Package: aghermann Version: 0.8.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1548 Depends: neurodebian-popularity-contest, libc6 (>= 2.8), libcairo2 (>= 1.2.4), libconfig++9, libfftw3-double3, libgcc1 (>= 1:4.1.1), libglib2.0-0 (>= 2.31.18), libgomp1 (>= 4.4), libgsl0ldbl (>= 1.9), libgtk-3-0 (>= 3.3.16), libitpp7, libpango1.0-0 (>= 1.14.0), libsamplerate0 (>= 0.1.7), libstdc++6 (>= 4.6), libunique-3.0-0 (>= 2.90.1), libvte-2.90-9 (>= 1:0.27.2) Suggests: edfbrowser Homepage: http://johnhommer.com/academic/code/aghermann Priority: optional Section: science Filename: pool/main/a/aghermann/aghermann_0.8.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 694444 SHA256: 187284a3cd87cf16e9976bf21f80868456ad410bea4ce7a664f435cf0bfeb7a0 SHA1: 426bc3e3eeae492eee666a47dd6f69d97fbef8bf MD5sum: 2aef2c30c948e475cc264d8900fd9a30 Description: Sleep-research experiment manager Aghermann is a program designed around a common workflow in sleep-research, complete with scoring facility; cairo subpixel drawing on screen or to file; conventional PSD and EEG Micrcontinuity profiles; Independent Component Analysis; artifact detection; and Process S simulation following Achermann et al, 1993. Package: biosig-tools Source: biosig4c++ Version: 1.4.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 679 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/biosig-tools_1.4.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 286554 SHA256: 5446015a9a5f3fcd651c5f2ebdadde043bb8b94c58dd55a7da6101f7846c568b SHA1: 1f757fb6f3a34accb2b54d913209f255e734bc3b MD5sum: 5c5585acc6f228680acdd1fb23209dae Description: format conversion tools for biomedical data formats Based on BioSig library, this package provides command line tools, such as . - save2gdf: converter between different file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF, BDF, CWFB. save2gdf can be also used to upload or retrieve data from a bscs server. Package: condor Version: 7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 13126 Depends: neurodebian-popularity-contest, debconf (>= 0.5) | debconf-2.0, libc6 (>= 2.7), libclassad3, libcomerr2 (>= 1.01), libcurl3 (>= 7.16.2), libexpat1 (>= 2.0.1), libgcc1 (>= 1:4.1.1), libglobus-common0 (>= 14), libglobus-ftp-control1 (>= 4), libglobus-gass-transfer2 (>= 7), libglobus-gram-client3 (>= 12), libglobus-gsi-credential1 (>= 5), libglobus-gsi-proxy-core0 (>= 6), libglobus-gsi-sysconfig1 (>= 5), libglobus-gss-assist3 (>= 8), libglobus-gssapi-gsi4 (>= 10), libglobus-io3 (>= 9), libglobus-rsl2 (>= 9), libglobus-xio0 (>= 3), libgsoap2, libk5crypto3 (>= 1.6.dfsg.2), libkrb5-3 (>= 1.10+dfsg~), libldap-2.4-2 (>= 2.4.7), libpcre3 (>= 8.10), libssl1.0.0 (>= 1.0.0), libstdc++6 (>= 4.6), libuuid1 (>= 2.16), libvirt0 (>= 0.5.0), python, perl, adduser, libdate-manip-perl Recommends: dmtcp Suggests: coop-computing-tools Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: science Filename: pool/main/c/condor/condor_7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 4642152 SHA256: eba7a6cc0ede6fc0fc060c7745dc831c0d925ef88f12dfa596b9281f2bc2b40c SHA1: 1c5b0c95d0362984e62b7378190363af06b8cca4 MD5sum: 3ec1e93d95f1d17a87c8c4ce3e55e890 Description: distributed workload management system 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 can set up an appropriate initial configuration at install time for a machine intended either as a member of an existing Condor pool or as a "Personal" (single machine) Condor pool. Package: condor-dbg Source: condor Version: 7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 32176 Depends: neurodebian-popularity-contest, condor (= 7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1) Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: debug Filename: pool/main/c/condor/condor-dbg_7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 12090168 SHA256: 33e6ca591cb39ffdee897ceb160f70b146645c5ca3cfbf264204202b2e0dca5e SHA1: 4ffcdf6fe8f818de104cb78bcec9af64b20f4d29 MD5sum: 5031dbfbe20b04c96c5e518df0ef757e Description: distributed workload management system - debugging symbols 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 the debugging symbols for Condor. Package: condor-dev Source: condor Version: 7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1542 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: devel Filename: pool/main/c/condor/condor-dev_7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 421918 SHA256: e5d49c5f912f8e551e7d6b12167f966b815b927545ca781c5810f4e9da35c74e SHA1: fa523023f69ad01735d98f5c11ac969f483e65a0 MD5sum: 7c8efbeb411f6d95f5fe843503278360 Description: distributed workload management system - development files 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 headers and libraries for development of Condor add-ons. Package: condor-doc Source: condor Version: 7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6155 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1_all.deb Size: 1334648 SHA256: 475b6c8425b6b73f5b3dd5e19b6b660dbeb77f7ff2f4400c39b8f7589f5d1801 SHA1: 5350154e1e2f98753a2d35d86a759f0ff6a7adfd MD5sum: 8a232078ed3de2b8c189b865d1b0cf78 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: debruijn Version: 1.6-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 130 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfftw3-double3, libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://www.cfn.upenn.edu/aguirre/wiki/public:de_bruijn_software Priority: extra Section: science Filename: pool/main/d/debruijn/debruijn_1.6-1~nd12.04+1+nd12.10+1+nd13.04+1_i386.deb Size: 45456 SHA256: 4192fd988eb86f76ffd0203b8ea2b8681fbc16c95e3e24340e6157b29949f9ee SHA1: 46a6ca113905327dd09cced37d6afc65ba034996 MD5sum: 488b6f1d862438aec226e6f5a99d3134 Description: De Bruijn cycle generator Stimulus counter-balance is important for many experimental designs. This command-line software creates De Bruijn cycles, which are pseudo-random sequences with arbitrary levels of counterbalance. "Path-guided" de Bruijn cycles may also be created. These sequences encode a hypothesized neural modulation at specified temporal frequencies, and have enhanced detection power for BOLD fMRI experiments. 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: eegview Version: 0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 19 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libeegdev0, libmcpanel0 (>= 0.0), libxdffileio0 (>= 0.0) Homepage: http://cnbi.epfl.ch/software/eegview.html Priority: extra Section: science Filename: pool/main/e/eegview/eegview_0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1_i386.deb Size: 10702 SHA256: b72cb86c50b070c6720c7efde62ec856bbf3f45815f7a9f3eb3783cfc8726c21 SHA1: 4d222c0608d0f212041a4f6a9c919d8e216f88c0 MD5sum: 9243c648fdf1e6538a7f3651c307c2c1 Description: Software to display EEG data in realtime This software allows one to display EEG signal in realtime as well as record them. It is the minimal recording panel needed to do simple experiment. Package: fail2ban Version: 0.8.8-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 359 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.8-1~nd12.10+1+nd13.04+1_all.deb Size: 112666 SHA256: 535eb08a977830c8417f8ceb6de39c792cddb5b9d78312e6ff3b6559456ac141 SHA1: acaab862549a930e4583f11c52ec5d6191f8b287 MD5sum: e082c92eedc9d49908655833c38e415d 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: fslview Version: 4.0.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 5913 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnewmat10ldbl, libnifti2, libqt4-qt3support (>= 4:4.5.3), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.7.0~beta1), libqwt5-qt4, libstdc++6 (>= 4.6), libvtk5.8, libvtk5.8-qt4 Recommends: fslview-doc, qt-assistant-compat Suggests: fsl-atlases Conflicts: fsl-fslview Replaces: fsl-fslview Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: science Filename: pool/main/f/fslview/fslview_4.0.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 2270052 SHA256: 0aca47b3716fee22f836e360bb62a2960d06b698a343eeee8cf2058d61cad490 SHA1: 16582cc033654b12548362e1dfefe821885cc512 MD5sum: 50e36093359e9ddfb532287268f2287a Description: viewer for (f)MRI and DTI data This package provides a viewer for 3d and 4d MRI data as well as DTI images. FSLView is able to display ANALYZE and NIFTI files. The viewer supports multiple 2d viewing modes (orthogonal, lightbox or single slices), but also 3d volume rendering. Additionally FSLView is able to visualize timeseries and can overlay metrical and stereotaxic atlas data. . FSLView is part of FSL. Package: fslview-doc Source: fslview Version: 4.0.1-1~nd12.10+1+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_4.0.1-1~nd12.10+1+nd13.04+1_all.deb Size: 2346478 SHA256: 43ee98b5316b423ced4814998959f36e7bf9dcec8e6f43f4c15fe9b2630a2680 SHA1: c6ee73e662c18b55a00256c4bd16adf3b012554a MD5sum: 67c5ecfe37664ec79e92eaec15eeead1 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: 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: libbiosig-dev Source: biosig4c++ Version: 1.4.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1338 Depends: neurodebian-popularity-contest, libbiosig1 (= 1.4.1-1~nd12.10+1+nd13.04+1) Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_1.4.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 419246 SHA256: c53aba78f038c39cc92a5188a1903a519880ffdde9a0f2595685d51329140141 SHA1: 3c516bae6e8107ae7e8698d8cc846ab2f7c75044 MD5sum: 1785e9107d12148f9d4d44e197e0aca2 Description: I/O library for biomedical data - development files BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://pub.ist.ac.at/~schloegl/biosig/TESTED . . This package provides header files and static library. Package: libbiosig1 Source: biosig4c++ Version: 1.4.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 811 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://biosig.sf.net/ Priority: extra Section: libs Filename: pool/main/b/biosig4c++/libbiosig1_1.4.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 329150 SHA256: f9524b9e759e8387c66a899534630359670b79477ceb91f2ef85a5a8a4e1a2a1 SHA1: f4469805a0c2dba70dd911166395a0b5d2b9b74d MD5sum: a2d53a9aa4e0f6a613d983453007994c Description: I/O library for biomedical data - dynamic library BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://pub.ist.ac.at/~schloegl/biosig/TESTED . . This package provides dynamic library. Package: libbiosig1-dbg Source: biosig4c++ Version: 1.4.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 323 Depends: neurodebian-popularity-contest, libbiosig1 (= 1.4.1-1~nd12.10+1+nd13.04+1) Homepage: http://biosig.sf.net/ Priority: extra Section: debug Filename: pool/main/b/biosig4c++/libbiosig1-dbg_1.4.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 118630 SHA256: ab6435df49234f2ea8a701100e38f62d0b397193a5c6a393000ee5a6036161ad SHA1: 555aa921107113f80eb46506a19e389857cc9180 MD5sum: 78c64694935417b930f3a553af7e0305 Description: I/O library for biomedical data - debug symbols BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://pub.ist.ac.at/~schloegl/biosig/TESTED . . This package provides debug symbols. Package: libclassad-dev Source: condor Version: 7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2163 Depends: neurodebian-popularity-contest, libclassad3 (= 7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1) Conflicts: libclassad0-dev Replaces: libclassad0-dev Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: libdevel Filename: pool/main/c/condor/libclassad-dev_7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 484114 SHA256: 691e04e8104094414e5de09009b3bba3abe3a54148610a3b86b96751a9eb1cb2 SHA1: d78519426f89f4547d86703a5bd7787fec235ad2 MD5sum: 83d3a46a80497c4c7bd6d4a774943ff2 Description: Condor classads expression language - development library Classified Advertisements (classads) are the lingua franca of