Saravanan666/pypi-org huggingface dataset:
Package: aircrack-gui
aircrack-guiAircrack-gui is a python gui foraircrack-ngusinggtk3.0.The priority was to make every step intuitive and easy)RequirementsPyGoObject version: > 3.38.0Python version: 3: stable, 2: not tested.Installationgit clone https://github.com/Cod3dDOT/aircrack-gui
cd aircrack-gui
pip install -r requirements.txtIMPORTANTYou must use aircrack-gui only on networks you have permission to.As this tool is only a gui, it will not work if the tools it uses behind the scenes do not work: python,aircrack-ng,hcxtools.UsageRun:cd aircrack-gui
python3 aircrack-gui.pyIf any interface will be found, a window will open with the option to choose an interface, scan, start airmon-ng or open aircrack-ng.Step 1: Set path (default: /home/SUDO_USER/Desktop/aircrack-ng/wifi/)Step 2: Press 'Scan for networks', wait for ~5 seconds (the main window can become unresponsive, that's normal). A new window will show up with a network list. Choose desired network, check that it has WPA2 encryption (right now WEP/WPA1 are not implemented), hit 'Start Airmon-ng on BSSID: NETWORK_BSSID'.Step 3: Aireplay-ng window will show up. Set amount of deauth packets to send (default: 10) and wait for station to appear (you can choose if several are found or type in a station mac address manually (format: xx-xx, xx:xx, xxxx)). Hit 'Run deauth (aireplay-ng)'. If you see 'Success' on top of the window, then a handshake was received successfully. If not, try changing the station or amount of packets.P.S: If no stations are found, your signal strength is probably too low. Signal strength can be checked when you select your network in Step 2 and is measured from 0 to 100, higher being better.Step 4: Now, you can close aireplay-ng window. In the main window, press 'Open aircrack-ng', select .cap file (capture file located wherever you set it to in Step 1). Select a wordlist, hit 'Start aircrack-ng / hashcat' and hope for the best ;)P.S If you want to convert your .cap to .22000 manually, visitofficial hashcat conversion website. Or, you can installhcxtoolsand .22000 files will be generated automatically.Command line argumentsCan be found by typingpython3 aircrack-gui.py -h.Application Options:
  --nokill                   Do not run 'airmon-ng check kill'. Will retain internet connection on other devices, but is probably a bad idea.
  --noclean                  Do not clean .csv files generated by airodump-ng when scanning for clients.
  --nolog                    Do not print anything to console.
  --display=DISPLAY          X display to useTodoChange network scanning from nmcli to airmon-ngChangelogChangelog0.0.6 --- Added command line arguments. Files, generated by airodump-ng (.csv, .netxml) are now automatically deleted. Minor log improvements and code formatting.0.0.5 --- Added automatic .cap to .22000 conversion when selecting hashcat andhcxtoolsare installed. Minor ui updates.0.0.4 --- Removed xterm windows (except for aircrack-ng/hashcat), fixed selected station deselecting/changing when updated.0.0.3 --- Hashcat support.0.0.2 --- README.md changes, etc.0.0.1 --- Initial commit.
Package: aircraft_classifiers_jme45
aircraft_classifiers_jme45Package for classifiers for aircraft classification, to be used in several other projects.
