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aironsuit | AIronSuitAIronSuit (Beta) is a Python library for automatic model design/selection and visualization purposes built to work withtensorflowas a backend. It aims to accelerate
the development of deep learning approaches for research/development purposes by providing components relying on cutting
edge approaches. It is flexible and its components can be
replaced by customized ones from the user. The user mostly focuses on defining the input and output,
and AIronSuit takes care of its optimal mapping.Key features:Automatic model design/selection withhyperopt.Parallel computing for multiple models across multiple GPUs when using a k-fold approach.Built-in model trainer that saves training progression to be visualized withTensorBoard.Machine learning tools fromAIronTools:model_constructor,block_constructor,layer_constructor, preprocessing utils, etc.Flexibility: the user can replace AIronSuit components by a customized one. For instance,
the model constructor can be easily replaced by a customized one.Installationpip install aironsuitExample# Databricks notebook sourceimportnumpyasnpfromhyperopt.hpimportchoicefromhyperoptimportTrialsfromtensorflow.keras.datasetsimportmnistfromtensorflow.keras.optimizersimportAdamimportosfromaironsuit.suitimportAIronSuitfromairontools.preprocessingimporttrain_val_splitfromairontools.constructors.models.unsupervisedimportImageVAEfromairontools.toolsimportpath_managementHOME=os.path.expanduser("~")OS_SEP=os.path.sep# COMMAND ----------# Example Set-Up #model_name='VAE_NN'working_path=os.path.join(HOME,'airon',model_name)+OS_SEPnum_classes=10batch_size=128epochs=30patience=3max_evals=3max_n_samples=Noneprecision='float32'# COMMAND ----------# Make/remove pathspath_management(working_path,modes=['rm','make'])# COMMAND ----------# Load and preprocess data(train_dataset,target_dataset),_=mnist.load_data()ifmax_n_samplesisnotNone:train_dataset=train_dataset[-max_n_samples:,...]target_dataset=target_dataset[-max_n_samples:,...]train_dataset=np.expand_dims(train_dataset,-1)/255# Split data per parallel modelx_train,x_val,_,meta_val,_=train_val_split(input_data=train_dataset,meta_data=target_dataset)# COMMAND ----------# VAE Model constructordefvae_model_constructor(latent_dim):# Create VAE model and compile itvae=ImageVAE(latent_dim)vae.compile(optimizer=Adam())returnvae# COMMAND ----------# Hyper-parameter spacehyperparam_space={'latent_dim':choice('latent_dim',np.arange(3,6))}# COMMAND ----------# Invoke AIronSuitaironsuit=AIronSuit(model_constructor=vae_model_constructor,force_subclass_weights_saver=True,force_subclass_weights_loader=True,path=working_path)# COMMAND ----------# Automatic Model Designprint('\n')print('Automatic Model Design\n')aironsuit.design(x_train=x_train,x_val=x_val,hyper_space=hyperparam_space,max_evals=max_evals,epochs=epochs,trials=Trials(),name=model_name,seed=0,patience=patience)aironsuit.summary()delx_train# COMMAND ----------# Get latent insightsaironsuit.visualize_representations(x_val,metadata=meta_val,hidden_layer_name='z',)More Examplessee usage examples inaironsuit/examples |
airontools | AIronToolsAIronTools (Beta) is a Python library that provides the user with higher level state-of-the-art deep learning tools built to work withtensorflowas a backend. The main goal of this repository is to enable fast model
design for both POCs and production.Key features:Out-of-the-box models ready to be used.Block constructor to build customised blocks/models.Layer constructor to build customised layers such as sequential, convolutional, self-attention or dense, and combinations of them.Preprocessing tools.On the fly non-topological hyper-parameter optimization. For now only the dropout regularization is compatible with this feature, in the future others such as l1 and l2 regularization will be compatible too.Latent representations for visualization purposes.Installationpip install airontoolsCustom Keras subclass to build a variational autoencoder (VAE) with airontools and compatible withaironsuitimportnumpyasnpimporttensorflowastffromairontools.constructors.models.unsupervised.vaeimportVAEfromnumpy.randomimportnormaltabular_data=np.concatenate([normal(loc=0.5,scale=1,size=(100,10)),normal(loc=-0.5,scale=1,size=(100,10)),])model=VAE(input_shape=tabular_data.shape[1:],latent_dim=3,)model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001))model.fit(tabular_data,epochs=10,)print("VAE evaluation:",float(model.evaluate(tabular_data)["loss"]))More examplessee usage examples inaironsuit/examples |
airoscriptng | Airoscript-ng python complete implementationFree software: GNU GENERAL PUBLIC LICENSE 2Documentation:http://airoscript-ng.readthedocs.org/en/masterFeaturesDynamic aircrack-ng API generation (under airoscriptng.aircrack)Threaded executionHackability assesmentScanning provides a list of best wireless hacking techniquesSession control (for better process control)Wireless monitor interfaces are nicely handled and reused if neccesaryXMLRPC server implementationTODOBetter parameter parsing & format for aircrack-ng parameters file, read them from manpagesImplement attacks on airoscript-ng classImplement cracking (and control of it, once cracked stop all attacks against that network)Build a user interface (probably more than one)History0.0.2 (2015-08-01)First usable thing, still no attacks“Hackability” property for apsIntegrated clients on AP objectExternal plugin supportReaver supportXMLRPC WorkingGeneral cleanup0.0.1 (2014-12-26)First release, monitor mode and scanning working |
airouter | AI Routerpip install airouterimportairouteroutputs=airouter.StreamedCompletion.create(model='gpt-3.5-turbo-1106',temperature=0.0,max_tokens=100,messages=[{'role':'user','content':'write a story about the big Large Language Models convention'}])importairouteroutputs=airouter.StreamedCompletion.create(model='text-bison-32k',temperature=0.0,max_tokens=100,messages=[{'role':'user','content':'write a story about the big Large Language Models convention'}])importairouteroutputs=airouter.StreamedCompletion.create(models=['gpt-3.5-turbo-1106','text-bison-32k'],temperature=0.0,max_tokens=100,messages=[{'role':'user','content':'write a story about the big Large Language Models convention'}])importairouteroutputs=airouter.StreamedCompletion.create_interactive(models=['gpt-3.5-turbo-1106','text-bison-32k'],temperature=0.0,max_tokens=100,messages=[{'role':'user','content':'write a story about the big Large Language Models convention'}]) |
airpage | No description available on PyPI. |
airpbx | No description available on PyPI. |
airpg | airpg: Automatically accessing the inverted repeats of archived plastid genomesA Python package for automatically accessing the inverted repeats of thousands of plastid genomes stored on NCBI NucleotideINSTALLATIONTo get the most recent stable version ofairpg, run:pip install airpgOr, alternatively, if you want to get the latest development version ofairpg, run:pip install git+https://github.com/michaelgruenstaeudl/airpg.gitEXAMPLE USAGETutorial 1: Very short survey (runtime ca. 5 min.; for the impatient)Survey of all plastid genomes of flowering plants submitted to NCBI Nucleotide within the past 10 days.Tutorial 2: Short survey (runtime ca. 15 min.; for testing)Survey of all plastid genomes of flowering plants submitted to NCBI Nucleotide within the current month.Tutorial 3: Medium survey (runtime ca. 5 hours)Survey of all plastid genomes of flowering plants submitted to NCBI Nucleotide in 2019 only. Note: The results of this survey are available on Zenodo via DOI10.5281/zenodo.4335906Tutorial 4: Full survey (runtime ca. 19 hours; with explanations)Survey of all plastid genomes of flowering plants submitted to NCBI Nucleotide from January 2000 until, and including, December 2020. Note: The results of this survey are available on Zenodo via DOI10.5281/zenodo.4335906CHANGELOGSeeCHANGELOG.mdfor a list of recent changes to the software. |
airphin | AirphinAirphin is a tool for migrating Airflow DAGs to DolphinScheduler Python API.InstallationFor now, it just for test and without publish to pypi but will be adding in the future.
You could still install locally by yourself.python-mpipinstall--upgradeairphinQuick StartHere will give a quick example to show how to migrate base on standard input.# Quick test the migrate rule for standard input# Can also add option `--diff` to see the diff detail of this migrateairphintest"from airflow.operators.bash import BashOperatortest = BashOperator(task_id='test',bash_command='echo 1',)"And you will see the migrated result in the standard output. Airphin can only migrate standard input, it can
also migrate file, directory and even can use in your python code. For more detail, please seeour usage.DocumentationThe documentation host on read the doc and is available athttps://airphin.readthedocs.io.Support StatementFor now, we support following statement from Airflow's DAG filesDAGBefore MigrationAfter Migrationfrom airflow import DAGfrom pydolphinscheduler.core.process_definition import ProcessDefinitionwith DAG(...) as dag: passwith ProcessDefinition(...) as dag: passOperatorsDummy OperatorBefore MigrationAfter Migrationfrom airflow.operators.dummy_operator import DummyOperatorfrom pydolphinscheduler.tasks.shell import Shellfrom airflow.operators.dummy import DummyOperatorfrom pydolphinscheduler.tasks.shell import Shelldummy = DummyOperator(...)dummy = Shell(..., command="echo 'airflow dummy operator'")Shell OperatorBefore MigrationAfter Migrationfrom airflow.operators.bash import BashOperatorfrom pydolphinscheduler.tasks.shell import Shellbash = BashOperator(...)bash = Shell(...)Spark Sql OperatorBefore MigrationAfter Migrationfrom airflow.operators.spark_sql_operator import SparkSqlOperatorfrom pydolphinscheduler.tasks.sql import Sqlspark = SparkSqlOperator(...)spark = Sql(...)Python OperatorBefore MigrationAfter Migrationfrom airflow.operators.python_operator import PythonOperatorfrom pydolphinscheduler.tasks.python import Pythonpython = PythonOperator(...)python = Python(...) |
airpiano | What is airpiano?takefuji (2022) airpiano for playing piano in the air [Source Code].https://doi.org/10.24433/CO.8862673.v1This is under review.airpiano has been downloaded by 23337 downloads worldwide as of Feb.12, 2024.When the paper is accepted, the contents of the air piano will be disclosed.
airpiano is a Python program to play airpiano composed of 123 lines code.
You can play 10 music notes by th right hand of five fingers.
airpiano is based on the-state-of-the-art hand gesture recognition using mediapipe.
airpiano uses musicalbeeps for generating music sounds by five fingers.
The paper on airpiano is under submission.
If the paper is accepted, the source code will be disclosed.Hand landmarks detected by MediaPipe are composed of 21 points in 2D image coordinates.https://mediapipe.dev/images/mobile/hand_landmarks.pngairpiano DEMO"twinkle twinkle little star"https://youtu.be/7KCmVyRpSCo"Mary had a little lamb"https://youtu.be/3J5xjW66MP8"Tulip"https://youtu.be/H7lwHb69ZgM"buzz buzz buzz"https://youtu.be/oG3_ZsW3XrQ"The Cuckoo"https://youtu.be/evyfpph-WOYHow to install airpianoPython3.7 or Python3.8 is recommended for running airpiano.
In order to run airpiano, the following libraries must be installed.$ pip install musicalbeeps$ pip install mediapipe$ pip install airpiano$ pip install airpiano --force-reinstall --no-cache-dir --no-binary :all:How to play airpianoMoving thumb finger plays C5 note.Moving index finger plays D5 note.Middle finger plays E5 note.Ring finger plays F5 note.Little finger plays G5 note.Moving thumb finger deeply plays A5 note.Moving thumb finger deeply and index finger plays B5 note.Moving thumb finger deeply and middle finger plays C6 note.Moving thumb finger deeply and ring finger plays D6 note.Moving thumb finger deeply and little finger plays E6 note.$ airpiano |
airpixel | No description available on PyPI. |
airplane | UNKNOWN |
airplanesdk | Airplane Python SDKSDK for writingAirplanetasks in Python.To learn more, seethe Airplane SDK docs. |
airplay | No description available on PyPI. |
air-pollution-check | air_pollution_checkA package for looking into the air pollution data, which can transfer the data into dataframe, add in the overall air pollution to the dataframe, visualize the air pollution data, and output the pollution level comparison from the past to the future for a specific location.Installation$pipinstallair_pollution_checkUsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licenseair_pollution_checkwas created by Yazhao Huang. It is licensed under the terms of the MIT license.Creditsair_pollution_checkwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate. |
airport | No description available on PyPI. |
airportAI-simulator | Airport simulator for evaluating airport gate assignment problem. Part of ariportAI project.PrerequisitesPython 3 is required to run this application. Cuda toolkit must be installed. If you are using Conda type:$ conda install cudatoolkitRefer tonumba requirements.InstallationInstalling from git:$ git clone https://github.com/burnpiro/airport-ai.git
$ cd airport-ai/simulator
$ pip install .Installing from pypi:$ pip install airportAI-simulatorUsageImportSimulatormodule and createSimulationobject.fromSimulatorimportSimulationsim=Simulation()# stepping simulationsim.step()# get agents and theier propertiesagents=sim.agentspos=agents[0].get_pos()# get flight scheduleflights=sim.get_flights()# get gates statusgates=sim.get_gates() |
airportcities | No description available on PyPI. |
airport-codes | No description available on PyPI. |
airportcountries | No description available on PyPI. |
airportfinder | airportfinderairportfinder is a Python library that provides a simple interface for looking up airport information by IATA code or airport name. It uses a JSON dataset of airports worldwide to provide accurate and up-to-date information.InstallationYou can install airportfinder using pip:pipinstallairportfinderUsagefromairportfinder.airportfinderimportAirports# Create an instance of the Airports classairports=Airports()# Look up an airport by IATA codeairport=airports.airport_iata('LGA')print(airport)# {'code': 'LGA', 'lat': '40.7731', 'lon': '-73.8756', 'name': 'LaGuardia Airport', 'city': 'Flushing', 'state': 'New York', 'country': 'United States', 'woeid': '12520509', 'tz': 'America/New_York', 'phone': '', 'type': 'Airports', 'email': '', 'url': '', 'runway_length': '7000', 'elev': '22', 'icao': 'KLGA', 'direct_flights': '82', 'carriers': '30'}# Look up an airport by nameairport=airports.airport_name('LaGuardia Airport')print(airport)# {'code': 'LGA', 'lat': '40.7731', 'lon': '-73.8756', 'name': 'LaGuardia Airport', 'city': 'Flushing', 'state': 'New York', 'country': 'United States', 'woeid': '12520509', 'tz': 'America/New_York', 'phone': '', 'type': 'Airports', 'email': '', 'url': '', 'runway_length': '7000', 'elev': '22', 'icao': 'KLGA', 'direct_flights': '82', 'carriers': '30'}The airport_iata and airport_name methods return the airport object corresponding to the provided IATA code or name. If the airport is not found, it returns None.Contributing
Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue on the GitHub repository.License
This project is licensed under the BSD 3-Clause License. See the LICENSE.txt file for details.Acknowledgments
This project uses airport data provided by Thomas Reynolds's "Airports" gist on GitHub:https://gist.github.com/tdreyno/4278655We would like to thank Thomas for his contributions to the open-source community and for making this data available to us. |
airportlib | No description available on PyPI. |
airport-management | No description available on PyPI. |
airport-py | airport-pyTiny MacOS only python library to integrate/use wi-fi scan results in your python projects. Wi-Fi scan data gathered by calling & parsing the results of a small MacOS cli utility called "airport"./System/Library/PrivateFrameworks/Apple80211.framework/Versions/Current/Resources/airportis the default path to program executable. Exporting environment variableAIRPORT_PATHwill update the defaults if you have your executable in a different path.Installationpip install airport-pyUsageimportairport# current connectionprintairport.info()InfoResult(agrCtlRSSI='-64',agrExtRSSI='0',agrCtlNoise='-92',agrExtNoise='0',state='running',op_mode='station',lastTxRate='702',maxRate='1300',lastAssocStatus='0',auth_80211='open',auth_link='wpa2-psk',BSSID='d0:3:4b:e9:55:99',SSID='AirPortTimeCapsule',MCS='5',channel='44,80')# wi-fi scanningscan_results=airport.scan()printmap(lambdar:(r.ssid,r.bssid,r.channel),scan_results[:3])[('kablo-ac','fd:4a:e9:6f:95:7e','52'),('kablo','fd:4a:e9:6f:95:7d','11'),('oz-hukuk','88:31:fd:4e:20:67','11')] |
airports_analytics | No description available on PyPI. |
airportsdata | Extensive database of location and timezone data for nearly every operational airport and landing strip in the world,
with 28,137 entries.Each entry consists of the following data:icao: ICAO 4-letter Location Indicator (Doc 7910) or (if none) an internal Pseudo-ICAO Identifier[1](28,137
entries);iata: IATA 3-letter Location Code (7,592 entries) or an empty string[2];name: Official name (diacritized latin script);city: City (diacritized latin script), ideally using the local language;subd: Subdivision (e.g. state, province, region, etc.), ideally using the local-language or English names ofISO 3166-2;country:ISO 3166-1alpha-2 country code
(plusXKfor Kosovo);elevation: MSL elevation of the highest point of the landing area, in feet (warning: it is often wrong);lat: Latitude (decimal) of theairport reference point;lon: Longitude (decimal) of theairport reference point;tz: Timezone expressed as atz database name(IANA-compliant) or an empty string for Antarctica;lid: U.S. FAA Location Identifier (12,567 entries), or an empty string.[1]Seeherefor an explanation on
how the Pseudo-ICAO Identifier is generated for airports and seaplane bases without an ICAO 4-letter Location
Indicator.[2]IATA Multi Airport Cities are not not airports so are not included, but we provide a database and a Python
function that returns the above data for all of their component airports of a IATA MAC. Please see documentationhere.Best efforts are placed to review all contributions for accuracy, but accuracy cannot be guaranteed nor should be
expected by users.Important notes:Timezone was originally sourced fromTimeZoneDBand is missing for Antarctica;No historical data (closed airports are removed).Please report any issues you may findhere.This project is a fork ofhttps://github.com/mwgg/Airports. All new data submitted in this fork have been validated
against nationalAeronautical Information Publications (AIP) or equivalent(or
ARINC database) andIATAbefore publishing.Raw dataA CSV (comma separated values) file, with headers and encoded in UTF-8, is downloadable from GitHubhere.PythonInstall fromPyPiusing pip:pipinstall-UairportsdataOnce installed, to load the data into a dict:importairportsdataairports=airportsdata.load()# key is the ICAO identifier (the default)print(airports['KJFK'])orimportairportsdataairports=airportsdata.load('IATA')# key is the IATA location codeprint(airports['JFK'])orimportairportsdataairports=airportsdata.load('LID')# key is the FAA LIDprint(airports['01AA'])Older Python versions are supported for 3 years after being obsoleted by a new major release (i.e. about 4 years
since their original release).LicenseReleased under theMIT License(see licensehere). |
airports-py | airports-pyA comprehensive Python library providing easy retrieval of airport data based on IATA, ICAO, city codes, country codes, and continents. Ideal for developers building applications related to aviation, travel, and geography in Python.FeaturesRetrieve airport data using IATA code.Retrieve airport data using ICAO code.Fetch data using city codes.Fetch data using country codes.Retrieve data based on continents.Built-in error handling for invalid input formats.Efficiently packaged with gzipped data.Comprehensive Data Access: Retrieve airport data using IATA code, ICAO code, city codes, country codes, and continents.Unique Link Integration: The first library to provide direct links toFlightRadar24,Radarbox, andFlightAwarefor each airport, giving users immediate access to live flight tracking and airport data.InstallationYou can installairports-pyusing pip:pipinstallairports-pyUsageHere's how you can use the library:fromairportsimportairport_data# Retrieve airport data using IATA codeairport_by_iata=airport_data.get_airport_by_iata("AAA")print(airport_by_iata)# Retrieve airport data using ICAO codeairport_by_icao=airport_data.get_airport_by_icao("NTGA")print(airport_by_icao)# Fetch data using city codesairport_by_city=airport_data.get_airport_by_city_code("NYC")print(airport_by_city)# Fetch data using country codesairport_by_country=airport_data.get_airport_by_country_code("US")print(airport_by_country)# Retrieve data based on continentsairport_by_continent=airport_data.get_airport_by_continent("AS")print(airport_by_continent)Using Command-Line Interface (CLI):You can also directly execute Python code from the CLI without entering the interactive shell. Navigate to the root of your project and run:python3-c"from airports import airport_data; result = airport_data.get_airport_by_iata('MAA'); print(result)"Replace'MAA'with other codes as needed.TestingTo test the library locally:Navigate to the root of the project:cdpath_to_airports-pyRun the tests using:python3-munittestdiscovertests-vThis command will discover and run all test files inside thetestsdirectory and provide a detailed output.Example Data FieldsFor Chennai International Airport:Field NameDataIATAMAAICAOVOMMTime ZoneAsia/KolkataCity CodeMAACountry CodeINNameChennai International AirportLatitude12.99Longitude80.1693Altitude (in feet)52StateTamil NaduCityPallavaramCountyKancheepuramState CodeTamil NaduAirport Typelarge_airportContinentASState AbbreviationIN-TNInternationalTRUEWikipedia LinkWikipediaOfficial WebsiteChennai AirportLocation ID12513629Phone Number044-2340551Runway Length (in meters)10050Flightradar24Flightradar24RadarboxRadarboxFlightaware LinkFlightawareSingapore Changi Airport:Field NameDataIATASINICAOWSSSTime ZoneAsia/SingaporeCity CodeSINCountry CodeSGNameSingapore Changi AirportLatitude1.35019Longitude103.994Altitude (in feet)22StateSingaporeCitySingaporeCountySingaporeState CodeSouth EastAirport Typelarge_airportContinentASState AbbreviationSG-04InternationalTRUEWikipedia LinkWikipediaOfficial WebsiteChangi AirportLocation ID12517525Phone Number(65) 6542 1122Runway Length (in meters)13200Flightradar24Flightradar24RadarboxRadarboxFlightawareFlightawareRunning the Project LocallyClone the repository:gitclonehttps://github.com/aashishvanand/airports-py.gitChange into the cloned directory:cdairports-pyInstall the necessary dependencies:pipinstall-rrequirements.txtTo run tests:python-munittestdiscovertestsLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.ContributingContributions, issues, and feature requests are welcome! Feel free to check theissues page. |