Condor, used for describing jobs, workstations, and other resources. There is a protocol for evaluating whether two classads match, which is used by the Condor central manager to determine the compatibility of jobs, and workstations where they may be run. . This package provides the static library and header files. Package: libclassad3 Source: condor Version: 7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 800 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libpcre3 (>= 8.10), libstdc++6 (>= 4.6) Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: science Filename: pool/main/c/condor/libclassad3_7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 272640 SHA256: 66a0e04e41d25c36771b5a039e0de344c2cd1dd7eafd890c1f8617d388c895da SHA1: 626139b52ac8ac6a88af1904fa2de4a754a747cb MD5sum: c5b3a76a4a6315360c6859eeb23f9150 Description: Condor classads expression language - runtime library Classified Advertisements (classads) are the lingua franca of Condor, used for describing jobs, workstations, and other resources. There is a protocol for evaluating whether two classads match, which is used by the Condor central manager to determine the compatibility of jobs, and workstations where they may be run. . This package provides the runtime library. Package: libinsighttoolkit4-dev Source: insighttoolkit4 Version: 4.2.1-2~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 25779 Depends: neurodebian-popularity-contest, libinsighttoolkit4.2 (= 4.2.1-2~nd12.10+1+nd13.04+1), libgdcm2-dev Recommends: libfftw3-dev, uuid-dev Suggests: insighttoolkit4-examples Conflicts: libinsighttoolkit-dev, libinsighttoolkit3-dev Replaces: libinsighttoolkit-dev Homepage: http://www.itk.org/ Priority: optional Section: libdevel Filename: pool/main/i/insighttoolkit4/libinsighttoolkit4-dev_4.2.1-2~nd12.10+1+nd13.04+1_i386.deb Size: 5275850 SHA256: ba9ec061432045f550b273c5ec6a13761fd8dfc4dbd895fc7877489257dabd55 SHA1: 820969441a9927cfc14f1591066753b582045489 MD5sum: 9bf7547dc7e2deeed23c181f20674d05 Description: Image processing toolkit for registration and segmentation - development 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 development files needed to build your own ITK applications. Package: libinsighttoolkit4.2 Source: insighttoolkit4 Version: 4.2.1-2~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 20377 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libgdcm2.2, libjpeg8 (>= 8c), libminc2-1, libpng12-0 (>= 1.2.13-4), libstdc++6 (>= 4.6), zlib1g (>= 1:1.2.3.3) Homepage: http://www.itk.org/ Priority: optional Section: libs Filename: pool/main/i/insighttoolkit4/libinsighttoolkit4.2_4.2.1-2~nd12.10+1+nd13.04+1_i386.deb Size: 6820470 SHA256: a59274fc382af253677dd8ed17eba8172366f04d0ef41a084ac93e478a313832 SHA1: d31ef04af11dd05501abacfc3d7cf08247f84c15 MD5sum: 2fa4b45917da66a3c0870b38a8ece561 Description: Image processing toolkit for registration and segmentation - runtime 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 libraries needed to run ITK applications. Package: libmcpanel-dev Source: mcpanel Version: 0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4 Depends: neurodebian-popularity-contest, libmcpanel0 (= 0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1) Homepage: http://cnbi.epfl.ch/software/mcpanel.html Priority: extra Section: libdevel Filename: pool/main/m/mcpanel/libmcpanel-dev_0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1_i386.deb Size: 2408 SHA256: 409c1f4d348b0b4c335499d37c5a5d678f7588035bdc65c129f704f2c180b3e7 SHA1: 5cd4e3eb18218f8bd730d88e1d13df94428e5827 MD5sum: 108bd991c2e7f228f8042e2cdd270bc0 Description: Library to display multichannel data in realtime (Developement files) This package provides a library written in C implementing a set of widgets designed to view in realtime multichannels signals. Despite it has been initially design to view signals coming from a BIOSEMI Activetwo EEG system, it is totally system agnostic and any user of other system might find it useful. . This package contains the files needed to compile and link programs which use mcpanel Package: libmcpanel0 Source: mcpanel Version: 0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 135 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.31.8), libgtk2.0-0 (>= 2.14.0), libpango1.0-0 (>= 1.14.0), librtfilter1 (>= 1.0) Homepage: http://cnbi.epfl.ch/software/mcpanel.html Priority: extra Section: libs Filename: pool/main/m/mcpanel/libmcpanel0_0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1_i386.deb Size: 48880 SHA256: 993a00a82f3fb5b5cebd48ae066b77557732de6411cf8de91c00af35b261b7b2 SHA1: b0a126e83cea00ebdaac09b87acab3357bd345dc MD5sum: 54cfefc125809ddfc2aabf732c55e39e Description: Library to display multichannel data in realtime This package provides a library written in C implementing a set of widgets designed to view in realtime multichannels signals. Despite it has been initially design to view signals coming from a BIOSEMI Activetwo EEG system, it is totally system agnostic and any user of other system might find it useful. Package: libmcpanel0-dbg Source: mcpanel Version: 0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: neurodebian-popularity-contest, libmcpanel0 (= 0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1) Homepage: http://cnbi.epfl.ch/software/mcpanel.html Priority: extra Section: debug Filename: pool/main/m/mcpanel/libmcpanel0-dbg_0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1_i386.deb Size: 6572 SHA256: f30713afa2881e8b3f238a6921383e41e42876d77146ae7b0633a544b4c9515a SHA1: 5effc30d4e54a0618d220ca2f47f6dd2cdf60317 MD5sum: 0b9869971d31693cc119d8bd252559a0 Description: Library to display multichannel data in realtime (Debugging symbols) This package provides a library written in C implementing a set of widgets designed to view in realtime multichannels signals. Despite it has been initially design to view signals coming from a BIOSEMI Activetwo