Saravanan666/pypi-org huggingface dataset:
Package: datamax-printer
Datamax python adapterThis piece of code allows you to control a datamax-o'neil label printer. You do not need knowledge of the datamax
programming language for simple tasks, this module takes care of it.The module was developed and tested on python 3.6 using a datamax o'neil e-class mark III printer. It still has very
limited functionality allowing you to print text labels and QR-codes. In case you need any other features feel free to
contribute.Install$pipinstalldatamax-printeror from source:$pythonsetup.pyinstallGetting startedfromdatamax_printerimportDPLPrinterprinter=DPLPrinter('<ip of the printer>')printer.configure()printer.start_document()printer.set_qr_code(285,120,'https://www.innetag.ch/',9)printer.set_label(300,60,'innetag.ch',9,10)printer.print()Please check theexample.pyfile inour GitHub repositoryfor a working example.RessourcesIn case you need more information about the datamax programming language, please check theofficial DPL documentation. It also
contains information about the different fonts available (see Appendix C).
Package: datamechanics-airflow-plugin
Data Mechanics Airflow PluginAn Airflow plugin to launch and monitor Spark applications on the Data Mechanics platform.EnvironmentPython >= 3.5apache-airflow >= 1.10.x. Compatible with Airflow 2.Installation and usageA tutorial to configure and use this plugin is available in theData Mechanics docs.The main difference between Airflow 1 and Airflow 2 is how to import the plugin:# Airflow 1fromairflow.operators.datamechanicsimportDataMechanicsOperator# Airflow 2fromdatamechanics_airflow_plugin.operatorimportDataMechanicsOperatorExample DAGsYou can see example DAGs forAirflow 1andAirflow 2.DevelopmentDevelopment instructions.Changelog1.1.12021-03-26FixedKill an app instead of deleting it when the Airflow task is marked as failed1.1.02021-03-10AddedSupport for Airflow templating in Data Mechanics operator's arguments (by @jj-ookla)Minimal support for Airflow 2 (the code is unchanged, only the doc and the contributor dev environment have changed)1.0.72020-09-11ChangedChanged bumversion config1.0.62020-09-11ChangedUpdated doc1.0.52020-09-11FixedDependency management1.0.02020-09-11ChangedConverted the existing plugin into a Python package
Package: datameta
# DataMeta - submission server for data and associated metadataData submission made easy! DataMeta allows you to easily define sample sheet columns, value
constraints for the sample sheet and columns which are associated with raw data file names.DataMeta is quick and easy to deploy on your local infrastructure and scales for high numbers of
users!![demo](./img/datameta.demo.gif?raw=true)## Quick InstallationCreate a directory for the datameta configuration (of your choice)` mkdir /usr/local/lib/datameta cd /usr/local/lib/datameta `Edit the configuration fileThe fields that require changing are marked with# CHANGE THIS. You may want
to perform additional adjustments to the compose file to fit your needs.Download the Docker compose file` curl-LOhttps://datameta.org/minimal/docker-compose.yml`Create the Docker volumes for persistent file and database storage` docker volume createdatameta-dbdocker volume createdatameta-filestorage`Start up your DataMeta Instance`docker-composeup-d`# DataMeta - submission server for data and associated metadataInitial version.
Package: data-migrator
Data-migrator (version 0.6.2) is a simple data-migration package for python lovers.Data-migrator is a declarative DSL for table driven data transformations, set up as
an open and extensive system. Use this to create data transformations for
changing databases as a result of changing code, initial loads to datalakes
(it contains a Kinesis provider)and more.ExampleData-migrator assumes data is extracted and loaded with client access.$mysqlsource_db-E'select id,a,b from table'-B|pythonmy_filter.py|mysqltarget_dbIt than offers a wide range of primitives with default settings to build complex transformations
fast, readable and extendablefromdata_migratorimportmodels,transformclassResult(models.Model):id=models.IntField(pos=0)# keep iduuid=models.UUIDField()# generate new uuid4 fielda=models.StringField(pos=1,default='NO_NULL',max_length=5,nullable='NULL',replacement=lambdax:x.upper())b=models.StringField(pos=2,name='my_b')if__name__=="__main__":transform.Transformer(models=[Result]).process()InstallationExecute the following command to installdata-migratorwithpip:pip install data-migratorSee theInstallation Instructionsin Documentation for
more instructions on installing, upgrading, and uninstallingdata-migrator.The project ismaintained at GitHub.Support and contributeQuestions, comments, bug reports and especially tested patches may be
submitted directly to theissue tracker.Everyone interacting with this codebase, issue trackers,
chat rooms, and mailing lists is expected to follow theCode of Conduct.
