Package: ants Version: 1.9+svn532-2~sid.nd1 Architecture: i386 Maintainer: Yaroslav Halchenko Installed-Size: 36240 Depends: libc6 (>= 2.3.6-6~), libgcc1 (>= 1:4.1.1), libinsighttoolkit3.18, libstdc++6 (>= 4.4.0) Suggests: fsl, gridengine-client Homepage: http://www.picsl.upenn.edu/ANTS/ Priority: extra Section: science Filename: pool/main/a/ants/ants_1.9+svn532-2~sid.nd1_i386.deb Size: 11265632 SHA256: ee708d498be8393c0f981b66273837cd1d85a4f06aa0b88e90f03409a29f0d94 SHA1: a5ffc775bc92043fccb02e31a71244defdc9946c MD5sum: 37bab022317a48cba5fca03ac8e1814e Description: advanced normalization tools for brain and image analysis Advanced Normalization Tools (ANTS) is an ITK-based suite of normalization, segmentation and template-building tools for quantitative morphometric analysis. Many of the ANTS registration tools are diffeomorphic, but deformation (elastic and BSpline) transformations are available. Unique components of ANTS include multivariate similarity metrics, landmark guidance, the ability to use label images to guide the mapping and both greedy and space-time optimal implementations of diffeomorphisms. The symmetric normalization (SyN) strategy is a part of the ANTS toolkit as is directly manipulated free form deformation (DMFFD). Package: arno-iptables-firewall Version: 1.9.2.k-3~sid.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 844 Depends: iptables (>= 1.2.11), gawk, debconf (>= 1.3.22) | cdebconf (>= 0.43), debconf (>= 0.5) | debconf-2.0, iproute Recommends: lynx, dnsutils Homepage: http://rocky.eld.leidenuniv.nl/ Priority: optional Section: net Filename: pool/main/a/arno-iptables-firewall/arno-iptables-firewall_1.9.2.k-3~sid.nd1_all.deb Size: 132466 SHA256: f27127b8c1dc917c0286a9387f8fa457376ded10b07a5908485636c27a2a14ff SHA1: 696de58c79bec6fd3efa3cf7dbbeecaa18d1ea8e MD5sum: da7a5641d17921fad83cbb534f2ebb22 Description: single- and multi-homed firewall script with DSL/ADSL support Unlike other lean iptables frontends in Debian, arno-iptables-firewall will setup and load a secure, restrictive firewall by just asking a few question. This includes configuring internal networks for internet access via NAT and potential network services (e.g. http or ssh). . However, it is in no way restricted to this simple setup. Some catch words of additional features, that can be enabled in the well documented configuration file are: DSL/ADSL, Port forwarding, DMZ's, portscan detection, MAC address filtering. Package: biosig-tools Source: biosig4c++ Version: 0.94.1+svn2521-1~pre0~sid.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 628 Depends: libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/biosig-tools_0.94.1+svn2521-1~pre0~sid.nd1_i386.deb Size: 243072 SHA256: d7cc138110003cd3f05673bc96a3ec99f082b65c044be5e225e311c95a4e5725 SHA1: f5dbdb0a2a51d621332a0be3a984a13d252b174c MD5sum: 21b298117371979cd0e74a87e7cdf711 Description: format conversion tools for biomedical data formats Based on libbiosig4c++ library, this package provides command line tools, such as * save2gdf: converter between different file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF, BDF, CWFB. save2gdf can be also used to upload or retrieve data from a bscs server. TODO... Extend? ship client/server? Package: caret-data Version: 5.6~dfsg.1-1 Architecture: all Maintainer: Michael Hanke Installed-Size: 236780 Homepage: http://brainmap.wustl.edu/caret Priority: optional Section: science Filename: pool/main/c/caret-data/caret-data_5.6~dfsg.1-1_all.deb Size: 175205418 MD5sum: e5f41497554088124975dfc27ba6378b Description: common data files for Caret This package provides online help, tutorials and atlas datasets for Caret. Package: cython Version: 0.13-1~nd+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4580 Depends: python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python2.6, libc6 (>= 2.3.6-6~) Suggests: gcc Homepage: http://cython.org/ Priority: optional Section: python Filename: pool/main/c/cython/cython_0.13-1~nd+1_i386.deb Size: 1117360 SHA256: 3294cdfa3975fccc7c036c7f682655415b097e72c912995137848e91e64a78f4 SHA1: f4cd5aaf276b9511d09d88326b9874d5c688180c MD5sum: ec697d214932e45fc788e7cc387f630c Description: C-Extensions for Python Cython is a language that makes writing C extensions for the Python language as easy as Python itself. Cython is based on the well-known Pyrex, but supports more cutting edge functionality and optimizations. . The Cython language is very close to the Python language, but Cython additionally supports calling C functions and declaring C types on variables and class attributes. This allows the compiler to generate very efficient C code from Cython code. . This makes Cython the ideal language for wrapping for external C libraries, and for fast C modules that speed up the execution of Python code. Python-Version: 2.5, 2.6 Package: cython-dbg Source: cython Version: 0.13-1~nd+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 7672 Depends: python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), libc6 (>= 2.3.6-6~), cython (= 0.13-1~nd+1) Suggests: gcc Homepage: http://cython.org/ Priority: extra Section: debug Filename: pool/main/c/cython/cython-dbg_0.13-1~nd+1_i386.deb Size: 2920060 SHA256: 2c4bc665bc543f0d9d7d675f37bf01376f7c725bb659c7bbb168bbfff1b52e64 SHA1: 6ac68b40d2d6858134a3559a7d567b1e59b894c1 MD5sum: b2621ac9fe5da867aed58d8dcb9aa69f Description: C-Extensions for Python (Debug Build of Cython) This package contains Cython libraries built against versions of Python configured with --pydebug. Python-Version: 2.5, 2.6 Package: libbiosig-dev Source: biosig4c++ Version: 0.94.1+svn2521-1~pre0~sid.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 1220 Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_0.94.1+svn2521-1~pre0~sid.nd1_i386.deb Size: 359384 SHA256: 17560a445b03e5c9157ee68df747a42b77f6af480d3a8b3b34a791d8a111ce58 SHA1: 4485b8ea51ffdaf8e35f620ea958b4d546800d9c MD5sum: 1cc7d0edbaeca860b72257d8f96551bf Description: library for accessing files in biomedical data formats A library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://hci.tugraz.at/schloegl/biosig/TESTED . . This package provides header files and static library. Package: libbiosig0 Source: biosig4c++ Version: 0.94.1+svn2521-1~pre0~sid.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 760 Depends: libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1) Homepage: http://biosig.sf.net/ Priority: extra Section: libs Filename: pool/main/b/biosig4c++/libbiosig0_0.94.1+svn2521-1~pre0~sid.nd1_i386.deb Size: 283592 SHA256: bfb68d75dde00820c9e47520f6004725abd37328cc888f06d8e34c72a12e26bb SHA1: 2983fa43cf53fbaadca454390b63346fe0631970 MD5sum: 2f85bf7a6ed7b8967edcb6af6b55bad8 Description: library for accessing files in biomedical data formats A library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://hci.tugraz.at/schloegl/biosig/TESTED . . This package provides dynamic library. Package: libjs-jquery Version: 1.2.6-1~apsy.0 Architecture: all Recommends: javascript-common Conflicts: jquery Replaces: jquery Installed-Size: 240 Maintainer: Debian Javascript Maintainers Source: jquery Priority: optional Section: web Filename: pool/main/j/jquery/libjs-jquery_1.2.6-1~apsy.0_all.deb Size: 65238 SHA256: fa858cf809b1885439cfb0d6e8ba64021a732b2e9b8493027f5d767973268d22 SHA1: 19177bbdd00962ac018ffa081149840aa7bbc469 MD5sum: 6dc346b0c5ffacbdf0e63f00d1f18485 Description: JavaScript library for dynamic web applications jQuery is a fast, concise, JavaScript Library that simplifies how you traverse HTML documents, handle events, perform animations, and add Ajax interactions to your web pages. jQuery is designed to change the way that you write JavaScript. Package: libodin-dev Source: odin Version: 1.8.1-2~sid.