EEG system, it is totally system agnostic and any user of other system might find it useful. . This package contains the debugging information of the library. Package: libvrpn-dev Source: vrpn Version: 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 659 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1), libvrpnserver0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libdevel Filename: pool/main/v/vrpn/libvrpn-dev_07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1_i386.deb Size: 174196 SHA256: c44f7ad756b31ea9f4563c4df4ceb56119ad37507f1c226c834975abc5e5e541 SHA1: a7350ed4f8bf1408d3ae518714546114797fb6c8 MD5sum: c858aae84df48b228adec9b743e1418c Description: Virtual Reality Peripheral Network (development files) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the development files Package: libvrpn0 Source: vrpn Version: 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 510 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libs Filename: pool/main/v/vrpn/libvrpn0_07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1_i386.deb Size: 184454 SHA256: dd2c652780033dccc1f2de3e839218acbd6431d164bba42361d6dc0106c02cfc SHA1: 51c912031c38ea3b772979befc6615c8f8fe18c7 MD5sum: 06b8c7b0262283cd10c41e9ba399c968 Description: Virtual Reality Peripheral Network (client library) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the client shared library Package: libvrpnserver0 Source: vrpn Version: 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1214 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libs Filename: pool/main/v/vrpn/libvrpnserver0_07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1_i386.deb Size: 425558 SHA256: 1c83bbe5d9b0ce8eebbc53eb108718597004382777f1e4a9b3ffe7e43be2e45a SHA1: 06faeefb4ae4622d25b5e20d4b73e6e27dacfc94 MD5sum: 2bc52092e7102fc7d0ad2480615b264b Description: Virtual Reality Peripheral Network (server library) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the shared library use in the VRPN server Package: libvw-dev Source: vowpal-wabbit Version: 7.2-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1856 Depends: neurodebian-popularity-contest, libvw0 (= 7.2-1~nd12.10+1+nd13.04+1) Homepage: http://hunch.net/~vw/ Priority: optional Section: libdevel Filename: pool/main/v/vowpal-wabbit/libvw-dev_7.2-1~nd12.10+1+nd13.04+1_i386.deb Size: 529316 SHA256: 9a1ac13868fe580e1f7647f451010f08fe507c79481d9b744a622a26efec1293 SHA1: c2454af7d22c080c70c14cf4a32c6297dcede64b MD5sum: ebef5c632aa6071631f77412d53d5053 Description: fast and scalable online machine learning algorithm - development files 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 development files needed to compile and link programs which use vowpal-wabbit's libraries. Package: libvw0 Source: vowpal-wabbit Version: 7.2-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 684 Depends: neurodebian-popularity-contest, libboost-program-options1.49.0 (>= 1.49.0-1), libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), zlib1g (>= 1:1.2.3.3) Homepage: http://hunch.net/~vw/ Priority: optional Section: libs Filename: pool/main/v/vowpal-wabbit/libvw0_7.2-1~nd12.10+1+nd13.04+1_i386.deb Size: 293550 SHA256: 26899e22d7204ca34db6da34c8837949238e27cb0efd6fb0e614bd371c503b7e SHA1: 4dd6ef6915a372a52c54b1ffeb8f18e5b68c247b MD5sum: 120a2cc5a1df2b9861e852d8156b4bef Description: fast and scalable online machine learning algorithm - dynamic library 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 vowpal-wabbit's dynamic libraries. Package: mriconvert Version: 2.0.250-1+nd1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2723 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.12.1), libwxgtk2.8-0 (>= 2.8.12.1) Homepage: http://lcni.uoregon.edu/~jolinda/MRIConvert/ Priority: optional Section: science Filename: pool/main/m/mriconvert/mriconvert_2.0.250-1+nd1~nd12.10+1+nd13.04+1_i386.deb Size: 891520 SHA256: 2de18fa186d056cf466118becced602c4992f7e1ed1e4bd01a3a5895ffccb681 SHA1: 0232db461a76b3b5266eb6814c9fb6944ed66a80 MD5sum: 0d8567ffe7d8de84f5a5ed76856f4b54 Description: medical image file conversion utility MRIConvert is a medical image file conversion utility that converts DICOM files to NIfTI 1.1, Analyze 7.5, SPM99/Analyze, BrainVoyager, and MetaImage volume formats. Package: mricron Version: 0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 14995 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libc6 (>= 2.3.6-6~), libcairo2 (>= 1.2.4), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.24.0), libpango1.0-0 (>= 1.14.0), libx11-6, mricron-data Suggests: mricron-doc, fsl Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron_0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1+nd13.04+1_i386.deb Size: 5785868 SHA256: 52d16f2c42f52418311f912094deeae10e19634c723c285b3d24d221afb04b4c SHA1: 9057c1e5b9e2ab081c14de7ed00769a46b12bebd MD5sum: 483e2853d5de5806d6bb52fa7514696d Description: magnetic resonance image conversion, viewing and analysis 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). . In addition to 'mricron', this package also provides 'dcm2nii' that supports converting DICOM and PAR/REC images into the NIfTI format, and 'npm' for non-parametric data analysis. Package: mricron-data Source: mricron Version: 0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1678 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1+nd13.04+1_all.deb Size: 1664070 SHA256: 8b2a654aad3dc98e12c6b0439b9c4341eaea214734690678a538fb503caa65b2 SHA1: ccfa17f62c2c6680f76e4d9aae1acc557f67a2db MD5sum: 2eb4ee496a8438e325c401235b731544 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: 0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 979 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1+nd13.04+1_all.deb Size: 735798 SHA256: 556dd4e3e50a13b8dfceff94cc5496708a250670042cf658dcbbd8de964e0c4b SHA1: a660fee601eb2aabd70f6bd5065c0051ce3f4858 MD5sum: dc44d660cc9199415f1bbfd44b0e801a 