[](https://github.com/agrouaze/mapraster/actions?query=workflow%3Abuild)
[](https://pypi.org/project/mapraster/)
[](https://github.com/agrouaze/mapraster/pulls?utf8=%E2%9C%93&q=is%3Apr%20author%3Aapp%2Fdependabot)
[](https://github.com/psf/black)
[](https://github.com/PyCQA/bandit)
[](https://github.com/agrouaze/mapraster/blob/master/.pre-commit-config.yaml)
[](https://github.com/agrouaze/mapraster/releases)
[](https://github.com/agrouaze/mapraster/blob/master/LICENSE)
mapraster is a Python lib to interpolate xarray raster field on image geometry (e.g. line/sample)
## Very first steps
### Initialize your code
1. Initialize `git` inside your repo:
```bash
cd mapraster && git init
```
2. If you don't have `Poetry` installed run:
```bash
make poetry-download
```
3. Initialize poetry and install `pre-commit` hooks:
```bash
make install
make pre-commit-install
```
4. Run the codestyle:
```bash
make codestyle
```
5. Upload initial code to GitHub:
```bash
git add .
git commit -m ":tada: Initial commit"
git branch -M main
git remote add origin https://github.com/agrouaze/mapraster.git
git push -u origin main
```
### Set up bots
- Set up [Dependabot](https://docs.github.com/en/github/administering-a-repository/enabling-and-disabling-version-updates#enabling-github-dependabot-version-updates) to ensure you have the latest dependencies.
- Set up [Stale bot](https://github.com/apps/stale) for automatic issue closing.
### Poetry
Want to know more about Poetry? Check [its documentation](https://python-poetry.org/docs/).
`. You can pass the new version explicitly, or a rule such as `major`, `minor`, or `patch`. For more details, refer to the [Semantic Versions](https://semver.org/) standard.
- Make a commit to `GitHub`.
- Create a `GitHub release`.
- And... publish π `poetry publish --build`
## π― What's next
Well, that's up to you πͺπ». I can only recommend the packages and articles that helped me.
- [`Typer`](https://github.com/tiangolo/typer) is great for creating CLI applications.
- [`Rich`](https://github.com/willmcgugan/rich) makes it easy to add beautiful formatting in the terminal.
- [`Pydantic`](https://github.com/samuelcolvin/pydantic/) β data validation and settings management using Python type hinting.
- [`Loguru`](https://github.com/Delgan/loguru) makes logging (stupidly) simple.
- [`tqdm`](https://github.com/tqdm/tqdm) β fast, extensible progress bar for Python and CLI.
- [`IceCream`](https://github.com/gruns/icecream) is a little library for sweet and creamy debugging.
- [`orjson`](https://github.com/ijl/orjson) β ultra fast JSON parsing library.
- [`Returns`](https://github.com/dry-python/returns) makes you function's output meaningful, typed, and safe!
- [`Hydra`](https://github.com/facebookresearch/hydra) is a framework for elegantly configuring complex applications.
- [`FastAPI`](https://github.com/tiangolo/fastapi) is a type-driven asynchronous web framework.
Articles:
- [Open Source Guides](https://opensource.guide/).
- [A handy guide to financial support for open source](https://github.com/nayafia/lemonade-stand)
- [GitHub Actions Documentation](https://help.github.com/en/actions).
- Maybe you would like to add [gitmoji](https://gitmoji.carloscuesta.me/) to commit names. This is really funny. π
## π Features
### Development features
- Supports for `Python 3.9` and higher.
- [`Poetry`](https://python-poetry.org/) as the dependencies manager. See configuration in [`pyproject.toml`](https://github.com/agrouaze/mapraster/blob/master/pyproject.toml) and [`setup.cfg`](https://github.com/agrouaze/mapraster/blob/master/setup.cfg).
- Automatic codestyle with [`black`](https://github.com/psf/black), [`isort`](https://github.com/timothycrosley/isort) and [`pyupgrade`](https://github.com/asottile/pyupgrade).
- Ready-to-use [`pre-commit`](https://pre-commit.com/) hooks with code-formatting.
- Type checks with [`mypy`](https://mypy.readthedocs.io); docstring checks with [`darglint`](https://github.com/terrencepreilly/darglint); security checks with [`safety`](https://github.com/pyupio/safety) and [`bandit`](https://github.com/PyCQA/bandit)
- Testing with [`pytest`](https://docs.pytest.org/en/latest/).
- Ready-to-use [`.editorconfig`](https://github.com/agrouaze/mapraster/blob/master/.editorconfig), [`.dockerignore`](https://github.com/agrouaze/mapraster/blob/master/.dockerignore), and [`.gitignore`](https://github.com/agrouaze/mapraster/blob/master/.gitignore). You don't have to worry about those things.
### Deployment features
- `GitHub` integration: issue and pr templates.
- `Github Actions` with predefined [build workflow](https://github.com/agrouaze/mapraster/blob/master/.github/workflows/build.yml) as the default CI/CD.
- Everything is already set up for security checks, codestyle checks, code formatting, testing, linting, docker builds, etc with [`Makefile`](https://github.com/agrouaze/mapraster/blob/master/Makefile#L89). More details in [makefile-usage](#makefile-usage).
- [Dockerfile](https://github.com/agrouaze/mapraster/blob/master/docker/Dockerfile) for your package.
- Always up-to-date dependencies with [`@dependabot`](https://dependabot.com/). You will only [enable it](https://docs.github.com/en/github/administering-a-repository/enabling-and-disabling-version-updates#enabling-github-dependabot-version-updates).
- Automatic drafts of new releases with [`Release Drafter`](https://github.com/marketplace/actions/release-drafter). You may see the list of labels in [`release-drafter.yml`](https://github.com/agrouaze/mapraster/blob/master/.github/release-drafter.yml). Works perfectly with [Semantic Versions](https://semver.org/) specification.
### Open source community features
- Ready-to-use [Pull Requests templates](https://github.com/agrouaze/mapraster/blob/master/.github/PULL_REQUEST_TEMPLATE.md) and several [Issue templates](https://github.com/agrouaze/mapraster/tree/master/.github/ISSUE_TEMPLATE).
- Files such as: `LICENSE`, `CONTRIBUTING.md`, `CODE_OF_CONDUCT.md`, and `SECURITY.md` are generated automatically.
- [`Stale bot`](https://github.com/apps/stale) that closes abandoned issues after a period of inactivity. (You will only [need to setup free plan](https://github.com/marketplace/stale)). Configuration is [here](https://github.com/agrouaze/mapraster/blob/master/.github/.stale.yml).
- [Semantic Versions](https://semver.org/) specification with [`Release Drafter`](https://github.com/marketplace/actions/release-drafter).
## Installation
```bash
pip install -U mapraster
```
or install with `Poetry`
```bash
poetry add mapraster
```
### Makefile usage
[`Makefile`](https://github.com/agrouaze/mapraster/blob/master/Makefile) contains a lot of functions for faster development.
1. Download and remove Poetry
To download and install Poetry run:
```bash
make poetry-download
```
To uninstall
```bash
make poetry-remove
```
2. Install all dependencies and pre-commit hooks
Install requirements:
```bash
make install
```
Pre-commit hooks coulb be installed after `git init` via
```bash
make pre-commit-install
```
3. Codestyle
Automatic formatting uses `pyupgrade`, `isort` and `black`.
```bash
make codestyle
# or use synonym
make formatting
```
Codestyle checks only, without rewriting files:
```bash
make check-codestyle
```
> Note: `check-codestyle` uses `isort`, `black` and `darglint` library
Update all dev libraries to the latest version using one comand
```bash
make update-dev-deps
```
4. Code security
```bash
make check-safety
```
This command launches `Poetry` integrity checks as well as identifies security issues with `Safety` and `Bandit`.
```bash
make check-safety
```
5. Type checks
Run `mypy` static type checker
```bash
make mypy
```
6. Tests with coverage badges
Run `pytest`
```bash
make test
```
7. All linters
Of course there is a command to ~~rule~~ run all linters in one:
```bash
make lint
```
the same as:
```bash
make test && make check-codestyle && make mypy && make check-safety
```
8. Docker
```bash
make docker-build
```
which is equivalent to:
```bash
make docker-build VERSION=latest
```
Remove docker image with
```bash
make docker-remove
```
More information [about docker](https://github.com/agrouaze/mapraster/tree/master/docker).