Based on FGVCA aircraft data (seehttps://arxiv.org/pdf/1306.5151.pdf), though this code allows one to use a subsetThis is not expected to be of sufficient general interest that other people might want to install it, but for my personal use it is useful to have it on pypi.Installationpip install aircraft_classifiers_jme45
Package: aircraft-list
aircraft_modelsDocumentationInstallationInstallation is easy usingpipand will install all required
libraries.$pipinstallaircraft-listHow to use aircraft_listfromaircraft_listimportaircraft_modelsaircrafts=aircraft_models()It returns a list of dictionaries which can be addressed as follows:foracinaircrafts:ac['manufacturer']#it returns the aircraft manufacturerac['model']#it returns the aircraft modelac['icao']#it returns the icao_code designatorac['type']#it returns the aircraft typeac['engine']#it returns the aircraft engine typeac['engine_number']#it returns the aircraft engine numberac['wake']#it returns the aircraft wake turbulence categoryThe list of dictionary is updated as per ICAO DOC 8643 - Aircraft Type Designator
Package: air-db
air-dbair-dbis a data access layer (DAL) to easily query atmospheric time series datasets from various sources.air-dbdoes not include any database. It is required to install corresponding database to workair-dbproperly.To install sample database:fromairdbimportDatabaseDatabase.install_sample()A database query can be implemented as follows:fromairdbimportDatabasedb=Database('samp',return_type='df')q=db.query(param=['so2','pm10'],city='istanbul',date=['>2010-05-10','<2012-10-07'],month=5)print(q)deldb# close connection to databaseand the output is:param      reg      city              sta                date     value
0      pm10  marmara  istanbul      çatladıkapı 2010-05-10 00:00:00  0.798218
1      pm10  marmara  istanbul      çatladıkapı 2010-05-10 01:00:00  0.946180
2      pm10  marmara  istanbul      çatladıkapı 2010-05-10 02:00:00  0.884385
3      pm10  marmara  istanbul      çatladıkapı 2010-05-10 03:00:00  0.537993
4      pm10  marmara  istanbul      çatladıkapı 2010-05-10 04:00:00  0.136689
...     ...      ...       ...              ...                 ...       ...
16123   so2  marmara  istanbul  şirinevler mthm 2012-05-31 19:00:00  0.697663
16124   so2  marmara  istanbul  şirinevler mthm 2012-05-31 20:00:00  0.615755
16125   so2  marmara  istanbul  şirinevler mthm 2012-05-31 21:00:00  0.489289
16126   so2  marmara  istanbul  şirinevler mthm 2012-05-31 22:00:00  0.385102
16127   so2  marmara  istanbul  şirinevler mthm 2012-05-31 23:00:00  0.039451

[16128 rows x 6 columns]
Package: air-df
base_handler -- tornado handler 基类主要提供参数检查，并初始化get、post、delete、option等方法plugins -- 插件集合1.OrmClient-- Sqlalchemy管理器支持类别：Mysql、Sqlserver、Postgresql。__enter__ 方法返回session 对象2.MongoClient-- MongoDB管理器MongoDB 连接管理器3.RedisClient-- Redis管理器Redis管理器，重写push、set（支持自定义文件夹）、get、delete方法4.LocalFaker-- 随机数据生成工具基于Faker，提供姓名、地址、邮箱、银行、公司、职业、md5、bool、密码、手机、身份证生成工具5.AlchemyEncoder-- sqlalchemy 模型转字典sqlalchemy 模型转字典，特殊类型处理类，使用方式
json.dumps(dict, cls=AlchemyEncoder)6.CryptoHelper-- 加密工具提供md5加密、aes加密、aes解密7.Tools-- 常用工具汇总get_birth_frommake_captchatransform_unixtransfrom_datetimemake_loggerget_excel_datasave_xlsexcel_color
Package: airdialogue-essentials
AirDialogueAirDialogue is a benchmark dataset for goal-oriented dialogue generation
research. This python library contains a collection of tookits that come with the dataset.AirDialogue paperAirDialogue datasetReference implementation:AirDialogue ModelWhat's NewJul 13,2020: Fixed a bug in BLEU evaluation. The current version gives higher BLEU scores. Support evaluation for different roles and add KL-divergence metric (see--infer_metrics).Jul 12,2020: We update theAirDialogue datasetto version v1.1. We fixed typos, misalignment between KB file and dialogue file. Please download and use the new data.PrerequisitesGeneralpython (verified on 3.7)wgetPython Packagestensorflow (tested on 1.15.0)tqdmnltkflask (for visualization)InstallTo install the bleeding edge from github, usepython setup.py installQuick StartScoringThe official scoring function evaluates the predictive results for a trained model and compare it to the AirDialogue dataset.airdialogue score --true_data PATH_TO_DATA_FILE --true_kb PATH_TO_KB_FILE \
    --infer_metrics bleu--infer_metricscan be one of (bleu:all|rouge:all|kl:all|bleu:brief|kl:brief).briefmode gives a single number metric. (bleu|kl) is equivalent to (belu:brief|kl:brief)Context GenerationContext generator generates a valid context-action pair without conversatoin history.airdialogue contextgen \
    --output_data PATH_TO_OUTPUT_DATA_FILE \
    --output_kb PATH_TO_OUTPUT_KB_FILE \
    --num_samples 100PreprocessingAirDialogue proprocess tookie tokenizes dialogue. Preprocess on AirDialogue data requires 50GB of ram to work.