Package: datamode
DatamodeQuickstart|Demo|Documentation|TutorialDatamode is a free, open source tool to quickly build data science pipelines. Our Python data visualization and transformation tool runs directly in Jupyter Notebook. We help you:Explore your dataReshape your dataDeploy your data and models to production.How will this help me?You're a data scientist. You deal with messy datasets all the time. You often build custom dataviz to explore your data.
You spend countless hours building and rechecking your data transformation code so that your model is accurate.Also, you usually have to ask Engineering to deploy your model, or worse, maintain the infrastructure yourself.
When the pipeline or your model break down, you spend a lot of time diagnosing the problem with Engineering.Datamode can help you with all of that.Try it outTry a live demo:http://bit.ly/datamode-live-demo.Or, go toQuickstartto try Datamode on your own data.QuickstartYou can install Datamode with pip. After that, run the two bottom lines in Jupyter Notebook.From your command line:pip install -U datamode
jupyter notebookIn your Jupyter notebook:# Then run this code in Jupyter:
from datamode.interface import quickshow
quickshow('https://raw.githubusercontent.com/datamode/datasets/master/movies.csv')Note: Datamode currently requires Python 3.6+ (64-bit). If you're having problems installing, see these pages:Install using pipInstall using condaCreate a Python 3.7 virtualenvCheck whether you're running 64-bit PythonExplore the dataset a little with Datamode, then try it on your own data.DocumentationMain Datamode documentationQuickstartTutorialTransformsDatasourcesFAQFeedback / ContributingIf you're trying out Datamode, we'd love to hear from you! We're especially interested in what's working/not working for you, and what's missing that you really need.Email us:feedback@datamode.com.If you want to contribute, seeContributing.
Package: datamodel-orm
DataModel ORMDataModel ORM is a Python library that provides a fast and efficient way to interact with databases using Pydantic models.InstallationUse the package managerpipto install DataModel.pip install datamodel_ormUsageThis example demonstrates how to use the User data model in Python. The User data model is defined using Pydantic and includes fields for id, name, and age.SetupFirst, import the necessary modules and define the User data model.from typing import Optional
from pydantic import Field
from data_model_orm import DataModel

class User(DataModel):
    id: Optional[int] = Field(json_schema_extra={"primary_key": True}, default=None)
    name: str
    age: intCreating the Data SourceCreate the data source for the User model. If the data source already exists, this operation will be ignored.User.create_source(ignore_if_exists=True)Saving a New UserTo save a user, instantiate the User class and call the save method.user = User(name="John", age=30)
user.save()
print(user.id)  # Prints the ID of the newly created userRetrieving a UserTo retrieve a user, use the get_one method and provide the user's name.user = User.get_one(name="John")Retrieving All Users with a Specific NameTo retrieve all users with a specific name, use the get_all method and provide the name.users = User.get_all(name="John")Deleting a UserTo delete a user, call the delete method on a User instance.user.delete()Please note that the actual usage may vary depending on the implementation of the DataModel class and the data source.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.
Package: datamodels
# datamodelsThe datamodels package builds on dataclasses and adds the following functionality:.dict()instance method – returns a dict of the data, making it simple to serialize to formats like JSON or YAML..from_data()class method – create a new instance by passing in an iterable key-value data source (such as a dict). Unlike the regular constructor, this only uses the values in the input data that match fields in the model — others are ignored. This is useful for pulling model data out of a larger data source.@validatordecorator to specify data validation methods..validate()class method – runs the attribute validator methods and returns adictwith the fields that have errors as keys and a list of errors as the values.The datamodels package is inspired by prior art: Django models and forms, the attrs package by which dataclasses was inspired, and the XML world, where schemas and data validation form a key part of production workflows.Installation:`bash pip install datamodels `
Package: datamodelsFrontier
Data ModelsDescriptionThis package contains three pydantic data models
Datamodels for use in the API tier and Processing tierHow to InstallThis package is a simple package with three pydantic data models.