airporttime | Convert local time to utc time by airport or vise-versa.This works thanks to data from opentraveldata. The data source is here:https://raw.githubusercontent.com/opentraveldata/opentraveldata/master/opentraveldata/optd_por_public.csvUsageHere is the usage:>>> # import the library
>>> import airporttime
>>> # we are also going to use the datetime library
>>> from datetime import datetime
>>> # create an instance of AirportTime at 'ORD'
>>> apt = airporttime.AirportTime(iata_code='ORD')
>>> # ** side note ** the apt object contains one attribute, basically an instance of a row of data
>>> print(apt.airport.__dict__)
{'iata_code': 'ORD', 'icao_code': 'KORD', 'faa_code': 'ORD', 'is_geonames': 'Y', 'geoname_id': '4887479', 'envelope_id': '', 'name': "Chicago O'Hare International Airport", 'asciiname': "Chicago O'Hare International Airport", 'latitude': '41.978603', 'longitude': '-87.904842', 'fclass': 'S', 'fcode': 'AIRP', 'page_rank': '0.4871606262308594', 'date_from': '', 'date_until': '', 'comment': '', 'country_code': 'US', 'cc2': '', 'country_name': 'United States', 'continent_name': 'North America', 'adm1_code': 'IL', 'adm1_name_utf': 'Illinois', 'adm1_name_ascii': 'Illinois', 'adm2_code': '031', 'adm2_name_utf': 'Cook County', 'adm2_name_ascii': 'Cook County', 'adm3_code': '14000', 'adm4_code': '', 'population': '0', 'elevation': '201', 'gtopo30': '202', 'timezone': 'America/Chicago', 'gmt_offset': '-6.0', 'dst_offset': '-5.0', 'raw_offset': '-6.0', 'moddate': '2018-03-29', 'city_code_list': 'CHI', 'city_name_list': 'Chicago', 'city_detail_list': 'CHI|4887398|Chicago|Chicago', 'tvl_por_list': '', 'iso31662': 'IL', 'location_type': 'A', 'wiki_link': 'https://en.wikipedia.org/wiki/O%27Hare_International_Airport', 'alt_name_section': "de|Flughafen Chicago O'Hare|=wuu|奥黑尔国际机场|=th|ท่าอากาศยานนานาชาติโอแฮร์|=uk|Аеропорт О'Хара|=ta|ஓஹேர் பன்னாட்டு வானூர்தி நிலையம்|=ru|Международный аэропорт Чикаго О'Хара|=ro|Aeroportul Internațional Chicago O'Hare|=pt|Aeroporto Internacional O'Hare|=pnb|اوہیر انٹرنیشنل ہوائی اڈہ|=ja|シカゴ・オヘア国際空港|=mr|ओ'हेर आंतरराष्ट्रीय विमानतळ|=ml|ഒ'ഹെയർ അന്താരാഷ്ട്ര വിമാനത്താവളം|=hu|O’Hare nemzetközi repülőtér|=he|נמל התעופה שיקגו או'הייר|=ko|오헤어 국제공항|=fr|Aéroport international O'Hare de Chicago|=fa|فرودگاه بین\u200cالمللی اوهر شیکاگو|=es|Aeropuerto Internacional O'Hare|=de|Chicago O’Hare International Airport|=cs|Letiště Chicago O'Hare International Airport|=ar|مطار أوهير الدولي|=en|Chicago O'Hare International Airport|p=|Orchard Field|=|O'Hare International Airport|=|Orchard Place/Douglas Field|=sv|Chicago O'Hare flygplats|p", 'wac': '41', 'wac_name': 'Illinois', 'ccy_code': 'USD', 'unlc_list': 'USORD|', 'uic_list': ''}
>>> # suppose you have a naive local datetime you want to convert
>>> naive_loc_time = datetime(2019, 1, 1, 10, 30)
>>> # convert your naive local date and time to utc
>>> tz_aware_utc_time = apt.to_utc(naive_loc_time)
>>> print(tz_aware_utc_time)
2019-02-02 16:30:00+00:00
>>> # convert your tz aware back to local time if you want to
>>> tz_aware_loc_time = apt.from_utc(tz_aware_utc_time)
>>> print(tz_aware_loc_time)
2019-02-02 10:30:00-06:00
>>> # ** side note ** this library internally tries to account daylight savings time (dst)
>>> print(apt._dst(naive_loc_time, tz_aware_loc_time.tzinfo))
False
>>> # if you would like to update the data file, you can use:
>>> airporttime.update() |
airport-weather-tracker | No description available on PyPI. |
airpress | airpressAirPress lets you create, sign and zip PKPass archives for Apple Wallet in runtime memory without a need for temporary files or directories.InstallationFrom PyPI:pip install airpressQuickstartfromairpressimportPKPass# PKPass compressor operates on `bytes` objects as input/outputp=PKPass(('icon.png',bytes(...)),('logo.png',bytes(...)),('pass.json',bytes(...)),...)p.sign(cert=bytes(...),key=bytes(...),password=bytes(...))# `password` argument is optional_=bytes(p)# Creates `bytes` object containing signed and compressed `.pkpass` archiveIn most cases you're likely to returnpkpassashttpresponse andbytesobject is exactly what you need.PKPasswill raise human-readable errors in case something is
wrong with pass package you're trying to sign and compress.Manage assets in pass packageAccessingPKPassassets that are already added to pass package is as easy as working with dictionary.Retrieve asset:icon=p['icon.png']It can also be used as alternative to add/update asset:p['icon.png']=bytes(...)Remove asset from pass package:delp['logo.png']Prepare Pass Type ID certificateIf you don't have your pass type certificate, follow this guide to create one.Export your developer certificate as.p12file and convert it into a pair of cert and key.pemfiles:openssl pkcs12 -in "Certificates.p12" -clcerts -nokeys -out certificate.pemopenssl pkcs12 -in "Certificates.p12" -nocerts -out key.pemYou will be asked for an export password (or export phrase), you may leave it blank or provide a passphrase.
It's this value that you later should supply to PKPass compressor (or leave blank).Example with local filesIn case you'd like to play around with locally stored files, or your server keeps assets in the same file storage
as source code, this example shows you how to read locally stored assets asbytesobjects, compresspkpassarchive
and save it to script's parent directory.importosfromairpressimportPKPass# Create empty pass packagep=PKPass()# Add locally stored assetsforfilenameinos.listdir('your_dir_with_assets'):withopen(os.path.join(os.path.dirname(__file__),'your_dir_with_assets',filename),'rb')asfile:data=file.read()# Add each individual asset to pass packagep.add_to_pass_package((filename,data))# Add locally stored credentialswithopen(os.path.join(os.path.dirname(__file__),'your_dir_with_credentials/key.pem'),'rb')askey,open(os.path.join(os.path.dirname(__file__),'your_dir_with_credentials/certificate.pem'),'rb')ascert:# Add credentials to pass packagep.key=key.read()p.cert=cert.read()p.password=bytes('passpass','utf8')# As we've added credentials to pass package earlier we don't need to supply them to `.sign()`# This is an alternative to calling .sign() method with credentials as arguments.p.sign()# Create pkpass file with pass datawithopen('your_dir_for_output/data.pkpass','wb')asfile:file.write(bytes(p))Hope you find this package useful!
I'd love to hear your feedback and suggestions for this tiny library as there's always room for improvement. |
airpt | No description available on PyPI. |
airpt1 | No description available on PyPI. |
airpt2 | No description available on PyPI. |
airptlib1 | No description available on PyPI. |
airpwn-ng | Packet injection for wifi; simplified. |
airpy | UsageInstallation:$ pip install airpyRun:$ airpy
Usage: airpy [OPTIONS] COMMAND [ARGS]...
AirPy : Documentation Installer for the Python
Options:
--help Show this message and exit.
Commands:
autopilot Auto install docs.
install Install offline doc of a Python module.
list List installed docs.
remove Remove an installed doc.
start Start a doc in a browser.Install a Documentation:$ airpy install requestsStart the Documentation in a browser:$ airpy start requestsRemove a Documentation:$ airpy remove requestsAirPy Autopilot : Install Documentation for Python packages already installed in your system.:$ airpy autopilot
$ airpy list
flask requests django sphinx wheel setuptools |
airpyllution | airpyllutionairpyllutionis a Python package for visualizing or obtaining future, historic and current air pollution data using theOpenWeather API. Our goal is to enable users the ability to explore air pollution levels in locations around the world by providing visual charts and graphs. We make the data accessible and easy to comprehend in just a few lines of code.Although there is an abundance of python weather packages and APIs in the Python ecosystem (e.g.python-weather,weather-forecast), this particular package looks at specifically air pollution data and uses theAir Pollution APIfrom OpenWeather. This is a unique package which provides simple and easy to use functions and allows users to quickly access and visualise data.The data returned from the API includes the polluting gases such as Carbon monoxide (CO), Nitrogen monoxide (NO), Nitrogen dioxide (NO2), Ozone (O3), Sulphur dioxide (SO2), Ammonia (NH3), and particulates (PM2.5 and PM10).Using the OpenWeatherMap API requires sign up to gain access to an API key.For more information about API call limits and API care recommendations please visit theOpenWeather how to startpage.FunctionsThis package contains 3 functions:get_air_pollution()get_pollution_history()get_pollution_forecast()get_air_pollution()Fetches the air pollution levels based on a location. Based on the values of the polluting gases, this package uses theAir Quality Indexto determine the level of pollution for the location and produces a coloured map of the area displaying the varying regions of air quality.get_pollution_history()Requires a start and end date and fetches historic air pollution data for a specific location. The function returns a data frame with the values of the polluting gases over the specified date range.get_pollution_forecast()Fetches air pollution data for the next 5 days for a specific location. The function returns a time series plot of the predicted pollution levels.Installation$pipinstallairpyllutionUsage and ExampleCreate anOpenWeather API KeyInstall airpyllutionRefer toReadTheDocsfor a usage guide and examples.To use the package, import the package with the following commands:from airpyllution.airpyllution import get_air_pollution
from airpyllution.airpyllution import get_pollution_history
from airpyllution.airpyllution import get_pollution_forecastRetrieve historic pollution data with specified date range and location:get_pollution_history(1606488670, 1606747870, 49.28, 123.12, api_key)Generate an interactive map containing current pollution data by location:get_air_pollution(49.28, 123.12, api_key, "Current Air Pollution")Generate a time-series line chart of forecasted air pollution data:import altair as alt
alt.renderers.enable("html");
get_pollution_forecast(49.28, 123.12, api_key)ContributorsChristopher Alexander (@christopheralex)Daniel King (@danfke)Mel Liow (@mel-liow)ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licenseairpyllutionwas created by Christopher Alexander, Daniel King, Mel Liow. It is licensed under the terms of theHippocratic License 3.0.Creditsairpyllutionwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate. |
airQ | airQ v0.3.3A near real time Air Quality Indication Data Collection Service( for India ), made with :heart:Consider putting :star: to show love & supportCompanion repo located at :airQ-insight, to power visualizationwhat does it do ?Air quality data collector, collected from180+ground monitoring stations( spread across India )UnreliableJSONdataset is fetched fromhere, which gives current hour's pollutant statistics, from all monitoring station(s), spread acrossIndia, which are then objectified, cleaned, processed & restructured into proper format and pushed into*.jsonfileAir quality data, given byminimum,maximum&averagepresence of pollutants such asPM2.5,PM10,CO,NH3,SO2,OZONE&NO2, along withtimeStamp, grouped under stations( from where these were collected )Automated data collection done using systemd( hourly )installationairQcan easily be installed from PyPI using pip.$pipinstallairQ--user# or may be use pip3$python3-mpipinstallairQ--user# if previous one doesn't workusageAfter installingairQ, run it using following command$cd# currently at $HOME$airQ# improper invokationairQ-AirQualityDataCollector$airQ`sink-file-path_(*.json)_`FormakingmodificationsonairQ-collecteddata(collectedpriortothisrun),passthatJSONpath,whileinvokingairQ;)BadInput
$airQ./data/data.json# proper invokationautomationWell my plan was to automate this data collection service, so that it'll keep running in hourly fashion, and keep refreshing datasetAnd for that, I've usedsystemd, which will use asystemd.timerto trigger execution ofairQevery hour i.e. after a delay of1h, counted from last execution ofairQ, periodicallyFor that we'll require to add two files,*.service&*.timer( placed in./systemd/)airQ.serviceWell our service isn't supposed to run always, only when timer trigger asks it to run, it'll run. So in[Unit]section, it's declared itWants,airQ.timer[Unit]
Description=Air Quality Data collection service
Wants=airQ.timerYou need to set absolute path of current working directory inWorkingDirectoryfield of[Service]unit declarationExecStartis the command, to be executed when this service unit is invoked byairQ.timer, so absolute installation path ofairQand absolute sink path( *.json )is requiredMake sure you updateUserfield, to reflect changes properly, as per your system.If you just add aRestartfield under[Service]unit & give it a valuealways, we can make this script running always, which is helpful for running Servers, but we'll trigger execution of script usingsystemd.timer, pretty much likecron, but much more used & supported in almost all linux based distros[Service]
User=anjan
WorkingDirectory=/absolute-path-to-current-working-directory/
ExecStart=/absolute-path-to-airQ /home/user/data/data.jsonThis declaration, makes this service a required dependency formulti-user.target[Install]
WantedBy=multi-user.targetairQ.timerPretty much same asairQ.service, onlyRequires,airQ.serviceas one strong dependency, because that's the service which is to be run when this timer expires[Unit]
Description=Air Quality Data collection service
Requires=airQ.serviceUnitfield specifies which service file to execute when timer expires.
You can simply skip this field, if you have created a./systemd/*.servicefile of same name as./systemd/*.timerAs we're interested in running this service every1h( relative to last execution of airQ.service ), we've specifiedOnUnitActiveSecfield to be1h[Timer]
Unit=airQ.service
OnUnitActiveSec=1hMakes it an dependency oftimers.target, so that this timer can be installed[Install]
WantedBy=timers.targetautomation in ACTIONNeed to place files present./systemd/*into/etc/systemd/system/, so thatsystemdcan find these service & timer easily.$sudocp./systemd/*/etc/systemd/system/We need to reloadsystemddaemon, to let it explore newly added service & timer unit(s).$sudosystemctldaemon-reloadLets enable our timer, which will ensure our timer will keep running even after system reboot$sudosystemctlenableairQ.timerTime to start this timer$sudosystemctlstartairQ.timerSo an immediate execution of our script to be done, and after completion of so, it'll again be executed1hlater, so that we get refreshed dataset.Check status of this timer$sudosystemctlstatusairQ.timerCheck status of this service$sudosystemctlstatusairQ.serviceConsider running your instance ofairQon Cloud, mine running onAWS LightSailvisualizationThis service is supposed to only collect data & properly structure it, but visualization part is done atairQ-insightHoping it helps:wink: |
airqdata | Air quality dataA toolkit to retrieve, analyze and visualize data from a variety of air quality
sensors.The scripts include tools towrap the APIs of various data providers, including Civic Lab Brussels'InfluencAirproject, theluftdaten.infoproject,madavi.deandirceline.berepresent sensors of those different providers as objects with a unified
interface to make it easy to interact with themretrieve sensor measurement data through API callscache those dataclean and combine the datadescribe measurements statistically - individual sensors or groups to
compareplot measurement time seriesfind sensors that are geographically close to a point of interest or to other
sensorsFor usage examples, see thedemonotebook.InstallationTo install airqdata from PyPI, runpip install airqdataA Python 3.5+ environment and several Python packages are required. When
installing airqdata with pip, those dependencies will be installed
automatically. Otherwise see requirements.txt and
install_requirements.sh in this repository.LegalThe scripts are licensed under theGPLv3.Data made available by the luftdaten.info project arelicensedunder theOpen Database License.Data published by the Belgian Interregional Environment Agency (IRCEL/CELINE)
arelicensedunder theCreative Commons Attribution 4.0
license. |
airQInsight | airQ-insightVisualization of Air Quality Indication Dataset collected byairQto get deeper insight, written with :heart:Accompanying website for Visualization of Air Quality, Coming soon ...:boss:motivationPrior to it, I wrote one simple data collection module, which collects Air Quality Indication Data from Govt. Of India'sOpen Data Platform.Now for getting a peek into that collected dataset, I'm writing this module, which can generate animated plot of pollutants( i.e. CO, NH3, NO2, OZONE, PM10, PM2.5, SO2 )for all monitoring stations, from which Pollution Control Board(s) collect data.installationThis python module is available on PyPI.$pipinstallairQInsight--user
$python3-mpipinstallairQInsight--user# if above one doesn't workusageAfter installingairQInsight, make sure you've put installation path( which is by default /home/user/.local/bin/ )in your system PATH variable.Now it can be invoked using its name directly.$airQInsight
airQInsight-AirQualityDataVisualizationSystem$airQInsight`path-to-data-file_(*.json)_``path-to-sink-directory`BadInputautomationIt'd be a good idea to automate this animated plot generation method usingsystemd, as we did in case ofairQ( which collected Air Quality Indication Data periodically ).Required UNIT files are suppliedhereYou'll require to make some changes in order to run this on your environment.Thanking you :wink: |
air-quality-cli | Air Quality Index CLIGet Air Quality index for your City.Installation$pipinstallair-quality-cliContentsAir Quality Index CLIInstallationContentsUsageInitializationSeach for air quality based on country or city nameSave your city to config for quick view.ContributingUsageInitializationOne time setup to initialize the CLI using API token.Get your API Token!$airinitSeach for air quality based on country or city name$airsearchkathmandu$airsearchNepalDemoSave your city to config for quick view.You can save stations from a number of locations to quickly view air quality there.Save the location$airaddkathmanduShow air quality from all your saved locations$airshowDemoContributingFor guidance on setting up a development environment and how to make a contribution toair-quality-cli, see thecontributing guidelines. |
air-quality-index | LinksDocs:palewi.re/docs/air-quality-index/Issues:github.com/datadesk/air-quality-index/issuesPackaging:pypi.python.org/pypi/air-quality-indexTesting:github.com/datadesk/air-quality-index/actions |
air-quotes | Air QuotesMock any proposal, reasonable or otherwise |
airr | InstallationInstall in the usual manner from PyPI:> pip3 install airr --userOr from thedownloadedsource code directory:> python3 setup.py install --userQuick StartDeprecation NoticeTheload_repertoire,write_repertoire, andvalidate_repertoirefunctions
have been deprecated for the new genericload_airr_data,write_airr_data, andvalidate_airr_datafunctions. These new functions are backwards compatible with
the Repertoire metadata format but also support the new AIRR objects such as GermlineSet,
RepertoireGroup, GenotypeSet, Cell and Clone. This new format is defined by the DataFile
Schema, which describes a standard set of objects included in a file containing
AIRR Data Model presentations. Currently, the AIRR DataFile does not completely support
Rearrangement, so users should continue using AIRR TSV files and its specific functions.