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: neurodebian-desktop Source: neurodebian Version: 0.31~nd12.10+1+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_0.31~nd12.10+1+nd13.04+1_all.deb Size: 115378 SHA256: ea27d4b5413313f3bacf2636461311058dbf67dc2efb94a185ef21a9827b1d9b SHA1: c58ffc48769ca9bb0aa28e29fdeaf99458efc737 MD5sum: 6b4418b053f34c2d176f864e236d7599 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: 0.31~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5762 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_0.31~nd12.10+1+nd13.04+1_all.deb Size: 5351248 SHA256: 20b3496a3dddd71651fcc1663feba113c1682d212e17dd4cd1dfc1219494fe09 SHA1: 5385e81663517ad533cc9dcef295f0be2a24670e MD5sum: 35409b00fce63b6164ba9e753b5e693b 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: 0.31~nd12.10+1+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_0.31~nd12.10+1+nd13.04+1_all.deb Size: 15242 SHA256: 203b9f9d26a55f432e5090e151ce7f09bed96f1329b45d9f9b1d13657859a8e3 SHA1: cfad4d5817354a6ce090b5c6c788b13893fc8e4a MD5sum: 0ca1609f39a61e9c517396686efcbd1c 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: 0.31~nd12.10+1+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_0.31~nd12.10+1+nd13.04+1_all.deb Size: 7542 SHA256: afb7778e22dba6945648fe213e8f4cc9c208ae324810845a97c02b32faef7622 SHA1: 6596b8c49b4724805d269908dfc8eecddadbe012 MD5sum: 63b49c8e8f825316a79513eb14a29dda 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: 0.31~nd12.10+1+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_0.31~nd12.10+1+nd13.04+1_all.deb Size: 6760 SHA256: 89585a170b94a85bc1241d995296770d8d9f81f6c366da97c308452d540980ce SHA1: d27b7a57312975cc31b0e8cac48f41215b9c2186 MD5sum: 045f0a04c84713469d0107bf73a37ee6 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: octave-biosig Source: biosig4c++ Version: 1.4.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 66 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), libbiosig1, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), liboctave1 (>= 3.6.2) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/octave-biosig_1.4.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 23656 SHA256: 1497c9919123fcb8e809a3a00d3f0468ebf01acc02b16c457cc02cd39e6a8aa5 SHA1: 6af4452d580400054065e6e0c6f725e9841f4f68 MD5sum: 0c809588befe1648db398b3b03b24560 Description: Octave bindings for BioSig library This package provides Octave bindings for BioSig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF. Package: opensesame Version: 0.27.1-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25107 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) Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27.1-1~nd12.10+1+nd13.04+1_all.deb Size: 24041884 SHA256: 42c6a3907d3423b4dc6982c8d55673acbbd4bedbe9ecf17f3cee2268fc535fda SHA1: 131dc9b069a917748e9447530a399f78dce1c793 MD5sum: 8ea82cfb55dd2019d9570ccc725553b0 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: 1.76.00.dfsg-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5340 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, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.76.00.dfsg-1~nd12.10+1+nd13.04+1_all.deb Size: 3177836 SHA256: 0f93fc1eb950a5934b7c0e504fef00c9b46e38a71850695b9b688830dbdfed31 SHA1: 3a5282853c0c6669cffd764bc1b530c77b2e37f4 MD5sum: 3336cbce97eea6694d04b83ba08d9e15 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: python-biosig Source: biosig4c++ Version: 1.4.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 190 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python-support (>= 0.90.0), libbiosig1, libc6 (>= 2.4), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libpython2.7 (>= 2.7), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: python Filename: pool/main/b/biosig4c++/python-biosig_1.4.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 55290 SHA256: 6537dfdaf7872f6d1a656a2102374c0079dbfb34a6702c787f9452d0acdbc1f0 SHA1: b43e85c2883f2a299aedcee4165507f5371f271d MD5sum: 6a90926a0847aeaae3684115886340ce Description: Python bindings for BioSig library This package provides Python bindings for BioSig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF. 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-brian-lib Source: brian Version: 1.4.1-1~nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 124 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python-support (>= 0.90.0), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian-lib_1.4.1-1~nd13.04+1_i386.deb Size: 52284 SHA256: e6f96fc6e7b864591abe7379dd4656162691f173315a0bfac9960ed15c02cff6 SHA1: 1d5d599ddb1fe1aafa1820c4aa58570399dc19fc MD5sum: fbef8a2a0459378d4eb5aca6b75b98b2 Description: simulator for spiking neural networks -- extensions Brian is a clock-driven simulator for spiking neural networks. . This package provides Python binary extensions. Package: python-dipy Source: dipy Version: 0.6.0-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2285 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.6.0-1~nd12.10+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_0.6.0-1~nd12.10+1+nd13.04+1_all.deb Size: 1586330 SHA256: 9ae0066ea9dd0827c19d4a38ef67434588eb327998c03f91b886f3d0c1ce6e18 SHA1: 6f4fe8d55b808fb237ff4d57616231d396cf1690 MD5sum: 79a32a472ed7bdf1918b72bb9b5a113d 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: 0.6.0-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5080 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_0.6.0-1~nd12.10+1+nd13.04+1_all.deb Size: 3615252 SHA256: 3970b0bb31d6fd6b2c731e67565849f92be42444129498f103111ff56212e1da SHA1: 3dd6d5bb930e50c23924f935a27d73b34ce2f7e9 MD5sum: 0e5cd4baee579fba43b730871827fa4f 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-dipy-lib Source: dipy Version: 0.6.0-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 877 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-support (>= 0.90.0), libc6 (>= 2.4) Provides: python2.7-dipy-lib Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy-lib_0.6.0-1~nd12.10+1+nd13.04+1_i386.deb Size: 345614 SHA256: 721ca0ae9fed603ed65c13096165cb701c00c5f88a25024a6b275deb1211dc7a SHA1: bdd916c991e89f568af0388f4802a8455f270b29 MD5sum: f484818ae27f32d8eb2b97dc304a9956 Description: toolbox for analysis of MR diffusion imaging data -- extensions Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides architecture-dependent builds of the extensions. Python-Version: 2.7 Package: python-joblib Source: joblib Version: 0.6.5-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 175 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.6.5-1~nd12.04+1+nd12.10+1+nd13.04+1_all.deb Size: 52746 SHA256: 894eaf78aaa0832a44b7b2b6f250decf901657e96430bdb566567962450a44ea SHA1: 981fca75d9d1006d67349fed9decdab0fed43b3c MD5sum: 5ade26a3f9cdd39dff5b712e33cabbfb 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-neo Source: neo Version: 0.2.1.1-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2415 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_0.2.1.1-1~nd12.10+1+nd13.04+1_all.deb Size: 1428844 SHA256: 15b33fd44dda6ae4ed10a9fa9909a644602fd9d774da5c29c93b4906aabc9d9f SHA1: 8f70077a144a0e8336afcd0ffa4bbff6c9bc6ffe MD5sum: 9d0eee7c888375f622002c9fc1452153 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-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: 0.7-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2544 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_0.7-1~nd13.04+1_all.deb Size: 567686 SHA256: 8ca9a13dbdc1c76a4996259331a9afeb3abee2070f80b72fcc8f037629251896 SHA1: 9839bbbb41a84092359a3bb4edb2dea3223f821a MD5sum: 4707eb5411696e2c30eb3f35dec428c5 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: 0.7-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14077 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_0.7-1~nd13.04+1_all.deb Size: 6682210 SHA256: c4cd5e8e1d569ae985b4cf122e585829661510176f2e198f3579bb0a37f72320 SHA1: fd02f4d5245fa120c83979b2e32e74bf3f96a09c MD5sum: e418a2313f730d4c59360637d6c9a62c 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: 1.6.1+hg2-g4bff8e3-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 291 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-nose Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.6.1+hg2-g4bff8e3-1~nd12.10+1+nd13.04+1_all.deb Size: 62100 SHA256: 64a8cf4cf5747a7cf77d347a415106897b547e7a88668496a480061112af091d SHA1: 7c45f18602568f0d607cc9f0ed352f9008afdc55 MD5sum: 70aa1ace74bb64e286215aeb426e1f0d 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: 0.11.0-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4619 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.6~), python-pandas-lib (>= 0.11.0-1~nd12.10+1+nd13.04+1) Recommends: python-scipy, python-matplotlib, python-tables, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt Suggests: python-pandas-doc Provides: python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.11.0-1~nd12.10+1+nd13.04+1_all.deb Size: 947182 SHA256: 1d5e56a393bd34ff67b28b6f59ee31db3cebc53fc75835b478d5763c5fdc6356 SHA1: 5df4d1e198b2a0954b67e0730a80dcddff9238da MD5sum: cc689361372a1bb68c42155bddbfee54 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-pandas-lib Source: pandas Version: 0.11.0-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3887 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python (>= 2.7), python (<< 2.8) Provides: python2.7-pandas-lib Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas-lib_0.11.0-1~nd12.10+1+nd13.04+1_i386.deb Size: 1358838 SHA256: 6c4f352e53ac63a3d26aae0806000a81c7017f0cc82f760ec285fd96de7eb712 SHA1: 3fb87bee3044b3a2e38b1ab80e8090fbcde95c57 MD5sum: 34b6c1f44c7edee54c2410e090795230 Description: low-level implementations and bindings for pandas This is an add-on package for python-pandas providing architecture-dependent extensions. . This package contains the Python 2 version. Python-Version: 2.7 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-pypsignifit Source: psignifit3 Version: 3.0~beta.20120611.1-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1488 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.6), python (<< 2.8), python (>= 2.7), python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy Homepage: http://psignifit.sourceforge.net Priority: extra Section: python Filename: pool/main/p/psignifit3/python-pypsignifit_3.0~beta.20120611.1-1~nd12.04+1+nd12.10+1+nd13.04+1_i386.deb Size: 425390 SHA256: 2bd1cc8c3df764d58ade43d75766c9c5ad2500e0c28587b536430ac0bb7bb0e6 SHA1: 100b3a8457467b26b5e8b53ae975df21aee96fe3 MD5sum: 15b622ac6a5533ba793064bde47f8dfb Description: psychometric analysis of psychophysics data in Python Psignifit allows fitting of psychometric functions to datasets while maintaining full control over a large number of parameters. Psignifit performs the calculation of confidence intervals as well as goodness-of-fit tests. In addition it offers: . * full Bayesian treatment of psychometric functions including Bayesian model selection and goodness of fit assessment * identification of influential observations and outlier detection * flexible shape definition of the psychometric function . This package provides the Python bindings. 