Saravanan666/pypi-org huggingface dataset:
Package: libquantum
libquantumDescriptionStandardized time-frequency representations
built on Gabor uncertainty principle for
logarithmic constant Q base 2 (binary) frequency bands
of quantized order N.NOTE:
Requirements upgraded to run on Apple M1 Silicon chip
with the RedVox SDK version 3.1.0 or later. Please read the notes on requirements.txtRefer to the open access publications:Garces (2020), Quantized Constant-Q Gabor Atoms for
Sparse Binary Representations of Cyber-Physical SignaturesGarces (2013), On Infrasound Standards, Part 1 Time, Frequency, and Energy ScalingFor the GT blast pulse, refer to:Garcés, M. A. (2019). Explosion Source Models,
Chapter in Infrasound Monitoring for Atmospheric Studies,
Second Edition, SpringerSchnurr, J. M., K. Kim, M. A. Garcés, A. Rodgers (2020).
Improved parametric models for explosion pressure signals
derived from large datasets,
Seism. Res. Lett.Kim, K, A. R. Rodgers, M. A. Garces, and S. C. Myers (2021).
Empirical Acoustic Source Model for Chemical Explosions in Air,
Bulletin of the Seismological Society of AmericaRecommended reading in chronological order:Gabor, D. Theory of Communication, Part 3. Electr. Eng. 1946, 93, 445–457.Shannon, C.E. The Mathematical Theory of Communication; University of Illinois Press: Urbana, IL, USA, 1998; [1949 first ed].Harris, F. J. On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform, Proceedings of the IEEE, 1978, 66 (1), 51-83.Cohen, L. Time-Frequency Analysis, Prentice-Hall, NI 07458, 1995.Stockwell, R. G., Mansina, L, and R. P. Lowe. Localization of the Complex Spectrum: The S Transform. Signal Processing, IEEE Transactions, 1996, 44 no. 4, 998-1001.Mallat, S. A Wavelet Tour of Signal Processing: The Sparse Way, 3rd ed.; Academic Press: Cambridge, MA, USA, 2009 [1998 first ed].InstallationpipinstalllibquantumMore details in theInstallation guide.ExamplesFull examples can be found in theexamples documentation.API DocumentationCheck theAPI Documentation.ResourcesFound an issue? Submit abug report.Apache License
Package: libra
LibraAn ergonomic machine learning library for non-technical users. Save time. Blaze through ML.Trending ContributorsInstallationInstall latest release version:pip install -U libraInstall directory from github:git clone https://github.com/Palashio/libra.git
cd libra
pip install .Alternatively you can build and use the docker image locally with:docker build . -f docker/libra-normal/Dockerfile -t libra
docker run -v /path/to/my/data:/data -it --rm libraOr if you havenvidia-dockerinstalled.docker build . -f docker/libra-gpu/Dockerfile -t libra-gpu
docker run -v /path/to/my/data:/data --gpus all -it --rm libra-gpuUsage: the basicsThe core functionality of libra works through theclientobject. A new client object should be created for every dataset that you want to produce results for. All information about the models that're built, the plots that are generated, and the metrics are created will be stored in the object.You can then call different queries on that client object, and the dataset you passed to it will be used.fromlibraimportclientnewClient=client('path/to/dataset')newClient.neural_network_query('please model the median number of households')Now, callingnewClient.info()will return a dictionary of all the information that was generated:dict_keys(['id','model','num_classes','plots','target','preprocessor','interpreter','test_data','losses','accuracy'])Other queries can also be called on the same object, and will be appended to themodelsdictionary.newClient.svm_query('predict the proximity to the ocean')newClient.model().keys()dict_keys(['regression_ANN',svm'])TutorialsFull documentation can be found atlibradocs.org.A list of resources can be found on ourawesome-librarepository.Asking for helpWelcome to the Libra community!If you have any questions, feel free to:read the docs.Search through the issues.Ask on stackoverflowwith the tag libra.Join our slack.DemosContactShoot me an email atps9cmk@virginia.eduif you'd like to get in touch!Follow me ontwitterfor updates and my insights about modern AI!
