Saravanan666/pypi-org huggingface dataset:
Package: pyhacores
Cores written withPyhaFree software: Apache Software License 2.0Available cores:CORDIC: CORDIC core.NCO: Numerically controlled oscillator, based on CORDIC.ToPolar: Converts IQ to polar form, returning equal tonp.abs()andnp.angle()/pi.Angle: Equal tonp.angle()/pi.Abs: Equal tonp.abs().FIR: Transposed form FIR filter, equal toscipy.signal.lfilter().MovingAverage: Hardware friendly implementation of moving average filter.DCRemoval: Filter out DC component, internally uses chainedMovingAverageblocks.CRC16: Calculate 16 bit CRC, galois based.HeaderCorrelator: Correlate against 16 bit package header.QuadratureDemodulator: Demodulate FM, FSK, GMSK…BladeRFSource: Convert BladeRF style I/Q (4 downto -11) into Pyha Complex (0 downto -17) typeBladeRFSink: Convert Pyha Complex style (0 downto -17) into BladeRF style I/Q (4 downto -11)ComplexConjugate: Complex conjugate with output registerComplexMultiply: Complex multiplycate with output register
Package: pyhacrf
Hidden alignment conditional random field for classifying string pairs -
a learnable edit distance.This package aims to implement the HACRF machine learning model with asklearn-like interface. It includes ways to fit a model to training
examples and score new example.The model takes string pairs as input and classify them into any number
of classes. In McCallum’s original paper the model was applied to the
database deduplication problem. Each database entry was paired with
every other entry and the model then classified whether the pair was a
‘match’ or a ‘mismatch’ based on training examples of matches and
mismatches.I also tried to use it as learnable string edit distance for normalizing
noisy text. SeeA Conditional Random Field for Discriminatively-trained
Finite-state String Edit Distanceby McCallum, Bellare, and Pereira,
and the reportConditional Random Fields for Noisy text normalisationby Dirko Coetsee.ExamplefrompyhacrfimportStringPairFeatureExtractor,Hacrftraining_X=[('helloooo','hello'),# Matching examples('h0me','home'),('krazii','crazy'),('non matching string example','no really'),# Non-matching examples('and another one','yep')]training_y=['match','match','match','non-match','non-match']# Extract featuresfeature_extractor=StringPairFeatureExtractor(match=True,numeric=True)training_X_extracted=feature_extractor.fit_transform(training_X)# Train modelmodel=Hacrf(l2_regularization=1.0)model.fit(training_X_extracted,training_y)# Evaluatefromsklearn.metricsimportconfusion_matrixpredictions=model.predict(training_X_extracted)print(confusion_matrix(training_y,predictions))>[[03]>[20]]print(model.predict_proba(training_X_extracted))>[[0.949148120.05085188]>[0.923977110.07602289]>[0.867560340.13243966]>[0.054388120.94561188]>[0.026412750.97358725]]DependenciesThis package depends onnumpy. The LBFGS optimizer inpylbfgsis
used, but alternative optimizers can be passed.InstallInstall by running:python setup.py installor from pypi:pip install pyhacrfDevelopingClone from repository, thenpip install -r requirements-dev.txt
cython pyhacrf/*.pyx
python setup.py installTo deploy to pypi, make sure you have compiled the *.pyx files to *.c
Package: pyhacrf-datamade
Hidden alignment conditional random field for classifying string pairs -
a learnable edit distance.Part of the Dedupe.io cloud service and open source toolset for de-duplicating and finding fuzzy matches in your data:https://dedupe.ioThis package aims to implement the HACRF machine learning model with asklearn-like interface. It includes ways to fit a model to training
examples and score new example.The model takes string pairs as input and classify them into any number
of classes. In McCallum’s original paper the model was applied to the
database deduplication problem. Each database entry was paired with
every other entry and the model then classified whether the pair was a
‘match’ or a ‘mismatch’ based on training examples of matches and
mismatches.I also tried to use it as learnable string edit distance for normalizing
noisy text. SeeA Conditional Random Field for Discriminatively-trained
Finite-state String Edit Distanceby McCallum, Bellare, and Pereira,
and the reportConditional Random Fields for Noisy text normalisationby Dirko Coetsee.ExamplefrompyhacrfimportStringPairFeatureExtractor,Hacrftraining_X=[('helloooo','hello'),# Matching examples('h0me','home'),('krazii','crazy'),('non matching string example','no really'),# Non-matching examples('and another one','yep')]training_y=['match','match','match','non-match','non-match']# Extract featuresfeature_extractor=StringPairFeatureExtractor(match=True,numeric=True)training_X_extracted=feature_extractor.fit_transform(training_X)# Train modelmodel=Hacrf(l2_regularization=1.0)model.fit(training_X_extracted,training_y)# Evaluatefromsklearn.metricsimportconfusion_matrixpredictions=model.predict(training_X_extracted)print(confusion_matrix(training_y,predictions))>[[03]>[20]]print(model.predict_proba(training_X_extracted))>[[0.949148120.05085188]>[0.923977110.07602289]>[0.867560340.13243966]>[0.054388120.94561188]>[0.026412750.97358725]]DependenciesThis package depends onnumpy. The LBFGS optimizer inpylbfgsis
