From 1a71883999700fada1d4a3398d62e12a1862c427 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20H=C3=B6lzer?= Date: Sat, 22 Nov 2025 18:56:34 +0100 Subject: [PATCH] Add arcticdem --- .python-version | 2 +- pixi.lock | 8443 ++++++++++++++---- pyproject.toml | 35 +- scripts/00grids.sh | 11 + scripts/01darts.sh | 11 + scripts/02alphaearth.sh | 23 + scripts/03era5.sh | 15 + scripts/alphaearth.sh | 17 - scripts/cds.py | 66 - scripts/era5.sh | 18 - scripts/rts.sh | 9 - src/entropice/alphaearth.py | 6 +- src/entropice/arcticdem.py | 308 + src/entropice/darts.py | 4 + src/entropice/grids.py | 70 +- src/entropice/inference.py | 4 +- src/entropice/paths.py | 2 + src/entropice/training_analysis_dashboard.py | 5 +- 18 files changed, 6981 insertions(+), 2068 deletions(-) create mode 100644 scripts/00grids.sh create mode 100644 scripts/01darts.sh create mode 100644 scripts/02alphaearth.sh create mode 100644 scripts/03era5.sh delete mode 100644 scripts/alphaearth.sh delete mode 100644 scripts/cds.py delete mode 100644 scripts/era5.sh delete mode 100644 scripts/rts.sh create mode 100644 src/entropice/arcticdem.py diff --git a/.python-version b/.python-version index e4fba21..24ee5b1 100755 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.12 +3.13 diff --git a/pixi.lock b/pixi.lock index dd67d3f..4021421 100644 --- a/pixi.lock +++ b/pixi.lock @@ -2,100 +2,467 @@ version: 6 environments: default: channels: + - url: https://conda.anaconda.org/nvidia/ - url: https://conda.anaconda.org/conda-forge/ + - url: https://conda.anaconda.org/rapidsai/ indexes: - https://pypi.org/simple + options: + channel-priority: disabled packages: linux-64: - - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-5_kmp_llvm.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-6_kmp_llvm.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda + - conda: 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a/pyproject.toml b/pyproject.toml index 27a43d1..2484a90 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ version = "0.1.0" description = "Add your description here" readme = "README.md" authors = [{ name = "Tobias Hölzer", email = "tobiashoelzer@hotmail.com" }] -# requires-python = ">=3.10,<3.13" +requires-python = ">=3.13,<3.14" dependencies = [ "aiohttp>=3.12.11", "bokeh>=3.7.3", @@ -28,19 +28,19 @@ dependencies = [ "mapclassify>=2.9.0", "matplotlib>=3.10.3", "netcdf4>=1.7.2", - "numba>=0.62.1", + "numba>=0.61.2", "numbagg>=0.9.3", - "numpy>=2.3.0", + "numpy>=2.2.6", "odc-geo[all]>=0.4.10", "opt-einsum>=3.4.0", - "pyarrow>=20.0.0", + "pyarrow>=18.1.0", "rechunker>=0.5.2", "requests>=2.32.3", "rich>=14.0.0", "rioxarray>=0.19.0", "scipy>=1.15.3", "seaborn>=0.13.2", - "smart-geocubes[gee,dask,stac,viz]>=0.0.9", + "smart-geocubes[stac]>=0.1.0", "stopuhr>=0.0.10", "ultraplot>=1.63.0", "xanimate", @@ -51,7 +51,13 @@ dependencies = [ "geocube>=0.7.1,<0.8", "streamlit>=1.50.0,<2", "altair[all]>=5.5.0,<6", - "h5netcdf>=1.7.3,<2", "streamlit-folium>=0.25.3,<0.26", "st-theme>=1.2.3,<2", + "h5netcdf>=1.7.3,<2", + "streamlit-folium>=0.25.3,<0.26", + "st-theme>=1.2.3,<2", + "xgboost>=3.1.1,<4", + "s3fs>=2025.10.0,<2026", + "xarray-spatial", + "cupy-xarray>=0.1.4,<0.2", ] [project.scripts] @@ -59,6 +65,7 @@ create-grid = "entropice.grids:main" darts = "entropice.darts:main" alpha-earth = "entropice.alphaearth:main" era5 = "entropice.era5:cli" +arcticdem = "entropice.arcticdem:cli" train = "entropice.training:main" dataset = "entropice.dataset:main" @@ -69,10 +76,20 @@ build-backend = "hatchling.build" [tool.uv] package = true +[[tool.uv.index]] +name = "nvidia" +url = "https://pypi.nvidia.com" +explicit = true + [tool.uv.sources] entropyc = { git = "ssh://git@github.com/AlbertEMC2Stein/entropyc", branch = "refactor/tobi" } entropy = { git = "ssh://git@forgejo.tobiashoelzer.de:22222/tobias/entropy.git" } xanimate = { git = "https://github.com/davbyr/xAnimate" } +xdem = { git = "https://github.com/GlacioHack/xdem" } +xarray-spatial = { git = "https://github.com/relativityhd/xarray-spatial" } +cudf-cu12 = { index = "nvidia" } +cuml-cu12 = { index = "nvidia" } +cuspatial-cu12 = { index = "nvidia" } [tool.ruff.lint.pyflakes] # Ignore libraries when checking for unused imports @@ -87,7 +104,8 @@ allowed-unused-imports = [ ] [tool.pixi.workspace] -channels = ["conda-forge"] +channels = ["nvidia", "conda-forge", "rapidsai"] +channel-priority = "disabled" platforms = ["linux-64"] [tool.pixi.activation.env] @@ -116,4 +134,5 @@ cupy = ">=13.6.0,<14" nccl = ">=2.27.7.1,<3" cudnn = ">=9.13.1.26,<10" cusparselt = ">=0.8.1.1,<0.9" -cuda-version = "12.1.*" +cuda-version = "12.9.*" +rapids = ">=25.10.0,<26" diff --git a/scripts/00grids.sh b/scripts/00grids.sh new file mode 100644 index 0000000..73c2bf7 --- /dev/null +++ b/scripts/00grids.sh @@ -0,0 +1,11 @@ +#! /bin/bash + +pixi run create-grid --grid hex --level 3 +pixi run create-grid --grid hex --level 4 +pixi run create-grid --grid hex --level 5 +pixi run create-grid --grid hex --level 6 +pixi run create-grid --grid healpix --level 6 +pixi run create-grid --grid healpix --level 7 +pixi run create-grid --grid healpix --level 8 +pixi run create-grid --grid healpix --level 9 +pixi run create-grid --grid healpix --level 10 diff --git a/scripts/01darts.sh b/scripts/01darts.sh new file mode 100644 index 0000000..8003fc0 --- /dev/null +++ b/scripts/01darts.sh @@ -0,0 +1,11 @@ +#! /bin/bash + +pixi run darts --grid hex --level 3 +pixi run darts --grid hex --level 4 +pixi run darts --grid hex --level 5 +pixi run darts --grid hex --level 6 +pixi run darts --grid healpix --level 6 +pixi run darts --grid healpix --level 7 +pixi run darts --grid healpix --level 8 +pixi run darts --grid healpix --level 9 +pixi run darts --grid healpix --level 10 diff --git a/scripts/02alphaearth.sh b/scripts/02alphaearth.sh new file mode 100644 index 0000000..98294db --- /dev/null +++ b/scripts/02alphaearth.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +pixi run alpha-earth download --grid hex --level 3 +pixi run alpha-earth download --grid hex --level 4 +pixi run alpha-earth download --grid hex --level 5 +pixi run alpha-earth download --grid healpix --level 6 +pixi run alpha-earth download --grid healpix --level 7 +pixi run alpha-earth download --grid healpix --level 8 +pixi run alpha-earth download --grid healpix --level 9 + +pixi run alpha-earth combine-to-zarr --grid hex --level 3 +pixi run alpha-earth combine-to-zarr --grid hex --level 4 +pixi run alpha-earth combine-to-zarr --grid hex --level 5 +pixi run alpha-earth combine-to-zarr --grid healpix --level 6 +pixi run alpha-earth combine-to-zarr --grid healpix --level 7 +pixi run alpha-earth combine-to-zarr --grid healpix --level 8 +pixi run alpha-earth combine-to-zarr --grid healpix --level 9 + +pixi run alpha-earth download --grid hex --level 6 +pixi run alpha-earth download --grid healpix --level 10 + +pixi run alpha-earth combine-to-zarr --grid hex --level 6 +pixi run alpha-earth combine-to-zarr --grid healpix --level 10 diff --git a/scripts/03era5.sh b/scripts/03era5.sh new file mode 100644 index 0000000..60f3e07 --- /dev/null +++ b/scripts/03era5.