Parameter job_type is a set of 5 bits separted by|, which reqpresentstrain|eval|infer|sp-train|sp-eval.
Parameter input_type can be eithercontextfor context only data ordialoguefor dialogue data with full history.airdialogue prepro \
  --data_file PATH_TO_DATA_FILE \
  --kb_file PATH_TO_KB_FILE \
  --output_dir "./data/airdialogue/" \
  --output_prefix 'train' --job_type '0|0|0|1|0' --input_type contextSimulatorSimulator is built on top of context generator that provides not only a context-action pair but also a full conversation history generated by two templated chatbot agents.airdialogue sim \
    --output_data PATH_TO_OUTPUT_DATA_FILE \
    --output_kb PATH_TO_OUTPUT_KB_FILE \
    --num_samples 100VisualizationVisualization tool displays the content of the raw json file.airdialogue vis --data_path ./data/airdialogue/json/Codalab simulatorTo simulate running the Codalab selfplay workflow, you can run the following script that replicates the bundle workflow
for the competition. This requires amodel/scripts/codalab_selfplay_step.shthat can be run asshscripts/codalab_selfplay_step.shout.txtdata.json[kb.json]More details can be found on theAirdialogue competition tutorial worksheeton Codalab.bashairdialogue/codalab/simulate_codalab.sh<path_to_data>/json/dev_data.json<path_to_data>/json/dev_kb.json<model_folder>
Package: airdropteatest
一、安装（python版本建议3.7以上）pipinstalldubborequests二、升级包pipinstall--upgradedubborequests三、示例获取dubbo服务详情# 导入importdubborequests# 获取dubbo服务详情data=dubborequests.search('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')获取服务下的所有方法# 导入importdubborequests# 获取dubbo服务下的所有方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')# 获取dubbo服务指定的方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService','login')通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口importdubborequestsfromdubborequestsimportConfig# 先配置zookeeper中心地址Config.zookeeper_url_list=['192.168.240.15:2181','192.168.240.15:2182','192.168.240.15:2183']invoke_data={"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# 通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口data=dubborequests.zk_invoke(*invoke_data)Telnet命令测试dubbo接口importdubborequestsinvoke_data={"ip":'xxxx',"port":7777,"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# Telnet命令测试dubbo接口data=dubborequests.telnet_invoke(*invoke_data)
Package: airdroptest
一、安装（python版本建议3.7以上）pipinstalldubborequests二、升级包pipinstall--upgradedubborequests三、示例获取dubbo服务详情# 导入importdubborequests# 获取dubbo服务详情data=dubborequests.search('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')获取服务下的所有方法# 导入importdubborequests# 获取dubbo服务下的所有方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')# 获取dubbo服务指定的方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService','login')通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口importdubborequestsfromdubborequestsimportConfig# 先配置zookeeper中心地址Config.zookeeper_url_list=['192.168.240.15:2181','192.168.240.15:2182','192.168.240.15:2183']invoke_data={"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# 通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口data=dubborequests.zk_invoke(*invoke_data)Telnet命令测试dubbo接口importdubborequestsinvoke_data={"ip":'xxxx',"port":7777,"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# Telnet命令测试dubbo接口data=dubborequests.telnet_invoke(*invoke_data)
Package: airdroptesttea
一、安装（python版本建议3.7以上）pipinstalldubborequests二、升级包pipinstall--upgradedubborequests三、示例获取dubbo服务详情# 导入importdubborequests# 获取dubbo服务详情data=dubborequests.search('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')获取服务下的所有方法# 导入importdubborequests# 获取dubbo服务下的所有方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService')# 获取dubbo服务指定的方法data=dubborequests.list('cn.com.xxx.sso.ehr.api.dubbo.SsoEmpInfoService','login')通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口importdubborequestsfromdubborequestsimportConfig# 先配置zookeeper中心地址Config.zookeeper_url_list=['192.168.240.15:2181','192.168.240.15:2182','192.168.240.15:2183']invoke_data={"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# 通过zookeeper获取服务的ip和端口, Telnet命令测试dubbo接口data=dubborequests.zk_invoke(*invoke_data)Telnet命令测试dubbo接口importdubborequestsinvoke_data={"ip":'xxxx',"port":7777,"service_name":"cn.com.xxxxx.sso.ehr.api.dubbo.SsoEmpInfoService","method_name":"login","data":{"account":"xxxx","password":"xxxx"}}# Telnet命令测试dubbo接口data=dubborequests.telnet_invoke(*invoke_data)