The module is called datamodels.Example imports:Command Line$pipinstalldatamodelsFrontierPythonfromdatamodelsFrontier.externalimportBarcodefromdatamodelsFrotnier.downstreamimportChildProductCode InformationExample Code:InventoryItemDimension(height_mm=3,width_mm=4,depth_mm=5,mass_kg=5)Each Model and Input ValueExternalThe models that can be imported are:InventoryItemDimension:height_mm: intwidth_mm: intdepth_mm: intmass_kg: intBarcode:barcode_type: strbarcode: strInventoryItem:inventory_item_id: strsku: strname: strcountry_of_origin: strharmonized_system_code: strhas_hazmat: boolbarcodes: list[Barcode]dimensions: InventoryItemDimensionalternative_inventory_items: list[str]thg_id: strstatus: strDownstreamChildProduct:id: strtitle: strbarcode: strreleaseDate: strrrp: strlength: strheight: strwidth: strweight: strreleaseDateEstimated: bool
Package: datamol
datamol - molecular processing made easyDocs|HomepageDatamol is a python library to work with molecules. It's a layer built on top ofRDKitand aims to be as light as possible.🐍 Simple pythonic API⚗️ RDKit first: all you manipulate arerdkit.Chem.Molobjects.✅ Manipulating molecules often relies on many options; Datamol provides good defaults by design.🧠 Performance matters: built-in efficient parallelization when possible with an optional progress bar.🕹️ Modern IO: out-of-the-box support for remote paths usingfsspecto read and write multiple formats (sdf, xlsx, csv, etc).Try OnlineVisitand try Datamol online.DocumentationVisithttps://docs.datamol.io.InstallationUse conda:mambainstall-cconda-forgedatamolQuick API Tourimportdatamolasdm# Common functionsmol=dm.to_mol("O=C(C)Oc1ccccc1C(=O)O",sanitize=True)fp=dm.to_fp(mol)selfies=dm.to_selfies(mol)inchi=dm.to_inchi(mol)# Standardize and sanitizemol=dm.to_mol("O=C(C)Oc1ccccc1C(=O)O")mol=dm.fix_mol(mol)mol=dm.sanitize_mol(mol)mol=dm.standardize_mol(mol)# Dataframe manipulationdf=dm.data.freesolv()mols=dm.from_df(df)# 2D vizlegends=[dm.to_smiles(mol)formolinmols[:10]]dm.viz.to_image(mols[:10],legends=legends)# Generate conformerssmiles="O=C(C)Oc1ccccc1C(=O)O"mol=dm.to_mol(smiles)mol_with_conformers=dm.conformers.generate(mol)# 3D viz (using nglview)dm.viz.conformers(mol,n_confs=10)# Compute SASA from conformerssasa=dm.conformers.sasa(mol_with_conformers)# Easy IOmols=dm.read_sdf("s3://my-awesome-data-lake/smiles.sdf",as_df=False)dm.to_sdf(mols,"gs://data-bucket/smiles.sdf")How to citePlease cite Datamol if you use it in your research:.CompatibilitiesVersion compatibilities are an essential topic for production-software stacks. We are cautious about documenting compatibility betweendatamol,pythonandrdkit.See below the associated versions of Python and RDKit, for which a minor version of Datamolhas been testedduring its whole lifecycle.It does not mean other combinations does not work but that those are not tested.datamolpythonrdkit0.12.x[3.10, 3.11][2023.03, 2023.09]0.11.x[3.9, 3.10, 3.11][2022.09, 2023.03]0.10.x[3.9, 3.10, 3.11][2022.03, 2022.09]0.9.x[3.9, 3.10, 3.11][2022.03, 2022.09]0.8.x[3.8, 3.9, 3.10][2021.09, 2022.03, 2022.09]0.7.x[3.8, 3.9][2021.09, 2022.03]0.6.x[3.8, 3.9][2021.09]0.5.x[3.8, 3.9][2021.03, 2021.09]0.4.x[3.8, 3.9][2020.09, 2021.03]0.3.x[3.8, 3.9][2020.09, 2021.03]CI StatusThe CI runs tests and performs code quality checks for the following combinations:The three major platforms: Windows, OSX and Linux.The two latest Python versions.The two latest RDKit versions.mainLib build & TestingCode Sanity (linting and type analysis)Documentation BuildLicenseUnder the Apache-2.0 license. SeeLICENSE.
Package: datamonitor
Python utility to monitor metadata KPIFree software: MIT licenseInstallationpip install datamonitorYou can also install the in-development version with:pip install https://github.com/infra-helpers/induction-monitoring/archive/master.zipDocumentationhttps://datamonitoring.readthedocs.io/en/latest/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.0 (2020-07-01)First release on PyPI.
