Package: fail2ban Version: 0.8.6-3~nd10.10+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~nd10.10+1_all.deb Size: 103504 SHA256: ae0b516a040b14b559b6d3b48a2f9a5593925bbde88946034733f844c74d0e32 SHA1: d689d048a4ea9f8cd35e9401f450079ba0ab5bee MD5sum: 2729dc868a77d6d0afae0d6cf8b63704 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~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 576 Depends: neurodebian-popularity-contest Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-5~nd10.10+1_all.deb Size: 87848 SHA256: 572c718327dc55f3f945f48c060a68ca3196aa73d23199712ca70b353c773452 SHA1: cce0dcbdb38fe3d62535c481e58b0faadd009ea8 MD5sum: 96b00556a8fb8912d20a2deac5e090f2 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~nd10.10+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 (>= 2.6.6-2ubuntu2~) 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~nd10.10+1_all.deb Size: 313472 SHA256: 8e70b68aded9129a5e5a63a7ec948fb9f16da34483120c6bd3581c2374a87928 SHA1: f6f18a63c8f46528948ecd0ded8136ef96ffa097 MD5sum: 0110be69f64baa287e8381636c94a966 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~nd10.10+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~nd10.10+1_all.deb Size: 2686066 SHA256: 235ef09f00ac0ac36d50f80b2d9495dcbc0fc86923cd5d84ae527d661bb9236e SHA1: 23bf935030b850a6b397a73bd534577348cc476c MD5sum: ea6c2b16eee539c49dc49598933a3dd8 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 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.9+svn2539.dfsg1-1~nd10.10+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~nd10.10+1_all.deb Size: 19425328 SHA256: 173253383805b6611843ae3386394a70c5e0a42274a74fff2ff5656d946b22b8 SHA1: 8fe4b72661d38416cc2712bdadfdb74884e61ba5 MD5sum: cdf8dc39b0b2f285bf975e056dfd109a 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~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40 Depends: neurodebian-popularity-contest, python2.6, python (>= 2.6.6-2ubuntu2~), python (<< 2.7), python-numpy Homepage: http://bitbucket.org/apdavison/lazyarray/ Priority: optional Section: python Filename: pool/main/l/lazyarray/python-lazyarray_0.1.0-1~nd10.10+1_all.deb Size: 7394 SHA256: 81470680682a7d7e57bd0303d63329d5c30009c8a8aa3901bf30e449c5ea088b SHA1: 163443900ab81047b2a652399d071fda6236d432 MD5sum: 20bf2ddee37451a1339ad26ab23a6b2f 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-mvpa Source: pymvpa Version: 0.4.8-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4092 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-lib (>= 0.4.8-1~nd10.10+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: python2.6-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.8-1~nd10.10+1_all.deb Size: 2193278 SHA256: 206cd14ecf1cb45a6f84b2389cca9cfae03392d6c5146fdd24daa308d8890b51 SHA1: 160ee9fc4966aa23ef8fb9a55ef865bcb943d7a1 MD5sum: d866def0f119476cd3865956fde87071 Description: multivariate pattern analysis with Python 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, 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: 2.6 Package: python-mvpa-doc Source: pymvpa Version: 0.4.8-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41252 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.8-1~nd10.10+1_all.deb Size: 8725250 SHA256: 135bcb5a99c285e39f5384f772ca3fb35cc99af58fdbbbafe02b513534e6eaec SHA1: 3138f617fd8b8581f679ee5803a4fbcf436f2160 MD5sum: 9d008d570ea6eff0f8bc37d6e5a1eecb 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-mvpa2 Source: pymvpa2 Version: 2.0.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4676 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-numpy, python-mvpa2-lib (>= 2.0.1-1~nd10.10+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 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.0.1-1~nd10.10+1_all.deb Size: 2334022 SHA256: 0b5238014183870a9097dd51587f4cff9c5cd59cb8c6a8d6f1f4d9b0bbfe4939 SHA1: 46ad3dac4d7a9944bcbfa3ff7aecc258e91cb67f MD5sum: 119b6d6465fc1190ae40cc6341f214c4 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 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.0.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24392 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~nd10.10+1_all.deb Size: 4549526 SHA256: 82115f5c7c91acf52647b8fa8572a8962e9603e95c96abd2a669f68685db1a11 SHA1: bc272d7eecdeb523fe022a2e52b453f086bf3a8d MD5sum: 67b7fa28662fadb2fa6a66d9f8984dc6 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~nd10.10+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 Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.5.3-1~nd10.10+1_all.deb Size: 497484 SHA256: 87e0dd8934265b16825ad45f9a6a7202f0f5150ae61a0515e544c5409828e3c2 SHA1: 0476f002515a6beccaf11e94b0737a41adc808d5 MD5sum: 7b1630590643e1edb90b38cb74f04483 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~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12976 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~nd10.10+1_all.deb Size: 5715172 SHA256: ab49e36b48606d6acead3eff177df83a1155012db4cecae2319106d7eabccd80 SHA1: 896006441da291b9f2dce94db2501f5aced925d7 MD5sum: 506b79f1ec3a11a20afbbc5aaec0aab6 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~nd10.10+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~nd10.10+1_all.deb Size: 71594 SHA256: a7c9859670978c8203b39df7ceb3684edd25eb0c715fb035ccd632e47cb3e72f SHA1: f2ce4d0819e44cc0128db1c5a7c3f5a60300a360 MD5sum: 7cfb8fb94e6f4177c4efa70e0b58f238 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-pynn Source: pynn Version: 0.7.2-1~nd10.10+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~nd10.10+1_all.deb Size: 166924 SHA256: e76f9d462d22c444ab9592ed43b27e22b1072ff68e8215aabc52793d0ddcfd9a SHA1: 4605af6bcc78f4da8c556d00bb5a0ccbe87d7d20 MD5sum: 9266d863c77a0bf0fd75addbaa924de5 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-surfer Source: pysurfer Version: 0.2+git29-g3a98681-1~nd10.10+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~nd10.10+1_all.deb Size: 25590 SHA256: 8515ec86476c23f9d4ea568f9804c1cfb8b40c6cc61a29162ed3ce13f64608ed SHA1: af01524c45a63baf54ea12acc605915fd940e09b MD5sum: f8fce5281a42a23c0dd06d856ded0e76 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 Package: spm8-common Source: spm8 Version: 8.4667~dfsg.1-1~nd10.10+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~nd10.10+1_all.deb Size: 10573706 SHA256: 89e7a1754f1f8113abdf1d33598dd151d0632944f701d431db37746a15afacb7 SHA1: 251f6d0090d8b9b51b74cf4b141e200d3d3215a6 MD5sum: 26a0351bec8804cf9f5e28bc728b3223 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~nd10.10+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~nd10.10+1_all.deb Size: 52167708 SHA256: 89a937d5f2f8040ddcbe651b89646acf704f98fa19af3cfb01af7c78ff2ca347 SHA1: de002aca59cb3438b4c5a5a70609b6e30cf238fe MD5sum: 254e61f0c8a0778f4bc9d5772a8e5f0b 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~nd10.10+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~nd10.10+1_all.deb Size: 8648928 SHA256: cc3118b97bb6ebbe40fb6b9fb0c737877e33b5057f3ac28e2ababe867c2a4072 SHA1: 68b4d10782ef304f9f5aa43b63ff2eacacea27b6 MD5sum: cff36651ee5c4bb444dfa7475d6d7efc 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.