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +# pixi run era5 download +# pixi run era5 enrich + +pixi run era5 spatial-agg --grid hex --level 3 +pixi run era5 spatial-agg --grid hex --level 4 +pixi run era5 spatial-agg --grid hex --level 5 +pixi run era5 spatial-agg --grid hex --level 6 + +pixi run era5 spatial-agg --grid healpix --level 6 +pixi run era5 spatial-agg --grid healpix --level 7 +pixi run era5 spatial-agg --grid healpix --level 8 +pixi run era5 spatial-agg --grid healpix --level 9 +pixi run era5 spatial-agg --grid healpix --level 10 diff --git a/scripts/alphaearth.sh b/scripts/alphaearth.sh deleted file mode 100644 index 2f5cae6..0000000 --- a/scripts/alphaearth.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/bash - -# uv run alpha-earth download --grid hex --level 3 -# uv run alpha-earth download --grid hex --level 4 -# uv run alpha-earth download --grid hex --level 5 -# uv run alpha-earth download --grid healpix --level 6 -# uv run alpha-earth download --grid healpix --level 7 -# uv run alpha-earth download --grid healpix --level 8 -# uv run alpha-earth download --grid healpix --level 9 - -uv run alpha-earth combine-to-zarr --grid hex --level 3 -uv run alpha-earth combine-to-zarr --grid hex --level 4 -uv run alpha-earth combine-to-zarr --grid hex --level 5 -uv run alpha-earth combine-to-zarr --grid healpix --level 6 -uv run alpha-earth combine-to-zarr --grid healpix --level 7 -uv run alpha-earth combine-to-zarr --grid healpix --level 8 -uv run alpha-earth combine-to-zarr --grid healpix --level 9 diff --git a/scripts/cds.py b/scripts/cds.py deleted file mode 100644 index efd5bfe..0000000 --- a/scripts/cds.py +++ /dev/null @@ -1,66 +0,0 @@ -"""Download ERA5 data from the Copernicus Data Store. - -Web platform: https://cds.climate.copernicus.eu -""" - -import os -import re -from pathlib import Path - -import cdsapi -import cyclopts -from rich import pretty, print, traceback - -traceback.install() -pretty.install() - -DATA_DIR = Path(os.environ.get("DATA_DIR", "../../data")) / "entropyc-rts" - - -def hourly(years: str): - """Download ERA5 data from the Copernicus Data Store. - - Args: - years (str): Years to download, seperated by a '-'. - - """ - assert re.compile(r"^\d{4}-\d{4}$").match(years), "Years must be in the format 'YYYY-YYYY'" - start_year, end_year = map(int, years.split("-")) - assert 1950 <= start_year <= end_year <= 2024, "Years must be between 1950 and 2024" - - dataset = "reanalysis-era5-single-levels" - client = cdsapi.Client(wait_until_complete=False) - - outdir = (DATA_DIR / "era5/cds").resolve() - outdir.mkdir(parents=True, exist_ok=True) - - print(f"Downloading ERA5 data from {start_year} to {end_year}...") - for y in range(start_year, end_year + 1): - for month in [f"{i:02d}" for i in range(1, 13)]: - request = { - "product_type": ["reanalysis"], - "variable": [ - "2m_temperature", - "total_precipitation", - "snow_depth", - "snow_density", - "snowfall", - "lake_ice_temperature", - "surface_sensible_heat_flux", - ], - "year": [str(y)], - "month": [month], - "day": [f"{i:02d}" for i in range(1, 32)], - "time": [f"{i:02d}:00" for i in range(0, 24)], - "data_format": "netcdf", - "download_format": "unarchived", - "area": [85, -180, 50, 180], - } - - outpath = outdir / f"era5_{y}_{month}.zip" - client.retrieve(dataset, request).download(str(outpath)) - print(f"Downloaded {dataset} for {y}-{month}") - - -if __name__ == "__main__": - cyclopts.run(hourly) diff --git a/scripts/era5.sh b/scripts/era5.sh deleted file mode 100644 index c2a49d6..0000000 --- a/scripts/era5.