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 15604 Homepage: http://od1n.sourceforge.net Priority: extra Section: libdevel Filename: pool/main/o/odin/libodin-dev_1.8.1-2~sid.nd1_i386.deb Size: 4027074 SHA256: 9b41cf08529574c0b8ccaaf01e533fecace14b7b73021149dcfbb8509453c33e SHA1: 25ead357fdfb304a2a31703fb25bc6d6e613a43d MD5sum: 050db6b0c2e2c58494ca7cb8fa42ad96 Description: static libraries and header for ODIN sequences This package provides static libraries and headers of the ODIN libraries odindata, adinpara, odinqt, odinseq and tjutils. They are required for building magnetic resonance imaging (MRI) sequences with ODIN. Package: mitools Source: odin Version: 1.8.1-2~sid.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 6240 Depends: libblitz0ldbl (>= 0.9), libc6 (>= 2.3.6-6~), libdcmtk1 (>= 3.5.4), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libnifti1 (>> 1.1.0-2), liboil0.3 (>= 0.3.10), libpng12-0 (>= 1.2.13-4), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.5.3), libqwt5-qt4, libstdc++6 (>= 4.4.0), libvia0, libvtk5.4, zlib1g (>= 1:1.1.4), dcmtk Recommends: grace Homepage: http://od1n.sourceforge.net Priority: extra Section: science Filename: pool/main/o/odin/mitools_1.8.1-2~sid.nd1_i386.deb Size: 2378956 SHA256: b1b3d6c40a319d5e65d2dc57c3a6c2b0760832a1f4b55c238a1221756e62e805 SHA1: 51dbeb78c59b390e6a5a5e999b359f1e147e5fde MD5sum: 493efff613cc2f0ad44be7de1f7f2e50 Description: view, convert and perform basic maths with medical image datasets The three contained tools micalc, miconv and miview are handy command-line utilities for converting, manipulating and viewing medical image data in various formats (DICOM, NIfTI, PNG, binary data, ...). Package: octave-biosig Source: biosig4c++ Version: 0.94.1+svn2521-1~pre0~sid.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 1396 Depends: libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libfftw3-3, libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.3), libhdf5-serial-1.8.4 | libhdf5-1.8.4, liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libncurses5 (>= 5.7+20100313), libreadline6 (>= 6.0), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/octave-biosig_0.94.1+svn2521-1~pre0~sid.nd1_i386.deb Size: 546148 SHA256: f08e9662db2639c74bc56d3ca9979f5bee5b17ae0bd520c5914fb0c64b93ce8d SHA1: 04ede855a972d1bcbbf45261c6d92d6b129e4e2f MD5sum: 9eb64646b14504187a1eec1704969484 Description: Octave bindings for BioSig4C++ library This package provides Octave bindings for biosig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDlibbiosig4c++. Package: odin Version: 1.8.1-2~sid.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 4000 Depends: libc6 (>= 2.3.6-6~), libgcc1 (>= 1:4.1.1), libqtcore4 (>= 4:4.5.3), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.4.0), libvtk5.4, mitools (= 1.8.1-2~sid.nd1), libodin-dev, libgsl0-dev, libc6-dev | libc-dev, g++, libblas-dev | libatlas-base-dev, xterm | x-terminal-emulator, gdb Recommends: liboil0.3-dev | liboil-dev, libdcmtk1-dev Homepage: http://od1n.sourceforge.net Priority: extra Section: science Filename: pool/main/o/odin/odin_1.8.1-2~sid.nd1_i386.deb Size: 1546836 SHA256: 9bc35c3dac3508ced6428d06d8171d3210be155c43ca9bd6ff377e890f554d50 SHA1: bcfdda136f8f346d12213be3ce4136f08d6e966b MD5sum: 802fde227e04a77e6a5f5e510e1d7a35 Description: develop, simulate and run magnetic resonance sequences ODIN is a framework for magnetic resonance imaging (MRI). It covers the whole toolchain of MRI, from low-level data acquisition to image reconstruction. In particular, it aims at rapid prototyping of MRI sequences. The sequences can be programmed using a high-level, object oriented, C++ programming interface. It provides advanced sequence analysis tools, such as interactive plotting of k-space trajectories, a user interface for a fast compile-link-test cycle and a powerful MRI simulator which supports different virtual samples. For fast and flexible image reconstruction, ODIN contains a highly customizable, multi-threaded data-processing framework. Package: openelectrophy Version: 0.0.svn143-1~sid.