From 05a370cac31f56f35d4e4b166e9152c7825cf338 Mon Sep 17 00:00:00 2001 From: f-PLT Date: Thu, 23 Oct 2025 15:46:13 -0400 Subject: [PATCH 1/6] Save progress --- geospatial_tools/radar/__init__.py | 0 geospatial_tools/radar/nimrod.py | 189 ++ geospatial_tools/utils.py | 98 + poetry.lock | 3643 ++++++++++++++++------------ pyproject.toml | 3 +- 5 files changed, 2383 insertions(+), 1550 deletions(-) create mode 100644 geospatial_tools/radar/__init__.py create mode 100644 geospatial_tools/radar/nimrod.py diff --git a/geospatial_tools/radar/__init__.py b/geospatial_tools/radar/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/geospatial_tools/radar/nimrod.py b/geospatial_tools/radar/nimrod.py new file mode 100644 index 0000000..1ed1690 --- /dev/null +++ b/geospatial_tools/radar/nimrod.py @@ -0,0 +1,189 @@ +import gzip +import shutil +from pathlib import Path +from typing import Any, Generator + +import numpy as np +import pandas as pd +import xarray as xr +from iris.analysis import MEAN +from iris.cube import Cube, CubeList +from iris.fileformats import netcdf +from iris.fileformats.nimrod import load_cubes + +from geospatial_tools.utils import parse_gzip_header + +FIVE_MIN = np.timedelta64(5, "m") + + +def extract_nimrod_from_archive(archive_file_path: str | Path, output_directory: str | Path = None) -> Path: + """ + Extract nimrod data from an archive file. If no output directory is provided, the extracted data will be saved to + the archive file's directory. + + Args: + archive_file_path: Path to the archive file + output_directory: Optional output directory. + + Returns: + Path to the extracted nimrod data file + """ + if isinstance(archive_file_path, str): + archive_file_path = Path(archive_file_path) + full_path = archive_file_path.resolve() + parent_folder = archive_file_path.parent + filename = archive_file_path.stem + + target_folder = None + if output_directory: + if isinstance(output_directory, str): + output_directory = Path(output_directory) + target_folder = output_directory + + if not target_folder: + target_folder = parent_folder / filename + + target_folder.mkdir(parents=True, exist_ok=True) + gzip_file_headers = parse_gzip_header(archive_file_path) + contained_filename = gzip_file_headers["original_name"] + + with gzip.open(full_path, "rb") as f_in: + if not contained_filename: + contained_filename = filename + print(f"Filename {contained_filename}") + out_path = target_folder / contained_filename + + with open(out_path, "wb") as f_out: + shutil.copyfileobj(f_in, f_out) + + return out_path + + +def load_nimrod_cubes(filenames: list[str | Path]) -> Generator[Cube | Any, Any, None]: + """ + + Args: + filenames: + output_name: + + Returns: + + """ + cubes = load_cubes(filenames) + # cubes = CubeList(cubes) + # merged_cubes = cubes.merge_cube() + # mean_cube = merged_cubes.collapsed("time", MEAN) + # netcdf.save(mean_cube, output_name) + # print(mean_cube) + return cubes + + +def load_nimrod_from_archive(filename): + """ + + Args: + filename: + + Returns: + + """ + nimrod_extracted_folder = extract_nimrod_from_archive(filename) + file_list = nimrod_extracted_folder.glob("*") + cubes = load_nimrod_cubes(file_list) + return cubes + + +def merge_nimrod_cubes(cubes): + """ + + Args: + cubes: + + Returns: + + """ + cubes = CubeList(cubes) + merged_cubes = cubes.merge_cube() + mean_cube = merged_cubes.collapsed("time", MEAN) + return mean_cube + + +def write_cube_to_file(cube, output_name): + """ + Save a nimrod cube to a Netcdf file. + + Args: + cube: + output_name: + + Returns: + """ + netcdf.save(cube, output_name) + + +def assert_dataset_time_dim_is_valid(dataset: xr.Dataset, time_dimension_name: str = "time") -> None: + """ + Ths function checks that the time dimension of a given dataset : + - Is composed of 5-minute time bins - Which is the