Saravanan666/pypi-org huggingface dataset:
Package: fathomdata
fathomdataPython package to make interacting with life sciences manufacturing data quick and intuitive. Getting the data should be the easy part.UsageAPI setupimport fathomdata as fd

fd.set_api_key('xxx')Get structured dataframes for documents that have been ingesteddocuments = fd.available_documents()
for index, row in documents.iterrows():
    document = fd.get_document(row['DocumentId'])
    print(document.get_materials_df())
    print(document.get_steps_df())
    print(document.get_parameters_df())Ingest a new document into the datasetnew_document_id = fd.ingest_document(path="/path/to/document.pdf")Create control charts for continuous process validationimport matplotlib.pyplot as plt

document_ids = documents['DocumentId'].tolist()

actuals = fd.get_parameter_actuals_across_documents(document_ids)
print(actuals)

titer_actuals = actuals.loc['Titer']
yield_actuals = actuals.loc['Yield']

first_document_params_df = fd.get_document(document_ids[0]).get_parameters_df()

titer_operating_limits = {
    'lower': first_document_params_df.at['Titer', 'Lower Operating Limit'],
    'upper': first_document_params_df.at['Titer', 'Upper Operating Limit']
}

yield_operating_limits = {
    'lower': first_document_params_df.at['Yield', 'Lower Operating Limit'],
    'upper': first_document_params_df.at['Yield', 'Upper Operating Limit']
}