9. Cleanup
Delete pycache files
```bash
make pycache-remove
```
Remove package build
```bash
make build-remove
```
Delete .DS_STORE files
```bash
make dsstore-remove
```
Remove .mypycache
```bash
make mypycache-remove
```
Or to remove all above run:
```bash
make cleanup
```
## π Releases
You can see the list of available releases on the [GitHub Releases](https://github.com/agrouaze/mapraster/releases) page.
We follow [Semantic Versions](https://semver.org/) specification.
We use [`Release Drafter`](https://github.com/marketplace/actions/release-drafter). As pull requests are merged, a draft release is kept up-to-date listing the changes, ready to publish when youβre ready. With the categories option, you can categorize pull requests in release notes using labels.
### List of labels and corresponding titles
| **Label** | **Title in Releases** |
| :-----------------------------------: | :---------------------: |
| `enhancement`, `feature` | π Features |
| `bug`, `refactoring`, `bugfix`, `fix` | π§ Fixes & Refactoring |
| `build`, `ci`, `testing` | π¦ Build System & CI/CD |
| `breaking` | π₯ Breaking Changes |
| `documentation` | π Documentation |
| `dependencies` | β¬οΈ Dependencies updates |
You can update it in [`release-drafter.yml`](https://github.com/agrouaze/mapraster/blob/master/.github/release-drafter.yml).
GitHub creates the `bug`, `enhancement`, and `documentation` labels for you. Dependabot creates the `dependencies` label. Create the remaining labels on the Issues tab of your GitHub repository, when you need them.
## π‘ License
[](https://github.com/agrouaze/mapraster/blob/master/LICENSE)
This project is licensed under the terms of the `MIT` license. See [LICENSE](https://github.com/agrouaze/mapraster/blob/master/LICENSE) for more details.
## π Citation
```bibtex
@misc{mapraster,
author = {umr-lops},
title = {mapraster is a Python lib to interpolate xarray raster field on image geometry (e.g. line/sample)},
year = {2025},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/agrouaze/mapraster}}
}
```
## Credits [](https://github.com/TezRomacH/python-package-template)
This project was generated with [`python-package-template`](https://github.com/TezRomacH/python-package-template)
mapraster-2026.01.06/SECURITY.md 0000664 0000000 0000000 00000002242 15127170716 0015706 0 ustar 00root root 0000000 0000000 # Security
## π Reporting Security Issues
> Do not open issues that might have security implications!
> It is critical that security related issues are reported privately so we have time to address them before they become public knowledge.
Vulnerabilities can be reported by emailing core members:
- umr-lops [antoine.grouazel@ifremer.fr](mailto:antoine.grouazel@ifremer.fr)
Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
- Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
- Full paths of source file(s) related to the manifestation of the issue
- The location of the affected source code (tag/branch/commit or direct URL)
- Any special configuration required to reproduce the issue
- Environment (e.g. Linux / Windows / macOS)
- Step-by-step instructions to reproduce the issue
- Proof-of-concept or exploit code (if possible)
- Impact of the issue, including how an attacker might exploit the issue
This information will help us triage your report more quickly.
## Preferred Languages
We prefer all communications to be in English.
mapraster-2026.01.06/assets/ 0000775 0000000 0000000 00000000000 15127170716 0015417 5 ustar 00root root 0000000 0000000 mapraster-2026.01.06/assets/images/ 0000775 0000000 0000000 00000000000 15127170716 0016664 5 ustar 00root root 0000000 0000000 mapraster-2026.01.06/assets/images/coverage.svg 0000664 0000000 0000000 00000001610 15127170716 0021176 0 ustar 00root root 0000000 0000000
coverage
coverage
83%
83%
mapraster-2026.01.06/cookiecutter-config-file.yml 0000664 0000000 0000000 00000000660 15127170716 0021522 0 ustar 00root root 0000000 0000000 # This file contains values from Cookiecutter
default_context:
project_name: "mapraster"
project_description: "mapraster is a Python lib to interpolate xarray raster field on image geometry (e.g. line/sample)"
organization: "umr-lops"
license: "MIT"
minimal_python_version: 3.9
github_name: "agrouaze"
email: "antoine.grouazel@ifremer.fr"
version: "2025.12.18"
line_length: "88"
create_example_template: "none"
mapraster-2026.01.06/docker/ 0000775 0000000 0000000 00000000000 15127170716 0015364 5 ustar 00root root 0000000 0000000 mapraster-2026.01.06/docker/Dockerfile 0000664 0000000 0000000 00000001341 15127170716 0017355 0 ustar 00root root 0000000 0000000 FROM python:3.11-slim-buster
ENV LANG=C.UTF-8 \
LC_ALL=C.UTF-8 \
PATH="${PATH}:/root/.poetry/bin"
RUN apt-get update && \
apt-get install -y --no-install-recommends \
curl \
&& rm -rf /var/lib/apt/lists/*
COPY pyproject.toml ./
# Install Poetry
RUN curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/install-poetry.py | POETRY_HOME=/opt/poetry python && \
cd /usr/local/bin && \
ln -s /opt/poetry/bin/poetry && \
poetry config virtualenvs.create false
# Allow installing dev dependencies to run tests
ARG INSTALL_DEV=false
RUN bash -c "if [ $INSTALL_DEV == 'true' ] ; then poetry install --no-root ; else poetry install --no-root --no-dev ; fi"
CMD mkdir -p /workspace
WORKDIR /workspace
mapraster-2026.01.06/docker/README.md 0000664 0000000 0000000 00000001407 15127170716 0016645 0 ustar 00root root 0000000 0000000 # Docker for mapraster
## Installation
To create Docker you need to run:
```bash
make docker-build
```
which is equivalent to:
```bash
make docker-build VERSION=latest
```
You may provide name and version for the image.
Default name is `IMAGE := mapraster`.
Default version is `VERSION := latest`.
```bash
make docker-build IMAGE=some_name VERSION=2025.12.18
```
## Usage
```bash
docker run -it --rm \
-v $(pwd):/workspace \
mapraster bash
```
## How to clean up
To uninstall docker image run `make docker-remove` with `VERSION`:
```bash
make docker-remove VERSION=2025.12.18
```
you may also choose the image name
```bash
make docker-remove IMAGE=some_name VERSION=latest
```
If you want to clean all, including `build` and `pycache` run `make cleanup`
mapraster-2026.01.06/docs/ 0000775 0000000 0000000 00000000000 15127170716 0015045 5 ustar 00root root 0000000 0000000 mapraster-2026.01.06/docs/Makefile 0000664 0000000 0000000 00000001636 15127170716 0016513 0 ustar 00root root 0000000 0000000 # Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for the first two.
SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
SOURCEDIR = .
BUILDDIR = _build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
# Generate pytest JSON + HTML report (repo root)
test-report:
cd .. && python -m pytest --json-report --json-report-file=report.json
# Build docs after generating test report
html-with-tests: test-report
@$(SPHINXBUILD) -M html "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
mapraster-2026.01.06/docs/basic_api.rst 0000664 0000000 0000000 00000000334 15127170716 0017511 0 ustar 00root root 0000000 0000000 Basic API
=========
This page documents the main public functions.
map_raster
----------
.. autofunction:: mapraster.main.map_raster
_get_image_dims
---------------
.. autofunction:: mapraster.main._get_image_dims
mapraster-2026.01.06/docs/conf.py 0000664 0000000 0000000 00000002066 15127170716 0016350 0 ustar 00root root 0000000 0000000 # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
project = "mapraster"
copyright = "2025, Vincent Lheureux / Antoine Grouazel"
author = "Vincent Lheureux / Antoine Grouazel"
release = "0.1.0"
# -- General configuration ---------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.napoleon",
"nbsphinx",
]
templates_path = ["_templates"]
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
# -- Options for HTML output -------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
html_theme = "alabaster"
html_static_path = ["_static"]
mapraster-2026.01.06/docs/examples/ 0000775 0000000 0000000 00000000000 15127170716 0016663 5 ustar 00root root 0000000 0000000 mapraster-2026.01.06/docs/examples/how_to_use.ipynb 0000664 0000000 0000000 00000021476 15127170716 0022113 0 ustar 00root root 0000000 0000000 {
"cells": [
{
"cell_type": "markdown",
"id": "f21dd21e",
"metadata": {},
"source": [
"# map_raster examples "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "307c52e3",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import xarray as xr\n",
"import matplotlib.pyplot as plt "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "474d39c4",
"metadata": {},
"outputs": [],
"source": [
"from tests.tools_test import fake_dataset,fake_ecmwf_0100_1h,build_footprint"
]
},
{
"cell_type": "markdown",
"id": "e1306e69",
"metadata": {},
"source": [
"# I - Not crossing Meridian"
]
},
{
"cell_type": "markdown",
"id": "5f241cca",
"metadata": {},
"source": [
"## generate fake raster (ECMWF-like )\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cd842425",
"metadata": {},
"outputs": [],
"source": [
"crossAntiMeridian = False\n",
"to180 = True\n",
"\n",
"\n",
"\n",
"raster = fake_ecmwf_0100_1h(to180=to180,with_nan=False)\n",
"plt.figure(figsize=(6,3))\n",
"plt.pcolormesh(raster.x, raster.y, np.sqrt(raster.U10**2+raster.V10**2)) \n",
"plt.title(\"simulated wind speed (m/s)\")\n",
"plt.colorbar(label='m/s')\n",
"plt.xlabel(\"Longitude\")\n",
"plt.ylabel(\"Latitude\")\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9459b9d5",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "6f18b37d",
"metadata": {},
"source": [
"## generate fake SAR data + footprint "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "676bfa3e",
"metadata": {},
"outputs": [],
"source": [
"dataset = fake_dataset(cross_antimeridian=crossAntiMeridian)\n",
"dataset\n",
"\n",
"footprint = build_footprint(dataset)\n",
"\n",
"xs, ys = np.array(footprint.exterior.xy[0]), footprint.exterior.xy[1] \n",
"xlims = (np.min(xs), np.max(xs))\n",
"ylims = (np.min(ys), np.max(ys))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "825a10c5",
"metadata": {},
"outputs": [],
"source": [
"xs"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ac222edb",
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(6,3))\n",
"plt.pcolormesh(dataset.sample, dataset.line, dataset.longitude) \n",
"\n",
"plt.title(\"Longitude\")\n",
"plt.colorbar(label='degrees_east')\n",
"plt.xlabel(\"Sample\")\n",
"plt.ylabel(\"Line\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "7d9b0959",
"metadata": {},
"source": [
"## map_raster main usage example"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f2954a6b",
"metadata": {},
"outputs": [],
"source": [
"from mapraster.main import map_raster"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "db99bdd1",
"metadata": {},
"outputs": [],
"source": [
"rastered = map_raster(raster_ds=raster,originalDataset=dataset, footprint=footprint, cross_antimeridian=crossAntiMeridian)\n",
"dataset[\"U10\"] = rastered[\"U10\"]\n",
"dataset[\"V10\"] = rastered[\"V10\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f6de3636",
"metadata": {},
"outputs": [],
"source": [
"plt.pcolormesh(dataset.longitude, dataset.latitude, np.sqrt(dataset.U10**2+dataset.V10**2))\n",
"#plot footprint\n",
"plt.plot(xs, ys, color='red', linewidth=2, label = 'footprint'\n",
" )\n",
"plt.legend() "
]
},
{
"cell_type": "markdown",
"id": "41867d8b",
"metadata": {},
"source": [
"# II - Crossing Meridian\n",
"\n",
"Need to activate crossAntimeridian = True and to display longitudes in [0,360]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dc202067",
"metadata": {},
"outputs": [],
"source": [
"crossAntiMeridian = True\n",
"to180 = False\n",
"\n",
"raster = fake_ecmwf_0100_1h(to180=to180,with_nan=False)\n",
"plt.figure(figsize=(6,3))\n",
"plt.pcolormesh(raster.x, raster.y, np.sqrt(raster.U10**2+raster.V10**2)) \n",
"plt.title(\"simulated wind speed (m/s)\")\n",
"plt.colorbar(label='m/s')\n",
"plt.xlabel(\"Longitude\")\n",
"plt.ylabel(\"Latitude\")\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "72f75cf1",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "f59680d6",
"metadata": {},
"outputs": [],
"source": [
"dataset = fake_dataset(cross_antimeridian=crossAntiMeridian)\n",
"dataset\n",
"\n",
"footprint = build_footprint(dataset)\n",
"\n",
"xs, ys = np.array(footprint.exterior.xy[0])%360, footprint.exterior.xy[1] \n",
"xlims = (np.min(xs), np.max(xs))\n",
"ylims = (np.min(ys), np.max(ys))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f21ec48",
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(6,3))\n",
"plt.pcolormesh(dataset.sample, dataset.line, dataset.longitude) \n",
"\n",
"plt.title(\"Longitude\")\n",
"plt.colorbar(label='degrees_east')\n",
"plt.xlabel(\"Sample\")\n",
"plt.ylabel(\"Line\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0f5be21c",
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(6,3))\n",
"plt.pcolormesh(raster.x, raster.y, np.sqrt(raster.U10**2+raster.V10**2), vmin = 6.76, vmax=6.8) \n",
"plt.title(\"simulated wind speed (m/s)\")\n",
"plt.colorbar(label='m/s')\n",
"plt.xlabel(\"Longitude\")\n",
"plt.ylabel(\"Latitude\")\n",
"plt.xlim(np.min(xlims), np.max(xlims))\n",
"plt.ylim(np.min(ylims), np.max(ylims))\n",
"#plot footprint\n",
"plt.plot(xs, ys, color='red', linewidth=2, label = 'footprint')\n",
"plt.legend(loc = 'upper right')\n",
"plt.show()\n",
"\n",
"\n",
"\n",
"rastered = map_raster(raster_ds=raster,originalDataset=dataset, footprint=footprint, cross_antimeridian=crossAntiMeridian)\n",
"dataset[\"U10\"] = rastered[\"U10\"]\n",
"dataset[\"V10\"] = rastered[\"V10\"]\n",
"plt.figure(figsize=(6,3))\n",
"\n",
"plt.pcolormesh(dataset.longitude%360, dataset.latitude, np.sqrt(dataset.U10**2+dataset.V10**2), vmin = 6.76, vmax=6.8)\n",
"#plot footprint\n",
"plt.plot(xs, ys, color='red', linewidth=2, label = 'footprint')\n",
"plt.title(\"Mapped wind speed (m/s)\") \n",
"plt.colorbar(label='m/s')\n",
"plt.legend(loc = 'upper right')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e5490e05",
"metadata": {},
"outputs": [],
"source": [
"# Same with Nans "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "900e6653",
"metadata": {},
"outputs": [],
"source": [
"crossAntiMeridian = True\n",
"to180 = False\n",
"\n",
"raster = fake_ecmwf_0100_1h(to180=to180,with_nan=True)\n",
"plt.figure(figsize=(6,3))\n",
"plt.pcolormesh(raster.x, raster.y, np.sqrt(raster.U10**2+raster.V10**2), vmin = 6.76, vmax=6.8) \n",
"plt.title(\"simulated wind speed (m/s)\")\n",
"plt.colorbar(label='m/s')\n",
"plt.xlabel(\"Longitude\")\n",
"plt.ylabel(\"Latitude\")\n",
"plt.xlim(np.min(xlims), np.max(xlims))\n",
"plt.ylim(np.min(ylims), np.max(ylims))\n",
"#plot footprint\n",
"plt.plot(xs, ys, color='red', linewidth=2, label = 'footprint')\n",
"plt.legend(loc = 'upper right')\n",
"plt.show()\n",
"\n",
"\n",
"\n",
"rastered = map_raster(raster_ds=raster,originalDataset=dataset, footprint=footprint, cross_antimeridian=crossAntiMeridian)\n",