native Nimrod format + - Contains a continuous time series, without any holes - which would lead to false statistics when resampling + + Args: + dataset: Merged nimrod cube + time_dimension_name: Name of the time dimension + + Returns: + Bool value indicating if the time bins are 5 minutes long and if there are no + gaps in the time series + """ + dataset_time_dimension = dataset[time_dimension_name] + if not dataset_time_dimension.to_index().is_monotonic_increasing: + raise AssertionError("Time is not sorted ascending") + if not dataset_time_dimension.to_index().is_unique: + duplicates = dataset_time_dimension.to_index()[dataset_time_dimension.to_index().duplicated(keep=False)] + raise AssertionError(f"Duplicate timestamps present: {duplicates[:10]} ...") + + difference_between_timesteps = dataset_time_dimension.diff(time_dimension_name) + if (difference_between_timesteps != FIVE_MIN).any(): + larger_time_gaps = np.nonzero((difference_between_timesteps != FIVE_MIN).compute())[0][:5] + raise AssertionError( + f"Non-5min gaps at positions {larger_time_gaps} " + f"(examples: {difference_between_timesteps.isel({time_dimension_name: larger_time_gaps}).values})" + ) + + start = pd.Timestamp(dataset_time_dimension.values[0]) + end = pd.Timestamp(dataset_time_dimension.values[-1]) + expected_index = pd.date_range(start=start, end=end, freq="5min", inclusive="both") + dataset_index = dataset_time_dimension.to_index() + missing_indexes = expected_index.difference(dataset_index) + if len(missing_indexes) > 0: + raise AssertionError(f"missing {len(missing_indexes)} stamps; first few: {missing_indexes[:10]}") + + +def resample_nimrod_timebox_30min_bins(filenames, output_name): + """ + This will resample nimrod data's bins to 30-minute interval instead of their + normal 5-minute interval. It uses a mean resampling, and creates time bins like + follows : + + ex. [[09h00, < 9h05], [09h05, < 9h10], ... ] -> [[09h00, < 9h30], [09h30, < 10h], ... ] + + Args: + filenames: + output_name: + + Returns: + + """ + ds = xr.open_mfdataset(filenames, combine="nested", concat_dim="time") + ds_30min = ds.resample(time="30min").mean() + ds_30min.to_netcdf(output_name) + + +extract_nimrod_from_archive( + "/home/francispelletier/projects/geospatial_tools/data/metoffice-c-band-rain-radar_uk_20180101_1km-composite.dat.gz/metoffice-c-band-rain-radar_uk_201801012355_1km-composite.dat.gz" +) +# cubes = load_nimrod_cubes( +# filenames="/home/francispelletier/projects/geospatial_tools/data/metoffice-c-band-rain-radar_uk_20180101_1km-composite.dat.gz/metoffice-c-band-rain-radar_uk_201801012355_1km-composite.dat/metoffice-c-band-rain-radar_uk_201801012355_1km-composite.dat", +# output_name="nimrod_cubes_30min") +# for cube in cubes: +# print(cube) diff --git a/geospatial_tools/utils.py b/geospatial_tools/utils.py index bfdd8d4..0ceaac5 100644 --- a/geospatial_tools/utils.py +++ b/geospatial_tools/utils.py @@ -1,13 +1,17 @@ """This module contains general utility functions.""" +from __future__ import annotations + import calendar import datetime import json import logging import os +import struct import sys import zipfile from pathlib import Path +from typing import Any, Optional import requests import yaml @@ -17,6 +21,13 @@ GEOPACKAGE_DRIVER = "GPKG" +# GZIP header positions +FTEXT = 0x01 +FHCRC = 0x02 +FEXTRA = 0x04 +FNAME = 0x08 +FCOMMENT = 0x10 + def create_logger(logger_name: str) -> logging.Logger: """ @@ -254,3 +265,90 @@ def create_date_range_for_specific_period( end_date = datetime.datetime(year + year_bump, end_month_range, last_day, 23, 59, 59) date_ranges.append(f"{start_date.isoformat()}Z/{end_date.isoformat()}Z") return date_ranges + + +def _read_cstring(f) -> bytes: + """Read a null-terminated byte string from the current position.""" + out = bytearray() + while True: + b = f.read(1) + if not b: # EOF + break + if b == b"\x00": # terminator + break + out += b + return bytes(out) + + +def parse_gzip_header(path: str | Path) -> dict[str, Any]: + """ + Parse the gzip header at the beginning of `path` (first member only). + + Raises ValueError if file doesn't look like gzip. + + Args: + path: Path to gzip file + + Returns: + dict: Returns a dict with keys + - compression_method (int) + - flags (int) + - mtime (int, Unix epoch or 0) + - xflags (int) + - os (int) + - original_name (Optional[str]) # FNAME + - comment (Optional[str]) # FCOMMENT + - header_end_offset (int) # file offset where compressed data starts + """ + p = Path(path) + with p.open("rb") as f: + # Magic + if f.read(2) != b"\x1f\x8b": + raise ValueError(f"{p} is not a gzip file (bad magic)") + + method_b = f.read(1) + flags_b = f.read(1) + if not method_b or not flags_b: + raise ValueError("Truncated header") + + compression_method = method_b[0] + flags = flags_b[0] + + # MTIME(4), XFL(1), OS(1) + mtime_bytes = f.read(4) + if 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version problem --- poetry.lock | 20 +++++++++++++++----- pyproject.toml | 2 +- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/poetry.lock b/poetry.lock index 7b6a901..a7b1ab8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. [[package]] name = "affine" @@ -1147,14 +1147,14 @@ test = ["pandas[test]", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "py [[package]] name = "dask-geopandas" -version = "0.4.3" +version = "0.5.0" description = "Parallel GeoPandas with Dask" optional = false python-versions = ">=3.10" groups = ["main"] files = [ - {file = "dask_geopandas-0.4.3-py3-none-any.whl", hash = "sha256:1cc159aecee7b3adc79383f1acc7c7e7c68130c57aea0643e190ac0149ef5bc4"}, - {file = "dask_geopandas-0.4.3.tar.gz", hash = "sha256:95d1a08fcb0dc47454628b89dee2e89dd63df6c31183746a58b4908a2dc21e96"}, + {file = "dask_geopandas-0.5.0-py3-none-any.whl", hash = "sha256:0b6c2a8f7fae94667c4af539e591fa7fb59c5c3206188fa51e461d395ef80240"}, + {file = "dask_geopandas-0.5.0.tar.gz", hash = "sha256:b1fc2a041550d609b87db9fcfad2e8217f7336e3436415da11ba9dc8437272b1"}, ] [package.dependencies] @@ -3291,6 +3291,16 @@ files = [ {file = "netCDF4-1.7.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:572f71459ef4b30e8554dcc4e1e6f55de515acc82a50968b48fe622244a64548"}, {file = "netCDF4-1.7.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7f77e72281acc5f331f82271e5f7f014d46f5ca9bcaa5aafe3e46d66cee21320"}, {file = "netCDF4-1.7.2-cp39-cp39-win_amd64.whl", hash = "sha256:d0fa7a9674fae8ae4877e813173c3ff7a6beee166b8730bdc847f517b282ed31"}, + {file = "netcdf4-1.7.2-cp310-cp310-macosx_13_0_x86_64.whl", hash = "sha256:16c3ba053930ed990e58827de6ab03184e407549004fb77438b98e5777e8cf3b"}, + {file = "netcdf4-1.7.2-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:142c9ed2db8a87a15ae0530c8a99f4f045435b0f495df733e9f111995e389d4f"}, + {file = "netcdf4-1.7.2-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:76cb3bbbbe4cd5fca612578eb105c16217380f7f93af2b549e8f38296bc906bb"}, + {file = "netcdf4-1.7.2-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:835ae7bcef666c967241baeeee9bef9376ddb7527297b24735597131f6f628e2"}, + {file = "netcdf4-1.7.2-cp310-cp310-win_amd64.whl", hash = "sha256:73bd7eda3cefb04c4076e76911f652f5ed56bf434e0a3958e367932953437557"}, + {file = "netcdf4-1.7.2-cp311-abi3-macosx_13_0_x86_64.whl", hash = "sha256:7e81c3c47f2772eab0b93fba8bb05b17b58dce17720e1bed25e9d76551deecd0"}, + {file = "netcdf4-1.7.2-cp311-abi3-macosx_14_0_arm64.whl", hash = "sha256:cb2791dba37fc98fd1ac4e236c97822909f54efbcdf7f1415c9777810e0a28f4"}, + {file = "netcdf4-1.7.2-cp311-abi3-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bf11480f6b8a5b246818ffff6b4d90481e51f8b9555b41af0c372eb0aaf8b65f"}, + {file = "netcdf4-1.7.2-cp311-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1ccc05328a8ff31921b539821791aeb20b054879f3fdf6d1d505bf6422824fec"}, + {file = "netcdf4-1.7.2-cp311-abi3-win_amd64.whl", hash = "sha256:999bfc4acebf400ed724d5e7329e2e768accc7ee1fa1d82d505da782f730301b"}, {file = "netcdf4-1.7.2.tar.gz", hash = "sha256:a4c6375540b19989896136943abb6d44850ff6f1fa7d3f063253b1ad3f8b7fce"}, ] @@ -6199,4 +6209,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.1" python-versions = ">=3.11,<3.13" -content-hash = "c13b2958022d24b395b97688aa71527a72b1253bd75ecc76adad51d93fbed51a" +content-hash = "4ef4d7de971af8e8d88db6d98588d7804b2e93a27750d87b8464f35a21a4826d" diff --git a/pyproject.toml b/pyproject.toml index b3706dd..be49750 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,13 +20,13 @@ pystac-client = "^0.7.7" planetary-computer = "^1.0.0" rioxarray = "^0.15.5" pyogrio = "^0.8.0" -dask-geopandas = "^0.4.1" pyarrow = "^17" numpy = "^2.1.1" pandas = "^2.2.2" typer = "^0.12.5" pillow = "^11.3.0" scitools-iris = "^3.13.1" +dask-geopandas = "^0.5.0" [tool.poetry.group.secondary.dependencies] jinja2 = ">=3.1.4" From 4ac4a5aa62b02592648b022a8a7fbdcf0bfaad0c Mon Sep 17 00:00:00 2001 From: f-PLT Date: Thu, 23 Oct 2025 22:23:08 -0400 Subject: [PATCH 3/6] Update docstrings about using workers on compute clusters --- geospatial_tools/raster.py | 14 +++++++++----- geospatial_tools/vector.py | 4 +++- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/geospatial_tools/raster.py b/geospatial_tools/raster.py index f7f6781..4ff3975 100644 --- a/geospatial_tools/raster.py +++ b/geospatial_tools/raster.py @@ -139,9 +139,12 @@ def clip_raster_with_polygon( Args: raster_image: Path to raster image to be clipped. polygon_layer: Polygon layer which polygons will be used to clip the raster image. - base_output_filename: Base filename for outputs. If `None`, will be taken from input polygon layer. + base_output_filename: Base filename for outputs. If `None`, will be taken from + input polygon layer. output_dir: Directory path where output will be written. - num_of_workers: The number of processes to use for parallel execution. Defaults to `cpu_count()`. + num_of_workers: The number of processes to use for parallel execution. If using + on a compute cluster, please set a specific amount (ex. 1 per CPU core requested). + Defaults to `cpu_count()`. logger: Logger instance Returns: @@ -260,12 +263,13 @@ def merge_raster_bands( merged_filename: Name of output raster file. merged_metadata: Dictionary of metadata to use if you prefer to great it independently. merged_band_names: Names of final output raster bands. For example : I have 3 images representing each - a single band; raster_file_list = ["image01_B0.tif", "image01_B1.tif", "image01_B2.tif"]. - With, merged_band_names, individual band id can be assigned for the final output raster; - ["B0", "B1", "B2"]. + a single band; raster_file_list = ["image01_B0.tif", "image01_B1.tif", "image01_B2.tif"]. + With, merged_band_names, individual band id can be assigned for the final output raster; + ["B0", "B1", "B2"]. logger: Logger instance Returns: + Path to merged raster """ if not merged_metadata: merged_metadata = create_merged_raster_bands_metadata(raster_file_list) diff --git a/geospatial_tools/vector.py b/geospatial_tools/vector.py index 9b441fe..ec3a5d8 100644 --- a/geospatial_tools/vector.py +++ b/geospatial_tools/vector.py @@ -281,7 +281,9 @@ def select_polygons_by_location( select_features_from: GeoDataFrame containing the polygons from which to select features from. intersected_with: Geodataframe containing the polygons that will be used to select features with via an intersect operation. - num_of_workers: Number of parallel processes to use for execution. Defaults to the min of number of CPU cores + num_of_workers: Number of