add readme and examples

README.md

@@ -1,3 +1,126 @@
----
-license: etalab-2.0
----
+---
+license: etalab-2.0
+tags:
+- climate
+- weather
+- forecasting
+pretty_name: 5 Minutes Radar Rainfall over mainland France
+size_categories:
+- 100K<n<1M
+---
+
+# 📡 5 Minutes Radar Rainfall over mainland France
+
+**Short name**: `radar-rainfall`
+**Source**: Météo-France
+**License**: Etalab 2.0 (Open License 2.0)
+
+## 🗂️ Dataset Summary
+
+This dataset provides high-resolution radar-based rainfall accumulation data over mainland France. Each file contains the rainfall accumulation (in hundredths of millimeters) over the **past 5 minutes**, with a **spatial resolution of 1 km**.
+
+The data is derived from the radar precipitation mosaic produced by **Météo-France**.
+
+* **Temporal resolution**: every 5 minutes
+* **Spatial resolution**: 1 km
+* **Grid size**: (1536, 1536)
+* **Coverage**: France mainland
+* **Projection**: Data is in a Stereographic projection and not in a regular Latitude/Longitude projection 🚨
+* **Period covered**: currently 2020–2025 (will be extended back to 2015 in future versions)
+* **Data format**: `.npz` (NumPy compressed archive)
+* **Total size**: approx. 15 GB/year (\~100,000 files/year)
+
+## 📁 Dataset Structure
+
+Files are organized in folders by year. Each `.npz` file corresponds to a single 5-minute time step.
+
+```bash
+radar-rainfall/
+├── 2020/
+│   ├── 202001010000.npz
+│   ├── 202001010005.npz
+│   └── ...
+├── 2021/
+│   └── ...
+└── ...
+```
+
+Each `.npz` file contains a 2D NumPy array representing the rainfall accumulation over the French territory during that 5-minute interval. Units are **hundredths of millimeters**.
+
+## 🧪 Example Usage
+
+To open and visualize a single `.npz` file:
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load the file
+data = np.load("2020/202004201700.npz")  # Adjust path as needed
+rain = data['arr_0']  # The array is stored under 'arr_0'
+print(rain.shape)  # Shape = (1536, 1536)
+
+# Negative values indicate no data, replace them with NaN:
+rain = np.where(rain < 0, np.nan, rain)
+
+# Visualize
+plt.imshow(rain, cmap="Blues")
+plt.colorbar(label="Rainfall (x0.01 mm / 5min)")
+plt.title("Rainfall Accumulation – 2020-04-20 17:00 UTC")
+plt.savefig("rainfall_20200420_1700.png")
+```
+
+![basic usage](rainfall_20200420_1700_basic.png)
+
+The provided `plots.py` module contains some utilities to make nice maps in a regular lat/lon grid.
+To convert data to mm/h and plot a beautiful map:
+
+```python
+import numpy as np
+from plots import plot_map_rain
+
+data = np.load("2020/202004201700.npz")
+rain = data['arr_0']
+rain = np.where(rain < 0, np.nan, rain)
+rain = rain / 100  # Convert from mm10-2 to mm
+rain = rain * 60 / 5  # Convert from mm in 5 minutes to mm/h
+
+plot_map_rain(
+    rain,
+    title="Rainfall Rate – 2020-04-20 17:00 UTC",
+    path="rainfall_20200420_1700_map.png"
+)
+```
+
+![nice map](rainfall_20200420_1700_map.png)
+
+## 🔍 Potential Use Cases
+
+* Precipitation **nowcasting** and short-term forecasting
+* Training datasets for **machine learning** or **deep learning** models in meteorology
+* **Visualization** and analysis of rainfall patterns
+* Research in hydrology, flood risk prediction, and climate science
+
+## 📜 Licensing
+
+The dataset is made available under the **Etalab Open License 2.0**, which permits free reuse, including for commercial purposes, provided proper attribution is given.
+
+More information: [https://www.etalab.gouv.fr/licence-ouverte-open-licence/](https://www.etalab.gouv.fr/licence-ouverte-open-licence/)
+
+## 📦 Citation
+
+If you use this dataset in your work, please cite it as:
+
+```
+@misc{radar_rainfall_france,
+  title        = {5 Minutes Radar Rainfall over French Mainland Territory},
+  author       = {Météo-France},
+  year         = {2025},
+  howpublished = {\url{https://huggingface.co/datasets/meteofrance/radar-rainfall}},
+  note         = {Distributed under Etalab 2.0 License}
+}
+```
+
+## 🙏 Acknowledgements
+
+Data provided by **Météo-France**. Processed and distributed by **Météo-France AI Lab** for open research and development purposes.