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash - -# uv run era5 download -# uv run era5 enrich - -# Can be summer, winter or yearly -agg=$1 - -echo "Running ERA5 spatial aggregation for aggregation type: $agg" - -uv run era5 spatial-agg --grid hex --level 3 --agg $agg -uv run era5 spatial-agg --grid hex --level 4 --agg $agg -uv run era5 spatial-agg --grid hex --level 5 --agg $agg - -uv run era5 spatial-agg --grid healpix --level 6 --agg $agg -uv run era5 spatial-agg --grid healpix --level 7 --agg $agg -uv run era5 spatial-agg --grid healpix --level 8 --agg $agg -uv run era5 spatial-agg --grid healpix --level 9 --agg $agg diff --git a/scripts/rts.sh b/scripts/rts.sh deleted file mode 100644 index 7ebb48e..0000000 --- a/scripts/rts.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -uv run rts --grid hex --level 3 -uv run rts --grid hex --level 4 -uv run rts --grid hex --level 5 -uv run rts --grid healpix --level 6 -uv run rts --grid healpix --level 7 -uv run rts --grid healpix --level 8 -uv run rts --grid healpix --level 9 diff --git a/src/entropice/alphaearth.py b/src/entropice/alphaearth.py index 7bb781c..2bdf366 100644 --- a/src/entropice/alphaearth.py +++ b/src/entropice/alphaearth.py @@ -30,6 +30,8 @@ ee.Initialize(project="ee-tobias-hoelzer") cli = cyclopts.App(name="alpha-earth") +# 7454521782,230147807,10000000. + @cli.command() def download(grid: Literal["hex", "healpix"], level: int): @@ -60,6 +62,8 @@ def download(grid: Literal["hex", "healpix"], level: int): .combine(ee.Reducer.mean(), sharedInputs=True) .combine(ee.Reducer.percentile([1, 5, 25, 75, 95, 99]), sharedInputs=True), geometry=geom, + scale=10, + bestEffort=True, ) # Add mean embedding values as properties to the feature return feature.set(mean_dict) @@ -85,7 +89,7 @@ def download(grid: Literal["hex", "healpix"], level: int): # Combine all batch results df = pd.concat(all_results, ignore_index=True) - embeddings_on_grid = grid.merge(df[[*bands, "cell_id"]], on="cell_id", how="left") + embeddings_on_grid = grid_gdf.merge(df[[*bands, "cell_id"]], on="cell_id", how="left") embeddings_file = get_annual_embeddings_file(grid, level, year) embeddings_on_grid.to_parquet(embeddings_file) print(f"Saved embeddings for year {year} to {embeddings_file}.") diff --git a/src/entropice/arcticdem.py b/src/entropice/arcticdem.py new file mode 100644 index 0000000..e419f89 --- /dev/null +++ b/src/entropice/arcticdem.py @@ -0,0 +1,308 @@ +import datetime +from dataclasses import dataclass +from math import ceil + +import cupy as cp +import cupy_xarray +import cupyx.scipy.signal +import cyclopts +import dask.array +import dask.distributed as dd +import icechunk.xarray +import numpy as np +import smart_geocubes +import xarray as xr +import xrspatial +import zarr +from cupyx.scipy.ndimage import binary_dilation, binary_erosion, distance_transform_edt +from rich import pretty, print, traceback +from stopuhr import stopwatch +from xrspatial.aspect import _run_cupy as aspect_cupy +from xrspatial.curvature import _run_cupy as curvature_cupy +from xrspatial.slope import _run_cupy as slope_cupy +from zarr.codecs import BloscCodec + +from entropice.paths import arcticdem_store + +traceback.install(show_locals=True, suppress=[cyclopts]) +pretty.install() + +zarr.config.set({"async.concurrency": 128}) + +cli = cyclopts.App(name="arcticdem") + + +@cli.command() +def download(): + adem = smart_geocubes.ArcticDEM32m(arcticdem_store) + adem.create(exists_ok=True) + with stopwatch("Download ArcticDEM data"): + adem.procedural_download(adem.extent.extent, None) + + +@dataclass +class KernelFactory: + res: int + size_px: int + + @property + def size(self): + return self.size_px * self.res + + @property + def inner(self): + return ceil(self.size / 3) + + @staticmethod + def _to_cupy_f32(kernel): + return