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 92 Depends: python, python-pyssdh (= 0.0.svn143-1~sid.nd1) Homepage: http://neuralensemble.org/trac/OpenElectrophy Priority: extra Section: science Filename: pool/main/o/openelectrophy/openelectrophy_0.0.svn143-1~sid.nd1_all.deb Size: 34360 SHA256: 15e2e7aefc8b1af85c120f648897950db56fb71fe5999c5a3ca51b1c70bc0fb4 SHA1: 84e8c88b4d56f44c987808ba5c54b1799a0403ee MD5sum: 0eaf72ffeedd568782315315e95b4dfe Description: data analysis framework for intra- and extra-cellular recordings This software aims to simplify data and analysis sharing for intra- and extra-cellular recordings. It supports time frequency plots, spike detection, spike rate calculation, and analysis of phase locked signals. . Data handling and storage utilizes a MySQL database, allowing to handle large amounts of data easily and efficiently. Therefore, a MySQL server running locally or on a remote machine is required. . This package provides the OpenElectrophy GUI. Package: psychopy Version: 1.62.01.dfsg-1~sid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3124 Depends: 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 Suggests: python-iolabs, python-pyepl Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.62.01.dfsg-1~sid.nd1_all.deb Size: 1393782 SHA256: 48c146abadb35e0a6346574f459ce8dff6493e8371d7a4c98cc2fb48a6992ffb SHA1: ef5b0e24cbae6f6fba904a9dd971817bf588152a MD5sum: 22744616b3e43ef5b3450b185748eb08 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 - High-level powerful scripting language (Python) - Simple syntax - 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-biosig Source: biosig4c++ Version: 0.94.1+svn2521-1~pre0~sid.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 908 Depends: libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: python Filename: pool/main/b/biosig4c++/python-biosig_0.94.1+svn2521-1~pre0~sid.nd1_i386.deb Size: 316956 SHA256: 2787bc37d9359b105fa794a2c6e25dc476018d8e11e9f5f17be6991b83532a98 SHA1: 10ba88d8641228cde59d31701863be564955d80e MD5sum: 9c3bfd71b26dbab6f9f16b7717fc9af6 Description: Python bindings for BioSig4C++ library This package provides Python bindings for biosig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDlibbiosig4c++. Package: python-dicom Source: pydicom Version: 0.9.5~rc1-1~sid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1864 Depends: python (>= 2.5), python-support (>= 0.90.0) 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_0.9.5~rc1-1~sid.nd1_all.deb Size: 372936 SHA256: e3abd85463e1a311df3588f4ac4b582da1e712e6d0b14cf6a5f87a5d92247862 SHA1: af591e04e34defa29892d5c1398a11de56d08191 MD5sum: 5546527ef55fde4ae81bc8bd50cb1ee0 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-griddata Source: griddata Version: 0.1.2-1~sid.apsy1 Architecture: i386 Maintainer: Michael Hanke Installed-Size: 244 Depends: libc6 (>= 2.7-1), python (<< 2.6), python (>= 2.4), python-support (>= 0.7.1), python-numpy, python-numpy-ext Recommends: python-matplotlib Provides: python2.4-griddata, python2.5-griddata Homepage: http://code.google.com/p/griddata-python/ Priority: optional Section: python Filename: pool/main/g/griddata/python-griddata_0.1.2-1~sid.apsy1_i386.deb Size: 61154 SHA256: 53efc2b4c9adf4ccdd35cb0266fd453f448119d952eebe85c0090d1b41d1fc29 SHA1: b335fe367c1a0233b495688df9af1a223520df30 MD5sum: dfad0b1ccba3fec9283ffa52e83cc495 Description: Python function to interpolate irregularly spaced data to a grid This module provides a single function, 'griddata', that fits a surface to nonuniformly spaced data points. It behaves basically like its equivalent in Matlab. Python-Version: 2.4, 2.5 Package: python-joblib Source: joblib Version: 0.4.