used, but alternative optimizers can be passed.InstallInstall by running:python setup.py installor from pypi:pip install pyhacrfDevelopingClone from repository, thenpip install -r requirements.txt
cython pyhacrf/*.pyx
python setup.py installTo deploy to pypi, make sure you have compiled the *.pyx files to *.c
Package: pyhafas
pyHaFASA python client for HaFAS public transport APIs.InstallationYou only need to install the pyhafas package, for example using pip:$pipinstallpyhafasThat’s it!Development setupFor development isrecommendedto use avenv.$python3-mvenv.venv
$source.venv/bin/activate
$pythonsetup.pydevelopBackgroundThere'sa company called HaConthat sellsa public transport management system called HAFAS. It isused by companies all over Europeto serve routing and departure information for apps. All those endpoints are similar, with the same terms and API routes, but have slightly different options, filters and sets of enabled features.Relatedhafas-client– JavaScript client for the HAFAS API.public-transport-enabler– Unleash public transport data in your Java project.Friendly Public Transport Format– A format for APIs, libraries and datasets containing and working with public transport data.db-hafas– JavaScript client for the DB HAFAS API.ContributingIf youhave a question,found a bugor want topropose a feature, have a look atthe issues page.
Package: pyhagl
Human Abstraction Gymnasium Language (HAGL)Goal of this library is to simplify work with environment. In some times when you create environment there are
necessity to use Box space because of library support, simplicity and etc. For exampleBox(-1, 1, (20,))And then, when implementing step method work with observation as numerical array:position = observation[0:2]Such work could be very uncomfortable. One of the existed solution is to use Dict space:Dict({
    "position": Box(-1, 1, (2,)),
    "velocity": Box(-1, 1, (2,))
})Dict can be transformed in Box space with flatten, unflatten and flatten_space functions from Gymnasium.
With HAGL it can be written in such way:class Observation:
    position = Position()
    velocity = Velocity()This allows to use objects in step function:position = observation.positionAnd then, using compile_type function or HAGLWrapper (for Gymnasium), HAGLParallelWrapper (for PettingZoo) this will be
transformed to Box space. Difference of HAGL and Dict is that:Can add method to class and use it in step functionCan use inheritanceWhen describe spaces with HAGL there are possibility to use usual python types for float, bool and arraysThere are some built-in types, for example Position and Velocity.As a result: HAGL can be used to automate transformation of Gymnasium space representation to Python object
Package: pyhaikunator
Heroku like random name generator.There is an djective and a noun list and it generates random name joining them by user provided delimiter.InstallJust install using pip:$ pip install pyhaikunatorUsagefromhaikunatorimportHaikunatorHaikunator.haikunate()# <-- 'icy-dream-4198'# drop random token at the endHaikunator.haikunate(0)# <-- 'quiet-tree'# supply a delimiterHaikunator.haikunate(delimiter='~')# <-- 'shy-feather-4125'# drop random token and supply delimiter at the same timeHaikunator.haikunate(0,' ')# <-- 'wandering glitter'# provide a larger range for random token at the endHaikunator.haikunate(100000,'🐮')# <-- spring🐮pine🐮71030TestsJust run module:$ python haikunator.pyLicenseMIT
Package: pyhail
Python Hail Retrieval Toolkit (pyhail) ⛈️📡🧊This toolkit provides a collection of hail retrieval techniques for
weather radar data using thePy-ARTtoolkit.Library DependenciesPy-ARTnumpynetCDF4scipyscikit-imageNotebook DependenciesmatplotlibcartopyHail Retrivals*Hail Size Discrimination Algorithm - HSDA (Ortega et al. 2016)Hail Differential Reflectivity - HDR (Depue et al. 2007)Maximum Expected Size of Hail - MESH witt1998 (Witt et al. 1998)Maximum Expected Size of Hail - MESH mh2019_75/mh2019_95 (Murillo and Homeyer 2019)Accumulated Hail - hAcc (Wallace et al. 2019)*Note that the Q confidence vector from Park et al. 2009 has not been implemented and all pixels are assigned a value of q=1.MESH is implemented for both pyart radar (PPI) and grid (Cartesian) data!Install using pypipip install pyhailInstall from sourceTo install pyhail, you can either download and unpack the zip file of the source code or use git to checkout the repository:git clone git@github.com:joshua-wx/pyhail.gitTo install in your home directory, use:python setup.py install --userUseExample Notebook
Package: pyhakoot
Note: The current version in this repository is incomplete and will not workAboutpyhakoot is a library to interact with the Kahoot API. KahootPY supports joining and interacting with quizzes and challenges.Installationpip install -U pyhakootUsagefrompyhakootimportclientbot=client()bot.join(12345,"KahootPY")defjoinHandle():passbot.on("Joined",joinHandle)Documentation:Work in progress. This is taking longer than expected because I am not good at python.