Also, therepertoire_templatefunction has been deprecated for theSchema.templatemethod, which can now be called on any AIRR Schema to create a blank object.Reading AIRR Data FilesTheairrpackage contains functions to read and write AIRR Data
Model files. The file format is either YAML or JSON, and the package provides a
light wrapper over the standard parsers. The file needs ajson,yaml, orymlfile extension so that the proper parser is utilized. All of the AIRR objects
are loaded into memory at once and no streaming interface is provided:import airr
# Load the AIRR data
data = airr.read_airr('input.airr.json')
# loop through the repertoires
for rep in data['Repertoire']:
print(rep)Why are the AIRR objects, such as Repertoire, GermlineSet, and etc., in a list versus in a
dictionary keyed by their identifier (e.g.,repertoire_id)? There are two primary reasons for
this. First, the identifier might not have been assigned yet. Some systems might allow MiAIRR
metadata to be entered but the identifier is assigned to that data later by another process. Without
the identifier, the data could not be stored in a dictionary. Secondly, the list allows the data to
have a default ordering. If you know that the data has a unique identifier then you can quickly
create a dictionary object using a comprehension. For example, with repertoires:rep_dict = { obj['repertoire_id'] : obj for obj in data['Repertoire'] }another example with germline sets:germline_dict = { obj['germline_set_id'] : obj for obj in data['GermlineSet'] }Writing AIRR Data FilesWriting an AIRR Data File is also a light wrapper over standard YAML or JSON
parsers. Multiple AIRR objects, such as Repertoire, GermlineSet, and etc., can be
written together into the same file. In this example, we use theairrlibrarytemplatemethod to create some blank Repertoire objects, and write them to a file.
As with the read function, the complete list of repertoires are written at once,
there is no streaming interface:import airr
# Create some blank repertoire objects in a list
data = { 'Repertoire': [] }
for i in range(5):
data['Repertoire'].append(airr.schema.RepertoireSchema.template())
# Write the AIRR Data
airr.write_airr('output.airr.json', data)Reading AIRR Rearrangement TSV filesTheairrpackage contains functions to read and write AIRR Rearrangement
TSV files as either iterables or pandas data frames. The usage is straightforward,
as the file format is a typical tab delimited file, but the package
performs some additional validation and type conversion beyond using a
standard CSV reader:import airr
# Create an iteratable that returns a dictionary for each row
reader = airr.read_rearrangement('input.tsv')
for row in reader: print(row)
# Load the entire file into a pandas data frame
df = airr.load_rearrangement('input.tsv')Writing AIRR Rearrangement TSV filesSimilar to the read operations, write functions are provided for either creating
a writer class to perform row-wise output or writing the entire contents of
a pandas data frame to a file. Again, usage is straightforward with theairroutput functions simply performing some type conversion and field ordering
operations:import airr
# Create a writer class for iterative row output
writer = airr.create_rearrangement('output.tsv')
for row in reader: writer.write(row)
# Write an entire pandas data frame to a file
airr.dump_rearrangement(df, 'file.tsv')By default,create_rearrangementwill only write therequiredfields
in the output file. Additional fields can be included in the output file by
providing thefieldsparameter with an array of additional field names:# Specify additional fields in the output
fields = ['new_calc', 'another_field']
writer = airr.create_rearrangement('output.tsv', fields=fields)A common operation is to read an AIRR rearrangement file, and then
write an AIRR rearrangement file with additional fields in it while
keeping all of the existing fields from the original file. Thederive_rearrangementfunction provides this capability:import airr
# Read rearrangement data and write new file with additional fields
reader = airr.read_rearrangement('input.tsv')
fields = ['new_calc']
writer = airr.derive_rearrangement('output.tsv', 'input.tsv', fields=fields)
for row in reader:
row['new_calc'] = 'a value'
writer.write(row)Validating AIRR data filesTheairrpackage can validate AIRR Data Model JSON/YAML files and Rearrangement
TSV files to ensure that they contain all required fields and that the fields types
match the AIRR Schema. This can be done using theairr-toolscommand
line program or the validate functions in the library can be called:# Validate a rearrangement TSV file
airr-tools validate rearrangement -a input.tsv
# Validate an AIRR DataFile
airr-tools validate airr -a input.airr.jsonCombining Repertoire metadata and Rearrangement filesTheairrpackage does not currently keep track of which AIRR Data Model files
are associated with which Rearrangement TSV files, though there is ongoing work to define
a standardized manifest, so users will need to handle those
associations themselves. However, in the data, AIRR identifier fields, such asrepertoire_id,
form the link between objects in the AIRR Data Model.
The typical usage is that a program is going to perform some
computation on the Rearrangements, and it needs access to the Repertoire metadata
as part of the computation logic. This example code shows the basic framework
for doing that, in this case doing gender specific computation:import airr
# Load AIRR data containing repertoires
data = airr.read_airr('input.airr.json')
# Put repertoires in dictionary keyed by repertoire_id
rep_dict = { obj['repertoire_id'] : obj for obj in data['Repertoire'] }
# Create an iteratable for rearrangement data
reader = airr.read_rearrangement('input.tsv')
for row in reader:
# get repertoire metadata with this rearrangement
rep = rep_dict[row['repertoire_id']]
# check the gender
if rep['subject']['sex'] == 'male':
# do male specific computation
elif rep['subject']['sex'] == 'female':
# do female specific computation
else:
# do other specific computation |
airrmap | No description available on PyPI. |
airrship | AIRRSHIP - Adaptive Immune Receptor Repertoire Simulation of Human Immunoglobulin ProductionAIRRSHIP simulates B cell receptor (BCR) sequences for use in benchmarking applications where BCR sequences of known origin are required.AIRRSHIP replicates the VDJ recombination process from haplotype through to somatic hypermutation. Recombination metrics are derived from a range of experimental sequences allowing faithful replication of true repertoires. Users may also control a wide range of parameters that influence allele usage, junctional diversity and somatic hypermutation rates. The current model extends to human heavy chain BCR sequences only.Installationpip install airrshipDocumentationFull documentation is availablehere.Example datasetsIf you do not wish to install and run AIRRSHIP yourself or want to explore the output yourself first, a small example repertoire is hosted at the AIRRSHIPGitHub repository. Larger example repertoire files are available atZenodo.PublicationsA preprint is currently available athttps://doi.org/10.1101/2022.12.20.521228. |
airrun | airrun致力于简单快速的在Android设备上运行airtest，减少在构建用例、获取性能监控、日志获取方面的复杂度，
并提基于airtest报告的新的报告生成工具，让测试人员更专注于编写测试用例。Python Version >= 3.6介绍用例分层和封装模板封装执行时区分用例监控 cpu、memory 信息输出图表统计信息图表重写 log 手机重写报告生成安装pip install airrunpipinstall--upgradeairrun使用python-mairrunversionshowversionandexitrunrunscriptinfoget&printauthor/title/descinfoofscriptreportgeneratereportofscript参数支持 run 方法pythonrun--package=com.tencent.mm--device=yourdevice用例编写查看testcase目录namedescremarkconfig本地配置pages/resource存放 资源文件page/*.py具体的页面信息main.py默认运行的 script 文件test_1.py给出的测试用例文件添加用例importloggingfromairtest.core.apiimport*fromairrun.common.marksimportairrun_setupfromtestcase.config.configimportLocalSettingfromtestcase.pagesimportmain_page_templatelogger=logging.getLogger(__name__)__author__="mengwei"__title__="Case 1"__desc__="""DEMO"""# 1 使用 airrun_setup 时，要传入测试的 package，测试的名称，全局不能重复@airrun_setup(package_name=LocalSetting.APP_PACKAGE_NAME,test_name="test_1",login_func=None)deftest_1():start_app(LocalSetting.APP_PACKAGE_NAME)time.sleep(5)main_page_template.dao_hang_template.tmplt_wo_de.assert_exists()main_page_template.dao_hang_template.tmplt_wo_de.click()分层编写报告生成基于 airtest 的报告生成，增加了每个用例的信息和全局的信息，支持自定义添加基于设备的报告 |
air-sans | A trame application for analysis, inspection, and reduction of multi-detector data produced by the Small-Angle Neutron Scattering (SANS) instruments.LicenseApache Software LicenseInstallingInstall the applicationpip install air-sansRun the applicationair-sans --data ./dataBinder usageTest it with Binder |
airscape | AirScape WHF interfaceAirScapehas a collection of connected whole house fans. They can be controlled via their app, but also have a published API. This project is an interface to control their fans locally via the REST API on the fan.API is documentedhereInvocationImport into your code and create aairscape.Fanobjectimportairscapefan=airscape.Fan('192.168.1.10',5)Constructor takes 2 arguments. The IP or hostname (if you DNS registered your fan) and the timeout for communicating with the fan.The timeout is optional and has a default value of 5.Fan ControlThe fan has 3 attributes to control:on/offspeedtimer>>>fan.is_onFalse>>>fan.is_on=True>>>fan.is_onTrue>>>fan.speed2>>>fan.speed=5>>>fan.speed5>>>fan.add_time()The speed can also be controled in incrememnts instead of setting directly>>>fan.speed3>>>fan.speed_up()>>>fan.speed4>>>fan.slow_down()>>>fan.speed3 |
airscooter | No description available on PyPI. |
airscooter_urban_physiology_plugin | No description available on PyPI. |
airscraper | AirscraperA simple scraper to download csv from any airtable shared view programatically, think of it as a programatic way of downloading csv from airtable shared view.
Use it if:You want to download a shared view periodicallyYou don't mind the shared view to be accessed basically without authorizationRequirementsBecause its a simple scraper, basically only beautifulsoup is neededBeautifulSoup4PandasInstallationUsing pip (Recommended)pip install airscraperBuild From SourceInstall build dependencies:pipinstall--upgradepipsetuptoolswheel
pipinstalltqdm
pipinstall--user--upgradetwineBuild the Packagepython setup.py bdist_wheelInstall the built Packagepip install --upgrade dist/airscraper-0.1-py3-none-any.whlUse it without adding python in front of itairscraper [url]Direct Execution (Testing Purpose)Clone this projectInstall the requirementspip install -r requirements.txtrun the codepython airscraper/airscraper.py [url]UsageCreate ashared view linkand use that link to download the shared view into csv. All[url]mentioned in the examples are referring to the shared view link you get from this step.As CLI# Print Result to Terminalpythonairscraper/airscraper.py[url]# Pipe the result to csv filepythonairscraper/airscraper.py[url]>[filename].csvAs Python PackagefromairscraperimportAirScraperclient=AirScraper([url])data=client.get_table().text# print the resultprint(data)# save as filewithopen('data.csv','w')asf:f.write(data)# use it with pandasfromioimportStringIOimportpandasaspddf=pd.read_csv(StringIO(data),sep=',')df.head()Helpusage: airscraper [-h] [-l LOCALE] [-tz TIMEZONE] view_url
Download CSV from Airtable Shared View Link, You can pass the result to file using
'> name.csv'
positional arguments:
view_url url generated from sharing view using link in airtable
optional arguments:
-h, --help show this help message and exit
-l LOCALE, --locale LOCALE
Your locale, default to 'en'
-tz TIMEZONE, --timezone TIMEZONE
Your timezone, use URL encoded string, default to
'Asia/Jakarta'What's nextCurrently I'm thinking of several things in mind:✅ Making this installed packageAdds accessibility to use it in FaaS Platform (most use case I could thought of are related to this)✅ Create a proper package that can be imported (so I could use it in my ETL script)✅ Fill in LICENSE and setup.py, (to be honest I have no idea yet what to put into it)It turns out there are a lot of resourcesout thereif you know what to look for :)ContributingIf you have similar problem or have any idea to improve this package please let me know in the issues or just hit me up on twitter@BanditelolRPDevelopmentIf you're going to try to develop it yourself, here's my overall workflow1. Create a virtual environmentI usually usedvenvon python 3.8 to create a new virtualenvironmentpython-mvenvvenv# and activate the environmentsourcevenv/bin/activate2. Create a virtual environmentInstall necessary requirements and install the package for development using editablepipinstallwheelspytest-q
pipinstall-rrequirements.txt
pipinstall-e.3. Play around with the codeYou can browse the notebook for explanation on how it works and some example use case, and I really appreciate helps in documentation and testing. Have fun! |
airscript | 这是AirScript Android 版本的提示文档贡献者: |
air-sdk | air_sdkThis project provides a Python SDK for interacting with the NVIDIA Air API (https://air.nvidia.com/api/).Click here for the full documentationPrerequisiteThe SDK requires python 3.7 or later. The safest way to install the SDK is to set up a virtual environment in python3.7:apt-get install python3.7python3.7 -m pip install virtualenvpython3.7 -m virtualenv venv37. venv37/bin/activateInstallationTo install the SDK, use pip:python3 -m pip install air-sdkUsage>>> from air_sdk import AirApi
>>> air = AirApi(username='<user>', password='<password>')Authentication OptionsUsing the API requires the use of either an API token, a username/password, or a bearer token.API tokenTo use an API token, one must first be generated. The easiest way to do this is via theAir UI.Once a token is generated:>>> air = AirApi(username='<username>', password='<api_token>')Username/PasswordTo use a username/password, an administrator of NVIDIA Air must provision a service account. Once the administrator provides the username and password:>>> air = AirApi(username='<username>', password='<password>')Bearer tokenGenerally, it's recommended to use anAPI Tokenover a bearer token. However, a bearer token might be used for testing or quick-and-dirty operations that might not need a long term API token. To use a bearer token, the calling user must have a nvidia.com account and have previously approved access for NVIDIA Air. Once a token is obtained:>>> air = AirApi(bearer_token='<bearer_token>')Interacting with the APIThe SDK provides various helper methods for interacting with the API. For example:>>> air.simulations.list()
[<Simulation sim1 c51b49b6-94a7-4c93-950c-e7fa4883591>, <Simulation sim2 3134711d-015e-49fb-a6ca-68248a8d4aff>]
>>> sim1 = air.simulations.get('c51b49b6-94a7-4c93-950c-e7fa4883591')
>>> sim1.title = 'My Sim'
>>> sim1.store()DevelopingContributions to the SDK are very welcome. All code must pass linting and unit testing before it will be merged.Requirementspython3 -m pip install .[dev]PoetryThe Poetry virtual environment manager should be the preferred way to manage and install dependencies of the SDK. General information about Poetry can be foundhere.In order to use Poetry, you'll need to install poetry on your machine.
If you're on a MacBook and havebrewinstalled, you can easily accomplish this through the following:brew install poetryAfter Poetry is installed, you can install all dependencies for the project by running the following:poetry install --all-extrasUnit testing./unit_test.shLintingruff checkPre-commit hooksIn this section, we delineate the usage of pre-commit hooks for this project.PrerequisitesThis section assumes that you've already accomplished the following:Cloned the codebase.Have a.gitconfiguration file in your local environment (this is where pre-commit will "hook" into git commits).Created a python virtual environment.Activated your virtual environment.Installed all dependencies withpoetry install --all-extrasIf you don't know whether you have accomplished all of the above, try runningpre-commit --versionorruff --versionin your terminal at the source directory of the codebase.Adding pre-commit to your environment.In order to use pre-commits, they need to be configured into your.gitdirectory. This is accomplished by running the following in your terminal:>>> pre-commit install
pre-commit installed at .git/hooks/pre-commit
>>>And that's it! You should now have pre-commit hooks activated on your local environment.If you would like to uninstall your pre-commit hooks, runpre-commit uninstall.It's important to note that you should NOT runpython3 -m pip uninstall pre-commitprior to uninstalling pre-commits from your.gitdirectory, or else you might be blocked from making commits to your project.
If this happens, re-install pre-commit with pip, runpre-commit uninstall, and thenpython3 -m pip uninstall pre-commit.Working with pre-commit hooks.Pre-commit hooks work with git to identify files which you've made changes to and then runs specific operations (such as linting or formatting) on those files.Currently, our primary hooks are performed byruff, a Python linter and formatter. When attempting to git commit a code change is made that doesn't comply with the defaultruffconfiguration and our custom configurations outlined in.ruff.toml,ruffwill attempt to resolve the issue automatically by fixing small linting issues and re-formatting your code.In cases where the pre-commit hooks cannot safely correct your code, errors will be printed in your console which typically will provide links to the pieces of code you must address in order to successfully commit the code.In general, the development workflow is identical to what typically occurs when working on the codebase:Write/modify some code.Rungit add .Rungit commit -m 'Your detailed commit message here.'However, prior to successfully executing step 3 above, all pre-commits configured in.pre-commit-config.yamlare run.
If theruffformatting and linting both pass, your commit will be made. Otherwise, you will need to address the issues and re-add/commit your code.
If the linter or formatter end up modifying your code, you will need togit add .these changes and commit them as well.Information on specific pre-commit hooks we use.We currently use the following hooks.Ruff (linter)Linting is the process of identifying and highlighting syntactical and stylistic trends and issues within code.
