Saravanan666/pypi-org huggingface dataset:
Package: iec-api
iec-apiA python wrapper for Israel Electric Company APIModule Usagefromiec_apiimportiec_clientasiecclient=iec.IecClient("123456789")try:awaitclient.manual_login()# login with user inputsexceptiec.exceptions.IECErroraserr:logger.error("Failed Login: (Code%d):%s",err.code,err.error)raisecustomer=awaitclient.get_customer()print(customer)contracts=awaitclient.get_contracts()forcontractincontracts:print(contract)reading=awaitclient.get_last_meter_reading(customer.bp_number,contracts[0].contract_id)print(reading)
Package: iecasdmx
Conversión de datos del Instituto de Estadística y Cartografía Andaluz a SDMXHerramienta interna de transformación de datos desde laAPI del IECAhacia el estandar SDMX,
haciendo uso de lasherramientas SDMX del Instituto Nacional de Estadística Italiano (ISTAT Toolkit).DespliegueEn un entorno con Python instalado, intsalar los requisitos de dependencias.pip3 install -r requirements.txtEjecuciónCon el directorio de trabajo en la raiz del proyecto ejecutar el fichero main.pyIECA2SDMX
└── iecasdmx
    └── main.py                    # Fichero de ejecuciónDocumentaciónIECA2SDMXInformación para desarrolladores del repositorioEjecutar Integración continua en localTox es una herramienta de automatización para python, se puede instalar con pip:pip install toxSus comandos son los siguientes:Ejecutar Tests, Lint y Compilar la documentacióntoxEjecutar teststox -e py38Ejecutar Linttox -e lintCompilar la documentacióntox -e docsPara compilar la documentación se hace uso del paquete make, se debe instalar en caso de no tenerlo presente en el entorno de trabajo.Integración continuaGithub está configurado con dos distintas comprobaciones.ToxSe ejecutará cada vez que se haga una Pull request y realizara el comando Tox completo. Indicandote si todo a ido bien.Publicación del paquete en PythonSe ejecutará cada vez que se haga una Pull Request a la rama "Main".Solamente pasará si el paquete no existe previamente en el repositorio de paquetes, por lo tanto cuando estemos seguros de que todo está finalizado deberemos hacer uso de los comandos:bumpversion minorDentro de la version actual del paquete, incrementa en 1 la subversionbumversion major (Incrementa la version)Incrementa la version del paquete
Package: iedb
IEDB API Tools Python SDKPython SDK for Immune Epitope Database (IEDB) Analysis Tools API. Includes support for the following prediction tools:MHC-I peptide binding:Determine sequence's ability to bind to MHC class I moleculeMHC-II peptide binding:Determine sequence's ability to bind to MHC class II moleculeT-cell epitope prediction:Use predictors to determine peptide's potential of being a T-cell epitopeMHC-Natural Peptide:Assess probability that peptide is naturally processed by the MHCAntibody epitope prediction:Predict B-cell epitopes from protein sequence(s)View theweb applicationInstallationRun the following to install:pipinstalliedbUsageimportiedb# Send POST request to MHC class I peptide binding prediction tool:mhci_res=iedb.query_mhci_binding(method="recommended",sequence="ARFTGIKTA",allele="HLA-A*02:01",length=8)# Send POST request to MHC class II peptide binding prediction tool:mhcii_res=iedb.query_mhcii_binding(method="nn_align",sequence="SLYNTVATLYCVHQRIDV",allele="HLA-DRB1*01:01",length=None)# Send POST request to T-cell epitope prediction tool:tcell_res=iedb.query_tcell_epitope(method="smm",sequence="SLYNTVATLYCVHQRIDV",allele="HLA-A*01:01",length=9,proteasome='immuno')# Send POST request to peptide prediction tool:pep_res=iedb.query_peptide_prediction(method="mhcnp",sequence="SLYNTVATLYCVHQRIDV",allele="HLA-A*02:01",length=9)# Send POST request to B-cell epitope prediction tool:bcell_res=iedb.query_bcell_epitope(method="Emini",sequence="VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRFKHLKTE",window_size=9)
Package: ieee754bin
This is a small Python extension which allows you to manipulate the
binary representation of floating-point values in the standard IEEE
754 64-bit format (= C double type or Python float type).Basically, the module includes methods to print integers and floating
point values in binary format, to convert floating-point values to and
from 64-bit integer representations, and to split/join floating-point
values into (sign, exponent, mantissa) tuples.
