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: 0.3.20.1+ds-1~nd10.10+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 (>= 2.6.6-2ubuntu2~) 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_0.3.20.1+ds-1~nd10.10+1_all.deb Size: 306330 SHA256: f123f9f99661fedcf06111afa7a734c29280e65214b31fd14f0a371d755937f2 SHA1: de5ae382c1524eeec110976991b6224b0da67c8e MD5sum: 5d84fbefbddbf4655c8a38ecf2d86001 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~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: 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-nipype Source: nipype Version: 0.5.2-1~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_0.5.2-1~nd10.10+1_all.deb Size: 497168 SHA256: 424e279b416a3740bc6de303672d6bce20b6a77c5253a6ccd00e363f33fb3adc SHA1: 8a03397b566a7c0feb787522acfd28bb49ae2c91 MD5sum: b8ccce34fae5fc43630649faa2360eea 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~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12952 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~nd10.10+1_all.deb Size: 5706504 SHA256: 7d59e982bb2f9263e37ca059d42185385f012a41550bfc14a11151ec14b7e8c7 SHA1: 410462b7dea5cd3a3cc8143172461dde49e64d4e MD5sum: c2efac9b9bae4b056163ea36e68a62de 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: 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.