Package: librabbitmq
Version:2.0.0Download:http://pypi.python.org/pypi/librabbitmq/Code:http://github.com/celery/librabbitmq/Keywords:rabbitmq, amqp, messaging, librabbitmq, rabbitmq-c, python,
kombu, celeryInstallationDownloading and installing from sourceUsing the development versionExamplesProducingConsumingPollOtherLicensePython bindings to the RabbitMQ C-libraryrabbitmq-c.
Supported by Kombu and Celery.InstallationInstall via pip:$ pip install librabbitmqor, install via easy_install:$ easy_install librabbitmqDownloading and installing from sourceDownload the latest version fromhttp://pypi.python.org/pypi/librabbitmq/Then install it by doing the following,:$ tar xvfz librabbitmq-0.0.0.tar.gz
$ cd librabbitmq-0.0.0
$ python setup.py build
# python setup.py install # as rootUsing the development versionYou can clone the repository by doing the following:$ git clone git://github.com/celery/librabbitmq.gitThen install it by doing the following:$ cd librabbitmq
$ make install        # or make developExamplesUsing with Kombu:>>> from kombu import Connection
>>> x = Connection("librabbitmq://")Stand-alone:>>> from librabbitmq import Connection

>>> conn = Connection(host="localhost", userid="guest",
...                   password="guest", virtual_host="/")

>>> channel = conn.channel()
>>> channel.exchange_declare(exchange, type, ...)
>>> channel.queue_declare(queue, ...)
>>> channel.queue_bind(queue, exchange, routing_key)Producing>>> channel.basic_publish(body, exchange, routing_key, ...)Consuming>>> def dump_message(message):
...     print("Body:'%s', Properties:'%s', DeliveryInfo:'%s'" % (
...         message.body, message.properties, message.delivery_info))
...     message.ack()

>>> channel.basic_consume(queue, ..., callback=dump_message)

>>> while True:
...    connection.drain_events()Poll>>> message = channel.basic_get(queue, ...)
>>> if message:
...     dump_message(message)
...     print("Body:'%s' Properties:'%s' DeliveryInfo:'%s'" % (
...         message.body, message.properties, message.delivery_info))Other>>> channel.queue_unbind(queue, ...)
>>> channel.close()
>>> connection.close()LicenseThis software is licensed under theMozilla Public License.
See theLICENSE-MPL-RabbitMQfile in the top distribution directory
for the full license text.
Package: librabbitmq-fork
Version:2.0.2-dev2Download:http://pypi.python.org/pypi/librabbitmq-fork/Code:http://github.com/henryykt/librabbitmq/Keywords:rabbitmq, amqp, messaging, librabbitmq, rabbitmq-c, python,
kombu, celeryInstallationDownloading and installing from sourceUsing the development versionExamplesProducingConsumingPollOtherLicenseNote: This is a (temporary) fork of librabbitmq with fixes for memory leaks
included inhttps://github.com/celery/librabbitmq/pull/145.Python bindings to the RabbitMQ C-libraryrabbitmq-c.
Supported by Kombu and Celery.InstallationInstall via pip:$ pip install librabbitmq-forkor, install via easy_install:$ easy_install librabbitmq-forkDownloading and installing from sourceDownload the latest version fromhttp://pypi.python.org/pypi/librabbitmq/Then install it by doing the following,:$ tar xvfz librabbitmq-0.0.0.tar.gz
$ cd librabbitmq-0.0.0
$ python setup.py build
# python setup.py install # as rootUsing the development versionYou can clone the repository by doing the following:$ git clone git://github.com/celery/librabbitmq.gitThen install it by doing the following:$ cd librabbitmq
$ make install        # or make developExamplesUsing with Kombu:>>> from kombu import Connection
>>> x = Connection("librabbitmq://")Stand-alone:>>> from librabbitmq import Connection

>>> conn = Connection(host="localhost", userid="guest",
...                   password="guest", virtual_host="/")

>>> channel = conn.channel()
>>> channel.exchange_declare(exchange, type, ...)
>>> channel.queue_declare(queue, ...)