Package: ieeh-power-system-data-model
IEEH Power System Data ModelA data model for the description of electrical power systems.IEEH Power System Data ModelField of ApplicationGeneral RemarksInstallationDevelopmentAttributionField of ApplicationThis data model is intended to describe electrical power systems. It provides a hierarchical structure/schema to describe unique entity relations as well as parameter sets.The data model is structured as the following schema:Grid TopologyThis is the base topology containing all elements of the exported grid:Branches (symmetrical: overhead lines, cables, fuses from type "branch")NodesTransformers (symmetrical: 2- or 3-winding)External gridsLoads (consumer, producer, grid assets, fuses from type)Topology CaseThis holds information about disabled elements to represent a specific operational case based on the base topology.Steadystate CaseThis holds information for a specific operational case such as:power draw/infeed of loadtap posistion of transformeroperating point of external gridGeneral RemarksPlease find below some important general remarks and assumptions to consider for consistent usage across different applications:The passive sign convention should be used for all types of loads (consumer as well as producer).Numeric values should be set using the SI unit convention.TopologyOnlysymmetricalgrid assets, e.g. transformer or line, are supported.TheRated Powershould always be defined positive (absolute value).The interaction between load models and controllers are depicted in the following schematic:InstallationJust install via pip:pipinstallieeh-power-system-data-modelDevelopmentInstallpdmWindows:(Invoke-WebRequest-Urihttps://raw.githubusercontent.com/pdm-project/pdm/main/install-pdm.py-UseBasicParsing).Content|python-Linux/Mac:curl-sSLhttps://raw.githubusercontent.com/pdm-project/pdm/main/install-pdm.py|python3-Or using pipx or pip:pipxinstallpdmpipinstall--userpdmClonepower-system-data-modelgit@github.com:ieeh-tu-dresden/power-system-data-model.gitcdpower-system-data-modelInstallpower-system-data-modelas a production toolpdminstall--prodInstallpower-system-data-modelin development modepdminstallFor development inVisual Studio Code, all configurations are already provided:ruffblackmypyAttributionPlease provide a link to this repository:https://github.com/ieeh-tu-dresden/power-system-data-modelPlease cite as:Institute of Electrical Power Systems and High Voltage Engineering - TU Dresden,Power System Data Model - A data model for the description of electrical power systems, Zenodo, 2023.https://doi.org/10.5281/zenodo.8087079.
Package: ieffel2
EiffelEiffel is a neural network architecture visualization tool. itâ€™s really simple to use. in order to start visualizing your network just insert a list of the number of nodes each layer has: e.g [2,5,5,1] â€“> returns a neural network with input 2 , output 1 (binary classify for example) and 2 hidden layers with 5 units each.Eiffel is intended for educational purposes.
Package: ielectric.fab
IntroductionMy fabric file, currently handles:* Sphinx upload docs
* pylons deploy
* utilities for releasing eggsChangelog0.1.5 (05.11.2010)correctly detect if supervisor is runningcorrectly bootstrap buildout0.1.4 (04.11.2010)add buildout support in pylons_deploy0.1.3 (03.11.2010)fix location of pylons deploy scripts0.1.2 (03.11.2010)fix pylons_init path0.1.1 (03.11.2010)IncludeCHANGELOG0.1 (03.11.2010)Initial release
Package: ielu
# Interactive Electrode Localization Utility (ielu)
This is a GUI utility to assist in preprocessing tasks for electrocorticography (ecog) and stereo eeg (seeg) data analysis. It includes:1. CT-to-MRI co-registration using Mutual Information (via Freesurfer)
1. Automatic electrode extraction and sorting into grids via pre-defined grid geometries
1. A GUI for manually tweaking electrode locations and grid information
1. Snapping electrode locations to a freesurfer cortical surface model
1. Exporting to montage files suitable for use in MNE-python# Dependencies
* A scientific python distribution such as anaconda or canopy
* pysurfer
* nibabel
* mne-python
* pymcubesNote - you can easily install these dependencies with tools such as pip and easy-install.# Quick start
Simply clone this repository, then evoke therunscript in the main folder. e.g.,cd gselu; ./run# More info
For more information about this package and how it works, see the [wiki](https://github.com/aestrivex/ielu/wiki).
