Package: fail2ban Version: 0.9.7-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1253 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python, python:any (>= 2.7.5-5~), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: mailx, system-log-daemon, monit, python-systemd Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.9.7-1~nd14.04+1_all.deb Size: 263184 SHA256: 1845d2b63439b839dd22c15acae7ed222a540a69b7e51e770cdc2c20c5bc3c35 SHA1: d80487781cb7548b2ecf033cbc8f2bf76eee298e MD5sum: 9065582859bbeec8910489a8a66df202 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. Following recommends are listed: . - iptables -- default installation uses iptables for banning. You most probably need it - whois -- used by a number of *mail-whois* actions to send notification emails with whois information about attacker hosts. Unless you will use those you don't need whois - python3-pyinotify -- unless you monitor services logs via systemd, you need pyinotify for efficient monitoring for log files changes Package: heudiconv Version: 0.1+git94-g85a2afb-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 128 Depends: neurodebian-popularity-contest, python, python-dcmstack, python-dicom, python-nibabel, python-numpy, python-nipype Recommends: mricron, dcm2niix Homepage: https://github.com/nipy/heudiconv Priority: optional Section: science Filename: pool/main/h/heudiconv/heudiconv_0.1+git94-g85a2afb-1~nd14.04+1_all.deb Size: 19564 SHA256: 94332ef1669da38c6f1f1e4d476b90274003985d8458b80a82ad8a8957fbf341 SHA1: 15a3d7abf9daf2c575f93b58039fc186d899d779 MD5sum: 0f513364573256843bd79dc3d31fc22a Description: DICOM converter with support for structure heuristics This is a flexible dicom converter for organizing brain imaging data into structured directory layouts. It allows for flexible directory layouts and naming schemes through customizable heuristics implementations. It only converts the necessary dicoms, not everything in a directory. It tracks the provenance of the conversion from dicom to nifti in w3c prov format. Package: python-mvpa2 Source: pymvpa2 Version: 2.6.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8479 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy, python:any (>= 2.7.1-0ubuntu2), python-mvpa2-lib (>= 2.6.1-1~nd14.04+1) Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-nipy, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, python-shogun, liblapack-dev, python-pprocess, python-statsmodels, python-joblib, python-duecredit, python-mock Suggests: fslview, fsl, python-mvpa2-doc, python-nose, python-openopt, python-rpy2 Provides: python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.6.1-1~nd14.04+1_all.deb Size: 5100910 SHA256: 063242a9b16f7500133f38449a03b47e814fe85266c33cbe64295afdd9b22d4b SHA1: 1fd566c2df77e14a226678af9a2026d104e5118c MD5sum: 33879e03e7691a79570bff05251c4e45 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: 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.6.1-1~nd14.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 31561 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2, python-mvpa2-tutorialdata, ipython-notebook Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.6.1-1~nd14.04+1_all.deb Size: 4735044 SHA256: 189d254392be454fd1b328b32ed3e24fd1c88156d8ae15634f43353d2ec39316 SHA1: 425e4fe1b116298b3ec074929e6ec0cd69ca5b40 MD5sum: ad2d5b46a9bfe9ff6210f6b5ed817797 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.), and example scripts. In addition the PyMVPA tutorial is also provided as IPython notebooks.