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: 0.3.20.1+ds-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1696 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_0.3.20.1+ds-1~nd11.04+1_all.deb Size: 306236 SHA256: 75dcf2c8e61406d58a055a0a807162b8a3145db6bfd19bc457b7d850812f4b29 SHA1: eb910407cca8d42748acfdd6ec9049deff0f15ee MD5sum: c14434474a074b361a760633a6a83f50 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: 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+svn2514.dfsg1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 53704 Depends: neurodebian-popularity-contest Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.9+svn2514.dfsg1-1~nd11.04+1_all.deb Size: 19474782 SHA256: 016bc72c2488941db29485aded811530063bbac2d6950c0a2be159e84385c833 SHA1: 98e118e6a19e1f923f2c16d5603f74bdb7b11f1d MD5sum: 7833b9144a7089f979abc55d0e1f7791 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-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-nipype Source: nipype Version: 0.5.2-1~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_0.5.2-1~nd11.04+1_all.deb Size: 497136 SHA256: 94a5c2a7c222a7a45897f0ae3897b8491a4d594fe102aad1e6ced302d49dce35 SHA1: 0c6b9b79ac7fb3a38d95c305ecc4afa6fe2e9faf MD5sum: c8716dd9cd1221aadcbfe51e9360e940 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: 0.5.2-1~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_0.5.2-1~nd11.04+1_all.deb Size: 5810256 SHA256: 2a5beacba131eaa30aa9ed0dc1ff9dbf0fb26274441a0b5c53636beddb920fc9 SHA1: 8ee0d34d2565184821b72914d2ad066266bdd680 MD5sum: 778df8bd8b87c94512b6e44adda34737 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-pandas Source: pandas Version: 0.7.1+git1-ga2e86c2-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2080 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-dateutil, python-pandas-lib (>= 0.7.1+git1-ga2e86c2-1~nd11.04+1) Recommends: python-scipy, python-matplotlib, python-tables, python-tz, python-xlrd, python-scikits.statsmodels 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_0.7.1+git1-ga2e86c2-1~nd11.04+1_all.deb Size: 399444 SHA256: 5934c2f30b23e8a2406a359d6aa60b2880ef8d99d58ba0cff85191454b72e819 SHA1: ef8f2a2f67641871fa25514eed3e79cf6298876c MD5sum: 39a635aea1973dca858d21bfa1d85120 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: 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.