### Run All Examples

Source: https://github.com/gribstream/python-client/blob/main/README.md

Execute all provided example scripts after installing the client from a source checkout. Some examples require an API key.

```bash
python examples/timeseries_rows.py
python examples/runs_forecast_aging.py
python examples/as_of_backtest.py
python examples/json_response.py
python examples/ensemble_members.py
python examples/shared_parameter_timeseries.py
python examples/multimodel_shared_compare.py
python examples/grid_filter.py
python examples/ndjson_stream.py
python examples/selector_lookup_to_query.py
python examples/payload_builder.py
python examples/catalog_discovery.py
python skewtlogp.py
```

--------------------------------

### Quickstart: Query Timeseries Data

Source: https://github.com/gribstream/python-client/blob/main/README.md

A basic example demonstrating how to query timeseries forecast data for a specific location and time range. It shows how to define coordinates and variables with aliases.

```python
from gribstream import GribStreamClient

with GribStreamClient() as client:
    rows = client.timeseries(
        dataset="gfs",
        from_time="2026-04-04T00:00:00Z",
        until_time="2026-04-04T06:00:00Z",
        coordinates=[client.coordinate(29.7604, -95.3698, "Houston")],
        variables=[client.variable("TMP", "2 m above ground", alias="temp_k")],
        min_lead_time="0h",
        max_lead_time="24h",
    )

for row in rows[:3]:
    print(row)
```

--------------------------------

### Install Optional Plotting Dependencies

Source: https://github.com/gribstream/python-client/blob/main/README.md

Install optional dependencies for plotting, required for examples like the Skew-T visualization. Use this command after checking out from source.

```bash
pip install -e '.[examples]'
```

--------------------------------

### Run Skew-T Example with Output

Source: https://github.com/gribstream/python-client/blob/main/README.md

Execute the Skew-T example script and specify an output file path for the generated PNG image. This example does not require authentication.

```bash
python skewtlogp.py --output out/skewt-houston.png
```

--------------------------------

### Install GribStream Python Client (PyPI)

Source: https://github.com/gribstream/python-client/blob/main/README.md

Install the GribStream Python client from PyPI using pip. This is the standard installation method for published versions.

```bash
pip install gribstream
```

--------------------------------

### Install GribStream Python Client (Source Checkout)

Source: https://github.com/gribstream/python-client/blob/main/README.md

Install the GribStream Python client from a source checkout. This involves setting up a virtual environment and installing in editable mode.

```bash
python -m venv .venv
source .venv/bin/activate
pip install -e .
```

--------------------------------

### Query with Exact Selectors

Source: https://github.com/gribstream/python-client/blob/main/README.md

Example of querying forecast data using an exact selector obtained from `get_dataset_parameter`. This ensures precise data retrieval.

```python
selector = parameter["variations"][0]["selector"]
selector["alias"] = "temp_k"

rows = client.timeseries(
    dataset="ifsoper",
    from_time="2026-04-04T00:00:00Z",
    until_time="2026-04-04T12:00:00Z",
    coordinates=[client.coordinate(51.5072, -0.1276, "London")],
    variables=[selector],
)
```

--------------------------------

### Request Streaming NDJSON Response Format

Source: https://github.com/gribstream/python-client/blob/main/README.md

Example of requesting forecast data in a streaming NDJSON format by setting `accept=NDJSON` and `stream=True`. This is efficient for large datasets.

```python
from gribstream import NDJSON

for row in client.timeseries(..., accept=NDJSON, stream=True):
    print(row)
```

--------------------------------

### Query Runs Data

Source: https://github.com/gribstream/python-client/blob/main/README.md

Example of querying all model runs within a specified time window using the `runs` method. This is useful for analyzing forecast aging or run-to-run variations.

```python
rows = client.runs(
    dataset="gfs",
    forecasted_from="2026-04-04T00:00:00Z",
    forecasted_until="2026-04-04T06:00:00Z",
    coordinates=[client.coordinate(29.7604, -95.3698, "Houston")],
    variables=[client.variable("TMP", "2 m above ground", alias="temp_k")],
    min_lead_time="0h",
    max_lead_time="18h",
)
```

--------------------------------

### Request JSON Response Format

Source: https://github.com/gribstream/python-client/blob/main/README.md

Example of requesting forecast data in JSON format by setting the `accept` parameter to `JSON`. This is an alternative to the default CSV parsing.

```python
from gribstream import JSON

rows = client.timeseries(..., accept=JSON)
```

--------------------------------

### Query Timeseries Data (Runs vs Timeseries)

Source: https://github.com/gribstream/python-client/blob/main/README.md

Demonstrates querying the freshest available run for each valid time using the `timeseries` method. This is suitable for general forecast lookups.

```python
rows = client.timeseries(
    dataset="gfs",
    from_time="2026-04-04T00:00:00Z",
    until_time="2026-04-04T06:00:00Z",
    coordinates=[client.coordinate(29.7604, -95.3698, "Houston")],
    variables=[client.variable("TMP", "2 m above ground", alias="temp_k")],
)
```

--------------------------------

### Set GribStream API Key

Source: https://github.com/gribstream/python-client/blob/main/README.md

Set your GribStream API key as an environment variable. This token is required for authentication when querying forecast data.

```bash
export GRIBSTREAM_API_KEY='YOUR_TOKEN_HERE'
```

--------------------------------

### Resolve and Use Shared Parameters

Source: https://github.com/gribstream/python-client/blob/main/README.md

Demonstrates how to resolve a shared parameter like 'wind_speed_10m' for a specific dataset and then use it in a `timeseries` query. This simplifies multi-model queries.

```python
with GribStreamClient() as client:
    wind_request = client.resolve_shared_parameter(
        "wind_speed_10m",
        dataset="ifsoper",
        alias="wind_mps",
    )

    rows = client.timeseries(
        dataset="ifsoper",
        from_time="2026-04-04T00:00:00Z",
        until_time="2026-04-04T12:00:00Z",
        coordinates=[client.coordinate(51.5072, -0.1276, "London")],
        variables=wind_request["variables"],
        expressions=wind_request.get("expressions"),
    )
```

--------------------------------

### Discover Dataset Parameters

Source: https://github.com/gribstream/python-client/blob/main/README.md

Use the `get_dataset_parameter` helper to find exact selectors for parameters within a dataset. This is useful for ensuring correct parameter names and levels.

```python
from gribstream import GribStreamClient

with GribStreamClient() as client:
    parameter = client.get_dataset_parameter("ifsoper", "2t")
    for variation in parameter["variations"]:
        print(variation["selector"])
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.