This process helps developers identify and correct programming errors or unconventional coding practices.We use the ruff linter for our linting needs. This is configured in our.pre-commit-config.yamlhere:# .pre-commit-config.yaml
repos:
...
hooks:
- id: ruff
args: [ --fix ]We've included the--fixargument, which requests thatruffattempt to correct safely fixable issues without asking us ahead of time. Ruff also has an--unsafe-fixesflag that we could add to address fixes that are more risky to correct via automation, however we are not using that flag at this time.To manually run the linter, runruff --fix path/to/your/directory/file.pyorruff --fix path/to/your/directory.If you would like to ignore a linting error on a specific piece of code, add a# noqa: CODE_TO_IGNOREtag next to the piece of code:def this_is_fully_linted(*args, **kwargs):
...
def this_is_fully_linted_except_F841(*args, **kwargs): # noqa: F841
...You can skip the linting of a specific code on entire files by adding# ruff: noqa: CODE_TO_IGNOREto the top of the file.# ruff: noqa: F841
def this_will_not_be_linted_for_F841(*args, **kwargs):
....
def this_also_will_not_be_linted_for_F841(*args, **kwargs):
....To skip linting entirely for the entire folder, simply put# ruff: noqaat the top of the file.Ruff (formatter)We use ruff for our code formatting needs.
Formatting is the process of styling existing code so that it abides by a predetermined set of standards.
This is important when working with large code-bases or when working with other developers since it makes large code-changes more uniform in style and easier to read.
When code is more readable, it enables developers to identifying errors in their code logic more easily and helps other developers understand your implementation of features when they are reviewing your code.We use ruff for our code formatting needs. This is configured in our.pre-commit-config.yamlhere:# .pre-commit-config.yaml
repos:
...
hooks:
- id: ruff-formatTo manually format your code, runruff format path/to/your/directory/file.pyorruff format path/to/your/directory.If you would like to suppress formatting on a section of code, you can wrap your code in# fmt: offand# fmt: on:# fmt: off
def this_function_will_not_be_formatted(arg1,
arg2,
arg3, arg4,
):
...
# fmt: onAlternatively, you can add# fmt: skipnext to a body of code to supress formatting:class ThisClassIsFormatted:
...
class ThisClassIsNotFormatted: # fmt: skip
...VS Code ExtensionThere is aruffVS Code extension you may add to your IDE if you are using VS Code.
Details on this extension can be foundhere.We have already configured the ruff linter and formatter as suggested extensions for the project. They can be found in.vcode/settings.json. |
air-sdk-python | air-sdk-pythonSDK Installationpipinstallair-sdk-pythonSDK Example UsageExampleimportairfromair.modelsimportshareds=air.Air(bearer_auth="<YOUR_BEARER_TOKEN_HERE>",)req=shared.Call(phone='874.397.6390 x76992',prompt_id=230621,metadata=shared.Metadata(),)res=s.call.initiate_call(req)ifres.objectisnotNone:# handle responsepassAvailable Resources and Operationscallinitiate_call- Initiate a callError HandlingHandling errors in this SDK should largely match your expectations. All operations return a response object or raise an error. If Error objects are specified in your OpenAPI Spec, the SDK will raise the appropriate Error type.Error ObjectStatus CodeContent Typeerrors.InitiateCallResponseBody401application/jsonerrors.InitiateCallCallResponseBody403application/jsonerrors.InitiateCallCallResponseResponseBody404application/jsonerrors.InitiateCallCallResponse422ResponseBody422application/jsonerrors.InitiateCallCallResponse500ResponseBody500application/jsonerrors.SDKError4x-5xx/Exampleimportairfromair.modelsimportshareds=air.Air(bearer_auth="<YOUR_BEARER_TOKEN_HERE>",)req=shared.Call(phone='874.397.6390 x76992',prompt_id=230621,metadata=shared.Metadata(),)res=Nonetry:res=s.call.initiate_call(req)excepterrors.InitiateCallResponseBodyase:print(e)# handle exceptionraise(e)excepterrors.InitiateCallCallResponseBodyase:print(e)# handle exceptionraise(e)excepterrors.InitiateCallCallResponseResponseBodyase:print(e)# handle exceptionraise(e)excepterrors.InitiateCallCallResponse422ResponseBodyase:print(e)# handle exceptionraise(e)excepterrors.InitiateCallCallResponse500ResponseBodyase:print(e)# handle exceptionraise(e)excepterrors.SDKErrorase:print(e)# handle exceptionraise(e)ifres.objectisnotNone:# handle responsepassServer SelectionSelect Server by IndexYou can override the default server globally by passing a server index to theserver_idx: intoptional parameter when initializing the SDK client instance. The selected server will then be used as the default on the operations that use it. This table lists the indexes associated with the available servers:#ServerVariables0https://chat.air.ai/api/v1NoneExampleimportairfromair.modelsimportshareds=air.Air(server_idx=0,bearer_auth="<YOUR_BEARER_TOKEN_HERE>",)req=shared.Call(phone='874.397.6390 x76992',prompt_id=230621,metadata=shared.Metadata(),)res=s.call.initiate_call(req)ifres.objectisnotNone:# handle responsepassOverride Server URL Per-ClientThe default server can also be overridden globally by passing a URL to theserver_url: stroptional parameter when initializing the SDK client instance. For example:importairfromair.modelsimportshareds=air.Air(server_url="https://chat.air.ai/api/v1",bearer_auth="<YOUR_BEARER_TOKEN_HERE>",)req=shared.Call(phone='874.397.6390 x76992',prompt_id=230621,metadata=shared.Metadata(),)res=s.call.initiate_call(req)ifres.objectisnotNone:# handle responsepassCustom HTTP ClientThe Python SDK makes API calls using therequestsHTTP library. In order to provide a convenient way to configure timeouts, cookies, proxies, custom headers, and other low-level configuration, you can initialize the SDK client with a customrequests.Sessionobject.For example, you could specify a header for every request that this sdk makes as follows:importairimportrequestshttp_client=requests.Session()http_client.headers.update({'x-custom-header':'someValue'})s=air.Air(client:http_client)AuthenticationPer-Client Security SchemesThis SDK supports the following security scheme globally:NameTypeSchemebearer_authhttpHTTP BearerTo authenticate with the API thebearer_authparameter must be set when initializing the SDK client instance. For example:importairfromair.modelsimportshareds=air.Air(bearer_auth="<YOUR_BEARER_TOKEN_HERE>",)req=shared.Call(phone='874.397.6390 x76992',prompt_id=230621,metadata=shared.Metadata(),)res=s.call.initiate_call(req)ifres.objectisnotNone:# handle responsepassMaturityThis SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning usage
to a specific package version. This way, you can install the same version each time without breaking changes unless you are intentionally
looking for the latest version.ContributionsWhile we value open-source contributions to this SDK, this library is generated programmatically.
Feel free to open a PR or a Github issue as a proof of concept and we'll do our best to include it in a future release!SDK Created bySpeakeasy |
airsea | This module is a translation of the original AIRSEA-2.0 MATLAB toolbox
routines for calculating the properties of airsea fluxes. |
airsensor-py | # airsensor-pyPython package for getting sensor values from an Ambient Air Sensor such as offered by [Rehau](https://www.amazon.co.uk/Rehau-USB-Stick-Ambient-Sensor/dp/B00ZXP6EI4).## Getting StartedThis section will introduce you to airsensor-py and how to install and use it for fun and profit.### PrerequisitesMake sure that python 3 + pip is installed.### Installing```pip install airsensor-py```## UsageYou can either use this package as a library in your code or using the bundled CLI. You'll have to run it as root or set up a udev rule to grant r/w access to the device to the user. For more instructions on that, see [here](https://github.com/tuxedo0801/usb-sensors-linux).### Library```from airsensor.core import AirSensorairsensor = AirSensor()voc = airsensor.get_voc()print(voc)> 450```### CLI```$ airsensor-py> 450```## Built With* [PyUSB](https://walac.github.io/pyusb/)## LicenseThis project is licensed under the MIT License - see the [LICENSE.txt](LICENSE.txt) file for details## ContributionWant to add a feature or report a bug? Open an issue or a pull request for me!## Acknowledgments* [A C implementation](https://code.google.com/archive/p/airsensor-linux-usb/) was done by Rodric Yates which I used to see how the interaction with the device works. |
airship | Airship allows users to synchronize saved games between cloud platforms. Requires subpackages for each cloud service; for example, install airship-steamcloud and airship-icloud to sync between these two services. |
airship-icloud | UNKNOWN |
airship-python | Airship Python SDK |
airship-steamcloud | UNKNOWN |
airsign | # AirSign for BitSharesA command line tool to interface with the BitShares network.## Installation### Install with `pip`:```pip3 install airsign```### Manual installation:```git clone https://github.com/xeroc/airsigncd airsignpython3 setup.py install --user```### Upgrade```$ pip install --user --upgrade airsign```## UsageYou can get a full length help by running:usage: airsign [-h] [--node NODE] [--rpcuser RPCUSER] [--rpcpassword RPCPASSWORD] [--nobroadcast] {set,addkey,listkeys,listaccounts,getbalance,transfer,approve} ...Command line tool to interact with the BitShares networkpositional arguments:{set,addkey,listkeys,listaccounts,getbalance,transfer,approve}sub-command helpset Set configurationaddkey Add a new key to the walletlistkeys List available keys in your walletlistaccounts List available accounts in your walletgetbalance Get balances of available account(s)transfer Transfer funds from your wallet to someone elseapprove approve funds from your wallet to someone elseoptional arguments:-h, --help show this help message and exit--node NODE Websocket URL for public BitShares API (default: "wss://bitshares.openledger.info/ws")--rpcuser RPCUSER Websocket user if authentication is required--rpcpassword RPCPASSWORDWebsocket password if authentication is required--nobroadcast Do not broadcast anything### Adding keysairsign comes with its own encrypted wallet to which keys need to be added:airsign addkey <posting-wif-key>On first run, you will be asked to provide a new passphrase that youwill need to provide every time you want to post on the BitShares network.If you chose an *empty* password, your keys will be stored in plain textwhich allows automated posting but exposes your private key to yourlocal user.### List available Keys and accountsairsign listkeysThis command will give the list of public keys to which the private keysare available.airsign listaccountsThis command tries to resolve the public keys into account names registeredon the network (experimental).### Get BalancesThe command `getbalance` only takes optional arguments if you want tosee the balance of a specific account, else it will show all balancesfor which a key has been added to the wallet (see above)$ airsign getbalancePlease unlock your existing wallet!Passphrase:wallet.xeroc:+-------------+----------+| Amount | Asset |+-------------+----------+| 61557.53766 | BTS || 1e-07 | OPEN.BTC || 107.5035 | MKR |+-------------+----------+live-coding:+------------+-------+| Amount | Asset |+------------+-------+| 9993.84531 | BTS |+------------+-------+### TransferTransfers can be initiated from any account for which the *active key*is installed. The `--amount` parameter can be applied several times tosend different assets from the same origin to the same recepient. Thecommand supports encrypted memos. If your account has a distinct memokey (usually identical to the active key), then you need to add that keyto your wallet aswell.airsign transfer --from <fromaccount> --to <recepient> --amount "1 USD" --amount "2 BTS" --memo <Text>The global `--nobroadcast` flag can be added before `transfer` toprevent airsign to broadcast the transaction to the BitShares network:airsign --nobroadcast transfer --from <fromaccount> --to <recepient> --amount "1 USD" --amount "2 BTS" --memo <Text>### Approval of ProposalsAny proposal can be approved using airsign:airsign approve <proposal-id>The default account will be used, unless stated otherwise withan `--account` parameter. The proposal-id must habe the form `1.10.x`. |
airsim | Python API for AirSimThis package contains Python APIs forAirSim.How to UseSee examples atcar/hello_car.pyormultirotor/hello_drone.py.DependenciesThis package depends onmsgpackand would automatically installmsgpack-rpc-python(this may need administrator/sudo prompt):pip install msgpack-rpc-pythonSome examples also requires opencv.More InfoMore information on AirSim Python APIs can be found at:https://github.com/Microsoft/AirSim/blob/main/docs/python.md |
airsim-adaptor | A customized airsim API adaptor for special purposesThis is a simple example package for special purpose usages ofMirosoft AirSim Unmanned Vehicle Simulator's python API. It utilizes the use of sBGC commands for gimbal emulation and Visca adaptor for Zoom/FOV controls.For more information:For more information onAirSimproject checkoutAirsim ProjectgitHub repo. |
airsim-autonomous-landing | Autonomous-Drone-Landing-On-Moving-Object-AirSimEasily land the drone on a moving or stationary target after the mission has completed!HOW TO USEfrom autonomous_landing import autonomous_landing
The object autonomous_landing(target_name, mission_function, mission_function_args) gets a name of the target, a function that you've created and want the drone to complete and the function's arguments if there are any.usage example:drone = autonomous_landing('LandingTarget_2', time.sleep, (5))
drone.RUN()In this case, the drone will complete a sleep function, for 5 seconds. You can create your own function with more advanced controls.After completing the mission, the drone will search for a target named 'LandingTarget_2' and will complete an autonomous landing on that target.(target name is from unreal)Tested on airsim for unreal 4.27.Runs on blocks project (deafult project for airsim) |
airsimdroneracinglab | Python API for AirSim Drone Racing LabThis package contains Python APIs forAirSim Drone Racing Lab, built onMicrosoft AirSimDependenciesThis package depends onmsgpackand would automatically installmsgpack-rpc-python(this may need administrator/sudo prompt):pip install msgpack-rpc-python |
airsimdroneracingvae | # Python API for AirSim-Drone-Racing-VAE-ImitationPython package for code -https://github.com/microsoft/AirSim-Drone-Racing-VAE-Imitation- associated with our paper Learning Controls Using Cross-Modal Representations: Bridging Simulation and Reality for Drone Racing:https://arxiv.org/abs/1909.06993, built on [Microsoft AirSim](https://github.com/microsoft/AirSim)## Dependencies
This package depends onmsgpackand would automatically installmsgpack-rpc-python(this may need administrator/sudo prompt):` pip installmsgpack-rpc-python` |
airsim-emulator | A customized airsim API Emulator for special purposesThis is a simple example package for special purpose usages ofMirosoft AirSim Unmanned Vehicle Simulator's python API. It utilizes the use of sBGC commands for gimbal emulation and Visca adaptor for Zoom/FOV controls.For more information:For more information onAirSimproject checkoutAirsim ProjectgitHub repo. |
airsim-gym | No description available on PyPI. |
airsimneurips | # Python API for AirSimThis package contains Python APIs for [Game of Drones: A NeurIPS 2019 Competition](https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing), built on [Microsoft AirSim](https://github.com/microsoft/AirSim)## Dependencies
This package depends onmsgpackand would automatically installmsgpack-rpc-python(this may need administrator/sudo prompt):` pip installmsgpack-rpc-python` |
airslate | airslateis a Python client library for theairSlateAPI v1.This Python client library has been designed to quickly and easily implement
the airSlate REST API. It offers a simple and elegant way to interact with
airSlate API resources within your Python applications. Sign up for our API here:https://developers.airslate.comProject Informationairslateis released under theApache Licence 2.0,
and the code onGitHub.
