diff --git a/.bandit.yml b/.bandit.yml deleted file mode 100644 index 86ad0206..00000000 --- a/.bandit.yml +++ /dev/null @@ -1,2 +0,0 @@ -exclude_dirs: - - scmdata/_version.py diff --git a/.coveragerc b/.coveragerc deleted file mode 100644 index cf8dbeab..00000000 --- a/.coveragerc +++ /dev/null @@ -1,14 +0,0 @@ -[run] -branch = True -source = scmdata -omit = *_version.py - -[report] -fail_under = 90 -exclude_lines = - pass - if self.debug: - pragma: no cover - raise NotImplementedError - if __name__ == .__main__.: -ignore_errors = True diff --git a/.github/actions/bump-version/action.yml b/.github/actions/bump-version/action.yml new file mode 100644 index 00000000..8c669c31 --- /dev/null +++ b/.github/actions/bump-version/action.yml @@ -0,0 +1,224 @@ +name: "Bump version" +description: > + Bump a Python project's version using `uv version --bump`, + update the CHANGELOG via towncrier, commit, tag, push, + and optionally land a second commit bumping main onto a pre-release version. + +# Vendored from climate-resource/github-actions bump-version so this public repo +# does not depend on a private actions repo. Keep behaviour in sync with the +# upstream action when updating. + +inputs: + bump-rule: + description: > + Whitespace-separated list of segments passed to `uv version --bump`. + Each segment becomes a separate `--bump ` argument, + so values like `patch`, `minor`, `major`, `stable`, `minor alpha`, `patch rc`, or `major beta` are all valid. + uv requires a release component (patch, minor, major, stable) alongside any prerelease segment (alpha, beta, rc, dev) when the current version is a stable release. + required: true + pre-release-bump: + description: > + Pre-release segment to bump main to after tagging, so future commits do not share the tagged version. + + One of: dev, alpha, beta, rc, none. + + Use 'none' to skip the second commit. + required: false + default: "dev" + pre-release-base: + description: > + Bump rule applied to the base version before adding the pre-release + segment in the second commit. Defaults to 'patch'. + + Use 'none' to skip bumping the base (only add the pre-release segment). + required: false + default: "patch" + update-changelog: + description: "If 'true', run towncrier to build the CHANGELOG before tagging." + required: false + default: "true" + commit-email: + description: "Email for the CI commit author." + required: false + default: "ci-runner@climate-resource.invalid" + workspace-packages: + description: > + Optional newline-separated list of uv workspace package names to mirror + the bumped version onto (in addition to the root project). + required: false + default: "" + lock: + description: "If 'true', run `uv lock` after each version change." + required: false + default: "true" + pre-commit-command: + description: > + Optional shell command run after the changelog build and before each bump commit. + + Use it to regenerate version-derived files (e.g. an OpenAPI schema) so they stay in sync in the tagged commit. + The commit uses `git commit -a`, so any files the command regenerates are picked up automatically. + The command must succeed; a non-zero exit aborts the bump. + required: false + default: "" + pre-commit-skip: + description: "If 'true', pass -n to git commit to skip pre-commit hooks." + required: false + default: "false" + push: + description: "If 'true', push the bump commit, tag, and pre-release commit." + required: false + default: "true" + +outputs: + base-version: + description: "The version before bumping." + value: ${{ steps.bump.outputs.base-version }} + new-version: + description: "The new tagged version (no 'v' prefix)." + value: ${{ steps.bump.outputs.new-version }} + tag: + description: "The new git tag (with 'v' prefix)." + value: ${{ steps.bump.outputs.tag }} + dev-version: + description: "The pre-release version landed on main after tagging (if any)." + value: ${{ steps.bump.outputs.dev-version }} + is-prerelease: + description: "'true' when the tagged version is a pre-release (a|b|rc|dev)." + value: ${{ steps.bump.outputs.is-prerelease }} + +runs: + using: "composite" + steps: + - name: Configure git identity + shell: bash + run: | + git config --global user.name "${GITHUB_ACTOR}" + git config --global user.email "${{ inputs.commit-email }}" + + - name: Bump, changelog, commit, tag + id: bump + shell: bash + env: + BUMP_RULE: ${{ inputs.bump-rule }} + PRE_RELEASE_BUMP: ${{ inputs.pre-release-bump }} + PRE_RELEASE_BASE: ${{ inputs.pre-release-base }} + UPDATE_CHANGELOG: ${{ inputs.update-changelog }} + WORKSPACE_PACKAGES: ${{ inputs.workspace-packages }} + RUN_LOCK: ${{ inputs.lock }} + PRE_COMMIT_COMMAND: ${{ inputs.pre-commit-command }} + COMMIT_SKIP_HOOKS: ${{ inputs.pre-commit-skip }} + DO_PUSH: ${{ inputs.push }} + run: | + set -euo pipefail + + commit_args=() + if [[ "${COMMIT_SKIP_HOOKS}" == "true" ]]; then + commit_args+=(-n) + fi + + mirror_workspace() { + local version="$1" + if [[ -z "${WORKSPACE_PACKAGES//[[:space:]]/}" ]]; then + return 0 + fi + while IFS= read -r pkg; do + pkg="${pkg// /}" + [[ -z "${pkg}" ]] && continue + echo "Mirroring version ${version} onto workspace package ${pkg}" + uv version --frozen --package "${pkg}" "${version}" + done <<< "${WORKSPACE_PACKAGES}" + } + + maybe_lock() { + if [[ "${RUN_LOCK}" == "true" ]]; then + uv lock + fi + } + + run_pre_commit_command() { + if [[ -n "${PRE_COMMIT_COMMAND}" ]]; then + echo "Running pre-commit command" + bash -c "${PRE_COMMIT_COMMAND}" + fi + } + + BASE_VERSION="$(uv version --short)" + echo "Bumping from version ${BASE_VERSION}" + + read -r -a bump_segments <<< "${BUMP_RULE}" + if [[ ${#bump_segments[@]} -eq 0 ]]; then + echo "bump-rule must not be empty" >&2 + exit 1 + fi + bump_cli_args=() + for seg in "${bump_segments[@]}"; do + bump_cli_args+=(--bump "${seg}") + done + + uv version --frozen "${bump_cli_args[@]}" + NEW_VERSION="$(uv version --short)" + echo "Bumped to version ${NEW_VERSION}" + + mirror_workspace "${NEW_VERSION}" + maybe_lock + + if [[ "${UPDATE_CHANGELOG}" == "true" ]]; then + uv run towncrier build --yes --version "v${NEW_VERSION}" + else + echo "Skipping changelog update" + fi + + run_pre_commit_command + + git commit "${commit_args[@]}" -a -m "bump: version ${BASE_VERSION} -> ${NEW_VERSION}" + git tag "v${NEW_VERSION}" + + if [[ "${DO_PUSH}" == "true" ]]; then + git push + git push --tags + fi + + if [[ "${NEW_VERSION}" =~ (a|b|rc|dev) ]]; then + IS_PRERELEASE="true" + else + IS_PRERELEASE="false" + fi + + echo "base-version=${BASE_VERSION}" >> "${GITHUB_OUTPUT}" + echo "new-version=${NEW_VERSION}" >> "${GITHUB_OUTPUT}" + echo "tag=v${NEW_VERSION}" >> "${GITHUB_OUTPUT}" + echo "is-prerelease=${IS_PRERELEASE}" >> "${GITHUB_OUTPUT}" + + # Skip the dev bump if the tagged version is already a pre-release or + # the caller opted out. + if [[ "${PRE_RELEASE_BUMP}" == "none" ]]; then + echo "Skipping pre-release bump (pre-release-bump=none)" + exit 0 + fi + if [[ "${IS_PRERELEASE}" == "true" ]]; then + echo "Skipping pre-release bump; tagged version is already a pre-release" + exit 0 + fi + + PREV_VERSION="${NEW_VERSION}" + bump_args=() + if [[ "${PRE_RELEASE_BASE}" != "none" ]]; then + bump_args+=(--bump "${PRE_RELEASE_BASE}") + fi + bump_args+=(--bump "${PRE_RELEASE_BUMP}") + uv version --frozen "${bump_args[@]}" + + DEV_VERSION="$(uv version --short)" + echo "Bumping main onto pre-release ${PREV_VERSION} > ${DEV_VERSION}" + + mirror_workspace "${DEV_VERSION}" + maybe_lock + run_pre_commit_command + + git commit "${commit_args[@]}" -a -m "bump(${PRE_RELEASE_BUMP}): version ${PREV_VERSION} > ${DEV_VERSION}" + + if [[ "${DO_PUSH}" == "true" ]]; then + git push + fi + + echo "dev-version=${DEV_VERSION}" >> "${GITHUB_OUTPUT}" diff --git a/.github/actions/setup-uv/action.yml b/.github/actions/setup-uv/action.yml new file mode 100644 index 00000000..755f16db --- /dev/null +++ b/.github/actions/setup-uv/action.yml @@ -0,0 +1,44 @@ +name: "Setup uv" +description: "Install uv (and optionally a Python version) with caching enabled" + +inputs: + python-version: + description: "Python version to install" + required: false + uv-version: + description: "Version of uv to install (defaults to the latest release)" + required: false + working-directory: + description: "Working directory for uv commands" + required: false + default: "." + + # Cache configuration + enable-cache: + description: "Enable caching of the uv cache" + required: false + default: "auto" + cache-dependency-glob: + description: "Glob pattern for cache dependency files" + required: false + default: "**/uv.lock" + cache-suffix: + description: "Suffix for the cache key" + required: false + cache-local-path: + description: "Path to a local cache directory" + required: false + +runs: + using: "composite" + steps: + - name: Setup uv + uses: astral-sh/setup-uv@v8.1.0 + with: + version: ${{ inputs.uv-version }} + python-version: ${{ inputs.python-version }} + working-directory: ${{ inputs.working-directory }} + enable-cache: ${{ inputs.enable-cache }} + cache-dependency-glob: ${{ inputs.cache-dependency-glob }} + cache-suffix: ${{ inputs.cache-suffix }} + cache-local-path: ${{ inputs.cache-local-path }} diff --git a/.github/actions/setup/action.yml b/.github/actions/setup/action.yml deleted file mode 100644 index 622953db..00000000 --- a/.github/actions/setup/action.yml +++ /dev/null @@ -1,57 +0,0 @@ -name: "Setup Python and Poetry" -description: "setup Python and Poetry with caches" - -inputs: - python-version: - description: "Python version to use" - required: true - venv-id: - description: "ID to identify cached environment (should be unique from other steps)" - required: true - poetry-dependency-install-flags: - description: "Flags to pass to poetry when running `poetry install --no-interaction --no-root`" - required: true - run-poetry-install: - description: "Should we run the poetry install steps" - required: false - default: true - - -runs: - using: "composite" - steps: - - name: Set up Python ${{ inputs.python-version }} - id: setup-python - uses: actions/setup-python@v4 - with: - python-version: ${{ inputs.python-version }} - - name: Load cached Poetry installation - id: cached-poetry - uses: actions/cache@v3 - with: - path: ~/.local # the path depends on the OS - key: poetry-2 # increment to reset cache - - name: Install Poetry - if: steps.cached-poetry.outputs.cache-hit != 'true' - uses: snok/install-poetry@v1 - - name: Load cached venv - if: ${{ inputs.run-poetry-install == 'true' }} - id: cached-poetry-dependencies - uses: actions/cache@v3 - with: - path: .venv - key: "venv-${{ inputs.venv-id }}-${{ inputs.python-version }}-${{ hashFiles('**/poetry.lock') }}" - # Install dependencies first so that our package doesn't appear in the cache by accident - - name: Install dependencies - if: ${{ inputs.run-poetry-install == 'true' }} - shell: bash - run: | - poetry config virtualenvs.create true - poetry config virtualenvs.in-project true - poetry env use ${{ inputs.python-version }} - poetry install --no-interaction --no-root ${{ inputs.poetry-dependency-install-flags }} - # Now run same command but let the package install too - - name: Install package - if: ${{ inputs.run-poetry-install == 'true' }} - shell: bash - run: poetry install --no-interaction ${{ inputs.poetry-dependency-install-flags }} diff --git a/.github/workflows/bump.yaml b/.github/workflows/bump.yaml index b0881d12..b2c50edf 100644 --- a/.github/workflows/bump.yaml +++ b/.github/workflows/bump.yaml @@ -1,19 +1,34 @@ name: Bump version +# Bumps the project version with `uv version --bump`, builds the changelog with +# towncrier, commits, tags and pushes, then lands a follow-up pre-release commit +# on main (e.g. 0.20.0 -> 0.20.1.dev0) so subsequent commits don't share the +# tagged version. This mirrors the process in climate-resource/github-actions, +# vendored here via ./.github/actions/{setup-uv,bump-version}. + on: workflow_dispatch: inputs: bump_rule: type: choice - description: How to bump the project's version (see https://python-poetry.org/docs/cli/#version) + description: > + How to bump the version. Whitespace-separated segments are passed to + `uv version --bump`; from a stable version a prerelease segment must + be combined with a release segment (e.g. "minor alpha"). options: - patch - minor - major - - prepatch - - preminor - - premajor - - prerelease + - stable + - patch alpha + - patch beta + - patch rc + - minor alpha + - minor beta + - minor rc + - major alpha + - major beta + - major rc required: true jobs: @@ -21,39 +36,22 @@ jobs: name: "Bump version and create changelog" if: "!startsWith(github.event.head_commit.message, 'bump:')" runs-on: ubuntu-latest - env: - CI_COMMIT_EMAIL: "ci-runner@scmdata.com" - + permissions: + contents: write steps: - name: Check out repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: fetch-depth: 0 - token: "${{ secrets.PERSONAL_ACCESS_TOKEN }}" + token: ${{ secrets.PERSONAL_ACCESS_TOKEN || github.token }} - # towncrier imports the package as part of building so just do the full - # setup here - - uses: ./.github/actions/setup + - name: Setup uv + uses: ./.github/actions/setup-uv with: python-version: "3.11" - venv-id: "bump" - # another test that we can import if we only install the main library - poetry-dependency-install-flags: "--only main" - - - name: Install towncrier - run: | - poetry run pip install towncrier - - name: Create bump and changelog - run: | - BASE_VERSION=`poetry version -s` - NEW_VERSION=`poetry version -s ${{ github.event.inputs.bump_rule }}` - poetry run towncrier build --yes --version v$NEW_VERSION - - git config --global user.name "$GITHUB_ACTOR" - git config --global user.email "$CI_COMMIT_EMAIL" - - git commit -a -m "bump: version $BASE_VERSION -> $NEW_VERSION" - git tag v$NEW_VERSION - git push && git push --tags - echo "Bumped to version $NEW_VERSION" + - name: Bump version + uses: ./.github/actions/bump-version + with: + bump-rule: ${{ inputs.bump_rule }} + commit-email: "ci-runner@scmdata.invalid" diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index a4ed450f..474ee66e 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -12,75 +12,75 @@ jobs: runs-on: ubuntu-latest steps: - name: Check out repository - uses: actions/checkout@v3 - - uses: ./.github/actions/setup + uses: actions/checkout@v4 + - uses: ./.github/actions/setup-uv with: - python-version: "3.10" - venv-id: "docs" - poetry-dependency-install-flags: "--all-extras --only 'main,dev'" + python-version: "3.11" + - name: Install dependencies + run: uv sync --all-extras # There are still a lot of mypy errors to resolve - name: mypy continue-on-error: true - run: MYPYPATH=stubs poetry run mypy src + run: MYPYPATH=stubs uv run mypy src docs: if: ${{ !github.event.pull_request.draft }} runs-on: ubuntu-latest steps: - name: Check out repository - uses: actions/checkout@v3 - - uses: ./.github/actions/setup + uses: actions/checkout@v4 + - uses: ./.github/actions/setup-uv with: - python-version: "3.10" - venv-id: "docs" - poetry-dependency-install-flags: "--all-extras --only 'main,docs,notebooks'" + python-version: "3.11" + - name: Install dependencies + run: uv sync --all-extras --group docs - name: docs # TODO: Enable -W option once the sphinx warnings are cleared up - run: poetry run sphinx-build --keep-going -T -b html docs/source docs/build + run: uv run sphinx-build --keep-going -T -b html docs/source docs/build tests: strategy: fail-fast: false matrix: os: [ "ubuntu-latest" ] - python-version: [ "3.10", "3.11" ] + python-version: [ "3.11", "3.12", "3.13" ] runs-on: "${{ matrix.os }}" steps: - name: Check out repository - uses: actions/checkout@v3 - - uses: ./.github/actions/setup + uses: actions/checkout@v4 + - uses: ./.github/actions/setup-uv with: python-version: "${{ matrix.python-version }}" - venv-id: "tests-${{ runner.os }}" - poetry-dependency-install-flags: "--all-extras" + - name: Install dependencies + run: uv sync --all-extras - name: Run tests run: | - poetry run pytest -r a -v src tests --doctest-modules --cov=src --cov-report=term-missing --cov-report=xml - poetry run coverage report + uv run pytest -r a -v src tests --doctest-modules --cov=src --cov-report=term-missing --cov-report=xml + uv run coverage report - name: Upload coverage reports to Codecov - uses: codecov/codecov-action@v3 + uses: codecov/codecov-action@v4 test-xarray-versions: + # Guard the minimum supported xarray (see the floor in pyproject.toml). + # Older xarray is incompatible with pandas >= 3, so the floor tracks the + # first pandas-3-compatible release; the latest xarray is covered by `tests`. runs-on: ubuntu-latest strategy: matrix: python-version: [ "3.11" ] - xarray-version: [ "0.18.2", "2025.01", "2025.03" ] + xarray-version: [ "2025.12.0" ] steps: - name: Checkout repository - uses: actions/checkout@v3 - - uses: ./.github/actions/setup + uses: actions/checkout@v4 + - uses: ./.github/actions/setup-uv with: python-version: "${{ matrix.python-version }}" - venv-id: "tests-${{ runner.os }}" - poetry-dependency-install-flags: "--all-extras" - - name: Install test dependencies - run: | - pip install xarray==${{ matrix.xarray-version }} + - name: Install dependencies + run: uv sync --all-extras - name: Test with pytest run: | - poetry run pytest -r a -v src tests + uv run --with "xarray==${{ matrix.xarray-version }}" pytest -r a -v src tests test-no-plotting: # This test makes sure that everything works even if the plotting @@ -88,52 +88,48 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.10"] + python-version: ["3.11"] steps: - name: Checkout repository - uses: actions/checkout@v3 - - uses: ./.github/actions/setup + uses: actions/checkout@v4 + - uses: ./.github/actions/setup-uv with: python-version: "${{ matrix.python-version }}" - venv-id: "tests-${{ runner.os }}" - poetry-dependency-install-flags: "--extras optional --with tests" + - name: Install dependencies (no plotting extras) + run: uv sync --no-default-groups --extra optional --group tests - name: Test with pytest run: | - poetry run pytest -r a -v src tests + uv run --no-sync pytest -r a -v src tests imports-without-extras: strategy: fail-fast: false matrix: os: [ "ubuntu-latest" ] - python-version: [ "3.10", "3.11" ] + python-version: [ "3.11", "3.13" ] runs-on: "${{ matrix.os }}" steps: - name: Check out repository - uses: actions/checkout@v3 - - uses: ./.github/actions/setup + uses: actions/checkout@v4 + - uses: ./.github/actions/setup-uv with: python-version: "${{ matrix.python-version }}" - venv-id: "imports-without-extras-${{ runner.os }}" - poetry-dependency-install-flags: "--only main" + - name: Install package without extras + run: uv sync --no-default-groups - name: Check importable without extras - run: poetry run python scripts/test-install.py + run: uv run --no-sync python scripts/test-install.py check-build: runs-on: ubuntu-latest steps: - name: Check out repository - uses: actions/checkout@v3 - - uses: ./.github/actions/setup + uses: actions/checkout@v4 + - uses: ./.github/actions/setup-uv with: - python-version: "3.10" - venv-id: "check-build-${{ runner.os }}" - run-poetry-install: false - poetry-dependency-install-flags: "not used" + python-version: "3.11" - name: Build package - run: | - poetry build --no-interaction + run: uv build - name: Check build run: | tar -tvf dist/scmdata-*.tar.gz --wildcards '*scmdata/py.typed' @@ -142,16 +138,14 @@ jobs: runs-on: ubuntu-latest steps: - name: Check out repository - uses: actions/checkout@v3 - - uses: ./.github/actions/setup + uses: actions/checkout@v4 + - uses: ./.github/actions/setup-uv with: - python-version: "3.10" - venv-id: "licence-check" - poetry-dependency-install-flags: "--all-extras" + python-version: "3.11" + - name: Install dependencies + run: uv sync --all-extras - name: Check licences of dependencies shell: bash run: | - TEMP_FILE=$(mktemp) - poetry export --without=tests --without=docs --without=dev > $TEMP_FILE - poetry run liccheck -r $TEMP_FILE -R licence-check.txt + ./scripts/check-licences.sh cat licence-check.txt diff --git a/.github/workflows/deploy.yaml b/.github/workflows/deploy.yaml index fd8b56cc..4e4737bd 100644 --- a/.github/workflows/deploy.yaml +++ b/.github/workflows/deploy.yaml @@ -14,20 +14,20 @@ jobs: runs-on: ubuntu-latest steps: - name: Check out repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: fetch-depth: 0 - - uses: ./.github/actions/setup + - uses: ./.github/actions/setup-uv with: - python-version: "3.10" - venv-id: "deploy" - poetry-dependency-install-flags: "--all-extras" + python-version: "3.11" + - name: Install dependencies + run: uv sync --all-extras - name: Run tests run: | - poetry run pytest -r a src tests --doctest-modules + uv run pytest -r a src tests --doctest-modules - name: Publish to PyPI env: - PYPI_TOKEN: "${{ secrets.PYPI_TOKEN }}" + UV_PUBLISH_TOKEN: "${{ secrets.PYPI_TOKEN }}" run: | - poetry config pypi-token.pypi $PYPI_TOKEN - poetry publish --build --no-interaction + uv build + uv publish diff --git a/.github/workflows/install-conda.yml b/.github/workflows/install-conda.yml index 25050dd2..71f654a6 100644 --- a/.github/workflows/install-conda.yml +++ b/.github/workflows/install-conda.yml @@ -12,7 +12,7 @@ jobs: fail-fast: false matrix: os: ["ubuntu-latest", "macos-latest", "windows-latest"] - python-version: ['3.10', '3.11'] + python-version: ['3.11', '3.12', '3.13'] steps: - name: Setup conda diff --git a/.github/workflows/install.yaml b/.github/workflows/install.yaml index b51444c4..df0acfda 100644 --- a/.github/workflows/install.yaml +++ b/.github/workflows/install.yaml @@ -12,7 +12,7 @@ jobs: fail-fast: false matrix: os: ["ubuntu-latest", "macos-latest", "windows-latest"] - python-version: [ "3.10", "3.11" ] + python-version: [ "3.11", "3.12", "3.13" ] steps: - name: Set up Python "${{ matrix.python-version }}" id: setup-python diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index bec1c2f7..51fbb02b 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -14,29 +14,29 @@ jobs: runs-on: ubuntu-latest steps: - name: Check out repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: fetch-depth: 0 - - uses: ./.github/actions/setup + - uses: ./.github/actions/setup-uv with: - python-version: "3.10" - venv-id: "release" - poetry-dependency-install-flags: "--all-extras" + python-version: "3.11" + - name: Install dependencies + run: uv sync --all-extras - name: Add version to environment run: | - PROJECT_VERSION=$(poetry version --short) + PROJECT_VERSION=$(uv version --short) echo "PROJECT_VERSION=$PROJECT_VERSION" >> $GITHUB_ENV - name: Run tests run: | - poetry run pytest -r a -v src tests --doctest-modules + uv run pytest -r a -v src tests --doctest-modules - name: Build package run: | - poetry build --no-interaction + uv build - name: Generate Release Notes run: | git log $(git describe --tags --abbrev=0 HEAD^)..HEAD --pretty='format:* %h %s' --no-merges >> ".github/release_template.md" - name: Create Release Draft - uses: softprops/action-gh-release@v1 + uses: softprops/action-gh-release@v2 with: body_path: ".github/release_template.md" draft: true diff --git a/.gitignore b/.gitignore index 7ff6bb07..579af1e0 100644 --- a/.gitignore +++ b/.gitignore @@ -144,6 +144,3 @@ dmypy.json # Mac stuff *.DS_Store - -# Poetry Lockfile -poetry.lock diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 4dadc0bc..da7242cb 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -30,21 +30,14 @@ repos: entry: found Copier update rejection files; review them and remove them language: fail files: "\\.rej$" - - repo: https://github.com/charliermarsh/ruff-pre-commit - rev: 'v0.1.6' + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: 'v0.15.18' hooks: - id: ruff args: [ --fix, --exit-non-zero-on-fix ] - - repo: https://github.com/psf/black - rev: '23.11.0' + - id: ruff-format + - repo: https://github.com/astral-sh/uv-pre-commit + rev: '0.11.21' hooks: - - id: black - # additional to the above, apply black to doctests in source code - - repo: https://github.com/keewis/blackdoc - rev: v0.3.8 - hooks: - - id: blackdoc - - repo: https://github.com/python-poetry/poetry - rev: '2.1.2' - hooks: - - id: poetry-check + # Keep uv.lock in sync with pyproject.toml + - id: uv-lock diff --git a/.pylintrc b/.pylintrc deleted file mode 100644 index 32776c49..00000000 --- a/.pylintrc +++ /dev/null @@ -1,24 +0,0 @@ -[MASTER] -ignore=_version.py -load-plugins= - pylint.extensions.bad_builtin, - pylint.extensions.check_elif, - pylint.extensions.comparetozero, - pylint.extensions.docparams, - pylint.extensions.emptystring, - pylint.extensions.overlapping_exceptions, - pylint.extensions.redefined_variable_type - -[MESSAGES CONTROL] -disable= - # handled by black - format - -[SIMILARITIES] -ignore-comments=no -ignore-docstrings=yes -ignore-imports=yes - -[DESIGN] - -max-args=10 diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 2ae6ed65..24f985d5 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -2,23 +2,16 @@ # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details -# Thank you also https://browniebroke.com/blog/specify-docs-dependency-groups-with-poetry-and-read-the-docs/ - # Required version: 2 -# Set the version of Python and other tools you might need +# Build the docs with uv. When build.commands is set, Read the Docs runs these +# commands verbatim and the `sphinx:`/`python:` keys are ignored. build: - os: ubuntu-22.04 + os: ubuntu-24.04 tools: - python: "3.10" - jobs: - post_create_environment: - - pip install poetry - - poetry config virtualenvs.create false - post_install: - - poetry install --with docs --all-extras - -# Build documentation in the docs/ directory with Sphinx -sphinx: - configuration: docs/source/conf.py + python: "3.11" + commands: + - pip install uv + - uv sync --all-extras --group docs + - uv run sphinx-build -T -b html docs/source $READTHEDOCS_OUTPUT/html diff --git a/Makefile b/Makefile index 5a3f3213..b1ab8a1c 100644 --- a/Makefile +++ b/Makefile @@ -23,42 +23,33 @@ help: ## print short description of each target .PHONY: checks checks: ## run all the linting checks of the codebase - @echo "=== pre-commit ==="; poetry run pre-commit run --all-files || echo "--- pre-commit failed ---" >&2; \ - echo "=== mypy ==="; MYPYPATH=stubs poetry run mypy src || echo "--- mypy failed ---" >&2; \ + @echo "=== pre-commit ==="; uv run pre-commit run --all-files || echo "--- pre-commit failed ---" >&2; \ + echo "=== mypy ==="; MYPYPATH=stubs uv run mypy src || echo "--- mypy failed ---" >&2; \ echo "======" -.PHONY: black -black: ## format the code using black - poetry run black src tests docs/source/conf.py scripts docs/source/notebooks/*.py - poetry run blackdoc src - .PHONY: ruff-fixes -ruff-fixes: ## fix the code using ruff - poetry run ruff src tests scripts docs/source/conf.py docs/source/notebooks/*.py --fix +ruff-fixes: ## fix and format the code using ruff + uv run ruff check --fix src tests scripts docs/source/conf.py docs/source/notebooks/*.py + uv run ruff format src tests scripts docs/source/conf.py docs/source/notebooks/*.py .PHONY: test test: ## run the tests - poetry run pytest src tests -r a -v --doctest-modules --cov=src + uv run pytest src tests -r a -v --doctest-modules --cov=src .PHONY: docs docs: ## build the docs - poetry run sphinx-build -T -b html docs/source docs/build/html + uv run sphinx-build -T -b html docs/source docs/build/html .PHONY: licence-check licence-check: ## Check that licences of the dependencies are suitable # Will likely fail on Windows, but Makefiles are in general not Windows # compatible so we're not too worried - poetry export --without=tests --without=docs --without=dev > $(TEMP_FILE) - poetry run liccheck -r $(TEMP_FILE) -R licence-check.txt - rm -f $(TEMP_FILE) + ./scripts/check-licences.sh .PHONY: virtual-environment virtual-environment: ## update virtual environment, create a new one if it doesn't already exist - poetry lock - # Put virtual environments in the project - poetry config virtualenvs.in-project true - poetry install --all-extras - poetry run pre-commit install + uv sync --all-extras + uv run pre-commit install # Set jupytext as the default viewer when opening notebooks - poetry run jupytext-config set-default-viewer + uv run jupytext-config set-default-viewer diff --git a/changelog/323.breaking.md b/changelog/323.breaking.md new file mode 100644 index 00000000..f897e744 --- /dev/null +++ b/changelog/323.breaking.md @@ -0,0 +1 @@ +Dropped support for Python 3.10; scmdata now requires Python >=3.11. The minimum supported ``xarray`` was raised to 2025.12 and ``scipy`` to 1.13, which are the versions required to work with pandas 3 and numpy 2 (Python 3.10's last ``xarray`` release predates pandas-3 support). diff --git a/changelog/323.fix.md b/changelog/323.fix.md new file mode 100644 index 00000000..63b067c7 --- /dev/null +++ b/changelog/323.fix.md @@ -0,0 +1 @@ +Fixed :meth:`ScmRun.from_nc` under pandas >= 2.1 (including pandas 3). The densified netCDF grid produced spurious all-NaN timeseries that pandas no longer drops automatically inside ``stack``, which caused incorrect round-trips and ``Index contains duplicate entries`` errors when an ``_id`` dimension was present. diff --git a/changelog/323.trivial.md b/changelog/323.trivial.md new file mode 100644 index 00000000..437c61e9 --- /dev/null +++ b/changelog/323.trivial.md @@ -0,0 +1 @@ +Migrated the development tooling from Poetry to [uv](https://docs.astral.sh/uv/) and switched formatting/linting fully to [ruff](https://docs.astral.sh/ruff/) (replacing black and blackdoc). The version-bump workflow now uses `uv version` with a vendored composite action. diff --git a/docs/source/conf.py b/docs/source/conf.py index 2ab2bf7f..28e51658 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -4,6 +4,7 @@ For the full list of built-in configuration values, see the documentation: https://www.sphinx-doc.org/en/master/usage/configuration.html """ + from functools import wraps from sphinxcontrib_autodocgen import AutoDocGen @@ -84,9 +85,7 @@ "modules": [scmdata], "generated_source_dir": "docs/source/api", # choose a different title for specific modules, e.g. the toplevel one - "module_title_decider": lambda modulename: "API Reference" - if modulename == "scmdata" - else modulename, + "module_title_decider": lambda modulename: "API Reference" if modulename == "scmdata" else modulename, } ] diff --git a/docs/source/notebooks/netcdf.py b/docs/source/notebooks/netcdf.py index 7fe01a75..5b742596 100644 --- a/docs/source/notebooks/netcdf.py +++ b/docs/source/notebooks/netcdf.py @@ -199,9 +199,7 @@ def new_timeseries( # noqa: PLR0913 # %% -ScmRun.from_nc(OUTPUT_DIR / "out-ssp585-sparse.nc").filter( - variable="Surface Temperature" -).line_plot() +ScmRun.from_nc(OUTPUT_DIR / "out-ssp585-sparse.nc").filter(variable="Surface Temperature").line_plot() # %% [markdown] # For such a data set, since both `run_id` and `paraset_id` vary, both could be added as dimensions @@ -247,9 +245,7 @@ def new_timeseries( # noqa: PLR0913 # %% -ScmRun.from_nc(OUTPUT_DIR / "out-ssp585-extras.nc").filter( - variable="Surface Temperature" -).line_plot() +ScmRun.from_nc(OUTPUT_DIR / "out-ssp585-extras.nc").filter(variable="Surface Temperature").line_plot() # %% [markdown] # If we use dimensions and extra such that our extra co-ordinates are not uniquely defined by the diff --git a/docs/source/notebooks/ops.py b/docs/source/notebooks/ops.py index 6998f143..3d50ca41 100644 --- a/docs/source/notebooks/ops.py +++ b/docs/source/notebooks/ops.py @@ -54,9 +54,9 @@ db_emms.tail() # %% -db_forcing = ScmRun( - "rcmip-radiative-forcing-annual-means-v4-0-0.csv", lowercase_cols=True -).drop_meta(["mip_era", "activity_id"], inplace=False) +db_forcing = ScmRun("rcmip-radiative-forcing-annual-means-v4-0-0.csv", lowercase_cols=True).drop_meta( + ["mip_era", "activity_id"], inplace=False +) db_forcing.head() @@ -86,9 +86,7 @@ # but give the output a different name as shown below. # %% -emms_co2_different_name = db_emms.filter( - variable="Emissions|CO2|MAGICC Fossil and Industrial" -).add( +emms_co2_different_name = db_emms.filter(variable="Emissions|CO2|MAGICC Fossil and Industrial").add( db_emms.filter(variable="Emissions|CO2|MAGICC AFOLU"), op_cols={"variable": "Emissions|CO2|Total"}, ) @@ -111,9 +109,7 @@ ax = plt.figure(figsize=(12, 7)).add_subplot(111) plt_df_forcing = run_append([db_forcing, non_co2_rf]) -plt_df_forcing.filter( - variable=["Effective Radiative Forcing", "Effective*CO2*"] -).lineplot(style="variable") +plt_df_forcing.filter(variable=["Effective Radiative Forcing", "Effective*CO2*"]).lineplot(style="variable") # %% [markdown] # We could also calculate the difference between some SSP and RCP scenarios. The @@ -140,9 +136,7 @@ # %% -ssp126_minus_rcp26 = db_forcing.filter( - scenario="ssp126", variable="Effective Radiative Forcing" -).subtract( +ssp126_minus_rcp26 = db_forcing.filter(scenario="ssp126", variable="Effective Radiative Forcing").subtract( db_forcing.filter(scenario="rcp26", variable="Radiative Forcing"), op_cols={ "scenario": "ssp126 - rcp26", @@ -195,9 +189,7 @@ # automatically returned as `dimensionless`. # %% -ssp126_to_rcp26 = db_forcing.filter( - scenario="ssp126", variable="Effective Radiative Forcing" -).divide( +ssp126_to_rcp26 = db_forcing.filter(scenario="ssp126", variable="Effective Radiative Forcing").divide( db_forcing.filter(scenario="rcp26", variable="Radiative Forcing"), op_cols={ "scenario": "ssp126 / rcp26", @@ -287,9 +279,7 @@ fig, axes = plt.subplots(figsize=(16, 9), ncols=2) erf_delta.lineplot(ax=axes[0], style="model") -erf_delta.filter(year=range(2000, 2500)).lineplot( - ax=axes[1], style="model", legend=False -) +erf_delta.filter(year=range(2000, 2500)).lineplot(ax=axes[1], style="model", legend=False) axes[1].set_ylim([-0.1, 0.2]) # %% [markdown] @@ -397,26 +387,20 @@ # %% ref_period = range(2010, 2040 + 1) -erf_total_for_shift_rel_to_ref_period = erf_total_for_shift.relative_to_ref_period_mean( - year=ref_period -) -erf_total_for_shift_rel_to_ref_period[ - "label" -] = f"rel. to {ref_period[0]} - {ref_period[-1]}" +erf_total_for_shift_rel_to_ref_period = erf_total_for_shift.relative_to_ref_period_mean(year=ref_period) +erf_total_for_shift_rel_to_ref_period["label"] = f"rel. to {ref_period[0]} - {ref_period[-1]}" # %% target = -5 evaluation_period = range(2020, 2030 + 1) -erf_total_for_shift_rel_to_ref_period_shifted = ( - erf_total_for_shift_rel_to_ref_period.adjust_median_to_target( - target=target, - evaluation_period=evaluation_period, - process_over=("scenario", "model"), - ) +erf_total_for_shift_rel_to_ref_period_shifted = erf_total_for_shift_rel_to_ref_period.adjust_median_to_target( + target=target, + evaluation_period=evaluation_period, + process_over=("scenario", "model"), +) +erf_total_for_shift_rel_to_ref_period_shifted["label"] = ( + f"rel. to {ref_period[0]} - {ref_period[-1]} (median of {evaluation_period[0]} - {evaluation_period[-1]} mean adjusted to {target})" # noqa: E501 ) -erf_total_for_shift_rel_to_ref_period_shifted[ - "label" -] = f"rel. to {ref_period[0]} - {ref_period[-1]} (median of {evaluation_period[0]} - {evaluation_period[-1]} mean adjusted to {target})" # noqa: E501 # %% pdf = run_append( diff --git a/docs/source/notebooks/processing.py b/docs/source/notebooks/processing.py index 46f30998..1a3229d8 100644 --- a/docs/source/notebooks/processing.py +++ b/docs/source/notebooks/processing.py @@ -276,9 +276,7 @@ magicc_output_categorisation_quantiles # %% -scmdata.processing.categorisation_sr15( - magicc_output_categorisation_quantiles, ["climate_model", "scenario"] -) +scmdata.processing.categorisation_sr15(magicc_output_categorisation_quantiles, ["climate_model", "scenario"]) # %% [markdown] # ## Set of summary variables @@ -318,11 +316,11 @@ index = ["climate_model", "scenario"] pivot_merge_unit = summary_stats.to_frame().reset_index() -pivot_merge_unit["statistic"] = pivot_merge_unit["statistic"] + pivot_merge_unit[ - "unit" -].apply(lambda x: f"({x})" if x else "") +pivot_merge_unit["statistic"] = pivot_merge_unit["statistic"] + pivot_merge_unit["unit"].apply( + lambda x: f"({x})" if x else "" +) pivot_merge_unit = pivot_merge_unit.drop("unit", axis="columns") -pivot_merge_unit = pivot_merge_unit.set_index( - list(set(pivot_merge_unit.columns) - {"value"}) -).unstack("statistic") +pivot_merge_unit = pivot_merge_unit.set_index(list(set(pivot_merge_unit.columns) - {"value"})).unstack( + "statistic" +) pivot_merge_unit diff --git a/docs/source/notebooks/scmrun.py b/docs/source/notebooks/scmrun.py index bda6fc5d..624f0c94 100644 --- a/docs/source/notebooks/scmrun.py +++ b/docs/source/notebooks/scmrun.py @@ -206,9 +206,7 @@ # %% # set_meta returns a new `ScmRun` with the updated metadata -ar4gwp100_converted.set_meta( - "unit_context", "updated-in-set_meta", variable="Emissions|CO2|*" -) +ar4gwp100_converted.set_meta("unit_context", "updated-in-set_meta", variable="Emissions|CO2|*") # %% # The original `ScmRun` was not modified by `set_meta` diff --git a/docs/source/notebooks/summary-statistics.py b/docs/source/notebooks/summary-statistics.py index 46bad239..07632019 100644 --- a/docs/source/notebooks/summary-statistics.py +++ b/docs/source/notebooks/summary-statistics.py @@ -157,9 +157,7 @@ def mean_and_invert(df, axis=0): # %% -summary_stats = runs.quantiles_over( - cols="run_id", quantiles=[0.05, 0.17, 0.5, 0.83, 0.95, "mean", "median"] -) +summary_stats = runs.quantiles_over(cols="run_id", quantiles=[0.05, 0.17, 0.5, 0.83, 0.95, "mean", "median"]) summary_stats # %% [markdown] diff --git a/docs/source/notebooks/time-operations.py b/docs/source/notebooks/time-operations.py index 5dbf444d..88dc3a64 100644 --- a/docs/source/notebooks/time-operations.py +++ b/docs/source/notebooks/time-operations.py @@ -95,9 +95,7 @@ # %% rcp26_middle_of_year = rcp26.interpolate( - target_times=sorted( - [dt.datetime(v, 7, 1) for v in set([v.year for v in rcp26["time"]])] - ) + target_times=sorted([dt.datetime(v, 7, 1) for v in set([v.year for v in rcp26["time"]])]) ) rcp26_middle_of_year["time operation"] = "middle of year" rcp26_middle_of_year.head() @@ -108,9 +106,7 @@ # Extrapolating is also supported by scmdata. # %% -rcp26_extrap = rcp26.interpolate( - target_times=sorted([dt.datetime(v, 7, 1) for v in range(1700, 2551)]) -) +rcp26_extrap = rcp26.interpolate(target_times=sorted([dt.datetime(v, 7, 1) for v in range(1700, 2551)])) rcp26_extrap["time operation"] = "extrapolated" rcp26_extrap.head() @@ -237,9 +233,7 @@ pdf.filter(variable=var_to_plot).lineplot(ax=ax, hue="time operation") ax = fig.add_subplot(122) -pdf.filter(variable=var_to_plot, year=range(1998, 2001)).lineplot( - ax=ax, hue="time operation" -) +pdf.filter(variable=var_to_plot, year=range(1998, 2001)).lineplot(ax=ax, hue="time operation") plt.tight_layout() @@ -257,9 +251,7 @@ pdf.filter(variable=var_to_plot).lineplot(ax=ax, hue="time operation") ax = fig.add_subplot(122) -pdf.filter(variable=var_to_plot, year=range(1998, 2001)).lineplot( - ax=ax, hue="time operation", legend=False -) +pdf.filter(variable=var_to_plot, year=range(1998, 2001)).lineplot(ax=ax, hue="time operation", legend=False) plt.tight_layout() @@ -269,15 +261,11 @@ # %% -rcp26_monthly.filter( - variable=var_to_plot, year=range(1998, 2001), month=[2, 3, 4, 5] -).timeseries() +rcp26_monthly.filter(variable=var_to_plot, year=range(1998, 2001), month=[2, 3, 4, 5]).timeseries() # %% -pdf.filter(variable=var_to_plot, year=range(1998, 2001)).timeseries().T.plot( - figsize=(16, 9) -) +pdf.filter(variable=var_to_plot, year=range(1998, 2001)).timeseries().T.plot(figsize=(16, 9)) # %% pdf.filter(variable=var_to_plot, year=range(1998, 2001)).timeseries().T.sort_index() diff --git a/docs/source/notebooks/xarray-compatibility.py b/docs/source/notebooks/xarray-compatibility.py index 55c44b98..95952abb 100644 --- a/docs/source/notebooks/xarray-compatibility.py +++ b/docs/source/notebooks/xarray-compatibility.py @@ -67,9 +67,7 @@ def get_data(years, n_ensemble_members, end_val, rand_pct): # %% -start.plumeplot( - quantile_over="ensemble_member", hue_var="variable", hue_label="Variable" -) +start.plumeplot(quantile_over="ensemble_member", hue_var="variable", hue_label="Variable") # %% [markdown] # However, we can cast to an xarray DataSet and then all the xarray methods become available too. @@ -98,9 +96,7 @@ def get_data(years, n_ensemble_members, end_val, rand_pct): # %% -xr_ds["gsat"].sel(ensemble_member=range(10)).plot.line( - hue="ensemble_member", add_legend=False -) +xr_ds["gsat"].sel(ensemble_member=range(10)).plot.line(hue="ensemble_member", add_legend=False) # %% [markdown] # Scatter plots. diff --git a/liccheck.ini b/liccheck.ini new file mode 100644 index 00000000..3aca97f4 --- /dev/null +++ b/liccheck.ini @@ -0,0 +1,44 @@ +# Dependency licence policy for `liccheck`. +[Licenses] +authorized_licenses: + bsd + bsd license + BSD 3-Clause + BSD-2-Clause + BSD-3-Clause + CC0 + CC0-1.0 + apache + apache 2.0 + apache software + apache software license + Apache License, Version 2.0 + Apache-2.0 + MPL-2.0 AND MIT + BSD-3-Clause AND 0BSD AND MIT AND Zlib AND CC0-1.0 + Historical Permission Notice and Disclaimer (HPND) + isc license + isc license (iscl) + gnu lgpl + lgpl with exceptions or zpl + LGPLv2+ + GNU Lesser General Public License v2 (LGPLv2) + GNU Lesser General Public License v2 or later (LGPLv2+) + mit + mit license + MIT + Mozilla Public License 2.0 (MPL 2.0) + MPL-2.0 + psf-2.0 + python software foundation + python software foundation license + zpl 2.1 +unauthorized_licenses: + agpl + gnu agpl + gpl v3 + gplv3 + gpl v2 + gplv2 + gpl v1 + gplv1 diff --git a/pyproject.toml b/pyproject.toml index e14c084a..19e63b04 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,106 +1,110 @@ -[tool.poetry] +[project] name = "scmdata" version = "0.19.0" description = "scmdata provides some useful data handling routines for dealing with data related to simple climate models (SCMs aka reduced complexity climate models, RCMs)" authors = [ - "Jared Lewis ", - "Zeb Nicholls ", - "Mika Pflüger ", + { name = "Jared Lewis", email = "jared.lewis@climate-resource.com" }, + { name = "Zeb Nicholls", email = "zebedee.nicholls@climate-energy-college.org" }, + { name = "Mika Pflüger", email = "mika.pflueger@climate-resource.com" }, ] readme = "README.md" -packages = [{include = "scmdata", from = "src"}] +license = { text = "BSD-3-Clause" } +requires-python = ">=3.11" keywords = [ "data", "simple climate model", "climate", - "scm" + "scm", ] classifiers = [ "Development Status :: 4 - Beta", "License :: OSI Approved :: BSD License", "Intended Audience :: Developers", "Operating System :: OS Independent", - "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] +dependencies = [ + "cftime>=1.5", + "numpy", + "openscm-units", + "packaging", + "pandas>=1.1", + "pint", + "pint-pandas", + "python-dateutil", + "tqdm", + "six", + "xarray>=2025.12", # earlier xarray is incompatible with pandas>=3 round-trips + "typing-extensions", ] -[tool.poetry.dependencies] -# TODO: get rid of this stupid upper level bound -python = ">=3.10,<4" -cftime = ">=1.5" -numpy = "*" -openscm-units = "*" -packaging = "*" -pandas = ">=1.1" -pint = "*" -pint-pandas = "*" -python-dateutil = "*" -tqdm = "*" -six = "*" -xarray = "*" -nc-time-axis = { version = ">=1.2.0", optional = true } -typing-extensions = "*" -matplotlib = { version = ">=3.7.1", optional = true } -seaborn = { version = ">=0.12.0", optional = true } -netCDF4 = { version = "*", optional = true } -openpyxl = { version = "*", optional = true } -xlrd = { version = "*", optional = true } -scipy = { version = "*", optional = true } -joblib = { version = "*", optional = true } -notebook = { version = ">=7", optional = true } -pyam-iamc = { version = "*", optional = true } - -[tool.poetry.extras] -plots = ["matplotlib", "seaborn" , "nc-time-axis"] -optional = ["netCDF4", "openpyxl", "xlrd", "scipy", "pyam-iamc", "joblib" ] -notebooks = ["notebook"] - -[tool.poetry.group.tests.dependencies] -pytest = "^7.3.1" -pytest-benchmark = "*" - -[tool.poetry.group.docs.dependencies] -myst-nb = "^0.17.0" -sphinx-rtd-theme = "^1.2.0" -sphinx-autodoc-typehints = "^1.23.0" -sphinx-autodocgen = "^1.3" -jupytext = "^1.14.5" -sphinx-copybutton = "^0.5.2" - -[tool.poetry.group.dev.dependencies] -pytest-cov = "^4.0.0" -coverage = "^7.2.0" -black = "23.11.0" -blackdoc = "0.3.8" -mypy = "^1.2.0" -ruff = "0.1.6" -pre-commit = "^3.3.1" -towncrier = "^23.6.0" -liccheck = "^0.9.1" -pandas-stubs = "<3" -setuptools = "<81" # liccheck imports pkg_resources, removed in setuptools 81 +[project.optional-dependencies] +plots = [ + "matplotlib>=3.7.1", + "seaborn>=0.12.0", + "nc-time-axis>=1.2.0", +] +optional = [ + "netCDF4", + "openpyxl", + "xlrd", + "scipy>=1.13", # first numpy-2-compatible scipy + "pyam-iamc", + "joblib", +] +notebooks = [ + "notebook>=7", +] -[tool.poetry.group.notebooks.dependencies] -myst-nb = "^0.17.0" -sphinx-rtd-theme = "^1.2.0" -sphinx-autodoc-typehints = "^1.23.0" -sphinx-autodocgen = "^1.3" -jupytext = "^1.14.5" -sphinx-copybutton = "^0.5.2" +[project.urls] +Homepage = "https://github.com/openscm/scmdata" +Documentation = "https://scmdata.readthedocs.io" +Repository = "https://github.com/openscm/scmdata" -[tool.poetry.requires-plugins] -poetry-plugin-export = ">=1.8" +[dependency-groups] +tests = [ + "pytest>=7.3.1", + "pytest-benchmark", + "pytest-cov>=4.0.0", + "coverage>=7.2.0", +] +docs = [ + "myst-nb>=0.17.0", + "sphinx-rtd-theme>=1.2.0", + "sphinx-autodoc-typehints>=1.23.0", + "sphinx-autodocgen>=1.3", + "jupytext>=1.14.5", + "sphinx-copybutton>=0.5.2", +] +dev = [ + "mypy>=1.2.0", + "ruff>=0.8", + "pre-commit>=3.3.1", + "towncrier>=23.6.0", + "liccheck>=0.9.1", + "pandas-stubs<3", + "setuptools<81", # liccheck imports pkg_resources, removed in setuptools 81 + "pip>=24.2", # liccheck imports pip internals, not installed in uv venvs by default + { include-group = "tests" }, + { include-group = "docs" }, +] [build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/scmdata"] [tool.coverage.run] source = ["src"] branch = true [tool.coverage.report] -fail_under = 95 +# Keep the existing gate (the previous .coveragerc, which took precedence, used 90). +fail_under = 90 skip_empty = true show_missing = true # Regexes for lines to exclude from consideration in addition to the defaults @@ -137,7 +141,11 @@ doctest_optionflags ="NORMALIZE_WHITESPACE" [tool.ruff] src = ["src"] -target-version = "py38" +target-version = "py311" +# Provide some leeway for long docstrings. +line-length = 110 + +[tool.ruff.lint] select = [ "E", # pycodestyle error "W", # pycodestyle warning @@ -158,11 +166,11 @@ ignore = [ "D200", # One-line docstring should fit on one line with quotes "D400", # First line should end with a period "TRY003", # Avoid specifying long messages outside the exception class + "UP007", # Keep Optional[X]/Union[X, Y] (consistent with other openscm/CR projects) + "PLC0415", # `import` outside top-level is intentional for optional-dependency lazy imports ] -# Provide some leeway for long docstring, this is otherwise handled by black -line-length = 110 -[tool.ruff.per-file-ignores] +[tool.ruff.lint.per-file-ignores] "test*.py" = [ "D", # Documentation not needed in tests "S101", # S101 Use of `assert` detected @@ -171,11 +179,16 @@ line-length = 110 "PLR5501", # Use `elif` instead of `else` then `if` "NPY002", # Replace legacy `np.random.rand` "S311", # Standard pseudo-random generators are not suitable for cryptographic purposes + "RUF043", # Pattern passed to pytest.raises(match=) need not be escaped/raw in tests + "RUF059", # Unpacked-but-unused variables are common in test fixtures + "RUF061", # Allow the legacy pytest.raises form in existing tests + "PLW0108", # Lambdas in test helpers are fine for readability ] "docs/source/notebooks/*" = [ "D100", # Missing docstring at the top of file "E402", # Module level import not at top of file "S101", # Use of `assert` detected + "NPY002", # Legacy np.random is fine for example notebooks ] "scripts/*" = [ "S101" # S101 Use of `assert` detected @@ -183,20 +196,22 @@ line-length = 110 "src/scmdata/ops.py" = [ "E501" # Line too long ] - "src/scmdata/processing.py" = [ "PLR2004" # Magic value used in comparison ] -[tool.ruff.pylint] +[tool.ruff.lint.pylint] max-args = 7 -[tool.ruff.isort] +[tool.ruff.lint.isort] known-first-party = ["src"] -[tool.ruff.pydocstyle] +[tool.ruff.lint.pydocstyle] convention = "numpy" +[tool.ruff.format] +docstring-code-format = true + [tool.towncrier] package = "scmdata" package_dir = "src" @@ -240,52 +255,3 @@ issue_format = "[#{issue}](https://github.com/openscm/scmdata/pulls/{issue})" directory = "trivial" name = "Trivial/Internal Changes" showcontent = false - -[tool.liccheck] -authorized_licenses = [ - "bsd", - "bsd license", - "BSD 3-Clause", - "BSD-2-Clause", - "BSD-3-Clause", - "CC0", - "CC0-1.0", - "apache", - "apache 2.0", - "apache software", - "apache software license", - "Apache License, Version 2.0", - "Apache-2.0", - # SPDX AND-expressions are not split by liccheck, so list as-is - "MPL-2.0 AND MIT", - "BSD-3-Clause AND 0BSD AND MIT AND Zlib AND CC0-1.0", - "Historical Permission Notice and Disclaimer (HPND)", - "isc license", - "isc license (iscl)", - "gnu lgpl", - "lgpl with exceptions or zpl", - "LGPLv2+", - "GNU Lesser General Public License v2 (LGPLv2)", - "GNU Lesser General Public License v2 or later (LGPLv2+)", - "mit", - "mit license", - "MIT", - "Mozilla Public License 2.0 (MPL 2.0)", - "MPL-2.0", - "psf-2.0", - "python software foundation", - "python software foundation license", - "zpl 2.1", -] -# This starting list is relatively conservative. Depending on the project, it -# may make sense to move some of these into the authorized list -unauthorized_licenses = [ - "agpl", - "gnu agpl", - "gpl v3", - "gplv3", - "gpl v2", - "gplv2", - "gpl v1", - "gplv1", -] diff --git a/scripts/check-licences.sh b/scripts/check-licences.sh new file mode 100755 index 00000000..f738583a --- /dev/null +++ b/scripts/check-licences.sh @@ -0,0 +1,20 @@ +#!/usr/bin/env bash +# Check that the licences of the project's dependencies are acceptable. +# +# liccheck unconditionally parses ./pyproject.toml with the unmaintained `toml` +# package, which cannot read our PEP 621 metadata (inline-table arrays, licence +# names containing commas). We therefore export the locked requirements and run +# liccheck from a scratch directory that contains no pyproject.toml, pointing it +# at the INI strategy file (liccheck.ini). +set -euo pipefail + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +WORK="$(mktemp -d)" +trap 'rm -rf "${WORK}"' EXIT + +uv export --no-default-groups --all-extras --no-hashes --no-emit-project > "${WORK}/requirements.txt" +cp "${ROOT}/liccheck.ini" "${WORK}/liccheck.ini" + +cd "${WORK}" +uv run --no-sync --project "${ROOT}" \ + liccheck -s liccheck.ini -r requirements.txt -R "${ROOT}/licence-check.txt" diff --git a/scripts/ops_examples.py b/scripts/ops_examples.py index 4f22670d..7459874a 100644 --- a/scripts/ops_examples.py +++ b/scripts/ops_examples.py @@ -1,6 +1,7 @@ """ Example of ops functionality """ + import numpy as np from scmdata import ScmRun @@ -47,17 +48,13 @@ world = nh.add(sh, op_cols={"region": "World"}) world.head() -fos_minus_afolu = fos.subtract( - afolu, op_cols={"variable": "Emissions|CO2|Fossil - AFOLU"} -) +fos_minus_afolu = fos.subtract(afolu, op_cols={"variable": "Emissions|CO2|Fossil - AFOLU"}) fos_minus_afolu.head() nh_minus_sh = nh.subtract(sh, op_cols={"region": "World|NH - SH"}) nh_minus_sh.head() -fos_times_afolu = fos.multiply( - afolu, op_cols={"variable": "Emissions|CO2|Fossil : AFOLU"} -) +fos_times_afolu = fos.multiply(afolu, op_cols={"variable": "Emissions|CO2|Fossil : AFOLU"}) fos_times_afolu.head() warming_per_co2 = start.filter(variable="*Temperature*").divide( diff --git a/scripts/test-install.py b/scripts/test-install.py index 0559ad5b..6dfb7ae6 100644 --- a/scripts/test-install.py +++ b/scripts/test-install.py @@ -5,6 +5,7 @@ Thanks https://stackoverflow.com/a/25562415/10473080 """ + import importlib import pkgutil diff --git a/src/scmdata/__init__.py b/src/scmdata/__init__.py index 2dc296c9..f1b66e6d 100644 --- a/src/scmdata/__init__.py +++ b/src/scmdata/__init__.py @@ -3,6 +3,7 @@ See README and docs for more info. """ + try: from importlib.metadata import version as _version except ImportError: @@ -18,4 +19,4 @@ from scmdata.database import ScmDatabase from scmdata.run import ScmRun, run_append -__all__ = ["ScmRun", "ScmDatabase", "run_append"] +__all__ = ["ScmDatabase", "ScmRun", "run_append"] diff --git a/src/scmdata/_base.py b/src/scmdata/_base.py index f7dba7a7..30513265 100644 --- a/src/scmdata/_base.py +++ b/src/scmdata/_base.py @@ -3,7 +3,7 @@ import numpy as np -class OpsMixin: +class OpsMixin: # noqa: PLW1641 # rich comparisons return new objects, not booleans; hashing is intentionally inherited """ Mixin for common binary operators """ diff --git a/src/scmdata/_typing.py b/src/scmdata/_typing.py index fde02901..9b2280c7 100644 --- a/src/scmdata/_typing.py +++ b/src/scmdata/_typing.py @@ -1,14 +1,16 @@ """ Commonly used typehints """ + from __future__ import annotations +from collections.abc import Callable from os import PathLike -from typing import Callable, Dict, Union +from typing import Union import pandas as pd FilePath = Union[str, "PathLike[str]"] MetadataValue = Union[str, int, float] -MetadataType = Dict[str, MetadataValue] +MetadataType = dict[str, MetadataValue] ApplyCallable = Callable[[pd.DataFrame], Union[pd.DataFrame, pd.Series, float]] diff --git a/src/scmdata/_xarray.py b/src/scmdata/_xarray.py index a2510a37..f0324d6a 100644 --- a/src/scmdata/_xarray.py +++ b/src/scmdata/_xarray.py @@ -1,6 +1,7 @@ """ Interface with `xarray `_ """ + import numpy as np import pint.errors import xarray as xr @@ -64,34 +65,22 @@ def to_xarray(self, dimensions=("region",), extras=(), unify_units=True): timeseries_dims = list(set(dimensions) - {"time"} - {"_id"}) self_unified_units = _unify_scmrun_units(self, unify_units) - timeseries = _get_timeseries_for_xr_dataset( - self_unified_units, timeseries_dims, extras - ) - non_dimension_extra_metadata = _get_other_metdata_for_xr_dataset( - self_unified_units, dimensions, extras - ) + timeseries = _get_timeseries_for_xr_dataset(self_unified_units, timeseries_dims, extras) + non_dimension_extra_metadata = _get_other_metdata_for_xr_dataset(self_unified_units, dimensions, extras) if extras: - ids, ids_dimensions = _get_ids_for_xr_dataset( - self_unified_units, extras, timeseries_dims - ) + ids, ids_dimensions = _get_ids_for_xr_dataset(self_unified_units, extras, timeseries_dims) else: ids = None ids_dimensions = None - for_xarray = _get_dataframe_for_xr_dataset( - timeseries, timeseries_dims, extras, ids, ids_dimensions - ) + for_xarray = _get_dataframe_for_xr_dataset(timeseries, timeseries_dims, extras, ids, ids_dimensions) xr_ds = xr.Dataset.from_dataframe(for_xarray) if extras: xr_ds = _add_extras(xr_ds, ids, ids_dimensions, self_unified_units) - unit_map = ( - self_unified_units.meta[["variable", "unit"]] - .drop_duplicates() - .set_index("variable")["unit"] - ) + unit_map = self_unified_units.meta[["variable", "unit"]].drop_duplicates().set_index("variable")["unit"] xr_ds = _add_units(xr_ds, unit_map) xr_ds = _add_scmdata_metadata(xr_ds, non_dimension_extra_metadata) xr_ds = _set_dimensions(xr_ds, dimensions) @@ -111,9 +100,7 @@ def _unify_scmrun_units(run, unify_units): "The following variables are reported in more than one unit. " "Found variable-unit combinations are:\n{}".format( variable_unit_table[ - variable_unit_table["variable"].isin( - more_than_one_unit_variables.index.values - ) + variable_unit_table["variable"].isin(more_than_one_unit_variables.index.values) ] ) ) @@ -155,9 +142,7 @@ def _get_timeseries_for_xr_dataset(run, dimensions, extras): def _get_other_metdata_for_xr_dataset(run, dimensions, extras): - other_dimensions = list( - set(run.meta.columns) - set(dimensions) - set(extras) - {"variable", "unit"} - ) + other_dimensions = list(set(run.meta.columns) - set(dimensions) - set(extras) - {"variable", "unit"}) other_metdata = run.meta[other_dimensions].drop_duplicates() if other_metdata.shape[0] > 1 and not other_metdata.empty: error_msg = ( @@ -226,17 +211,13 @@ def _get_dataframe_for_xr_dataset(timeseries, dimensions, extras, ids, ids_dimen timeseries.columns.names = ["time"] - if ( - len(timeseries.index.unique()) != timeseries.shape[0] - ): # pragma: no cover # emergency valve + if len(timeseries.index.unique()) != timeseries.shape[0]: # pragma: no cover # emergency valve # shouldn't be able to get here because any issues should be caught # by initial creation of timeseries but just in case raise AssertionError("something not unique") for_xarray = ( - timeseries.T.stack([*dimensions, "_id"]) - if add_id_dimension - else timeseries.T.stack(dimensions) + timeseries.T.stack([*dimensions, "_id"]) if add_id_dimension else timeseries.T.stack(dimensions) ) return for_xarray @@ -249,11 +230,7 @@ def _add_extras(xr_ds, ids, ids_dimensions, run): if id_dimension in ids: ids_extra = ids.reset_index().set_index(id_dimension) else: - ids_extra = ( - run.meta[[extra, id_dimension]] - .drop_duplicates() - .set_index(id_dimension) - ) + ids_extra = run.meta[[extra, id_dimension]].drop_duplicates().set_index(id_dimension) extra_coords[extra] = ( id_dimension, @@ -268,9 +245,7 @@ def _add_extras(xr_ds, ids, ids_dimensions, run): def _add_units(xr_ds, unit_map): for data_var in xr_ds.data_vars: unit = unit_map[data_var] - if ( - not isinstance(unit, str) and len(unit) > 1 - ): # pragma: no cover # emergency valve + if not isinstance(unit, str) and len(unit) > 1: # pragma: no cover # emergency valve # should have already been caught... raise AssertionError(f"Found multiple units ({unit}) for {data_var}") diff --git a/src/scmdata/database/_database.py b/src/scmdata/database/_database.py index afa8960b..b350d32d 100644 --- a/src/scmdata/database/_database.py +++ b/src/scmdata/database/_database.py @@ -1,6 +1,7 @@ """ Database for handling large datasets in a performant, but flexible way """ + import os import os.path @@ -86,12 +87,10 @@ def _get_backend(self, backend, backend_config): cls = backend_classes[backend.lower()] return cls(**backend_config) except KeyError: - raise TypeError(f"Unknown database backend: {backend}") # noqa: TRY200 + raise TypeError(f"Unknown database backend: {backend}") else: if not isinstance(backend, BaseDatabaseBackend): - raise TypeError( - "Backend must be an instance of scmdata.database.BaseDatabaseBackend" - ) + raise TypeError("Backend must be an instance of scmdata.database.BaseDatabaseBackend") return backend def __repr__(self): diff --git a/src/scmdata/database/backends/base.py b/src/scmdata/database/backends/base.py index b66cd757..422c3d54 100644 --- a/src/scmdata/database/backends/base.py +++ b/src/scmdata/database/backends/base.py @@ -4,7 +4,6 @@ All other database backends should be based upon this interface """ - from abc import ABC, abstractmethod diff --git a/src/scmdata/database/backends/netcdf.py b/src/scmdata/database/backends/netcdf.py index 1cf72675..59deeebd 100644 --- a/src/scmdata/database/backends/netcdf.py +++ b/src/scmdata/database/backends/netcdf.py @@ -59,9 +59,7 @@ def get_key(self, sr): Path in which to save the data without spaces or special characters """ levels = { - database_level: sr.get_unique_meta( - database_level, no_duplicates=True - ).replace(os.sep, "_") + database_level: sr.get_unique_meta(database_level, no_duplicates=True).replace(os.sep, "_") for database_level in self.kwargs["levels"] } @@ -186,8 +184,7 @@ def get(self, filters): load_files = [ v for vlist in [ - glob.glob(os.path.join(self.kwargs["root_dir"], g), recursive=True) - for g in globs_to_check + glob.glob(os.path.join(self.kwargs["root_dir"], g), recursive=True) for g in globs_to_check ] for v in vlist ] diff --git a/src/scmdata/errors.py b/src/scmdata/errors.py index 14fdc677..a6b75b12 100644 --- a/src/scmdata/errors.py +++ b/src/scmdata/errors.py @@ -1,6 +1,7 @@ """ Custom errors and exceptions used by scmdata """ + import pandas as pd @@ -21,10 +22,7 @@ def __init__(self, meta): dup = dup.rename(columns={"size": "repeats"}) dup = dup[dup.repeats > 1] - msg = ( - "Duplicate metadata (numbers show how many times the given " - f"metadata is repeated).\n{dup}" - ) + msg = f"Duplicate metadata (numbers show how many times the given metadata is repeated).\n{dup}" super().__init__(msg) @@ -51,10 +49,7 @@ def __init__(self, time_index): dup = time_index.value_counts() dup = dup[dup > 1] - msg = ( - "Duplicate times (numbers show how many times the given time is " - f"repeated):\n{dup}" - ) + msg = f"Duplicate times (numbers show how many times the given time is repeated):\n{dup}" super().__init__(msg) diff --git a/src/scmdata/filters.py b/src/scmdata/filters.py index 5b083273..5e0208ed 100644 --- a/src/scmdata/filters.py +++ b/src/scmdata/filters.py @@ -7,7 +7,8 @@ import datetime import re import time -from typing import Any, Iterable, List, Optional, Union +from collections.abc import Iterable +from typing import Any, Union import numpy as np import pandas as pd @@ -44,7 +45,7 @@ def find_depth( s: str, level: Union[int, str], separator: str = HIERARCHY_SEPARATOR, -) -> List[Any]: +) -> list[Any]: """ Find all values which match given depth from a filter keyword. @@ -109,7 +110,7 @@ def apply_test(val): def pattern_match( # pylint: disable=too-many-arguments,too-many-locals meta_col: pd.Index, values: Union[Iterable[MetadataValue], MetadataValue], - level: Optional[MetadataValue] = None, + level: MetadataValue | None = None, regexp: bool = False, separator: str = HIERARCHY_SEPARATOR, ) -> np.ndarray: @@ -153,9 +154,7 @@ def pattern_match( # pylint: disable=too-many-arguments,too-many-locals """ matches = np.array([False] * len(meta_col), dtype=bool) _values: Iterable[Any] = ( - [values] - if not isinstance(values, Iterable) or isinstance(values, str) - else values + [values] if not isinstance(values, Iterable) or isinstance(values, str) else values ) for s in _values: @@ -164,8 +163,7 @@ def pattern_match( # pylint: disable=too-many-arguments,too-many-locals comparison_value = np.nan use_string_comparison = isinstance(comparison_value, str) or ( - not np.isnan(comparison_value) - and pd.api.types.is_string_dtype(meta_col.categories.dtype) + not np.isnan(comparison_value) and pd.api.types.is_string_dtype(meta_col.categories.dtype) ) if use_string_comparison: @@ -188,27 +186,21 @@ def pattern_match( # pylint: disable=too-many-arguments,too-many-locals subset = [m for m in meta_col.categories if pattern.match(str(m))] if level is not None: - depth = find_depth( - meta_col, str(comparison_value), level, separator=separator - ) + depth = find_depth(meta_col, str(comparison_value), level, separator=separator) subset = set(subset).intersection(set(depth)) matches |= meta_col.isin(subset) else: s_float = float(comparison_value) if np.isnan(s_float): - matches |= [ - c == -1 for c in meta_col.codes - ] # nan's are missing from categoricals + matches |= [c == -1 for c in meta_col.codes] # nan's are missing from categoricals else: matches |= np.isclose(s_float, meta_col.astype(float)) return matches -def years_match( - data: Iterable[Any], years: Union[Iterable[int], np.ndarray, int] -) -> np.ndarray: +def years_match(data: Iterable[Any], years: Union[Iterable[int], np.ndarray, int]) -> np.ndarray: """ Match years in time columns for data filtering. @@ -243,9 +235,7 @@ def years_match( return is_in(data, years) -def month_match( - data: Iterable[Any], months: Union[Iterable[Union[str, int]], int, str] -) -> np.ndarray: +def month_match(data: Iterable[Any], months: Union[Iterable[Union[str, int]], int, str]) -> np.ndarray: """ Match months in time columns for data filtering. @@ -265,9 +255,7 @@ def month_match( return time_match(data, months, ["%b", "%B"], "tm_mon", "month") -def day_match( - data: Iterable[Any], days: Union[List[str], List[int], int, str] -) -> np.ndarray: +def day_match(data: Iterable[Any], days: Union[list[str], list[int], int, str]) -> np.ndarray: """ Match days in time columns for data filtering. @@ -311,7 +299,7 @@ def hour_match(data: Iterable[Any], hours: Union[Iterable[int], int]) -> np.ndar def time_match( data: Iterable[Any], times: Union[Iterable[Union[str, int]], int, str], - conv_codes: List[str], + conv_codes: list[str], strptime_attr: str, name: str, ) -> np.ndarray: @@ -350,18 +338,13 @@ def time_match( increasing integers (i.e. "Nov-Feb" will not work, one must use ["Nov-Dec", "Jan-Feb"] instead) """ - times_list: List[Union[int, str]] = ( - [times] if isinstance(times, (int, str)) else times - ) + times_list: list[Union[int, str]] = [times] if isinstance(times, (int, str)) else times def conv_strs(strs_to_convert, conv_codes, name): res = None for conv_code in conv_codes: try: - res = [ - getattr(time.strptime(t, conv_code), strptime_attr) - for t in strs_to_convert - ] + res = [getattr(time.strptime(t, conv_code), strptime_attr) for t in strs_to_convert] break except ValueError: continue @@ -373,15 +356,14 @@ def conv_strs(strs_to_convert, conv_codes, name): if isinstance(times_list[0], str): to_delete = [] - to_append: List[Any] = [] + to_append: list[Any] = [] for i, timeset in enumerate(times_list): # ignore type as already established we're looking at strings if "-" in timeset: # type: ignore ints = conv_strs(timeset.split("-"), conv_codes, name) # type: ignore if ints[0] > ints[1]: error_msg = ( - "string ranges must lead to increasing integer ranges," - f" {timeset} becomes {ints}" + f"string ranges must lead to increasing integer ranges, {timeset} becomes {ints}" ) raise ValueError(error_msg) diff --git a/src/scmdata/groupby.py b/src/scmdata/groupby.py index 83358205..b5d9e5c3 100644 --- a/src/scmdata/groupby.py +++ b/src/scmdata/groupby.py @@ -5,8 +5,8 @@ from __future__ import annotations import warnings -from collections.abc import Iterable -from typing import TYPE_CHECKING, Any, Callable, Generic, Iterator, TypeVar, Union +from collections.abc import Callable, Iterable, Iterator +from typing import TYPE_CHECKING, Any, Generic, TypeVar, Union import numpy as np import pandas as pd @@ -25,8 +25,10 @@ from xarray.computation import ops if TYPE_CHECKING: + from typing import Concatenate + from pandas.core.groupby.generic import DataFrameGroupBy - from typing_extensions import Concatenate, ParamSpec + from typing_extensions import ParamSpec P = ParamSpec("P") Q = ParamSpec("Q") @@ -48,9 +50,7 @@ class RunGroupBy(ImplementsArrayReduce, Generic[GenericRun]): GroupBy object specialized to grouping ScmRun objects """ - def __init__( - self, run: GenericRun, groups: Iterable[str], na_fill_value: float = -10000 - ): + def __init__(self, run: GenericRun, groups: Iterable[str], na_fill_value: float = -10000): self.run = run self.group_keys = groups @@ -65,8 +65,7 @@ def __init__( if any([pd.api.types.is_numeric_dtype(m[c]) for c in m]): if (m == na_fill_value).any(axis=None): raise ValueError( - "na_fill_value conflicts with data value. Choose a na_fill_value " - "not in meta" + "na_fill_value conflicts with data value. Choose a na_fill_value not in meta" ) else: m = m.fillna(na_fill_value) @@ -82,9 +81,7 @@ def _try_fill_value(v: MetadataValue) -> MetadataValue: pass return v - groups: Iterable[ - MetadataValue | tuple[MetadataValue, ...] - ] = self._grouper.groups + groups: Iterable[MetadataValue | tuple[MetadataValue, ...]] = self._grouper.groups for indices in groups: if not isinstance(indices, Iterable) or isinstance(indices, str): indices_clean: tuple[MetadataValue, ...] = (indices,) @@ -95,9 +92,7 @@ def _try_fill_value(v: MetadataValue) -> MetadataValue: filter_kwargs = {k: v for k, v in zip(self.group_keys, indices_clean)} res = self.run.filter(**filter_kwargs) # type: ignore if not len(res): - raise ValueError( - f"Empty group for {list(zip(self.group_keys, indices_clean))}" - ) + raise ValueError(f"Empty group for {list(zip(self.group_keys, indices_clean))}") yield res def __iter__(self) -> Iterator[GenericRun]: @@ -240,9 +235,7 @@ def map(self, func, *args, **kwargs): warnings.warn("Use RunGroupby.apply instead", DeprecationWarning) return self.apply(func, *args, **kwargs) - def _combine( - self, applied: Iterable[GenericRun | (pd.DataFrame | None)] - ) -> GenericRun: + def _combine(self, applied: Iterable[GenericRun | (pd.DataFrame | None)]) -> GenericRun: """ Recombine the applied objects like the original. """ @@ -344,9 +337,7 @@ def joblib_parallel_processor( *args: Q.args, **kwargs: Q.kwargs, ) -> Iterable[ApplyCallableReturnType[RunLike]]: - prepped_groups = ( - joblib.delayed(func)(group, *args, **kwargs) for group in groups - ) + prepped_groups = (joblib.delayed(func)(group, *args, **kwargs) for group in groups) applied = processor(prepped_groups) return applied diff --git a/src/scmdata/netcdf.py b/src/scmdata/netcdf.py index 68682df2..d9ce4175 100644 --- a/src/scmdata/netcdf.py +++ b/src/scmdata/netcdf.py @@ -3,6 +3,7 @@ Reading and writing :class:`ScmRun ` to disk as binary """ + from __future__ import annotations try: @@ -13,9 +14,10 @@ nc = None has_netcdf = False +from collections.abc import Iterable from datetime import datetime from logging import getLogger -from typing import TYPE_CHECKING, Any, Iterable +from typing import TYPE_CHECKING, Any import pandas as pd import xarray as xr @@ -91,6 +93,13 @@ def _read_nc(cls: BaseScmRun, fname: FilePath) -> BaseScmRun: def _reshape_to_scmrun_dataframe(dataframe, loaded): + # ``Dataset.to_dataframe`` densifies the full (dimensions x _id) grid, so + # combinations that were never written appear as rows where every data + # variable is NaN. Drop them up front; otherwise, once the ``_id`` level is + # removed below, the remaining index is no longer unique and ``unstack`` + # fails with "Index contains duplicate entries". + dataframe = dataframe.dropna(subset=list(loaded.data_vars), how="all") + index_cols = list(set(dataframe.columns) - set(loaded.data_vars)) dataframe = dataframe.set_index(index_cols, append=True) if "_id" in dataframe.index.names: @@ -103,12 +112,15 @@ def _reshape_to_scmrun_dataframe(dataframe, loaded): # Can revert to below once https://github.com/pandas-dev/pandas/issues/47071 is resolved # dataframe = dataframe.stack("variable").unstack("time").reset_index() dataframe = dataframe.stack("variable").unstack("time") + # A variable may exist for only some dimension combinations, so stacking + # produces all-NaN timeseries for the combinations where it is absent. + # pandas < 2.1 dropped these inside ``stack`` (dropna=True default); pandas + # >= 2.1 keeps them, so drop the fully empty timeseries explicitly. + dataframe = dataframe.dropna(how="all") dataframe.columns = pd.Index(dataframe.columns.values) dataframe = dataframe.reset_index() - unit_map = { - data_var: loaded[data_var].attrs["units"] for data_var in loaded.data_vars - } + unit_map = {data_var: loaded[data_var].attrs["units"] for data_var in loaded.data_vars} dataframe["unit"] = dataframe["variable"].map(_var_to_nc).map(unit_map).values return dataframe diff --git a/src/scmdata/offsets.py b/src/scmdata/offsets.py index 7c95845a..5778bc0d 100644 --- a/src/scmdata/offsets.py +++ b/src/scmdata/offsets.py @@ -4,7 +4,8 @@ Provides similar functionality to https://pandas.pydata.org/pandas-docs/stable/user_gui de/timeseries.html#dateoffset-objects """ -from typing import Iterable + +from collections.abc import Iterable import cftime from xarray.coding import cftime_offsets @@ -121,11 +122,9 @@ def generate_range( start_cf = cftime.DatetimeGregorian(*start.timetuple()[:6]) end_cf = cftime.DatetimeGregorian(*end.timetuple()[:6]) - res = cftime_offsets.cftime_range( - offset.rollback(start_cf), offset.rollforward(end_cf), freq=offset - ) + res = cftime_offsets.cftime_range(offset.rollback(start_cf), offset.rollforward(end_cf), freq=offset) return [date_cls(*dt.timetuple()[:6]) for dt in res] -__all__ = ["to_offset", "generate_range"] +__all__ = ["generate_range", "to_offset"] diff --git a/src/scmdata/ops.py b/src/scmdata/ops.py index 5263eea4..591d77d6 100644 --- a/src/scmdata/ops.py +++ b/src/scmdata/ops.py @@ -220,9 +220,7 @@ def subtract(self, other, op_cols, **kwargs): idealised World|NH idealised MtC / yr Emissions|CO2|AFOLU 1.0 5.0 World|SH idealised MtC / yr Emissions|CO2|AFOLU 3.0 7.0 - >>> fos_minus_afolu = fos.subtract( - ... afolu, op_cols={"variable": "Emissions|CO2|Fossil - AFOLU"} - ... ) + >>> fos_minus_afolu = fos.subtract(afolu, op_cols={"variable": "Emissions|CO2|Fossil - AFOLU"}) >>> # The rows align and the units are handled automatically >>> fos_minus_afolu.head() time 2010-01-01 2020-01-01 @@ -329,9 +327,7 @@ def add(self, other, op_cols, **kwargs): idealised World|NH idealised MtC / yr Emissions|CO2|AFOLU 1.0 5.0 World|SH idealised MtC / yr Emissions|CO2|AFOLU 3.0 7.0 - >>> fos_plus_afolu = fos.add( - ... afolu, op_cols={"variable": "Emissions|CO2|Fossil + AFOLU"} - ... ) + >>> fos_plus_afolu = fos.add(afolu, op_cols={"variable": "Emissions|CO2|Fossil + AFOLU"}) >>> # The rows align and the units are handled automatically >>> fos_plus_afolu.head() time 2010-01-01 2020-01-01 @@ -436,9 +432,7 @@ def multiply(self, other, op_cols, **kwargs): idealised World|NH idealised MtC / yr Emissions|CO2|AFOLU 1.0 5.0 World|SH idealised MtC / yr Emissions|CO2|AFOLU 3.0 7.0 - >>> fos_times_afolu = fos.multiply( - ... afolu, op_cols={"variable": "Emissions|CO2|Fossil * AFOLU"} - ... ) + >>> fos_times_afolu = fos.multiply(afolu, op_cols={"variable": "Emissions|CO2|Fossil * AFOLU"}) >>> # The rows align and the units are handled automatically >>> fos_times_afolu.convert_unit("(GtC / yr) ** 2").head() time 2010-01-01 2020-01-01 @@ -543,9 +537,7 @@ def divide(self, other, op_cols, **kwargs): idealised World|NH idealised MtC / yr Emissions|CO2|AFOLU 1.0 5.0 World|SH idealised MtC / yr Emissions|CO2|AFOLU 3.0 7.0 - >>> fos_divide_afolu = fos.divide( - ... afolu, op_cols={"variable": "Emissions|CO2|Fossil / yrFOLU"} - ... ) + >>> fos_divide_afolu = fos.divide(afolu, op_cols={"variable": "Emissions|CO2|Fossil / yrFOLU"}) >>> # The rows align and the units are handled automatically >>> fos_divide_afolu.convert_unit("dimensionless").head() time 2010-01-01 2020-01-01 @@ -647,9 +639,7 @@ def cumsum(self, out_var=None, check_annual=True): # Check that all intervals are uniform and equal years = self["year"] if check_annual and not (years.diff().iloc[1:] == 1).all(): - raise ValueError( - 'Annual data are required for "cumsum" integration. Use ScmRun.resample first' - ) + raise ValueError('Annual data are required for "cumsum" integration. Use ScmRun.resample first') ts = self.timeseries() ur = scmdata.units.get_unit_registry() @@ -732,9 +722,7 @@ def cumulative_trapezoid(self, out_var=None): ) time_unit = "s" - times_in_s = self.time_points.values.astype(f"datetime64[{time_unit}]").astype( - "int" - ) + times_in_s = self.time_points.values.astype(f"datetime64[{time_unit}]").astype("int") ts = self.timeseries() ur = scmdata.units.get_unit_registry() @@ -742,11 +730,7 @@ def cumulative_trapezoid(self, out_var=None): # If required, we can remove the hard-coding of initial, it just requires # some thinking about unit handling _initial = 0.0 - out = pd.DataFrame( - scipy.integrate.cumulative_trapezoid( - y=ts, x=times_in_s, axis=1, initial=_initial - ) - ) + out = pd.DataFrame(scipy.integrate.cumulative_trapezoid(y=ts, x=times_in_s, axis=1, initial=_initial)) out.index = ts.index out.columns = ts.columns @@ -862,9 +846,7 @@ def delta_per_delta_time(self, out_var=None): out = ts.diff(periods=1, axis="columns") if not out.iloc[:, 0].isna().all(): # pragma: no cover - raise AssertionError( - "Did pandas change their API? The first timestep is not all nan." - ) + raise AssertionError("Did pandas change their API? The first timestep is not all nan.") out = out.iloc[:, 1:] / time_deltas_in_s @@ -934,9 +916,7 @@ def linear_regression(self): def _convert_linear_regression_raw_to_pdf(raw, key_to_keep, unit): pdf_dicts = [] for r in raw: - transformed = { - k: v for k, v in r.items() if k not in ["gradient", "intercept", "unit"] - } + transformed = {k: v for k, v in r.items() if k not in ["gradient", "intercept", "unit"]} if unit is None: transformed[key_to_keep] = r[key_to_keep].magnitude transformed["unit"] = str(r["gradient"].units) @@ -1013,7 +993,7 @@ def linear_regression_scmrun(self): ( times_numpy, times_in_s, - time_unit, + _time_unit, gradients, intercepts, meta, @@ -1021,8 +1001,7 @@ def linear_regression_scmrun(self): out_shape = (meta.shape[0], len(times_in_s)) regression_timeseries = ( - np.broadcast_to(gradients, out_shape[::-1]).T * times_in_s - + intercepts[:, np.newaxis] + np.broadcast_to(gradients, out_shape[::-1]).T * times_in_s + intercepts[:, np.newaxis] ) out = type(self)( @@ -1109,18 +1088,10 @@ def adjust_median_to_target( groups = list(set(self.meta.columns) - set(process_over)) - current_medians = ( - self.filter(year=evaluation_period) - .timeseries() - .mean(axis=1) - .groupby(groups) - .median() - ) + current_medians = self.filter(year=evaluation_period).timeseries().mean(axis=1).groupby(groups).median() if check_groups_identical: - npt.assert_allclose( - current_medians, current_medians.iloc[0], **check_groups_identical_kwargs - ) + npt.assert_allclose(current_medians, current_medians.iloc[0], **check_groups_identical_kwargs) # have to align ourselves, see # https://github.com/pandas-dev/pandas/issues/43321 diff --git a/src/scmdata/plotting.py b/src/scmdata/plotting.py index a567541f..9bab2f23 100644 --- a/src/scmdata/plotting.py +++ b/src/scmdata/plotting.py @@ -3,6 +3,7 @@ See the example notebook 'plotting-with-seaborn.ipynb' for usage examples """ + import warnings from itertools import cycle @@ -190,9 +191,7 @@ def plumeplot( # pragma: no cover # noqa: PLR0913, PLR0912, PLR0915 Pre-calculate the quantiles, then plot - >>> summary_stats = ScmRun( - ... scmrun.quantiles_over("ensemble_member", quantiles=[0.05, 0.5, 0.95]) - ... ) + >>> summary_stats = ScmRun(scmrun.quantiles_over("ensemble_member", quantiles=[0.05, 0.5, 0.95])) >>> summary_stats.plumeplot(pre_calculated=True) # doctest: +ELLIPSIS (, ...) @@ -207,9 +206,7 @@ def plumeplot( # pragma: no cover # noqa: PLR0913, PLR0912, PLR0915 import matplotlib.patches as mpatches import matplotlib.pyplot as plt except ImportError: # pragma: no cover - raise ImportError( # noqa:TRY200 - "matplotlib is not installed. Run 'pip install matplotlib'" - ) + raise ImportError("matplotlib is not installed. Run 'pip install matplotlib'") if not pre_calculated: quantiles = [v for qv in quantiles_plumes for v in qv[0]] @@ -246,9 +243,7 @@ def plumeplot( # pragma: no cover # noqa: PLR0913, PLR0912, PLR0915 missing_quantile = False for qt in q: if qt not in quantiles: - warnings.warn( - f"Quantile {qt} not available for {hue_value} {style_value}" - ) + warnings.warn(f"Quantile {qt} not available for {hue_value} {style_value}") missing_quantile = True if missing_quantile: @@ -273,12 +268,8 @@ def plumeplot( # pragma: no cover # noqa: PLR0913, PLR0912, PLR0915 p = ax.fill_between( xaxis, - _get_1d_or_raise( - hsdf.filter(quantile=q[0]), hue_var, style_var - ), - _get_1d_or_raise( - hsdf.filter(quantile=q[1]), hue_var, style_var - ), + _get_1d_or_raise(hsdf.filter(quantile=q[0]), hue_var, style_var), + _get_1d_or_raise(hsdf.filter(quantile=q[1]), hue_var, style_var), label=label, **pkwargs, ) @@ -311,9 +302,7 @@ def plumeplot( # pragma: no cover # noqa: PLR0913, PLR0912, PLR0915 p = ax.plot( xaxis, - _get_1d_or_raise( - hsdf.filter(quantile=q[0]), hue_var, style_var - ), + _get_1d_or_raise(hsdf.filter(quantile=q[0]), hue_var, style_var), label=label, linewidth=linewidth, **pkwargs, @@ -326,18 +315,14 @@ def plumeplot( # pragma: no cover # noqa: PLR0913, PLR0912, PLR0915 _palette[hue_value] = p.get_color() else: - raise ValueError( - "quantiles to plot must be of length one or two, " - f"received: {q}" - ) + raise ValueError(f"quantiles to plot must be of length one or two, received: {q}") if label not in quantile_labels: quantile_labels[label] = p # Fake the line handles for the legend hue_val_lines = [ - mlines.Line2D([0], [0], color=_palette[hue_value], label=hue_value) - for hue_value in plotted_hues + mlines.Line2D([0], [0], color=_palette[hue_value], label=hue_value) for hue_value in plotted_hues ] legend_items = [ @@ -364,10 +349,7 @@ def plumeplot( # pragma: no cover # noqa: PLR0913, PLR0912, PLR0915 *style_val_lines, ] elif dashes is not None: - warnings.warn( - "`dashes` was passed but no lines were plotted, the style settings " - "will not be used" - ) + warnings.warn("`dashes` was passed but no lines were plotted, the style settings will not be used") ax.legend(handles=legend_items, loc="best") diff --git a/src/scmdata/processing.py b/src/scmdata/processing.py index 554ca482..1069c97d 100644 --- a/src/scmdata/processing.py +++ b/src/scmdata/processing.py @@ -4,6 +4,7 @@ These functions are intended to be able to be used directly with :meth:`scmdata.ScmRun.process_over`. """ + import numpy as np import pandas as pd import tqdm.autonotebook as tqdman @@ -140,9 +141,7 @@ def calculate_crossing_times_quantiles( 3 2006 4 2006 dtype: object - >>> calculate_crossing_times_quantiles( - ... crossing_times, groupby=["climate_model", "scenario"] - ... ) + >>> calculate_crossing_times_quantiles(crossing_times, groupby=["climate_model", "scenario"]) climate_model scenario quantile x_model a_scenario 0.05 2006.0 0.50 2007.0 @@ -165,9 +164,7 @@ def calculate_crossing_times_quantiles( crossing_times_quantiles = crossing_times_full.groupby(groupby).quantile( q=quantiles, interpolation=interpolation ) - out = crossing_times_quantiles.where( - crossing_times_quantiles < out_nan_threshold, other=pd.NA - ) + out = crossing_times_quantiles.where(crossing_times_quantiles < out_nan_threshold, other=pd.NA) out.index = out.index.set_names("quantile", level=-1) return out @@ -175,9 +172,7 @@ def calculate_crossing_times_quantiles( def _assert_only_one_value(scmrun, col): if len(scmrun.get_unique_meta(col)) > 1: - raise ValueError( - f"More than one value for {col}. " "This is unlikely to be what you want." - ) + raise ValueError(f"More than one value for {col}. This is unlikely to be what you want.") def _get_exceedance_fraction(ts, group_cols): @@ -205,9 +200,7 @@ def _set_index_level_to(inp, col, val): return inp -def calculate_exceedance_probabilities( - scmrun, threshold, process_over_cols, output_name=None -): +def calculate_exceedance_probabilities(scmrun, threshold, process_over_cols, output_name=None): """ Calculate exceedance probability over all time @@ -266,9 +259,7 @@ def calculate_exceedance_probabilities( return out -def calculate_exceedance_probabilities_over_time( - scmrun, threshold, process_over_cols, output_name=None -): +def calculate_exceedance_probabilities_over_time(scmrun, threshold, process_over_cols, output_name=None): """ Calculate exceedance probability at each point in time @@ -398,9 +389,7 @@ def calculate_peak_time(scmrun, output_name=None, return_year=True): if return_year: out = out.apply(lambda x: x.year) - out = _set_peak_output_name( - out, output_name, "Year of peak" if return_year else "Time of peak" - ) + out = _set_peak_output_name(out, output_name, "Year of peak" if return_year else "Time of peak") return out @@ -473,9 +462,7 @@ def _get_comp_series(res): peak_median = _get_comp_series(calculate_peak(scmrun.filter(quantile=0.5))) peak_p33 = _get_comp_series(calculate_peak(scmrun.filter(quantile=0.33))) peak_p66 = _get_comp_series(calculate_peak(scmrun.filter(quantile=0.66))) - end_of_century_median = _get_comp_series( - calculate_peak(scmrun.filter(quantile=0.5, year=2100)) - ) + end_of_century_median = _get_comp_series(calculate_peak(scmrun.filter(quantile=0.5, year=2100))) categories[peak_median > 2.0] = "Above 2C" categories[peak_median <= 1.5] = "Below 1.5C" @@ -490,9 +477,7 @@ def _get_comp_series(res): still_uncategorised = categories.isna() peak_p66_lte_2 = peak_p66 <= 2.0 # p exceed < 0.34 - categories[ - still_uncategorised & (peak_median <= 2.0) & ~peak_p66_lte_2 - ] = "Higher 2C" + categories[still_uncategorised & (peak_median <= 2.0) & ~peak_p66_lte_2] = "Higher 2C" categories[still_uncategorised & peak_p66_lte_2] = "Lower 2C" if categories.isna().any(): # pragma: no cover # emergency valve @@ -506,7 +491,7 @@ def _calculate_quantile_groupby(base, index, quantile): def _raise_missing_variable_error(name, requested, scmrun): - msg = "{} `{}` is not available. " "Available variables:{}".format( + msg = "{} `{}` is not available. Available variables:{}".format( name, requested, scmrun.get_unique_meta("variable") ) raise ValueError(msg) @@ -670,16 +655,12 @@ def calculate_summary_stats( # noqa: PLR0912, PLR0913, PLR0915 scmrun_categorisation = scmrun.filter(variable=categorisation_variable) if scmrun_categorisation.empty: - _raise_missing_variable_error( - "categorisation_variable", categorisation_variable, scmrun - ) + _raise_missing_variable_error("categorisation_variable", categorisation_variable, scmrun) _categorisation_quantile_cols = categorisation_quantile_cols if isinstance(_categorisation_quantile_cols, str): _categorisation_quantile_cols = [_categorisation_quantile_cols] - if not all( - [v in scmrun_categorisation.meta for v in _categorisation_quantile_cols] - ): + if not all([v in scmrun_categorisation.meta for v in _categorisation_quantile_cols]): msg = ( f"categorisation_quantile_cols `{categorisation_quantile_cols}` not in `scmrun`. " f"Available columns:{scmrun.meta.columns.tolist()}" @@ -700,9 +681,7 @@ def calculate_summary_stats( # noqa: PLR0912, PLR0913, PLR0915 "SR1.5 category", ) ] - func_calls_args_kwargs = ( - exceedance_prob_calls + peak_calls + peak_time_calls + categorisation_calls - ) + func_calls_args_kwargs = exceedance_prob_calls + peak_calls + peak_time_calls + categorisation_calls if progress: iterator = tqdman.tqdm(func_calls_args_kwargs) @@ -716,8 +695,7 @@ def get_result(func, args, kwargs, name): return res series = [ - get_result(func, args, kwargs, name).reorder_levels(_index) - for func, args, kwargs, name in iterator + get_result(func, args, kwargs, name).reorder_levels(_index) for func, args, kwargs, name in iterator ] if not peak_return_year: series = [s.astype("object") for s in series] diff --git a/src/scmdata/pyam_compat.py b/src/scmdata/pyam_compat.py index e41a3578..2eb2e186 100644 --- a/src/scmdata/pyam_compat.py +++ b/src/scmdata/pyam_compat.py @@ -1,6 +1,7 @@ """ Imports and classes required to ensure compatibility with Pyam is intelligently handled. """ + import datetime from dateutil import parser @@ -32,10 +33,7 @@ def convert_str_to_datetime(inp): self.data["time"] = self.data["time"].apply(convert_str_to_datetime) - not_datetime = [ - not isinstance(x, (datetime.datetime, datetime.date)) - for x in self.data["time"] - ] + not_datetime = [not isinstance(x, (datetime.datetime, datetime.date)) for x in self.data["time"]] if any(not_datetime): bad_values = self.data[not_datetime]["time"] error_msg = ( diff --git a/src/scmdata/run.py b/src/scmdata/run.py index 7736d181..1909d198 100644 --- a/src/scmdata/run.py +++ b/src/scmdata/run.py @@ -5,6 +5,7 @@ model data. ScmRuns are able to hold multiple model runs which aids in analysis of ensembles of model runs. """ + from __future__ import annotations import copy @@ -13,15 +14,13 @@ import os import pathlib import warnings +from collections.abc import Callable, Iterable, Mapping, Sequence from logging import getLogger from typing import ( TYPE_CHECKING, Any, - Callable, - Iterable, Literal, - Mapping, - Sequence, + Self, TypeVar, cast, overload, @@ -34,7 +33,6 @@ import pandas.io.common import pint from dateutil import parser -from typing_extensions import Self import scmdata.units @@ -69,8 +67,10 @@ if TYPE_CHECKING: + from typing import Concatenate + from numpy.typing import NDArray - from typing_extensions import Concatenate, ParamSpec + from typing_extensions import ParamSpec from scmdata.groupby import RunGroupBy @@ -102,9 +102,7 @@ def _read_file( # pylint: disable=missing-return-doc return _format_data(_read_pandas(str(filename), *args, **kwargs), required_cols) -def _read_pandas( - fname: str, *args: Any, lowercase_cols: bool = False, **kwargs: Any -) -> pd.DataFrame: +def _read_pandas(fname: str, *args: Any, lowercase_cols: bool = False, **kwargs: Any) -> pd.DataFrame: """ Read a file and return a :class:`pandas.DataFrame`. @@ -195,9 +193,7 @@ def _format_data( # pylint: disable=missing-return-doc Not all required metadata columns are present or the time axis cannot be understood """ - df: pd.DataFrame = ( - input_df.to_frame() if isinstance(input_df, pd.Series) else input_df - ) + df: pd.DataFrame = input_df.to_frame() if isinstance(input_df, pd.Series) else input_df # reset the index if meaningful entries are included there if list(df.index.names) != [None]: @@ -216,9 +212,7 @@ def _format_data( # pylint: disable=missing-return-doc return df, meta -def _format_long_data( - df: pd.DataFrame, required_cols: Sequence[str] -) -> tuple[pd.DataFrame, pd.DataFrame]: +def _format_long_data(df: pd.DataFrame, required_cols: Sequence[str]) -> tuple[pd.DataFrame, pd.DataFrame]: # check if time column is given as `year` (int) or `time` (datetime) cols = set(df.columns) if "year" in cols and "time" not in cols: @@ -238,9 +232,7 @@ def _format_long_data( return df, meta -def _format_wide_data( - df: pd.DataFrame, required_cols: Sequence[str] -) -> tuple[pd.DataFrame, pd.DataFrame]: +def _format_wide_data(df: pd.DataFrame, required_cols: Sequence[str]) -> tuple[pd.DataFrame, pd.DataFrame]: cols = set(df.columns) - set(required_cols) time_cols = False extra_cols: list[str] = [] @@ -266,10 +258,7 @@ def _format_wide_data( extra_cols.append(i) # some other string if not time_cols: - msg = ( - "invalid column format, must contain some time (int, float or datetime) " - "columns!" - ) + msg = "invalid column format, must contain some time (int, float or datetime) columns!" raise ValueError(msg) all_cols_set = set(tuple(required_cols) + tuple(extra_cols)) @@ -322,17 +311,12 @@ def _from_ts( num_ts = len(input_df.columns) for c_name, col in columns.items(): - col_list = ( - [col] if isinstance(col, str) or not isinstance(col, Iterable) else col - ) + col_list = [col] if isinstance(col, str) or not isinstance(col, Iterable) else col if len(col_list) == num_ts: continue if len(col_list) != 1: - error_msg = ( - f"Length of column '{c_name}' is incorrect. It should be length " - f"1 or {num_ts}" - ) + error_msg = f"Length of column '{c_name}' is incorrect. It should be length 1 or {num_ts}" raise ValueError(error_msg) columns[c_name] = col_list * num_ts @@ -455,9 +439,7 @@ def __init__( ... "unit": ["unspecified"], ... } >>> multi_value_init = ScmRun(d, index, columns=col_2) - >>> pd.testing.assert_frame_equal( - ... single_value_init.meta, multi_value_init.meta - ... ) + >>> pd.testing.assert_frame_equal(single_value_init.meta, multi_value_init.meta) metadata: Optional dictionary of metadata for instance as a whole. @@ -503,9 +485,7 @@ def __init__( self._init_timeseries(data, index, columns, copy_data=copy_data, **kwargs) else: self._df = pd.DataFrame(dtype=float) - self._meta = pd.MultiIndex.from_frame( - pd.DataFrame(data=[], columns=list(self.required_cols)) - ) + self._meta = pd.MultiIndex.from_frame(pd.DataFrame(data=[], columns=list(self.required_cols))) self._time_points = TimePoints([]) if self._duplicated_meta(): @@ -531,23 +511,16 @@ def _init_timeseries( raise ValueError("`index` argument is required") if columns is not None: - (_df, _meta) = _from_ts( - data, index=index, required_cols=self.required_cols, **columns - ) + (_df, _meta) = _from_ts(data, index=index, required_cols=self.required_cols, **columns) elif isinstance(data, (pd.DataFrame, pd.Series)): (_df, _meta) = _format_data(data, self.required_cols) elif (IamDataFrame is not None) and isinstance(data, IamDataFrame): - (_df, _meta) = _format_data( - data.data.copy() if copy_data else data.data, self.required_cols - ) + (_df, _meta) = _format_data(data.data.copy() if copy_data else data.data, self.required_cols) else: if not isinstance(data, (str, pathlib.PurePath)): - if isinstance(data, (list, tuple)) and isinstance( - data[0], (str, pathlib.PurePath) - ): + if isinstance(data, (list, tuple)) and isinstance(data[0], (str, pathlib.PurePath)): raise ValueError( # noqa: TRY004 - "Initialising from multiple files not supported, " - "use `scmdata.run.ScmRun.append()`" + "Initialising from multiple files not supported, use `scmdata.run.ScmRun.append()`" ) error_msg = f"Cannot load {type(self)} from {type(data)}" raise TypeError(error_msg) @@ -598,18 +571,14 @@ def __getitem__(self, key: Any) -> Any: Provides direct access to "time", "year" as well as the columns in :attr:`meta`. If key is anything else, the key will be applied to :attr:`_data`. """ - _key_check = ( - [key] if isinstance(key, str) or not isinstance(key, Iterable) else key - ) + _key_check = [key] if isinstance(key, str) or not isinstance(key, Iterable) else key if key == "time": return pd.Series(self._time_points.to_index(), dtype="object") if key == "year": return pd.Series(self._time_points.years()) if set(_key_check).issubset(self.meta_attributes): try: - return self._meta_column(key).astype( - self._meta_column(key).cat.categories.dtype - ) + return self._meta_column(key).astype(self._meta_column(key).cat.categories.dtype) except ValueError: return self._meta_column(key).astype(float) @@ -671,10 +640,7 @@ def __setitem__( verify_integrity=False, ) else: - msg = ( - "Invalid length for metadata, `{}`, must be 1 or equal to the " - "number of timeseries, `{}`" - ) + msg = "Invalid length for metadata, `{}`, must be 1 or equal to the number of timeseries, `{}`" raise ValueError(msg.format(len(meta), len(self))) if self._duplicated_meta(): @@ -696,13 +662,7 @@ def _indent(s): else: time_str = ["Start: N/A", "End: N/A"] time_str = _indent("\n".join(time_str)) - return "<{} (timeseries: {}, timepoints: {})>\nTime:\n{}\nMeta:\n{}".format( - self.__class__.__name__, - len(self), - len(self.time_points), - time_str, - meta_str, - ) + return f"<{self.__class__.__name__} (timeseries: {len(self)}, timepoints: {len(self.time_points)})>\nTime:\n{time_str}\nMeta:\n{meta_str}" # noqa: E501 def _binary_op( self, @@ -725,9 +685,7 @@ def _binary_op( f"as self ({self.shape[1]}) are supported" ) else: - raise ValueError( - f"operations with {other_ndim}d data are not supported" - ) + raise ValueError(f"operations with {other_ndim}d data are not supported") def _perform_op(run: Self) -> Self: if isinstance(other, pint.Quantity): @@ -735,9 +693,8 @@ def _perform_op(run: Self) -> Self: data = run.values * ur(run.get_unique_meta("unit", True)) use_pint = True except KeyError: # pragma: no cover # emergency valve - raise KeyError( # noqa: TRY200 - "No `unit` column in your metadata, cannot perform operations " - "with pint quantities" + raise KeyError( + "No `unit` column in your metadata, cannot perform operations with pint quantities" ) else: data = run.values @@ -752,9 +709,7 @@ def _perform_op(run: Self) -> Self: res_stacked = np.vstack(res) if use_pint: - run._df.iloc[:, :] = np.asarray( - res_stacked.magnitude.T, dtype=float - ) + run._df.iloc[:, :] = np.asarray(res_stacked.magnitude.T, dtype=float) run["unit"] = str(res_stacked.units) else: run._df.iloc[:, :] = np.asarray(res_stacked.T, dtype=float) @@ -888,9 +843,7 @@ def timeseries( elif time_axis == "year": columns = self._time_points.years() elif time_axis == "year-month": - columns = ( - self._time_points.years() + (self._time_points.months() - 0.5) / 12 - ) + columns = self._time_points.years() + (self._time_points.months() - 0.5) / 12 elif time_axis == "days since 1970-01-01": def calc_days(x): @@ -976,7 +929,7 @@ def shape(self) -> tuple[int, int]: return self._df.T.shape @property - def values(self) -> NDArray[np.float_]: + def values(self) -> NDArray[np.float64]: """ Timeseries values without metadata @@ -1010,9 +963,7 @@ def meta(self) -> pd.DataFrame: """ Metadata """ - df = pd.DataFrame( - self._meta.to_list(), columns=self._meta.names, index=self._df.columns - ) + df = pd.DataFrame(self._meta.to_list(), columns=self._meta.names, index=self._df.columns) return df[sorted(df.columns)] @@ -1064,9 +1015,7 @@ def set_meta( if "inplace" in filter_kwargs: raise ValueError("Inplace updating of metadata is not supported") - filtered_run = self.filter( - keep=keep, log_if_empty=log_if_empty, **filter_kwargs - ) + filtered_run = self.filter(keep=keep, log_if_empty=log_if_empty, **filter_kwargs) filtered_run[dimension] = value res = run_append( [ @@ -1198,13 +1147,7 @@ def filter( ret = copy.copy(self) if not inplace else self _keep_times, _keep_rows = self._apply_filters(kwargs) - if ( - not keep - and len(_keep_rows) - and len(_keep_times) - and sum(~_keep_rows) - and sum(~_keep_times) - ): + if not keep and len(_keep_rows) and len(_keep_times) and sum(~_keep_rows) and sum(~_keep_times): raise ValueError( "If keep==False, filtering cannot be performed on the temporal axis " "and with metadata at the same time" @@ -1373,16 +1316,14 @@ def get_unique_meta( self, meta: str, no_duplicates: Literal[True], - ) -> MetadataValue: - ... + ) -> MetadataValue: ... @overload def get_unique_meta( self, meta: str, no_duplicates: Literal[False] = ..., - ) -> list[MetadataValue]: - ... + ) -> list[MetadataValue]: ... def get_unique_meta( self, @@ -1419,9 +1360,7 @@ def get_unique_meta( vals: list[MetadataValue] = self._meta.get_level_values(meta).unique().to_list() if no_duplicates: if len(vals) != 1: - raise ValueError( - f"`{meta}` column is not unique (found values: {vals})" - ) + raise ValueError(f"`{meta}` column is not unique (found values: {vals})") return vals[0] @@ -1488,12 +1427,8 @@ def interpolate( # TODO: Extend TimeseriesConverter to handle 2d inputs for i in range(len(res)): - target_data[:, i] = timeseries_converter.convert_from( - res._df.iloc[:, i].values - ) - res._df = pd.DataFrame( - target_data, columns=res._df.columns, index=target_time_points.to_index() - ) + target_data[:, i] = timeseries_converter.convert_from(res._df.iloc[:, i].values) + res._df = pd.DataFrame(target_data, columns=res._df.columns, index=target_time_points.to_index()) res._time_points = target_time_points return res @@ -1593,9 +1528,7 @@ def resample(self, rule: str = "AS", **kwargs: Any) -> Self: for more information about possible arguments. """ orig_dts = self["time"] - target_dts = generate_range( - orig_dts.iloc[0], orig_dts.iloc[-1], to_offset(rule) - ) + target_dts = generate_range(orig_dts.iloc[0], orig_dts.iloc[-1], to_offset(rule)) return self.interpolate(list(target_dts), **kwargs) def time_mean(self, rule: str) -> Self: @@ -1634,15 +1567,8 @@ def group_annual_mean_beginning_of_year(x): return x.year return x.year + 1 - ts_resampled = ( - self.timeseries() - .T.groupby(group_annual_mean_beginning_of_year) - .mean() - .T - ) - ts_resampled.columns = ts_resampled.columns.map( - lambda x: dt.datetime(x, 1, 1) - ) + ts_resampled = self.timeseries().T.groupby(group_annual_mean_beginning_of_year).mean().T + ts_resampled.columns = ts_resampled.columns.map(lambda x: dt.datetime(x, 1, 1)) return type(self)(ts_resampled) if rule == "AC": @@ -1651,9 +1577,7 @@ def group_annual_mean(x): return x.year ts_resampled = self.timeseries().T.groupby(group_annual_mean).mean().T - ts_resampled.columns = ts_resampled.columns.map( - lambda x: dt.datetime(x, 7, 1) - ) + ts_resampled.columns = ts_resampled.columns.map(lambda x: dt.datetime(x, 7, 1)) return type(self)(ts_resampled) if rule == "A": @@ -1663,12 +1587,8 @@ def group_annual_mean_end_of_year(x): return x.year return x.year - 1 - ts_resampled = ( - self.timeseries().T.groupby(group_annual_mean_end_of_year).mean().T - ) - ts_resampled.columns = ts_resampled.columns.map( - lambda x: dt.datetime(x, 12, 31) - ) + ts_resampled = self.timeseries().T.groupby(group_annual_mean_end_of_year).mean().T + ts_resampled.columns = ts_resampled.columns.map(lambda x: dt.datetime(x, 12, 31)) return type(self)(ts_resampled) raise ValueError(f"`rule` = `{rule}` is not supported") @@ -1682,8 +1602,7 @@ def process_over( op_cols: dict[str, str] | None = None, as_run: type[GenericRun] = ..., **kwargs: Any, - ) -> GenericRun: - ... + ) -> GenericRun: ... @overload def process_over( @@ -1694,8 +1613,7 @@ def process_over( op_cols: dict[str, str] | None = None, as_run: Literal[False] = False, **kwargs: Any, - ) -> pd.DataFrame: - ... + ) -> pd.DataFrame: ... @overload def process_over( @@ -1706,8 +1624,7 @@ def process_over( op_cols: dict[str, str] | None = None, as_run: Literal[True] = ..., **kwargs: Any, - ) -> Self: - ... + ) -> Self: ... def process_over( # noqa: PLR0912 self, @@ -1800,9 +1717,7 @@ def process_over( # noqa: PLR0912 if na_override is not None: ts_idx = ts.index.to_frame() if ts_idx[ts_idx == na_override].any().any(): - raise ValueError( - f"na_override clashes with existing meta: {na_override}" - ) + raise ValueError(f"na_override clashes with existing meta: {na_override}") ts.index = pd.MultiIndex.from_frame(ts_idx.fillna(na_override)) group_cols = list(set(ts.index.names) - set(cols)) @@ -1961,7 +1876,6 @@ def groupby(self, *group: str | Iterable[str]) -> RunGroupBy[Self]: >>> for group in run.groupby("scenario"): ... print(group) - ... Time: Start: 2005-01-01T00:00:00 @@ -2021,7 +1935,6 @@ def apply( ... if variable == "Surface Temperature": ... return arr * 2 ... return arr - ... >>> run = ScmRun( ... data=[[1, 2], [3, 4]], @@ -2160,18 +2073,11 @@ def convert_unit( to_not_convert_filtered = ret.filter(**kwargs, keep=False, log_if_empty=False) already_correct_unit = to_convert_filtered.filter(unit=unit, log_if_empty=False) - if ( - "unit_context" in already_correct_unit.meta_attributes - and not already_correct_unit.empty - ): + if "unit_context" in already_correct_unit.meta_attributes and not already_correct_unit.empty: self._check_unit_context(already_correct_unit, context) - to_convert = to_convert_filtered.filter( - unit=unit, log_if_empty=False, keep=False - ) - to_not_convert: Self = run_append( - [to_not_convert_filtered, already_correct_unit] - ) + to_convert = to_convert_filtered.filter(unit=unit, log_if_empty=False, keep=False) + to_not_convert: Self = run_append([to_not_convert_filtered, already_correct_unit]) if "unit_context" in to_convert.meta_attributes and not to_convert.empty: self._check_unit_context(to_convert, context) @@ -2345,8 +2251,7 @@ def append_timewise( ts_other = other.timeseries(meta=align_columns) except NonUniqueMetadataError as exc: error_msg = ( - "Calling ``other.timeseries(meta=align_columns)`` must " - "result in umabiguous timeseries" + "Calling ``other.timeseries(meta=align_columns)`` must result in umabiguous timeseries" ) raise ValueError(error_msg) from exc @@ -2386,9 +2291,7 @@ def to_iamdataframe(self) -> LongDatetimeIamDataFrame: # pragma: no cover from .pyam_compat import LongDatetimeIamDataFrame if LongDatetimeIamDataFrame is None: - raise ImportError( - "pyam is not installed. Features involving IamDataFrame are unavailable" - ) + raise ImportError("pyam is not installed. Features involving IamDataFrame are unavailable") return LongDatetimeIamDataFrame(self.timeseries()) @@ -2442,9 +2345,7 @@ def reduce(self, func, dim=None, axis=None, **kwargs): input_data = self.values if axis is None or axis == 1: - raise NotImplementedError( - "Cannot currently reduce along the time dimension" - ) + raise NotImplementedError("Cannot currently reduce along the time dimension") if axis is not None: data = func(input_data, axis=axis, **kwargs) @@ -2651,17 +2552,13 @@ def run_append( # noqa: PLR0912, PLR0915 orig_dtypes = orig_ts_index.to_frame().dtypes # Convert index to str - ts.index = pd.MultiIndex.from_frame( - ts.index.to_frame().astype(str).reset_index(drop=True) - ) + ts.index = pd.MultiIndex.from_frame(ts.index.to_frame().astype(str).reset_index(drop=True)) deduped_ts = ts.groupby(ts.index, as_index=True).mean() ret._df = deduped_ts.reset_index(drop=True).T - new_meta = pd.DataFrame.from_records( - deduped_ts.index.values, columns=ts.index.names - ) + new_meta = pd.DataFrame.from_records(deduped_ts.index.values, columns=ts.index.names) # Convert back from str for c in nan_cols[nan_cols].index: @@ -2674,9 +2571,7 @@ def run_append( # noqa: PLR0912, PLR0915 if metadata is not None: ret.metadata = metadata else: - ret.metadata = _merge_metadata( - [r.metadata if hasattr(r, "metadata") else {} for r in runs] - ) + ret.metadata = _merge_metadata([r.metadata if hasattr(r, "metadata") else {} for r in runs]) return ret diff --git a/src/scmdata/testing.py b/src/scmdata/testing.py index 9e39a4be..edb4f1d4 100644 --- a/src/scmdata/testing.py +++ b/src/scmdata/testing.py @@ -27,9 +27,7 @@ def _assert_frame_equal(left, right, **kwargs): pdt.assert_frame_equal(left.T, right.T, **kwargs) -def assert_scmdf_almost_equal( - left, right, allow_unordered=False, check_ts_names=True, rtol=1e-5, atol=1e-8 -): +def assert_scmdf_almost_equal(left, right, allow_unordered=False, check_ts_names=True, rtol=1e-5, atol=1e-8): """ Check that left and right :class:`ScmRun ` are equal. @@ -63,9 +61,7 @@ def assert_scmdf_almost_equal( df1_index = np.argsort(left.meta.index) df2_index = np.argsort(right.meta.index) _assert_frame_equal(left.meta, right.meta, check_like=True) - npt.assert_allclose( - left.values[df1_index], right.values[df2_index], rtol=rtol, atol=atol - ) + npt.assert_allclose(left.values[df1_index], right.values[df2_index], rtol=rtol, atol=atol) else: # ignore differing meta index labels @@ -74,9 +70,7 @@ def assert_scmdf_almost_equal( right_ts = right.timeseries() # check metadata columns are same set if set(left_sorted.index.names) != set(right_ts.index.names): - raise AssertionError( - f"{set(left_sorted.index.names)} != {set(right_ts.index.names)}" - ) + raise AssertionError(f"{set(left_sorted.index.names)} != {set(right_ts.index.names)}") right_sorted = right.timeseries(left_sorted.index.names).sort_index() # this checks both the index (i.e. sorted meta) and values are the same diff --git a/src/scmdata/time.py b/src/scmdata/time.py index 4e504caf..45c1e015 100644 --- a/src/scmdata/time.py +++ b/src/scmdata/time.py @@ -6,7 +6,7 @@ """ from datetime import datetime -from typing import Any, Dict, Optional, Type +from typing import Any import cftime import numpy as np @@ -32,10 +32,7 @@ def _float_year_to_datetime(inp: float) -> np.datetime64: year = int(inp) fractional_part = inp - year return np.datetime64(year - 1970, "Y") + np.timedelta64( - int( - (datetime(year + 1, 1, 1) - datetime(year, 1, 1)).total_seconds() - * fractional_part - ), + int((datetime(year + 1, 1, 1) - datetime(year, 1, 1)).total_seconds() * fractional_part), "s", ) @@ -133,9 +130,7 @@ def to_index(self) -> pd.Index: """ return pd.Index(self._values.astype(object), dtype=object, name="time") - def as_cftime( - self, date_cls: Type[cftime.datetime] = cftime.DatetimeGregorian - ) -> list: + def as_cftime(self, date_cls: type[cftime.datetime] = cftime.DatetimeGregorian) -> list: """ Format time points as :class:`cftime.datetime` @@ -237,18 +232,10 @@ def __init__( source_time_points: np.ndarray, target_time_points: np.ndarray, interpolation_type: str = "linear", - extrapolation_type: Optional[str] = "linear", + extrapolation_type: str | None = "linear", ): - self.source = ( - np.array(source_time_points) - .astype(_TARGET_DTYPE) - .astype(_TARGET_TYPE, copy=True) - ) - self.target = ( - np.array(target_time_points) - .astype(_TARGET_DTYPE) - .astype(_TARGET_TYPE, copy=True) - ) + self.source = np.array(source_time_points).astype(_TARGET_DTYPE).astype(_TARGET_TYPE, copy=True) + self.target = np.array(target_time_points).astype(_TARGET_DTYPE).astype(_TARGET_TYPE, copy=True) self.interpolation_type = interpolation_type self.extrapolation_type = extrapolation_type @@ -275,14 +262,12 @@ def points_are_compatible(self, source: np.ndarray, target: np.ndarray) -> bool: bool Can I convert between the time points? """ - if self.extrapolation_type is None and ( - source[0] > target[0] or source[-1] < target[-1] - ): + if self.extrapolation_type is None and (source[0] > target[0] or source[-1] < target[-1]): return False return True - def _get_scipy_extrapolation_args(self, values: np.ndarray) -> Dict[str, Any]: + def _get_scipy_extrapolation_args(self, values: np.ndarray) -> dict[str, Any]: if self.extrapolation_type == "linear": return {"fill_value": "extrapolate"} if self.extrapolation_type == "constant": diff --git a/src/scmdata/timeseries.py b/src/scmdata/timeseries.py index 00263793..d3208f2f 100644 --- a/src/scmdata/timeseries.py +++ b/src/scmdata/timeseries.py @@ -6,13 +6,13 @@ import copy import datetime as dt -from typing import Any, Callable, List, Literal, Optional, Union +from collections.abc import Callable +from typing import Any, Literal, Self, Union import numpy as np import pint import xarray as xr from openscm_units import unit_registry as ur -from typing_extensions import Self from ._base import OpsMixin from .time import TimePoints, TimeseriesConverter @@ -88,10 +88,7 @@ def __init__(self, data, time=None, **kwargs): if isinstance(data, xr.DataArray): if time is not None: - raise TypeError( - "If data is an :class:`xarray.DataArray` instance, time must be " - "`None`" - ) + raise TypeError("If data is an :class:`xarray.DataArray` instance, time must be `None`") if data.dims != ("time",): raise ValueError( @@ -103,8 +100,7 @@ def __init__(self, data, time=None, **kwargs): else: if time is None: raise TypeError( - "If data is not an :class:`xarray.DataArray` instance, `time` " - "must not be `None`" + "If data is not an :class:`xarray.DataArray` instance, `time` must not be `None`" ) if "coords" in kwargs: @@ -268,9 +264,9 @@ def reindex(self, time: np.ndarray, **kwargs: Any) -> "TimeSeries": def interpolate( self, - target_times: Union[np.ndarray, List[Union[dt.datetime, int]]], + target_times: Union[np.ndarray, list[Union[dt.datetime, int]]], interpolation_type: Literal["linear"] = "linear", - extrapolation_type: Optional[Literal["linear", "constant"]] = "linear", + extrapolation_type: Literal["linear", "constant"] | None = "linear", ) -> "TimeSeries": """ Interpolate the timeseries onto a new time axis diff --git a/src/scmdata/units.py b/src/scmdata/units.py index 586c1cdd..94cd43bb 100644 --- a/src/scmdata/units.py +++ b/src/scmdata/units.py @@ -5,8 +5,10 @@ built on top of :mod:`pint` and includes some additional quantity definitions to support the tracking of emissions timeseries. """ + import warnings -from typing import Optional, Sequence, Union +from collections.abc import Sequence +from typing import Union import numpy as np import openscm_units @@ -27,7 +29,7 @@ class UnitConverter: Converts numbers between two units. """ - def __init__(self, source: str, target: str, context: Optional[str] = None): + def __init__(self, source: str, target: str, context: str | None = None): """ Initialize. @@ -69,8 +71,7 @@ def __init__(self, source: str, target: str, context: Optional[str] = None): if np.isnan(t1) or np.isnan(t2): warn_msg = ( - f"No conversion from {source} to {target} available, nan will be returned " - "upon conversion" + f"No conversion from {source} to {target} available, nan will be returned upon conversion" ) warnings.warn(warn_msg) diff --git a/stubs/joblib.pyi b/stubs/joblib.pyi index a716011a..c7cfec64 100644 --- a/stubs/joblib.pyi +++ b/stubs/joblib.pyi @@ -1,12 +1,11 @@ -from typing import Any, Callable, Iterable, ParamSpec, TypeVar +from collections.abc import Callable, Iterable +from typing import Any, ParamSpec, TypeVar T = TypeVar("T") P = ParamSpec("P") Q = ParamSpec("Q") -def delayed( - func: Callable[P, T] -) -> Callable[..., tuple[Callable[P, T], Q.args, Q.kwargs]]: ... +def delayed(func: Callable[P, T]) -> Callable[..., tuple[Callable[P, T], Q.args, Q.kwargs]]: ... class Parallel: def __init__(self, *args: Any, n_jobs: int, backend: str, **kwargs: Any): ... diff --git a/tests/conftest.py b/tests/conftest.py index a2b73bb2..3f158c28 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,6 +1,7 @@ """ Fixtures and data for tests. """ + import datetime as dt from collections import namedtuple from contextlib import contextmanager @@ -185,9 +186,7 @@ def check_context(): with pytest.warns(None) as record: yield if record: - pytest.fail( - f"The following warnings were raised: {[w.message for w in record.list]}" - ) + pytest.fail(f"The following warnings were raised: {[w.message for w in record.list]}") return check_context @@ -442,9 +441,7 @@ def plumeplot_scmrun(): { "base": append_scm_df_base, "other": { - "data": np.array( - [[3, 3.5, 3.7], [1, 7, 11], [-2, 1, -1.4], [-3, -4, -5]] - ).T, + "data": np.array([[3, 3.5, 3.7], [1, 7, 11], [-2, 1, -1.4], [-3, -4, -5]]).T, "index": append_scm_df_pairs_times, "columns": { "scenario": ["a_scenario", "b_scenario", "b_scenario2", "a_scenario3"], @@ -484,9 +481,7 @@ def plumeplot_scmrun(): { "base": append_scm_df_base, "other": { - "data": np.array( - [[-1, 0, 1], [3, 4, 4.5], [0.1, 0.2, 0.3], [-4, -8, 10]] - ).T, + "data": np.array([[-1, 0, 1], [3, 4, 4.5], [0.1, 0.2, 0.3], [-4, -8, 10]]).T, "index": append_scm_df_pairs_times, "columns": { "scenario": append_scm_df_pairs_scens, @@ -496,9 +491,7 @@ def plumeplot_scmrun(): }, "duplicate_rows": 4, "expected": { - "data": np.array( - [[0, 3, 4], [1.75, 3.5, 3.25], [1.05, 3.6, 0.15], [-2.5, -5, 6.5]] - ).T, + "data": np.array([[0, 3, 4], [1.75, 3.5, 3.25], [1.05, 3.6, 0.15], [-2.5, -5, 6.5]]).T, "index": append_scm_df_pairs_times, "columns": { "scenario": append_scm_df_pairs_scens, @@ -582,9 +575,7 @@ def rcp26(): test_combinations = [] -def create_time_points( - start_time: np.datetime64, period_length: np.timedelta64, points_num: int -): +def create_time_points(start_time: np.datetime64, period_length: np.timedelta64, points_num: int): end_time_output = start_time + (points_num - 1) * period_length return np.linspace( start_time.astype("datetime64[s]").astype(float), diff --git a/tests/integration/test_plotting_integration.py b/tests/integration/test_plotting_integration.py index 50bed478..e14ac2bf 100644 --- a/tests/integration/test_plotting_integration.py +++ b/tests/integration/test_plotting_integration.py @@ -38,28 +38,20 @@ def test_plumeplot(plumeplot_scmrun, quantiles_plumes): @sample_quantiles_plumes def test_plumeplot_pre_calculated(plumeplot_scmrun, quantiles_plumes): quantiles = [v for qv in quantiles_plumes for v in qv[0]] - summary_stats = ScmRun( - plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles) - ) + summary_stats = ScmRun(plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles)) summary_stats.plumeplot( quantiles_plumes=quantiles_plumes, pre_calculated=True, ) -@pytest.mark.xfail( - _check_pandas_less_110(), reason="pandas<=1.1.0 spews warnings everywhere" -) +@pytest.mark.xfail(_check_pandas_less_110(), reason="pandas<=1.1.0 spews warnings everywhere") def test_plumeplot_pre_calculated_no_plume_for_one(plumeplot_scmrun): quantiles_plumes = (((0.05, 0.95), 0.5), ((0.5,), 1.0)) quantiles = [v for qv in quantiles_plumes for v in qv[0]] - summary_stats = ScmRun( - plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles) - ) + summary_stats = ScmRun(plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles)) # drop out the plume for one of the climate models - summary_stats = summary_stats.filter( - climate_model="a_model", quantile=[0.05, 0.95], keep=False - ) + summary_stats = summary_stats.filter(climate_model="a_model", quantile=[0.05, 0.95], keep=False) with pytest.warns(UserWarning) as record: warnings.filterwarnings("ignore", category=DeprecationWarning) @@ -82,17 +74,11 @@ def test_plumeplot_pre_calculated_no_plume_for_one(plumeplot_scmrun): def test_plumeplot_pre_calculated_no_median_for_one(plumeplot_scmrun): quantiles_plumes = (((0.05, 0.95), 0.5), ((0.5,), 1.0)) quantiles = [v for qv in quantiles_plumes for v in qv[0]] - summary_stats = ScmRun( - plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles) - ) + summary_stats = ScmRun(plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles)) # drop out the plume for one of the climate models - summary_stats = summary_stats.filter( - climate_model="a_model", quantile=0.5, keep=False - ) + summary_stats = summary_stats.filter(climate_model="a_model", quantile=0.5, keep=False) - warn_msg = re.escape( - "Quantile 0.5 not available for a_scenario Surface Air Temperature Change" - ) + warn_msg = re.escape("Quantile 0.5 not available for a_scenario Surface Air Temperature Change") with pytest.warns(UserWarning, match=warn_msg): summary_stats.plumeplot( quantiles_plumes=quantiles_plumes, @@ -100,21 +86,17 @@ def test_plumeplot_pre_calculated_no_median_for_one(plumeplot_scmrun): ) -@pytest.mark.xfail( - _check_pandas_less_110(), reason="pandas<=1.1.0 spews warnings everywhere" -) +@pytest.mark.xfail(_check_pandas_less_110(), reason="pandas<=1.1.0 spews warnings everywhere") def test_plumeplot_pre_calculated_no_plume_for_one_no_median_for_other( plumeplot_scmrun, ): quantiles_plumes = (((0.05, 0.95), 0.5), ((0.5,), 1.0)) quantiles = [v for qv in quantiles_plumes for v in qv[0]] - summary_stats = ScmRun( - plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles) - ) + summary_stats = ScmRun(plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles)) # drop out the plume for one of the climate models - summary_stats = summary_stats.filter( - climate_model="a_model", quantile=[0.05, 0.95], keep=False - ).filter(climate_model="a_model_2", quantile=0.5, keep=False) + summary_stats = summary_stats.filter(climate_model="a_model", quantile=[0.05, 0.95], keep=False).filter( + climate_model="a_model_2", quantile=0.5, keep=False + ) with pytest.warns(UserWarning) as record: warnings.filterwarnings("ignore", category=DeprecationWarning) @@ -138,21 +120,17 @@ def test_plumeplot_pre_calculated_no_plume_for_one_no_median_for_other( ) -@pytest.mark.xfail( - _check_pandas_less_110(), reason="pandas<=1.1.0 spews warnings everywhere" -) +@pytest.mark.xfail(_check_pandas_less_110(), reason="pandas<=1.1.0 spews warnings everywhere") def test_plumeplot_pre_calculated_no_plume_for_one_no_median_for_other_different_styles( plumeplot_scmrun, ): quantiles_plumes = (((0.05, 0.95), 0.5), ((0.5,), 1.0)) quantiles = [v for qv in quantiles_plumes for v in qv[0]] - summary_stats = ScmRun( - plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles) - ) + summary_stats = ScmRun(plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles)) # drop out the plume for one of the climate models - summary_stats = summary_stats.filter( - climate_model="a_model", quantile=[0.05, 0.95], keep=False - ).filter(climate_model="a_model_2", quantile=0.5, keep=False) + summary_stats = summary_stats.filter(climate_model="a_model", quantile=[0.05, 0.95], keep=False).filter( + climate_model="a_model_2", quantile=0.5, keep=False + ) with pytest.warns(UserWarning) as record: warnings.filterwarnings("ignore", category=DeprecationWarning) @@ -163,25 +141,13 @@ def test_plumeplot_pre_calculated_no_plume_for_one_no_median_for_other_different ) assert len(record) == 3, record - assert ( - record[0].message.args[0] - == "Quantile 0.05 not available for a_scenario a_model" - ) - assert ( - record[1].message.args[0] - == "Quantile 0.95 not available for a_scenario a_model" - ) - assert ( - record[2].message.args[0] - == "Quantile 0.5 not available for a_scenario_2 a_model_2" - ) + assert record[0].message.args[0] == "Quantile 0.05 not available for a_scenario a_model" + assert record[1].message.args[0] == "Quantile 0.95 not available for a_scenario a_model" + assert record[2].message.args[0] == "Quantile 0.5 not available for a_scenario_2 a_model_2" def test_plumeplot_warns_dashes_without_lines(scm_run): - warn_msg = re.escape( - "`dashes` was passed but no lines were plotted, the style " - "settings will not be used" - ) + warn_msg = re.escape("`dashes` was passed but no lines were plotted, the style settings will not be used") with pytest.warns(UserWarning, match=warn_msg): scm_run.plumeplot( quantiles_plumes=(((0.17, 0.83), 0.7),), @@ -196,9 +162,7 @@ def test_plumeplot_non_unique_lines(plumeplot_scmrun): scenario = "a_scenario" variable = "Surface Air Temperature Change" - summary_stats = ScmRun( - plumeplot_scmrun.quantiles_over(quantile_over, quantiles=(0.05, 0.5, 0.95)) - ) + summary_stats = ScmRun(plumeplot_scmrun.quantiles_over(quantile_over, quantiles=(0.05, 0.5, 0.95))) error_msg = re.escape( "More than one timeseries for " @@ -301,9 +265,7 @@ def test_plumeplot_values(plumeplot_scmrun, quantiles_plumes, time_axis, linewid dashes = {"a_scenario": "-", "a_scenario_2": "--"} quantiles = [v for qv in quantiles_plumes for v in qv[0]] - summary_stats = ScmRun( - plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles) - ) + summary_stats = ScmRun(plumeplot_scmrun.quantiles_over("ensemble_member", quantiles=quantiles)) summary_stats.plumeplot( ax=mock_ax, quantiles_plumes=quantiles_plumes, @@ -355,12 +317,8 @@ def _make_fill_between_call(idf, cm, scen, quant_alpha): return call( xaxis, - _get_with_empty_check( - idf.filter(climate_model=cm, scenario=scen, quantile=quantiles[0]) - ), - _get_with_empty_check( - idf.filter(climate_model=cm, scenario=scen, quantile=quantiles[1]) - ), + _get_with_empty_check(idf.filter(climate_model=cm, scenario=scen, quantile=quantiles[0])), + _get_with_empty_check(idf.filter(climate_model=cm, scenario=scen, quantile=quantiles[1])), alpha=alpha, color=palette[cm], label=f"{quantiles[0] * 100:.0f}th - {quantiles[1] * 100:.0f}th", @@ -372,9 +330,7 @@ def _make_plot_call(idf, cm, scen, quant_alpha): return call( xaxis, - _get_with_empty_check( - idf.filter(climate_model=cm, scenario=scen, quantile=quantiles[0]) - ), + _get_with_empty_check(idf.filter(climate_model=cm, scenario=scen, quantile=quantiles[0])), color=palette[cm], linestyle=dashes[scen], linewidth=linewidth, @@ -387,24 +343,15 @@ def _make_plot_call(idf, cm, scen, quant_alpha): plume_qa = [q for q in quantiles_plumes if len(q[0]) == 2] fill_between_calls = [ - _make_fill_between_call(summary_stats, cm, scen, qa) - for cm, scen in cm_scen_combos - for qa in plume_qa + _make_fill_between_call(summary_stats, cm, scen, qa) for cm, scen in cm_scen_combos for qa in plume_qa ] # debug by looking at mock_ax.fill_between.call_args_list - assert all( - [ - _is_in_calls(c, mock_ax.fill_between.call_args_list) - for c in fill_between_calls - ] - ) + assert all([_is_in_calls(c, mock_ax.fill_between.call_args_list) for c in fill_between_calls]) line_qa = [q for q in quantiles_plumes if len(q[0]) == 1] plot_calls = [ - _make_plot_call(summary_stats, cm, scen, qa) - for cm, scen in cm_scen_combos - for qa in line_qa + _make_plot_call(summary_stats, cm, scen, qa) for cm, scen in cm_scen_combos for qa in line_qa ] # debug by looking at mock_ax.plot.call_args_list @@ -414,9 +361,9 @@ def _make_plot_call(idf, cm, scen, quant_alpha): def test_plumeplot_no_dashes_single_dash_style(plumeplot_scmrun): # if there's only a single variable and it is used for style, without # specifying dashes then all the lines should come out solid - ax, legend_items = plumeplot_scmrun.filter( - variable="Surface Air Temperature Change" - ).plumeplot(style_var="variable") + ax, legend_items = plumeplot_scmrun.filter(variable="Surface Air Temperature Change").plumeplot( + style_var="variable" + ) assert legend_items[-1].get_linestyle() == "-" @@ -427,9 +374,7 @@ def test_plumeplot_no_dashes_single_dash_style(plumeplot_scmrun): # sensible error if missing style etc. def test_error_missing_palette(plumeplot_scmrun): # extra definitions are fine - plumeplot_scmrun.plumeplot( - palette={"a_scenario": "blue", "a_scenario_2": "red", "b_scenario": "green"} - ) + plumeplot_scmrun.plumeplot(palette={"a_scenario": "blue", "a_scenario_2": "red", "b_scenario": "green"}) # missing definitions raise palette_miss = {"a_scenario_2": "red", "b_scenario": "green"} @@ -440,14 +385,10 @@ def test_error_missing_palette(plumeplot_scmrun): def test_error_missing_style(plumeplot_scmrun): # extra definitions are fine - plumeplot_scmrun.plumeplot( - dashes={"Surface Air Temperature Change": "-", "GMST": "--"} - ) + plumeplot_scmrun.plumeplot(dashes={"Surface Air Temperature Change": "-", "GMST": "--"}) # missing definitions raise dashes_miss = {"GMST": "--"} - error_msg = re.escape( - f"Surface Air Temperature Change not in dashes: {dashes_miss}" - ) + error_msg = re.escape(f"Surface Air Temperature Change not in dashes: {dashes_miss}") with pytest.raises(KeyError, match=error_msg): plumeplot_scmrun.plumeplot(dashes=dashes_miss) diff --git a/tests/integration/test_processing.py b/tests/integration/test_processing.py index c4ed0e2b..55e623af 100644 --- a/tests/integration/test_processing.py +++ b/tests/integration/test_processing.py @@ -102,9 +102,7 @@ def _get_expected_crossing_times(exp_vals, conv_to_year): ), ) @crossing_times_year_conversions -def test_crossing_times( - threshold, exp_vals, return_year, conv_to_year, test_processing_scm_df -): +def test_crossing_times(threshold, exp_vals, return_year, conv_to_year, test_processing_scm_df): call_kwargs = _get_calculate_crossing_times_call_kwargs(return_year) res = scmdata.processing.calculate_crossing_times( test_processing_scm_df, @@ -123,18 +121,12 @@ def test_crossing_times( "end_year", ( 5000, - pytest.param( - 10**3, marks=pytest.mark.xfail(reason="ScmRun fails to initialise #179") - ), - pytest.param( - 10**4, marks=pytest.mark.xfail(reason="ScmRun fails to initialise #179") - ), + pytest.param(10**3, marks=pytest.mark.xfail(reason="ScmRun fails to initialise #179")), + pytest.param(10**4, marks=pytest.mark.xfail(reason="ScmRun fails to initialise #179")), ), ) @crossing_times_year_conversions -def test_crossing_times_long_runs( - end_year, return_year, conv_to_year, test_processing_scm_df -): +def test_crossing_times_long_runs(end_year, return_year, conv_to_year, test_processing_scm_df): test_processing_scm_df = test_processing_scm_df.timeseries(time_axis="year").rename( {2100: end_year}, axis="columns" ) @@ -210,9 +202,7 @@ def _get_expected_crossing_time_quantiles( return out -@pytest.mark.parametrize( - "groups", (["model", "scenario"], ["climate_model", "model", "scenario"]) -) +@pytest.mark.parametrize("groups", (["model", "scenario"], ["climate_model", "model", "scenario"])) @pytest.mark.parametrize( "quantiles,exp_quantiles", ( @@ -261,9 +251,7 @@ def test_crossing_times_quantiles( if interpolation is not None: call_kwargs["interpolation"] = interpolation - res = scmdata.processing.calculate_crossing_times_quantiles( - crossing_times, **call_kwargs - ) + res = scmdata.processing.calculate_crossing_times_quantiles(crossing_times, **call_kwargs) if _check_pandas_less_120(): check_dtype = False @@ -282,9 +270,7 @@ def test_crossing_times_quantiles_datetime_error( return_year=False, ) with pytest.raises(NotImplementedError): - scmdata.processing.calculate_crossing_times_quantiles( - crossing_times, ["model", "scenario"] - ) + scmdata.processing.calculate_crossing_times_quantiles(crossing_times, ["model", "scenario"]) @pytest.mark.parametrize( @@ -304,9 +290,7 @@ def test_crossing_times_quantiles_datetime_error( (3000, 2805, [2025.4, 2027.0, np.nan]), ), ) -def test_crossing_times_quantiles_nan_fill_values( - nan_fill_value, out_nan_threshold, exp_vals -): +def test_crossing_times_quantiles_nan_fill_values(nan_fill_value, out_nan_threshold, exp_vals): data = np.array( [ [1.3, 1.35, 1.5, 1.52], @@ -365,9 +349,7 @@ def test_crossing_times_quantiles_nan_fill_values( pdt.assert_series_equal(res, exp, check_dtype=check_dtype) -output_name_options = pytest.mark.parametrize( - "output_name", (None, "test", "test other") -) +output_name_options = pytest.mark.parametrize("output_name", (None, "test", "test other")) def _get_calculate_exceedance_probs_call_kwargs(output_name): @@ -394,9 +376,7 @@ def _get_calculate_exeedance_probs_expected_name(output_name, threshold): ), ) @output_name_options -def test_exceedance_probabilities_over_time( - output_name, threshold, exp_vals, test_processing_scm_df -): +def test_exceedance_probabilities_over_time(output_name, threshold, exp_vals, test_processing_scm_df): call_kwargs = _get_calculate_exceedance_probs_call_kwargs(output_name) res = scmdata.processing.calculate_exceedance_probabilities_over_time( test_processing_scm_df, @@ -406,9 +386,7 @@ def test_exceedance_probabilities_over_time( ) exp_idx = pd.MultiIndex.from_frame( - test_processing_scm_df.meta.drop( - "ensemble_member", axis="columns" - ).drop_duplicates() + test_processing_scm_df.meta.drop("ensemble_member", axis="columns").drop_duplicates() ) exp = pd.DataFrame( @@ -442,9 +420,7 @@ def test_exceedance_probabilities_over_time_multiple_res( threshold=threshold, ) - exp_idx = pd.MultiIndex.from_frame( - start.meta.drop(["ensemble_member"], axis="columns").drop_duplicates() - ) + exp_idx = pd.MultiIndex.from_frame(start.meta.drop(["ensemble_member"], axis="columns").drop_duplicates()) exp = pd.DataFrame( exp_vals, @@ -476,9 +452,7 @@ def test_exceedance_probabilities_over_time_multiple_grouping( ) exp_idx = pd.MultiIndex.from_frame( - start.meta.drop( - ["climate_model", "ensemble_member"], axis="columns" - ).drop_duplicates() + start.meta.drop(["climate_model", "ensemble_member"], axis="columns").drop_duplicates() ) exp = pd.DataFrame( @@ -506,9 +480,7 @@ def test_exceedance_probabilities_over_time_multiple_grouping( ), ) @output_name_options -def test_exceedance_probabilities( - output_name, threshold, exp_val, test_processing_scm_df -): +def test_exceedance_probabilities(output_name, threshold, exp_val, test_processing_scm_df): call_kwargs = _get_calculate_exceedance_probs_call_kwargs(output_name) res = scmdata.processing.calculate_exceedance_probabilities( test_processing_scm_df, @@ -518,9 +490,7 @@ def test_exceedance_probabilities( ) exp_idx = pd.MultiIndex.from_frame( - test_processing_scm_df.meta.drop( - "ensemble_member", axis="columns" - ).drop_duplicates() + test_processing_scm_df.meta.drop("ensemble_member", axis="columns").drop_duplicates() ) exp = pd.Series(exp_val, index=exp_idx) @@ -546,9 +516,7 @@ def test_exceedance_probabilities_multiple_res( threshold=threshold, ) - exp_idx = pd.MultiIndex.from_frame( - start.meta.drop("ensemble_member", axis="columns").drop_duplicates() - ) + exp_idx = pd.MultiIndex.from_frame(start.meta.drop("ensemble_member", axis="columns").drop_duplicates()) exp = pd.Series(exp_vals, index=exp_idx) exp.name = _get_calculate_exeedance_probs_expected_name(None, threshold) @@ -574,9 +542,7 @@ def test_exceedance_probabilities_multiple_grouping( ) exp_idx = pd.MultiIndex.from_frame( - start.meta.drop( - ["ensemble_member", "climate_model"], axis="columns" - ).drop_duplicates() + start.meta.drop(["ensemble_member", "climate_model"], axis="columns").drop_duplicates() ) exp = pd.Series(exp_vals, index=exp_idx) @@ -601,13 +567,9 @@ def test_exceedance_probabilities_multiple_grouping( ), ) def test_requires_preprocessing(test_processing_scm_df, col, func, kwargs): - test_processing_scm_df[col] = [ - str(i) for i in range(test_processing_scm_df.shape[0]) - ] + test_processing_scm_df[col] = [str(i) for i in range(test_processing_scm_df.shape[0])] - error_msg = ( - f"More than one value for {col}. " "This is unlikely to be what you want." - ) + error_msg = f"More than one value for {col}. This is unlikely to be what you want." with pytest.raises(ValueError, match=error_msg): func( test_processing_scm_df, @@ -722,9 +684,7 @@ def test_peak_time(output_name, return_year, conv_to_year, test_processing_scm_d @crossing_times_year_conversions -def test_peak_time_multi_variable( - return_year, conv_to_year, test_processing_scm_df_multi_climate_model -): +def