# Quickstart

Get started with Cecil in a few steps.

# Sign up

Run the following command in your terminal and follow the prompt. You will receive an email with instructions to generate your API key. By signing up, you agree to our [Dataset Pricing](https://docs.cecil.earth/datasets) and [Terms](https://drive.google.com/file/d/14nmnpIuONJsv1rRAsOuCf5lnNypCSqxk/).

```bash
/bin/bash -c "$(curl -fsSL https://cecil.earth/sign-up.sh)"
```

**What's included with your new account**

Your new account gives you immediate access to 30+ free geospatial datasets — including Hansen Global Forest Change, JRC Forest Cover, WorldPop, WRI Aqueduct, and more. You can create up to 100 subscriptions covering up to 20,000 hectares total.

Commercial datasets (such as Planet Forest Carbon, Kanop Monitoring, and Chloris Biomass) require a verified account. To get verified and unlock commercial datasets, contact us at [support@cecil.earth](mailto:support@cecil.earth) or visit the [billing page](https://docs.cecil.earth/billing).

# Install the SDK

Install the [SDK](https://docs.cecil.earth/sdk) in your project [virtual environment](https://docs.python.org/3/tutorial/venv.html). Python ≥ 3.10 is required.

```bash
pip install cecil
```

Configure the [SDK](https://docs.cecil.earth/sdk) by setting the `CECIL_API_KEY` environment variable. Make sure to store your API key in an encrypted vault or secrets manager. Never store API keys in code or plain/text files.

```bash
# Linux and macOS
export CECIL_API_KEY="my-api-key"

# Windows
set CECIL_API_KEY=my-api-key
```

# Create an AOI

Create an area of interest (AOI) using the `geometry` object of a GeoJSON in `EPSG:4326` coordinate reference system (CRS). Learn more about the AOI specification in the [SDK](https://docs.cecil.earth/sdk).

```python
import cecil

client = cecil.Client()

aoi = client.create_aoi(
    external_ref="Chaco Region",
    geometry={
        "type": "Polygon",
        "coordinates": [[
            [-60.3095, -20.9585],
            [-60.3095, -20.99875],
            [-60.2665, -20.99875],
            [-60.2665, -20.9585],
            [-60.3095, -20.9585],
        ]]
    }
)

print(aoi)
```

# Acquire a dataset

Create a subscription for your AOI using a `dataset_id` from our available [datasets](https://docs.cecil.earth/datasets). This step runs in the background and **may take minutes, hours, or days** to process depending on the data provider.

```python
import cecil

client = cecil.Client()

subscription = client.create_subscription(
    external_ref="Hansen Global Forest Change - Chaco Region",
    aoi_id="my-aoi-id",
    dataset_id="9659ec1d-7091-4f8b-9db5-e9fe07d2f508",
)

print(subscription)
```

# Analyse your dataset

Install [matplotlib](https://matplotlib.org/) for data visualisation.

```bash
pip install matplotlib
```

Visualise forest loss over time. You may get an **empty dataset** when you call `load_xarray()` which means the previous step is still processing. Please wait for provider processing accordingly.

```python
import cecil
import matplotlib.pyplot as plt

client = cecil.Client()

ds = client.load_xarray("my-subscription-id")

# Pixel values above 0 indicate forest loss years.
forest_loss = ds["loss_year"].where(ds["loss_year"] > 0)

# Convert values from 1-24 to 2001-2024.
forest_loss_years = forest_loss + 2000

forest_loss_years.plot(cmap="plasma").colorbar.set_ticks(
    [2001, 2005, 2010, 2015, 2020, 2024]
)

plt.gca().set_title("Hansen Global Forest Change - Chaco Region")
plt.gca().set_aspect("equal")
plt.gca().set_facecolor("#101010")

plt.show()
```

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