It’s rigorously tested on Python 3.7+. If you’d like to contribute toairslateyou’re most welcome!SupportShould you have any question, any remark, or if you find a bug, or if there is
something you can’t do with theairslate, pleaseopen an issue.Release Information0.2.1 (2021-02-08)FeaturesProvided ability to get slate addon file.Added new resources:airslate.resources.slate_addon.SlateAddonFiles- represent slate addon files resourceAdded new entities:airslate.entities.addons.SlateAddon- represent slate addon entityairslate.entities.addons.SlateAddonFile- represent slate addon file entityThe base entity class as well as all derived classes now provide the following methods:has_one()- create an instance of the related entityfrom_one()- create an instance of the current class from the provided dataTrivial/Internal ChangesChange default string representation of entities. Now it has the
following form<EntityName: id=ID, type=TYPE>.Full changelog.Creditsairslateis written and maintained byairSlate.A full list of contributors can be found inGitHub’s overview. |
airsmodel | AIRS ModelThis package contains the data model for sending requests to ARLAS Item Registration Services.Item Schemaairs_modelAbstractExtensibleStatusIdentifiableCustom PropertiesAdditional PropertiesAccess RestrictionsDefined InCan be instantiatedNoUnknown statusNoForbiddenAllowednonemodel.schema.jsonItem Typeobject(Item)Item PropertiesPropertyTypeRequiredNullableDefined bycollectionMergedOptionalcannot be nullItemcatalogMergedOptionalcannot be nullItemidMergedOptionalcannot be nullItemgeometryMergedOptionalcannot be nullItembboxMergedOptionalcannot be nullItemcentroidMergedOptionalcannot be nullItemassetsMergedOptionalcannot be nullItempropertiesMergedOptionalcannot be nullItemAdditional PropertiesAnyOptionalcan be nullcollectioncollectionis optionalType: merged type (Name of the collection the item belongs to.)cannot be nulldefined in:Itemcollection Typemerged type (Name of the collection the item belongs to.)any ofUntitled string in ItemUntitled null in Itemcatalogcatalogis optionalType: merged type (Name of the catalog the item belongs to.)cannot be nulldefined in:Itemcatalog Typemerged type (Name of the catalog the item belongs to.)any ofUntitled string in ItemUntitled null in Itemididis optionalType: merged type (Unique item identifier. Must be unique within the collection.)cannot be nulldefined in:Itemid Typemerged type (Unique item identifier. Must be unique within the collection.)any ofUntitled string in ItemUntitled null in Itemgeometrygeometryis optionalType: merged type (Defines the full footprint of the asset represented by this item, formatted according to `RFC 7946, section 3.1 (GeoJSON) <https://tools.ietf.org/html/rfc7946>`_)cannot be nulldefined in:Itemgeometry Typemerged type (Defines the full footprint of the asset represented by this item, formatted according to `RFC 7946, section 3.1 (GeoJSON) <https://tools.ietf.org/html/rfc7946>`_)any ofUntitled object in ItemUntitled null in Itembboxbboxis optionalType: merged type (Bounding Box of the asset represented by this item using either 2D or 3D geometries. The length of the array must be 2*n where n is the number of dimensions. Could also be None in the case of a null geometry.)cannot be nulldefined in:Itembbox Typemerged type (Bounding Box of the asset represented by this item using either 2D or 3D geometries. The length of the array must be 2*n where n is the number of dimensions. Could also be None in the case of a null geometry.)any ofUntitled array in ItemUntitled null in Itemcentroidcentroidis optionalType: merged type (Coordinates (lon/lat) of the geometry's centroid.)cannot be nulldefined in:Itemcentroid Typemerged type (Coordinates (lon/lat) of the geometry's centroid.)any ofUntitled array in ItemUntitled null in Itemassetsassetsis optionalType: merged type (A dictionary mapping string keys to Asset objects. All Asset values in the dictionary will have their owner attribute set to the created Item.)cannot be nulldefined in:Itemassets Typemerged type (A dictionary mapping string keys to Asset objects. All Asset values in the dictionary will have their owner attribute set to the created Item.)any ofUntitled object in ItemUntitled null in Itempropertiespropertiesis optionalType: merged type (Item properties)cannot be nulldefined in:Itemproperties Typemerged type (Item properties)any ofPropertiesUntitled null in ItemAdditional PropertiesAdditional properties are allowed and do not have to follow a specific schemaItem DefinitionsDefinitions group AssetReference this group by using{"$ref":"airs_model#/$defs/Asset"}PropertyTypeRequiredNullableDefined bynameMergedOptionalcannot be nullItemsizeMergedOptionalcannot be nullItemhrefMergedOptionalcannot be nullItemasset_typeMergedOptionalcannot be nullItemasset_formatMergedOptionalcannot be nullItemstorage__requester_paysMergedOptionalcannot be nullItemstorage__tierMergedOptionalcannot be nullItemstorage__platformMergedOptionalcannot be nullItemstorage__regionMergedOptionalcannot be nullItemairs__managedMergedOptionalcannot be nullItemairs__object_store_bucketMergedOptionalcannot be nullItemairs__object_store_keyMergedOptionalcannot be nullItemtitleMergedOptionalcannot be nullItemdescriptionMergedOptionalcannot be nullItemtypeMergedOptionalcannot be nullItemrolesMergedOptionalcannot be nullItemextra_fieldsMergedOptionalcannot be nullItemgsdMergedOptionalcannot be nullItemeo__bandsMergedOptionalcannot be nullItemsar__instrument_modeMergedOptionalcannot be nullItemsar__frequency_bandMergedOptionalcannot be nullItemsar__center_frequencyMergedOptionalcannot be nullItemsar__polarizationsMergedOptionalcannot be nullItemsar__product_typeMergedOptionalcannot be nullItemsar__resolution_rangeMergedOptionalcannot be nullItemsar__resolution_azimuthMergedOptionalcannot be nullItemsar__pixel_spacing_rangeMergedOptionalcannot be nullItemsar__pixel_spacing_azimuthMergedOptionalcannot be nullItemsar__looks_rangeMergedOptionalcannot be nullItemsar__looks_azimuthMergedOptionalcannot be nullItemsar__looks_equivalent_numberMergedOptionalcannot be nullItemsar__observation_directionMergedOptionalcannot be nullItemproj__epsgMergedOptionalcannot be nullItemproj__wkt2MergedOptionalcannot be nullItemproj__geometryMergedOptionalcannot be nullItemproj__bboxMergedOptionalcannot be nullItemproj__centroidMergedOptionalcannot be nullItemproj__shapeMergedOptionalcannot be nullItemproj__transformMergedOptionalcannot be nullItemAdditional PropertiesAnyOptionalcan be nullnamenameis optionalType: merged type (Asset's name. But be the same as the key in the `assets` dictionary.)cannot be nulldefined in:Itemname Typemerged type (Asset's name. But be the same as the key in the `assets` dictionary.)any ofUntitled string in ItemUntitled null in Itemsizesizeis optionalType: merged type (Asset's size in Bytes.)cannot be nulldefined in:Itemsize Typemerged type (Asset's size in Bytes.)any ofUntitled integer in ItemUntitled null in Itemhrefhrefis optionalType: merged type (Absolute link to the asset object.)cannot be nulldefined in:Itemhref Typemerged type (Absolute link to the asset object.)any ofUntitled string in ItemUntitled null in Itemasset_typeasset_typeis optionalType: merged type (Type of data (ResourceType))cannot be nulldefined in:Itemasset_type Typemerged type (Type of data (ResourceType))any ofUntitled string in ItemUntitled null in Itemasset_formatasset_formatis optionalType: merged type (Data format (AssetFormat))cannot be nulldefined in:Itemasset_format Typemerged type (Data format (AssetFormat))any ofUntitled string in ItemUntitled null in Itemstorage__requester_paysstorage__requester_paysis optionalType: merged type (Is the data requester pays or is it data manager/cloud provider pays. Defaults to false. Whether the requester pays for accessing assets)cannot be nulldefined in:Itemstorage__requester_pays Typemerged type (Is the data requester pays or is it data manager/cloud provider pays. Defaults to false. Whether the requester pays for accessing assets)any ofUntitled boolean in ItemUntitled null in Itemstorage__tierstorage__tieris optionalType: merged type (Cloud Provider Storage Tiers (Standard, Glacier, etc.))cannot be nulldefined in:Itemstorage__tier Typemerged type (Cloud Provider Storage Tiers (Standard, Glacier, etc.))any ofUntitled string in ItemUntitled null in Itemstorage__platformstorage__platformis optionalType: merged type (PaaS solutions (ALIBABA, AWS, AZURE, GCP, IBM, ORACLE, OTHER))cannot be nulldefined in:Itemstorage__platform Typemerged type (PaaS solutions (ALIBABA, AWS, AZURE, GCP, IBM, ORACLE, OTHER))any ofUntitled string in ItemUntitled null in Itemstorage__regionstorage__regionis optionalType: merged type (The region where the data is stored. Relevant to speed of access and inter region egress costs (as defined by PaaS provider))cannot be nulldefined in:Itemstorage__region Typemerged type (The region where the data is stored. Relevant to speed of access and inter region egress costs (as defined by PaaS provider))any ofUntitled string in ItemUntitled null in Itemairs__managedairs__managedis optionalType: merged type (Whether the asset is managed by AIRS or not.)cannot be nulldefined in:Itemairs__managed Typemerged type (Whether the asset is managed by AIRS or not.)any ofUntitled boolean in ItemUntitled null in Itemairs__managed Default ValueThe default value is:trueairs__object_store_bucketairs__object_store_bucketis optionalType: merged type (Object store bucket for the asset object.)cannot be nulldefined in:Itemairs__object_store_bucket Typemerged type (Object store bucket for the asset object.)any ofUntitled string in ItemUntitled null in Itemairs__object_store_keyairs__object_store_keyis optionalType: merged type (Object store key of the asset object.)cannot be nulldefined in:Itemairs__object_store_key Typemerged type (Object store key of the asset object.)any ofUntitled string in ItemUntitled null in Itemtitletitleis optionalType: merged type (Optional displayed title for clients and users.)cannot be nulldefined in:Itemtitle Typemerged type (Optional displayed title for clients and users.)any ofUntitled string in ItemUntitled null in Itemdescriptiondescriptionis optionalType: merged type (A description of the Asset providing additional details, such as how it was processed or created. CommonMark 0.29 syntax MAY be used for rich text representation.)cannot be nulldefined in:Itemdescription Typemerged type (A description of the Asset providing additional details, such as how it was processed or created. CommonMark 0.29 syntax MAY be used for rich text representation.)any ofUntitled string in ItemUntitled null in Itemtypetypeis optionalType: merged type (Optional description of the media type. Registered Media Types are preferred. See MediaType for common media types.)cannot be nulldefined in:Itemtype Typemerged type (Optional description of the media type. Registered Media Types are preferred. See MediaType for common media types.)any ofUntitled string in ItemUntitled null in Itemrolesrolesis optionalType: merged type (Optional, Semantic roles (i.e. thumbnail, overview, data, metadata) of the asset.)cannot be nulldefined in:Itemroles Typemerged type (Optional, Semantic roles (i.e. thumbnail, overview, data, metadata) of the asset.)any ofUntitled array in ItemUntitled null in Itemextra_fieldsextra_fieldsis optionalType: merged type (Optional, additional fields for this asset. This is used by extensions as a way to serialize and deserialize properties on asset object JSON.)cannot be nulldefined in:Itemextra_fields Typemerged type (Optional, additional fields for this asset. This is used by extensions as a way to serialize and deserialize properties on asset object JSON.)any ofUntitled object in ItemUntitled null in Itemgsdgsdis optionalType: merged type (Ground Sampling Distance (resolution) of the asset)cannot be nulldefined in:Itemgsd Typemerged type (Ground Sampling Distance (resolution) of the asset)any ofUntitled number in ItemUntitled null in Itemeo__bandseo__bandsis optionalType: merged type (An array of available bands where each object is a Band Object. If given, requires at least one band.)cannot be nulldefined in:Itemeo__bands Typemerged type (An array of available bands where each object is a Band Object. If given, requires at least one band.)any ofUntitled array in ItemUntitled null in Itemsar__instrument_modesar__instrument_modeis optionalType: merged type (The name of the sensor acquisition mode that is commonly used. This should be the short name, if available. For example, WV for "Wave mode" of Sentinel-1 and Envisat ASAR satellites.)cannot be nulldefined in:Itemsar__instrument_mode Typemerged type (The name of the sensor acquisition mode that is commonly used. This should be the short name, if available. For example, WV for "Wave mode" of Sentinel-1 and Envisat ASAR satellites.)any ofUntitled string in ItemUntitled null in Itemsar__frequency_bandsar__frequency_bandis optionalType: merged type (The common name for the frequency band to make it easier to search for bands across instruments. See section "Common Frequency Band Names" for a list of accepted names.)cannot be nulldefined in:Itemsar__frequency_band Typemerged type (The common name for the frequency band to make it easier to search for bands across instruments. See section "Common Frequency Band Names" for a list of accepted names.)any ofUntitled string in ItemUntitled null in Itemsar__center_frequencysar__center_frequencyis optionalType: merged type (The center frequency of the instrument, in gigahertz (GHz).)cannot be nulldefined in:Itemsar__center_frequency Typemerged type (The center frequency of the instrument, in gigahertz (GHz).)any ofUntitled number in ItemUntitled null in Itemsar__polarizationssar__polarizationsis optionalType: merged type (Any combination of polarizations.)cannot be nulldefined in:Itemsar__polarizations Typemerged type (Any combination of polarizations.)any ofUntitled string in ItemUntitled null in Itemsar__product_typesar__product_typeis optionalType: merged type (The product type, for example SSC, MGD, or SGC)cannot be nulldefined in:Itemsar__product_type Typemerged type (The product type, for example SSC, MGD, or SGC)any ofUntitled string in ItemUntitled null in Itemsar__resolution_rangesar__resolution_rangeis optionalType: merged type (The range resolution, which is the maximum ability to distinguish two adjacent targets perpendicular to the flight path, in meters (m).)cannot be nulldefined in:Itemsar__resolution_range Typemerged type (The range resolution, which is the maximum ability to distinguish two adjacent targets perpendicular to the flight path, in meters (m).)any ofUntitled number in ItemUntitled null in Itemsar__resolution_azimuthsar__resolution_azimuthis optionalType: merged type (The azimuth resolution, which is the maximum ability to distinguish two adjacent targets parallel to the flight path, in meters (m).)cannot be nulldefined in:Itemsar__resolution_azimuth Typemerged type (The azimuth resolution, which is the maximum ability to distinguish two adjacent targets parallel to the flight path, in meters (m).)any ofUntitled number in ItemUntitled null in Itemsar__pixel_spacing_rangesar__pixel_spacing_rangeis optionalType: merged type (The range pixel spacing, which is the distance between adjacent pixels perpendicular to the flight path, in meters (m). Strongly RECOMMENDED to be specified for products of type GRD.)cannot be nulldefined in:Itemsar__pixel_spacing_range Typemerged type (The range pixel spacing, which is the distance between adjacent pixels perpendicular to the flight path, in meters (m). Strongly RECOMMENDED to be specified for products of type GRD.)any ofUntitled number in ItemUntitled null in Itemsar__pixel_spacing_azimuthsar__pixel_spacing_azimuthis optionalType: merged type (The azimuth pixel spacing, which is the distance between adjacent pixels parallel to the flight path, in meters (m). Strongly RECOMMENDED to be specified for products of type GRD.)cannot be nulldefined in:Itemsar__pixel_spacing_azimuth Typemerged type (The azimuth pixel spacing, which is the distance between adjacent pixels parallel to the flight path, in meters (m). Strongly RECOMMENDED to be specified for products of type GRD.)any ofUntitled number in ItemUntitled null in Itemsar__looks_rangesar__looks_rangeis optionalType: merged type (Number of range looks, which is the number of groups of signal samples (looks) perpendicular to the flight path.)cannot be nulldefined in:Itemsar__looks_range Typemerged type (Number of range looks, which is the number of groups of signal samples (looks) perpendicular to the flight path.)any ofUntitled number in ItemUntitled null in Itemsar__looks_azimuthsar__looks_azimuthis optionalType: merged type (Number of azimuth looks, which is the number of groups of signal samples (looks) parallel to the flight path.)cannot be nulldefined in:Itemsar__looks_azimuth Typemerged type (Number of azimuth looks, which is the number of groups of signal samples (looks) parallel to the flight path.)any ofUntitled number in ItemUntitled null in Itemsar__looks_equivalent_numbersar__looks_equivalent_numberis optionalType: merged type (The equivalent number of looks (ENL).)cannot be nulldefined in:Itemsar__looks_equivalent_number Typemerged type (The equivalent number of looks (ENL).)any ofUntitled number in ItemUntitled null in Itemsar__observation_directionsar__observation_directionis optionalType: merged type (Antenna pointing direction relative to the flight trajectory of the satellite, either left or right.)cannot be nulldefined in:Itemsar__observation_direction Typemerged type (Antenna pointing direction relative to the flight trajectory of the satellite, either left or right.)any ofUntitled string in ItemUntitled null in Itemproj__epsgproj__epsgis optionalType: merged type (EPSG code of the datasource.)cannot be nulldefined in:Itemproj__epsg Typemerged type (EPSG code of the datasource.)any ofUntitled integer in ItemUntitled null in Itemproj__wkt2proj__wkt2is optionalType: merged type (PROJJSON object representing the Coordinate Reference System (CRS) that the proj:geometry and proj:bbox fields represent.)cannot be nulldefined in:Itemproj__wkt2 Typemerged type (PROJJSON object representing the Coordinate Reference System (CRS) that the proj:geometry and proj:bbox fields represent.)any ofUntitled string in ItemUntitled null in Itemproj__geometryproj__geometryis optionalType: merged type (Defines the footprint of this Item.)cannot be nulldefined in:Itemproj__geometry Typemerged type (Defines the footprint of this Item.)any ofUntitled undefined type in ItemUntitled null in Itemproj__bboxproj__bboxis optionalType: merged type (Bounding box of the Item in the asset CRS in 2 or 3 dimensions.)cannot be nulldefined in:Itemproj__bbox Typemerged type (Bounding box of the Item in the asset CRS in 2 or 3 dimensions.)any ofUntitled array in ItemUntitled null in Itemproj__centroidproj__centroidis optionalType: merged type (Coordinates representing the centroid of the Item (in lat/long).)cannot be nulldefined in:Itemproj__centroid Typemerged type (Coordinates representing the centroid of the Item (in lat/long).)any ofUntitled undefined type in ItemUntitled null in Itemproj__shapeproj__shapeis optionalType: merged type (Number of pixels in Y and X directions for the default grid.)cannot be nulldefined in:Itemproj__shape Typemerged type (Number of pixels in Y and X directions for the default grid.)any ofUntitled array in ItemUntitled null in Itemproj__transformproj__transformis optionalType: merged type (The affine transformation coefficients for the default grid.)cannot be nulldefined in:Itemproj__transform Typemerged type (The affine transformation coefficients for the default grid.)any ofUntitled array in ItemUntitled null in ItemAdditional PropertiesAdditional properties are allowed and do not have to follow a specific schemaDefinitions group BandReference this group by using{"$ref":"airs_model#/$defs/Band"}PropertyTypeRequiredNullableDefined bynamestringRequiredcannot be nullItemcommon_nameMergedOptionalcannot be nullItemdescriptionMergedOptionalcannot be nullItemcenter_wavelengthMergedOptionalcannot be nullItemfull_width_half_maxMergedOptionalcannot be nullItemsolar_illuminationMergedOptionalcannot be nullItemquality_indicatorsMergedOptionalcannot be nullItemAdditional PropertiesAnyOptionalcan be nullnamenameis requiredType:string(The name of the band (e.g., B01, B8, band2, red).)cannot be nulldefined in:Itemname Typestring(The name of the band (e.g., B01, B8, band2, red).)name Constraintsmaximum length: the maximum number of characters for this string is:300common_namecommon_nameis optionalType: merged type (The name commonly used to refer to the band to make it easier to search for bands across instruments. See the list of accepted common names.)cannot be nulldefined in:Itemcommon_name Typemerged type (The name commonly used to refer to the band to make it easier to search for bands across instruments. See the list of accepted common names.)any ofUntitled string in ItemUntitled null in Itemdescriptiondescriptionis optionalType: merged type (Description to fully explain the band. CommonMark 0.29 syntax MAY be used for rich text representation.)cannot be nulldefined in:Itemdescription Typemerged type (Description to fully explain the band. CommonMark 0.29 syntax MAY be used for rich text representation.)any ofUntitled string in ItemUntitled null in Itemcenter_wavelengthcenter_wavelengthis optionalType: merged type (The center wavelength of the band, in micrometers (μm).)cannot be nulldefined in:Itemcenter_wavelength Typemerged type (The center wavelength of the band, in micrometers (μm).)any ofUntitled number in ItemUntitled null in Itemfull_width_half_maxfull_width_half_maxis optionalType: merged type (Full width at half maximum (FWHM). The width of the band, as measured at half the maximum transmission, in micrometers (μm).)cannot be nulldefined in:Itemfull_width_half_max Typemerged type (Full width at half maximum (FWHM). The width of the band, as measured at half the maximum transmission, in micrometers (μm).)any ofUntitled number in ItemUntitled null in Itemsolar_illuminationsolar_illuminationis optionalType: merged type (The solar illumination of the band, as measured at half the maximum transmission, in W/m2/micrometers.)cannot be nulldefined in:Itemsolar_illumination Typemerged type (The solar illumination of the band, as measured at half the maximum transmission, in W/m2/micrometers.)any ofUntitled number in ItemUntitled null in Itemquality_indicatorsquality_indicatorsis optionalType: merged type (Set of indicators for estimating the quality of the datacube variable (band).)cannot be nulldefined in:Itemquality_indicators Typemerged type (Set of indicators for estimating the quality of the datacube variable (band).)any ofIndicatorsUntitled null in ItemAdditional PropertiesAdditional properties are allowed and do not have to follow a specific schemaDefinitions group DimensionTypeReference this group by using{"$ref":"airs_model#/$defs/DimensionType"}PropertyTypeRequiredNullableDefined byDefinitions group GroupReference this group by using{"$ref":"airs_model#/$defs/Group"}PropertyTypeRequiredNullableDefined bytimestampMergedOptionalcannot be nullItemrastersMergedOptionalcannot be