Package: airduct
airductSimple Pipeline Scheduler in PythonLinksGithubDocumentationInstalling$ pip install airductor$ poetry add airductQuickstartCreate a file and put into a folder/python-module.fromairductimportschedule,taskschedule(name='ExampleFlow',run_at='* * * * *',flow=[task('e1f1'),[task('e1f2'),task('e1f3',can_fail=True)],[task('e1f4')]])asyncdefe1f1():print('e1f1 - An async function!')defe1f2():print('e1f2 - Regular functions work too')asyncdefe1f3():print('e1f3')asyncdefe1f4():print('e1f4')Run:$ airduct schedule --path /path/to/folderBy default it uses a sqlite in-memory database. If using the in-memory database, it will also automatically run as a worker, in addition to a scheduler. If you wish to use a non in-memory sqlite database, you will need to also run a worker (could be on same box, or separate) See the documentation for more info.
Package: aireplication
ai-replicationCheckout at:https://pypi.org/manage/project/aireplication/releases/Usagefromaireplication.ultils.dataimportTimeSeriesGenerator,Datasetconfig={"dataset_name":"GYEONGGI2955","features":["Amount of Consumption","Temperature"],"prediction_feature":"Amount of Consumption",# Feature to use for prediction"input_width":168,"output_length":1,"train_ratio":0.9}dataset=Dataset(dataset_name=config["dataset_name"])# data = dataset.dataloader.export_a_single_sequence()data=dataset.dataloader.export_the_sequence(config["features"])print("Building time series generator...")tsf=TimeSeriesGenerator(data=data,config=config,normalize_type=1,shuffle=False)# Get modelmodel=get_model(model_name=args.model_name,config=config)# Train modelhistory=model.fit(x=tsf.data_train[0],# [number_recoder, input_len, number_feature]y=tsf.data_train[1],# [number_recoder, output_len, number_feature]validation_data=tsf.data_valid)List of dataset is availableconfig1 = {"dataset_name":"GYEONGGI2955","features":["AmountofConsumption","Temperature"],"prediction_feature":"AmountofConsumption",# Feature to use for prediction"input_width":168,"output_length":1,"train_ratio":0.9}config2 = {"dataset_name":"CNU_ENGINEERING_7","features":["temperatures","humidity","pressure","energy"]# Features to use for trainingprediction_feature:"energy",# Feature to use for prediction"input_width":168,"output_length":1,"train_ratio":0.9}Publishing the packagepipinstalltwine
pythonsetup.pysdist
twineuploaddist/*- Note: Testing case:twineupload--repositorytestpypidist/*
Package: airflint
airflintEnforce Best Practices for all your Airflow DAGs. ⭐⚠️airflint is still in alpha stage and has not been tested with real world Airflow DAGs. Please report any issues you face viaGitHub Issues, thank you. 🙏🧑‍🏫 RulesUse function-level imports instead of top-level imports[^1][^2] (seeTop level Python Code)Use jinja template syntax instead ofVariable.get(seeAirflow Variables)[^1]: There is a PEP forLazy Importstargeted to arrive in Python 3.12 which would supersede this rule.[^2]: To remove top-level imports after runningUseFunctionLevelImportsrule, use a tool such asautoflake.based on officialBest PracticesRequirementsairflint is tested with:Main version (dev)Released version (0.3.1-alpha)Python3.9, 3.10, 3.11.0-alpha - 3.11.03.9, 3.10Apache Airflow>= 2.0.0>= 2.3.0🚀 Get startedTo install it fromPyPIrun:pip install airflintNOTE:It is recommended to install airflint into your existing airflow environment with all your providers included. This wayUseJinjaVariableGetrule can detect alltemplate_fieldsand airflint works as expected.Then just call it like this:pre-commitAlternatively you can add the following repo to yourpre-commit-config.yaml:-repo:https://github.com/feluelle/airflintrev:v0.3.1-alphahooks:-id:airflintargs:["-a"]# Use -a to apply the suggestionsadditional_dependencies:# Add all package dependencies you have in your dags, preferable with version spec-apache-airflow-apache-airflow-providers-cncf-kubernetesTo complete theUseFunctionlevelImportsrule, please add theautoflakehook after theairflinthook, as below:-repo:https://github.com/pycqa/autoflakerev:v1.4hooks:-id:autoflakeargs:["--remove-all-unused-imports","--in-place"]This will remove unused imports.