Package: moose Version: 3.1.5+10.1 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 13458 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 9) Filename: ./amd64/moose_3.1.5+10.1_amd64.deb Size: 2695792 MD5sum: 6165de2a417a9f0ed34205e7cb796c3b SHA1: 6fdd3db8992925f426546136bd3b04efa7b4cdfe SHA256: 81e8208c738b3eee5113d53004dd0393fd5f2a309c0dc54b6f16eff52d2cd7e1 Section: science Priority: optional Homepage: http://moose.ncbs.res.in Description: the Multiscale Object-Oriented Simulation Environment MOOSE is designed for large, detailed multiscale simulations including computational neuroscience and Systems Biology. . MOOSE spans the range from single molecules to subcellular networks, from single cells to neuronal networks, and to still larger systems. MOOSE uses Python for scripting compatibility with a large range of software and analysis tools. It recognizes model definition standards including SBML, NeuroML, and GENESIS model file formats. . MOOSE is open source software, licensed under the LGPL (Lesser GNU Public License). It has absolutely no warranty. Package: moose Source: xppaut Version: 8.0+11.2 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 1935 Depends: libc6 (>= 2.29), libx11-6 Filename: ./amd64/moose_8.0+11.2_amd64.deb Size: 687916 MD5sum: a795c5b266ea8761b01f4966156ad7cb SHA1: e71acabb4ea0982cfc6d97e58d2e96fa173de6ac SHA256: da6415195499e3ef40ceecbb94d07020f8fe528223738da3bda758aab53928b7 Section: science Priority: optional Homepage: http://moose.ncbs.res.in Description: XPPAUT is a tool for solving * differential equations, * difference equations, * delay equations, * functional equations, * boundary value problems, and * stochastic equations. The code brings together a number of useful algorithms and is extremely portable. All the graphics and interface are written completely in Xlib which explains the somewhat idiosyncratic and primitive widgets interface. Package: nest Version: 2.20.git-1+7.1 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 23143 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.4), libgomp1 (>= 4.9), libgsl23 (>= 2.5), libgslcblas0 (>= 2.4), libltdl7 (>= 2.4.6), libncurses6 (>= 6), libpython3.8 (>= 3.8.2), libreadline8 (>= 6.0), libstdc++6 (>= 9), libtinfo6 (>= 6) Recommends: python3-matplotlib, python3-scipy Filename: ./amd64/nest_2.20.git-1+7.1_amd64.deb Size: 6448060 MD5sum: a2a79bbcd44b2b5ac11e20afbd54e83d SHA1: d44d6ca2f2621edd6c6fc878fdae5436bfd8203c SHA256: 3dcc43d942cfbb9904baefb3325aa603848b7af21f4091ab1c7cc3d4d602e7c8 Section: biology Priority: optional Homepage: http://github.com/madhavPdesai/ahir Description: NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons. Package: nest-dbg Source: nest Version: 2.20.git-1+7.1 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 7 Filename: ./amd64/nest-dbg_2.20.git-1+7.1_amd64.deb Size: 1248 MD5sum: f1a375bdcb4acf4f0c95782d8989dc8b SHA1: 5e3128093e9fd651045ce978e69650f5484c4a1c SHA256: 20d0b762be06af0560d36615953ed8b8d993516ada607cfde8d59b91c9307240 Section: debug Priority: extra Homepage: http://github.com/madhavPdesai/ahir Description: debugging symbols for nest Package: nest-dev Source: nest Version: 2.20.git-1+7.1 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 2777 Filename: ./amd64/nest-dev_2.20.git-1+7.1_amd64.deb Size: 332344 MD5sum: 23d9473713e81e6405402814e463016f SHA1: 6e18ec074f9a32b5abc2efdf568af41b62afb3fc SHA256: bc5365035121a6c3b3663e3b9068171db4d6bc687ee63a02fb80567745ccd56a Section: libdevel Priority: optional Multi-Arch: same Homepage: http://github.com/madhavPdesai/ahir Description: NEST development package This package contains C++ header files. Package: smoldyn Version: 2.64.4-1+2.1 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 11 Filename: ./amd64/smoldyn_2.64.4-1+2.1_amd64.deb Size: 6224 MD5sum: 1d886d6464a5cf8f4146e3d2be38e26d SHA1: 8729e893e30102c598ee35e99288784107957927 SHA256: 1aeeee75df00a343e9458ed0fe8f7b885d4c80c61aaa155f2b719787db1714c6 Section: science Priority: optional Homepage: http://smoldyn.org Description: is a computer program for cell-scale biochemical simulations. It simulates each molecule of interest individually to capture natural stochasticity and to yield nanometer-scale spatial resolution. It treats other molecules implicitly, enabling it to simulate hundreds of thousands of molecules over several minutes of real time. Simulated molecules diffuse, react, are confined by surfaces, and bind to membranes much as they would in a real biological system. Package: stimfit Version: 0.16.git+37.2 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 903 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libfftw3-double3 (>= 3.3.5), libgcc-s1 (>= 3.0), libhdf5-103, liblapack3 | liblapack.so.3, libstdc++6 (>= 9) Filename: ./amd64/stimfit_0.16.git+37.2_amd64.deb Size: 276168 MD5sum: 187abb8aa3109d7e8bc0ab43d797ca13 SHA1: 5e601a46e08bbfeda13d8db20324df2389414f46 SHA256: 4fd5d33e9f1dd41439de241a97f7e580c065e5da39589abce1e7465d7349bee1 Section: science Priority: optional Description: A 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: tippecanoe Version: 1.35.0+8.1 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 1211 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libsqlite3-0 (>= 3.5.9), libstdc++6 (>= 5.2), zlib1g (>= 1:1.2.0.2) Filename: ./amd64/tippecanoe_1.35.0+8.1_amd64.deb Size: 345600 MD5sum: aca3c3924873539df31c7f5c3ebd64c4 SHA1: d4f594e857677b33fff8a2bc9aebf7b3a19ede01 SHA256: ba7365437e547b86b32716b5378834b532fd3a7b39b9e2acbe1958dc0147076e Section: science Priority: optional Homepage: http://gitlab.com/mapbox/tippecanoe Description: The goal of Tippecanoe is to enable making a scale-independent view of your data, so that at any level from the entire world to a single building, you can see the density and texture of the data rather than a simplification from dropping supposedly unimportant features or clustering or aggregating them.