nullItemquality_indicatorsMergedOptionalcannot be nullItemtimestamptimestampis optionalType: merged type (The timestamp of this temporal group.)cannot be nulldefined in:Itemtimestamp Typemerged type (The timestamp of this temporal group.)any ofUntitled integer in ItemUntitled null in Itemrastersrastersis optionalType: merged type (The rasters belonging to this temporal group.)cannot be nulldefined in:Itemrasters Typemerged type (The rasters belonging to this temporal group.)any ofUntitled array in ItemUntitled null in Itemquality_indicatorsquality_indicatorsis optionalType: merged type (Set of indicators for estimating the quality of the datacube group. The indicators are group based.)cannot be nulldefined in:Itemquality_indicators Typemerged type (Set of indicators for estimating the quality of the datacube group. The indicators are group based.)any ofIndicatorsUntitled null in ItemDefinitions group IndicatorsReference this group by using{"$ref":"airs_model#/$defs/Indicators"}PropertyTypeRequiredNullableDefined bytime_compacityMergedOptionalcannot be nullItemspatial_coverageMergedOptionalcannot be nullItemgroup_lightnessMergedOptionalcannot be nullItemtime_regularityMergedOptionalcannot be nullItemtime_compacitytime_compacityis optionalType: merged type (Indicates whether the temporal extend of the temporal slices (groups) are compact or not compared to the cube temporal extend. Computed as follow: 1-range(group rasters) / range(cube rasters).)cannot be nulldefined in:Itemtime_compacity Typemerged type (Indicates whether the temporal extend of the temporal slices (groups) are compact or not compared to the cube temporal extend. Computed as follow: 1-range(group rasters) / range(cube rasters).)any ofUntitled number in ItemUntitled null in Itemspatial_coveragespatial_coverageis optionalType: merged type (Indicates the proportion of the region of interest that is covered by the input rasters. Computed as follow: area(intersection(union(rasters),roi)) / area(roi)))cannot be nulldefined in:Itemspatial_coverage Typemerged type (Indicates the proportion of the region of interest that is covered by the input rasters. Computed as follow: area(intersection(union(rasters),roi)) / area(roi)))any ofUntitled number in ItemUntitled null in Itemgroup_lightnessgroup_lightnessis optionalType: merged type (Indicates the proportion of non overlapping regions between the different input rasters. Computed as follow: area(intersection(union(rasters),roi)) / sum(area(intersection(raster, roi))))cannot be nulldefined in:Itemgroup_lightness Typemerged type (Indicates the proportion of non overlapping regions between the different input rasters. Computed as follow: area(intersection(union(rasters),roi)) / sum(area(intersection(raster, roi))))any ofUntitled number in ItemUntitled null in Itemtime_regularitytime_regularityis optionalType: merged type (Indicates the regularity of the extends between the temporal slices (groups). Computed as follow: 1-std(inter group temporal gaps)/avg(inter group temporal gaps))cannot be nulldefined in:Itemtime_regularity Typemerged type (Indicates the regularity of the extends between the temporal slices (groups). Computed as follow: 1-std(inter group temporal gaps)/avg(inter group temporal gaps))any ofUntitled number in ItemUntitled null in ItemDefinitions group PropertiesReference this group by using{"$ref":"airs_model#/$defs/Properties"}PropertyTypeRequiredNullableDefined bydatetimeMergedOptionalcannot be nullItemstart_datetimeMergedOptionalcannot be nullItemend_datetimeMergedOptionalcannot be nullItemprogrammeMergedOptionalcannot be nullItemconstellationMergedOptionalcannot be nullItemsatelliteMergedOptionalcannot be nullIteminstrumentMergedOptionalcannot be nullItemsensorMergedOptionalcannot be nullItemsensor_typeMergedOptionalcannot be nullItemannotationsMergedOptionalcannot be nullItemgsdMergedOptionalcannot be nullItemsecondary_idMergedOptionalcannot be nullItemdata_typeMergedOptionalcannot be nullItemitem_typeMergedOptionalcannot be nullItemitem_formatMergedOptionalcannot be nullItemmain_asset_formatMergedOptionalcannot be nullItemmain_asset_nameMergedOptionalcannot be nullItemobservation_typeMergedOptionalcannot be nullItemdata_coverageMergedOptionalcannot be nullItemwater_coverageMergedOptionalcannot be nullItemlocationsMergedOptionalcannot be nullItemcreate_datetimeMergedOptionalcannot be nullItemupdate_datetimeMergedOptionalcannot be nullItemview__off_nadirMergedOptionalcannot be nullItemview__incidence_angleMergedOptionalcannot be nullItemview__azimuthMergedOptionalcannot be nullItemview__sun_azimuthMergedOptionalcannot be nullItemview__sun_elevationMergedOptionalcannot be nullItemstorage__requester_paysMergedOptionalcannot be nullItemstorage__tierMergedOptionalcannot be nullItemstorage__platformMergedOptionalcannot be nullItemstorage__regionMergedOptionalcannot be nullItemeo__cloud_coverMergedOptionalcannot be nullItemeo__snow_coverMergedOptionalcannot be nullItemeo__bandsMergedOptionalcannot be nullItemprocessing__expressionMergedOptionalcannot be nullItemprocessing__lineageMergedOptionalcannot be nullItemprocessing__levelMergedOptionalcannot be nullItemprocessing__facilityMergedOptionalcannot be nullItemprocessing__softwareMergedOptionalcannot be nullItemdc3__quality_indicatorsMergedOptionalcannot be nullItemdc3__compositionMergedOptionalcannot be nullItemdc3__number_of_chunksMergedOptionalcannot be nullItemdc3__chunk_weightMergedOptionalcannot be nullItemdc3__fill_ratioMergedOptionalcannot be nullItemcube__dimensionsMergedOptionalcannot be nullItemcube__variablesMergedOptionalcannot be nullItemacq__acquisition_modeMergedOptionalcannot be nullItemacq__acquisition_orbit_directionMergedOptionalcannot be nullItemacq__acquisition_typeMergedOptionalcannot be nullItemacq__across_trackMergedOptionalcannot be nullItemacq__along_trackMergedOptionalcannot be nullItemacq__archiving_dateMergedOptionalcannot be nullItemacq__download_orbitMergedOptionalcannot be nullItemacq__request_idMergedOptionalcannot be nullItemacq__quality_averageMergedOptionalcannot be nullItemacq__quality_computationMergedOptionalcannot be nullItemacq__receiving_stationMergedOptionalcannot be nullItemacq__reception_dateMergedOptionalcannot be nullItemacq__spectral_modeMergedOptionalcannot be nullItemsar__instrument_modeMergedOptionalcannot be nullItemsar__frequency_bandMergedOptionalcannot be nullItemsar__center_frequencyMergedOptionalcannot be nullItemsar__polarizationsMergedOptionalcannot be nullItemsar__product_typeMergedOptionalcannot be nullItemsar__resolution_rangeMergedOptionalcannot be nullItemsar__resolution_azimuthMergedOptionalcannot be nullItemsar__pixel_spacing_rangeMergedOptionalcannot be nullItemsar__pixel_spacing_azimuthMergedOptionalcannot be nullItemsar__looks_rangeMergedOptionalcannot be nullItemsar__looks_azimuthMergedOptionalcannot be nullItemsar__looks_equivalent_numberMergedOptionalcannot be nullItemsar__observation_directionMergedOptionalcannot be nullItemproj__epsgMergedOptionalcannot be nullItemproj__wkt2MergedOptionalcannot be nullItemproj__geometryMergedOptionalcannot be nullItemproj__bboxMergedOptionalcannot be nullItemproj__centroidMergedOptionalcannot be nullItemproj__shapeMergedOptionalcannot be nullItemproj__transformMergedOptionalcannot be nullItemgenerated__has_overviewMergedOptionalcannot be nullItemgenerated__has_thumbnailMergedOptionalcannot be nullItemgenerated__has_metadataMergedOptionalcannot be nullItemgenerated__has_dataMergedOptionalcannot be nullItemgenerated__has_cogMergedOptionalcannot be nullItemgenerated__has_zarrMergedOptionalcannot be nullItemgenerated__date_keywordsMergedOptionalcannot be nullItemgenerated__day_of_weekMergedOptionalcannot be nullItemgenerated__day_of_yearMergedOptionalcannot be nullItemgenerated__hour_of_dayMergedOptionalcannot be nullItemgenerated__minute_of_dayMergedOptionalcannot be nullItemgenerated__monthMergedOptionalcannot be nullItemgenerated__yearMergedOptionalcannot be nullItemgenerated__seasonMergedOptionalcannot be nullItemgenerated__tltrbrblMergedOptionalcannot be nullItemgenerated__band_common_namesMergedOptionalcannot be nullItemgenerated__band_namesMergedOptionalcannot be nullItemgenerated__geohash2MergedOptionalcannot be nullItemgenerated__geohash3MergedOptionalcannot be nullItemgenerated__geohash4MergedOptionalcannot be nullItemgenerated__geohash5MergedOptionalcannot be nullItemAdditional PropertiesAnyOptionalcan be nulldatetimedatetimeis optionalType: merged type (datetime associated with this item. If None, a start_datetime and end_datetime must be supplied.)cannot be nulldefined in:Itemdatetime Typemerged type (datetime associated with this item. If None, a start_datetime and end_datetime must be supplied.)any ofUntitled string in ItemUntitled null in Itemstart_datetimestart_datetimeis optionalType: merged type (Optional start datetime, part of common metadata. This value will override any start_datetime key in properties.)cannot be nulldefined in:Itemstart_datetime Typemerged type (Optional start datetime, part of common metadata. This value will override any start_datetime key in properties.)any ofUntitled string in ItemUntitled null in Itemend_datetimeend_datetimeis optionalType: merged type (Optional end datetime, part of common metadata. This value will override any end_datetime key in properties.)cannot be nulldefined in:Itemend_datetime Typemerged type (Optional end datetime, part of common metadata. This value will override any end_datetime key in properties.)any ofUntitled string in ItemUntitled null in Itemprogrammeprogrammeis optionalType: merged type (Name of the programme)cannot be nulldefined in:Itemprogramme Typemerged type (Name of the programme)any ofUntitled string in ItemUntitled null in Itemconstellationconstellationis optionalType: merged type (Name of the constellation)cannot be nulldefined in:Itemconstellation Typemerged type (Name of the constellation)any ofUntitled string in ItemUntitled null in Itemsatellitesatelliteis optionalType: merged type (Name of the satellite)cannot be nulldefined in:Itemsatellite Typemerged type (Name of the satellite)any ofUntitled string in ItemUntitled null in Iteminstrumentinstrumentis optionalType: merged type (Name of the instrument)cannot be nulldefined in:Iteminstrument Typemerged type (Name of the instrument)any ofUntitled string in ItemUntitled null in Itemsensorsensoris optionalType: merged type (Name of the sensor)cannot be nulldefined in:Itemsensor Typemerged type (Name of the sensor)any ofUntitled string in ItemUntitled null in Itemsensor_typesensor_typeis optionalType: merged type (Type of sensor)cannot be nulldefined in:Itemsensor_type Typemerged type (Type of sensor)any ofUntitled string in ItemUntitled null in Itemannotationsannotationsis optionalType: merged type (Human annotations for the item)cannot be nulldefined in:Itemannotations Typemerged type (Human annotations for the item)any ofUntitled string in ItemUntitled null in Itemgsdgsdis optionalType: merged type (Ground Sampling Distance (resolution))cannot be nulldefined in:Itemgsd Typemerged type (Ground Sampling Distance (resolution))any ofUntitled number in ItemUntitled null in Itemsecondary_idsecondary_idis optionalType: merged type (Secondary identifier)cannot be nulldefined in:Itemsecondary_id Typemerged type (Secondary identifier)any ofUntitled string in ItemUntitled null in Itemdata_typedata_typeis optionalType: merged type (Type of data)cannot be nulldefined in:Itemdata_type Typemerged type (Type of data)any ofUntitled string in ItemUntitled null in Itemitem_typeitem_typeis optionalType: merged type (Type of data (ResourceType))cannot be nulldefined in:Itemitem_type Typemerged type (Type of data (ResourceType))any ofUntitled string in ItemUntitled null in Itemitem_formatitem_formatis optionalType: merged type (Data format (ItemFormat))cannot be nulldefined in:Itemitem_format Typemerged type (Data format (ItemFormat))any ofUntitled string in ItemUntitled null in Itemmain_asset_formatmain_asset_formatis optionalType: merged type (Data format of the main asset (AssetFormat))cannot be nulldefined in:Itemmain_asset_format Typemerged type (Data format of the main asset (AssetFormat))any ofUntitled string in ItemUntitled null in Itemmain_asset_namemain_asset_nameis optionalType: merged type (Name of the main asset (AssetFormat))cannot be nulldefined in:Itemmain_asset_name Typemerged type (Name of the main asset (AssetFormat))any ofUntitled string in ItemUntitled null in Itemobservation_typeobservation_typeis optionalType: merged type (Type of observation (ObservationType))cannot be nulldefined in:Itemobservation_type Typemerged type (Type of observation (ObservationType))any ofUntitled string in ItemUntitled null in Itemdata_coveragedata_coverageis optionalType: merged type (Estimate of data cover)cannot be nulldefined in:Itemdata_coverage Typemerged type (Estimate of data cover)any ofUntitled number in ItemUntitled null in Itemwater_coveragewater_coverageis optionalType: merged type (Estimate of water cover)cannot be nulldefined in:Itemwater_coverage Typemerged type (Estimate of water cover)any ofUntitled number in ItemUntitled null in Itemlocationslocationsis optionalType: merged type (List of locations covered by the item)cannot be nulldefined in:Itemlocations Typemerged type (List of locations covered by the item)any ofUntitled array in ItemUntitled null in Itemcreate_datetimecreate_datetimeis optionalType: merged type (Date of item creation in the catalog, managed by the ARLAS Item Registration Service)cannot be nulldefined in:Itemcreate_datetime Typemerged type (Date of item creation in the catalog, managed by the ARLAS Item Registration Service)any ofUntitled integer in ItemUntitled null in Itemupdate_datetimeupdate_datetimeis optionalType: merged type (Update date of the item in the catalog, managed by the ARLAS Item Registration Service)cannot be nulldefined in:Itemupdate_datetime Typemerged type (Update date of the item in the catalog, managed by the ARLAS Item Registration Service)any ofUntitled integer in ItemUntitled null in Itemview__off_nadirview__off_nadiris optionalType: merged type (The angle from the sensor between nadir (straight down) and the scene center. Measured in degrees (0-90).)cannot be nulldefined in:Itemview__off_nadir Typemerged type (The angle from the sensor between nadir (straight down) and the scene center. Measured in degrees (0-90).)any ofUntitled number in ItemUntitled null in Itemview__incidence_angleview__incidence_angleis optionalType: merged type (The incidence angle is the angle between the vertical (normal) to the intercepting surface and the line of sight back to the satellite at the scene center. Measured in degrees (0-90).)cannot be nulldefined in:Itemview__incidence_angle Typemerged type (The incidence angle is the angle between the vertical (normal) to the intercepting surface and the line of sight back to the satellite at the scene center. Measured in degrees (0-90).)any ofUntitled number in ItemUntitled null in Itemview__azimuthview__azimuthis optionalType: merged type (Viewing azimuth angle. The angle measured from the sub-satellite point (point on the ground below the platform) between the scene center and true north. Measured clockwise from north in degrees (0-360).)cannot be nulldefined in:Itemview__azimuth Typemerged type (Viewing azimuth angle. The angle measured from the sub-satellite point (point on the ground below the platform) between the scene center and true north. Measured clockwise from north in degrees (0-360).)any ofUntitled number in ItemUntitled null in Itemview__sun_azimuthview__sun_azimuthis optionalType: merged type (Sun azimuth angle. From the scene center point on the ground, this is the angle between truth north and the sun. Measured clockwise in degrees (0-360).)cannot be nulldefined in:Itemview__sun_azimuth Typemerged type (Sun azimuth angle. From the scene center point on the ground, this is the angle between truth north and the sun. 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properties are allowed and do not have to follow a specific schemaDefinitions group RasterReference this group by using{"$ref":"airs_model#/$defs/Raster"}PropertyTypeRequiredNullableDefined bytypeobjectRequiredcannot be nullItempathstringRequiredcannot be nullItemidstringRequiredcannot be nullItemtypetypeis requiredType:object(RasterType)cannot be nulldefined in:Itemtype Typeobject(RasterType)pathpathis requiredType:string(Path)cannot be nulldefined in:Itempath Typestring(Path)ididis requiredType:string(Id)cannot be nulldefined in:Itemid Typestring(Id)Definitions group RasterTypeReference this group by using{"$ref":"airs_model#/$defs/RasterType"}PropertyTypeRequiredNullableDefined bysourcestringRequiredcannot be nullItemformatstringRequiredcannot be nullItemsourcesourceis requiredType:string(Source)cannot be nulldefined in:Itemsource Typestring(Source)formatformatis requiredType:string(Format)cannot be nulldefined in:Itemformat Typestring(Format)Definitions group VariableTypeReference this group by using{"$ref":"airs_model#/$defs/VariableType"}PropertyTypeRequiredNullableDefined by |
airspeed | No description available on PyPI. |
airspeed-ext | No description available on PyPI. |
airsspy | airsspyA package to help working with the Ab initio Random Structure Searching (AIRSS)
using Atomic simulation environment (ase).
Supports building a search seed for AIRSS in a interactive python environments.
One of the important steps for performing a successful search with AIRSS is to have sensible seed for generating
random structures, which are subsequently relaxed using the method of choice.
In general, AIRSS only relies on a few simple parameters to generate random structure, such as numbers of atoms,
numbers of species and cell volume.
However, for complicated search involving surfaces and/or interfaces, hand-building seed files becomes a
tedious or impossible job to do.
ASE has a suite of tools for manipulate atomic structure which can be very helpful for building structures,
and here, for building search seeds.AIRSS is a open source code licensed under GPLv2,
this package does not contain any source code of AIRSS nor links to it.What this doesAllow preparing seed for AIRSS using ASE'satomsinterfaceAllow ase's calculators to be used in AIRSS to do relaxationsTry interactivelyInteractive jupyter notebook examples can be found in theexamplesfolder.
Click thebinderbadge above to launch these examples in a pre-built environment and try it in your browser!Dependencesase: The atomic simulation environmentcastepinput: A light weight writer/reader for the input files ofCASTEP.InstallationThe package can be installed from pypi together with the dependencies:pip install airsspyAlternative, one can also install directly from the repository (defaults to the master branch):pip install git+https://github.com/zhubonan/airsspyUsageAssuming you are familiar withase, python and has some basic knowledge of AIRSS.
To prepare a seed for generating asensiblerandom structure:fromairsspyimportSeedAtomsseed=SeedAtoms('C6')seed.buiid.varvol=20seed.build.symmops=(2,4)# Can also access per `atom` tags/ketwords just like in ASEforiinrange(0,6,2):atom=seed[i]atom.tagname='CX'atom.posamp=2To write the seed file onto the disk:atoms.write_seed('C6.cell')# With IPython# Use the buildcell executable to generate the file!buildcell<C6.cell>C6-rand.cellTo generate a cell we can create aBuildcellinstance,
which is helping wrapper to thebuildcellprogram of AIRSS:fromairsspyimportBuildcellbuidcell=Buildcell(seed)random_atoms=builcell.generate()A shortcut is also available as an method of theSeedAtoms:random_atoms=seed.build_random_atoms()LimitationsDue to the lack oftimeoutargument ofPopen.communicatein python 2.7,
communication with thebuildcellis not available. Hence, direct generation and
retrieval of the random structure are not supported in python. However, it is
still possible to write the seed out and call the program externally. |
airstack | airstack-python-sdkThe Airstack Python SDK is a library that allows Python developers to integrate Airstack's blockchain functionalities into their applications. With this SDK, developers can perform various tasks, such as querying and fetching data from smart contracts, displaying NFT assets.Installationpip3 install airstackGetting startedTo use the SDK you will need airstack api-key, which you can find in your profile setting section in airstack web, once you have it you can initialise the Airstackclient with the api-key.fromairstack.execute_queryimportAirstackClientapi_client=AirstackClient(api_key='api-key')Methodsexecute_queryThe execute query method query the data and return the data in asynchronous, it returns query_response which has below dataquery_response.data: data returned by the queryquery_response.status_code: status code of the query responsequery_response.error: any error that occurred while loading the queryExamplefromairstack.execute_queryimportAirstackClientapi_client=AirstackClient(api_key='api-key')execute_query_client=api_client.create_execute_query_object(query=query,variables=variables)query_response=awaitexecute_query_client.execute_query()execute_paginated_queryNote:pagination methods only works with queries that has support for pagination, and the query passed to method must have a cursor as argument for it to work.Theexecute_paginated_querymethod provides a simple way to paginate the data returned by a query. It works the same as theexecute_querymethod, it returns query_response which has below data:query_response.data: data returned by the queryquery_response.status_code: status code of the query responsequery_response.error: any error that occurred while loading the queryquery_response.has_next_page: a boolean indicating whether there is another page of data after the current pagequery_response.has_prev_page: a boolean indicating whether there is another page of data before the current pagequery_response.get_next_page: a function that can be called to fetch the next page of dataquery_response.get_prev_page: a function that can be called to fetch the previous page of dataExamplefromairstack.execute_queryimportAirstackClientapi_client=AirstackClient(api_key='api-key')execute_query_client=api_client.create_execute_query_object(query=query,variables=variables)query_response=awaitexecute_query_client.execute_paginated_query()ifquery_response.has_next_page:next_page_response=awaitquery_response.get_next_pageifnext_page_response.has_prev_page:prev_page_response=awaitquery_response.get_prev_page |
airstore | No description available on PyPI. |
airstorm | airstormAirstorm is a dynamic Python ORM forAirtable. It allows you to easily interact with any base using Python with minimal setup.Main FeaturesObject-oriented interface.Dynamic generation of data models from schema.Automatic foreign-key resolution.Caching layer to avoid abusing the Airtable API.InstallationpipinstallairstormUsagefromairstorm.baseimportBasejamba_juice=Base('your_base_id','your_api_key',{'your':'schema'})smoothy=jamba_juice.Smoothie('some_smoothie_id')# Get your table record.forfruitinsmoothy.fruits:# Get linked record in a breeze.print(fruit.name)# Access any field data.print(smoothy.fruits.names)# Access to mutliple record field data at once.Getting the SchemaUnfortunatly currently this part of the process is not ideal.
Because Airtable does not provide access to the schema via their API, you'll have to "download" the schema manually via a web browser of choice.The followinggistis a script that you can run on a Chrome console from the Airtable baseAPI pageto get back the JSON schema that airtstorm is expecting to be fed with.RoadmapField validation where possible.Push changes. Currently we are read-only.Downlading schema automatically using pyppeteer.Pythonic formulas. |
airsupply | No description available on PyPI. |
airsupply-django-shorturls | django-shorturlsA custom URL shortening app for Django, including easyrev=canonicalsupport.Most code was originally by Simon Willison; seehttps://simonwillison.net/2009/Apr/11/revcanonical/for details. Improved
slightly and packaged by Jacob Kaplan-Moss. Currently maintained by
Ben Firshman.Patches welcome:http://github.com/bfirsh/django-shorturlsUsageSo, you want to host your own short URLs on your Django site:In your settings, define a set of prefixes for short URLs:SHORTEN_MODELS={'A':'myapp.animal','V':'myapp.vegetable','M':'myapp.mineral'}The keys are string prefixes; they can be any string, actually,
but since we’re going for short a single character is probably good.Values are the (hopefully-familiar)"<app-name>.<model-class>"used
by Django to identify a model. Remember:app-nameis the
(case-sensitive) last bit of your app’s name inINSTALLED_APPS, and<model-class>is your model class’s name, lowercased.Make sure your models have aget_absolute_url()method defined.Wire up the redirect view by adding to your URLconf:('^short/',include('shorturls.urls'))If you’d like to quickly link to shortened URLs in your templates, stick"shorturls"inINSTALLED_APPS, and then in your templates do:{%loadshorturl%}<ahref="{%shorturlobject%}">...</a>(whereobjectis a model instance).Alternatively:{%loadshorturl%}{%revcanonicalobject%}This generates the whole<linkrev="canonical"href="...">tag for
you.That’s it.If you’d like more control, keep reading.SettingsAvailable settings are:SHORTEN_MODELSYou’ve seen this one.SHORT_BASE_URLIf defined, theshorturlandrevcanonicaltemplate tags will
prefix generated URLs with this value. Use this if you’ve got a shorter
domain name you’d like to use for small URLs.For example, givenSHORT_BASE_URL ='http://exm.pl/',{% shorturl obj %}would return something likehttp://exm.pl/AbCd.SHORTEN_FULL_BASE_URLThe domain to redirect to when redirecting away from the small URL.
Again, you’ll use this if your short URL base and your “real” site
differ.If not defined, the redirect view will try to guess the proper domain by
consulting thedjango.contrib.sitesframework, if installed, or the
requested domain, if not.SHORTURLS_DEFAULT_CONVERTERThe converter that is used to translate between short URLs and model IDs.
Defaults to the built in base 62 conversion.Available converters:shorturls.baseconv.base62Base 62 encoding.shorturls.baseconv.base32Douglas Crockford’s base 32.shorturls.baseconv.hexconvHex encoding.shorturls.baseconv.binBinary encoding, because why not. |
airsupply-lambda-tools | No description available on PyPI. |
airt | artpyThis file will become your README and also the index of your
documentation.InstallpipinstallartpyHow to useFill me in please! Don’t forget code examples:1+12 |
airtable | Python interface to the Airtable’s REST API -https://airtable.com-For javascript enthusiasts:https://github.com/Airtable/airtable.jsInstallationAirtable Python usesRequests.py:
make sure you have it installed by running$ pip install requestsGetting startedOnce you have createda new
baseand a new table through the Web interface, you’re ready to start using
Airtable Python.fromairtableimportairtableat=airtable.Airtable('BASE_ID','API_KEY')at.get('TABLE_NAME')Here’s an example of response from the Restaurant’s example base{u'records':[{u'fields':{u'Diet':u'Kosher or Halal',u'Friendly Restaurants':[u'recr0ITqq9C1I92FL',u'recGeAJLw0ZkbwdXZ'],u'Icon':[{u'filename':u'no-pig.jpg',u'id':u'attzKGOBbjndOx0FU',u'size':34006,u'thumbnails':{u'large':{u'height':202,u'url':u'https://dl.airtable.com/trmtq3BaRoa0sWnyffWZ_large_no-pig.jpg',u'width':256},u'small':{u'height':36,u'url':u'https://dl.airtable.com/yzuRv5CyRs2PVH4fDvCe_small_no-pig.jpg',u'width':46}},u'type':u'image/jpeg',u'url':u'https://dl.airtable.com/DyGOjAASze6AIkQxFpDv_no-pig.jpg'}],u'People':[u'Annie',u'Maryam']},u'id':u'rec5sD6mBBd0SaXof'},...API ReferenceThe available methods closely mimick theREST
API:GetGiven a table name, fetched one or multiple records.at.get(table_name,table_name,record_id=None,limit=0,offset=None,filter_by_formula=None,view=None,max_records=0,fields=[])wheretable_name (required) is a string representing the table name
record_id (optional) is a string, which fetches a specific item by id. If not specified, all items are fetched
limit (optional) is an integer, and it can only be specified if record_id is not present, and limits the number of items fetched (see pageSize in the AirTable documentation)
offset is a string representing the record id from which we start the offset
filter_by_formula (optional) is a string to filter the retrieving records (see filterByFormula in the AirTable documentation)
max_records (optional) is the total number of records that will be returned (see maxRecords in the AirTable documentation)
fields (optional) is a list of strings with the field names to be returnedIterateGiven a table name, fetched all records.at.iterate(table_name,table_name,limit=0,offset=None,filter_by_formula=None,view=None,max_records=0,fields=[])wheretable_name (required) is a string representing the table name
limit (optional) is an integer, and it can only be specified if record_id is not present, and limits the number of items fetched (see pageSize in the AirTable documentation)
offset is a string representing the record id from which we start the offset
filter_by_formula (optional) is a string to filter the retrieving records (see filterByFormula in the AirTable documentation)
max_records (optional) is the total number of records that will be returned (see maxRecords in the AirTable documentation)
fields (optional) is a list of strings with the field names to be returnedNote: this returns a generator instead, which you can use to loop
each record:# example with similar results of at.getresult={"records":[]}forrinself.at.iterate(self.table,fields=fields):result["records"].append(r)CreateCreates a new entry in a table, and returns the newly created entry with
its new ID.at.create(table_name,data)wheretable_name (required) is a string representing the table name
data (required) is a dictionary containing the fields and the resepective valuesUpdateUpdatessomefields in a specific entry in the table. Fields which are
not explicitely included will not get updatedat.update(table_name,record_id,data)wheretable_name (required) is a string representing the table name
record_id (required) is a string representing the item to update
data (required) is a dictionary containing the fields (and the resepective values) to be updatedUpdate AllLike the previous method, but updates all fields, clearing the ones that
are not included in the request.at.update_all(table_name,record_id,data)DeleteDelete a specific record from the tableat.delete(table_name,record_id)wheretable_name (required) is a string representing the table name
record_id (required) is a string representing the item to updateReleaseTo release, tag the Git repo with a new version number, push that tag to GitHub then Travis CI will
do the rest. |
airtable-async | No description available on PyPI. |
airtable-async-ti | No description available on PyPI. |
airtablecache | airtablecacheThis project contains source code and supporting files for a package which caches data from airtable to specified location |
airtable-cacher | Airtable CacherThis plugin is fork of the work done by Ron Mountjoy onAirtable Cachinghttps://github.com/rmountjoy92/AirtableCachingCachingFirst you must setup a recurring script that will cache the table.from airtable_cacher import Base
airtable = Base(<AIRTABLE_BASE_ID>, <AIRTABLE_API_KEY>)
"""
Main Function
"""
airtable.cache_table(<AIRTABLE_PRODUCTS_TABLE>)You can optionally supply a third argument toBase()for setting the json folder like so:from airtable_cacher import Base
airtable = Base(<AIRTABLE_BASE_ID>, <AIRTABLE_API_KEY>, "my_json_folder")Caching imagesIf you'd like to cache images, you can do so by supplying an optional fourth argumentAccessing cached datafrom airtable_cacher import Table
products_table = Table(<AIRTABLE_BASE_ID>,<AIRTABLE_PRODUCTS_TABLE>)If you have supplied a custom JSON folder path in the caching, you supply that as an optional third argument inTable()from airtable_cacher import Table
products_table = Table(<AIRTABLE_BASE_ID>,<AIRTABLE_PRODUCTS_TABLE>, "my_json_folder")To get all records then userecords = products_table.all() |
airtable-caching | Airtable CachingUtility for caching api responses from the airtable-python-wrapper and provides an ORM style interface for querying cached records.Dummy api key and base keys provided below for testing. Please do not modify anything using this key, or I will have to take this option away.Installationpipinstallairtable-cachingStep 1 - Import classesfromairtable_cachingimportBase,TableStep 2 - cache a tablebase=Base(base_id="appjMwyFviPaM9I0L",api_key="keyqhxncgPbSySJQN")base.cache_table("Table 1")Step 3 - Access cached datatable=Table(base_id="appjMwyFviPaM9I0L",table_name="Table 1")# get single record by it's airtable record IDtable.get('rec4trz5QrB6aWJBw')# get all records in the tabletable.query().all()# get all records and resolve linked fieldstable.query(resolve_fields={"Table 2":"Link to Table 2"}).all()# get the first record in tabletable.query().first()# get the last record in tabletable.query().last()# filtering records in the querytable.query().filter_by({"Name":"Data entry 1 from Table 1 from Base 1"}).all()# ordering records in the querytable.query().order_by("Number").all()# ordering records in the query (descending)table.query().order_by("Number",desc=True).all()Defining a custom cache folder locationBy default this stores all cached data as .json files in airtable_caching/json. You can optionally pass a custom folder location to the Base and Table classes.importoscustom_json_folder=os.path.join(os.path.dirname(__file__),"custom_json")base=Base(base_id="appjMwyFviPaM9I0L",api_key="keyqhxncgPbSySJQN",json_folder=custom_json_folder,)table=Table(base_id="appjMwyFviPaM9I0L",table_name="Table 1",json_folder=self.custom_json_folder,)Changelog0.0.4UpdatedREADME and docstrings0.0.3Changed(BREAKING) Base and Table classes no longer use base name, now they use base id (see docs)added option for supplying custom cache locationAddedDocumentationTests0.0.1 - 0.0.2Initial release |
airtable-export | airtable-exportExport Airtable data to files on diskInstallationInstall this tool usingpip:$ pip install airtable-exportUsageYou will need to know the following information:Your Airtable base ID - this is a string starting withapp...Your Airtable API key - this is a string starting withkey...The names of each of the tables that you wish to exportYou can export all of your data to a folder calledexport/by running the following:airtable-export export base_id table1 table2 --key=keyThis example would create two files:export/table1.ymlandexport/table2.yml.Rather than passing the API key using the--keyoption you can set it as an environment variable calledAIRTABLE_KEY.Export optionsBy default the tool exports your data as YAML.You can also export as JSON or asnewline delimited JSONusing the--jsonor--ndjsonoptions:airtable-export export base_id table1 table2 --key=key --ndjsonYou can pass multiple format options at once. This command will create a.json,.ymland.ndjsonfile for each exported table:airtable-export export base_id table1 table2 \
--key=key --ndjson --yaml --jsonSQLite database exportYou can export tables to a SQLite database file using the--sqlite database.dboption:airtable-export export base_id table1 table2 \
--key=key --sqlite database.dbThis can be combined with other format options. If you only specify--sqlitethe export directory argument will be ignored.The SQLite database will have a table created for each table you export. Those tables will have a primary key column calledairtable_id.If you run this command against an existing SQLite database records with matching primary keys will be over-written by new records from the export.Request optionsBy default the tool usespython-httpx's default configurations.You can override theuser-agentusing the--user-agentoption:airtable-export export base_id table1 table2 --key=key --user-agent "Airtable Export Robot"You can override thetimeout during a network read operationusing the--http-read-timeoutoption. If not set, this defaults to 5s.airtable-export export base_id table1 table2 --key=key --http-read-timeout 60Running this using GitHub ActionsGitHub Actionsis GitHub's workflow automation product. You can use it to runairtable-exportin order to back up your Airtable data to a GitHub repository. Doing this gives you a visible commit history of changes you make to your Airtable data - likethis one.To run this for your own Airtable database you'll first need to add the following secrets to your GitHub repository:AIRTABLE_BASE_IDThe base ID, a string beginning `app...`AIRTABLE_KEYYour Airtable API keyAIRTABLE_TABLESA space separated list of the Airtable tables that you want to backup. If any of these contain spaces you will need to enclose them in single quotes, e.g. 'My table with spaces in the name' OtherTableWithNoSpacesOnce you have set those secrets, add the following as a file called.github/workflows/backup-airtable.yml:name:Backup Airtableon:workflow_dispatch:schedule:-cron:'320***'jobs:build:runs-on:ubuntu-lateststeps:-name:Check out repouses:actions/checkout@v2-name:Set up Pythonuses:actions/setup-python@v2with:python-version:3.8-uses:actions/cache@v2name:Configure pip cachingwith:path:~/.cache/pipkey:${{ runner.os }}-pip-restore-keys:|${{ runner.os }}-pip--name:Install airtable-exportrun:|pip install airtable-export-name:Backup Airtable to backups/env:AIRTABLE_BASE_ID:${{ secrets.AIRTABLE_BASE_ID }}AIRTABLE_KEY:${{ secrets.AIRTABLE_KEY }}AIRTABLE_TABLES:${{ secrets.AIRTABLE_TABLES }}run:|-airtable-export backups $AIRTABLE_BASE_ID $AIRTABLE_TABLES -v-name:Commit and push if it changedrun:|-git config user.name "Automated"git config user.email "[email protected]"git add -Atimestamp=$(date -u)git commit -m "Latest data: ${timestamp}" || exit 0git pushThis will run once a day (at 32 minutes past midnight UTC) and will also run if you manually click the "Run workflow" button, seeGitHub Actions: Manual triggers with workflow_dispatch.DevelopmentTo contribute to this tool, first checkout the code. Then create a new virtual environment:cd airtable-export
python -mvenv venv
source venv/bin/activateOr if you are usingpipenv:pipenv shellNow install the dependencies and tests:pip install -e '.[test]'To run the tests:pytest |
airtable-fdw | Airtable Foreign Data WrapperInstallationRequirementsPostgreSQL 9.1+ withMulticornextension installed.Loading extension and defining FDW serverEnsure multicorn is loaded and define Foreign Data Wrapper for airtablecreateextensionifnotexistsmulticorn;createserverifnotexistsmulticorn_airtable_srvforeigndatawrappermulticornoptions(wrapper'airtable_fdw.AirtableFDW');UsageDefine table ascreateforeigntableschema.table_name("_id"varcharoptions(rowid'true'),-- column used as rowid, may be any name,-- should appear only onece"Some text column"varchar,"Some numeric column"numeric,"Some date column"date,"Some complex column"json,-- can be used for complex fields but see example below"Some json nullable column"jsonoptions(nulljson'true'),-- keep nulls as json ('null'::json instead of null::json)"Some computed column"varcharoptions(computed'true')-- column that won't be modified with update-- may appear multiple times)servermulticorn_airtable_srvoptions(api_key'...',-- api access keybase_key'...',-- database identifiertable_name'...',-- name of table to read fromview_name'...',-- optional view name, if not present raw table will be readrowid_column'...'-- optional rowid column name will be used if no column has `rowid` option set);If complex column - likeCollaborator- appears in table it is read from AirTable API as ajsonand could be treated asjsonor as a complex, custom defined type.createtypeAirtableCollaboratoras(idvarchar,emailvarchar,"name"varchar);createforeigntableschema.table_name("_id"varcharoptions(rowid'true'),"editor"AirtableCollaboratoroptions(complextype_fields'id,email,name',complextype_send'email'))servermulticorn_airtable_srvoptions(api_key'...',base_key'...',table_name'...');where:complextype_fields 'id,email,name'indicates how record string should be constructed fromjson- so{"id": "someid", "email": "[email protected]", "name":"My Name"}will be converted to(someid,[email protected],My Name)and will be correctly casted toAirtableCollaboratortype.complextype_send 'email'means that when this field is modified onlyemailfield will be sent to APIUsage TipsUseANDinWHEREclause whenever possible,ORs are not handled well (at all?) bymulticornso unconditional queries are sent to Airtable (watch the quota!).IfORis required try to replace it withIN (...)FeaturesConfigurable to read from given base / table / viewSQLWHEREclause transformed toformulaquery (so number of requests to API is optimized)BatchINSERT/UPDATE/DELETEsupport for complex types - json is parsed to complex type on read (SELECT), and single, selected field is set on write (INSERT,UPDATE) |
airtableio | Async API wrapper for Airtable.Powered by aiohttp.https://airtable.com/InstallationpipinstallairtableioSimple UsagefromairtableioimportAirtableimportasyncioTOKEN=""APP_ID=""my_airtable=Airtable(TOKEN,APP_ID)asyncdefcall():result=awaitmy_airtable.get_records("Table")print(result)if__name__=="__main__":loop=asyncio.get_event_loop()loop.create_task(call())loop.run_forever() |
airtablemock | A mock library to help test Python code accessing Airtable using thePython library.It keeps tables in RAM and can do basic operations.InstallationThe easiest way is using pip:pipinstallairtablemockUsageIn your test, you patch the whole airtable library:[email protected](mycode.__name__+'.airtable')classTestMyCode(unittest.TestCase):deftest_foo():# This is a client for the base "baseID", it will not access the real# Airtable service but only the mock one which keeps data in RAM.client=airtablemock.Airtable('baseID','apiKey')# Populate the table.client.create('table-foo',{'field1':1,'field2':'two'})# Run your code that uses Airtable, it should transparently uses the table# above.mycode.run()# Access the table again to check if anything was modified.records=client.get('table-foo')…ReleaseTo create a new release of airtablemock, tag the Git repo and run:pythonsetup.pysdistbdist_wheeltwineuploaddist/airtablemock-* |
airtable-orm | Python ORM for Airtable tablesInstallationpip install airtable-ormUsagefromdataclassesimportdataclass# Importing libraryfromairtable_ormimportAirtableORM# Initialize the object and get the sessionorm=AirtableORM("airtable://:<YOUR API KEY>@<YOUR APP KEY>")session=orm.get_session()# Create your data class with properties same as your Airtable tables# The name of the class muse be matched to the Airtable table name@dataclassclassMyEntity:id:strname:str# Create new object for the dataclassnew_entity=MyEntity("id#1","My name")# Run add() to create new entrysession.add(new_entity)# Run commit() to save the data to Airtablesession.commit()To list all the data as objectdata=session.query(MyEntity).all()for_indata:print(_.name) |
airtable-pg-sync | Airtable Postgres SyncThe goal of this library is to provide an out-of-the-box solution for replicating
an entire Airtable base in a Postgres schema. There are two modes of operation:One-off-sync: This mode will replicate the Airtable base in the specified Postgres schema
and then exit. This is useful for creating snapshots of the base for analysis or for storage as a backup.Perpetual sync: This mode will replicate the Airtable base in the specified Postgres schema
and then continue to watch for changes in the base. When a change is detected, the
change will be applied to the Postgres schema. This is useful for creating a
replica of the base that can be used for analysis in real time.This library will produce a Postgres table and view for each of the tables in the specified Airtable base.