❤️ ContributingI am looking for contributors who are interested in..testing airflint with real world Airflow DAGs and reporting issues as soon as they face themoptimizing the ast traversing for existing rulesadding new rules based on best practices or bottlenecks you have experienced during Airflow DAGs authoringdocumenting about what is being supported in particular by each ruledefining supported airflow versions i.e. some rules are bound to specific Airflow features and versionFor questions, please don't hesitate to open a GitHub issue.
Package: airflow-add-ons
# airflow-add-onsAirflow utilities to extend Airflow operators and connectors## Where to get it
The source code is currently hosted on GitHub at:https://github.com/pualien/airflow-add-onsBinary installers for the latest released version are available at the [Python
package index](https://pypi.org/project/airflow-add-ons/).`sh pip installairflow-add-ons`
Package: airflow-ad-query
Airflow Data Query PluginA user-friendly data query tool for Apache Airflow. With this plugin, you can execute SQL queries against databases
connected in your Airflow environment and view the results directly on the Airflow UI.FeaturesConnect to databases using existing Airflow connections.Execute SQL queries and view results directly on the Airflow UI.Save query results as CSV files.UsageInstall the plugin using pip:pipinstallairflow-data-queryAdd the plugin to the Airflow plugins folder:cp-rairflow_data_query$AIRFLOW_HOME/pluginsNavigate to the Airflow UI,and click on "Data Query".Connect to a database using an existing Airflow connection and start executing your SQL queries.ThanksApache AirflowBase on thisLicenseThis project is licensed under the Apache 2.0 License.
Package: airflow-aggua-plugin
Airflow Aggua API - PluginApache Airflow plugin that exposes aggua secure API endpoints similar to the officialAirflow API (Stable) (1.0.0), providing richer capabilities. Apache Airflow version 2.1.0 or higher is necessary.Requirementsapache-airflowmarshmallowInstallationpython3-mpipinstallairflow-aggua-apiAuthenticationAirflow Aggua API plugin uses the same auth mechanism asAirflow API (Stable) (1.0.0). So, by default APIs exposed via this plugin respect the auth mechanism used by your Airflow webserver and also complies with the existing RBAC policies. Note that you will need to pass credentials data as part of the request. Here is a snippet from the official docs when basic authorization is used:curl-XPOST'http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/dags/{dag_id}?update_mask=is_paused'\-H'Content-Type: application/json'\--user"username:password"\-d'{"is_paused": true}'Using the Custom APIAll the supported endpoints are exposed in the below format:http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/aggua/{ENDPOINT_NAME}Following are the names of endpoints which are currently supported.serializedDagsserializedDagsDescription:Get the serialized representation of a DAG.Endpoint:http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/aggua/serializedDagsMethod:GETGET request query parameters :limit (optional) - number - The number of items to return. default = 10.offset (optional) - number - The number of items to skip before starting to collect the result set.Endpoint:http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/aggua/serializedDags/{dag_id}Method:GETGet request path parameter:dag_id - string - the DAG ID.
Package: airflow-alt-ldap
An alternative LDAP backend for airflow=======================================The default LDAP backend works with OpenLDAP if the memberOf overlay isactivated (see http://www.openldap.org/doc/admin24/overlays.html#Reverse%20Group%20Membership%20Maintenance)I.e., users must present the `memberOf` attribute to know what group theybelong to. If your LDAP server only has groups with `memberUid` (or anyother key like `member`) listing the users belonging to the group, thenyou need something different. This is what this module attemps to provide.Installation============Using pip:```pip install airflow-alt-ldap```Configuration=============Activate authentication via this LDAP backend in `airflow.cfg` config:```[webserver]authenticate = Trueauth_backend = airflow-alt-ldap.auth.backend.ldap_auth```Then you can configure that module using the following keys (example conf to be adapted):```uri = ldap://localhost:389user_basedn = ou=people,dc=nexmo,dc=comuser_filter = uid=*user_name_attr = uidgroup_basedn = ou=groups,dc=nexmo,dc=comgroup_member_attr = memberUidgroup_filter = cn=*superuser_filter = cn=admingroupdata_profiler_filter = cn=datagroupbind_user = uid=binddn,dc=example,dc=combind_password = MyAwesomePassword# cacert = /etc/ca/ldap_ca.crt# Set search_scope to one of them:  BASE, LEVEL , SUBTREE# Set search_scope to SUBTREE if using Active Directory, and not specifying an Organizational Unitsearch_scope = SUBTREE```
Package: airflow-api-plugin
Airflow Aggua API - PluginApache Airflow plugin that exposes aggua secure API endpoints similar to the officialAirflow API (Stable) (1.0.0), providing richer capabilities. Apache Airflow version 2.1.0 or higher is necessary.Requirementsapache-airflowmarshmallowInstallationpython3-mpipinstallairflow-aggua-apiAuthenticationAirflow Aggua API plugin uses the same auth mechanism asAirflow API (Stable) (1.0.0). So, by default APIs exposed via this plugin respect the auth mechanism used by your Airflow webserver and also complies with the existing RBAC policies. Note that you will need to pass credentials data as part of the request. Here is a snippet from the official docs when basic authorization is used:curl-XPOST'http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/dags/{dag_id}?update_mask=is_paused'\-H'Content-Type: application/json'\--user"username:password"\-d'{"is_paused": true}'Using the Custom APIAll the supported endpoints are exposed in the below format:http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/aggua/{ENDPOINT_NAME}Following are the names of endpoints which are currently supported.serializedDagsserializedDagsDescription:Get the serialized representation of a DAG.Endpoint:http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/aggua/serializedDagsMethod:GETGET request query parameters :limit (optional) - number - The number of items to return. default = 10.offset (optional) - number - The number of items to skip before starting to collect the result set.Endpoint:http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/aggua/serializedDags/{dag_id}Method:GETGet request path parameter:dag_id - string - the DAG ID.
Package: airflow-arcgis-plugin
airflow-arcgisSimple hooks and operators for exporting data from ArcGIS.Import PostgreSQL table data into ArcGIS feature layer or perform incremental updates.WIPFeaturesPostgresToArcGISOperator- exporting/syncing a PostgreSQL table to ArcGISInstallUsing pip:pip3installairflow-arcgis-pluginUsageCreate a connection of typeHTTPin Airflow namedhttp_agoto store your ArcGIS base url (e.g.https://detroitmi.maps.arcgis.com/), username and password.You can also pass in an override connection name in your DAG definition.This plugin is published as a pip package. Refer to the example DAG for available parameters.