Package: condor-doc Source: condor Version: 7.8.8~dfsg.1-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 6118 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.8~dfsg.1-2~nd13.04+1_all.deb Size: 1459864 SHA256: 70fd12501ed9c750b5e148e55bc2335501daaaf6b77ff80dc728277c4a68f4f6 SHA1: c659a0a5d89550d08188b5f04afba63c467db9fd MD5sum: 32fab8d8211465476741f9d83cf1ec9e Description: distributed workload management system - documentation 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 systems, 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: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8109 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1_all.deb Size: 7224822 SHA256: e03522059ac09830cf48fd4f41780c0e6fcc7c4d1f3c331f213dbc6743c49565 SHA1: d9675743da0b53adc7682ebacfc6a4922f7a0880 MD5sum: 8a6520b56c5bf5302d61f9eb27b6a847 Description: sample EEG data for EEGLAB tutorials EEGLAB is sofwware for processing continuous or event-related EEG or other physiological data. . This package provide some tutorial data files shipped with the EEGLAB distribution. Package: fail2ban Version: 0.8.11-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 525 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx, system-log-daemon Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.11-1~nd13.04+1_all.deb Size: 176804 SHA256: a1ddf948737bfad1dade04b91770093e16e834ed00e8fe8621ae102aaa8c9923 SHA1: b517df4725e0c09d5049252020687866b5c94d75 MD5sum: 6050230544f444689795cf113f3f3dfb 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. Package: fis-gtm Version: 6.0-003-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, fis-gtm-6.0-003 Provides: mumps Homepage: http://sourceforge.net/projects/fis-gtm Priority: optional Section: database Filename: pool/main/f/fis-gtm/fis-gtm_6.0-003-2~nd13.04+1_all.deb Size: 15118 SHA256: 62a3d1064045f0c9adc75259bdd44bece502402aa6af5a7a910453caea3a8d7d SHA1: 407dbe9fde129c6716b52b6b44a54c23d118cb80 MD5sum: 12b9bdc8bd351655f2d686c5efe6c92c Description: metapackage for the latest version of FIS-GT.M database GT.M is a database engine with scalability proven in large real-time transaction processing systems that have thousands of concurrent users, individual database file sizes to the Terabyte range (with virtually unlimited aggregate database sizes). Yet the light footprint of GT.M allows it to also scale down for use in small applications and software appliances (virtual machines). . The GT.M data model is hierarchical associative memory (i.e., multi-dimensional array) that imposes no restrictions on the data types of the indexes or content - the application logic can impose any schema, dictionary or data organization suited to its problem domain. (Database engines that do not impose schemas, but which allow layered application software to impose and use whatever schema that is appropriate to the application are popularly referred to as "document oriented", "schemaless" or "schema-free" databases.) . GT.M's compiler for the standard M (also known as MUMPS) scripting language implements full support for ACID (Atomic, Consistent, Isolated, Durable) transactions, using optimistic concurrency control and software transactional memory (STM) that resolves the common mismatch between databases and programming languages. Its unique ability to create and deploy logical multi-site configurations of applications provides unrivaled continuity of business in the face of not just unplanned events, but also planned events, including planned events that include changes to application logic and schema. . This metapackage always depends from the default fis-gtm version. Package: fslview-doc Source: fslview Version: 4.0.1-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2874 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-2~nd13.04+1_all.deb Size: 2346538 SHA256: 21191091e9505223e32987c04eb1acbe3829ae341eef5525489f97a1a2d457bf SHA1: 33c8b353b6b9e7b5824704f2f58440ec2d4ad6f0 MD5sum: 66d92ab90f1579986c945c1cf4e2f90f Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gmsl Version: 1.1.3-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 78 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.3-2~nd13.04+1_all.deb Size: 16296 SHA256: 1330ca8671c52ce66d25a9c4e7342060578d234e9ff8d6d0d2c8d7ee5d4a069e SHA1: 8d7d2c207db0415ea391b037cc2854e453320dbd MD5sum: 1cca8ecb1df3278f8b133028c491886e Description: extra functions to extend functionality of GNU Makefiles The GNU Make Standard Library (GMSL) is a collection of functions implemented using native GNU Make functionality that provide list and string manipulation, integer arithmetic, associative arrays, stacks, and debugging facilities. . Note that despite the name of this project, this library is NOT standard and is NOT written or distributed by the GNU project. Package: guacamole Source: guacamole-client Version: 0.8.3-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 475 Depends: neurodebian-popularity-contest, guacd Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole_0.8.3-1~nd13.04+1_all.deb Size: 428594 SHA256: d55f6232980dc1ef9045b48139a91d4dba50bbf0ccd4f1b9985e49b867d8e1d7 SHA1: 190bdcbe59f8d3376a740ea165eaf0d2e2a6a89f MD5sum: 6d1a33f6ffebe3d47914824494caee9c Description: HTML5 web application for accessing remote desktops Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. Package: guacamole-tomcat Source: guacamole-client Version: 0.8.3-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole-tomcat_0.8.3-1~nd13.04+1_all.deb Size: 6954 SHA256: 86720dba22910a24bf42d89d963cc85eebdb3473d0b2cd3d024e303fe3c08d58 SHA1: dadcc37c8a43d8983e56f5b9c8b7c034e36907c4 MD5sum: 65573fafba4caf4845f917da4c2539d0 Description: Tomcat-based Guacamole install with VNC support Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. . This metapackage depends on Tomcat, Guacamole, and the VNC support plugin for guacamole. Guacamole is automatically installed and configured under Tomcat. Package: impressive Version: 0.10.3+svn61-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 306 Depends: neurodebian-popularity-contest, python-opengl, python-pygame, python-imaging, poppler-utils | xpdf-utils (>= 3.02-2), perl Recommends: pdftk Suggests: ghostscript, latex-beamer Conflicts: keyjnote (<< 0.10.2r-0) Replaces: keyjnote (<< 0.10.2r-0) Provides: keyjnote Homepage: http://impressive.sourceforge.net/ Priority: optional Section: x11 Filename: pool/main/i/impressive/impressive_0.10.3+svn61-1~nd13.04+1_all.deb Size: 155300 SHA256: 159c171633fe48243aa2aa1b42c22cc6bc58f5cdcef3a7c52064018c6795da18 SHA1: 1cd3a49e36b88578c5ac668a511248b23c9f6c45 MD5sum: bb6cdb07320f44bd39856b18f751cbae Description: PDF presentation tool with eye candies Impressive is a program that displays presentation slides using OpenGL. Smooth alpha-blended slide transitions are provided for the sake of eye candy, but in addition to this, Impressive offers some unique tools that are really useful for presentations. Some of them are: * Overview screen * Highlight boxes * Spotlight effect * Presentation scripting and customization Package: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd12.04+1+nd12.10+1+nd13.04+1_all.deb Size: 9762 SHA256: 1b70856f5e29f4ff05b8faf310211265c11d0e9b6b78f8bd46101218ed941444 SHA1: 727d17fe0fae14cde238acfc92e4c478e60c84af MD5sum: 60d09f476bd15e66eb89d054f2be4951 Description: utility for pushing DICOM data to the INCF datasharing server A command line utility for anonymizing and sending DICOM data to the XNAT image database at the International Neuroinformatics Coordinating Facility (INCF). This tool is maintained by the INCF NeuroImaging DataSharing (NIDASH) task force. Package: insighttoolkit4-examples Source: insighttoolkit4 Version: 4.2.1-2~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2677 Depends: neurodebian-popularity-contest Suggests: libinsighttoolkit4-dev Conflicts: insighttoolkit-examples Replaces: insighttoolkit-examples Homepage: http://www.itk.org/ Priority: optional Section: devel Filename: pool/main/i/insighttoolkit4/insighttoolkit4-examples_4.2.1-2~nd12.10+1+nd13.04+1_all.deb Size: 2408122 SHA256: 65e6c7472a8a92b3ce0c45d17ffb3483e73a2dac7e24c4ba5175cbf31ca7ea99 SHA1: f8cb4f79f753bfd22d7a9a07470b3c8814a656dd MD5sum: 074628f846a92252222964aefd06d892 Description: Image processing toolkit for registration and segmentation - examples ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the source for example programs. Package: ipython01x Version: 0.13.2-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4665 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython01x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x_0.13.2-1~nd12.10+1+nd13.04+1_all.deb Size: 1286080 SHA256: b735f17a5d75039971d01bafcae7e4eb301a795277414ecebd0064d375dad79c SHA1: a5085547449b24edb309ce17e432dde037a2d232 MD5sum: 0789a3e20beda824754862ced7bcdf5e Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.13.2-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16686 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.2-1~nd12.10+1+nd13.04+1_all.deb Size: 7246220 SHA256: f98bd7d37f0204febffa5709f09eb5124cc1f376e53aa7d79d9538ad05cec0f7 SHA1: aa53cd815c0dc66eb3991d7ce8c5fd84b9b4fff9 MD5sum: 811804ffc2f242722732f1bfd66a90bf Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-notebook Source: ipython01x Version: 0.13.2-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-notebook_0.13.2-1~nd12.10+1+nd13.04+1_all.deb Size: 910 SHA256: e712c2d41ec3b89d4633e58ccdc1f15893b9ac1296d4b9aae04e193ba2aae489 SHA1: 2afc184179ee99033f71f6b7f4cc6ac7e8deff23 MD5sum: 8a3db4e3c7b89648db43bb4aa8dc9b3d Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython01x-parallel Source: ipython01x Version: 0.13.2-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython01x/ipython01x-parallel_0.13.2-1~nd12.10+1+nd13.04+1_all.deb Size: 834 SHA256: 8dae62722abf91c76d20ca6d926382fa4197efa7d815c1973ab67ade497cd432 SHA1: b39f45f0df6f3fff98b23c54fb2cbadc6db01b1f MD5sum: aca06ab4c7848644ab4e9807593aa026 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython01x-qtconsole Source: ipython01x Version: 0.13.2-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.13.2-1~nd12.10+1+nd13.04+1_all.deb Size: 920 SHA256: 9438c824e04211686d5b9bb4c9a3a5eeedaa619255054650c85642c7e7a97395 SHA1: a1e7af1ada5039a1022157445773c5cf9c931597 MD5sum: af5c4bc8bc33c1c950d56f879b3d1e90 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11748 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython1x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython1x/ipython1x_1.1.0+git7-gf5891e9-1~nd13.04+1_all.deb Size: 4486534 SHA256: 94324cd485722db64cddadc7bfd1ebf13e71ff6e45c532a24dfd4952f380a287 SHA1: f6f1a41353f31ba8f0886ca23e778e989db1aec9 MD5sum: 9362cc9f7293e4a0fb8597dc71456e6f Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-doc Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10403 Depends: neurodebian-popularity-contest, libjs-jquery, ipython1x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython1x/ipython1x-doc_1.1.0+git7-gf5891e9-1~nd13.04+1_all.deb Size: 4191858 SHA256: fbeb5290528cfca96b94b65cbfa132d97608f6f5a651294aed932d7423b27bf3 SHA1: d6f03dbf53b09e44d6b43a614646a182e75fc7c2 MD5sum: 9b59122dee64e866196af3d1690393ae Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-notebook Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-notebook_1.1.0+git7-gf5891e9-1~nd13.04+1_all.deb Size: 914 SHA256: ab5fbda2005a1d46b4250a354bc3448b549f11c15b8b1676d224361f108f7ba7 SHA1: 7242d729a32fe408f059430a5968889d0ee717f6 MD5sum: 05a7f10ee24405419cf88a47f39ddd71 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython1x-parallel Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython1x/ipython1x-parallel_1.1.0+git7-gf5891e9-1~nd13.04+1_all.deb Size: 838 SHA256: 43315410a21249c1c7d2d5a4ebdba410e73469d8af202da572e571c6159adc29 SHA1: 723104e9bf1bde1e5cea3eef9e7e34a0c5a10c7e MD5sum: 3a7f4a7e5c24c95d2e5c3f08a5815d3d Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython1x-qtconsole Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-qtconsole_1.1.0+git7-gf5891e9-1~nd13.04+1_all.deb Size: 926 SHA256: a0c02b115f2b0311d514a7c1752b7d93847cc8d1a0947ae9e730cf7245590b06 SHA1: d8425631e03d417deb9841e72d2cddb0f89ea711 MD5sum: a4d2ae8a03caea83f7430a598f7b0d4b Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: libmia-2.0-doc Source: mia Version: 2.0.13-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12977 Depends: neurodebian-popularity-contest, libjs-jquery Enhances: libmia-2.0-dev Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/libmia-2.0-doc_2.0.13-1~nd13.04+1_all.deb Size: 778794 SHA256: a27c7f64692b847ab2d3c3beaa9b41810055bd2eebeec0dbb85c85441b6b298b SHA1: 3e13b1a709d58c78f44ac3a570eac301456a9372 MD5sum: 426e7747fea1774f40ddfad9b2d9eb9e Description: library for 2D and 3D gray scale image processing, documentation libmia comprises a set of libraries and plug-ins for general purpose 2D and 3D gray scale image processing and basic handling of triangular meshes. The libraries provide a basic infrastructure and generic algorithms, that can be specialized by specifying the apropriate plug-ins. This package provides the Doxygen generated API reference. Package: libmialm-doc Source: libmialm Version: 1.0.7-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 232 Depends: neurodebian-popularity-contest Suggests: devhelp Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/libm/libmialm/libmialm-doc_1.0.7-2~nd13.04+1_all.deb Size: 25102 SHA256: f8de6602cafe72cf37dee0373dcc59c165e2241e04497a7feb21898fe3bf8c1f SHA1: c910483d2ccecbe624859a282676baeb37fd65a5 MD5sum: 0647a0bcaa78adafc9973ec5b16e4b4b Description: Documentation for the MIA landmark library This library implements handling for landmarks and 3D view positioning for optimal landmark visibility, and in-and output of these landmarks. This library is part of the MIA tool chain for medical image analysis. This package contains the library documentation. Package: mia-tools-doc Source: mia Version: 2.0.13-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1134 Depends: neurodebian-popularity-contest Enhances: mia-tools Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/mia-tools-doc_2.0.13-1~nd13.04+1_all.deb Size: 72790 SHA256: ee3a4b4aec826682d9db0129ee48d93528e1a1677bd906ee8fb55b9290c4eacf SHA1: 767900133589f8ea59fbcbfa6fd3abc55010b53f MD5sum: 752a1e81809d66f44decdc745fa5cf6d Description: Cross-referenced documentation of the MIA command line tools Cross referenced documentation of the command line tools and plug-ins that are provided by the MIA gray scale image processing tool chain. These lines tools to provide the means to run general purpose image processing tasks on 2D and 3D gray scale images, and basic operations on triangular meshes interactively from the command line. Supported image processing algorithms are image filtering, combining, image registration, motion compensation for image series, and the estimation of various statistics over images. Package: mricron-data Source: mricron Version: 0.20130828.1~dfsg.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1710 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20130828.1~dfsg.1-1~nd13.04+1_all.deb Size: 1664524 SHA256: 179f46350fc86243f6af340e0ba92f4d8cbafb46d0c085be129f29f8ac846159 SHA1: cfa43237268d891d2472016ee77e8c7470298c30 MD5sum: cb9e0cda7870731a8d15ff9e016e002b Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20130828.1~dfsg.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1019 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20130828.1~dfsg.1-1~nd13.04+1_all.deb Size: 737510 SHA256: f6522a77a07ff991e70a16fbeb0a146e3744049373534f465c99da6da03aabe7 SHA1: fdfd8b08dfb6c00c23ceb6b8b139ba16d80e09a1 MD5sum: c16e9f95bb777af9a6fa7517187931d4 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: mrtrix-doc Source: mrtrix Version: 0.2.11-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3520 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.11-1~nd13.04+1_all.deb Size: 3317828 SHA256: c2fdca5d4469af324b396660001238f4289922a55cc45813d5bc97060df0fab8 SHA1: 2c21e943a5b80e042f73fdfd2b768fe556fe1110 MD5sum: dfa6593cfe9bb6d76eb51cd2de717334 Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: neurodebian-desktop Source: neurodebian Version: 0.32~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 142 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, gnome-icon-theme, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.32~nd13.04+1_all.deb Size: 115558 SHA256: fb2ce5fb8bdc68b9538ded1fe8b38d840b2070be56005c60fff83a190d84bd7a SHA1: 8e4efae8e33e6bafcfd3656f9036801ce96c04fd MD5sum: abebd793bb8fd1467bdda10a7a65fe87 Description: neuroscience research environment This package contains NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring most popular neuroscience tools automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.32~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6842 Depends: devscripts, cowbuilder, neurodebian-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring, apt-utils Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.32~nd13.04+1_all.deb Size: 6433088 SHA256: 57c6da06449724a2baf893712cdaa5f8ba7d0ce73c335c643788d70775686b01 SHA1: 3f6a9999cc43c704b76e480da1d7041a90e88a51 MD5sum: f3c750d80bce68b591bd1c44c72c6540 Description: NeuroDebian development tools neuro.debian.net sphinx website sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-guest-additions Source: neurodebian Version: 0.32~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm | lightdm, zenity Recommends: chromium-browser, update-manager-gnome, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.32~nd13.04+1_all.deb Size: 15368 SHA256: a17f7ae3ceef942087450f8d19e2b87b142f5787ec1981beb22a29499f735bde SHA1: c801838145de1ee7c04f30b6f725c5922174205d MD5sum: e931791983e6ca2f27adfa619982b7d0 Description: NeuroDebian guest additions (DO NOT INSTALL OUTSIDE VIRTUALBOX) This package configures a Debian installation as a guest operating system in a VirtualBox-based virtual machine for NeuroDebian. . DO NOT install this package unless you know what you are doing! For example, installation of this package relaxes several security mechanisms. Package: neurodebian-keyring Source: neurodebian Version: 0.32~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.32~nd13.04+1_all.deb Size: 7624 SHA256: 239d23454e0e47195450883b29b63d8f417dd5719c390765a69518f4e6af075f SHA1: 8e8b3228adaa59e3178d615bf671324fb96e8eb6 MD5sum: f7f0dd6a1bc84b7d45235919ecc716c0 Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.32~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.32~nd13.04+1_all.deb Size: 6842 SHA256: 227ff47eaa0092133dd93682f02f830406c759d1af8ce4f4fc197e77cb62aad7 SHA1: 2cb7bc299aef28713e49f41b48191dfee2f59291 MD5sum: 75e389175704744299d52ede1a19e280 Description: Helper for NeuroDebian popularity contest submissions This package is a complement to the generic popularity-contest package to enable anonymous submission of usage statistics to NeuroDebian in addition to the popcon submissions to the underlying distribution (e.g. Debian or Ubuntu) popcon server. . Your participation in popcon is important for following reasons: - Popular packages receive more attention from developers, bugs are fixed faster and updates are provided quicker. - Assure that we do not drop support for a previous release of Debian or Ubuntu while are active users. - User statistics could be used by upstream research software developers to acquire funding for continued development. . It has an effect only if you have decided to participate in the Popularity Contest of your distribution, i.e. Debian or Ubuntu. You can always enable or disable your participation in popcon by running 'dpkg-reconfigure popularity-contest' as root. Package: nifti2dicom-data Source: nifti2dicom Version: 0.4.6-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 606 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.6-2~nd13.04+1_all.deb Size: 615168 SHA256: fece278a877eb7992684c0f84f93112a1edbd3d378a624202c80ca8a53216347 SHA1: bd83f73dfe317e221dbd28b4db9479971aae6860 MD5sum: 45a36880d6ed84bbe5c36705b331c45f Description: data files for nifti2dicom This package contains architecture-independent supporting data files required for use with nifti2dicom, such as such as documentation, icons, and translations. Package: nuitka Version: 0.5.0.1+ds-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2022 Depends: neurodebian-popularity-contest, g++-4.8 | g++-4.7 | g++-4.6 (>= 4.6.1) | g++-4.5 | g++-4.4 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python (>= 2.7.1-0ubuntu2) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6), strace Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.5.0.1+ds-1~nd13.04+1_all.deb Size: 553682 SHA256: abae9143d6e4acc316afcd94c760b10b39eaa4a863c2d67190e380ec8afeea6d SHA1: 56fdd17e74ff9a94cf558d02da655882ee5f2898 MD5sum: d97b075557f42bb05ce263ef18a9de74 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 at all. Instead they can be used in the same way as pure Python objects. Package: opensesame Version: 0.27.4-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26639 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~), python-qscintilla2, gnome-icon-theme Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1), expyriment (>= 0.5.2), ipython-qtconsole (>= 0.12), python-markdown Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27.4-2~nd13.04+1_all.deb Size: 25359352 SHA256: be473efbb1a3c74f86b5d05bd4d841ec3bee3c0e6e61451bd2aa3673834c9c22 SHA1: 1f1e9c48706fb0075bcdce01889f29457ab7efad MD5sum: db4fbffa3d4dc3eb61e2c40cc52fc9a4 Description: graphical experiment builder for the social sciences This graphical environment provides an easy to use, point-and-click interface for creating psychological experiments. In addition to a powerful sketchpad for creating visual stimuli, OpenSesame features a sampler and synthesizer for sound playback. For more complex tasks, OpenSesame supports Python scripting using the built-in editor with syntax highlighting. Package: packaging-tutorial Version: 0.8~nd0+nd13.04+1 Architecture: all Maintainer: Lucas Nussbaum Installed-Size: 1550 Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.8~nd0+nd13.04+1_all.deb Size: 1488406 SHA256: 2080837f62cdfe9c3cdd6abbe134a1672da2a73335dd213fc8c80732244c40a9 SHA1: 15eaae4fe59ba4f53545db6406d8f51e61692eea MD5sum: 27e78cb1c4edc8953cf73e9a71350c36 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: 1.79.00+git16-g30c9343.dfsg-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12186 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, python-pyo, libavbin0, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.79.00+git16-g30c9343.dfsg-1~nd13.04+1_all.deb Size: 8109658 SHA256: d348a51d0d94f052bb8300a1ca7526ccd90e923b93b02cf856b44d36c0d60919 SHA1: 72b7fa77df11a89b72ad018f4960184d7a402fba MD5sum: ffd2d612a22696e4f3de3bf70383f721 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: 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.11.20131230.dfsg1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49724 Depends: neurodebian-popularity-contest Recommends: subversion Suggests: gnuplot Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.11.20131230.dfsg1-1~nd13.04+1_all.deb Size: 19962518 SHA256: c93ff41e4c2a528001ef32eecc6c1430e76e51a73cbe56b8a94aa3f7ec9b0bfe SHA1: e637a2b858b5da789e07f4084be112eed6e29b27 MD5sum: cf34483bc33eea896386c8130da290e4 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-brian Source: brian Version: 1.4.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2336 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.1-1~nd13.04+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.4.1-1~nd13.04+1_all.deb Size: 549182 SHA256: 9eced6619fb84aea48b5a3fcbc2866e03897ae230c42de1363a01e9dd5b54f91 SHA1: 3ca4a641e5ded2df230142d5a767dacb02e2c1fd MD5sum: 1e3f0dd840e5c40b39c063a9ec88305d Description: simulator for spiking neural networks Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include: - a system for specifying quantities with physical dimensions - exact numerical integration for linear differential equations - Euler, Runge-Kutta and exponential Euler integration for nonlinear differential equations - synaptic connections with delays - short-term and long-term plasticity (spike-timing dependent plasticity) - a library of standard model components, including integrate-and-fire equations, synapses and ionic currents - a toolbox for automatically fitting spiking neuron models to electrophysiological recordings Package: python-brian-doc Source: brian Version: 1.4.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6811 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-brian Homepage: http://www.briansimulator.org/ Priority: extra Section: doc Filename: pool/main/b/brian/python-brian-doc_1.4.1-1~nd13.04+1_all.deb Size: 2247196 SHA256: d155efe0ac801294667b2a44eb58bcb3b0298e73ff97556f8829de8710828f81 SHA1: c247d3f619f28dacd1fe308e61c674ee3556c2f1 MD5sum: bc1ad205f7e25c67460319c0ce2c290c Description: simulator for spiking neural networks - documentation Brian is a clock-driven simulator for spiking neural networks. . This package provides user's manual (in HTML format), examples and demos. Package: python-dipy Source: dipy Version: 0.7.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2952 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.7.1-1~nd13.04+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.7-dipy Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.7.1-1~nd13.04+1_all.deb Size: 1884556 SHA256: 43c72681dd633079d38711f4c765e843b2efbfe65f733e0207339a9adccde441 SHA1: cc6a307448fa6190ba8983255c1b1ce569dfc11a MD5sum: 6fa78c1e2eb61a83fff11165bab0e9d9 Description: toolbox for analysis of MR diffusion imaging data Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. It features: - Reconstruction algorithms, e.g. GQI, DTI - Tractography generation algorithms, e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space, e.g. MNI space - Reading many different file formats, e.g. Trackvis or NIfTI - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing Python-Version: 2.7 Package: python-dipy-doc Source: dipy Version: 0.7.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9459 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: extra Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.7.1-1~nd13.04+1_all.deb Size: 7616700 SHA256: 8b9b96810a5c8a2687fea02c1873abc8ffbb5e0b20340334d64ce60ba0e82a5d SHA1: 1079f6e74d736d15dd6f9f761fb1d8f716d69c3f MD5sum: d2662d2cadb9e9a942f37d07d4973e06 Description: toolbox for analysis of MR diffusion imaging data -- documentation Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. Package: python-joblib Source: joblib Version: 0.7.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 182 Depends: neurodebian-popularity-contest, python (>= 2.6), 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_0.7.1-1~nd13.04+1_all.deb Size: 54852 SHA256: 34de23a238a4de92b1b8ccf9459e78ff31134f79260dde329140d39488198105 SHA1: 1ca2b03462493c6e6cbeef719af1bab45c022e93 MD5sum: 258dd84c5aec75e280895604964962c9 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-mdp Source: mdp Version: 3.3+git6-g7bbd889-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1495 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy Recommends: python-scipy, python-libsvm, python-joblib, python-scikits-learn | python-sklearn, python-pp Suggests: python-py, shogun-python-modular Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.3+git6-g7bbd889-1~nd12.10+1+nd13.04+1_all.deb Size: 478658 SHA256: f64b49dd6826c89a25593bc9a05d154599fed318b5bb9a3c8e99c6ce9ba6dd5a SHA1: 1d63f0f452c4d60af96494263e7f75b724ee7a56 MD5sum: 4d9fb528cbf2c338a7562b20dcfd8a4d Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 2. Package: python-mne Version: 0.7.3-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6208 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-sklearn, python-matplotlib, python-joblib (>= 0.4.5), xvfb, xauth, libgl1-mesa-dri, help2man Recommends: python-nose, mayavi2 Suggests: python-dap, ipython Provides: python2.7-mne Homepage: http://martinos.org/mne Priority: optional Section: python Filename: pool/main/p/python-mne/python-mne_0.7.3-1~nd13.04+1_all.deb Size: 4053356 SHA256: 1859e5d1d072111c55949e4c5189ad5675bb318c9d7e31d82840ac22a531094b SHA1: 62a3641be07443909cb2a261edc72d151b743dfe MD5sum: 6d95517850ff369c73ce19cda7513b18 Description: Python modules for MEG and EEG data analysis This package is designed for sensor- and source-space analysis of MEG and EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. Package: python-mpi4py-doc Source: mpi4py Version: 1.3.1+hg20131106-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 256 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3.1+hg20131106-1~nd13.04+1_all.deb Size: 73120 SHA256: 0d1ac85177ba65bb8362f9af8645361a0d352ae7b56575cd5be8c81f2b98b1f1 SHA1: b966da7d5a6d6e6b1c991dbed503f58be1e14e41 MD5sum: a90f44ff9721a35d7f3f388834bbb808 Description: bindings of the MPI standard -- documentation MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides HTML rendering of the user's manual. Package: python-mvpa2 Source: pymvpa2 Version: 2.2.0-4~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4226 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python-mvpa2-lib (>= 2.2.0-4~nd13.04+1) Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, shogun-python-modular, liblapack-dev, python-pprocess 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_2.2.0-4~nd13.04+1_all.deb Size: 2394650 SHA256: 4f35a258e43481d1880690d5d4a064b484ef31b4a3e8dcc4399f897cb9a04b91 SHA1: 2b4b4479626a5a2662f63bdf33cc9a4f0a0976b3 MD5sum: 5b03c7cab4f3413c26dd08072e07c132 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: 2.2.0-4~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 19868 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_2.2.0-4~nd13.04+1_all.deb Size: 5309128 SHA256: fecfa813b4f22c98945bfbae66d11286a3358fcecbb778ba4b3f43ccd5e2222f SHA1: 1d2adb60f07769dd46138bbfc057cac99ce7bd1c MD5sum: 457c830f5f395d23660da5057208b0b9 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. Package: python-neo Source: neo Version: 0.3.3-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2913 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy (>= 1:1.3~), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Suggests: python-nose Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.3.3-1~nd13.04+1_all.deb Size: 1503960 SHA256: a89c10210269720f42fa1d4986f09ecaa13fc4ea78f1d75fc5ed7ddc6352827f SHA1: c883688f279ec7aaac4c0cc5ac8d1ff27f183533 MD5sum: 015d0c523b10e217eb75e781759c8a80 Description: Python IO library for electrophysiological data formats NEO stands for Neural Ensemble Objects and is a project to provide common classes and concepts for dealing with electro-physiological (in vivo and/or simulated) data to facilitate collaborative software/algorithm development. In particular Neo provides: a set a classes for data representation with precise definitions, an IO module with a simple API, documentation, and a set of examples. . NEO offers support for reading data from numerous proprietary file formats (e.g. Spike2, Plexon, AlphaOmega, BlackRock, Axon), read/write support for various open formats (e.g. KlustaKwik, Elan, WinEdr, WinWcp, PyNN), as well as support common file formats, such as HDF5 with Neo-structured content (NeoHDF5, NeoMatlab). . Neo's IO facilities can be seen as a pure-Python and open-source Neuroshare replacement. Package: python-neurosynth Source: neurosynth Version: 0.3-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 83 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-nibabel, python-ply Recommends: python-nose, fsl-mni152-templates Suggests: python-testkraut Homepage: http://neurosynth.org Priority: extra Section: python Filename: pool/main/n/neurosynth/python-neurosynth_0.3-1~nd13.04+1_all.deb Size: 32522 SHA256: 6f15ee7476bbfde85706a4c2f3d03cda6040abdbd999b3894242c38a6b5e5e15 SHA1: 6d50b9fdc6cc95d65b5a36dff938dc20a53f85a6 MD5sum: 490b03520a88f2609b103c8f9e81d538 Description: large-scale synthesis of functional neuroimaging data NeuroSynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. This Python module at the moment provides functionality for processing the database of collected terms and spatial coordinates to generate associated spatial statistical maps. Package: python-nibabel Source: nibabel Version: 1.3.0-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4152 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: python2.7-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.3.0-1~nd12.04+1+nd12.10+1+nd13.04+1_all.deb Size: 1816392 SHA256: 5471327f1b831976c7f5fd2e640cea5ad293f6fe3d5dc5c90cad27576dd31c7b SHA1: 24a7b6779e812cb2e0141392940f64f5f9654eab MD5sum: c0d6269ba81e7cde8a02b4ecf5d09a9a 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: 2.7 Package: python-nibabel-doc Source: nibabel Version: 1.3.0-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2446 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.3.0-1~nd12.04+1+nd12.10+1+nd13.04+1_all.deb Size: 441852 SHA256: c393586f5d860ad1367c994624f64e56f5142eb810b1c7eb398b3a4514ba1e59 SHA1: d709ae60593669a10a6ba1bce7e3597b4ec0cdf9 MD5sum: debae32aab1b2c3078bcd33a7e0a3dbd 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-nipype Source: nipype Version: 0.9.2-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3521 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: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.9.2-1~nd13.04+1_all.deb Size: 768506 SHA256: 6031eca5799cfec9f8d0037cfd1424c66967f861b44c89e1f3b559f8924171a0 SHA1: 5cd1b910d0a1997524c2ed2e26a6e86439dd5ff9 MD5sum: bde560f80b6cc7937bb7aaf91e010225 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: 0.9.2-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16563 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_0.9.2-1~nd13.04+1_all.deb Size: 7621042 SHA256: 18db5684e0554436f54dedafb4d3d73295c326ebfc9ac827fd2cb49cce9ff085 SHA1: ed3323cfa8556d3310f3077224f8075a34005955 MD5sum: 5e2862423bb79616ae1dad000bee10c7 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: 1.7.0+ds1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 452 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-nose, python-pil, python-imaging Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.7.0+ds1-1~nd13.04+1_all.deb Size: 91950 SHA256: 85b2b60b87f158f379a654ac99c15239a6095024efc9eb2b97ba2e2e4052c4e5 SHA1: d26ab1a664555bb012e58aa4fc3ad4ca3ac9b876 MD5sum: b88ed9a7467757e920c2ede8e5826755 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-pandas Source: pandas Version: 0.13.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7145 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.6~), python-pandas-lib (>= 0.13.1-1~nd13.04+1), python-six Recommends: python-scipy, python-matplotlib, python-tables, python-numexpr, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt, python-bs4, python-html5lib Suggests: python-pandas-doc Provides: python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.13.1-1~nd13.04+1_all.deb Size: 1424164 SHA256: 9b82099d9334ef976ea07612d12d52f0459129afb54972bf1f3a97a37bd4fa15 SHA1: 8675b1321d5eca649026e015279620fbb8e8b6a5 MD5sum: b22c4831f1fa581415597e93d7d88bb8 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 . This package contains the Python 2 version. Package: python-patsy Source: patsy Version: 0.2.1-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 542 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy Recommends: python-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python-patsy_0.2.1-2~nd13.04+1_all.deb Size: 141588 SHA256: 4a8f8281baa93259779245035bf908c2aae32c49ca12c18254b711e424f86db1 SHA1: 9183b4262d8853365f88bcb36efa3a536d2e03ce MD5sum: c4224925cc6e1d71fa5b69574ec751d2 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 2 version. Package: python-patsy-doc Source: patsy Version: 0.2.1-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 827 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-patsy Homepage: http://github.com/pydata/patsy Priority: optional Section: doc Filename: pool/main/p/patsy/python-patsy-doc_0.2.1-2~nd13.04+1_all.deb Size: 271954 SHA256: eb3f3e7eefcf670563190265682b6c20ba7fee0bab2da9cf3f07438c99fdba8b SHA1: dc228a83b4e3ca0d0c31fa373d41a8cec4facde6 MD5sum: 6f963190f5954f63d7e18a061f04cd3c Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pprocess Source: pprocess Version: 0.5-1+nd0~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 716 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.boddie.org.uk/python/pprocess.html Priority: optional Section: python Filename: pool/main/p/pprocess/python-pprocess_0.5-1+nd0~nd13.04+1_all.deb Size: 108522 SHA256: 5c2afaaea25f2e23d0c8b715a21196da047034de4e26227bd3e42a6b5f30d7aa SHA1: a10d6cc71e421d327f2a3b83e719bc67cc3630a7 MD5sum: 550613922b0b145cd729e657a220a429 Description: elementary parallel programming for Python The pprocess module provides elementary support for parallel programming in Python using a fork-based process creation model in conjunction with a channel-based communications model implemented using socketpair and poll. On systems with multiple CPUs or multicore CPUs, processes should take advantage of as many CPUs or cores as the operating system permits. Python-Version: 2.7 Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 813 Depends: neurodebian-popularity-contest, python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0+git12-g365f8e3-2~nd13.04+1_all.deb Size: 818248 SHA256: db03e0e6da1604340b0e261b32b5fbfeb29c5ce61c377a0143c68970b8c84527 SHA1: 66261c89106edb13ad1415cfdc8f980ec0b0b383 MD5sum: 634c2710263e8349bd60a73fd742d18d Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides common files such as images. Package: python-pynn Source: pynn Version: 0.7.5-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 763 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_0.7.5-1~nd12.10+1+nd13.04+1_all.deb Size: 175824 SHA256: 8739a9484b7006b204e2cd7d530501ee969178ae5f0e291ef1d7243a6a5cfa6b SHA1: 6a89717511066991e428fc8e7efec413b51d4008 MD5sum: c6787a9174e58de883cd75f9682891f7 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-pyxnat Source: pyxnat Version: 0.9.1+git39-g96bf069-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 862 Depends: neurodebian-popularity-contest, python-lxml, python-simplejson, python-httplib2 (>= 0.7.0) Recommends: python-networkx, python-matplotlib Homepage: http://packages.python.org/pyxnat/ Priority: extra Section: python Filename: pool/main/p/pyxnat/python-pyxnat_0.9.1+git39-g96bf069-1~nd12.10+1+nd13.04+1_all.deb Size: 190396 SHA256: a742f5f47842ee38e7491416ea0de9e721716c69c4cf32cae51ad2367a8d89c2 SHA1: c687df5e68cc79fba85924061a7f8e3d9732903e MD5sum: 4e5a040be6b4430eab9ed7922df103ee Description: Interface to access neuroimaging data on XNAT servers pyxnat is a simple Python library that relies on the REST API provided by the XNAT platform since its 1.4 version. XNAT is an extensible database for neuroimaging data. The main objective is to ease communications with an XNAT server to plug-in external tools or Python scripts to process the data. It features: . - resources browsing capabilities - read and write access to resources - complex searches - disk-caching of requested files and resources Package: python-scikits-learn Source: scikit-learn Version: 0.14.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 35 Depends: neurodebian-popularity-contest, python-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.14.1-1~nd13.04+1_all.deb Size: 33350 SHA256: 3b26ce343682816c9d20ab5a0ec2951c46ce233a3874719adcd28d08ff32db49 SHA1: dc45e381d04f62a84296a6173bc4a434a6e001fd MD5sum: ff188f7c78cc6f31413834f373b03bad 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-scikits.statsmodels Source: statsmodels Version: 0.5.0-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9 Depends: neurodebian-popularity-contest, python-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.5.0-1~nd13.04+1_all.deb Size: 5636 SHA256: edd8cc37a13fde303ab919fcf0382e1537107b555ee4063bb0fdf252aefb265f SHA1: 47674665c8a46c2db0e7fec5f3788e0508a769ea MD5sum: 5c856d0a92039b7a0d29eb5a2a90a834 Description: transitional compatibility package for statsmodels migration Provides old namespace (scikits.statsmodels) and could be removed if dependent code migrated to use statsmodels for clarity of the namespace. Package: python-skimage Source: skimage Version: 0.9.3-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6267 Depends: neurodebian-popularity-contest, libfreeimage3, python-numpy, python-scipy (>= 0.10), python-skimage-lib (>= 0.9.3-1~nd13.04+1), python (>= 2.7.1-0ubuntu2), python (<< 2.8) Recommends: python-imaging, python-pil, python-matplotlib (>= 1.0), python-nose, python-qt4 Suggests: python-opencv, python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.9.3-1~nd13.04+1_all.deb Size: 4538304 SHA256: 70a57093bb1b630c026bc7cacb220b8745bef71790b64b81f3ee91e94bc3fe29 SHA1: f58745e31575ab0834728c289ee0351e1f0d3a8b MD5sum: ac02970dbde3897c72124b6c1d595a18 Description: Python modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 2 module. Package: python-skimage-doc Source: skimage Version: 0.9.3-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17726 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-skimage Homepage: http://scikit-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.9.3-1~nd13.04+1_all.deb Size: 14618750 SHA256: 96d3ce3d82f535b0cf4761e0ab80b663a0d3834c95f07554b0b2567c276a59f7 SHA1: a97e414b8b7f6f32a00963a443ccf564b8fa868a MD5sum: ebbd55b17d3d641ab3331eecc156900a Description: Documentation and examples for scikit-image This package contains documentation and example scripts for python-skimage. Package: python-sklearn Source: scikit-learn Version: 0.14.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3549 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-sklearn-lib (>= 0.14.1-1~nd13.04+1), python-joblib (>= 0.4.5) Recommends: python-nose, python-matplotlib 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: python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.14.1-1~nd13.04+1_all.deb Size: 1103558 SHA256: 75ce8de7cb4410c4e7f27119a369af11a2db1bd3501ae92fcd62cb7ebc55eb62 SHA1: 921fd43cc36b78d3894d05c4f18fea9cebbdc974 MD5sum: 32cca45f7d6720d58c8c74e0e106b14a 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) Package: python-sklearn-doc Source: scikit-learn Version: 0.14.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 579 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_0.14.1-1~nd13.04+1_all.deb Size: 190030 SHA256: 9cc5098d90f84e0c2b60d6e4edda4253068e584e95289c621c637c879f8186c4 SHA1: 3e2ec356df17a5cf548f3c6d01ff1d43a3efcff3 MD5sum: 3448a9c8ad7df5f8045002acd3af159c Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-spyderlib Source: spyder Version: 2.2.5+dfsg-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4009 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), libjs-sphinxdoc (>= 1.0), libjs-jquery, libjs-mathjax, python-qt4 Recommends: ipython-qtconsole, pep8, pyflakes (>= 0.5.0), pylint, python-matplotlib, python-numpy, python-psutil (>= 0.3.0), python-rope, python-scipy, python-sphinx Suggests: tortoisehg, gitk Breaks: spyder (<< 2.0.12-1) Replaces: spyder (<< 2.0.12-1) Provides: python2.7-spyderlib Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: python Filename: pool/main/s/spyder/python-spyderlib_2.2.5+dfsg-1~nd13.04+1_all.deb Size: 1847880 SHA256: 379f4058e083e07aa6eacb71e7c8a0660492df755ff7cd485b1448eae26e97d4 SHA1: e3c7c59a3341f43168d321a74e13607cf3ebd756 MD5sum: 794e56f72fc037355fc68d1dd24408e9 Description: python IDE for scientists Originally written to design Spyder (the Scientific PYthon Development EnviRonment), the spyderlib Python library provides ready-to-use pure-Python widgets: source code editor with syntax highlighting and code introspection/analysis features, NumPy array editor, dictionary editor, Python console, etc. It's based on the Qt Python binding module PyQt4 (and is compatible with PySide since v2.2). Package: python-spykeutils Source: spykeutils Version: 0.4.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2017 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-scipy, python-quantities, python-neo (>= 0.2.1), python-nose, python-sphinx Recommends: python-guidata, python-guiqwt, python-tables, libjs-jquery, libjs-underscore, python-sklearn (>= 0.11), python-joblib (>= 0.4.5) Provides: python2.7-spykeutils Homepage: https://github.com/rproepp/spykeutils Priority: extra Section: python Filename: pool/main/s/spykeutils/python-spykeutils_0.4.1-1~nd13.04+1_all.deb Size: 401300 SHA256: 7e36271bebc926fd6a4137f162575178a763ebd5b4204d62d790f4d93d95f5a5 SHA1: 3046c2f4388c1ef502b2b67d596b9eb185eee1b4 MD5sum: 4182e8a63e9605ed55f30a3bacf36789 Description: utilities for analyzing electrophysiological data spykeutils is a Python library for analyzing and plotting data from neurophysiological recordings. It can be used by itself or in conjunction with Spyke Viewer, a multi-platform GUI application for navigating electrophysiological datasets. Package: python-statsmodels Source: statsmodels Version: 0.5.0-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20432 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-statsmodels-lib (>= 0.5.0-1~nd13.04+1), python-patsy Recommends: python-pandas, python-matplotlib, python-nose, python-joblib Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Provides: python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.5.0-1~nd13.04+1_all.deb Size: 4682438 SHA256: 2a21d2b08addd50290ec03381e2ee92c1e7be25e9a5ef0abd907c4416442f933 SHA1: c98e31786fde183d1d5ab82dccd28a1547f61941 MD5sum: bbfda8bb9802b1ab252f1414f429f82a Description: Python module for the estimation of statistical models statsmodels Python module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Package: python-statsmodels-doc Source: statsmodels Version: 0.5.0-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 32549 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-statsmodels Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.5.0-1~nd13.04+1_all.deb Size: 9253286 SHA256: 6fe726032db301674769285750d43dabeadc30ff9771efcc14e2095f0f911177 SHA1: be795b20ee9adfe7fcb982602430c5d89c70e8b7 MD5sum: 2d2d70302b9c703bc34ff802b3d9830b Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-surfer Source: pysurfer Version: 0.3+git15-gae6cbb1-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 93 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-nibabel, python-imaging, mayavi2, python-argparse, ipython Recommends: mencoder Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.3+git15-gae6cbb1-1~nd12.04+1+nd12.10+1+nd13.04+1_all.deb Size: 28126 SHA256: d027822bf6edf7f8e4a4726abfa4bff05a553010e617229cf66c9ebc39f260ae SHA1: ae3d0fa877af7818bb783d737f8a8df882765042 MD5sum: db1308bda1624dc4cacc037c4519bbc7 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: 2.7 Package: python3-mdp Source: mdp Version: 3.3+git6-g7bbd889-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1457 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-numpy Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python3-mdp_3.3+git6-g7bbd889-1~nd12.10+1+nd13.04+1_all.deb Size: 472534 SHA256: d3239c36162185a4ef03148ce61949c6f7a9e74670d56c88722e4200721b57dc SHA1: 2562033ca9918601e8f0cd28eff8146a5a610d52 MD5sum: 735efdc181c99b658b0e9276ea66e384 Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 3. Package: python3-pandas Source: pandas Version: 0.13.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7078 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-dateutil, python3-tz, python3-numpy (>= 1:1.6~), python3-pandas-lib (>= 0.13.1-1~nd13.04+1) Recommends: python3-scipy, python3-matplotlib, python3-numexpr, python3-tables, python3-bs4, python3-html5lib, python3-six Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.13.1-1~nd13.04+1_all.deb Size: 1418108 SHA256: ff4f0da169caf29e5deb238dd13f05a1c26ec06919f9d5ceceab4ecf8de2730a SHA1: 8e510359df3d15aad1795e5dd416d0f3c95caa36 MD5sum: 201fc8f06af52051de848fe36c1e48a5 Description: data structures for "relational" or "labeled" data - Python 3 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 . This package contains the Python 3 version. Package: python3-patsy Source: patsy Version: 0.2.1-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 537 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-numpy Recommends: python3-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python3-patsy_0.2.1-2~nd13.04+1_all.deb Size: 140894 SHA256: eb5bfa48cc76fca948b402aef445a9d3a0cdcf2d9f98614cac0c92055ba95fdc SHA1: 0f8d0299ae15ca0caf4b46f1f967eedeaa8c0794 MD5sum: 4cb4170c8a7aaa09a0e878833ec7576b Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 3 version. Package: python3-skimage Source: skimage Version: 0.9.3-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6178 Depends: neurodebian-popularity-contest, libfreeimage3, python3-numpy, python3-scipy (>= 0.10), python3-skimage-lib (>= 0.9.3-1~nd13.04+1), python3 (>= 3.2.3-3~) Recommends: python3-imaging, python3-pil, python3-matplotlib (>= 1.0), python3-nose Suggests: python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python3-skimage_0.9.3-1~nd13.04+1_all.deb Size: 4532470 SHA256: 4989d126acf8958f76b2f68cf79647afe8691c728f77de03475724b66b1fb20e SHA1: 8fdf86d214857739d84c324e38b7916a16813234 MD5sum: 97688f6d6ac6038b1e1cfeda08e9281e Description: Python 3 modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 3 module. Package: spm8-common Source: spm8 Version: 8.5236~dfsg.1-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 18499 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.5236~dfsg.1-1~nd12.10+1+nd13.04+1_all.deb Size: 10744012 SHA256: 8cbaf2ef8a3621e1a45f8046a5a4b3f5018599cd5fd061dbdab3f6f636db55dc SHA1: 358a47eca3c2a8e45383012ebb1db5ed7e315d4f MD5sum: 2ea4c1626f3ecc1a0f8b72afe93d7325 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: 8.5236~dfsg.1-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 72987 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd12.10+1+nd13.04+1_all.deb Size: 52166644 SHA256: adf5ccd28c33d360fddc672827cc45f128dd95d107e6fc80ec7b5f01013199b8 SHA1: 555fd27c1066c8cdc3f9d5ca88e1a5604af0f318 MD5sum: 007b37221423d90e041d57b47f112413 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: 8.5236~dfsg.1-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9242 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd12.10+1+nd13.04+1_all.deb Size: 8990888 SHA256: 26ace888d49d3abb1c4525fff11e3538aaf54bdeb7bd0f37720b5a32c211055d SHA1: 3e7bd77be9b08a8227586a6138fba786fa7a5365 MD5sum: 823a54d07acd71360de860ff3f62e019 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. Package: spyder Version: 2.2.5+dfsg-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 94 Depends: neurodebian-popularity-contest, python, python-spyderlib (= 2.2.5+dfsg-1~nd13.04+1) Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: devel Filename: pool/main/s/spyder/spyder_2.2.5+dfsg-1~nd13.04+1_all.deb Size: 36074 SHA256: 95beb4bfca945126e17f36852c4a4ce3cc8f7e7875c21f22d8e7b6de27cb0e73 SHA1: b6377d55d2fb282b643d0e6aca5498fcd1b905eb MD5sum: c15381c5f383a2cb05d27e0ab7c20525 Description: python IDE for scientists Spyder (previously known as Pydee) is a free open-source Python development environment providing MATLAB-like features in a simple and light-weighted software Package: spykeviewer Version: 0.4.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1125 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.4.0), python-neo (>= 0.2.1), python-matplotlib, python-scipy, python-nose, python-sphinx, python-tables Recommends: libjs-jquery, libjs-underscore, ipython-qtconsole (>= 0.12) Homepage: http://www.ni.tu-berlin.de/software/spykeviewer Priority: extra Section: python Filename: pool/main/s/spykeviewer/spykeviewer_0.4.1-1~nd13.04+1_all.deb Size: 576652 SHA256: c600edbdde9bf11d4ea2e95768886e9610acfb6c9e32aa8176ce6a6698494286 SHA1: c0d20eb31bc7eda04e1d953055d6e0e7621c56bd MD5sum: 4151349463bbbc36f0e94761724dea20 Description: graphical utility for analyzing electrophysiological data Spyke Viewer is a multi-platform GUI application for navigating, analyzing and visualizing electrophysiological datasets. Based on the Neo framework, it works with a wide variety of data formats. Spyke Viewer includes an integrated Python console and a plugin system for custom analyses and plots. Package: stabilitycalc Version: 0.1-1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy, python-nifti Recommends: python-dicom Homepage: https://github.com/bbfrederick/stabilitycalc Priority: extra Section: science Filename: pool/main/s/stabilitycalc/stabilitycalc_0.1-1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1_all.deb Size: 28806 SHA256: f4d0a92bd82ccf13995bd023e60482c9bb6e5bcc276aacf8d6d71f47c726ac91 SHA1: 5d37dfd09ba13dca96797dc779dfd58616624062 MD5sum: 64c06762e13f6bd6a33f11383612ccc0 Description: evaluate fMRI scanner stability Command-line tools to calculate numerous fMRI scanner stability metrics, based on the FBIRN quality assurance test protocal. Any 4D volumetric timeseries image in NIfTI format is support input. Output is a rich HTML report. Python-Version: 2.7 Package: testkraut Version: 0.0.1-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 359 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, libjs-underscore, libjs-jquery, python-argparse Recommends: strace, python-scipy, python-colorama, python-apt Homepage: https://github.com/neurodebian/testkraut Priority: extra Section: python Filename: pool/main/t/testkraut/testkraut_0.0.1-1~nd12.10+1+nd13.04+1_all.deb Size: 99674 SHA256: 4bb183a82062eea15a25438731463d7d3a57ac1fd8bbe3f38101b6c3c08c6db2 SHA1: 045ce201ed946332c8971bc0b8f92774aa49e90b MD5sum: 7b33e1503852f85c3d82cd5c0d1c0e54 Description: test and evaluate heterogeneous data processing pipelines This is a framework for software testing. That being said, testkraut tries to minimize the overlap with the scopes of unit testing, regression testing, and continuous integration testing. Instead, it aims to complement these kinds of testing, and is able to re-use them, or can be integrated with them. . In a nutshell testkraut helps to facilitate statistical analysis of test results. In particular, it focuses on two main scenarios: . * Comparing results of a single (test) implementation across different or changing computational environments (think: different operating systems, different hardware, or the same machine before an after a software upgrade). * Comparing results of different (test) implementations generating similar output from identical input (think: performance of various signal detection algorithms). . While such things can be done using other available tools as well, testkraut aims to provide a lightweight, yet comprehensive description of a test run. Such a description allows for decoupling test result generation and analysis – opening up the opportunity to “crowd-source” software testing efforts, and aggregate results beyond the scope of a single project, lab, company, or site. Python-Version: 2.7 Package: vowpal-wabbit-doc Source: vowpal-wabbit Version: 7.3-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 70918 Depends: neurodebian-popularity-contest Recommends: vowpal-wabbit Homepage: http://hunch.net/~vw/ Priority: optional Section: doc Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-doc_7.3-1~nd13.04+1_all.deb Size: 50202370 SHA256: 9dddf9b5b7a2237d4f2a93efd545d867f3d204f1d05ab4d462bf15ea4b55e986 SHA1: 8e49788bf4e88ece7e82ef40224840f79f2eee52 MD5sum: 55654e0768d8a01e3e339294bbb06565 Description: fast and scalable online machine learning algorithm - documentation Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains examples (tests) for vowpal-wabbit.