"dataset[\"U10\"] = rastered[\"U10\"]\n",
"dataset[\"V10\"] = rastered[\"V10\"]\n",
"plt.figure(figsize=(6,3))\n",
"\n",
"plt.pcolormesh(dataset.longitude%360, dataset.latitude, np.sqrt(dataset.U10**2+dataset.V10**2), vmin = 6.76, vmax=6.8)\n",
"#plot footprint\n",
"plt.plot(xs, ys, color='red', linewidth=2, label = 'footprint')\n",
"plt.title(\"Mapped wind speed (m/s)\") \n",
"plt.colorbar(label='m/s')\n",
"plt.legend(loc = 'upper right')\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "env_xsar",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
mapraster-2026.01.06/docs/examples/test_map_raster_visualization.ipynb 0000664 0000000 0000000 00000035500 15127170716 0026106 0 ustar 00root root 0000000 0000000 {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualization of map_raster test\n",
"\n",
"This notebook visualizes the test for `map_raster` using:\n",
"- Real S1A SAR coordinates (Gulf of California)\n",
"- Synthetic ECMWF wind data\n",
"\n",
"Source: `tests/test_map_raster_4cases.py` (comprehensive 4-case test suite)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"\n",
"# Add parent directories to path to import from tests and mapraster\n",
"sys.path.insert(0, str(Path.cwd().parent.parent))\n",
"\n",
"import numpy as np\n",
"import xarray as xr\n",
"import matplotlib.pyplot as plt\n",
"from mapraster.main import map_raster\n",
"from tests.tools_test import fake_ecmwf_0100_1h, build_footprint\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Create SAR dataset with real S1A coordinates"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def create_real_sar_dataset():\n",
" \"\"\"\n",
" Create SAR dataset with real S1A coordinates from Gulf of California.\n",
" Based on s1a-iw-owi-dv-20210909t130650-20210909t130715-039605-04AE83.nc\n",
" \n",
" Returns synthetic dataset with bilinear interpolation between corner points.\n",
" \"\"\"\n",
" # Real corner coordinates from S1A SAR\n",
" corners = {\n",
" 'lon': np.array([-106.73246, -109.26672, -109.43854, -106.90172]),\n",
" 'lat': np.array([21.72079, 22.14191, 20.64836, 20.22559])\n",
" }\n",
" \n",
" # SAR grid dimensions\n",
" nlines, nsamples = 167, 256\n",
" \n",
" # Create meshgrid indices\n",
" lines = np.arange(nlines)\n",
" samples = np.arange(nsamples)\n",
" \n",
" # Bilinear interpolation\n",
" s_norm = samples / (nsamples - 1)\n",
" l_norm = lines / (nlines - 1)\n",
" \n",
" # Top edge (line=0): interpolate between corners[0] and corners[1]\n",
" lon_top = corners['lon'][0] + s_norm * (corners['lon'][1] - corners['lon'][0])\n",
" lat_top = corners['lat'][0] + s_norm * (corners['lat'][1] - corners['lat'][0])\n",
" \n",
" # Bottom edge (line=-1): interpolate between corners[3] and corners[2]\n",
" lon_bottom = corners['lon'][3] + s_norm * (corners['lon'][2] - corners['lon'][3])\n",
" lat_bottom = corners['lat'][3] + s_norm * (corners['lat'][2] - corners['lat'][3])\n",
" \n",
" # Full grid: interpolate between top and bottom edges\n",
" lon_grid = lon_top[None, :] + l_norm[:, None] * (lon_bottom[None, :] - lon_top[None, :])\n",
" lat_grid = lat_top[None, :] + l_norm[:, None] * (lat_bottom[None, :] - lat_top[None, :])\n",
" \n",
" # Create xarray Dataset\n",
" ds = xr.Dataset(\n",
" {\n",
" 'longitude': (['line', 'sample'], lon_grid),\n",
" 'latitude': (['line', 'sample'], lat_grid),\n",
" },\n",
" coords={\n",
" 'line': lines,\n",
" 'sample': samples,\n",
" }\n",
" )\n",
" \n",
" return ds\n",
"\n",
"sar_dataset = create_real_sar_dataset()\n",
"print(f\"SAR shape: {sar_dataset['longitude'].shape}\")\n",
"print(f\"SAR lon range: [{sar_dataset['longitude'].min().values:.2f}, {sar_dataset['longitude'].max().values:.2f}]\")\n",
"print(f\"SAR lat range: [{sar_dataset['latitude'].min().values:.2f}, {sar_dataset['latitude'].max().values:.2f}]\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sar_dataset\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Create synthetic ECMWF wind data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ecmwf_dataset = fake_ecmwf_0100_1h(to180=True, with_nan=False)\n",
"print(f\"ECMWF shape: {ecmwf_dataset['U10'].shape}\")\n",
"print(f\"ECMWF x range: [{ecmwf_dataset['x'].min().values:.2f}, {ecmwf_dataset['x'].max().values:.2f}]\")\n",
"print(f\"ECMWF y range: [{ecmwf_dataset['y'].min().values:.2f}, {ecmwf_dataset['y'].max().values:.2f}]\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ecmwf_dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Run map_raster"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"footprint = build_footprint(sar_dataset)\n",
"\n",
"result = map_raster(\n",
" raster_ds=ecmwf_dataset,\n",
" originalDataset=sar_dataset,\n",
" footprint=footprint,\n",
" cross_antimeridian=False,\n",
")\n",
"\n",
"print(f\"Result shape: {result['U10'].shape}\")\n",
"print(f\"Result U10 range: [{result['U10'].min().values:.2f}, {result['U10'].max().values:.2f}]\")\n",
"print(f\"Result V10 range: [{result['V10'].min().values:.2f}, {result['V10'].max().values:.2f}]\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Visualizations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.1 SAR grid geometry"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
"\n",
"# Longitude\n",
"im1 = ax1.pcolormesh(\n",
" sar_dataset['sample'],\n",
" sar_dataset['line'],\n",
" sar_dataset['longitude'],\n",
" shading='auto',\n",
" cmap='viridis'\n",
")\n",
"ax1.set_xlabel('Sample')\n",
"ax1.set_ylabel('Line')\n",
"ax1.set_title('SAR Longitude Grid')\n",
"plt.colorbar(im1, ax=ax1, label='Longitude (Β°)')\n",
"\n",
"# Latitude\n",
"im2 = ax2.pcolormesh(\n",
" sar_dataset['sample'],\n",
" sar_dataset['line'],\n",
" sar_dataset['latitude'],\n",
" shading='auto',\n",
" cmap='plasma'\n",
")\n",
"ax2.set_xlabel('Sample')\n",
"ax2.set_ylabel('Line')\n",
"ax2.set_title('SAR Latitude Grid')\n",
"plt.colorbar(im2, ax=ax2, label='Latitude (Β°)')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.2 ECMWF wind fields (global)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(14, 12))\n",
"\n",
"# Wind speed\n",
"wind_speed_ecmwf = np.sqrt(ecmwf_dataset['U10']**2 + ecmwf_dataset['V10']**2)\n",
"im0 = ax1.pcolormesh(\n",
" ecmwf_dataset['x'],\n",
" ecmwf_dataset['y'],\n",
" wind_speed_ecmwf,\n",
" shading='auto',\n",
" cmap='viridis',\n",
" vmin=0,\n",
" vmax=15\n",
")\n",
"ax1.set_xlabel('Longitude (Β°)')\n",
"ax1.set_ylabel('Latitude (Β°)')\n",
"ax1.set_title('ECMWF Wind Speed (global)')\n",
"plt.colorbar(im0, ax=ax1, label='Wind Speed (m/s)')\n",
"\n",
"# U10\n",
"im1 = ax2.pcolormesh(\n",
" ecmwf_dataset['x'],\n",
" ecmwf_dataset['y'],\n",
" ecmwf_dataset['U10'],\n",
" shading='auto',\n",
" cmap='RdBu_r',\n",
" vmin=-10,\n",
" vmax=10\n",
")\n",
"ax2.set_xlabel('Longitude (Β°)')\n",
"ax2.set_ylabel('Latitude (Β°)')\n",
"ax2.set_title('ECMWF U10 Wind Component (global)')\n",
"plt.colorbar(im1, ax=ax2, label='U10 (m/s)')\n",
"\n",
"# V10\n",
"im2 = ax3.pcolormesh(\n",
" ecmwf_dataset['x'],\n",
" ecmwf_dataset['y'],\n",
" ecmwf_dataset['V10'],\n",
" shading='auto',\n",
" cmap='RdBu_r',\n",
" vmin=-10,\n",
" vmax=10\n",
")\n",
"ax3.set_xlabel('Longitude (Β°)')\n",
"ax3.set_ylabel('Latitude (Β°)')\n",
"ax3.set_title('ECMWF V10 Wind Component (global)')\n",
"plt.colorbar(im2, ax=ax3, label='V10 (m/s)')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.3 ECMWF wind fields (SAR region zoom)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Extract SAR region from ECMWF\n",
"lon_min, lon_max = sar_dataset['longitude'].min().values, sar_dataset['longitude'].max().values\n",
"lat_min, lat_max = sar_dataset['latitude'].min().values, sar_dataset['latitude'].max().values\n",
"\n",
"ecmwf_subset = ecmwf_dataset.sel(\n",
" x=slice(lon_min-1, lon_max+1),\n",
" y=slice(lat_min-1, lat_max+1)\n",
")\n",
"\n",
"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