cp.asarray(kernel.astype("float32")) + + def ring(self): + return self._to_cupy_f32(xrspatial.convolution.annulus_kernel(self.res, self.res, self.size, self.inner)) + + def tri(self): + kernel = np.ones((self.size_px, self.size_px), dtype=float) + kernel[self.size_px // 2, self.size_px // 2] = 0 # Set the center cell to 0 + return self._to_cupy_f32(xrspatial.convolution.custom_kernel(kernel)) + + def vrm(self): + kernel = np.ones((self.size_px, self.size_px), dtype=float) / self.size_px**2 + return self._to_cupy_f32(xrspatial.convolution.custom_kernel(kernel)) + + +def tpi_cupy(chunk, kernels: KernelFactory): + kernel = kernels.ring() + kernel = kernel / cp.nansum(kernel) + tpi = chunk - cupyx.scipy.signal.convolve2d(chunk, kernel, mode="same") + return tpi + + +def tri_cupy(chunk, kernels: KernelFactory): + kernel = kernels.tri() + c2 = chunk**2 + focal_sum = cupyx.scipy.signal.convolve2d(chunk, kernel, mode="same") + focal_sum_squared = cupyx.scipy.signal.convolve2d(c2, kernel, mode="same") + tri = np.sqrt((kernel.size - 1) * c2 - 2 * chunk * focal_sum + focal_sum_squared) + return tri + + +def ruggedness_cupy(chunk, slope, aspect, kernels: KernelFactory): + slope_rad = slope * (cp.pi / 180) + aspect_rad = aspect * (cp.pi / 180) + aspect_rad = cp.where(aspect_rad == -1, 0, aspect_rad) + + xy = cp.sin(slope_rad) + z = cp.cos(slope_rad) + x = xy * cp.sin(aspect_rad) + y = xy * cp.cos(aspect_rad) + + kernel = kernels.vrm() + + # Calculate sums of x, y, and z components in the neighborhood + x_sum = cupyx.scipy.signal.convolve2d(x, kernel, mode="same") + y_sum = cupyx.scipy.signal.convolve2d(y, kernel, mode="same") + z_sum = cupyx.scipy.signal.convolve2d(z, kernel, mode="same") + + # Calculate the resultant vector magnitude + vrm = 1 - np.sqrt(x_sum**2 + y_sum**2 + z_sum**2) + return vrm + + +def _get_xy_chunk(chunk: np.array, x: np.array, y: np.array, block_info=None) -> tuple[cp.array, cp.array]: + chunk_loc = block_info[0]["chunk-location"] + d = 15 + cs = 3600 + + # Calculate safe slice bounds for edge chunks + y_start, y_end = max(0, cs * chunk_loc[0] - d), min(len(y), cs * chunk_loc[0] + cs + d) + x_start, x_end = max(0, cs * chunk_loc[1] - d), min(len(x), cs * chunk_loc[1] + cs + d) + + # Extract coordinate arrays with safe bounds + y_chunk = cp.asarray(y[y_start:y_end]) + x_chunk = cp.asarray(x[x_start:x_end]) + + # Handle cases where the extracted chunk doesn't match expected dimensions + if len(y_chunk) != chunk.shape[0] or len(x_chunk) != chunk.shape[1]: + print( + f"Adjusting coordinate chunk sizes: y_chunk {len(y_chunk)} vs chunk {chunk.shape[0]}, " + f"x_chunk {len(x_chunk)} vs chunk {chunk.shape[1]}" + ) + # Pad coordinates to match chunk dimensions if needed + if len(y_chunk) < chunk.shape[0]: + # Pad with the edge values + pad_start = chunk.shape[0] - len(y_chunk) + y_chunk = cp.pad(y_chunk, (pad_start, 0), mode="edge") + if len(x_chunk) < chunk.shape[1]: + pad_start = chunk.shape[1] - len(x_chunk) + x_chunk = cp.pad(x_chunk, (pad_start, 0), mode="edge") + + yy = cp.repeat(y_chunk.reshape(-1, 1), x_chunk.shape[0], axis=1) + xx = cp.repeat(x_chunk.reshape(1, -1), y_chunk.shape[0], axis=0) + return xx, yy + + +def _enrich_chunk(chunk: np.array, x: np.array, y: np.array, block_info=None) -> np.array: + res = 32 # 32m resolution + small_kernels = KernelFactory(res=res, size_px=3) # ~3x3 kernels (96m) + medium_kernels = KernelFactory(res=res, size_px=7) # ~7x7 kernels (224m) + large_kernels = KernelFactory(res=res, size_px=15) # ~15x15 kernels (480m) + + # Check if there is data in the chunk + if np.all(np.isnan(chunk)): + # Return an array of NaNs with the expected shape + return np.full( + (12, chunk.shape[0] - 2 * large_kernels.size_px, chunk.shape[1] - 2 * large_kernels.size_px), + np.nan, + dtype=np.float32, + ) + + chunk = cp.asarray(chunk) + + # Interpolate missing values in chunk (for patches smaller than 7x7 pixels) + mask = cp.isnan(chunk) + mask &= ~binary_dilation(binary_erosion(mask, iterations=3, brute_force=True), iterations=3, brute_force=True) + if cp.any(mask): + # Find indices of valid values + indices = distance_transform_edt(mask, return_distances=False, return_indices=True) + chunk = chunk[tuple(indices)] + + # TPI + tpi_small = tpi_cupy(chunk, small_kernels) + tpi_medium = tpi_cupy(chunk, medium_kernels) + tpi_large = tpi_cupy(chunk, large_kernels) + # Slope + slope = slope_cupy(chunk, res, res) + # Aspect + aspect = aspect_cupy(chunk) + xx, yy = _get_xy_chunk(chunk, x, y, block_info) + aspect_correction = cp.arctan2(yy, xx) * (180 / cp.pi) + 90 + aspect = (aspect + aspect_correction) % 360 + # Curvature + curvature = curvature_cupy(chunk, res) + # TRI + tri_small = tri_cupy(chunk, small_kernels) + tri_medium = tri_cupy(chunk, medium_kernels) + tri_large = tri_cupy(chunk, large_kernels) + # Ruggedness + vrm_small = ruggedness_cupy(chunk, slope, aspect, small_kernels) + vrm_medium = ruggedness_cupy(chunk, slope, aspect, medium_kernels) + vrm_large = ruggedness_cupy(chunk, slope, aspect, large_kernels) + + # Stack on GPU, then move to CPU once + res_gpu = cp.stack( + [ + tpi_small, + tpi_medium, + tpi_large, + slope, + aspect, + curvature, + tri_small, + tri_medium, + tri_large, + vrm_small, + vrm_medium, + vrm_large, + ], + axis=0, + ) + + # Trim edges on GPU + e = large_kernels.size_px + res_gpu = res_gpu[:, e:-e, e:-e] + + # Single CPU transfer at the end + res = cp.asnumpy(res_gpu) + return res + + +def _enrich(terrain: xr.DataArray): + features = [ + "tpi_small", + "tpi_medium", + "tpi_large", + "slope", + "aspect", + "curvature", + "tri_small", + "tri_medium", + "tri_large", + "vrm_small", + "vrm_medium", + "vrm_large", + ] + output_chunks = (len(features), *tuple(terrain.data.chunks)) + enriched_da = dask.array.map_overlap( + _enrich_chunk, + terrain.data, + # dask.array.from_array(terrain.y.data.reshape(-1, 1), chunks=(terrain.data.chunks[0][0], 3600)).repeat( + # terrain.x.size, axis=1 + # ), + # dask.array.from_array(terrain.x.data.reshape(1, -1), chunks=(3600, terrain.data.chunks[1][0])).repeat( + # terrain.y.size, axis=0 + # ), + x=terrain.x.to_numpy(), + y=terrain.y.to_numpy(), + depth=15, # large_kernels.size_px + chunks=output_chunks, + new_axis=0, + dtype=np.float32, + meta=np.array((), dtype=np.float32), + trim=False, + boundary=np.nan, + token="enrich_arcticdem", + ) + enriched_da = xr.DataArray( + enriched_da, + dims=("feature", "y", "x"), + coords={ + "feature": features, + "y": terrain.y, + "x": terrain.x, + }, + ) + return enriched_da, features + + +@cli.command() +def enrich(): + with ( + dd.LocalCluster(n_workers=7, threads_per_worker=2, memory_limit="30GB") as cluster, + dd.Client(cluster) as client, + ): + print(client) + print(client.dashboard_link) + + accessor = smart_geocubes.ArcticDEM32m(arcticdem_store) + + # Garbage collect from previous runs + accessor.repo.garbage_collect(datetime.datetime.now(datetime.UTC)) + + adem = accessor.open_xarray() + # session = adem.repo.readonly_session("main") + # adem = xr.open_zarr(session.store, mask_and_scale=False, consolidated=False).set_coords("spatial_ref") + del adem.y.attrs["_FillValue"] + del adem.x.attrs["_FillValue"] + enriched, new_features = _enrich(adem.dem) + + for feature in new_features: + adem[feature] = enriched.sel(feature=feature) + print(adem[new_features]) + # subset = adem[new_features].isel(x=slice(190800, 220000), y=slice(61200, 100000)) + encodings = {feature: {"compressors": [BloscCodec(clevel=5)]} for feature in new_features} + # subset.to_zarr("test2.zarr", mode="w", encoding=encodings) + + session = accessor.repo.writable_session("main") + icechunk.xarray.to_icechunk( + adem[new_features], + session, + mode="a", + encoding=encodings, + ) + session.commit("Add terrain features: TPI, slope, aspect, curvature, TRI, ruggedness") + + print("Enrichment complete.") + + +if __name__ == "__main__": + cli() diff --git a/src/entropice/darts.py b/src/entropice/darts.py index cd829e4..07d6ac5 100644 --- a/src/entropice/darts.py +++ b/src/entropice/darts.py @@ -71,6 +71,10 @@ def extract_darts_rts(grid: Literal["hex", "healpix"], level: int): darts_counts = grid_gdf[darts_counts_columns] grid_gdf["darts_rts_count"] = darts_counts.dropna(axis=0, how="all").sum(axis=1) + darts_density_columns = [c for c in grid_gdf.columns if c.startswith("darts_") and c.endswith("_rts_density")] + darts_density = grid_gdf[darts_density_columns] + grid_gdf["darts_rts_density"] = darts_density.dropna(axis=0, how="all").max(axis=1) + output_path = get_darts_rts_file(grid, level) grid_gdf.to_parquet(output_path) print(f"Saved RTS labels to {output_path}") diff --git a/src/entropice/grids.py b/src/entropice/grids.py index 6bb8dcf..531b2dc 100644 --- a/src/entropice/grids.py +++ b/src/entropice/grids.py @@ -4,6 +4,7 @@ Author: Tobias Hölzer Date: 09. June 2025 """ +from concurrent.futures import ProcessPoolExecutor, as_completed from typing import Literal import cartopy.crs as ccrs @@ -16,8 +17,9 @@ import matplotlib.pyplot as plt import numpy as np import xarray as xr import xdggs -import xvec # noqa: F401 +import xvec from rich import pretty, print, traceback +from rich.progress import track from shapely.geometry import Polygon from shapely.ops import transform from stopuhr import stopwatch @@ -64,6 +66,24 @@ def get_cell_ids(grid: Literal["hex", "healpix"], level: int): return cell_ids +def _get_cell_polygon(hex0_cell, resolution: int) -> tuple[list[Polygon], list[str], list[float]]: + hex_batch = [] + hex_id_batch = [] + hex_area_batch = [] + + hex_cells = h3.cell_to_children(hex0_cell, resolution) + + for hex_id in hex_cells: + boundary_coords = h3.cell_to_boundary(hex_id) + hex_polygon = Polygon(boundary_coords) + hex_polygon = transform(lambda x, y: (y, x), hex_polygon) # Convert from (lat, lon) to (lon, lat) + hex_area = h3.cell_area(hex_id, unit="km^2") + hex_batch.append(hex_polygon) + hex_id_batch.append(hex_id) + hex_area_batch.append(hex_area) + return hex_batch, hex_id_batch, hex_area_batch + + @stopwatch("Create a global hex grid") def create_global_hex_grid(resolution): """Create a global hexagonal grid using H3. @@ -76,30 +96,44 @@ def create_global_hex_grid(resolution): """ # Generate hexagons + # For resolutions >=5, use multiprocessing to speed up the process hex0_cells = h3.get_res0_cells() - if resolution > 0: - hex_cells = [] - for hex0_cell in hex0_cells: - hex_cells.extend(h3.cell_to_children(hex0_cell, resolution)) - - else: - hex_cells = hex0_cells - # Initialize lists to store hex information hex_list = [] hex_id_list = [] hex_area_list = [] - # Convert each hex ID to a polygon - for hex_id in hex_cells: - boundary_coords = h3.cell_to_boundary(hex_id) - hex_polygon = Polygon(boundary_coords) - hex_polygon = transform(lambda x, y: (y, x), hex_polygon) # Convert from (lat, lon) to (lon, lat) - hex_area = h3.cell_area(hex_id, unit="km^2") - hex_list.append(hex_polygon) - hex_id_list.append(hex_id) - hex_area_list.append(hex_area) + if resolution >= 5: + with ProcessPoolExecutor(max_workers=20) as executor: + future_to_hex = { + executor.submit(_get_cell_polygon, hex0_cell, resolution): hex0_cell for hex0_cell in hex0_cells + } + for future in track( + as_completed(future_to_hex), description="Creating hex polygons...", total=len(hex0_cells) + ): + hex_batch, hex_id_batch, hex_area_batch = future.result() + hex_list.extend(hex_batch) + hex_id_list.extend(hex_id_batch) + hex_area_list.extend(hex_area_batch) + else: + if resolution > 0: + hex_cells = [] + for hex0_cell in track(hex0_cells, description="Generating hex ids...", total=len(hex0_cells)): + hex_cells.extend(h3.cell_to_children(hex0_cell, resolution)) + + else: + hex_cells = hex0_cells + + # Convert each hex ID to a polygon + for hex_id in track(hex_cells, description="Creating hex polygons...", total=len(hex_cells)): + boundary_coords = h3.cell_to_boundary(hex_id) + hex_polygon = Polygon(boundary_coords) + hex_polygon = transform(lambda x, y: (y, x), hex_polygon) # Convert from (lat, lon) to (lon, lat) + hex_area = h3.cell_area(hex_id, unit="km^2") + hex_list.append(hex_polygon) + hex_id_list.append(hex_id) + hex_area_list.append(hex_area) # Create GeoDataFrame grid = gpd.GeoDataFrame({"cell_id": hex_id_list, "cell_area": hex_area_list, "geometry": hex_list}, crs="EPSG:4326") diff --git a/src/entropice/inference.py b/src/entropice/inference.py index a3c9f31..846b29c 100644 --- a/src/entropice/inference.py +++ b/src/entropice/inference.py @@ -36,7 +36,7 @@ def predict_proba(grid: Literal["hex", "healpix"], level: int, clf: ESPAClassifi print(f"Predicting probabilities for {len(data)} cells...") # Predict in batches to avoid memory issues - batch_size = 100_000 + batch_size = 10_000 preds = [] for i in range(0, len(data), batch_size): batch = data.iloc[i : i + batch_size] @@ -45,7 +45,7 @@ def predict_proba(grid: Literal["hex", "healpix"], level: int, clf: ESPAClassifi X_batch = batch.drop(columns=cols_to_drop).dropna() cell_ids = batch.loc[X_batch.index, "cell_id"].to_numpy() cell_geoms = batch.loc[X_batch.index, "geometry"].to_numpy() - X_batch = X_batch.to_numpy(dtype="float32") + X_batch = X_batch.to_numpy(dtype="float64") X_batch = torch.asarray(X_batch, device=0) batch_preds = clf.predict(X_batch).cpu().numpy() batch_preds = gpd.GeoDataFrame( diff --git a/src/entropice/paths.py b/src/entropice/paths.py index dc1be26..779632e 100644 --- a/src/entropice/paths.py +++ b/src/entropice/paths.py @@ -27,6 +27,8 @@ WATERMASK_DIR.mkdir(parents=True, exist_ok=True) TRAINING_DIR.mkdir(parents=True, exist_ok=True) RESULTS_DIR.mkdir(parents=True, exist_ok=True) +arcticdem_store = DATA_DIR / "arcticdem32m.icechunk.zarr" + watermask_file = WATERMASK_DIR / "simplified_water_polygons.shp" dartsl2_file = DARTS_DIR / "DARTS_NitzeEtAl_v1-2_features_2018-2023_level2.parquet" diff --git a/src/entropice/training_analysis_dashboard.py b/src/entropice/training_analysis_dashboard.py index d8c49a2..d1f7952 100644 --- a/src/entropice/training_analysis_dashboard.py +++ b/src/entropice/training_analysis_dashboard.py @@ -111,7 +111,10 @@ def get_available_result_files() -> list[Path]: if result_file.exists() and state_file.exists() and preds_file.exists() and settings_file.exists(): result_files.append(search_dir) - return sorted(result_files, reverse=True) # Most recent first + def _key_func(path: Path): + return path.stat().st_mtime + + return sorted(result_files, key=_key_func, reverse=True) # Most recent first def load_and_prepare_results(file_path: Path, settings: dict, k_bin_width: int = 40) -> pd.DataFrame: