Saravanan666/pypi-org huggingface dataset:
Package: hescore-hpxml
HPXML to Home Energy Score TranslatorThis translator script takes an HPXML file or directory of files as an
input and generates HEScore inputs from it. The HEScore inputs are
exported as json.Details of the translation assumptions as well instructions for use can
be found inthe
documentation.InstallationUse avirtualenv.
(Good idea, but not strictly required.)Install using pip:pip install hescore-hpxmlTo get the latest and greatest, clone this repository, cd into the
directory and install as follows:pip install -e .How to useUse the command line script:hpxml2hescore examples/house1.xmlTo get some guidance on how to use the script:hpxml2hescore -h
Package: hesiod53-jumpcloud
# hesiod53-jumpcloudCreate a hesiod53 configuration file populated via jumpcloud. See the
example_configuration.ymlOnly dumps users who have an SSH key.# InstallationThis library depends on python-ldap which requires installing some system librarieslibldap2-devlibsasl2-devlibssl-devNow you can install this librarypip install hesiod53-jumpcloud# Usagejumpcloud myconfig.yml > myusers.yml
Package: hesitate
Likeassert, but less… assertive.Are you skipping on writing yourassertstatements because of performance reasons? Skip no more!
Hesitate will, through probabilistic means, make your Design by Contract-assertions faster!How?By not executing them.…Yeah.Hesitate will measure the execution time on every assert statement you write, and execute the slow
ones less frequently than the fast ones. You decide on what you think is a reasonable amount of time
per assertion on average, and Hesitate will control how often your assertions run in order to match
that.It looks like this:# worker.pydefdo_work(data):assertdata_is_valid(data)# Super slow!returnwork_work(data)# main.pyimporthesitatehesitate.attach_hook(target_timing=0.002,# 2 milliseconds per assert on average)importworkerworker.do_work(invalid_data)# Might not raise AssertionError! Who knows?Hesitate works with an AST rewriter influenced topy.test. It hooks on to Python’s module loader
where it intercepts and rewrites code on the fly as it is loaded. Hesitate stores assertion timings
in a plain dictionary, so everything is forgotten when the application quits. This makes it most
useful for long-living processes, such as application servers.InstallationpipinstallhesitateCallhesitate.attach_hook()as soon as possible when your application starts. It must be done
before any modules you want Hesitate to plug in to are loaded.attach_hooktakes three optional arguments:initial_probability- the probability an assert is initially run, before its timing is known.
The default is 1.0, which causes all asserts to trigger on their first run. Lower this value to
avoid flooding the system with potentially slow assertions each time the application starts.target_timing- the expected duration each assert should converge on, in seconds. The default
is 0.1.convergence_factor- the speed with which the measurements of an assert converges. The timing
of an assert is low pass filtered, this controls how fast the filter follows. 1.0 means that it
always uses the new value. 0.0 means that it will never change from its initial value. The default
is 0.7.Python SupportHesitate is tested with Python 2.7, 3.2, 3.3, 3.4, and PyPy 2.4.Django IntegrationHesitate comes pre-packaged with a Django application that integrates with your settings, and
supports Django 1.6 and 1.7.INSTALLED_APPS=(# Hesitate should be loaded first of all apps!'hesitate.wrappers.django',# The rest of your apps here)HESITATE_ENABLED=True# Optional, defaults to the inverse of DEBUG# These parameters reflect the attach_hook parameters and are all optional:HESITATE_INITIAL_PROBABILITY=1.0HESITATE_TARGET_TIMING=0.1HESITATE_CONVERGENCE_FACTOR=0.7
Package: hesmapy
HesmapyHESMA Python - Tools for reading HESMA modelsDocumentationFull documentation can be foundhere.InstallationCurrently, the package is only available throughpip.pip install hesmapyIn case you want to build the documentation, you'll need to install the optional dependencies as well.
Seepyproject.tomlfor a list of needed packages (mostlysphinxrelated packages)Contentshesmapycontains three main modules, one for each of the main HESMA categories (Hydro, RT, Tracer).
It provides utilties to read and write files in the standardised HESMA file formats, as well as
some utilities such as plotting. These utilities are mostly intended for use by HESMA itself and
might be rather limited in their functionality.HydroHydro1DThis module contains the tools for one-dimensional hydro models. See below for some basic usage examples.Loading models:importhesmapy.baseashpmodel=hp.load_hydro_1d("examples/hydro/hydro_1d.json")Get the data from a model as apd.DataFrame:df=model.get_data()Plot a model:model.plot(show_plot=True)Model files can be written by providing either apd.DataFrame,dictor severalnp.ndarray.
See the documentation for more details.RT(Not yet implemented)Tracer(Not yet implemented)
Package: hesong-ipsc-busnetcli
IPSC专用的消息总线客户端的Python包装。它用于向Python开发者提供SmartBus的客户端功能。Features直接封装IPSC Data Bus的C语言客户端库ipsc-bus-client采用Python标准库的ctypes进行C语言动态/共享库的封装。所以安装时不需要进行编译，理论上同时支持多种Python（如pypy,ironpython,jython）运行时（只要该Python实现的标准库支持ctypes）完整的客户端功能包装,其功能基本上与C语言实现的客户端一对一。Warningipsc-bus-client目前只提供了Linux-x86_64支持。Install通过pip安装使用pip工具从PyPI安装pipinstall--userhesong-ipsc-busnetcli从源代码安装gitclonehttps://bitbucket.org/hesong-opensource/ipsc-bus-client-python.gitcdipsc-bus-client-pythonpythonsetup.pyinstallCHANGELOG3.2.1Date:2018-08-07Change去除 Python 3.0-3.3 的支持重构 SphinxDocs 文档Fix自定义 Error 类构造函数的一处错误Addpylintrc 配置文件Optimize优化包结构许多来自 linter 的优化AUTHORSLiu Xue Yan <liu_xue_yan@foxmail.com>
Package: hesperides-cli
HESPERIDES CLIPython 3 lib & CLI (Command Line Interface) forHesperidesA changelog is available here:CHANGELOG.mdInstallationhesperides-cliis published onPypi. It can be installed simply withpip:pip install hesperides-cliDevelopper environment installationAfter cloning this repo and optionally creating avirtualenv:pip install -e .Releasing a new versionWith a valid~/.pypirc:updateCHANGELOG.mdbump version inhesperidescli/hesperidescli.py&git commit&git pushpython setup.py sdisttwine upload dist/*perform a release on GitGub, including the description added toCHANGELOG.mdUsagehesperides [OPTIONS] COMMAND [ARGS]To get all hesperides commands, just type:hesperidesConfigureThis client configuration is stored locally in~/.hesperides/:To set up your environment and create a local user profile by answering to a few questions:hesperides set-conf [PROFILE_NAME]If you have multiple profiles, you can switch between them with:hesperides use-profile [PROFILE_NAME]To try your configuration, type one of these commands:hesperides get-versions
hesperides get-userUsage with DockerYou can using the followingDockerfileas a starting point:FROM python:3
RUN pip install hesperides-cli
ARG USERNAME
ARG PASSWORD
RUN hesperides set-conf --username $USERNAME --password $PASSWORD --hesperides-endpoint https://hesperides.example --ignore-ssl-warnings
ENTRYPOINT ["hesperides"]The resulting Docker image will contain some secret credentials, which isnota good practice,
but makes it really easy to use for demos:docker build --build-arg USERNAME=... --build-arg PASSWORD=... -t hesperides-cli .
docker run --rm hesperides-cli get-versionsIf your goal is instead to generate files in your Docker image at startup time,
you can start by putting something like this in yourENTRYPOINT:hesperides write-files --app $APP --ptf $PTFExporting properties as environment variablesFor this use case, you can useget-properties --export.Here is an example of usage, that could be included in a Gitlab CD/CI step for instance:tmp_file=$(mktemp) && hesperides get-properties --app APP --ptf PTF --path '#ROOT#test-module#1.1#WORKINGCOPY' --export > $tmp_file && source $tmp_file && rm $tmp_fileLocal files generation with zero calls to the backend APIcf.hesperidescli.localWarning for Windows bash usersWhen using this tool with a Python version installed through Windows,
in combination with a Linux-like shell environment (e.g.Git Bash),
the files generated bywrite-filesmay not be written were you expect,
because your Python executable will interpret Unix path like/var/tmpas relative to your current drive (C:,D:...).In this situation, use this command to find out where is the location of your root (/) for Python:python -c 'import os; os.chdir("/"); print(os.getcwd())'
Package: hespi
HErbarium Specimen sheet PIpelineHespi takes images of specimen sheets from herbaria and first detects the various components of the sheet.Hespi first takes a specimen sheet and detects the various components of it using the Sheet-Component Model.
Then any full database label detected is cropped and this is given to the Label-Field Model
which detects different textual fields written on the label.
A Label Classifier is also used to determine the type of text written on the label.
If it is printed or typewritten, then the text of each field is given to an Optical Character Recognition (OCR) engine
and if there is handwriting, then each field is given to the Handwritten Text Recognition (HTR) engine.
Finally, the result of the fields is post-processed before being written into
an HTML report, a CSV file and text files.The stages of the pipeline are explained in thedocumentation for the pipeline.InstallationInstall hespi using pip:pipinstallhespiThe first time it runs, it will download the required model weights from the internet.It is recommended that you also installTesseractso that this can be used in the text recognition part of the pipeline.To install the development version, see thedocumentation for contributing.UsageTo run the pipeline, use the executablehespiand give it any number of images:hespiimage1.jpgimage2.jpgThis will prompt you to specify an output directory. You can set the output directory with the command with the--output-dirargument:hespiimages/*.tif--output-dir./hespi-outputThe detected components and text fields will be cropped and stored in the output directory.
There will also be a CSV file with the filenamehespi-results.csvin the output directory with the text recognition results for any institutional labels found.More information on the command line arguments can be found in theCommand Line Referencein the documentation.There is another command line utility calledhespi-toolswhich provides additional functionality.
See thedocumentationfor more information.Training with custom dataTo train the model with custom data, see the documention.CreditsRobert Turnbull, Emily Fitzgerald, Karen Thompson and Jo Birch from the University of Melbourne.This research was supported by The University of Melbourne’s Research Computing Services and the Petascale Campus Initiative.
The authors thank collaborators Niels Klazenga, Heroen Verbruggen, Nunzio Knerr, Noel Faux, Simon Mutch, Babak Shaban, Andrew Drinnan, Michael Bayly and Hannah Turnbull.This pipeline depends onYOLOv8,torchapp,
Microsoft’sTrOCR.Logo derived from artwork byka reemov.See the documentation for more information for references in BibTeX format or use the command:hespi-toolsbibtex
Package: hesseflux
hessefluxprovides functions used in the processing and post-processing of
the Eddy covariance flux data of theICOSecosystem siteFR-Hes.About hessefluxhessefluxcollects functions used for processing Eddy covariance data of theICOSecosystem siteFR-Hes.The post-processing functionality for Eddy flux data is similar to the R-packageREddyProcand includes basically the steps described inPapale et al.
(Biogeosciences, 2006)plus some extensions such as the daytime method of flux
partitioning (Lasslop et al., Global Change Biology 2010).DocumentationThe complete documentation forhessefluxis available at:https://mcuntz.github.io/hesseflux/Quick usage guidePost-processing Eddy covariance data that is in europe-fluxdata.eu formatAn example script that makes all the steps described inPapale et al.
