diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..ff6fd09 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,38 @@ +name: CI + +on: + push: + branches: [main] + pull_request: + +concurrency: + group: ci-${{ github.ref }} + cancel-in-progress: true + +jobs: + test: + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.11", "3.12"] + + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + + - name: Create virtual environment + run: uv venv --python ${{ matrix.python-version }} .venv + + - name: Install project with dev extras + run: uv pip install --python .venv -e ".[dev]" + + - name: Lint (ruff) + run: .venv/bin/ruff check . + + - name: Run tests + run: .venv/bin/pytest -q diff --git a/README.md b/README.md index 29ca664..fc7af4f 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,90 @@ # simlab-tools -Research toolchain for SimLab. -Main functionalities are listed below, grouped by topic. +Shared research tooling for SIM lab projects: loading weather data, geodata, and +transferring files to and from object storage. The package is organised into +topical subpackages, each keeping its heavy dependencies behind an optional +extra so consumers install only what they use. -## Online Storage (SwitchDrive) +## Installation + +Install straight from GitHub, selecting the extras you need: + +```bash +# Object storage only +uv pip install "simlab-tools[storage] @ git+https://github.com/simlab-vs/simlab-tools.git" + +# Geodata utilities +uv pip install "simlab-tools[geo] @ git+https://github.com/simlab-vs/simlab-tools.git" + +# Everything +uv pip install "simlab-tools[all] @ git+https://github.com/simlab-vs/simlab-tools.git" +``` + +Available extras: `storage`, `geo`, `smoothing`, `weather`, `all`, and `dev` +(the full toolkit plus test/lint tooling). + +## Subpackages + +### `simlab_tools.storage` — object storage (extra: `storage`) + +Transfer files to and from S3-compatible buckets (e.g. SwitchCloud) with +progress bars and multipart transfers. + +```python +from simlab_tools.storage import get_s3_client, download_files_from_bucket + +client = get_s3_client("https://zhw-a.s3.cloud.switch.ch") # reads S3_ACCESS_KEY_ID / S3_SECRET_ACCESS_KEY +download_files_from_bucket(client, "ofen", "dejection_cones_dem", "data/", file_extensions=[".tif"]) +``` + +`get_s3_client` accepts explicit `key_id` / `key_secret` arguments, or falls back +to the `S3_ACCESS_KEY_ID` and `S3_SECRET_ACCESS_KEY` environment variables. + +For backwards compatibility, the storage functions are also importable from the +top-level package: `from simlab_tools import get_s3_client`. + +### `simlab_tools.geo` — geodata (extra: `geo`) + +- `raster`: `load_and_merge_rasters`, `export_multiband_geotiff`, + `convert_raster_to_xarray` — load/merge raster tiles, write GeoTIFFs, wrap + arrays as rioxarray `DataArray`s. +- `terrain`: `compute_slope_components`, `create_geometry_mask` — DEM slope + gradients and polygon rasterisation. +- `swisstopo`: `Point`, `BoundingBox`, `query_layer`, `query_layer_from_tiles`, + `merge_river_segments` — a client for the swisstopo / geo.admin.ch REST API. + +```python +from simlab_tools.geo import load_and_merge_rasters, compute_slope_components + +mosaic, x, y, transform, crs = load_and_merge_rasters(["tile_a.tif", "tile_b.tif"]) +slopes = compute_slope_components(mosaic, pixel_size=0.5) +``` + +### `simlab_tools.smoothing` — 1D smoothers (extra: `smoothing`) + +Interchangeable 1D curve smoothers behind a common `smooth(x, y)` protocol: +`MovingAverageSmoother`, `SplineSmoother`, `SavGolSmoother`, `APLRSmoother`, and +a `get_smoother(method)` factory. + +### `simlab_tools.logging` — context-aware logging + +`configure_logging`, `set_site_context` and `SiteFormatter` attach a per-context +key (e.g. a site or job id) to every log record, keeping logs attributable across +concurrent workers. + +### `simlab_tools.weather` — weather data (planned) + +Placeholder for provider-agnostic loaders of weather observations and forecasts. +The public surface is documented in `simlab_tools/weather/__init__.py`; +implementations are tracked as future work. + +## Development + +```bash +uv pip install -e ".[dev]" +ruff check . +pytest +``` + +Continuous integration (ruff + pytest on Python 3.11 and 3.12) runs on every push +and pull request via GitHub Actions. diff --git a/examples/s3_demo.ipynb b/examples/s3_demo.ipynb index f0e6a9f..6728fba 100644 --- a/examples/s3_demo.ipynb +++ b/examples/s3_demo.ipynb @@ -24,9 +24,9 @@ "from pathlib import Path\n", "\n", "from simlab_tools import (\n", + " download_files_from_bucket,\n", " get_s3_client,\n", " upload_files_to_bucket,\n", - " download_files_from_bucket,\n", ")" ] }, diff --git a/pyproject.toml b/pyproject.toml index b90ec4a..c264e0c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,21 +2,48 @@ name = "simlab-tools" version = "0.1.0" description = "Shared utilities for the SIM lab" +readme = "README.md" requires-python = ">=3.11" -dependencies = [ +dependencies = [] + +[project.optional-dependencies] +# Transfer files to/from S3-compatible object storage. +storage = [ "boto3>=1.35.0", - "moto[all]>=5.1.20", "mypy-boto3-s3>=1.35.0", - "pytest>=9.0.2", "tqdm>=4.66.0", ] - -[project.optional-dependencies] -dev = ["pytest>=8.0.0"] - -[tool.pytest.ini_options] -testpaths = ["tests"] -python_files = "test_*.py" +# Raster handling, terrain analysis and geodata API clients. +geo = [ + "numpy>=1.26", + "affine>=2.4", + "rasterio>=1.3", + "rioxarray>=0.15", + "xarray>=2024.1", + "pandas>=2.0", + "geopandas>=0.14", + "shapely>=2.0", + "requests>=2.32", +] +# 1D signal/curve smoothers. +smoothing = [ + "numpy>=1.26", + "scipy>=1.11", + "aplr>=10.0", +] +# Weather observations and forecasts (planned; see simlab_tools.weather). +weather = [] +# Everything above, for consumers that want the full toolkit. +all = [ + "simlab-tools[storage,geo,smoothing,weather]", +] +# Development / CI: full toolkit plus test and lint tooling. +dev = [ + "simlab-tools[all]", + "pytest>=8.0.0", + "moto[all]>=5.1.20", + "ruff>=0.6.0", +] [build-system] requires = ["hatchling"] @@ -24,3 +51,18 @@ build-backend = "hatchling.build" [tool.hatch.build.targets.wheel] packages = ["src/simlab_tools"] + +[tool.pytest.ini_options] +testpaths = ["tests"] +python_files = "test_*.py" + +[tool.ruff.lint] +select = ["E", "F", "I", "D"] + +[tool.ruff.lint.per-file-ignores] +# Allow undocumented tests and empty package docstrings. +"tests/*" = ["D"] +"**/__init__.py" = ["D104"] + +[tool.ruff.lint.pydocstyle] +convention = "numpy" diff --git a/src/simlab_tools/__init__.py b/src/simlab_tools/__init__.py index 0fc1f76..7631a22 100644 --- a/src/simlab_tools/__init__.py +++ b/src/simlab_tools/__init__.py @@ -1,21 +1,45 @@ -"""Shared utilities for the SIM lab.""" - -from simlab_tools.s3 import ( - download_file, - download_files_from_bucket, - download_specific_files_from_bucket, - get_s3_client, - list_bucket_contents, - upload_file, - upload_files_to_bucket, +"""Shared utilities for the SIM lab. + +Organised into topical subpackages: + +- :mod:`simlab_tools.storage` -- transfer files to/from S3-compatible buckets. +- :mod:`simlab_tools.geo` -- raster handling, terrain analysis and geodata APIs. +- :mod:`simlab_tools.weather` -- weather observations and forecasts (planned). +- :mod:`simlab_tools.logging` -- context-aware logging configuration. +- :mod:`simlab_tools.smoothing` -- 1D signal/curve smoothers. + +Each subpackage keeps its heavy dependencies behind an optional extra +(``storage``, ``geo``, ``smoothing``), so consumers only install what they use. +""" + +__version__ = "0.1.0" + +# Names re-exported from :mod:`simlab_tools.storage` for backwards compatibility +# with ``from simlab_tools import get_s3_client``. Resolved lazily so that +# importing the top-level package does not require the ``storage`` extra. +_STORAGE_EXPORTS = frozenset( + { + "get_s3_client", + "list_bucket_contents", + "upload_file", + "upload_files_to_bucket", + "download_file", + "download_files_from_bucket", + "download_specific_files_from_bucket", + } ) -__all__ = [ - "get_s3_client", - "list_bucket_contents", - "upload_file", - "upload_files_to_bucket", - "download_file", - "download_files_from_bucket", - "download_specific_files_from_bucket", -] +__all__ = ["__version__", *sorted(_STORAGE_EXPORTS)] + + +def __getattr__(name: str): + """Lazily resolve backwards-compatible storage exports (PEP 562).""" + if name in _STORAGE_EXPORTS: + from simlab_tools import storage + + return getattr(storage, name) + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") + + +def __dir__() -> list[str]: + return sorted(__all__) diff --git a/src/simlab_tools/geo/__init__.py b/src/simlab_tools/geo/__init__.py new file mode 100644 index 0000000..3b90727 --- /dev/null +++ b/src/simlab_tools/geo/__init__.py @@ -0,0 +1,43 @@ +"""Geodata utilities: raster handling, terrain analysis and geodata