Package: pyhakuna
pyhakunapyhakunais a client to access the API of the time keeping servicehakuna.ch.The Hakuna APIis – unfortunately – personal and currently does not allow to access company-wide data.RequirementsThis tool requiresPython 3.Installationpip3install-UpyhakunaUse the same command to update to a new version.UsagefrompyhakunaimportHakunah=Hakuna("instance-name","api-token")u=h.personal().users().me()The API tries to follow the structure of theHakuna API documentation.Developmentpython3-mvenv.venvsource.venv/bin/active
python3-mpipinstall-Upipsetuptoolswheel
python3-mpipinstall-rrequirements.txt
python3-mpipinstall-e.ReleaseSeehttps://packaging.python.org/tutorials/packaging-projects/.tl;dr:rm-rfdist*.egg-info
python3-mpipinstall-Ubuildtwine
python3-mbuild
python3-mtwineuploaddist/*# Username: __token__Licensing and CopyrightThis code is copyrighted.
But it can be used under the terms of theMIT licensefor your own purposes.
It builds upon the following third party modules:requests, a simple HTTP client for Python, which is made available under the terms of theApache Software License.Open source software rocks 🎸!
Package: pyhalboy
PyHalboyA library for all things hypermedia. insipred byHalboyandHalboy.jsCreate hypermedia resourcesMarshal to and from plain python dictsNavigate JSON+HAL APIsAPIResourcesWith PyHalboy you can create resources, and pull information from them.frompyhalboyimportResourcediscountResource=Resource().add_link('self','/discounts/1256').add_property('discountPercentage',10)itemResources=[Resource().add_link('self','/items/534').add_property('price',25.48)]resource=Resource().add_link('self','/orders/123').add_link('creator','/users/rob').add_resource('discount',discountResource).add_resource('items',itemResources).add_property('state','dispatching')resource.get_link('self')//{href:'/orders/123'}resource.get_href('self')//'/orders/123'resource.get_property('state')//'dispatching'resource.get_resource('creator').get_property('discountPercentage')//10resource.get_resource('items')[0].get_property('price')//25.48MarshallingYou can create HAL resources from plain JS objects, and vice versa.frompyhalboyimportResourceitemResources=[Resource().add_link('self','/items/534').add_property('price',25.48)]resource=newResource().add_link('self','/orders/123').add_link('creator','/users/rob').add_resource('items',itemResources).add_property('state','dispatching')resource.to_object()//{//_links:{//self:{href:'/orders/123'},//creator:{href:'/users/rob'}//},//_embedded:{//items:[{//_links:{//self:{href:'/items/534'}//},//price:25.48//}]//},//state:'dispatching'//}Resource.from_object(resource.to_object()).get_href('self')//'/orders/123'NavigationProvided you're calling a HAL+JSON API, you can discover the API and navigate
through its links. When you've found what you want, you callnavigator.resource()and you get a plain old HAL resource, which you can inspect
using any of the methods above.frompyhalboyimportNavigator//GET/-200OK//{//"_links":{//"self":{//"href":"/"//},//"users":{//"href":"/users"//},//"user":{//"href":"/users/{id}",//"templated":true//}//}//}discovery_result=Navigator.discover('https://api.example.com/')users_result=discovery_result.get('users')users_result.status()//200users_result.location()//'https://api.example.com/users'robResult=discovery_result.get('user',{id:'rob'})robResult.location()//'https://api.example.com/users/rob'sue_result=discovery_result.post('user',{'id':'sue','name':'Sue','title':'Dev'})sue_result.location()//'https://api.example.com/users/sue'sue_result.resource().get_property('title')//'Dev'ContributionFeel free to submit PRs and raise bugs.publishingpythonsetup.pyinstallsdistbdist_wheeltwineupload--repository-urlhttps://test.pypi.org/legacy/dist/*
Package: pyhaloxml
PyHaloXMLPython code to read/write .annotation files from Halo. Can export as .geojson for further analysis.ExamplesExample 1: Move rectangles to a different layer and save as.annotation.Example 2: Check if annotations have holes in them and save as.geojson.Example 3: Show the wkt representation of the shapely polygon.Example 4: Create a .annotation file from coordinates.Installationpip install pyhaloxmlLoading speedRust is used to match the negative regions to positive regions, thanks toWim Pomp!It takes 41 seconds to load a 30.9MB file with 856454 vertices in 5769 regions with 731 holes.DevelopmentInstallRustClone the repositorypip install -e .