Package: iemail
IEmailDescriptionIEmail is a PyPI package that provides a simple way to get a temporary email address and receive emails anonymously. It is a wrapper forIEmail, an online service that allows users to create temporary email addresses for free.The project is released onpypi.org, and you can view it athttps://pypi.org/project/iemail/.InstallationYou can install IEmail with pip as usual.pip3installiemailUsage# Import the modulefromiemailimportAccount# Create an accounttest=Account(username='test',prefix='mymail')# Address: test@mymail.iemail.eu.org# See the address and passwordprint(test.address)# Check the email's inboxprint(a.get_message(latest=0))# latest: 0 means the latest email, 1 means the second latest email, and so on.# If there is no email, it will raise IndexError.print(a.get_messages())# Get All the emails.ContactIf you have any questions or concerns, please feel free to contact us ati@iemail.eu.org.
Package: iemap
iemap-moduleThis module provides simple functions to interact with the iemap API.QuickstartCreate a virtual environment and install the module in it:pipinstalliemapIn your script or notebook, import the module:fromiemapimportIEMAPCreate an instance of theIEMAPclass, using youruserandpassword:api=IEMAP(user,password)Use thelogin()method for API login:api.login()Use themy_projects()method to get a list of your projects:api.my_projects()
Package: ie_mbdbl2018_ahirane
The package provides a function called linear_congruence_random_generator(), wich returns a randon number based on the method of linear congruences using the following parameters:
a = 22695477
b = 1
m = 2**32
x0=1649836483Also it provides and object called c_linear_congruences.
It should be intialized providing the parameters x0,a,b and m.
The object provides a method called linear_congruence_random_generator()
wich will return a random number based on the method of linear congruences
using the parameters of the object initialization.
Package: iembdfa
Included in this module are 5 libraries that will help you during your data science adventures and help you save some of that valuable time you would rather spend on modelling rather than on data cleaning.Module NamesDataCleaning.pyAutoInterpolation.pyDateCatVar.pyGeneticFeature.pyRatioVar.pyHistory0.9 (2016-06-18)First release on PyPI.
Package: ienv
ienv!!WARNING!! THIS IS A ONE WAY PROCESS !!WARNING!!Have you got 30GB of SciPy on your disk because every time someone wants to
add two numbers together they install a whole lab on your machine? Are your
fifty copies of PyTorch and TensorFlow weighing heavy on your SSD?Why not throw caution to the wind and replace everyhing in the site-packages
dir with symlinks? It's not like you're going to need them anyway. And nobody
will ever write to them and mess up every venv on your machine. Right?!!WARNING!! THIS RECKLESS AND STUPID !!WARNING!!Usagepipinstallienv
ienv.venvRecoveryPull requests welcome!All the files are there, I've just not written anything to bring them back yet.
Ever, probably.CreditsMostly written by ChatGPT just to see if it could do it. With a bit of guidance
it actually could, but it can't learn like that so it's like a student that
nods along and you think it's listening and it's really just playing along and
tricking you into doing its homework. But to be honest it was either that or
copilot anyway.LicenseThey say you get what you pay for, sometimes less. This is one of those times.
As free software distributed under the WTFPL (with one additional clause); this
is one of the times when you pay for what you get.
Package: ieo
# ieo
Irish Earth Observation (IEO) remote sensing data processing Python moduleImage processing library for Landsat and MODIS to Irish Transverse Mercator ProjectionThis library, at present, is designed to import data from Landsat and MODIS sensors. The library will convert Landsat Thematic Mapper (TM), Enhanced Thematic Mapper+ (ETM+), and Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data from Universal Transverse Mercator (UTM) zones 29 and 30 North to Irish Transverse Mercator (ITM). It will also convert specific MODIS data products from MODIS Sinusoidal to ITM. In all cases, outputs are in an ENVI (Harris Geospatial, Boulder, CO, USA) format. Data from the Landsat 1-5 Multispectral Scanner (MSS) instruments are not currently supported.Currently, only Landsat functionality is being actively maintained by the author, with Sentinel-2 support to be added soon.