APIs. + +Submodules +---------- +- ``raster``: load, merge, export and wrap raster tiles (rasterio / rioxarray). +- ``terrain``: DEM slope components and geometry masks. +- ``swisstopo``: client for the swisstopo / geo.admin.ch REST API. +- ``types``: shared file-path type aliases. +""" + +from simlab_tools.geo.raster import ( + convert_raster_to_xarray, + export_multiband_geotiff, + load_and_merge_rasters, +) +from simlab_tools.geo.swisstopo import ( + BoundingBox, + Point, + merge_river_segments, + query_layer, + query_layer_from_tiles, +) +from simlab_tools.geo.terrain import compute_slope_components, create_geometry_mask +from simlab_tools.geo.types import FileName, FileNames + +__all__ = [ + # raster + "load_and_merge_rasters", + "export_multiband_geotiff", + "convert_raster_to_xarray", + # terrain + "compute_slope_components", + "create_geometry_mask", + # swisstopo + "Point", + "BoundingBox", + "query_layer", + "query_layer_from_tiles", + "merge_river_segments", + # types + "FileName", + "FileNames", +] diff --git a/src/simlab_tools/geo/raster.py b/src/simlab_tools/geo/raster.py new file mode 100644 index 0000000..3c3cdff --- /dev/null +++ b/src/simlab_tools/geo/raster.py @@ -0,0 +1,159 @@ +"""Utility functions to work with raster data. + +This includes: + - loading and merging (potentially with downsampling) raster data + - producing interactive 3D visualizations of DEM data + +""" + +import affine +import numpy as np +import rasterio +import rioxarray # noqa: F401 (registers the .rio xarray accessors) +import xarray as xr +from rasterio.crs import CRS +from rasterio.merge import merge +from rasterio.transform import xy + +from simlab_tools.geo.types import FileName, FileNames + + +def load_and_merge_rasters( + rasters_paths: FileNames, + downsample_factor: int = 1, +) -> tuple[np.ndarray, np.ndarray, np.ndarray, affine.Affine, CRS]: + """Load and merge multiple raster files into a single mosaic with coordinates. + + Parameters + ---------- + rasters_paths : FileNames + List of paths to raster files to merge + downsample_factor : int, optional + Factor by which to downsample the resolution (default: 1, no downsampling) + + Returns + ------- + tuple[np.ndarray, np.ndarray, np.ndarray, affine.Affine, CRS] + - mosaic: Merged raster data with shape (bands, height, width) + - x_coords: 1D array of x coordinates (length: width) + - y_coords: 1D array of y coordinates (length: height) + - transform: Affine transformation matrix + - crs: Coordinate reference system + """ + rasters = [] + for path in rasters_paths: + src = rasterio.open(path) + rasters.append(src) + + # Get the resolution and CRS from the first raster + base_res = rasters[0].res[0] + crs = rasters[0].crs + target_res = base_res * downsample_factor + + mosaic, out_trans = merge(rasters, res=target_res) + + for raster in rasters: + raster.close() + + # Create geo-referenced coordinate arrays + nbands, nrows, ncols = mosaic.shape + + # For x-coordinates: convert all column positions at row 0 + x_coords, _ = xy(out_trans, np.zeros(ncols), np.arange(ncols), offset='center') + + # For y-coordinates: convert all row positions at column 0 + _, y_coords = xy(out_trans, np.arange(nrows), np.zeros(nrows), offset='center') + + return mosaic, x_coords, y_coords, out_trans, crs + + +def export_multiband_geotiff( + data: np.ndarray, + output_path: FileName, + transform: affine.Affine, + crs: CRS, + band_descriptions: list[str] | None = None, +) -> None: + """Export multi-band raster data as a GeoTIFF. + + Parameters + ---------- + data: np.ndarray + Data array with shape (bands, height, width), or (height, width) for + single-band data + output_path: FileName + path to output GeoTIFF file + transform: affine.Affine + affine transform from rasterio + crs: CRS + coordinate reference system + band_descriptions: list[str] | None + optional list of band descriptions + """ + # Ensure data is 3D (bands, height, width) + if data.ndim == 2: + data = data[np.newaxis, :, :] + + nbands, height, width = data.shape + + with rasterio.open( + output_path, + "w", + driver="GTiff", + height=height, + width=width, + count=nbands, + dtype=data.dtype, + crs=crs, + transform=transform, + ) as dst: + for i in range(nbands): + dst.write(data[i], i + 1) + if band_descriptions and i < len(band_descriptions): + dst.set_band_description(i + 1, band_descriptions[i]) + + +def convert_raster_to_xarray( + raster: np.ndarray, + raster_x: np.ndarray, + raster_y: np.ndarray, + transform, + crs, +) -> xr.DataArray: + """Create xarray DataArray from raster arrays with geospatial metadata. + + This function wraps raster data and coordinate grids into an xarray DataArray + with proper spatial metadata using rioxarray. + + Parameters + ---------- + raster : np.ndarray + Raster data with shape (bands, height, width) + raster_x : np.ndarray + 1D array of x coordinates (length: width) + raster_y : np.ndarray + 1D array of y coordinates (length: height) + transform : affine.Affine + Affine transform from rasterio + crs : Any + Coordinate reference system (e.g., from rasterio) + + Returns + ------- + xr.DataArray + DEM with rioxarray spatial metadata (CRS and transform) + + """ + dem = xr.DataArray( + raster, + coords={ + "band": np.arange(1, raster.shape[0] + 1), + "y": raster_y, + "x": raster_x, + }, + dims=["band", "y", "x"], + ) + dem.rio.write_crs(crs, inplace=True) + dem.rio.write_transform(transform, inplace=True) + + return dem diff --git a/src/simlab_tools/geo/swisstopo.py b/src/simlab_tools/geo/swisstopo.py new file mode 100644 index 0000000..2ca4729 --- /dev/null +++ b/src/simlab_tools/geo/swisstopo.py @@ -0,0 +1,452 @@ +"""Utilities for querying the Swisstopo API. + +This module provides functions to interact with the Swiss Federal Office of Topography +(swisstopo) API services. + +Querying is done on a per layer basis. The user should provide the name of the layer +to be queried (for example all:ch.swisstopo.geologie-geocover), together with a geometry +(point, bbox or tiles list) defining the region to query. Results are returned +in a GeoPandas GeoDataFrame. + +Available layers for the REST API can be found +[here](https://data.geo.admin.ch/). Short name descriptions +for these layers are available +[here](https://api3.geo.admin.ch/rest/services/api/MapServer/layersTable). + Further documentation about the API can be found +[here](https://docs.geo.admin.ch/). + +""" + +import logging +from collections import defaultdict +from typing import cast + +import geopandas as gpd +import pandas as pd +import rasterio +import requests +import shapely.geometry +from shapely.geometry import LineString, MultiLineString, box +from shapely.ops import linemerge, unary_union + +from simlab_tools.geo.types import FileNames + + +class Point: + """A point geometry with an associated coordinate reference system.""" + + def __init__(self, coords: tuple[float, float], epsg_code: str = "2056"): + """Create a point. + + Parameters + ---------- + coords : tuple[float, float] + The (x, y) coordinates of the point. + epsg_code : str, optional + EPSG code for the coordinate reference system. Default is "2056" + (EPSG:2056, LV95). Supported reference systems are: 21781 (LV03), + 2056 (LV95), 4326 (WGS84) and 3857 (Web Pseudo-Mercator). + """ + self.x, self.y = coords + self.epsg_code = epsg_code + + +class BoundingBox: + """An axis-aligned bounding box with an associated CRS.""" + + def __init__( + self, bbox: tuple[float, float, float, float], epsg_code: str = "2056" + ): + """Create a bounding box from (minx, miny, maxx, maxy).""" + self.minx, self.miny, self.maxx, self.maxy = bbox + if self.minx >= self.maxx or self.miny >= self.maxy: + raise ValueError( + f"Invalid bounding box: minX ({self.minx}) must be less than " + f"maxX ({self.maxx}), and minY ({self.miny}) must be less than " + f"maxY ({self.maxy})" + ) + self.epsg_code = epsg_code + + +def query_layer_from_tiles( + layer: str, + tile_files: FileNames, + return_geometry: bool = True, + filter_to_extent: bool = False, +) -> pd.DataFrame: + """Query the Swisstopo geocover geological layer for multiple raster tiles. + + This function queries each tile individually to avoid API limitations with large + bounding boxes. For each tile, it extracts the bounds and queries the SwissTopo + API, then merges all results and dissolves touching polygons with the same ids + + Parameters + ---------- + tile_files : FileNames + List of paths to raster tile files (e.g., GeoTIFF files). + return_geometry : bool, optional + Whether to return geometry information in the response. Default is True. + filter_to_extent : bool, optional + Whether to filter results to only include features entirely within the + merged tile extent. Default is False. + + Returns + ------- + pd.DataFrame + GeoDataFrame with parsed features and attributes + + """ + all_gdfs = [] + tile_bounds = [] # Collect bounds of all tiles for filtering + + for tile_file in tile_files: + with rasterio.open(tile_file) as src: + if src.crs is not None: + epsg_code = src.crs.to_epsg() + else: + epsg_code = "2056" + + bbox = BoundingBox( + ( + src.bounds.left, + src.bounds.bottom, + src.bounds.right, + src.bounds.top, + ), + epsg_code, + ) + gdf_tile = query_layer( + layer=layer, + geometry=bbox, + return_geometry=return_geometry, + ) + all_gdfs.append(gdf_tile) + + # Collect bounds for later filtering + if filter_to_extent: + tile_bounds.append( + box( + src.bounds.left, + src.bounds.bottom, + src.bounds.right, + src.bounds.top, + ) + ) + + # Merge results and remove duplicates. + gdf = gpd.GeoDataFrame(pd.concat(all_gdfs, ignore_index=True)) + gdf_unique = gdf.drop_duplicates(subset=["id"], keep="first").copy() + + # Extract LaufNr from gwlnr if available + # LaufNr (course number) is the last 4 digits of gwlnr and indicates river order. + # 0 for main water bodies, >0 for tributaries. + if ( + "gwlnr" in gdf_unique.columns + and gdf_unique["gwlnr"] + .apply(lambda x: isinstance(x, str) and len(x) >= 4) + .any() + ): + gdf_unique["laufnr"] = gdf_unique["gwlnr"].str[-4:].astype(int) + else: + # If gwlnr is not available or not in expected format, create laufnr with pd.NA + gdf_unique["laufnr"] = pd.NA + + # Filter to merged extent if requested + if filter_to_extent: + # Create union of all tile extents + merged_extent = unary_union(tile_bounds) + # Keep polygons that intersect (not just within), then clip to extent + gdf_unique = gdf_unique[gdf_unique.intersects(merged_extent)] + gdf_unique = gdf_unique.clip(merged_extent) + + return gdf_unique + + +def query_layer( + layer: str, + geometry: Point | BoundingBox, + return_geometry: bool = True, +) -> pd.DataFrame: + """Query a given layer from the Swisstopo API. + + Parameters + ---------- + layer: str + Name of the layer to query. + geometry: Point | BoundingBox, + Geometry defining the region to query. + return_geometry : bool, optional + Whether to return geometry information in the response. Default is True. + + Returns + ------- + gpd.GeoDataFrame + GeoDataFrame with parsed features and attributes + + Raises + ------ + requests.exceptions.RequestException + If the API request fails. + + """ + base_url = "https://api3.geo.admin.ch/rest/services/ech/MapServer/identify" + + params = { + "tolerance": 0, + "layers": layer, + "returnGeometry": "true" if return_geometry else "false", + "sr": geometry.epsg_code, + } + if isinstance(geometry, Point): + params["geometryType"] = ("esriGeometryPoint",) + params["geometry"] = f"{geometry.x},{geometry.y}" + elif isinstance(geometry, BoundingBox): + params["geometryType"] = ("esriGeometryEnvelope",) + params["geometry"] = ( + f"{geometry.minx},{geometry.miny},{geometry.maxx},{geometry.maxy}", + ) + + response = requests.get(base_url, params=params) + response.raise_for_status() + gdf_tile = _parse_api_response(response.json(), geometry.epsg_code) + + return gdf_tile + + +def _parse_api_response(response: dict, crs) -> pd.DataFrame: + """Parse Swisstopo API response into a GeoDataFrame. + + Parameters + ---------- + response : dict + JSON response from the geocover API + crs : str or pyproj.CRS + Coordinate reference system for the geometries + + Returns + ------- + gpd.GeoDataFrame + GeoDataFrame with parsed features and attributes + + """ + if "results" not in response or len(response["results"]) == 0: + logging.warning("API returned no results for this query") + return gpd.GeoDataFrame() + + features = [] + for result in response["results"]: + attributes = result.get("attributes", {}) + geometry_data = result.get("geometry", None) + + # Parse geometry if available. + geom = None + if geometry_data: + # ESRI format - handle different geometry types + if "rings" in geometry_data and geometry_data["rings"]: + # Polygon geometry + rings = geometry_data["rings"] + if len(rings) == 1: + geom = shapely.geometry.Polygon(rings[0]) + else: + # Multiple rings - could be multipolygon or polygon with holes + polygons = [shapely.geometry.Polygon(ring) for ring in rings] + geom = shapely.geometry.MultiPolygon(polygons) + elif "paths" in geometry_data and geometry_data["paths"]: + # Polyline geometry + paths = geometry_data["paths"] + if len(paths) == 1: + geom = shapely.geometry.LineString(paths[0]) + else: + geom = shapely.geometry.MultiLineString(paths) + elif "x" in geometry_data and "y" in geometry_data: + # Point geometry + geom = shapely.geometry.Point(geometry_data["x"], geometry_data["y"]) + elif isinstance(geometry_data, dict): + # Try standard GeoJSON format as fallback + geom = shapely.geometry.shape(geometry_data) + + feature = { + "id": result.get("id", None), + **attributes, + "geometry": geom, + } + + features.append(feature) + + gdf = gpd.GeoDataFrame(features, crs=crs) + + return gdf + + +def merge_river_segments( + rivers_gdf: gpd.GeoDataFrame, + group_by: str = "gewissnr", + preserve_metadata: bool = True, +) -> gpd.GeoDataFrame: + """Merge river segments belonging to the same river network. + + Groups river segments by a common identifier (typically 'gewissnr') and merges + them into single features. Segments are connected geometrically where they touch + using shapely's linemerge operation. + + Parameters + ---------- + rivers_gdf : gpd.GeoDataFrame + GeoDataFrame with river segments from VEC25 layer. Must contain + the column specified in `group_by` parameter. + group_by : str, optional + Column name to group segments by (default: "gewissnr"). + Segments with the same value are merged into one river. + preserve_metadata : bool, optional + Whether to preserve metadata from original segments in the merged result. + Default: True. If True, adds columns: 'segment_count', 'merged_id', + 'original_ids'. + + Returns + ------- + gpd.GeoDataFrame + GeoDataFrame with merged rivers. Each row represents one unique river + (one gewissnr value). Geometries are LineString or MultiLineString. + + Notes + ----- + **Merging Approach**: + + This function uses `shapely.ops.linemerge()` to connect segments geometrically + where their endpoints touch. This approach: + - Connects touching segments into continuous LineStrings + - Handles disconnected tributaries as MultiLineString components + - Does not depend on elevation data (more robust to DEM noise) + - Preserves river network topology naturally + + **Connectivity**: + - Segments with same gewissnr that touch are merged into LineString + - Disconnected segments (tributaries, branches) remain as MultiLineString + - Merging is purely geometric - relies on endpoint proximity + + **Metadata Preservation**: + If preserve_metadata=True, adds these columns: + - 'segment_count': number of original segments merged + - 'original_ids': list of original segment IDs + - Other columns: takes value from first segment in group + + The 'id' column is always set to the gewissnr value for all rivers + (both single and multi-segment) to provide consistent identification. + + Examples + -------- + >>> # Merge VEC25 rivers using gewissnr + >>> rivers = query_layer_from_tiles( + ... "all:ch.swisstopo.vec25-gewaessernetz_referenz", tiles + ... ) + >>> rivers_merged = merge_river_segments(rivers, group_by="gewissnr") + >>> print(f"Before: {len(rivers)} segments, After: {len(rivers_merged)} rivers") + + >>> # Merge without metadata preservation + >>> rivers_merged = merge_river_segments( + ... rivers, group_by="gewissnr", preserve_metadata=False + ... ) + """ + # Validate group_by column exists + if group_by not in rivers_gdf.columns: + raise ValueError( + f"Column '{group_by}' not found in rivers_gdf. " + f"Available columns: {list(rivers_gdf.columns)}" + ) + + # Create working copy + working_gdf = rivers_gdf.copy() + + # Remove segments with missing gewissnr + original_count = len(working_gdf) + working_gdf = working_gdf.dropna(subset=[group_by]) + excluded = original_count - len(working_gdf) + if excluded > 0: + logging.info(f" Excluded {excluded} segment(s) with missing {group_by}") + + if len(working_gdf) == 0: + logging.warning("No segments remaining after filtering") + return gpd.GeoDataFrame(columns=rivers_gdf.columns, crs=rivers_gdf.crs) + + # Group segments by gewissnr + river_groups = defaultdict(list) + for idx, row in working_gdf.iterrows(): + gewissnr_value = row[group_by] + river_groups[gewissnr_value].append( + { + "index": idx, + "row": row, + "geometry": row.geometry, + "id": row.get("id", idx), + } + ) + + logging.info( + f" Grouped {len(working_gdf)} segment(s) into " + f"{len(river_groups)} unique river(s)" + ) + + # Process each river group + merged_features = [] + + for gewissnr, segments in river_groups.items(): + # Handle single-segment rivers + if len(segments) == 1: + seg_data = segments[0] + merged_row = seg_data["row"].copy() + + if preserve_metadata: + merged_row["segment_count"] = 1 + merged_row["original_ids"] = [seg_data["id"]] + + # Set id to gewissnr for consistency + merged_row["id"] = gewissnr + + merged_features.append(merged_row) + continue + + # Handle multi-segment rivers + # Collect all line geometries from segments + all_lines = [] + for seg_data in segments: + geom = seg_data["geometry"] + + # Extract LineStrings from both LineString and MultiLineString + if isinstance(geom, MultiLineString): + all_lines.extend(list(geom.geoms)) + elif isinstance(geom, LineString): + all_lines.append(geom) + # Skip other geometry types (shouldn't happen for rivers) + + if not all_lines: + # No valid line geometries + continue + + # Use linemerge to connect touching segments + if len(all_lines) == 1: + merged_geom = all_lines[0] + else: + # linemerge connects segments where endpoints touch + # Returns LineString if all connected, MultiLineString if disconnected parts + merged_geom = linemerge(all_lines) + + # Create merged row using first segment as template + template_row = segments[0]["row"].copy() + template_row.geometry = merged_geom + + if preserve_metadata: + template_row["segment_count"] = len(segments) + template_row["original_ids"] = [s["id"] for s in segments] + + # Set id to gewissnr for consistency + template_row["id"] = gewissnr + + merged_features.append(template_row) + + # Construct output GeoDataFrame + merged_gdf = gpd.GeoDataFrame(merged_features, crs=rivers_gdf.crs).reset_index( + drop=True + ) + + logging.info(f" Merge result: {len(merged_gdf)} river(s)") + return cast(gpd.GeoDataFrame, merged_gdf) diff --git a/src/simlab_tools/geo/terrain.py b/src/simlab_tools/geo/terrain.py new file mode 100644 index 0000000..2b26d46 --- /dev/null +++ b/src/simlab_tools/geo/terrain.py @@ -0,0 +1,116 @@ +"""Terrain analysis functions for geomorphological studies. + +This module provides functions for computing terrain derivatives (slopes, aspects) +and masking operations based on geometric regions. +""" + +import numpy as np +from rasterio.features import geometry_mask + + +def compute_slope_components( + raster: np.ndarray, pixel_size: float = 1.0 +) -> dict[str, np.ndarray]: + """Compute slope gradients from a DEM raster. + + Uses NumPy's second-order central-difference gradient for efficient + calculation of terrain slopes. The gradients represent the rate of + elevation change in the x and y directions. + + Parameters + ---------- + raster : np.ndarray + DEM raster with shape (1, height, width) or (height, width). + Elevation values should be in meters. + pixel_size : float, optional + Ground sampling distance in metres (default 1.0). Must match the DEM + resolution so that gradients are in m/m rather than m/pixel. + + Returns + ------- + dict[str, np.ndarray] + Dictionary containing: + - 'slopes_x': gradient in x direction (m/m or rise/run) + - 'slopes_y': gradient in y direction (m/m or rise/run) + - 'slopes_magnitude': magnitude of gradient vector (m/m) + + Notes + ----- + The magnitude can be converted to slope angle in degrees using: + ``angle_degrees = np.arctan(slopes_magnitude) * 180 / np.pi`` + + Examples + -------- + >>> raster, x, y, trans, crs = load_and_merge_rasters(tiles) + >>> slopes = compute_slope_components(raster, pixel_size=0.5) + >>> slope_angles = np.arctan(slopes['slopes_magnitude']) * 180 / np.pi + >>> print(f"Mean slope: {slope_angles.mean():.2f} degrees") + """ + # Compute gradients along both axes; spacing converts pixel differences to metres. + slopes_x, slopes_y = np.gradient(raster.squeeze(), pixel_size, pixel_size) + + # Calculate magnitude from x and y components + slopes_magnitude = np.sqrt(slopes_x**2 + slopes_y**2) + + return { + "slopes_x": slopes_x, + "slopes_y": slopes_y, + "slopes_magnitude": slopes_magnitude, + } + + +def create_geometry_mask( + geometries: list, + shape: tuple[int, int], + transform, + invert: bool = True, +) -> np.ndarray: + """Create a boolean mask from polygon geometries. + + This function rasterizes vector geometries (polygons) into a boolean mask + that can be used to clip or filter raster data. + + Parameters + ---------- + geometries : list + List of shapely geometries (typically polygons) to rasterize + shape : tuple[int, int] + Output shape (height, width) matching the raster dimensions + transform : affine.Affine + Affine transform mapping pixel coordinates to geographic coordinates + invert : bool, optional + If True (default), mask is True inside polygons and False outside. + If False, mask is True outside polygons and False inside. + + Returns + ------- + np.ndarray + Boolean mask with shape (height, width). By default (invert=True), + True values indicate pixels inside the geometries. + + Examples + -------- + >>> # Mask DEM to only show areas within geological features + >>> geometries = [geom for geom in gdf.geometry if geom is not None] + >>> mask = create_geometry_mask( + ... geometries, + ... shape=(dem.shape[1], dem.shape[2]), + ... transform=transform, + ... invert=True + ... ) + >>> dem_masked = dem.values[mask] + >>> print(f"Elevation range: {dem_masked.min():.1f} - {dem_masked.max():.1f} m") + """ + # Create mask using rasterio: False inside geometries, True outside + mask = geometry_mask( + geometries, + out_shape=shape, + transform=transform, + invert=False, # False means: mask is False inside, True outside + ) + + # Invert if requested (default behavior: True inside polygons) + if invert: + mask = ~mask + + return mask diff --git a/src/simlab_tools/geo/types.py b/src/simlab_tools/geo/types.py new file mode 100644 index 0000000..f7ab7d8 --- /dev/null +++ b/src/simlab_tools/geo/types.py @@ -0,0 +1,7 @@ +"""Shared type aliases for geodata utilities.""" + +from collections.abc import Iterable +from pathlib import Path + +FileName = str | Path +FileNames = Iterable[FileName] diff --git a/src/simlab_tools/logging.py b/src/simlab_tools/logging.py new file mode 100644 index 0000000..bf342d6 --- /dev/null +++ b/src/simlab_tools/logging.py @@ -0,0 +1,56 @@ +"""Context-aware logging configuration for parallel processing. + +A :class:`contextvars.ContextVar` carries a per-context key (for example a site +or job id) that is injected into every log record, so logs stay attributable +even when work runs concurrently across threads or async tasks. +""" + +import logging +from contextvars import ContextVar +from pathlib import Path + +site_context: ContextVar[str] = ContextVar("site_context", default="GLOBAL") + + +def set_site_context(site_id: str) -> None: + """Set the current site context for logging.""" + site_context.set(site_id) + + +class SiteFormatter(logging.Formatter): + """Formatter that includes site_id from contextvars.""" + + def format(self, record: logging.LogRecord) -> str: + """Format log record with site_id injected.""" + record.site_id = site_context.get() + return super().format(record) + + +def configure_logging( + log_file: Path | None = None, + level: int = logging.INFO, +) -> None: + """Configure logging with site-aware formatting. + + Parameters + ---------- + log_file : Path | None + Path to log file. If None, logs only to console. + level : int + Logging level (default: INFO). + """ + fmt = "%(asctime)s | %(levelname)-8s | [%(site_id)s] | %(message)s" + datefmt = "%Y-%m-%d %H:%M:%S" + formatter = SiteFormatter(fmt, datefmt=datefmt) + + root = logging.getLogger() + root.handlers.clear() + + handlers: list[logging.Handler] = [logging.StreamHandler()] + if log_file is not None: + handlers.append(logging.FileHandler(log_file, mode="w")) + + for handler in handlers: + handler.setFormatter(formatter) + + logging.basicConfig(level=level, handlers=handlers, force=True) diff --git a/src/simlab_tools/smoothing.py b/src/simlab_tools/smoothing.py new file mode 100644 index 0000000..8f58812 --- /dev/null +++ b/src/simlab_tools/smoothing.py @@ -0,0 +1,467 @@ +"""Elevation profile smoothers using the Protocol pattern. + +This module provides a modular framework for smoothing river elevation profiles. +Each smoother implements the ElevationSmoother protocol and can be used +interchangeably. +""" + +from typing import Protocol + +import numpy as np +from aplr import APLRRegressor + + +class ElevationSmoother(Protocol): + """Protocol for river elevation profile smoothers.""" + + def smooth( + self, + cumulative_distance: np.ndarray, + elevation: np.ndarray, + ) -> np.ndarray: + """Smooth elevation profile, returning smoothed values. + + Parameters + ---------- + cumulative_distance : np.ndarray + Cumulative distance along river in meters (1D array). + elevation : np.ndarray + Elevation values at each distance point (1D array). + + Returns + ------- + np.ndarray + Smoothed elevation values (same length as input). + """ + ... + + +def _window_size_to_points( + cumulative_distance: np.ndarray, + window_size_meters: float, +) -> int: + """Convert window size from meters to number of points. + + Ensures the result is an odd number >= 3 for symmetric windows. + + Parameters + ---------- + cumulative_distance : np.ndarray + Cumulative distance array to compute mean spacing. + window_size_meters : float + Window size in meters. + + Returns + ------- + int + Number of points for the window (odd, >= 3). + """ + spacing = np.mean(np.diff(cumulative_distance)) + window_points = max(3, int(window_size_meters / spacing)) + + # Ensure odd number for symmetric window + if window_points % 2 == 0: + window_points += 1 + + return window_points + + +class MovingAverageSmoother: + """Simple moving average smoother with reflection padding. + + Applies convolution with a uniform kernel after padding the data + using reflection to avoid edge artifacts. + + Parameters + ---------- + window_size_meters : float + Smoothing window size in meters (default: 5.0). + """ + + def __init__(self, window_size_meters: float = 5.0) -> None: + self.window_size_meters = window_size_meters + + def smooth( + self, + cumulative_distance: np.ndarray, + elevation: np.ndarray, + ) -> np.ndarray: + """Smooth elevation profile using moving average. + + Parameters + ---------- + cumulative_distance : np.ndarray + Cumulative distance along river in meters. + elevation : np.ndarray + Elevation values at each distance point. + + Returns + ------- + np.ndarray + Smoothed elevation values. + """ + if len(elevation) < 3: + return elevation.copy() + + window_points = _window_size_to_points( + cumulative_distance, self.window_size_meters + ) + + # Don't smooth if window is larger than the data + if window_points >= len(elevation): + return elevation.copy() + + # Create uniform kernel for moving average + kernel = np.ones(window_points) / window_points + + # Pad the data using reflection to avoid edge effects + pad_width = window_points // 2 + elevation_padded = np.pad(elevation, pad_width, mode="reflect") + + # Apply convolution + smoothed_padded = np.convolve(elevation_padded, kernel, mode="same") + + # Remove padding to get back to original length + smoothed = smoothed_padded[pad_width:-pad_width] + + return smoothed + + +class SplineSmoother: + """Cubic spline smoother using scipy.interpolate.UnivariateSpline. + + Parameters + ---------- + window_size_meters : float + Controls the smoothing factor - larger values produce smoother results + (default: 5.0). + """ + + def __init__(self, window_size_meters: float = 5.0) -> None: + self.window_size_meters = window_size_meters + + def smooth( + self, + cumulative_distance: np.ndarray, + elevation: np.ndarray, + ) -> np.ndarray: + """Smooth elevation profile using cubic spline. + + Parameters + ---------- + cumulative_distance : np.ndarray + Cumulative distance along river in meters. + elevation : np.ndarray + Elevation values at each distance point. + + Returns + ------- + np.ndarray + Smoothed elevation values. + """ + if len(elevation) < 3: + return elevation.copy() + + from scipy.interpolate import UnivariateSpline + + window_points = _window_size_to_points( + cumulative_distance, self.window_size_meters + ) + + # Don't smooth if window is larger than the data + if window_points >= len(elevation): + return elevation.copy() + + # Smoothing factor based on window size + smoothing_factor = (len(elevation) / window_points) * np.var(elevation) + + # Create spline (k=3 for cubic spline) + spline = UnivariateSpline( + cumulative_distance, + elevation, + s=smoothing_factor, + k=min(3, len(elevation) - 1), + ) + + # Evaluate spline at original distance points + return spline(cumulative_distance) + + +class SavGolSmoother: + """Savitzky-Golay filter smoother. + + Applies a polynomial fit in a sliding window to smooth the data + while preserving higher moments than simple averaging. + + Parameters + ---------- + window_size_meters : float + Smoothing window size in meters (default: 5.0). + polyorder : int + Order of the polynomial to fit (default: 2). + Must be less than window_length in points. + """ + + def __init__(self, window_size_meters: float = 5.0, polyorder: int = 2) -> None: + self.window_size_meters = window_size_meters + self.polyorder = polyorder + + def smooth( + self, + cumulative_distance: np.ndarray, + elevation: np.ndarray, + ) -> np.ndarray: + """Smooth elevation profile using Savitzky-Golay filter. + + Parameters + ---------- + cumulative_distance : np.ndarray + Cumulative distance along river in meters. + elevation : np.ndarray + Elevation values at each distance point. + + Returns + ------- + np.ndarray + Smoothed elevation values. + """ + if len(elevation) < 3: + return elevation.copy() + + from scipy.signal import savgol_filter + + window_points = _window_size_to_points( + cumulative_distance, self.window_size_meters + ) + + # Don't smooth if window is larger than the data + if window_points >= len(elevation): + return elevation.copy() + + # Ensure polyorder < window_length + polyorder = min(self.polyorder, window_points - 1) + + # Apply Savitzky-Golay filter + return savgol_filter(elevation, window_points, polyorder) + + +class APLRSmoother: + """APLR (Automatic Piecewise Linear Regression) smoother. + + Fits a piecewise linear function to the elevation profile, automatically + detecting changepoints where the gradient changes. + + Provides extra outputs (segments, model) via attributes after smoothing. + + Parameters + ---------- + min_observations_in_split : int + Minimum number of observations per segment (default: 20). + m : int + Maximum boosting steps (default: 3000). + v : float + Learning rate (default: 0.5). + """ + + def __init__( + self, + min_observations_in_split: int = 20, + m: int = 3000, + v: float = 0.5, + ) -> None: + self.min_observations_in_split = min_observations_in_split + self.m = m + self.v = v + self.model_: APLRRegressor | None = None + self.segments_: list[dict] | None = None + + def smooth( + self, + cumulative_distance: np.ndarray, + elevation: np.ndarray, + ) -> np.ndarray: + """Smooth elevation profile using APLR. + + After calling this method, the fitted model is available via + `self.model_` and extracted segments via `self.segments_`. + + Parameters + ---------- + cumulative_distance : np.ndarray + Cumulative distance along river in meters. + elevation : np.ndarray + Elevation values at each distance point. + + Returns + ------- + np.ndarray + Smoothed elevation values. + """ + if len(elevation) < 3: + self.model_ = None + self.segments_ = None + return elevation.copy() + + X = cumulative_distance.reshape(-1, 1) + + model = APLRRegressor( + m=self.m, + v=self.v, + max_interaction_level=0, # No interactions (univariate) + min_observations_in_split=self.min_observations_in_split, + verbosity=0, + random_state=42, + ) + + model.fit(X, elevation, X_names=["distance"]) + smoothed = model.predict(X, cap_predictions_to_minmax_in_training=False) + + self.model_ = model + self.segments_ = self._extract_segments(model, cumulative_distance, smoothed) + + return smoothed + + def _extract_segments( + self, + model: APLRRegressor, + distance: np.ndarray, + elevation_smoothed: np.ndarray, + gradient_tolerance: float = 0.01, + ) -> list[dict]: + """Extract river segments from a fitted APLR model. + + Parameters + ---------- + model : APLRRegressor + Fitted APLR model. + distance : np.ndarray + Cumulative distance array. + elevation_smoothed : np.ndarray + Smoothed elevation array. + gradient_tolerance : float + Minimum gradient change (m/m) to identify a breakpoint. + + Returns + ------- + list[dict] + Segments with keys: 'start', 'end', 'gradient', 'gradient_percent', + 'gradient_angle', 'elevation_start', 'elevation_end'. + """ + # Get shape from APLR model + shape = model.get_main_effect_shape(0) + intercept = model.get_intercept() + + # Sort by x-values (distance) + shape_x = np.array(sorted(shape.keys())) + shape_y = np.array([shape[xi] for xi in shape_x]) + intercept + + # Compute gradients between consecutive points + dx = np.diff(shape_x) + dy = np.diff(shape_y) + gradients = dy / dx + + # Find where gradient changes (breakpoints) + gradient_changes = np.abs(np.diff(gradients)) > gradient_tolerance + breakpoint_indices = np.where(gradient_changes)[0] + 1 + + # Build segment boundaries from breakpoints + breakpoint_x = shape_x[breakpoint_indices] + boundaries = np.concatenate([[distance.min()], breakpoint_x, [distance.max()]]) + + # Build segments + segments = [] + for i in range(len(boundaries) - 1): + start, end = boundaries[i], boundaries[i + 1] + + start_idx = np.searchsorted(distance, start) + end_idx = np.searchsorted(distance, end, side="right") - 1 + end_idx = max(end_idx, start_idx + 1) + + if end_idx < len(distance): + elev_start = elevation_smoothed[start_idx] + elev_end = elevation_smoothed[end_idx] + length = distance[end_idx] - distance[start_idx] + + if length > 0: + grad = (elev_end - elev_start) / length + segments.append( + { + "start": float(start), + "end": float(end), + "gradient": float(grad), + "gradient_percent": float(grad * 100), + "gradient_angle": float(np.degrees(np.arctan(abs(grad)))), + "elevation_start": float(elev_start), + "elevation_end": float(elev_end), + } + ) + + # Handle edge case: no segments found + if not segments: + grad = (elevation_smoothed[-1] - elevation_smoothed[0]) / ( + distance[-1] - distance[0] + ) + return [ + { + "start": float(distance.min()), + "end": float(distance.max()), + "gradient": float(grad), + "gradient_percent": float(grad * 100), + "gradient_angle": float(np.degrees(np.arctan(abs(grad)))), + "elevation_start": float(elevation_smoothed[0]), + "elevation_end": float(elevation_smoothed[-1]), + } + ] + + return segments + + +# Registry mapping method names to smoother classes +SMOOTHER_REGISTRY: dict[str, type] = { + "moving_average": MovingAverageSmoother, + "spline": SplineSmoother, + "savgol": SavGolSmoother, + "aplr": APLRSmoother, +} + + +def get_smoother( + method: str, + window_size_meters: float = 5.0, + **kwargs, +) -> ElevationSmoother: + """Get a smoother instance by method name. + + Parameters + ---------- + method : str + Smoothing method: 'moving_average', 'spline', 'savgol', or 'aplr'. + window_size_meters : float + Window size for window-based smoothers (default: 5.0). + **kwargs + Additional arguments passed to the smoother constructor. + + Returns + ------- + ElevationSmoother + A smoother instance. + + Raises + ------ + ValueError + If the method is not recognized. + """ + if method not in SMOOTHER_REGISTRY: + valid_methods = ", ".join(sorted(SMOOTHER_REGISTRY.keys())) + raise ValueError( + f"Unknown smoothing method: {method}. Valid options: {valid_methods}" + ) + + smoother_cls = SMOOTHER_REGISTRY[method] + + # Handle different constructor signatures + if method == "aplr": + # APLR doesn't use window_size_meters + return smoother_cls(**kwargs) + else: + return smoother_cls(window_size_meters=window_size_meters, **kwargs) diff --git a/src/simlab_tools/storage/__init__.py b/src/simlab_tools/storage/__init__.py new file mode 100644 index 0000000..3f72c83 --- /dev/null +++ b/src/simlab_tools/storage/__init__.py @@ -0,0 +1,21 @@ +"""Storage utilities: transferring files to and from S3-compatible buckets.""" + +from simlab_tools.storage.s3 import ( + download_file, + download_files_from_bucket, + download_specific_files_from_bucket, + get_s3_client, + list_bucket_contents, + upload_file, + upload_files_to_bucket, +) + +__all__ = [ + "get_s3_client", + "list_bucket_contents", + "upload_file", + "upload_files_to_bucket", + "download_file", + "download_files_from_bucket", + "download_specific_files_from_bucket", +] diff --git a/src/simlab_tools/s3.py b/src/simlab_tools/storage/s3.py similarity index 83% rename from src/simlab_tools/s3.py rename to src/simlab_tools/storage/s3.py index 9987dac..b575f82 100644 --- a/src/simlab_tools/s3.py +++ b/src/simlab_tools/storage/s3.py @@ -9,25 +9,49 @@ from mypy_boto3_s3 import S3Client from tqdm import tqdm +# Default multipart transfer configuration shared by up- and downloads. +_TRANSFER_CONFIG = TransferConfig( + multipart_threshold=1024 * 25, # 25 MB threshold + max_concurrency=10, + use_threads=True, +) -def get_s3_client(endpoint_url: str, key_id: str, key_secret: str) -> S3Client: - """Initializes an S3 client. + +def get_s3_client( + endpoint_url: str, + key_id: Optional[str] = None, + key_secret: Optional[str] = None, +) -> S3Client: + """Initialize an S3 client for an S3-compatible endpoint. Parameters ---------- endpoint_url: str - Url to the S3 endpoint. - key_id: str - Id of the secret key to use for S3 access. - key_secret: str - Secret key for S3 access. + Url to the S3 endpoint (e.g. ``https://zhw-a.s3.cloud.switch.ch``). + key_id: str, optional + Access key id for S3 access. If omitted, falls back to the + ``S3_ACCESS_KEY_ID`` environment variable. + key_secret: str, optional + Secret key for S3 access. If omitted, falls back to the + ``S3_SECRET_ACCESS_KEY`` environment variable. Returns ------- S3Client - Client with connection opened to the given S3 endpoint. + Client with a connection opened to the given S3 endpoint. + + Raises + ------ + KeyError + If credentials are not provided and the corresponding environment + variables are not set. """ + if key_id is None: + key_id = os.environ["S3_ACCESS_KEY_ID"] + if key_secret is None: + key_secret = os.environ["S3_SECRET_ACCESS_KEY"] + s3_client = boto3.client( "s3", aws_access_key_id=key_id, @@ -55,12 +79,6 @@ def upload_file(client: S3Client, local_file: Path, bucket: str, key: str) -> No """ file_size = local_file.stat().st_size - config = TransferConfig( - multipart_threshold=1024 * 25, # 25MB threshold - max_concurrency=10, - use_threads=True, - ) - with tqdm( total=file_size, unit="B", @@ -69,10 +87,10 @@ def upload_file(client: S3Client, local_file: Path, bucket: str, key: str) -> No desc=local_file.name, ) as pbar: client.upload_file( - Filename=local_file, + Filename=str(local_file), Bucket=bucket, Key=key, - Config=config, + Config=_TRANSFER_CONFIG, Callback=lambda bytes_transferred: pbar.update(bytes_transferred), ) @@ -100,9 +118,9 @@ def upload_files_to_bucket( files: list[Path] List of pathlib.Path objects for each of the files to upload. base_folder: Optional[Path] - If the files to upload are all situated in subfolder hierarchies of `base_folder`, - then this can be provided as an argument to specify that the subfolder hierarchy of `base_folder` - should be re-created in the S3 bucket. + If the files to upload are all situated in subfolder hierarchies of + `base_folder`, this can be provided to specify that the subfolder + hierarchy of `base_folder` should be re-created in the S3 bucket. """ uploaded_count = 0 @@ -151,12 +169,6 @@ def download_file(client: S3Client, bucket: str, key: str, local_file: Path) -> head_response = client.head_object(Bucket=bucket, Key=key) file_size = head_response["ContentLength"] - config = TransferConfig( - multipart_threshold=1024 * 25, # 25MB threshold - max_concurrency=10, - use_threads=True, - ) - with tqdm( total=file_size, unit="B", @@ -168,7 +180,7 @@ def download_file(client: S3Client, bucket: str, key: str, local_file: Path) -> Bucket=bucket, Key=key, Filename=str(local_file), - Config=config, + Config=_TRANSFER_CONFIG, Callback=lambda bytes_transferred: pbar.update(bytes_transferred), ) @@ -251,7 +263,8 @@ def download_files_from_bucket( print(f"\nError downloading {key}: {e}\n") print( - f"Download complete: {downloaded_count}/{len(all_objects)} files downloaded successfully" + f"Download complete: {downloaded_count}/{len(all_objects)} " + "files downloaded successfully" ) @@ -281,8 +294,8 @@ def list_bucket_contents( List of object metadata dicts with keys 'Key', 'Size', 'LastModified'. """ - # Need paginator cause otherwise we are limited to listing - # 1000 objectws with list_objects. + # A paginator is needed here: list_objects_v2 returns at most 1000 objects + # per call, whereas the paginator transparently fetches every page. paginator = client.get_paginator("list_objects_v2") pages = paginator.paginate(Bucket=bucket, Prefix=prefix) @@ -341,5 +354,6 @@ def download_specific_files_from_bucket( print(f"\nError downloading {key}: {e}\n") print( - f"Download complete: {downloaded_count}/{len(s3_keys)} files downloaded successfully" + f"Download complete: {downloaded_count}/{len(s3_keys)} " + "files downloaded successfully" ) diff --git a/src/simlab_tools/weather/__init__.py b/src/simlab_tools/weather/__init__.py new file mode 100644 index 0000000..14039a7 --- /dev/null +++ b/src/simlab_tools/weather/__init__.py @@ -0,0 +1,20 @@ +"""Weather data utilities for observations and forecasts. + +.. note:: + This subpackage is a placeholder. It defines the intended public surface for + loading meteorological data across SIM lab projects but does not yet ship + working loaders. + +Planned API +----------- +- ``observations``: load historical / near-real-time station observations + (temperature, precipitation, ...) for a location or region. +- ``forecasts``: load gridded or point weather forecasts. + +Both are expected to expose small, provider-agnostic ``load_*`` functions that +return tidy ``pandas``/``xarray`` structures, mirroring the style of the +:mod:`simlab_tools.geo` utilities. Implementations are tracked as future work; +see the project README for status. +""" + +__all__: list[str] = [] diff --git a/tests/conftest.py b/tests/conftest.py index d1f856b..247f8ce 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,9 +1,9 @@ -"""Pytest fixtures for S3 testing with moto.""" +"""Shared pytest fixtures: mocked S3 (moto) and synthetic rasters.""" import os -from pathlib import Path import boto3 +import numpy as np import pytest from moto import mock_aws @@ -72,3 +72,38 @@ def tmp_files(tmp_path): files.append(file4) return tmp_path, files + + +@pytest.fixture +def ramp_geotiff(tmp_path): + """Write a small single-band GeoTIFF with a linear west-east elevation ramp. + + Returns + ------- + tuple[Path, numpy.ndarray] + Path