075a3d32e3df366 MD5sum: 9cd521ca408c23c7573675f90882245b Description: multivariate pattern analysis with Python Python module to ease pattern classification analyses of large datasets. 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 a development snaphot. The latest released version is provided by the python-mvpa package. Python-Version: 2.5, 2.6 Package: python-mvpa-snapshot-lib Source: pymvpa-snapshot Version: 0.6.0~rc2-1~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 264 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm2, python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy Conflicts: python-mvpa-lib Provides: python2.5-mvpa-snapshot-lib, python2.6-mvpa-snapshot-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot-lib_0.6.0~rc2-1~nd70+1_i386.deb Size: 58376 SHA256: d9be0e497db4b44e6b3993cf1309646b12367d3c92cebae5816577700468934f SHA1: fa455e142ae3881ef28f542390a03266d1aa0761 MD5sum: 90ba61f8644f5286cae68e19d3a8bfa4 Description: low-level implementations and bindings for PyMVPA This is an add-on package for the PyMVPA framework. It provides a low-level implementation of an SMLR classifier and custom Python bindings for the LIBSVM library. . This is a package of a development snaphot. The latest released version is provided by the python-mvpa-lib package. Python-Version: 2.5, 2.6 Package: python-openpyxl Source: openpyxl Version: 1.5.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 412 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_1.5.0-1~nd70+1_all.deb Size: 57462 SHA256: e3114ad3b18968fdf4fb27bbb4797cb8640eeca9cf99c071896fed13a0209d22 SHA1: 51ea411fa10e23ebc6b1fd1be7e8f126bd3306f9 MD5sum: 53ef1d4f937edfcc1c852ba0a7c0723f 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-stfio Source: stimfit Version: 0.10.12-2~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 516 Depends: neurodebian-popularity-contest, libc6 (>= 2.2), libgcc1 (>= 1:4.1.1), libhdf5-serial-1.8.4 | libhdf5-1.8.4, libpython2.6 (>= 2.6), libstdc++6 (>= 4.4.0), python-numpy Recommends: python-matplotlib, python-scipy Homepage: http://www.stimfit.org Priority: optional Section: python Filename: pool/main/s/stimfit/python-stfio_0.10.12-2~nd70+1_i386.deb Size: 212916 SHA256: 69b3f532ff609c6b276adf64e5b8ba7918fcc5e957cfe559eb9794b871d043e4 SHA1: 7d4958e177935f9c1f865378c70c5f89e87d3b5d MD5sum: 87ff8ea2c0d6aec514d92932c10101c3 Description: A Python module to read common electrophysiology file formats. The stfio module allows you to read common electrophysiology file formats from Python. Axon binaries (abf), Axon text (atf), HEKA (dat), CFS (dat/cfs), Axograph (axgd/axgx) are currently supported. Package: stimfit Version: 0.10.12-2~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2004 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.3.6-6~), libfftw3-3, libgcc1 (>= 1:4.1.1), libhdf5-serial-1.8.4 | libhdf5-1.8.4, libpython2.6 (>= 2.6), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.10.1), libwxgtk2.8-0 (>= 2.8.10.1), python-wxgtk2.8 (>= 2.8.9), python-numpy, python-matplotlib Recommends: python-scipy Homepage: http://www.stimfit.org Priority: optional Section: science Filename: pool/main/s/stimfit/stimfit_0.10.12-2~nd70+1_i386.deb Size: 734998 SHA256: 3284ae66445a592011a7371faaef30a0d46e8ebd97b412a60ce6bd95117cf16b SHA1: 88f1fcb22741e27662a3b61b3346b3c5acc0a764 MD5sum: bfe152c263a263ced8c83ada478f23c3 Description: A program for viewing and analyzing electrophysiological data Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. Package: stimfit-dbg Source: stimfit Version: 0.10.12-2~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 12740 Depends: neurodebian-popularity-contest, stimfit Recommends: python-matplotlib, python-scipy, python-stfio Homepage: http://www.stimfit.org Priority: extra Section: debug Filename: pool/main/s/stimfit/stimfit-dbg_0.10.12-2~nd70+1_i386.deb Size: 4842544 SHA256: d4ef1b8e19ed593ab4820306cb86ae046237d77aba42fea00ae7b6a802f41e1f SHA1: 8a5e9cb030b3f79d9e2f10077d07f17bc7ca0bc3 MD5sum: 1b17197bf9d917755c8f4c1aad489e30 Description: Debug symbols for stimfit Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. Package: voxbo Version: 1.8.5~svn1246-1~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 9696 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), libfontconfig1 (>= 2.8.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libpng12-0 (>= 1.2.13-4), libqt4-network (>= 4:4.5.3), libqt4-qt3support (>= 4:4.5.3), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.6.1), libstdc++6 (>= 4.4.0), libx11-6, libxext6, libxi6, libxrender1, zlib1g (>= 1:1.1.4) Suggests: mni-colin27-nifti, matlab-spm8 Homepage: http://www.voxbo.org Priority: extra Section: science Filename: pool/main/v/voxbo/voxbo_1.8.5~svn1246-1~nd70+1_i386.deb Size: 3704676 SHA256: e287d12a4f8562cc6ed2f8e64d64938cfa33a64e2a0edaf34fd1a52d7da63e78 SHA1: f361d60af81addd6abc74b53da16da063985c7e3 MD5sum: 5f54ecfba6b9c661369ce81d661a53db Description: processing, statistical analysis, and display of brain imaging data This is a toolkit for analysis of functional neuroimaging (chiefly fMRI) experiments and voxel-based lesion-behavior mapping. VoxBo supports the modified GLM (for autocorrelated data), as well as the standard GLM for non-autocorrelated data. The toolkit is designed to be interoperable with AFNI, FSL, SPM and others.