test_peak_time_multi_variable(return_year, conv_to_year, test_processing_scm_df_multi_climate_model): test_processing_scm_df_multi_climate_model["variable"] = [ str(i) for i in range(test_processing_scm_df_multi_climate_model.shape[0]) ] @@ -739,9 +699,7 @@ def test_peak_time_multi_variable( dt.datetime(2007, 1, 1), ] * 2 - res = scmdata.processing.calculate_peak_time( - test_processing_scm_df_multi_climate_model, **call_kwargs - ) + res = scmdata.processing.calculate_peak_time(test_processing_scm_df_multi_climate_model, **call_kwargs) if conv_to_year: exp_vals = [v.year if conv_to_year else v for v in exp_vals] @@ -778,9 +736,7 @@ def sr15_inferred_temperature_quantiles(test_data_path): for p in [0.67, 0.5, 0.34]: quantile = 1 - p cm_q = cm_median.reset_index() - cm_q["variable"] = cm_q["variable"].str.replace( - "MED", f"P{int(np.round(quantile * 100, 0))}" - ) + cm_q["variable"] = cm_q["variable"].str.replace("MED", f"P{int(np.round(quantile * 100, 0))}") cm_q = cm_q.set_index(cm_median.index.names).sort_index() cm_q.iloc[:, :] = 10 for t in [2.0, 1.5]: @@ -800,9 +756,7 @@ def sr15_inferred_temperature_quantiles(test_data_path): @pytest.fixture(scope="session") def sr15_temperatures_unmangled_names(sr15_inferred_temperature_quantiles): out = sr15_inferred_temperature_quantiles.copy() - out["quantile"] = out["variable"].apply( - lambda x: float(x.split("|")[-1].strip("P")) / 100 - ) + out["quantile"] = out["variable"].apply(lambda x: float(x.split("|")[-1].strip("P")) / 100) out["variable"] = out["variable"].apply(lambda x: "|".join(x.split("|")[:-1])) return out @@ -875,9 +829,7 @@ def test_categorisation_sr15_multi_variable(sr15_temperatures_unmangled_names): inp = sr15_temperatures_unmangled_names.copy() inp["variable"] = range(inp["variable"].shape[0]) - error_msg = ( - "More than one value for variable. " "This is unlikely to be what you want." - ) + error_msg = "More than one value for variable. This is unlikely to be what you want." with pytest.raises(ValueError, match=error_msg): scmdata.processing.categorisation_sr15(inp, index=["model", "scenario"]) @@ -898,9 +850,7 @@ def test_categorisation_sr15_no_quantile(sr15_temperatures_unmangled_names): ) with pytest.raises(MissingRequiredColumnError, match=error_msg): scmdata.processing.categorisation_sr15( - sr15_temperatures_unmangled_names.filter(quantile=0.5).drop_meta( - "quantile" - ), + sr15_temperatures_unmangled_names.filter(quantile=0.5).drop_meta("quantile"), index=["model", "scenario"], ) @@ -1107,9 +1057,7 @@ def test_calculate_summary_stats( # noqa: PLR0912, PLR0915 exp.append(tmp) peaks = scmdata.processing.calculate_peak(inp) - peak_times = scmdata.processing.calculate_peak_time( - inp, return_year=exp_peak_return_year - ) + peak_times = scmdata.processing.calculate_peak_time(inp, return_year=exp_peak_return_year) for q in exp_peak_quantiles: peak_q = peaks.groupby(exp_index).quantile(q) peak_q.name = exp_peak_naming_base.format(q) @@ -1120,9 +1068,7 @@ def test_calculate_summary_stats( # noqa: PLR0912, PLR0915 exp.append(peak_q) exp.append(peak_time_q) - inp_categories = scmdata.ScmRun( - inp.quantiles_over("ensemble_member", quantiles=[0.33, 0.5, 0.66]) - ) + inp_categories = scmdata.ScmRun(inp.quantiles_over("ensemble_member", quantiles=[0.33, 0.5, 0.66])) sr15_cats = scmdata.processing.categorisation_sr15( inp_categories, exp_index, @@ -1138,21 +1084,15 @@ def test_calculate_summary_stats( # noqa: PLR0912, PLR0915 call_kwargs = {} if exceedance_probabilities_thresholds is not None: - call_kwargs[ - "exceedance_probabilities_thresholds" - ] = exceedance_probabilities_thresholds + call_kwargs["exceedance_probabilities_thresholds"] = exceedance_probabilities_thresholds if exceedance_probabilities_output_name is not None: - call_kwargs[ - "exceedance_probabilities_naming_base" - ] = exceedance_probabilities_output_name + call_kwargs["exceedance_probabilities_naming_base"] = exceedance_probabilities_output_name inp_renamed = inp.copy() inp_renamed["variable"] = exp_exceedance_probabilities_variable if exceedance_probabilities_variable is not None: - call_kwargs[ - "exceedance_probabilities_variable" - ] = exceedance_probabilities_variable + call_kwargs["exceedance_probabilities_variable"] = exceedance_probabilities_variable if peak_quantiles is not None: call_kwargs["peak_quantiles"] = peak_quantiles @@ -1213,8 +1153,7 @@ def test_calculate_summary_stats_no_exceedance_probability_var( test_processing_scm_df_multi_climate_model, ): error_msg = re.escape( - "exceedance_probabilities_variable `junk` is not available. " - "Available variables:{}".format( + "exceedance_probabilities_variable `junk` is not available. Available variables:{}".format( test_processing_scm_df_multi_climate_model.get_unique_meta("variable") ) ) @@ -1230,8 +1169,7 @@ def test_calculate_summary_stats_no_peak_variable( test_processing_scm_df_multi_climate_model, ): error_msg = re.escape( - "peak_variable `junk` is not available. " - "Available variables:{}".format( + "peak_variable `junk` is not available. Available variables:{}".format( test_processing_scm_df_multi_climate_model.get_unique_meta("variable") ) ) @@ -1247,8 +1185,7 @@ def test_calculate_summary_stats_no_categorisation_variable( test_processing_scm_df_multi_climate_model, ): error_msg = re.escape( - "categorisation_variable `junk` is not available. " - "Available variables:{}".format( + "categorisation_variable `junk` is not available. Available variables:{}".format( test_processing_scm_df_multi_climate_model.get_unique_meta("variable") ) ) diff --git a/tests/integration/test_scmrun_convert_units.py b/tests/integration/test_scmrun_convert_units.py index a2e8fe41..4037bc4f 100644 --- a/tests/integration/test_scmrun_convert_units.py +++ b/tests/integration/test_scmrun_convert_units.py @@ -55,6 +55,4 @@ def test_convert_to_scen_units(): conv_factor, _, ) in var_start_units_scen_units_conv_factor: - np.testing.assert_allclose( - res.filter(variable=variable).timeseries(), conv_factor, rtol=1e-15 - ) + np.testing.assert_allclose(res.filter(variable=variable).timeseries(), conv_factor, rtol=1e-15) diff --git a/tests/integration/test_scmrun_speed.py b/tests/integration/test_scmrun_speed.py index 1500b3a2..d617fefe 100644 --- a/tests/integration/test_scmrun_speed.py +++ b/tests/integration/test_scmrun_speed.py @@ -102,9 +102,7 @@ def setup(): def append_runs_different_times(other): return scmdata.run_append([big_scmrun, other]) - res = benchmark.pedantic( - append_runs_different_times, setup=setup, iterations=1, rounds=2 - ) + res = benchmark.pedantic(append_runs_different_times, setup=setup, iterations=1, rounds=2) assert len(res) == 2 * len(big_scmrun) assert res.shape[1] == 2 * big_scmrun.shape[1] @@ -131,9 +129,7 @@ def set_meta(): def test_set_meta_reduce_uniqueness(benchmark, big_scmrun): def set_meta(): - big_scmrun["to_squash"] = big_scmrun["to_squash"].apply( - lambda x: "a" if x.startswith("a") else "b" - ) + big_scmrun["to_squash"] = big_scmrun["to_squash"].apply(lambda x: "a" if x.startswith("a") else "b") return big_scmrun @@ -159,9 +155,7 @@ def test_append_multiple_same_time(benchmark, big_scmrun, n_to_append): to_append = [] for i in range(n_to_append): tmp = big_scmrun.copy() - tmp["ensemble_member"] = range( - i * big_scmrun.shape[0], (i + 1) * big_scmrun.shape[0] - ) + tmp["ensemble_member"] = range(i * big_scmrun.shape[0], (i + 1) * big_scmrun.shape[0]) to_append.append(tmp) def append(): @@ -228,18 +222,12 @@ def round_trip(): return res res = benchmark.pedantic(round_trip, iterations=1, rounds=1) - scmdata.testing.assert_scmdf_almost_equal( - start, res, allow_unordered=True, check_ts_names=False - ) + scmdata.testing.assert_scmdf_almost_equal(start, res, allow_unordered=True, check_ts_names=False) @pytest.mark.parametrize("as_run", [True, False]) def test_process_over(benchmark, big_scmrun, as_run): def round_trip(arr): - return arr.process_over( - "variable", "sum", as_run=as_run, op_cols={"variable": "squashed"} - ) + return arr.process_over("variable", "sum", as_run=as_run, op_cols={"variable": "squashed"}) - benchmark.pedantic( - lambda: big_scmrun.groupby("region").apply(round_trip), iterations=1, rounds=1 - ) + benchmark.pedantic(lambda: big_scmrun.groupby("region").apply(round_trip), iterations=1, rounds=1) diff --git a/tests/integration/test_xarray_integration.py b/tests/integration/test_xarray_integration.py index 7886b14c..c2aea42a 100644 --- a/tests/integration/test_xarray_integration.py +++ b/tests/integration/test_xarray_integration.py @@ -5,27 +5,19 @@ def test_xarray_plot_line(plumeplot_scmrun): pytest.importorskip("matplotlib") pytest.importorskip("nc_time_axis") - plumeplot_scmrun["climate_model"] = plumeplot_scmrun.get_unique_meta( - "climate_model" - )[0] + plumeplot_scmrun["climate_model"] = plumeplot_scmrun.get_unique_meta("climate_model")[0] xr_ds = plumeplot_scmrun.to_xarray(dimensions=("scenario", "ensemble_member")) - xr_ds["Surface Air Temperature Change"].median(dim="ensemble_member").plot.line( - hue="scenario" - ) - xr_ds["Surface Air Temperature Change"].sel(scenario="a_scenario").plot.line( - hue="ensemble_member" - ) + xr_ds["Surface Air Temperature Change"].median(dim="ensemble_member").plot.line(hue="scenario") + xr_ds["Surface Air Temperature Change"].sel(scenario="a_scenario").plot.line(hue="ensemble_member") @pytest.mark.parametrize("scatter_kwargs", ({}, dict(hue="scenario"))) def test_xarray_plot_scatter(plumeplot_scmrun, scatter_kwargs): pytest.importorskip("matplotlib") - plumeplot_scmrun["climate_model"] = plumeplot_scmrun.get_unique_meta( - "climate_model" - )[0] + plumeplot_scmrun["climate_model"] = plumeplot_scmrun.get_unique_meta("climate_model")[0] gmst = plumeplot_scmrun.copy() gmst["variable"] = "Surface Air Ocean Blended Temperature Change" plumeplot_scmrun = plumeplot_scmrun.append(gmst) diff --git a/tests/unit/test_database.py b/tests/unit/test_database.py index 0399d82c..42122cbd 100644 --- a/tests/unit/test_database.py +++ b/tests/unit/test_database.py @@ -40,9 +40,7 @@ def get(self, filters, ext="*.nc"): return self.keys # not doing any filtering... def save(self, sr): - key = os.path.join( - *[sr.get_unique_meta(k, True) for k in self.kwargs["levels"]] - ) + key = os.path.join(*[sr.get_unique_meta(k, True) for k in self.kwargs["levels"]]) self.keys[key] = sr return key @@ -228,9 +226,7 @@ def test_netcdf_save_missing_meta(self, tdb, start_scmrun): def test_netcdf_save_duplicate_meta(self, tdb, start_scmrun): with tempfile.TemporaryDirectory() as tempdir: backend = NetCDFDatabaseBackend(levels=("climate_model",), root_dir=tempdir) - msg = re.escape( - "`climate_model` column is not unique (found values: ['cmodel_a', 'cmodel_b'])" - ) + msg = re.escape("`climate_model` column is not unique (found values: ['cmodel_a', 'cmodel_b'])") with pytest.raises(ValueError, match=msg): backend.save(start_scmrun) @@ -248,9 +244,7 @@ def test_netcdf_save(self, mock_to_nc, tdb, start_scmrun): mock_get_out_filepath.assert_called_once() mock_get_out_filepath.assert_called_with( - climate_model=inp_scmrun.get_unique_meta( - "climate_model", no_duplicates=True - ), + climate_model=inp_scmrun.get_unique_meta("climate_model", no_duplicates=True), variable=inp_scmrun.get_unique_meta("variable", no_duplicates=True), region=inp_scmrun.get_unique_meta("region", no_duplicates=True), scenario=inp_scmrun.get_unique_meta("scenario", no_duplicates=True), @@ -262,9 +256,7 @@ def test_netcdf_save(self, mock_to_nc, tdb, start_scmrun): @patch("scmdata.database.backends.netcdf.ensure_dir_exists") @patch.object(ScmRun, "to_nc") - def test_netcdf_save_non_unique_meta( - self, mock_to_nc, mock_ensure_dir_exists, tdb, start_scmrun - ): + def test_netcdf_save_non_unique_meta(self, mock_to_nc, mock_ensure_dir_exists, tdb, start_scmrun): with tempfile.TemporaryDirectory() as tempdir: backend = NetCDFDatabaseBackend(levels=tdb.levels, root_dir=tempdir) @@ -280,9 +272,7 @@ def test_netcdf_save_non_unique_meta( mock_get_out_filepath.assert_called_once() mock_get_out_filepath.assert_called_with( - climate_model=inp_scmrun.get_unique_meta( - "climate_model", no_duplicates=True - ), + climate_model=inp_scmrun.get_unique_meta("climate_model", no_duplicates=True), variable=inp_scmrun.get_unique_meta("variable", no_duplicates=True), region=inp_scmrun.get_unique_meta("region", no_duplicates=True), scenario=inp_scmrun.get_unique_meta("scenario", no_duplicates=True), @@ -298,9 +288,7 @@ def test_netcdf_save_non_unique_meta( def test_save_to_database_single_file_non_unique_levels(tdb, start_scmrun): with pytest.raises( ValueError, - match=re.escape( - "`climate_model` column is not unique (found values: ['cmodel_a', 'cmodel_b'])" - ), + match=re.escape("`climate_model` column is not unique (found values: ['cmodel_a', 'cmodel_b'])"), ): tdb._backend.save(start_scmrun) @@ -310,11 +298,7 @@ def test_database_save(tdb, start_scmrun): tdb.save(start_scmrun) expected_calls = len( - list( - start_scmrun.groupby( - ["climate_model", "variable", "region", "scenario", "ensemble_member"] - ) - ) + list(start_scmrun.groupby(["climate_model", "variable", "region", "scenario", "ensemble_member"])) ) assert len(tdb._backend.keys) == expected_calls @@ -326,9 +310,7 @@ def test_database_save_weird(tdb, start_scmrun, ch): tdb.save(start_scmrun) assert len(start_scmrun.filter(variable=weird_var_name)) - assert_scmdf_almost_equal( - tdb.load(variable=weird_var_name), start_scmrun.filter(variable=weird_var_name) - ) + assert_scmdf_almost_equal(tdb.load(variable=weird_var_name), start_scmrun.filter(variable=weird_var_name)) replace_ch = "-" if ch not in "." else ch exp = pd.DataFrame( @@ -360,9 +342,7 @@ def test_database_save_weird_end(tdb, start_scmrun, ch): tdb.save(start_scmrun) assert len(start_scmrun.filter(variable=weird_var_name)) - assert_scmdf_almost_equal( - tdb.load(variable=weird_var_name), start_scmrun.filter(variable=weird_var_name) - ) + assert_scmdf_almost_equal(tdb.load(variable=weird_var_name), start_scmrun.filter(variable=weird_var_name)) exp = pd.DataFrame( [ @@ -399,14 +379,10 @@ def test_database_save_weird_slash(tdb, start_scmrun): def test_database_loaded(tdb_with_data): assert os.path.exists( - os.path.join( - tdb_with_data._root_dir, "cmodel_a", "variable", "cmodel_a__variable.nc" - ) + os.path.join(tdb_with_data._root_dir, "cmodel_a", "variable", "cmodel_a__variable.nc") ) assert os.path.exists( - os.path.join( - tdb_with_data._root_dir, "cmodel_b", "variable", "cmodel_b__variable.nc" - ) + os.path.join(tdb_with_data._root_dir, "cmodel_b", "variable", "cmodel_b__variable.nc") ) out_names = glob( @@ -432,9 +408,7 @@ def test_database_loaded(tdb_with_data): ) def test_database_load_data(tdb_with_data, start_scmrun, filter): loaded_ts = tdb_with_data.load(**filter) - assert_scmdf_almost_equal( - loaded_ts, start_scmrun.filter(**filter), check_ts_names=False - ) + assert_scmdf_almost_equal(loaded_ts, start_scmrun.filter(**filter), check_ts_names=False) def test_database_load_data_extras(tdb_with_data): @@ -485,9 +459,7 @@ def test_database_overwriting(tdb_with_data, start_scmrun): ) loaded_ts = tdb_with_data.load() - assert_scmdf_almost_equal( - loaded_ts, run_append([start_scmrun, start_scmrun_2]), check_ts_names=False - ) + assert_scmdf_almost_equal(loaded_ts, run_append([start_scmrun, start_scmrun_2]), check_ts_names=False) def test_database_save_duplicates(tdb_with_data, start_scmrun): diff --git a/tests/unit/test_groupby.py b/tests/unit/test_groupby.py index 2fdc6889..d83d3db6 100644 --- a/tests/unit/test_groupby.py +++ b/tests/unit/test_groupby.py @@ -102,9 +102,7 @@ def func(r): with pytest.warns(DeprecationWarning, match="Use RunGroupby.apply instead"): res = scm_run.groupby("scenario").map(func) - assert_scmdf_almost_equal( - res, scm_run * 2, allow_unordered=True, check_ts_names=False - ) + assert_scmdf_almost_equal(res, scm_run * 2, allow_unordered=True, check_ts_names=False) def test_groupby_integer_metadata(): diff --git a/tests/unit/test_netcdf.py b/tests/unit/test_netcdf.py index 69c7fa9e..428d739b 100644 --- a/tests/unit/test_netcdf.py +++ b/tests/unit/test_netcdf.py @@ -37,21 +37,15 @@ def test_run_to_nc(scm_run, use_pathlib): npt.assert_allclose( ds.variables["Primary_Energy"][0, :], - scm_run.filter(variable="Primary Energy", scenario="a_scenario").values[ - 0 - ], + scm_run.filter(variable="Primary Energy", scenario="a_scenario").values[0], ) npt.assert_allclose( ds.variables["Primary_Energy"][1, :], - scm_run.filter( - variable="Primary Energy", scenario="a_scenario2" - ).values[0], + scm_run.filter(variable="Primary Energy", scenario="a_scenario2").values[0], ) npt.assert_allclose( ds.variables["Primary_Energy__Coal"][0, :], - scm_run.filter( - variable="Primary Energy|Coal", scenario="a_scenario" - ).values[0], + scm_run.filter(variable="Primary Energy|Coal", scenario="a_scenario").values[0], ) @@ -209,9 +203,7 @@ def test_nc_to_run_4d(scm_run): with tempfile.TemporaryDirectory() as tempdir: out_fname = join(tempdir, "out.nc") - run_to_nc( - scm_run, out_fname, dimensions=("scenario", "climate_model", "run_id") - ) + run_to_nc(scm_run, out_fname, dimensions=("scenario", "climate_model", "run_id")) assert exists(out_fname) @@ -399,9 +391,7 @@ def test_run_to_nc_with_extras(scm_run, dtype): # make an extra column which maps 1:1 with scenario unique_scenarios = scm_run["scenario"].unique().tolist() - run_id = ( - scm_run["scenario"].apply(lambda x: unique_scenarios.index(x)).astype(dtype) - ) + run_id = scm_run["scenario"].apply(lambda x: unique_scenarios.index(x)).astype(dtype) scm_run["run_id"] = run_id run_to_nc(scm_run, out_fname, dimensions=("scenario",), extras=("run_id",)) @@ -421,21 +411,15 @@ def test_run_to_nc_with_extras(scm_run, dtype): npt.assert_allclose( ds.variables["Primary_Energy"][0, :], - scm_run.filter(variable="Primary Energy", scenario="a_scenario").values[ - 0 - ], + scm_run.filter(variable="Primary Energy", scenario="a_scenario").values[0], ) npt.assert_allclose( ds.variables["Primary_Energy"][1, :], - scm_run.filter( - variable="Primary Energy", scenario="a_scenario2" - ).values[0], + scm_run.filter(variable="Primary Energy", scenario="a_scenario2").values[0], ) npt.assert_allclose( ds.variables["Primary_Energy__Coal"][0, :], - scm_run.filter( - variable="Primary Energy|Coal", scenario="a_scenario" - ).values[0], + scm_run.filter(variable="Primary Energy|Coal", scenario="a_scenario").values[0], ) @@ -484,21 +468,15 @@ def test_nc_methods(scm_run): @patch("scmdata.netcdf.has_netcdf", False) def test_no_netcdf(scm_run): - with pytest.raises( - ImportError, match="netcdf4 is not installed. Run 'pip install netcdf4'" - ): + with pytest.raises(ImportError, match="netcdf4 is not installed. Run 'pip install netcdf4'"): run_to_nc(scm_run.__class__, "ignored") - with pytest.raises( - ImportError, match="netcdf4 is not installed. Run 'pip install netcdf4'" - ): + with pytest.raises(ImportError, match="netcdf4 is not installed. Run 'pip install netcdf4'"): nc_to_run(scm_run, "ignored") def test_nc_read_failure(scm_run, test_data_path, caplog): - test_fname = join( - test_data_path, "netcdf-scm_tas_Amon_bcc-csm1-1_rcp26_r1i1p1_209001-211012.nc" - ) + test_fname = join(test_data_path, "netcdf-scm_tas_Amon_bcc-csm1-1_rcp26_r1i1p1_209001-211012.nc") with pytest.raises(Exception): nc_to_run(scm_run.__class__, test_fname) @@ -665,9 +643,7 @@ def test_run_to_nc_loop_tricky_variable_name(scm_run, start_variable): scm_run["variable"] = scm_run["variable"].apply( lambda x: x.replace("Primary Energy|Coal", start_variable) ) - scm_run["unit"] = scm_run["variable"].apply( - lambda x: "EJ/yr" if x != start_variable else "MJ / yr" - ) + scm_run["unit"] = scm_run["variable"].apply(lambda x: "EJ/yr" if x != start_variable else "MJ / yr") with tempfile.TemporaryDirectory() as tempdir: out_fname = join(tempdir, "out.nc") @@ -770,8 +746,6 @@ def test_nc_to_run_does_not_emit_use_cftime_futurewarning(scm_run): nc_to_run(scm_run.__class__, out_fname) use_cftime_warnings = [ - w for w in caught - if issubclass(w.category, FutureWarning) - and "use_cftime" in str(w.message) + w for w in caught if issubclass(w.category, FutureWarning) and "use_cftime" in str(w.message) ] assert not use_cftime_warnings, [str(w.message) for w in use_cftime_warnings] diff --git a/tests/unit/test_ops.py b/tests/unit/test_ops.py index 855010df..0570de41 100644 --- a/tests/unit/test_ops.py +++ b/tests/unit/test_ops.py @@ -60,9 +60,7 @@ def base_multiple_scmrun(): @pytest.fixture def other_multiple_scmrun(): - return get_multiple_ts( - data=np.array([[-1, 0, 3.2], [11.1, 20, 32.3]]).T, scenario="Scenario B" - ) + return get_multiple_ts(data=np.array([[-1, 0, 3.2], [11.1, 20, 32.3]]).T, scenario="Scenario B") def convert_to_pint_name(unit): @@ -96,18 +94,14 @@ def perform_op(base, other, op, reset_index): @patch("scmdata.ops.has_scipy", False) def test_no_scipy(scm_run): - with pytest.raises( - ImportError, match="scipy is not installed. Run 'pip install scipy'" - ): + with pytest.raises(ImportError, match="scipy is not installed. Run 'pip install scipy'"): scm_run.cumulative_trapezoid() @OPS_MARK @pytest.mark.filterwarnings("ignore:divide by zero") def test_single_timeseries(op, base_single_scmrun, other_single_scmrun): - res = getattr(base_single_scmrun, op)( - other_single_scmrun, op_cols={"variable": "Emissions|CO2|AFOLU"} - ) + res = getattr(base_single_scmrun, op)(other_single_scmrun, op_cols={"variable": "Emissions|CO2|AFOLU"}) exp_ts = perform_op(base_single_scmrun, other_single_scmrun, op, "variable") exp_ts["variable"] = "Emissions|CO2|AFOLU" @@ -129,9 +123,7 @@ def test_single_timeseries(op, base_single_scmrun, other_single_scmrun): @OPS_MARK @pytest.mark.filterwarnings("ignore:divide by zero") def test_multiple_timeseries(op, base_multiple_scmrun, other_multiple_scmrun): - res = getattr(base_multiple_scmrun, op)( - other_multiple_scmrun, op_cols={"scenario": "A to B"} - ) + res = getattr(base_multiple_scmrun, op)(other_multiple_scmrun, op_cols={"scenario": "A to B"}) exp_ts = perform_op(base_multiple_scmrun, other_multiple_scmrun, op, "scenario") exp_ts["scenario"] = "A to B" @@ -140,9 +132,7 @@ def test_multiple_timeseries(op, base_multiple_scmrun, other_multiple_scmrun): exp_ts["unit"] = exp_ts["unit"].apply(convert_to_pint_name).values elif op == "multiply": - exp_ts["unit"] = ( - exp_ts["unit"].apply(lambda x: convert_to_pint_name(f"({x})**2")).values - ) + exp_ts["unit"] = exp_ts["unit"].apply(lambda x: convert_to_pint_name(f"({x})**2")).values elif op == "divide": exp_ts["unit"] = "dimensionless" @@ -157,8 +147,7 @@ def test_missing_series_error(): other = get_multiple_ts(region=["World|R5LAM", "World|R5OECD"]) error_msg = re.escape( - "No equivalent in `other` for " - "[('model', 'mod'), ('region', 'World|R5REF'), ('scenario', 'scen')]" + "No equivalent in `other` for [('model', 'mod'), ('region', 'World|R5REF'), ('scenario', 'scen')]" ) with pytest.raises(KeyError, match=error_msg): base.add(other, op_cols={"variable": "Warming plus Cumulative emissions CO2"}) @@ -168,10 +157,7 @@ def test_different_unit_error(): base = get_single_ts(variable="Surface Temperature", unit="K") other = get_single_ts(variable="Cumulative Emissions|CO2", unit="GtC") - error_msg = re.escape( - "Cannot convert from 'kelvin' ([temperature]) to " - "'gigatC' ([mass] * [carbon])" - ) + error_msg = re.escape("Cannot convert from 'kelvin' ([temperature]) to 'gigatC' ([mass] * [carbon])") with pytest.raises(DimensionalityError, match=error_msg): base.add(other, op_cols={"variable": "Warming plus Cumulative emissions CO2"}) @@ -201,9 +187,7 @@ def test_warming_per_gt(): base = get_single_ts(variable="Surface Temperature", unit="K") other = get_single_ts(variable="Cumulative Emissions|CO2", unit="GtC") - res = base.divide( - other, op_cols={"variable": "Warming per Cumulative emissions CO2"} - ) + res = base.divide(other, op_cols={"variable": "Warming per Cumulative emissions CO2"}) exp_ts = perform_op(base, other, "divide", ["variable", "unit"]) exp_ts["variable"] = "Warming per Cumulative emissions CO2" @@ -254,9 +238,7 @@ def perform_pint_op(base, pint_obj, op): @OPS_MARK def test_scalar_ops_pint(op): scalar = 1 * unit_registry("MtC / yr") - start = get_multiple_ts( - variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"] - ) + start = get_multiple_ts(variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"]) exp_ts = perform_pint_op(start, scalar, op) exp = ScmRun(exp_ts) @@ -291,9 +273,7 @@ def test_scalar_ops_pint(op): @pytest.mark.xfail(reason="pint doesn't recognise ScmRun") def test_scalar_divide_pint_by_run(): scalar = 1 * unit_registry("MtC / yr") - start = get_multiple_ts( - variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"] - ) + start = get_multiple_ts(variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"]) exp_ts = perform_pint_op(start, scalar, "divide_inverse") exp = ScmRun(exp_ts) @@ -308,9 +288,7 @@ def test_scalar_divide_pint_by_run(): @pytest.mark.xfail(reason="pint doesn't recognise ScmRun") def test_scalar_multiply_pint_by_run(): scalar = 1 * unit_registry("MtC / yr") - start = get_multiple_ts( - variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"] - ) + start = get_multiple_ts(variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"]) exp_ts = perform_pint_op(start, scalar, "multiply_inverse") exp = ScmRun(exp_ts) @@ -325,9 +303,7 @@ def test_scalar_multiply_pint_by_run(): @pytest.mark.parametrize("op", ["add", "subtract"]) def test_scalar_ops_pint_wrong_unit(op): scalar = 1 * unit_registry("Mt CH4 / yr") - start = get_multiple_ts( - variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"] - ) + start = get_multiple_ts(variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"]) error_msg = re.escape( "Cannot convert from 'gigatC / yr' ([mass] * [carbon] / [time]) " @@ -348,9 +324,7 @@ def test_scalar_ops_pint_wrong_unit(op): @pytest.mark.filterwarnings("ignore:divide by zero") def test_vector_ops_pint(op): vector = np.arange(3) * unit_registry("MtC / yr") - start = get_multiple_ts( - variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"] - ) + start = get_multiple_ts(variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"]) exp_ts = perform_pint_op(start, vector, op) exp = ScmRun(exp_ts) @@ -386,9 +360,7 @@ def test_vector_ops_pint(op): @pytest.mark.parametrize("start_unit", ("GtC / yr", ["Mt CH4 / yr", "GtC / yr"])) def test_vector_ops_pint_wrong_unit(op, start_unit): vector = np.arange(3) * unit_registry("Mt