The table will take the Airtable table id for its name and the field ids for its column names. The view will have the
same name as the Airtable table and the column names will be the same as the Airtable column names.
For most analysis use cases it makes sense to use the view as it is more readable, but for applications requiring
robustness with respect to column name changes the table should be used.InstallationTo install the library, run the following command:pipinstallairtable-postgres-syncPermissionsTo use this library, you will need to create a personal access token in Airtable. This
token will need to have the following scopes:data.records:readschema.bases:readwebhook:manageYou will also need to give the Postgres user that you are using read and write access to the schema
you are syncing to.UsageTo use the library, you will need to create a config file. The config file defines
all the parameters that are needed to connect to Airtable and Postgres, as well as how
your program will listen for changes. The file must be in YAML format and must contain
the following fields:AIRTABLE_PG_SYNC:DB_INFO:HOST:# Postgres hostPORT:# Postgres portUSER:# Postgres userPASSWORD:# Postgres passwordDB_NAME:# Postgres database nameSCHEMA_NAME:# Postgres schema nameAIRTABLE_INFO:BASE_ID:# Airtable base id to syncPAT:# Airtable personal access tokenLISTENER_INFO:WEBHOOK_URL:# The url that Airtable will send change notifications toPORT:# The port to listen for change notifications onThe library can be used in two ways:As a command line toolTo trigger a one-time sync, run the following command:airtable-pg-syncone-time-sync--config/path/to/config.ymlTo trigger a perpetual sync, run the following command:airtable-pg-syncperpetual-sync--config/path/to/config.ymlAs a python libraryTo trigger a sync from within a python program, run the following code:fromairtable_pg_syncimportSyncSync(config_path="/path/to/config.yml",perpetual=True/False).run()Testing and DeploymentWhen testing this library for your use case thengrokservice is very useful. It allows you to listen
for requests sent over the internet to your PC (ie the webhook POST requests).For deployment it is recommended that you run the library in an AWS EC2 type service. A t2.micro instance should suffice.Bugs, Feature Requests, and ContributionsIf you find a bug or have a feature request, please open an issue
onGitHub.
Any contributions are welcome and appreciated. If you would like to
contribute, please open a pull request onGitHub.Ideas for contributions:Add support for other databasesAdd support for Postgres -> Airtable syncLicenseThis library is licensed under the MIT License. See theLICENSEfile |
airtable-python | airtable-pythonairtable-pythonis an API wrapper for Airtable, written in PythonUse this library if you are interested in Oauth authentication and webhook notifications.Installingpip install airtable-pythonUsagefrom airtable.client import Client
client = Client(client_id, client_secret, redirect_uri, code_verifier)Note: If you already have an access token, you can initiate Client without any parameters and directly set token with the access token you have as a dictionary client.set_token({"access_token": token})Get access_tokenTo get the access token using Oauth2 follow the next steps.
Checkhttps://airtable.com/developers/web/api/oauth-referencefor more info:Get authorization URL to receive codeurl = client.authorization_url(state)Get access token using codetoken = client.token_creation(code)Set access tokenclient.set_token(access_token)If your access token expired, you can get a new one using refresh_token:response = client.refresh_access_token(refresh_token)And then set access token again...Get current useruser = client.get_current_user()List Basesbases = client.list_bases()List Tablestables = client.list_base_tables(baseId)List recordsrecords = client.list_records(
baseId,
tableId,
pageSize=None,
maxRecords=None,
filter_field=None,
filter_value=None,
sort_field=None,
sort_direction=None
)
# baseId and tableId are required
# sort_direction options are 'desc' or 'asc'Create Recordsrecords = [
{
"fields": {
"Projects": "Project from python",
"Status": "In progress",
"Complete?": False
}
},
]
records = client.create_records(baseId, tableId, records)Update Recorddata = {
"Status": "Complete",
"Complete?": True
}
record = client.update_record(baseId, tableId, recordId, data)Update Multiple Recordsrecords = [
{
"id": "recB4UscNECOHnT7y",
"fields": {
"Number": 10000,
},
},
{
"id": "recFkktx671jlvyj8",
"fields": {
"Number": 20000,
},
},
]
updated_records = client.update_record(baseId, tableId, records)List Collaborators (needs enterprise scopes access)collabs = client.list_collaborators(baseId) |
airtable-python-wrapper | Airtable Python WrapperAirtable API Client Wrapper for PythonInstallingpip install airtable-python-wrapperDocumentationFull documentation here:http://airtable-python-wrapper.readthedocs.io/Usage ExampleBelow are some of the methods available in the wrapper.For the full list and documentation visit thedocsYou can see the wrapper in action in thisJupyter Notebook.airtable = Airtable('base_id', 'table_name')
airtable.get_all(view='MyView', maxRecords=20)
airtable.insert({'Name': 'Brian'})
airtable.search('Name', 'Tom')
airtable.update_by_field('Name', 'Tom', {'Phone': '1234-4445'})
airtable.delete_by_field('Name', 'Tom')LicenseMIT0.15.3Adds escaping formula field references #1200.15.2Added Batch update method0.15.1Fix:batch delete0.15.0Breaking: Drop Api config from ENV variable - useapi_keyarg insteadBreaking: Drop Python 2 / IronPython SupportFeature: On HTTP Errors, Raise Original Exception, but with Helpful Errors addedFix: #86 formulas with string values0.14.0Removed:mirror()method.Feature: Configurable request timeout0.13.0Fixed: Python 2 compatibility issuesStart CI testing on all supported Python versionsBreaking: Drop implicit support for Python 3.4, officially support 3.7 and 3.8.0.12.0Fixed: Rewrote testsFixed: Improve CI and deployment0.11.2Fixed: Add sdist and universal for 2.7 distFixed: Long dist set to markdown0.11.0Feature: Merged PR#17 - Added typecast to update, update_by_field, replace, replace_by_field0.10.1Feature: Added typcase option to batch_insert0.10.0Feature: Merged PR#17 - typecase kwarg0.9.1Feature: Better exception message for 422 (Issue #16)Fix: 2.7 Compat with sys.implementation0.9.0Docs: Revised Docs strings to show kebab case kwargsFix: Url Escape (PR#1)0.8.0Docs: New Documentation on Parameter filters DocsDocs: More documentation and examples.Feature: Search now uses filterByFormulaAdded Formula Generator0.7.3Removed Unencoded Debug Msg due to IronPython Bug #2420.7.2Merge Fix0.7.1-alphaMoved version to sep file to fix setup.py errorRemoved urlencode importAdded Explicit Raise for 422 errors with Decoded Urls0.7.0-dev1Feature: Added airtable.get() method to retrieve recordFix: sort/field string input to allow sting or listFix: AirtableAuth DocsFix: Keyargs Docs0.6.1-dev1Bugfix: Fix Setup to install six.pyBugfix: Fix AitableAuth Docs0.6.0-dev1Implemented Sort FilterImplemented FilterByFormulaImplemented all param filters as classesAdded Aliases for ParametersRenamed get() to get_iter()0.5.0-dev10.4.0Added replace()Added mirror()0.3.0Initial Work |
airtable-schema | Airtable-schemaSchema management for Airtable! |
airtable-to-sqlite | Airtable to SQliteTable of ContentsInstallationUsageDatabase formatAlternativesFuture developmentLicenseInstallationpip install airtable-to-sqliteUsageThe tool is primarily intended to be used through the command line. Once installed, you can use it like this:airtable-to-sqliteapp123456789This will fetch the base with the IDapp123456789and save it to a file calledBaseName.dbin your current directory.AuthenticationFor the tool to work you need to authenticate with Airtable API using a personal access token. To generate a token visityour Airtable account.There are two ways to use the token. You can set it as an environment variable (calledAIRTABLE_PERSONAL_ACCESS_TOKEN), and the tool will pick it up:exportAIRTABLE_PERSONAL_ACCESS_TOKEN=patABCDE123456789
airtable-to-sqliteapp123456789Or you can pass it to the tool directly:airtable-to-sqlite--personal-access-tokenpatABCDE123456789app123456789Download more than one baseYou can add more Base IDs to download more than one Base.airtable-to-sqliteapp123456789app567891234These will be saved toBase1Name.dbandBase2Name.dbrespectively.Customise the output fileTo customise the name of the file where the database will be saved, just pass the--outputparameter. So for example:airtable-to-sqlite--output"output.db"app123456789Will save the file tooutput.db. If you include{}in the filename it will be replaced with the name of the base. So for example:airtable-to-sqlite--output"db/{}/output.db"app123456789Will result in a file saved todb/BaseName/output.db.The string{}must be included if more than one Base is requested, omitting it will produce an error.Use IDs instead of namesBy default, the tool will use the names of tables, fields and bases. You can use the--prefer-idsflag to tell the tool to use the IDs instead.If this flag is used it will mean the filenames use the Base ID (egapp123456789.dbinstead ofBaseName.db), the table names will use IDs instead of names, and columns within the tables will be named using IDs.This may be helpful if your table or field names contain characters that can't be used in sqlite. You can use the_meta_tableand_meta_fieldtables (see below) to find the names of the tables and columnsDatabase formatEach table within the Airtable Base gets in own table within the database. Each of these tables always contains two default fields, and then the rest of the data from the table. The additional fields are:_id: The airtable ID for the record. This is set as the primary key_createdTime: The date and time the record was created.All fields are stored in the database, with the exception of fields with the typemultipleRecordLinks, which are instead stored in a linking table.Where possible, the tool will attempt to assign an appropriate column type to each field. Note that constraints on these fields are not enforced by sqlite by default, so the database may contain invalid data.Storage for linked recordsWhere a field has the typemultipleRecordLinks, i.e. where it is a record that links to other records in another table, a linking table is created. The name of this table is{table_name}_{field_id}, and it always contains two columns, with foreign key constraints to their tables:recordId: the record in the original tableotherRecordId: the record in the linked tableNote that these fields contain many-to-many relationships, so values in both fields may appear more than once.Meta tablesIn addition to the main data tables from the Base, the tool creates tables holding metadata about the original Base and the export process. These tables are:_meta_tableA record for each table in the Base. Fields are:id: (str) Airtable Table IDname: (str) Table nameprimaryFieldId: (str) ID of the primary field_meta_fieldA record for each field in each table in the Base. Fields are (only the first 4 fields are mandatory):id: (str) Airtable Field IDname: (str) Field Nametype: (str) Type of fieldtableId: (str) Airtable Table IDoptions: (json) Any remaining options not covered by other variableslinkedTableId: (str) For fields of typemultipleRecordLinks, the other table looked upisReversed: (bool)prefersSingleRecordLink: (bool)inverseLinkFieldId: (str)isValid: (bool)recordLinkFieldId: (str)icon: (str)color: (str)referencedFieldIds: (str)result: (str)precision: (str)symbol: (str)_meta_field_choiceWhere a field has choices (e.g. where it is a single or multiple select field), this table contains the options. Fields are:id: (str) Choice IDname: (str) Choice Namecolor: (str)fieldId: (str) Airtable Field ID_meta_viewA record for each view in the Base. Fields are:id: (str) Airtable View IDname: (str) View nametableId: (str) Airtable Table IDThis table doesn't contain enough information to reconstruct the view._meta_settingsEach record contains a key value pair with a piece of metadata, for example the original Base ID and Base Name. Fields are:key: (str)value: (str)Alternativesairtable-exportby @simonwFuture developmentPotential future developments include:Viewer/editor for exported filesGreater coverage of available fieldsexport to Excel (could be a separate tool)Contributions are very welcome.Licenseairtable-to-sqliteis distributed under the terms of theMITlicense. |
airtable-view-fetcher | Tool to scrape data from an Airtable shared view |
airtag-bat | No description available on PyPI. |
airt-client | Python client for airt serviceDocsFor full documentation, Please follow the below link:https://docs.airt.ai/How to installIf you don't have the airt library already installed, please install it using pip.pip install airt-clientHow to useTo access the airt service, you must first create a developer account. Please fill out the signup form below to get one:https://bit.ly/3hbXQLYAfter successful verification, you will receive an email with the username and password for the developer account.Once you have the credentials, use them to get an access token by callingClient.get_tokenmethod. It is necessary to get an access token; otherwise, you won't be able to access all of the airt service's APIs. You can either pass the username, password, and server address as parameters to theClient.get_tokenmethod or store them in the environment variablesAIRT_SERVICE_USERNAME,AIRT_SERVICE_PASSWORD, andAIRT_SERVER_URLIn addition to the regular authentication with credentials, you can also enable multi-factor authentication (MFA) and single sign-on (SSO) for generating tokens.To help protect your account, we recommend that you enable multi-factor authentication (MFA). MFA provides additional security by requiring you to provide unique verification code (OTP) in addition to your regular sign-in credentials when performing critical operations.Your account can be configured for MFA in just two easy steps:To begin, you need to enable MFA for your account by calling theUser.enable_mfamethod, which will generate a QR code. You can then
scan the QR code with an authenticator app, such as Google Authenticator and follow the on-device instructions to finish the setup in your smartphone.Finally, activate MFA for your account by callingUser.activate_mfaand passing the dynamically generated six-digit verification code from your
smartphone's authenticator app.You can also disable MFA for your account at any time by calling the methodUser.disable_mfamethod.Single sign-on (SSO) can be enabled for your account in three simple steps:Enable the SSO for a provider by calling theUser.enable_ssomethod with the SSO provider name and an email address. At the moment,
we only support"google"and"github"as SSO providers. We intend to support additional SSO providers in future releases.Before you can start generating new tokens with SSO, you must first authenticate with the SSO provider. Call theClient.get_tokenwith
the same SSO provider you have enabled in the step above to generate an SSO authorization URL. Please copy and paste it into your
preferred browser and complete the authentication process with the SSO provider.After successfully authenticating with the SSO provider, call theClient.set_sso_tokenmethod to generate a new token and use it automatically
in all future interactions with the airt server.For more information, please check:Tutorialwith more elaborate example, andAPIwith reference documentation.Here's a minimal example showing how to use airt services to train a model and make predictions.In the below example, the username, password, and server address are stored inAIRT_SERVICE_USERNAME,AIRT_SERVICE_PASSWORD, andAIRT_SERVER_URLenvironment variables.0. Get token#| include: false
# Do not remove "# hide" from this cell. Else this cell will appear in documentation
import os
# setting the environment variable
os.environ["AIRT_SERVICE_USERNAME"] = "johndoe"
os.environ["AIRT_SERVICE_PASSWORD"] = os.environ["AIRT_SERVICE_SUPER_USER_PASSWORD"]# Importing necessary libraries
from airt.client import Client, DataSource, DataBlob
# Authenticate
Client.get_token()1. Connect data# The input data in this case is a CSV file stored in an AWS S3 bucket. Before
# you can use the data to train a model, it must be uploaded to the airt server.
# Run the following command to upload the data to the airt server for further
# processing.
data_blob = DataBlob.from_s3(
uri="s3://test-airt-service/ecommerce_behavior_csv"
)
# Display the upload progress
data_blob.progress_bar()
# Once the upload is complete, run the following command to preprocess and
# prepare the data for training.
data_source = data_blob.to_datasource(
file_type="csv",
index_column="user_id",
sort_by="event_time"
)
# Display the data preprocessing progress
data_source.progress_bar()
# When the preprocessing is finished, you can run the following command to
# display the head of the data to ensure everything is fine.
print(data_source.head())100%|██████████| 1/1 [01:00<00:00, 60.62s/it]
100%|██████████| 1/1 [00:35<00:00, 35.39s/it]
event_time event_type product_id \
user_id
10300217 2019-11-06 06:51:52+00:00 view 26300219
253299396 2019-11-05 21:25:44+00:00 view 2400724
253299396 2019-11-05 21:27:43+00:00 view 2400724
272811580 2019-11-05 19:38:48+00:00 view 3601406
272811580 2019-11-05 19:40:21+00:00 view 3601406
288929779 2019-11-06 05:39:21+00:00 view 15200134
288929779 2019-11-06 05:39:34+00:00 view 15200134
310768124 2019-11-05 20:25:52+00:00 view 1005106
315309190 2019-11-05 23:13:43+00:00 view 31501222
339186405 2019-11-06 07:00:32+00:00 view 1005115
category_id category_code \
user_id
10300217 2053013563424899933 None
253299396 2053013563743667055 appliances.kitchen.hood
253299396 2053013563743667055 appliances.kitchen.hood
272811580 2053013563810775923 appliances.kitchen.washer
272811580 2053013563810775923 appliances.kitchen.washer
288929779 2053013553484398879 None
288929779 2053013553484398879 None
310768124 2053013555631882655 electronics.smartphone
315309190 2053013558031024687 None
339186405 2053013555631882655 electronics.smartphone
brand price \
user_id
10300217 sokolov 40.54
253299396 bosch 246.85
253299396 bosch 246.85
272811580 beko 195.60
272811580 beko 195.60
288929779 racer 55.86
288929779 racer 55.86
310768124 apple 1422.31
315309190 dobrusskijfarforovyjzavod 115.18
339186405 apple 915.69
user_session
user_id
10300217 d1fdcbf1-bb1f-434b-8f1a-4b77f29a84a0
253299396 b097b84d-cfb8-432c-9ab0-a841bb4d727f
253299396 b097b84d-cfb8-432c-9ab0-a841bb4d727f
272811580 d18427ab-8f2b-44f7-860d-a26b9510a70b
272811580 d18427ab-8f2b-44f7-860d-a26b9510a70b
288929779 fc582087-72f8-428a-b65a-c2f45d74dc27
288929779 fc582087-72f8-428a-b65a-c2f45d74dc27
310768124 79d8406f-4aa3-412c-8605-8be1031e63d6
315309190 e3d5a1a4-f8fd-4ac3-acb7-af6ccd1e3fa9
339186405 15197c7e-aba0-43b4-9f3a-a815e31ade402. Train# We assume that the input data for training a model includes the client_column
# target_column, and timestamp column, which specify the time of an event.
from datetime import timedelta
model = data_source.train(
client_column="user_id",
target_column="event_type",
target="*purchase",
predict_after=timedelta(hours=3),
)
# Display model training progress
model.progress_bar()
# Once the model training is complete, call the following method to display
# multiple evaluation metrics to evaluate the model's performance.
print(model.evaluate())100%|██████████| 5/5 [00:00<00:00, 126.62it/s]
eval
accuracy 0.985
recall 0.962
precision 0.9343. Predict# Finally, you can use the trained model to make predictions by calling the
# method below.
predictions = model.predict()
# Display model prediction progress
predictions.progress_bar()
# If the dataset is small enough, you can use the following method to download
# the prediction results as a pandas DataFrame.
print(predictions.to_pandas().head())100%|██████████| 3/3 [00:10<00:00, 3.38s/it]
Score
user_id
520088904 0.979853
530496790 0.979157
561587266 0.979055
518085591 0.978915
558856683 0.977960 |
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