Setup and Usage

To use the Hyperion API, start by setting up the client with an access token. Then, define a payload to specify your query parameters and call the appropriate method to fetch the data.

The examples below show how to configure the client, apply filters to refine your request, and handle the results efficiently.

⮞ Setup the client.

from synmax.hyperion import HyperionApiClient, ApiPayload
access_token = 'your access token goes here'
client = HyperionApiClient(access_token=access_token)

For a more detailed guide on setting up the Hyperion client, please see the Quickstart page.

⮞ Use parameters to narrow down your request.

payload = ApiPayload(start_date='2021-05-01', end_date='2022-06-25', sub_region=['S LA', 'West - TX'])

Tip

Narrow down your request to receive a faster response. Using no parameters will fetch the entire dataset which may take a very long time.

⮞ Call the function.

result_df = client.well_completion(payload)

Endpoint	Function Name
completions	well_completion(payload)
dailyfrackedfeet	daily_fracked_feet(payload)
dailyproduction	daily_production(payload)
ducsbyoperator	ducs_by_operator(payload)
fraccrews	frac_crews(payload)
longtermforecast	long_term_forecast(payload)
operatorclassification	fetch_operator_classification()
pipelinescrapes	pipeline_scrapes(payload)
pipelinescrapestatus	fetch_pipeline_scrape_status()
productionbywell	production_by_well(payload)
regions	fetch_regions()
rigs	rigs(payload)
shorttermforecast	short_term_forecast(payload)
shorttermforecastdeclines	short_term_forecast_declines(payload)
shorttermforecasthistory	short_term_forecast_history(payload)
shorttermforecasthistorydates	short_term_forecast_history_dates()
shorttermforecastaggregatedhistory	short_term_forecast_aggregated_history(payload)
TILs	fetch_tils()
wells	wells(payload)

⮞ Print the results or save them for later.

# Print the entire response as a string.
print(result_df.to_string())

# Create a CSV file from the output.
result_df.to_csv('output.csv')

# Print the number of items per column.
print(result_df.count())

For more information on outputting to different formats, please see the pandas DataFrame documentation.