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 244 Depends: python, 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_0.4.5-1~nd+1_all.deb Size: 37904 SHA256: dfe34a5a97ff2ccf92ce7deb4a61c2a7c20b1d1f7355aaeb0625f2c65d92531a SHA1: 06c5d78e3cf51f90b60a8a6a6fa745c758e13bb2 MD5sum: f7af9b3c460973f846f370383ca22193 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-mvpa Source: pymvpa Version: 0.4.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4152 Depends: python (>= 2.5), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-lib (>= 0.4.5-1~nd+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_0.4.5-1~nd+1_all.deb Size: 2167538 SHA256: ef59abd523cb2a25602db157335858c76be23a860ce1cbc69acccae734da943e SHA1: da884e0fa1249e3f8d9f248b4bf9af485cc2533c MD5sum: 066f06deebccb91dc35dafa463d1400b 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: 0.4.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41052 Depends: libjs-jquery Suggests: python-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.5-1~nd+1_all.deb Size: 9067604 SHA256: cf0b284af9f9414dc02204ac31625343d80f2b96644fbd718863bc506a8f60f6 SHA1: 7ad38563e0ceedd361e1c42c2b4ecc569d56a9d7 MD5sum: c2e96d3144191dfd8a181b7192052d74 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: 0.4.5-1~nd+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 288 Depends: 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 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_0.4.5-1~nd+1_i386.deb Size: 67984 SHA256: 60c6766f3d6e89abcb38c2046137bfd605e8c006512713382e0f7ceca288cabc SHA1: deec96bc7e409e72c62227e51e2f842cf7f3b019 MD5sum: 0837bb5a24ba318ad21aa0468b70cef2 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: 0.5.0.dev+783+gde39-1~sid.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 4432 Depends: python (>= 2.4), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-snapshot-lib (>= 0.5.0.dev+783+gde39-1~sid.nd1) 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-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_0.5.0.dev+783+gde39-1~sid.nd1_all.deb Size: 2225766 SHA256: 11d984831fbf38243f7886014f8f40aa3094c3841a4ed3966896888a4c42adf7 SHA1: 0059c6b5352a7034086efed13ec93f3c78526a41 MD5sum: f884a7cf433a74b2acbf5b9fb2fc36e6 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: 0.5.0.dev+783+gde39-1~sid.nd1 Architecture: i386 Maintainer: Experimental Psychology Maintainers Installed-Size: 284 Depends: 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_0.5.0.dev+783+gde39-1~sid.nd1_i386.deb Size: 66098 SHA256: e4951ef5ad37d3a995da8f1bec0eb054f8e8487c696fb1bc61ba88008ed8902c SHA1: bfe7751b734c903ef482e7566f181c07e10d3430 MD5sum: df0a54280d48d55c86089fb696ab1d31 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-nibabel Source: nibabel Version: 1.0.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2784 Depends: python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom Suggests: python-nibabel-doc Provides: python2.5-nibabel, python2.6-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.0.0-1~nd+1_all.deb Size: 1061210 SHA256: 0505371d82e6a344e73ce30b5d1b7200f8a104bc3ebf561ddb5dbc4544f210f2 SHA1: 3bb315251ab12b46a378e1df67107d032fd81ced MD5sum: 7924017354df1fa9c4963e13ac694244 Description: Python bindings to various neuroimaging data formats This package 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 tool for conversion of PAR/REC to NIfTI images. Python-Version: 2.5, 2.6 Package: python-nibabel-doc Source: nibabel Version: 1.0.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2724 Depends: libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.0.0-1~nd+1_all.deb Size: 404008 SHA256: bda2ded9fa849d866db78959a43e71862b9a66cb02b663d7612112aac2ac4da1 SHA1: 1462cfbd7f63745042f8522b354cf186c51b9b1b MD5sum: c403118f483919e980093dbedbcb8e0c 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-nibabel-snapshot Source: nibabel-snapshot Version: 1.0.0.dev+137+gf1c6-1~sid.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 964 Depends: python (>= 2.5), python-support (>= 0.90.0), python-numpy, libjs-jquery, python-scipy Conflicts: python-nibabel Provides: python2.5-nibabel-snapshot, python2.6-nibabel-snapshot Homepage: http://nipy.sourceforge.net/nibabel Priority: optional Section: python Filename: pool/main/n/nibabel-snapshot/python-nibabel-snapshot_1.0.0.dev+137+gf1c6-1~sid.nd1_all.deb Size: 469776 SHA256: 674d6faa8c47cc5d2abded6bf10d56d3c7b2041b70390b254d6bed4fe0b89f92 SHA1: 1e06be036a09d6114c43bcccf080aa256f7c7a69 MD5sum: 26e58a8ca88e85dfba68eae891bdcdeb Description: Python bindings to various neuroimaging data formats Currently supported formats are: . * ANALYZE (including SPM2 and SPM99 variants) * MINC * NIfTI * PAR/REC . This package also provides a commandline tool for conversion of PAR/REC to NIfTI images. Python-Version: 2.5, 2.6 Package: python-nipype Source: nipype Version: 0.3.3-1~sid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1752 Depends: python (>= 2.5), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits Recommends: python-nifti, ipython, python-nose, python-networkx Suggests: fsl, afni, lipsia, python-nipy Provides: python2.5-nipype, python2.6-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.3.3-1~sid.nd1_all.deb Size: 277514 SHA256: 368910b5558d3586e86bb6d919354e15c980d15156e87379ed5b6d92e9944637 SHA1: e8079a3aef6b6ea8822867bb23ea127d228e82c7 MD5sum: fb3c525781e1a4416582704f8041bd71 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: 0.3.3-1~sid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3640 Depends: libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.3.3-1~sid.nd1_all.deb Size: 840650 SHA256: a2adc848d29e0eac4f7f7d1e323d06441cc7a2a947d1f608364e3a2a14c4bd8d SHA1: fd337addc385ad76b1a6f1e19b9eafea534a8a62 MD5sum: 2cbce98a7cd66dac74d132b310f68328 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-pyoptical Source: pyoptical Version: 0.2-1~sid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 72 Depends: python-serial Enhances: psychopy, python-pyepl Homepage: http://github.com/esc/pyoptical Priority: extra Section: python Filename: pool/main/p/pyoptical/python-pyoptical_0.2-1~sid.nd1_all.deb Size: 6946 SHA256: 61b96afae4d2c43351ad598253b8b38fff6b0c2d99669f49f431b8d8678f89be SHA1: 8442b14c93a7d2c3718d78655dc85fef951bcfaf MD5sum: 1eaea3d3d51bcd440299d8aa65220111 Description: python interface to the CRS 'OptiCAL' photometer The 'OptiCAL' is a photometer that is produced by Cambridge Research Systems (CRS). This device is a standard tool for gamma-calibration of display devices in vision research. This package provides a free-software replacement for the Windows-software distributed by the manufacturer that allows querying an OptiCAL via a serial connection. pyoptical can be used as a library for third-party applications or as a standalone command line tool. Python-Version: 2.5, 2.6 Package: python-pyssdh Source: openelectrophy Version: 0.0.svn143-1~sid.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 792 Depends: python-support (>= 0.90.0), python-numpy, python-scipy, python-qt4, python-mysqldb, python-matplotlib Recommends: g++ | c++-compiler, python-mdp Suggests: mysql-server Provides: python2.4-pyssdh, python2.5-pyssdh Homepage: http://neuralensemble.org/trac/OpenElectrophy Priority: extra Section: python Filename: pool/main/o/openelectrophy/python-pyssdh_0.0.svn143-1~sid.nd1_all.deb Size: 119482 SHA256: 047337422d8c671d1ca38e938384c985fc1fac566d178123b6cb5ee4d1fccc51 SHA1: 2fb56ca17ad07ee58955caf8de17a4cd24d3d85a MD5sum: 1790628c9012a2ae40aff02998bd9c41 Description: data analysis framework for intra- and extra-cellular recordings This software aims to simplify data and analysis sharing for intra- and extra-cellular recordings. It supports time frequency plots, spike detection, spike rate calculation, and analysis of phase locked signals. . Data handling and storage utilizes a MySQL database, allowing to handle large amounts of data easily and efficiently. Therefore, a MySQL server running locally or on a remote machine is required. . This package provides the OpenElectrophy Python module. Python-Version: 2.4, 2.5 Package: python-scikits-learn Source: scikit-learn Version: 0.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1112 Depends: python (>= 2.5), python-numpy (<< 1:1.5), python-numpy (>= 1:1.4.1), python-support (>= 0.90.0), python-scipy, python-scikits-learn-lib (>= 0.5-1~nd+1) Recommends: python-nose, python-psyco, python-matplotlib, python-joblib (>= 0.4.5) Suggests: python-dap, python-scikits-optimization, python-scikits-learn-doc Provides: python2.5-scikits-learn, python2.6-scikits-learn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-scikits-learn_0.5-1~nd+1_all.deb Size: 203456 SHA256: 58c467196d1137788a286ffb6d48b9ae3b9b2e22584baf87712e27a5544ffd48 SHA1: 5d612867c0a038af1f6bb8571bd067e553c2b504 MD5sum: 7ce3bb3fef42c063e45e673e49b36ec6 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: 2.5, 2.6 Package: python-scikits-learn-doc Source: scikit-learn Version: 0.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6392 Depends: libjs-jquery Suggests: python-scikits-learn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-scikits-learn-doc_0.5-1~nd+1_all.deb Size: 3201136 SHA256: 8350c31d9a0bb34541963174ff236e7af1fcfaaa746a32c7a08c03387ab7ad47 SHA1: e933290cd8548dcd3fafef6b6156db92ae1983c1 MD5sum: c11dc8c29cd24fa58cf8ce2566ca4b78 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-scikits-learn. Package: python-scikits-learn-lib Source: scikit-learn Version: 0.5-1~nd+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1244 Depends: libc6 (>= 2.3.6-6~), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), libsvm2, python (<< 2.7), python (>= 2.5), python-numpy (<< 1:1.5), python-numpy (>= 1:1.4.1), python-support (>= 0.90.0) Provides: python2.5-scikits-learn-lib, python2.6-scikits-learn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-scikits-learn-lib_0.5-1~nd+1_i386.deb Size: 416156 SHA256: 1561d50adb2ea52f225346a88a27b4d093060a7ac0bfa990af5a12ff43f85d0e SHA1: c14251cdeea14a02c82f7197d2146914e2ca8766 MD5sum: d078142c744506c379112a5ccaf318f9 Description: low-level implementations and bindings for scikits-learn This is an add-on package for python-scikits-learn. It provides low-level implementations and custom Python bindings for the LIBSVM library. Python-Version: 2.5, 2.6 Package: python-scikits-statsmodels Source: statsmodels Version: 0.2.0+bzr1990-1~sid.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 9644 Depends: python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-rpy Provides: python2.5-scikits-statsmodels, python2.6-scikits-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: optional Section: python Filename: pool/main/s/statsmodels/python-scikits-statsmodels_0.2.0+bzr1990-1~sid.nd1_all.deb Size: 1874480 SHA256: 1d5be1691f554290e8c195284ef8fbdffd1ef948df40cb4e42f23ed1ea454724 SHA1: 95f607c3b8e4ec04a0a3530abcc4d0f381decba7 MD5sum: 775433657571640365080d6f54bba54c Description: classes and functions for the estimation of statistical models scikits.statsmodels is a pure Python package that 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 avalable for each estimation problem. Python-Version: 2.5, 2.6 Package: python-scikits-statsmodels-doc Source: statsmodels Version: 0.2.0+bzr1990-1~sid.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 2184 Depends: libjs-jquery Suggests: python-scikits-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: optional Section: doc Filename: pool/main/s/statsmodels/python-scikits-statsmodels-doc_0.2.0+bzr1990-1~sid.nd1_all.deb Size: 307526 SHA256: e87b964cce23b84e481622b0dd5b20db420f9da592f62f56b48372e1f162a76b SHA1: a5b070d9b9ea9046491c3efc92582ae85ccd7c95 MD5sum: f52b8e834c2a71aaf9541efd3bd7326b Description: documentation and examples for python-scikits-statsmodels This package contains HTML documentation and example scripts for python-scikits-statsmodels. Package: python-sphinx Source: sphinx Version: 1.0.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4580 Depends: python (>= 2.4), python-support (>= 0.90.0), python-docutils (>= 0.5), python-pygments (>= 0.8), python-jinja2 (>= 2.2), libjs-jquery Recommends: python (>= 2.6) | python-simplejson, python-imaging Suggests: jsmath Homepage: http://sphinx.pocoo.org/ Priority: optional Section: python Filename: pool/main/s/sphinx/python-sphinx_1.0.1-1~nd+1_all.deb Size: 1233522 SHA256: 9d2412a330c57af8bd8adff6703a5e3c70346a1b892e02a8bb18d751b374ccba SHA1: 5f994974763ed9fa89766248c7ff86510b9fe1b2 MD5sum: a7aa0438348ec0b070a3b32c8f566511 Description: tool for producing documentation for Python projects Sphinx is a tool for producing documentation for Python projects, using reStructuredText as markup language. . Sphinx features: * HTML, CHM, LaTeX output, * Cross-referencing source code, * Automatic indices, * Code highlighting, using Pygments, * Extensibility. Existing extensions: - automatic testing of code snippets, - including doctrings from Python modules. Package: r-noncran-psychofun Version: 0.5.0-1~sid.apsy0 Architecture: all Depends: r-base-core (>= 2.4.0) Installed-Size: 600 Maintainer: Experimental Psychology Maintainers Source: psychofun Priority: optional Section: math Filename: pool/main/p/psychofun/r-noncran-psychofun_0.5.0-1~sid.apsy0_all.deb Size: 70968 MD5sum: ad3d95b1a239fa17cae77a362e9f8639 Description: Bayesian Inference for Psychometric Functions The package provides routines for inference about the parameters of psychometric functions. It provides routines for maximum a posteriori estimation and Markov chain Monte Carlo sampling from the posterior over model parameters. . This package is in many ways the successor of the psignifit package. Package: sigviewer Version: 0.3.0+svn362-1~pre1~sid.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 1424 Depends: libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libfftw3-3, libgcc1 (>= 1:4.1.1), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.4.0), zlib1g (>= 1:1.1.4) Homepage: http://sigviewer.sourceforge.net Priority: extra Section: science Filename: pool/main/s/sigviewer/sigviewer_0.3.0+svn362-1~pre1~sid.nd1_i386.deb Size: 598398 SHA256: bfc2d2fde86e125b569826313754962fa6840e3dc388126424eb037732954489 SHA1: 0b5d4a1f9a96ad206ce6ff365896f9646467af7f MD5sum: 46b2f25545ab1b9b9008cf821edb39a2 Description: GUI viewer for biosignals such as EEG, EMG, and ECG SigViewer is a viewing and scoring software for biomedical signal data. It relies on biosig4c++ library which supports a number of data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://hci.tugraz.at/schloegl/biosig/TESTED . . Besides displaying biosignals, SigViewer supports creating annotations to select artifacts or specific events. Package: svgtune Version: 0.1.0-1~sid.nd1 Architecture: all Maintainer: Yaroslav Halchenko Installed-Size: 64 Depends: python, python-lxml Suggests: inkscape Homepage: http://github.com/yarikoptic/svgtune Priority: optional Section: graphics Filename: pool/main/s/svgtune/svgtune_0.1.0-1~sid.nd1_all.deb Size: 6746 SHA256: 7e2eef77f108ebbb53afb7238f1a87fb77f8e500267878f69eef8e5123bb4ddb SHA1: 8cb46ecfad1a596ec83d7c7fc635056ce54b86b9 MD5sum: a6c69c60a2d9de936346fdaf80e2f7bc Description: tool to generate a set of .svg files out of a single .svg file svgtune is just a little helper to generate a set of .svg files out of a single .svg file, by tuning respective groups/layers visibility, transparency or anything else. . It might come very handy for generation of incremental figures to be embedded into the presentation in any format which inkscape could render using original .svg file (e.g. pdf, png).