parallel processes to use for execution. If using + on a compute cluster, please set a specific amount (ex. 1 per CPU core requested). + Defaults to the min of number of CPU cores or number (cpu_count()) join_type: predicate: The predicate to use for selecting features from. Available predicates are: From e4b1dfeda1a3e1bfef1add3c98a44f3545f91fea Mon Sep 17 00:00:00 2001 From: f-PLT Date: Thu, 23 Oct 2025 22:30:33 -0400 Subject: [PATCH 4/6] Update notebook test and sanitize_file.cfg for different environments --- tests/test_notebooks/sanitize_file.cfg | 6 +- .../test_planetary_computer_sentinel2.ipynb | 837 +++++++++--------- 2 files changed, 424 insertions(+), 419 deletions(-) diff --git a/tests/test_notebooks/sanitize_file.cfg b/tests/test_notebooks/sanitize_file.cfg index b7efa2a..cd58dcd 100644 --- a/tests/test_notebooks/sanitize_file.cfg +++ b/tests/test_notebooks/sanitize_file.cfg @@ -8,4 +8,8 @@ replace: TIME-STAMP [regex3] regex: took\s+[+-]?\d+\.\d+\s+seconds\s+to\s+write -replace: took TIME-STAMP seconds to write \ No newline at end of file +replace: took TIME-STAMP seconds to write + +[regex4] +regex: \/([^ !$`&*()+]|(\\[ !$`&*()+]))+geospatial[-_]tools +replace: PROJECT_PATH diff --git a/tests/test_notebooks/test_planetary_computer_sentinel2.ipynb b/tests/test_notebooks/test_planetary_computer_sentinel2.ipynb index 85647d0..632d565 100644 --- a/tests/test_notebooks/test_planetary_computer_sentinel2.ipynb +++ b/tests/test_notebooks/test_planetary_computer_sentinel2.ipynb @@ -2,15 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, "id": "26590ffa", "metadata": { "ExecuteTime": { - "end_time": "2025-05-22T18:12:05.636797Z", - "start_time": "2025-05-22T18:12:04.226800Z" + "end_time": "2025-10-24T02:26:45.182290Z", + "start_time": "2025-10-24T02:26:34.621503Z" } }, - "outputs": [], "source": [ "import shutil\n", "\n", @@ -22,23 +20,25 @@ "from geospatial_tools.raster import clip_raster_with_polygon\n", "from geospatial_tools.stac import Asset\n", "from geospatial_tools.vector import create_vector_grid_parallel, select_polygons_by_location, to_geopackage" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 2, "id": "bbf8d1aede2a4fd2", "metadata": { "ExecuteTime": { - "end_time": "2025-05-22T18:12:05.679842Z", - "start_time": "2025-05-22T18:12:05.677670Z" + "end_time": "2025-10-24T02:26:45.308968Z", + "start_time": "2025-10-24T02:26:45.298373Z" } }, - "outputs": [], "source": [ "TEST_TMP_DIR = TESTS_DIR / \"test_notebooks/tmp_sentinel2\"\n", "TEST_TMP_DIR.mkdir(exist_ok=True)" - ] + ], + "outputs": [], + "execution_count": 2 }, { "cell_type": "markdown", @@ -63,35 +63,44 @@ }, { "cell_type": "code", - "execution_count": 3, "id": "fcb67d1eb21aafc1", "metadata": { "ExecuteTime": { - "end_time": "2025-05-22T18:12:05.766407Z", - "start_time": "2025-05-22T18:12:05.727177Z" + "end_time": "2025-10-24T02:26:45.842939Z", + "start_time": "2025-10-24T02:26:45.714239Z" } }, - "outputs": [], "source": [ "USA_POLYGON_FILE = DATA_DIR / \"usa_polygon_5070.gpkg\"\n", "S2_USA_GRID_FILE = DATA_DIR / \"s2_grid_usa_polygon_5070.gpkg\"\n", "usa_polygon = gpd.read_file(USA_POLYGON_FILE)\n", "s2_grid = gpd.read_file(S2_USA_GRID_FILE)" - ] + ], + "outputs": [], + "execution_count": 3 }, { "cell_type": "code", - "execution_count": 4, "id": "314e7c068786ad4d", "metadata": { "ExecuteTime": { - "end_time": "2025-05-22T18:12:05.789822Z", - "start_time": "2025-05-22T18:12:05.780131Z" + "end_time": "2025-10-24T02:26:45.935556Z", + "start_time": "2025-10-24T02:26:45.903989Z" } }, + "source": [ + "usa_polygon" + ], "outputs": [ { "data": { + "text/plain": [ + " AFFGEOID GEOID NAME \\\n", + "0 0100000US US United States \n", + "\n", + " geometry \n", + "0 MULTIPOLYGON (((-2116048.733 3142966.552, -211... " + ], "text/html": [ "
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