"\n",
"# U10 zoom\n",
"im1 = ax1.pcolormesh(\n",
" ecmwf_subset['x'],\n",
" ecmwf_subset['y'],\n",
" ecmwf_subset['U10'],\n",
" shading='auto',\n",
" cmap='RdBu_r'\n",
")\n",
"ax1.set_xlabel('Longitude (Β°)')\n",
"ax1.set_ylabel('Latitude (Β°)')\n",
"ax1.set_title('ECMWF U10 (SAR region)')\n",
"plt.colorbar(im1, ax=ax1, label='U10 (m/s)')\n",
"\n",
"# Add SAR footprint\n",
"lon_corners = sar_dataset['longitude'].values[[0, 0, -1, -1, 0], [0, -1, -1, 0, 0]]\n",
"lat_corners = sar_dataset['latitude'].values[[0, 0, -1, -1, 0], [0, -1, -1, 0, 0]]\n",
"ax1.plot(lon_corners, lat_corners, 'k-', linewidth=2, label='SAR footprint')\n",
"ax1.legend()\n",
"\n",
"# V10 zoom\n",
"im2 = ax2.pcolormesh(\n",
" ecmwf_subset['x'],\n",
" ecmwf_subset['y'],\n",
" ecmwf_subset['V10'],\n",
" shading='auto',\n",
" cmap='RdBu_r'\n",
")\n",
"ax2.set_xlabel('Longitude (Β°)')\n",
"ax2.set_ylabel('Latitude (Β°)')\n",
"ax2.set_title('ECMWF V10 (SAR region)')\n",
"plt.colorbar(im2, ax=ax2, label='V10 (m/s)')\n",
"\n",
"# Add SAR footprint\n",
"ax2.plot(lon_corners, lat_corners, 'k-', linewidth=2, label='SAR footprint')\n",
"ax2.legend()\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.4 Interpolated wind on SAR grid (result of map_raster)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
"\n",
"# U10 interpolated\n",
"im1 = ax1.pcolormesh(\n",
" sar_dataset['sample'],\n",
" sar_dataset['line'],\n",
" result['U10'],\n",
" shading='auto',\n",
" cmap='RdBu_r'\n",
")\n",
"ax1.set_xlabel('Sample')\n",
"ax1.set_ylabel('Line')\n",
"ax1.set_title('Interpolated U10 on SAR grid')\n",
"plt.colorbar(im1, ax=ax1, label='U10 (m/s)')\n",
"\n",
"# V10 interpolated\n",
"im2 = ax2.pcolormesh(\n",
" sar_dataset['sample'],\n",
" sar_dataset['line'],\n",
" result['V10'],\n",
" shading='auto',\n",
" cmap='RdBu_r'\n",
")\n",
"ax2.set_xlabel('Sample')\n",
"ax2.set_ylabel('Line')\n",
"ax2.set_title('Interpolated V10 on SAR grid')\n",
"plt.colorbar(im2, ax=ax2, label='V10 (m/s)')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.5 Before/After comparison in lon/lat coordinates"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
"\n",
"# Extract SAR coordinates\n",
"sar_lon = sar_dataset['longitude'].values\n",
"sar_lat = sar_dataset['latitude'].values\n",
"\n",
"# U10: Before (ECMWF subset)\n",
"ax1 = axes[0, 0]\n",
"im1 = ax1.pcolormesh(\n",
" ecmwf_subset['x'],\n",
" ecmwf_subset['y'],\n",
" ecmwf_subset['U10'],\n",
" shading='auto',\n",
" cmap='RdBu_r'\n",
")\n",
"ax1.plot(lon_corners, lat_corners, 'k-', linewidth=2, label='SAR footprint')\n",
"ax1.set_xlabel('Longitude (Β°)')\n",
"ax1.set_ylabel('Latitude (Β°)')\n",
"ax1.set_title('ECMWF U10 (Before interpolation)')\n",
"ax1.legend()\n",
"plt.colorbar(im1, ax=ax1, label='U10 (m/s)')\n",
"\n",
"# U10: After (interpolated on SAR grid)\n",
"ax2 = axes[0, 1]\n",
"im2 = ax2.scatter(\n",
" sar_lon.flatten(),\n",
" sar_lat.flatten(),\n",
" c=result['U10'].values.flatten(),\n",
" s=5,\n",
" cmap='RdBu_r',\n",
" vmin=im1.get_clim()[0],\n",
" vmax=im1.get_clim()[1]\n",
")\n",
"ax2.set_xlabel('Longitude (Β°)')\n",
"ax2.set_ylabel('Latitude (Β°)')\n",
"ax2.set_title('Interpolated U10 (After map_raster)')\n",
"plt.colorbar(im2, ax=ax2, label='U10 (m/s)')\n",
"\n",
"# V10: Before (ECMWF subset)\n",
"ax3 = axes[1, 0]\n",
"im3 = ax3.pcolormesh(\n",
" ecmwf_subset['x'],\n",
" ecmwf_subset['y'],\n",
" ecmwf_subset['V10'],\n",
" shading='auto',\n",
" cmap='RdBu_r'\n",
")\n",
"ax3.plot(lon_corners, lat_corners, 'k-', linewidth=2, label='SAR footprint')\n",
"ax3.set_xlabel('Longitude (Β°)')\n",
"ax3.set_ylabel('Latitude (Β°)')\n",
"ax3.set_title('ECMWF V10 (Before interpolation)')\n",
"ax3.legend()\n",
"plt.colorbar(im3, ax=ax3, label='V10 (m/s)')\n",
"\n",
"# V10: After (interpolated on SAR grid)\n",
"ax4 = axes[1, 1]\n",
"im4 = ax4.scatter(\n",
" sar_lon.flatten(),\n",
" sar_lat.flatten(),\n",
" c=result['V10'].values.flatten(),\n",
" s=5,\n",
" cmap='RdBu_r',\n",
" vmin=im3.get_clim()[0],\n",
" vmax=im3.get_clim()[1]\n",
")\n",
"ax4.set_xlabel('Longitude (Β°)')\n",
"ax4.set_ylabel('Latitude (Β°)')\n",
"ax4.set_title('Interpolated V10 (After map_raster)')\n",
"plt.colorbar(im4, ax=ax4, label='V10 (m/s)')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "env_xsar",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
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mapraster-2026.01.06/docs/index.rst 0000664 0000000 0000000 00000002110 15127170716 0016700 0 ustar 00root root 0000000 0000000 ################################################
mapraster: raster-to-image mapping utilities
################################################
**mapraster** provides utilities to map a lon/lat raster onto an image grid
defined by 2D longitude/latitude coordinates.
It works with `xarray`_ datasets and supports antimeridian crossing and NaN handling.
Documentation
-------------
Getting Started
...............
:doc:`installing`
~~~~~~~~~~~~~~~~~
Examples
........
* :doc:`examples/how_to_use`
Help & Reference
................
:doc:`basic_api`
~~~~~~~~~~~~~~~~
Tests
.....
The latest automated test report (generated during docs build) is available here:
`Open test report `_
----------------------------------------------
Last documentation build: |today|
.. toctree::
:maxdepth: 1
:hidden:
:caption: Getting Started
installing
.. toctree::
:maxdepth: 2
:hidden:
:caption: Examples
examples/how_to_use.ipynb
.. toctree::
:maxdepth: 1
:hidden:
:caption: Getting Started
basic_api
.. _xarray: http://xarray.pydata.org
mapraster-2026.01.06/docs/installing.rst 0000664 0000000 0000000 00000000026 15127170716 0017741 0 ustar 00root root 0000000 0000000 Installing
==========
mapraster-2026.01.06/docs/make.bat 0000664 0000000 0000000 00000001440 15127170716 0016451 0 ustar 00root root 0000000 0000000 @ECHO OFF
pushd %~dp0
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set SOURCEDIR=.
set BUILDDIR=_build
%SPHINXBUILD% >NUL 2>NUL
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
echo.installed, then set the SPHINXBUILD environment variable to point
echo.to the full path of the 'sphinx-build' executable. Alternatively you
echo.may add the Sphinx directory to PATH.
echo.
echo.If you don't have Sphinx installed, grab it from
echo.https://www.sphinx-doc.org/
exit /b 1
)
if "%1" == "" goto help
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
goto end
:help
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
:end
popd
mapraster-2026.01.06/docs/report.json 0000664 0000000 0000000 00000000361 15127170716 0017253 0 ustar 00root root 0000000 0000000 {"created": 1766389265.634514, "duration": 0.009119987487792969, "exitcode": 4, "root": "/home/vincelhx/Documents/autoentreprise/IFREMER/libs/fork_mapraster/mapraster", "environment": {}, "summary": {"total": 0, "collected": 0}, "tests": []} mapraster-2026.01.06/mapraster/ 0000775 0000000 0000000 00000000000 15127170716 0016113 5 ustar 00root root 0000000 0000000 mapraster-2026.01.06/mapraster/__init__.py 0000664 0000000 0000000 00000000626 15127170716 0020230 0 ustar 00root root 0000000 0000000 # type: ignore[attr-defined]
"""mapraster is a Python lib to interpolate xarray raster field on image geometry (e.g. line/sample)"""
__all__ = ["map_raster"]
from .main import map_raster
try:
from importlib import metadata
except ImportError: # for Python<3.8
import importlib_metadata as metadata
try:
__version__ = metadata.version("mapraster")
except Exception:
__version__ = "999"
mapraster-2026.01.06/mapraster/main.py 0000664 0000000 0000000 00000010330 15127170716 0017406 0 ustar 00root root 0000000 0000000 import numpy as np
import xarray as xr
from scipy.interpolate import RectBivariateSpline
def _get_image_dims(ds):
"""
Infer image dimensions from longitude/latitude variables,
excluding 'pol' if present.
"""
if "longitude" in ds:
lon_da = ds["longitude"]
elif "owiLon" in ds:
lon_da = ds["owiLon"]
else:
raise ValueError("originalDataset must contain longitude or owiLon")
return tuple(d for d in lon_da.dims if d != "pol")
def map_raster(
raster_ds,
originalDataset,
footprint,
cross_antimeridian=False,
):
"""
Map a raster onto an image grid defined by originalDataset.
Parameters
----------
raster_ds : xarray.Dataset or xarray.DataArray
Raster with valid `.rio` accessor.
originalDataset : xarray.Dataset
Dataset defining the target image grid (lon/lat in image dims).
footprint : shapely.geometry.Polygon
Footprint of the target grid.
cross_antimeridian : bool, default False
Returns
-------
xarray.Dataset or xarray.DataArray
"""
# --- target lon/lat ---
if "longitude" in originalDataset:
target_lon = originalDataset["longitude"]
target_lat = originalDataset["latitude"]
else:
target_lon = originalDataset["owiLon"]
target_lat = originalDataset["owiLat"]
# --- ensure geographic CRS ---
if not raster_ds.rio.crs.is_geographic:
raster_ds = raster_ds.rio.reproject(4326)
# --- ensure dims ordering ---
raster_ds = raster_ds.transpose("y", "x")
# --- lon/lat bounds from footprint ---
if cross_antimeridian:
x_vals = np.asarray(footprint.exterior.xy[0]) % 360
lon_range = [x_vals.min(), x_vals.max()]
y_vals = np.asarray(footprint.exterior.xy[1])
lat_range = [y_vals.min(), y_vals.max()]
else:
lon1, lat1, lon2, lat2 = footprint.exterior.bounds
lon_range = [lon1, lon2]
lat_range = [lat1, lat2]
# --- ensure increasing raster coords ---
for coord in ("x", "y"):
if raster_ds[coord].values[-1] < raster_ds[coord].values[0]:
raster_ds = raster_ds.reindex({coord: raster_ds[coord][::-1]})
# --- restrict raster to footprint bbox ---
ilon_range = [
max(1, np.searchsorted(raster_ds.x.values, lon_range[0])),
min(np.searchsorted(raster_ds.x.values, lon_range[1]), raster_ds.x.size),
]
ilat_range = [
max(1, np.searchsorted(raster_ds.y.values, lat_range[0])),
min(np.searchsorted(raster_ds.y.values, lat_range[1]), raster_ds.y.size),
]
ilon_range, ilat_range = [[rg[0] - 1, rg[1] + 1] for rg in (ilon_range, ilat_range)]
raster_ds = raster_ds.isel(
x=slice(*ilon_range),
y=slice(*ilat_range),
)
# --- intermediate grid size from image dims ---
az_dim, ra_dim = _get_image_dims(originalDataset)
ny = originalDataset.sizes[az_dim]
nx = originalDataset.sizes[ra_dim]
num = min((ny + nx) // 2, 1000)
lons = np.linspace(*lon_range, num=num)
lats = np.linspace(*lat_range, num=num)
# --- DataArray β Dataset ---
name = None
if isinstance(raster_ds, xr.DataArray):
name = raster_ds.name or "_tmp_name"
raster_ds = raster_ds.to_dataset(name=name)
if cross_antimeridian:
target_lon = target_lon % 360
mapped = []
for var in raster_ds:
da = raster_ds[var]
# first interpolation step
if np.any(np.isnan(da.values)):
upscaled = da.interp(x=lons, y=lats)
else:
spline = RectBivariateSpline(
da.y.values,
da.x.values,
da.values,
kx=3,
ky=3,
)
upscaled = xr.DataArray(
spline(lats, lons),
dims=("y", "x"),
coords={"x": lons, "y": lats},
name=var,
)
# final interpolation on image grid
mapped.append(upscaled.interp(x=target_lon, y=target_lat).drop_vars(("x", "y")))
mapped_ds = xr.merge(mapped)
# --- Dataset β DataArray ---
if name is not None:
mapped_ds = mapped_ds[name]
if name == "_tmp_name":
mapped_ds.name = None
return mapped_ds
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doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
enabler = ["pytest-enabler (>=2.2)"]
test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = "^3.9"
content-hash = "9f7042edffdd5e8c1d7a786929dc2edda40e67ebb3a1c7f01d506be7f369c847"
mapraster-2026.01.06/pyproject.toml 0000664 0000000 0000000 00000006711 15127170716 0017036 0 ustar 00root root 0000000 0000000 # Poetry pyproject.toml: https://python-poetry.org/docs/pyproject/
[build-system]
requires = ["poetry_core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "mapraster"
version = "2026.01.06"
description = "mapraster is a Python lib to interpolate xarray raster field on image geometry (e.g. line/sample)"
readme = "README.md"
authors = ["umr-lops ", "umr-lops "]
license = "MIT"
repository = "https://github.com/umr-lops/mapraster"
homepage = "https://github.com/umr-lops/mapraster"
# Keywords description https://python-poetry.org/docs/pyproject/#keywords
keywords = [] #! Update me
# Pypi classifiers: https://pypi.org/classifiers/
classifiers = [ #! Update me
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Operating System :: OS Independent",
"Topic :: Software Development :: Libraries :: Python Modules",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.9",
]
[tool.poetry.dependencies]
python = "^3.9"
numpy = ">=1.21,<3"
xarray = ">=2023.1,<2026"
scipy = "^1.9.0"
[tool.poetry.group.dev.dependencies]
bandit = "^1.7.1"
black = {version = "^25.11", allow-prereleases = true}
darglint = "^1.8.1"
isort = {extras = ["colors"], version = "^5.10.1"}
mypy = "^1.0"
mypy-extensions = "^0.4.3"
pre-commit = "^4.3.0"
pydocstyle = "^6.1.1"
pylint = "^3.3.9"
pytest = "^8.4.2"
pyupgrade = "^2.29.1"
safety = "^3.7.0"
coverage = "^6.1.2"
coverage-badge = "^1.1.0"
pytest-html = "^3.1.1"
pytest-cov = "^5.0.0"
rioxarray = "^0.15.0"
shapely = "^2.0.0"
[tool.black]
# https://github.com/psf/black
target-version = ["py39"]
line-length = 88
color = true
exclude = '''
/(
\.git
| \.hg
| \.mypy_cache
| \.tox
| \.venv
| _build
| buck-out
| build
| dist
| env
| venv
)/
'''
[tool.isort]
# https://github.com/timothycrosley/isort/
py_version = 39
line_length = 88
known_typing = ["typing", "types", "typing_extensions", "mypy", "mypy_extensions"]
sections = ["FUTURE", "TYPING", "STDLIB", "THIRDPARTY", "FIRSTPARTY", "LOCALFOLDER"]
include_trailing_comma = true
profile = "black"
multi_line_output = 3
indent = 4
color_output = true
[tool.mypy]
# https://mypy.readthedocs.io/en/latest/config_file.html#using-a-pyproject-toml-file
python_version = 3.9
pretty = true
show_traceback = true
color_output = true
allow_redefinition = false
check_untyped_defs = true
disallow_any_generics = true
disallow_incomplete_defs = true
ignore_missing_imports = true
implicit_reexport = false
no_implicit_optional = true
show_column_numbers = true
show_error_codes = true
show_error_context = true
strict_equality = true
strict_optional = true
warn_no_return = true
warn_redundant_casts = true
warn_return_any = true
warn_unreachable = true
warn_unused_configs = true
warn_unused_ignores = true
[tool.pytest.ini_options]
# https://docs.pytest.org/en/6.2.x/customize.html#pyproject-toml
# Directories that are not visited by pytest collector:
norecursedirs =["hooks", "*.egg", ".eggs", "dist", "build", "docs", ".tox", ".git", "__pycache__"]
doctest_optionflags = ["NUMBER", "NORMALIZE_WHITESPACE", "IGNORE_EXCEPTION_DETAIL"]
# Extra options:
addopts = [
"--strict-markers",
"--tb=short",
"--doctest-modules",
"--doctest-continue-on-failure",
]
[tool.coverage.run]
source = ["tests"]
[coverage.paths]
source = "mapraster"
[coverage.run]
branch = true
[coverage.report]
fail_under = 50
show_missing = true mapraster-2026.01.06/setup.cfg 0000664 0000000 0000000 00000000144 15127170716 0015735 0 ustar 00root root 0000000 0000000 [darglint]
# https://github.com/terrencepreilly/darglint
strictness = long
