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add transsect tool function
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‎App/functions/delta-dtm-transsect-plot/.gitignore

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data/

‎App/functions/delta-dtm-transsect-plot/Dockerfile

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FROM python:3.12
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ENV PYTHONUNBUFFERED=1
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WORKDIR /app/
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# Install uv
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# Ref: https://docs.astral.sh/uv/guides/integration/docker/#installing-uv
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COPY --from=ghcr.io/astral-sh/uv:0.5.11 /uv /uvx /bin/
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# Place executables in the environment at the front of the path
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# Ref: https://docs.astral.sh/uv/guides/integration/docker/#using-the-environment
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ENV PATH="/app/.venv/bin:$PATH"
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# Compile bytecode
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# Ref: https://docs.astral.sh/uv/guides/integration/docker/#compiling-bytecode
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ENV UV_COMPILE_BYTECODE=1
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# uv Cache
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# Ref: https://docs.astral.sh/uv/guides/integration/docker/#caching
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ENV UV_LINK_MODE=copy
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RUN apt-get update && apt-get install -y libgdal-dev libgl1
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# Install dependencies
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# Ref: https://docs.astral.sh/uv/guides/integration/docker/#intermediate-layers
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RUN --mount=type=cache,target=/root/.cache/uv \
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--mount=type=bind,source=uv.lock,target=uv.lock \
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--mount=type=bind,source=pyproject.toml,target=pyproject.toml \
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uv sync --frozen --no-install-project
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ENV PYTHONPATH=/app
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COPY ./pyproject.toml ./uv.lock /app/
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COPY ./src /app/src
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# Sync the project
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# Ref: https://docs.astral.sh/uv/guides/integration/docker/#intermediate-layers
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync
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CMD ["uvicorn", "src.main:app", "--host", "0.0.0.0", "--port", "8000"]

‎App/functions/delta-dtm-transsect-plot/README.md

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# Delta DTM Line Plot
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A FastAPI-based web service that generates elevation profile plots along transect lines using the Deltares Digital Terrain Model (Delta DTM) dataset.
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## Overview
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This application provides an API endpoint that accepts a LineString geometry and returns an elevation profile plot along that line. It uses the Delta DTM dataset stored in Azure Blob Storage to extract elevation data.
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![Example Elevation Profile](./images/transect_plot.png)
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## For Scientists
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### How It Works
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The core functionality is implemented in `src/transsect_plot.py` and follows these steps:
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1. **Data Selection**: When given a LineString geometry, the application identifies which Delta DTM tiles intersect with the line.
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2. **Data Retrieval**: The relevant raster tiles are fetched from Azure Blob Storage.
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3. **Data Processing**:
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- The raster tiles are merged into a single data array
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- Elevation values are sampled along the transect line
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- Distances along the transect are calculated in meters
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4. **Visualization**: A matplotlib figure is generated showing the elevation profile.
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### Key Functions
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- `get_intersecting_item_ids()`: Identifies which Delta DTM tiles intersect with the input line
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- `get_datasets()`: Asynchronously fetches the raster data for the intersecting tiles
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- `merge_and_mask_datasets()`: Combines multiple raster datasets into a single dataset
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- `transsect()`: Samples elevation values along the transect line
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- `get_distances()`: Calculates distances along the transect in meters
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- `create_profile_plot()`: Generates the matplotlib figure
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- `plot_transsect()`: Main function that orchestrates the entire process
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### Modifying the Scientific Logic
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If you want to modify how the elevation data is processed or visualized:
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1. The sampling density can be adjusted in the `transsect()` function by changing the `n_samples` parameter
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2. The plot appearance can be customized in the `create_profile_plot()` function
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3. The distance calculation in `get_distances()` uses a simple approximation - you may want to implement a more accurate geodesic calculation
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## For Developers
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### API Endpoints
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- `POST /line-plot`: Accepts a GeoJSON LineString and returns a PNG image of the elevation profile
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- `GET /`: Simple welcome message
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### Installation & Local Development
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#### Prerequisites
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- Python 3.11+
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- [uv](https://github.com/astral-sh/uv) for dependency management
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#### Setup
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1. Clone the repository
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2. Install dependencies:
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```bash
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uv sync
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```
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3. Run the application:
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```bash
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uv run src/main.py
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```
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4. Access the API documentation at http://localhost:8000/docs
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### Docker Deployment
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The application includes a Dockerfile for containerized deployment:
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```bash
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# Build the Docker image
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docker build -t delta-dtm-line-plot .
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# Run the container
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docker run -p 8000:8000 delta-dtm-line-plot
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```
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### Environment Variables
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- No specific environment variables are required for basic operation
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- The application uses hardcoded URLs for Azure Blob Storage access
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### Performance Considerations
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- The application uses asynchronous I/O for fetching raster data
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- For high-traffic deployments, consider implementing caching for frequently requested transects
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- Memory usage scales with the number and size of intersecting raster tiles
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‎App/functions/delta-dtm-transsect-plot/images/transect_plot.png