>>> channel.queue_bind(queue, exchange, routing_key)Producing>>> channel.basic_publish(body, exchange, routing_key, ...)Consuming>>> def dump_message(message):
...     print("Body:'%s', Properties:'%s', DeliveryInfo:'%s'" % (
...         message.body, message.properties, message.delivery_info))
...     message.ack()

>>> channel.basic_consume(queue, ..., callback=dump_message)

>>> while True:
...    connection.drain_events()Poll>>> message = channel.basic_get(queue, ...)
>>> if message:
...     dump_message(message)
...     print("Body:'%s' Properties:'%s' DeliveryInfo:'%s'" % (
...         message.body, message.properties, message.delivery_info))Other>>> channel.queue_unbind(queue, ...)
>>> channel.close()
>>> connection.close()LicenseThis software is licensed under theMozilla Public License.
See theLICENSE-MPL-RabbitMQfile in the top distribution directory
for the full license text.
Package: librair
Synopsislibrairis a Python package for harvesting data from different library catalogs. It has been developed as part of theDikon project(2019–2020) at theInterdisciplinary Centre for European Enlightenment Studies.InstallationYou can install this package viaPyPI:pipinstalllibrair… or via this repository:pipinstall-egit+https://github.com/herreio/librair.git#egg=librair… or by cloning the repository:gitclonehttps://github.com/herreio/librair.gitcdlibrair# create and activate virutalenvutils/setupsourceenv/bin/activateDocumentationThe documentation can be found onRead the Docs.To build the documentation from the files found at docs:cddocsmakehtml
Package: librapid-cuda-11.4.0
What is LibRapid?LibRapid is a high performance Array library, supporting a wide range of optimised calculations which can be performed on the CPU or GPU (via CUDA). All calculations are vectorised with SIMD instructions and are run on multiple threads (if necessary) to make them as fast as possible on any given machine.There are also a wide range of helper functions and classes to aid the development of your own project.LibRapid is highly templated, meaning it can conform to exactly your needs with minimal compile-times and even support for custom datatypes.Current Development StageAt the current point in time, LibRapid C++ is under rapid development by me (pencilcaseman).I am currently doing my A-Levels and do not have time to work on the library as much as I would like, so if you or someone you know might be willing to support the development of the library, feel free to create a pull request or chat to us onDiscord. Any help is greatly appreciated!Future PlansMy goal for LibRapid is to develop the C++ interface further, at least initially. At some point I want to add Python and Javascript interfaces (in that order) to increase the range of people who can benefit from the library, but the most important thing is the performance of the underlying C++ code.
Package: librapid-cuda-11.5.0
What is LibRapid?LibRapid is a high performance Array library, supporting a wide range of optimised calculations which can be performed on the CPU or GPU (via CUDA). All calculations are vectorised with SIMD instructions and are run on multiple threads (if necessary) to make them as fast as possible on any given machine.There are also a wide range of helper functions and classes to aid the development of your own project.LibRapid is highly templated, meaning it can conform to exactly your needs with minimal compile-times and even support for custom datatypes.Current Development StageAt the current point in time, LibRapid C++ is under rapid development by me (pencilcaseman).I am currently doing my A-Levels and do not have time to work on the library as much as I would like, so if you or someone you know might be willing to support the development of the library, feel free to create a pull request or chat to us onDiscord. Any help is greatly appreciated!Future PlansMy goal for LibRapid is to develop the C++ interface further, at least initially. At some point I want to add Python and Javascript interfaces (in that order) to increase the range of people who can benefit from the library, but the most important thing is the performance of the underlying C++ code.
Package: librapid-cuda-11.6.0
What is LibRapid?LibRapid is a high performance Array library, supporting a wide range of optimised calculations which can be performed on the CPU or GPU (via CUDA). All calculations are vectorised with SIMD instructions and are run on multiple threads (if necessary) to make them as fast as possible on any given machine.There are also a wide range of helper functions and classes to aid the development of your own project.LibRapid is highly templated, meaning it can conform to exactly your needs with minimal compile-times and even support for custom datatypes.Current Development StageAt the current point in time, LibRapid C++ is under rapid development by me (pencilcaseman).I am currently doing my A-Levels and do not have time to work on the library as much as I would like, so if you or someone you know might be willing to support the development of the library, feel free to create a pull request or chat to us onDiscord. Any help is greatly appreciated!Future PlansMy goal for LibRapid is to develop the C++ interface further, at least initially. At some point I want to add Python and Javascript interfaces (in that order) to increase the range of people who can benefit from the library, but the most important thing is the performance of the underlying C++ code.