Package: pyham
MotivationpyHam is a library to facilitate the analysis of hierarchical orthologous groups.
It also contains a few tools to run standard type of analysis.Currently, Ham is limited to analyze HOGs that are stored in an orthoXML file.
More information on the schema of orthoxml and some examples are
available athttp://orthoxml.org.For extended documentation we refer to the docs folder that contain information
on common use cases and API documentation of the library.How to cite pyHamIf you use pyHam in your work, please consider citing:Clément-Marie Train, Miguel Pignatelli, Adrian Altenhoff, Christophe Dessimoz; iHam and pyHam: visualizing and processing hierarchical orthologous groups, Bioinformatics, bty994, https://doi.org/10.1093/bioinformatics/bty994InstallationHam is written in python3 (also compatible with python2), with little external dependencies, i.e.
currently ete3, lml, six. The setup script should resolve these
dependencies automatically.
Consider using pip to install the package directly from a checked out git repopython-mpipinstall--upgradepippipinstallpyhamExampleWe prepare a ready-to-use example (see example folder) with few python scripts to use main pyHam features.
You just have to run the following command in bash:pythonrun_hog_queries.pypythonrun_treeProfile.pypythonrun_iHam.pyGetting startedWe create a small introductory blog post about HOGs and pyHam athttp://lab.dessimoz.org/blog/2017/06/29/pyham. We highly recommend you to read it before starting using pyHam.We also create an ipython notebook to help you with basic uses of pyHam API and embedded tools athttp://zoo.cs.ucl.ac.uk/tutorials/tutorial_pyHam_get_started.html.Table of compatibilitySupport for pyHam by various HOG inference resources.ResourceSpecies tree formatOrthoXMLSUPPORTOMA browserPhyloXMLandNewickAll HOGs, orone HOG at a timeYESOMA standalonePhyloXML and NewickAll HOGsYESEnsemblNewickone HOG at a timeYESHieranoidDBNewickone HOG at a timeYESDocumentationYou can the full documentation of pyHam athttp://zoo.cs.ucl.ac.uk/doc/pyham/index.html
Package: pyHamiltonPSD
The Precision Syringe Drive (PSD) is a compact syringe pump designed to perform
all liquid handling operations including dispensing, serial dispensing and diluting.
There are four types of PSD: 4/6/4 smooth flow/6 smooth flow.
The intention of this library is to provide a simple form of interaction with the pump.
The pump is capable to execute various commands, which can be found in the manual.
The pyHamiltonPSD library goal is to make this commands easy to be used in a Python environment.
The library can be used to run commands in the command line, or to create programs that agregate
multiple commands as scenarios.Change Log1.0.0 (21/05/2021)First Release1.0.1 (09/06/2021)Small correction1.0.2 (15/06/2021)Small correction1.1.0 (23/07/2021)Adding new scripts for syringe movement and velocity speed
-> syringeMovement(..)
-> velocityConfiguration(..)
-> setMaximumMicroStepVelocityPerMinute(..) - used only for PSD smooth flowMethod definePump is updated, it has a new parameter called ‘syringe’
Package: pyHand
A package which converts text to handwrittenwe need to first import the package - import pyHandthen to get to know the information regarding this package you need to use the following command - pyHand.info()for converting you text to handwritten you will use the following command - pyHand.write("Sample Text")then it will ask you to ive the file name : then you can find the handwritten text in png file with name you gave above@SujithSouryaYedida
Package: pyhandle
PYHANDLEPyHandle is a Python client library for interaction with aHandle Systemserver, providing basic create, read, update and delete capabilities for Handles. The library offers a client for the HTTP REST interface. It also includes a client that interacts directly with a Handle server SQL back-end and a client that creates customized Batch files (containing Batch operations that can be performed using the GenericBatch command utility provided by the Handle System), but those two are no longer maintained.PyHandle currently supports Python >=3.5 (tested up to 3.10), and requires at least a Handle System server 9. The library requires OpenSSL v1.0.1 or higher. Python 2.7 and 3.10 are supported.PyHandle is based on a prior development of theEUDAT projectunder the name B2Handle.