Package: ie-pandas
IE PandasThis is Team C's final project in Advanced Python.A simple implementation of dataframe functionalityThe library is available inPypiInstallingThe easiest way to install ie_pandas is throughpippipinstallie_pandasTo use it in your project, you must first import the libraryfromie_pandasimportDataframeYou can create a frame by the following 4 methods:A list of listsA numpy array of listsA dictionary of lists with keys being column names and values being the values for that columnA dictionary of numpy arrays (same as with lists)dictionary={'c0':[1,3,5],'c1':[7,6,2],'c2':[2,4,7],'c3':[5,3,9]}df=DataFrame(dictionary)FunctionalityCreate dataframes from list of lists, numpy arrays, dictionaries of lists and numpy arraysdictionary={'c0':[1,3,5],'c1':[7,6,2],'c2':[2,4,7],'c3':[5,3,9]}df=DataFrame(dictionary)# You may optionally pass along two parameters, cols and index# cols determines the column names (if blank they will be numerical strings)# index determines the row names (if blank they will be numbers)df=DataFrame(dictionary,cols=["col0","col1","col2","col3"],index=["row1","row2","row3"])Access columns by namedf['column_1']Access rows by position or by row namedf.get_index(1)# ordf.get_index('row_1')Access data like a numpy array by namedf[0:2,1:3]Modify dataframedf[0,0]=3Sum, median, mean, min, max methods (only work for numerical columns)df.mean()Since the underlying object of the dataframe is a numpy array, you may perform aditional functionality likedf[:,1:2].sum()Visualize relationships between 2 entirely numerical columns (only for numerical columns)df.visualize(df[:,2],df[:,3])# ordf.visualize(df["c1"],df["c2"])DependenciesIE_Pandas only requires the following packages:Numpy (>=1.16)Matplotlib (>=3.0.2)However, for development purposes, the following packages are needed:Pytest (>= 4.2)Pytest-cov (>= 2.6)Black (for PEP8 compliance)DevelopmentFor development purposes, you may download the files directly and install the library locally by placing your terminal in the downloaded folder and doingpipinstall--editable.[dev]Then, to execute the tests you just need to runpytest--covIE_Pandas Coding StyleIE_Pandas complies to PEP8 and usesblackfor coding standardsVersioningSemVeris used for versioning.LicenseThis project is licensed under the MIT License - see theLicensefile for details
Package: iepy
IEPY is an open source tool forInformation Extractionfocused on Relation Extraction.To give an example of Relation Extraction, if we are trying to find a
birth date in:“John von Neumann (December 28, 1903 – February 8, 1957) was a Hungarian and
American pure and applied mathematician, physicist, inventor and polymath.”then IEPY’s task is to identify “John von Neumann” and
“December 28, 1903” as the subject and object entities of the “was born in”
relation.It’s aimed at:usersneeding to perform Information Extraction on a large dataset.scientistswanting to experiment with new IE algorithms.FeaturesA corpus annotation toolwith aweb-based UIAn active learning relation extraction toolpre-configured with convenient defaults.A rule based relation extraction toolfor cases where the documents are semi-structured or high precision is required.A web-based user interface that:Allows layman users to control some aspects of IEPY.Allows decentralization of human input.A shallow entity ontology with coreference resolution viaStanford CoreNLPAn easily hack-able active learning core,
ideal for scientist wanting to experiment with new algorithms.InstallationInstall the required packages:sudoapt-getinstallbuild-essentialpython3-devliblapack-devlibatlas-devgfortranopenjdk-7-jreThen simply install withpip:pipinstalliepyFull details about the installation is available on theRead the Docspage.Running the testsIf you are contributing to the project and want to run the tests, all you have to do is:Make sure your JAVAHOME is correctly set.Read more about it hereIn the root of the project runnosetestsLearn moreThe full documentation is available onRead the Docs.AuthorsIEPY is © 2014Machinalisin collaboration
with theNLP Group at UNC-FaMAF. Its primary
