Package: python-kwant-doc Source: kwant Version: 1.3.3-1~bpo9+1 Architecture: all Maintainer: Christoph Groth Installed-Size: 6691 Depends: libjs-sphinxdoc (>= 1.0), libjs-mathjax Homepage: http://kwant-project.org/ Priority: optional Section: doc Filename: pool/main/k/kwant/python-kwant-doc_1.3.3-1~bpo9+1_all.deb Size: 4293366 SHA256: 1b5ca4c52a7a94d2c05b428dfcc8705b34163618ae30e0079342620ed5d29dfd SHA1: 095d2bd68ddcf9421a8c92cef33846d649c92f87 MD5sum: c48ac697f312c6842f89bd9774416068 Description: Kwant documentation Kwant is a Python package for numerical quantum transport calculations. It exposes the natural concepts of the theory of quantum transport (lattices, symmetries, electrodes, orbital/spin/electron-hole degrees of freedom) in a simple and transparent way. Kwant offers direct support for calculations of transport properties (conductance, noise, scattering matrix), dispersion relations, modes, wave functions, various Green’s functions, and out-of-equilibrium local quantities. Other computations involving tight-binding Hamiltonians can be implemented easily. . This package contains the documentation for Kwant in HTML format. Package: python-tinyarray Source: tinyarray Version: 1.2.1-1~bpo9+1 Architecture: amd64 Maintainer: Christoph Groth Installed-Size: 149 Depends: python (<< 2.8), python (>= 2.7~), python:any (<< 2.8), python:any (>= 2.7~), libc6 (>= 2.14), libgcc1 (>= 1:4.0), libstdc++6 (>= 5.2) Homepage: http://kwant-project.org/tinyarray/ Priority: optional Section: python Filename: pool/main/t/tinyarray/python-tinyarray_1.2.1-1~bpo9+1_amd64.deb Size: 41700 SHA256: 986d45bdb005b4b5dcc759a710c325b4d154d7289cf55d6a7d07016b5975594f SHA1: 9bba8eca6fc2ac512945b6861ce2f6b5a3199af6 MD5sum: 18740a924a3d6559ea24a99203063d76 Description: Arrays of numbers, optimized for small sizes (Python 2) Tinyarray is a numerical array module for Python. The multi-dimensional arrays it provides are best thought of as (possibly nested) tuples of numbers that, unlike Python's built-in tuples, support mathematical operations. Like tuples, tinyarrays are hashable and immutable and thus can be used as dictionary keys. The module's interface is a subset of that of NumPy and hence should be familiar to many Python programmers. Tinyarray has been heavily optimized for small arrays: For example, common operations on 1-d arrays of length 3 run 3-7 times faster than with NumPy. When storing many small arrays, memory consumption is reduced by a factor of 3. In summary, Tinyarray is a more efficient alternative to NumPy when many separate small numerical arrays are to be used. . This package installs the library for Python 2. Package: python-tinyarray-dbgsym Source: tinyarray Version: 1.2.1-1~bpo9+1 Auto-Built-Package: debug-symbols Architecture: amd64 Maintainer: Christoph Groth Installed-Size: 211 Depends: python-tinyarray (= 1.2.1-1~bpo9+1) Homepage: http://kwant-project.org/tinyarray/ Priority: extra Section: debug Filename: pool/main/t/tinyarray/python-tinyarray-dbgsym_1.2.1-1~bpo9+1_amd64.deb Size: 179768 SHA256: f808006e9ac9744993debcf08b9f94b91f1f1833f859fa80e134340a0dc8980e SHA1: 930db8456919e2541b68fe9e75a9575a77c165f3 MD5sum: 8782e34bafdc8ed7a883186afa330d4c Description: Debug symbols for python-tinyarray Build-Ids: c0f7e3ba29161256c1449d6c65a8c5fdac87046c Package: python3-kwant Source: kwant Version: 1.4.1-1~bpo9+1 Architecture: amd64 Maintainer: Christoph Groth Installed-Size: 3556 Depends: python3 (<< 3.6), python3 (>= 3.5~), python3-numpy (>= 1.11), python3-scipy (>= 0.17), python3-tinyarray (>= 1.2), python3:any (>= 3.5~), libc6 (>= 2.14), libgfortran3 (>= 4.3), libmumps-4.10.0, libmumps-scotch-4.10.0 Recommends: python3-matplotlib (>= 1.5.1), python3-sympy (>= 0.7.6) Suggests: python-kwant-doc, python3-pytest (>= 2.8) Homepage: http://kwant-project.org/ Priority: optional Section: python Filename: pool/main/k/kwant/python3-kwant_1.4.1-1~bpo9+1_amd64.deb Size: 825058 SHA256: 264e685b34e2a3ffdec8f0aef4550c52b645e9da5333eef411bd1d939cba08a0 SHA1: 14add89b399d8f6937648b7e80330ba76bc996f0 MD5sum: 3f9d476a29529d9ad26de00c80049836 Description: Python package for numerical quantum transport calculations Kwant is a Python package for numerical quantum transport calculations. It exposes the natural concepts of the theory of quantum transport (lattices, symmetries, electrodes, orbital/spin/electron-hole degrees of freedom) in a simple and transparent way. Kwant offers direct support for calculations of transport properties (conductance, noise, scattering matrix), dispersion relations, modes, wave functions, various Green’s functions, and out-of-equilibrium local quantities. Other computations involving tight-binding Hamiltonians can be implemented easily. . This package contains Kwant for Python 3. 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The multi-dimensional arrays it provides are best thought of as (possibly nested) tuples of numbers that, unlike Python's built-in tuples, support mathematical operations. Like tuples, tinyarrays are hashable and immutable and thus can be used as dictionary keys. The module's interface is a subset of that of NumPy and hence should be familiar to many Python programmers. Tinyarray has been heavily optimized for small arrays: For example, common operations on 1-d arrays of length 3 run 3-7 times faster than with NumPy. When storing many small arrays, memory consumption is reduced by a factor of 3. In summary, Tinyarray is a more efficient alternative to NumPy when many separate small numerical arrays are to be used. . This package installs the library for Python 3. 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