(Biogeosciences, 2006)is given in the example directory. It is simply called:pythonpostproc_europe-fluxdata.pyhesseflux_example.cfgThe script is governed by a configuration file in Python’s standardconfigparserformat. The example configuration filehesseflux_example.cfgis
highly commented. See theUser Guidefor a step by step guide through the
script and the configuration file.InstallationThe easiest way to install is viapip:pipinstallhessefluxRequirementsnumpyscipypandaspyjamsLicensehessefluxis distributed under the MIT License. See theLICENSEfile for
details.Copyright (c) 2009-2022 Matthias CuntzThe project structurehessefluxhas borrowed heavily fromwelltestpybySebastian Müller.
Package: hessianfree
Hessian-free optimization for deep networksSetupQuick startInstall the package via:pipinstallhessianfreeTo make sure things are working, open a python interpreter and enter:importhessianfreeashfhf.demos.xor()A simple xor training example will run, at the end of which it will display
the target and actual outputs from the network.Developer installUse this if you want to track the latest changes from the repository:gitclonehttps://github.com/drasmuss/hessianfree.gitcdhessianfreepythonsetup.pydevelop--userRequirementspython 2.7 or 3.5numpy 1.9.2matplotlib 1.3.1optional: scipy 0.15.1, pycuda 2015.1.3, scikit-cuda 0.5.1, pytest 2.7.0(older versions may work, but are untested)FeaturesAll the standard features of Hessian-free optimization from Martens (2010) and
Martens and Sutskever (2011) are implemented (Gauss-Newton approximation, early
termination, CG backtracking, Tikhonov damping, structural damping, etc.).  In
addition, the code has been designed to make it easy to customize the network
you want to train, without having to modify the internal computations of the
optimization process.Works for feedforward and recurrent deep networks (or mixtures of the two)Standard nonlinearities built in (e.g., logistic, tanh, ReLU, softmax), and
support for custom nonlinearitiesStandard loss functions (squared error, cross entropy, sparsity constraints),
and support for custom loss functionsVarious weight initialization methods (although Hessian-free optimization
doesn’t usually require much tweaking)Customizable connectivity between layers (e.g., skip connections)Efficient implementation, taking advantage of things like activity cachingOptional GPU acceleration if PyCUDA and scikit-cuda are installedGradient checking (and Gauss-Newton matrix checking) implemented to help with
debuggingInputs can be predefined or generated dynamically by some other system (like
an environmental simulation)Different optimizers can be swapped out for comparison (e.g., Hessian-free
versus SGD)DocumentationView the documentation athttp://pythonhosted.org/hessianfree/In addition, examples illustrating the main features of the code can be found
indemos.py.
Package: hestia-earth-aggregation
Hestia Aggregation EngineDocumentationOfficial documentation can be found onRead the Docs.Additional models documentation can be found in thesource folder.InstallInstall the module:pipinstallhestia_earth.aggregationUsageimportosfromhestia_earth.aggregationimportaggregateos.environ['API_URL']='https://api.hestia.earth'aggregates=aggregate(country_name='Japan')
Package: hestia-earth-earth-engine
Hestia Earth EngineHestia's utilities to make queries to Earth Engine.Getting StartedSign up for aGoogle Cloud AccountEnable theEarth Engine APICreate aService Account with Earth Engine accessSet the service account JSON credentials in the following file:ee-credentials.jsonSet the following environment variable:# path to the ee-credentials.json file saved at previous step
EARTH_ENGINE_KEY_FILE=./ee-credentials.jsonInstallInstall Python3(we recommend using Python3.6minimum)Install the module:pipinstallhestia_earth.earth_engineUsagefromhestia_earth.earth_engineimportinit_gee,run# call this only once during a sessioninit_gee()# fetch sand content for a specific locationdata={"ee_type":"raster","collections":[{"collection":"users/hestiaplatform/T_SAND","fields":"first"}],"coordinates":[{"latitude":-11.77,"longitude":-45.7689}]}run(data)assertdata==[81]
Package: hestia-earth-extend-bibliography
Hestia Extend BibliographyInstallpipinstallhestia_earth.extend_bibliographyUsagefromhestia_earth.extend_bibliographyimportextend# nodes is a Dict {nodes: []}result=extend(nodes,# pass in the desired crendentials for each APImendeley_username=MENDELEY_USERNAME,mendeley_password=MENDELEY_PASSWORD,# enable Mendeley SDKmendeley_api_url=MENDELEY_API_URL,# enable Mendeley through API URL (default to using the SDK if not provided)wos_api_key=WOS_API_KEY,# enable WoS REST APIwos_api_user=WOS_API_USER,wos_api_pwd=WOS_API_PASSWORD,# enable WoS SOAP APIenable_unpaywall=False,# enable Unpaywall as fallbackenable_crossref=False,# enable CrossRef as last fallback (slower))
Package: hestia-earth-models
Hestia Engine ModelsHestia's set of models for running calculations or retrieving data using external datasets and internal lookups.DocumentationDocumentation for every model can be found in theHestia API Documentation.InstallInstall python3(we recommend using python3.6minimum)Install the module:pipinstallhestia_earth.modelsSet the following environment variables:API_URL=https://api.hestia.earth
WEB_URL=https://hestia.earthUsagefromhestia_earth.models.pooreNemecek2018importrun# cycle is a JSONLD node Cyclerun('no3ToGroundwaterSoilFlux',cycle_data)Using Spatial ModelsWe have models that can gap-fill geographical information on aSite.
If you want to use thse models:Install the library:pip install hestia_earth.earth_engineFollow theGetting Started instructions.Using Ecoinvent Modelecoinvent is a consistent, transparent, and well validated life cycle inventory database.
We use ecoinvent data to ascertain the environmental impacts of activities that occur outside of our system boundary, for example data on the environmental impacts of extracting oil and producing diesel, or the impacts of manufacturing plastics.
To include these data in your environmental impact assessments using Hestia, you must own a suitableecoinvent license.Please contact us atcommunity@hestia.earthfor instructions to download the required file to run the model.
Once downloaded, copy the file in thehestia_earth/models/data/ecoinventV3folder.
Thank you!
Package: hestia-earth-orchestrator
Hestia Engine OrchestratorOrchestrate your different models to run on a Cycle, an ImpactAssessment or a Site.DocumentationDocumentation can be found in thesource folder.InstallInstall the library:pipinstallhestia_earth.orchestratorYou can now install your own models or follow the steps below to use the default Hestia models.Install the Hestia ModelsIf you want to use thehestia default models, follow these steps:Install the library:pipinstallhestia_earth.modelsDownload the latest configuration files:curlhttps://gitlab.com/hestia-earth/hestia-engine-orchestrator/-/raw/master/scripts/download_config.sh?inline=false-odownload_config.sh&&chmod+xdownload_config.sh# pip default install directory is /usr/local/lib/python<version>/site-packages./download_config.sh<pipinstalldirectory>Using your own modelsYou can create your own models in addition (or instead of) the default set of models provided by Hestia.The model needs to expose only one method:defrun(key:str,data):...It will be given the data that has been given to the orchestrator, i.e. by calling:fromhestia_earth.orchestratorimportrunmy_data={'@type':'Cycle','inputs':[]}config={"models":[{"key":"inputs","model":"my_model","value":"my_model_value","runStrategy":"add_if_missing_key"}]}run(my_data,config)Will be calling in your own modelmy_model.py:defrun('my_model_value',my_data:dict):...Usage# will work with either Cycle or Sitefromhestia_earth.orchestratorimportrun# cycle is a JSONLD node cyclecycle={'@type':'Cycle',...}result=run(cycle,'/path/to/my-config.json')# configuration stored in a fileprint(result)
Package: hestia-earth-utils
Hestia UtilsInstallInstall the module:pipinstallhestia_earth.utilsAdd this to your environment variables:API_URL=https://api.hestia.earth
