Saravanan666/pypi-org huggingface dataset:
Package: airbyte-source-zendesk-sell
Zendesk Sell SourceThis is the repository for the Zendesk Sell configuration based source connector.
For information about how to use this connector within Airbyte, seethe documentation.If you are a community contributor, follow the instructions in thedocumentationto generate the necessary credentials. Then create a filesecrets/config.jsonconforming to thesource_zendesk_sell/spec.yamlfile.
Note that any directory namedsecretsis gitignored across the entire Airbyte repo, so there is no danger of accidentally checking in sensitive information.
Seeintegration_tests/sample_config.jsonfor a sample config file.If you are an Airbyte core member, copy the credentials in Lastpass under the secret namesource zendesk-sell test credsand place them intosecrets/config.json.Viaairbyte-ci(recommended):airbyte-ciconnectors--name=source-zendesk-sellbuildAn image will be built with the tagairbyte/source-zendesk-sell:dev.Viadocker build:dockerbuild-tairbyte/source-zendesk-sell:dev.Then run any of the connector commands as follows:docker run --rm airbyte/source-zendesk-sell:dev spec
docker run --rm -v $(pwd)/secrets:/secrets airbyte/source-zendesk-sell:dev check --config /secrets/config.json
docker run --rm -v $(pwd)/secrets:/secrets airbyte/source-zendesk-sell:dev discover --config /secrets/config.json
docker run --rm -v $(pwd)/secrets:/secrets -v $(pwd)/integration_tests:/integration_tests airbyte/source-zendesk-sell:dev read --config /secrets/config.json --catalog /integration_tests/configured_catalog.jsonYou can run our full test suite locally usingairbyte-ci:airbyte-ciconnectors--name=source-zendesk-selltestCustomizeacceptance-test-config.ymlfile to configure tests. SeeConnector Acceptance Testsfor more information.
If your connector requires to create or destroy resources for use during acceptance tests create fixtures for it and place them inside integration_tests/acceptance.py.All of your dependencies should go insetup.py, NOTrequirements.txt. The requirements file is only used to connect internal Airbyte dependencies in the monorepo for local development.
We split dependencies between two groups, dependencies that are:required for your connector to work need to go toMAIN_REQUIREMENTSlist.required for the testing need to go toTEST_REQUIREMENTSlistYou've checked out the repo, implemented a million dollar feature, and you're ready to share your changes with the world. Now what?Make sure your changes are passing our test suite:airbyte-ci connectors --name=source-zendesk-sell testBump the connector version inmetadata.yaml: increment thedockerImageTagvalue. Please followsemantic versioning for connectors.Make sure themetadata.yamlcontent is up to date.Make the connector documentation and its changelog is up to date (docs/integrations/sources/zendesk-sell.md).Create a Pull Request: useour PR naming conventions.Pat yourself on the back for being an awesome contributor.Someone from Airbyte will take a look at your PR and iterate with you to merge it into master.
Package: airbyte-source-zendesk-sunshine
Zendesk Sunshine SourceThis is the repository for the Zendesk Sunshine configuration based source connector.
For information about how to use this connector within Airbyte, seethe documentation.If you are a community contributor, follow the instructions in thedocumentationto generate the necessary credentials. Then create a filesecrets/config.jsonconforming to thesource_zendesk_sunshine/spec.yamlfile.
Note that any directory namedsecretsis gitignored across the entire Airbyte repo, so there is no danger of accidentally checking in sensitive information.
Seeintegration_tests/sample_config.jsonfor a sample config file.If you are an Airbyte core member, copy the credentials in Lastpass under the secret namesource zendesk-sunshine test credsand place them intosecrets/config.json.Viaairbyte-ci(recommended):airbyte-ciconnectors--name=source-zendesk-sunshinebuildAn image will be built with the tagairbyte/source-zendesk-sunshine:dev.Viadocker build:dockerbuild-tairbyte/source-zendesk-sunshine:dev.Then run any of the connector commands as follows:docker run --rm airbyte/source-zendesk-sunshine:dev spec
docker run --rm -v $(pwd)/secrets:/secrets airbyte/source-zendesk-sunshine:dev check --config /secrets/config.json
docker run --rm -v $(pwd)/secrets:/secrets airbyte/source-zendesk-sunshine:dev discover --config /secrets/config.json
docker run --rm -v $(pwd)/secrets:/secrets -v $(pwd)/integration_tests:/integration_tests airbyte/source-zendesk-sunshine:dev read --config /secrets/config.json --catalog /integration_tests/configured_catalog.jsonYou can run our full test suite locally usingairbyte-ci:airbyte-ciconnectors--name=source-zendesk-sunshinetestCustomizeacceptance-test-config.ymlfile to configure tests. SeeConnector Acceptance Testsfor more information.