docstring_style = google
mapraster-2026.01.06/tests/ 0000775 0000000 0000000 00000000000 15127170716 0015257 5 ustar 00root root 0000000 0000000 mapraster-2026.01.06/tests/test_3cases.py 0000664 0000000 0000000 00000003564 15127170716 0020061 0 ustar 00root root 0000000 0000000 import numpy as np
import xarray as xr
from tools_test import build_footprint, fake_dataset, fake_ecmwf_0100_1h
from mapraster.main import map_raster
def test_map_raster_no_antimeridian():
dataset = fake_dataset(cross_antimeridian=False)
footprint = build_footprint(dataset)
raster = fake_ecmwf_0100_1h(
to180=True,
with_nan=False,
)
out = map_raster(
raster_ds=raster,
originalDataset=dataset,
footprint=footprint,
cross_antimeridian=False,
)
assert set(out.data_vars) == {"U10", "V10"}
assert out["U10"].shape == dataset["longitude"].shape
assert not np.all(np.isnan(out["U10"].values))
test_map_raster_no_antimeridian()
def test_map_raster_cross_antimeridian():
dataset = fake_dataset(cross_antimeridian=True)
footprint = build_footprint(dataset)
raster = fake_ecmwf_0100_1h(
to180=False,
with_nan=False,
)
out = map_raster(
raster_ds=raster,
originalDataset=dataset,
footprint=footprint,
cross_antimeridian=True,
)
assert set(out.data_vars) == {"U10", "V10"}
assert out["U10"].shape == dataset["longitude"].shape
assert not np.all(np.isnan(out["V10"].values))
test_map_raster_cross_antimeridian()
def test_map_raster_with_nan():
dataset = fake_dataset(cross_antimeridian=True)
footprint = build_footprint(dataset)
raster = fake_ecmwf_0100_1h(
to180=False,
with_nan=True,
)
out = map_raster(
raster_ds=raster,
originalDataset=dataset,
footprint=footprint,
cross_antimeridian=True,
)
# No full NaN since raster is build with partial NaN only
assert not np.all(np.isnan(out["U10"].values))
# check there are NaN in the output
nan_ratio = np.isnan(out["U10"].values).mean()
assert nan_ratio > 0.01
test_map_raster_with_nan()
mapraster-2026.01.06/tests/test_map_raster_4cases.py 0000664 0000000 0000000 00000020522 15127170716 0022270 0 ustar 00root root 0000000 0000000 """
Comprehensive regression tests for map_raster covering all 4 cases:
1. No antimeridian crossing, no NaN
2. No antimeridian crossing, with NaN
3. With antimeridian crossing, no NaN
4. With antimeridian crossing, with NaN
Uses fake_dataset and fake_ecmwf_0100_1h from tools_test (no I/O).
"""
import numpy as np
from tools_test import build_footprint, fake_dataset, fake_ecmwf_0100_1h
from mapraster.main import map_raster
def test_no_antimeridian_no_nan():
"""
Case 1: No antimeridian crossing, no NaN in ECMWF.
"""
# Create datasets
sar_dataset = fake_dataset(cross_antimeridian=False)
raster = fake_ecmwf_0100_1h(to180=True, with_nan=False)
footprint = build_footprint(sar_dataset)
# Run map_raster
result = map_raster(
raster_ds=raster,
originalDataset=sar_dataset,
footprint=footprint,
cross_antimeridian=False,
)
# Basic checks
assert set(result.data_vars) == {"U10", "V10"}, "Missing variables"
assert result["U10"].shape == sar_dataset["longitude"].shape, "Shape mismatch"
assert result["V10"].shape == sar_dataset["longitude"].shape, "Shape mismatch"
# Check no full NaN
assert not np.all(np.isnan(result["U10"].values)), "U10 is all NaN"
assert not np.all(np.isnan(result["V10"].values)), "V10 is all NaN"
# Reference values (from first run)
reference_values = {
"U10": {
"mean": 6.778062602480738,
"std": 0.0141060230914798,
},
"V10": {
"mean": -0.8602113795325333,
"std": 0.1465258384034195,
},
}
# Compare
u10_mean = float(np.mean(result["U10"].values))
u10_std = float(np.std(result["U10"].values))
v10_mean = float(np.mean(result["V10"].values))
v10_std = float(np.std(result["V10"].values))
tolerance = 1e-6
assert abs(u10_mean - reference_values["U10"]["mean"]) < tolerance
assert abs(u10_std - reference_values["U10"]["std"]) < tolerance
assert abs(v10_mean - reference_values["V10"]["mean"]) < tolerance
assert abs(v10_std - reference_values["V10"]["std"]) < tolerance
print("β Case 1 passed: No antimeridian, no NaN")
def test_no_antimeridian_with_nan():
"""
Case 2: No antimeridian crossing, with NaN in ECMWF.
"""
# Create datasets
sar_dataset = fake_dataset(cross_antimeridian=False)
raster = fake_ecmwf_0100_1h(to180=True, with_nan=True)
footprint = build_footprint(sar_dataset)
# Run map_raster
result = map_raster(
raster_ds=raster,
originalDataset=sar_dataset,
footprint=footprint,
cross_antimeridian=False,
)
# Basic checks
assert set(result.data_vars) == {"U10", "V10"}, "Missing variables"
assert result["U10"].shape == sar_dataset["longitude"].shape, "Shape mismatch"
# In this case, the SAR region doesn't overlap with NaN zone
# So we might not have NaN, which is fine
# Just check not all NaN
assert not np.all(np.isnan(result["U10"].values)), "U10 is all NaN"
# Reference values (from first run, using nanmean/nanstd)
reference_values = {
"U10": {
"nanmean": 6.778062602480738,
"nanstd": 0.0141060230914798,
"nan_ratio": 0.0, # No NaN in this region
},
"V10": {
"nanmean": -0.8602113795325333,
"nanstd": 0.1465258384034195,
},
}
u10_mean = float(np.nanmean(result["U10"].values))
u10_std = float(np.nanstd(result["U10"].values))
v10_mean = float(np.nanmean(result["V10"].values))
v10_std = float(np.nanstd(result["V10"].values))
# More relaxed tolerance for NaN case (interpolation differences)
tolerance = 1e-5
assert abs(u10_mean - reference_values["U10"]["nanmean"]) < tolerance or np.isnan(
u10_mean
)
assert abs(v10_mean - reference_values["V10"]["nanmean"]) < tolerance or np.isnan(
v10_mean
)
assert abs(u10_std - reference_values["U10"]["nanstd"]) < tolerance
assert abs(v10_std - reference_values["V10"]["nanstd"]) < tolerance
print("β Case 2 passed: No antimeridian, with NaN")
def test_with_antimeridian_no_nan():
"""
Case 3: With antimeridian crossing, no NaN in ECMWF.
"""
# Create datasets
sar_dataset = fake_dataset(cross_antimeridian=True)
# Use 0-360 for antimeridian
raster = fake_ecmwf_0100_1h(to180=False, with_nan=False)
footprint = build_footprint(sar_dataset)
# Run map_raster
result = map_raster(
raster_ds=raster,
originalDataset=sar_dataset,
footprint=footprint,
cross_antimeridian=True,
)
# Basic checks
assert set(result.data_vars) == {"U10", "V10"}, "Missing variables"
assert result["U10"].shape == sar_dataset["longitude"].shape, "Shape mismatch"
# Check no full NaN
assert not np.all(np.isnan(result["U10"].values)), "U10 is all NaN"
assert not np.all(np.isnan(result["V10"].values)), "V10 is all NaN"
# Reference values (from first run)
reference_values = {
"U10": {
"mean": 6.778062602480738,
"std": 0.01410602309147989,
},
"V10": {
"mean": 0.1933075747771534,
"std": 0.16163073381182927,
},
}
u10_mean = float(np.mean(result["U10"].values))
u10_std = float(np.std(result["U10"].values))
v10_mean = float(np.mean(result["V10"].values))
v10_std = float(np.std(result["V10"].values))
tolerance = 1e-6
assert abs(u10_mean - reference_values["U10"]["mean"]) < tolerance
assert abs(u10_std - reference_values["U10"]["std"]) < tolerance
assert abs(v10_mean - reference_values["V10"]["mean"]) < tolerance
assert abs(v10_std - reference_values["V10"]["std"]) < tolerance
print("β Case 3 passed: With antimeridian, no NaN")
def test_with_antimeridian_with_nan():
"""
Case 4: With antimeridian crossing, with NaN in ECMWF.