example.py

@@ -0,0 +1,33 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from plots import plot_map_rain, project_to_latlon
+
+# Load the file
+data = np.load("2020/202004201700.npz")  # Adjust path as needed
+rain = data["arr_0"]  # The array is stored under 'arr_0'
+print("Array shape:", rain.shape)  # Shape = (1536, 1536)
+
+# Negative values indicate no data, replace them with NaN:
+rain = np.where(rain < 0, np.nan, rain)
+
+# Visualize
+print("Making basic plot...")
+plt.imshow(rain, cmap="Blues")
+plt.colorbar(label="Rainfall (x0.01 mm / 5min)")
+plt.title("Rainfall Accumulation – 2020-04-20 17:00 UTC")
+plt.savefig("rainfall_20200420_1700_basic.png")
+plt.close()
+
+print("Converting and projecting rainfall data...")
+rain = rain / 100  # Convert from mm10-2 to mm
+rain = rain * 60 / 5  # Convert from mm to mm/h
+da_reproj = project_to_latlon(rain)
+print(da_reproj)
+
+print("Plotting projected rainfall data...")
+plot_map_rain(
+    data=da_reproj,
+    title="Rainfall Rate – 2020-04-20 17:00 UTC",
+    path="rainfall_20200420_1700_map.png",
+)