authors are:Rafael Carrascosa <rcarrascosa@machinalis.com> (rafacarrascosa at github)Javier Mansilla <jmansilla@machinalis.com> (jmansilla at github)Gonzalo García Berrotarán <ggarcia@machinalis.com> (j0hn at github)Franco M. Luque <francolq@famaf.unc.edu.ar> (francolq at github)Daniel Moisset <dmoisset@machinalis.com> (dmoisset at github)You can follow the development of this project and report issues athttp://github.com/machinalis/iepyYou can join the mailing listhere
Package: iers
Author:Behrouz SafariLicense:MITiersAnalysis data of the International Earth Rotation and Reference Systems Service (IERS)InstallationInstall the latest version ofiersfromPyPI:pip install iersRequirements arenumpyandpandas.ExamplesLet's read a file:fromiersimportcreate_dfadr='../data/eop/eopc01/'f=adr+'eopc01.iau2000.1846-now'df=create_df(f)Let's open all files we have downloaded from iers server:fromiersimportcreate_df,files_dcfiles=[iforiinfiles_dc.keys()ifi[:3]!='TEM']adrs=[files_dc[i]['adr']foriinfiles]fori,finenumerate(files):df=create_df('C:/Moi/_py/Astronomy/Earth/IERS/data/'+adrs[i]+f)print(df)print(len(files_dc[f]['cols']))print('-'*80)See more examples atastrodatascience.net
Package: ieseg-credscore
Credit ScoringModuleClass: 2022 & 2023OverviewOdds based Grouping (OBG)OBGEncoderpred_var: Name of predictor variable. Values can be either continuous or categorical.target_var: Name of binary target variable..fitdf: DataFrame containing pred_var and target_var.max_delta: max difference between odds for merging two levels. default: 0.05min_bins: minimum number of bins. default: 3q: number of quantiles when converting continuous variable to categorical. default: 10.transformdf: Transform pred_var based on fitted bins.impute: Boolean indicating whether to impute missing values. default: Falseimpute_value: Category level to impute missing values with. default: 'Missing' or 'nan'.fit_transformdf: DataFrame containing pred_var and target_var. Transform pred_var based on fitted bins.fit_dict: dictionary containing the matched category levels and fitted bins.lookup: dictionary containing cutoff values for continuous variable (empty if pred_var is categorical).Weight of Evidence (WOE)WOEEncoderpred_var: Name of predictor variable.Values can be either continuous or categorical.target_var: Name of binary target variable.target_value: Value indicating event. default: 1..fitdf: DataFrame containing pred_var and target_var.stop_limit: Stops WOE based merging of the predictor's classes/levels in case the resulting information value (IV) decreases more than (e.g. 0.05 = 5%) compared to the preceding binning step. stop_limit=0 will skip any WOE based merging. Increasing the stop_limit will simplify the binning solution and may avoid overfitting. Accepted value range: 0 to 0.5. default: 0.1.q: number of quantiles when converting continuous variable to categorical. default: 10.transformdf: Transform pred_var based on fitted binsimpute: Boolean indicating whether to impute missing values. default: Falseimpute_value: Category level to impute missing values with. default: 'Missing' or 'nan'.fit_transformdf: DataFrame containing pred_var and target_var. Transform pred_var based on fitted bins..test_limitdf: DataFrame containing pred_var and target_var to test stop limits at 1%, 2.5%, 5% and 10%.fit_dict: dictionary containing the matched category levels and fitted bins.lookup: dictionary containing cutoff values for continuous variable (empty if pred_var is categorical).
Package: ieseg-recsys
Recommendation SystemsModuleClass: 2022 & 2023OverviewModel evaluation (eval.py):Regression metricsRMSEMAEClassification metricsPrecisionRecallF1Ranking metricsNDCGeval.evaluatecomputes all above mentioned metricsEvaluate Top-N recommendationsHRMAPContent based Recommender System (model.py)Helper functions (utils.py)get_top_n: Compute Top-N recommendations from predictionspredict_user_topn: Compute Top-N recommendations for a userUseful Links
Package: iesugrace-django-polls
Polls is a simple Django app to conduct Web-based polls. For each
question, visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “polls” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'polls',
]Include the polls URLconf in your project urls.py like this:path('polls/', include('polls.urls')),Runpython manage.py migrateto create the polls models.Start the development server and visithttp://127.0.0.1:8000/admin/to create a poll (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/polls/to participate in the poll.