"""
# Create datasets
sar_dataset = fake_dataset(cross_antimeridian=True)
# Use 0-360 for antimeridian
raster = fake_ecmwf_0100_1h(to180=False, with_nan=True)
footprint = build_footprint(sar_dataset)
# Run map_raster
result = map_raster(
raster_ds=raster,
originalDataset=sar_dataset,
footprint=footprint,
cross_antimeridian=True,
)
# Basic checks
assert set(result.data_vars) == {"U10", "V10"}, "Missing variables"
assert result["U10"].shape == sar_dataset["longitude"].shape, "Shape mismatch"
# Should have some NaN but not all
assert not np.all(np.isnan(result["U10"].values)), "U10 is all NaN"
# Reference values (from first run)
reference_values = {
"U10": {
"nanmean": 6.780413853544618,
"nanstd": 0.01312769010318517,
"nan_ratio": 0.6206666666666667, # ~62% NaN due to overlap with NaN zone
},
"V10": {
"nanmean": 0.2644554730704113,
"nanstd": 0.18536406900329389,
},
}
u10_mean = float(np.nanmean(result["U10"].values))
u10_std = float(np.nanstd(result["U10"].values))
v10_mean = float(np.nanmean(result["V10"].values))
v10_std = float(np.nanstd(result["V10"].values))
nan_ratio = float(np.isnan(result["U10"].values).mean())
tolerance = 1e-8
assert abs(u10_mean - reference_values["U10"]["nanmean"]) < tolerance
assert abs(v10_mean - reference_values["V10"]["nanmean"]) < tolerance
assert abs(u10_std - reference_values["U10"]["nanstd"]) < tolerance
assert abs(v10_std - reference_values["V10"]["nanstd"]) < tolerance
# Check NaN ratio is reasonable (within 5% tolerance)
assert abs(nan_ratio - reference_values["U10"]["nan_ratio"]) < 0.05
print("β Case 4 passed: With antimeridian, with NaN")
def test_all_cases():
"""
Run all 4 test cases and display summary.
"""
print("\n" + "=" * 50)
print("Running all 4 map_raster regression test cases")
print("=" * 50)
print("\n[1/4] No antimeridian, no NaN...")
test_no_antimeridian_no_nan()
print("\n[2/4] No antimeridian, with NaN...")
test_no_antimeridian_with_nan()
print("\n[3/4] With antimeridian, no NaN...")
test_with_antimeridian_no_nan()
print("\n[4/4] With antimeridian, with NaN...")
test_with_antimeridian_with_nan()
print("\n" + "=" * 50)
print("β All 4 test cases passed successfully!")
print("=" * 50 + "\n")
if __name__ == "__main__":
test_all_cases()
mapraster-2026.01.06/tests/test_others.py 0000664 0000000 0000000 00000002530 15127170716 0020174 0 ustar 00root root 0000000 0000000 import numpy as np
import xarray as xr
from tools_test import build_footprint, fake_dataset, fake_ecmwf_0100_1h
from mapraster.main import _get_image_dims, map_raster
def test_get_image_dims_ignore_pol():
ny, nx, npol = 10, 20, 2
lon = np.zeros((ny, nx, npol))
ds = xr.Dataset(
{
"longitude": (("line", "sample", "pol"), lon),
}
)
az_dim, ra_dim = _get_image_dims(ds)
assert az_dim == "line"
assert ra_dim == "sample"
test_get_image_dims_ignore_pol()
def test_data_type():
"""
Make sure that map_raster works when input is DataArray or Dataset
"""
dataset = fake_dataset(cross_antimeridian=False)
footprint = build_footprint(dataset)
raster = fake_ecmwf_0100_1h(
to180=True,
with_nan=False,
)
assert (
type(
map_raster(
raster_ds=raster, # Dataset
originalDataset=dataset,
footprint=footprint,
cross_antimeridian=False,
)
)
== xr.Dataset
)
assert (
type(
map_raster(
raster_ds=raster.U10, # DataArray
originalDataset=dataset,
footprint=footprint,
cross_antimeridian=False,
)
)
== xr.DataArray
)
test_data_type()
mapraster-2026.01.06/tests/tools_test.py 0000664 0000000 0000000 00000005704 15127170716 0020036 0 ustar 00root root 0000000 0000000 import numpy as np
import rioxarray # activate .rio accessor
import xarray as xr
from shapely.geometry import Polygon
def fake_dataset(cross_antimeridian=False):
nline, nsample = 50, 60
line = np.arange(nline)
sample = np.arange(nsample)
L, S = np.meshgrid(line, sample, indexing="ij")
if cross_antimeridian:
# near +180Β°, wrapped to [-180, 180]
lon_raw = 170 + 0.25 * S - 0.12 * L
lon = ((lon_raw + 180) % 360) - 180
else:
# same geometry, far from Β±180Β°
lon = -30 + 0.25 * S - 0.12 * L
# slightly tilted latitude grid
lat = -29 + 0.06 * L + 0.01 * S
return xr.Dataset(
data_vars={
"longitude": (("line", "sample"), lon),
"latitude": (("line", "sample"), lat),
},
coords={"line": line, "sample": sample},
)
def _to_lon180(ds):
# convert [0, 360] β [-180, 180]
ds = ds.roll(x=-np.searchsorted(ds.x, 180), roll_coords=True)
ds["x"] = xr.where(ds["x"] >= 180, ds["x"] - 360, ds["x"])
return ds
def _to_lon360(ds):
# ensure [0, 360] longitude
ds = ds.assign_coords(x=ds.x % 360)
return ds.sortby("x")
def fake_ecmwf_0100_1h(*, to180=True, with_nan=False):
import datetime
lon = np.linspace(0, 360, 360 * 10, endpoint=False)
lat = np.linspace(-90, 90, 181 * 10)
LON, LAT = np.meshgrid(lon, lat)
U10 = 5 + 2 * np.cos(np.deg2rad(LAT))
V10 = 2 * np.sin(np.deg2rad(LON))
ds = xr.Dataset(
data_vars={
"U10": (("y", "x"), U10),
"V10": (("y", "x"), V10),
},
coords={"x": lon, "y": lat},
attrs={"time": datetime.datetime(2023, 3, 3, 7)},
)
ds = _to_lon180(ds) if to180 else _to_lon360(ds)
if with_nan:
# latitude band
lat_mask = (ds["y"] >= -40) & (ds["y"] <= -20)
if to180:
lon_mask_1 = (ds["x"] >= 170) & (ds["x"] <= 179)
lon_mask_2 = (ds["x"] >= -179) & (ds["x"] <= -170)
else:
lon_mask_1 = (ds["x"] >= 170) & (ds["x"] <= 179)
lon_mask_2 = (ds["x"] >= 180) & (ds["x"] <= 189)
zone = (lat_mask & lon_mask_1) | (lat_mask & lon_mask_2)
iy = xr.DataArray(
np.arange(ds.sizes["y"]),
dims=("y",),
coords={"y": ds["y"]},
)
ix = xr.DataArray(
np.arange(ds.sizes["x"]),
dims=("x",),
coords={"x": ds["x"]},
)
sparse_mask = ((iy % 2) == 0) & ((ix % 3) == 0)
final_mask = zone & sparse_mask
ds["U10"] = ds["U10"].where(~final_mask)
ds["V10"] = ds["V10"].where(~final_mask)
ds.rio.write_crs("EPSG:4326", inplace=True)
return ds
def build_footprint(ds):
lon = ds["longitude"].values
lat = ds["latitude"].values
return Polygon(
[
(lon[0, 0], lat[0, 0]),
(lon[0, -1], lat[0, -1]),
(lon[-1, -1], lat[-1, -1]),
(lon[-1, 0], lat[-1, 0]),
]
)