Package: caffe-cpu
Priority: optional
Section: metapackages
Installed-Size: 36
Maintainer: Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
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 <debian-science-maintainers@lists.alioth.debian.org>
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 <debian-science-maintainers@lists.alioth.debian.org>
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 <benjamin@physionet.org>
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 <benjamin@physionet.org>
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 <debian-science-maintainers@lists.alioth.debian.org>
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 <debian-science-maintainers@lists.alioth.debian.org>
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 <pkg-protobuf-devel@lists.alioth.debian.org>
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 <pkg-protobuf-devel@lists.alioth.debian.org>
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 <pkg-protobuf-devel@lists.alioth.debian.org>
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 <pkg-protobuf-devel@lists.alioth.debian.org>
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 <pkg-protobuf-devel@lists.alioth.debian.org>
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 <pkg-protobuf-devel@lists.alioth.debian.org>
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 <benjamin@physionet.org>
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 <benjamin@physionet.org>
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 <pkg-protobuf-devel@lists.alioth.debian.org>
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 <cjwatson@debian.org>
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 <python-modules-team@lists.alioth.debian.org>
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 <openstack-devel@lists.alioth.debian.org>
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 <openstack-devel@lists.alioth.debian.org>
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 <debian@onerussian.com>
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 <stender@debian.org>
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 <python-modules-team@lists.alioth.debian.org>
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 <python-modules-team@lists.alioth.debian.org>
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 <openstack-devel@lists.alioth.debian.org>
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 <morph@debian.org>
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 <morph@debian.org>
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 <openstack-devel@lists.alioth.debian.org>
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 <openstack-devel@lists.alioth.debian.org>
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 <pkg-protobuf-devel@lists.alioth.debian.org>
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 <team@neuro.debian.net>
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 <python-modules-team@lists.alioth.debian.org>
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 <python-modules-team@lists.alioth.debian.org>
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 <python-modules-team@lists.alioth.debian.org>
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 <cjwatson@debian.org>
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 <team@neuro.debian.net>
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 <team@neuro.debian.net>
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 <team@neuro.debian.net>
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 <debian-science-maintainers@lists.alioth.debian.org>
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 <debian-science-maintainers@lists.alioth.debian.org>
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 <barry@debian.org>
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 <barry@debian.org>
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 <benjamin@physionet.org>
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 <debian-science-maintainers@lists.alioth.debian.org>
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 <python-modules-team@lists.alioth.debian.org>
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 <openstack-devel@lists.alioth.debian.org>
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 <debian@onerussian.com>
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 <stender@debian.org>
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 <python-modules-team@lists.alioth.debian.org>
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 <openstack-devel@lists.alioth.debian.org>
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 <morph@debian.org>
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 <morph@debian.org>
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 <openstack-devel@lists.alioth.debian.org>
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 <pkg-protobuf-devel@lists.alioth.debian.org>
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 <python-modules-team@lists.alioth.debian.org>
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 <python-modules-team@lists.alioth.debian.org>
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 <cjwatson@debian.org>
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 <team@neuro.debian.net>
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 <team@neuro.debian.net>
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 <debian-science-maintainers@lists.alioth.debian.org>
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 <debian-science-maintainers@lists.alioth.debian.org>
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 <barry@debian.org>
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 <benjamin@physionet.org>
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 <debian-science-maintainers@lists.alioth.debian.org>
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 <benjamin@physionet.org>
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 <benjamin@physionet.org>
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.