WEB_URL=https://www.hestia.earthUsageTo download a file from the Hestia API:fromhestia_earth.schemaimportSchemaTypefromhestia_earth.utils.apiimportdownload_hestiacycle=download_hestia('cycleId',SchemaType.CYCLE)sandContent=download_hestia('sandContent',SchemaType.TERM)To search for a specific Node on Hestia:fromhestia_earth.schemaimportSchemaTypefromhestia_earth.utils.apiimportfind_node_exactsource=find_node_exact(SchemaType.SOURCE,{'bibliography.title':'My Bibliography'})To get a lookup table from local file system:fromhestia_earth.schemaimportSchemaTypefromhestia_earth.utils.lookupimportload_lookupdf=load_lookup('path/to/my/lookup.csv')To get a lookup table from Hestia:fromhestia_earth.schemaimportSchemaTypefromhestia_earth.utils.lookupimportdownload_lookupdf=download_lookup('crop.csv')
Package: hestia-earth-validation
Hestia Data ValidationInstallpipinstallhestia_earth.validationUsagefromhestia_earth.validationimportvalidate# for each node, this will return a list containing all the errors/warnings (empty list if no errors/warnings)errors=validate(nodes)Note 1: if you want to validate existing data (with@typeand@idfields), please set the following environment variable:VALIDATE_EXISTING_NODES=trueNote 2: to skip searching for terms using the Hestia API, please enable mocking:ENABLE_MOCKING=true
Package: hetbuilder
Hetbuilder - builds heterostructure interfacesBuilds 2D heterostructure interfaces via coincidence lattice theory.InstallationBuild-time dependenciesRequires a C++17 compiler andcmake.It is also recommended to preinstallspglibandpybind11.
Otherwise, these will be built during the installation from the submodules.Installing with AnacondaCreate a clean conda environment:condaenvcreate-nhetbuilderpython=3.9Then install the build-time dependencies first:condainstall-cconda-forgecxx-compilergitpipcmakespglibpybind11Then, you can install the project from pip:pipinstallhetbuilderIf that does not work, try directly installing from git:pipinstallgit+https://github.com/romankempt/hetbuilder.gitInstalling with pipPyPI does not provide the library files ofspglib. These will be built from the submodules at installation time, which might be time-consuming.On Unix, you can install acxx-compilerwith:sudoaptinstallbuild-essentialFirst stepsThe installation exposes a multi-leveltyperCLI utility calledhetbuilder:hetbuilder--helpThebuildutility is fully implemented.
You can use any ASE-readable structure format to specify the lower and upper layer. They should be recognizable as two-dimensional, e.g., by having a zero vector in thez-direction.hetbuilderbuildgraphene.xyzMoS2.cifThis should open amatplotlibinterface looking like this:DocumentationDocumentation is available atRead the Docs.TestingTests can be run in the project directory withpytest-vtestsCitingIf you use this tool, please cite 10.5281/zenodo.4721346.RequirementsAtomic Simulation EnvironmentSpace Group LibarySciPymatplotlibpybind11typer
Package: heteroverlap
heteroverlapThis project is used for regression-based heterogeneity analysis to identify overlapping subgroup structure in high-dimensional data. For more details, please check our paper.InstallationInstall the package from pypi orpip install heteroverlapfrom sourcegit clone https://github.com/foliag/subgroup
cd subgroup
pip install .License & citationThe content of this repository is released under the terms of theMIT license.
Please consider citing our paper if you use it.@article{luo2022regression,
author = {Luo, Ziye and Yao, Xinyue and Sun, Yifan and Fan, Xinyan},
title = {Regression-based heterogeneity analysis to identify overlapping subgroup structure in high-dimensional data},
journal = {Biometrical Journal},
volume = {n/a},
number = {n/a},
pages = {},
keywords = {heterogeneity analysis, high-dimensional data, overlapping subgroup structure, penalization, regression},
doi = {https://doi.org/10.1002/bimj.202100119},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.202100119},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202100119}
}