Package: condor-doc Source: condor Version: 7.7.5~dfsg.1-2~nd10.04+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~nd10.04+1_all.deb Size: 1276632 SHA256: 2f663efdf3a6030d172ea826daea57a4c90692ff86d9e8967197d0ce7fcb672f SHA1: cf5fddcf714ce449e947f2b2f313948123994a3d MD5sum: 991a45eaa0eab99dda5c7eb32c04a05b 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: debian-handbook Version: 6.0+20120509~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23215 Depends: neurodebian-popularity-contest Homepage: http://debian-handbook.info Priority: optional Section: doc Filename: pool/main/d/debian-handbook/debian-handbook_6.0+20120509~nd+1_all.deb Size: 21998670 SHA256: b33f038d8363175473cc056a5f98fc7af52386a466b45d4b2e42d2f25233a3ed SHA1: 7a0b369b4548a3f4fb61aa1ef9efa2ddf2b319e2 MD5sum: 3e3d2cf990fcc5ed1ed6bdbfb5c1c3dd Description: reference book for Debian users and system administrators Accessible to all, the Debian Administrator's Handbook teaches the essentials to anyone who wants to become an effective and independent Debian GNU/Linux administrator. . It covers all the topics that a competent Linux administrator should master, from the installation and the update of the system, up to the creation of packages and the compilation of the kernel, but also monitoring, backup and migration, without forgetting advanced topics like SELinux setup to secure services, automated installations, or virtualization with Xen, KVM or LXC. . The Debian Administrator's Handbook has been written by two Debian developers — Raphaël Hertzog and Roland Mas. . This package contains the English book covering Debian 6.0 “Squeeze”. Package: fail2ban Version: 0.8.6-3~nd10.04+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~nd10.04+1_all.deb Size: 103498 SHA256: a6cbd680d17b38454e3879b3522595363274e259ef8ae384b74d96a59ca06b64 SHA1: 0576ec90baa2aaa0a359cf970d0c053ff5cad722 MD5sum: 9323fb420bc9e8eea74a61a8c69ef6e5 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-6~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 548 Depends: neurodebian-popularity-contest Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-6~nd10.04+1_all.deb Size: 85888 SHA256: eb7e328d32a3c95d598f79eeb7d2d7220060e1ff6d64cdc10006103ff31f05ff SHA1: 9d08af477dc490067cb014fa48899f7f817d8c29 MD5sum: 8d6e307534fdb0341a2b3d722b92aba5 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: 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~nd10.04+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~nd10.04+1_all.deb Size: 2686168 SHA256: 1f7d59546b5e3699e5d19ab7f0f165145e9bcb90ac39a82030fe18c91febb253 SHA1: efa6b54eb5a5c707893cdd27c615e4d765a2387f MD5sum: 259c06ccf3d27a310b076f3a12e36563 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+svn2579.dfsg1-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 53608 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+svn2579.dfsg1-1~nd10.04+1_all.deb Size: 19434404 SHA256: 1e0a6939d26a5debd944bf1acaece3dff5cbdbd14591a07ec90f6e5c3c1918ed SHA1: 8e3ba262934eff7f7568d77be4558b6156996bb4 MD5sum: afac568d1e1f06470c7b17554c57ec0c 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-joblib Source: joblib Version: 0.6.4-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 252 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.6.4-1~nd10.04+1_all.deb Size: 51620 SHA256: 9d83cd8e775edde66d30039af3cee2c528368704e9aeb63957690f596353b56d SHA1: 514e33f2d3a356638c2125d803ec7e2585ae28a6 MD5sum: 9e5e3001134a790b4e432801b490319d Description: tools to provide lightweight pipelining in Python Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers: - transparent disk-caching of the output values and lazy re-evaluation (memoize pattern) - easy simple parallel computing - logging and tracing of the execution . Joblib is optimized to be fast and robust in particular on large, long-running functions and has specific optimizations for numpy arrays. Package: python-mvpa Source: pymvpa Version: 0.4.8-1~nd10.04+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~nd10.04+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~nd10.04+1_all.deb Size: 2195398 SHA256: 7429e8b63f838a1821b8f444ff85f95162caf21916382f9439b45b2ae1d0cb44 SHA1: c9795cc37ac3b277a8687a7b8e099f0ba776529c MD5sum: 3e21707251da18a094a5a7e40f37fc47 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~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41240 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~nd10.04+1_all.deb Size: 8772578 SHA256: 9d3dff72aee22b019c206e41988f805c39b2b972580a868baf56b828ca835aea SHA1: 11ea76c7b25370bd570dff4433e2eb9205f2b720 MD5sum: 16f6ef2ef23880e96db96360f11835cf 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~nd10.04+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~nd10.04+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 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.0.1-1~nd10.04+1_all.deb Size: 2334026 SHA256: 4a58541ad97394a58211939b980b860659fe056cd630e5e15d11ad611807cc32 SHA1: 49bfa448832880d61ac042187e0b9b05f8832b9e MD5sum: 48d1ac6d1ef51d4a09e1edac13a67029 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~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24600 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~nd10.04+1_all.deb Size: 4645956 SHA256: 6514d6724dddecd62110a47f42d70a150e7072921c0ec68d27f52445927e756b SHA1: 04d437c403c04ab3c084ba887374e42b1616310f MD5sum: d1ca97256a86a83fb921c8c888903a0f 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-nibabel Source: nibabel Version: 1.2.