If your connector requires to create or destroy resources for use during acceptance tests create fixtures for it and place them inside integration_tests/acceptance.py.All of your dependencies should go insetup.py, NOTrequirements.txt. The requirements file is only used to connect internal Airbyte dependencies in the monorepo for local development.
We split dependencies between two groups, dependencies that are:required for your connector to work need to go toMAIN_REQUIREMENTSlist.required for the testing need to go toTEST_REQUIREMENTSlistYou've checked out the repo, implemented a million dollar feature, and you're ready to share your changes with the world. Now what?Make sure your changes are passing our test suite:airbyte-ci connectors --name=source-zendesk-sunshine testBump the connector version inmetadata.yaml: increment thedockerImageTagvalue. Please followsemantic versioning for connectors.Make sure themetadata.yamlcontent is up to date.Make the connector documentation and its changelog is up to date (docs/integrations/sources/zendesk-sunshine.md).Create a Pull Request: useour PR naming conventions.Pat yourself on the back for being an awesome contributor.Someone from Airbyte will take a look at your PR and iterate with you to merge it into master.
Package: airbyte-source-zenefits
How to access the token from ZenefitsLogin into the Zenefits portal.Follow the steps in the given linkHere, This will generate and Bearer token for the user which can be used to interact with the API using the source-zenefits connector.This is the repository for the Zenefits configuration based source connector.
For information about how to use this connector within Airbyte, seethe documentation.If you are a community contributor, follow the instructions in thedocumentationto generate the necessary credentials. Then create a filesecrets/config.jsonconforming to thesource_zenefits/spec.yamlfile.
Note that any directory namedsecretsis gitignored across the entire Airbyte repo, so there is no danger of accidentally checking in sensitive information.
Seeintegration_tests/sample_config.jsonfor a sample config file.If you are an Airbyte core member, copy the credentials in Lastpass under the secret namesource zenefits test credsand place them intosecrets/config.json.Viaairbyte-ci(recommended):airbyte-ciconnectors--name=source-zenefitsbuildAn image will be built with the tagairbyte/source-zenefits:dev.Viadocker build:dockerbuild-tairbyte/source-zenefits:dev.Then run any of the connector commands as follows:docker run --rm airbyte/source-zenefits:dev spec
docker run --rm -v $(pwd)/secrets:/secrets airbyte/source-zenefits:dev check --config /secrets/config.json
docker run --rm -v $(pwd)/secrets:/secrets airbyte/source-zenefits:dev discover --config /secrets/config.json
docker run --rm -v $(pwd)/secrets:/secrets -v $(pwd)/integration_tests:/integration_tests airbyte/source-zenefits:dev read --config /secrets/config.json --catalog /integration_tests/configured_catalog.jsonYou can run our full test suite locally usingairbyte-ci:airbyte-ciconnectors--name=source-zenefitstestCustomizeacceptance-test-config.ymlfile to configure tests. SeeConnector Acceptance Testsfor more information.
If your connector requires to create or destroy resources for use during acceptance tests create fixtures for it and place them inside integration_tests/acceptance.py.All of your dependencies should go insetup.py, NOTrequirements.txt. The requirements file is only used to connect internal Airbyte dependencies in the monorepo for local development.
We split dependencies between two groups, dependencies that are:required for your connector to work need to go toMAIN_REQUIREMENTSlist.required for the testing need to go toTEST_REQUIREMENTSlistYou've checked out the repo, implemented a million dollar feature, and you're ready to share your changes with the world. Now what?Make sure your changes are passing our test suite:airbyte-ci connectors --name=source-zenefits testBump the connector version inmetadata.yaml: increment thedockerImageTagvalue. Please followsemantic versioning for connectors.Make sure themetadata.yamlcontent is up to date.Make the connector documentation and its changelog is up to date (docs/integrations/sources/zenefits.md).Create a Pull Request: useour PR naming conventions.Pat yourself on the back for being an awesome contributor.Someone from Airbyte will take a look at your PR and iterate with you to merge it into master.