plots.py

@@ -0,0 +1,199 @@
+"""
+This module contains functions for plotting rainfall rate data using Cartopy and Matplotlib.
+It includes utilities for color mapping, coordinate transformations, and plotting.
+"""
+
+from pathlib import Path
+from typing import Tuple
+
+import cartopy.feature as cfeature
+import matplotlib.colors as mcolors
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+from cartopy.crs import Globe, PlateCarree, Stereographic
+from matplotlib.axes import Axes
+from pyproj import CRS, Transformer
+from scipy.interpolate import griddata
+from scipy.spatial import cKDTree
+
+########################################################################################
+#                           PROJECTIONS AND COORDINATES                                #
+########################################################################################
+
+# Original radar projection
+PROJ_WKT = """
+PROJCS["unknown",GEOGCS["unknown",DATUM["unknown",SPHEROID["unknown",6378137,298.252840776245]],
+PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],
+PROJECTION["Polar_Stereographic"],PARAMETER["latitude_of_origin",45],
+PARAMETER["central_meridian",0],PARAMETER["false_easting",0],PARAMETER["false_northing",0],
+UNIT["metre",1],AXIS["Easting",SOUTH],AXIS["Northing",SOUTH]]
+"""
+GEOTRANSFORM = (
+    -619652.0953618084,
+    1000.0,
+    0.0,
+    -3526818.459196719,
+    0.0,
+    -999.9999999999997,
+)
+
+
+def project_to_latlon(arr: np.ndarray) -> xr.DataArray:
+    """Convert a 2D array from the original projection to lat/lon coordinates."""
+    x0, dx, _, y0, _, dy = GEOTRANSFORM
+    height, width = arr.shape
+
+    # Create meshgrid of coordinates
+    x_coords = x0 + np.arange(width) * dx
+    y_coords = y0 + np.arange(height) * dy
+    xx, yy = np.meshgrid(x_coords, y_coords)
+
+    # Transform grid coords to lat/lon
+    crs_src = CRS.from_wkt(PROJ_WKT)
+    crs_dst = CRS.from_epsg(4326)  # WGS84
+    to_latlon = Transformer.from_crs(crs_src, crs_dst, always_xy=True)
+    lon, lat = to_latlon.transform(xx, yy)
+
+    # Creation of the source DataArray
+    da_src = xr.DataArray(arr, dims=("y", "x"), coords={"x": x_coords, "y": y_coords})
+    da_src = da_src.assign_coords(lon=(("y", "x"), lon), lat=(("y", "x"), lat))
+
+    # Regular grid in lat/lon
+    res_deg = 0.01  # ~1 km
+    lat_target = np.arange(lat.min(), lat.max(), res_deg)
+    lon_target = np.arange(lon.min(), lon.max(), res_deg)
+    lon_grid, lat_grid = np.meshgrid(lon_target, lat_target)
+
+    # Interpolation with griddata
+    points = np.column_stack((lon.ravel(), lat.ravel()))
+    values = arr.ravel()
+    data_interp = griddata(points, values, (lon_grid, lat_grid), method="nearest")
+
+    # The nearest neighbor interpolation can create artefacts on the edges
+    # so we mask values using a maximum distance
+    tree = cKDTree(points)
+    distances, _ = tree.query(
+        np.column_stack((lon_grid.ravel(), lat_grid.ravel())), k=1
+    )
+
+    # Max radius: diagonal of a target pixel
+    max_dist = np.sqrt(2) * res_deg
+    mask = distances > max_dist
+
+    # Mask the interpolated data
+    data_interp_flat = data_interp.ravel()
+    data_interp_flat[mask] = np.nan
+    data_interp = data_interp_flat.reshape(lon_grid.shape)
+
+    # Create the final DataArray with the reprojected data
+    da_reproj = xr.DataArray(
+        data_interp,
+        dims=("lat", "lon"),
+        coords={"lat": lat_target, "lon": lon_target},
+        name="data",
+    )
+    # Invert latitude axis to match the original orientation
+    da_reproj = da_reproj[::-1, :]
+    return da_reproj
+
+
+########################################################################################
+#                           COLORS AND COLORMAPS                                       #
+########################################################################################
+
+
+def hex_to_rgb(hex):
+    """Converts a hexadecimal color to RGB."""
+    return tuple(int(hex[i : i + 2], 16) / 255 for i in (0, 2, 4))
+
+
+COLORS_RR = [  # 14 colors
+    hex_to_rgb("E5E5E5"),
+    hex_to_rgb("6600CBFF"),
+    hex_to_rgb("0000FFFF"),
+    hex_to_rgb("00B2FFFF"),
+    hex_to_rgb("00FFFFFF"),
+    hex_to_rgb("0EDCD2FF"),
+    hex_to_rgb("1CB8A5FF"),
+    hex_to_rgb("6BA530FF"),
+    hex_to_rgb("FFFF00FF"),
+    hex_to_rgb("FFD800FF"),
+    hex_to_rgb("FFA500FF"),
+    hex_to_rgb("FF0000FF"),
+    hex_to_rgb("991407FF"),
+    hex_to_rgb("FF00FFFF"),
+]
+"""list of str: list of colors for the rainfall rate colormap"""
+
+CMAP_RR = mcolors.ListedColormap(COLORS_RR)
+"""ListedColormap : rainfall rate colormap"""
+
+BOUNDARIES_RR = [
+    0,
+    0.1,
+    0.4,
+    0.6,
+    1.2,
+    2.1,
+    3.6,
+    6.5,
+    12,
+    21,
+    36,
+    65,
+    120,
+    205,
+    360,
+]
+"""list of float: boundaries of the rainfall rate colormap"""
+
+NORM_RR = mcolors.BoundaryNorm(BOUNDARIES_RR, CMAP_RR.N, clip=True)
+"""BoundaryNorm: norm for the reflectivity colormap"""
+
+########################################################################################
+#                           PLOTTING FUNCTIONS                                         #
+########################################################################################
+
+
+def plot_ax_rainfall_rate(
+    ax: Axes,
+    data: np.ndarray,
+    extent: Tuple[float],
+    cmap=CMAP_RR,
+    norm=NORM_RR,
+    title: str = "",
+):
+    """Plot a rainfall rate image on a given axis."""
+    img = ax.imshow(data, extent=extent, cmap=cmap, norm=norm, interpolation="none")
+    states_provinces = cfeature.NaturalEarthFeature(
+        category="cultural",
+        name="admin_1_states_provinces_lines",
+        scale="10m",
+        facecolor="none",
+    )
+    ax.add_feature(states_provinces, edgecolor="lightgrey", linewidth=0.5)
+    ax.add_feature(cfeature.BORDERS.with_scale("10m"), edgecolor="black", linewidth=1)
+    ax.coastlines(resolution="10m", color="black", linewidth=1)
+    ax.set_title(title, fontsize=15)
+    ax.gridlines(
+        crs=PlateCarree(),
+        draw_labels=True,
+        linewidth=0.4,
+        color="lightgrey",
+        linestyle=":",
+    )
+    return img
+
+
+def plot_map_rain(data: xr.DataArray, title: str, path: Path) -> None:
+    """Plot a rainfall rate map."""
+    projection = PlateCarree()
+    extent = [data.lon.min(), data.lon.max(), data.lat.min(), data.lat.max()]
+    fig, ax = plt.subplots(subplot_kw={"projection": projection}, figsize=(10, 7))
+    img = plot_ax_rainfall_rate(ax, data.values, title=title, extent=extent)
+    cb = fig.colorbar(img, ax=ax, orientation="horizontal", fraction=0.04, pad=0.05)
+    cb.set_label(label="Precipitation in mm/h", fontsize=12)
+    plt.tight_layout()
+    plt.savefig(path)
+    plt.close()