to the written GeoTIFF and the elevation array it contains. + Pixel size is 1 metre, CRS is EPSG:2056 (Swiss LV95). + """ + import rasterio + from rasterio.transform import from_origin + + height, width = 8, 10 + # Elevation increases by 2 m per column: a constant east-west gradient. + data = np.tile(np.arange(width, dtype="float32") * 2.0, (height, 1)) + transform = from_origin(2_600_000, 1_200_000, 1.0, 1.0) + + path = tmp_path / "ramp.tif" + with rasterio.open( + path, + "w", + driver="GTiff", + height=height, + width=width, + count=1, + dtype="float32", + crs="EPSG:2056", + transform=transform, + ) as dst: + dst.write(data, 1) + + return path, data diff --git a/tests/test_geo_raster.py b/tests/test_geo_raster.py new file mode 100644 index 0000000..3c7f312 --- /dev/null +++ b/tests/test_geo_raster.py @@ -0,0 +1,84 @@ +"""Tests for simlab_tools.geo.raster.""" + +import numpy as np +import rasterio +import xarray as xr + +from simlab_tools.geo.raster import ( + convert_raster_to_xarray, + export_multiband_geotiff, + load_and_merge_rasters, +) + + +class TestLoadAndMergeRasters: + """Tests for load_and_merge_rasters.""" + + def test_shapes_and_coords(self, ramp_geotiff): + """A single tile loads back with matching shape and coordinate lengths.""" + path, data = ramp_geotiff + + mosaic, x_coords, y_coords, transform, crs = load_and_merge_rasters([path]) + + assert mosaic.shape == (1, *data.shape) + assert x_coords.shape[0] == data.shape[1] + assert y_coords.shape[0] == data.shape[0] + assert crs.to_epsg() == 2056 + np.testing.assert_allclose(mosaic[0], data) + + def test_downsampling_reduces_resolution(self, ramp_geotiff): + """A downsample factor of 2 halves each spatial dimension.""" + path, data = ramp_geotiff + + mosaic, _, _, _, _ = load_and_merge_rasters([path], downsample_factor=2) + + assert mosaic.shape[1] == data.shape[0] // 2 + assert mosaic.shape[2] == data.shape[1] // 2 + + +class TestExportMultibandGeotiff: + """Tests for export_multiband_geotiff.""" + + def test_single_band_roundtrip(self, ramp_geotiff, tmp_path): + """A 2D array is written as a single-band GeoTIFF and reads back equal.""" + path, data = ramp_geotiff + with rasterio.open(path) as src: + transform, crs = src.transform, src.crs + + out = tmp_path / "out.tif" + export_multiband_geotiff(data, out, transform, crs) + + with rasterio.open(out) as src: + assert src.count == 1 + np.testing.assert_allclose(src.read(1), data) + + def test_band_descriptions_written(self, ramp_geotiff, tmp_path): + """Band descriptions are stored on the output raster.""" + path, data = ramp_geotiff + with rasterio.open(path) as src: + transform, crs = src.transform, src.crs + + stacked = np.stack([data, data + 1.0]) + out = tmp_path / "multi.tif" + export_multiband_geotiff( + stacked, out, transform, crs, band_descriptions=["elev", "elev_plus"] + ) + + with rasterio.open(out) as src: + assert src.count == 2 + assert src.descriptions == ("elev", "elev_plus") + + +class TestConvertRasterToXarray: + """Tests for convert_raster_to_xarray.""" + + def test_returns_dataarray_with_crs(self, ramp_geotiff): + """The wrapped array carries dims, coords and CRS metadata.""" + path, data = ramp_geotiff + mosaic, x_coords, y_coords, transform, crs = load_and_merge_rasters([path]) + + da = convert_raster_to_xarray(mosaic, x_coords, y_coords, transform, crs) + + assert isinstance(da, xr.DataArray) + assert da.dims == ("band", "y", "x") + assert da.rio.crs.to_epsg() == 2056 diff --git a/tests/test_logging.py b/tests/test_logging.py new file mode 100644 index 0000000..ff87df2 --- /dev/null +++ b/tests/test_logging.py @@ -0,0 +1,70 @@ +"""Tests for simlab_tools.logging.""" + +import logging + +from simlab_tools.logging import ( + SiteFormatter, + configure_logging, + set_site_context, + site_context, +) + + +def _make_record(msg: str = "hello") -> logging.LogRecord: + return logging.LogRecord( + name="test", + level=logging.INFO, + pathname=__file__, + lineno=1, + msg=msg, + args=(), + exc_info=None, + ) + + +class TestSiteFormatter: + """Tests for SiteFormatter and the site context variable.""" + + def test_defaults_to_global(self): + """With no context set the formatter injects the GLOBAL default.""" + token = site_context.set("GLOBAL") + try: + formatter = SiteFormatter("[%(site_id)s] %(message)s") + assert formatter.format(_make_record("hi")) == "[GLOBAL] hi" + finally: + site_context.reset(token) + + def test_set_site_context_is_injected(self): + """set_site_context changes the site_id injected into records.""" + token = site_context.set("GLOBAL") + try: + set_site_context("site-42") + formatter = SiteFormatter("[%(site_id)s] %(message)s") + assert formatter.format(_make_record("hi")) == "[site-42] hi" + finally: + site_context.reset(token) + + +class TestConfigureLogging: + """Tests for configure_logging.""" + + def test_console_only(self): + """Without a log file, only a stream handler is attached.""" + configure_logging(level=logging.DEBUG) + root = logging.getLogger() + assert root.level == logging.DEBUG + assert any(isinstance(h, logging.StreamHandler) for h in root.handlers) + + def test_writes_to_file(self, tmp_path): + """With a log file, records are written using the site format.""" + log_file = tmp_path / "run.log" + configure_logging(log_file=log_file) + set_site_context("s1") + + logging.getLogger("x").info("processing") + for handler in logging.getLogger().handlers: + handler.flush() + + contents = log_file.read_text() + assert "[s1]" in contents + assert "processing" in contents diff --git a/tests/test_s3.py b/tests/test_s3.py index 4ff58c2..b49e8be 100644 --- a/tests/test_s3.py +++ b/tests/test_s3.py @@ -1,13 +1,13 @@ """Test suite for simlab_tools.s3 module.""" -from pathlib import Path - import pytest -from simlab_tools.s3 import ( +import simlab_tools +from simlab_tools.storage.s3 import ( download_file, download_files_from_bucket, download_specific_files_from_bucket, + get_s3_client, list_bucket_contents, upload_file, upload_files_to_bucket, @@ -47,8 +47,8 @@ def test_upload_file_content_matches(self, test_bucket, tmp_path): # Download and verify content response = client.get_object(Bucket=bucket, Key="content_test.txt") - downloaded_content = response["Body"].read().decode("utf-8") - assert downloaded_content == test_content + downloaded_content = response["Body"].read().decode("utf-8") + assert downloaded_content == test_content class TestUploadFilesToBucket: """Tests for upload_files_to_bucket function.""" @@ -84,7 +84,9 @@ def test_upload_with_base_folder_preserves_structure(self, test_bucket, tmp_file # Check that subdirectory structure is preserved assert any("subdir/" in key for key in keys) - def test_upload_without_base_folder_flattens_structure(self, test_bucket, tmp_files): + def test_upload_without_base_folder_flattens_structure( + self, test_bucket, tmp_files + ): """Test that without base_folder files are flattened.""" client, bucket = test_bucket tmp_path, files = tmp_files @@ -283,3 +285,48 @@ def test_list_with_no_matches(self, populated_bucket): result = list_bucket_contents(client, bucket, file_extensions=[".xyz"]) assert result == [] + + +class TestGetS3Client: + """Tests for get_s3_client credential handling (no network calls).""" + + def test_explicit_credentials(self): + """Explicit credentials produce a client bound to the given endpoint.""" + client = get_s3_client("https://example.invalid", "id", "secret") + + assert client.meta.endpoint_url == "https://example.invalid" + creds = client._request_signer._credentials + assert creds.access_key == "id" + assert creds.secret_key == "secret" + + def test_credentials_from_env(self, monkeypatch): + """Credentials fall back to the S3_* environment variables.""" + monkeypatch.setenv("S3_ACCESS_KEY_ID", "env-id") + monkeypatch.setenv("S3_SECRET_ACCESS_KEY", "env-secret") + + client = get_s3_client("https://example.invalid") + + creds = client._request_signer._credentials + assert creds.access_key == "env-id" + assert creds.secret_key == "env-secret" + + def test_missing_env_raises(self, monkeypatch): + """A missing credential env var raises KeyError.""" + monkeypatch.delenv("S3_ACCESS_KEY_ID", raising=False) + monkeypatch.delenv("S3_SECRET_ACCESS_KEY", raising=False) + + with pytest.raises(KeyError): + get_s3_client("https://example.invalid") + + +class TestBackwardCompatibleExports: + """The historical top-level ``simlab_tools`` API is still resolvable.""" + + def test_lazy_top_level_reexport(self): + """``simlab_tools.get_s3_client`` resolves to the storage function.""" + assert simlab_tools.get_s3_client is get_s3_client + + def test_unknown_attribute_raises(self): + """Unknown attributes still raise AttributeError.""" + with pytest.raises(AttributeError): + simlab_tools.does_not_exist diff --git a/tests/test_smoothing.py b/tests/test_smoothing.py new file mode 100644 index 0000000..ab4a2db --- /dev/null +++ b/tests/test_smoothing.py @@ -0,0 +1,85 @@ +"""Tests for simlab_tools.smoothing.""" + +import numpy as np +import pytest + +from simlab_tools.smoothing import ( + APLRSmoother, + MovingAverageSmoother, + SavGolSmoother, + SplineSmoother, + get_smoother, +) + + +@pytest.fixture +def noisy_ramp(): + """A linear ramp with reproducible additive noise.""" + rng = np.random.default_rng(0) + distance = np.arange(200, dtype="float64") + signal = 0.5 * distance + noisy = signal + rng.normal(0, 1.0, size=distance.shape) + return distance, signal, noisy + + +class TestGetSmoother: + """Tests for the get_smoother factory.""" + + @pytest.mark.parametrize( + "method,cls", + [ + ("moving_average", MovingAverageSmoother), + ("spline", SplineSmoother), + ("savgol", SavGolSmoother), + ("aplr", APLRSmoother), + ], + ) + def test_returns_expected_class(self, method, cls): + """Each registered method maps to its smoother class.""" + assert isinstance(get_smoother(method), cls) + + def test_unknown_method_raises(self): + """An unknown method name raises ValueError.""" + with pytest.raises(ValueError): + get_smoother("does_not_exist") + + +class TestWindowSmoothers: + """Behavioural tests for the window-based smoothers.""" + + @pytest.mark.parametrize("method", ["moving_average", "spline", "savgol"]) + def test_smoothing_reduces_noise(self, method, noisy_ramp): + """Smoothing brings the noisy signal closer to the true ramp.""" + distance, signal, noisy = noisy_ramp + smoother = get_smoother(method, window_size_meters=15.0) + + smoothed = smoother.smooth(distance, noisy) + + assert smoothed.shape == noisy.shape + assert np.mean((smoothed - signal) ** 2) < np.mean((noisy - signal) ** 2) + + @pytest.mark.parametrize("method", ["moving_average", "spline", "savgol"]) + def test_short_input_returned_unchanged(self, method): + """Inputs shorter than three points are returned as a copy.""" + smoother = get_smoother(method) + distance = np.array([0.0, 1.0]) + elevation = np.array([10.0, 12.0]) + + result = smoother.smooth(distance, elevation) + + np.testing.assert_array_equal(result, elevation) + + +class TestAPLRSmoother: + """Tests for the APLR smoother.""" + + def test_smooth_populates_segments(self, noisy_ramp): + """After smoothing, the fitted model and segments are available.""" + distance, _, noisy = noisy_ramp + smoother = APLRSmoother(min_observations_in_split=20, m=200) + + smoothed = smoother.smooth(distance, noisy) + + assert smoothed.shape == noisy.shape + assert smoother.model_ is not None + assert smoother.segments_ and "gradient" in smoother.segments_[0] diff --git a/tests/test_swisstopo.py b/tests/test_swisstopo.py new file mode 100644 index 0000000..6737b19 --- /dev/null +++ b/tests/test_swisstopo.py @@ -0,0 +1,129 @@ +"""Tests for simlab_tools.geo.swisstopo (no network access).""" + +import geopandas as gpd +import pytest +from shapely.geometry import LineString, Polygon + +from simlab_tools.geo import swisstopo +from simlab_tools.geo.swisstopo import ( + BoundingBox, + Point, + merge_river_segments, + query_layer, +) + + +class TestGeometryTypes: + """Tests for the Point and BoundingBox helpers.""" + + def test_point_defaults_to_lv95(self): + """A Point stores its coordinates and defaults to EPSG:2056.""" + p = Point((2_600_000, 1_200_000)) + assert (p.x, p.y) == (2_600_000, 1_200_000) + assert p.epsg_code == "2056" + + def test_bounding_box_valid(self): + """A valid bounding box exposes its corners.""" + bbox = BoundingBox((0, 0, 10, 5)) + assert (bbox.minx, bbox.miny, bbox.maxx, bbox.maxy) == (0, 0, 10, 5) + + def test_bounding_box_rejects_inverted_extent(self): + """minX >= maxX (or minY >= maxY) raises ValueError.""" + with pytest.raises(ValueError): + BoundingBox((10, 0, 0, 5)) + + +class TestParseApiResponse: + """Tests for the private _parse_api_response helper.""" + + def test_parses_polygon_feature(self): + """An ESRI polygon result becomes a GeoDataFrame row with a Polygon.""" + response = { + "results": [ + { + "id": 1, + "attributes": {"gewissnr": "abc"}, + "geometry": {"rings": [[[0, 0], [0, 1], [1, 1], [1, 0], [0, 0]]]}, + } + ] + } + + gdf = swisstopo._parse_api_response(response, "2056") + + assert len(gdf) == 1 + assert gdf.iloc[0]["gewissnr"] == "abc" + assert isinstance(gdf.iloc[0].geometry, Polygon) + + def test_empty_results_returns_empty_frame(self): + """A response with no results yields an empty GeoDataFrame.""" + gdf = swisstopo._parse_api_response({"results": []}, "2056") + assert gdf.empty + + +class TestQueryLayer: + """Tests for query_layer with a stubbed HTTP layer.""" + + def test_query_layer_parses_response(self, monkeypatch): + """query_layer issues a request and parses the JSON into a GeoDataFrame.""" + + class _FakeResponse: + def raise_for_status(self): + return None + + def json(self): + return { + "results": [ + { + "id": 7, + "attributes": {"name": "river"}, + "geometry": {"paths": [[[0, 0], [1, 1]]]}, + } + ] + } + + captured = {} + + def _fake_get(url, params=None): + captured["url"] = url + captured["params"] = params + return _FakeResponse() + + monkeypatch.setattr(swisstopo.requests, "get", _fake_get) + + gdf = query_layer("some:layer", BoundingBox((0, 0, 10, 10))) + + assert len(gdf) == 1 + assert isinstance(gdf.iloc[0].geometry, LineString) + assert captured["params"]["layers"] == "some:layer" + + +class TestMergeRiverSegments: + """Tests for merge_river_segments.""" + + def test_touching_segments_merge_into_one_river(self): + """Two touching segments with the same id merge into a single feature.""" + gdf = gpd.GeoDataFrame( + { + "gewissnr": ["r1", "r1"], + "id": [1, 2], + "geometry": [ + LineString([(0, 0), (1, 0)]), + LineString([(1, 0), (2, 0)]), + ], + }, + crs="EPSG:2056", + ) + + merged = merge_river_segments(gdf, group_by="gewissnr") + + assert len(merged) == 1 + assert merged.iloc[0]["segment_count"] == 2 + assert merged.iloc[0]["id"] == "r1" + + def test_missing_group_column_raises(self): + """Merging on an absent column raises ValueError.""" + gdf = gpd.GeoDataFrame( + {"geometry": [LineString([(0, 0), (1, 0)])]}, crs="EPSG:2056" + ) + with pytest.raises(ValueError): + merge_river_segments(gdf, group_by="gewissnr") diff --git a/tests/test_terrain.py b/tests/test_terrain.py new file mode 100644 index 0000000..92ad9cc --- /dev/null +++ b/tests/test_terrain.py @@ -0,0 +1,64 @@ +"""Tests for simlab_tools.geo.terrain.""" + +import numpy as np +from rasterio.transform import from_origin +from shapely.geometry import box + +from simlab_tools.geo.terrain import compute_slope_components, create_geometry_mask + + +class TestComputeSlopeComponents: + """Tests for compute_slope_components.""" + + def test_constant_gradient_ramp(self): + """A constant east-west ramp yields a constant x-gradient of its slope.""" + # Elevation rises 3 m per column (axis 1), flat along rows (axis 0). + raster = np.tile(np.arange(10, dtype="float64") * 3.0, (6, 1)) + + slopes = compute_slope_components(raster, pixel_size=1.0) + + np.testing.assert_allclose(slopes["slopes_y"], 3.0) + np.testing.assert_allclose(slopes["slopes_x"], 0.0) + np.testing.assert_allclose(slopes["slopes_magnitude"], 3.0) + + def test_pixel_size_scales_gradient(self): + """Doubling the pixel size halves the gradient (m per metre).""" + raster = np.tile(np.arange(10, dtype="float64") * 3.0, (6, 1)) + + coarse = compute_slope_components(raster, pixel_size=2.0) + + np.testing.assert_allclose(coarse["slopes_y"], 1.5) + + def test_accepts_leading_band_axis(self): + """A (1, H, W) raster is squeezed and produces (H, W) gradients.""" + raster = np.tile(np.arange(10, dtype="float64") * 3.0, (6, 1))[np.newaxis] + + slopes = compute_slope_components(raster, pixel_size=1.0) + + assert slopes["slopes_magnitude"].shape == (6, 10) + + +class TestCreateGeometryMask: + """Tests for create_geometry_mask.""" + + def test_mask_true_inside_polygon(self): + """With invert=True the mask is True inside the polygon footprint.""" + transform = from_origin(0, 10, 1.0, 1.0) # 10x10 grid, 1 m pixels + # Polygon covering the top-left 4x4 pixel block in world coords. + poly = box(0, 6, 4, 10) + + mask = create_geometry_mask([poly], shape=(10, 10), transform=transform) + + assert mask.dtype == bool + assert mask[:4, :4].all() + assert not mask[6:, 6:].any() + + def test_invert_false_flips_mask(self): + """invert=False marks the outside of the polygon instead.""" + transform = from_origin(0, 10, 1.0, 1.0) + poly = box(0, 6, 4, 10) + + inside = create_geometry_mask([poly], (10, 10), transform, invert=True) + outside = create_geometry_mask([poly], (10, 10), transform, invert=False) + + np.testing.assert_array_equal(inside, ~outside) diff --git a/uv.lock b/uv.lock index c0124ff..5d7da3d 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,26 @@ version = 1 revision = 3 requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'emscripten'", + 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