Package: caffe-cpu Priority: optional Section: metapackages Installed-Size: 36 Maintainer: Debian Science Maintainers Architecture: amd64 Source: caffe Version: 1.0.0~rc4-1~pn1 Depends: caffe-tools-cpu (= 1.0.0~rc4-1~pn1), python3-caffe-cpu (= 1.0.0~rc4-1~pn1), libcaffe-cpu1 (= 1.0.0~rc4-1~pn1), libopenblas-base | libatlas3-base | libblas.so.3 Suggests: libcaffe-cpu-dev (= 1.0.0~rc4-1~pn1), caffe-doc (= 1.0.0~rc4-1~pn1) Conflicts: caffe-cuda Filename: pool/main/c/caffe/caffe-cpu_1.0.0~rc4-1~pn1_amd64.deb Size: 7280 MD5sum: d73c501b1bcf8b0865997dfc2fd5819d SHA1: cdda02e434e4962c403b386d36106e42836dd1e5 SHA256: de9045634a7c3cad5b680ada0b9b771d11375e72e2cc017abfd3450aaab13ccf Description: Fast, open framework for Deep Learning (Meta) Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors. . This metapackage pulls CPU_ONLY version of caffe: * caffe-tools-cpu * libcaffe-cpu* * python3-caffe-cpu And suggests these packages: * libcaffe-cpu-dev * caffe-doc . Note, this CPU_ONLY version cannot co-exist with the CUDA version. Homepage: http://caffe.berkeleyvision.org Package: caffe-doc Priority: optional Section: doc Installed-Size: 17014 Maintainer: Debian Science Maintainers Architecture: all Source: caffe Version: 1.0.0~rc4-1~pn1 Filename: pool/main/c/caffe/caffe-doc_1.0.0~rc4-1~pn1_all.deb Size: 7891846 MD5sum: 7a10059767df45fa303588c292903207 SHA1: 6e777a9121c361d0c575e99644c2d93f31c82f4d SHA256: 936729c78351af32509f7285973e79b662ce00f091aca4f80464430592ba14f2 Description: Caffe's doxygen docs and examples Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors. . This package contains doxygen documents (both HTML version and PDF version) and some other documents and examples. Homepage: http://caffe.berkeleyvision.org Multi-Arch: foreign Package: caffe-tools-cpu Priority: optional Section: science Installed-Size: 478 Maintainer: Debian Science Maintainers Architecture: amd64 Source: caffe Version: 1.0.0~rc4-1~pn1 Depends: libcaffe-cpu1 (= 1.0.0~rc4-1~pn1), libboost-python1.55.0, libboost-system1.55.0, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgflags2, libgoogle-glog0, libleveldb1, libopencv-core2.4, libopencv-highgui2.4, libopencv-imgproc2.4, libprotobuf9, libpython3.4 (>= 3.4.2~rc1), libstdc++6 (>= 4.9) Conflicts: caffe-tools-cuda Filename: pool/main/c/caffe/caffe-tools-cpu_1.0.0~rc4-1~pn1_amd64.deb Size: 107328 MD5sum: 6da865ee8229aa4c614e0ff66210577b SHA1: 5c97ed17e652c4cdb1dddb241b098177487ad713 SHA256: 689dd218c1c95c3e6d62e034f4669f294bc25be4f6a86254e349069c28e71ad6 Description: Tools for fast, open framework for Deep Learning (CPU_ONLY) Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors. . It contains caffe executables, configured as CPU_ONLY. Multi-Arch: foreign Homepage: http://caffe.berkeleyvision.org Package: ecgpuwave Priority: optional Section: science Installed-Size: 212 Maintainer: Benjamin Moody Architecture: amd64 Version: 1.3.3-0~pn1 Depends: libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.6), libquadmath0 (>= 4.6), libwfdb10 (>= 10.5.11) Filename: pool/main/e/ecgpuwave/ecgpuwave_1.3.3-0~pn1_amd64.deb Size: 61062 MD5sum: cae7d6435d4381d1c01ac5ee56ab6d4d SHA1: 355d75435b2be7a1bc7891cdeb3de8e1b480cc25 SHA256: c691082099166ae9c7185aaf71497ca60b86f0136ee663fbc17f1ddb5dc88253 Description: QRS detector and waveform limit locator ecgpuwave analyses an ECG signal, detecting the QRS complexes and locating the beginning, peak, and end of the P, QRS, and ST-T waveforms. The QRS detector is based on the algorithm of Pan and Tompkins with some improvements that make use of slope information. Optionally, QRS annotations can be provided as input, permitting the use of external QRS detectors such as sqrs or manually-edited annotations. Package: islandhack Priority: optional Section: net Installed-Size: 84 Maintainer: Benjamin Moody Architecture: amd64 Version: 0.1-0+pn1 Depends: libwww-perl, libhttp-daemon-perl, openssl, libc6 (>= 2.4), perl Pre-Depends: multiarch-support Filename: pool/main/i/islandhack/islandhack_0.1-0+pn1_amd64.deb Size: 14376 MD5sum: f7c42a14a58f137510ae5323f393fab5 SHA1: 0871e66db5ea905c32f58c6041ce0cf3178979e4 SHA256: c4a905bec2334e9a2e3ec40d27de37f2c9727ea70a54814ee1838dab2f91d34c Description: run a program with simulated WWW access islandhack runs a program in an environment that provides simulated access to HTTP and FTP sites, serving all files from a local cache. It can be used to run programs that expect to be able to download particular files from the Web, without actually relying on remote servers or connecting to an outside network at all. Package: libcaffe-cpu-dev Priority: optional Section: libdevel Installed-Size: 1274 Maintainer: Debian Science Maintainers Architecture: amd64 Source: caffe Version: 1.0.0~rc4-1~pn1 Depends: libcaffe-cpu1 (= 1.0.0~rc4-1~pn1) Suggests: caffe-doc Conflicts: libcaffe-cuda-dev Filename: pool/main/c/caffe/libcaffe-cpu-dev_1.0.0~rc4-1~pn1_amd64.deb Size: 121732 MD5sum: 67fd7b20a119d5b247cea8efdc5fe18e SHA1: ccfb4002bad846d38cded6e76b2fc4a448d70858 SHA256: c9e29fab2991217e897e1be987839984b32eb219924f699045cf013598900886 Description: development files for Caffe (CPU_ONLY) Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors. . It contains development files of caffe. Homepage: http://caffe.berkeleyvision.org Multi-Arch: foreign Package: libcaffe-cpu1 Priority: optional Section: libs Installed-Size: 3779 Maintainer: Debian Science Maintainers Architecture: amd64 Source: caffe Version: 1.0.0~rc4-1~pn1 Depends: libblas3 | libblas.so.3, libboost-python1.55.0, libboost-system1.55.0, libboost-thread1.55.0, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgflags2, libgoogle-glog0, libhdf5-8, libleveldb1, liblmdb0 (>= 0.9.7), libopencv-core2.4, libopencv-highgui2.4, libopencv-imgproc2.4, libprotobuf9, libpython3.4 (>= 3.4.2~rc1), libstdc++6 (>= 4.9) Conflicts: libcaffe-cuda1 Filename: pool/main/c/caffe/libcaffe-cpu1_1.0.0~rc4-1~pn1_amd64.deb Size: 791776 MD5sum: 1f3a0b0e3da34f132a9385faf8835177 SHA1: 1988a5f764dbca144fc882bd4720816f54322d67 SHA256: 5f19b1770f6b1e2830e32ec89fc6e2ca5eff7e15587bfa7349b149b563b77257 Description: library of Caffe, deep learning framework (CPU_ONLY) Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors. . It contains caffe shared library, configured as CPU_ONLY. Multi-Arch: same Homepage: http://caffe.berkeleyvision.org Package: libprotobuf-dev Priority: optional Section: libdevel Installed-Size: 8541 Maintainer: Debian protobuf maintainers Architecture: amd64 Source: protobuf Version: 3.2.0-0~pn1 Depends: zlib1g-dev, libprotobuf10 (= 3.2.0-0~pn1), libprotobuf-lite10 (= 3.2.0-0~pn1) Filename: pool/main/p/protobuf/libprotobuf-dev_3.2.0-0~pn1_amd64.deb Size: 1010688 MD5sum: c2cdcc6e40e52efff6761bc33d9ce8b9 SHA1: d1283424e212c6b77ab885efec6dc20f8e5bf9a4 SHA256: 1e6089d6b8536245bd79cc201b279ccd4c7196ecbe29af98ae45a7ce2fe8a171 Description: protocol buffers C++ library (development files) Protocol buffers are a flexible, efficient, automated mechanism for serializing structured data - similar to XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. You can even update your data structure without breaking deployed programs that are compiled against the "old" format. . Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats. . This package contains the development headers and static libraries needed for writing C++ applications. Homepage: https://github.com/google/protobuf/ Multi-Arch: same Package: libprotobuf-java Priority: optional Section: java Installed-Size: 775 Maintainer: Debian protobuf maintainers Architecture: all Source: protobuf Version: 3.2.0-0~pn1 Filename: pool/main/p/protobuf/libprotobuf-java_3.2.0-0~pn1_all.deb Size: 666730 MD5sum: 55da45abff2e02f2b9d5201dcfa2b71b SHA1: d74e6f540384d66f73f5afccb06e15df578ea5d1 SHA256: 346702d43b3ecafa64823795ca8227b79078ca22b5b7e937fd22f50fe5718b22 Description: Java bindings for protocol buffers Protocol buffers are a flexible, efficient, automated mechanism for serializing structured data - similar to XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. You can even update your data structure without breaking deployed programs that are compiled against the "old" format. . Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats. . This package contains the Java bindings for the protocol buffers. You will need the protoc tool (in the protobuf-compiler package) to compile your definition to Java classes, and then the modules in this package will allow you to use those classes in your programs. Homepage: https://github.com/google/protobuf/ Package: libprotobuf-lite10 Priority: optional Section: libs Installed-Size: 416 Maintainer: Debian protobuf maintainers Architecture: amd64 Source: protobuf Version: 3.2.0-0~pn1 Depends: libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.9), zlib1g (>= 1:1.1.4) Filename: pool/main/p/protobuf/libprotobuf-lite10_3.2.0-0~pn1_amd64.deb Size: 132144 MD5sum: 0cb744883f4442931814513cb09c2aa2 SHA1: e1aad028517697f71f80e22170398b84cd62c051 SHA256: e623ae05c0c3d9ab8e48a27a65c3be8926426c428b6cd3a2c3545142e7502049 Description: protocol buffers C++ library (lite version) Protocol buffers are a flexible, efficient, automated mechanism for serializing structured data - similar to XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. You can even update your data structure without breaking deployed programs that are compiled against the "old" format. . Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats. . This package contains the runtime library needed for C++ applications whose message definitions have the "lite runtime" optimization setting. Homepage: https://github.com/google/protobuf/ Multi-Arch: same Package: libprotobuf10 Priority: optional Section: libs Installed-Size: 2744 Maintainer: Debian protobuf maintainers Architecture: amd64 Source: protobuf Version: 3.2.0-0~pn1 Depends: libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.9), zlib1g (>= 1:1.1.4) Filename: pool/main/p/protobuf/libprotobuf10_3.2.0-0~pn1_amd64.deb Size: 726254 MD5sum: cd6055b03c312136d059b93dbd481b64 SHA1: 7c14ab51f5b589dff435b9b2ecf37ff42ef14aab SHA256: 8da6b0c3e8a65bb3bd2e14b6c8a6fbac8d67fa290f285d97f652891d14e2619f Description: protocol buffers C++ library Protocol buffers are a flexible, efficient, automated mechanism for serializing structured data - similar to XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. You can even update your data structure without breaking deployed programs that are compiled against the "old" format. . Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats. . This package contains the runtime library needed for C++ applications. Multi-Arch: same Homepage: https://github.com/google/protobuf/ Package: libprotoc-dev Priority: optional Section: libdevel Installed-Size: 5618 Maintainer: Debian protobuf maintainers Architecture: amd64 Source: protobuf Version: 3.2.0-0~pn1 Replaces: libprotobuf-dev (<< 2.1.0) Depends: libprotoc10 (= 3.2.0-0~pn1), libprotobuf-dev (= 3.2.0-0~pn1) Breaks: libprotobuf-dev (<< 2.1.0) Filename: pool/main/p/protobuf/libprotoc-dev_3.2.0-0~pn1_amd64.deb Size: 731010 MD5sum: 7ce0cc0a528487101a2bbfd0c2eee444 SHA1: e82fa17ef6540f5258a19ac02536d4f7d131dad3 SHA256: 2055f91df6b2d77ade2c6b37e3b243d3d4b7ed1d3b5c157534066680865cca90 Description: protocol buffers compiler library (development files) Protocol buffers are a flexible, efficient, automated mechanism for serializing structured data - similar to XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. You can even update your data structure without breaking deployed programs that are compiled against the "old" format. . Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats. . This package contains the development headers and static library needed for writing protobuf compilers. Multi-Arch: same Homepage: https://github.com/google/protobuf/ Package: libprotoc10 Priority: optional Section: libs Installed-Size: 2546 Maintainer: Debian protobuf maintainers Architecture: amd64 Source: protobuf Version: 3.2.0-0~pn1 Depends: libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libprotobuf10, libstdc++6 (>= 4.9), zlib1g (>= 1:1.1.4) Filename: pool/main/p/protobuf/libprotoc10_3.2.0-0~pn1_amd64.deb Size: 664220 MD5sum: 42908cd7839a0bf167165bb661a44853 SHA1: f435f62f61c7bd576846e11c82bc971aa672934e SHA256: c206df2888de8da652439516326881774b66366d000dffad21bbbc7178d3c269 Description: protocol buffers compiler library Protocol buffers are a flexible, efficient, automated mechanism for serializing structured data - similar to XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. You can even update your data structure without breaking deployed programs that are compiled against the "old" format. . Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats. . This package contains the runtime library needed for the protocol buffer compiler. Homepage: https://github.com/google/protobuf/ Multi-Arch: same Package: libwfdb-dev Priority: optional Section: libdevel Installed-Size: 347 Maintainer: Benjamin Moody Architecture: amd64 Source: wfdb Version: 10.5.25~pre2-0~pn1 Depends: libwfdb10 (= 10.5.25~pre2-0~pn1), libc6 (>= 2.3.4) Filename: pool/main/w/wfdb/libwfdb-dev_10.5.25~pre2-0~pn1_amd64.deb Size: 204208 MD5sum: fb542302881fa1d0ab4b6e28dc70086f SHA1: dbf3aceb08548a205c954d1950ffd44b148583d3 SHA256: c5a1c702eba253cc3b650aef24a2d971a980a18fb50e4d959f50e33844421d80 Description: WFDB developer's toolkit The WFDB (Waveform Database) library supports creating, reading, and annotating digitized signals in a wide variety of formats. Input can be from local files or directly from web or FTP servers. Although created for use with physiologic signals such as those in PhysioBank (http://www.physionet.org/physiobank/), the WFDB library supports a broad range of general-purpose signal processing applications. . This package includes files needed to develop new WFDB applications in C, C++, and Fortran, examples in C and in Fortran, and miscellaneous documentation. Package: libwfdb10 Priority: optional Section: libs Installed-Size: 146 Maintainer: Benjamin Moody Architecture: amd64 Source: wfdb Version: 10.5.25~pre2-0~pn1 Depends: libc6 (>= 2.14), libcurl3-gnutls (>= 7.16.2) Pre-Depends: multiarch-support Recommends: wfdb Filename: pool/main/w/wfdb/libwfdb10_10.5.25~pre2-0~pn1_amd64.deb Size: 44596 MD5sum: 3cd5aa321a9e88ba5a7b5c3f0c53b3d4 SHA1: 854f54ff2a20237aafd61532775cff32183b7b7d SHA256: 3fb504c3ecef0915429c2650bda7fad32c45641ec445676bfabc4be9220c10b9 Description: Waveform Database library The WFDB (Waveform Database) library supports creating, reading, and annotating digitized signals in a wide variety of formats. Input can be from local files or directly from web or FTP servers. Although created for use with physiologic signals such as those in PhysioBank (http://www.physionet.org/physiobank/), the WFDB library supports a broad range of general-purpose signal processing applications. . This package contains the shared library. Multi-Arch: same Package: protobuf-compiler Priority: optional Section: devel Installed-Size: 139 Maintainer: Debian protobuf maintainers Architecture: amd64 Source: protobuf Version: 3.2.0-0~pn1 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libprotobuf10, libprotoc10 (= 3.2.0-0~pn1), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Filename: pool/main/p/protobuf/protobuf-compiler_3.2.0-0~pn1_amd64.deb Size: 55770 MD5sum: 52d0937e13a272139e2196a3d248e158 SHA1: f64e32c04acddf207c1638bc4b0e35a4c1ccc2e7 SHA256: cc15c16106272feccc807c3b795c8f458cc99a7a69088471cd43bbce3c9e15b5 Description: compiler for protocol buffer definition files Protocol buffers are a flexible, efficient, automated mechanism for serializing structured data - similar to XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. You can even update your data structure without breaking deployed programs that are compiled against the "old" format. . Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats. . This package contains the protocol buffer compiler that is used for translating from .proto files (containing the definitions) to the language binding for the supported languages. Homepage: https://github.com/google/protobuf/ Multi-Arch: foreign Package: pypy-six Priority: optional Section: python Installed-Size: 53 Maintainer: Colin Watson Architecture: all Source: six Version: 1.10.0-3 Depends: pypy Filename: pool/main/s/six/pypy-six_1.10.0-3_all.deb Size: 14358 MD5sum: bab5e642c3d69e7cd043d5dea3bd0a77 SHA1: 2200ccca3d27395cb2a7dd5859b0112d025cce91 SHA256: 4674214520b03a2019ac9e7687b609ca286d82819e871bc5e750e825e3cd40d0 Description: Python 2 and 3 compatibility library (PyPy interface) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six on the PyPy module path. It is complemented by python-six and python3-six. Multi-Arch: foreign Homepage: https://pythonhosted.org/six/ Package: python-decorator Priority: optional Section: python Installed-Size: 43 Maintainer: Debian Python Modules Team Architecture: all Version: 4.0.11-1 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool/main/p/python-decorator/python-decorator_4.0.11-1_all.deb Size: 13262 MD5sum: 05d9d9ea43b2eb5c93fe168be65eb71c SHA1: ab413ac46d76e4134805582531146927df2a5467 SHA256: d4f72b36358c1db27b7eb20b0727fdee6d5cdbd409f8a343dacc9badb17a3e15 Description: simplify usage of Python decorators by programmers Python 2.4 decorators have significantly changed the way Python programs are structured. * decorators help reduce boilerplate code; * decorators help the separation of concerns; * decorators enhance readability and maintainability; * decorators are very explicit. Still, as of now, writing custom decorators correctly requires some experience and is not as easy as it could be. For instance, typical implementations of decorators involve nested functions and we all know that flat is better than nested. The aim of the decorator module it to simplify the usage of decorators for the average programmer and to popularize decorators usage giving examples of useful decorators, such as memoize, tracing, redirecting_stdout, locked, etc. Enhances: python-pylons Homepage: https://pypi.python.org/pypi/decorator Package: python-funcsigs Priority: optional Section: python Installed-Size: 93 Maintainer: PKG OpenStack Architecture: all Version: 1.0.2-3~bpo8+1 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-funcsigs-doc Filename: pool/main/p/python-funcsigs/python-funcsigs_1.0.2-3~bpo8+1_all.deb Size: 13530 MD5sum: 62fdd2ca08a3e9e6d08f9764e373e433 SHA1: a7c9e26f870af74edc988e82289a2f535fefc4be SHA256: 194768cfba101146b36c154289780f1085d0f4735e2927232a9df255af4191c7 Description: function signatures from PEP362 - Python 2.7 funcsigs is a backport of the PEP 362 function signature features from Python 3.3's inspect module. The backport is compatible with Python 2.6, 2.7 as well as 3.2 and up. . This package contains the Python 2.7 module. Homepage: http://funcsigs.readthedocs.org Package: python-funcsigs-doc Priority: optional Section: doc Installed-Size: 135 Maintainer: PKG OpenStack Architecture: all Source: python-funcsigs Version: 1.0.2-3~bpo8+1 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool/main/p/python-funcsigs/python-funcsigs-doc_1.0.2-3~bpo8+1_all.deb Size: 25542 MD5sum: de9c848e1c214e7539640af51ada526c SHA1: 9d5db1eec90429a8eb74b7b45b8a93b952287f60 SHA256: 9432c21b9cc83d34fd99fa45b269dc89c38296211c790252f4c65649b1f0e345 Description: function signatures from PEP362 - doc funcsigs is a backport of the PEP 362 function signature features from Python 3.3's inspect module. The backport is compatible with Python 2.6, 2.7 as well as 3.2 and up. . This package contains the documentation. Homepage: http://funcsigs.readthedocs.org Package: python-joblib Priority: optional Section: python Installed-Size: 488 Maintainer: Yaroslav Halchenko Architecture: all Source: joblib Version: 0.10.3+git55-g660fe5d-1 Depends: python (>= 2.7), python (<< 2.8), python:any (>= 2.6.6-7~) Recommends: python-numpy, python-pytest, python-simplejson Filename: pool/main/j/joblib/python-joblib_0.10.3+git55-g660fe5d-1_all.deb Size: 115320 MD5sum: d93769592d85a1fcd3b91bf3ca499451 SHA1: 7d7c78ec82869ebf14eaacef60588a298491ab87 SHA256: 3c108e6648e6f069dec75144a805f71fd4c8a2a6a9cecdb6edf409804025fa60 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. . This package contains the Python 2 version. Homepage: http://packages.python.org/joblib/ Package: python-keras Priority: optional Section: science Installed-Size: 1146 Maintainer: Daniel Stender Architecture: all Source: keras Version: 2.0.2+git20170403+64d24215-0~pn1 Depends: python-numpy, python-scipy, python-h5py, python-six, python-theano, python-yaml, python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool/main/k/keras/python-keras_2.0.2+git20170403+64d24215-0~pn1_all.deb Size: 151218 MD5sum: c76bcff79eb1374236a36d21f54bb756 SHA1: 9ef93168f50d980e3c8fc76102eca460133c5087 SHA256: 86e5ce571ae1524b6cbd8c0a93084776d3edf7c0d93f06f6cc253b856bf97fcc Description: high-level framework for deep learning (Python 2) Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation. . Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing). . It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian, but coming up). Homepage: http://keras.io/ Package: python-mock Priority: extra Section: python Installed-Size: 315 Maintainer: Debian Python Modules Team Architecture: all Version: 2.0.0-3~bpo8+1 Depends: python-funcsigs (>= 1), python-pbr (>= 1.3), python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-mock-doc Filename: pool/main/p/python-mock/python-mock_2.0.0-3~bpo8+1_all.deb Size: 60174 MD5sum: e168812d0b22617848ec30f27572174b SHA1: 09d668c8427b4c8f1a5d1d565d32e067a9897a8b SHA256: 775e0e3c7fd75dafe204561550a0d9b171d0c6704513f94693d0f2e031f9c599 Description: Mocking and Testing Library mock provides a core mock.Mock class that is intended to reduce the need to create a host of trivial stubs throughout your test suite. After performing an action, you can make assertions about which methods / attributes were used and arguments they were called with. You can also specify return values and set specific attributes in the normal way. Homepage: https://github.com/testing-cabal/mock Package: python-mock-doc Priority: extra Section: doc Installed-Size: 217 Maintainer: Debian Python Modules Team Architecture: all Source: python-mock Version: 2.0.0-3~bpo8+1 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool/main/p/python-mock/python-mock-doc_2.0.0-3~bpo8+1_all.deb Size: 53502 MD5sum: 6c97b655da6b05a553cd180c69a79e89 SHA1: b1d2cea71582137180e4eed1f602a77f675603e5 SHA256: 35341f56958c4140590bdf237e97a063484eaf5b88d62278fdb6f4057bbd50ee Description: Mocking and Testing Library (Documentation) mock provides a core mock.Mock class that is intended to reduce the need to create a host of trivial stubs throughout your test suite. After performing an action, you can make assertions about which methods / attributes were used and arguments they were called with. You can also specify return values and set specific attributes in the normal way. . This package contains the documentation. Homepage: https://github.com/testing-cabal/mock Package: python-nose-parameterized Priority: optional Section: python Installed-Size: 74 Maintainer: PKG OpenStack Architecture: all Version: 0.6.0-0~pn0 Depends: python (>= 2.7), python (<< 2.8) Pre-Depends: dpkg (>= 1.15.6~) Suggests: python-nose-parameterized-doc Filename: pool/main/p/python-nose-parameterized/python-nose-parameterized_0.6.0-0~pn0_all.deb Size: 11374 MD5sum: df98ae4ae8ad6016f2bab8c0ef00e674 SHA1: 3ae7b9ff8582f373dfc2fcd75fc34137a46eb29b SHA256: 3054901b7a08cf1c9f5c0bde9d4a889a3dba868c08c2ea92fbbde3a7f922d706 Description: decorator for parameterized testing with Nose - Python 2.x nose-parameterized is a decorator for parameterized testing of Python code with nose. . The provided decorators make it simple to pass lists, iterables, tuples or callables to the test functions. This allows you to separate the data from the test without having to subclass unittest.testcase. . This package contains the Python 2.x module. Homepage: https://github.com/wolever/nose-parameterized Package: python-numpy Priority: optional Section: python Installed-Size: 10531 Maintainer: Sandro Tosi Architecture: amd64 Version: 1:1.12.0-2~pn0 Provides: python-f2py, python-numpy-abi9, python-numpy-api10, python-numpy-dev, python2.7-numpy Depends: python (<< 2.8), python (>= 2.7~), python2.7, python:any (>= 2.7.5-5~), libblas3 | libblas.so.3, libc6 (>= 2.14), liblapack3 | liblapack.so.3 Suggests: gcc (>= 4:4.6.1-5), gfortran, python-dev, python-nose (>= 1.0), python-numpy-dbg, python-numpy-doc Filename: pool/main/p/python-numpy/python-numpy_1.12.0-2~pn0_amd64.deb Size: 1918544 MD5sum: 555b94070deadd78bbf25b5878ade6ca SHA1: 0d72d9220319b896e46f3d28e22e35d3cb4982a5 SHA256: f885c61127f822c91e25cdd930f68252daaec5f50cd357601a1b8a7d5c717927 Description: Numerical Python adds a fast array facility to the Python language Numpy contains a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran code, and useful linear algebra, Fourier transform, and random number capabilities. . Numpy replaces the python-numeric and python-numarray modules which are now deprecated and shouldn't be used except to support older software. Homepage: http://www.numpy.org/ Package: python-numpy-dbg Priority: extra Section: debug Installed-Size: 25516 Maintainer: Sandro Tosi Architecture: amd64 Source: python-numpy Version: 1:1.12.0-2~pn0 Replaces: python-numpy (<< 1:1.7.1-1) Depends: python-dbg, python-numpy (= 1:1.12.0-2~pn0), libblas3 | libblas.so.3, libc6 (>= 2.14), liblapack3 | liblapack.so.3 Breaks: python-numpy (<< 1:1.7.1-1) Filename: pool/main/p/python-numpy/python-numpy-dbg_1.12.0-2~pn0_amd64.deb Size: 4178978 MD5sum: c4df16a64c3dcee5c31fae8ab1fea3e6 SHA1: c5945f0499d06b80084540386089ecd9b6c15a0f SHA256: 8863a27df519765cd2d0cacbe6a88f1acf09088fde749738481ee158cb6419e5 Description: Fast array facility to the Python language (debug extension) Numpy contains a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran code, and useful linear algebra, Fourier transform, and random number capabilities. . Numpy replaces the python-numeric and python-numarray modules which are now deprecated and shouldn't be used except to support older software. . This package contains the extension built for the Python debug interpreter. Homepage: http://www.numpy.org/ Package: python-pbr Priority: optional Section: python Installed-Size: 274 Maintainer: PKG OpenStack Architecture: all Version: 1.8.0-4.1~bpo8+1 Depends: python-pkg-resources, python-six (>= 1.9.0), python2.7, python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool/main/p/python-pbr/python-pbr_1.8.0-4.1~bpo8+1_all.deb Size: 47326 MD5sum: b4d599f973e21fd772d57a1ae6334b09 SHA1: d7c1505bf40fe29cb1a4821296e5a0634349e292 SHA256: c451897e9f9d37a20222fc19cc223b163718bd1b89868caf54e27295bcfc83bb Description: inject useful and sensible default behaviors into setuptools - Python 2.x PBR (Python Build Reasonableness) is a library that injects some useful and sensible default behaviors into your setuptools run. PBR can: * Manage version number based on git revisions and tags (Version file). * Generate AUTHORS file from git log * Generate ChangeLog from git log * Generate Sphinx autodoc stub files for your whole module * Store your dependencies in a pip requirements file * Use your README file as a long_description * Smartly find packages under your root package . PBR is only mildly configurable. The basic idea is that there's a decent way to run things and if you do, you should reap the rewards, because then it's simple and repeatable. If you want to do things differently, cool! But you've already got the power of Python at your fingertips, so you don't really need PBR. . PBR builds on top of `d2to1` to provide for declarative configuration. It then filters the `setup.cfg` data through a setup hook to fill in default values and provide more sensible behaviors. . This package provides support for Python 2.x. Homepage: http://pypi.python.org/pypi/pbr Package: python-pbr-doc Priority: optional Section: doc Installed-Size: 216 Maintainer: PKG OpenStack Architecture: all Source: python-pbr Version: 1.8.0-4.1~bpo8+1 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool/main/p/python-pbr/python-pbr-doc_1.8.0-4.1~bpo8+1_all.deb Size: 48156 MD5sum: 26c2a43a0dd668d8816b89fd31b2c981 SHA1: 215d892c056a345b0e8ec7ac380fa37542ec64a8 SHA256: 7a775070b01eb9c2b267036e72838d106aaaa244879d7f5e8f32ba544136c82e Description: inject useful and sensible default behaviors into setuptools - doc PBR (Python Build Reasonableness) is a library that injects some useful and sensible default behaviors into your setuptools run. PBR can: * Manage version number based on git revisions and tags (Version file). * Generate AUTHORS file from git log * Generate ChangeLog from git log * Generate Sphinx autodoc stub files for your whole module * Store your dependencies in a pip requirements file * Use your README file as a long_description * Smartly find packages under your root package . PBR is only mildly configurable. The basic idea is that there's a decent way to run things and if you do, you should reap the rewards, because then it's simple and repeatable. If you want to do things differently, cool! But you've already got the power of Python at your fingertips, so you don't really need PBR. . PBR builds on top of `d2to1` to provide for declarative configuration. It then filters the `setup.cfg` data through a setup hook to fill in default values and provide more sensible behaviors. . This package provides the documentation. Homepage: http://pypi.python.org/pypi/pbr Package: python-protobuf Priority: optional Section: python Installed-Size: 2432 Maintainer: Debian protobuf maintainers Architecture: amd64 Source: protobuf Version: 3.2.0-0~pn1 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libprotobuf10, libstdc++6 (>= 4.3), python (<< 2.8), python (>= 2.7~), python-pkg-resources, python-six (>= 1.9), python:any (>= 2.7.5-5~) Filename: pool/main/p/protobuf/python-protobuf_3.2.0-0~pn1_amd64.deb Size: 307742 MD5sum: ced4c8b2c3c105caec3f437a8a75a38c SHA1: ec53feae3df89f9504b24e4dda38062c42df698d SHA256: 0eefebecc0097f07346eb81b8911398661cdff147b67ddd4e4567b1ce9bdd8e7 Description: Python bindings for protocol buffers Protocol buffers are a flexible, efficient, automated mechanism for serializing structured data - similar to XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. You can even update your data structure without breaking deployed programs that are compiled against the "old" format. . Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats. . This package contains the Python bindings for the protocol buffers. You will need the protoc tool (in the protobuf-compiler package) to compile your definition to Python classes, and then the modules in this package will allow you to use those classes in your programs. Homepage: https://github.com/google/protobuf/ Package: python-scikits-learn Priority: optional Section: oldlibs Installed-Size: 95 Maintainer: NeuroDebian Team Architecture: all Source: scikit-learn Version: 0.18-5~pn0 Depends: python-sklearn Filename: pool/main/s/scikit-learn/python-scikits-learn_0.18-5~pn0_all.deb Size: 69668 MD5sum: 991ed13bd9cc71c27707bb486f57dc58 SHA1: e0ca238a35ec7213eddeadb1460d5f5f0edce69b SHA256: 9b94d4c0fc515d938d62ce71bcf172988499230f1dd83a06d87d2dc4f05c1db2 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. Homepage: http://scikit-learn.sourceforge.net Package: python-scipy Priority: extra Section: python Installed-Size: 38147 Maintainer: Debian Python Modules Team Architecture: amd64 Version: 0.18.1-2~pn1 Provides: python2.7-scipy Depends: python-decorator (>= 4.0.11), python-numpy (>= 1:1.8.0), python-numpy-abi9, python (<< 2.8), python (>= 2.7~), python:any (>= 2.7.5-5~), libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.6), liblapack3 | liblapack.so.3, libquadmath0 (>= 4.6), libstdc++6 (>= 4.1.1) Recommends: g++ | c++-compiler, python-dev, python-imaging Suggests: python-scipy-doc Filename: pool/main/p/python-scipy/python-scipy_0.18.1-2~pn1_amd64.deb Size: 9130876 MD5sum: d90bc1662425cb149d532eb4beb8f5ba SHA1: 60689ce9e55079047bdb476dc5e04b5534864c59 SHA256: 9b4a0ecc8b757159d6e931a3f69c51ff576c442694cc342198ae548a88603314 Description: scientific tools for Python SciPy supplements the popular NumPy module (python-numpy package), gathering a variety of high level science and engineering modules together as a single package. . SciPy is a set of Open Source scientific and numeric tools for Python. It currently supports special functions, integration, ordinary differential equation (ODE) solvers, gradient optimization, genetic algorithms, parallel programming tools, an expression-to-C++ compiler for fast execution, and others. Homepage: http://www.scipy.org/ Package: python-scipy-dbg Priority: extra Section: debug Installed-Size: 108678 Maintainer: Debian Python Modules Team Architecture: amd64 Source: python-scipy Version: 0.18.1-2~pn1 Depends: python-dbg (<< 2.8), python-numpy-dbg (>= 1:1.5.1), python-scipy (= 0.18.1-2~pn1), python-numpy (>= 1:1.8.0), python-numpy-abi9, python-dbg (>= 2.7~), libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.6), liblapack3 | liblapack.so.3, libquadmath0 (>= 4.6), libstdc++6 (>= 4.1.1) Filename: pool/main/p/python-scipy/python-scipy-dbg_0.18.1-2~pn1_amd64.deb Size: 17013700 MD5sum: b8f2c16973916754b08baa287e381eaa SHA1: 8574595585ddaa8554f36e8b4742102bc3e81b58 SHA256: 66e84b7a5e7d17e0b7deefae501ecd27ab11ae5fe03a7d7b1221e691c7a2f979 Description: scientific tools for Python - debugging symbols SciPy supplements the popular NumPy module (python-numpy package), gathering a variety of high level science and engineering modules together as a single package. . SciPy is a set of Open Source scientific and numeric tools for Python. It currently supports special functions, integration, ordinary differential equation (ODE) solvers, gradient optimization, genetic algorithms, parallel programming tools, an expression-to-C++ compiler for fast execution, and others. . This package provides debugging symbols for python-scipy. Homepage: http://www.scipy.org/ Package: python-scipy-doc Priority: extra Section: doc Installed-Size: 46137 Maintainer: Debian Python Modules Team Architecture: all Source: python-scipy Version: 0.18.1-2~pn1 Depends: libjs-jquery, libjs-mathjax, libjs-underscore, libjs-sphinxdoc (>= 1.0) Filename: pool/main/p/python-scipy/python-scipy-doc_0.18.1-2~pn1_all.deb Size: 20455186 MD5sum: 445f5ff26167fdfe76e5602f01562ccb SHA1: a844860b3e6089846a81ae983b8bf5a105e4a604 SHA256: b2ac79612f0f33e5598bab33be4910ef45bb0ce816c8815515be6ca04f2299f0 Description: scientific library for Python - documentation SciPy supplements the popular NumPy module (python-numpy package), gathering a variety of high level science and engineering modules together as a single package. . SciPy is a set of Open Source scientific and numeric tools for Python. It currently supports special functions, integration, ordinary differential equation (ODE) solvers, gradient optimization, genetic algorithms, parallel programming tools, an expression-to-C++ compiler for fast execution, and others. . This package contains documentation for the SciPy library. Homepage: http://www.scipy.org/ Package: python-six Priority: optional Section: python Installed-Size: 56 Maintainer: Colin Watson Architecture: all Source: six Version: 1.10.0-3 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool/main/s/six/python-six_1.10.0-3_all.deb Size: 14358 MD5sum: 91c2c9528b727696f67b58b1cdb73003 SHA1: 2f21e7339e67bfdf0d9101666fadc0f99e0282fd SHA256: 547c1f63a8cf07d99a7a79da562a5a938bfaa08b292c1fa479afdebafbb955fa Description: Python 2 and 3 compatibility library (Python 2 interface) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six on the Python 2 module path. It is complemented by python3-six and pypy-six. Homepage: https://pythonhosted.org/six/ Multi-Arch: foreign Package: python-sklearn Priority: optional Section: python Installed-Size: 6543 Maintainer: NeuroDebian Team Architecture: all Source: scikit-learn Version: 0.18-5~pn0 Replaces: python-scikits-learn (<< 0.9~) Provides: python2.7-sklearn Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-sklearn-lib (>= 0.18-5~pn0), python-joblib (>= 0.9.2) Recommends: python-nose, python-matplotlib Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Breaks: python-scikits-learn (<< 0.9~) Filename: pool/main/s/scikit-learn/python-sklearn_0.18-5~pn0_all.deb Size: 1385252 MD5sum: 7fbddfdb17a474266c06d4e985bbdeff SHA1: 1298a38ca85a3110837e19e7afedf8fb4d8e0d4e SHA256: 747ae1f186e524c20c8544f1d192fa90aa2eb4fce83bf8ddbf416f8c93781a1b 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) Enhances: python-mdp, python-mvpa2 Homepage: http://scikit-learn.sourceforge.net Package: python-sklearn-doc Priority: optional Section: doc Installed-Size: 27326 Maintainer: NeuroDebian Team Architecture: all Source: scikit-learn Version: 0.18-5~pn0 Replaces: python-scikits-learn-doc Depends: libjs-jquery, libjs-underscore Suggests: python-sklearn Conflicts: python-scikits-learn-doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.18-5~pn0_all.deb Size: 5239564 MD5sum: 1ee2baab14f7520a239c26485704412c SHA1: e6731a69d55d9bf804b5f684f61b8b24b9bd08a2 SHA256: 5cd46625db58e61bd679c28f8f4318e4262f72adb01bb9e97a0eb1e2912f5b67 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Homepage: http://scikit-learn.sourceforge.net Package: python-sklearn-lib Priority: optional Section: python Installed-Size: 6279 Maintainer: NeuroDebian Team Architecture: amd64 Source: scikit-learn Version: 0.18-5~pn0 Replaces: python-scikits-learn-lib Provides: python2.7-sklearn-lib Depends: libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python-numpy (>= 1:1.8.0), python-numpy-abi9, python (<< 2.8), python (>= 2.7~) Conflicts: python-scikits-learn-lib Filename: pool/main/s/scikit-learn/python-sklearn-lib_0.18-5~pn0_amd64.deb Size: 1369482 MD5sum: 6759ac5134b5133bf56244b3639d1f1e SHA1: aca4deeee0adcc97a257ee1c998657662cefeafc SHA256: 1e6a84df2c75a802d09fba6d2ebf14cc8854344954a28aa05cd3489d25cd88ff 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. Homepage: http://scikit-learn.sourceforge.net Package: python-theano Priority: optional Section: python Installed-Size: 12261 Maintainer: Debian Science Maintainers Architecture: amd64 Source: theano Version: 0.9.0-0~pn1 Depends: python-numpy, python-scipy, python-six (>= 1.9.0), python:any (<< 2.8), python:any (>= 2.7.5-5~), python-dev, libblas-dev | libblas.so Recommends: python-pydot, python-nose, python-nose-parameterized, theano-doc Suggests: nvidia-cuda-toolkit, python-pycuda Filename: pool/main/t/theano/python-theano_0.9.0-0~pn1_amd64.deb Size: 2094604 MD5sum: 131c2342a777feaf427f2b0b1b554360 SHA1: e40e375ee8c00bc3e307a2808373a1baf011ae9d SHA256: 87c5a7c55d78955022140e1a569ff9299208202eb0aa8eafc5e8ef896d55e220 Description: CPU/GPU math expression compiler for Python Theano is a Python library that allows one to define and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It provides a high-level Numpy like expression language for functional description of calculation, rearranges expressions for speed and stability, and generates native machine instructions for fast calculation. Optionally, highly accelerated computations could be carried out on graphics cards processors. . This package contains Theano for Python 2. Homepage: http://www.deeplearning.net/software/theano/ Package: python-theano Priority: optional Section: python Installed-Size: 11062 Maintainer: Debian Science Maintainers Architecture: amd64 Source: theano Version: 0.8.2-6 Depends: python-numpy, python-scipy, python-six (>= 1.9.0), python:any (<< 2.8), python:any (>= 2.7.5-5~), python-dev, libblas-dev | libblas.so Recommends: python-pydot, python-nose, python-nose-parameterized, theano-doc Suggests: nvidia-cuda-toolkit, python-pycuda Filename: pool/main/t/theano/python-theano_0.8.2-6_amd64.deb Size: 1952444 MD5sum: 84d8548f1847f55c9ac626c7ac2b2191 SHA1: f5af752c81dc2a3da45701a1e402807c45664b5f SHA256: 1c8f1c49cb338d6f109b46ed7144c991ef0f30d05cefe5cab35f4e89e0012b00 Description: CPU/GPU math expression compiler for Python Theano is a Python library that allows one to define and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It provides a high-level Numpy like expression language for functional description of calculation, rearranges expressions for speed and stability, and generates native machine instructions for fast calculation. Optionally, highly accelerated computations could be carried out on graphics cards processors. . This package contains Theano for Python 2. Homepage: http://www.deeplearning.net/software/theano/ Package: python-wheel Priority: optional Section: python Installed-Size: 211 Maintainer: Barry Warsaw Architecture: all Source: wheel Version: 0.29.0-2 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-keyring, python-keyrings.alt, python-xdg Suggests: python-setuptools Filename: pool/main/w/wheel/python-wheel_0.29.0-2_all.deb Size: 51734 MD5sum: 80734705baab54b8e94563d6986f9950 SHA1: db36272b4883f66140921702e7e3eb13296f930f SHA256: 1f45b430e72523b3ac65985164cf574845dabcc88e01802f612a126fc5fb46a8 Description: built-package format for Python A wheel is a ZIP-format archive with a specially formatted filename and the `.whl` extension. It is designed to contain all the files for a PEP 376 compatible install in a way that is very close to the on-disk format. . The wheel project provides a `bdist_wheel` command for setuptools. Wheel files can be installed with `pip`. . This is the Python 2 compatible package. Homepage: https://bitbucket.org/pypa/wheel Package: python-wheel-common Priority: optional Section: python Installed-Size: 22 Maintainer: Barry Warsaw Architecture: all Source: wheel Version: 0.29.0-2 Depends: python3, python3-wheel Filename: pool/main/w/wheel/python-wheel-common_0.29.0-2_all.deb Size: 9680 MD5sum: 221170276147926b8309c6b4aaf9683e SHA1: 1357367d7e49530b19e38f8c02f39f5ab9e1076d SHA256: 1dd45c2e0cfdccea7dd9ac4a9182a077565b8bf80324a9fb07c8bb577a4c2c7c Description: built-package format for Python A wheel is a ZIP-format archive with a specially formatted filename and the `.whl` extension. It is designed to contain all the files for a PEP 376 compatible install in a way that is very close to the on-disk format. . The wheel project provides a `bdist_wheel` command for setuptools. Wheel files can be installed with `pip`. . These are the command line scripts and manpages. Homepage: https://bitbucket.org/pypa/wheel Package: python-xgboost Priority: optional Section: python Installed-Size: 3956 Maintainer: Benjamin Moody Architecture: amd64 Source: xgboost Version: 0.6+git20160810-0~pn1 Depends: python (<< 2.8), python (>= 2.7~), python-numpy, python-scipy, python:any (>= 2.7.5-5~), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgomp1 (>= 4.9), libstdc++6 (>= 4.9) Filename: pool/main/x/xgboost/python-xgboost_0.6+git20160810-0~pn1_amd64.deb Size: 926358 MD5sum: 48605d05d0584c264fb72814e7fb8aee SHA1: ed267f9bc6a1dd9b789162c07d05bd314032597f SHA256: 50992a768661cc77dd241edc07b4ad555302ceb82508617f01fe00f012357be2 Description: scalable, distributed gradient boosting library (Python 2) XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples. . This package provides the xgboost library for Python 2. Package: python3-caffe-cpu Priority: optional Section: python Installed-Size: 3015 Maintainer: Debian Science Maintainers Architecture: amd64 Source: caffe Version: 1.0.0~rc4-1~pn1 Depends: libcaffe-cpu1 (= 1.0.0~rc4-1~pn1), libboost-python1.55.0, libboost-system1.55.0, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgoogle-glog0, libprotobuf9, libpython3.4 (>= 3.4.2~rc1), libstdc++6 (>= 4.2.1), cython3, ipython3, python3 (<< 3.5), python3 (>= 3.4~), python3-dateutil, python3-gflags, python3-h5py, python3-leveldb, python3-matplotlib, python3-networkx, python3-nose, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3-pandas, python3-pil, python3-protobuf, python3-scipy, python3-six (>= 1.1.0), python3-skimage, python3-yaml Conflicts: python3-caffe-cuda Filename: pool/main/c/caffe/python3-caffe-cpu_1.0.0~rc4-1~pn1_amd64.deb Size: 685058 MD5sum: e527a52b78576a76d2eed8ceb32f73a6 SHA1: 6fe7fafdabf403f398a9b442e6866420c56f5759 SHA256: 97adc105ebea18f051739ffe45ab954c31a7f219af3aa66c614b6ccf2ae90467 Description: Python3 interface of Caffe (CPU_ONLY) Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors. . It contains python3 interface of caffe, configured as CPU_ONLY. Multi-Arch: foreign Homepage: http://caffe.berkeleyvision.org Package: python3-decorator Priority: optional Section: python Installed-Size: 43 Maintainer: Debian Python Modules Team Architecture: all Source: python-decorator Version: 4.0.11-1 Depends: python3:any (>= 3.3.2-2~) Filename: pool/main/p/python-decorator/python3-decorator_4.0.11-1_all.deb Size: 13326 MD5sum: 1f5f4a308a64e610713f32e260cfcecc SHA1: d2a9c1425ba7e4ef0b5c4927112ad6b43cbccb8e SHA256: 63cca50dc848d1d68c4ac8d4b5a6f8886f592977822019673529f797938b5091 Description: simplify usage of Python decorators by programmers Python 2.4 decorators have significantly changed the way Python programs are structured. * decorators help reduce boilerplate code; * decorators help the separation of concerns; * decorators enhance readability and maintainability; * decorators are very explicit. Still, as of now, writing custom decorators correctly requires some experience and is not as easy as it could be. For instance, typical implementations of decorators involve nested functions and we all know that flat is better than nested. The aim of the decorator module it to simplify the usage of decorators for the average programmer and to popularize decorators usage giving examples of useful decorators, such as memoize, tracing, redirecting_stdout, locked, etc. Homepage: https://pypi.python.org/pypi/decorator Package: python3-funcsigs Priority: optional Section: python Installed-Size: 93 Maintainer: PKG OpenStack Architecture: all Source: python-funcsigs Version: 1.0.2-3~bpo8+1 Depends: python3:any (>= 3.3.2-2~) Suggests: python-funcsigs-doc Filename: pool/main/p/python-funcsigs/python3-funcsigs_1.0.2-3~bpo8+1_all.deb Size: 13610 MD5sum: 6da75f65190d508cdc48cfb1d46b8039 SHA1: 0c1e009f0fe391479d983431e8f06957c80845fa SHA256: 2c8fd730d32edb6b8ac4c46677425ce2bedcc84b20b8bb05c8036703867a302e Description: function signatures from PEP362 - Python 3.x funcsigs is a backport of the PEP 362 function signature features from Python 3.3's inspect module. The backport is compatible with Python 2.6, 2.7 as well as 3.2 and up. . This package contains the Python 3.x module. Homepage: http://funcsigs.readthedocs.org Package: python3-joblib Priority: optional Section: python Installed-Size: 483 Maintainer: Yaroslav Halchenko Architecture: all Source: joblib Version: 0.10.3+git55-g660fe5d-1 Depends: python3:any (>= 3.3.2-2~) Recommends: python3-numpy, python3-pytest, python3-simplejson Filename: pool/main/j/joblib/python3-joblib_0.10.3+git55-g660fe5d-1_all.deb Size: 112620 MD5sum: 094c46ccaa0e3a7b333f86ebbd46a349 SHA1: 229d7e722d3cc21b30297a496e965f147f87aa45 SHA256: 4ad3279722dcff9959fdcd9e1bb59aea5ba57df7a4dbfdc66c87ac210fb638a4 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. . This package contains the Python 3 version. Homepage: http://packages.python.org/joblib/ Package: python3-keras Priority: optional Section: science Installed-Size: 1342 Maintainer: Daniel Stender Architecture: all Source: keras Version: 2.0.2+git20170403+64d24215-0~pn1 Depends: python3-numpy, python3-scipy, python3-h5py, python3-six, python3-theano, python3-yaml, python3:any (>= 3.3.2-2~) Filename: pool/main/k/keras/python3-keras_2.0.2+git20170403+64d24215-0~pn1_all.deb Size: 195526 MD5sum: 6099b56ff5b9a9a6b018f8bb315f24d9 SHA1: 3b84533864e125f919d65e0afe7d8aab6aab56b7 SHA256: 73b9336ab3f76babad36848829045c3bd33c833d146d57bfc1d6fe51fb4ff920 Description: high-level framework for deep learning (Python 3) Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation. . Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing). . It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian, but coming up). Homepage: http://keras.io/ Package: python3-mock Priority: extra Section: python Installed-Size: 315 Maintainer: Debian Python Modules Team Architecture: all Source: python-mock Version: 2.0.0-3~bpo8+1 Depends: python3-pbr (>= 1.3), python3-six, python3:any (>= 3.3.2-2~) Suggests: python-mock-doc Filename: pool/main/p/python-mock/python3-mock_2.0.0-3~bpo8+1_all.deb Size: 60252 MD5sum: 8d59cada944ba285541d796ebf652eb5 SHA1: 55a8e4b009b70d259395b84c94ab1723b04aff97 SHA256: eed98f5c39f455a971b26267de143ab0c976f2bd0e622578931318c7cadf3932 Description: Mocking and Testing Library (Python3 version) mock provides a core mock.Mock class that is intended to reduce the need to create a host of trivial stubs throughout your test suite. After performing an action, you can make assertions about which methods / attributes were used and arguments they were called with. You can also specify return values and set specific attributes in the normal way. . This package contains the Python 3 version of the library. Homepage: https://github.com/testing-cabal/mock Package: python3-nose-parameterized Priority: optional Section: python Installed-Size: 73 Maintainer: PKG OpenStack Architecture: all Source: python-nose-parameterized Version: 0.6.0-0~pn0 Depends: python3:any (>= 3.3.2-2~) Pre-Depends: dpkg (>= 1.15.6~) Suggests: python-nose-parameterized-doc Filename: pool/main/p/python-nose-parameterized/python3-nose-parameterized_0.6.0-0~pn0_all.deb Size: 11282 MD5sum: 3e044b2eb30931cbadd1bd8bc19bc49f SHA1: c1068539b58bf40579c5e0dc24cd87cfbbc704c6 SHA256: fd95bc8edc92cc4d92e1f0ef6a53c7b1112846371967ae21b3570f3a37d6f822 Description: Decorator for parameterized testing with Nose - Python 3.x nose-parameterized is a decorator for parameterized testing of Python code with nose. . The provided decorators make it simple to pass lists, iterables, tuples or callables to the test functions. This allows you to separate the data from the test without having to subclass unittest.testcase. . This package contains the Python 3.x module. Homepage: https://github.com/wolever/nose-parameterized Package: python3-numpy Priority: optional Section: python Installed-Size: 10510 Maintainer: Sandro Tosi Architecture: amd64 Source: python-numpy Version: 1:1.12.0-2~pn0 Provides: python3-f2py, python3-numpy-abi9, python3-numpy-api10, python3-numpy-dev, python3.4-numpy Depends: python3 (<< 3.5), python3 (>= 3.4~), python3.4, libblas3 | libblas.so.3, libc6 (>= 2.14), liblapack3 | liblapack.so.3 Suggests: gcc (>= 4:4.6.1-5), gfortran, python-numpy-doc, python3-dev, python3-nose (>= 1.0), python3-numpy-dbg Filename: pool/main/p/python-numpy/python3-numpy_1.12.0-2~pn0_amd64.deb Size: 1916700 MD5sum: 1a2e0bf5164a9f37320a8a11afe40973 SHA1: 075e98604624e9963b115b084c79b7a6578d8616 SHA256: 85607b8714d8c0da765676f295f4acb2f0e2ae663fe816ce3e351a9e8b3e2108 Description: Fast array facility to the Python 3 language Numpy contains a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran code, and useful linear algebra, Fourier transform, and random number capabilities. . Numpy replaces the python-numeric and python-numarray modules which are now deprecated and shouldn't be used except to support older software. . This package contains Numpy for Python 3. Homepage: http://www.numpy.org/ Package: python3-numpy-dbg Priority: extra Section: debug Installed-Size: 31861 Maintainer: Sandro Tosi Architecture: amd64 Source: python-numpy Version: 1:1.12.0-2~pn0 Replaces: python3-numpy (<< 1:1.7.1-1) Depends: python3-dbg, python3-numpy (= 1:1.12.0-2~pn0), libblas3 | libblas.so.3, libc6 (>= 2.14), liblapack3 | liblapack.so.3 Breaks: python3-numpy (<< 1:1.7.1-1) Filename: pool/main/p/python-numpy/python3-numpy-dbg_1.12.0-2~pn0_amd64.deb Size: 5193720 MD5sum: d200d6e12147383d5baf33b16e8e8491 SHA1: 5c9386148f6632b3daa2c419c648d26afc06a553 SHA256: a5693ed2bff23cf46596cd741027ab7a5849136ed21e94823062b2c4c6e432ac Description: Fast array facility to the Python 3 language (debug extension) Numpy contains a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran code, and useful linear algebra, Fourier transform, and random number capabilities. . Numpy replaces the python-numeric and python-numarray modules which are now deprecated and shouldn't be used except to support older software. . This package contains the extension built for the Python 3 debug interpreter. Homepage: http://www.numpy.org/ Package: python3-pbr Priority: optional Section: python Installed-Size: 182 Maintainer: PKG OpenStack Architecture: all Source: python-pbr Version: 1.8.0-4.1~bpo8+1 Depends: python3-pkg-resources, python3-six (>= 1.9.0), python3, python3:any (>= 3.3.2-2~) Filename: pool/main/p/python-pbr/python3-pbr_1.8.0-4.1~bpo8+1_all.deb Size: 34252 MD5sum: 0c57f4dad153145c4b563e3a8d396366 SHA1: 5305d81433407d849d4a2af186546c0fe5d37213 SHA256: a3abb6b4830e71b50379ce42a8e91ce978381c0d5c22145709d78a6ba8a79926 Description: inject useful and sensible default behaviors into setuptools - Python 3.x PBR (Python Build Reasonableness) is a library that injects some useful and sensible default behaviors into your setuptools run. PBR can: * Manage version number based on git revisions and tags (Version file). * Generate AUTHORS file from git log * Generate ChangeLog from git log * Generate Sphinx autodoc stub files for your whole module * Store your dependencies in a pip requirements file * Use your README file as a long_description * Smartly find packages under your root package . PBR is only mildly configurable. The basic idea is that there's a decent way to run things and if you do, you should reap the rewards, because then it's simple and repeatable. If you want to do things differently, cool! But you've already got the power of Python at your fingertips, so you don't really need PBR. . PBR builds on top of `d2to1` to provide for declarative configuration. It then filters the `setup.cfg` data through a setup hook to fill in default values and provide more sensible behaviors. . This package provides support for Python 3.x. Homepage: http://pypi.python.org/pypi/pbr Package: python3-protobuf Priority: optional Section: python Installed-Size: 2424 Maintainer: Debian protobuf maintainers Architecture: amd64 Source: protobuf Version: 3.2.0-0~pn1 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libprotobuf10, libstdc++6 (>= 4.3), python3 (<< 3.5), python3 (>= 3.4~), python3-pkg-resources, python3-six (>= 1.9) Filename: pool/main/p/protobuf/python3-protobuf_3.2.0-0~pn1_amd64.deb Size: 307636 MD5sum: 824965072034f95d970e48606fab8d0b SHA1: 0be817e7e5b2b1b9ce4e273100d90c8b915a5ed2 SHA256: 024b2a6ecb8f710764cafe95b3aa8113fa47fb80693f8584e1fe01570215044f Description: Python 3 bindings for protocol buffers Protocol buffers are a flexible, efficient, automated mechanism for serializing structured data - similar to XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. You can even update your data structure without breaking deployed programs that are compiled against the "old" format. . Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats. . This package contains the Python 3 bindings for the protocol buffers. You will need the protoc tool (in the protobuf-compiler package) to compile your definition to Python classes, and then the modules in this package will allow you to use those classes in your programs. Homepage: https://github.com/google/protobuf/ Package: python3-scipy Priority: extra Section: python Installed-Size: 33760 Maintainer: Debian Python Modules Team Architecture: amd64 Source: python-scipy Version: 0.18.1-2~pn1 Depends: python3-decorator (>= 4.0.11), python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3 (<< 3.5), python3 (>= 3.4~), libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.6), liblapack3 | liblapack.so.3, libquadmath0 (>= 4.6), libstdc++6 (>= 4.1.1) Recommends: g++ | c++-compiler Suggests: python-scipy-doc Filename: pool/main/p/python-scipy/python3-scipy_0.18.1-2~pn1_amd64.deb Size: 8697884 MD5sum: 84749d1e94653236787016186724ea29 SHA1: 1d160687600c72337063e166c2e9af0dd10bf9f3 SHA256: 7b4ba8b5278a4515a743488d1db032d234dcbbbd830769a986abf1d27a3ac749 Description: scientific tools for Python 3 SciPy supplements the popular NumPy module (python-numpy package), gathering a variety of high level science and engineering modules together as a single package. . SciPy is a set of Open Source scientific and numeric tools for Python. It currently supports special functions, integration, ordinary differential equation (ODE) solvers, gradient optimization, genetic algorithms, parallel programming tools, an expression-to-C++ compiler for fast execution, and others. . This package provides the Python 3 version. Homepage: http://www.scipy.org/ Package: python3-scipy-dbg Priority: extra Section: debug Installed-Size: 111968 Maintainer: Debian Python Modules Team Architecture: amd64 Source: python-scipy Version: 0.18.1-2~pn1 Depends: python3-dbg (<< 3.5), python3-numpy-dbg (>= 1:1.7.2), python3-scipy (= 0.18.1-2~pn1), python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3-dbg (>= 3.4~), libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.6), liblapack3 | liblapack.so.3, libquadmath0 (>= 4.6), libstdc++6 (>= 4.1.1) Filename: pool/main/p/python-scipy/python3-scipy-dbg_0.18.1-2~pn1_amd64.deb Size: 17626048 MD5sum: 6e0f573ecbc37beea4ed47d5243cba6a SHA1: 8cb2f0478aa28a00e640d534d7764af5c3cfc161 SHA256: 4f4fe46aeac327cdbca7db47ef48a4259cac54dead1fe733d531bcf98a2ead26 Description: scientific tools for Python 3 - debugging symbols SciPy supplements the popular NumPy module (python-numpy package), gathering a variety of high level science and engineering modules together as a single package. . SciPy is a set of Open Source scientific and numeric tools for Python. It currently supports special functions, integration, ordinary differential equation (ODE) solvers, gradient optimization, genetic algorithms, parallel programming tools, an expression-to-C++ compiler for fast execution, and others. . This package provides debugging symbols for python3-scipy. Homepage: http://www.scipy.org/ Package: python3-six Priority: optional Section: python Installed-Size: 56 Maintainer: Colin Watson Architecture: all Source: six Version: 1.10.0-3 Depends: python3:any (>= 3.4~) Filename: pool/main/s/six/python3-six_1.10.0-3_all.deb Size: 14426 MD5sum: 866e0526b94f60212ce4aa440cef02ea SHA1: ee7ae8597afd2380606d1f8c62088f266fd971d0 SHA256: 597005e64cf70e4be97170a47c33287f70a1c87a2979d47a434c10c9201af3ca Description: Python 2 and 3 compatibility library (Python 3 interface) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six on the Python 3 module path. It is complemented by python-six and pypy-six. Multi-Arch: foreign Homepage: https://pythonhosted.org/six/ Package: python3-sklearn Priority: optional Section: python Installed-Size: 6543 Maintainer: NeuroDebian Team Architecture: all Source: scikit-learn Version: 0.18-5~pn0 Depends: python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-sklearn-lib (>= 0.18-5~pn0), python3-joblib (>= 0.9.2) Recommends: python3-nose, python3-matplotlib Suggests: python3-dap, python-sklearn-doc, ipython3 Filename: pool/main/s/scikit-learn/python3-sklearn_0.18-5~pn0_all.deb Size: 1385106 MD5sum: 946af8f5e1d39b7ebc982e03f73d8cb4 SHA1: 63cef92a4c7620685f4c5bf241582f16d3927ef0 SHA256: 50ba9fcc767cc310720727b299ccad58650a85db91aa92ff03e8fdea94c74321 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) . This package contains the Python 3 version. Homepage: http://scikit-learn.sourceforge.net Enhances: python3-mdp, python3-mvpa2 Package: python3-sklearn-lib Priority: optional Section: python Installed-Size: 7873 Maintainer: NeuroDebian Team Architecture: amd64 Source: scikit-learn Version: 0.18-5~pn0 Depends: libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3 (<< 3.5), python3 (>= 3.4~) Filename: pool/main/s/scikit-learn/python3-sklearn-lib_0.18-5~pn0_amd64.deb Size: 1276442 MD5sum: c0193fbd899d44a6fba3c42be6f5e38f SHA1: 120d960746048dc967930532a794048ef409c343 SHA256: 391a0a5f27e283047652b0429d1e1a9cd6edcd1b4d34d5fc328d5348f61bfc69 Description: low-level implementations and bindings for scikit-learn - Python 3 This is an add-on package for python-sklearn. It provides low-level implementations and custom Python bindings for the LIBSVM library. . This package contains the Python 3 version. Homepage: http://scikit-learn.sourceforge.net Package: python3-theano Priority: optional Section: python Installed-Size: 12261 Maintainer: Debian Science Maintainers Architecture: amd64 Source: theano Version: 0.9.0-0~pn1 Depends: python3-numpy, python3-scipy, python3-six (>= 1.9.0), python3:any (>= 3.3.2-2~), python3-dev, libblas-dev | libblas.so Recommends: python3-pydot, python3-nose, python3-nose-parameterized, theano-doc Suggests: nvidia-cuda-toolkit, python3-pycuda Filename: pool/main/t/theano/python3-theano_0.9.0-0~pn1_amd64.deb Size: 2094688 MD5sum: 680ae216e0e5be0ff96a04b66b91ac56 SHA1: 5545c5e6dc9d4a06c754f52be9b359d74c903f92 SHA256: 4bd77198fad478938469f87ececcf38b6928e8d178ba8b63ba024a898f8b6a23 Description: CPU/GPU math expression compiler for Python 3 Theano is a Python library that allows one to define and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It provides a high-level Numpy like expression language for functional description of calculation, rearranges expressions for speed and stability, and generates native machine instructions for fast calculation. Optionally, highly accelerated computations could be carried out on graphics cards processors. . This package contains Theano for Python 3. Homepage: http://www.deeplearning.net/software/theano/ Package: python3-theano Priority: optional Section: python Installed-Size: 11062 Maintainer: Debian Science Maintainers Architecture: amd64 Source: theano Version: 0.8.2-6 Depends: python3-numpy, python3-scipy, python3-six (>= 1.9.0), python3:any (>= 3.3.2-2~), python3-dev, libblas-dev | libblas.so Recommends: python3-pydot, python3-nose, python3-nose-parameterized, theano-doc Suggests: nvidia-cuda-toolkit, python3-pycuda Filename: pool/main/t/theano/python3-theano_0.8.2-6_amd64.deb Size: 1952398 MD5sum: 12bfdac3383440ff9b75d17cb0b1fbd3 SHA1: 9ee1e40586ee550f8416b2150b9b1e2a21c72c99 SHA256: 6e8ce44637f2d49fa7ca01d9480ce99d2d516395798ea7c165b7640488c1ba4d Description: CPU/GPU math expression compiler for Python 3 Theano is a Python library that allows one to define and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It provides a high-level Numpy like expression language for functional description of calculation, rearranges expressions for speed and stability, and generates native machine instructions for fast calculation. Optionally, highly accelerated computations could be carried out on graphics cards processors. . This package contains Theano for Python 3. Homepage: http://www.deeplearning.net/software/theano/ Package: python3-wheel Priority: optional Section: python Installed-Size: 211 Maintainer: Barry Warsaw Architecture: all Source: wheel Version: 0.29.0-2 Depends: python3:any (>= 3.3.2-2~) Recommends: python3-keyring, python3-keyrings.alt, python3-xdg Suggests: python3-setuptools Filename: pool/main/w/wheel/python3-wheel_0.29.0-2_all.deb Size: 51822 MD5sum: 7f0c0929619f857313adc9b50a012543 SHA1: 7bccb19dc2712dfe0692bc529082dea2ebf8fcb7 SHA256: aaadbaff27adaa022dca3528e94bbf4d0a0740b05c0df473084afc4032a2292b Description: built-package format for Python A wheel is a ZIP-format archive with a specially formatted filename and the `.whl` extension. It is designed to contain all the files for a PEP 376 compatible install in a way that is very close to the on-disk format. . The wheel project provides a `bdist_wheel` command for setuptools. Wheel files can be installed with `pip`. . This is the Python 3 compatible package. Homepage: https://bitbucket.org/pypa/wheel Package: python3-xgboost Priority: optional Section: python Installed-Size: 3954 Maintainer: Benjamin Moody Architecture: amd64 Source: xgboost Version: 0.6+git20160810-0~pn1 Depends: python, python3 (>= 3~), python3-numpy, python3-scipy, python3:any (>= 3.3.2-2~), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgomp1 (>= 4.9), libstdc++6 (>= 4.9) Filename: pool/main/x/xgboost/python3-xgboost_0.6+git20160810-0~pn1_amd64.deb Size: 925840 MD5sum: 73c9fe14e153a78b52836777a72cb093 SHA1: 41f0a3a31e3d74812a40598e19bfc67ae926cea1 SHA256: bc37ce3d7518fc96202f30d8aaf05e6037ffa32195b93676e77380528e21e0b5 Description: scalable, distributed gradient boosting library (Python 3) XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples. . This package provides the xgboost library for Python 3. Package: theano-doc Priority: optional Section: doc Installed-Size: 8730 Maintainer: Debian Science Maintainers Architecture: all Source: theano Version: 0.8.2-6 Depends: libjs-sphinxdoc (>= 1.0) Recommends: python-theano, python3-theano Filename: pool/main/t/theano/theano-doc_0.8.2-6_all.deb Size: 2733002 MD5sum: a1585f5cfe4f685f6d1c72e67c690c1d SHA1: 9a4df8ae7258fab3b84aae0c3ac2e7cc09675d35 SHA256: c355e339d6fd5d2b6c4c16c36ff45572eb8162847bda147fa68ab060085ad28c Description: CPU/GPU math expression compiler for Python (docs) Theano is a Python library that allows one to define and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It provides a high-level Numpy like expression language for functional description of calculation, rearranges expressions for speed and stability, and generates native machine instructions for fast calculation. Optionally, highly accelerated computations could be carried out on graphics cards processors. . This package contains the documentation for Theano. Homepage: http://www.deeplearning.net/software/theano/ Package: wfdb Priority: optional Section: science Installed-Size: 1452 Maintainer: Benjamin Moody Architecture: amd64 Version: 10.5.25~pre2-0~pn1 Depends: libc6 (>= 2.14), libexpat1 (>= 2.0.1), libwfdb10 (>= 10.5.11) Filename: pool/main/w/wfdb/wfdb_10.5.25~pre2-0~pn1_amd64.deb Size: 422306 MD5sum: ad83e00abae0561bc37cac37c330cbba SHA1: 78c923a82bfddbfd8e9dfceca46bc785dd193c54 SHA256: 04662f44d90463fd589edba6383c23d9a77d51c1404cfaadfe68998f54637be7 Description: Waveform Database Software Package The WFDB (Waveform Database) library supports creating, reading, and annotating digitized signals in a wide variety of formats. Input can be from local files or directly from web or FTP servers. Although created for use with physiologic signals such as those in PhysioBank (http://www.physionet.org/physiobank/), the WFDB library supports a broad range of general-purpose signal processing applications. . This package contains about 60 applications for creating, reading, transforming, analyzing, annotating, and viewing digitized signals, especially physiologic signals. Applications include digital filtering, event detection, signal averaging, power spectrum estimation, and many others. . This package also contains shared data files and tools that are required by many programs that use the WFDB library. Multi-Arch: foreign Package: wfdb-app-toolbox Priority: optional Section: science Installed-Size: 8727 Maintainer: Benjamin Moody Architecture: amd64 Version: 0.9.9+src-0~pn1 Depends: libc6 (>= 2.7), libwfdb10 (>= 10.5.11), wfdb, ecgpuwave, octave (>= 3.8), octave-signal Filename: pool/main/w/wfdb-app-toolbox/wfdb-app-toolbox_0.9.9+src-0~pn1_amd64.deb Size: 3178988 MD5sum: 4d4208a5228780d943f5fdfea3464bdc SHA1: 84dc4e6304d2177961b78f2e7a5e5ff7dff1c06a SHA256: ae2e12db80168565738cf7fd4545f6c9fbe58ddaed8e125530eb33ac7e5aec6d Description: WFDB Toolbox for MATLAB and Octave A collection of functions for reading, writing, and processing physiologic signals and time series in the formats used by PhysioBank databases (among others). The Toolbox is compatible with 64-bit MATLAB and GNU Octave on GNU/Linux, Mac OS X, and MS-Windows.