CH4 / yr") - start = get_multiple_ts( - variable="Emissions|Gas", unit=start_unit, scenario=["scen_a", "scen_b"] - ) + start = get_multiple_ts(variable="Emissions|Gas", unit=start_unit, scenario=["scen_a", "scen_b"]) error_msg = re.escape( "Cannot convert from 'gigatC / yr' ([mass] * [carbon] / [time]) " @@ -429,9 +401,7 @@ def perform_op_float_int(base, scalar, op): @OPS_MARK @pytest.mark.parametrize("scalar", (1, 1.0)) def test_scalar_ops_float_int(op, scalar): - start = get_multiple_ts( - variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"] - ) + start = get_multiple_ts(variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"]) exp_ts = perform_op_float_int(start, scalar, op) exp = ScmRun(exp_ts) @@ -457,9 +427,7 @@ def test_scalar_ops_float_int(op, scalar): @OPS_MARK @pytest.mark.parametrize("shape", ((2, 2), (3, 2), (3, 3, 3))) def test_wrong_shape_ops(op, shape): - start = get_multiple_ts( - variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"] - ) + start = get_multiple_ts(variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"]) other = np.arange(np.prod(shape)).reshape(shape) @@ -482,9 +450,7 @@ def test_wrong_shape_ops(op, shape): @OPS_MARK -@pytest.mark.parametrize( - "vector", (np.arange(3).astype(int), np.arange(3).astype(float)) -) +@pytest.mark.parametrize("vector", (np.arange(3).astype(int), np.arange(3).astype(float))) @pytest.mark.filterwarnings("ignore:divide by zero") def test_vector_ops_float_int(op, vector): start = get_multiple_ts( @@ -516,15 +482,11 @@ def test_vector_ops_float_int(op, vector): @OPS_MARK def test_wrong_length_ops(op): - start = get_multiple_ts( - variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"] - ) + start = get_multiple_ts(variable="Emissions|CO2", unit="GtC / yr", scenario=["scen_a", "scen_b"]) other = np.arange(start.shape[1] - 1) - error_msg = re.escape( - "only vectors with the same number of timesteps as self (3) are supported" - ) + error_msg = re.escape("only vectors with the same number of timesteps as self (3) are supported") with pytest.raises(ValueError, match=error_msg): if op == "add": start + other @@ -542,9 +504,7 @@ def test_wrong_length_ops(op): raise NotImplementedError(op) -@pytest.mark.xfail( - _check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument" -) +@pytest.mark.xfail(_check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument") @pytest.mark.parametrize("out_var", (None, "new out var")) # We can add initial back if use case arises. At the moment I can't see an easy # way to make the units behave. @@ -560,14 +520,10 @@ def test_cumulative_trapezoid(out_var): else: exp_var = out_var - exp = get_single_ts( - data=np.array([0, 1.5, 4]), index=[1, 2, 3], variable=exp_var, unit="gigatC" - ) + exp = get_single_ts(data=np.array([0, 1.5, 4]), index=[1, 2, 3], variable=exp_var, unit="gigatC") # rtol is because our calculation uses seconds, which doesn't work out # quite the same as assuming a regular year - assert_scmdf_almost_equal( - res, exp, allow_unordered=True, check_ts_names=False, rtol=1e-3 - ) + assert_scmdf_almost_equal(res, exp, allow_unordered=True, check_ts_names=False, rtol=1e-3) def test_cumulative_trapezoid_time_handling_big_jumps(): @@ -583,9 +539,7 @@ def test_cumulative_trapezoid_time_handling_big_jumps(): def test_cumulative_trapezoid_time_handling_all_over_jumps(): - start = get_single_ts( - data=[1, 2, 3, 3, 1.8], index=[10, 10.1, 11, 20, 50], unit="GtC / yr" - ) + start = get_single_ts(data=[1, 2, 3, 3, 1.8], index=[10, 10.1, 11, 20, 50], unit="GtC / yr") res = start.cumulative_trapezoid() @@ -594,9 +548,7 @@ def test_cumulative_trapezoid_time_handling_all_over_jumps(): third = second + 2.5 * 0.9 fourth = third + 3 * 9 fifth = fourth + 2.4 * 30 - npt.assert_allclose( - res.values.squeeze(), [first, second, third, fourth, fifth], rtol=1e-3 - ) + npt.assert_allclose(res.values.squeeze(), [first, second, third, fourth, fifth], rtol=1e-3) @pytest.mark.parametrize( @@ -630,9 +582,7 @@ def test_integration_nan_handling(method, exp): ) -@pytest.mark.xfail( - _check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument" -) +@pytest.mark.xfail(_check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument") def test_cumulative_trapezoid_multiple_ts(): variables = ["Emissions|CO2", "Heat Uptake", "Temperature"] start = get_multiple_ts( @@ -658,9 +608,7 @@ def test_cumulative_trapezoid_multiple_ts(): exp_comp.get_unique_meta("unit", no_duplicates=True), ) - assert_scmdf_almost_equal( - res_comp, exp_comp, allow_unordered=True, check_ts_names=False, rtol=1e-3 - ) + assert_scmdf_almost_equal(res_comp, exp_comp, allow_unordered=True, check_ts_names=False, rtol=1e-3) @pytest.mark.parametrize("out_var", (None, "new out var")) @@ -721,25 +669,19 @@ def test_cumsum_multiple_ts(): exp_comp.get_unique_meta("unit", no_duplicates=True), ) - assert_scmdf_almost_equal( - res_comp, exp_comp, allow_unordered=True, check_ts_names=False - ) + assert_scmdf_almost_equal(res_comp, exp_comp, allow_unordered=True, check_ts_names=False) def test_integrate_deprecated(): dat = [1, 2, 3] start = get_single_ts(data=dat, index=[2020, 2021, 2024], unit="GtC / yr") - match = ( - "integrate has been deprecated in preference of cumsum and cumulative_trapezoid" - ) + match = "integrate has been deprecated in preference of cumsum and cumulative_trapezoid" with pytest.warns(DeprecationWarning, match=match): start.integrate() -@pytest.mark.xfail( - _check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument" -) +@pytest.mark.xfail(_check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument") @pytest.mark.parametrize("out_var", (None, "new out var")) def test_delta_per_delta_time(out_var): dat = [1, 2, 3] @@ -752,14 +694,10 @@ def test_delta_per_delta_time(out_var): else: exp_var = out_var - exp = get_single_ts( - data=np.array([1, 1]), index=[1.5, 2.5], variable=exp_var, unit="GtC / yr^2" - ) + exp = get_single_ts(data=np.array([1, 1]), index=[1.5, 2.5], variable=exp_var, unit="GtC / yr^2") # rtol is because our calculation uses seconds, which doesn't work out # quite the same as assuming a regular year - assert_scmdf_almost_equal( - res, exp, allow_unordered=True, check_ts_names=False, rtol=1e-3 - ) + assert_scmdf_almost_equal(res, exp, allow_unordered=True, check_ts_names=False, rtol=1e-3) def test_delta_per_delta_time_handling_big_jumps(): @@ -775,9 +713,7 @@ def test_delta_per_delta_time_handling_big_jumps(): def test_delta_per_delta_time_handling_all_over_jumps(): - start = get_single_ts( - data=[1, 2, 3, 3, 1.8], index=[10, 10.1, 11, 20, 50], unit="GtC" - ) + start = get_single_ts(data=[1, 2, 3, 3, 1.8], index=[10, 10.1, 11, 20, 50], unit="GtC") res = start.delta_per_delta_time().convert_unit("GtC / yr") @@ -807,9 +743,7 @@ def test_delta_per_delta_time_nan_handling(): ) -@pytest.mark.xfail( - _check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument" -) +@pytest.mark.xfail(_check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument") def test_delta_per_delta_time_multiple_ts(): variables = ["Emissions|CO2", "Heat Uptake", "Temperature"] start = get_multiple_ts( @@ -849,14 +783,10 @@ def test_delta_per_delta_time_multiple_ts(): exp_comp.get_unique_meta("unit", no_duplicates=True), ) - assert_scmdf_almost_equal( - res_comp, exp_comp, allow_unordered=True, check_ts_names=False, rtol=1e-3 - ) + assert_scmdf_almost_equal(res_comp, exp_comp, allow_unordered=True, check_ts_names=False, rtol=1e-3) -@pytest.mark.xfail( - _check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument" -) +@pytest.mark.xfail(_check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument") def test_linear_regression(): dat = [1, 2, 3] start = get_single_ts(data=dat, index=[1970, 1971, 1972], unit="GtC / yr") @@ -881,15 +811,11 @@ def test_linear_regression_handling_big_jumps(): def test_linear_regression_handling_all_over_jumps(): - start = get_single_ts( - data=[1, 2, 3, 3, 1.8], index=[10, 10.1, 11, 20, 50], unit="GtC" - ) + start = get_single_ts(data=[1, 2, 3, 3, 1.8], index=[10, 10.1, 11, 20, 50], unit="GtC") res = start.linear_regression() - npt.assert_allclose( - res[0]["gradient"].to("GtC / yr").magnitude, -0.00439, rtol=1e-3 - ) + npt.assert_allclose(res[0]["gradient"].to("GtC / yr").magnitude, -0.00439, rtol=1e-3) def test_linear_regression_nan_handling(): @@ -912,9 +838,7 @@ def test_linear_regression_nan_handling(): assert np.isnan(res[0]["intercept"]) -@pytest.mark.xfail( - _check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument" -) +@pytest.mark.xfail(_check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument") def test_linear_regression_multiple_ts(): variables = ["Emissions|CO2", "Heat Uptake", "Temperature", "Temperature Ocean"] start = get_multiple_ts( @@ -931,9 +855,7 @@ def test_linear_regression_multiple_ts(): if r["variable"] == "Emissions|CO2": npt.assert_allclose(r["gradient"].to("Mt CO2 / yr").magnitude, 1, rtol=1e-3) elif r["variable"] == "Heat Uptake": - npt.assert_allclose( - r["gradient"].to("J / m^2 / yr").magnitude, -1, rtol=1e-3 - ) + npt.assert_allclose(r["gradient"].to("J / m^2 / yr").magnitude, -1, rtol=1e-3) elif r["variable"] == "Temperature": npt.assert_allclose(r["gradient"].to("K / yr").magnitude, 5, rtol=1e-3) elif r["variable"] == "Temperature Ocean": @@ -942,9 +864,7 @@ def test_linear_regression_multiple_ts(): raise NotImplementedError(r["variable"]) -@pytest.mark.xfail( - _check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument" -) +@pytest.mark.xfail(_check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument") @pytest.mark.parametrize( "unit,exp_values", ( @@ -980,9 +900,7 @@ def test_linear_regression_gradient(unit, exp_values): pdt.assert_frame_equal(res, exp, rtol=1e-3, check_like=True) -@pytest.mark.xfail( - _check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument" -) +@pytest.mark.xfail(_check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument") @pytest.mark.parametrize( "unit,exp_values", ( @@ -1026,9 +944,7 @@ def test_linear_regression_intercept(unit, exp_values): pdt.assert_frame_equal(res, exp, rtol=1e-3, check_like=True) -@pytest.mark.xfail( - _check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument" -) +@pytest.mark.xfail(_check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument") def test_linear_regression_scmrun(): start = get_multiple_ts( data=np.array([[1, 2, 3], [-1, -2, -3], [0, 8, 10], [0, 5, 10]]).T, @@ -1048,9 +964,7 @@ def test_linear_regression_scmrun(): scenario=["a", "b", "c", "d"], ) - assert_scmdf_almost_equal( - res, exp, allow_unordered=True, check_ts_names=False, rtol=1e-3 - ) + assert_scmdf_almost_equal(res, exp, allow_unordered=True, check_ts_names=False, rtol=1e-3) @pytest.mark.parametrize( @@ -1169,9 +1083,7 @@ def test_adjust_median_to_target_single_ts(evaluation_period, exp_shift, target_ exp = start.copy() - exp_shift + target_median - res = start.adjust_median_to_target( - target_median, evaluation_period, process_over="ensemble_member" - ) + res = start.adjust_median_to_target(target_median, evaluation_period, process_over="ensemble_member") assert_scmdf_almost_equal(res, exp, allow_unordered=True, check_ts_names=False) @@ -1188,9 +1100,7 @@ def test_adjust_median_to_target_single_ts(evaluation_period, exp_shift, target_ ), ) @pytest.mark.parametrize("target_median", (0.43, 1.0)) -def test_adjust_median_to_target_multiple_ts( - evaluation_period, exp_shift, target_median -): +def test_adjust_median_to_target_multiple_ts(evaluation_period, exp_shift, target_median): start = get_single_ts( np.array([[1, 2, 3], [2, 3.5, 4], [3, 4, 5]]).T, [2000, 2010, 2020], @@ -1238,15 +1148,9 @@ def test_adjust_median_to_target_process_over(process_over): ensemble_member=[0, 1, 2], ) index = [2000, 2010, 2020] - start = get_single_ts( - np.array([[1, 2, 3], [2, 3.5, 4], [3, 4, 5]]).T, index, **get_ts_kwargs - ) + start = get_single_ts(np.array([[1, 2, 3], [2, 3.5, 4], [3, 4, 5]]).T, index, **get_ts_kwargs) - if ( - process_over is None - or process_over == "not supplied" - or process_over in ["scenario", ["scenario"]] - ): + if process_over is None or process_over == "not supplied" or process_over in ["scenario", ["scenario"]]: exp = get_single_ts( np.array([[0.5, 1.5, 2.5], [0.25, 1.75, 2.25], [0.5, 1.5, 2.5]]).T, index, @@ -1332,13 +1236,11 @@ def test_custom_registry(custom_unit_registry): pop = get_single_ts(variable="Population", unit="million population") with patch("scmdata.units.get_unit_registry", return_value=custom_unit_registry): - res = emms.divide( - pop, op_cols={"variable": "Emissions|CO2 per capita"} - ).convert_unit("ktCO2 / yr / population") + res = emms.divide(pop, op_cols={"variable": "Emissions|CO2 per capita"}).convert_unit( + "ktCO2 / yr / population" + ) assert res.get_unique_meta("unit", True) == "ktCO2 / yr / population" - with pytest.raises( - UndefinedUnitError, match="'million' is not defined in the unit registry" - ): + with pytest.raises(UndefinedUnitError, match="'million' is not defined in the unit registry"): emms.divide(pop, op_cols={"variable": "Emissions|CO2 per capita"}) diff --git a/tests/unit/test_plotting.py b/tests/unit/test_plotting.py index a7aa2113..d5625209 100644 --- a/tests/unit/test_plotting.py +++ b/tests/unit/test_plotting.py @@ -21,9 +21,7 @@ def test_plotting_long_data(scm_run): assert len(long_data) == scm_run.shape[0] * scm_run.shape[1] exp = scm_run.filter(year=2005, scenario="a_scenario2").values.squeeze() - obs = long_data[ - (long_data.scenario == "a_scenario2") & (long_data.time.dt.year == 2005) - ].value.squeeze() + obs = long_data[(long_data.scenario == "a_scenario2") & (long_data.time.dt.year == 2005)].value.squeeze() assert exp == obs @@ -50,16 +48,12 @@ def hide_seaborn(monkeypatch): def test_no_matplotlib(scm_run, hide_matplotlib): - with pytest.raises( - ImportError, match="matplotlib is not installed. Run 'pip install matplotlib'" - ): + with pytest.raises(ImportError, match="matplotlib is not installed. Run 'pip install matplotlib'"): scm_run.plumeplot() def test_no_seaborn(scm_run, hide_seaborn): - with pytest.raises( - ImportError, match="seaborn is not installed. Run 'pip install seaborn'" - ): + with pytest.raises(ImportError, match="seaborn is not installed. Run 'pip install seaborn'"): scm_run.lineplot() diff --git a/tests/unit/test_pyam_compat.py b/tests/unit/test_pyam_compat.py index 72363a0a..546a783f 100644 --- a/tests/unit/test_pyam_compat.py +++ b/tests/unit/test_pyam_compat.py @@ -14,9 +14,7 @@ def test_to_int_value_error(test_iam_df): idf.loc[postion, "time"] = bad_val if pd.__version__.startswith("1"): - error_msg = re.escape( - f"Unknown string format: {bad_val} present at position {postion}" - ) + error_msg = re.escape(f"Unknown string format: {bad_val} present at position {postion}") else: error_msg = re.escape(f"Invalid time domain: {bad_val}") diff --git a/tests/unit/test_run.py b/tests/unit/test_run.py index cc349f77..4d842889 100644 --- a/tests/unit/test_run.py +++ b/tests/unit/test_run.py @@ -37,10 +37,7 @@ @pytest.fixture def scm_run_interpolated(scm_run): return scm_run.interpolate( - [ - dt.datetime(y, 1, 1) - for y in range(scm_run["year"].min(), scm_run["year"].max() + 1) - ] + [dt.datetime(y, 1, 1) for y in range(scm_run["year"].min(), scm_run["year"].max() + 1)] ) @@ -48,8 +45,7 @@ def test_init_df_year_converted_to_datetime(test_pd_df): res = ScmRun(test_pd_df) assert (res["year"].unique() == [2005, 2010, 2015]).all() assert ( - res["time"].unique() - == [dt.datetime(2005, 1, 1), dt.datetime(2010, 1, 1), dt.datetime(2015, 1, 1)] + res["time"].unique() == [dt.datetime(2005, 1, 1), dt.datetime(2010, 1, 1), dt.datetime(2015, 1, 1)] ).all() @@ -70,17 +66,13 @@ def test_init_df_year_converted_to_datetime(test_pd_df): def test_init_df_formats(test_pd_run_df, in_format): if in_format == "pd.Series": idx = ["climate_model", "model", "scenario", "region", "variable", "unit"] - test_init = test_pd_run_df.melt(id_vars=idx, var_name="year").set_index( - [*idx, "year"] - )["value"] + test_init = test_pd_run_df.melt(id_vars=idx, var_name="year").set_index([*idx, "year"])["value"] elif in_format == "year_col": idx = ["climate_model", "model", "scenario", "region", "variable", "unit"] test_init = test_pd_run_df.melt(id_vars=idx, var_name="year") elif in_format == "year_col_index": idx = ["climate_model", "model", "scenario", "region", "variable", "unit"] - test_init = test_pd_run_df.melt(id_vars=idx, var_name="year").set_index( - [*idx, "year"] - ) + test_init = test_pd_run_df.melt(id_vars=idx, var_name="year").set_index([*idx, "year"]) elif in_format == "time_col": idx = ["climate_model", "model", "scenario", "region", "variable", "unit"] test_init = test_pd_run_df.melt(id_vars=idx, var_name="year") @@ -106,15 +98,12 @@ def test_init_df_formats(test_pd_run_df, in_format): test_init = test_pd_run_df[test_pd_run_df.columns[::-1]] elif in_format == "str_times": test_init = test_pd_run_df.copy() - test_init.columns = test_init.columns.map( - lambda x: f"{x}/1/1" if isinstance(x, int) else x - ) + test_init.columns = test_init.columns.map(lambda x: f"{x}/1/1" if isinstance(x, int) else x) res = ScmRun(test_init) assert (res["year"].unique() == [2005, 2010, 2015]).all() assert ( - res["time"].unique() - == [dt.datetime(2005, 1, 1), dt.datetime(2010, 1, 1), dt.datetime(2015, 1, 1)] + res["time"].unique() == [dt.datetime(2005, 1, 1), dt.datetime(2010, 1, 1), dt.datetime(2015, 1, 1)] ).all() assert "Start: 2005" in res.__repr__() assert "End: 2015" in res.__repr__() @@ -146,10 +135,7 @@ def test_init_df_missing_time_columns_error(test_pd_df): test_init.columns[test_init.columns.map(lambda x: isinstance(x, int))], axis="columns", ) - error_msg = re.escape( - "invalid column format, must contain some time (int, float or datetime) " - "columns!" - ) + error_msg = re.escape("invalid column format, must contain some time (int, float or datetime) columns!") with pytest.raises(ValueError, match=error_msg): ScmRun(test_init) @@ -198,10 +184,7 @@ class MyRun(BaseScmRun): def test_init_multiple_file_error(): - error_msg = re.escape( - "Initialising from multiple files not supported, use " - "`scmdata.run.ScmRun.append()`" - ) + error_msg = re.escape("Initialising from multiple files not supported, use `scmdata.run.ScmRun.append()`") with pytest.raises(ValueError, match=error_msg): ScmRun(["file_1", "filepath_2"]) @@ -289,9 +272,7 @@ def test_init_with_ts(test_ts, test_pd_df): ) tdf = get_test_pd_df_with_datetime_columns(test_pd_df) - pd.testing.assert_frame_equal( - df.timeseries().reset_index(), tdf, check_like=True, check_names=False - ) + pd.testing.assert_frame_equal(df.timeseries().reset_index(), tdf, check_like=True, check_names=False) b = ScmRun(test_pd_df) @@ -306,13 +287,9 @@ def test_init_with_scmdf(test_scm_run_datetimes, test_scm_datetime_run): assert_scmdf_almost_equal(df, test_scm_datetime_run, check_ts_names=False) -@pytest.mark.parametrize( - "years", [["2005.0", "2010.0", "2015.0"], ["2005", "2010", "2015"]] -) +@pytest.mark.parametrize("years", [["2005.0", "2010.0", "2015.0"], ["2005", "2010", "2015"]]) def test_init_with_years_as_str(test_pd_df, years): - df = copy.deepcopy( - test_pd_df - ) # This needs to be a deep copy so it doesn't break the other tests + df = copy.deepcopy(test_pd_df) # This needs to be a deep copy so it doesn't break the other tests cols = copy.deepcopy(test_pd_df.columns.values) cols[-3:] = years df.columns = cols @@ -331,9 +308,7 @@ def test_init_with_years_as_str(test_pd_df, years): def test_init_with_year_columns(test_pd_df): df = ScmRun(test_pd_df) tdf = get_test_pd_df_with_datetime_columns(test_pd_df) - pd.testing.assert_frame_equal( - df.timeseries().reset_index(), tdf, check_like=True, check_names=False - ) + pd.testing.assert_frame_equal(df.timeseries().reset_index(), tdf, check_like=True, check_names=False) def test_init_with_decimal_years(): @@ -376,9 +351,7 @@ def test_init_df_with_extra_col(test_pd_df): tdf = get_test_pd_df_with_datetime_columns(tdf) assert extra_col in df.meta - pd.testing.assert_frame_equal( - df.timeseries().reset_index(), tdf, check_like=True, check_names=False - ) + pd.testing.assert_frame_equal(df.timeseries().reset_index(), tdf, check_like=True, check_names=False) def test_init_df_without_required_arguments(test_run_ts): @@ -446,9 +419,7 @@ def test_init_with_copy_dataframe(copy_data, test_pd_df): def test_init_duplicate_columns(test_pd_df): - exp_msg = ( - "Duplicate times (numbers show how many times the given " "time is repeated)" - ) + exp_msg = "Duplicate times (numbers show how many times the given time is repeated)" inp = pd.concat([test_pd_df, test_pd_df[2015]], axis=1) with pytest.raises(DuplicateTimesError) as exc_info: ScmRun(inp) @@ -796,9 +767,7 @@ def test_filter_day(test_scm_run_datetimes, test_day): def test_filter_hour(test_scm_run_datetimes, test_hour): obs = test_scm_run_datetimes.filter(hour=test_hour) test_hour = [test_hour] if isinstance(test_hour, int) else test_hour - expected_rows = ( - test_scm_run_datetimes["time"].apply(lambda x: x.hour).isin(test_hour) - ) + expected_rows = test_scm_run_datetimes["time"].apply(lambda x: x.hour).isin(test_hour) expected = test_scm_run_datetimes["time"].loc[expected_rows].unique() unique_time = obs["time"].unique() @@ -827,9 +796,7 @@ def test_filter_time_exact_match(test_scm_run_datetimes): def test_filter_time_range(test_scm_run_datetimes): error_msg = r".*datetime.datetime.*" with pytest.raises(TypeError, match=error_msg): - test_scm_run_datetimes.filter( - year=range(dt.datetime(2000, 6, 17), dt.datetime(2009, 6, 17)) - ) + test_scm_run_datetimes.filter(year=range(dt.datetime(2000, 6, 17), dt.datetime(2009, 6, 17))) def test_filter_time_range_year(test_scm_run_datetimes): @@ -855,9 +822,7 @@ def test_filter_time_range_month(test_scm_run_datetimes, month_range): def test_filter_time_range_month_unrecognised_error(test_scm_run_datetimes): fail_filter = "Marb-Jun" error_msg = re.escape( - "Could not convert month '{}' to integer".format( - [m for m in fail_filter.split("-")] - ) + "Could not convert month '{}' to integer".format([m for m in fail_filter.split("-")]) ) with pytest.raises(ValueError, match=error_msg): test_scm_run_datetimes.filter(month=fail_filter) @@ -865,10 +830,7 @@ def test_filter_time_range_month_unrecognised_error(test_scm_run_datetimes): @pytest.mark.parametrize("month_range", [["Mar-Jun", "Nov-Feb"]]) def test_filter_time_range_round_the_clock_error(test_scm_run_datetimes, month_range): - error_msg = re.escape( - "string ranges must lead to increasing integer ranges, " - "Nov-Feb becomes [11, 2]" - ) + error_msg = re.escape("string ranges must lead to increasing integer ranges, Nov-Feb becomes [11, 2]") with pytest.raises(ValueError, match=error_msg): test_scm_run_datetimes.filter(month=month_range) @@ -884,11 +846,7 @@ def test_filter_time_range_day(test_scm_run_datetimes, day_range): def test_filter_time_range_day_unrecognised_error(test_scm_run_datetimes): fail_filter = "Thud-Sat" - error_msg = re.escape( - "Could not convert day '{}' to integer".format( - [m for m in fail_filter.split("-")] - ) - ) + error_msg = re.escape("Could not convert day '{}' to integer".format([m for m in fail_filter.split("-")])) with pytest.raises(ValueError, match=error_msg): test_scm_run_datetimes.filter(day=fail_filter) @@ -897,9 +855,7 @@ def test_filter_time_range_day_unrecognised_error(test_scm_run_datetimes): def test_filter_time_range_hour(test_scm_run_datetimes, hour_range): obs = test_scm_run_datetimes.filter(hour=hour_range) - expected_rows = ( - test_scm_run_datetimes["time"].apply(lambda x: x.hour).isin(hour_range) - ) + expected_rows = test_scm_run_datetimes["time"].apply(lambda x: x.hour).isin(hour_range) expected = test_scm_run_datetimes["time"][expected_rows].unique() unique_time = obs["time"].unique() @@ -959,9 +915,7 @@ def test_filter_keep_false_time_and_metadata(scm_run): def test_filter_keep_false_successive(scm_run): - df = scm_run.filter(variable="Primary Energy|Coal", keep=False).filter( - year=2005, keep=False - ) + df = scm_run.filter(variable="Primary Energy|Coal", keep=False).filter(year=2005, keep=False) obs = df.filter(scenario="a_scenario").timeseries().values.ravel() npt.assert_array_equal(obs, [6, 6]) @@ -1014,9 +968,7 @@ def test_filter_timeseries_different_length(): # This is different to how `ScmDataFrame` deals with nans # Nan and empty timeseries remain in the Run df = ScmRun( - pd.DataFrame( - np.array([[1.0, 2.0, 3.0], [4.0, 5.0, np.nan]]).T, index=[2000, 2001, 2002] - ), + pd.DataFrame(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, np.nan]]).T, index=[2000, 2001, 2002]), columns={ "model": ["a_iam"], "climate_model": ["a_model"], @@ -1027,9 +979,7 @@ def test_filter_timeseries_different_length(): }, ) - npt.assert_array_equal( - df.filter(scenario="a_scenario2").timeseries().squeeze(), [4.0, 5.0, np.nan] - ) + npt.assert_array_equal(df.filter(scenario="a_scenario2").timeseries().squeeze(), [4.0, 5.0, np.nan]) npt.assert_array_equal(df.filter(year=2002).timeseries().squeeze(), [3.0, np.nan]) exp = pd.Series(["a_scenario", "a_scenario2"], name="scenario") @@ -1040,9 +990,7 @@ def test_filter_timeseries_different_length(): def test_filter_timeseries_nan_meta(): df = ScmRun( - pd.DataFrame( - np.array([[1.0, 2.0], [4.0, 5.0], [7.0, 8.0]]).T, index=[2000, 2001] - ), + pd.DataFrame(np.array([[1.0, 2.0], [4.0, 5.0], [7.0, 8.0]]).T, index=[2000, 2001]), columns={ "model": ["a_iam"], "climate_model": ["a_model"], @@ -1055,9 +1003,7 @@ def test_filter_timeseries_nan_meta(): def with_nan_assertion(a, b): assert len(a) == len(b) - assert all( - [(v == b[i]) or (np.isnan(v) and np.isnan(b[i])) for i, v in enumerate(a)] - ) + assert all([(v == b[i]) or (np.isnan(v) and np.isnan(b[i])) for i, v in enumerate(a)]) res = df.filter(scenario="*")["scenario"].unique() exp = ["a_scenario", "a_scenario2", np.nan] @@ -1201,10 +1147,7 @@ def test_set_meta_keep(scm_run): # Note that the order has changed here (hence the sort_values) pd.testing.assert_frame_equal( - res.meta[["variable", "region"]] - .drop_duplicates() - .sort_values(by="variable") - .reset_index(drop=True), + res.meta[["variable", "region"]].drop_duplicates().sort_values(by="variable").reset_index(drop=True), pd.DataFrame( { "variable": ["Primary Energy", "Primary Energy|Coal"], @@ -1216,9 +1159,7 @@ def test_set_meta_keep(scm_run): def test_set_meta_inplace(scm_run): - with