Package: librapid-cuda-11.7.0
What is LibRapid?LibRapid is a high performance Array library, supporting a wide range of optimised calculations which can be performed on the CPU or GPU (via CUDA). All calculations are vectorised with SIMD instructions and are run on multiple threads (if necessary) to make them as fast as possible on any given machine.There are also a wide range of helper functions and classes to aid the development of your own project.LibRapid is highly templated, meaning it can conform to exactly your needs with minimal compile-times and even support for custom datatypes.Current Development StageAt the current point in time, LibRapid C++ is under rapid development by me (pencilcaseman).I am currently doing my A-Levels and do not have time to work on the library as much as I would like, so if you or someone you know might be willing to support the development of the library, feel free to create a pull request or chat to us onDiscord. Any help is greatly appreciated!Future PlansMy goal for LibRapid is to develop the C++ interface further, at least initially. At some point I want to add Python and Javascript interfaces (in that order) to increase the range of people who can benefit from the library, but the most important thing is the performance of the underlying C++ code.
Package: librapy
# Python Libra Client Library## OverviewThis library implements the core client facilities for interacting with the
Libra blockchain. The intent is to demonstrate Libra’s APIs without either
requiring reading extensive documentation or navigating Libra’s Rust code base.## Componentslcs.pyimplements Libra Canonical Serialization## Requirementsgrpcio-toolsfor gRPC and Protocol Bufferspynaclfor cryptographic operations (Curve25519, Ed25519, Sha3)## UpdatingThis library includes both proto and mvir files for communicating with Libra
validators and the Libra VM, respectively. These files are borrowed from the
upstream Libra distribution and may be changed without warning. To enable easy
transition to newer versions this library includes to scripts to retrieve the
appropriate files and add them herein:update_programs.shcompiles all the basic transaction Libra move scripts into
Move IR so that they can aeqgransferred to the validators when executing a
transaction.update_proto.shretrieves all the proto files for communicating to the Libra
admission control node and converts it into Python proto and gRPC libraries.
Package: libra_py
Citing libra_py=============The library libra_py is an academic project. The time and resources spent developing fastFM are therefore justifiedby the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry `citation.bib <http://jmlr.org/papers/v17/15-355.bib>`_).Bayer, I. "fastFM: A Library for Factorization Machines" Journal of Machine Learning Research 17, pp. 1-5 (2016)libra_py: A Package for sparsity problem============================================Supported Operating Systems---------------------------fastFM has a continuous integration / testing servers (Travis) for **Linux (Ubuntu 14.04 LTS)**and **OS X Mavericks**. Other OS are not actively supported.Usage-----.. code-block:: pythonfrom fastFM import alsfm = als.FMRegression(n_iter=1000, init_stdev=0.1, rank=2, l2_reg_w=0.1, l2_reg_V=0.5)fm.fit(X_train, y_train)y_pred = fm.predict(X_test)Tutorials and other information are available `here <http://arxiv.org/abs/1505.00641>`_.The C code is available as `subrepository <https://github.com/ibayer/fastFM-core>`_ and providesa stand alone command line interface. If you have still **questions** after reading the documentation please open a issue at GitHub.+----------------+------------------+-----------------------------+| Family         | Solver           | Loss                        |+================+==================+=============================+| Gaussian       | LBI_Linear       | Square Loss                 |+----------------+------------------+-----------------------------+| Binomial       | LBI_Logit        | Logit Model                 |+----------------+------------------+-----------------------------+*Supported solvers and tasks*Installation------------**binary install**``pip install libra_py``Tests-----
Package: libra_py_001_01
