Package: condor-doc Source: condor Version: 7.7.5~dfsg.1-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5988 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.7.5~dfsg.1-2~nd11.10+1_all.deb Size: 1283190 SHA256: a3fe08c8c42baadd71d095430f9b275b3c62864b6960a751589f3e748ca74cee SHA1: d88e0e0031805bd910c75c7c4fa42d007eace128 MD5sum: 2ed14d91a1c7767817461d84206ace13 Description: documentation for Condor Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor, Condor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing system, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . This package provides Condor's documentation in HTML and PDF format, as well as configuration and other examples. Package: fail2ban Version: 0.8.6-3~nd11.10+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~nd11.10+1_all.deb Size: 103454 SHA256: f0c8f860708a30f13a5dced8fdcd1633e9dd31d0f0d5508520393e6b90976e1c SHA1: 7f79a01d8470e34f8638513ee3be3eb8e69f4ba2 MD5sum: 398cd3a1faa72db92f156eb70a617810 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 596 Depends: neurodebian-popularity-contest Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-5~nd11.10+1_all.deb Size: 89344 SHA256: 96a2a501b7e1b3b6e1c1d8d5b355f9f5b484a8824e2c632d8b431e2cd82a7159 SHA1: 810457cafe53bc25de11dcc6b1281e3c5430c1e4 MD5sum: 0b1a78886cfb6ebcb1a8aba2fee2b9ac 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~nd11.10+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~nd11.10+1_all.deb Size: 305826 SHA256: e049107b8acfbbb33b6420595488129692882f421fe5d0c6847b4a1ca3235bb1 SHA1: ae27a2f0baf15a279314bc3ad1702c53a0a9c5a1 MD5sum: 5bc20b08607e50a608166b712807eb6a 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~nd11.10+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~nd11.10+1_all.deb Size: 2686068 SHA256: d5461b0a690d2cbc46ddb10f4f7486b2d5c070300e7dd38007d392b44a29f2aa SHA1: 9aa8eaa67d029bd6db5ed17c732feedb9b8a49d3 MD5sum: b89e5f6aa4d3a43d5208b89a566e9650 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~nd11.10+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~nd11.10+1_all.deb Size: 19474792 SHA256: a09189f4e486cbd14048c5c92936080943bb4a23588037fc8c550d09afbbea0c SHA1: 84171b5a0c223f1b30782cb2aff8b95c7949e35a MD5sum: c89dd9c596c0c3231f61d107665b2447 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36 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~nd11.10+1_all.deb Size: 7320 SHA256: a6173047b4834e63ae2a25dd4d1df3d705b14a56a878e9e6ada4828e22938a38 SHA1: 8727ceef57f94c0c47c1394ac652d0860eebdfb5 MD5sum: f45dad983f038670b80cf5904d49f7f6 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~nd11.10+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~nd11.10+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~nd11.10+1_all.deb Size: 2334038 SHA256: a36ec894fd538863ef7728633b4463b42b9832178ad8e200a45e4ddb242cbe6f SHA1: 0d5c4143262f8ef37457f6086df5aee33889e25e MD5sum: 2efe8398ca2690d54110a1b288662dc4 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24424 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~nd11.10+1_all.deb Size: 4565406 SHA256: eda8dfc6644a35b936f1da9112b18d90532886d065ef76c6730d6273e0c3e41e SHA1: 82efec329facfee3f0f6e661bcf01f3155dc3228 MD5sum: cfa91bdb16834e1af904ca2909cf17ea 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: 0.5.3-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2924 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~nd11.10+1_all.deb Size: 501224 SHA256: 13e99320e82427890b37d87ee88b3b00d586d6f770c9a7a12c46465165d684fe SHA1: 340296aa43edcbfda8fef9d82da755cc6a80dc84 MD5sum: 307a5323d67150dfb598acf199394ec8 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12988 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~nd11.10+1_all.deb Size: 5715138 SHA256: 1bc6d7e975bb4022c925ebeb795f6984cdf1b014063a20796618b476d10bc442 SHA1: 35eab7ea3dac4efd6d4019f3a2d7f091a5c7edb6 MD5sum: 9bda26a515b441e96b3b0abe8c9a6b9c 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~nd11.10+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~nd11.10+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~nd11.10+1_all.deb Size: 399450 SHA256: 456505bdd758a97e3501801567b956941aa7e54953c22a7fc52e872e1638cdc5 SHA1: 7ab51c5396d0fd059ad67b6529721d952a388c61 MD5sum: 91b611d80614be08b72f3502f2628f57 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~nd11.10+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~nd11.10+1_all.deb Size: 166958 SHA256: 1d7440018106e39b1a02f31bad03fdbb48a515c5cd155bee364aecf00f9cece6 SHA1: a1a6c6853900544ce740ae694ab0b53f5b858c02 MD5sum: 05f28ac37f8a660f6eb51ed27deef398 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 504 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~nd11.10+1_all.deb Size: 60204 SHA256: db3834ba60ab0a28385895e01e87ee77196d468252a2196c4396322aba9b8032 SHA1: 3debe5c10b56978500cc395f0a1ea825f08ab3ff MD5sum: 31cd2ec5a63a5d73d902c83d66008e7c 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~nd11.10+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~nd11.10+1_all.deb Size: 3099084 SHA256: d8b37cc7f01c974f61250aaa99fd02504e6d2ee11a9f9be255fa878a858d27d2 SHA1: 2e290faa04800332eac7be983c6de44be0daf629 MD5sum: 80fb1a6f527ee063704798b0de74ef28 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20736 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-scikits.statsmodels Conflicts: python-scikits-statsmodels-doc Replaces: python-scikits-statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-scikits.statsmodels-doc_0.3.1-4~nd11.10+1_all.deb Size: 2666076 SHA256: 70084b0225c7c438ff33eef9c5229f338b28878caa444f5778b82b8427f10967 SHA1: cacf440471a8de730029ce8ee8cddd04b6e6279a MD5sum: 49e51765e58d515de79e107055338e8b Description: documentation and examples for python-scikits.statsmodels This package contains HTML documentation and example scripts for python-scikits.statsmodels. Package: spm8-common Source: spm8 Version: 8.4667~dfsg.1-1~nd11.10+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~nd11.10+1_all.deb Size: 10573720 SHA256: f0321ca7b21561e4d36761c8b822a658935beed84da70603e6c85c49c8df54a7 SHA1: a90f50f9eaf6dc3c4dafb24896e014b1f46ad17e MD5sum: 60163a673bd38fc493e44f6ff60f68e9 Description: analysis of brain imaging data sequences Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the platform-independent M-files. Package: spm8-data Source: spm8 Version: 8.4667~dfsg.1-1~nd11.10+1 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~nd11.10+1_all.deb Size: 52167722 SHA256: e19c67624dae801e90d3d2ac4513fd291a52c679dda6a17539f9b81281400e8b SHA1: a4516b85aed0b0507ffb9fb655f77fee607896b2 MD5sum: 6f8b8c766b3a7a13083842d70d61f325 Description: data files for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provide the data files shipped with the SPM distribution, such as various stereotaxic brain space templates and EEG channel setups. Package: spm8-doc Source: spm8 Version: 8.4667~dfsg.1-1~nd11.10+1 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~nd11.10+1_all.deb Size: 8648920 SHA256: de82f4ec4f61b92d16f055186e425295077205616998b56a507c773648c00eb2 SHA1: 4f226546eddd4adcc049cf993c2dd1bebefb696c MD5sum: f78a3cda6f161c0975499c70718050ee 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.