Package: fail2ban Version: 0.8.6-3~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 612 Depends: neurodebian-popularity-contest, python (>= 2.4), python-central (>= 0.6.11), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-gamin Suggests: mailx Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.6-3~nd11.04+1_all.deb Size: 103468 SHA256: d3327c84e7ab3f07be88f6a4e6942b129518be2cb722866360b24df0e4d01ba3 SHA1: 5050c018f615ff82fbda3d048fffbfd56cd1dd33 MD5sum: 771a9dcf792f6851068d12e917cf49e8 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. Python-Version: current, >= 2.4 Package: libfreenect-doc Source: libfreenect Version: 1:0.1.2+dfsg-5~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 592 Depends: neurodebian-popularity-contest Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-5~nd11.04+1_all.deb Size: 89240 SHA256: 09d93cfe7aa07762b2202d68b07fa7ff8665ccbf2a2387bbdf113e988d60f9d7 SHA1: 5bcda233b27aaff9cf306a3ac6c16b0596b4e1cc MD5sum: a55a2f7bc52572fd20c5a8e83f3b58e5 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: nuitka Version: 0.3.21+ds-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1676 Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5, 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_0.3.21+ds-1~nd11.04+1_all.deb Size: 313362 SHA256: 9984f8cd2984f509dfc3c51a8250cb7124d061ce95397338d3133aff0244b076 SHA1: 907e25bbe4b081b3e493b9ac0cbcea1061338f37 MD5sum: 8bfb0eee6b5e4981797d2610d01f2774 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: packaging-tutorial Version: 0.5~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1155 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.5~nd+1_all.deb Size: 1111034 SHA256: 3410f99232ee6a8cff11c2d97b4cd50f56d4ae5d71f5dadaa077d92457842996 SHA1: 8756d44b1a608c8c0e29fde5813d6146e67c5026 MD5sum: 7d653f7b7bc96d627e73720627567851 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: 1.73.05.dfsg-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5264 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_1.73.05.dfsg-1~nd11.04+1_all.deb Size: 2686084 SHA256: a5e80dd7d62448a9ef86dd6842fe53d39fd7d05715c0ed9a926c3f8b5a530a17 SHA1: 0c2a6d390828c1d5cbb102be82c59adde5218ccc MD5sum: 885af829407f5e6fcc8fe08e1db4b932 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: 2.6, 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.9+svn2539.dfsg1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 53584 Depends: neurodebian-popularity-contest Recommends: subversion Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.9+svn2539.dfsg1-1~nd11.04+1_all.deb Size: 19425358 SHA256: 14a70635245b3f026c66351bf09207a274fee1d3ef4114a1d0ccb870e28d62eb SHA1: a460f8c1bfa98afa417f264f62c187feb22680f7 MD5sum: 7b4cd504332692c5a1b8f7e54f2ac016 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-lazyarray Source: lazyarray Version: 0.1.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40 Depends: neurodebian-popularity-contest, python2.7 | python2.6, 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~nd11.04+1_all.deb Size: 7434 SHA256: 406466499e0bc085da82ed8c52f44c0d3923019fcbd6cb8d6488e4353828019d SHA1: 9981902e387971ec03dbd4213cae64843ae940a1 MD5sum: 7dfc493b2e0805c3d3a6e140ecf43e68 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: 3.2+git78-g7db3c50-3~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1912 Depends: neurodebian-popularity-contest, python2.7 | python2.6, 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_3.2+git78-g7db3c50-3~nd11.04+1_all.deb Size: 482294 SHA256: 771806dadb9ad024c311981dee450efe4656f4dac51247810c959f438f0e777f SHA1: e4d58e174a40bb1a07c0f31b7a7a7ce8c32867cf MD5sum: 76a9b4acffab5a72b197a2ba1202938b 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-mvpa2 Source: pymvpa2 Version: 2.0.1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4676 Depends: neurodebian-popularity-contest, python (>= 2.4), python-numpy (>= 1:1.5.1), python-numpy (<< 1:1.6), python-support (>= 0.90.0), python-mvpa2-lib (>= 2.0.1-1~nd11.04+1) Recommends: python-nibabel, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab, python-h5py, python-psutil Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy2, python-mvpa2-doc, python-sklearn Provides: python2.6-mvpa2, python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.0.1-1~nd11.04+1_all.deb Size: 2334024 SHA256: e3a10aa880182d8e228929f53ef35e9f742b8b21e1d79a374d5c29245c7b10f8 SHA1: ea6ae44b834c04e7a90002e0cee9cdffcfd26b1f MD5sum: 1b1b883464fe932335bc82b25f329319 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: 2.6, 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.0.1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24408 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.0.1-1~nd11.04+1_all.deb Size: 4564902 SHA256: e32a5088ce450b715a58aea5d1efd09d76572c25154852be9aff5cbeda836109 SHA1: 14c48f914b17c4563ec2f10e3783a1d685f0f178 MD5sum: abe564612759f520f5c5672e1eac58b6 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.) as well as example scripts. Package: python-neo Source: neo Version: 0.2.