Cimis Python API Docs | dltHub

Cimis is a REST API providing California Irrigation Management Information System (CIMIS) weather station and Spatial CIMIS evapotranspiration (ETo) and solar radiation data (daily/hourly) for California. The REST API base URL is https://et.water.ca.gov/api and all requests require an application key (appKey) query parameter.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Cimis data in under 10 minutes.

---

What data can I load from Cimis?

Here are some of the endpoints you can load from Cimis:

---

How do I authenticate with the Cimis API?

Registered CIMIS users receive an application key (appKey). Include appKey as a query string parameter (appKey=YOUR-APP-KEY) on every request. Responses default to JSON when Accept: application/json is set or when using the JSON examples.

1. Get your credentials

1. Register an account on the CIMIS site (https://wwwcimis.water.ca.gov or the CIMIS Home/Register page). 2) Log in to your account. 3) Open the account Edit/Manage page and scroll to the end. 4) Click the 'Get AppKey' button; the system will issue an AppKey for use in API requests.

2. Add them to .dlt/secrets.toml

Copy
[sources.cimis_source]
app_key = "YOUR_APP_KEY_HERE"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

Copy
dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Cimis API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

Copy
python cimis_pipeline.py

If everything is configured correctly, you'll see output like this:

Copy
Pipeline cimis_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cimis_data
The duckdb destination used duckdb:/cimis.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline cimis_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

---

Python pipeline example

This example loads data and station from the Cimis API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def cimis_source(app_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://et.water.ca.gov/api",
            "auth": {
                "type": "api_key",
                "app_key": app_key,
            },
        },
        "resources": [
            {"name": "data", "endpoint": {"path": "api/data", "data_selector": "Data.Providers[].Records"}},
            {"name": "station_list", "endpoint": {"path": "api/station", "data_selector": "Stations"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cimis_pipeline",
        destination="duckdb",
        dataset_name="cimis_data",
    )
    load_info = pipeline.run(cimis_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

---

How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("cimis_pipeline").dataset()
sessions_df = data.data.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM cimis_data.data LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("cimis_pipeline").dataset()
data.data.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Cimis data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

---

Troubleshooting

Authentication failures

If you receive HTTP 403 or error code ERR1006 (INVALID APP KEY), verify the appKey query parameter is present and correct. Ensure you are using the issued AppKey string exactly and that it's not expired or revoked.

Not Found / target errors

HTTP 404 responses may be returned with ERR1019 (STATION NOT FOUND), ERR1031 (UNSUPPORTED ZIP CODE), or ERR1034 (COORD NOT IN CA) when targets contain invalid station numbers, unsupported zip codes, or coordinates outside California. Use the /api/station and zipcodes endpoints to validate targets before requesting data.

Bad request / parameter errors

HTTP 400 responses include ERR1025 (INVALID COORDINATE), ERR2006 (INVALID TARGET), ERR1010/ERR1011/ERR1012 (date-related faults), ERR1032 (INVALID UNIT OF MEASURE) and ERR1035 (DATA ITEM NOT FOUND). Check date ranges (no dates before 1982-06-07 and no future dates), target formatting (do not mix station/zip/coordinate/address types), unitOfMeasure values ('E' or 'M'), and valid dataItems.

Data volume limits and hourly/spatial quirks

ERR2112 (DATA VOLUME VIOLATION) occurs when a request exceeds allowed data size; reduce date range or number of targets. Spatial CIMIS (SCS) does not support hourly data — requests mixing SCS targets with hourly dataItems will error (ERR2007/HLY COORDINATES FAULT). When querying zip codes, prioritizeSCS=Y or N affects whether SCS or WSN provides results; responses may include multiple Providers with their own Records arrays.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

Copy
dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack