Package: eyesthatblink Version: 1.0-12 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 6057 Depends: libatkmm-1.6-1v5 (>= 2.24.0), libboost-filesystem1.62.0, libboost-regex1.62.0, libboost-system1.62.0, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libglibmm-2.4-1v5 (>= 2.54.0), libgtkmm-2.4-1v5 (>= 1:2.24.0), libnotify4 (>= 0.7.0), libopencv-core3.1, libopencv-imgproc3.1, libopencv-objdetect3.1, libopencv-videoio3.1, libsigc++-2.0-0v5 (>= 2.2.0), libstdc++6 (>= 7), libxcb-randr0 (>= 1.1), libxcb-util1 (>= 0.4.0), libxcb1 Filename: ./amd64/eyesthatblink_1.0-12_amd64.deb Size: 651754 MD5sum: bde6592c8032887b86b262e420e991df SHA1: 1b482036e5b7be70622227a9baccbb6ac7f7a762 SHA256: 3be4e5a6f0850a9dd517db958af976612de775d962c8dc65d99e63a803a661fe Section: science Priority: optional Homepage: http://eyesthatblink.com Description: Eyes That Blink Package: moose Source: xppaut Version: 8.0+9.1 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 2107 Depends: libc6 (>= 2.23), libx11-6 Filename: ./amd64/moose_8.0+9.1_amd64.deb Size: 745558 MD5sum: 41f6a34a2c1aa7bdc785881c8b8c1844 SHA1: 72033a9c6725181442782676a0488e8ac3ff9dba SHA256: d413e1f290947652354a54a936da892b949aa82d1c90ff76e5abe219e7c0689b 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: 2.16.0+4.1 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 24123 Depends: libc6 (>= 2.14), libgcc1 (>= 1:3.4), libgomp1 (>= 4.9), libgsl23, libgslcblas0, libltdl7 (>= 2.4.6), libpython3.6 (>= 3.6.0~b2), libreadline7 (>= 6.0), libstdc++6 (>= 5.2), libtinfo5 (>= 6) Recommends: python3-matplotlib, python3-scipy Filename: ./amd64/nest_2.16.0+4.1_amd64.deb Size: 6879560 MD5sum: 4267d1bc158fa5c82e09163fe85e571f SHA1: 4d3355e73621f27294d33041f2d99b364b1062c5 SHA256: c45298f7d505c7d87d09c9cf3e9175d9bf313fcfd903f917ed198a2a57670e88 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: 2.16.0+4.1 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 7 Filename: ./amd64/nest-dbg_2.16.0+4.1_amd64.deb Size: 1086 MD5sum: f6c4d9339c0b49acffb0082785a76b19 SHA1: fdea6a76922d5085c81490a4e67617466a109b02 SHA256: 07b36980b9d7995c72f2bd9ce503fe3843413ff931d04cf2814b97dc702fc39b Section: debug Priority: extra Homepage: http://github.com/madhavPdesai/ahir Description: debugging symbols for nest Package: nest-dev Source: nest Version: 2.16.0+4.1 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 2597 Filename: ./amd64/nest-dev_2.16.0+4.1_amd64.deb Size: 320784 MD5sum: fc731f6f8ec1ac48024845f5bfe5a267 SHA1: 3b308d29ae5a51054b1ace812eadf0e5aee976b2 SHA256: b2e78f08139376e2b2d9519c26fc0e8f27f349fb6a937cde060467dca2326c66 Section: libdevel Priority: optional Multi-Arch: same Homepage: http://github.com/madhavPdesai/ahir Description: NEST development package This package contains C++ header files. Package: tippecanoe Version: 1.35.0+8.1 Architecture: amd64 Maintainer: Dilawar Singh Installed-Size: 1150 Depends: libc6 (>= 2.14), libgcc1 (>= 1:3.0), 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: 340800 MD5sum: 28a9e70d27af6555b84a71e92056c429 SHA1: e7ecf5038c6249f1db6add886b82fdc135daa41d SHA256: b9778ae29ef22f9f08ee403cdaedc4dfe29f1dac661d71d415d474e700a6b25e 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.