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2492 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy (>= 1:1.5.1), python-numpy (<< 1:1.6), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.2.0-1~nd11.04+1_all.deb Size: 1372780 SHA256: 766354875c026c79a2470a546aae4ea79c6a287bf91a636b846abd84a352745b SHA1: f17942409e9b082acf591e5ef10c537508e7c6d1 MD5sum: 0d12a5e4cddee2c85fe72faffc061698 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-nipype Source: nipype Version: 0.5.3-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2916 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: python2.6-nipype, python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.5.3-1~nd11.04+1_all.deb Size: 497486 SHA256: c30eb1c51fbe8498da8f656c2c6aa4538755d4a2fd1b10a1cf0481670c5a2661 SHA1: 75574facc22ea41b966dab3ad107212178cb71a0 MD5sum: 5b0e18b086bd63ec0e16a5d4ebaa706b 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: 0.5.3-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13056 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_0.5.3-1~nd11.04+1_all.deb Size: 5795616 SHA256: 36e7c0d868830f251617ffcff5cabd06ad005e879867dbb70cbd24acd98936e4 SHA1: fb27ba14a96558bf647b82d85b78c800190e52a1 MD5sum: 56c4dc19502e00c51338187c6b94e032 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-openpyxl Source: openpyxl Version: 1.5.8-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 504 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~nd11.04+1_all.deb Size: 71602 SHA256: 8535bb65d52179942e3bff84c89eb7e77ea74ed2d00a5b3f4aacaaaef66caa2e SHA1: 5079d085996610fa3adc507374b37a21bdbedb3d MD5sum: bdf8ab57b76bc68bb57a54ec565a4ae5 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: 0.7.3-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2220 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-dateutil, python-pandas-lib (>= 0.7.3-1~nd11.04+1) Recommends: python-scipy, python-matplotlib, python-tables, python-tz, python-xlrd, python-scikits.statsmodels, python-openpyxl, python-xlwt Suggests: python-pandas-doc Provides: python2.6-pandas, python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.7.3-1~nd11.04+1_all.deb Size: 460914 SHA256: 4db83900523de2a81465ee1de273c90efe39ace7dd6e731444c7c37d71addfba SHA1: 3dc4ee1157163fa22e13b1dbccdada1f68b301ae MD5sum: 3e068f630295bbb058f3192b7e06849b 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 Package: python-pynn Source: pynn Version: 0.7.2-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 968 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_0.7.2-1~nd11.04+1_all.deb Size: 166958 SHA256: f29742844aa602d72bc06838d8dfae6bd5095b17559ec3738e41949f4607c0e9 SHA1: 1bec7c081d5a8cfc56bd2349e1e11be98fb562ff MD5sum: dcef8eb19c37db95dfb42a1245fc9fd2 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-quantities Version: 0.10.1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 508 Depends: neurodebian-popularity-contest, python2.7 | python2.6, 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~nd11.04+1_all.deb Size: 60344 SHA256: 7198da79d70c3b046900ef5c9934b2b64d8d005f9135f485eb2176a5d43ae0b6 SHA1: 55272357a667712976a080c6dbee2dce5817a06e MD5sum: 186aa6d0a5d0d575d368ac1961e72874 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.statsmodels Source: statsmodels Version: 0.3.1-4~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13276 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-rpy Conflicts: python-scikits-statsmodels Replaces: python-scikits-statsmodels Provides: python2.6-scikits.statsmodels, python2.7-scikits.statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.3.1-4~nd11.04+1_all.deb Size: 3099052 SHA256: a3783f4a0225f1740a8b525e78faa5543cc8834fc5d5c90eabda9613e7a7c5b3 SHA1: fb89ac3a90e29472587037cb3d04afc00ce0c92c MD5sum: d23ba58c04184484f6a96ae209b6da25 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 available for each estimation problem. Python-Version: 2.6, 2.7 Package: python-scikits.statsmodels-doc Source: statsmodels Version: 0.3.1-4~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 18740 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~nd11.04+1_all.deb Size: 1877710 SHA256: 8daa6c45ed85370df37e7b64df1c588ffef9755206bc21404601db42fa69f946 SHA1: 44e41d473a07c8acd1d5f6ef4306c66c018107c4 MD5sum: 73b386a9ee0240feab087607b1750fb7 Description: documentation and examples for python-scikits.statsmodels This package contains HTML documentation and example scripts for python-scikits.statsmodels. Package: python-surfer Source: pysurfer Version: 0.2+git29-g3a98681-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 140 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-scipy, python-nibabel, python-imaging, mayavi2, python-argparse, ipython Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.2+git29-g3a98681-1~nd11.04+1_all.deb Size: 25592 SHA256: 0d1cbc1bd9b6028c58f64c56228063ea136026e6ab391c46326737a9b6561b6b SHA1: 572e23dbb3e9801289d3c9340b0dcc668b501fe9 MD5sum: 55ff2b157e6199a8c214809dfe8b6aef 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: 2.6, 2.7 Package: spm8-common Source: spm8 Version: 8.4667~dfsg.1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 22352 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~nd11.04+1_all.deb Size: 10573730 SHA256: 1ed89a6bdc612f421a81d873339a371d525db78787353ef924ff9c84959ba3c0 SHA1: 3f9ca30b40af16c9789bce60d5f248a7b3dfe446 MD5sum: d640a888ad262e3cdf83dcf52c835f16 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73084 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.4667~dfsg.1-1~nd11.04+1_all.deb Size: 52167688 SHA256: ce55009bec2f76b96ce90263f625630697db37418b8a3252f4c26ad1dbccca70 SHA1: 483782bd568d9f95d9062bf168dcfb1a766742f2 MD5sum: 99c20008937ad48f7e8163d887be2af4 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~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9380 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.4667~dfsg.1-1~nd11.04+1_all.deb Size: 8648920 SHA256: 735926447772fa98fadc948f4b83de1a4be1cb628df1d508c178185e575bc57f SHA1: 8b47dfda11247dc24e77c835e83129ccf8c379f5 MD5sum: baba74d55f63322228a92cd8ecb4901d 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.