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4432 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Provides: python2.6-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.2.0-1~nd10.04+1_all.deb Size: 1814382 SHA256: 4eed701b0a92ad12b3b3000e22309e3142711a9c73bf56285bd6fa23c71ffd57 SHA1: 76a07fa856009157708d438138b1558284c6905a MD5sum: ca50b3138097e67622b84f53ec11c976 Description: Python bindings to various neuroimaging data formats NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package also provides a commandline tools: . - dicomfs - FUSE filesystem on top of a directory with DICOMs - nib-ls - 'ls' for neuroimaging files - parrec2nii - for conversion of PAR/REC to NIfTI images Python-Version: 2.6 Package: python-nibabel-doc Source: nibabel Version: 1.2.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2840 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.2.0-1~nd10.04+1_all.deb Size: 418902 SHA256: 6bbe7c4fae5037636743daa7f363328067b627c23bcb5e492883bf6fe2d9bab3 SHA1: a94c08db01f2d73267643a5595ffe9f57eea540e MD5sum: 73900db811168842baa769558ac1e438 Description: documentation for NiBabel NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package provides the documentation in HTML format. Package: python-openopt Source: openopt Version: 0.38+svn1589-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1612 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.6-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd10.04+1_all.deb Size: 245206 SHA256: fa59841cc93f8aca5496a7459a5901fd178766472cb795a9d0d29f42ba9ba8c4 SHA1: 872744e09191a35ef0b5c3a59c8cd2cf14a0908a MD5sum: 925ae988aa5c48249f2b1b47dc0d5066 Description: Python module for numerical optimization Numerical optimization framework developed in Python which provides connections to lots of solvers with easy and unified OpenOpt syntax. Problems which can be tackled with OpenOpt * Linear Problem (LP) * Mixed-Integer Linear Problem (MILP) * Quadratic Problem (QP) * Non-Linear Problem (NLP) * Non-Smooth Problem (NSP) * Non-Linear Solve Problem (NLSP) * Least Squares Problem (LSP) * Linear Least Squares Problem (LLSP) * Mini-Max Problem (MMP) * Global Problem (GLP) . A variety of solvers is available (e.g. IPOPT, ALGENCAN). Python-Version: 2.6 Package: python-openpyxl Source: openpyxl Version: 1.5.8-1~nd10.04+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~nd10.04+1_all.deb Size: 71622 SHA256: 1d1a28113078057de46cacad522ad075face1bc8aabbf9f69ca825e482697ab9 SHA1: 9a92ed3547c55b8f588b188bf1589811069a57ba MD5sum: d0c8d3a553f4a1c00cf2019f4398a51d 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~nd10.04+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~nd10.04+1_all.deb Size: 183132 SHA256: 97e62471ecd72f25ee0463eb8af08f3276ef5c51d95808861de359c02b35d9bf SHA1: d6399665ba70a05e8e7ca46de3c0f2f4ad596501 MD5sum: 8d05e7dfb68300ff41f4a889bc77eab6 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-scikits-learn Source: scikit-learn Version: 0.11.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 96 Depends: neurodebian-popularity-contest, python-sklearn, python (>= 2.6), python-support (>= 0.90.0) Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.11.0-1~nd10.04+1_all.deb Size: 22868 SHA256: 0d8b1aefa5c62ffc25305049765b979d817b1eb72cfba2dcd81b02c9f366eeb0 SHA1: 4b8e95695f8f0bb301a17ec7282e99bb13d4fa95 MD5sum: 24d77290866d7332a9da602d1515520c Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-sklearn Source: scikit-learn Version: 0.11.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3072 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.11.0-1~nd10.04+1) Recommends: python-nose, python-psyco, python-matplotlib, python-joblib (>= 0.4.5) Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Enhances: python-mdp, python-mvpa2 Breaks: python-scikits-learn (<< 0.9~) Replaces: python-scikits-learn (<< 0.9~) Provides: python2.6-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.11.0-1~nd10.04+1_all.deb Size: 890882 SHA256: 754c41915844b246cf59b0d30cdefb3d944fe08c48bf109e0be18991273aad8f SHA1: fedbe2a243da58c283623ed772f251bf7d01f4bc MD5sum: e71a8f7802223669b0acf7c64f387f34 Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Python-Version: 2.6 Package: python-sklearn-doc Source: scikit-learn Version: 0.11.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29240 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.11.0-1~nd10.04+1_all.deb Size: 16235326 SHA256: 96143ff04ce43cdc1f9846d0e1a1eb4679bd8b6c1b8094a31f6292da61ff2a09 SHA1: 1e907689c22afc300655b7932b89a6c02326c35b MD5sum: 9cd25a12bbf04493cefe2e2b27dd738a Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-surfer Source: pysurfer Version: 0.2+git29-g3a98681-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 144 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~nd10.04+1_all.deb Size: 25986 SHA256: 9fa55d9c357e564804664ebd4e5b30f6979188d58288786213d0c76c121c8331 SHA1: 49a05205131449071299068bd76c36b59c31114b MD5sum: 24139f79323a4dc86162da7d0e711163 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~nd10.04+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~nd10.04+1_all.deb Size: 10573676 SHA256: 885839fee58c5da6818fb8d61615c3a3b8a2c6cdf17455d12b325ebfc6335dbb SHA1: 44051a62ef000e395e598372db51c0c97d013f67 MD5sum: 1403e1d413153dbfc75a2fc87c537a6a 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~nd10.04+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~nd10.04+1_all.deb Size: 52167700 SHA256: 892fd5132f9f07c9c18ba513240f89b078807ca90a5709153732acd61ae75ca0 SHA1: bf466428a78d725441b20d810f923df0110a5906 MD5sum: f8d0da1cd8deccb95f4f3dc0700b9c7b 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~nd10.04+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~nd10.04+1_all.deb Size: 8648932 SHA256: 0f720d86c9e975b3033db96e9c37f54f9349d5371f577788b5214cfcd25a50b4 SHA1: d47adec8d0a15e91be7ec799f3a84ac20099301d MD5sum: 0a60d682d6cb6b05f84a5916092e813d 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.