Citing libra_py_001_01=============The library libra_py is an academic project. The time and resources spent developing fastFM are therefore justifiedby the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry `citation.bib <http://jmlr.org/papers/v17/15-355.bib>`_).Bayer, I. "fastFM: A Library for Factorization Machines" Journal of Machine Learning Research 17, pp. 1-5 (2016)libra_py: A Package for sparsity problem============================================Supported Operating Systems---------------------------fastFM has a continuous integration / testing servers (Travis) for **Linux (Ubuntu 14.04 LTS)**and **OS X Mavericks**. Other OS are not actively supported.Usage-----.. code-block:: pythonfrom fastFM import alsfm = als.FMRegression(n_iter=1000, init_stdev=0.1, rank=2, l2_reg_w=0.1, l2_reg_V=0.5)fm.fit(X_train, y_train)y_pred = fm.predict(X_test)Tutorials and other information are available `here <http://arxiv.org/abs/1505.00641>`_.The C code is available as `subrepository <https://github.com/ibayer/fastFM-core>`_ and providesa stand alone command line interface. If you have still **questions** after reading the documentation please open a issue at GitHub.+----------------+------------------+-----------------------------+| Family         | Solver           | Loss                        |+================+==================+=============================+| Gaussian       | LBI_Linear       | Square Loss                 |+----------------+------------------+-----------------------------+| Binomial       | LBI_Logit        | Logit Model                 |+----------------+------------------+-----------------------------+*Supported solvers and tasks*Installation------------**binary install**``pip install libra_py``Tests-----
Package: libra_py_001_02
Citing libra_py_001_01=============The library libra_py is an academic project. The time and resources spent developing fastFM are therefore justifiedby the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry `citation.bib <http://jmlr.org/papers/v17/15-355.bib>`_).Bayer, I. "fastFM: A Library for Factorization Machines" Journal of Machine Learning Research 17, pp. 1-5 (2016)libra_py: A Package for sparsity problem============================================Supported Operating Systems---------------------------fastFM has a continuous integration / testing servers (Travis) for **Linux (Ubuntu 14.04 LTS)**and **OS X Mavericks**. Other OS are not actively supported.Usage-----.. code-block:: pythonfrom fastFM import alsfm = als.FMRegression(n_iter=1000, init_stdev=0.1, rank=2, l2_reg_w=0.1, l2_reg_V=0.5)fm.fit(X_train, y_train)y_pred = fm.predict(X_test)Tutorials and other information are available `here <http://arxiv.org/abs/1505.00641>`_.The C code is available as `subrepository <https://github.com/ibayer/fastFM-core>`_ and providesa stand alone command line interface. If you have still **questions** after reading the documentation please open a issue at GitHub.+----------------+------------------+-----------------------------+| Family         | Solver           | Loss                        |+================+==================+=============================+| Gaussian       | LBI_Linear       | Square Loss                 |+----------------+------------------+-----------------------------+| Binomial       | LBI_Logit        | Logit Model                 |+----------------+------------------+-----------------------------+*Supported solvers and tasks*Installation------------**binary install**``pip install libra_py``Tests-----
Package: libra_py_001_03
Citing libra_py_001_02=============The library libra_py is an academic project. The time and resources spent developing fastFM are therefore justifiedby the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry `citation.bib <http://jmlr.org/papers/v17/15-355.bib>`_).Bayer, I. "fastFM: A Library for Factorization Machines" Journal of Machine Learning Research 17, pp. 1-5 (2016)libra_py: A Package for sparsity problem============================================Supported Operating Systems---------------------------fastFM has a continuous integration / testing servers (Travis) for **Linux (Ubuntu 14.04 LTS)**and **OS X Mavericks**. Other OS are not actively supported.Usage-----.. code-block:: pythonfrom fastFM import alsfm = als.FMRegression(n_iter=1000, init_stdev=0.1, rank=2, l2_reg_w=0.1, l2_reg_V=0.5)fm.fit(X_train, y_train)y_pred = fm.predict(X_test)Tutorials and other information are available `here <http://arxiv.org/abs/1505.00641>`_.The C code is available as `subrepository <https://github.com/ibayer/fastFM-core>`_ and providesa stand alone command line interface. If you have still **questions** after reading the documentation please open a issue at GitHub.+----------------+------------------+-----------------------------+| Family         | Solver           | Loss                        |+================+==================+=============================+| Gaussian       | LBI_Linear       | Square Loss                 |+----------------+------------------+-----------------------------+| Binomial       | LBI_Logit        | Logit Model                 |+----------------+------------------+-----------------------------+*Supported solvers and tasks*Installation------------**binary install**``pip install libra_py``Tests-----