AsB2Handlewas developed with a specific scope - Handle operations in the EUDAT project - in mind, it has been improved and made more generic to cater to a broader audience.We advice you to use PyHandle isteard of B2HADNLE.InstallationYou can install the PyHandle library as follows:gitclonehttps://github.com/EUDAT-B2SAFE/PYHANDLE.gitcdPYHANDLE/
pythonsetup.pyinstallThe library is also available on PyPi and can be installed via pip:pipinstallpyhandleFor more information on the methods offered by the library, please consult thetechnical documentation.Instantiate:One of the core steps is to instantiate the file with the needed credentials.
A guide on how to use them is the following:credentials_file = './credentials/creds.json'
# Path must be relative to current working dir
# JSON file must contain absolute paths, or paths relative to the creds.json file!!
creds = pyhandle.clientcredentials.PIDClientCredentials.load_from_JSON(credentials_file)
client = pyhandle.handleclient.PyHandleClient('rest').instantiate_with_credentials(
        creds, HTTPS_verify=https_verify)Link to documentationCheck out the technical documentationhere.Check out the overall documentationhere.LicenseCopyright 2015-2022, Deutsches Klimarechenzentrum GmbH, GRNET S.A., SURFsaraThe PYHANDLE library is licensed under the Apache License,
Version 2.0 (the "License"); you may not use this product except in
compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.Github contributionsDevs:Please make contributions based on the devel branch, then issue a PR to the devel branch.Small contributions (e.g. typos, README, ...) can be pushed directly to devel if you have permissions.Owners:Run unit tests on PRMerged PR into develIf/when changes are ready for a next release, bump the version number (no PR, but push directly to the repo)Run unit tests again on this devel branch.Merge devel into master (no PR, directly merge themwith --no-ff to keep historyand push to master)Add "-dev" to the incremented version number on develSend release to pypi:
Package: pyhandlers
# pyhandlers
[![Downloads](https://pepy.tech/badge/pyhandlers)](https://pepy.tech/project/pyhandlers)
—–Table of Contents[Installation](#installation)[License](#license)## Installationpyhandlers is distributed on [PyPI](https://pypi.org) as a universal
wheel and is available on Linux/macOS and Windows and supports
Python 3.6+.`bash $ pip install pyhandlers `## Licensepyhandlers is distributed under the terms of the
[MIT License](https://choosealicense.com/licenses/mit).
Package: pyhandmouse
pyhandmouse카메라로 손을 포착하여 클릭을 하는 앱 입니다.
현재는 단순한 단일클릭과 뷰하는 화면만 있습니다.
추후 더블클릭,드래그, 정교해진 뷰화면 등 다양하게 개발할 예정입니다.
Package: py-handoff
py-handoff: Like Handoff, but in Pythonpy-handoff is an application used to share clipboard contents across your devices connected to the same LAN.UsageOn each Windows/macOS/Linux device connected to the same LAN, run the following command:pip install py-handoff
    py-handoffNow all your devices would share their clipboard.Optional Environment VariablesPY_HANDOFF_DISCOVERY_PORTBy setting "PY_HANDOFF_DISCOVERY_PORT=<port_number: int>", you can customize the port that py-handoff used to discover other py-handoff applications in the same LAN through UDP; defaults to 5005.PY_HANDOFF_DISCOVERY_KEYBy setting "PY_HANDOFF_DISCOVERY_KEY=<secret_key:str>", you can customize the secret key py-handoff used to encode/decode messages during the discovery phase; defaults to "D0AA67DD-C285-45A2-B7A7-F5277F613E3C".PY_HANDOFF_CLIPBOARD_LISTENER_PORTBy setting "PY_HANDOFF_CLIPBOARD_LISTENER_PORT=<port_number: int>", you can customize the port that py-handoff used to accept clipboard contents sent by other py-handoff applications in the same LAN through TCP; defaults to 6000.PY_HANDOFF_CLIPBOARD_SIZE_LIMIT_IN_MBBy setting "PY_HANDOFF_CLIPBOARD_SIZE_LIMIT_IN_MB=<size: int>", you can customize the maximum size of clipboard contents in MB that py-handoff would send to other py-handoff applications that have been discovered; defaults to 128 MB.Keep in mind that all py-handoff applications can only recognize and communicate with each other if and only if they havethe same PY_HANDOFF_DISCOVERY_PORTandthe same PY_HANDOFF_DISCOVERY_KEY.