fig, axes = plt.subplots(2, 1, sharex=True, figsize=(8,12))
titer_control_chart = fd.create_control_chart(axes[0], titer_actuals, titer_operating_limits['lower'], titer_operating_limits['upper'])
yield_control_chart = fd.create_control_chart(axes[1], yield_actuals, yield_operating_limits['lower'], yield_operating_limits['upper'])
Package: fathom-global-client-sdk
Fathom Python SDK clientThe Python SDK client for communicating with the Fathom APIExample usage:"""An example usage of the Fathom API client."""importosfromfathom.sdk.v2importClient,point,polygon,write_tiffsif__name__=="__main__":client=Client(os.getenv("FATHOM_CLIENT_ID"),os.getenv("FATHOM_CLIENT_SECRET"),os.getenv("FATHOM_API_ADDR"),)layer_ids=["GLOBAL-1ARCSEC-00_OFFSET-1in10-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v0.0.0_test","GLOBAL-1ARCSEC-00_OFFSET-1in100-PLUVIAL-DEFENDED-DEPTH-2020-PERCENTILE50-v0.0.0_test",]# Get from pointspt=point(lat=51.996147,lng=-2.159495)points_response=client.geo.get_points(([pt]),layer_ids)# Or using a polygonpoly=polygon([point(51.45,0),point(51.55,0),point(51.55,-0.1),point(51.45,-0.1),point(51.45,0),])polygon_response=client.geo.get_polygon(poly,layer_ids)write_tiffs(polygon_response,output_dir,)
Package: fathom-lib
Fathom Python libraryThis is a library that helps in defining model pipelines.Modules:component_autogeneration- generates schemas for defining Machine Learning componentsexperimentation- generates scripts for mlflow to run experimentsmetrics- defines non-standard metrics for predictive maintenance modelsmodels- wrappers for predictive maintenance models (XGBoostAFT)transformers- defines non-standard scikit-learn transformationsvalidation- defines validation algorithms for predictive maintanenceDevelopmentRun testspytest tests
Package: fathomnet
fathomnet-pyfathomnet-pyis a client-side API to help scientists, researchers, and developers interact withFathomNetdata.>>>fromfathomnet.apiimportboundingboxes>>>boundingboxes.find_concepts()['2G Robotics structured light laser','55-gallon drum',...]>>>fromfathomnet.apiimportimages>>>images.find_by_concept('Nanomia')[AImageDTO(id=2274942,uuid='cdbfca66-284f-48ac-a36f-7b2ac2b43533',url='https://fathomnet.org/static/m3/framegrabs/MiniROV/images/0056/02_18_37_20.png',...),...]>>>fromfathomnet.apiimporttaxa>>>taxa.find_children('mbari','Bathochordaeus')[Taxa(name='Bathochordaeus stygius',rank='species'),Taxa(name='Bathochordaeus charon',rank='species'),Taxa(name='Bathochordaeus mcnutti',rank='species')]>>>fromfathomnet.apiimportxapikey>>>xapikey.auth('NuCLjlNUlgHchtgDB01Sp1fABJVcWR')# your API key hereAuthHeader(type='Bearer',token='eyJhbGciOiJI...')Thefathomnet-pyAPI offers native Python interaction with the FathomNet REST API, abstracting away the underlying HTTP requests.Installingfathomnet-pyfathomnet-pyis available on PyPI:$python-mpipinstallfathomnetAPI Reference available onRead the Docs
Package: fathon
fathon (v1.3.3)Current version is available for Linux (x86_64 and ARM64), macOS (x86_64), and Windows (64bit).Python package for detrended fluctuation analysis (DFA) and related algorithms.fathonprovides five main algorithms, namelyDFA(Detrended Fluctuation Analysis)MFDFA(Multifractal Detrended Fluctuation Analysis)DCCA(Detrended Cross-Correlation Analysis)MFDCCA(Multifractal  Detrended Cross-Correlation Analysis)HT(Time-dependent Hurst exponent)MFDFAandMFDCCAalso provides methods for the mass exponent τ and the multifractal spectrum f(α).DCCAhas methods to compute the cross-correlation coefficient ρ_DCCA and the corresponding confidence intervals.RequirementsPython 3.8+numpy (version >= 1.24.4 for Python < 3.12, version >= 1.26.2 for Python >= 3.12)Installationpip install fathon
Package: fatiando
Website and documentation:www.fatiando.orgDevelopment documentation:www.fatiando.org/devMailing list (get updates and ask for help):groups.google.com/d/forum/fatiandoOverviewOur goal is provide a comprehensive and extensible framework
for geophysical data analysis and the development of new methodologies.Research:Make your research morereproducibleby writing a Python script or Jupyter
notebook instead of clicking through complicated menus.Development:Don’t start from scratch! Build upon the existing tools in Fatiando to develop
new methods.Teaching:Combine Fatiando with the Jupyter notebook to make rich, interactive documents.