Package: datamountaineer-schemaregistry
[![Build Status](https://travis-ci.org/datamountaineer/python-serializers.svg?branch=master)](https://travis-ci.org/datamountaineer/python-serializers)[![PyPI](https://img.shields.io/badge/PyPi-0.3-blue.svg)](https://pypi.python.org/pypi/datamountaineer-schemaregistry/0.3)# Python Schema Registry Client and Serializers/DeserializersA Python client used to interact with [Confluent](http://confluent.io/)'s[schema registry](https://github.com/confluentinc/schema-registry).  Supports Python 3.5.  This also works within a virtual env.The API is heavily based off of the existing Java API of [Confluent schema registry](https://github.com/confluentinc/schema-registry).The serializers/deserializers use [fastavro](https://github.com/tebeka/fastavro) for reading and writing by default.When one does not want to use `fastavro`, it can be disabled by providing `fast_avro=False` to the `MessageSerializer` constructor and Apache Avro's `avro` package will be used instead.# InstallationRun `python setup.py install` from the source root.or via pip```pip3 install datamountaineer-schemaregistry```# Example UsageSetup```pythonfrom datamountaineer.schemaregistry.client import SchemaRegistryClientfrom datamountaineer.schemaregistry.serializers import MessageSerializer, Util# Initialize the clientclient = SchemaRegistryClient(url='http://registry.host')```Schema operations```python# register a schema for a subjectschema_id = client.register('my_subject', avro_schema)# fetch a schema by IDavro_schema = client.get_by_id(schema_id)# get the latest schema info for a subjectschema_id,avro_schema,schema_version = client.get_latest_schema('my_subject')# get the version of a schemaschema_version = client.get_version('my_subject', avro_schema)# Compatibility testsis_compatible = client.test_compatibility('my_subject', another_schema)# One of NONE, FULL, FORWARD, BACKWARDnew_level = client.update_compatibility('NONE','my_subject')current_level = client.get_compatibility('my_subject')```Encoding to write back to Kafka. Encoding by id is the most efficent as it avoids an extra trip to the Schema Registry tolookup the schema id.```python# Message operations# encode a record to put onto kafkaserializer = MessageSerializer(client)#build your avrorecord = get_obj_to_put_into_kafka()# use the schema id directlyencoded = serializer.encode_record_with_schema_id(schema_id, record)```Encode by schema only.```python# use an existing schema and topic# this will register the schema to the right subject based# on the topic name and then serializeencoded = serializer.encode_record_with_schema('my_topic', avro_schema, record)```Reading messages```python# decode a message from kafkamessage = get_message_from_kafka()decoded_object = serializer.decode_message(message)```# Release Notes**0.3*** Testing, setup, and import improvements from PR #4# Testing```pip3 install pytest mockpy.test -s -rxs -v```# LicenseThe project is licensed under the Apache 2 license.
Package: data-mountains
data-mountains# requirementsimportpandasaspdimportgeopandasasgpdfromshapely.geometryimportPolygon,Point,LineString,GeometryCollectionfromshapely.opsimportunary_union,transformimportaltairasaltimportmathimportmatplotlib.pyplotaspltInstallpipinstalldata_mountainsHow to use# import the libraryimportdata_mountains.mountainiseasdm# some census output area population density datapopden_oa=pd.read_csv('https://gist.github.com/joel-lbth/b35c0ac7a3652c3f34441d25c45ea84a/raw/d69075dc707b5bdb06c9b640d43db1e5b2ffcb64/lbth_census_2021_pop_density_oa.csv')# some census output area population weighted centroidsoa=gpd.read_file("https://gist.github.com/joel-lbth/00f24602797d51d02d2177ed82f9295d/raw/b6c26621504a0df20ef6db1c93e5f99bce911d0a/lbth_oa21_pop_centroids.geojson")# add data attribute to each point by merging on common identifiergdf=oa.merge(popden_oa,left_on='oa21cd',right_on='GEOGRAPHY_CODE')# turn each point in a data sized mountaingdf=dm.points_to_peaks(gdf=gdf,column='OBS_VALUE',range=[0,0.01])# use Altair to create the visualdm.plot(gdf=gdf,fill='#eee',stroke='#333')
Package: dataMovies
Table of ContentsDescription 😊Maintainers 👨👩👧👦Prerequisites 🏁Installation ✅Usage 😁Run tests ⚙Clone the repositoryrun unit testsDescription 😊DataMovies is a data-science focused library that allows you to request movies/series dataset via ImDb’s API.Available for now:search all users reviews for a specific moviesearch all ratings distributions for a specific moviestatistic by ratingsratings per age range for manratings per age range for womanratings per age range for allMaintainers 👨👩👧👦@rizerkrof@Zuwhity@HaozGUPrerequisites 🏁Python >= 3.8pipInstallation ✅Fromofficial repository:pip install dataMoviesUsage 😁Look at theexampleto know more about the use. Do not forget to check thedocumentationto see all features information!Run tests ⚙Clone the repositorygit clone https://github.com/rizerkrof/libray-dataMovies.git
cd libray-dataMoviesrun unit testspython3 -m unittest discover --top-level-directory=. --start-directory=./tests/tests_dataMovies/
Package: datamuse-cli
datamuse-cliA command line interface that displays results from the Datamuse API.Install:pip install datamuse-cliUsage:>datamusesky# Show results for "sky">datamusesky:color# Show results for the word "sky" related to the topic "color".>datamusesky:color,human# Show results for the word "sky" related to the topics "color, human".Example Usage:> datamuse thyme -m 7

 🔡synonyms/similars:                       💭evocative:
╶─────────────────────────────────────────╴╶─────────────────────────────────────────╴
 herb, oregano, rosemary, marjoram,         marjoram, oregano, thymus, eyebright,
 parsley, tarragon, fennel                  harebell, thymol, hyssop


 📣sound[saʊnd]:              🧮similar spelling:          👂rhymic:
╶───────────────────────────╴╶───────────────────────────╴╶──────────────────────────╴
 them, theme, tham, thein,    rhyme, theme, chyme, tyme,   time, paradigm, sublime,
 thiem, thieme                thymy, thome                 rhyme, prime, crime, lime


 ___ + thyme:                 thyme + ___:                 thyme...:
╶───────────────────────────╴╶───────────────────────────╴╶──────────────────────────╴
 wild, fresh, lemon, common,  oil, leaved, mixture,        thymectomy, thymene,
 little, sweet, chopped       sauce, leaf, blossoms, bank  thymelaeaceae, thyme
                                                           camphor, thymes,
                                                           thyme-leaved speedwellArguments-h, --help         show this help message and exit
-m, --max          maximum number of words to display
-v, --version      show program's version number and exitDependenciesrichaiohttp
Package: datamx
This file is part of pvoutput-publisherpvoutput-publisher 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.PySunSpec-Read 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 pvoutput-publisher.  If not, see <https://www.gnu.org/licenses/>.
Package: datanalyse
datanalyseThe package name datanalyse is a suitcase word for data and analyse - mainly of experimental data from the Combined Energy Lab at TU Dresden, but perhaps useful for others as well. However, due to its absolute alpha status, I cannot recommend it to anybody in the world but me.It is more or less a tool-kit for my daily work as an experimentalist and data scientist and makes use of the fantastic packages pandas, pathlib and matplotlib.
Package: datanashor
DataNashorLeague of Legends live replay data parser packageWhat is DataNashor?DataNashor is a League of Legends live replay data parser package.DataNashor parses live data from League Client using Riot's officialLive Client Data API.Installation> pip install datanashorGetting StartedParse all replay files in the replay directoriesfromdatanashor.parserimportReplayParserparser=ReplayParser()parser.parse()To parse Client Metadata from lockfileLeague of Legends Client MUST be running.fromdatanashor.parserimportReplayParserparser=ReplayParser(current_game_version='12.20')parser.get_client_metadata()PrerequisitesLeague of Legends KR Client
Windows System
Python 3.9+
Package: datanchor
Setup and UsagePre-Requisites:pip3 install requestspip3 install psutilSetup:pip3 install "datanchor==1.0"Usage Example below. Note:dataobject in the example should be replaced with the response you receive from Datanchor encrypt service.from app.datanchor import Datanchor

def main():
    d = Datanchor()
    data = {
        "data": "U2FsdGVkX1+bvTL3NjzTIMHS4it8++O33BlKmGbz1Ts=",
        "context": "U2FsdGVkX19Tws0WtFmpHbr2C5lkeqCGvJyZ03EIZp/OKxYcFm8u4HXVQIij6QRj1IGHLKiussrr8/5qTmGjt2dJ2EbqJcs4rlX5O2wNc2X1SreCY0T+wlPM/a4/i46W",
        "username": "U2FsdGVkX1+anPgxa0gXsi8Ka/0S0B1ttrm1qZUfeso=",
        "key": "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"
    }

    print(d.decrypt(data))

main()