pytest.raises( - ValueError, match="Inplace updating of metadata is not supported" - ): + with pytest.raises(ValueError, match="Inplace updating of metadata is not supported"): scm_run.set_meta("test", "value", inplace=True) @@ -1322,17 +1263,13 @@ def test_timeseries(scm_run): "years": [2005, 2010, 2015], "value": [1, 6, 6], } - exp = pd.DataFrame(dct).pivot_table( - index=["model", "scenario"], columns=["years"], values="value" - ) + exp = pd.DataFrame(dct).pivot_table(index=["model", "scenario"], columns=["years"], values="value") obs = scm_run.filter(variable="Primary Energy", scenario="a_scenario").timeseries() npt.assert_array_equal(obs, exp) def test_timeseries_meta(scm_run): - obs = scm_run.filter(variable="Primary Energy").timeseries( - meta=["scenario", "model"] - ) + obs = scm_run.filter(variable="Primary Energy").timeseries(meta=["scenario", "model"]) npt.assert_array_equal(obs.index.names, ["scenario", "model"]) @@ -1520,9 +1457,7 @@ def test_median_over(test_processing_scm_df): ) obs = test_processing_scm_df.process_over("scenario", "median") - pd.testing.assert_frame_equal( - exp, obs.reset_index(), check_like=True, check_names=False - ) + pd.testing.assert_frame_equal(exp, obs.reset_index(), check_like=True, check_names=False) def test_arb_function_over(test_processing_scm_df): @@ -1531,17 +1466,13 @@ def same(df): obs = test_processing_scm_df.process_over("scenario", same) - pd.testing.assert_frame_equal( - test_processing_scm_df.timeseries(), obs, check_like=True - ) + pd.testing.assert_frame_equal(test_processing_scm_df.timeseries(), obs, check_like=True) def add_2(df): return df + 2 obs = test_processing_scm_df.process_over("scenario", add_2) - pd.testing.assert_frame_equal( - (test_processing_scm_df + 2).timeseries(), obs, check_like=True - ) + pd.testing.assert_frame_equal((test_processing_scm_df + 2).timeseries(), obs, check_like=True) def test_arb_function_returns_none(test_processing_scm_df): @@ -1552,9 +1483,7 @@ def add_2_only_coal(df): # implicit return None for any other variables obs = test_processing_scm_df.process_over("scenario", add_2_only_coal) - exp = ( - test_processing_scm_df.filter(variable="Primary Energy|Coal") + 2 - ).timeseries() + exp = (test_processing_scm_df.filter(variable="Primary Energy|Coal") + 2).timeseries() pd.testing.assert_frame_equal( exp, obs, @@ -1785,9 +1714,7 @@ def test_quantiles_over(test_processing_scm_df): cols=["model", "scenario"], quantiles=[0, 0.5, 1, "mean", "median"], ) - pd.testing.assert_frame_equal( - exp.set_index(obs.index.names), obs, check_like=True, check_names=False - ) + pd.testing.assert_frame_equal(exp.set_index(obs.index.names), obs, check_like=True, check_names=False) def test_quantiles_over_operation_in_kwargs(test_processing_scm_df): @@ -1917,9 +1844,7 @@ def test_relative_to_ref_period_mean(test_processing_scm_df, tfilter): obs_ts = obs.timeseries() exp_ts = exp.timeseries() - pd.testing.assert_frame_equal( - exp_ts.reorder_levels(obs_ts.index.names), obs_ts, check_like=True - ) + pd.testing.assert_frame_equal(exp_ts.reorder_levels(obs_ts.index.names), obs_ts, check_like=True) def test_append(scm_run): @@ -1936,9 +1861,7 @@ def test_append(scm_run): assert "col1" in scm_run.meta_attributes # assert that merging of meta works as expected - npt.assert_array_equal( - df.meta.sort_values(["scenario", "variable"])["col1"].values, [5, 6, 7, 2] - ) + npt.assert_array_equal(df.meta.sort_values(["scenario", "variable"])["col1"].values, [5, 6, 7, 2]) pd.testing.assert_series_equal( df.meta.sort_values(["scenario", "variable"])["col2"].reset_index(drop=True), pd.Series([np.nan, np.nan, np.nan, "b"]), @@ -1994,9 +1917,7 @@ def test_append_duplicates_order_doesnt_matter(scm_run): obs = res.filter(scenario="a_scenario2").timeseries().squeeze() exp = [2.0, 7.0, 7.0, 2.0, 7.0, 5.0] - npt.assert_array_equal( - res._time_points.years(), [2005, 2010, 2015, 2020, 2030, 2040] - ) + npt.assert_array_equal(res._time_points.years(), [2005, 2010, 2015, 2020, 2030, 2040]) npt.assert_almost_equal(obs, exp) @@ -2008,18 +1929,13 @@ def test_append_duplicate_times(test_append_scm_runs, duplicate_msg): expected = test_append_scm_runs["expected"] if duplicate_msg and not isinstance(duplicate_msg, str): - exp_msg = ( - "Duplicate metadata (numbers show how many times the given " - "metadata is repeated)." - ) + exp_msg = "Duplicate metadata (numbers show how many times the given metadata is repeated)." with pytest.raises(NonUniqueMetadataError) as exc_info: base.append(other, duplicate_msg=duplicate_msg) error_msg = exc_info.value.args[0] assert error_msg.startswith(exp_msg) - pd.testing.assert_frame_equal( - pd.concat([base.meta, other.meta]), exc_info.value.meta - ) + pd.testing.assert_frame_equal(pd.concat([base.meta, other.meta]), exc_info.value.meta) return @@ -2036,9 +1952,7 @@ def test_append_duplicate_times(test_append_scm_runs, duplicate_msg): else: assert not mock_warn_taking_average - pd.testing.assert_frame_equal( - res.timeseries(), expected.timeseries(), check_like=True - ) + pd.testing.assert_frame_equal(res.timeseries(), expected.timeseries(), check_like=True) @pytest.mark.filterwarnings("ignore::DeprecationWarning") @@ -2174,9 +2088,7 @@ def test_run_append_empty(scm_run): def test_append_chain_column_order_time_interpolation(scm_run): base, other, other_2, exp = get_append_col_order_time_dfs(scm_run) - res = scm_run.append(other, duplicate_msg="warn").append( - other_2, duplicate_msg="warn" - ) + res = scm_run.append(other, duplicate_msg="warn").append(other_2, duplicate_msg="warn") pd.testing.assert_frame_equal( res.timeseries().sort_index(), @@ -2209,9 +2121,7 @@ def test_append_inplace_preexisting_nan(scm_run): # make sure underlying hasn't changed when not appending inplace pd.testing.assert_frame_equal(original_ts, scm_run.timeseries()) - exp = pd.concat( - [scm_run.timeseries().reset_index(), other.timeseries().reset_index()] - ) + exp = pd.concat([scm_run.timeseries().reset_index(), other.timeseries().reset_index()]) exp["junk"] = np.nan exp = exp.set_index(res.meta_attributes) @@ -2327,9 +2237,7 @@ def test_append_timewise_align_columns_many_to_many(scm_run_interpolated): start.filter(year=range(join_year, 3000)), ) - for scenario, df in ( - res.filter(year=range(1, join_year)).timeseries().groupby("scenario") - ): + for scenario, df in res.filter(year=range(1, join_year)).timeseries().groupby("scenario"): # check that correct history has been written into all timeseries exp_vals = history.filter(scenario=scenario).values.squeeze() res_vals = df.values.squeeze() @@ -2338,8 +2246,7 @@ def test_append_timewise_align_columns_many_to_many(scm_run_interpolated): def test_append_timewise_ambiguous_history(scm_run_interpolated): error_msg = re.escape( - "Calling ``other.timeseries(meta=align_columns)`` must " - "result in umabiguous timeseries" + "Calling ``other.timeseries(meta=align_columns)`` must result in umabiguous timeseries" ) with pytest.raises(ValueError, match=error_msg): scm_run_interpolated.append_timewise( @@ -2366,9 +2273,7 @@ def test_append_timewise_no_match(scm_run_interpolated): join_year = 2010 base = start.filter(year=range(join_year, 3000)) - other = start.filter( - year=range(1, join_year), variable="Primary Energy", scenario="a_scenario" - ) + other = start.filter(year=range(1, join_year), variable="Primary Energy", scenario="a_scenario") other["scenario"] = "other" other["model"] = "test" @@ -2382,9 +2287,7 @@ def test_append_timewise_no_match(scm_run_interpolated): allow_unordered=True, ) - for variable, df in ( - res.filter(year=range(1, join_year)).timeseries().groupby("variable") - ): + for variable, df in res.filter(year=range(1, join_year)).timeseries().groupby("variable"): # check that correct other has been written into all timeseries exp_vals = other.filter(variable=variable) if exp_vals.empty: @@ -2457,9 +2360,7 @@ def test_interpolate_nan(source): extrapolation_type="linear", ) - npt.assert_array_almost_equal( - res.values.squeeze(), [1.0, 2.0, 3.0, 4.0, 5.0], decimal=4 - ) + npt.assert_array_almost_equal(res.values.squeeze(), [1.0, 2.0, 3.0, 4.0, 5.0], decimal=4) def test_interpolate_nan_constant(): @@ -2480,9 +2381,7 @@ def test_interpolate_nan_constant(): extrapolation_type="constant", ) - npt.assert_array_almost_equal( - res.values.squeeze(), [1.0, 2.0, 3.0, 3.0, 3.0], decimal=4 - ) + npt.assert_array_almost_equal(res.values.squeeze(), [1.0, 2.0, 3.0, 3.0, 3.0], decimal=4) @pytest.mark.parametrize( @@ -2648,12 +2547,7 @@ def group_annual_mean_beginning_of_year(x): return x.year return x.year + 1 - ts_resampled = ( - test_scm_df_monthly.timeseries() - .T.groupby(group_annual_mean_beginning_of_year) - .mean() - .T - ) + ts_resampled = test_scm_df_monthly.timeseries().T.groupby(group_annual_mean_beginning_of_year).mean().T ts_resampled.columns = ts_resampled.columns.map(lambda x: dt.datetime(x, 1, 1)) pd.testing.assert_frame_equal(res.timeseries(), ts_resampled, check_like=True) @@ -2679,9 +2573,7 @@ def test_time_mean_year(test_scm_df_monthly): def group_annual_mean(x): return x.year - ts_resampled = ( - test_scm_df_monthly.timeseries().T.groupby(group_annual_mean).mean().T - ) + ts_resampled = test_scm_df_monthly.timeseries().T.groupby(group_annual_mean).mean().T ts_resampled.columns = ts_resampled.columns.map(lambda x: dt.datetime(x, 7, 1)) pd.testing.assert_frame_equal(res.timeseries(), ts_resampled, check_like=True) @@ -2709,12 +2601,7 @@ def group_annual_mean_end_of_year(x): return x.year return x.year - 1 - ts_resampled = ( - test_scm_df_monthly.timeseries() - .T.groupby(group_annual_mean_end_of_year) - .mean() - .T - ) + ts_resampled = test_scm_df_monthly.timeseries().T.groupby(group_annual_mean_end_of_year).mean().T ts_resampled.columns = ts_resampled.columns.map(lambda x: dt.datetime(x, 12, 31)) pd.testing.assert_frame_equal(res.timeseries(), ts_resampled, check_like=True) @@ -2828,9 +2715,7 @@ def test_filter_empty_times(): np.testing.assert_equal(result, expected) -@pytest.mark.xfail( - _check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument" -) +@pytest.mark.xfail(_check_pandas_less_110(), reason="pandas<=1.1.0 does not have rtol argument") @pytest.mark.parametrize( ("target_unit", "input_units", "filter_kwargs", "expected", "expected_units"), [ @@ -2867,9 +2752,7 @@ def test_filter_empty_times(): ), ], ) -def test_convert_unit( - scm_run, target_unit, input_units, filter_kwargs, expected, expected_units -): +def test_convert_unit(scm_run, target_unit, input_units, filter_kwargs, expected, expected_units): scm_run["unit"] = input_units obs = scm_run.convert_unit(target_unit, **filter_kwargs) @@ -2903,9 +2786,7 @@ def test_convert_unit_inplace(scm_run): assert ret is None assert (scm_run["unit"] != units).all() - npt.assert_array_almost_equal( - scm_run.filter(year=2005).values.squeeze(), [1000.0, 500.0, 2000.0] - ) + npt.assert_array_almost_equal(scm_run.filter(year=2005).values.squeeze(), [1000.0, 500.0, 2000.0]) def test_convert_unit_target_unit_in_input(): @@ -3054,33 +2935,25 @@ def test_unit_context_no_existing_contexts(scm_run, context): "unit_context", no_duplicates=True ) assert np.isnan( - res.filter(variable=to_convert, keep=False).get_unique_meta( - "unit_context", no_duplicates=True - ) + res.filter(variable=to_convert, keep=False).get_unique_meta("unit_context", no_duplicates=True) ) def _check_context_or_nan(run, variable, exp, keep=True): if exp is None: assert np.isnan( - run.filter(variable=variable, keep=keep).get_unique_meta( - "unit_context", no_duplicates=True - ) + run.filter(variable=variable, keep=keep).get_unique_meta("unit_context", no_duplicates=True) ) else: assert ( - run.filter(variable=variable, keep=keep).get_unique_meta( - "unit_context", no_duplicates=True - ) + run.filter(variable=variable, keep=keep).get_unique_meta("unit_context", no_duplicates=True) == exp ) @pytest.mark.parametrize("to_not_convert_matches", (True, False)) @pytest.mark.parametrize("context", (None, "AR4GWP100")) -def test_unit_context_both_have_existing_context( - scm_run, context, to_not_convert_matches -): +def test_unit_context_both_have_existing_context(scm_run, context, to_not_convert_matches): to_convert = "*Coal" scm_run["unit_context"] = context @@ -3103,9 +2976,7 @@ def _set_not_convert_context(v): @pytest.mark.parametrize("to_not_convert_matches", (True, False)) @pytest.mark.parametrize("context", (None, "AR4GWP100")) -def test_unit_context_both_have_existing_context_error( - scm_run, context, to_not_convert_matches -): +def test_unit_context_both_have_existing_context_error(scm_run, context, to_not_convert_matches): to_convert = "*Coal" scm_run["unit_context"] = "junk" @@ -3132,16 +3003,8 @@ def test_unit_context_to_convert_has_existing_context(scm_run, context): _check_context_or_nan(start, to_convert, context) _check_context_or_nan(start, to_convert, None, keep=False) - assert ( - res.filter(variable=to_convert).get_unique_meta("unit", no_duplicates=True) - == "GJ/yr" - ) - assert ( - res.filter(variable=to_convert, keep=False).get_unique_meta( - "unit", no_duplicates=True - ) - == "EJ/yr" - ) + assert res.filter(variable=to_convert).get_unique_meta("unit", no_duplicates=True) == "GJ/yr" + assert res.filter(variable=to_convert, keep=False).get_unique_meta("unit", no_duplicates=True) == "EJ/yr" @pytest.mark.parametrize("context", ("AR5GWP100", "AR4GWP100")) @@ -3165,16 +3028,10 @@ def test_unit_context_to_convert_has_existing_context_error(scm_run, context): @pytest.mark.parametrize("context", ("AR5GWP100", "AR4GWP100", None)) @pytest.mark.parametrize("to_not_convert_context", ("AR5GWP100", "AR4GWP100")) -def test_unit_context_to_not_convert_has_existing_context( - scm_run, context, to_not_convert_context -): +def test_unit_context_to_not_convert_has_existing_context(scm_run, context, to_not_convert_context): to_convert = "*Coal" - to_not_convert = scm_run.filter(variable=to_convert, keep=False).get_unique_meta( - "variable" - ) - start = scm_run.convert_unit( - "MJ/yr", variable=to_not_convert, context=to_not_convert_context - ) + to_not_convert = scm_run.filter(variable=to_convert, keep=False).get_unique_meta("variable") + start = scm_run.convert_unit("MJ/yr", variable=to_not_convert, context=to_not_convert_context) _check_context_or_nan(start, to_convert, None) _check_context_or_nan(start, to_convert, to_not_convert_context, keep=False) @@ -3185,16 +3042,8 @@ def test_unit_context_to_not_convert_has_existing_context( _check_context_or_nan(res, to_convert, context) _check_context_or_nan(res, to_not_convert, to_not_convert_context) - assert ( - res.filter(variable=to_convert).get_unique_meta("unit", no_duplicates=True) - == "GJ/yr" - ) - assert ( - res.filter(variable=to_convert, keep=False).get_unique_meta( - "unit", no_duplicates=True - ) - == "MJ/yr" - ) + assert res.filter(variable=to_convert).get_unique_meta("unit", no_duplicates=True) == "GJ/yr" + assert res.filter(variable=to_convert, keep=False).get_unique_meta("unit", no_duplicates=True) == "MJ/yr" @pytest.mark.parametrize( @@ -3211,9 +3060,7 @@ def test_unit_context_to_not_convert_has_existing_context( ), ) @pytest.mark.parametrize("duplicated_meta_once_converted", [True, False]) -def test_convert_unit_multiple_units( - start_units, target_unit, duplicated_meta_once_converted -): +def test_convert_unit_multiple_units(start_units, target_unit, duplicated_meta_once_converted): if duplicated_meta_once_converted: climate_model = "climate_model" else: @@ -3361,10 +3208,7 @@ def test_read_from_disk(test_file, test_kwargs, test_data_path, use_pathlib): if use_pathlib: fname = pathlib.Path(fname) loaded = ScmRun(fname, **test_kwargs) - assert ( - loaded.filter(variable="Emissions|N2O", year=1767).timeseries().values.squeeze() - == 0.010116813 - ) + assert loaded.filter(variable="Emissions|N2O", year=1767).timeseries().values.squeeze() == 0.010116813 def _write_two_timeseries_csv(tmp_path): @@ -3422,9 +3266,7 @@ def test_read_csv_python_engine_omits_low_memory(tmp_path): def test_read_from_disk_different_number_of_digits_years(test_data_path): - loaded = ScmRun( - os.path.join(test_data_path, "different_number_of_digits_years.csv") - ) + loaded = ScmRun(os.path.join(test_data_path, "different_number_of_digits_years.csv")) # make sure data sorts correctly assert loaded["year"].iloc[-1] == loaded["year"].max() @@ -3479,22 +3321,15 @@ def test_get_meta(scm_run): @pytest.mark.parametrize("no_duplicates", [True, False]) def test_get_meta_no_duplicates(scm_run, no_duplicates): if no_duplicates: - assert ( - scm_run.get_unique_meta("climate_model", no_duplicates=no_duplicates) - == "a_model" - ) + assert scm_run.get_unique_meta("climate_model", no_duplicates=no_duplicates) == "a_model" error_msg = re.escape( - "`variable` column is not unique (found values: {})".format( - scm_run["variable"].unique().tolist() - ) + "`variable` column is not unique (found values: {})".format(scm_run["variable"].unique().tolist()) ) with pytest.raises(ValueError, match=error_msg): scm_run.get_unique_meta("variable", no_duplicates=no_duplicates) else: - assert scm_run.get_unique_meta( - "climate_model", no_duplicates=no_duplicates - ) == ["a_model"] + assert scm_run.get_unique_meta("climate_model", no_duplicates=no_duplicates) == ["a_model"] assert scm_run.get_unique_meta("variable", no_duplicates=no_duplicates) == [ "Primary Energy", "Primary Energy|Coal", @@ -3538,9 +3373,7 @@ def test_drop_meta_missing(scm_run, label): assert "variable" in scm_run.meta.columns -@pytest.mark.parametrize( - "label", [["other", "climate_model"], ["climate_model", "other"]] -) +@pytest.mark.parametrize("label", [["other", "climate_model"], ["climate_model", "other"]]) def test_drop_meta_missing_one(scm_run, label): with pytest.raises(KeyError): scm_run.drop_meta(label) @@ -3684,9 +3517,7 @@ def test_lineplot_time_axis(scm_run, time_axis, mod_func): ), ), ) -def test_timeseries_time_axis_non_unique_raises( - method_to_call, time_axis, non_unique_vals, exp_raise -): +def test_timeseries_time_axis_non_unique_raises(method_to_call, time_axis, non_unique_vals, exp_raise): start = ScmRun( data=np.arange(len(non_unique_vals)), index=non_unique_vals, @@ -3772,12 +3603,8 @@ def test_append_long_run(tax1, tax2): "unit": "GtC", "region": "World", } - run1 = ScmRun( - data=np.arange(len(tax1)), index=tax1, columns={"scenario": "run1", **mdata} - ) - run2 = ScmRun( - data=np.arange(len(tax2)), index=tax2, columns={"scenario": "run2", **mdata} - ) + run1 = ScmRun(data=np.arange(len(tax1)), index=tax1, columns={"scenario": "run1", **mdata}) + run2 = ScmRun(data=np.arange(len(tax2)), index=tax2, columns={"scenario": "run2", **mdata}) res = run_append([run1, run2]) @@ -3837,9 +3664,7 @@ def test_append_inplace(scm_run, inplace, use_cls_method): ) @inplace_params @pytest.mark.parametrize("use_cls_method", [True, False]) -def test_append_metadata( - scm_run, metadata_1, metadata_2, metadata, expected, inplace, use_cls_method -): +def test_append_metadata(scm_run, metadata_1, metadata_2, metadata, expected, inplace, use_cls_method): run1 = scm_run.copy() run1["ensemble_member"] = 1 run1.metadata = metadata_1 @@ -3920,10 +3745,7 @@ def test_non_unique_metadata_error_formatting(): exp = exp[exp > 1] exp.name = "repeats" exp = exp.to_frame().reset_index() - error_msg = ( - "Duplicate metadata (numbers show how many times the given " - f"metadata is repeated).\n{exp}" - ) + error_msg = f"Duplicate metadata (numbers show how many times the given metadata is repeated).\n{exp}" with pytest.raises(NonUniqueMetadataError, match=re.escape(error_msg)): raise NonUniqueMetadataError(meta) diff --git a/tests/unit/test_testing.py b/tests/unit/test_testing.py index da9b42a3..d004903d 100644 --- a/tests/unit/test_testing.py +++ b/tests/unit/test_testing.py @@ -15,6 +15,4 @@ def test_almost_equal_message(scm_run): msg = r"\'a_scenario\', \'EJ/yr\', \'Primary Energy\'" with pytest.raises(AssertionError, match=msg): - scmdata.testing.assert_scmdf_almost_equal( - scm_run, other, allow_unordered=True, check_ts_names=False - ) + scmdata.testing.assert_scmdf_almost_equal(scm_run, other, allow_unordered=True, check_ts_names=False) diff --git a/tests/unit/test_timeseries.py b/tests/unit/test_timeseries.py index 5a4be276..699e27e5 100644 --- a/tests/unit/test_timeseries.py +++ b/tests/unit/test_timeseries.py @@ -15,9 +15,7 @@ @pytest.fixture(scope="function") def ts(): - times = np.asarray( - [datetime(2000, 1, 1), datetime(2001, 1, 1), datetime(2002, 1, 1)] - ) + times = np.asarray([datetime(2000, 1, 1), datetime(2001, 1, 1), datetime(2002, 1, 1)]) return TimeSeries([1, 2, 3], time=times) @@ -61,9 +59,7 @@ def test_timeseries_init_xarray(): ( [[1, 2], [2, 4]], np.array([[1, 2], [2, 4]]), - xr.DataArray( - [[1, 2], [2, 4]], coords=[("time", [2000, 2001]), ("lat", [-45, 45])] - ), + xr.DataArray([[1, 2], [2, 4]], coords=[("time", [2000, 2001]), ("lat", [-45, 45])]), ), ) def test_timeseries_init_2d_data(data): @@ -85,20 +81,14 @@ def test_timeseries_init_xarray_no_time_coord(coord_name): raw_data = [-1, -2, -3] time_points = [20, 2020, 5050] data = xr.DataArray(raw_data, coords=[(coord_name, time_points)]) - error_msg = ( - "If data is an :class:`xarray.DataArray` instance, its only dimension must " - "be named `'time'`" - ) + error_msg = "If data is an :class:`xarray.DataArray` instance, its only dimension must be named `'time'`" with pytest.raises(ValueError, match=re.escape(error_msg)): TimeSeries(data) @pytest.mark.parametrize("data", ([1, 2, 3], (1, 2, 3), np.array([1, 2, 3]))) def test_timeseries_init_no_time_list(data): - error_msg = ( - "If data is not an :class:`xarray.DataArray` instance, `time` must not be " - "`None`" - ) + error_msg = "If data is not an :class:`xarray.DataArray` instance, `time` must not be `None`" with pytest.raises(TypeError, match=re.escape(error_msg)): TimeSeries(data) @@ -149,9 +139,7 @@ def test_timeseries_mul(ts, inplace): @pytest.fixture(scope="function") def ts_gtc_per_yr_units(): - times = np.asarray( - [datetime(2000, 1, 1), datetime(2001, 1, 1), datetime(2002, 1, 1)] - ) + times = np.asarray([datetime(2000, 1, 1), datetime(2001, 1, 1), datetime(2002, 1, 1)]) return TimeSeries([1, 2, 3], time=times, attrs={"unit": "GtC / yr"}) @@ -441,9 +429,7 @@ def test_interpolate(combo): npt.assert_array_almost_equal(res.values.squeeze(), combo.target_values) -@pytest.mark.parametrize( - "dt", [datetime, cftime.datetime, cftime.DatetimeNoLeap, cftime.Datetime360Day] -) +@pytest.mark.parametrize("dt", [datetime, cftime.datetime, cftime.DatetimeNoLeap, cftime.Datetime360Day]) def test_extrapolation_long(dt): source = np.arange(800, 1000) source_times = [dt(y, 1, 1) for y in source] @@ -460,9 +446,7 @@ def test_extrapolation_long(dt): npt.assert_array_almost_equal(res.values.squeeze(), target, decimal=0) -@pytest.mark.parametrize( - "dt", [datetime, cftime.datetime, cftime.DatetimeNoLeap, cftime.Datetime360Day] -) +@pytest.mark.parametrize("dt", [datetime, cftime.datetime, cftime.DatetimeNoLeap, cftime.Datetime360Day]) def test_extrapolation_nan(dt): source = np.arange(2000, 2005, dtype=float) source_times = [dt(int(y), 1, 1) for y in source] diff --git a/tests/unit/test_timeseries_convertor.py b/tests/unit/test_timeseries_convertor.py index 04c0890f..289f4438 100644 --- a/tests/unit/test_timeseries_convertor.py +++ b/tests/unit/test_timeseries_convertor.py @@ -17,9 +17,7 @@ def test_no_scipy(scm_run): "linear", ) - with pytest.raises( - ImportError, match="scipy is not installed. Run 'pip install scipy'" - ): + with pytest.raises(ImportError, match="scipy is not installed. Run 'pip install scipy'"): timeseriesconverter.convert_to([1, 2, 3]) @@ -51,8 +49,7 @@ def test_none_extrapolation_error(combo): ) error_msg = re.escape( - "Target time points are outside the source time points, use an " - "extrapolation type other than None" + "Target time points are outside the source time points, use an extrapolation type other than None" ) with pytest.raises(InsufficientDataError, match=error_msg): TimeseriesConverter(combo.source, target, combo.interpolation_type, None) diff --git a/tests/unit/test_xarray.py b/tests/unit/test_xarray.py index c1d9ca21..078bd451 100644 --- a/tests/unit/test_xarray.py +++ b/tests/unit/test_xarray.py @@ -20,12 +20,7 @@ def do_basic_to_xarray_checks(res, start_run, dimensions, extras): assert data_var.units in unit # all other metadata should be in attrs - for meta_col in ( - set(start_run.meta.columns) - - set(dimensions) - - set(extras) - - {"variable", "unit"} - ): + for meta_col in set(start_run.meta.columns) - set(dimensions) - set(extras) - {"variable", "unit"}: meta_val = start_run.get_unique_meta(meta_col, True) assert res.attrs[f"scmdata_metadata_{meta_col}"] == meta_val @@ -100,9 +95,7 @@ def test_to_xarray_extras_no_id_coord(scm_run, extras): for xarray_extra_val, extra_xarray_coord in zip(xarray_vals, xarray_coords): scm_run_extra_val = ( - scm_run_meta[scm_run_meta[extra_dims] == extra_xarray_coord][extra_col] - .unique() - .tolist() + scm_run_meta[scm_run_meta[extra_dims] == extra_xarray_coord][extra_col].unique().tolist() ) assert len(scm_run_extra_val) == 1 scm_run_extra_val = scm_run_extra_val[0] @@ -138,9 +131,7 @@ def test_to_xarray_extras_no_id_coord(scm_run, extras): ), ), ) -def test_to_xarray_extras_with_id_coord( - scm_run, extras, dimensions, expected_dimensions -): +def test_to_xarray_extras_with_id_coord(scm_run, extras, dimensions, expected_dimensions): df = scm_run.timeseries() val_cols = df.columns.tolist() df = df.reset_index() @@ -194,16 +185,10 @@ def test_to_xarray_extras_with_id_coord( scm_run_filter = row.to_dict() scm_run_spot = scm_run.filter(**scm_run_filter) - xarray_sel = { - k: v for k, v in scm_run_filter.items() if k in xarray_timeseries.dims - } - xarray_spot = xarray_timeseries.sel(**xarray_sel)[ - scm_run_filter["variable"] - ] + xarray_sel = {k: v for k, v in scm_run_filter.items() if k in xarray_timeseries.dims} + xarray_spot = xarray_timeseries.sel(**xarray_sel)[scm_run_filter["variable"]] - npt.assert_array_equal( - scm_run_spot.values.squeeze(), xarray_spot.values.squeeze() - ) + npt.assert_array_equal(scm_run_spot.values.squeeze(), xarray_spot.values.squeeze()) @pytest.mark.parametrize("ch", "!@#$%^&*()~`+={}]<>,;:'\".") @@ -253,9 +238,7 @@ def test_to_xarray_multiple_units_error(scm_run): "The following variables are reported in more than one unit. 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