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: 0.13-2~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 30 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_0.13-2~nd12.10+1+nd13.04+1_all.deb Size: 28190 SHA256: 0c041873c65fc1dcadceeac96d38d63f1b3ef47d6707e71febaffcff5ad2eb78 SHA1: 88d299e0b99a92dd4f89b05c930017f3b392296f MD5sum: 7504e0dcc44c9499a5b8840737416d45 Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-sklearn Source: scikit-learn Version: 0.13-2~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3035 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.13-2~nd12.10+1+nd13.04+1) Recommends: python-nose, python-matplotlib, python-joblib (>= 0.4.5) Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Enhances: python-mdp, python-mvpa2 Breaks: python-scikits-learn (<< 0.9~) Replaces: python-scikits-learn (<< 0.9~) Provides: python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.13-2~nd12.10+1+nd13.04+1_all.deb Size: 1007508 SHA256: be7c75d6eeb31ab0f4e97115dbd9700813073b40be8e88bd2fb87c87f50e2848 SHA1: 02e20398cd16bcf02b2d34821369b78b72f74182 MD5sum: 9a413e6d6aeecaa972d56f964e6487bb Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Python-Version: 2.7 Package: python-sklearn-doc Source: scikit-learn Version: 0.13-2~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 42182 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_0.13-2~nd12.10+1+nd13.04+1_all.deb Size: 30707390 SHA256: 32bb8c160adf124ac9534b27eb8c7f4a9f2d7e13c90a7939082c3ccfcfda3d42 SHA1: 61165be85e2b4956daca297d2736200c899da57a MD5sum: 5a293c7ec8aebb0d20a46d31d9883970 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-sklearn-lib Source: scikit-learn Version: 0.13-2~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2350 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python-support (>= 0.90.0) Conflicts: python-scikits-learn-lib Replaces: python-scikits-learn-lib Provides: python2.7-sklearn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn-lib_0.13-2~nd12.10+1+nd13.04+1_i386.deb Size: 920570 SHA256: 59532fbbd3a72d128cd709efe326b51aae54ce51578db8479fe2247222ec6965 SHA1: 91321f30e5c75532239968484d4c453f58c2f045 MD5sum: 1a68c99e77a3dd8b65798660b3b91304 Description: low-level implementations and bindings for scikit-learn This is an add-on package for python-sklearn. It provides low-level implementations and custom Python bindings for the LIBSVM library. Python-Version: 2.7 Package: python-spykeutils Source: spykeutils Version: 0.2.1-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1393 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_0.2.1-1~nd12.10+1+nd13.04+1_all.deb Size: 320710 SHA256: f659c3dcafce0c359ab001951708c3759f0cb2899d2d9faa1913052de86df138 SHA1: 8a1ce39877423aa938035ef066ed6eabb80056a6 MD5sum: 8f84d8f204631a740247f7374c676e5a 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-stfio Source: stimfit Version: 0.12.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 757 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7.1-0ubuntu2), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, libbiosig1, libc6 (>= 2.4), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libhdf5-7, libpython2.7 (>= 2.7), libstdc++6 (>= 4.2.1) Recommends: python-matplotlib, python-scipy Provides: python2.7-stfio Homepage: http://www.stimfit.org Priority: optional Section: python Filename: pool/main/s/stimfit/python-stfio_0.12.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 273558 SHA256: b2754ff8ef37e2686106f3fd2a86484f9559cff5134273b69995f4538d07a59d SHA1: 6c011dd68d9b56a805bbba243893eba01036a122 MD5sum: cd12bc345642f9bc32f0113a7d8ae6c9 Description: Python module to read common electrophysiology file formats. The stfio module allows you to read common electrophysiology file formats from Python. Axon binaries (abf), Axon text (atf), HEKA (dat), CFS (dat/cfs), Axograph (axgd/axgx) are currently supported. 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: python-traits4 Source: python-traits Version: 4.0.0-1~cbp1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1 Architecture: i386 Bugs: mailto:bugs@neuro.debian.net Maintainer: NeuroDebian Team Installed-Size: 1662 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python (<< 2.8), python (>= 2.7), python-support (>= 0.90.0) Suggests: python-traitsui Conflicts: python-traits (>= 4.0~) Homepage: http://pypi.python.org/pypi/traits Priority: optional Section: python Filename: pool/main/p/python-traits/python-traits4_4.0.0-1~cbp1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1_i386.deb Size: 338370 SHA256: c65bcafa75da76cf07ad3f82c619d45c6587a4e9232133ac99aa483dc7cf98f1 SHA1: b2ee7268590847385f596c7f3e780f903e0d57eb MD5sum: da1b4ba4998e09f36f3e5d7a86af2629 Description: Manifest typing and reactive programming for Python The traits package provides a metaclass with special attributes that are called traits. A trait is a type definition that can be used for normal Python object attributes, giving the attributes some additional characteristics: * Initialization: A trait attribute can have a default value * Validation: A trait attribute is manifestly typed. * Delegation: The value of a trait attribute can be contained in another object * Notification: Setting the value of a trait attribute can fired callbacks * Visualization: With the TraitsUI package, GUIs can be generated automatically from traited objects. Uploaders: Yaroslav Halchenko , Michael Hanke Vcs-Browser: http://git.debian.org/?p=pkg-exppsy/python-traits4.git Vcs-Git: git://git.debian.org/git/pkg-exppsy/python-traits4.git 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: 0.11.0-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4567 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-dateutil, python3-tz, python3-numpy (>= 1:1.6~), python3-pandas-lib (>= 0.11.0-1~nd12.10+1+nd13.04+1) Recommends: python3-scipy, python3-matplotlib, python3-tables Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.11.0-1~nd12.10+1+nd13.04+1_all.deb Size: 943432 SHA256: 9eb4b9222b38f3ff7dc2e51e126e539e2331b594f84e760f02dc922d73b32aef SHA1: 8d7618a58b18651f92ee220839aa77e1e3aed952 MD5sum: a586d026af85a2c6d4a651e3e92467d1 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-pandas-lib Source: pandas Version: 0.11.0-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3819 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), python3-numpy (>= 1:1.7-0~b1), python3-numpy-abi9, python3 (>= 3.3), python3 (<< 3.4) Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas-lib_0.11.0-1~nd12.10+1+nd13.04+1_i386.deb Size: 1333064 SHA256: 967cdc5ff8972de0bc80c45a50dca206dea8bacfeffa54d0bde006d719cd9eb2 SHA1: 94a3dd1d6690e3402b3de58577e790ef917728df MD5sum: 85733d02022284989735cbc34f13f8c1 Description: low-level implementations and bindings for pandas - Python 3 This is an add-on package for python-pandas providing architecture-dependent extensions. . This package contains the Python 3 version. 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: spykeviewer Version: 0.2.1-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 848 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.2.1), python-neo (>= 0.2.1), python-matplotlib, python-nose, python-scipy, 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_0.2.1-1~nd12.10+1+nd13.04+1_all.deb Size: 460122 SHA256: a1a76469ad302e8fb6f97adcb2158b0c934df780cf7f5b233437acd79f4bcc80 SHA1: a4313ff1faa6ae33446522c9b10dfe69c13cf5b2 MD5sum: 9734884c0ddbb63547d7aadfb295ddaf 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: stimfit Version: 0.12.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2306 Depends: neurodebian-popularity-contest, libbiosig1, libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.4), libcholmod1.7.1 (>= 1:3.4.0), libfftw3-double3, libgcc1 (>= 1:4.1.1), libhdf5-7, liblapack3 | liblapack.so.3 | libatlas3-base, libpython2.7 (>= 2.7), libstdc++6 (>= 4.2.1), libwxbase2.8-0 (>= 2.8.12.1), libwxgtk2.8-0 (>= 2.8.12.1), python (>= 2.7.1-0ubuntu2), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python2.7, python-wxgtk2.8 (>= 2.8.9), python-matplotlib Recommends: python-scipy Homepage: http://www.stimfit.org Priority: optional Section: science Filename: pool/main/s/stimfit/stimfit_0.12.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 829300 SHA256: 14647929454502da4cd70bc72f0f65933fd5710a9de9f344ac45e8838aea29fe SHA1: fa39a91688a2763d9d0aefd22de2baa78c4ecf98 MD5sum: eab20cd3810111dfeb53fa83017b1082 Description: Program for viewing and analyzing electrophysiological data Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. Package: stimfit-dbg Source: stimfit Version: 0.12.1-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 10654 Depends: neurodebian-popularity-contest, stimfit Recommends: python-matplotlib, python-scipy, python-stfio Homepage: http://www.stimfit.org Priority: extra Section: debug Filename: pool/main/s/stimfit/stimfit-dbg_0.12.1-1~nd12.10+1+nd13.04+1_i386.deb Size: 4057168 SHA256: bc13cf1ae6c8a723d6a1872448f7c464af5597add0b13d202da8d5d8a6288363 SHA1: af07ce70486599e7605810274bf379d76600e8bf MD5sum: 879f8b4bea8dd80f389d44bddf98b2ee Description: Debug symbols for stimfit Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. 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 Version: 7.2-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 45 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libvw0 (= 7.2-1~nd12.10+1+nd13.04+1) Suggests: vowpal-wabbit-doc Homepage: http://hunch.net/~vw/ Priority: optional Section: science Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit_7.2-1~nd12.10+1+nd13.04+1_i386.deb Size: 20886 SHA256: 542a17ef1fd21a65508ede9c82bdae868a70322308d45b12a52ca7c7581f940b SHA1: dd360189a0fa779d1a1c7c22dfd393de7d2816f2 MD5sum: b8f07eca9040d5697aa490a0669195bd Description: fast and scalable online machine learning algorithm 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 Package: vowpal-wabbit-dbg Source: vowpal-wabbit Version: 7.2-1~nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 5477 Depends: neurodebian-popularity-contest, vowpal-wabbit (= 7.2-1~nd12.10+1+nd13.04+1) Homepage: http://hunch.net/~vw/ Priority: extra Section: debug Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-dbg_7.2-1~nd12.10+1+nd13.04+1_i386.deb Size: 2120078 SHA256: 02ba136e0232cdf5911dcabfa245437441d82cef283c798b822567ee05da1b6c SHA1: 68decc79d7e289fc86b7926490e2e194e239f873 MD5sum: e6e0354fceb9975461c5cbbb9da67a63 Description: fast and scalable online machine learning algorithm - debug files 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 debug symbols for the binaries shipped by vowpal-wabbit packages. Package: vowpal-wabbit-doc Source: vowpal-wabbit Version: 7.2-1~nd12.10+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_7.2-1~nd12.10+1+nd13.04+1_all.deb Size: 50202306 SHA256: bff603a80e099779a6e59e179b519f491ae3a21a34e554d4271d9989c5786d1c SHA1: d574744d3ecb4bf57b35726a13848d5b09cc33bc MD5sum: 1d0fb1795be60a140cf7eba2b98ccbe5 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. Package: vrpn Version: 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 284 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libvrpn0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1), libvrpnserver0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: utils Filename: pool/main/v/vrpn/vrpn_07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1_i386.deb Size: 64378 SHA256: 11584fd469c60b38d7722fb52e4224dcd11b9f87b9baced4969e2eedb73064cd SHA1: 895242ad867135ee02f52140ac3783e118fb4ba9 MD5sum: 23e840b1017aece1283396ee89026933 Description: Virtual Reality Peripheral Network (executables) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the executables like the VRPN server. Package: vrpn-dbg Source: vrpn Version: 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4162 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1), libvrpnserver0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1), vrpn (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: debug Filename: pool/main/v/vrpn/vrpn-dbg_07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1_i386.deb Size: 1592158 SHA256: c8966dd758e36af76a5820259c7e138488d7aa67cfac882f1c133eb5d78b56f0 SHA1: 49c835f7a4162ee9dda93a87cb15aee9025a8f69 MD5sum: 2ad28e221c53bfa49d1f9b936298d15e Description: Virtual Reality Peripheral Network (debugging symbols) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the debugging symbols of the libraries and executables.