Great for teaching fundamental concepts of geophysics!Getting startedInstall Fatiandoand its
dependencies.Browse theGalleryandCookbookfor examples of what
Fatiando can do.Take a look at theDocumentationfor
a detailed reference of the library.Get involved in the development through ourDeveloper Guide.Participating and asking for helpSubscribeto our Google Groups mailing list to stay informed and ask for
help:groups.google.com/d/forum/fatiandoWe’ll post updates to the list about new releases and features, events, and
future plans for the project.Get involvedto help us shape the project and make it even better!Another option for reaching out and reporting bugs is toopen an issue on Github.We have anopen developmentprocess where everything is discussed throughGithub issues. Anyone can
comment and give feedback. See ourRoadmap for v1.0to get a feeling for where
the project is headed.Your input is welcome!CitingIf you use it in your research, please cite Fatiando in your publications as:Uieda, L., V. C. Oliveira Jr, and V. C. F. Barbosa (2013), Modeling the
Earth with Fatiando a Terra, Proceedings of the 12th Python in Science
Conference, pp. 91 - 98.Pleasealso cite the method papersof individual functions/classes.
References are available in the documentation of each module.See theCITATION.rstfile or theCiting sectionof the docs for more
information.LicenseFatiando a Terra is free software: you can redistribute it and/or modify it
under the terms of theBSD 3-clause License. A copy of this license is
provided inLICENSE.txt.
Package: fatld
Four And Two Level DesignsThefatldpackage contains functionality to generate and characterize designs with four-and-two-level (FATL) factors.
Design characteristics include word length pattern, defining relation, and number of clear interactions.
For more information about the package see the documentation athttps://abohyndoe.github.io/fatld/.
A large collection of FATL designs can be explored interactively using a web app athttps://abohyndoe.shinyapps.io/fatldesign-selection-tool/.UsageThe package can be used from Python:>>>importfatld>>>D=fatld.Design(runsize=32,m=1,cols=[21,27,29])>>>D.wlp()[1,3,3,0,0]>>>D.defining_relation()['A1cef','A3deg','A1cdeh']>>>print("There are%s2-2 interactions clear from any main effect or other two-factor interaction."%D.clear('2-2'))Thereare62-2interactionsclearfromanymaineffectorothertwo-factorinteraction.>>>print("The design contains%sfour-level factors and%stwo-level factors"%(D.m,D.n))Thedesigncontains1four-levelfactorsand6two-levelfactorsFor more examples see the documentation.InstallationThe Python interface to the package is available on pypi. Installation can be done using the following command:pipinstallfatldDevelopmentAll development is done in a virtual environment based onpoetry, activate it using:poetryshellCode styleTry to follow thePEP 8style guide. A usefull tool for automated formatting isblack. We do allow lines upto 120 characters.Document functions using theNumpy docstringconventionLinting is based onruff, configuration is found in thepyproject.tomlfile.Tests are ran usingpytestand a coverage report can be generated usingcoverageinside the virtual environment:coveragerun-mpytesttests
coveragereport-mSubmitting codeIf you would like to contribute, please submit a pull request. (See theGithub Hello Worldexample, if you are new to Github).By contributing to the repository you state you own the copyright to those contributions and agree to include your contributions as part of this project under the BSD license.Bugs reports and feature requestsTo submit a bug report or feature request use theGithub issue tracker.
Search for existing and closed issues first. If your problem or idea is not yet addressed, please open a new issue.ContactFor further information please contact alexandre dot bohyn at kuleuven dot be
Package: fat-macho
fat-machoPython wrapper of thefat-machoRust crate.Installationpipinstallfat-machoUsageGenerate a Mach-O fat binaryfromfat_machoimportFatWriterwriter=FatWriter()withopen("x86_64_thin_file_path","rb")asf:writer.add(f.read())withopen("arm64_thin_file_path","rb")asf:writer.add(f.read())# Get Mach-O fat binary as bytesfat_bytes=writer.generate()# Write to filewriter.write_to("fat_file_path")LicenseThis work is released under the MIT license. A copy of the license is provided in theLICENSEfile.
Package: fatpack
fatpackPython package for fatigue analysis of data series. The package
requiresnumpy.InstallationEither install from the github repository (latest version),pip install git+https://github.com/gunnstein/fatpack.gitinstall from the python package indexpip install fatpackor from the conda-forge:conda install --channel=conda-forge fatpackUsageThe package provides functionality for rainflow cycle counting, defining
endurance curves, mean and compressive stress range correction
and racetrack filtering. The code example below shows how fatigue damage
can be calculated:importnumpyasnpimportfatpack# Assume that `y` is the data series, we generate one herey=np.random.normal(0.,30.,size=10000)# Extract the stress ranges by rainflow countingS=fatpack.find_rainflow_ranges(y)# Determine the fatigue damage, using a trilinear fatigue curve# with detail category Sc, Miner's linear damage summation rule.Sc=90.0curve=fatpack.TriLinearEnduranceCurve(Sc)fatigue_damage=curve.find_miner_sum(S)An example is included (example.py) which extracts rainflow cycles,
generates the rainflow matrix and rainflow stress spectrum, see the
figure presented below. The example is a good place to start to get
into the use of the package.Additional examples are found in theexamples folder.SupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow.
Create a branch, add commits, andopen a pull request.
Package: fatsecret
Looking for new maintainerLife and career have taken me in a different direction and I no longer have time to keep this updated. I've attempted to get this merged back into the original with no success. If you're interested in continuing this then let me know.pyfatsecretThis library provides a lightweight python wrapper for the Fatsecret API with the goal of making it easier to visualize the data retrieved from the API. To that end, this library will usually return lists of identical elements for ease of plotting, discarding extra header fields that the Fatsecret API otherwise includes. All API calls return either a single or list of JSON dictionaries.InstallationInstall the module via pip$pipinstallfatsecretor easy_install::$easy_installfatsecretConfigRegister for a developer account atFatsecret. You will need your Consumer Key and Consumer Secret key for your application.UsageFatsecret supports both delegated and public calls. Only through delegated calls can you access Fatsecret user profile data.If you're only interested in the public data you only require a session to make HTTP requests:fromfatsecretimportFatsecretfs=Fatsecret(consumer_key,consumer_secret)Once you have created a session then you can start reading from Fatsecret's public food and recipe databasefoods=fs.foods_search("Tacos")Refer to thedocumentationfor further examples and detailContributingAll contributions are welcome! I'm not actively maintaining this library, so there's a good chance that any API changes have not been implemented in this wrapper.
Package: fatslim
FATSLiM (Fast Analysis Toolbox for Simulations of Lipid Membranes and its goalis to provide an efficient, yet robust, tool to extract physical parameters from MD trajectories.The main objective of FATSLiM is to decrease as possible the amount of time needed to
analyze MD trajectories: the ultimate goal is to process a several-gigabyte-big file is just a
few minutes or less. This is why a rather important part of FATSLiM’s development is
focused on code optimization and simplification in order to maximize its efficiency.
Package: fattails
Fat TailsPython package and Jupyter notebooks for fat-tailed statistics. Inspired by Nassim Taleb's Technical Incerto.ContentNotebooksSee thenotebooks/README.mdfor more detail.My favourite notebooks so far:Central Limit Theorem: How the sum of Uniform values is GaussianS&P500: How geometric average return is impossibleGameStop: January 2021 was not an outlier if you assume Power Law tails.FunctionsQuick Access:fattails.mad(): Calculates mean absolute deviation.fattails.plot_MS_moments(): Plots the cumulative max/sum ratio of moments 1 to 4.Other:fattails.metrics.get_survival_probability(): Calculate survival probabilities for a given dataset.fattails.metrics.calculate_moments(): Generate dataframe with the chosen moments for each datapointfattails.metrics.max_over_sum(): Calculate the cumulative max/sum ratioExample:$ pip install fattails
$ python

>>> import fattails
>>>
>>>
>>> fattails.mad([1,2,3]) # Calculate Mean Absolute Deviation of [1,2,3]
0.6666666666666666
>>>
>>>
>>> fattails.metrics.get_survival_probability([1,2,3]) # Get survival probability for each value in your data
0    0.75
1    0.50
2    0.25
Name: survival_probability, dtype: float64DerivationsHow much data do I need?ContactJump straight todiscussionsor check out thecontributing guidelines. Feedback and suggestions are really appreciated!ContributorsSpecial thanks to:Daniel Reti:github.com/dretttExternal ResourcesTechnical Incerto Book One:Free PDF:researchers.one,arxiv.orgErrata:fooledbyrandomness.comMore Links:Incerto Reading Club:Website,GitHubStatisticalConsequencesOfFatTails: Code and Links collected byMarcos Carreira
Package: fatter
Fatter is a program for manipulating fat graphs.Fatter can be run as a Python 2, Python 3 orSage Pythonmodule. For even more speed (~25% more)
consider running fatter with the -O optimise bytecode option.InstallationFatteris available on thePython Package
Index. The preferred method for installing the latest
stable release is to usepip(included in Python 2.7.9 and Python 3.4 by default):> python -m pip install fatter --user --upgradeWarningIn order to use the fatter GUI on OS X, users must first update
their copy of Tk/Tcl as describedhere.
Flipper has been tested withActiveTcl 8.5.18.
Additionally, if running fatter under Sage, users must then reinstall sage python
by using the command:> sage -f pythonWarningAs of Sage 6.9, Sage no longer appears to load packages from the user directory.
Therefore users may need to either install fatter directly into Sage (which may require
superuser privileges) or add the path to fatter to their SAGE_PATH environment variable.UsageOnce installed, start the fatter GUI by using:> python -m fatter.appCitingIf you find fatter useful in your research, please consider citing it. A suggested
reference is:Mark Bell. fatter (Computer Software).
pypi.python.org/pypi/fatter, 2016. Version <<version number>>the BibTeX entry:@Misc{fatter,
        author = {Bell, Mark},
        title = {fatter (Computer Software)},
        howpublished = {\url{pypi.python.org/pypi/fatter}},
        year = {2016},
        note = {Version <<version number>>}
}or the BibItem:\bibitem{fatter} Mark Bell: \emph{fatter (Computer Software)},
        \url{pypi.python.org/pypi/fatter}, (2016),
        Version <<version number>>.DevelopmentThe latest development version of fatter is available fromBitBucket.
Alternatively, you can clone the mercurial repository directly using
the command:> hg clone https://bitbucket.org/mark_bell/fatterAnd then install using the command:> python setup.py install --user
Package: fattura24
# Python SDK for fattura24 api### API Referencehttps://www.fattura24.com/api-documentazione/### Installationpip install fattura24### Usageimport fattura24fe = fattura24.FEDocument(api_key=settings.FATTURA24_API_KEY)res = fe.send()if res.findtext(‘returnCode’) == ‘0’:doc_number = res.findtext(‘docNumber’)
doc_id = res.findtext(‘docId’)
Package: fattura-elettronica-reader
fattura-elettronica-readerValidate, extract, and generate printables of electronic invoice files
received from theSistema di Interscambio.fattura-elettronica-readerDocumentationAPI examplesCLI helpsLicenseChangelog and trusted sourceCrypto donationsDocumentationhttps://docs.franco.net.eu.org/fattura-elettronica-readerAPI examplesfattura-elettronica-reader has apublic API.
This means for example that you can you easily read invoice files within
another Python program:>>>importfattura_elettronica_reader>>>data={'patched':True,'configuration_file':str(),'write_default_configuration_file':False,'extract_attachments':True,'metadata_file':'myfile.xml','invoice_xslt_type':'ordinaria','no_invoice_xml_validation':False,'force_invoice_schema_file_download':False,'generate_html_output':True,'invoice_filename':str(),'no_checksum_check':False,'force_invoice_xml_stylesheet_file_download':False,'ignore_attachment_extension_whitelist':False,'ignore_attachment_filetype_whitelist':False,'ignore_signature_check':False,'ignore_signers_certificate_check':False,'force_trusted_list_file_download':False,'keep_original_file':True,'ignore_assets_checksum':False,'destination_directory':'/dev/shm/invoices',}>>>fattura_elettronica_reader.assert_data_structure(source='invoice',file_type='p7m',data=data)>>>fattura_elettronica_reader.pipeline(source='invoice',file_type='p7m',data=data,)Have a look at theinvoicespage in theftutorialsrepository.CLI helps$fattura_elettronica_reader--helpLicenseCopyright (c) 2018 Enio Carboni - ItalyCopyright (C) 2019-2023 Franco Masotti (franco \D\o\T masotti {-A-T-} tutanota \D\o\T com)fattura-elettronica-reader is free software: you can redistribute it
and/or modify it under the terms of the GNU General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.fattura-elettronica-reader is distributed in the hope that it will be
useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
Public License for more details.You should have received a copy of the GNU General Public License along
with fattura-elettronica-reader. If not, seehttp://www.gnu.org/licenses/.Changelog and trusted sourceYou can check the authenticity of new releases using my public key.Changelogs, instructions, sources and keys can be found atblog.franco.net.eu.org/software#fattura-elettronica-reader.Crypto donationsBitcoin:bc1qnkflazapw3hjupawj0lm39dh9xt88s7zal5mwuMonero:84KHWDTd9hbPyGwikk33Qp5GW7o7zRwPb8kJ6u93zs4sNMpDSnM5ZTWVnUp2cudRYNT6rNqctnMQ9NbUewbj7MzCBUcrQEYDogecoin:DMB5h2GhHiTNW7EcmDnqkYpKs6Da2wK3zPVertcoin:vtc1qd8n3jvkd2vwrr6cpejkd9wavp4ld6xfu9hkhh0
Package: fatush
Translation ToolkitA collection of scripts to streamline the translation of markdown files using vector stores and deep learning.OverviewThis toolkit provides a set of Python scripts designed to simplify the translation process for markdown files. The scripts leverage embedding models to enhance the accuracy of document retrieval and improve the overall translation workflow.ScriptsInitialization Scriptsearch_word.pyThesearch_word.pyscript initializes a search engine for retrieving relevant documents based on embeddings. It is designed to work with markdown files in multiple languages. The CLI app is based onTyper.UsageInstallation:Install the module itself:install the toolset:pip install fatushthen run the script:fatush runInstall the whole project:Clone this repository:git clone https://github.com/alperiox/fatush.gitChange the directory to the project folder:cd fatushInstall the required dependencies using pip or poetry:pip install -r requirements.txtorpoetry installConfiguration:Run the initialization script:python fatush/search_word.py runFollow the prompts to set up the initial configuration.If theconfig.yamlfile is not found, the script will fetch documents from the FastAPI repo and create the necessary configuration file.Processing Documents:The script will process the documents based on the provided configuration.It'll split the documents line by line and then calculate their embeddings to set up a FAISS vector store.Loading Embedding Model:The used embedding model isall-MiniLM-L6-v2, which is rather popular for vectorstores.Vector Store:If the vector store path is not found in the configuration, it will be constructed and loaded. It'll be loaded automatically otherwise.Currently, the only implemented vector store isFAISS.Search Engine Initialization:The search engine is initialized with the loaded embedding model and vector store.The script will start the inference process to provide relevant search results.TODOs:Integration testsHopefully, a web application based on FastAPIYet another tool for automatically suggesting initial translations for the given textCurrently, the scripts don't cover all exceptions. This may require you to reconfigure the script by deleting the config file and the downloaded repository if you do not run the script directly without configuring any options.Configuration Options forsearch_word.pysource_lang: Source language code (e.g., 'en').translation_lang: Translation language code (e.g., 'tr').docs_path: Path to the documents (default is the current working directory).vectorstore_path: Path to the vector store (default is the current working directory).NoteSince the project is built on my experience with translating FastAPI documentation, a nicer abstraction is a must for a more generally usable toolset. That is because there are several hard-coded variables at the moment, like fetching the documentation from the FastAPI repository.