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‎App/functions/delta-dtm-transsect-plot/pyproject.toml

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[project]
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name = "delta-dtm-line-plot"
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version = "0.1.0"
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description = "Add your description here"
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# readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"aiohttp>=3.11.6",
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"azure-storage-blob>=12.24.1",
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"duckdb>=1.1.3",
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"fastapi>=0.115.8",
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"fastparquet>=2024.11.0",
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"geopandas>=1.0.1",
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"jupyter>=1.1.1",
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"matplotlib>=3.10.0",
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"pyarrow>=19.0.1",
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"pydantic-shapely>=1.0.0a4",
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"pystac-client>=0.8.5",
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"rioxarray>=0.18.2",
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"uvicorn>=0.34.0",
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"xarray>=2024.10.0",
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]
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["src"]
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[dependency-groups]
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dev = [
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"mypy>=1.15.0",
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"pytest>=8.3.4",
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]

‎App/functions/delta-dtm-transsect-plot/src/main.py

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import io
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import traceback
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from typing import Annotated
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import fastapi
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import matplotlib
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matplotlib.use("AGG")
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import matplotlib.pyplot as plt
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import uvicorn
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from fastapi import BackgroundTasks, Body
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from fastapi.responses import Response
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from pydantic_shapely import FeatureBaseModel, GeometryField
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from shapely import LineString
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from src.transsect_plot import plot_transsect
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app = fastapi.FastAPI(
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title="Delta DTM Line Plot API",
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description="API for generating elevation profile plots along a transect line",
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version="1.0.0",
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)
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class LineStringModel(FeatureBaseModel, geometry_field="line"):
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line: Annotated[LineString, GeometryField()]
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@app.post(
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"/line-plot",
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response_class=Response,
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summary="Generate an elevation profile plot along a transect line",
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description="Takes a LineString geometry and returns a plot of the elevation profile along that line",
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responses={
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200: {
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"content": {"image/png": {}},
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"description": "A PNG image of the elevation profile plot",
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},
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500: {
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"description": "Internal server error",
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},
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},
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)
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async def line_plot(
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line_string: Annotated[
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LineStringModel.GeoJsonDataModel, # type: ignore
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Body(
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example={
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"type": "Feature",
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"geometry": {
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"type": "LineString",
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"coordinates": [[4.9, 52.37], [5.12, 52.09]],
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},
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"properties": {},
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}
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),
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],
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background_tasks: BackgroundTasks,
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) -> Response:
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try:
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# Use the existing plot_transsect function to generate the figure
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line: LineString = line_string.geometry.to_shapely()
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fig = await plot_transsect(line)
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# Create an in-memory bytes buffer
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buf = io.BytesIO()
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# Save the figure to the in-memory buffer
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fig.savefig(buf, format="png")
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# Close the figure to free up resources
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plt.close(fig)
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# Seek to the beginning of the buffer
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buf.seek(0)
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# Return the image as a response with appropriate headers
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background_tasks.add_task(buf.close)
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background_tasks.add_task(plt.close, fig)
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return Response(
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content=buf.getvalue(),
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media_type="image/png",
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headers={"Content-Disposition": 'attachment; filename="transect_plot.png"'},
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)
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except Exception as e:
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# Log the error
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print("Error generating plot:")
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print(traceback.format_exc())
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# Raise an HTTP exception
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raise fastapi.HTTPException(
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status_code=500,
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detail=f"Error generating plot: {str(e)}",
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)
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@app.get("/", include_in_schema=False)
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async def root():
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return {
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"message": "Welcome to the Delta DTM Line Plot API. Use /line-plot endpoint to generate elevation profile plots."
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}
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if __name__ == "__main__":
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uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)

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