Package: aircal
AircalAircal is a library that exports future DAG runs as events to Google Calendar.Having DAG run as events in the calendar may help you:visualize the utilization of your airflow workers to better spread your jobsdetermine when a certain DAG should be finished to monitor the service.The library will also observe the changes to your DAGs and synchronize it with the calendar:add runs for the freshly added DAGschange start and/or end time when an existing DAG changes the schedule (or the execution time changes significantly)delete run events when a DAG is removed (or paused)Tip: run the sync script regularly, perhaps, with you know, Airflow :)The library only support DAG schedules that use the standard cron syntax. The rest will be ignored (with a warning).Warning: This is an beta stage software. Expect occassional bugs and rough edges (PR welcome).Installation & setuppip install aircalAlternatively you can clone the repo and install it from there:pip install -e .Google API credentials are required to create events in the calendar. You can obtain themhere. Storecredentials.jsoninto a directory accessible by your code.The library is modifying and deleting calendar events. I highly recommend creating a new calendar to be used by this software:"add calendar" -> "create new calendar" in Google calendar settings.UsageSeeexample.pyfor an example of the potential pipeline that can be run on the regular intervals.
Package: aircalc
air_calculatorWriting letters with a pen on paper is very different from drawing letters in the air.Drawing "-" minus operator and "+" operator in the air are extremely difficult so that these operators in the current system are replaced by "W" and "P" respectively.When drawing letters with fingers in the air, letters that are difficult
to recognize or write in the air need to be replaced with letters
that can be accurately recognized by artificial intelligence.
For example, replace the number "1" with "L" in the air.How to install necessary libraries$ pip install pytesseractFor Windows users, you should also install the latest tesseracthttps://github.com/UB-Mannheim/tesseract/wikiAnd add tesseract.exe of Tesseract-OCR directory PATH in .profile or .bashrc.$ pip install mediapipeFinally install aircalc$ pip install aircalcfingermath.py is renamed as aircalc.pyHow to run aircalcaircalc is a program for drawing a math expression
in the air for possible calculation.aircalc is based on two open source libraries including mediapipe and tesseract.There are six states of five fingers recognized by mediapipe library:
0-finger, 1-finger, 2-finger, 3-finger, 4-finger, and 5-finger respectively.A pen of index finger tip is used for drawing an expression 
by fingers. 

0-finger can move the pen without drawing. 
1-finger can draw lines in the air. 
2-finger can move the pen without drawing. 
3-finger can delete the last touches of drawn letters for correction.
4-finger can call tesseract for transforming the hand-writing 
images to the digital text for possible calculation. 
For several seconds, 4-finger can terminate the program 
for showing the answer of the hand-drawn expression.
5-finger can move the pen without drawing.Continuous 4-finger state can terminate and exit this program.0-finger or 5-finger is equivalent to 2-finger.The saved picture is tranformed into digital text using the state-of-the-art
optical character recognition.Writing letters with a pen on paper is very different from drawing letters in the air.Of the 0 to 9 digits, 1 is the least recognizable number.Drawing "L" in the air represents "1"."S" or "5" in the air represents "5"."P" in the air represents "+" plus operator."W" or "-" represents "-" minus operator."V" in the air represents "/" division operator."M" in the air represents "*" multiplication operator."&" in the air represents "**" exponential operator.Drawing two letters "a" and "A" in the air represents the sqrt() function.
Therefore, the string "a13A" or "aL3A" represents sqrt(13).$ aircalchttps://youtu.be/med_jrFTMPAsqrt(6)*2=?1-3=?                                 10+2=?4-5-3=?[2-3/5=?[34*5=?[2**8=?[2&9V3 --> 2**9/3[aLLAV3=? -> sqrt(11)/3[
Package: aircloak-tools
Python Aircloak ToolsTools for querying an Aircloak api.This package contains two main components:Aircloak Api: Wrapper around psycopg to query Aircloak directly.Explorer: An interface to Diffix Explorer for data analytics.Aircloak ApiThe main aim is to provide an Aircloak-friendly wrapper aroundpsycopg2, and in particular to
provide clear error messages when something doesn't go as planned.Query results are returned aspandasdataframes.ExplorerUsesDiffix Explorerto return enhanced statistics. Please see the project homepage for further information about Explorer.InstallationThe package can be installed in youir local environment using pip:pipinstallaircloak-toolsTo use Explorer Features you will also need to runDiffix Explorer.ExampleThe following code shows how to initiate a connection and execute a query.As a pre-requisite you should have a username and password for the postgres interface of an
Aircloak installation (ask your admin for these). Assign these values toAIRCLOAK_PG_USERandAIRCLOAK_PG_PASSWORDenvironment variables.importaircloak_toolsasacAIRCLOAK_PG_HOST="covid-db.aircloak.com"AIRCLOAK_PG_PORT=9432AIRCLOAK_PG_USER=environ.get("AIRCLOAK_PG_USER")AIRCLOAK_PG_PASSWORD=environ.get("AIRCLOAK_PG_PASSWORD")TEST_DATASET="cov_clear"withac.connect(host=AIRCLOAK_PG_HOST,port=AIRCLOAK_PG_PORT,user=AIRCLOAK_PG_USER,password=AIRCLOAK_PG_PASSWORD,dataset=TEST_DATASET)asconn:assert(conn.is_connected())tables=conn.get_tables()print(tables)feeling_now_counts=conn.query('''select feeling_now, count(*), count_noise(*)from surveygroup by 1order by 1 desc''')The easiest way to use Diffix Explorer is with the Docker image ondocker hub. More detailed information on running Diffix Explorer is available at theproject repo. As an example, you can use explorer to generate sample data based on the anonymized dataset as follows:fromaircloak_toolsimportexplorerEXPLORER_URL="http://localhost"EXPLORER_PORT=5000DATASET="gda_banking"TABLE="loans"COLUMNS=["amount","duration"]session=explorer.explorer_session(base_url=EXPLORER_URL,port=EXPLORER_PORT)result=explorer.explore(session,DATASET,TABLE,COLUMNS)assertresult['status']=='Complete'print(f'{COLUMNS[0]:>10}|{COLUMNS[1]:>10}')forrowinresult['sampleData']:print(f'{row[0]:>10}|{row[1]:>10}')# Should print something like:##    amount |  duration#     33000 |        12#     43000 |        36#     57000 |        12#     91000 |        24#     97000 |        48#    101000 |        60## etc.
Package: aircloudy
aircloudyAircloudy is an unofficial python library that allow management of RAC (Room Air Conditioner) compatible with Hitachi Air Cloud.This project IS NOT endorsed by Hitachi and is distributed as-is without warranty.Table of ContentsInstallationUsageLicenseDevelopmentInstallationpip install aircloudyUsagefrom__future__importannotationsimportasynciofromaircloudyimportHitachiAirCloud,InteriorUnit,compute_interior_unit_diff_descriptiondefprint_changes(dict:dict[int,tuple[InteriorUnit|None,InteriorUnit|None]])->None:for(id,change)indict.items():print(f"Change on interior unit{id}: "+compute_interior_unit_diff_description(change[0],change[1]))asyncdefmain()->None:asyncwithHitachiAirCloud("your@email.com","top_secret")asac:ac.on_change=print_changesunit_bureau=next((iuforiuinac.interior_unitsifiu.name=="Bureau"),None)ifunit_bureauisNone:raiseException("No unit named `Bureau`")awaitac.set(unit_bureau.id,"ON")awaitac.set(unit_bureau.id,requested_temperature=21,fan_speed="LV3")awaitasyncio.sleep(30)asyncio.run(main())Licenseaircloudyis distributed under modified HL3 license. SeeLICENSE.txt.Developmentpoetry run task lintpoetry run task checkpoetry run task testpoetry run task coverageNotesNot read/used field from notification :iduFrostWashStatus: IduFrostWashStatus
        active: bool
        priority: int
        astUpdatedA: int
        subCategory = None
        errorCode = None
specialOperationStatus: SpecialOperationStatus
        active: bool
        priority: int
        lastUpdatedAt: int
        subCategory = None
        errorCode = None
errorStatus: ErrorStatus
        active: bool
        priority: int
        lastUpdatedAt: int
        subCategory: str
        errorCode = None
cloudId: str
opt4: int
holidayModeStatus: HolidayModeStatus
        active: bool
        priority: int
        lastUpdatedAt: int
        subCategory = None
        errorCode = None
SysType: intNot read/used field from API:userId: str
iduFrostWash: bool
specialOperation: bool
criticalError: bool
zoneId: str
Package: airconditioner-webthing
airconditioner_webthingAn airconditioner web thing connectorThis project provides awebthing APIto Midea air conditionersThe airconditioner_webthing package exposes a http webthing endpoint which supports controlling the air conditioner via http. E.g.# webthing has been started on host 192.168.0.23

curl http://192.168.0.23:7122/properties 

{
   "outdoor_temperature": 4,
   "indoor_temperature": 22,
   "target_temperature": 23,
   "operational_mode": "heat",
   "fan_speed": 102,
   "power": false,
   "run_util": ""
}To install this software you may usePIPpackage manager such as shown belowsudo pip install airconditioner_webthingAfter this installation you may start the webthing http endpoint inside your python code or via command line usingsudo aircon --command listen --port 7122 --ip 10.31.33.90 --id 957548654462565Here, the webthing API will be bound to the local port 7122. Additionally, the ip address of the air conditioner has to be set
as well as the device id of the air conditioner. To discovery the device id you may usemidea-msmartlibrary as shon belowmidea-discover -a YOUR_ACCOUNT -p YOUR_PASSWORDAlternatively to thelistencommand, you can use theregistercommand to register and start the webthing service as systemd unit.
By doing this the webthing service will be started automatically on boot. Starting the server manually using thelistencommand is no longer necessary.sudo aircon --command register --port 7122 --ip 10.31.33.90 --id 957548654462565
Package: air-connection-app
REST API using Flask and Python [Air connections search]Project consist of:images - folder with images sourcestests - folder with UnitTests, Pytestapp.py - main application resourcedb.py - resource for creation and for fill the databaserequirements.txt - file for install packageslist.csv - list of flights for databasepytest.ini - pytest fileconfig.cfg - file with config, flake8For using application you need to do:Install Python 3.6 or higherInstall requirementspip install -r requirements.txtCreate database for applicationpython db.py$ python db.py

20 Record TransferredThere are 3 environments to choose from with different databases, choose one:testing - for local testing,dev - need to be configured manuallyprod - need to be configured manuallyRun applicationpython app.py testing$ python app.py
 * Serving Flask app "app" (lazy loading)
 * Environment: production
 * Debug mode: on
 * Restarting with stat
 * Debugger is active!
 * Debugger PIN: 231-078-686
 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)Follow the linkhttp://127.0.0.1:5000/EndpointsEndpointRequestDescription/flightGETGet All Flights/flight/idGETGet Single Flight/flightPOSTCreate a Flight/flight/idPUTUpdate a Flight/flight/idDELETEDelete Flight/flight/searchGETFlight search between 2 citiesStart Using:1. Get All Flightscurl-XGEThttp://127.0.0.1:5000/flight2. Get Single Flightcurl-XGEThttp://127.0.0.1:5000/flight/103. Create a Flightcurl-XPOST-H"Content-Type: application/json"-d'{"source": "Barcelona", "destination": "Palma de Mallorca", "flight_company": "Vueling", "flight_number": "FR3745", "flight_time": "55m", "free_seats": "5", "price": "50$"}'http://127.0.0.1:5000/flight4. Update a Flightcurl-XPUT-H"Content-Type: application/json"-d'{"source": "Barcelona", "destination": "Palma de Mallorca", "flight_company": "Ryanair", "flight_number": "FR3785", "flight_time": "50m", "free_seats": "8", "price": "150$"}'http://127.0.0.1:5000/flight/115. Delete Flightcurl-XDELETEhttp://127.0.0.1:5000/flight/116. Flight search between 2 citieshttp://127.0.0.1:5000/flight/search?c1=Milan&c2=BarcelonaUnitTests:Download the project:Follow thelinkChoose branche (feature/jdobc-255)- Using LinterInstall packagespip install flake8Run flake8flake8$ flake8
0- Using PyTestInstall pytest from pippip install pytestRun pytestpytest -v- Coverage by PyTest:Install packagespip install pytest-covRun commandpytest --cov=. --cov-config='.coveragerc' -c='pytest.ini'GitLab CI Multi-project:Flake8PytestCoverageBuild artifactUpload artifact to s3Deploy terraform codeLogging for AWS CloudWatchHow to run the application locally in the test environment ?run applicatin:python app.py testingLog output file "air-connection-app.log" will be created automaticallyÐ¢Ð¾ see the streaming of application logs in AWS you need to deploy the application in AWS usingterraform templateand go toCloudWatch logs
Package: aircopy
The CLI software to copy airtable data to Billinge group databaseFree software: 3-clause BSD licenseDocumentation: (COMING SOON!)https://st3107.github.io/aircopy.InstallationInstall pip on your machine. Run the following code in the terminal.pip install aircopyUsageRun the following command to find out the usage in the terminal.aircopy----helpFeaturesTODO
Package: aircot
Classify aircraft in TAKAirCOT is software for classifying aircraft within the Team Awareness Kit (TAK) ecosystem of products.AirCOT is used by these TAK compatible products:adsbcot: ADSBCOT is software for monitoring and analyzing aviation surveillance data via the Team Awareness Kit (TAK) ecosystem of products.AirTAK: AirTAK is a low SWaP-C device for monitoring and analyzing aviation surveillance data via the Team Awareness Kit (TAK) ecosystem of products.Documentation is available here.LicenseCopyright Sensors & Signals LLChttps://www.snstac.comLicensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.