Package: condor-doc Source: condor Version: 7.7.5~dfsg.1-2~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5972 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.7.5~dfsg.1-2~nd60+1_all.deb Size: 1277028 SHA256: dbd49b3e2a4584b46acd43b9dbac6c31ed0890256b7ae6e7355adc522986a8fe SHA1: 9aba8e474593399abd146734a1d934d4605122d5 MD5sum: b90c478686ed8c09cb95fd7ce2361e55 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: debhelper Version: 9.20120115~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1700 Depends: neurodebian-popularity-contest, perl, file (>= 3.23), dpkg-dev (>= 1.16.0), html2text, binutils, po-debconf, man-db (>= 2.5.1-1) Suggests: dh-make Conflicts: dpkg-cross (<< 1.18), python-central (<< 0.5.6), python-support (<< 0.5.3) Homepage: http://kitenet.net/~joey/code/debhelper/ Priority: optional Section: devel Filename: pool/main/d/debhelper/debhelper_9.20120115~nd60+1_all.deb Size: 692314 SHA256: fdf94200725e9a8275b080987fe4af75a1ae42bee7e65eb495d071e1c79127f6 SHA1: 12eee77fe5f679ef83881451ce3dd987a0e5371f MD5sum: e82d0707a4052e21e403dee452096894 Description: helper programs for debian/rules A collection of programs that can be used in a debian/rules file to automate common tasks related to building debian packages. Programs are included to install various files into your package, compress files, fix file permissions, integrate your package with the debian menu system, debconf, doc-base, etc. Most debian packages use debhelper as part of their build process. Package: fail2ban Version: 0.8.6-3~nd60+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~nd60+1_all.deb Size: 103500 SHA256: 3f363bbb4aab43fe62721452194b2b6d50a314c03822b4f3fba2a64760050edb SHA1: 6a88525f9cead8210d6b8ddb74efb15c205002fa MD5sum: 32b9330132d95ff6eeb12af858302894 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~nd60+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~nd60+1_all.deb Size: 87834 SHA256: f24771598330352f0cb5b95d0a02bb287a2bdff35bc5cbeda82ca0ebfa190975 SHA1: aae946f33789780f10ab2f5f2b6b966de51e5644 MD5sum: ba181d9d11f5924f07196ca0e5bb671d 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: psychopy Version: 1.73.05.dfsg-1~nd60+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~nd60+1_all.deb Size: 2686046 SHA256: e7cfdd260f00c953e69bcff47c23638c7ac4156167f1c545e18f13274fe7db2b SHA1: 49e1639990eac52454e24690c01536503a9b408a MD5sum: bdcff2a248dffc93f2cd6cdee0a01a16 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: 2.5, 2.6 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.9+svn2514.dfsg1-1~nd60+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~nd60+1_all.deb Size: 19475000 SHA256: 6dffbb98f314ab1860aba26b5bdbe73ae7f82337aeb942069c73f438768fa4d6 SHA1: 7a93aae7ad1ee2d1316e8f9ed5427c19da51d48e MD5sum: 21fc7364d31be486a0e38bd5bb45e668 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~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40 Depends: neurodebian-popularity-contest, python2.6 | python2.5, python (>= 2.6.6-3+squeeze3~), 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~nd60+1_all.deb Size: 7430 SHA256: 4add14c7519849f9f98eba7976f7389e162a5cd8d03e6bc778c64a42536fa109 SHA1: 89e6fc5f690f0797b4cee2e135c7ee0a21ea8187 MD5sum: ef8002ce29ca25685369e74069640fc1 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-nipype Source: nipype Version: 0.5.2-1~nd60+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 Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.5.2-1~nd60+1_all.deb Size: 500804 SHA256: 88c66203246453d1de7006c708fdab2b4d95e735ed2c47b4467bf1b5ab888220 SHA1: 987afdf484dab84de3f45cfe80ab17d65d8c665b MD5sum: 88c61ee80ba2c85c7d096531ac560304 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~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13736 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~nd60+1_all.deb Size: 6233576 SHA256: 24a175f0e90269f2d5a189bb99f7742928f6b0ebb625be0c475744475ea1566a SHA1: ae1c1bb321ece3493702a4997f3ec2dc2c1eae98 MD5sum: 3a288664ad26b57c97152c41b02d5cfa 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~nd60+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~nd60+1) Recommends: python-scipy, python-matplotlib, python-tables, python-tz, python-xlrd, python-scikits.statsmodels Suggests: python-pandas-doc Provides: python2.5-pandas, python2.6-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.7.1+git1-ga2e86c2-1~nd60+1_all.deb Size: 399452 SHA256: 5e4e371c4b69231036b3abe5c095a888c1e93ece520e8310c3d90f4a7d966cf1 SHA1: cf69accb5aee17d1f0a19bed29738b27635b7f1d MD5sum: faecf52242c00c85f0f21bee10383d90 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~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 984 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~nd60+1_all.deb Size: 183120 SHA256: 0fc0788d76c60ad36879dbec3f6fe324426e9304a85fff48d2fd921cf2720da7 SHA1: 9a7c97579eb34b88c5d4e6ea5b3c9482e7a862dc MD5sum: 1b0f1eeae562cbde0e1957f7f34a8718 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: spm8-common Source: spm8 Version: 8.4667~dfsg.1-1~nd60+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~nd60+1_all.deb Size: 10573690 SHA256: 8aa9f613db4d596b62c16b09218f3b0c7b0d598d936da22209e971b3369e5c89 SHA1: fd12e9e25ed9aa391403e70d2666fe40a0fe6bc6 MD5sum: 00d2e40ee0e82c296451f0a7940e6aa0 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~nd60+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~nd60+1_all.deb Size: 52167706 SHA256: 162599ca35a4e161c1a068d6dc58c5e3047e4caaa3a915b590edbdb8fe39af08 SHA1: a6159ab4fb7476743b2f9379b5542f04e0c9b75a MD5sum: 5b9abe3d26aa01b34f036ba69ebd591c 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~nd60+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~nd60+1_all.deb Size: 8648912 SHA256: d5745267f4bd85af983f231f84633d2d7b9235150fe4ec2b02dbb9a3a366ab90 SHA1: 459809faf98947b5f7e8e3df7d7f9825913c134a MD5sum: b503c563c050f4cbab6dc052b709ec54 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.